Women's groups, covariate shocks, and resilience: An evidence synthesis of past shocks to inform a response to COVID-19.

Background: Interventions with women's groups are increasingly seen as an important strategy for advancing women's empowerment, health, and economic outcomes in low- and middle-income countries, with the potential to increase the resiliency of members and their communities during widespread covariate shocks, such as coronavirus disease 2019 (COVID-19). Methods: This evidence synthesis compiles evidence from past shocks on women's group activities and the extent to which women's groups mitigate the effects of shocks on members and communities. We reviewed 90 documents from academic databases, organizational reports, and additional gray literature, and included literature diverse in geography, type of women's group, and shock. Results: The literature suggests that covariate shocks tend to disrupt group activities and reduce group resources, but linkages to formal institutions can mitigate this impact by extending credit beyond the shock-affected resource pool. Evidence was largely supportive of women's groups providing resilience to members and communities, though findings varied according to shock severity, group purpose and structure, and outcome measures. Further, actions to support individual resilience during a shock, such as increased payment flexibility, may run counter to group resilience. The findings of the evidence synthesis are largely consistent with emerging evidence about women's groups and COVID-19 in South Asia and sub-Saharan Africa. Conclusions: We finalize the paper with a discussion on policy implications, including the importance of sustainable access to financial resources for women's group members; equity considerations surrounding the distribution of group benefits and burdens; and the potential for meaningful partnerships between women's groups and local governments and/or non-governmental organizations (NGOs) to enhance community response amidst crises.

Early detection of COVID-19 outbreaks using textual analysis of electronic medical records.

PURPOSE: Our objective was to develop a tool promoting early detection of COVID-19 cases by focusing epidemiological investigations and PCR examinations during a period of limited testing capabilities. METHODS: We developed an algorithm for analyzing medical records recorded by healthcare providers in the Israeli Defense Forces. The algorithm utilized textual analysis to detect patients presenting with suspicious symptoms and was tested among 92 randomly selected units. Detection of a potential cluster of patients in a unit prompted a focused epidemiological investigation aided by data provided by the algorithm. RESULTS: During a month of follow up, the algorithm has flagged 17 of the units for investigation. The subsequent epidemiological investigations led to the testing of 78 persons and the detection of eight cases in four clusters that were previously gone unnoticed. The resulting positive test rate of 10.25% was five time higher than the IDF average at the time of the study. No cases of COVID-19 in the examined units were missed by the algorithm. CONCLUSIONS: This study depicts the successful development and large scale deployment of a textual analysis based algorithm for early detection of COVID-19 cases, demonstrating the potential of natural language processing of medical text as a tool for promoting public health.