State-Level Outcomes of the Affordable Care Act (2010-2015)

Coverage, Employer Provisions, Medicare Benefits and Medicaid Changes

By Gabriela Swider [source]

About this dataset

Welcome to the ACA State Data, a comprehensive set of metrics that offer invaluable insights into how the 2010 Patient Protection and Affordable Care Act (Obamacare) has impacted the American healthcare system. With this dataset, you can be sure to land on factual conclusions and leave no stone unturned when it comes to tracking any aspect of health insurance coverage.

This wide-ranging dataset provides a detailed snapshot of each state's impact from Obamacare, including coverage gains, employer coverage, individual market coverage, Medicaid expansion and Medicare savings. Analyze data points such as Uninsured Rate (2010 & 2015), Percentage Point Decrease in Uninsured Rate (2010-2015), People Gaining Coverage (2010-2015%), Lifetime Limit Pre-ACA (2008-10), premium savings compared pre/post ACA rates; all charted by state to help draw out patterns in changes before/after Obamacare took effect.

Ever since its inception more than five years ago, there has been an enduring debate about whether or not Obamacare should be repealed; let's use this dataset to delve deeper into these outcomes with the facts and figures found here! Join us down the rabbit hole as we dig into all that this truly unique dataset has on offer – will you find evidence that confirms your opinion on Obama Care's successes or failures?

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How to use the dataset

This tutorial will guide you through how to make use of this dataset.

Orienting Yourself

The first step is familiarizing yourself with the structure of this dataset. It contains metrics related to coverage gains, employer coverage, individual market coverage, Medicaid, and Medicare for all 50 US states across a 5 year period (2010-2015). For each metric there will be an associated data type e.g. percentage points decreased in uninsured rate or people with employer coverage (integer). Different metrics also contain different years - while some are solely limited to 2015 others are tracking from 2010 onwards such as percentage point decreases in uninsured rate or number who gained coverage over that period respectively. The last column describes more about each metric so it’s helpful when analyzing data for clarity and understanding contextually what information is being measured by each metric column title listed below:

* Uninsured Rate (2010 %)    
* Uninsured Rate (2015 %)     
* Percentage Point Decrease in Uninsured Rate (2010 percentages)     
* People Gaining Coverage between 2010 - 2015 (%)   
* People With Employer Coverage                                    
* Gained Coverage By Staying On Parents' Plan Until Age 26                          

  Total With Lifetime Limit on Health Benefits Pre - ACA %                  

    Children With Lifetime Limit On Benefits Pre-ACA         

      Adult Males With Lifetime Limit On Benefits Pre_ACA      

        Adult Females With Lifetime Limit On Benefits Pre - ACA           

          Total w/ PriV COV No Cost Sharing For Prev Services Between 2013      & 2015

             Male W/ Private Cov No Cost Sharing For Prev Services 2013      & 2015       

               Female W/ Private Cov No Cost Sharing For Prev Services        Between 2013     & 2015      AvG Annual GRowth In FamPremiums For Emp   Cov 2000             - 10    AVG Annual Growth In FamPremiumsForEmp     Cov 2010           A Family Em Prem Savings CompCont GROwthPreAcA     RATE 20 1 5

Research Ideas

• Comparing the insurance coverage gains of different states in response to the ACA at a state-level, such as changes in uninsured rate and employer sponsored coverage.
• Examining how the premiums for employer sponsored health insurance have changed over time, pre and post ACA, at a state-level.
• Analyzing lifetime limits on healthcare services prior to the ACA (2008-2010) which could be used to compare access to care for children vs adults in states across the country

Acknowledgements

If you use this dataset in your research, please credit the original authors. [Data So...

This map shows where people have Medicaid or means-tested healthcare coverage in the US (ages under 65). This is shown by State, County, and Census Tract, and uses the most current ACS 5-year estimates.

