BioMed Research International

BioMed Research International / 2017 / Article

Research Article | Open Access

Volume 2017 |Article ID 2615105 | 13 pages | https://doi.org/10.1155/2017/2615105

Patient Payment and Unhealthy Behavior: A Comparison across European Countries

Academic Editor: Nikolaos Siafakas
Received15 Jun 2016
Revised29 Nov 2016
Accepted13 Dec 2016
Published05 Feb 2017

Abstract

Introduction. Prior research has documented that unhealthy behaviors result in greater health care use and greater health care costs. However, there are few studies on out-of-pocket expenditure paid by those engaging in unhealthy behaviors. We provide cross-country evidence on the association of smoking, alcohol consumption, and obesity with health care use and health care cost as well as out-of-pocket payments among the elderly in Europe. Method. Using SHARE dataset for 13 European countries, the study uses a sequential logit model to analyze use and payments for outpatient and inpatient health care service in addition to a two-part model for the analysis of use and payments for prescribed drugs. Results. Former smoking is associated with a higher rate of health care use. However, current smoking is associated with lower health care use. Former smoking is also associated with paying higher amount of out-of-pocket payments. Alcohol consumption is associated with lower health care use. Conclusion. We do not find systematic evidence that unhealthy behaviors among elderly (50+) are associated with more utilization of health care and more out-of-pocket payments. The results can be of interest for policies that aim to make people more responsible toward their health behaviors.

1. Introduction

Prior research has documented that unhealthy behaviors result in greater health care use and consequently greater health care costs [14]. In a solidarity-based health care system, like in most European countries, the burden of the costs is incurred by all individuals (citizens), who contribute to the system funding and not only by those who cause the costs. This issue has been criticized, as it seems unfair that the costs of a choice for an unhealthy behavior are paid for by those who have chosen a healthy lifestyle [5]. Thus, there is an ongoing debate about incorporating some elements of individual responsibility in the health care system and providing incentives for a better health-related behavior [6, 7]. For instance, some studies suggest a carrot and stick approach for this purpose: carrot like the discount in premium for those who have a healthier lifestyle and a stick like a copayment for those who have a medical problem due to their unhealthy lifestyle. There are also some other suggestions like waiting list for those who have an unhealthy lifestyle [8].

This debate is based on the expectation that unhealthy behaviors increase use and costs of health care. Many studies have investigated the correctness of this assumption [917]. Regarding smoking, previous studies [911] have shown that current smokers use outpatient care less often than never smokers, but, if anything, the number of physician visits is higher for current smokers than for never smokers. Regarding inpatient care, previous studies report more hospitalization either by current smokers [9] or by former smokers [11] or only among female former smokers [10] compared to never smokers. Some studies have looked at other health-related behaviors too, though there are fewer studies on this than on the effect of smoking. For instance, one study [10] has shown that obesity and overweight are strongly related to a higher probability of outpatient visits in both genders and with the probability of hospitalization in women. Another study [17] has found that, among 50- to 84-year-old women in England, around one in eight hospital admissions are likely to be attributable to overweight or obesity, which means 420,000 extra hospital admissions and two million extra days spent in hospital annually.

Many studies have shown that smoking, obesity, overweight, and alcohol consumption lead to more medical expenditure at least in the short run [9, 12, 13, 18]. However, there is no such consensus about the long-run effects of unhealthy behaviors on health care costs, particularly in case of smoking. Some studies indicate that the extra costs caused by smokers are compensated for by their higher mortality rate at earlier stages of life than nonsmokers, concluding that smoking cessation would lead to increased health care costs due to nonsmoker longevity [12, 19]. In contrast, other studies show that in spite of smokers having a shorter lifespan than nonsmoker, the total lifetime medical expenditure is still higher among smokers, and it increases with the amount of smoking [20].

Most previous studies have commonly used a top-down approach using data at the population level. At the same time, these studies have mostly looked at the cost of unhealthy behaviors from a societal perspective. There are few studies investigating out-of-pocket expenditure paid by those who engaged in unhealthy behaviors. If it can be shown that those who engage in unhealthy behavior and use the health care system more often also pay more out of their pocket, it means that they have already paid some of their way.

