Password Required

This content is password protected. To view it please enter your password below:

Web article banners (26)

As Dementia Rises, More Older Americans Are Getting Care at Home. It's Not Without Challenges.

Families face the financial burden of paying out-of-pocket for care not covered by Medicare and Medicaid and the emotional toll of day-to-day caregiving.

Today, an estimated 7.2 million Americans ages 65 and older live with dementia. While conversations around dementia often evoke nursing homes, most older Americans living with dementia are actually aging in place in their homes. Home-based care has become more common over the last decade, partly because it is more affordable and aligns with what many people prefer.  

People living with dementia often face both medical and practical barriers to obtaining care, including challenges with memory, decision-making, and mobility. These difficulties make access to effective care at home not just helpful but essential to supporting their ability to remain safely in the community. 

Despite its benefits, home-based care is not without challenges, including the financial burden of paying out-of-pocket for services not covered by Medicare and Medicaid and the emotional toll on family members who often take on day-to-day caregiving responsibilities. Understanding how home-based care works for the growing population of older adults with dementia is critical for improving how dementia is managed in the community and providing better support for older adults and their families. 

Home-based care typically falls into two categories: home health care and home care. Home health care refers to medical services provided by licensed professionals, including skilled nursing care, physical therapy, and medical social services. It is covered by Medicare if prescribed by a doctor or nurse practitioner. Home care refers to non-medical services to assist with housekeeping and the activities of daily living, such as bathing and dressing. Medicare does not cover home care unless it is provided with medical care, but Medicaid covers home care in some states.  

Older Adults With Dementia Are Twice as Likely to Use Home Health Care 

Based on data from the Health and Retirement Study (HRS) and Medicare claims between 2012 and 2018, Julia Burgdorf and her colleagues found that approximately 30% of home health care users had a dementia diagnosis.1 Older adults with dementia were twice as likely to use home health care compared to those without dementia.  

About half of those with dementia were referred to home health care without a preceding hospitalization, compared to slightly less than a third of those without dementia. This finding highlights the importance of home health care as a key source of clinical care for older adults with dementia, not just for recovery care, Burgdorf and her colleagues said. 

Once enrolled in home health care, people with dementia received care more times (an average of 1.4 times compared to 1 time) and for longer periods (median of 56 versus 40 days) than people without dementia. They were also more likely to receive personal care, medical social work, and speech-language pathology services than those without dementia.  

However, among people who received services, those with dementia had fewer visits for skilled nursing and physical therapy, the researchers found. 

“Existing prospective payment structures incentivize HHC providers to limit the number of visits in order to lower costs and maximize profits,” they wrote. And “the 2020 implementation of a new Medicare HHC payment model, the Patient-Driven Groupings Model (PDGM), may further incentivize limiting visits for people living with dementia.” 

The new payment model aims to reimburse providers based on how sick patients are—but it doesn’t directly account for dementia status, Burgdorf and team note. It also reduces reimbursement for community referrals, though many people with dementia enter care this way. Differences in coverage between Medicare Advantage (private plans) and fee-for-service (the traditional government-run plan), along with workforce shortages and fragmented care systems, may create additional barriers for people with dementia, they explain. 

Burgdorf and colleagues suggest several ways to improve home health care for people with dementia, including: 

  • Developing care models tailored to patients’ specific needs. 
  • Improving information sharing, especially during the transition from hospital to home, such as identifying a patient’s dementia status in referral documents.  
  • Including family caregivers in care planning.  
  • Developing better methods to assess and assist caregivers, including providing additional resources and training.  
  • Developing new care approaches that bring in services like social work and speech-language pathology earlier in the care process to improve both quality of care for patients and support for caregivers.

Home Care Costs Create Heavy Financial Burden for People With Dementia

Many older adults end up paying for home care out of pocket because long-term care is not included in Medicare, Medicaid coverage is inconsistent across states, and few people have private long-term care insurance, according to another analysis.  

“The financial burden of out-of-pocket payment for home care is significant, particularly among people with dementia and those with limited income and wealth,” conclude Karen Shen and colleagues, who used Health and Retirement Study (HRS) data (2002-2018) to measure the financial burden of home care.  

Home care also often results in ongoing expenses over a long period of time, unlike most other health care costs, they found. 

In recent years, people with dementia made up one-third of an estimated 3 million people who received home care and almost half (45%) of the over 600,000 people who paid for at least some of this care out of pocket, according to the researchers.2 Among those with dementia who paid out-of-pocket, half (51%) spent over $1,000 per month, compared to one-fourth (26%) of those without dementia. Additionally, people with dementia were much more likely to pay for full-time help, defined as 40 hours or more per week, compared to those without dementia (46% versus 22%). 

Although higher-income individuals are more likely to pay out-of-pocket, many people with lower incomes also do so, largely because those with fewer financial resources are disproportionately affected by disability and dementia, the authors note. In fact, about half of those paying for home care out of pocket were poor or near-poor, defying the common perception that private home care is used only by individuals with higher incomes. 

Those who are poor and have dementia experienced disproportionate financial burdens, as they spent 87% of their household income on home care, compared to 32% spent by their peers without dementia, and 22% spent by high-income individuals with dementia (See figure). 

These findings suggest that those with dementia and limited financial resources may not be getting the care they need. 

“Policies aimed at easing the financial burden of home care are essential, particularly for low-income individuals with dementia who experience the greatest financial burden,” argue Shen and her colleagues. 

They recommend policies to reduce unmet care needs and financial hardship while also making the system more equitable and responsive to the realities of aging at home with dementia, including: 

  • Expanding access to Medicaid home- and community-based services to help more individuals receive support, as many states currently impose strict eligibility rules, asset limits, and waitlists that limit access.  
  • Encouraging long-term care savings and expanding the availability of long-term care insurance to help people avoid steep out-of-pocket costs later in life.  
  • Adding a home care benefit to Medicare, which currently does not cover non-medical home care—though such a move would be expensive and require careful implementation.  

To make these programs financially sustainable, they recommend targeting benefits to the most vulnerable individuals and incorporating cost-sharing mechanisms so that those with more resources help shoulder the cost of care. 

Figure: The Poorest Americans With Dementia Spent 87% of Their Monthly Household Income on Home Care
Percent of monthly household income spent on home care for older adults, by patient’s dementia status and income level, 2002–2018 

Note: “Poor”: 100% of the Federal Poverty Level (FPL); “Near-poor”: 100-200% of FPL; “Moderate-income”: 200-400% of FPL; and “Upper-income”: >400% of FPL.

Source: Karen Shen et al., “Paying for Home Care Out-of-Pocket Is Common and Costly Across the Income Spectrum Among Older Adults,Health Affairs Scholar 3, no. 1 (2025). 

Caregivers Are Underusing Respite Care—Which Could Shoulder Some of the Burden 

Even with paid home care, most dementia care still falls on family and friends. In the United States, over 11 million unpaid caregivers provide over 15 billion hours of dementia care every year, according to Yeunkyung Kim and his colleagues.3 One way to give caregivers a break is through respite care—short-term care that lets family members step away for a few hours or days. Ultimately, respite care aims to help sustain caregiver health and delay the institutionalization of the people in their care.  

Yet its use remains limited. Only 16% of Black caregivers used respite services compared to 32% of white caregivers in 2015, representing a significant gap of 12 percentage points, Kim and team found. Although this racial gap had been reduced or eliminated by 2017, respite care use remained low among both Black and white caregivers. Data are from the National Health and Aging Trends Study (NHATS) and the National Study of Caregiving (NSOC) from 2015, 2017, and 2021. 

Even though there have been efforts to expand access, too many caregivers are still doing this difficult work without enough support. This highlights the persistence of structural and informational barriers to care—including financial cost, lack of awareness, cultural expectations, and insufficient supply of respite providers.  

The underuse of respite care represents a missed opportunity to support the mental and physical health of caregivers, and thereby also the stability of dementia care at home. 

Kim and colleagues recommend several strategies to improve access to respite care for families supporting older adults with dementia, including integrating respite services more fully into long-term care systems, particularly through expanded support in Medicaid-funded programs like home- and community-based services waivers.  

Better outreach and clearer communication could also raise awareness of available services, since many caregivers remain uninformed or face fragmented information, the researchers note. There is a clear need for more flexible respite options that can accommodate the diverse cultural, financial, and scheduling needs of caregivers. For example, offering evening or weekend respite hours for those who work during the day, or providing in-home options for caregivers who are uncomfortable with facility-based care, could make a meaningful difference. 

Simplifying program design, reducing waitlists, and ensuring consistent availability are also key to increasing use of these services, they said. 

 

References

1. Julia G. Burgdorf et al., “Variation in Home Healthcare Use by Dementia Status Among a National Cohort of Older Adults,The Journals of Gerontology, Series A: Biological Sciences and Medical Sciences 79, no. 3 (2024) 

2. Karen Shen et al., “Paying for Home Care Out-of-Pocket Is Common and Costly Across the Income Spectrum Among Older Adults,” Health Affairs Scholar 3, no. 1 (2025). 

 3. Yeunkyung Kim et al., “Trend in Respite Use by Race Among Caregivers for People Living With Dementia,” The Journals of Gerontology, Series A: Biological Sciences and Medical Sciences 79, Supplement 1 (2024): S42-S49

Web article banners (13)

7 Trends Reshaping the Health and Lifespans of America’s Rapidly Aging Population

Experts point to key dynamics challenging policymakers, health care planners, and families

Early onset chronic disease, a growing caregiving gap, and climate change are among the major trends affecting the health and well-being of older Americans and their families, according to leading scholars from across the country.

Before a standing-room-only crowd at the 2025 meeting of the Population Association of America in Washington, D.C., experts identified seven key themes that are challenging policymakers, planners, and families as the U.S. population rapidly ages.

1. U.S. Life Expectancy Gains Have Stalled, Lagging Behind Other Higher-Income Countries 

The United States has experienced the earliest and greatest slowdown in life expectancy improvements among higher-income countries, reported Eileen Crimmins of the University of Southern California/University of California-Los Angeles Center on Biodemography and Population Health.

“We have horrible life expectancy—and it’s getting worse and worse,” she said, pointing to the diverging line for the United States in Figure 1. Though premature deaths from heart disease and stroke have declined, Americans today are unhealthy for a longer portion of their lives, coping with chronic diseases and conditions such as diabetes, hypertension, arthritis, cancer, and heart problems.

While we “have a long way to go” to improve the health of the U.S. population, Crimmins said, new research into biomarkers gathered through blood and other medical tests is offering clues into what speeds up or slows down the aging process, including stress levels, income, and social connections over a lifetime.

Figure 1: The United States Experienced the Earliest and Greatest Slowdown in Life Expectancy Improvements Among Higher-Income Countries
Trends in Life Expectancy Among 23 Higher-Income Countries, 1980-2024

Source: United Nations, World Population Prospects.

2. Americans Are Spending More of Their Later Years With Chronic Diseases Rooted in Their Early Lives

While advances in health care have succeeded in preventing many early deaths, older people are spending more time living with chronic diseases today than two decades ago, Crimmins’ forthcoming research shows. Americans spent more years after age 65 living with diabetes, cancer, heart disease, arthritis, and high blood pressure in 2018 than in 1998, she has found.

Scott Lynch of the Duke Center for Population Health and Aging agrees. Over the past century, complex chronic conditions like cardiovascular disease and cancer have replaced infectious diseases like pneumonia and tuberculosis as the leading causes of death, he noted. In addition to biomarker research, longitudinal studies that follow individuals over decades have contributed to a growing understanding that events and conditions in childhood and adolescence shape health and lifespans in adulthood and old age, he said.

Improving the health of children and young people has profound effects later in life, argued William Dow of the Center on the Economics and Demography of Aging at the University of California, Berkeley. He pointed to new research showing that people with Medicaid insurance in childhood have better health as adults. “By reducing disability and keeping people in the labor force, Medicaid is actually paying for itself,” Dow said.

3. The United States Faces a Caregiving Crisis As the Cadre of Traditional Caregivers Shrinks

Family members provide most of the care that enables older people to live safely in their own homes, said V. Joseph Hotz of the Center for Healthy Aging Behaviors and Longitudinal Investigations at the University of Chicago. Among care recipients ages 65 and older, 69% receive only informal home care from friends and relatives, whereas just 5% receive only formal paid home care  (Figure 2).

But a care gap is emerging as the baby boom generation ages. The traditional caregiver population (ages 45 to 64) is shrinking while the number of oldest-old Americans—those most likely to need care—is growing. By 2040, there are expected to be just three traditional caregivers per person ages 80 or older—down from a 6:1 ratio in 2025, according to Census Bureau projections.

But there is some good news, Hotz adds. While research finds that adult children feel less obliged to care for stepparents, new evidence suggests that an increasing share of adult children are stepping up when older parents are in need (for example, having trouble buying food). His own analysis shows that childless older people received as much help from their siblings, other relatives, and friends as their peers received from their adult children.

