- Email: [email protected]
Disparities in patient reports of communications to inform decision making in the DECISIONS survey
Patient Education and Counseling 87 (2012) 198–205
Contents lists available at SciVerse ScienceDirect
Patient Education and Counseling journal homepage: www.elsevier.com/locate/pateducou
Medical Decision Making
Disparities in patient reports of communications to inform decision making in the DECISIONS survey§ Brian J. Zikmund-Fisher a,b,c,*, Mick P. Couper c,d, Angela Fagerlin b,c,e a
Department of Health Behavior and Health Education, University of Michigan, Ann Arbor, USA Division of General Medicine, Department of Internal Medicine, University of Michigan, Ann Arbor, USA c Center for Bioethics and Social Sciences in Medicine, University of Michigan, Ann Arbor, USA d Survey Research Center, Institute for Social Research, University of Michigan, Ann Arbor, USA e Center for Clinical Management Research, VA Ann Arbor Healthcare System, Ann Arbor, USA b
A R T I C L E I N F O
A B S T R A C T
Article history: Received 26 January 2011 Received in revised form 3 June 2011 Accepted 11 August 2011
Objective: To identify patient- and decision-type predictors of two key aspects of informed decision making: discussing the cons (not just the pros) of medical interventions and asking patients what they want to do. Methods: Using data from 2473 members of the DECISIONS survey, a nationally representative sample of U.S. adults age 40+, we used logistic regression analysis to identify which patient characteristics predicted patient reports of healthcare providers discussing cons or eliciting preferences about one of 9 common medical decisions. Results: Multiple demographic characteristics predicted both discussions of cons and elicitations of preferences, although the specific characteristics varied between decision contexts. In particular, African-American respondents reported being more likely to receive a discussion of the cons of cancer screening (OR = 1.69, p < 0.05) yet less likely to have been asked their opinion about either getting a cancer screening test (OR = 0.56, p < 0.05) or initiating medications (OR = 0.53, p < 0.05). Significant cross-decision variations remained even after controlling for patient characteristics. Conclusions: Important disparities in patient communication and involvement appear to exist both between different types of medical decisions and between different types of patients. Practice implications: Providers must make sure to consistently discuss the cons of treatment and to solicit input from all patients, especially African-Americans. ß 2011 Elsevier Ireland Ltd. All rights reserved.
Keywords: Decision making Patient education as topic Patient participation Minority health
1. Introduction Few medical interventions offer benefits with no risks. In fact, there is often a significant correlation between the benefits and risks of treatment. While medications can help manage chronic conditions, surgeries can restore functionality, and screening tests can identify incipient disease, each of these medical actions almost always comes with potential complications or side effects. As a result, an essential tenet of informed decision making about medical care is that patients need to understand both reasons why they might want to take medical action (‘‘pros’’) and reasons why not doing so might be a better choice (‘‘cons’’) [1]. Yet, research has shown that decision quality for preference-sensitive decisions is
§ An earlier version of this manuscript was presented to the International Shared Decision Making conference, Boston, MA, June 17, 2009. * Corresponding author at: Department of Health Behavior and Health Education, 1415 Washington Heights, Ann Arbor, MI 48109-2029, USA. Tel.: +1 734 936 9179; fax: +1 734 763 7379. E-mail address: [email protected] (B.J. Zikmund-Fisher).
0738-3991/$ – see front matter ß 2011 Elsevier Ireland Ltd. All rights reserved. doi:10.1016/j.pec.2011.08.002
substandard as patients often have unrealistic expectations regarding risks and benefits of treatment. In addition, physicians often do not have an adequate understanding of patients’ values, which often leads to overutilization of treatment [2]. Given the aforementioned problems, it is critical to ask patients about their treatment preferences, and doing so is one of the most basic forms of patient involvement in medical care [1]. Even patients who wish to defer to their provider’s judgments will often value the opportunity to feel engaged in the process [3–5]. Explicit inquiries about patient preferences provide patients with unambiguous opportunities to ask questions, clarify understanding. More importantly, eliciting patient preferences is the most direct way to incorporate patients’ values into a shared medical decision making process [6]. Furthermore, the role of patients’ preferences in decision making is increasing. Previously, patients relied more on physicians’ recommendations and did not participate in the decision to the degree they do today [7]. Yet, as medical science identifies interventions with marginal benefits but also non-trivial risks or costs, there are an increasing number of decisions for
B.J. Zikmund-Fisher et al. / Patient Education and Counseling 87 (2012) 198–205
which both clinicians and patients believe that the ‘‘best choice’’ is dependent up on the preferences, values, and goals of the patient. Shared decision making is particularly essential in such dual equipoise situations [8]. However, for patients to be able to determine how each choice is related to their preferences, values, and goals, patients must be informed of (and understand) the pros and cons of each treatment choice. Involvement of patients in discussions of the risks and benefits of treatment choices is critical because the perception of the pros and cons can vary significantly across stakeholders (patients, family members, clinicians). In fact, Weinstein et al. recently argued ‘‘One of the ethical imperatives of patient-centered care is the balanced, evidence-based presentation of risks and benefits by providers to patients.’’ [9]. Yet, medical decisions vary substantially in how easy or hard it is to achieve this goal. Some decisions require only limited facts in order to effectively inform patient decision making, while others require detailed information about key risks or benefits or awareness of a broad range of possible outcomes. When informational needs are non-trivial, other concerns, such as the time constraints present in most clinical encounters today, further challenge physicians’ ability to communicate effectively about treatment choices and associated risks and benefits. Such constraints likely affect patient-provider communications in certain contexts (e.g., primary care) more than others. The recently published National Survey of Medical Decisions (referred to as the DECISIONS Study) was the first study to look at how a broad spectrum of common medical decisions are made by a nationally representative sample of patients. Its data have been used to document how frequently U.S. adults are considering common medical decisions about prescription medications, cancer screening tests, and elective surgeries [10], how informed patients feel they are about these decisions [11], the extent of patients’ actual knowledge [12], patient use of different sources of information [13], and the specific patterns of decision making that exist for different types of decisions (e.g., cancer screening test decisions) [14–18]. When the DECISIONS study asked patients about their communications with providers, an interesting contrast emerged. In some ways, patients reported that health care providers were remarkably consistent: Providers were reported as unfailingly discussing pros and giving recommendations for what to do (usually to do the action) with the vast majority of patients in every decision examined [19]. The consistency with which pros were discussed did not, however, carry forward to discussions of cons, which were reported as having been provided for some decisions but very infrequently for others [19]. Furthermore, whether or not patients reported having been asked what they wanted to do also varied widely across conditions [19]. As noted above, there are many reasons why discussions of cons and elicitation of patient preferences are particularly essential for informed decision making. As a result, the observed variability in patient reports of these behaviors in the DECISIONS data was concerning. However, the published papers that have used the DECISIONS data did not test whether patient characteristics were related to the likelihood of reporting discussions of cons or elicitations of patient preferences. As a result, it has remained unclear whether the previously reported variations in these behaviors across decision types was exclusively a function of the type of decision being made or whether, instead or in addition, it derived from different patient populations facing each type of decision. If certain types of patients are indeed more prone to have patient–provider communications that lack these important elements, it is imperative that we identify those factors that predict which patients are least likely to have balanced and engaging discussions with health care providers in order to reduce disparities and design appropriate corrective interventions. To that
199
end, this paper reports a new analysis of the DECISIONS dataset that examines the individual characteristics that predict these outcomes while controlling for the substantial cross-decision variation in these reported provider behaviors. 2. Methods 2.1. Design The DECISIONS study involved a list-assisted random-digit-dial (RDD) telephone survey of a national probability sample of English-speaking U.S. adults 40 years of age and older conducted between November 2006 and May 2007. See Zikmund-Fisher et al. [10] for complete details of the sampling, instrument development, and data collection methodologies. All procedures and instruments received approval from the University of Michigan and VA Ann Arbor Medical Center institutional review boards prior to data collection. Primary data collection in the DECISIONS survey occurred in core question modules that examined nine common types of decisions regarding prescription medications (for hypertension, hypercholesterolemia, or depression), cancer screening tests (breast, prostate, and colorectal), and elective surgeries (hip/knee replacement, cataracts, and lower back pain). As detailed in the Appendix to Zikmund-Fisher et al. [10], each primary respondent (randomly selected from eligible adults in the household) was randomly assigned to complete up to two core modules, with the probability of assignment inversely related to the expected prevalence of that condition (estimated from various national survey data sources), as compared to the other modules for which the respondent was eligible. Respondents were eligible to complete a core module if they reported having initiated medications, been tested, or had surgery within the preceding two years or having discussed medications, testing, or surgery with a health care provider in that same time period. Each of the 9 core modules included a set of questions about patient–provider communications and patient involvement in decision making, as well as other questions related to each of the specific decisions not reported here. The four questions that addressed discussions of pros and cons of treatment and provider and patient preferences are shown in Table 1. In this manuscript, we focus on the two questions that addressed the degree that health care providers discussed cons and elicited patient preferences about the target decision. Because the degree that cons were discussed is appropriately a function of specific patient circumstances, subject to reporting variance due to patients’ subjective ratings, and produced a highly skewed distribution in the DECISIONS data, we dichotomize this variable into ‘‘none’’ vs. ‘‘a little’’/‘‘some’’/‘‘a lot’’ for analysis purposes. Respondents also answered a set of questions about demographic characteristics (including race/ethnicity), economic status, access to health care, and trust in their primary care providers. 2.2. Analyses All analyses include selection, nonresponse, post-stratification, and module-randomization weights to adjust for different selection probabilities and nonresponse (for details, see [10]) When combined, these weights permit generalization to the population of adult patients age 40 and older who considered each of these nine medical decisions. We used STATA 11’s svy command set to estimate multivariate logistic regression models while adjusting for this weighting and stratification structure [20]. As in the previously published paper on these DECISIONS variables [19], all analyses of the medication modules included only those individuals who reported either initiating medication
B.J. Zikmund-Fisher et al. / Patient Education and Counseling 87 (2012) 198–205
200
Table 1 DECISIONS questions related to discussions of pros and cons, statements of provider opinions, and elicitation of patient preferences. ‘‘How much did the health care provider discuss with you the reasons to_____?’’ (Not at all, A little, Some, A lot) ‘‘How much did the health care provider discuss with you the reasons not to_____?’’ (Not at all, A little, Some, A lot) ‘‘Did the health care provider express an opinion about whether or not you should_____?’’ (Yes, No) ‘‘Did the health care provider ask you what your preference was with regard to whether or not to_____?’’ (Yes, No)
therapy or having discussed medication initiation. While decisions regarding discontinuation of medications are extremely important, they are qualitatively different in circumstance and character from decisions to initiate therapy and are hence excluded here.
