Utah and Nebraska State Medicaid Expansions in 2020 and AI/AN Health Insurance Access

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Introduction

The Affordable Care Act (ACA), enacted in 2010 and implemented in 2014, included two provisions to expand health insurance coverage, the creation of health insurance marketplaces, and giving states the option to expand Medicaid coverage to families with incomes to 138% of the federal poverty level.[1]

One goal of the ACA was to expand health insurance coverage of the American Indian and Alaska Native (AI/AN) population. In 2013, prior to ACA implementation, 24% of AI/ANs reported having no health insurance coverage, as measured by the 2013 American Community Survey 1-Year Estimate. In 2015, after the implementation of ACA, 17% of AI/ANs were uninsured. There were 25 states who adopted Medicaid Expansion during the first year of eligibility in 2014. In expansion states, the percentage of the AI/AN population without insurance dropped from 23% pre-expansion to 15% post-expansion.[2] Similarly, the National Indian Health Board compared the AI/AN uninsured rate between the 2008-2012 Five-Year ACS and the 2017-21 Five Year ACS, and estimated that the national AI/AN uninsured rate fell from 24.2% to 14.8%[3]

As of April 2024, 41 states have adopted the ACA’s Medicaid Expansion program[4]. The most recent states to adopt the program are:

  • North Carolina, which implemented expansion in December 2023.
  • South Dakota, which implemented expansion in July 2023.
  • Oklahoma, which implemented expansion in June 2021.
  • Missouri, which implemented expansion in October 2021.
  • Utah, which implemented expansion in January 2020.
  • Nebraska, which implemented expansion in October 2020.

At the time of the drafting of this report, the most recent year of American Community Survey 1-Year Public Use Microdata Sample (PUMS) is from 2021. Therefore, Utah and Nebraska are the most recent expansion states with available post-expansion ACS data. In both Utah and Nebraska, Medicaid Expansion was adopted by ballot initiative in November 2018. In both states, there were efforts by the state legislature to limit and delay implementation.[5][6]

Despite similar origin stories, the two expansions led to different outcomes for AI/AN residents in each state. In Utah, the AI/AN uninsured rate dropped from 32.1% in 2019 to 19.5% in 2021. The rate of Medicaid coverage increased from 12.8% to 14.8%. In Nebraska the AI/AN uninsured remained almost unchanged (from 30.8% to 29.9%). The AI/AN Medicaid coverage rate decreased by 3%, while the non-AI/AN Medicaid coverage rate increased by 3% (Table 1).[7]

Table 1: Uninsured and Medicaid Coverage Rates for AI/AN and non-AI/AN respondents (Ages 19-64)

Uninsured 2019 Uninsured 2021 Medicaid 2019 Medicaid 2021
Utah AI/AN 32.1% 19.5% 12.8% 14.8%
Utah non-AI/AN 12.1% 11.4% 6.3% 8.3%
Nebraska AI/AN 30.8% 29.9% 15.5% 12.9%
Nebraska non-AI/AN 11.4% 9.4% 6.8% 10.3%

Data and Methods

In interpreting changes over time, it is difficult to disentangle the effect of state Medicaid expansion from national conditions such as the Covid-19 pandemic, national economic conditions, and Federal health policy. To better understand specific factors, I employed two logistic regression[8] models on insurance coverage, one model using the ACS 2019 1-year data and a second model using the ACS 2021 1-year data. This model excludes respondents under 19 and over 64 to avoid complications from CHIP and Medicare policies.

To isolate state-specific factors from national effects, I created two ‘dummy’ variables. The variable “Utah” is coded as 1 if the respondent resides in Utah and 0 if the respondent resides in any of the other 49 states or the District of Columbia. The variable “Nebraska” is coded as 1 if the respondent resides in Nebraska and 0 if the respondent resides in any of the other 49 states.

The model includes the “AI/AN” variable, this variable is coded as 1 if the respondent self-identifies as American Indian or Alaska Native, and 0 if the respondent does not. The variable is coded as 1 even if the respondent identifies with multiple other racial categories. According to the Medicaid and CHIP Access Commission, “There are several challenges to enrolling eligible AI/AN people in Medicaid, including geographical remoteness, limited access to Internet or phone service, language barriers, cultural factors, distrust of government programs, or lack of knowledge of the benefits of coverage.”[9]

To isolate the State-level Indian health policy context, the model includes interaction effects between the state dummy variables and the AI/AN variable.

The ACS health insurance question administered to respondents includes IHS usage listed as a type of “coverage” but IHS is not coded as “coverage” when Census derives an “Any Insurance Coverage” variable from the response option. Depending on the availability of services, IHS users may not perceive a need for additional coverage, so it is important to include this variable in the model.

In the research literature on the uninsured, age and income are consistent determinants of coverage, so the model includes age and family income as a percentage of the federal poverty line.[10]

Results

In 2019, non-AI/AN residents of Utah had 7.9% higher odds of coverage as compared to the rest of the United States. The logistic regression model also produces an estimate of the marginal effect of each variable, when all other variables are set to their means. In 2019, Utah residents had a 0.7% higher marginal probability of coverage. The “Utah Effect” in 2021 is not substantially different for non-AI/AN residents (Table 2).

In 2019 non-AI/AN Nebraska residents had a 1.6% higher marginal probability of coverage as compared to the rest of the U.S. In 2021, non-AI/AN residents had 2.6% higher marginal probability of coverage, as compared to the rest of the U.S. It is possible that this difference reflects increased access to health insurance from the Medicaid expansion.

In 2019, the national AI/AN population (excluding Utah and Nebraska) had a 0.9% lower marginal probability of coverage as compared to the national non-AI/AN population. It is interesting to note that this AI/AN coverage ‘disadvantage’ is independent of family income and may reflect aforementioned coverage barriers. The magnitude of the disadvantage doubles between 2019 and 2021, despite policy efforts to preserve coverage.

The interaction term between Utah residency and AI/AN status can be interpreted as either:

  • The coverage disadvantage of Utah residency among AI/AN respondents, OR
  • The coverage disadvantage of AI/AN status among Utah residents.

Among AI/ANs in 2019, AI/AN Utahans have a 6.4% lower marginal probability of coverage than AI/ANs elsewhere. Among Utahans in 2019, AI/AN Utahans have a 6.4% lower probability of coverage than non-AI/AN Utahans. In 2021, the Utah X AI/AN interaction term coefficient is not significantly different from zero, suggesting that Utah’s Medicaid expansion possibly ameliorated disadvantageous coverage barriers specific to AI/ANs in Utah.

Table 2: Odds Ratio on Insurance Coverage ACS 2019 and ACS 2021

2019 Odds Ratio 2019 Marginal Effect at Means 2021 Odds Ratio 2021 Marginal Effect at Means
Utah 1.079** 0.7% 1.061* 0.5%
Nebraska 1.185** 1.6% 1.358** 2.6%
AI/AN 0.917** -0.9% 0.770** -2.7%
Utah X AI/AN 0.593** -6.4% 0.984 -1.1%
Nebraska X AI/AN 0.638* -5.3% 0.376** -13.3%
IHS User 0.405** -12.6% .532** -7.6%
Age In Years 1.009** 0.1% 1.008** 0.1%
Family income as % of fed poverty line 1.004** 0.4% 1.004** 0.0%

** 99% Statistical Significance
*95% Statistical Significance

While non-AI/AN Nebraskans saw an improvement in probability of coverage between 2019 and 2021, the opposite effect is observed for Nebraska AI/AN’s. The coverage disadvantage of Nebraska residency among AI/AN respondents increased from 5.3% to 13.3%. More research is required to determine how this occurred in the context of Medicaid expansion.

