Is the emergency department used as a substitute or a complement to primary care in Medicaid?

Policies to decrease low-acuity emergency department (ED) use have traditionally assumed that EDs are a substitute for unavailable primary care (PC). However, such policies can exacerbate ED overcrowding, rather than ameliorate it, if patients use EDs to complement, rather than substitute, their PC use. We tested whether Medicaid managed care enrolees visit the ED for nonemergent and PC treatable conditions to substitute for or to complement PC. Based on consumer choice theory, we modelled county-level monthly ED visit rate as a function of PC supply and used 2012-2015 New York Statewide Planning and Research Cooperative System (SPARCS) outpatient data and non-linear least squares method to test substitution vs complementarity. In the post-Medicaid expansion period (2014-2015), ED and PC are substitutes state-wide, but are complements in highly urban and poorer counties during nights and weekends. There is no evidence of complementarity before the expansion (2012-2013). Analyses by PC provider demonstrate that the relationship between ED and PC differs depending on whether PC is provided by physicians or advanced practice providers. Policies to reduce low-acuity ED use via improved PC access in Medicaid are likely to be most effective if they focus on increasing actual appointment availability, ideally by physicians, in areas with low PC provider supply. Different aspects of PC access may be differently related to low-acuity ED use.

US trends in social isolation, social engagement, and companionship ⎯ nationally and by age, sex, race/ethnicity, family income, and work hours, 2003-2020.

Social connectedness is essential for health and longevity, while isolation exacts a heavy toll on individuals and society. We present U.S. social connectedness magnitudes and trends as target phenomena to inform calls for policy-based approaches to promote social health. Using the 2003-2020 American Time Use Survey, this study finds that, nationally, social isolation increased, social engagement with family, friends, and 'others' (roommates, neighbors, acquaintances, coworkers, clients, etc.) decreased, and companionship (shared leisure and recreation) decreased. Joinpoint analysis showed that the pandemic exacerbated upward trends in social isolation and downward trends in non-household family, friends, and 'others' social engagement. However, household family social engagement and companionship showed signs of progressive decline years prior to the pandemic, at a pace not eclipsed by the pandemic. Work hours emerged as a structural constraint to social engagement. Sub-groups allocated social engagement differently across different relationship roles. Social engagement with friends, others, and in companionship plummeted for young Americans. Black Americans experienced more social isolation and less social engagement, overall, relative to other races. Hispanics experienced much less social isolation than non-Hispanics. Older adults spent more time in social isolation, but also relatively more time in companionship. Women spent more time with family while men spent more time with friends and in companionship. And, men's social connectedness decline was steeper than for women. Finally, low-income Americans are more socially engaged with 'others' than those with higher income. We discuss potential avenues of future research and policy initiatives that emerge from our findings.

Are Increasing Trends in Opioid-Related Hospitalizations Attributable to Increases in Diagnosis Recordability? Evidence from 2 Large States.

Based on calculations using all-listed diagnoses, the Agency for Healthcare Research and Quality (AHRQ) reports increasing national trends in opioid-related hospitalizations. It is unclear whether the reported increases are attributable to increases in available diagnosis fields. We leveraged increases in available diagnosis fields, ie, diagnosis recordability, in 2 states to examine their effects on opioid-related hospitalizations, graphically and with nonlinear least squares. Hospitalization data from Texas (1999-2011, N = 36 593 049) and New York (2005-2015Q3, N = 27 582 208) were aggregated to quarter-year in each state. Opioid-related hospitalizations were identified using the same International Classification of Diseases, Ninth Revision, Clinical Modification (ICD-9-CM) Diagnosis Codes as AHRQ. In Texas, the increase in diagnosis recordability resulted in a 29.9% discrete shift in the number of recorded opioid diagnoses and a 3-fold increase in the slope. In New York, a smaller discrete shift (3.1%) and a 3-fold increase in the slope were identified, although a more pronounced change in the trend occurred 5 years earlier (slope change from flat to increasing). Increases in recordability lead to a broader definition of opioid-related hospitalizations, if all-listed diagnoses are used; we found that more hospitalizations are identified using the postchange definition than with the prechange definition (9.7% more in Texas and 4.9% more in New York after 4 years). We conclude that reported increases in opioid-related hospitalizations are partially attributable to increases in diagnosis recordability. Cross-state and temporal comparisons of opioid-related hospitalization rates based on all-listed diagnoses can misrepresent the true relative extent of opioid-related hospital use and therefore of the opioid epidemic.

