Racial and Ethnic Disparities in Blood Pressure and Glycemic Control in the US Community Health Center Patient Population

Abstract

Objective:

To describe blood pressure and glycemic control by racial/ethnic group in the US Community Health Center (CHC) patient population, and whether center characteristics, proxying for higher resource levels and better quality of care, were associated with greater rates of controlled cardiometabolic conditions.

Methods:

Data came from the Uniform Data System, representing aggregate patient clinical data for individual health centers in 2019. Descriptive analyses were conducted weighting by health center patient populations to produce race-specific national rates of blood pressure and glycemic control, and linear regression is used to test whether cardiometabolic control rates varied by center characteristics.

Results:

Hypertension was controlled for 67.2% of non-Hispanic White, 66.9% of Hispanic, and 56.7% of non-Hispanic Black patients. Diabetes was controlled for 70.7% of non-Hispanic White, 65.7% of Hispanic, and 66.1% of non-Hispanic Black patients. The rate of blood pressure control was 2.54 to 3.99 percentage points higher across racial/ethnic groups in health centers that adopted a patient-centered medical home (PCMH) model of care relative to non-PCMH centers, while glycemic control was 1.08 to 2.27 pp. higher as a function of PCMH certification. Results for other center characteristics did not show consistent patterns across racial groups or outcomes.

Conclusion:

This study documented racial and ethnic health disparities in the CHC patient population after major expansion of the CHC program. CHCs with PCMH certification have improved clinical outcomes among patients with hypertension and diabetes across racial/ethnic groups relative to centers without this certification.

Keywords

community health centers primary care patient centered medical home health disparities hypertension diabetes

Introduction

Diabetes prevalence has increased in the US in recent decades, with socially marginalized racial and ethnic groups disproportionately affected.¹ In 2018, the age-adjusted percentage of adults with diabetes was 12.5% for Black and Hispanic Americans compared to 7.8% for Whites. Similarly, for Black relative to White Americans, hypertension is more common (32.8% vs 24%) and more likely uncontrolled when diagnosed.² Diabetes and hypertension, in turn, increase mortality risk, especially when uncontrolled.^3,4 Thus, disparities in the prevalence and control of cardiometabolic conditions likely contribute to Black-White disparities in diabetes- and heart disease-related mortality and Hispanic-White disparities in diabetes-related mortality.⁵ Improving prevention and management of cardiometabolic conditions for Black and Hispanic Americans is essential to reduce racial/ethnic disparities in mortality.

The US Department of Health and Human Services has the elimination of health disparities as a foundational principle of the Healthy People initiative,⁶ with the Community Health Center (CHC) program playing a central role.^7,8 CHCs are located in medically underserved areas where they serve a racially diverse, low-income population. Notably, the CHC program was substantially expanded over the past 2 decades.^9,10 Under the Bush Administration, federal funding doubled to $2.1 billion and the program grew to 1200 health centers.¹⁰ The program was expanded further under the Obama Administration as part of the Affordable Care Act (ACA) in 2010,¹¹ and by 2021 the number of designated health centers totaled approximately 1400 with over 13 500 delivery sites.^9,12 The number of patients served by health centers also increased, partly from Medicaid expansion under the ACA,¹³ nearly tripling from 2000 to 2021.⁹ CHCs currently serve over 30 million patients across the United States,⁹ with 24% of patients identifying as Black and 43% as Hispanic/Latino.¹⁴ More than 70% of patients live below the federal poverty level and 34% are uninsured.¹⁵ CHCs thus represent a critical source of health care for a segment of the population who otherwise encounters barriers to care.^16,17

5CHC patients also may be in worse health compared to other low-income patients, as fair or poor health is twice as common for health center patients compared to other low-income groups, and diabetes is 50% more common.¹⁵ Although CHC patients have elevated health burdens, racial/ethnic health disparities may be smaller within CHCs relative to the national population. One study found poorer glycemic and blood pressure control for Black relative to White patients and poorer glycemic control for Hispanic relative to White patients in the CHC population, but these disparities were smaller than published national estimates.¹² Another survey of 7 health centers found no ethnic differences in glycemic control among previously diagnosed diabetic CHC patients.¹⁸ While these findings on attenuated health disparities in CHCs are encouraging, research on health disparities in CHCs predates the recent expansion of the CHC program and increase in the number of patients.