Figure 2: Most Older Americans Receive Only Unpaid Help From Family and Friends
Type of Care Received by Adults Ages 65+ and 85+

 

Source: Jonathan Gruber and Kathleen M. McGarry, “Long-term Care in the United States,” National Bureau of Economic Research, Working Paper 31881, November 2023, DOI 10.3386/w31881.

4. Older Adults’ Social Ties Are More Important to Health and Longevity Than Many Realize

Compared with their peers who have supportive families and robust social networks, socially isolated older people face a greater risk of early death, dementia, heart disease, diabetes, and a host of other conditions, explained Debra Umberson of the Center on Aging and Population Sciences at the University of Texas at Austin. “The evidence is increasingly convincing, overwhelmingly persuasive,” she said.

Inflammation, depression, hypervigilance, alcohol consumption, and the disadvantages of lower levels of education all play a role in poorer outcomes among older adults, Umberson said. Social isolation is a modifiable risk factor; the challenge is “identifying who is most at risk, why, and what can be done.”

Research suggests that “isolation begins to increase as early as adolescence and continues steadily through the life course,” she reported. Black Americans, people living in poverty, and sexual and gender minority populations tend to experience higher levels of isolation than other groups. Experiencing the death of a family member, extreme weather events (like Hurricane Katrina, which dispersed community members), pandemics (as we saw with COVID-19), incarceration, and deportation can also disrupt families and communities.

5. Medicare Advantage Plans Could Lead to Cost Savings and More Home-Based Services, But May Shift More Caregiving to Families

In 2024, more than half of Americans ages 65 and older (54%) were enrolled in the Medicare Advantage program, up from just 19% in 2007. The dramatic change from a fee-for-service model to a privately run managed care model has vast implication for aging Americans, said Dan Polsky of the Johns Hopkins’ Economics of Alzheimer’s Disease and Services (HEADS) Center. Medicare Advantage plans may offer efficiency and flexibility that can lower recipients’ costs and increase access to home-based care, which most Americans say they prefer, according to Polsky. But new findings by Lauren Nicholas suggest that unpaid family caregivers may be providing more end-of-life home care for people with dementia, essentially moving costs from the formal system of payment to unpaid family members, he reported.

At the same time, traditional Medicare is not without innovations: A new program is exploring ways to meet the health care needs of both people with dementia and their caregivers, Polsky noted.

In the pipeline are new disease-modifying pharmaceutical treatments for dementia, but they require an early diagnosis, which only a fraction of people receive. Should these treatments scale, it could cost the Medicare program tens of billion dollars a year, presenting an additional challenge to the already-strained Medicare budget. Implementing new early diagnosis techniques and providing cost-effective new treatments will present complicated hurdles for the health-care delivery system, Polsky suggested.

6. Americans Caring for Relatives With Dementia See Increasing Demands on Their Time and Constraints on Their Employment

The more than 5 million family members and friends who provide unpaid care for older adults with dementia have high—and increasing—demands on their time, reported Jennifer Wolff of the HEADS Center, based on her team’s research using nationally representative data.

On average, the time that family caregivers spent helping older adults with dementia grew by almost 50% between 2011 and 2022, going from 21 hours per week to 31 hours (Figure 3). By contrast, time spent assisting older adults without dementia fell during the same period.

Wolff and team show that more than half (52%) of dementia caregivers lived with the person they were caring for in 2022, up from 39% in 2011. And the share able to hold jobs—outside their caregiving work—dropped from 43% to 35% during the same period.

Noting that the number of individuals affected by dementia is projected to triple in the next 30 years, Wolff underscored the importance of monitoring unpaid caregivers and developing interventions to support them. Some strategies could include providing direct financial assistance and tax relief, supporting flexible work arrangements and paid family leave, and using digital tools and remote monitoring technologies to help caregivers manage care more efficiently and connect with support networks.

Figure 3: Family Caregivers Are Spending 50% More Time Caring for Older Adults With Dementia
Average Weekly Family Caregiving Hours, by the Dementia Status of Older Adults (65+), 2011 and 2022

Source: Jennifer L. Wolff, Jennifer C. Cornman, and Vicki A. Freedman, “The Number of Family Caregivers Helping Older US Adults Increased From 18 Million to 24 Million, 2011–22,” Health Affairs 44, no. 2 (2025): 189-95.

 

7. Extreme Weather Events and Warmer Temperatures Pose Unique Challenges for Older People

More devastating fires, storms, and hurricanes, along with greater climate variability are the “new normal,” said Elizabeth Frankenberg of the Carolina Population Center at the University of North Carolina at Chapel Hill.

People who experience these events not only face an increased risk of death and disease but also lost livelihoods, diminished assets, and poor quality of life for months, years, and even decades to come, she noted.

Older people can be uniquely vulnerable due to reduced physical mobility, cognitive decline, diminished temperature regulation, and changes in economic resources, access to safety net programs, and the availability of social and family networks. Further, their ability to cope with change may be influenced by anxiety around uncertainty, a deep attachment to where they live, and difficulty making life-changing decisions.

To effectively plan for, mitigate, and adapt to severe weather events and temperature changes, demographers should team up with engineers to better understand the level of vulnerability in specific risky locations, Frankenberg said. For example, older people with lower incomes and limited mobility may need emergency support in places with rising sea levels or that are prone to wildfires.

How Future Research Can Address Unanswered Questions About Population Aging

The experts noted several promising areas for future research that can improve the health and well-being of older adults, including:

  • Advanced data collection: Expand the use of wearables, MRIs, sound files, and other innovative data collection methods to better understand social interactions and biological processes.
  • Macro-level demographic analysis: Better integrate micro-level research on households and individuals with population-level questions and policy implications.
  • Biological mechanisms of aging: Focus on understanding physiological dysregulation, molecular and cellular changes, and other factors that determine fast versus slow aging trajectories.
  • Early-life interventions: Evaluate social and economic interventions earlier in the life course to prevent later health problems.
  • Real-world care delivery for people with dementia: Study how new diagnostic technologies and treatments can be effectively integrated into primary care systems.
  • Measurement harmonization: Develop better methods to harmonize measures of dementia across different surveys and data sources.
  • Root causes of isolation: Better understand why people become isolated, focusing on people, place, climate, and social disruption factors.
  • Caregiving workforce: Better quantify and understand the economic value of unpaid family caregiving as the care-to-support ratio declines.
  • Medicare Advantage impacts: Study how the shift to private Medicare plans affects care quality and costs, especially for vulnerable populations.

The researchers emphasized that many of these priorities require sustained investment in longitudinal data collection and interdisciplinary collaboration across aging research centers.


The scholars featured above lead many of the 15 research centers on the demography and economics of aging and Alzheimer’s disease and Alzheimer’s related dementias supported by the National Institute on Aging (NIA) of the National Institutes of Health for the past 30 years.

A coordinating center based at the University of Michigan supports the dissemination of findings from the centers in partnership with PRB.

 

06-25-ARC-Chartbook-new-cover

New Data Reveal Appalachia’s Economic Improvements, Key Vulnerabilities Compared to the Rest of the U.S. Economy

Report from ARC and PRB finds decreased unemployment, increased labor force participation, and higher homeownership in Appalachia—but the Region still lags behind the U.S. in population and income growth, as well as post-secondary education attainment.

New data released by the Appalachian Regional Commission (ARC) and PRB in the 15th annual update of The Appalachian Region: A Data Overview from the 2019-2023 American Community Survey shows that rates of labor force participation and homeownership continue to improve in Appalachia.

Drawing from the latest American Community Survey and comparable 2023 Census Population Estimates, the report, known as “The Chartbook,” contains more than 300,000 data points comparing Appalachia’s regional, subregional and state data with the rest of the nation.

Key improvements in the region’s economic indicators are as follows:

Decrease in unemployment rates and higher labor force participation

  • Appalachia’s unemployment rate decreased by 0.8 percentage points between 2014-2018 to 2019-2023, compared to a 0.4 percentage point decrease in the rest of the U.S.
  • Appalachia’s labor force participation rate among civilians ages 25 to 64 was 1.5 percentage points higher in 2019-2023 than it was in 2014-2018, slightly outpacing the national increase of 1.2 points.

Homeownership bypasses national average

  • Among occupied housing units, homeownership in the region was 6.7 percentage points higher than in the U.S. overall.
  • However, housing unit vacancy in the Appalachian Region was 3.4 percentage points higher than the national average.

Below average number of cost burdened households

  • The share of households in Appalachia that are cost burdened – where housing costs are 30% or more of monthly income—is 6.7 percentage points lower than the U.S. average.
  • In Appalachia and nationally, housing cost burden is highest among the youngest and oldest renters.

“While Appalachia continues to make progress toward reaching economic parity with the rest of the country, it’s important to recognize there is still work to be done,” said ARC Federal Co-Chair Gayle Manchin. “ARC will continue to partner on the local, state, and federal levels to prioritize the future of Appalachia’s 13 states and remains committed to ensuring Appalachians have access to the education, job training and infrastructure they need for prosperous lives in the places they love.”

“This year’s Chartbook highlights important economic advances, not only in Maryland but across the Appalachian Region—including gains in employment and homeownership,” said ARC 2025 States’ Co-Chair, Maryland Governor Wes Moore. “By working together, we continue to uplift our most vulnerable populations, promoting a better, brighter future for all families across Appalachia.”

Despite positive trends, several data points revealed key challenges affecting Appalachian economies compared to the rest of the nation:

Despite population increase, growth lags

  • Appalachia’s population is growing – but more slowly than the nation as a whole.
  • Growth in the region was 4.3 percentage points lower than the national average between 2010 and 2023.
  • In addition, Appalachia’s population is, on average, 2.2 years older than the U.S. population, with 1 in 5 Appalachian residents age 65 or older.

Post-secondary educational attainment remains behind national average

  • 27.3% of Appalachians hold a bachelor’s degree or higher, falling behind the national average of 35%.

Greater share of Appalachians live in poverty

  • At $64,588, the median household income in Appalachia is nearly $14,000 below the U.S. average of $78,538.
  • More than 14% of Appalachians live in poverty or “deep” poverty.

“The data point to bright spots but also guide us to areas where targeted efforts could improve well-being for Appalachians across the region,” said Sara Srygley, a senior research associate at PRB. “Decisionmakers and advocates can use the Chartbook to create the changes they want to see in their communities.”

The data shows that Appalachia’s rural areas continue to be at increased risk for economic distress compared to its urban areas. Appalachia’s 107 rural counties are also more uniquely challenged, compared to 841 similarly designated rural counties across the rest of the U.S., as rural Appalachian counties continue to lag behind on indicators including educational attainment and household income.

The data also highlights key differences between Appalachia’s subregions, including:

  • Northern Appalachia has the highest share of adults with a bachelor’s degree or more in the science and engineering field at 32.4%.
  • North Central Appalachia has the highest share of veterans among the subregions.
  • Central Appalachia saw an increase in digital device ownership and internet access, although broadband access remains a challenge.
  • South Central Appalachia experienced one of the most significant decreases in cost-burdened households compared to other subregions.
  • Southern Appalachia has the highest mean and median incomes—and income per capita is increasing more than in the other subregions.

In addition to the written report co-authored by the Population Reference Bureau, ARC offers companion web pages on Appalachia’s population, employment, education, income and poverty, computer and broadband access, and rural Appalachian counties compared to the rest of rural America’s counties. For more information, visit www.arc.gov/chartbook.


About the Appalachian Regional Commission

The Appalachian Regional Commission is an economic development entity of the federal government and 13 state governments focusing on 423 counties across the Appalachian Region. ARC’s mission is to innovate, partner, and invest to build community capacity and strengthen economic growth in Appalachia to help the region achieve socioeconomic parity with the nation.

Caregiver helps older woman put on her jacket

Fact Sheet: Trends in Family Care for Older Americans

In the United States, over 24 million people provide unpaid care for older adults—a 32% increase from a decade ago

As the large Baby Boom generation enters advanced ages, more family members and other unpaid helpers are stepping in as caregivers. In just over a decade, the number of family caregivers regularly assisting older adults with daily activities at home grew by 32%, increasing from 18.2 million to 24.1 million between 2011 and 2022.1

While the caregiving cadre has grown, who’s getting care has also changed. Older Americans receiving family care are younger, better educated, and less likely to have dementia than they were in 2011, report Jennifer L. Wolff of Johns Hopkins University, independent consultant Jennifer C. Cornman, and Vicki A. Freedman of the University of Michigan.

The increase in family caregiving partly reflects the rising share of older adults with multiple chronic conditions, such as heart disease, hypertension, stroke, and cancer. And while the share of older adults with dementia has declined, unpaid caregivers average twice as many hours each week caring for people with dementia than without dementia (about 31 hours versus 14), Wolff and team found (see Figure 1).

