Preferences for More or Less Health Care and Association With Health Literacy of Men Eligible for Prostate-Specific Antigen Screening in Australia | Cancer Screening, Prevention, Control | JAMA Network Open | JAMA Network
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Table 1.  Participant Characteristics
Participant Characteristics
Table 2.  Regression Models of Primary and Secondary Outcomes, With the RR and Associated Test Statistic and P value
Regression Models of Primary and Secondary Outcomes, With the RR and Associated Test Statistic and P value
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Carter  HB, Albertsen  PC, Barry  MJ,  et al.  Early detection of prostate cancer: AUA guideline.   J Urol. 2013;190(2):419-426. doi:10.1016/j.juro.2013.04.119 PubMedGoogle ScholarCrossref
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Grossman  DC, Curry  SJ, Owens  DK,  et al; US Preventive Services Task Force.  Screening for prostate cancer: US Preventive Services Task Force recommendation statement.   JAMA. 2018;319(18):1901-1913. doi:10.1001/jama.2018.3710 PubMedGoogle Scholar
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Kensler  KH, Pernar  CH, Mahal  BA,  et al.  Racial and ethnic variation in psa testing and prostate cancer incidence following the 2012 USPSTF recommendation.   J Natl Cancer Inst. 2021;113(6):719-726. doi:10.1093/jnci/djaa171 PubMedGoogle ScholarCrossref
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O’Connor  A, O’Brien-Pallas  LL. Decisional conflict. In: Mcfarlane  GK, Mcfarlane  EA, eds.  Nursing Diagnosis and Intervention. Mosby; 1989:486-496.
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Moynihan  R, Nickel  B, Hersch  J,  et al.  What do you think overdiagnosis means: a qualitative analysis of responses from a national community survey of Australians.   BMJ Open. 2015;5(5):e007436. doi:10.1136/bmjopen-2014-007436 PubMedGoogle Scholar
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Pickles  K, Kazda  L, Barratt  A, McGeechan  K, Hersch  J, McCaffery  K.  Evaluating two decision aids for Australian men supporting informed decisions about prostate cancer screening: a randomised controlled trial.   PLoS One. 2020;15(1):e0227304. doi:10.1371/journal.pone.0227304 PubMedGoogle Scholar
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Vernooij  RWM, Lytvyn  L, Pardo-Hernandez  H,  et al.  Values and preferences of men for undergoing prostate-specific antigen screening for prostate cancer: a systematic review.   BMJ Open. 2018;8(9):e025470. doi:10.1136/bmjopen-2018-025470 PubMedGoogle Scholar
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Scherer  LD, Shaffer  VA, Caverly  T, DeWitt  J, Zikmund-Fisher  BJ.  Medical maximizing-minimizing predicts patient preferences for high- and low-benefit care.   Med Decis Making. 2020;40(1):72-80. doi:10.1177/0272989X19891181 PubMedGoogle ScholarCrossref
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Scherer  LD, Kullgren  JT, Caverly  T,  et al.  Medical maximizing-minimizing preferences predict responses to information about prostate-specific antigen screening.   Med Decis Making. 2018;38(6):708-718. doi:10.1177/0272989X18782199 PubMedGoogle ScholarCrossref
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Chew  LD, Griffin  JM, Partin  MR,  et al.  Validation of screening questions for limited health literacy in a large VA outpatient population.   J Gen Intern Med. 2008;23(5):561-566. doi:10.1007/s11606-008-0520-5 PubMedGoogle ScholarCrossref
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Scherer  LD, Zikmund-Fisher  BJ.  Eliciting medical maximizing-minimizing preferences with a single question: development and validation of the MM1.   Med Decis Making. 2020;40(4):545-550. doi:10.1177/0272989X20927700Google ScholarCrossref
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Marteau  TM, Dormandy  E, Michie  S.  A measure of informed choice.   Health Expect. 2001;4(2):99-108. doi:10.1046/j.1369-6513.2001.00140.x PubMedGoogle ScholarCrossref
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Smith  SM, Fabrigar  LR, Norris  ME.  Reflecting on six decades of selective exposure research: progress, challenges, and opportunities.   Soc Personal Psychol Compass. 2008;2(1):464-493. doi:10.1111/j.1751-9004.2007.00060.x Google ScholarCrossref
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Hersch  J, Jansen  J, Barratt  A,  et al.  Women’s views on overdiagnosis in breast cancer screening: a qualitative study.   BMJ. 2013;346:f158. doi:10.1136/bmj.f158 PubMedGoogle ScholarCrossref
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Peters  E, Västfjäll  D, Gärling  T, Slovic  P.  Affect and decision making: a “hot” topic.   J Behav Decis Mak. 2006;19(2):79-85. doi:10.1002/bdm.528 Google ScholarCrossref
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Hersch  J, McGeechan  K, Barratt  A,  et al.  How information about overdetection changes breast cancer screening decisions: a mediation analysis within a randomised controlled trial.   BMJ Open. 2017;7(10):e016246. doi:10.1136/bmjopen-2017-016246PubMedGoogle Scholar
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McCaffery  KJ, Dodd  RH, Cvejic  E,  et al.  Health literacy and disparities in COVID-19-related knowledge, attitudes, beliefs and behaviours in Australia.   Public Health Res Pract. 2020;30(4):30342012. doi:10.17061/phrp30342012 PubMedGoogle Scholar
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Muscat  DM, Morris  GM, Bell  K,  et al.  Benefits and harms of hypertension and high-normal labels: a randomized experiment.   Circ Cardiovasc Qual Outcomes. 2021;14(4):e007160. doi:10.1161/CIRCOUTCOMES.120.007160 PubMedGoogle Scholar
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Murray  K, Hargis  P, Scherer  L.  MP12-18 Medical Minimizer-Maximizer Scale (MMS) and patient perceptions of results in a clinical setting.   J Urol. 2020;203:e151-e152. doi:10.1097/JU.0000000000000832.018 Google ScholarCrossref
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    Original Investigation
    Public Health
    October 12, 2021