Alongside efforts to expand the CHC program, the Bureau of Primary Health care (BPHC) has implemented multiple initiatives to improve the quality of care delivered. As part of the Affordable Care Act, BPHC was authorized to support the adoption of the Patient Centered Medical Home (PCMH) model of care in health centers.¹⁹ PCMH is a nationally recognized model of care that seeks to improve quality by ensuring a team-based approach to care that is patient-centered, comprehensive, and coordinated across different providers and community services.²⁰ The PCMH model emphasizes the use of electronic health records and health information technology to promote patient engagement strategies and quality improvement activities.²⁰ PCMH-certified health centers report better performance on clinical measures than health centers that are not PCMH-certified.^19,21,22 As of today, 1058 or 77% of all health centers are PCMH-certified,⁹ yet the impact of BPHC’s quality improvement initiatives on racial and ethnic disparities is unknown.

Current Study

This study uses 2019 data from the Uniform Data System (UDS) to describe rates of glycemic and blood pressure control among CHC patients by racial/ethnic group. The extent to which racial/ethnic disparities are present in the CHC patient population is not well understood, despite a substantial expansion of the program and patients served in the past 2 decades. Additionally, we examine how rates of blood pressure and glycemic control among racial/ethnic groups and related disparities are associated with CHC characteristics that proxy for higher resource levels and better quality of care (ie, number of patients served, CHC designation years, and PCMH recognition status). Prior research has shown that both organization size and duration in the health center program are associated with better clinical performance outcomes, but no studies have examined how racial disparities vary by such center characteristics.¹² Larger and more established health centers likely have higher resource levels relative to smaller and newer clinics. Furthermore, well-established health centers may be more integrated into the community, as providers have had more opportunities to interact and gain the trust of local residents—and thereby may be more effective at promoting health behaviors and improving outcomes.^23,24 Importantly, rates of blood pressure control and glycemic control have been shown to respond to quality-of-care initiatives, such that health centers may improve levels of controlled cardiometabolic conditions in their patient population through practices such as effective monitoring, coordinated care, and medication management.^25,26 Identifying how rates and disparities in control of cardiometabolic conditions vary across CHC characteristics could support further BPHC quality improvement initiatives aimed at eliminating health disparities.

Methods

Data came from the HRSA Uniform Data System (UDS) and represent the 2019 reporting year. In total, 2019 UDS records covered 1457 CHCs, of which 95% were HRSA-funded CHCs while 5% were look-alike (unfunded) CHCs. All CHCs were required to report annually on their patient demographics, patient outcomes, staffing, patient use of services, costs, and revenue. A health center patient was defined as anyone who received a health center service within the reporting year.

While CHC reports generally refer to the aggregated characteristics and health outcomes for the total patient population, outcomes related to blood pressure and glycemic control are for adults (ages 18-85 and 18-75 years, respectively) and are disaggregated by race/ethnicity. We focus on patients identifying as non-Hispanic White (White); non-Hispanic African American/Black (Black); and Hispanic, any race (Hispanic) due to the substantial racial/ethnic disparities between Black and Hispanic relative to White Americans, and because these groups have sufficient representation in the CHC population and at individual centers to support estimation of CHC-level disparities. Patient race and ethnicity were self-reported at the time of intake. When race was self-reported but not ethnicity, health centers were instructed to presume that patients are non-Hispanic. Estimates for other racial/ethnic groups were not included due to a relatively small sample size.

Blood Pressure Control

The percentage of hypertensive patients with controlled blood pressure was reported by race/ethnicity for patients aged 18 to 85 years with a diagnosis of hypertension in the first 6 months of the reporting period. Blood pressure control was defined when the most recent in-clinic blood pressure recording was lower than 140/90. If blood pressure was recorded multiple times on the same day, the lowest measurement was used. If no blood pressure measurements were recorded during the 1-year reporting period (after a qualifying diagnosis), the patient’s blood pressure was assumed not controlled. This approach is consistent with recommendations from the Joint National Committee and Healthcare Effectiveness Data and Information Set (HEDIS).²⁷ Patients who were pregnant, were in hospice care, had end stage renal disease, or were undergoing dialysis or renal transplant were excluded in CHC reports.

