Mental Health Staffing at HRSA-Funded Health Centers May Improve Access to Care
Abstract
HRSA-funded health centers provide primary care regardless of patients’ ability to pay. A nationwide study found that patients at centers where mental health staff were colocated were more likely to receive mental health treatment—even when only one such staff person was employed for every 2,000 patients.
Abstract
Objective:
The study objective was to examine the association between mental health staffing at health centers funded by the Health Resources and Services Administration (HRSA) and patients’ receipt of mental health treatment.
Methods:
Data were from the 2014 HRSA-funded Health Center Patient Survey and the 2013 Uniform Data System. Colocation of any mental health staff, including psychiatrists, psychologists, and other licensed staff, was examined. The outcomes of interest were whether a patient received any mental treatment and received any such treatment on site (at the health center). Analyses were conducted with multilevel generalized structural equation logistic regression models for 4,575 patients ages 18–64.
Results:
Patients attending health centers with at least one mental health full-time equivalent (FTE) per 2,000 patients had a higher predicted probability of receiving mental health treatment (32%) compared with those attending centers with fewer than one such FTE (24%) or no such staffing (22%). Among patients who received this treatment, those at health centers with no staffing had a significantly lower predicted probability of receiving such treatment on site (28%), compared with patients at health centers with fewer than one such FTE (49%) and with at least one such FTE (65%). The predicted probability of receiving such treatment on site was significantly higher if there was a colocated psychiatrist versus no psychiatrist (58% versus 40%).
Conclusions:
Colocating mental health staff at health centers increases the probability of patients’ access to such treatment on site as well as from off-site providers.
Highlights
•
Colocated mental health staff at health centers funded by the Health Resources and Services Administration may offer a promising way to increase patients’ access to mental health treatment.
•
Attending a health center with at least one full-time equivalent (FTE) mental health staff increased the probability of a patient’s receipt of mental health treatment, compared with those attending centers with fewer than one mental health FTE or no mental health staff.
•
Attending health centers with mental health staffing increased predicted probability of patients’ receipt of mental health treatment on site, and this probability was significantly higher if the health center had a colocated psychiatrist.
•
Colocated mental health staff at health centers can help low-income and uninsured patients receive mental health treatment, and further efforts to develop colocated mental health capacity at health centers are warranted.
In 2018, 47.6 million adults in the United States had some form of mental illness, but only 43% received treatment for their condition (1). Unmet mental health needs have been attributed to stigma avoidance, mistrust of the mental health care system, and lack of access to services because of the cost of care and insufficient insurance coverage (2, 3). This cost barrier is particularly concerning, because uninsured and low-income groups are at a higher risk of mental illness (4).
Research shows that most adults with mental health conditions end up seeking and receiving treatment within the primary care setting (5). Integrating mental health providers into primary care settings can help primary care providers (PCPs) improve care coordination for these patients (6). Other studies have shown that the availability of mental health services within primary care settings has enhanced access to mental health care and promoted treatment (7–9). Yet, the existing literature has focused on specialized settings and partnerships that are not generalizable to community-based settings (10, 11).
Health centers funded by the Health Resources and Services Administration (HRSA) are a primary source of providers of care for uninsured and low-income patients across the United States. Health centers (also known as federally qualified health centers or community health centers) provide comprehensive and affordable primary care to everyone, regardless of ability to pay, and in 2018, they delivered care to more than 28 million Americans (12, 13). Health centers also reported providing mental health services to more than 2 million patients in the same year, including individual or group counseling and psychotherapy, 24-hour crisis services, and case management services. The strategic location of health centers in medically underserved areas and their experience providing culturally appropriate care highlight the potential of health centers to improve access to mental health services among low-income patients (12). Past evidence from unrepresentative samples indicated limited provision of mental health services, but emerging evidence indicates an increase in service delivery (14–17).
We aimed to address these gaps in the literature by examining associations between colocation of mental health staff at health centers and patients’ receipt of mental health treatment anywhere and patients’ receipt of mental health treatment on site versus elsewhere. We examined both number and type of colocated mental health staff (i.e., psychiatrists, psychologists, and other licensed staff).
