The Impact of the Vermont Support and Services at Home Program on Healthcare Expenditures

Objective: The Support and Services at Home (SASH) program in Vermont aims to coordinate care and assist participants in accessing the health care and support services they need to maintain their health and age comfortably and safely in their homes. Most program participants are residents of U.S. Department of Housing and Urban Development (HUD)-assisted properties or Low-Income Housing Tax Credit (LIHTC) properties. Our objective is to estimate the impact of the first 5 1/2 years of the SASH program on the Medicare expenditures of these participants.Methods: We use a difference-in-differences model, comparing the change in the expenditures among the SASH participants with the change in the expenditures for a comparison group of Medicare beneficiaries in HUD-assisted or LIHTC properties that did not host the SASH program.Results: Our findings indicate that participants—particularly dual-eligible participants— in SASH panels that are overseen by the Cathedral Square Corporation, and in the subset of those panels that are in an urban county, experience slower growth in total Medicare expenditures and expenditures for hospital care, emergency department visits, and specialist physician visits relative to the comparison group.Conclusions: Although we do not find that the SASH program has a significant impact on Medicare expenditures for all participants in our sample, the favorable results among a subset of panels, containing nearly one-half of the SASH participants in HUD-assisted or LIHTC properties, provides evidence that a housing-plus-services model has the potential to slow the growth of healthcare costs

The Economic Impact of Smoke-Free Laws on Restaurants and Bars in 9 States

Introduction: Smoke-free air laws in restaurants and bars protect patrons and workers from involuntary exposure to secondhand smoke, but owners often express concern that such laws will harm their businesses. The primary objective of this study was to estimate the association between local smoke-free air laws and economic outcomes in restaurants and bars in 8 states without statewide smoke-free air laws: Alabama, Indiana, Kentucky, Mississippi, Missouri, South Carolina, Texas, and West Virginia. A secondary objective was to examine the economic impact of a 2010 statewide smoke-free restaurant and bar law in North Carolina. Methods: Using quarterly data from 2000 through 2010, we estimated dynamic panel data models for employment and sales in restaurants and bars. The models controlled for smoke-free laws, general economic activity, cigarette sales, and seasonality. We included data from 216 smoke-free cities and counties in the analysis. During the study period, only North Carolina had a statewide law banning smoking in restaurants or bars. Separate models were estimated for each state. Results: In West Virginia, smoke-free laws were associated with a significant increase of approximately 1% in restaurant employment. In the remaining 8 states, we found no significant association between smoke-free laws and employment or sales in restaurants and bars. Conclusion: Results suggest that smoke-free laws did not have an adverse economic impact on restaurants or bars in any of the states studied; they provided a small economic benefit in 1 state. On the basis of these findings, we would not expect a statewide smoke-free law in Alabama, Indiana, Kentucky, Missouri, Mississippi, South Carolina, Texas, or West Virginia to have an adverse economic impact on restaurants or bars in those states

Bayesian Inference in a Sample Selection Model

This paper develops methods of Bayesian inference in a sample selection model. The main feature of this model is that the outcome variable is only partially observed. We first present a Gibbs sampling algorithm for a model in which the selection and outcome errors are normally distributed. The algorithm is then extended to analyze models that are characterized by nonnormality. Specifically, we use a Dirichlet process prior and model the distribution of the unobservables as a mixture of normal distributions with a random number of components. The posterior distribution in this model can simultaneously detect the presence of selection effects and departures from normality. Our methods are illustrated using some simulated data and an abstract from the RAND health insurance experiment

Bayesian moment-based inference in a regression model with misclassification error

We present a Bayesian analysis of a regression model with a binary covariate that may have classification (measurement) error. Prior research demonstrates that the regression coefficient is only partially identified. We take a Bayesian approach which adds assumptions in the form of priors on the unknown misclassification probabilities. The approach is intermediate between the frequentist bounds of previous literature and strong assumptions which achieve point identification, and thus preferable in many settings. We present two simple algorithms to sample from the posterior distribution when the likelihood function is not fully parametric but only satisfies a set of moment restrictions. We focus on how varying amounts of information contained in a prior distribution on the misclassification probabilities change the posterior of the parameters of interest. While the priors add information to the model, they do not necessarily tighten the identified set. However, the information is sufficient to tighten Bayesian inferences. We also consider the case where the mismeasured binary regressor is endogenous. We illustrate the use of our Bayesian approach in a simulated data set and an empirical application investigating the association between narcotic pain reliever use and earnings

Community Health Center Efficiency: The Role of Grant Revenues in Health Center Efficiency

Objective: To test the relationship between external environments, organizational characteristics, and technical efficiency in federally qualified health centers (FQHCs). We tested the relationship between grant revenue and technical efficiency in FQHCs. Data Sources/Study Design: Secondary data were collected in each year from the Uniform Data System (UDS) on 644 eligible U.S.-based FQHCs between 2005 and 2007. The study employs a retrospective longitudinal cohort design with instrumental variables. Principal Findings: Increased grant revenues did not increase the probability that a health center would be on the efficiency frontier. However, increased grant revenues had a negative association with technical efficiency for health centers that were not fully efficient. Conclusion: If all health centers were operating efficiently, anywhere from 39 to 45 million patient encounters could have been delivered instead of the actual total of 29 million in 2007. Policy makers should consider tying grant revenues to performance indicators, and future work is needed to understand the mechanisms through which diseconomies of scale are present in FQHCs

The relation between tobacco taxes and youth and young adult smoking: What happened following the 2009 U.S. federal tax increase on cigarettes?

