Walsh, Elliot
Research Dissemination Coordinator

e wals0421@umn.edu

What Are Adverse Childhood Experiences (ACEs)? ACEs Definition, Data Challenges, and Resources

Basics Blog Introduction

SHADAC has created a series of “Basics Blogs” to familiarize readers with common terms, concepts, and topics that are frequently covered.

Children’s health is impacted by a number of factors – family life and income, housing, neighborhood, nutrition, health care access and cost, amongst others.

Adverse Childhood Experiences (ACEs) is a broad term for a range of negative experiences people may experience as children that can have profound implications for their health. Sometimes ACEs may occur in the form of a single traumatic event, such as a violent incident, but ACEs may also be the result of recurring, lower-grade stress, such as continual food insecurity.

Many research studies conducted over the course of decades have found ACEs to be linked not only with short-term health implications for children, but also long-term health burden that can last well into adulthood. Unfortunately, they are quite common. One SHADAC brief reports that nearly half (46.3%) of U.S. children have experienced at least one adverse childhood experience, with the CDC reporting that 1 in 6 adults have experienced four or more ACEs in their childhood.

There is ample evidence that ACEs are linked to increased risk of chronic health conditions (asthma, chronic obstructive pulmonary disease, stroke, obesity, etc.), engaging in coping behaviors with health risks (drug use, heavy drinking, unprotected sex, etc.), and long-term behavioral and/or mental health issues (depression, anxiety, substance use disorders, etc.). Evidence also shows that those who experience ACEs have poorer health outcomes, lower educational attainment, and higher unemployment rates.

In this blog, we will provide you with:

• Adverse childhood experience definition and examples

• Data collection challenges

• Comments on ACEs and health equity

• Resources on ACEs and related topics from SHADAC

Gaining a better understanding of ACEs can help people recognize them. With that knowledge, people can seek to prevent ACEs and seek health care that recognizes them as underlying risk factors that can influence people’s health. Understanding ACEs and disparities in their prevalence may help to improve public health and enhance health equity. We can work towards reducing the prevalence of childhood trauma and stresses that can comprise ACEs, as well as their impacts on health in both the short- and the long-term for different and diverse communities.

What Are Adverse Childhood Experiences?

First coined by Vincent Felitti, Robert Anda, and their colleagues in a study published in 1998, Adverse Childhood Experiences (ACEs) are defined by the CDC as, “potentially traumatic events that occur in childhood.”

There is no single, comprehensive definition of what experiences do and don’t constitute ACEs. Initial definitions focused heavily on childhood abuse and neglect, and dysfunction and violence in the household, but some experts have proposed broader definitions that encompass systemic issues, such as housing instability and food insecurity, which research suggests can have similar long-term health impacts.

Some examples of potential adverse childhood experiences include:

• Experiencing neglect or abuse (e.g., emotional, physical, sexual)

• Separation from a parent, guardian, or caregiver due to death, incarceration or divorce

• Witnessing violence and/or domestic violence

• Family member attempting or dying by suicide

• Growing up in a household with substance abuse and/or mental health problems

• Housing instability and food insecurity

• Peer-based bullying

• Experiencing or witnessing racism

These only represent a small number of what could be considered an adverse childhood experience.

ACEs and Health Equity

It has been established by a number of studies that certain populations experience ACEs at a higher prevalence. In the SHADAC study on ACEs and Health Equity, we found that, “Black children had the highest rate of ACEs exposure, at 63.7%, followed closely by American Indian and Alaska Native children, at 63.0%. Each of those was significantly different from the total population rate of 46.3%.”

Figure 1. Percent of children with one or more adverse childhood experiences by race and ethnicity, 2016-2019

The CDC reports groups with the highest prevalence of ACEs in the U.S. include:

• Women

• Racial and ethnic minorities (particularly American Indian or Alaska Native (AIAN) people and multiracial people)

• Members of the LGBTQ+ community

• Those considered low-income

• Those who are unemployed or can’t work

• Adults with less than a high school education

It follows, then, that these groups with a higher prevalence of ACEs would also experience the health impacts of these events at higher rates, which goes on to impact quality of life, overall health, and can impact the children of people in these groups as well. This can perpetuate a cycle of generational trauma and continue to widen the disparities between these and other groups.

It is also worth noting that even with the finding that these groups have higher prevalence of ACEs exposure compared to the total population, it could still be an overall underestimation of ACE prevalence because of demographic data collection challenges both for minority groups and for collection of ACE data in general.

You can learn more about demographic data collection and its impacts in the SHADAC brief on The Collection of Race, Ethnicity, Language Data on Medicaid Applications.

Adverse Childhood Experiences and Data Collection Challenges

As noted earlier, there are challenges when it comes to ACEs data collection.

One such challenge is who the data is collected from. Many surveys are filled out by a parent or guardian reporting on their child. This can result in underestimation of ACEs experienced by the child for a few reasons. Firstly, the parent or guardian filling out the survey could be unaware of a traumatic experience their child has had. Additionally, the parent or guardian could feel uncomfortable or unable to report that their child has had certain adverse experiences, like abuse or experiencing a parent’s mental health issues.