[1] HealthCare.gov. “Medicaid Expansion & What It Means for You.” Accessed November 7, 2023. https://www.healthcare.gov/medicaid-chip/medicaid-expansion-and-you/.

[2] Samantha Artiga, Petry Ubri, and Julia Foutz. “Medicaid and American Indians and Alaska Natives.” Issue Brief. Henry J. Kaiser Family Foundation, September 2017. https://files.kff.org/attachment/issue-brief-medicaid-and-american-indians-and-alaska-natives.

[3] Rochelle Ruffer. “State Health Insurance Status Report.” National Indian Health Board, July 2023. https://www.nihb.org/resources/NIHB%20State%20Health%20Insurance%20Status%20Report_July%202023.pdf.

[4] Published: “Status of State Medicaid Expansion Decisions: Interactive Map.” KFF (blog), October 4, 2023. https://www.kff.org/medicaid/issue-brief/status-of-state-medicaid-expansion-decisions-interactive-map/.

[5] Maresh, Sarah. “Expanding Medicaid in Nebraska and the Fight to Stop Harmful Waivers, Tiered Benefits.” National Health Law Program, November 2, 2022. https://healthlaw.org/expanding-medicaid-in-nebraska-and-the-fight-to-stop-harmful-waivers-tiered-benefits/.

[6] Musumeci, MaryBeth, Madeline Guth, Robin Rudowitz, and Cornelia Hall Published. “From Ballot Initiative to Waivers: What Is the Status of Medicaid Expansion in Utah?” KFF (blog), November 15, 2019. https://www.kff.org/medicaid/issue-brief/from-ballot-initiative-to-waivers-what-is-the-status-of-medicaid-expansion-in-utah/.

[7] Steven Ruggles, Sarah Flood, Matthew Sobek, Danika Brockman, Grace Cooper, Stephanie Richards, and Megan Schouweiler. IPUMS USA: Version 13.0 [ACS 2019 & ACS 2021]. Minneapolis, MN: IPUMS, 2023. https://doi.org/10.18128/D010.V13.0

[8] J. Scott Long. Regression Models for Categorical and Limited Dependent Variables. Vol. 7. Advanced Quantitative Techniques in the Social Sciences Series. Sage Publications, 1997.

[9] “Medicaid’s Role in Health Care for American and Alaska Natives.” Issue Brief. Medicaid and CHIP Access Commission, February 2021. https://www.macpac.gov/wp-content/uploads/2021/02/Medicaids-Role-in-Health-Care-for-American-Indians-and-Alaska-Natives.pdf.

[10] “Who Went Without Health in 2019, and Why?” Congressional Budget Office, September 2020. https://www.cbo.gov/system/files/2020-09/56504-Health-Insurance.pdf.

Driving Success: Best Practices for GPRA Compliance and Performance

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GPRA/GPRAMA OVERVIEW:

The Government Performance and Results Act (GPRA) of 1993 is a federal law that mandates agencies to demonstrate effective and efficient use of congressional funds.1 The Indian Health Service (IHS) has been reporting GPRA data for over a decade.1 This data helps the IHS to evaluate its progress towards achieving its goals and objectives, which ultimately benefits the health of Native Americans.

To ensure that federal agencies make informed decisions, the Government Performance and Results Modernization Act (GPRAMA) was introduced in 2010 as an update to GPRA. This act requires federal agencies to leverage performance data in their decision-making processes. The GPRA and its modernization counterpart, the GPRAMA, play crucial roles in shaping how federal agencies are held accountable for their performance and results. For instance, in the context of healthcare services, GPRAMA shows Congress how well the IHS is providing care to American Indians and Alaska Natives who use IHS federal, tribal, and urban Indian health facilities.2

The IHS started reporting under GPRAMA in FY 2013, which involves a smaller set of measures compared to GPRA. 1 The 26 clinical GPRA/GPRAMA measures are collected throughout the GPRA year (October 1 to September 30) using the Integrated Data Collection System (IDCS) and exported to the National Data Warehouse (NDW).1 This data is cumulative and aggregates results from all reporting clinics, including federal, tribal, and urban Indian health programs, into national outcomes.1

Regardless of the electronic health record (EHR) system they use, urban Indian health programs can report GPRA data.1 This makes it easier for them to contribute to a comprehensive national database and ensures that the IHS has access to the information it needs to make informed decisions. By leveraging this data, the IHS can continue to provide effective and efficient healthcare services to Native Americans across the country.

COMPARATIVE UIO PERFORMANCE OF GPRA/GPRAMA MEASURES:3-5

GPRA/GPRAMA measures are indicators of how well the agency has provided clinical care to its patients. Overall, they measure how well the IHS has done in the prevention and treatment of certain diseases, and the improvement of overall health. The table below outlines the measures of performance of Urban Indian Organizations (UIOs) over three years (2018, 2019, 2020), comparing actual results against set goals. The measures are categorized into the “Top 3” and “Bottom 3” based on performance. In the “Top 3” category, Statin Therapy consistently exceeded its goals, demonstrating a significant improvement. Nephropathy and CVD Statin Therapy measures also performed well, with Nephropathy showing yearly increases in actual achievements. However, the “Bottom 3” measures faced challenges, with Exclusive/Mostly Breastfeeding meeting its goal in 2019, while IPV/DV Screening and Adult Immunizations fell short of their goals every year reviewed.  In 2020, two new measures, Topical Fluoride and Retinopathy Exam, were introduced as part of the GPRA tracking. However, in their first year, they did not meet their targets. This data underscores the substantial progress in some areas and the need for enhanced focus and improvement in others, particularly in preventative screenings and healthcare interventions.

TOP ACHIEVERS AND AREAS OF IMPROVEMENT (2018-2020) 3-5

   Top 3 Actual Goal Bottom 3 Actual Goal
2
0
1
8		 1 Statin Therapy 61.6% 37.5% Exclusive/Mostly Breastfeeding (Age of 2 Mos) 25.0% 39.0%
2 Nephropathy 50.2% 34.0% IPV/DV Screening 30.1% 41.6%
3 CVD Statin Therapy 41.9% 26.6% Mammography Screening 33.6% 42.0%
2
0
1
9		 1 Statin Therapy 61.4% 37.5% Adult Immunizations 33.5% 54.9%
2 Nephropathy 51.6% 34.0% Exclusive/Mostly Breastfeeding (Age of 2 Mos) 28.6% 28.6%
3 CVD Statin Therapy 40.3% 26.6% IPV/DV Screening 32.6% 41.6%
2
0
2
0		 1 Statin Therapy 62.6% 51.6% Adult Immunizations 25.9% 59.7%
2 CVD Statin Therapy 42.8% 35.7% Topical Fluoride 14.1% 34.5%
3 HIV Screening Ever 31.5% 28.4% Retinopathy Exam 36.0% 53.5%

Definitions of Performance Metrics See Appendix*

BEST PRACTICES TO ADDRESS PROBLEMS AND CHALLENGES IN GPRA DATA:7

UIOs face several challenges in their efforts to collect data and achieve GPRA metrics. These challenges include the need to troubleshoot issues, secure healthcare center buy-in, provide adequate training, lack of employee capacity, and implement technology updates and requirements.

