The ACA: Impacts on Health, Access, and Employment
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Abstract
On March 23, 2010, President Barack Obama signed the Patient Protection and Affordable Care Act (ACA) into law. This comprehensive health care reform legislation sought to expand health care coverage to millions of Americans, control health care costs, and improve the overall quality of the health care system. The ACA required that all US citizens and legal residents have qualifying health insurance by 2014. In this paper we give readers a brief overview of the effects of the ACA based on recent research. We then turn our attention to the possibility of using the ACA expansion to answer important underlying questions, such as: To what extent does the holding of insurance lead to improvements in access to care? To what extent does the holding of coverage lead to improvements in health? In mental health? Are there likely general equilibrium effects on labor force participation, hours worked, employment setting, and indeed even the probability of marrying? By necessity, researchers’ ability to answer these questions depends on the availability of data, so we discuss current and potential data sources relevant for answering these questions. We also look to what has been studied about the health reform in Massachusetts and early Medicaid expansions to speculate what we can expect to learn about the effects of the ACA on these outcomes in the future.
Appendix A: Data Sources
A variety of data sources have been used to date in studying the effects of the ACA, each with its own strengths and limitations. This appendix highlights the most common national data sources used in the preliminary research in addition to several independent surveys that have been used or developed in response to the legislation.
National Data Sourcs[27]
American Community Survey
The American Community Survey (ACS) is an annual survey administered by the U.S. Census Bureau providing data to inform how billions of dollars of federal and state funds should be distributed each year. Up to approximately 2 million households have been randomly selected each year since 2000 to complete the survey, and selected individuals are obliged to complete the questionnaire. The ACS is one of the most commonly used sources of information on health insurance coverage; however, it does not provide information on access to health care or individuals’ health status and does not follow individuals longitudinally.
Behavioral Risk Factor Surveillance System
Originally implemented in 15 states in 1984, the Behavioral Risk Factor Surveillance System (BRFSS) became a nationwide cross-sectional telephone survey beginning in 1993. BRFSS is one of the leading sources of public health data for adults over age 18, gathering information on a variety of health-related risk behaviors and events, chronic conditions, and access to preventive and interventional health services. Although BRFSS provides a wealth of health and access measures, it does not gather information on private ESI coverage or labor force participation. Additionally, although there is a fixed set of national core questions, there is some level of variation in state data collection (Sonier 2012).
Current Population Survey
The Current Population Survey (CPS), also administered by the Census Bureau, is the nation’s primary source of labor force statistics. In addition to providing economic data, the CPS collects a wealth of demographic information. Approximately 60,000 households are surveyed monthly, providing a cross-sectional profile of the US population. The CPS Annual Social and Economic Supplement (ASEC) has been widely used to study the ACA, as it provides information on private ESI coverage and a variety of labor force status, earnings, educational attainment, and family formation outcomes. The CPS ASEC, however, does not capture information on access to care or health status.
In 2014, the Census Bureau redesigned the health insurance coverage questions of the CPS ASEC in order to address measurement issues and include the coverage categories available under the ACA. As a result, the most recent uninsurance estimates are lower than those of previous years and health coverage data for the 2014 CPS ASEC are not comparable with those of previous releases.
Medical Expenditure Panel Survey
The Medical Expenditure Panel Survey (MEPS), administered by the Department of Health and Human Services, is a large-scale survey of individuals and families, health care providers, and employers in the US and has provided information on health care usage, cost, and insurance coverage since 1996. MEPS has two major components: the Household Component (MEPS-HC) and Insurance Component (MEPS-IC). MEPS-HC data are gathered from a nationally representative subsample of households that completed the previous year’s National Health Interview Survey (see below). MEPS-HC follows households for two full calendar years, allowing researchers to study changes and trends in coverage, access, and health status over time. Each MEPS-HC cohort consists of approximately 12,000 households, of which over 5000 are at or below 138 percent FPL. Although the survey was not designed for state or local estimates, state-level identifiers are available (Sonier 2012; Cohen and Cohen 2013).
National Health Interview Survey
The National Health Interview Survey (NHIS) is a cross-sectional household interview survey administered by the Census Bureau that is used to monitor national health trends. Originally developed in 1957, the current questionnaire was implemented in 1997 and annually surveys approximately 35,000 households with 87,500 persons. The NHIS collects extensive data on health insurance coverage, in addition to an abundance of information on access to care, health, labor force participation, and family formation. Since NHIS interviewees compose the MEPS-HC population, these two data sources can be linked to allow health insurance coverage rates and health status transitions to be observed in individuals over a 3-year period, though the resulting sample sizes may be relatively small (Cohen and Cohen 2013).
Survey of Income and Program Participation
The Census Bureau’s Survey of Income and Program Participation (SIPP) consists of a series of household panels, each lasting approximately 4 years. SIPP collects data on a variety of economic factors, including information on health insurance coverage (including ESI), health care access, personal health, and workforce participation. The last published panel is from 2008, and the 2014 data are forthcoming.