The aim of our study is to address the following two questions: first, whether or not an unhealthy lifestyle is associated with more utilization of health care and, second, whether the extra cost, if any, is paid out-of-pocket by those who engage in an unhealthy behavior or is paid out of a collective pocket. In this study, we look at daily smoking, body mass index (BMI), and heavy drinking to proxy health-related behaviors. We use individual-level data on the utilization of outpatient and inpatient care as well as data on the utilization of medication from 13 European countries. Specifically, we use data from the second wave of the Survey of Health, Aging and Retirement in Europe (SHARE), which targets the elderly population (aged 50+). A sequential logit model is used for the analysis of use and payments for outpatient and inpatient health care service in addition to a two-part model for the analysis of use and payments for prescribed drugs. We contribute to previous research by providing evidence on the internal medical costs of unhealthy behavior among elderly in a broad range of European countries. The results can be of interest for developing policies that aim to make people more responsible toward their health behavior.

2. Methods

2.1. Study Population

To study the association of health-related behavior with health care use and payments among the elderly in European countries, we use data from the second wave of the Survey on Health, Aging and Retirement in Europe (SHARE, release 2.5.0). SHARE is a multidisciplinary and cross-national panel dataset with microlevel data on health, socioeconomic status, and social and family networks. The survey is conducted every two years, starting from 2004, using nationally representative samples of individuals aged 50 or over in Europe. The most recent wave for our analysis was the second wave of SHARE which included our questions of interest about out-of-pocket expenditure while including more countries than the first wave. The second wave was conducted in 2006-2007 in 14 European countries, namely, Austria, Germany, Sweden, Netherlands, Spain, Italy, France, Denmark, Greece, Switzerland, Belgium, Czech Republic, Poland, and Ireland [21]. We exclude Ireland because the imputed variables generated by SHARE were not available for that country. Thus, overall, our sample size consists of 33281 respondents from 13 countries.

2.2. Out-of-Pocket Expenditures

In the second wave of SHARE, one part of the questionnaire was devoted to out-of-pocket expenses where the following question was asked: “not counting your health insurance premium or reimbursements from employers, about how much did you pay out-of-pocket for your outpatient care in last twelve months?” The same question was asked for inpatient care and prescribed drugs. The respondents were instructed to consider out-of-pocket payments as every expense that is not covered by their health insurance:(i)For outpatient care: all expenses for consultations for all health professionals including dentists, for all labs, exam, or therapies prescribed by doctor, and for outpatient surgery(ii)For inpatient care: all expenses for staying in medical, surgical, psychiatric or in any other specialized wards(iii)For their prescribed drugs not including self-medication or drugs not prescribed by a doctor. For these variables, we use imputed values generated by SHARE. For each variable, five imputed values were provided. The median of the imputed values was used. All amounts in local currencies were converted to Euro using nominal exchange rate corresponding to the year of interview.

2.3. Analytical Model

Out-of-pocket payments are an outcome of a consecutive process. At the first sequence, the service should be used, then the payment should be made, and subsequently the amount of payment should be determined. Therefore, a sequential logit model was used to study the association between health behaviors (i.e., smoking, alcohol consumption, and obesity) and the odds ratios of passing through each transition from utilization of outpatient and inpatient care to the size of out-of-pocket payments for these services (Figure 1). For prescribed drugs, we were not able to distinguish between the service use and the payment for that service as we did for outpatient and inpatient care. Thus, a two-part model was used to model the probability of payment at first and then the amount of payment for those who paid (Figure 2). Three models were estimated: for outpatient care, for inpatient care, and for prescribed drugs. The analyses were done jointly for all countries as well as separately for each country.