Did Medicaid expansion matter in states with generous Medicaid?

OBJECTIVES: It is unclear whether the Medicaid expansion under the Affordable Care Act had an effect on coverage in states with relatively generous pre-expansion Medicaid eligibility levels. We examined the effect of the Medicaid expansions on Medicaid coverage in 4 generous states: New York, Vermont, Massachusetts, and Delaware. STUDY DESIGN: We used the American Community Survey (2011-2016) to estimate effects on coverage among nonelderly adults with incomes up to 138% of the federal poverty level. METHODS: We estimated differences in differences (DID) in marginal probabilities following probit models, comparing New York, Vermont, Massachusetts, and Delaware with nonexpansion states on the East Coast. RESULTS: There is strong evidence of the effect in New York: DID estimates ranged from 3.3 to 5.2 percentage points. There is weak or no evidence of coverage gains in the other 3 states. Pronounced effects were found among the racial/ethnic majority (white, non-Hispanic white, and nonblack populations) in New York, as well as the working poor and previously eligible in New York and Massachusetts. CONCLUSIONS: Even in states with relatively generous pre-expansion Medicaid programs, the expansion can produce nontrivial coverage gains, as evidenced by New York. Our findings of spillover effects may indicate the relative importance and success of a simplified enrollment process and increased media coverage in boosting enrollment in Medicaid. Our subgroup analyses highlight a potential need to improve access to office-based care to accommodate the growing population of the working poor on Medicaid and potential changes in the Medicaid risk pool served by managed care organizations and subsequent decreases in capitated payments.

Harnessing Expert Judgment to Support Clinical Decisions When the Evidence Base Is Weak.

PURPOSE: In the process of developing an evidence-based decision dashboard to support treatment decisions for patients with newly diagnosed prostate cancer, we found that the clinical evidence base is insufficient to provide high-quality comparative outcome data. We therefore sought to determine if clinically acceptable outcome estimates could be created using a modified version of the Sheffield Elicitation Framework (SHELF), a formal method for eliciting judgments regarding probability distributions of expected decision outcomes. METHODS: We asked a panel of 3 urologists, 4 radiation oncologists, and 2 medical oncologists to estimate the probabilities of 11 treatment outcomes based on their clinical experience and an annotated evidence summary. The estimates were elicited using a Microsoft Excel spreadsheet containing a self-guided, adapted version of the SHELF Roulette method distributed via email. We created combined outcome estimates by taking the mean values of the panel members' upper and lower 95% bounds for each outcome. The combined estimates were then distributed via email to the panel for final approval. RESULTS: Eight of the 9 responses were judged to be correct applications of the SHELF method and included in the combined outcome estimates. The final set of outcome estimates was unanimously accepted by the clinician panel members and used to create a decision dashboard suitable for clinical use and evaluation. CONCLUSIONS: Many important health care decisions need to be made in situations where the evidence base is inadequate. Use of a formal protocol for eliciting expert judgments is feasible and can be used to promote evidence-based practice by providing a powerful tool to facilitate the combination of professional judgment with research evidence and patient preferences to guide clinical decisions.

Political orientation, political environment, and health behaviors in the United States.

Political orientation (Republican/Democrat and conservative/liberal) and political environment (geo-spatial political party affiliated voting patterns) are both associated with various health outcomes, including mortality. Modern disease etiology in the U.S. suggests that many of our health outcomes derive from behaviors and lifestyle choices. Thus, we examine the associations of political orientation and political environment with health behaviors. We used the Annenberg National Health Communication Survey (ANHCS) data, which is a nationally representative U.S. survey fielded continuously from 2005 through 2012. The health behaviors studied include health information search, flu vaccination, excessive alcohol consumption, tobacco consumption, exercise, and dietary patterns. Democrats/liberals had higher odds of cigarette smoking and excessive drinking compared to Republicans/conservatives. Whereas, Republicans/conservatives ate fewer servings and fewer varieties of fruit and vegetables; ate more high fat and processed foods; and engaged in less in-depth health information searches compared to Democrats/liberals. Also, conservatives had lower odds of exercise participation than liberals; whereas Republicans had lower odds of flu vaccination. Greater Republican vote share in the 2008 and 2012 presidential elections at the state and/or county levels was associated with higher odds of flu vaccination and smoking cigarettes and lower odds of avoiding fat/calories, avoiding fast/processed food, eating a variety of fruits and vegetables, and eating more servings of fruit. We use the distinct cognitive-motivational styles attributed to political orientation in discussing the findings. Health communication strategies could leverage these relationships to produce tailored and targeted messages as well as to develop and advocate for policy.

