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Published in final edited form as: Drug Alcohol Depend. 2022 Feb 26;233:109381. doi: 10.1016/j.drugalcdep.2022.109381

Medicaid Expansion and opioid overdose mortality among socioeconomically disadvantaged populations in the US: a difference in differences analysis

Umedjon Ibragimov 1,*, Russell W Mansfield 1, Courtney R Yarbrough 2, Janet R Cummings 2, Melvin D Livingston 1, Regine Haardoerfer 1, Stephanie Beane 1, Monica M Fadanelli 1, Danielle F Haley 3, Hannah LF Cooper 1
PMCID: PMC8971012  NIHMSID: NIHMS1786643  PMID: 35259679

Abstract

Background:

Opioid-related overdoses are a major cause of mortality in the US. Medicaid Expansion is posited to reduce opioid overdose-related mortality (OORM), and may have a particularly strong effect among people of lower socioeconomic status. This study assessed the association between state Medicaid Expansion and county-level OORM rates among individuals with low educational attainment.

Methods:

This quasi-experimental study used lagged multilevel difference-in-difference models to test the relationship of state Medicaid Expansion to county-level OORM rates among people with a high-school diploma or less. Longitudinal (2008–2018) OORM data on 2,978 counties nested in 48 states and the District of Columbia (DC) were drawn from the National Center for Health Statistics. The state-level exposure was a time-varying binary-coded variable capturing pre- and post-Medicaid Expansion under the Affordable Care Act (an “on switch”-type variable).

The main outcome was annual county-level OORM rates among low-education adults adjusted for potential underreporting of OORM.

Findings:

The adjusted county-level OORM rates per 100,000 among the study population rose on average from 10.26 (SD=13.56) in 2008 to 14.51 (SD=18.20) in 2018. In the 1-year lagged multivariable model that controlled for policy and sociodemographic covariates, the association between state Medicaid Expansion and county-level OORM rates was statistically insignificant.

Conclusions:

We found no evidence that expanding Medicaid eligibility reduced OORM rates among adults with lower educational attainment. Future work should seek to corroborate our findings and also identify – and repair – breakdowns in mechanisms that should link Medicaid Expansion to reduced overdoses.

Keywords: Medicaid expansion, opioid epidemic, opioid overdose-related mortality rates, difference-in-difference modelling

1. Introduction

Fatal overdoses remain a significant public health crisis in the US. Overdoses claimed more than 70 thousand lives in 2019 and the COVID-19 pandemic accelerated this mortality (Ahmad, Rossen, & Sutton, 2021). The vast majority of overdose deaths are attributable to opioids (Wilson, Kariisa, Seth, Smith IV, & Davis, 2020). Socioeconomically disadvantaged individuals are at particularly high risk of a fatal overdose (Altekruse, Cosgrove, Altekruse, Jenkins, & Blanco, 2020).

Medicaid Expansion under the Affordable Care Act (ACA) is a major structural intervention with a potential to reduce opioid overdose mortality rates among socioeconomically disadvantaged populations. First, Medicaid Expansion may reduce risky opioid use by improving access to substance use disorder (SUD) treatment among low-income populations previously ineligible for coverage under traditional Medicaid policies (Beronio, Glied, & Frank, 2014). Empirical evidence supports this hypothesis: states that expanded Medicaid under ACA had higher rates of admissions to SUD treatment programs (Meinhofer & Witman, 2018; Saloner & Maclean, 2020) and of prescriptions for medications for opioid use disorder (MOUD) such as buprenorphine and naltrexone compared to non-expansion states (Saloner, Levin, Chang, Jones, & Alexander, 2018; Sharp et al., 2018). Second, Medicaid Expansion may reduce overdoses by addressing mental health disorders, a major risk factor for SUD and overdoses (Suffoletto & Zeigler, 2020; Webster, 2017). Past research indicates that Medicaid expansion is associated with improved mental health and with better access to mental health care and medications among low-income individuals (Fry & Sommers, 2018; Winkelman & Chang, 2018). While some have hypothesized that ACA may increase overdose rates by increasing opioid pain relievers (OPR) prescriptions (see, e.g. Adolphsen, 2017), research has found no evidence of a positive association between Medicaid Expansion and OPR prescription rates (Cher, Morden, & Meara, 2019; Saloner et al., 2018; Sharp et al., 2018) or OPR-related overdoses (Kravitz-Wirtz et al., 2020; Swartz & Beltran, 2019).

