Drivers of old-age dependence and long-term care usage in Switzerland: A structural equation model approach

Rudnytskyi, Iegor; Wagner, Joël Article Drivers of old-age dependence and long-term care usage in Switzerland: A structural equation model approach Risks Provided in Cooperation with: MDPI – Multidisciplinary Digital Publishing Institute, Basel Suggested Citation: Rudnytskyi, Iegor; Wagner, Joël (2019) : Drivers of old-age dependence and longterm care usage in Switzerland: A structural equation model approach, Risks, ISSN 2227-9091, MDPI, Basel, Vol. 7, Iss. 3, pp. 1-20, https://doi.org/10.3390/risks7030092 This Version is available at: https://hdl.handle.net/10419/257930 Standard-Nutzungsbedingungen: Die Dokumente auf EconStor dürfen zu eigenen wissenschaftlichen Zwecken und zum Privatgebrauch gespeichert und kopiert werden. Sie dürfen die Dokumente nicht für öffentliche oder kommerzielle Zwecke vervielfältigen, öffentlich ausstellen, öffentlich zugänglich machen, vertreiben oder anderweitig nutzen. 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If the documents have been made available under an Open Content Licence (especially Creative Commons Licences), you may exercise further usage rights as specified in the indicated licence. https://creativecommons.org/licenses/by/4.0/ risks Article Drivers of Old-Age Dependence and Long-Term Care Usage in Switzerland—A Structural Equation Model Approach Iegor Rudnytskyi 1,† and Joël Wagner 1,2,*,† 1Department of Actuarial Science, Faculty HEC, University of Lausanne, Extranef, 1015 Lausanne, Switzerland 2Swiss Finance Institute, University of Lausanne, Extranef, 1015 Lausanne, Switzerland *Correspondence: [email protected] † These authors contributed equally to this work. Received: 16 July 2019; Accepted: 19 August 2019; Published: 26 August 2019   Abstract: Long-term care (LTC) encompasses a set of services provided to impaired and dependent elderly people. To assess the level of the dependence several scales are used, including activities of daily living (ADL), instrumental ADL (IADL) and functional limitations. Once an elderly person fails to perform these activities independently, he or she requires special assistance. Help can be provided as informal care by relatives and as formal care by professionals. The aim of this research is to study individual characteristics that relate to the demand of LTC and to analyze the relation between formal and informal care. We base our study on data from the Swiss Health Survey focusing on respondents aged over 65 years. Using the structural equation modeling technique, we develop a statistical model that considers the dependence concept as a latent variable. This hidden dependence variable combines three indices linked to the limitations in ADL, in IADL and functional limitations. Accounting for causality links between covariates enables us to include the indirect effect of pathologies on the receipt of LTC mediated via dependence. In our model, we do not assume a causal relationship between formal and informal care. From our results, we observe a significant impact of pathologies as well as of the socio-demographic factors on the demand for LTC. The relationship between formal and informal care is found to be of both a complementary and substitutional nature. Keywords: old-age dependence; long-term care (LTC); structural equation model; pathology 1. Introduction The population is aging in many countries in the world and the speed of the process is expected to increase in the future (Lutz et al. 2008). The dramatic growth of the number of elderly triggers a higher demand for long-term care (LTC) services. The latter are defined by the International Labour Office as the “support that is needed by older persons with limited ability to care for themselves due to physical or mental conditions, including chronic diseases and multimorbidity” (Scheil-Adlung 2015, p. 2) . Such care can be delivered as informal care by persons from the social network, that is, partners, children, friends or neighbors of a dependent elderly person. Further, formal LTC embodies services and assistance in personal care and domestic tasks by specifically trained and qualified persons. Formal LTC is associated with higher expenditures and raises direct financial challenges on the health policies. As a result, informal care, although coming at a cost to the caregivers, is an important alternative to formal care (Weaver and Weaver 2014). Therefore, investigating the link between formal and informal care is of particular importance (see also, for example, Fuino and Wagner 2019). In policymaking and when defining incentives, it is highly relevant whether the relationship is substitutional or complementary. Risks 2019,7, 92; doi:10.3390/risks7030092 www.mdpi.com/journal/risks Risks 2019,7, 92 2 of 20 The aim of the present study is to investigate the drivers of old-age dependence and LTC usage in a structural equation modeling (SEM) framework. Relying on data from the Swiss Health Survey (SHS) for the model calibration, our focus is threefold: First, we are interested in the the measurement of dependence, linking dependence with the indices related to the limitations in activities of daily living (ADL), in instrumental ADL (IADL) and to functional limitations. Second, we relate the latent dependence variable to selected covariates including the age, the body mass index (BMI) and selected pathologies. Finally, we