VIII. Behavioral Health Care Provider Model Components

Published 2022

In this module:

This module describes the data and assumptions used in the Health Workforce Simulation Model (HWSM) to project the future supply and demand for behavioral health care providers. Behavioral health care includes treatment of mental health and substance abuse conditions, stress-linked physical symptoms, and patient activation and health behaviors.1

Some behavioral health occupations overlap those we cover in other modules. Behavioral health includes psychiatrists (see the Physicians module), psychiatric/mental health nurse practitioners (see the Advanced Practice Nurses module), and psychiatric/mental health physician assistants (see the Physician Assistants module). Some behavioral health services are provided by occupations not part of the behavioral health workforce as defined here for modeling—such as primary care providers. This module describes the data and assumptions to model supply and demand projections for:

  • psychologists
  • substance abuse and behavioral disorder counselors (addiction counselors)
  • mental health counselors
  • school counselors
  • child, family, and school social workers
  • health care social workers
  • mental health and substance abuse social workers
  • marriage and family therapists (MFTs)
  • psychiatric aides & technicians (demand modeling only)2 3

For modeling purposes, the definition of psychologists, counselors, therapists, and social workers has evolved over the years. HRSA’s 2016 report3 included all psychologists trained at the master’s degree-level or higher, whereas HRSA’s subsequent projections include doctorate-level psychologists only. This change was based on feedback from the American Psychological Association that master’s-level psychologists generally work as counselors rather than as clinical psychologists.

Due to data challenges distinguishing between clinical social workers and social workers in non-clinical roles, the criteria used to identify social workers for modeling have evolved across studies. Prior HRSA projections included social workers trained at the master’s degree-level or higher only and that restriction applies to the most recent projections as well. A major data constraint is that national data sources such as the American Community Survey (ACS) and the Occupational Employment and Wage Statistics (OEWS) do not identify which social workers are clinical social workers. Starting with the projections developed in 2022, we model three types of social workers: (1) child, family, and school, (2) health care, and (3) mental health and substance abuse. Some modeling assumptions are the same across the three types of social workers, and when referring simply to social workers the discussion implies all three types of social workers.

Also, starting in 2022, for modeling, we updated the definition for mental health counselors, school counselors, and marriage and family therapists to include only providers with a master’s degree or higher.

For the 2018 report2 , the research team sought assistance from professional associations representing behavioral health professions to identify the best available data sources, discuss trends affecting workforce supply and demand, and provide feedback on preliminary findings. The information provided in this technical documentation and in HRSA reports do not necessarily reflect the views of the associations that responded to our invitation to participate in the workforce study. There may not be a clear consensus on all assumptions. Individuals from the following associations participated:

  • Association of Social Work Boards (ASWB)
  • American Academy of Physician Assistants (AAPA)
  • American Association for Marriage and Family Therapy (AAMFT)
  • American Psychiatric Association (APA)
  • American Psychiatric Nurses Association (APNA)
  • American Psychological Association (APA)
  • American School Counselor Association (ASCA)
  • Association for Addiction Professionals (NAADAC)
  • College of Psychiatric and Neurologic Pharmacists (CPNP)
  • Council on Social Work Education (CSWE)
  • National Board for Certified Counselors (NBCC)

The remainder of this module describes how HWSM models supply and demand for behavioral health providers.

Modeling Supply

Supply modeling consists of the following steps:

  • estimate the number and characteristics of the current supply
  • model the number and characteristics of annual new entrants to supply
  • model annual attrition from the behavioral health workforce
  • model workforce behavior such as hours worked patterns and geographic mobility

Estimating the Base Year Workforce Supply

The data sources for estimating starting supply in 2020, by state, are the following:

  • Psychologists: American Psychological Association for the de-duplicated state totals of licensed psychologists4
  • Addiction counselors, marriage and family therapists, social workers, and psychiatric technicians and aides: Bureau of Labor Statistics (BLS) Occupational Employment and Wage Statistics (OEWS)5 6 7
  • Mental health counselors: National Board for Certified Counselors (NBCC) for the de-duplicated state totals of licensed mental health counselors8
  • School counselors: Department of Education National Center for Education Statistics (NCES)9

The above data sources indicating the total number of licensed or active providers by state do not contain provider characteristics required for the HWSM microsimulation approach. To fill this gap, we used 2016-2020 ACS data which include demographic information for each occupation.

