Search

Care management is becoming increasingly offered in the U.S. as a means of helping patients better manage their chronic diseases and possibly avoid worsening or exacerbations of their conditions, which can result in potentially avoidable and costly healthcare services. Since care management can be provided using different intervention methods and by different entities, we sought to compare different models of care management delivery on their long-term medical savings. Specifically, we compare health plan provided care management to provider delivered care management. Evaluation of the effectiveness of care management programs can be challenging because it can take time for patients to make recommended changes and then demonstrate healthcare savings associated with those changes. In this study, we modeled the unknown form of the time-varying program effects using a spline-based technique in the Bayesian framework, where the number and locations of knots were treated unknown and learned via reversible jump Markov chain Monte Carlo. We also addressed additional modeling challenges from features seen in our healthcare cost data such as highly right-skewed outcomes with non-constant variances and extra zeros. To provide a more robust analysis, we incorporated a follow-up period of up to 4 years that is longer than the most of the published studies on care management. The results of this work demonstrate that cost savings do accrue with specific models of care management. In particular, the embedded model of care management was significantly more effective than the health plan provided care management in controlling medical costs after 2 years of engagement, and the savings increased over time. This information should help policy makers and employer groups achieve a better understanding of the potential value of care management. [ABSTRACT FROM AUTHOR]

Various approaches have been used to select control groups in observational studies: (1) from within the intervention area; (2) from a convenience sample, or randomly chosen areas; (3) from areas matched on area-level characteristics; and (4) nationally. The consequences of the decision are rarely assessed but, as we show, it can have complex impacts on confounding at both the area and individual levels. We began by reanalyzing data collected for an evaluation of a rapid response service on rates of unplanned hospital admission. Balance on observed individual-level variables was better with external than local controls, after matching. Further, when important prognostic variables were omitted from the matching algorithm, imbalances on those variables were also minimized using external controls. Treatment effects varied markedly depending on the choice of control area, but in the case study the variation was minimal after adjusting for the characteristics of areas. We used simulations to assess relative bias and means-squared error, as this could not be done in the case study. A particular feature of the simulations was unexplained variation in the outcome between areas. We found that the likely impact of unexplained variation for hospital admissions dwarfed the benefits of better balance on individual-level variables, leading us to prefer local controls in this instance. In other scenarios, in which there was less unexplained variation in the outcome between areas, bias and mean-squared error were optimized using external controls. We identify some general considerations relevant to the choice of control population in observational studies. [ABSTRACT FROM AUTHOR]

Information asymmetry has well known efficiency consequences for health insurance markets. Unlike private insurance, Medicaid programs are designed to attract clinically and socioeconomically vulnerable patients, and allow limited enrollee cost sharing. This suggests that the Medicaid expansion authorized by the Affordable Care Act may attract an adversely selected population that poses unknown utilization, and hence, financial risks to its federal and state financers. In this study, I test for information asymmetry by examining whether unobserved determinants of Medicaid enrollment are correlated with unobserved determinants of emergency department and primary care visits, using the Medical Expenditure Panel Survey. I use instrumental variables, correlation tests, and a simulation analysis to test for correlated unobservables. I find evidence of information asymmetry in Medicaid for emergency care use, but not primary care visits, once certain demographic and health variables are controlled. Since new Medicaid enrollees may not have 'baseline' claims or diagnostic data, it may be helpful to collect information on their self-rated health status and disease history, such as obesity and heart attacks. Controlling for these variables may improve forecasts of health care use among Medicaid enrollees, and can reduce estimation bias in non-experimental studies of Medicaid. [ABSTRACT FROM AUTHOR]

Given a shifted exponential distribution, the exact sampling distribution of the Gini index has been derived by Moothathu (Ann Inst Stat Math 37: 473-479, ). However, direct derivation of confidence interval of the Gini index via inverting the sampling distribution requires highly intensive computational power. In this note, we propose an exact interval estimation procedure through the concept of generalized confidence interval introduced by Weerahandi (J Am Stat Assoc 88: 899-905, ). The proposed method is much more computationally efficient and can be readily extended for Type-II doubly censored data. The idea is further developed for the interval estimation of the difference of Gini indexes from two shifted exponential distributions. Numerical studies show that the generalized interval estimator is better than commonly used asymptotic approximation and bootstrap in term of coverage probability, particularly for small to median sample size. The proposed approaches are illustrated via an application to a previously published data set. [ABSTRACT FROM AUTHOR]

Research has suggested that accessible primary healthcare may help control hospitalization for certain conditions that are often considered either preventable or treatable through primary healthcare. However, only limited research has tested this widely accepted hypothesis. We extend research on preventable hospitalization in the framework of cost-effectiveness analysis. Using hospitalization data from South Carolina, we show how access to primary healthcare may affect the volume and cost of hospitalizations for ambulatory care sensitive conditions. Our statistical models for effectiveness and cost take advantage of spatial methods to adjust for neighborhood characteristics. This adjustment also addresses correlations among variables describing each given area, providing appropriate results despite those correlations. We also use propensity scores to adjust for county level variation in access to primary healthcare. We observe that the mean differential volume and cost of lower extremity amputation associated with diabetes was significantly lower in areas served by community health centers than in other areas. This result provides support for the hypothesis that increasing access to primary healthcare may help to control the volume and cost of preventable hospitalization for some conditions. [ABSTRACT FROM AUTHOR]

