Drivers of Old-Age Dependence and Long-Term Care Usage in Switzerland—A Structural Equation Model Approach
Abstract
:1. Introduction
2. Development of Research Hypotheses
2.1. Health Indicators
2.2. Socio-Demographic Indicators
2.3. Social Environment Factors
2.4. Relationship between Informal and Formal Care
3. Swiss Health Survey Data and Descriptive Statistics
3.1. SHS Dataset
3.2. Descriptive Statistics
3.3. Exogenous Characteristics Affecting Dependence
3.4. Exogenous Characteristics Affecting LTC Usage
4. Model Setup, Results and Discussion
4.1. Structural Equation Model
4.2. Measurement of Dependence
4.3. Regression Model for Dependence
4.4. Regression Model for LTC Usage
4.5. Results and Discussion
4.6. Measurement of Dependence
4.7. Regression Model for Dependence
4.8. Regression Models for LTC Usage
5. Concluding Remarks
Author Contributions
Funding
Acknowledgments
Conflicts of Interest
References
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1 | Detailed information about the survey is available through the website https://www.bfs.admin.ch/bfs/fr/home/statistiques/sante/enquetes/sgb.html. |
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. |
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. |
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. |
6 | While developing the models presented below, we have tested alternative specifications, including, for example, the effect of the gender on dependence and the direct effect of the age on LTC usage. None of the tested combinations yields a better goodness-of-fit nor significance of the characteristics. Further, we do not consider interaction terms in order to keep the model simple. |
7 | The values of range from zero to CHF 150,000 yielding a high scale of variance, when compared to the other variables. In order to attain better robustness while calibrating the model, we standardize the income and consider , where and are the sample mean and the sample standard deviation , respectively. |
8 | We have tested an alternative specification of our model (Equations (5) and (6)) that includes the interaction term 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 (, and 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). |
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 | 3841 | (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 | ||
N | 4026 | (100.0) | 292 | 1165 | 970 |
Variables | ADL | IADL | FUN |
---|---|---|---|
1.00 | 0.67 | 0.49 | |
0.67 | 1.00 | 0.63 | |
0.49 | 0.63 | 1.00 | |
Std. dev. | 0.08 | 0.14 | 0.08 |
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 |
Income (in CHF) | ||||||
0–1749 | 995 | (24.7) | 94.0 | 6.0 | 83.5 | 16.5 |
1750–2999 | 1013 | (25.2) | 91.8 | 8.2 | 85.1 | 14.9 |
3000–4999 | 1008 | (25.0) | 94.2 | 5.8 | 87.5 | 12.5 |
5000+ | 1010 | (25.1) | 93.8 | 6.2 | 88.9 | 11.1 |
Language region | ||||||
German | 2778 | (69.0) | 94.3 | 5.7 | 86.5 | 13.5 |
French | 925 | (23.0) | 91.1 | 8.9 | 87.2 | 12.8 |
Italian | 323 | (8.0) | 92.3 | 7.7 | 81.1 | 18.9 |
Number of persons in household | ||||||
1 | 1189 | (29.5) | 87.5 | 12.5 | 84.4 | 15.6 |
2 | 2561 | (63.6) | 95.9 | 4.1 | 87.4 | 12.6 |
276 | (6.9) | 96.0 | 4.0 | 83.3 | 16.7 | |
Children (outside the household) | ||||||
Yes | 3316 | (82.4) | 93.8 | 6.2 | 86.2 | 13.8 |
No | 710 | (17.6) | 91.8 | 8.2 | 86.8 | 13.2 |
ADL scale () | ||||||
0 | 3734 | (92.7) | 95.8 | 4.2 | 89.7 | 10.3 |
0.000–0.249 | 215 | (5.3) | 69.3 | 30.7 | 49.8 | 50.2 |
0.250–0.499 | 49 | (1.2) | 49.0 | 51.0 | 18.4 | 81.6 |
0.500–0.749 | 14 | (0.3) | 35.7 | 64.3 | 0.0 | 100.0 |
0.750–1.000 | 14 | (0.3) | 57.1 | 42.9 | 50.0 | 50.0 |
IADL scale () | ||||||
0 | 2861 | (71.1) | 97.9 | 2.1 | 94.8 | 5.2 |
0.001–0.249 | 935 | (23.2) | 88.6 | 11.4 | 75.6 | 24.4 |
0.250–0.499 | 121 | (3.0) | 65.3 | 34.7 | 34.7 | 65.3 |
0.500–0.749 | 51 | (1.3) | 43.1 | 56.9 | 17.6 | 82.4 |
0.750–1.000 | 58 | (1.4) | 56.9 | 43.1 | 6.9 | 93.1 |
Functional limitations scale () | ||||||
