Background
Health care utilization is influenced by the predisposition of patients and their ability and need to use health care services.1 Andersen's Behavioral Model of Health Services Use1 takes into account factors that could impact health care utilization inclusive of predisposing (age, gender), enabling (financial income, insurance), and need factors (perceived health status). Farmer et al2 further suggests that patients' relationship with their primary care providers and ease of access to attend appointments were particularly important to patients who lived in more remote regions.
Ultimately, patients' health-seeking behaviors are partially determined by what they value and are measured by their willingness to accept available options after considering the potential trade-offs between these options. Patients' preferences also vary depending on when they participate in research relative to their care pathway; as noted by Brown et al,3 43% of patients (N=683) with early-stage breast cancer changed their preferences after a medical consultation.
Choice-based techniques such as discrete choice experiments (DCE)4 have been successfully used to evaluate genetic counseling for cancer,5 screening programs,6 and cancer treatments.7 Few DCE studies have been performed on how patients value different aspects of health care appointments, how these preferences may differ, and how this might affect health care utilization.8,9
We hypothesized that different patient- and health care–related characteristics will influence patient preferences about attending health care appointments to varying degrees.10 Prior assumptions were made about the behavior of included characteristics and were described in detail in an earlier publication.10 For example, we hypothesize that in an ideal world, patients will prefer to travel shorter versus longer distances to health care appointments. We also included key variables described in a systematic analysis of studies using Andersen's Behavioral Model.11 These variables included age, marital status, gender, education, employment status, financial situation, ethnicity, health insurance, and perceived health status (an example of the DCE questionnaire is provided in supplemental eAppendix 1, available online with this article at JNCCN.org).
A DCE was chosen as the preference-elicitation method in our study to
Quantify and establish the relative importance of different health care appointment–related attributes,
Identify similarities and differences in preferences among patient subgroups, and
Determine willingness to pay (WTP) estimates for health care appointment–related attributes.
Methods and Study Design
A full description of the methods has been described previously.10 The most salient methodological aspects of the study are summarized in the following sections.
Participants and Setting
Our study sample consisted of English-speaking patients who were diagnosed with cancer from January 2009 and attended any 1 of 3 adult oncology services across southwestern Victoria, Australia. Eligible patients who attended an appointment at the oncology services during the period July to November 2014 were given a questionnaire by the administrative or medical staff members. Patients were able to provide informed consent; there were no exclusion criteria.
A letter introducing the aim of the study, the DCE questionnaire, and a reply paid envelope were distributed to 512 eligible participants. Additional information about sociodemographic, disease-related, and treatment-related characteristics were collected. The questionnaire was designed to be self-completed and participants did not receive assistance from research staff.
The Barwon Health Human Research Ethics Committee approved the study. A returned questionnaire was assumed to represent implied consent.
The DCE Questionnaire
The DCE enabled the comparison of multiple appointment-related features in hypothetical scenarios to simulate decision-making in reality; choice responses were then analyzed to identify patients' preferences for different types of health care appointments. Respondents were asked to choose from several options, wherein each option was defined by its appointment attributes. Each attribute takes one of a number of possible levels. The relative importance of each attribute-level combination was estimated to reflect patient preferences and trade-offs.
Attributes and Levels: Attributes and levels were derived from the published literature and an analysis of qualitative data collected in interviews (N=21) from both patients with cancer and health care professionals (HCPs) caring for them.10 This approach generated a comprehensive range of attributes that are potentially of importance to patients when they attend health care appointments (Table 1).
Experimental Design of DCE:The combination of 6 attributes with 3 levels yielded 729 (=36) possible appointment profiles. A smaller fractional factorial design (FFD) was selected according to the D-efficiency criteria.12 The final FFD design randomly blocked 128 choice sets into 16 blocks with 8 choice tasks (supplemental eAppendix 1).
Attributes and Levels Used in the Discrete Choice Experiment
For each choice task, respondents were asked to choose between Appointment A and Appointment B (Figure 1). There was no opt-out option, because most patients in this situation were likely to attend health care appointments due to the factors included in the design. We expected that all of the included attributes were important to the respondents,10 rather than to assume, a priori, that parameters were zero.13–15
Eleven patients who had participated in the qualitative interviews were invited to pilot test the DCE; one did not return the questionnaire. Respondents were provided with descriptions of the attributes and levels, and were given the opportunity to comment on the DCE design and layout. Five respondents found the DCE questionnaire complex and were unable to complete the questions. Subsequently, changes were made to include an example choice task that demonstrated how to choose between the 2 appointment options.
Statistical Analysis
The analysis was undertaken using a range of logistic regression models, which are commonly used to explore situations in which the dependent variable (ie, appointment choice) is binary. A conditional logit function (CL)16 is widely used in choice modeling, but it assumes all responses come from a common decision-maker, thus limiting consideration of response heterogeneity. The mixed logistic regression (ML) model17 overcomes some of these limitations, because it (1) allows regression coefficients to be drawn from a distribution and vary across respondents, implying that different respondents may have different preferences, and (2) accounts for different scenarios that the same respondent may face.
