Search tips
Search criteria

Results 1-25 (125)

Clipboard (0)

Select a Filter Below

Year of Publication
more »
2.  Estimating the Expected Value of Sample Information Using the Probabilistic Sensitivity Analysis Sample 
Medical Decision Making  2015;35(5):570-583.
Health economic decision-analytic models are used to estimate the expected net benefits of competing decision options. The true values of the input parameters of such models are rarely known with certainty, and it is often useful to quantify the value to the decision maker of reducing uncertainty through collecting new data. In the context of a particular decision problem, the value of a proposed research design can be quantified by its expected value of sample information (EVSI). EVSI is commonly estimated via a 2-level Monte Carlo procedure in which plausible data sets are generated in an outer loop, and then, conditional on these, the parameters of the decision model are updated via Bayes rule and sampled in an inner loop. At each iteration of the inner loop, the decision model is evaluated. This is computationally demanding and may be difficult if the posterior distribution of the model parameters conditional on sampled data is hard to sample from. We describe a fast nonparametric regression-based method for estimating per-patient EVSI that requires only the probabilistic sensitivity analysis sample (i.e., the set of samples drawn from the joint distribution of the parameters and the corresponding net benefits). The method avoids the need to sample from the posterior distributions of the parameters and avoids the need to rerun the model. The only requirement is that sample data sets can be generated. The method is applicable with a model of any complexity and with any specification of model parameter distribution. We demonstrate in a case study the superior efficiency of the regression method over the 2-level Monte Carlo method.
PMCID: PMC4471064  PMID: 25810269
expected value of sample information; economic evaluation model; Monte Carlo methods; Bayesian decision theory; computational methods; nonparametric regression; generalized additive model.
3.  Accounting for Heterogeneity in Relative Treatment Effects for Use in Cost-Effectiveness Models and Value-of-Information Analyses 
Medical Decision Making  2015;35(5):608-621.
Cost-effectiveness analysis (CEA) models are routinely used to inform health care policy. Key model inputs include relative effectiveness of competing treatments, typically informed by meta-analysis. Heterogeneity is ubiquitous in meta-analysis, and random effects models are usually used when there is variability in effects across studies. In the absence of observed treatment effect modifiers, various summaries from the random effects distribution (random effects mean, predictive distribution, random effects distribution, or study-specific estimate [shrunken or independent of other studies]) can be used depending on the relationship between the setting for the decision (population characteristics, treatment definitions, and other contextual factors) and the included studies. If covariates have been measured that could potentially explain the heterogeneity, then these can be included in a meta-regression model. We describe how covariates can be included in a network meta-analysis model and how the output from such an analysis can be used in a CEA model. We outline a model selection procedure to help choose between competing models and stress the importance of clinical input. We illustrate the approach with a health technology assessment of intravenous immunoglobulin for the management of adult patients with severe sepsis in an intensive care setting, which exemplifies how risk of bias information can be incorporated into CEA models. We show that the results of the CEA and value-of-information analyses are sensitive to the model and highlight the importance of sensitivity analyses when conducting CEA in the presence of heterogeneity. The methods presented extend naturally to heterogeneity in other model inputs, such as baseline risk.
PMCID: PMC4471065  PMID: 25712447
cost-effectiveness analysis; Bayesian meta-analysis; value of information
4.  Can streamlined multi-criteria decision analysis be used to implement shared decision making for colorectal cancer screening? 
Current US colorectal cancer screening guidelines that call for shared decision making regarding the choice among several recommended screening options are difficult to implement. Multi-criteria decision analysis (MCDA) is an established methodology well suited for supporting shared decision making. Our study goal was to determine if a streamlined form of MCDA using rank order based judgments can accurately assess patients’ colorectal cancer screening priorities.
We converted priorities for four decision criteria and three sub-criteria regarding colorectal cancer screening obtained from 484 average risk patients using the Analytic Hierarchy Process (AHP) in a prior study into rank order-based priorities using rank order centroids. We compared the two sets of priorities using Spearman rank correlation and non-parametric Bland-Altman limits of agreement analysis. We assessed the differential impact of using the rank order-based versus the AHP-based priorities on the results of a full MCDA comparing three currently recommended colorectal cancer screening strategies. Generalizability of the results was assessed using Monte Carlo simulation.
