Consumer Health Information and the Demand for Physician Visits.

HEALTH ECONOMICS Health Econ. 24: 1619–1631 (2015) Published online 22 October 2014 in Wiley Online Library (wileyonlinelibrary.com). DOI: 10.1002/hec.3117

CONSUMER HEALTH INFORMATION AND THE DEMAND FOR PHYSICIAN VISITS CHRISTIAN SCHMID Department of Economics, University of Bern, Bern, Switzerland

ABSTRACT The present study empirically investigates the effect of consumer health information on the demand for physician visits. Using a direct information measure based on questions from the Swiss Health Survey, we estimate a Poisson hurdle model for office visits. We find that information has a negative effect on health care utilization, contradicting previous findings in the literature. We consider differences in the used information measures to be the most likely explanation for the different findings. However, our results suggest that increasing consumer health information has the potential to reduce health care expenditures. Copyright © 2014 John Wiley & Sons, Ltd. Received 22 October 2013; Revised 18 September 2014; Accepted 23 September 2014 JEL classification:

KEY WORDS:

I11; I18; C24

consumer health information; health care demand; physician visits; Poisson hurdle model

1. INTRODUCTION The insight that health information is valuable to patients dates back to the seminal work by Arrow (1963) who examines consumer information in the health care market. On the one hand, patients have incomplete information about their own health conditions, the marginal benefits of medical care, their physicians’ quality, and options for medical care. Consequently, consumer health information can reduce such uncertainties and is thus commonly assumed to improve patients’ decisions about medical care (Arrow, 1963; Kenkel, 1990).1 On the other hand, health care providers are suspected to exploit their information advantage for financial gains while acting as agents for their patients, that is, to induce demand for their own services (see, e.g., Stano, 1987; Labelle et al., 1994; McGuire, 2000). If this so-called demand inducement hypothesis is true, patients with high health information would be less prone to this exploitation and would therefore circumvent unnecessary health care expenditures. In either case, consumer health information is associated with utility gains. Although the importance of information in health care markets is well known, only a few empirical investigations on consumer health information exist. Using the medical occupations of patients’ family members as

1

Correspondence to: Department of Economics, University of Bern, Schanzeneckstrasse 1, CH-3001 Bern, Switzerland. E-mail: [email protected]

Hereafter, we consider medical care to be an input into the patient’s production of health assuming that health is a durable capital stock (Grossmann, 1972). An increase in health is associated with direct utility gains and indirect utility gains resulting from a greater efficiency of both leisure time and consumption. Thus, patients decide on the utility maximizing investment in health on the basis of the marginal benefits and costs of medical care.

Copyright © 2014 John Wiley & Sons, Ltd.

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a proxy, Hay and Leahy (1982) find a positive effect of information on physician visits.2 More precisely, consumer health information is found to have a positive effect on the likelihood of visiting a physician but does not significantly alter the number of office visits. Kenkel (1990) corroborates these findings using an information measure based on survey questions about the symptoms of four diseases. More recently, Dwyer and Liu (2013) investigate the effect of health information from non-physician sources while also controlling for patients’ trust in physicians. They find a positive effect of consumer health information on the number of physician visits but a decrease in the likelihood of visiting the emergency room. Consequently, these empirical results contradict the demand inducement hypothesis in which the theory predicts that a mainly (weakly) negative effect of consumer information on health care demand exists (Dranove, 1988; Xie et al., 2006; De Jaegher, 2012).3 Additionally, Hsieh and Lin (1997) find a positive effect of consumer information on the demand for preventive care using a symptom-based measure.4 In summary, consumer health information is found to increase the demand for physician visits and other medical services. These findings indicate that poorly informed consumers can underestimate the marginal benefits of health care. The present study empirically investigates the effect of consumer health information on the demand for physician visits. This paper differs from the previous literature, particularly from Kenkel (1990), in the following two ways. First, our measure of consumer information is based on survey questions about health competence, that is, the ability to address health care issues. In other words, we do not assess consumer knowledge using a symptom-based measure of health information. Second, we account for the Poisson-like distribution of physician visits by applying a Poisson hurdle model in our estimation strategy. Our empirical results indicate that the number of office visits decreases with a higher level of consumer health information, but we find no effect on the likelihood of visiting a physician. However, inferring that supplier-induced demand exists from this negative effect is not possible because we cannot exclude other potential interpretations. Nevertheless, our results clearly contradict previous findings, and we examine several potential explanations for these discrepancies. As our results are neither sensitive to the estimation strategy nor endogenous information acquisition, we consider the differences in the information measure to be the most likely explanation. The remainder of this paper is structured in the following manner. First, the next section presents the empirical approach. Section 3 provides the details about the data including a short explanation about the Swiss health care system. Next, in Section 4, we present the main results, perform several robustness checks, and discuss some limitations of the analysis. Finally, the last section contains some concluding remarks. 2. EMPIRICAL APPROACH Like Hay and Leahy (1982) and Kenkel (1990), we estimate a two-part model, that is, a non-nested hurdle model using a Poisson regression approach (Mullahy, 1986). Hence, we assume that the statistical processes governing the decision of visiting a physician and the decision on the number of visits are clearly distinct and different. Note that the variable of interest, the number of physician visits, is likely to exhibit overdispersion, meaning that the (conditional) variance is larger than the (conditional) mean. This overdispersion would violate the assumption of the basic Poisson model, and a generalized specification should be applied for reasons of efficiency. A common generalization is the negative-binomial regression model (Cameron and Trivedi, 1986;

