Health Services Research © Health Research and Educational Trust DOI: 10.1111/1475-6773.12345 RESEARCH ARTICLE
Patient Preferences for Features of Health Care Delivery Systems: A Discrete Choice Experiment Axel C. Muhlbacher, € Susanne Bethge, Shelby D. Reed, and Kevin A. Schulman Objective. To estimate the relative importance of organizational-, procedural-, and interpersonal-level features of health care delivery systems from the patient perspective. Data Sources/Study Setting. We designed four discrete choice experiments (DCEs) to measure patient preferences for 21 health system attributes. Participants were recruited through the online patient portal of a large health system. We analyzed the DCE data using random effects logit models. Data Collection/Extraction Methods. DCEs were performed in which respondents were provided with descriptions of alternative scenarios and asked to indicate which scenario they prefer. Respondents were randomly assigned to one of the three possible health scenarios (current health, new lung cancer diagnosis, or diabetes) and asked to complete 15 choice tasks. Each choice task included an annual out-of-pocket cost attribute. Principal Findings. A total of 3,900 respondents completed the survey. The out-ofpocket cost attribute was considered the most important across the four different DCEs. Following the cost attribute, trust and respect, multidisciplinary care, and shared decision making were judged as most important. The relative importance of out-of-pocket cost was consistently lower in the hypothetical context of a new lung cancer diagnosis compared with diabetes or the patient’s current health. Conclusions. This study demonstrates the complexity of patient decision making processes regarding features of health care delivery systems. Our findings suggest the importance of these features may change as a function of an individual’s medical conditions. Key Words. Patient preference, choice behavior, delivery of health care, discrete choice models
Delivering optimal patient-centered care may require restructuring the current health care system. Plans to design new health care delivery systems or to improve current systems should consider the preferences of patients at the organizational, process, and interpersonal levels. Historically, health system reforms 704
Patient Preferences for Features of Health Care Delivery Systems
705
have been conceptualized in a top-down manner by governments and health care administrators with little regard to patients’ preferences (Wismar and Busse 2002). Patients often have little choice among health care systems. Their options are typically restricted to the health systems with which their health plans have established payment contracts. Thus, the ability to study revealed preferences for features of health care systems using observational data is limited. Stated preference methods, such as discrete choice experiments (DCEs), provide a means to elicit information about which features of health care systems patients would most highly value if they had the opportunity to choose (Louviere, Hensher, and Swait 2000; Ryan and Farrar 2000; Bridges 2003; Bridges et al. 2008). Grounded in random utility theory, which posits that individuals act rationally to maximize their own utility (McFadden 1974), stated preference methods were developed in marketing and psychology to investigate trade-offs between crucial attributes of products to understand purchasing behavior (McFadden 1974; Thurstone 1974; Ryan and Hughes 1997; Ryan and Gerard 2003). The theory defines the value of a product as the sum of an individual’s utilities for the product’s attributes. Therefore, the value of any product or service, real or hypothetical, can be estimated as a function of its underlying features (Lancaster 1966, 1971). In DCEs, respondents must choose between two or more options (i.e., “choice sets” in DCE nomenclature) that are characterized by varying levels of the options’ relevant features (i.e., attributes). For example, a patient may be asked to choose between two possible drug treatments that vary with regard to the risks of side effects and positive outcomes. We designed and executed a DCE to characterize patients’ values for specific features of health care delivery systems by identifying how varying attributes at the organizational, procedural, and interpersonal levels influences patients’ choices and how these choices may change as a function of health status. Our goal was to provide quantitative information about the relative importance of each attribute so that patients’ priorities could be considered in the design (or modification) of health care delivery systems. We followed a
Address correspondence to Axel C. M€ uhlbacher, Ph.D., Hochschule Neubrandenburg, Brodaer Straße 2, 17033 Neubrandenburg Germany; e-mail: [email protected]. Axel C. M€ uhlbacher, Ph.D., and Susanne Bethge, M.Sc., are with the Institute of Health Economics and Health Care Management, Neubrandenburg University of Applied Sciences, Neubrandenburg, Germany. Shelby D. Reed, Ph.D., and Kevin A. Schulman, M.D., are with the Department of Medicine, Duke Clinical Research Institute, Duke University School of Medicine, Durham, NC. Axel C. M€ uhlbacher, Ph.D., is also a Senior Fellow at the Department of Medicine, Duke Clinical Research Institute, Duke University School of Medicine, Durham, NC and he received a Harkness Fellowship during the time of the study.
