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A method commonly used in health care research is the waiting room (WR) survey. While patients are waiting for their appointment, they are asked to complete a questionnaire measuring their attitudes, behaviors and other characteristics. In this paper we synthesize practical guidelines for WR surveys by comparing the method with two similar approaches: public intercept (PI) surveys and drop-off-pick-up (DOPU) surveys. In this comparison we use the Total Survey Error approach Groves (Survey Methodology, Wiley, New York, 2004); (Groves in Public Opinion Quarterly 74(5): 849-879, 2010) and apply it to three case examples in which one of the three surveys is used. We take into account measurement (validity, measurement- and processing error) and representation (coverage-, sampling- and nonresponse error). From our review, we conclude that waiting room surveys, though limited to patients and their caregivers, can provide useful information on patients' perspective on health care. Response rates in waiting rooms are usually high, but often not even reported. We recommend adjustment for sampling bias by taking into account the number of visits to the hospital per respondent and sample times proportionate to the number of sample members expected on a particular time. These surveys also allow for collection of para-data; i.e., relevant information in the circumstances of a request to participate in survey research, and behavior of surveyors can easily be controlled, or investigated in an experimental design. [ABSTRACT FROM AUTHOR]

Analysis of public policy affecting dual eligibles requires accurate identification of survey respondents eligible for both Medicare and Medicaid. Doing so for Medicaid is particularly challenging given the complex eligibility rules tied to income and assets. In this paper we provide guidance on how to best identify eligible respondents in household surveys that have limited income or asset information, such as the National Health Interview Survey, American Community Survey, Current Population Survey, and Medical Expenditure Panel Survey. We show how two types of errors—false negative and false positive errors—are impacted by incorporating limited income or asset information, relative to the commonly-used approach of solely comparing total income to the income threshold. With the 2018 Health and Retirement Study, which has detailed income and asset information, we mimic the income and asset information available in those other household surveys and quantify how errors change when imposing income or asset tests with limited information. We show that incorporating all available income and asset data results in the lowest number of errors and the lowest overall error rates. We recommend that researchers adjust income and impose the asset test to the fullest extent possible when imputing Medicaid eligibility for Medicare enrollees. [ABSTRACT FROM AUTHOR]

The World Health Organization Disability Assessment Schedule (WHODAS 2.0) is the official generic assessment instrument of WHO for disability and health. It is considered to be applicable across cultures in adult populations. The paper describes the development, cultural adaptation and validation of the Greek version of the official WHODAS 2.0, 36-item assessment schedule. The research tested the psychometric properties of this version in order to identify if it can be considered a valid and reliable instrument for measuring health and disability in the Greek population and culture. Field test studies were conducted in two hundred participants of the general population, aged over 61 years old, in a Greek Open Care Centre for the elderly. The study was carried out between July 2015 and September 2016. The linguistic adaptation of the original instrument was performed according to the most recent guidelines. Ratings of the different items were statistically analyzed to investigate the construct, criterion and concurrent validity and reliability of the instrument. Construct validity is confirmed in the Greek version of WHODAS 2.0. Concurrent validity is well established between the scores of WHODAS 2.0 and SF-36 v2. Cronbach’s α (0.673–0.901) and ICC (0.793–0.885) values indicate satisfactory reliability. The study supports the validity and reliability of the official Greek version of WHODAS 2.0, which can be used to assess effectively the quality of life and disability levels of the intended population and inform policy-making. Findings will be further tested in larger and diversified samples in subsequent parts of the study. [ABSTRACT FROM AUTHOR]

The need to harmonize different outcome metrics is a common problem in research synthesis and economic evaluation of health interventions and technology. The purpose of this paper is to describe the use of multidimensional item response theory (IRT) to equate different scales which purport to measure the same construct at the item level. We provide an overview of multidimensional IRT in general and the bi-factor model which is particularly relevant for applications in this area. We show how both the underlying true scores of two or more scales that are intended to measure the same latent variable can be equated and how the item responses from one scale can be used to predict the item responses for a scale that was not administered but are necessary for the purpose of economic evaluations. As an example, we show that a multidimensional IRT model predicts well both the EQ-5D descriptive system and the EQ-5D preference index from SF-12 data which cannot be directly used to perform an economic evaluation. Results based on multidimensional IRT performed well compared to traditional regression methods in this area. A general framework for harmonization of research instruments based on multidimensional IRT is described. [ABSTRACT FROM AUTHOR]

