Browsing by Subject "LMICs"

Researchers have been searching for ways to improve outcomes for orphaned and separated children (OSC) worldwide. OSC have a particularly high rate of mental health disorders and lower emotional wellbeing. Stigma has been shown to be a predictor of mental health disorders and emotional wellbeing for HIV and children in poverty. However, no research has been conducted with OSC examining the relationship between self-stigma and emotional wellbeing. Using Round 10 of the Positive Outcomes for Orphans (POFO) study with 2013 orphans from Kenya, Ethiopia, Tanzania, India, and Cambodia, a linear model was implemented to examine the association between self-stigma and emotional wellbeing. Through the building of a linear regression model, self-stigma was shown to be a strong predictor of emotional wellbeing as measured by the Strengths and Difficulties Questionnaire (SDQ). This indicates that self-stigma may be a significant factor to address when looking at ways to improve emotional wellbeing among orphans.

Background: Traumatic brain injury (TBI) has a disproportionate burden on low- and middle-income countries (LMICs) and cost-effective and culturally relevant measures are necessary to improve TBI care. This study aims to characterize emergency healthcare providers’ decision making when treating TBI patients, develop a machine learning-based model to predict TBI patient outcome, and conduct a decision curve analysis (DCA) to evaluate model clinical applicability. Methods: This study is twofold: 1) a secondary analysis of a TBI data registry with 4142 patients and 2) a survey examining physicians decision-making in treating 50 TBI patients in real time. Results: Five machine learning models were developed with AUCs ranging from 70.86% (Single C5.0 Ruleset) to 85.67% (Ensemble Model). DCA showed that all models exhibited a greater net benefit over ranges of clinical thresholds. The survey collected information on 50 patients providing insight on tools used by physicians in real-time when treating TBI patients as well as the unmet need patients at KCMC faced. Conclusions: This study is the first to use machine learning modeling and DCA in the context of TBI prognosis in Sub-Saharan Africa. Prognostic models have great potential within the decision-making process for treating TBI patients in LMIC health systems and such utility can be expanded through determining different threshold probabilities for various interventions.

Background: Perinatal depression in low- and middle-income countries (LMICs) is common and associated with many negative outcomes. Although effective interventions exist, many cases in LMICs remain untreated due to a lack of human resources. Task-sharing approaches such as Thinking Healthy program were proved to be feasible to expand access to treatment in LMICs but were facing certain barriers to scale up. In this study, we adapted Thinking Healthy Program to the artificial intelligence system called Tess (named Zuri in Kenya) to provide automated psychological support for perinatal women in Kenya. The objective was to gather preliminary data on feasibility, acceptability, and perceived impacts of the automated psychological support on perinatal women in Kenya.

Methods: Women were recruited from two public hospitals in Kiambu County, Kenya. After enrollment, each woman was matched to another new participant with similar maternity status and was randomly assigned to have a 1-week or 2-week baseline period. We prompted participants to rate their mood every 3 days throughout the study. We also reviewed system logs and conducted in-depth interviews to determine feasibility, acceptability, and perceived impacts of the intervention.

Results: 647 women were invited to participate; 86 of them completed the SMS screening and 41 of them enrolled in the study. Among all the enrolled participants, 27 of them (65.9%) sent at least one message to Zuri and 31 (75.6%) of them submitted at least three ratings. 14 women (34.1%) engaged with Zuri beyond registration. Free chats were a big part of the participants’ interactions with Zuri. During free chats, the most common intervention module was mindfulness-based meditation, and the most common rapport-building module was discussion about the women’s passion. Most interviewees expressed positive attitudes towards Zuri. They also reported some positive changes in their life after using Zuri.

Conclusion: The preliminary data showed that Zuri was feasible, acceptable, and had some perceived impacts among a sample of pregnant women and new mothers in Kenya. Automated psychological support is still in its infancy, but it has great potential to close the large treatment gap that exists in many LMICs.

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Introduction: Acute coronary syndromes (ACS) result in significant morbidity and mortality in low-and-middle-income countries (LMICs). Fifty percent of deaths in this region are from a cardiac etiology. Not much is known about the epidemiology of ACS in Brazil. Our aim was to describe the correlation between social determinants of health and access-to-care markers as related to ACS mortality and its geographic distribution in the country. Methods: Using the Brazilian National Health Database (DATASUS) and other nationally aggregated data sources, socioeconomic (SE) parameters, cardiovascular risk (CV) factors and an accessibility index for high complexity cardiac care centers (with hemodynamic monitoring and cardiac interventions) were obtained. To account for spatial dependency, geographic weighted regression (GWR) analysis was performed for all the predictor variables with respect to the outcome of deaths. Results: There were 776,449 ACS-related deaths from 2012 to 2018. The highest ACS mortality rate was in the South region of Brazil (104.7 per 100,000 population). The GWR analysis showed regional variability of socioeconomic factors as correlated with ACS mortality. A low accessibility-index in the North and Northeast regions of Brazil was strongly associated with ACS deaths. Conclusions: Spatial analysis allows for estimation of the local heterogeneity in the relationship between SE components, CV risk factors and access-to-care markers as related to ACS mortality. Such analyses allow for improved understanding of the burden of ACS in Brazil.