Glycemic Control

The percentage of diabetic patients with uncontrolled glycemia was reported by race/ethnicity for patients aged 18- to -75 years with a recorded diagnosis of type 1 or type 2 diabetes. Poorly controlled glycemia was defined when patients had a hemoglobin A1c (HbA1c) measurement exceeding 9.0% during the measurement period. CHCs were advised to report poor glycemic control for current patients with diabetes whose HbA1c was not assessed within the 1-year measurement period. This measure is consistent with HEDIS recommendations for assessing poor diabetes care.²⁸ Patients who were pregnant, had end-stage renal disease, or were in hospice care were excluded from CHC reports. We recoded rates of uncontrolled glycemia to represent the percentage of diabetic patients with controlled glycemia for consistency with controlled blood pressure.

Independent variables

Center-level characteristics with potential to influence patient outcomes and health disparities include the health center size, number of years participating in the CHC program, and PCMH recognition status. Health center size, proxying as an indicator for level of resources, was divided into quartiles based on the number of patients served.¹² Quartile groupings are commonly used in research when there is an absence of meaningful cutpoints, as they handle outlier observations and allow for examination of non-linear differences. Number of years in CHC program was coded as a categorical variable, representing 5 years or less in the CHC program, 6 to 10 years, and more than 10 years. These categories were selected to align with BPHC’s oversight process which requires each CHC undergo a comprehensive programmatic review at least once per project period, with such reviews intended to address non-compliance issues related to clinical quality improvement and quality assurance.²⁹ While a health center’s project period is currently 3 years, 5-year project periods were historically used.³⁰ PCMH certification was coded as a binary variable (certified, not certified) based on designation of at least 1 site location being PCMH-certified at the end of 2019.

Covariates

Patient characteristics have been shown to correlate with clinical quality outcomes for CHCs.¹² Also, racial/ethnic disparities may vary based on age, sex, income, and insurance status.^31
-34 Thus, we controlled for the following center-level patient socio-demographic characteristics: poverty rate; composition of insurance types; mean age; and percentage female. Income categories were reported as the percentage of patients in select federal poverty level categories (ie, 0%-100%, 101%-150%, 151%-200%) from which we computed the CHC-level patient poverty rate using the 100% threshold. We coded rates for the following insurance types: private insurance (omitted as the reference in statistical models), uninsured, Medicaid, and Medicare (inclusive of Medicare-Medicaid dual eligibles). Average patient age among adults was derived from reports for patient counts in given age intervals. Center-level total patient socio-demographic characteristics were included as proxies for individual racial groups, as no race-specific reports were available in the UDS.

Additionally, we controlled for area urban-rural status, categorized as urban (coded as 1) or rural or sparsely populated (coded as 0). Urban-rural status in the UDS aligns with HRSA’s definition of rural communities and was calculated according to metro census tracts and Rural-Urban Commuting Area (RUCA) codes (HRSA, 2022).

Analysis Plan

Rates of glycemic control and blood pressure control are presented by race/ethnicity for CHC patients. To produce national rates of glycemic and blood pressure control among CHC patients, we applied analytic weights defined by the center-level racial/ethnic population total with the respective condition (ie, diabetes and hypertension). A small number of centers reported outcomes using a sample rather than their full patient population, and, for these centers, we used the sample as the weight. Analytic weights were applied using the aweight option in Stata, with the weights representing the number of patients from which center-level averages are derived.

To test how center characteristics relate to patient health outcomes, we used linear regression with race-specific estimates of glycemic and blood pressure control as outcomes. We simultaneously estimated associations between center characteristics and cardiometabolic outcomes while adjusting for aggregate patient characteristics and area urban-rural status. In these models, we applied analytic weights using the population for the respective racial/ethnic group, such that estimates refer to the relationship in the national patient populations who have the relevant cardiometabolic conditions. We reported regression coefficients and 95% confidence intervals, with the test of statistical significance being whether the confidence intervals include 0. When cardiometabolic control outcomes showed consistent differences by center characteristics, we computed estimated marginal means to visualize the results. In particular, we estimated cardiometabolic control rates as a function of PCMH certification for each racial/ethnic group.

