Social Support for Elders May Help Prevent Cardiovascular Disease and Death

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

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

Revealing Vulnerability to COVID-19 in Urban American Indian and Alaska Native Communities

Urban Native communities exist and thrive across this country, with 38 areas served by an Urban Indian Organization (UIO). These UIOs have been an indispensable source of culturally competent care to these communities during the COVID-19 pandemic as you can see in our previous post: Visualizing COVID-19: A Year in Urban Indian Organization Service Areas.

However, a lack of COVID-19 statistics continue to obscure the full burden that UIOs wrestle with. We know that cities are affected by COVID-19, but how are AI/AN communities affected within these cities?  A single count of cases across a county assumes that the extent of the pandemic is uniform within the county, but we know that’s not the case.  Especially in urban areas, neighborhoods and communities wildly vary in terms of resources, systemic deprivation, and the ability to resist natural disasters such as a pandemic.  

Resilience and vulnerability to natural disasters can be illustrated using the Social Vulnerability Index (SVI), a metric developed by the CDC and the Agency of Toxic Substances and Disease Registry. The SVI has been utilized for research in racial and geographic disparities in COVID-19 response, such as in AlabamaLos Angeles, and Louisiana and has also been used as a reference resource by public health departments. Research has shown that historically marginalized communities tend to live in areas of higher vulnerability, and areas of high SVI also have seen the most COVID-19 cases and deaths. Put together, this means that the full extent of the COVID-19 pandemic for the Urban AI/AN populations is not reflected in public county-level COVID-19 statistics. 

IS EVERYONE EQUALLY VULNERABLE WITHIN A CITY? 

Public health statisticians too often overlook urban AI/AN communities . And while AI/AN communities are often proportionately small when compared to the total population of the cities in which they live, great lessons can be learned from including them in analysis. AI/AN people do not live in an evenly dispersed pattern in most cities.  In fact, in many cities, they live concentrated in areas of disproportionately high social vulnerability, compared to the white population in the same city. This makes the presence of UIOs even more crucial, as they deliver life-saving services to areas of the highest need.

Urban AI/AN populations are more clustered in higher SVI census tracts than the white population in 30 out of 38 UIO service areas.  Further, in tracts that are extremely vulnerable – defined here as the top 10% most vulnerable tracts nationally – the concentration of AI/AN people is at least twice that of the local white population in 18 service areas.   

In the link below you will find an interactive map showing all 38 UIO service areas with information about the COVID-19 pandemic, the Urban AI/AN population, and the social vulnerability in each city. You can navigate this map by clicking on the black outline of any service areas to zoom in on summary statistics. Each map and chart can be magnified further by clicking on it. You can return to the main map by moving your cursor to the far right and clicking the home button or return to the prior page by clicking the arrow. You will also see a button on the main map with a glossary showing explaining all our methods and data sources used for this analysis.  Images may take a few moments to load. 

Clicking on a service area, the tool shows

  1. The county’s COVID-19 case and death count, illustrating that available COVID-19 statistics are coarse and uninformative for an urban setting.
  2. The urban AI/AN population is not equally spread across the service area. 
  3. Service areas and the neighborhoods within them vary in their level of vulnerability.  In many service areas the AI/AN population is more concentrated in areas that are more vulnerable to COVID-19.

These three points are illustrated in the red, green, and blue maps on each of the service area info-cards.

After selecting a service area, click the “SVI Graphics” button on the lower right and you will be navigated to statistics and visualizations about the relationship between SVI and service areas AI/AN population. Nearly all service areas show that more of the urban AI/AN population live in the most vulnerable tracts in their cities (Figure D).  Equally, in many UIO service areas, fewer AI/AN people live in the most resilient areas compared to the white population, forming a downwards-sloping distribution curve (Figure E).  In the most vulnerable areas, the ratio of AI/AN population to the same city’s white population is often high, representing the disproportionate vulnerability to disasters such as the pandemic (Figure F).

