VISIT STATE HEALTH COMPARE
Publication
Factors in Financial Burdens Related to Health Care for Californians
Though robust Affordable Care Act (ACA) implementation efforts in California have resulted in larger numbers of residents with health care coverage, Californians are now facing coverage-related issues regarding rising costs of care and increasing financial burdens as a result of lack of affordable health care.
In this brief prepared for the California Health Care Foundation (CHCF), SHADAC researchers Colin Planalp, MPA, and Lacey Hartman, MPP, use a multitude of measures from the California Health Insurance Study (CHIS) to measure affordability of health care coverage in California in 2018.
Foundations of Analysis
As found in previous surveys, a number of Californians have reported spending large portions of income on medical bills, with corresponding ramifications such as trouble paying off medical bills, credit card debts, and affording basic necessities such as food and housing. Other consequences, such as skipping or delaying care have also been reported by recent surveys.1
In order to better understand these affordability issues, SHADAC used 2018 data from the California Health Insurance Study (CHIS), the latest year of available data, to perform an analysis of the following cost-of-care measures:
- Health insurance deductibles
- Trouble paying medical bills - Difficulty paying for basic necessities among those reporting trouble paying medical bills
- Medical bill-related credit card debt
SHADAC examined statistically significant changes over time—between 2013, the last year before ACA implementation in 2014, as well as between 2017 and 2018—and between certain demographic groups and the overall state population.
Key Findings
Deductibles
In 2018, 31% of individuals in California reported a deductible of more than $2,000, which was roughly double the rate of 16% in 2013—a statistically significant difference.2 Significant increases in the proportion of California residents with a $2,000 deductible also occurred in key subgroups, including all income levels (up to 138% of FPG, 139% to 249% of FPG, 250% to 399% of FPG, and more than 400% of FPG); Asian, Latin(x), and White Californians; individuals living in both urban and rural areas of the state; and Californians with employer-sponsored insurance (ESI).
Trouble Paying Medical Bills
In 2018, 10% of Californians reported having trouble paying medical bills. Compared to this state average, subgroups reporting a higher percentage of trouble paying medical bills included: individuals with an income level at 139%-249% of Federal Poverty Guidelines (FPG); individuals reporting as multiracial; and uninsured individuals and those with Medicare coverage
Medical Bill-related Burdens
Of those reporting trouble paying medical bills in 2018, 44% said they used credit card debt in order to pay their medical bills. Of that population using credit cards, 35% of people incurring debt of $4,000 or more. Across key subpopulations, those reporting using credit card debt to pay medicals bills at a significantly higher percentage than the statewide average included: individuals with an income level 250% to 399% FPG; White Californians; and those with individual-market coverage. Additionally, the report found a statewide average of 32% of individuals who reported trouble paying medical bills also said those bills caused difficulties affording basic necessities, such as housing and food.
Looking ahead
Though this brief was written before both the arrival of the COVID-19 pandemic and the release of 2019 employer-sponsored insurance (ESI) measures, it is important to note that trends of increasing costs of care and medical bill-related trouble and financial burden found in 2018 are being echoed in data for 2019.
For instance, SHADAC’s recently released annual employer-sponsored insurance report found that Californians experienced significant increases in premiums from 2018 to 2019. Single-coverage premiums rose by $397 from $6,229 to $6,939, and family-coverage premiums rose by $1,221 from $19,567 to $20,788—both of which mark continued concerns around health care affordability for Californians even before the arrival of COVID-19 and our understanding of how that public health crisis will affect these same issues.
Notes
1 Ben-Porath, E., Hachey, E., Sutton, J., & Su, J. (2020). Health Care Priorities and Experiences of California Residents: Findings from the California Health Policy Survey. Retrieved from https://www.chcf.org/wp-content/uploads/2020/02/HealthPolicySurvey2020.pdf
2 “High deductibles” are defined as $1,300 for individual or $2,600 for family-coverage ESI.
