Blog & News
Supporting Families: Associations between Neighborhood Support and Child Health Outcomes
August 24, 2023:Background
The circumstances of peoples’ lives have great influence over their health, often much more than genetics or access to health care—a concept known as “social determinants of health.” One such social determinant is social capital, which includes the networks of relationships that people rely on for help and to help others to exist and function in society.
We wanted to understand how one measure of social capital—living in a supportive neighborhood—may affect people’s health status and other risk factors. Our study focused on children, using data from the National Survey of Children’s Health (NSCH), which asks parents about their kids’ health, certain risk factors, and questions about their perceptions of social support in their neighborhoods.
Parents reported the extent to which they agreed—from “strongly disagree” to “strongly agree”—with three related questions about giving and receiving help in their neighborhoods. Children were defined as living in a supportive neighborhood if their parents agreed with all three questions and “strongly agree” with at least one of them:
- People in this neighborhood help each other out.
- We watch out for each other's children in this neighborhood.
- When we encounter difficulties, we know where to go for help in our community.
Study approach
The NSCH includes nationally representative data for children age 0-17 in all 50 states. This survey provides important information on child and family characteristics, as well as parental perceptions of neighborhood support. In the NSCH, children with special health care needs are identified with a five-item screener based on whether they have health needs such as prescription medication or specialized therapies for a condition expected to last longer than 12 months. We assessed which groups of children were more likely to live in supportive neighborhoods, and how this support correlated with child health and access to health care. We pooled 2020-2021 data (n=88,891) to enhance our ability examine the relationship between neighborhood support and the health status of children with greater precision and reliability.
To understand how supportive neighborhoods relate to children’s health, we focused on five child health measures including access to and use of health care, and exposure to a set of health risk factors known as adverse childhood experiences (ACEs). The measures were based on parents’ responses:
Health status
- Parent-reported child health status, measured as fair/poor vs. excellent/very good/good
- Parent-reported oral health status, measured as fair/poor vs. excellent/very good/good
Health care access and use
- Emergency room (ER) visit in the past year (one or more)
- Any past-year forgone health care in the past year (medical, dental, vision, or mental health care)
ACEs
- Exposure to two or more ACEs. This composite measure was based on parent recollection for a list of potentially traumatic experiences in the child’s household, such as difficulty affording basic necessities, divorce, death of a parent, exposure to violence in the home or neighborhood, and living with someone experiencing mental illness or substance abuse.
Findings
We found that slightly over half (56.4%) of all children lived in neighborhoods that their parents reported as supportive (Figure 1). The lowest rates of children living in supportive neighborhoods were reported for subgroups of children who were black (44.8%) or Hispanic (45.3%); who lived in households in which English was not the primary language (44.5%); who had family incomes below the federal poverty level (45.6%); or whose parents had an education of high school or less (46.3%). Children with special health care needs were also less likely to live in supportive neighborhoods (50.5%). The differences in children’s neighborhood support by race/ethnicity, household language, income, education, and special health care needs were significant and consistent in our tabulation of population rates and multivariate analysis.
Figure 1. Neighborhood support by child characteristics
Importantly, we also found that children living in supportive neighborhoods had fewer ER visits, better access to health care (less forgone health care); better health status (less likely to have fair/poor health and oral health); and a lower prevalence of having two or more ACEs (Figure 2). Exposure to multiple ACEs is a risk factor for a number of long-term health conditions. The significant association between neighborhood support and better child outcomes shown in Figure 2 remained consistent in multivariate analysis.
Figure 2. Rates of child outcomes by neighborhood support
Conclusions
Our study highlights the importance of one measure of social capital—living in supportive neighborhoods—for a range of child outcomes including ER use, forgone care, overall and oral health status, and ACEs. This research bolsters the arguments for policies that support social networks and supports in the communities where children live, such as school and community health resources.
For children, community resources are important for their long-term outcomes. Strong neighborhoods support population health in nuanced ways that go beyond a neighborhood’s more obvious features, such as parks where people can exercise and grocery stores where people can buy healthy food. For example, previous research has shown that living in supportive neighborhoods with social networks to care for kids can mitigate the negative impact of traumatic experiences on child mental health. Our study found that neighborhood support is also associated with a reduced likelihood of ACEs and a range of beneficial child health outcomes.
This analysis was originally presented at the Academy Health Annual Research Meeting in Seattle, WA, on 6.26.2023.
