Adolescent childbearing in sub-Saharan Africa

This article examines whether increased years of schooling exercised a consistent impact on delayed childbearing in sub-Saharan Africa. Data were drawn from Demographic and Health Surveys conducted in eight countries over the period 1987-1999. Multiple logistic regressions were used to assess trends and determinants in the probability of first birth during adolescence. Girls' education from about the secondary level onwards was found to be the only consistently significant covariate. No effect of community aggregate education was discernible, after controlling for urbanity and other individual-level variables. The results reinforce previous findings that improving girls' education is a key instrument for raising ages at first birth, but suggest that increases in schooling at lower levels alone bear only somewhat on the prospects for fertility decline among adolescents.adolescence, Africa, developing countries, education, fertility, fertility determinants

The Network Survival Method for Estimating Adult Mortality: Evidence From a Survey Experiment in Rwanda.

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 network survival method, 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 (n = 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

Assessing and adjusting for differences between HIV prevalence estimates derived from national population-based surveys and antenatal care surveillance, with applications for Spectrum 2013

Objective(s): To assess differences between HIV prevalence estimates derived from national population surveys and antenatal care (ANC) surveillance sites and to improve the calibration of ANC-derived estimates in Spectrum 2013 to more appropriately account for differences between these data. Design: Retrospective analysis of national population survey and ANC surveillance data from 25 countries with generalized epidemics in sub-Saharan Africa and 8 countries with concentrated epidemics. Methods: Adult national population survey and ANC surveillance HIV prevalence estimates were compared for all available national population survey data points for the years 1999–2012. For sub-Saharan Africa, a mixed-effects linear regression model determined whether the relationship between national population and ANC estimates was constant across surveys. A new calibration method was developed to incorporate national population survey data directly into the likelihood for HIV prevalence in countries with generalized epidemics. Results were used to develop default rules for adjusting ANC data for countries with no national population surveys. Results: ANC surveillance data typically overestimate population prevalence, although a wide variation, particularly in rural areas, is observed across countries and survey years. The new calibration method yields similar point estimates to previous approaches, but leads to an average 44% increase in the width of 95% uncertainty intervals. Conclusion: Important biases remain in ANC surveillance data for HIV prevalence. The new approach to model-fitting in Spectrum 2013 more appropriately accounts for this bias when producing national estimates in countries with generalized epidemics. In countries with concentrated epidemics, local sex ratios should be used to calibrate ANC surveillance estimates

Improving estimates of children living with HIV from the Spectrum AIDS Impact Model

Objective: Estimated numbers of children living with HIV determine programmatic and treatment needs. We explain the changes made to the UNAIDS estimates between 2015 and 2016, and describe the challenges around these estimates. Methods: Estimates of children newly infected, living with HIV, and dying of AIDS are developed by country teams using Spectrum software. Spectrum files are available for 160 countries, which represent 98% of the global population. In 2016, the methods were updated to reflect the latest evidence on mother-to-child HIV transmission and improved assumptions on the age children initiate antiretroviral therapy. We report updated results using the 2016 model and validate these estimates against mother-to-child transmission rates and HIV prevalence from population-based surveys for the survey year. Results: The revised 2016 model estimates 27% fewer children living with HIV in 2014 than the 2015 model, primarily due to changes in the probability of mother-to-child transmission among women with incident HIV during pregnancy. The revised estimates were consistent with population-based surveys of HIV transmission and HIV prevalence among children aged 5–9 years, but were lower than surveys among children aged 10–14 years. Conclusions: The revised 2016 model is an improvement on previous models. Paediatric HIV models will continue to evolve as further improvements are made to the assumptions. Commodities forecasting and programme planning rely on these estimates, and increasing accuracy will be critical to enable effective scale-up and optimal use of resources. Efforts are needed to improve empirical measures of HIV prevalence, incidence, and mortality among children

Childhood Mortality in Kenya: An examination of trends and determinants in the late 1980s to mid 1990s

After Independence in the early 1960s, child mortality in Kenya fell rapidly. Until around 1980, the under 5 mortality rate (U5MR), the probability of dying by age 5, fell at an annual rate of about 4 percent per annum. This rate of decline slowed in the early 1980s, to about 2 per cent per annum. Recent data from the 1998 Kenya Demographic and Health Survey showed that, far from declining, the U5MR increased by as much as 25 percent from the late 1980s to the mid 1990s. This adverse trend coincided with a number of other adverse trends: stagnation in growth of per capita income, declining levels of immunization, falling school enrolment, and the emergence of an HIV/AIDS epidemic. On a more positive note, fertility fell by about 30 percent from the mid 1980s to the mid 1990s. Controversy surrounds the factors responsible for the increase in child mortality in the 1990s, and the objective of this paper is to clarify the situation. Data from the 1993 and 1998 DHSs have been merged into a single data set, and multivariate analysis used to examine the factors associated with mortality risks in childhood. Dummy variables were used to represent different three-year time periods, from 1984-86 to 1996-98. Socioeconomic controls, including mother¹s education, an indicator of household wealth, urban/rural residence, and indicators of health service utilization, plus controls for reproductive dynamics such as age of mother at the birth, birth order, sex and preceding birth interval, were developed. In addition, an indicator of the HIV epidemic, the prevalence of HIV in the district of birth at the time of each child¹s birth, was developed. With no controls, the models confirmed an increase in mortality of about 25 percent. Including socioeconomic and biodemographic controls tended to strengthen the upward trend in mortality; in other words, had there been no changes in these factors, child mortality would have been expected to decline. Introducing controls for health variables immunization, pregnancy and delivery care, prevalence of childhood diseases and maternal and child malnutrition ­ also did not alter the underlying trends substantially. Thus rising child mortality could not be explained by socioeconomic, biodemographic or health status factors. Including the prevalence of HIV in the models, however, changed the underlying trends fundamentally, from sharp increase to monotonic decline. Although models of this sort cannot demonstrate causation, only association, the HIV epidemic appears to be the most probable cause of the recent increases in child mortality in Kenya. Of the health variables, the only one found to be significantly protective was immunization coverage