The Financing Fault Line in Kids’ Mental Health

by Xiayun Tan, PhD and Robin Ghertner, MPP

BLUF:

• For-profit facilities now make-up a quarter of all children’s mental health facilities, growing by 130 percent from 2021 to 2024.

  • This far out-paced non-profit and public facilities in 2024, which grew around 30 percent.

• For-profit facilities are far less likely to accept public funding and far more likely to take cash, creating divergent access pathways depending on a family’s type of coverage and ability to pay.

  • Three-quarters of for-profits accepted Medicaid, compared to 95 percent of non-profit and public facilities.

  • 95 percent of for-profits accepted cash payments, compared to less than 90 percent of non-profit and public facilities.

• Access to care seems to be increasingly driven by families’ ability to pay, not need for services.

Financing Treatment is at the Center of the Growing Child Mental Health Crisis

Previous Wonk analysis pointed to the worsening of children’s mental health; particularly among kids in foster care.

The mental health system’s capacity to treat children—especially those in foster care—depends as much on how services are financed as on how many facilities exist.

As federal funding shifts following the One Big Beautiful Bill Act (PL 119-21) and state budgets tighten in response, understanding how children’s mental health facilities are actually financed is essential—and mostly missing from the debate.

We’re filling this gap using the latest data from the National Substance Use and Mental Health Services Survey (N-SUMHSS).

We looked at what funding sources children’s mental health facilities accept, and differences by who operates a facility - whether it’s a for-profit, non-profit, or government-run facility. The data don’t permit looking at funding amounts.

All differences we talk about are statistically significant at p<0.05. Details on our approach can be found in the Methodological appendix.

Who Pays for Mental Health?

Over 8,000 facilities nationwide provide mental health treatment for children. Understanding how those facilities are financed is central to any discussion of access or reform.

A recent premium Wonk analysis showed that Medicaid is the dominant payer for children’s mental health treatment.

But it operates within a complex patchwork of public and private sources that vary by state and ownership structure (Weil, 2025).

Facilities can take funding from several sources:

• Medicaid

• Private insurance

• Cash payments

• Other federal sources, such as the Community Mental Health Services Block Grant (totaling over $1 billion for FY2023), the Community Services Block Grant, and the Social Services Block Grant.

• Other state and local sources, including funds from state mental health agencies or child and family services agencies. (Note: we can’t separate state from local in the data).

• Other sources, such as private foundations

Facilities routinely make decisions about which payers they will take.

Lower reimbursement, administrative burden, or reporting requirements can make some funding streams functionally inaccessible — especially for smaller or privately financed providers.

More Facilities, More For-Profits

Between 2021 and 2024, there was a 50 percent increase in mental health facilities serving children–from about 5,500 to about 8,800.

But the expansion wasn’t evenly distributed among provider types.

It was in large part driven by a rapid expansion in for-profit providers.

Three ownership types make up the landscape:

• For-profit facilities, which grew by 135 percent, to 2,329 in 2024– making up 26 percent of all facilities.

• Non-profits, which grew by 35 percent, now make up 61 percent of all facilities.

• Public facilities – run by federal, state, local and tribal governments– which grew by 30 percent, and now make up 12 percent.

The growth in for-profits is not uniform across states. Their footprint ranges from less than 3 percent of all facilities (Kansas) to 70 percent (Indiana).

States with less than 10 percent of facilities being for-profit include: Kansas, South Dakota, Missouri, Vermont, South Carolina, Alaska, New York, New Hampshire, and Oregon.

States with over 50 percent include: Indiana, North Carolina, Utah, and Maryland.

Fewer For-Profit Facilities Accept Medicaid, More Likely to Take Cash

The impact of ownership structure on access shows up most clearly in which payment sources facilities are willing to take.

Three quarters of for-profits accepted Medicaid in 2024, compared to 95 percent of non-profit and public facilities (Figure 2). That number actually went down from 2021, when 80 percent of for-profits accepted Medicaid.

Even fewer for-profits accepted other government sources - 60 percent accepting state and local funding, and half accepting federal sources.

Nearly 90 percent of non-profit and public facilities accepted state and local funding. Two-thirds of non-profits and around 80 percent of public facilities relied upon non-Medicaid federal dollars.

Cash pay is another story. Nearly 95 percent of for-profits accepted cash payments from patient families, compared to 88 of non-profits and 86 percent of public facilities.

Will the Growth in For-Profits and Shift Away from Public Funding Continue?

Medicaid remains the backbone of treatment for children and youth, but participation varies by facility type. Even small declines in acceptance among for-profit providers can narrow options for publicly insured families, particularly where capacity is already limited.

The financing structure of children’s mental health care is facing a turning point.

The growth in for-profits and their ability to choose which funding source to accept suggest that access could become more uneven over time—especially for families who rely on Medicaid and other public sources.

These trends are not distributed evenly across the country. States vary widely in the share of for-profit facilities, which could create regional exacerbations.

These findings don’t show a collapse in access—they signal early indicators of imbalance.

Tracking how financing patterns evolve will be critical to understanding where access is tightening, and what might stabilize it.

Methodological Details

This study used the National Substance Use and Mental Health Services Survey (N-SUMHSS), a comprehensive national source of data on substance use disorder and mental health treatment facilities’ geographic location, service provision, and operation characteristics. N-SUMHSS provides a unique source of information especially on privately operated facilities (86% of mental health treatment facilities are privately operated in 2024). To be eligible and included in the N-SUMHSS, mental health facilities must provide specialty MH treatment services. Solo or small practices not licensed or accredited as a mental health clinic or a mental health center are excluded from N-SUMHSS.

In our analysis, mental health treatment facilities who self-reported accepting client age groups of young children (0-5), children (6-12), or adolescents (13-17) are coded as child-serving mental health treatment facilities. Facilities reported various types of client payment, insurance, or funding accepted for mental health treatment services.

For easy and clearer demonstration, we combined non-Medicaid state level payment and funds, including state-financed health insurance plan other than Medicaid, state mental health agency (or equivalent) funds, state welfare or child and family services agency funds, state corrections or juvenile justice agency funds, state education agency funds, and other state government funds.

We then combined the state level and “county or local government funds” where 95% of facilities who accept county or local government funds also accept at least one state level non-Medicaid payment and funds. In addition, we combined “private or community foundation” funds and “other”, both of which account for lower than 7% of all child-serving facilities.

We exclude Medicare payment because children can only get Medicare if they are disabled under very specific circumstances. According to CMS enrollment numbers, in 2023 only 1,223 children were on Medicare.

Facilities self reported their ownership - whether they are operated by private for-profit organization, private non-profit organization. We coded the facility as publicly operated if they are operated by state government, local, county, or community government, tribal government, or federal government.

Statistical proportion tests are conducted. While not all results are statistically significant, all proportion differences highlighted in the report are statistically significant at 0.05 level.

There are several limitations of N-SUMHSS. First, it does not capture the monetary value of the payment, insurance, or funding from different sources, nor the compositions of all the income sources. It is a simple yes/no response on acceptance.

N-SUMHSS cannot drill down below the state level. Our data can’t tell us how providers are distributed within states, whether private facilities are more likely to be in areas with limited treatment options, and the breakdown between rural and urban areas.

The data can’t tell us about facility capacity. That’s key because the presence of a facility that accepts public funding doesn’t mean that the facility can accept new patients.

Or that the facility will prioritize kids whose treatment will be paid by public sources, who may have lower payment rates than other sources.

Other research can give us hints into what is likely going on within states. We expect rural areas to be most affected by capacity constraints. Research shows that rural counties have fewer mental health facilities; less than 30 percent of rural counties had at least 1 youth-serving mental health facilities between 2017 and 2019 (Graves, et al. 2020).

References

Graves, Janessa M., Demetrius A. Abshire, Jessica L. Mackelprang, Solmaz Amir, and Ashley Beck. 2020. “Association of rurality with availability of youth mental health facilities with suicide prevention services in the US.” JAMA Network Open 3(10): e2021471. doi:10.1001/jamanetworkopen.2020.21471.

Weil, A. R. (Ed.). (2025). Addressing the Child and Adolescent Mental Health Crisis. Aspen Health Strategy Group

When an agency waits until the final weeks of a fiscal year to renew the grants it already promised, it raises a question: is this delay a signal of system constraint—or the start of a deeper change?

At the Administration for Children and Families (ACF), over a third of continuation grants for FY 2025—more than $890 million—were awarded in September alone.

As non-competitive renewals of multi-year awards, continuations are usually the surest, steadiest part of ACF’s grantmaking. These routine awards are typically only denied if there’s a meaningful dip in appropriations or a grantee performance issue.

That makes this year’s timing an anomaly worth watching. The dollars may have gone out the door —we documented that in a recent piece— but the delay itself may be the more revealing data point.

What Continuation Grants Are

Most of ACF’s funding to states and localities moves through grants. These are formal awards to states, tribes, territories, and local organizations to operate child and family programs—ranging from foster care and child welfare services to Head Start and community economic support.

Most grants are competitive in their first year, open to new applicants each cycle. Grants can be awarded for up to 5-years.

After the first year, awards become continuations, renewing multi-year commitments based on expected funding from Congress and ACF.

Continuation awards are often an administrative follow-through: the second, third, or fourth year of a grant that’s already underway.

These awards are for projects and programs that have had close communication between awardees and agency staff. Awardees typically know what to expect for follow-on years. Whether or not this is good practice, it is how ACF has awarded most grants.

Historically, continuations don’t require a new competition, just confirmation that a grantee is performing as expected and Congress provided enough money to keep the award going.

Because they represent ongoing obligations—essentially promises already made—they can be awarded at any time during the fiscal year, and are typically predictable for grantees. That’s what makes the timing of these awards a useful thing to watch.

HHS recently adopted new regulations that allow terminating a grant “if an award no longer effectuates the program goals or agency priorities.” (2 CFR 200.340(a)(4)). This applies to both new grants and continuations.

What the Numbers Show

In fiscal year 2025, ACF issued 2,728 continuation awards, representing about 22 percent of all ACF grant awards and totaling $8.5 billion, or roughly 13 percent of total funding.

In terms of volume, that looks consistent with prior years. What stands out is when those dollars were released.

More than a third of continuation grants—1,017 awards worth $890 million—were issued in September, the final month of the fiscal year. By comparison, in 2021-2024, between 3 and 9 percent of continuations were awarded that late. In fact, the bulk of continuations were awarded in June of those prior fiscal years.

September Continuations Were Concentrated in A Few Offices

Almost every office had a larger share of continuations awarded in September, but they were clustered heavily in a few ACF offices:

• The Children’s Bureau (CB): all 80 continuations issued in September, totaling $76.3 million

• Office of Community Services (OCS): all 14 continuations were issued in September, totaling $6.2 million

• Family and Youth Services Bureau (FYSB): nearly all were issued in September (96 percent), totaling $182 million

• In contrast, only 4 percent of continuations from the Office of Head Start (OHS) were issued in September. That totalled $62 million.

This is an operational signal worth unpacking.

What Awarding Continuations Later May Mean

So what does it mean when continuations are awarded towards the end of the fiscal year? The answer isn’t obvious—but it is important.

At first glance, it could seem like bureaucratic burden creates a delay. With fewer staff to manage grants, (particularly the closure of regional offices) this is certainly part of the story.