The Feasibility of Sophisticated Multicriteria Support for Clinical Decisions.

BACKGROUND: Multicriteria decision-making (MCDM) methods are well-suited to serve as the foundation for clinical decision support systems. To do so, however, they need to be appropriate for use in busy clinical settings. We compared decision-making processes and outcomes of patient-level analyses done with a range of multicriteria methods that vary in ease of use and intensity of decision support, 2 factors that could affect their ease of implementation into practice. METHODS: We conducted a series of Internet surveys to compare the effects of 5 multicriteria methods that differ in user interface and required user input format on decisions regarding selection of a preferred method for lowering the risk of cardiovascular disease. The study sample consisted of members of an online Internet panel maintained by Fluidsurveys, an Internet survey company. Study outcomes were changes in preferred option, decision confidence, preparation for decision making, the Values Clarification and Decisional Uncertainty subscales of the Decisional Conflict Scale, and method ease of use. RESULTS: The frequency of changes in the preferred option ranged from 9% to 38%, P < 0.001, and rose progressively as the level of decision support provided by the MCDM method increased. The proportion of respondents who rated the method as easy ranged from 57% to 79% and differed significantly among MCDM methods, P = 0.003, but was not consistently related to intensity of decision support or ease of use. CONCLUSION: Decision support based on MCDM methods is not necessarily limited by decreases in ease of use. This result suggests that it is possible to develop decision support tools using sophisticated multicriteria techniques suitable for use in routine clinical care settings.

Should Health Numeracy Be Assessed Objectively or Subjectively?

INTRODUCTION: Because current evidence suggests that numeracy affects how people make decisions, it is an important factor to account for in studies assessing the effectiveness of medical decision support interventions. Subjective and objective numeracy assessment methods are available that vary in theoretical background, skills assessed, known relationship with decision making skills, and ease of implementation. The best way to use these tools to assess numeracy when conducting medical decision-making research is currently unknown. METHODS: We conducted Internet surveys comparing numeracy assessments obtained using the subjective numeracy scale (SNS) and 5 objective numeracy scales. Each study participant completed the SNS and 1 objective numeracy measure. Following each assessment, participants indicated willingness to repeat the assessment and rated its user acceptability. RESULTS: The overall response rate was 78%, resulting in a total sample size of 673. Spearman correlations between the SNS and the objective numeracy measures ranged from 0.19 to 0.44. Acceptability assessments for the short form of the Numeracy Understanding in Medicine Instrument and the SNS did not differ significantly. The other objective scales all had lower acceptability ratings than the SNS. CONCLUSIONS: These findings are consistent with prior research suggesting that objective and subjective numeracy scales measure related but distinct constructs. Due to current uncertainty regarding which construct is more likely to influence the effectiveness of decision support interventions, these findings warrant further investigation to determine the proper use of objective versus subjective numeracy assessments in medical decision-making research. Pending additional information, a reasonable approach is to measure both objective and subjective numeracy so that the full range of actual and perceived numeracy skills can be taken into account.

Prehospital Trauma Triage Decision-making: A Model of What Happens between the 9-1-1 Call and the Hospital.

We describe the decision-making process used by emergency medical services (EMS) providers in order to understand how 1) injured patients are evaluated in the prehospital setting; 2) field triage criteria are applied in-practice; and 3) selection of a destination hospital is determined. We conducted separate focus groups with advanced and basic life support providers from rural and urban/suburban regions. Four exploratory focus groups were conducted to identify overarching themes and five additional confirmatory focus groups were conducted to verify initial focus group findings and provide additional detail regarding trauma triage decision-making and application of field triage criteria. All focus groups were conducted by a public health researcher with formal training in qualitative research. A standardized question guide was used to facilitate discussion at all focus groups. All focus groups were audio-recorded and transcribed. Responses were coded and categorized into larger domains to describe how EMS providers approach trauma triage and apply the Field Triage Decision Scheme. We conducted 9 focus groups with 50 EMS providers. Participants highlighted that trauma triage is complex and there is often limited time to make destination decisions. Four overarching domains were identified within the context of trauma triage decision-making: 1) initial assessment; 2) importance of speed versus accuracy; 3) usability of current field triage criteria; and 4) consideration of patient and emergency care system-level factors. Field triage is a complex decision-making process which involves consideration of many patient and system-level factors. The decision model presented in this study suggests that EMS providers place significant emphasis on speed of decisions, relying on initial impressions and immediately observable information, rather than precise measurement of vital signs or systematic application of field triage criteria.