Next, we calculated center-level racial/ethnic disparities in both blood pressure and glycemic control as the absolute difference in rates between White and Black patients and between White and Hispanic patients, with more positive values indicating larger racial/ethnic disparities. We included center-level racial/ethnic disparities in both blood pressure and glycemic control as outcomes and tested CHC characteristics as predictors, adjusting for the covariates described above. As estimates of racial/ethnic disparities may be imprecise for centers with small sample sizes, we applied analytic weights to account for CHC differences in patient population size. The choice of weights was complicated by the use of 2 populations in our calculation of the outcome; as our primary concern was with low reliability of estimated racial disparities, we used the CHC-specific smaller sample of the 2 racial/ethnic groups as the analytic weight. In sensitivity tests, we estimated unweighted models but required samples to have a minimum of 100 patients with the respective condition (ie, hypertension and diabetes) for each racial/ethnic group. All analyses were conducted using Stata v15.1, with regress and margins commands.

Results

Descriptive statistics for the total CHC patient population in 2019 are presented in Table 1. Of all patients for whom race/ethnicity is known, 36.9% identified as White, 18.8% as Black, and 37.5% as Hispanic. The patient population was economically disadvantaged relative to the national population, as evidenced by two-thirds of patients from families with incomes under the federal poverty line and more than 1-quarter uninsured.

Table 1.

Socio-Demographic Characteristics of CHC Patient Population (n = 30 431 643).

Patient characteristics	% or mean^a	SD^b
Race/Ethnicity, %
Non-Hispanic White	36.9	31.6
Non-Hispanic Black	18.8	23.8
Hispanic, any race	37.5	28.3
Female, %	57.5	6.3
Age, mean	43.2	2.3
Poverty rate, %	66.7	18.8
Insurance type, %
Uninsured	27.1	19.5
Medicaid	37.2	19.1
Medicare	13.9	9.0
Private	21.7	13.6

Statistic weighted by center population to refer to national CHC patient population.

SD refers to variation between 1456 centers.

Race/ethnicity-specific estimates of blood pressure and glycemic control are shown in Table 2. A comparison across racial/ethnic groups indicates that national rates of blood pressure control were 10.5 percentage points (pp) lower for Black patients with a hypertension diagnosis relative to White patients. This is equivalent to an excess of 119 942 Black hypertensive patients with uncontrolled blood pressure if rates were comparable to White patients (ie, 10.5% fewer of 1 142 304 Black hypertensive patients). Rates of glycemic control were 4.6 and 5.0 pp lower for Black and Hispanic patients with diabetes relative to Whites. These differences suggest that an excess of 23 172 Black and 45 597 Hispanic diabetic patients have uncontrolled HbA1c than would be the case if rates were comparable to non-Hispanic Whites. The use of race-specific analytic weights prevents a formal test of whether differences are statistically significant. However, in independent samples t-tests when analytic weights were not used, racial/ethnic differences in blood pressure and glycemic control rates showed a similar rank ordering and were statistically significant (all P < .001), with the exception of differences in blood pressure control for Hispanic relative to White patients; note, these tests refer to differences in center averages rather than national rates.

Table 2.

Race-Specific National Rates of Blood Pressure and Glycemic Control, 2019.

	Patients with hypertension	Blood pressure control	Patients with diabetes	Glycemic control
Racial/ethnic group	n	%	n	%
Non-Hispanic White	1 972 766	67.2	823 570	70.7
Non-Hispanic Black	1 142 304	56.7	511 232	66.1
Hispanic, any race	1 272 801	66.9	919 824	65.7

Sample sizes for individual racial/ethnic groups are derived using the center-level population with the respective condition. To calculate national rates, weights are applied representing the number of patients per racial/ethnic group from which center-level rates of control were reported.

Results from regression models testing CHC characteristics as predictors of race-specific rates of blood pressure and glycemic control are shown in Tables 3 and 4. Estimates indicate a consistent finding of improved outcomes across racial/ethnic groups with PCMH certification. In particular, the blood pressure control rate was 2.54 to 3.99 pp higher as a function of PCMH certification for each racial group. The glycemic control rate was 1.08 to 2.27 pp higher for PCMH-certified relative to non-PCMH-certified CHCs, although the estimate for Black patients was not statistically significant. Estimated marginal means are shown in Figure 1 and depict the rates of glycemic and blood pressure control across racial groups as a function of PCMH certification. Results for other CHC characteristics did not show consistent patterns across racial/ethnic groups or outcomes. One exception is that both blood pressure and glycemic control rates for White patients were higher in more established centers, with newer centers having lower rates of control relative to centers in the CHC program for 6 to 10 and 11 years or more. Blood pressure control for White patients also varied as a function of center size, with the smallest CHCs having lower rates of control relative to the 3 larger quartiles—although the fourth quartile was not significantly different. Differences by CHC size were of a similar pattern but not statistically significant for Hispanic patients.