derive the factors that relate to formal and informal LTC utilization. Our model includes the gender, the education and income levels, the language region, the number of persons in the household and the presence of children outside of the household. Thereby, modeling formal and informal care usage separately, we study the prevalent correlation between both types of care. Instead of relying on a single regression model and one dependence measure, the SEM technique allows to measure dependence by several scales ensuring the robustness of the results. Thereby, we mediate the effect of medical conditions on the receipt of LTC via dependence. Using SHS data from the 2017 wave, we find that strokes, heart attacks, bronchitis and diabetes significantly increase the level of dependence. We observe this effect to a lower extent in the case of osteoporosis, arthritis and cancer. Further, higher dependence is associated with higher ages and higher deviations from the average BMI. Being a woman increases the probability to require LTC while neither the education level nor the monthly income significantly changes the probability. Residents from French-speaking cantons report a higher probability of receiving formal care. A higher number of persons living in the same household decreases the consumption of formal care but increases the use of informal care indicating a substitution effect. However, we do not observe any impact of the presence of children outside of the household. Our findings for Switzerland confirm findings from a European study based on the SHARE dataset (cf. Fuino et al. 2019). Finally, we report on the positive correlation between formal and informal care that indicates complementarity. The remainder of the paper is organized as follows: In Section 2we discuss the characteristics that can influence the demand for LTC by reviewing the existing literature and presenting the empirical background. We also derive four research hypotheses. Section 3introduces the available data from the Swiss Health Survey, presents the variables for the empirical model and provides descriptive statistics. In Section 4, we define a statistical model based on SEM, outline our estimation results and review the main findings. We conclude in Section 5. 2. Development of Research Hypotheses In the following, we review the existing literature relating selected risk factors to LTC dependence and care utilization. We derive four research hypotheses. 2.1. Health Indicators Age has been extensively studied in conjunction with LTC dependence. The decline of the general health is associated with the natural process of aging through the frailty syndrome (Fried et al. 2001) which triggers impairment and dependence. Research by House et al. (1990) and Rahmqvist (2001) underlines the relationship between age and self-rated health that is significantly associated with dependence. Further, age is one of the key demographic covariates for assessing the utilization of care facilities (see, for example, Langa et al. 2001a;Michaud and van Soest 2008; Kim and Lim 2015; Coe and Van Houtven 2009; etc.). Another health-related variable is the BMI. While Pi-Sunyer (1991) finds obesity to be a major health risk, Wu et al. (2013) also observe that underweight can affect self-rated health. Using data from European countries, the recent work by Fuino et al. (2019) links underweight and obesity to dependence. Finally, the impact of diseases on the demand for LTC is widely accepted. We start by mentioning, for example, Wong et al. (2010) who study how diagnoses trigger the utilization of different forms of formal care. They find significant influence among diagnoses of cancer, mental diseases (Schizophrenia, dementia and Alzheimer’s disease) and others. Similar results are derived in the papers by Shapiro and Tate (1985); Langa et al. (2001a); Risks 2019,7, 92 3 of 20 Hanaoka and Norton (2008) ; Michaud and van Soest (2008) ;De Meijer et al. (2011); Prince et al. (2013) and GBD DALYs and HALE Collaborators (2017) . Most of these studies consider the direct effect of diseases. We will emphasize on the importance to measure the mediated effect through the dependence status and state our first conjecture as follows: Conjecture 1. The indirect effect of physical diseases, the age and the BMI on the utilization of formal and informal care mediated via dependence is significant and positive. 2.2. Socio-Demographic Indicators In the care literature, the gender of the elderly person is found to play a vital role in predicting and explaining the demand for LTC. Evidence suggests to use the gender as an explanatory variable: women tend to live longer and become disabled at higher ages (Barczyk and Kredler 2018), the type of care provided to women significantly differs from the one provided to men (e.g., Denton 1997) and informal caregivers are mostly female individuals entailing higher receipt of informal care by their male spouses (Kemper 1992 and Wagner and Brandt 2018). Two other essential components are the education and the wealth of the elderly person. Rodrigues et al. (2018) and Freedman and Martin (1999) conclude that the use of LTC significantly differs across the accumulated wealth and Kemper (1992) shows the significant and positive association between monthly income and the probability to receive formal care. Further, the education level is positively associated with the probability of hospitalization in the paper by