Related Reading
New State Health Compare estimates on Adverse Childhood Experiences aim to improve understanding of health inequities Blog
The Kids Aren't Alright: Adverse Childhood Experiences and Implications for Health Equity, Issue Brief
Neighborhood Support Matters for Child Health, Blog
Blog & News
Interventions Likely Prevented Childhood Poverty from Rising During the Pandemic
July 20, 2023:Research has shown that since the start of the COVID-19 public health crisis, aggressive government interventions, aimed at reducing broader economic fallout as well as individual-level hardship, have been largely sucessful.1 To preempt a potential increase in poverty due to pandemic-related job loss, income loss, and health insurance coverage loss, the federal government implemented policies such as a temporary Child Tax Credit (CTC), COVID-19 rescue payments, requirements for states to maintain current Medicaid enrollment and coverage, and increased SNAP (food stamp) allotments.1,2 Evidence suggests that these targeted actions may have contributed to a small but statistically significant decrease in nationwide childhood poverty between 2019 and 2021, falling from 19.1% to 18.7%, as seen in data available on SHADAC’s State Health Compare tool.
Childhood poverty is defined as children whose household incomes are below 100% of the Federal Poverty Guidelines, which was $30,000 in 2021, for a family of four.3 |
In this blog, SHADAC presents 2021 estimates of childhood poverty at the national and state level based on our analysis of the U.S. Census Bureau’s American Community Survey (ACS) Public Use Microdata Sample (PUMS) file and compares them to 2019 data, as 2020 childhood poverty estimates were not released due to pandemic impacts on data collection. (Please see a previous blog for more technical detail.) We also highlight notable differences in childhood poverty estimates for different racial and ethnic populations.
Highlights from 2021 Data
Compared to the pre-COVID-era figures previously reported by SHADAC, 2021 estimates for childhood poverty were significantly lower both at the overall national level and in five particular states — Arizona, Connecticut, Missouri, Tennessee, and Utah — while no states saw any significant increases in their childhood poverty rates.3 These data highlight the surprising fact that childhood poverty declined at a time when many expected it to rise for the reasons highlighted above.
Childhood poverty rates remain highest in southern states, specifically in Mississippi, followed by Louisiana, New Mexico, the District of Columbia (D.C.), and Alabama. Consistent with 2019 data, states in the Northwest, Northeast, and Midwest regions tend to have the lowest rates of childhood poverty, with New Hampshire having the lowest rate at 9.0%, followed by Utah, Vermont, Minnesota, and North Dakota.
5 states with the lowest rates | 5 states with the highest rates | |||
New Hampshire | 9.0% | Mississippi | 29.4% | |
Utah | 9.1% | Louisiana | 27.7% | |
Vermont | 9.7% | New Mexico | 27.2% | |
Minnesota | 10.2% | District of Columbia | 27.1% | |
North Dakota | 10.3% | Alabama | 23.8% |
Current survey data only allow for demographic disaggregation by race and ethnicity, specifically for African American/Black, Hispanic/Latino, non-Hispanic White, and Other/Multiple Race populations. In 2021, estimates show that one-third of children (33.9%) who identified as African American/Black were living in poverty. In addition, a quarter of children (25.7%) who identified as Hispanic/Latino were living in poverty, while 16.4% of children who identified as being of Other/Multiple races and 11.6% of those who identified as White were living in poverty, respectively.
Figure 1. Percent of children considered to be poor (<100% FPG) by state, 2021
Understanding Poverty Trends Pre- and Post-Pandemic
Despite concerns that the economic consequences of the COVID-19 pandemic would increase rates of childhood poverty, data show the opposite – a small but statistically significant decline. Combined with other research that uses measures of economic well-being, this falling poverty rate suggests that targeted pandemic-era government actions likely succeeded in their efforts to prevent further hardship during this latest public health crisis.
These positive data trends are welcome news, especially given that childhood poverty can have lasting negative effects. For example, children living in poverty are twice as likely to be exposed to adverse childhood experiences, according to a February 2023 SHADAC brief on Adverse Childhood Experiences (ACEs). Childhood poverty can also contribute to both a greater likelihood of disease onset in adulthood and limited chances for socioeconomic mobility or educational opportunity.4
Conclusions
Specific pandemic-era interventions implemented by the federal government likely contributed to a drop in childhood poverty rates between 2019 and 2021. Although programs such as the Child Tax Credit were short-lived and enacted in response to the COVID-19 public health crisis, their success in helping to reduce overall childhood poverty rates bears consideration as longer-term mechanisms to address this issue. Additionally, while overall poverty rates for children declined during this time period, evidence showing disparities in childhood poverty rates by race and ethnicity remains – likely influenced by factors such as systemic racism, social marginalization, and community-level disinvestment.4 For these reasons among others, continued research, monitoring, and advocacy efforts are vital in the ongoing fight against childhood poverty.