2

The idea to compare physicians (and their families) with other professional groups dates back to Bunker and Brown (1974) and has been extended by Domenighetti et al. (1993). However, their analysis assumes that lawyers are considered by their physicians to be ‘special patients’ as lawyers might cause more litigation. Thus, their results are based on the similarity of the prevalence between physicians and other professional groups (compared with the rest of the population) and not the medical occupation per se. 3 Note that Xie et al. (2006) and De Jaegher (2012) also derive specific circumstances where (private) information may lead to more inducement. More precisely, the increase in information necessary to cause a reduction in demand inducement depends on the relative distribution of information among population subgroups and on the extent of the physicians’ incentives to overprescribe, respectively. 4 Besides utilization, empirical results by Pauly and Satterthwaite (1981) and Haas-Wilson (1990) suggest that a high reputation enables physicians to increase the price for their services, and Dafny and Dranove (2008) show that the market shares of high quality Medicare Health Maintenance Organizations increase as soon as consumers become aware of the differences in quality. Copyright © 2014 John Wiley & Sons, Ltd.

Health Econ. 24: 1619–1631 (2015) DOI: 10.1002/hec

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CONSUMER HEALTH INFORMATION

Winkelmann and Zimmermann, 1995; Winkelmann, 2008), where the density is given by yj . C yj / j f .yj / D Pr.Yj D yj / D for yj D 0; 1; 2; : : : ; N; ./.1 C yj / C j C j

(2.1)

in which EŒYj jxj j D exp.xj0 ˇ/, xj is a vector that contains the explanatory variables, ./ is the standard gamma function, and ˛1 , where ˛ denotes the variance of the gamma distribution. Note that overdispersion may be due to unobserved heterogeneity in health care utilization. This heterogeneity can be fj D taken into account by adding an individual error term to the random mean function for Yj , that is, 0 exp.xj ˇ/ exp."j / assuming that exp."j / follows a gamma distribution with mean 1 and variance ˛. The fj in the standard Poisson distribution. Finally, negative-binomial distribution can then be obtained by inserting the resulting distribution in (2.1) has conditional mean EŒYj jxj D j and variance VarŒYj jxj D j C ˛2j . In the case of ˛ D 0, the negative-binomial distribution collapses to the standard Poisson distribution. As ˛ has to be estimated, one can test whether the negative-binomial model is appropriate or not. However, the restriction to strictly positive numbers implies a truncation. The density of the truncated negative-binomial regression model is given by f yj jyj > 0 D Pr Yj D yj jyj > 0 D 1

f .yj / ;

(2.2)

Cj

. Following Grootendorst where f .yj / corresponds to the density given by (2.1) and Pr.Yj D 0/ D C j (1995), we assume a sample with iid distributed observations (individuals), where j D 1; : : : ; n individuals have a positive utilization of medical services, and the remaining j D n C 1; : : : ; N individuals do not visit a physician. The likelihood of the entire sample is then given by LD

n Y

N Y Pr yj > 0jxj f yj jyj > 0; xj Pr yj D 0jxj

j D1

(2.3)

j DnC1

or, because the likelihood function can factor into two multiplicative terms, L1 D

n Y

N Y Pr yj > 0jxj Pr yj D 0jxj

j D1

(2.4)

j DnC1

L2 D

n Y

f yj jyj > 0; xj :

(2.5)

j D1

The first term depends solely on parameters in the hurdle component of the model such as the binary choice visiting a physician. By contrast, the second term depends exclusively on parameters in the level component of the model such as the number of visits given yj > 0. Because of this separability, the binary probability model can be estimated separately from the truncated negative-binomial model without losing any information (Mullahy, 1998). Therefore, we estimate a logit model for the binary choice visiting a physician and a truncated negative-binomial model for the number of visits. The vector of explanatory variables, xj , contains the controls described in the next section. 3. DATA The data used in this paper are taken from the Swiss Health Survey conducted in 2007 by the Swiss Federal Statistical Office. Note that the Swiss health care system seems to be well-suited for our empirical analysis for Copyright © 2014 John Wiley & Sons, Ltd.