Weaver and Weaver (2014). Further references that use education and income include papers by Huisman et al. (2003); Hanaoka and Norton (2008); De Meijer et al. (2011); Shapiro and Tate (1985); Tomiak et al. (2000). Fuino and Wagner (2019) find a significant effect of the pre-retirement income on LTC duration. Cultural aspects are important drivers for the usage of formal and informal LTC. A number of studies report on the so-called North-South gradient in Europe (Bolin et al. 2008; Bonsang 2009; Torbica et al. 2015 ; and Barczyk and Kredler 2018). For example, Bolin et al. (2008) provide evidence that in Southern European countries informal care plays a very important role, when compared to Central Europe. This is also derived by Fuino et al. (2019). Switzerland has three cultural clusters linked to the spoken languages, namely German-, French- and Italian-speaking parts. The presence of this categorization is well covered, for example, in Fuino and Wagner (2018) . Let us stress the paper by Gentili et al. (2017) who observe that French-speaking population is more likely to enter a nursing home with higher dependency level and age, when compared to elderly from the German-speaking part. Conjecture 2. Socio-demographic variables such as gender, education, income and Swiss language regions have a significant direct effect on the utilization of formal and informal care. 2.3. Social Environment Factors Aspects from the social environment are often used to predict the demand for various types of LTC. In case of informal care, the assistance is mostly provided by spouses (partners) and children, followed by friends and neighbors (Christianson 1988). The number of such caregivers, as well as their specific characteristics (age, gender, proximity, etc.) are associated with the use of informal LTC. For instance, Bakx et al. (2015) indicate the spouse’s characteristics as being significant drivers of professional services in the Netherlands. Balia and Brau (2014) account for the distance to the nearest child and find it relevant to the average number of hours of informal care. Selected caregivers’ characteristics can help to define the type of care that is demanded (see Conjecture 4 below). Further, Finlayson (2002) provides a comprehensive and exhaustive list of references for which the relation between the socio-related factors and formal care is investigated. Most of the reviewed studies focus on the number of informal caregivers (typically represented as the number of persons in the household) and various dummy variables, such as living alone, the presence of children, the presence Risks 2019,7, 92 4 of 20 of the spouse in the household and the marital status. For example, Newman et al. (1990) emphasize the relationship of living alone and institutional care. As in the case of informal care, factors like the gender and the age of potential caregivers are also considered. Jette et al. (1995) outline a strong increase in the risk of entering the nursing home, in cases of elderly people with a male caregiver, when compared to those with a female available spouse. Fuino et al. (2019) find that the presence of the partner in the household significantly impacts the usage of professional LTC services. The number of persons in the household incorporates the information whether or not the individual has a potential caregiver, regardless whether this is the spouse or a child. Since at higher ages, children mostly live separately from their elderly parents, we account for the presence of children outside of the household. Conjecture 3. The utilization of formal and informal care strongly depends on the social environment and the household structure. Formal and informal care are negatively, respectively positively, associated with the household size and the presence of children outside household. 2.4. Relationship between Informal and Formal Care The interaction between formal and informal care has been studied extensively during the past decades. The primary question is whether the two types of care are substitutes or complements. Previous empirical studies outline mixed results. Chappell (1985) compares elderly people on their utilization of home-based care using structured interview. The author finds that the relation is complementary and that formal care does not substitute to informal care. Further, Langa et al. (2001b) observe that at-home care services increase the number of hours in case elderly people need greater social support indicating a complementary relationship. Christianson (1988) reports that the increase of formal services has no significant impact on informal care utilization while using an experimental design methodology. Finally, a small substitutional effect has been shown by Pezzin et al. (1996). Another stream of research emphasizes on the importance of accounting for endogeneity of informally provided care which may otherwise lead to inconsistent findings. To cope with this issue, several studies use an instrumental variable approach pioneered by Van Houtven and Norton (2004) and Lo Sasso and Johnson (2002). Information on family structures, in particular children’s characteristics are used as instruments: Charles and Sevak (2005) use the proximity, the gender and the marital status of children, Bolin et al. (2008) employ the age of the oldest child and Bonsang (2009) exploits the proportion of daughters and the distance to the nearest child. In these studies, informal care is found to be to some extent a substitute of formal care. Several recent studies by Weaver and Weaver (2014) ; Kim and Lim (2015); Torbica et al. (2015) and Mommaerts (2018) confirm these results. Most studies implicitly assume the causality of one form of care on the other, for example, the effect of receiving informal care on the utilization of formal care. However, the decision on which type of services to use is made simultaneously and the causal link is not fully obvious. Given the complex nature and in order to address a potential bias, we let formal and informal care correlate rather than explain each other. This is possible in a SEM framework. Conjecture 4. The usage of formal and informal care is positively correlated. 