Economic outcomes among microfinance group members receiving community-based chronic disease care: Cluster randomized trial evidence from Kenya.

BACKGROUND: Poverty can be a robust barrier to HIV care engagement. We assessed the extent to which delivering care for HIV, diabetes and hypertension within community-based microfinance groups increased savings and reduced loan defaults among microfinance members living with HIV. METHODS: We analyzed cluster randomized trial data ascertained during November 2020-May 2023 from 57 self-formed microfinance groups in western Kenya. Groups were randomized 1:1 to receive care for HIV and non-communicable diseases in the community during regular microfinance meetings (intervention) or at a health facility during routine appointments (standard care). Community and facility care provided clinical evaluations, medications, and point-of-care testing. The trial enrolled 900 microfinance members, with data collected quarterly for 18-months. We used a two-part model to estimate intervention effects on microfinance shares purchased, and a negative binomial regression model to estimate differences in loan default rates between trial arms. We estimated effects overall and by participant characteristics. RESULTS: Participants' median age and distance from a health facility was 52 years and 5.6 km, respectively, and 50% reported earning less than $50 per month. The probability of saving any amount (>$0) through purchasing microfinance shares was 2.7 percentage points higher among microfinance group members receiving community vs. facility care. Community care recipients and facility care patients saved $44.90 and $25.24 over 18-months, respectively, and the additional amount saved by community care recipients was statistically significant (p = 0.036). Overall and in stratified analyses, loan defaults rates were not statistically significantly different between community and facility care patients. CONCLUSIONS: Receiving integrated care in the community was significantly associated with modest increases in savings. We did not find any significant association between community-delivered care and reductions in loan defaults among HIV-positive microfinance group members. Longer follow up examination and formal mediation analyses are warranted.

Classification of Listeria monocytogenes persistence in retail delicatessen environments using expert elicitation and machine learning.

Increasing evidence suggests that persistence of Listeria monocytogenes in food processing plants has been the underlying cause of a number of human listeriosis outbreaks. This study extracts criteria used by food safety experts in determining bacterial persistence in the environment, using retail delicatessen operations as a model. Using the Delphi method, we conducted an expert elicitation with 10 food safety experts from academia, industry, and government to classify L. monocytogenes persistence based on environmental sampling results collected over six months for 30 retail delicatessen stores. The results were modeled using variations of random forest, support vector machine, logistic regression, and linear regression; variable importance values of random forest and support vector machine models were consolidated to rank important variables in the experts' classifications. The duration of subtype isolation ranked most important across all expert categories. Sampling site category also ranked high in importance and validation errors doubled when this covariate was removed. Support vector machine and random forest models successfully classified the data with average validation errors of 3.1% and 2.2% (n = 144), respectively. Our findings indicate that (i) the frequency of isolations over time and sampling site information are critical factors for experts determining subtype persistence, (ii) food safety experts from different sectors may not use the same criteria in determining persistence, and (iii) machine learning models have potential for future use in environmental surveillance and risk management programs. Future work is necessary to validate the accuracy of expert and machine classification against biological measurement of L. monocytogenes persistence.