To begin to address these challenges, it is essential to prioritize Electronic Medical Record (EMR) reminders or alerts for clinicians, and updated medical coding, taxonomies, and ICD-10 codes. These updates will allow for more efficient and streamlined healthcare services and reporting.

Another critical challenge is the lack of healthcare center buy-in. The prioritization of GPRA within Urban Indian Organizations is an essential aspect of enhancing the quality of care. By visualizing benchmarks as indicators of care quality, as opposed to solely numerical targets, UIOs can integrate these standards into their daily operations. It is recommended that sites hold themselves accountable for adhering to these standards for both internal assessment and external reporting purposes. In instances where metrics are not met, it is advisable that they are considered as opportunities for Quality Assurance Projects.

Establishing a dedicated GPRA team comprising of both medical and non-medical staff can ensure that responsibilities are clearly defined, thereby making the targets more attainable and allowing providers to focus more on patient interactions.7 Effective coordination and communication are crucial to overcoming these challenges. Therefore, establishing a GPRA Coordination Committee and holding regular structured interactions, such as morning huddles or weekly medical meetings, provide ample opportunities to discuss GPRA metrics. These interactions facilitate the sharing of insights and strategizing on how best to further improve metrics, fostering a collaborative environment for continuous improvement. Furthermore, ensuring the availability, improvement, and sharing of GPRA data is fundamental. Data should be readily accessible, regularly updated, and shared with all stakeholders, including medical and quality improvement staff, to allow for early identification of issues and collaborative problem-solving.7

Encouraging internal development and the use of technology can further enhance the effectiveness of GPRA measures.7 UIOs should foster local solutions and employ information technology to track care delays and community health statuses. Innovations such as electronic clinical reminders and specialized clinics that provide comprehensive care and use incentives can significantly improve patient outcomes.  Additionally, learning from peers or other UIOs by visiting sites that exhibit best practices can provide invaluable insights into effective strategies and areas requiring improvement. This peer learning helps in directly understanding what works and what doesn’t from those who have experienced it first-hand.

It is important to focus on the actual health outcomes achieved through the implementation of GPRA measures, rather than merely the activities performed. Adhering to an outcomes-focused approach ensures that the efforts are not solely geared towards meeting metrics but genuinely improving patient health, which is the ultimate goal of these measures. In conclusion, addressing the challenges faced by Urban Indian Organizations is essential to improving healthcare services for communities. By prioritizing the necessary updates and providing adequate training, we can ensure that healthcare centers are fully invested in delivering high-quality care to all patients, and by promoting effective communication, we can continuously improve our services.

For more information on best practices visit: https://www.ihs.gov/crs/toolbox/.

For more information on GPRA Performance Measures visit: https://ihs.gov/sites/crs/themes/responsive2017/display_objects/documents/crsv24/GPRA-FY-2022-2023-2024.pdf

APPENDIX

References:

  1. Claymore, V., & Boney, M. (2024, March). UIO GPRA/GPRAMA UPDATES. Indian Health Services. Online; Online.
  2. Indian Health Services. (n.d.). Understanding the Government Performance and Results Act (GPRA)/ GPRA Modernization Act (GPRAMA). https://www.ihs.gov/crs/includes/themes/responsive2017/display_objects/documents/toolbox/GPRAHandoutforPatients.pdf
  3. Weahkee, R., & Mueller, R. (2018). 2018 Indian Health Service Urban Indian Health Organizations GPRA/GPRAMA Results. Rockville, MD; Indian Health Service.
  4. Weahkee, R., & Mueller, R. (2019). 2019 Indian Health Service Urban Indian Health Organizations GPRA/GPRAMA Results. Rockville, MD; Indian Health Service.
  5. Weahkee, R., & Mueller, R. (2020). 2020 Indian Health Service Urban Indian Health Organizations GPRA/GPRAMA Results. Rockville, MD; Indian Health Service.
  6. Indian Health Services. (n.d.). 2011 GPRA Best Practices. 2011 GPRA Meeting. https://www.ihs.gov/california/tasks/sites/default/assets/File/GPRA/2011GPRAMtg-GPRABestPractices.pdf

Definitions:

  • Adult Composite Immunizations: Percentage of adults age 19 and older who receive recommended age-appropriate vaccinations.
  • Breastfeeding Rates: Percentage of patients who, at the age of 2 months, were either exclusively or mostly breastfed.
  • Cancer Screening: Mammogram Rates: Percentage of women ages 52 to 74 years of age, who have had mammography screening within the previous two years.
  • Diabetes: Statin Therapy to Reduce CVD Risk in Patients with Diabetes: Percentage of patients with diagnosed diabetes who received a prescription for statin therapy.
  • Diabetes: Nephropathy Assessment: Percentage of patients with diagnosed diabetes assessed for nephropathy.
  • Diabetes: Retinopathy: Percentage of patients with diagnosed diabetes who received an annual retinal exam
  • Domestic (Intimate Partner) Violence Screening: Percentage of women who are screened for domestic violence at health care facilities.
  • HIV Screening Ever: Percentage of patients who were ever screened for HIV.
  • Statin Therapy for the Prevention and Treatment of Cardiovascular Disease: Percentage of patients with CVD or at high risk for CVD who receive a statin therapy prescription
  • Topical Fluorides: Percentage of patients ages 1-15 who received one or more topical fluoride applications.

Life Expectancy Rates for American Indian and Alaska Native People Dropped Drastically During the COVID-19 Pandemic

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Overview:

COVID-19 has impacted life expectancy across the globe, reversing trends of life expectancy gains.1,2 American Indian and Alaska Native (AI/AN) people have lower life expectancies than the non-Hispanic White (NHW) population. While the gap between life expectancies for the AI/AN and White populations decreased in the late 20th century and early 21st century, during the COVID-19 Pandemic, the difference in AI/AN and NHW life expectancies grew by over ten years. When looking at the top-of-the-decade life expectancies for each population, a 10+ year difference in life expectancy was last seen between the two populations in 1940.

Life Expectancy Definition:

Life expectancy at birth is the calculated estimate of years a given population (i.e., sex, race/ethnicity, place of residence, etc.) is expected to live if born in the given year, based on current mortality and life trends.3

Life expectancy for the AI/AN population from 1940-1990 was calculated using Indian Health Service (IHS) patient records to calculate life expectancy, so those decades would include single-race or combination-race IHS patients in the given timeframe. From 2000-2021, life expectancy for the AI/AN population was measured using National Vital Statistics System (NVSS) Centers for Disease Control and Prevention (CDC) data, which only included the single-race-identified AI/AN population.

Explanation of Data:

Historically, AI/AN people experienced significantly lower life expectancies than NHW people in the United States. Currently, the life expectancy for an AI/AN person born in 2021 is 65.2 years, 11.2 years less than an NHW person’s life expectancy born in the same year (76.4 years).4 The life expectancy for AI/AN people in 2021 was comparable to the overall life expectancy for Americans during the first two years of US military involvement in World War II (1942 and 1943).5,6

  • 2021 -> AI/AN pop. 65.2 years4
  • 1942 -> All Races/All Sexes US 66.2 years5
  • 1943 -> All Races/All Sexes US 63.3 years5

In addition, within the inaugural year of the COVID-19 pandemic, a universal decline in life expectancies was observed across the US. However, the decrease was particularly larger within the AI/AN community, where life expectancy decreased by nearly five years between 2019 and 2020. In contrast, the life expectancy for NHW populations diminished by approximately 1.5 years during the same period. This stark contrast underscores the profound inequities experienced among AI/AN people in the United States, highlighting the catastrophic repercussions of the COVID-19 pandemic on Native communities. Table 1 showcases the historic gap in life expectancies for AI/AN people compared to NHW people in the United States.

