THE RELATIONSHIP BETWEEN MEDICAID EXPANSION AND OPIOID OVERDOSE MORTALITY
Hannah Klukoff
Abstract: Approximately 500,000 people died from an opioid overdose between 1999 and 2019. Despite the 2017 declaration by The Department of Health and Human Services that the opioid crisis was a public health emergency, projections suggest that at least another 500,000 opioid-related deaths will occur between 2020 and 2032. In recent years, states have had the option to expand their Medicaid programs, including increased access to treatment for all substance use disorders, particularly treatment for Opioid Use Disorder (OUD) , as part of those expansions. A small literature has examined the relationship between Medicaid expansion and factors related to opioid mortality, such as access to OUD treatment. These studies have yielded varying results. This thesis uses state-level opioid overdose data to add a new data point to this otherwise unsettled body of literature. I also make a novel contribution by exploring the question of whether there is variation in the relationship between Medicaid expansion and opioid overdose mortality according to states’ racial composition, workforce composition, and unemployment rates. I find evidence of a positive and statistically significant relationship between Medicaid expansion and opioid overdose deaths. I further find that Medicaid expansion may be more closely related to opioid overdose mortality in states with larger Black populations, smaller American Indian and Alaska Native populations, smaller Hispanic populations, lower unemployment rates and lower numbers of blue-collar workers. My results may indicate the need to reconsider the ways that Medicaid policy is being leveraged to combat the opioid crisis.
I. Introduction
According to the Centers for Disease Control and Prevention (CDC) (2021), approximately 500,000 people died from opioid overdoses between 1999 and 2019. In 2017, the Acting Secretary of the Department of Health and Human Services (HHS) declared the opioid crisis a public health emergency after death rates reached an average of 91 deaths per day (HHS, 2017). Today, an average of 136 people die from an opioid overdose every day (CDC, 2021). However, despite the increased attention toward the opioid crisis, projections suggest that at least another half of a million people will die from an opioid overdose between 2020 and 2032 (Lim et al., 2022).
Numerous facets of life in the United States have been impacted by the destructive effects of the opioid crisis. In the 2020 Economic Report of the President, the Council of Economic Advisors (CEA) estimated that the opioid crisis amounted to costs of $2.5 trillion during 2015 to 2018. This estimate accounts for healthcare, treatment, criminal justice costs and forgone labor productivity. The crisis has also harmed child well-being. Opioid use and dependency among pregnant women (both prescribed by a doctor and illicit) increased by 127% between 1998 and 2011 (Maeda et al., 2014). Studies have shown that children who are exposed to parental opioid misuse are at increased risk of developing mental health problems and substance use disorders later in life (Winstanley & Stover, 2019); are more likely to experience trauma from the loss of a parent due to an overdose (Brundage & Levine, 2019); are more likely to have contact with the foster care system (Ghertner et al. 2018); and are more likely to struggle academically (Darolia & Tyler, 2020). Further, evidence suggests that the opioid crisis has particularly acute implications for vulnerable and historically disadvantaged populations such as racial and ethnic minorities (Larochelle et al., 2021), poor pregnant women (National Academies of Sciences, Engineering, and Medicine, 2017; Brundage & Levine, 2019), poor children and adolescents (Ghertner et al., 2018) and formerly incarcerated people (National Academies of Sciences, Engineering, and Medicine, 2017).
Medication-Assisted Treatment (MAT), usually in combination with cognitive behavioral therapy, is the standard treatment for Opioid Use Disorder (OUD), the clinical phrase used to refer to misuse of opioids (American Psychiatric Association [APA], 2013). Access to treatment and other forms of addiction-related services is often limited among lower income and more vulnerable populations (Priester et al., 2016). While states are required to cover certain mental health services and Substance Use Disorder (SUD) treatments — such as medically necessary inpatient hospital stays — under their Medicaid programs, there is wide variation in which (if any) additional services states choose to cover (Medicaid and CHIP Payment and Access Commission [MACPAC], n.d.). In recent years, states have had the option to expand their Medicaid programs and have included increased access to SUD treatment (particularly treatment for OUD) in these expansions.
Using state-level opioid overdose mortality data from the CDC, this paper examines state Medicaid expansions and opioid overdose death rates between 2010 and 2019. I add a new data point to a body of literature that is otherwise unsettled, as existing literature on the relationship between Medicaid expansion and opioid overdose mortality has produced varying results. Additionally, this study is, to my knowledge, the first to explore variation in the relationship between Medicaid expansion and opioid overdose deaths according to states’ racial composition, workforce composition, and unemployment rates.
