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How large should a cause of death be in order to be included in mortality trend analysis? Deriving a cut-off point from retrospective trend analyses in 21 European countries
OBJECTIVES: The International Classification of Diseases (ICD-10) distinguishes a large number of causes of death (CODs) that could each be studied individually when monitoring time-trends. We aimed to develop recommendations for using the size of CODs as a criterion for their inclusion in long-term...
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Formato: | Online Artículo Texto |
Lenguaje: | English |
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BMJ Publishing Group
2020
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Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7044923/ https://www.ncbi.nlm.nih.gov/pubmed/31969361 http://dx.doi.org/10.1136/bmjopen-2019-031702 |
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author | Mitratza, Marianna Kardaun, Jan W P F Kunst, Anton E |
author_facet | Mitratza, Marianna Kardaun, Jan W P F Kunst, Anton E |
author_sort | Mitratza, Marianna |
collection | PubMed |
description | OBJECTIVES: The International Classification of Diseases (ICD-10) distinguishes a large number of causes of death (CODs) that could each be studied individually when monitoring time-trends. We aimed to develop recommendations for using the size of CODs as a criterion for their inclusion in long-term trend analysis. DESIGN: Retrospective trend analysis. SETTING: 21 European countries of the WHO Mortality Database. PARTICIPANTS: Deaths from CODs (3-position ICD-10 codes) with ≥5 average annual deaths in a 15-year period between 2000 and 2016. PRIMARY AND SECONDARY OUTCOME MEASURES: Fitting polynomial regression models, we examined for each COD in each country whether or not changes over time were statistically significant (with α=0.05) and we assessed correlates of this outcome. Applying receiver operating characteristicROC curve diagnostics, we derived COD size thresholds for selecting CODs for trends analysis. RESULTS: Across all countries, 64.0% of CODs had significant long-term trends. The odds of having a significant trend increased by 18% for every 10% increase of COD size. The independent effect of country was negligible. As compared to circulatory system diseases, the probability of a significant trend was lower for neoplasms and digestive system diseases, and higher for infectious diseases, mental diseases and signs-and-symptoms. We derived a general threshold of around 30 (range: 28–33) annual deaths for inclusion of a COD in trend analysis. The relevant threshold for neoplasms was around 65 (range: 61–70) and for infectious diseases was 20 (range: 19–20). CONCLUSIONS: The likelihood that long-term trends are detected with statistical significance is strongly related to COD size and varies between ICD-10 chapters, but has no independent relation to country. We recommend a general size criterion of 30 annual deaths to select CODs for long-term mortality-trends analysis in European countries. |
format | Online Article Text |
id | pubmed-7044923 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2020 |
publisher | BMJ Publishing Group |
record_format | MEDLINE/PubMed |
spelling | pubmed-70449232020-03-09 How large should a cause of death be in order to be included in mortality trend analysis? Deriving a cut-off point from retrospective trend analyses in 21 European countries Mitratza, Marianna Kardaun, Jan W P F Kunst, Anton E BMJ Open Epidemiology OBJECTIVES: The International Classification of Diseases (ICD-10) distinguishes a large number of causes of death (CODs) that could each be studied individually when monitoring time-trends. We aimed to develop recommendations for using the size of CODs as a criterion for their inclusion in long-term trend analysis. DESIGN: Retrospective trend analysis. SETTING: 21 European countries of the WHO Mortality Database. PARTICIPANTS: Deaths from CODs (3-position ICD-10 codes) with ≥5 average annual deaths in a 15-year period between 2000 and 2016. PRIMARY AND SECONDARY OUTCOME MEASURES: Fitting polynomial regression models, we examined for each COD in each country whether or not changes over time were statistically significant (with α=0.05) and we assessed correlates of this outcome. Applying receiver operating characteristicROC curve diagnostics, we derived COD size thresholds for selecting CODs for trends analysis. RESULTS: Across all countries, 64.0% of CODs had significant long-term trends. The odds of having a significant trend increased by 18% for every 10% increase of COD size. The independent effect of country was negligible. As compared to circulatory system diseases, the probability of a significant trend was lower for neoplasms and digestive system diseases, and higher for infectious diseases, mental diseases and signs-and-symptoms. We derived a general threshold of around 30 (range: 28–33) annual deaths for inclusion of a COD in trend analysis. The relevant threshold for neoplasms was around 65 (range: 61–70) and for infectious diseases was 20 (range: 19–20). CONCLUSIONS: The likelihood that long-term trends are detected with statistical significance is strongly related to COD size and varies between ICD-10 chapters, but has no independent relation to country. We recommend a general size criterion of 30 annual deaths to select CODs for long-term mortality-trends analysis in European countries. BMJ Publishing Group 2020-01-21 /pmc/articles/PMC7044923/ /pubmed/31969361 http://dx.doi.org/10.1136/bmjopen-2019-031702 Text en © Author(s) (or their employer(s)) 2020. Re-use permitted under CC BY. Published by BMJ. https://creativecommons.org/licenses/by/4.0/This is an open access article distributed in accordance with the Creative Commons Attribution 4.0 Unported (CC BY 4.0) license, which permits others to copy, redistribute, remix, transform and build upon this work for any purpose, provided the original work is properly cited, a link to the licence is given, and indication of whether changes were made. See: https://creativecommons.org/licenses/by/4.0/. |
spellingShingle | Epidemiology Mitratza, Marianna Kardaun, Jan W P F Kunst, Anton E How large should a cause of death be in order to be included in mortality trend analysis? Deriving a cut-off point from retrospective trend analyses in 21 European countries |
title | How large should a cause of death be in order to be included in mortality trend analysis? Deriving a cut-off point from retrospective trend analyses in 21 European countries |
title_full | How large should a cause of death be in order to be included in mortality trend analysis? Deriving a cut-off point from retrospective trend analyses in 21 European countries |
title_fullStr | How large should a cause of death be in order to be included in mortality trend analysis? Deriving a cut-off point from retrospective trend analyses in 21 European countries |
title_full_unstemmed | How large should a cause of death be in order to be included in mortality trend analysis? Deriving a cut-off point from retrospective trend analyses in 21 European countries |
title_short | How large should a cause of death be in order to be included in mortality trend analysis? Deriving a cut-off point from retrospective trend analyses in 21 European countries |
title_sort | how large should a cause of death be in order to be included in mortality trend analysis? deriving a cut-off point from retrospective trend analyses in 21 european countries |
topic | Epidemiology |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7044923/ https://www.ncbi.nlm.nih.gov/pubmed/31969361 http://dx.doi.org/10.1136/bmjopen-2019-031702 |
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