Estimates Of Under-5 Mortality Research Articles

Exclusive Breastfeeding, Child Mortality, and Economic Cost in Sub-Saharan Africa.

Sub-Saharan Africa remains the region with the highest under-5 mortality (U5M) rates globally. Emerging evidence revealed that exclusive breastfeeding (EBF) rates are significantly associated with a decreased risk for child mortality. Our aim with this study is to fill the gap of knowledge regarding the economic impact of EBF practices in relation to U5M in sub-Saharan African countries. Data were gathered from the World Bank's database during the period 2000-2018. A meta-analytical approach was used to evaluate heterogeneity of country estimates and to perform an estimate of the prevalence of EBF and economic cost by country. The association between estimates of U5M and EBF prevalence was estimated and used to perform the total cumulative nonhealth gross domestic product loss (TCNHGDPL) attributable to U5M in 2018 and 2030. The prevalence of EBF increased by 1%, and U5M reduced significantly by 3.4 per 1000 children each year during 2000-2018. A U5M reduction of 5.6 per 1000 children could be expected if EBF prevalence improved by 10%. The TCNHGDPL in sub-Saharan Africa had a total value higher than $29 billion in 2018. The cost of U5M is estimated to increase to ∼$42 billion in 2030. If EBF prevalence improve by 10%, the related TCNHGDPL was estimated to be $27 billion in 2018 and $41 billion in 2030, therefore saving ∼$1 billion. Sub-Saharan Africa should imperatively prioritize and invest in essential approaches toward EBF implementation.

Measuring and correcting bias in indirect estimates of under-5 mortality in populations affected by HIV/AIDS: a simulation study

BackgroundIn populations that lack vital registration systems, under-5 mortality (U5M) is commonly estimated using survey-based approaches, including indirect methods. One assumption of indirect methods is that a mother’s survival and her children’s survival are not correlated, but in populations affected by HIV/AIDS this assumption is violated, and thus indirect estimates are biased. Our goal was to estimate the magnitude of the bias, and to create a predictive model to correct it.MethodsWe used an individual-level, discrete time-step simulation model to measure how the bias in indirect estimates of U5M changes under various fertility rates, mortality rates, HIV/AIDS rates, and levels of antiretroviral therapy. We simulated 4480 populations in total and measured the amount of bias in U5M due to HIV/AIDS. We also developed a generalized linear model via penalized maximum likelihood to correct this bias.ResultsWe found that indirect methods can underestimate U5M by 0–41% in populations with HIV prevalence of 0–40%. Applying our model to 2010 survey data from Malawi and Tanzania, we show that indirect methods would underestimate U5M by up to 7.7% in those countries at that time. Our best fitting model to correct bias in U5M had a root median square error of 0.0012.ConclusionsIndirect estimates of U5M can be significantly biased in populations affected by HIV/AIDS. Our predictive model allows scholars and practitioners to correct that bias using commonly measured population characteristics. Policies and programs based on indirect estimates of U5M in populations with generalized HIV epidemics may need to be reevaluated after accounting for estimation bias.

Error and bias in under-5 mortality estimates derived from birth histories with small sample sizes

BackgroundEstimates of under-5 mortality at the national level for countries without high-quality vital registration systems are routinely derived from birth history data in censuses and surveys. Subnational or stratified analyses of under-5 mortality could also be valuable, but the usefulness of under-5 mortality estimates derived from birth histories from relatively small samples of women is not known. We aim to assess the magnitude and direction of error that can be expected for estimates derived from birth histories with small samples of women using various analysis methods.MethodsWe perform a data-based simulation study using Demographic and Health Surveys. Surveys are treated as populations with known under-5 mortality, and samples of women are drawn from each population to mimic surveys with small sample sizes. A variety of methods for analyzing complete birth histories and one method for analyzing summary birth histories are used on these samples, and the results are compared to corresponding true under-5 mortality. We quantify the expected magnitude and direction of error by calculating the mean error, mean relative error, mean absolute error, and mean absolute relative error.ResultsAll methods are prone to high levels of error at the smallest sample size with no method performing better than 73% error on average when the sample contains 10 women. There is a high degree of variation in performance between the methods at each sample size, with methods that contain considerable pooling of information generally performing better overall. Additional stratified analyses suggest that performance varies for most methods according to the true level of mortality and the time prior to survey. This is particularly true of the summary birth history method as well as complete birth history methods that contain considerable pooling of information across time.ConclusionsPerformance of all birth history analysis methods is extremely poor when used on very small samples of women, both in terms of magnitude of expected error and bias in the estimates. Even with larger samples there is no clear best method to choose for analyzing birth history data. The methods that perform best overall are the same methods where performance is noticeably different at different levels of mortality and lengths of time prior to survey. At the same time, methods that perform more uniformly across levels of mortality and lengths of time prior to survey also tend to be among the worst performing overall.

Examining small area estimation techniques for public health intervention: Lessons from application to under-5 mortality data in Uganda

In Uganda, estimates of under-5 mortality are available only at national and regional levels. None exist at decentralized levels of governance or district level. Using small area statistical techniques in a Hierarchical Bayesian Framework, we applied a modeling approach to determine whether we could learn how to target health interventions to reduce under-5 mortality at the district level. Our modeling approach has an advantage over the commonly used Standardized Mortality Ratios, as it estimates the relative risk of under-5 mortality for a particular district. Using data from Uganda's Demographic and Health Survey in 2006, we were able to estimate relative risk of under-5 mortality for each district. Our findings reveal the evidence of district-to-district variations in under-5 mortality with potential spatial clustering. We believe that this information will be useful in Uganda, as interventions can be targeted at districts with higher relative risk of under-5 mortality. Discussion of these results at district level could increase funding for primary health-care activities. Our analysis also suggests the utility of small area techniques for other countries and other health problems.

A response to criticism of our estimates of under-5 mortality in Iraq, 1980–98

According to estimates published in this journal, the number of deaths of children under 5 in Iraq in the period 1991–98 resulting from the Gulf War of 1991 and the subsequent imposition of sanctions by the United Nations was between 400,000 and 500,000. These estimates have since been held to be implausibly high by a working group set up by an Independent Inquiry Committee appointed by the United Nations Secretary-General. We believe the working group's own estimates are seriously flawed and cannot be regarded as a credible challenge to our own. To obtain their estimates, they reject as unreliable the evidence of the 1999 Iraq Child and Maternal Mortality Survey—despite clear evidence of its internal coherence and supporting evidence from another, independent survey. They prefer to rely on the 1987 and 1997 censuses and on data obtained in a format that had elsewhere been rejected as unreliable 30 years earlier.