Search

Purpose: To evaluate the effect of behavioral, empowerment-focused interventions on the incidence of pregnancy-related school dropout among girls in Nairobi's informal settlements. Method: Retrospective data on pregnancy-related school dropout from two cohorts were analyzed using a matched-pairs quasi-experimental design. The primary outcome was the change in the number of school dropouts due to pregnancy from 1 year before to 1 year after the interventions. Results: Annual incidence of school dropout due to pregnancy decreased by 46% in the intervention schools (from 3.9% at baseline to 2.1% at follow-up), whereas the comparison schools remained essentially unchanged (p < 0.029). Sensitivity analysis shows that the findings are robust to small levels of unobserved bias. Conclusions: Results suggest that these behavioral interventions significantly reduced the number of school dropouts due to pregnancy. As there are limited promising studies on behavioral interventions that decrease adolescent pregnancy in low-income settings, this intervention may be an important addition to this toolkit.

Intimate partner violence (IPV) has myriad negative health and economic consequences for women and families. We hypothesized that empowering women through a combination of formal business training, microfinance, and IPV support groups would decrease IPV and improve women's economic status. The study included adult female survivors of severe IPV. Women living in Korogocho received the intervention and women in Dandora served as a standard of care (SOC) group, but received the intervention at the end of the follow-up period. Women in the intervention groups (n = 82, SOC group, n = 81) received 8 weeks of business training, assistance creating a business plan, a small initial loan (about US$60), and weekly business and social support meetings. The two primary outcome measures included change in: (a) average daily profit margin, and (b) incidence of severe IPV. Exploratory analysis also looked at incidence of violence against children and women's self-efficacy. Average daily profit margin in the intervention group increased by 351 Kenyan Shillings (about US$3.5) daily (95% CI = [172, 485]). IPV directed against participating women decreased from a baseline of 2.1 to 0.26 incidents, a difference of 1.84 incidents (95% CI = [1.32, 2.36]). Violence against children in the household in the prior 3 months decreased from 1.1 to 0.55 incidents, a difference of 0.55 incidents (95% CI = [0.16, 1.03]). Finally, the intervention appears to have increased self-efficacy scores by 0.42 points (95% CIs 0.13, 0.71). In a low-resource urban environment, employing three complementary interventions resulted in higher daily profit margins and lower IPV in the intervention compared with the SOC group. These data support the notion that employing multiple interventions concomitantly might possess synergistic, beneficial effects, and hold promise to address profound poverty and interrupt the devastating cycle of IPV. [ABSTRACT FROM AUTHOR]

Objective: Evaluate the quality of clinical order practice patterns machine-learned from clinician cohorts stratified by patient mortality outcomes., Materials and Methods: Inpatient electronic health records from 2010 to 2013 were extracted from a tertiary academic hospital. Clinicians (n = 1822) were stratified into low-mortality (21.8%, n = 397) and high-mortality (6.0%, n = 110) extremes using a two-sided P-value score quantifying deviation of observed vs. expected 30-day patient mortality rates. Three patient cohorts were assembled: patients seen by low-mortality clinicians, high-mortality clinicians, and an unfiltered crowd of all clinicians (n = 1046, 1046, and 5230 post-propensity score matching, respectively). Predicted order lists were automatically generated from recommender system algorithms trained on each patient cohort and evaluated against (i) real-world practice patterns reflected in patient cases with better-than-expected mortality outcomes and (ii) reference standards derived from clinical practice guidelines., Results: Across six common admission diagnoses, order lists learned from the crowd demonstrated the greatest alignment with guideline references (AUROC range = 0.86-0.91), performing on par or better than those learned from low-mortality clinicians (0.79-0.84, P < 10 -5 ) or manually-authored hospital order sets (0.65-0.77, P < 10 -3 ). The same trend was observed in evaluating model predictions against better-than-expected patient cases, with the crowd model (AUROC mean = 0.91) outperforming the low-mortality model (0.87, P < 10 -16 ) and order set benchmarks (0.78, P < 10 -35 )., Discussion: Whether machine-learning models are trained on all clinicians or a subset of experts illustrates a bias-variance tradeoff in data usage. Defining robust metrics to assess quality based on internal (e.g. practice patterns from better-than-expected patient cases) or external reference standards (e.g. clinical practice guidelines) is critical to assess decision support content., Conclusion: Learning relevant decision support content from all clinicians is as, if not more, robust than learning from a select subgroup of clinicians favored by patient outcomes., (Copyright © 2018 The Authors. Published by Elsevier Inc. All rights reserved.)

Clinical order patterns derived from data-mining electronic health records can be a valuable source of decision support content. However, the quality of crowdsourcing such patterns may be suspect depending on the population learned from. For example, it is unclear whether learning inpatient practice patterns from a university teaching service, characterized by physician-trainee teams with an emphasis on medical education, will be of variable quality versus an attending-only medical service that focuses strictly on clinical care. Machine learning clinical order patterns by association rule episode mining from teaching versus attending-only inpatient medical services illustrated some practice variability, but converged towards similar top results in either case. We further validated the automatically generated content by confirming alignment with external reference standards extracted from clinical practice guidelines.