As an example of reading the figures and statistics, let us look at the Minneapolis/Saint Paul service area. As of April 14th, 2021, the area had 161,171 COVID-19 cases and 2,522 COVID-19 deaths cumulatively since the start of data collection (Figure A). These statistics are reported at the county level for Hennepin and Ramsey counties. But the Urban AI/AN community is not equally distributed across these counties (Figure B).  The Twin cities are wildly unequal in terms of community resources. Census tracts in the Minneapolis/Saint Paul service area range from among the most resilient in the country to the top 1% most vulnerable in the country.  The Urban AI/AN population tends to live in the more vulnerable tracts in this city (Figure C). Clicking on the “SVI Graphics” button will take you to an analysis showing the association between AI/AN-race and SVI in the Twin Cities. In this Metropolitan area there are 51 tracts that are in the top 10% most vulnerable in the nation. 21.7% of the local urban AI/AN population live in these most vulnerable tracts. Meanwhile only 4.5% of the city’s white population lives in those vulnerable tracts (Figure D). Figure E groups the census tracts into blocks of five, each approximately 1% of the service area, and orders them by their vulnerability. It then plots the percentage of the white and AI/AN populations that live in these tracts. You can see along the entire continuum of vulnerability in the Twin Cities, the AI/AN population is more likely to live in high-SVI neighborhoods and less likely to live in low-SVI areas than the white population.  Figure F shows the magnitude of this problem by measuring the ratio the concentration of AI/AN and white residents in areas of differing SVI. At some points (the 50 most vulnerable tracts) the AI/AN population is more than five times as concentrated in these areas than the white population.  Taken as a whole, these figures act as a corrective to county-level data, revealing a level of vulnerability to the virus that is not reflected in COVID-19 statistics. 

CONCLUSIONS 

In many urban areas, AI/AN people face additional vulnerability to the pandemic (and other disasters) simply because of where they live.  This compounds with the other challenges we know they face.  Explore the interactive map in other areas to see that this relationship holds across the country.  Relying on one county-level number for cases and deaths during to the pandemic effectively masks the vulnerabilities that AI/AN community’s face within those counties.  

Yet, due to a lack of accurate racial data on cases and deaths, county-level data is often the only thing key stakeholders see. It is more important than ever to push for accurate and reliable statistics for COVID-19 cases and deaths, particularly for racial minorities.  Efforts have already been made to collect and publish better statistics showing racial breakdown of COVID-19 cases and deaths.  Urban AI/AN communities need to ensure that they too are being counted.  Ensuring that more specific geographic and racial COVID-19 data is in the hands of those who rightfully own and can effectively utilize it is crucial to alleviate the burden faced by Urban AI/AN people.  As we have shown, even a proportionally “small population” can face a massively disproportionate burden in their home cities. This problem should be revealed and treated as a priority.  

By Alexander Zeymo & Andrew Kalweit, posted on Monday August 2, 2021

This post is supported by the Centers for Disease Control and Prevention of the U.S. Department of Health and Human Services (HHS) as part of a financial assistance award (NOFO OT18-1802, titled Strengthen Public Health Systems and Services through National Partnerships to Improve and Protect the Nation’s Health) funded by CDC/HHS. The contents are those of the author(s) and do not necessarily represent the official views of, nor an endorsement, by CDC/HHS, or the U.S. Government.

VISUALIZING COVID-19: A YEAR IN URBAN INDIAN ORGANIZATION SERVICE AREAS

URBAN NATIVE COMMUNITIES: AN OVERLOOKED POPULATION 

Urban Native communities often battle a set of myths – especially the stereotype that American Indian and Alaska Native (AI/AN) people only live on reservations or rural areas. Urban AI/AN people often must prove that they exist in order to obtain the resources they need to address the health disparities their communities face. The first year of the COVID-19 pandemic has only made data on the needs of Urban AI/AN communities more important. However, some public health systems have characteristically overlooked AI/AN people and contributed to widespread disparities. 

First, a list of realities: 

  • The COVID-19 pandemic, which has plagued our country for over a year now, was first detected in UIO service areas.  The first U.S. case was reported in the Seattle area by late January2 and community transmission detected in Santa Clara County by late February.3  Both areas experienced the first known COVID-19 deaths due to community transmission by mid to late February.4,5   
  • In the first year of pandemic data, there were more than 28,646,373 confirmed cases of COVID19 nationally, and 514,117 reported deaths associated with COVID-19 (as of February 28, 2021.)   
  • Cities have been the most affected by the pandemic, accounting for 84.5% of cases (n = 24,197,682) and 82.9% of deaths (n = 426,271) shown in figures 1. 
  • However, different cities have had different experiences over the past year due to state and local differences in mitigation measures and resource allocation. 
  • Each urban AI/AN community is different. Yet across the nation, Urban AI/AN people face specific disparities that put them at a higher risk of severe COVID-19 or transmission of the virus.  Urban AI/AN people are about three times more likely to live in poverty, be uninsured, or have diabetes compared to their non-Hispanic White neighbors. And Urban AI/AN people are 1.5-1.8 times as likely to live in multigenerational or crowded housing, smoke, or have asthma. 
  • There are 41 Urban Indian Organization (UIOs) with over 70 facilities, located in 38 cities across the country.  Each provides their local AI/AN community with culturally-competent services.