Independent Surveys[28]
Commonwealth Fund Tracking Surveys
The Commonwealth Fund has developed several surveys to collect data on health insurance coverage, including the Health Insurance Tracking Survey (conducted June to July 2011), Health Insurance Tracking Survey of Young Adults (conducted November 2011), and Affordable Care Act Tracking Survey (conducted July to September 2013, and April to June 2014). Each of these surveys provides point-in-time estimates of insurance coverage rates and is designed to be nationally representative.
Gallup Healthways Well-Being Index
The Gallup Healthways Well-Being Index seeks to measure the various physical health and economic indicators, as well as other aspects of people’s lives, which encompass individual or communal well-being. Conducted via telephone since 2008, this survey provides information on insurance coverage, health care access, and health, allowing researchers to study more nuanced impacts of the ACA.
Health Reform Monitoring Survey
Developed and implemented by the Urban Institute, the Health Reform Monitoring Survey (HRMS) is a nationally representative quarterly survey of the non-elderly US population collecting data relevant to the ACA. Since 2013, the HRMS has provided information on health care affordability, access to care, and self-reported health measures. HRMS questions are based upon those in several national surveys, including the ACS, BRFSS, CPS ASEC, and NHIS.
RAND Health Reform Opinion Study
The RAND Health Reform Opinion Study (RHORS) is a longitudinal study of public opinion regarding the ACA. Administered monthly since November 2013 to 5500 adults, the RHROS asks three main questions regarding individual opinions of the ACA in addition to two health insurance coverage questions and a question related to ACA-related current events.
Other Data Sources
Several other specialized data sets have been used to study the ACA. The Centers for Medicare and Medicaid Services (CMS) provides detailed administrative data for individuals with Medicare or Medicaid coverage and has been a useful source of information for studying Medicaid expansion. The Healthcare Cost and Utilization Project (HCUP) supplies administrative data pertaining to inpatient populations and emergency department use in its State Inpatient Databases (SIDs), State Emergency Department Databases (SEDDs), Nationwide Inpatient Sample (NIS), and Nationwide Emergency Department Sample (NEDS). The National Survey of Drug Use and Health (NSDUH) has detailed measures on access to mental health care and related behavioral health outcomes and has been used to study how the ACA has affected individuals with mental health needs. Finally, researchers have utilized spending data contained in the Consumer Expenditure Survey (CE) in order to see how health care costs and expenditures have been affected by the reform. We expect these and other specialized administrative and provider data sources to become increasingly important as researchers study more specialized patterns and trends emerging from the legislation.
Large Scale Public Data Sources.
| Data structure | Number of observations | Geographic identifiers | Insurance measures | Health care access measures | Health measures | Labor market measures | |
|---|---|---|---|---|---|---|---|
| American Community Survey (ACS) | Cross-sectional | 0.5–2.2 million households | State, county, census tracta | X | X | ||
| Behavioral Risk Factor Surveillance System (BRFSS) | Cross-sectional | 0.5 million individuals | State, county | X | X | X | |
| Current Population Survey (CPS) | Cross-sectional | 60,000 households | State, county | X | X | X | |
| Medical Expenditure Panel Survey (MEPS) – Household Component | Panel | 12,000–14,000 individuals | State, countyb | X | X | X | X |
| National Health Interview Survey (NHIS) | Cross-sectional | 35,000 households | State, countyb | X | X | X | X |
| Survey of Income and Program Participation (SIPP) | Panel | 14,000–52,000 households | State | X | X | X | X |
aACS multi-year data are representative down to the census-tract level, and public use microdata sample (PUMS) data are available down to the public use microdata area (PUMA) level.
bCounty level data are available only in restricted use files.
Independent Surveys.a
| Sponsoring organization | Data structure | Number of observations | Geographic identifiers | Insurance measures | Private ESI measures | Health care access measures | Health measures | Labor market measures | |
|---|---|---|---|---|---|---|---|---|---|
| Commonwealth Fund Tracking Surveys | Commonwealth Fund | Cross-sectional | 1000–2000 individuals | Nationally representative | X | X | |||
| Gallup Healthways Well-Being Index (WBI) | Gallup | Cross-sectional | N/S | Nationally representative | X | X | X | ||
| Health Reform Monitoring Survey (HRMS) | Urban Institute | Cross-sectional | 7500 individuals | Nationally representative | X | X | X | X | |
| RAND Health Reform Opinion Study (RHROS) | RAND | Panel | 5500 | Nationally representative | X | X |
aSee Long et al. (2015) for a detailed discussion of the nonfederal surveys providing information on the ACA.
Appendix B: Summary of Preliminary Findings for Young Adults
Preliminary Findings of Effects of ACA on Coverage for Young Adults.
