Health literacy and outpatient physician visits: An analysis of the Swiss health survey

Transcription

1 Health literacy and outpatient physician visits: An analysis of the Swiss health survey WIG Working Paper, Mark Pletscher January 10, 2013 Abstract Health literacy has become an increasingly important topic in public health. It has been shown that limited health literacy is associated with poor health and high health care cost. Economic theory suggests that health literacy is also related to health care utilization. While there are quite a few studies on the relationship between health literacy and the use of hospital services and preventive interventions, outpatient physician visits are less well covered in the literature. The objective of this study is to test whether health literacy is connected to the utilization of outpatient physician services. Five count data models are assessed by means of three goodness of fit measures and a test for overdispersion. The final LOG-ZTNB model shows no significant association between health literacy and the probability to have seen a physician of any kind. The number of specialist visits, however, is highest for people with very good health literacy who have been in specialist care. Well educated people have fewer GP visits when they are in GP treatment but are more likely to have seen a specialist. These results are in opposition to predictions of the Grossman model or the theory of supplier induced demand. JEL-Classification: I11, I18, C21 Keywords: Health literacy, outpatient service utilization, hurdle model, Swiss health survey Zurich University of Applied Sciences, Winterthur Institute for Health Economics, St. Georgenstrasse 70, CH-8400 Winterthur, Switzerland. mark.pletscher@zhaw.ch. Phone: Fax: I am grateful to Prof. Dr. Claude Jeanrenaud for his support and to participants of the ECHE 2012 conference for their helpful comments. 1

2 1 Introduction Health literacy has become an important public health topic as it has been identified to be an important contributor to health inequalities (Kickbusch, 2002). Health literacy can be defined as "the capacity to obtain, interpret and understand basic health information and services and the competence to use such information and services to enhance health" (United States Department of Health and Human Services, 2000). Limited health literacy is not just a problem of developing countries or of the very poor but is also quite prevalent in industrialized countries. A review of 85 studies conducted in the U.S. found an average reported rate of limited health literacy of 26% (Paasche-Orlow et al., 2005a). Recent results indicate that limited health literacy is also quite prevalent in Switzerland. 25% of participants in a health literacy survey reported difficulties in understanding health related information in the media (ISPMZ, 2006). 16% of participants in an UNIVOX survey thought AIDS could be cured and only one third of respondents could deduct the correct dosage schedule of aspirin from a patient information leaflet (Neck-Häberli et al., 2008). Limited health literacy is associated with poor health outcomes (Berkman et al., 2004; DeWalt et al., 2004; Nielsen- Bohlman et al., 2004) and higher health care costs (Eichler et al., 2009; Spycher, 2006; Vernon et al., 2007; Berkman et al., 2004). One possible explanation for these inequalities is different health service utilization. It has been shown that people with limited health literacy use diagnostic procedures less frequently, have lower immunization rates, are more likely to be admitted to emergency departments, and have more inpatient hospital stays. Outpatient physician visits, however, are not well covered in the literature (Baker et al., 2004) and most studies analyzing service utilization are limited to very specific patient groups. The objective of this paper is to investigate the relationship between health literacy and outpatient physician visits based on data from a Swiss population survey. This paper is structured as follows: Previous results on the association between health literacy and health care utilization are summarized in section 2, and hypotheses on outpatient physician visits are formulated in section 3. In section 4 the dataset is described and possible candidates for control variables are identified. In section 5 the econometric model and the set of covariates are selected and specified in order to provide good model fit. Finally, the esimation results are presented and discussed in sections 6 and 7. 2

3 2 Literature Most evidence on the relationship between health literacy and service utilization covers hospitalizations, emergency department visits and preventive interventions. A systematic review by Berkman et al. (2011) gives a good summary of studies on service utilization in these domains. The review contains only one study addressing outpatient physician visits in the U.S. (Baker et al., 2004), which did not find any correlation between health literacy and the utilization of outpatient physician care. However, the study is not representative for Switzerland as it was conducted among a sample of medicare managed care enrollees and only covered certain types of physicians. 2.1 Hospitalization and emergency department rates A study among new Medicaid enrollees in 4 managed care sites in the U.S. showed that they were more likely to have used emergency room care if they had inadequate health literacy in the Short Test of Functional Health Literacy in Adults (STOFHLA) (Howard et al., 2005). Those patients consequently had higher emergency room costs while the difference in overall inpatient cost was only marginally significant. Outpatient cost did not differ significantly at all between participants with inadequate and adequate health literacy. Individuals with marginal health literacy, however, had lower outpatient costs than those with adequate health literacy. A study among 192 participants diagnosed with heart failure showed that the number of emergency departments visits related to heart failure was significantly lower in patients who were able to accurately read and interpret prescription labels (Murray et al., 2009). Patients who turned out to be adequately health literate in the STOFHLA also had fewer all causehospital admissions than those with marginal and inadequate health literacy. Asthma Patients with inadequate health literacy (STOFHLA) reported to have been more often to a hospital during the previous 12 months than those with marginal and adequate health literacy (Paasche-Orlow et al., 2005b). Similarly, children suffering from asthma had more hospital and emergency department visits if their parents reading ability was below a ninth grade level (DeWalt et al., 2007). Hope et al. (2004) also find that people who have difficulties reading standard prescriptions visit emergency departments more often. A study among 489 elderly medicare patients investigated the pathways between health literacy, health behavior, health status and health service utilization (Cho et al., 2008). It 3

