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Background: Understanding socioeconomic disparities in all-cause and cause-specific mortality can help inform prevention and treatment strategies. Objectives: 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. Methods: 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. Results: 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. Conclusions: 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. [ABSTRACT FROM AUTHOR]

Background:Women continue to die in unacceptably large numbers around the world as a result of pregnancy, particularly in sub-Saharan Africa and Asia. Part of the problem is a lack of accurate, population-based information characterising the issues and informing solutions. Population surveillance sites, such as those operated within the INDEPTH Network, have the potential to contribute to bridging the information gaps. Objective: To describe patterns of pregnancy-related mortality at INDEPTH Network Health and Demographic Surveillance System sites in sub-Saharan Africa and southeast Asia in terms of maternal mortality ratio (MMR) and cause-specific mortality rates. Design: Data on individual deaths among women of reproductive age (WRA) (15-49) resident in INDEPTH sites were collated into a standardised database using the INDEPTH 2013 population standard, the WHO 2012 verbal autopsy (VA) standard, and the InterVA model for assigning cause of death. Results: These analyses are based on reports from 14 INDEPTH sites, covering 14,198 deaths amongWRAover 2,595,605 person-years observed. MMRs varied between 128 and 461 per 100,000 live births, while maternal mortality rates ranged from 0.11 to 0.74 per 1,000 person-years. Detailed rates per cause are tabulated, including analyses of direct maternal, indirect maternal, and incidental pregnancy-related deaths across the 14 sites. Conclusions: As expected, these findings confirmed unacceptably high continuing levels of maternal mortality. However, they also demonstrate the effectiveness of INDEPTH sites and of the VA methods applied to arrive at measurements of maternal mortality that are essential for planning effective solutions and monitoring programmatic impacts. [ABSTRACT FROM AUTHOR]

Influenza causes severe illness and deaths, and global surveillance systems use different clinical case definitions to identify patients for diagnostic testing. We used data collected during January 2007–July 2010 at hospital-based influenza surveillance sites in western Kenya to calculate sensitivity, specificity, positive predictive value, and negative predictive value for eight clinical sign/symptom combinations in hospitalized patients with acute respiratory illnesses, including severe acute respiratory illness (SARI) (persons aged 2–59 months: cough or difficulty breathing with an elevated respiratory rate or a danger sign; persons aged ⩾5 years: temperature ⩾38 °C, difficulty breathing, and cough or sore throat) and influenza-like illness (ILI) (all ages: temperature ⩾38 °C and cough or sore throat). Overall, 4800 persons aged ⩾2 months were tested for influenza; 416 (9%) had laboratory-confirmed influenza infections. The symptom combination of cough with fever (subjective or measured ⩾38 °C) had high sensitivity [87·0%, 95% confidence interval (CI) 83·3–88·9], and ILI had high specificity (70·0%, 95% CI 68·6–71·3). The case definition combining cough and any fever is a simple, sensitive case definition for influenza in hospitalized persons of all age groups, whereas the ILI case definition is the most specific. The SARI case definition did not maximize sensitivity or specificity. [ABSTRACT FROM AUTHOR]

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 (<0.001) and among Infants HR = 2.07 (<0.006) in the Kombewa HDSS. Among those deaths in which VA was performed, the top causes of death were HIV/AIDS (12.6%), Malaria (10.3%), Pneumonia (10.1%), Acute abdomen (7.0%), Stroke (5.2%) and TB (4.9%) for the whole population in general. Stroke, acute abdomen heart diseases and Pneumonia were common causes of death (CODs) among the elderly over the age of 65. Conclusions: The analysis established the main CODs among people of all ages within the area served by the Kombewa HDSS. We hope that information generated from this study will help better address preventable deaths in the surveyed community as well as help mitigate negative health impacts in other rural communities throughout the Western Kenya region. [ABSTRACT FROM AUTHOR]