Correção de sub-registros de óbitos e proporção de internações por causas mal definidas

Abstract

To propose a correction approach for underreporting and relocation of ill-defined causes of morbidity and mortality in the National Health System Mortality and Hospital Information Systems. Modified James-Stein empirical Bayes estimators for events in delimited geographic areas were applied as a correction approach for underreporting in Brazilian municipalities in 2001. There was an increase of 55,671 deaths in the Mortality Information System, an underreporting correction of 5.85%. It was more effective at the age groups under five (8.1%) and 70 years old and more (6.4%); for neonatal (8.7%) and ill-defined (8.0%) causes of death; and in the states of Maranhão (10.6%), Bahia (9.5%) and Alagoas (8.8%). Relocation of ill-defined causes of mortality changed the structure of proportional mortality in the Northern and Northeastern regions, and increased the proportion of deaths due to cardiovascular diseases and reduced those due to external and neonatal causes. Relocation of ill-defined causes of hospital admissions did not affect hospital proportional morbidity. The results of underreporting correction were consistent with previous studies, in terms of age groups, causes and geographic areas. Relocation of ill-defined causes of death was spatially consistent. The approach studied may be applicable on Brazilian Health Information since it can be implemented in computational algorithms. Some improvements, however, may be considered, like estimation approaches based on time-space event distribution.