If a child is filling out the survey themselves, some of the questions may be inappropriate to ask of young children, the children may be reluctant to report some forms of trauma, or they may not even recognize that an event was a traumatic one.

As SHADAC researcher Colin Planalp mentions in his brief on ACEs and health equity, “Another approach [for data collection] could be to survey adults about their experiences as children, but that approach may also face some of the same limitations of reluctance to report some forms of trauma, and adults may simply not recall traumatic experiences that occurred decades ago. And by surveying adults about their own traumatic experiences as children, those data would by nature be retrospective— telling us little about the prevalence and types of ACEs among today’s children, which may change over time.”

Another issue is the feasibility of including all types of ACEs in a survey. As we mentioned in the previous section, we only listed a small number of possible ACEs. Surveys often condense or simplify the number of ACEs that it asks about – it simply isn’t feasible to list out every single type of ACE or trauma, which likely results in an overall underestimation of experienced ACEs in the population.

Adverse Childhood Experiences: Resources from SHADAC

Hopefully this has given you a general overview on the question, “What are ACEs?” But, as you could probably glean, this is a complex topic with many factors to consider and learn about. ACEs and their impact touch upon a number of issues, including health care cost & access, mental health care, housing, generational trauma, drug and substance use, structural oppression (like racism, homophobia, ableism, etc.), children’s health, and more. Each factor exists amongst and with the others, creating complex systems to unravel and intersectional identities to consider.

Additionally, this is a relatively new area of study, with that original Felitti et al. study published in 1998. New information and research continue to expand our understanding of ACEs, health disparities, and their impacts on health later in life – for example, a study published in Health Affairs in 2024 found a link between ACEs and adults experiencing financial and housing problems, social network problems, and food insecurity, and found that “those with ACEs had substantially higher [health care] utilization and 26.3 percent higher expenditures.”

Continued study and research can help us identify these issues, understand the groups impacted, and work towards both prevention and post-trauma support. Working on this issue has implications to improve the health of children and adults, reduce risk of chronic and mental health conditions, reduce related costs, and, hopefully, reduce the impact and prevalence of childhood trauma.

Keep learning about ACEs and related topics with some of the following SHADAC products:

Changing Dynamics in the Opioid Crisis Since the COVID-19 Pandemic

Soaring rates of fatal fentanyl, methamphetamine, and cocaine overdoses have worsened and spread to previously insulated groups, including teenagers and the elderly

It has been more than a decade since the U.S. Centers for Disease Control and Prevention (CDC) declared a national opioid “epidemic,” also known as the opioid crisis. In that time, hundreds of thousands of people have died, with the impact rippling out amongst friends, family, and communities: One in three U.S. adults know someone who has died of an overdose.¹ 

Despite knowledge of this crisis and efforts to reduce fatal overdoses and impacts of substance use disorder, we have seen the opioid epidemic continually worsen and evolve. Evidence suggests that increased regulations on prescription opioids pushed many to illicit alternatives.² The COVID-19 pandemic coincided with a huge increase in overdose deaths. Fentanyl, a once lesser-known contributor to the epidemic, has skyrocketed in its impact. Once insulated groups, like adolescents and the elderly, have seen overdose rates rise.

In this new brief from SHADAC, we examine changes in drug overdose death rates, and the types of drugs that cause these fatal overdoses, during the pandemic period (2019-2022). We also look at the history of the opioid epidemic: its beginnings, factors that influenced its evolution, and what it looks like now.

With this brief focusing on the four most common causes of drug overdoses — fentanyl, prescription opioids, methamphetamine, and cocaine — just some of the findings from this pandemic period include:

• Fatal overdoses from fentanyl increased 99%
• Fatal overdoses from methamphetamine increased 108%, and 
• Fatal overdoses from cocaine increased 69%

While overdose death rates generally increased across all the measured racial and ethnic groups, “we […] found evidence of dramatic and growing disparities across racial and ethnic groups in rates of fatal overdoses, with the crisis increasingly harming American Indian and Alaska Native people and Black people during the pandemic era,” lead author and Senior Researcher Colin Planalp says. 

In this brief, we explore these findings and more, including looking deeper at the impacts of the opioid crisis on different communities, demographics, and states. Click here to read the brief in full. The appendix for this piece is available here.

