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Using the 4 Pillars to Increase Vaccination Among High-Risk Adults: Who Benefits?
Mary Patricia Nowalk, PhD, RD; Krissy K. Moehling, MPH; Song Zhang, MS; Jonathan M. Raviotta, MPH; Richard K. Zimmerman, MD, MPH; and Chyongchiou J. Lin, PhD
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Impact of Health Reform on Young Adult Prescription Medication Utilization
Amy Pakyz, PharmD, PhD, MS; Hui Wang, PhD; and Peter Cunningham, PhD
Electronic Reminder's Role in Promoting Human Papillomavirus Vaccine Use
Jaeyong Bae, PhD; Eric W. Ford, PhD, MPH; Shannon Wu, BA; and Timothy Huerta, PhD, MS
Improving Antibiotic Stewardship: A Stepped-Wedge Cluster Randomized Trial
Adam L. Sharp, MD, MS; Yi R. Hu, MS; Ernest Shen, PhD; Richard Chen, MD; Ryan P. Radecki, MD, MS; Michael H. Kanter, MD; and Michael K. Gould, MD, MS
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Joseph A. Ladapo, MD, PhD; and Dave A. Chokshi, MD, MSc
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Lillian Min, MD, MSHS; Christine T. Cigolle, MD, MS; Steven J. Bernstein, MD, MPH; Kathleen Ward, MPA; Tisha L. Moore, MPH; Jinkyung Ha, PhD; and Caroline S. Blaum, MD, MS
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Chad Ellimoottil, MD, MS; John D. Syrjamaki, MPH; Benedict Voit, MBA; Vinay Guduguntla, BS; David C. Miller, MD, MPH; and James M. Dupree, MD, MPH

Impact of Health Reform on Young Adult Prescription Medication Utilization

Amy Pakyz, PharmD, PhD, MS; Hui Wang, PhD; and Peter Cunningham, PhD
The dependent coverage provision was associated with an increase in total and private expenditures and a decrease in out-of-pocket medication expenditures paid, especially among higher-income groups.
ABSTRACT

Objectives: To assess the impact of the Affordable Care Act dependent coverage provision on outpatient prescription expenditures among young adults and to characterize medication types that are prescribed for young adults. 

Study Design: Quasi-experimental. 

Methods: Using data from the Medical Expenditure Panel Survey from 2007 to 2009 and 2011 to 2013, difference-in-differences (DID) analyses estimated the provision’s impact among young adults aged 19 to 25 years versus those aged 26 to 34 years. Five outcomes related to prescription medication access and utilization were evaluated, including whether young adults were unable to get necessary medications in the past year due to cost and whether there were changes in total, out-of-pocket (OOP), and private expenditures and the share of total expenditures paid OOP. DID analyses were also carried out for medication expenditures by class. 

Results: There were a total of 19,165 young adults aged 19 to 25 years and 23,892 aged 26 to 34 years. After adjusting for demographic and socioeconomic factors, provision implementation (DID coefficient P ≤.05) was associated with an increase in total expenditures (by 3.8 percentage points), including those paid by private insurance (7.6 percentage points), and decreases in OOP expenditures and the share of total expenditures paid OOP (4.4 and 1.2 percentage points, respectively). Commonly used medications were anti-infectives, central nervous system agents, and hormones. Expenditures significantly increased for anti-infectives and decreased for hormones and psychotherapeutics. 

Conclusions: The dependent coverage provision was associated with an increase in prescription medication expenditures, especially for anti-infectives, among young adults. The amount of expenditures paid by private insurers increased, whereas they decreased for OOP spending. 