Decision Aids: The Effect of Labeling Options on Patient Choices and Decision Making.

BACKGROUND: Conscious and unconscious biases can influence how people interpret new information and make decisions. Current standards for creating decision aids, however, do not address this issue. METHOD: Using a 2×2 factorial design, we developed surveys that contained a decision scenario (involving a choice between aspirin or a statin drug to lower risk of heart attack) and a decision aid. Each aid presented identical information about reduction in heart attack risk and likelihood of a major side effect. They differed in whether the options were labeled and the amount of decisional guidance: information only (a balance sheet) versus information plus values clarification (a multicriteria decision analysis). We sent the surveys to members of 2 Internet survey panels. After using the decision aid, participants indicated their preferred medication. Those using a multicriteria decision aid also judged differences in the comparative outcome data provided for the 2 options and the relative importance of achieving benefits versus avoiding risks in making the decision. RESULTS: The study sample size was 536. Participants using decision aids with unlabeled options were more likely to choose a statin: 56% versus 25% (P < 0.001). The type of decision aid made no difference. This effect persisted after adjustment for differences in survey company, age, gender, education level, health literacy, and numeracy. Participants using unlabeled decision aids were also more likely to interpret the data presented as favoring a statin with regard to both treatment benefits and risk of side effects (P ≤ 0.01). There were no significant differences in decision priorities (P = 0.21). CONCLUSION: Identifying the options in patient decision aids can influence patient preferences and change how they interpret comparative outcome data.

Balance Sheets Versus Decision Dashboards to Support Patient Treatment Choices: A Comparative Analysis.

BACKGROUND: Growing recognition of the importance of involving patients in preference-driven healthcare decisions has highlighted the need to develop practical strategies to implement patient-centered shared decision-making. The use of tabular balance sheets to support clinical decision-making is well established. More recent evidence suggests that graphic, interactive decision dashboards can help people derive deeper a understanding of information within a specific decision context. We therefore conducted a non-randomized trial comparing the effects of adding an interactive dashboard to a static tabular balance sheet on patient decision-making. METHODS: The study population consisted of members of the ResearchMatch registry who volunteered to participate in a study of medical decision-making. Two separate surveys were conducted: one in the control group and one in the intervention group. All participants were instructed to imagine they were newly diagnosed with a chronic illness and were asked to choose between three hypothetical drug treatments, which varied with regard to effectiveness, side effects, and out-of-pocket cost. Both groups made an initial treatment choice after reviewing a balance sheet. After a brief "washout" period, members of the control group made a second treatment choice after reviewing the balance sheet again, while intervention group members made a second treatment choice after reviewing an interactive decision dashboard containing the same information. After both choices, participants rated their degree of confidence in their choice on a 1 to 10 scale. RESULTS: Members of the dashboard intervention group were more likely to change their choice of preferred drug (10.2 versus 7.5%; p = 0.054) and had a larger increase in decision confidence than the control group (0.67 versus 0.075; p < 0.03). There were no statistically significant between-group differences in decisional conflict or decision aid acceptability. CONCLUSION: These findings suggest that clinical decision dashboards may be an effective point-of-care decision-support tool. Further research to explore this possibility is warranted.

Who avoids going to the doctor and why? Audience segmentation analysis for application of message development.