| Study | Data source(s) | Study design | Findings |
|---|---|---|---|
| Antwi et al. (2013) | SIPP | – Difference-in-differences model with state fixed effects – Study period: 2008–2011 – Treatment group: ages 19–25; comparison group: ages 16–18 and 27–29 | – 2.6 million young adults added parental ESI after ACA implementation – Parental ESI rates rose prior to law taking effect |
| Antwi et al. (2015a) | NIS | – Difference-in-differences model with year, seasonality, and hospital fixed effects – Study period: 2007–2011 – Treatment group: ages 19–25; comparison group: ages 27–29 | – 12.5% reduction in uninsurance rate among hospitalized young adults ages 19–25 |
| Chua and Sommers (2014) | MEPS | – Difference-in-differences – Study period: 2002–2011 – Treatment group: ages 19–25; comparison group: ages 26–34 | – 7.2pp increase in probability of insurance coverage among 19- to 25-year-olds as compared to 26- to 34-year-olds |
| Cohen and Martinez (2015) | NHIS | – 2014 NHIS coverage estimates | – Number of uninsured young adults ages 19–25 fell from 26.5% in 2013 to 20.0% in 2014 |
| Collins et al. (2012) | Commonwealth Fund Health Insurance Tracking Survey | – Survey results weighted to correct for sample design and nonresponse – Survey period: November 2010–November 2011 | – 13.7 million young adults remained on or joined parent’s health insurance plan between November 2010 and November 2011, and 6.6 million would not have been able to do so prior to the ACA – 39% of young adults ages 19–29 were uninsured at some point in 2011 – 70% of young adults with incomes below 133% FPL were uninsured at some point in 2011 |
| Collins et al. (2014) | Commonwealth Fund Health Insurance Tracking Survey | – Survey results weighted to correct for sample design and nonresponse – Survey periods: July–September 2013, April–June 2014 | – 5.7 million fewer uninsured young adults ages 19–34 in April to June 2014 cohort than July to September 2013 cohort – Uninsurance rate declined from 28% to 18% during this time |
| Depew (2013) | SIPP | – Difference-in-differences model with state and year fixed effects – Study period: 2001–2011 – Treatment group: ages 19–25; comparison group: ages 26–29 | – Females ages 19–25 were 3.2pp more likely to have health insurance after the ACA than females ages 26–29 – Similar males 4.7pp more likely to be insured |
| Lloyd et al. (2014) | CPS | – Difference-in-differences – Study period: pre-ACA 2004–2009; post- ACA 2010–2011 – Treatment group: ages 19–23 (excluding students) and ages 24–25 (all); comparison group: ages 27–30 (all) | – Percentage of young adults age 26 and under with non-spousal insurance rose 7.2pp between pre-ACA period (2004–2009) and period immediately after implementation (2010–2011) – Percentage of uninsured young adults decreased 4.5pp |
| Kotagal et al. (2014) | BRFSS NHIS | – Difference-in-differences – Study period: pre-ACA 2009; post-ACA 2012 – Treatment group: ages 19–25; comparison group: ages 26–34 | – 68.3% to 71.1% increase in coverage for young adults ages 19–25 between 2009 and 2012 |
| Martinez and Cohen (2014) | NHIS | – NHIS coverage estimates | – 4.5 million young adults gained coverage between the implementation of the dependent coverage provision and second quarter of 2014 |
| McMorrow et al. (2015) | NHIS | – Examined temporal coverage trends – Study period: 2009–2014 | – Uninsurance rate among 19–25 year-olds fell from 30% in 2009 to 19% in second quarter of 2014 – Dependent coverage expansion disproportionately affected higher-income young adults – Largest reductions in Medicaid expansion states |
| Mulcahy et al. (2013) | IMS Health CDM Database | – Difference-in-differences model – Study period: 2009–2011 – Treatment group: ages 19–25; comparison group: ages 26–31 | – 1.7pp decrease in proportion of ED visits by uninsured young adults ages 19–25 between January 2009 and December 2011 compared to adults ages 26–31 – 3.1pp increase in private coverage rates of nondiscretionary ED visits by young adults |
| O’Hara and Brault (2013) | ACS | – Difference-in-differences model with state effects – Study period: 2008–2011 – Treatment group: ages 19–25; comparison group: ages 26–29 | – Private insurance rate increased 4.6pp from 2010–2011 for young adults ages 19–25, corresponding to net increase in coverage of 1.4 million individuals and net decrease in uninsurance of 1.3 million |
| Scott et al. (2015) | National Trauma Data Bank | – Difference-in-differences model with facility-level fixed effects – Study period: pre-ACA 2007–2009; post-ACA 2011–2012 – Study population: individuals with trauma experience – Treatment group: ages 19–25; comparison group: ages 26–34 | – Uninsurance rate decreased 3.4pp among young adult trauma patients ages 19–25, as compared to similar adults ages 26–34 – Largest decrease among men and non-Hispanic Whites |
| Slusky (2012) | CPS | – Difference-in-differences model with age, state, and time fixed effects – Study period: pre-ACA 2005–2009; post-ACA 2011 – Treatment group: ages 19–25; comparison group: ages 16–18 and 27–29 | – Insurance coverage rate rose 3pp to 4pp for young adults ages 19–25 – Parental insurance coverage rates rose by 7pp–9pp and self-coverage fell by 4pp–5pp |
| Sommers et al. (2013) | NHIS CPS ASEC | – Difference-in-differences – Study period: 2005–2010 – Treatment group: ages 19–25; comparison group: ages 26–34 | – 6.7pp increase in proportion of young adults ages 19–25 gaining dependent coverage between September 2011 and September 2012, as compared to adults ages 26–35 |
| Wallace and Sommers (2015) | BRFSS | – Difference-in-differences model with state and time fixed effects – Study period: 2005–2012 – Treatment group: ages 19–25; comparison group: ages 26–34 | – 6.6pp increased likelihood of having health insurance post-reform for young adults ages 19–25, as compared to adults ages 26–34 |
Preliminary Findings of Effects of ACA on Access to Health Care for Young Adults.