We hypothesized that patients at health centers with colocated staff would have a higher predicted probability of receiving mental health treatment anywhere, because mental health staff would improve PCPs’ awareness of mental health conditions and provide guidance on screening and detection of these conditions. We also hypothesized that among patients who received mental health treatment, patients in colocated health centers would have a higher predicted probability of receiving such treatment on site because of elimination of access barriers.
Methods
Data and Sample
We used the 2014 Health Center Patient Survey (HCPS), an in-person survey of health center patients conducted between October 2014 and April 2015. HCPS included information on patient demographic characteristics, health care utilization, health conditions, and patient experiences at 169 health centers. The survey was designed to use a three-stage sampling method to provide a nationally representative sample of parent organization, clinics within the parent organization, and patients at clinics. Patients were prescreened to ensure that they had at least one prior visit to the health center. They were interviewed in the waiting room when they registered for an appointment. We merged the HCPS data with the 2013 Uniform Data System (UDS) report to include health center characteristics. UDS includes health center aggregated administrative data provided to HRSA for the past calendar year on staffing, revenues, patient demographic characteristics, and services delivered. Because UDS data reflect health center resources at the end of the calendar year, the 2013 UDS data were used to provide the best estimate of mental health staffing in place for the beginning of 2014 and the period when patients in the HCPS reported receiving mental health treatment. Our project was determined exempt by the University of California, Los Angeles, Institutional Review Board.
Because of differential service needs among children and the elderly, we restricted our sample to adult patients ages 18–64 (N=5,040) (18). Because we were interested in the impact of colocated mental health staffing on health center patients specifically, we excluded 416 patients who did not identify the health center where they were interviewed as their usual source of care. We also excluded 49 patients with missing responses to questions about their mental health condition. Our final sample included 4,575 patients.
Independent Variables
The primary predictor of interest for our analysis was mental health staffing at a patient’s health center, defined as the ratio of total full-time mental health equivalent staff (FTE) per 2,000 patients and categorized into more meaningful categories of none, less than one (i.e., part-time), and one or more (at least one FTE). Mental health staff included psychiatrists, licensed clinical psychologists, licensed clinical social workers, and other licensed mental health care providers (including psychiatric social workers, psychiatric nurse practitioners, family therapists, and other licensed master’s-level clinicians) (19). In the absence of data on optimal or average panel sizes for mental health providers, we standardized this size of mental health FTE per 2,000 patients on the basis of the calculation that in 1 year (50 weeks, excluding vacation) one FTE would be able to provide approximately 2,000 1-hour consultations. The second independent variable of interest was the type of colocated mental health providers. Mental health providers offer various services depending on their specialty. Psychiatrists are the only mental health providers who are licensed to prescribe psychotropic medications and are more likely than other mental health providers to treat serious mental illness. Others, such as psychologists and licensed clinical social workers, are more likely to provide counseling or other therapy requiring multiple visits over time. We created separate indicator variables for whether health centers had any psychiatrist, any psychologist, and any other licensed mental health staff.
We controlled for the number of patients per PCP FTE to account for likely provision of mental health services by PCPs with smaller panel sizes. We created three categories indicating small (under 1,200 patients [reference]), medium (1,200–1,999), and large (2,000 or more) panels per PCP.
We also controlled for the ratio of clinic support staff to PCP as a measure reflecting the capacity of PCPs to collaborate with mental health staff in the care of patients with mental health conditions. Clinical support staff included registered nurses, licensed practical and vocational nurses, home health and visiting nurses, clinical nurse specialists, public health nurses, medical assistants, and nurses’ aides. We categorized this ratio into two or less (reference), three to four, and greater than four clinical support staff per PCP. We also included an indicator for rural location of the health center, because rural areas often have a shortage of mental health professionals. To reflect the overall capacity in size and service offerings of a health center, we used an indicator for the number of clinics within the health center organization as low (10 or fewer clinics [reference]), medium (11–19), and large (20 or more).
As a measure of patient demand for mental health services, we included the percentage of patients diagnosed as having depression (regardless of primary diagnosis) at the patient’s respective health center. We categorized this variable into low (≤5% of patients [reference group]), medium (more than 5% but less than 15%), and high (≥15%). We also included the percentage of total revenue from Medicaid managed care contracts at the patient’s health center as a proxy for potential incentives to provide more integrated care and categorized this variable into no revenue (reference group) versus less than 25%, and 25% or more.