3. Results The DECISIONS study resulted in a total of 3010 completed interviews, which represented 86.5% of the individuals confirmed by interviewers as being eligible to participate and a weighted American Association of Public Opinion Research RR4 response rate [21] of 51.6% (see [10] for details). This rate is comparable to that obtained by the widely cited Centers for Disease Control and Prevention’s Behavioral Risk Factor Surveillance System (2006 median response rate: 51.4%) [18–20] and reflects the continuing challenges facing all researchers attempting to conducting RDD
telephone surveys in the general population [22]. The average interview length was 27.7 min. Table 2 reports both the unweighted and weighted sample characteristics of the 2473 DECISIONS respondents who completed one or more of the 9 question modules regarding initiation of prescription medications, cancer screening tests, or elective surgeries. As expected, the demographics of respondents did vary across the 9 decisions, reflecting in particular the age and gender differences between, for example, the set of patients discussing cancer screening tests vs. cataract removal surgery. 3.1. Prevalence of informed decision making interactions Table 3 summarizes the overall prevalence of the four key aspects of the informed decision making process that were measured in DECISIONS (using the questions in Table 1) and previously reported in [19]. As noted above, discussions of pros and statements of providers’ opinions were both extremely common and did not vary much at all across decisions. This consistency of patient experience was not replicated, however, in our two focal variables. Discussions of cons varied from a low of 20% to a high of 80%, and reports of elicitations of patient preferences varied from 34% to 80% across the 9 decisions. Our primary research question is whether the observed variations in these key elements of the informed decision making process occur only between different types of decisions (a question addressed in the previously published analyses) or whether individual patient characteristics also influence their prevalence
Table 2 Sample characteristics. N (%) in sample
Characteristic
Range of weighted % in 9 decisions studied
593 754 550 576
(24.0%) (30.5%) (22.2%) (23.3%)
34.1% 28.2% 19.3% 18.4%
[4.4%, 48.0%] [10.6%, 36.9%] [13.0%, 23.5%] [12.3%, 61.5%]
Male: married Male: single Female married Female single
737 220 829 687
(29.8%) (8.9%) (33.5%) (27.8%)
32.9% 8.3% 35.3% 23.4%
[23.6%, 43.2%]a [5.8%, 12.1%]a [20.9%, 38.0%]a [17.0%, 35.6%]a
Education: Education: Education: Education: Education:
179 869 522 490 413
(7.2%) (35.1%) (21.1%) (19.8%) (16.7%)
8.2% 33.8% 22.0% 20.5% 15.5%
[3.3%, 18.9%] [31.2%, 42.3%] [16.6%, 24.9%] [10.5%, 25.1%] [8.6%, 18.7%]
Age: Age: Age: Age:
40s 50s 60s 70+
Weighted %
Hispanic African-American
95 (3.8%)
6.4%
[3.7%, 7.9%]
198 (8.0%)
18.3%
[11.8%, 23.2%]
Has health insurance
2302 (93.1%)
91.0%
[86.4%, 98.6%]
Employed
1291 (52.2%)
Annual Annual Annual Annual Annual
income: income: income: income: income:
<$25 k/year $25 k X < $50 k $50 k X < $75 k $75 k X < $100 k $100 k
Health: Health: Health: Health: Health:
excellent very good good fair poor
PCP: none PCP: yes, not trusted PCP: yes, trusted
54.7%
[20.1%, 63.1%]
490 594 531 325 533
(19.8%) (24.0%) (21.5%) (13.1%) (21.5%)
20.9% 24.6% 20.0% 12.1% 22.3%
[11.0%, 39.9%] [22.6%, 33.6%] [10.9%, 23.2%] [5.3%, 15.9%] [11.7%, 30.2%]
429 869 777 286 112
(17.4%) (35.1%) (31.4%) (11.6%) (4.5%)
15.9% 33.4% 33.0% 12.5% 5.3%
[5.7%, 23.2%] [27.4%, 36.7%] [28.6%, 40.9%] [7.1%, 19.2%] [2.7%, 18.2%]
153 (6.2%) 375 (15.2%) 1942 (78.6%)
7.5% 15.8% 76.7%
[4.9%, 12.0%] [8.9%, 18.0%] [74.7%, 80.7%]
Notes: PCP = primary care provider. Sample restricted to 2473 respondents who were asked 1 or more decision modules but excludes those only asked about medication discontinuation decisions. Response categories may not sum to 100% due to rounding. a Excludes breast and prostate cancer modules due to their gender specificity.
B.J. Zikmund-Fisher et al. / Patient Education and Counseling 87 (2012) 198–205
201
Table 3 Prevalence of discussions of cons and elicitations of patient preferences in 9 common medical decisions. Decision process variable
Overall prevalence
95% C.I.
‘‘Pros’’ discussed at all? ‘‘Cons’’ discussed at all? Healthcare provider had an opinion? Patient asked his/her preference?
94.9% 40.6% 81.9% 50.5%
[94.0%, [38.0%, [79.9%, [48.0%,
Range in 9 decisions studied 95.8%] 43.2%] 83.7%] 52.9%]
[91.0%, [19.6%, [77.5%, [33.6%,
97.6%] 79.8%] 85.0%] 80.1%]
Data from [19]. Note: Table reports weighted prevalence estimates adjusting for the sampling design as well as the probability of being randomly assigned to complete each decision module.
Table 4 Logistic regression models predicting whether patients reported that healthcare providers discussed reasons not to take medical action (‘‘cons’’).
N (unique decisions)
Blood pressure medications Cholesterol medications Depression medications Colon cancer screening Breast cancer screening Prostate cancer screening Knee/hip replacement surgery Cataract surgery Lower back pain surgery Male: married Male: single Female: married Female: single White race African-American Multi-/other race Hispanic Age (per 10 years) Has health insurance Education (1–5) Health (1–5) Income (1–5) Primary care provider: none Primary care provider: untrusted Primary care provider: trusted * ** ***
Medications
Cancer screening tests
Surgeries
1285
2059
447
OR
95% C.I.
(Referent) 1.23 1.28
– [0.87, 1.73] [0.83, 1.96]
(Referent) 1.76 1.43 1.36 (Referent) 0.74 1.05 1.26 0.82** 1.63 1.00 0.92 1.31*** (Referent) 0.64 0.92
– [0.99, [0.93, [0.78, – [0.41, [0.60, [0.59, [0.71, [0.91, [0.86, [0.76, [1.12, – [0.31, [0.47,
3.12] 2.19] 2.35] 1.33] 1.86] 2.69] 0.94] 2.90] 1.16] 1.12] 1.53] 1.36] 1.81]
OR
95% C.I.
(Referent) 0.71 1.20
– [0.51, 1.01] [0.78, 1.86]
(Referent) 0.62 0.87 0.61 (Referent) 1.69* 1.43 0.83 0.95 1.00 0.85* 0.96 1.00 (Referent) 1.92 2.15*
– [0.34, [0.54, [0.35, – [1.04, [0.73, [0.40, [0.82, [0.53, [0.73, [0.82, [0.86, – [0.91, [1.11,
1.16] 1.40] 1.06] 2.76] 2.80] 1.71] 1.10] 1.88] 0.98] 1.13] 1.17] 4.07] 4.20]
OR
95% C.I.