But continuation awards are designed to be easier to award and more predictable for both the agency and grantees. There’s no rigorous application review, and grantees have likely planned out their budgets well ahead of time.

For many programs, they essentially move on autopilot. Program offices typically have their continuation packages ready many months before the end of the fiscal year.

That raises a deeper question: are these delays a sign of ordinary backlog—or of system constraint inside ACF?

Several factors are in play – and they aren’t mutually exclusive:

• Staffing limits. Cuts to regional and grants management staff may have slowed routine approvals and processes.

• Heightened internal review. Legal and policy offices took longer while they were under greater scrutiny from the Office of Management and Budget or the Department of Government Efficiency (DOGE).

  • In April, HHS launched its “Defend the Spend” initiative and added grant efficiency reviews to practically all grant actions.

  • In August, the White House issued new guidance to agencies to increase scrutiny of all grants, and this likely equally applied to continuations.

• Policy signaling. Slower processing could reflect leadership pressure to underspend or delay commitments.

  • It could also be indicative of internal consideration of phasing down certain grant awards—prompting extensive review and slower approval across the board.

It’s almost certain that both staffing shortages and heightened review had an effect.

But often, even if short-staffed, agencies will either formally or informally advise grantees about the status of continuations. This time around, for many grantees, this simply didn’t happen. Many experienced unexpected radio silence.

Whether the cause is bureaucratic, a policy shift, or both, it marks a change in posture.

Grantees have come to expect predictability from ACF. Successfully compete for an award and you can plan on the funds throughout the grant cycle.

Delays and limited communication at this scale raise the question of whether that predictability still holds, or whether a new process will be instituted. How to read this signal will be clarified by how ACF manages continuations throughout 2026, and what is communicated to new grantees about their continuations.

Methodological Details

Data from this analysis come from the HHS Tracking Accountability in Government Grants System, a management and reporting platform developed that pulls together financial assistance data from across HHS. TAGGS is managed by the HHS Office of Grants to maintain compliance with various federal reporting requirements. Data in TAGGS are updated daily.

We pulled data on continuation grants from ACF issued from fiscal years 2021 through 2025, and focused on the latest date that awards were issued. We tabulated the data by the program office within ACF managing the grant (not always the office that issued the grant).

By Robin Ghertner, Director of Strategic Policy Intelligence

BLUF

• The Administration for Children and Families obligated a total of $63.9 billion in grants during fiscal year 2025, on a total of 12,166 awards.

• Since the Trump Administration began, ACF grant funding decreased by about 3 percent, representing $1.9 billion. These decreases affected a total of 1,599 awards.

  • $1 billion of the decrease was from the Office of Refugee Resettlement.

• When compared with the prior year, this funding decrease is nearly the same amount.

  • In fiscal year 2025, ACF reduced funding by 3 percent.

  • In fiscal year 2024 ACF reduced funding by 4 percent.

• For programs funded by the Children’s Bureau (CB), most of the decreases in obligations were not likely cuts, as the amount deobligated in 2025 was in line with FY2024.

  • Title IV-E funding for foster care and adoption assistance made up over 80 percent of all CB funding — $4 billion and $3 billion respectively. Foster care deobligations totaled $90 million (2 percent), while adoption assistance deobligations totaled $38 million dollars (1 percent). Neither of these decreases were meaningfully different from 2024.

• Cuts in 2026 may still be coming, either through new appropriations or through new administrative action by ACF.

The Push to Cut—and What ACF Actually Did

Immediately after taking office, the Trump Administration moved to claw back spending that was already appropriated and, in large part, obligated.

The Office of Management and Budget and Department of Government Efficiency (DOGE) put out guidance, embedded new staff charged with leading cuts, and directed agencies to establish aggressive targets for cuts.

Many early cuts were tied with funding perceived to support DEI or LGBTQI+ initiatives, but the larger mandate was broader: cut spending across the board.

At HHS, agencies in particular were told to cut funding deeply. The Centers for Disease Control and Prevention (CDC) and the National Institutes of Health (NIH) drew headlines for high profile cuts to research and vaccines.

But what actually happened at the ACF—the second-largest grantmaker in HHS after the Centers for Medicare and Medicaid Services—has gone largely unexamined. Until now.

This is where the Wonk steps in. Now that fiscal year 2025 is over, the numbers tell the story: what ACF promised to fund, and how much of that promise was rolled back.

This analysis focuses on grants; we’ll dive into contracts later on.

A Critical Note on Methods

Federal agencies move money in two main ways:

• Grants: The government funds others – such as states, tribes, or non-profit organizations – to carry out public purposes aligned with federal priorities. Grants include cooperative agreements, a special type that has more direct federal involvement in design or implementation.

• Contracts: The government buys goods or services for its own use.

It’s important to know that this analysis focuses on grant obligations—the government’s commitments to fund those awards—not actual spending.

Because the fiscal year has ended, we can safely say that obligations equate actual spending.

But reductions in those obligations, called deobligations, can happen for several reasons.

While they can reflect policy decisions to change a grant’s scope or terminate it entirely (i.e. cuts), deobligations are most often done as part of routine business that reflect administrative or technical adjustments.

The data I use are from USA Spending, the main data source on federal grant financing. Unfortunately, the data can’t reliably tell us the reason for a funding decrease.

To test whether the Trump Administration actively reduced ACF grants, we compared FY2025 deobligations with FY2024, the final year of the Biden Administration.

Similar rates suggest business as usual; sharper drops would signal cuts and a policy shift.

The data can also only tell us about funding on awards through 2025, not future commitments.

Awards can be issued for up to 5 years, with each year new funds added through a continuation award. I can only analyze data that tell us about funding on an award in FY2025; the data can’t tell us anything about future obligations.

For more on our approach, assumptions, and how grants and obligations work, refer to the Methods section at the end.

Despite the Rhetoric, ACF’s Nearly $64 Billion in Grant Awards Was Largely Untouched

ACF awarded a total of $63.9 billion in grants FY2025, on a total of 12,166 awards.

Since the Trump Administration began, ACF grant funding decreased by about 3 percent, representing $1.9 billion. These decreases affected a total of 1,599 awards.

$1.9 billion sounds like a lot (and it is), but context matters.

When compared to the previous year, ACF’s decrease in grant funding by nearly the same amount. In fiscal year 2025, ACF reduced funding by 3 percent; whereas in fiscal year 2024 ACF reduced funding by 4 percent. That difference is equivalent to a rounding error.

Let’s break down where ACF spent its grant funding, and where decreases happened.

Where ACF Spent the Most Dollars, and Where They Decreased the Most

ACF’s Office of Family Assistance (OFA), which oversees TANF, funded over $17 billion in grant awards, the most among ACF offices.

This is followed by the Office of Head Start, which oversees Head Start and other early education programs, with nearly $11.7 billion in grant funding.

Figure 1 lays out total funding for fiscal year 2025, and reductions in funding obligations after President Trump took office.

The Children’s Bureau (CB) – funding most child welfare programs and services – awarded $8.6 billion in fiscal year 2025.

It decreased obligations by $181 million since President Trump took office.

There was no meaningful difference in the decrease in obligations between FY2024 and FY2025.

Meanwhile, the Office of Refugee Resettlement (ORR), which oversees refugee resettlement and programs for unaccompanied children, cut the most grant funding by just over $1 billion, representing a 30 percent decrease in obligated funding.

All ACF offices show some reduction in obligations, but ORR’s decline stands out. Its grant obligations dropped far more than in prior years—and unlike the routine deobligations seen elsewhere, this decrease largely reflects policy change.

ORR’s grant obligations are directly tied to the number of refugees entering the country, and the number of unaccompanied children entering HHS custody.

Policy choices by the Administration caused both of those numbers to decline almost immediately after President Trump took office.

Additionally, ORR dramatically shifted its approach to serving these populations, cutting services like legal aid and permanency initiatives.

ACF’s Grant Counts Tell a Similar Story

The Office of Head Start issued the most number of grant awards in FY2025: 3,158. The Children’s Bureau came in second, with almost 2,500, followed by the Office of Child Care, with just under 2,300.

Figure 2 gives the number of grant awards and awards with deobligations since inauguration.

The Children’s Bureau had the most grant awards with deobligations—613 awards, totaling almost $182 million.

The Office of Head Start had 423 grant awards with deobligations, totaling almost $315 million.

Diving into Children’s Bureau Funding

Digging into the data further, Figure 3 breaks down CB funding by program area, and how each area was impacted by grant decreases. Some takeaways:

• Most of the decreases are all in line with decreases from FY2024, which suggests that most programs were not actual cuts but rather standard deobligations.

• Title IV-E for foster care and adoption assistance make up over 80 percent of all CB funding - $4 billion and $3 billion respectively.

  • Title IV-E funding decreased by $90 million (2 percent), while adoption assistance funding decreased by $38 million dollars (1 percent). Neither of these decreases were meaningfully different from 2024.

• The Chafee Education and Training Vouchers Program—with $41 million in awards—lost the greatest percentage of its funding among all programs (21 percent).

  • This decrease is comparable to 2024, which suggests this program was not targeted for policy purposes.

    • Rather, it is most likely due to issues we’ve previously covered with state difficulty disbursing these funds. Documented in a report from GAO, this has drawn bipartisan interest.

• Title IV-E prevention funds totaled just under $138 million, and had a decrease of $8.7 million after inauguration.

• Two programs saw substantial less funding deobligated in 2025 compared to 2024: the Chafee Foster Care Program for Successful Transition to Adulthood, and the Adoption and Legal Guardianship Incentive Payments Program.

  • Both of these programs had over 30 percent of funds deobligated in FY2024, whereas in FY2025 less than 7 percent of funds were deobligated.

ACF Grants Held Steady—but the System May Not Feel It That Way

ACF’s grant totals barely moved in FY2025, but stability on paper doesn’t mean stability in practice.

Staff cuts in ACF mean less technical assistance for states to clarify complex rules and requirements. Approvals for state plans or waiver requests, guidance on complex issues, and grant reviews may take longer.

Or they may be more cursory. The closing of ACF regional offices is particularly relevant, as now more requests from states and other grantees have to be processed by central offices that aren’t equipped for it.

Many states already saw delays in approval of continuation awards on ACF grants for FY2026, a historically routine process. Continuations can be signed at any point during the fiscal year.

But many grantees experienced radio silence from ACF on the status of their awards until they were finally awarded towards the end of the year. This level of uncertainty has made it difficult for states to plan their own budgets, staffing levels, and contract commitments.

Beyond ACF, cuts across HHS and other federal agencies still ripple into child welfare. Cuts to other federal funding sources can affect finances and service options for child welfare agencies.

State and local agencies often receive funding from, or rely on services funded by, other parts of HHS, such as its behavioral health agency, the Substance Abuse and Mental Health Services Administration.

They also rely on other arms of the federal government, including the Department of Justice, the Department of Education and the Department of Housing and Urban Development.

The most consequential pressure point, though, is Medicaid. The One Big Beautiful Bill Act (OBBBA) cuts nearly $1 trillion in Medicaid spending reductions over the next decade.

Even if those dollars don’t flow directly to child welfare agencies, they underwrite much of the care families and children receive—mental health and substance use treatment, developmental supports, and medical services for children in foster care.