Correlations between the two sets of priorities for the seven criteria ranged from 0.55 to 0.92. The proportions of absolute differences between rank order-based and AHP-based priorities that were more than ± 0.15 ranged from 1% to 16%. Differences in the full MCDA results were minimal and the relative rankings of the three screening options were identical more than 88% of the time. The Monte Carlo simulation results were similar.
Rank order-based MCDA could be a simple, practical way to guide individual decisions and assess population decision priorities regarding colorectal cancer screening strategies. Additional research is warranted to further explore the use of these methods for promoting shared decision making.
PMCID: PMC4055507  PMID: 24300851
5.  Knowledge, Attitudes, and Self-Efficacy as Predictors of Preparedness for Oncology Clinical Trials: A Mediational Model 
This study used the Ottawa Decision Support Framework to evaluate a model examining associations between clinical trial knowledge, attitudinal barriers to participating in clinical trials, clinical trials self-efficacy, and clinical trial preparedness among 1256 cancer patients seen for their first outpatient consultation at a cancer center. As an exploratory aim, moderator effects for gender, race/ethnicity, education, and metastatic status on associations in the model were evaluated.
Patients completed measures of cancer clinical trial knowledge, attitudinal barriers, self-efficacy and preparedness. Structural equation modeling (SEM) was conducted to evaluate whether self-efficacy mediated the association between knowledge and barriers with preparedness.
The SEM explained 26% of the variance in cancer clinical trial preparedness. Self-efficacy mediated the associations between attitudinal barriers and preparedness, but self-efficacy did not mediate the knowledge-preparedness relationship.
Findings partially support the Ottawa Decision Support Framework and suggest that assessing patients’ level of self-efficacy may be just as important as evaluating their knowledge and attitudes about cancer clinical trials.
PMCID: PMC3991731  PMID: 24246567
Cancer clinical trials; Ottawa Decision Support Framework; cancer patients
6.  Blocks, Ovals, or People? Icon Type Affects Risk Perceptions and Recall of Pictographs 
Numerous research studies have demonstrated that icon arrays (also called “pictographs”) are an effective method of communicating risk statistics and appear particularly useful to less numerate and less graphically literate people. Yet research is very limited regarding whether icon type affects how people interpret and remember these graphs.
1504 people age 35 to 75 from a demographically-diverse online panel completed a cardiovascular risk calculator based on Framingham data using their actual age, weight, and other health data. Participants received their risk estimate in an icon array graphic that used one of 6 types of icons: rectangular blocks, filled ovals, smile/frown faces, an outline of a person’s head and shoulders, male/female “restroom” person icons (gender matched), or actual head-and-shoulder photographs of people of varied races (gender matched). In each icon array, blue icons represented cardiovascular events and grey icons represented those who would not experience an event. We measured perceived risk magnitude, approximate recall, and opinions about the icon arrays, as well as subjective numeracy and an abbreviated measure of graphical literacy.
Risk recall was significantly higher with more anthropomorphic icons (restroom icons, head outlines, and photos) than with other icon types, and participants rated restroom icons as most preferred. However, while restroom icons resulted in the highest correlations between perceived and actual risk among more numerate and more graphically literate participants, they performed no better than other icon types among less numerate/graphically literate participants.
Icon type influences both risk perceptions and risk recall, with restroom icons in particular resulting in improved outcomes. However, optimal icon types may depend on numeracy and/or graphical literacy skills.
PMCID: PMC3991751  PMID: 24246564
risk; patient education as topic; patient-provider communication; decision aids; visual aids
7.  Numbers matter to informed patient choices: A randomized design across age and numeracy levels 
How drug adverse events (AEs) are communicated in the United States may mislead consumers and result in low adherence. Requiring written information to include numeric AE-likelihood information might lessen these effects, but providing numbers may disadvantage less skilled populations.
To determine risk comprehension and willingness to use a medication when presented with numeric or non-numeric AE-likelihood information across age, numeracy, and cholesterol-lowering-drug-usage groups.
In a cross-sectional internet survey (N=905; American Life Panel, 5/15/08–6/18/08), respondents were presented with a hypothetical prescription medication for high cholesterol. AE likelihoods were described using one of six formats (non-numeric: Consumer-Medication-Information (CMI)-like list, risk labels; numeric: percentage, frequency, risk-labels-plus-percentage, risk-labels-plus-frequency). Main outcome measures were risk comprehension (recoded to indicate presence/absence of risk overestimation and underestimation), willingness to use the medication (7-point scale; not likely=0, very likely=6), and main reason for willingness (chosen from eight predefined reasons).