II. Background
Medicaid was first established in 1965 as Title XIX of the Social Security Act. When Medicaid was first established, it was meant to provide health insurance to those receiving cash assistance — i.e., the lowest-income families, low-income elderly and people with disabilities. Since its inception, Medicaid has been expanded several times by Congress and state governments, broadening the base of individuals who are eligible for coverage. Since its inception, Medicaid has been an entitlement program, meaning anyone who qualifies for coverage is eligible for benefits. There are no waiting lists and no caps on enrollment and the program is designed to take on more people in times of economic downturn.
Each state shares the costs of its Medicaid program with the federal government. States have broad leeway to design and implement their own Medicaid programs within federal guidelines and their Medicaid spending is reimbursed based on a formula called the Federal Medical Assistance Percentage (FMAP) which is dependent on the state’s per capita income. Though participation in Medicaid is voluntary, all states participate. Arizona was the last to opt-in in 1982 (Center for Health Care Strategies [CHCS], 2019).
Despite Medicaid’s reputation as a popular and effective safety net program, its stringent eligibility categories and income limits have historically been designed to ensure that only very low-income children, families, pregnant women, people with disabilities and some elderly people were eligible for benefits (Congressional Research Service [CRS], 2021). The program’s structure has left a large swath of people uninsured. Typically, these were adults with no dependent children who were not offered employer sponsored insurance through their jobs and whose incomes were not low enough to make them financially eligible for Medicaid, but who also could not afford to purchase insurance on their own (Tolbert & Orgera, 2020). The closing of this “coverage gap” was one of President Barack Obama’s primary goals when he signed into law in 2010 the Patient Protection and Affordable Care Act, or more commonly known as the Affordable Care Act (ACA) (Silvers, 2013). The ACA sought to make health insurance more affordable and accessible through the creation of state and federal health insurance marketplaces, tax credits intended to offset insurance premiums, cost-sharing mechanisms designed to lower the cost of purchasing private health insurance and expansion of the Medicaid program (Silvers, 2013; CHCS, 2019).
The phrase “Medicaid expansion” refers to the part of the ACA that increases the program’s eligibility thresholds to include all adults whose incomes are below 138% Federal Poverty Level (FPL), effectively ending the previous categorical eligibility system (Centers for Medicare and Medicaid Services [CMS], 2021; Kaiser Commission on Medicaid and the Uninsured, 2013). The original ACA sought to make Medicaid expansion mandatory and to penalize non-compliant states by taking away their federal match dollars (Kaiser Family Foundation [KFF], 2012). However, several states declared Medicaid expansion to be unconstitutional and filed a lawsuit challenging this provision of the ACA (National Federation of Independent Business [NFIB] v. Sebelius). On June 28th, 2012, the U.S. Supreme Court ruled that Medicaid expansion in and of itself was not unconstitutional, but that the federal government could not take away FMAP money as a punishment for a state’s failure to expand coverage (KFF, 2012). This ruling effectively changed Medicaid expansion from a sweeping national policy reform to a state option (CHCS, 2019).
Scholars have observed stark differences in a variety of health outcomes between low-income populations in states that have chosen to expand their Medicaid programs and those that have not (Han et al., 2015; Bellerose et al., 2022; Bauchner & Maddox, 2019). One such outcome is the provision of treatment for OUD. Because the federal government requires that Medicaid expansion benefit packages include mental and behavioral health services and treatment for all SUD, Medicaid programs are theoretically well positioned to provide OUD treatment (Orgera & Tolbert, 2019). Some studies have found that Medicaid expansion is associated with increased access to coverage for prescriptions for buprenorphine (one of the three most commonly used medications for OUD treatment) (Wen et al., 2017) and increased access to MAT for formerly incarcerated people (Khatri et al., 2021).
In 2018, President Trump signed the Substance Use Disorder Prevention that Promotes Opioid Recovery and Treatment for Patients and Communities (SUPPORT) Act, which included many Medicaid-specific provisions (Musumeci & Tolbert, 2018). Additionally, states have the flexibility to offer added services and treatment for OUD as part of their Medicaid programs through “1115 demonstration waivers,” or through the more recent enhanced federal matching rate for crisis intervention services approved under the American Rescue Plan Act (ARPA) (Guth, 2021). However, researchers at the Pew Charitable Trust (2022) find that low-income individuals are less likely to be able to access these services and may be less able to afford treatment for OUD on their own. I posit that this dynamic is likely to be even more pronounced for low-income people who live in states that have chosen not to expand Medicaid.
III. Literature Review
There are two competing theories in the literature on the relationship between Medicaid expansion and opioid overdose mortality. The first is that greater access to health insurance through Medicaid expansion means that more people will potentially have access to prescription opioids, increasing their likelihood of becoming addicted and dying of an overdose. The second is that Medicaid expansion increases access to treatment for OUD, resulting in fewer overdose deaths (Figure 1 below illustrates these theoretical links). There is a considerable body of research that examines the relationship between Medicaid expansion and both opioid prescriptions and treatment accessibility. There is also a substantial amount of literature on the relationship between Medicaid expansion and opioid overdose mortality, though the results of these studies vary.
