17,285 research outputs found
Interpretable Subgroup Discovery in Treatment Effect Estimation with Application to Opioid Prescribing Guidelines
The dearth of prescribing guidelines for physicians is one key driver of the
current opioid epidemic in the United States. In this work, we analyze medical
and pharmaceutical claims data to draw insights on characteristics of patients
who are more prone to adverse outcomes after an initial synthetic opioid
prescription. Toward this end, we propose a generative model that allows
discovery from observational data of subgroups that demonstrate an enhanced or
diminished causal effect due to treatment. Our approach models these
sub-populations as a mixture distribution, using sparsity to enhance
interpretability, while jointly learning nonlinear predictors of the potential
outcomes to better adjust for confounding. The approach leads to
human-interpretable insights on discovered subgroups, improving the practical
utility for decision suppor
Learning to Address Health Inequality in the United States with a Bayesian Decision Network
Life-expectancy is a complex outcome driven by genetic, socio-demographic,
environmental and geographic factors. Increasing socio-economic and health
disparities in the United States are propagating the longevity-gap, making it a
cause for concern. Earlier studies have probed individual factors but an
integrated picture to reveal quantifiable actions has been missing. There is a
growing concern about a further widening of healthcare inequality caused by
Artificial Intelligence (AI) due to differential access to AI-driven services.
Hence, it is imperative to explore and exploit the potential of AI for
illuminating biases and enabling transparent policy decisions for positive
social and health impact. In this work, we reveal actionable interventions for
decreasing the longevity-gap in the United States by analyzing a County-level
data resource containing healthcare, socio-economic, behavioral, education and
demographic features. We learn an ensemble-averaged structure, draw inferences
using the joint probability distribution and extend it to a Bayesian Decision
Network for identifying policy actions. We draw quantitative estimates for the
impact of diversity, preventive-care quality and stable-families within the
unified framework of our decision network. Finally, we make this analysis and
dashboard available as an interactive web-application for enabling users and
policy-makers to validate our reported findings and to explore the impact of
ones beyond reported in this work.Comment: 8 pages, 4 figures, 1 table (excluding the supplementary material),
accepted for publication in AAAI 201
Temperature enhanced effects of ozone on cardiovascular mortality in 95 large US communities, 1987-2000 - assessment using the NMMAPS data
A few studies examined interactive effects between air pollution and temperature on health outcomes. This study is to examine if temperature modified effects of ozone and cardiovascular mortality in 95 large US cities. A nonparametric and a parametric regression models were separately used to explore interactive effects of temperature and ozone on cardiovascular mortality during May and October, 1987-2000. A Bayesian meta-analysis was used to pool estimates. Both models illustrate that temperature enhanced the ozone effects on mortality in the northern region, but obviously in the southern region. A 10-ppb increment in ozone was associated with 0.41 % (95% posterior interval (PI): -0.19 %, 0.93 %), 0.27 % (95% PI: -0.44 %, 0.87 %) and 1.68 % (95% PI: 0.07 %, 3.26 %) increases in daily cardiovascular mortality corresponding to low, moderate and high levels of temperature, respectively. We concluded that temperature modified effects of ozone, particularly in the northern region
Causal Impact of the Hospital Readmissions Reduction Program on Hospital Readmissions and Mortality
Estimating causal effects of the Hospital Readmissions Reduction Program
(HRRP), part of the Affordable Care Act, has been very controversial.
Associational studies have demonstrated decreases in hospital readmissions,
consistent with the intent of the program, although analyses with different
data sources and methods have differed in estimating effects on patient
mortality. To address these issues, we define the estimands of interest in the
context of potential outcomes, we formalize a Bayesian structural time-series
model for causal inference, and discuss the necessary assumptions for
estimation of effects using observed data. The method is used to estimate the
effect of the passage of HRRP on both the 30-day readmissions and 30-day
mortality. We show that for acute myocardial infarction and congestive heart
failure, HRRP caused reduction in readmissions while it had no statistically
significant effect on mortality. However, for pneumonia, HRRP had no
statistically significant effect on readmissions but caused an increase in
mortality.Comment: 10 pages, 1 figure, 2 table
Labor-Force Heterogeneity as a Source of Agglomeration Economies in an Empirical Analysis of County-Level Determinants of Food Plant Entry
Results of this study show that a heterogeneous labor force serves to attract new food manufacturing investment. We conduct analysis for SIC 20, Food and Kindred Product Manufacturing, and disaggregate analysis on all nine three-digit SIC food industries. Heterogeneity variables are a significant factor in nearly all specifications. We also examine which factors create the greatest increases in the expected number of new establishments. Areas with a high degree of labor heterogeneity are found to have large advantages. Labor heterogeneity is among the most important factors attracting food manufacturing to urban areas over rural areas.agglomeration externalities, business location determinants, food manufacturing, labor heterogeneity, rural development, Labor and Human Capital,
Labor-Force Heterogeneity as a Source of Agglomeration Economies in an Empirical Analysis of County-Level Determinants of Food Plant Entry
Results of this study show that a heterogeneous labor force serves to attract new food manufacturing investment. We conduct analysis for SIC 20, Food and Kindred Product Manufacturing, and disaggregate analysis on all nine three-digit SIC food industries. Heterogeneity variables are a significant factor in nearly all specifications. We also examine which factors create the greatest increases in the expected number of new establishments. Areas with a high degree of labor heterogeneity are found to have large advantages. Labor heterogeneity is among the most important factors attracting food manufacturing to urban areas over rural areas.agglomeration externalities, business location determinants, food manufacturing, labor heterogeneity, rural development
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