Changes in the quality of cause-of-death statistics in Brazil: garbage codes among registered deaths in 1996-2016

Abstract

Background: Registered causes in vital statistics classified as garbage codes (GC) are considered indicators of quality of cause-of-death data. Our aim was to describe temporal changes in this quality in Brazil, and the leading GCs according to levels assembled for the Global Burden of Disease (GBD) study. We also assessed socioeconomic differences in the burden of different levels of GCs at a regional level. Methods: We extracted data from the Brazilian Mortality Information System from 1996 to 2016. All three and four digit ICD-10 codes considered as GC were selected and classified into four categories, according to the GBD study proposal. GCs levels 1 and 2 are the most damaging unusable codes, or major GCs. Proportionate distribution of deaths by GC levels according selected variables were performed. Age-standardized mortality rates after correction of underreporting of deaths were calculated to investigate temporal relationships as was the linear association adjusted for completeness between GC rates in states and the Sociodemographic Index (SDI) from the GBD study, for 1996-2005 and 2006-2016. We classified Brazilian states into three classes of development by applying tertiles cutoffs in the SDI state‐level estimates. Results: Age-standardized mortality rates due to GCs in Brazil decreased from 1996 to 2016, particularly level 1 GCs. The most important GC groups were ill-defined causes (level 1) in 1996, and pneumonia unspecified (level 4) in 2016. At state level, there was a significant inverse association between SDI and the rate of level 1-2 GCs in 1996-2005, but both SDI and completeness had a non-expected significant direct association with levels 3-4. In 2006-2016, states with higher SDIs tended to have lower rates of all types of GCs. Mortality rates due to major GCs decreased in all three SDI classes in 1996-2016, but GCs levels 3-4 decreased only in the high SDI category. States classified in the low or medium SDI groups were responsible for the most important decline of major GCs. Conclusion: Occurrence of major GCs are associated with socioeconomic determinants over time in Brazil. Their reduction with decreasing disparity in rates between socioeconomic groups indicates progress in reducing inequalities and strengthening cause-of-death statistics in the country.

Incidence and mortality rates of lip, oral cavity, and pharynx cancers in Brazil: time-trend and age-period-cohort analysis from the last 30 years, Global Burden of Disease Study

“…The data reported were mapped to a list of underlying causes in the GBD causes of a hierarchy of death 13 . Uninformative causes of death codes (the "garbage codes") are redistributed among appropriate underlying causes of death 14 . The population-based cancer records (RCBP, in Portuguese) were the sources used to calculate the incidence estimates 15 .…”

“…Moreover, the ICD change from version 9 to 10, in 1996, may have led to different results concerning the classification of the underlying cause of death, but this change is not expected to modify the trends described herein, as we have conducted an analysis that included the last 30 years. There was an improvement in data quality over the period: the completeness of death counts increased from 80%, in 1980-1991, to 95%, in 2000-2010 50 , but a large regional variation still persists in a pattern similar to the distribution of ill-defined causes of death 14 . A quality factor is that mouth and lip cancers are considered as a group, even though they have distinct risk factors and characteristics, which might have influenced the estimates shown in this study, especially in relation to incidence.…”

Evolution of the structure and results of Primary Health Care in Brazil between 2008 and 2019

“…Some studies [22][23][24] show that in low-income locations access to diagnostic and therapeutic support services is lower, which can generate a higher number of recorded deaths from unclear causes, underestimating PCSC mortality. Therefore, the method developed by Cavalini and Ponce de Leon 25 was used to correct both under-reporting and unclear causes.…”

Biases arising from linked administrative data for epidemiological research: a conceptual framework from registration to analyses

“…While in countries such as Scotland where publicly funded healthcare data in theory enumerates the whole population, in practice coverage is only 98% [49] and among this population some additional care will be provided privately. Additionally, the 100MCohort is a considerable investment, and is built on decades of work to improve statistics in Brazil [50]. Thus, in some countries much work may be required to create the necessary data infrastructure for similar cohorts to be created.…”