The aim of the current study was to learn how people integrate attitudes about multiple health conditions to make a decision about genetic testing uptake.
This study recruited 294 healthy young adults from a parent research project, the Multiplex Initiative, conducted in a large health care system in Detroit, Michigan. All participants were offered a multiplex genetic test that assessed risk for 8 common health conditions (e.g., type 2 diabetes). Data were collected from a baseline survey, a web-based survey, and at the time of testing.
Averaging attitudes across diseases predicted test uptake but did not contribute beyond peak attitudes, the highest attitude toward testing for a single disease in the set. Peak attitudes were found sufficient to predict test uptake.
The effects of set size and mode of presentation could not be examined because these factors were constant in the multiplex test offered.
These findings support theories suggesting that people use representative evaluations in attitude formation. The implication of these findings for further developments in genetic testing is that the communication and impact of multiplex testing may need to be considered in the light of a bias toward peak attitudes.
cognitive psychology; judgment and decision psychology; patient choice modeling; social judgment theory
The impact of choice on consumer decision-making is controversial in U.S. health policy.
Our objective was to determine how choice set size influences decision-making among Medicare beneficiaries choosing prescription drug plans.
We randomly assigned members of an internet-enabled panel age 65 and over to sets of prescription drug plans of varying sizes (2, 5, 10, and 16) and asked them to choose a plan. Respondents answered questions about the plan they chose, the choice set, and the decision process. We used ordered probit models to estimate the effect of choice set size on the study outcomes.
Both the benefits of choice, measured by whether the chosen plan is close to the ideal plan, and the costs, measured by whether the respondent found decision-making difficult, increased with choice set size. Choice set size was not associated with the probability of enrolling in any plan.
Medicare beneficiaries face a tension between not wanting to choose from too many options and feeling happier with an outcome when they have more alternatives. Interventions that reduce cognitive costs when choice sets are large may make this program more attractive to beneficiaries.
Medicare Part D; prescription drugs; choice; decision-making; insurance
Electronic personal health records offer a promising way to communicate medical test results to patients. We compared the usability of tables and horizontal bar graphs for presenting medical test results electronically.
We conducted experiments with a convenience sample of 106 community-dwelling adults. In the first experiment, participants viewed either table or bar graph formats (between subjects) that presented medical test results with normal and abnormal findings. In a second experiment, participants viewed table and bar graph formats (within subjects) that presented test results with normal, borderline, and abnormal findings.
Participants required less viewing time when using bar graphs rather than tables. This overall difference was due to superior performance of bar graphs in vignettes with many test results. Bar graphs and tables performed equally well with regard to recall accuracy and understanding. In terms of ease of use, participants did not prefer bar graphs to tables when they viewed only one format. When participants viewed both formats, those with experience with bar graphs preferred bar graphs, and those with experience with tables found bar graphs equally easy to use. Preference for bar graphs was strongest when viewing tests with borderline results.
Compared to horizontal bar graphs, tables required more time and experience to achieve the same results, suggesting that tables can be a more burdensome format to use. The current practice of presenting medical test results in a tabular format merits reconsideration.
electronic personal health record; medical test results; format; usability
The Institute of Medicine (IOM) has called for a new health care paradigm that integrates patient values into discussions of the risks and benefits of treatment. Although CVD affects one-third of Americans, little is known about how adults regard the potential harms or complications of treatment.
We sought to determine the preferences of community dwelling adults for 15 potential harms or complications resulting from treatment of cardiovascular disease (CVD).
In a telephone survey, adults over 18 years of age residing on Long Island, New York were asked to score the preferences for 15 potential harms or complications of treatment of CVD on a scale from 0 to 100. All statistical analyses were based on non-parametric methods. Multivariable general linear model analyses were performed to identify demographic factors associated with the score assigned for each adverse outcome.
The 807 individuals surveyed generated 723 unique sequences of scores for the 15 outcomes. The ranking of scores from least to most acceptable was stroke, major myocardial infarction (MI), cognitive dysfunction, renal failure, death, prolonged ventilator support, heart failure, angina, sternal wound infection, major bleeding, re-operation, prolonged recovery in a nursing home, cardiac readmission, minor MI and percutaneous coronary intervention. Demographic factors accounted for less than 7% of the observed variation in the score attributed to each outcome.