4 was found that health literacy is negatively associated with hospital admissions and emergency department visits and that this relationship is rather direct than mediated by health related behavior or preventive care. Overall Berkman et al. (2011) conclude that the evidence on the relationship between health literacy and the utilization of hospital and emergency department care was moderate. An analysis of the relationship between health literacy and the utilization of medical care among adolescent HIV-positive patients showed, that such analyses are very sensitive to the subgroup chosen for the analysis (Murphy et al., 2010). 2.2 Preventive interventions Several studies showed that individuals with limited health literacy use fewer preventive interventions. One study showed a positive relationship between health literacy of elderly Americans (National Assessment of Adult Literacy health literacy scale) and the likelihood to have received an influenza vaccination, a mammography or dental care in the previous 12 months (Bennett et al., 2009). The authors suggest that health literacy could be a mediator between sociodemographic variables and the use of preventive health services. Other studies confirm the lower immunization rates of people with limited health literacy (Scott et al., 2002; Sudore et al., 2006; Howard et al., 2005). A statistical analysis of a large representative U.S. national dataset (2003 National Assessment of Adult Literacy) confirmed the positive association between health literacy and the use of preventive interventions such as Papanicolaou smear, dental checkup, vision checkup, influenza vaccination, prostate cancer screening, mammography, osteoporosis screening and colon cancer screening (White et al., 2008). Significance and Strength of the effects differed considerably between certain age and gender groups. Peterson et al. (2007) also find that a smaller portion of patients with limited or inadequate health literacy (REALM Score) are up to date with their colorectal cancer tests but the observed difference was not statistically significant at a sample size of 99 participants. Nevertheless participants with limited health literacy reported more barriers to fecal occult blood testing or colonoscopy. 1 Latinas who participated in a study in Philadelphia were also less likely to have ever had a 1 The proposed items included the following: not understanding what to do, embarrassing, time consuming, fear of finding something wrong, fear of pain, having to follow special diet and take a laxative, cost concerns, not having problems or symptoms, possibility of bleeding or colon tearing, transportation problems. 4

5 mammography if their functional health literacy (STOFHLA) was measured to be low (Guerra et al., 2005b). Similarly Garbers (2004) found in a sample of latinas in New York that those with inadequate health literacy (STOFHLA) were less likely to have ever had a Papanicolaou test. 3 Hypotheses The objective of this paper is to assess whether utilization of outpatient physician services is linked to health literacy. Impaired health among people with limited health literacy could be related to different user behavior in the health care system. People with little knowledge of the health system might not recognize symptoms of a disease that should be checked by a physician and if they do so, they may have difficulties in choosing and finding an appropriate physician for their problem. Once they are in treatment, people with limited health literacy might have difficulties in communicating their needs or preferences and they might have less influence on physicians choices which could lead to unhealthy decisions about further treatment. The Grossman model The health capital approach suggests that people are not mere consumers of health but also produce health using time and money in order to maintain a health capital stock that makes them feel good and allows them to earn good money and to enjoy their leisure time (Grossman, 1972). A rational agent will consume a utility maximizing amount of medical care which implies that marginal benefit of the last unit equals its marginal cost. In the Grossman model, education affects decisions through two channels: 1. A high stock of human capital implies high returns in the labor market and thus high benefits of improving health. On the other hand a high wage increases the cost of time used in the production of health (Grossman, 1972, 8). 2. Education can increase the efficiency of the production of health (Grossman, 1972, 25). One can assume that the second effect also applies to health literacy and allows predictions about the correlation between health literacy and health care demand. The health capital model implies that health literacy improves the marginal product of medical services and other health inputs, because people are more capable of following a treatment or because they choose the right provider in the first place without wasting resources. In the Grossman model a higher efficiency in the production of health implies a higher optimal health capital stock (Grossman, 1972, 36). At higher productivity the same amount of health can be 5

