IV. Nursing Model Components

Published 2024

In this module:

This module describes the data, methods, and assumptions used in the Health Workforce Simulation Model (HWSM) to model supply and demand for registered nurses (RNs) and licensed practical/vocational nurses (LPNs). The latest year for which reliable supply data are available is the “base year.” The base year is currently 2021. The period from the base year through the last year for which projections are made is the “projection period.” The projection period is 2021-2036.

Modeling supply

The microsimulation approach to modeling supply used in HWSM begins by creating a database with information on each nurse in the base year supply of nurses. Moving through the projection period is simulated by:

  • adding new entrants to the nursing workforce each year,
  • removing those who leave the RN/LPN workforce during the year,
  • adjusting hours worked based on age of nurses in the new year, and
  • adjusting for nurses moving between states during the year.

Details of the supply modeling process are found in the Supply Modeling Overview module. The data, methods, and assumptions specific to nurses are found in the following subsections.

Estimating base year workforce supply

The base year supply of nurses is taken from the 2022 NSSRN sample frame for each state. This frame approximates the states’ nursing workforce on characteristics such as age, sex, highest education level, and employment status. Employment status indicates whether a nurse is (1) employed or actively looking for employment, or (2) not employed and not looking for employment. Each states’ sample is built by drawing a random sample, the size of the estimated number of available nurses in the state, from the NSSRN data described above. For example, if the NSSRN sample frame indicates 100,000 RNs are licensed in a particular state then 100,000 records are drawn (with replacement) from the NSSRN respondents from that state. Each nurse in the NSSRN has a sample weight that reflects the number of nurses in the state with whom they share age, sex, education level, and employment status characteristics. A nurse with popular characteristics might have double the sample weight of a nurse with a unique set of characteristics. The nurse with double the sample weight has double the probability of being drawn for the sample. Nurses drawn from the NSSRN sample frame are included in the starting supply sample then placed back in the NSSRN data. Thus, nurses in the NSSRN data may be chosen as many times as they are selected at random. The sample sizes of nurses in the NSSRN from the District of Columbia, Delaware, Montana, and Wyoming were too small for the Census Bureau to identify their state in the NSSRN public use file. Therefore, the samples of RNs drawn from the NSSRN for these states were drawn from nurses with unknown states but in their states’ Census Division. For example, nurses sampled for the Montana HWSM starting supply came from either Montana or Wyoming, the two states in the Mountain Census Division for which state name was not available in the NSSRN data. Nurses in a state’s starting supply sample with the characteristic “employed” were drawn from all nurses in the NSSRN employed in that state. However, nurses with the characteristic “unemployed” were drawn from all nurses in the NSSRN living in that state.

Data to create the starting supply samples of LPNs by state come from the 2021 Occupational Employment and Wage Statistics (OEWS) survey and 2017-2021 American Community Survey (ACS). State-level estimates of the size of LPN supply in 2021 come from the OEWS. The ACS sample size in the 2021 file was too small to accurately estimate the size of the LPN supply by state. The approach described above for RNs also was used to create a starting sample of LPNs in each state by sampling the state’s LPNs in the 2017-2021 ACS data. We used ACS sample weights in the sampling process to match the characteristics of LPNs in the starting sample to those of LPNs in the state. The state supply samples include each LPN’s race/ethnicity, sex, age, and employment status.

For seven states (Arkansas, Maryland, Ohio, Oklahoma, Oregon, New Jersey, and Texas) the modeling team gained access to licensing data with RN and LPN counts and demographics after conducting an outreach campaign to relevant entities in each state. However, we did not use the counts from this data to determine starting supply. Using licensure data could place some nurses in nurse compact states in their home state even though they work in a different state, which would create less accurate estimates of nurse supply by state. However, we did use the licensure data to sample both RNs and LPNs into the starting supply instead of the NSSRN and ACS data for these states. This allows the nurses’ characteristics in each of these states to come from a database of all the state’s nurses instead of surveys that only contact about 1% of nurses in the state. For Kentucky, the modeling team obtained licensure data that provided information on whether the nurse was practicing in Kentucky. Hence, for Kentucky there was no need to remove duplications who might be practicing in other states.