Blog & News
New Brief Explores State Approaches to Risk Adjustment Based on Social Factors and Strategies for Filling Data Gaps
August 2020:
The role that social factors, such as housing instability, food insecurity, and access to transportation, have in influencing health has been a growing focus in health policy and health care. However, the concept of “social determinants of health” (SDOH), the idea that individuals have unique circumstances that can affect their health care outcomes, is not new to health care. In fact, the concept of “social determinants of health” (SDOH)—the idea that individual’s social circumstances can affect their health—is being used in some states to augment existing health care risk-adjustment tools, which are often used in setting payment rates or calculating performance on quality measures.
A new State Health & Value Strategies (SHVS) brief, authored by SHADAC researchers, details three approaches taken by organizations in two states, Minnesota and Massachusetts, to solving a key challenge to risk adjusting based on social factors: Where to find data on social risk factors? The brief’s profiled examples employed three approaches to filling the data gap—using survey data, using claims and administrative data, and using a combination of both.
Minnesota Medicaid ACO
In 2018, Minnesota launched an update to the state Medicaid program’s accountable care organization (ACO) model, known as “Integrated Health Partnerships” (IHPs). Among other changes, the updated IHP program introduced a new population-based payment that is now adjusted based on the social risk factors (in addition to medical conditions) of IHP program beneficiaries. The rationale for the change was to support activities typically not reimbursed by Medicaid, recognizing that some beneficiaries have greater risk factors—including social risk factors.
To create its new risk-adjustment methodology, Minnesota leaned on earlier research conducted by state Medicaid staff that had used claims and other administrative data to study the relationship between social risk factors and health outcomes. Informed by that research, the state developed a risk-adjustment approach that used beneficiary-level claims and administrative records that would adjust payments based on records of individual beneficiaries’ own social risk factors, such as an income level that would be considered as “deep poverty” (based on Medicaid eligibility data) and homelessness (based on self-reported information or analysis of beneficiary addresses).
Minnesota Community Measurement
Another entity in the same state, Minnesota Community Measurement (MNCM), is a non-profit organization that collects, analyzes, and publishes health care quality and cost data. MNCM undertook an effort to incorporate social risk factors into its risk-adjustment approach based on recommendations from the organization’s board of directors and another stakeholder committee.
Unlike Medicaid agencies, MNCM does not have access to detailed data on individual patients beyond basic demographics. However, because health care providers report ZIP codes for individual patients to MNCM, they were able to develop a geographically based index measure drawn from the U.S. Census Bureau’s American Community Survey (ACS), using the social risk factors associated with the ZIP codes where individual patients live as a proxy for their own social risk factors. Although it is not a Medicaid agency, the approach used by MNCM to risk adjust based on social risk factors could be readily duplicated by a state Medicaid agency because it uses publically available data.
Massachusetts Medicaid ACO and MCO
In Massachusetts, the state’s Medicaid program has incorporated measures of beneficiaries’ social risk factors into its methodology for risk adjusting payments to Medicaid Managed Care Organizations (MCOs) since 2016 and Accountable Care Organizations (ACOs) since 2018. Part of the state’s rationale for accounting for social risk factors in its payments to MCOs and ACOs was to “mitigate the incentive (they) might otherwise have to limit care or avoid members with greater health care needs,” recognizing that individuals with social risk factors may face additional needs and greater challenges to optimal health outcomes.1,2
Unlike the prior examples, Massachusetts has developed a hybrid approach using both administrative and survey data. Similar to Minnesota Medicaid, Massachusetts uses various elements of Medicaid claims data and administrative data tied to individual beneficiaries from within the Medicaid agency and other departments, such as data on disability status and housing instability. And similar to MNCM, Massachusetts created a geographically based index of other social risk factors using data from the ACS. By using those dual approaches, Massachusetts was able to fill more gaps in social risk factor data than either single data source was able to address on its own.
Massachusetts also took an initial step toward collecting new data on individual beneficiaries’ own social risk factors. The state began using ICD-10 Z-codes for homelessness as part of its methodology for risk adjusting based on social factors. Although Z-codes for homelessness already existed, using them as part of the state’s risk adjustment methodology—which informs payments to health care providers—gives providers an incentive for reliably screening for and documenting whether individual patients are experiencing homelessness.