| Study | Data source(s) | Study design | Findings |
|---|---|---|---|
| Abraham (2014) | MEPS | – Calculations based on quasi-experimental literature | – Using 2008–2011 data, ACA is estimated to increase office visits between 33 million and 149 million annually |
| Antwi et al. (2015a) | NIS | – Difference-in-differences model with year, seasonality, and hospital fixed effects – Study period: 2007–2011 – Treatment group: ages 19–25; comparison group: ages 27–29 | – 3.5% increase in inpatient visits among young adults ages 19–25, as compared to 27- to 29-year-olds – 9.0% increase in visits related to mental illness |
| Antwi et al. (2015b) | HCUP NEDS | – Difference-in-differences model with age, year, and seasonality fixed effects – Study period: pre-ACA 2007–2009; post-ACA 2011 – Treatment group: ages 19–25; comparison group: ages 27–34 | – Quarterly emergency department visit rate decreased 1.6pp per 1000 young adults ages 19–25, compared to adults ages 27–34 – Largest decreases for women, weekday visits, nonurgent conditions, and conditions that could be treated elsewhere |
| Barbaresco et al. (2015) | BRFSS | – Difference-in-differences model with age, state, and time fixed effects – Study period: 2007–2013 – Treatment group: ages 23–25; comparison group: ages 27–29 | – ACA dependent coverage provision increased likelihood of having primary care physician by 1.8pp–3.9pp – Decreased likelihood of forgoing medical care due to cost by 2.2pp–2.8pp among 23- to 25-year-old adults – Decreased likelihood of receiving flu vaccine by 2.1pp–2.7pp |
| Busch et al. (2014) | MEPS | – Difference-in-differences model with age and time fixed effects – Study period: pre-ACA 2007–2009; post-ACA 2010–2011 – Treatment group: ages 19–25; comparison group: ages 26–29 | – Significant reduction in share of young adults ages 19–25 with annual out-of-pocket expenditures exceeding $1500 (4.2%–2.9%) following ACA, as compared to those ages 26–29 |
| Chen et al. (2015) | MEPS | – Difference-in-differences – Study period: pre-ACA 2008–2009; post-ACA 2011–2012 – Treatment group: ages 19–26; comparison group:ages 27–30 | – White and African American young adults ages 19–26 had significantly lower total health spending in 2011 and 2012 as compared to those ages 27–30 |
| Chua and Sommers (2014) | MEPS | – Difference-in-differences – Study period: 2002–2011 – Treatment group: ages 19–25; comparison group: ages 26–34 | – No significant changes in health care use (outpatient, primary care, or emergency department visits; hospitalizations; prescription fills) – 3.7pp decrease in out-of-pocket expenditure rate among 19–25 year-olds as compared to 26–34 year olds, corresponding to an 18% reduction in out-of-pocket expenditures |
| Golberstein et al. (2015b) | NIS California SID and SEDD | – Difference-in-differences model with age and time fixed effects – Study period: 2005–2011 – Treatment group: ages 19–25; comparison group: ages 26–29 | – 0.14 more inpatient admissions for psychiatric diagnoses per 1000 among 19- to 25-year- olds than 26- to 29-year-olds – 0.45 fewer psychiatric emergency department visits per 1000 California 19- to 25-year-olds than 26- to 29- year-olds |
| Han et al. (2014) | MEPS | – Difference-in-differences – Study period: pre-ACA 2009; post-ACA 2011–2012 – Treatment group: ages 19–25; comparison group: ages 26–30 | – Adults ages 19–25 were significantly more likely to receive a dental checkup, blood pressure measurement, and routine health checkup after implementation than those ages 26–30 – No significant change in flu vaccination or pap smear rates |
| Hernandez-Boussard et al. (2014) | HCUP SIDs HCUP SEDDs | – Difference-in-differences model with age, state, and year fixed effects – Study period: 2009–2011 – Treatment group: ages 19–25; comparison group: ages 26–31 | – Decrease of 2.7 emergency department visits per 1000 young adults ages 19–25 |
| Kotagal et al. (2014) | BRFSS NHIS | – Difference-in-differences – Study period: pre-ACA 2009; post-ACA 2012 – Treatment group: ages 19–25; comparison group: ages 26–34 | – Decrease in likelihood of having a usual source of care for young adults ages 19–25 and adults ages 26–34 between 2009 and 2012, though larger decrease for latter group – No significant change in rates of young adults receiving routine checkup or flu shot in last year |
| Lau et al. (2014) | MEPS | – Pre-post design with multivariate logistic regression – Study period: pre-ACA 2009; post-ACA 2011 – Study group: ages 18–25 | – Between 2009 and 2011, young adults ages 18–25 had significantly higher rates of routine exam receipt (44% vs. 48%), blood pressure screening rates (65% vs. 68%), cholesterol screening rates (24% vs. 29%), and annual dental visit rates (55% vs. 61%) – Insurance status fully accounted for differences in routine exam and blood pressure screening rates |