The Relationship Between Medicaid Expansion and Access to Prescription Opioids
The general consensus in the literature suggests that Medicaid expansion is not strongly related to the rate at which opioids are prescribed to Medicaid beneficiaries. In their analysis of Medicaid prescription reimbursement data for 2011 through 2016, Sharp et al. (2018) conclude that there was an increase during this period in the number of prescriptions for opioids that were covered by Medicaid, but that the difference between expansion and non-expansion states was not statistically significant. Swartz & Beltran (2019) analyze data from 2008 through 2016 and find that opioid prescription availability was at its peak in 2010 (well before many states opted to expand Medicaid in 2014). They also find that Medicaid expansion is positively associated with opioid overdose deaths overall, but not with an increase in prescription-overdose-related deaths specifically. Thus, this study does not support the theory that higher rates of opioid overdose mortality in states that have expanded Medicaid are being mediated by increased prescription opioid availability.
Baicker et al. (2017) collected experimental data related to Oregon’s early expansion of their Medicaid program to ascertain the impact of expanded Medicaid coverage on the use of prescription medications. The authors’ results suggest that Medicaid coverage did not cause a significant increase in the possession of prescription opioids or of analgesics overall, and that coverage resulted in a significant reduction in the use of medication originally prescribed to someone other than the beneficiary (Baicker et al., 2017). The latter finding has implications for Medicaid’s role (or lack thereof) in the opioid crisis because the sources of misused pharmaceutical drugs are most often friends or family, according to a review of the literature (Hulme et al., 2018).
The Relationship Between Medicaid Expansion and Access to OUD Treatment
Several studies show that Medicaid expansion is associated with increased access to treatment for people with OUD (Sharp et al., 2018), but that this relationship may be conditional upon provider location (Wen et al., 2017; Gertner et al, 2020). In the same study discussed above, Sharp et al. (2018) find that the prescription rates of buprenorphine and naltrexone (two of the three medications used in medication assisted treatment for OUD) increased by less than 50% in non-expansion states but by more than 200% in expansion states between 2011 and 2016. In combination with the finding previously discussed, this study suggests that Medicaid beneficiaries are no more likely to be prescribed opioids after expansion than they were before, but that the expansion of Medicaid may increase access to MAT for people with OUD.
Until recently, qualified practitioners were required to obtain a Drug Addiction Treatment Act (DATA) waiver (also called an “X-Waiver”) to prescribe buprenorphine to individuals with OUD. Wen et al. (2017) use quarterly drug utilization and spending data from CMS for the years 2011 through 2014 to investigate the impact of Medicaid expansion on utilization of buprenorphine for OUD treatment and provider capacity to prescribe it. The results of this study suggest that Medicaid expansion is associated with a 70% increase in buprenorphine utilization for MAT covered by Medicaid and that an observed 10% increase in the number of physicians who had waivers to treat up to 100 patients for OUD was associated with a 45% increase in the number of Medicaid-covered buprenorphine prescriptions (Wen et al., 2017).
Gertner et al. (2020) use panel data from the Drug Enforcement Administration on methadone and buprenorphine dispensed between 2006 and 2017 to assess the impact of Medicaid expansion on MAT using these two medications. This study finds that, within their sample as a whole, Medicaid expansion is not significantly associated with an increase in the amount of methadone or buprenorphine dispensed. However, the authors also found that Medicaid expansion was positively associated with the dispensing of buprenorphine, in states with high concentrations of DATA-waivered practitioners (Gertner et al., 2020). This finding supports Wen et al.’s (2017) assertion that the requirement of DATA waivers may be stunting the effect of Medicaid expansion on OUD treatment.
The Relationship Between Medicaid Expansion and Opioid Overdose Mortality
Some studies find no evidence of a statistically significant relationship between Medicaid expansion and opioid overdose deaths (Averett et al., 2019; Ibragimov et al., 2022; Auty & Griffith, 2022). For example, Averett et al. (2019) use state-level data from 2010 through 2017 and find that a state’s Medicaid expansion status is not significantly related to its opioid overdose mortality rates. Ibragimov et al. (2022) build upon this work by using county-level opioid overdose mortality data from 2008 through 2018 and limiting their sample to individuals with low educational attainment as a proxy for Medicaid eligibility. The authors find that there is not a significant relationship between Medicaid expansion and opioid overdose deaths when they control for sociodemographic and policy-related variables in one and two-year lagged models (Ibragimov et al., 2022). Auty & Griffith (2022) use state-level data from 2013 through 2020 to assess whether Medicaid expansion impacted opioid overdose mortality during the COVID-19 pandemic. The authors found that both expansion and non-expansion states experienced significant increases in opioid overdose deaths since pre-pandemic years, but that these increases were not statistically different from one another (Auty & Griffith, 2022).