We combined multiple years of ACS data to ensure a sufficient sample size in each state to draw a representative sample (with replacement) of the workforce population by demographic. The sample size equals the state-level estimates of licensed or active providers from the above data sources. For occupations with licensed provider counts, including psychologists, marriage and family therapists, and mental health counselors, we draw a random sample from all such providers (active and non-active) in the ACS. For occupations with active provider counts, including social workers, addiction counselors, and school counselors, we instead draw a random sample of “active” providers. For the ACS draw, “active” status is based on an employment status variable. Inactive respondents are those the ACS describes as “not in the labor force.”

For social workers, school counselors, marriage and family therapists, and mental health counselors, we sample from the population of those occupations in the ACS with an education level of master’s degree or higher.

HWSM uses a microsimulation modeling approach that simulates labor force participation decisions at the individual provider level. For modeling, we used the above data sources to create a database containing a simulated population of the behavioral health workforce in each state. This database has an individual record for each worker by occupation, state, age, sex, and race/ethnicity. HWSM uses this database as the starting point to project future supply.

Modeling New Entrants

A simulated population for each occupation models yearly additions to the workforce. The simulated population reflects the annual graduates by demographics (Exhibit VIII-1).

We used the 2020 Integrated Postsecondary Education Data System (IPEDS) data to determine the number of annual new graduates for each behavioral health occupation, by sex and race. IPEDS collects the number of new graduates by sex, and race, for each Classification of Instructional Programs (CIP) code and degree level. We included only master’s degrees for the most appropriate CIP code in this analysis for all behavioral health occupations, except addiction counselors. The number of addiction counselors includes those with associate’s, bachelor’s, and master’s degrees.

To calculate the age distribution of new entrants to the workforce, we compared the number of providers of a particular age (e.g., age 29) in 2016-2020 ACS data to the number who were one year older (e.g., age 30) in the subsequent year’s file. The number of providers at age 30, above the number of providers at age 29, reflects the net number of new providers entering the workforce at age 29-30 in subsequent years of the survey. With this information, we can estimate the approximate age distribution of new entrants to the workforce. We used multiple years of data to increase the sample size by individual age and occupation. We also used the state distributions of providers aged 30-39 years to assign future new entrants in the model to a state.

Modeling Workforce Participation

Workers licensed and/or trained and thus eligible to work as behavioral health providers may choose not to be in the workforce. Each simulation year, HWSM models the likelihood that a provider under age 50 becomes inactive and does not count as a full-time equivalent (FTE) in the supply. For active workers, HWSM calculates expected hours worked as described in the next section. Inactive providers under age 50 remain in the simulation, as they have a calculated probability of being active in subsequent years. This differs from attrition, where a person retires or permanently leaves the workforce.

Labor force participation rates for health care workers are calculated directly for individuals under age 50 using 2016-2020 ACS data. We use a logistic regression approach where the dependent variable is whether the person works at least one hour per week in their profession. Explanatory variables include age group, sex, and race/ethnicity. Separate regressions are estimated for each occupation.

For providers aged 50 years and over, we modeled the annual probability of retiring, with retirement probability increasing with age. Attrition patterns for each profession are based on 2016-2020 ACS data. These patterns are constructed based on a question asking whether the person is currently employed and whether they were employed last year. Health care workers, flagged for attrition, drop out of the supply entirely and do not return in later years. Attrition probabilities vary by occupation and age. The sample size in ACS is insufficient to model attrition by sex or race/ethnicity. Exhibit VIII-2 summarizes the cumulative probability that behavioral health providers will be active in the workforce after age 50.

Modeling Hours Worked

HWSM contains predictive equations of weekly hours worked, estimated by Ordinary Least Squares regression using 2016-2020 ACS data. We estimated separate regressions for each occupation. The dependent variable is total hours worked per week. Explanatory variables are provider age group (age <35, 35-44, 45-54, 55-59, 60-64, 65-69, and 70-74), sex, and race/ethnicity. ACS survey year is included as a set of explanatory variables to control for differences by year in the number of hours worked.

Exhibit VIII-3 and Exhibit VIII-4 summarize weekly hours worked for non-Hispanic White males and females, respectively, by provider age and occupation. Female providers tend to work slightly fewer hours per week than their male colleagues, on average. Older providers still active in the workforce work fewer hours than their younger colleagues. Average hours worked varies by provider race and ethnicity, with the race/ethnicity effect being small and differing by provider age group, sex, and occupation.

