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Michel Boudreaux, Health Policy and Management, and Anuj Gangopadhyaya, Urban Institute

Wade Jacobsen, Criminology and Criminal Justice

Utilization of cost-effective essential preventive health services increased after the implementation of the Affordable Care Act’s (ACA) provision that non-grandfathered private insurers provide cost-effective preventive services without cost sharing in 2010. Little is known, however, whether this change is also observed among Asians in the US. We examined patterns of preventive services utilization among Asian subgroups relative to non-Latino whites (whites) after the implementation of the ACA’s preventive services provisions. Using 2013–2016 Medical Expenditure Panel Survey data, we examined utilization trends in preventive services among Asian Indians, Chinese, Filipinos, and other Asians relative to whites. We also ran logistic regression models to estimate the likelihood of having received each of the seven essential preventive services (routine checkups, flu vaccinations, cholesterol screenings, blood pressure checkups, Papanicolaou “pap” tests, mammograms, and colorectal cancer screenings). Compared to whites, Asians had higher rates of utilization of routine checkups, cholesterol screenings, and flu vaccinations, but they had lower utilization rates of blood pressure checkups, pap tests, and mammograms. The patterns of preventive services utilization differed across the Asian subgroups. All Asian subgroups, except for Filipinos, were less likely to have pap tests or mammograms than whites. Moreover, we observed a decreasing trend in having pap tests, mammograms, or colorectal cancer screenings among all Asian subgroups between 2013 and 2016. Our findings suggest that there are low cancer screening rates across Asian subgroups. This indicates the need for programs tailored to specific Asian subgroups to improve cancer screening.

Gang membership is believed to impede success in the legitimate economic market while simultaneously supporting success in the illegal market. We extend the study of the economic effects of gang membership by using a within-and between-individual analytic design, decomposing gang membership into multiple statuses (i.e., entering a gang,continuously in a gang, leaving a gang, and inactive gang membership), examining legal and illegal earnings simultaneously, and accounting for factors endogenous to gang membership that may contribute to economic achievement.By using panel data from 1,213 individuals who participated in the Pathways to Desistance Study to conduct a multi level path analysis, we find that active gang membership status is unrelated to legal earnings. Alternatively,entering a gang is associated with increased illegal earnings, attributable to changes in delinquent peers and drug use, whereas leaving a gang has a direct relationship with decreased illegal earnings. Our results indicate that the positive economic effect of gang membership (i.e., illegal earnings and total earnings) is short-lived and that, on balance,the sum of the gang membership experience does not “pay”in terms of overall earnings.

From education to health to criminal justice, government regulation and policy decisions have important effects on social and individual experiences. New data science tools applied to data created by government agencies have the potential to enhance these meaningful decisions. However, certain institutional barriers limit the realization of this potential. First, we need to provide systematic training of government employees in data analytics. Second we need a careful rethinking of the rules and technical systems that protect data in order to expand access to linked individual-level data across agencies and jurisdictions, while maintaining privacy. Here, we describe a program that has been run for the last three years by the University of Maryland, New York University, and the University of Chicago, with partners such as Ohio State University, Indiana University/Purdue University, Indianapolis, and the University of Missouri. The program—which trains government employees on how to perform applied data analysis with confidential individual-level data generated through administrative processes, and extensive project-focused work—provides both online and onsite training components. Training takes place in a secure environment. The aim is to help agencies tackle important policy problems by using modern computational and data analysis methods and tools. We have found that this program accelerates the technical and analytical development of public sector employees. As such, it demonstrates the potential value of working with individual-level data across agency and jurisdictional lines. We plan to build on this initial success by creating a larger community of academic institutions, government agencies, and foundations that can work together to increase the capacity of governments to make more efficient and effective decisions.

Children exposed to "Sesame Street" were more likely to be enrolled in the correct grade level for their age at middle and high school

Montgomery County Executive, Maryland Governor disagree on Thin Blue Line Flag

Sought-for freedoms require action in both domains, she says

A major concern arising from ubiquitous tracking of individuals’ online activity is that algorithms may be trained to predict personal sensitive information, even for users who do not wish to reveal such information. Although previous research has shown that digital trace data can accurately predict sociodemographic characteristics, little is known about the potentials of such data to predict sensitive outcomes. Against this background, we investigate in this article whether we can accurately predict voting behavior, which is considered personal sensitive information in Germany and subject to strict privacy regulations. Using records of web browsing and mobile device usage of about 2,000 online users eligible to vote in the 2017 German federal election combined with survey data from the same individuals, we find that online activities do not predict (self-reported) voting well in this population. These findings add to the debate about users’ limited control over (inaccurate) personal information flows.

Mary Zaki and her colleagues published a working paper analyzing the effects of work requirements on SNAP participation, beneficiary composition, and labor supply