More fine-grained analyses sometimes detect a stronger relationship between Medicaid expansion and opioid overdose mortality. Kravitz-Wirtz et al. (2020) analyze disaggregated county-level opioid overdose mortality data on deaths involving each opioid class from 2001 through 2017. The authors find that Medicaid expansion is associated with a 6% reduction in overall opioid overdose mortality rates and in deaths related to the use of each class of opioids except for methadone (Kravitz-Wirtz et al., 2020). For example, heroin overdose rates were 11% lower among counties in states that expanded Medicaid than among counties in states that did not expand Medicaid (Kravitz-Wirtz et al., 2020).
Abouk et al. (2021) use county-level data from 2007 through 2017 to investigate the relationship between Medicaid expansion and opioid overdose deaths, but they also control for trends in the market for manufactured illicit fentanyl. The authors study Eastern and Western states separately and find that Medicaid expansion is associated with an increase in opioid overdose deaths only in Eastern states and only from synthetic opioids and heroin overdoses (those most likely to be mixed with manufactured fentanyl). Additionally, the authors find no evidence that overdose deaths attributable to natural/semisynthetic opioids or methadone (the types that one would be most likely to gain access to through a prescription via Medicaid eligibility) increased more quickly in Eastern expansion states than in Eastern non-expansion states. They also find no evidence of an association between Medicaid expansion and opioid overdose mortality in the Western states.
The Present Study
A review of the literature suggests that Medicaid expansion is not directly related to an influx in new opioid prescriptions and may help low-income people with OUD to access treatment (Sharp et al., 2019; Swartz & Beltran, 2019; Baicker et al., 2017; Wen et al., 2017; Gertner et al, 2020), but that the relationship between expansion and opioid overdose mortality is nonetheless unclear. Some studies find evidence of a negative relationship (Kravitz-Wirtz et al., 2021), some find that there is no relationship at all (Averett et al., 2019; Ibragimov et al., 2022; Auty & Griffith, 2022), and Abouk et al. (2021) finds that the relationship varies geographically and may be mediated by state-specific conditions such as the illicit fentanyl market. The present study includes a more comprehensive set of occupational, demographic, and policy controls than did previous studies. For example, most research on this topic accounts for the two most popular opioid related laws, Good Samaritan laws and Naloxone Access laws, but does not control for other potentially relevant policies and programs such as:
states’ MAT licensure requirements,
Opioid Treatment Program (OTP) operation regulations,
and additional state training regulations, which could influence the ease with which Medicaid beneficiaries receive treatment.
I control for all of these policy variables in my regressions. Additionally, I add to the literature by investigating potential variation in my relationship of interest according to states’ unemployment rates, workforce composition and racial composition.
IV. Conceptual Framework
Based on my review of the literature, it is unclear whether there is a positive or a negative association (or none at all) between Medicaid expansion and opioid overdose mortality. As discussed above, there are competing theories about the nature of this relationship. For instance, some scholars emphasize the fact that expansion may lead to increased access to opioid prescriptions via Medicaid expansion, which could lead to higher mortality rates (Sharp et al., 2018; Swartz & Beltran, 2019; Baicker et al., 2017). Other studies focus on the increased access to MAT after Medicaid is expanded, which could decrease deaths by overdose (Sharp et al., 2018; Wen et al., 2017; Gertner et al., 2020). The literature also highlights other state-level opioid-related policies that may be related to overdose mortality rates. My model accounts for these policies and for economic conditions and demographic characteristics whose omission might otherwise bias my estimates of interest. These factors are represented graphically in Figure 2 and are described in the sections that follow.
Policy Factors
Independent of whether a state expands its Medicaid program, many states have laws on the books that are meant to increase the likelihood that someone experiencing an overdose receives medical attention in order to prevent death (GAO, 2021; Abouk et al., 2019; Abouk et al., 2021). In addition, all states have licensure requirements for practitioners who administer medication for MAT (Wen et al., 2017; Gertner et al., 2020).
Good Samaritan Laws: Good Samaritan laws are one of two kinds of measures that attempt to increase the likelihood that medical attention is provided to someone who is experiencing a drug overdose by protecting the individual who calls in the medical emergency from drug or other criminal charges. These laws vary by state in terms of the protections offered, but according to a 2021 report by the GAO, 48 states and Washington D.C. currently have some form of a Good Samaritan Law related to opioids on their books.