Research on determinants of workplace presenteeism is based on the assumption that presenteeism is the outcome of a decision process with two options: taking sick leave, or attending work despite illness. However, frequency measures of sickness presenteeism are usually employed as the dependent variables in previous research on the determinants of the decision process. Monte Carlo simulations show that the analytical strategies used in previous research are likely to identify determinants of vulnerability, but will underestimate the determinants that affect the decision process under consideration. As a solution to this shortcoming, an alternative analytical strategy is presented, which uses an empirical estimator of the individual presenteeism propensity. [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]

Confounding is widely recognized in settings where all variables are fully observed, yet recognition of and statistical methods to address confounding in the context of latent class regression are slowly emerging. In this study we focus on confounding when regressing a distal outcome on latent class; extending standard confounding methods is not straightforward when the treatment of interest is a latent variable. We describe a recent 1-step method, as well as two 3-step methods (modal and pseudoclass assignment) that incorporate propensity score weighting. Using simulated data, we compare the performance of these three adjusted methods to an unadjusted 1-step and unadjusted 3-step method. We also present an applied example regarding adolescent substance use treatment that examines the effect of treatment service class on subsequent substance use problems. Our simulations indicated that the adjusted 1-step method and both adjusted 3-step methods significantly reduced bias arising from confounding relative to the unadjusted 1-step and 3-step approaches. However, the adjusted 1-step method performed better than the adjusted 3-step methods with regard to bias and 95 % CI coverage, particularly when class separation was poor. Our applied example also highlighted the importance of addressing confounding-both unadjusted methods indicated significant differences across treatment classes with respect to the outcome, yet these class differences were not significant when using any of the three adjusted methods. Potential confounding should be carefully considered when conducting latent class regression with a distal outcome; failure to do so may results in significantly biased effect estimates or incorrect inferences. [ABSTRACT FROM AUTHOR]

A bivariate mixed-effects location-scale model is proposed for estimation of means, variances, and covariances of two continuous outcomes measured concurrently in time and repeatedly over subjects. Modeling the two outcomes jointly allows examination of BS and WS association between the outcomes and whether the associations are related to covariates. The variance-covariance matrices of the BS and WS effects are modeled in terms of covariates, explaining BS and WS heterogeneity. The proposed model relaxes assumptions on the homogeneity of the within-subject (WS) and between-subject (BS) variances. Furthermore, the WS variance models are extended by including random scale effects. Data from a natural history study on adolescent smoking are used for illustration. 461 students, from 9th and 10th grades, reported on their mood at random prompts during seven consecutive days. This resulted in 14,105 prompts with an average of 30 responses per student. The two outcomes considered were a subject's positive affect and a measure of how tired and bored they were feeling. Results showed that the WS association of the outcomes was negative and significantly associated with several covariates. The BS and WS variances were heterogeneous for both outcomes, and the variance of the random scale effects were significantly different from zero. [ABSTRACT FROM AUTHOR]

In most nonrandomized observational studies, differences between treatment groups may arise not only due to the treatment but also because of the effect of confounders. Therefore, causal inference regarding the treatment effect is not as straightforward as in a randomized trial. To adjust for confounding due to measured covariates, the average treatment effect is often estimated by using propensity scores. Typically, propensity scores are estimated by logistic regression. More recent suggestions have been to employ nonparametric classification algorithms from machine learning. In this article, we propose a weighted estimator combining parametric and nonparametric models. Some theoretical results regarding consistency of the procedure are given. Simulation studies are used to assess the performance of the newly proposed methods relative to existing methods, and a data analysis example from the Surveillance, Epidemiology and End Results database is presented. [ABSTRACT FROM AUTHOR]

I present an improved methodology for estimating local prevalence rates using classical econometric methods. I provide information on the variation within national mental health surveys associated with ICD versus DSM coding. Conditional on the validity of national survey responses, I estimate precise and statistically significant models associated with binary measures of mental health diagnoses. I also present estimates from polychotomous discrete choice allowing for covariance in errors. Focusing on binary discrete measures, empirical results from NCS-R and NSADMHP are qualitatively similar though very different from NHIS. I speculate that, to a significant degree, this occurs because both NCS-R and NSADMHP rely on popular screening tools to mechanically diagnosis sample participants, while NHIS relies on self-diagnosis. I also discuss the effects on local prevalence estimates caused by unobserved community-specific effects. Finally, I use the results to make policy statements about the provision of public mental health services in central Virginia; the results document a severe shortage of services for people who are unlikely to be able to afford services in the private market. [ABSTRACT FROM AUTHOR]

