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Population-based estimates of age and comorbidity specific life expectancy: a first application in Swedish males

Research output: Contribution to journalArticlepeer-review

Mieke Van Hemelrijck, Eugenio Ventimiglia, David Robinson, Rolf Gedeborg, Lars Holmberg, Pär Stattin, Hans Garmo

Original languageEnglish
Article number35
JournalBMC medical informatics and decision making
Volume22
Issue number1
DOIs
Accepted/In press24 Jan 2022
Published8 Feb 2022

Bibliographical note

Funding Information: This project was made possible by the continuous work of the National Prostate Cancer Register of Sweden (NPCR Swe) steering group. Funding Information: Funding came from the Swedish Research Council 825-2008-5910, Stockholm Cancer Society, the Swedish Council for Working Life and Social Research, and Västerbotten County Council. Funding contributed to data collection and maintenance of the data source. Publisher Copyright: © 2022, The Author(s).

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King's Authors

Abstract

Introduction: For clinical decision-making, an estimate of remaining lifetime is needed to assess benefit against harm of a treatment during the remaining lifespan. Here, we describe how to predict life expectancy based on age, Charlson Comorbidity Index (CCI) and a Drug Comorbidity Index (DCI), whilst also considering potential future changes in CCI and DCI using population-based data on Swedish men. Methods: Simulations based on annual updates of vital status, CCI and DCI were used to estimate life expectancy at population level. The probabilities of these transitions were determined from generalised linear models using prostate cancer-free comparison men in PCBaSe Sweden. A simulation was performed for each combination of age, CCI, and DCI. Survival curves were created and compared to observed survival. Life expectancy was then calculated as the area under the simulated survival curve. Results: There was good agreement between observed and simulated survival curves for most ages and comorbidities, except for younger men. With increasing age and comorbidity, there was a decrease in life expectancy. Cross-validation based on six regions in Sweden also showed that simulated and observed survival was similar. Conclusion: Our proposed method provides an alternative statistical approach to estimate life expectancy at population level based on age and comorbidity assessed by routinely collected information on diagnoses and filled prescriptions available in nationwide health care registers.

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