    Preferences for More or Less Health Care and Association With Health Literacy of Men Eligible for Prostate-Specific Antigen Screening in Australia

    Author Affiliations
    • 1University of Sydney, Sydney Health Literacy Lab, School of Public Health, Faculty of Medicine and Health, Sydney, Australia
    • 2Division of Cardiology, University of Colorado School of Medicine, Aurora
    • 3VA Denver Center for Innovation, Denver, Colorado
    JAMA Netw Open. 2021;4(10):e2128380. doi:10.1001/jamanetworkopen.2021.28380
    Key Points

    Question  What is the association of individuals’ preferences for more or less health care with knowledge about prostate cancer overdiagnosis and informed choice?

    Findings  In this survey study of 2993 men in Australia, higher scores on the Medical Maximizing-Minimizing Scale, indicating stronger preferences for more health care, were associated with reduced relative risk of making an informed choice, having adequate conceptual and correct numerical knowledge of prostate cancer screening outcomes, and correct understanding of overdiagnosis.

    Meaning  These findings may help clinicians to better tailor discussions with patients about low-value care, such as prostate cancer screening, for which benefit is uncertain.

    Abstract

    Importance  Understanding personal factors that influence diverse responses to health care information, such as preferences for more or less health care, might be beneficial to more effective communication and better involvement in health care choices.

    Objective  To determine whether individuals’ preferences for more or less health care are associated with informed choice and understanding of overdiagnosis in routine prostate cancer screening and to examine associations among preferences, educational status, and health literacy.

    Design, Setting, and Participants  This survey study included a community-based sample of men in Australia aged 45 to 60 years eligible for prostate-specific antigen (PSA) screening, recruited via an international social research company. Survey data were collected online from June 27 to July 26, 2018. Data were analyzed in April 2020.

    Exposures  Participants were randomized to 1 of 2 versions of an online decision aid (full-length or abbreviated) about PSA screening and completed an online survey that included a measure of preference for more or less health care, the Medical Maximizer-Minimizer Scale (MMS), in which higher score indicates preference for more health care.