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[1] Kennedy-Hendricks, A., Ettman, C. K., Gollust, S. E., Bandara, S. N., Abdalla, S. M., Castrucci, B. C., & Galea, S. (2024). Experience of Personal Loss Due to Drug Overdose Among US Adults. JAMA health forum, 5(5), e241262. https://pubmed.ncbi.nlm.nih.gov/38819798/

[2] Pitt, A.L., Humphreys, K., & Brandeau, M.L. (2018). Modeling Health Benefits and Harms of Public Policy Responses to the US Opioid Epidemic. American Journal of Public Health, 108(10), 1394-1400. https://ajph.aphapublications.org/doi/pdfplus/10.2105/AJPH.2018.304590

 

Walsh, Elliot
Hest, Robert
Stewart, Andrea

Children’s Health Insurance in 2023: Exploring Rising Uninsured Rates for Low-Income Children

• The total U.S. population
• Nonelderly adults (age 19-64), and 
• Children (age 0-18)

As seen in our full blog post on the 2023 ACS data release and our blog & infographic on the 2023 CPS data release, we found that overall rates of public coverage and private coverage remained unchanged in 2023. However, it is important to note that alongside that indicator of coverage stability at a national level, we also found that:

• Changes in uninsured rates for nonelderly adults' (age 19-64) varied depending on Medicaid expansion status and poverty level
• Rates of uninsured children (age 0-18) rose significantly

These findings sparked questions: After two years of falling rates, what could be contributing to rising uninsurance among children? Changes in nonelderly adults’ uninsured rates between 2022 and 2023 varied by poverty level and the status of Medicaid expansion in their state – could either of these factors also be impacting children’s uninsured rates in the newly released data?

While more research into causes behind these changes is needed, SHADAC began by looking at the ACS HI-11 tables, which include data specific to health insurance coverage rates for children below 200% of the Census poverty threshold. 

Keep reading to see some of our findings on how the insurance coverage of low-income children (those with family incomes below 200% of the Census poverty threshold) changed between 2022 and 2023 both nationally and at the state level.

Nationally, Children’s Uninsured Rates Increased while Public Coverage Rates Decreased

Looking at 2023 ACS data for children below 200% of the Census poverty threshold, we found that uninsured rates for this group mirrored overall trends at a national level, rising by 0.4 percentage points (PP) to 7.3%. This significant increase was likely driven by a 0.6PP decrease in public coverage (bringing that national rate down to 72.6%) and a statistically unchanged rate of private coverage (27.0%). 

Further, low-income children’s uninsured rate was 1.9PP higher than the 5.4% rate of uninsured children overall, a statistic which saw its own significant rise from 5.1% in 2022.

State-Level Coverage Changes for Low-Income Children

When we delve into state-level data for low-income children, we see that Louisiana (+1.5PP), Michigan (+1.2PP), South Carolina (+1.6PP), and Texas (+1.5PP) all saw significant increases in uninsurance, rising to 5.5%, 4.3%, 8.0%, and 15.4%, respectively.   

As with the national uninsured rate, our analysis found that a couple of these states mirror a simultaneous decrease in public coverage that likely influenced the increase in uninsurance. For example, Michigan saw a 3.1PP decrease in public coverage, bringing it down to 73.2%, and Texas saw a 2.5PP decrease in public coverage, bringing it down to 65.0%. 

Again, similar to trends at the national level, none of the states that saw increased uninsurance for low-income children saw any significant changes in private coverage among that group. 

No states saw decreases in uninsurance. 

Looking Closer at Public & Private Coverage Rates for Low-Income Children   

Looking at public coverage, states saw a wide range of changes:

• Four states, Arkansas (-4.8PP ), Michigan (-3.1PP), Nebraska (-5.4PP), and Texas (2.5PP), saw significant decreases in public coverage.
• Kansas (+4.5PP), Tennessee (+3.2PP), and Wyoming (+15.8PP) saw significant increases in public coverage

Looking at private coverage, states also saw a wide range of changes:

• Alaska (-8.1PP), Kansas (-5.3PP), Tennessee (-3.8PP), and Wisconsin (-4.9PP) all saw decreases in private coverage
• Kentucky (+3.4PP), Nebraska (+6.5PP), and New York (+2.5PP) all saw increases in private coverage

One thing to note here is that Nebraska, the state with the largest significant decrease in public coverage for low-income children, did not appear on the list of states with significant increases in uninsurance for the same group. But it did also see the largest increase in private coverage for this group, which could have mitigated any change in uninsurance.

Conclusion & Discussion 

By looking deeper into low-income children’s health insurance coverage in 2023, we hoped to find more information on why uninsured rates have increased both overall and specifically for this group. 

At the national level and in a small number of states, we observed increases in uninsurance that coincided with decreases in public coverage. This could be a result of the unwinding of the Medicaid continuous coverage requirement that began during 2023. According to an issue brief on children’s poverty and health insurance coverage published by KFF in January 2024, Medicaid covers 8 in 10 children living in poverty. This brief goes on to report that based on reported unwinding data from 23 states, almost 4 in 10 of those disenrolled from Medicaid during the unwinding are children, likely impacting the many children living in poverty with this public coverage.

However, this story of falling public coverage and rising uninsurance was not consistent across the states. We did not observe significant changes in uninsurance in most states, either because decreases in one type of coverage were offset by increases in another type of coverage, or because there was little movement in coverage overall. 