6 achieved with less medical care. Under certain conditions, the Grossman model predicts that people with limited health literacy are in worse health but use more medical care than those with adequate health literacy, since they need to invest more to maintain their stock of health capital. 2 Hypothesis 1: People with limited health literacy use more medical care and therefore have more outpatient physician visits than people with adequate health literacy. Limited access to care Most studies on the use of preventive diagnostic procedures report lower utilization rates of people with limited health literacy. Since many screening tests such as mammography or coloscopy are conducted by specialists, these results indicate fewer outpatient visits to specialists. People who do not know whom to contact for a certain health problem or who have difficulties in the communication with physicians may also find it difficult to see a provider. If people with limited health literacy have limited access to specialist treatment, they are expected to be less likely to have visited a physician at least once. Hypothesis 2: People with limited health literacy are less likely to have seen a physician. Supplier induced demand It has been shown that the concept of rational agents is not appropriate in health care markets as patients cannot make treatment decisions themselves but need the advice of medical experts. Applying the game theoretic concept of "agency" to decision making in health care, one can see that patients as principals cannot fully monitor the behavior of physicians, which leads to undesired outcomes when the goals of physicians conflict with those of patients. The concept of supplier induced demand suggests that physicians exploit their information advantage and initiate treatments that are in their own but not necessarily in their patients best interest (Labelle et al., 1994). In this context, people who know more about health and the health care system may be less prone to such physician induced overutilization of outpatient physician services. As shown by (Schmid, 2012) this hypothesis can be tested by the hurdle model employed in this study. If the assumption that the decision for the first contact is made independently by the patient while the decision about the further course of treatment is made jointly by the patient and the physician, health literacy should have a negative effect in the second part of the hurdle 2 For a detailed derivation of the reduced form of the demand for medical services please see Breyer et al. (2004, ). 6

7 model. Hypothesis 3: People with limited health literacy have more outpatient physician visits after the first contact with a physician. 4 Data This study exploits the fact that five questions regarding health literacy were added to the 2007 wave of the Swiss health survey (SHS). This survey is conducted every 5 years among Swiss residents above 15 years of age living in private households. After an initial telephone interview, respondents receive a written questionnaire with questions that require consultation of documents or that are too personal to be answered over the phone. The 2007 wave covers participants, of which returned the written questionnaire. The data covers a wide range of domains like overall health, diseases, health related resources, medical service utilization, health insurance as well as health related behavior and living conditions. 4.1 Outpatient physician visits The main dependent variable is the number of visits to private practices of physicians during the previous 12 months. Respondents were asked about the total number of physician visits and, among others, about the number of visits to GPs and specialists. Thus, it is possible to analyze these groups separately. Female participants were also asked about their visits to gynecologists. These consultations are excluded from the number of physician visits percent of respondents [%] number of visits in the previous 12 months all physicians specialists general practitioners Figure 1: Frequency distribution of outpatient physician visits 7

8 Figure 1 shows that physician visits are distributed unequally in all categories. Many people have never visited a physician while few people are treated frequently. Almost 20% of respondents have never seen a physician at all, more than 30% have never been at a GP s and more than 60% have never visited a specialist. The average number of visits is 4.07 for any type of physician, 2.31 for GPs and 1.47 for specialists. 4.2 Health literacy The explanatory variable of interest is the self-assessed knowledge in consumer and patient behavior in the health and insurance system. In the SHS respondents were asked to rate their state of knowledge in four domains on a five level scale: Personal health behavior (e.g. nutrition, physical activity), general consumer behavior (e.g. buying healthy food or over the counter drugs), consumer behavior in the health and insurance system (e.g. communication with a physician, choice of health insurance), and voting behavior in health-related issues. 3 In this study, only the self-rated state of knowledge in consumer and patient behavior in the health and insurance system is used to test the hypotheses. According to personal communication with one of the creators of the health literacy questions used in the Swiss Health survey, this variable is intended to measure navigational health literacy, the ability to find and choose the right providers in the health care system. This concept of health literacy seems appropriate to test the formulated hypotheses as it is likely to make health care utilization more efficient in the production of health. Different from Schmid (2012) I do not create a combined measure of all health literacy variables, since the construction algorithm would be somehow arbitrary and it is not clear what such a combined variable measures. Figure 2 shows that 49% of our sample felt confident or very confident about their knowledge in consumer and patient behavior in the health and insurance system. 24% chose the central category, and 18% assessed their state of knowledge on the lower two levels, indicating limited health literacy. Thus, this measure yields a prevalence of limited health literacy similar to other studies. 4.3 Covariates In the following I identify several variables as candidates for control variables based on the literature. The final set of variables will be selected according to the Akaike information criterion 3 The original wording of the question in German, French and Italian is presented in the appendix. 8

