SHADAC Staff

p 612.624.4802
e shadac@umn.edu

October 21st Webinar - Rising Suicide Rates: Examining Trends and Variations through State-level Data

Over the past two decades, the rise in suicide death rates has continued along an accelerating climb. According to new vital statistics data from the Centers for Disease Control and Prevention (CDC), the United States suicide rate reached another historic high of 14.2 per 100,000 people in 2018, up from 14.0 in the previous year. 

This increase in suicide deaths has not followed a consistent trend; rather, growth has accelerated more recently. From 2000 to 2009 the suicide death rate grew by 13 percent, but from 2009 to 2018 the rate grew by 21 percent. While these trends predate the COVID-19 pandemic, early evidence has indicated this crisis is taking a significant toll on mental health, and these data therefore represent an important baseline from which the effects of the pandemic will be measured.

During the webinar, SHADAC Research Fellow Carrie Au-Yeung used national and state-level data on suicide deaths to examine this growing public health issue, highlighting concerning trends and variations in suicide deaths by geographic locations such as regions, states, and metropolitan areas, as well as by specific subpopulation groups such as age, sex, and race/ethnicity.

Ms. Au-Yeung was also joined by SHADAC Research Fellow Robert Hest, who explained how to access and use the data on suicide deaths through SHADAC’s State Health Compare website. Senior Research Fellow Colin Planalp also joined the webinar for a question and discussion session following the presentation.

Related Resources
​Suicide Rates on the Rise: Examining Continuing Trends and Variation across the Nation and in the States from 2000 to 2018 (Briefs)
U.S. Suicide Death Rate Reached Record High in 2018: SHADAC Briefs Examine the Numbers among Subgroups and States 
State Health Compare Data Offer Baseline for Measuring Pandemic's Impact on Suicide, Drug Overdose Death Rates

National Suicide Prevention Lifeline: 1-800-273-8255
If you’re thinking about suicide, are worried about a friend or loved one, or would like emotional support, the Lifeline network is available 24/7 across the United States. For more suicide prevention resources, visit https://suicidepreventionlifeline.org/

Comparing Federal Government Surveys That Count the Uninsured: 2020

October 2020:

With the release of new insurance coverage estimates from surveys conducted by the U.S. Census Bureau, the Agency for Healthcare Research and Quality (AHRQ), and the Centers for Disease Control and Prevention (CDC), SHADAC has updated our annual “Comparing Federal Government Surveys that Count the Uninsured” brief.

The brief provides an annual update to comparisons of uninsurance estimates from four federal surveys:

• The American Community Survey (ACS)
• The Current Population Survey (CPS)
• The Medical Expenditure Panel Survey - Household Component (MEPS-HC)
• The National Health Interview Survey (NHIS)

In this brief, SHADAC presents current and historical national estimates of uninsurance along with the most recent available state-level estimates from these surveys. We also discuss the main reasons for variation in the estimates across the different surveys as well as possible reasons for incomparability of estimates across and within the surveys.

Download a PDF of the Comparing Federal Government Surveys Brief.

Last year’s brief with data from 2019, and certain 2018 data, can be accessed here.

 

SHADAC Staff

p 612.624.4802
e shadac@umn.edu

Exploring Strategies to Fill Gaps in Medicaid Race, Ethnicity, and Language Data

The following content is cross-posted from State Health and Value Strategies. It was first published on October 1, 2020.
Authors: Elizabeth Lukanen and Emily Zylla, SHADAC

As states seek to address the social determinants of health and advance health equity, they face longstanding and persistent challenges in collecting complete, accurate, and consistent race, ethnicity and language (REL) data. This expert perspective provides an overview of current REL data collection standards; ideas for increasing completeness in data by engaging the enrollee and enrollment assisters, and modifying enrollment and renewal interface; and provides suggestions for how states could leverage alternative sources of data in order to improve REL data completeness.

Background
Collecting standardized patient demographic and language data across health care systems is an important first step toward improving population health, identifying areas of health disparities, and addressing health equity and social risk factors. As seen during the recent COVID-19 pandemic, for example, disaggregated demographic data is critical to identifying problems, implementing solutions, and evaluating impacts. To-date, the amount of unknown race data varies in Medicaid programs across the country. Recently the Centers for Medicaid and Medicaid Services (CMS) classified 14 states’ Medicaid race and ethnicity data as “high concern” because more than 20 percent of the data was missing. An additional five states with more than 50 percent of their data missing were classified as unusable.¹ A review of the literature confirms that the deficiencies in REL data have changed over time from one of accuracy to one of completeness. The most commonly cited REL data collection challenges include:

• Lack of mandatory reporting standards
• Rapidly changing demographics
• Evolution in how people self-identify
• Voluntary reporting
• Lack of understanding on why the data are important
• Mistrust and enrollee concerns about how data will be used

Current Standards for Collecting Race and Ethnicity Data
There is a robust body of research, guidance, and tools related to the collection of REL data in health care settings. The Department of Health and Human Services (HHS) provides guidance (but does not mandate) to use the minimum standards developed by the US Office of Management and Budget (OMB), established in 1997 (Figure 1). These standards were updated after the passage of the ACA.

