Background
Health care use has long been an important issue in health policy. Defined as the meeting of supply and demand of health care, health care use includes in particular, hospital stays and physician visits, besides many other goods and services, such as pharmaceuticals or physiotherapy. Providing health care resources requires substantial financial efforts, and knowledge about determinants of health care use is important for health policy in order to manage health care use.
Andersen and Newman [
1] developed a theoretical framework for analyzing determinants of health care use in 1973. Over the years, this behavioral model has been further developed to a version published in 1995 [
2], which distinguishes three categories of
determinants of health care use:
predisposing,
enabling, and
need factors. The Andersen model is a widely used framework to examine determinants of health care use [
3]. Therefore, this conceptual framework was used to select independent variables such as age or employment status.
Predisposing factors include, for example, demographic variables, like age and gender or the social structure. Social structure refers to the manner in which, a person can cope with problems or possess adequate means to solve problems.
Enabling resources are financial and organizational factors that enable persons to use health care services. They are a prerequisite for the use of health care services [
1]. The Andersen behavioral model distinguishes between personal and family resources as well as community-related resources.
Need factors represent perceived or evaluated need for care, i.e. they either are based on the individual view upon his or her own health, or are assessed by a professional or by means of objective measures.
The differentiation into the three components of possible determinants in Andersen’s behavioral model is – beyond scientific reasons – also policy-oriented. Predisposing and enabling factors may influence health care seeking and might hint at inequity in access to health care. Thus, the goal of achieving equitable access to health care means that the influence of predisposing and enabling factors ought to be reduced, with need factors remaining the main reason for using health care services.
Many studies have analyzed health care use based on Andersen’s behavioral model [
3]. It is well known that the actual determinants found in empirical studies are not always related to need factors. For example, for the German health care context, it has been recognized that women tend to use services in the outpatient sector more frequently than men do [
4]. Health care use that is not related to need factors, however, might point to underuse, overuse or misuse of these services [
5]. Therefore, it is essential to identify predisposing and enabling factors affecting health care use.
For the German health care context, some studies have thoroughly described the use of health care services [
6,
7]. Germany’s health care system is characterized by a health insurance system that provides comprehensive protection against health care expenses. About 90% of the population are insured by social statutory health insurance (SHI) funds, while the remaining 10% are covered by private health insurances (PHI). For most members of SHI, the membership is compulsory, which is in particular, the case for employees below a certain income-threshold. Self-employed and employees above the income-threshold may opt for PHI. Contributions to SHI are income-related and independent of health status, whereas the contrary is the case for PHI. Both types of health insurance cover most expenses of inpatient and outpatient treatment, as well as for pharmaceuticals. Patients are guaranteed access to General Practitioners (GP), as well as specialists without further requirements, while hospital care can be used when being referred to by an outpatient physician or in case of an emergency. Patients of a SHI had to pay a small co-payment (€10 per quarter) until 2013 when using outpatient physician services, and €10 per day in a hospital. The waiting time for appointments with outpatient physicians is usually short [
8]. As it is the case with other developed nations, in Germany, most visits to outpatient physicians and hospital stays are caused by the older population [
9]. Accordingly, the older population accounts for a large proportion of health care costs, and the number of older people is likely to increase substantially in the next decades.
Beyond describing health care use in Germany, patterns of health care use have been analyzed comprehensively by various studies [
4,
10‐
12]. Yet, these studies were limited to cross-sectional designs. Consequently, these studies gave important insights into associations between, certain predisposing as well as enabling factors and health care use. However, causal mechanisms between them still need to be investigated. This is important to derive interventional strategies.
There are only a few longitudinal studies investigating the predictors of health care use [
13‐
15]. However, these longitudinal studies mainly used a
static set of baseline predictors to predict subsequent health care use. Thus, it remains an open question how
changes in independent variables affect health care use. Consequently, the aim of the longitudinal study was to investigate how changes in predisposing, enabling, and need factors affect health care use in a representative sample of community-dwelling older persons. This might help to gain insights into the causal relationship between the independent variables and health care use. Knowing the predictors of health care use is important for health policy. It is a main goal of the German health care system to provide universal access to health care and reduce barriers to access. If predisposing factors and enabling resources, rather than need factors were related to health care use, this would point to inadequacy of health care use and inequity in access to health care, which policy makers would have to address. For the target group of individuals in the second half of life aged 40 years and above, little is known about determinants of health care use. However, due to demographic shifts, the number of individuals in old age is likely to increase substantially, emphasizing the importance of this study and the need to focus on elderly adults. Thus, this study might be important for policy-makers to identify approaches to modify health care behavior.
