On the estimation of population cause-specific mortality fractions from in-hospital deaths
Abstract Background Almost 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 countr...
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doaj-b05d67059bb74e97845a62a48c0d9b902020-11-24T21:42:11ZengBMCBMC Medicine1741-70152019-02-0117111010.1186/s12916-019-1267-zOn the estimation of population cause-specific mortality fractions from in-hospital deathsGail M. Williams0Ian Douglas Riley1Riley H. Hazard2Hafizur R. Chowhury3Nurul Alam4Peter Kim Streafield5Veronica Tallo6Diozele Sanvictores7Marilla Lucero8Tim Adair9Alan D. Lopez10School of Public Health, University of QueenslandSchool of Population and Global Health, University of MelbourneSchool of Population and Global Health, University of MelbourneSchool of Population and Global Health, University of MelbourneInternational Centre for Diarrhoeal Disease ResearchInternational Centre for Diarrhoeal Disease ResearchResearch Institute for Tropical MedicineResearch Institute for Tropical MedicineResearch Institute for Tropical MedicineSchool of Population and Global Health, University of MelbourneSchool of Population and Global Health, University of MelbourneAbstract Background Almost 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). Methods We 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. Results We 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. Conclusions Statistical 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.http://link.springer.com/article/10.1186/s12916-019-1267-zBangladeshPhilippinesVital registrationCause of deathDeath certificate |
collection |
DOAJ |
language |
English |
format |
Article |
sources |
DOAJ |
author |
Gail M. Williams Ian Douglas Riley Riley H. Hazard Hafizur R. Chowhury Nurul Alam Peter Kim Streafield Veronica Tallo Diozele Sanvictores Marilla Lucero Tim Adair Alan D. Lopez |
spellingShingle |
Gail M. Williams Ian Douglas Riley Riley H. Hazard Hafizur R. Chowhury Nurul Alam Peter Kim Streafield Veronica Tallo Diozele Sanvictores Marilla Lucero Tim Adair Alan D. Lopez On the estimation of population cause-specific mortality fractions from in-hospital deaths BMC Medicine Bangladesh Philippines Vital registration Cause of death Death certificate |
author_facet |
Gail M. Williams Ian Douglas Riley Riley H. Hazard Hafizur R. Chowhury Nurul Alam Peter Kim Streafield Veronica Tallo Diozele Sanvictores Marilla Lucero Tim Adair Alan D. Lopez |
author_sort |
Gail M. Williams |
title |
On the estimation of population cause-specific mortality fractions from in-hospital deaths |
title_short |
On the estimation of population cause-specific mortality fractions from in-hospital deaths |
title_full |
On the estimation of population cause-specific mortality fractions from in-hospital deaths |
title_fullStr |
On the estimation of population cause-specific mortality fractions from in-hospital deaths |
title_full_unstemmed |
On the estimation of population cause-specific mortality fractions from in-hospital deaths |
title_sort |
on the estimation of population cause-specific mortality fractions from in-hospital deaths |
publisher |
BMC |
series |
BMC Medicine |
issn |
1741-7015 |
publishDate |
2019-02-01 |
description |
Abstract Background Almost 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). Methods We 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. Results We 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. Conclusions Statistical 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. |
topic |
Bangladesh Philippines Vital registration Cause of death Death certificate |
url |
http://link.springer.com/article/10.1186/s12916-019-1267-z |
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