We apply three separate panel data estimation methods to examine the diffusion of technologies at the state-level. These methods include the Hausman-Taylor random effects model, the fixed effects vector decomposition, and generalized estimating equations. We discuss the assumptions required of each and assess the stability of our policy results across the three models for a longitudinal study of the diffusion of newer psychotropic technologies. We find a reasonable level of consistency among marginal effects for time varying independent variables between our three estimation methods but some discrepancy in the estimated measure of precision in our empirical application. We find a number of policy conclusions are quite stable across estimation methods and may be of interest to state-level mental health policy decision makers. [ABSTRACT FROM AUTHOR]

The estimation of treatment effects is one of the primary goals of statistics in medicine. Estimation based on observational studies is subject to confounding. Statistical methods for controlling bias due to confounding include regression adjustment, propensity scores and inverse probability weighted estimators. These methods require that all confounders are recorded in the data. The method of instrumental variables (IVs) can eliminate bias in observational studies even in the absence of information on confounders. We propose a method for integrating IVs within the framework of Cox's proportional hazards model and demonstrate the conditions under which it recovers the causal effect of treatment. The methodology is based on the approximate orthogonality of an instrument with unobserved confounders among those at risk. We derive an estimator as the solution to an estimating equation that resembles the score equation of the partial likelihood in much the same way as the traditional IV estimator resembles the normal equations. To justify this IV estimator for a Cox model we perform simulations to evaluate its operating characteristics. Finally, we apply the estimator to an observational study of the effect of coronary catheterization on survival. [ABSTRACT FROM AUTHOR]

We demonstrate how to apply regression risk analysis to compute risk ratios and risk differences from logistic regression models using complex survey data. We validate the use of regression risk analysis for complex survey design. First, we derive formulas for adjusted risk ratios (ARRs) and adjusted risk differences (ARDs) adjusted for weighting, stratification, and clustering. Then we use Monte Carlo data to show why correcting statistics for complex survey design is important. We show how to calculate and interpret ARRs using a publicly available data set with a binary outcome. Regression risk analysis can be applied to complex survey data to calculate correct ARRs and ARDs from logistic regression. [ABSTRACT FROM AUTHOR]

For over two decades, disease management (DM) has been touted as an intervention capable of producing large scale cost savings for health care purchasers. However, the preponderance of scientific evidence suggests that these programs do not save money. This finding is not surprising given that the theorized causal mechanism by which the intervention supposedly influences the outcome has not been systematically assessed. Mediation analysis is a statistical approach to identifying causal pathways by testing the relationships between the treatment, the outcome, and an intermediate variable that is posited to mediate the relationship between the treatment and outcome. This analysis can therefore help identify how to make DM interventions effective by determining the causal mechanisms between intervention components and the desired outcome. DM interventions can then be optimized by eliminating those activities that are ineffective or even counter-productive. In this article we seek to promote the application of mediation analysis to DM program evaluation by describing the two principal frameworks generally followed in causal mediation analysis; structural equation modeling and potential outcomes. After comparing several approaches within these frameworks using real and simulated data, we find that some methods perform better than others under the conditions imposed upon the models. We conclude that mediation analysis can assist DM programs in developing and testing the causal pathways that enable interventions to be effective in achieving desired outcomes. [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]

The common approach to modeling healthcare cost data is to use aggregated total cost from multiple categories or sources (e.g. inpatient, outpatient, prescriptions, etc.) as the dependent variable. However, this approach could hide the differential impact of covariates on the different cost categories. An alternative is to model each cost category separately. This could also lead to wrong conclusions due to failure to account for the interdependence among the multiple cost outcomes. Therefore, we propose a multivariate generalized linear mixed model (mGLMM) that allows for joint modeling of longitudinal cost data from multiple sources. We assessed four different approaches, (1) shared random intercept, (2) shared random intercept and slope, (3) separate random intercepts from a joint multivariate distribution, and (4) separate random intercepts and slopes from a joint multivariate distribution. Each of these approaches differs in the way they account for the correlation among the multiple cost outcomes. Comparison was made via goodness of fit measures and residual plots. Longitudinal cost data from a national cohort of 740,195 veterans with diabetes (followed from 2002-2006) was used to demonstrate joint modeling. Among examined models, the separate random intercept approach exhibited the lowest AIC/BIC in both log-normal and gamma GLMMs. However, for our data example, the shared random intercept approach seemed to be sufficient as the more complex models did not lead to qualitatively different conclusions. [ABSTRACT FROM AUTHOR]