Individuals given non-numeric information were more likely to overestimate risk, less willing to take the medication, and gave different reasons than those provided numeric information across numeracy and age groups (e.g., among less numerate: 69% and 18% overestimated risks in non-numeric and numeric formats, respectively; among more numerate: these same proportions were 66% and 6%). Less numerate middle-aged and older adults, however, showed less influence of numeric format on willingness to take the medication.
It is unclear whether differences are clinically meaningful although some differences are large.
Providing numeric AE-likelihood information (compared to non-numeric) is likely to increase risk comprehension across numeracy and age levels. Its effects on uptake and adherence of prescribed drugs should be similar across the population, except perhaps in older, less numerate individuals.
PMCID: PMC3991753  PMID: 24246563
risk comprehension; risk communication; pharmaceutical decision making; adherence; numeracy; aging; informed decision making; statins
8.  Automatically Annotating Topics in Transcripts of Patient-Provider Interactions via Machine Learning 
Annotated patient-provider encounters can provide important insights into clinical communication, ultimately suggesting how it might be improved to effect better health outcomes. But annotating outpatient transcripts with Roter or General Medical Interaction Analysis System (GMIAS) codes is expensive, limiting the scope of such analyses. We propose automatically annotating transcripts of patient-provider interactions with topic codes via machine learning.
We use a conditional random field (CRF) to model utterance topic probabilities. The model accounts for the sequential structure of conversations and the words comprising utterances. We assess predictive performance via 10- fold cross-validation over GMIAS-annotated transcripts of 360 outpatient visits (over 230,000 utterances). We then used automated in place of manual annotations to reproduce an analysis of 116 additional visits from a randomized trial that used GMIAS to assess the efficacy of an intervention aimed at improving communication around antiretroviral (ARV) adherence.
With respect to six topic codes, the CRF achieved a mean pairwise kappa compared with human annotators of 0.49 (range: 0.47, 0.53) and a mean overall accuracy of 0.64 (range: 0.62, 0.66). With respect to the RCT re-analysis, results using automated annotations agreed with those obtained using manual ones. According to the manual annotations, the median number of ARV-related utterances without and with the intervention was 49.5 versus 76, respectively (paired sign test p=0.07). Using automated annotations, the respective numbers were 39 versus 55 (p=0.04).
While moderately accurate, the predicted annotations are far from perfect. Conversational topics are intermediate outcomes; their utility is still being researched.
This foray into automated topic inference suggests that machine learning methods can classify utterances comprising patient-provider interactions into clinically relevant topics with reasonable accuracy.
PMCID: PMC3991772  PMID: 24285151
9.  Advance Care Planning Norms May Contribute to Hospital Variation in End-of-life ICU Use: A Simulation Study 
There is wide variation in end-of-life (EOL) intensive care unit (ICU) use among academic medical centers (AMCs).
To develop hypotheses regarding medical decision-making factors underlying this variation.
High-fidelity simulation experiment involving a critically and terminally ill elder, followed by a survey and debriefing cognitive interview and evaluated using triangulated quantitative-qualitative comparative analysis.
2 AMCs in the same state and health care system with disparate EOL ICU use.
Hospital-based physicians responsible for ICU admission decisions.
Treatment plan, prognosis, diagnosis, qualitative case perceptions and clinical reasoning.
Main Results
Sixty-seven of 111 (60%) eligible physicians agreed to participate; 48 (72%) could be scheduled. There were no significant between-AMC differences in 3-month prognosis or treatment plan, but there were systematic differences in perceptions of the case. Case perceptions at the low-intensity AMC seemed to be influenced by the absence of a DNR order in the context of norms of universal code status discussion and documentation upon admission, whereas case perceptions at the high-intensity AMC seemed to be influenced by the patient’s known metastatic gastric cancer in the context of norms of oncologists’ avoiding code status discussions.
In this simulation study of 2 AMCs, hospital-based physicians had different perceptions of an identical case. We hypothesize that different advance care planning norms may have influenced their decision-making heuristics.
PMCID: PMC4026761  PMID: 24615275
terminal care; palliative care; intensive care; physician decision making; heuristics; cancer; simulation; variation; Medicare; national health policy; qualitative research
10.  A marginal benefit approach for vaccinating influenza “superspreaders” 
There is widespread recognition that interventions targeting “superspreaders” are more effective at containing epidemics than strategies aimed at the broader population. However, little attention has been devoted to determining optimal levels of coverage for targeted vaccination strategies, given the nonlinear relationship between program scale and the costs and benefits of identifying and successfully administering vaccination to potential superspreaders.