Key takeaways
A key challenge to risk adjusting based on social factors is obtaining data regarding patients’ relevant social risk factors. To address that limitation, the three examples profiled in this brief highlight possible approaches to filling those gaps that other states’ Medicaid programs could reproduce or build from:
- Using survey data as a proxy for individuals’ own social risk factors;
- Repurposing existing administrative data pertaining to social risk factors;
- And taking initial steps toward collecting data on social risk factors, such as through systematic use of billing Z-codes.
1 Seifert, R.W., & Love, K.A. (2018). What to know about ACOs: An Introduction to MassHealth Accountable Care Organizations. Retrieved from https://www.bluecrossmafoundation.org/sites/default/files/download/publication/ACO_Primer_July2018_Final.pdf
2 Massachusetts’ revised risk adjustment methodology also includes some measures that are not necessarily social risk factors, such as interaction measures specific to children—ages 0-14 and 15-20—between age and medical risk scores, to account for the particular health care costs for children with complex medical situations compared to healthy children.
Publication
SHADAC Article in Journal of Aging & Social Policy Urges States to Use COVID-19 Flexible Medicaid Authority for LTSS Eligibility
In response to the current public health emergency presented by COVID-19, especially the health risks pertaining to low-income older adults and disabled persons, states have been given new authority with regard to Medicaid in order to ease traditional complications and restrictions surrounding eligibility. A new article from SHADAC Director and UMN School of Public Health Professor Lynn A. Blewett, PhD, and SHADAC Research Fellow Robert Hest, MPP, focuses specifically on how this state-level Medicaid program flexibility, along with recent emergency waivers, can expand Medicaid financial eligibility for long-term supports and services (LTSS) for these at-risk individuals.
Traditionally, Medicaid LTSS eligibility criteria for states (though federal standards are also a key component) have been based on financial rules and functional needs assessments. Due to complexities surrounding these eligibility requirements, many beneficiaries are at risk of losing coverage throughout the year. Under public health emergency authority granted to states during the COVID-19 pandemic, however, mechanisms such as state plan amendments (SPAs), section 1115 and 1135 waivers, and 1915(c) Appendix K can be used by states to ease these difficulties and ensure that eligible individuals get coverage, including:
- Reducing administrative burdens for applicants
- Streamlining eligibility redeterminations
- Extending deadlines to conduct evaluations/assessments
- Temporarily suspending authorization requirements
- Relaxing eligibility requirements
While states are adopting these flexible measures to expand eligibility, they are simultaneously facing increasing pressures to curb state spending as budgets are constrained during the pandemic. Medicaid spending, most especially for LTSS, is a prime target for cuts as it accounts for a large majority of states’ budgets. However, the article argues that LTSS provided by Medicaid is an essential service for low-income older adults and disabled individuals who are at particular risk from COVID-19, and therefore it is critical that eligibility and flexibility be maintained in order to meet the increasing demand for services created by the coronavirus.
Read the full article in the Journal of Aging & Social Policy.
Blog & News
An Updated Brief Examines State Health Compare Estimates on State Funding for Public Health
May 13, 2020:
As states have urgently strategized responses to the sudden arrival of the initial wave of the coronavirus pandemic and begun to prepare for the possibility of a second wave in the fall, states’ per-capita public health funding is a metric that has garnered new attention. Estimates for this measure not only reveal a state’s prioritization of investment in public health infrastructure, but also levels of preparedness for health emergencies such as COVID-19. With this understanding, a number of media outlets have used the public health spending rankings chart provided by SHADAC’s State Health Compare to publish articles which highlight the challenges that states on the lower end of the public health spending spectrum now face in attempting to respond to the COVID-19 crisis.
In light of this information, and with the release of the latest estimates for public health spending for 2019, SHADAC has just recently updated our brief on estimates of state-provided funding for the State Health Compare measure, “Public Health Funding.” Though this updated brief highlights state public health funding data from 2015–2019, data is available on the State Health Compare website for every year, starting in 2005 (with the exception of data year 2006 for which no estimates are available).
The Big Picture: Wide Variation between States
There is a wide gap between state public health funding among states, with 2019 state-provided funding ranging from $7 per capita in Missouri to $363 per capita in the District of Columbia (D.C.). In 2019, the median state, Kentucky spent $35 per capita in public health funding.
