8
views
0
recommends
+1 Recommend
0 collections
    0
    shares
      • Record: found
      • Abstract: found
      • Article: found
      Is Open Access

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

      Read this article at

      Bookmark
          There is no author summary for this article yet. Authors can add summaries to their articles on ScienceOpen to make them more accessible to a non-specialist audience.

          Abstract

          Background

          In 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).

          Methods

          The 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.

          Results

          Completeness 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.

          Conclusions

          GLM 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.

          Related collections

          Most cited references 18

          • Record: found
          • Abstract: not found
          • Article: not found

          On the statistical analysis of capture experiments

           R Huggins (1989)
            Bookmark
            • Record: found
            • Abstract: not found
            • Article: not found

            Some Practical Aspects of a Conditional Likelihood Approach to Capture Experiments

             R Huggins (1991)
              Bookmark
              • Record: found
              • Abstract: found
              • Article: not found

              Civil registration and vital statistics: progress in the data revolution for counting and accountability.

              New momentum for civil registration and vital statistics (CRVS) is building, driven by the confluence of growing demands for accountability and results in health, improved equity, and rights-based approaches to development challenges, and by the immense potential of innovation and new technologies to accelerate CRVS improvement. Examples of country successes in strengthening of hitherto weak systems are emerging. The key to success has been to build collaborative partnerships involving local ownership by several sectors that span registration, justice, health, statistics, and civil society. Regional partners can be important to raise awareness, set regional goals and targets, foster country-to-country exchange and mutual learning, and build high-level political commitment. These regional partners continue to provide a platform through which country stakeholders, development partners, and technical experts can share experiences, develop and document good practices, and propose innovative approaches to tackle CRVS challenges. This country and regional momentum would benefit from global leadership, commitment, and support.
                Bookmark

                Author and article information

                Contributors
                timothy.adair@unimelb.edu.au
                Journal
                Popul Health Metr
                Popul Health Metr
                Population Health Metrics
                BioMed Central (London )
                1478-7954
                4 September 2020
                4 September 2020
                2020
                : 18
                Affiliations
                [1 ]GRID grid.457035.0, ISNI 0000 0001 2289 3995, Brazilian Institute of Geography and Statistics (IBGE), ; Level 8, 500 Republic of Chile Avenue, Rio de Janeiro, RJ 20031-170 Brazil
                [2 ]GRID grid.414596.b, ISNI 0000 0004 0602 9808, Ministry of Health, ; SRTVN 701, Via W5 Norte, PO700 Building, 6th floor-DASNT, Brasilia, DF 70723-040 Brazil
                [3 ]GRID grid.1008.9, ISNI 0000 0001 2179 088X, University of Melbourne, Melbourne School of Population and Global Health, , The University of Melbourne, ; Level 5, Building 379, 207 Bouverie Street, Carlton, Victoria 3010 Australia
                [4 ]GRID grid.8430.f, ISNI 0000 0001 2181 4888, Tele-Health/Federal University of Minas Gerais, ; Pres. Antônio Carlos, 6627-Pampulha, Belo Horizonte, MG 31270-901 Brazil
                Article
                223
                10.1186/s12963-020-00223-2
                7650525
                32887639
                © The Author(s) 2020

                Open AccessThis article is licensed under a Creative Commons Attribution 4.0 International License, which permits use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons licence, and indicate if changes were made. The images or other third party material in this article are included in the article's Creative Commons licence, unless indicated otherwise in a credit line to the material. If material is not included in the article's Creative Commons licence and your intended use is not permitted by statutory regulation or exceeds the permitted use, you will need to obtain permission directly from the copyright holder. To view a copy of this licence, visit http://creativecommons.org/licenses/by/4.0/. The Creative Commons Public Domain Dedication waiver ( http://creativecommons.org/publicdomain/zero/1.0/) applies to the data made available in this article, unless otherwise stated in a credit line to the data.

                Funding
                Funded by: Bloomberg Philanthropies
                Award ID: Data for Health Initiative
                Categories
                Research
                Custom metadata
                © The Author(s) 2020

                Comments

                Comment on this article