We examined the relationship between the proportion of time spent in the mammography exam room relative to the total visit time and the rate of repeat visits across mammography facilities. We hypothesized that the means for these proportions would be greater among facilities with high repeat rates compared to those with low repeat rates. The beta distribution was used to model the proportions. However, not all of the observations were observed as some were (right- or interval-) censored due to the inability to observe complete information (e.g., time of arrival at the facility) to calculate the proportions for some observations. Using a maximum likelihood approach, we estimated the mean proportion of time spent during appointments on the actual exams. To analyze these data we utilized survival analysis methods, which typically are applied to data on time scales, to analyze these data restricted to the range (0, 1). We found that facilities with high repeat mammography rates on average had a greater proportion of their visits spent receiving the exams than the low repeat rate facilities ( p = 0.0003). Women from low repeat rate facilities spent on average 45 % of their appointment time engaging in the exam; thus, these women averaged more time waiting for the exam to begin than receiving the exam. Conversely, those from high repeat rate facilities averaged more of their appointment time engaging in the mammographic exams. These findings were consistent with our hypothesis that women would have greater satisfaction with their mammography appointments-and thus be more likely to return for routine screening-in facilities where their appointments were more efficient. [ABSTRACT FROM AUTHOR]

Observational healthcare databases represent a valuable resource for health economics, outcomes research, quality of care, drug safety, epidemiology and comparative effectiveness research. The methods used to identify a population for study in an observational healthcare database with the desired drug exposures of interest are complex and not consistent nor apparent in the published literature. Our research evaluates three drug classification systems and their impact on prevalence in the analysis of observational healthcare databases using opioids as a case in point. The standard terminologies compiled in the Observational Medical Outcomes Partnership's Common Data Model vocabulary were used to facilitate the identification of populations with opioid exposures. This study analyzed three distinct observational healthcare databases and identified patients with at least one exposure to an opioid as defined by drug codes derived through the application of three classification systems. Opioid code sets were created for each of the three classification systems and the number of identified codes was summarized. We estimated the prevalence of opioid exposure in three observational healthcare databases using the three defined code sets. In addition we compared the number of drug codes and distinct ingredients that were identified using these classification systems. We found substantial variation in the prevalence of opioid exposure identified using an individual classification system versus a composite method using multiple classification systems. To ensure transparent and reproducible research publications should include a description of the process used to develop code sets and the complete code set used in studies. [ABSTRACT FROM AUTHOR]

Generalized additive models for location, scale, and shape (GAMLSS) are a class of semi-parametric models with potential applicability to health care cost data. We compared the bias, accuracy, and coverage of GAMLSS estimators with two distributions [gamma and generalized inverse gaussian (GIG)] using a log link to the generalized linear model (GLM) with log link and gamma family and the log-transformed OLS. The evaluation using simulated gamma data showed that the GAMLSS and GLM gamma model had similar bias, accuracy, and coverage and outperformed the GAMLSS GIG. When applied to simulated GIG data, the GLM gamma was similar or improved in bias, accuracy, and coverage compared to the GAMLSS GIG and gamma; furthermore, the GAMLSS estimators produced wildly inaccurate or overly-precise results in certain circumstances. Applying all models to empirical data on health care costs after a fall-related injury, all estimators produced similar coefficient estimates, but GAMLSS estimators produced spuriously smaller standard errors. Although no single alternative was best for all simulations, the GLM gamma was the most consistent, so we recommend against using GAMLSS estimators using GIG or gamma to test for differences in mean health care costs. Since GAMLSS offers many other flexible distributions, future work should evaluate whether GAMLSS is useful when predicting health care costs. [ABSTRACT FROM AUTHOR]

Bed days is a potentially useful metric of efficiency in clinical studies involving the hospital admission decision. However, this metric involves excess zeros, possible overdispersion, and possible clustering (in multi-site studies). A random effects negative binomial hurdle model can account for each of these issues. We extend this model to include site-level correlation between the two component parts and implement best linear unbiased prediction-type estimation with restricted maximum quasi-likelihood. This approach offers computational advantages over maximum likelihood in a generalized linear mixed model setting. Simulations show that the proposed approach performs well for fixed effects and variance components under a plausible range of bivariate correlation. The Emergency Department Community Acquired Pneumonia study motivates this work and illustrates the methods. [ABSTRACT FROM AUTHOR]

Estimating the causal effect of an active ingredient in a randomized treatment may be complicated when participants receive heterogeneous doses of the active ingredient. For example, homeless veterans with mental illness were randomized to receive either case management only, or case management plus special access to a Section 8 housing voucher. However, a quarter of those randomized to the housing voucher group did not actually use the voucher, while a few of those randomized to case management actually used a housing voucher. Our goal is to estimate the causal effect of the proposed active ingredient (i.e., using a housing voucher), as opposed to the effect of being randomized to the active ingredient. The proposed instrumental variable methodology assumes that the outcome is proportional to the dose of the active ingredient and tests the significance of the instrumental variable estimate using a permutation test such as the Hodges-Lehman aligned rank statistic proposed by Rosenbaum for pair-matched data (Rosenbaum, Observational studies, ; The design of observational studies, ). This method removes unmeasured confounding when the assumptions of monotonicity and the exclusion restrictions hold; however, in practice these assumptions may not hold perfectly. Consequently, we extend and strengthen this approach by incorporating optimal full matching on the propensity to use the active ingredient, to further reduce bias and variability in the IV estimation. [ABSTRACT FROM AUTHOR]