2008-2024. American Lung Association. Cessation Coverage. Medicaid data compiled by the Centers for Disease Control and Prevention’s Office on Smoking and Health were obtained from the State Tobacco Cessation Coverage Database, developed and administered by the American Lung Association. Data from 2008-2012 are reported on an annual basis; beginning in 2013 data are reported on a quarterly basis. Data include state-level information on Medicaid coverage of approved medications by the Food and Drug Administration (FDA) for tobacco cessation treatment; types of counseling recommended by the Public Health Service (PHS) and barriers to accessing cessation treatment. Note: Section 2502 of the Patient Protection and Affordable Care Act requires all state Medicaid programs to cover all FDA-approved tobacco cessation medications as of January 1, 2014. However, states are currently in the process of modifying their coverage to come into compliance with this requirement. Data in the STATE System on Medicaid coverage of tobacco cessation medications reflect evidence of coverage that is found in documentable sources, and may not yet reflect medications covered under this requirement.

From the Centers for Medicare & Medicaid Services: Hospital price transparency helps Americans know the cost of a hospital item or service before receiving it. Starting January 1, 2021, each hospital operating in the United States will be required to provide clear, accessible pricing information online about the items and services they provide ... This information will make it easier for consumers to shop and compare prices across hospitals and estimate the cost of care before going to the hospital.

Although most hospitals comply with the letter of the law, it's still hard to compare costs across providers or even see the difference between charges to insurers vs cash payers. Alex Stein from DoltHub organized an effort to assemble over 300M rows from over 1800 hospitals. This dataset is the result.

The U.S. Department of Health and Human Services (HHS) via the Health Resources and Services Administration (HRSA) is releasing American Rescue Plan payments to providers and suppliers who have served rural Medicaid, Children's Health Insurance Program (CHIP), and Medicare beneficiaries from January 1, 2019 through September 30, 2020. The dataset will be updated as additional payments are released. Data does not reflect recipients’ attestation status, returned payments, or unclaimed funds.

Content

The Affordable Care Act created the new Pre-Existing Condition Insurance Plan (PCIP) program to make health insurance available to Americans denied coverage by private insurance companies because of a pre-existing condition. Coverage for people living with such conditions as diabetes, asthma, cancer, and HIV/AIDS has often been priced out of the reach of most Americans who buy their own insurance, and this has resulted in a lack of coverage for millions. The temporary program covers a broad range of health benefits and is designed as a bridge for people with pre-existing conditions who cannot obtain health insurance coverage in today’s private insurance market. To learn more, visit PCIP.gov or HealthCare.gov.

Note: * Massachusetts and Vermont are guarantee issue states that have already implemented many of the broader market reforms included in the Affordable Care Act that take effect in 2014. Existing commercial plans offering guaranteed coverage at premiums comparable to PCIP are already available in both states.

Context

This is a dataset hosted by the Centers for Medicare & Medicaid Services (CMS). The organization has an open data platform found here and they update their information according the amount of data that is brought in. Explore CMS's Data using Kaggle and all of the data sources available through the CMS organization page!

• Update Frequency: This dataset is updated daily.

Acknowledgements

This dataset is maintained using Socrata's API and Kaggle's API. Socrata has assisted countless organizations with hosting their open data and has been an integral part of the process of bringing more data to the public.

Cover photo by Lily Banse on Unsplash
Unsplash Images are distributed under a unique Unsplash License.

In the U.S., every hospital that receives payments from Medicare and Medicaid is mandated to provide quality data to The Centers for Medicare and Medicaid Services (CMS) annually. This data helps gauge patient satisfaction levels across the country. While overall hospital scores can be influenced by the quality of customer services, there may also be variations in satisfaction based on the type of hospital or its location.

Year: 2016 - 2020

The Star Rating Program, implemented by The Centers for Medicare & Medicaid Services (CMS), employs a five-star grading system to evaluate the experiences of Medicare beneficiaries with their respective health plans and the overall healthcare system. Health plans receive scores ranging from 1 to 5 stars, with 5 stars denoting the highest quality.

Benefits:

Historical Analysis: With data spanning from 2016 to 2020, researchers and analysts can observe trends over time, understanding how patient satisfaction has evolved over these years.