(referent) 0.68 2.66* (Referent) 0.60 0.40* 0.53 (Referent) 0.69 0.70 0.20 0.76* 0.76 1.01 1.06 1.30 (Referent) 0.77 1.80
— [0.33,1.44] [1.22,5.78] – [0.19, 1.92] [0.20, 0.82] [0.22, 1.24] – [0.29, 1.60] [0.13, 3.77] [0.02, 2.31] [0.58, 0.99] [0.21, 2.75] [0.78, 1.32] [0.79, 1.42] [1.00, 1.68] – [0.21, 2.85] [0.68, 4.82]
p < 0.05. p < 0.01. p < 0.001.
in healthcare consultations. We also sought to test whether the cross-decision variations would remain significant after controlling for the differences in the patient populations making these different types of medical decisions. 3.2. Predictors of discussions of cons To explore these questions, Table 4 reports the results of three logistic regression models predicting discussions of cons regarding medication initiation, cancer screening, and surgery decisions, respectively. In medication decisions, older participants were significantly less likely to report having had a discussion of reasons not to take medications (OR = 0.82 per 10 years, p < 0.01), while more affluent participants were significantly more likely to report having had such discussions (OR = 1.31 per step on a 1–5 scale, p < 0.001). Neither of these factors, however, had any influence on discussions of the cons of cancer screening tests. Instead, having a trusted primary care provider (OR = 2.15, p < 0.05) and being African-American (OR = 1.69, p < 0.05) significantly increased the likelihood of hearing about reasons not to screen, while higher levels of education decreased the chances of discussing cons (OR = 0.85 per step on a 1–5 scale, p < 0.05). With regards to elective surgery decisions, age was again a significant negative predictor of discussions of cons (OR = 0.76, p < 0.05), as was being a married woman (OR = 0.40 vs. married men, p < 0.05). It is notable, also, that both single men and
single women also had lower rates of receiving discussions of cons than married men did (ORs = 0.60 and 0.53, respectively), although the effects are non-significant in part due to small sample size. The above analyses were conducted separately for each type of decision to allow for the possibility that the predictors of discussions of cons might vary substantially across different types of decisions. The results shown in Table 4 suggest that indeed they do. For example, while married men were, in general, more likely than unmarried men and all women to report having discussed the cons of cancer screening tests or surgeries, they appear much less likely to report discussing the cons of hypertension, cholesterol, or depression medications with their healthcare providers. AfricanAmericans reported being much more likely to have discussed the cons of cancer screening than white respondents, but no such effect is observed (and the odds ratios trend in the opposite direction) for medication and surgery decisions. Because we conducted three separate analyses, however, we do not have direct tests of the variability of discussions of cons across different types of decisions. Nonetheless, we do find confirmation that patients discussing surgery for lower back pain were significantly more likely to recall having had a discussion of reasons not to have surgery than even patients considering knee or hip replacement surgery (OR = 2.66, p < 0.05), even after controlling for differences in the characteristics of these two patient populations.
B.J. Zikmund-Fisher et al. / Patient Education and Counseling 87 (2012) 198–205
202
Fig. 1. Illustrative predicted probabilities of patient reporting having had a discussion of cons, by decision type. Table 5 Logistic regression models predicting whether patients reported that healthcare providers asked about their preferences.
N (unique decisions)
Blood pressure medications Cholesterol medications Depression medications Colon cancer screening Breast cancer screening Prostate cancer screening Knee/hip replacement surgery Cataract surgery Lower back pain surgery Male: married Male: single Female: married Female: single White race African-American Multi-/other race Hispanic Age (per 10 years) Has health insurance Education (1–5) Health (1–5) Income (1–5) Primary care provider: none Primary care provider: untrusted Primary care provider: trusted * ** ***
Medications
Cancer screening tests
Surgeries
1275
2018
442
OR
95% C.I.
(Referent) 0.96 2.59***
– [0.68, 1.37] [1.62, 4.13]
(Referent) 0.68 1.27 0.93 (Referent) 0.53* 1.01 1.19 0.77*** 1.75 0.99 0.97 0.94 (Referent) 0.82 1.67
– [0.39, [0.85, [0.59, – [0.32, [0.55, [0.54, [0.66, [0.96, [0.85, [0.84, [0.82, – [0.36, [0.80,
1.17] 1.89] 1.47] 0.87] 1.88] 2.60] 0.89] 3.19] 1.15] 1.12] 1.08] 1.85] 3.49]
OR
95% C.I.
(Referent) 1.36* 1.77**
– [1.01, 1.84] [1.16, 2.70]
(Referent) 0.46** 0.63* 0.70 (Referent) 0.56* 1.12 0.54 1.01 1.52 0.91 0.92 0.88* (Referent) 1.48 1.76*
– [0.27, [0.42, [0.44, – [0.36, [0.64, [0.29, [0.90, [0.90, [0.81, [0.80, [0.78, – [0.83, [1.05,
0.78] 0.95] 1.12] 0.88] 1.96] 1.01] 1.13] 2.57] 1.03] 1.04] 1.00] 2.65] 2.94]
OR
95% C.I.
(Referent) 0.60 0.83 (Referent) 0.70 0.63 0.41* (Referent) 1.43 0.50 0.99 0.74* 0.42 0.98 0.91 0.87 (Referent) 1.12 1.83
— [0.29, [0.36, – [0.24, [0.28, [0.19, – [0.57, [0.14, [0.24, [0.56, [0.09, [0.76, [0.71, [0.68, – [0.32, [0.62,
1.25] 1.92] 2.05] 1.43] 0.85] 3.61] 1.85] 4.06] 0.98] 1.83] 1.25] 1.17] 1.11] 3.93] 5.35]
p < 0.05. p < 0.01. p < 0.001.
To illustrate the magnitude of the above effects, Fig. 1 depicts the average predicted probability of reporting a discussion of cons for various combinations of decision type and individual characteristics, adjusted for all remaining variables in the model. When combined, the size of the effects can clearly be substantial. For example, we predict that an African-American patient with less than a high school education but a trusted primary care provider (PCP) would receive a discussion of the cons of cancer screening tests about 40% of the time. By contrast, the average White patient with a post-graduate education but no primary care provider is predicted to learn about cons only 10% of the time. 3.3. Predictors of elicitations of patient preferences Table 5 reports the results of the parallel logistic regression analyses examining predictors of whether patients reported that healthcare providers asked their opinions about whether or not to initiate medications, have a cancer screening test, or have surgery. Regarding discussions about medications, both older
and African-American patients were significantly less likely to report having been asked their opinion (age: OR = 0.77 per 10 years, p < 0.001; African-American: OR = 0.53, p < 0.05). African-Americans were also less likely to report being asked their preference about cancer screening tests (OR = 0.56, p < 0.05). In addition, more affluent patients were less likely to be asked their opinion about cancer screening tests (OR = 0.88 per step on a 1–5 scale, p < 0.05), as were single men and married women (single men: OR = 0.46 vs. married men, p < 0.01; married women: OR = 0.63, p < 0.05). Having a trusted primary care provider, however, increased the likelihood of being asked one’s preferences about cancer screening (OR = 1.76, p < 0.05). With regards to elective surgeries, older patients were again less likely to be asked what they wanted to do (OR = 0.74 per 10 years, p < 0.05), as were single women (OR = 0.41 vs. married men; p < 0.05). We illustrate the combined effects of these factors using illustrative examples shown in Fig. 2. Looking across decision types, the largest shift in covariate relationships occurs with African-Americans, who were
B.J. Zikmund-Fisher et al. / Patient Education and Counseling 87 (2012) 198–205
203
Fig. 2. Illustrative predicted probabilities of a patient reporting having been asked for his/her opinion, by decision type.
significantly less likely to be asked their opinions about medications or screening but somewhat more likely than Whites to be asked their opinions about surgery decisions (OR = 1.43, ns). Age was a significant predictor of preference elicitations for medications and surgery, but no evidence of such effects existed for cancer screening tests (OR = 1.01, ns). Lastly, an examination of the decision type coefficients in Table 5 clearly shows that the substantial cross-decision variation in the prevalence of patient preference elicitations is not merely a product of different patient populations. Even after controlling for all of our individual patient characteristics, DECISIONS respondents were significantly more likely to report having been asked their opinions for discussions of depression medications than blood pressure medications (OR = 2.59, p < 0.001) and discussions of breast or prostate cancer screening tests versus colon cancer screening (breast: OR = 1.36, p < 0.05; prostate: OR = 1.77, p < 0.01). 4. Discussion and conclusions 4.1. Discussion In order for patients to make informed decisions about their medical care, they must consistently learn about the cons of medical interventions, i.e., the reasons they might not want to undertake medical actions, in addition to learning about reasons to take action. In order for patients to be full participants in decision making about their care, they need to be reliably and explicitly asked about what they want to do. Yet, the DECISIONS data suggest that neither of these criteria is being met in the context of consultations about many common medical decisions. While previous research had shown that discussions of cons and elicitations of patient preferences varied across different types of medical decisions [19], the present analyses extend that work by showing that those variations persist even after controlling for a wide range of patient characteristics. Perhaps more importantly, we also identify several of these characteristics as independently predictive within the context of specific types of medical decisions (medications, cancer screening, and/or elective surgeries). Thus, our findings suggest that certain types of patients may be disproportionately likely to experience patient–provider interactions about these decisions that omit these key elements of informed decision making. For example, an affluent White patient in his 40s making a decision about hypertension medications appears likely to both receive a balanced discussion that includes both reasons to take medications (pros) and reasons not to do so (cons) and then to be asked explicitly for his opinion about what to do. Yet, a poorer, African-American man in his 70s appears much less likely to have either of these things occur.