Naloxone Access Laws: Naloxone Access laws are meant to increase the availability of this drug – which counteracts the effects of opioid overdose – to people who may be in a position to need to administer it (e.g., shelters, first responders, and the family and friends of those who use opioids). Naloxone access laws can authorize providers to prescribe naloxone to anyone who may have need of it, authorize pharmacies to provide naloxone without a prescription, or both. All 50 states and D.C. have Naloxone Access laws in place: 45 use both methods of providing access, five authorize direct access through a pharmacy but not through a provider, and one authorizes access through a provider but not directly through pharmacies (GAO, 2021).
State MAT Licensure Requirements: As discussed above, in order to prescribe buprenorphine to individuals with OUD, qualified practitioners were also required to obtain an X waiver until very recently. Waivers allowed practitioners to treat either 30, 100 or 275 patients at one time for OUD and required varying levels of training and certification (SAMHSA, 2022). Some states still impose additional requirements for licensure for practitioners to provide services or have additional regulations in place for the operation of OTPs beyond what was required by the federal X waiver. The availability of physicians and other waivered practitioners who can prescribe buprenorphine varies by state and may influence whether people are able to receive the treatment to which they theoretically have access through their insurance (Medicaid or otherwise) (Auty & Griffith, 2022; Stein et al., 2015; Dick et al., 2015; Jones et al., 2015; Knudson et al., 2015).
Economic Factors
As previously discussed, Medicaid is an entitlement program that is meant to support more people during times of economic downturn. Lower socioeconomic status is also associated with a higher risk of opioid overdose death (Ghertner & Groves, 2018). Many aggregate-level studies (Averett et al., 2019; Kravitz-Wirtz et al., 2020; Abouk et al., 2020; Gertner et al., 2020; Wen et al., 2017; Auty & Griffith, 2022) account for this factor by controlling for unemployment rate, poverty rate, and income. Additionally, there may be a correlation between opioid overdose deaths and employment in occupations that result in higher rates of musculoskeletal injury and chronic pain – for example, work that involves manual labor or in service occupations (Shaw et al., 2020).
Demographic Factors
The opioid crisis has been shown to be disproportionately concentrated among certain demographic groups (CDC, 2022). White non-Hispanic males are often presented as being the most affected by the opioid crisis (Kravitz-Wirtz et al., 2020; Abouk et al., 2020; Auty & Griffith, 2022; Ibragimov et al., 2022), but studies have shown that opioid overdose death rates have risen significantly in recent years among both American Indian/Alaska Native and Black people, and that American Indian/Alaska Native males had the highest overdose mortality rate in 2020 (Larochelle et al., 2021; CDC, 2022).
V. Data and Methods
My empirical analyses use state-level panel data for all 50 states and the District of Columbia from 2010 to 2019. Data for my key independent variable are drawn from the Kaiser Family Foundation’s Status of State Action on the Medicaid Expansion Decision table (KFF, 2022). The data for my dependent variable are drawn from the CDC Wide-ranging Online Data for Epidemiological Research (WONDER) database. Specifically, I obtain data on opioid overdose mortality rates for the years 2010 through 2019 from the CDC WONDER Multiple Cause of Death (1999-2020) database. My dependent variable accounts for deaths among those who are 64 and under (and who are therefore not eligible for Medicare), in order to focus on the population for whom Medicaid expansion would have the greatest impact, and whose underlying cause of death was coded using any of the following International Classification of Disease, Tenth Revision (ICD-10) codes: X40-X44 (overdose-unintentional); X60-X64 (overdose-suicide); X85 (overdose-homicide); and Y10-Y14 (overdose-undetermined). Among these underlying cause-of-death codes, the type of opioid was identified using the following ICD-10 codes: T40.0 (opium); T40.1 (heroin); T40.2 (natural and semisynthetic opioids); T40.3 (methadone); T40.4 (synthetic opioids other than methadone); and T40.6 (other and unspecified narcotics). My measure of opioid overdose mortality accounts for deaths that are attributed to more than one type of opioid, so these categories are not mutually exclusive.
As discussed in the previous section, I also control for certain state-level demographic characteristics, economic factors and state-specific policies. Data on state population demographics come from the United States Census Bureau’s American Community Survey (ACS) 1-year estimates. These include the percentage of the population that is White, Black, or American Indian/Alaska Native, the percentage of the population that is Hispanic and the percentage of the population that is male. Data on economic factors including the share of the population living below the poverty line, median inflation-adjusted household income (expressed in $2019) and the percentage of the population working in a blue-collar job also come from ACS 1-year estimates. Data on state unemployment rates come from the Bureau of Labor Statistics. Data on opioid-related state policies are taken from the Prescription Drug Abuse Policy System (PDAPS), a database funded by the National Institute on Drug Abuse to track state laws regarding drug abuse and access to treatment. Specifically, I extract data on the following programs and policies: Good Samaritan Overdose Prevention Laws; Naloxone Overdose Prevention Law; and Requirements for Licensure and Operations of Medications for Opioid Use Disorder Treatment.
