Long-term care models and dependence probability tables by acuity level: New empirical evidence from Switzerland

Abstract

Due to the demographic changes and population aging occurring in many countries, the financing of long-term care (LTC) poses a systemic threat. The scarcity of knowledge about the probability of an elderly person needing help with activities of daily living has hindered the development of insurance solutions that complement existing social systems. In this paper, we consider two models: a frailty level model that studies the evolution of a dependent person through mild, moderate and severe dependency states to death and a type of care model that distinguishes between care received at home and care received in an institution. We develop and interpret the expressions for the state-and time-dependent transition probabilities in a semi-Markov framework. Then, we empirically assess these probabilities using a novel longitudinal dataset covering all LTC needs in Switzerland over a 20-year period. As a key result, we are the first to derive dependence probability tables by acuity level, gender and age for the Swiss population. We find that the transition probabilities differ significantly by gender, age and time spent in the frailty level and type of care states.

Old-age care prevalence in Switzerland: drivers and future development

“…On a first stage, our study could be improved by analyzing the impact of further sociodemographic factors, e.g., the social class and the nationality on the prevalence rates. By accessing the underlying longitudinal data, further analyses could consider the age at entry and the time spent in the different states of dependence, the probability of different paths and evaluate their effect on the overall dependence (Fuino and Wagner, 2018). Finally, pathologies, functional limitations in ADLs and causes of death are factors affecting the dependence of elderly (Marengoni et al, 2011;Barnett et al, 2012;Biessy, 2016) and their analysis might lead to a better understanding of prevalence rate drivers.…”

“…For example, when controlling for the time to death, some variables like the age lose importance in shaping the LTC need (Zweifel et al, 2004). Furthermore, the application of multistate models for explaining the evolution of dependency requires to consider frailty levels and types of care (Czado and Rudolph, 2002) as well as the time spent in dependency (Fuino and Wagner, 2018). Some authors have considered the importance of culture in determining the demand for care in Switzerland.…”

Multi-State Health Transition Modeling Using Neural Networks

“…Second, our approach allows us to incorporate several socioeconomic and lifestyle factors in the modeling of health transition intensities and allows for linear and nonlinear relationships between these variables. Many existing studies on multi-state health transition models use GLM models with a limited number of factors such as age, time, and long-term care duration (see, e.g., Fong et al, 2015;Li et al, 2017;Fuino and Wagner, 2018;Hanewald et al, 2019).…”

“…Our application involves modeling the health transitions between different health states. Previous studies on multi-state health transition modeling have modeled the different transition processes separately (e.g., Fong et al, 2015;Shao et al, 2017;Fuino and Wagner, 2018;Hanewald et al, 2019). However, as the health states are linked to each other, the morbidity and mortality transitions are also linked (e.g., Alter and Riley, 1989;Johansson, 1991).…”

Long-term care insurance research and trajectory

“…The slow insurance development can be accounted for by a number of supply and demand factors, some of which this special issue discusses and advances in its study. Such supply factors include questions associated with risk insurability, asymmetric information and pricing (Fuino and Wagner 2018a). As for demand factors, potential cognitive biases in risk perception, awareness of caregiving needs and the burden on family (Sloan and Norton 1997), and more especially the crowding out of public assistance (Brown and Finkelstein 2008) and of family support (Pauly 1990) are relevant drivers.…”