Evaluating treatment effects in non-randomized studies is challenging due to the potential unmeasured confounding and complex form of observed confounding. Propensity score based approaches, such as matching or weighting, are commonly used to handle observed confounding variables. The instrumental variable (IV) method is known to guard against unmeasured confounding if a good instrument can be identified. We propose to combine both methods to estimate the long-term treatment effect in a longitudinal psychiatric study. The NIMH collaborative Multi-site Treatment study of children with Attention-deficit/hyperactivity disorder (ADHD) compared different treatment strategies for children diagnosed with ADHD (known as MTA study). The first 14 months is a randomized study and the participants are allowed to choose their desired treatment strategies afterwards. Follow-up measurements are taken at 24, 36 and 72 months. Randomization is often considered as a good instrument since it is not associated with any covariate, observed or unobserved. We first apply a randomization based IV method to estimate the self-selected medication effect on outcome at the end of 72 months. However this approach yields results with huge standard errors due to randomization's weak relationship with later treatment selection. We then consider the self-selection right after the randomization as an instrument, because it is associated with later treatment selection and it is unlikely to affect the outcome directly given the 5-year time lapse. To better control the confounding due to observed factors, propensity score matching is used to create a subpopulation with comparable covariate distributions across different self-selected treatments. Using MTA data, matching-enhanced IV estimation yields the most sensible result, while other estimation strategies tend to imply a spurious significant effect. Also, our simulation study shows that the matching-enhanced IV estimation outperforms non-matched methods in terms of relative bias. [ABSTRACT FROM AUTHOR]

As the amount of data generated continues to increase, consideration of individuals' privacy is a growing concern. As a result, there has been a vast quantity of research done on methods of statistical disclosure control. Some of these methods propose to release a randomized version of the data rather than the actual data. While methods of this type certainly offer some layer of protection, there is still the potential for private information to be disclosed. Quantifying the level of privacy provided by these methods is often difficult. In the past, a method for assessing privacy using the receiver operating characteristic (ROC) curve based on ideas related to differential privacy was proposed. However, the method was only demonstrated for univariate randomized releases. Here, the ROC-based privacy measure is extended to the release of randomized vectors. [ABSTRACT FROM AUTHOR]

Joint modeling of longitudinal and survival data can provide more efficient and less biased estimates of treatment effects through accounting for the associations between these two data types. Sponsors of oncology clinical trials routinely and increasingly include patient-reported outcome (PRO) instruments to evaluate the effect of treatment on symptoms, functioning, and quality of life. Known publications of these trials typically do not include jointly modeled analyses and results. We formulated several joint models based on a latent growth model for longitudinal PRO data and a Cox proportional hazards model for survival data. The longitudinal and survival components were linked through either a latent growth trajectory or shared random effects. We applied these models to data from a randomized phase III oncology clinical trial in mesothelioma. We compared the results derived under different model specifications and showed that the use of joint modeling may result in improved estimates of the overall treatment effect. [ABSTRACT FROM AUTHOR]

We develop a new tool for assessing the sensitivity of findings on treatment effectiveness to differential follow-up rates in the two treatment conditions being compared. The method censors the group with the higher response rate to create a synthetic respondent group that is then compared with the observed cases in the other condition to estimate a treatment effect. Censoring is done under various assumptions about the strength of the relationship between follow-up and outcomes to determine how informative differential dropout can alter inferences relative to estimates from models that assume the data are Missing at Random. The method provides an intuitive measure for understanding the strength of the association between outcomes and dropout that would be required to alter inferences about treatment effects. Our approach is motivated by translational research in which treatments found to be effective under experimental conditions are tested in standard treatment settings. In such applications, follow-up rates in the experimental setting are likely to be substantially higher than in the standard setting, especially when observational data are used in the evaluation. We test the method on a case study evaluation of the effectiveness of an evidence-supported adolescent substance abuse treatment program (Motivational Enhancement Therapy/Cognitive Behavioral Therapy-5) delivered by community-based treatment providers relative to its performance in a controlled research trial. In this case study, follow-up rates in the community-based settings were extremely low (54 %) compared to the experimental setting (95 %) giving raise to concerns about non-ignorable drop-out. [ABSTRACT FROM AUTHOR]

Community colleges have increased post-secondary educational access for disadvantaged youth, but it is unknown how community college degrees fit into the educational gradient of health status disparities. Using data from high school graduates in the National Longitudinal Study of Adolescent Health, we compared young adults ages 26-31 whose highest degrees were high school diplomas ( n = 5,584), sub-baccalaureate credentials (sub-BAs include community college certificates and associate's degrees) ( n = 2,415), and baccalaureate degrees (BAs) ( n = 3,303) on measures of hypertension, obesity, smoking, sleep problems, dyslipidemia, and depression. Comparisons used multivariate Poisson regression with robust standard errors after exact and nearest-neighbor Mahalanobis matching within propensity score calipers on 23 baseline factors measured in 1995. High school graduates and sub-BAs differed significantly on 3 of 23 baseline factors. After matching, sub-BAs were 16 % less likely to smoke daily than if they had only a high school diploma but did not differ in other health status measures. Sub-BAs and BAs differed significantly on 14 of 23 baseline factors. After matching, BAs were 60 % less likely to smoke daily, 14 % less likely to be obese, and 38 % less likely to have been diagnosed with depression. Sub-BA degrees are accessible to high school graduates irrespective of academic backgrounds and predict lower smoking prevalence. BAs are less accessible to high school graduates and predict lower chances of smoking, depression, and obesity. [ABSTRACT FROM AUTHOR]

