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Recommendations for Monitoring Access to Care among Medicaid Beneficiaries at the State-level

Publication Date
Jan 31, 2013
By: Mike Davern
 
Through an environmental scan and a technical expert panel meeting, this project identified major data sources and indicators that currently exist at the state level to measure access to care for Medicaid beneficiaries; assessed how well they performed across key dimensions of timelines, relevance, accuracy and accessibility; considered consumer perspectives, provider reports and measures of realized access; identified indicators that can be used to monitor access to care for Medicaid beneficiaries from these data sources and highlighted new opportunities and continuing challenges for continuous tracking of Medicaid access in the future.
"

Study Background & Objectives

This report presents the findings of a study entitled "Developing a System for Measuring Access to Care for Medicaid Beneficiaries," sponsored by the Office of the Assistant Secretary for Planning and Evaluation (ASPE) and U.S. Department of Health and Human Services (HHS), under contract (Task Order No. HHS23337020T, Contract No. HHSP23320095647WC) to NORC at the University of Chicago. ASPE is undertaking the project in partnership with the Center for Medicaid and CHIP Services at the Centers for Medicare and Medicaid Services (CMS).

Project Objectives

ASPE initiated this project to address department-wide interest in a federal-level system to measure and track access to care for Medicaid beneficiaries. With the passage of the Affordable Care Act (ACA), several new policies will go into effect in 2013 and 2014 that may affect access to care for Medicaid beneficiaries, including an increase in Medicaid's primary care payments and Medicaid expansion and increased enrollment. ASPE and CMS are interested in measuring baseline data and creating a system to monitor the impact of these policies on access to care for Medicaid beneficiaries.

The goal of the project is to provide guidance and recommendations for ASPE, CMS, and states to create a better system of collecting and utilizing data in order to understand access at the state-level for Medicaid beneficiaries. The project was designed to address the following objectives:

■        Determine the data sources and indicators that currently exist to measure access to care for Medicaid beneficiaries at the state-level

►      Assess how well the data sources perform across four key dimensions: timeliness, relevance, accuracy, and accessibility

►      Identify indicators that can be used to monitor access to care for Medicaid beneficiaries within a state over time and access to care indicators which can be used to make comparisons across states

■        Identify new opportunities and challenges for continuous tracking of Medicaid beneficiaries' access to care in the future

Project Methods

To address the project objectives, NORC collaborated with ASPE and CMS to conduct an environmental scan and convene a Technical Expert Panel (TEP). The environmental scan included a review of relevant published literature and white papers. NORC also reviewed survey and administrative data sources which can be used to measure Medicaid beneficiaries' perceptions of access to care, provider reports of access to care, and realized access to care. These data sources were identified through consultation with federal and non-federal experts and were selected for further discussion by the TEP if they met the criteria of including access to care measures, collecting data from all fifty states and the District of Columbia, and collecting data on a regular basis. NORC summarized findings from the environmental scan in a briefing book that was provided to all TEP members and referenced throughout the TEP meeting.

The TEP was convened at HHS's Humphrey Building in Washington, DC, on Wednesday, June 6, 2012. There were sixteen panelists in attendance, including directors of two state Medicaid programs; experts from Federal government agencies, such as CMS and the National Center for Health Statistics (NCHS) at the Centers for Disease Control and Prevention (CDC); researchers from organizations like Urban Institute, SHADAC, Mathematica Policy Research, and the Kaiser Family Foundation; and other leaders from private industry. In addition to these panelists, the meeting was attended by federal stakeholders from various divisions within ASPE and CMS. The full list of panelists and stakeholders in attendance is included in Appendix A.

After the meeting, NORC developed an online feedback tool to better assess panelists' opinions regarding potential indicators and data sources for monitoring Medicaid beneficiaries' access to care. The tool asked respondents to rate the relevance of different indicators of access, organized by consumer perceptions, provider reports, and realized access. Next respondents were asked to rate the accuracy, relevance, timeliness, accessibility, and overall importance of potential data sources. Lastly, respondents answered open-ended questions to provide additional information pertinent to developing a system to monitor Medicaid beneficiaries' access to care at the state-level.

Framework for Monitoring Access

In order to measure, understand, and track access to care issues from multiple perspectives, ASPE and CMS approach access using three domains:

■        Consumer Perceptions of Access: What does the person or family enrolled in Medicaid experience? Can they access primary care and specialty care? Do they have a usual source of care? Do they confront barriers in gaining access to care?

■        Provider Reports on Access: What do providers report regarding access to care for Medicaid beneficiaries at their practice?

■        Realized Access: What services are beneficiaries actually using? What are the characteristics of the providers serving Medicaid beneficiaries?

ASPE and CMS consider all three domains important. The objective is to identify data sources and indicators that provide a relatively complete picture of access in each of these three domains at the state-level.

Findings for Potential Data Sources for State-level Medicaid Access

Several data sources for measuring access to care for Medicaid beneficiaries were considered as potential sources for constructing access indicators. The primary criteria for selecting data sources included coverage of all states (even if current sample sizes do not permit state-level estimates) and the presence of access-related variables. There are other quality data sources examining access issues for Medicaid beneficiaries, including state-specific surveys,

The TEP weighed the strengths and limitations of four data sources for monitoring consumer perceptions of access including the Behavioral Risk Factor Surveillance System (BRFSS), the National Health Interview Survey (NHIS), the National Survey on Drug Use and Health (NSDUH), and the household component of the Medical Expenditure Panel Survey (MEPS). Additional detail on these data sources is included in Appendix B. Two datasets emerged as the best choices given HHS's needs. The BRFSS and NHIS could each be part of a system used by HHS to monitor Medicaid access at the state-level.