    Main Outcomes and Measures  The primary outcome was informed choice; knowledge, attitudes, and intentions about screening for prostate cancer were also measured.

    Results  Of 3722 participants who began the survey, 2993 (80.4%) completed it (mean [SD] age, 52.15 [6.65] years). Stronger preferences for more heath care were observed in those without tertiary education (mean difference, 0.15; 95% CI, 0.09-0.22; P < .001) and with inadequate health literacy (mean difference, 0.16; 95% CI, 0.09-0.22; P < .001). After controlling for health and demographic variables, a 1-unit increase in MMS score was associated with reduced relative risk (RR) of making an informed choice (RR, 0.78; 95% CI, 0.74-0.82; P < .001) and of having adequate conceptual knowledge (RR, 0.87; 95% CI, 0.84-0.90; P < .001), correct numerical knowledge (RR, 0.93; 95% CI, 0.89-0.97; P = .001), and correct understanding of overdiagnosis (RR, 0.84; 95% CI, 0.79-0.90; P < .001). A 1-unit increase in MMS score was associated with a more positive attitude toward screening (RR, 1.18; 95% CI, 1.15-1.21; P < .001) and more positive intention to screen (RR, 1.20; 95% CI, 1.16-1.25; P < .001) after adjusting for control variables.

    Conclusions and Relevance  This survey study examined associations between preferences for more or less health care and knowledge about overdiagnosis and informed choice among men in Australia. These results may motivate clinicians to elicit individual patient preferences to facilitate tailored discussions with patients about low-value care, such as prostate cancer screening, for which benefit is uncertain.

    Introduction

    The Medical Maximizer-Minimizer theory proposes that people’s maximizing vs minimizing orientation is a stable trait that influences the way individuals use health care across time and contexts.1 Individuals with a maximizing orientation tend to prefer to take medical action when it comes to their health, even when there is significant burden of doing so and a low chance of benefit. By contrast, individuals with a minimizing orientation usually prefer to avoid medical tests and treatments, especially when there is significant treatment burden and a low chance of benefit. The Medical Maximizer-Minimizer Scale (MMS), a 10-item questionnaire developed and validated by Scherer and colleagues,2 assesses people’s preferences for more or less medical care and quantifies the orientation into a numerical score. Consistent with its theoretical underpinnings, the scale has been found to project health care utilization in a variety of contexts.2 People scoring highly on the scale (ie, people with a preference for more health care and medical intervention) have been shown to use more prescription medications2 and visit a physician more frequently,3 be more likely to get imaging tests3 and blood draws,2 and report more overnight hospital stays in the past 10 years2 compared with people with a preferences for less care. These associations are present even though individuals preferring more health care are typically no more ill than those preferring less and are equally likely to have health insurance.3

    Routine population screening for prostate cancer using the prostate-specific antigen (PSA) test is not endorsed internationally, but it is widely used in clinical practice.4,5 Evolving evidence and guidelines continue to prompt substantial debate regarding the utility of PSA screening, particularly in higher-risk subgroups.6 The US Preventive Services Task Force (USPSTF) and Australian National Health and Medical Research Council (NHMRC) currently recommend a shared decision-making approach, whereby PSA screening should not occur unless an individual expresses a preference for screening after being informed of and understanding the benefits and harms.7,8 This is a shift from the USPSTF’s previous recommendation in 2012 that explicitly recommended against PSA screening in men of all ages. The major downside of PSA screening is the risk of overdiagnosis and overtreatment. Overdiagnosis occurs when a diagnosis is “correct” according to current professional standards, but the diagnosis or associated treatment has a low probability of benefiting the person.9 Cancer overdiagnosis refers to the detection of cancers that, in the absence of screening, would not present symptomatically.10

    Some studies11,12 have suggested that guideline changes could have important implications for overdiagnosis; for example, PSA testing rates declined in the US following the 2012 USPSTF recommendation against PSA screening, which coincided with a decrease in early stage cancers detected, potentially diminishing rates of overdiagnosis. Associations of the 2018 USPSTF recommendation with screening behavior and patient outcomes will be informative to observe.

