Author:
Lundgren Moa,Segernäs Anna,Nord Magnus,Alwin Jenny,Lyth Johan
Abstract
Abstract
Background
A small proportion of the older population accounts for a high proportion of healthcare use. For effective use of limited healthcare resources, it is important to identify the group with greatest needs. The aim of this study was to explore frequency and reason for hospitalisation and cumulative mortality, in an older population at predicted high risk of hospital admission, and to assess if a prediction model can be used to identify individuals with the greatest healthcare needs. Furthermore, discharge diagnoses were explored to investigate if they can be used as basis for specific interventions in the high-risk group.
Methods
All residents, 75 years or older, living in Östergötland, Sweden, on January 1st, 2017, were included. Healthcare data from 2016 was gathered and used by a validated prediction model to create risk scores for hospital admission. The population was then divided into groups by percentiles of risk. Using healthcare data from 2017–2018, two-year cumulative incidence of hospitalisation was analysed using Gray´s test. Cumulative mortality was analysed with the Kaplan–Meier method and primary discharge diagnoses were analysed with standardised residuals.
Results
Forty thousand six hundred eighteen individuals were identified (mean age 82 years, 57.8% women). The cumulative incidence of hospitalisation increased with increasing risk of hospital admission (24% for percentiles < 60 to 66% for percentiles 95–100). The cumulative mortality also increased with increasing risk (7% for percentiles < 60 to 43% for percentiles 95–100). The most frequent primary discharge diagnoses for the population were heart diseases, respiratory infections, and hip injuries. The incidence was significantly higher for heart diseases and respiratory infections and significantly lower for hip injuries, for the population with the highest risk of hospital admission (percentiles 85–100).
Conclusions
Individuals 75 years or older, with high risk of hospital admission, were demonstrated to have considerable higher cumulative mortality as well as incidence of hospitalisation. The results support the use of the prediction model to direct resources towards individuals with highest risk scores, and thus, likely the greatest care needs. There were only small differences in discharge diagnoses between the risk groups, indicating that interventions to reduce hospitalisations should be personalised.
Trial registration
clinicaltrials.gov Identifier: NCT03180606, first posted 08/06/2017.
Funder
County Council of Östergötland and Linköping University
Swedish Research Council
Medical Research Council of Southeastern Sweden
Linköping University
Publisher
Springer Science and Business Media LLC
Reference50 articles.
1. Schoenman JA, Chockley N. The concentration of health care spending. National Institute for Health Care Management Research Educational Foundation; 2012. https://nihcm.org/assets/articles/databrief3final.pdf. Cited 2023 March 22.
2. Holle M, Wolff T, Herant M. Trends in the concentration and distribution of Health Care expenditures in the US, 2001–2018. JAMA Netw open. 2021;4(9):e2125179-e.
3. Tanke MA, Feyman Y, Bernal-Delgado E, Deeny SR, Imanaka Y, Jeurissen P, et al. A challenge to all. A primer on inter-country differences of high-need, high-cost patients. PLoS One. 2019;14(6):e0217353.
4. Cohen SB, Yu W. Statistical brief# 354: the concentration and persistence in the level of health expenditures over time: estimates for the US population, 2008–2009. Rockville: Agency for Healthcare Research and Quality; 2012.
5. Nägga K, Dong H-J, Marcusson J, Skoglund SO, Wressle E. Health-related factors associated with hospitalization for old people: comparisons of elderly aged 85 in a population cohort study. Arch Gerontol Geriatr. 2012;54(2):391–7.