Methods
Sample
Data came from the public release of the German Ageing Survey (DEAS). This is a population-based, representative (national probability sampling) survey of the community-dwelling population aged 40 years and older in Germany. Data was provided by the Research Data Centre of the German Centre of Gerontology (DZA). Baseline recruitment took place in 1996; 10,608 randomly selected inhabitants of Germany above the age of 40 years were asked via mail to participate in the study. Addresses had been taken from registry offices who own data of all inhabitants due to compulsory registration. In the waves 3 and 4, individuals who consented to participate were interviewed face-to-face by trained staff via computer-assisted personal interviewing (CAPI). These interviews covered, for example, general socio-demographic information and many general topics of aging. Following the interview, participants were asked to answer a standardized questionnaire that included more personal topics like psychological factors (e.g., satisfaction with life or self-efficacy) or illnesses. The data used in this study were derived from the subsamples who sent back the questionnaire.
We restricted our analysis to the waves 3 (2008) and 4 (2011), since physical activities were comprehensively assessed only from wave 3 onwards. 8200 individuals participated in the third wave, whereas 4855 individuals participated in the fourth wave. The response rate for the third wave was 38%, and it was 56% in the fourth wave. The survey in 2008 considered a cross-sectional sample as well as panel sample of study participants, whereas the most recent survey in 2011 considered only panel participants. Thus, the sample sizes differed considerably between those two waves. 6205 community-dwelling individuals were interviewed for the first time in the third wave, whereas 1995 individuals had already been interviewed in former waves. For example, reasons for not participating in the fourth wave were that 23% did not participate anymore, 10% could not be contacted, 5% for reasons of health, and 3% had died or moved to another country. More details for the third wave have been reported elsewhere [
16].
Individuals were only included in our regression analysis if they had
changes in the outcome variables between the third and fourth wave, resulting in 1372 individuals (with specialist visits as outcome variable). Please refer to the chapter “Statistical analysis” for more details regarding regression analysis. Further details regarding the sampling frame and the sample composition can also be found elsewhere [
17].
Research carried out on humans in the German Ageing Survey were in compliance with the Helsinki declaration.
Outcome: health care use
Assessed health care use included outpatient physician services and hospital treatment in the past 12 months, measured as:
-
Number of visits to GP, including home visits
-
Number of visits to specialists, including home visits: Internists, gynecologists, ophthalmologists, orthopedists, ear, nose, and throat specialists, neurologists, psychiatrists, dermatologists, urologists, and other specialists (open answer)
-
Number of days in hospital
Individuals reported the frequency of visits to GPs and specialists (“never”, “once”, “2–3 times”, “4–6 times”, “7–12 times”, “more often” (open answer). They were recoded as follows: never = 0; once = 1; 2–3 = 2.5; 4–6 times = 5, 7–12 times = 9.5; more often = 13. The hospital stays were dichotomized (0 = no hospital stay, 1 = hospital stay).
Independent variables
Predisposing factors were included as follows: Age, gender, place of birth (whether Germany or abroad), family status (married, living together with spouse, others (married, living separated from spouse; divorced; widowed; never married)), employment status (working; retired; not employed) and educational level (International Standard Classification of Education (ISCED) [
18]). The ISCED contains the following categories: low (ISCED 0–2: respondents without formal vocational qualification), medium (ISCED 3–4: respondents with vocational training at work or at school, including respondents with a higher general school certificate without professional training) and high (ISCED 5–6: respondents with completed professional development training (professional, master or technical school, university of cooperative education or academies) and respondents with completed university studies (university or university of applied science).
Enabling resources were included as self-rated accessibility of doctors and (log) monthly equivalence income (new Organisation for Economic Co-operation and Development (OECD) equivalence scale) in Euro. The self-rated accessibility was quantified based on the item “There are not enough doctors and pharmacies in the vicinity” (no; yes).
Need factors covered morbidity and subjective health. Morbidity was quantified by using the total number of chronic diseases (adapted from the Charlson Comorbidity Index [
19]) such as cancer, depression, diabetes or osteoporosis. Subjective health was included based on a self-rating scale: 1 = “very good” to 5 = “very bad”. Additionally, lifestyle factors represented need factors, operationalized as physical activity, excess weight and current smoking status (yes; no). Physical activity was quantified by summing the total approximated time per week (in hours and minutes) for (i) endurance sports (e.g., swimming, long-distance running, jogging, cycling), (ii) team sports or games (e.g., handball, soccer, tennis, volleyball, basketball, squash, badminton), and (iii) strength training or combat sports (e.g., weightlifting, bodybuilding, karate, judo, including activities in a gym) [
20]. Excess weight was self-reported and defined according to the World Health Organization (WHO) thresholds for Body Mass Index (BMI): underweight (BMI < 18.5 kg/m
2), normal weight (18.5 kg/m
2 ≤ BMI < 25 kg/m
2), overweight (25 kg/m
2 ≤ BMI < 30 kg/m
2), and obesity (BMI ≥ 30 kg/m
2).
