Impact of Medicaid Expansion on Healthcare Utilization
Impact of Medicaid Expansion on Healthcare Utilization
The Patient Protection and Affordable Care Act (PPACA) came into law in 2010. Originally,
 according to the Act, a state would lose its federal Medicaid funding if it did not expand
 its Medicaid eligibility to include all persons earning below 138% of the federal poverty
 level on January 1, 2014. However, in a Supreme Court Case in 2012 this was ruled as
 unconstitutional and Medicaid expansion in 2014 was made optional. Twenty four states and the
 District of Columbia opted to expand their Medicaid programs on January 1, 2014 and the
 remaining 26 states opting against it. Section 1115 of the Social Security Act allows states
 to alter the federal Medicaid requirements to promote the overall state Medicaid program.
 Among those states that expanded Medicaid Arkansas, Arizona and Iowa adopted approved Section
 115 Waivers to expand their Medicaid programs. The variability in the states' decisions
 regarding Medicaid expansion presented researchers with the opportunity to study the impacts
 of Medicaid expansion on various facets of health care.

 There is a growing body of evidence suggesting that implementation of the coverage expansions
 under the PPACA and Medicaid expansion led to significant decreases in rate of uninsured
 persons, increase in access to health care and improvements in affordability of healthcare.
 Along with improving access and affordability of health care, the PPACA aimed at reducing the
 growth rate of health care expenditures by reducing wasteful use of resources such as
 preventable inpatient and emergency department (ED) visits. According to previous research,
 access to primary care and insurance coverage are significantly and negatively associated
 with experiencing preventable inpatient and ED visits. Historically, racial/ethnic minorities
 have had lower rates of access to primary care and insurance and higher rates of preventable
 inpatient and ED visits which might change with implementation of PPACA. Within states that
 have expanded Medicaid, adopting different methods of expansion may also impact patterns of
 inpatient and ED utilization and disparities in those. In the current political scenario and
 looming uncertainty over the future of PPACA and the possibilities of modifying the PPACA it
 might benefit policy makers to gain knowledge on the early impact of Medicaid expansions and
 different approaches to expanding Medicaid under the PPACA. This study seeks to determine the
 impact of Medicaid expansion and different types of Medicaid expansion on overall and
 preventable inpatient and ED utilization and disparities in those through a three-state
 comparison between Kentucky, Arkansas and Florida.

 Another major reform under PPACA was in the area of substance use disorder treatment. Despite
 the high societal burden exerted by patients with substance use disorders treatment rates
 among them have been low. The most common reasons cited for the poor access to treatment have
 been lack of insurance coverage. The PPACA required all insurance plans sold after January 1,
 2014 to cover substance use disorder treatments. Additionally, plans were required to cover
 screening, brief intervention and referral to treatment for substance use disorders. This
 might potentially lead to changes in treatment rates and sources of payments for substance
 use disorder treatment. Further, the promotion of integration between substance use disorder
 treatment and primary care might lead to increased referrals by healthcare professionals to
 substance use treatment. Thus, in this study we also seek to assess the impact of Medicaid
 expansion on admission to substance use disorder treatment facilities and changes in sources
 of payments and rate of health care referrals to those treatment facilities.
Background The Patient Protection and Affordable Care Act (PPACA) was signed into law by
 President Obama on March 23, 2010. Two of the most contentious clauses under the PPACA were
 penalizing all persons who lacked insurance after January 1, 2014 and expansion of Medicaid
 insurance to all adults with incomes at or below 138% of the federal poverty level. The
 constitutionality of the act came under scrutiny and led to the "National Federation of
 Independent Businesses vs Sebelius" case wherein the Supreme Court upheld most of the
 provisions of the PPACA but decided to give states the option of not expanding Medicaid and
 at same time retain their Federal funding for the program. This Supreme Court decision
 resulted in Medicaid expansion becoming optional for states. As of January 1, 2014 24 states
 and the District of Columbia elected to expand Medicaid coverage as per the PPACA
 requirements. This variation in Medicaid expansion provided natural experiments to
 investigate the impact of expansion decisions.