BackgroundOn April 1, 2009, the federal government raised cigarette taxes from $0.39 to$1.01 per pack. This study examines the impact of this increase on a range of smoking behaviors among youth aged 12 to 17 and young adults aged 18 to 25.MethodsData from the 2002–2011 National Survey on Drug Use and Health (NSDUH) were used to estimate the impact of the tax increase on five smoking outcomes: (1) past year smoking initiation, (2) past-month smoking, (3) past year smoking cessation, (4) number of days cigarettes were smoked during the past month, and (5) average number of cigarettes smoked per day. Each model included individual and state-level covariates and other tobacco control policies that coincided with the tax increase. We examined the impact overall and by race and gender.ResultsThe odds of smoking initiation decreased for youth after the tax increase (odds ratio (OR) = 0.83, p < 0.0001). The odds of past-month smoking also decreased (youth: OR = 0.83, p < 0.0001; young adults: OR = 0.92, p < 0.0001), but the odds of smoking cessation remained unchanged. Current smokers smoked on fewer days (youth: coefficient = - 0.97, p = 0.0001; young adults: coefficient = - 0.84, p < 0.0001) and smoked fewer cigarettes per day after the tax increase (youth: coefficient = - 1.02, p = 0.0011; young adults: coefficient = - 0.92, p < 0.0001).ConclusionsThe 2009 federal cigarette tax increase was associated with a substantial reduction in smoking among youths and young adults. The impact of the tax increase varied across male, female, white and black subpopulations

Total Cost of Care Lower among Medicare Fee-for-Service Beneficiaries Receiving Care from Patient-Centered Medical Homes

Objective. To compare health care utilization and payments between NCQA-recognized patient-centered medical home (PCMH) practices and practices without such recognition.Data Sources. Medicare Part A and B claims files from July 1, 2007 to June 30, 2010, 2009 Census, 2007 Health Resources and Services Administration and CMS Utilization file, Medicare’s Enrollment Data Base, and the 2005 American Medical Association Physician Workforce file.Study Design. This study used a longitudinal, nonexperimental design. Three annual observations (July 1, 2008–June 30, 2010) were available for each practice. We compared selected outcomes between practices with and those without NCQA PCMH recognition.Data Collection Methods. Individual Medicare fee-for-service (FFS) beneficiaries and their claims and utilization data were assigned to PCMH or comparison practices based on where they received the plurality of evaluation and management services between July 1, 2007 and June 30, 2008.Principal Findings. Relative to the comparison group, total Medicare payments, acute care payments, and the number of emergency room visits declined after practices received NCQA PCMH recognition. The decline was larger for practices with sicker than average patients, primary care practices, and solo practices.Conclusions. This study provides additional evidence about the potential of the PCMH model for reducing health care utilization and the cost of care

Binary Misclassification and Identification in Regression Models

We study a regression model with a binary explanatory variable that is subject to misclassification errors. The regression coefficient is then only partially identified. We derive several results that relate different assumptions about the misclassification probabilities and the conditional variances to the size of the identified set

A Bayesian analysis of binary misclassification

We consider Bayesian inference about the mean of a binary variable that is subject to misclassification error. If the error probabilities are not known, or cannot be estimated, the parameter is only partially identified. For several reasonable and intuitive prior distributions of the misclassification probabilities, we derive new analytical expressions for the posterior distribution. Our results circumvent the need for Markov chain Monte Carlo simulation. The priors we use lead to regions in the identified set that are a posteriori more likely than others

Many-instruments Asymptotic Approximations Under Nonnormal Error Distributions

In this paper we derive an alternative asymptotic approximation to the sampling distribution of the limited information maximum likelihood estimator and a bias corrected version of the two-stage least squares estimator. The approximation is obtained by allowing the number of instruments and the concentration parameter to grow at the same rate as the sample size. More specifically, we allow for potentially nonnormal error distributions and obtain the conventional asymptotic distribution and the results of Bekker (1994, Econometrica 62, 657–681) and Bekker and Van der Ploeg (2005, Statistica Neerlandica 59, 139–267) as special cases. The results show that when the error distribution is not normal, in general both the properties of the instruments and the third and fourth moments of the errors affect the asymptotic variance. We compare our findings with those in the recent literature on many and weak instruments