The BRFSSOther limitations of BRFSS include concerns over state-level variation in administration and sampling design - particularly coverage of cell-phone-only households - as well as response rates, which are low in some states relative to other federally-sponsored surveys. However, design modifications made in 2011 included the use of cell phone interviewing and the proportion of interviews conducted with cell-phone-only households is expected to grow over time.CDC's National Center for Health Statistics (NCHS) conducts the NHISRelevant to the inclusion of the NHIS as a data source for consumer perceptions of access is the NHIS sample augmentation and enhanced health care access and utilization questions that began in 2011.The primary limitation of the NHIS is the sample size and, consequently, whether the NHIS data can be used to generate state-level estimates of access for those enrolled in Medicaid. There are two distinct sampling issues that help inform the answer to this question. The first issue is whether the survey's sample is designed to produce unbiased estimates of the state's population (i.e., whether it can be used to produce state-representative data). The second issue is whether the effective sample size (i.e., the size of the sample after it is adjusted for complex survey design and weighting) for a specific measure produces a sufficiently precise estimate for the policy purposes needed.

The NHIS's current sample design allows for it to meet the design criteria. In 2011, the NHIS produced state-level insurance estimates for thirty-two states. In 2016 the NHIS is looking to change its sample design to produce representative estimates for even more states. The second issue of adequate precision is much harder to answer. An estimate of 15% of a population experiencing access problems in State X based on 100 effective sample size cases would have a standard error of 3.5% and a 95% confidence interval of 15%, plus or minus 7%. (An estimate of 15% based off of 50 effective sample cases would have a standard error of 5%, the same estimate with 200 effective sample size cases would have a standard error of 2.5%, and the same estimate with 400 effective sample size cases would have a standard error of 1.8%.)

Because the level of precision needed for an estimate to be policy relevant is dependent on how the estimate will be used, the decision of where to draw the line is not fixed.Two additional surveys were presented to the TEP for consideration as sources of indicators to measure consumer perceptions of access to care: the household component of MEPS and the NSDUH. Lack of an adequate sample size to generate state-level estimates and long lag times for release of data files limited any further consideration of MEPS. NSDUH was considered for its ability to capture subsets of Medicaid enrollees with mental illness and/or substance abuse issues, and for its questions specific to mental health and substance abuse. However, the relatively small number of Medicaid beneficiariesConducted annually by NCHS, NAMCSFunded by the Office of the National Coordinator for Health Information Technology (ONC) and conducted by NCHS, NAMCS-EMRThe company SK&AMSIS was identified as the main data source for monitoring realized access. However, there are several limitations and caveats to using MSIS for cross-state comparisons. Variation in benefit packages, program design, percent of the Medicaid population with fee-for-service (FFS) coverage, and data coding and reporting make cross-state comparisons challenging. Additionally, because it is expected that most newly eligible Medicaid beneficiaries will be enrolled in managed care plans, the lack of data on managed care Medicaid beneficiaries is a significant issue. In the short term, the use of MSIS for monitoring access will hinge on ASPE's and CMS's ability to engage with MSIS data experts to understand and control for state-level effects in the measures. Until these issues are identified and better understood, other types of comparisons may improve understanding and monitoring of access issues at the state-level, including: 1) comparing access for different eligibility populations across states; 2) comparing access within a given state across different eligibility groups over time; and 3) using a normative benchmark for care (e.g., are diabetic Medicaid enrollees meeting guidelines for visits and services?). As the data are utilized for monitoring access and the issues hindering state-level comparisons are identified, long-term solutions can be established to improve standardized reporting and to develop a fuller understanding of the factors that account for differences among states.

A recent report published by Mathematica Policy ResearchThe T-MSIS was identified as a potential data source when more states report data after 2013. The T-MSIS offers several advantages over the MSIS, such as the inclusion of the National Provider Identifier (NPI); more timely access to the data (within sixty days); integrated databases (including provider and managed care files),; and a more robust infrastructure, including automated data validation and analytic reporting. Currently, twelve states are using T-MSIS as part of Phase I. Phase II will add another four states by the end of 2012 and Phase III will add the remaining thirty four by the end of 2013.

Consumer Perceptions: BRFSS

Major Limitation: Before 2013 there is no Medicaid specific measure and beyond 2013 it is unknown if there will be a specific measure of Medicaid coverage. Income is only measured categorically. Does not include data on children under 18.

Consumer Perceptions: NHIS

Major Limitation: State estimates only available for 32 largest states and a smaller number of states are likely to have an adequate effective sample size for the Medicaid enrolled population. Access to state data is limited to NCHS RDC.

Provider Reports: NAMCS-EMR

Major Limitation: Access to state-level identifiers limited to NCHS RDC.

Realized Access:
MSIS


Major Limitation: Limited understanding of state-level variation and ability to make cross-state comparisons. Largely limited to Medicaid Fee For Service.

% of low-income adults with access to a usual source of care

% of Medicaid population with a usual source of care

% physicians accepting new patients (by primary care providers and specialists)

% eligible FFS beneficiaries with at least one service

% of low-income adults who forewent receiving care because it was unaffordable

Type of usual source of care

% physicians accepting new Medicaid patients (by primary care providers and specialists)

% eligible FFS beneficiaries with at least one ambulatory care visit

% of low-income adults without a preventive care visit in the last 2 years

% with Medicaid coverage that delayed medical care due to cost

% of physician patient population with Medicaid/CHIP (by primary care providers and specialists)

% eligible FFS beneficiaries with at least one specialty care (aggregate) visit

Interval since last doctor visit

% physician revenue from Medicaid (by primary care providers and specialists)

% eligible FFS beneficiaries with at least one specialty care (specific) visit

% who experienced trouble finding a general doctor or provider

% physicians accepting new Medicaid patients in practices with mid-level providers (e.g., NP, PA) (by primary care providers and specialists)

Number of participating providers (using NPI)

% with Medicaid coverage who were not accepted as new patients

Number and range of FFS beneficiaries served per provider

% with Medicaid coverage who visited doctors' offices that did not accept their form of health insurance

% with Medicaid coverage who delayed getting medical care because they could not get an appointment soon enough

 

These indicators can be stratified to examine access to care issues for different types of Medicaid beneficiaries. It should be acknowledged that stratifying the sample by demographic or other variables will exacerbate the sample size issues. However, the Exhibit 2 includes recommendations for potential stratifying variables when the state sample size permits.