 Inpatient care accounts for the largest share of national health care expenditures in the
 United States. There are several provisions in the PPACA which encourage coordination of care
 between providers through use of patient centered medical homes and base provider
 reimbursements on patient outcomes as opposed to volume of care delivered. Through such
 reforms the PPACA aimed at reducing wasteful use of resources such as preventable
 hospitalizations. Previous research has shown that inpatient use, specifically preventable
 hospitalizations can be sensitive to coverage gains and can serve as an indicator of access
 to primary care. Thus, assessing how insurance expansion under the PPACA has impacted overall
 inpatient utilization and rate of preventable hospitalizations may provide insight on how
 successful PPACA has been in replacing high cost wasteful services with lower cost primary
 care services. Wherry et al, using data from National Health Interview Survey, determined a
 positive association between the PPACA Medicaid expansion and rate of overnight
 hospitalizations, whereas Sommers et al, using data from a survey fielded in Arkansas,
 Kentucky and Texas found no significant association between overnight hospitalizations and
 Medicaid Expansion. They did not detect any differences in probability of overnight
 hospitalization and method of expansion (Arkansas vs Kentucky). However, their study was
 limited by a small sample size and based on results of a survey. Additionally, due to lack of
 administrative data they were unable to assess outcomes which might be more sensitive to
 increased access to primary care such as preventable hospitalizations. To the best of our
 knowledge only one study conducted thus far, assessed the impact of Medicaid expansion under
 PPACA on overall inpatient utilization and rates of preventable hospitalization. However,
 their study was limited to California, additionally different counties had different levels
 of expansions, with some expanding coverage up to 133% FPL, some up to 200% FPL and some were
 below 100% FPL. None of the studies thus far have determined the impact of different methods
 of Medicaid expansion under the PPACA on rates of all-cause and preventable hospitalizations
 using administrative data. One of the goals of the PPACA was to curb health care costs by
 reducing the volume of care delivered at ED. Certain measures incorporated in the PPACA such
 as increasing the number of insured persons and hence increasing access to care outside the
 ED and integration of health care delivery may contribute to reducing ED utilization. Two
 studies assessing the impact of Medicaid expansion on ED use also analyzed rates of
 ambulatory care sensitive conditions (ACSC), emergent but primary care treatable conditions
 and non-emergent ED visits. However, one study was limited to only the state of Maryland
 lacking a comparison state which did not expand Medicaid and the other was limited to only a
 single for-profit investor owned chain of hospitals in 6 states which expanded Medicaid and
 14 which didn't. Furthermore, none of the studies have assessed how different methods of
 Medicaid expansion under the PPACA impact rates of overall and non-emergent ED use.
 Racial/ethnic disparities in various facets of health care such as insurance coverage and
 overall access to health care is well documented in literature. Combined with the coverage
 provisions, additional reforms made by the PPACA such as, elevating the National Center on
 Minority Health and Health Disparities at the National Institutes of Health from a Center to
 a full Institute, might lead to narrowing of these disparities. The studies that have
 assessed the impact of Medicaid expansion under PPACA on disparities in inpatient use or ED
 use have relied primarily on self-reported data which is subject to cognitive, non-response,
 recall and other biases. Furthermore, granular measures of inpatient or ED use such as
 preventable hospitalizations and preventable ED visits cannot be accurately obtained from
 self-reported data. None of the studies thus far have determined the impact of Medicaid
 expansion or different methods expansions under PPACA on disparities in types of inpatient or
 emergency department utilization.

 Persons with substance use disorders (SUD) have significantly higher health-care costs,
 higher rates of suicide attempts and disabilities compared to the general population thus
 posing a burden to society overall. Despite this, in 2012, nearly 90% of the persons aged 12
 and older who required treatment for SUDs did not receive adequate treatment. The lack of
 insurance or insurance not covering SUD treatment services leading to the patient's inability
 to pay for them are some of the most commonly cited barriers to treatment. The coverage
 expansions under the PPACA have led to nearly 1.6 million Americans suffering from SUDs in
 Medicaid expansion states gaining health insurance coverage. Further, under the PPACA, SUD
 treatment is one of the ten essential health benefits that all plans must offer beginning in
 2014. The PPACA also requires all plans to adhere to the federal Mental Health Parity and
 Addiction Equity Act of 2008 (MHPAEA). Under this act all plans which offer mental health and
 SUD treatment benefits to their beneficiaries must make the benefits no more restrictive than
 medical benefits. The PPACA mandates all plans to cover screening, brief intervention and
 referral to treatment (SBIRT) for SUDs. Along with mandating SBIRT the PPACA also encourages
 Accountable Care Organizations and Patient Centered Medical Homes which might increase
 coordinated care for patients suffering from SUDs. The efforts for increasing care
 coordination and incorporating SBIRT for SUD in primary might translate to increase in number
 of health care referrals for SUD treatment which has historically, predominantly been through
 law enforcement agencies. Only one study has examined the impact of 2014 coverage expansions
 on the eligible adult population (18-64 year olds). The authors used the National Survey on
 Drug Use and Health data and found a significant increase in mental health treatment
 utilization without any significant changes in treatment of SUDs. However, they did not draw
 comparisons between states that expanded Medicaid vs states that did not. The study also did
 not examine changes in sources of payment for the SUD treatment following Medicaid expansion.