In addition, a new study estimates that the number of new dementia cases will double over the next 40 years as the population ages—setting the stage for more demands on dementia caregivers and more changes to the caregiving landscape.

“Understanding the changing composition and experiences of family caregiving has never been more important, but it is challenging to assess,” the researchers write. “[It] requires consistent measurement for well-characterized, generalizable samples of people who receive and provide help.”

The nationally representative National Study of Caregiving and the National Health and Aging Trends Study offer important insights. The two studies provide a snapshot of the family caregivers that help Americans ages 65+ who live in the community (i.e., at home or with a relative) or in a residential care setting other than a skilled nursing facility, such as an assisted or independent living facility, a personal care home, or a continuing care retirement community.

Family caregivers include relatives and unpaid helpers, like neighbors and friends, who assist with personal care tasks like bathing and dressing; mobility tasks like getting out of bed and getting around the house; and household activities such as laundry, food preparation, shopping, and managing money.

Dementia Caregivers See Increasing Demands on Their Time, Employment Woes

On average, the time that family caregivers spent helping older adults with dementia increased by almost 50% over the decade, rising from 21.4 hours per week in 2011 to 31.0 hours in 2022. By contrast, time spent assisting older adults without dementia fell from 15.3 hours a week in 2011 to 13.9 hours in 2022 (Figure 1).

Figure 1: Family Caregivers Are Spending 50% More Time Caring for Older Adults With Dementia
Average Weekly Family Caregiving Hours, by the Dementia Status of Older Adults (65+), 2011 and 2022

Source: Jennifer L. Wolff, Jennifer C. Cornman, and Vicki A. Freedman, “The Number of Family Caregivers Helping Older US Adults Increased From 18 Million to 24 Million, 2011–22,” Health Affairs 44, no. 2 (2025): 189-95.

 

People caring for older adults with dementia have high—and increasing—demands on their time. More than half (51.7%) of dementia caregivers lived with the person they were caring for in 2022, up from 39.4% in 2011, Wolff and team report. And the share able to hold jobs—outside their caregiving work—dropped from 42.5% to 34.6% during the same period.

Among caregivers with formal jobs, the share who reported challenges with their employment—including working fewer hours or being less productive—increased over the decade, regardless of whether they cared for someone for dementia.

“Challenges are exacerbated when caregivers are in poor health themselves; have a lack of choice in assuming the caregiving role; and, for the substantial proportion of family caregivers who are employed, work in low-wage jobs with limited flexibility,” the researchers note.

Care Recipients Are Mainly Older Women, but the Share of Men Receiving Care Is Growing

Which older Americans get family care? As in the past, they tend to be female, non-Hispanic white women who are married or widowed. But growing numbers of family care recipients are male and have some college education. More are also separated and divorced compared to 2011, reflecting national trends.

Adult Children Continue to Care for Their Parents

Who’s providing care? Family caregivers continue to be largely female and married, and most report being in good health. In 2022, adult children still made up the largest share of family caregivers for older adults, at 40.7%, but this represents a significant decline since 2011 (Figure 2).

Figure 2: The Share of Adult Children Caring for Older Relatives Has Declined
Relationship of Family Caregivers to Adults Ages 65 and Older Receiving Care, 2011 and 2022

Source: Jennifer L. Wolff, Jennifer C. Cornman, and Vicki A. Freedman, “The Number of Family Caregivers Helping Older US Adults Increased From 18 Million to 24 Million, 2011–22,” Health Affairs 44, no. 2 (2025): 189-95.

 

In 2022, adult children accounted for about half (49.1%) of family caregivers for older adults with dementia, compared with 38.4% of caregivers for those without dementia. Just 17.7% of family caregivers for older adults with dementia were spouses, compared with 24.5% of family caregivers for people without dementia.

A sizeable share of family caregivers (17.0%) had children under age 18 at home in 2022, and 6% to 13% viewed their care responsibilities for older adults as a source of financial, physical, or emotional difficulty.

Despite these challenges, the researchers report a decline in the use of support groups (4.1% to 2.5%) and respite services (12.9% to 9.3%) between 2011 and 2022.

Trends and Policy Implications

Many caregivers face extraordinary demands and should be the focus of support services, Wolff and colleagues say. They single out those caring for older adults with dementia or nearing the end of life, as well as caregivers “from racial and ethnic minority groups who are more likely to assist people who have extensive care needs in circumstances that involve scare economic resources.”

Family care needs are likely to rise as the number of U.S. adults ages 85 and older is  projected to triple by 2050. The researchers note that the number of family caregivers rose even as the long-term use of skilled nursing facilities among older Americans dropped and community living increased. The challenges these caregivers continue to face is “sobering,” they write, including competing time demands from work and child care while spending an average of 17 hours per week on care. In addition, about 1 in 8 family caregivers report financial, physical, or emotional difficulties related to their caregiving roles, percentages that were largely unchanged over the 11 years examined.

Policies and programs to help reduce the financial, physical, and emotional burden of caregiving exist, but do not represent a coherent strategy, the researchers say. “Local, state, and federal policies are a patchwork that is uneven in availability and largely symbolic in magnitude,” they argue. Addressing the needs of family caregivers will require a “cohesive framework in support of the care economy.”

 

 

References

1.  Jennifer L. Wolff, Jennifer C. Cornman, and Vicki A. Freedman, “The Number of Family Caregivers Helping Older US Adults Increased From 18 Million to 24 Million, 2011–22,” Health Affairs 44, no. 2 (2025): 189-95.

Holding It Together Webinar (Twitter) (1)

Webinar: The Pros and Cons of Virtual vs. In-Person Interaction for Older Adults

In this March 4, 2025 webinar, three researchers discussed how social media and new technologies may enhance—or limit—social connectedness and emotional well-being among older adults.

  • Karen L. Fingerman (University of Texas at Austin) focused on the relationships between social media use, social ties, and emotional well-being in later life.
  • Ellen L. Compernolle (NORC at the University of Chicago) summarized research on loneliness among older adults from Chicago during and before the COVID-19 Pandemic.
  • Steven W. Cole (University of California at Los Angeles) described how in-person and virtual social environments influenced gene expression during the COVID-19 Pandemic.

This webinar was hosted by PRB and the Coordinating Center for the Centers on the Demography and Economics of Aging and Alzheimer’s Disease and Related Dementias, with funding from the National Institute on Aging.

Like this event?

Support our work

 

Stay up to date on future PRB events.

Subscribe to our newsletter

Holding It Together Webinar (Twitter) (1)

Webinar: How State Contexts Impact Population Health

In this Nov. 14, 2024 webinar, two distinguished researchers discussed how U.S. state policies and systems can affect racial and regional inequities in health and longevity.

  • Tyson H. Brown (Duke University) focused on innovative and best practices for measuring and modeling state-level structural racism to advance aging health research.
  • Jennifer Karas Montez (Syracuse University) summarized findings from recent studies that have connected the dots between changes in states’ policy contexts in recent decades and changes in population health.

This webinar was hosted by PRB and the Coordinating Center for the Centers on the Demography and Economics of Aging and Alzheimer’s Disease and Related Dementias, with funding from the National Institute on Aging.

Like this event?

Support our work

 

Stay up to date on future PRB events.

Subscribe to our newsletter


Transcript

Mark Mather, PRB: Hi everyone, thank you for joining today’s webinar on how state contexts impact Population Health. I am Mark Mather. I’m with the Population Reference Bureau, or PRB, and I work in collaboration with the Coordinating Center for the Demography and Economics of Aging and Alzheimer’s Disease and Related Dementias at the University of Michigan. And our goal is to help publicize research on health and well-being, especially among older adults. I also want to acknowledge the National Institute on Aging for making this webinar possible, and my PRB colleague, Toshiko Kaneda, for helping us to organize this event.

I’m excited to introduce our two speakers today. We have Dr. Tyson Brown, who’s a Professor of Sociology, Associate Professor in medicine, and Associate Scientific Director of social sciences of the Duke Aging Center at Duke University and Dr. Jennifer Montez, who is a Professor of Sociology, Gerald B Kramer faculty scholar in aging studies, Director of the center for Aging and Policy Studies, and Co-director of the Policy, Place and Population Health Lab at Syracuse University.

We’re also going to put two links in the chat, I’m not going to read their long bios, but if you’re interested in learning more about the speakers and their research, we will be putting links in the chat where you can get more information. And finally, just a couple of housekeeping notes; we’re going to hold the Q&A until the end. But if you do have a question, you can type it into the Q&A box at any time during the webinar. And finally, this webinar is being recorded, we will send you a link to that recording after the event, probably within a day or two. And with that, I will go ahead and turn it over to you, Tyson.

Dr. Tyson Brown, Duke University: Well, thank you so much. I look forward to hearing your feedback during our presentation. I will go ahead and just start sharing my screen. Again, thank you so much for inviting me to present my work, and I’m really excited to talk with you all today about how we can operationalize state level structural racism and its impact on population, health and aging. And as a race scholar, gerontologist, and population health scientist, you know, the primary focus of my research is on quantifying and mapping structural racism, as well as estimating its impact on aging and health equity. And so today, I’ll be giving an overview discussing what I see as important theoretical and methodological issues, as well as promising avenues to address them, and in doing so, advance the scientific study of structural racism and health.

So, before discussing the role of states, I think it’s really useful to zoom out to highlight how various frameworks can help us better understand how social context influence health. So, the social ecological model emphasizes that population health is shaped by complex interplay of social factors across multiple levels, from individual behaviors to broader societal forces. And this model highlights how health outcomes are not solely a product of individual choices, but are influenced by interpersonal relationships, community structures, institutional policies, and broader sociopolitical contexts. And by analyzing each of these, we gain insights into how policies, societal norms, and community structures can either benefit or harm population health. And this is true for many social forces, including structural racism.

And so, most of the research on links between structural racism and health have focused on structural racism at the neighborhood or the county level, which are really important context and really sort of the micro-level, or rather the more proximate contextual sources or determinants of health. However, other special units and policies such as states have received comparatively less attention. And this is a really major limitation in literature, because U.S. states obviously are important legal, administrative and political units and states have always provided an important context for racial stratification, including the roles that they most obviously played with respect to chattal slavery, Jim Crow and anti-miscegenation laws. So importantly, states continue to have a great deal of autonomy, and they’re heterogeneous in terms of the racialized policies and practices. This is evident with respect to things like disenfranchisement laws, aggressive policing practices, punitive sentencing, as well as the expansion or contraction of safety net resources.

And so, as a result, my research focuses on conceptualizing states as racialized institutional actors that influence population health.

So, in several recent studies, my research team scopes the literature on social racism to really identify the central tenets of structural racism theories and draw upon them to study state level structural racism. We highlight how the analytical crux for measuring structural racism, which is a complex, interconnected and dynamic network, really lies in examining its features and mechanics that undergird racial inequalities.

And so, failure to align measurement tools with these core features compromises the validity of research. Because measures that are incongruent with salient aspects of structural racism can distort findings. So, to address this issue, we draw on interdisciplinary theories and evidence to really dissect core features of social racism, with a focus on their implications for measurement and modeling.

And so, our studies on and the broader literature really underscore the importance of measuring modeling core features of structural racism, including things like the fact that it’s a distributional system, involves relational power dynamics, manifestations of racial inequities, that it’s a multi-level phenomenon operating at macro and micro level contexts, that it’s multifaceted and interconnected in nature. And this plays out across societal domains, that there are specific racial actors implicated, that it’s an institutionalized phenomenon that it involves racial schemas, logics, as well as socio historical context and intersections with other systems of oppression. And so structural racism is embedded in major political, economic, medical, criminal, legal and numerous other social institutions and aspects of society. So, in these forthcoming articles that are listed here, we offer concrete recommendations for conceptualizing, measuring, and modeling structural racism in ways aligned with theory. And so, the aims of these studies is to provide field guides for rigorous, theory driven measurement approaches, proposing best practices for the scientific study of structural racism in health research.

And to provide a couple concrete examples of ways to measure structural racism, as well as its effects on health, I’ll briefly describe several recent studies in this vein. And so, the first one addresses two main research questions. The first is, does state level structural racism across societal remains reflect an underlying latent construct? And latent constructs are phenomena that are not directly observable but can be estimated through statistical approaches that really capture manifestations of the phenomena. And then secondly, the question is structural racism associated with worse health outcomes among Blacks and whites in the U.S.

So, to examine variability in state level structural racism and its relationship with health among Black and white adults, we combined indicators of structural racism from several sources of publicly available data. And then we link these to geo coded individual health and demographic data from the Health and Retirement Study, as well as CDC data on COVID mortality, as well as the Behavioral Risk Factor Surveillance study. And so, this table shows each of the domains and indicators of structural racism that we examine. We use publicly available data to examine structural racism for the year circa 2010, spanning several domains including the criminal legal system, education, economic resources, political participation, as well as residential segregation.