3. Swiss Health Survey Data and Descriptive Statistics 3.1. SHS Dataset We base our analysis on the SHS data provided by the Swiss Federal Statistical Office (2019). 1 The survey is conducted every five years and has started in 1992 yielding six sets of survey data. 1 Detailed information about the survey is available through the website https://www.bfs.admin.ch/bfs/fr/home/ statistiques/sante/enquetes/sgb.html. Risks 2019,7, 92 5 of 20 The overall target population of the survey are individuals aged over 15 years permanently living in an individual household in Switzerland. This means in particular that individuals who live in hospitals or medical-social establishments including long-term care institutions are not eligible for an interview 2 The interview is split into two parts, an interview over-the-phone complemented with a paper- or electronic-based questionnaire. Computer-assisted telephone interviewing, computer-assisted personal interviewing and telephone interviews with a close person are used to ensure the quality of the data. 3 In the framework of the SHS, the health concept and the quality of life depends on four key aspects: the resources and services in the health sector, the lifestyle and health behavior, the natural and man-made environment and the social environment. Therefore, the interviews cover themes such as sociodemographic characteristics (e.g., gender, age, marital status, education, income, household structure), lifestyle and behavioral risks (e.g., tobacco and alcohol consumption, gambling, physical activity), health (e.g., body measurements, chronic diseases, physical disorders) and health services (e.g., medical consultations, hospitalizations). In the present research we use the most recent wave of the survey conducted in 2017. It includes raw responses of 22,134 individuals from the phone-based interviews and 18,832 from the written interviews. The survey data are complemented by a file containing indices that summarize groups of raw questions in a convenient, consistent and condensed format. We merge the survey data and indices together along each unique identifier. In our study, we focus only on respondents aged 65 years or older, since LTC utilization occurs at higher ages (Balia and Brau 2014). Restricting the age to be between 65 and 99 years yields a dataset containing 5114 observations. We remove entries with missing values in the variables used in our study and are finally left with 4026 observations. In the SHS, the level of the dependence is assessed by the number of limitations in ADL, IADL and functional limitations. Five items used for the ADL are similar to the ones used in the original Katz scale (Katz et al. 1963): eating, getting in/out of bed, dressing, toileting and taking a bath or a shower. The SHS scale, however, does not include continence which is used in the Katz scale. Survey participants are asked how they can perform each of these activities within the following answers: “Yes, without difficulty”, “Yes, with some difficulty”, “Yes, with great difficulty” and “Not at all”. In order to convert the categorical answers to numeric ones, we assign to the answers values of 0, 1 / 3, 2 / 3 and 1, respectively. Then, we sum up these values over all the activities and divide by five, the number of items of the scale. The resulting ADL variable takes values between 0 and 1 and not only indicates whether or not a respondent is dependent but also relates, to some extent, to the degree of dependency. In a similar manner we construct two variables linked to the limitations in IADL and to the functional limitations. The instrumental activities studied in the SHS include eight items: meal preparation, calling, shopping, doing laundry, doing light housework, occasionally doing heavy housework, doing accounting and using public transport. The answers are again coded as above and the variable IADL is constructed by averaging over the items. Finally, functional limitations encompass limitations in reading a book or a newspaper, in following a conversation with at least two persons, in walking without assistance and in speaking without difficulty. We construct the FUN variable using these four activities as available from the SHS data. We now discuss the demographic covariates that we consider in our study. In the survey, the age AGE is represented as an integer number of years in 2017, while the gender variable SEX takes two values, male and female. Further, the SHS contains information on the individuals’ monthly income INC in CHF. The value does not include compulsory contributions to social insurance but takes into account supportive payments made or received. The question on education allows for 20 different 2 Given that non-institutionalized individuals are not covered by the survey our sample is not representative of the overall LTC needs at national level and we must be careful when interpreting the results. 