We used Andersen’s model of health care utilization and access to inform selection of relevant patient characteristics (20). For predisposing factors, we controlled for sociodemographic factors, such as patient’s race-ethnicity, gender, and age, as proxies for perceived stigma (21). We also included marital status as a proxy for protective impact of a patient’s social network from depression or anxiety. We included limited English language proficiency and education as proxies for patients’ familiarity with the health care system and their ability to communicate with health care professionals. We added a variable indicating whether patients reported that they would definitely recommend the health center to their friends or family as a proxy for patients’ experience that may influence their decision to seek care in the future.
For enabling factors, we included a measure of poverty and type of insurance as financial factors that enable patients to access mental health services. We used several indicators of need for services, including a variable to account for the patient’s level of psychological distress that used the Kessler 6 (K6) diagnostic questions (22). We identified patients with low, moderate, and severe psychological distress. Because substance use disorders are highly correlated with mental health conditions, we also included an indicator variable for whether a patient reported wanting or needing substance use disorder treatment or counseling in the past year in the HCPS. We also used the patient’s self-reported health status, dichotomized as excellent or very good versus good, fair, or poor health.
Dependent Variables
The first outcome of interest was whether a patient reported receiving any mental health treatment or counseling in the past year. The second outcome of interest was whether the patient received all, some, or no mental health services on site (at the patient’s health center). We combined the first two categories (all and some) to indicate at least some mental health visits on site because of small sample sizes. (Further detail of the construction of dependent and independent variables is provided in a table in an online supplement to this article.)
Statistical Analysis
We used Stata, version 15, for our statistical analyses. After running descriptive statistics and checking for collinearity problems, we developed multivariate logistic regression models to assess the independent association of colocation of any mental health staff and type of mental health staff with patients’ receipt of any mental health treatment. We ran separate weighted multilevel generalized structural equation models to examine association of mental health staffing with patients’ receipt of any mental health treatment and with patients’ receipt of mental health services on site at the health center, controlling for clustering of patients within health centers. For ease of interpreting results of these models, we used the margins command to obtain absolute predicted probabilities. Both models included a single aggregated survey weight across health center organizations, sites, and patients within those sites to account for the complex survey design of HCPS. We conducted stratified analyses by the levels of K6 score and patients with specific mental health conditions to assess whether there were differences in results based on mental health status.
Results
Table 1 provides health center and patient characteristics. Most patients (71%) went to health centers with limited mental health staff (fewer than one mental health FTE per 2,000 patients), and 10% went to health centers with at least one mental health FTE. In the entire sample, most patients (73%) reported no mental health visits in the past 12 months, 16% had at least some mental health visits on site at the health center, and 11% had all mental health visits off site. Across all patients, 45% were scored as having mild or no psychological distress, 40% as having moderate psychological distress, and 15% as having severe psychological distress. The proportion of patients who reported moderate or severe psychological distress was higher at health centers with at least one mental health FTE (68%), compared with health centers with fewer than one mental health FTE or no mental health staff (56% and 46%, respectively).
