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The under-five mortality rate (U5MR) is a critical and widely available population health indicator. Both the MDGs and SDGs define targets for improvement in the U5MR, and the SDGs require spatial disaggregation of indicators. We estimate trends in the U5MR for Admin-1 subnational areas using 122 DHS surveys in 35 countries in Africa and assess progress toward the MDG target reductions for each subnational region and each country as a whole. In each country, direct weighted estimates of the U5MR from each survey are calculated and combined into a single estimate for each Admin-1 region across five-year periods. Our method fully accounts for the sample design of each survey. The region-time-specific estimates are smoothed using a Bayesian, space-time model that produces more precise estimates (when compared to the direct estimates) at a one-year scale that are consistent with each other in both space and time. The resulting estimated distributions of the U5MR are summarized and used to assess subnational progress toward the MDG 4 target of two-thirds reduction in the U5MR during 1990-2015. Our space-time modeling approach is tractable and can be readily applied to a large collection of sample survey data. Subnational, regional spatial heterogeneity in the levels and trends in the U5MR vary considerably across Africa. There is no generalizable pattern between spatial heterogeneity and levels or trends in the U5MR. Subnational, small-area estimates of the U5MR: (i) identify subnational regions where interventions are still necessary and those where improvement is well under way; and (ii) countries where there is very little spatial variation and others where there are important differences between subregions in both levels and trends. More work is necessary to improve both the data sources and methods necessary to adequately measure subnational progress toward the SDG child survival targets.

We show that Bayesian population reconstruction, a recent method for estimating past populations by age, works for data of widely varying quality. Bayesian reconstruction simultaneously estimates age-specific population counts, fertility rates, mortality rates, and net international migration flows from fragmentary data, while formally accounting for measurement error. As inputs, Bayesian reconstruction uses initial bias-reduced estimates of standard demographic variables. We reconstruct the female populations of three countries: Laos, a country with little vital registration data where population estimation depends largely on surveys; Sri Lanka, a country with some vital registration data; and New Zealand, a country with a highly developed statistical system and good quality vital registration data. In addition, we extend the method to countries without censuses at regular intervals. We also use it to assess the consistency of results between model life tables and available census data, and hence to compare different model life table systems.

Traditionally health statistics are derived from civil and/or vital registration. Civil registration in low- to middle-income countries varies from partial coverage to essentially nothing at all. Consequently the state of the art for public health information in low- to middle-income countries is efforts to combine or triangulate data from different sources to produce a more complete picture across both time and space – data amalgamation. Data sources amenable to this approach include sample surveys, sample registration systems, health and demographic surveillance systems, administrative records, census records, health facility records and others. We propose a new statistical framework for gathering health and population data – Hyak – that leverages the benefits of sampling and longitudinal, prospective surveillance to create a cheap, accurate, sustainable monitoring platform. Hyak has three fundamental components: •Data amalgamation: A sampling and surveillance component that organizes two or more data collection systems to work together: (1) data from HDSS with frequent, intense, linked, prospective follow-up and (2) data from sample surveys conducted in large areas surrounding the Health and Demographic Surveillance System (HDSS) sites using informed sampling so as to capture as many events as possible;•Cause of death: Verbal autopsy to characterize the distribution of deaths by cause at the population level; and•Socioeconomic status (SES): Measurement of SES in order to characterize poverty and wealth.We conduct a simulation study of the informed sampling component of Hyak based on the Agincourt HDSS site in South Africa. Compared with traditional cluster sampling, Hyak's informed sampling captures more deaths, and when combined with an estimation model that includes spatial smoothing, produces estimates of both mortality counts and mortality rates that have lower variance and small bias.

Accurate estimates of the under-5 mortality rate (U5MR) in a developing world context are a key barometer of the health of a nation. This paper describes new models to analyze survey data on mortality in this context. We are interested in both spatial and temporal description, that is, wishing to estimate U5MR across regions and years, and to investigate the association between the U5MR and spatially-varying covariate surfaces. We illustrate the methodology by producing yearly estimates for subnational areas in Kenya over the period 1980 - 2014 using data from demographic health surveys (DHS). We use a binomial likelihood with fixed effects for the urban/rural stratification to account for the complex survey design. We carry out smoothing using Bayesian hierarchical models with continuous spatial and temporally discrete components. A key component of the model is an offset to adjust for bias due to the effects of HIV epidemics. Substantively, there has been a sharp decline in U5MR in the period 1980 - 2014, but large variability in estimated subnational rates remains. A priority for future research is understanding this variability. Temperature, precipitation and a measure of malaria infection prevalence were candidates for inclusion in the covariate model., 36 pages, 11 figures