The discrepancy between African Americans’ and Whites’ experiences with their physicians is not likely due to a lack of desire by African-American participants. In a recent study, Levinson et al. showed that basically all patients (96%) want to be informed of all available treatment options and to be asked their treatment preferences. Importantly, this desire did not differ by race [5]. As we were not privy to the DECISIONS participants’ conversations with their healthcare providers, we have no way of ascertaining whether observed disparities for African-American patients are due to differences in the information conveyed, differences in the quality of the patient–physician relationship/ communication, or differences in patients’ perceptions of the clinic visits. Still, our findings are suggestive of racial disparities in patient–provider interactions that are consistent with prior work. While previous research found few racial differences in terms of whether the content of Braddock’s 9 informed decision making elements were present in patient–physician conversations, observer ratings of the relationship between patients and physicians (in terms of responsiveness, respect, and listening) differed significantly by the race of the patients, as were patients’ ratings of physician communication and overall satisfaction with the clinic appointment [23]. Similarly, Cooper-Patrick et al. have shown that African Americans perceive their clinic visits as less participatory than due their White counterparts, even after adjusting for adjusting for patient age, gender, education, marital status, health status, and length of the patient–physician relationship [24]. More recently, African-American patients have been found to ask fewer questions in oncology consultations, suggesting that they subsequently receive less information from their doctors [25]. Our conclusions are limited first and foremost by the fact that DECISIONS only recorded patients’ recollections of what occurred in clinical consultations rather than actually observing the encounters in question. While eligibility was limited to those patients who had made decisions within a 2 year window, respondents may have had difficulty remembering specific details and may therefore have guessed at what was said or done. As a result, variations in the whether a discussion of cons or an elicitation of preferences was particularly salient may have resulted in biased recall. In particular, patients may be more or less likely to report these provider behaviors if the medication, test, or surgery was seen as successful outcomes versus if it turned out poorly. Nonetheless, these data accurately reflect how patients felt about their interactions with their healthcare providers about these specific medical decisions, and our data suggest that such perceptions are significantly related to both decision type and particular patient characteristics. We use the term ‘‘suggest’’ in part because we recognize that spurious relationships are possible given the multiple analyses conducted (we did not statistically
204
B.J. Zikmund-Fisher et al. / Patient Education and Counseling 87 (2012) 198–205
adjust for multiple comparisons due to the limited sample size available in DECISIONS). Nonetheless, we believe that the totality of our results provides strong evidence that discussions of cons and elicitations of patient preferences are likely to be both situationand patient-dependent. We also acknowledge that we did not independently measure the characteristics of the healthcare providers involved in these conversations, and it is possible that the patient characteristics are not inherently related to the observed variations in communications but instead are proxying for variations in the type and characteristics of the healthcare provider being seen.
research involved substantial collaboration between the University of Michigan (UM) research team and FIMDM representatives, the research grant was awarded in compliance with UM’s policies which bar funder interference in scholarly work. Design of the survey and control of the research data rested with the UM investigator team, and Dr. Zikmund-Fisher had full access to all the data in the study and takes responsibility for the integrity of the data and the accuracy of the data analysis. Dr. Zikmund-Fisher is supported by a Mentored Research Scholar Grant from the American Cancer Society (MRSG-06-130-01-CPPB). Conflicts of interest
4.2. Practice implications The authors have no conflicts of interest to disclose. Our results suggest that the question for both healthcare practitioners and researchers must not be whether patients, as a group, are given balanced discussions of their options about common medical interventions and involved in decision making about their care, but rather which patients experience such interactions, and under what circumstances. While the optimal level of patient involvement in medical decision making is both complex and highly situation dependent, the variability documented here is concerning given that the standards used in these analyses were quite minimal (i.e., any discussion of cons or asking of patients what they wanted to do). Providers need to be aware that their tendency to discuss reasons not to initiate prescription medications, undergo cancer screening tests, or have elective surgeries and their tendency to elicit patient preferences about these decisions appear to be potentially dependent on both who the patient is and what type of decision it is. If this relationship reflects patterns in provider behavior and not just patient recollections, then healthcare providers need to consider ways to make their interactions more egalitarian. While perhaps unintentional, discussing cons and eliciting preferences with only a subset of patients may unwittingly create differences in how patients are cared for (or at least how they believe they are cared for) and, as a result, potentially alter treatment decisions in unexpected and perhaps non-optimal ways. 4.3. Conclusion The prevalence of key elements of informed decision making in patient–provider interactions appears to be remarkably variable based on both individual patient characteristics and the type of medical decision being discussed. The DECISIONS data strongly suggest that important disparities in patient communication and involvement exist, with African-American and older patients, in particular, appearing less likely to report that certain types of patient–provider interactions included discussions of cons and/or elicitations of patient preferences. These conclusions are tempered, however, by the cross-sectional and retrospective nature of our data. We urge additional prospective research studies to confirm the existence of such disparities in health communications and to identify their causes. For example, do individual providers actually treat different groups of patients differently, or are the variations in the types of providers that different groups of patients see what really underlies the observed disparities in the patient experience of medical decision making? Only by simultaneously assessing both cross-patient and cross-provider variations in patient– provider communication dynamics can the root causes of such disparities be identified. Role of funding The Foundation for Informed Medical Decision Making (FIMDM) provided the funding for this research. While the
Acknowledgement None. References [1] Braddock 3rd CH, Edwards KA, Hasenberg NM, Laidley TL, Levinson W. Informed decision making in outpatient practice: time to get back to basics. J Amer Med Assoc 1999;282:2313–20. [2] O’Connor AM, Wennberg JE, Legare F, Llewellyn-Thomas HA, Moulton BW, Sepucha KR, et al. Toward the ‘tipping point’: decision aids and informed patient choice. Health Aff 2007;26:716–25. [3] Beisecker AE, Beisecker TD. Patient information-seeking behaviors when communicating with doctors. Med Care 1990;28:19–28. [4] Degner LF, Kristjanson LJ, Bowman D, Sloan JA, Carriere KC, O’Neil J, et al. Information needs and decisional preferences in women with breast cancer. J Amer Med Assoc 1997;277:1485–92. [5] Levinson W, Kao A, Kuby A, Thisted RA. Not all patients want to participate in decision making: a national study of public preferences. J Gen Intern Med 2005;20:531–5. [6] Charles C, Gafni A, Whelan T. Shared decision-making in the medical encounter: what does it mean? (or it takes at least two to tango) Soc Sci Med 1997;44:681–92. [7] Woolf SH, Chan ECY, Harris R, Sheridan SL, Braddock 3rd CH, Kaplan RM, et al. Promoting informed choice: transforming health care to dispense knowledge for decision making. Ann Intern Med 2005;143:293–300. [8] Elwyn G, Frosch DL, Rollnick S. Dual equipoise shared decision making: definitions for decision and behaviour support interventions. Implementation Sci 2009;4:75. [9] Weinstein JN, Clay K, Morgan TS. Informed patient choice: patient-centered valuing of surgical risks and benefits. Health Aff 2007;26:3726–30. [10] Zikmund-Fisher BJ, Couper MP, Singer E, Levin C, Fowler Jr FJ, Ziniel S, et al. The DECISIONS study: a nationwide survey of U.S. adults regarding nine common medical decisions. Med Decision Making 2010;30:20S–34S. [11] Sepucha KR, Fagerlin A, Couper MP, Levin CA, Singer E, Zikmund-Fisher BJ. How does feeling informed relate to being informed? The DECISIONS survey. Med Decision Making 2010;30:77S–84S. [12] Fagerlin A, Sepucha KR, Couper MP, Levin CA, Singer E, Zikmund-Fisher BJ. Patients’ knowledge about 9 common health conditions: the DECISIONS survey. Med Decision Making 2010;30:35S–52S. [13] Couper MP, Singer E, Levin CA, Fowler Jr FJ, Fagerlin A, Zikmund-Fisher BJ. Use of the internet and ratings of information sources for medical decisions: results from the DECISIONS survey. Med Decision Making 2010;30:106S–14S. [14] Dillard AJ, Couper MP, Zikmund-Fisher BJ. Perceived risk of cancer and patient reports of participation in decisions about screening: the DECISIONS study. Med Decision Making 2010;30:96S–105S. [15] Hoffman RM, Couper MP, Zikmund-Fisher BJ, Levin CA, McNaughton-Collins M, Helitzer DL, et al. Prostate cancer screening decisions: results from the National Survey of Medical Decisions (DECISIONS study). Arch Intern Med 2009;169:1611–8. [16] Hoffman RM, Lewis CL, Pignone MP, Couper MP, Barry MJ, Elmore JG, et al. Decision-making processes for breast, colorectal, and prostate cancer screening: the DECISIONS survey. Med Decision Making 2010;30:53S–64S. [17] Lewis CL, Couper MP, Levin CA, Pignone MP, Zikmund-Fisher BJ. Plans to stop cancer screening tests among adults who recently considered screening. J Gen Intern Med 2010;25:859–64. [18] Ratanawongsa N, Zikmund-Fisher BJ, Couper MP, Van Hoewyk J, Powe NR. Race, ethnicity, and shared decision making for hyperlipidemia and hypertension treatment: the DECISIONS survey. Med Decision Making 2010;30: 65S–76S. [19] Zikmund-Fisher BJ, Couper MP, Singer E, Ubel PA, Ziniel S, Fowler Jr FJ, et al. Deficits and variations in patients’ experience with making 9 common medical decisions: the DECISIONS survey. Med Decision Making 2010;30:85S–95S. [20] Stata Statistical Software, 11th ed., College Station, Texas: Stata Corporation; 2009.
B.J. Zikmund-Fisher et al. / Patient Education and Counseling 87 (2012) 198–205 [21] AAPOR. Standard definitions: final dispositions of case codes and outcome rates for surveys, 4th ed., Lenexa, Kansas: American Association for Public Opinion Research; 2006. [22] Lepkowski JM, Tucker NC, Brick JM, De Leeuw ED, Japec L, Lavrakas PJ, et al., editors. Advances in telephone survey methodology. New York: Wiley; 2007. [23] Levinson W, Hudak PL, Feldman JJ, Frankel RM, Kuby A, Bereknvei S, et al. It’s not what you say racial disparities in communication between orthopedic surgeons and patients. Med Care 2008;46:410–6.
205
[24] Cooper-Patrick L, Gallo JJ, Gonzales JJ, Vu TH, Powe NR, Nelson C, et al. Race gender partnership in the patient–physician relationship. J Amer Med Assoc 1999;282:583–9. [25] Eggly S, Harper FWK, Penner LA, Gleason MJ, Foster T, Albrecht TL. Variation in question asking during cancer clinical interactions: a potential source of disparities in access to information. Patient Educ Couns 2011;82:63–8.