9 percent of respondents [%] not confident at all very confident Figure 2: Frequency distribution of health literacy (AIC) in a stepwise procedure. Possible candidates are variables that may be correlated with both, health literacy and health care utilization. Table 1 contains sample means and descriptions of all candidate variables. Education: As I assume that the concept of education in the Grossman model actually relates to health literacy, I need to control for other effects of education since these two concepts are very closely related. 5 dummies for the educational level are used as candidates for control variables. Because the dual education of an apprenticeship together with professional school is the standard education in Switzerland, 57.9% have a professional education as their highest degree. An A-level degree (Matura / Bacchalauréat) is rated higher than a professional education but does not qualify for any profession and therefore is rarely reported as the highest degree (3.5%). 9.0% have a higher professional education and 20.7% a university degree (Universität / Fachhochschule) as highest degree. Lifestyle: Response to the question about the self-rated state of knowledge may also be affected by people s attitudes toward health. People who pursue a healthy lifestyle may feel more competent in health related topics. Thus, a dummy indicating whether "thoughts on maintaining health affect lifestyle" is used as a possible control variable. A large majority of 89.4% of respondents reported a health oriented lifestyle. Age: Older people do not only use more health care but are also more likely to have limited health literacy (Kirsch et al., 1993). Average age in our sample is 50 years. Sex: Although I subtracted visits to gynecologists from the number of specialist visits, 9

10 women have different utilization patterns especially at certain ages. If there are some differences in health literacy between men and women, this would be relevant for our model. As it can be expected in a telephone interview women are slightly overrepresented with a share of 54.9% of our sample. 4 Foreign nationality: Many studies in the systematic review by Berkman et al. (2011) and the analysis by Kirsch et al. (1993) show that limited health literacy is particularly prevalent among migrants. Since migrants are more likely to be in medical treatment in Switzerland (Guggisberg et al., 2011) a dummy for non-swiss nationality is used. Foreigners are quite underrepresented in our sample with a proportion of only 9.6%. Language region: Wang and Schmid (2007) showed quite pronounced regional differences in health literacy in Switzerland and service utilization patterns also vary between language regions (Lieberherr et al., 2010). When other regional differences are controlled for, this variable is thought to catch some concept of culture. The language region needs to be coded as one dummy for non-german speaking parts of Switzerland since some of the evaluated models do not converge with two separate dummies for French and Italian speaking areas. 37.7% of respondents live in French or Italian speaking parts of Switzerland. Urbanity: It has been shown in other countries that rural and urban areas differ with respect to average health literacy levels (Zahnd et al., 2009). Although Switzerland is very densely populated and much more homogeneous than the United States, average health literacy may still be lower in rural communities. A study on the availability and utilization of outpatient services in Switzerland showed that especially outpatient services are used more frequently in urban areas (Baschung et al., 2008). Thus, a dummy for residence in an urban area is added to the set of candidate variables. Density of service providers: Patients often do not know for themselves what type and amount of services they need and consult service providers in order to make such decisions. Demand for medical services is therefore not independent of their provision and the concept of supplier induced demand suggests that medical services are used more frequently the more providers are in the market (Labelle et al., 1994). At the same time a high density of physicians can give people the feeling of good knowledge about the health system as they face fewer obstacles in finding a physician. Provider density in the canton where respondents live is represented by the number of GPs, specialists and pharmacies per inhabitants (Swiss Federal Statistical 4 Sampling weights supplied with the SHS cannot be used in this analysis as the final count data model cannot be approximated with these weights. 10

11 Office, 2011). On average respondents in our sample live in an area where specialists, 45.3 GPs and 23.6 pharmacies per inhabitants are in business. Health: Poor health induces health care utilization and the connection between health literacy and health status is a major motivation of health literacy research. In the SHS, health is measured by the self-rated health question from the Minimum European Health Module (MEHM) established by the statistical office of the European Union (EU). 87.7% of respondents rated their health as good or very good, 9.8% chose the central category and only 2.5% reported bad or very bad health. The small proportion of people with very bad health could become an obstacle in maximizing the likelihood functions of our count data models, when the sample becomes "thinned out" due to very rare combinations of explanatory variables. Therefore maximum health is used as baseline in the regressions. Income: According to Kirsch et al. (1993) individuals with low income are particularly affected by limited health literacy and income is known to be related to health care utilization in Switzerland (Van Doorslaer and Masseria, 2004). The income variable contains the monthly household income adjusted for the number of adults and children in the household. The average reported equivalized household income is CHF per month. Insurance plan: Health literacy defined as state of knowledge in patient behavior in the health and insurance system may also be associated with the choice of alternative managed care insurance plans and the deductible which create incentives for certain utilization patterns. Two dummies for having an alternative managed care insurance plan or a deductible above the minimum size of CHF for adults and CHF 0.- for teenagers is used. Again, these variables cannot be split into categories as in some models the likelihood function cannot be maximized. Only 15.7% of respondents have an alternative insurance model, that imposes some restrictions on their choice of provider or that requires them to contact a gatekeeper before being treated by a physician. With 67.2% of our sample, a majority of insurees chose a deductible above the minimum. 5 Model Model selection and specification will be carried out in the following steps: First, I compare five count data models by means of likelihood based goodness of fit measures, a test for overdispersion and the Vuong test. Secondly, control variables are selected from the set of candidate variables 11

