Development of multivariable models to predict perinatal depression before and after delivery using patient reported survey responses at weeks 4-10 of pregnancy.

BACKGROUND: Perinatal depression is estimated to affect ~ 12% of pregnancies and is linked to numerous negative outcomes. There is currently no model to predict perinatal depression at multiple time-points during and after pregnancy using variables ascertained early into pregnancy. METHODS: A prospective cohort design where 858 participants filled in a baseline self-reported survey at week 4-10 of pregnancy (that included social economics, health history, various psychiatric measures), with follow-up until 3 months after delivery. Our primary outcome was an Edinburgh Postnatal Depression Score (EPDS) score of 12 or more (a proxy for perinatal depression) assessed during each trimester and again at two time periods after delivery. Five gradient boosting machines were trained to predict the risk of having EPDS score > = 12 at each of the five follow-up periods. The predictors consisted of 21 variables from 3 validated psychometric scales. As a sensitivity analysis, we also investigated different predictor sets that contained: i) 17 of the 21 variables predictors by only including two of the psychometric scales and ii) including 143 additional social economics and health history predictors, resulting in 164 predictors. RESULTS: We developed five prognostic models: PND-T1 (trimester 1), PND-T2 (trimester 2), PND-T3 (trimester 3), PND-A1 (after delivery 1) and PND-A2 (delayed onset after delivery) that calculate personalised risks while only requiring that women be asked 21 questions from 3 validated psychometric scales at weeks 4-10 of pregnancy. C-statistics (also known as AUC) ranged between 0.69 (95% CI 0.65-0.73) and 0.77 (95% CI 0.74-0.80). At 50% sensitivity the positive predictive value ranged between 30%-50% across the models, generally identifying groups of patients with double the average risk. Models trained using the 17 predictors and 164 predictors did not improve model performance compared to the models trained using 21 predictors. CONCLUSIONS: The five models can predict risk of perinatal depression within each trimester and in two post-natal periods using survey responses as early as week 4 of pregnancy with modest performance. The models need to be externally validated and prospectively tested to ensure generalizability to any pregnant patient.

Leveraging Digital Technology in Conducting Longitudinal Research on Mental Health in Pregnancy: Longitudinal Panel Survey Study.

BACKGROUND: Collecting longitudinal data during and shortly after pregnancy is difficult, as pregnant women often avoid studies with repeated surveys. In contrast, pregnant women interact with certain websites at multiple stages throughout pregnancy and the postpartum period. This digital connection presents the opportunity to use a website as a way to recruit and enroll pregnant women into a panel study and collect valuable longitudinal data for research. These data can then be used to learn new scientific insights and improve health care. OBJECTIVE: The objective of this paper is to describe the approaches applied and lessons learned from designing and conducting an online panel for health care research, specifically perinatal mood disorders. Our panel design and approach aimed to recruit a large sample (N=1200) of pregnant women representative of the US population and to minimize attrition over time. METHODS: We designed an online panel to enroll participants from the pregnancy and parenting website BabyCenter. We enrolled women into the panel from weeks 4 to 10 of pregnancy (Panel 1) or from weeks 28 to 33 of pregnancy (Panel 2) and administered repeated psychometric assessments from enrollment through 3 months postpartum. We employed a combination of adaptive digital strategies to recruit, communicate with, and build trust with participants to minimize attrition over time. We were transparent at baseline about expectations, used monetary and information-based incentives, and sent personalized reminders to reduce attrition. The approach was participant-centric and leveraged many aspects of flexibility that digital methods afford. RESULTS: We recruited 1179 pregnant women-our target was 1200-during a 26-day period between August 25 and September 19, 2016. Our strategy to recruit participants using adaptive sampling tactics resulted in a large panel that was similar to the US population of pregnant women. Attrition was on par with existing longitudinal observational studies in pregnant populations, and 79.2% (934/1179) of our panel completed another survey after enrollment. There were 736 out of 1179 (62.4%) women who completed at least one assessment in both the prenatal and postnatal periods, and 709 out of 1179 (60.1%) women who completed the final assessment. To validate the data, we compared participation rates and factors of perinatal mood disorders ascertained from this study with prior research, suggesting reliability of our approach. CONCLUSIONS: A suitably designed online panel created in partnership with a digital media source that reaches the target audience is a means to leverage a conveniently sized and viable sample for scientific research. Our key lessons learned are as follows: sampling tactics may need to be adjusted to enroll a representative sample, attrition can be reduced by adapting to participants' needs, and study engagement can be boosted by personalizing interactions with the flexibility afforded by digital technologies.

Perinatal depressive symptoms often start in the prenatal rather than postpartum period: results from a longitudinal study.

Depressive symptoms during and after pregnancy confer risks for adverse outcomes in both the mother and child. Postpartum depression is traditionally diagnosed after birth of the child. Perinatal depression is a serious, prevalent heterogeneous syndrome that can occur during the period from conception through several months after childbirth. Onset and course are not well understood. There is a paucity of longitudinal studies of the disorder that include the antenatal period in population-based samples. We used an Internet panel of pregnant women recruited in 2 cohorts; 858 ascertained in the first and 322 ascertained in the third trimesters of pregnancy. We recruited the second cohort in order to assure sufficient sample to examine depressive symptoms later in pregnancy and in the postpartum period. Assessments included standard psychometric measures, health history, and pregnancy experience. The Edinburgh Postnatal Depression Scale was used for the assessment of depressive symptoms. Nearly 10% of women entered the pregnancy with depressive symptoms. Prevalence was about the same at 4 weeks and 3 months postpartum. During pregnancy, prevalence increased to 16% in the third trimester. Among incident cases, 80% occurred during pregnancy, with 1/3 occurring in the first trimester. We describe predictors of incident depressive symptoms and covariates associated with time-to-onset which include health history (psychiatric and medical) and social support covariates. The majority of incident depressive symptoms occur during pregnancy rather than afterward. This finding underscores the mandate for mental health screening early in pregnancy and throughout gestation. It will be important to find safe and effective interventions that prevent, mitigate, or delay the onset of depressive symptoms that can be implemented during pregnancy.