Not all nurses with an active license are active in nursing. HWSM contains prediction equations, estimated using the NSSRN (for RNs) and the ACS (for LPNs), that return the probability that nurses are employed in a nursing position or are actively seeking employment as a nurse. Nurses not in the workforce are still included in the starting supply microsimulation file but not counted as part of the starting year full-time equivalent (FTE) supply total. As described later, nurses that are inactive in the starting year have a chance to become active again in later years.

Modeling new entrants

New entrants reflect the number of nurses entering the workforce for the first time upon completion of a nursing program and certification. These graduates must pass the National Council Licensure Examination (NCLEX) to practice as a nurse. The NCSBN administers the NCLEX and reports the number of RNs who are first time takers and the number who passed on the first try by nurse education level:

  • Bachelor of Science in Nursing (BSN): an undergraduate-level degree from an accredited college or university
  • Associate Degree in Nursing (ADN): a 2-year degree from an accredited college or technical program
  • Diploma: typically, a hospital-based nursing school requiring 2-3 years of training

NCSBN also reports the number of people taking and passing the National Council Licensure Examination for Practical Nurses (NCLEX-PN) exam to become an LPN.

For RNs who fail the exam on the first try (often around 20% depending on education level) and retake the exam, information is not provided on their education level.1 Hence, relying on annual pass numbers (rather than calculating eventual passing rates) would not provide exact number of RNs entering the workforce by education level. Education level is important in HWSM and is correlated with propensity to leave RN work to become an APRN, hours worked patterns, and geographic mobility patterns.

Modeling the annual number of new entrants starts with state-level numbers of first time, U.S. educated candidates taking the NCLEX and the pass rates by education level. In 2022, there were 187,990 first-time, U.S.-educated takers of the NCLEX-RN across the 50 states and District of Columbia.2 Of these, 98,909 RNs had completed a baccalaureate degree. Another 85,760 RNs had completed a diploma, associate degree, or special program. (Because of small numbers for diploma and special program graduates, we combine them with the associate degree graduates for modeling). We assume that nurses who initially fail the NCLEX will retake the test up to two more times and as such calculated initial state-level estimates of the eventual NCLEX passers, by education level. The initial state-level estimates of passers are compared by education level with NCSBM reports of aggregate numbers. Estimates are scaled by education level to equal the reported total. For LPNs, we use reported numbers of total NCLEX-PN passers in each state.

Pass rates for the initial and subsequent tests differ by state and by nurse education level. At the national level, this modeling assumption gives an eventual pass rate of:

  • 96.6% of RNs trained at the baccalaureate level
  • 95.7% of RNs trained at the associate or diploma level
  • 93.2% of LPNs

Historically HWSM has excluded data of international students who pass NCLEX when calculating new entrants. The primary reason is the lack of data on how many of these students subsequently practice in the U.S. The number of internationally educated RNs who passed the NCLEX-RN decreased from 14,931 in 2019 to 14,040 in 2022 (Exhibit IV-1). About half of these RNs came from the Philippines. Other geographic areas with larger numbers of NCLEX-RN passers include India, Puerto Rico, Nigeria, Kenya, Canada, Nepal, and South Korea. The number of internationally-trained LPNs passing the NCLEX-PN is relatively small (224 in 2021 and 205 in 2022), representing less than 1% of new LPNs trained.

Exhibit IV-1 summarizes the number of internationally-trained RNs who passed the NCLEX-RN and the proportion of passers who are U.S.-trained over the past two decades. Available data over the past 10 years (2013-2022), indicate that 94.6% of NCLEX-RN passers are U.S.- trained and the number of internationally-trained passers averaged 9,179 RNs per year.

The Status Quo supply scenario models that annually the number of nurses eventually passing the NCLEX is 109,580 RNs (baccalaureate level), 85,172 RNs (associate or diploma level), and 44,376 LPNs. Our analysis of the 2018 NSSRN indicates that about 16,000 LPNs will further their education and become RNs each year. The counts of nurses taking the NCLEX-RN include the 16,000 LPNs. They are also included in the attrition from the LPN workforce, as described later.