2.4. Health Behaviors

Indicators of smoking, alcohol consumption, and preobesity (overweight) or obesity were included in the models as independent variables. For smoking, current and former daily smokers were compared with those who had never smoked daily for a period of at least one year (reference group). Alcohol consumption was assessed using the question about the frequency of excessive alcohol use in the last three months. Excessive alcohol use was defined as drinking more than two glasses of any alcoholic beverage (e.g., beer, cider, wine, spirits, or cocktails) almost every day or five or six times a week. Respondents were categorized into three groups: excessive alcohol users, not excessive alcohol users, and those who had never drank in the past 3 months (reference group). Weight problems were measured on the basis of body mass index (BMI) calculated from self-reported height and weight. Respondents were classified into three categories: obese (), preobese (), and those with (reference group). It should be noted that obesity and overweight are not health behaviors but the results of health-related behaviors such as nutrition and physical activity. In this study, we use them to proxy those health-related behaviors.

2.5. Other Explanatory Variables

All models controlled for age, gender, living with spouse or partner, years of education, household size, annual household net income quartile, the number of chronic diseases, the number of symptoms, and a dummy variable indicating the self-perceived health status (very good or excellent and less than very good or excellent). Previous studies have shown that risk tolerance is a significant predictor of unhealthy behavior such as smoking and drinking [22]. At the same time, risk aversion can affect health care utilization of those who engaged in unhealthy behaviors. So the model should include a measure of risk aversion. SHARE asks the individual when deciding about making an investment if they are willing to take a substantial, above average, or average financial risk to earn substantial, above average, or average return, or they are not willing to take any financial risk at all. We use the answer to this question as a proxy for risk aversion. However, the reference group is those who said that this question is not applicable to them to represent the most risk adverse individuals.

3. Results

3.1. Descriptive Statistics

The sample characteristics are presented in Table 1. It should be noted that all results presented below refer to the elderly population (aged 50+). Of all participants, 20% are current daily smokers, 28% former daily smokers, and 52% never smokers. Greece and France have the highest and the lowest prevalence of current smokers (29% and 14%, resp.). Netherlands and Greece have the highest and the lowest proportion of former smokers (40% and 17%, resp.). Austria and Netherlands have the highest and the lowest proportion of never smokers (65% and 37%, resp.). Of all participants, nearly 7% are heavy drinkers, 62% occasional drinkers, and 31% never drinkers. The highest prevalence of heavy drinking is reported in Netherlands and Belgium (11%), and the lowest prevalence is in Sweden (1.5%). Denmark and Sweden have the highest proportion of occasional drinkers (about 81%), and Spain has the lowest proportion (37%). Spain has the highest proportion of never drinkers (57%), and Denmark has the lowest one (8%). Weight problems appear the most prevalent unhealthy behavior as only 37% of all respondents report a normal weight. About 19% of all respondents are obese and 42% are preobese (overweight). Obesity is most prevalent in Poland and Spain (nearly 25% of the participants in these countries). Preobesity is most prevalent in Greece (47.5%). Switzerland has the highest number of individuals whose weight is in the normal range while Spain has the lowest number (47.5% versus 27%).


AustriaBelgiumCzech RepublicDenmarkFranceGermanyGreeceItalyNetherlandPolandSpainSwedenSwitzerlandTotal

Daily smoking
Current (%)15.417.521.226.413.816.828.917.022.325.715.414.518.819.8
Former (%)20.231.318.835.228.827.017.126.040.428.421.236.125.227.9
Never (%)64.451.360.038.457.456.254.157.037.345.963.446.456.052.3
119931392801257029222529323129692605244822002713145032776

Alcohol consumption
Heavy (%)3.510.84.010.210.24.75.18.610.81.75.61.46.96.6
Light (%)65.168.155.781.663.672.952.745.765.550.537.181.078.962.3
Not at all (%)31.321.240.48.226.322.442.245.723.747.957.317.614.331.1
133131382797255829052539323629752619244822172705144532913

Body Mass Index
24.518.924.214.616.017.720.218.514.925.725.016.012.819.0
38.440.546.838.338.144.847.544.541.441.047.439.437.142.3
35.038.328.244.843.836.731.736.142.531.926.642.147.437.2
2.22.20.72.42.20.80.60.91.31.41.12.52.71.5
116030702769254928402491321529122526242520162682143332088

Outpatient OOP
Mean33017684392247137322605378123489273698320
SD5466011559505424796191432788248974263120561477
1781866246923391147714501010446158207230690011558

Inpatient OOP
Mean2296237591847615918781639233127934761128509
SD485222794780127926232523125290186143812030271763
235407345125330134461240243311101833

Prescribed drug OOP
Mean19928261242116115185260135333166179204200
SD2913951053422012163031215213291441421544551226
10702590218417099121825222519733761958608203075620216

Out-of-pocket payment if made.