12 Table 1: Sample means and variable description variable mean description format docvis number of visits to any physician N 0 gpvis number of visits to GP N 0 specvis number of visits to specialists N 0 hl health literacy = 1 0,1 hl health literacy = 2 0,1 hl health literacy = 3 0,1 hl health literacy = 4 0,1 hl health literacy = 5 0,1 educ mandatory schooling 0,1 educ professional education 0,1 educ A-level degree 0,1 educ higher professional education 0,1 educ university degree 0,1 lifestyle health oriented lifestyle 0,1 age age [years] sex sex 0,1 foreign foreign nationality 0,1 latin French or Italian speaking part 0,1 urban Residence in an urban area 0,1 specdens Number of specialists per residents N 0 gpdens Number of GPs per residents N 0 pharmdens Number of pharmacies per residents N 0 health self-rated health (EHIS/MEHM) = very bad 0,1 health self-rated health (EHIS/MEHM) = bad 0,1 health self-rated health (EHIS/MEHM) = fair 0,1 health self-rated health (EHIS/MEHM) = good 0,1 health self-rated health (EHIS/MEHM) = very good 0,1 income monthly equivalized household income [1 000 CHF] modalt alternative insurance plan 0,1 dedhigh deductible above minimum 0,1 N

13 in a stepwise procedure according to the AIC. Third, higher order terms of age and income are evaluated. Interaction terms are not used for this analysis as the final LOG-ZTNB model does not maximize with those interaction terms that are selected by AIC. 5.1 Model selection In the following I compare a poisson model (PRM), a negative binomial model with a quadratic variance term (NBRM2), a zero inflated poisson model (ZIP), a zero inflated negative binomial model (ZINB) and a logit-zero truncated negative binomial hurdle model (LOG-ZTNB). 5 The objective of this model comparison is not only to find the best model for the analysis of the relationship between health literacy and outpatient physician visits, but also to find a good model for further studies on outpatient health care utilization in the SHS. Count data models basically need to deal with two aspects of count data: overdispersion and excess of zeros. When the conditional variance is significantly larger than the conditional mean, negative binomial models are superior to poisson models as they allow a more flexible specification of the dispersion parameter. The size and the p-value of the dispersion parameter in a negative binomial regression can be used as a test for overdispersion. Excess of zeros can be dealt with by modeling the zeroes as the product of a finite mixture of a binary process and a count process (zero inflated models) or by modeling the zeros and the positive counts as outcomes of two separate processes (hurdle models). A Vuong test can be used to test whether the handling of zeros in a zero inflated model adds some information compared to the plain version of the model. Zero inflated models have the disadvantage that they are sometimes hard to estimate while hurdle models are based on a strong prior assumption that needs to be justified. To compare the five models and to run the overdispersion and Vuong tests I estimate the models with all candidate control variables. 6 As shown in table 2 the dispersion parameter α in the NBRM model is always estimated significantly different from zero which indicates overdispersion and favors the negative binomial family over the poisson family. The z-values from the Vuong tests comparing the zero inflated versions of the models to their plain counterparts are always above the thresholds for 99% significance which indicates that dealing with zeros adds relevant information to the model. Table 2 shows the results of the model comparison for all three dependent variables. All three goodness of fit measures, the log-likelihood, the AIC and the BIC, 5 The econometric models are described in detail in the appendix. 6 Provider density was not used in this model selection exercise as the likelihood function of the zero-inflated models cannot be maximized with these variables. 13

14 favor the LOG-ZTNB hurdle model over the other alternatives. Although the difference between the LOG-ZTNB and the ZINB or NBRM models is not very large, the assumption underlying the hurdle model, that service utilization is determined by two independent processes seems fulfilled in Switzerland. In a Swiss mandatory health insurance plan people face no restrictions on the type or amount of medical services they consume, and a large part of the cost is covered by the insurance company. In addition there are hardly any national screening programs that induce people to use a certain diagnostic test. Thus, it seems plausible to assume that the decision to seek care is made by patients while the decision about the further course of treatment is made in an interaction between patients and their treating physicians. This model allows to discriminate between provider induced service utilization and patients effective demand. Thus, the predictions from the Grossman model should mainly be tested in the binary part of the model since the second part also includes providers influence on further service utilization. A limitation of this model is that it assumes that only the first decision to seek care during a year is made by the patient alone. This is not very accurate as even the first visit during the previous 12 month can have been initiated by a physician before that period. Similarly, a patient can experience several disease episodes and make several independent decisions to seek treatment. Table 2: Goodness of fit, Vuong test, overdispersion test (1) (2) (3) (4) (5) PRM NBRM ZIP ZINB LOG-ZTNB all physician visits ll AIC BIC Vuong alpha 1.02 GP visits ll AIC BIC Vuong alpha 1.03 specialist visits ll AIC BIC Vuong alpha 4.00 t-statistics in parentheses, * p<0.1, ** p<0.05, *** p<0.01 Figure 3 shows the difference between the predicted and the observed probability of each count for each of the five models. The solid green line of the LOG-ZTNB hurdle models are closer to the zero line for all three dependent variables indicating better fit than the other models. The hurdle model is best in predicting zeros because a logit estimation is designed such that overall predicted probability of success equals the observed probability. In the specialist model the 14