We developed a framework for such an assessment derived from a transmission model of seasonal influenza parameterized to emulate typical seasonal influenza epidemics in the US. We used this framework to estimate how the marginal benefit of expanded targeted vaccination changes with the proportion of the target population already vaccinated.
The benefit of targeting additional superspreaders varies considerably as a function of both the baseline vaccination coverage and proximity to the herd immunity threshold. The general form of the marginal benefit function starts low, particularly for severe epidemics, increases monotonically until its peak at the point of herd immunity, and then plummets rapidly.
We present a simplified transmission model, primarily designed to convey qualitative insight rather than quantitative precision. With appropriate contact data, future work could address more complex population structures, such as age structure and assortative mixing patterns. Our illustrative example highlights the general economic and epidemiological findings of our method, but does not contrive to address intervention design, policy, and resource allocation issues related to practical implementation of this particular scenario.
Our approach offers a means of estimating willingness to pay for search costs associated with targeted vaccination of superspreaders, which can inform policies regarding whether a targeted intervention should be implemented and, if so, up to what levels.
PMCID: PMC4209160  PMID: 24740238
Infectious disease < INTERNAL MEDICINE; Vaccination < GLOBAL HEALTH; Economic Analysis (general) < ECONOMICS (HEALTH); Willingness to pay < ECONOMICS (HEALTH); MATHEMATICAL MODELS AND DECISION ANALYSIS
11.  Estimation and Validation of a Multi-Attribute Model of Alzheimer's Disease Progression 
To estimate and validate a multi-attribute model of the clinical course of Alzheimer's Disease (AD) from mild AD to death in a high-quality prospective cohort study; to estimate the impact of hypothetical modifications to AD progression rates on costs associated with Medicare and Medicaid services.
Data and Methods
We estimated sex-specific longitudinal Grade of Membership (GoM) models for AD patients (103 males; 149 females) in the initial cohort of the Predictors Study (1989–2001) based on 80 individual measures obtained every six months for 10 years. We replicated these models for AD patients (106 males; 148 females) in the second Predictors Study cohort (1997–2007). Model validation required that the disease-specific transition parameters be identical for both Predictors Study cohorts. Medicare costs were estimated from the National Long Term Care Survey.
Sex-specific models were validated using the second Predictors Study cohort with the GoM transition parameters constrained to the values estimated for the first Predictors Study cohort; 57–61 of the 80 individual measures contributed significantly to the GoM models. Simulated, cost-free interventions in the rate of progression of AD indicated that large potential cost offsets could occur for patients at the earliest stages of AD.
AD progression is characterized by a small number of parameters governing changes in large numbers of correlated indicators of AD severity. The analysis confirmed that the progression of AD represents a complex multidimensional physiological process that is similar across different study cohorts. The estimates suggested that there could be large cost offsets to Medicare and Medicaid from the slowing of AD progression among patients with mild AD. The methodology appears generally applicable in AD modeling.
PMCID: PMC4392765  PMID: 21183754
Clinical assessment; outcomes; staging of dementia
12.  Health Related Quality of Life in HIV-infected and at-risk Women: The Impact of Illicit Drug Use and Hepatitis C on a Community Preference Weighted Measure* 
To assess the impact of illicit drug use and chronic hepatitis C virus (HCV) on health related quality of life (HRQoL) in women with HIV or at-risk for HIV infection.
Cross-sectional analysis of data from the Women's Interagency Health Study (WIHS) of women with HIV (n=2508) and at high risk of HIV infection (n=889) in the US. A Short-Form-6D (SF-6D) HRQoL measure derived from the Medical Outcomes Study–HIV (MOS-HIV) questionnaire, HIV infection status, CD4 cell count (a measure of immune status), antiretroviral treatment, current illicit drug use (heroin and/or cocaine), and HCV status were assessed at a recent study visit. We developed multivariate linear regression models adjusting for age, race/ethnicity, education, and testing for interactions.