In observational studies of treatments or interventions, propensity score (PS) adjustment is often useful for controlling bias in estimation of treatment effects. Regression on PS is used most often and can be highly efficient, but it can lead to biased results when model assumptions are violated. The validity of stratification on PS depends on fewer model assumptions, but this approach is less efficient than regression adjustment when the regression assumptions hold. To investigate these issues, we compare stratification and regression adjustments in a Monte Carlo simulation study. We consider two stratification approaches: equal frequency strata and an approach that attempts to choose strata that minimize the mean squared error (MSE) of the treatment effect estimate. The regression approach that we consider is a generalized additive model (GAM) that estimates treatment effect controlling for a potentially nonlinear association between PS and outcome. We find that under a wide range of plausible data generating distributions the GAM approach outperforms stratification in treatment effect estimation with respect to bias, variance, and thereby MSE. We illustrate each approach in an analysis of insurance plan choice and its relation to satisfaction with asthma care. [ABSTRACT FROM AUTHOR]

Methods for estimating average treatment effects (ATEs), under the assumption of no unmeasured confounders, include regression models; propensity score (PS) adjustments using stratification, weighting, or matching; and doubly robust estimators (a combination of both). Researchers continue to debate about the best estimator for outcomes such as health care cost data, as they are usually characterized by an asymmetric distribution and heterogeneous treatment effects,. Challenges in finding the right specifications for regression models are well documented in the literature. Propensity score estimators are proposed as alternatives to overcoming these challenges. Using simulations, we find that in moderate size samples ( n = 5,000), balancing on PSs that are estimated from saturated specifications can balance the covariate means across treatment arms but fails to balance higher-order moments and covariances amongst covariates. Therefore, unlike regression model, even if a formal model for outcomes is not required, PS estimators can be inefficient at best and biased at worst for health care cost data. Our simulation study, designed to take a 'proof by contradiction' approach, proves that no one estimator can be considered the best under all data generating processes for outcomes such as costs. The inverse-propensity weighted estimator is most likely to be unbiased under alternate data generating processes but is prone to bias under misspecification of the PS model and is inefficient compared to an unbiased regression estimator. Our results show that there are no 'magic bullets' when it comes to estimating treatment effects in health care costs. Care should be taken before naively applying any one estimator to estimate ATEs in these data. We illustrate the performance of alternative methods in a cost dataset on breast cancer treatment. [ABSTRACT FROM AUTHOR]

This study uses instrumental variable (IV) models with genetic instruments to assess the effects of maternal smoking on the child's risk of orofacial clefts (OFC), a common birth defect. The study uses genotypic variants in neurotransmitter and detoxification genes relateded to smoking as instruments for cigarette smoking before and during pregnancy. Conditional maximum likelihood and two-stage IV probit models are used to estimate the IV model. The data are from a population-level sample of affected and unaffected children in Norway. The selected genetic instruments generally fit the IV assumptions but may be considered 'weak' in predicting cigarette smoking. We find that smoking before and during pregnancy increases OFC risk substantially under the IV model (by about 4-5 times at the sample average smoking rate). This effect is greater than that found with classical analytic models. This may be because the usual models are not able to consider self-selection into smoking based on unobserved confounders, or it may to some degree reflect limitations of the instruments. Inference based on weak-instrument robust confidence bounds is consistent with standard inference. Genetic instruments may provide a valuable approach to estimate the 'causal' effects of risk behaviors with genetic-predisposing factors (such as smoking) on health and socioeconomic outcomes. [ABSTRACT FROM AUTHOR]

We seek to determine whether hospitals providing high-quality care are associated with different environmental and organizational factors than hospitals providing lower quality of care. We address this question using congestion analysis, which is an extension of data envelopment analysis (DEA). We employ a rich data set comprised of urban hospitals operating in 34 states in 2004. The data include measures of inputs, outputs, case mix, and indicators of patient safety, which we use to derive separate best-practice frontiers for high-, medium-, and low-quality hospitals. By comparing the frontier of the high-quality hospitals with the lower quality hospital frontiers, we find that higher quality hospitals perform better on the criteria of overall technical efficiency and pure technical efficiency than lower quality institutions. We found that a variety of characteristics, including expenditures, percentage of admissions that are births, Medicaid share of admissions, and government ownership are associated with differences in performance among the medium-quality hospitals. Expenditures and birth rate were associated with differences within low-quality hospital performance, as were variables related to scope and depth of activities, such as capital expenditures per bed and teaching status. We find evidence that hospitals should be able to improve both the quality and efficiency of their operations by adopting, where possible, the characteristics of the highest performing institutions. [ABSTRACT FROM AUTHOR]