3 Elderly persons are often affected by several pathologies (multimorbidity). Despite the process of interviewing in two parts and integrating a close person to ensure the quality of the data, the registered diseases may not be correctly reported, especially in the case of dependent elderly people. This should be kept in mind when considering the results. Risks 2019,7, 92 6 of 20 answers for the highest completed level. These answers are summarized in EDU , a compact variable along three categories including primary, secondary and tertiary education levels. The language of the interview is a good indicator for the linguistic region LNG of the respondent, namely, German-, French- and Italian-speaking areas. Finally, the survey contains information on the height and the weight, making it possible to compute the body mass index (BMI). Typically the distribution of the BMI has a bell shape and absolute deviations from the average BMI are associated with a poorer health status. Therefore, we consider absolute deviations dBMI from the sample mean. To reflect the structure of the household we use the number of persons in the household NPR counting the actual persons living in the same household during more than four days per week. Furthermore, as respondents are asked whether or not they have children outside the household, we include that number in CHO. The set of pathologies contained in the SHS includes the following diseases: asthma AST , arthritis ART , osteoporosis OST , chronic bronchitis BRH , heart attack ATT , stroke STR , cancer CNR (any type) and diabetes DBT . 4 The binary variables representing these diseases have two levels, yes and no, that define whether a respondent has had the illness or problems related to it during the past 12 months. Finally, the utilization of formal FCR and informal ICR care are represented by binary variables indicating whether the respondent has used services over the past 12 months. Formal care encompasses care services provided by professionals at home, including nursing care, help with domestic tasks, personal care, meals-on-wheels and transport services. Informal care covers assistance in physical care, shopping, preparing meals and administrative tasks provided by a spouse or partner, relatives, neighbors or friends. 3.2. Descriptive Statistics In this section, we present descriptive statistics of the respondents’ characteristics. Our first conjecture assumes that the effect of diseases, age and BMI on the utilization of LTC is mediated via dependence. Furthermore, the second and the third conjecture focus on the direct effect of individuals’ demographic characteristics as well as the social environment on the receipt of LTC. Therefore, we split exogenous variables into two subgroups depending on the factor they are affecting directly, that is, dependence or the utilization of LTC. 3.3. Exogenous Characteristics Affecting Dependence Descriptive statistics of the variables that directly affect dependence are given in Table 1. For each variable and value we provide the number of respondents N and their share within the total data set. For the three scales ADL , IADL and FUN that measure the dependence, we report the conditional average dependence value considering only non-zero values. Values of the overall sample conditional means (see bottom of the table) are 0.21, 0.17 and 0.14 for ADL , IADL and FUN , respectively. Among the 4026 respondents, 292 individuals have reported a non-zero value of ADL , while the numbers of individuals reporting non-zero values of IADL respectively FUN are 1165 and 970. The two last numbers are higher, since ADL represent activities that are necessary for fundamental functioning and therefore dependence in ADL implies the dependence in less severe IADL and functional limitations. This is in line with, for example, (Spector et al. 1987) who find that the items “shopping” and “transportation” in IADL show lower levels of dependence when compared to ADL. The three dependence indicators are well related to dependence and highly correlate among each others. Table 2reports information on the correlation and standard deviations of ADL,IADL and FUN. 4 The data does not contain information on mental diseases except for depression. Further, the causal link for depression does not have a clear direction: a respondent can be depressed because of the dependence status, and, conversely, clinical depression can cause dependence. Thus, the absence of questions on mental diseases in the SHS does not allow us to study the role of mental pathologies. Risks 2019,7, 92 7 of 20 Table 1. Descriptive statistics of the exogenous characteristics affecting dependence. Characteristics N(%) ADL IADL FUN Age 65–69 1282 (31.8) 0.20 0.16 0.13 70–74 1145 (28.4) 0.18 0.13 0.13 75–79 832 (20.7) 0.24 0.14 0.14 80–84 483 (12.0) 0.15 0.16 0.14 85–89 218 (5.4) 0.26 0.25 0.20 90–94 53 (1.3) 0.22 0.29 0.19 95–99 13 (0.3) 0.27 0.43 0.19 BMI deviation 0.00–1.30 1004 (24.9) 0.19 0.15 0.15 1.31–2.62 1009 (25.1) 0.25 0.16 0.13 2.63–4.64 1005 (25.0) 0.17 0.14 0.13 4.65+ 1008 (25.0) 0.22 0.20 0.16 Asthma No 3 841 (95.4) 0.20 0.17 0.14 Yes 185 (4.6) 0.23 0.16 0.15 Arthritis No 2615 (65.0) 0.23 0.17 0.13 Yes 1411 (35.0) 0.19 0.16 0.15 