    An informed choice is one that is based on relevant knowledge, consistent with the decision maker’s values, and behaviorally implemented.13 While the harms of overdiagnosis and overtreatment are particularly important to convey and understand if informed choice is to be achieved, they are also particularly difficult for physicians to explain and for patients to grasp.14,15 There is large variability in screening preferences and behavior among well-informed men.16

    A 2020 study by Scherer et al17 found that MMS scores uniquely explained 29% of the variance in preferences for low benefit care. Previous studies have reported an association between MMS scores and men’s attitudes and intentions regarding prostate screening.18 In particular, another study by Scherer et al18 found that most men (both maximizers and minimizers) were initially interested in receiving PSA screening, but after being informed of the harms and low likelihood of benefit, men with a preference for more care remained enthusiastic about screening whereas men with a preference for less care became less likely to accept screening. This finding may indeed reflect good, preference-concordance choice. However, because knowledge about PSA screening was not assessed, the study was unable to examine whether maximizers and minimizers differed in terms of whether they made an informed choice. If maximizers (or minimizers) are ignoring evidence that is inconsistent with their preferences, then they might be making choices that are suboptimal because they are not well informed.

    Our previous study involving almost 3000 men found that both long and abbreviated versions of a decision aid for prostate screening supported informed choice, and men in the longer decision aid group had significantly better understanding of overdiagnosis.15 The aim of this study is to investigate whether individuals’ preferences for more or less health care (measured using the MMS) were associated with informed choice and understanding of overdiagnosis in the context of PSA screening. Scherer et al18 have reported that individuals with higher levels of education tend to prefer less health care. In Australia, higher educated men with private health insurance are more likely to receive PSA tests than other demographic groups.5 We also explored associations between MMS scores and education and health literacy.

    Methods

    This survey study was approved by the Human Research Ethics Committee of the University of Sydney. Completion and submission of the questionnaire was considered evidence of participant consent. This study followed the American Association for Public Opinion Research (AAPOR) reporting guideline.

    We used a nonprobability internet panel and report participation rates. In 2018, an online randomized clinical trial was conducted in Australia comparing the effectiveness and acceptability of 2 versions of a decision aid (full length vs abbreviated) about prostate cancer screening.15 Findings of the decision aid trial have been published elsewhere.15 Participant preferences for more or less health care (assessed using the MMS) were measured in the baseline survey and are the focus of this analysis.

    Survey

    The online survey was developed using internationally accepted, validated scales and items in previously published studies. The survey was built and administered using Qualtrics (SAP), an online survey platform.

    Standard sociodemographic data were obtained from participants at baseline, including age, education, and private health insurance, as well as measures of health literacy adequacy19 and past experience with PSA testing. The MMS items were also included at baseline.

    The MMS is a 10-item measure that assesses a person’s maximizing or minimizing tendencies (ie, preferences for more vs less health care) on a scale, from 1, indicating strongly disagree (strong minimizing) to 7, strongly agree (strong maximizing). MMS items capture the individual’s general health care preferences via their level of agreement or disagreement with statements such as “Doing everything to fight illness is always the right choice” and “It is important to treat disease even when it does not make a difference in quality of life.” The mean score for each participant is computed (score range, 1-7), with a higher score indicating a preference toward seeking health care at a greater frequency compared with those scoring lower on the scale.2 Generally, MMS scores are normally distributed around approximately the midpoint of the scale.2,17,18,20

    After viewing the online decision aid, all participants responded to further questions in the online survey. These items were not assessed at baseline, only after the intervention, and included conceptual and numerical knowledge of prostate cancer, screening, and overdiagnosis; previous awareness of overdiagnosis; attitudes toward screening, diagnosis, and treatment; future screening intention; and perceived risk of developing prostate cancer. All survey items are presented in eTable 1 in the Supplement, and a detailed summary of the measures and analysis is reported elsewhere.15