| Licensed mental health FTE | |||||||||||||
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
| per 2,000 patientsb | |||||||||||||
| Total | No mental health FTE | Fewer than one | At least one | ||||||||||
| (N=4,575) | (N=547, 19%) | (N=3,543, 71%) | (N=485, 10%) | ||||||||||
| Characteristic | N | % | SE | N | % | SE | N | % | SE | N | % | SE | pc |
| Patient mental health utilization (in past 12 months) | <.01 | ||||||||||||
| No mental health visits | 3,419 | 73 | 2 | 459 | 81 | 3 | 2,695 | 74 | 2 | 291 | 50 | 11 | |
| All mental health visits off site | 506 | 11 | 1 | 66 | 13 | 2 | 395 | 11 | 1 | 45 | 12 | 4 | |
| At least some mental health visits on site | 650 | 16 | 2 | 22 | 6 | 2 | 453 | 15 | 2 | 149 | 37 | 8 | |
| Health center | |||||||||||||
| Organizational capacity | |||||||||||||
| PCP panel sized | .94 | ||||||||||||
| <1,200 patients (reference) | 866 | 17 | 4 | 144 | 21 | 11 | 602 | 15 | 5 | 120 | 23 | 12 | |
| 1,200–1,999 patients | 2,565 | 58 | 6 | 221 | 60 | 15 | 1,990 | 58 | 7 | 354 | 50 | 19 | |
| ≥2,000 patients | 1,144 | 25 | 5 | 182 | 19 | 11 | 951 | 27 | 6 | 11 | 27 | 21 | |
| Ratio of clinic support staff to PCP FTE | .67 | ||||||||||||
| ≤2 (reference) | 1,306 | 34 | 6 | 170 | 43 | 15 | 971 | 33 | 7 | 165 | 26 | 13 | |
| >2–≤4 | 2,710 | 54 | 6 | 262 | 47 | 16 | 2,128 | 53 | 7 | 320 | 74 | 13 | |
| >4 | 559 | 12 | 4 | 115 | 10 | 8 | 444 | 14 | 5 | 0 | — | — | |
| Rural location | 1,506 | 47 | 6 | 304 | 72 | 16 | 1,001 | 43 | 7 | 201 | 28 | 14 | .13 |
| N of center’s clinic sites | .15 | ||||||||||||
| ≤10 (reference) | 2,298 | 60 | 6 | 407 | 80 | 12 | 1,756 | 57 | 7 | 135 | 49 | 19 | |
| 11–19 | 1,173 | 23 | 5 | 140 | 20 | 12 | 975 | 25 | 6 | 58 | 10 | 8 | |
| ≥20 | 1,104 | 17 | 4 | 0 | — | — | 812 | 18 | 5 | 292 | 41 | 18 | |
| Demand for mental health services: % of patients at center with a diagnosis of depression | .05 | ||||||||||||
| ≤5% (reference) | 1,244 | 30 | 5 | 304 | 52 | 15 | 930 | 28 | 6 | 10 | 1 | 1 | |
| >5%–<15% | 2,915 | 59 | 6 | 226 | 34 | 14 | 2,362 | 64 | 6 | 327 | 70 | 15 | |
| ≥15% | 416 | 11 | 4 | 17 | 14 | 13 | 251 | 8 | 3 | 148 | 30 | 15 | |
| Funding incentives for integrated care: % of total revenue from Medicaid managed care | .58 | ||||||||||||
| None (reference) | 2,259 | 56 | 6 | 293 | 48 | 16 | 1,751 | 58 | 7 | 215 | 55 | 18 | |
| <25% | 1,974 | 38 | 6 | 234 | 51 | 16 | 1,470 | 34 | 6 | 270 | 45 | 18 | |
| ≥25% | 342 | 6 | 2 | 20 | 2 | 2 | 322 | 8 | 3 | 0 | — | — | |
| Patients | |||||||||||||
| Predisposing factor | |||||||||||||
| Female | 2,946 | 66 | 2 | 369 | 74 | 4 | 2,311 | 66 | 3 | 266 | 48 | 10 | .04 |
| Age | .06 | ||||||||||||
| 18–25 | 427 | 16 | 2 | 265 | 62 | 4 | 1,691 | 50 | 2 | 247 | 51 | 4 | |
| 26–49 (reference) | 2,203 | 52 | 2 | 47 | 9 | 2 | 350 | 18 | 2 | 30 | 17 | 4 | |
| 50–64 | 1,945 | 31 | 2 | 235 | 29 | 4 | 1,502 | 32 | 3 | 208 | 32 | 5 | |
| Race-ethnicity | .24 | ||||||||||||
| Non-Hispanic White (reference) | 1,081 | 48 | 4 | 144 | 60 | 5 | 770 | 44 | 5 | 167 | 53 | 15 | |
| Hispanic, Latino | 1,616 | 25 | 3 | 183 | 19 | 6 | 1,313 | 28 | 4 | 120 | 15 | 3 | |
| Non-Hispanic Black | 1,074 | 20 | 2 | 159 | 18 | 6 | 813 | 21 | 3 | 102 | 18 | 9 | |
| Other | 804 | 6 | 1 | 61 | 3 | 2 | 647 | 6 | 1 | 96 | 14 | 6 | |
| Not married, no domestic partner (reference: married or has domestic partner) | 2,717 | 59 | 2 | 308 | 49 | 5 | 2,075 | 61 | 2 | 334 | 61 | 3 | .06 |
| Education | .28 | ||||||||||||
| Less than high school (reference) | 1,980 | 34 | 2 | 270 | 39 | 4 | 1,533 | 33 | 3 | 177 | 35 | 7 | |