Exhibit 2. Potential Stratifying Variables by Data Source

Data Source

Stratifying Variables

BRFSS

Race, ethnicity, age, gender, categorical household income, insurance coverage, state, MSA

NHIS

Race, ethnicity, age, gender, family income relative to poverty, Medicaid enrollment, type of insurance coverage, state, urban/rural residence

NAMCS & NAMCS-EMR

Physician Characteristics: State, race, ethnicity, age, gender, physician type (MD, DO), specialty group, employment

Practice Characteristics: Office setting, practice ownership, practice size, solo, MSA

MSIS

Race, ethnicity, age, sex, basis of eligibility category, TANF, dual-eligible

Long-Term Recommendations to Improve the Data Infrastructure for State-level Assessments of Medicaid Access

The data sources currently available for measuring state-level access to care for Medicaid beneficiaries reveal several limitations for developing a robust system for monitoring access. There are several high-priority measures which the TEP identified that lack state-based estimates, and each of the existing survey sources for measuring consumer perceptions at the state-level have limitations. However, even if these state measures existed, there is no population household survey that has access measures for both adults and children which can be used to derive estimates in all fifty states and the District of Columbia. Current surveys provide access data that are robust only for healthy adults and for children in the thirty-two states made possible by 2011 NHIS sample augmentation (selected states), and only when the effective sample size of the Medicaid population is sufficient to provide precise estimates for policy analysis. There are many causes for concern, including access issues for subpopulations (such as people with disabilities and racial and ethnic minorities) and the inability to perform sub-state analysis. The ability of the data products to produce reliably comparable state estimates will be a challenge for surveys (due to sample design and sample size) and administrative data collected by the states (due to differences in data collection practices and definitions across states). Funding and infrastructure for data linkage work is insufficient to support current needs (e.g., the ability to link survey data with administrative records data—, such as linking provider surveys with provider characteristics information from administrative data, or linking household surveys with corresponding administrative data). Finally, T-MSIS implementation is not currently taking place in all states, and many states still contribute to MSIS only.

To improve the quality of the recommended indicators, several enhancements to existing data sources can be pursued. Additional funding for BRFSS will be required to include the question on type of health insurance coverage beyond 2013. Additionally, working with states to better understand what the data represent, given differences in sampling, survey administration, and periodicity of some questions, will provide additional insight into understanding variability in access to care issues across the states. The potential exists to add access questions to the American Community Survey (ACS), and some TEP members and their organizations were already considering suggestions. The TEP did not seriously consider the ACS as a data source because it was not perceived to have access measures, although it does have health insurance and income data with large annual state sample sizes. NCHS should be supported in its efforts to enhance the sample design of the NHIS to improve the survey's ability to make state estimates by 2016. Work could also be conducted with NCHS to explore the possibility of long-term support for increasing the sample size of NAMCS.

Continued support for the development, deployment, and adoption of T-MSIS will facilitate the measurement of Medicaid access in the future. Furthermore, CMS is developing additional datasets and systems which will complement T-MSIS. For example, the MACPro system is a new online system designed to facilitate submission of state applications to amend existing state plans or waivers, propose new options under the Medicaid and CHIP programs, and submit key administrative information. MACPro will modernize the CMS and state information exchange by offering an electronic workflow to CMS reviewers as well as our state partners. MACPro will be the official system of record for these submissions and changes. MACPro will be implemented in phases with the first release in early 2013. Additional Medicaid authorities will be included in future releases with all authorities incorporated into MACPro in 2014. MACPro will provide important contextual data to better utilize T-MSIS data by:

■        Enabling states to share program information with each other for benchmarking purposes

■        Providing structured data about a state's program which can be used to compare state programs and can be integrated and analyzed with expenditure data and granular operational data

Additional, long-term improvements could take the form of data linkage opportunities, sample size expansion, and the creation of surveys that specifically examine Medicaid access. Additional ways to work with the Census Bureau should be explored in order to improve researcher access to linked administrative and survey data within a secure working environment and ultimately to increase the utility of these linked data for health policy analysis. There is general agreement that many of the data sources contain the necessary access variables, but there is a need to expand the sample size of the NHIS and NAMCS. To provide a more robust understanding of consumer perceptions of access, a survey targeting Medicaid beneficiaries could be developed to facilitate state comparisons and attain the power to compare beneficiaries by key sub-populations (e.g., racial/ethnic minorities, disability status). The development of a state-based Medicaid Consumer Assessment of Healthcare Providers and Systems (CAHPS) could also be considered to support measurement of consumer assessments of Medicaid.

Additionally, TEP members and stakeholders recommend that ASPE and CMS continue working closely with states, not only in the development of the measures, but also once measures are in place. In an ideal system, HHS would share a report with each state that shows its data compared to national averages. When indicators are far outside the mean or moving in the wrong direction, HHS could engage with the states to determine the potential causes (e.g., data analysis issues, data interpretation issues, reporting issues, or programmatic/policy issues). This would not only improve understanding of the access issues, but also have the potential to improve claims data. As noted by one TEP member, "Data used is data improved."

Appendix A. TEP Meeting Attendance

Panelists

Panelist

Institutional Affiliation

Maggie Anderson

North Dakota Medicaid
Director

Deborah Bachrach

Manatt
Counsel, Healthcare Transaction & Policy

Dave Baugh

Mathematica Policy Research
Senior Researcher

Kathleen T. Call

State Health Access Data Assistance Center (SHADAC)
Professor, University of Minnesota School of Public Health

Stephen Cha

Center for Medicaid and CHIP Services (CMS)
Medical Director

William Clark

Center for Medicare and Medicaid Innovation (CMS)
Director, Division of Research on State and Special Populations

Robin A. Cohen

National Center for Health Statistics (NCHS)
Senior Statistician, Division of Health Interview Statistics

Kim Elliott

Arizona Medicaid
Administrator for Clinical Quality Management

Janet Freeze

Center for Medicaid and CHIP Services (CMS)
Director, Division of Reimbursement & State Financing

James Gorman

Center for Medicaid and CHIP Services (CMS)
Director, Information Analysis and Technical Assistance

