nursing research studies on infertility

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Infertility management in primary care

Thable, Angela MN, NP; Duff, Elsie PhD, NP; Dika, Cheryl MN, NP

Angela Thable is an instructor at the University of Manitoba, College of Nursing, Winnipeg, Manitoba, Canada.

Elsie Duff is an instructor at the University of Manitoba, College of Nursing, Winnipeg, Manitoba, Canada.

Cheryl Dika is an instructor at the University of Manitoba, College of Nursing, Winnipeg, Manitoba, Canada.

The authors have disclosed no financial relationships related to this article.

Copyright © 2020 The Author(s). Published by Wolters Kluwer Health, Inc. This is an open-access article distributed under the terms of the Creative Commons Attribution-Non Commercial-No Derivatives License 4.0 (CCBY-NC-ND), where it is permissible to download and share the work provided it is properly cited. The work cannot be changed in any way or used commercially without permission from the journal.

Infertility is a growing issue for couples. Primary care NPs can manage initial treatment as well as address the emotional and financial burdens of patients experiencing infertility. NPs can provide timely access to investigations and treatment, helping patients achieve their goal of pregnancy sooner.

Rising infertility rates have prompted primary care NPs to begin the infertility work-up while patients are in the process of being referred to a specialist or awaiting an appointment. The initial primary care visit can serve as the first opportunity to address fertility concerns. Continuity of care in fertility treatment is key, especially in this vulnerable population that wants to feel supported and understood, which includes ongoing communication between patient and provider. 1 NPs are in a position to support continuity of care for patients seeking fertility assistance and play an essential role in the health history, physical exams, and lab investigations of both partners. Primary care NPs can initiate timely pharmacologic infertility management. Assessment, diagnosis, and treatment of fertility issues within primary care can effectively reduce the emotional and financial burden on couples, helping some avoid lengthy wait times and achieve pregnancy sooner.

Infertility is defined as the failure to conceive after 12 or more months of regular, unprotected sexual intercourse. 2 The prevalence of infertility is steadily increasing due to delayed childbearing and rising rates of male infertility. 3 Female age is one of the most important factors affecting fertility. 4 Female fertility naturally declines with age, so women in their 20s and 30s should be counseled about the age-related risk of infertility. A full infertility workup is recommended for all women age 35 and older who have had consistent unprotected vaginal intercourse for 6 months and for all women under age 35 who have had consistent unprotected vaginal intercourse for 12 months or more and have not achieved pregnancy. 4 Investigations may still be warranted for women under age 35 who have not been trying to conceive for a full 12 months, when risk factors are present. Risk factors may include: the presence of only one ovary; known or suspected uterine/tubal/peritoneal disease or stage III-IV endometriosis; or recurrent pregnancy loss (two or more). 5

Discussions about fertility should occur as part of a larger conversation about reproductive health issues, including contraception and sexual health practices prior to conception planning. 4 One study found that the social and emotional impact of being unable to conceive can be devastating for patients, with high rates of anxiety (23.2%) and depression (17%) among women unable to conceive on their own. 1 Even when eventually connected with a fertility specialist—20% of patients will wait 2 or more years for the referral—rates of self-discontinuation from care are high. 1 In patients with health insurance that covered fertility treatments where finances were not the primary barrier, dropout rates were still staggeringly high between 46% and 58%. 1 NPs can address and support patients with emotional or psychological hurdles who require counseling, coping strategies, and couple-based interventions, again illustrating the significance of continuity of care and close follow-up prior to and after referral to specialists. 1

Sexual and gynecologic history

Infertility can be a complex issue, so it is important to assess both partners, preferably together. A thorough menstrual history is needed, with details of the patient's normal menstrual cycle, including length between cycles, duration and amount of bleeding, as well as age of menarche. An earlier age of menarche is associated with a higher risk of developing endometriosis. 6 In addition, the presence or absence of subjective and objective signs of ovulation (also called molimina) throughout the cycle can help identify ovulatory versus nonovulatory states; molimina includes breast tenderness, food cravings, fatigue, and fluid retention. 7 Lack of ovulation with certain conditions, including polycystic ovary syndrome (PCOS), often causes cycle irregularities such as amenorrhea or oligomenorrhea. 7 Pelvic pain with chronic, cyclic, persistent, or progressive patterns may signify endometriosis. 8 Women may also complain of dyspareunia, dyschezia, dysuria, or menorrhagia, all of which require further investigation. 8

Both partners should be assessed for a history of chlamydia or gonorrhea infections, which can lead to pelvic inflammatory disease (PID). Chlamydia and gonorrhea are important preventable causes of PID and infertility. 9 Female patients should be assessed for any history of leiomyomas, also known as uterine fibroids, as these are the most common benign tumors in adult women. 10 Leiomyomas can cause a wide array of symptoms, including menorrhagia, iron-deficiency anemia, pelvic pain and pressure, and subfertility. 10

Previous successful pregnancy followed by the development of infertility, known as secondary infertility, is a key component of the patient's history, which should include information about the overall health of each pregnancy and the circumstances of the delivery. This history can reveal individual risk factors that may have contributed to secondary infertility including the presence of recurrent pregnancy loss (marked by greater than two early pregnancy losses), previous infertility treatments, and traumatic deliveries, which may have caused injury to or scarring of the female genital tract. If positive findings are noted during initial screening, the NP should treat or refer accordingly. If conditions are discovered that may warrant surgical intervention, such as the presence of leiomyomas, the NP should immediately refer the patient to the appropriate specialist.

Pregnancy is a chance occurrence with a cumulative pregnancy rate of 25% when female age is above 35, with this rate dropping the longer couples attempted pregnancy. 11 In order to maximize the chances of conception, couples can be taught about the fertile period, which generally includes the 5 days prior to ovulation as well as the day of ovulation. 12 Couples can engage in frequent sexual intercourse to ensure maximum coverage of the fertile period.

Male factors solely account for 20% of all cases of infertility and are considered contributory, along with female factors, in another 30% of cases. 13 It is therefore important to discuss any potential erectile or ejaculatory dysfunction and determine if the male partner previously fathered any children. 14 Patients should refrain from using barriers for sperm such as over-the-counter products that contain spermicides. The uterotubal junction is also a barrier for sperm anatomically, physiologically, and/or as a mucus barrier to sperm passage; therefore, postcoital behaviors that affect anatomical passage, such as voiding or douching the vagina immediately after intercourse, should be discouraged for infertile couples. 15 That is, consider eliminating activities or products to minimize barriers to sperm's linear progressive motility and the passage to the uterotubal junction for couple with infertility. 15

Surgical history . The female patient's history should also include any prior abdominal or pelvic surgery, such as dilation and curettage from an elective, spontaneous or therapeutic abortion, or fallopian tube surgery from a previous ectopic pregnancy. Patients need to be assessed for a history of any loop electrosurgical excision procedures, cryosurgery, cone biopsy, or laser vaporization subsequent to abnormal cervical cancer screening. Male patients need to be screened for previous genitourinary surgeries that may impact fertility, including vasectomy reversal, surgery for testicular cancer, or surgical repair of cryptorchidism. 13 If a condition that may warrant surgical intervention is revealed, the NP should immediately refer the patient to the appropriate specialist.

Past and present health status

Past and present health status should be assessed and documented in detail, highlighting any triggers or precipitating events that may contribute to infertility. It is well known that cancer and its treatments can impair male fertility. 13,14 Conditions such as cystic fibrosis and mumps are well known to cause male sterility and need to be ruled out. 13,16 Any type of surgical instrumentation near or in the reproductive tract can lead to scar tissue formation and adhesions, which can impact future fertility. 5,8,17 Women with some autoimmune disorders are at an increased risk of infertility unrelated to direct effects of antibodies on fertilization and implantation. 18

Mental and emotional health . Couples' emotional health is often overlooked in primary care settings and needs to be included in fertility assessments. Rates of sexual dissatisfaction, depression, anxiety, and marital discord are staggeringly high among couples faced with a diagnosis of infertility. 1,19,20 Particularly, high rates of anxiety and depression among infertile women represent a significant barrier to achieving pregnancy. 1 NPs can identify patients early who may require referrals to mental health professionals through the use of screening with the Hospital Anxiety and Depression Scale, the Beck Depression Inventory, or the Center for Epidemiologic Studies-Depression Scale. 1 Current research supports the notion that psychological interventions can double the pregnancy rate in infertile women who participated as compared with the control group. 1

Lifestyle associations . It is important to explore the couples' social history to identify possible risk factors for infertility and assist with preconception planning. Preconception planning should include the standard use of prenatal vitamins with folic acid for all women wanting to achieve pregnancy. Both partners should be encouraged to make healthy lifestyle choices that promote overall good health, including exploration of nutrition, and avoidance of any toxicities to the fetus. 1,21 Females should be encouraged to take a prenatal vitamin with folic acid (400 to 800 mcg daily) to limit the risk of neural tube defects. 21 Males should be aware that the use of hot tubs or saunas may elevate testicular temperatures and can lead to altered spermatogenesis. 14

Family history . A thorough family history may highlight a possible genetic predisposition to several conditions implicated in infertility. Genetic conditions known to affect fertility rates include: PCOS, Fragile X syndrome, premature ovarian insufficiency, endometriosis, and cystic fibrosis. 7,8,18,22 A referral to a genetic counselor can be considered for patients with positive family histories of genetic conditions. This is ideally done in anticipation of childbearing years to determine overall risk.

Medications and other substances . There are a vast number of prescription medications and illicit drugs involved in male and female infertility. These substances contribute to infertility through both primary and secondary pathways. (See Medications and other substances affecting fertility .) Many medications and substances affect male spermatogenesis, including marijuana, anabolic steroids, and nicotine. 13,14 Medications used in the treatment and maintenance of benign prostatic hypertrophy and lower urinary tract symptoms can severely affect male fertility, and include alpha-adrenergic blockers (such as tamsulosin or doxazosin) and 5-alpha reductase inhibitors (finasteride, dutasteride). These types of medications can cause retrograde ejaculation, impotence, diminished libido, and an overall decrease in ejaculate volume. 17 When appropriate, these medications should be discontinued or replaced with an alternate agent, but would likely require consultation with a urologist for collaborative management.

Medications commonly used to treat hypertension and congestive heart failure can have detrimental effects on male fertility; however, more research in this area is needed. For instance, propranolol can cause low libido through lower testosterone levels and erectile dysfunction. When appropriate, safer agents should be substituted, which may include amlodipine or hydrochlorothiazide that cause fewer male fertility related adverse reactions than beta-blockers, angiotensin-converting enzyme inhibitors, or other types of calcium channel blockers. 17

The most widely recognized and extensively researched medications known to significantly impact both male and female fertility belong to the psychotherapeutic, antipsychotic, and tricyclic antidepressant drug classes. Although antidepressant treatment can effectively improve the manifestation of major depression, it may induce or exacerbate symptoms of sexual dysfunction. 23 Symptoms of sexual dysfunction can significantly affect quality of life and may include decreased libido, anorgasmia, delayed ejaculation, erectile dysfunction, and dyspareunia. These adverse reactions often lead to nonadherence and subsequent relapses in depressive symptoms. Serotonergic antidepressants are frequently associated with the onset of sexual dysfunction, affecting more than 70% of sexually active patients. 23 The risks and benefits of these medications should be addressed on an individual basis prior to discontinuation.

Since alcohol is the most widely used recreational substance, it is crucial to understand its implications on female and male fertility. Recent studies suggest that heavy alcohol consumption, defined as more than eight standard drinks per week for women, may diminish ovarian reserves and fecundability. 13,24,25 Heavy alcohol consumption in males, defined as more than 15 standard drinks per week, can also negatively affect fertility by reducing the quality of semen, with occasional cases of azoospermia. 24,25 Further, it has been well documented that alcohol use disorder and acute alcohol intoxication are associated with sexual dysfunction, including low libido and erectile/ejaculatory dysfunction. 25 The impact of light to moderate alcohol consumption on male and female fertility is not fully understood, but does not appear to be statistically significant. 13,25 Alcohol use disorder is often not discussed within the primary care setting in relation to potential causes of infertility and should be explored with all patients.

In addition, female fertility can be adversely affected by opiate use, regardless of whether these medications are being used recreationally or for chronic pain disorders. Although research is ongoing, it appears that opiate use can negatively affect the hypothalamic-pituitary-ovarian axis system. 26 Therefore, a comprehensive medication history is important and counseling regarding recreational use of opioids should be addressed at the preconception stage when possible.

Over-the-counter medications are sold to millions of patients all over the world every day. Aspirin, ibuprofen, and naproxen are nonsteroidal anti-inflammatory drugs (NSAIDs) that prevent pain by inhibiting the enzymes involved in prostaglandin synthesis. 27 Prostaglandins are essential for female reproduction; preovulatory levels of prostaglandins increase to facilitate ovulation and implantation. 27 The inhibition of prostaglandin synthesis can vary by the type of NSAID, with aspirin being the least likely to cause issues and naproxen the most likely. 27 The use of daily low-dose aspirin in seemingly healthy women is not recommended when trying to conceive. Only women diagnosed with antiphospholipid antibody syndrome may benefit from daily low-dose aspirin in addition to low-molecular-weight heparin and should be managed by an obstetrician. 28

Physical exam

The specific causes of couples' fertility issues are unlikely to be revealed on physical exam. However, a few common causes of infertility are detectable on physical exam, particularly those related to PCOS or endometriosis. Female patients with elevated androgen levels often have a high body mass index (BMI), excessive hair growth on the face, acne, and alopecia, all of which are strong indicators of PCOS. It is important to note that older women with PCOS may have lower rates of hyperandrogenic symptoms and may have regained their regular menstrual cycles. 7 In addition, high and low BMIs can impact overall fertility and should be documented.

The focused female physical exam should include palpation of the thyroid gland for size and the presence of any nodules. A pelvic exam should be completed to assess patency of the cervical os; the presence of cervical polyps; potential deformities of the cervix or vagina; vaginal discharge or lesions suggesting the presence of an active infection; cervical motion tenderness; and any adnexal masses or tenderness. 18 The general size, shape, and mobility of the uterus should be assessed as nodularity and thickened pelvic anatomy, particularly of the uterosacral ligaments, vagina, rectovaginal space, pouch of Douglas, and adnexa, are possible findings in endometriosis. 8

Physical exam of the male partner is generally not required, unless risk factors have been identified in the health history, which would then prompt a focused physical exam that is beyond the scope of this article.

Lab investigations

Female . Lab investigations allow the detection of hormonal imbalances. (See Normal hormone-based lab values .) Initial investigations for female patients should include measurement of follicle-stimulating hormone (FSH), luteinizing hormone (LH), and estradiol levels during day 2 to 4 of the menstrual cycle. Ovulation can be confirmed by measuring progesterone levels on day 21 of the menstrual cycle. Women who perform basal body temperature monitoring, and know their day of ovulation, can have progesterone levels measured 7 days postovulation. All other lab investigations can be done at any time in the menstrual cycle and should include the following: thyroid stimulating hormone (TSH); prolactin levels; serology for hepatitis B, hepatitis C, HIV, and syphilis; rubella and varicella titers; and urine or cervical swab for chlamydia and gonorrhea. 9,29

Additional lab tests should be considered based on patients' risk factors and physical exam results. These tests may include a complete blood cell count, comprehensive metabolic panel, hemoglobin A1C, lipid panel, and an antimüllerian hormone (AMH) level. AMH is expressed by preantral and early antral follicles and reflects the remaining primordial follicle pool or egg reserve. It is the best biochemical marker of ovarian function, even when compared with FSH, which has more cycle-to-cycle variability. 30 Results of AMH tests are age-specific because levels normally decline with age, along with reproductive health and capability. Abnormal serum hormone results should prompt an immediate referral to a fertility specialist for further evaluation and treatment.

If PCOS is suspected, testosterone levels should also be measured. A pelvic ultrasound can aid in assessing for multiple follicles/cysts, which are typically evident in patients with PCOS, and can also detect other uterine abnormalities. It is important to note that pelvic ultrasound is not solely required to diagnose PCOS, as per the Rotterdam criteria. 7

Male . Despite being a poor predictor of overall male fertility, a semen analysis (SA) remains the cornerstone test when assessing male fertility. As outlined by the WHO, the most recently updated normal reference range for semen analysis is: 32

• volume ≥1.5 mL
• sperm concentration ≥15 x 10 6 /mL
• total sperm number ≥39 x 10 6
• total motility ≥40%
• vitality (% live) ≥58%
• sperm morphology (% normal) ≥4%.

In order to achieve the most accurate results, the specimen should be collected after a period of abstinence (2 to 7 days), kept warm, and appropriately transported to a testing facility within 60 minutes of ejaculation. 32,33 If an abnormal result is found, a repeat SA should be done within 3 months. Any abnormal SA indicators should prompt immediate referral to a urologist and a fertility specialist. In addition to SA, male partners should also be tested for serology of hepatitis B, hepatitis C, HIV, and syphilis.

Initial pharmacotherapy options

In North America, wait times for fertility specialists are highly variable due to the number of fertility clinics available, the number of fertility specialists employed per clinic, and the overall size of the population. For patients exhibiting an anovulatory clinical picture, ovulation induction medications can be safely and effectively initiated in primary care settings. Currently, clomiphene is the only oral agent approved for ovulation induction in North America; however, letrozole is fast becoming a preferred agent, however, it is being used off label. Initial manufacturer concerns expressed for the use of letrozole included concerns over fetal toxicity and malformation; however, further studies have demonstrated safety and the Canadian Fertility and Andrology Society (CFAS) supports the use of letrozole for the treatment of ovulatory dysfunction and unexplained infertility. 34

Clomiphene . Clomiphene citrate (Clomid or Serophene) is a well-known and trusted medication used in the management of ovulatory disorders, including PCOS. 34 Ovulation induction agents can increase the risk of multiple pregnancy, ovarian hyperstimulation syndrome, and thrombosis, and may increase the risk of ovarian cancer in women who remain nulliparous; therefore, patients should be counseled about these risks. 31 Patients who present with PCOS and amenorrhea will often require synthetic progesterone, such as medroxyprogesterone to induce menses before starting an ovulation induction cycle. After administration of the synthetic progesterone and ovulation induction agent, ovulation needs to be confirmed by a serum progesterone lab on menstrual cycle day 21, or 7 days before expected menses. 31 If ovulation has not occurred (progesterone <5 ng/mL), the clomiphene dose can be increased. 34-36

Letrozole . Research has shown that letrozole (Femara) is a superior ovulation induction agent when compared with clomiphene and is often used as first-line treatment. 34,35 Both letrozole and clomiphene have the ability to affect the recruitment of multiple follicles, which can increase the risk of a multiple gestation pregnancy. As such, starting to middle range dosages are recommended in primary care settings. 35,36 For patients with higher-than-average risk of pregnancy complications, including those with previous pregnancy-induced preeclampsia, an increased risk of multiple gestation pregnancy may not be ideal. Therefore, ovulation induction may not be advisable within the primary care setting for these patients. It is important to note that dosing within the higher range of either induction medication should always be accompanied by ultrasound monitoring. Ultrasound can help ensure that two or fewer follicles have been recruited. Unfortunately, ultrasound technology is often not available within primary care settings; therefore, patients requiring higher doses of ovulation induction medications should await specialist referral.

Metformin . Metformin has historically been used to treat women with PCOS by increasing levels of sex hormone binding globulin, which may then reduce levels of insulin, free testosterone, and LH. 36 However, metformin is no longer supported in the literature as monotherapy for ovulation induction, unless the patient has diabetes or displays signs of metabolic syndrome. 36 When metformin was used in combination with clomiphene, only slight improvements in ovulation rates were observed, falling just barely within the statistically significant threshold. 36 In addition, metformin is known to cause a variety of untoward adverse reactions, including abdominal bloating, nausea, cramps, and diarrhea, and should always be started at the lowest possible dose, with slow titration upward.

When treating patients in primary care who meet criteria for infertility, obtain all required lab results including SA while initiating a referral to a fertility specialist. Due to long wait times, it is important to start the referral process early, as it can always be cancelled if pregnancy is achieved while being managed in primary care. Referral to other specialists should be initiated when positive subjective and objective findings are discovered, as previously mentioned.

Although infertility rates are steadily increasing, timely diagnosis and management of fertility issues within primary care settings is lacking. A thorough health history is the first critical step in diagnosing infertility, allowing NPs to identify possible etiologies or comorbidities and guiding further investigations and treatment options. Emotional and psychological hurdles can be identified early through screening tools, as they represent a significant barrier to achieving pregnancy.

NPs provide continuity of care within the primary care model allowing ongoing patient communication and timely follow-up, which were identified as barriers in the literature when accessing specialist care. Assessments and treatments within the primary care setting should not negate a referral to a fertility specialist, who can offer more comprehensive consultation and treatment options. Primary care NPs are in an ideal position to identify clients at risk and initiate early investigations and treatments with the goal of optimizing patients' fertility outcomes and quality of life.

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endometriosis; fertile period; fertility specialist; infertility; ovulation; polycystic ovary syndrome (PCOS); pregnancy; semen analysis

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• Research article
• Open access
• Published: 04 September 2019

An investigation of the effects of infertility on Women’s quality of life: a case-control study

• Katayoun Bakhtiyar 1 ,
• Ramin Beiranvand 1 ,
• Arash Ardalan 2 ,
• Farahnaz Changaee 3 ,
• Mohammad Almasian 4 ,
• Afsaneh Badrizadeh 5 ,
• Fatemeh Bastami 6 &
• Farzad Ebrahimzadeh   ORCID: orcid.org/0000-0003-3897-8242 7  

BMC Women's Health volume  19 , Article number:  114 ( 2019 ) Cite this article

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Human instinctively desire to have offspring. Infertility can cause painful emotional experiences throughout the life mainly known as quality of life impairment. This study aimed to investigate the impact of infertility on a woman’s quality of life.

A number of 180 infertile and 540 fertile women participated in this matched case-control study. The cases were selected through a combination of multistage stratified and cluster sampling methods. For each infertile woman three fertile women were randomly selected. The data gathering instrument consisted of demographic variables and the WHOQOL-BREF questionnaire. Data collection was conducted through interview with participants. The multivariate marginal model and SPSS software 21 were used for data analyses with a significance level of 0.05.

The results of the multivariate modeling show infertility can potentially affect various aspects of women’s quality of life such as physical health ( p  <  0.001), mental health (p <  0.001), social health (p <  0.001) and the total score of quality of life (p <  0.001) significantly.

An infertile woman practice a relatively lower scores in QOL sub-scales of mental, physical and environmental health; while they experience a higher social health score than a fertile woman.

Peer Review reports

Reproduction is known as an essential human desire so that infertility may cause a great deal of psychosocial impairment [ 1 ]. According to WHO, infertility is defined as a disease of the reproductive system in which pregnancy does not occur after 1 year of continued intercourse [ 2 ]. Infertility is considered as a global concern which affects many aspects of life in both genders [ 3 ]. The rates even go up to 186 million people around the world [ 4 ]. About 10 percents of couples are currently suffering from infertility in Iran [ 5 ].

Infertility may work as a painful emotional experience [ 6 , 7 ]. It can cause a lot of psychological issues including stress, anxiety, depression, diminished self-esteem, declined sexual satisfaction, and reduced quality of life [ 8 , 9 , 10 ]. The resulted psychosocial issues affect the female gender adversely more than her spouse [ 4 ], especially in societies where there are prejudices against women [ 9 , 10 , 11 ]. As such, an infertile woman may show a relatively high level of frustration and anger which affect her relationship with family, friends and even her spouse. Likewise, infertile women are more likely to develop mental illnesses, marital dissatisfaction, and impaired quality of life compared to the individuals of fertile group [ 9 , 11 , 12 ].

According to WHO, quality of life is a concept used to describe development, growth, and well-being which reflects individuals’ perceptions of their position in the community as well as their goals, expectations, standards, and priorities [ 13 , 14 ]. Attitudes toward women’s infertility are often influenced by ethnic and cultural groups [ 15 ]. In the eastern societies, the community mainly expects women to play a role as a mother. This will cause many psychosocial concerns if pregnancy does not occur for any reason [ 16 ]. Therefore, more studies are required among eastern societies to reveal the impact of social, cultural and individual factors on an infertile woman’s quality of life [ 17 ].

Studying the quality of life among infertile women alarms the health authorities and subsequently let them spend a great deal of effort to help the infertile couples in one way or another [ 5 ]. There are already a few studies on the quality of life among infertile women in Iran; although those are largely descriptive and just follow a cross-sectional method which lacks a comparison group to analyze the impact of infertility on different aspects of life [ 5 , 18 , 19 ]. Most of these studies have been conducted using SF-36, a quality of life assessment questionnaire which evaluates the physical aspects of life quality [ 18 ]. There are multiple ethnical groups living in the country which requires researchers to run further studies in different regions as well. This study basically aimed to investigate the effect of infertility on a woman’s quality of life among population of Lorestan, Iran.