Individual community-dwelling adults living on Long Island, New York assign unique values to their preferences for potential harms encountered following treatment of CVD. Thus, risk-benefit discussions and treatment decisions regarding CVD should be harmonized to the value system of each individual.
With the increasing complexity of decisions in pediatric medicine, there is a growing need to understand the pediatric decision-making process.
To conduct a narrative review of the current research on parent decision making about pediatric treatments and identify areas in need of further investigation.
Articles presenting original research on parent decision making were identified from MEDLINE (1966–6/2011), using the terms “decision making,” “parent,” and “child.” We included papers focused on treatment decisions but excluded those focused on information disclosure to children, vaccination, and research participation decisions.
We found 55 papers describing 52 distinct studies, the majority being descriptive, qualitative studies of the decision-making process, with very limited assessment of decision outcomes. Although parents’ preferences for degree of participation in pediatric decision making vary, most are interested in sharing the decision with the provider. In addition to the provider, parents are influenced in their decision making by changes in their child’s health status, other community members, prior knowledge, and personal factors, such as emotions and faith. Parents struggle to balance these influences as well as to know when to include their child in decision making.
Current research demonstrates a diversity of influences on parent decision making and parent decision preferences; however, little is known about decision outcomes or interventions to improve outcomes. Further investigation, using prospective methods, is needed in order to understand how to support parents through the difficult treatment decisions.
randomized trial methodology; risk factor evaluation; population-based studies; scale development/validation; patient decision making; provider decision making; risk communication or risk perception; health state preferences, utilities, and valuations; judgment and decision psychology; informed consent; education; pediatrics
We examined physician diagnostic certainty as one reason for cross-national medical practice variation. Data are from a factorial experiment conducted in the United States, the United Kingdom, and Germany, estimating 384 generalist physicians’ diagnostic and treatment decisions for videotaped vignettes of actor patients depicting a presentation consistent with coronary heart disease (CHD). Despite identical vignette presentations, we observed significant differences across health care systems, with US physicians being the most certain and German physicians the least certain (p < .0001). Physicians were least certain of a CHD diagnoses when patients were younger and female (p < .0086), and there was additional variation by health care system (as represented by country) depending on patient age (p < .0100) and race (p < .0021). Certainty was positively correlated with several clinical actions, including test ordering, prescriptions, referrals to specialists, and time to follow-up.
clinical decision making; medical practice variation; health disparities
Preference-based measures of health-related quality of life all use the same dead = 0.00 to perfect health = 1.00 scale, but there are substantial differences among measures.
The objective is to examine agreement in classifying patients as better, stable, or worse.
The EQ-5D, Health Utilities Index Mark 2 and Mark 3, Quality of Well-Being – Self-Administered, Short-Form 36 (Short-Form 6D), and disease-targeted measures were administered prospectively in two clinical cohorts.
The study was conducted at academic medical centers: University of California, Los Angeles; University of California, San Diego; University of Wisconsin-Madison; and University of Southern California.
Patients undergoing cataract extraction surgery with lens replacement completed the 25-item National Eye Institute Visual Function Questionnaire (NEI-VFQ-25). Patients newly refereed to congestive heart failure specialty clinics completed the Minnesota Living with Heart Failure Questionnaire (MLHF).
In both cohorts subjects completed surveys at baseline, one and six months. The NEI-VFQ-25 and MLHF were used as gold standards to assign patients to categories of change. Agreement was assessed using kappa.
376 cataract patients were recruited. Complete data for baseline and the one-month follow-up were available on all measures for 210 cases. Using criteria specified by Altman, agreement was poor for six of nine pairs of comparisons and fair for three pairs. 160 heart failure patients were recruited. Complete data for baseline and the six-month follow-up were available for 86 cases. Agreement was negligible for five pairs and fair for one.
The study was conducted on selected patients at a few academic medical centers.
The results underscore the lack of interchangeability among different preference-based measures.
Mathematical and simulation models are increasingly used to plan for and evaluate health sector responses to disasters, yet no clear consensus exists regarding best practices for the design, conduct, and reporting of such models. We examined a large selection of published health sector disaster response models to generate a set of best practice guidelines for such models.
