Christopher J L Murray|Alan D. López|Jeremy Barofsky|Chloe Bryson?Cahn|Rafael Lozano
Estimating Population Cause-Specific Mortality Fractions from in-Hospital Mortality: Validation of a New Method
Institute for Health Metrics and Evaluation|University of Washington|University of Queensland|Harvard Global Health Institute|Harvard Global Health Institute|Secretaria de Salud
causeofdeath data many developing countries available information deaths hospital cause available many low middleincome countries representative sample deaths population propose method estimate population causespecific mortality fractions csmfs using data already collected many middleincome lowincome developing nations yet rarely used inhospital death recordsfor given cause death communitys hospital deaths equal total community deaths multiplied proportion deaths occurring hospital estimate proportion dying hospital estimate proportion dying population using deaths hospital propose estimate proportion deaths age sex cause group die hospital subset population vital registration systems function another population evaluated method using nearly complete vital registration vr data mexico 19982005 records whether death occurred hospital validation test used 45 disease categories validated method two ways nationally communities first investigated methods accuracy changes decrease amount mexican vr used estimate proportion age sex cause group dying hospital decreasing vr data used first step 100 9 produces 12 maximum relative error estimated true csmfs even mexico collected full vr information capital city 9 population estimation method would produce average relative error csmfs across 45 causes 10 second used vr data capital zone distrito federal estado de mexico estimated csmfs three lowestdevelopment states estimation method gave average relative error 20 23 31 guerrero chiapas oaxaca respectivelywhere accurate international classification diseases icdcoded causeofdeath data available deaths hospital vr covering subset population demonstrated population csmfs estimated low average error addition showed case mexico method substantially reduce error biased hospital data even applied areas widely different levels development countries icdcoded deaths hospital method potentially allows use existing data inform health policy