And so, I should mention that this measurement approach is informed by theoretical perspectives as well as previously validated measures of state level structural racism.

So, the next step is to test whether state level structural racism reflects this underlying latent construct. And so, we’re especially interested in measuring structural racism in ways that are in line with structural theories, as I mentioned. And a latent variable approach is well suited for minimizing measurement error and capturing conceptual properties of a complex system that’s difficult to quantify or directly measure, such as structural racism. And so along those lines, the nine indicators of structural racism that I mentioned were used to develop a latent measure of structural racism. And we used confirmatory factor analysis to measure the extent to which structural racism across these domains are reflective of an underlying construct of structural racism and doing so we systematically evaluated model fit using varying model specifications related to correlated errors and also dimensionality. This figure here illustrates the factor structure of the latent construct. These correlated errors are all intended to address common sources of variation that are independent of the effects of structural racism on the various indicators. And so all consider the measurement model that allows for other common sources of variance in the indicators that are largely conceptually motivated, has a good fit with the data, and it generates reasonable parameter estimates. Furthermore, this measurement approach is largely consistent with many of the theoretical tenets that I outlined above. And I should note that we made this measure publicly available from the journals website in case you’re interested in exploring. So now we’ll turn to addressing the second research question, which is how does structural racism shape health among Black and white adults? Eco social theory has become a leading framework for understanding how macro level discriminatory environments impact health, and the theory suggests that structural racism has deleterious effects on Black people’s health. That part’s pretty much straight forward. We would anticipate that from the theory. And there are competing hypotheses about how structural racism may influence the health of whites, with some suggesting that whites benefit from sexual racism, while others are positing that they’re harmed by it, and others suggesting that their health may be unaffected by structural racism. I’m glad to talk more about these competing hypotheses during in more detail during the Q&A, if you’d like.

But let’s go ahead and jump into the results. What’s going on here? I’ll explain it in just a second. But one of the things I want to mention is that, you know, since replication is a hallmark of good science, we examine the relationship between structural racism and six different health outcomes in both the Health and Retirement Study and the Behavioral Risk Factor Surveillance study. For a little bit of context, the HRS is considered one of the premier data sources for studying health among adults over the age of 50 in the U.S. is nationally representative over samples of Black adults and the birth. This is the largest health survey in the in the United States. So, you have over 300 respondents. So, you’ve got tremendous statistical power. And so, we found remarkably consistent results across health outcomes and across the two data sets. And six out of the six cases, you can see that there are that higher levels of structural racism are predictive of worse health among Black people, remarkably consistent. But we see a very different story for whites. And one instance, structural racism exposure at the state level is predictive of better health for whites and in five out of six cases, there is no statistically significant relationship between structural racism and whites’ health. So, you find, you know, shockingly consistent results and just, you know, these health outcomes are some of the most commonly used, especially when studying older adults. And so, I think that there’s this, you know, there’s a real signal that we’re picking up. And we had much more confidence in our findings because it was so consistent across these health outcomes and across these health studies. So, in a recent study, we also examined the relationship between a latent measure of structural racism and Black-white inequities in Covid 19 mortality rates. Right.

So, the regression estimates indicate that the relationship between social racism and Black, white inequality in Covid 19 mortality is positive and statistically significant, both in the bivariate model and net of covariates. And so, this again is a state level measure and collectively the findings you know, I would argue that the collectively our findings across these studies suggest that our latent variable approach has really strong predictive validity and we actually did measure it against benchmarked it against other metrics such as sort of a summative index that assumed equal weighting of the indicators and did not take into account correlations between their errors when we found that the latent measure actually explained more variation in health outcomes and also explained significantly more variation of any of the individual level indicators. And so that again, provides some evidence that there’s really there are some real benefits to taking a latent measurement approach. So, I think it’s also essential to measure and map cultural forms of racism that reflect racial schemas, logics and practices.

So broadly you know, we really need to be considering the roles of things like racialized violence, animus, resentment, hate speech, and biases, all of which have been shown to vary by place including across states. And so, in addition to well-established survey data resources, we should also be utilizing data and methods to capture utilizing, you know, more innovative data measures to capture anti-Blackness through geo coded data from things like internet search engines and computational approaches, scraping websites and even experiments. So, this map shows the spatial distribution of a latent measure that Reid DeAngelis and I are developing using these types of data and measures. And preliminary results show that it’s predictive of population health inequities. And so, we’re really encouraged about the possibility of combining these latent measures of racial schemas and logics, sort of cultural measures with the more institutional measures and looking at how the interplay between them and their relative contributions to population health inequities.

I should also mention that in a recent study published in the Journal of Health and Social Behavior, Patricia Home and Brittany King and I, we introduced a state level structural intersectionality approach to population health. And what it does is it really demonstrates an application of social intersectionality using administrative data sources similar to the ones that I discussed in the previous studies. To examine the relationship between macro level structural racism as well as structural sexism and economic inequality and looking at how they interplay between them, as well as their joint and individual contributions to health inequities across U.S. states. Right. And so, this study, I think, can really serve as a springboard and a data source for similar studies that are aiming to extend this research, in studying how state level structural, intersectional oppressions differentially shape health outcomes for various demographic groups. I should also note that numerous studies also really highlight the dynamic role that policies and politics play in shaping health inequities.

Obviously, Jennifer Montez is a new data set, is an excellent resource for studying the role of states in this regard. There are also some studies by biomedical engineer Jackie John and other colleagues that used novel, a novel database on racism related laws which has been shown to predict health. So, these and other data sources on racial policies such as three strikes laws, welfare reform, banning critical race theory and racialized disenfranchisement can really serve as important resources for research on health inequities. And, you know, the history of structural racism in the U.S. has really important implications for how we should really be approaching the measurement of it and quantifying its effects on these inequities. And there is suggest that historical racism directs, constructs and continues to mold contemporary structural racism as well as health outcomes. And so, in that vein you know, there have been several empirical studies that have shown that polities that had larger enslaved populations in 1860, have greater present-day inequities and poverty and economic mobility and also higher levels of contemporary pro-white bias, and that these can be linked to contemporary health outcomes as well, and that historical redlining practices underlie contemporary residential segregation patterns. This is becoming a well-documented social fact, as well as the fact that New Deal policies expanded the white middle class and are directly implicated in modern Black-white inequities and wealth. And so, it really shows the long arm of history in shaping contemporary health outcomes and moving forward. I think a couple of examples of measures to consider include variation and exposure to things like slavery, Jim Crow, lynchings, anti-miscegenation laws, stunned downtowns, the number of folks who are doing really good work in that space. We can think about exclusion from the economic benefits of the New Deal and GI bills, as well as racialized voter suppression.

And so, in many respects, I think what this literature is showing is that what’s past is prologue. And we’re seeing this with new laws, for example, the disproportionately disenfranchised Black and brown Americans. And in closing, I’d just like to highlight a couple, what I see as exciting opportunities for future research that I think will really advance our understanding of the links between racism and aging, health inequities, which I think can be used to inform ultimately efficacious racial equity solutions. So, for example, future studies should really better utilize longitudinal data to better understand the temporal dimensions of the relationships between structural racism and health such as the potential impacts of things like sensitive periods, durations of exposure, as well as causal effects. Right.

And moving forward, you know, I plan to use system dynamics modeling to explicitly model interdependence between forms of structural racism as well as feedback loops and lagged effects. I think that’s a really exciting area that we can start to really overlay temporal dimensions and a life force perspective with our understanding of the processes that ultimately lead to these wide health inequities that get larger and larger throughout the life course.

And also, multilevel research are really needed in this space in order to better understand the cross level structural races and linkages and the joint effects on racial stratification. So obviously, my talk here has been highlighting the role and really funny evidence of the role of states, which are really key. I hope that made that is clear, but of course, other levels also matter, right? and ultimately, you know, this is multifactorial and we should really be thinking about how structural racism across different levels, including state level influence, health.

And then, you know, as the data ecosystem is rapidly expanding, scholars are increasingly calling for a wider use of new sort of big data sources and social science research. And so, I’d just like to echo and expand upon these calls by recommending the use of these new approaches to address salient new research questions about how structural oppression shapes racial inequality.

And so harnessing digital trace data is especially useful as traditional data sources become increasingly costly, increasingly logistically complex and are becoming less representative of the target populations due to declining response rates as well as other selective forces. Also, moving forward, you know, I’d recommend testing the extent to which structural oppression affects inequality indirectly through intermediary social pathways such as unequal access to resources, things like education, income, health care, autonomy, and also exposure to risks, things like toxins, housing instability, victimization, as well as involvement in the criminal legal system and other pathogenic social conditions.

And so ultimately, this is really important. And to do this, we really need to address the and build upon really strengthen the data infrastructure on linking structural racism to health and the current data landscape for structural racism is rather confusing and scattershot.

As I alluded to at the beginning of the talk, you know, we’re willing to build a publicly available data infrastructure on structural racism to catalyze future research on its effects. And creating a data hub will reduce inefficiencies by increasing data sharing, coordination and innovation and so it’s essential that the data include contextual information at multiple spatial and temporal scales, obviously, including the state level. And I see a number of challenges and opportunities to really build a user-friendly data resource like this.

And currently, the process of linking existing data sets to contextual data can be cumbersome, but there are some wonderful opportunities and some recent examples of innovations that reduce these barriers and make it a lot easier, which Professor Montez will be discussing with us. So, I look forward to the Q&A.

Mark Mather, PRB: Great. Thanks so much, Tyson and as a reminder, if you do have a question, we have a Q&A box. So, I encourage you to type your questions into that box at any time. We’re going to take all of those questions at the end of the webinar. So, I will now turn it over to you, Jennifer.

Jennifer Karas Montez, Syracuse University: Great. Thank you. Tyson, I really appreciate your presentation. I love the historical perspective. It’s so important. And I can’t wait to read the 2025 annual review piece.

So, what I’m going to do with my time is give a high-level summary of some of the work that’s been going on using U.S. states to understand why U.S. life expectancy has not been keeping pace with other high-income countries over the last several decades. I don’t know if you can see this thing here where I’m going to try to hide it. Okay, great.

So, this work I’ve been involved in for several years now with this wonderful team. And I want to acknowledge also the funding that we received from NIA to do this work. So, I want to start off by just talking about why we’re focusing on states and state policies. If our goal is to understand what’s going on for the U.S. overall. Um, Just touch on some of the key learnings that we’ve uncovered over the last 5 or 6 years. Talk about how did we get here? Like, how did state policies end up being so important in explaining U.S. population health? And then where do we go from here? So, why are we focusing on states to understand U.S. Life expectancy. Well, this figure you’re looking at here has a line, a wiggly line for all 50 states. It shows their life expectancy trends from 1959 to 2019. And I pointed out Connecticut and Oklahoma, and I’ll do that a lot during the presentation because there are really interesting case study pretty illustrative of what’s happening. So, you can see these two states that we think of today is wildly different in so many ways, they weren’t always that different. So in1959, these two states had the exact same life expectancy. But you can see they’ve taken very different trajectories over this time period. And you’ll notice that, you know, there are some states, like Connecticut who, you know, are approaching, you know, 80, 81, 82 in terms of life expectancy. But there’s a lot of states that have kind of flatlined, if you will, since the mid-80s. And our thinking, our team’s thinking is that if we can explain why we have this divergence in life expectancy across states and why we have so many states that just really haven’t made progress in decades, then that gives us another piece to the puzzle in terms of why U.S. life expectancy is not doing what we’d like it to do.

So, I want to give you one other view of the same data, and in this view, instead of plotting all 50 states, I’m just going to plot the range in life expectancy across the states for every year. And this is what it looks like. And this data runs from 1970 to 2014. Because the study that I’m using this from examine those years. But what I want you to see here is that until 1984, states were actually becoming more alike in terms of their life expectancy. The range was shrinking. And in 1984, something happens and the range and life expectancy just keeps getting bigger and bigger and bigger. And if I were to continue this chart out through present day, you would see that range just grow year after year after year. So, you know, there are a number of explanations, a number of hypotheses for what might be happening here.