 Arkansas, Kentucky and Florida responses. Arkansas was the first state to secure an approval
 for its "Private Option" demonstration project to implement the Medicaid expansion under the
 PPACA. Arkansas adopted a premium assistance strategy, which involved using federal funds to
 provide individual commercial health insurance for all the newly Medicaid eligible persons
 earning up to 138% of the FPL by placing them into one of the federally qualified health
 plans. On May 9, 2013 Governor Beshear declared that Kentucky would go ahead with Medicaid
 expansion as proposed under the PPACA. Kentucky decided to carry out the Medicaid expansion
 by placing the newly eligible persons in this pre-existing managed Medicaid program. Despite
 the state's vehement opposition to the PPACA, Governor Rick Scott expressed support for a
 "limited Medicaid expansion" through a federally funded and privately administered managed
 care plan. However, Florida opted against the Medicaid expansion. This resulted in almost
 764,000 individuals not having any affordable coverage options by virtue of not being
 eligible for Medicaid coverage or Marketplace subsidies.

 Comparing inpatient and emergency room utilization between these three states will highlight
 the impact Medicaid expansion and different approaches to Medicaid expansion might have on
 health service utilization. Further, using national level data, comparing the change in rate
 of SUD treatment admissions after Medicaid expansion in states that expanded Medicaid vs
 states that did not will determine the impact of Medicaid expansion on SUD treatment
 utilization.

 Objectives and Specific Aims

 1. To determine the impact of Medicaid expansion and type of Medicaid expansion (purchase
 commercial insurance vs traditional Medicaid expansion) on all cause inpatient
 utilization and preventable hospitalizations and if effect of Medicaid expansion on
 inpatient utilization differs by race/ethnicity.

 1. Using a differences in differences approach we will compare the rate of all-cause
 hospitalizations and preventable hospitalizations for adults aged 19-64 between
 2013 and 2014 in the two states that expanded Medicaid vs Florida. A sub-group
 analysis comparing Arkansas and Kentucky will reveal if different approaches to
 expansion influenced inpatient utilization.

 2. Using a differences in differences in differences approach we will determine
 whether the impact of Medicaid expansion on inpatient utilization for adults aged
 19-64 differed by racial/ethnic groups (Hispanics, Non-Hispanic Whites and
 Non-Hispanic Blacks) between the three states.

 2. To determine the impact of Medicaid expansion and type of Medicaid expansion on ED
 utilization and non-emergent ED use and if the effect of Medicaid expansion on ED
 utilization differs by race/ethnicity.

 1. Using a differences in differences approach we will compare the rate of ED visits
 and non-emergent ED visits for adults aged 19-64 between 2013 and 2014 in the two
 states that expanded Medicaid vs Florida. A sub-group analysis comparing Arkansas
 and Kentucky will reveal if different approaches to expansion influenced ED
 utilization.

 2. Using a differences in differences in differences approach we will determine
 whether the impact of Medicaid expansion on emergency department utilization, for
 adults aged 19-64, differed by racial/ethnic sub-groups (Hispanics, Non-Hispanic
 Whites and Non-Hispanic Blacks) between the three states.

 3. To determine the impact of Medicaid expansion on the rate of SUD treatment admissions in
 facilities receiving some public support.