Benchmarking: Hospitals can compare their performance against national averages or against peer institutions to see where they stand.

Identifying Areas for Improvement: By analyzing specific metrics and feedback, hospitals can pinpoint areas where their services may be lacking and need enhancement.

Policy and Decision Making: Governments and healthcare administrators can use the data to make informed decisions about healthcare policies, funding allocations, and other strategic decisions.

Research and Academic Purposes: Academics and researchers can use the dataset for various studies, including correlational studies, predictions, and more.

Geographical Insights: The dataset may provide insights into regional variations in patient satisfaction, helping to identify areas or states with particularly high or low scores.

Understanding Factors Affecting Satisfaction: By correlating satisfaction scores with other variables (e.g., hospital type, size, location), it might be possible to determine which factors play the most significant role in patient satisfaction.

Performance Evaluation: Hospitals can use the data to evaluate the efficacy of any interventions or changes they've made over the years in terms of improving patient satisfaction.

Enhancing Patient Trust: Demonstrating transparency and a commitment to improvement can enhance patient trust and loyalty.

Informed Patients: By making such data publicly available, potential patients can make more informed decisions about where to seek care based on the satisfaction ratings of previous patients.

Source: https://data.cms.gov/provider-data/archived-data/hospitals

ImportanceThe Affordable Care Act (ACA) has expanded access to health insurance for millions of Americans, but the impact of Medicaid expansion on healthcare delivery and utilization remains uncertain.ObjectiveTo determine the early impact of the Medicaid expansion component of ACA on hospital and ED utilization in California, a state that implemented the Medicaid expansion component of ACA and Florida, a state that did not.DesignAnalyze all ED encounters and hospitalizations in California and Florida from 2009 to 2014 and evaluate trends by payer and diagnostic category. Data were collected from State Inpatient Databases, State Emergency Department Databases and the California Office of Statewide Health Planning and Development.SettingHospital and ED encounters.ParticipantsPopulation-based study of California and Florida state residents.ExposureImplementation of Medicaid expansion component of ACA in California in 2014.Main outcomes or measuresChanges in ED visits and hospitalizations by payer, percentage of patients hospitalized after an ED encounter, top diagnostic categories for ED and hospital encounters.ResultsIn California, Medicaid ED visits increased 33% after Medicaid expansion implementation and self-pay visits decreased by 25% compared with a 5.7% increase in the rate of Medicaid patient ED visits and a 5.1% decrease in rate of self-pay patient visits in Florida. In addition, California experienced a 15.4% increase in Medicaid inpatient stays and a 25% decrease in self pay stays. Trends in the percentage of patients admitted to the hospital from the ED were notable; a 5.4% decrease in hospital admissions originating from the ED in California, and a 2.1% decrease in Florida from 2013 to 2014.Conclusions and relevanceWe observed a significant shift in payer for ED visits and hospitalizations after Medicaid expansion in California without a significant change in top diagnoses or overall rate of these ED visits and hospitalizations. There appears to be a shift in reimbursement burden from patients and hospitals to the government without a dramatic shift in patterns of ED or hospital utilization.

