Background:The epidemiology of COVID-19 remains speculative in Africa. To the best of our knowledge, no study, using robust methodology provides its trajectory for the region or accounts for local context. This paper is the first systematic attempt to provide prevalence, incidence, and mortality estimates across Africa.Methods: Caseloads and incidence forecasts are from a co-variate-based instrumental variable regression model. Fatality rates from Italy and China were applied to generate mortality estimates after making relevant health system and population-level characteristics related adjustments between each of the African countries.Results: By June 30 2020, around 16.3 million people in Africa will contract 403 to 98,358,799). Northern and Eastern Africa will be the most and least areas affected. Cumulative cases on June 30 are expected to reach around 2.9 million (95% CI 465,028 to 18,286,358) in Southern Africa, 2.8 million (95% CI 517,489 to 15,056,314) in Western Africa, and 1.2 million (95% CI 229,111 to 6,138,692) in Central Africa. Incidence for the month of April 2020 is expected to be highest in Djibouti, 32.8 per 1000 (95% CI 6.25 to 171.77), while Morocco will experience among the highest fatalities (1,045 deaths, 95% CI 167 to 6,547). Conclusion:Less urbanized countries with low levels of socio-economic development (hence least connected to the world), are likely to register lower and slower transmissions at the early stages of an epidemic. However, the same enabling factors that worked for their benefit can hinder interventions that have lessened the impact of COVID-19 elsewhere.

BackgroundHealthcare financing through health insurance is gaining traction as developing countries strive to achieve universal health coverage and address the limited access to critical health services for specific populations including pregnant women and their children. However, these reforms are taking place despite limited evaluation of impact of health insurance on maternal health in developing countries including Kenya. In this study we evaluate the association of health insurance with access and utilization of obstetric delivery health services for pregnant women in Kenya.MethodsNationally representative data from the Kenya Demographic and Health Survey 2008–09 was used in this study. 4082 pregnant women with outcomes of interest - Institutional delivery (Yes/No – delivery at hospital, dispensary, maternity home, and clinic) and access to skilled birth attendants (help by a nurse, doctor, or trained midwife at delivery) were selected from 8444 women ages 15–49 years. Linear and logistic regression, and propensity score adjustment are used to estimate the causal association of enrollment in insurance on obstetric health outcomes.ResultsMothers with insurance are 23 percentage points (p < 0.01) more likely to deliver at an institution and 20 percentages points (p < 0.01) more likely have access to skilled birth attendants compared to those not insured. In addition mothers of lower socio-economic status benefit more from enrollment in insurance compared to mothers of higher socio-economic status. For both institutional delivery and access to skilled birth attendants, the average difference of the association of insurance enrollment compared to not enrolling for those of low SES is 23 percentage points (p < 0.01), and 6 percentage points (p < 0.01) for those of higher SES.ConclusionsEnrolling in health insurance is associated with increased access and utilization of obstetric delivery health services for pregnant women. Notably, those of lower socio-economic status seem to benefit the most from enrollment in insurance.Electronic supplementary materialThe online version of this article (doi:10.1186/s12913-017-2397-7) contains supplementary material, which is available to authorized users.

Background: Reducing maternal morbidity and mortality remains a top global health agenda especially in high HIV/AIDS endemic locations where there is increased likelihood of mother to child transmission (MTCT) of HIV. Social health insurance (SHI) has emerged as a viable option to improve population access to health services, while improving outcomes for disenfranchised populations, particularly HIV+ women. However, the effect of SHI on healthcare access for HIV+ persons in limited resource settings is yet to undergo rigorous empirical evaluation. This study analyzes the effect of health insurance on obstetric healthcare access including institutional delivery and skilled birth attendants for HIV+ pregnant women in Kenya. Methods: We analyzed cross-sectional data from HIV+ pregnant women (ages 15-49 years) who had a delivery (full term, preterm, miscarriage) between 2008 and 2013 with their insurance enrollment status available in the electronic medical records database of a HIV healthcare system in Kenya. We estimated linear and logistic regression models and implemented matching and inverse probability weighting (IPW) to improve balance on observable individual characteristics. Additionally, we estimated heterogeneous effects stratified by HIV disease severity (CD4 < 350 as "Severe HIV disease", and CD4 > 350 otherwise). Findings: Health Insurance enrollment is associated with improved obstetric health services utilization among HIV+ pregnant women in Kenya. Specifically, HIV+ pregnant women covered by NHIF have greater access to institutional delivery (12.5-percentage points difference) and skilled birth attendants (19-percentage points difference) compared to uninsured. Notably, the effect of NHIF on obstetric health service use is much greater for those who are sicker (CD4 < 350)-20 percentage points difference. Conclusion: This study confirms conceptual and practical considerations around health insurance and healthcare access for HIV+ persons. Further, it helps to inform relevant policy development for health insurance and HIV financing and delivery in Kenya and in similar countries in sub-Saharan Africa in the universal health coverage (UHC) era.

Migori County is located in western Kenya bordering Lake Victoria and has traditionally performed poorly on important health metrics, including child mortality and HIV prevalence. The Lwala Community Alliance is a non-governmental organization that serves to promote the health and well-being of communities in Migori County through an innovative model utilizing community health workers, community committees, and high-quality facility-based care. This has led to improved outcomes in areas served, including improvements in childhood mortality. As the Lwala Community Alliance expands to new programming areas, it has partnered with multiple academic institutions to rigorously evaluate outcomes. We describe a repeated cross-sectional survey study to evaluate key health metrics in both areas served by the Lwala Community Alliance and comparison areas. This will allow for longitudinal evaluation of changes in metrics over time. Surveys will be administered by trained enumerators on a tablet-based platform to maintain high data quality.