In short: ACF’s grants may look steady, but that doesn’t mean families and agencies won’t feel the broader effects of the Trump Administration’s cuts.

And of course, with inflationary pressures eroding the purchasing power of every dollar, these funds can’t buy as much as they did even a year ago either.

Cuts Could Still Come in 2026—Because of, or Despite, the Budget Impasse

The lack of significant funding cuts in FY2025 doesn’t mean they aren’t coming in 2026. One mechanism is what ultimately happens with federal appropriations, currently stalled in Congress.

It’s possible a final spending agreement could provide lower funding that reduces resources for ACF grantees.

That pathway would take some time to play out; cuts in appropriations would be more likely to affect grantees in FY2027 than in 2026, since that’s when new grant awards would begin performance.

Even if federal appropriations are relatively flat for ACF, ACF has several ways to reduce grant funding in 2026, even for awards already issued.

The Administration has already shown a willingness to test strategies of questionable legal durability, like impoundment of appropriated funds or the cancellation of agreements based on their divergence with Administration policy, even when commitments have been made to grantees.

The delays in approval of continuation awards mentioned above is itself a signal to watch.

In the past, agencies would not approve grant continuations for two reasons: lack of funding availability (i.e. Congress didn’t appropriate enough funds) and performance issues. This has been a relatively rare occurrence, at least in ACF.

Performance issues historically have to be well-documented by the government, often require an opportunity to improve, and can be challenged.

But as of October 1, HHS adopted new federal regulations that permit ACF to terminate a grant “if an award no longer effectuates the program goals or agency priorities.” (2 CFR 200.340(a)(4), issued April 22, 2024).

It remains to be seen how HHS and ACF will interpret this text, and the extent to which it could be used to terminate grants or reduce award amounts. It’s also unclear how such action would stand up to legal challenges.

And beyond appropriated amounts for 2026 (when a budget is passed), the White House will be issuing its first full budget request in February. This will be a clear indicator of ACF’s priorities for future fiscal years, and signal levels and program areas for funding.

Methodological Details

Grants and Obligations

Grants are funding to non-federal entities—typically state, local, tribal or territorial governments, non-profit organizations, and academic institutions—to conduct work in pursuit of specific goals outlined by the federal government. Cooperative agreements are a type of grant that permit greater involvement of the government during implementation.

Grants often span multiple fiscal years (up to 5), and grantees can carry forward award amounts from one fiscal year to the next. This means that funds spent in fiscal year 2025 may have been originally obligated in 2024 or earlier.

An obligation is the government’s legal commitment to make a certain amount of funding available to a grantee. It’s the maximum amount the government will fund—it does not necessarily mean the funds have to or will be spent. It can be thought of as a ceiling to the grant award. So when a grant has a $1 million obligation, that means the government can fund the grantee up to $1 million, but does not have to.

With available data, I can only examine obligations, not actual spending. For the total amount, since the fiscal year has closed, total obligations should equate total spending, so I am comfortable calling obligations the actual awarded amount. But for deobligations, we cannot simply call them cuts.

Decreasing an obligation does not mean cutting funds. A federal office will often deobligate funds as part of routine grant administration, without it having anything to do with a policy decision. There are four broad reasons why an agency may reduce obligated funds:

1. Administrative or budgetary adjustments. Grantees may not have used the full funding amount or funding may have expired (if appropriations are time limited).

2. Grantee performance or compliance. If a grantee performs poorly or fails an audit, the agency may reduce funding.

3. Technical adjustments. Obligated funding amounts could be entered in error, or the agency may have initially obligated the maximum amount to permit future funding at a later date.

4. Policy shifts. The agency may choose to reallocate grant funds for a different purpose; it may also choose to reduce a grant’s scope or cancel it entirely because it no longer pursues a desired goal. This is the least common reason for a deobligation.

Only the last reason would be an indication that the Trump Administration has actively cut funding.

Unfortunately, I cannot reliably determine the reason for a funding decrease.

To assess whether the Administration actively cut funding, I compared relative funding decreases from FY2025 to FY2024, the last year of the Biden Administration. Where the reductions are comparable, we can safely say that the Trump Administration didn’t substantially cut FY2025 funding.

Data Source

USA Spending is the best source of government-wide spending on grants and contracts. It pulls data from agency-specific systems. I pulled all actions on ACF grants across the fiscal year by office and program. Actions include increased obligations, decreased obligations, as well as modifications with no funding change. To capture total obligated amounts, I summed across all changes in obligations. To identify awards with deobligations, I identified which awards had actions with any decrease in obligations over the course of the year. To calculate total deobligations, I summed the decreases in obligations across all actions for each award, and then rolled them up to program area and office.

Translating the latest research (July-August) for decision-makers in child and family policy.

From the Director’s Desk

From Wonk Founding Director of Strategic Policy Intelligence Robin Ghertner, MPP

Welcome to the first edition of our monthly Children & Family Research Roundup!

A common (and perhaps mythical) refrain among researchers is that it takes 17 years for research to influence decisions.

At the Wonk we are always looking for ways to bring research insights faster to decision-making. Ergo, the Research Roundup.

In the Research Roundup, my team digs through the latest research from various fields related to child and family policy so you don’t have to.

We filter through the technical jargon and pull out the key intel you need to do your jobs better.

This edition highlights some of the most meaningful studies published in July and August of 2025.

This isn’t an annotated bibliography. It’s a translator to help you wrap your head around what research can - and sometimes what it cannot - tell us.

We hope that the Research Roundup helps you engage with us to make research more meaningful to you. In the Roundup we’ll invite you to answer some questions about the info we share.

We’ll use the answers to help us improve the product, and we’ll occasionally summarize the results so you can see what other colleagues are thinking about.

We’ll also be hosting virtual research roundtables based on these Roundups, where you can engage with the smartest thinkers on child welfare policy. Premium subscribers get to help shape those discussions.

The Research Roundup is part of our suite of products for Wonk Briefing Room members.

This first, free edition gives you a taste of how we can help you engage with research in actionable ways. Join the Wonk Briefing Room here.

Let’s get into it!

- Robin Ghertner, MPP, Founding Director of Strategic Policy Intelligence

New Research for Policymakers to Drive Decisions

Substance use continues to be a major driver of child welfare system involvement.

A new study this month found that parental substance use disorder (SUD) continues to predict child welfare system involvement. Taylor and colleagues found that:

• Children who have mothers with SUD are more likely to be involved in child welfare systems.

• All types of SUD were linked with increased risk of child welfare system involvement, according to data from multiple provider systems in Allegheny County, PA.

• Additional findings included that use of stimulants and opioids are related to more intensive system involvement, and that infants were at greatest risk.

Takeaways: Decision-makers need to recognize that the effects of the substance use are not uniform across types of substances and child and family demographics.

One-size-fits-all substance use disorder policies may simultaneously over and under correct for unique challenges affecting communities.

Housing and economic support may prevent child maltreatment and improve child welfare outcomes.

New releases in July and August emphasized that concrete supports and community-wide approaches can be effective prevention tools.

Housing assistance for families involved with child welfare can promote family reunification for children in foster care, and may have a role in preventing foster care placement.

• Supportive housing increased family reunification out of foster care, according to a review of the most rigorous studies on housing interventions in child welfare from Bai and colleagues.

  • However, housing supports had a minimal impact at preventing out of home placement.

  • This doesn’t mean every housing intervention doesn’t work - just that it’s likely dependent on the specific target population as well as details of the intervention.

• Case in point: Housing supports had positive effects on both family reunification and preventing entry into foster care in an evaluation of a San Francisco based intervention. The new study from Chapin Hall found that:

  • The Bringing Families Home program offers permanent supportive housing and rapid re-housing, along with voluntary supportive services.

  • Adequate resources to sustain the program were among the greatest implementation challenges, according to the evaluation team at Chapin Hall.

• Economic stimulus may have a role in mitigating the conditions that lead to overdose deaths, which predicts higher rates of child welfare involvement.

  • The Wonk’s very own Ghertner and Ali analyzed national data and found that the overdose problem was less severe in counties that received more economic stimulus during the COVID pandemic.

  • While this study was not tied directly to child welfare, as we noted above, substance use is a major driver of child welfare involvement, so there’s a good chance that fewer overdoses means fewer kids entering foster care.

Takeaways: Concrete supports - in this case, housing and cash - hold promise as prevention mechanisms.

They are not a panacea and policymakers need to consider budgetary constraints among other factors (note: none of the studies from this month estimated budgetary costs of the interventions).

Appraisals of housing and social service programs are key starting points for understanding upstream child welfare prevention.

New Insights on Promising Practices in Child and Family Services

Foster youth do better when decisions about services, and delivery of those services, are not done in isolation.

Two studies this month assessed the impact of including key parties in care decisions, and described how increasing collaboration among providers improves continuity of care over time and contexts.

This leads to services being more responsive to family needs.

• Integrated care models improve access, care coordination, and continuity, relative to the sole provision of primary care, according to a systematic review of studies on integrated care models (i.e. combined primary care and behavioral health). Quick and colleagues also found that integrated care models:

  • Can overcome some of the challenges traditional practice faces, such as fragmented service providers.

  • Are most successful when services are provided in the same location (called “co-location”) and systematically share data on patients.

  • Can be hard to implement, mainly due to financial and workforce constraints.

• Including foster parents in case planning and service delivery improves collaboration toward case goals. Thompson and colleagues added to the research base on this topic and found, among other things:

  • Foster parents aren’t as involved in case planning as they could be, and data from Florida shows that keeping them engaged with children’s service providers and the courts may improve case outcomes.

Takeaways: Child welfare leaders may want to consider who is involved in case planning and service decisions, and how foster parents and other providers could be more involved.

Additionally, leaders can consider their level of service integration, and what resources and practices would be needed for more integration. Systems will face tradeoffs in shifting resources to encourage more integration.

Child maltreatment leads to difficulties with emotional regulation later in life.

• Adults who experienced childhood maltreatment had difficulties with emotional regulation, according to a recent rigorous experiment by Armbruster-Genç and colleagues.

  • In particular, they had trouble switching from suppressing emotions, to responding more proactively to emotional events.

  • Though the study didn’t suggest interventions, the findings point to the need to address behavioral health issues early for children who have experienced maltreatment.

• Multidimensional Treatment Foster Care (MTFC) is one such intervention, designed as a behavioral health treatment alternative to residential placement for foster youth. It targets youth with chronic behavioral health challenges - like emotional disturbance - and has been found to be effective at reducing behavioral incidents in previous studies.

  • However, a new systematic review found mixed results on the long-term benefits for youth, relative to usual care services (meaning some studies showed MTFC was associated with long-term outcomes, and others did not).

  • Krishnapillai and colleagues conclude that administrators should consider the costs of MTFC relative to usual care when deciding whether to use it.

Takeaways: Child welfare leaders may want to consider how they’re addressing the unique emotional needs of foster youth in their systems, and the budgetary, staffing, or other resource tradeoffs with taking different interventions tailored to those needs.

Interventions are rarely effective for every child, with their success depending on factors like how closely providers stick to the model, the availability of resources to implement the model, and the extent to which the model has been adapted to the unique circumstances of children.

