Abbreviation: PPD, postpartum depression.
All 4,844 individuals in the ontology were subclasses of one or more of the 8 confirmed risk factor classes, or subclasses of the 47 potential risk factors for which ICD codes existed. For history of depression, anxiety, and PPD, as well as lack of support postpregnancy, there were no ICD codes with the “history of” or “post pregnancy” temporal designations. A star (*) in Table 2 indicates the ontology class under which the ICD codes (individuals) for these conditions are located; however, the temporal relationship would need to be determined by a researcher or medical professional. For example, a patient with a history of PPD would be diagnosed with one of the ICD codes that was an individual of the Postpartum_Depression class. Researchers could then specify a time range when pulling patient codes from EHRs to determine whether patients had a history of PPD or current PPD. Furthermore, there were 23 potential risk factors for which there existed no ICD codes; thus, these risk factors are designated by a double star (**).
The object properties within the PDO are included in Table 3 . Object properties were critical for demonstrating the relationships of risk factors with PPD, as well as detailing injury relationships for the investigation of traumatic brain injury (TBI) as a potential risk factor. There were 13 object properties in total with seven classified as pregnancy- or mental health–related and two specifically showing the relationships among PPD and its risk factors. There were nine object properties with the general domain class OWL: Thing due to the domain spanning the entire ontology; for example, there were risk factors in all three superclasses of the PDO.
Domain class | Object property | Conjunction | Range class | Related to mental health, pregnancy, or other |
---|---|---|---|---|
owl:Thing | only | Postpartum_Depression_ Risk_Status | Pregnancy Mental health | |
owl:Thing | only some | Postpartum_Depression_ Risk_Type | Pregnancy Mental health | |
owl:Thing | NA | ICD_Code_Version | Pregnancy | |
owl:Thing | some | Postpartum_Depression | Pregnancy Mental health | |
Inviable_ Pregnancy_ Condition | only | Infant_or_Fetus_ Inviability | Pregnancy | |
owl:Thing | | only | Postpartum_Psychosis_ Risk_Status | Pregnancy Mental health |
owl:Thing | only | Psychotic_Status | Mental health | |
Injury_Type | only | Injury_Depth | Other | |
Injury_Type | only | Injury_Trauma | Other | |
TBI_Related_ Injury | only | Injury_Area | Other | |
owl:Thing | only | Condition_Time | Other | |
owl:Thing | only | Condition | Other | |
owl:Thing | only | Medical_Procedure_ Encounter_or_Treatment | Other |
Abbreviations: ICD, International Classification of Diseases; PPD, postpartum depression.
The PPD risk factors spanned the ontology and were categorized as several distinct types, leading to general owl:Thing domain classes for both PPD-related object properties. To differentiate among the types of risk factors, the object property has_PPD_risk_type was created with a range of Postpartum_Depression_Risk_Type. This class was further subdivided into the four risk factor type classes. To demonstrate whether the literature supported classes as confirmed or potential risk factors, the object property has_PPD_risk_factor_status was formed with a range of Postpartum_Depression_Risk_Status. This class included three subclasses identifying variables as confirmed, potential or not risk factors; this latter categorization included the class Mistaken_for_PPD which contained conditions with symptoms similar to those of PPD that could lead to an incorrect diagnosis.
The object property has_PPD_risk_factor_status required the use of the conjunction ‘only’ to relate it to a class and exclude the possibility of a confirmed risk factor also being a potential risk factor or not a risk factor. For example, Abuse_Violence_Type—the class representing all forms of abuse—had the object property relationship has_PPD_risk_factor_status ‘only’ Confirmed_PPD_Risk_Factor. In contrast, the object property has_PPD_risk_type allowed the conjunctions ‘only’ and ‘some’ to relate classes due to the polyhierarchical structure of the PDO. Abuse_Violence_Type had the object property relationship has_PPD_risk_type ‘only’ Instability_Mediated_by_Outside_Factor_Related_PPD_Risk_Type because it could not be classified as one of the other three types. However, History_of_Postpartum_Depression used the conjunction ‘some’ because it could be considered both a mental condition and pregnancy- or birth-related risk type, and ‘some’ specifies an “at least one” relationship. 32 Fig. 4 shows an example of the relationships and individuals for the confirmed risk factor abuse and, more specifically, sexual abuse.
Individuals and the has_PPD_risk_factor_status object property showing abuse as an example. In Fig. 4 , blue arrows show a has subclass relationship, the yellow arrow shows a has_PPD_risk_factor_status only relationship with the Confirmed_PPD_Risk_Factor class, and the purple arrows signify a has individual relationship. ICD codes for sexual abuse are shown here, with two overlapping the Physical_Abuse class. ICD, International Classification of Disease; PPD, postpartum depression.
Three more pregnancy- or mental health–related object properties were particularly important in the ontology. The object property has_ICD_version was used among the 4,844 individuals of the PPD risk factors to show whether the codes were from version ICD-9 or -10; no specified conjunction was necessary because the object property connected individuals. Fig. 5 represents the other object property illustrating the variety of PPD symptoms included in the final version of the PDO. The is_symptom_of object property had a range of Postpartum_Depression, and the conjunction ‘some’ was used because these symptoms are at least related to Postpartum_Depression, yet they could also be related to other conditions. Any symptoms and signs with synonymous or similar descriptions were included using the rdfs:label annotation.
Symptoms of PPD as shown in the PDO. In Fig. 5 , a representation of several PPD symptoms is visualized, in which the blue arrows show a has subclass relationship and the orange arrows signify an is_symptom_of some relationship with the Postpartum_Depression class. The yellow boxes show clustering of synonyms and similar terms under the rdfs:label annotation. PDO, postpartum depression ontology; PPD, postpartum depression.
There were six object properties classified as “Other.” Due to the relationship among traumatic injury, TBI, physical abuse, and depression, 33 34 35 a clinical expert (M.R.B.) recommended that we include TBI in the ontology as a potential PPD risk factor. Thus, the injury class required three object properties to relate five important subclasses of the injury superclass: Injury_Type, Injury_Area, Injury_Depth, Injury_Trauma, and TBI_Related_Injury. The class Injury_Type was related to the classes Injury_Depth and Injury_Trauma by the object properties has_injury_depth and has_injury_trauma_type , respectively, while TBI_Related_Injury was related to Injury_Area by the object property has_injury_area. All used the conjunction ‘only’ to exclude false injury descriptions; for example, the class Traumatic_Brain_Injury used the conjunction ‘only’ to provide closure to the object property has_injury_trauma_type ‘only’ Traumatic_Injury such that a nontraumatic cause of a brain injury was excluded.
Of the remaining three object properties classified as “Other,” the is_during object property was particularly important. Since the ontology was created to represent the PPD knowledge base, time was defined relative to pregnancy and delivery. The is_during object property was used to form many complex class expressions which have more than one conjunction. For example, the class Gestational_Diabetes was equivalent to Diabetes ‘and’ is_during ‘only’ Time_During_Pregnancy. In other words, gestational diabetes is a type of diabetes that only occurs during pregnancy. In another example, the object property defined the meaning of a very preterm baby, which is a baby born between 28 weeks of gestation up to 31 weeks 6 days gestation. 36 In this case, the object property would be used as follows: Very_Preterm is_during ‘only’ (28_Weeks_Gestation ‘or’ 29_Weeks_Gestation ‘or’ 30_Weeks_Gestation ‘or’ 31_Weeks_Gestation).