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several reasons.5 First, Swiss residents are covered by a mandatory health insurance plan, which is offered by approximately 60 private insurance companies.6 The coverage is determined by federal regulations and notably includes almost all ambulatory services with the exception of a few forms of alternative medicine. Thus, our analysis is unlikely to be confounded by unobserved differences in health plans. Second, all health insurance companies are required to contract with every licensed health care provider. General practitioners and specialists delivering non-hospital ambulatory services are generally self-employed and paid on a fee-for-service basis, and patients can freely choose their physician.7 In addition, patients have some choice regarding their cost sharing, that is, they can select among seven possible deductible levels. Thus, our analysis is unlikely to be confounded by unobserved differences in physician choice restrictions and we can adequately control for the patients’ cost sharing. The survey data are derived from a random sample of the Swiss resident population aged between 15 and 99 years. The survey consists of a phone interview and an additional paper-based form, which was answered by 14,393 individuals. The data include detailed information about individuals’ health status and insurance, health-related behaviors, utilization of medical services, health expertise, and their socioeconomic background. Table I provides the variable definitions and descriptive statistics of both the dependent and the explanatory variables. The measure for the individuals’ health expertise is constructed using answers to four questions about the respondents’ self-confidence of health issues. Specifically, the respondents were asked to self-evaluate their own abilities in dealing with health care issues. In the first question, the respondents were asked about the importance of critically questioning given health information. The range of possible answers was from 1 (very important) to 4 (not important). In addition, the fifth possible answer was ‘I cannot assess’, which implies that the respondent does not have the ability to evaluate health information. The other three questions were related to patients’ communication behaviors, such as with a physician, and general knowledge about health issues and consumption behavior in buying and using over-the-counter drugs. In all three questions, the range of possible answers was 1 (feel very certain) up to 5 (do not feel certain at all). We combine these four categorical variables into one dummy variable denoted INFO where 1 indicates that an individual answered all questions with at least ‘important’ or ‘feel certain’. We expect this to be a good proxy for the individual’s health information. Kenkel (1990) proceeds in a similar fashion using 10 questions about symptoms associated with diabetes, heart disease, cancer, and tuberculosis. More recently, Hsieh and Lin (1997) use 20 questions about the health effects and the symptoms associated with high blood pressure and diabetes. While these information measures are quite objective, they are narrow because of the restriction on a few diseases. By comparison, our information measure is more subjective but broader. In addition, we do not sum up the answers because we use categorical variables. Considering the empirical literature about the effect of consumer health information on health care demand, another important variable is the individual’s occupation. In the following analysis, we use a dummy variable denoted MED. OCCUPATION. This variable takes the value of 1 if the individual is a health care worker including a physician, dentist, ophthalmologist, pharmacist, or physical therapist. Additionally, the physician density is of special interest, because the density is found to be positively correlated with the demand for medical care. This positive relationship is commonly considered as evidence for demand inducement (Evans, 1974; Fuchs, 1978; Wilensky and Rossiter, 1983; Reinhardt, 1985; Stano, 1985; Cromwell and Mitchell, 1986; Carlsen and Grytten, 1998; Xirasagar and Lin, 2006; Peacock and Richardson, 2007). However, there are at least two possible explanations for this positive correlation. On the one hand, physicians may indeed induce 5

Two comprehensive summaries of the Swiss health care system are provided by the Organisation for Economic Co-operation and Development and the European Observatory on Health Care Systems in the review of the Swiss Health System (OECD/World Health Organization, 2011) and in the Health Systems in Transition (HiT) series (World Health Organization, 2000), respectively. 6 Note that the health insurers are required to accept all patients who wish to enroll such that refusing patients on the basis of health status, age, sex, etc. is prohibited. 7 Some insurance providers offer health plans where the individuals are bounded to a specific (group of) health care provider, for example, Health Maintenance Organization (HMO) or Preferred Provider Organization (PPO). According to the Swiss Health Survey, only 14.69% of Swiss residents chose a group health plan in 2007. Notice that individuals’ insurance coverage is not altered by this choice, and the selected health care provider can be changed easily. Copyright © 2014 John Wiley & Sons, Ltd.


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Table I. Variable definitions and descriptive statistics Variable VISITSa PVISIT a PHARM a PPHARM a ALT a PALT a DIABa OSTEOa CANCa PREV a INFO MED. OCCUPATION PHYSICIAN DENSITY PHADENS Health, subjective GOOD FAIR POOR Health, symptom-based

GOOD FAIR POOR Health, other CHRONIC

ACCIDENT PREGNANT DEPRESSION LIFESTYLE Insurance deductible MEDIUM HIGH Insurance ALTERNATIVE MED. ADD. HOSPCOV b HMO/PPOb Employment PART-TIME NON Household income LOW MEDIUM HIGH Age category 36–50 years 51–65 years > 65 years

Definition

Mean

Standard deviation

Number of physician visits within 1 year (including home visits) D 1 if positive physician visits Number of pharmacy visits within 1 year D 1 if positive pharmacy visits Number of visits related to alternative medical care within 1 year D 1 if positive utilization of alternative medical care D 1 if the respondent has tested his/her blood sugar level D 1 if the respondent has tested his/her bone density D 1 if the respondent had a checkup related to cancer D 1 if the respondent has DIAB, OSTEO, and/or CANC D 1 D 1 if the respondent as a high information level D 1 if the respondent has a medical occupation, namely, physician, dentist, optometrist, pharmacist, and physical therapist Number of physicians with practice per 1000 residents (by canton) Number of pharmacies and drugstores per 1000 residents (by canton) Self-reported health (reference group: very good) D 1 if the respondent reports health status as good D 1 if the respondent reports health status as fair D 1 if the respondent reports health status as poor Symptom-based health, included diseases: pain in the back, adynamia, abdominal pain, looseness or costiveness, sleep disorder, headache, heart palpitation or extra-systole, pain or pressure in the chest, joint pain or pain in the limbs, and pain in the hands; objective health is constructed by summing the indicators (range: 0 to 20, reference group: very good) D 1 for values 2 and 3 D 1 for values 4 and 5 D 1 for values between 6 and 20

