TY - JOUR
T1 - Trajectories at the end of life
T2 - A controlled investigation of longitudinal Health Services Consumption data
AU - Cohen-Mansfield, Jiska
AU - Brill, Shai
N1 - Publisher Copyright:
© 2016 Elsevier Ireland Ltd
PY - 2016/12/1
Y1 - 2016/12/1
N2 - Background Knowledge of individual-level trajectories of Health Services Consumption (HSC) at End-of-Life (EoL) is scarce. Such research is needed for understanding and planning health expenditures. Objective To explore individual-level EoL trajectories in the Israeli population. This approach differs from past studies which aggregated across populations or disease groups. Data sources We used HMO (Health Maintenance Organization) longitudinal data for HSC of persons ages 65–90 who died during 2010–2011 (n = 35,887) and of an age by sex matched sample of persons who were alive by mid-2012 (n = 48,560). Design HSC per quarter was calculated for each individual. Trajectory-types of HSC were described through k-means cluster analysis. Extraction methods Data were extracted from computerized HMO files. HSC was computed as a standardized function of HMO costs for each individual. Results In both samples, low HSC trajectories were the most common. However, among the deceased, all trajectories had higher HSC than those who were alive; the low HSC trajectory cluster represented a smaller percentage of the sample; and all relevant trajectories included a HSC peak. In contrast, the most common trajectory among the living was a flat low HSC. Clusters differed significantly by sex, disease status, and age. Conclusion This methodology shows the utility of individual-level analysis of HSC at end-of-life and should inform future research and current debates concerning EoL care and resource distribution.
AB - Background Knowledge of individual-level trajectories of Health Services Consumption (HSC) at End-of-Life (EoL) is scarce. Such research is needed for understanding and planning health expenditures. Objective To explore individual-level EoL trajectories in the Israeli population. This approach differs from past studies which aggregated across populations or disease groups. Data sources We used HMO (Health Maintenance Organization) longitudinal data for HSC of persons ages 65–90 who died during 2010–2011 (n = 35,887) and of an age by sex matched sample of persons who were alive by mid-2012 (n = 48,560). Design HSC per quarter was calculated for each individual. Trajectory-types of HSC were described through k-means cluster analysis. Extraction methods Data were extracted from computerized HMO files. HSC was computed as a standardized function of HMO costs for each individual. Results In both samples, low HSC trajectories were the most common. However, among the deceased, all trajectories had higher HSC than those who were alive; the low HSC trajectory cluster represented a smaller percentage of the sample; and all relevant trajectories included a HSC peak. In contrast, the most common trajectory among the living was a flat low HSC. Clusters differed significantly by sex, disease status, and age. Conclusion This methodology shows the utility of individual-level analysis of HSC at end-of-life and should inform future research and current debates concerning EoL care and resource distribution.
KW - End of life care
KW - Health care costs
KW - Health policy
KW - Health services
KW - International health
UR - http://www.scopus.com/inward/record.url?scp=85001110960&partnerID=8YFLogxK
U2 - 10.1016/j.healthpol.2016.09.017
DO - 10.1016/j.healthpol.2016.09.017
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AN - SCOPUS:85001110960
VL - 120
SP - 1395
EP - 1403
JO - Health Policy
JF - Health Policy
SN - 0168-8510
IS - 12
ER -