| Lipton and Decker (2015) | NHIS | – Difference-in-differences model with age and year fixed effects – Study period: 2008–2012 – Treatment group: women ages 19–25; comparison group: women ages 18 or 26 | – ACA increased likelihood of HPV vaccine initiation by 7.7pp and HVP vaccine completion by 5.8pp for women ages 19–25 relative to women age 18 or 26 |
| Saloner and Le Cook (2014) | NSDUH | – Difference-in-differences – Study period: 2008–2012 – Treatment group: ages 18–25; comparison group: ages 26–35 | – 5.3pp increase in mental health treatment rates for young adults ages 18–25 with possible mental health disorders, as compared to 26- to 35-year-old adults |
| Scott et al. (2015) | National Trauma Data Bank | – Difference-in-differences model with facility-level fixed effects – Study period: pre-ACA 2007–2009; post-ACA 2011–2012 – Study population: individuals with trauma patient experience – Treatment group: ages 19–25; comparison group: ages 26–34 | – No significant changes in use of intensive care among young adult trauma patients ages 19–25, as compared to similar adults ages 26–34 |
| Slusky (2012) | CPS ASEC BRFSS CE | – Difference-in-differences model with age, state, and time fixed effects – Study period: pre-ACA 2005–2009; post-ACA 2011 – Treatment group: ages 19–25; comparison group: ages 16–18 and 27–29 | – Young adults ages 19–25 are 2–3pp more likely to have personal doctor than individuals ages 16–18 and 27–29 – Young adults 1–2pp less likely to forgo care due to cost – Young adults spent average of $45 to $60 per 3 months less on health insurance |
| Sommers et al. (2013) | NHIS CPS ASEC | – Difference-in-differences – Study period: 2005–2010 – Treatment group: ages 19–25; comparison group: ages 26–34 | – Significant reduction in number of young adults ages 19–25 delaying care or not receiving needed care due to cost, as compared to adults ages 26–34 |
| Vujicic et al. (2014) | NHIS | – Difference-in-differences – Study period: pre-ACA 2008–2010; post-ACA 2011–2012 – Treatment group: ages 19–25; comparison group: ages 26–34 | – Coverage of private dental benefits increased 6.9pp for young adults ages 19–25 between 2008 and 2012, compared to those ages 26–34 – Dental care utilization increased 3.3pp for young adults vs. those ages 26–34 during this time |
| Wallace and Sommers (2015) | BRFSS | – Difference-in-differences model with state and time fixed effects – Study period: 2005–2012 – Treatment group: ages 19–25; comparison group: ages 26–34 | – Young adults ages 19–25, 2.4pp more likely to have usual source of care and 1.9pp less likely to be unable to receive care due to cost than adults ages 26–34 |
Preliminary Findings of Effects of ACA on Health Outcomes for Young Adults.
| Study | Data source(s) | Study design | Findings | ||
|---|---|---|---|---|---|
| Barbaresco et al. (2015) | BRFSS | – | Difference-in-differences model with age, state, and time fixed effects | – | 2.1pp–2.4pp increased probability of self-reported excellent health among 23- to 25-year-olds |
| – | Study period: 2007–2013 | ||||
| – | Treatment group: ages 23–25; comparison group: ages 27–29 | ||||
| Carlson et al. (2014) | CPS | – | Difference-in-differences | – | Better self-reported health statuses among young adults ages 19–25 |
| – | Study period: pre-ACA 2008–2009; post-ACA 2010–2011 | ||||
| – | Treatment group: ages 19–25; comparison group: ages 28–34 | ||||
| Chua and Sommers (2014) | MEPS | – | Difference-in-differences | – | 6.2pp increase in reporting excellent physical health and 4.0pp increase in reporting excellent mental health for 19- to 25-year-olds, as compared to 26- to 34-year-olds |
| – | Study period: 2002–2011 | ||||
| – | Treatment group: ages 19–25; comparison group: ages 26–34 | ||||
| Kotagal et al. (2014) | BRFSS | – | Difference-in-differences | – | No significant change in health status for 19- to 25-year-olds compared to 26- to 34-year-olds between 2009 and 2012 |
| NHIS | – | Study period: pre-ACA 2009; post-ACA 2012 | |||
| – | Treatment group: ages 19–25; comparison group: ages 26–34 | ||||
| Scott et al. (2015) | National Trauma Data Bank | – | Difference-in-differences model with facility-level fixed effects | – | No significant changes in mortality among young adult trauma patients ages 19–25, as compared to similar adults ages 26–34 |
| – | Study period: pre-ACA 2007–2009; post-ACA 2011–2012 | ||||
| – | Study population: individuals with trauma patient experience | ||||
| – | Treatment group: ages 19–25; comparison group: ages 26–34 | ||||
| Wallace and Sommers (2015) | BRFSS | – | Difference-in-differences model with state and time fixed effects | – | Young adults ages 19–25 had 0.8pp decreased likelihood of reporting fair/poor health than those ages 26–34 |
| – | Study period: 2005–2012 | ||||
| – | Treatment group: ages 19–25; comparison group: ages 26–34 | ||||
Preliminary Findings of Effects of ACA on Labor Market Outcomes for Young Adults.