Am J Manag Care. 2017;23(11):670-676
Takeaway Points
  • The dependent coverage provision was associated with increased prescription total expenditures by 3.8 percentage points among young adults aged 19 to 25 years in the 3-year time period following implementation versus prior. This included an increase in expenditures paid by private insurance (7.6 percentage points) and decreases in out-of-pocket (OOP) expenditures (4.4 percentage points) and share of total expenditures paid OOP (1.2 percentage points). 
  • Provision effects were especially noted among young adults with higher household income levels. For total expenditures, there was a significant 11.3 percentage-point increase among young adults in the highest income group (≥300% of federal poverty level [FPL]), while significant decreases of 13.2 and 13.0 percentage points were noted among the ≤100% and 101% to 299% of FPL groups, respectively. Among young adults, 6 therapeutic classes composed more than 80% of all outpatient prescription medications: anti-infectives; central nervous system agents; and hormone, respiratory, psychotherapeutic, and topical medications.
Among the major objectives of the Affordable Care Act (ACA) legislation, passed in March 2010, was greater access to healthcare services through increasing insurance coverage and decreasing the number of uninsured Americans—estimated to be more than 41 million before enactment.1 The consequences of being uninsured are well documented and, besides financial considerations, include delays in care for serious conditions and receiving fewer preventive services.2-4 Greater access was accomplished through several provisions, including expansion of Medicaid and establishment of insurance marketplaces. Another strategy was the dependent coverage provision that specified that young adults, who had a high uninsured rate prior to the ACA,5,6 were eligible to stay on their parents’ plans until the start of the first plan year after they turn 26, regardless of residency, marital status, and financial dependency, effective September 23, 2010. 

Previously, investigators showed that the provision led to an increase in young adults who were insured, had a primary care doctor, and had coverage for emergency care.7-13 Others have documented the provision’s spillover effects in terms of increasing dental insurance rates.14 Chua and colleagues evaluated the provision’s effects on medical spending, healthcare use, and overall health.15 They found significant increases in the probabilities of insurance coverage and the reporting of excellent physical and mental health and a significant decrease in out-of-pocket (OOP) expenditures among young adults aged 19 to 25 years in the first whole-implementation year, 2011, compared with a group aged 26 to 34 years. However, they did not find an increase in healthcare utilization in the form of primary care, emergency department, or hospital visits, nor changes in prescription medications filled. Look et al16 evaluated the ACA's impact on health and medication insurance coverage, medication utilization, and expenditures among young adults. They found that in 2011, compared with a control group, health and medication coverage increased by 4.9 and 5.5 percentage points, respectively, among young adults. There was no change in total medication utilization as measured by fills, although there was a 30% decrease in OOP medication expenditures. Shane et al evaluated changes in health insurance and prescription medication utilization through 2012 and found increases in insurance coverage among young adults, but no significant changes in fills in 2012 compared with 2011.17 

Using a nationally representative sample, the Medical Expenditure Panel Survey (MEPS), this study builds on the work of others16,17 in evaluating changes in medication utilization among young adults in the longer term. Specifically, using a difference-in-differences (DID) approach, this study assessed the ACA’s impact on young adult healthcare services utilization regarding facets of outpatient medication utilization, including overall expenditures, through 2013. MEPS includes data regarding the medication name and expenditure amounts by payer, offering a dataset well suited to investigate this topic. Further, as there are few data that assess medication use patterns among young adults, a secondary aim was to examine the types that are commonly prescribed and whether or not expenditures by type were affected by the ACA. 

METHODS

Data Source

The data source for this study was the Household Component Public Use Files of MEPS, a nationally representative sample of the noninstitutionalized civilian population. In 2013, close to 14,000 households were interviewed, representing 35,068 individuals.18 The MEPS data include healthcare utilization, expenditures, medications, insurance coverage, and demographic and health characteristics.

This was a retrospective analysis that used data pooled from 2007 to 2009 and from 2011 to 2013 representing pre- and postimplementation periods of the dependent coverage provision, respectively. 2010 was not used because the dependent coverage provision was implemented during this year. Young adults aged 19 to 25 years served as the target group, whereas those aged 26 to 34 years were the comparison group, as defined previously.15 This age group is close in age to the target group and presumably similar on aspects related to health utilization, but is not affected by the provision. 

Prescription Medicine Utilization and Expenditures

During each survey round, respondents were asked about outpatient medications they obtained, both new and refills; this information is included in the Prescribed Medicines file. Data collected included medication name, quantity dispensed, and expenditure amounts. Information was verified by pharmacy providers (provided that interviewees give permission to contact their pharmacies and the pharmacies respond to requests; about three-fourths of pharmacies responded in 2011) who relay information concerning medication fill/refill dates, the National Drug Code, medication name, strength and quantity dispensed, total expenditures, and payment sources.19 For nonresponses, expenditures were imputed from pharmacy data for another person’s obtainment of the same medication. Using a Generic Product Identifier code, MEPS coders classified medications into 16 major categories according to Lexicon Plus (Cerner Multum Inc; Denver, Colorado): anti-infective, antineoplastic, central nervous system (CNS), hormonal, topical, cardiovascular, gastrointestinal, respiratory, nutritional, metabolic, psychotherapeutic, genitourinary, coagulation, immunologic, alternative, and miscellaneous agents.20 

The MEPS medication data have been validated. When MEPS data were matched with Medicare Part D claims data to validate prescription data, it was found that household respondents were good at reporting the total number of fills/refills and that they overreported the number of fills per drug, but underreported the number of drugs filled.21 Concordance between sources was greater for chronic as opposed to intermittently used medications, such as anti-infective and pain medications. 