4.0707 0.8091 0.9142 0.3674 1.9358 0.3177 0.6775 0.1223 0.7461 0.9123 0.1759

7.4797

D 1 if the respondent was under medical treatment because of at least one chronic disease, including migraine, asthma, diabetes, arthrosis, stomach ulcer, osteoporosis, chronic bronchitis, high blood pressure, heart attack, stroke, renal disease, cancer, allergy, and depression D 1 if the respondent had an accident at work, at home, road accident, and/or sporting accident D 1 if the respondent was pregnant within the last 12 months D 1 if the respondent suffers from clinical depression D 1 if the respondent reports that health and healthy behavior is important for his/her lifestyle (Reference group: below CHF 1000) D 1 for deductibles between CHF 1000 and CHF 2000 D 1 for deductible equal to CHF 2000 or above D 1 if the respondent has an additional insurance for alternative medicine D 1 if the respondent has a supplementary insurance for hospitalization D 1 if the respondent has a HMO or PPO health plan Gainfully employed (reference group: fully employed) D 1 if the respondent reports part-time working D 1 if the respondent reports to be non-working monthly income (reference group: very low) D 1 if household income equals CHF 4500 - 5999 D 1 if household income equals CHF 6000 - 8999 D 1 if household income equals CHF 9000 and more (Reference group: 15 to 30 years) D 1 if age between 36 and 50 years D 1 if age between 51 and 65 years D 1 if age above 65 years


0.0386 1.9928 0.3200

2.0675 6:0512

0.5377 0.1187

0.6680 0.1049 0.0272

0.3381 0.2417 0.1220

0.5253 0.1184 0.0082 0.0516 0.8863

0.2488 0.1466 0.5700 0.7288 0.1469 0.2445 0.3859 0.1816 0.2914 0.2562 0.2910 0.2560 0.2202


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Table I. Variable definitions and descriptive statistics (continued) Variable Socioeconomic FEMALE MIGRANT b COUPLEb NBKIDSb IHELPb Educationb SECONDARY TERTIARY Risk factorsb BMI SMOKE ALCOHOL DRUG ABUSE PSYCH. DISTRESS Physical activityb SOMETIMES FREQUENT Locus of controlb AVERAGE STRONG Degree of urbanityb

Definition

Mean

D 1 if the respondent is female D 1 if the respondent has a migration background D 1 if the respondent is not single Number of children in the household D 1 if the respondent received informal help given daily difficulties Education level (reference group: mandatory school) D 1 if respondent completed a secondary education D 1 if respondent completed a tertiary education

0.5617 0.2768 0.6480 0.4194 0.1203

Body mass index D 1 if the respondent is a smoker D 1 if the respondent drinks alcohol D 1 if the respondent abused (illegal) drugs during the past 2 years D 1 if the respondent has psychological distress Frequency of physical activity per week (reference group: never) D 1 if respondent reports once or twice a week D 1 if respondent reports three or more times per week Mastery level of the respondent on the basis of three questionnaires (reference group: weak control) D 1 if the respondent has average control D 1 if the respondent has strong control Degree of urbanization (14 categories)

24.320 0.2607 0.8549 0.0387 0.1676

Standard deviation

0.8389

0.6037 0.2758 4.0515

0.4379 0.4020

0.4001 0.3891

a

Dependent variables. The corresponding coefficients are not reported in the results tables in this article. Full tables of results can be found in the Supporting Information. b

more demand in areas with high competition. On the other hand, physicians may settle down and practice in areas with high demand. Moreover, additional physicians in a certain area may lower the individual’s cost of health care because of better access.8 Nevertheless, the described effects may significantly alter the demand for health care, and therefore, controlling for the physician density seems to be appropriate. The corresponding variable denoted PHYSICIAN DENSITY is defined as the number of physicians per thousand residents. The data are retrieved at the cantonal level from the Swiss Federal Statistical Office. Finally, two categories of variables are expected to affect the demand for medical care. The first category consists of variables that capture the individuals’ health status. These variables are coded such that higher values indicate lower health levels. Moreover, the healthiest group of individuals is always the reference group. We include a self-assessment of health status and some more objective health measures. The latter consist of a variable for the health status based on the symptoms and severity of 10 different diseases and three dummy variables for chronic diseases, an accident within the last year, and pregnancy during the last 12 months. Finally, we include a dummy variable that indicates a depression, and an additional categorical variable measures the individual’s psychological distress with values between 0 (normal) and 2 (high). As the healthiest respondents serve as the reference group for all these health status measures, we expect a positive effect on utilization for these variables. The second category contains variables about individuals’ insurance and, therefore, the price of medical care. In particular, we include a variable with three categories for the deductible; these categories range from 1 (below CHF 1000) to 3 (above CHF 2000). We expect a negative effect on the utilization of medical services, because an increase in the deductible implies a higher copayment for the individual. In addition, we include a dummy variable where 1 indicates a supplementary insurance that covers alternative medicine. The expected effect is positive, because the overall coverage increases and the price decreases.