Data Corner: New Data Sources to Know and How to Use Them

The CDC updated resources that provide data useful for monitoring trends related to child welfare and well-being.

These child welfare dashboards and performance management. Centralizing national and state indicators relevant to placement stability, permanency timelines, and youth well-being is key for ongoing research and evaluation.

• The Child Development Data & Statistics Hub

  • What it Is: is a one-stop directory linking to KIDS COUNT, the National Survey of Children’s Health, Census data, and other data sources and tools.

  • What it’s Good For: Federal data to track child development at the national and state levels.

• The CDC updated its “FastStats: Child Health” to offer quick access to the latest indicators such as access to care, child health status, mortality, and others.

  • What it Is: These measures offer vital context in quality reviews and monitoring.

  • What it’s Good For: These population-level health statistics can be used by child welfare professionals as a baseline for comparing the needs of children in care. These indicators may also be useful for assessing whether services are meeting the needs of target populations.

Takeaways: Health data on children can provide important context for child welfare outcomes. It can also be used to help target interventions more effectively.

What’s Your Take?

We want to know what stands out to you from this month’s research roundup, and why.

• Who do you wish had access to this information? Are there types of organizations, policymakers, or advocates that would benefit from any of these insights?

• Based on any of the insights shared, what shifts in child and family policy would you recommend to state or federal policy makers?

• What studies from July to August this year did we miss?

Send your thoughts to robin@childwelfarewonk.com.

Studies discussed in this roundup

Bai, R., Kennedy, R., Collins, C., Tumin, D., & Reis, H. L. (2025). Effectiveness of housing assistance for child welfare-involved families: A systematic review with meta-analysis. The American Journal of Orthopsychiatry. https://doi.org/10.1037/ort0000875

Ghertner, R., & Ali, M. M. (2025). Economic stimulus for households during the COVID-19 pandemic may have mitigated rise in overdose deaths. General Hospital Psychiatry, 96, 193–194.https://doi.org/10.1016/j.genhosppsych.2025.07.019

Krishnapillai, A., Oad, L., Cassidy, T., Kimber, M., & Santesso, N. (2025). The effects of multidimensional treatment foster care for maltreated children and adolescents: A systematic review and meta-analysis. Child Protection and Practice, 5, 100173.https://doi.org/10.1016/j.chipro.2025.100173

Quick, C., Meimers, M. C., Buchele, E., Krawciw, M., Hughes, D., & Rohn, A. (2025). Integrated Healthcare for Youth in Foster Care: A Narrative Review. Journal of Pediatric Health Care.https://doi.org/10.1016/j.pedhc.2025.06.012

Rhodes, E., Brooks, L., & Van Drunen, M. (2025). Bringing Families Home San Francisco: 2025 Annual Evaluation Report. Chapin Hall.https://www.chapinhall.org/wp-content/uploads/Chapin-Hall_Bringing-Families-Home-Report_July-2025.pdf

Taylor, J., Bandara, S., Powell, T. W., Blumberger, L., Galbraith, J., Kessler, L., Ko, P. J., & Kennedy-Hendricks, A. (2025). Risk of Child Welfare System Involvement Among Mothers With Substance Use Disorder. American Journal of Preventive Medicine, 0(0).https://doi.org/10.1016/j.amepre.2025.108034

Thompson, H. M., Colvin, M. L., Cooley, M. E., Dowdy-Hazlett, T., & Pasarariu, A. (2025). Foster parent inclusion and collaboration in case planning and implementation: Perspective of Florida foster parents and child welfare workers. Child Protection and Practice, 5, 100148. https://doi.org/10.1016/j.chipro.2025.100148

By Robin Ghertner, MPP, Founding Director of Strategic Policy Intelligence

Here at the Wonk, we love data.

You’ve seen it in the Data Drops we’ve put out. It reflects something deeper about our work; separating signal from noise to lift up what matters in policy.

If you’re not in the weeds of federal data but want to know how it shapes policy and decisions, this piece is for you.

I’m going to lay out some of the context for current cuts to federal health surveys and why those cuts matter for those of us working on child and family issues.

Before I joined the Wonk to oversee our data and analytic work, I spent 15 years leading teams in several federal agencies focused on using data to improve decision making.

I’ve seen numerous changes – mostly improvements – in how the federal government builds data systems.

The Stakes

You already know that reorganizations at the Department of Health and Human Services (HHS), staffing cuts, and budget pressures are impacting child and family policy.

What you may not know is that those actions will lead to changes to survey data that inform our understanding of children’s health and wellbeing. Here are a few examples.

Moving Key Health Data Offices to the Office of the Secretary

This spring, HHS proposed reorganizations involving two of the main sources of high-quality health data.

In May HHS proposed moving the National Center for Health Statistics (NCHS) out of CDC to the Office of the Secretary – one of the biggest potential changes to health data.

NCHS is in charge of national surveys and the National Vital Statistics System. Among many uses, NCHS data inform child fatality reviews, disability statistics, family formation trends, and child welfare risk factors.

Earlier in March, HHS planned to also move the Agency for Healthcare Research and Quality (AHRQ; wonks pronounce it “ark”) to the Office of the Secretary. AHRQ supports science and improved health care practices.

It also runs the Medical Expenditure Panel Survey (MEPS), which we and others have used to understand psychotropics, mental health, and other issues child welfare populations face.

These moves could change the entire orientation of NCHS and AHRQ.

These offices are supposed to produce objective data that are measured consistently over time – meaning you can trust that a number for 2025 is directly comparable to a number for 2015.

The Office of the Secretary is political by definition – this is where political appointees make decisions about funding, regulations, and other policies.

NCHS and AHRQ would be directly subject to the whims of changing political leadership, no longer having the same autonomy to determine their own priorities.

The data they produce may lose consistency and objectivity, which in turn could affect the decisions federal and state governments make.

Staff Cuts to the Most Important Behavioral Health Survey

The National Survey on Drug Use and Health (NSDUH), run by the Substance Abuse and Mental Health Services Administration for decades, tracks mental health and substance use, treatment access, and many other things.

It gathers essential information for anticipating pressures on child welfare systems and shaping prevention efforts that drive policy.

The NSDUH is housed in SAMHSA’s Center for Behavioral Health Services and Quality.

The NSDUH lead and the entire office that runs it were let go in April’s Reduction in Force.

Without staff to lead the survey effort, it’s questionable whether it can continue without significant data problems.

Gold Standard for Maternal and Infant Health in Limbo

At the end of January, the Pregnancy Risk Assessment Monitoring System (PRAMS) was put on hold, putting at risk both current availability of data and future collections (Handler et al., 2025).

PRAMS is a main source for research on maternal and infant health, run out of CDC’s National Center for Chronic Disease Prevention and Health Promotion.

Cutting Surveys to Save Money is Not New

Survey data collection is expensive.

For years the federal government has looked to save money by being smarter about the data it collects. Surveys are often one of the targets for cost efficiencies.

Over the past 15 years, I’ve been in budget debates with leadership in multiple agencies over the future of supplemental survey questions to the Current Population Survey.

These supplements cover a range of topics, from civic engagement to child support. When agency budgets are thin, it can be hard to justify spending money on these surveys, especially when other data sources look like they can fill the same needs.

Our health and human services systems automatically collect massive amounts of data through administrative records and insurance claims.

It seems reasonable that we can save money by using these data, rather than paying for surveys.

In truth, surveys are experiencing lowering response rates (Czajka and Beyler, 2016), making it harder and more expensive to get enough people to respond to make the results meaningful.

Surveys Feed Research and Drive Policy

To a non-research crowd this might seem like semantics, but these complementary approaches do different things.

I won’t rehash what a lot of smart people have written on the pros and cons of surveys relative to other data sources (Graeff and Baur 2020).

What I think matters most for child welfare is that survey data are intentionally designed to be of high quality for research.

Other sources - case records, claims, or third-party commercial data – are built for reporting, operations, or billing, not for research.

While these other sources are extremely valuable for many research topics, they have limitations.

For example, analyzing foster care records misses everyone who is not in foster care, meaning you have no one to compare to. In health claims data, if information is not needed for billing purposes, it may be incomplete, inaccurate, or outright wrong.

Administrative or claims data can also be biased against specific populations based on how they interact with the systems (including the service providers) producing the data.

Surveys can overcome all of these issues. Yes, they aren’t perfect, but they are not obsolete.

Getting Insight on Substance Use in Child Welfare Populations Is an Example of Why We Need Surveys

Parental substance use is one of the key drivers of child welfare system involvement. The Children’s Bureau reported that 32% of kids in FY2023 entered foster care because of parental substance use.

That analysis is based on the Adoption and Foster Care Analysis and Reporting System (AFCARS), a mandatory federal data collection used to study trends in foster care. But that number is most certainly an undercount.

There are numerous problems with how AFCARS collects substance use data, related to how state systems collect the data.

These problems lead to underreporting and inconsistency across states. (Give me some time and I’ll get my act together to publish something on it).

AFCARS also only includes kids in child welfare systems. If you want to know whether substance use has gotten worse for families in child welfare systems, you need to compare them to families not in the system.

We have to rely on surveys like the NSDUH, the NCSH, and National Survey of Child and Adolescent Well-Being to reliably measure substance use trends.

This isn’t just about research. Back in the early 2010s, we would have missed the full signal that the opioid crisis was pushing more kids into foster care if we just relied on AFCARS.

What's at Stake in Child Welfare Policy

Child welfare agencies face so many challenges today that solid data help address.

Administrators, providers, and families want to know if interventions work. They want to know if certain groups are being excluded from services.

They want to know how to prioritize strapped budgets.

Pick the topic that you are working on right now, and I can point to a health survey that can inform it.

Do you want to know how mental health and treatment differ for children with experience in foster care? Turn to the National Survey of Children’s Health, which we did recently.

Do you want to know how psychotropic use differs between children on Medicaid and those not, to design interventions before children enter foster care? That’s something we looked at with the MEPS.

Do you want to know which behavioral health issues homeless youth are facing and how it may have changed? The Youth Risk Behavioral Surveillance System can give you insight.

Understanding many child welfare policy issues relies on surveys run by CDC, SAMHSA, and AHRQ.

If those agencies face cuts or reorgs that diffuse their mission, those surveys may either go away or lose their value.

It may not happen overnight.

But losing these data sources would raise questions about where decision makers on child and family issues can turn for the insights they need.

References

Handler, A. S., Johnson, K., Rankin, K. M., Velonis, A. J., James, A. R., & Kotelchuck, M. (2025). Shuttering the Pregnancy Risk Assessment Monitoring System (PRAMS): A Dangerous Attack on US Mothers and Infants. American Journal of Public Health, 115(6), 848–850. https://doi.org/10.2105/AJPH.2025.308107

Czajka, J. L., & Beyler, A. (2016). Declining Response Rates in Federal Surveys: Trends and Implications. Office of the Assistant Secretary for Planning and Evaluation, U.S. Department of Health and Human Services. https://aspe.hhs.gov/sites/default/files/private/pdf/255531/Decliningresponserates.pdf

Graeff, P., & Baur, N. (2020). Digital Data, Administrative Data, and Survey Compared: Updating the Classical Toolbox for Assessing Data Quality of Big Data, Exemplified by the Generation of Corruption Data. Historical Social Research / Historische Sozialforschung, 45(3), 244–269.