This exploratory study examines the prevalent and detrimental health care phenomenon of patient delay in order to inform formative research leading to the design of communication strategies. Delayed medical care diminishes optimal treatment choices, negatively impacts prognosis, and increases medical costs. Various communication strategies have been employed to combat patient delay, with limited success. This study fills a gap in research informing those interventions by focusing on the portion of patient delay occurring after symptoms have been assessed as a sign of illness and the need for medical care has been determined. We used CHAID segmentation analysis to produce homogeneous segments from the sample according to the propensity to avoid medical care. CHAID is a criterion-based predictive cluster analysis technique. CHAID examines a variety of characteristics to find the one most strongly associated with avoiding doctor visits through a chi-squared test and assessment of statistical significance. The characteristics identified then define the segments. Fourteen segments were produced. Age was the first delineating characteristic, with younger age groups comprising a greater proportion of avoiders. Other segments containing a comparatively larger percent of avoiders were characterized by lower income, lower education, being uninsured, and being male. Each segment was assessed for psychographic properties associated with avoiding care, reasons for avoiding care, and trust in health information sources. While the segments display distinct profiles, having had positive provider experiences, having high health self-efficacy, and having an internal rather than external or chance locus of control were associated with low avoidance among several segments. Several segments were either more or less likely to cite time or money as the reason for avoiding care. And several older aged segments were less likely than the remaining sample to trust the government as a source for health information. Implications for future research are discussed.

Regulatory focus affects physician risk tolerance.

Risk tolerance is a source of variation in physician decision-making. This variation, if independent of clinical concerns, can result in mistaken utilization of health services. To address such problems, it will be helpful to identify nonclinical factors of risk tolerance, particularly those amendable to intervention-regulatory focus theory suggests such a factor. This study tested whether regulatory focus affects risk tolerance among primary care physicians. Twenty-seven primary care physicians were assigned to promotion-focused or prevention-focused manipulations and compared on the Risk Taking Attitudes in Medical Decision Making scale using a randomization test. Results provide evidence that physicians assigned to the promotion-focus manipulation adopted an attitude of greater risk tolerance than the physicians assigned to the prevention-focused manipulation (p = 0.01). The Cohen's d statistic was conventionally large at 0.92. Results imply that situational regulatory focus in primary care physicians affects risk tolerance and may thereby be a nonclinical source of practice variation. Results also provide marginal evidence that chronic regulatory focus is associated with risk tolerance (p = 0.05), but the mechanism remains unclear. Research and intervention targeting physician risk tolerance may benefit by considering situational regulatory focus as an explanatory factor.

Predictors of avoiding medical care and reasons for avoidance behavior.

BACKGROUND: Delayed medical care has negative health and economic consequences; interventions have focused on appraising symptoms, with limited success in reducing delay. OBJECTIVE: To identify predictors of care avoidance and reasons for avoiding care. METHODS: Using the Health Information National Trends Survey (2007), we conducted logistic regressions to identify predictors of avoiding medical visits deemed necessary by the respondents; and, we then conducted similar analyses on reasons given for avoidance behavior. Independent variables included geographic, demographic, socioeconomic, personal health, health behavior, health care system, and cognitive characteristics. RESULTS: Approximately one third of adults avoided doctor visits they had deemed necessary. Although unadjusted associations existed, avoiding needed care was not independently associated with geographic, demographic, and socioeconomic characteristics. Avoidance behavior is characterized by low health self-efficacy, less experience with both quality care and getting help with uncertainty about health, having your feelings attended to by your provider, no usual source of care, negative affect, smoking daily, and fatalistic attitude toward cancer. Reasons elicited for avoidance include preference for self-care or alternative care, dislike or distrust of doctors, fear or dislike of medical treatments, time, and money; respondents also endorsed discomfort with body examinations, fear of having a serious illness, and thoughts of dying. Distinct predictors distinguish each of these reasons. CONCLUSIONS: Interventions to reduce patient delay could be improved by addressing the health-related behavioral, belief, experiential, and emotional traits associated with delay. Attention should also be directed toward the interpersonal communications between patients and providers.

How Older Persons Structure Information in the Decision to Seek Medical Care.

Typical models of the decision to seek care consider information as a single conceptual object. This paper presents an alternative that allows multiple objects. For older persons seeking care, results support this alternative. Older decision-makers that segregate information into multiple conceptual objects assessed separately are characterized by socio-demographic (younger age, racial category, non-Hispanic, higher education, higher income, and not married), health status (better general health for men and worse general health for women, fewer known illnesses), and neuropsychological (less memory loss for men, trouble concentrating and trouble making decisions for men) factors. Results of this study support the conclusion that older persons are more likely to integrate information, and individuals with identifiable characteristics are more likely to do so than others. The theory tested in this study implies a potential explanation for misutilization of care (either over or under-utilization).