John Holahan

Urban Institute
Director, Health Policy Center

Martha Kelly

Acumen, LLC
Senior Research Associate II

Sharon Long

Urban Institute
Senior Fellow

Julia Paradise

Henry J. Kaiser Family Foundation
Associate Director, Kaiser Commission on Medicaid and the Uninsured

Chris Peterson

MACPAC
Director of Eligibility, Enrollment and Benefits

Alan Weil

National Academy for State Health Policy
Executive Director

 

Federal & Association Stakeholders

Name

Institutional Affiliation

Andy Bindman

ASPE, Office of Health Policy
UCSF, Prof. of Medicine, Health Policy, Epidemiology & Biostatistics

Nancy DeLew

ASPE
Associate Deputy Assistant Secretary for Health Policy

Kristin Fan

CMS, Center for Medicaid and CHIP Services (CMCS)
Deputy Director, Financial Management Group

Dianne Heffron

CMS , Center for Medicaid and CHIP Services (CMCS)
Director, Financial Management Group

Julia Hinckley

CMS
Senior Advisor to the Director of the Center for Medicaid, CHIP, Survey, and Certification

Abby Kahn

National Association of Medicaid Directors (NAMD)

Policy Analyst

Rick Kronick

ASPE
Deputy Assistant Secretary for Health Policy

Marsha Lillie-Blanton

CMS
Director, Division of Quality, Evaluation, and Health Outcomes

Karen Llanos

CMS
Technical Director, Division of Quality, Evaluation, and Health Outcomes

Cindy Mann

CMS
Deputy Administrator and Director, Center for Medicaid and CHIP Services (CMCS)

Wilma Robinson

ASPE
Senior Health Policy Analyst, Office of Health Policy

Karyn Schwartz

ASPE
Office of Health Policy

Jeremy Silanskis

CMS, Center for Medicaid and CHIP Services, Financial Management Group, Division of Reimbursement & State Financing

Ben Sommers

ASPE, Office of Health Policy, Senior Advisor

Harvard, School of Public Health, Asst. Prof. of Health Policy and Economics

Megan Thomas

CMS, Center for Medicaid and CHIP Services (CMCS)
Health Insurance Specialist, Family and Children's Health Program Group (FCHPG), Division of Quality, Evaluation, and Health Outcomes (DQEHO)

Penny Thompson

CMS
Deputy Director, Center for Medicaid and CHIP Services (CMCS)

 

Appendix B. Summary Information on Key Data Sets for Measuring Access to Care for Medicaid Enrollees

Summary Information on Data Sources for Consumer Perception

BRFSS

NHIS

 

NSDUH

Sponsor

A partnership between CDC and State Health Departments with CDC providing a core level of support in every state

NCHS

AHRQ

SAMHSA

Target Population

Adults (non-institutionalized)

Households (civilian, non-institutionalized)

Households (civilian, non-institutionalized)

Individuals 12 or older (civilian, non-institutionalized)

Sample Frame

Random Digit Dialing to telephone numbers (landline only through 2010) and landline and Cell beginning with the 2011 BRFSS data file to be released in the summer of 2102

Address Based Sample Frame developed by the Census with oversamples of Blacks, Hispanics, and Asians

NHIS

Address-based sampling using a national sample frame developed by RTI for SAMHSA

Data Collection Mode

CATI

CAPI

CAPI

CAPI & ACASI

Response Rate

54.6 (2010, 50-state median)

82.0% (2011, Household Module)

57.2% (2009, full-year file)

74.7% (2010)

Survey Period

Throughout the year

Throughout the year

Panel over 2-yr period

Throughout the year

Sample Size

Approx. 350,000 persons (only one individual interviewed per HH)

Approx. 35,000 households containing 87,500 individuals

In 2009, 13,875 households containing 34,920 individuals

Approx. 70,000 persons

State Estimates

All 50 states and DC, and many large counties and large Metropolitan areas

States are not identified on Public Use File, but direct estimates are derived by NCHS for the largest 30 states preferably using Cross year pooling

No

States not identified in Public Use data but SAMHSA does release small area estimates for a pre-defined set of cross-tabulations

Years

Since 1984, annually

Since 1957 and annually since 1962

Since 1996

Since 1988, annually

Data Lag

4-6 months

6 months for full data, early release analytic reports available sooner

2 Years

9 months

Relevant Measures

Usual Source of Care and Unmet Need; Cancer Screenings obtained

Usual Source of Care, Visits, and Unmet Need, Medical Costs Burden (2011). Includes FFS vs. MC Medicaid.

Usual Source of Care, Visits, Unmet need (including dental and Rx), Patient Experience, and Costs.

Visits and Mental Health Costs

Limitations

No specific measure of Medicaid coverage and income data is not precisely measured

Cannot provide state-level estimates for all states by year. Also sample size of Medicaid recipients selected as sample adult or sample child within many states will be small

Small sample size within states and the data lag time is longer

There is no established process for gaining access to states identifiers (e.g., the NHIS allows access through the Census Bureau RDC and NCHS RDC system)

Source: Dataset websites and SHADAC

Summary Information on Data Sources for Provider Reports

 

Provider Reports on Access

NAMCS

NAMCS-EMR

SK&A Physician Access

Sponsor

NCHS

NCHS

SK&A

Target Population

Non-Federal employed office-based physicians primarily engaged in direct patient care

Non-Federal employed office-based physicians primarily engaged in direct patient care

Practicing physicians at medical offices

Sample Frame

AMA and AOA lists within PSUs

NAMCS

AMA-based, with additional cleaning and verification

Data Collection Mode

In-person PAPI (majority using Census field staff)

Mail

Phone

Response Rate

65.4% (2007)

61% (2011)

Unavailable

Survey Period

Year round

Feb-June (2011)

Year round

Sample Size

In 2012, 15,590 office-based physicians and 6,336 community health center providers

In 2010, 10,301 office-based, non-Federal physicians

740,000 practicing physicians at medical offices

State Estimates

Designed to produce estimates for the largest 34 states with 2012 sample size increase

