Evidence map›Paper›PMID 32887639›Full record

ArticlePopulation health metrics2020

Estimating completeness of national and subnational death reporting in Brazil: application of record linkage methods.

Luiz Fernando Lima Costa, Marli de Mesquita Silva Montenegro, Dacio de Lyra Rabello Neto, Antonio Tadeu Ribeiro de Oliveira, Jose Eduardo de Oliveira Trindade, Tim Adair, Maria de Fatima Marinho

Abstract read
In one paragraph

Article in Population health metrics, 2020. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 24 papers, 1 of them a synthesis that pooled it.

0numbers the graph read from it
0cells of the map it votes in
24citing papers in PubMed, 1 pooled it
–field-weighted citation impact
1 · What the graph read from it

What it found

Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.

The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.

2 · The registry

The trial behind it

Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.

Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.

3 · Its place in the literature

Who cites it

24 citing papers in PubMed, 1 synthesis or guideline pooled it.

  1. Pooled it
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  6. Observational
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  15. Mortality trend due to chronic kidney disease in Brazil: an ecological study.Epidemiologia e servicos de saude : revista do Sistema Unico de Saude do Brasil · 2023
    Article
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4 · The record

Corrections and comments

PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.

5 · Who and what money

Authors and funding

7 authors.

Luiz Fernando Lima CostaBrazilian Institute of Geography and Statistics (IBGE), Level 8, 500 Republic of Chile Avenue, Rio de Janeiro, RJ, 20031-170, Brazil.
Marli de Mesquita Silva MontenegroMinistry of Health, SRTVN 701, Via W5 Norte, PO700 Building, 6th floor-DASNT, Brasilia, DF, 70723-040, Brazil.
Dacio de Lyra Rabello NetoMinistry of Health, SRTVN 701, Via W5 Norte, PO700 Building, 6th floor-DASNT, Brasilia, DF, 70723-040, Brazil.
Antonio Tadeu Ribeiro de OliveiraBrazilian Institute of Geography and Statistics (IBGE), Level 8, 500 Republic of Chile Avenue, Rio de Janeiro, RJ, 20031-170, Brazil.
Jose Eduardo de Oliveira TrindadeBrazilian Institute of Geography and Statistics (IBGE), Level 8, 500 Republic of Chile Avenue, Rio de Janeiro, RJ, 20031-170, Brazil.
Tim AdairUniversity of Melbourne, Melbourne School of Population and Global Health, The University of Melbourne, Level 5, Building 379, 207 Bouverie Street, Carlton, Victoria, 3010, Australia. timothy.adair@unimelb.edu.au.ORCID 0000-0002-1562-4452
Maria de Fatima MarinhoTele-Health/Federal University of Minas Gerais, Pres. Antônio Carlos, 6627-Pampulha, Belo Horizonte, MG, 31270-901, Brazil.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

backgroundIn Brazil, both the Civil Registry (CR) and Ministry of Health (MoH) Mortality Information System (SIM) are sources of routine mortality data, but neither is 100% complete. Deaths from these two sources can be linked to facilitate estimation of completeness of mortality reporting and measurement of adjusted mortality indicators using generalized linear modeling (GLM).

methodsThe 2015 and 2016 CR and SIM data were linked using deterministic methods. GLM with covariates of the deceased's sex, age, state of residence, cause of death and place of death, and municipality-level education decile and population density decile, was used to estimate total deaths and completeness nationally, subnationally and by population sub-group, and to identify the characteristics of unreported deaths. The empirical completeness method and Global Burden of Disease (GBD) 2017 estimates were comparators at the national and state level.

resultsCompleteness was 98% for SIM and 95% for CR. The vast majority of deaths in Brazil were captured by either system and 94% were reported by both sources. For each source, completeness was lowest in the north. SIM completeness was consistently high across all sub-groups while CR completeness was lowest for deaths at younger ages, outside facilities, and in the lowest deciles of municipality education and population density. There was no clear municipality-level relationship in SIM and CR completeness, suggesting minimal dependence between sources. The empirical completeness method model 1 and GBD completeness estimates were each, on average, less than three percentage points different from GLM estimates at the state level. Life expectancy was lowest in the northeast and 7.5 years higher in females than males.

conclusionsGLM using socio-economic and demographic covariates is a valuable tool to accurately estimate completeness from linked data sources. Close scrutiny of the quality of variables used to link deaths, targeted identification of unreported deaths in poorer, northern states, and closer coordination of the two systems will help Brazil achieve 100% death reporting completeness. The results also confirm the validity of the empirical completeness method.

Indexed as

Medical Record LinkageMortalityAged, 80 and overAge DistributionBrazilCause of DeathCost of IllnessData AccuracyGlobal HealthHumansLife ExpectancyRegistriesBrazilCapture-recapture methodsCivil registration and vital statisticsGeneralized linear modelingMortalityRecord linkageRegistration completeness

Identifiers

PMID32887639
PMCPMC7650525

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Registered trials

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Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.