Study population and sampling methods

They were selected by means of a combination of multistage stratified and cluster sampling methods from population of Lorestan, Iran. We came up with a total of nine clusters. Each cluster contributes to a town in Lorestan, Iran. Five clusters (towns) were randomly selected out of them by sampling with varying probabilities; so that the more densely populated town, the higher chance of being selected.

There were two strata in each city for infertile women: The first stratum consisted of women who were being cared in a gynecology hospital or an infertility clinic, for which a non-probability consecutive sampling method was used. That means the information of an infertile woman was collected consecutively until the number of cases and their information were completed. The second stratum consisted of women who have been visited in a gynecology office. A total of 2--4 offices were selected in each geographical area using systematic random sampling method. We utilized a non-probability consecutive sampling method in each gynecologist office.

The control group consisted of fertile women who were matched for age, educational levels, and the duration of marriage with cases. For each infertile woman, three fertile women who met the matching criteria and lived in the same area were selected. In order to find the control subjects the investigators went to the same city block the infertile women were selected from. Then for each infertile woman they previously selected for the purpose of the study, they matched three fertile ones through a consecutive non-probability method. Data were gathered by trained interviewers.

The inclusion criteria for both groups comprised of giving consent for participating in the study, residing in the Lorestan province, as well as having monogamy with husband, lack of a psychological problems or an experience of stressful event related to the issue of infertility during the past 3 months, and no current use of alcohol or drugs.

Infertility was defined as not being able to achieve pregnancy after 1 year of having regular, unprotected intercourse . The inclusion criteria for the control group included no development of pregnancy during the course of study and a minimum gap of at least 4 months between the last given birth and the beginning of the study. A number of 120 fertile women were estimated to be suitable for the case group, however, considering the design effect; we had to select 180 individuals in the end. Since we matched three control subjects for each case, a number of 540 women were selected for the control group. The sample size eventually came up to 720 individuals.

The questionnaire consisted of two parts. First part of the questionnaire included demographic and background information of the participants such as age, occupational status, educational levels of the couple, duration of marriage, residential property ownership, address of residence, income, fertility and the status of spouse’s employment. The second part of the questionnaire consisted of the WHOQOL-BREF general measurement of life quality [ 20 ]. The internal consistency coefficient (Cronbach’s alpha) was evaluated and reported as satisfactory for all the sub-scales of the questionnaire, except for the social relation subscales (physical health dimension: α = 0.75, mental health dimension = 0.74, social health dimension = 0.70, and environmental health dimension = 0.75). We did not try to use SF-36 quality of life questionnaire for the purpose of our study because it only measures health-related quality of life but social and environmental health components of life quality [ 20 ].

Statistical analysis

Frequency distribution tables, means, standard deviations and bar charts were used to describe the variables. Since individual-to-individual method of matching was used and the data was of a matched quadruplet type, the marginal model, and more specifically, the generalized estimating equations (GEE) method in parameter estimation was used for both univariate and multivariate data modeling. GEE is basically used to estimate the parameters of a generalized linear model with a possible unknown correlation between outcomes [ 21 ].

At first, the demographic and background variables between the fertile and infertile groups were compared through marginal model/GEE. In these GEE methods, a logit link function, along with exchangeable structure for covariance matrix was used. In each separate GEE, “Infertility status” was considered as dependent variable and a single demographic predictor was used as independent variable. We employed another marginal model/GEE to determine the relationship between quality of life scores and demographic variables. In these GEEs, an identity link function was deployed, and in each separate GEE, the quality of life score was considered as dependent variable while the only predictor was a single demographic variable.

The study was controlled for the effect of confounding factors. Since we aimed to investigate the impact of infertility on women quality of life, variables with a P -value of less than 0.25 in the aforementioned univariate approach were selected and entered into the multivariate model [ 22 , 23 ]. Those demographic and background variables which were significantly associated with infertility and quality of life were considered as confounding variables.

For multivariate modeling, we utilized the identity link function along with an exchangeable structure for working correlation matrix in our GEE model. The quality of life scores and infertility status were considered as the dependent and independent variables, respectively. Confounding variables such as residential property ownership status, history of underlying diseases and consanguineous marriage were selected for the model. SPSS version 21 was used for data analyses with a significance level of 0.05.

We selected 180 infertile women and 540 fertile women from different cities of Lorestan, Iran for the purpose of our study. The mean age of cases and controls came up to 33.19 ± 5.9 and 33.11 ± 4.9, respectively (Table  1 ). Primary infertility was recognized as the most common reason for inability to reproduce (91.1%). The most frequent methods of treatment were IVF (45.6%) and medical therapy (43.8%). A proportion of 70.6% of cases and 69.4% of the controls were a housewife (Table 1 ). The prevalence of underlying diseases was higher among infertile women (20%) than the fertile ones (10.4%) ( P  = 0.023). Table  2 compares the demographic variables of cases to each dimension of women’s quality of life in Lorestan, Iran.

Among infertile women, 52% of those who obtained a score of ≥70 on social dimension of quality of life were illiterate or had an educational level as of primary school. A proportion of 93% of infertile women were a housewife. Among infertile women, 67% of husbands were illiterate and 22% were unemployed. There was a significant difference between the mean scores of mental health in consanguineous and non-consanguineous married women ( P  = 0.01). The mean scores of both mental health and social health dimensions showed significant relationship with cost of treatment for infertility ( P  = 0.023) and ( P  = 0.025), respectively. There were also significant differences between the mean scores of mental, social, as well as environmental health dimensions and the method of treatment for infertility (P = < 0.001), ( P  = 0.005) and ( P  = 0.019), respectively (Table 2 ).

The results of the study showed that there is a significant statistical relationship between some of the independent variables and physical dimension of quality of life. For example; people aged 35 years or younger, those who had married for less than 10 years, women with an university educational level, individuals with no history of underlying diseases, as well as fertile and employed women had a higher score of physical dimension of life quality compared to the individuals of other categories ( p  <  0.05).

In addition, women younger than 35 years of age, those with an university educational level, individuals who were employed, people with no history of underlying diseases, women with an educated spouse, those with low costs of treatment for infertility, women who owned a house, as well as women with less than 10 years of marital life, those with no family marriage, fertile women, and infertile women under medical therapy only all had a higher average score of mental health dimension of quality of life compared to the individuals of the other subgroups ( p  < 0.05).

Likewise, people who owned a house, those with an university educational level, women whose spouses had university education, employed women and infertile women under medical therapy only experienced a higher environmental health dimension of quality of life compared to the people of other categories (p < 0.05 for all). In addition, women with a marital relationship of over 10 years, undereducated or early school-age women, those whose spouses were not educated or just had elementary education, housewives, women living in permissive or paternal homes, infertile women, women with underlying illnesses, infertile women who suffered from a treatment cost of more than $ 1500 per month, and infertile women who received IVF treatment had a higher social dimension of quality of life score compared to the women of other categories ( P  < 0.05). Likewise, women with a marital relationship of more than 10 years, women whose spouses were undereducated or had elementary education, women with underlying diseases, and infertile women had a higher overall score of quality of life compared to the other categories (P < 0.05) (Table 2 , Fig.  1 ).

The mean scores of different dimensions of life quality among fertile and infertile women

Table  3 demonstrates the multivariate modeling for the impact of infertility on various aspects of women’s quality of life using the GEE method. Based on GEE 1 and 2 models which were analyzed both with and without adjustment for confounding variables, the effect of infertility on physical health dimension of life quality was significant ( P  < 0.001). After adjusting for confounding variables, the mean score of the physical dimension of quality of life among infertile women was 3.6 units lower than that of the fertile ones. Likewise, the effect of infertility on mental health dimension of quality of life was significant ( P  < 0.001). After controlling for confounding variables, the mean score of mental health dimension of life quality among infertile women was about 16.0 units less than that of the fertile ones.

According to both GEE 1 and 2 models, the effects of infertility on the environmental dimension of life quality was insignificant ( P  = 0.477) and ( P  = 0.460), respectively. However, the impact of infertility on the social dimension of quality of life was found to be statistically significant ( P  < 0.001). After adjusting for confounding variables, the mean score of the social dimension of life quality among infertile women was 20.0 units more than that of the fertile ones. Finally, the effect of infertility on the total score of life quality was statistically significant (P < 0.001). After controlling for confounding variables, the mean score of life quality was 21.6 units more than that of fertile ones (Table 3 ).

The results of the current study showed an infertile woman experiences a relatively low quality of life by several dimensions in Iran. A few modalities of life quality such as physical, mental, and environmental health subscales scored lower among infertile Iranian woman than that of the fertile ones. Our research supports the findings of previous studies on this cause and effect relationship [ 24 , 25 , 26 ]. The social health dimension of life quality among infertile women however attained a higher score than that of the control group. This might have caused a large overall score of quality of life among infertile women.

According to the studies, Iranian women generally experience only an average overall health-related quality of life [ 27 , 28 , 29 ]. Nejat et al. was able to show that the mean score of Iranian women’s quality of life levels lower than that of other nation’s population of women in almost all sub-scales. The difference looked remarkable especially when physical and mental components of health came into the account [ 30 ]. Likewise, a study by Mirghafourvand showed a lower overall quality of life score among Iranian women than that of the Brazilian ones [ 27 ]. There are however studies that oppose the above findings which believe health-related quality of life among Iranian women scores higher than that of Turkish and Canadian ones [ 31 , 32 ]. The difference in the results of the Iranian’s studies might be due to the diversity in socio-economic contexts, characteristics of the participants, sampling methods or a combination of all [ 27 ]. Likewise, use of different scales in these studies can cause difficulties comparing findings [ 33 ].

The findings of social health dimension in our study caused a significant difference in the overall score of the quality of life between groups. We found infertile women to have a higher social health score compared to the control group. This contradicts the results of previous studies [ 11 , 19 ]; and might be due to the achieved score of ≥70 among 16% of infertile women. Likewise, the level of education and occupational status of women and their spouses did not match the distribution of education and occupation of the population. In this subgroup, a woman with a relatively low educational level played the role of a housewife, while she did not own a house; she enjoyed a greater social health. This might be due to the fact that an infertile woman receives more social support due to different reasons such as personal or familial relationships. In fact, an excellent social support can improve the physical and mental health; thus, it provides a relatively high social well-being and quality of life [ 34 , 35 ].

The educational levels of couples and the occupational status of women predicted the quality of life in our study. According to the results, the educational status of a couple, women’s employment circumstances, and the status of ownership of a residential property affected a few dimensions of quality of life such as physical, mental, environmental and social health. As such, a couple with high educational level, an employed woman, and a homeowner enjoyed a better physical, mental, and environmental health. The results of few studies also indicate high educational level is associated with a high quality of life [ 36 , 37 , 38 , 39 ]. Therefore, low level of education can be linked to an increased probability of poverty, as well as a relatively low level of health, undesirable health behaviors, and an increased risk of mortality [ 27 ].

The results of the present study showed age range can affect the physical and mental health. Physical and mental health of the women younger than 35 was found to be significantly better than that of the women of older age groups. This is because a young woman has fewer physical and medical issues, more energy and ability to work, and higher self-esteem than an older one. A few studies have demonstrated a woman younger than 30 years of age experiences a better quality of life than an older woman [ 25 , 33 , 40 ]. A study of mental, environmental, and social health of women have brought up supportive results [ 41 ].

Duration of marriage can also affect the various dimensions of quality of life. Based on the findings, a woman experienced a relatively high physical, mental, and environmental quality of life within the first 10 years of marriage. Rostami et al. reported a woman in her first or second decade of marriage, while she is older; she is less satisfied with her marriage compared to a younger woman. This might be due to a negative assessment of physical appearance which adversely affects marital satisfaction. Therefore, it reduces a woman’s quality of life [ 34 , 42 , 43 ].

According to the present studies, having underlying illnesses can affect the various dimensions of life quality. As such, a woman with no underlying illnesses has a better physical, mental, and environmental health scores compared to an ill woman [ 24 , 44 , 45 ]. Proulex et al. was able to show that overall health had a significant relationship with almost all dimensions of quality of life [ 46 ]. Likewise, Maroufzadeh et al. showed infertile couples are more likely to have underlying illnesses such as anxiety (49.6%) and depression (33%) [ 12 ]. In fact, chronic diseases such as depression, diabetes, different types of cancer, etc., adversely affect those aspects of a woman’s quality of life which are related to overall health; thus, managing the above conditions may lead to a relatively better quality of life [ 27 ].

Our study has many strong points and we are perfectly confident in the validity of the results. The fact that it was a case–control study within cohort of Lorestan, Iran, enabled us to minimize the risk of selection bias. In addition, the design of the study allowed us to examine the link between infertility and quality of life from all socioeconomic classes. We were also able to examine a large number of variables as likely predictors of quality of life following failure to reproduction. Nonetheless, our study has a limitation as well. The fact that it is a non-longitudinal case-control study; we have had difficulty controlling it for some confounding variables. Therefore, prospective longitudinal studies are recommended for future studies on this link.

Mental, physical, and environmental health components of quality of life may be adversely affected among infertile women, although the social health subscale may not. Other modalities such as educational attainment, employment, house ownership, and major illnesses also influence the quality of life. Given the fact that the quality of life among women of reproductive age affects the long-term health of each family member, health policy makers, family counselors, and psychologists are required to pay special attention to physical, mental, and environmental health dimensions of a woman’s life which adversely affects her quality of life.

Availability of data and materials

The datasets used and/or analyzed during the current study are provided by the corresponding author on a reasonable request.

Abbreviations

Generalized Estimating Equations

In Vitro Fertilisation

36-Item Short Form Survey

World Health Organization

WHO Quality of Life-BREF

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Acknowledgements

The researchers would like to express their gratitude to the participants and the staff of the health centers of Lorestan, Iran.

This study was funded by Lorestan University of Medical Sciences as a research project under registration number 1285.

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FE and KB have made substantial contributions to the conception and design, writing and revision of the manuscript. MA and AB participated in the study design and data acquisition. RB and AA were involved in drafting and revising the manuscript, which was critically important for the intellectual content. FB and F Ch provided the final draft of the manuscript. All authors read and approved the final manuscript.

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Bakhtiyar, K., Beiranvand, R., Ardalan, A. et al. An investigation of the effects of infertility on Women’s quality of life: a case-control study. BMC Women's Health 19 , 114 (2019). https://doi.org/10.1186/s12905-019-0805-3

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Objectives To assess the prevalence, risk factors and psychological impact of infertility among females. This review summarises the available evidence, effect estimates and strength of statistical associations between infertility and its risk factors.

Study design Systematic review and meta-analysis.

Data sources MEDLINE, CINAHL and ScienceDirect were searched through 23 January 2022.

Eligibility criteria The inclusion criteria involved studies that reported the psychological impact of infertility among women. We included cross-sectional, case–control and cohort designs, published in the English language, conducted in the community, and performed at health institution levels on prevalence, risk factors and psychological impact of infertility in women.

Data extraction and synthesis Two reviewers independently extracted and assess the quality of data using the Joanna Briggs Institute Meta-Analysis. The outcomes were assessed with random-effects model and reported as the OR with 95% CI using the Review Manager software.

Results Thirty-two studies with low risk of bias involving 124 556 women were included. The findings indicated the overall pooled prevalence to be 46.25% and 51.5% for infertility and primary infertility, respectively. Smoking was significantly related to infertility, with the OR of 1.85 (95% CI 1.08 to 3.14) times higher than females who do not smoke. There was a statistical significance between infertility and psychological distress among females, with the OR of 1.63 (95% CI 1.24 to 2.13). A statistical significance was noted between depression and infertility among females, with the OR of 1.40 (95% CI 1.11 to 1.75) compared with those fertile.

Conclusions The study results highlight an essential and increasing mental disorder among females associated with infertility and may be overlooked. Acknowledging the problem and providing positive, supportive measures to females with infertility ensure more positive outcomes during the therapeutic process. This review is limited by the differences in definitions, diagnostic cut points, study designs and source populations.

PROSPERO registration number CRD42021226414.

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All data relevant to the study are included in the article or uploaded as online supplemental information.

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https://doi.org/10.1136/bmjopen-2021-057132

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Strengths and limitations of this study

Meta-analysis of studies according to Preferred Reporting Items for Systematic Reviews and Meta-Analyses guidelines.

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Only studies with a low risk of bias were included in the analyses.

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The search was restricted to English-language articles only.

Introduction

Infertility is defined by the World Health Organization (WHO) as the inability to conceive after 1 year (or longer) of unprotected intercourse. 1 It is classified as primary or secondary. Primary infertility is denoted for those women who have not conceived previously. 2 In secondary infertility, there is at least one conception, but it fails to repeat. 2 In 2002, the WHO estimated that infertility affects approximately 80 million people in all parts of the world. 3 It affects 10%–15% of couples in their lifetime. 4 5 The prevalence of infertility is concerned, it is high (up to 21.9%): primary infertility at 3.5% and secondary infertility at 18.4%. 6 It is generally accepted that infertility rates are not estimated correctly. The reasons could hinder the measurement of the prevalence, imperfect measurement methods, and unknown kinds of infertility resulting from cultural biases. 7

Infertility is a multidimensional stressor requiring several kinds of emotional adjustments. 4 It is associated with dysfunction in sexual relationships, anxiety, depression, difficulties in marital life and identity problems. 8 The impact of infertility may be long-lasting, even beyond the initial period of childlessness has passed. 9 10 In the general population, major depression is two to three times as common among women as among men. 11 In the United States, the 12-month prevalence of any mood disorder is 14.1% in females and 8.5% in males, whereas any anxiety disorder is 22.6% in females and 11.8% in males. 12 Thus, depression is one of the most common negative emotions associated with infertility, 13 14 which the local social and cultural context may influence.

Determining the psychological impact of infertility among women worldwide provides a better assessment than discrete primary studies. Identifying this impact helps gain a clear understanding of the issue and serves as a basis for an appropriate preventive strategy. In addition, it applies to primary prevention that could potentially prevent conditions affecting adverse psychological well-being. We aimed to perform a systematic review and meta-analysis on infertility among females with regard to its pooled prevalence, risk factors and psychological impact in observational studies conducted worldwide. This review will summarise the available evidence, effect estimates, and strength of statistical associations between infertility and its risk factors.

Materials and methods

Study design and search strategy.

A systematic review and meta-analysis of studies were conducted to assess the psychological impact of infertility among women. The study followed the Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) guidelines. 15

A systematic search was performed in MEDLINE (PubMed), CINAHL (EBSCOhost) and ScienceDirect. The search was done using text words such as “infertility,” “prevalence,” “risk factor,” “psychology,” “mental,” “quality of life,” “anxiety, “depression” and “stress.” The search terms were flexible and tailored to various electronic databases ( online supplemental file ). All studies published from the inception of these databases until 23 January 2022 were retrieved to assess their eligibility for inclusion in this study. The search was restricted to full-text and English-language articles. To find additional potentially eligible studies, reference lists of included citations were cross-checked.

Supplemental material

Eligibility criteria.

The inclusion criteria involved studies that reported the psychological impact of infertility among women. Studies with cross-sectional, case-control and cohort designs, published in the English language, conducted in the community, and performed at health institution levels were included. Case series/reports, conference papers, proceedings, articles available only in an abstract form, editorial reviews, letters of communication, commentaries, systematic reviews and qualitative studies were excluded.

Study selection and screening

All records identified by our search strategy were exported to the EndNote software. Duplicate articles were removed. Two independent reviewers screened the titles and abstracts of the identified articles. The full text of eligible studies was obtained and read thoroughly to assess their suitability. A consensus discussion was held in the event of a conflict between the two reviewers, and a third reviewer was consulted. The search method is presented in the PRISMA flow chart showing the studies that were included and excluded with reasons for exclusion ( figure 1 ).

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Flow diagram showing the included studies for systemic review and meta-analysis on the prevalence, risk factors and psychological impact of infertility among women.

Quality assessment and bias

A critical appraisal was undertaken to assess data quality using the Joanna Briggs Institute Meta-Analysis for cross-sectional, case–control and cohort studies. 16 Two reviewers performed bias assessments independently. The risk of bias was considered low when more than 70% of the answers were ‘yes,’ moderate when 50%–69% of the answers were ‘yes,’ and high when up to 49% of the answers were ‘yes’. Studies that showed a high and moderate risk of bias were excluded from the review.

Data extraction process

Two reviewers independently extracted data using the NVivo software (V.12). The process included the first author, publication year, study location, study design and setting, study population, sample size, psychological impact, infertility definition and data in calculating effect estimates for psychological impact.

Result synthesis and statistical analysis

The outcomes were reported as the odds ratio (OR) and 95% confidence interval (CI). The analysis was performed using the Review Manager software (V.5.4; Nordic Cochrane Centre, Copenhagen, Denmark). A random-effects model was employed to pool data. The I 2 statistic was used to assess heterogeneity, with a guide as outlined: 0%–40% might not be important; 30%–60% may represent moderate heterogeneity; 50%–90% may represent substantial heterogeneity, and 75%–100% may represent considerable heterogeneity. 17 A subgroup analysis was performed based on countries (developed and developing) and comorbidity (presence and absence of comorbidity) if an adequate number of studies were available. Funnel plots were used to assess publication bias if indicated.

Characteristics of included studies

A total of 3169 articles were retrieved through an electronic search using different search terms. Forty-eight duplicate records were removed. Of the 3168 articles screened for eligibility, 3065 were excluded by their title and abstract evaluation. The full text of 103 articles was searched. Subsequently, 62 articles were excluded: 46 did not present the main outcomes, 6 were performed in different populations, 5 were review articles, 4 had only abstracts and 1 was published in a non-English language ( figure 1 ). A total of 41 studies underwent quality assessment, of which nine had moderate and high risk of bias.

Finally, 32 studies with low risk of bias were explored in the review: 22 were cross-sectional, 8 were case–control and 2 were cohort studies. Different countries were involved. Five studies were conducted in Iran, 18–22 four in Turkey, 23–26 three in Italy, 27–29 three in America, 30–32 three in Sweden, 33–35 two in India, 36 37 two in the Netherlands, 38 39 one in Finland, 9 two in Africa, 40 41 one in Saudi Arabia, 42 one in Japan, 43 two in China 44 45 one in Pakistan 46 and two in Greece. 47 48 The smallest sample size was 87, 47 and the largest was 98 320. 39 Overall, this study included 124 556 women ( table 1 ).

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Summary of research articles included in this systemic review and meta-analysis of infertility (n=32)

Of the included studies, 20 were conducted in a hospital-based setting, 4 9 23 33 37 in a community-based setting and 2 18 46 in both hospital-based and community-based settings. A slight difference in the prevalence of infertility was observed in the review. A lower prevalence (10.4%) of infertility 9 was observed in a community-based setting, and a higher prevalence (79.3%) 44 47 was noted in a hospital-based setting. The overall pooled prevalence of infertility was 46.25% (95% CI 37.73% to 54.77%; I 2 =100%). Twenty-four articles were included for the estimation of pooled prevalence of infertility among females ( figure 2 ). The funnel plot was asymmetry with smaller studies and lower prevalence being missing on the left side. The results of the assessment of bias based on the funnel plot asymmetry were not shown but available on request. Out of this, nine were used for the estimation of pooled prevalence of primary infertility.

Forest plot depicting the prevalence of infertility. IV, inverse variance.

The overall pooled prevalence of primary infertility was 51.5% (95% CI 32.74% to 70.26%; I 2 =100%) ( figure 1 ). The lowest prevalence (18%) of primary infertility was reported in a hospital-based study, 27 and the highest prevalence (91.1%) was observed in both community- and hospital-based studies conducted in Iran 18 ( figure 2 ).

Risk factors of infertility

In this study, risk factors such as age, body mass index (BMI), smoking and family income were evaluated for their association with infertility. Five studies were included to assess age older than 35 years as a risk factor for infertility regarding the association between age and infertility among females. 9 18 32 37 The pooled meta-regression analysis showed no significant difference in the occurrence of infertility in females aged 35 years or older compared with those younger than 35 years, with the odds being 1.10 (95% CI 0.83 to 1.45; I 2 =41%). Similarly, there was no association between BMI and infertility in four studies, 9 32–34 with odds of 1.11 (95% CI 0.91 to 1.36; I 2 =66%). However, smoking was found to be significantly related to infertility in three studies, 9 33 34 with the odds being 1.85 (95% CI 1.08 to 3.14; I 2 =94%) times higher compared with those who do not smoke ( figure 3 ). There was no difference observed (OR 0.85; 95% CI 0.59 to 1.23; I 2 =34%) regarding the association between low income and infertility in five studies. 9 20 24 37 46

Forest plot depicting the risk factors associated with infertility. IV, inverse variance.

