Explore the vast range of titles available.

\"Model life tables provide ways of deriving accurate mortality schedules or predicting future trends from scanty data. In settings where accurate data are unavailable, these provide invaluable tools for estimating mortality conditions among populations. Constructing model life tables requires the availability of accurate empirical life tables that depict the different patterns of age-specific risks of death in the populations covered. Starting in the early 1960s, a number of field-based research stations were established to collect longitudinal data on births, deaths and migrations, covering some geographic areas in sub-Saharan Africa and Asia. In the late 1990s, these research sites teamed up to form an international network, INDEPTH.

Assessments of age-specific mortality and life expectancy have been done by the UN Population Division, Department of Economics and Social Affairs (UNPOP), the United States Census Bureau, WHO, and as part of previous iterations of the Global Burden of Diseases, Injuries, and Risk Factors Study (GBD). Previous iterations of the GBD used population estimates from UNPOP, which were not derived in a way that was internally consistent with the estimates of the numbers of deaths in the GBD. The present iteration of the GBD, GBD 2017, improves on previous assessments and provides timely estimates of the mortality experience of populations globally. The GBD uses all available data to produce estimates of mortality rates between 1950 and 2017 for 23 age groups, both sexes, and 918 locations, including 195 countries and territories and subnational locations for 16 countries. Data used include vital registration systems, sample registration systems, household surveys (complete birth histories, summary birth histories, sibling histories), censuses (summary birth histories, household deaths), and Demographic Surveillance Sites. In total, this analysis used 8259 data sources. Estimates of the probability of death between birth and the age of 5 years and between ages 15 and 60 years are generated and then input into a model life table system to produce complete life tables for all locations and years. Fatal discontinuities and mortality due to HIV/AIDS are analysed separately and then incorporated into the estimation. We analyse the relationship between age-specific mortality and development status using the Socio-demographic Index, a composite measure based on fertility under the age of 25 years, education, and income. There are four main methodological improvements in GBD 2017 compared with GBD 2016: 622 additional data sources have been incorporated; new estimates of population, generated by the GBD study, are used; statistical methods used in different components of the analysis have been further standardised and improved; and the analysis has been extended backwards in time by two decades to start in 1950. Globally, 18·7% (95% uncertainty interval 18·4–19·0) of deaths were registered in 1950 and that proportion has been steadily increasing since, with 58·8% (58·2–59·3) of all deaths being registered in 2015. At the global level, between 1950 and 2017, life expectancy increased from 48·1 years (46·5–49·6) to 70·5 years (70·1–70·8) for men and from 52·9 years (51·7–54·0) to 75·6 years (75·3–75·9) for women. Despite this overall progress, there remains substantial variation in life expectancy at birth in 2017, which ranges from 49·1 years (46·5–51·7) for men in the Central African Republic to 87·6 years (86·9–88·1) among women in Singapore. The greatest progress across age groups was for children younger than 5 years; under-5 mortality dropped from 216·0 deaths (196·3–238·1) per 1000 livebirths in 1950 to 38·9 deaths (35·6–42·83) per 1000 livebirths in 2017, with huge reductions across countries. Nevertheless, there were still 5·4 million (5·2–5·6) deaths among children younger than 5 years in the world in 2017. Progress has been less pronounced and more variable for adults, especially for adult males, who had stagnant or increasing mortality rates in several countries. The gap between male and female life expectancy between 1950 and 2017, while relatively stable at the global level, shows distinctive patterns across super-regions and has consistently been the largest in central Europe, eastern Europe, and central Asia, and smallest in south Asia. Performance was also variable across countries and time in observed mortality rates compared with those expected on the basis of development. This analysis of age-sex-specific mortality shows that there are remarkably complex patterns in population mortality across countries. The findings of this study highlight global successes, such as the large decline in under-5 mortality, which reflects significant local, national, and global commitment and investment over several decades. However, they also bring attention to mortality patterns that are a cause for concern, particularly among adult men and, to a lesser extent, women, whose mortality rates have stagnated in many countries over the time period of this study, and in some cases are increasing. Bill & Melinda Gates Foundation.