Case study evaluation results.
The case study evaluation was performed in two rounds of five case studies by R.B.M. and M.R.B ( Supplementary Table S3 , available in the online version). In the first set of 158 sentence chunks ( Fig. 3 ), 60 chunks (38%) were identified as relevant features by the two evaluators, and all 60 were annotated similarly. The remaining 98 chunks (62%) were only identified by one evaluator due to varying expertise; these were discussed to reach a consensus opinion for all. This process was repeated for the second set of case studies with 214 sentence chunks ( Fig. 3 ). Of these,106 chunks (49.5%) were identified as relevant features by both evaluators with 96 annotated similarly and 10 without consensus. This lack of consensus was due to differing interpretations of the sentence chunks. For example, “I wasn't eating or drinking enough water, which meant my body wasn't making breastmilk” was interpreted by R.B.M. as “breastfeeding difficulties,” whereas M.R.B. interpreted this as “nutrition issues.” For these 10 chunks and 108 (50.5%) identified by only one evaluator, a second discussion led to a consensus on classification of relevant features.
Table 4 shows the results of the analysis comparing the unique relevant features identified through case studies to the PDO in both rounds of evaluation. During the initial evaluation, 79 unique relevant features were analyzed. In total, 30.4% of all unique relevant features were explicitly included in the first PPD ontology; when similar classes for unique relevant features without an explicit class in the ontology were considered, 45.6% of the unique relevant features were covered. All 14 PPD treatments in the initial evaluation were not encapsulated, so they were added to a new class of features called PPD_Treatment. Furthermore, of the 11 PPD risk factors that were not already included, 10 were potential risk factors and one—History_of_Anxiety—was identified as a confirmed risk factor. A subsequent survey of the literature on History_of_Anxiety was performed to fulfill the criteria required in Fig. 2 .
Initial evaluation ( = 79) | Final evaluation ( = 88) | |||
---|---|---|---|---|
Unique relevant feature type | Included in first version of PDO | Total | Included in final version of PDO | Total |
PPD symptoms | 13 (30.2%) | 43 | 31 (67.4%) | 46 |
PPD risk factors | 11 (50.0%) | 22 | 27 (90.0%) | 30 |
PPD treatments | 0 (0.0%) | 14 | 10 (83.3%) | 12 |
Abbreviations: PDO, postpartum depression ontology; PPD, postpartum depression.
The final evaluation involved 88 unique relevant features of which 77.3% were already explicitly included in the second PPD ontology and 86.4% were covered when similar classes were evaluated. Interestingly, only three new potential risk factors were identified out of the 30 risk factor chunks, demonstrating a 90.0% accuracy in the ontology's goal of characterizing PPD risk factors. Most treatments were already included (83.3%), whereas several new symptoms were identified.
We used the Pellet reasoner to evaluate the final ontology and 154 inconsistencies were found. All inconsistencies resulted from multiple inheritance of ICD code individuals under the three disjoint superclasses: Condition, Maternal_Descriptor, and Medical_Procedure_Encounter_or_Treatment ( Supplementary Table S1 , available in the online version).
One major source of inconsistencies were ICD codes describing high-risk pregnancies which are classified as a descriptor of pregnancy risk in the ontology. For example, ICD-10 code O09.811, which is defined as “Supervision of pregnancy resulting from assisted reproductive technology, first trimester,” is an individual of the High_Risk and Assisted_Reproductive_Technology_Cycle classes. However, these are located under the Maternal_Descriptor and Medical_Procedure_Encounter_or_Treatment superclasses, respectively. ICD codes classified as multiple gestations also exhibited many inconsistencies. The ICD-9 code V27.3, which is defined as “Outcome of delivery, twins, one liveborn and one stillborn,” exhibited multiple inheritance of the classes Infant_Stillbirth (Condition) and Multiple_Gestation (Maternal_Descriptor).
The inconsistencies identified by the Pellet reasoner demonstrated that the three superclasses of the PDO should not be disjoint; some ICD codes are both descriptive in nature, as well as indicate a condition or procedure. All inconsistencies were deemed to be valid from a logical and clinical perspective, and we modified the ontology accordingly. The finalized ontology available on NCBO reflects the latest and most correct and updated version of the ontology. If any changes are made postpublication of this manuscript, those will also be shared with the community via the NCBO BioPortal at: https://bioportal.bioontology.org/ontologies/PARTUMDO . For an overview of the entire ontology's hierarchy please see Fig. 6 .
A graphical overview of the Postpartum Depression Ontology Superclasses and Direct Subclasses of the Ontology.
As the first PPD-specific ontology to our knowledge, the PDO was built inclusive of ICD codes to represent the PPD knowledge domain in an EHR-accessible format for researchers and medical professionals. After a literature search and two rounds of evaluation, the ontology encompasses treatments, symptoms, risk factors, and other related conditions, as well as personal descriptors and procedures. Most importantly, the PDO compiles 78 known and potential PPD risk factors that were identified through the literature, case studies, and clinical expertise. To date, no studies have considered all of these factors together, yet understanding the totality of risk factors is critical given the high prevalence of PPD 3 4 and its serious consequences. 5 6 The PDO developed here designates eight variables as confirmed risk factors and 70 as potential risk factors in an effort to inform not only diagnoses but also to identify and improve prevention strategies.
The literature search revealed many risk factors, yet very few had sufficient evidence to support a causal relationship with PPD. Despite agreement among most papers reviewed that a history of mental illness was one of the strongest risk factors for PPD development, 7 8 9 25 37 38 39 other variables investigated often had contradictory significance levels, indicating the possibility of a bidirectional relationship or an absence of sufficient evidence. Given the importance of understanding risk factors and the lack of agreement in the field, we designed this ontology as a resource to discover which areas in the field required further research. Thus, the object property and class combination of “ has_PPD_risk_factor_status ‘only’ Potential_PPD_Risk_Factor” directs researchers to identify conditions that have not been adequately studied and promotes investigation of these conditions to add to the field through the inclusion of the relevant ICD codes.
The confirmed risk factor section of the ontology promotes adjustment for factors as confounding variables in future risk factor studies, as well as helps medical professionals to identify patients at risk. Since ICD codes were included in the ontology for each of the confirmed PPD risk factors, medical professionals may offer more individualized care by using these codes to supplement screening tools such as the PPD-specific Edinburgh Postnatal Depression Scale (EPDS), 40 the Center for Epidemiologic Studies Depression (CES-D) scale, 41 the Beck Depression Inventory, 42 and the Patient Health Questionnaire-9 (PHQ-9). 43 Additionally, researchers can now more easily identify larger cohorts of women at risk for PPD by using the ICD codes included in EHRs; this population is a critical target for future intervention studies.