Per Person Public Health Funding, 2019
Top States for State Public Health Funding
Eight states occupied the top five funding spots over the course of five years, from 2015 through 2019 (the most recent five years for which data are available): Alaska, D.C., Hawaii, Idaho, New Mexico, New York, North Dakota, and West Virginia. Alaska, D.C., and Hawaii were among the top five states for each of the five years. In 2019, the top five states for per-capita state public health funding were Alaska, D.C., Hawaii, Idaho, and New Mexico.
Bottom States for Public Health Funding
Eight states also occupied the bottom five funding spots at various times over the five-year period (2015 through 2019). Arizona, Indiana, Kansas, Mississippi, Missouri, Nevada, North Carolina, and Ohio moved among the bottom five positions for state-provided public health funding during this time period. Both Missouri and Nevada consistently ranked among the bottom five states for each year of this period. In 2019, the bottom five states with regard to public health spending were: Kansas, Missouri, Nevada, North Carolina, and Ohio.
About the Estimates
Estimates of state funding for public health come from data collected by Trust for America's Health (TFAH), which obtains its calculations using budget documents that are publicly available through state government websites. Depending on which information is available, TFAH uses executive budget documents listing actual expenditures, estimated expenditures, or final appropriates; appropriations bills enacted by the state's legislature; and documents from legislative analysis offices to calculate public health spending. TFAH defines "public health" broadly to include most state-level health funding with the exception of Medicaid, CHIP, or comparable health coverage programs. SHADAC standardizes these estimates of state public health funding to the estimated population of each state to create per-capita estimates.
Why the Variation among States on this Measure?
According to Trust for America's Health, comparisons of public health funding levels across states are difficult because every state allocates and reports its budget in different ways, and states vary widely in the budget details they provide. Non-methodological sources of inter-state variation in state public health funding may also include the relative performance of individual state economies (since state public health funding is often cut during economic downturns) as well as the relative tax bases of individual states along with state population counts.
Explore Additional Public Health Data at State Health Compare
Visit State Health Compare to explore national and state-level estimates for the following public health indicators:
- Weight Assessment in Schools
- School Nutrition Standards Stronger than USDA
- School Required to Provide Physical Activity
- Smoke Free Campuses
- Cigarette Tax Rates
State Health Compare also features a number of other indicator categories, including: health insurance coverage, cost of care, access to and utilization of care, care quality, health behaviors, health outcomes, and social determinants of health.
Blog & News
Research Assistant
SHADAC Resource on Required Evaluation Plans for DSRIP/Delivery System Reforms through 1115 Waivers
October 14, 2019:Introduction
SHADAC researchers have extensive experience providing technical assistance to states on payment and delivery system reform, often supporting the evaluation and monitoring of these activities. Recently, we have been working with a subset of states pursuing innovative policies under the Medicaid 1115 waiver opportunity, which requires a rigorous independent evaluation. To support peer learning around the evaluation of delivery and payment reform, SHADAC has reviewed publicly available evaluation plans related to one waiver type, the Delivery System Reform Incentive Payment (DSRIP) programs, and created a resource summarizing state evaluation approaches.
Delivery System Reform Incentive Payment Programs[1]
Delivery System Reform Incentive Payment (DSRIP) programs are a subset of Medicaid 1115 waiver initiatives that aim to improve a state’s healthcare delivery system through performance-based payments to hospital systems and providers. The Centers for Medicare and Medicaid Services (CMS) approved the first DSRIP waiver in 2010.[2] At their core, DSRIP waivers provide incentive payments to hospitals and other providers to support various projects aimed at improving care quality and patient outcomes and reducing cost. To participate in the program, healthcare organizations pick from a menu of projects (crafted by CMS in collaboration with the state) to implement and evaluate within their system. Payments are made to providers based on milestones that are met and quality improvement achievements. Under this construct, CMS and states assume that the value added by systems, infrastructure, and payment reforms will outweigh the costs of implementation and incentive payment.