Table 1.

Historic Life Expectancy (at Birth) for AI/AN Population Compared to NHW Population

Year Life Expectancy for AI/AN Life Expectancy for NHW
1940 51.6 y/o7 62.1 y/o7
1950 60.0 y/o7 69.1 y/o7
1960 61.7 y/o7 70.6 y/o7
1970 65.1 y/o7 71.1 y/o7
1980 71.1 y/o7 74.4 y/o7
1990 73.2 y/o 76.1 y/o8
2000 73.1 y/o9 77.3 y/o10
2010 73.5 y/o9 78.9 y/o10
2019 71.8 y/o9 78.8 y/o4
2020 67.1 y/o4 77.4 y/o4
2021 65.2 y/o4 76.4 y/o4

Note: The table includes a summary of AI/AN and NHW populations’ life expectancy (at birth) for all sexes data by the decade in the same table. See Limitations of Data for more information on population.

Figure 1.

Historic Life Expectancy (at Birth) for AI/AN and White Populations in the United States

Note: See Table 1 for more information on population data for data sources for each decade.

Chart: Historic Life Expectancy (at Birth) for AI/AN and White Populations* in the United States by Decade

Limitations of the Data:

Life data tables for the AI/AN population were not produced by the CDC/US Census Bureau until 2019.8 Much of the life expectancy data comes from IHS and then is linked by researchers with data from NVSS, the US Census Bureau, or other government agencies, to collect an estimate on the AI/AN population’s life expectancy for a given year. For example, NVSS grouped different races/ethnicities into “White” and “Nonwhite” until 1969.11 Historic life expectancies for the Black population had to use “Nonwhite” data until additions were made to the racial categories for vital statistics data in 1970.12 Despite the addition of “Black” as a racial category in the ‘70s, the non-Hispanic AI/AN demographic was not included as a racial category for the NVSS United States Life Tables until the publication of the United States Life Tables, 2019 in 2022.13 However, the United States Life Tables, 2019 only include single-race-identified AI/AN people due to the 1997 Office of Management and Budget’s (OMB) standards on classifications for race and ethnicity.13–15 It is important to note that in 2019, the National Center for Health Statistics published a study  in the  Mortality and Morbidity Weekly Report  that sounded the alarm of a Public Health Emergency for the existing health disparities within the  AI/AN population, stating,

“My hope is that policymakers and health care providers recognize the very large disparity between this population and all other populations in this country… they have the worst health profile and mortality risk in this country. So, I would call it an emergency. It is something that should be taken very seriously.” 16

The study also showed that AI/AN people have higher mortality rates for most of the top leading causes of death and in most age groups compared with White, Black, and Hispanic people.

Specifically, the data in Table 1 for the life expectancy for AI/AN people from 1940-1990 used IHS records to calculate AI/AN life expectancy, while the NHW life expectancy used census data. The IHS data only uses a sample of AI/AN people who received services or care from an IHS facility. The IHS user population is not representative of all AI/AN people within the United States in that timeframe or in another timeframe.7,17

2000-2021 data for the AI/AN population use a more inclusive population from NVSS that encompasses more AI/AN people compared to IHS datasets, which are inclusive of AI/AN-identified people who do not receive care from IHS. However, due to NVSS following OMB standards for reporting, only single-race-identified AI/AN people were included in the AI/AN population.4,18

Additionally, death records for AI/AN people are frequently misclassified for race. There is continuous documentation of racial misclassification of AI/AN people in death certificates. This racial misclassification would undercount the mortality rate of AI/AN people due to their deaths being labeled as another race/ethnicity and counted towards that indicated race. Given the limited data publicly available on the AI/AN population, the NCUIH Research and Data Team could not account for the misclassification of the death rate. NVSS life tables did try to account for racial misclassification by linking death records to the 2010 decennial census.14 However, the census underreports AI/AN people, and thus, misclassification could still persist in NVSS life expectancy calculations.19

Works Referenced:

  1. Soucheray, S. WHO: COVID-19 pandemic reversed decade of life expectancy gains | CIDRAP. Center for Infectious Disease Research and Policy, University of Minnesota https://www.cidrap.umn.edu/covid-19/who-covid-19-pandemic-reversed-decade-life-expectancy-gains (2024).
  2. World Health Organization. COVID-19 eliminated a decade of progress in global level of life expectancy. World Health Organization https://www.who.int/news/item/24-05-2024-covid-19-eliminated-a-decade-of-progress-in-global-level-of-life-expectancy (2024).
  3. Centers for Disease Control and Prevention National Center for Health Statistics. NVSS – Life Expectancy. National Center for Health Statistics https://www.cdc.gov/nchs/nvss/life-expectancy.htm (2023).
  4. Arias, E., Tejada-Vera, B., Kochanek, K. & Ahmad, F. Provisional Life Expectancy Estimates for 2021. https://www.cdc.gov/nchs/data/vsrr/vsrr023.pdf (2022) doi:10.15620/cdc:118999.
  5. Bastian, B., Tejada-Vera, B. & Arias, E. Mortality Trends in the United States, 1900-2018. National Center for Health Statistics https://www.cdc.gov/nchs/data-visualization/mortality-trends/index.htm (2020).
  6. Library of Congress. World War II | Great Depression and World War II, 1929-1945 | U.S. History Primary Source Timeline | Classroom Materials at the Library of Congress | Library of Congress. Library of Congress, Washington, D.C. 20540 USA https://www.loc.gov/classroom-materials/united-states-history-primary-source-timeline/great-depression-and-world-war-ii-1929-1945/world-war-ii/.
  7. Snipp, C. M. American Indians: The First of This Land. (Russell Sage Foundation, 1989).
  8. Products – Life Tables – Decennial Tables – 1989-1991. https://www.cdc.gov/nchs/products/life_tables/89liftbl.htm (2019).
  9. Dwyer-Lindgren, L. et al. Life expectancy by county, race, and ethnicity in the USA, 2000–19: a systematic analysis of health disparities. The Lancet 400, 25–38 (2022).
  10. Arias, E. United States Life Tables, 2010. https://www.cdc.gov/nchs/data/nvsr/nvsr63/nvsr63_07.pdf (2014).
  11. Grove, R. & Hetzel, A. Vital Statistics Rates in the United States 1940-1960. https://www.cdc.gov/nchs/data/vsus/vsrates1940_60.pdf (1968).
  12. Sanghi, S. & Smaldone, A. The Evolution of the Racial Gap in U.S. Life Expectancy. https://www.stlouisfed.org/on-the-economy/2022/january/evolution-racial-gap-us-life-expectancy (2022).
  13. Arias, E. & Xu, J. United States Life Tables, 2019. National Vital Statistics Reports 70, (2022).
  14. Arias, E., Xu, J. & Kochanek, K. United States Life Tables, 2021. National Vital Statistics Reports: From the Centers for Disease Control and Prevention, National Center for Health Statistics, National Vital Statistics System 72, (2023).
  15. Arias, E. & Xu, J. United States Life Tables, 2020. National Vital Statistics Reports 71, (2022).
  16. Arias, E., Xu, J., Curtin, S., Bastian, B. & Tejada-Vera, B. Mortality Profile of the Non-Hispanic American Indian or Alaska Native Population, 2019. Natl Vital Stat Rep 70, 1–27 (2021).
  17. YOUNG, T. K. Recent health trends in the Native American population. Population Research and Policy Review 16, 147–167 (1997).
  18. Life expectancy by county, race, and ethnicity in the USA, 2000–2019: a systematic analysis of health disparities. Lancet 400, 25–38 (2022).
  19. Zeymo, A. Urban American Indian Undercount in the 2020 Census Went Underreported. National Council of Urban Indian Health Research Blog https://ncuih.org/2023/08/28/urban-american-indian-undercount-in-the-2020-census-went-underreported/ (2023).