These regression models are based on the random utility model (RUM) framework, in which each respondent n is assumed to be rational and maximize utility (U),18 wherein they must choose an
An example of a choice task used in the discrete choice experiment.
Citation: Journal of the National Comprehensive Cancer Network J Natl Compr Canc Netw 14, 3; 10.6004/jnccn.2016.0036
The respondents are assumed to choose the appointment option that maximizes utility; hence we estimated the following model:
Uappointment= ß0 + ß1Expertise1 + ß2Expertise2 + ß3Drknows1 + ß4Drknows2 + ß5WaitTimes1 + ß6WaitTimes2 + ß7Support1 + ß8Support2 + ß9TravelTimes1 + ß10TravelTimes2 + ß11Cost_Cont + ε where Uappointment represents the utility of a hypothetical appointment scenario, ß0 is a constant that indicates the general respondents' preferences for Appointment A versus Appointment B, and ß1 through ß10 are the coefficients that reflect the relative importance of each attribute level compared with its reference level. All variables other than cost were treated as binary variables, whereas the cost parameter was modeled as a continuous variable (Cost_Cont) to allow estimation of WTP estimates around each attribute-level combination.
A positive (negative) ß coefficient indicates whether the attribute-level combination was preferred (not preferred) relative to the reference level. The higher the absolute value of the ß coefficient, the stronger the preference for that level relative to the reference level.
Subgroup analysis was undertaken using CL to explore differences in preferences among sociodemographic, disease-related, and treatment-related characteristics. Where possible, the subgroups were prespecified to ensure adequate sample size for estimation, thus enabling greater confidence in the comparison of results among subgroups (Table 2).
Results from the regression analyses are presented as parameter estimates, with 95% CIs and P values.
Subgroups and Their Descriptions
Results
Respondent Characteristics
A total of 512 questionnaires were distributed to patients across 3 hospital sites; the participation rate was 36% (N=185). There were 128 completed questionnaires and 57 partially completed questionnaires. The total number of responses resulted in 2,960 discrete-choice observations (sample sizes and sampling probabilities are provided in supplemental eAppendix 2). Respondents took an average of 17 minutes to complete the questionnaire (SD, 10; 2–90).
Patient characteristics are summarized in Table 3. Mean age for all respondents was 61 years (SD, 14; 22–92) with mean time since diagnoses of 34 months (SD, 46; 1–300).
Respondents' Preferences
Both CL (chi-square, 396; 11 df; P< .001; pseudo R2=0.249) and ML (chi-square=76; 11 df; P<.001) models indicated that all the attributes of the appointments were highly statistically significant relative to the reference level (Table 4). These attributes performed in line with prior assumptions,10 which confirms the theoretical validity of the study.
The most important attributes for determining patient preferences were expertise of the HCP (cancer specialist vs nurse/general practitioner [GP]: ß=1.98; P<.001) and familiarity of the doctors with their medical history (usual GP vs new practice: ß=1.61; P<.001); travel time (30 minutes vs 2–3 hours: ß=0.96; P<.001) was least likely to influence patient preferences.
Additional results from the ML model relate to the SD of the independent parameters. The last column of Table 4 shows that the derived SD for waiting time (3 weeks), unavailability of family/friends to stay overnight if required, and travel times were not statistically significant; these indicated that the distributional information for these parameters was contained within the mean with minimal heterogeneity. The statistically significant SD for expertise of HCP, familiarity of doctors with medical history, waiting time (6 weeks), and nonaccompaniment by family/friends suggests there is considerable heterogeneity around the mean for these parameters.
WTP Estimates Between Attributes: WTP estimates were derived from the CL model by comparing the coefficients for each attribute-level combination with the coefficient of cost. Figure 2A shows that, as expected, they reflected the magnitude of the coefficients in the CL regressions. Respondents' WTP for Expertise2 was $680 (CI, $470–$891), which indicated that patients were on average willing to pay $680 to consult cancer specialists rather than
Characteristics of Respondents in the Discrete Choice Experiment
Variation in Patient Preferences Across Subgroups: The WTP estimates for all appointment-related attributes were analyzed across major cities, inner regional and outer regional residences (Figure 2B). Expertise of the HCP and familiarity of the doctors with patients were the most important attributes for respondents from major cities (cancer specialist vs nurse/GP: ß=0.98; P<.001; usual GP vs new practice: ß=1.09; P<.001) and inner regional areas (cancer specialist vs nurse/GP: ß=1.41; P<.001; usual GP vs new practice: ß=1.04; P<.001). The degree of specialist care (cancer specialist vs nurse/GP: ß=1.14; P<.001) and waiting times (1 vs 6 weeks: ß=1.19; P<.001) strongly influenced the preferences of patients in outer regional areas.
When preferences were analyzed by gender, men strongly favored specialist care (cancer specialist vs nurse/GP: ß=1.56; P<.001) and being accompanied by family/friends (accompaniment vs nonaccompaniment: ß=1.11; P<.001). Being accompanied by family/friends (accompaniment vs nonaccompaniment: ß=0.50; P<.001) was much less important to women than the familiarity (usual GP vs new practice: ß=1.08; P<.001) and expertise (cancer specialist vs nurse/GP: ß=1.03; P<.001) of the HCP.