Table 3.

Regression Results for Community Health Center Characteristics as Predictors of Race-Specific Blood Pressure Control Rates.

	White	Black	Hispanic
Predictor variables	Est. (95% CI)	Est. (95% CI)	Est. (95% CI)
PCMH-certified (ref. = not certified)	2.62 (1.45, 3.80)	3.99 (2.65, 5.34)	2.54 (1.35, 3.74)
Center age (ref. = 5 years or less)
6-10 years	1.50 (−0.89, 3.89)	−0.29 (−3.09, 2.51)	1.49 (−0.98, 3.96)
11 years or more	2.81 (0.79, 4.82)	−0.19 (−2.66, 2.29)	0.72 (−1.19, 2.62)
Center size (ref. = first quartile)
Second quartile	2.62 (0.75, 4.49)	0.36 (−2.17, 2.89)	1.39 (−1.31, 4.09)
Third quartile	2.46 (0.62, 4.30)	1.37 (−1.12, 3.87)	1.43 (−1.17, 4.03)
Fourth quartile	1.77 (−0.04, 3.57)	0.23 (−2.23, 2.70)	1.89 (−0.66, 4.43)

Unstandardized regression coefficients are presented, referring to differences in blood pressure control rates by predictors. Bold font indicates statistical significance based on 95% confidence intervals. Race-specific CHC-level analytic weights are applied such that coefficients refer to the total CHC population with hypertension for a given racial/ethnic group. All models include CHC-level controls for percent female, mean age, poverty rate, income type composition, and urban status.

Table 4.

Regression Results for Community Health Center Characteristics as Predictors of Race-Specific Glycemic Control Rates.

	White	Black	Hispanic
Predictor variables	Est. (95% CI)	Est. (95% CI)	Est. (95% CI)
PCMH-certified (ref. = not certified)	2.27 (0.87, 3.68)	1.08 (−0.47, 2.63)	1.49 (0.17, 2.81)
Center age (ref. = 5 years or less)
6-10 years	3.83 (1.01, 6.65)	2.93 (−.23, 6.09)	2.57 (−0.11, 5.25)
11 years or more	3.28 (0.87, 5.68)	1.52 (−1.26, 4.31)	−0.51 (−2.62, 1.61)
Center size (ref. = first quartile)
Second quartile	−0.26 (−2.57, 2.04)	0.65 (−2.30, 3.60)	1.17 (−1.80, 4.14)
Third quartile	1.15 (−1.11, 3.42)	−0.28 (−3.18, 2.62)	1.00 (−1.87, 3.86)
Fourth quartile	0.05 (−2.19, 2.29)	1.08 (−1.81, 3.96)	1.33 (−1.48, 4.13)

Unstandardized regression coefficients are presented, referring to differences in glycemic control rates by predictors. Bold font indicates statistical significance based on 95% confidence intervals. Race-specific CHC-level analytic weights are applied such that coefficients refer to the total CHC population with diabetes for a given racial/ethnic group. All models include CHC-level controls for percent female, mean age, poverty rate, insurance type composition, and urban status.

Figure 1.

Estimated marginal means showing glycemic and blood pressure control rates for PCMH-certified and non-certified CHCs by race/ethnicity.

Next, we tested whether CHC characteristics were associated with center-level White-Black and White-Hispanic disparities in blood pressure and glycemic control. Model results are presented in Tables 5 and 6 and include estimates when weighting based on the patient population of the smaller racial/ethnic group and when excluding centers with fewer than 100 patients in either group. Findings were generally inconsistent across weighted and unweighted models, and showed few consistent associations between CHC characteristics and racial/ethnic disparities across outcomes. For instance, PCMH certification was not associated with disparities in blood pressure control or glycemic control in most models. The exception was that, in the unweighted model, PCMH certification was associated with larger White-Hispanic disparities in glycemic control. Estimates for the age of CHCs showed no reliable differences in racial disparities in the outcomes while findings were mixed for CHC size based on the patient population, with some estimates indicating more substantial racial disparities for CHCs with larger patient populations relative to the smallest quartile.