The Importance of EHR Interoperability for Urban AIAN Veterans

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In a recent ranking of the health care systems of high-income countries, the Commonwealth fund ranked the United States the worst out of the 11 analyzed. Along with the well-documented problems of high cost and poor access, the ranking also focused on the administrative inefficiencies in the American System. Unlike other countries, the United States has failed to implement a functioning system of portable Electronic Health Records (EHRs).i This failure leads to the “blocking” of crucial health information and the impairment of the “safety, quality, and effectiveness of care provided to patients.”ii The ability for patients to access their EHRs across multiple providers is important for better care coordination.

In an attempt to address this failure, in 2009 President Obama signed the Health Information Technology for Economic and Clinical Health (HITECH) Act to promote the adoption of EHRs by Medical Providers. Unfortunately, the HITECH Act did not provide adequate incentives to encourage the use of EHRs. By 2015, only 6% of physicians had the ability to share patient data across EHR systems.iii This failure effects the quality of care of every American, but the challenges are greatest for patients who must regularly interact with multiple disconnected healthcare providers, such as American Indians and Alaska Natives.

All federally-recognized American Indians and Alaska Natives (AIANs) are entitled to receive care through the Indian health system managed by the Indian Health Service (IHS). AIANs have historically served in the U.S. military at a higher rate than any other populationiv, and these veterans are also entitled to receive healthcare through the veterans’ healthcare system managed by the Veterans Health Administration (VHA). However, AIAN veterans report significant challenges because IHS systems and VHA systems are not currently interoperable to connect and share patient information with each other.

The 144,844 AIAN Veterans who live in Urban Areasv face an even more complex challenge. Many cities with large populations of AIAN Veterans contain no IHS facilities. IHS-funded Urban Indian Organizations (UIOs) serve 39 cities. Still, UIOs do not provide inpatient healthcare services, so Urban AIAN Veterans must interact with local healthcare systems to fully meet healthcare needs, which do not currently speak to each other.

In the most recent American Community Survey Five-Year Dataset (ACS 2017-21), 9.4% of Urban AIAN Veterans (under age 65) report being “currently covered” by the “Indian Health Service” and 39.4% report being “enrolled for VA health care.” Interestingly, 4.5% report overlapping coverage from IHS and VA.vi

Table 1 lists the percentage of Urban AIAN covered by IHS and VA for the 20 cities with the largest Urban AIAN Veteran Populations from ACS. Unsurprisingly, IHS coverage is lowest in cities which contain no IHS facilities and no Urban Indian facilities (Washington-DC, Houston, Atlanta). The exception are Veterans in Denver and Dallas which report an approximately 5% IHS coverage rate even though those cities do not contain IHS facilities.vii It is possible that some veterans are categorizing their use of services by UIOs as “IHS Coverage” when responding to the American Community Survey. IHS is highest in cities with multiple IHS facilities (Phoenix, Oklahoma City).viii In general, in cities where IHS coverage is high, overlap with VA coverage is also high.

Table 1: IHS and VA Coverage in Twenty Metro Areas with Largest Urban AIAN Veteran Population.

Metro Area

% Covered by IHS

 % Covered by VA % Covered by IHS and VA

2021 Urban AIAN Veterans
Phoenix, AZ 26.6 36.3 13.0 5015
Los Angeles, CA 1.6 38.4 0.4 4780
New York, NY 7.9 28.5 4.9 4149
Washington, DC 0.2 27.4 0.2 4033
Seattle, WA 6.2 38.5 5.1 3947
Dallas, TX 4.6 42.3 1.7 3843
Houston, TX 1.8 42.1 0.7 3633
San Diego, CA 7.6 50.2 3.8 2890
Riverside, CA 11.9 41.5 7.5 2738
Chicago, IL 2.9 39.2 1.1 2667
Denver, CO 5.4 37.5 1.9 2596
Portland, OR 7.5 33.6 2.5 2559
Oklahoma City, OK 42.3 37.8 22.4 2389
San Antonio, TX 2.8 53.0 0.8 2349
Albuquerque, NM 47.5 35.4 21.0 2097
Atlanta, GA 2.2 35.4 0.8 2059
Las Vegas, NV 6.6 38.7 3.2 2034
Jacksonville, FL 0.9 37.3 0.9 2022
Austin, TX 3.1 49.2 2.6 1895
San Francisco, CA 0.3 27.8 0.0 1873

There are multiple issues with interpreting these results. The ACA asks about IHS “coverage plans” in the context of a question about health insurance, so it is unclear how a respondent should answer if they receive some healthcare through IHS, but also some from other providers outside the IHS health system. It is also unclear if patients receiving care from IHS-funded Urban Indian Organizations would include themselves as part of “Indian Health Service” coverage.

Adding another layer of complexity, Urban AIAN Veterans must also manage the overlap between the IHS and VA healthcare systems and their insurers (Figure 1).

Figure 1: Urban AIAN Veterans Overlapping Coverage

Figure 1: Urban AIAN Veterans Overlapping Coverage

Only 1.3% of Urban AIAN Veterans receive coverage solely through IHS. Overlap with other healthcare systems and insurers is more common. 1.8% of Urban AIAN Veterans receive services from IHS while enrolled as a beneficiary of Medicaid. 1.2% of Urban AIAN Veterans receive services from both IHS and VA without any insurance coverage. 2.8% of Urban AIAN Veterans receive services from both IHS and VA while enrolled in private insurance. 10.1% of Urban AIAN Veterans receive coverage solely through IHS. 21.2% of Urban AIAN veterans receive services from the VA, while also enrolled in private health insurance.