Self-identification is the preferred means of obtaining information about an individual's race and ethnicity

• Allow respondent to select multiple responses (as opposed to a “multi-racial” category)
• Collect race and ethnicity data using two questions, starting with ethnicity
• At a minimum, Office of Management and Budget (OMB) categories include:

• Optional questions are recommended for collecting data on specific language spoken
  • Do you speak a language other than English at home?
    • Yes
    • No
  • What is this language?
    • Spanish
    • Other Language (Identify) 

HHS also recommends collecting more granular data that reflects the population of interest/locally relevant choices, with the following considerations:

• Make sure the categories can be aggregated back to the minimum categories above (see Figure 2.)
• Consider conducting analysis of Census data to select these options
• Rely on established coding systems such as the CDC code set for race and ethnicity
• Although “Other” is not an official OMB designation, the US Census and OMB also recommend including a “some other race” option.

The number of response categories a state offers often will depend on format and space available (e.g., web vs paper) and whether or not a state has the staffing capacity to recode write-in responses. States are encouraged to report additional granularity where it is supported by sample size and as long as the additional detail can be aggregated back to the minimum standard set of race and ethnicity categories.

Race and Ethnicity: New Census Research
In preparation for the 2020 census, the agency conducted research to improve race/ethnicity collection.[2] The goals of the project were to increase the accuracy and reliability of reporting in the major OMB racial and ethnic categories; collect detailed data for myriad groups; and obtain lower item nonresponse rates. The research had several major findings, including:

• Reinforcing the importance of allowing multiple responses
• Suggesting that “Mark all that apply” or “Select all that apply” is better than “Select one or more”
• Using race/ethnicity terminology is less confusing than using terms like “category,” which can suggest a hierarchy
• Data collection is improved when there is a dedicated “Middle Eastern or North African” response category for race (currently classified as “White”)
• Data collection is improved when a “write-in line” is used to collect detailed American Indian/Alaskan Native (AIAN) responses, however including a limited number of conceptual checkboxes (i.e., American Indian, Alaska Native, and Central/South American Indian) decreased detailed reporting for the AIAN category. According to Census Bureau research there are hundreds of very small detailed AIAN tribes, villages, and indigenous groups.  Listing the six largest American Indian groups and Alaska Native groups as checkboxes, for example, would represent only about 10 percent of the entire AIAN population. Therefore, providing a distinct write-in area was determined to be the best overall approach for eliciting detailed responses across AIAN communities and identities.

Overall, the research found evidence that a providing a combined race/ethnicity question with detailed checkboxes resulted in increased use of OMB standard categories, decreased nonresponse, and improved accuracy.

Strategies for Improving and Revising Collection Standards
Research continues to inform REL data collection efforts, and practices continue to evolve. Data collection standards change in response to research, but also in response to the rapidly changing demographics and fluidity of racial and ethnic self-identification. States should review data collection practices periodically, as well as the overall demographics of the state’s population to assure REL response options are appropriate, but only modify collection practices if needed. If modifications are needed, make sure changes do not impact the ability to compare/link your data to other sources (e.g., other state agency data, data from providers, etc.). If possible, engage enrollees and their advocates in decision-making related to how REL data are collected and get feedback on any changes that are made.

The following suggestions are strategies that states could employ to help increase REL data completeness in their Medicaid programs.

Engage Enrollees
Some Medicaid enrollees may be sensitive about answering questions about their race/ethnicity or preferred language because of concerns about discrimination. Consider the following steps to help enrollees overcome that concern:

• Conduct outreach to ask the enrollee community, and community leaders, for their input about how to best collect this information
  • Proactively seek advice on why data are not being provided and how the state can improve data collection
  • Collect input from stakeholders into how data are collected, defined, and used (e.g., A/B user testing, testing what educational messages work, etc.)
  • Report findings back to stakeholders when data are available
• Develop a communication strategy focused on the importance of REL data collection, addressing concerns about collecting this data, and providing enrollees information specifically on:
  • How are REL data used and NOT used? (e.g., can the information be used to enforce immigration laws)
  • The state’s privacy policy
  • Who has access to these data and how these data are protected
  • Where results are reported