FTE supply projections account for changing demographics of the workforce and the average hours worked per week differs by age, sex, race, and occupation. We convert the expected number of hours worked by each individual to FTE supply by dividing the total person-hours worked by 40. This creates a uniform standard of 1 FTE as working 40 hours per week, regardless of the occupation. It also means that the initial FTE of an occupation will differ from the actual number of active providers.

Modeling State-Level Supply and Migration

For occupations with sufficient state-level data, HWSM models future movement of behavioral health workers across states. In the ACS and OEWS, some occupations do not have totals reported in every U.S. state (particularly for less populated states and behavioral occupations with fewer workers). State-level supply estimates are unavailable for addiction counselors and social workers.

The model accounts for annual movement between states in two steps. First, logistic regression10 using 2016-2020 ACS data estimates the probability of a behavioral health provider under the age of 50 migrating to another state based on their age group, sex, the population of the state from which the person moved, and a year indicator. The assumption is that states with large populations have more opportunities for intra-state movements and thus experience fewer inter-state movements. Comparing each person’s move probability to a random number between 0 and 1 determines which providers move each year.

Second, the likelihood that each person who moves will relocate to a specific state is based on the proportion of providers in that occupation moving to that state, as observed in ACS data. The assigned new state is based on the distribution of destination states for individuals who moved in the 2011-2020 ACS data. The model uses more years of ACS data for this step because the number of ACS respondents in each occupation moving to smaller population states is low. For example, if 10% of psychologists who relocated according to ACS moved to California, then each psychologist who moves in the simulation has a 10% probability of moving to California. This approach ensures that cross-state migration has no impact at the national level in terms of the number and characteristics of modeled providers.

Supply Scenarios Modeled

The Status Quo supply scenario models the continuation of current numbers and characteristics of new graduates from the Health Resources and Services Administration’s (HRSA’s) behavioral health training expansion programs (minus graduates from the Behavioral Health Workforce Education and Training Program (BHWET) and the Opioid Workforce Expansion Program (OWEP), which had been funded through the 2021-2022 academic year). The scenario extrapolates current patterns of hours worked, attrition, and cross-state migration to behavioral health supply in future years.

Alternative supply scenarios model the sensitivity of future supply to changes in key assumptions. These alternative scenarios illustrate the uncertainty of behavioral health supply determinants changes over time. Two scenarios model a 10% increase and a 10% decrease in number of new providers trained each year, respectively. Two scenarios model if providers retire 2 years earlier or 2 years later, relative to current patterns. For example, under the early retirement scenario, a provider who would have retired at age 65 instead retires at age 63. Under the delayed retirement scenario, a provider who would have retired at age 65 instead retires at age 67.

In addition, we model a scenario projecting what current and future supply of behavioral health workers would have been if BHWET and OWEP had never been funded. This scenario removes from the starting supply the people who were in the workforce because of BHWET and OWEP funding—approximately 660 psychologists; 2,990 mental health counselors; 380 MFTs; 9,640 mental health & substance abuse social workers; 1,130 psychiatric NPs; 330 school counselors; 320 child, family, and school social workers, and 60 addiction counselors.

Modeling Demand

The approach to modeling demand for behavioral health providers follows the common model outlined in the other modules. The main demand components are (1) a constructed population file for each county in the U.S. with a representative sample of the population from 2020 through 2035, (2) prediction equations of the demand (i.e., annual expected use) for health care services, and (3) provider staffing ratios. This section provides additional details on modeling demand for behavioral health providers.

Adapting HWSM to model demand for behavioral health services and providers requires addressing data limitations. One major limitation is that the population database that underlies the model and the prediction equations for health care use lack variables related to mental health status and substance abuse.

The Behavioral Risk Factor Surveillance System (BRFSS) used to construct the population file for the community-based population does not collect data on respondents’ substance abuse status but does collects some information on mental health:

  • Now thinking about your mental health, which includes stress, depression, and problems with emotions, for how many days during the past 30 days was your mental health not good?
  • During the past 30 days, for about how many days did poor physical or mental health keep you from doing your usual activities, such as self-care, work, or recreation?
  • Because of a physical, mental, or emotional condition, do you have serious difficulty concentrating, remembering, or making decisions?

The Medical Expenditure Panel Survey (MEPS) used to develop prediction equations for health care use, also collects some information on mental health status:

  • Perceived mental health status: Excellent, Very Good, Good, Fair, Poor

For HWSM to include mental health status as a predictor for health care use, the same type of mental health status information needs to be collected by the surveys used to construct the population file (e.g., ACS or BRFSS) and the files used to model health care use (e.g., MEPS).