706
HSR: Health Services Research 51:2 (April 2016)
detailed checklist developed by experts in the field (Bridges et al. 2011) throughout the design and analysis of the DCE.
M ETHODS The design and analytic methods applied in this DCE were motivated by our research question, which was to discern which organizational-, procedural-, and interpersonal-level features of a health care delivery system are most important to patients and whether their importance differs when patients are confronted with different medical conditions. Although our focus was on measuring patient preferences, our aim was to inform individuals involved in designing and managing patient-centered health care delivery systems. Attributes and Levels Guided by existing conceptual frameworks, we developed a list of patient-relevant attributes of health care delivery systems. These existing frameworks included the chronic care model described by Wagner (1998), which captures all relevant aspects of delivery systems needed to improve health care for persons with chronic illness, and a framework for primary care organizations described by Hogg and colleagues (2008). This framework also makes the important distinction between structural and performance domains and uses further distinctions within each domain. Our preliminary list of attributes also aligned well with the recently published modification of the World Health Organization’s Innovative Care for Chronic Conditions framework (Oni et al. 2014). To refine the list of all decision-relevant attributes and corresponding levels within each attribute, we conducted key informant interviews (n = 9) and focus groups (n = 20). After developing the list, we completed cognitive interviews (n = 9) with individuals as they were reading the attributes and their levels. We then made revisions to the attributes to improve the validity and reliability of each item. Consistent with the models described above, these processes yielded a conceptual framework representative of preference-sensitive health care delivery systems incorporating individual-level, procedurallevel, and organizational-level features containing 21 attributes (Appendix Figure S1).
Patient Preferences for Features of Health Care Delivery Systems
707
Construction of Tasks, Experimental Design, and Preference Elicitation Discrete choice tasks with 21 attributes would be too cognitively burdensome for individuals to complete. Ryan and Gerard (2003) suggest including 4–8 attributes per choice task. To reduce the number of attributes in each choice task to a manageable level, we deconstructed the conceptual framework into four single DCEs. Each of the attributes and their corresponding levels were described using simple, patient-friendly language (Appendix S1). We chose the levels to represent realistic possibilities in contemporary health systems but limited them to three levels to allow respondents to discriminate between them. After developing the four DCEs, we conducted pretest interviews with nine individuals who demonstrated that they understood the choice tasks and made logical trade-offs. To evaluate whether patients’ priorities for various characteristics of health care delivery systems may be influenced by their medical conditions, we randomly assigned each respondent to complete the DCEs for one of the three scenarios. In one scenario, respondents were asked to consider their current health status (i.e., status quo). In the other two scenarios, respondents were asked to imagine how they would make choices if they were recently diagnosed with diabetes mellitus, a chronic but manageable disease, or recently diagnosed with lung cancer, an acute diagnosis that is incurable. Therefore, the last two scenarios were explained as hypothetical situations. The choice scenarios presented within a DCE are based on an experimental design that combines the attributes and levels. To generate this experimental design, we used Sawtooth Software (Sawtooth Software, Inc, Orem, Utah). To reduce respondent burden, we limited the number of unique choice tasks to 14 and required that the first task choice was repeated at the end of the experiment to control for consistency between answers. Thus, each respondent had to complete 15 choice tasks in total, as he or she was randomly assigned to 1 of the DCEs. We chose a balanced overlapping design that created 140 choice sets to maximize D-efficiency. We divided the choice sets into 10 questionnaire versions, each consisting of the 14 unique choice tasks. The experimental design was identical for each of the four different DCEs. We replicated these questionnaire versions for each of the three patient scenarios (i.e., status quo, diabetes, lung cancer). We randomly assigned respondents to one questionnaire version, and patients completed the questionnaire according to the patient scenario to which they were assigned. An example choice set is shown in Appendix Figure S2.