Dependent Variables

There were 5 dependent variables related to medications. These included 1 binary (yes/no) variable related to medication access (whether or not there was a time in the past year that they were unable to get necessary prescribed medications due to cost) and 4 continuous variables related to utilization: total prescription expenditures, total paid by self/family (OOP), total paid by private insurance, and share of total expenditures paid OOP. 

Analyses

DID methods tested the impact of the dependent coverage provision. Specifically, a multivariate DID model was used:

Outcome = B0 + B1 group + B2 time period + B3 (group) × (time period) + other covariates

The coefficient B1 group represents the target group, young adults aged 19 to 25 years, and B2, the postpolicy period, 2011 to 2013. The B3 coefficient is the output of interest, representing an interaction term that captures the difference of the provision effect on the target and comparison groups. 

Other demographic and health status factors that may affect medication utilization were included: age (continuous), gender, race/ethnicity (white non-Hispanic, Hispanic, black non-Hispanic, Asian/Pacific Islander, or other), region (Northeast, Midwest, South, or West), marital status (married, single/never married, widowed, or divorced/separated), household income (poor: ≤100% of the federal poverty level [FPL]; near-poor to low income: 101%-299% of the FPL; or middle to high income: ≥300% of FPL), self-perceived health and mental health status (excellent, very good, good, fair, or poor), education (less than high school diploma, high school diploma or General Educational Development credential, some college, or degree), health insurance (none, private, or public), having usual source of healthcare (yes/no), having chronic conditions (yes/no; asthma, high blood pressure, high cholesterol, or arthritis), and employment status (yes/no). 

Separate models were constructed for the different outcomes. For expenditures, log transformed ordinary least squares regressions were conducted, adjusting for covariates. Analyses were also carried out in which individual postpolicy years (2011, 2012, 2013) were treated as an interaction term [(group) × (year)] to assess whether the ACA’s impact on outcomes studied increased, decreased, or leveled off throughout postpolicy years. To discern whether the ACA had a differential impact depending upon household income, the outcomes were stratified by income levels, as outlined previously, in additional models. Multivariate DID models were also carried out for expenditures by medication class. 

Descriptive analyses characterized medication use patterns by quantifying the proportion of different classes out of total medications prescribed to target and comparison group members before and after dependent coverage provision implementation. Chi-square and Student’s t tests were used, as appropriate, to ascertain for differences in demographic and socioeconomic factors between groups. Given that the members of the target population of the provision were those covered by private insurance, sensitivity analyses were carried out whereby all individuals who were publicly insured were removed from the sample to evaluate result robustness.

To obtain nationally representative estimates, appropriate survey-weighting procedures accounting for the MEPS national probability design were used, as well as procedures to generate robust standard errors and estimates to take into account the complex survey design. Expenditures were adjusted to 2013 US$ based on the MEPS Personal Healthcare Expenditure Component Index, as recommended when pooling prescription medication expenditures.22 Statistical tests were 2-tailed with alpha level of 0.05. 

RESULTS

For all years, there were 19,165 (weighted = 177,426,653) young adults aged 19 to 25 years and 23,892 (weighted = 221,590,555) aged 26 to 34 years. eAppendix I (eAppendices available at ajmc.com) displays population characteristics for both groups. There were several significant differences across marital status, education, and income groups. For example, there was a lower proportion of comparison group members who were single/never married (42%), who had less than a high school diploma (11%), and who had an income ≤100% FPL (14%) compared with target group members (85%, 22%, and 19%, respectively; P <.001). There were also differences across health measures, with more target individuals having self-perceived health and mental health statuses of “excellent” (42% and 52% vs 33% and 47%, respectively; P <.001). 

 
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