Patient characteristics included in HWSM are hypothesized to be correlated with receipt of mental health services. These include:

  • demographics
  • family income level
  • presence of chronic disease
  • insurance type (especially insured by Medicaid)
  • lifestyle choices
  • metropolitan/nonmetropolitan location
  • State of residency

A second data limitation is that recorded information on provider-patient encounters is unavailable for some behavioral health occupations. MEPS does contain information on ambulatory visits to psychologists and social workers (as well as to psychiatrists as discussed in the module on modeling the physician workforce). Because MEPS visits are based on data extraction from medical records, visits to social workers are likely to clinical social workers.

To model annual ambulatory (office and outpatient) visits to psychologists and social workers, we used negative binomial regression:

  • the dependent variable is the number of annual visits to a specific type of provider
  • the explanatory variables are
    • demographics
    • lifestyle variables
    • family income
    • insurance information
    • presence of select chronic conditions
    • resident county level of rurality

MEPS does not specifically identify visits to mental health counselors, marriage & family therapists, or addiction counselors. These occupations are not differentiated in MEPS, but rather are listed under the “all other non-physician” category. Therefore, to construct prediction equations for patients’ annual use of ambulatory services for these occupations, we modeled the relationship between patient characteristics and total annual ambulatory behavioral health visits available in MEPS. Total visits include visits to psychiatrists, psychologists, social workers, and the “all other non-physicians” category, indicating a visit for mental health or substance abuse. Using this broad category of behavioral health visits assumes that patient characteristics correlated with a greater number of mental health visits (psychiatrists, psychologists, social workers or other non-physicians) correlate with greater use of services provided by mental health counselors, marriage family therapists, and addiction counselors. Using projected total behavioral health visits as the workload driver overstates the actual number of visits to individual provider occupations. However, it serves as a scalar for the projected growth rate in demand and geographic variation in demand. The above prediction equations apply only to modeling services in provider office and outpatient settings. Different assumptions and prediction equations are used for modeling demand for these occupations in other care delivery or employment settings such as nursing homes, schools, etc.

Because HWSM contains no information on the use of illicit drugs, alcohol consumption, and mental health variables, such as presence or severity of depression, the predicted demand for behavioral health services for each individual will be biased toward the national average. We made a further adjustment to account for geographic variation in demand for addiction counselors.

As illustrated in Exhibit VIII-5, the prevalence of substance use disorder (SUD) varies across states for both adults and children. Publications based on the National Survey on Drug Use and Health (NSDUH) provide state averages for SUD prevalence for adults (age 18+) and adolescents (age 12-17). Due to sample size concerns, reported state prevalence estimates generally combine two years of data—such as the combined 2017-2018 surveys, and the combined 2018-2019 surveys. SUD state prevalence estimates based only on the 2020 NSDUH are published, but the 2020 estimates use different criteria than the pre-2020 estimates and thus are not directly comparable to earlier years. Due to COVID-19-related concerns around data collection in 2020 and the availability of only a single year of data, we averaged the two prior state estimates reported that cover 2017-2018 and 2018-2019.11 12

Among adults, SUD prevalence for the District of Columbia averages 1.642 times the national average, while Florida is 0.822 times the national average (Exhibit VIII-5). Among adolescents, SUD prevalence in Montana is 1.442 times the national average, while Georgia is 0.802 times the national average. We use these state-level multiplicative scalars for adults and for children and adolescents to adjust demand up or down for addiction counselors to reflect state variation in SUD prevalence. While these scalars changed the state-level projections, the effect on national-level projections when projected to 2035 is small.

To model demand, HWSM estimates the FTE distribution of providers across employment settings, and projects FTE demand growth for each setting over time. Workload drivers are projected for each setting. Using national ratios of workload per FTE, HWSM then projects future demand for providers by setting.

National estimates of total FTE providers in each setting are based on the combined 2019 and 2020 ACS and the 2021 OEWS (Exhibit VIII-6). Total workload measures were divided by FTE supply in 2020 to calculate staffing ratios, by occupation and care delivery setting (Exhibit VIII-7). The Status Quo demand scenario models the continuation of current provider-to-visits ratios during the projection period. It also models state-level demand for services and providers, by applying national patterns to the characteristics of each state’s population.

