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Detecting Mortality Trends in the Netherlands Across 625 Causes of Death

Author

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  • Marianna Mitratza

    (Department of Public Health, Amsterdam UMC, University of Amsterdam, 1105 AZ Amsterdam, The Netherlands)

  • Anton E. Kunst

    (Department of Public Health, Amsterdam UMC, University of Amsterdam, 1105 AZ Amsterdam, The Netherlands)

  • Jan W. P. F. Kardaun

    (Department of Public Health, Amsterdam UMC, University of Amsterdam, 1105 AZ Amsterdam, The Netherlands
    Department of Health and Care, Statistics Netherlands, 2090 HA The Hague, The Netherlands)

Abstract

Cause of death (COD) data are essential to public health monitoring and policy. This study aims to determine the proportion of CODs, at ICD-10 three-position level, for which a long-term or short-term trend can be identified, and to examine how much the likelihood of identifying trends varies with COD size. We calculated annual age-standardized counts of deaths from Statistics Netherlands for the period 1996–2015 for 625 CODs. We applied linear regression models to estimate long-term trends, and outlier analysis to detect short-term changes. The association of the likelihood of a long-term trend with COD size was analyzed with multinomial logistic regression. No long-term trend could be demonstrated for 216 CODs (34.5%). For the remaining 409 causes, a trend could be detected, following a linear (211, 33.8%), quadratic (126, 20.2%) or cubic model (72, 11.5%). The probability of detecting a long-term trend increased from about 50% at six mean annual deaths, to 65% at 22 deaths and 75% at 60 deaths. An exceptionally high or low number of deaths in a single year was found for 16 CODs. When monitoring long-term mortality trends, one could consider a much broader range of causes of death, including ones with a relatively low number of annual deaths, than commonly used in condensed lists.

Suggested Citation

  • Marianna Mitratza & Anton E. Kunst & Jan W. P. F. Kardaun, 2019. "Detecting Mortality Trends in the Netherlands Across 625 Causes of Death," IJERPH, MDPI, vol. 16(21), pages 1-9, October.
    • Handle: RePEc:gam:jijerp:v:16:y:2019:i:21:p:4150-:d:281021
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    References listed on IDEAS

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    1. C. S. Wong & W. K. Li, 1998. "A note on the corrected Akaike information criterion for threshold autoregressive models," Journal of Time Series Analysis, Wiley Blackwell, vol. 19(1), pages 113-124, January.
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