0 | 3056 | (75.9) | 96.1 | 3.9 | 90.5 | 9.5 |
0.001–0.249 | 803 | (19.9) | 89.3 | 10.7 | 80.3 | 19.7 |
0.250–0.499 | 147 | (3.7) | 64.6 | 35.4 | 40.8 | 59.2 |
0.500–0.749 | 15 | (0.4) | 60.0 | 40.0 | 0.0 | 100.0 |
0.750–1.000 | 5 | (0.1) | 60.0 | 40.0 | 20.0 | 80.0 |
Total | 4026 | 100.0 | 93.4 | 6.6 | 86.3 | 13.7 |
Formal Care | |||
---|---|---|---|
Informal Care | No | Yes | Total |
No | 3354 | 119 | 3473 |
Yes | 408 | 145 | 553 |
Total | 3762 | 264 | 4026 |
Variables | Type | Description | Values |
---|---|---|---|
Exogenous | Age | from 65 to 99 | |
Exogenous | Deviations from BMI | from 0 to 42.4 | |
Exogenous | Asthma | yes, no | |
Exogenous | Arthritis | yes, no | |
Exogenous | Osteoporosis | yes, no | |
Exogenous | Bronchitis | yes, no | |
Exogenous | Heart attack | yes, no | |
Exogenous | Stroke | yes, no | |
Exogenous | Cancer | yes, no | |
Exogenous | Diabetes | yes, no | |
Latent | Dependence level | – | |
Manifest | Level of ADL limitations | from 0 to 1 | |
Manifest | Level of IADL limitations | from 0 to 1 | |
Manifest | Level of functional limitations | from 0 to 1 | |
Exogenous | Gender | male, female | |
Exogenous | Education level | primary, secondary, tertiary | |
Exogenous | Standardized monthly income | from 0.8 to 33.1 | |
Exogenous | Language region | German, French, Italian | |
Exogenous | Number of persons in the household | from 0 to 9 | |
Exogenous | Children outside of the household | yes, no | |
Endogenous | Use of formal care | yes, no | |
Endogenous | Use of informal care | yes, no |
Model | Dependence Measurement (1) | ||
---|---|---|---|
Std. dev. | Sig. | ||
1.000 | |||
2.312 | (0.027) | *** | |
0.964 | (0.012) | *** |
Variables | Dependence (2) | ||
---|---|---|---|
Std. dev. | Sig. | ||
Age | |||
0.002 | (0.0001) | *** | |
BMI deviations | |||
0.001 | (0.0003) | *** | |
Asthma (baseline: No) | |||
Yes | 0.000 | (0.0046) | |
Arthritis (baseline: No) | |||
Yes | 0.008 | (0.0021) | *** |
Osteoporosis (baseline: No) | |||
Yes | 0.012 | (0.0028) | *** |
Bronchitis (baseline: No) | |||
Yes | 0.035 | (0.0034) | *** |
Heart attack (baseline: No) | |||
Yes | 0.034 | (0.0055) | *** |
Stroke (baseline: No) | |||
Yes | 0.071 | (0.0047) | *** |
Cancer (baseline: No) | |||
Yes | 0.007 | (0.0049) | |
Diabetes (baseline: No) | |||
Yes | 0.020 | (0.0026) | *** |
Variables | Formal Care (5) | Informal Care (6) | ||||
---|---|---|---|---|---|---|
Std. dev. | Sig. | Std. dev. | Sig. | |||
Dependence | ||||||
6.498 | (0.277) | *** | 8.304 | (0.238) | *** | |
Gender (baseline: Male) | ||||||
Female | 0.232 | (0.079) | ** | 0.350 | (0.059) | *** |
Education (baseline: Primary) | ||||||
Secondary | (0.081) | 0.000 | (0.066) | |||
Tertiary | 0.019 | (0.106) | 0.058 | (0.083) | ||
Income | ||||||
(0.043) | 0.030 | (0.023) | ||||
Language region (baseline: German) | ||||||
French | 0.223 | (0.079) | ** | (0.065) | ||
Italian | 0.051 | (0.127) | 0.133 | (0.091) | ||
Number of persons in household | ||||||
(0.056) | *** | 0.108 | (0.038) | ** | ||
Children (outside household) (baseline: No) | ||||||
Yes | (0.084) | 0.044 | (0.069) |
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Rudnytskyi, I.; Wagner, J. Drivers of Old-Age Dependence and Long-Term Care Usage in Switzerland—A Structural Equation Model Approach. Risks 2019, 7, 92. https://doi.org/10.3390/risks7030092
Rudnytskyi I, Wagner J. Drivers of Old-Age Dependence and Long-Term Care Usage in Switzerland—A Structural Equation Model Approach. Risks. 2019; 7(3):92. https://doi.org/10.3390/risks7030092
Chicago/Turabian StyleRudnytskyi, Iegor, and Joël Wagner. 2019. "Drivers of Old-Age Dependence and Long-Term Care Usage in Switzerland—A Structural Equation Model Approach" Risks 7, no. 3: 92. https://doi.org/10.3390/risks7030092
APA StyleRudnytskyi, I., & Wagner, J. (2019). Drivers of Old-Age Dependence and Long-Term Care Usage in Switzerland—A Structural Equation Model Approach. Risks, 7(3), 92. https://doi.org/10.3390/risks7030092