Abstract
BackgroundAlmost all countries without complete vital registration systems have data on deaths collected by hospitals. However, these data have not been widely used to estimate cause of death (COD) patterns in populations because only a non-representative fraction of people in these countries die in health facilities. Methods that can exploit hospital mortality statistics to reliably estimate community COD patterns are required to strengthen the evidence base for disease and injury control programs. We propose a method that weights hospital-certified causes by the probability of death to estimate population cause-specific mortality fractions (CSMFs).MethodsWe used an established verbal autopsy instrument (VAI) to collect data from hospital catchment areas in Chandpur and Comilla Districts, Bangladesh, and Bohol province, the Philippines, between 2011 and 2014, along with demographic covariates for each death. Hospital medical certificates of cause of death (death certificates) were collected and mapped to the corresponding cause categories of the VAI. Tariff 2.0 was used to assign a COD for community deaths. Logistic regression models were created for broad causes in each country to calculate the probability of in-hospital death, given a set of covariate values. The reweighted CSMFs for deaths in the hospital catchment population, represented by each hospital death, were calculated from the corresponding regression models.ResultsWe collected data on 4228 adult deaths in the Philippines and 3725 deaths in Bangladesh. Short time to hospital and education were consistently associated with in-hospital death in the Philippines and absence of a disability was consistently associated with in-hospital death in Bangladesh. Non-communicable diseases (excluding stroke) and stroke were the leading causes of death in both the Philippines (33.9%, 19.1%) and Bangladesh (46.1%, 21.1%) according to the reweighted method. The reweighted method generally estimated CSMFs that fell between those derived from hospitals and those diagnosed by Tariff 2.0.ConclusionsStatistical methods can be used to derive estimates of cause-specific probability of death in-hospital for Bangladesh and the Philippines to generate population CSMFs. In regions where hospital death certification is of reasonable quality and routine verbal autopsy is not applied, these estimates could be applied to generate cost-effective and robust CSMFs for the population.
Highlights
Almost all countries without complete vital registration systems have data on deaths collected by hospitals
The disadvantage of using hospital data to extrapolate cause-specific mortality fractions (CSMFs) to whole populations is that hospital deaths are a biased sample of all deaths and are unlikely to accurately represent the distribution of deaths by cause in the population
As a solution to the variability of Pasj between regions and the difficulty in determining it, we propose an empirical methodology that allows researchers to use hospitals deaths to predict population CSMFs while correcting for factors that affect place of death
Summary
Almost all countries without complete vital registration systems have data on deaths collected by hospitals. These data have not been widely used to estimate cause of death (COD) patterns in populations because only a non-representative fraction of people in these countries die in health facilities. Well-functioning vital registration (VR) systems are essential to monitor health progress and inform health policy They are a critical input for public health analyses, understanding epidemiological patterns, and allocating scarce resources for public health and medical care. The disadvantage of using hospital data to extrapolate cause-specific mortality fractions (CSMFs) to whole populations is that hospital deaths are a biased sample of all deaths and are unlikely to accurately represent the distribution of deaths by cause in the population
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