To investigate the relationship between Medicaid expansion and opioid overdose mortality, I estimate a two-way (state and year) fixed effects regression model. State fixed effects control for state characteristics that are unchanging over time, but that vary by state. For example, cultural norms and social stigma regarding the seeking of treatment for substance use are likely to be relatively unchanging over my period of analysis, but to differ by state. Year fixed effects control for characteristics that change over time but are fixed across states at a given point in time—for example, changes to federal Medicaid policy or the declaration of the opioid crisis as an official public health emergency by HHS. The inclusion of state and year fixed effects in my regression substantially reduces the extent of bias in my coefficients of interests. I therefore estimate the following regression model, with the state-year as the unit of analysis:
OVERDOSEit=0+1MedEx+it+it+it+ t+ i+it,
where i is a state index, t is a year index, it represents my policy control variables, ∝it represents my demographic control variables, it represents my economic control variables,t represents year fixed effects, i represents state fixed effects, it is an error term and 1 is my coefficient of interest. My dependent variable measures the opioid overdose mortality rate per 100,000 population members in a given state in a given year. My analytic sample consists of 508 state-year observations (50 states and D.C. * 10 years for all states except North Dakota, for whom the state-years 2011 and 2012 were dropped). Overdose mortality rate data were not available for North Dakota for the years 2011 and 2012 due to CDC data suppression rules, so those observations were dropped from my dataset. Other than the aforementioned observations, there were no other missing values in my dataset. Definitions for all variables included in the model are provided in Table 1.
VI. Descriptive Statistics
Table 2 presents descriptive statistics for my dependent and key independent variables, as well as demographic, economic and policy control variables. All estimates are weighted by average state population size over the period of analysis. The weighted average opioid overdose mortality rate in my sample is 12.04 per 100,000 population members, although there is substantial variation across state-year observations. The lowest opioid overdose mortality rate was 1.8 per 100,000 people (North Dakota in 2013) and the highest was 55.6 (West Virginia in 2017). During my period of analysis (2010-2019), 36% of state-year observations expanded their Medicaid programs at some point. This corresponds to 34 of 50 states; see Appendix Table A1 for an overview of the timing of states’ expansions.
Table 3 reports descriptive statistics disaggregated according to the Medicaid expansion status of the state-years in my sample. Expansion states are more likely than non-expansion states to have laws on the books that are meant to decrease opioid overdose deaths, which demonstrates the importance of controlling for these factors in my regressions. During the period of analysis, 88% of states that expanded Medicaid had also passed a Good Samaritan law compared to 33% of non-expansion states. Additionally, 95% of expansion states had a naloxone access law on the books compared to 49% of non-expansion states. Despite these policy differences, expansion states have significantly higher opioid overdose mortality rates than non-expansion states (16.42 compared to 9.56 deaths per 100,000 people in a state). However, non-expansion states appear to be worse-off economically, with significantly higher poverty and unemployment rates on average and lower median household incomes than expansion states. Non-expansion states also have higher proportions of workers in blue-collar occupations.
VII. Regression Results
My main regression results are presented in Table 4, and the results of a set of subgroup analyses are reported in Table 5. I report robust standard errors in parentheses beneath each coefficient, and all regressions are weighted by average state population size over my period of analysis. In Table 4, Model (1) is a simple bivariate regression of dependent variable (opioid overdose mortality) on my key independent variable (Medicaid expansion). Model (2) introduces time-varying controls that account for demographic, economic and policy characteristics. Model (3) adds state fixed effects to control for unobserved characteristics that remain fixed over time but vary by state. Finally, Model (4) layers on year fixed effects, which control for unobserved characteristics that change over time but are fixed across states at a given point in time.
In Table 5, I report the results of specifications that build upon model (4) to test whether my relationship of interest varies according to the share of the state’s population that falls into various race categories, state unemployment rates, or the share of the population that works in blue-collar occupations. All of these subgroup analyses are based on initially continuous control variables. I dichotomize each of these moderating controls to indicate whether a state is above or below the within-sample median each year. I then interact the dichotomized moderating variable with my key independent variable.
Main Regression Results
The results of models (1) through (4) suggest a positive and significant relationship between Medicaid expansion and opioid overdose mortality, although the magnitude of this relationship shrinks considerably with the addition of state and year fixed effects. This finding is consistent with the Swartz & Beltran’s (2019) and Abouk et al.’s (2021) findings of a positive and statistically significant relationship between these two variables. Model (1) indicates that Medicaid expansion is associated with an increase of a little more than six and a half deaths per 100k in opioid overdose mortality. In Model (2), which includes time-varying and state-level controls, the Medicaid expansion coefficient remains significant, but its magnitude is reduced by about a third. With the addition of state and year fixed effects in models (3) and (4), this coefficient’s magnitude decreases considerably, but the relationship remains positive and significant. Model (4), which is my fully specified regression and includes all covariates and state and year fixed effects, suggests that Medicaid expansion is associated with an increase of a little less than two deaths per 100k in opioid overdose mortality and is statistically significant. As indicated in Table 2, the weighted average opioid overdose mortality rate is 12.04 deaths per 100k, so a 1.98 deaths per 100k increase represents a somewhat modest, but nonetheless notable, proportional shift relative to the mean (an increase of approximately 16%).
