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In this paper, we assess whether or not organic agriculture has a positive impact on local economies. We first identify organic agriculture hotspots (clusters of counties with positively correlated high numbers of organic operations) using spatial statistics. Then, we estimate a treatment effects model that classifies a county's membership in an organic hotspot as an endogenous treatment variable. By modeling what a hotspot county's economic indicators would have been had the county not been part of a hotspot, this model captures the effect of being in a hotspot on a county's economic indicators. We perform the same analysis for general agricultural farm hotspots to confirm that the benefits associated with organic production hotspots are, in fact, due to the organic component. Our results show that organic hotspot membership leads to a lower county-level poverty rate and a higher median household income. A similar result is not found when investigating the impact of general agriculture hotspots. On the other hand, our result is robust to alternative hotspot definitions based on type of organic operations to alternative methods of estimating average treatment effects on the treated. These results provide strong motivation for considering hotspots of organic handling operations, which refers to middlemen such as processors, wholesalers and brokers, and hotspots of organic production to be local economic development tools, and may be of interest to policymakers whose objective is to promote rural development. Our results may incentivize policymakers to specifically focus on organic development, rather than the more general development of agriculture, as a means to promote economic growth in rural areas, and may further point them in the direction of not only encouraging the presence of organic operations, but of fostering the development of clusters or hotspots of these operations. [ABSTRACT FROM AUTHOR]

Background: Underlying blood pressure is that observed in the absence of antihypertensive treatment or, among those treated, the estimate of that which would be observed without treatment. This study aims to examine the relationships between diabetes or obesity and underlying systolic blood pressure adjusted for antihypertensive treatment by several methods.Methods: Data from two population studies were analyzed-an American Indian community in Arizona and the National Health and Nutrition Examination Surveys. Antihypertensive treatment was accounted for using: no adjustment; antihypertensive use as a covariate; blood pressure dichotomized into normotension and hypertension; addition of a fixed treatment effect; non-parametric algorithm; and censored normal regression.Results: The magnitude of association at each time point differed by adjustment method particularly where there was a difference in prevalence of antihypertensive use between people with and without diabetes or obesity. The common methods of ignoring antihypertensive treatment or including it as a covariate in a regression model underestimated the effects of diabetes and obesity on underlying blood pressure, compared to the recommended method of the censored normal regression.Conclusion: Proper accounting for antihypertensive treatment is needed in interpreting variables that affect blood pressure. [ABSTRACT FROM AUTHOR]

The literature assessing the efficacy of the Supplemental Nutrition Assistance Program (SNAP), formerly known as the Food Stamp Program, has long puzzled over positive associations between SNAP receipt and various undesirable health outcomes such as food insecurity. Assessing the causal impacts of SNAP, however, is hampered by two key identification problems: endogenous selection into participation and extensive systematic underreporting of participation status. Using data from the National Health and Nutrition Examination Survey (NHANES), we extend partial identification bounding methods to account for these two identification problems in a single unifying framework. Specifically, we derive informative bounds on the average treatment effect (ATE) of SNAP on child food insecurity, poor general health, obesity, and anemia across a range of different assumptions used to address the selection and classification error problems. In particular, to address the selection problem, we apply relatively weak nonparametric assumptions on the latent outcomes, selected treatments, and observed covariates. To address the classification error problem, we formalize a new approach that uses auxiliary administrative data on the size of the SNAP caseload to restrict the magnitudes and patterns of SNAP reporting errors. Layering successively stronger assumptions, an objective of our analysis is to make transparent how the strength of the conclusions varies with the strength of the identifying assumptions. Under the weakest restrictions, there is substantial ambiguity; we cannot rule out the possibility that SNAP increases or decreases poor health. Under stronger but plausible assumptions used to address the selection and classification error problems, we find that commonly cited relationships between SNAP and poor health outcomes provide a misleading picture about the true impacts of the program. Our tightest bounds identify favorable impacts of SNAP on child health. [ABSTRACT FROM PUBLISHER]

Informal care provided to the elderly by their children is proposed as a less expensive alternative to institutional long-term care. This paper explores how the elderly's consumption of medical care changes in response to changes in the informal care they receive from their children. Many earlier studies have ignored both the endogeneity of informal care and the complicated nature of health care utilization data. This paper develops a two-part model with informal care treated as an endogenous regressor and imposes exclusion restrictions on the selection process. The model is fitted using the Bayesian Markov Chain Monte Carlo (MCMC) methods, in particular the Gibbs sampler and the Metropolis-Hasting algorithm. The average treatment effects and the distributions of the treatment effects are obtained via posterior simulation. The results indicate that informal care provides a substitute for nursing home care and hospital inpatient care, but it does not affect paid home health care on average. The treatment effects are heterogeneous. The largest substitution effects occur for nursing home and hospital inpatient care at the intensive margin. The policy analysis suggests that informal care policies targeting the group that incurs the largest substitution effect may help to reduce government spending on Medicaid and Medicare. [ABSTRACT FROM AUTHOR]