Statistical analysis
Fixed effects (FE) regressions were used to estimate the effect of time-dependent regressors on health care use. When time-constant unobserved factors, such as genetic disposition, are systematically correlated with the predictors, random effects (RE) regressions lead to inconsistent estimates [
21]. Contrarily, FE regressions provide consistent estimates even when time-constant unobserved factors are correlated with the predictors. Consequently, in our case, FE regressions are the method of choice (indicated by Hausman tests).
FE regressions only use intraindividual changes (within-variation). This means that changes within individuals over time were examined. For this reason, the FE estimator is also called ‘within-estimator’. Consequently, solely time-dependent variables (such as income or self-rated health) can be included as predictors in FE regressions. Thus, time-constant variables, such as gender or education, cannot be included as independent variables in FE regressions. However, time-constant can be included in the model as moderator variables. Consequently, in additional models, it was tested whether need factors – proven to be significant in main regression models – were moderated by the predisposing factors gender or education, respectively.
While the predictors of hospital stays (binary) were estimated by using conditional FE logistic regressions, the predictors of GP and specialist visits (count data) were estimated by using FE Poisson regressions. The level of significance was set at α = .05. Statistical analysis was conducted using Stata Release 14 (Stata Corp., College Station, Texas;
http://www.stata.com/new-in-stata).
Results
Sample characteristics
The pooled (wave 3 and wave 4) median for GP visits was 2.5 and the median for specialist visits was 2. 90% of participants had at least one visit to a GP during the 12 months preceding the interview and about 65% used services provided by a specialist. Moreover, about 20% were hospitalized in the last 12 months.
Since we were interested in the intra-individual changes, individuals were only included in the sample if they had changes in the outcome variable between wave 3 and wave 4. Descriptive statistics for individuals included in FE regression analysis with GP visits as outcome variable are depicted in Table
1. As for the time-constant variables (not included in FE regressions as independent variables), the majority was female (52.2%) and had a medium educational level (50.8%). In addition, 86.9% of the individuals were born in Germany.
Table 1
Sample Characteristics for Individuals included in fixed effects regressions (with GP visits as outcome variable, waves 3–4, pooled)
| Low education: N (%) | 173 (7.9) |
Medium education: N (%) | 1112 (50.8) |
High education: N (%) | 906 (41.3) |
Place of birth: Germany: N (%) | 1902 (86.9) |
Predisposing factors | Age (in years): Mean (SD) | 64.3 (11.2) |
Married, living together with spouse: N (%) | 1952 (74.2) |
Working: N (%) | 860 (32.7) |
Retired: N (%) | 1498 (57.0) |
Other: not employed: N (%) | 272 (10.3) |
Enabling resources | Equivalence income: Mean (SD) | 1794.8 (1782.4) |
Self-rated accessibility of doctors = Yes (accessible): N (%) | 2112 (80.3) |
Need factors | Self-rated health (from “very good” to “very bad”): Mean (SD) | 2.4 (0.8) |
Number of chronic diseases: Mean (SD) | 2.4 (1.8) |
Underweight: N (%) | 20 (0.8) |
Normal weight: N (%) | 1029 (39.1) |
Overweight: N (%) | 1069 (40.6) |
Obesity: N (%) | 512 (19.5) |
Currently smoking: N (%) | 366 (13.9%) |
Physical activities (in hours per week): Mean (SD) | 2.9 (3.6) |
Observations | 2630 |
Mean age was 64.3 years (±11.2 years), ranging from 40 to 95 years. The majority were married, living together with their spouse (74.2%), retired (57.0%) and had a mean net income of €1,794.8 (±€1,782.4). 80.3% were satisfied with the accessibility of doctors. The mean self-rated health was 2.4 (±0.8) and the mean number of chronic diseases was 2.4 (±1.8). Furthermore, most of the individuals either had a normal weight (39.1%) or were overweight (40.6%). Only 13.9% were current smokers. The mean duration of physical activities (in hours per week) was 2.9 (±3.6).
The descriptive statistics for individuals included in FE regression analysis with specialist visits as outcome variable was almost the same. However, descriptive statistics for individuals included in FE regression analysis with hospital stay as outcome variable were somewhat different. For example, individuals had a mean self-rated health of 2.7 (±0.9) and a mean number of chronic diseases of 3.1 (±2.0). The median for GP visits was 2.5 and the median for specialist visits was 4.0.