HIV-infected women with ≤200 CD4 cells/uL had lower mean HRQoL scores (0.69) than either HIV-infected women with >200 CD4 cells/uL (0.78) or HIV-uninfected women (0.80) (p<0.01). In multivariate analysis, illicit drug use, chronic HCV, and low CD4 count were independently associated with lower HRQoL. There was a differential effect of HCV and illicit drug use for HIV-infected women depending on CD4 cell count: HIV-infected women with >200 CD4 cells/uL had a significantly greater reduction in HRQoL associated with illicit drug use (−0.063) and chronic HCV (−0.036) than women with ≤200 CD4 cells/uL (−0.017, −0.005 respectively).
Poorly-controlled HIV, illicit drug use, and chronic HCV are associated with lower HRQoL. Illicit drug use and chronic HCV have greater HRQoL impacts for HIV-infected women with well-controlled HIV versus those with poorly-controlled HIV, which may affect clinical and policy priorities.
PMCID: PMC3980200  PMID: 24106234
HIV; HRQoL; Opiate Dependence; Health Utility; Illicit Drug use; HCV
13.  Do men make informed decisions about prostate cancer screening? Baseline results from the “Take the Wheel” Trial 
The efficacy of prostate cancer (CaP) screening with the prostate specific antigen test is debated. Most medical organizations recommend that men make individual, informed decisions about whether to undergo screening. Informed decision-making (IDM) requires: adequate knowledge about CaP as well as the risks and benefits of screening; confidence in the ability to participate in decision-making at a personally desired level (decision self-efficacy); and decision-making that reflects one’s values and preferences (decisional consistency).
Baseline data from a randomized trial in 12 worksites were analyzed. Men aged 45+ (n=812) completed surveys documenting screening history, screening preferences and decisions, CaP knowledge, decision self-efficacy and decisional consistency. Psychosocial and demographic correlates of IDM were also assessed.
Approximately half of the sample had a prior PSA test, although only 35% reported having made an explicit screening decision. Across the sample, CaP knowledge was low (mean=56%), though decision self-efficacy was high (mean=78%), and the majority of men (81%) made decisions consistent with their stated values. Compared with those who were undecided, men who made an explicit screening decision had significantly higher levels of knowledge, greater decisional self-efficacy, and were more consistent in terms of making a decision in alignment with their values. They tended to be White, have high levels of income and education, and had discussed screening with their health care provider.
Many men undergo CaP screening without being fully informed about the decision. These findings support the need for interventions aimed at improving IDM about screening, particularly among men of color, those with lower levels of income and education, and those who have not discussed screening with their provider.
PMCID: PMC4386607  PMID: 20484092
16.  [No title available] 
PMCID: PMC3818438  PMID: 23811760
17.  [No title available] 
PMCID: PMC3946215  PMID: 24106235
18.  [No title available] 
PMCID: PMC3948210  PMID: 24125789
19.  Too Much of a Good Thing? When to Stop Catch-Up Vaccination 
During the 20th century, deaths from a range of serious infectious diseases decreased dramatically due to the development of safe and effective vaccines. However, infant immunization coverage has increased only marginally since the 1960s, and many people remain susceptible to vaccine-preventable diseases. “Catch-up vaccination” for age groups beyond infancy can be an attractive and effective means of immunizing people who were missed earlier. However, as newborn vaccination rates increase, catch-up vaccination becomes less attractive: the number of susceptible people decreases, so the cost to find and vaccinate each unvaccinated person may increase; additionally, the number of infected individuals decreases, so each unvaccinated person faces a lower risk of infection. This paper presents a general framework for determining the optimal time to discontinue a catch-up vaccination program. We use a cost-effectiveness framework: we consider the cost per quality-adjusted life year gained of catch-up vaccination efforts, as a function of newborn immunization rates over time and consequent disease prevalence and incidence. We illustrate our results with the example of hepatitis B catch-up vaccination in China. We contrast results from a dynamic modeling approach with an approach that ignores the impact of vaccination on future disease incidence. The latter approach is likely to be simpler for decision makers to understand and implement because of lower data requirements.
PMCID: PMC4247340  PMID: 23858015
vaccine; epidemic control; hepatitis B
20.  Patient Time Requirements for Anticoagulation Therapy with Warfarin 
Most patients receiving warfarin are man- aged in outpatient office settings or anticoagulation clinics that require frequent visits for monitoring.
To measure the amount and value of time required of patients for chronic anticoagulation therapy with warfarin.
Prospective observation of a cohort of adult patients treated at a university-based anticoagulation program.
