ArticleBMC medical informatics and decision making2022
Population-based estimates of age and comorbidity specific life expectancy: a first application in Swedish males.
Article in BMC medical informatics and decision making, 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 8 papers.
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The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
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Who cites it
8 citing papers in PubMed, 18 citations in OpenAlex.
- Extent of prostate cancer cases not registered in The National Cancer Register of Sweden and consequences for estimates of prostate cancer incidence and mortality.Acta oncologica (Stockholm, Sweden) · 2026Article
- Temporal trends in diagnostic work-up, treatment, and mortality in locally advanced prostate cancer in 2016-2024: nationwide, population-based study in Sweden.Acta oncologica (Stockholm, Sweden) · 2026Article
- Potential gains from radical treatment of men with prostate cancer according to life expectancy.BJUI compass · 2025Article
- Natural History of Nonmetastatic Prostate Cancer Managed With Watchful Waiting.JAMA network open · 2024Article
- Number of medically prescribed pharmaceutical agents as predictor of mortality risk: a longitudinal, time-variable analysis in the EPIC-Heidelberg cohort.Scientific reports · 2024Article
- Survival Trend in Individuals With De Novo Metastatic Prostate Cancer After the Introduction of Doublet Therapy.JAMA network open · 2023Article
- Variations in the Uptake of Active Surveillance for Prostate Cancer and Its Impact on Outcomes.European urology open science · 2023Article
- Charlson comorbidity index applied to shunted idiopathic normal pressure hydrocephalus.Scientific reports · 2023Article
Corrections and comments
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Authors and funding
7 authors at 4 institutions in 4 countries.
Funding
No grant is acknowledged in the PubMed record.
Abstract
introductionFor 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.
methodsSimulations 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.
resultsThere 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.
conclusionOur 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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