Missingness patterns in a comprehensive instrument identifying psychosocial and substance use risk in antenatal care.

BACKGROUND: Psychosocial vulnerabilities (e.g. inadequate social support, financial insecurity, stress) and substance use elevate risks for adverse perinatal outcomes and maternal mental health morbidities. However, various barriers, including paucity of validated, simple and usable comprehensive instruments, impede execution of the recommendations to screen for such vulnerabilities in the first antenatal care visit. The current study presents findings from a newly implemented self-report tool created to overcome screening barriers in outpatient antenatal clinics. METHODS: This was a retrospective chart-review of 904 women who completed the Profile for Maternal & Obstetric Treatment Effectiveness (PROMOTE) during their first antenatal visit between June and December 2019. The PROMOTE includes the 4-item NIDA Quick Screen and 15 additional items that each assess a different psychosocial vulnerability. Statistical analysis included evaluation of missing data, and exploration of missing data patterns using univariate correlations and hierarchical clustering. RESULTS: Three quarters of women (70.0%) had no missing items. In the entire sample, all but four PROMOTE items (opioid use, planned pregnancy, educational level, and financial state) had < 5% missing values, suggesting good acceptability and feasibility. Several respondent-related characteristics such as lower education, less family support, and greater stress were associated with greater likelihood of missing items. Instrument-related characteristics associated with missing values were completing the PROMOTE in Spanish or question positioning at the end of the instrument. CONCLUSIONS AND IMPLICATIONS: Conducting a comprehensive screening of theoretically and clinically meaningful vulnerabilities in an outpatient setting is feasible. Study findings will inform modifications of the PROMOTE and subsequent digitisation.

Applying machine learning methods to psychosocial screening data to improve identification of prenatal depression: Implications for clinical practice and research.

We utilized machine learning (ML) methods on data from the PROMOTE, a novel psychosocial screening tool, to quantify risk for prenatal depression for individual patients and identify contributing factors that impart greater risk for depression. Random forest algorithms were used to predict likelihood for being at high risk for prenatal depression (Edinburgh Postnatal Depression Scale; EPDS ≥ 13 and/or positive self-injury item) using data from 1715 patients who completed the PROMOTE. Performance matrices were calculated to assess the ability of the PROMOTE to accurately classify patients. Probability for depression was calculated for individual patients. Finally, recursive feature elimination was used to evaluate the importance of each PROMOTE item in the classification of depression risk. PROMOTE data were successfully used to predict depression with acceptable performance matrices (accuracy = 0.80; sensitivity = 0.75; specificity = 0.81; positive predictive value = 0.79; negative predictive value = 0.97). Perceived stress, emotional problems, family support, age, major life events, partner support, unplanned pregnancy, current employment, lifetime abuse, and financial state were the most important PROMOTE items in the classification of depression risk. Results affirm the value of the PROMOTE as a psychosocial screening tool for prenatal depression and the benefit of using it in conjunction with ML methods. Using such methods can help detect underreported outcomes and identify what in patients' lives makes them more vulnerable, thus paving the way for effective individually tailored precision medicine.