While the PDO incorporates ICD codes for EHR analyses, it was important to include information beyond its coverage of medical diagnoses and the information typically included in EHRs such as age and weight. Specifically, we added classes characterizing the mother's intentions and perceptions of support, self, and the pregnancy that may only be present in clinical notes or not at all. This type of information is rarely—if ever—included in ontologies with mental health sections, yet negative perceptions of reality and illusions often influence mental health unfavorably. 44 Furthermore, outcomes that differ from the mother's expectations, such as intent to breastfeed but inability to do so, have been linked to an increased risk of PPD; 45 thus, this type of information is crucial to include. While perceptions of reality may be difficult to diagnose, this PDO can improve the current self-assessment screening tools 40 41 42 43 that may suffer from self-reporting bias, 46 as well as suggest topics about intentions and perceptions to discuss with patients such as expecting to lose pregnancy weight quickly.
In addition to classes that were added to characterize intentions, perceptions, and other variables missing from ICD codes, the use of polyhierarchical modeling and multiple inheritance was crucial in building the PDO. The polyhierarchy was particularly important for risk factor classification; simply creating a list of risk factors would have been insufficient, as this would not allow the categorization of risk factors into multiple risk types. In addition, multiple inheritance was critical for ICD codes which often had more than one parent class. For example, the ICD-10 code T74.11XA, which is defined as “Adult physical abuse, confirmed, initial encounter,” had the parent classes Abuse_Violence_Type, Adult_Victim, Confirmed_Abuse, and Physical_Abuse. It was necessary to identify each aspect of the code—confirmed abuse, abuse type, and victim age—because these variables could play a role in risk factor research, such as determining age of sustained abuse or type of sustained abuse as carrying a greater risk of developing PPD. Thus, the development of the PDO through these modeling choices allowed the characterization of the PPD knowledge domain's complexity.
During the evaluation of the PDO, clinical case studies provided a unique focus on PPD treatments and symptoms that were often absent from the literature and ICD codes. None of the treatments identified from the first set of case studies were included in the ontology which partially accounts for the low total percent coverage (30.4%); when treatments were removed from analysis, there was a small increase to 36.9% coverage. Further, comprehensive coverage of PPD symptoms was difficult because many of the symptom names used in the case studies were similar to other symptoms described by different women. Even though the coverage of symptoms doubled between evaluations, there was only 67.4% coverage in the final evaluation, suggesting the need to continue updating the ontology with more symptoms and to increase the use of the rdfs:label annotation. Moreover, despite their absence from ICD codes, which limits EHR research, these symptoms may be included in clinical notes or screening results that could aid in the development of better screening tools or in the choice of treatments by medical professionals.
To date, this PDO evaluation is limited by the relatively low number of case studies reviewed and the use of two evaluators. Another limitation is that one of the evaluators (R.B.M.) was the developer of the ontology; however, the second evaluator (M.R.B) produced similar results in the evaluation and a consensus opinion with regard to correctness was derived from comparison of the evaluations, demonstrating the usability and reliability of the ontology independent of the developer. Future work will involve a secondary evaluation with additional case studies and new evaluators to assess the strength of the ontology in its inclusion of all information relevant to a PPD diagnosis. Newly constructed ontologies will be assessed for relevance to the PDO and new terms will be included; one such example is the semantic-based verbal autopsy framework for maternal death 47 which includes many pregnancy complications that could increase the risk of PPD in cases of maternal survival. Additionally, further refinement of PPD symptoms and treatments is required, as the number of relevant features in those categories added after the two rounds of evaluation suggest that there are more PPD features. However, we are confident that most treatments, symptoms, and risk factors are included either explicitly or through a similar feature in the ontology due to the final evaluation yielding 86.4% coverage. In fact, the inclusion of 90.0% of risk factors—which is the most important category for the PDO's target population—demonstrates that these are sufficiently characterized.
Beyond a deeper exploration and evaluation of the current PDO, our future work will involve incorporating more relevant ICD codes into the PDO. Currently, the ICD-9 and -10 codes included as individuals in the ontology are categorized under the eight confirmed PPD risk factors and the 47 potential PPD risk factors for which there are relevant ICD codes. As the literature expands, future iterations of the ontology will be updated with additional risk factors and their ICD codes, 48 as well as include codes from ICD version 11. ICD-11 will be available to Member States in 2022 to replace earlier versions; 49 thus, the relevant ICD codes from all versions must be included as individuals, so that researchers can identify the most up-to-date codes to use in EHR studies. Finally, the terms in the PDO will be mapped to SNOMED CT, ICD-9-CM, and ICD-10-CM to improve accessibility and standardization of resources.
The PDO is a comprehensive ontology of the PPD knowledge base designed to include information needed for a PPD diagnosis. We made our ontology readily accessible via the NCBO BioPortal (available at: https://bioportal.bioontology.org/ontologies/PARTUMDO ) for researchers to utilize and incorporate into their future work. Our evaluation focused on the use of case studies to demonstrate its coverage and usefulness. Interestingly, the PDO adds a new dimension to the knowledge base by compiling researched risk factors and designating them as confirmed (known) or potential PPD risk factors, with ICD codes used in EHRs included for these risk factors. The PDO can therefore illuminate areas of PPD that require further investigation, as well as supplement the current PPD screening techniques employed, promoting more clarity in the field for researchers and potentially improving the standard of care provided by medical professionals. As an ontology, the PDO provides much more detail than would typically be available in disease concept sets because it provides relationships between concepts and other useful information for conducting research studies on PPD.
Postpartum pepression (PPD) remains an understudied research area despite its high prevalence. This study contributes an ontology to aid in the identification of patients with PPD and to enable future analyses with electronic health record (EHR) data. In addition to PPD, relevant comorbidities that have been reported in the literature that are related to PPD are included. The ontology is freely available on the NCBO BioPortal web site and was constructed using Protégé-OWL. Our ontology will enable future researchers to study PPD using EHR data as it contains important information with regards to structured (e.g., billing codes) and unstructured data (e.g., synonyms of symptoms not coded in EHRs) and also the connections between diseases and comorbidities. The PDO is publicly available through the National Center for Biomedical Ontology (NCBO) BioPortal ( https://bioportal.bioontology.org/ontologies/PARTUMDO ) which will enable other informaticists to utilize the PDO to study PPD in other populations.
Correct Answer: The correct answer is option a. Our ontology is made freely available on the NCBO BioPortal web site. The NCBO was founded as one of the National Centers for Biomedical Computing supported by the NHGRI, NHLBI, and the NIH Common Fund.
Correct Answer: The correct answer is option d. Our ontology includes diagnostic codes for both ICD-9 and ICD-10 for researchers using those terminologies. These can be easily mapped to SNOMED-CT using existing OHDSI Common Data Model mappings.
Correct Answer: The correct answer is option d. While ICD-9 has diagnosis codes for depression, there is no explicit code for “postpartum depression.” However, in ICD-10, an explicit code for postpartum depression appears, and ICD-11 also contains specific codes.
Funding We thank the University of Pennsylvania for generous funds to support this project (R.B.M., S.P.C., and M.R.B.). Support also provided by the Penn Injury Science Center (B.A.D'A., S.P.C., and M.R.B.) which is an Injury Control Research Center funded by the Centers for Disease Control and Prevention (CDC; grant no.: R49CE003083). Support also provided by the NIH NINDS brain injury training grant (grant no.: T32 NS 043126) supporting A.C.B. with mentors M.R.B. and A.L.C.S.