708
HSR: Health Services Research 51:2 (April 2016)
Instrument Design and Data Collection We designed the survey to be an online, self-administered survey. In addition to the DCE, we included survey items to elicit sociodemographic information (e.g., age, sex, employment status, and income), patient experience with health care delivery programs, and participants’ health-related information (e.g., selfrated health, chronic diseases, and hospitalizations). Based on pretesting, the survey was expected to take 35 minutes to complete. Potential survey respondents included all individuals with access to the Duke HealthView platform (now called Duke MyChart), an online portal that offers individuals personalized and secure access to view their medical records and test results, schedule appointments, request prescriptions, and communicate with medical care teams. It is available to patients who registered with the Duke University Health System. Participants were informed about the study on the HealthView website and invited to take part. An interested person could access the survey via a link in the information box. Eligible participants were 18 years or older. Before completing the survey, each respondent provided informed consent online. The institutional review board of the Duke University Health System approved the study. Sample size calculations represent a challenge in DCEs (Bridges et al. 2011). Minimum sample size depends on a number of criteria, including the question format, the complexity of the choice task, the desired precision of the results, and the need to conduct subgroup analyses (Louviere, Hensher, and Swait 2000). The minimum sample size according to the Orme’s calculation (based on two alternatives per choice set, 14 total choice sets for each participant, and three levels for each attribute) was 54, which was the lowest possible sample size for main effects estimation for each hypothetical medical condition scenario (i.e., status quo, diabetes, lung cancer). Statistical Analyses We applied random effects logit models to analyze the data from the four DCEs. The dependent variables represent the respondents’ preferred options for the choice tasks presented. The independent variables were the attributes and levels within each content block. We applied effects coding to calculate coefficients for each level (Bech and Gyrd-Hansen 2005). The “best” level by content was coded positive, and the “worst” level was used as the reference level. Thus, the effect coding coefficients show the difference from the grand mean. It is likely that the middle category of a three-level attribute will be close
Patient Preferences for Features of Health Care Delivery Systems
709
to the grand mean and, in this way, less likely to significantly differ from the grand mean. By calculating a coefficient for each level of each attribute, linearity assumptions relating the levels of the attribute to the odds of being chosen could be tested and confirmed. The random effects logit models were also calculated separately for the three medical condition scenarios sets (i.e., status quo, diabetes, and lung cancer). We did not specify hypotheses about the relative importance for specific attributes across the three scenarios; thus, these analyses were exploratory. Importance Weights of Attribute Levels The coefficients for each attribute level were estimated with the random effects logit models. These coefficients were exponentiated and reported as odds ratios. Odds ratios greater than 1 represent positive utilities, wherein respondents assign greater importance to the attribute level. Conversely, odds ratios less than 1 represent negative utilities and lower probability of a respondent choosing an alternative when this attribute level is shown (relative to the average). Mean Relative Importance In a DCE, utility is measured on an arbitrary scale, and the overall importance of each attribute is conditional upon the range of attribute levels chosen for the experiment. We computed the mean relative importance of each attribute by computing the difference between the highest and lowest coefficients for levels of the attribute. Then, we normalized the scale by