| High school graduate | 1,268 | 29 | 2 | 125 | 24 | 2 | 1,001 | 32 | 2 | 142 | 21 | 7 | |
| More than high school | 1,327 | 37 | 2 | 152 | 37 | 4 | 1,009 | 35 | 2 | 166 | 44 | 6 | |
| Limited English proficiency | 1,418 | 17 | 2 | 138 | 11 | 4 | 1,203 | 19 | 3 | 77 | 13 | 3 | .13 |
| Would not or would only somewhat recommend health center to family or friends (reference: would definitely recommend) | 840 | 14 | 1 | 102 | 9 | 2 | 649 | 16 | 2 | 89 | 14 | 5 | .19 |
| Enabling factor | |||||||||||||
| Income ≤100% of the federal poverty level (reference: >100%) | 3,004 | 57 | 3 | 340 | 49 | 6 | 2,341 | 60 | 3 | 323 | 51 | 7 | .13 |
| Insurance coverage | .43 | ||||||||||||
| Uninsured (reference) | 1,241 | 30 | 3 | 197 | 25 | 5 | 942 | 31 | 4 | 102 | 27 | 7 | |
| Medicaid | 2,531 | 54 | 4 | 227 | 51 | 5 | 2,002 | 54 | 5 | 302 | 56 | 7 | |
| Other or missing response | 803 | 16 | 2 | 123 | 24 | 6 | 599 | 15 | 2 | 81 | 17 | 4 | |
| Need factor | |||||||||||||
| Level of psychological distress as measured by K6e | .08 | ||||||||||||
| Mild or none (reference) | 2,029 | 45 | 3 | 270 | 55 | 7 | 1,585 | 44 | 3 | 174 | 32 | 7 | |
| Moderate | 1,797 | 40 | 3 | 202 | 33 | 6 | 1,377 | 40 | 2 | 218 | 57 | 8 | |
| Severe | 749 | 15 | 1 | 75 | 13 | 3 | 581 | 16 | 2 | 93 | 11 | 5 | |
| Reported past-year want of or need for counseling or treatment for substance use | 356 | 5 | 1 | 40 | 4 | 2 | 263 | 6 | 1 | 53 | 5 | 3 | .89 |
| Excellent or very good self-reported health (reference: good, fair, or poor health) | 775 | 20 | 2 | 107 | 16 | 3 | 580 | 19 | 2 | 88 | 28 | 4 | .12 |
a
Source: Uniform Data System 2013 and Health Center Patient Survey 2014. Ns are unweighted, and percentages are weighted.
b
FTE, full-time equivalent.
c
From chi-square test; reference group for comparison is indicated.
d
PCP, primary care provider.
e
K6 Score, Kessler K6 nonspecific distress scale.
Logistic regression results indicated that the predicted probability of receiving any mental health treatment in the past year was 22% for patients at health centers with no mental health staffing, compared with 32% for patients at health centers with at least one mental health FTE (Table 2). This difference was significant (p<0.05). Our analysis showed that the probability of receiving care at a health center with limited mental health staff (fewer than one FTE) was not significantly different than the probability of receiving care at a health center with no mental health staff. The type of mental health staff employed at the health center also did not make a significant difference in the probability of a patient’s receipt of any mental health treatment.
| Marginal | p>|z| for | |||
|---|---|---|---|---|
| Model and variableb | % | SEc | differenced | difference |
| Model 1: any licensed mental health staff (primary predictor) | ||||
| No mental health staff (reference) | 22 | 2 | ||
| Fewer than one mental health FTE per 2,000 patients | 24 | 1 | 2 | .370 |
| At least one mental health FTE per 2,000 patients | 32 | 4 | 10 | .049 |
| Model 2: type of mental health staff (primary predictor) | ||||
| No psychiatrist FTE on staff (reference) | 24 | 2 | ||
| Any psychiatrist FTE on staff | 26 | 2 | 2 | .507 |
| No clinical psychologist FTE on staff (reference) | 25 | 1 | ||
| Any clinical psychologist FTE on staff | 25 | 2 | 0 | .881 |
| No other licensed mental health provider FTE on staff (reference) | 25 | 3 | ||
| Any other licensed mental health provider FTE on staff | 25 | 1 | 0 | .909 |
a
Source: Uniform Data System 2013 and Health Center Patient Survey 2014.