The United Nations’ Sustainable Development Goal 3 is to ensure health and well-being for all at all ages with a specific target to end malaria by 2030. Aligned with this goal, the primary objective of this study is to determine the effectiveness of utilizing local spatial variations to uncover the statistical relationships between malaria incidence rate and environmental and behavioral factors across the counties of Kenya. Two data sources are used—Kenya Demographic and Health Surveys of 2000, 2005, 2010, and 2015, and the national Malaria Indicator Survey of 2015. The spatial analysis shows clustering of counties with high malaria incidence rate, or hot spots, in the Lake Victoria region and the east coastal area around Mombasa; there are significant clusters of counties with low incidence rate, or cold spot areas in Nairobi. We apply an analysis technique, geographically weighted regression, that helps to better model how environmental and social determinants are related to malaria incidence rate while accounting for the confounding effects of spatial non-stationarity. Some general patterns persist over the four years of observation. We establish that variables including rainfall, proximity to water, vegetation, and population density, show differential impacts on the incidence of malaria in Kenya. The El-Nino–southern oscillation (ENSO) event in 2015 was significant in driving up malaria in the southern region of Lake Victoria compared with prior time-periods. The applied spatial multivariate clustering analysis indicates the significance of social and behavioral survey responses. This study can help build a better spatially explicit predictive model for malaria in Kenya capturing the role and spatial distribution of environmental, social, behavioral, and other characteristics of the households.

Rising psychiatric emergency department (ED) presentations pose significant financial and administrative burdens to hospitals. Alternative psychiatric emergency services programs have the potential to alleviate this strain by diverting non-emergent mental health issues from EDs. This study explores one such program, the Boston Emergency Services Team (BEST), a multi-channel psychiatric emergency services provider intended for the publicly insured and uninsured population. BEST provides evaluation and treatment for psychiatric crises through specialized psychiatric EDs, a 24/7 hotline, psychiatric urgent care centers, and mobile crisis units. This retrospective review examines the sociodemographic and clinical characteristics of 225,198 BEST encounters (2005–2016). Of note, the proportion of encounters taking place in ED settings decreased significantly from 70 to 58% across the study period. Findings suggest that multi-focal, psychiatric emergency programs like BEST have the potential to reduce the burden of emergency mental health presentations and improve patient diversion to appropriate psychiatric care.

Background: The global push to achieve the 90-90-90 targets designed to end the HIV epidemic has called for the removing of policy barriers to prevention and treatment, and ensuring financial sustainability of HIV programs. Universal health insurance is one tool that can be used to this end. In sub-Saharan Africa, where HIV prevalence and incidence remain high, the use of health insurance to provide comprehensive HIV care is limited. This study looked at the factors that best predict social health insurance enrollment among HIV positive pregnant women using data from the Academic Model Providing Access to Healthcare (AMPATH) in western Kenya. Methods: Cross-sectional clinical encounter data were extracted from the electronic medical records (EMR) at AMPATH. We used univariate and multivariate logistic regressions to estimate the predictors of health insurance enrollment among HIV positive pregnant women. The analysis was further stratified by HIV disease severity (based on CD4 cell count <350 and 350>) to test the possibility of differential enrollment given HIV disease state. Results: Approximately 7% of HIV infected women delivering at a healthcare facility had health insurance. HIV positive pregnant women who deliver at a health facility had twice the odds of enrolling in insurance [2.46 Adjusted Odds Ratio (AOR), Confidence Interval (CI) 1.24–4.87]. They were 10 times more likely to have insurance if they were lost to follow-up to HIV care during pregnancy [9.90 AOR; CI 3.42–28.67], and three times more likely to enroll if they sought care at an urban clinic [2.50 AOR; 95% CI 1.53–4.12]. Being on HIV treatment was negatively associated with health insurance enrollment [0.22 AOR; CI 0.10–0.49]. Stratifying the analysis by HIV disease severity while statistically significant did not change these results. Conclusions: The findings indicated that health insurance enrollment among HIV positive pregnant women was low mirroring national levels. Additionally, structural factors, such as access to institutional delivery and location of healthcare facilities, increased the likelihood of health insurance enrollment within this population. However, behavioral aspects, such as being lost to follow-up to HIV care during pregnancy and being on HIV treatment, had an ambiguous effect on insurance enrollment. This may potentially be because of adverse selection and information asymmetries. Further understanding of the relationship between insurance and HIV is needed if health insurance is to be utilized for HIV treatment and prevention in limited resource settings.

Objective Electronic health records (EHR) hold promise for conducting large-scale analyses linking individual characteristics to health outcomes. However, these data often contain a large number of missing values at both the patient and visit level due to variation in data collection across facilities, providers, and clinical need. This study proposes a stepwise framework for imputing missing values within a visit-level EHR dataset that combines informative missingness and conditional imputation in a scalable manner that may be parallelized for efficiency. Results For this study we use a subset of data from AMPATH representing information from 530,812 clinic visits from 16,316 Human Immunodeficiency Virus (HIV) positive women across Western Kenya who have given birth. We apply this process to a set of 84 clinical, social and economic variables and are able to impute values for 84.6% of variables with missing data with an average reduction in missing data of approximately 35.6%. We validate the use of this imputed dataset by predicting National Hospital Insurance Fund (NHIF) enrollment with 94.8% accuracy.