Abstract: The objective of the study on which the paper is based was to explore the application of fully Bayesian methods for before–after road safety studies. Several variations of the methodology were evaluated with a simulated dataset in which hypothetical treatments with no safety effect were randomly assigned to high accident locations to mimic the common site selection process in road jurisdictions. It was confirmed that the fully Bayesian method by estimating no safety effect can account for the regression-to-the-mean that results from this biased site selection process. The fully Bayesian method was then applied to California rural intersection data to evaluate the safety effect of conversion from stop to signalized control. The results were then compared with those from the empirical Bayesian method, currently the accepted approach for conducting unbiased before–after evaluations. This comparison was generally favorable in that FB can provide similar results as EB. [Copyright &y& Elsevier]

Health care expenditures and use are challenging to model because these dependent variables typically have distributions that are skewed with a large mass at zero. In this article, we describe estimation and interpretation of the effects of a natural experiment using two classes of nonlinear statistical models: one for health care expenditures and the other for counts of health care use. We extend prior analyses to test the effect of the ACA's young adult expansion on three different outcomes: total health care expenditures, office-based visits, and emergency department visits. Modeling the outcomes with a two-part or hurdle model, instead of a single-equation model, reveals that the ACA policy increased the number of office-based visits but decreased emergency department visits and overall spending.

This study aimed to replicate and extend the findings of previous investigations looking at treatment responses in antiepileptic clinical trials over time and to examine the effects of subject age and duration of treatment. To address the potential biases of published literature, we examined the reported data from 14 investigational antiepileptic drugs (AEDs) (34 trials, 59 treatment arms, 10,783 patients) reviewed and approved by the US FDA (1996-2016). For each treatment arm, we recorded drug and placebo response (percent reduction in seizure frequency), calculated effect sizes, and examined these measures over time. Regression analysis showed that placebo response has increased significantly over time (R 2 =0.292, p=0.001) from 5% to 20% reduction in seizure frequency in 20years. Response to drug treatment appears to have increased in parallel but the trend was not statistically significant (p=0.143). Effect sizes have remained stable over time (p=0.084). Treatment duration was not related to treatment response or outcomes. Including younger patients in trials appeared to predict lower drug response (β=1.44, p=0.012) and effect size (β=0.014, p=0.047) but not placebo response (p=0.141). These FDA-reviewed and source-verified data support and extend prior findings from published literature that response to placebo treatment is noticeably increasing over time, nearly quadrupling in magnitude, while AED efficacy remains the same due to a parallel increase in drug response. The rise in placebo response appears to be an ongoing phenomenon rather than a mere historical artifact. Future design and interpretation of data from clinical trials of investigational antiepileptic drugs can be informed by these observations., (Copyright © 2017 The Authors. Published by Elsevier Inc. All rights reserved.)

R788 (fostamatinib) is an oral prodrug that is rapidly converted into a relatively selective spleen tyrosine kinase (SYK) inhibitor R406, evaluated for the treatment of rheumatoid arthritis (RA). This analysis aimed at developing a pharmacodynamic model for efficacy using pooled ACR20 data from two phase II studies in patients with rheumatoid arthritis (TASKi1 and TASKi2), describing the effect of fostamatinib as a function of fostamatinib exposure (dose, R406 plasma concentration) and other explanatory variables. The exposure-response relationship of fostamatinib was implemented into a continuous time Markov model describing the time course of transition probabilities between the three possible states of ACR20 non-responder, responder, and dropout at each visit. The probability of transition to the ACR20 response state was linearly (at the rate constant level) related to average R406 plasma concentrations and the onset of this drug effect was fast. Further, increases of fostamatinib dose resulted in increased dropout and subsequent loss of efficacy. This analysis provided an increased understanding of the exposure-response relationship, and provided support for fostamatinib 100 mg BID an appropriate dose regimen for further clinical evaluation., (© 2014, The American College of Clinical Pharmacology.)

Propensity score methods are being increasingly used as a less parametric alternative to traditional regression to balance observed differences across groups in both descriptive and causal comparisons. Data collected in many disciplines often have analytically relevant multilevel or clustered structure. The propensity score, however, was developed and has been used primarily with unstructured data. We present and compare several propensity-score-weighted estimators for clustered data, including marginal, cluster-weighted, and doubly robust estimators. Using both analytical derivations and Monte Carlo simulations, we illustrate bias arising when the usual assumptions of propensity score analysis do not hold for multilevel data. We show that exploiting the multilevel structure, either parametrically or nonparametrically, in at least one stage of the propensity score analysis can greatly reduce these biases. We applied these methods to a study of racial disparities in breast cancer screening among beneficiaries of Medicare health plans., (Copyright © 2013 John Wiley & Sons, Ltd.)