    The primary outcome was informed choice. It was based on the 3-dimensional framework by Marteau et al,21 which includes 3 constructs combining adequate knowledge of possible outcomes of screening and consistency between a respondent’s attitude toward the screening test (positive or negative) and intention to undergo a PSA test to determine the proportion of respondents who made an informed (or uninformed) choice.21

    Study Population and Data Collection

    A community sample of men residing in Australia, aged 45 to 60 years, were recruited via an international survey sampling company (Survey Sampling International [SSI]) to participate in a 15-minute survey. SSI has an extensive online database panel of 600 000 members in Australia. Participants listed on the database have indicated a willingness to participate in online research in exchange for credits toward small rewards. SSI approached panel members who met this study’s eligibility criteria. Potential participants were emailed a web link to the online survey. All interested participants were directed to an online participant information statement; subsequent completion and submission of the questionnaire was considered evidence of consent. By virtue of being on the survey sampling database, all participants had already consented to being involved in online research.

    All participants were recruited through the SSI database. SSI charges a set fee for providing the required number and type of survey participants. We requested quota sampling to ensure strong representation of men with lower educational attainment (ie, school-level qualifications only) and across the relevant age groups. Men who had previously been diagnosed with prostate cancer were excluded.

    Survey data were collected from June 27 to July 26, 2018, through the Qualtrics platform. All men completed the same questionnaire.

    Statistical Analysis

    Associations between MMS score and participant characteristics were explored using pairwise correlations and independent-sample t tests. Generalized linear models (with a Poisson distribution, log-link function, and robust SEs) were performed to obtain adjusted prevalence ratios (PRs) and relative risks (RRs) (with 95% CIs) per 1-unit increase in the MMS score (controlling for other variables, including decision aid group allocation) for making an informed choice, having a positive attitude toward PSA screening, previous awareness and correct understanding of overdiagnosis, having adequate knowledge of possible screening outcomes, and positive intention to screen. MMS score was treated as a continuous variable in all analyses. Analyses were conducted using Stata/IC statistical software version 15.1 (StataCorp). The threshold for statistical significance was set at 2-sided P < .05 for all analyses. Data were analyzed in April 2020.

    Results
    Participant Characteristics

    Of 3722 participants who began the survey, 2993 men (80.4%) completed it and were included in analysis. Participants had a mean (SD) age of 52.15 (4.65) years, 1209 participants (40.4%) had no tertiary education, 1717 participants (57.4%) were privately insured, and most participants (2650 participants [88.5%]) had adequate health literacy (Table 1). The overall mean (SD) MMS score was 4.47 (0.90). The distribution of MMS score was approximately normal within the sample.

    Univariable Unadjusted Analyses

    There was no association between preferences for more or less health care and age (r = 0.027; 95% CI, −0.063 to 0.009; P = .14). A stronger preference for more care was observed in participants without tertiary education (mean [SD] MMS score, 4.56 [0.86]) compared with those with tertiary education (mean [SD] MMS score, 4.41 [0.92]; mean difference, 0.15; 95% CI, 0.09 to 0.22; t2991 = 4.56; P < .001; Cohen d = 0.17). Similarly, stronger preferences for more health care was reported by participants with inadequate health literacy (mean [SD] MMS score, 4.61 [0.84]) compared with those with adequate health literacy (mean [SD] MMS score, 4.45 [0.90]; mean difference, 0.16; 95% CI, 0.09 to 0.22; t2991 = 3.07; P < .001; Cohen d = 0.18).

    Awareness and personal history of PSA testing were measured at baseline; 1155 participants (38.6%) reported a previous PSA test, and 1655 participants (55.3%) indicated that they had heard of the PSA test. For each 1-unit increase in MMS score, the prevalence of having heard about the PSA test significantly decreased by 9% (PR, 0.91; 95% CI, 0.88 to 0.94; χ21 = 27.18; P < .001); yet there was no association between MMS score and previously having undergone a PSA test (PR, 0.99; 95% CI, 0.94 to 1.04; χ21 = 0.22, P = .64).