The psychological impact of infertility

In this study, psychological impact—including distress, depression and anxiety—was evaluated. Four studies were included to assess the distress caused by infertility. 9 20 39 43 The pooled meta-regression analysis showed a statistical significance between infertility and psychological distress among females, with the odds being 1.63 (95% CI 1.24 to 2.13; I 2 =57%) ( figure 4 ).

Forest plot depicting the psychological impact of infertility. IV, inverse variance.

Eight studies were included to assess the association between depression and infertility among females. 9 19 29 30 32–35 Four studies showed significant 9 30 34 35 associations, and four showed no significant 19 29 32 33 associations. The pooled meta-regression analysis showed a statistical significance between depression and infertility among females, with the odds being 1.40 (95% CI 1.11, 1.75; I 2 =50%) compared with those fertile. However, there was no association between anxiety and infertility in the six studies, 9 19 29 32–34 with a pooled meta-regression analysis of OR of 1.68 (95% CI 0.71, 3.98; I 2 =98%) ( figure 4 ).

Infertility is a worldwide public health agenda affecting an individual’s personal, social and economic life and the family as a whole. This study was conducted to determine the pooled prevalence and risk factors of infertility among females. In this meta-analysis, the pooled prevalence of infertility and primary infertility among females was 45.85% (95% CI 37.12% to 54.57%) and 51.5% (95% CI 32.74% to 70.26%), respectively. The prevalence of infertility among females in this study is higher than in a review conducted in 2007 (between 3.5% and 16.7%). 49 It is because most of the sample size for the research articles in this meta-analysis is from an infertility clinic. Regarding primary infertility, it is similar to a review in Africa at 49.9% (95% CI 41.34% to 58.48%). 50

Various risk factors were assessed in terms of their association with infertility among females. Age was not found to be associated with infertility; however, a study on a sample comprising 7172 couples showed that the odds of being diagnosed with unexplained and tubal factor infertility are almost twice as high in women older than 35 years as those younger than 30 years. 51 There was no association noted between BMI and infertility among females. Vahratian and Smith 52 found that a large proportion of females seeking medical help to become pregnant are obese, and the risk of infertility is three times higher in those obese than nonobese. 53 Smoking is a crucial risk factor for females, and it shows that females who smoke have a 1.8 times higher risk of developing infertility than those who do not. One study pointed toward a significant association with a 60% increase in the risk of infertility among females who smoke cigarettes. 54 A meta-analysis identified the pertinent literature available from 1966 through late 1997 and reported an OR of 1.60 for infertility among females who smoke compared with those who do not across all study designs. 54

Infertility among females has a vast impact on psychological distress. In the current study, females with infertility have a 1.6 times higher risk of being psychologically distressed than those fertile. This is similar to a study in Taiwan, 55 which found that 40.2% of the females with infertility suffer from mental disorders. A review of studies conducted in many countries suggested that women endure the major burdens caused by infertility and experience intense anxiety from being blamed for their failure to give birth. 56 Infertility also contributes to the risk of having depression, with females suffering from infertility having a 1.4 times higher chance of being depressed, whereas other studies showed 67.0% 57 and 35.3% 58 of women with infertility were depressed. Recent research has shown that prevalence can range from 11% 35 to 27% 55 and 73%. 57 Another study in Sweden 35 reported that major depression was the most common disorder among couples suffering from infertility, with a prevalence of 10.9% in females and 5.1% in males. It shows that infertility increases the risk of depression. Therefore, it should be considered a serious warning and given a particular focus.

The risk of anxiety in females with infertility is also high. A meta-analysis by Kiani and Simbar 59 showed a pooled prevalence of 36.17% (95% CI 22.47% to 49.87%) among females having anxiety because of infertility. In another systematic review, Sawyer et al 60 reported a 14.8% prevalence of anxiety in females with infertility and a prevalence of 14.0% among women in their prenatal and postnatal periods. In most societies, having a child is closely related to a woman’s identity. Being a mother is equated with being female, 59 which results in high levels of stress and a sense of worthlessness in those childless. 61 In addition, a female who cannot conceive is at risk of social insecurity and becomes anxious because she foresees a future with no child to take care of them in old age or case of illness. 62

Strengths and limitations

This study showed the prevalence of infertility worldwide and the risk of psychological problems among such females, including studies from different countries. It also focused on the quantitative aspect of the problem to get a better view of the intervention.

However, this study is not without limitations. The differences in definitions, diagnostic cut points, study designs, and source populations make performing a meta-analysis on infertility difficult. On the contrary, there are diverse instruments to determine psychological distress, depression and anxiety that make comparing results difficult. Another limitation was the use of various instruments to assess psychological problems in the general population. None of the tools was developed specifically to investigate the incidence of factors concerning females. Although the risk factors identified in this review are not new, calling attention to the psychological impact of infertility is worthwhile.

Conclusions

This study identified that the risk of psychological distress among females with infertility is 60% higher than that among the general population. Furthermore, the risks of anxiety and depression are 60% and 40% times higher, respectively. These results highlight an important and increasing mental disorder among females that may be overlooked. Psychological distress should concern attending physicians and should be assessed to avoid any unwanted events from happening. Acknowledging the problem and taking positive, supportive measures to help females with infertility ensure more positive outcomes during the therapeutic process.

Ethics statements

Patient consent for publication.

Not applicable.

Acknowledgments

The authors would like to thank Madam Nurul Azurah Mohd Roni, a librarian from Hamdan Tahir Library, for her assistance with the database searches.

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Supplementary materials

Supplementary data.

This web only file has been produced by the BMJ Publishing Group from an electronic file supplied by the author(s) and has not been edited for content.

• Data supplement 1

Contributors Conceptualisation, NHNH, MNN and ISB; methodology, NHNH, MNN and ISB; validation MNN and NHNH; formal analysis, MNN and NANMA; investigation, NANMA; resources, MNN and NHNH; data curation, NHNH and NANMA; writing of original draft preparation and NANMA; writing of review and editing, NHNH, MNN, ISB and NANMA; visualisation, NHNH, MNN and ISB; project administration, NHNH; all authors have read and agreed to the published version. MNN is the guarantor for this review.

Funding The authors have not declared a specific grant for this research from any funding agency in the public, commercial or not-for-profit sectors.

Competing interests None declared.

Patient and public involvement Patients and/or the public were not involved in the design, or conduct, or reporting, or dissemination plans of this research.

Provenance and peer review Not commissioned; externally peer reviewed.

Supplemental material This content has been supplied by the author(s). It has not been vetted by BMJ Publishing Group Limited (BMJ) and may not have been peer-reviewed. Any opinions or recommendations discussed are solely those of the author(s) and are not endorsed by BMJ. BMJ disclaims all liability and responsibility arising from any reliance placed on the content. Where the content includes any translated material, BMJ does not warrant the accuracy and reliability of the translations (including but not limited to local regulations, clinical guidelines, terminology, drug names and drug dosages), and is not responsible for any error and/or omissions arising from translation and adaptation or otherwise.

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StatPearls [Internet]. Treasure Island (FL): StatPearls Publishing; 2024 Jan-.

StatPearls [Internet].

Female infertility.

Matthew H. Walker ; Kyle J. Tobler .

Affiliations

Last Update: December 19, 2022 .

• Continuing Education Activity

Infertility is a medical condition that can cause psychological, physical, mental, spiritual, and medical detriments to the patient. The unique quality of this medical condition involves affecting both the patient and the patient's partner as a couple. Although male infertility is an important part of any infertility discussion, this topic reviews the evaluation, management, and treatment of female infertility. To understand infertility, one must understand normal fecundability, the probability of achieving pregnancy in one menstrual cycle. This activity reviews the evaluation, management, and treatment of female infertility and highlights the interprofessional healthcare team's role in improving care for this patient population.

• Identify the epidemiology of female infertility.
• Evaluate the most common findings of female infertility.
• Determine how best to manage female infertility.
• Communicate the importance of improving healthcare coordination among the interprofessional team to enhance and improve outcomes for female patients with infertility.
• Introduction

Infertility is a medical condition that can cause psychological, physical, mental, spiritual, and medical detriments to the patient. The unique quality of this medical condition involves affecting both the patient and the patient's partner as a couple. Although male infertility is an important part of any infertility discussion, this topic reviews the evaluation, management, and treatment of female infertility. To understand infertility, one must understand normal fecundability, the probability of achieving pregnancy in 1 menstrual cycle. This basic understanding helps the healthcare team properly counsel the patient on referrals and provide basic education and understanding of this medical condition. 

The research community has established a fecundability rate multiple times, which has helped establish normal pregnancy rates to assist in diagnosing infertility. The largest study identified that 85% of women would conceive within 12 months. Based on this study's findings, fecundability is 25% in the first 3 months of unprotected intercourse and then decreased to 15% for the remaining 9 months. [1]  This research has helped the American Society of Reproductive Medicine (ASRM) establish when a couple should undergo an infertility evaluation. The ASRM recommends initiating an evaluation for infertility after failing to achieve pregnancy within 12 months of unprotected intercourse or therapeutic donor insemination in women younger than 35 years or within 6 months in women older than 35. [2]

The World Health Organization (WHO) performed a large multinational study to determine gender distribution and infertility etiologies. In 37% of infertile couples, female infertility was the cause; in 35% of couples, both male and female causes were identified; in 8%, there was male factor infertility. [3] In the same study, the most common identifiable factors of female infertility are as follows:

• Ovulatory disorders: 25%
• Endometriosis: 15%
• Pelvic adhesions: 12%
• Tubal blockage: 11%
• Other tubal/uterine abnormalities: 11%
• Hyperprolactinemia: 7%

These causes are further investigated in later portions of this topic; male and unknown factors are outside the scope of this topic. Even though these factors are not discussed here, it is important to realize that male factor infertility represents a substantial portion of the identifiable factors causing infertility.

• Epidemiology

In a study conducted by the National Survey of Family Growth that interviewed 12,000 women in the United States, the prevalence of infertility decreased with the increase in the woman’s age. [4]  As a woman gets older, her chances of infertility increase. In women aged 15 to 34 years, infertility rates ranged from 7.3% to 9.1%. In women ages 35 to 39 years old, the infertility rates increased to 25%. Lastly, women from ages 40 to 44 years had a 30% chance of infertility. [4] Worldwide, infertility rates are higher in Eastern Europe, North Africa, and the Middle East. Worldwide, 2% of women aged 20 to 44 were never able to have a live birth, and 11% with a previous live birth were unable to have an additional birth. [5]

• Pathophysiology

Anovulation

Ovulatory disorders make up 25% of the known causes of female infertility. Oligo-ovulation or anovulation results in infertility because no oocyte are released monthly. Without an oocyte, there is no opportunity for fertilization and pregnancy. To help with treatment and further classification, the World Health Organization subdivided ovulatory disorders into 4 classes:

• Hypogonadotropic hypogonadal anovulation: Hypothalamic amenorrhea
• Normogonadotropic normoestrogenic anovulation: Polycystic ovarian syndrome (PCOS)
• Hypergonadotropic hypoestrogenic anovulation: Premature ovarian failure
• Hyperprolactinemic anovulation: Pituitary adenoma

Hypothalamic amenorrhea or functional hypothalamic amenorrhea (FHA) is associated with eating disorders and excessive exercise, which results in a decrease in hypothalamic GnRH secretion. [6]  The decreased caloric intake, associated weight loss, or excessive exercise leads to elevated cortisol, which causes a suppression of GnRH. [7] The decreased or absent pulsatility of GnRH results in a decrease in the release of gonadotropins, follicle-stimulating hormone (FSH), and luteinizing hormone (LH) from the anterior pituitary gland. These 2 deficiencies result in abnormal follicle growth, anovulation, and low estrogen levels. [8]  The FSH and LH have variations ranging from normal to low, but the hormone ratio resembles a prepubertal female, with FSH higher than LH. [8]  

The most common type of Normogonadotropic normoestrogenic anovulation is PCOS. PCOS accounts for 80% to 85% of all anovulatory patients and affects 8% of all reproductive-aged females. [9]  PCOS can be diagnosed using the Rotterdam criteria, which requires at least 2 of the 3 below-listed criteria in the absence of other pathological causes:

• Oligoovulation/anovulation
• Clinical signs of hyperandrogenism or serological elevations of androgens
• Polycystic ovaries demonstrated with ultrasound  [10]

Infertility caused by PCOS is thought to be associated with a dysfunction in developing a mature follicle, leading to anovulation. The FSH and estrogen are be within normal laboratory limits. The LH can either be normal or elevated. The pathophysiology behind PCOS and infertility is not well understood; classically, abnormal pulsatility of GnRH is described as a possible underlying cause. Correlating the high number of arrested follicles and polycystic-appearing ovaries is the elevation of the anti-Mullerian hormone (AMH). [11]  

Hypergonadotropic hypoestrogenic anovulation is the category of premature ovarian insufficiency and ovarian resistance associated with females' age. As mentioned before, a woman’s age affects fertility due to a well-studied phenomenon of a steady decline in the quality and quantity of the patient’s oocytes. In quantity, the female fetus at 20 weeks gestation has roughly 6 million follicles. The newborn has approximately 1 million follicles. At the onset of puberty, the number of follicles decreases to 300,000. [12] The rate of follicle loss continues throughout a woman’s life and increases in rate around her mid-thirties. [13] External factors are also associated with decreased follicular quantity. The most notable and highly researched is cigarette smoking. Fecundability and follicular quantity are both inversely proportional to the amount of cigarette smoking. Early menopause (under 40 years old) is also associated with cigarette smoking; there is a more than 30% increase in early menopause among ever-smokers. [14]

Ovarian quality is also essential to overall fertility. The loss of the oocyte quality throughout a woman’s life is associated with meiotic nondisjunction, resulting in aneuploidy. This is thought to be related to accumulated damage throughout life and age-related changes in the granulosa cells. [15]  As women age, there is a significant increase in the number of meiotic nondisjunction events and corresponding aneuploid or chromosomally abnormal oocytes and embryos. [15]

Primary ovarian insufficiency (POI) is defined as hypergonadotropic hypogonadism before the age of 40. This disease is characterized by a lack of folliculogenesis, a decrease in estrogen, loss of oocytes, and infertility. [16] The most common cause of POI is Turner syndrome, monosomy of the sex chromosomes leading to a 45X karyotype. [17]  Further discussion of Turner syndrome is outside the scope of this manuscript. 

As mentioned above, the WHO recognizes prolactinemia as a leading cause of female infertility; however, the ASRM recently published guidelines that the initial workup does not need to include prolactin. [2]  Prolactin causes suppression of hypothalamic GnRH secretion, leading to a low LH, resulting in anovulation, corresponding oligomenorrhea, or amenorrhea. Prolactin serum values of 20 to 50 ng/mL cause insufficient progesterone release from the corpus luteum, which shortens the luteal phase. Although controversial, prolactinemia is described as an etiology of luteal phase defects leading to infertility. Prolactin values of 50 to 100 ng/mL cause amenorrhea or oligomenorrhea due to abnormal feedback on the hypothalamic-pituitary-ovarian axis. A concentration greater than 100 ng/mL is associated with overt hypogonadism and amenorrhea and is most commonly associated with pituitary adenomas. [18]  

Endometriosis

Endometriosis is defined as endometrial tissue outside the uterine cavity. The diagnosis is based on the histological identification of endometrial glands or stroma outside the uterus. Endometriosis is most commonly found in the pelvis but can spread throughout the entire abdomen and affects 10% to 15% of reproductive-age women. [19] Of women with endometriosis, 40% to 50% experience infertility. [20] Endometriosis is categorized into 4 stages, according to the American Society of Reproductive Medicine, with stage I being minimal and stage IV severe. Endometriosis is known to cause infertility, but the pathophysiology is thought to change according to the stage. [21] For stages I and II, infertility is believed to be associated with inflammation and increased production of prostaglandins and cytokines, macrophages, and natural killer cells. [22] The inflammation impairs ovarian and tubal function, resulting in defective follicular formation, fertilization, and implantation. [23] Stages III and IV are associated with pelvic adhesions or masses that distort pelvic anatomy; this inherently impairs tubal motility, oocyte release, and sperm motility. [24] Also, advanced endometriosis is hypothesized to impair folliculogenesis, reducing fertilization potential. [25]  

Pelvic/Tubal Adhesions

Pelvic and tubal adhesions, along with uterine and tubal abnormalities, account for a large portion of female infertility. Infectious processes within the abdomen are the leading cause of pelvic/tubal adhesions; the most common infectious process to affect infertility is pelvic inflammatory disease (PID). The microorganism that carries the greatest risk of infertility in association with PID is Chlamydia trachomatis . One in 4 women with tubal factor infertility has positive antibodies to chlamydia, which are inversely proportional to pregnancy rates. [26] The number of PID episodes and the severity play a role in the likelihood of infertility. One study demonstrated that the pregnancy rates following PID were 89% after 1 episode, 77% after two episodes, and 46% after 3 episodes. [27] Regarding PID severity of mild, moderate, and severe, the live-birth rates were 90%, 82%, and 57%, respectively. [28]

Hydrosalpinges, are a tubal abnormality caused by acute and chronic inflammation that damages the structural integrity of the fallopian. This damage leads to tubal obstruction, which blocks the distribution of physiologic fluid in the fallopian tube and results in fluid accumulation. The belief is that hydrosalpinges impair fertility through the retrograde flow of toxins and prostaglandins into the endometrium, creating a hostile environment for implantation by impairing endometrial receptivity. [29]  The literature has demonstrated that patients undergoing in-vitro fertilization have a 50% decrease in pregnancy if a hydrosalpinx is present. [30]

Uterine Causes

Uterine causes of infertility are associated with either space-occupying lesions or reduced endometrial receptivity. In regards to uterine leiomyomas (fibroids), 1 meta-analysis demonstrated that only submucosal or intracavitary fibroids impaired implantation and pregnancy rates compared to other infertile controls. [31] Congenital uterine abnormalities (CUA), although rare, are also associated with infertility. Most commonly found are uterine septums, which are also associated with recurrent pregnancy loss. Interestingly, one study demonstrated that the prevalence of CUA in the fertile and infertile population is the same. [32]  Infertility due to CUA is thought to account for roughly 8% of the female causes of infertility; however, 25% of women with late first-trimester or second-trimester miscarriages are found to have CUAs. [33]  A detailed discussion of the classifications and pathophysiology of CUAs is outside the scope of this manuscript.

• History and Physical

Infertility evaluation is indicated in women with unsuccessful pregnancies after 12 months of unprotected regular intercourse or 6 months if they are over 35 years old. [34] This topic focuses on female infertility, but it is essential to remember that a male infertility evaluation is needed and should be initiated simultaneously. For women planning to use donor sperm, they should receive the same female infertility workup before inseminations. [2] The key aspects of the history of the infertile woman are listed below:

• Duration of infertility
• Obstetrical history
• Menstrual history, including molimina
• Medical, surgical, and gynecological history to include a history of sexually transmitted infections
• Focusing on the male partner, which includes issues with erection and ejaculation
• Social and lifestyle history to include cigarettes, alcohol, and illicit drug use, exercise, and diet, occupation
• Family history, screening for genetic issues, history of venous thrombotic events, recurrent pregnancy loss, and infertility

 The physical exam should include the following:

• Vital signs and body mass index
• Thyroid evaluation
• Breast exam for galactorrhea
• Signs of androgen excess: dermatological and external genitalia exam
• The appearance of abnormal vaginal or cervical anatomy
• Pelvic masses or tenderness
• Uterine enlargement or irregularity
• Transvaginal ultrasonography is often done at the bedside as part of the initial physical exam

The 5 diagnostic evaluation categories are:

• Semen analysis
• Assessment of ovarian function and reserve
• Assessment of the uterine cavity
• Assessment of the fallopian tubes
• Endocrinological serum studies

The evaluation and interpretation of a semen analysis are outside the scope of this review; however, the importance of this test as part of the initial evaluation before initiating treatments cannot be reiterated enough.

Assessment of ovarian function can be as simple as the menstrual cycle history. Women with predictable, regular cycles and predictable menstrual flow and molimina (bloating, fatigue, breast tenderness) are more than likely ovulating. At-home urinary LH predictor kits detect a mid-cycle LH surge, which is indirect evidence of ovulation and helps identify the fertile window. [35]  Ovulation can also be detected by a cycle day 21 progesterone serum level or, more accurately, a mid-luteal phase progesterone level. The lab should be taken approximately 1 week before menses, and a progesterone lab value greater than 3 ng/mL is evidence of ovulation. [36] A more invasive, more accurate, but likely unnecessary means of determining ovulation is daily ultrasounds for following the growth and disappearance of a follicle. [37]

There are multiple available tests to assess ovarian reserve, and this topic only discusses the 2 most common: Cycle day 3 FSH and estradiol and Anti-Mullerian hormone (AMH). The theory of day 3 FSH and estradiol is that women with good ovarian reserve have early sufficient ovarian hormones from small follicles to allow FSH to remain lower. According to multiple examples in the literature, it is most important to identify women with a reduced follicle count that produces insufficient hormones, causing a lack of inhibition, resulting in an elevated FSH. FSH levels less than 10 IU/mL demonstrate likely normal ovarian reserve, 10 to 20 IU/mL is intermediate, and an FSH greater than 20 IU/mL is a poor prognosis for spontaneous ovulation due to low ovarian reserve. According to one study, the pregnancy rates per natural menstrual cycle, corresponding to the FSH levels above, are 32%, 17% to 19%, and 3%. [38] Day 3 estradiol less than 80 pg/mL is normal with adequate ovarian reserve. Values greater than 80 pg/mL resulted in lower pregnancy rates, and values >100 pg/mL had a 0% pregnancy rate. [39]

AMH is a hormone expressed by preantral and antral follicles, representing a marker of ovarian function that can be measured at any time during a woman’s cycle. [40] The AMH levels gradually decline throughout a woman’s natural reproductive life to undetectable levels at menopause. [41] AMH levels seem to be a good predictor of exogenous gonadotropin response [42] :

• Less than 0.5 ng/mL: Predicts difficulty getting more than 3 follicles to grow
• Less than 1.0 ng/mL:  Shows limited egg supply that may require more aggressive ovulation induction protocols
• 1.0 to 3.5 ng/mL: Shows normal values
• Greater than 3.5: Shows ample supply and may require mild induction to prevent ovarian hyperstimulation syndrome

It is important to remember that the available ovarian reserve tests are reliable in predicting ovulation induction difficulties but are not diagnostic in predicting live births. They should not be used to exclude patients from in vitro fertilization (IVF) treatment. [43]  

Antral follicle counts, measuring the number of follicles less than 9mm in the ovaries with transvaginal ultrasound in the early follicular phase, is also an accurate measure of ovarian reserve and predictive of ovarian response to stimulation. [44]

Tubal Evaluation

The gold standard for the evaluation of tubal patency is laparoscopy with chromopertubation. Laparoscopy is indicated as a first-line diagnostic test for suspected pelvic adhesions, endometriosis, or other pelvic pathologies; however, due to high specificity and being less invasive, the hysterosalpingogram (HSG) is more commonly used for the first-line evaluation for tubal patency and abnormalities. [45] The ability to detect abnormalities is best for proximal occlusion, followed by distal occlusions; however, the HSG has poor predictability for intrauterine and tubal adhesions. [46] Another added benefit of the HSG is increased pregnancy and live birth rates with oil-soluble media. A meta-analysis showed that after HSG, pregnancy, and live birth rates increased compared to controls (overall response, 2.98; 95% CI 1.05-6.37). [47]  

Uterine Cavity

The gold standard for assessing the uterine cavity is hysteroscopy, which allows direct visualization of the intrauterine pathology and provides an opportunity for immediate surgical correction. Although hysteroscopy is considered the gold standard, a less invasive approach is more commonly utilized with a saline infusion sonogram (SIS). The SIS is highly sensitive and specific for all intrauterine abnormalities and is adequate as a screening tool before infertility treatment, with or without 3-D model rendering. [48]

• Treatment / Management

Lifestyle Changes

Women with extremes in body mass index (BMI) frequently present with infertility and ovulatory dysfunction. [49] Women with a BMI of less than 17 kg/m^2 with a history of intense exercise regimens or women with eating disorders are likely to develop hypogonadotropic hypogonadism, which causes decreased pituitary gonadotropin secretions. [50] In The United States, controlled ovarian stimulation using exogenous gonadotropins is used to induce ovulation; however, in Europe, women who fail to respond to therapy can receive pulsatile GnRH therapy. [51] One study demonstrated the importance of behavioral change in inducing ovulation. Of the women who received individual-directed therapy to correct energy deficiencies or behavior problems, 87% resumed regular ovarian function to correct the abnormal BMI. [52]