Subgroup Analyses Results
As previously discussed, models (5) through (9) in Table 5 incorporate a set of interaction terms to test whether the relationship between Medicaid expansion and opioid overdose mortality varies according to dichotomized versions of a subset of my covariates. These subgroup analyses are motivated by the prevailing perception in the United States that the opioid crisis is concentrated among White people of relatively higher socioeconomic status (Hansen, 2016).
Race Interactions
In model (5), I interact my Medicaid expansion indicator with a “high Black population” dummy variable. The results of this specification suggest that there is indeed a difference in my relationship of interest between states that have Black populations above versus below the within-sample national median. Specifically, Medicaid expansion is associated with a decrease of about 1.7 deaths per 100k in states with small Black populations; this estimate’s p-value is close to conventional thresholds of statistical significance (p = 0.138). In states with large Black populations, Medicaid expansion is associated with a just under five deaths per 100k increase (-1.705 + 6.414) in opioid overdose mortality. As indicated by the results of the F-test located at the bottom of Table 5, this relationship is statistically significant. These results thus indicate that there is a different relationship between Medicaid expansion and opioid overdose mortality in states with higher versus lower Black populations. This finding is further corroborated by the statistically significant coefficient on my interaction term, which specifically measures the difference between states with high and low populations of Black people.
In model (6), I interact the Medicaid expansion dummy with the “high American Indian and Alaska Native (AIAN) population” indicator. My results again suggest that there is a difference in my relationship according to the racial composition of states’ populations. Specifically, Medicaid expansion is associated with a statistically significant increase in opioid overdose mortality of almost 6 deaths per 100k in states with low AIAN populations but about a 1.6 deaths per 100k decrease (-7.41 + 5.775) in mortality among states with large AIAN populations. The results of the F-test for this model indicate that the latter is just above the cut-off for conventional significance. Further, the Medicaid-High AIAN interaction is statistically significant at all levels.
In model (7), I interact the Medicaid expansion indicator with a “high Hispanic population” dummy variable. The results of this specification provide suggestive, but not definitive, evidence that there is a difference in the relationship between Medicaid expansion and opioid overdose mortality for states that have large Hispanic populations versus small Hispanic populations. Specifically, the association between my dependent and key independent variables is not statistically significant among states that have small Hispanic populations, but in states with large Hispanic populations, Medicaid expansion is associated with a statistically significant increase of just under one death per 100k (-2.963 + 3.867) in opioid overdose mortality. These results would appear to indicate that my relationship of interest varies according to the size of states’ Hispanic populations. However, the coefficient on my interaction term, which specifically measures the difference in this relationship between the two groups of states, is not statistically significant. Taken together, these results provide mildly suggestive evidence that Medicaid expansion may be more closely related to opioid overdose mortality in states with lower Hispanic populations than those with higher Hispanic populations.
Other Interactions
In model (8), I interact my Medicaid expansion variable with a “high unemployment” dummy variable. Again, my results provide suggestive, but not definitive, evidence that there may be a difference in the relationship between Medicaid expansion and opioid overdose mortality according to states’ unemployment levels. Specifically, in states with low unemployment, Medicaid expansion is associated with a marginally significant increase in opioid overdose mortality of about two and a half deaths per 100k. In states with high unemployment, Medicaid expansion is associated with an increase in opioid overdose deaths of a little over 1 death per 100k (-1.269 + 2.634), but this relationship is not statistically significant. These results also appear to indicate that there is a difference in the relationship between Medicaid expansion and opioid overdose deaths in high versus low unemployment states. However, the coefficient on my interaction variable, which measures this difference, is not statistically significant. Overall, these results hint at the possibility that Medicaid expansion may be more closely related to opioid overdose mortality in states with low unemployment than in states with high unemployment.
Finally, in model (11), I interact the Medicaid expansion indicator with a “high blue-collar” dummy. These results also offer suggestive, but not definitive, evidence that there is a difference in the relationship between Medicaid expansion and opioid overdose mortality between states with high versus low proportions of blue-collar workers. Specifically, Medicaid expansion is associated with a marginally significant increase in opioid overdose mortality of a little over one and a half deaths per 100k among states that have a low proportion of blue-collar workers. In states that have a high proportion of blue-collar workers, Medicaid expansion is associated with an increase in opioid overdose deaths of approximately 2.4 per 100k (1.627 + 0.803), but this relationship is not statistically significant. While these results appear to suggest a difference in the relationship between Medicaid expansion and opioid overdose mortality for high-and-low-blue-collar states, we see once again that the coefficient on my interaction term is not statistically significant. Overall, these results thus suggest that there is a limited amount of evidence to suggest that Medicaid expansion may be more closely related to opioid overdose mortality in states with low numbers of blue-collar workers than in states with high numbers of blue-collar workers.
