Algorithms for enhancing public health utility of national causes-of-death data - PubMed (original) (raw)
Algorithms for enhancing public health utility of national causes-of-death data
Mohsen Naghavi et al. Popul Health Metr. 2010.
Abstract
Background: Coverage and quality of cause-of-death (CoD) data varies across countries and time. Valid, reliable, and comparable assessments of trends in causes of death from even the best systems are limited by three problems: a) changes in the International Statistical Classification of Diseases and Related Health Problems (ICD) over time; b) the use of tabulation lists where substantial detail on causes of death is lost; and c) many deaths assigned to causes that cannot or should not be considered underlying causes of death, often called garbage codes (GCs). The Global Burden of Disease Study and the World Health Organization have developed various methods to enhance comparability of CoD data. In this study, we attempt to build on these approaches to enhance the utility of national cause-of-death data for public health analysis.
Methods: Based on careful consideration of 4,434 country-years of CoD data from 145 countries from 1901 to 2008, encompassing 743 million deaths in ICD versions 1 to 10 as well as country-specific cause lists, we have developed a public health-oriented cause-of-death list. These 56 causes are organized hierarchically and encompass all deaths. Each cause has been mapped from ICD-6 to ICD-10 and, where possible, they have also been mapped to the International List of Causes of Death 1-5. We developed a typology of different classes of GCs. In each ICD revision, GCs have been identified. Target causes to which these GCs should be redistributed have been identified based on certification practice and/or pathophysiology. Proportionate redistribution, statistical models, and expert algorithms have been developed to redistribute GCs to target codes for each age-sex group.
Results: The fraction of all deaths assigned to GCs varies tremendously across countries and revisions of the ICD. In general, across all country-years of data available, GCs have declined from more than 43% in ICD-7 to 24% in ICD-10. In some regions, such as Australasia, GCs in 2005 are as low as 11%, while in some developing countries, such as Thailand, they are greater than 50%. Across different age groups, the composition of GCs varies tremendously - three classes of GCs steadily increase with age, but ambiguous codes within a particular disease chapter are also common for injuries at younger ages. The impact of redistribution is to change the number of deaths assigned to particular causes for a given age-sex group. These changes alter ranks across countries for any given year by a number of different causes, change time trends, and alter the rank order of causes within a country.
Conclusions: By mapping CoD through different ICD versions and redistributing GCs, we believe the public health utility of CoD data can be substantially enhanced, leading to an increased demand for higher quality CoD data from health sector decision-makers.
Figures
Figure 1
Number of country-years of cause-of-death data by ICD revision from 1950 to 2008 used in this study based on publicly available datasets. Between 1950-1957, 40 countries used ICD-6 and sent data to WHO using the same Tab A format as ICD-7. ICD-6 has 235 country-years and 50.9 million deaths.
Figure 2
Percentage of garbage codes by type of GCs and ICD version, all ages. Between 1950-1957, 40 countries used ICD-6 and sent data to WHO using the same Tab A format as ICD-7. ICD-6 has 235 country-years and 50.9 million deaths.
Figure 3
Fraction of all deaths coded to GCs for all years available in each GBD region by year.
Figure 4
Fraction of deaths assigned to GCs in the latest ICD-10 year since 2000.
Figure 5
Percentage of all deaths coded to GCs by age in all country-years of ICD-10 data.
Figure 6
Ratio of the number of deaths from maternal causes after redistribution to the number of deaths before GC redistribution across 4,434 country-years of ICD-coded mortality data.
Figure 7
A & 7B Distribution of all ICD-10 coded cause-of-death data for 878 country-years by age and 24 causes before and after GC redistribution. A01 - Tuberculosis in all type, A02 - HIV/AIDS, A03 - Sexually transmitted diseases except HIV, A04 - Intestinal Infectious, A05 - Vaccine Preventable, A06 - Malaria, A07 - Parasitic and Vector born disease, A08 - Meningitis/Encephalitis/Hepatitis, A09 - Respiratory Infections, A10 - Maternal conditions, A11 - Neonatal conditions, A12 - Nutritional deficiencies, B14 - Neoplasms, B15 - Diabetes, B16 - Endocrine, nutritional, blood, and immune disorders, B17 - Mental/behavioral and neurological conditions, B18 - Cardiovascular and circulatory diseases, B19 - Respiratory diseases, B20 - Digestive diseases, B21 - Genitourinary/skin/musculoskeletal diseases, B22 - Congenital anomaly, C23 - Unintentional injuries, C24 - Intentional injuries, GAR - Garbage
Figure 8
Age-standardized death rate for ischemic heart diseases in Italy before and after GC redistribution.
Figure 9
Age-standardized death rate for all digestive disease except cirrhosis in France before and after GC redistribution.
Figure 10
Age-standardized death rate for nutritional deficiencies in El Salvador before and after GC redistribution.
Figure 11
Age-standardized death rate for transport injuries in Bulgaria before and after GC redistribution.
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