Since 2010, designed to produce state-level estimates

Yes

Years

Annually since 1989

Annually since 2008

N/A

Data Lag

Approx 12 months

Approx 12 months

Updated files available daily

Relevant Measures

New patient acceptance, Medicaid patient acceptance, % revenue from Medicaid, NPI

New patient acceptance, Medicaid patient acceptance, % revenue from Medicaid (2011)

New patient acceptance, Insurance plans accepted, NPI

Limitations

Restricted access

Restricted access

Intended census, but unclear representativeness of survey, Unclear if data distinguish between acceptance of Medicare and Medicaid in a systematic way; Medicaid data may not by collected from physicians during repeat administrations

Source: Dataset websites and SHADAC

Summary Information on Data Sources for Realized Access

 

Provider Reports on Access

MSIS

T-MSIS

HRSA UDS

HCUP

(SID & SEDD)

Sponsor

CMS

CMS

HRSA

AHRQ

Data Source is Inclusive of:

Medicaid eligibles and beneficiaries

Medicaid eligibles and beneficiaries

Grantees of HRSA's primary care programs

SID: hospital admissions in each state

SEDD: ED discharges that do not result in an admission

Data Product Production Schedule

Quarterly

Monthly

Data reported annually in the first quarter of the year

Annually

Data Set Coverage

50 states + District of Columbia

50 states + District of Columbia

Varies (e.g., 7,900 service sites in 2009; 1,128 grantees in 2011)

SID has 44 states.

SEDD has 27 states.

State Estimates

Yes

Yes

Yes

Yes

Years

Quarterly since FY 1998

Monthly starting in 2011 as pilot initiative

Annually since 2007

HCUP since 1988, SID since 1990, and SEDD since 1999

Relevant Measures

400 data elements, including: Encounter total claims count; Total Medicaid paid amount; eligibles and beneficiaries count

1,000 data elements, including new claims files on managed care, third-party liability, coordination of benefits, and provider

Health center-level data on patient demographics, services provided, staffing, clinical indicators, utilization rates, costs, and revenues of grantee health centers

SID & SEDD: payer source; diagnoses; procedures

Add'l SID measures: admission and discharge status; total charges; length of stay

Limitations

Inconsistency of variables reported across states; Missing data; Interpretation of data; Broad technical issues

Currently 12 states participating

Limited coverage since only includes grantee sites.

Not all states participate

Source: Dataset websites

Appendix C. BRFSS Module on Health Care Access for 2013

1 Do you have Medicare?

(298)

1            Yes

2            No

7            Don't know/Not sure

9 Refused

Note: Medicare is a coverage plan for people age 65 or over and for certain disabled people.

2 Are you CURRENTLY covered by any of the following types of health insurance or health

coverage plans?

(299-312)

(Select all that apply)

Please Read:

01 Your employer

02 Someone else's employer

03 A plan that you or someone else buys on your own

04 Medicaid or Medical Assistance [or substitute state program name]

05 The military, CHAMPUS, or the VA [or CHAMP-VA]

06 The Indian Health Service [or the Alaska Native Health Service]

07 Some other source

88 None

77 Don't know/Not sure

99 Refused

CATI Note: If PPHF State go to core 3.2

3 Other than cost, there are many other reasons people delay getting needed medical care.

Have you delayed getting needed medical care for any of the following reasons in the past 12 months? Select the most important reason.

(313)

Please read

1 You couldn't get through on the telephone.

2 You couldn't get an appointment soon enough.

3 Once you got there, you had to wait too long to see the doctor.

4 The (clinic/doctor's) office wasn't open when you got there.

5 You didn't have transportation.

Do not read:

(314-338)

6 Other ____________

specify

8 No, I did not delay getting medical care/did not need medical care

7 Don't know/Not sure

9 Refused

CATI Note: If PPHF State, go to core 3.4

CATI Note: If Q3.1 = 1 (Yes) continue, else go to Q4b

4a In the PAST 12 MONTHS was there any time when you did NOT have ANY health

insurance or coverage?

(339)

1 Yes [Go to Q5]

2 No [Go to Q5]

7 Don't know/Not sure [Go to Q5]

9 Refused [Go to Q5]

CATI Note: If Q3.1 = 2, 7, or 9 continue, else go to next question (Q5)

4b About how long has it been since you last had health care coverage?

(340)

1 6 months or less

2 More than 6 months, but not more than 1 year ago

3 More than 1 year, but not more than 3 years ago

4 More than 3 years

5 Never

7 Don't know/Not sure

9 Refused

5 How many times have you been to a doctor, nurse, or other health professional in the past 12 months?

(341-342)

_ _ Number of times

8 8 None

7 7 Don't know/Not sure

9 9 Refused

6 Was there a time in the past 12 months when you did not take your medication as prescribed because of cost? Do not include over-the-counter (OTC) medication.

(343)

1 Yes

2 No

Do not read:

3 No medication was prescribed.

7 Don't know/Not sure

9 Refused

7 In general, how satisfied are you with the health care you received? Would you say—

(344)

1 Very satisfied

2 Somewhat satisfied

3 Not at all satisfied

Do not read

8 Not applicable

7 Don't know/Not sure

9 Refused

 

8 Do you currently have any medical bills that are being paid off over time?

(345)

INTERVIEWER NOTE:

This could include medical bills being paid off with a credit card, through personal loans, or bill paying arrangements with hospitals or other providers. The bills can be from earlier years as well as this year.

1 Yes

2 No

7 Don't know/Not sure

9 Refused

CATI Note: If PPHF state, Go to core section 4.

Appendix D. Additional Documentation for Recommended Set of Access Indicators

Consumer Perceptions from BRFSS

Indicator: Adults who forewent receiving care because it was unaffordable

Description/Purpose: Number of adults who needed care but could not obtain it due to cost, in the last 12 months

Variable name: MEDCOST

Data sources: BRFSS 2011

Universe:Adults age 18+

Question wording: Was there a time in the past 12 months when you needed to see a doctor but could not because of cost?

