Improving the public health utility of global cardiovascular mortality data: the rise of XXXX .

@entity1 @entity712 @entity101 @entity741 @entity1030 @entity204 @entity166 @entity65
BACKGROUND: High-quality, cause-specific mortality data are critical for effective health policy. Yet vague cause of @entity204 codes, such as @entity712 , are highly prevalent in global mortality data. We propose an empirical method correcting mortality data for the use of @entity712 as an underlying cause of @entity204 . METHODS: We performed a regression analysis stratified by sex, age, and country development status on all available ICD-10 mortality data, consisting of 142 million deaths across 838 country-years. The analysis yielded predicted fractions with which to redistribute @entity712 -attributed deaths to the appropriate underlying causes of @entity204 . Age-adjusted @entity204 rates and rank causes of @entity204 before and after correction were calculated. RESULTS: @entity712 accounts for 3.1% of all deaths in the dataset. @entity741 has the highest redistribution proportion for ages 15-49 and 50+ in both sexes and country development levels, causing gains in age-adjusted @entity204 rates in both developed and developing countries. @entity1030 and @entity101 also make significant rank gains. Reproductive-aged @entity1 in developing country-years yield the most diverse range of @entity712 causes. CONCLUSIONS: @entity741 becomes the No. 1 cause of @entity204 in several developed countries, including France and Japan, underscoring the @entity65 in high-income countries. Age-adjusted @entity204 rate increases for @entity741 in low- and middle-income countries, such as Argentina and South Africa, highlight the rise of the @entity65 in regions where public health efforts have historically focused on @entity166 . This method maximizes the use of available data, providing better evidence on major causes of @entity204 to inform policymakers in allocating finite resources.