Alternative supply scenarios modeled include training 10% more or 10% fewer nurses relative to current numbers. These scenarios illustrate the sensitivity of supply projections to the number of nurses being educated each year and the uncertainty that the annual number of graduates passing the NCLEX will change over the projection horizon.

Demographic information for the new entrants to the workforce came from the 2022 NSSRN for RNs and the 2017-2021 ACS for LPNs. Due to concerns regarding the race and ethnicity estimates of the NSSRN, we compared the new nurse race and ethnicity distributions in the model to published estimates from the American Association of Colleges of Nursing (AACN).3 The RN subset consists of those nurses who received their degree in nursing in the year 2000 or later. The LPN subset contains only those LPNs under age 35 for the sex and race distribution, as graduation year is unavailable in ACS. Exhibit IV-2 summarizes the demographic characteristics of new nurses.

Modeling workforce attrition

In this section, we describe analyses and assumptions regarding nurses who permanently leave the RN or LPN workforce. A permanent departure from the nursing workforce includes retirement from the workforce, or a career change out of nursing. This also includes departures from the LPN workforce for nurses who become RNs, and departures from the RN workforce for nurses who become APRNs. This differs from a temporary departure (discussed later) such as for child rearing, illness, or other reasons where the nurse might return to employment. We model three types of nurse workforce attrition: (1) Nurses under age 50 who leave the workforce, often to change occupations; (2) Nurses aged 50 and older who leave the workforce, presumably retiring; and (3) Nurses who transition from LPN to RN, or from RN to APRN.

Attrition of nurses under age 50

An analysis of the 2022 NSSRN asked nurses in 2023 if they were still working in the nursing position that they held on December 31st, 2021. An annualized 1.53% of nurses responded that they were no longer in that nursing position or any other nursing position and no longer had any desire to be a part of the nursing workforce.

Attrition of nurses aged 50 or older

For nurses aged 50 and older, we model retirement intention which includes nurses intending to leave the workforce for chronic illness, to care for family members and for other reasons. Attrition does not account for unexpected deaths or other unexpected reasons for leaving the workforce. There is insufficient data to model mortality rates for nurses. Using national average mortality will likely overstate mortality rates for nurses and is presumably correlated with nurses’ stated intentions to retire.

Multiple approaches have been explored and used to estimate nurse attrition patterns. Prior to 2016, ACS-derived attrition rates by age and sex were used for RNs age 50 and younger. A challenge with ACS data is that ACS does not collect occupation data if a respondent has been out of the workforce for five or more years. However, if a trained nurse respondent remains in the workforce but changes to a non-nursing occupation, their occupation will indicate the current occupation instead of nurse. The approach to model attrition patterns changed in 2016 to use refined estimates of nurse attrition patterns based on licensure data from Oregon, South Carolina, and Texas. In this latest 2023 update, the attrition patterns are based on the most recent data available—which is the 2022 NSSRN for RNs and the 2017-2021 ACS for LPNs.

Retirement for RNs is based on a question in the 2022 NSSRN that asks nurses when they plan to retire. Those who say that they plan to retire within a year are considered as retiring at their current age. RNs who say they plan to retire in 1-2 years are considered as retiring at the age they would be in 1-2 years and with half the weight due to the two-year time span covered. Finally, RNs who say they plan to retire in 3-5 years are considered as retiring at the age they would be in 3-5 years and with a third of the weight due to the three-year time span covered. We assume that all other nurses remain in the workforce at their current age. For LPNs, the ACS identifies individuals who are not in the labor force at the time of the survey but were in the labor force one year prior to taking the survey. For individuals aged 50 and older, we assume that a combination of responses represents a permanent departure from the workforce (retirement). Retirement patterns in the model differ by age, nurse type, and education level (RN with baccalaureate degree, RN with diploma or associate degree, or LPN). Samples sizes are insufficient to estimate retirement patterns by nurse sex, race/ethnicity, or other factors such as region. For each age and nurse type combination, the number of nurses retiring in the next year (in the NSSRN) or in the past year (in the ACS) is divided by the total number of nurses in that combination, which results in a probability of retirement at that age for that type of nurse. For nurses aged 70 and older, the sample sizes are small and estimates of retirement patterns fluctuate accordingly.