Contents lists available at SciVerse ScienceDirect
Patient Education and Counseling journal homepage: www.elsevier.com/locate/pateducou
Medical Decision Making
Disparities in patient reports of communications to inform decision making in the DECISIONS survey§ Brian J. Zikmund-Fisher a,b,c,*, Mick P. Couper c,d, Angela Fagerlin b,c,e a
Department of Health Behavior and Health Education, University of Michigan, Ann Arbor, USA Division of General Medicine, Department of Internal Medicine, University of Michigan, Ann Arbor, USA c Center for Bioethics and Social Sciences in Medicine, University of Michigan, Ann Arbor, USA d Survey Research Center, Institute for Social Research, University of Michigan, Ann Arbor, USA e Center for Clinical Management Research, VA Ann Arbor Healthcare System, Ann Arbor, USA b
A R T I C L E I N F O
A B S T R A C T
Article history: Received 26 January 2011 Received in revised form 3 June 2011 Accepted 11 August 2011
Objective: To identify patient- and decision-type predictors of two key aspects of informed decision making: discussing the cons (not just the pros) of medical interventions and asking patients what they want to do. Methods: Using data from 2473 members of the DECISIONS survey, a nationally representative sample of U.S. adults age 40+, we used logistic regression analysis to identify which patient characteristics predicted patient reports of healthcare providers discussing cons or eliciting preferences about one of 9 common medical decisions. Results: Multiple demographic characteristics predicted both discussions of cons and elicitations of preferences, although the specific characteristics varied between decision contexts. In particular, African-American respondents reported being more likely to receive a discussion of the cons of cancer screening (OR = 1.69, p < 0.05) yet less likely to have been asked their opinion about either getting a cancer screening test (OR = 0.56, p < 0.05) or initiating medications (OR = 0.53, p < 0.05). Significant cross-decision variations remained even after controlling for patient characteristics. Conclusions: Important disparities in patient communication and involvement appear to exist both between different types of medical decisions and between different types of patients. Practice implications: Providers must make sure to consistently discuss the cons of treatment and to solicit input from all patients, especially African-Americans. ß 2011 Elsevier Ireland Ltd. All rights reserved.
Keywords: Decision making Patient education as topic Patient participation Minority health
1. Introduction Few medical interventions offer benefits with no risks. In fact, there is often a significant correlation between the benefits and risks of treatment. While medications can help manage chronic conditions, surgeries can restore functionality, and screening tests can identify incipient disease, each of these medical actions almost always comes with potential complications or side effects. As a result, an essential tenet of informed decision making about medical care is that patients need to understand both reasons why they might want to take medical action (‘‘pros’’) and reasons why not doing so might be a better choice (‘‘cons’’) [1]. Yet, research has shown that decision quality for preference-sensitive decisions is
§ An earlier version of this manuscript was presented to the International Shared Decision Making conference, Boston, MA, June 17, 2009. * Corresponding author at: Department of Health Behavior and Health Education, 1415 Washington Heights, Ann Arbor, MI 48109-2029, USA. Tel.: +1 734 936 9179; fax: +1 734 763 7379. E-mail address: [email protected] (B.J. Zikmund-Fisher).
0738-3991/$ – see front matter ß 2011 Elsevier Ireland Ltd. All rights reserved. doi:10.1016/j.pec.2011.08.002
substandard as patients often have unrealistic expectations regarding risks and benefits of treatment. In addition, physicians often do not have an adequate understanding of patients’ values, which often leads to overutilization of treatment [2]. Given the aforementioned problems, it is critical to ask patients about their treatment preferences, and doing so is one of the most basic forms of patient involvement in medical care [1]. Even patients who wish to defer to their provider’s judgments will often value the opportunity to feel engaged in the process [3–5]. Explicit inquiries about patient preferences provide patients with unambiguous opportunities to ask questions, clarify understanding. More importantly, eliciting patient preferences is the most direct way to incorporate patients’ values into a shared medical decision making process [6]. Furthermore, the role of patients’ preferences in decision making is increasing. Previously, patients relied more on physicians’ recommendations and did not participate in the decision to the degree they do today [7]. Yet, as medical science identifies interventions with marginal benefits but also non-trivial risks or costs, there are an increasing number of decisions for
B.J. Zikmund-Fisher et al. / Patient Education and Counseling 87 (2012) 198–205
which both clinicians and patients believe that the ‘‘best choice’’ is dependent up on the preferences, values, and goals of the patient. Shared decision making is particularly essential in such dual equipoise situations [8]. However, for patients to be able to determine how each choice is related to their preferences, values, and goals, patients must be informed of (and understand) the pros and cons of each treatment choice. Involvement of patients in discussions of the risks and benefits of treatment choices is critical because the perception of the pros and cons can vary significantly across stakeholders (patients, family members, clinicians). In fact, Weinstein et al. recently argued ‘‘One of the ethical imperatives of patient-centered care is the balanced, evidence-based presentation of risks and benefits by providers to patients.’’ [9]. Yet, medical decisions vary substantially in how easy or hard it is to achieve this goal. Some decisions require only limited facts in order to effectively inform patient decision making, while others require detailed information about key risks or benefits or awareness of a broad range of possible outcomes. When informational needs are non-trivial, other concerns, such as the time constraints present in most clinical encounters today, further challenge physicians’ ability to communicate effectively about treatment choices and associated risks and benefits. Such constraints likely affect patient-provider communications in certain contexts (e.g., primary care) more than others. The recently published National Survey of Medical Decisions (referred to as the DECISIONS Study) was the first study to look at how a broad spectrum of common medical decisions are made by a nationally representative sample of patients. Its data have been used to document how frequently U.S. adults are considering common medical decisions about prescription medications, cancer screening tests, and elective surgeries [10], how informed patients feel they are about these decisions [11], the extent of patients’ actual knowledge [12], patient use of different sources of information [13], and the specific patterns of decision making that exist for different types of decisions (e.g., cancer screening test decisions) [14–18]. When the DECISIONS study asked patients about their communications with providers, an interesting contrast emerged. In some ways, patients reported that health care providers were remarkably consistent: Providers were reported as unfailingly discussing pros and giving recommendations for what to do (usually to do the action) with the vast majority of patients in every decision examined [19]. The consistency with which pros were discussed did not, however, carry forward to discussions of cons, which were reported as having been provided for some decisions but very infrequently for others [19]. Furthermore, whether or not patients reported having been asked what they wanted to do also varied widely across conditions [19]. As noted above, there are many reasons why discussions of cons and elicitation of patient preferences are particularly essential for informed decision making. As a result, the observed variability in patient reports of these behaviors in the DECISIONS data was concerning. However, the published papers that have used the DECISIONS data did not test whether patient characteristics were related to the likelihood of reporting discussions of cons or elicitations of patient preferences. As a result, it has remained unclear whether the previously reported variations in these behaviors across decision types was exclusively a function of the type of decision being made or whether, instead or in addition, it derived from different patient populations facing each type of decision. If certain types of patients are indeed more prone to have patient–provider communications that lack these important elements, it is imperative that we identify those factors that predict which patients are least likely to have balanced and engaging discussions with health care providers in order to reduce disparities and design appropriate corrective interventions. To that
199
end, this paper reports a new analysis of the DECISIONS dataset that examines the individual characteristics that predict these outcomes while controlling for the substantial cross-decision variation in these reported provider behaviors. 2. Methods 2.1. Design The DECISIONS study involved a list-assisted random-digit-dial (RDD) telephone survey of a national probability sample of English-speaking U.S. adults 40 years of age and older conducted between November 2006 and May 2007. See Zikmund-Fisher et al. [10] for complete details of the sampling, instrument development, and data collection methodologies. All procedures and instruments received approval from the University of Michigan and VA Ann Arbor Medical Center institutional review boards prior to data collection. Primary data collection in the DECISIONS survey occurred in core question modules that examined nine common types of decisions regarding prescription medications (for hypertension, hypercholesterolemia, or depression), cancer screening tests (breast, prostate, and colorectal), and elective surgeries (hip/knee replacement, cataracts, and lower back pain). As detailed in the Appendix to Zikmund-Fisher et al. [10], each primary respondent (randomly selected from eligible adults in the household) was randomly assigned to complete up to two core modules, with the probability of assignment inversely related to the expected prevalence of that condition (estimated from various national survey data sources), as compared to the other modules for which the respondent was eligible. Respondents were eligible to complete a core module if they reported having initiated medications, been tested, or had surgery within the preceding two years or having discussed medications, testing, or surgery with a health care provider in that same time period. Each of the 9 core modules included a set of questions about patient–provider communications and patient involvement in decision making, as well as other questions related to each of the specific decisions not reported here. The four questions that addressed discussions of pros and cons of treatment and provider and patient preferences are shown in Table 1. In this manuscript, we focus on the two questions that addressed the degree that health care providers discussed cons and elicited patient preferences about the target decision. Because the degree that cons were discussed is appropriately a function of specific patient circumstances, subject to reporting variance due to patients’ subjective ratings, and produced a highly skewed distribution in the DECISIONS data, we dichotomize this variable into ‘‘none’’ vs. ‘‘a little’’/‘‘some’’/‘‘a lot’’ for analysis purposes. Respondents also answered a set of questions about demographic characteristics (including race/ethnicity), economic status, access to health care, and trust in their primary care providers. 2.2. Analyses All analyses include selection, nonresponse, post-stratification, and module-randomization weights to adjust for different selection probabilities and nonresponse (for details, see [10]) When combined, these weights permit generalization to the population of adult patients age 40 and older who considered each of these nine medical decisions. We used STATA 11’s svy command set to estimate multivariate logistic regression models while adjusting for this weighting and stratification structure [20]. As in the previously published paper on these DECISIONS variables [19], all analyses of the medication modules included only those individuals who reported either initiating medication
B.J. Zikmund-Fisher et al. / Patient Education and Counseling 87 (2012) 198–205
200
Table 1 DECISIONS questions related to discussions of pros and cons, statements of provider opinions, and elicitation of patient preferences. ‘‘How much did the health care provider discuss with you the reasons to_____?’’ (Not at all, A little, Some, A lot) ‘‘How much did the health care provider discuss with you the reasons not to_____?’’ (Not at all, A little, Some, A lot) ‘‘Did the health care provider express an opinion about whether or not you should_____?’’ (Yes, No) ‘‘Did the health care provider ask you what your preference was with regard to whether or not to_____?’’ (Yes, No)
therapy or having discussed medication initiation. While decisions regarding discontinuation of medications are extremely important, they are qualitatively different in circumstance and character from decisions to initiate therapy and are hence excluded here.
