Background: Mortality statistics are key inputs for evidence based health policy at national level. Little is known of the empirical basis for mortality statistics in China, which accounts for roughly one-fifth of the world's population. An adequate description of the evolution of mortality registration in China and its current situation is important to evaluate the usability of the statistics derived from it for international epidemiology and health policy. Current situation: The Chinese vital registration system currently covers 41 urban and 85 rural centres, accounting for roughly 8 % of the national population. Quality of registration is better in urban than in rural areas, and eastern than in western regions, resulting in significant biases in the overall statistics. The Ministry of Health introduced the Disease Surveillance Point System in 1980, to generate cause specific mortality statistics from a nationally representative sample of sites. Currently, the sample consists of 145 urban and rural sites, covering populations from 30,000 – 70,000, and a total of about 1 % of the national population. Causes of death are derived through a mix of medical certification and 'verbal autopsy' procedures, applied according to standard guidelines in all sites. Periodic evaluations for completeness of registration are conducted, with subsequent corrections for under reporting of deaths. Conclusion: Results from the DSP have been used to inform health policy at national, regional and global levels. There remains a need to critically validate the information on causes of death, and a detailed validation exercise on these aspects is currently underway. In general, such sample based mortality registration systems hold much promise as models for rapidly improving knowledge about levels and causes of mortality in other low-income populations.

Background Vital registration of causes of death in China is incomplete with poor coverage of medical certification. Information on the leading causes of mortality will continue to rely on verbal autopsy (VA) methods. A new international VA form is being considered for data collection in China, but it first needs to be validated to determine its operating characteristics. Methods Detailed medical records and clinical evidence for 3290 deaths (mostly adults) among residents of six cities representative of the urban Chinese population were reviewed by a panel of physicians and coded by experts to establish a reference underlying cause of death. Independently, families of the deceased were interviewed using a structured symptomatic questionnaire and a separate death certificate was prepared for each matching case (2102). Validity of the VA procedure was assessed using standard measurement criteria of sensitivity, specificity, and positive predictive value. Results VA methods perform reasonably well in identifying deaths from several leading causes of adult deaths including stroke, several major cancer sites (lung, liver, stomach, oesophagus, and colorectal), and transport accidents. Sensitivity was less satisfactory in detecting deaths from several causes of major public health concern in China including ischaemic heart disease, chronic obstructive pulmonary disease, diabetes, and tuberculosis, and was particularly poor in diagnosing deaths from viral hepatitis, hypertension, and kidney diseases. Conclusions VA is an imprecise tool for detecting leading causes of death among adults. However, much of the misclassification generally occurs within broad cause groups (e.g. CVD, respiratory diseases, and liver diseases). Moreover, compensating patterns of misclassification would appear to suggest that, in urban China at least, the method yields population-level cause-specific estimates that are reasonably reliable. These results suggest the possible utility of these methods in rural China, to back up the low coverage of medical certification of cause of death owing to poor access to health facilities there.

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.