8

In fact, Auster and Oaxaca (1981) show that it is almost impossible to identify demand inducement using the physician density.



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Table II. Health information and physician visits hurdle model Variable

Hurdle

INFO MED. OCCUPATION PHYSICIAN DENSITY Health, subjective GOOD FAIR POOR Health, symptom-based GOOD FAIR POOR Health, other CHRONIC ACCIDENT PREGNANT DEPRESSION LIFESTYLE Deductible MEDIUM HIGH Insurance ALTERNATIVE MED. Employment PART-TIME NON Income LOW MEDIUM HIGH Age 36–50 years 51–65 years > 65 years Socioeconomic FEMALE

Count

0.01106 0.07403*** 0.04889***

(0.01428) (0.02544) (0.01551)

0.55079*** 1.23914*** 0.12605

(0.20787) (0.36325) (0.21270)

0.02106 0.11791*** 0.00000

(0.01377) (0.02506) (0.00000)

0.74476*** 2.73308*** 7.83676***

(0.18104) (0.46669) (1.73166)

0.03487** 0.06773*** 0.12320***

(0.01388) (0.01599) (0.02270)

0.66911*** 1.00309*** 1.39256***

(0.22393) (0.24808) (0.33368)

0.11041*** 0.17967***

(0.01275) (0.02608)

0.16070*** 0.06505***

(0.03377) (0.01813)

1.67858*** 1.47211*** 2.27778*** 2.05344*** 0.95762***

(0.21988) (0.26308) (0.42217) (0.46392) (0.28821)

0.06339*** 0.11124***

(0.01364) (0.01700)

0.93236*** 0.98297***

(0.19132) (0.26416)

0.01157

(0.01189)

0.87921***

(0.19118)

0.03981*** 0.02616

(0.01526) (0.02032)

0.77427*** 1.25571***

(0.22641) (0.31077)

0.04611** 0.06705*** 0.10691***

(0.02211) (0.02129) (0.02197)

0.01277 0.17326 0.49739

(0.28102) (0.28159) (0.33078)

0.04700*** 0.03660** 0.04492*

(0.01516) (0.01792) (0.02458)

0.10892***

(0.01387)

0.85493*** 0.93551*** 1.01315** 0.07436

(0.27669) (0.33105) (0.42280) (0.21884)

p 0:01, p 0:05, and p 0:10. Marginal effects after estimation of a truncated negative-binomial hurdle model for physician visits (total N D 7827 and N D 6483 given positive utilization) within 1 year using data from the Swiss Health Survey 2007. Robust standard errors in parentheses. Additionally controlling for the variables described in Table I, see Supporting Information for further details.

4. RESULTS AND DISCUSSION Table II reports the main results of the logit estimation for the hurdle part given by the likelihood in (2.4) and reports the estimates obtained from the truncated negative-binomial model for the count part given by the likelihood in (2.5).9 First, the estimated coefficient on INFO is not significantly different from zero in the hurdle component but is significantly negative at the 1% level in the count component. The latter suggests that consumer information reduces the number of physician visits by 0.55 per year. This negative effect could provide some evidence for supplier-induced demand. On the other hand, well-informed patients might be better able to adapt the physician’s advice and, therefore, are more efficient in producing their own health and require fewer physician visits. Obviously, the negative estimate could also be a mixture of these two effects. Although it 9

Note that the estimated ˛ is 1.595 and, given a standard error of 0.151, significantly different from zero. Hence, the choice of the negative-binomial model compared with the standard Poisson model is appropriate.