The need for interoperability is clear, especially for Urban AIAN Veterans who may navigate multiple systems of care, that would benefit from coordination. In 2016, the 21st Century Cures Act passed and is currently being implemented with increasing requirements for interoperabilityix. In response to these requirements, IHS is piloting a “Four Directions Hubs” which connects IHS to the Joint Higher Information Exchange of the VA through the national Ehealth exchange as a positive reinforcement of the required effort. The pilot was implemented at 4 IHS sites with preliminary success. It is important that this effort can be scaled up to improve the multiple healthcare systems accessed by Urban AIAN Veterans to support the full I/T/U system.

i “Mirror, Mirror 2021: Reflecting Poorly,” August 4, 2021. https://doi.org/10.26099/01dv-h208.

ii Office of the National Coordinator for Health IT (ONC). 2015. Report to Congress on Health Information Blocking. April 2015. Available at: https://www.healthit.gov/sites/default/files/reports/info_blocking_040915.pdf

iii Reisman, Miriam. “EHRs: The Challenge of Making Electronic Data Usable and Interoperable.” Pharmacy and Therapeutics 42, no. 9 (September 2017): 572–75. https://www.ncbi.nlm.nih.gov/pmc/articles/PMC5565131/.

iv Proclamation on National Native American Heritage Month, 86 C.F.R. § 60545 (2021), available at https://www.whitehouse.gov/briefing-room/presidential-actions/2021/10/29/a-proclamation-on-national-native-american-heritage-month-2021/.

v U.S. Census Bureau, 2017-2021 American Community Survey 5-year Public Use Microdata Samples (2022), retrieved from https://usa.ipums.org/usa/sda/. Urban Veterans are defined as respondents who 1. Reside in a Public Use Microdata Areas (PUMA) which lies fully or partially within a Metropolitan Area with a population of 50,000 or more; 2. Were formerly in the armed forces or armed forces. CODEBOOK for Variable Descriptions: https://sda.usa.ipums.org/sdaweb/docs/us2019c/DOC/nes.htm

vi Ibid.

vii Locations. “Locations | Indian Health Service (IHS).” Accessed June 6, 2023. https://www.ihs.gov/locations/.

viii Ibid.

ix AAMC. “Electronic Health Records: What Will It Take to Make Them Work?” Accessed June 6, 2023. https://www.aamc.org/news/electronic-health-records-what-will-it-take-make-them-work.

Urban American Indian Undercount in the 2020 Census Went Underreported

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Figure 1: Interactive National Map of Urban Indian Organization (UIO) Service Areas showing the Distribution of American Indian/Alaska Natives (AI/AN) and Self-Response Rate (SRR)

There have been concerns for growing inaccuracies in the U.S. Decennial Census in recent decades, particularly affecting marginalized communities.  The most recent Decennial Census seems to have continued these inaccuracies, undercounting Black, Hispanic and American Indian and Alaska Native (AI/AN) peoples.i,ii The undercount for the AI/AN population living on reservation in the 2020 Decennial Census was reported in several publications after Census results were first announced by the Census Bureau .iii,iv, v,vi However, the undercount of the off-reservation AI/AN population, which includes urban AI/AN people, did not receive similar coverage by  the mainstream media, despite similar effects. Failure to report on the undercount of the urban AI/AN population contributes to the existing research gap and may impact federal health policy relating to this population.

Background: Undercount on Reservations and in the U.S. Balance

 After the Decennial Census, the U.S. Census Bureau conducted a Post-Enumeration Survey, to estimate the accuracy of the census, and estimating over- or undercount by state and demographic groupvii. In a demographic analysis of the undercount, the U.S. Census separated the nation’s AI/AN population into three groups:viii

  1. “On Reservation”: “the AI/AN population living within a federally or state recognized Indian reservation”
  2. “American Indian Areas Off Reservation”: “populations of AI/AN on lands not considered a reservation” This includes trust lands, tribal jurisdiction statistical areas, tribal designated statistical areas, Alaska Native Regional Corporations, and Alaska Native village statistical areas.
  3. “Balance of the United States”: everything else, including the many large cities across the county.

The Census undercount report found that in 2020, there was a statistically significant undercount of AI/AN people in both “On Reservation” and “Balance of the United States” populations. This undercount is shown in Table 4 from the U.S. Census Demographic Analysis which is provided in Figure 2 below. We recognize that “Balance of the United States” is not precisely the same as urban areas, since it also includes many non-urban areas that are not part of Tribal lands.  This adds another layer of inaccuracy, since inaccuracies specific to the urban AI/AN population count is hidden within the “Balance of the United States” undercount.  The majority of AI/AN people (over 70%) live in the “Balance of the United States”, including in the 38 metropolitan areas where Urban Indian Organizations (UIOs) are present.ix, x

Figure 2: Net Coverage Error Rates for Household Populations in the United States by Race and Hispanic Origin from the U.S. Census Post-Enumeration Survey Demographic Analysis.

Source: U.S. Census Bureau: Decennial 2020 Census: Post-Enumeration Survey Demographic Analysis.

The undercount for the “Balance of the United States” AI/AN population, which includes urban AI/AN populations, was mostly unreported by mainstream media sources. These sources pointed to the large undercount in the African-American, Hispanic, and the AI/AN population living “On Reservation”, but they failed to mention the statistically significant undercount among the AI/AN population in the “Balance of the United States”. In fact, one institution published “the American Indian or Alaska Native population not living on reservations was not miscounted.” This statement is incorrect, since the U.S. Census Demographic Analysis showed a statistically significant undercount of 0.9% in the AI/AN population in the “Balance of the United States” in 2020.xi This dismissal continues the erasure of the urban AI/AN population in data, news, and the consciousness of the public.

Census Enumeration Process: Self-Response Rate

To understand better how undercounting can occur we must take a closer look at the process of collecting data for the Census.  The first stage of the Census is the self-response operations. This is when each household in the country is sent the US Census survey to fill out and return. The 2020 Decennial Census was the first-time respondents were allowed to return their census online instead of by phone or mail. Self-response is considered the “gold standard” of census taking because of its high accuracy.xii Some areas followed an Update/Enumerate (UE) method, where census enumerators were sent to interview households and update the address frame, or an Update/Leave (UL), where the enumerator goes to the location to update the address frame and then leaves census forms there. These areas included Remote Alaska areas and American Indian areas that requested for initial interviews to be done by an enumerator. After the period for self-response expires, the U.S. Census undergoes Non-Response Follow-Up (NRFU) operations which include sending enumerators to confirm vacancies, conduct in person interviews and using administrative records to impute demographic information about incomplete enumerations.xiii Despite efforts to improve accurate coverage during NRFU, enumerations from NRFU increase the chance of erroneous enumeration and omission compared to enumerations from the self-response phase.xiv

NCUIH Self-Response Rate Analysis

NCUIH analyzed the cumulative self-response rate (SRR) by census tract within the 38 cities served by UIOs and compared it against the AI/AN population in those tracts.xv,xvi We chose to limit our analysis to UIO service areas in order to focus on an urban AI/AN population with access to the culturally competent healthcare and community services provided by these UIOs. In this analysis, we combined those who identified as AI/AN alone as well as those that identified as AI/AN in combination with other races, henceforth referred to as “AI/AN alone or in combination”.  To protect identification of AI/AN people, census tracts with fewer than 20 AI/AN people were excluded. Additionally, census tracts where the population was less than 0.5% AI/AN were also excluded for the same reason. Finally, to respect the sovereignty and privacy of Tribes with land in UIO service areas, census tracts with American Indian land in them were also excluded. In all, 20,423 census tracts were included in our analysis, while 1,228 census tracts were excluded for any of the previously listed reasons.

In our analysis we used graphs and linear regression to determine the association of AI/AN population concentration and SRR, for all UIO service areas aggregated as well as for each service area separately.  We also used maps to show the distribution of AI/AN population within these UIO service areas, and the relationship with SRR.

SRR Results

Our regression analysis indicated a statistically significant negative association between AI/AN population and SRR. For every 1% increase in the AI/AN percentage of the tract population, the self-response rate decreases by 0.68 percentage points.