    Likelihood of perceived medium– to high–lifetime risk of prostate cancer (compared with no or low risk) increased by 8% per 1-unit increase in MMS score (RR, 1.08; 95% CI, 1.02 to 1.14; χ21 = 7.85; P = .005). However, MMS scores were not associated with a difference in perceived risk of prostate cancer when participants were asked about their risk compared with the typical man of their age (RR, 1.07; 95% CI, 0.95 to 1.20; χ21 = 1.22; P = .27).

    Multivariable Adjusted Analyses
    Informed Choice

    Having a preference for more health care was associated with lower rates of informed choice (Table 2). For each 1-unit increase in MMS score, the likelihood of making an informed choice was reduced by 22% (RR, 0.78; 95% CI, 0.74 to 0.82). The association between higher MMS score and reduced likelihood of making an informed choice was also maintained when previous awareness of overdiagnosis was added into the regression model (RR, 0.80; 95% CI, 0.76 to 0.84; χ21 = 77.48; P < .001).

    Attitude Toward Screening

    After adjusting for the control variables, the MMS score was associated with attitude toward screening. The likelihood of having a positive attitude toward screening increased by 18% for each 1-unit increase in the MMS score (RR, 1.18; 95% CI, 1.15 to 1.21; χ21 = 168.5; P < .001).

    Awareness and Understanding of Overdiagnosis

    After viewing the decision aid, participants were asked if they could recall seeing or hearing the term overdiagnosis before. Approximately half of participants (1563 participants [52.2%]) indicated that they had heard the term overdiagnosis before, but overall, understanding of overdiagnosis information was low.12 Regarding preferences for more or less care, after adjusting for control variables, the chance of having previous awareness of overdiagnosis reduced by 17% for each 1-unit increase in the MMS score (RR, 0.83, 95% CI, 0.80 to 0.86; χ21 = 100.80; P < .001).

    After adjusting for the same control variables, the chance of having a correct understanding of overdiagnosis was reduced by 16% for each 1-unit increase in the MMS score (RR, 0.84; 95% CI, 0.79 to 0.90; χ21 = 30.81; P < .001). There was no association of greater understanding of overdiagnosis with health literacy adequacy (RR, 1.11; 95% CI, 0.91 to 1.35; χ21 = 1.10; P = .29).

    Conceptual and Numerical Knowledge

    The chance of scoring correctly on conceptual knowledge items was reduced by 13% for each 1-unit increase in MMS score, after adjusting for all other control variables (RR, 0.87; 95% CI, 0.84 to 0.90; χ21 = 73.54; P < .001). Individuals with adequate health literacy were more likely to respond correctly to conceptual knowledge questions compared with those with inadequate health literacy (RR, 1.31; 95% CI, 1.15 to 1.48; χ21 = 17.45; P < .001).

    After adjusting for other model variables, the likelihood of having correct numerical knowledge was reduced by 7% per 1-unit increase in MMS score (RR, 0.93; 95% CI, 0.89 to 0.97; χ21 = 10.73; P = .001). There was no association of health literacy adequacy with numerical knowledge (RR, 1.11; 95% CI, 0.96 to 1.28; χ21 = 2.01; P = .16).

    Intention to Screen

    After adjusting for all other model variables, the chance of having positive screening intention increased by 20% per 1-unit increase in MMS score (RR, 1.20; 95% CI, 1.16 to 1.25; χ21 = 109.87; P < .001). Individuals with adequate health literacy were more likely to have positive screening intention compared with those with inadequate health literacy (RR, 1.19; 95% CI, 1.06 to 1.34; χ21 = 9.14, P = .003).

    Modification of Decision Aid Associations by MMS

    The inclusion of an interaction term between MMS scores and decision aid received in the regression models found no difference in the association of decision aids with either informed choice (χ21 = 0.26; P = .61) or intention to screen outcomes (χ21 = 0.78; P = .38). Regression models were repeated post hoc to examine whether the association between MMS outcomes differed by whether or not a participant had previously had a PSA test, including an interaction terms between MMS score and previous PSA test (eTable 2, eFigure 1, and eFigure 2 in the Supplement).

