Effects of Federal Policy to Insure Young Adults: Evidence from the 2010 Affordable Care Act Dependent Coverage Mandate

Abstract

Brown Bag seminar and the fall 2012 Association for Public Policy Analysis and Management (APPAM) conference. The authors have no conflicts of interests to disclose related to this paper. The views expressed herein are those of the authors and do not necessarily reflect the views of the National Bureau of Economic Research. NBER working papers are circulated for discussion and comment purposes. They have not been peerreviewed or been subject to the review by the NBER Board of Directors that accompanies official NBER publications.

An ACA Provision Increased Treatment For Young Adults With Possible Mental Illnesses Relative To Comparison Group

“…2 The provision initially did not require insurers to cover people who already had an offer of employer-sponsored insurance, but this exemption was phased out in 2014. 2 Early estimates suggest the provision increased the number of insured people in the target population by over two million [3][4][5] and increased the use of emergency department and hospital services. 6,7 The dependent coverage provision could be especially important for young adults because mental health and substance use disorders peak in young adulthood, 8 and insurance coverage has historically been low among young adults.…”

Access to health insurance and the use of inpatient medical care: Evidence from the Affordable Care Act young adult mandate

“…We present sample means of our treatment and control groups in Table 1 Table 2 contains regression results from Equation [1], where the dependent variable is the number of admissions at the quarter, hospital, and group (control vs. treatment) level. The results indicate that the expansion had a statistically significant impact on overall inpatient visits for non-pregnancy-related conditions.…”

“…For each of the 11 quarters between January 2007 and March 2010, we re-estimate Equation [1] assuming a placebo date for the ACA law and create a distribution of the results from the replications. We perform this test for both non-pregnancy-related and mental illness admissions, as well as for our subsample analysis of young adults who reside in high-income zip codes and for the analysis of 25-vs. 27-year-olds.…”

“…We study treatment intensity using a DD regression model that is similar to Equation [1] except that the unit of observation for this model is at the individual level rather than the hospital-quarter level. In the Massachusetts context, Kolstad and Kowlaski (2012) argue that if health insurance expansion alters observable characteristics of patients who seek care, then including these characteristics in a regression framework would blunt any estimated impact of the reform.…”

“…We do the same in our evaluation of the impact of the mandate on treatment intensity. For models in which we include patient characteristics, we include a new vector Z ighst not present in Equation [1]; this vector contains patient demographics and clinical indicators such as age, gender, race/ethnicity, whether the patient was admitted on the weekend, Charlson Index, the number of diagnosis codes (up to nine codes), an indicator for each of 27 comorbidity measures calculated using the HCUP Comorbidity Software, and indicator variables for Major Diagnostic Categories (MDCs). We estimate our continuous outcomes with ordinary least squares and our binary outcomes with a linear probability model.…”