Values/labels:

Frequency

Percent

Weighted Percentage

1

Yes

63,828

12.65

16.91

2

No

439,274

87.09

82.84

7

Don't know/not sure

1,023

0.20

0.18

9

Refused

283

0.06

0.06

 

Potential recodes:

Stratification: Race, ethnicity, age, gender, categorical household income, insurance coverage, state, MSA

Indicator: Adults who have usual source of care

Description/Purpose: Number of adults who identify one person that they perceive as their personal doctor or health care provider

Variable name: PERSDOC2

Data sources: BRFSS 2011

Universe: Adults age 18+

Question wording: Do you have one person you think of as your personal doctor or health care provider? (If "No" ask "Is there more than one or is there no person who you think of as your personal doctor or health care provider?".)

Values/labels:

Frequency

Percent

Weighted Percentage

1

Yes, only one

389,557

77.23

70.77

2

More than one

40,883

8.11

6.85

3

No

72,366

14.35

21.93

7

Don't know/Not Sure

1,062

0.21

0.28

9

Refused

540

0.11

0.16

 

Potential recodes: 1 AND 2 - YES; 3 - NO; 7, 8, 9 = missing

Stratification: Race, ethnicity, age, gender, categorical household income, insurance coverage, state, MSA

Indicator: Interval since last routine care visit

Description/Purpose: Length of time since adult last visited a doctor for routine care

Variable name: CHECKUP1

Data sources: BRFSS 2011

Universe: Adults age 18+

Question wording: About how long has it been since you last visited a doctor for a routine checkup? A routine checkup is a general physical exam, not an exam for a specific injury, illness, or condition.

Values/labels:

Frequency

Percentage

Weighted Percentage

1

Within past year (anytime less than 12 months ago)

360,620

71.49

66.08

2

Within past 2 years (1 year but less than 2 years ago)

60,075

11.91

13.68

3

Within past 5 years (2 years but less than 5 years ago)

36,083

7.15

8.94

4

5 or more years ago

36,007

7.14

8.65

7

Don't know/Not sure

5,989

1.19

1.15

8

Never

5,032

1.00

1.38

9

Refused

602

0.12

0.12

 

Potential recodes: 3 AND 4 = More than 2 years ago; 7, 8, 9 = missing

Stratification: Race, ethnicity, age, gender, categorical household income, insurance coverage, state, MSA

Consumer Perceptions from NHIS

Indicator: Persons that delayed medical care due to cost

 

Description/Purpose: All persons that delayed medical care due to cost in the last 12 months

Variable name: PDMED12M

Data sources: NHIS 2011 Person file

Universe: All persons that need care and did not receive it due to cost.

Question wording: DURING THE PAST 12 MONTHS, has medical care been delayed for {person} because of worry about the cost? (Do not include dental care)

Values/labels:

Frequency

Percent

1

Yes

9080

8.91

2

No

92718

91.01

7

Refused

26

0.03

8

Not ascertained

0

0.00

9

Don't know

51

0.05

 

Potential recodes:

Stratification: Race, ethnicity, age, gender, family income relative to poverty, Medicaid enrollment, type of insurance coverage, state, urban/rural residence

Indicator: Interval since last doctor visit

Description/Purpose: Duration of time since last doctor's visit

Variable name: AMDLONGR

Data sources: NHIS 2011 Sample Adult file

Universe: Sample adults aged 18+ years

Question wording: About how long has it been since you last saw or talked to a doctor or other health care professional about your own health? Include doctors seen while a patient in a hospital.

Values/labels:

Frequency

Percent

0

Never

355

1.08

1

6 months or less

22130

67.03

2

More than 6 mos, but not more than 1 yr ago

4807

14.56

3

More than 1 yr, but not more than 2 yrs ago

2543

7.70

4

More than 2 yrs, but not more than 5 yrs ago

1796

5.44

5

More than 5 years ago

1056

3.20

7

Refused

21

0.06

8

Not ascertained

250

0.76

9

Don't know

56

0.17

 

Potential recodes: <= 1 year, 1 year and more

Stratification: Race, ethnicity, age, gender, family income relative to poverty, Medicaid enrollment, type of insurance coverage, state, urban/rural residence

Indicator: Adult: Usual source of care

Description/Purpose: Whether or not an adult reported having a usual source of care that they can rely on when sick or in need of medical advice

Variable name: AUSUALPL

Data sources: NHIS 2011 Sample Adult file

Universe: Sample adults aged 18+ years

Question wording: Is there a place that you USUALLY go to when you are sick or need advice about your health?

Values/labels:

Frequency

Percent

1

Yes

27494

83.28

2

There is NO place

5061

15.33

3

There is MORE THAN ONE place

348

1.05

7

Refused

10

0.03

8

Not ascertained

92

0.28

9

Don't know

9

0.03

 

Potential recodes:

Stratification: Race, ethnicity, age, gender, family income relative to poverty, Medicaid enrollment, type of insurance coverage, state, urban/rural residence

Indicator: Adults: Type of usual source of care

Description/Purpose: For adults who report a usual source of care, what type of resources do they typically rely on?

Variable name: APLKIND

Data sources: NHIS 2011 Sample Adult file

Universe: Sample adults aged 18+ years with one or more usual place(s) to go when sick/in need of health advice

Question wording: What kind of place do you go to most often - a clinic, doctor's office, emergency room, or some other place?

Values/labels:

Frequency

Percent

1

Clinic or health center

6835

24.55

2

Doctor's office or HMO

19539

70.18

3

Hospital emergency room

468

1.68

4

Hospital outpatient department

453

1.63

5

Some other place

320

1.15

6

Doesn't go to one place most often

218

0.78

7

Refused

3

0.01

8

Not ascertained

3

0.01

9

Don't know

3

0.01

 

Potential recodes:

Stratification: Race, ethnicity, age, gender, family income relative to poverty, Medicaid enrollment, type of insurance coverage, state, urban/rural residence

Indicator: Adults who experienced trouble finding a doctor

Description/Purpose: Number of adults who experienced trouble finding a doctor in the past 12 months

Variable name: APRVTRYR

Data sources: NHIS 2011 Sample Adult file

Universe: Sample adults aged 18+ years

Question wording: DURING THE PAST 12 MONTHS, did you have any trouble finding a general doctor or provider who would see you?