Our team has been focusing on the possibility that this divergence in life expectancy across states might be partly explained by the divergence in policies across states. So let me give you a couple of visuals of how states have diverged in their policies. So, this chart, it’s a little messy at first. It has 50 arrows. There’s an arrow here for every state. It’s not very important to figure out which state is which arrow. What these arrows indicate is how each state overall policy context has changed between 1970 and 2014. So, Jake Greenbank is a wonderful political scientist, created this overall measure of state’s policy context. And so, what I’m showing you here is where each state started on that measure in 1970. That’s the beginning of every arrow. And then where each state ended up in 2014 on that measure. And that’s the arrowhead. And so, what I want you to see is that we’ve got most states moving away from the center in terms of their overall policy context. Okay. So, they’re diverging just like states life expectancies are diverging. And again, I picked out Connecticut and Oklahoma so that you can see, you know, in the 70s, these states weren’t that different in terms of their policy context, but they sure are today. So, you might be thinking, well, give me some examples of how are these policies changed over time. So, I’m going to give you just four examples. This summary measure you’re looking at here has over 120 policies all wrapped in it. I’m just going to pull out four of those, four that we know are important for population health. So, let’s take minimum wage and earned income tax credit. Again, two policies that have pretty good evidence that these are important for population health. You can see in 1990, there’s effectively no difference between these two states. In in 1999, neither state offered an ITC. So, there’s absolutely no difference between these two states in these two very important economic policies. But by 2019, you can see these two states are wildly different in these two core economic policies. Let me give you one more example. I’m going to show you how these states have differed in terms of two very important health behavior policies. So, in 1990, these two states had almost identical taxes on a pack of cigarettes and pretty similar number of laws meant to increase the safety of firearms. But by 2019, these states, again wildly different. And it’s not just that a state like Connecticut in blue is moving in one direction in states like Oklahoma or just stuck. But you can see as states like Oklahoma are actually rolling back some of these policies that we know are good for population health. And you can see this most clearly in the firearm safety laws here, where the number of those laws has been rolled back in Oklahoma. So, let’s go back to this chart then. Now what I want to do is give you one more view of how state policy context have changed during this time period. So, what I’m going to do is I’m going to again take this overall summary measure of states policy context, and I’m going to plot the range across the states for every year in that measure. And this is what it looks like. So again, in 1970 until 1981, states were becoming more alike in terms of their overall policy contexts. I mean, it’s almost unimaginable today. But then after 1981, they become increasingly different, increasingly polarized, increasingly divergent. Whatever term you want to use in terms of their overall policy context. And so, if you were here five minutes ago, you’re thinking, I have seen a pattern similar to this before, and you have in fact. So, what I have here is now overlaying the two charts that show how the range in states policy has changed over time.

And the range and states life expectancy has changed over time. So bottom line, as states policy contexts are becoming more similar so were states. Life expectancies three years after states becoming start becoming more dissimilar in their policy environment, states start becoming more dissimilar in their life expectancy. So, at this point in our team’s journey, we thought we had developed some pretty compelling descriptive evidence that there’s a relationship here now. Is it causal? That’s what we wanted to find out. So, I’m going to walk you through some pretty high-level summaries of what we have. Think we’ve learned so far about whether that relationship is more than just a correlation.

So, we’ve found that actually U.S. life expectancy trends would have been significantly steeper if state policies hadn’t changed the way that they did. And that if we were to change all state policy, so all 50 states, if we were to change them all, to have either a very liberal or a very conservative policy context, we could alter us life expectancy by about 2 to 3 years, which is huge. That would put the U.S. about average among its peer countries, as opposed to being very securely at the bottom right now.

Changing state policies in this way would also alter the number of working age deaths each year by about 220,000, again, a very substantial number. So, in more recent work, we’ve been trying to put a more a finer understanding of where are these effects taking place for which causes of death are these effects happening and can we better understand the time lags under which these policies might have an effect? Do we see that policies have an immediate effect on working age mortality? Do we see that it takes a year or two years? How many years does it take? And so, I’m just going to summarize a piece of those findings. So, what you’re looking at here is how much the mortality rate of working age women would change if a state policy changed. And this is specifically CVD mortality among working age women. So, a lot of dots here. So let me walk you through what’s going on. So, look at the far left under the criminal justice policies. So, our analysis suggests that if we were to examine what would happen to working age women, CVD mortality during the very same year that a state’s criminal justice policies went from very conservative to very liberal. That was a lot of words. Let me say it again. So again, this is what we estimate.

What happened to working age women’s CVD mortality, If a state changed its criminal justice policies from conservative to liberal. So, we find that if we were to examine that in the very same year that that that policy was made, we would get a non-significant change in women’s CVD mortality. A year later, we start to see an effect, and within three years later, we start to see a significant reduction in women’s CVD mortality.

So, what this is saying is that if you believe these counterfactual analyses that it takes a while to see the benefits, the mortality benefits of moving from a conservative to a liberal criminal justice policy environment in the States. I’ll just walk you through a couple of other policies, gun safety policy. We find that we see almost an immediate effect in the reduction of women, CVD mortality when gun policies go from conservative to liberal.

And that effect seems to be stronger with each passing year. But there are other policies like health and welfare where we see an immediate effect, but over time that effect attenuates. So, what we take away from an analysis like this is that. The timing matters in our studies. And we don’t often do a very good job of taking timing into account. But imagine the implications. If I were to have only done this study examining no lag or a one-year lag, I would have walked away saying some policies don’t matter when they actually do or saying some do when they actually don’t matter after a given period of time.

And the other thing to take away from this is that not only do we need to pay more attention to it these lag times, but the appropriate lag time might depend on the specific policy that some policies just may take a longer time period to see a population health effect than others.

Another high level finding from our work is that the policies that have changed and polarized the most since the 80s happened to be the same policies that have the strongest associations with life expectancy and working age mortality, and those policies are mainly around labor and firearms.

And then the last finding I want to highlight, and this is not from our group, but it’s important. And I and I’m anticipating some of your questions later on, is that the growing disparities in working age mortality across states do not appear to be due to the changing socio-economic composition of a state’s populations.

So that’s, again, a very high level, a very selective set of what we’ve learned so far. So, what I want to do now is talk about how did we get here. And I want to do this because I want to make sure you, you leave this session realizing that the changes in these state’s policies, these are not exogenous changes, right? There are forces behind the scenes turning the knobs. So, if we want to move our analysis, you know, up the causal chain we need to be looking at who’s changing the knobs and why are they changing them. So, I’m going to point out, maybe my thing. It’s not moving. Okay.

So, I’m going to point out four interlocking forces. And I’ve got several citations down here. Really great in-depth books that you can read on this by sociologists, political scientists and others, a historian. And if you want just the quick and dirty that the very last article in the Milbank Quarterly covers these, these four issues.

So how did we get here? Four things really come together. One is the devolution of federal authority to the states, where states have gained increasing policymaking authority, especially since the Reagan administration. The other thing that has been happening that has given states this increasing power is there has been a proliferation of states enacting preemption laws to take away local authority, to do any number of things from raise the minimum wage, to mandate paid leave, to banning fracking. And so not only have states been given this additional authority by the federal government, they’ve been taking away authority from the states at the same time. Now, those two trends are not in and of themselves problematic but as a result of that, states have made very different decisions about their policies’ environments.

Some of those decisions have been unduly influenced by corporations, their interest groups and wealthy donors and some of the influences coming from the nationalization of political parties. And what I mean by that is, you know, we used to have a Republican version of Oklahoma and a different Republican version of Texas. But now there’s increasingly a Republican version across the U.S., just like there is an increasing Democrat version of policy context across the U.S. And I’m just highlighting this very quickly and again, I’ve given you some citations if you’re interested in digging into that further.

So, I want to conclude with talking about where do we go from here? And some of these recommendations are going to echo what Tyson presented earlier. I think my mouse is acting up here and I’m just pulling these ten ideas from editorial in the Milbank Quarterly this year on ten ways to better understand how shifting state policy contexts affect Americans health.

I’m not going to go through all ten, but I’m going to talk about some that keep me up at night. One of the things that we have got to do a better job with is giving more attention to policy bundles or indices. There is a very strong correlation across policies within any given state. If you were to tell me a state’s minimum wage, I can tell you almost everything else about that state. I can tell you can give you a good idea of what its tobacco taxes are, or whether it has an earned income tax credit, whether they’re right to work laws. Policies are becoming increasingly bundled. And so, the tendency in a lot of our work is to create summary indices or other kinds of indices, which I think is the right direction to go. But at the same time, I think we need some general guidance on how to use these indices responsibly. And there was a really great commentary by Harper and Andy last year around their concerns of using these kinds of indices. I don’t want to persuade anyone not to use indices, particularly because there is no single policy that can explain the difference between states. We wouldn’t choose a single policy to try to describe why U.S. life expectancy is different than that in Sweden, for example, because they’re just two completely different policy context and the same is true across states.

The other thing that I want to draw attention to, and this is going to echo what Tyson said, is that going forward, we need to pay more attention to lag times. We need to understand how these state level exposures accumulate across the life course. You know, by the time people are, you know, in the 30s, 40s and 50s, they have likely experience multiple different state policy context. They might have moved and so they experienced different context, or they might have stayed in their own state of residence. But, you know, as you saw earlier, states policy contexts are very dynamic and they’re changing.

So how do we capture this cumulative exposure to different contexts over people’s life course? And at the same time, how do we understand how the health effects of those exposures also evolve over time? So, I think, you know, version 2.0 of this work is has got to get just into this messy detail about how these dynamics, how to capture the dynamic interplay across the life course. And then the last point I want to draw on here is that I think, you know, as a group of researchers interested in this topic.

We need to develop an easy to implement method for accounting for inter-state migration. It is the question that comes up any time that those of us who are doing this work present it, and there’s no agreed upon way to account for it, it’s very, very difficult to account for it. In most surveys. You just don’t have the information. But can we not come up with some sort of agreed upon way, you know, baseline way to take this into account so that we can better isolate the effects of state policies on people’s health.

That is all I have. I’m going to stop here and say thank you so much. And I look forward to your questions.

Mark Mather, PRB: Thank you so much, Jennifer. We are now going to move to the Q&A. And so, as I mentioned before, if you have a question, we have a Q&A box. You’re welcome to put your questions into the box at the bottom of the screen.

And I will start with you, Dr. Tyson. There’s a question that came in through the chat asking if you could say a bit more about why different types of structural injustice vary in their geographic distribution. This person would have expected them to align more than they do.

Dr. Tyson Brown, Duke University: Yeah, that’s a great question. So, I’ll start by just noting that, you know, in my research, I argue that because racism is dynamic, flexible and really adaptive to socio historical context, that there are likely distinct contemporary racialized regimes, and that these regimes are characterized by different manifestations and modalities of racism across place. And so, whereas contemporary discrimination, discriminatory, legal and cultural forms of racism are especially pronounced in states in Southern and Appalachian regions. Findings from my research really illustrates how contemporary structural racism that’s manifest in discriminatory institutional contexts and racial inequities, that this is particularly severe in states and Midwestern and Northeastern regions.

And so, although the historical and modern roots of place-based differences in social races have not fully understood, you know, scholars have posited that that really the extreme degrees of contemporary structural racism reflected by racial inequality and institutional contexts in many northern states, them in part from institutionalized policies and practices of social control, racialized social control through exclusion and subordination. So, you can think about examples of resource hoarding, of redlining, of racial covenants and discriminatory policing and that these white supremacist tactics were increasingly deployed after the Great Migration because northern whites perceived the increasing Black population as a threat. Right, and so, given the fluid, shapeshifting nature of structural racism, it’s really important that future research investigate the etiology and consequences of these distinct contemporary racialized regimes across time and place. But that’s a really astute observation and frankly, we don’t know and so this is a really exciting time to be doing this research and I think it’s a really important time as well.

Mark Mather, PRB: For you, Jennifer, there’s a question in the chat, to what extent do changes in interstate migration due to increasing political polarization?

I think they’re talking about people moving because of the policy context. Perhaps, would that lead to a select group of unhealthier people in more conservative states and conversely, healthier people in in liberal states? Thinking about the change in interstate migration over time.

Jennifer Karas Montez, Syracuse University: That is a great question. So, I will say that in the ways that we have tried to account for interstate migration and also the study that I mentioned in the talk, we’ve not found that it is a major contributor. So that being said, I am not sure that that will those kind of null findings will hold up post 2020 due to both increasing polarization and people potentially moving to places that are more aligned with their political values, and the fact that, you know, we’re seeing a lot of people moving to red states, to Texas, to Florida, I mean, even Oklahoma. So, I don’t know, you know, on the whole how that’s all going to play out. But my working hypothesis at this time is that what we know about who moves and who doesn’t move might be different pre versus post 2020.

Mark Mather, PRB: Thank you. This question is I guess mainly for you, Tyson. But Jennifer, you’re welcome to respond in your future directions. You mentioned digital trace data, Tyson, could you expand a little bit more on what that type of data looks like, why it may be a future direction?

Dr. Tyson Brown, Duke University: I’d be glad to. So just a level set. Digital trace data includes things like social media activity, web browsing histories, search engine queries, e-commerce transactions, text messages, emails all where we live our lives today, right? And digitally and so there’s a lot of data out there and in my work, I argue that harnessing the data revolution, which is the rapid, you know, growth in data generation and storage analysis that’s driven by technological advances big data and digitization and transforming how information is used in decision making you know in research and everyday life. Right.