Despite evidence supporting pathways through which Medicaid Expansion might reduce overdose mortality, past empirical studies have reached conflicting conclusions about the Expansion/OORM overdose association. Some have found a positive relationships (Swartz & Beltran, 2019; Yan, Sloan, Boscardin, Guo, & Dudley, 2021); others have found a negative relationship; (Kravitz-Wirtz et al., 2020); while still others have found no association (Averett, Smith, & Wang, 2019). Discordant results may, in part, be a product of the challenges of overdose ascertainment and target populations. Past research has explored the Medicaid Expansion/overdose relationship in the general population, ignoring the possibility that expansion’s effects may be greatest among its intended target population, people of lower socioeconomic status (Averett et al., 2019; Kravitz-Wirtz et al., 2020; Swartz & Beltran, 2019; Yan et al., 2021). In addition, no studies accounted for potential underreporting of deaths related to opioids, a major source of bias (Ruhm, 2018).

Answering the call for more detailed exploration of the links between health insurance policies and overdose deaths, we analyzed the association between state Medicaid Expansion and county-level opioid overdose-related mortality (OORM) rates among individuals with low educational attainment, a proxy for socioeconomic disadvantage, using 2008–2018 US mortality data.

2. Materials and methods

We estimated the effect of Medicaid expansion on OORM using a quasi-experimental difference-in-difference (DiD) design with county-level National Center for Health Statistics (2020) 2008–2018 data from 2,978 counties nested in 48 states and the District of Columbia (DC). Due to lack of reliable data on OORM among Medicaid beneficiaries, we attempted to approximate this population by limiting our sample to individuals with low education attainment, since on average people with lower education tend to have lower income (U.S. Bureau of Labor Statistics, 2018), and are more likely to be covered by Medicaid (US Census Bureau, 2021). This approach is consistent with past research on mortality-related outcomes of Medicaid Expansion (Austin, Naumann, & Short, 2021). We excluded data from Georgia and Rhode Island due to high missingness (>90%) of education data in the mortality dataset.

2.1. Measures.

The outcome was first calculated to capture the annual county-level OORM rate in each county among adults (aged 25–64) whose highest educational attainment was a high-school diploma/GED or less, per 100,000 adults of the same age and education level (see Formula 1).

No. opioid overdose related deaths in adults (aged 25−64) with a high school diploma or GED or lessYeariCountyjNo. adults (aged 25−64) with a high school diploma or GED or less YeariCountyj×100,000 (Formula 1)

The lower age limit restricted the sample to individuals with complete data on high school diploma/GED attainment, and the upper limit excluded adults eligible for Medicare coverage. We then adjusted the outcome to account for unreported opioid overdoses (Ruhm, 2018) (see Supplemental Table S1 for detailed description, references and data sources for all variables).

The exposure of interest was state Medicaid Expansion adopted under ACA, including early Medicaid Expansion (states that adopted §1115 policy waivers to expand Medicaid prior to ACA start date) (Sommers, Kenney, & Epstein, 2014). This variable was coded “1” in each state/year observation when Medicaid Expansion was active, and “0” otherwise. We excluded other §1115 waivers (adopted post-ACA) or similar policies expanding eligibility for health insurance in this exposure variable, because these policies did not uniformly mandate coverage of SUD treatment services (Kaiser Family Foundation, 2021).

Other annual time-varying covariates included state drug-related policies (prescription drug monitoring program, Good Samaritan and Naloxone dispensing laws and medical marijuana laws), gleaned from the Prescription Drug Abuse Policy System, and county-level sociodemographic characteristics and general mortality rate. To account for possible differences in fentanyl-related overdoses between states that ever-expanded Medicaid under ACA vs. those that did not (Table 1), and given fentanyl’s much higher toxicity compared to other opioids, we controlled the annual percentage of deaths attributed to synthetic opioids (which includes fentanyl and its analogs) at the county-level.