Engage Enrollment Assisters
Enrollment assisters (term used broadly) play a large roll in application submission and renewal. In many cases, assisters serve as trusted source of information and are well positioned to talk to enrollees about providing REL data. Like the enrollees, however, assisters may not know why REL data are being collected, how data are used and might skip questions to save time. Assisters pride themselves on being neutral and may not be comfortable making recommendations to clients about providing data. Consider the following steps to help enrollment assisters collect complete and accurate REL data:

• Develop trainings, role playing, talking points, and fact sheets that address key questions about REL data collection broadly, but also provide specific tips regarding client engagement. (Training and tools should be framed as information to educate their clients, not coerce them.) Trainings can address:
  • Where in the application/renewal form or online process are REL questions asked and how can discussions about the importance of these questions be incorporated into assister workflows?
  • Where can the assister send people if the client has questions or complaints about response options
• For assisters where Medicaid has a direct or contractual relationship (call center employees, contractors, grantees):
  • Require training on the topic
  • Make enrollee discussions about REL data part of the formalized work flow
• For the broader assister community
  • Communicate with umbrella organizations that support smaller community based organizations as a way to build trust and spread the message

Modify the Enrollment/Renewal Interface
Since the implementation of the ACA, states have made significant progress in streamlining and simplifying their enrollment processes and systems. As required by the ACA, all states now have to accept Medicaid applications via multiple modes including in person, mail, telephone, and online. The following strategies could help states increase the collection of REL data through their online enrollment and renewal platforms.

• Include text, or links to text, explaining the importance of collecting REL data and how the information will be used in online forms
  • Utilize hover text with links
  • Provide reminder prompts: “Are you sure you want to skip this question?”
• Use research-tested messages to explain the value of collecting race, ethnicity, and language data (Figure 3)

A research study tested four messages related to the collection of race/ethnicity data and measured individual’s comfort in providing that information:

1. Quality Monitoring: “…review the treatment that all patients receive and make sure everyone gets the highest quality of care”
2. Government Recommendation: “Several government agencies recommend we collect information…as part of a national effort to make sure all patients have access to quality health care”
3. Needs Assessment: “…this will help us decide who to hire, how to train our staff better, and what health information is most helpful for our patients”
4. Personal Gain: “…help us make sure you get the best care possible…”

Telling patients that this information would be used for monitoring quality provided the most comfort

Source: Baker, D.W., et al. (2005). Patients’ attitudes toward health care providers collecting information about their race and ethnicity. J Gen Intern Med, 20(10), 895-900.

• Increase opportunities for enrollees to provide REL data for collection if data is missing
  • Prompt near the end of the application
  • Prompt when the enrollee logs in to account (e.g., when they log in to re-enroll or report changes)
  • Send special email reminder to complete this information a short time after enrollment or renewal

Leverage Alternative Data Sources
Another potential strategy is to use other data sources to fill gaps or validate Medicaid data. This is a particularly successful strategy if the data source in question is more complete (e.g., Minnesota has robust provider quality reporting standards that include REL data collection). States could compare Medicaid data against other data sources to look for systematic gaps (e.g., implausibly low rates of certain races/ethnicities). In some cases, states are collecting data at several points in time – e.g., eligibility determination, enrollment into a health plan, and at the time of treatment. These data can be used together and to validate the strongest and most complete data source. Potential data sources might include:

• Encounter data collected directly or from managed care organizations
• Data from health and social needs assessments
• Vital records data
• Data from other state agencies

Leveraging other data is not without challenges and might be best approached initially as a pilot initiative. For example, data use agreements take time to execute and data linking can face technical challenges and take up scarce analyst time. Also, data from other sources often has similar issues related to quality and completeness or different issues.

October 13th Webinar - "From Telephone to Multi-mode Surveys: State Case Studies and Conversations"

Date: October 13, 2020

Time: 11:30 AM Central / 12:30 PM Eastern

 

For years response rates for random digit dial (RDD) telephone surveys have been falling and costs of conducting these surveys have been rising. With growing concerns about sample coverage and costs, many ongoing government, academic and commercial surveys are making the transition from reliance on RDD sample frames with interviewers collecting data only by telephone to address-based sample (ABS) frames with letters mailed to residents and data collected through multiple self-administered modes including web or paper surveys.

This panel documents how this transition unfolded for three states...