In the data tool below, Figure 3, a scatter plot with a line-of-best fit is plotted relating the percentage of AI/AN people and the SRR for each UIO service area. Each point in the scatter plot represents each census tract. You can change the UIO service areas by clicking on the arrow tabs at the top of the tool. Additionally, the data tool includes a drop-down menu to select a specific service area of interest.

Figure 3: Interactive Graph showing Association between AI/AN Population Concentration and SRR in UIO Service Areas

The map data tool at the top of the page, Figure 1, can be used to explore service areas in more detail.

  • To view one of the infocards about a particular UIO service area, click on one of the circles where a UIO is located, and the tool will zoom into a card showing information about self-response rates and the AI/AN population in the service area.
    • Each card includes the total AI/AN alone or in combination population in that area as of the 2020 Decennial Census as well as two maps, one showing the self-response rates by census tract and another showing the AI/AN population percentage by census tracts.
    • Census tracts that were excluded in the analysis were not shown in these maps.
    • The card also includes the average self-response rate for the tracts in the area in addition to the percentage of tracts with low SRR (<60%) and the percentage with high SRR (80%+).
    • Each card includes the percentage of the AI/AN population that lives in the low SRR tracts and the percentage that live in the high SRR tracts.
  • There are two hidden zoom areas for western Montana and northern California to select a UIO more precisely in those regions.
  • To return to the entire map, click on the “home” button on the right side of the data tool, or scroll out with the mouse wheel.
  • The data tool also includes a glossary of terms and data sources used to do this analysis at the bottom right of the “home” map.

In sixteen UIOs service areas, AI/AN population is more strongly associated with lower self-response rate, which you can see where the percentage of AI/AN population in low SRR tracts is higher than the tracts with an SRR less than 60%.

Discussion of NCUIH SRR Analysis Results

Others have demonstrated the relationship between low self-response rate and increased inaccuracy in the U.S. Census.xii,xvii,xviii Between the significant undercount of AI/AN people in cities and the underreporting of the undercount, the urban AI/AN community remains partially hidden from the general population’s and decision maker’s view. The implications of this undercount to the urban AI/AN community are many, including inaccurate reference data needed to inform survey design aiming to record urban AI/AN respondents.19xix This will only exacerbate the gap in research on this overlooked population. Additionally, this undercount could jeopardize health in the population, as federal spending calculations, including the budget for the Indian Health Service, are affected.xx,xxi,xxii

As the U.S. Bureau of the Census undergoes its planning of the 2030 Census procedures, we need to have voices and evidence showing the arbitrary inequities in enumeration. Research such as this shows the need for changes, as we cannot forget that urban AI/AN people exist and thrive, and issues and inequities that affect this community cannot be overlooked.

i US Census Bureau Newsroom Archive. (2012, May) Census Bureau Releases Estimates of Undercount and Overcount in the 2010 Census. U.S. Census Bureau. https://www.census.gov/newsroom/releases/archives/2010_census/cb12-95.html#:~:text=The%202010%20Census%20undercounted%202.1%20percent%20of%20the,0.7%20percent%20was%20not%20statistically%20different%20from%20zero.

ii Kreiger, N (2019, Aug). The US Census and the People’s Health: Public Health Engagement from Enslavement and “Indians not Taxed” to Census Tracts and Health Equity (1780-2018). American Journal of Public Health: 109(8):1092-1100. doi: 10.2105/AJPH.2019.305017.

iii Wines, M. and Cramer M. (2022) “2020 Census Undercounted Hispanics, Black and Native American Residents.” The New York Times. https://www.nytimes.com/2022/03/10/us/census-undercounted-population.html.

iv Schneider, M. (2022) “Some Minority Groups Missed at Higher Rates in 2020 US Census.” AP News. https://apnews.com/article/us-census-bureau-hispanics-census-2020-d284cdbe32fd9ad1a1ad3794cd4d0362.

v Wang, H.L. (2022) “The 2020 census had big undercounts of Black People, Latinos and Native Americans.” National Public Radio. https://www.npr.org/2022/03/10/1083732104/2020-census-accuracy-undercount-overcount-data-quality.

vi Ax, J. (2022) “U.S. Census Undercounted Latinos, Black People and Native Americans.” Reuters. https://www.reuters.com/world/us/us-census-undercounted-black-people-latinos-native-americans-officials-say-2022-03-10/.

vii Marra, E., and Kennel, T. (2022, Mar) Source and Accuracy of the 2020 Post-Enumeration Survey Person Estimates. U.S. Census Bureau. https://www2.census.gov/programs-surveys/decennial/coverage-measurement/pes/2020-source-and-accuracy-pes-estimates.pdf

viii Khubba, S., Heim, K., and Hong, J. (2022) “National Census Coverage Estimates for People in the United States by Demographic Characteristics; 2020 Post-Enumeration Survey Estimation Report.” U.S. Census Bureau. https://www2.census.gov/programs-surveys/decennial/coverage-measurement/pes/national-census-coverage-estimates-by-demographic-characteristics.pdf.

ix Urban Indian Health Institute (2023) Urban Indian Health. Urban Indian Health Institute. https://www.uihi.org/urban-indian-health/#:~:text=7%20out%20of%2010%20American%20Indians%20and%20Alaska,Indians%20and%20Alaska%20Natives%20live%20in%20urban%20areas.

x Whittle, J (2017, Sep) Most Native Americans Live In Cities, not Reservations. Here are their Stories. The Guardian. https://www.theguardian.com/us-news/2017/sep/04/native-americans-stories-california.

xi Benson, S. (2022) “Census undercounted Black people, Hispanics, and Native Americans in 2020”.Politico. https://www.politico.com/news/2022/03/10/2020-census-undercount-black-people-hispanics-native-americans-00016138.

xii Salvo, J.J., Jacoby, A., and Lobo, A.P. (2020) Census 2020: Why Increasing Self-Response is Key to a Good Count. Significance. https://academic.oup.com/jrssig/article/17/1/30/7029480.

xiii Fontenot, A.J. (2022, Feb) 2020 Census Operational Plan: A New Design for the 21st Century. U.S. Census Bureau. https://www2.census.gov/programs-surveys/decennial/2020/program-management/planning-docs/2020-oper-plan5-and-memo.pdf.

xiv O’Hare, W.P. (2020) Are Self-Participation Rates Predictive of Accuracy in the U.S. Census? International Journal of Social Science Studies: 8(6). https://doi.org/10.11114/ijsss.v8i64967.

xv CRVRDO. (2021) US Census PL 94-171 Redistricting Data. United States Census Bureau. https://www.census.gov/programs-surveys/decennial-census/about/rdo/summary-files.html.

xvi United States Census Bureau. (2021) Decennial Census Self-Response Rates (2020, 2010). United States Census Bureau. https://www.census.gov/data/developers/data-sets/decennial-response-rates.html.

xvii Center for Urban Research (2020). “Census Self-Response Rates Mapped: 2000, 2010, 2020.” The Graduate Center: City University of New York. https://www.gc.cuny.edu/center-urban-research/research-projects/census-maps.

xviii Center for Urban Research (2020). “HRC 2020: Hard-to-Count Maps.” The Graduate Center: City University of New York. https://www.censushardtocountmaps2020.us/?latlng=40.01079%2C-98.08594&z=4&promotedfeaturetype=states&baselayerstate=3&rtrYear=sR2020latest&infotab=info-rtrselfresponse&filterQuery=false.

