Microfinance, retention in care, and mortality among patients enrolled in HIV 2 Care in East Africa

Objective: To measure associations between participation in community-based microfinance groups, retention in HIV care, and death among people with HIV (PWH) in low-resource settings. Design and methods: We prospectively analyzed data from 3609 patients enrolled in an HIV care program in western Kenya. HIV patients who were eligible and chose to participate in a Group Integrated Savings for Health Empowerment (GISHE) microfinance group were matched 1 : 2 on age, sex, year of enrollment in HIV care, and location of initial HIV clinic visit to patients not participating in GISHE. Follow-up data were abstracted from medical records from January 2018 through February 2020. Logistic regression analysis examined associations between GISHE participation and two outcomes: retention in HIV care (i.e. >1 HIV care visit attended within 6 months prior to the end of follow-up) and death. Socioeconomic factors associated with HIV outcomes were included in adjusted models. Results: The study population was majority women (78.3%) with a median age of 37.4 years. Microfinance group participants were more likely to be retained in care relative to HIV patients not participating in a microfinance group [adjusted odds ratio (aOR) = 1.31, 95% confidence interval (CI) 1.01–1.71; P = 0.046]. Participation in group microfinance was associated with a reduced odds of death during the follow-up period (aOR = 0.57, 95% CI 0.28–1.09; P = 0.105). Conclusion: Participation in group-based microfinance appears to be associated with better HIV treatment outcomes. A randomized trial is needed to assess whether microfinance groups can improve clinical and socioeconomic outcomes among PWH in similar settings

Incidence and time-varying predictors of HIV and sexually transmitted infections among male sex workers in Mexico City

Background Male sex workers are at high-risk for acquisition of sexually transmitted infections (STIs), including human immunodeficiency virus (HIV). We quantified incidence rates of STIs and identified their time-varying predictors among male sex workers in Mexico City. Methods From January 2012 to May 2014, male sex workers recruited from the largest HIV clinic and community sites in Mexico City were tested for chlamydia, gonorrhea, syphilis, hepatitis, and HIV at baseline, 6-months, and 12-months. Incidence rates with 95% bootstrapped confidence limits were calculated. We examined potential time-varying predictors using generalized estimating equations for a population averaged model. Results Among 227 male sex workers, median age was 24 and baseline HIV prevalence was 32%. Incidence rates (per 100 person-years) were as follows: HIV [5.23; 95% confidence interval (CI): 2.15–10.31], chlamydia (5.15; 95% CI: 2.58–9.34), gonorrhea (3.93; 95% CI: 1.88–7.83), syphilis (13.04; 95% CI: 8.24–19.94), hepatitis B (2.11; 95% CI: 0.53–4.89), hepatitis C (0.95; 95% CI: 0.00–3.16), any STI except HIV (30.99; 95% CI: 21.73–40.26), and any STI including HIV (50.08; 95% CI: 37.60–62.55). In the multivariable-adjusted model, incident STI (excluding HIV) were lower among those who reported consistently using condoms during anal and vaginal intercourse (odds ratio = 0.03, 95% CI: 0.00–0.68) compared to those who reported inconsistently using condoms during anal and vaginal intercourse. Conclusions Incidence of STIs is high among male sex workers in Mexico City. Consistent condom use is an important protective factor for STIs, and should be an important component of interventions to prevent incident infections

Characterizing the neighborhood risk environment in multisite clinic-based cohort studies: A practical geocoding and data linkages protocol for protected health information.

BackgroundMaintaining patient privacy when geocoding and linking residential address information with neighborhood-level data can create challenges during research. Challenges may arise when study staff have limited training in geocoding and linking data, or when non-study staff with appropriate expertise have limited availability, are unfamiliar with a study's population or objectives, or are not affordable for the study team. Opportunities for data breaches may also arise when working with non-study staff who are not on-site. We detail a free, user-friendly protocol for constructing indices of the neighborhood risk environment during multisite, clinic-based cohort studies that rely on participants' protected health information. This protocol can be implemented by study staff who do not have prior training in Geographic Information Systems (GIS) and can help minimize the operational costs of integrating geographic data into public health projects.MethodsThis protocol demonstrates how to: (1) securely geocode patients' residential addresses in a clinic setting and match geocoded addresses to census tracts using Geographic Information System software (Esri, Redlands, CA); (2) ascertain contextual variables of the risk environment from the American Community Survey and ArcGIS Business Analyst (Esri, Redlands, CA); (3) use geoidentifiers to link neighborhood risk data to census tracts containing geocoded addresses; and (4) assign randomly generated identifiers to census tracts and strip census tracts of their geoidentifiers to maintain patient confidentiality.ResultsCompletion of this protocol generates three neighborhood risk indices (i.e., Neighborhood Disadvantage Index, Murder Rate Index, and Assault Rate Index) for patients' coded census tract locations.ConclusionsThis protocol can be used by research personnel without prior GIS experience to easily create objective indices of the neighborhood risk environment while upholding patient confidentiality. Future studies can adapt this protocol to fit their specific patient populations and analytic objectives