Coverage estimates for 2024 (to be released next fall) will give a more complete picture of coverage during the unwinding and may clarify how coverage evolved during this dynamic period. 

Tatiana "Annie" Gowlovech
Research Assistant

What is Health Equity? — A SHADAC Basics Blog

Basics Blog Introduction

SHADAC has created a series of “Basics Blogs” to familiarize readers with common terms, concepts, and topics that are frequently covered.

This Basics Blog will focus on the concept of health equity. We will answer and provide explanations for questions like:

• What is health equity?
• What is the definition of health equity?
• How can we talk about health equity in public health work? 
• What are other supplemental terms and their definitions that are used in the health equity space?

With that foundation set, we then move into an example, using a study on provider discrimination data drawn from the Minnesota Health Access Survey (MNHA) to understand how to apply a health equity lens to public health work and analysis. Finally, we end with some further thoughts and considerations on our evolving understand of this complex concept. 

Keep on reading below to learn more about health equity.

Health Equity Definitions 

The Robert Wood Johnson Foundation, a philanthropic organization dedicated to advancing health equity and dismantling barriers in health care, defines health equity below:

It is important to note, though, that since health equity is conceptual, there is not a singular definition of it. This definition can also change and evolve, so it’s important to continue to educate yourself on health equity as time goes on.

What Is the Difference Between Equity and Equality? 

While these two concepts are similar, equity and equality are not one in the same. 

Equality is defined as giving the same treatment to everyone, regardless of individual needs or differences. 

Equity is defined as giving treatment that is specific to individual needs, which allows everyone a fair chance at being successful. 

The graphic below from the Robert Wood Johnson Foundation provides a visual representation of the difference between equality and equity. In the ‘Equality’ example, all people are given the same mode of transportation – they are given equal treatment. However, because the individual’s needs are not considered, the outcome is not necessarily equal as can be seen in the visual. In the ‘Equity’ example, each individual is given a mode of transportation that works for their needs, leading to a more equitable outcome as can be seen in the example. 

Source: Joan Barlow, “We Used Your Insights to Update Our Graphic on Equity” Robert Wood Johnson Foundation, November 2022, We Used Your Insights to Update Our Graphic on Equity (rwjf.org). Reproduced with permission of the Robert Wood Johnson Foundation, Princeton, N.J.

Additional Terminology Definitions

The topic of health equity is complex, with many factors feeding into it. To further understand health equity, it is important to define these additional factors and terms as well. While this list is not exhaustive, the terms below all refer to larger barriers and systems that must be addressed in order to reach equity in health and health care.

Health Disparities: Avoidable differences in health outcomes experienced by people with one characteristic (race, gender, sexual orientation, etc.) as compared to the socially dominant group (e.g., white, male, cis-gender, heterosexual, etc.). 

Measuring disparities can benchmark progress toward achieving health equity.

Social Determinants of Health (SDOH): The daily context in which people live, work, play, pray, and age that affect health. SDOH encompass multiple levels of experience from social risk factors (such as socioeconomic status, education, and employment) to structural and environmental factors (such as structural racism and poverty created by economic, political, and social policies). 

These factors are known quantities that contribute to social and health inequities.

Social Inequities: Differences between groups that are unfair, unjust, systemic, and avoidable. Social inequities can be characterized by race, ethnicity, gender, age, sexual orientation, income, etc. 

Social inequities can lead to poor health outcomes and further perpetuate systems and circumstances leading to health disparities (systemic racism, ableism, etc.). 

Structural Racism: A complex system rooted in historical and current realities of differential access to power and opportunity for different racial groups. This system is embedded within and across laws, structures, and institutions in a society or organization. This includes laws, inherited disadvantages (e.g., the intergenerational impact of trauma) and advantages (e.g., intergenerational transfers of wealth), and standards and norms rooted in racism. 

Structural racism contributes to a number of social determinants of health (SDOH). Public health systems must prioritize addressing structural racism as a primary barrier to health equity.

As you can see, many of these terms contribute to each other or are a result of another—truly showing how complex the topic of health equity is. 

Using a Health Equity Lens on Provider Discrimination—Challenges and Interventions

Now that we have gone over some helpful terminology to keep in mind, let’s look at how to apply a health equity lens when looking at a real-life public health example.

The example we will go over in this section is related to LGBTQ+ health equity and discrimination. With roughly 13.9 million adults in the United States identifying as LGBTQ+, this example is both relevant and important for conversations on equity. Individuals of minoritized sexual orientation and gender identity have been historically marginalized, underrepresented in data, and faced with many health disparities, compared to their heterosexual, cisgender counterparts. These disparities exist for LGBTQ+ people not only at the national level, but also at the community, organizational, and smaller societal levels. 

Thus, in June 2022, President Biden signed Executive Order 14075: Advancing Equality for Lesbian, Gay, Bisexual, Transgender, Queer, and Intersex Individuals. This executive order is a sign of progress toward breaking down systemic barriers and more relatedly for this blog, empowering the Secretary of Health and Human Services (HHS) to “promote the adoption of promising policies and practices to support health equity” for this population.  