 1. Using a differences in differences in differences approach we will compare the rate
 of SUD treatment admissions from 2010-2014 for persons aged 18-54 vs those aged
 12-17 in the states that expanded Medicaid in 2014 vs states that did not expand
 Medicaid in 2014.

 2. Using a differences in differences in differences approach we will compare the
 changes in sources of payment for the SUD treatment admissions from 2010-2014 for
 persons aged 18-54 vs those aged 12-17 in the states that expanded Medicaid in 2014
 vs states that did not expand Medicaid in 2014.

 3. Using a differences in differences in differences approach we will compare the rate
 of health care referral for the SUD treatment admissions from 2010-2014 for persons
 aged 18-54 vs those aged 12-17 in the states that expanded Medicaid in 2014 vs
 states that did not expand Medicaid in 2014.

 Methods Impact of Private Option, Traditional Medicaid Expansion and Opting Against Medicaid
 Expansion on Inpatient Utilization: A Three State Comparison The investigators will use a
 longitudinal cross-sectional quasi-experimental difference in difference study design to
 assess the impact of Medicaid expansion and method of expansion on our inpatient utilization
 measures. We will determine the change in rates of all-cause and preventable hospitalization
 from 2013 - 2014 and contrast the change between states based on expansion/method of
 expansion status. The investigators will use a longitudinal cross-sectional
 quasi-experimental difference in difference in difference study design to assess the impact
 of Medicaid expansion and method of expansion on racial/ethnic disparities on our inpatient
 utilization measures. The investigators will determine the change in rates of all-cause and
 preventable hospitalization from 2013 - 2014 for each race/ethnicity and contrast the change
 between states based on expansion/method of expansion status.

 Data CDC - Wonder Bridged-Race Population Estimates The national center for health statistics
 (NCHS) combines 31 race categories used in the Census to four race categories: Asian or
 Pacific Islander, Black or African American, American Indian or Alaska Native, White and
 provides age, gender and ethnicity (Hispanic/Non-Hispanic) specific population counts for
 each county of the United States. These files are available for public use on CDC Wonder
 website. We will use these files to obtain the total population counts that will serve as
 denominators for our utilization metrics for each county of Arkansas, Florida and Kentucky
 for 2013 and 2014.

 HCUP SID For our inpatient utilization measures, the investigators will acquire the
 Healthcare Cost and Utilization Project (HCUP) State Inpatient Database (SID) from Arkansas,
 Kentucky and Florida respectively. The SIDs capture the particular state's hospital inpatient
 discharge records for the given year and contain them in a uniform format to allow
 inter-state comparisons. The unit of analysis in the dataset is a hospital discharge. In
 addition to information each hospitalization such as length of stay, charges, admit and
 discharge dates, the SIDs also contain clinical information such as the diagnosis recorded
 and procedures performed during each hospitalization and demographics of the hospitalized
 patient such as birth date, zip code of residence, gender, race, ethnicity, payer type etc.

 Analysis The investigators will depict the rates of outcomes for each state be quarter and
 determine if there is any change in trends between 2013 and 2014 or if there is an immediate
 change between 2013 and 2014 by using interrupted time series analysis. The investigators
 will calculate the crude rates of all-cause and preventable hospitalization for each state
 for each year. Using the US 2014 population as the reference population we will carry out
 direct standardization to compute the age and sex adjusted rates of all-cause and preventable
 hospitalizations for each state for each year. The investigators will also carry out direct
 standardization for race/ethnicity subgroups. Next, we will perform multivariate regression
 analysis to estimate the excess change in inpatient utilization due to Medicaid expansion.
 The investigators will use a Poisson distribution for our outcome variables. The population
 at risk will be the total population count of each the cohort as obtained from NCHS data.
 Standard errors will be clustered at the emergency management region level to account for
 nesting of each cohort within a emergency management region. The model will include emergency
 management region level fixed effects, to account for time invariant emergency management
 region level factors, dummies for age-group, gender race/ethnicity, and year. The
 investigators will create indicators for period (2013 and 2014) and expansion status
 (expanded Medicaid or not). The investigators will interact period indicator with expansion
 status indicator to determine the excess change in the outcome measures for states which
 expanded Medicaid (AR and KY) vs state that did not expand Medicaid in 2014 (FL). The same
 model specification will be used to determine the impact of method of Medicaid expansion on
 rates of all-cause and preventable hospitalizations. For this analysis, the investigators
 will compare the 2013 and 2014 data from Arkansas and Kentucky. To assess if the impact of
 Medicaid expansion on our outcome measures differed by racial/ethnic groups in the three
 states we will use a multivariate difference in difference in difference model. The data will
 be restricted to Non-Hispanic White, Hispanic and Non-Hispanic Black cohorts. In addition to
 the terms in the prior models, we will include interactions between race/ethnicity and state
 indicators to control for baseline disparities in the outcome in the states and interactions
 between race/ethnicity and time to control for longitudinal trends in disparities overall.
 The indicators for Non-Hispanic Blacks and Hispanics will be interacted with period
 indicators and state indicators. The coefficients of these interactions will provide us with
 the excess change in the outcome measures between Hispanic vs Non-Hispanic Whites and
 Non-Hispanic Blacks vs Non-Hispanic Whites in states which expanded Medicaid (AR and KY) vs
 state that did not expand Medicaid in 2014 (Referent = FL).