Table 1:

County-level opioid overdose-related deaths and county-level covariates by state Medicaid expansion status, 48 states and District of Columbia (2008–2018)a

Variables (n=2,978 counties) Baseline (2008) Endline (2018)
Ever expanded Medicaidb No Medicaid expansionc Ever expanded Medicaidb No Medicaid expansionc
Mean (standard deviation)
Overdose-related variables
Unadjusted opioid overdose deaths per 100,000 people of lower education attainment, ages 25 to 64 8.23 (14.20) 6.59 (10.57) 16.02 (20.22) 11.43 (15.91)
Adjustedd opioid overdose deaths per 100,000 people of lower education attainment, ages 25 to 64 (main outcome) 11.54 (15.99) 9.67 (12.23) 18.19 (20.95) 12.80 (16.50)
Unadjusted opioid overdose deaths per 100,000 people of higher education attainment, ages 25 to 64 2.69 (2.69) 2.23 (5.07) 5.53 (7.53) 4.21 (7.15)
Adjustedd opioid overdose deaths per 100,000 people of higher education attainment, ages 25 to 64 3.75 (6.73) 3.34 (6.03) 6.51 (8.03) 4.91 (7.79)
Adjustedd percent of opioid overdoses due to synthetic opioids 10.3 (6.5) 11.5 (7.6) 52.3 (29.8) 42.3 (28.6)
Unadjusted percent of opioid overdoses due to synthetic opioids 9.5 (6.4) 11.7 (8.4) 51.8 (30.0) 41.9 (28.9)
County Demographics:
Population Density (population/square mile) 468.37 (3,039.13) 160.58 (503.47) 494.08 (3,171.20) 174.09 (570.72)
Percent of White residents 86.0 (15.6) 84.0 (16.3) 84.7 (16.1) 83.6 (16.8)
Percent of Black residents 5.6 (10.2) 9.0 (14.9) 5.9 (10.3) 9.1 (15.0)
Percent of Non-Hispanic residents 93.1 (10.3) 91.5 (14.2) 91.5 (11.3) 89.7 (15.4)
Percent of 25 to 64 years old 52.7 (3.6) 51.3 (3.5) 51.7 (3.3) 50.2 (3.3)
Percent of male residents ages 25 to 64 50.1 (2.5) 49.9 (2.2) 50.4 (2.8) 50.2 (2.6)
Percent of families in poverty 10.4 (5.3) 11.5 (5.5) 10.4 (5.6) 11.2 (5.6)
General mortality, ages 25 to 64 (per 100,000) 424.32 (192.34) 457.22 (160.61) 466.62 (190.40) 561.53 (1,899.55)
General mortality among lower education attainment stratum, ages 25 to 64 (per 100,000) 618.84 (354.31) 637.65 (229.03) 724.41 (287.60) 1067.17 (13,012.51)
General mortality among higher education attainment stratum, ages 25 to 64 (per 100,000) 238.05 (117.19) 266.73 (128.38) 252.82 (109.12) 498.34 (9,418.18)
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a

Not including data from the states of Rhode Island and Georgia, and Kalawao County, Hawaii

b

Counties in states that ever-expanded Medicaid under ACA (n=942)

c

Counties in states that never expanded Medicaid under ACA (n=2,036)

d

Adjustments were made using Ruhm (2018) method to account for underreported opioid overdoses

2.2. Analytic strategy.

We used a linear two-way fixed effects model to estimate the DiD effect of Medicaid Expansion on county-level annual OORM rates. Medicaid Expansion and all state policy covariates were lagged by one year to account for the delay in the possible impact of policies on the outcome. Initially we tested a base model controlling for only state and year fixed effects, followed by three adjusted models: 1) including sociodemographic and mortality-related covariates; and 2) including policy-related covariates only, and 3) including all covariates (Equation 1).

Ycst=β0+β1ACAst-1+Ss+Tt+Xcst+Pst-1 (1)

Where Ycst is the county specific OORM rate; ACAst is a binary “on switch”-type indicator for whether Medicaid expansion had occurred in state s at least one year prior; Ss is a series of state fixed effects; Tt is a set of year fixed effects; Xcst is a vector of time-varying county specific percentage of deaths due to synthetic opioids (deaths with ICD-10 code “T40.4: Poisoning by other synthetic narcotics,” which include fentanyl) and sociodemographic factors; and Pst-1 is a vector of state specific opioid related policies lagged by one year. All models were estimated the standard errors accounting for clustering at the state level.