Table 5.

Regression Results for Community Health Center Characteristics as Predictors of Racial/Ethnic Disparities in Blood Pressure Control Rates.

	White-Black disparity		White-Hispanic disparity
	Weighted	Unweighted	Weighted	Unweighted
Predictor variables	Est. (95% CI)	Est. (95% CI)	Est. (95% CI)	Est. (95% CI)
PCMH certified (ref. = not certified)	0.01 (−0.79, 0.81)	0.86 (−0.16, 1.89)	−0.07 (−0.84, 0.70)	0.66 (−0.39, 1.70)
Center age (ref. = 5 years or less)
6-10 years	1.40 (−0.08, 2.88)	0.55 (−1.24, 2.34)	−0.06 (−1.57, 1.44)	−0.97 (−2.83, 0.90)
11 years or more	0.45 (−0.80, 1.71)	0.01 (−1.51, 1.53)	−0.01 (−1.23, 1.22)	−0.78 (−2.37, 0.81)
Center size (ref. = first quartile)
Second quartile	1.75 (0.31, 3.19)	N/A¹	1.63 (0.04, 3.22)	N/A¹
Third quartile	1.48 (0.07, 2.89)	−0.03 (−1.06, 1.00)	1.15 (−0.39, 2.69)	−0.15 (−1.22, 0.91)
Fourth quartile	1.86 (0.47, 3.24)	−0.34 (−1.36, 0.68)	1.16 (−0.34, 2.66)	−0.36 (−1.42, 0.70)

Unstandardized regression coefficients are presented, referring to racial/ethnic disparities in blood pressure control rates by predictors. Disparities are calculated at the center level as the absolute difference in blood pressure control rates for White relative to Black patients, and between White relative to Hispanic patients. Bold font indicates statistical significance based on 95% confidence intervals. Weighted model estimates use the smaller sample of the corresponding racial/ethnic groups, and unweighted estimates are derived using an alternative sample requiring that centers have at least 100 patients with hypertension for each racial/ethnic group. All models include CHC-level controls for percent female, mean age, poverty rate, insurance type composition, and urban status.

In unweighted models, first and second quartiles for center size are combined as there are otherwise a small number of centers in the first quartile due to the criterion of 100 patients per racial/ethnic group.

Table 6.

Regression Results for Community Health Center Characteristics as Predictors of Racial/Ethnic Disparities in Glycemic Control Rates.

	White-Black disparity		White-Hispanic disparity
	Weighted	Unweighted	Weighted	Unweighted
Predictor variables	Est. (95% CI)	Est. (95% CI)	Est. (95% CI)	Est. (95% CI)
PCMH-certified (ref. = not certified)	0.12 (−0.82, 1.06)	0.45 (−0.82, 1.71)	0.95 (−0.05, 1.95)	1.34 (0.02, 2.66)
Center age (ref. = 5 years or less)
6-10 years	1.59 (−0.10, 3.28)	0.30 (−1.94, 2.53)	0.00 (−1.96, 1.96)	−0.63 (−3.12, 1.86)
11 years or more	−0.71 (−2.14, 0.73)	−1.03 (−2.90, 0.84)	−0.27 (−1.90, 1.36)	−0.15 (−2.24, 1.95)
Center size (ref. = first quartile)
Second quartile	0.33 (−1.39, 2.06)	N/A¹	0.27 (−1.80, 2.35)	N/A¹
Third quartile	0.74 (−0.95, 2.43)	0.17 (−1.13, 1.48)	0.17 (−1.85, 2.19)	0.64 (−0.74, 2.02)
Fourth quartile	1.46 (−0.21, 3.13)	0.92 (−0.37, 2.21)	0.31 (−1.67, 2.29)	0.59 (−0.79, 1.97)

Unstandardized regression coefficients are presented, referring to racial/ethnic disparities in glycemic control rates by predictors. Disparities are calculated at the center level as the absolute difference in glycemic control rates for White relative to Black patients, and between White relative to Hispanic patients. Bold font indicates statistical significance based on 95% confidence intervals. Weighted model estimates use the smaller sample of the corresponding racial/ethnic groups, and unweighted estimates are derived using an alternative sample requiring that centers have at least 100 patients with diabetes for each racial/ethnic group. All models include CHC-level controls for percent female, mean age, poverty rate, insurance type composition, and urban status.