The infant mortality rate (IMR) is a critical indicator of population health, but its measurement is subject to response bias in countries without complete vital registration systems who rely instead on birth histories collected via sample surveys. One of the most salient bias is the fact that child deaths in these birth histories tend to be reported with a large amount of heaping at age 12 months. Because of this issue, analysts and international agencies do not directly use IMR estimates based on surveys such as Demographic and Health Surveys (DHS); they rely instead on mortality models such as model life tables. The use of model life tables in this context, however, is arbitrary, and the extent to which this approach appropriately addresses bias in DHS-based IMR estimates remains unclear. This hinders our ability to monitor IMR levels and trends in low-and middle-income countries. The objective of this study is to evaluate age heaping bias in DHS-based IMR estimates and propose an improved method for adjusting this bias. Our method relies on a recently-developed log-quadratic model that can predict age-specific mortality by detailed age between 0 and 5. The model's coefficients were derived from a newly constituted database, the Under-5 Mortality Database (U5MD), that represents the mortality experience of countries with high-quality vital registration data. We applied this model to 204 DHS surveys, and compared unadjusted IMR values to IMR values adjusted with the log-quadratic model as well as with the classic model life table approach. Results show that contrary to existing knowledge, age heaping at age 12 months rarely generates a large amount of bias in IMR estimates. In most cases, the unadjusted IMR values were not deviating by more than +/- 5% from the adjusted values. The model life table approach, by contrast, introduced an unwarranted, downward bias in adjusted IMR values. We also found that two regions, Sub-Saharan Africa and South Asia, present age patterns of under-5 mortality that strongly depart from the experience represented in the U5MD. For these countries, neither the existing model life tables nor the log-quadratic model can produce empirically-supported IMR adjustments. Age heaping at age 12 months produces a smaller amount of bias in DHS-based IMR estimates than previously thought. If a large amount of age heaping is present in a survey, the log-quadratic model allows users to evaluate, and whenever necessary, adjust IMR estimates in a way that is more informed by the local mortality pattern than existing approaches. Future research should be devoted to understanding why Sub-Saharan African and South Asian countries have such distinct age patterns of under-five mortality.

Background Stroke poses a growing human and economic burden in South Africa. Excess sugar consumption, especially from sugar-sweetened beverages (SSBs), has been associated with increased obesity and stroke risk. Research shows that price increases for SSBs can influence consumption and modelling evidence suggests that taxing SSBs has the potential to reduce obesity and related diseases. This study estimates the potential impact of an SSB tax on stroke-related mortality, costs and health-adjusted life years in South Africa. Methods A proportional multi-state life table-based model was constructed in Microsoft Excel (2010). We used consumption data from the 2012 South African National Health and Nutrition Examination Survey, previously published own and cross price elasticities of SSBs and energy balance equations to estimate changes in daily energy intake and BMI arising from increased SSB prices. Stroke relative risk, and prevalent years lived with disability estimates from the Global Burden of Disease Study and modelled disease epidemiology estimates from a previous study, were used to estimate the effect of the BMI changes on the burden of stroke. Results Our model predicts that an SSB tax may avert approximately 72 000 deaths, 550 000 stroke-related health-adjusted life years and over ZAR5 billion, (USD400 million) in health care costs over 20 years (USD296-576 million). Over 20 years, the number of incident stroke cases may be reduced by approximately 85 000 and prevalent cases by about 13 000. Conclusions Fiscal policy has the potential, as part of a multi-faceted approach, to mitigate the growing burden of stroke in South Africa and contribute to the achievement of the target set by the Department of Health to reduce relative premature mortality (less than 60 years) from non-communicable diseases by the year 2020.

A critical analysis of the World Bank's strategy to combat HIV/AIDS in Africa. The World Bank's Commitment to HIV/AIDS in Africa examines the development challenges posed by HIV/AIDS in Sub-Saharan Africa and outlines a comprehensive agenda for action. This report reaffirms the World Bank's dedication to supporting African countries in achieving their Universal Access targets, integrating AIDS into national development plans, and strengthening national systems. This agenda provides a roadmap for policymakers, development practitioners, and researchers seeking to understand and address the complexities of the HIV/AIDS epidemic in Africa. Discover how the World Bank is working with partners to: * Provide sustainable funding for HIV/AIDS programs * Promote evidence-based strategies for prevention and treatment * Strengthen governance and accountability * Build capacity in key sectors This report is essential reading for anyone committed to global health and development in Africa.