Is personality associated with health care use by older adults?

CONTEXT: The patterns of health care utilization in the United States pose well-established challenges for public policy. Although economic and sociological research has resulted in considerable knowledge about what influences the use of health services, the psychological literature in this area is underdeveloped. Importantly, it is not known whether personality traits are associated with older adults' use of acute and long-term care services. METHODS: Data were collected from 1,074 community-dwelling seniors participating in a Medicare demonstration. First they completed a self-report questionnaire measuring the "Big Five" personality traits: Neuroticism, Extraversion, Openness to Experience, Agreeableness, and Conscientiousness. During the next two years, the participants maintained daily journals of their use of health care services. We used regression models based on the Andersen behavioral model of health care utilization to test for associations. FINDINGS: Our hypothesis that higher Neuroticism would be associated with greater health care use was confirmed for three services-probability of any emergency department (ED) use, likelihood of any custodial nursing home use, and more skilled nursing facility (SNF) days for SNF users-but was disconfirmed for hospital days for those hospitalized. Higher Openness to Experience was associated with a greater likelihood of custodial home care use, and higher Agreeableness and lower Conscientiousness with a higher probability of custodial nursing home use. For users, lower Openness was associated with more ED visits and SNF days, and lower Conscientiousness with more ED visits. For many traits with significant associations, the predicted use was 16 to 30 percent greater for people high (low) versus low (high) in specific traits. CONCLUSIONS: Personality traits are associated with Medicare beneficiaries' use of many expensive health care services, findings that have implications for health services research and policy. Accordingly, person-centered interventions, population-based translational effectiveness programs, and other personalized approaches that leverage the profound advances in personality psychology in recent decades should be considered.

Development and initial evaluation of a treatment decision dashboard.

BACKGROUND: For many healthcare decisions, multiple alternatives are available with different combinations of advantages and disadvantages across several important dimensions. The complexity of current healthcare decisions thus presents a significant barrier to informed decision making, a key element of patient-centered care.Interactive decision dashboards were developed to facilitate decision making in Management, a field marked by similarly complicated choices. These dashboards utilize data visualization techniques to reduce the cognitive effort needed to evaluate decision alternatives and a non-linear flow of information that enables users to review information in a self-directed fashion. Theoretically, both of these features should facilitate informed decision making by increasing user engagement with and understanding of the decision at hand. We sought to determine if the interactive decision dashboard format can be successfully adapted to create a clinically realistic prototype patient decision aid suitable for further evaluation and refinement. METHODS: We created a computerized, interactive clinical decision dashboard and performed a pilot test of its clinical feasibility and acceptability using a multi-method analysis. The dashboard summarized information about the effectiveness, risks of side effects and drug-drug interactions, out-of-pocket costs, and ease of use of nine analgesic treatment options for knee osteoarthritis. Outcome evaluations included observations of how study participants utilized the dashboard, questionnaires to assess usability, acceptability, and decisional conflict, and an open-ended qualitative analysis. RESULTS: The study sample consisted of 25 volunteers - 7 men and 18 women - with an average age of 51 years. The mean time spent interacting with the dashboard was 4.6 minutes. Mean evaluation scores on scales ranging from 1 (low) to 7 (high) were: mechanical ease of use 6.1, cognitive ease of use 6.2, emotional difficulty 2.7, decision-aiding effectiveness 5.9, clarification of values 6.5, reduction in decisional uncertainty 6.1, and provision of decision-related information 6.0. Qualitative findings were similarly positive. CONCLUSIONS: Interactive decision dashboards can be adapted for clinical use and have the potential to foster informed decision making. Additional research is warranted to more rigorously test the effectiveness and efficiency of patient decision dashboards for supporting informed decision making and other aspects of patient-centered care, including shared decision making.

Cardiac resynchronization and quality of life in patients with minimally symptomatic heart failure.