Now we will look specifically at a relevant measure from the Minnesota Health Access Survey (MNHA) – provider discrimination by sexual orientation and gender identity (SOGI) in Minnesota – to demonstrate how we can perform studies, analyze results, and identify accessible solutions to health disparities by viewing them through a health equity lens. 

The Data  

Data coming from the 2021-2023 Minnesota Health Access Survey (MNHA) reveals that among all adults in Minnesota, over half of the transgender/non-binary population (56.3%) reported experiencing Sexual Orientation and/or Gender Identity (SOGI) based discrimination from health care providers – significantly higher compared with cisgender adults’ reported experiences of discrimination (6.7%). 

Nearly 9 in 10 transgender/non-binary adults (88.1%) and about one in four (24.1%) cisgender adults who identified as gay/lesbian reported discrimination. Two thirds of transgender/non-binary adults (66.1%) and about a quarter of cisgender adults (23.9%) that chose the ‘none of these’ option for sexual orientation also reported discrimination. Discrimination among people who identify as bisexual/pansexual is also high, and not statistically different for transgender/non-binary adults (40.5%) and cisgender adults (31.6%). 

Experiencing discrimination has been shown to negatively affect both mental and physical health, as individuals and members of historically marginalized communities report worse health status and may forgo or delay care to avoid further discrimination. 

Now, let’s look at potential ways to equitably address instances of provider discrimination by sexual orientation and gender identity at various levels. 

Community Level 

There are various ways to approach the issue of addressing provider discrimination. One potential solution is to make changes to education for the overall medical community, such as implementing cultural competency and communication training.

Cultural competency describes services, practices, and processes that are responsive to diverse practices, assets, needs, beliefs, and languages for an array of individuals and communities. Examples of incorporating cultural competence in health care include:

• Promoting awareness and knowledge
• Recognizing one’s biases
• Engaging in cultural competence & bias training
• Using accessible language
• Familiarizing oneself with the local community
• Recruiting diverse team members

Embracing these strategies can help immensely in understanding and reducing stigma as well as strengthening relationships between providers and the unique communities they serve.

Incorporating cultural competence into care can therefore offer meaningful ways to make strides toward reducing reported discrimination rates, such as those from individuals reporting SOGI-based provider discrimination in Minnesota. When cultural competence is practiced, better and more equitable health outcomes are observed for patients. Improved trust and communication between providers and patients can lead to more regular interactions with the health care system (like routine preventative visits), which can in turn lead to better treatments, health outcomes, and even better overall health status, another known issue for LGBTQ+ individuals.

Organizational Level 

Addressing provider discrimination at a higher organizational level can be done through changing health systems and how they operate. Structural issues, such as payment/reimbursement structures, lack of time for visits, and workforce diversity may also contribute to the discrimination felt by different individuals. 

Structural competency, which is the belief that inequalities in health must be conceptualized in relation to the institutions and social conditions that determine health related resources, can then play a crucial role in promoting equitable care alongside cultural competency by addressing issues at a structural level. This concept can be implemented in health systems through training in five core competencies: 

1. Recognizing the structures that shape clinical interactions
2. Developing an extra-clinical language of structure
3. Rearticulating “cultural” formations in structural terms
4. Observing and imagining structural interventions
5. Developing structural humility

Structural training and education helps medical professionals more effectively understand the economic and social determinants of health (SDOH) that occur for their patients before any interaction with the health care system occurs. In the instance of individuals belonging to the LGBTQ+ community, these can take the shape of lack of health insurance coverage or access barriers. Additionally, organizations can invest in structural changes to advance health equity through improved data systems that are able to track populations’ diverse makeup (race, ethnicity, sexual orientation, gender identity, primary language), alongside SDOH (income, education, employment) and health needs (utilization of care, treatment, and health outcomes), to better understand who they are serving and how to improve services. 

Societal Level

On a larger scale, the state of Minnesota has made it a goal to ensure all Minnesotans receive high quality health care, free of provider or system bias. 

Citing the 2021 Minnesota Health Access Survey (MNHA) data on responses to unfair treatment, the Minnesota Department of Health (DHS) recognized the disparity between the higher rates of unfair treatment by Black Minnesotans (39%) and Transgender Minnesotans (50%) compared to White Minnesotans and cis-gendered men (9% and 12%, respectively), and created a measurable goal to directly address this issue. 

By 2027, MN DHS states that it hopes to reduce the percentage of transgender and non-binary Minnesotans and Black Minnesotans reporting unfair treatment by their providers by 50%. This goal is a meaningful step toward health equity for individuals with different racial and ethnic backgrounds and different gender identities.

Shifting Definitions and Future Considerations

In the example above, examining reported rates of provider discrimination in Minnesota and potential solutions at different levels helps to understand not only a practical example of how better understanding of health equity and systemic issues can clarify what the major barriers to advancing equity look like, but also how they can be addressed. 