About the Data
The measure used for these estimates, “Percent of children considered to be poor (<100%FPG),” can be accessed through SHADAC’s online data tool, State Health Compare. These estimates were produced through SHADAC’s analysis of the American Community Survey (ACS) Public Use Microdata Sample (PUMS).
References
1. Collyer, S., Hardy, B., & Wimer, C. (2023, March 1). The antipoverty effects of the expanded Child Tax Credit across states: Where were the historic reductions felt? Brookings Institute. https://www.brookings.edu/research/the-antipoverty-effects-of-the-expanded-child-tax-credit-across-states-where-were-the-historic-reductions-felt/
2. United States Census Bureau. (2023, May 2). The Impact of the 2021 Expanded Child Tax Credit on Child Poverty. https://www.census.gov/library/working-papers/2022/demo/SEHSD-wp2022-24.htm
3. State Health Access Data Assistance Center. (2023, April 18). FPG vs. FPL: What’s the Difference? SHADAC.https://www.shadac.org/news/fpg-vs-fpl-whats-difference
4. Woolf, S. H. (2017). Progress In Achieving Health Equity Requires Attention to Root Causes. Health Affairs.https://www.healthaffairs.org/doi/10.1377/hlthaff.2017.0197
Blog & News
Analyzing Forgone Care by Coverage Type: New on State Health Compare
June 29, 2023:For most Americans, having health insurance is an important first step to being able to access and afford health care when it is needed.
As shown in recent data published on SHADAC’s State Health Compare, adults with health insurance coverage were at least 79% less likely to report having forgone (gone without) medical care when they needed it because of cost than adults without health insurance coverage. Whereas 63 percent of adults without health coverage reported forgone care, no more than 14 percent of those with coverage reported forgone care. This statistic illustrates the ability of health insurance coverage to protect against unexpected health care costs.
However, these data indicate that health insurance coverage’s ability to protect against unexpected health care costs varies by state and by type of health insurance coverage. The chart below shows the percent of adults (age 18+) who could not get medical care when needed due to cost by state and source of health insurance coverage. In the U.S., adults with some types of coverage such as Medicare (5 percent) or employer/military coverage (6 percent) were less likely to have gone without care because of cost compared with other types of coverage, such as individual coverage (10 percent) or Medicaid/CHIP coverage (14 percent).
It is important to note that the size of these differences in the protective power of health insurance coverage varied substantially by state. For example, where 11 percent of Georgia adults with employer coverage went without care, just 3 percent of employer-covered adults in the District of Columbia went without care. And similar state differences existed for other types of coverage:
- For adults with Medicare coverage, 9 percent of Louisianans had forgone care versus 3 percent of Vermonters.
- For adults with individual coverage, 16 percent of Nevadans had forgone care versus 3 percent of Vermonters.
- For adults with Medicaid/CHIP coverage, 25 percent of Oklahomans had forgone care versus 7 percent of South Dakotans.
- For adults with no health insurance coverage, 77 percent of Nevadans had forgone care versus 44 percent of South Dakotans.
These state differences highlight the inconsistency in the quality of health insurance coverage across the U.S., often related to differences in states’ markets for private health insurance coverage (e.g., individual coverage or employer coverage), differences in state Medicaid and CHIP programs, and differences in health care costs across the states.
Differences by coverage type call attention to variation in the ability of different types of health insurance coverage to protect covered adults from the risks of expensive or unexpected health care needs. However, these differences also highlight disparities in the underlying economic and health vulnerabilities of adults covered by different types of coverage.
Accessing BRFSS Coverage Data on State Health Compare
The data presented here come from SHADAC analysis of the 2021 Behavioral Risk Factor Surveillance System (BRFSS), a survey conducted in partnership between states and the Centers for Disease Control, and are available on SHADAC’s State Health Compare web tool.
Data on detailed health insurance coverage types were collected among all participating states in the BRFSS for the first time in 2021. These new data have allowed State Health Compare to include breakdowns by detailed coverage type for all eight BRFSS measures that feature 2021 data and are the first source of information about these important measures of access and affordability of care, outcomes, and health behaviors for adults with specific types of coverage (e.g., Medicaid/CHIP).