Women with a BMI greater than 27 kg/m^2 with anovulation can improve ovulation with weight loss alone. Multiple studies have shown that losing 10% of body weight restores normal ovulation in 50% to 100% of women in less than 1 year. [53] Even though weight loss is important for many aspects of a patient's life, one study showed that obese women who received counseling and interventions for weight loss before infertility treatment did not have higher pregnancy or live birth rates compared to obese women who had infertility treatment without weight-loss interventions. Therefore, a specific BMI is not required to initiate fertility treatment. [54]

Controlled Ovarian Hyperstimulation

The first-line medication for infertility of unknown origin and the medication most providers use is clomiphene citrate (CC). Clomiphene is a selective estrogen receptor modulator (SERM) with estrogen antagonists and agonist effects that ultimately increase gonadotropin release from the anterior pituitary. Clomiphene treats WHO class 2 anovulation effectively but is ineffective in WHO class 1 and class 3 anovulation. Clomiphene is dosed starting at 50mg starting on cycle days 2, 3, 4, or 5 for 5 sequential days. The couple is encouraged to have intercourse every other day for 1 week, beginning 5 days after the last pill. However, the odds of pregnancy may be increased when clomiphene is combined with intrauterine insemination (IUI). There is little difference in the results of ovulation, pregnancy, or live birth regarding which day the medication is started, between cycle days 2 to 5. [55]

Another commonly used oral medication for ovulation induction is letrozole. Letrozole is an aromatase inhibitor that prevents estrogen production by preventing the conversion of androstenedione and testosterone to estrone and estradiol. [56]  Letrozole is FDA-indicated for the extended adjuvant treatment of breast cancer, and its use for ovulation induction is considered off-label. However, an abundance of scientific literature and multiple committee opinions support both the efficacy and safety of its use in ovulation induction. Letrozole is dosed starting at 2.5, 5, or 7.5 mg/day on cycle days 3, 4, 5, 6, and 7, with intercourse every other day 5 days after completing the medication, which is similar to clomiphene. According to ACOG, letrozole should be considered the first-line treatment for women with PCOS over clomiphene. [57] The benefits of letrozole over clomiphene are:

• Higher rate of monofollicular development and a corresponding decrease in twin gestations
• Shorter half-life
• No antiestrogenic effects on the endometrium and central nervous system
• Lower estradiol levels, which is a benefit for women with breast cancer undergoing IVF 

Letrozole's beneficial profile compared to clomiphene may replace clomiphene as a first-line treatment. [58]

Gonadotropin therapy is a more intensive medical regimen used for WHO Class 1, 2, or 3 anovulatory disorders. Gonadotropins are beneficial as a second-line treatment option for women who fail to conceive after multiple cycles of clomiphene. There is one study that showed an increased live birth rate with gonadotropins compared to continued clomiphene usage. [59]  Multiple dosing protocols are used depending on the provider's preferences and on the infertility treatment decided. Those protocols are outside the scope of this review. However, it is important to discuss that close monitoring is required while using more invasive and intensive gonadotropins. To evaluate mature follicles, transvaginal ultrasounds are used to monitor follicular growth every 2 to 3 days during the late follicular phase. A mature follicle is over 18 mm in diameter, and estradiol levels are greater than 200 pg/mL. Once a mature follicle is identified, a 250 mg recombinant HCG subcutaneous injection or intramuscular injection of 10,000U of urinary-derived HCG is given to trigger ovulation. [60] Once the trigger shot is given, an IUI occurs 24-36 hours later. IUIs can be used in combination with all ovarian induction agents, and IUI with medication is encouraged to increase pregnancy rates, which is discussed below. There is unclear evidence as to the superiority of allowing a natural LH surge versus an HCG trigger before IUI. A meta-analysis completed in 2010 showed no clear evidence of 1 treatment option over the other, and the treatment choice should be based on cost, hospital staffing restrictions, and patient convenience. [61]  

Tubal and Pelvic Adhesions 

In vitro fertilization (IVF) is the first-line treatment for bilateral tubal factor infertility. Tubal corrective surgeries have worse pregnancy outcomes and have an increased risk of ectopic pregnancy. Women with severe tubal disease, including hydrosalpinx, are encouraged to have a bilateral salpingectomy to increase the pregnancy rate of IVF. [62] For women with mild distal tubal disease, fimbrioplasty is an option to allow for multiple pregnancies without IVF. One small study showed that the pregnancy rate was equal to IVF for mild tubal disease, but the risk of ectopic pregnancy was 15% compared to 0.7% for IVF treatment. [63]

The patients with a prior bilateral salpingectomy or tubal ligation for contraception are an important tubal factor population. It is always important for healthcare providers to discuss the risk of regret with all women who desire tubal ligation. The chance of pregnancy after tubal reanastomosis depends on the patient's age, the type of ligation, and the tubal length available. Younger women who had a ring or a clip with more than 4 cm of tubal length are the best candidates and have comparable pregnancy rates to IVF. [64]  However, the time to pregnancy is significantly longer following tubal surgery as compared to IVF.

Uterine Abnormalities

There is unclear evidence on the effect of leiomyomas on infertility and live birth rates. It is recommended that the patient receive a complete infertility workup before further investigation into fibroids. The most important aspect of fibroids is the location. Fibroids that impinge on the endometrium and distort the uterine cavity result in impaired implantation and increased miscarriage rates. [65] Women with submucosal or submucosal-intramural fibroids that distort the uterine cavity have been proven to have decreased pregnancy rates. With the removal of these fibroids, pregnancy, and live birth rates increase. [66] The first-line treatment for the removal of the most detrimental fibroids is operative hysteroscopy. Other uterine pathologies like uterine synechiae and septa are more related to recurrent pregnancy loss but are capable of causing infertility. Operative hysteroscopy has shown a marked reduction in pregnancy loss for women with both synechiae and septa. [67]   Asymptomatic polyps have also been shown to cause infertility. One study showed a polypectomy on asymptomatic infertile women before IUI increased pregnancy rates from 28% to 63%. [68]  

IVF Procedures

This section discusses an overview of IVF procedures without discussing medication protocols as the most effective treatment option for infertility. Step 1 is controlled ovarian hyperstimulation with injectable gonadotropins, most commonly. Thirty-six hours after a trigger shot or HCG injection, a specialist performs a transvaginal ultrasonography-guided needle aspiration and oocyte retrieval. After retrieval, the oocytes are transferred to a special media, and normal sperm is transferred to the dish for insemination. If there is abnormal sperm, intracytoplasmic sperm injection (ICSI) is performed. ICSI is a procedure that places a single spermatozoon directly into the egg cytoplasm. After fertilization, the embryo is assessed and graded. The embryos are then transferred on Day 3 or Day 5. Preimplantation genetic testing (PGT) is an additional IVF procedure that helps detect known parental genetic mutations or balanced translocation. PGT can also be used to detect aneuploidy, both monosomies and trisomies, from all 23 chromosome pairs. Apart from a known parental carrier for a genetic mutation or balanced translocation, PGT is likely beneficial for advanced maternal age, repeated IVF failures with high-grade embryos, recurrent pregnancy loss, and unexplained infertility.

• Differential Diagnosis

Infertility is a highly complex disorder with significant effects on the couple as a whole. It is important to remember that there can be, and regularly are, multiple causes of infertility. The differential diagnosis for infertility can be extensive, and a thorough workup is required to ensure no harmful disease process is missed. Due to the expansive nature of this discussion, this topic focuses on the differential diagnosis of PCOS due to its high prevalence in the infertile population. 

The differential diagnosis for patients with suspected PCOS includes:

• Androgen-producing ovarian tumors
• Adrenal tumors
• Nonclassic congenital adrenal hyperplasia
• Cushing syndrome
• Prolactinemia disorders
• Thyroid disorders

The investigation of PCOS should include total testosterone, DHEA-S, and 17-Hydroxyprogesterone for evaluation of a virilizing ovarian or adrenal tumor or nonclassical congenital adrenal hyperplasia (CAH). There is a suggestion that DHEA-S should only be reserved for women with severe virilization because an asymptomatic, slightly elevated DHEA-S level does not affect management. Additionally, prolactin and thyroid-stimulating hormones should be measured.

• The upper limit of normal for female testosterone is 45 to 60 ng/dL. [69]
• A testosterone value greater than 150 ng/dL warrants investigation for ovarian and adrenal androgen-secreting tumors. [69]
• DHEA-S of greater than 500 to 700 mcg/dL warrants further investigation of an adrenal tumor. [70]
• A fasting 17-hydroxyprogesterone greater than 200 ng/dL collected during the follicular phase warrants an ACTH stimulation test, and a value greater than 500 ng/dL is diagnostic for nonclassical congenital adrenal hyperplasia. [71]

The clinical signs and further evaluation of these disorders are outside the scope of this paper.

This section covers the pregnancy rates per cycle for each treatment modality. The data are mostly from evaluating unexplained infertility but are also consistent with known causes. The rates of IVF vary drastically according to multiple individual factors. The following pregnancy rates were collected from a retrospective analysis of 45 separate studies:

• No treatment: 1.3% to 3.8%
• IUI alone: 4%
• Clomiphene citrate (CC) alone: 5.6%
• CC with IUI: 8.3%
• Gonadotropins alone: 7.7%
• Gonadotropins with IUI: 17.1%
• IVF: 20.7%  [72]

Letrozole alone and letrozole with IUI result in similar pregnancy rates as CC plus IUI and can be used for women for who IVF is not an option and who have failed CC plus IUI. [73]

In 2009, a study showed that women who failed CC plus IUI should go straight to IVF instead of gonadotropins plus IUI before IVF. This study resulted in less time to achieve pregnancy, fewer treatment cycles, and lower total financial cost per delivery. [74] The IVF pregnancy rates have increased since the paper reported above; however, the data still proves that IVF results in the highest pregnancy rates of all treatment options.

The research above suggests that providers can use clomiphene alone as a first-line treatment for infertility. This is no longer true. In 2008, a randomized control trial showed that clomiphene alone had lower live birth rates than expectant management, respectively 14% and 17%. [75] This paper did show a benefit regarding patient satisfaction versus expectant management; however, most women in both categories were satisfied with their care. In light of this study, ASRM published a committee opinion stating that clomiphene with timed intercourse should be discouraged as a first-line treatment for unexplained infertility. [75]

• Complications

The 3 primary complications associated with infertility treatments are multiples, ectopic pregnancy, and ovarian hyperstimulation syndrome.

Multiple Gestations

The risk of multiples has been a problem for artificial reproductive technologies since the practice's inception. In the US, 32% of assisted reproductive technology pregnancies were multiples compared to 3.4% of naturally conceived births. [76]  In 2009, according to the Centers for Disease Control (CDC), the chances of singleton, twin, or higher-order pregnancies with IVF fresh embryo transfer were 62%, 29%, and 3%, respectively. [77]  The oral ovarian induction agents, clomiphene and letrozole, have a lower risk of multiple gestations than gonadotropins, with the percentages of twins, triplets, and quadruplets of 7%, 0.5%, and 0.3%, respectively. Gonadotropins have a 13% chance of multiple gestations, including triplets. [78]  

Currently, the ASRM and CDC have strongly promoted the use of elective single embryo transfer (eSET) for good prognosis patients. With the use of eSET, the rates of twins and triplets dropped to less than 1%. Additionally, there is active debate about whether gonadotropins should be used for ovulation induction outside of an IVF protocol due to the high risk of multiples. [79]  As the use of IVF increases, the use of gonadotropins in conjunction with IUI is likely to continue to decrease.

Ectopic Pregnancy

Ectopic pregnancy following treatment of infertile patients is another risk that requires extensive counseling. There is a two-to-threefold increase in ectopic pregnancies among infertility patients. This is thought to be associated with a high percentage of tubal factor infertility. [80] The highest associated risk of ectopic pregnancy is after tubal surgery to correct tubal factor infertility. Rates of ectopic pregnancy following tubal reconstructive surgery are approximately 9%, with other reports as high as 30%. [63] The risk of ectopic pregnancy with IVF fresh embryo transfer is higher than frozen embryo transfer, but the overall rate of ectopic pregnancies with IVF is roughly 1.3%. [81] There does not seem to be an increase in ectopic pregnancies with the use of ovulation induction agents combined with IUI versus natural conception; however, in a large study comparing the ovulation induction agents, clomiphene, letrozole, and gonadotropins had ectopic pregnancy rates of 4%, 6%, and 8%, respectively. [82]  

Ovarian Hyperstimulation Syndrome 

Ovarian hyperstimulation syndrome (OHSS) is an iatrogenic complication of controlled ovarian hyperstimulation that results in a broad range of signs and symptoms, ranging from abdominal distention, nausea, vomiting, enlarged ovaries, third-spacing of fluids, renal failure, and venous thrombosis, acute respiratory distress syndrome, electrolyte derangements, cardiac arrhythmias, and sepsis. If severe OHSS is not treated and monitored, mortality can result from the listed complications. The different stages of OHSS were classified by Golan et al. in 1989. [83] The underlying pathophysiological feature is increased capillary permeability, resulting in a fluid shift into the third space. Women at the highest risk of developing OHSS are those patients with greater than 20 mature follicles who also receive an HCG trigger shot. The incidence of moderate and severe OHSS with IVF ranges from 6% to 1%, respectively. [84] The diagnosis, prevention, and management of OHSS are outside the scope of this manuscript.

• Deterrence and Patient Education

Women should see their providers for a referral to an infertility subspecialist if they are unable to achieve pregnancy after 1 year of unprotected timed intercourse or if she is older than 35 years of age, 6 months of unprotected timed intercourse. [2] It is important to explain that infertility can result from both female and male factors or a combination of the two. This is important to remember, as most couples with infertility seek care through the female partner's health provider, potentially overlooking the male contribution. The medications and procedures available for female infertility are well-studied and have a well-known risk profile. The patient needs to be aware of the risk of multiple gestations, ectopic pregnancy, and OHSS. Finally, the patient needs to understand the odds of pregnancy per treatment cycle for each modality. The inability to conceive, even with IVF, is a possibility that needs to be fully understood before dedicating a large number of resources required for infertility treatment.

• Enhancing Healthcare Team Outcomes

As mentioned at the beginning of this topic, infertility is a devastating diagnosis and should be considered a disease process by all healthcare team members. The author believes the best way to improve the physical, emotional, social, and interpersonal stressors of infertility for the patient is to complete an immediate and thorough investigation into both partners. The evaluation is straightforward and can be completed before referral to a fertility subspecialist. This expedites and enhances the specialist's ability to initiate follow-up studies and treatments. The use of either clomiphene or letrozole with timed intercourse alone can be used to correct a known cause of anovulation but should not prolong the referral to a subspecialist. All primary care providers need to set realistic expectations of the chances of pregnancy and the possibility of complications when counseling couples suffering from infertility. Lastly, this manuscript is not all-encompassing but provides a basic foundation of knowledge to feel comfortable discussing a patient's family planning goals, initiating an evaluation, and reviewing available treatments.  

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Disclosure: Matthew Walker declares no relevant financial relationships with ineligible companies.

Disclosure: Kyle Tobler declares no relevant financial relationships with ineligible companies.

This book is distributed under the terms of the Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 International (CC BY-NC-ND 4.0) ( http://creativecommons.org/licenses/by-nc-nd/4.0/ ), which permits others to distribute the work, provided that the article is not altered or used commercially. You are not required to obtain permission to distribute this article, provided that you credit the author and journal.

• Cite this Page Walker MH, Tobler KJ. Female Infertility. [Updated 2022 Dec 19]. In: StatPearls [Internet]. Treasure Island (FL): StatPearls Publishing; 2024 Jan-.

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Top 10 priorities for future infertility research: an international consensus development study

Collaborators.

• Priority Setting Partnership for Infertility : Hisham AlAhwany ,  Ofra Balaban ,  Yusuf Beebeejaun ,  Jacky Boivin ,  Jan J A Bosteels ,  Arianna D'Angelo ,  Leona F Dann ,  Christopher J De Jonge ,  Elyce du Mez ,  Rui A Ferriani ,  Marie-Odile Gerval ,  Lynda J Gingel ,  Ellen M Greenblatt ,  Geraldine Hartshorne ,  Charlie Helliwell ,  Charlotte Helliwell ,  Lynda J Hughes ,  Junyoung Jo ,  Jelena Jovanović ,  Ludwig Kiesel ,  Chumnan Kietpeerakool ,  Elena Kostova ,  Tansu Kucuk ,  Robyn L Lawrence ,  Nicole Lee ,  Katy E Lindemann ,  Olabisi M Loto ,  Peter J Lutjen ,  Michelle MacKinven ,  Mariano Mascarenhas ,  Helen McLaughlin ,  David J Mills ,  Selma M Mourad ,  Linh K Nguyen ,  Robert J Norman ,  Maja Olic ,  Kristine L Overfield ,  Maria Parker-Harris ,  David G Ramos ,  Aleksandra Rendulic ,  Sjoerd Repping ,  Roberta Rizzo ,  Pietro Salacone ,  Catherine H Saunders ,  Rinku Sengupta ,  Ioannis A Sfontouris ,  Natalie R Silverman ,  Helen L Torrance ,  Eleonora P Uphoff ,  Sarah A Wakeman ,  Tewes Wischmann ,  Bryan J Woodward ,  Mohamed A Youssef

Affiliations

• 1 King's Fertility, Fetal Medicine Research Institute, London, UK; Institute for Women's Health, University College London, London, UK. Electronic address: [email protected].
• 2 ARC Fertility, Cupertino, California, United States.
• 3 Patient and Public Participation Group, Priority Setting Partnership for Infertility, University of Auckland, Auckland, New Zealand.
• 4 Institute of Applied Health Sciences, University of Aberdeen, Aberdeen, UK.
• 5 Department of Obstetrics and Gynaecology, University of Auckland, Auckland, New Zealand.
• 6 Women's Network, Royal College of Obstetricians and Gynecologists, London, UK.
• 7 Resolve: The National Infertility Association, Virginia, United States.
• 8 School of Psychology, University of Waikato, Hamilton, New Zealand.
• 9 Centre for Reproductive Medicine and Biology, University Medical Centre Maastricht, Maastricht, The Netherlands.
• 10 Department of Obstetrics and Gynaecology, Liverpool Women's NHS Foundation Trust, Liverpool, UK.
• 11 Fertility Europe, Belgium.
• 12 Department of Obstetrics and Gynaecology, Münster University Hospital, Münster, Germany.
• 13 Center for Research, Innovation and Training in Reproduction and Infertility, Center for Reproductive Sciences, University of California, San Francisco, California, United States; International Federation of Fertility Societies, Mount Royal, New Jersey, United States.
• 14 Auckland District Health Board, Auckland, New Zealand.
• 15 Department of Obstetrics and Gynaecology, University of Melbourne, Victoria, Australia.
• 16 MRC Centre for Reproductive Health, University of Edinburgh, Edinburgh, UK.
• 17 Robinson Research Institute and Adelaide Medical School, University of Adelaide, Adelaide, Australia.
• 18 Department of Women and Children's Health, Kings College London, London, UK.
• 19 Freya, Gorinchem, The Netherlands.
• 20 Department of Obstetrics and Gynaecology, Penn State College of Medicine, Pennsylvania.
• 21 Fertility Plus, Auckland, New Zealand.
• 22 Reproductive Medicine Unit, University College Hospital, London, UK.
• 23 Department of Obstetrics and Gynaecology, Monash University, Melbourne, Australia.
• 24 Department of Obstetrics and Gynaecology, University of Auckland, Auckland, New Zealand; Fertility Associates, Auckland, New Zealand.
• 25 Cochrane Gynaecology and Fertility, University of Auckland, Auckland, New Zealand.
• 26 Department of Obstetrics and Gynaecology, The University of Hong Kong, Hong Kong; Shenzhen Key Laboratory of Fertility Regulation, The University of Hong Kong-Shenzhen Hospital, China.
• 27 Department of Urology, University of Illinois at Chicago College of Medicine, Chicago, Illinois.
• 28 Osakidetza OSI, Bilbao, Spain.
• 29 Pharmacy, Science, and Technology, University of Medicine, Targu Mures, Romania; Center for Reproductive Medicine, Amsterdam Reproduction and Development Institute, Amsterdam University Medical Centres, Amsterdam, The Netherlands.
• 30 Department of Obstetrics and Gynaecology, University of Auckland, Auckland, New Zealand; Auckland District Health Board, Auckland, New Zealand.
• 31 King's Fertility, Fetal Medicine Research Institute, London, UK.
• 32 British Fertility Society, Middlesex, UK.
• 33 Sahlgrenska Academy, Dept of Obstetrics and Gynecology, University of Gothenburg, Sahlgrenska University Hospital, Göteborg, Sweden.
• 34 Fertility Network UK, London, UK.
• 35 Centre for Biostatistics, University of Manchester, Manchester Academic Health Science Centre, Manchester, UK.
• 36 Center for Reproductive Medicine, Amsterdam Reproduction and Development Institute, Amsterdam University Medical Centres, Amsterdam, The Netherlands.
• 37 Department of Obstetrics and Gynaecology, University of Medicine and Pharmacy at Ho Chi Minh City, Ho Chi Minh City, Vietnam.
• 38 Australian Centre for Public and Population Health Research, Faculty of Health, University of Technology Sydney, Australia.
• 39 Department of Obstetrics and Gynaecology, University of Auckland, Auckland, New Zealand; Cochrane Gynaecology and Fertility, University of Auckland, Auckland, New Zealand.
• PMID: 33272617
• DOI: 10.1016/j.fertnstert.2020.11.014

Study question: Can the priorities for future research in infertility be identified?

Summary answer: The top 10 research priorities for the four areas of male infertility, female and unexplained infertility, medically assisted reproduction, and ethics, access, and organization of care for people with fertility problems were identified.

What is known already: Many fundamental questions regarding the prevention, management, and consequences of infertility remain unanswered. This is a barrier to improving the care received by those people with fertility problems.

Study design, size, duration: Potential research questions were collated from an initial international survey, a systematic review of clinical practice guidelines, and Cochrane systematic reviews. A rationalized list of confirmed research uncertainties was prioritized in an interim international survey. Prioritized research uncertainties were discussed during a consensus development meeting. Using a formal consensus development method, the modified nominal group technique, diverse stakeholders identified the top 10 research priorities for each of the categories male infertility, female and unexplained infertility, medically assisted reproduction, and ethics, access, and organization of care.

Participants/materials, setting, methods: Healthcare professionals, people with fertility problems, and others (healthcare funders, healthcare providers, healthcare regulators, research funding bodies and researchers) were brought together in an open and transparent process using formal consensus methods advocated by the James Lind Alliance.

Main results and the role of chance: The initial survey was completed by 388 participants from 40 countries, and 423 potential research questions were submitted. Fourteen clinical practice guidelines and 162 Cochrane systematic reviews identified a further 236 potential research questions. A rationalized list of 231 confirmed research uncertainties were entered into an interim prioritization survey completed by 317 respondents from 43 countries. The top 10 research priorities for each of the four categories male infertility, female and unexplained infertility (including age-related infertility, ovarian cysts, uterine cavity abnormalities, and tubal factor infertility), medically assisted reproduction (including ovarian stimulation, IUI, and IVF), and ethics, access, and organization of care, were identified during a consensus development meeting involving 41 participants from 11 countries. These research priorities were diverse and seek answers to questions regarding prevention, treatment, and the longer-term impact of infertility. They highlight the importance of pursuing research which has often been overlooked, including addressing the emotional and psychological impact of infertility, improving access to fertility treatment, particularly in lower resource settings, and securing appropriate regulation. Addressing these priorities will require diverse research methodologies, including laboratory-based science, qualitative and quantitative research, and population science.

Limitations, reasons for caution: We used consensus development methods, which have inherent limitations, including the representativeness of the participant sample, methodological decisions informed by professional judgement, and arbitrary consensus definitions.

Wider implications of the findings: We anticipate that identified research priorities, developed to specifically highlight the most pressing clinical needs as perceived by healthcare professionals, people with fertility problems, and others, will help research funding organizations and researchers to develop their future research agenda.