Mixed-effects regression models have extraordinary capacity to capture the essence of complex longitudinal data. In this paper we present analyses from a longitudinal study on 157 patients with osteoarthritis (OA) where real-time momentary self-reported pain and physical activity level were collected. We illustrate the application of heterogeneous mixed effect (HME) models to examine the effect of time-varying activity level assessments (sedentary vs. moderate/vigorous) during the 30 min prior to the pain recording on mean pain levels and variability. We also examine the variation in pain associated with activity level and the degree that participant characteristics (weight-bearing vs. non-weight bearing joint w/OA, body mass index, white vs. non-white race, gender) and psychosocial factors (mood, tension, pain catastrophizing) influence pain variation. We fit separate HME models for each characteristic or factor. These HME models are fit with standard statistical software, but there are challenges to fitting and interpreting these models. Once fit the models provide a powerful tool to help guide researchers in understanding highly individualized pain trajectories. [ABSTRACT FROM AUTHOR]

The quality of propensity scores is traditionally measured by assessing how well they make the distributions of covariates in the treatment and control groups match, which we refer to as 'good balance'. Good balance guarantees less biased estimates of the treatment effect. However, the cost of achieving good balance is that the variance of the estimates increases due to a reduction in effective sample size, either through the introduction of propensity score weights or dropping cases when propensity score matching. In this paper, we investigate whether it is best to optimize the balance or to settle for a less than optimal balance and use double robust estimation to adjust for remaining differences. We compare treatment effect estimates from regression, propensity score weighting, and double robust estimation with varying levels of effort expended to achieve balance using data from a study about the differences in outcomes by HIV status in heterosexually active homeless men residing in Los Angeles. Because of how costly data collection efforts are for this population, it is important to find an alternative estimation method that does not reduce effective sample size as much as methods that aggressively aim to optimize balance. Results from a simulation study suggest that there are instances in which we can obtain more precise treatment effect estimates without increasing bias too much by using a combination of regression and propensity score weights that achieve a less than optimal balance. There is a bias-variance tradeoff at work in propensity score estimation; every step toward better balance usually means an increase in variance and at some point a marginal decrease in bias may not be worth the associated increase in variance. [ABSTRACT FROM AUTHOR]

The SF-36 is a commonly used tool for measuring health status in a general population. Despite the overall moderate to high validity scores, certain communicative dynamics of the questionnaire deserve attention. Our aim was to examine how pragmatic dynamics, semantic context-sensitivity and epistemic reflection may influence answers to the SF-36. We applied a three-step Gricean analysis, including identification of the items in which pragmatic dynamics are most likely to have a significant effect, examination of how Gricean maxims might affect the answers given to the items identified, and assessment of whether the combined influence of linguistic context-sensitivity and pragmatic norms is benign. We found that the pragmatic dynamics of scalar implicatures are crucial to the interpretation of answer options but generally benign to its purpose. Regarding context, we raised concerns about the answer option 'Ved ikke' (Don't know); rather than representing a neutral midpoint, the answer is compatible with both a positive and a negative answer option. Whereas scalar implicatures are helpful to the purpose of SF-36, other contextual effects appear to be more worrisome. However, since pragmatic norms of communication, semantic context-sensitivity, and attention to epistemic error possibilities can all be expected to shape answers to the SF-36, we think that all three factors belong in a description of how the questionnaire works. [ABSTRACT FROM AUTHOR]

To slow the spread of COVID-19, most countries implemented stay-at-home orders, social distancing, and other nonpharmaceutical mitigation strategies. To understand individual preferences for mitigation strategies, we piloted a web-based Respondent Driven Sampling (RDS) approach to recruit participants from four universities in three countries to complete a computer-based Discrete Choice Experiment (DCE). Use of these methods, in combination, can serve to increase the external validity of a study by enabling recruitment of populations underrepresented in sampling frames, thus allowing preference results to be more generalizable to targeted subpopulations. A total of 99 students or staff members were invited to complete the survey, of which 72% started the survey (n = 71). Sixty-three participants (89% of starters) completed all tasks in the DCE. A rank-ordered mixed logit model was used to estimate preferences for COVID-19 nonpharmaceutical mitigation strategies. The model estimates indicated that participants preferred mitigation strategies that resulted in lower COVID-19 risk (i.e. sheltering-in-place more days a week), financial compensation from the government, fewer health (mental and physical) problems, and fewer financial problems. The high response rate and survey engagement provide proof of concept that RDS and DCE can be implemented as web-based applications, with the potential for scale up to produce nationally-representative preference estimates. [ABSTRACT FROM AUTHOR]