Characterizing the neighborhood risk environment in multisite clinic-based cohort studies: A practical geocoding and data linkages protocol for protected health information

Background Maintaining patient privacy when geocoding and linking residential address information with neighborhood-level data can create challenges during research. Challenges may arise when study staff have limited training in geocoding and linking data, or when non-study staff with appropriate expertise have limited availability, are unfamiliar with a study’s population or objectives, or are not affordable for the study team. Opportunities for data breaches may also arise when working with non-study staff who are not on-site. We detail a free, user-friendly protocol for constructing indices of the neighborhood risk environment during multisite, clinic-based cohort studies that rely on participants’ protected health information. This protocol can be implemented by study staff who do not have prior training in Geographic Information Systems (GIS) and can help minimize the operational costs of integrating geographic data into public health projects. Methods This protocol demonstrates how to: (1) securely geocode patients’ residential addresses in a clinic setting and match geocoded addresses to census tracts using Geographic Information System software (Esri, Redlands, CA); (2) ascertain contextual variables of the risk environment from the American Community Survey and ArcGIS Business Analyst (Esri, Redlands, CA); (3) use geoidentifiers to link neighborhood risk data to census tracts containing geocoded addresses; and (4) assign randomly generated identifiers to census tracts and strip census tracts of their geoidentifiers to maintain patient confidentiality. Results Completion of this protocol generates three neighborhood risk indices (i.e., Neighborhood Disadvantage Index, Murder Rate Index, and Assault Rate Index) for patients’ coded census tract locations. Conclusions This protocol can be used by research personnel without prior GIS experience to easily create objective indices of the neighborhood risk environment while upholding patient confidentiality. Future studies can adapt this protocol to fit their specific patient populations and analytic objectives

Macro-enabled excel file.

Macro-enabled Excel file that can be used to (1) Link census tracts containing patient geocoded addresses to indicators of neighborhood crime and socioeconomic disadvantage using the census tract geoidentifier, and (2) Assign randomly generated identification numbers to census tracts and strip them of geoidentifiers to maintain patient confidentiality. (XLSM)</p

Multilevel Resilience and HIV Virologic Suppression Among African American/Black Adults in the Southeastern United States

To assess overall and by neighborhood risk environments whether multilevel resilience resources were associated with HIV virologic suppression among African American/Black adults in the Southeastern United States. This clinical cohort sub-study included 436 African American/Black participants enrolled in two parent HIV clinical cohorts. Resilience was assessed using the Multilevel Resilience Resource Measure (MRM) for African American/Black adults living with HIV, where endorsement of a MRM statement indicated agreement that a resilience resource helped a participant continue HIV care despite challenges or was present in a participant's neighborhood. Modified Poisson regression models estimated adjusted prevalence ratios (aPRs) for virologic suppression as a function of categorical MRM scores, controlling for demographic, clinical, and behavioral characteristics at or prior to sub-study enrollment. We assessed for effect measure modification (EMM) by neighborhood risk environments. Compared to participants with lesser endorsement of multilevel resilience resources, aPRs for virologic suppression among those with greater or moderate endorsement were 1.03 (95% confidence interval: 0.96-1.11) and 1.03 (0.96-1.11), respectively. Regarding multilevel resilience resource endorsement, there was no strong evidence for EMM by levels of neighborhood risk environments. Modest positive associations between higher multilevel resilience resource endorsement and virologic suppression were at times most compatible with the data. However, null findings were also compatible. There was no strong evidence for EMM concerning multilevel resilience resource endorsement, which could have been due to random error. Prospective studies assessing EMM by levels of the neighborhood risk environment with larger sample sizes are needed