15 LOG-ZTNB hurdle model is not much better in predicting the probability of each count than the other negative binomial models. However, the small superiority in predicting zeros is highly relevant for this dependent variable since more than 60% of respondent in our sample report zero specialist visits. Table 3: Difference between predicted and observed probability all physician visits 0.2 observed - predicted probabilities Count PRM NBRM ZIP ZINB LOG-ZTNB GP visits 0.2 observed - predicted probabilities Count PRM NBRM ZIP ZINB LOG-ZTNB specialist visits 0.4 observed - predicted probabilities Count PRM NBRM ZIP ZINB LOG-ZTNB Legend: Positive deviations show underpredictions, negative deviations overpredictions of the probability of a certain count outcome. 15

16 5.2 Variable selection The stepwise variable selection procedure begins with a model with all candidate variables. In each step the variable without which the model fit improves most is removed until no improvement by removal can be achieved. Table 4 also reports the BIC which penalizes the number of parameters harder than the AIC. However, I focus on the AIC as it is designed to find the model which describes an unknown reality most adequately. The BIC tries to find a true model among all tested alternatives, which is an assumption I reject for this study. Density of GP does not add sufficient information to the model and is removed from all three estimations. Foreign nationality and language region is not relevant for the number of GP and all physicians models but is included in the specialist model. Residence in an urban area is removed from the GP model as this extra parameter does not add enough information to the model. Table 4: Stepwise backward variable selection all physician visits GP visits specialist visits Variable AIC BIC Variable AIC BIC Variable AIC BIC all variables all variables all variables remove gpdens remove foreign remove gpdens remove foreign remove latin remove latin remove urban remove gpdens The final model consists of all candidate variables except for the removed variables. 5.3 Higher order terms of age and income Age and income are the only continuous variables in our model. I test whether the models fit the data better with higher order terms of these two variables. Again I use the AIC as criterion for the inclusion of higher order terms. Higher order terms are only used together with the terms of lower order. If they were used without terms of lower order, e.g. without the linear term, the relationship between the dependent and the explanatory variable must have a global extremum at age=0 or income=0. Table 5 shows that higher order terms of age and income improve the fit of all three models. Age is used in a quartic specification in the "all physician" and GP models and in a cubic specification in the specialist model. Income enters all models in a quartic specification. 6 Results As presented in table 6 health literacy is not correlated with the probability to have used outpatient physician services. Thus our estimation fails to confirm hypothesis 1 derived from 16

17 Table 5: Selection of higher order terms all physician visits GP visits specialist visits AIC BIC AIC BIC AIC BIC age linear quadratic cubic quartic income linear quadratic cubic quartic Bold face numbers mark the term that improves model fit most. the role of education in the Grossman model. I neither find evidence that people with limited health literacy face obstacles when trying to get access to care. The result that patients with the highest level of health literacy see specialists more often once they are in specialist treatment, conflicts with the idea that better informed patients are less prone to supplier induced demand. However, the marginal effect on the number of specialist visits is very small (table 7). Education, however, increases the probability to have been in specialist care. The higher the educational level, the higher is the probability to have visited a specialist. At the same time, people with a university degree have fewer GP visits, when they have been in GP care. If everybody in our sample had a university degree instead of only mandatory schooling the number of GP visits of those being treated would be by 0.5 lower (table 7). The probability to have been in specialist care would increase by 12.4 percentage points. The fact that education is connected to specialist visits like the Grossman model would predict, but health literacy is not, could mean that education does not work by increasing efficiency of health production as assumed in the model. The negative coefficient in the positive part of the GP model, however, could indicate that well educated people are less prone to demand inducement by GPs. People whose lifestyle is affected by thoughts of maintaining their health are significantly more likely to have seen a GP, a specialist or any physician. A health oriented lifestyle on average increases the probability to have visited on these providers by 3.72 to 6.63 percentage points. All marginal effects of age in 7 are positive except for the number of specialist visits after the first contact. The regression coefficients of the higher order terms show that the number of specialist visits among those with a positive number follows an inverse u-shape. It is perceivable that older people are more likely to be referred to a GP or a hospital when they consult a specialist. Treatments of many chronic diseases such as atrial fibrillation are initiated by specialists 17