Sensitivity analyses (1) estimated a DiD model limited to people with more than a high-school diploma/GED) as a placebo test; and (2) applied a 2-year lag between the policy exposures and the outcome. We conducted the analyses using SAS 9.4 (Cary, NC) software in June 2020–March 2021.

3. Results

There were 190,723 opioid overdose-related deaths among people with lower education attainment in 2,978 US counties nested in 48 states and DC in 2008–2018. The adjusted county-level OORM rates among people with lower education attainment ages 25–64 rose on average from 10.26 (SD=13.56) in 2008 to 14.51 (SD=18.20) per 100,000 in 2018. Adjusted OORM rates were higher in states that eventually expanded Medicaid under the ACA across all years of observations (RD=3.63, 95%CI: 2.66, 4.60), with rate difference ranging from 1.87 in 2008 to 5.39 per 100,000 in 2018 (Table 1).

The 1-year lagged model with no covariates except for state and year as fixed effects (Table 2) found a positive and statistically significant association between state Medicaid Expansion and county-level adjusted OORM rates (rate difference [RD]=2.68, 95% CI: 1.74, 3.62). In the model controlling for sociodemographic covariates the association was non-significant, while in the model with policy-related covariates only, the focal coefficient became positive and statistically significant (RD=1.18; 95%CI: 0.22, 2.13). When controlling for all covariates, however, this association is statistically insignificant (see Table 2).

Table 2:

Difference-in-difference modelling of the association between 1-year lagged Medicaid expansion and county-level opioid overdose-related mortality rates among people of lower education attainment, 2008–2018

Variables Crude Model Multivariable Model 1 Multivariable Model 2 Final Multivariable Model
Rate Difference 95% Confidence Interval p-value Rate Difference 95% Confidence Interval p-value Rate Difference 95% Confidence Interval p-value Rate Difference 95% Confidence Interval p-value
Intercept 7.81 5.32 10.29 <.0001 9.72 7.46 11.98 <.0001 9.19 6.70 11.69 <.0001 9.85 7.59 12.12 <.0001
Medicaid Expansion a 2.68 1.74 3.62 <.0001 0.07 −0.76 0.90 0.8723 1.18 0.22 2.13 0.0154 0.56 −0.35 1.46 0.2297
state -- -- -- <.0001 -- -- -- <.0001 -- -- -- <.0001 -- -- -- <.0001
Year -- -- -- <.0001 -- -- -- <.0001 -- -- -- <.0001 -- -- -- <.0001
Overall mortality rate (ages 25–64) b -- -- -- -- 0.00 0.00 0.00 0.1706 0.00 0.00 0.00 0.1832
Adjusted percent of opioid overdoses due to synthetic opioids b -- -- -- -- 0.35 0.34 0.37 <.0001 0.35 0.34 0.37 <.0001
Population density (per square mile) b -- -- -- -- 0.00 0.00 0.00 0.5203 0.00 0.00 0.00 0.517
Percent White alone b -- -- -- -- 0.12 0.09 0.15 <.0001 0.12 0.09 0.15 <.0001
Percent Non-Hispanic b -- -- -- -- 0.05 0.02 0.07 0.0003 0.05 0.02 0.07 0.0002
Percent of people 25–64 years old b -- -- -- -- 0.20 0.10 0.29 <.0001 0.20 0.10 0.29 <.0001
Percent male b -- -- -- -- −0.17 −0.29 −0.05 0.0045 −0.17 −0.29 −0.06 0.0038
Percent of families in poverty b -- -- -- -- 0.32 0.24 0.40 <.0001 0.32 0.24 0.40 <.0001
S1115 waiver in state a -- 3.84 2.47 5.22 <.0001 0.49 −0.67 1.65 0.4058
Good Samaritan law in state a -- 0.24 −0.46 0.95 0.504 −1.69 −2.38 −1.00 <.0001
Naloxone law in state a -- -- -- -- 3.12 2.26 3.98 <.0001 1.96 1.17 2.75 <.0001
Prescription Drug Monitoring Program in state a -- -- -- -- 5.70 4.60 6.80 <.0001 0.54 −0.51 1.58 0.3114
Medical Marijuana Law in state a -- -- -- -- 3.35 2.26 4.44 <.0001 −0.12 −1.08 0.83 0.8021
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a

Lagged by 1 year

b

Recoded to mean by year

NOTE: All counties from Georgia and Rhode Island were removed from these models due to high missingness (>90%) of education data in the mortality dataset for 2 and 6 years correspondingly. Kalawao County, Hawaii was removed from these models because its small population led to unstable estimates.