Data drives Child Welfare Wonk. From the beginning we’ve brought you original data analyses that cut through the noise to surface what matters.

Now we’re scaling that effort; inviting sharp researchers to drop new data-driven insights you won’t find anywhere else.

These fast, focused analyses are made for decision makers; rigorous, fluff-free, and aimed at the underlying structural tensions that actually matter in policy decisions.

The OBBBA’s Impact on Children’s Coverage

Children with Chronic Health Conditions Are More Likely to Lose Medicaid Coverage due to New Community Engagement Requirements

By Meredith Dost, PhD and Robin Ghertner, MPP

The One Big Beautiful Bill Act’s (P.L. 119-21, OBBBA) made significant structural changes to Medicaid.

One of those is a first-ever national creation of community engagement requirements, known colloquially as work requirements.

We found that those community engagement requirements will lead to fewer children and parents enrolled in Medicaid, including those with chronic health conditions and behavioral health issues. 

These losses will be primarily due to administrative burden; increased red tape and enrollment system complexity that can lead to otherwise eligible children losing coverage.

We created ballpark projections for how this may affect, in particular, children with chronic health conditions.

• Up to 68,000 children could lose Medicaid because their parents lose coverage as a result of the work requirements. 

• This includes up to 14,500 children with chronic health conditions who could lose coverage. 

• They would lose coverage because the burden of maintaining enrollment is too high, or because states commit errors, NOT because they are actually ineligible

• At least 1.9 million children have a parent who is at risk of losing Medicaid coverage.

• Children with chronic conditions are more likely (12.8%) to have a parent at risk of losing coverage, compared to Medicaid children without a chronic condition (5.1%).

• Children on Medicaid who take psychotropic medications are more likely (10.1%) to have a parent at risk of losing coverage compared to those not taking these medications (5.3%).

OBBBA doesn’t target children, but they will be affected

One key change to Medicaid from OBBBA is the introduction of community engagement requirements in every state.

Adults ages 19–64 will be required to prove that they work, complete community service, or attend school for at least 80 hours per month, or qualify for an exemption.

OBBBA’s changes aren’t supposed to affect children’s coverage — but they will through indirect impact. And, as a consequence, child welfare systems need to be prepared.

For one thing, Medicaid pays for behavioral health and other supportive services that many parents need to safely care for their children.

Losing Medicaid-funded services may put their children at risk of entering the child welfare system (Brown et al., 2019). 

That’s because parental substance use disorders and other behavioral health conditions are a major driver of child welfare involvement.

In 2023, the Children’s Bureau reported that  38 percent of entries to foster care were at least in part because of parental drug or alcohol use. Given data reporting issues, that percentage is likely much higher.

Furthermore, when parents lose coverage, even when their children remain eligible, the children may also lose coverage. 

Children are more likely to remain covered when their parents are enrolled in Medicaid (Sommers, 2006; Devoe et al., 2015).

We know from the experience of families subject to work requirements in Arkansas, Georgia, and New Hampshire that fewer eligible adults maintained enrollment for two reasons. 

First, administrative burden: it’s hard for anyone to navigate and comply with complex eligibility rules and requirements (Sommers et al., 2020). 

Second, state Medicaid systems must create brand-new processes and IT systems to implement the requirements, which are costly, time-intensive, and error-prone.

Interruptions to children’s health care coverage have many negative consequences, from absenteeism in schools (Roy et al., 2021) to poorer health outcomes (Cha et al., 2023). 

All of this in turn undermines healthy child development and can lead to system involvement. 

Our own work here at the Wonk shows that children in foster care already have higher behavioral health diagnoses, which may be due to undiagnosed issues before entering foster care.

The conversation on Medicaid community engagement requirements hasn’t addressed the impact on children with chronic conditions and behavioral health issues

There’s been little discussion about how children with chronic illnesses or behavioral health conditions will be affected. 

Topline discussion has focused on who work requirements impact  directly; adults who do not have a disability or dependent children under the age of 14.

That misses the policy’s indirect impact on children’s coverage. 

We haven’t seen anyone estimate how many children - particularly those with chronic health conditions - could lose coverage. 

These conditions require preventive and reactive health care and can be very costly. Both chronic and behavioral health conditions are on the rise in children (Forrest, 2025) .

We estimate that, from 2020–2022, an annual average of 3.2 million children on Medicaid were treated for a chronic health condition; anything from asthma to autism. 

It is critical to understand how the new work requirements will raise the risk of disenrollment for this population of children who are eligible for Medicaid and have demonstrable need for services.

In this analysis, we present two sets of ballpark estimates to analyze this issue.

These are admittedly rough, given the uncertainty about how things will play out, but it’s critical that decisionmakers have something to start with.  

First, we provide projections for how many children may lose Medicaid coverage, with a focus on certain health conditions.

Second, we provide estimates on the number of children on Medicaid whose parents may lose Medicaid coverage. These estimates are stronger methodologically (though still not perfect).

So let’s dive in.

Our Approach to Projecting the Effect on Children’s Medicaid Enrollment

We don’t want to drown you in the methods, but it’s important to understand some things up front about the assumptions we have to make. 

National work requirements have never been implemented in Medicaid so there’s limited research to draw upon.

If you are interested in the details, jump to the Methodological Details section at the end.

For the previous House version of the final bill, CBO estimated that 10.9 million could lose Medicaid coverage by 2034 due to OBBBA’s changes to the Medicaid program, including work requirements.

We haven’t seen anyone publish estimates on how children will be affected under the OBBBA.

We quantify how the burden of implementing work requirements will affect enrollment.

We are interested in people likely to lose coverage because of administrative burden: red tape and unprepared systems. 

This includes some adults who do meet work requirements but are disenrolled because the system is too complex and they are unable to re-enroll. It also includes many of their children.

Based on experience with Arkansas’s and New Hampshire’s implementation of Medicaid work requirements, this is very common. (Georgia had unique experiences that we didn’t use to directly inform our analysis).

When parents lose coverage, their children often do as well - what we call a “reverse welcome mat” (Hudson and Moriya, 2019). 

We use the distinct experiences of work requirements in Arkansas and New Hampshire to create a range of projections. This approach has been taken by others (Karpman et al, 2025; Mann et al., 2025). 

Arkansas and New Hampshire both tried out work requirements in Medicaid, and had very different infrastructure to enforce compliance. 

Arkansas had relatively stronger data systems to automatically assess compliance and was therefore able to streamline eligibility and verification processes. 

New Hampshire was on the opposite end of the spectrum. In both cases, a number of eligible people lost coverage due to system errors and administrative burdens.

Our analysis uses the latest data from the Medical Expenditure Panel Survey (MEPS) for years 2020–2022. 

Other data may provide more accurate estimates of all children, but MEPS is the best national survey on health conditions, medication use, and interaction with the health care system. 

MEPS allows us to hone in on the children we have been focusing on at the Wonk: those with behavioral health challenges and those who are at risk of entering foster care.

Up to 68,000 Children May Lose Medicaid Coverage, Including up to 14,500 With Chronic Conditions

Figure 1 presents our rough projections of the number of children who may lose Medicaid coverage as a result of a parent’s disenrollment:

• Between 55,000 and 68,000 children could lose Medicaid because their parents lose coverage as a result of the work requirements. 

  • They would lose coverage because the burden of maintaining enrollment is too high, or because states commit errors.

• For kids with different conditions, coverage losses vary:

  • Between 11,500 and 14,500 children with chronic health conditions.

  • Between 9,000 and 11,000 children with a mental or behavioral health diagnosis.

  • Between 11,000 and 13,500 children taking psychotropic medications.

These numbers are imprecise and are intended to give an idea of the scale of what may happen.

The projections are short-term — we would consider these the immediate effects in the first year or so of implementation.

As state systems get better at automating verification, there may be less drop-off in enrollment among those eligible. 

But we have to start where states are now, not where they will be years down the road.

Parents Will Lose Coverage, With Higher Losses Among Children With Chronic Conditions and Taking Psychotropic Medication

An estimated 5.8 percent of all children on Medicaid (1.9 million) have a parent who is subject to the OBBBA community engagement requirements and at risk of losing Medicaid coverage. 

As Figure 2 shows, there are important differences based on children’s health conditions:

• Children with chronic conditions are more likely (12.8%) to have a parent subject to the requirements, compared to Medicaid children without a chronic condition (5.1%).

• Children with a mental or behavioral health condition were just as likely as those without such a condition to have a parent at risk of losing coverage. 

  • While the estimates differ (8.2% vs. 5.5%), they do not show a statistically significant difference.

Children taking psychotropic medications are more likely (10.1%) to have a parent subject to the requirements compared to those not taking these medications (5.3%).

Our Main Takeaway: Implementing this Policy Will Lead to Children With Chronic Conditions Losing Coverage

Though children are not directly subject to the OBBBA community engagement requirements to be eligible for Medicaid, they are nevertheless affected by them. 

Children with chronic conditions and those taking psychotropic medications are especially likely to lose Medicaid coverage as a result of their parents losing coverage.

Children’s Behavioral Health May Suffer, and Lead to Further System Involvement

Children with mental or behavioral health conditions were no more likely to lose coverage, or have a parent lose coverage, than children without such conditions. 

This is counter-intuitive; given their greater need, we would expect legislative protection against such coverage loss. 

But the law doesn’t provide any special protection for these children. 

The fact that children taking psychotropic medications are more likely to have parents who lose coverage is also a signal worth noting, as it could impact continuity of treatment. 

What This Means for Child Welfare Systems and Others Providing Care to Children

More children may enter the system with undiagnosed conditions and no treatment.

More families may enter the system when parents do not have access to treatment they need to keep their children safe.

It will fall on child welfare systems to address these conditions, in large part through Medicaid.

What Does This Mean to You?

We want to hear your thoughts about what this analysis means to you in your work, and what further questions it raises for you.

Feel free to write us and answer any of these questions that motivate you:

• Which groups of children are you most concerned about losing coverage?

• What federal changes can help state Medicaid and child welfare agencies be prepared for unintended consequences on children’s Medicaid coverage?

• What approaches can state agencies — Medicaid or in children and family services — take to reduce loss of coverage for eligible children and families who face a high compliance burden?

Methodological Details

Here we describe the data, coding decisions, and methodological approach used in this analysis. 

We use a nationally representative survey commissioned by the U.S. Department of Health and Human Services called the Medical Expenditure Panel Survey (MEPS), for years 2020-2022 (latest available). The MEPS surveys families and individuals along with their medical providers and employers to provide in-depth data on the cost and use of health care in the U.S. For each year, we link the full-year consolidated data files with the medical conditions file and the prescriptions file. We then identified parental relationships in the MEPS and attached parents’ variables to their children’s observations to be able to identify which children had parents who were subject to Medicaid community engagement requirements.

Finally, we pool all three years’ linked files together and merge in the pooled linkage file for common variance structure which allows us to compute accurate estimates that take into account the complex sampling structure. We divide the person-level survey weight (PERWT) by three, the total number of pooled years, to compute estimates that represent annual averages. All analyses were conducted in R and primarily used the “survey” package.