OBJECTIVES: This study compared the quality of life (QOL) of patients with cardiac resynchronization therapy (CRT) and an implantable cardioverter-defibrillator (ICD) to patients with an ICD only. BACKGROUND: CRT with ICD is associated with a reduction in heart failure risk among minimally symptomatic patients. It is unknown whether this improves QOL. METHODS: This study uses the MADIT-CRT (Multicenter Automatic Defibrillator Implantation Trial With Cardiac Resynchronization Therapy) data. The MADIT-CRT enrolled 1,820 patients at 110 centers across 14 countries. Patients had ischemic cardiomyopathy (New York Heart Association [NYHA] functional class I or II) or nonischemic cardiomyopathy (NYHA functional class II only), sinus rhythm, an ejection fraction of 30% or less, and prolonged intraventricular conduction with a QRS duration of 130 ms or more. QOL was evaluated on the 1,699 patients with baseline and follow-up measures using the Kansas City Cardiomyopathy Questionnaire (KCCQ). Six dimensions (Physical Limitation, Symptom Stability, Symptom Frequency, Symptom Burden, Quality of Life, and Social Limitations) and 3 summary scores (Total Symptom, Clinical Summary, and Overall Summary) were analyzed. RESULTS: During an average follow-up of 2.4 years, the CRT-ICD group had greater improvement than the ICD-only group on all KCCQ measures (p < 0.05 on each scale). These differences were significant among patients with left bundle branch block conduction disturbance (n = 1,204, p < 0.01 on each scale), but not among patients without left bundle branch block (n = 494). CONCLUSIONS: Compared with patients with ICD only, CRT-ICD is associated with greater improvement in QOL among relatively asymptomatic patients, specifically among those with left bundle branch conduction disturbance.

Identifying sincerity of effort based on the combined predictive ability of multiple grip strength tests.

STUDY DESIGN: Retrospective Cohort. INTRODUCTION: Detecting sincerity of effort (SOE) of grip strength remains a frustrating and elusive task for hand therapists because there are no valid, reliable, or widely accepted assessments for identifying feigned effort. Some therapists use various combinations of different SOE tests in an attempt to identify feigned effort, but there is lack of evidence to support this practice. PURPOSE: The present study examined the ability of a combination of three grip strength tests commonly used in the clinic to detect SOE: the five rung grip test, rapid exchange grip test, and coefficient of variation. A secondary purpose was to compare the predictive ability between the logistic and linear regression models. METHODS: Healthy participants (n=146) performed the three SOE tests exerting both maximal and submaximal efforts. We compared the ability of two regression models, the logistic and linear models, to predict sincere versus insincere efforts. RESULTS: Combining the three tests predicted SOE better than each test alone. Yet, the full logistic model, which was the best predictor of SOE, explained only 42% of variance and correctly classified only 58% of the efforts. CONCLUSIONS: Our findings do not support the clinical practice of combining these three tests to detect SOE. LEVEL OF EVIDENCE: Not applicable.

How well do commonly used data presentation formats support comparative effectiveness evaluations?

BACKGROUND: Good decisions depend on an accurate understanding of the comparative effectiveness of decision alternatives. The best way to convey data needed to support these comparisons is unknown. OBJECTIVE: To determine how well 5 commonly used data presentation formats convey comparative effectiveness information. METHODS: The study was an Internet survey using a factorial design. Participants consisted of 279 members of an online survey panel. Study participants compared outcomes associated with 3 hypothetical screening test options relative to 5 possible outcomes with probabilities ranging from 2 per 5000 (0.04%) to 500 per 1000 (50%). Data presentation formats included a table, a "magnified" bar chart, a risk scale, a frequency diagram, and an icon array. Outcomes included the number of correct ordinal judgments regarding the more likely of 2 outcomes, the ratio of perceived versus actual relative likelihoods of the paired outcomes, the intersubject consistency of responses, and perceived clarity. RESULTS: The mean number of correct ordinal judgments was 12 of 15 (80%), with no differences among data formats. On average, there was a 3.3-fold difference between perceived and actual likelihood ratios (95% confidence interval = 3.0-3.6). Comparative judgments based on flowcharts, icon arrays, and tables were all significantly more accurate and consistent than those based on risk scales and bar charts (P < 0.001). The most clearly perceived formats were the table and the flowchart. Low subjective numeracy was associated with less accurate and more variable data interpretations and lower perceived clarity for icon displays, bar charts, and flow diagrams. CONCLUSIONS: None of the data presentation formats studied can reliably provide patients, especially those with low subjective numeracy, with an accurate understanding of comparative effectiveness information.