Key Table Information.Table Title.Allocation of Medicaid/Means-Tested Public Coverage.Table ID.ACSDT1Y2024.B992707.Survey/Program.American Community Survey.Year.2024.Dataset.ACS 1-Year Estimates Detailed Tables.Source.U.S. Census Bureau, 2024 American Community Survey, 1-Year Estimates.Dataset Universe.The dataset universe of the American Community Survey (ACS) is the U.S. resident population and housing. For more information about ACS residence rules, see the ACS Design and Methodology Report. Note that each table describes the specific universe of interest for that set of estimates..Methodology.Unit(s) of Observation.American Community Survey (ACS) data are collected from individuals living in housing units and group quarters, and about housing units whether occupied or vacant. For more information about ACS sampling and data collection, see the ACS Design and Methodology Report..Geography Coverage.ACS data generally reflect the geographic boundaries of legal and statistical areas as of January 1 of the estimate year. For more information, see Geography Boundaries by Year.Estimates of urban and rural populations, housing units, and characteristics reflect boundaries of urban areas defined based on 2020 Census data. As a result, data for urban and rural areas from the ACS do not necessarily reflect the results of ongoing urbanization..Sampling.The ACS consists of two separate samples: housing unit addresses and group quarters facilities. Independent housing unit address samples are selected for each county or county-equivalent in the U.S. and Puerto Rico, with sampling rates depending on a measure of size for the area. For more information on sampling in the ACS, see the Accuracy of the Data document..Confidentiality.The Census Bureau has modified or suppressed some estimates in ACS data products to protect respondents' confidentiality. Title 13 United States Code, Section 9, prohibits the Census Bureau from publishing results in which an individual's data can be identified. For more information on confidentiality protection in the ACS, see the Accuracy of the Data document..Technical Documentation/Methodology.Information about the American Community Survey (ACS) can be found on the ACS website. Supporting documentation including code lists, subject definitions, data accuracy, and statistical testing, and a full list of ACS tables and table shells (without estimates) can be found on the Technical Documentation section of the ACS website.Sample size and data quality measures (including coverage rates, allocation rates, and response rates) can be found on the American Community Survey website in the Methodology section.Data are based on a sample and are subject to sampling variability. The degree of uncertainty for an estimate arising from sampling variability is represented through the use of a margin of error. The value shown here is the 90 percent margin of error. The margin of error can be interpreted roughly as providing a 90 percent probability that the interval defined by the estimate minus the margin of error and the estimate plus the margin of error (the lower and upper confidence bounds) contains the true value. In addition to sampling variability, the ACS estimates are subject to nonsampling error (for a discussion of nonsampling variability, see ACS Technical Documentation). The effect of nonsampling error is not represented in these tables.Users must consider potential differences in geographic boundaries, questionnaire content or coding, or other methodological issues when comparing ACS data from different years. Statistically significant differences shown in ACS Comparison Profiles, or in data users' own analysis, may be the result of these differences and thus might not necessarily reflect changes to the social, economic, housing, or demographic characteristics being compared. For more information, see Comparing ACS Data..Weights.ACS estimates are obtained from a raking ratio estimation procedure that results in the assignment of two sets of weights: a weight to each sample person record and a weight to each sample housing unit record. Estimates of person characteristics are based on the person weight. Estimates of family, household, and housing unit characteristics are based on the housing unit weight. For any given geographic area, a characteristic total is estimated by summing the weights assigned to the persons, households, families or housing units possessing the characteristic in the geographic area. For more information on weighting and estimation in the ACS, see the Accuracy of the Data document.Although the American Community Survey (ACS) produces population, demographic and housing unit estimates, the decennial census is the official source of population totals for April 1st of each decennial year. In between censuses, the Census Bureau's Population Estimates Program produces and disseminates the official estimates of the population for the nation, states, counties, cities, and...

description:

Medically Unlikely Edits (MUEs) define for each HCPCS / CPT code the maximum units of service (UOS) that a provider would report under most circumstances for a single beneficiary on a single date of service.

Practitioner services also refers to ambulatory surgical centers.
DME refers to provider claims for durable medical equipment.

The CMS National Correct Coding Initiative (NCCI) promotes national correct coding methodologies and reduces improper coding which may result in inappropriate payments of Medicare Part B claims and Medicaid claims. NCCI procedure-to-procedure (PTP) edits define pairs of Healthcare Common Procedure Coding System (HCPCS)/Current Procedural Terminology (CPT) codes that should not be reported together for a variety of reasons. The purpose of the PTP edits is to prevent improper payments when incorrect code combinations are reported. The edits in this dataset are active for the dates indicated within. This file should NOT be used by state Medicaid programs as their edit file. Current Procedural Terminology (CPT) codes, descriptions and other data only are copyright 2017 American Medical Association. All rights reserved. CPT is a registered trademark of the American Medical Association. Applicable FARSDFARS Restrictions Apply to Government Use. Fee schedules, relative value units, conversion factors and/or related components are not assigned by the AMA, are not part of CPT, and the AMA is not recommending their use. The AMA does not directly or indirectly practice medicine or dispense medical services. The AMA assumes no liability for the data contained or not contained herein.