We create each of the recoded variables as follows:

• Parent exempt from Medicaid community engagement requirements is any individual who has a dependent child in their household and meets any of the following criteria, per the OBBB:

  • Age 65+

  • Has a dependent child in their household ages <= 13

  • Disabled, based on Supplemental Security Income (SSI) recipient, which is consistent with how the Supplemental Nutrition Assistance Program (SNAP) classifies disability status

  • Is a student, which was identified by a response that they were not currently working due to being in school

  • Blind, based on if the individual is treated for “blindness” or “low vision”

  • Has a substance use disorder, based on if the individual is treated for any specific substance use disorder (e.g., alcohol, opioid, tobacco)

  • Not on Medicaid (Medicaid is self-reported)

• Parent subject to Medicaid community engagement requirements is any individual who has a dependent child in their household and meets none of the above exemptions.

• Chronic health condition: We classified 20 health conditions as “chronic” based on the classification scheme developed and utilized by the U.S. Department of Health and Human Services. This definition has been used in prior scholarly work (Davis-Ajami et al, 2019; Goodman et al, 2013). An individual is coded as having a chronic condition if they are treated for any of the 20 conditions, which include: arthritis, asthma, autism spectrum disorder, cancer, cardiac arrhythmias, chronic kidney disease, chronic obstructive pulmonary disease, congestive heart failure, coronary artery disease, dementia, depression, diabetes, hepatitis, HIV infection, hyperlipidemia, hypertension, osteoporosis, schizophrenia, stroke, and substance use disorders.

• Mental or behavioral health condition: An individual is coded as having a mental or behavioral health condition if they are treated for any mental, behavioral, or neurodevelopmental disorder (includes all MEPS condition codes beginning with “MBD”).  

• Psychotropic medication use: An individual is coded as taking at least one psychotropic medication if they take a psychotherapeutic agent or a central nervous system agent. These include antidepressants, antipsychotics, anticonvulsants, antimanic, antiparkinsonian, anxiolytics-sedatives, benzodiazepines-barbiturates, central nervous system agents, hypnotics, and stimulants.

Our projections in Table 1 shows the number of children who may lose coverage using the above definition of a parent subject to Medicaid community engagement requirements. To compute how many children may lose coverage, we multiplied the total number of children with a parent subject to the requirements by 25% or 30%, corresponding with half of the percentages that New Hampshire and Arkansas, respectively, were able to automatically determine exempt or compliant using state-held data. We use half of the percentages because we identified nearly all individuals who would be exempt, and data from Arkansas show that about half of the people the state identified as exempt or compliant were compliant due to having wages consistent with working > 80 hours. Then, we multiply the resulting number by 72% or 82%, which are the proportion of adults subject to work requirements in Arkansas and New Hampshire, respectively, who were estimated (Karpman et al, 2025; Mann et al, 2025) to have lost coverage as a direct result of the requirements. 

We do not estimate a scenario with no to very little state automation of work requirement administration, as in Georgia, because OBBBA requires states to automate to the best of their ability. Finally, we multiply the resulting number by 5.7%, a likely conservative estimate of the “unwelcome mat” effect of children losing Medicaid coverage because their parents did, even if children (or their parents) are eligible, due to procedural reasons.

Limitations

Our approach has two key limitations. 

First, like any survey, MEPS undercounts Medicaid enrollment, primarily due to respondents’ underreporting and survey non-response, which is in part mediated by weighting. One study found that the 2007 MEPS undercounts Medicaid enrollment by 11% (Bernard et al., 2012), and it references an internal federal government memo which estimated the 2006 MEPS Medicaid undercount at 12% (Banthin and Sing, 2006). Compared to the Community Population Survey (CPS), MEPS is a much better estimate of Medicaid coverage: CPS has been estimated to undercount Medicaid by around 40% (Davern et al., 2009; Hartman, 2022). The MEPS Medicaid undercount is on par with, to slightly worse than, the American Community Survey (ACS) undercount, which estimates have found ranges from 3.9%-15.5% (McIntyre, 2024; Boudreaux et al., 2019; Hest, 2023), getting larger in recent years. Our estimates should therefore be conservative, underestimates of the number of children affected.

The second limitation is that experiences from two states’ implementing work requirements is not representative of all states. Using Arkansas and New Hampshire has the benefit that they had different system capacities which may reflect a range of how prepared states are. New Hampshire provides a case of having some, though not high, capacity to identify exempt and compliant individuals in state data systems, and Arkansas represents a state with high data systems capacity. 

In the time since these work requirements were implemented (and eventually blocked by the courts), the unwinding of expanded Medicaid coverage from COVID-19-era policy (from April 2023 through 2024) occurred, which is also informative about states programs’ recent capacity across the U.S. States had to redetermine eligibility for all Medicaid recipients, and despite technological advances and streamlining of enrollment and verification processes, state Medicaid programs struggled to navigate this process. 69% of those disenrolled from Medicaid were actually eligible (Tolbert and Corallo, 2024), but they were disenrolled due to procedural reasons or state errors. 4.2 million children lost coverage in this unwinding (Alker et al., 2024), and federal estimates indicate that about three-quarters of these kids were likely disenrolled for procedural reasons (ASPE, 2022). 

When the states begin implementing these national work requirements, there will be a range of state capacity, and we would expect that technological capacity and procedures have improved and will continue to improve over time based on implementation experiences.

References

Alker, J., OSorio, A., Brooks, T., & Park, E. (2024). Child Medicaid Disenrollment Data Shows Wide Variation in State Performance as Continuous Coverage Pandemic Protections Lifted. 

Center for Children and Families, Georgetown Unviersity McCourt School of Public Policy. https://ccf.georgetown.edu/2024/05/02/child-medicaid-disenrollment-data-shows-wide-variation-in-state-performance-as-continuous-coverage-pandemic-protections-lifted/

ASPE. (2022). Unwinding the Medicaid Continuous Enrollment Provision: Projected Enrollment Effects and Policy Approaches (Issue Brief HP-2022-20). Office of the Assistant Secretary for Planning and Evaluation, U.S. Department of Health and Human Services. https://aspe.hhs.gov/reports/unwinding-medicaid-continuous-enrollment-provision

Banthin, J. S., & Sing, M. (2006). How Medicaid Enrollment Estimates from MEPS Compare with Administrative Totals (Working Paper). Agency for Health Research and Quality.

Bernard, D., Cowan, C., Selden, T., Cai, L., Catlin, A., & Heffler, S. (2012). Reconciling Medical Expenditure Estimates from the MEPS and NHEA, 2007. Medicare & Medicaid Research Review, 2(4), mmrr.002.04.a09. https://doi.org/10.5600/mmrr.002.04.a09

Boudreaux, M., Noon, J. M., Fried, B., & Pascale, J. (2019). Medicaid expansion and the Medicaid undercount in the American Community Survey. Health Services Research, 54(6), 1263–1272. https://doi.org/10.1111/1475-6773.13213

Brown, E. C. B., Garrison, M. M., Bao, H., Qu, P., Jenny, C., & Rowhani-Rahbar, A. (2019). Assessment of Rates of Child Maltreatment in States With Medicaid Expansion vs States Without Medicaid Expansion. JAMA Network Open, 2(6), e195529. https://doi.org/10.1001/jamanetworkopen.2019.5529

Cha, P., Danielson, C., & Escarce, J. J. (2023). Young Children’s Mental Health Improves Following Medicaid Expansion to Low-Income Adults. Academic Pediatrics, 23(3), 686–691. https://doi.org/10.1016/j.acap.2022.09.009

Davern, M., Klerman, J. A., Baugh, D. K., Call, K. T., & Greenberg, G. D. (2009). An Examination of the Medicaid Undercount in the Current Population Survey: Preliminary Results from Record Linking. Health Services Research, 44(3), 965–987. https://doi.org/10.1111/j.1475-6773.2008.00941.x

DeVoe, J. E., Crawford, C., Angier, H., O’Malley, J., Gallia, C., Marino, M., & Gold, R. (2015). The Association Between Medicaid Coverage for Children and Parents Persists: 2002–2010. Maternal and Child Health Journal, 19(8), 1766–1774. https://doi.org/10.1007/s10995-015-1690-5

Forrest, C. B., Koenigsberg, L. J., Eddy Harvey, F., Maltenfort, M. G., & Halfon, N. (2025). Trends in US Children’s Mortality, Chronic Conditions, Obesity, Functional Status, and Symptoms. JAMA. https://doi.org/10.1001/jama.2025.9855

Hartman, L. (2022). Understanding the Undercount of Medicaid Enrollees in the 2020 Current Population Survey Health Insurance Coverage Data. State Health Access Data Assistance Center. https://www.shadac.org/news/understanding-undercount-medicaid-enrollees-2020-current-population-survey-health-insurance

Hest, R. (2023). Tracking the Medicaid Undercount in the 2021 ACS Coverage Data. State Health Access Data Assistance Center. https://shadac-pdf-files.s3.us-east-2.amazonaws.com/s3fs-public/publications/Medicaid_Undercount_ACS_1.23.pdf

Hudson, J. L., & Moriya, A. S. (2017). Medicaid Expansion For Adults Had Measurable ‘Welcome Mat’ Effects On Their Children. Health Affairs, 36(9), 1643–1651. https://doi.org/10.1377/hlthaff.2017.0347

Karpman, M., & Haley, J. M. (2025). Assessing Potential Coverage Losses among Medicaid Expansion Enrollees under a Federal Medicaid Work Requirement. Urban Institute. https://www.urban.org/sites/default/files/2025-03/Assessing-Potential-Coverage-Losses-among-Medicaid-Expansion-Adults-under-a-Federal-Medicaid-Work-Requirement.pdf?utm_source=80m.beehiiv.com&utm_medium=referral&utm_campaign=the-reality-of-work-requirements-designed-to-cut-not-to-put-people-to-work

Mann, C., Serafi, K., Eder, J., Polk, E., & Toups Tranchina, M. (2025). No Place to Hide: Children Will Be Hurt by Medicaid Cuts. Manatt Health. https://lpfch.org/wp-content/uploads/2025/05/No-Place-to-Hide-Children-Will-Be-Hurt-by-Medicaid-Cuts-Brief-FINAL-05-06-2025.pdf

McIntyre, A., Smith, R. B., & Sommers, B. D. (2024). Survey-Reported Coverage in 2019-2022 and Implications for Unwinding Medicaid Continuous Eligibility. JAMA Health Forum, 5(4), e240430. https://doi.org/10.1001/jamahealthforum.2024.0430

Roy, S., Wilson, F. A., Chen, L.-W., Kim, J., & Yu, F. (2022). The Link Between Medicaid Expansion and School Absenteeism: Evidence From the Southern United States. Journal of School Health, 92(2), 123–131. https://doi.org/10.1111/josh.13111

Sommers, B. D. (2006). Insuring children or insuring families: Do parental and sibling coverage lead to improved retention of children in Medicaid and CHIP? Journal of Health Economics, 25(6), 1154–1169. https://doi.org/10.1016/j.jhealeco.2006.04.003

Sommers, B. D., Chen, L., Blendon, R. J., Orav, E. J., & Epstein, A. M. (2020). Medicaid Work Requirements In Arkansas: Two-Year Impacts On Coverage, Employment, And Affordability Of Care. Health Affairs, 39(9), 1522–1530. https://doi.org/10.1377/hlthaff.2020.00538

Tolbert, J., & Corallo, B. (2024). An Examination of Medicaid Renewal Outcomes and Enrollment Changes at the End of the Unwinding. KFF. https://www.kff.org/medicaid/issue-brief/an-examination-of-medicaid-renewal-outcomes-and-enrollment-changes-at-the-end-of-the-unwinding/

Data drives Child Welfare Wonk. From the beginning we’ve brought you original data analyses that cut through the noise to surface what matters.