3. Results The DECISIONS study resulted in a total of 3010 completed interviews, which represented 86.5% of the individuals confirmed by interviewers as being eligible to participate and a weighted American Association of Public Opinion Research RR4 response rate [21] of 51.6% (see [10] for details). This rate is comparable to that obtained by the widely cited Centers for Disease Control and Prevention’s Behavioral Risk Factor Surveillance System (2006 median response rate: 51.4%) [18–20] and reflects the continuing challenges facing all researchers attempting to conducting RDD
telephone surveys in the general population [22]. The average interview length was 27.7 min. Table 2 reports both the unweighted and weighted sample characteristics of the 2473 DECISIONS respondents who completed one or more of the 9 question modules regarding initiation of prescription medications, cancer screening tests, or elective surgeries. As expected, the demographics of respondents did vary across the 9 decisions, reflecting in particular the age and gender differences between, for example, the set of patients discussing cancer screening tests vs. cataract removal surgery. 3.1. Prevalence of informed decision making interactions Table 3 summarizes the overall prevalence of the four key aspects of the informed decision making process that were measured in DECISIONS (using the questions in Table 1) and previously reported in [19]. As noted above, discussions of pros and statements of providers’ opinions were both extremely common and did not vary much at all across decisions. This consistency of patient experience was not replicated, however, in our two focal variables. Discussions of cons varied from a low of 20% to a high of 80%, and reports of elicitations of patient preferences varied from 34% to 80% across the 9 decisions. Our primary research question is whether the observed variations in these key elements of the informed decision making process occur only between different types of decisions (a question addressed in the previously published analyses) or whether individual patient characteristics also influence their prevalence
Table 2 Sample characteristics. N (%) in sample
Characteristic
Range of weighted % in 9 decisions studied
593 754 550 576
(24.0%) (30.5%) (22.2%) (23.3%)
34.1% 28.2% 19.3% 18.4%
[4.4%, 48.0%] [10.6%, 36.9%] [13.0%, 23.5%] [12.3%, 61.5%]
Male: married Male: single Female married Female single
737 220 829 687
(29.8%) (8.9%) (33.5%) (27.8%)
32.9% 8.3% 35.3% 23.4%
[23.6%, 43.2%]a [5.8%, 12.1%]a [20.9%, 38.0%]a [17.0%, 35.6%]a
Education: Education: Education: Education: Education:
179 869 522 490 413
(7.2%) (35.1%) (21.1%) (19.8%) (16.7%)
8.2% 33.8% 22.0% 20.5% 15.5%
[3.3%, 18.9%] [31.2%, 42.3%] [16.6%, 24.9%] [10.5%, 25.1%] [8.6%, 18.7%]
Age: Age: Age: Age:
40s 50s 60s 70+
Weighted %
Hispanic African-American
95 (3.8%)
6.4%
[3.7%, 7.9%]
198 (8.0%)
18.3%
[11.8%, 23.2%]
Has health insurance
2302 (93.1%)
91.0%
[86.4%, 98.6%]
Employed
1291 (52.2%)
Annual Annual Annual Annual Annual
income: income: income: income: income:
<$25 k/year $25 k X < $50 k $50 k X < $75 k $75 k X < $100 k $100 k
Health: Health: Health: Health: Health:
excellent very good good fair poor
PCP: none PCP: yes, not trusted PCP: yes, trusted
54.7%
[20.1%, 63.1%]
490 594 531 325 533
(19.8%) (24.0%) (21.5%) (13.1%) (21.5%)
20.9% 24.6% 20.0% 12.1% 22.3%
[11.0%, 39.9%] [22.6%, 33.6%] [10.9%, 23.2%] [5.3%, 15.9%] [11.7%, 30.2%]
429 869 777 286 112
(17.4%) (35.1%) (31.4%) (11.6%) (4.5%)
15.9% 33.4% 33.0% 12.5% 5.3%
[5.7%, 23.2%] [27.4%, 36.7%] [28.6%, 40.9%] [7.1%, 19.2%] [2.7%, 18.2%]
153 (6.2%) 375 (15.2%) 1942 (78.6%)
7.5% 15.8% 76.7%
[4.9%, 12.0%] [8.9%, 18.0%] [74.7%, 80.7%]
Notes: PCP = primary care provider. Sample restricted to 2473 respondents who were asked 1 or more decision modules but excludes those only asked about medication discontinuation decisions. Response categories may not sum to 100% due to rounding. a Excludes breast and prostate cancer modules due to their gender specificity.
B.J. Zikmund-Fisher et al. / Patient Education and Counseling 87 (2012) 198–205
201
Table 3 Prevalence of discussions of cons and elicitations of patient preferences in 9 common medical decisions. Decision process variable
Overall prevalence
95% C.I.
‘‘Pros’’ discussed at all? ‘‘Cons’’ discussed at all? Healthcare provider had an opinion? Patient asked his/her preference?
94.9% 40.6% 81.9% 50.5%
[94.0%, [38.0%, [79.9%, [48.0%,
Range in 9 decisions studied 95.8%] 43.2%] 83.7%] 52.9%]
[91.0%, [19.6%, [77.5%, [33.6%,
97.6%] 79.8%] 85.0%] 80.1%]
Data from [19]. Note: Table reports weighted prevalence estimates adjusting for the sampling design as well as the probability of being randomly assigned to complete each decision module.
Table 4 Logistic regression models predicting whether patients reported that healthcare providers discussed reasons not to take medical action (‘‘cons’’).
N (unique decisions)
Blood pressure medications Cholesterol medications Depression medications Colon cancer screening Breast cancer screening Prostate cancer screening Knee/hip replacement surgery Cataract surgery Lower back pain surgery Male: married Male: single Female: married Female: single White race African-American Multi-/other race Hispanic Age (per 10 years) Has health insurance Education (1–5) Health (1–5) Income (1–5) Primary care provider: none Primary care provider: untrusted Primary care provider: trusted * ** ***
Medications
Cancer screening tests
Surgeries
1285
2059
447
OR
95% C.I.
(Referent) 1.23 1.28
– [0.87, 1.73] [0.83, 1.96]
(Referent) 1.76 1.43 1.36 (Referent) 0.74 1.05 1.26 0.82** 1.63 1.00 0.92 1.31*** (Referent) 0.64 0.92
– [0.99, [0.93, [0.78, – [0.41, [0.60, [0.59, [0.71, [0.91, [0.86, [0.76, [1.12, – [0.31, [0.47,
3.12] 2.19] 2.35] 1.33] 1.86] 2.69] 0.94] 2.90] 1.16] 1.12] 1.53] 1.36] 1.81]
OR
95% C.I.
(Referent) 0.71 1.20
– [0.51, 1.01] [0.78, 1.86]
(Referent) 0.62 0.87 0.61 (Referent) 1.69* 1.43 0.83 0.95 1.00 0.85* 0.96 1.00 (Referent) 1.92 2.15*
– [0.34, [0.54, [0.35, – [1.04, [0.73, [0.40, [0.82, [0.53, [0.73, [0.82, [0.86, – [0.91, [1.11,
1.16] 1.40] 1.06] 2.76] 2.80] 1.71] 1.10] 1.88] 0.98] 1.13] 1.17] 4.07] 4.20]
OR
95% C.I.