This study represents a new application of Veterans Affairs (VA) administrative data for measuring VA system-wide performance of colorectal cancer (CRC) screening adherence among veterans at average-risk for CRC. Our new measurement has two features: it is specifically designed for average-risk populations and is applied repeatedly at the veteran level each year over the study period. Using 1997-2007 VA administrative data, we developed an algorithm that first constructed 7 independent cohorts of average-risk veterans eligible for CRC screening, one for each year from 2001 to 2007, and then appended the seven cohorts together to form 2001-2007 veteran-level panel data. Veterans in a cohort for a given year were considered adherent if they received fecal occult blood test (FOBT) during that given year, or received flexible sigmoidoscopy (FS), double-contrast barium enema (DCBE), or colonoscopy during that given year or the 4 previous years. The main analysis shows that VA CRC screening rates increased from 30.11% in 2001 to 35.51% in 2004, but declined to 31.54% in 2007. Among the screened, the proportion adherent to colonoscopy increased over the 7-year period while the proportion adherent to FOBT, FS, or DCBE decreased during the same period. Sensitivity analyses, including use of a 10-year retrospective window for determining the screening adherence of colonoscopy, show the robustness of the main analysis. This new algorithm demonstrates that VA administrative data may be used for assessing VA performance of CRC screening adherence of average-risk veterans. Furthermore, our panel data may enhance understanding of factors associated with CRC screening adherence. [ABSTRACT FROM AUTHOR]

With health care reform passing in the United States, much effort is directed toward developing and disseminating comparative information on standardized processes of care for health care providers. We propose the use of Bayesian multilevel item response theory models to estimate hospital quality from multiple process measures and to assess geographical variation in hospital quality. Our approach fully incorporates the nesting structure of measures, patients, hospitals, and various levels of geographical units to provide a summary of hospital quality. A national dataset of patients treated for a heart attack, heart failure, or pneumonia illustrates our methods. We find considerable geographical differences in hospital quality for these conditions with variations across census regions and states accounting for slightly more than 10% of the total variation. Some states performed well for all three conditions (e.g., the respective posterior probabilities of having better than the national average performance was close to 1 in Iowa, New Jersey, South Dakota, and Wisconsin). In contrast, quality of other states varied across conditions (e.g., the corresponding posterior probability was close to 1 in Massachusetts for heart attack and heart failure quality, but less than .5 for pneumonia care). Our framework provides a comprehensive approach to assessment of hospital performance at both regional and national levels, and might be informative for policy development. [ABSTRACT FROM AUTHOR]

For many surgical procedures, apparent volume-outcome relationships may reflect differences in patient risk-profiles as well as quality of care. As some important patient profile differences may be unobserved, we use fixed effects (FE) regression to estimate the relationship between operative mortality and surgeon and hospital volumes, and compare this method with the more commonly used random effects (RE) regression approach. The 1998 and 1999 Medicare Inpatient and Denominator files for Medicare Fee for Service enrollees aged 65-99. Operative mortality rates are estimated for different surgeon and hospital volume tertiles (high, medium, low) using FE and RE regression methods, adjusted for patient demographics and morbidities. The data were collected by the Centers for Medicare and Medicaid Services (CMS). FE regression estimates that lowest volume tertile hospitals have 1.4 and lowest volume tertile surgeons have 1.6 additional operative deaths (for every 100 CABG surgeries) compared to their highest volume tertile counterparts. The corresponding RE estimates are 0.5 and 1.4 respectively. The substantially higher FE hospital volume effect compared to RE indicates the presence of unobserved 'protective' characteristics in lower volume providers, including a less complicated patient profile. Lower hospital and surgeon volumes are associated with substantially higher excess operative mortality from CABG surgeries than previously estimated. [ABSTRACT FROM AUTHOR]

When assessing the impact of a family or youth intervention, program evaluators often consider whether and how baseline characteristics moderate the effect of treatment. Parametric and non-parametric approaches are both possible, but each has limitations. In this article, we argue for and illustrate the use of semi-parametric methods. A hybrid of parametric and non-parametric approaches, semi-parametric modeling techniques combine the strengths of each approach. This article demonstrates how this approach can be used to evaluate whether an intervention effect was larger for those with most serious problems at baseline. Using data from the Fast Track project-a comprehensive intervention designed to prevent serious conduct problems among children at high risk-we investigated the moderating effect of two key baseline characteristics: parent-reported and teacher-reported problem behavior. Our analyses do not demonstrate a significant moderation of the intervention effect. [ABSTRACT FROM AUTHOR]