assigning the most important attribute 10 units and measured the other attributes’ importance relative to this change. The mean relative importance score for each factor expresses an improvement from the worst level to the best level for an attribute on a scale from 0 to 10. Marginal Rate of Substitution and Willingness to Pay Because coefficients and utilities do not have a certain 0 point (Lancsar, Louviere, and Flynn 2007; Louviere, Hensher, and Swait 2000), we used the “outof pocket costs” attribute (which was identical in all DCE versions) to transform the estimates to a uniform scale. Inclusion of the price proxy for different health care delivery programs ($500 per year; $1,000 per year; $2,000 per
710
HSR: Health Services Research 51:2 (April 2016)
year; $3,000 per year) allowed estimation of the marginal rate of substitution between the costs and the other attributes as they express these “part-worth” values (Ryan and Hughes 1997; Gerard et al. 2008). The theory of welfare economics allows the estimation of willingness to pay based on patients’ willingness to trade between alternatives as long as a cost attribute is included (Lancsar, Louviere, and Flynn 2007; Louviere et al. 2007). By including the “out-of-pocket cost” attribute as a price proxy in each of the four DCEs, we are able to estimate the money value of each attribute, of combinations of attributes, or even the whole choice set (Ryan and Hughes 1997; Gerard et al. 2008; Telser, Becker, and Zweifel 2008; Becker et al. 2008), translating “intangible” values into assessable values (Sch€ offski, Glaser, and Schulenburg 1998). In this approach, estimates of willingness to pay are restricted to quantifying the monetary value of changes in hypothetical health care delivery systems. From a practical perspective, when including a cost attribute within a DCE, it is important to display acceptable and realistic level spans. Otherwise, participants may be unwilling to trade, may give biased responses, or may even refuse to answer (Drummond et al. 2005; Sculpher et al. 2005). For example, participants may engage in “protest choosing,” in which they trade only against the cost attribute and do not trade between all available attributes (Ryan and Hughes 1997; Gerard et al. 2008). Also, the inclusion of a “zero cost” attribute could have led patients to use a simplifying heuristic in which they always select the scenario with zero cost without considering other features. Therefore, we decided not to include a zero cost level. In addition, willingness to pay is nearly always positively associated with income, so it is necessary to adjust for income in the estimation. Despite this adjustment, there may be other factors, such as household assets or education status that could differentially affect the impact of income on willingness to pay across participants. Following standard consumer theory, the marginal rate of substitution between attributes can be obtained by calculating the ratio of the partial derivatives of each attribute. Willingness to pay represents the mean maximum monetary equivalent of an improvement in a single attribute, estimated by dividing the marginal utility of a change in attribute levels by the marginal utility of a change in the cost attribute. Therefore, willingness to pay can be calculated as the difference between the utility provided by a specified outcome at the lowest level (or initial level) and the utility provided after the change to the highest level, divided by the marginal utility of income. (Sometimes referred to as “implicit price” and usually represented by the coefficient of the payment attribute, this is also the utility provided by $1.00.) Hicksian
Patient Preferences for Features of Health Care Delivery Systems
711
compensating variation measures a change in expected utility due to a change in the level of provision in the attribute or attributes by weighting this change by the marginal utility of income. The distribution of welfare effects can be reported by bootstrap techniques (Krinsky and Robb 1986). The present study represents a cross-sectional study, and therefore no interpretation regarding possible discounting effects is possible.