Urban Indian Organizations have been on the front lines of the pandemic since it began. They are coping with increased pandemic-related need, despite limited budgets and resources. But how do UIO service areas compare to the rest of the country?  How have their needs and conditions changed over time?  

CHARTING THE PANDEMIC IN SERVICE AREAS  

Hotspots and outbreaks of the novel coronavirus have transitioned across the USA, through Southern California, the Southwest, and the Northeast as seen in figures 2 and 3.  In each region, Urban Indian communities face the most extreme brunt of the pandemic, with stretched resources and high social vulnerabilities to the virus in their clientele.   

Figure 2: Evolution of New 14-Day COVID-19 Cases from 03-01-2020 to 02-28-2021  

Figure 3: Evolution of Case Rate (New 14-Day COVID-19 Cases per 100,000 population) from 03-01-2020 to 02-28-2021 6

In fact, each UIO service area has been in the top 10 percent counties by number of new cases at least once in the past year. Further, each service area has been a high risk of transmission zone for at least 11 weeks in the past year. But each has faced a different challenge.   

To see how conditions have fared in different UIO service areasuse the interactive map below. 

This interactive map allows you to zoom in on each UIO service area, and see how this area has fared over the last year compared to the rest of the country. To zoom in on a service area, click the thick black lines outlining the UIO regions. You may need to zoom in with your mouse-wheel on some areas, particularly where multiple UIOs are clustered like in the Bay area and western MontanaYou can then zoom into figures and maps using your mouse or by clicking on the figuresTo zoom back out, move to the far right hand side and you should see two buttons. You can return to the starting national map by clicking the “home” button, and zoom-out to the previous page using the “up-arrow” button. Learn more about our data sources and the measurements used by clicking the glossary button on the lower right hand side of the map.

For example, let’s walk through how to use this with the example of Los Angeles County, where roughly 165,513 AI/AN people live (see figure A below).  By February 28th 2021, there were 1,190,894 confirmed COVID-19 cases and 21,328 deaths.  Since March 2020, Los Angeles has been in the top 10 counties in the nation by 14-day rolling average of new cases for 43 weeks (figure B). During weeks it was not in the top 10 counties, LA was still in the top 32 (or 10%) of counties – which are signified by the red and gold line respectively.  Figure C shows how the 14-day rolling new case average has changed over the course of the pandemic, specific to this service area. You can see that cases peaked locally in December-February with a smaller peak in late July. Figure D shows these new cases in the form of a transmission rate, which factors in the population of the Los Angeles area and compares this with the CDC COVID-19 risk categories. Case rates above the red line indicate “high transmission risk” and the gold line indicates the cutoff for “substantial transmission risk”7. As you can see, Los Angeles county has been at high transmission risk category for 25 weeks, or 48% of the last year. Figure E and F shows the number of new deaths over time.

Figure A                                                     Figure B

 

                                                                                                  

As you can see, Los Angeles county has been consistently ranked at the top of US counties by number of new cases every week since May 2020, has experienced peaks and valleys, and has generally been a very “high transmission” county. Such a large and persistent burden will affect the AI/AN population that lives there and the providers that serve them. 

Figure C                                                                                                  Figure D

Figure E                                                                                                 Figure F

CONCLUSIONS 

The story is the similar across the country, in all 38 urban service areas. Please investigate other areas. Some cities saw peaks in the first wave of spring 2020, others in the late summer, and many more in the winter of 2020-2021. Yet at any given time, there was usually a UIO serving in the top counties in the NationNative populations exist in each of these areas, but are often overlooked as a small population.  Even when racial data on cases and deaths doesn’t exist or include AI/AN people, it is important to remember that UIOs have been on the frontlines providing culturally-competent care in the hotspots of the pandemic. We must remember that UIOs are struggling against these surges every day, as are the people they serve.    

NCUIH hopes this data tool brings some awareness of the magnitude of this issueas millions of AI/AN people continue to live and struggle against coronavirus.  We also hope this data is helpful for UIOs in their communication, advocacy, and grant writing activities.   

Remember, you can always ask NCUIH for data analysis or technical assistance via our website. Stay tuned for our second COVID-19 Data Tools postwhere we will dive into the specific vulnerabilities to COVID-19 that AI/AN communities face within cities.

By Alexander Zeymo & Andrew Kalweit, posted on Monday June 28, 2021

This post is supported by the Centers for Disease Control and Prevention of the U.S. Department of Health and Human Services (HHS) as part of a financial assistance award (NOFO OT18-1802, titled Strengthen Public Health Systems and Services through National Partnerships to Improve and Protect the Nation’s Health) funded by CDC/HHS. The contents are those of the author(s) and do not necessarily represent the official views of, nor an endorsement, by CDC/HHS, or the U.S. Government.