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is not possible to determine the dominant effect and whether demand inducement exists, we are at least unable to reject the inducement hypothesis. Second, medical professionals have a significantly lower overall demand as the coefficient of MED. OCCUPATION is negative and is significant at the 1% level in both the hurdle and the count component. This finding provides some evidence that individuals working in the health care sector have a different consumption pattern than non-medical professionals. On the one hand, medical professionals might have a different attitude toward medical care or health, and individuals working in the health care sector may have access to informal care, such as through a ‘professional courtesy’ (Bunker and Brown, 1974). On the other hand, a treatment received by a medical professional might be more effective as the individual has more knowledge. For example, a nurse can change a bandage on his/her own, or a pharmacist can monitor his/her own drug interactions with a newly prescribed medication. Thus, the effect of medical occupation on health care demand should be interpreted cautiously, because the profession is unlikely to measure mere consumer information. Finally, the density coefficient in the hurdle component has a positive effect on utilization. The density coefficient most likely captures an availability effect, because physicians are unlikely to be able to influence the individual’s decision to visit before they are actually contacted. Note that Swiss physicians do not advertise their own services. Moreover, the coefficient in the count component is not significantly different from zero indicating that there is no relationship between office visits and the number of physicians in a certain area. As expected, better health leads to a lower probability of visiting a physician and less utilization given a positive demand. Moreover, the magnitude of the effect increases when the health status is further decreased. While only the supplementary insurance for alternative medicine is associated with an increase in the number of office visits, an increase in the deductible always reduces the likelihood of visiting a physician as well as the utilization. Note that income and being female positively affects the likelihood of visiting a physician but has no effect on the number of visits. The estimated effect of age exhibits a somehow counterintuitive negative sign but is comparable with the related literature (Hay and Leahy, 1982; Kenkel, 1990). One possible explanation is that age is highly correlated with the included health measures, which most likely capture the health status very well. Another possibility is that older individuals substitute physician care with other medical services (e.g., hospital care) or that the elderly receive basic care in places such as the retirement homes, which might decrease the need for physicians’ services. Finally, older people might also have lower opportunity cost of being ill ceteris paribus. While most of our results exhibit the expected algebraic sign and are comparable with estimates in the literature, the estimates for the information measure differ substantially from the results of other empirical studies. One potential explanation for these differences might be that consumer information is endogenous because of unobserved heterogeneity that drives both the demand for medical services and the process acquiring health information (Dwyer and Liu, 2013). Therefore, we follow Kenkel (1990) and apply the method described in Wooldridge (2002, pp. 663–668) to replicate the results of Kenkel (1990) using the present data. In short, we regress INFO on the full set of covariates and include the predicted residuals, IRESIDUALS, to estimate again the hurdle model. We additionally include the inverse mills ratio, lambda, to correct for sample selection. The standard errors are then bootstrapped using 2000 iterations. As evident from Table III (second and third columns), the estimated coefficients are not considerably different from the main results presented in Table II but are less precisely estimated. In particular, we cannot reject the null hypothesis that health information has no effect on utilization. However, as shown in the first column of Table III, the coefficients on IRESIDUALS equate 0.136 .0:135/ in the hurdle part and 1.172 .2:223/ in the count component (standard errors in parentheses). Thus, we do not reject the null hypothesis that INFO is exogenous (Wooldridge, 2002, p. 665). In other words, endogeneity is not an issue. Thus, our approach seems to be appropriate, and, most importantly, we do not alter our conclusions on the effects of information. Another possible explanation is that INFO does not capture the same type of information as the information measures of Kenkel (1990) or Dwyer and Liu (2013). In this regard, the literature identifies at least three types of information that are associated with improved decisions about medical care (Haas-Wilson, 2001). First, diagnostic information affects the patient’s ability to determine what is causing illnesses and symptoms. Second, physician-specific quality information enables the patient to improve his/her physician choice Copyright © 2014 John Wiley & Sons, Ltd.


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Table III. Physician visits hurdle model with endogenous health information acquisition Dependent variable (1) INFO INFO MED. OCCUPATION Education SECONDARY TERTIARY PHYSICIAN DENSITY Health, subjective GOOD FAIR POOR Health, symptom-based GOOD FAIR POOR Health, other CHRONIC ACCIDENT PREGNANT DEPRESSION LIFESTYLE Deductible MEDIUM HIGH Insurance ALTERNATIVE MED. Employment PART-TIME NON Income LOW MEDIUM HIGH Age 36–50 years 51–65 years > 65 years FEMALE lambda IRESIDUALS

(2) PVISIT 0.15299

(3) VISITS

(0.13394)

1.39261

(2.23796)

0.14620***

(0.02678)

0.04843*** 0.07328*** 0.00835

(0.01510) (0.01713) (0.00730)

0.02707***

(0.00936)

0.01702

(0.12903)

0.01017 0.02643 0.05948**

(0.01133) (0.01968) (0.02413)

0.01197 0.11891*** 0.37127***

(0.01085) (0.02635) (0.15692)

1.08137*** 2.76102*** 3.63639**

(0.24278) (0.39576) (1.56657)

0.00082 0.03192*** 0.02435

(0.01122) (0.01166) (0.01603)

0.03866*** 0.06448*** 0.11051***

(0.01095) (0.01339) (0.02251)

0.19455 0.33758 0.88473***

(0.23444) (0.26949) (0.32731)

0.00935 0.01006 0.02962 0.03867** 0.08459***

(0.00937) (0.01272) (0.03190) (0.01766) (0.01070)

0.11864*** 0.15589***

(0.01052) (0.01950)

0.65894*** 0.39800

(0.27783) (0.29102)

0.09534*** 0.07296***

(0.02805) (0.01874)

1.47224*** 0.00023

(0.36270) (0.34579)

0.00686 0.02185

(0.01115) (0.01430)

0.04502*** 0.08524***

(0.01131) (0.01410)

0.34885 0.04910

(0.21968) (0.33822)

0.03358***

(0.00886)

0.02350**

(0.01100)

0.61289***

(0.18802)

0.04370*** 0.02374*

(0.01237) (0.01312)

0.04594*** 0.03183**

(0.01408) (0.01466)

0.32567 0.46040*

(0.25293) (0.25923)

0.00528 0.01830 0.04677***

(0.01328) (0.01166) (0.01250)

0.03101** 0.03929*** 0.08643***

(0.01472) (0.01322) (0.01458)

0.35620 0.07290 0.03421

(0.24793) (0.23710) (0.28375)

0.05723*** 0.11326*** 0.09648*** 0.00547

(0.01124) (0.01257) (0.01478) (0.01039)

0.02149 0.00896 0.06191*** 0.09440***

(0.01466) (0.01978) (0.02232) (0.01051)