Summary Background Understanding the effects of socioeconomic disparities in health outcomes is important to implement specific preventive actions. We assessed socioeconomic disparities in mortality indicators in a rural South African population over the period 2001–13. Methods We used data from 21 villages of the Agincourt Health and socio-Demographic Surveillance System (HDSS). We calculated the probabilities of death from birth to age 5 years and from age 15 to 60 years, life expectancy at birth, and cause-specific and age-specific mortality by sex (not in children

Background Virtually all low- and middle-income countries are undergoing an epidemiological transition whose progression is more varied than experienced in high-income countries. Observed changes in mortality and disease patterns reveal that the transition in most low- and middle-income countries is characterized by reversals, partial changes and the simultaneous occurrence of different types of diseases of varying magnitude. Localized characterization of this shifting burden, frequently lacking, is essential to guide decentralised health and social systems on the effective targeting of limited resources. Based on a rigorous compilation of mortality data over two decades, this paper provides a comprehensive assessment of the epidemiological transition in a rural South African population. Methods We estimate overall and cause-specific hazards of death as functions of sex, age and time period from mortality data from the Agincourt Health and socio-Demographic Surveillance System and conduct statistical tests of changes and differentials to assess the progression of the epidemiological transition over the period 1993–2013. Results From the early 1990s until 2007 the population experienced a reversal in its epidemiological transition, driven mostly by increased HIV/AIDS and TB related mortality. In recent years, the transition is following a positive trajectory as a result of declining HIV/AIDS and TB related mortality. However, in most age groups the cause of death distribution is yet to reach the levels it occupied in the early 1990s. The transition is also characterized by persistent gender differences with more rapid positive progression in females than males. Conclusions This typical rural South African population is experiencing a protracted epidemiological transition. The intersection and interaction of HIV/AIDS and antiretroviral treatment, non-communicable disease risk factors and complex social and behavioral changes will impact on continued progress in reducing preventable mortality and improving health across the life course. Integrated healthcare planning and program delivery is required to improve access and adherence for HIV and non-communicable disease treatment. These findings from a local, rural setting over an extended period contribute to the evidence needed to inform further refinement and advancement of epidemiological transition theory. Electronic supplementary material The online version of this article (doi:10.1186/s12889-017-4312-x) contains supplementary material, which is available to authorized users.

Summary Background Antiretroviral therapy (ART) substantially decreases morbidity and mortality in people living with HIV. In this study, we describe population-level trends in the adult life expectancy and trends in the residual burden of HIV mortality after the roll-out of a public sector ART programme in KwaZulu-Natal, South Africa, one of the populations with the most severe HIV epidemics in the world. Methods Data come from the Africa Centre Demographic Information System (ACDIS), an observational community cohort study in the uMkhanyakude district in northern KwaZulu-Natal, South Africa. We used non-parametric survival analysis methods to estimate gains in the population-wide life expectancy at age 15 years since the introduction of ART, and the shortfall of the population-wide adult life expectancy compared with that of the HIV-negative population (ie, the life expectancy deficit). Life expectancy gains and deficits were further disaggregated by age and cause of death with demographic decomposition methods. Findings Covering the calendar years 2001 through to 2014, we obtained information on 93 903 adults who jointly contribute 535 42 8 person-years of observation to the analyses and 9992 deaths. Since the roll-out of ART in 2004, adult life expectancy increased by 15·2 years for men (95% CI 12·4–17·8) and 17·2 years for women (14·5–20·2). Reductions in pulmonary tuberculosis and HIV-related mortality account for 79·7% of the total life expectancy gains in men (8·4 adult life-years), and 90·7% in women (12·8 adult life-years). For men, 9·5% is the result of a decline in external injuries. By 2014, the life expectancy deficit had decreased to 1·2 years for men (−2·9 to 5·8) and to 5·3 years for women (2·6–7·8). In 2011–14, pulmonary tuberculosis and HIV were responsible for 84·9% of the life expectancy deficit in men and 80·8% in women. Interpretation The burden of HIV on adult mortality in this population is rapidly shrinking, but remains large for women, despite their better engagement with HIV-care services. Gains in adult life-years lived as well as the present life expectancy deficit are almost exclusively due to differences in mortality attributed to HIV and pulmonary tuberculosis. Funding Wellcome Trust, the Bill & Melinda Gates Foundation, and the National Institutes of Health.

Understanding the distribution of socioeconomic status (SES) and its temporal dynamics within a population is critical to ensure that policies and interventions adequately and equitably contribute to the well-being and life chances of all individuals. This study assesses the dynamics of SES in a typical rural South African setting over the period 2001-2013 using data on household assets from the Agincourt Health and Demographic Surveillance System. Three SES indices, an absolute index, principal component analysis index and multiple correspondence analysis index, are constructed from the household asset indicators. Relative distribution methods are then applied to the indices to assess changes over time in the distribution of SES with special focus on location and shape shifts. Results show that the proportion of households that own assets associated with greater modern wealth has substantially increased over time. In addition, relative distributions in all three indices show that the median SES index value has shifted up and the distribution has become less polarized and is converging towards the middle. However, the convergence is larger from the upper tail than from the lower tail, which suggests that the improvement in SES has been slower for poorer households. The results also show persistent ethnic differences in SES with households of former Mozambican refugees being at a disadvantage. From a methodological perspective, the study findings demonstrate the comparability of the easy-to-compute absolute index to other SES indices constructed using more advanced statistical techniques in assessing household SES.