| Study | Data source(s) | Study design | Findings |
|---|---|---|---|
| Abramowitz (2015) | ACS | – Difference-in-differences model with age, state, and year fixed effects – Study period: 2008–2012 – Treatment group: ages 20–25; comparison group: ages 16–18 and 27–29 | – 20- to 25-year-old women 0.35pp (6%) less likely to marry than 16- to 18- and 27- to 29-year-old counterparts – Significant 0.006pp reduction in cohabitation for young adults ages 20–25 |
| Antwi et al. (2013) | SIPP | – Difference-in-differences model with state fixed effects – Study period: 2008–2011 – Treatment group: ages 19–25; comparison group: ages 16–18 and 27–29 | – Young adults worked 3% fewer hours – Young adults 5.8% less likely to work FT – No evidence dependent mandate significantly affected likelihood of employment |
| Bailey (2013) | ACS | – Difference-in-differences – Study period: 2005–2011 – Treatment group: ages 19–25; comparison group: ages 27–33 | – Significant 13% to 24% increase in self-employment among young adults ages 19–25 – Individuals receiving health insurance through dependent coverage more likely to start a small business |
| Bailey and Chorniy (2016) | CPS | – Difference-in-differences model with state and cohort fixed effects – Study period: 2008–2013 – Treatment group: ages 20–25; comparison group: ages 16–18 and ages 27–29 | – No significant effect on job mobility for young adults ages 20–25 |
| Depew (2013) | SIPP | – Difference-in-differences model with state and year fixed effects – Study period: 2001–2011 – Treatment group: ages 19–25; comparison group: ages 26–29 | – ACA reduced labor force participation by 2.4pp for females and 2.2pp for males ages 19–25 – 1.8pp increase in likelihood of being full time student for both males and females ages 19–25 – Females under age 26 2.6pp less likely to be married |
| Gollu (2014) | MEPS | – Difference-in-differences – Study period: 2009–2011 – Treatment group: ages 23–25; comparison group: ages 26–30 | – 4.3% decrease in likelihood of employment among young adults ages 23–25 – No significant change among 19- to 22-year-olds |
| Heim et al. (2015) | Administrative tax records from IRS CDW | – Difference-in-differences model with age and year fixed effects – Study period: 2008–2012 – Treatment group: ages 19–25; comparison group: ages 27–29 | – No significant changes in labor-related outcomes after ACA implementation, including employment status, job characteristics, and enrollment in postsecondary program |
| Slusky (2012) | CPS ASEC BRFSS CE | – Difference-in-differences model with age, state, and time fixed effects – Study period: pre-ACA 2005–2009; post-ACA 2011 – Treatment group: ages 19–25; comparison group: ages 16–18 and 27–29 | – Mandate caused shift from full-time to part-time work and from private 4-year to public 2-year colleges for young adults ages 19–25 |
Appendix C: Summary of Preliminary Findings for General Population
Preliminary Findings of Effects of ACA on Coverage for General Population.
| Study | Data source(s) | Study design | Findings |
|---|---|---|---|
| Carman and Eibner (2014) | HROS | – Survey results weighted to correct for sample design and nonresponse – Survey period: September 2013–March 2014 | – 9.3 million adults ages 18–64 gained insurance coverage between September 2013 and March 2014 – Uninsurance rate fell from 20.5% to 15.8% during this time |
| Collins et al. (2015) | Commonwealth Fund Health Insurance Tracking Survey | – Survey results weighted to correct for sample design and nonresponse – Survey period: July–December 2014 | – Number of uninsured working-age adults fell from 37 million (20% of population) in 2010 to 29 million (16% of population) in second half of 2014 – Uninsurance rate fell from 15% to 10% for non- Hispanic Whites, 24% to 18% for African Americans, and 39% to 34% for Latinos between 2010 and 2014 |
| Collins et al. (2014) | Commonwealth Fund Health Insurance Tracking Survey | – Survey results weighted to correct for sample design and nonresponse – Survey periods: July–September 2013, April–June 2014 | – Approximately 9.5 million fewer uninsured adults ages 19–64 in April to June 2014 cohort than July to September 2013 cohort, corresponding to a decline in the uninsurance rate from 20% to 15% |
| Cohen and Martinez (2015) | NHIS | – 2014 NHIS coverage estimates | – 36 million Americans (11.5%) were without insurance coverage at time of interview |
| Davidoff et al. (2015) | MEPS | – Simulation model – Study sample: adult cancer survivors ages 18–64 | – 19% of adult cancer survivors expected to be Medicaid eligible under ACA, including 30% of uninsured survivors and 39% of those reporting financial hardship |
| Department of Health and Human Services (2014) | CMS | – CMS enrollment figures from October 2013 to March 2014 | – Over 8 million individuals selected a plan through the Marketplace during the first open enrollment period as of March 31, 2014, 2.2 million (28%) of whom were young adults ages 18–34 |