Participants completed a questionnaire and a prospective diary of the time required for 1 visit to the anticoagulation clinic, including travel, waiting, and the clinic visit. The authors reviewed subjects’ medical records to obtain additional information, including the frequency of visits to the anti- coagulation clinic. They used the human capital method to estimate the value of time.
Eighty-five subjects completed the study. The mean (median) total time per visit was 147 minutes (123). Subjects averaged 15 visits per year (14) and spent 39.0 hours (29.3) per year on their visits. Other anticoagulation-related activities, such as communication with providers, pharmacy trips, and extra time preparing food, added an average of 52.7 hours (19.0) per year. The mean annual value of patient time spent traveling, waiting, and attending anticoagulation visits was $707 (median $591). The mean annual value when also including other anticoagulation-related activities was $1799 (median $1132).
The time required of patients for anticoagulation visits was considerable, averaging approximately 2.5 hours per visit and almost 40 hours per year. Methods for reducing patient time requirements, such as home-based testing, could reduce costs for patients, employers, and companions.
PMCID: PMC4181607  PMID: 19773584
anticoagulation; warfarin; time; human capital method; health economics
21.  The Impact of a Novel Computer-Based Decision Aid on Shared Decision-Making for Colorectal Cancer Screening: A Randomized Trial (Running head: SDM for CRC Screening) 
Eliciting patients’ preferences within a framework of shared decision-making (SDM) has been advocated as a strategy for increasing colorectal cancer (CRC) screening adherence. Our objective was to assess the effectiveness of a novel decision aid on SDM in the primary care setting.
An interactive, computer-based decision aid for CRC screening was developed and evaluated within the context of a randomized controlled trial. A total of 665 average-risk patients (mean age, 57 years; 60% female; 63% Black, 6% Hispanics) were allocated to one of two intervention arms (decision aid alone, decision aid plus personalized risk assessment) or a control arm. The interventions were delivered just prior to a scheduled primary care visit. Outcome measures (patient preferences, knowledge, satisfaction with the decision making process [SDMP], concordance between patient preference and test ordered, and intentions) were evaluated using pre/post-study visit questionnaires and electronic scheduling.
Overall, 95% of patients in the intervention arms identified a preferred screening option based on values placed on individual test features. Mean cumulative knowledge, SDMP and intention scores were significantly higher for both intervention groups compared with the control group. Concordance between patient preference and test ordered was 59%. Patients who preferred colonoscopy were more likely to have a test ordered than those who preferred an alternative option (83% vs. 70%; P<0.01). Intention scores were significantly higher when the test ordered reflected patient preferences.
Our interactive computer-based decision aid facilitates SDM but overall effectiveness is determined by the extent to which providers comply with patient preferences.
PMCID: PMC4165390  PMID: 20484090
22.  The Numeracy Understanding in Medicine Instrument (NUMi): A Measure of Health Numeracy Developed Using Item Response Theory 
Health numeracy can be defined as the ability to understand medical information presented with numbers, tables and graphs, probability, and statistics and to use that information to communicate with one’s health care provider, take care of one’s health, and participate in medical decisions.
To develop the Numeracy Understanding in Medicine Instrument (NUMi) using Item Response Theory scaling methods.
A 20 item test was formed drawing from an item bank of numeracy questions. Items were calibrated using responses from 1000 participants and a 2 parameter Item Response Theory (IRT) model. Construct validity was assessed by comparing scores on the NUMi to established measures of print and numeric health literacy, mathematic achievement, and cognitive aptitude.
Community and clinical populations in the Milwaukee and Chicago metropolitan areas.
Twenty-nine percent of the 1000 respondents were Hispanic, 24% Non-Hispanic white, and 42% Non-Hispanic black. Forty-one percent (41%) had no more than a high school education. The mean score on the NUMi was 13.2 (SD 4.6) with a Cronbach’s alpha of 0.86. Difficulty and discrimination IRT parameters of the 20 items ranged from −1.70 to 1.45 and 0.39 to 1.98, respectively. Performance on the NUMi was strongly correlated with the WRAT-arithmetic test (0.73, p<0.001), the Lipkus expanded numeracy scale (0.69, p<0.001), the Medical Data Interpretation Test (0.75, p<0.001), and the Wonderlic Cognitive Ability Test (0.82, p<0.001). Performance was moderately correlated to the Short Test of Functional Health Literacy (0.43, p<0.001).
The NUMi was found to be most discriminating among respondents with a lower than average level of health numeracy.
The NUMi can be applied in research and clinical settings as a robust measure of the health numeracy construct.