0.16860 0.13508 0.16183 0.44297** 4.98685*** 1.17206

(0.24390) (0.35510) (0.36369) (0.22322) (1.23783) (2.22259)

0.13557

(0.13518)

p 0:01, p 0:05 and p 0:10 (1) Regression of INFO on the given covariates using data from the Swiss Health Survey 2007 (N D 12;231) to obtain predictions for the residuals, IRESIDUALS. The latter is used in the estimation of the hurdle part (2) (logit, N D 11;985) and, together with the inverse mills ratio, lambda, in the count part (3) (negative-binomial model, N D 9727). The presented results for (2) and (3) are marginal effects with bootstrapped standard errors using 2000 iterations.

in terms of quality and specialty. Third, treatment information signifies the patient’s ability to evaluate the physician’s advice such as distinguishing between necessary treatment and induced demand.10 Knowledge of symptoms (e.g., Kenkel 1990) might rather measure diagnostic information and, therefore, improve the decision (process) of visiting a physician. In fact, Kenkel concludes that information plays the dominant role in increasing the probability of health service utilization. In addition, searching for health information as used

10

The nomenclature of the different information types is not standardized throughout the literature. While Dranove (1988) uses the term ‘diagnostic skills’ for the patient’s ability to distinguish between necessary treatment and induced demand, Haas-Wilson (2001) uses the term ‘diagnostic information’ for the patient’s ability to determine what is causing illnesses and symptoms.



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in Dwyer and Liu (2013) could be an indication of anxiety or risk aversion, which affects both the probability to visit a physician and the number of visits. Moreover, health information from sources like the internet can result in a higher demand for health care services because of larger concerns about the own health, or consumers might lack the ability to understand the given information (Suziedelyte, 2012). However, our information measure is related to critically questioning given health information and the ability to communicate with a physician, such that it most likely captures the patient’s ability to evaluate and apply the physician’s advice. Although we are not able to determine to which extent this ability is captured by INFO, it is likely to measure some other type of consumer information than previously used measures. Thus, we regard differences in the information measure as an important explanation for the differences between our estimates and the results in the related literature.11 Nevertheless, regarding the underlying questions in the questionnaire, INFO might capture more than just the consumer’s ability to evaluate the (treatment) advice of the physician. A consumer with a high level of health information might choose health care services more appropriately (for instance visit the pharmacy instead of the physician). Therefore, we estimate the hurdle model for pharmacy visits and the utilization of alternative medical services. In addition, Parente et al. (2005) and Hsieh and Lin (1997) show that the knowledge of insurance benefits and (diagnostic) information, respectively, increase the demand for preventive care, which in turn is mostly associated with a decrease in either the probability or the severity of illness (Phelps, 1978; Kenkel, 1994; Tian et al., 2010). Thus, we estimate binary choice models (logit) for preventive care related to diabetes, osteoporosis, cancer, and an aggregate of these three measures. However, INFO does not affect the probabilities of using substitutes or preventive care and has no effect on the number of visits of the considered substitutes for physician services (cf. Supporting Information). Although we do not jointly estimate the consumer’s demand for preventive care and the demand for physician visits, these results provide further evidence that INFO captures rather the ability to evaluate the (treatment) advice of the physician than the ability to choose health care services appropriately. Another issue is the fact that consumer preferences may vary in a non-random way between patients with different information levels. In that case, our estimates of INFO would be biased and additionally consist of the preference effect. However, one would expect that preferences (positively) affect both the decision to visit a physician as well as the number of visits. As INFO has only a negative effect on the latter, it is unlikely to capture preferences. Moreover, we control for the respondents’ opinions on healthy behaviors by including LIFESTYLE, which possibly captures health (care) preference to a large extent and reduces the potential bias. Although non-randomness in consumer preferences relative to consumer information is not testable, we conclude that unobserved variation in consumer preferences is unlikely to be an issue. Finally, we perform some robustness tests. First, we reduce the number of controls from 32 to 12 and again estimate the negative-binomial model. Second, we estimate the count part of the hurdle model with ordinary least squares, a negative-binomial model without truncation at 0, and Tobit. In addition, we estimate the (zero-truncated) Poisson model without accounting for the overdispersion. The corresponding estimates are qualitatively and quantitatively comparable with our main results but are less precisely estimated (cf. Supporting Information). The latter is not surprising, as the other estimation methods are less efficient compared with the negative-binomial model. Finally, we test several specifications of the INFO variable in terms of included questionnaire answers and the threshold level. Regarding the sign and the significance of the effect, the results are not notably altered by excluding one questionnaire answer or by changing the threshold (for instance to ‘very certain’) of some questionnaire answers. In summary, we conclude that our results are robust against changes in the model specification, estimation method, and the construction of INFO. Although our results seem to be robust, our analysis has some limitations. The outcome variables are rather raw measures for the utilization of medical services, and we cannot control for the length of a visit, its quality, and the effect on the individual’s health. Some unobserved quality or physician-specific differences that may affect the demand and are correlated with INFO would bias the estimates and might invalidate our conclusions. 11

Note that a similar argument can be made for MED. OCCUPATION as we observe the occupation of the respondent, whereas Bunker and Brown (1974) and Hay and Leahy (1982) observe the profession of family members.