Discussion

Community health centers are at the forefront of government-wide efforts to achieve health equity by improving outcomes among low-income individuals and reducing racial/ethnic health disparities.⁷ Because CHCs are often the only source of medical care for millions of low-income Americans, monitoring health outcomes by racial/ethnic group for the CHC patient population and evaluating the impact of quality improvement initiatives is essential for eliminating racial and ethnic health disparities.

Our findings show that, among the CHC population in 2019, blood pressure control rates were 10.5 percentage points lower for Black (56.7%) relative to White (67.2%) patients with hypertension, and that glycemic control rates were 4.6 and 5.0 percentage points lower for Black and Hispanic relative to White patients with diabetes. These estimates are consistent with previous findings showing racial/ethnic health disparities for both blood pressure and glycemic control, but indicate that disparities may have widened over time.¹² In particular, among CHC patients in 2009, rates of blood pressure control were highest (62.5%) among non-Hispanic White patients and lowest (56.1%) for non-Hispanic Black patients, with a difference of 6.4 percentage points.¹² Thus, while blood pressure control appears to have improved for White patients from 2009 to 2019, the rate remained similar for Black patients, contributing to a widening disparity. Racial/ethnic disparities in glycemic control were similar in the 2009 CHC population relative to our estimates for 2019, although all groups appear to have experienced around a 2-percentage point decline in glycemic control. However, the populations for whom these estimates of blood pressure and glycemic control were derived are not directly comparable, given the large increase in patients served during this time. The 2009 UDS data set included 1131 CHCs and approximately 20 million patients, while the 2019 UDS included 1457 CHCs and over 30 million patients. Thus, while CHCs began providing care to more people over this 10-year period and potentially improved health outcomes as a result, our results indicate that goals to reduce health disparities were not being met.

That PCMH certification was associated with improved cardiometabolic control rates in the present study is consistent with prior research.^19,21,35 One study using UDS data from 2012 to 2015 showed that PCMH-certified health centers reported higher performance on 9 of 11 clinical measures, and that rates of diabetes and hypertension control were higher in PCMH-certified health centers during reporting years 2013, 2014, and 2015.²¹ Another study showed increases in clinical performance were proportional to the number of CHC sites that achieved PCMH status, particularly for CHCs with at least 50% of their sites certified.³⁵ While these studies examined outcomes for the total patient population, no prior research, to our knowledge, investigates the relationship between PCMH certification and racial/ethnic health disparities across CHCs. Our study found that PCMH certification was associated with improved glycemic and blood pressure control across all racial/ethnic groups. Moreover, we found that PCMH certification was not associated with the magnitude of racial or ethnic disparities for either outcome. Future research may consider whether the number of sites with PCMH certification and/or length of time a CHC is PCMH certified is associated with racial/ethnic health disparities.

We found no consistent differences in rates of cardiometabolic control by the size of CHCs and duration of their funding across racial/ethnic groups. The exception was for non-Hispanic White patients who had higher rates of both glycemic and blood pressure control at more established health centers relative to newer ones. These findings contrast with prior research showing that larger CHCs had better diabetes control for both non-Hispanic White patients and non-Hispanic Black patients, as well as better hypertension control for Hispanic patients.¹² Prior research also found differences by length of time in CHC program, with older health centers having higher rates of diabetes control for both Hispanic and non-Hispanic White patients and higher rates of blood pressure control for Hispanic patients.¹² Disparate findings may be attributed to differences in statistical models and our inclusion of the PCMH status variable. More established health centers noted in previous studies may have incorporated patient-centered models of care to improve health outcomes, while the recent expansion of PCMH certification across the program has generated greater parity in resources and treatment options, regardless of size and maturity.