Even now as the definition of health equity is not set in stone and continues to evolve, we see opportunity for solutions to advance health equity--both in general, and for people with minoritized sexual and gender identities--to evolve as well. However, this does not apply only to health equity, as the definitions and understanding of many of the terms described above have evolved over time. As society and demographics continue to shift, so does our insight and understanding of these concepts. 

There is a need for more involvement at the community, organizational, and societal level in advancing health equity. While there is progress in the right direction, there are still structural barriers that need to be overcome and met with applicable solutions that lead to positive systemic change.

Interested in learning more about health equity? Now that you have the SHADAC Basics down, try some of the following resources to continue to expand your knowledge:

• Brief: Sexual Orientation and Gender Identity Data - New and Updated Information on Federal Guidance and Medicaid Data Collection Practices
• Brief: Minnesota’s COVID-19 vaccine campaign left vulnerable groups with lagging rates
• Annotated Bibliography: Underlying Factors of Medicaid Inequities

Impact on Vital Community Conditions: Underlying Factors of Medicaid Inequities Annotated Bibliography

*Click here to jump to the 'Impact on Vital Community Conditions' annotated bibliography*

The State Health Access Data Assistance Center (SHADAC) with support from the Robert Wood Johnson Foundation (RWJF) and in collaboration with partner organizations is exploring whether a new national Medicaid Equity Monitoring Tool could increase accountability for state Medicaid programs to advance health equity while also improving population health.

During the first phase of this project, a conceptual wireframe for the potential tool was created. This wireframe includes five larger sections, organized by various smaller domains, which would house the many individual concepts, measures, and factors that can influence equitable experiences and outcomes within Medicaid (see full wireframe below).

While project leaders and the Advisory Committee appointed at the beginning of the project all agree that the Medicaid program is a critical safety net, they specifically identified the importance and the need for an “Underlying Factors” section of the tool. This section aims to compile academic research and grey literature sources that explain and provide analysis for the underlying factors and root causes that may contribute to inequities in Medicaid.

These topics are complex and intersectional – the hope for the “Underlying Factors” section of a future equity monitoring tool is to break these topics down into clear, manageable, and understandable information. Researchers performed a data scan to find recent (2018 and onward) and relevant material, specifically selecting resources for inclusion that provide:

• Historical context of Medicaid inequities
• Information on how underlying factors perpetuate inequities in Medicaid
• Potential solutions for alleviating inequities within Medicaid

Once selected, researchers compiled sources in an organized annotated bibliography, providing a summary of each source and its general findings. This provides users with a curated and thorough list of resources they can use to understand the varied and interconnecting root causes of Medicaid inequities. Researchers plan to continually update this curated selection as new research and findings are identified and/or released.

Sections of the full annotated bibliography include:

• Systemic Racism
• Systemic / Structural Ableism
• Sexual Orientation, Gender Identity, and Gender Affirming Care Discrimination
• Reproductive Oppression in Health Care
• Impact on Vital Community Conditions 

 

 

This page is dedicated to a single section from the full annotated bibliography:

Impact on Vital Community Conditions

 

Find each of the sources and their summaries below. Resources are in chronological order with the most recent perspectives at the end - articles from the same year are ordered alphabetically by author, not by month of publication. 

 

Want to stay updated on the latest products and information released as a part of the Medicaid Equity Monitoring Tool project? Find the project page here, and sign up for SHADAC’s newsletters here to be notified of our latest releases.

Underlying Factors Annotated Bibliography: Impact on Vital Community Conditions

Have a source you'd like to submit for inclusion in our annotated bibliography? Contact us here to propose a source for inclusion.

Click on the arrows to expand / collapse each source.

Semprini, J., Ali, A. K., & Benavidez, G. A. (2023). Medicaid Expansion Lowered Uninsurance Rates Among Nonelderly Adults In The Most Heavily Redlined Areas. Health Affairs, 42(10), 1439–1447. https://doi.org/10.1377/hlthaff.2023.00400

Author(s): Jason Semprini, University of Iowa, College of Public Health; Abinasir K. Ali, University of Iowa, College of Public Health; and Gabriel A. Benavidez, Department of Epidemiology and Biostatistics, University of South Carolina 

Article Type: Peer-reviewed journal 

While there has been ample research on the effect of Medicaid expansion on reducing individual-level racial and ethnic disparities in health insurance coverage, authors here attempt to fill the gap in research on how Medicaid expansion is affected by root causes of health inequities, such structural racism, as measured by the historically racist policy of residential redlining. The federal government’s rating system for mortgage investments, which benefited white, upper middle-class families and penalized minoritized communities and the working-class, has had lasting effects even after it was outlawed in the late 1960s. These effects include substantial wealth gaps, under-resourced communities, and poorer health outcomes. The authors use data from national surveys and a difference-in-differences design to explore how exposure to historical redlining may have influenced the effect of Medicaid expansion on population-level insurance rates for non-Hispanic Black, non-Hispanic White, and Hispanic nonelderly adults. When comparing uninsurance rates in Medicaid expansion and non-expansion states before and after the passage of the Affordable Care Act, authors found that Medicaid expansion had the greatest impact on lowering uninsurance rates in areas with the highest level of historic redlining. Even though no statistically significant differences were observed by race and ethnicity within each redline category, authors conclude that “Medicaid expansion may have helped to reduce some of the negative consequences of structural racism…” and emphasize the importance of studying contextual factors when evaluating health programs and policies. 