• California
• Massachusetts
• Minnesota

...with ongoing surveys used to document trends in health insurance coverage, access to and affordability of healthcare. State analysts and survey experts highlight their experiences, successes and challenges faced in this transition sharing lessons learned. We encourage a conversation among panelists and the audience concerning the rewards and challenges of transitioning from RDD telephone to ABS multi-mode surveys.

Moderator

 Dr. Kathleen Call, Faculty and SHADAC Investigator

 University of Minnesota School of Public Health

 

Panelists

 Sarah Hagge, Research Scientist

 Minnesota Department of Health

 

 Todd Hughes, Director of the California Health Interview Survey (CHIS)

 University of California Los Angeles

 

 Zi Zhang, Director

 Massachusetts Center for Health Information & Analysis

 

Slides from this webinar are also available to view and download. Please feel free to contact us at either astewart@umn.edu or shadac@umn.edu if you have any follow-up questions on the webinar content or would like to speak to a specific presenter regarding their portion of the presentation.

SHADAC Staff

p 612.624.4802
e shadac@umn.edu

New Subsidized Marketplace Data and Other Data Tables Now Available from the 2019 American Community Survey (ACS)

The U.S. Census Bureau recently released 2019 estimates of income, poverty, and health insurance coverage from both the Current Population Survey Annual Social and Economic Supplement (CPS ASEC) and the American Community Survey (ACS).

Along with the new estimates, several new data sets and features from the surveys are also now available for this year, including new estimates of subsidized marketplace insurance coverage, which is the main subject of this post.

New Data
In 2019, for the first time, the American Community Survey (ACS) asked respondents if they or a family member received a “tax credit or subsidy based on family income” to help pay for their coverage.¹ These subsidies are only available through the Affordable Care Act (ACA) marketplaces for individuals who are eligible based on their family income. By adding this question, researchers at the Census Bureau and other data users are now able to create estimates for the number and percent of the population who receive subsidized ACA marketplace coverage.

As part of a high-level analysis, SHADAC researchers found that at the national level, approximately 1.6% of the civilian noninstitutionalized population reported having subsidized marketplace coverage—representing nearly 5.3 million individuals.

Across the states, rates of subsidized marketplace coverage ranged significantly from a low of 0.7% in West Virginia and D.C. to a high of 3.4% in Florida and Utah. 

The five states with the largest populations of individuals with subsidized marketplace coverage were California, Florida New York, North Carolina, and Texas. More than 40% of total marketplace enrollees lived in one of these five states, and of that subsection of enrollees, nearly 3 in 5 lived in either California or Florida.

Eleven states (Florida, Idaho, Maine, Montana, Nebraska, North Carolina, South Carolina, South Dakota, Utah, Wisconsin, and Wyoming) had rates of subsidized marketplace coverage that were significantly higher than the national rate in 2019. Of these states, only Montana and Maine had implemented Medicaid expansion for the majority of 2019, which expands the portion of the population eligible for ACA subsidies. (Montana implemented Medicaid expansion as of January 1, 2019, and Maine implemented expansion on January 10, 2019.)

Twenty states (Alaska, Arizona, Arkansas, Connecticut, Delaware, Hawaii, Iowa, Illinois, Indiana, Kentucky, Louisiana, Massachusetts, Maryland, Minnesota, Mississippi, New Mexico, New York, Ohio, Washington, and West Virginia) and D.C. had rates of subsidized marketplace coverage that were significantly lower than the national rate in 2019. Of these 20 states and D.C., only Mississippi had chosen not to expand Medicaid as of January 1, 2019.

Nineteen states had rates of subsidized marketplace coverage that were not statistically different from the national rate.

New Data Tables and Geographic Breakdowns
Along with the new question and corresponding data table on subsidized coverage discussed above, other new data tables available from the ACS this year include:

• Population: a new table on place of birth shows the year of entry among the foreign-born population for the nine largest country of birth groups. Estimates are divided between year of entry before 2010 and year of entry beginning 2010 and later.
• Households and Families: two new tables provide information regarding (1) couples who live together with biological children, stepchildren, or adopted children of the main householder who are under 18 and have not been married; and (2) married couples, cohabiting couples, and single householders (male or female) with no spouse or partner present who also live with either relatives or their own children under 18.
• Quality Measure: this new table provides the unweighted total population sample for the nation, states, counties, and places.

Extensive modifications have also been made to existing ACS data tables, a full listing of which can be found here.

In addition to data table changes, the Census Bureau has also created an updated posting regarding geographic entities of varying sizes and designations (cities, towns, townships, school districts, Native American reservations, etc.) that have either come into existence, been absorbed into other entities, or have been dissolved in 2019. A full listing of all new, modified, or removed geographic breakdowns used for the 2019 ACS estimates can be found here.