 Sensitivity Analysis The investigators will use a negative binomial distribution for our
 outcome measures in place of a Poisson distribution. In the second sensitivity analysis, for
 our measure of all-cause hospitalizations, the investigators will exclude all maternal
 discharges. In a third sensitivity analysis, the investigators will include only
 hospitalizations for adults aged 27-64 to exclude all young adults who might have experienced
 coverage gains prior to 2014 due to the "young-adult mandate". In a fourth sensitivity
 analysis, the investigators will include only those hospitalizations that took place in
 community hospitals. In a fifth sensitivity analysis, the investigators will collapse the
 racial/ethnic groups and define each cohort based on year, state, emergency management
 region, age-group and sex. The investigators will do this in order to incorporate those
 admissions in the count of the outcome measures where the patient's race/ethnicity is
 missing. Sixth, instead of grouping the data annually the investigators will group it by
 quarter and perform a segmented regression analysis. Lastly, the investigators will calculate
 all the outcome variables as rates per 10,000 persons and use weighted ordinary least squares
 regression analysis to determine if our interpretations change.

 Impact of Private Option, Traditional Medicaid Expansion and Opting Against Medicaid
 Expansion on Emergency Department Utilization: A Three State Comparison The investigators
 will use a longitudinal cross-sectional quasi-experimental difference in difference study
 design to assess the impact of Medicaid expansion and method of expansion on the inpatient
 utilization measures. The investigators will determine the change in rates of overall and
 types of ED visits from 2013 - 2014 and contrast the change between states based on
 expansion/method of expansion status. The investigators will use a longitudinal
 cross-sectional quasi-experimental difference in difference in difference study design to
 assess the impact of Medicaid expansion and method of expansion on racial/ethnic disparities
 on our ED utilization measures. The investigators will determine the change in rates of
 overall and types of ED visits from 2013 - 2014 for each race/ethnicity and contrast the
 change between states based on expansion/method of expansion status.

 Data CDC - Wonder Bridged-Race Population Estimates HCUP SEDD For our outcome measures the
 investigators will acquire the Healthcare Cost and Utilization Project (HCUP) State Emergency
 Department Database (SEDD) for Arkansas, Kentucky and Florida respectively. The SEDDs capture
 all the ED visits for the particular year which take place in EDs that are affiliated with
 hospitals and that do not result in hospitalizations. In addition to information on each ED
 visit such as charges and admit dates, the SEDDs also contain clinical information such as
 the diagnosis recorded and procedures performed during each visit and demographics of the
 patient such as birth date, zip code of residence, gender, race, ethnicity, payer type etc.