Career progression: LPN-to-RN and RN-to-APRN

The other modeled reason for nurses leaving the nursing workforce of their current nurse type is career progression. HWSM changes education level based on the probability that an LPN will become an RN or an RN will become an APRN (nurse practitioner, nurse midwife, or nurse anesthetist). The NCLEX examination data used to determine the number of new RN entrants includes those who formerly practiced as LPNs, so changing LPNs to RNs would introduce double counting issues. Instead, career progression from LPN to RN is treated as attrition from the LPN workforce. Similarly, RNs who become APRNs are also included in the attrition count because APRNs are not part of the nursing model component. APRNs are modeled in other HRSA reports with physicians and physician assistants.

To determine the probability of an LPN becoming an RN, the supply model uses a similar process to the retirement pattern described previously. LPN responses from the 2015 ACS are combined with 2018 NSSRN data on registered nurses whose highest degree was awarded in 2015 and who reported having an LPN license. An attrition pattern is then created for LPNs by dividing the number of LPNs who become RNs in 2015 by the total number of responses in the combined NSSRN and ACS data for each age from 20 to 49. For RNs who become APRNs, the process is similar, except that there is no need to use ACS data. We created a subset of responses from the NSSRN including RNs active in 2015 and APRNs who achieved their highest education level in 2015. We created an attrition pattern by each individual age using the nurses’ age in 2015. Though ACS data later than 2015 are available, we used 2015 data due to anomalies in the NSSRN data on the number of LPNs becoming RNs in later years.

We combined the career progression and retirement patterns to form a single attrition pattern that the model uses the same way regardless of attrition reason. The model generates a random number between 0 and 1 every year of the simulation for each individual in the simulation. If this number is less than the attrition probability for that age and nurse type, the model removes the nurse from the supply output for that year and every subsequent year.

Exhibit IV-3 shows the attrition pattern for RNs with a baccalaureate degree. These nurses have a small annual probability of leaving the workforce due to becoming APRNs, with retirement probability accelerating after about age 55.

Exhibit IV-4 shows the attrition pattern for RNs with a diploma or associate degree. Each year some RNs trained at the associate or diploma level complete RN-to-BSN programs and transition to a different education level in HWSM. This is counted as attrition for the purposes of this exhibit and explains the steep drop in RNs at this education level among the workforce under 40 years of age. However, these nurses are switched to the baccalaureate degree level in the model and do not leave the nursing workforce.

Exhibit IV-5 shows the attrition pattern for LPNs. The steep drop in remaining active in the LPN workforce indicates that about half of new LPNs at age 20 will eventually become an RN. This does not mean that half of the current supply of LPNs will become RNs, as LPNs who stay in that career for longer will be overrepresented compared to those who only briefly practice as an LPN.

Modeling workforce participation

Activity status for nurses is modeled using prediction equations derived from the 2022 NSSRN for RNs and 2017-2021 ACS for RNs and LPNs. This analysis focuses on nurse clinicians under age 50 as the activity status for clinicians aged 50 and over is modeled as attrition. The dependent variable is whether the nurse is active in the nursing workforce (employed or actively seeking employment) or not active. Explanatory variables are the same as those used to model hours worked: sex, race/ethnicity, and 5-year age groups.

The overall activity rates for RNs and LPNs under age 50 were 85% and 87%, respectively. The odds of being employed vary by nurse characteristics, in particular age (Exhibit IV-6). Nurses are more likely to be active in the workforce as they age, with RNs at the baccalaureate level having a 14% higher odds of being active at ages 45-49 compared to those 30 and under. Non-Hispanic Black LPNs have 55% higher odds of being active in nursing compared to non-Hispanic White LPNs.