RESULTS Our study included 3,900 individuals who provided consent and completed the survey between April and August 2011. Table 1 summarizes the characteristics of the study sample. The majority of the participants were women (65.5 Table 1: Respondent Characteristics Characteristic Sex, no. (%) Men Women Marital status, no. (%) Married Single Divorced or separated In a committed relationship, but not married Widowed Unknown Level of education, no. (%) Less than high school graduate High school graduate or equivalent Completed some college, but no degree Completed technical or community college College or university degree or higher Not sure Self-rated health, no. (%) Excellent Very good Good Fair Poor Not sure
Respondents (N = 3,900)
DUHS Patients* (N = 444,807)
1,347 (34.5) 2,553 (65.5)
177,616 (39.9) 267,167 (60.1)
2,431 (62.3) 568 (14.6) 432 (11.1) 311 (8.0) 158 (4.1) —†
189,015 (42.5) 85,515 (19.2) 27,120 (6.1) —† 23,169 (5.2) 119,183 (26.8)
47 (1.2) 293 (7.5) 665 (17.1) 394 (10.1) 2,499 (64.1) 2 (
Patient Preferences for Features of Health Care Delivery Systems: A Discrete Choice Experiment Axel C. Muhlbacher, € Susanne Bethge, Shelby D. Reed, and Kevin A. Schulman Objective. To estimate the relative importance of organizational-, procedural-, and interpersonal-level features of health care delivery systems from the patient perspective. Data Sources/Study Setting. We designed four discrete choice experiments (DCEs) to measure patient preferences for 21 health system attributes. Participants were recruited through the online patient portal of a large health system. We analyzed the DCE data using random effects logit models. Data Collection/Extraction Methods. DCEs were performed in which respondents were provided with descriptions of alternative scenarios and asked to indicate which scenario they prefer. Respondents were randomly assigned to one of the three possible health scenarios (current health, new lung cancer diagnosis, or diabetes) and asked to complete 15 choice tasks. Each choice task included an annual out-of-pocket cost attribute. Principal Findings. A total of 3,900 respondents completed the survey. The out-ofpocket cost attribute was considered the most important across the four different DCEs. Following the cost attribute, trust and respect, multidisciplinary care, and shared decision making were judged as most important. The relative importance of out-of-pocket cost was consistently lower in the hypothetical context of a new lung cancer diagnosis compared with diabetes or the patient’s current health. Conclusions. This study demonstrates the complexity of patient decision making processes regarding features of health care delivery systems. Our findings suggest the importance of these features may change as a function of an individual’s medical conditions. Key Words. Patient preference, choice behavior, delivery of health care, discrete choice models
Delivering optimal patient-centered care may require restructuring the current health care system. Plans to design new health care delivery systems or to improve current systems should consider the preferences of patients at the organizational, process, and interpersonal levels. Historically, health system reforms 704
Patient Preferences for Features of Health Care Delivery Systems
705
have been conceptualized in a top-down manner by governments and health care administrators with little regard to patients’ preferences (Wismar and Busse 2002). Patients often have little choice among health care systems. Their options are typically restricted to the health systems with which their health plans have established payment contracts. Thus, the ability to study revealed preferences for features of health care systems using observational data is limited. Stated preference methods, such as discrete choice experiments (DCEs), provide a means to elicit information about which features of health care systems patients would most highly value if they had the opportunity to choose (Louviere, Hensher, and Swait 2000; Ryan and Farrar 2000; Bridges 2003; Bridges et al. 2008). Grounded in random utility theory, which posits that individuals act rationally to maximize their own utility (McFadden 1974), stated preference methods were developed in marketing and psychology to investigate trade-offs between crucial attributes of products to understand purchasing behavior (McFadden 1974; Thurstone 1974; Ryan and Hughes 1997; Ryan and Gerard 2003). The theory defines the value of a product as the sum of an individual’s utilities for the product’s attributes. Therefore, the value of any product or service, real or hypothetical, can be estimated as a function of its underlying features (Lancaster 1966, 1971). In DCEs, respondents must choose between two or more options (i.e., “choice sets” in DCE nomenclature) that are characterized by varying levels of the options’ relevant features (i.e., attributes). For example, a patient may be asked to choose between two possible drug treatments that vary with regard to the risks of side effects and positive outcomes. We designed and executed a DCE to characterize patients’ values for specific features of health care delivery systems by identifying how varying attributes at the organizational, procedural, and interpersonal levels influences patients’ choices and how these choices may change as a function of health status. Our goal was to provide quantitative information about the relative importance of each attribute so that patients’ priorities could be considered in the design (or modification) of health care delivery systems. We followed a
Address correspondence to Axel C. M€ uhlbacher, Ph.D., Hochschule Neubrandenburg, Brodaer Straße 2, 17033 Neubrandenburg Germany; e-mail: [email protected]. Axel C. M€ uhlbacher, Ph.D., and Susanne Bethge, M.Sc., are with the Institute of Health Economics and Health Care Management, Neubrandenburg University of Applied Sciences, Neubrandenburg, Germany. Shelby D. Reed, Ph.D., and Kevin A. Schulman, M.D., are with the Department of Medicine, Duke Clinical Research Institute, Duke University School of Medicine, Durham, NC. Axel C. M€ uhlbacher, Ph.D., is also a Senior Fellow at the Department of Medicine, Duke Clinical Research Institute, Duke University School of Medicine, Durham, NC and he received a Harkness Fellowship during the time of the study.