(referent) 0.68 2.66* (Referent) 0.60 0.40* 0.53 (Referent) 0.69 0.70 0.20 0.76* 0.76 1.01 1.06 1.30 (Referent) 0.77 1.80
— [0.33,1.44] [1.22,5.78] – [0.19, 1.92] [0.20, 0.82] [0.22, 1.24] – [0.29, 1.60] [0.13, 3.77] [0.02, 2.31] [0.58, 0.99] [0.21, 2.75] [0.78, 1.32] [0.79, 1.42] [1.00, 1.68] – [0.21, 2.85] [0.68, 4.82]
p < 0.05. p < 0.01. p < 0.001.
in healthcare consultations. We also sought to test whether the cross-decision variations would remain significant after controlling for the differences in the patient populations making these different types of medical decisions. 3.2. Predictors of discussions of cons To explore these questions, Table 4 reports the results of three logistic regression models predicting discussions of cons regarding medication initiation, cancer screening, and surgery decisions, respectively. In medication decisions, older participants were significantly less likely to report having had a discussion of reasons not to take medications (OR = 0.82 per 10 years, p < 0.01), while more affluent participants were significantly more likely to report having had such discussions (OR = 1.31 per step on a 1–5 scale, p < 0.001). Neither of these factors, however, had any influence on discussions of the cons of cancer screening tests. Instead, having a trusted primary care provider (OR = 2.15, p < 0.05) and being African-American (OR = 1.69, p < 0.05) significantly increased the likelihood of hearing about reasons not to screen, while higher levels of education decreased the chances of discussing cons (OR = 0.85 per step on a 1–5 scale, p < 0.05). With regards to elective surgery decisions, age was again a significant negative predictor of discussions of cons (OR = 0.76, p < 0.05), as was being a married woman (OR = 0.40 vs. married men, p < 0.05). It is notable, also, that both single men and
single women also had lower rates of receiving discussions of cons than married men did (ORs = 0.60 and 0.53, respectively), although the effects are non-significant in part due to small sample size. The above analyses were conducted separately for each type of decision to allow for the possibility that the predictors of discussions of cons might vary substantially across different types of decisions. The results shown in Table 4 suggest that indeed they do. For example, while married men were, in general, more likely than unmarried men and all women to report having discussed the cons of cancer screening tests or surgeries, they appear much less likely to report discussing the cons of hypertension, cholesterol, or depression medications with their healthcare providers. AfricanAmericans reported being much more likely to have discussed the cons of cancer screening than white respondents, but no such effect is observed (and the odds ratios trend in the opposite direction) for medication and surgery decisions. Because we conducted three separate analyses, however, we do not have direct tests of the variability of discussions of cons across different types of decisions. Nonetheless, we do find confirmation that patients discussing surgery for lower back pain were significantly more likely to recall having had a discussion of reasons not to have surgery than even patients considering knee or hip replacement surgery (OR = 2.66, p < 0.05), even after controlling for differences in the characteristics of these two patient populations.
B.J. Zikmund-Fisher et al. / Patient Education and Counseling 87 (2012) 198–205
202
Fig. 1. Illustrative predicted probabilities of patient reporting having had a discussion of cons, by decision type. Table 5 Logistic regression models predicting whether patients reported that healthcare providers asked about their preferences.
N (unique decisions)
Blood pressure medications Cholesterol medications Depression medications Colon cancer screening Breast cancer screening Prostate cancer screening Knee/hip replacement surgery Cataract surgery Lower back pain surgery Male: married Male: single Female: married Female: single White race African-American Multi-/other race Hispanic Age (per 10 years) Has health insurance Education (1–5) Health (1–5) Income (1–5) Primary care provider: none Primary care provider: untrusted Primary care provider: trusted * ** ***
Medications
Cancer screening tests
Surgeries
1275
2018
442
OR
95% C.I.
(Referent) 0.96 2.59***
– [0.68, 1.37] [1.62, 4.13]
(Referent) 0.68 1.27 0.93 (Referent) 0.53* 1.01 1.19 0.77*** 1.75 0.99 0.97 0.94 (Referent) 0.82 1.67
– [0.39, [0.85, [0.59, – [0.32, [0.55, [0.54, [0.66, [0.96, [0.85, [0.84, [0.82, – [0.36, [0.80,
1.17] 1.89] 1.47] 0.87] 1.88] 2.60] 0.89] 3.19] 1.15] 1.12] 1.08] 1.85] 3.49]
OR
95% C.I.
(Referent) 1.36* 1.77**
– [1.01, 1.84] [1.16, 2.70]
(Referent) 0.46** 0.63* 0.70 (Referent) 0.56* 1.12 0.54 1.01 1.52 0.91 0.92 0.88* (Referent) 1.48 1.76*
– [0.27, [0.42, [0.44, – [0.36, [0.64, [0.29, [0.90, [0.90, [0.81, [0.80, [0.78, – [0.83, [1.05,
0.78] 0.95] 1.12] 0.88] 1.96] 1.01] 1.13] 2.57] 1.03] 1.04] 1.00] 2.65] 2.94]
OR
95% C.I.
(Referent) 0.60 0.83 (Referent) 0.70 0.63 0.41* (Referent) 1.43 0.50 0.99 0.74* 0.42 0.98 0.91 0.87 (Referent) 1.12 1.83
— [0.29, [0.36, – [0.24, [0.28, [0.19, – [0.57, [0.14, [0.24, [0.56, [0.09, [0.76, [0.71, [0.68, – [0.32, [0.62,
1.25] 1.92] 2.05] 1.43] 0.85] 3.61] 1.85] 4.06] 0.98] 1.83] 1.25] 1.17] 1.11] 3.93] 5.35]
p < 0.05. p < 0.01. p < 0.001.
To illustrate the magnitude of the above effects, Fig. 1 depicts the average predicted probability of reporting a discussion of cons for various combinations of decision type and individual characteristics, adjusted for all remaining variables in the model. When combined, the size of the effects can clearly be substantial. For example, we predict that an African-American patient with less than a high school education but a trusted primary care provider (PCP) would receive a discussion of the cons of cancer screening tests about 40% of the time. By contrast, the average White patient with a post-graduate education but no primary care provider is predicted to learn about cons only 10% of the time. 3.3. Predictors of elicitations of patient preferences Table 5 reports the results of the parallel logistic regression analyses examining predictors of whether patients reported that healthcare providers asked their opinions about whether or not to initiate medications, have a cancer screening test, or have surgery. Regarding discussions about medications, both older
and African-American patients were significantly less likely to report having been asked their opinion (age: OR = 0.77 per 10 years, p < 0.001; African-American: OR = 0.53, p < 0.05). African-Americans were also less likely to report being asked their preference about cancer screening tests (OR = 0.56, p < 0.05). In addition, more affluent patients were less likely to be asked their opinion about cancer screening tests (OR = 0.88 per step on a 1–5 scale, p < 0.05), as were single men and married women (single men: OR = 0.46 vs. married men, p < 0.01; married women: OR = 0.63, p < 0.05). Having a trusted primary care provider, however, increased the likelihood of being asked one’s preferences about cancer screening (OR = 1.76, p < 0.05). With regards to elective surgeries, older patients were again less likely to be asked what they wanted to do (OR = 0.74 per 10 years, p < 0.05), as were single women (OR = 0.41 vs. married men; p < 0.05). We illustrate the combined effects of these factors using illustrative examples shown in Fig. 2. Looking across decision types, the largest shift in covariate relationships occurs with African-Americans, who were
B.J. Zikmund-Fisher et al. / Patient Education and Counseling 87 (2012) 198–205
203
Fig. 2. Illustrative predicted probabilities of a patient reporting having been asked for his/her opinion, by decision type.
significantly less likely to be asked their opinions about medications or screening but somewhat more likely than Whites to be asked their opinions about surgery decisions (OR = 1.43, ns). Age was a significant predictor of preference elicitations for medications and surgery, but no evidence of such effects existed for cancer screening tests (OR = 1.01, ns). Lastly, an examination of the decision type coefficients in Table 5 clearly shows that the substantial cross-decision variation in the prevalence of patient preference elicitations is not merely a product of different patient populations. Even after controlling for all of our individual patient characteristics, DECISIONS respondents were significantly more likely to report having been asked their opinions for discussions of depression medications than blood pressure medications (OR = 2.59, p < 0.001) and discussions of breast or prostate cancer screening tests versus colon cancer screening (breast: OR = 1.36, p < 0.05; prostate: OR = 1.77, p < 0.01). 4. Discussion and conclusions 4.1. Discussion In order for patients to make informed decisions about their medical care, they must consistently learn about the cons of medical interventions, i.e., the reasons they might not want to undertake medical actions, in addition to learning about reasons to take action. In order for patients to be full participants in decision making about their care, they need to be reliably and explicitly asked about what they want to do. Yet, the DECISIONS data suggest that neither of these criteria is being met in the context of consultations about many common medical decisions. While previous research had shown that discussions of cons and elicitations of patient preferences varied across different types of medical decisions [19], the present analyses extend that work by showing that those variations persist even after controlling for a wide range of patient characteristics. Perhaps more importantly, we also identify several of these characteristics as independently predictive within the context of specific types of medical decisions (medications, cancer screening, and/or elective surgeries). Thus, our findings suggest that certain types of patients may be disproportionately likely to experience patient–provider interactions about these decisions that omit these key elements of informed decision making. For example, an affluent White patient in his 40s making a decision about hypertension medications appears likely to both receive a balanced discussion that includes both reasons to take medications (pros) and reasons not to do so (cons) and then to be asked explicitly for his opinion about what to do. Yet, a poorer, African-American man in his 70s appears much less likely to have either of these things occur.
