Economic vulnerabilities, mental health, and coping strategies among Tanzanian youth during COVID-19.

BACKGROUND: The COVID-19 pandemic has exacerbated struggles for youth living in poor households. Youth in rural Tanzania are particularly vulnerable given widespread poverty, lack of formal sector employment opportunities, and health risks. We examine influences of the pandemic on economic insecurity and mental health and explore the coping strategies employed by youth and their households. METHODS: We conducted mixed-method data collection with youth (N = 760 quantitative and N = 44 qualitative interviews) and households (n = 542) via mobile phone among a sub-set of a cohort from an on-going longitudinal sample in two rural regions in Tanzania. In addition to phone interviews, we collected data bi-weekly via SMS messaging. We present mixed-methods, descriptive analysis of the outcomes and longitudinally compare quantitative outcomes pre- and post-COVID-19, within the same individuals. RESULTS: Adverse economic impacts were most salient, and to cope, youth engaged in more labor and domestic chores. Compared to prior the COVID-19 pandemic, youth reported spending more time caring for elderly or sick household members and gathering firewood or nuts. CONCLUSIONS: These findings underscore the potential opportunity to promote policies and programs which address risks youth face. Recommended measures include expansion and adaptation of social protection policies, strengthened food and nutrition surveillance and referral systems, and scaling up community-based mental health programming.

Health services availability and readiness moderate cash transfer impacts on health insurance enrolment: evidence from the LEAP 1000 cash transfer program in Ghana.

BACKGROUND: Expanding health insurance coverage is a priority under Sustainable Development Goal 3. To address the intersection between poverty and health and remove cost barriers, the government of Ghana established the National Health Insurance Scheme (NHIS). Government further linked NHIS with the Livelihood Empowerment Against Poverty (LEAP) 1000 cash transfer program by waiving premium fees for LEAP 1000 households. This linkage led to increased NHIS enrolment, however, large enrolment gaps remained. One potential reason for failure to enroll may relate to the poor quality of health services. METHODS: We examine whether LEAP 1000 impacts on NHIS enrolment were moderated by health facilities' service availability and readiness. RESULTS: We find that adults in areas with the highest service availability and readiness are 18 percentage points more likely to enroll in NHIS because of LEAP 1000, compared to program effects of only 9 percentage points in low service availability and readiness areas. Similar differences were seen for enrolment among children (20 v. 0 percentage points) and women of reproductive age (25 v. 10 percentage points). CONCLUSIONS: We find compelling evidence that supply-side factors relating to service readiness and availability boost positive impacts of a cash transfer program on NHIS enrolment. Our work suggests that demand-side interventions coupled with supply-side strengthening may facilitate greater population-level benefits down the line. In the quest for expanding financial protection towards accelerating the achievement of universal health coverage, policymakers in Ghana should prioritize the integration of efforts to simultaneously address demand- and supply-side factors. TRIAL REGISTRATION: This study is registered in the International Initiative for Impact Evaluation's (3ie) Registry for International Development Impact Evaluations ( RIDIE-STUDY-ID-55942496d53af ).

Geographically weighted machine learning model for untangling spatial heterogeneity of type 2 diabetes mellitus (T2D) prevalence in the USA.

Type 2 diabetes mellitus (T2D) prevalence in the United States varies substantially across spatial and temporal scales, attributable to variations of socioeconomic and lifestyle risk factors. Understanding these variations in risk factors contributions to T2D would be of great benefit to intervention and treatment approaches to reduce or prevent T2D. Geographically-weighted random forest (GW-RF), a tree-based non-parametric machine learning model, may help explore and visualize the relationships between T2D and risk factors at the county-level. GW-RF outputs are compared to global (RF and OLS) and local (GW-OLS) models between the years of 2013-2017 using low education, poverty, obesity, physical inactivity, access to exercise, and food environment as inputs. Our results indicate that a non-parametric GW-RF model shows a high potential for explaining spatial heterogeneity of, and predicting, T2D prevalence over traditional local and global models when inputting six major risk factors. Some of these predictions, however, are marginal. These findings of spatial heterogeneity using GW-RF demonstrate the need to consider local factors in prevention approaches. Spatial analysis of T2D and associated risk factor prevalence offers useful information for targeting the geographic area for prevention and disease interventions.