706
HSR: Health Services Research 51:2 (April 2016)
detailed checklist developed by experts in the field (Bridges et al. 2011) throughout the design and analysis of the DCE.
M ETHODS The design and analytic methods applied in this DCE were motivated by our research question, which was to discern which organizational-, procedural-, and interpersonal-level features of a health care delivery system are most important to patients and whether their importance differs when patients are confronted with different medical conditions. Although our focus was on measuring patient preferences, our aim was to inform individuals involved in designing and managing patient-centered health care delivery systems. Attributes and Levels Guided by existing conceptual frameworks, we developed a list of patient-relevant attributes of health care delivery systems. These existing frameworks included the chronic care model described by Wagner (1998), which captures all relevant aspects of delivery systems needed to improve health care for persons with chronic illness, and a framework for primary care organizations described by Hogg and colleagues (2008). This framework also makes the important distinction between structural and performance domains and uses further distinctions within each domain. Our preliminary list of attributes also aligned well with the recently published modification of the World Health Organization’s Innovative Care for Chronic Conditions framework (Oni et al. 2014). To refine the list of all decision-relevant attributes and corresponding levels within each attribute, we conducted key informant interviews (n = 9) and focus groups (n = 20). After developing the list, we completed cognitive interviews (n = 9) with individuals as they were reading the attributes and their levels. We then made revisions to the attributes to improve the validity and reliability of each item. Consistent with the models described above, these processes yielded a conceptual framework representative of preference-sensitive health care delivery systems incorporating individual-level, procedurallevel, and organizational-level features containing 21 attributes (Appendix Figure S1).
Patient Preferences for Features of Health Care Delivery Systems
707
Construction of Tasks, Experimental Design, and Preference Elicitation Discrete choice tasks with 21 attributes would be too cognitively burdensome for individuals to complete. Ryan and Gerard (2003) suggest including 4–8 attributes per choice task. To reduce the number of attributes in each choice task to a manageable level, we deconstructed the conceptual framework into four single DCEs. Each of the attributes and their corresponding levels were described using simple, patient-friendly language (Appendix S1). We chose the levels to represent realistic possibilities in contemporary health systems but limited them to three levels to allow respondents to discriminate between them. After developing the four DCEs, we conducted pretest interviews with nine individuals who demonstrated that they understood the choice tasks and made logical trade-offs. To evaluate whether patients’ priorities for various characteristics of health care delivery systems may be influenced by their medical conditions, we randomly assigned each respondent to complete the DCEs for one of the three scenarios. In one scenario, respondents were asked to consider their current health status (i.e., status quo). In the other two scenarios, respondents were asked to imagine how they would make choices if they were recently diagnosed with diabetes mellitus, a chronic but manageable disease, or recently diagnosed with lung cancer, an acute diagnosis that is incurable. Therefore, the last two scenarios were explained as hypothetical situations. The choice scenarios presented within a DCE are based on an experimental design that combines the attributes and levels. To generate this experimental design, we used Sawtooth Software (Sawtooth Software, Inc, Orem, Utah). To reduce respondent burden, we limited the number of unique choice tasks to 14 and required that the first task choice was repeated at the end of the experiment to control for consistency between answers. Thus, each respondent had to complete 15 choice tasks in total, as he or she was randomly assigned to 1 of the DCEs. We chose a balanced overlapping design that created 140 choice sets to maximize D-efficiency. We divided the choice sets into 10 questionnaire versions, each consisting of the 14 unique choice tasks. The experimental design was identical for each of the four different DCEs. We replicated these questionnaire versions for each of the three patient scenarios (i.e., status quo, diabetes, lung cancer). We randomly assigned respondents to one questionnaire version, and patients completed the questionnaire according to the patient scenario to which they were assigned. An example choice set is shown in Appendix Figure S2.
