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Many studies in health services research rely on regression models with a large number of covariates or predictors. In this article, we introduce novel methodology to estimate and perform model selection for high-dimensional non-parametric multivariate regression problems, with application to many healthcare studies. We particularly focus on multi-responses or multi-task regression models. Because of the complexity of the dependence between predictors and the multiple responses, we exploit model selection approaches that consider various level of groupings between and within responses. The novelty of the method lies in its ability to account simultaneously for between and within group sparsity in the presence of non-linear effects. We also propose a new set of algorithms that can identify inactive and active predictors that are common to all responses or to a subset of responses. Our modeling approach is applied to uncover factors that impact healthcare expenditure for children insured through the Medicaid benefits program. We provide important findings on the association between healthcare expenditure and a large number of well-cited factors for two neighboring states, Georgia and North Carolina, which have similar demographics but different Medicaid systems. We also validate our methods with a benchmark cancer data set and simulated data examples. [ABSTRACT FROM AUTHOR]

We introduce a modeling approach for characterizing heterogeneity in healthcare utilization using massive medical claims data. We first translate the medical claims observed for a large study population and across five years into individual-level discrete events of care called utilization sequences. We model the utilization sequences using an exponential proportional hazards mixture model to capture heterogeneous behaviors in patients’ healthcare utilization. The objective is to cluster patients according to their longitudinal utilization behaviors and to determine the main drivers of variation in healthcare utilization while controlling for the demographic, geographic, and health characteristics of the patients. Due to the computational infeasibility of fitting a parametric proportional hazards model for high-dimensional, large-sample size data we use an iterative one-step procedure to estimate the model parameters and impute the cluster membership. The approach is used to draw inferences on utilization behaviors of children in the Medicaid system with persistent asthma across six states. We conclude with policy implications for targeted interventions to improve adherence to recommended care practices for pediatric asthma. Supplementary materials for this article are available online. [ABSTRACT FROM AUTHOR]

Accessibility and equity across populations are important measures in public health. This paper is specifically concerned with potential spatial accessibility, or the opportunity to receive care as moderated by geographic factors, and with horizontal equity, or fairness across populations regardless of need. Both accessibility and equity were goals of the 2009 vaccination campaign for the novel H1N1a influenza virus, including during the period when demand for vaccine exceeded supply. Distribution system design can influence equity and accessibility at the local level. We develop a general methodology that integrates optimization, game theory, and spatial statistics to measure potential spatial accessibility across a network, where we quantify spatial accessibility by travel distance and scarcity. We estimate and make inference on local (census-tract level) associations between accessibility and geographic, socioeconomic, and health care infrastructure factors to identify potential inequities in vaccine accessibility during the 2009 H1N1 vaccination campaign in the U.S. We find that there were inequities in access to vaccine at the local level and that these were associated with factors including population density and health care infrastructure. Our methodology for measuring and explaining accessibility leads to policy recommendations for federal, state, and local public health officials. The spatial-specific results inform the development of equitable distribution plans for future public health efforts. [ABSTRACT FROM AUTHOR]

Objectives. To evaluate how met need for accessibility and availability of primary care among nonelderly individuals in Georgia will be affected by the Patient Protection and Affordable Care Act (ACA) over the next 10 years. Methods. We used a stock-and-flow model to predict the number of available visits from 2013 to 2025, regression models to project needed visits, and an optimization model to estimatemet need. The outputs of thesemodelswere used to estimate unmet need and the availability and accessibility of primary care. Results. Our findings showed that the number of primary care providers will increase by 9.2% to 11.7% by 2025 and that the number of needed visits will increase by 20%. Under Medicaid expansion, the percentage of met need will increase from 67% to 80%. Accessibility will improve by 20% under expansion, and availability will decrease by 13% to 19% under expansion. Conclusions. The ACAs' provisions will reduce unmet need and positively affect accessibility while reducing availability in some communities. Increased need because of a larger Medicaid population under Medicaid expansion will not be a significant burden on the privately insured population. [ABSTRACT FROM AUTHOR]

This paper elaborates the conceptual underpinnings needed to understand and influence change in complex socio-technical systems. The nature of causality is first addressed, followed by consideration of the nature of complexity. It is argued that, at least from a practical perspective, the difficulty in understanding causality increases as complexity increases. The possibility of influencing change is addressed in terms of concepts, principles and models for analysis and design in a range of domains or contexts. [ABSTRACT FROM AUTHOR]