eAppendix 1. Selected methodology details
A. Dataset selection
B. Modeled work requirement exemption and fulfilment criteria (eTable 1)
C. Approach to state-level reweighting
D. Simulation of work requirements in non-waiver states
eAppendix 2. Sensitivity Analyses
A. Excluding potentially-undocumented immigrants
B. Using an expanded set of work requirement fulfilment criteria (existing criteria plus participation in Supplemental Nutrition Assistance Program, Temporary Assistance for Needy Families, or job search and/or training activities) (eTable 2)
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Silvestri DM, Holland ML, Ross JS. State-Level Population Estimates of Individuals Subject to and Not Meeting Proposed Medicaid Work Requirements. JAMA Intern Med. 2018;178(11):1552–1555. doi:10.1001/jamainternmed.2018.4196
Several states have sought waivers from the US Secretary of Health and Human Services to impose work requirements as a condition of Medicaid eligibility, as permitted under §1115 of the Social Security Act.1 Implications of such requirements depend on differences in states’ proposed policies, Medicaid eligibility criteria, and population characteristics. To estimate the potential impact of work requirements on Medicaid eligibility, we derived state-level proportions of Medicaid-eligible individuals subject to and not already meeting requirements using nationally representative population survey data, both for states proposing work requirements and those not doing so, including the District of Columbia.
We conducted a cross-sectional analysis of noninstitutionalized civilians participating in the 2014 Survey on Income and Program Participation (SIPP), a publicly available longitudinal, nationally representative US Census Bureau household survey providing monthly information on government assistance programs, income dynamics, labor involvement (including multiple jobs), and social context. We report on data for March 2013 because their structure and content enable modeling of Medicaid eligibility and diverse work requirement exemption and fulfillment criteria.
At the time of writing, 11 states had submitted waiver applications proposing work requirements. For each, we modeled Medicaid eligibility using state-specific income thresholds,2 and work requirement exemption and fulfillment criteria using submitted applications (eTable 1 in the Supplement).3 Because SIPP state sample sizes preclude reliable subpopulation estimates using state identifiers, we sequentially applied each state’s eligibility and work requirement criteria to the entire SIPP population, leveraging the full data set to resemble each state’s population through a technique of iterative proportional fitting (eAppendix 1C in the Supplement).4,5 To do this, we repetitively recalibrated SIPP observation weights for each state until weighted proportions of 8 population variables (age, sex, race, marital status, income, disability, school enrollment, employment) matched corresponding state reference distributions from the March 2013 Current Population Survey Supplement.4,5 Using resulting newly calibrated state-specific weights, we computed state-specific proportions of Medicaid-eligible individuals subject to (not exempt from) and not meeting work requirements.
To simulate requirements in 40 nonwaiver states, we repeated this process. Because these states had not proposed work requirements, we assessed the impact on each state of existing submitted policies (eAppendix 1D in the Supplement), and we report the 2 scenarios with lowest and highest resulting proportions of Medicaid-eligible individuals subject to but not meeting requirements. State-specific analyses were performed for all Medicaid-eligible individuals, and specifically for nondisabled Medicaid-eligible adults, using Stata/SE, version 14.0 (StataCorp).
Among the 11 states with submitted waiver applications, 3.9% to 29.2% of Medicaid-eligible individuals, including 21.8% to 54.0% of nondisabled Medicaid-eligible adults, were subject to (not exempt from) proposed work requirements (Table 1). Proportions were higher in states expanding Medicaid under the Affordable Care Act and varied similarly among the 40 nonwaiver states (Table 2).
Among states with submitted waiver applications, just 0.3% to 5.4% of Medicaid-eligible individuals, including 1.6% to 10.6% of nondisabled Medicaid-eligible adults, were subject to but did not meet proposed work requirements (Table 1). Proportions were slightly higher in Medicaid expansion states and varied similarly among nonwaiver states (Table 2).
This state-level analysis of Medicaid work requirements finds that almost all Medicaid-eligible individuals may already meet proposed work requirements or exemptions prior to implementation. We modeled requirements among Medicaid-eligible individuals, and estimates may differ when applied to those both eligible and enrolled. Data constraints precluded consideration of certain beneficiaries (those receiving long-term and home-base care, Transitional Medical Assistance); however, these individuals would likely meet exemption or work requirement criteria—accentuating our findings. Although we used 1 medical frailty indicator, states may implement this exemption differently. Finally, although data are from 2013, subsequent labor market strengthening suggests that even more Medicaid-eligible individuals may meet requirements today. Our approach augments prior work by providing comprehensive and robust estimates for all states,6 leveraging the SIPP’s unique detail regarding exemptions and employment characteristics to closely mirror proposed policies. In light of these findings, policymakers should consider whether administrative costs and beneficiary burdens imposed by work requirements are justified by their narrow projected reach.
Accepted for Publication: June 29, 2018.
Corresponding Author: David M. Silvestri, MD, MBA, Yale University School of Medicine, PO Box 208088, New Haven, CT 06520 (email@example.com).
Published Online: September 10, 2018. doi:10.1001/jamainternmed.2018.4196
Author Contributions: Dr Silvestri had full access to all of the data in the study and takes responsibility for the integrity of the data and the accuracy of the data analysis.
Study concept and design: Silvestri, Ross.
Acquisition, analysis, or interpretation of data: All authors.Drafting of the manuscript: Silvestri.
Critical revision of the manuscript for important intellectual content: All authors.
Statistical analysis: Silvestri.
Administrative, technical, or material support: Holland.
Study supervision: Ross.
Conflict of Interest Disclosures: In the past 36 months, Dr Ross has received research support through Yale University from Johnson and Johnson to develop methods of clinical trial data sharing, from Medtronic, Inc and the US Food and Drug Administration (FDA) to develop methods for postmarket surveillance of medical devices (U01FD004585), from the FDA to establish Yale-Mayo Clinic Center for Excellence in Regulatory Science and Innovation (CERSI) program (U01FD005938), from the Blue Cross Blue Shield Association to better understand medical technology evaluation, from the Centers of Medicare and Medicaid Services to develop and maintain performance measures that are used for public reporting (HHSM-500-2013-13018I), from the Agency for Healthcare Research and Quality (R01HS022882), from the National Heart, Lung, and Blood Institute of the National Institutes of Health (R01HS025164), and from the Laura and John Arnold Foundation to establish the Good Pharma Scorecard at Bioethics International and to establish the Collaboration for Research Integrity and Transparency at Yale. No other disclosures are reported.
Funding/Support: This work was supported in part by Clinical and Translational Science Awards Grant TL1 TR001864 from the National Center for Advancing Translational Science, a component of the National Institutes of Health.
Role of the Funder/Sponsor: The funding agency had no role in the design and conduct of the study; collection, management, analysis, and interpretation of the data; preparation, review, or approval of the manuscript; and decision to submit the manuscript for publication.
Disclaimer: Dr Ross is Associate Editor of JAMA Internal Medicine, but he was not involved in any of the decisions regarding review of the manuscript or its acceptance.
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