| Department of Health and Human Services (2015a) | Gallup Healthways WBI | – Survey results weighted to correct for sample design and nonresponse – Survey data through March 4, 2015 | – 16.4 million previously uninsured Americans have gained coverage since ACS took effect – 14.1 million adults gained coverage between October 2013 and March 4, 2015 |
| Department of Health and Human Services (2015b) | CMS | – CMS enrollment figures from November 2014 to February 2015 | – 11.7 million Americans selected or were automatically reenrolled into a 2015 plan through the Marketplace during the second open enrollment period |
| Garfield and Damico (2016) | CPS ASEC | – Weighted estimates directly from 2014 CPS ASEC data | – Nearly 4 million poor uninsured adults in nonexpansion states who would have received coverage if residing in expansion state |
| Garfield et al. (2015) | CPS ASEC | – Weighted estimates directly from 2014 CPS ASEC data | – Nearly 4 million poor uninsured adults fall into coverage gap |
| Garfield and Young (2015) | Kaiser Survey of Low-Income Americans and the ACA | – Survey estimates – Survey period: September 2 to December 15, 2014 | – Approximately 11 million non-elderly adults gained coverage in 2014 – 30 million individuals remained uninsured in 2014 |
| Kates et al. (2014) | CDC’s Medical Monitoring Project (MMP) | – Weighted survey estimates – 2009 data projected through 2013 | – Nearly 200,000 individuals with HIV could gain coverage from the ACA |
| Levy (2015) | Gallup Healthways WBI | – Survey estimates – Survey period: fourth quarter of 2013 to first quarter of 2015 | – Uninsurance rate at 11.9% in first quarter of 2015, down 5.2pp from 2013 and the lowest WBI estimate since survey began in 2008 – Largest decline among low-income and Hispanic individuals |
| Long et al. (2014) | HRMS | – Survey results weighted to correct for sample design and nonresponse – Survey period: September 2013–September 2014 | – Uninsurance rate fell 36.3% in Medicaid expansion states and 23.9% in nonexpansion states – Most adults in coverage gap are considered working poor, and the coverage gap disproportionately affects individuals of color |
| Long et al. (2015) | HRMS | – Survey results weighted to correct for sample design and nonresponse – Survey period: September 2013–March 2015 | – Number of uninsured adults fell 15 million (42.5%) between September 2013 and March 2015 – Uninsurance rate declined 52.5% in Medicaid expansion states and 30.6% in nonexpansion states – Largest coverage gains among adults in expansion states who are ages 18–30, low-income, Hispanic, or male – Coverage gap between non-Hispanic Whites and non-Hispanic nonWhites fell from 7.2pp to 3.1pp between September 2013 and March 2015 |
| Schartzer et al. (2014) | HRMS | – Survey results weighted to correct for sample design and nonresponse – Survey period: September 2013–June 2014 | – 13.9% of adults lacked coverage as of June 2014 – Uninsured adults more concentrated in Medicaid nonexpansion states and South and more likely to be unmarried, Spanish-speaking, and have less than a high school education – 60.6% of remaining uninsured adults lived in Medicaid nonexpansion states as of June 2014 |
| Smith and Medalia (2015) | CPS ASEC ACS | – Enrollment figures weighted to be nationally representative – Study period: 2013–2014 | – 10.4% of Americans (33.0 million) were without coverage for entire 2014 calendar year, a 2.9pp decrease from 2013 (13.3% of or 41.8 million Americans) – Approximately 2/3 of Americans covered by private insurance, 1/3 by public insurance – Non-Hispanic Whites had lowest uninsurance rate (7.9%), followed by Asians (9.3%), Blacks (11.8%), and Hispanics (19.9%) |
| Sommers et al. (2014c) | Gallup Healthways WBI | – Regression estimates – Survey population: ages 18–64 – Survey period: January 2012–June 2014 | – Percentage of adults without insurance fell 4.2– 7.1pp between fourth quarter of 2013 and second quarter of 2014 – Subgroup changes: 4.0pp decrease among non- Hispanic Whites, 6.8pp decrease among non- Hispanic Blacks, 7.7pp decrease among Hispanics (all significant) – Significant decline for adults below 138% FPL in Medicaid expansion states; decline insignificant in nonexpansion states |
| Sommers et al. (2014a) | Official Medicaid enrollment figures | – Difference-in-differences – Study period: 2008–2011 – Treatment groups: CT, DC, MN, CA; comparison groups: nearby states | – Statistically significant 4.9pp increase in Medicaid enrollment in Connecticut following expansion – Washington, DC had 3.7pp increase in Medicaid coverage (not significant) – Medicaid enrollment rates highest among adults reporting health limitations |
Preliminary Findings of Effects of ACA on Access to Health Care for General Population.