Osteoporosis No 3639 (90.4) 0.21 0.16 0.14 Yes 387 (9.6) 0.20 0.19 0.16 Bronchitis No 3833 (95.2) 0.20 0.16 0.14 Yes 193 (4.8) 0.24 0.23 0.17 Heart attack No 3984 (99.0) 0.20 0.17 0.14 Yes 42 (1.0) 0.43 0.22 0.19 Stroke No 3983 (98.9) 0.21 0.16 0.14 Yes 43 (1.1) 0.22 0.35 0.22 Cancer No 3851 (95.7) 0.21 0.17 0.14 Yes 175 (4.3) 0.15 0.16 0.12 Diabetes No 3652 (90.7) 0.20 0.16 0.14 Yes 374 (9.3) 0.23 0.23 0.18 Total 0.21 0.17 0.14 N4026 (100.0) 292 1 165 970 Table 2. Correlation coefficients and standard deviation of the dependence indicator variables. Variables ADL IADL FUN ADL 1.00 0.67 0.49 IADL 0.67 1.00 0.63 FUN 0.49 0.63 1.00 Std. dev. 0.08 0.14 0.08 Risks 2019,7, 92 8 of 20 From Table 1we see that the dataset mainly consists of respondents aged below 85 (93%). We observe that the ADL variable does not show a perfectly increasing trend through age groups, which can be explained by the fact that, with a higher number of limitations, elderly people enter nursing homes and are no more represented in the SHS. Further, we note that the average IADL and FUN values consistently increase with the age. We group respondents according to their absolute deviations from the sample BMI mean. To form four classes we use sample quartiles that are bounded at deviations of 1.31 (25%), 2.63 (50%) and 4.65 (75%). We find that dependency levels increase with BMI deviation. For the group with the largest BMI deviation (4.65 + ), the averages of all scales are larger when compared to the groups with lower deviations. Finally, we cover statistics along selected diseases. Arthritis comes with the highest number of reported dependence cases, which equals to 1411 respondents and corresponds to 35.0% of the sample. In contrast, the lowest proportion of diseased (1.0%) have had a heart attack (42 cases). Nevertheless heart attacks relate to the highest average ADL (0.43). Such differences can be explained by severe rehabilitation measures after myocardial infarction (Witt et al. 2004). Heart attack is followed by bronchitis, asthma, diabetes and stroke. These pathologies also relate to an important increase in limitations among respondents who have reported such diseases when compared to those who have not. Cancer, 5 arthritis and osteoporosis have a lower ADL index. The only diseases that lead to a decreasing IADL are asthma, arthritis and cancer. The highest increase in limitations comes from stroke. The latter often leads to a strong cognitive decline or even dementia associated with limitations in instrumental activities, especially in those that require cognitive abilities (Brainin et al. 2015) . Similar results are obtained for FUN . Both cardio-vascular diseases, heart attack and stroke, yield the highest values of FUN . Overall, we conclude that most diseases substantially increase the dependence indicators. 3.4. Exogenous Characteristics Affecting LTC Usage The second set of exogenous covariates directly relates to LTC usage. In addition to gender, education, income, language group and the household composition, we consider three variables that describe the state of dependence, that is, ADL , IADL and FUN . For each of these exogenous variables we provide the shares of respondents who have reported formal and informal care utilization. These figures are shown in Table 3. Among the 4026 observations, 6.6% of the respondents have used formal care, while more than double of this share (13.7%) have required informal assistance. Table 3. Descriptive statistics of the exogenous characteristics affecting long-term care (LTC) usage. Characteristics N(%) Formal Care Informal Care without (%) with (%) without (%) with (%) Gender Male 1943 (48.3) 95.6 4.4 90.0 10.0 Female 2083 (51.7) 91.5 8.5 82.8 17.2 Education Primary 905 (22.5) 91.0 9.0 83.1 16.9 Secondary 2126 (52.8) 93.7 6.3 86.8 13.2 Tertiary 995 (24.7) 95.1 4.9 87.9 12.1 5 The SHS data does not contain any specific information on the type of cancer except for prostate and breast cancer for male respectively female respondents. Although, for the latter, the data does not indicate which person has a positive test, we know that the number of male (female) respondents having examined for prostate (breast) cancer is 1439 (1644 for a pap test and 1649 for mammography), among which 450 (243) individuals have made this test because of pain or similar symptoms. Risks 2019,7, 92 15 of 20 4.6. Measurement of Dependence We choose to set the scale of the latent variable DEP to its first indicator ADL in Equation (1) . When the level of limitations in ADL is high, this scale assumes higher values of DEP associated with a higher level of dependence. We report factor loadings λ as well as their standard deviation and statistical significance in Table 6. The factor loadings of the other two indicators, the level of limitations with IADL and of functional limitations, have positive and significant values of 2.312 and 0.964, implying that three variables reliably measure the latent construct. 