The annual out-of-pocket payments, if any, for outpatient care, inpatient care, and prescribed drugs are, on average, 320, 509, and 200, respectively. The highest amount of out-of-pocket payments for outpatient care are reported in Switzerland (700), for inpatient care in Greece (1878), and for prescribed drugs in Poland (333). The lowest amounts of out-of-pocket payments for these three services are reported in the Czech Republic. The sample characteristics regarding sociodemographic and health status are presented in Appendix A1 in Supplementary Material available online at https://doi.org/10.1155/2017/2615105.

3.2. Outpatient Out-of-Pocket Payments

Table 2 presents the results of a sequential logit for out-of-pocket payments for outpatient care. As mentioned in Methods, the model estimates the odds ratio of passing three transitions: whether or not the service is used, whether or not an out-of-pocket payment is made, and whether a high or a low amount is paid.


Either used or not Either paid or not Either paid high or low amount
OR 95% CIOR 95% CIOR 95% CI
LULULU

Daily smoking
Current smoker0.720.660.790.930.861.011.010.901.14
Former smoker1.151.051.261.070.991.151.101.0021.21
Alcohol consumption
Not excessive alcohol use0.970.891.061.171.091.260.940.841.04
Excessive alcohol use0.730.630.851.020.901.171.020.841.24
Body mass index
Preobese1.060.981.151.020.951.090.980.891.07
Obese0.980.881.090.930.851.011.030.911.15
Age1.011.011.010.990.991.001.001.001.01
Female1.361.261.471.171.101.261.111.021.22
Living with partner0.940.891.000.950.911.001.030.961.10
Years of education1.021.011.031.031.021.041.031.021.04
Willingness to take financial risk
Substantial financial risk1.170.811.710.860.601.221.220.771.93
Above average financial risk1.251.031.501.381.131.681.160.961.40
The average financial risk1.120.991.261.331.201.471.211.061.38
Not willing to take any risk1.040.951.131.050.971.131.000.901.10
Household size0.910.880.950.950.920.990.980.931.04
Annual household net income
1th quartile0.800.710.890.960.871.060.800.690.91
2th quartile0.990.891.101.131.031.240.850.760.96
3th quartile1.080.981.191.060.971.150.910.811.01
Household net asset
1th quartile0.880.790.980.840.760.920.870.760.98
2th quartile0.970.881.070.930.851.010.910.811.02
3th quartile0.970.881.071.020.941.110.990.881.10
Self-perceived health1.601.481.731.070.991.161.351.231.49
Number of chronic diseases1.971.872.071.000.971.031.141.091.18
Number of symptoms1.271.231.321.021.001.041.101.071.13
Country dummies
Austria1.881.482.380.810.651.011.380.912.08
Belgium2.872.403.447.186.268.241.010.801.26
Czech republic2.291.922.740.440.370.520.880.631.23
Denmark1.110.951.302.271.962.631.100.851.42
France3.983.254.870.690.590.810.940.701.26
Germany2.221.862.666.885.967.942.131.682.70
Greece1.060.911.235.474.746.321.180.931.51
Italy1.571.331.853.022.613.481.050.821.35
Poland0.650.550.760.390.310.471.070.721.60
Spain1.651.371.990.600.490.731.290.891.89
Sweden0.810.700.9558.5491.011.000.801.26
Switzerland1.501.251.809.608.1011.371.270.991.64

With reference to Netherlands.
Significant at 1% level, significant at 5% level, and significant at 10% level.