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Wonk Data Drop: Comparing State Medicaid Spending for Children in Foster Care

What decisionmakers need to know about trends in Medicaid spending for children in foster care, as they prepare for Medicaid funding cuts.

*This article was updated on 8/13/2025 for clarity.

By Brett Greenfield, PhD and Robin Ghertner, MPP

BLUF:

• On average, states with more kids in foster care spend more on Medicaid per child, though there are exceptions.

• The amount of Medicaid dollars states spend on children in foster care varies dramatically.

• In 2021 the average Medicaid spending relative to the number of children in foster care was at least $27.4 million.

• This ranged from $2.1 million (DC) and $144 million (California). They probably spent much more, as our analysis is likely an undercount given children in foster care tend to use more services than their counterparts.

• Cuts to Medicaid may result in expenditures on foster care children going up.

• State leaders need to plan for potential increases in foster care Medicaid expenditures absent any other funding or policy change. Foster care may also take up a larger share of state spending.

Medicaid’s Role in Serving Children in Foster Care

The U.S. Congressional Budget Office estimates that the “One Big Beautiful Bill Act” will cut $1 trillion in Medicaid funds over 10 years.

Foster care systems rely on Medicaid to pay for health services to children in care. Medicaid also addresses conditions faced by children and caregivers that could lead to children’s removal (Center for Health Care Strategies, 2020).

A majority of children who enter foster care are already receiving Medicaid services prior to entry (Greenfield, 2024).

OBBBA’s Impact on Medicaid for Children in Foster Care

Though the effect of the new law is to reduce Medicaid spending, based on what we know about how Medicaid financing works for children in foster care, states may not actually spend less on Medicaid for this population.

The reality is, we don’t fully know how foster care systems will be affected by the cuts, because Medicaid has a complex relationship with foster care.

There are viable scenarios in which spending for this population actually goes up.

Our Analysis: The Relationship Between Foster Care and Medicaid Spending

We dove into state trends in foster care caseloads and Medicaid expenditures on children to highlight how fluctuations in one relate to the other.

We used data from a couple different sources for 2021, the most recent year of complete data.

There is no reliable source of data on national Medicaid spending on children in foster care. Surveys and claims data cannot capture this population.

To approximate the relationship between spending and foster care caseloads, we did two analyses.

First, we compared Medicaid spending for all enrolled children (not just those in foster care) to the number of children served by foster care systems.

Second, we estimated the total expenditures on children in foster care using the estimate of per child Medicaid spending. We provide an estimate of total spending, and then create a standardized index of that total based on each state’s foster care caseload to make state spending more comparable.

These estimates are a lower bound, because kids in foster care use more Medicaid services than those not in foster care.

Some research suggests that state Medicaid spending doubles for children in foster care compared to those not in care (Kaferly et al, 2023).

This is for many reasons – for example, states are required to conduct health screenings for all children in foster care, driving increased services that address the effects of the circumstances that led to their need for foster care placement.

We don’t double our estimates in the charts, but we think it’s a good way to approximate how much states are actually spending.

Children going into foster care may also be more likely to have poor health, particularly chronic conditions. And Medicaid has to cover services for this population by law.

What Our Analysis Found

Generally speaking, states with more children enrolled in foster care spend more on Medicaid per child.

However, the relationship isn’t straightforward, and most states don’t conform exactly to this trend.

Figure 1 shows per child Medicaid spending and foster care caseloads for 2021. Notable takeaways:

• Annual Medicaid spending for states, per enrolled child ranged from $572 (Florida) to $5,825 (Alaska) in 2021.

• Our estimated percent of Medicaid spending that goes to children in foster care ranged from 0.58% (New Jersey) to 4% (North Dakota) of total Medicaid spending

• States served between 2.6 and 20 children in foster care for every 1,000 children in the population.

• On average, states with more kids in foster care spend more on Medicaid per child.

  • For example, Alaska and Vermont had both higher Medicaid spending and higher foster care caseloads than most states.

  • In these two states, Medicaid spent over $4,000 per enrolled child, and they had over 12 kids in foster care per 1,000 children.

• But there are plenty of exceptions.

  • For example, Delaware spent the third most on Medicaid, but has one of the lowest foster care caseloads.

  • Montana is in the middle of states on Medicaid spending, and has the second highest foster care rate.

Because children in foster care have to be covered by Medicaid, changes to Medicaid – whether to overall eligibility and enrollment or in the services that Medicaid covers – will not likely translate into lower expenditures on this population.

State Medicaid Spending for Children in Foster Care Varies Dramatically

Though we can’t easily get precise estimates of Medicaid spending on children in foster care, we can get rough approximations.

Figure 2 shows estimates of state spending on children in foster care. Keep in mind, these are lower bounds on the estimates.

We use the actual spending for all children (not just those in foster care), but as we said before, states are spending more on children in foster care so the actual spend is likely much higher.

We create a standardized index of total spending based on each state’s foster care caseload rate. States differ in their Medicaid spending, their total population of children, as well as the number of kids in foster care. This index helps us compare what states likely spend on children in foster care.

For example, from Figure 2 we see that Medicaid spent over $17m on kids in foster care in Alaska, compared to less than $15m in Alabama. Alaska had a much higher foster care caseload rate, and as a result the difference in our standardized index is much greater - 99 for Alaska and 13 for Alabama.

These are rough estimates – the Wonk technical term is “back of the envelope” – and meant to be illustrative, not exact.

The trend is our focus. It’s important for grounding discussions among decisionmakers about Medicaid and foster care in a rapidly evolving policy and fiscal environment. Notable takeaways:

• In 2021, the total amount of state Medicaid spending on children in foster care was on average $27.4 million.

• But our estimates of Medicaid spending on children in foster care vary dramatically across states.

  • This ranged from $2.1 million (DC) to $144 million (California).

• This is a lower bound, because we know that children in foster care utilize more Medicaid-funded services on average than those not in foster care. Medicaid likely spends much more than these estimates

• If we take into account our best guess for how much more is spent on Medicaid services for children in foster care, states would spend from around $4.2 million (DC) to $288 million (California).

• The middle state – Iowa – would spend around $33.7 million on children in foster care.

• The standardized spending on children in foster care also varies substantially.

  • States' standardized spend index - the total spend adjusted by the foster care rate – less and was between 4 (Florida) and 99 (Alaska). States with a lower index spent less Medicaid dollars on kids in foster care than those with a higher index.

  • These estimates allow direct comparisons across states because they are adjusted by the size of the foster care population.

States with lower Federal Medical Assistance Percentage (FMAP) rates will likely carry a greater burden of Medicaid cuts.

The previous estimates showed total Medicaid spending, combining the federal and state shares.

But because states have different FMAP rates, the relative burden of spending on children in foster care isn’t the same for every state.

Figure 3 breaks down the state and federal share of our estimates of Medicaid spending on children in foster care.

• For states with the highest FMAP rates (e.g. New Mexico and Alabama), the state burden is relatively lower.

• We estimate that New Mexico, for example, spent less than $2 million on children in foster care, and the federal government spent over $7.6 million.

• California - with one of the lower FMAP rates - spent over $63 million receiving over $81 million from the federal government.

• Texas - in the middle in terms of FMAP rates - received a larger federal share, nearly $86 million.

As federal funding for Medicaid goes down, the states with higher FMAP rates are likely to see foster care take up a greater total proportion of their spending.

States may also see higher foster care expenditures following Medicaid cuts. Medicaid covers a variety of services that prevent involvement in foster care, such as behavioral health services for children and caregivers.

Changes to Medicaid financing will limit states ability to bolster access to these and other services, particularly in areas with already limited provider capacity that also disproportionately rely on Medicaid funding (like rural communities).

States may also limit optional Medicaid benefits, which they did during and after the Great Recession (Schubel et al., 2025).

This can have spillover effects for foster care systems. If families cannot get services, children may be more likely to enter foster care (Gross et al., 2025; Puls et al., 2021). Once there, they may require more services from Medicaid.

State leaders need to plan for increases in foster care Medicaid expenditures. When federal funds are squeezed, foster care may take up a larger share of state spending.

At the federal level, these insights about complexity across states are important for assessing the impact of current and future policy decisions around foster care and Medicaid.

This also underscores that changes to Medicaid can have significant effects on this population, even if those changes are not specific to children in foster care

References

Center for Health Care Strategies. (2020). How can Medicaid-funded services support children, youth, and families involved with child protection? Casey Family Programs. https://www.casey.org/medicaid-funded-services/

Greenfield, B. (2024). Timing of Medicaid Enrollment for Children Engaged with Child Welfare Services in Two States. Office of the Assistant Secretary for Planning and Evaluation, U.S. Department of Health and Human Services. https://aspe.hhs.gov/sites/default/files/documents/ab3a421123cfbaca8804e8d3a60d8d0b/Medicaid-Enrollment-Child-Welfare.pdf

Gross, M., Keating, B., Miller, R., Radel, L., & Abbott, Marissa. (2025). Prevalence and Characteristics of Children Entering Foster Care to Receive Behavioral Health or Disability Services. Office of the Assistant Secretary for Planning and Evaluation, U.S. Department of Health and Human Services. https://aspe.hhs.gov/reports/custody-relinquishment-prevalence

Kaferly, J., Orsi, R., Alishahi, M., Hosokawa, P., Sevick, C., & Gritz, R. M. (2023). Primary Care and Behavioral Health Services Use Differ Among Medicaid-Enrolled Children by Initial Foster Care Entry Status. International Journal on Child Maltreatment: Research, Policy and Practice, 6(2), 255–285. https://doi.org/10.1007/s42448-022-00142-9

Puls, H. T., Hall, M., Anderst, J. D., Gurley, T., Perrin, J., & Chung, P. J. (2021). State Spending on Public Benefit Programs and Child Maltreatment. Pediatrics, 148(5), e2021050685. https://doi.org/10.1542/peds.2021-050685

Schubel, J., Barkoff, A., Kaye, H. S., Cohen, M. A., & Tavares, J. (n.d.). History Repeats? Faced With Medicaid Cuts, States Reduced Support For Older Adults And Disabled People. Health Affairs Forefront. https://doi.org/10.1377/forefront.20250414.154091

Data drives Child Welfare Wonk. From the beginning we’ve brought you original data analyses that cut through the noise to surface what matters.

Now we’re scaling that effort; inviting sharp researchers to drop new data-driven insights you won’t find anywhere else.

These fast, focused analyses are made for decision makers; rigorous, fluff-free, and aimed at the underlying structural tensions that actually matter in policy decisions.

The Other Pediatric Mental Health Crisis: Children in Foster Care Face an Alarming Burden 

Robin Ghertner, MPP

BLUF:

• The percentage of children in foster care with disorders grew by 14 percentage points between 2018 and 2023 – much faster than children not in foster care.

• In 2023, children in foster care were nearly 20 percentage points more likely to have a mental health condition than children not in foster care.