We reviewed a spectrum of published disaster response models addressing public health or healthcare delivery, focusing in particular on the type of disaster and response decisions considered, decision makers targeted, choice of outcomes evaluated, modeling methodology, and reporting format. We developed initial recommendations for best practices for creating and reporting such models and refined these guidelines after soliciting feedback from response modeling experts and from members of the Society for Medical Decision Making.
We propose six recommendations for model construction and reporting, inspired by the most exemplary models: Health sector disaster response models should address real-world problems; be designed for maximum usability by response planners; strike the appropriate balance between simplicity and complexity; include appropriate outcomes, which extend beyond those considered in traditional cost-effectiveness analyses; and be designed to evaluate the many uncertainties inherent in disaster response. Finally, good model reporting is particularly critical for disaster response models.
Quantitative models are critical tools for planning effective health sector responses to disasters. The recommendations we propose can increase the applicability and interpretability of future models, thereby improving strategic, tactical, and operational aspects of preparedness planning and response.
Disaster Planning; Mass Casualty Incidents; Computer Simulation; Cost-benefit Analysis; Guideline
Shared Decision Making (SDM) and Decision Aids (DAs) increase patients’ involvement in healthcare decisions and enhance satisfaction with their choices. Studies of SDM and DAs have primarily occurred in academic centers and large health systems, but most primary care is delivered in smaller practices and over 20% of Americans live in rural areas where poverty, disease prevalence and limited access to care may increase the need for SDM and DAs.
To explore perceptions and practices of rural primary care clinicians regarding SDM and DAs.
Cross sectional survey.
Setting and Participants
Primary care clinicians affiliated with the Oregon Rural Practice-based Research Network (ORPRN).
Surveys were returned by 181 of 231 eligible participants (78%), 174 could be analyzed. Two-thirds of participants were physicians, 84% practiced family medicine, and 55% were male. Sixty five percent of respondents were unfamiliar with the term “SDM”, but following definition, 97% reported they found the approach useful for conditions with multiple treatment options. Over 90% of clinicians perceived helping patients make decisions regarding chronic pain and health behavior change as moderate/hard in difficulty. Although 69% of respondents preferred that patients play an equal role in making decisions, they estimate this happens only 35% of the time. Time was reported as the largest barrier to engaging in SDM (63%). Respondents were receptive to using DAs to facilitate SDM in printed (95%) or web-based formats (72%) and topic preference varied by clinician specialty and decision difficulty.
Rural clinicians recognized the value of SDM and were receptive to using DAs in multiple formats. Integration of DAs to facilitate SDM in routine patient care may require addressing practice operation and reimbursement.
Primary Care; Translating Research Into Practice; Shared Decision Making – Decision Aid Tools; Decision Aids – Decision Aid Tools; Survey Methods – Statistical Methods
Type 2 diabetes genetic risk testing might motivate at-risk patients to adopt diabetes prevention behaviors. However, the influence of literacy and numeracy on patient response to diabetes genetic risk is unknown.
The authors investigated the association of health literacy, genetic literacy, and health numeracy with patient responses to diabetes genetic risk.
Design and Measurements
Overweight patients at high phenotypic risk for type 2 diabetes were recruited for a clinical trial of diabetes genetic risk testing. At baseline, participants predicted how their motivation for lifestyle modification to prevent diabetes might change in response to hypothetical scenarios of receiving “high” and “low” genetic risk results. Responses were analyzed according to participants’ health literacy, genetic literacy, and health numeracy.
Two-thirds (67%) of participants (n = 175) reported very high motivation to prevent diabetes. Despite high health literacy (92% at high school level), many participants had limited health numeracy (30%) and genetic literacy (38%). Almost all (98%) reported that high-risk genetic results would increase their motivation for lifestyle modification. In contrast, response to low-risk genetic results varied. Higher levels of health literacy (P = 0.04), genetic literacy (P = 0.02), and health numeracy (P = 0.02) were associated with an anticipated decrease in motivation for lifestyle modification in response to low-risk results.
While patients reported that high-risk genetic results would motivate them to adopt healthy lifestyle changes, response to low-risk results varied by patient numeracy and literacy. However, anticipated responses may not correlate with true behavior change. If future research justifies the clinical use of genetic testing to motivate behavior change, it may be important to assess how patient characteristics modify that motivational effect.
genetics; diabetes; internal medicine; risk communication or perception; decision aids and tools; numeracy; individual differences; health literacy; judgment and decision psychology
Most patients with dementia will, at some point, need a proxy health care decision maker. It is unknown whether persons with various degrees of cognitive impairment can reliably report their health related preferences.
