Predicting mortality and visualizing health care spending by predicted mortality in Danes over age 65

Scientific Reports, vol. 13, pp. 1203

Abstract

Health care expenditure in the last year of life makes up a high proportion of medical spending across the world. This is often framed as waste, but this framing is only meaningful if it is known at the time of treatment who will go on to die. We analyze the distribution of health care spending by predicted mortality for the Danish population over age 65 over the year 2016, with one-year mortality predicted by a machine learning model based on sociodemographics and use of health care services for the two years before entry into follow-up. While a reasonably good model can be built, extremely few individuals have high ex-ante probability of dying, and those with a predicted mortality of more than 50% account for only 2.8% of total health care expenditure. Decedents outspent survivors by a factor of more than ten, but compared to survivors with similar predicted mortality they spent only 2.5 times as much. Our results suggest that while spending in the last year of life is indeed high, this is nearly all spent in situations where there is a reasonable expectation that the patient can survive.

Authors 5

1. Anne Vinkel Hansen corresponding

   University of Copenhagen · Statistics Denmark

   Affiliation as printed

   Center for Healthy Aging, University of Copenhagen, Copenhagen, Denmark. anviha@dcm.aau.dk

   Methods and Analysis, Statistics Denmark, , Danmarks Statistik, Sejrøgade 11, 2100, Copenhagen, Denmark. anviha@dcm.aau.dk

   Methods and Analysis, Statistics Denmark, , Danmarks Statistik, Sejrøgade 11, 2100, Copenhagen, Denmark

2. Laust Hvas Mortensen

   University of Copenhagen · Statistics Denmark

   Affiliation as printed

   Methods and Analysis, Statistics Denmark, , Danmarks Statistik, Sejrøgade 11, 2100, Copenhagen, Denmark

   Section of Epidemiology, Department of Public Health, University of Copenhagen, Copenhagen, Denmark

3. Claus Thorn Ekstrøm

   University of Copenhagen

   Affiliation as printed

   Section of Biostatistics, Department of Public Health, University of Copenhagen, Copenhagen, Denmark

4. Stella Trompet Aachen Department of Internal Medicine Section of Gerontology and Geriatrics

   Leiden University Medical Center

   Affiliation as printed

   Department of Internal Medicine, Section of Gerontology and Geriatrics, Leiden University Medical Center, Leiden, the Netherlands

5. Rudi G. J. Westendorp

   University of Copenhagen · Statistics Denmark

   Affiliation as printed

   Center for Healthy Aging, University of Copenhagen, Copenhagen, Denmark

   Methods and Analysis, Statistics Denmark, , Danmarks Statistik, Sejrøgade 11, 2100, Copenhagen, Denmark

   Section of Epidemiology, Department of Public Health, University of Copenhagen, Copenhagen, Denmark