Study funding/ competing interest(s): The study was funded by the Auckland Medical Research Foundation, Catalyst Fund, Royal Society of New Zealand, and Maurice and Phyllis Paykel Trust. Geoffrey Adamson reports research sponsorship from Abbott, personal fees from Abbott and LabCorp, a financial interest in Advanced Reproductive Care, committee membership of the FIGO Committee on Reproductive Medicine, International Committee for Monitoring Assisted Reproductive Technologies, International Federation of Fertility Societies, and World Endometriosis Research Foundation, and research sponsorship of the International Committee for Monitoring Assisted Reproductive Technologies from Abbott and Ferring. Siladitya Bhattacharya reports being the Editor-in-Chief of Human Reproduction Open and editor for the Cochrane Gynaecology and Fertility Group. Hans Evers reports being the Editor Emeritus of Human Reproduction. Andrew Horne reports research sponsorship from the Chief Scientist's Office, Ferring, Medical Research Council, National Institute for Health Research, and Wellbeing of Women and consultancy fees from Abbvie, Ferring, Nordic Pharma, and Roche Diagnostics. M. Louise Hull reports grants from Merck, grants from Myovant, grants from Bayer, outside the submitted work and ownership in Embrace Fertility, a private fertility company. Neil Johnson reports research sponsorship from Abb-Vie and Myovant Sciences and consultancy fees from Guerbet, Myovant Sciences, Roche Diagnostics, and Vifor Pharma. José Knijnenburg reports research sponsorship from Ferring and Theramex. Richard Legro reports consultancy fees from Abbvie, Bayer, Ferring, Fractyl, Insud Pharma and Kindex and research sponsorship from Guerbet and Hass Avocado Board. Ben Mol reports consultancy fees from Guerbet, iGenomix, Merck, Merck KGaA and ObsEva. Ernest Ng reports research sponsorship from Merck. Craig Niederberger reports being the Co Editor-in-Chief of Fertility and Sterility and Section Editor of the Journal of Urology, research sponsorship from Ferring, and retains a financial interest in NexHand. Jane Stewart reports being employed by a National Health Service fertility clinic, consultancy fees from Merck for educational events, sponsorship to attend a fertility conference from Ferring, and being a clinical subeditor of Human Fertility. Annika Strandell reports consultancy fees from Guerbet. Jack Wilkinson reports being a statistical editor for the Cochrane Gynaecology and Fertility Group. Andy Vail reports that he is a Statistical Editor of the Cochrane Gynaecology & Fertility Review Group and of the journal Reproduction. His employing institution has received payment from HFEA for his advice on review of research evidence to inform their 'traffic light' system for infertility treatment 'add-ons'. Lan Vuong reports consultancy and conference fees from Ferring, Merck and Merck Sharp and Dohme. The remaining authors declare no competing interests in relation to the present work. All authors have completed the disclosure form.

Trial registration number: Not applicable.

Keywords: Consensus science methods; and research priorities; infertility; modified Delphi method; modified Nominal Group Technique; reproductive medicine.

Copyright © 2020 The Authors. Published by Elsevier Inc. All rights reserved.

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Article Contents

Introduction, materials and methods, supplementary data, acknowledgments.

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Top 10 priorities for future infertility research: an international consensus development study †   ‡

Members of the Priority Setting Partnership for Infertility are listed in the  Appendix.

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J M N Duffy, G D Adamson, E Benson, S Bhattacharya, S Bhattacharya, M Bofill, K Brian, B Collura, C Curtis, J L H Evers, R G Farquharson, A Fincham, S Franik, L C Giudice, E Glanville, M Hickey, A W Horne, M L Hull, N P Johnson, V Jordan, Y Khalaf, J M L Knijnenburg, R S Legro, S Lensen, J MacKenzie, D Mavrelos, B W Mol, D E Morbeck, H Nagels, E H Y Ng, C Niederberger, A S Otter, L Puscasiu, S Rautakallio-Hokkanen, L Sadler, I Sarris, M Showell, J Stewart, A Strandell, C Strawbridge, A Vail, M van Wely, M Vercoe, N L Vuong, A Y Wang, R Wang, J Wilkinson, K Wong, T Y Wong, C M Farquhar, Priority Setting Partnership for Infertility , Top 10 priorities for future infertility research: an international consensus development study  , Human Reproduction , Volume 35, Issue 12, December 2020, Pages 2715–2724, https://doi.org/10.1093/humrep/deaa242

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Can the priorities for future research in infertility be identified?

The top 10 research priorities for the four areas of male infertility, female and unexplained infertility, medically assisted reproduction and ethics, access and organization of care for people with fertility problems were identified.

Many fundamental questions regarding the prevention, management and consequences of infertility remain unanswered. This is a barrier to improving the care received by those people with fertility problems.

Potential research questions were collated from an initial international survey, a systematic review of clinical practice guidelines and Cochrane systematic reviews. A rationalized list of confirmed research uncertainties was prioritized in an interim international survey. Prioritized research uncertainties were discussed during a consensus development meeting. Using a formal consensus development method, the modified nominal group technique, diverse stakeholders identified the top 10 research priorities for each of the categories male infertility, female and unexplained infertility, medically assisted reproduction and ethics, access and organization of care.

Healthcare professionals, people with fertility problems and others (healthcare funders, healthcare providers, healthcare regulators, research funding bodies and researchers) were brought together in an open and transparent process using formal consensus methods advocated by the James Lind Alliance.

The initial survey was completed by 388 participants from 40 countries, and 423 potential research questions were submitted. Fourteen clinical practice guidelines and 162 Cochrane systematic reviews identified a further 236 potential research questions. A rationalized list of 231 confirmed research uncertainties was entered into an interim prioritization survey completed by 317 respondents from 43 countries. The top 10 research priorities for each of the four categories male infertility, female and unexplained infertility (including age-related infertility, ovarian cysts, uterine cavity abnormalities and tubal factor infertility), medically assisted reproduction (including ovarian stimulation, IUI and IVF) and ethics, access and organization of care were identified during a consensus development meeting involving 41 participants from 11 countries. These research priorities were diverse and seek answers to questions regarding prevention, treatment and the longer-term impact of infertility. They highlight the importance of pursuing research which has often been overlooked, including addressing the emotional and psychological impact of infertility, improving access to fertility treatment, particularly in lower resource settings and securing appropriate regulation. Addressing these priorities will require diverse research methodologies, including laboratory-based science, qualitative and quantitative research and population science.

We used consensus development methods, which have inherent limitations, including the representativeness of the participant sample, methodological decisions informed by professional judgment and arbitrary consensus definitions.

We anticipate that identified research priorities, developed to specifically highlight the most pressing clinical needs as perceived by healthcare professionals, people with fertility problems and others, will help research funding organizations and researchers to develop their future research agenda.

The study was funded by the Auckland Medical Research Foundation, Catalyst Fund, Royal Society of New Zealand and Maurice and Phyllis Paykel Trust. G.D.A. reports research sponsorship from Abbott, personal fees from Abbott and LabCorp, a financial interest in Advanced Reproductive Care, committee membership of the FIGO Committee on Reproductive Medicine, International Committee for Monitoring Assisted Reproductive Technologies, International Federation of Fertility Societies and World Endometriosis Research Foundation, and research sponsorship of the International Committee for Monitoring Assisted Reproductive Technologies from Abbott and Ferring. Siladitya Bhattacharya reports being the Editor-in-Chief of Human Reproduction Open and editor for the Cochrane Gynaecology and Fertility Group. J.L.H.E. reports being the Editor Emeritus of Human Reproduction . A.W.H. reports research sponsorship from the Chief Scientist’s Office, Ferring, Medical Research Council, National Institute for Health Research and Wellbeing of Women and consultancy fees from AbbVie, Ferring, Nordic Pharma and Roche Diagnostics. M.L.H. reports grants from Merck, grants from Myovant, grants from Bayer, outside the submitted work and ownership in Embrace Fertility, a private fertility company. N.P.J. reports research sponsorship from AbbVie and Myovant Sciences and consultancy fees from Guerbet, Myovant Sciences, Roche Diagnostics and Vifor Pharma. J.M.L.K. reports research sponsorship from Ferring and Theramex. R.S.L. reports consultancy fees from AbbVie, Bayer, Ferring, Fractyl, Insud Pharma and Kindex and research sponsorship from Guerbet and Hass Avocado Board. B.W.M. reports consultancy fees from Guerbet, iGenomix, Merck, Merck KGaA and ObsEva. E.H.Y.N. reports research sponsorship from Merck. C.N. reports being the Co Editor-in-Chief of Fertility and Sterility and Section Editor of the Journal of Urology , research sponsorship from Ferring and retains a financial interest in NexHand. J.S. reports being employed by a National Health Service fertility clinic, consultancy fees from Merck for educational events, sponsorship to attend a fertility conference from Ferring and being a clinical subeditor of Human Fertility . A.S. reports consultancy fees from Guerbet. J.W. reports being a statistical editor for the Cochrane Gynaecology and Fertility Group. A.V. reports that he is a Statistical Editor of the Cochrane Gynaecology & Fertility Review Group and the journal Reproduction . His employing institution has received payment from Human Fertilisation and Embryology Authority for his advice on review of research evidence to inform their ‘traffic light’ system for infertility treatment ‘add-ons’. N.L.V. reports consultancy and conference fees from Ferring, Merck and Merck Sharp and Dohme. The remaining authors declare no competing interests in relation to the present work. All authors have completed the disclosure form.

The ultimate aim of infertility research is to improve clinical practice and optimize the chances of people with fertility problems achieving parenthood. For this to be possible, research needs to address questions that are pertinent to people with infertility, be conducted using appropriate methods, and be reported in a comprehensive, transparent and accessible manner ( Duffy et al. , 2017 ). The first step in research production is to identify appropriate questions. Traditionally, research funding organizations and researchers have identified, refined and prioritized their own research agenda. It is unlikely that such prioritization has used formal consensus methods, engaged wider stakeholders, including people with fertility problems, and was independent of commercial interests. There has been modest improvement in some countries, including the Netherlands, the UK and the USA, which has emphasized the importance of including patients and the public in developing research priorities ( Graham et al. , 2020 ).

Sir Iain Chalmers, founder of the Cochrane Collaboration, has advocated for research priorities to be jointly identified by healthcare professionals, patients and communities ( Chalmers and Glasziou, 2009 ). He established the James Lind Alliance, which brings together healthcare professionals, patients and others, in priority setting partnerships. Using formal consensus methods, each priority setting partnership engages in an open and transparent process to identify and prioritize unanswered research questions, known as research uncertainties, in a particular area of health care ( James Lind Alliance, 2018 ). The expectation is that prioritized research uncertainties will establish the future research agenda of funding organizations and researchers. As a result, it is hoped that the gap will close between what research is needed and what research is pursued ( Wilkinson et al. , 2019a ).

An international collaboration has brought healthcare professionals, people with fertility problems and others together within a Priority Setting Partnership for Infertility to develop future research priorities for male infertility, female and unexplained infertility, medically assisted reproduction and ethics, access and organization of care.

An international multidisciplinary steering group, including healthcare professionals, people with fertility problems and researchers, was established to provide a diverse range of perspectives to inform key methodological decisions. The steering group was convened during the development of the study protocol, before the launch of the initial survey and interim prioritization survey, and before the consensus development meeting. A systematic review of registered, progressing and completed priority setting research settings was completed to assist with the planning and delivery of the study ( Graham et al. , 2020 ).

Research uncertainties related to infertility associated with endometriosis, miscarriage and polycystic ovary syndrome were not considered because of other current or completed research prioritization initiatives ( Horne et al. , 2017 ; Prior et al. , 2017 ).

Research priorities were developed in a three-stage process using consensus methods advocated by the James Lind Alliance (2018) . Potential research uncertainties were gathered through an online survey of healthcare professionals, people with fertility problems and others. Healthcare professionals, including embryologists, fertility specialists and gynecologists, were recruited through the British Fertility Society, Core Outcomes in Women’s Health (CROWN) initiative, Cochrane Gynaecology and Fertility Group, Fertility and Sterility Forum, Reproductive Medicine Clinical Study Group and Royal College of Obstetricians and Gynecologists. People with fertility problems were recruited through Fertility Europe, an umbrella organization of more than 20 European patient organizations, including Fertility Network UK and Freya, Fertility New Zealand, RESOLVE: The National Infertility Association, and the Women’s Voices Involvement Panel hosted by the Royal College of Obstetricians and Gynecologists. Other people could register to participate, including healthcare funders, healthcare regulators and researchers. Recruitment was supported by an active social media campaign. Potential participants received an explanatory video abstract, a plain-language summary and survey instructions. Before completing the survey, participants provided demographic details, including age, gender and geographical location, and information pertaining to their professional or personal experience of infertility. Participants were invited to suggest up to five research questions related to infertility that they considered unanswered.

After the survey had closed, the survey responses were examined in detail within an iterative process. Individual responses were reviewed by at least two members of the steering group. Responses were excluded if they included questions that did not fit the scope of the study, were not answerable by research, related to a specific person or situation or were ambiguous. Incomplete responses were also excluded. The remaining responses were formatted into appropriate research questions.

In addition, research recommendations were identified from a systematic review of clinical practice guidelines and Cochrane systematic reviews. Clinical practice guidelines relevant to infertility were identified by searching bibliographical databases, including Embase, International Guideline Library and MEDLINE, from 2007 to July 2017. Research recommendations were extracted verbatim from clinical practice guidelines. Using a data extraction tool available to the Cochrane Gynaecology and Fertility Group, research recommendations were extracted from individual Cochrane reviews evaluating potential fertility treatments. Research recommendations from clinical practice guidelines and Cochrane systematic reviews were reviewed by two members of the steering group and formatted into appropriate research questions. Differences in opinion were resolved by discussion with the steering group.

The long list of potential research questions was organized by allocating individual research questions in four categories: male infertility; female and unexplained infertility, including age-related infertility, ovarian cysts, uterine cavity abnormalities and tubal factor infertility; medically assisted reproduction including ovarian stimulation, IUI and IVF; and ethics, access and organization of care. These categories were identified in consultation with the steering group. Duplicate research questions were removed. Research questions were checked against the published research evidence, including clinical practice guidelines, Cochrane systematic reviews and randomized trials, and those questions considered to be already answered were removed.

The long list of confirmed research uncertainties was entered into an interim prioritization survey. Initial survey participants were invited to participate in the survey. In addition, healthcare professionals, people with fertility problems and others were recruited using the same methods as the initial survey. Before completing the survey, participants provided demographic details, including age, gender and geographical location, and information pertaining to their professional or personal experience of infertility. Participants were invited to select the research uncertainties they considered most important. After the survey had closed, questions were ranked based on the frequency they had been chosen by participants.

The top 15 research uncertainties in each category were discussed during a consensus development meeting (data are presented in the Supplementary Table S1 ). A formal consensus development method, the modified nominal group technique, was used to identify the top 10 research uncertainties for each category ( James Lind Alliance, 2018 ). Healthcare professionals, people with fertility problems and others who had completed the initial or interim prioritization survey were invited to participate. The modified nominal group technique does not depend on statistical power. In consultation with the steering group, the aim was to recruit between 15 and 30 participants, as this number has yielded sufficient results and assured validity in other settings ( Murphy et al. , 1998 ).

Before the consensus development meeting, participants provided demographic details, including age, gender and geographical location, and information pertaining to their professional or personal experience of infertility. Following an introductory session, participants were assigned to one of two groups, each with a facilitator, to discuss the ranking of prioritized research uncertainties. The assignments were pre-specified to ensure a mixture of healthcare professionals, people with fertility problems and others. The groups were provided with a set of cards with an individual research uncertainty printed on each. Each participant was asked to contribute their opinions on the research uncertainties they felt most and least strongly about. Following this initial discussion, participants were invited to discuss the ordering of the research uncertainties. By the end of the session, the research uncertainties were placed in ranked order. The rankings from the two groups were aggregated into a single ranking order and presented to the entire group. Participants were invited to discuss the ordering of the research uncertainties. By the end of the discussion, the research uncertainties were placed in a final ranked order.

The National Research Ethics Service, UK, advised the study did not require formal review.

The initial survey was completed by 179 healthcare professionals (46%), 153 people with fertility problems (39%) and 56 others (14%), from 40 countries ( Table I ). Four hundred and twenty-three responses were submitted ( Fig. 1 ). Following review, 136 responses (32%) were excluded. Clinical practice guidelines relevant to infertility were identified by searching bibliographical databases; the search strategy identified 3680 records. After excluding 731 duplicate records, 2949 titles and abstracts were screened. Thirty-two potentially relevant clinical practice guidelines were evaluated. Fourteen clinical practice guidelines met the inclusion criteria, including two guidelines related to infertility in general ( Loh et al. , 2014 ; National Institute for Health and Care Excellence, 2017 ), five guidelines related to male infertility (American Urological Association, 2010; Jarvi et al. , 2010 ; Jungwirth et al. , 2018 ), five guidelines related to uterine anomalies ( Kroon et al. , 2011 ; American Association of Gynecologic Laparoscopists, 2012 ; Carranza-Mamane et al. , 2015 ; Practice Committee of the American Society for Reproductive Medicine, 2016 a, 2017 ) and two guidelines related to medically assisted reproduction ( Practice Committee of the American Society for Reproductive Medicine, 2016b ; Penzias et al. , 2017 ). Thirteen research recommendations were extracted from the clinical practice guidelines. The Cochrane Gynaecology and Fertility Group provided research recommendations from 162 Cochrane systematic reviews. Two hundred and twenty-three potential research questions were extracted from these research recommendations. A long list of 533 potential research uncertainties was reviewed, 241 duplicate research uncertainties were removed and 51 research uncertainties which had been answered by research were also removed.

Overview of the process of identifying research uncertainties .

Characteristics of the participants in a survey to identify the priorities for future infertility research.

A rationalized list of 231 confirmed research uncertainties was developed, which included 34 research uncertainties related to male infertility, 48 research uncertainties related to female and unexplained infertility, 101 research uncertainties related to medically assisted reproduction and 48 research uncertainties related to ethics, access and organization of care. These confirmed research uncertainties were entered into an interim prioritization survey, which was completed by 143 healthcare professionals, 119 people with fertility problems and 55 others, from 43 countries.

Nineteen healthcare professionals, 14 people with personal experience of infertility and 8 others, from 11 countries, participated in the consensus development meeting. The modified nominal group technique was used to prioritize the top 10 research uncertainties for male infertility, female and unexplained infertility, medically assisted reproduction and ethics, access and organization of care. Fifteen highly prioritized research uncertainties for each category were discussed during the consensus development meeting ( Supplementary Table SI ). The 15 highly prioritized research uncertainties were initially discussed by two separate groups and at the end of the discussion, they ranked the research uncertainties. The first-round ranking is presented in Supplementary Table SI . The rankings from the two groups were aggregated into a single ranking order and discussed by the entire group ( Supplementary Table SI ). Participants were encouraged to discuss and finalize the rank order of the research priorities. The top 10 research priorities are presented in Fig. 2 .

The top 10 priorities for future infertility research in each of the four categories .

The Priority Setting Partnership for infertility has brought together healthcare professionals, people with fertility problems and others to identify the top 10 research priorities for future infertility research. These research priorities are diverse and seek answers to questions regarding prevention, treatment and the longer-term impact, as well as wider contextual issues related to access and public health policy. They highlight the importance of pursuing research which has often been overlooked, including addressing the emotional and psychological impact of infertility, improving access to fertility treatment, particularly in lower resource settings, and securing appropriate regulation. Addressing these priorities will require diverse research methodologies, including laboratory-based science, qualitative and quantitative research and population science.

Strengths and limitations

The James Lind Alliance (2018) has published guidance to inform the design of research priority setting studies. This study has followed this guidance to ensure the research priorities were developed using a clear and transparent process using formal consensus development methods. The study design, development and delivery were also informed by a systematic review of research priority setting studies relevant to women’s health ( Graham et al. , 2020 ). With 388 respondents from 40 countries participating in the initial survey, 317 respondents from 43 countries participating in the interim prioritization survey, and 41 participants from 11 countries included in the consensus development meeting, the global participation achieved in this study should secure the generalizability of the results within an international context. The study included people with fertility problems and they were able to suggest potential research uncertainties during the initial survey, share their views regarding the importance of research uncertainties during the interim prioritization survey and participate fully in the consensus development meeting which prioritized the final research priorities.

This consensus study is not without limitations. Consideration should be given to the representativeness of the study’s participants. For example, when considering the initial survey, there was a higher response from participants who identified as living in Europe (115 participants; 30%). To participate in the initial survey and interim prioritization survey, English proficiency and literacy, a computer and internet access were required. We appreciate that limitations in the representativeness of the sample could impact upon the research uncertainties suggested and prioritized. There is uncertainty regarding the optimal consensus development method to prioritize research uncertainties, and methodological research is required to evaluate different approaches to priority setting and the use of different consensus methods. Further contextual information, including the number of people the research priority impacts upon, the feasibility of answering the research priority, and the resources required to address the research uncertainty could have assisted participants to prioritize research uncertainties. Future methodological research should evaluate the use of contextual information in research priority studies.

Reflections on the research priorities

Reproductive medical care for men has lagged behind that for women. Setting impactful and tractable priorities for male reproduction is consequently a critically important task. For diagnosis, the variation in morphology is extraordinary and counting sperm is challenging, severely limiting our ability to make predictions of male reproductive potential from the standard semen analysis, and begging the question: are there other, better tests of sperm? We need to explore how overall health affects male fertility and whether treating other diseases improves it. Because a man does not live in a vacuum, we need to understand how the environment affects male reproduction. When considering the treatment of male infertility, men often ask what they can do to improve their fertility, and well-conducted studies into diet and nutraceuticals are essential. The endocrine system drives the making of sperm and further evidence is required to understand if hormonal therapy could improve the production of sperm and improve live birth rates.

The priorities for unexplained infertility seek answers to several challenging and long-standing questions, including the prevention of age-related infertility and exploring the role of fibroids, polyps, intrauterine adhesions and uterine septa in unexplained infertility. It is also surprising that it remains unclear what the first-line treatment is for couples with unexplained infertility, IVF or IUI, and the timing of the superior treatment for that couple.

When considering medically assisted reproduction, new large prospective cohorts that consider all variables and use advance methodology will be required to address casual relationships related to implantation failure. Similar complexity will exist when studying oocyte yield and quality over subsequent IVF cycles, even though similar stimulation protocols have been used. The three research priorities concerning the effectiveness of IVF are seeking to identify optimal ovarian stimulation protocols in poor responders, sperm selection techniques and embryo selection. These contrast with the research priorities which explore if, when, and how IUI should be used. To answer these effectiveness questions, well-designed randomized controlled trials will be required ( Wilkinson et al. , 2019b ). The psychological impact of fertility treatment is brought into sharper focus with research priorities related to the emotional and psychological impact of repeated fertility treatment failure and in children following gamete donation. Strong involvement of patient representatives, psychologists and behavioral scientists will be required to establish the appropriate qualitative and quantitative studies to address these important priorities.

The research priorities for ethics, access and organization of care broadly fall into two overarching themes: access and infertility as a public health issue. When considering access, cost is a major barrier to appropriate care, which is reflected in the research priorities aiming to explore interventions to reduce the cost of fertility treatment and increase the availability of fertility treatment in lower-resources settings. Turning to infertility as a public health issue, prevention of infertility should be a key priority for public health initiatives. We need to determine the minimum standard of care that people with fertility problems should expect, especially if we are seeking reimbursements for this care.

Wider context

A prioritized list of research uncertainties, developed to specifically highlight the most pressing clinical needs as perceived by healthcare professionals, people with fertility problems and others, should help funding organizations and researchers to set their future research agenda. The selected list of research uncertainties should serve to focus a discussion regarding the allocation of limited resources.

Many of the research priorities will require national and international collaboration. Several countries, including China, the Netherlands, the UK and the USA, have developed national networks to undertake infertility research ( Devall et al. , 2020 ). Further development of national infrastructure is required. Collaboration should spread beyond national boundaries and develop within an international context. It is hoped the development of a prioritized research agenda could be an important enabler to deepen international collaboration. Development of generic infrastructure could help foster collaboration, including the use of minimum data sets, known as core outcome sets, low-cost data repositories and standardized approaches to the reporting of research. A core outcome set has recently been developed for future infertility trials ( Duffy et al. , 2018 ). Over 400 healthcare professionals, researchers and patients, from 40 countries, have used formal consensus development methods to identify a core outcome set for infertility ( Duffy et al. , 2020a ). Consensus definitions have also been agreed for individual core outcomes ( Duffy et al. , 2020b ). It is hoped the core outcome set will provide generic tools to collect outcomes during research, provide concise guidance regarding statistical analysis and standardize the approach to research reporting ( Duffy et al. , 2019 ).

Research priorities identified in this study correspond with research priorities identified by the Priority Setting Partnership for Miscarriage, including determining the emotional and psychological impact of miscarriage, investigating the modifiable risk factors which cause miscarriage and identifying specific comorbidities which cause miscarriage ( Prior et al. , 2017 ). Other similarities exist when considering the research uncertainties prioritized by the Priority Setting Partnership for Endometriosis and International Polycystic Ovary Syndrome Network ( Horne et al. , 2017 ).