So, I think this is a really important and, and it’s especially critical, to use digital trace data to really capture what we termed as backstage cultural dimensions of racism. Right. And so, you don’t have to rely upon what people responses that they give in surveys because there’s social desirability. There’s a number of reasons that we may be getting not we may not be getting the full picture from survey data, which is still really important and even administrative, that can be really important. But there’s something unique about how we live our lives online that you know, they’re basically receipts. You know your digital life doesn’t lie, right? and we have a lot of digital traces, and so I see it as really, a really valuable way to complement more traditional data sources that many of us as demographers have used.

Mark Mather, PRB: In place and again, for you at this person, notice that there’s not a lot of overlap in the map to measures of structural racism with high values in the Midwest and West, and cultural racism with high values in the southeast and on the state level similar, I guess, to the other question, but how do you envision these measures potentially capturing different pathways for racism to impact population health?

Dr. Tyson Brown, Duke University: Yeah. Well, I guess see my previous response. You know, again, getting back to these contemporary racializing regimes, I would refer to the person who asked the question to the recent study. So, there’s a 2024 article in the Journal of Health and Social Paper. Between this author by myself and Patricia Home and also a couple forthcoming in your articles and your view of sociology and review of public health will be really digging deep with some of these issues and so I think this is really important. You know, the latter part of the question was, you know, what do I see as there, you know, sort of unique or joint pathways in shaping health. Right? So, whether you have sort of the institutional manifestations and you’ve got the more ideological, cultural dimensions and that is an empirical question that we really don’t know, but many of us are working on that. And so, you know, I hope that in a year we’ll have better answer and especially five years from now. But I think that really is one of the frontiers for understanding population health in general and more broadly, and then more specifically thinking about racial inequities in health including at the state level.

Mark Mather, PRB: Your question for either or both of you, is there an agreed upon way to incorporate different races living under alternative government structure like Native Americans or tribal lands, into measures of structural racism and population health when they might not be present in our standard data resources.

Dr. Tyson Brown, Duke University: I didn’t fully understand the question, but I will just note that I think it began by is there an agreement? and I would say no. You know, these are debate, you know, like any fields of science, there are contested debates. There are new revelations, there’s discoveries, we’re always innovating and I think that we’re always learning more about these sorts of racialized practices and processes and certainly I would note that, you know, really there is no one size fits all and that there are real limitations to all the ways in which we measure these things and how folks are coded, whether it’s self-reported, whether it’s by the interviewer, whether it’s, you know, there are other ways of getting at sort of trying to triangulate to figure out someone’s racial identity or status. But, none of them are perfect. And so, this is part of the job of trying to advance the field and do so in responsible manner. So, I appreciate the question.

Mark Mather, PRB: Do you want to say anything to that, Jennifer?

Jennifer Karas Montez, Syracuse University: Sure. I’ll just add that, I mean what the question reminds me of is just, you know, the need to examine heterogeneity more carefully and, you know, the need to go below the state level to understand some of these processes. You know, there the states are just super interesting unit of analysis. I mean, you know, we call them institutional actors. And, you know, I mean, Tyson made a great pitch for why states are important, but they’re not the only thing that matters. And so, I think to get at the kinds of detailed analyses that that question would require going below the state level.

I would also say that what it seems like is that, you know, states and probably also local environments too, seem to matter most for the health of people who are marginalized. We have this term colleges of firewall, right? So that if you have a college degree or higher, it almost doesn’t matter where you live, you’re going to have good health. But with each, you know, lower level of education, that the place that you live matters more and more for shaping your health. And so, but again I mean that those kinds of analyses are going to benefit from going below the state level.

Mark Mather, PRB: Great. And I just had one question for you, Tyson. You had mentioned things like hate speech and racial animus. And I am curious about how do we measure those types of structures, partly because they might be reported differently in different local jurisdictions or not reported at all in certain places. So, I’m wondering, is that where the social media comes in to capture some of those things, or is it a combination of social media and some of the criminal justice data?

Dr. Tyson Brown, Duke University: Yeah. Great question. I think that it’s like you mentioned, you know, I think it is really important to triangulate and use a variety of data sources. They all have their strengths and limitations and certainly reporting on official Bureau of Justice statistics on hate crimes as well. It’s got its limitations, to say the least. And there’s certainly variation across place about how and when they report if they report and so that data you know, it’s got major limitations but I think that we can get at things by looking at the proportion of hate groups and indexing it to the population size. Right?

So, I think that’s really revealing, you know, when we map that data, I think that, as you mentioned also that using things like social media data really can get at some of these sorts of things by whether it’s Google searches or people’s activity on other social media platforms and there’s also experiments that folks have done, including things like the AIT that get at sort of underlying logics and subconscious biases as well as survey data. I think that, you know, there survey data on racial resentment. If you map it, it’s in a rather predictable way, it varies across states in a predictable way and it’s highly correlated with other measures of racial essentialism and norms and things along those lines. So, I think that all of the above, frankly, you know, they all have their relative advantages and disadvantages.

Mark Mather, PRB: Well, we are at time, so I think we need to close here. But Tyson and Jennifer, thank you so much for joining this discussion. It’s an important and timely topic and for those who stuck around, thanks for joining. As I mentioned, this is being recorded. We will send you a link to that recording in the next few days. So, thanks everyone.

10-24-TRA Social ties_b

More Than a Feeling: How Social Connection Protects Health in Later Life

Older adults’ social ties are more important for physical and mental health than previously thought, new research shows.

Older Americans with strong social connections are healthier and live longer than their socially isolated peers. Increasingly, researchers are finding that the components of good health are not only the absence of mental disorders and physical disease but also the presence of robust social relationships.

This brief explores recent research probing the dynamics of social connection and health supported by the National Institute on Aging. The findings point to myriad ways in which social ties bolster health—from slowing aging and boosting cancer-fighting hormones to preventing depression and protecting memory. Health policymakers and program planners can use this evidence to inform a variety of interventions—particularly those aimed at reducing social isolation in vulnerable groups—to support longer and healthier lives for older Americans.

Strong Social Ties Slow Aging, Lower Risk of Death

Studies have shown that both the quality and quantity of our social ties shape our mental and physical health, health behaviors, and mortality risk. Researchers are studying multiple aspects of our social lives—from the levels of social support we receive to our activities and the strength of our social networks—to understand how they link to health outcomes.

Healthy, supportive relationships with family and friends may slow aging, concluded a research team from the University of California, Los Angeles (UCLA) and the University of Southern California (USC).1 According to their DNA, older adults with the most supportive relationships with spouses, adult children, other family members, and friends were aging one to two years slower than those who lacked such ties, they found. The pace of such aging is important—rapid epigenetic aging at younger chronological ages can contribute to the early onset of chronic disease and disability and premature death.

 

Senior adults in a senior home cheerfully playing a board game around a table.

The healthiest older adults had strong social connections and just a 4% risk of dying within five years.

The authors, led by Kelly E. Rentscher, based their analysis on aging-related molecular changes in DNA among older adults participating in the nationally representative Health and Retirement Study (HRS). Even after factoring in smoking, alcohol use, and other lifestyle factors known to accelerate aging, the protective role of strong social relationships persisted, they reported.

Supportive relationships with spouses and children helped slow the pace of aging by more than three weeks per year, they found. Having support from other family members and friends also helped slow the pace of aging, by about three weeks per year and more than two weeks per year, respectively.

Older adults with the most supportive relationships were aging one to two years slower than those who lacked such ties.

Overall, the findings affirm that both the presence of relationships and their quality mattered for longevity. The new study may support interventions with the potential to “prevent, slow, or reverse accelerated aging and extend the healthspan and lifespan,” the authors wrote.

Other ongoing research underscores the important role of social factors in the overall health of older adults. Linda Waite and Yiang Li from the University of Chicago found that the healthiest older adults had strong social connections and just a 4% risk of dying within five years, while those with the poorest health had weaker social connections and a 57% risk of dying within five years.2 Key factors linked to longevity included robust social networks and partnered sexual activity, highlighting the importance of social integration in maintaining health in later life. Their preliminary findings are based on nationally representative data from the National Social Life, Health, and Aging Project (NSHAP).

Social Connections May Improve the Well-Being of Patients With Cancer and Other Health Conditions

More new research poses that social connections may also improve the well-being of cancer patients by boosting protective hormones.

A research team from six major universities found that in ovarian cancer patients, social support was associated with higher levels of oxytocin—a hormone linked to some protection against cancer.3 Specifically, having a more positive outlook, a sense of purpose, and a role in caring for others at the time of cancer surgery were associated with higher oxytocin levels. (However, a person’s perceived closeness with others was not related to higher oxytocin levels.)

“Nurturance is consistent with the oxytocin-focused ‘tend and befriend’ hypothesis of female coping and stress response,” wrote the team, led by Michaela G. Cuneo at the University of Iowa. Thus, even though this research is in the early stages, feeling needed by others could have protective health effects for women with ovarian cancer.

Similarly, another team of researchers at the National Cancer Institute and the University of Wisconsin–Madison found that strong, supportive relationships between cancer patients and their caregivers were associated with better self-reported health for both parties.4 This was especially the case for those dealing with lung cancer, where social support was linked to better self-reported health 12 months after diagnosis. Dannielle E. Kelley and coauthors speculate that there may be a beneficial “partner effect” for lung cancer that can counter internalized and social stigmas associated with the disease for former smokers.

 

Woman with daughter visiting her mother at hospital, she is talking with doctor and showing love and care for her mother.

Strong, supportive relationships between cancer patients and their caregivers were associated with better self-reported health for both parties.

Social connections might also help a patient adopt more healthful behaviors after diagnosis—but the benefits could depend on their education level. According to Won-tak Joo of the University of Florida, college graduates have more robust health conversations with people in their social networks at the time of diagnosis, which may help explain why they are more successful at improving their health behaviors and show a better prognosis than those with lower education levels.5

“The cultivation of health discussion may be more active in earlier stages of illness when patients require external support to adapt to new lives with diseases,” writes Joo. For older adults with lower levels of education, Joo finds that both social networks and health conversations decline with disease diagnosis, suggesting a need for interventions to help this group.

Researchers are also studying how social ties affect the health of older people with disabilities. Karen L. Fingerman of the University of Texas at Austin and colleagues find that those with limiting disabilities were more likely to attend medical appointments when spending time with friends and family.6 And Sophie Mitra of Fordham University and coauthors report that older adults with disabilities have as much close, regular contact with partners, family, and friends as those without disabilities.7 For those with disabilities and others with serious medical conditions, social connectedness may lead to help with activities that improve their health and well-being.

Connectedness also affects our health in ways that ultimately impact our sleep. A recent study from China’s Xi’an Jiaotong University, the University of Texas at Austin, and the University of Maryland found that socially isolated older adults—those with smaller and less intimate relationships—had more depressive symptoms, were lonelier, and had more chronic diseases and pain, all of which contributed to greater sleep difficulty.8 Dan Zhang and coauthors argue that improving older adults’ social connections could enhance their mental, physical, and sleep health.

A Lifetime of Experiences Shape Social Connections at Older Ages

Some groups of people are more likely to be socially isolated than others, report Debra Umberson of the University of Texas at Austin and Rachel Donnelly of Vanderbilt University.9 Older married women may become socially isolated when a spouse needs round-the-clock care, while men who either never marry or divorce may begin experiencing social isolation in young adulthood.

In addition, non-Hispanic Black and Hispanic older adults are more likely to experience social isolation than non-Hispanic older white adults, they found (see Figure 1). Black and Hispanic Americans’ social isolation could be related to the impact of lifetime discrimination and financial stress, the researchers suggest. Using HRS data, the study measured isolation among adults ages 50 and older based on whether they are married or cohabiting, participate in volunteer activities, and have contact with parents, children, and neighbors.

Figure 1. Older Non-Hispanic Black Americans Experience Higher Levels of Social Isolation Than Other Groups
Mean Levels of Social Isolation Among U.S. Adults Ages 50 and Older in the Health and Retirement Study, by Race/Ethnicity

Source: Debra Umberson and Rachel Donnelly, “Social Isolation: An Unequally Distributed Health Hazard,” Annual Review of Sociology 49, no. 1 (2023): 379-99. 

 

Another study out of Johns Hopkins University estimated that nearly one in four (24%) of older Americans living in the community are socially isolated, and one in 25 (4%) are severely isolated.10 To measure isolation, they examined participants’ living arrangements, religious attendance, social activities, and the number of people they spoke with about important matters. The study used data from the National Health and Aging Trends Study, which includes a nationally representative sample of Medicare beneficiaries ages 65 and older.

Being unmarried, male, and having low education and income levels increased the odds of being socially isolated, according to the analysis, led by Thomas Cudjoe. Specifically, men were four times as likely as women to be severely isolated, while people with annual incomes below $30,000 were twice as likely as people with incomes over $60,000 to be severely isolated. These findings offer “easily identifiable” factors to help program planners target those most at risk, the researchers wrote, noting that living arrangements, discussion networks, and social activities can all be modified to improve social connections.