Conflict of Interest None declared.
This research did not involve human subjects.
Conceived study design: R.B.M. and M.R.B. Developed methodology: R.B.M., B.A.D'A., and M.R.B. Provided clinical advice pertinent to study problem: A.L.C.S. and A.C.B. Wrote paper: R.B.M. and M.R.B. Reviewed, edited, and approved final manuscript: R.B.M., A.C.B., B.A.D'A., S.P.C., A.L.C.S., and M.R.B.
The lies i told myself with postpartum depression, 6 steps to rediscovering yourself when dealing with postpartum depression..
Posted July 16, 2024 | Reviewed by Michelle Quirk
Let’s be honest, early motherhood, starting from pregnancy all the way through the toddler years, is extremely hard. As a new mom, we struggle with minimal sleep, painful physical recovery, extreme hormonal fluctuations, and often profound identity changes, all while caring for our new child around the clock.
But if your experience of motherhood changes from being hard into feelings of despair, helplessness, or overwhelming anxiety , please know that you are not alone. One in five new moms will experience postpartum depression and anxiety, which can start unexpectedly any time during the first year after childbirth.
While postpartum depression and anxiety are the most common complications of childbirth , many new moms are never taught the symptoms, how to support themselves and their families, or even how to find help. As a result, many of us struggle alone for far too long.
After having my child, I felt OK at first—I was struggling with sleep deprivation, breastfeeding challenges, and physical recovery from childbirth, but mentally I was still holding on and felt like myself. Then week six came and my world turned upside down—suddenly I couldn’t sleep at all, and I fell down a mental black hole where I felt completely alone, helpless, and overwhelmed with anxiety and despair.
All I could think about, day in and day out, were these three, simple statements:
These statements were lies—negative thoughts that clouded every minute of every day.
The joy, love, and happiness I believed a new mom should feel were vastly different than my reality during those first 18 months of my child’s life. Society, social media , and even Hollywood had taught me that this was supposed to be one of the happiest times of my life; but, instead, it was filled with darkness, shame , and a complete loss of who I was.
I had no idea what was happening to me. As a first-time mom, I spent months during my pregnancy planning to care for my child in every imaginable scenario, but I never thought about or prepared to care for my own mental health.
After I hit my lowest point when intrusive thoughts kicked in, I realized I needed help—and with medication from my health care provider and regular appointments with a maternal mental health therapist, I started to slowly but surely find myself again.
During this process, I learned that those three negative statements I told myself constantly truly were lies caused by postpartum depression. I have rephrased those lies to be the truth:
If you tell yourself similar lies or are struggling postpartum, these six key actions helped me heal and find myself again:
If you are struggling with postpartum depression and anxiety, the negative thoughts you tell yourself about motherhood are likely lies.
Medication, therapy , and self-help strategies can help support your recovery, so don’t suffer in silence. There is help. And, remember, you are a great mom, this will end, and you will feel like yourself again.
Kelly Siebold is a postpartum depression and anxiety survivor, Founder and CEO of Thrive Postpartum, and podcast host who helps moms struggling with maternal mental health issues after experiencing them herself.
Sticking up for yourself is no easy task. But there are concrete skills you can use to hone your assertiveness and advocate for yourself.
Outcomes are not contingent on having a primary care visit. Error bars indicate 95% CI.
Study Protocol
eFigure. Examples of Study Messages and Reminders
eTable 1. Outcome Definition Table
eTable 2. Comparison of the Intervention Components Between Groups
eTable 3. Reasons the Primary Care Appointment Could Not Be Scheduled
eTable 4. Secondary Outcomes by Health Condition Subgroups
eTable 5. Primary Outcome by Population Subgroups (Heterogeneity Analysis)
Data Sharing Statement
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Clapp MA , Ray A , Liang P , James KE , Ganguli I , Cohen JL. Postpartum Primary Care Engagement Using Default Scheduling and Tailored Messaging : A Randomized Clinical Trial . JAMA Netw Open. 2024;7(7):e2422500. doi:10.1001/jamanetworkopen.2024.22500
© 2024
Question What is the impact of a behavioral economic intervention designed to reduce patient administrative burden and information gaps on primary care practitioner (PCP) visit completion in the postpartum period?
Findings In this randomized clinical trial of 360 patients with or at risk for a chronic condition, default PCP scheduling, tailored messages, and reminders increased postpartum PCP visit rates by 19 percentage points. The intervention also resulted in more individuals receiving important screening tests and services provided by their PCP.
Meaning The findings of this trial suggest that a multifaceted and relatively low-resource behavioral economic intervention may improve postpartum health and well-being.
Importance More than 30% of pregnant people have at least 1 chronic medical condition, and nearly 20% develop gestational diabetes or pregnancy-related hypertension, increasing the risk of future chronic disease. While these individuals are often monitored closely during pregnancy, they face major barriers when transitioning to primary care following delivery, due in part to a lack of health care support for this transition.
Objective To evaluate the impact of an intervention designed to improve postpartum primary care engagement by reducing patient administrative burden and information gaps.
Design, Setting, and Participants An individual-level randomized clinical trial was conducted from November 3, 2022, to October 11, 2023, at 1 hospital-based and 5 community-based outpatient obstetric clinics affiliated with a large academic medical center. Participants included English- and Spanish-speaking pregnant or recently postpartum adults with obesity, anxiety, depression, diabetes, chronic hypertension, gestational diabetes, or pregnancy-related hypertension and a primary care practitioner (PCP) listed in their electronic health record.
Intervention A behavioral economics–informed intervention bundle, including default scheduling of postpartum PCP appointments and tailored messages.
Main Outcome and Measures Completion of a PCP visit for routine or chronic condition care within 4 months of delivery was the primary outcome, ascertained directly by reviewing the patient’s electronic health record approximately 5 months after their estimated due date. Intention-to-treat analysis was conducted.
Results A total of 360 patients were randomized (control, 176; intervention, 184). Individuals had a mean (SD) age of 34.1 (4.9) years and median gestational age of 36.3 (IQR, 34.0-38.6) weeks at enrollment. The distribution of self-reported race and ethnicity was 6.8% Asian, 7.4% Black, 68.6% White, and 15.0% multiple races or other. Most participants (75.4%) had anxiety or depression, 16.1% had a chronic or pregnancy-related hypertensive disorder, 19.5% had preexisting or gestational diabetes, and 40.8% had a prepregnancy body mass index of 30 or greater. Medicaid was the primary payer for 21.2% of patients. Primary care practitioner visit completion within 4 months occurred in 22.0% (95% CI, 6.4%-28.8%) of individuals in the control group and 40.0% (95% CI, 33.1%-47.4%) in the intervention group. In regression models accounting for randomization strata, the intervention increased PCP visit completion by 18.7 percentage points (95% CI, 9.1-28.2 percentage points). Intervention participants also had fewer postpartum readmissions (1.7% vs 5.8%) and increased receipt of the following services by a PCP: blood pressure screening (42.8% vs 28.3%), weight assessment (42.8% vs 27.7%), and depression screening (32.8% vs 16.8%).