BACKGROUND Estimating the total fertility rate is challenging for many developing countries because of limited data and varying data quality. A standardized, reproducible approach to produce estimates that include an uncertainty assessment is desired. METHODS We develop a method to estimate and assess uncertainty in the total fertility rate over time, based on multiple imperfect observations from different data sources including surveys and censuses. We take account of measurement error in observations by decomposing it into bias and variance and assess both by linear regression on a variety of data quality covariates. We estimate the total fertility rate using a local smoother, and assess uncertainty using the weighted likelihood bootstrap. RESULTS We apply our method to data from seven countries in West Africa and construct estimates and uncertainty intervals for the total fertility rate. Based on cross-validation exercises, we find that accounting for differences in data quality between observations gives better calibrated confidence intervals and reduces bias. CONCLUSIONS When working with multiple imperfect observations from different data sources to estimate the total fertility rate, or demographic indicators in general, potential biases and differences in error variance have to be taken into account to improve the estimates and their uncertainty assessment.

Background: Research training for public health professionals is key to the future of public health and policy in Africa. A growing number of schools of public health are connected to health and socio-demographic surveillance system field sites in developing countries, in Africa and Asia in particular. Linking training programs with these sites provides important opportunities to improve training, build local research capacity, foreground local health priorities, and increase the relevance of research to local health policy. Objective: To increase research training capacity in public health programs by providing targeted training to students and increasing the accessibility of existing data. Design: This report is a case study of an approach to linking public health research and training at the University of the Witwatersrand. We discuss the development of a sample training database from the Agincourt Health and Socio-demographic Surveillance System in South Africa and outline a concordant transnational intensive short course on longitudinal data analysis offered by the University of the Witwatersrand and the University of Colorado-Boulder. This case study highlights ways common barriers to linking research and training can be overcome. Results and Conclusions: This collaborative effort demonstrates that linking training to ongoing data collection can improve student research, accelerate student training, and connect students to an international network of scholars. Importantly, the approach can be adapted to other partnerships between schools of public health and longitudinal research sites. Keywords: Africa; public health training; data accessibility; research capacity; health policy (Published: 31 August 2010) Citation: Global Health Action 2010, 3: 5287 - DOI: 10.3402/gha.v3i0.5287

Background: Literature is limited on the effects of high prevalence HIV on fertility in the absence of treatment, and the effects of the introduction of sustained access to antiretroviral therapy (ART) on fertility. We summarize fertility patterns in rural northeast South Africa over 21 years during dynamic social and epidemiological change. Methods: We use data for females aged 15-49 from the Agincourt health and socio-demographic surveillance system (1993-2013). We use discrete time event history analysis to summarize patterns in the probability of any birth. Results: Overall fertility declined in 2001-2003, increased in 2004-2011, and then declined in 2012-2013. South Africans showed a similar pattern. Mozambicans showed a different pattern, with strong declines prior to 2003 before stalling during 2004-2007, and then continued fertility decline afterwards. There was an inverse gradient between fertility levels and household socioeconomic status. The gradient did not vary by time or nationality. Conclusions: The fertility transition in rural South Africa shows a pattern of decline until the height of the HIV/AIDS pandemic, with a resulting stall until further decline in the context of ART rollout. Fertility patterns are not homogenous among groups.

The Agincourt Health and Demographic Surveillance System has since 2001 conducted a biannual household asset survey in order to quantify household socio-economic status (SES) in a rural population living in northeast South Africa. The survey contains binary, ordinal and nominal items. In the absence of income or expenditure data, the SES landscape in the study population is explored and described by clustering the households into homogeneous groups based on their asset status. A model-based approach to clustering the Agincourt households, based on latent variable models, is proposed. In the case of modeling binary or ordinal items, item response theory models are employed. For nominal survey items, a factor analysis model, similar in nature to a multinomial probit model, is used. Both model types have an underlying latent variable structure - this similarity is exploited and the models are combined to produce a hybrid model capable of handling mixed data types. Further, a mixture of the hybrid models is considered to provide clustering capabilities within the context of mixed binary, ordinal and nominal response data. The proposed model is termed a mixture of factor analyzers for mixed data (MFA-MD). The MFA-MD model is applied to the survey data to cluster the Agincourt households into homogeneous groups. The model is estimated within the Bayesian paradigm, using a Markov chain Monte Carlo algorithm. Intuitive groupings result, providing insight to the different socio-economic strata within the Agincourt region., Comment: Published in at http://dx.doi.org/10.1214/14-AOAS726 the Annals of Applied Statistics (http://www.imstat.org/aoas/) by the Institute of Mathematical Statistics (http://www.imstat.org)