Values/labels:

Frequency

Percent

1

Yes

1109

3.36

2

No

31762

96.21

7

Refused

14

0.04

8

Not ascertained

104

0.32

9

Don't Know

25

0.08

 

Potential recodes:

Stratification: Race, ethnicity, age, gender, family income relative to poverty, Medicaid enrollment, type of insurance coverage, state, urban/rural residence

Indicator: Adults who were not accepted as new patients

Description/Purpose: Number of adults who reported not being accepted by doctors as new patients in the last 12 months

Variable name: ADRNANP

Data sources: NHIS 2011 Sample Adult file

Universe: Sample adults aged 18+ years

Question wording: DURING THE PAST 12 MONTHS, were you told by a doctor's office or clinic that they would not accept you as a new patient?

Values/labels:

Frequency

Percent

1

Yes

913

2.77

2

No

31948

96.77

7

Refused

15

0.05

8

Not ascertained

108

0.33

9

Don't Know

30

0.09

 

Potential recodes:

Stratification: Race, ethnicity, age, gender, family income relative to poverty, Medicaid enrollment, type of insurance coverage, state, urban/rural residence

Indicator: Adults who visited doctors' offices that did not accept their form of health insurance

Description/Purpose: Number of adults whose form of health care coverage was refused by a doctor's office or clinic during the past 12 months.

Variable name: ADRNAI

Data sources: NHIS 2011 Sample Adult file

Universe: Sample adults aged 18+ years

Question wording: DURING THE PAST 12 MONTHS, were you told by a doctor's office or clinic that they did not accept your health care coverage?

Values/labels:

Frequency

Percent

1

Yes

1110

3.36

2

No

31724

96.09

7

Refused

15

0.05

8

Not ascertained

110

0.33

9

Don't know

55

0.17

 

Potential recodes:

Stratification: Race, ethnicity, age, gender, family income relative to poverty, Medicaid enrollment, type of insurance coverage, state, urban/rural residence

Indicator: Adults who delayed getting medical care because they could not get an appointment soon enough

Description/Purpose: Number of adults who reported delaying medical care in the past 12 months because they could not get an appointment soon enough

Variable name: AHCDLYR2

Data sources: NHIS 2011 Sample Adult file

Universe: Sample adults aged 18+ years

Question wording: There are many reasons people delay getting medical care. Have you delayed getting care for any of the following reasons in the PAST 12 MONTHS? ..... You couldn't get an appointment soon enough

Values/labels:

Frequency

Percent

1

Yes

2013

6.10

2

No

30839

93.41

7

Refused

18

0.05

8

Not ascertained

121

0.37

9

Don't know

23

0.07

 

Potential recodes:

Stratification: Race, ethnicity, age, gender, family income relative to poverty, Medicaid enrollment, type of insurance coverage, state, urban/rural residence

Provider Reports from NAMCS-EMR

Indicator: Percent of revenue from Medicaid

Description/Purpose: Mean percentage of patient care revenue that comes from Medicaid

Variable name: PRMAID

Data sources: 2011 NAMCS EMR Supplement MICRO DATA FILE

Universe: Physicians practicing in an ambulatory care setting

Question wording: At the reporting location, what percentage of your patient care revenue comes from the following … Medicaid?

Values/labels: -9 BLANK

0-100 Continuous

Potential recodes: 0%, 1-25%, 26-50%, 51-75%, 76%-100%; and 0%, 1-100%

Stratification: Physician Characteristics: State, race, ethnicity, age, sex, physician type (MD, DO), specialty group, employment

Practice Characteristics: Office setting, practice ownership, practice size, solo, MSA

Indicator: Accepting New Patients

Description/Purpose: Percentage of physicians currently accepting new patients into the practice

Variable name: ACEPTNEW

Data sources: 2011 NAMCS EMR Supplement MICRO DATA FILE

Universe: Physicians practicing in an ambulatory care setting

Question wording: At the reporting location, are you currently accepting new patients?

Values/labels: -9 BLANK

-8 DON'T KNOW

1 Yes

2 No

Potential recodes:

Stratification: Physician Characteristics: State, race, ethnicity, age, sex, physician type (MD, DO), specialty group, employment

Practice Characteristics: Office setting, practice ownership, practice size, solo, MSA

Indicator: Accepting New Medicaid Patients

Description/Purpose: Percentage of physicians currently accepting new patients with Medicaid into the practice

Variable name NMEDCAID

Data sources: 2011 NAMCS EMR Supplement MICRO DATA FILE

Universe: Ambulatory care physicians who are accepting new Medicaid patients into practice [ACETPNEW=1]

Question wording: From those "new" patients, which of the following types of payment do you accept? … Medicaid?

Values/labels: -9 BLANK

-8 DON'T KNOW

-7 NOT APPLICABLE

1 Yes

2 No

Potential recodes:

Stratification: Physician Characteristics: State, race, ethnicity, age, sex, physician type (MD, DO), specialty group, employment

Practice Characteristics: Office setting, practice ownership, practice size, solo, MSA

Indicator: Percent of Patients with Medicaid/CHIP

Description/Purpose: Mean percentage patients with Medicaid/CHIP

Variable name: PCTMCAID

Data sources: 2011 NAMCS EMR Supplement MICRO DATA FILE

Universe: All physicians in an ambulatory setting

Question wording: At the reporting location, what percent of your current patients have Medicaid/CHIP?

Values/labels: -9 BLANK

0-100 Continuous

Potential recodes: 0%, 1-50%, 51% or higher

Stratification: Physician Characteristics: State, race, ethnicity, age, sex, physician type (MD, DO), specialty group, employment

Practice Characteristics: Office setting, practice ownership, practice size, solo, MSA

Indicator: Access to Mid-level Providers

Description/Purpose: Percentage of physicians accepting new Medicaid patients in practices with mid-level providers

Variable name: MIDLEVP1

Data Sources: 2011 NAMCS EMR Supplement MICRO DATA FILE

Universe: Ambulatory care physicians who are accepting new Medicaid patients into practice [ACETPNEW=1]

Question wording: How many mid-level providers (i.e., nurse practitioners, physician assistants, and nurse midwives) are associated with the reporting location?