The discrepancy between African Americans’ and Whites’ experiences with their physicians is not likely due to a lack of desire by African-American participants. In a recent study, Levinson et al. showed that basically all patients (96%) want to be informed of all available treatment options and to be asked their treatment preferences. Importantly, this desire did not differ by race [5]. As we were not privy to the DECISIONS participants’ conversations with their healthcare providers, we have no way of ascertaining whether observed disparities for African-American patients are due to differences in the information conveyed, differences in the quality of the patient–physician relationship/ communication, or differences in patients’ perceptions of the clinic visits. Still, our findings are suggestive of racial disparities in patient–provider interactions that are consistent with prior work. While previous research found few racial differences in terms of whether the content of Braddock’s 9 informed decision making elements were present in patient–physician conversations, observer ratings of the relationship between patients and physicians (in terms of responsiveness, respect, and listening) differed significantly by the race of the patients, as were patients’ ratings of physician communication and overall satisfaction with the clinic appointment [23]. Similarly, Cooper-Patrick et al. have shown that African Americans perceive their clinic visits as less participatory than due their White counterparts, even after adjusting for adjusting for patient age, gender, education, marital status, health status, and length of the patient–physician relationship [24]. More recently, African-American patients have been found to ask fewer questions in oncology consultations, suggesting that they subsequently receive less information from their doctors [25]. Our conclusions are limited first and foremost by the fact that DECISIONS only recorded patients’ recollections of what occurred in clinical consultations rather than actually observing the encounters in question. While eligibility was limited to those patients who had made decisions within a 2 year window, respondents may have had difficulty remembering specific details and may therefore have guessed at what was said or done. As a result, variations in the whether a discussion of cons or an elicitation of preferences was particularly salient may have resulted in biased recall. In particular, patients may be more or less likely to report these provider behaviors if the medication, test, or surgery was seen as successful outcomes versus if it turned out poorly. Nonetheless, these data accurately reflect how patients felt about their interactions with their healthcare providers about these specific medical decisions, and our data suggest that such perceptions are significantly related to both decision type and particular patient characteristics. We use the term ‘‘suggest’’ in part because we recognize that spurious relationships are possible given the multiple analyses conducted (we did not statistically
204
B.J. Zikmund-Fisher et al. / Patient Education and Counseling 87 (2012) 198–205
adjust for multiple comparisons due to the limited sample size available in DECISIONS). Nonetheless, we believe that the totality of our results provides strong evidence that discussions of cons and elicitations of patient preferences are likely to be both situationand patient-dependent. We also acknowledge that we did not independently measure the characteristics of the healthcare providers involved in these conversations, and it is possible that the patient characteristics are not inherently related to the observed variations in communications but instead are proxying for variations in the type and characteristics of the healthcare provider being seen.
research involved substantial collaboration between the University of Michigan (UM) research team and FIMDM representatives, the research grant was awarded in compliance with UM’s policies which bar funder interference in scholarly work. Design of the survey and control of the research data rested with the UM investigator team, and Dr. Zikmund-Fisher had full access to all the data in the study and takes responsibility for the integrity of the data and the accuracy of the data analysis. Dr. Zikmund-Fisher is supported by a Mentored Research Scholar Grant from the American Cancer Society (MRSG-06-130-01-CPPB). Conflicts of interest
4.2. Practice implications The authors have no conflicts of interest to disclose. Our results suggest that the question for both healthcare practitioners and researchers must not be whether patients, as a group, are given balanced discussions of their options about common medical interventions and involved in decision making about their care, but rather which patients experience such interactions, and under what circumstances. While the optimal level of patient involvement in medical decision making is both complex and highly situation dependent, the variability documented here is concerning given that the standards used in these analyses were quite minimal (i.e., any discussion of cons or asking of patients what they wanted to do). Providers need to be aware that their tendency to discuss reasons not to initiate prescription medications, undergo cancer screening tests, or have elective surgeries and their tendency to elicit patient preferences about these decisions appear to be potentially dependent on both who the patient is and what type of decision it is. If this relationship reflects patterns in provider behavior and not just patient recollections, then healthcare providers need to consider ways to make their interactions more egalitarian. While perhaps unintentional, discussing cons and eliciting preferences with only a subset of patients may unwittingly create differences in how patients are cared for (or at least how they believe they are cared for) and, as a result, potentially alter treatment decisions in unexpected and perhaps non-optimal ways. 4.3. Conclusion The prevalence of key elements of informed decision making in patient–provider interactions appears to be remarkably variable based on both individual patient characteristics and the type of medical decision being discussed. The DECISIONS data strongly suggest that important disparities in patient communication and involvement exist, with African-American and older patients, in particular, appearing less likely to report that certain types of patient–provider interactions included discussions of cons and/or elicitations of patient preferences. These conclusions are tempered, however, by the cross-sectional and retrospective nature of our data. We urge additional prospective research studies to confirm the existence of such disparities in health communications and to identify their causes. For example, do individual providers actually treat different groups of patients differently, or are the variations in the types of providers that different groups of patients see what really underlies the observed disparities in the patient experience of medical decision making? Only by simultaneously assessing both cross-patient and cross-provider variations in patient– provider communication dynamics can the root causes of such disparities be identified. Role of funding The Foundation for Informed Medical Decision Making (FIMDM) provided the funding for this research. While the
Acknowledgement None. References [1] Braddock 3rd CH, Edwards KA, Hasenberg NM, Laidley TL, Levinson W. Informed decision making in outpatient practice: time to get back to basics. J Amer Med Assoc 1999;282:2313–20. [2] O’Connor AM, Wennberg JE, Legare F, Llewellyn-Thomas HA, Moulton BW, Sepucha KR, et al. Toward the ‘tipping point’: decision aids and informed patient choice. Health Aff 2007;26:716–25. [3] Beisecker AE, Beisecker TD. Patient information-seeking behaviors when communicating with doctors. Med Care 1990;28:19–28. [4] Degner LF, Kristjanson LJ, Bowman D, Sloan JA, Carriere KC, O’Neil J, et al. Information needs and decisional preferences in women with breast cancer. J Amer Med Assoc 1997;277:1485–92. [5] Levinson W, Kao A, Kuby A, Thisted RA. Not all patients want to participate in decision making: a national study of public preferences. J Gen Intern Med 2005;20:531–5. [6] Charles C, Gafni A, Whelan T. Shared decision-making in the medical encounter: what does it mean? (or it takes at least two to tango) Soc Sci Med 1997;44:681–92. [7] Woolf SH, Chan ECY, Harris R, Sheridan SL, Braddock 3rd CH, Kaplan RM, et al. Promoting informed choice: transforming health care to dispense knowledge for decision making. Ann Intern Med 2005;143:293–300. [8] Elwyn G, Frosch DL, Rollnick S. Dual equipoise shared decision making: definitions for decision and behaviour support interventions. Implementation Sci 2009;4:75. [9] Weinstein JN, Clay K, Morgan TS. Informed patient choice: patient-centered valuing of surgical risks and benefits. Health Aff 2007;26:3726–30. [10] Zikmund-Fisher BJ, Couper MP, Singer E, Levin C, Fowler Jr FJ, Ziniel S, et al. The DECISIONS study: a nationwide survey of U.S. adults regarding nine common medical decisions. Med Decision Making 2010;30:20S–34S. [11] Sepucha KR, Fagerlin A, Couper MP, Levin CA, Singer E, Zikmund-Fisher BJ. How does feeling informed relate to being informed? The DECISIONS survey. Med Decision Making 2010;30:77S–84S. [12] Fagerlin A, Sepucha KR, Couper MP, Levin CA, Singer E, Zikmund-Fisher BJ. Patients’ knowledge about 9 common health conditions: the DECISIONS survey. Med Decision Making 2010;30:35S–52S. [13] Couper MP, Singer E, Levin CA, Fowler Jr FJ, Fagerlin A, Zikmund-Fisher BJ. Use of the internet and ratings of information sources for medical decisions: results from the DECISIONS survey. Med Decision Making 2010;30:106S–14S. [14] Dillard AJ, Couper MP, Zikmund-Fisher BJ. Perceived risk of cancer and patient reports of participation in decisions about screening: the DECISIONS study. Med Decision Making 2010;30:96S–105S. [15] Hoffman RM, Couper MP, Zikmund-Fisher BJ, Levin CA, McNaughton-Collins M, Helitzer DL, et al. Prostate cancer screening decisions: results from the National Survey of Medical Decisions (DECISIONS study). Arch Intern Med 2009;169:1611–8. [16] Hoffman RM, Lewis CL, Pignone MP, Couper MP, Barry MJ, Elmore JG, et al. Decision-making processes for breast, colorectal, and prostate cancer screening: the DECISIONS survey. Med Decision Making 2010;30:53S–64S. [17] Lewis CL, Couper MP, Levin CA, Pignone MP, Zikmund-Fisher BJ. Plans to stop cancer screening tests among adults who recently considered screening. J Gen Intern Med 2010;25:859–64. [18] Ratanawongsa N, Zikmund-Fisher BJ, Couper MP, Van Hoewyk J, Powe NR. Race, ethnicity, and shared decision making for hyperlipidemia and hypertension treatment: the DECISIONS survey. Med Decision Making 2010;30: 65S–76S. [19] Zikmund-Fisher BJ, Couper MP, Singer E, Ubel PA, Ziniel S, Fowler Jr FJ, et al. Deficits and variations in patients’ experience with making 9 common medical decisions: the DECISIONS survey. Med Decision Making 2010;30:85S–95S. [20] Stata Statistical Software, 11th ed., College Station, Texas: Stata Corporation; 2009.
B.J. Zikmund-Fisher et al. / Patient Education and Counseling 87 (2012) 198–205 [21] AAPOR. Standard definitions: final dispositions of case codes and outcome rates for surveys, 4th ed., Lenexa, Kansas: American Association for Public Opinion Research; 2006. [22] Lepkowski JM, Tucker NC, Brick JM, De Leeuw ED, Japec L, Lavrakas PJ, et al., editors. Advances in telephone survey methodology. New York: Wiley; 2007. [23] Levinson W, Hudak PL, Feldman JJ, Frankel RM, Kuby A, Bereknvei S, et al. It’s not what you say racial disparities in communication between orthopedic surgeons and patients. Med Care 2008;46:410–6.
205
[24] Cooper-Patrick L, Gallo JJ, Gonzales JJ, Vu TH, Powe NR, Nelson C, et al. Race gender partnership in the patient–physician relationship. J Amer Med Assoc 1999;282:583–9. [25] Eggly S, Harper FWK, Penner LA, Gleason MJ, Foster T, Albrecht TL. Variation in question asking during cancer clinical interactions: a potential source of disparities in access to information. Patient Educ Couns 2011;82:63–8.