Calling social isolation “an unequally distributed health hazard,” Umberson and Donnelly urge future researchers to undertake “a systematic assessment of social conditions that foster isolation over the life course” to better understand the root causes and identify ways to reduce isolation among those most at risk.

Being unmarried, male, and having low education and income levels increased the odds of being socially isolated.

“We need to understand why people become more isolated over their lives, because social isolation is a public health issue,” Umberson said. “People became more concerned about isolation in the wake of Covid-19 because we were all more isolated for several years, but this is a problem that’s likely to become more serious, not less.”11

The stability of older adults’ lives may also contribute to richer social networks. While younger adults experienced turnover in social networks after a major life transition, such as getting married or having children, older adults maintained relatively stable social networks after such changes, including retirement, changes in marital status, or becoming empty nesters, found Jordan Weiss and team at the University of California, Berkeley.12 The authors suggest that for older adults, having stable, long-term (often decades-old) relationships make for more reliable networks.

A person’s temperament also may influence their lifelong social ties. Using NSHAP data, James Iveniuk at the University of Toronto finds that among older Americans, personality traits such as extraversion and agreeableness were associated with stronger social ties than openness, conscientiousness, or neuroticism.13 Thus, certain personality traits may strengthen the social connections linked to health benefits.

Social Networks May Protect Mental Health and Prevent Cognitive Decline

Older adults’ social networks may protect both their mental health and cognitive abilities. Getting help with daily activities may be an important reason why—since many older adults need assistance bathing, getting in and out of bed, and doing other tasks, some built-in social interaction can accompany aging. But feelings of closeness and companionship may also help stave off memory loss, loneliness, and depression—and may matter as much or more than geographic proximity or number of family or friends, new research shows.

To try to understand the importance of relationship quality, Sarah Patterson of the University of Michigan and Rachel Margolis of the University of Western Ontario looked at four groups of older adults with different types of family connections: those who were geographically and emotionally close with family; those who were kinless and without a partner or children; those who were distanced and lived far from family; and those who were disconnected and had no family members in their social network or did not know where they lived.14

A senior Hispanic couple walking with their two adult daughters at the park on a sunny autumn day. They are side by side, holding hands, conversing.

The closeness of the relationships—especially with family—buffered loneliness.

“We were interested in understanding how much the presence of family ties matters for older adults’ well-being but also in measuring the quality of those relationship ties,” said Patterson.15

They found that older adults who lived near family members and discussed important concerns with them were less likely to report unmet need for help with daily activities than the other groups (see Figure 2). Meanwhile, those who reported no partner or family or disconnection had the poorest mental health and socialized less often—even less than those who lived far away from their family.

Figure 2. Older Adults Who Are Close to Family Get More Help With Activities
Share of adults ages 70 and older reporting unmet need for help with activities by type of family connections, 2015–2019

Source: Sarah E Patterson and Rachel Margolis, “Family Ties and Older Adult Well-Being: Incorporating Social Networks and Proximity,” The Journals of Gerontology: Series B, Volume 78, no. 12 (December 2023): 2080–89.

 

The findings suggest that the presence and strength of family ties matter for older adults’ mental health. “As families continue to evolve, researchers should strive to capture the size and shape of family networks, as well as the level of connection that older adults have with those kin,” the authors note.

In fact, social connections can also produce distress. Stephanie T. Child and Leora E. Lawton of UC Berkeley found that social companionship and emergency help mattered most to older adults, whereas having more people from whom they sought advice was related to more psychological distress.16 The findings suggest that mental well-being may be enhanced by enjoyable and helpful relationships, while those that are more demanding may detract from it. Data are from the UC Berkeley Social Networks Study (UCNets), which includes a locally representative sample from across the San Francisco Bay Area in California.

Companionship and emergency help mattered most to older adults.

In a similar study, the Berkeley authors found that those who were more dissatisfied with their social networks also experienced more loneliness and isolation.17 Interestingly, it wasn’t the number of connections but the closeness of the relationships—especially with family—that buffered loneliness. Further, having a romantic partner helped older adults feel less isolated.

As Child and Lawton write, “evaluations about one’s own social network, including whether someone feels satisfied in the number or quality of connections they have to call on for social engagement or support, may be a more meaningful precursor of loneliness.”

Similarly, social engagement may be connected to cognitive benefits. Using Michigan Cognitive Aging Project data, Abbey M. Hamlin at the University of Michigan and colleagues find important differences by race in both social engagement and its connection to cognition.18 Older white, non-Hispanic adults engaged in more social activities than their Black peers, and those activities were linked to better episodic memory—or the recall of information from the past—and thus better cognitive health. The findings suggest that social isolation is not only more prevalent among older non-Hispanic Black adults, but also that it may be taking a toll on their cognitive well-being.

Marriage Has Diverse Effects on Older Adults’ Health

Several recent studies build on the well-established link between marriage and better physical and psychological health in old age, particularly for men. They examine some of the ways marriage may benefit health as well as the connections between marriage and other forms of social interaction.

New research finds that marriage can help men be less socially isolated throughout their lives. Umberson of University of Texas at Austin, Zhiyong Lin of University of Texas at San Antonio, and Hyungmin Cha of USC show that men tend to be more isolated in adolescence and young adulthood, while women tend to experience isolation in later life.19 Their analysis of HRS data shows that levels of social isolation increase with age for both men and women.

But gender patterns differ by marital history (see Figure 3). Among older adults in stable marriages, women are less isolated than men until age 60, but by age 68, men are slightly less isolated than women. This gender gap shrinks at older ages for those who have experienced marital disruptions, possibly because chronic health issues contribute more to social isolation among women, the authors note.

Figure 3. Social Isolation Increases With Age, but Gender Patterns Differ by Marital History
Age Trajectories of Social Isolation Among Adults Ages 50 and Older, by Gender and Relationship History, 1998-2012

Source: Debra Umberson, Zhiyong Lin, and Hyungmin Cha, “Gender and Social Isolation Across the Life Course,” Journal of Health and Social Behavior 63, no. 3 (2022): 319-35. 

 

There may be an unexpected physiological explanation for some of marriage’s health benefits. Drawing on lessons from primate research and using stool samples from a subset of participants in the long-running Wisconsin Longitudinal Study, researchers affiliated with the University of Wisconsin-Madison find that spouses in self-described close marriages tend to have more diverse and healthful gut microbiota compared with siblings, people without a partner, or married couples in less close relationships.20 Less diverse gut microbiota is related to obesity, cardiac disease, type 2 diabetes, and other inflammatory disorders, Kimberly Dill-McFarland and coauthors note.

Not all marriages are equal when it comes to social support and its potential health effects. Both men and women in same-sex marriages are more likely than those in different-sex marriages to offer concrete support to a spouse in distress, such as taking over chores or giving extra personal time, found Mieke Thomeer of the University of Alabama at Birmingham, Amanda Pollitt of Northern Arizona University, and Umberson.21 The team used a survey of 378 midlife couples ages 35 to 65.

 

Wide angle shot featuring a Pacific Islander woman and her Caucasian husband enjoying nature on a sunny day. They are holding hands and smiling.

Among older adults in stable marriages, men are less isolated than women.

Relationships can be a source of stress as well as support, and individuals in a marriage with a difficult or demanding partner experience a similar degree of loneliness as single people and more loneliness than other married people, another study finds.22 Shira Offer’s research at UC  Berkeley draws on UCNets data to identify these differences and finds that the same is true for tough relationships with adult children.

Two other studies offer new insights into the mental health toll of the loss of a spouse due to death, separation, or divorce. People with less than a high school education face a higher risk of losing a spouse than people with more education, research using HRS data shows. But higher education levels do not lessen symptoms of depression when divorce, separation, or death does occur, find Claudia Recksiedler of the German Youth Institute and Robert S. Stawski of Oregon State University.23

People who lose a spouse often receive helpful support from social networks. Using NSHAP data, James Iveniuk of the Wellesley Institute and coauthors find that friends and family of older adults respond with social support after the death of a spouse, but less so when a close friend or other confidant dies.24

 

Good Neighbors (and Neighborhoods) Are Good for Well-Being

Multiple studies have shown that a neighborhood’s physical features—from broken sidewalks and high crime to plentiful parks and low air pollution—are related to older residents’ health and quality of life.25 Not surprisingly, the places older people call home also shape their social connections, thereby influencing both their physical and mental health.

Neighborhood social ties may promote sensory health, a study using NSHAP data shows. Older adults who have more social connections in their neighborhoods report better self-rated vision than those who have fewer connections, find Alyssa Goldman of Boston College and Jayant Pinto of the University of Chicago.26 More social ties may lead to more time spent engaging with people and places outside of the home, protecting visual abilities, the researchers suggest. Good vision is key to older adults’ ability to safely navigate their environment, they add.

For caregivers, social support can counteract the negative effects of living in less-connected neighborhoods. Researchers at the University of California, Davis and the University of Michigan show that neighborhoods with low social cohesion—lacking a sense of community and trust among neighbors—can take a toll on mental health in the absence of social support.27 This is particularly true for dementia caregivers, who face a high risk of depression related to the emotional and physical burden of their work. But dementia caregivers living in neighborhoods with low social cohesion had fewer symptoms of depression if they had family and friends to talk to and help with daily tasks, Oanh Meyer and team found.

 

Group of senior men of various backgrounds having a friendly chat in a front yard.

Local opportunities for social connection may strengthen social ties.

Community-level interventions focused on increasing neighborhood connections—such as caregiver support groups in disadvantaged neighborhoods—could be important for maintaining caregiver health, the research team suggests.

The proximity of one’s close friends also makes a difference for mental health, reports Keunbok Lee of UCLA.28 Older adults with fewer confidants who live nearby show more severe depression symptoms when faced with traumatic events than those with more close friends in their neighborhood, according to Lee’s study of UCNets data.

When faced with traumatic events, older adults with fewer confidants living nearby showed more severe depression symptoms  than those with more close friends in their neighborhood.

Local opportunities for social connection may strengthen social ties and help prevent suicide. A new study finds that suicide rates are much lower among working-age adults, including people ages 51 to 64, in counties with more places for people to connect, such as public libraries, community centers, religious groups, coffee shops, diners, and entertainment and sports venues.29 These findings held true even when the researchers accounted for differences in health care availability, age, education, race/ethnicity, and proximity to metropolitan areas.

Gathering places, part of the social infrastructure, may buffer suicide risk and improve mental health by boosting social connections, reducing social isolation, and facilitating social support, trust, and information and resource sharing, report Xue Zhang and Danielle Rhubart of Penn State University and Shannon Monnat of Syracuse University. Local governments should consider partnering “with market-based services and social service agencies to increase the availability, access, and use of spaces that promote social interaction,” they write. In addition to helping to lower suicide rates, building more robust social infrastructure may also support overall health, they suggest.

Living Alone Linked to Social Isolation and a Variety of Health Risks

Research has established that living alone at older ages raises the risk of poor health, early death, and dementia. New evidence demonstrates that living alone for extended periods increases the risk of dementia more strongly than previously thought.30 Every two years of living alone is linked to about a 10% increase in the risk of dementia, according to study authors Benjamin A. Shaw of the University of Illinois Chicago, Tse-Chuan Yang of the University of Albany, and Seulki Kim of the University of Nebraska.

Social isolation may explain this dynamic. Their analysis, based on HRS data that tracked more than 18,000 older Americans for 18 years (2000 to 2018), suggests that a lack of mental stimulation combined with limited day-to-day companionship may increase stress “that, over time, could accumulate and eventually lead to cognitive impairment.”

Even two years of living alone is linked to about a 10% increase in the risk of dementia.

Another recent study shows that the impact of social isolation extends to diet and nutrition. Analysis of data from an HRS nutrition survey shows that older adults—particularly men—living alone with no adult children or friends in their neighborhood had the lowest fruit and vegetable intake.31 Lack of motivation to cook and eat healthy may explain this pattern, according to Yeon Jin Choi, Jennifer A. Ailshire, and Eileen M. Crimmins of USC.

Because fruits and vegetables provide key nutrients for maintaining health and protecting against age-related diseases, the researchers recommend local agencies consider ways to improve social engagement among older adults who live alone to boost health outcomes. They also suggest providing help with grocery shopping (such as transportation) and meal preparation (including home-delivered meals).

Virtual Interaction Cannot Fully Replace the Health Benefits of Face-to-Face Contact

Phone calls and Zoom or FaceTime gatherings replaced in-person get-togethers for many people during the COVID-19 pandemic shutdowns, but a growing body of research suggests virtual interaction cannot fully replace face-to-face contact. Two recent studies, one led by Namkee Choi at the University of Texas at Austin and the other by Louise Hawkley at NORC at the University of Chicago, show that older adults who had less in-person time with family and friends and more phone calls during the first year of the pandemic were more likely to experience loneliness.32

Senior man looks at his phone on a park bench.