PMCID: PMC4162626  PMID: 22635285
23.  Measuring risk perceptions: What does the excessive use of 50% mean? 
Risk perceptions are central to good health decisions. People can judge valid probabilities, but use 50% disproportionately. We hypothesized that 50% is more likely than other responses to reflect not knowing the probability, especially among individuals with low education and numeracy, and evaluated the usefulness of eliciting “don’t know” explanations.
Respondents (n=1020) judged probabilities for “living” or “dying” in the next 10 years, indicating whether they gave a good estimate or did not know the chances. They completed demographics, medical history, and numeracy questions.
Overall, 50% was more likely than other probabilities to be explained as “don’t know” (vs. “a good estimate.”) Correlations of using 50% with low education and numeracy were mediated by expressing “don’t know.” Judged probabilities for survival and mortality explained as “don’t know” had lower correlations with age, diseases, and specialist visits.
When judging risks, 50% may reflect not knowing the probability, especially among individuals with low numeracy and education. Probabilities expressed as “don’t know” are less valid. Eliciting uncertainty could benefit theoretical models and educational efforts.
PMCID: PMC4152727  PMID: 21521797
24.  Estimating the Cost of No-shows and Evaluating the Effects of Mitigation Strategies 
To measure the cost of non-attendance (“no-shows”) and benefit of overbooking and interventions to reduce no-shows for an outpatient endoscopy suite.
We used a discrete event simulation model to determine improved overbooking scheduling policies and examine the effect of no-shows on procedure utilization and expected net gain, defined as the difference in expected revenue based on CMS reimbursement rates and variable costs based on the sum of patient waiting time and provider and staff overtime. No-show rates were estimated from historical attendance (18% on average, with a sensitivity range of 12 to 24%). We then evaluated the effectiveness of scheduling additional patients and the effect of no-show reduction interventions on the expected net gain.
The base schedule booked 24 patients per day. The daily expected net gain with perfect attendance is $4,433.32. The daily loss attributed to the base case no-show rate of 18% is $725.42 (16.36% of net gain), ranging from $472.14 to $1,019.29 (10.7% to 23.0% of net gain). Implementing no-show interventions reduced net loss by $166.61 to $463.09 (3.8% to 10.5% of net gain). The overbooking policy of 9 additional patients per day resulted in no loss in expected net gain when compared to the reference scenario.
No-shows can significantly decrease the expected net gain of outpatient procedure centers. Overbooking can help mitigate the impact of no-shows on a suite’s expected net gain and has a lower expected cost of implementation to the provider than intervention strategies.
PMCID: PMC4153419  PMID: 23515215
25.  Estimating a Preference-Based Index from the Clinical Outcomes in Routine Evaluation–Outcome Measure (CORE-OM) 
Medical Decision Making  2013;33(3):381-395.
Background. The Clinical Outcomes in Routine Evaluation–Outcome Measure (CORE-OM) is used to evaluate the effectiveness of psychological therapies in people with common mental disorders. The objective of this study was to estimate a preference-based index for this population using CORE-6D, a health state classification system derived from the CORE-OM consisting of a 5-item emotional component and a physical item, and to demonstrate a novel method for generating states that are not orthogonal. Methods. Rasch analysis was used to identify 11 emotional health states from CORE-6D that were frequently observed in the study population and are, thus, plausible (in contrast, conventional statistical design might generate implausible states). Combined with the 3 response levels of the physical item of CORE-6D, they generate 33 plausible health states, 18 of which were selected for valuation. A valuation survey of 220 members of the public in South Yorkshire, United Kingdom, was undertaken using the time tradeoff (TTO) method. Regression analysis was subsequently used to predict values for all possible states described by CORE-6D. Results. A number of multivariate regression models were built to predict values for the 33 health states of CORE-6D, using the Rasch logit value of the emotional state and the response level of the physical item as independent variables. A cubic model with high predictive value (adjusted R2 = 0.990) was selected to predict TTO values for all 729 CORE-6D health states. Conclusion. The CORE-6D preference-based index will enable the assessment of cost-effectiveness of interventions for people with common mental disorders using existing and prospective CORE-OM data sets. The new method for generating states may be useful for other instruments with highly correlated dimensions.
PMCID: PMC4107796  PMID: 23178639
condition specific; CORE-6D; CORE-OM; health state valuation; mental health; preference-based index; time trad-eoff

Results 1-25 (125)