This study investigates individual-level misreporting among Medicaid recipients in Louisiana from 2007 to 2017. It explores whether the type of individual who misreports varies over time, including following a major policy shift (the implementation of the Affordable Care Act). Results are based on a series of biennial Medicaid Bias Studies from 2007 to 2017 in which Medicaid recipients are asked about their health care coverage, allowing us to identify individuals who misreport their status. Study participants are (1) randomly selected from state Medicaid files or (2) matched participants from a statewide health insurance survey designed to estimate uninsured rates. Survey respondents are asked a battery of questions designed to identify health insurance coverage for themselves and for each member residing within their household. Responses are then matched to Medicaid enrollment data to identify misreporting, including whether household members are misreported as uninsured, covered by an employer, or covered by some other government program. The results reveal that the level of Medicaid misreporting varies over time and that misreporting declined significantly following the Louisiana's Medicaid expansion. As a result, careful estimation of Medicaid misreporting continues to be an important source of bias in estimates of health insurance coverage. [ABSTRACT FROM AUTHOR]

To generate a Hospital Medical Surge Preparedness Index that can be used to evaluate hospitals across the United States in regard to their capacity to handle patient surges during mass casualty events. Data from the American Hospital Association's annual survey, conducted from 2005 to 2014. Our sample comprised 6239 hospitals across all 50 states, with an annual average of 5769 admissions. An extensive review of the American Hospital Association survey was conducted and relevant variables applicable to hospital inpatient services were extracted. Subject matter experts then categorized these items according to the following subdomains of the "Science of Surge" construct: staff, supplies, space, and system. The variables within these categories were then analyzed through exploratory and confirmatory factor analyses, concluding with the evaluation of internal reliability. Based on the combined results, we generated individual (by hospital) scores for each of the four metrics and an overall score. The exploratory factor analysis indicated a clustering of variables consistent with the "Science of Surge" subdomains, and this finding was in agreement with the statistics generated through the confirmatory factor analysis. We also found high internal reliability coefficients, with Cronbach's alpha values for all constructs exceeding 0.9. A novel Hospital Medical Surge Preparedness Index linked to hospital metrics has been developed to assess a health care facility's capacity to manage patients from mass casualty events. This index could be used by hospitals and emergency management planners to assess a facility's readiness to provide care during disasters. [ABSTRACT FROM AUTHOR]

Recurrent event data is frequently encountered in biomedical research. Estimators describing the time to the next event occurrence could be useful information in healthcare settings to deliver targeted, preemptive, patient-specific care. Distribution estimation of recurrence times is varied based on both the underlying assumptions of the estimator (e.g., dependence vs. independence of recurrence times within individual) and estimator construct. In an example among Veterans with spinal cord injury, distributional changes in recurrence times of health complications subsequent lower extremity (LE) fracture are explored. Veterans with LE fracture (cases) were matched to Veterans without LE fracture (controls) on demographic (age, race), level of injury (paraplegia vs. tetraplegia), extent of injury, Veterans Affairs connected service status, and comorbidities using Mahalanobis metric matching. Stratified distributional estimates of recurrence times between successive morbidity outcomes are compared between Veterans with/without LE fracture using three estimators: independent identically distributed product limit estimator, Wang-Chang product limit estimator, and a gamma frailty maximum likelihood estimator. It can be seen that the estimator selection can provide a very different showcasing of a recurrence time distribution. There is a change in the time-to-recurrence of recurrent urinary tract infections and pressure ulcers for fracture cases directly following LE fracture, however testing if this difference is statistically significant remains unclear. Causal inference of gap time analyses in observational data with recurrent events is considered and a call for methods in this area is much warranted. [ABSTRACT FROM AUTHOR]

Provider profiling as a means to describe and compare the performance of health care professionals has gained momentum in the past decade. As a key component of pay-for-performance programs profiling has been increasingly used to identify top-performing providers. However, rigorous examination of the performance of statistical methods for profiling when used to classify top-performing providers is lacking. The objective of this study was to compare the classification accuracy of three methods for identifying providers exceeding performance thresholds and to analyze data on satisfaction with mental health care providers at Group Health Cooperative using these methods. Questionnaire data on patient satisfaction with mental health care providers at Group Health Cooperative was collected between April 2008 and January 2010. Simulated data were used to compare the classification accuracy of alternative statistical methods. We evaluated sensitivity, specificity, and root mean squared error of alternative statistical methods using simulated data. For Group Health providers, we compared agreement of alternative approaches to classification. We found that when between-provider variability in performance was low, all three methods exhibited poor classification accuracy. When used to evaluate mental health care provider performance, we found substantial uncertainty in the estimates and poor agreement across methods. Based on these findings, we recommend providing uncertainty estimates for provider rankings and caution against the use of any classification method when between-provider variability is low. [ABSTRACT FROM AUTHOR]