 

Londhe, S., Ritter, G., & Schlesinger, M. (2019). Medicaid Expansion in Social Context: Examining Relationships Between Medicaid Enrollment and County-Level Food Insecurity. Journal of Health Care for the Poor and Underserved, 30(2), 532–546. https://doi.org/10.1353/hpu.2019.0033     

Author(s): Shilpa Londhe, Department of Health Policy and Management, Yale School of Public Health; Grant Ritter, Heller School for Social Policy and Management, Brandeis University, Mark Schlesinger, Department of Health Policy and Management, Yale School of Public Health 

Article Type: Peer-reviewed journal 

This article studies the relationship between county Medicaid enrollment and food insecurity in over 350 counties. The authors acknowledge that food insecurity prevalence ranges widely across the country, from as low as 4 percent up to 39 percent in some states. The authors observe that counties who expanded their Medicaid program in 2012 had significantly reduced food insecurity compared to their baseline year of 2009, and compared to counties that expanded later in calendar year 2014. While the authors acknowledge that food insecurity and lack of insurance coverage are likely related, they conclude that Medicaid likely offers greater financial security overall, which alludes to many improvements for low-income families, with overcoming food insecurity being one positive of many likely effects.  

 

Bowen, S., Elliott, S., & Hardison-Moody, A. (2021). The structural roots of food insecurity: How racism is a fundamental cause of food insecurity. Sociology Compass, 15(7). https://doi.org/10.1111/soc4.12846    

Author(s): Sarah Bowen, Professor of Sociology at North Carolina State University; Sinikka Elliot, Associate Professor of Sociology at the University of British Columbia; Annie Hardison-Moody, Associate Professor of Agricultural and Human Sciences at North Carolina State University 

Article Type: Peer-reviewed journal 

This article, written by scholars in the US and Canada and funded by the US Department of Agriculture, provides a detailed overview of the patterns of food insecurity in the recent past. These patterns of food insecurity include that it is associated with lower income households, mostly cyclical as opposed to chronic, more prevalent in households with children and in households headed by women, and households headed by people of color and people with disabilities. Food insecurity also has negative effects on physical and mental health as well as academic performance and risk of hospitalization among children. Authors summarize evidence on the association between food insecurity and poverty, other forms of hardship, housing insecurity, and neighborhood support systems, but argue that there is a more fundamental cause for food insecurity: racism and persistent unequal access for people of color to opportunities and resources. To combat food insecurity, structural change is needed. While the article does not go into detail about specific recommendations for the Medicaid program, it references advocating for Medicaid expansion and against “...punitive and stringent policies that disproportionately harm people of color…,” which can be the price for access to assistance.

 

Charania, S. (2021). How Medicaid and States Could Better Meet Health Needs of Persons Experiencing Homelessness. AMA Journal of Ethics, 23(11), E875-880. https://doi.org/10.1001/amajethics.2021.875

 

Author(s): Sana Charania, George Washington University’s School of Public Health in Washington, DC 

Article Type: Peer-reviewed policy reform brief 

This peer-reviewed policy reform brief includes a list of strategies that state Medicaid programs can pursue with providers to alleviate health care stressors of those experiencing homelessness. The author remarks that in 2020, over half a million people experience homelessness on any given night in the United States. According to survey data from a decade earlier, about a quarter of “…sheltered persons experiencing homelessness had a severe mental illness and 35 percent had problems with substance use”. The author states that there is a need for better supportive housing options and discusses studies in several states that showed providing permanent housing and needed services (such as behavioral health care) to individuals who were previously homeless led to decreased inpatient or emergency department visits and lower health care costs. The author emphasizes that it is not entirely on state Medicaid programs to reach those experiencing homelessness; clinicians and hospitals have a role to play in resolving biases about this very stigmatized group of people and assessing the basic needs of their patients. The author does highlight the importance of Medicaid expansion under the ACA – citing the improved health conditions of those experiencing homelessness and an increase in coverage in expansion states.

 

 

Dennett, J. M., & Baicker, K. (2022). Medicaid, Health, and the Moderating Role of Neighborhood Characteristics. Journal of Urban Health, 99(1), 116–133. https://doi.org/10.1007/s11524-021-005792

 

Author(s): Julia M. Dennett, Yale University School of Public Health, New Haven, CT; Katherine Baicker, University of Chicago Harris School of Public Policy, Chicago, IL and the National Bureau of Economic Research, Cambridge, MA.