Demand Scenarios Modeled

As with other health occupations modeled, HWSM models the demand for behavioral health providers under a Status Quo scenario and a Reduced Barriers scenario, as described in other modules. Both scenarios assume that current demand is equal to supply at the national level. However, the demand for psychiatrists starts at 6,464 active psychiatrists higher than supply to reflect the number of providers required to remove all mental health professional shortage area (HPSA) designations.13 These two scenarios extrapolate current levels of care into the future, with the Reduced Barrier scenario modeling greater access to care.

For the behavioral health workforce, we model three additional Unmet Needs demand scenarios based on the assumption that current supply is inadequate to meet population needs.14 “An unmet mental health need exists when someone has a mental health problem but doesn’t receive formal care, or when the care received is insufficient or inadequate."15 The Reduced Barriers and Unmet Needs scenarios are consistent with HRSA’s strategic plan to “address health disparities through access to quality services.”16 This scenario also illustrates the wide disparities in use of behavioral health services. Modeling this scenario helps quantify the additional workforce required to meet SAMHSA’s aim to improve behavioral health equity, which is defined as “the right to access quality health care for all populations regardless of the individual’s race, ethnicity, gender, socioeconomic status, sexual orientation, geographical location and social conditions through prevention and treatment of mental health and substance use conditions and disorders.”17

The following is a brief description of each scenario:

  1. The Status Quo scenario models a continuation of recent (2015-2019) national patterns of care use extrapolated to the future population. This scenario captures geographic variation in demographics, health risk factors, disease prevalence, insurance coverage, level of rurality, and household income that can affect demand for behavioral health services. Similarly, the scenario captures population growth and aging—and the associated implications for disease prevalence and other health risk factors—over the projection horizon. The scenario evaluates whether the nation’s future behavioral health workforce is sufficient to provide at least the current level of care.

  2. The Reduced Barriers scenario estimates the number of FTEs required if populations that historically faced barriers to accessing behavioral health services demonstrated care use patterns comparable to populations perceived to have fewer barriers to accessing care. The scenario modifies the health care use patterns of nonmetropolitan county residents, racial and ethnic minority populations, and people without health insurance to the health care use patterns of their peers living in metropolitan counties, who are non-Hispanic White, and who have health insurance. Each of the three components of this scenario (non-metropolitan versus metropolitan county of residence, minority versus non-Hispanic White, and uninsured versus insured) is modeled in isolation and together to quantify the magnitude of each factor on demand for services. This hypothetical scenario describes the implications for behavioral health provider demand if policies and programs reduced access-based disparities to health care services. The impact of reducing barriers to accessing care is modeled only for care provided in ambulatory settings and hospital settings. Demand projections for behavioral health services delivered in nursing homes and residential care facilities equal those of the Status Quo scenario.

    For this scenario, we model the American School Counselors Association (ASCA) recommendation of 1 school counselor per 250 students.18 This would substantially increase the number of school counselors, from the current ratio of about a counselor per 415 students. This ratio has improved in recent years, steadily declining from 491 students per counselor during the 2013-2014 school year to 415 during the 2020-2021 school year. Employment of school counselors is driven in large part by school funding decisions, with school districts and government investing more in hiring school counselors, as seen by recent trends. There is substantial variation across states in the student per counselor ratio.19 HWSM does not adjust for geographic differences in socioeconomic factors or other determinants of need.20 ASCA notes that “students of color and students from low-income families are often shortchanged, receiving unequal access to school counselors or attending a school with too few school counselors.”21

    This scenario helps quantify the impact of each factor when a certain level of change of each factor is expected. For example, when a proportion of uninsured population is expected to be covered by insurance, a rough estimate on the impact to the demand for the health workforce can be proportionally developed.

  3. The Unmet Needs 1 scenario models the lower-bound assumption that 11.2% of the nation’s behavioral health needs are currently unmet among adults with any mental illness (AMI) or adolescents with a major depressive episode (MDE). The National Institute of Mental Health (NIMH) reports that there were 52.9 million adult Americans (aged 18 and older) with AMI in 2020 (Exhibit VIII-8).22 This amounts to approximately 21% of the U.S. adult population. NIMH findings from the 2020 National Survey on Drug Use and Health (NSDUH) show that less than half of adults with AMI received treatment (24.3 million). This implies that 28.6 million adults with AMI did not receive treatment in 2020. This amounts to approximately 11.4% of the U.S. adult population and 8.6% of the entire U.S. population.