Cross-sectional and retest study. We performed health state valuations (HSVs) of current and hypothetical future health states on 47 pairs of patients with mild to moderate cognitive impairment and their caregivers using computer-based standard gamble, time trade-off, and rating scale techniques.
Patients’ mean (SD) age was 74.6 (9.3) years. About half of the patients were women (48%) as were most caregivers (73%) who were on average younger (mean age = 66.2 years, SD = 12.2). Most participants were White (83%); 17% were African-American. The mean (SD) Mini-Mental State Examination (MMSE) score of patients was 24.2 (4.6) of 30. All caregivers and 77% of patients (36/47) completed all 18 components of the HSV exercise. Patients who completed the HSV exercise were slightly younger (mean age (SD) = 74.1 (8.5) vs. 75.9 (11.8); p = .569) and had significantly higher MMSE scores (mean score (SD) = 25.0 (4.3) vs. 21.4 (4.4); p = .018). Although MMSE scores below 20 did not preclude the completion of all 18 HSV ratings, being classified as having moderate cognitive impairment was associated with a lower likelihood of completing all scenario ratings (44 % vs. 82%). Patient and caregiver responses showed good consistency across time and across techniques, and were logically consistent.
Obtaining HSVs for current and hypothetical health states was feasible for most patients with mild cognitive impairment and many with moderate cognitive impairment. HSV assessments were consistent and reasonable.
health state valuation; utilities; dementia; cognitive impairment; computer-based techniques; reliability
scale dependency; herd protection; epidemiological model; resource allocation
The relationship of primary care provider’s (PCP) CRC screening strategies to completion of screening is poorly understood.
To describe PCP test recommendation patterns, associated factors, and their relationship to patient test completion.
This cross-sectional study used a PCP survey, in-depth PCP interviews, and electronic medical records.
Kaiser Permanente Northwest HMO.
132 PCPs and 49,259 eligible patients aged 51–75.
Patterns related to PCP CRC screening recommendations, based upon frequency of recommending fecal occult blood testing (FOBT), flexible sigmoidoscopy (FS), and colonoscopy. We compared PCP demographics, CRC screening-test influences, concerns, decision making and counseling processes, and rates of patient CRC screening completion by PCP group.
We identified four CRC screening-recommendation groups: a “Balanced” group (n=54; 40.9%) that recommended the tests nearly equally; an “FOBT” group (n=31; 23.5%) that largely recommended FOBT; an “FOBT& FS” (n=25; 18.9%); and a “Colonoscopy & FOBT” (n=22; 16.7%) group that recommended these tests nearly equally. Internal medicine (vs. family medicine) PCPs were more common in groups recommending endoscopy more frequently. The FOBT and FOBT&FS groups were most influenced by clinical guidelines. Groups recommending more endoscopy were most concerned that FOBT generates a lot of false positives and FOBT misses a lot of cancers. The FOBT and FOBT&FS groups were more likely to recommend a specific screening strategy compared to the Colonoscopy & FOBT and Balanced groups, which were more likely to let the patient decide. CRC screening rates did not differ by group.
Small numbers within PCP groups
Specialty, the influence of guidelines, test concerns, and the “jointness” of the test selection decision distinguished CRC screening recommendation patterns. All patterns were associated with similar overall screening rates.
colorectal cancer screening; primary care recommendations
The decision aid called “Adjuvant Online” (Adjuvant! for short) helps breast cancer patients make treatment decisions by providing numerical estimates of treatment efficacy (e.g., 10-y relapse or survival). Studies exploring how patients’ numeracy interacts with the estimates provided by Adjuvant! are lacking. Pooling across 2 studies totaling 105 women with estrogen receptor–positive, early-stage breast cancer, the authors explored patients’ treatment expectations, perceived benefit from treatments, and confidence of personal benefit from treatments. Patients who were more numerate were more likely to provide estimates of cancer-free survival that matched the estimates provided by Adjuvant! for each treatment option compared with patients with lower numeracy (odds ratios of 1.6 to 2.4). As estimates of treatment efficacy provided by Adjuvant! increased, so did patients’ estimates of cancer-free survival (0.37 > rs > 0.48) and their perceptions of treatment benefit from hormonal therapy (rs = 0.28) and combined therapy (rs = 0.27). These relationships were significantly more pronounced for those with higher numeracy, especially for perceived benefit of combined therapy. Results suggest that numeracy influences a patient’s ability to interpret numerical estimates of treatment efficacy from decision aids such as Adjuvant!.
