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Objectives: To examine whether community health centers (CHCs) are effective in offsetting mental health emergency department (ED) visits. Data Sources and Study Setting: The HRSA Uniform Data System and the HCUP State ED Databases for Florida patients during 2012-2019. Study Design: We identified CHC-year-specific service areas using patient origin zip codes. We then estimated panel data models for number of ED mental health visits per capita in a CHC's service area. Models measured CHC mental health utilization as number of visits, unique patients, and intensity (visits per patient). Principal Findings: CHC mental health utilization increased approximately 100% during 2012-2019. Increased CHC mental health provision was associated with small reductions in ED mental health utilization. An annual increase of 1000 CHC mental health care visits (5%) was associated with 0.44% fewer ED mental health care visits (p = 0.153), and an increase of 1000 CHC mental health care patients (15%) with 1.9% fewer ED mental health care visits (p = 0.123). An increase of 1 annual mental health visit per patient was associated with 16% fewer ED mental health care visits (p = 0.011). Conclusions: Results suggest that mental health provision in CHCs may reduce reliance on hospital EDs, albeit minimally. Policies that promote alignment of services between CHCs and local hospitals may accelerate this effect. KEYWORDS community health centers, hospital emergency departments, mental health services, 1 | INTRODUCTION Mental health disorders affect nearly 1 in 5 U.S. adults and children, (1,2) yet the health care system fails to provide adequate access to mental health care [...]

Objective: To describe the national rate of social risk factor screening adoption among federally qualified health centers (FQHCs), examine organizational factors associated with social risk screening adoption, and identify barriers to utilizing a standardized screening tool in 2020. Data Source: 2020 Uniform Data System, a 100% sample of all US FQHCs (N = 1375). Study Design: We used multivariable linear probability models to assess the association between social risk screening adoption and key FQHC characteristics. We used descriptive statistics to describe variations in screening tool types and barriers to utilizing standardized tools. We thematically categorized open-ended responses about tools and barriers. Data Collection: None. Principal Findings: In 2020, 68.9% of FQHCs screened patients for any social risk factors. Characteristics associated with a greater likelihood of screening adoption included having high proportions of patients best served in a language other than English (18.8 percentage point [PP] increase, 95% CI: 6.0, 31.6) and being larger in size (10.3 PP increase, 95% CI: 0.7, 20.0). Having higher proportions of uninsured patients (14.2 PP decrease, 95% CI: -25.5, -0.3) and participating in Medicaid-managed care contracts (7.3 PP decrease, 95% CI: -14.2, -0.3) were associated with lower screening likelihood. Among screening FQHCs, the Protocol for Responding to and Assessing Patients' Assets, Risks, and Experiences (PRAPARE) was the most common tool (47.1%). Among non-screening FQHCs, common barriers to using a standardized tool included lack of staff training to discuss social issues (25.2%), inability to include screening in patient intake (21.7%), and lack of funding for addressing social needs (19.2%). Conclusions: Though most FQHCs screened for social risk factors in 2020, various barriers have prevented nearly 1 in 3 FQHCs from adopting a screening tool. Policies that provide FQHCs with resources to support training and workflow changes may increase screening uptake and facilitate engagement with other sectors. KEYWORDS cross-sector collaboration, federally qualified health centers, safety net, social determinants of health, social risk factor screening What is known on this topic * The COVID-19 pandemic elucidated the importance of cross-sector collaborations between health care, public health, and social services to jointly advance health equity, which may be facilitated through social risk screening. * Federally qualified health centers (FQHCs) are safety-net providers that serve over 30 million low-income people in the United States, and many FQHCs collect social risk data. * FQHCs are pivotal health care partners because of their social risk screening efforts, but there is limited data about the characteristics and barriers associated with social risk screening at FQHCs. What this study adds * Using a 100% sample of US FQHCs, we identified the patient- and organization-level characteristics associated with social risk screening at FQHCs in 2020, the first year of the COVID-19 pandemic. * Despite high rates of social risk screening at FQHCs, one in three FQHCs did not screen; FQHCs with higher proportions of uninsured patients and FQHCs smaller in size were less likely to screen. * In the first-known national examination of barriers to standardized social risk screening at FQHCs, common barriers included lack of staff training, inability to include screening in workflow, and limited funding., 1 | INTRODUCTION The onset of the Coronavirus 2019 (COVID-19) pandemic disrupted communities globally. In the United States (US), the impact of the pandemic was especially devastating for marginalized communities [...]

In 2020 and 2021, health centers received federal funding to support their COVID-19 pandemic response, yet little is known about how the funds were distributed. This study identified ten sources of funding distributed to 1,352 centers, ranging from $19 to $1.22 billion per center. When we examined patient and organizational characteristics by quartiles of funding per patient, health centers in the highest-funded quartile (quartile 4) were more likely rural and in the South; employed lower percentages of physicians; and had the highest percentages of sicker, uninsured, and unhoused patients. Centers in the lowest-funded quartile (quartile 1) were more likely urban, employed lower percentages of nurse practitioners, and had the highest percentages of Medicaid enrollees. With the end of pandemic-related funding in 2023, combined with Medicaid unwinding concerns, targeted investment is needed to mitigate a financial cliff and help maintain health centers’ capacity to provide high-quality services to those most in need.

Objective: To evaluate the impact of TEAM UP--an initiative that fully integrates behavioral health services into pediatric primary care in three Boston-area Community Health Centers (CHCs)--on health care utilization and costs. Data Sources: 2014-2017 claims data on continuously enrolled children from a Massachusetts Medicaid managed care plan. Study Design: We used a difference-in-difference approach with inverse probability of treatment weights to compare outcomes in children receiving primary care at TEAM UP CHCs versus comparison site CHCs, in the pre (2014-2016q2)- versus post (2016q3-2017)-intervention periods. Utilization outcomes included emergency department visits, inpatient admissions, primary care visits, and outpatient/professional visits (all cause and those with mental health (MH) diagnoses). Cost outcomes included total cost of care (inpatient, outpatient, professional, pharmacy). We further assessed differential effects by baseline MH diagnosis. Principal Findings: After 1.5 years, TEAM UP was associated with a relative increase in the rate of primary care visits (IRR = 1.15, 95% CI 1.04-1.27, or 115 additional visits/1000 patients/quarter), driven by children with a MH diagnosis at baseline. There was no significant change in avoidable health care utilization or cost. Conclusions: Expanding the TEAM UP behavioral health integration model to other sites has the potential to improve primary care engagement in low-income children with MH needs. KEYWORDS behavioral health integration, cost, mental health, pediatrics, utilization, 1 | INTRODUCTION Approximately 1 in 5 US children have a behavioral health problem, (1) with low-income children bearing a disproportionate burden of risk. (2) Children with behavioral health problems, [...]