4.7. Regression Model for Dependence We now highlight the regression model (2) for which the dependence DEP is the outcome variable. The β coefficient estimates, standard deviations and their significance are reported in Table 7. Expectedly, the impact of the age on the dependence is significant and positive, meaning that individuals are expected to have higher levels of dependence at higher ages. Indeed, as mentioned before, higher ages trigger the frailty syndrome associated with poorer health implying strong impairment. The deviation from normal BMI is also found to affect the state of dependence significantly. The positive value of the coefficient indicates that for one standard deviation increase of BMI deviation, the value of the hidden variable of dependence will increase by 0.001 standard deviations. Deviations from the normal BMI are typically considered as indication of severe diseases. For instance, a low BMI is a risk factor for fractures (De Laet et al. 2005) while an increased BMI is associated with an increased risk for various types of cancer (Renehan et al. 2008), cardio-vascular diseases (Gregg et al. 2005) and stroke (Kurth et al. 2002 and Rexrode et al. 1997). All considered diseases, except asthma and cancer, have a statistically significant impact on dependence. Headen (1993) finds that asthma decreases the hazard of entering the nursing home by 25% when compared to those who do not have a respiratory disease. At the same time, respiratory diseases increase the hazard of death by the same share. This result allows us to assume that chronic asthma skips the state of dependence leading directly to death of an individual. We note that stroke, bronchitis and heart attack show the highest coefficients. (Tomiak et al. 2000) find that, after mental diseases, stroke is a major factor of entering a nursing home. The aftermath of cardio-vascular diseases is typically associated with a long rehabilitation process among survivors (Torbica et al. 2015) and, therefore, yields higher utilization of care services. We could not find similar results in the case of bronchitis which is typically combined with other chronic obstructive pulmonary diseases (COPD). Since many researchers also consider asthma as COPD and combine it together with bronchitis, it is hard to distinguish the separate effect of these pathologies. Individuals diagnosed with diabetes have a higher risk of becoming dependent. According to Charles and Sevak (2005) who also find diabetes to increase the probability of entering the nursing home, diabetes is associated with a severe condition of heart illness. Further, even though arthritis and osteoporosis are two different diseases with different diagnoses and symptoms, in the context of dependence, they show similar results. Finally, we report that cancer does not affect the dependence status significantly. The explanation is similar to asthma: cancer often leads to earlier death than other diseases and comes with lower needs for care. 4.8. Regression Models for LTC Usage When analyzing the drivers of demand for formal and informal LTC through the γ coefficients statistics reported in Table 8, we observe a significant association with dependence. Further, the link between dependence and care is even stronger for informal care. In line with the consistent observations from previous studies , we find a significant effect of the gender variable: female respondents tend to have a higher probability to receive LTC. This can be explained by the lower mortality and higher frailty at high ages among women. Neither education nor income plays a significant role in explaining the usage of LTC. At first glance, this result might look confusing because both education and income Risks 2019,7, 92 16 of 20 are indicators of socio-economic status and also proxies of the profession. However, the risk related to the earlier profession of the respondent is also likely to be incorporated in the observed chronic diseases that enter the model via dependence. Another possible explanation is that in Switzerland about 65% of costs related to health care are provided by compulsory health insurance and only 20% are paid out-of-pocket (Gentili et al. 2017). This holds true in particular for at-home based care (recipients of institutional care are rather absent from the survey data). Therefore, there may be little incentive to save money by substituting formal care by informal care. The respondents who have completed the survey in French show significantly higher receipt of formal care and lower usage of informal care. This result coincides with the conclusion made by Gentili et al. (2017) who highlight differences in German- and French-speaking parts of Switzerland. Their main finding is that the French-speaking population enters nursing homes only with higher dependence status. As our sample does not contain elderly people residing in nursing homes, respondents of the German-speaking part probably have less severe dependence and therefore trigger less use of formal and informal care. Regional differences in Switzerland can be linked both to cultural aspects and to policy-related incentives from different cantons (see, e.g., Fuino and Wagner 2018,2019). Finally, let us briefly discuss the socio-related variables. The presence of children outside the household does not play a relevant role. We find that a higher number of persons in the household reduces formal care usage. The increase of the number of persons in the household by one standard deviation triggers a significant 0.298 decrease of standard deviation of formal care. At the same time, we observe a significant 0.108 increase in informal care. 8 The opposite signs of these coefficients indicate a substitutional relationship between both types of care: the decrease in formal care is complemented with an increase of informal care delivered by household cohabitants. Overall, however, we observe a