DSRIP Evaluation Activities and Requirements
There are both federal-led and state-led evaluations of DSRIP programs. At the federal level, CMS has contracted with Mathematica Policy Research to produce meta-analyses, case studies, and implementation reports for many 1115 waiver strategies, including specific evaluations of DSRIP programs.[3] Notably, CMS published two separate but related analyses of the implementation of DSRIP progress and outcome measurement and of key DSRIP demonstration design strategies to reward performance, as well as an Interim Evaluation Report of existing DSRIP demonstrations. Most recently, CMS published its approach to the final outcomes evaluation for DSRIP programs approaching conclusion.[4] In addition, CMS has developed state-level independent evaluation guidelines for states pursuing a variety eligibility, coverage, and delivery system reform waivers. States, including those with DSRIP waivers, are required to adhere to these evaluation guidelines and report regularly on both their monitoring and evaluation efforts.
SHADAC’s Catalogue of DSRIP Evaluation Plans
With CMS’s heightened expectations and improved guidance for program evaluation, SHADAC felt that reviewing state evaluations of DSRIP programs could serve as a useful learning opportunity for states interested in delivery reform or other systems change efforts.[5] We have summarized these activities in a resource that includes program characteristics as well as evaluation design characteristics being used in the seven states for which evaluation plans or reports are publicly available. Evaluation design characteristics include:
- Program Implementation Timeframe
- State and Federal Funding
- Timeline for Evaluation Deliverables
- Evaluation Characteristics (Budget, contractor, logic model)
- Evaluation Methods (Qualitative Analysis, Randomized Controlled Trial, etc.)
- Evaluation Comparison Group Levels (Beneficiary, Program, Provider, Project, etc.)
- Beneficiary Comparison Groups
- Secondary Data Sources
- Primary and Qualitative Data Sources
States can use this resource in the following ways:
- Search for programs implementing similar reforms and learn more about evaluation approaches.
- Identify whether there are any available evaluation reports.
- Learn independent contractor budgets.
- Determine whether states are proposing control groups in the event that a state is interested in reaching out to learn more details about the success of the approach.
- Determine whether states are proposing randomized control trials.
SHADAC used the following approach to selecting states for inclusion in the catalogue. We cross-referenced MACPAC’s summary of existing DSRIP or DSRIP-like waivers[6] with CMS’s federal evaluator’s list of DSRIP programs. We also confirmed that these states were in the Kaiser Family Foundation’s Medicaid Waiver Tracker.[7] To investigate how states are evaluating their DSRIP initiatives, we looked for evaluation designs published by the DSRIP states of interest on both CMS’s state waiver repository and state government websites. According to federal guidance, evaluation plans should be submitted to CMS within 180 days of waiver approval, and posted publicly within 30 days of evaluation plan approval. Of the DSRIP and DSRIP-like states, we identified seven states with publicly accessible and approved evaluation designs—California, Massachusetts, New Hampshire, New York, Oregon, Texas, and Washington. The work of three states (MA, CA, and TX) pre-dates the implementation of the Affordable Care Act in 2014.
| SHADAC Resource on Required Evaluation Plans for DSRIP / Delivery System Reforms Through 1115 Waivers | |||||||
|---|---|---|---|---|---|---|---|
| CALIFORNIA | MASSACHUSETTS | NEW HAMPSHIRE | NEW YORK | OREGON | TEXAS | WASHINGTON | |
| Program | Public Hospital Redesign Incentives in Medi-Cal (PRIME) |
Delivery System Reform Incentive Payment | Building Capacity for Transformation | Medicaid Redesign Team Waiver Amendment* | Oregon Health Plan (formerly Hospital Transformation Performance) Program | Healthcare Transformation and Quality Improvement Program** | Transformation through Accountable Communities of Health |
| Program Implementation | 2010-2015 (DSRIP); 2016-2020 (PRIME) |
2011-2017 (DSTI); 2017-2022 6th waiver extension (DSRIP) |