BRFSS measures to explore further:
Notes: All differences described in this analysis are statistically significant at the 95% confidence level. Estimates are representative of the civilian non-institutionalized population 18 years and over and are based on SHADAC analysis of the 2021 Behavioral Risk Factor Surveillance System public use files. Florida data are not available in 2021 because the state was unable to collect sufficient data, and 2021 national data do not include Florida. Other state-level data were not available due to suppression where the number of sample cases was too small.
Blog & News
Now Available on State Health Compare: One Brand New Measure and Five Updated Measures
May 26, 2023:Estimates for five measures of health care access, affordability, and use have now been updated on SHADAC’s State Health Compare. One new measure, Had Telehealth Visit, has also been added to State Health Compare. The new and updated measures are all produced using data from the National Health Interview Survey (NHIS), conducted by the National Center for Health Statistics (NCHS). SHADAC produces these state-level measures using restricted-access data through the Minnesota Research Data Center (MnRDC). SHADAC’s State Health Compare is the only source of state-specific data for these measures which are essential for monitoring individuals’ access to and use of medical care, along with their ability to afford care.
These measures now contain data through 2021, using two-year pooled periods (i.e., 2019-2020, 2020-2021). The measures can be broken down by Total, Age, and Coverage Type (Public, Private, Uninsured).
Updated and new measures include:
NEW: Had Telehealth Visit
This measure describes the percent of individuals who had a medical appointment by video or phone during the past twelve months.
Had Usual Source of Medical Care
This measure captures rates of individuals who had a usual place of medical care other than an emergency department during the past twelve months.
Had General Doctor or Provider Visit
This measure provides rates of individuals who had any visit to a general doctor or provider within the last year.
Had Emergency Department Visit
This measure looks at rates of individuals who had any type of visit to an emergency department in the past twelve months.
Trouble Paying Medical Bills
This measure examines rates of individuals who had trouble paying off medical bills during past twelve months.
Made Changes to Medical Drugs
This measure highlights rates of individuals who were prescribed medication in the past twelve months who made changes to their medical drugs due to cost during the past twelve months. This includes delaying filling a prescription, taking less medicine, or skipping doses to save money.
Click here to explore these measures on State Health Compare!
Notes: Data for State Health Compare’s Had Dental Visit measure is only asked in the NHIS rotating core and thus was not asked in the 2021 survey. We will be able to update that measure with new data after both the 2022 and 2023 data are out in mid-2024.
All measures are representative of the civilian noninstitutionalized population.
Data Source: The estimates were produced using restricted NHIS data in the MnRDC. Measures have been updated with data through 2021 using two-year pooled periods, including (a) 2011–2012 through 2017–2018 and (b) 2019-2020 through 2020-2021, except in the case of the telehealth measure for which data is only available from 2020-2021.
Blog & News
Monitoring Broadband Expansion and Disparities using Data from State Health Compare
April 13, 2023:Introduction
The onset of the COVID-19 pandemic underscored the importance of telehealth services to people across the U.S. and the crucial role of broadband internet access in providing those services. Telehealth continues to be a popular and often necessary way for patients to access care, and federal and state governments are now making significant investments to expand and ensure affordable access to broadband internet.1
For these reasons, it is important to monitor changes in the share of the population with broadband internet as an indicator of access to care. SHADAC analysis of 2021 American Community Survey (ACS) data indicates households’ access to broadband internet has increased by 3.8 percentage points (PP) since 2019, rising to 90.1% (from 86.3%).
State Broadband Efforts
The percentage of households with broadband access varies across the states, ranging from a low of 81.8% in Mississippi to a high of 93.4% in Washington. Though every state experienced some increase in household broadband access, a few states showed larger expansions in access.
Out of all states, Arkansas saw the largest increase at 5.4PP, rising to 85.7% from 80.3%. Former Arkansas Governor Asa Hutchinson and the Arkansas General Assembly had made broadband a “top priority,” working to expand the state Broadband Office by increasing staffing and enhancing services.2, 3 The state also developed the Arkansas Rural Connect (ARC) grant program to expand broadband access in rural communities, and they recently announced a first-in-the-nation partnership with national non-profit EducationSuperHighway to develop best practices and programs to address broadband affordability.4
Rates of broadband access increased by similar amounts in South Carolina, rising to 87.8% from 82.4%. South Carolina has engaged in significant broadband expansion initiatives over the past few years, approving nearly $30 million in broadband expansion projects in early 2021 and spending nearly $50 million in CARES Act funding on broadband-related projects in 2020.5
Despite gains in access, disparities by income level remain
Despite overall growth in broadband internet access, there are still sizable disparities in access between households of different income levels.