As depicted in Table 2, current smokers are less likely (OR = 0.72) than never smokers to use outpatient care. However, there is no statistically significant difference between current smokers and never smokers in terms of out-of-pocket payments. Former smokers are more likely (OR = 1.15) than never smokers to use outpatient care. They are also more likely (OR = 1.10) to pay higher amounts than never smokers if any out-of-pocket payment is made, although the odds ratio of out-of-pocket payments is not statistically significant. Heavy drinkers are less likely (OR = 0.73) than never drinkers to use outpatient care. For out-of-pocket payments, the results do not show statistically significant differences between heavy drinkers and never drinkers. However, light drinkers are more likely (OR = 1.17) to pay out of pocket in case of using outpatient care, although they are as likely as never drinkers to use outpatient care. The findings related to obesity and preobesity do not appear statistically significant.

3.3. Inpatient Out-of-Pocket Payments

Table 3 presents, in turn, the odds ratios for whether or not an inpatient care service is used, for whether or not an out-of-pocket payment is made, and for whether a high or a low amount is paid.


Either used or not Either paid or not Either paid high or low amount
OR 95% CIOR 95% CIOR 95% CI
LULULU

Daily smoking
Current1.010.911.120.850.651.111.060.771.44
Former1.371.271.490.940.761.161.030.821.30
Alcohol consumption
Not excessive alcohol use0.700.650.761.140.941.380.930.741.17
Excessive alcohol use0.530.450.621.380.902.131.040.631.72
Body mass index
Preobese0.880.820.950.990.821.210.950.771.19
Obese0.900.820.990.860.681.090.930.711.23
Age1.011.011.011.000.991.011.011.001.02
Female0.780.730.850.990.811.210.840.681.04
Living with partner1.020.971.070.990.861.121.000.861.17
Years of education1.011.001.021.031.001.060.980.961.01
Willingness to take financial risk
Substantial financial risk0.770.491.213.581.1111.560.520.141.86
Above average financial risk1.000.811.241.210.592.481.220.722.06
The average financial risk0.990.871.111.270.931.751.020.711.45
Not willing to take any risk0.980.901.071.140.921.420.790.621.01
Household size0.970.941.011.010.911.121.110.951.29
Annual household net income
1th quartile0.950.841.061.020.761.380.900.641.26
2th quartile0.990.901.101.160.881.520.730.530.99
3th quartile1.050.951.161.290.991.670.810.601.09
Household net asset
1th quartile0.980.891.090.920.711.200.930.691.26
2th quartile0.920.831.021.070.821.390.830.621.11
3th quartile0.900.821.001.000.771.300.820.611.11
Self-perceived health2.091.882.321.010.761.351.100.821.49
Number of chronic diseases1.221.191.261.010.941.081.081.001.17
Number of symptoms1.171.151.201.051.001.100.990.941.04
Country dummies
Austria2.452.013.0078.6040.63152.050.840.252.77
Belgium1.361.151.6090.0248.24168.000.810.252.62
Czech republic1.180.991.391.810.913.600.940.243.66
Denmark1.231.031.480.250.080.802.190.1728.10
France1.140.961.359.154.9217.040.970.293.27
Germany1.411.191.6882.5143.81155.410.920.282.98
Greece0.570.470.6933.0117.2663.141.010.303.42
Italy1.020.861.212.931.505.740.690.192.55
Poland1.010.851.212.461.254.870.670.182.55
Spain0.930.771.132.191.034.660.800.193.42
Sweden1.120.931.34354.04168.46744.080.790.242.58
Switzerland1.401.131.7335.7718.4869.240.900.273.05

With reference to Netherlands.
Significant at 1% level, significant at 5% level, and significant at 10% level.

Former smokers are more likely (OR = 1.37) to use inpatient care than never smokers but when they use the service, there is no statistically significant difference in terms of out-of-pocket payments. In contrast, both heavy and light drinkers are less likely (OR = 0.53 and OR = 0.70, resp.) than never drinker to be hospitalized. Obese and preobese individuals are also less likely (OR = 0.88 and OR = 0.90, resp.) to use inpatient care than those with normal weight.