The HIV pandemic has led to dramatic increases and inequalities in adult mortality, and the diffusion of antiretroviral treatment, together with demographic and socioeconomic shifts in sub-Saharan Africa, has further changed mortality patterns. We describe all-cause and cause-specific mortality patterns in rural South Africa, analyzing data from the Agincourt health and socio-demographic surveillance system from 1994 to 2009 for those aged 5 years and older. Mortality increased during that period, particularly after 2002 for ages 30-69. HIV/AIDS and TB deaths increased and recently plateaued at high levels in people under age 60. Noncommunicable disease deaths increased among those under 60, and recently also increased among those over 60. There was an inverse gradient between mortality and household SES, particularly for deaths due to HIV/AIDS and TB and noncommunicable diseases. A smaller and less consistent gradient emerged for deaths due to other communicable diseases. Deaths due to injuries remained an important mortality risk for males but did not vary by SES. Rural South Africa continues to have a high burden of HIV/AIDS and TB mortality while deaths from noncommunicable diseases have increased, and both of these cause-categories show social inequalities in mortality.

The Agincourt health and socio-demographic surveillance system (HDSS), located in rural northeast South Africa close to the Mozambique border, was established in 1992 to support district health systems development led by the post-apartheid ministry of health. The HDSS (90 000 people), based on an annual update of resident status and vital events, now supports multiple investigations into the causes and consequences of complex health, population and social transitions. Observational work includes cohorts focusing on different stages along the life course, evaluation of national policy at population, household and individual levels and examination of household responses to shocks and stresses and the resulting pathways influencing health and well-being. Trials target children and adolescents, including promoting psycho-social well-being, preventing HIV transmission and reducing metabolic disease risk. Efforts to enhance the research platform include using automated measurement techniques to estimate cause of death by verbal autopsy, full 'reconciliation' of in- and out-migrations, follow-up of migrants departing the study area, recording of extra-household social connections and linkage of individual HDSS records with those from sub-district clinics. Fostering effective collaborations (including INDEPTH multi-centre work in adult health and ageing and migration and urbanization), ensuring cross-site compatibility of common variables and optimizing public access to HDSS data are priorities.

This article examines the influence of maternal status, socioeconomic status of the household, and household composition on the mobility of children aged 0–14 in Mpumalanga Province, South Africa, from 1999 to 2008. Using data from the Agincourt Health and Demographic Surveillance System, we found that children whose mothers were temporary migrants, living elsewhere, or dead had higher odds of moving than children whose mothers were coresident. Older children and children living in richer households faced lower odds of mobility. For children whose mothers were coresident, there was no effect of maternal substitutes on child mobility. However, among children whose mothers were temporary migrants or living elsewhere, the presence of prime-aged and elderly females lowered the odds of mobility. For maternal orphans, the presence of elderly women in the household lowered their odds of mobility. The results underscore the importance of examining the conditions under which children move in order to strengthen service delivery targeted at safeguarding children’s well-being.

Background In a given population the age pattern of mortality is an important determinant of total number of deaths, age structure, and through effects on age structure, the number of births and thereby growth. Good mortality models exist for most populations except those experiencing generalized HIV epidemics and some developing country populations. The large number of deaths concentrated at very young and adult ages in HIV-affected populations produce a unique ‘humped’ age pattern of mortality that is not reproduced by any existing mortality models. Both burden of disease reporting and population projection methods require age-specific mortality rates to estimate numbers of deaths and produce plausible age structures. For countries with generalized HIV epidemics these estimates should take into account the future trajectory of HIV prevalence and its effects on age-specific mortality. In this paper we present a parsimonious model of age-specific mortality for countries with generalized HIV/AIDS epidemics. Methods and Findings The model represents a vector of age-specific mortality rates as the weighted sum of three independent age-varying components. We derive the age-varying components from a Singular Value Decomposition of the matrix of age-specific mortality rate schedules. The weights are modeled as a function of HIV prevalence and one of three possible sets of inputs: life expectancy at birth, a measure of child mortality, or child mortality with a measure of adult mortality. We calibrate the model with 320 five-year life tables for each sex from the World Population Prospects 2010 revision that come from the 40 countries of the world that have and are experiencing a generalized HIV epidemic. Cross validation shows that the model is able to outperform several existing model life table systems. Conclusions We present a flexible, parsimonious model of age-specific mortality for countries with generalized HIV epidemics. Combined with the outputs of existing epidemiological and demographic models, this model makes it possible to project future age-specific mortality profiles and number of deaths for countries with generalized HIV epidemics.