Values/labels: -9 BLANK

0-100 Continuous

Potential recodes: 0, 1-4, 5-9, 10+, and 0, 1+

Stratification: Physician Characteristics: State, race, ethnicity, age, sex, physician type (MD, DO), specialty group, employment

Practice Characteristics: Office setting, practice ownership, practice size, solo, MSA

Realized Access from MSIS

Indicator: Percent of state population who are eligible beneficiaries

Description/Purpose: To identify proportion of population eligible for services.

Data sources: ELIGIBLE (numerator), To be derived from an Extant source such as the American Community Survey of the Current Population Survey (denominator)

Time period: Monthly/quarterly/annually

Universe: All state residents.

Numerator: All eligible beneficiaries with at least 1 month FFS eligibility during <time period>.

Denominator: State population count for <time period>.

Type: Continuous

Values/labels: 0 - 100%

Potential recodes: Categories

Stratification: Race, ethnicity, age, sex, basis of eligibility category, TANF, dual-eligible

Indicator: Percent of eligible beneficiaries that are beneficiaries served

Description/Purpose: To identify proportion of eligible beneficiaries that use services.

Data sources: CLAIMSXX (numerator), ELIGIBLE (denominator)

Time period: Monthly/quarterly/annually

Universe: All state residents.

Numerator: Sum of eligible beneficiaries with at least 1 month FFS eligibility AND one claim during <time period>.

Denominator: All eligible beneficiaries with at least 1 month FFS eligibility during <time period>.

Type: Continuous

Values/labels: 0 - 100%

Potential recodes: Categories

Stratification: Race, ethnicity, age, sex, basis of eligibility category, TANF, dual-eligible

Indicator: Percent of Eligible Beneficiaries with at least one Ambulatory Care Visit

Description/Purpose: Indicator of regular/usual treatment among target population.

Data sources: CLAIMOT (numerator), ELIGIBLE (denominator)

Time period: Monthly/quarterly/annually

Universe: All eligible beneficiaries with at least 3 months FFS eligibility during <time period>.

Numerator: Sum of ambulatory care visits (TYPE-OF-VISIT = <select>) for eligible beneficiaries with at least 3 months FFS eligibility during <time period>.

Denominator: All eligible beneficiaries with at least 3 months FFS eligibility during <time period>.

Type: Continuous

Values/labels: 0 - 100%

Potential recodes: Categories

Stratification: Race, ethnicity, age, sex, basis of eligibility category, TANF, dual-eligible

Notes: Number of visits based on claims for a unique beneficiary, provider ID and single service day. May need to combine with SPECIALTY-CODE, coding varies by state.

 

Indicator: Percent of Eligible Beneficiaries with at least one Specialty Care (Aggregate) Visit

Description/Purpose: Indicator of regular/usual treatment among target population.

Data sources: CLAIMOT (numerator), ELIGIBLE (denominator)

Time period: Monthly/quarterly/annually

Universe: All eligible beneficiaries with at least 1 month FFS eligibility during <time period>.

Numerator: Sum of all specialty care visits (SPECIALTY-CODE=<select codes>) for eligible beneficiaries with at least 1 month FFS eligibility during <time period>.

Denominator: All eligible beneficiaries with at least 1 month FFS eligibility during <time period>.

Type: Continuous

Values/labels: 0 - 100%

Potential recodes: Categories

Stratification: Race, ethnicity, age, sex, basis of eligibility category, TANF, dual-eligible

Notes: No standard coding for this field, state-specific.

Indicator: Percent of Eligible Beneficiaries with at least one Specialty Care (Specific) Visit

Description/Purpose: Indicator of regular/usual treatment among target population.

Data sources: CLAIMOT (numerator), ELIGIBLE (denominator)

Time period: Monthly/quarterly/annually

Universe: All eligible beneficiaries with at least 1 month FFS eligibility during <time period>.

Numerator: Sum of specialty care visits (SPECIALTY-CODE=<specialty of interest>) for eligible beneficiaries with at least 1 month FFS eligibility during <time period>.

Denominator: All eligible beneficiaries with at least 1 month FFS eligibility during <time period>.

Type: Continuous

Values/labels: 0 - 100%

Potential recodes: Categories

Stratification: Race, ethnicity, age, sex, basis of eligibility category, TANF, dual-eligible

Notes: No standard coding for this field, state-specific. This variable can be repeated for all specialty codes of interest.

 

Indicator: Number of Unique Beneficiaries Served Per Provider

Description/Purpose: Indicates level of beneficiaries served by provider.

Data sources: ELIGIBLE, CLAIMSXX (numerator), CLAIMSOT (denominator)

Time period: Monthly/quarterly/annually

Universe: Sum of unique beneficiaries with at least 1 month FFS eligibility AND one claim during <time period>.

Numerator: Sum of unique beneficiaries with at least 1 month FFS eligibility AND one claim during <time period>.

Denominator: Sum of unique providers with at least one (SERVICE CODE) during <time period>.

Type: Continuous

Values/labels: 0 - 100%

Potential recodes: Categories

Stratification: Race, ethnicity, age, sex, basis of eligibility category, TANF, dual-eligible

Endnotes

http://www.cdc.gov/brfss/about.htm; accessed October 1, 2012.

http://www.cdc.gov/rdc/index.htm; accessed December 9, 2012.

http://www.cdc.gov/nchs/ahcd/about_ahcd.htm; accessed October 1, 2012.

http://www.skainfo.com/acquire.php, accessed October 1, 2012.

"The Availability and Usability of Behavioral Health Organization Encounter Data in MAX 2009." MAX Medicaid Policy Brief #14. Mathematica Policy Research, December 2012, Document No. PP12-107..

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Medicaid