    Discussion

    This survey study contributes important new information to the understanding of medical maximizing-minimizing orientation, particularly regarding its association with knowledge-related outcomes. We found that individuals with a preference for more health care showed reduced understanding of overdiagnosis and less adequate knowledge about screening in general. MMS scores were associated with informed choice, ie, a choice that is based on relevant knowledge and shows consistency between the decision maker’s values and choice (in this case, operationalized as behavioral intention). Having a stronger preference for more care was associated with a lower likelihood of making an informed choice about screening for prostate cancer. Of particular note in this study was that maximizing was associated with greater risk of reduced understanding of overdiagnosis than were education and health literacy.

    People’s innate preferences for more or less health care may guide what they direct their attention to and what they are willing to believe.18 Studies have shown that people will attend to and interpret information in a way that confirms their existing positions and ignore or dispute contradictory information.22 Such attentional and confirmatory biases can lead to heightened or reduced sensitivity to particular information. One possible explanation for our findings is that individuals with a strong preference for more health care gave less attention to information about screening harms, such as overdiagnosis, while those with a strong preference for less health care spent more time understanding and verifying reasons for not screening. Resultant knowledge possibly then influenced screening intentions. Unfortunately, timing data were not captured, so we are unable to evaluate whether men spent more or less time looking at particular information, such as overdiagnosis information, in the decision aids. Future research could capture timing data to test this hypothesis.

    Alternatively, people who strongly prefer more health care might dismiss, reject, or disbelieve particular information about their preferred screening choice. They might not believe or accept the information about, for instance, the potential downsides of prostate screening or they may distrust the data presented. Women involved in focus groups about overdiagnosis of breast cancer often did not see information on overdiagnosis as relevant to their decision-making about screening, and most retained their initial (positive) perspectives on attending screening, perhaps reflecting the influence of broad and long-standing encouragement to be screened.23 A tendency to avoid or reject certain information in the context of health decisions is problematic because it can undermine engagement with potentially valuable risk information, such as overdiagnosis, that is perceived as threatening or unwanted.24

    Differences in preferences for prostate screening were associated with differences in knowledge. However, we cannot say whether these differences were driven by an emotional motivation to decide to screen without attending to the information, or a failure to attend to and encode the information, or participants simply not believing or accepting it. Medical maximizing-minimizing orientation may influence screening intentions directly via the affect pathway (ie, via personal experiences of feeling or emotion). Affect can play multiple roles in medical decision-making,25 including influencing screening intentions.26 Men with an orientation toward more health care, for instance, might simply prefer to have a PSA screening test because of a “gut feeling” that it is the right thing to do in that moment.27 This theory may be partially supported by our data.

    We found that MMS scores were lower for participants with tertiary education compared with those without, similar to previous findings by Scherer et al.18 Studies have shown that willingness to vaccinate28 or undergo cervical screening29 is often lower among people with less education, lower health literacy, and lower socioeconomic status. Other studies have reported that people with less education and lower health literacy have higher preferences for medication for cardiovascular disease30 and for more invasive surgery for papillary thyroid cancer.31 Whether or not preferences for more or less health care in people with less education and lower health literacy is a state or trait characteristic is an important question and needs more attention.

    Implications for Clinical Practice

    Understanding personal factors, such as individuals’ diverse approaches to health care, might be beneficial in more effective communication and better involvement of individuals in health care choices, such as deciding to undergo screening or not. Some studies have suggested that the utility of the MMS lies in improving patient-clinician communication and the shared decision-making process in the clinical setting.32 Nurses, health coaches, and nonclinical members of a care team may be well placed to have in-depth conversations with patients regarding preferences for health care and could play a valuable role in administering and discussing MMS scores with patients considering PSA testing. Clinicians could use their patients’ orientation toward more or less health care (as measured by the MMS) as a useful starting point for tailored discussions, although some patients might have reservations about sharing their MMS score with their clinician.32 The challenge is to ensure that men who are considering having a PSA test have the opportunity to make an informed decision, which includes understanding the harms of screening, such as overdiagnosis.