Conclusions and Relevance The findings of this randomized clinical trial suggest that the current lack of support for postpartum transitions to primary care is a missed opportunity to improve recently pregnant individual’s short- and long-term health. Reducing patient administrative burdens may represent relatively low-resource, high-impact approaches to improving postpartum health and well-being.
Trial Registration ClinicalTrials.gov Identifier: NCT05543265
Although the chronic disease burden in pregnancy is high and increasing in the US, most people with chronic conditions do not successfully transition to primary care management following delivery. 1 - 9 More than 30% of pregnant people have diabetes, hypertension, or obesity, and 11% to 22% have anxiety or depression. 10 - 12 Furthermore, common pregnancy-related conditions (eg, gestational diabetes and pregnancy-related hypertension), which combined affect nearly 20% of pregnancies, confer an increased risk of developing chronic disease. 13 - 18 Strong evidence underpins the benefits of managing chronic conditions through primary care and of managing these conditions earlier in life. 19 - 22 However, while pregnant people with these conditions are often carefully monitored during pregnancy, many receive no routine care after their pregnancy, and nearly half of those with chronic conditions do not see their primary care practitioner (PCP) at all in the postpartum year. 23 The abrupt drop off from high health system engagement and motivation during pregnancy to limited or no health care encounters post partum has been termed a postpartum cliff. 24 Low rates of postpartum primary care engagement reflect a missed opportunity to improve the prevention and management of chronic disease.
Postpartum transitions from obstetric to primary care are encouraged by guidelines yet stymied by numerous barriers. Specifically, the American College of Obstetrics and Gynecology 25 recommends that all individuals have a comprehensive postpartum visit within 12 weeks of their delivery; at that time, obstetric care clinicians typically counsel patients on the importance of ongoing primary care follow-up. Yet, a range of systemic, financial, and behavioral barriers often prevents postpartum people from successfully transitioning to primary care. 26 - 30 Patient administrative burden (eg, appointment scheduling, information seeking, and insurance/billing issues) is increasingly recognized as a barrier to accessing care. 31 In a 2021 survey, 33% of patients reported that they delayed or did not seek health care because of the administrative burden. 31 The results of this burden may be amplified in the postpartum period when new parents are sleep deprived and face many competing demands, including caring for their newborn and family. This study aimed to increase patient engagement in primary care after the immediate postpartum period for pregnant individuals with conditions that convey a long-term health risk by reducing administrative burden and motivating continued health activation through an intervention based on insights from behavioral economics.
This study was an individual-level, 2-group, 1:1 stratified randomized clinical trial of the effectiveness of a behavioral economics–informed intervention to increase the rate of postpartum primary care visit completion. The study was conducted at 1-hospital based obstetric clinic and 5 community-based obstetric clinics from November 3, 2022, to October 11, 2023 (trial protocol and analysis plan included in Supplement 1 ). The Mass General Brigham Human Subjects Committee approved this study, and analyzed data were deidentified. Individuals provided verbal consent to participate and received financial compensation. The Consolidated Standards of Reporting Trials ( CONSORT ) reporting guideline was followed in reporting the study and its results.
Patients who had obesity (prepregnancy body mass index ≥30; calculated as weight in kilograms divided by height in meters squared), anxiety or depressive mood disorder, type 1 or 2 diabetes, chronic hypertension, gestational diabetes, or pregnancy-related hypertension listed in their electronic health record (EHR) were eligible to participate. Patients at high risk for hypertensive disorders of pregnancy, defined as those who would be recommended for low-dose aspirin by US Preventative Services Task Force guidelines, were eligible. Patients with these conditions were prioritized for inclusion in the study as they were more likely to have ongoing care needs after pregnancy. Also, this study was limited to patients who had a PCP listed or identified in their EHR, as the barriers and solutions to postdelivery primary care reengagement are different than establishing care with a new PCP; a preliminary analysis of patients receiving obstetric care at the study institution revealed that 90% had a PCP listed in the EHR. Other eligibility criteria included (1) pregnant or recently post partum (defined as up to 2 weeks after their estimated due date [EDD]), (2) receipt of prenatal care at the study institution or its affiliated clinics, (3) enrolled and elected to receive messages in the study institution’s EHR patient portal, (4) primary language of English or Spanish, (5) age 18 years or older at the time of enrollment, and (6) not actively undergoing a workup for or known to have fetal demise at the time of enrollment.
Eligible patients were approached in person and via telephone during the eligibility window (up to 2 weeks after their EDD). Those who consented to participate in the study were also asked to consent to receive text (SMS) messages separately. Individuals were randomized using a randomization table created by the statistician (K.E.J.) and uploaded directly into the REDCap randomization module, which was blinded to the primary investigators and study staff. The assignment sequence was stratified by 2 variables that were determined a priori to be important to ensure balance: visit with a PCP within 3 years before the EDD and site of prenatal care (hospital campus vs community-based obstetric clinic). Patients were randomized after they consented and completed a baseline survey.
The intervention was designed to increase the rate of postpartum primary care visit completion within 4 months after the patient’s EDD. The bundle included a targeted introduction message about the importance of seeing their PCP after delivery and informed them that, to support them in this, a study staff member would be making an appointment on their behalf; they were allowed to opt out or communicate about scheduling preferences. For those who did not opt out, the study staff called the PCP office and requested that a health care maintenance or annual visit be scheduled within the target 4-month window. If a patient had already seen their PCP for an annual visit within the year, they were scheduled for this visit when they were next eligible (ie, 1 year after their last annual examination), even if outside the 4-month study follow-up period. For those who had appointments scheduled, study-specific appointment reminders were sent approximately 1 month after the EDD and 1 week before the scheduled appointment via the EHR patient portal and SMS, and both used salient labeling to describe the visit; examples are shown in the eFigure in Supplement 2 . If the PCP worked in the same health system and an appointment was scheduled, an electronic message was sent to the PCP from the study staff about the appointment scheduled by the study staff. For those for whom an appointment could not be scheduled, similar reminders were sent on the importance of PCP follow-up and encouraged the patient to contact their PCP office directly to schedule. Reminders included best practice wording from behavioral economic nudge mega-studies, including that the appointment had been reserved for them. 32 Using salient labeling, the appointment was described as the postpartum-to–primary care transition appointment. Patients in the control group received 1 message within 2 weeks of the EDD with a generic recommendation for PCP follow-up after delivery.
The primary outcome was completing a primary care visit for routine or chronic condition care within 4 months of the patient’s EDD. Specifically, we considered the outcome to have occurred if the patient attended a health care maintenance (ie, annual examination) visit or a problem-based visit in which obesity, anxiety and/or depression, diabetes, or hypertension were addressed with a primary care clinician within 4 months after their EDD. This definition was chosen to include visits most likely to reflect primary care reengagement after delivery instead of a visit for an acute illness or issue. This time frame was selected for 2 reasons: to capitalize on the increased health activation and motivation that have been noted during pregnancy and because these patients were more likely to have conditions that required ongoing and active management outside of the traditional postpartum period (up to 12 weeks after delivery). We considered practitioners affiliated with internal medicine, family medicine, pediatric and adolescent medicine, and gynecology practices to provide primary care; however, we did not count designated postpartum visits to be primary care visits.