2016-2020 | 2014-2019 | 2014-2018 (HTPP, DSRIP-like); 2017-2022 | 2011-2017 (DSRIP); 2017-2022 (DSRIP renewal & final funding) | 2017-2021 |
| State and Federal Funding | $7.46 billion (PRIME) | $1.80 billion (DSRIP) | $150 million | $12.84 billion | $600 million | $10.83 billion (renewal) | $1.13 billion |
| Program Description | Performance-based funding made available to PRIME entities, which include California's Designated Public Hospitals and Municipal Public Hospitals for implementation of projects in three domains (outpatient delivery system reform, targeted high risk or high cost, and resource utilization efficiency) and demonstration of improved outcomes, such as clinical event outcomes, potentially preventable events, and patient experience measures. | Supports the implementation of three ACO models, the formation and payment of Behavioral Health and Long Term Services and Supports Community Partners and Community Service Agencies, and statewide infrastructure, workforce capacity, and operations to enable successful reform implementation. | Performance-based funding made available to seven regionally based Integrated Delivery Networks (IDNs), which are required to implement six integrated physical and behavioral health care projects to improve coordination and linkages, reduce gaps, and meet needs of beneficiaries with behavioral health disorders. | Formation of 25 Performing Provider Systems (PPS) offering targeted, integrated care, which is incentivized through value-based payments. | Extension of Oregon's performance incentive program for Medicaid coordinated care organizations (CCOs) allowing diagnostic-related group (DRG) hospitals (i.e., larger, urban hospitals) to earn incentive payments for reporting and improving their performance on select quality measures. | Three components of the waiver, one of which relates to delivery system reform (two other components include Medicaid Managed Care expansion and Uncompensated Care Pool). Delivery system reform calls for performance-based incentive payments made available to provider members of Regional Healthcare Partnerships for implementation of projects and demonstration of increased access to, coordination and quality care and improved outcomes. | Performance-based funding made available to nine regionally-based Accountable Communities of Health (ACHs), which are self-governing organizations with multiple partners responsible for managing and coordinating evidence-based projects to transform care delivery and improve health. |
| CMS Evaluation Deliverable Timeline - Planned | |||||||
| Evaluation Design Approved | 8/23/2017 | 1/31/2019 | 9/5/2017 | 3/13/2018 | 2/14/2018 | 8/2/2018 | 10/26/2017 |
| Interim Evaluation Report Due | 3/30/2019 | 6/30/2021 | 8/21/2019 | 6/30/2019 | 6/30/2021 | 3/28/2022 | 8/1/2021 |
| Summative Evaluation Report Due | 12/31/2021 | 12/31/2023 | 2/23/2022 | 3/28/2021 | 12/31/2023 | 9/18/2024 | 5/30/2022 |
| CMS Evaluation Report Available | X | ||||||
| Other State Monitoring or Evaluation Reports Available | Final Evaluation - Bridge to Reform, DSRIP | 2018 Statewide Annual Report for the NEW YORK STATE Delivery System Reform Incentive Payment (DSRIP) Program | Oregon Hospital Transformation Performance Program Year 4 Performance | Previous DSRIP Cycle - Final Evaluation- Healthcare Transformation and Quality Improvement | |||
| Evaluation Characteristics | |||||||
| Logic Model | X | X | X | X | |||
| Evaluation Contractor | UCLA Center for Health Policy Research | University of Maryland Medical System | TBD | Research Foundation of the State University of New York (SUNY), Institute for Health System Evaluation | Oregon Health and Science University, Center for Health Systems Effectiveness | Texas University | TBD |
| Evaluation Subcontractor | University at Albany, SUNY; Boston University School of Public Health; University of Maryland School of Public Health | Providence Center for Outcomes Research and Education | |||||
| Budget | $2.2 million | $6 million | $2 million | Not publicly available | $650,000 | $6 million (all components) | Over $4 million |
| Evaluation Methods | |||||||
| Qualitative analysis | X | X | X | X | X | X | X |
| Randomized, controlled trial | |||||||
| Quasi-experimental (e.g., difference in differences, hierarchical linear models, or interrupted time series) | X | X | X | X | X | X | |