patient decision making; risk communication or risk perception; numeracy; breast cancer/mammography; health literacy
Standard errors of measurement (SEMs) of health related quality of life (HRQoL) indexes are not well characterized. SEM is needed to estimate responsiveness statistics and provides guidance on using indexes on the individual and group level. SEM is also a component of reliability.
To estimate SEM of five HRQoL indexes.
The National Health Measurement Study (NHMS) was a population based telephone survey. The Clinical Outcomes and Measurement of Health Study (COMHS) provided repeated measures 1 and 6 months post cataract surgery.
3844 randomly selected adults from the non-institutionalized population 35 to 89 years old in the contiguous United States and 265 cataract patients.
The SF6-36v2™, QWB-SA, EQ-5D, HUI2 and HUI3 were included. An item-response theory (IRT) approach captured joint variation in indexes into a composite construct of health (theta). We estimated: (1) the test-retest standard deviation (SEM-TR) from COMHS, (2) the structural standard deviation (SEM-S) around the composite construct from NHMS and (3) corresponding reliability coefficients.
SEM-TR was 0.068 (SF-6D), 0.087 (QWB-SA), 0.093 (EQ-5D), 0.100 (HUI2) and 0.134 (HUI3), while SEM-S was 0.071, 0.094, 0.084, 0.074 and 0.117, respectively. These translate into reliability coefficients for SF-6D: 0.66 (COMHS) and 0.71 (NHMS), for QWB: 0.59 and 0.64, for EQ-5D: 0.61 and 0.70 for HUI2: 0.64 and 0.80, and for HUI3: 0.75 and 0.77, respectively. The SEM varied considerably across levels of health, especially for HUI2, HUI3 and EQ-5D, and was strongly influenced by ceiling effects.
Repeated measures were five months apart and estimated theta contain measurement error.
The two types of SEM are similar and substantial for all the indexes, and vary across the range of health.
To estimate the benefit of PSA-based screening for prostate cancer from the patient and societal perspectives.
A partially observable Markov decision process (POMDP) model was used to optimize PSA screening decisions. We considered age-specific prostate cancer incidence rates and the mortality rates from prostate cancer and competing causes. Our model trades off the potential benefit of early detection with the cost of screening and loss of patient quality of life due to screening and treatment. PSA testing and biopsy decisions are made based on the patient’s probability of having prostate cancer. Probabilities are inferred based on the patient’s complete PSA history using Bayesian updating.
The results of all PSA tests and biopsies done in Olmsted County, Minnesota from 1993 to 2005 (11,872 men and 50,589 PSA test results).
Perspective and Outcome Measures
Patients’ perspective: maximize expected quality-adjusted life years (QALYs); societal perspective: maximize the expected monetary value based on societal willingness to pay for QALYs and the cost of PSA testing, prostate biopsies and treatment.
From the patient perspective the optimal policy recommends stopping PSA testing and biopsy at age 76. From the societal perspective the stopping age is 71. The expected incremental benefit of optimal screening over the traditional guideline of annual PSA screening with threshold 4.0 ng/mL for biopsy is estimated to be 0.165 QALYs per person from the patient perspective, and 0.161 QALYs per person from the societal perspective. PSA screening based on traditional guidelines is found to be worse than no screening at all.
PSA testing done with traditional guidelines underperforms and therefore underestimates the potential benefit of screening. Optimal screening guidelines differ significantly depending on the perspective of the decision maker.
Online tools such as Adjuvant! provide tailored estimates of the possible outcomes of adjuvant therapy options available to breast cancer patients. The graphical format typically displays four outcomes simultaneously: survival, mortality due to cancer, other cause mortality, and incremental survival due to adjuvant treatment.