• Medicaid cuts could make access to treatment even worse. More kids with disorders are at risk of being untreated, putting a greater burden on foster care systems.

The Big Picture: A Crisis Within a Crisis

In 2021, 25% of children had a diagnosis for a mental, behavioral, or developmental disorder.1

The COVID-19 pandemic contributed to a surge in children’s mental health care needs, high use of psychotropic medications, and increased suicidality.

For example, anxiety rose by 29 percent between 2016 and 2020.2

The American Academy of Pediatrics and other health organizations declared a national emergency in child mental health.3

This crisis is particularly acute for children in foster care, who face a significantly higher burden of these disorders.4 Research has shown that kids in foster care have high prevalence of mental health conditions and use of psychotropic medications.5 

These disorders may arise as a response to either the conditions that led to being removed from their homes or due to their experiences while in foster care.6

What Was Missing Before Our Analysis

We haven’t yet seen the most recent data trends for children in foster care. The post-COVID landscape of mental health conditions, treatments and providers has changed substantially. 

This analysis uses nationally-representative data from the National Survey of Children’s Health, sponsored by the Health Resources and Services Administration and conducted by the US Census Bureau.

In the survey, parents were asked whether a health care provider had told them their child currently has a range of mental health problems, including neurological and developmental disorders (defined under the figure). 

What We Found: Faster Rise, Sharper Disparities

Children in foster care saw a 14-point increase in reported disorders—from 23% in 2018 to nearly 37% in 2023.

Children in foster care were nearly 20 percentage points more likely to have one of these disorders compared to non-foster care children in 2023.

As shown in the figure, in 2023 almost 36.9% of children in foster care had a disorder, compared to 17.8% of children not in foster care.

And while all children experienced increases in mental health issues, the increase was much higher for those in foster care.

Prevalence for children not in foster care only grew by 4 points, compared to the 14 point rise for children in foster care. In 2018, 14.1% of children not in foster care had a disorder, rising to 17.8% in 2023. 

Why This Difference Matters for Decision Makers

There are key issues for leaders to consider when grappling with the policy response to the rising need for pediatric mental health services, based on what evidence suggests drives this difference.

We expect more understanding to emerge as we further analyze this issue.

• Trauma and instability: Children entering foster care have experienced neglect, abuse, or chronic instability.7 

• Diagnosis on entry: Many children are newly screened after removal, surfacing previously undiagnosed conditions.8 

• Eligibility and access: Children in foster care are enrolled in Medicaid and receive mandatory health screenings—exposing unmet needs.

• Children with disorders are more likely to be removed from their families. 

• If families can’t support their children with disorders and don’t have access to therapies, children may come to the attention of the child welfare system. 

• Some parents relinquish custody solely to access treatment. One study suggests that up to 5% of children enter foster care to access behavioral health services.9 10 

If parents can’t get treatment for their children, more may end up in foster care.

And if conditions aren’t appropriately diagnosed early enough, they can worsen; more kids will enter the system with heightened needs.

What This Means for Systems and Strategy

Federal and State Policymakers

Federal and state policymakers concerned with these trends will need to come up with approaches to financing and monitoring services in the new budgetary environment. 

Mental Health Providers

Mental health providers need to understand what is different about children in foster care, and why they may need different approaches to treatment.

It also might mean their own staffing and financing systems may need to adapt to heightened health needs. 

Child Welfare Administrators 

Child welfare system administrators and practitioners – including congregate care providers – already see the evolving complexity of needs of the children in their care. 

They will face financial, policy, and logistical challenges in ensuring availability of appropriate therapies available. 

Philanthropy and Non-Governmental Actors

And non-governmental players will need to understand their own role in increasing availability of services.

Hospitals already faced challenges in caring for children while awaiting access to mental health care.

Now they face these rising pressures with less financial resources. Limits on Medicaid financing tools like provider taxes constrain funding right as demand rises.

Private funders face complex tensions. Principal among them, Medicaid cuts mean there are more treatment needs to be filled by private funds.

But they face new strategic tradeoffs, given other constraints related to ongoing market volatility and changes to tax law. 

Medicaid Cuts Aren’t Isolated—they Reshape the System

Cuts to Medicaid and other parts of the health care system – even if not directly for foster care – will likely have consequences on families in foster care. 

Children enter foster care for many reasons. One is inability to access mental health care. Medicaid funds nearly all of that care – both directly for children on Medicaid but also indirectly, by increasing the number of providers in a community and defraying their uncompensated care costs. 

When Medicaid expenditures for children in foster care go up, other Medicaid-funded services can be negatively affected.

Children in foster care make up a relatively small percentage of the total Medicaid caseload.

If more children with disorders are in foster care, the proportional cost burden on Medicaid will increase.

The more Medicaid dollars states spend on foster care, the less funds they have to spend on other Medicaid populations.

Robin Ghertner is Child Welfare Wonk’s Founding Director of Strategic Policy Intelligence.

Data drives Child Welfare Wonk. From the beginning we’ve brought you original data analyses that cut through the noise to surface what matters.

Now we’re scaling that effort; inviting sharp researchers to drop new data-driven insights you won’t find anywhere else.

These fast, focused analyses are made for decision makers; rigorous, fluff-free, and aimed at the underlying structural tensions that actually matter in policy decisions.

Children in poverty and on Medicaid are more likely to use psychotropic medication, with gaps between medication use and diagnosis

By Meredith Dost, PhD and Robin Ghertner, MPP

BLUF

Psychotropic medications are being used by millions of children in the U.S.

But gaps between diagnosis and treatment suggest systemic misalignment: many kids are medicated without a diagnosis, while many with diagnoses aren’t receiving medication at all.

Our analysis indicates that utilization patterns are higher among children in poverty and on Medicaid, raising questions about access and quality in pediatric mental and behavioral healthcare.

• Nearly 9% of all children took psychotropic medications in 2021-2022.

• 11% of all children had a mental or behavioral health diagnosis.

• 3 in 10 children using psychotropics did not have a formal mental or behavioral health diagnosis.

• More than 4 in 10 children with a diagnosis were not taking psychotropics.

• Children were more likely to use psychotropic drugs if they were in poverty or on Medicaid, despite being no more likely to have a relevant diagnosis than their counterparts.

Why Psychotropics Matter for Policies Affecting Children

The recent Make America Healthy Again Commission report raised broad concerns about prescribing psychotropics - also called psychiatric medication - to children.

Long before that, there has been an ongoing policy and practice debate about these medications in child welfare and children’s behavioral health.

The evidence on the effectiveness of psychotropic medications on children is limited.¹In particular, observers have raised concerns about the inappropriate or over prescribing of medications to children.²

Understanding who gets these medications and whether they have a relevant diagnosis can inform policy discussions about oversight and access to both these medications and other treatments children may need.

What’s Missing in the Psychotropic Medication Debate; data on current trends.

Any effective policy deliberation needs grounding in facts.

We know psychotropic prescribing increased in the early 2000s.³ A recent study found nearly 28% of children in foster care were prescribed at least one psychotropic medication.⁴

We also know that low-income children are 2-3 times more likely to have mental health problems.⁵

In this analysis we provide insight into current national trends to help decision-makers decipher what’s going on:

• How broadly are psychotropics currently used in the population of children nationally?

• How many children with and without related behavioral health conditions are using these medicines?

• Are there differences across family income and Medicaid receipt?

Approach

To answer these questions we use a nationally-representative survey commissioned by the U.S. Department of Health and Human Services called the Medical Expenditure Panel Survey (MEPS), for years 2021-2022 (latest available).

The MEPS surveys families and individuals along with their medical providers and employers to provide in-depth data on the cost and use of health care in the U.S.

What the Data Tell Us

Nationally, almost 9% of all American children received a prescription for at least one psychotropic medication.

11% of children were diagnosed with a mental or behavioral health condition in 2021-2022.

Looking more closely at the relationship between diagnosis and medication (see Table 1), the data reveal a significant disconnect:

• Nearly 70 percent of children using psychotropic medications had a diagnosed mental or behavioral health condition.

  • That means that about 30 percent of children using these drugs did not have a formal diagnosis.

  • This calls into question whether these medications are being appropriately prescribed, and whether they are prescribed to manage behavior in lieu of clinical care or other interventions.

• More than half (56 percent) of children diagnosed with a mental or behavioral health condition took at least one psychotropic medication.

  • That means that over 40% of children with a condition were not taking psychotropics.

Table 1⁶ presents national estimates of the prevalence of psychotropic medication use and mental or behavioral health diagnoses, as well as how medication use and diagnoses relate to each other.

Children in poverty were more likely to use psychotropic medication than those not in poverty.

Similarly, children on Medicaid were more likely than children not on Medicaid to use these medications.

Figure 1 presents the prevalence of child psychotropic medication use and related diagnoses by subgroups of the population, focusing on poverty status and Medicaid receipt.

• 11.8% of children in poverty (with family incomes up to 100% of the Federal Poverty Level) used psychotropics, the highest across income groups. As a comparison, 7.2% of children with family income between 200 and 400% of poverty used psychotropics.

• 10.6% of children receiving Medicaid used psychotropics, compared to 7.7% of non-recipients.

At the same time, kids in poverty or on Medicaid were not more likely to be diagnosed with a mental or behavioral health condition compared with other children.

Even though there are differences shown in the figure, the differences are not statistically significant - the black lines show overlapping confidence intervals.

We can’t say for certain that the differences are meaningful.

Figure 1.⁷ Children in poverty and on Medicaid were more likely to use psychotropic medication but not more likely to have a mental health diagnosis

Child Psychotropic Use and Mental Health Diagnosis by Family Income as % of Federal Poverty Level and Medicaid receipt

Why are children in poverty and Medicaid more likely to use psychotropic medications?

It’s possible children in poverty and on Medicaid may be more likely to use these drugs because other therapies are unavailable or unaffordable.

There are other factors involved, such as difficulties in coordinating care and collaborating across systems - particularly foster care systems.

MEPS data can’t reveal much about these different factors, so we have to look to other data sources to uncover the reasons.

Over or Under-Utilization?

This analysis doesn’t give a definitive answer; our suspicion is that it is mixed.

3 in 10 kids using them don’t have a diagnosis, which points to a possible problem in too much or inappropriate prescribing.

But not all kids with a diagnosis use medication. Some may be getting an alternative therapy, but some may be receiving no treatment.

One-size-fits-all is rarely an effective policy approach. As policymakers in behavioral health and child welfare think about addressing psychotropics, they need to consider the different aspects of the issue.

Children without access to effective, alternative therapies could be left with no treatment if access to psychotropic medication is limited. Research is unclear on the later consequences of psychotropic use relative to no treatment at all.

The likely concurrent over-prescribing of psychotropics and limited access to effective mental and behavioral health treatment points to a balancing act that policymakers face to avoid exacerbating the issue in either direction.

So what about differences among other subgroups of children?

In a follow-on analysis, we will explore differences in prescribing and diagnosis by racial and ethnic groups as well as the interaction between race and poverty.

Meredith Dost is the National Poverty Fellow at the Institute for Research on Poverty at UW-Madison. This work does not represent the views of the university.

Robin Ghertner is Child Welfare Wonk’s Founding Director of Strategic Policy Intelligence.