| Descriptive statistics | X | X | X | X | X | X | |
| Cost effectiveness/ROI | X | X | |||||
| Social network analysis | X | ||||||
| Evaluation Comparisons | |||||||
| Beneficiary level | X | X | X | X | X | X | |
| Program/provider or network/project level | X | X | X | X | X | ||
| Subgroups | X | X | X | ||||
| Beneficiary-level Comparison Groups | |||||||
| Propensity score or exact or distance matched | X | X | Planned | X | |||
| Pre-intervention or baseline group | X | X | X | Planned | X | X | |
| Beneficiaries or providers not participating in program | X | X | |||||
| Beneficiaries with no BH disorders | X | ||||||
| Secondary Data Sources | |||||||
| Medicaid administrative data, including claims, eligibility, enrollment, demonstration monitoring data | X | X | X | X | X | X | X |
| Medicare administrative data | X | ||||||
| All-payer claims data | X | X | X | ||||
| Other non-Medicaid or Medicare program data (e.g., mental health, substance use, social or human services, emergency department data) | X | X | X | X | |||
| Non-claim discharge data (e.g., NH psychiatric hospital, MA safety net hospital and uncompensated care cost reports) | X | X | X | ||||
| Medical records | X | X | X | ||||
| Vital records | X | X | X | ||||
| Census data (e.g., ACS, CPS) | X | X | X | ||||
| Registry data | X | X | |||||
| EDIE | X | ||||||
| HEDIS | X | X | |||||
| CAHPS | X | X | X | ||||
| BRFFS | X | X | |||||
| Other (e.g., WA Pregnancy Risk Assessment Monitoring System, MA opioid overdose data) | X | X | |||||
| Primary and Qualitative Data Sources | |||||||
| Document review | X | X | X | X | X | X | X |
| Literature review | X | ||||||
| Beneficiary surveys | X | X | X | X | X | ||
| Provider surveys | X | X | X | X | X | X | |
| Stakeholder interviews | X | X | X | X | X | X | X |
| Stakeholder focus groups | X | X | X | ||||
| Site visits/case studies | X | ||||||
| *Information from state is from the evaluation report, NOT the evaluation design plan. **Evaluation plans relate to the delivery system component of the waiver only. |
|||||||
If you have feedback on the usefulness of this catalogue, or have an interest in seeing a catalogue like this for other program types, please contact SHADAC at shadac@umn.edu.
[1] Gates, A., Rudowitz, R., & Guyer, J. (2014, September 29). An Overview of Delivery System Reform Incentive Payment (DSRIP) Waivers [PDF file]. Retrieved from https://www.kff.org/medicaid/issue-brief/an-overview-of-delivery-system-reform-incentive-payment-waivers/
[2] Medicaid and CHIP Payment and Access Commission (MACPAC). (2018). Delivery System Reform Incentive Payment Programs. Retrieved from https://www.macpac.gov/wp-content/uploads/2018/03/Delivery-System-Reform-Incentive-Payment-Programs.pdf
[3] Center for Medicare and Medicaid Services (CMS). (2019). 1115 Demonstration Federal Evaluation and Meta-Analysis. Retrieved from https://www.medicaid.gov/medicaid/section-1115-demo/evaluation-reports/federal-evaluation-and-meta-analysis/index.html
[4] Baller, J., Verbitsky Savitz, N., & Heeringa, J. (2019). Delivery System Reform Incentive Payments. Retrieved from https://www.medicaid.gov/medicaid/section-1115-demo/downloads/evaluation-reports/final-eval-dsgn-dlvry-incntv-pyments.pdf
[5] While DSRIP programs appear to be phasing out of popularity and existing waivers are soon to wrap up, the concepts behind the creation of DSRIP programs continue to be tested under different initiatives. Hinton, E., Musumeci, M., Rudowitz, R., Antonisse, L., & Hall, C. (2019, February 12). Section 1115 Medicaid Demonstration Waivers: The Current Landscape of Approved and Pending Waivers [PDF file]. Retrieved from https://www.kff.org/medicaid/issue-brief/section-1115-medicaid-demonstration-waivers-the-current-landscape-of-approved-and-pending-waivers/
[6] Medicaid and CHIP Payment and Access Commission (MACPAC). (2018). Delivery System Reform Incentive Payment Programs. Retrieved from https://www.macpac.gov/wp-content/uploads/2018/03/Delivery-System-Reform-Incentive-Payment-Programs.pdf
[7] Kaiser Family Foundation (KFF). (2019, September 18). Medicaid Waiver Tracker: Approved and Pending Section 1115 Waivers by State. Retrieved from https://www.kff.org/medicaid/issue-brief/medicaid-waiver-tracker-approved-and-pending-section-1115-waivers-by-state/
