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Background:\ud Understanding socioeconomic disparities in all-cause and cause-specific mortality can help inform prevention and treatment strategies.\ud \ud Objectives:\ud To quantify cause-specific mortality rates by socioeconomic status across seven health and demographic surveillance systems (HDSS) in five countries (Ethiopia, Kenya, Malawi, Mozambique, and Nigeria) in the INDEPTH Network in sub-Saharan Africa.\ud \ud Methods:\ud We linked demographic residence data with household survey data containing living standards and education information we used to create a poverty index. Person-years lived and deaths between 2003 and 2016 (periods varied by HDSS) were stratified in each HDSS by age, sex, year, and number of deprivations on the poverty index (0–8). Causes of death were assigned to each death using the InterVA-4 model based on responses to verbal autopsy questionnaires. We estimated rate ratios between socioeconomic groups (2–4 and 5–8 deprivations on our poverty index compared to 0–2 deprivations) for specific causes of death and calculated life expectancy for the deprivation groups.\ud \ud Results:\ud Our pooled data contained almost 3.5 million person-years of observation and 25,038 deaths. All-cause mortality rates were higher among people in households with 5–8 deprivations on our poverty index compared to 0–2 deprivations, controlling for age, sex, and year (rate ratios ranged 1.42 to 2.06 across HDSS sites). The poorest group had consistently higher death rates in communicable, maternal, neonatal, and nutritional conditions (rate ratios ranged 1.34–4.05) and for non-communicable diseases in several sites (1.14–1.93). The disparities in mortality between 5–8 deprivation groups and 0–2 deprivation groups led to lower life expectancy in the higher-deprivation groups by six years in all sites and more than 10 years in five sites.\ud \ud Conclusions:\ud We show large disparities in mortality on the basis of socioeconomic status across seven HDSS in sub-Saharan Africa due to disparities in communicable disease mortality and from non-communicable diseases in some sites. Life expectancy gaps between socioeconomic groups within sites were similar to the gaps between high-income and lower-middle-income countries. Prevention and treatment efforts can benefit from understanding subpopulations facing higher mortality from specific conditions.

Background: Reliable data on the HIV epidemic is critical for the measurement of the impact of HIV response and for the implementation of further interventions. Methods: We used mortality data from the Kombewa health and demographic surveillance systems (HDSS) from January 1, 2011 to December 31, 2015 to examine the space–time pattern of HIV-associated mortality. HIV mortality rate was calculated per 1000 persons living with HIV (for comparison with regional and national averages) and per 1000 person-years (p-y) for comparison with data from other HDSS sites. We used the Optimized Hot Spot Analysis to examine whether HIV-associated deaths would form statistically significant local aggregation in the 5-year period. P-value of

Background: The vast majority of deaths in the health and Kombewa demographic surveillance system (HDSS) study area are not registered and reported through official systems of vital registration. As a result, few data are available regarding causes of death in this population. Objectives: To describe causes of death among residents of all ages in the Kombewa HDSS, located in rural Western Kenya. Methods: Verbal autopsy (VA) interviews at the site were conducted using the modified 2007 and later 2012 standardized WHO questionnaires. Assignment of causes of death was made using the InterVA-4 model version 4.02. Cox regression model, adjusted for sex, was built to evaluate the influence of age on mortality. Results: There were a total of 5196 deaths recorded between 2011 and 2015 at the site. VA interviews were successfully completed for 3903 of these deaths (75.1%). Mortality rates were highest among neonates HR = 38.54 (

Background This study analyzes the consequences of maternal death to households in Western Kenya, specifically, neonatal and infant survival, childcare and schooling, disruption of daily household activities, the emotional burden on household members, and coping mechanisms. Methods The study is a combination of qualitative analysis with matched and unmatched quantitative analysis using surveillance and survey data. Between September 2011 and March 2013 all households in the study area with a maternal death were surveyed. Data were collected on the demographic characteristics of the deceased woman; household socio-economic status; a history of the pregnancy that led to the death; schooling experiences of surviving school-age children; and disruption to household functioning due to the maternal death. These data were supplemented by in-depth and focus group discussions. Quantitative data on neonatal and infant survival from a demographic surveillance system in the study area were also used. Descriptive and bivariate analyses were conducted with the quantitative data, and qualitative data were analyzed through text analysis using NVivo. Results More than three-quarters of deceased women performed most household tasks when healthy. After the maternal death, the responsibility for these tasks fell primarily on the deceased’s husbands, mothers, and mothers-in-law. Two-thirds of the individuals from households that suffered a maternal death had to shift into another household. Most children had to move away, mostly to their grandmother’s home. About 37% of live births to women who died of maternal causes survived till age 1 year, compared to 65% of live births to a matched sample of women who died of non-maternal causes and 93% of live births to surviving women. Older, surviving children missed school or did not have enough time for schoolwork, because of increased housework or because the loss of household income due to the maternal death meant school fees could not be paid. Respondents expressed grief, frustration, anger and a sense of loss. Generous family and community support during the funeral and mourning periods was followed by little support thereafter. Conclusion The detrimental consequences of a maternal death ripple out from the woman’s spouse and children to the entire household, and across generations.

Background This study explores the consequences of a maternal death to households in rural Western Kenya focusing particularly on the immediate financial and economic impacts. Methods Between September 2011 and March 2013 all households in the study area with a maternal death were surveyed. Data were collected on the demographic characteristics of the deceased woman; household socio-economic status; a history of the pregnancy and health care access and utilization; and disruption to household functioning due to the maternal death. These data were supplemented by in-depth and focus group discussions. Results The health service utilization costs associated with maternal deaths were significantly higher, due to more frequent service utilization as well as due to the higher cost of each visit suggesting more involved treatments and interventions were sought with these women. The already high costs incurred by cases during pregnancy were further increased during delivery and postpartum mainly a result of higher facility-based fees and expenses. Households who experienced a maternal death spent about one-third of their annual per capita consumption expenditure on healthcare access and use as opposed to at most 12% among households who had a health pregnancy and delivery. Funeral costs were often higher than the healthcare costs and altogether forced households to dis-save, liquidate assets and borrow money. What is more, the surviving members of the households had significant redistribution of labor and responsibilities to make up for the lost contributions of the deceased women. Conclusion Kenya is in the process of instituting free maternity services in all public facilities. Effectively implemented, this policy can lift a major economic burden experienced by a very large number of household who seek maternal health services which can be catastrophic in complicated cases that result in maternal death. There needs to be further emphasis on insurance schemes that can support households through catastrophic health spending.