Background: Mortality statistics systems with reliable cause-of-death data constitute a major resource for effective health planning; however, many developing countries lack such information systems. Brazil has a long history of registering deaths, and a critical assessment of the quality of current cause-of-death statistics in its five different regions is crucial to identify strengths and weaknesses in the data, and present options for improvement.
Methods: Quality of cause-of-death data from 2002 to 2004 was evaluated using an assessment framework based on four main attributes: generalizability, reliability, validity and policy relevance. A set of nine criteria: coverage, completeness, consistency of cause patterns with general mortality levels, consistency of cause specific mortality proportions over time, content validity, proportion of ill-defined causes and non-specific codes, incorrect or improbable age or sex patterns, timeliness, and geographical disaggregation were used to assess the four attributes of data quality.
Results: Completeness of death registration varies from 72 to 80% in the northeast regions, compared with 85-90% in the Southeast and Centre-West regions, and 94-97% in the wealthier South region. The proportion of ill-defined deaths is an important problem in reported causes of death from almost all regions. Lack of adequate evidence limits the assessment of content validity of registered causes of death. Coverage, consistency of causes with general level of mortality, consistency over time, age and sex patterns, timeliness and usability of statistics for subnational purposes were judged to be reasonable and increase confidence in using the statistics.
Conclusions: There is considerable heterogeneity in the quality of cause-of-death statistics across Brazilian regions, especially for criteria such as completeness and ill-defined causes. These factors can influence generalizability and validity of reported causes of death, and must be considered in the interpretation and use of data for secondary descriptive analyses such as burden of disease estimation at regional level, with suitable adjustments to account for bias. The differences identified in this study could be a useful guide for defining measures and investments needed to improve data quality in Brazil.