In summary, the results of my fully specified regression suggest that there is a positive relationship between Medicaid expansion and opioid overdose mortality. The results of my subgroup analysis suggest Medicaid expansion may be more closely related to opioid overdose mortality in states with larger Black populations, smaller AIAN populations, smaller Hispanic populations, lower unemployment rates and lower numbers of blue-collar workers. In the following section, I discuss the limitations of my analysis and the implications of these findings for policy and future research.
VIII. Conclusion
My results suggest that there is a moderate, positive, and statistically significant relationship between Medicaid expansion and opioid overdose mortality at the state level. This finding is generally consistent with those of Swartz & Beltran’s (2019) and Abouk et al.’s (2021), although the latter study finds that these two variables are only related in Eastern states.
It is important to note that my results are likely affected by omitted variable bias, as some potentially relevant factors are difficult to measure and were therefore excluded from my regressions. For instance, I do not control for the physician-to-population ratio. I hypothesize that this variable is negatively correlated with opioid overdose deaths, given that easier access to primary care physicians may reduce the incentive to substitute prescription opioids with heroin or other illegal drugs and may increase incentives to seek professional help among those suffering from OUD. However, I suspect that the physician-to-population ratio is positively correlated with Medicaid expansion, as studies tend to find that Medicaid expansion states have more primary care physicians (Saloner et al., 2018) and MAT providers (Meinhofer & Witman, 2018) than non-expansion states. Thus, the omission of this variable is likely exerting downward bias on my estimate of interest. This means that my fully specified regression may understate the magnitude of the positive relationship between Medicaid expansion and opioid overdose mortality.
The above discussion of the physician-to-population ratio highlights another limitation of my study: even if I had access to data on this variable, it would not necessarily be a useful measure for a state-level analysis, given that there is substantial geographic variation in physician-to-population ratio within states (Naylor et al., 2019). Previous studies of opioid overdose mortality use county-level data (Abouk et al., 2021; Ibragimov et al., 2022; Kravitz-Wirtz et al., 2020) to allow for a more fine-grained analysis than I was able to conduct. I was constrained to perform my analysis at the state level because county-level data on opioid overdose deaths were not available due to CDC data suppression rules.
Perhaps the strongest source of bias in my regression stems from the omission of a control for the size of a state’s illicit fentanyl market. The contribution of illicitly manufactured fentanyl to drug overdoses in general—but to opioid overdoses in particular—has increased considerably in the past decade (Jones et al., 2018). However, the relationship between the size of a state’s fentanyl market and its Medicaid expansion status is unknown. Therefore, the sign of any bias resulting from my omission of any control for this factor is also unknown.
My analysis may also be affected by measurement error in my opioid overdose data. The specific drugs that lead to an overdose death are often not listed on death certificates, which leads to an undercount in opioid overdose mortality rates (Rhume, 2018). One study suggests that the true number of annual opioid-involved overdose deaths is between 20 and 35%higher than that suggested by official estimates (Rhume, 2018). Future research should attempt, either through more sophisticated statistical methods or the inclusion of more accurate measures, to correct for this undercount.
The policy implications of this study are a mixed bag. My results could signal the need for a reconsideration of the way that Medicaid policy is being leveraged to combat the opioid crisis on a state-by-state basis. Medicaid expansion may allow for greater access to MAT practitioners and to OUD services, but this increased access may have a limited impact if it does not result in broadly increased use of those services, if they are of low quality, or if those who access these services do not use them properly (e.g., if they fail to complete recommended programs). Thus, additional research is warranted on the efficacy of opioid-related services that are offered to Medicaid beneficiaries. Although X-Waivers are no longer a federal requirement (SAMHSA, 2023), many states still mandate varying levels of additional training for practitioners and impose various regulations on OTPs; these requirements have the potential to act as barriers to treatment. Further research should focus on the ways in which Medicaid policy can be strengthened to improve access to, and the quality of, MAT as a way of combating our growing opioid crisis.
While this thesis found a relationship between Medicaid Expansion and opioid mortality for some populations, this thesis does not conclude that expansion was bad for health outcomes overall. As mentioned, expansion allowed for increased access to opioids, but also aimed to increase access to MAT practitioners and to OUD services. Thus, if states increase focus on these programs, Medicaid expansion could potentially provide better support for improved health outcomes.
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