18 Table 6: Logit-negative binomial 2 hurdle model regression results all physicians general practitioners specialists logit ztnb logit ztnb logit ztnb Pr(y>0) E(y y>0) Pr(y>0) E(y y>0) Pr(y>0) E(y y>0) hl (0.74) ( 0.82) (0.26) (0.35) (0.71) ( 1.22) hl ( 0.71) ( 0.04) ( 0.37) ( 0.67) (0.10) (0.94) hl ( 0.20) ( 0.19) ( 0.66) ( 0.06) ( 0.24) (0.70) hl (0.32) ( 0.03) (0.18) ( 1.21) (0.08) (2.17) educ (1.32) ( 1.18) (0.44) ( 1.77) (3.34) ( 1.99) educ ( 0.30) (0.36) ( 0.58) ( 0.69) (2.56) (0.80) educ (1.22) (1.08) ( 0.28) ( 1.58) (3.63) (0.74) educ (1.17) ( 0.81) ( 0.57) ( 2.63) (5.63) (0.01) lifestyle ( 4.95) (1.52) (4.10) (0.96) (2.27) (1.82) age (2.07) (1.86) (1.54) (0.12) ( 4.46) (2.91) age ( 2.65) ( 2.21) ( 2.16) ( 0.47) (4.57) ( 2.81) age (3.02) (2.46) (2.61) (0.75) ( 4.37) (2.43) age ( 3.15) ( 2.62) ( 2.80) (0.93) female (15.11) (2.40) (1.69) (0.83) (1.30) ( 1.94) foreign ( 0.81) ( 1.89) latin (2.82) ( 0.65) urban (1.33) (0.95) (1.13) (2.09) specdens (0.63) (2.80) ( 1.94) (1.17) (4.55) (0.62) pharmdens (2.15) ( 4.01) (2.63) ( 4.75) ( 2.22) (0.11) health (.) (9.26) (.) (9.56) (4.78) (3.61) health (4.13) (20.87) (6.93) (17.27) (12.58) (10.67) health (9.64) (18.05) (12.13) (17.88) (14.65) (9.57) health (5.67) (9.42) (7.88) (9.44) (7.27) (4.27) income (2.28) ( 0.33) (1.30) ( 1.53) (4.78) ( 0.64) income ( 1.23) (0.92) ( 0.41) (1.53) ( 3.25) (1.17) income (0.98) ( 0.96) (0.06) ( 1.31) (2.68) ( 1.30) income ( 0.86) (0.87) (0.17) (1.07) ( 2.37) (1.33) modalt (1.48) ( 2.44) (2.73) ( 2.53) ( 2.14) ( 2.99) dedhigh ( 7.12) ( 4.97) ( 7.46) ( 3.52) ( 6.80) ( 2.86) constant ( 1.48) ( 1.15) ( 0.97) (0.36) ( 0.51) ( 0.06) lnalpha pseudo R-sq log likelihood N t-statistics in parentheses, * p<0.1, ** p<0.05, *** p<0.01. coefficients in the logit estimation are logs of odds ratios, coefficients in the ZTNB estimation are logs of incidence rate ratios. All 34 individuals with very poor health have seen a GP and thus are dropped. Sample weights provided with the SHS are not used because some models do not converge with these weights. 18

19 but managed by GPs. People living in the French and Italian speaking part of Switzerland are more likely to have seen a specialist. For the model of all physician visits and visits to GPs the language region was a negligible variable. The fact that it is positively correlated with the probability to have seen a specialist even when provider density is controlled for indicates that there is also a habit of going directly to a specialist in these parts of Switzerland. It is unclear, however, why the number of specialist visits is not higher among those who have been in treatment. One possibility is that capacities of specialists are used up when a higher proportion of the population uses their services. Another explanation could be referrals to other types of healthcare providers, e.g. physiotherapists. These substitutional effects between different types of services could be estimated in a simultaneous equations model for count data (Windmeijer and Silva, 1996). Density of specialists in the canton where a respondent lives, increases the probability to have seen a specialist and reduces the probability to have visited a GP. This rather stands for better access to care than supplier induced demand as the positive part of those regressions is not affected by provider density. Fot the combined measure of all physician visits density of specialists is positively correlated with the number of visits after treatment initiation. However, this cannot be seen as prove for supplier induced demand as provider density is very likely to be endogenous and physicians very likely move to cantons where demand is high. In cantons with a higher density of pharmacies people are more likely to have visited a GP and less likely to have seen a specialist. Pharmacies may act as a kind of gate keeper, sending people with health problems rather to GPs than specialists. Among those who have seen a physician a higher density of pharmacies goes along with fewer physician visits. This could be the result of reduced incentives to induce demand when no drugs can be sold to the patient. Since physician dispensing does not lie in the focus of this study, physicians right to sell medication is not controlled for in this study. Income is significantly associated with the probability to have seen a physician at least once. Once a treatment has been initiated, income is not significantly associated with the number of physician visits. This seems plausible in the face of the minimum deductible of CHF which is used after very few consultations. If every household earned CHF more per month, our model would predict an increase in the probability to have been in specialist care of 2.01 percentage points. This is a very modest effect. As it can be expected health is negatively associated with utilization of outpatient physician 19