positive and significant correlation coefficient of 0.441 between the error terms eFCR and eICR . This result indicates that on a baseline both types of care are complementary. To summarize this section, we can put the observations in relation with our initial conjectures laid out in Section 2. By showing the significant effects of pathologies, age and BMI on dependence as well as the significance of dependence on both formal and informal care, we provide the necessary evidence to support the first conjecture. Further, we partially validate the second hypothesis: the gender and the linguistic regions are proven to significantly affect the usage for LTC. Education and income are not directly significant in the models (5) and (6) . Further, while the presence of children outside of the household does not have a significant effect, the number of persons in the household impacts the demand for both types of LTC. We find that cohabitants act as informal caregivers reducing the demand for formal care. This yields to partial validation of Conjecture 3. Finally, our findings support the hypothesis of a positive correlation between formal and informal LTC. 5. Concluding Remarks In this paper, we investigate and analyze the factors associated with dependence and the usage of formal and informal LTC. We develop a statistical model in a framework of structural equation modeling and calibrate it using the data of 4 026 observations from the Swiss Health Survey. We model the concept of dependence as a latent variable with three manifest indicators, namely limitations in ADL, limitations in IADL and functional limitations. We allow for a direct effect of age, BMI and selected diseases on dependence which, in turn, is affecting the receipt of LTC. In other words, the effect 8 We have tested an alternative specification of our model (Equations (5) and (6)) that includes the interaction term SEX ·NPR between the gender and the number of persons in the household. Although including the interaction term does not improve the goodness-of-fit of the model (see also Footnote 6), it is worthwhile studying if the effect of the number of persons in the household (a proxy for being single or widowed respectively living in a couple) is the same for males and females in terms of need for care. When including the interaction term, several coefficients ( SEX , NPR and SEX ·NPR for informal care) are not significant. Nonetheless, we find that the coefficient of the interaction term is positive and significant for formal care usage. This means that female respondents with male spouses use more formal care. This asymmetric effect can be related to the fact that men are often worse care providers (compare, e.g., with the findings in Fuino et al. 2019). Risks 2019,7, 92 17 of 20 of such factors is mediated via dependence. In addition, we account for socio-demographic factor and factors related to the social environment as drivers of the demand and usage for LTC. Finally, we let formal and informal care correlate to investigate the nature of the relation. Our main findings can be summarized as follows: (1) the indirect effects of age, BMI and pathologies on the receipt of LTC are significant while mediated via the latent dependence variable, (2) socio-demographic and social environment variables as the gender, the linguistic region and the number of persons in the household play a significant role in determining the LTC needs and (3) the relation of formal and informal care can be both substitutional and complementary depending on a context. Our observations underline the importance to incorporate the path analysis instead of directly assuming a causal link between formal and informal care. We also add to the understanding of the relationship between formal and informal care. This paper reinforces the importance of medical characteristics of elderly people, when studying the evolution of needs of impaired elderly people. Finally, our results give indications and insights for the rapid development of new LTC insurance products. The purpose of this paper is to explain selected factors that affect LTC use, rather than to predict the demand for LTC. Therefore, the study is limited in potential applications to the process of insurance products’ pricing, for example, due to the choice of methodology and the bias of the sample. However, our research can serve as a basis for an underwriting questionnaire to assess the risk presented by new customers. Author Contributions: Conceptualization, J.W.; formal analysis, I.R.; writing–original draft preparation, I.R.; writing–review and editing, I.R. and J.W.; supervision, J.W. Funding: Financial support from the Swiss National Science Foundation (grant no. 100018_169662) is gratefully acknowledged. Acknowledgments: Data from the “Enquête suisse sur la santé 1992, 1997, 2002, 2007, 2012 et 2017” has been provided by the Swiss Federal Statistical Office to the authors (contract no. 180239, ref. 624.110-1). Conflicts of Interest: The authors declare no conflict of interest. References Bakx, Pieter, Claudine De Meijer, Frederik Schut, and Eddy van Doorslaer. 2015. Going Formal or Informal, Who Cares? The Influence of Public Long-Term Care Insurance. Health Economics 24: 631–43. [CrossRef] [PubMed] Balia, Silvia, and Rinaldo Brau. 2014. A Country for Old Men? Long-Term Home Care Utilization in Europe. Health Economics 23: 1185–212. 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