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Mortality estimates for WHO SEAR countries: Problems and prospects

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Rao, Chalapati
Bundhamcharoen, Kanitta
Kelly, Matthew
Tangcharoensathien, Viroj

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BMJ Publishing Group

Abstract

Cause-specific mortality estimates for 11 countries located in the WHO's South East Asia Region (WHO SEAR) are generated periodically by the Global Burden of Disease (GBD) and the WHO Global Health Estimates (GHE) analyses. A comparison of GBD and GHE estimates for 2019 for 11 specific causes of epidemiological importance to South East Asia was undertaken. An index of relative difference (RD) between the estimated numbers of deaths by sex for each cause from the two sources for each country was calculated, and categorised as marginal (RD=±0%-9%), moderate (RD=±10%-19%), high (RD=±20%-39%) and extreme (RD>±40%). The comparison identified that the RD was >10% in two-thirds of all instances. The RD was ' high' or ' extreme' for deaths from tuberculosis, diarrhoea, road injuries and suicide for most SEAR countries, and for deaths from most of the 11 causes in Bangladesh, DPR Korea, Myanmar, Nepal and Sri Lanka. For all WHO SEAR countries, mortality estimates from both sources are based on statistical models developed from an international historical cause-specific mortality data series that included very limited empirical data from the region. Also, there is no scientific rationale available to justify the reliability of one set of estimates over the other. The characteristics of national mortality statistics systems for each WHO SEAR country were analysed, to understand the reasons for weaknesses in empirical data. The systems analysis identified specific limitations in structure, organisation and implementation that affect data completeness, validity of causes of death and vital statistics production, which vary across countries. Therefore, customised national strategies are required to strengthen mortality statistics systems to meet immediate and long-term data needs for health policy and research, and reduce dependence on current unreliable modelled estimates.

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BMJ Global Health

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Open Access

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Creative Commons Attribution licence

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