b
FTE, full-time equivalent.
c
Delta method SE.
d
Differences are based on exact numbers, not rounded numbers displayed in table.
The predicted probabilities of onsite mental health treatment among patients who reported receiving any mental health treatment in the past year are shown in Table 3. Patients at health centers with no mental health staffing had a significantly lower predicted probability of receiving mental health treatment on site (28%), compared with those at health centers with fewer than one mental health FTE (49%) and those at health centers with at least one mental health FTE (65%).
| Difference | ||||
|---|---|---|---|---|
| from | p>|z| for | |||
| Model and variableb | % | SEc | base leveld | difference |
| Model 1: any licensed mental health staff (primary predictor) | ||||
| No mental health staff (reference) | 28 | 6 | ||
| Fewer than one mental health FTE per 2,000 patients | 49 | 3 | 22 | .001 |
| At least one mental health FTE per 2,000 patients | 65 | 7 | 38 | <.001 |
| Model 2: type of mental health staff (primary predictor) | ||||
| No psychiatrist FTE on staff (reference) | 40 | 4 | ||
| Any psychiatrist FTE on staff | 58 | 4 | 17 | .002 |
| No clinical psychologist FTE on staff (reference) | 50 | 3 | ||
| Any clinical psychologist FTE on staff | 50 | 3 | 0 | .935 |
| No other licensed mental health provider FTE on staff (reference) | 42 | 5 | ||
| Any other licensed mental health provider FTE on staff | 51 | 3 | 9 | .133 |
a
Source: Uniform Data System 2013 and Health Center Patient Survey 2014. The sample includes only patients who reported they received mental health treatment within the past 12 months (on site or off site).
b
FTE, full-time equivalent.
c
Delta method SE.
d
Differences are based on exact numbers, not rounded numbers displayed in table.
In the model assessing the relationship between type of mental health staff at a patient’s health center and where the patient’s mental health treatment took place, the predicted probability of receiving mental health treatment on site was significantly higher if the health center had any psychiatrist on staff versus no psychiatrist (58% versus 40%). However, no other categories of mental health staff were significantly associated with this outcome. (Tables with full regression models are included in the online supplement.) Sensitivity analyses showed similar results as described above for patients with depression or generalized anxiety and for those with moderate K6 scores but not for patients with panic disorder, schizophrenia, or bipolar disorder (N=1,013) or those with mild or severe K6 scores (N=749) (see table in online supplement).
Discussion
Our study found that although most health centers had some mental health staff in 2013, only a small proportion had at least one mental health FTE per 2,000 patients in that year. We found that the predicted probability of receipt of any mental health treatment (on or off site) was higher only if the health center had at least one mental health FTE; however, the type of mental health FTE did not make a significant difference in this case. We also found that having any colocated mental health staff increased the predicted probability of patients’ receipt of at least some mental health treatment on site. When different categories of mental health staff were considered separately, we found that having a colocated psychiatrist—either part-time or full-time—increased this predicted probability but having other types of staff did not.
Our results are consistent with previous research suggesting that integrated care through colocation of mental health staff increases access to or utilization of mental health services (23, 24). This increase likely results from a greater capacity to diagnose and treat patients’ mental health conditions, rather than referring them off site (24). Our findings highlight the importance of having at least one mental health FTE to increase the predicted probability of patients’ receipt of such treatment. This finding likely indicates that part-time capacity does not adequately support systematic screening of patients because of limited availability of mental health staff to engage in warm hand-offs with primary care patients and to diagnose and treat all patients who need such care (25). The stratified analyses suggested that colocation of mental health staffing may have benefited patients with moderate psychological distress or less complex mental health conditions that could be treated in primary care settings. Those with mild psychological distress may have received mental health care from PCPs.