3.4. Prescribed Drug Out-of-Pocket Payment

Table 4 presents the results of the two-part model for the out-of-pocket payments for prescribed drugs. As previously noted, the first part is a logit model estimating the odds of out-of-pocket payment. The second part is a log-linear model estimating the amount of out-of-pocket payments.


First part: logit
Either paid or not
Second part: log transformation
The amount of OOP (log)
OR95% CICoeff95% CI
LULU

Daily smoking
Current smoker0.810.750.870.00
Former smoker1.040.971.110.06
Alcohol consumption
Not excessive alcohol use1.040.971.11−0.21
Excessive alcohol use0.950.851.08−0.26
BMI
Preobese1.101.041.170.02
Obese1.081.001.170.07
Age1.001.001.010.01
Female1.331.251.410.03
Living with partner0.940.900.98−0.02
Years of education1.031.031.040.01
Willingness to take financial risk
Substantial financial risk1.270.931.730.05
Above average financial risk1.181.011.37
The average financial risk1.241.131.36
Not willing to take any risk1.151.081.23
Household size0.940.910.97
Annual household net income
1th quartile0.910.830.99
2th quartile1.010.931.09
3th quartile1.091.011.17
Household net asset
1th quartile1.050.971.14
2th quartile1.101.021.19
3th quartile1.070.991.15
Self-perceived health1.921.802.05
Number of chronic diseases1.471.421.51
Number of symptoms1.121.101.14
Country dummies
Austria31.1925.4838.18
Belgium31.7027.2136.93
Czech republic21.4618.4424.98
Denmark14.8012.7517.18
Germany16.2313.9818.85
Greece18.8016.2421.75
France2.301.992.66
Italy12.9211.1614.95
Poland22.8019.3926.81
Spain2.171.852.55
Sweden22.2119.0525.90
Switzerland9.127.7510.730.750.620.88
Constant2.902.703.10

With reference to Netherlands.
Significant at 1% level, significant at 5% level, and significant at 10% level.

Current smokers are less likely than never smokers (OR = 0.81) to make out-of-pocket payments for prescribed drugs. The odds of an out-of-pocket payment are not statistically different for former and never smokers. However, former smokers, on average, incur 6% more out-of-pocket expenditure than never smokers. The odds of out-of-pocket payments are not statistically significant for heavy and light drinkers; but if any payment has been made, light and heavy drinking are associated with 21% and 26% decrease in the amount of out-of-pocket payments compared with never drinkers. Those who are preobese are more likely to pay out of pocket for prescribed drugs. However, it has no effect on the amount of payment. In contrast, being obese is not related to the odds of an out-of-pocket payment, while, in case of payment size, it increases the amount of out-of-pocket payments by 7%.

In order to show the bivariate associations of smoking, alcohol consumption, and obesity with our dependent variables of interest, we have also reported the results of uncontrolled models as Appendixes A2–A4.

3.5. Per-Country Analysis

The stratified regression analysis by country shows some differences compared with the aggregate results presented above. Specifically, as depicted in Table 5, light drinkers in Germany (OR = 0.61), Spain (OR = 0.71), and Poland (OR = 0.68) are less likely, but those who in Greece (OR = 1.39) are more likely to use outpatient care than never drinkers. In France and Poland, overweight appears to be associated with higher odds (OR = 1.83 and OR = 1.47, resp.) of outpatient care use. However, in Sweden, obesity is associated with lower odds of outpatient care use (OR = 0.62). In contrast, in Poland, it is associated with higher odds of outpatient care use (OR = 0.62). The results of per-country analysis also show that current smokers in Austria, Netherlands, and Poland are less likely (OR = 0.37, OR = 0.71, and OR = 0.58, resp.) and former smokers in Italy and Belgium are more likely (OR = 1.36 and OR = 1.24, resp.) to pay out-of-pocket for outpatient care. In Netherlands and Switzerland, heavy drinkers are more likely (OR = 1.73 and OR =2.62, resp.) to pay out-of-pocket, while, in Greece and Belgium, they are less likely (OR = 0.58 and OR = 0.73, resp.) to do so. In Sweden and Belgium, preobesity is associated with higher odds of out-of-pocket payment for outpatient care (OR = 1.62 and OR = 1.37, resp.). However, in Italy, Denmark, and Czech Republic, obesity is associated with lower odds of out-of-pocket payment (OR = 0.77, OR = 0.75, and OR = 0.66, resp.). In contrast, in Belgium, it is associated with higher odds of out-of-pocket payments (OR = 1.37). Regarding the amount of out-of-pocket payments, the only statistically significant results are observed in Denmark for former smokers (OR = 1.53) and in Switzerland for light drinkers (OR = 0.59).