Discussion
All of the attributes explored in this study were important to patients when choosing between hypothetical health care appointments. The relative importance (from most important to least) of these appointment attributes was expertise of the HCP, familiarity of the doctor with their medical history, waiting time for appointments, availability of social support, and travel times to appointments. Consistent with our results, a DCE9 indicated that general practice patients strongly preferred seeing a doctor of their choice and were willing to trade off speed of access for continuity of care.
By the year 2020, it is estimated that the provision of oncology services will not meet the demand for care from patients with cancer.19 Hence, our patients' preferences for HCP with higher levels of cancer expertise and those familiar with their medical history
Conditional Logistic and Mixed Logistic Regression Models of Patient Preferences
The presence of a social support network was also very important to patients with cancer. These social networks may provide patients with both practical and emotional support that could improve their adherence and compliance to treatment.23 A meta-analysis24 suggested that higher levels of perceived social support, a larger social network, and being married were associated with relative risk reductions in cancer mortality of 25%, 20%, and 12%, respectively. A subgroup analysis of our population also revealed that male patients placed more importance on the presence of social networks compared with female patients. This observation is in keeping with a population-based study25 that examined the relationship between perceived social support and delay in seeking medical attention among patients with cancer. The authors found that the presence of partner support significantly decreased the time from symptoms to medical presentation for male patients. Future campaigns could harness these sex-based differences to improve health care services and service delivery through encouraging men to discuss their health with partners, and to empower their partners to be more attentive to men's health.25
Distance traveled was the least important factor that influenced our population's preferences, especially for respondents from major cities who preferred higher levels of specialist care, and those from outer regional areas who presumably had limited access to specialist health care locally. Previous research has estimated that the median travel time for patients in urban areas to access health care was 12.6 minutes, whereas those from rural areas could travel up to 42 minutes.26 However, those with complex illnesses such as cancer may be “forced” to travel longer distances for their specialist care. Hence, our study reaffirms that patients' willingness to travel27 may not shaped by distance
(A) Willingness to pay (WTP) for appointment-related attributes, mean WTP (AUD$) and 95% CI. (B) WTP for appointment-related attributes, mean WTP (AUD$) and 95% CI across major cities, inner regional, and outer regional residences.
Citation: Journal of the National Comprehensive Cancer Network J Natl Compr Canc Netw 14, 3; 10.6004/jnccn.2016.0036
Our study has several limitations. First, DCEs may not represent complex real-life situations given the limited number of attributes and levels tested. Second, misinterpretation of the information and the use of cognitive shortcuts by respondents could oversimplify the tasks and may result in choices that are not considered rational in the economic sense. Third, the DCE results are not representative of all patients with cancer, because we only explored English-speaking adult patients who attended adult oncology services.
However, our study provides valuable information about how patients weigh their care options and trade off different health care appointment features, demonstrating preference heterogeneity among respondents. We studied patient preferences about any health care appointments and did not stratify these visits into initial, follow-up, or treatment appointments. We believe this article provides novel evidence and information with respect to the “average” patient with cancer. We also examined multiple characteristics that influence patient preferences toward health care appointments, and derived WTP estimates in hypothetical appointment settings.
The WTP estimates in our study could guide the cost evaluation of health care models to cope with the increasing costs of medical care.28 The Health and Hospitals Fund (HHF)29 was established in Australia in 2009 to provide funding for developing regional cancer services, upgrading regional health infrastructure, and supporting clinical training in regional settings. The allocated HHF funding for Australian health care models emphasizes some of the attributes that were found to be important to patients in our study, including ease of access to specialist care.29
A patient-centered approach to developing current health care models is likely to improve patient and HCP satisfaction while ensuring that health care funds are allocated in the patients' best interests. A better understanding of patient preferences will guide future investment opportunities as policymakers aim to bridge the gap between the optimal health care models for patient-centered service delivery and the current medical infrastructure.
To further understand the relationship between stated (those elicited in the DCE) and revealed preferences (actual health seeking behavior),30 future research plans include understanding if and how much patients' preferences in relation to their treatment and management plans impact their survival outcomes. Potential confounding factors will be accounted for by analyzing information from the Evaluation of Cancer Outcomes31 database that collects information about patients with cancer presenting to southwestern Victoria, Australia.
Acknowledgments
The authors wish to thank all of the patients and health care professionals who participated in the interviews and discrete choice experiment study, as well as the medical oncologists and reception staff who assisted with participant recruitment across southwestern Victoria, Australia.
See JNCCN.org for supplemental online content.
The authors have disclosed that they have no financial interests, arrangements, affiliations, or commercial interests with the manufacturers of any products discussed in this article or their competitors. Dr. Wong is supported by clinical postgraduate research scholarships from the National Health and Medical Research Council (APP1074400), Rotary Bowel Scan, and Barwon South Western Regional Integrated Cancer Services.
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