Main regression analysis
Table
2 presents the results of FE-regressions for GP visits and specialist visits. GP visits increased with changes in employment status (predisposing factor) from ‘working’ to ‘retired’ or ‘not employed’. Other predisposing factors as well as enabling resources did not affect GP visits significantly. GP visits increased with decreased self-rated health and the number of chronic diseases (both need factors). Furthermore, while GP visits increased with an increased duration of physical activities, the other lifestyle factors (BMI categories and the current smoking status) did not affect this outcome variable significantly.
Table 2
Predictors of GP and Specialist visits. Results of fixed effects poisson regressions (Waves 3–4)
Predisposing factors | Age (in years) | 0.00576 | −0.00776 |
| (0.00691) | (0.00886) |
Other marital statuses (Ref.: Married, living together with spouse) | −0.146 | −0.0811 |
| (0.0960) | (0.106) |
Retired (Ref.: Working) | 0.165* | −0.0138 |
| (0.0801) | (0.126) |
Other: not employed | 0.181* | 0.0822 |
| (0.0759) | (0.101) |
Enabling resources | (Log) equivalence income | 0.0754 | 0.0993 |
| (0.0544) | (0.0718) |
Self-rated accessibility of doctors (Ref.: No accessibility of doctors) | 0.00891 | 0.0131 |
| (0.0375) | (0.0539) |
Need factors | Self-rated health (from “very good” to “very bad”) | 0.108*** | 0.200*** |
| (0.0268) | (0.0338) |
Number of chronic diseases | 0.0425** | 0.0557** |
| (0.0140) | (0.0174) |
Underweight (Ref.: Normal weight) | −0.147 | 0.0770 |
| (0.177) | (0.217) |
Overweight | −0.0217 | −0.156* |
| (0.0570) | (0.0726) |
Obesity | −0.00928 | −0.238* |
| (0.0778) | (0.113) |
Currently smoking (Ref.: Currently not smoking) | 0.0463 | −0.188 |
| (0.111) | (0.163) |
Physical activities (in hours per week) | 0.0204** | 0.0161+
|
| (0.00739) | (0.00867) |
Observations | 2630 | 2744 |
Number of Individuals | 1315 | 1372 |
Specialist visits also increased with decreased self-rated health and the number of chronic diseases. Additionally, specialist visits increased with changes from ‘normal weight’ to excess weight. None of the predisposing factors nor the enabling resources affected specialist visits significantly.
Table
3 depicts the results of FE-regressions for hospital stay. The probability of hospitalization significantly increased with higher age, changes in the employment status from ‘working’ to’not employed’, and decreased self-rated health. Other predictors did not reach statistical significance.
Table 3
Predictors of hospital stay. Results of conditional fixed effects logistic regressions (Waves 3–4)
Predisposing factors | Age (in years) | 0.909* |
| (0.842–0.982) |
Other marital statuses (Ref.: Married, living together with spouse) | 0.989 |
| (0.315–3.099) |
Retired (Ref.: Working) | 0.739 |
| (0.284–1.928) |
Other: not employed | 2.372* |
| (1.013–5.556) |
Enabling resources | (Log) equivalence income | 0.751 |
| (0.407–1.385) |
Self-rated accessibility of doctors (Ref.: No accessibility of doctors) | 1.042 |
| (0.629–1.726) |
Need factors | Self-rated health (from “very good” to “very bad”) | 1.770*** |
| (1.349–2.322) |
Number of chronic diseases | 1.084 |
| (0.942–1.248) |
Underweight (Ref.: Normal weight) | 0.509 |
| (0.0484–5.350) |
Overweight | 0.929 |
| (0.484–1.786) |
Obesity | 0.878 |
| (0.337–2.284) |
Currently smoking (Ref.: Currently not smoking) | 0.534 |
| (0.169–1.687) |
Physical activities (in hours per week) | 1.042 |
| (0.966–1.124) |
Observations | 770 |
Number of Individuals | 385 |
Pseudo R2
| 0.08 |
Additional models
In additional models (not shown), it was tested whether the effect of need factors found to be significant were moderated by education and sex (i.e. education x self-rated health, education x number of chronic diseases; sex x self-rated health; sex x number of chronic diseases). However, the interaction terms did not reach statistical significance.
Conclusions
While our study investigated the quantitative use of health care, further research is required regarding the access and quality of care. Furthermore, our study investigated outpatient physician and hospital treatment, whereas further research is required regarding the prevention and health promotion.
Our findings underline the importance of need factors for health care use in Germany. This effect did not vary by gender nor educational level. Besides, our analyses show that it is important to take into account time-constant unobserved factors. Virtually none of the predisposing factors nor enabling resources affect health care use. This might indicate that individuals use health care services adequately, i.e. when medically indicated, stressing the meaning of need factors for the German health care system.