Modeling hours worked

Forecasting equations model the correlation between weekly hours worked to nurse age, sex, and race/ethnicity by nurse type and education level. Data for all variables came from the 2022 NSSRN for RNs and the 2017-2021 ACS for RNs and LPNs. Year was included in the regression because multiple years of ACS data were analyzed. Total projected nurse hours worked were converted to FTEs by dividing by 40, as starting in 2017 workforce projections for all the health occupations modeled using HWSM have defined an FTE as 40 hours per week for the full year.

Ordinary Least Squares regression coefficients reveal the following (Exhibit IV-7):

  • Average weekly hours worked decline among older nurses, especially from age 65 onward.
  • On average, male RNs work about 3 more hours and male LPNs work about 2 more hours than their female peers, controlling for any differences in age, race/ethnicity and education level.
  • Non-Hispanic Black nurses work more hours than nurses in other race/ethnicity groups.

These prediction equations have low predictive power for explaining weekly hours worked for specific nurses, as illustrated by the low R-squared values. Other predictors of labor force participation that are not included in HWSM, but are likely correlated with nurse demographics, are family composition (including marital status, presence of young children in the family, and older family members who need caregivers), nurse health, wages, and local economic conditions.4 Still, at the aggregate level the prediction equations allow HWSM to capture the implications of changing nurse demographics and education level on FTE supply.

Modeling cross-state migration

We model that nurses initially enter the workforce in the state where they took the NCLEX exam. We then model cross-state migration based on prediction equations estimated using logistic regression with the 5-year (2017-2021) ACS file combined with the 5-year (2012-2016) ACS file. Multiple 5-year files were used to increase sample size, as the number of nurses observed changing states is low as a proportion of the total number of nurses. Cross-state migration models whether a person moves out of a state. It then models whether a person moves into a state. Of the 81,874 baccalaureate-level RNs in the 10-year file (with different nurses surveyed each year), 2,719 (3.3%) indicated working in a different state compared to a year ago. Of the 39,990 RNs at the diploma or associate degree level in this file, there were 749 (1.9%) who indicated working in a different state compared to a year ago. Of the 27,421 LPNs in this file, there were 546 (2.0%) who indicated working in a different state compared to a year ago.

Analysis of nurse cross-state migration patterns found in Exhibit IV-8 suggests that:

  • The probability of migration declines with age. Nurses aged 30 and below have the highest probability of migrating to another state.
  • Male nurses are more likely to move than female nurses.
  • Non-Hispanic White RNs with a baccalaureate and Hispanic LPNs are more likely to relocate compared to other race/ethnicity groups in the same nurse type (Exhibit IV-8).

Using the ACS sample weights, the findings suggest that approximately 43,000 RNs with a baccalaureate, 11,300 RNs with a diploma or associate degree, and 9,000 LPNs change states annually. When modeling cross-state migration patterns, HWSM uses the above equations to generate a probability that each nurse will migrate out of the state. The model then compares this probability to a random number between 0 and 1 using a uniform distribution. If the random number is below the estimated probability of moving, then the nurse moves out of that state.

We ensure that the national number and characteristics of nurses moving out of states matches the number and characteristics of nurses moving into states. When the model moves a nurse out of state, it generates a random number. It compares that number against a cumulative distribution created from the national distribution column in Exhibit IV-9. This gives each nurse a chance to move to a specific state based on how frequently nurses in the past have been observed moving to that state. For example, between 2017 and 2021, of the estimated 9,000 LPNs who moved to another state each year, approximately 1.9% moved to Alabama and 5.9% moved to California. Over time, projections of the number of nurses exiting a state change based on the characteristics of nurses in that state and the overall number of nurses. The variation across states and years reflects the modeling of migration determinants. It also reflects the use of the random number generator to move nurses across the various states based on the geographic distributions described previously.