CountryATBECHCZDEDKESFRGRITNLPLSE
OROROROROROROROROROROROROR

Service use (0 = no; 1 = yes)
Current smoker0.840.870.770.980.800.82
Former smoker1.230.931.011.121.171.061.311.081.150.84
Light alcohol consumption1.361.200.750.830.911.241.091.060.90
Heavy alcohol consumption1.960.840.540.960.841.040.911.17
Preobese0.971.060.851.261.011.000.991.001.220.900.85
Obese0.841.041.041.071.350.721.590.871.091.02
Payments (0 = no; 1 = yes)
Current smoker1.031.310.891.151.161.240.980.920.89
Former smoker0.771.091.251.151.051.381.050.900.840.82
Light alcohol consumption0.810.861.221.001.270.931.231.28
Heavy alcohol consumption0.731.481.061.070.930.901.171.222.16
Preobese1.011.110.821.061.141.021.040.920.89
Obese1.040.860.960.920.960.980.961.13
High payments (0 = no; 1 = yes)
Current smoker0.870.880.941.930.791.181.260.801.201.060.841.071.22
Former smoker1.290.881.091.431.072.091.171.110.871.080.901.16
Light alcohol consumption1.330.921.140.780.980.820.861.081.041.12
Heavy alcohol consumption0.361.180.823.121.090.812.791.121.080.661.491.11
Preobese0.920.910.940.991.330.891.191.001.232.211.11
Obese0.870.931.271.101.610.990.96

Significant at 1% level, significant at 5% level, and significant at 10% level.
AT, Austria; BE, Belgium; CH, Switzerland; CZ, Czech Republic; DE, Germany; DK, Denmark; ES, Spain; FR, France; GR, Greece; IT, Italy; NL, Netherlands; PL, Poland; SE, Sweden.

We also observe some country-specific results regarding prescribed drugs. As depicted in Table 6, the odds of out-of-pocket payments for prescribed drugs appear to be statistically significant not only for smoking and being preobese but also for other behaviors in some countries. Specifically, in Greece and Switzerland, light drinking is associated with higher odds (OR = 1.22 and OR=1.61, resp.) but in Czech Republic and Poland with lower odds (OR = 0.70) of out-of-pocket payments for prescribed drugs. In Greece, Belgium, and Poland, obesity is associated with higher odds of out-of-pocket payments for prescribed drugs (OR = 1.31, OR = 1.54, and OR = 1.72, resp.). Regarding the amounts of out-of-pocket payments for prescribed drugs, the results are not statistically significant for current smoking and obesity in aggregate models. However, as depicted in the second part of Table 6, in Germany, current smoking is associated with a higher amount of out-of-pocket payments. In Germany and Poland, preobesity is associated with a higher amount of out-of-pocket payments, while, in Spain, it is associated with lower amount of out-of-pocket payments.


ATBECHCZDEDKESFRGRITNLPLSE

Payments (0 = no; 1 = yes)OROROROROROROROROROROROROR

Current smoker
Former smoker
Light alcohol consumption
Heavy alcohol consumption
Pre-obese
Obese

Linear log transformationCoefCoefCoefCoefCoefCoefCoefCoefCoefCoefCoefCoefCoef

Current smoker
Former smoker
Light alcohol consumption
Heavy alcohol consumption