Answering the prioritized research questions would represent a significant step forward for our specialty. The steering group recognizes the important role of research which stems from the intellectual curiosity of individuals, fundamental research which does not have an immediate clinical application and research which is funded by special interest groups raising funding for the topic of their particular interest. A blended research strategy should offer the optimal pathway to improving clinical care and patient outcomes.

Perhaps the most important part of this process has been the strengthening of relationships between partner organizations, healthcare professionals and people with lived experience of infertility. The prioritized list of uncertainties that require research should help funding organizations and researchers to set their future research agenda. Our approach should ensure that future research has the necessary reach and relevance to inform clinical practice and to improve patient outcomes.

Supplementary data are available at Human Reproduction online.

We would like to thank the initial survey, interim prioritization survey and consensus development meeting participants, and colleagues at the Cochrane Gynaecology and Fertility Group, University of Auckland, New Zealand.

Authors’ roles

Study concept and design: J.M.N.D., S.B., B.C., C.C., J.L.H.E., R.G.F., S.F., L.C.G., A.W.H., N.P.J., Y.K., J.M.L.K., R.S.L., S.L., B.W.M., H.N., E.H.Y.N., C.N., A.S.O., L.P., S.R.H., M.S., J.S., A.S., C.S., A.V., M.v.W., M.A.V., N.L.V., A.Y.W., R.W., J.W. and C.M.F. Acquisition of data: J.M.N.D., S.B., K.B., C.B., C.C., J.L.H.E., R.G.F., A.F., S.F., L.C.G., A.W.H., N.P.J., Y.K., J.M.L.K., R.S.L., S.L., B.W.M., E.H.Y.N., C.N., A.S.O., L.P., S.R.H., M.S., J.S., A.S., C.S., A.V., M.v.W., M.A.V., N.L.V., A.Y.W., R.W., J.W. and C.M.F. Analysis and interpretation of data: J.M.D., G.D.A., E.B., S.B., S.B., M.B., K.B., B.C., C.C., J.L.H.E., R.G.F., A.F., S.F., L.C.G., E.G., M.H., A.W.H., M.L.H., N.P.J., V.J., Y.K., J.M.L.K., R.S.L., S.L., J.M., D.M., B.W.M., D.E.M., H.N., E.H.Y.N., C.N., A.S.O., L.P., S.R.H., L.S., I.S., M.S., J.S., A.S., C.S., A.V., M.v.W., M.V., N.L.V., A.Y.W., R.W., J.W., K.W., T.W. and C.M.F. Drafting of the manuscript: J.M.D., B.C., S.L., H.N., C.N., M.S., M.v.W., M.V., R.W., J.W. and C.M.F. Critical revision of the manuscript for important intellectual content: G.D.A., E.B., S.B., S.B., M.B., B.K., C.C., J.L.H.E., R.G.F., A.F., S.F., L.C.G., E.G., M.H., A.W.H., M.L.H., N.P.J., V.J., Y.K., J.M.L.K., R.S.L., J.M., M.M., D.M., B.W.M., D.M., E.H.Y.N., A.S.O., L.P., S.R.H., L.S., I.S., J.S., A.S., C.S., A.V., N.L.V., A.Y.W., K.W. and T.W. Statistical analysis: J.M.D., J.W. and A.V. Study supervision: C.M.F.

This research was funded by the Catalyst Fund, Royal Society of New Zealand, Auckland Medical Research Foundation and Maurice and Phyllis Paykel Trust. The funders had no role in the design and conduct of the study, the collection, management, analysis or interpretation of data or manuscript preparation. B.W.M. is supported by a National Health and Medical Research Council Practitioner Fellowship (GNT1082548). Siladitya Bhattacharya was supported by the Auckland Foundation Seelye Travelling Fellowship.

Conflict of interest

G.D.A. reports research sponsorship from Abbott, personal fees from Abbott and LabCorp, a financial interest in Advanced Reproductive Care, committee membership of the FIGO Committee on Reproductive Medicine, International Committee for Monitoring Assisted Reproductive Technologies, International Federation of Fertility Societies and World Endometriosis Research Foundation, and research sponsorship of the International Committee for Monitoring Assisted Reproductive Technologies from Abbott and Ferring. Siladitya Bhattacharya reports being the Editor-in-Chief of Human Reproduction Open and editor for the Cochrane Gynaecology and Fertility Group. J.H.L.E. reports being the Editor Emeritus of Human Reproduction . A.W.H. reports research sponsorship from the Chief Scientist’s Office, Ferring, Medical Research Council, National Institute for Health Research and Wellbeing of Women and consultancy fees from AbbVie, Ferring, Nordic Pharma and Roche Diagnostics. M.L.H. reports grants from Merck, grants from Myovant, grants from Bayer, outside the submitted work and ownership in Embrace Fertility, a private fertility company. N.P.J. reports research sponsorship from AbbVie and Myovant Sciences and consultancy fees from Guerbet, Myovant Sciences, Roche Diagnostics and Vifor Pharma. J.M.L.K. reports research sponsorship from Ferring and Theramex. R.S.L. reports consultancy fees from AbbVie, Bayer, Ferring, Fractyl, Insud Pharma and Kindex and research sponsorship from Guerbet and Hass Avocado Board. B.W.M. reports consultancy fees from Guerbet, iGenomix, Merck, Merck KGaA and ObsEva. E.H.Y.N. reports research sponsorship from Merck. C.N. reports being the Co Editor-in-Chief of Fertility and Sterility and Section Editor of the Journal of Urology , research sponsorship from Ferring and retains a financial interest in NexHand. J.S. reports being employed by a National Health Service fertility clinic, consultancy fees from Merck for educational events, sponsorship to attend a fertility conference from Ferring and being a clinical subeditor of Human Fertility . A.S. reports consultancy fees from Guerbet. J.W. reports being a statistical editor for the Cochrane Gynaecology and Fertility Group. A.V. reports that he is a Statistical Editor of the Cochrane Gynaecology & Fertility Review Group and the journal Reproduction . His employing institution has received payment from Human Fertilisation and Embryology Authority for his advice on review of research evidence to inform their ‘traffic light’ system for infertility treatment ‘add-ons’. N.L.V. reports consultancy and conference fees from Ferring, Merck and Merck Sharp and Dohme. The remaining authors declare no competing interests in relation to the present work. All authors have completed the disclosure form.

Priority Setting Partnership for Infertility

Dr Hisham AlAhwany, University of Nottingham, UK; Ofra Balaban, CHEN: Patient Fertility Association, Israel; Faith Barton, UK; Dr Yusuf Beebeejaun, King’s Fertility, Fetal Medicine Research Institute, UK; Professor Jacky Boivin, Cardiff University, UK; Professor Jan J. A. Bosteels, Imelda Hospital, Belgium; Professor Carlos Calhaz-Jorge, Faculdade de Medicina da Universidade de Lisboa, Portugal; Dr Arianna D’Angelo, Wales Fertility Institute, UK; Dr Leona F. Dann, Health Quality and Safety Commission, New Zealand; Professor Christopher J. De Jonge, University of Minnesota Medical Center, United States; Elyce du Mez, University of Auckland, New Zealand; Professor Rui A. Ferriani, University of Sao Paulo, Brazil; Dr Marie-Odile Gerval, Chelsea and Westminster Hospital NHS Foundation Trust, UK; Lynda J. Gingel, UK; Dr Ellen M. Greenblatt, Mount Sinai Fertility, University of Toronto, Toronto; Professor Geraldine Hartshorne, University of Warwick, UK; Charlie Helliwell, New Zealand; Charlotte Helliwell, New Zealand; Lynda J. Hughes, The Fertility Clinic, London Health Sciences Centre, Canada; Dr Junyoung Jo, Conmaul Hospital of Korean Medicine, Republic of Korea; Jelena Jovanović, Serbia; Professor Ludwig Kiesel, University of Münster, Germany; Dr Chumnan Kietpeerakool, Khon Kaen University, Thailand; Dr Elena Kostova, Cochrane Gynaecology and Fertility, New Zealand; Professor Tansu Kucuk, Acibadem Maslak Hospital, Turkey; Rajesh Kumar, National Foundation for the Deaf, New Zealand; Robyn L. Lawrence, The Liggins Institute, The University of Auckland, New Zealand; Nicole Lee, Canada; Katy E. Lindemann, UK; Professor Olabisi M. Loto, Obafemi Awolowo University, Nigeria; Associate Professor Peter J. Lutjen, Monash University, Australia; Michelle MacKinven, Fertility New Zealand; New Zealand; Dr Mariano Mascarenhas, Leeds Teaching Hospital NHS Trust, UK; Helen McLaughlin, Endometriosis UK, UK; David J. Mills, UK; Dr Selma M. Mourad, Isala Hospital Zwolle, The Netherlands; Linh K. Nguyen, Vietnam; Professor Robert J. Norman, Robinson Research Institute, University of Adelaide, Adelaide; Maja Olic, NGO Counselling Center for In Vitro Fertilisation, Serbia; Kristine L. Overfield, NISIG: National Infertility Support and Information Group, Ireland; Maria Parker-Harris, UK; David G. Ramos, Spain; Aleksandra Rendulic, Serbia; Sjoerd Repping, Amsterdam University Medical Centres, The Netherlands; Professor Roberta Rizzo, University of Ferrara, Italy; Professor Pietro Salacone, Italy; Catherine H. Saunders, The Dartmouth Institute for Health Policy and Clinical Practice, USA; Dr Rinku Sengupta, UK; Dr Ioannis A. Sfontouris, Eugonia: Assisted Reproduction Unit, Greece; Natalie R. Silverman, The Fertility Podcast, UK; Dr Helen L. Torrance, University Medical Center Utrecht, The Netherlands; Dr Eleonora P. Uphoff, UK; Dr Sarah A. Wakeman, Fertility Associates, New Zealand; Professor Tewes Wischmann, Heidelberg University, Germany; Dr Bryan J. Woodward, UK; and Mohamed A. Youssef, Cairo University, Egypt.

This article has not been externally peer reviewed.

This article has been published simultaneously in Fertility and Sterility.

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Author notes

Members of the Priority Setting Partnership for Infertility are listed in the   Appendix .

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• Standardizing definitions and reporting guidelines for the infertility core outcome set: an international consensus development study †   ‡

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• Published: 27 December 2022

Investigating different dimensions of infertile women’s quality of life: a descriptive cross-sectional study

• Zahra Kiani 1 ,
• Masoumeh Simbar 1 , 2 ,
• Sepideh Hajian 3 ,
• Farid Zayeri 4 ,
• Farzaneh RashidiFakari 5 &
• Fatemeh Jalali Chimeh 2  

BMC Public Health volume  22 , Article number:  2436 ( 2022 ) Cite this article

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Infertility is a major challenge in the life of women which affects their quality of life. Infertile women's quality of life is a relatively new field of research that has recently been considered by health researchers. However, there has been no standard tool for measuring different aspects of infertile women's quality of life with female factors, and general and specific tools of infertile couples have been used to assess their quality of life. This study, thus, aimed to analyze different aspects of the quality of life of infertile women.

This descriptive cross-sectional study was conducted on 320 infertile women referred to a teaching hospital affiliated with Mazandaran University of Medical Sciences and private infertility treatment centers in Sari, Iran. Demographic and fertility characteristics and the quality of life questionnaire for infertile women questionnaire (a 25-item tool was designed which measured 7 factors of psychological effects, sexual life with infertility family and social effects, infertility-related concerns, physical effects, adaptive approaches and factors preventing infertility adaptation), were recruited for data gathering. Data were analyzed using SPSS version 22. Descriptive statistics (percentage, mean, standard deviation), correlation coefficient, independent sample t-test, and multiple linear regression were used. P -values less than 0.05 were considered statistically significant.

The total mean score of infertile women's quality of life was 65.68 ± 8.91%. Findings were indicative of infertile women's quality of life in the dimensions of adaptive approach (70.48 ± 15.02%), psychological (67.88 ± 12.06%), family and social (64.63 ± 10.76%), physical, 63.42 ± 11.36%), inhibitory factors/ factors preventing adaptation (60.98 ± 8.24%), related concerns (51.52 ± 10.21%) and sexual life (40.12 ± 14.28%). According to the final multiple linear regression model, women's education (B = 2.57, p  < 0.001), spouse's education (B = 1.56, p  = 0.046), economic status (B = 1.64, p  < 0.001), age of women (B = -0.62, p  < 0.001), age of spouse (B = -0.65, p  < 0.001), duration of infertility (B = -0.36, p  = 0.024) and duration of marriage (B = -0.39, p  = 0.022) were the final predictors of the quality of life score in infertile women of the study.

Given that infertility causes extensive changes in individuals, families, and social dimensions of infertile women, it can affect their quality of life. We can take steps to improve the health of infertile women by promoting various dimensions of their quality of life.

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Infertility refers to the lack of pregnancy after one year of a sexual relationship in absence of any contraceptive method [ 1 ]. The prevalence of infertility has increased compared to previous decades [ 2 ] and, according to the World Health Organization (WHO), there are about 60 to 80 million infertile couples worldwide and the prevalence of it varies in different parts of the world [ 3 ]. The results of a recent study in Iran have shown that the prevalence of infertility in Iran (20.2%) is higher than the global average and one-fifth of Iranian couples are infertile. As such, infertility is supposed to be a national problem [ 4 ].

Infertility causes women to experience changes in various aspects of their lives and leads to emotional, psychological, and social disorders. Moreover, there is a conflict between worries and coping strategies in infertile women that can affect their quality of life [ 5 ]. Infertile women experience different personal, family, and social problems. A review of studies in many parts of the world showed that women carry the main burden of infertility and have to tolerate a great deal of anxiety as they are blamed for their inability to childbearing [ 6 ]. Infertile women face the high costs of infertility treatments, frequent doctor visits, and scheduled sexual intercourse that affect their quality of life [ 5 ]. Given the fact that pregnancy is one of the most important goals of women, infertility can instill a sense of lack and deficiency in infertile women [ 7 ].

Furthermore, infertility can decrease sexual attractiveness and sexual desire in women. Change in one's sexual desires is a significant issue that can affect their quality of life [ 8 ]. However, few studies have shown that the quality of marital relationships in infertile couples is higher than in fertile ones, and infertility causes couples to get closer to each other and talk more about their worries in the future [ 9 , 10 ].

At a social level, many infertile women suffer from social isolation and despair. In developing societies, a woman is considered complete only when she becomes a mother. Causing inequality between men and women, this issue leads to gender-related suffering among women [ 11 ]. Infertility, as a general stigma, brings about devastating consequences such as feelings of insignificance and worthlessness for infertile women, which may in turn affect their quality of life [ 12 ].

There are various definitions for quality of life. Researchers usually define the quality of life by focusing on one of the three areas of well-being and peace in life, economic and social power and physical symptoms, illness, and disability [ 13 ]. The WHO defines quality of life as people's perceptions of their position in life in terms of the cultural systems and values ​​in which they live, and how these perceptions are related to their goals, expectations, standards, and concerns. This concept is a very broad one which includes physical and mental health, autonomy level, social relationships, personal beliefs, and the individual's relationship with the environment [ 14 ]. Infertile women's quality of life is an important issue that has recently been considered by health researchers. Moreover, women play a pivotal role in different stages of individuals' life and hence, their quality of life can significantly affect the health of the individual and society [ 15 ].

Focusing on clinical aspects of infertile women's quality of life, a cross-sectional study surveyed 106 infertile women who were referred to infertility centers in Brazil in 2020. The 36-Item Short Form Survey (SF-36) Quality of Life Questionnaire was used in this study to assess the quality of life of infertile women. According to the results, the scores obtained by infertile women were low and the increased duration of infertility was shown to have an adverse effect on their quality of life [ 16 ]. Massarotti et al. conducted a study in 2019 to evaluate the effect of infertility treatments on quality of life as well as on the level of anxiety and depression in infertile women in three groups based on the type of infertility factor (male, female, and both factors). They used the Hospital Anxiety and Depression Scale (HADS) and the Fertility Quality of Life questionnaire (FertiQoL). The findings showed that infertile women with female factors had lower scores of quality of life compared to the other two groups. Moreover, their scores for anxiety and depression were high [ 17 ]. Similar results were obtained in the study of Jahromi et al. where the FertiQoL questionnaire was used [ 18 ]. In a systematic review study, it was noted that although infertility had a negative effect on couples' mental health and sexual relationships, it did not affect the quality of life of infertile couples [ 19 ].

In studies conducted around the world, data collection tools for assessing the quality of life of infertile women are general tools such as World Health Organization Quality of Life (WHOQOL) [ 14 ] and SF-36 [ 20 ] or specific questionnaires for infertile couples such as Quality of Life in Infertile Couples Questionnaire (QOLICQ) by Yaghmaei et al. (2009) [ 21 ] and FertiQoL by Boivin et al. (2011) [ 22 ]. On the one hand, general tools are not suitable for measuring changes in illness or health problems and, on the other, specific tools ignore the type of infertility factor which, according to the literature review, can affect the quality of life. Given the significance of accurate measurement of the quality of life dimensions in infertile women with the female factor, we decided to use a standard tool designed for assessing the quality of life of infertile women with female factor and examine the different dimensions of the quality of life of infertile women with the female factor [ 23 ]. The quality of life questionnaire for infertile women (QOL-QIW) measures the quality of life and its aspects by the seven dimensions of the questionnaire, including 1) Psychologic; 2) Sexual life; 3) Family and social; 4) Infertility-related concerns; 5) Physical; 6) Adaptive approaches; and 7) Preventive factors of adaptation [ 23 ]. This tool helps to show the needs in different dimensions of these women's quality of life and also to plan future needs-based interventions to improve the women's quality of life. Since the quality of life of infertile women has not been assessed with a valid tool yet, this study aimed to assess the quality of life of women with female infertility factors by using QOL-QIW in Iran.

Type of study

This descriptive cross-sectional study was conducted from June to December 2020.

Participants and the research setting

The participants included infertile women with female infertility who have referred to a teaching hospital affiliated with Mazandaran University of Medical Sciences and private infertility centers in Sari, Iran. The hospital is the largest medical training center of Northern University of Medical Sciences of Iran and the referral center of the province and neighboring provinces in the fields of neurosurgery, gynecology, obstetrics, IVF, vascular, thoracic, multiple trauma, orthopedics, cancer, and super-specialized center for all internal trends, ICU and NICU.

Inclusion criteria for the research subjects were as follows

One year of infertility with female factor diagnosed by a specialist, women whose infertility was not due to male factor, formal and permanent marriage, living currently with their spouses, no adoption, no severe mental illness (including major depression, schizophrenia, and mania), interested in participating in the study, no experience of mourning in the last 6 months and no use of psychiatric medication at the time of the study.

Exclusion criteria

Unwillingness to participate in the study.

Sample size

Using the following formula and a pilot study on 40 infertile women with a mean quality of life score of 79.78%, standard deviation of 14.5, and accuracy of 0.2, the sample size was calculated to be 320.

Sampling method

The convenience sampling method was used in this study.

Data collection tool

The questionnaire of demographic characteristics and a valid and reliable quality of life questionnaire for infertile women (QOL-QIW), designed by Kiani et al. [ 23 ], were used in this study.

Demographic and fertility characteristics questionnaire

The validity of this 12-item questionnaire was assessed by experts (reproductive health specialist, psychologist, psychiatrist, midwife, gynecologist, and quality of life specialist). The questions of this questionnaire consist of information about age, education, occupation, economic status, place of residence, duration of the marriage, duration of infertility, type of infertility, and infertility treatment.

Infertile women's quality of life tool of Kiani et al. [ 23 ] was designed in a sequential exploratory study. In the first stage of this questionnaire, a qualitative study with a content analysis approach was used to explain the concept and dimensions of infertile women's quality of life. Then, in the second stage, a quantitative study with an inductive-deductive approach was designed and its psychometric properties were evaluated based on the codes extracted from the qualitative stage, expert opinion and literature review, quality of life questionnaire for infertile women by using the basic steps of Waltz et al. tool, which includes the selection of a conceptual model for identifying the components of the measurement process, explanation of the objectives for the measurement, creation of a roadmap or blueprint, as well as the development of the tool including the use of the mentioned steps, along with the regulation of the items and scoring rules. Finally, a 25-item tool was designed which measured 7 factors of psychological effects (4 items), sexual life with infertility (3 items), family and social effects (3 items), infertility-related concerns (5 items), physical effects (3 items), adaptive approaches (4 items) and factors preventing infertility adaptation (3 items), where the means scale- content validity index (S-CVI) and Item- content validity index (I-CVI) were 0.94 and 0.92 respectively, and the reliability of the tool was confirmed with the Cronbach's alpha of 0.87 and intra-cluster correlation of 0.97. The criteria for interpretability and ease of use were also acceptable. The options of the quality of life questionnaire for infertile women are based on a 5-point Likert scale. The points of the Likert scale include never (5), rarely (4), sometimes (3), most of the time (2), and always (1), and questions 19, 20, 21, and 22 are scored inversely. Each person's scores ranged from 0 to 100.

The correlation coefficient between a generic instrument with the SF-36 questionnaire and a specific instrument the QOL-QIW was 0.61; considering that there was a correlation coefficient of 0.5 to 0.7 between the quality of life questionnaire for infertile women and the SF-36 questionnaire the concurrent validity was good.

Data collection method

Given the prevalence of the Covid-19 pandemic and the high risks of virus transmission during the process of completing the questionnaires, electronic sampling was used through the Google platform. Going to a teaching hospital affiliated with Mazandaran University of Medical Sciences and private infertility treatment centers in Sari, the researcher identified the infertile women who were eligible for the study. After explaining the research objectives, obtaining written electronic informed consent, and getting their mobile number if they were willing, the electronic questionnaire was sent to them via WhatsApp application and completed by them in infertility centers or whenever they wanted. In cases where the subjects would not like to give their phone number, the researcher's phone, which was specifically used for this purpose, was provided to them considering health protocols.

Data analysis method

The data were entered into the Statistical Package for the Social Sciences (SPSS) software version 22. Descriptive statistics were used for presenting and describing the information in tables and the calculation of percentage, mean and standard deviation. Inferential statistics were also used for analysis and the relationships. First, the distribution of quantitative variables was investigated using the Kolmogorov–Smirnov test, and parametric statistical tests were used in the case of the normal distribution; otherwise, nonparametric tests were used. The correlation coefficient, independent sample t-test, and multiple linear regression were used for data analysis. P -values less than 0.05 were considered statistically significant.

Ethics approval

This study was approved by the ethics code of IR.SBMU.PHARMACY.REC.1400.002 in Shahid Beheshti University of Medical Sciences. All methods were carried out in accordance with relevant guidelines and regulations. Participation in the study was fully voluntary and contingent on consent and all methods were carried out under relevant guidelines and regulations. To maintain the confidentiality of participants’ data, the questionnaires were completed anonymously with no identification number. At the beginning of each questionnaire, the consent option to participate in the research was placed and all participants provided informed consent to include in the study.

The data of 320 infertile women showed that the mean age of them, the mean duration of the marriage, and the mean duration of infertility were 31.79 ± 5.58, 9.08 ± 3.61, and 4.21 ± 6.21 years respectively. Other demographic characteristics are shown in Table 1 .

The mean score of infertile women's quality of life was 65.68 ± 8.91%. The highest mean score was in the dimension of adaptive approaches and the lowest one belonged to the dimension of sexual life with infertility (Table 2 ).

As the results of the t-test shown in Table 3 , there was a statistically significant difference in the quality of life scores based on the type of infertility in infertile women ( P  < 0.001), and women with primary infertility had a lower quality of life mean score. There were no significant differences in the quality of life of the women with a different family history of infertility, women’s jobs, and place of residence between groups ( P  > 0.05).

Infertile women's quality of life had the highest positive and significant correlation with women's education ( P  < 0.001, r  = 0.75). It had also a negative and significant correlation with the age of the women and their spouses, duration of the marriage, and duration of infertility. The highest inverse relationships were with the duration of marriage ( r  = -0.65) and the duration of infertility ( r  = -0.59) respectively ( P  < 0.001) (Table 4 ).

Multiple linear regression was used to determine the prediction of variables. First, the variance inflation index (VIF) was used to examine multicollinearity and the results showed that there was no overlap between the variables (VIF < 10). As the results showed, 65% of the variance of the quality of life total score in infertile women was explained by these variables.

According to the final multiple linear regression model, women's education (B = 2.57, < 0.001), spouse's education (B = 1.56, p  = 0.046), economic status (B = 1.64, p  < 0.001), age of women (B = -0.62, p  < 0.001), age of spouse (B = -0.65, p  < 0.001), duration of infertility (B = -0.36, p  = 0.024) and duration of marriage (B = -0.39, p  = 0.022) were the final predictors of the quality of life score in infertile women of the study (Table 5 ).