xix Jacobson, L. (2020) “Could an Undercounted 2020 Census Affect a Range of Federal Statistics.” Politifact. https://www.politifact.com/article/2020/aug/26/could-undercounted-2020-census-afflict-range-feder/#:~:text=Problems%20with%20the%20basic%20population%20count%20could%20have,spending%2C%20education%2C%20income%2C%20inequality%2C%20and%20poverty%2C%20experts%20said.

xx Macagnone, M (2020, June) Census Trying to Fix History of Undercounting Minorities. Roll Coll: Policy. https://rollcall.com/2020/06/18/census-trying-to-fix-history-of-undercounting-minorities/.

xxi Hale, K (2020, Mar) Being Undercounted in the U.S. Census Costs Minority Communities Millions of Dollars. Forbes. https://www.forbes.com/sites/korihale/2020/03/24/being-undercounted-in-the-us-census-costs-minority-communities-millions-of-dollars/?sh=5f24d6ce3aa0.

xxii George Washington Institute of Public Policy (2020, Apr) Counting for Dollars 2020: The Role of the Decennial Census in the Geographic Distribution of Federal Funds. The George Washington University. https://gwipp.gwu.edu/counting-dollars-2020-role-decennial-census-geographic-distribution-federal-funds.

Be a Good Relative: What We Learned

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Wonderful news; the National Council of Urban Indian Health has just completed its Be a Good Relative Campaign (BAGR). NCUIH is committed to the continuation and promotion of vaccine equity for Urban Indians. The BAGR campaign was launched to provide educational material on vaccines culturally tailored to Native and Urban Native communities. This series of four videos, promoted on February 16th, April 20th, April 29th, and June 16th.

These videos have been one of our most successful campaigns yet, reaching many people thanks to enhanced promotion for the third and fourth videos. The first video (#BeAGoodRelative Campaign: Flu Immunization) had 311 total views, with 5 reshares and 23 likes. It received 1,318 impressions. The second video posted on April 20th (#BeAGoodRelative Campaign: COVID-19 Myths vs Facts) was viewed 4,473 times, retweeted 104 times, and liked 656 times. The link was clicked 75 times.  The impressions on this video were 17,936 and the engagements were 1,275. The third video (#BeAGoodRelative Campaign: Annual Vaccines) had 249 total views, with 5 reshares and 4 likes. The last video (#BeAGoodRelative Campaign: Youth Immunization), was also quite successful. The video was viewed 3,182 times, retweeted 96 times, and received 472 likes. The link was clicked 53 times. The impressions on this video were 11,063 and the engagements were 819.

Feedback surveys showed that the second and final BAGR videos were effective at reaching and engaging the Urban Native community on vaccination.

One Third of respondents did not receive a COVID-19 vaccine. 66% were very likely to get vaccinated after watching the video, and only 5% were still not likely to get vaccinated.

Further, of the 271 AI/AN people who responded to the feedback survey, they overwhelmingly agreed that the video represented their community. People who worked at facilities that serviced American Indians and Alaska Natives also agreed that these videos were representative of the communities they serve.

Be a Good Relative: What We Learned

 

Be a Good Relative: What We Learned

 

Respondents were also asked what factors were the most important when considering getting a vaccine. There was a range of sentiments, the most prevalent of which was a desire to protect oneself against the virus and the disease. However, there were also a substantial group of respondents who indicated concerns for the safety of the vaccine as well as potential side effects. Additionally, respondents indicated that they would consider the effectiveness of the vaccine in protecting against the virus, a desire to protect their family or community, as well as trying to stop the pandemic. Some of the less common concerns were the perceived cost of acquiring the vaccine, access to the vaccine and equity, as well as more trials for effectiveness and safety. Knowing this we can see what sort of messaging would be most receptive to our community.

NCUIH thanks the Urban Indian community and everyone who viewed and provided feedback to our #BeAGoodRelative Campaign. We have learned much about effective vaccination messaging for our community. Moving forward, we will continue to share materials to promote vaccination and vaccine equity that our community would find helpful and useful.

Social Support for Elders May Help Prevent Cardiovascular Disease and Death

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Researchers from the University of Washington Seattle released a report on Valentine’s day that shows that increasing social support could not only improve depressive symptoms, but also prevents cardiovascular disease and even pre-mature death in older American Indian and Alaska Native (AI/AN) people.

Researchers studied AI/AN adults who participated in the Strong Heart Family Study from 12 communities in over 3 regions between 2000-2003 and, “ .” There was a correlation between those who had reported depressive symptoms, lower quality of life, isolation, heart disease, and death.

Participants in the study were middle-aged adults. The depressive symptoms cited were emotions such as anger self-criticism, and cynicism, and were matched with poor quality of life and isolation. However, better social support saw lower cynicism levels, anger, and trauma. Researchers found that depression and a poorer quality of life, along with social isolation created a higher risk for mortality and cardiovascular events. However, social support lowered that risk. Overall, the study suggests that social support leads to better mood and quality of life in AI/AN elders, and may even lower cynicism, stress, and overall disease risk.

Urban Indian Organizations already provide so much programming and support for their elders, not only in the form of social connections, but also health services. It is a priority to connect elders to one another and other resources to help address social determinants of health and close the health inequalities that urban Indian communities face in the United States.

Suicide Statistics in AI/AN Communities on the Rise: Recent Updates from the CDC

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During November 2021, the CDC released a report on “Provisional Numbers and Rates of Suicide by Month and Demographic Characteristics: United States, 2020”, which covers initial pandemic-era data on suicide rates nationwide.

One of the most important findings for the AI/AN community that the study found is that, “for males, age-adjusted suicide rates were higher in 2020 than in 2019 for non-Hispanic Black, Non-Hispanic AI/AN, and Hispanic males and lower for non-Hispanic White and non-Hispanic Asian males.” Suicide is a complex multifaceted public health issue, which affects the AI/AN community at disproportionate rates. Although suicide rates decreased for many non-Hispanic White groups within the country, they increased for many other ethnic minority groups including non-Hispanic American Indian or Alaskan Native people. Due to a lack of overall research on issues pertaining to the AI/AN community and the complex issues related to suicide, studies like this are necessary to highlight the impact of mental and behavioral healthcare on AI/AN communities (and the supports that are needed). Without data, organizations and the nation are unable to provide accurate support and solutions.

The COVID-19 pandemic has caused many public health issues to be amplified, such as ongoing issues with mental health, substance abuse, financial difficulties, as well as many other factors. Due to the COVID-19 pandemic, many of the possible risk factors associated with suicidal behavior may have increased, which increases the concern that deaths by suicide in 2020 might have increased as well. This report details the numbers of deaths by suicide by the demographics of sex and race and Hispanic origin, by month for the year 2020. These statistics are then compared with final 2019 rates. Rates are compared year-to-year to monitor changes within key demographics by year.

These provisional estimates are based on 99% of all 2020 death records received and processed by the National Center for Health Statistics, but this likely is still an undercount of AI/AN deaths. Since 1979, over 45% of people who self-identified as AI/AN on a national survey had their race misclassified after death (usually as White).

 

 

Citations:

Centers for Disease Control and Prevention. (n.d.). Vital Statistics Rapid Release – cdc.gov. CDC.gov. Retrieved January 20, 2022, from https://www.cdc.gov/nchs/data/vsrr/VSRR016.pdf