To test whether simpler formats that only present baseline and incremental survival would improve comprehension of the relevant risk statistics and/or affect treatment intentions.
Randomized experimental manipulation of risk graphics shown included in Internet-administered survey vignettes about adjuvant therapy decisions for breast cancer patients with ER+ tumors.
Demographically diverse, stratified random samples of women ages 40–74 recruited from an Internet research panel.
Participants were randomized to view either pictographs (icon arrays) that displayed all four possible outcomes or pictographs that showed only survival outcomes.
Comprehension of key statistics, task completion times, graph evaluation ratings, and perceived interest in adjuvant chemotherapy.
In the primary study (N=832), participants who viewed survival-only pictographs had better accuracy when reporting the total chance of survival with both chemotherapy and hormonal therapy (63% vs. 50%, p<0.001; higher graph evaluation ratings (M=7.98 vs. 7.67, p=0.04), and less interest in adding chemotherapy to hormonal therapy (43% vs. 50%, p=0.04; adjusted OR=0.68, p=0.008). A replication study (N=714) confirmed that participants who viewed survival-only graphs had higher graph evaluation ratings (M=8.06 vs. 7.72, p=0.04) and reduced interest in chemotherapy (OR=0.67, p=0.03).
Studies used general public samples; actual patients may process risk information differently.
Taking a “less is more” approach by omitting redundant mortality outcome statistics can be an effective method of risk communication and may be preferable when using visual formats such as pictographs.
decision aids; risk; patient education as topic; audiovisual aids
Women with localized breast cancer face difficult decisions about adjuvant therapy. Several decision aids are available to help women choose between treatment options. Decision aids are known to affect treatment choices and may therefore affect patient survival. The authors aimed to model the effects of the Adjuvant! decision aid on expected survival in women with early stage breast cancer.
Patients and Methods
Data were obtained from a randomized trial of Adjuvant! (n =395). To calculate the effects of the decision aid on survival, the authors used the Adjuvant! survival predictions as a surrogate endpoint. Data from each arm were entered separately into statistical models to estimate change in survival associated with receiving the Adjuvant! decision aid.
Most women (~85%) chose a treatment option that maximized predicted survival. The effects of the decision aid on outcome could not be modeled because a small number of women (n =12, 3%) chose treatment options associated with a large (5%–14%) loss in survival. These women—most typically estrogen receptor positive but refusing hormonal therapy—were equally divided between Adjuvant! and control groups and were not distinguished by medical or demographic factors.
Expected benefit from treatment is a key variable in understanding patient behavior. A small number of women refuse adjuvant treatment associated with large increases in predicted survival, even when they are explicitly informed about the degree of benefit they would forgo. Investigation of the effects of decision aids on cancer survival is unlikely to be fruitful due to power considerations.
Adjuvant!; breast cancer; decision aids; women’s health; oncology; outcomes research
Health State Preferences, Utilities, and Valuation; Health status indicators; Spine diseases; Quality of life; Economic evaluation; SPORT; Scale Validation; Cost-utility Analysis; Cost-effectiveness Analysis
Microsimulation models are important decision support tools for screening. However, their complexity creates a barrier, making it difficult to understand models and, as a result, limiting realization of their full potential. Therefore, it is important to develop documentation that clarifies assumptions. We demonstrate this problem and explore a solution for the natural history, using three independently developed colorectal cancer screening models.
We begin by projecting the cost-effectiveness of colonoscopy screening for the three microsimulation models. Next, we provide a conventional presentation of each of them, including information that would usually be published with a decision analysis. Finally, for the three models, we provide the simulated reduction in clinical cancer incidence following a one-time complete removal of adenomas and preclinical cancers. We denote this measure as maximum clinical incidence reduction (MCLIR).
There are considerable between-model differences in projected effectiveness. Conventional documentation describes model structure and associated parameter values. Given only this information, it is very difficult to compare models, largely because differences in structure make parameter values incomparable. In contrast, the MCLIR clearly shows the differences in assumptions on the key issue of the natural history: the dwell time of progressive preclinical disease, explaining between-model differences in projected effectiveness.
The simulated “maximum clinical incidence reduction” adds to the insight in dwell time, the critical characteristic of the natural history of disease, and how it differs between models. Inclusion of the MCLIR as a standard description would clarify the implications of assumptions for models applied to screening questions.