Alternative specifications for the primary outcome were compared in sensitivity analyses: (1) self-reported PCP visits within 4 months after the EDD, obtained from a survey sent approximately 5 months after the EDD; (2) primary outcome restricted to visits with the patient’s designated PCP; (3) primary outcome restricted to patients whose PCP was affiliated with the study institution’s health system; (4) primary outcome expanded to include any PCP visit (not only routine or chronic condition care) within 4 months after a patient’s EDD; and (5) primary outcome expanded to include any completed or scheduled PCP visit within 1 year of a patient’s EDD.
We examined secondary outcomes measuring unscheduled care: obstetric triage visit, emergency department or urgent care use, and readmission within 4 months after the delivery. We also measured the likelihood of a patient having a PCP visit that included specific primary care services within 4 months: weight screening, blood pressure screening, mood screening, plan for diabetes screening, plan for mental health care, and contraception planning. Content of care outcomes were also compared within population subgroups related to the eligibility health condition. All outcomes are defined in detail in eTable 1 in Supplement 2 .
The primary and most secondary outcomes were ascertained directly by reviewing the patient’s EHR approximately 5 months after their EDD. Study staff that performed the review were blinded to the group assignment. Secondary self-reported outcomes were obtained by an electronic survey sent to patients approximately 5 months after their EDD.
Based on a historical cohort, we estimated that 33% of the targeted study population would have a PCP visit within 4 months of delivery. We estimated the intervention would increase the rate of PCP visit attendance by at least 15 percentage points (pp), a conservative estimate based on a prior study that examined default scheduling of postpartum obstetric care appointments (24-pp increase). 33 Assuming an α level of .05 and power of 80% and using a 2-sided z test, 334 patients were needed to detect a 15-pp difference. To account for individuals who may be lost to follow-up or withdraw, we planned to randomize 360 patients.
Patients were examined by intention-to-treat analysis. Patients who were lost to follow-up (ie, transferred obstetric care before delivery) or withdrew before the outcome assessment were excluded. Baseline patient characteristics and the percentage of patients who accessed the study messages in the EHR patient portal are reported. Primary and secondary outcomes were compared using χ 2 , t tests, and Fisher exact test, where appropriate. The pp difference in outcomes between the 2 groups was estimated using a linear probability regression model that included 2 indicator terms for the randomization strata, which were defined a priori.
A heterogeneity analysis was performed to understand the potential impact of the intervention among patient factors known or hypothesized to be disproportionately affected by administrative burdens. The primary outcome was compared among subgroups based on site of prenatal care (hospital- vs community-based clinic), chronic conditions (anxiety and/or depression, diabetes, hypertension, obesity, and multimorbidity, defined as >1 of the listed conditions), race (self-described Asian, Black, White, other [including American Indian or Alaska Native and Native Hawaiian or Other Pacific Islander], or multiple) and ethnicity (self-described Hispanic or non-Hispanic), individual earnings/income (≤$30 000, $30 001-$75 000, or >$75 000), primary payer for delivery hospitalization (Medicaid or private/other), and self-reported physical and mental health status at the time of enrollment. 31 , 34 As known racial disparities exist in maternal health outcomes, including maternal morbidity and mortality, we examined the outcome by race to understand whether there was a differential impact among subgroups.
Stata, version 16.1 (StataCorp LLC) was used for the analysis. P values are reported for the primary outcome; P < .05 was considered statistically significant. As this project was not designed to have statistical power to detect the intervention’s impact on secondary outcomes or differences across subgroups, multiple hypothesis testing was not planned or prespecified. Results from secondary analyses are presented with 95% CIs that were not adjusted for multiple hypothesis testing. These secondary analyses should be considered exploratory, and results may not be reproducible.
Initially, 574 patients were identified as likely to be eligible based on predefined eligibility filters within the EHR ( Figure 1 ). Upon EHR review, 35 individuals were determined ineligible. Of those confirmed eligible, 77 could not be contacted and 102 declined. Thus, 360 patients were randomized: 176 to the control group and 184 to the intervention group. Six patients were excluded from the final analysis because they transferred their care to another institution before delivery (3 in each group). One patient in the intervention group withdrew from the study before the end of the follow-up period. The final number of patients analyzed in each group was 173 in the control group and 180 in the intervention group. Among study participants, 345 of 353 (97.7%) accessed study-related messages in the online patient portal. The proportion of patients in the intervention group who received each component of the intervention bundle is included in eTable 2 in Supplement 2 ; the study staff scheduled appointments for 137 participants (76.1%), of whom only 6 (4.4%) did not present and did not cancel their appointment. The most common reason the study staff did not schedule an appointment was that a PCP appointment was already scheduled (21 of 43 [48.8%]) (eTable 3 in Supplement 2 provides the full list). Of all participants, 61.8% completed the online electronic survey 5 months after the EDD.
The intervention and control groups were balanced in all baseline patient characteristics ( Table 1 ). Individuals included in the trial had a mean (SD) age of 34.1 (4.9) years and median gestational age of 36.3 (IQR, 34.0-38.6) weeks at enrollment. The distribution of self-reported race and ethnicity was 6.8% Asian, 7.4% Black, 68.6% White, and 15.0% multiple races or other; 2.3% declined to report their race. The distribution of self-reported ethnicities was 22.1% Hispanic and 75.4% non-Hispanic; 2.5% declined to report their ethnicity. Of the eligibility conditions, which were not mutually exclusive, 75.4% of all participants had anxiety or depression, 16.1% had a chronic or pregnancy-related hypertensive disorder, 19.5% had preexisting or gestational diabetes, and 40.8% had a prepregnancy body mass index of 30 or greater. Medicaid was the primary payer for the delivery encounter for 21.2% of patients. When surveyed, 11.6% reported their physical health and 19.6% reported their mental health as fair or poor. At enrollment, 34.3% of the participants had not seen any PCP within the previous 3 years and 29.2% were receiving obstetric care at one of the hospital’s satellite or affiliated health center clinics.
Table 2 reports the effects of the intervention on completion of a primary care visit for routine or chronic condition care within 4 months of the patient’s EDD. This primary outcome occurred in 40.0% (95% CI, 33.1%-47.4%) of the intervention group and 22.0% (95% CI, 6.4%-28.8%) of the control group ( P < .001). When adjusted using linear probability regression models for prespecified randomization strata, the intervention increased the primary outcome by 18.7 (95% CI, 9.1-28.2) pp. The effects on the primary outcome were similar in the sensitivity analyses ( Table 3 ). There were no significant effects on obstetric triage visits or emergency department or urgent care use. However, the intervention group had fewer postpartum readmissions: 1.7% (95% CI, 0.5%-5.1%) vs 5.8% (95% CI, 3.1%-10.4%) ( Table 2 ).