    While NIMH does not provide data on adolescents (aged 12 to 17) with AMI, it reports on adolescents that had an MDE in the past 12 months. In 2020, 4.1 million adolescents reported having had an MDE in the past 12 months, of which 41.6% (1.7 million adolescents) received treatment. This implies 2.4 million did not receive treatment, which amounts to approximately 9.6% of the adolescent population in 2020. Combining the data for adults and adolescents suggests that the percentage of the population aged 12 and above that have a mental health need but who received no services is approximately 11.2%.

    The 11.2% estimate of unmet needs under this scenario might be a lower bound for unmet needs, as it only counts people with mental health issues who received no services. This estimate omits people with mental health issues who received some services but perceive the level of services insufficient to meet all their mental health needs. This estimate also omits estimates of unmet need for substance abuse issues.

  1. The Unmet Needs 2 scenario models the assumption that 22.8% of the nation’s behavior health needs are currently unmet among adults and adolescents with mental health and substance abuse issues. In addition to data on adults and adolescents with mental health needs and treatment services, NSDUH also collects data on substance abuse issues, along with data on treatment services for 2020.24

    The NSDUH reports that 40.3 million Americans aged 12 and above had a substance abuse disorder (SUD) in 2020 and that only about 5% of these individuals received treatment. This means that 38.4 million people had an unmet treatment need for SUD in 2020, accounting for approximately 13.9% of the U.S. population aged 12 and above (see SAMHSA [2021] Figures 27 and 45).24

    Because approximately 17.7 million people with SUD also had AMI, simply adding the unmet need for adults with AMI to the unmet need for SUD would overstate the combined percentage. Exhibit VIII-9 uses data from the NSDUH to approximate a combined unmet need percentage.

    The analysis above suggests 60.3 million Americans with AMI/MDE or SUD, or both, did not receive treatment in 2020. Given a total U.S. population aged 12 and above of 276.9 million (see SAMHSA [2021] supplemental Table 12.1A)24 , that implies that about 21.8% of the adult and adolescent population had an unmet need for mental health or substance abuse treatment.

    Because the definition of mental health needs for adolescents (i.e., one or more MDEs during the prior 12 months) differs from the definition for adults (i.e., AMI), these estimates should be thought of as approximations.

  1. The Elevated and Unmet Needs scenario combines the increase in demand estimated by the Reduced Barriers scenario with the unmet needs estimated by the Unmet Needs 2 scenario. This scenario might be thought of as an upper bound for demand for behavioral health services. It incorporates the ideas that historically underserved populations and communities would use more behavioral health services if barriers to receiving care were removed, but also that the level of services used by the reference population (i.e., non-Hispanic white, insured, and living in a metropolitan area) may also increase to meet unmet needs.

The rationale for modeling the above demand scenarios is that the health system continues to improve access to and comprehensiveness of behavioral health services. These trends include:

  • better affordability through increased coverage by insurance
  • more intensive use of screening
  • efforts to decrease stigma
  • better understanding of how to address behavioral health issues
  • increased integration of primary care and behavioral health
  • greater use of team-based care with a broader range of behavioral health providers that provides opportunities for task shifting and providing a more comprehensive range of behavioral health services
  • greater use of technology such as telemedicine to reduce barriers to accessing care in rural areas and improve care to patients with mental health needs in emergency departments, nursing facilities, and other employment settings

Historically, the unmet needs gap was modeled at 20%, reflecting a likely lower bound on the true level of unmet need.26 27 However, there are several caveats with the unmet needs estimates used in the scenarios described above.

  • These estimates are based on adults and adolescents who self-report their behavioral health conditions and having received any services. The NSDUH does not clinically evaluate whether the services received are sufficient or whether perceived unmet needs are necessary. Adults with AMI who receive no services possibly have lower mental health needs, on average, as compared to adults with AMI who receive some services or who received services but still perceive having an unmet need.28 29 People with serious mental illness (SMI) who perceive having unmet needs may be more likely to require a greater level of additional care than those with AMI who perceive having unmet needs.
  • Estimates of behavioral health needs for children under age 12 are not collected in the NSDUH, though any downward bias in the national numbers of unmet behavioral needs is likely to be small.
  • Even within the behavioral health workforce, the level of unmet need likely varies by occupation. For example, the unmet needs estimate for addiction counselors may differ from the unmet needs estimate for mental health providers.
  • There is some evidence that unmet need has increased with the COVID-19 pandemic.30 The prevalence of anxiety or a depressive disorder among adults has increased and the percentage of adults reporting an unmet mental health need increased between August 2020 and February 2021.
Date Last Reviewed: