Search

In countries lacking comprehensive systems of civil registration and vital statistics, the collection of demographic data through mobile surveys is increasingly feasible, as mobile phone coverage has rapidly expanded. However, mobile phone users are often younger, male, with higher education, and wealthier, potentially biasing demographic estimates based on phone surveys. We assess selection bias in estimates of under-five mortality and fertility using data from 34 Demographic and Health Surveys. We use logistic regressions to characterize individuals who own or have access to a mobile phone in terms of sociodemographic background characteristics, and Poisson regressions to estimate the association between mobile phone ownership (or access)and total fertility and under-five mortality rates. We further evaluate the potential reasons why estimates by mobile phone ownership may differ using a set of behavioral characteristics. Fertility and mortality rates would be biased downwards if estimates are only based on women who own or have access to mobile phones. Under-five mortality estimates can be adjusted through post-stratification using age, educational level, area of residence, wealth, and marital status as weights. However, fertility estimates remain biased even after adjusting for these covariates. This difference is associated with behavioral factors that are not captured by the post-stratification variables, but for which there are also differences between mobile phone owners and non-owners. In many countries, modern contraceptive use is positively associated with mobile phone ownership, which may complicate fertility estimates based on mobile phone surveys. Mobile phone surveys need to include background questions on socioeconomic status to be able to weight and adjust mortality estimates ex post-facto. Fertility estimates from mobile phone surveys will be biased unless further research uncovers the mechanisms driving the bias.

Background The Sustainable Development Goals (SDGs), set in 2015 by the UN General Assembly, call for all countries to reach an under-5 mortality rate (U5MR) of at least as low as 25 deaths per 1000 livebirths and a neonatal mortality rate (NMR) of at least as low as 12 deaths per 1000 livebirths by 2030. We estimated levels and trends in under-5 mortality for 195 countries from 1990 to 2019, and conducted scenario-based projections of the U5MR and NMR from 2020 to 2030 to assess country progress in, and potential for, reaching SDG targets on child survival and the potential under-5 and neonatal deaths over the next decade. Methods Levels and trends in under-5 mortality are based on the UN Inter-agency Group for Child Mortality Estimation (UN IGME) database on under-5 mortality, which contains around 18 000 country-year datapoints for 195 countries—nearly 10 000 of those datapoints since 1990. The database includes nationally representative mortality data from vital registration systems, sample registration systems, population censuses, and household surveys. As with previous sets of national UN IGME estimates, a Bayesian B-spline bias-reduction model (B3) that considers the systematic biases associated with the different data source types was fitted to these data to generate estimates of under-5 (age 0–4 years) mortality with uncertainty intervals for 1990–2019 for all countries. Levels and trends in the neonatal mortality rate (0–27 days) are modelled separately as the log ratio of the neonatal mortality rate to the under-5 mortality rate using a Bayesian model. Estimated mortality rates are combined with livebirths data to calculate the number of under-5 and neonatal deaths. To assess the regional and global burden of under-5 deaths in the present decade and progress towards SDG targets, we constructed several scenario-based projections of under-5 mortality from 2020 to 2030 and estimated national, regional, and global under-5 mortality trends up to 2030 for each

Studies have shown that children in rural areas face excess risks of dying, but the little research on spatial inequalities in adult mortality has reached mixed conclusions. We examine rural–urban differences in mortality in 53 low- and middle-income countries. We consider how the rural–urban mortality gap evolves from birth to age 60 by estimating mortality based on birth and sibling histories from 138 Demographic and Health Surveys run between 1992 and 2018. We observe excess rural mortality until age 15, finding the largest differences between urban and rural sectors among 1–59-month-olds. While we cannot claim higher mortality among urban adults than those in rural areas, we find a reduced gap between the sectors over the life course and a diminishing urban advantage in adult mortality with age. This shift over the life course reflects a divergence in the epidemiologic transition between the rural and urban sectors.

BACKGROUND In low- and middle-income countries, mortality levels are commonly derived from retrospective reports on deceased relatives collected in sample surveys and censuses. These data sources are potentially affected by recall errors. OBJECTIVE Using high-quality data collected by the Nouna Health and Demographic Surveillance System (HDSS) in Burkina Faso, we evaluate the reliability of mortality estimates based on the 2006 national census. METHODS We extracted from the census database all records referring to the population under surveillance in the HDSS. Life tables were estimated from recent household deaths reported in the census and compared to those obtained from the prospective mortality data. To evaluate age errors and assess their impact on mortality, we linked census and HDSS records at the individual level for the surviving population and the deceased. Indirect estimates of mortality were also calculated based on the reported survival of children and parents. RESULTS Life expectancies at birth derived from recent household deaths pointed to a lower mortality than monitored in the HDSS, with a difference of 4 years for men and 8 years for women. Underreporting of deaths among the population aged 60 and above accounted for more than half of these differences. Age errors were small for the surviving population and larger for the deceased, but their effects on mortality estimates were modest. Indirect estimates of child mortality were consistent with the HDSS data, but orphanhood-based estimates were implausibly low. CONCLUSION Additional elicitation questions should be asked during the census interviews to improve the collection of data on recent household deaths. CONTRIBUTION Mortality rates derived from recent household deaths can seriously underestimate mortality. In Burkina Faso the downward bias in the 2006 census was larger among females and was mostly attributable to underreporting of deaths.

Optimal health and development from preconception to adulthood are crucial for human flourishing and the formation of human capital. The Nurturing Care Framework, as adapted to age 20 years, conceptualises the major influences during periods of development from preconception, through pregnancy, childhood, and adolescence that affect human capital. In addition to mortality in children younger than 5 years, stillbirths and deaths in 5–19-year-olds are important to consider. The global rate of mortality in individuals younger than 20 years has declined substantially since 2000, yet in 2019 an estimated 8·6 million deaths occurred between 28 weeks of gestation and 20 years of age, with more than half of deaths, including stillbirths, occurring before 28 days of age. The 1000 days from conception to 2 years of age are especially influential for human capital. The prevalence of low birthweight is high in sub-Saharan Africa and even higher in south Asia. Growth faltering, especially from birth to 2 years, occurs in most world regions, whereas overweight increases in many regions from the preprimary school period through adolescence. Analyses of cohort data show that growth trajectories in early years of life are strong determinants of nutritional outcomes in adulthood. The accrual of knowledge and skills is affected by health, nutrition, and home resources in early childhood and by educational opportunities in older children and adolescents. Linear growth in the first 2 years of life better predicts intelligence quotients in adults than increases in height in older children and adolescents. Learning-adjusted years of schooling range from about 4 years in sub-Saharan Africa to about 11 years in high-income countries. Human capital depends on children and adolescents surviving, thriving, and learning until adulthood.