Figure 2 compares the secondary outcomes related to the content or provision of care between the 2 groups. Intervention group participants had a higher likelihood of having a PCP visit with a weight screening (42.8%; 95% CI, 35.7%-50.1% vs 27.7%; 95% CI, 21.6%-34.9%), blood pressure screening (42.8%; 95% CI, 35.7%-50.1% vs 28.3%; 95% CI, 22.1%-35.1%), and mood screening (32.8%; 95% CI, 26.3%-40.0% vs 16.8%; 95% CI, 11.9%-23.1%). Intervention group participants were also more likely to have a PCP visit with a plan documented about their mental health (37.2%; 95% CI, 30.5%-44.5%) vs 23.1%; 95% CI, 17.4%-30.0%) and with a documented contraception plan (19.4%; 95% CI, 14.3%-25.9% vs 11.0%; 95% CI, 7.1%-16.6%). There was no significant difference in a documented plan for diabetes screening between the 2 groups. Comparisons of the secondary outcomes related to the content of care among subgroups of health conditions are reported in eTable 4 in Supplement 2 ; many comparisons were limited by small sample sizes.
There was treatment effect heterogeneity across health conditions, demographic characteristics, and baseline self-reported physical and mental health status (eTable 5 in Supplement 2 ). While the study was not powered to detect outcomes within subgroups, the intervention was associated with increases in PCP visits among nearly all subgroups examined.
Among pregnant people with common comorbidities, a behavioral economics–informed intervention bundle, including default appointment scheduling, tailored messaging, and nudge reminders, increased PCP visit completion within 4 months post partum by 18.7 pp, a nearly 2-fold increase. The primary finding was robust to multiple definitions or variations of the primary outcome, including self-reported PCP visit attendance. The effects on the primary outcome appeared largely consistent among population subgroups, although small sample sizes limited power in these comparisons. Not only did the intervention increase PCP visit completion, it also resulted in more individuals receiving important screening tests and services. There were no observed changes in emergent or urgent care visits between the 2 groups. However, any potential effects of facilitated primary care engagement on emergent care use are more likely to occur later in the postpartum year or beyond, and we intend to measure longer-term care use and outcomes in future studies.
Our results suggest that behavioral economic–informed interventions that reduce patient administrative burden have the potential to be relatively low-resource, high-impact approaches to increasing primary care use, a critical priority in the context of decreasing and inequitable primary care engagement in the US. 35 , 36 Behavioral economics research examines how people make predictable decision errors and tests interventions that leverage these insights to remove behavioral barriers (nudges). 37 - 47 These interventions often try to make it easier for people to make choices they already want to undertake but do not. In kind, the underlying hypothesis of the present study was that many postpartum individuals with or at high risk for chronic conditions who have a PCP assigned want to receive care by their PCP but face multiple barriers to primary care engagement in the postpartum period, including identifying who their PCP is and scheduling with them. Our study design was built to address 2 common behavioral barriers, namely, inattention and status quo bias, and demonstrated how default primary care appointment scheduling—a salient label for the appointment—and tailored SMS messages and appointment reminders can increase postpartum primary care engagement. Similar approaches have motivated other health behaviors, including in obstetric and postpartum care. 47 - 51
This study builds on prior efforts to improve postpartum health and well-being. 33 , 52 - 58 Our study is most closely aligned with the intervention research on postpartum care navigation in which patient navigators identify and holistically address patient-level barriers to care and assist with care coordination. 52 , 59 Although obstetric care navigators hold great promise for improving postpartum health care use, that level of intervention intensity and cost may not be necessary for most postpartum people needing primary care. Results from this study suggest that reducing some patient administrative barriers may be a relatively resource conscious but highly effective approach to encouraging postpartum primary care transitions. Specifically, we demonstrated this intervention could be delivered consistently, with the successful scheduling of an annual visit appointment for 76.1% of participants and a low no-show appointment rate of only 4.4%. Future work should focus on examining the health impacts and cost-effectiveness of the intervention.
The study had several limitations. First, the study tested a bundled intervention; we were unable to measure the effectiveness of the individual components for increasing PCP visits. Next, we observed health care encounters within a single health system, although the health system is large (>1300 PCPs). This study was conducted in Massachusetts, in which pregnancy-related Medicaid coverage extends for 12 months post partum and may impact the generalizability of our findings. We could not observe PCP visits for clinicians who do not use or are not affiliated with the health system’s common EHR. As an alternate measure, we examined self-reports of PCP visits, which was highly consistent with results using EHR data. However, the response rate of 61.8% (balanced across treatment and control groups) may also limit the generalizability of self-reported outcomes. This study focused on individuals who had an identified PCP at enrollment; given the limited availability of PCPs in certain areas, the effect of the intervention may be lessened for individuals seeking to establish care with a new PCP. In addition, the study was not powered to detect differences in many secondary outcomes related to the content of primary care within health conditions, and larger studies are needed to ascertain the impact of the intervention on the quality of primary care for specific conditions.
In this randomized clinical trial, a behavioral economics–informed intervention to improve postpartum transitions to primary care substantially increased postpartum primary care visit completion for patients with or at risk for common comorbidities. Targeting this population at a time of high health activation and motivation, this intervention represents a potentially scalable approach to increasing primary care engagement and ongoing health condition management in the postpartum months and beyond. Ongoing follow-up related to this study seeks to analyze condition-specific management (ie, the content and quality of care provided in the postpartum period) and long-term health outcomes. Similarly, as many individuals still did not attend a PCP appointment within 4 months even with the assistance of this intervention, additional investigations should focus on identifying and addressing remaining barriers to transitioning to primary care after pregnancy.
Accepted for Publication: May 2, 2024.
Published: July 16, 2024. doi:10.1001/jamanetworkopen.2024.22500
Open Access: This is an open access article distributed under the terms of the CC-BY License . © 2024 Clapp MA et al. JAMA Network Open .
Corresponding Author: Mark A. Clapp, MD, MPH, Massachusetts General Hospital, 45 Fruit St, Boston, MA 02459 ( [email protected] ).
Author Contributions: Drs Clapp and Cohen had full access to all of the data in the study and take responsibility for the integrity of the data and the accuracy of the data analysis.
Concept and design: Clapp, Ray, Cohen.
Acquisition, analysis, or interpretation of data: Clapp, Liang, James, Ganguli, Cohen.
Drafting of the manuscript: Clapp, Liang, Cohen.
Critical review of the manuscript for important intellectual content: Clapp, Ray, James, Ganguli, Cohen.
Statistical analysis: Clapp, Liang, James, Cohen.
Obtained funding: Clapp, Cohen.
Administrative, technical, or material support: Clapp, Ray, Liang.
Supervision: Clapp, Cohen.
Conflict of Interest Disclosures: Dr Clapp reported holding equity from the Delfina Care Scientific Advisory Board outside the submitted work. Dr Ganguli reported receiving grants from the National Institute on Aging, Commonwealth Fund, and Arnold Ventures, and personal fees from FPrime outside the submitted work. Dr Cohen reported receiving grants from the National Academy of Medicine and the National Academy on Aging during the conduct of the study. No other disclosures were reported.
Funding/Support: This study was funded by the National Institute on Aging via the Massachusetts Institute of Technology Roybal Center for Translational Research to Improve Health Care for the Aging (P30AG064190) and the National Bureau of Economic Research Roybal Center for Behavior Change in Health (P30AG034532). Additional support was provided by the National Academy of Medicine’s Health Catalyst Award.
