Estimation of postpartum depression risk from electronic health records using machine learning.

BACKGROUND: Postpartum depression is a widespread disorder, adversely affecting the well-being of mothers and their newborns. We aim to utilize machine learning for predicting risk of postpartum depression (PPD) using primary care electronic health records (EHR) data, and to evaluate the potential value of EHR-based prediction in improving the accuracy of PPD screening and in early identification of women at risk. METHODS: We analyzed EHR data of 266,544 women from the UK who gave first live birth between 2000 and 2017. We extracted a multitude of socio-demographic and medical variables and constructed a machine learning model that predicts the risk of PPD during the year following childbirth. We evaluated the model's performance using multiple validation methodologies and measured its accuracy as a stand-alone tool and as an adjunct to the standard questionnaire-based screening by Edinburgh postnatal depression scale (EPDS). RESULTS: The prevalence of PPD in the analyzed cohort was 13.4%. Combing EHR-based prediction with EPDS score increased the area under the receiver operator characteristics curve (AUC) from 0.805 to 0.844 and the sensitivity from 0.72 to 0.76, at specificity of 0.80. The AUC of the EHR-based prediction model alone varied from 0.72 to 0.74 and decreased by only 0.01-0.02 when applied as early as before the beginning of pregnancy. CONCLUSIONS: PPD risk prediction using EHR data may provide a complementary quantitative and objective tool for PPD screening, allowing earlier (pre-pregnancy) and more accurate identification of women at risk, timely interventions and potentially improved outcomes for the mother and child.

Increased rates of unattained developmental milestones among Israeli children between 2016 and 2020: a national report.

BACKGROUND: The early years of children's lives are critical for their healthy development. Although children's growth and development rates may vary, a significant delay during early childhood could indicate a medical or a developmental disorder. Developmental surveillance is used worldwide by healthcare providers in routine encounters, as well as by educators and parents, to elicit concerns about child development. In this work, we used a national dataset of developmental assessments to describe temporal trends of milestone attainment rates and associations between milestone attainment and various sociodemographic factors. METHODS: The study included 1,002,700 children ages birth until 6 years with 4,441,689 developmental visits between the years 2016 and 2020. We used the Israeli developmental scale to assess the annual rates of failure to attain language, social and motoric milestones by the entire population, as well as by subgroups stratified by sociodemographic factors. In addition, we evaluated the rates of parental concern for child development and of the nurse's report of development inadequate for age. We used multivariable logistic regression to analyze the impact of different sociodemographic factors on the odds of failure to attain milestones, while controlling for confounding. RESULTS: Milestone failure rates progressively increased over the examined years in all developmental domains, and most prominently in the language domain. Conversely, the rates of parental concern for developmental delay remained constant. In multivariable analysis, higher risk of milestone attainment failure was observed in children whose mothers were divorced, unemployed, immigrant, had lower education, of Bedouin origin or were over 40 years old when giving birth. CONCLUSIONS: This report describes national trends of child development in the gross motor, fine motor, language, and social domains. A periodic report of these trends should be published to objectively evaluate subgroups in need for intervention, and to assess the effectiveness of intervention programs in attempt to maximize the developmental potential of children in Israel.

Estimating the effect of cesarean delivery on long-term childhood health across two countries.

Assessing the impact of cesarean delivery (CD) on long-term childhood outcomes is challenging as conducting a randomized controlled trial is rarely feasible and inferring it from observational data may be confounded. Utilizing data from electronic health records of 737,904 births, we defined and emulated a target trial to estimate the effect of CD on predefined long-term pediatric outcomes. Causal effects were estimated using pooled logistic regression and standardized survival curves, leveraging data breadth to account for potential confounders. Diverse sensitivity analyses were performed including replication of results in an external validation set from the UK including 625,044 births. Children born in CD had an increased risk to develop asthma (10-year risk differences (95% CI) 0.64% (0.31, 0.98)), an average treatment effect of 0.10 (0.07-0.12) on body mass index (BMI) z-scores at age 5 years old and 0.92 (0.68-1.14) on the number of respiratory infection events until 5 years of age. A positive 10-year risk difference was also observed for atopy (10-year risk differences (95% CI) 0.74% (-0.06, 1.52)) and allergy 0.47% (-0.32, 1.28)). Increased risk for these outcomes was also observed in the UK cohort. Our findings add to a growing body of evidence on the long-term effects of CD on pediatric morbidity, may assist in the decision to perform CD when not medically indicated and paves the way to future research on the mechanisms underlying these effects and intervention strategies targeting them.