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.
Highlights
IntroductionAs part of the vital statistics registries, they cover entire national populations, extend over long periods of time and are accessible
Mortality statistics are a key source of information in public health, epidemiology and medicine.As part of the vital statistics registries, they cover entire national populations, extend over long periods of time and are accessible
The general aim of this study was to determine the level of detail, in terms of Cause of death (COD) size, at which the ICD-10 classification can be best used for presenting mortality trends in national populations
Summary
As part of the vital statistics registries, they cover entire national populations, extend over long periods of time and are accessible. Among others, these statistics are used to monitor cause-specific mortality, in order to identify trends that may inform disease prevention, screening and surveillance. These statistics are used to monitor cause-specific mortality, in order to identify trends that may inform disease prevention, screening and surveillance Both short- and long-term mortality trends can be monitored to identify changes that may prompt health policy actions. Trends in the short term, defined as a maximum period of a year, are important to identify sudden population-level changes and events, such as outbreaks of communicable diseases. Long-term mortality trends, defined as secular changes, as observed across several years, may reflect gradual changes in the incidence or case-fatality of specific diseases or injuries
Sign-in/Register to view highlights & summary Sign-in/Register to access full text options 
Talk to us
Join us for a 30 min session where you can share your feedback and ask us any queries you have
Schedule a callMore From: International journal of environmental research and public health
Disclaimer: All third-party content on this website/platform is and will remain the property of their respective owners and is provided on "as is" basis without any warranties, express or implied. Use of third-party content does not indicate any affiliation, sponsorship with or endorsement by them. Any references to third-party content is to identify the corresponding services and shall be considered fair use under The CopyrightLaw.