In the United States, African Americans are more likely to experience lower quality patient/provider communication and less shared decision making (SDM) than whites, which may be an important contributor to racial health disparities. Patient factors have not been fully explored as a potential contributor to communication disparities.
The authors analyzed cross-sectional data from a survey of 974 patients with diabetes seen at 34 community health centers (HC) in 17 midwestern and west-central states. They used ordinal and logistic regression models to investigate racial differences in patients’ preferences for SDM and in patients’ behaviors that may facilitate SDM (initiating discussions about diabetes care).
The response rate was 67%. In bivariate and multivariate analyses, race was not associated with patient preference for a shared role in the 3 measured SDM domains: agenda setting (odds ratio [OR]: 1.13 [0.86, 1.49]), information sharing (OR: 1.26 [0.97, 1.64]), or decision making (OR: 1.16 [0.85, 1.59]). African Americans were more likely to report initiating discussions with their physicians about 4 of 6 areas of diabetes care—blood pressure measurement (66% v. 52%, P < 0.001), foot examination (54% v. 47%, P = 0.04), eye examination (57% v. 46%, P = 0.002), and microalbumin testing (38% v. 29%, P = 0.01)—but not HbA1c testing (39% v. 43%, P = 0.31) or cholesterol testing (53% v. 51%, P = 0.52). In multivariate analysis, African Americans were still more likely to report initiating conversations about diabetes care (OR: 1.78 [1.10, 2.89]).
The authors found that African Americans in this study preferred shared decision making as much as whites and were more likely to report initiating more discussions with their doctors about their diabetes care. This research suggests that, among diabetes patients receiving care at community health centers, patient preference or patient behaviors may be an unlikely cause of racial differences in shared decision making.
randomized trial methodology; risk factor evaluation; population-based studies; scale development/validation
The decision to participate in a research intervention or to undergo medical treatment should be both informed and voluntary.
The aim of the present study was to develop an instrument to measure the perceived voluntariness of parents making decisions for their seriously ill children.
A total of 219 parents completed questionnaires within 10 days of making such a decision at a large, urban tertiary care hospital for children. Parents were presented with an experimental form of the Decision Making Control Instrument (DMCI), a measure of the perception of voluntariness. Data obtained from the 28-item form were analyzed using a combination of both exploratory and confirmatory factor analytic techniques.
The 28 items were reduced to nine items representing three oblique dimensions of Self-Control, Absence of Control, and Others’ Control. The hypothesis that the three-factor covariance structure of our model was consistent with that of the data was supported. Internal consistency for the scale as a whole was high (0.83); internal consistency for the subscales ranged from 0.68 to 0.87. DMCI scores were associated with measures of affect, trust, and decision self-efficacy, supporting the construct validity of the new instrument.
The DMCI is an important new tool that can be used to inform our understanding of the voluntariness of treatment and research decisions in medical settings.
voluntariness; decision making control; informed consent; ethics
Background. When planning to use a validated prediction model in new patients, adequate performance is not guaranteed. For example, changes in clinical practice over time or a different case mix than the original validation population may result in inaccurate risk predictions. Objective. To demonstrate how clinical information can direct updating a prediction model and development of a strategy for handling missing predictor values in clinical practice. Methods. A previously derived and validated prediction model for postoperative nausea and vomiting was updated using a data set of 1847 patients. The update consisted of 1) changing the definition of an existing predictor, 2) reestimating the regression coefficient of a predictor, and 3) adding a new predictor to the model. The updated model was then validated in a new series of 3822 patients. Furthermore, several imputation models were considered to handle real-time missing values, so that possible missing predictor values could be anticipated during actual model use. Results. Differences in clinical practice between our local population and the original derivation population guided the update strategy of the prediction model. The predictive accuracy of the updated model was better (c statistic, 0.68; calibration slope, 1.0) than the original model (c statistic, 0.62; calibration slope, 0.57). Inclusion of logistical variables in the imputation models, besides observed patient characteristics, contributed to a strategy to deal with missing predictor values at the time of risk calculation. Conclusions. Extensive knowledge of local, clinical processes provides crucial information to guide the process of adapting a prediction model to new clinical practices.
clinical prediction rules; methodology; decision rules; provider decision making; statistical methods