Role of the Funder/Sponsor: The funding organizations had no role in the design and conduct of the study; collection, management, analysis, and interpretation of the data; preparation, review, or approval of the manuscript; and decision to submit the manuscript for publication.
Meeting Presentation: Initial results from this study were presented at the University of Pennsylvania Center for Health Incentives and Behavioral Economics Research Conference; October 30, 2023; Philadelphia, Pennsylvania; the 2023 Association for Public Policy Analysis and Management Fall Research Conference; November 10, 2023; Atlanta, Georgia; the American Economic Association Annual Meeting; January 7, 2024; San Antonio, Texas; and the 2024 American Society for Health Economists Conference; June 18, 2024; San Diego, California.
Data Sharing Statement: See Supplement 3 .
Additional Contributions: We thank Fowsia Warsame, BA, for serving as a paid clinical research coordinator for this trial; Hasan Quadri for serving as a paid data analyst; and Amanda Lee, MPP, and Fatima Vakil, BS, from the Jameel Poverty Action Lab for providing in-kind research management support.
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The present thesis examines correlates of adolescent PPD, as well as an intervention for low-income adolescent mothers with symptoms of PPD. Study 1 investigated the relationship between depressive symptom severity and potential individual vulnerability and social context factors among a sample of adolescent mothers (N=102).
This Senior Thesis is brought to you for free and open access by the Student Scholarship at ... endanger to developing postpartum depression. Problem Statement . ... 6 weeks and 6 months postpartum. Although an initial sample size of 7, 589 women were enrolled in the study, only 3, 233 mothers
Background. Pregnancy is a complex process that can lead to dramatic changes in female's physical, psychological, and social roles. Since pregnancy and birth-giving are both major life events and traumatic processes, postpartum is often considered to be the most risky stage for women to develop depression [1, 2].Postpartum depression (PPD) is a cross-disciplinary disorder between obstetrics ...
postpartum period is a high-risk time for the beginning of postpartum depression in mothers. Postpartum depression is undertreated. Many women believe that sadness at what should be a joyous time is embarrassing, while others are affected by society's overall stigma around mental health care. The study aims to explore mothers' holistic ...
Postpartum depression occurs in 8% to 15% of new mothers (author, year). The third and. most severe form is called postpartum psychosis, and it affects 1 to 2 women per 1,000. deliveries (author, year). Postpartum psychosis is a psychiatric sickness that requires. emergency hospitalization and treatment.
Example 2: Qualitative Research The research problem is very relevant to the actual practice of nursing because it regards how patients deal with suffering, ... Original Thesis Statement: Postpartum depression is a serious mental health condition that significantly impacts the physical, emotional, and social well-being of mothers. ...
This essay sample was donated by a student to help the academic community. Papers provided by EduBirdie writers usually outdo students' samples. Postpartum depression (PPD) affects a substantial ratio of first-time adolescent mothers. After childbirth, Adolescent mothers face unique, demanding situations that could make them more vulnerable.
Postpartum Depression Among Adolescent Mothers: Examining and Treating Low-income Adolescents with Symptoms of Postpartum Depression Thesis directed by Professor Sona Dimidjian Postpartum depression (PPD) among adult women is a prevalent and impairing problem, with evidence suggesting risk of adverse consequences for mothers and their infants. Few
adult mothers' experience postpartum depression. Postpartum depression is a serious mental health issue that affects women irrespective of age, race or ethnicity. Although there has been an influx of postpartum depression literature, few studies employ a sociological perspective, and even fewer focus on Mexican Americans.
P ostpartum depression (PPD) is a mood disorder that a ects 10 to 15% of new. mothers. In the United States the prevalence of PPD ranges from 7 to 20%, but. most studies suggest rates between 10 ...
The purpose of this thesis was to develop evidence-based best practice recommendations. for nurses caring for postpartum mothers who are at risk for developing postpartum depression. These recommendations are shown in Table 1 and are based on the literature reviewed in chapter. 2.
Introduction. The postpartum period is recognized as the time when many women are vulnerable to a variety of emotional symptoms.[] The most prevalent mental or emotional problem associated with childbirth is postpartum depression (PPD).[2,3] A latest review reported its prevalence to be 1.9 to 82.1% and 5.2 to 74.0% in developing and developed countries, respectively, using a self-reported ...
Postpartum depression (PPD) has a significant negative impact on the child's emotional, mental as well as intellectual development if left untreated, which can later have long-term complications. Later in life, it also results in the mother developing obsessive-compulsive disorder and anxiety. Many psychological risk factors are linked with PPD.
Introduction. Already in the late '90s, Kirby Deater‐Deckard (Citation 1998) established that parenting stress linked to adult functioning, the quality of the parent-child relationships, and child functioning.Furthermore, research has established a link between postpartum depression and parental stress, concluding that postpartum depression is the most reliable predictor for parental ...
PROGRAM ON POSTPARTUM DEPRESSION SCORES Holly Batenic-Healy, M.S.N. Thesis Advisor: Ella Heitzler, Ph.D. ABSTRACT Postpartum depression is a significant mental health condition affecting both mothers and infants. Traditional treatment with medication has proven to have low adherence, and thus
Activity During Pregnancy and Postpartum Depression. Studies have shown that women's mood and cardiorespiratory fitness improve when they engage in moderate-intensity physical activity in the weeks and months after giving birth to a child. We will write. a custom essay specifically for you by our professional experts.
It was found that in twins, the heritability of perinatal depression was estimated at 54%. Thus, the heredity of perinatal depression is ~ 50% (54% in the design of twins and 44% in the design of sibling), and the heredity of non‐perinatal depression is 32%.
Postpartum depression: Proposal for prevention through an integrated care and support network. September 1997. Applied and Preventive Psychology 6 (4):169-178. DOI: 10.1016/S0962-1849 (97)80006-6 ...
Experience of Postpartum Depression: A Review of the Literature, Issues in Mental Health Nursing To link to this article: https://doi.or g/10.1080/01612840.2019.1688437 Published online: 08 Apr 2020.
The typical signs of postpartum depression include the presence of sleep disorder, fatigue, crying, anxiety, changes in appetite, and feelings of inadequacy (Tharpe, Farley, & Jordan, 2017). The patient has these symptoms, which allowed for establishing the diagnosis. Drug therapy included the prescription of tricyclic antidepressants, as they ...
Postpartum depression: The birth of a baby can trigger a jumble of powerful emotions, from excitement and joy to fear and anxiety. But it can also result in something you might not expect — depression.
Importance and Prevalence of Postpartum Depression. While approximately 15 to 85% of women experience the "baby blues" or some form of sadness in the 2 weeks following delivery, 1 postpartum depression (PPD) is a more severe and longer lasting mental illness that is detrimental to both the mother and newborn. PPD is classified as an episode of major depressive disorder (MDD) that can occur ...
Key points. One in five new moms will get postpartum depression and anxiety during the first 12 months after childbirth. Reframing negative thoughts and realigning motherhood expectations are ...
This study builds on prior efforts to improve postpartum health and well-being. 33,52-58 Our study is most closely aligned with the intervention research on postpartum care navigation in which patient navigators identify and holistically address patient-level barriers to care and assist with care coordination. 52,59 Although obstetric care ...