Phone calls are an important source of social connection for older adults with impaired vision or hearing.

Older people with impaired hearing or vision may be an exception—phone and video chats appear to have protected them in 2020 from depressive symptoms, find Amanda Zhang and colleagues at the University of Chicago.33 One reason may be that phone calls are important for the mental health and mood of people with small social networks, replacing some of the day-to-day interactions that shrink with age and physical impairment, report Yijung K. Kim and Karen L. Fingerman of the University of Texas at Austin, based on another study.34

Older adults who had less in-person time with family and friends and more phone calls during the first year of the pandemic were more likely to experience loneliness.

At the root of these mixed findings on digital versus in-person interaction may be the immune system. A team of researchers from Colorado State University and UCLA show that face-to-face interaction protects health-promoting immune functions in ways that digital contact does not.35 They examined the gene activity that stimulates inflammation and inhibits antiviral responses in the blood of adult study participants during the COVID-19 social distancing period. Participants who had mainly online social contact had higher levels of such unhealthy gene activity than those who had more in-person social contact.

“Digitally mediated social relations do not appear to substantially offset the absence of in-person/offline social connection,” the research team concluded.

 

Policy Implications

As many as one in four older Americans are socially isolated and face an increased risk of poor health and early death. The research documented in this report underscores the links between strong social ties and longer, healthier lives. U.S. Surgeon General Vivek H. Murthy has called for making social connectedness a national priority, “the same way we have prioritized other critical public health issues such as tobacco, obesity, and substance use disorders.”36 His recent advisory, Our Epidemic of Loneliness and Isolation, identifies multiple actions based on growing research evidence, including:  

  • Strengthening social infrastructure: Social ties are not just built by person-to-person interactions, but by the physical elements (parks, libraries, sidewalks, and benches) and the programs and policies in place. Communities can design environments, establish and expand programs, and invest in institutions that bring people together.
  • Enacting pro-connection public policies: National, state, local, and tribal governments can play a role in establishing policies for accessible public transportation, paid family leave, and other supports that can enable more connection among communities and families.

Social Isolation and Loneliness Among Older Adults: Opportunities for the Health Care System, a recent report from the National Academies of Sciences, Engineering, and Medicine, details ways health care organizations can address social isolation among older people by better educating their staff to intervene and aligning with other community agencies. Specific recommendations include:

  • Partnering directly with ride-sharing programs to help older adults’ get to medical appointments and community events.
  • Working with community organizations to integrate social activities and in-person interaction into hospital discharge planning, care coordination, and transitional care planning.

“Our relationships are a source of healing and well-being hiding in plain sight,” Murthy said, “one that can help us live healthier, more fulfilled, and more productive lives.”37

 

Disabled senior man accessing the bus on mobility scooter

Accessible public transportation can help improve older adults’ health by connecting them to both medial care and social activities.


 

References

  1. Kelly E. Rentscher et al., “Social Relationships and Epigenetic Aging in Older Adulthood: Results From the Health and Retirement Study,” Brain, Behavior, and Immunity 114, (2023): 349-59.
  2. Linda Waite and Yiang Li, “Bringing the Social World Into Our Understanding of Health,” paper presented at the annual meeting of the Population Association of America, Columbus, April 2024.
  3. Michaela G. Cuneo et al., “Positive Psychosocial Factors and Oxytocin in the Ovarian Tumor Microenvironment,” Psychosomatic Medicine 83, no. 5 (2021): 417-22.
  4. Dannielle E. Kelley, et al., “Dyadic Associations Between Perceived Social Support and Cancer Patient and Caregiver Health: An Actor-Partner Interdependence Modeling Approach,” Psycho-oncology 28, no. 7 (2019): 1453-60.
  5. Won-Tak Joo, “Educational Gradient in Social Network Changes at Disease Diagnosis,” Social Science & Medicine 317 (2023): 115626.
  6. Karen L. Fingerman, et al., “Functional Limitations, Social Integration, and Daily Activities in Late Life,” The Journals of Gerontology: Series B, Psychological Sciences and Social Sciences 76, no. 10 (2021): 1937-47.
  7. Sophie Mitra, Debra L. Brucker, and Katie M Jajtner, “Wellbeing at Older Ages: Towards an Inclusive and Multidimensional Measure,” Disability and Health Journal 13, no. 4 (2020): 100926.
  8. Dan Zhang et al. “What Could Interfere with a Good Night’s Sleep? The Risks of Social Isolation, Poor Physical and Psychological Health Among Older Adults in China,” Research on Aging 44, nos. 7-8 (2022): 519-30.
  9. Debra Umberson and Rachel Donnelly, “Social Isolation: An Unequally Distributed Health Hazard,” Annual Review of Sociology 49, no. 1 (2023): 379-99.
  10. Thomas K. M. Cudjoe et al., “The Epidemiology of Social Isolation: National Health and Aging Trends Study,” The Journals of Gerontology: Series B, Psychological Sciences and Social Sciences 75, no. 1 (2020): 107-13.
  11. Kaulie Watson, “Social Isolation Can Begin as Early as Adolescence, Research Shows,” The University of Texas at Austin College of Liberal Arts, July 20, 2023.
  12. Jordan Weiss, Leora E. Lawton, and Claude S. Fischer, “Life Course Transitions and Changes in Network Ties Among Younger and Older Adults,” Advances in Life Course Research 52 (2022): 100478.
  13. James Iveniuk, “Social Networks, Role-Relationships, and Personality in Older Adulthood,” The Journals of Gerontology: Series B, Psychological Sciences and Social Sciences, 74, no. 5, July 2019, 815–26.
  14. Sarah E. Patterson and Rachel Margolis, “Family Ties and Older Adult Well-Being: Incorporating Social Networks and Proximity,” The Journals of Gerontology: Series B Psychological Sciences and Social Sciences, 78, no. 12 (2023): 2080-9.
  15. Jon Meerdink, “New Paper Explores the Impact of Family Ties on Older Adults,” University of Michigan Institute for Social Research, November 15, 2023.
  16. Stephanie T. Child and Leora E. Lawton, “Personal Networks and Associations With Psychological Distress Among Young and Older Adults,” Social Science & Medicine  246 (2020): 112714.
  17. Stephanie T. Child and Leora Lawton, “Loneliness and Social Isolation Among Young and Late Middle-Age Adults: Associations With Personal Networks and Social Participation,” Aging & Mental Health 23, no. 2 (2019): 196-204.
  18. Abbey M. Hamlin et al., “Social Engagement and Its Links to Cognition Differ Across Non-Hispanic Black and White Older Adults,” Neuropsychology 36, no. 7 (2022): 640-50.
  19. Debra Umberson, Zhiyong Lin, and Hyungmin Cha, “Gender and Social Isolation Across the Life Course,” Journal of Health and Social behavior 63, no. 3 (2022): 319-35.
  20. Kimberly A. Dill-McFarland et al., “Close Social Relationships Correlate With Human Gut Microbiota Composition,” Scientific Reports 9, no. 1 (2019): 703.
  21. Mieke Beth Thomeer, Amanda Pollitt, and Debra Umberson, “Support in Response to a Spouse’s Distress: Comparing Women and Men in Same-Sex and Different-Sex Marriages,” Journal of Social and Personal Relationships 38, no. 5 (2021): 1513-34.
  22. Shira Offer, “They Drive Me Crazy: Difficult Social Ties and Subjective Well-Being,” Journal of Health and Social Behavior 61, no. 4 (2020): 418-36.
  23. Claudia Recksiedler and Robert S. Stawski, “Marital Transitions and Depressive Symptoms Among Older Adults: Examining Educational Differences,” Gerontology 65, no. 4 (2019): 407-18.
  24. James Iveniuk, Peter Donnelly, and Louise Hawkley, The Death of Confidants and Changes in Older Adults’ Social Lives,” Research on Aging 42, nos. 7-8 (2020): 236-46.
  25. Mark Mather and Paola Scommegna, “How Neighborhoods Affect the Health and Well-Being of Older Americans,” Today’s Research on Aging, no. 35 (2017).
  26. Alyssa Goldman and Jayant Pinto, “Sensory Health Among Older Adults in the United States: A Neighborhood Context Approach,” The Journals of Gerontology: Series B; Psychological Sciences and Social Sciences, 79, no. 5, (May).
  27. Oanh L. Meyer et al., “Neighborhood Characteristics and Caregiver Depressive Symptoms in the National Study of Caregiving,” Journal of Aging and Health 34, no. 6-8 (2022): 1005-15.
  28. Keunbok Lee, “Different Discussion Partners and Their Effect on Depression Among Older Adults,” Social Sciences 10, no. 6 (2021): 215.
  29. Xue Zhang, Danielle Rhubart, and Shannon Monnat, “Social Infrastructure Availability and Suicide Rates Among Working-Age Adults in the United States,” Socius 10 (2024).
  30. Benjamin A Shaw, Tse-Chuan Yang, and Seulki Kim, “Living Alone During Old Age and the Risk of Dementia: Assessing the Cumulative Risk of Living Alone,” The Journals of Gerontology: Series B, Psychological Sciences and Social Sciences 78, no. 2 (2023): 293-301.
  31. Yeon Jin Choi, Jennifer A. Ailshire, and Eileen M. Crimmins, “Living Alone, Social Networks in Neighbourhoods, and Daily Fruit and Vegetable Consumption Among Middle-Aged and Older Adults in the USA,” Public Health Nutrition 23,18 (2020): 3315-23.
  32. Namkee G. Choi et al., “COVID-19 and Loneliness Among Older Adults: Associations With Mode of Family/Friend Contacts and Social Participation,” Clinical Gerontologist 45, no. 2 (2022): 390-402 and Louise C. Hawkley et al., “Can Remote Social Contact Replace In-Person Contact to Protect Mental Health Among Older Adults?Journal of the American Geriatrics Society 69, no. 11 (2021): 3063-5.
  33. Amanda Zhang et al., “Can Digital Communication Protect Against Depression for Older Adults With Hearing and Vision Impairment During COVID-19?The Journals of Gerontology: Series B, Psychological Sciences and Social Sciences 78, no. 4 (2023): 629-38.
  34. Yijung K. Kim, and Karen L. Fingerman, “Daily Social Media Use, Social Ties, and Emotional Well-Being in Later Life,” Journal of Social and Personal Relationships 39, no. 6 (2022): 1794-1813.
  35. Jeffrey G. Snodgrass et al. “Social Connection and Gene Regulation During the COVID-19 Pandemic: Divergent Patterns for Online and In-Person Interaction,” Psychoneuroendocrinology 144 (2022): 105885.
  36. U.S. Department of Health and Human Services, “New Surgeon General Advisory Raises Alarm About the Devastating Impact of the Epidemic of Loneliness and Isolation in the United States,” May 3, 2023.
  37. U.S. Department of Health and Human Services, “New Surgeon General Advisory Raises Alarm About the Devastating Impact of the Epidemic of Loneliness and Isolation in the United States.”
A female Indian doctor at work with a family of patients.

2024 World Population Data Sheet Media Brief

This media brief shows journalists how population data can be used to report stories about primary health care, which touches every part of our society.

With current data on more than 200 countries and territories, PRB’s World Population Data Sheet offers essential context for journalists reporting on policy, public services, health, climate, and other critical issues shaped by population shifts. The Data Sheet provides a comprehensive view of where and how populations live, projecting trends in growth, decline, and factors affecting population change. Each year’s special focus takes an in-depth look at a topic like climate adaptation so we can better understand what the data show and why it matters. PRB provides media resources that guide journalists on how to report on these complex topics for their communities.

This media brief on primary health care (PHC), a special focus of the 2024 World Population Data Sheet, identifies the components, actors, and systems of this holistic step toward universal health coverage. It includes definitions and highlights data that can be used to report stories about how PHC touches every part of our society.

AI_Image

2023 World Population Data Sheet Media Briefs

These two media briefs provide journalists with tools to report stories on climate change using population data to understand who it affects and how their lives are changing.

With current data on more than 200 countries and territories, PRB’s World Population Data Sheet offers essential context for journalists reporting on policy, public services, health, climate, and other critical issues shaped by population shifts. The Data Sheet provides a comprehensive view of where and how populations live, projecting trends in growth, decline, and factors affecting population change. Each year’s special focus takes an in-depth look at a topic like climate adaptation so we can better understand what the data show and why it matters. PRB provides media resources that guide journalists on how to report on these complex topics for their communities.

Two media briefs explore climate adaptation and resilience, a special focus of the 2023 World Population Data Sheet. They demystify key concepts and provide journalists with causes, consequences, and examples of this issue.

  • Brief 1 explains climate change, adaptation, and resilience; and defines these often-misunderstood terms.
  • Brief 2 unpacks how to use climate and demographic data to report on climate change and adaptation.