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1.
PLOS Glob Public Health ; 3(11): e0002426, 2023.
Artigo em Inglês | MEDLINE | ID: mdl-37910476

RESUMO

In Myanmar 84% of deaths occur in the community, of which half are unregistered and none have a reliable cause of death (COD) recorded. Since 2018, Myanmar has introduced improved registration practices and verbal autopsy (VA) to assess whether such methods can produce policy relevant information on community COD. Community health midwives and public health supervisors grade II collected VAs on over 80,000 deaths which occurred between January 2018 and December 2019 in a nationwide sample of 42 townships in Myanmar. Electronic methods were used to collect and consolidate data. The most probable COD was assigned using the SmartVA Analyze 2.0 computer algorithm. Completeness of VA death reporting increased to 71% in 2019. Most adult (12+ years) deaths (82%) were due to non-communicable diseases, primarily stroke, ischemic heart disease and chronic respiratory disease, for both men and women. VA results were consistent with Global Burden of Disease (GBD) Study estimates, except for cirrhosis in men, which was more common, and had a younger age distribution of death than the GBD. Large scale implementation of improved death registration practices and COD diagnosis using VA is feasible and provides plausible, timely, disaggregated and policy relevant information on the leading causes of community death. Addressing the burden of non-communicable diseases, particularly cirrhosis in young men, is an important public health priority in Myanmar. Improving completeness of VA death reporting in poorly performing townships and in neonates, children and women will further improve the policy utility of the VA data.

2.
BMJ Glob Health ; 6(5)2021 05.
Artigo em Inglês | MEDLINE | ID: mdl-34059494

RESUMO

INTRODUCTION: The measurement of progress towards many Sustainable Development Goals (SDG) and other health goals requires accurate and timely all-cause and cause of death (COD) data. However, existing guidance to countries to calculate these indicators is inadequate for populations with incomplete death registration and poor-quality COD data. We introduce a replicable method to estimate national and subnational cause-specific mortality rates (and hence many such indicators) where death registration is incomplete by integrating data from Medical Certificates of Cause of Death (MCCOD) for hospital deaths with routine verbal autopsy (VA) for community deaths. METHODS: The integration method calculates population-level cause-specific mortality fractions (CSMFs) from the CSMFs of MCCODs and VAs weighted by estimated deaths in hospitals and the community. Estimated deaths are calculated by applying the empirical completeness method to incomplete death registration/reporting. The resultant cause-specific mortality rates are used to estimate SDG Indicator 23: mortality between ages 30 and 70 years from cardiovascular diseases, cancers, chronic respiratory diseases and diabetes. We demonstrate the method using nationally representative data in Myanmar, comprising over 42 000 VAs and 7600 MCCODs. RESULTS: In Myanmar in 2019, 89% of deaths were estimated to occur in the community. VAs comprised an estimated 70% of community deaths. Both the proportion of deaths in the community and CSMFs for the four causes increased with older age. We estimated that the probability of dying from any of the four causes between 30 and 70 years was 0.265 for men and 0.216 for women. This indicator is 50% higher if based on CSMFs from the integration of data sources than on MCCOD data from hospitals. CONCLUSION: This integration method facilitates country authorities to use their data to monitor progress with national and subnational health goals, rather than rely on estimates made by external organisations. The method is particularly relevant given the increasing application of routine VA in country Civil Registration and Vital Statistics systems.


Assuntos
Objetivos , Adulto , Idoso , Autopsia , Causas de Morte , Feminino , Humanos , Masculino , Pessoa de Meia-Idade , Mianmar , Probabilidade
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