BackgroundIn many developed countries, the economic crisis started in 2008 producing a serious contraction of the financial resources spent on healthcare. Identifying which individuals will require more resources and the moment in their lives these resources have to be allocated becomes essential. It is well known that a small number of individuals with complex healthcare needs consume a high percentage of health expenditures. Conversely, little is known on how morbidity evolves throughout life. The aim of this study is to introduce a longitudinal perspective to chronic disease management.MethodsData used relate to the population of the county of Baix Empordà in Catalonia for the period 2004–2007 (average population was N = 88,858). The database included individual information on morbidity, resource consumption, costs and activity records. The population was classified using the Clinical Risk Groups (CRG) model. Future morbidity evolution was simulated under different assumptions using a stationary Markov chain. We obtained morbidity patterns for the lifetime and the distribution function of the random variable lifetime costs. Individual information on acute episodes, chronic conditions and multimorbidity patterns were included in the model.ResultsThe probability of having a specific health status in the future (healthy, acute process or different combinations of chronic illness) and the distribution function of healthcare costs for the individual lifetime were obtained for the sample population. The mean lifetime cost for women was €111,936, a third higher than for men, at €81,566 (all amounts calculated in 2007 Euros). Healthy life expectancy at birth for females was 46.99, lower than for males (50.22). Females also spent 28.41 years of life suffering from some type of chronic disease, a longer period than men (21.9).ConclusionsFuture morbidity and whole population costs can be reasonably predicted, combining stochastic microsimulation with a morbidity classification system. Potential ways of efficiency arose by introducing a time perspective to chronic disease management.
High consumption of healthcare resources is associated with complex chronic morbidity. A model based on age, morbidity, and prior utilization is able to predict high-cost risk and identify a target population requiring proactive care.
The CRG grouping system aids analysis at different levels for clinical administration. Due to its composition, this system allows better understanding of the use, costs and quality of the set of services received by a population.
In 1999, Zweifel, Felder, and Meiers questioned conventional wisdom on ageing and healthcare expenditure (HCE). According to these authors, the positive association between age and HCE is due to an increasing age-specific mortality and the high cost of dying. After a weighty academic debate, a new consensus was reached on the importance of proximity to death when analysing HCE. Nevertheless, the influence of individual health status remains unknown. The objective of our study is to analyse the influence individual health status has on HCE, when compared to proximity to death and demographic effects and considering a comprehensive view of healthcare services and costs. We examined data concerning different HCE components of N = 61,473 persons aged 30 to 95 years old. Using 2-part models, we analysed the probability of use and positive HCE. Regardless of the specific group of healthcare services, HCE at the end of life depends mainly on the individual health status. Proximity to death approximates individual morbidity when it is excluded from the model. The inclusion of morbidity generally improves the goodness of fit. These results provide implications for the analysis of ageing population and its impact on HCE that should be taken into account.
scite is a Brooklyn-based organization that helps researchers better discover and understand research articles through Smart Citations–citations that display the context of the citation and describe whether the article provides supporting or contrasting evidence. scite is used by students and researchers from around the world and is funded in part by the National Science Foundation and the National Institute on Drug Abuse of the National Institutes of Health.
hi@scite.ai
10624 S. Eastern Ave., Ste. A-614
Henderson, NV 89052, USA
Copyright © 2024 scite LLC. All rights reserved.
Made with 💙 for researchers
Part of the Research Solutions Family.