| Study | Data source(s) | Study design | Findings | ||
|---|---|---|---|---|---|
| Aitken et al. (2015) | IMS Institute for Healthcare Informatics databases | – | Descriptive analysis | – | Medicaid patients in expansion states filled prescriptions 25.4% more in 2014 than in 2013 |
| – | Prescription fill rate increased 2.8% in nonexpansion states | ||||
| Clemans-Cope et al. (2013) | MEPS | – | Multivariate regression analysis | – | Expanding Medicaid to all low-income uninsured adults projected to increase likelihood of having usual source of care by 28.6pp |
| – | Study sample: low-income adults ages 19–64 with one or more chronic health conditions and either full-year Medicaid coverage or uninsured | ||||
| Collins et al. (2014) | Commonwealth Fund Health Insurance Tracking Survey | – | Survey results weighted to correct for sample design and nonresponse | – | By June 2014, 60% of adults with Marketplace or Medicaid coverage reported visiting a doctor or hospital or filling a prescription |
| – | Survey periods: July–September 2013, April–June 2014 | – | 62% reported they would not have accessed this care previously | ||
| Collins et al. (2015) | Commonwealth Fund Health Insurance Tracking Survey | – | Survey results weighted to correct for sample design and nonresponse | – | Number of adults not receiving needed care due to cost fell from 80 million (43% of population) in 2012 to 66 million (36%) in 2014 |
| – | Survey period: July–December 2014 | ||||
| Li et al. (2015) | Insurance, disease, and risk factor rates gathered from literature | – | State-transition simulation model considering Medicaid expansion status as of January 2014 | – | State expansions as of January 2014 projected to increase treatment rate by 5.1% for adults with hypertension |
| – | Model assumes an expansion to 13.9 million adults | ||||
| – | Study population: adults ages 25–64 | ||||
| Sommers et al. (2014c) | Gallup Healthways WBI | – | Regression estimates | – | 2.2pp increased likelihood of having a personal doctor and 2.7pp decrease in inability to afford care among adults ages 18–64 between fourth quarter of 2013 and second quarter of 2014 |
| – | Survey population: ages 18–64 | ||||
| – | Survey period: January 2012–June 2014 | ||||
| Wagner et al. (2014) | Pre-published rates gathered from literature (Kaiser Commission on Medicaid and the Uninsured, Congressional Budget Office, BRFSS) | – | Microsimulation model for 2013–2017 | – | ACA projected to result in additional 466,153 individuals being tested for HIV and 2598 new diagnoses of HIV by 2017 |
| – | Considered state Medicaid expansion status as of July 2013 | ||||
Preliminary Findings of Effects of ACA on Health Outcomes for General Population.
| Study | Data source(s) | Study design | Findings |
|---|---|---|---|
| Kaufman et al. (2015) | Private clinical laboratory database | – Chi squared tests for statistical significance – Study period: 2013–2014 – Study population: adults ages 19–64 without previous diabetes diagnosis | – 23% increase in number of Medicaid patients with newly identified diabetes between 2013 and 2014 in 26 Medicaid expansion states – Nonexpansion states experienced 0.4% increase in new diabetes diagnoses |
| Li et al. (2015) | Insurance, disease, and risk factor rates gathered from literature | – State-transition simulation model considering Medicaid expansion status as of January 2014 – Model assumes an expansion to 13.9 million adults – Study population: adults ages 25–64 | – Medicaid coverage gains expected to lead to 111,000 fewer coronary heart disease events, 63,000 fewer stroke events, and 95,000 fewer cardiovascular disease-related deaths by 2015 |
Preliminary Findings of Effects of ACA on Labor Market Outcomes for General Population.
| Study | Data source(s) | Study design | Findings | ||
|---|---|---|---|---|---|
| Garrett and Kaestner (2015) | CPS | – | Regression analysis with state and time fixed-effects | – | No evidence 2014 ACA policies adversely affected labor force participation, employment, or hours worked |
| – | Study period: 2000–2014 | ||||
| – | ACA policies associated with 1.8pp increase in employment and 0.5pp increase in part-time employment among non-elderly adults with high school diploma or less | ||||
| Gooptu et al. (2016) | CPS | – | Difference-in-differences model with state and year fixed effects | – | No significant evidence that Medicaid expansions increased job turnover rates or affected wages |
| – | Study period: January 2005–August 2014 | ||||
| – | Treatment group: low-educated adults in Medicaid expansion states; comparison group: similar individuals in nonexpansion states | ||||
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Articles in the same Issue
- Frontmatter
- Articles
- Public Provision and Cross-Border Health Care
- Physician Self-Referral of Physical Therapy Services for Patients with Low Back Pain: Implications for Use, Types of Treatments Received and Expenditures
- The ACA: Impacts on Health, Access, and Employment
- Estimating Regression-Based Medical Care Expenditure Indexes for Medicare Advantage Enrollees
- Evidence of Inefficiencies in Practice Patterns: Regional Variation in Medicare Medical and Drug Spending
- The Impact of Delayed Hepatitis C Viral Load Suppression on Patient Risk: Historical Evidence from the Veterans Administration
Articles in the same Issue
- Frontmatter
- Articles
- Public Provision and Cross-Border Health Care
- Physician Self-Referral of Physical Therapy Services for Patients with Low Back Pain: Implications for Use, Types of Treatments Received and Expenditures
- The ACA: Impacts on Health, Access, and Employment
- Estimating Regression-Based Medical Care Expenditure Indexes for Medicare Advantage Enrollees
- Evidence of Inefficiencies in Practice Patterns: Regional Variation in Medicare Medical and Drug Spending
- The Impact of Delayed Hepatitis C Viral Load Suppression on Patient Risk: Historical Evidence from the Veterans Administration