One of the most pressing issues in the confidentiality literature is the quantification of privacy. One proposal, $$ \epsilon $$-differential privacy, moves away from absolute guarantees of privacy to relative guarantees. However, the selection of an appropriate $$ \epsilon $$ is difficult because its interpretation is unclear. Further, when comparing different privacy preserving techniques to one another, a direct comparison cannot be made by simply comparing the respective values of $$ \epsilon $$. The aim of this work is to provide a measure that allows for direct comparison across different privacy schemes and is more easily interpreted. In turn, this will aid in policy debate pertaining to how much privacy is acceptable. Our proposal sets the problem in a hypothesis testing framework and uses the area under the receiver-operator characteristic (ROC) curve as a measure of privacy. [ABSTRACT FROM AUTHOR]

In order to create an empirically derived parsimonious typology of physician financial incentives that will be useful for future research, we used data from the nationally representative 2004–2005 Community Tracking Study Physician Survey (N = 6,628). Linear regression analyses informed by economic theory were used to identify the combinations of incentives associated with an overall financial incentive to expand services to individual patients. The approach was validated using two nonparametric methods (CART analysis and data mining techniques) and by examining the relationship between the resulting typology and other measures of physician behavior including hours worked, visit volume, and specialty-adjusted income. Of the 6,628 physicians surveyed, approximately 25% (1,605) reported an overall incentive to increase services and 75% (5,023) reported either neutral incentives or incentives to decrease services. Men, who were approximately 75% of respondents, were slightly more likely to report incentives to increase services ( P < 0.05). There were no differences in reported incentives according to specialty. We created two typologies (one with eleven categories and the other with a collapsed set of six categories) based on combinations of variables measuring ownership, base compensation methods, and financial incentives. The percentage with an overall incentive to increase services ranges from 6% for employed physicians compensated via fixed salary to 36.7% for owners in low capitation environments with either individual or practice level productivity incentives. The criterion validity of the typology was established by examining the relationship with adjusted physician income, hours worked, and visit volume, which showed generally consistent relationships in the expected direction. A parsimonious typology consisting of six mutually exclusive groups reasonably captures the continuum of incentives to increase service delivery experienced by physicians. [ABSTRACT FROM AUTHOR]

Standard approaches for determining weights when calculating a composite measure of health care quality from individual quality indicators (QIs) include equal weighting, opportunity-based weights, and judgment-based weights. Benefit-of-the-doubt approaches have not been used in the health services area, though one has been used to calculate composite measures for profiling countries. Underlying these approaches is the assumption that relative performance on a set of indicators is, at least to some extent, a revealed preference by the organizational unit about the relative importance of the indicators. A benefit-of-the-doubt approach recognizes these revealed preferences by assigning higher weights to indicators on which performance is better and lower weights to indicators on which performance is poorer. We consider two benefit-of-the-doubt approaches. The first uses simple linear programming (LP) models; the second uses data envelopment analysis (DEA), the way in which the benefit-of-the-doubt approach has been previously implemented. In both cases, constraints are added to limit weight adjustments to some percentage of policy-determined baseline weights. Using both standard and benefit-of-the-doubt approaches, composite scores are calculated from data on five QIs from 32 Department of Veterans Affairs (VA) nursing homes. We examine the tradeoff between the level of allowable weight adjustment and impact on facility rankings. If weights are constrained to be within 75% of baseline weights, all approaches identify pretty much the same high performing facilities. Weights from benefit-of-the-doubt approaches, because they are able to reflect local preferences and conditions, should be attractive to facilities and, in a collaborative environment, to policy makers. [ABSTRACT FROM AUTHOR]

The determinants for the number of outpatient hospital treatments across Danish municipalities are analysed using balanced panel data from the period 1998–2004. The determinants include socio-demographic variables, home help service, residential homes capacity, proxy variables for morbidity, utilisation of primary care services, accessibility of hospitals and a number of other factors. Panel effects in the form of intra-municipal correlation and heterogeneity across years are controlled for. Spatial spillover effects across municipalities will be investigated in order to disclose the spatial dynamics of outpatient treatments. The results reveal substantial heterogeneity and dependency across time, as well as presence of a significant spatial spillover effect. Reverse causalities among the number of hospital outpatient treatments and certain health systems characteristics are further controlled for. The results are shown to be highly sensitive to such adjustments, as the effects of determinants—including those over which the municipalities exert some control—are seriously overestimated if such features are ignored. [ABSTRACT FROM AUTHOR]

Differential item functioning (DIF) in tests and multi-item surveys occurs when a lack of conditional independence exists between the response to one or more items and membership to a particular group, given equal levels of proficiency. We develop an approach to detecting DIF in the context of item response theory (IRT) models based on computing a diagnostic which is the posterior mean of a p-value. IRT models are fit in a Bayesian framework, and simulated proficiency parameters from the posterior distribution are retained. Monte Carlo estimates of the p-value diagnostic are then computed by comparing the fit of nonparametric regressions of item responses on simulated proficiency parameters and group membership. Some properties of our approach are examined through a simulation experiment. We apply our method to the analysis of responses from two separate studies to the BASIS-24, a widely used self-report mental health assessment instrument, to examine DIF between the English and Spanish-translated version of the survey. [ABSTRACT FROM AUTHOR]