Medically Unlikely Edits (MUEs) define for each HCPCS / CPT code the maximum units of service (UOS) that a provider would report under most circumstances for a single beneficiary on a single date of service.

Practitioner services also refers to ambulatory surgical centers.
DME refers to provider claims for durable medical equipment.

The CMS National Correct Coding Initiative (NCCI) promotes national correct coding methodologies and reduces improper coding which may result in inappropriate payments of Medicare Part B claims and Medicaid claims. NCCI procedure-to-procedure (PTP) edits define pairs of Healthcare Common Procedure Coding System (HCPCS)/Current Procedural Terminology (CPT) codes that should not be reported together for a variety of reasons. The purpose of the PTP edits is to prevent improper payments when incorrect code combinations are reported. The edits in this dataset are active for the dates indicated within. This file should NOT be used by state Medicaid programs as their edit file. Current Procedural Terminology (CPT) codes, descriptions and other data only are copyright 2017 American Medical Association. All rights reserved. CPT is a registered trademark of the American Medical Association. Applicable FARSDFARS Restrictions Apply to Government Use. Fee schedules, relative value units, conversion factors and/or related components are not assigned by the AMA, are not part of CPT, and the AMA is not recommending their use. The AMA does not directly or indirectly practice medicine or dispense medical services. The AMA assumes no liability for the data contained or not contained herein.

Effective Jan. 1, 2026. Copyright Notice: Current Dental Terminology © 2025 American Dental Association. All rights reserved.

Where applicable, please refer to Oregon Administrative Rules, Prioritized List placement and Guideline Notes listed for each code for complete information regarding benefit coverage and limitations.

Dental/Denturist Services Rules (Chapter 410, Division 123): https://secure.sos.state.or.us/oard/displayDivisionRules.action?selectedDivision=1711

Early and Periodic Screening, Diagnostic and Treatment (EPSDT) Rules (Chapter 410, Division 151): https://secure.sos.state.or.us/oard/displayDivisionRules.action?selectedDivision=8155

Prioritized List and Guideline Notes: https://www.oregon.gov/oha/hpa/dsi-herc/Pages/index.aspx

For medical-surgical services, refer to the code groups at https://www.oregon.gov/oha/HSD/OHP/Pages/Policy-Medical-Surgical.aspx.

To find fee-for-service reimbursement rates, view the OHP Fee-for-Service Fee Schedule at http://www.oregon.gov/oha/hsd/ohp/pages/fee-schedule.aspx. This schedule represents a given point in time and may not include payable codes that were added to MMIS after the posted fee schedule date.

TennCare is the American state of Tennessee’s Medicaid program that provides health care for over 1.6 million Tennesseens. TennCare covers approximately 20 percent of the state’s population, 50 percent of the state’s births, and 50 percent of the state’s children. TennCare is one of the oldest Medicaid-managed care programs in the country, having begun on January 1, 1994. In the interest of promoting research into public health and improving outcomes of care, TennCare may make deidentified or identifiable records available for research purposes.