 Outcome Measures

 1. ED visit.

 2. Type of ED visit:

 1. Preventable/Avoidable.

 2. Emergent. Analytic data structure Similar to previous aim, only difference would be
 in terms of the outcomes. Analysis Similar to previous aim, only difference would
 be in terms of the outcomes. Sensitivity Analysis The investigators will use a
 negative binomial distribution for our outcome measures in place of a Poisson
 distribution. In a second sensitivity analysis, we will include ED visits for
 adults aged 27-64 to exclude all young adults who might have experienced coverage
 gains prior to 2014 due to the "young-adult mandate". In a third sensitivity
 analysis the investigators will change our definition of preventable visits and
 emergent visits. The investigators will define a preventable visit as any visit
 whose probability of being preventable is ≥ 0.5 and any visit whose probability of
 being emergent is ≥ 0.5 will be defined as such. In a fourth sensitivity analysis
 the investigators will include only those ED visits that took place in community
 hospitals. In a fifth sensitivity analysis we will collapse the racial/ethnic
 groups and define each cohort based on year, state, emergency management region,
 age-group and sex. The investigators will do this in order to incorporate those ED
 visits in the count of the outcome measures where the patient's race/ethnicity is
 missing. Sixth, instead of grouping the data annually we will group it by quarter
 and perform a interrupted time series analysis. Seventh, the probability of ED
 visit being preventable will be defined as sum of probability of ED visit being
 non-emergent and emergent but primary care treatable and the probability of ED
 visit being emergent will be defined as sum of probability of ED visit being
 emergent and ED care needed. Lastly, the investigators will calculate all the
 outcome variables as rates per 10,000 persons and use weighted ordinary least
 squares regression analysis to determine if our interpretations change.

 Early Impact of Medicaid Expansion on Public Sector Substance Abuse Treatment: Evidence from
 the Affordable Care Act We will use a longitudinal cross-sectional quasi-experimental
 difference in difference in difference study design. We will determine the changes in rates
 of admissions to SUD treatment facilities, changes in the sources of payment and changes in
 the rate of health care referrals from 2010 - 2014 for persons aged 18-54 vs 12-17 and
 compare the changes between them based on states' Medicaid expansion status.

 Data Treatment Episode Data Set - Admissions (TEDS-A) TEDS-A is a nationwide administrative
 database of admissions to specialty SUD treatment facilities. It consists of facilities that
 receive some public source of funding for providing SUD treatment. Some of the states collect
 data only on admissions that are publically funded whereas others also collect privately
 funded admissions from facilities that receive some public funding. TEDS-A includes data on
 the demographics of the patient including age-group, gender, race/ethnicity, state of
 residence, substances abused, type of facility, payment source for the admission for a
 sub-group of states and source of referral to the treatment. It covers nearly 80% of SUD
 treatment admissions in the US.

 Current Population Survey (CPS) The CPS reports monthly information on population
 demographics including age, race/ethnicity, gender, state of residence etc. We will use this
 data to get information on total population counts for our cohorts developed on the basis of
 age-group (12-14, 15-17, 18-29, 30-39, 40-54), gender, race/ethnicity (Non-Hispanic White,
 Hispanic, Non-Hispanic Blacks, Others), state and year and state-level covariates. The
 investigators will exclude New Hampshire and Michigan from our data to avoid ambiguity since
 these states expanded later in 2014. To assess impact of Medicaid expansion on changes in
 payment source for SUD admissions the investigators will include only those states which
 provide primary payer information in the TEDS files for at least 85% of the admissions in
 each of the years.

 Outcome Measures

 1. Treatment admissions.

 2. Source of payment

 1. Treatment admissions privately funded.

 2. Treatment admissions funded by Medicaid.

 3. Treatment admissions self-funded.

 4. Treatment admissions that are free/ or funded by other government sources.

 3. Treatment admissions through a health care source of referral Analytic Data Structure We
 will sum all the outcome measures from the TEDS-A data by state, year, age, gender, and
 race/ethnicity. The investigators will ink the CPS data and the aggregate level TEDS
 file files. The investigators will gather the covariate information from the CPS data at
 the state-year level which will give us our final analytic dataset to assess the impact
 of Medicaid expansion on SUD treatment in publically funded facilities.

