Predicting mortality and visualizing health care spending by predicted mortality in Danes over age 65
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Predicting mortality and visualizing health care spending by predicted mortality in Danes over age 65. / Hansen, Anne Vinkel; Mortensen, Laust Hvas; Ekstrøm, Claus Thorn; Trompet, Stella; Westendorp, Rudi.
In: Scientific Reports, Vol. 13, No. 1, 1203, 2023.Research output: Contribution to journal › Journal article › Research › peer-review
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TY - JOUR
T1 - Predicting mortality and visualizing health care spending by predicted mortality in Danes over age 65
AU - Hansen, Anne Vinkel
AU - Mortensen, Laust Hvas
AU - Ekstrøm, Claus Thorn
AU - Trompet, Stella
AU - Westendorp, Rudi
N1 - © 2023. The Author(s).
PY - 2023
Y1 - 2023
N2 - 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.
AB - 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.
KW - Humans
KW - Aged
KW - Health Expenditures
KW - Delivery of Health Care
KW - Health Facilities
KW - Denmark/epidemiology
U2 - 10.1038/s41598-023-28102-4
DO - 10.1038/s41598-023-28102-4
M3 - Journal article
C2 - 36681729
VL - 13
JO - Scientific Reports
JF - Scientific Reports
SN - 2045-2322
IS - 1
M1 - 1203
ER -
ID: 333967393