The UDS Reporting System has notable limitations that do not permit a more thorough analysis of health disparities, especially by restricting the ability to control for race-specific characteristics. While the UDS allows for estimation of racial disparities in the full CHC population, data on patient socio-demographic characteristics and most health outcomes are reported as aggregate statistics for all patients, not by race/ethnicity. Without knowledge of whether racial and ethnic groups differ by age, sex/gender, income, or health insurance status within centers, estimates of racial and ethnic disparities may be misidentified. As an example, racial disparities vary by age, with Black women having a more accelerated age-related risk of cardiovascular disease—likely from “weathering” due to disproportionate exposure to environmental stressors^36,37–such that disparities increase with age. For instance, 1 study showed Black women, 49- to 55-years-old, had biologically aged an average of 7.5 years more than White women.³⁶ Racial/ethnic disparities in cardiovascular and metabolic conditions also vary by sex, economic resources, and insurance status.^31
-34 Thus, consideration of differences in patient characteristics (eg, by age and income) is essential when estimating racial/ethnic disparities in the CHC population, yet this is not possible with the current data reporting system. These measurement limitations impact the generalizability of this study, preventing direct comparisons between the magnitude of racial/ethnic disparities within CHCs patients, and those found in other low-income populations. However, UDS reporting requirements are changing, such that health centers will be required to submit de-identified, patient-level data in alignment with Fast Healthcare Interoperability Resources (FHIR). This UDS modernization initiative is part of a national effort to improve data exchange capabilities while standardizing reporting requirements across federal programs and healthcare industries.³⁸ The advent of patient-level UDS data presents improved opportunities to investigate whether the CHC program is reducing racial/ethnic disparities. While the roll out of mandatory patient-level reporting has been postponed for calendar year 2023, health centers have the option of submitting de-identified, patient level data for UDS reporting.

Another study limitation is the exclusion of other racial/ethnic groups beyond the 3 identified. While CHCs report UDS data for a number of additional racial/ethnic groups (American Indians, Native Hawaiians, Native Alaskans, and Asian Americans), these groups did not have sufficient representation at individual centers to support estimation of CHC-level disparities.

This study reports rates of blood pressure and glycemic control by race/ethnicity, identifying substantial disparities among the CHC patient population. We showed that the adoption of the PCMH model may lead to higher rates of blood pressure and glycemic control across racial/ethnic groups but not reduce existing racial/ethnic disparities. While access to primary care is important, prior estimates indicate that utilization of health care only accounts for approximately 10% of variation in health outcomes, with social and behavioral factors accounting for a substantially higher share.^39,40 Within the health center patient population, disparities are associated with inequities in the social and built environment such that health centers with higher scores on the Social Deprivation Index (SDI) have worse health outcomes compared to health centers located in areas with less social deprivation.⁴¹ Thus, there is an urgent need for research to identify the determinants of disparities among these primarily low-income patients and how CHCs may be effective in reducing disparities in the control of cardiometabolic conditions. CHCs may need to intervene on community-level influences in the social and built environment in order to meaningfully reduce racial/ethnic disparities in health outcomes. In fact, the first CHC grant proposal at the program’s inception explicitly stated that CHC interventions must address social/environmental health determinants such as housing, nutrition, water supplies and sanitation in order to improve health and reduce disparities.⁸ While the program has evolved over several decades, Section 330 of the Public Health Service Act (42 U.S.C 254b), Subsection b(2)(C) defines additional services that are appropriate for CHCs to provide alongside primary health services, specifically noting the detection and alleviation of unhealthful conditions associated with air quality, water supply, housing, and other environmental factors related to health. Thus, the use of grant funds to support activities such as community development, collaborative urban planning and other collective impact initiatives may be justified as they pertain to the services listed in the statute.⁴² While future research is needed to determine whether access to CHC services alone is associated with meaningful reductions in racial/ethnic disparities for CHC patients, current findings indicate CHCs have a limited influence on attenuating health disparities. Future research may help identify how CHCs can optimize collaboration across community organizations in addressing social and environmental health determinants to more meaningfully reduce health disparities.

Footnotes

Declaration of Conflicting Interests

The author(s) declared no potential conflicts of interest with respect to the research, authorship, and/or publication of this article.

Funding

The author(s) disclosed receipt of the following financial support for the research, authorship, and/or publication of this article: Research reported in this publication was supported by the National Institute on Minority Health and Health Disparities of the National Institutes of Health under Award Number R21MD014281. The content is solely the responsibility of the authors and does not necessarily represent the official views of the National Institutes of Health.

ORCID iD

Brittany Alosi

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