This indicator provides information about medically underserved areas and/or populations (MUA/Ps), as determined by the federal Health Resources and Services Administration (HRSA). Each designated area includes multiple census tracts.State Primary Care Offices submit applications to HRSA to designate specific areas within counties as MUA/Ps. The MUA/P designation is made using the Index of Medical Underservice (IMU) score, which includes four components: provider per 1,000 population, percent of population under poverty, percent of population ages 65 years and older, and infant mortality rate. The IMU scores ranges from 0-100. Lower scores indicate higher needs. An IMU score of 62 or below qualifies for designation as an MUA/P. Note: if an area is not designated as an MUA/P, it does not mean it is not underserved, only that an application has not been filed for the area and that official designation has not been given.The MUAs within Los Angeles County consist of groups of urban census tracts (namely service areas). MUPs have a shortage of primary care health services for a specific population within a geographic area. These populations may face economic, cultural, or language barriers to health care, such as: people experiencing homelessness, people who are low-income, people who are eligible for Medicaid, Native Americans, or migrant farm workers. All the MUPs that have been designated within Los Angeles County are among low-income populations of selected census tract groups. Due to the nature of the designation process, a census tract may be designated as both an MUA and an MUP and as multiple MUAs. MUA/P designations help establish health maintenance organizations or community health centers in high-need areas.For more information about the Community Health Profiles Data Initiative, please see the initiative homepage.

In comparison to vaginal deliveries, caesarean (C-section) deliveries have been linked to poorer health outcomes for both mother and infant; nevertheless, the rate of C-section deliveries continues to rise worldwide (Keag et al., 2018; Negrini et al., 2021). Using the Natality Data (2020) from the US National Center for Health Statistics (NCHS) (N=3,619,826), this study analyzes structural inequalities and systemic racism that exist within ubiquitous social institutions, such as hospitals (Bonilla-Silva, 1997; Meyer & Rowan, 1977). We estimate multinomial logistical regression models to predict differences in risk of C-section deliveries across racialized and non-racialized mothers, as well as for immigrant women. We also investigate the effect of source of payment for the birth, both as a signal of socioeconomic status of the mother and cost to the hospital.

This data set uses the following exclusion criteria: breech/transverse births, non-hospital births, "other" methods of payment. "Other" method of payment refers to any payment method used for the delivery other than private insurance, Medicaid, or self-pay. Moreover, any records missing data on race of mother were also excluded.

This dataset includes the primary language of newly Medi-Cal eligible individuals who identified their primary language as English, Spanish, Vietnamese, Mandarin, Cantonese, Arabic, Other Non-English, Armenian, Russian, Farsi, Korean, Tagalog, Other Chinese Languages, Hmong, Cambodian, Portuguese, Lao, French, Thai, Japanese, Samoan, Other Sign Language, American Sign Language (ASL), Turkish, Ilacano, Mien, Italian, Hebrew, and Polish, by reporting period. The primary language data is from the Medi-Cal Eligibility Data System (MEDS) and includes eligible individuals without prior Medi-Cal eligibility. This dataset is part of the public reporting requirements set forth in California Welfare and Institutions Code 14102.5.

BackgroundHealth literacy experts and the American Medical Association have developed recommended communication techniques for healthcare providers given that effective communication has been shown to greatly improve health outcomes. The purpose of this study was to determine the number and types of communication techniques routinely used by Maryland physicians.MethodsIn 2010, a 30-item survey was mailed to a random sample of 1,472 Maryland family physicians and pediatricians, with 294 surveys being returned and usable. The survey contained questions about provider and practice characteristics, and 17 items related to communication techniques, including seven basic communication techniques. Physicians’ use of recommended communication techniques was analyzed using descriptive statistics, analysis of variance, and ordinary least squares regression.ResultsFamily physicians routinely used an average of 6.6 of the 17 total techniques and 3.3 of the seven basic techniques, whereas pediatricians routinely used 6.4 and 3.2 techniques, respectively. The use of simple language was the only technique that nearly all physicians routinely utilized (Family physicians, 91%; Pediatricians, 93%). Physicians who had taken a communications course used significantly more techniques than those who had not. Physicians with a low percentage of patients on Medicaid were significantly less likely to use the recommended communication techniques compared to those providers who had high proportion of their patient population on Medicaid.ConclusionsOverall, the use of recommended communication techniques was low. Additionally, many physicians were unsure of the effectiveness of several of the recommended techniques, which could suggest that physicians are unaware of valuable skills that could enhance their communication. The findings of this study suggest that communications training should be given a higher priority in the medical training process in the United States.

Demographic Characteristics of Patient Subgroups with the most predictive sequences.