    More work is needed to examine communication strategies that help people direct attention to and consider information that goes against their usual preference for more or less care. Education and awareness of cognitive biases are important so that individuals can recognize their habitual thinking and attentional processes and try to reorient from their defaults toward more critical, evidence-based thinking. For example, cognitive (eg, using a consider-the-opposite procedure) or technological (eg, by providing graphical in addition to statistical information) debiasing strategies could encourage critical thinking and eliminate framing effects.33

    The burden of decision-making should not lie solely with patients; health care practitioners should also be aware of their own perspectives and the possibility of cognitive biases influencing them. Future research could also explore clinician maximizing-minimizing orientation and associations with their approach to communication, screening, and diagnostic behavior. Changing clinician behavior (eg, only offering prostate cancer screening to patients for whom there is likely to be a strong benefit vs indiscriminate screening) and restructuring health care systems are key to reducing overdiagnosis from a systems perspective.34

    Limitations

    This study has some limitations. Although the study used an online sample that is not fully representative of the male population of Australia aged 45 to 60 years, it allowed for a large and demographically diverse community sample, generally representative of the range of men who might be offered or are considering prostate cancer screening, and included a considerable proportion of men with lower educational attainment. This was not a longitudinal study, so we only measured MMS scores and knowledge, attitudes, and screening intentions once (ie, MMS was only measured at baseline). However, we collected a number of variables, which enabled a detailed analysis of the association of medical maximizing with key variables, with adjustment for confounders.

    Conclusions

    This survey study is the first study, to our knowledge, to examine associations between people’s orientation toward more or less health care and knowledge about overdiagnosis and informed choice. A stronger preference for more health care was observed in participants without tertiary education and with inadequate health literacy. Preference for more health care was negatively associated with informed choice and understanding of overdiagnosis, even when controlling for education and health literacy. Understanding personal factors that influence diverse responses to health care information might be beneficial in contributing to more effective communication and better involving individuals in choices, such as deciding whether to undergo screening. Clinicians could use MMS scores as a useful starting point for tailored discussions with patients.

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    Article Information

    Accepted for Publication: July 29, 2021.

    Published: October 12, 2021. doi:10.1001/jamanetworkopen.2021.28380

    Open Access: This is an open access article distributed under the terms of the CC-BY License. © 2021 Pickles K et al. JAMA Network Open.

    Corresponding Author: Kristen Pickles, PhD, Sydney School of Public Health, The University of Sydney, NSW Edward Ford Building (A27), Room 127A, 2006, Australia (kristen.pickles@sydney.edu.au).

    Author Contributions: Dr Pickles had full access to all of the data in the study and takes responsibility for the integrity of the data and the accuracy of the data analysis.

    Concept and design: Pickles, Hersch, Barratt, McCaffery.

    Acquisition, analysis, or interpretation of data: Pickles, Scherer, Cvejic, Hersch, Barratt.

    Drafting of the manuscript: Pickles, Scherer, Cvejic, Barratt.

    Critical revision of the manuscript for important intellectual content: All authors.

    Statistical analysis: Cvejic.

    Obtained funding: Pickles, Hersch, Barratt.

    Administrative, technical, or material support: Pickles.

    Supervision: Scherer, Barratt.

    Conflict of Interest Disclosures: Dr Scherer reported receiving grants from the National Institutes of Health National Cancer Institute during the conduct of the study. No other disclosures were reported.

    Funding/Support: This study was cofunded by the Prostate Cancer Foundation of Australia and Wiser Healthcare. Wiser Healthcare is funded by the National Health and Medical Research Council Program (grant No. 1113532).

    Role of the Funder/Sponsor: The funders had no role in the design and conduct of the study; collection, management, analysis, and interpretation of the data; preparation, review, or approval of the manuscript; and decision to submit the manuscript for publication.

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