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However, this would only materialize if the bias was so large that the sign changes. In addition, these issues might be resolved by using more detailed data on utilization and consumer information. Regarding the estimation procedure, there are two critical issues. First, by applying a two-part model, we implicitly treat the whole year as one illness episode. In fact, we ignore the possibility that an individual decides several times to visit the physician once (per decision). Second, in some estimations the sample size is reduced to 7827 observations because of missing data. In particular, unreported income and deductible levels account for nearly two thirds of the missing data. However, we do not find any systematic differences in the dependent variables conditional on the missing values. Moreover, the estimates with the reduced set of controls using up to 9804 observations are comparable with our main results. We therefore conclude that sample selection is not an issue. In summary, our study exhibits some minor drawbacks, but they are unlikely to invalidate our main results. Thus, we conclude that an increase in consumer health information leads to a reduction of physician visits and, therefore, has the potential to reduce health care expenditures. 5. CONCLUSION This study empirically investigates whether and to what extent the demand for physician visits is affected by consumer health information. We use a direct measure of health information that is based on survey questions related to patients’ health expertise. Contrary to Hay and Leahy (1982) and Kenkel (1990), we find that better informed patients do not differ in the likelihood of visiting a physician but exhibit a smaller demand for office visits. These findings could be regarded as evidence for supplier-induced demand. On the other hand, wellinformed patients might be more efficient in producing their own health. A similar argument can be made for individuals working in the health care sector as we observe a smaller overall demand within this group. Thus, information and abilities might be substitutes for office visits. Although we are unable to determine which effect prevails and whether demand inducement exists, our results suggest that consumer health information enables patients to reduce the number of office visits. Interestingly, our estimates differ substantially from previous findings in the aforementioned literature. We consider several possible explanations for these differences including endogenous information acquisition and differences in the estimation strategy and the information measure. However, the robustness tests provide evidence that the estimation strategy does not matter qualitatively, and our replication of Kenkel (1990) indicates that endogeneity is not an issue. On the other hand, our consumer health information measure is not based on disease symptoms and differs substantially from previously used information measures. Consequently, we consider differences in the latter to be the most likely explanation for the different estimates. Further research could explore in detail how decisions about medical care are affected by different types of consumer health information. In particular, the behavior of physicians confronted with a heterogeneously informed patient population demanding certain services has been rarely investigated so far (a notable exception is Shih and Tai-Seale, 2012). Future empirical work in this direction may shed light on the joint decision making between physicians and patients and thus help to understand outcomes in health care markets. In principle, the policy implications of this study vary depending on which effect prevails and whether demand inducement exists. For instance, if the latter was the case, one should adapt the health care system such that the incentives to induce demand are dampened, for example, by altering the reimbursement system. On the other hand, if information is a substitute for office visits, changing the health care system might be inadequate and one might instead consider shared-decision making models, for instance. However, independent of these possible explanations, better informed patients seem to have a lower demand for physician visits. In other words, consumer health information improves the efficiency in the health care sector in any situation. Therefore, policies to increase the average information level in the population may prove very effective in lowering health care expenditures, because they would address simultaneously different sources of inefficiencies. In addition, such policies are particularly appealing because they might be implemented without the need to fundamentally adapt the health care system. However, as we only examine physician visits, the presented results do not allow for conclusions about the overall benefits and costs of such a policy. Beyond the aforementioned limitations, Copyright © 2014 John Wiley & Sons, Ltd.


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our analysis is also limited in that we cannot estimate the long-term effect of an increase in information. These issues could be tackled if more detailed data were available. Nevertheless, our study presents clear evidence that consumer health information can affect patient’s behavior in ways that reduce the number of office visits, therefore resulting in lower health care expenditures.

ACKNOWLEDGEMENTS AND DISCLOSURE

I am grateful to Robert E. Leu, Michael Gerfin, Stefan Boes, Boris Kaiser, Stephan Fretz, and seminar participants at Bern (2012) and conference participants at Bochum (2013) for helpful comments on an earlier version of this paper. I also thank two anonymous referees, whose comments led to substantial improvements. None of the mentioned is responsible for remaining errors and shortcomings. The manuscript was written while working as an employed researcher at a public university and not funded by a third party. There are neither financial nor non-financial conflicts. Anonymity has been ensured by the Swiss Federal Statistical Office in delivering anonymized data; individuals cannot be identified with the given data. No other ethical considerations apply.

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SUPPORTING INFORMATION Additional supporting information may be found in the online version of this article at the publisher’s web site.



The impact of health information technology and e-health on the future demand for physician services.

Consumer health information and the not-for-profit health agency.

Consumer health information system urged.

Consumer values and the demand for professional services.

Analysis of online information searching for cardiovascular diseases on a consumer health information portal.

A questions-based investigation of consumer mental-health information.

Consumer medicine information.

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Virtual physician visits venture into mainstream use.

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Tax incentives and the demand for private health insurance.

Pharmacist and Physician Interpretation of Abbreviations for Acetaminophen Intended for Use in a Consumer Icon.

The physician and health promotion.

[Evaluation of the Heidelberg cancer information service by the consumer].