 Analysis The investigators will compare the demographics of the admissions and covariates
 between states that expanded Medicaid vs states that did not expand Medicaid for each year of
 the study period. The investigators will conduct t-tests/chi-square to determine significant
 differences for continuous and categorical variables respectively. The investigators will sum
 up all the outcome measures for each state and plot them year wise on separate graphs to
 visually examine the trends. The investigators will perform multivariate regression analysis
 to estimate the excess change in our outcome measures attributable to Medicaid expansion. The
 investigators will use a Poisson distribution for outcome variables. The population at-risk
 will be the population count of each cohort. The model will include state level fixed
 effects, to account for time invariant state level factors, and dummies for age-group,
 gender, period indicator (pre-expansion vs post-expansion), race/ethnicity, state-year level
 covariates (marital status, educational attainment, and proportion unemployed), and time
 varying state-policies (eg. legalizing medical marijuana). The investigators will create
 indicators for period before and after Medicaid expansion (2014 and 2010-2013) and expansion
 status of state (expanded Medicaid or not) and an indicator for treatment group (12-17 vs
 18-54). The investigators will interact period indicator with expansion status indicator to
 control for overall trend in outcome in states that expanded vs states that did not expand.
 The investigators will interact the treatment indicator with period indicator to control for
 overall trend in outcome among treatment group (18-54 year olds) vs non-treatment group
 (12-17 year olds). The investigators will also control for baseline differences in the
 outcome among treatment group (18-54 year olds) vs non-treatment group (12-17 year olds) in
 states that expanded vs states that did not expand by interacting expansion status with
 treatment group indicator. The model will include a difference in difference in difference
 indicator to assess the effect of Medicaid Expansion on the outcome measure among treatment
 group (18-54 year olds) vs non-treatment group (12-17 year olds). The difference in
 difference in difference indicator will be specified by interacting the expansion, treatment
 and period indicators.

 Sensitivity Analysis The investigators will conduct a number of sensitivity analyses to test
 the robustness of our model and study subject selection criteria. First, The investigators
 will use a negative binomial distribution for the outcome counts instead of Poisson
 distribution. Second, The investigators will exclude persons aged 18-29 to eliminate the
 population that had an increase in access to insurance in 2010 due to the "young adult
 mandate". The age-group that was impacted by the mandate was 18-26. However, due to the
 availability of age-groups in the TEDS-A data (18-20, 21-25 and 26-29) The investigators
 decided to include the entire 18-29 age-group. Third, consistent with previous literature The
 investigators will exclude admissions only for detoxification since these are considered to
 be forerunners for treatments rather than treatment itself. Fourth, The investigators will
 determine the impact of Medicaid expansion only on rate of first time admission. Finally, The
 investigators will conduct a weighted least squares regression for each of our outcome
 measures. Here the weights will be the population counts for each cohort. The outcomes will
 be as follows: a. rate of treatment admissions, privately funded admissions, Medicaid funded
 admissions, self-funded admissions, admissions payed for by charity or other government
 organizations, admissions through health care referrals per 10,000 persons.

 Limitations Proposed studies on the impact of Medicaid expansion on inpatient and ED
 utilization have several limitations. The investigators analyze data from only three states,
 hence our findings might not be generalizable to other states of the US. Although Florida is
 a southern state, it is significantly different in terms of demographic composition from
 Arkansas and Kentucky. However, since the rate of uninsured in the three states was similar
 prior to Medicaid expansion we believe that it would serve as a reasonable control.
 Additionally, The investigators do not have access to data from other southern states which
 did not expand Medicaid in 2014 and which might be better controls such as Texas, Tennessee,
 Mississippi, Louisiana or Georgia. The investigators have access to only one year of
 post-reform data hence The investigators might not be able to capture the full impact of the
 health care reform. Since The investigators have only one year of pre-reform data we might
 not be able to fully account for differences in trends of the outcomes pre-reform in our
 three states. Our third study is subject to limitations as well. TEDS-A data does not include
 all the substance abuse treatment centers. Thus, The investigators may potentially miss some
 of the admissions. Our findings might be biased by showing higher rates of admissions in
 states have greater public funding by virtue of them contributing more data. Further since
 the funding may fluctuate annually, The investigators cannot ascertain whether the changes in
 treatment rates are a function of the fluctuations in reporting or due to Medicaid expansion.
 The investigators mitigate this limitation to some extent by including the 12-17 age group as
 a control group which will not be impacted by the Medicaid expansion but if there are changes
 in reporting rates for states The investigators expect them to have equal effect on 12-17
 years olds as on 18-54 year olds. Not all states provide information on source of funding,
 thus limiting the generalizability of our findings about the changes in source of payment.
 However, previous research has shown no differences in states that provide data on sources of
 funding vs states that do not.
Inclusion Criteria:

 -  Age 19-64 for aims 1 and 2

 -  Age 12-54 for aim 3

 Exclusion Criteria:

 -  None