Augmenting U.S. Census data on industry and occupation of respondents The U.S. Census Bureau classifies survey respondents into hundreds of detailed industry and occupation categories. The classification systems change periodically, creating breaks in time series. Standard crosswalks and unified category systems bridge the periods but these often leave sparse or empty cells, or induce sharp changes in time series. We propose a methodology to predict standardized industry, occupation, and related variables for each employed respondent in the public use samples from recent Censuses of Population and CPS data. Unlike earlier approaches, predictions draw from micro data on each individual and large training data sets. Tests of the resulting xe2x80x9caugmentedxe2x80x9d data sets can evaluate their consistency with known trends, smoothness criteria, and benchmarks.

Supervised Mixture Models for Population Health We examine a machine learning approach for deriving insights from observational healthcare data in order to improve public health. Our goal is to simultaneously identify patient subpopulations with differing health risks and find the distinct risk factors or determinants associated with each subpopulation. Here, we develop a supervised Gaussian Mixture Model (GMM) approach for subpopulation modeling that combines GMMs with L1logistic regression. We demonstrate the approach on an analysis of high cost drivers of Medicaid expenditures for inpatient stays associated with Newborn, Pregnancy, and Circulatory Systems diagnostic categories. These conditions were chosen because they had the highest total inpatient expenditures in New York State (NYS) in 2016. When compared with stateoftheart learning methods (random forests, boosting, deep learning), our approach provides comparable prediction performance but also extracts insightful explanations of the subpopulation structure and risk factors within each subpopulation. Sequentially applying unsupervised learning methods and then applying logistic regression fails to yield equally meaningful results: the unsupervised subpopulations are homogeneous and moderately predictable, while some of our subpopulations are highly predictable with easytoidentify drivers of cost. Focusing on newborns, we unveil subpopulations indicative of the landscape of healthcare in NYS: about 90% of the discharges are healthy New York City babies and about 1% are costly complex cases. Subpopulations indicate regional disparities: for example newborns from Central, Southern and Western NY are of higher risk for highcost stays associated with substance abuse. The results indicate the promise of the approach for future population health studies based on electronic health care records.