To the current knowledge of researchers, this study for the first time examined different dimensions of the quality of life of infertile women with female factors by using a specific tool. Using this tool, seven dimensions of psychological dimension, sexual life with infertility, family and social effects, infertility-related concerns, physical effects, adaptive approaches, and factors preventing adaptation were examined. According to the results, the quality of life scores of women was 65.68 ± 8.91percent and the highest and lowest scores were related to the adaptive approach and sexual dimension, respectively. Most studies have used questionnaires on the quality of life of infertile couples by Yaghmaei et al. (2009) and the quality of life of infertile couples by Boivin et al. (2011). As the findings of these studies have indicated, the quality of life of infertile women is lower than men, and disorders such as anxiety, social dysfunction, and depression strongly affect the quality of life of women [ 24 , 25 , 26 , 27 ]. Moreover, a systematic study reviewed the studies that used the WHOQOL and SF-36 questionnaires. According to the results of this review, the quality of life of infertile couples was reported to be low in two studies that had used the SF-36 questionnaire, while it was high in a study that had used the WHOQOL questionnaires. As the couples were more educated in this study, they felt that infertility had brought them closer and they could solve this problem together [ 19 ].

The mean score of infertile women was 67.88 ± 12.06 percent in the psychological dimension. Infertility often causes a lot of stress in women that may affect their quality of life [ 28 ]. In the study conducted by Moghadam et al. where they used the SF-36 questionnaire, the mean score of psychological dimension was 61.69 ± 17.78 percent [ 29 ]. The results of the study by Bornstein et al. (2020) showed that infertile women are more affected by psychological aspects of their lives than men and are more depressed and anxious as well [ 30 ]. Although these results have not been obtained in some studies [ 19 , 31 ], most studies indicated that the mental vulnerability of infertile women is greater than men. In a meta-analysis study conducted by Kiani et al. (2020), the overall prevalence of anxiety in infertile women was reported to be 36.17%, which was 54.24% in middle- and low-income countries [ 32 ]. Another meta-analysis study revealed in 2021 that the overall prevalence of depression in infertile women was 39.78%, while it was 44.32% in low- and middle-income countries [ 33 ]. Having children and parenting are traditionally considered to be the most prominent features of the female gender role and that is why infertility is considered in traditional communities to be a feminine deficit. Additionally, infertility-related sufferings in women are more severe and deeper than in men. As such, women are supposed to be more vulnerable, and the social structures that cause this suffering and the resulting consequences that affect the quality of life of these women are different from infertile men. Therefore, women face different emotional problems which differentiate their quality of life from that of men [ 23 ]. Using this questionnaire, the current study examined this dimension specifically in women with a female factor.

Based on the results of our study, the women obtained the lowest score in the dimension of the quality of sexual life. Sexual behaviors of infertile women have different physiological, cultural, social, and family dimensions and are influenced by the process of diagnosis and level of infertility treatment, prevalent beliefs about infertility, sexual response cycle, and socio-cultural context [ 34 ]. Moreover, the nature of the sexual behaviors of infertile people may change and they do these behaviors only for obedience and childbearing not enjoying their sexual life [ 6 ]. Most studies have shown that infertility reduces the number of sexual intercourse [ 35 , 36 ] and sexual desire and satisfaction [ 37 , 38 ], leading to sexual dysfunction in couples [ 34 , 39 , 40 ]. There is no sexual attraction for infertile women and their sexual desire is reduced. As an important issue, sexual desire can affect one's quality of life [ 8 ]. However, it has been shown in many studies that infertile couples have a better marital relationship than fertile couples, and infertile couples are closer to each other as they try to solve their problems together and give comfort to each other [ 9 , 10 ]. In their study, Kohan et al. investigated the sexual life of infertile people. They found that infertile women felt a kind of reduction of their feminine characteristics and deficit during infertility that had a negative effect on their sexual life [ 41 ]. According to many studies, as women are aware of their childbearing role, their inability in becoming mothers challenges their feminine characteristics, leading to some dysfunctions in their sexual relations [ 42 ].

The infertile women obtained a mean score of 64.63 ± 10.76 percent in the dimension of quality of family and social life. This dimension has also been referred to in other studies where infertile women obtained average scores. The issue of fertility is highly significant in most cultures and the desire for having a child is a human stimulus to continue living, and any lack of pregnancy can lead to adverse effects on family and social relationships [ 43 ]. Almin et al. have shown that infertility affects family relationships and, affected by the detrimental effects of infertility, many people try to stay away from their families [ 44 ]. As shown by some studies, women are more likely than men to bear the burden of infertility and are exposed to negative social stigmas which may affect their relationship with other family and society members [ 37 , 45 ]. To hide the problem, most infertile women often try to stay away from family and friends [ 46 ]. It has also been argued in some studies that the concept of infertility and the social relations of infertile people change over time. Additionally, the hopes of infertility treatment have caused the social relations of many infertile people to not change much after infertility [ 5 ].

Infertility-related concerns obtained the lowest score after the sexual dimension, and the mean score of women was 51.52 ± 10.21 percent in this dimension. The questions of this dimension were concerned with the effect of age on infertility, repetition of treatment, and harms of assisted reproductive techniques on the fetus, which are not available in other questionnaires. Given the nature of the problem and the impact on quality of life, these items indicate that this tool is specifically designed for the quality of life of infertile women with a female factor and has specific questions for addressing their concerns. Becoming a mother and raising a child is a turning point in women's lives and lack of childbearing will cause them anxiety [ 47 ]. In most low- and middle-income countries, women are not employed and infertility treatment costs impose some burden on their husbands, leading to more anxiety in them [ 32 ]. Furthermore, infertility treatment requires frequent visits to the doctors and the use of various medications that, imposing great economic burdens, can affect the health of individuals and their quality of life [ 48 , 49 ]. According to some studies, infertility can lead to divorce, which has occupied the mind of couples since the beginning of infertility and has increased their panic in this regard [ 50 ]. The low chance of remarriage for women and the condemnation of single life in traditional societies put additional pressure on women compared to men, causing more worry in them and affecting their quality of life [51). Moreover, as they do not have a child to take care of them in old age or during an illness, these women may be afraid of loneliness in the future [ 51 ]. Fear of loneliness in the future [ 51 ] and fear of divorce [ 52 ] are important factors that increase the incidence of depression in women and have negative effects on the infertility treatment response [ 53 ].

Physical dimension was another aspect of this questionnaire whose mean score was 63.42 ± 11.36 percent. In studies that used general and specific quality of life questionnaires, similar to our study, the score of the physical dimension of infertile women was lower than the overall total score [ 16 , 54 ]. In fact, with its complex treatments and various stresses, infertility has been found characteristic of chronic physical diseases that cause physical fatigue in individuals [ 55 ]. Moreover, the medicines used in the treatment of infertility have often many gastrointestinal, neurological, cardiovascular, dermal, skeletal, and muscular side effects [ 56 ]. Sabarre et al. also have referred to weakness and lethargy of infertile people during the treatment process [ 57 ]. Similarly, Bakhtiyar et al. (2019) indicated that because of receiving infertility services, infertile women had frequent visits to infertility centers that could cause physical fatigue in them [ 58 ]. All of these factors can affect the quality of life of infertile women negatively.

The infertile women in our study obtained the highest score in the dimension of adaptive approach which was almost 5 points higher than the total mean score of quality of life. In this dimension, women received the highest percentage of score in the item related to trusting in God. Given the Iranian culture, religious beliefs play an important role in dealing with problems of life, based on which the high scores of women in this dimension can be partly justified. Although communication with God is one of the spiritual issues that plays a significant role in relieving people and helping them cope with infertility, its role is under discussion in different societies [ 59 ]. In some societies, childbearing is even considered to be a means of achieving piety [ 48 ]. Receiving social support from others and medical staff is one of the strategies that may help infertile women to deal with infertility [ 60 ]. Based on studies, infertile people who have received social support have a better quality of life and use better-coping strategies [ 61 ]. On the other hand, infertility is considered in some studies to be a social disgrace or stigma. The more the burden of this stigma in society, the more infertility is considered to be a threat to self-esteem. In such a condition, infertile women have less social support and their coping ability will be gradually reduced. Generally speaking, these problems are more prominent in women than men that may affect their quality of life [ 62 ].

As studies have shown, adaptive approaches have been among the ways of dealing with infertility. Although infertility is a threat to women's quality of life, it largely depends on their ability in responding to problems. Self-control is a psychological skill by which people can control their infertility-related emotions and come to a better understanding of their existential abilities that, ultimately, leads to personal peace and comfort [ 63 ]. In the studies conducted by Pasha et al. [ 64 ] Arsalan et al. [ 65 ], self-control reduced anxiety, depression, and stress in infertile women and improved the outcomes of treatment. By contrast, those who do not have enough knowledge about themselves and their abilities are at risk and, because of their weaknesses, do not have enough ability to do things, which can lead to their social isolation and reduces their quality of life [ 66 ].

Concerning the dimension of factors preventing adaptation, the mean score of the women was 60.98 ± 8.24 percent which was about 5 points lower than the mean total score of quality of life. At the Cairo International Conference on Population and Development in 1994, the problem of infertility was emphasized as an important health priority. However, this problem has been overlooked not only in developing countries but at most levels of international health management [ 67 ]. This view is a kind of underestimation of the infertility problem, which justifies the lack of governmental centers, lack of funding, specialists, and cost-effective treatment options [ 68 ]. Current disorganized infertility policies in the field of treatment and distribution have led to an inadequate distribution of public and private centers [ 69 ]. Many policies in Iran are only to increase the fertility rate, and economic problems in the provision of medicine and infertility treatment have unfortunately been overlooked. Current policies in Iran do not consider prevention, early diagnosis and referral, primary supportive care, and access to infertility services. These problems reduce the ability in coping with the problem of infertility [ 70 ].

Furthermore, because of patriarchal beliefs for the survival and continuity of the generation, women's reproductive inability can lead to gender-related inequalities. In such societies, infertility is considered a purely feminine issue, and, thus, women consider themselves inferior to men and are doubly pressured [ 11 ]. The salient difference between developed and developing societies in the issue of childbearing is that not having children in developed societies can be voluntary and considered an option for women to grow [ 71 ], but in developing countries, female infertility means a kind of disease and a feminine defective identity [ 72 ]. If men play their roles as partners and supporters of women, reproductive health indicators and treatment outcomes will surely be improved. As such, programs that involve men alongside women will be more successful [ 73 ].

It was revealed in our study that women with primary infertility have a lower quality of life. Similar results were obtained in the studies of Khayata et al. [ 74 ] and Shahraki et al. [ 75 ]. Because of having no child and experiencing no pregnancy, primary infertile women are more concerned than secondary infertile women, which can further affect the quality of life of these women with primary infertility [ 75 ].

As revealed in this study, the age of women, the age of the spouse, the duration of the marriage, and the duration of infertility are inversely correlated with the quality of life of infertile women. Women generally have better physical, mental, and environmental quality in the first 10 years of their marital life. However, with the passage of time and prolonged infertility, they grow a negative evaluation of themselves that may reduce their marital satisfaction and, consequently, have some negative effects on their quality of life [ 58 ]. Aging in men and women has a negative effect on the quality of life because it is a risk factor for reduced fertility and the chances of successful treatment [ 34 , 37 ]. Similar findings have been found in different studies [ 76 , 77 ], but it is noteworthy that education improves the quality of life of infertile women. Women's education is one of the most important factors which can empower women and make them use adaptive approaches in dealing with problems [ 78 , 79 ]. Husbands' education makes them more aware of infertility, their wives feel less worried and their quality of life is higher [ 32 ]. Because most housewives are concerned about financing infertility treatment, improving their economic situation will reduce their worries about infertility treatment and can improve their quality of life [ 5 , 22 ].

Limitations of the study

The cross-sectional design of the present research was a limitation of the study as the direction of causality of the relationship may be questionable in this study. Given the fact that this study used for the first time the questionnaire of quality of life of infertile women with female factor, it was almost impossible to compare it accurately with other studies which had used different tools. Thus, as this tool is a valid and reliable one, it can be used in other studies in different societies.

Assessment of the different aspects of the quality of life of infertile women including psychological effects, sexual life with infertility, family and social effects, infertility-related concerns, physical effects, adaptive approaches, and factors preventing infertility adaptation showed the lowest scores in the infertility-related concerns, and the sexual life aspects. Therefore, these are the most important dimensions of infertile women’s quality of life that need special consideration for intervention. Increased duration of marriage and duration of fertility decreases women's quality of life. Infertile women's quality of life is an important and challenging issue whose accurate measurement is critically significant. Given the use of a standard tool for assessing the quality of life of infertile women, the information extracted from this study can be used in planning and intervention for improving the quality of life of infertile women.

Availability of data and materials

The data that support the findings of this study are available from Masoumeh Simbar but restrictions apply to the availability of these data, which were used under license for the current study, and so are not publicly available.

Abbreviations

World Health Organization

36-Item Short Form Survey

Hospital Anxiety and Depression Scale

Fertility Quality of Life Questionnaire

Quality of Life in Infertile Couples Questionnaire

World Health Organization Quality of Life

The quality of life questionnaire for infertile women

Scale- Content Validity Index

Item- Content Validity Index

Variance Inflation Index

Statistical Package for the Social Sciences

Standard Deviation

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Acknowledgements

The authors express their gratitude to women participating in the research, employees of infertility centers and all the researchers whose articles were analyzed, and the Nursing and Midwifery Faculty of the University of Medical Sciences for their support and contribution to this work.

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Proteomics Research Center and Department of Biostatistics, Faculty of Allied Medical Sciences, Shahid Beheshti University of Medical Sciences, Tehran, Iran

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ZK and MS, SH, and FZ conceived the study, interpreted the results, and co-wrote the manuscript. ZK, FZ, MS, FR, and FJ collected the data, helped with data interpretation, and co-wrote the manuscript. All the authors read and approved the final manuscript.

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Kiani, Z., Simbar, M., Hajian, S. et al. Investigating different dimensions of infertile women’s quality of life: a descriptive cross-sectional study. BMC Public Health 22 , 2436 (2022). https://doi.org/10.1186/s12889-022-14924-w

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Effectiveness of psychosocial interventions for infertile women: A systematic review and meta-analysis with a focus on a method-critical evaluation

Roles Conceptualization, Data curation, Formal analysis, Investigation, Methodology, Writing – original draft, Writing – review & editing

* E-mail: [email protected]

Affiliation Institute of Medical Psychology, Centre for Psychosocial Medicine, University Hospital, Heidelberg, Germany

Roles Project administration, Supervision, Writing – review & editing

Roles Conceptualization, Data curation, Investigation, Project administration, Supervision, Visualization, Writing – review & editing

• Franziska Kremer, 
• Beate Ditzen, 
• Tewes Wischmann

• Published: February 28, 2023
• https://doi.org/10.1371/journal.pone.0282065
• Peer Review
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Approximately seven to nine percent of couples of reproductive age do not get pregnant despite regular and unprotected sexual intercourse. Various psychosocial interventions for women and men with fertility disorders are repeatedly found in the literature. The effects of these interventions on outcomes such as anxiety and depression, as well as on the probability of pregnancy, do not currently allow for reliable generalisable statements. This review includes studies published since 2015 performing a method-critical evaluation of the studies. Furthermore, we suggest how interventions could be implemented in the future to improve anxiety, depression, and pregnancy rates.

The project was registered with Prospero (CRD42021242683 13 April 2021). The literature search was conducted according to the Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) guidelines. Six databases were searched and 479 potential studies were discovered. After reviewing the full texts, ten studies were included for the synthesis. Not all studies reported the three outcomes: four studies each for depression, three for anxiety and nine studies for pregnancy rates were included in the meta-analysis, which was conducted using the Comprehensive meta-analysis (CMA) software.

Psychosocial interventions do not significantly change women’s anxiety (Hedges’ g -0,006; CI: -0,667 to 0,655; p = 0,985), but they have a significant impact on depression in infertile women (Hedges’ g -0,893; CI: -1,644 to -0,145; p = 0,026). Implementations of psychosocial interventions during assisted reproductive technology (ART) treatment do not increase pregnancy rates (odds ratio 1,337; 95% CI 0,983 to 1,820; p = 0,064). The methodological critical evaluation indicates heterogeneous study design and samples. The results of the studies were determined with different methods and make comparability difficult. All these factors do not allow for a uniform conclusion.

Methodological critical evaluation

Study design (duration and timing of intervention, type of intervention, type of data collection) and samples (age of women, reason for infertility, duration of infertility) are very heterogeneous. The results of the studies were determined with different methods and make comparability difficult. All these factors do not allow for a uniform conclusion.

In order to be able to better compare psychosocial interventions and their influence on ART treatment and thus also to achieve valid results, a standardised procedure to the mentioned factors is necessary.

Citation: Kremer F, Ditzen B, Wischmann T (2023) Effectiveness of psychosocial interventions for infertile women: A systematic review and meta-analysis with a focus on a method-critical evaluation . PLoS ONE 18(2): e0282065. https://doi.org/10.1371/journal.pone.0282065

Editor: Giovanni Buzzaccarini, San Raffaele Institute: IRCCS Ospedale San Raffaele, ITALY

Received: December 5, 2022; Accepted: February 7, 2023; Published: February 28, 2023

Copyright: © 2023 Kremer et al. This is an open access article distributed under the terms of the Creative Commons Attribution License , which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited.

Data Availability: All relevant data are within the manuscript and its Supporting information files.

Funding: After this work is accepted, a request will be made to cover publication costs. If the costs are covered: For the publication fee we acknowledge financial support by Deutsche Forschungsgemeinschaft within the funding programme „Open Access Publikationskosten“ as well as by Heidelberg University.

Competing interests: The authors have declared that no competing interests exist.

Introduction

Infertility affects 48.5 million people worldwide [ 1 ] including 15% of couples of reproductive age [ 2 ]. In Germany, about eight percent of couples of fertile age are involuntarily childless, and about 25,000 couples undergo assisted reproductive technology (ART) in Germany each year [ 3 ]. The World Health Organization (WHO) defines infertility as "a disease of the reproductive system defined by the failure to achieve a clinical pregnancy after 12 months or more of regular unprotected sexual intercourse" [ 4 ].

The unfulfilled desire to have children and the various ART procedures usually place a considerable burden on the couple. Childlessness is often perceived as a life crisis, the emotional burden of which is equivalent to that of a traumatic event [ 5 ]. Some studies indicate an increased risk of developing symptoms of psychological distress, depression and anxiety in infertile patients, even though there have been no previous psychological problems in their medical history. This is particularly the case when treatment does not result in a clinical pregnancy or live birth [ 6 – 10 ]. ART also has a psychological impact on men, although they tend to be less affected by the treatments than women [ 11 ].

Stressful life events such as ART treatment can trigger a physiological stress response, e.g., by potentially altering the regulation of sex hormone signalling, which can lead to a reduction in reproductive potential [ 12 ]. It has therefore been suggested that increased psychological stress may be associated with a lower pregnancy rate [ 13 , 14 ]. Hence, several studies have investigated possible associations between psychosocial interventions focusing on psychological distress and ART treatment outcomes [ 14 – 22 ]. However, the results are heterogeneous. Regarding the effectiveness of psychosocial interventions for fertility disorders on the quality of life of affected individuals (especially anxiety and depression), the findings are also inconsistent. Two recent reviews [ 23 , 24 ] published during our data collection showed an improvement in pregnancy rates through psychosocial interventions (RR = 1,12 [ 24 ]; RR = 1,25 [ 23 ]). Both author teams included RCTs only.

The aim of this systematic review and meta-analysis–which has been undertaken without prior knowledge of Katyal et al. [ 24 ] and Dube et al. [ 23 ]—is to investigate the effects of psychosocial interventions on the psychological factors anxiety and depression as well as on pregnancy rates of women undergoing ART treatment compared to women undergoing ART treatment only and not receiving psychosocial interventions (treatment as usual). In this meta-analysis, the attention is exclusively on the psychosocial aspects and not on medical content. In addition, we focus on a method-critical evaluation of these studies.

Literature search and screening criteria

A protocol (see S1 File ) was developed in advance and the systematic review was registered with Prospero. A systematic literature search was conducted according to the PRISMA statement (Preferred Reporting Items for Systematic Reviews and Meta-Analyses) [ 25 ] which is shown in Fig 1 . The PRISMA checklist can be found in the appendix ( S1 Table ). A total of six databases (CINAHL, Cochrane, PsychInfo, Psyndex, PubMed, Web of Science,) were searched for studies reporting on psychosocial interventions for infertile women and anxiety and/or depression and/or pregnancy rates. Medical keywords (MesH) or a comparable method were used to identify the search terms ( S2 File ).

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Diagram showing the flow of information through the different phases of the systematic review and meta-analysis.

https://doi.org/10.1371/journal.pone.0282065.g001

The initial search was conducted by the authors F.K. and T.W. in May 2021 and updated in April 2022. Empirical studies published since April 2015 were included. The reason for the temporal selection period from April 2015 is the Cochrane Review [ 26 ] which can be classified as high quality. These authors included publications up to March 2015. They concluded that the low quality of the selected studies did not allow for a meta-analysis and provided recommendations for future studies. For this reason, we examined studies from 2015 onwards.

The database searches yielded 479 records, with a further 11 records identified through citation snowballing and experts in the field. The references of the articles selected for review and other related systematic reviews were also screened to look for other relevant articles. After removing 34 duplicates found in more than one database, 456 records remained ( Fig 1 ).

Selection of studies

The systematic review with meta-analysis included studies that all met the following a priori criteria ( Table 1 ).

https://doi.org/10.1371/journal.pone.0282065.t001

The exclusion criteria of this review were as follows: studies that do not provide detailed information on the duration of infertility, treatment type, treatment cycle and duration and number of sessions of interventions; and studies that were only published in conference supplements or proceedings and whose authors did not respond to repeated email requests for further data ( S2 Table ).

Application of the inclusion criteria : By applying the inclusion criteria to the information contained in the title and abstracts, the number of records was reduced to 53. After screening the full texts, a total of 17 studies from 53 publications could be included in the review. The screening and selection of abstracts were carried out by F. K. T. W. reviewed 40 randomly selected abstracts. The agreement rate between the two authors was 97.5%. Potential conflicts were resolved within a group of two until consensus could be reached.

In the case of missing or ambiguous information in the full text, the corresponding author of the publication in question was searched for electronically and contacted with the kind request to provide the missing or additional information. If no current address of the corresponding author could be found, the co-authors were contacted. A total of 23 authors were contacted of which 11 authors responded. We would like to take this opportunity to thank all authors who responded for their cooperation.

Another seven articles had to be excluded because the data were insufficient or the authors did not respond to the email. Of these final ten studies, four articles reported on anxiety, four reported on depression, and a total of nine reported on pregnancy rates. No study captured live birth rates.

Data extraction

The following data were extracted: (1) general information: first author, year of publication, country of origin as well as journal and impact factor; (2) number of women, men or couples; (3) characteristics of the intervention: type, timing, number and duration of sessions, duration of intervention, setting, persons implementing, and measurement points; and (4) outcome measures: anxiety, depression, and pregnancy rate; (5) Quality criteria: power analysis, loss to follow within reasons, randomization, study design and participation criteria ( S3 File ).

One study [ 27 , 28 ] have two publications on the same research project. Missing data from the main publication were supplemented and extracted by the second publication. A detailed overview of the extracted data can be found in the online Resource 2.

To determine the quality of evidence, the GRADE approach was used [ 29 – 36 ]. RCTs are first categorised as high-quality evidence in the GRADE system, while observational studies are classified as low-quality data supporting estimates of intervention effects. Five factors can degrade the quality of evidence, whereas three factors can improve it. Each outcome’s evidence quality falls into one of four categories, ranging from extremely high to very low. This is performed in order to evaluate the quality of evidence for each outcome across all trials. This does not imply that each study is evaluated individually. Rather, GRADE is "outcome-based": Grading is performed for each result, and quality might vary from one outcome to the next within single research and a body of evidence.

Calculating effect size

The reporting of the results of the studies were inconsistent and incomplete. Effect size calculation was therefore only possible for ten studies that reported state anxiety, depression or pregnancy rates. Given the available data, an effect size calculation was performed for mean differences of groups with unequal sample sizes within a pre-post control design as described by Morris and DeShon [ 37 , 38 ]. An online calculator was used, which is available on the Open Access website: www.psychometrica.de/effect_size.html .

Computate the meta-analysis

The software Comprehensive Meta-Analysis Version 3 (CMA) [ 39 ] was used to calculate the random-effects meta-analysis. Hedges’ g was calculated for continuous outcomes and the odds ratio for binary outcomes, each with a 95% CI with two-sided p values for each outcome.