708
HSR: Health Services Research 51:2 (April 2016)
Instrument Design and Data Collection We designed the survey to be an online, self-administered survey. In addition to the DCE, we included survey items to elicit sociodemographic information (e.g., age, sex, employment status, and income), patient experience with health care delivery programs, and participants’ health-related information (e.g., selfrated health, chronic diseases, and hospitalizations). Based on pretesting, the survey was expected to take 35 minutes to complete. Potential survey respondents included all individuals with access to the Duke HealthView platform (now called Duke MyChart), an online portal that offers individuals personalized and secure access to view their medical records and test results, schedule appointments, request prescriptions, and communicate with medical care teams. It is available to patients who registered with the Duke University Health System. Participants were informed about the study on the HealthView website and invited to take part. An interested person could access the survey via a link in the information box. Eligible participants were 18 years or older. Before completing the survey, each respondent provided informed consent online. The institutional review board of the Duke University Health System approved the study. Sample size calculations represent a challenge in DCEs (Bridges et al. 2011). Minimum sample size depends on a number of criteria, including the question format, the complexity of the choice task, the desired precision of the results, and the need to conduct subgroup analyses (Louviere, Hensher, and Swait 2000). The minimum sample size according to the Orme’s calculation (based on two alternatives per choice set, 14 total choice sets for each participant, and three levels for each attribute) was 54, which was the lowest possible sample size for main effects estimation for each hypothetical medical condition scenario (i.e., status quo, diabetes, lung cancer). Statistical Analyses We applied random effects logit models to analyze the data from the four DCEs. The dependent variables represent the respondents’ preferred options for the choice tasks presented. The independent variables were the attributes and levels within each content block. We applied effects coding to calculate coefficients for each level (Bech and Gyrd-Hansen 2005). The “best” level by content was coded positive, and the “worst” level was used as the reference level. Thus, the effect coding coefficients show the difference from the grand mean. It is likely that the middle category of a three-level attribute will be close
Patient Preferences for Features of Health Care Delivery Systems
709
to the grand mean and, in this way, less likely to significantly differ from the grand mean. By calculating a coefficient for each level of each attribute, linearity assumptions relating the levels of the attribute to the odds of being chosen could be tested and confirmed. The random effects logit models were also calculated separately for the three medical condition scenarios sets (i.e., status quo, diabetes, and lung cancer). We did not specify hypotheses about the relative importance for specific attributes across the three scenarios; thus, these analyses were exploratory. Importance Weights of Attribute Levels The coefficients for each attribute level were estimated with the random effects logit models. These coefficients were exponentiated and reported as odds ratios. Odds ratios greater than 1 represent positive utilities, wherein respondents assign greater importance to the attribute level. Conversely, odds ratios less than 1 represent negative utilities and lower probability of a respondent choosing an alternative when this attribute level is shown (relative to the average). Mean Relative Importance In a DCE, utility is measured on an arbitrary scale, and the overall importance of each attribute is conditional upon the range of attribute levels chosen for the experiment. We computed the mean relative importance of each attribute by computing the difference between the highest and lowest coefficients for levels of the attribute. Then, we normalized the scale by assigning the most important attribute 10 units and measured the other attributes’ importance relative to this change. The mean relative importance score for each factor expresses an improvement from the worst level to the best level for an attribute on a scale from 0 to 10. Marginal Rate of Substitution and Willingness to Pay Because coefficients and utilities do not have a certain 0 point (Lancsar, Louviere, and Flynn 2007; Louviere, Hensher, and Swait 2000), we used the “outof pocket costs” attribute (which was identical in all DCE versions) to transform the estimates to a uniform scale. Inclusion of the price proxy for different health care delivery programs ($500 per year; $1,000 per year; $2,000 per