Article Type: Peer-reviewed journal 

This article is an analysis of whether key neighborhood characteristics, such as “socioeconomic deprivation (which is a score that reflects neighborhood ethnicity, education, employment, poverty, and housing/crowding), food access, park access and green space, attributes that promote active living, and land use” influence the effect of health insurance coverage on health outcomes. Using data collected in 2009 and 2010 about participants in the Oregon Health Insurance living in the Portland area, some of whom had access to Medicaid coverage and some of whom did not, authors found neighborhood characteristics played only a limited role in moderating the impacts of coverage on select health outcomes. The study’s null findings imply that Medicaid expansion benefited many across the board, regardless of neighborhood. Also implied is that the relationship between coverage and neighborhood characteristics and health outcomes is complex. Authors note several study limitations, including other factors to consider in the definition of neighborhood characteristics and their inability to conduct subgroup analyses. They also call for future research to inform policy.

 

 

Satcher, L. A. (2022). (Un) Just Deserts: Examining Resource Deserts and the Continued Significance of Racism on Health in the Urban South. Sociology of Race and Ethnicity, 8(4), 483–502. https://doi.org/10.1177/23326492221112424

 

Author(s): Lacee Satcher, Professor of Sociology and Environmental Studies at Boston College 

Article Type: Peer-reviewed journal 

This peer-reviewed article summarizes research examining the relationship between resource scarcity (measured in terms of multiply-deserted areas (MDAs)) and health. The author goes on to discuss how this relationship varies according to race and class composition of urban neighborhoods in the South and its implications for public programs, including Medicaid. MDAs were constructed based on three types of resource deserts: food, green spaces, and pharmacy deserts. Health outcomes examined included adults with diabetes, obesity, asthma, and no leisure-time physical activity. Results show that, compared to less resource-scarce areas, MDAs are associated with higher disease prevalence as well as higher rates of inactivity. Co-occurring resource scarcity has more influence on outcomes and activity levels than single-resource scarcity resulting in greater stress and negative impacts on health. “While there have been efforts to increase food access or greenspace for low-income, predominantly Black neighborhoods via farmer’s markets and community gardens, understanding that these neighborhoods are experiencing compounded, co-occurring resource scarcity calls for a more comprehensive policy intervention or community initiative that increases access to healthy foods, greenspace, and prescription medicines.” The author explains further that, “reducing disparities in prescription access and adherence via expansion of Medicare Part D and Medicaid are important, but study findings suggest that policy efforts to reduce disparities should also increase spatial access to pharmacies.”

 

 

Wei, Y., Qiu, X., Sabath, M. B., Yazdi, M. D., Yin, K., Li, L., Peralta, A. A., Wang, C., Koutrakis, P., Zanobetti, A., Dominici, F., & Schwartz, J. D. (2022). Air Pollutants and Asthma Hospitalization in the Medicaid Population. American Journal of Respiratory and Critical Care Medicine, 205(9). https://doi.org/10.1164/rccm.202107-1596oc    

Author(s): Yaguang Wei, Xinye Qiu, Mahdieh Danesh Yazdi, Longxiang Li, Adjani A. Peralta,  Cuicui Wang, Petros Koutrakis, Antonella Zanobetti, are all from the Department of Environmental Health at Harvard T.H. Chan School of Public Health; Matthew Benjamin Sabath Francesca Dominici are from the Department of Biostatistics at Harvard T.H. Chan School of Public Health; Kanhua Yin is from the Department of Epidemiology at Harvard T.H. Chan School of Public Health; and Joel D. Schwartz is associated with both the Department of Environmental Health at Harvard T.H. Chan School of Public Health as well as the Department of Surgery, Massachusetts General Hospital, Harvard Medical School. 

Article Type: Peer-reviewed journal

This peer reviewed study analyzes inpatient claims of Medicaid beneficiaries from 2000 to 2012 by zip code in order to determine the effect of three common air pollutants (nitrogen dioxide, ozone pollution, and particulate matter) on asthma hospitalizations. The researchers found a positive relationship between short-term exposures and increased risk of asthma hospitalization for those with one asthma admission during the study period, but air pollutants appeared to be less of a factor for those with multiple asthma admissions during the study period. A community-level analysis found higher risk of asthma hospitalization for people living in low population density zip codes, people with higher average BMI, and people living a longer distance to the nearest hospital. There were no significant differences in risk of asthma hospitalizations by race or ethnicity. These results emphasize the importance of both individual and contextual factors in assessing the quality of health care for the Medicaid population, where the impact of air pollutant exposures on asthma susceptibility differed by severity and place characteristics.   

 

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[1]  State Health & Value Strategies (SHVS). (2021). Talking about anti-racism and health equity: Discussing racism. https://www.shvs.org/wp-content/uploads/2021/08/Talking-About-Anti-Racism-Health-Equity-1-of-3.pdf