Hedges’g, like Cohen’s d, is an effect size based on standardised mean differences. Especially for small samples (n < 20), Cohen’s d yields biased results. Both Cohen’s d and Hedge’s g use pooled variances; however, g pools with Bessel correction (n-1), which provides a better estimate, especially for small samples. Both d and g overestimate the effect size, albeit only slightly. The interpretation also follows Cohen’s rules of thumb in each case [ 40 , 41 ].

To check data for heterogeneity, we performed a visual inspection by examining the similarity of the point estimates, the overlap of the confidence intervals and the results of the statistical heterogeneity tests displayed at the bottom of a Forest plot. If greater similarity of point estimates and greater overlap of confidence intervals is observed, this means less heterogeneity [ 33 ] The P-value determined by the Chi-square test is the probability for the null hypothesis that there is no heterogeneity between the studies. Furthermore, I2 describes the percentage of variability in the effect estimates that is due to heterogeneity and not to sampling error (chance) The statistic I2 ranges from 0 to 100% and indicates the extent of heterogeneity. A larger I2 indicates greater heterogeneity. An I2 below 40% may indicate insignificant heterogeneity, while an I2 above 75% indicates considerable heterogeneity [ 42 ].

For advanced analysis the ’Trim and Fill’ method by Duval and Tweedie [ 39 ] was used to calculate publication bias. The approach first removes the asymmetric studies from the right to find the unbiased effect (in an iterative process), and then fills the plot by reinserting the trimmed studies on the right as well as their imputed counterparts to the left the mean effect. The program is looking for missing studies based on a fixed effect model (by convention), and is looking for missing studies only to the right side of the mean effect. We did not perform further analyses for anxiety and depression as tests for funnel plot asymmetry should only be performed in meta-analysis including at least 10 studies [ 43 ].

Due to the small number of original studies, no moderator analyses were calculated. The focus of this review was on the method-critical evaluation.

A total of ten studies published between April 2015 and May 2022 were included in the systematic review [ 27 , 44 – 52 ]. All articles were written in English. The study characteristics extracted from the original studies are listed in Table 2 .

https://doi.org/10.1371/journal.pone.0282065.t002

Study characteristics

All studies were open-label randomised controlled trials (RCT). A more detailed analysis can be found in section of the method-critical evaluation.

The outcome measures were inconsistent across all variables:

Three studies reported state anxiety scores with State-Trait Anxiety Inventory (STAI) [ 53 ]. Anxiety was also evaluated with the Generalised Anxiety Disorder-7 (GAD- 7) scale [ 54 ] and Beck Anxiety Inventory (BAI) [ 55 ]. The convergent validity between the mentioned questionnaires is low [ 56 ]. For this reason, these values cannot be compared with each other. The questionnaire that was used most frequently was therefore used in this analysis.

Five trials reported depression scores: of these, 4 used the Beck Depression Inventory (BDI) scale [ 57 ], with three using a translation of that questionnaire. Another study used the Patient Health Questionnaire PHQ [ 58 ]. The convergent validity between the two questionnaires can be classified as "closely correlated" [ 59 ]. However, the authors [ 45 ] reported Wald Chi-squared values for the results. Mail requests for raw values or other more usable values were not answered. For this reason, the results could not be included in the analysis.

Overall, nine studies reported pregnancy rates. However, the results were recorded differently. For example, Domar et al. [ 47 ] defined a woman as “pregnant” after a positive 7-week fetal heart ultrasound. Frederiksen [ 48 ] determines a pregnancy as clinical pregnancy, i.e., a vaginal ultrasound examination showing at least one gestational sac with fetal heartbeat performed 5 weeks after embryo transfer. Other studies used Beta hcG as evidence of pregnancy. Two author teams mentioned a pregnancy test [ 49 ] and a blood pregnancy test [ 51 ] as evidence, respectively. One study collected self-reports from the patients [ 46 ].

Participants characteristics

The sample sizes ranged from 49 to 186 women with a median of 116 and a mean of 113. A total of 1,129 women were scientifically examined. Only the study by [ 48 ] collected data from men. Considering that the vast majority of research has only collected data on women, this review also focuses on women. The age of the women was presented differently. Eight out of ten studies reported age as mean [ 28 , 45 – 51 ], with the youngest participants in [ 27 ] with a mean of 29 years and the oldest participants in Domar et al.´s study [ 47 ] with a mean of 34,85 years. Two other studies provided age groups and frequencies [ 44 , 52 ].

Whether women were receiving ART at the time of the intervention was often not specifically noted in the studies. Participants in six trials were beginning, in four trials were undergoing an ART ( Table 3 ).

https://doi.org/10.1371/journal.pone.0282065.t003

Intervention characteristics

The psychosocial interventions ranged from music therapy [ 44 ] to gratitude, mindfulness [ 45 ], relaxation techniques such as progressive muscle relaxation [ 27 ] and diaphragmatic breathing [ 28 , 46 , 47 ], yoga [ 50 ], assertiveness training [ 46 ], cognitive-behavioural stress reduction, imagination, expressive writing [ 48 ] and laughter therapy [ 51 ]. One study also included nutrition and exercise [ 49 ]. Domar et al.´s study [ 47 ] adapted the intervention to the individual phases of treatment (stimulation phase, waiting period). It is important to note that no study used a single form of intervention solely, as each trial used a combination of different methods.

The number of sessions ranged from one [ 44 ] to on a daily basis during the treatment cycle, whereby the intervention was done as homework and not guided by a professional person each time [ 47 ]. The duration of the sessions ranged from 20 [ 48 ] to 120 minutes [ 49 ]. The duration of the intervention ranged from 2 * 28 minutes [ 44 ] to twelve months. The participants were able to decide how long and how often they would use the intervention at home during the twelve-month period [ 47 ].

Not every intervention required trained staff. For example, in the study of Aba [ 44 ] a CD for music therapy was used and in Domar et al. [ 47 ] information leaflets were given out.

The interventions were mostly individual sessions, but some had some group sessions. Interventions are detailed in Table 2 .

Effects of the psychosocial interventions

We have aggregated the results in a Table "Summary of results" to give an overview ( Table 4 ).

https://doi.org/10.1371/journal.pone.0282065.t004

Three studies examined the effect of psychosocial interventions on anxiety scores using the STAI. These scores range from 20 to 80, with a higher score indicating greater anxiety. A cut-off score of 39 to 40 has been suggested to identify clinically significant anxiety symptoms for the State Anxiety Scale [ 60 ]. In the reports on which this paper is based, state STAI scores in women undergoing fertility treatment ranged from 33.39 to 45.11 ( Table 5 ). In two out of three studies in this review, participants had mild clinical anxiety symptoms before the intervention, with scores ranging from 43.11 to 44.87. After the intervention, anxiety scores decrease significant in all intervention groups.

https://doi.org/10.1371/journal.pone.0282065.t005

For three studies that reported anxiety in the experimental and control groups, the heterogeneity between studies showed Q = 0,298, df = 2 (p = 0,862), and I2 = 0,00%. In general, the effects of the intervention are likely to be heterogeneous, if there is a low p value or a high Q statistic in respect to the degree of freedom [ 42 ]. There appears to be heterogeneity in this analysis. I2 describes the percentage of variability in effect estimates that is due to heterogeneity rather than sampling error. I2 is a useful statistic for quantifying inconsistency. The importance of the observed value of I2 depends on magnitude and direction of effects, and strength of evidence for heterogeneity (e.g. P value from the Chi2 test, or a confidence interval for I2: uncertainty in the value of I2 is substantial when the number of studies is small) [ 42 ]. Since only three studies could be included, this value and also the meta-analysis are not meaningful. For the sake of completeness, the other values are reported. The effect size Hedges´ g of anxiety ( Fig 2 ) was -0,006 (95% CI: -0,667, 0,655), and the anxiety between the experimental group and the control group showed no statistically significant difference (Z = -0,019, p = 0,985). Nevertheless, the calculated analysis is not to be considered due to heterogeneity and small sample size.

Hedges´ g, confidence interval (CI), Z-Value, p-Value and forest plot for anxiety (created with CMA Software [ 39 ]).

https://doi.org/10.1371/journal.pone.0282065.g002

Depression.

All studies that reported depression as an outcome showed a significant improvement in depression scores. The BDI is a 21-question multiple-choice self-assessment inventory with a maximum score of 63 [ 57 ]. Scores ranged from 0–13 (no depression), 14–19 (mild depression), 20–28 (moderate depression) and 29–63 (severe depression). As shown in Table 6 , the women had BDI scores ranging from no depression to moderate depression before the intervention [ 50 ]. We found a statistically significant decrease in all four studies.

https://doi.org/10.1371/journal.pone.0282065.t006

Based on the examination of four studies that included depression in the intervention and control groups, the heterogeneity between studies showed yielded the following results: Q = 3,250, df = 3 (p = 0,355), and I2 = 8%. The data were homogeneous and consistent. The effect size Hedges´ g of depression ( Fig 3 ) was -0,893 (95% CI: -1,677, -0,108), and the depression between the experimental group and the control group showed a statistically significant difference (Z = -2,230, p = 0,026).

Hedges´ g, confidence interval (CI), Z-Value, p-Value and forest plot for depression (created with CMA Software (Borenstein et al. 2015) [ 39 ]). * p value < .05.

https://doi.org/10.1371/journal.pone.0282065.g003

Pregnancy rates.

Nine studies reported pregnancy rates—one study had two intervention groups and is therefore included twice in the analysis [ 45 ]. The following results are obtained for these trials: Q = 13,183, df = 9 (p = 0,155), I2 = 31,7308%. There is less heterogeneity and less inconsistency in this analysis. Psychosocial interventions have no significant effect on pregnancy rate with a odds ratio (OR) of 1,337 (95% CI 0,983; 1,820) with Z = 1,850 (p = 0,064) ( Fig 4 ).

Hedges´s g, confidence interval (CI), Z-Value, p-Value and forest plot for pregnancy rates (created with CMA Software [ 39 ]).

https://doi.org/10.1371/journal.pone.0282065.g004

Advanced analysis for pregnancy rates . A visual check was made to see if there was a publication bias [ 34 ]. Under the random effect model the point estimate and 95% confidence interval for the combined studies was 1,33732 (0,98280; 1,81968). Using Trim and Fill these values were unchanged. The method suggests that no studies were missing. This is visually underlined: the white and black diamond lie on top of each other in the funnel plot and do not deviate ( Fig 5 ).

Including the ’Trim and Fill’-method (black diamond), looking for missing studies (created with CMA Software [ 39 ]).

https://doi.org/10.1371/journal.pone.0282065.g005

The sensitivity analysis yielded a robust result. Different calculations were carried out in which one study was removed in each case. The final result did not differ from the model calculated with all included studies.

The mean effect size was estimated as 1,337, and the confidence interval provided information on the precision of this estimate. The 95% confidence interval was 1,071 to 1,669. The estimated prediction interval was 0,672 to 2,658 in log units.

Method-critical evaluation

Power analysis.

We evaluate how different studies performed their power analysis:

Czamanski-Cohen et al. [ 27 ] and Fata and Tokat [ 52 ] used a study [ 61 ] as a basis for calculating the power analysis, which investigate whether hypnosis during embryo transfer (ET) contributes to successful IVF/ET outcome. In this case-control study from Levitas et al. [ 61 ], the methodological challenge was to establish an optimal match between the hypnosis and control cases. Parameters of the study were analysed to assess their impact on conception. Duration of infertility was not one of the matching criteria between the hypnosis and control groups. It was found to be significantly longer in the control group patients. This context should be taken into account. Furthermore, the underlying sample size on which the calculations were based was not the number of women but the number of cycles.

Bai et al. [ 45 ] used the effect size d = 0.59 (medium effect [ 41 ]) for the calculation of the power analysis, which was determined in the meta-analysis by Frederiksen et al. [ 19 ]. In this meta-analysis, it is critical to note that the effect sizes of the RCTs regarding increased pregnancy rates are smaller than in the other non-RCTs studies analysed [ 17 ]. Furthermore, Frederiksen et al. [ 19 ] made some miscalculations in their meta-analysis [ 62 ].

Most authors utilised different software to calculate the power and described their calculations and values they used [ 44 , 47 , 49 , 51 ]. Frederiksen et al. [ 48 ] did not describe the sample size estimation or power calculation in detail. Kalhori et al. [ 50 ] presented the formula they used, so the calculation is replicable.

In the Clifton et al. study [ 46 ], the sample size is based on the feasibility and recommendations for pilot studies that precede clinical trials [ 63 ].

Randomisation

Randomisations were carried out differently. Some studies used a number generator for each participation (e.g., [ 46 ], others used block randomisation (e.g., [ 48 ]) and one used a permuted block algorithm stratified according to age and anxiety levels [ 44 ]. All these types of randomisations are legitimate.

The creation of the random sequence should be done by an independent person, usually a statistician, who is not involved in the conduct of the RCT [ 64 ]. Bai et al. [ 45 ] used the possibility of this allocation type. In two groups of authors, it was explicitly mentioned that the randomisation was done by a person, who was not responsible for the intervention [ 47 , 51 ].

In addition, one study of this review explicitly mentioned that participants were not informed about the hypotheses and content of the intervention [ 45 ].

Detailed information about randomisation and blinding can be found in Table 7 .

https://doi.org/10.1371/journal.pone.0282065.t007

Eligibility criteria

The majority of authors of this review [ 28 , 44 – 46 , 49 – 52 ] defined the following, among others, as exclusion criteria: the participants should not have mental illness (other formulations were: perception disorders, psychiatric disorder, no suicidal ideation/intent, psychotic disorder, eating disorder, substance abuse or dependence nor axis I Diagnostic and statistical manual of mental disorders IV-TR diagnosis).

In most studies participants were not pre-screened for psychiatric disorders (e.g., DSM-IV axis I psychiatric illness), among others affective and anxiety disorders in the clinically significant range. No semi-structured interviews or similar established survey instruments were conducted (e.g., Structured Clinical Interview for DSM-5 Disorders [ 65 ]) Although these clinically relevant disorders were mentioned in the exclusion criteria, no valid screening by mental health professionals took place. The participants were asked about this with specially designed personal information form. However, the self-assessment ought to be confirmed by external assessment as well. Two studies have examined this in more detail. Clifton et al. [ 46 ] assessed the exclusion criteria by an internet-based MINI International Neuropsychiatric Interview for DSM-IV [ 66 ]. In Hamzehgardeshi et al. [ 49 ] the participants were diagnosed by a psychologist. The more detailed procedure and the qualification of the psychologist were not described.

Intervention

No study used only one form of intervention. Each trial used a combination of different methods. The interventions are diverse ( Table 2 ) Not only psychosocial interventions are included. Physical activity (general: [ 49 ] Hatha Yoga: [ 46 ]) and nutrition [ 49 ] are found in some interventions. This wide variety of interventions makes it extremely difficult to show a causal relationship in the case of a significant effect. It is therefore impossible to identify the active ingredient(s) of the intervention.

Furthermore, there are large differences in the duration of the interventions and the number of the sessions. The authors [ 44 ] applied the intervention on a single day. Respectively, the music therapy group received twenty-eight minutes of music therapy one hour before and after the embryo transfer.

Clifton et al. [ 46 ] provided participants with ten online modules that lasted less than 60 minutes each. A therapist gave feedback after pre-assessment, after each module and was available by email. Participants could decide for themselves when they wanted to work on the modules. Afterwards, it was analysed how many modules they had completed. Thirty-nine per cent of the participants completed all ten modules. Furthermore, the time of study participation differed massively between the intervention and control groups: the control group was in the study for an average of 90 days and the intervention group was in the study for 233 days, which is 2.6 times longer. The time factor alone could explain the higher pregnancy rates in the intervention study. Clifton’s study is the only one whose confidence interval does not include 1 ( Fig 4 ) and was significant. The interpretation of this significant result must be related to the different lengths of stay in the intervention group.

The participants from Domar et al. [ 47 ] were instructed to read the intervention cards or use the relaxation methods independently, during the 12-month observation period. The intervention should be used daily during the treatment cycle. It was self-administered intervention without a delivery person.

The duration of a session was a minimum of 20 minutes [ 52 ] and up to 120 minutes plus homework [ 49 ]. The question of what minimum or maximum duration should be required for the intervention cannot be answered unambiguously because of the different durations.

Time of the measurement

Baseline was mostly collected at the start of ART treatment or before the intervention. There were large differences between the studies with the post measurement concerning the survey of anxiety and depression:

• on oocyte pick-up day [ 51 ],
• 3–7 days before embryo transfer [ 50 ],
• on the day of embryo transfer [ 52 ],
• post embryo transfer [ 44 ]
• three days before pregnancy test [ 45 , 47 ],
• on the day of the pregnancy test [ 28 ]
• three months after intervention [ 48 ]
• ten weeks after start of treatment for control group compared to end of program (on average 223 day) for intervention group [ 46 ].

The study by Aba et al. is an exception. The pre- and post-measurements were taken on the same day, before and after embryo transfer.

Due to the different measurement times, it is extremely difficult to make a uniform statement about the effectiveness of psychosocial intervention on anxiety and depression scores as well as pregnancy rates.

This is a systematic review and meta-analysis of the effects of psychosocial interventions in women with fertility disorders. Only one included study collected data from men, so conclusions about the effect of psychosocial interventions on men are not possible. Based on a total of ten studies included in this systematic review, we found a significant large effect for depression, no significant effect for anxiety and pregnancy rates. The four studies that resulted in significant reductions in depression scores used the following psychosocial interventions: cognitive restructuring, emotional expression, assertiveness training as well as relaxation techniques (incl. diaphragmatic breathing) and Yoga [ 46 ]; expressive writing [ 48 ]; breathing with mindfulness and Yoga [ 50 ] and progressive muscle relaxation and laughter therapy [ 51 ].

A comparison with published reviews indicates: De Liz and Strauss [ 15 ] identified a decrease in anxiety in their meta-analysis. After psychotherapy ended, the decrease in depressive symptoms was greater in patients after 6 months. Effects of the interventions on pregnancy rates were not detectable. Hämmerli [ 18 ] showed no significant effect of psychological interventions on mental health (depression, anxiety, psychological distress). Nevertheless, there was evidence for positive effects of psychological interventions on pregnancy rates when the duration of the psychological intervention was used as a moderator. Longer duration interventions improved anxiety and depression scores. One explanation for the positive effects on pregnancy probabilities could be, for example, increased sexual intercourse after the psychological interventions. Also conceivable is an effect of the high dropout rates of couples who did not become pregnant during ART treatment [ 62 ]. In another review by Ying et al. [ 17 ] the effects of psychosocial interventions on anxiety levels and pregnancy rates could not be confirmed due to methodological problems (related to measurement time points and dropout rates). None of the studies reviewed showed efficacy in improving depression or stress levels of individuals or couples undergoing IVF treatment. Furthermore, Verkuijlen’s team of authors [ 26 ] did not conduct a meta-analysis for the following reasons: They concluded that there was considerable clinical heterogeneity in terms of participant characteristics, type of intervention, delivery of the intervention, duration of the intervention and outcome measures. The pooled estimate would not have represented a clinically meaningful summary.

Katyal et al. [ 24 ] found an improvement in pregnancy rates in their meta-analysis. This increased when only long-duration interventions were considered, which excluded music therapy interventions (comparable with [ 23 ], see below).

The systematic review and meta-analysis of Dube et al. [ 23 ] (which has been published exactly at the time of completion of this review) showed that psychosocial interventions for women had a 25% higher probability of becoming pregnant than those who did not receive treatment. Art/music therapy, yoga, acupuncture and massage therapy were excluded as psychosocial interventions because they were not considered psychologically based. We did not make this exclusion and also examined studies that included music therapy and Yoga (as part of an intervention set). In addition, their review includes studies that recruited participants who were not specifically being treated for infertility with medication, as well as studies that included a mix of participants with and without medical treatment. Furthermore, the moderator analysis with region as moderator showed a reduction in effect size from large to small. Studies conducted within the Middle East showed a lager effect size as studies conducted in other regions worldwide. Our analysis regarding pregnancy rates also includes one study conducted in the Middle East. Anxiety and depression scores were slightly but statistically significantly improved by the interventions in the analysis of Dube et al. [ 23 ]. Our review also showed an improvement in anxiety in women with fertility disorders.

Due to the small number of studies and the heterogeneous results of other reviews, we focused on a method-critical evaluation of our original studies. In the following, recommendations are made that can provide a basis for future intervention studies in order to obtain clear statements about effects and effectiveness of psychosocial interventions.

Future intervention studies should calculate power a priori and make a careful selection of the underlying data (such as effect size). Ideally, software should be used for this as it is less prone to error. Publications should clearly set out the data on which they are based. This allows for transparency and reproducibility of the study.

One of the most important components of a RCT is concealed allocation. This means that neither the providers, the investigators nor the participants know whether the next eligible participant will receive the treatment or the control intervention. This should be concealed until the time when participants are ready to receive the intervention. In this way, unnecessary adjustments to whether a participant should be enrolled or not, can be avoided. This is very important in situations where blinding of the intervention is not possible [ 64 ]. Ideally, random allocation of study participants to individual groups should be carried out using an established system in the future. Furthermore, we recommend that randomisations carried out by people who are not responsible for the implementation of the intervention and the data analysis.

If a person’s mental illness is defined as an exclusion criterion for participation in a study, then this should be done by a mental health professional using an established (screening) instrument. Self-reporting by study participants is not sufficient.

At this point, however, it should be taken into account that there is evidence of an increased risk of developing symptoms of mental distress, depression and anxiety in infertile patients, even if they have no history of mental health problems. This is especially the case if the treatment does not lead to a clinical pregnancy or live birth [ 67 ]. This means that it should also be investigated whether psychosocial interventions are effective in women or couples with elevated anxiety and depression levels. Pedro et al. [ 68 ] were able to show in their study that women who achieve a BDI score > 13 are five times more likely to discontinue fertility treatment, which ultimately reduces the success rate of ART treatment.

Throughout, all interventions are a mixture of different methods/interventions (as outlined above). This complicates the process of identifying the appropriate effective ingredient for a possibly successful intervention. For this reason, we recommend to use one form of intervention (for example, only progressive muscle relaxation or only cognitive restructuring). Once individual methods have been assessed, the next step is to combine them with other interventions that have already been evaluated.

Even if a single intervention is determined to be ineffective, the time dimension should be considered. Similar to the dose of a medication, it remains to be explored whether an intervention requires a certain length of time. The question of whether an intervention is successful after 10 minutes or after 20 or some other time window should be examined.

Pregnancy tests

The recording of pregnancy rates over time influences the supposed effectiveness of an intervention. The more time passes (from 2 weeks after embryo transfer to 7-week fetal heart ultrasound), the more the rate of premature abortions increases. No study surveyed the live birth rate. Unfortunately, even if a pregnancy can be induced, this does not necessarily mean a live birth. This is another reason that the results cannot be easily compared because of this heterogeneity. Future studies should record the live birth rate in addition to the occurrence of a pregnancy.

The results of this recent systematic review show serious methodological inadequacies in all studies to date. It is therefore not possible to draw conclusions of psychosocial interventions influencing quality of life (anxiety, depression) and pregnancy rates in women with fertility disorders. Further conclusions on the effects of psychosocial interventions for fertility disorders can only be made if, future studies are carefully planned and designed.

Therefore, the effectiveness of psychosocial interventions on anxiety, depression and pregnancy rates cannot be clearly assessed in this review with methodological evaluation. Future study designs should include a single intervention and establish uniform time points for measurements. Study participants should receive only one ART treatment cycle to allow comparison of results. Data should be collected not only up to the pregnancy test, but ideally up to 9 months later.

Supporting information

S1 table. prisma checklist..

https://doi.org/10.1371/journal.pone.0282065.s001

S2 Table. Excluded studies.

Inspection and exclusion within the qualitative synthesis.

https://doi.org/10.1371/journal.pone.0282065.s002

S3 Table. CMA Data for anxiety.

Data for anxiety used in CMA software to calculate analysis.

https://doi.org/10.1371/journal.pone.0282065.s003

S4 Table. CMA Data for depression.

Data for depression used in CMA software to calculate analysis.

https://doi.org/10.1371/journal.pone.0282065.s004

S5 Table. CMA Data for pregnancy rates.

Data for pregnancy rates used in CMA software to calculate analysis.

https://doi.org/10.1371/journal.pone.0282065.s005

S1 File. Protocol.

Effects of psychosocial interventions in couples with fertility disorders: protocol of a systematic review and a planned meta-analysis.

https://doi.org/10.1371/journal.pone.0282065.s006

S2 File. Search strategy and words.

Search strategy and search words for all databases.

https://doi.org/10.1371/journal.pone.0282065.s007

S3 File. Extracted and underlying data of included studies.

General information, sociodemographic data and reported outcomes.

https://doi.org/10.1371/journal.pone.0282065.s008

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