20 care. As maximum health is chosen as the baseline here positive and increasing coefficients imply higher utilization with poorer health. People who are insured in a managed care plan with either restricted choice of providers or the obligation to contact a gatekeeper before seeing a physician have significantly fewer physician visits when they have been in treatment. This can be seen as the savings due to the management by the managed care organization. Since many of these managed care insurance plans require people to see their GP before they can use the services of any other provider, these insurees are more likely to have seen a GP and less likely to have seen a specialist. This can be seen as the consequence of successful gate-keeping by GPs. However, I cannot prove these supposed substitutional effects by our single equations formulation of the LOG-ZTNB hurdle model. In addition, it is unclear whether such a shift from specialist care to GP care is cost saving or not. Having an above minimum deductible significantly reduces outpatient physician care utilization. The reduction in the probability to have used the services of one of the tested providers is reduced between 6.64 and 8.27 percentage points. Although the effect of a higher deductible on the number of specialist visits is significant, it is very small when a patient has already been in specialist care. This seems plausible as even high deductibles may be used up after very few specialist visits. 7 Discussion This analysis did not show a systematic relationship between health literacy and outpatient physician visits. The fact that education is negatively correlated with the probability to have seen a specialist but health literacy is not, indicates that education is not related to health care utilization through the pathway the Grossman model would suggest. If education increases health capital and reduces health care demand by making the production of health more efficient, then this should be even more true for health literacy. This also applies to the smaller number of GP visits of well educated people in GP care. If well educated people are more likely to be referred to a specialist because they are more capable to evade demand inducement, then patients with higher health literacy should also be. It can be concluded that it is not clear what self-rated knowledge in consumer behavior in the health an insurance system really measures. It could rather reflect confidence than health literacy. If people with certain attitudes are more likely to rate their knowledge as high, e.g. those favoring alternative medicine, then our health literacy 20

21 Table 7: Average marginal effects all physicians general practitioners specialists logit ztnb logit ztnb logit ztnb Pr(y>0) E(y y>0) Pr(y>0) E(y y>0) Pr(y>0) E(y y>0) hl (0.73) ( 0.81) (0.26) (0.35) (0.71) ( 0.83) hl ( 0.72) ( 0.04) ( 0.37) ( 0.65) (0.10) (0.71) hl ( 0.20) ( 0.19) ( 0.66) ( 0.06) ( 0.24) (0.53) hl (0.32) ( 0.03) (0.17) ( 1.18) (0.08) (1.59) educ (1.28) ( 1.14) (0.44) ( 1.69) (3.49) ( 1.11) educ ( 0.29) (0.36) ( 0.58) ( 0.71) (2.52) (0.49) educ (1.21) (1.08) ( 0.28) ( 1.58) (3.67) (0.52) educ (1.15) ( 0.81) ( 0.57) ( 2.58) (5.90) (0.00) lifestyle (4.60) (1.60) (3.99) (1.00) (2.31) (1.46) age (1.69) (0.26) (7.09) (4.03) (1.84) ( 1.66) female (15.35) (2.41) (1.69) (0.84) (1.31) ( 1.43) foreign ( 0.82) ( 1.47) latin (2.81) ( 0.50) urban (1.32) (0.96) (1.13) (1.54) specdens (0.63) (2.79) ( 1.94) (1.17) (4.57) (0.47) pharmdens (2.16) ( 4.01) (2.64) ( 4.78) ( 2.22) (0.08) health (.) (3.73) (.) (3.92) (5.19) (1.42) health (21.16) (9.27) (10.65) (7.86) (16.85) (2.20) health (13.16) (12.53) (14.75) (12.53) (15.04) (2.35) health (5.42) (10.92) (7.73) (10.98) (7.69) (2.12) income (3.90) (0.79) (2.50) ( 1.16) (6.59) (0.21) modalt (1.51) ( 2.58) (2.78) ( 2.69) ( 2.17) ( 2.21) dedhigh ( 7.59) ( 4.79) ( 7.66) ( 3.49) ( 6.71) ( 1.87) N t-statistics in parentheses, * p<0.1, ** p<0.05, *** p<0.01. Marginal effects in the logit estimation are changes of the expected probability of positive use in percentage points. Marginal effects in the ZTNB estimation is the change in the expected number of visits. All 34 individuals with very poor health have seen a GP and thus are dropped. 21