710
HSR: Health Services Research 51:2 (April 2016)
year; $3,000 per year) allowed estimation of the marginal rate of substitution between the costs and the other attributes as they express these “part-worth” values (Ryan and Hughes 1997; Gerard et al. 2008). The theory of welfare economics allows the estimation of willingness to pay based on patients’ willingness to trade between alternatives as long as a cost attribute is included (Lancsar, Louviere, and Flynn 2007; Louviere et al. 2007). By including the “out-of-pocket cost” attribute as a price proxy in each of the four DCEs, we are able to estimate the money value of each attribute, of combinations of attributes, or even the whole choice set (Ryan and Hughes 1997; Gerard et al. 2008; Telser, Becker, and Zweifel 2008; Becker et al. 2008), translating “intangible” values into assessable values (Sch€ offski, Glaser, and Schulenburg 1998). In this approach, estimates of willingness to pay are restricted to quantifying the monetary value of changes in hypothetical health care delivery systems. From a practical perspective, when including a cost attribute within a DCE, it is important to display acceptable and realistic level spans. Otherwise, participants may be unwilling to trade, may give biased responses, or may even refuse to answer (Drummond et al. 2005; Sculpher et al. 2005). For example, participants may engage in “protest choosing,” in which they trade only against the cost attribute and do not trade between all available attributes (Ryan and Hughes 1997; Gerard et al. 2008). Also, the inclusion of a “zero cost” attribute could have led patients to use a simplifying heuristic in which they always select the scenario with zero cost without considering other features. Therefore, we decided not to include a zero cost level. In addition, willingness to pay is nearly always positively associated with income, so it is necessary to adjust for income in the estimation. Despite this adjustment, there may be other factors, such as household assets or education status that could differentially affect the impact of income on willingness to pay across participants. Following standard consumer theory, the marginal rate of substitution between attributes can be obtained by calculating the ratio of the partial derivatives of each attribute. Willingness to pay represents the mean maximum monetary equivalent of an improvement in a single attribute, estimated by dividing the marginal utility of a change in attribute levels by the marginal utility of a change in the cost attribute. Therefore, willingness to pay can be calculated as the difference between the utility provided by a specified outcome at the lowest level (or initial level) and the utility provided after the change to the highest level, divided by the marginal utility of income. (Sometimes referred to as “implicit price” and usually represented by the coefficient of the payment attribute, this is also the utility provided by $1.00.) Hicksian
Patient Preferences for Features of Health Care Delivery Systems
711
compensating variation measures a change in expected utility due to a change in the level of provision in the attribute or attributes by weighting this change by the marginal utility of income. The distribution of welfare effects can be reported by bootstrap techniques (Krinsky and Robb 1986). The present study represents a cross-sectional study, and therefore no interpretation regarding possible discounting effects is possible.
RESULTS Our study included 3,900 individuals who provided consent and completed the survey between April and August 2011. Table 1 summarizes the characteristics of the study sample. The majority of the participants were women (65.5 Table 1: Respondent Characteristics Characteristic Sex, no. (%) Men Women Marital status, no. (%) Married Single Divorced or separated In a committed relationship, but not married Widowed Unknown Level of education, no. (%) Less than high school graduate High school graduate or equivalent Completed some college, but no degree Completed technical or community college College or university degree or higher Not sure Self-rated health, no. (%) Excellent Very good Good Fair Poor Not sure
Respondents (N = 3,900)
DUHS Patients* (N = 444,807)
1,347 (34.5) 2,553 (65.5)
177,616 (39.9) 267,167 (60.1)
2,431 (62.3) 568 (14.6) 432 (11.1) 311 (8.0) 158 (4.1) —†
189,015 (42.5) 85,515 (19.2) 27,120 (6.1) —† 23,169 (5.2) 119,183 (26.8)
47 (1.2) 293 (7.5) 665 (17.1) 394 (10.1) 2,499 (64.1) 2 (