For example, though 91.1% of Minnesota households have broadband access (higher than the national average), only 73.1% of low-income households in Minnesota (under $25,000 per year) have broadband internet (below the national average of 74.7%).
In general, households with an income under $25,000 per year have the lowest percentage of broadband access, and households with an income over $50,000 per year have the highest percentage, though that difference varies by state. South Dakota shows the largest gap at 26.2PP: only 67.8% of low-income households have broadband access, compared to 94% of high-income households. Mississippi has a sizable disparity between income levels as well, with a 25.4PP difference between high- and low-income households.
Delaware and Oregon see the smallest disparities between income levels, showing a gap of only 15.8PP and 15.9PP between high- and low-income households, respectively. Those two states have some of the highest percentages of broadband access for low-income households as well, with 79.7% of low-income households in Delaware reporting broadband access and 79.5% in Oregon.
Considering these disparities, some states are taking steps to address broadband affordability;
The 2022 Virginia Telecommunication Initiative guidelines include grant scoring criteria that encourage applicants to be aligned with the state’s efforts to bring low-income households affordable access to broadband internet.6
The Minnesota Office of Broadband Development recently began the process of establishing a statewide digital equity plan which would focus on addressing internet service affordability and reducing gaps in device access, and digital skills.7
In 2021, California passed a historic law directing $6 billion toward improving broadband access and affordability, with multiple provisions intended to improve internet speed, increase access, and lower internet costs for consumers.8, 9
Conclusion
On both national and state levels, access to broadband internet improved from 2019 to 2021. States have begun several promising broadband expansion initiatives and are using available federal and state grant funding to bolster broadband infrastructure and affordability. However, as states continue working toward greater broadband access and navigating an influx of funding for expansion projects, it is necessary to ensure those improvements are specifically targeted to address existing disparities in access.
The U.S. Census Bureau recently launched a data dashboard exploring the impact of federal broadband initiatives on local economies, including different access measures and displays for data on local employment statistics, wages and income, home values, and more. This is a helpful tool for visualizing the local effects of broadband infrastructure investment; explore it here. |
About the Data
The data cited here can be accessed through SHADAC’s online data tool, State Health Compare, using the measure “Percent of households with a broadband internet subscription” for the years 2019-2021. The estimates come from SHADAC’s analysis of the American Community Survey (ACS) Public Use Microdata Sample (PUMS). All differences described are statistically significant at the 95% confidence level unless otherwise specified.
1 Broadband Expansion Initiatives—The Council of State Governments. (2022, May 11). The Council of State Governments. https://www.csg.org/2022/05/11/broadband-expansion-initiatives/
2 AR Rural Connect. (n.d.). Arkansas Department of Commerce – Broadband Office. https://broadband.arkansas.gov/ar-rural-connect/
3 Connecting Arkansas: A Path to Economic Prosperity. (2021). Arkansas Department of Commerce. https://www.arkleg.state.ar.us/Calendars/Attachment?committee=410&agenda=4735&file=Exhibit+C+-Arkansas+Broadband+Plan.pdf
4 Governor Hutchinson Announces First State Partnership with EducationSuperHighway to Close Broadband Affordability Gap. (2022, November 30). https://www.arkansasedc.com/news-events/newsroom/detail/2022/11/30/governor-hutchinson-announces-first-state-partnership-with-educationsuperhighway-to-close-broadband-affordability-gap
5 Broadband and the Coronavirus Aid, Relief, and Economic Security or “CARES Act.” (2021). South Carolina Office of Regulatory Staff. https://ors.sc.gov/sites/ors/files/Documents/Broadband/Broadband%20CARES%20Act%20Update_1.04.2021.pdf
6 2022 Virginia Telecommunication Initiative (VATI) Program Guidelines and Criteria. (2022). Virginia Department of Housing and Community Development (DHCD). https://dhcd.virginia.gov/sites/default/files/Docx/vati/2022-vati-guidelines-and-criteria.pdf
7 Digital Inclusion. (n.d.). Minnesota Department of Employment and Economic Development. Retrieved March 2, 2023, from https://mn.gov/deed/programs-services/broadband/adoption/
8 Broadband Implementation for California. (2021). https://www.cpuc.ca.gov/industries-and-topics/internet-and-phone/broadband-implementation-for-california
9 SB 156 Fact Sheet: Meeting the Digital Divide. (2021). California Telehealth Policy Coalition. https://www.cchpca.org/2022/01/SB156_factsheet_0921_r3_091621-4.pdf