We also found that colocation of psychiatrists significantly increased the predicted probability of receipt of onsite treatment among patients who received any mental health treatment. This finding likely reflects the fact that psychiatrists are able to prescribe medications for patients who would otherwise be referred elsewhere for this care and would have trouble finding providers willing to accept Medicaid coverage (26).
The cross-sectional design of HCPS limited our ability to determine a causal relationship between mental health staffing at health centers and patients’ receipt of treatment, even though data on such staffing were collected the year before patients reported receiving their mental health treatment. It is possible that health centers’ staffing levels changed from 2013 to 2014, when patients were surveyed. Additionally, for health centers with multiple clinics, staffing (colocation) information in the UDS referred to staffing at the organizational level, not clinic level. It is possible some patients may have received mental health services at a different clinic within the health center organization. Nevertheless, when health center providers can refer patients to treatment providers within the same organization, there are fewer barriers to accessing services in general, because of health centers’ mission to provide care to all patients regardless of the ability to pay. Consequently, our data highlight the value of having at least one colocated mental health FTE within the health center organization, whether it is a single-site or multisite health center. We also lacked data to assess continuity of care of patients with the same mental health provider, which may have played a role in patients’ decision to seek mental health services at the health center (27).
Finally, because of the sensitive nature of mental health issues, patients may have underreported such visits or felt uncomfortable responding to the K6 questions (28). Our measure of psychological distress captured problems only within the past 30 days, rather than the past year, and may thus have underrepresented the level of mental health need in this population. Despite these limitations, our study is the first to use nationally representative data to examine the relationship between mental health staffing at health centers and low-income and uninsured patients’ receipt of mental health treatment.
Conclusions
Our findings indicate that colocating mental health staff at health centers can help low-income and uninsured patients receive mental health treatment. However, health centers face challenges in employing such staff in proportion to the needs of their patients. Health centers compete with other employers for mental health staff who are linguistically and culturally competent and are willing to work for less competitive salaries, particularly in the case of psychiatrists (26, 29). As a potential model for improving colocation of these providers in community clinics, HRSA has implemented the Behavioral Health Workforce Education and Training Program to recruit mental health providers; in addition, HRSA periodically disburses mental health workforce grants to promote colocation of such staff (30). In recent years, HRSA has further invested in increasing access to integrated mental health care by awarding $200 million in 2017 through Access Increases in Mental Health and Substance Abuse Services, over $350 million in 2018 through Expanding Access to Quality Substance Use Disorder and Mental Health Services, and nearly $200 million in 2019 through Integrated Behavioral Health Services (31, 32). However, even though colocation is likely to improve overall outcomes of care and increase efficiencies, colocation alone does not guarantee integrated care delivery at health centers. Full integration requires intensive efforts to promote close interaction between PCPs and mental health staff in patient care and requires commitment from the leadership, change in workflows, and provider buy-in (33). Integration can be promoted by providing financial incentives and technical assistance to health centers through such programs and resources from the Center for Integrated Solutions of the Substance Abuse and Mental Health Services Administration and HRSA.
Our findings indicate that further effort to develop mental health capacity at health centers is warranted and that additional research is required to study use patterns of these patients to obtain insights into the role of colocated mental health staff in access to mental health services, quality of care provided, and improvements in mental and physical health (29). Further studies could also quantitatively examine the application of tele–mental health services, particularly in rural areas where there is a shortage of mental health providers.
Supplementary Material
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Received: 14 May 2020
Revision received: 15 October 2020
Accepted: 17 November 2020
Published online: 2 June 2021
Published in print: September 01, 2021
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Competing Interests
The authors report no financial relationships with commercial interests.
Funding Information
This research was funded by grant 2T32HS000046 from the Agency for Healthcare Research and Quality (AHRQ) U.S. Department of Health and Human Services (HHS); by contract HHSH250201300023I with the Health Resources and Services Administration (HRSA); and by grant TL1TR001883 from the National Center for Advancing Translation Science, National Institutes of Health (NIH). The views expressed in this publication are solely the opinions of the authors and do not necessarily reflect the official policies of HHS, AHRQ, NIH, or HRSA, nor does mention of the department or agency names imply endorsement by the U.S. government.
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