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
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 |
|
Deborah Bachrach |
Manatt |
|
Dave Baugh |
Mathematica Policy Research |
|
Kathleen T. Call |
State Health Access Data Assistance Center (SHADAC) |
|
Stephen Cha |
Center for Medicaid and CHIP Services (CMS) |
|
William Clark |
Center for Medicare and Medicaid Innovation (CMS) |
|
Robin A. Cohen |
National Center for Health Statistics (NCHS) |
|
Kim Elliott |
Arizona Medicaid |
|
Janet Freeze |
Center for Medicaid and CHIP Services (CMS) |
|
James Gorman |
Center for Medicaid and CHIP Services (CMS) |
|
John Holahan |
Urban Institute |
|
Martha Kelly |
Acumen, LLC |
|
Sharon Long |
Urban Institute |
|
Julia Paradise |
Henry J. Kaiser Family Foundation |
|
Chris Peterson |
MACPAC |
|
Alan Weil |
National Academy for State Health Policy |
Federal & Association Stakeholders
|
Name |
Institutional Affiliation |
|---|---|
|
Andy Bindman |
ASPE, Office of Health Policy |
|
Nancy DeLew |
ASPE |
|
Kristin Fan |
CMS, Center for Medicaid and CHIP Services (CMCS) |
|
Dianne Heffron |
CMS , Center for Medicaid and CHIP Services (CMCS) |
|
Julia Hinckley |
CMS |
|
Abby Kahn |
National Association of Medicaid Directors (NAMD) Policy Analyst |
|
Rick Kronick |
ASPE |
|
Marsha Lillie-Blanton |
CMS |
|
Karen Llanos |
CMS |
|
Cindy Mann |
CMS |
|
Wilma Robinson |
ASPE |
|
Karyn Schwartz |
ASPE |
|
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) |
|
Penny Thompson |
CMS |
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) |
|
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.shadac.org/content/state-survey-research-activity; accessed October 1, 2012.
http://www.cdc.gov/nchs/nhis/about_nhis.htm; accessed October 1, 2012.
http://www.amstat.org/meetings/jsm/2012/onlineprogram/AbstractDetails.cfm?abstractid=306359; accessed October 10, 2012. Full paper forthcoming.
http://www.cdc.gov/rdc/index.htm; accessed December 9, 2012.
http://www.samhsa.gov/data/NSDUH/2k11MH_FindingsandDetTables/2K11MHDetTabs/NSDUH-MHDetTabsSect3pe2011.htm#Tab3.4N; accessed December 9, 2012.
http://www.cdc.gov/nchs/ahcd/about_ahcd.htm; accessed October 1, 2012.
http://www.cdc.gov/nchs/ahcd/namcs_participant.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..
http://www.mesconference.org/wp-content/uploads/2012/08/Monday_TMSIS_Gorman.pdf; accessed December 9, 2012.