Background and Aim The prevalence of type 2 diabetes (T2D) is rapidly increasing in Sub-Saharan Africa (SSA). T2D increases the risk of premature death and reduces quality of life and work productivity. This population life table modelling analysis evaluated the impact of T2D in terms of productivity-adjusted life years (PALYs) on the South African working-age population. Research Design and Methods Life table modelling was employed to simulate the follow-up of individuals aged 20–65 with T2D in South Africa (SA). Two life table models were developed to simulate health outcomes for a SA cohort with and without diabetes. The difference in the number of deaths, years of life lost (YLL), and PALYs lost between the two cohorts represented the burden of diabetes. Scenarios were simulated in which the proportions of gross domestic productivity (GDP), productivity indices, labour force dropout, and mortality risk trends were adjusted to lower and upper uncertainty bounds. Data were sourced from the International Diabetes Federation, Statistics SA, and both publicly available and published sources. We utilised the World Health Organization (WHO) standard annual discount rate of 3% for YLL and PALYs. Results In 2019, an estimated 9.5% (7.68% men and 11.37% women) or 3.2 million total working-age people had T2D in SA. Simulated follow-up until retirement predicted 669,427 excess mortality, a loss of 6.2 million years of life (9.3%) and 13 million PALYs (30.6%) in SA. On average, this resulted in 3.1 PALYs lost per person. Based on the GDP per full-time employee in 2019, the PALYs loss equated to US$223 billion, or US$69,875 per person. Conclusions This study emphasises the significant impact of T2D on society and the economy. Relatively modest T2D prevention and treatment management enhancement could lead to substantial economic benefits in SA.

In South Africa, life expectancy increased considerably after the government introduced its antiretroviral therapy (ART) program in 2004. The impact of the national ART program on life expectancy may be underestimated if child mortality is not accounted for in formal evaluations. We measured the extent to which life expectancy gains from 2006 to 2017 were attributable to declines in HIV/AIDS mortality, accounting for all age groups, including infants and children. To calculate life expectancies, we constructed period life tables using age-specific mortality rates estimated by Thembisa, a South Africa-based HIV epidemic model that integrates pediatric HIV data sources. We modeled counterfactual scenarios, a worst-case and best-case, to discern life expectancy gains related to HIV mortality versus other causes of mortality. We reported outcomes as life expectancy gains and life-years saved per person at varying ages. In South Africa, life expectancy at birth was 65.1 years in 2017, compared to 54.0 years in 2006. Of these 11.1 life-years gained, we found that 8.9 life-years were attributable to HIV mortality reductions. In people under 49 years old, most gains were attributable to HIV reduction. Gains from HIV reduction and other causes became equivalent at about age 49. In people over 60, most gains were attributable to causes other than HIV/AIDS reduction.

We investigate whether sub-Saharan African countries are affected by an \"urban mortality penalty\" repeating the history of industrialized countries during the nineteenth century. We analyze Demographic and Health Surveys from several sub-Saharan African countries for differences in child and adult mortality between rural and urban areas. For the first decade of the 2000s, our findings indicate that child mortality is higher in rural than in urban areas for all countries. On average, child mortality rates are 13.6 percent in rural areas and 10.8 percent in urban areas. In contrast, average urban adult mortality rates (14.1 percent) have exceeded rural adult mortality rates (12.4 percent). Child mortality rates are on average 65 percent higher in urban slums than in formal settlements. Child mortality rates in slum areas are, however, still lower than or equal to those in rural areas for most countries in our sample.

Acemoglu, Johnson, and Robinson (2001) established that economic institutions today are correlated with expected mortality of European colonialists. David Albouy argues this relationship is not robust. He drops all data from Latin America and much of the data from Africa, making up almost 60 percent of our sample, despite much information on the mortality of Europeans in those places during the colonial period. He also includes a “campaign” dummy that is coded inconsistently; even modest corrections undermine his claims. We also show that limiting the effect of outliers strengthens our results, making them robust to even extreme versions of Albouy's critiques. (JEL D02, E23, F54, I12, N40, O43, P14)

Adult death rates are a critical indicator of population health and well-being. Wealthy countries have high-quality vital registration systems, but poor countries lack this infrastructure and must rely on estimates that are often problematic. In this article, we introduce the , a new approach for estimating adult death rates. We derive the precise conditions under which it produces consistent and unbiased estimates. Further, we develop an analytical framework for sensitivity analysis. To assess the performance of the network survival method in a realistic setting, we conducted a nationally representative survey experiment in Rwanda ( = 4,669). Network survival estimates were similar to estimates from other methods, even though the network survival estimates were made with substantially smaller samples and are based entirely on data from Rwanda, with no need for model life tables or pooling of data from other countries. Our analytic results demonstrate that the network survival method has attractive properties, and our empirical results show that this method can be used in countries where reliable estimates of adult death rates are sorely needed.