In sensitivity analyses, the multivariable model with a two-year lag (see Supplementary Table S2) and both 1- and 2-year lag models for higher education strata produced non-significant results (see Supplementary Tables S2–S4).

4. Discussion

Our nationwide, population-based study found no evidence that Medicaid Expansion reduced OORM rates among people with lower education attainment. This more refined model does not corroborate previous empirical studies reporting either positive or negative associations between Medicaid expansion and OORM rates (Kravitz-Wirtz et al., 2020; Swartz & Beltran, 2019; Yan et al., 2021). Our capacity to directly compare our results with past evidence, however, is limited, since to improve the rigor we narrowed analyses to less educated individuals to approximate the low-income population eligible for Medicaid; adjusted the outcome for unreported opioid overdoses; and controlled for the percentage of fatal overdoses from synthetic opioids including fentanyl.

Our findings are somewhat unexpected given the evidence that Medicaid expansion improved SUD and mental health treatment utilization (Fry & Sommers, 2018; Meinhofer & Witman, 2018; Saloner et al., 2018; Saloner & Maclean, 2020; Sharp et al., 2018; Winkelman & Chang, 2018).. Possible explanations for our findings may lie in breakdowns in the potential mechanisms linking Medicaid Expansion to reduced OORM rates. These mechanisms may include absence of increase in SUD treatment utilization among low-income adults in the states that adopted ACA (Olfson, Wall, Barry, Mauro, & Mojtabai, 2018); limited growth in Naloxone prescribing attributed to Medicaid (Frank & Fry, 2019); and geographic barriers to SUD treatment that may be particularly formidable for low-income individuals (Amiri et al., 2018; Kuo et al., 2013). Alternatively, lack of association may be due to protective effects of Medicaid Expansion on OORM (reducing overdoses via improved access to treatment) being offset by its harmful effects (increasing overdoses via easier access to ORPs), as suggested by some researchers (Averett et al., 2019).

The main potential limitation of our study is measurement error. While we used educational attainment to approximate low-income status as a Medicaid eligibility criterion, income-based Medicaid eligibility and educational attainment may be not highly correlated (Chang & Davis, 2013). Low-education populations may have lower OORM rates than Medicaid populations, which also may explain the non-significant findings in our analyses.

5. Conclusions.

We found no evidence that expanding Medicaid eligibility may reduce OORM rates among adults with low education attainment. Future work should seek to corroborate our findings, perhaps with a more accurate measure of Medicaid eligibility, and explore – and try to repair – breakdowns in the mechanisms that should be linking Medicaid Expansion to overdose prevention, including low uptake of SUD treatment and suboptimal naloxone distribution.

Supplementary Material

1

Highlights:

  • Compared opioid deaths among less educated people by state’s Medicaid expansion

  • States that expanded Medicaid had higher adjusted rates of opioid deaths

  • No evidence that Medicaid expansion influences opioid death rates in less educated

Acknowledgements:

We express our gratitude to the National Center for Health Statistics of the US Centers for Disease Control and Prevention for sharing the detailed mortality database for all US counties, 2008–2018. The findings and conclusions in this report are those of the authors and do not represent the official position of NIDA or the Centers for Disease Control and Prevention.

Role of funding source:

This study was supported by National Institute on Drug Abuse, grants R01DA046197 (PI Cooper) and K01DA046307 (PI Haley). The funding source had no involvement in the design or implementation of the study.

Footnotes

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Declarations of interest: none

Conflict of interest: no conflict declared

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Supplementary Materials

1
NIHMS1786643-supplement-1.doc (140.5KB, doc)

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