Supply Scenarios Modeled

Nurse supply is modeled under a Status Quo scenario that models the continuation of recent numbers and characteristics of nurses completing their nursing education, and recent patterns of labor force participation. Labor force participation decisions include attrition (retirement, career change out of nursing, or career advancement from LPN to RN or from RN to APRN), being temporarily out of the workforce, and hours worked patterns. Labor force participation varies by nurse demographics and education level. The Status Quo scenario models the continuation of these patterns taking into account the changing demographic and changing education levels of the nursing workforce.

Alternative supply scenarios modeled include the impacts of:

  • retiring two years earlier or delaying retirement by two years, on average
  • graduating 10% more or 10% fewer nurses annually than the Status Quo

The early or delayed retirement scenarios simply shift workforce attrition patterns for nurses aged 50 and older by ±2 years. For example, a nurse who would have retired at age 62 under the Status Quo scenario would now retire at age 60 under the Early Retirement scenario and at age 64 under the Delayed Retirement scenario.

Modeling demand

Demand modeling for nurses follows the overall HWSM demand modeling approach described in the other modules. HWSM applies prediction equations to the simulated U.S. population data to estimate use of health care services in the settings where nurses work. Projected demand for health care services is the driver of projected demand for nurses. For example, projected growth in hospital inpatient days and emergency visits are the drivers of project demand for nurses employed in hospital inpatient and emergency department settings, respectively. For work settings outside the traditional health care system, growth in the population most likely to use the services is the driver of growth in demand for nurses (Exhibit IV-10). For example, projected growth in demand for school-based nurses is based on projected growth in the population of children ages 5 to 17.

As illustrated in Exhibit IV-10, nurses are found in almost all care delivery settings. Data from the ACS estimates the portion of national FTE nurses providing care in each setting. The national starting supply of nurses is multiplied by these proportions to estimate the number of FTEs in each work setting. Additionally, the starting estimates are adjusted by setting to incorporate nursing starting shortage estimates based on analysis of the literature. For RNs an assumed overall starting shortage of 180,647 FTEs is distributed across inpatient, academia, nursing home and residential care settings. The FTE starting year shortfall estimate is based on the following. One, a Nursing Solutions Inc. (NSI)5 report shows that RN vacancy rates across hospitals remained relatively constant between 8.0 and 9.0 percent from 2018 to 2020, jumping to 9.9% in 2021 and 17.0% in 2022. Some of these vacancies in prior years likely were covered by traveling nurses, and some vacancies are the result of time lags between when a nurse leaves a position and when a replacement is found. Therefore, it is assumed that vacancy rates above 8.0% reflect a shortfall—suggesting that the latest data of a 17.0% vacancy rate is indicative of about a 9% shortfall of RNs in hospitals.6 Two, the American Association of Colleges of Nursing (AACN) reports a shortfall of 2,166 full-time faculty vacancies for nursing programs, or an 8.8% shortfall.7 Three, we estimate a 5.0% shortfall of RNs in nursing homes (6,630 RNs) and a 4.2% shortfall of RNs in assisted living facilities (2,180 RNs) based on a comparison of 2021 and 2022 OEWS data suggesting a decline in supply in those settings—despite demand continuing to increase. The 180,647 FTE shortfall number recalibrates the above RN vacancy estimates to a 40-hour FTE. These numbers suggest that starting year supply adequacy is about 95% of demand—with the shortfall estimate being possibly conservative.

For LPNs we assumed an overall starting shortage of 27,212 FTEs distributed across office, home health, nursing home and residential care settings. These shortfall estimates are based on a comparison of OEWS data for 2020-2022. The number of LPN positions reported in the OEWS data decline from early pandemic to current levels. For LPNs in nursing homes the drop in employment between 2020 and 2021 can be partially explained by the drop in number of nursing home residents during the first year of the COVID-19 pandemic.8 Hence, for nursing homes we used the drop in employment from 2021 to 2022 as a proxy for the size of the shortfall. Lacking data on the number of vacancies in nursing homes, this drop in employment is consistent with reported staffing challenges in long term care and nursing homes losing more than 200,000 workers over the course of the COVID-19 pandemic.9

A staffing measure for each setting was calculated by dividing the number of FTE nurses working in that setting (plus estimated shortfalls) in the base year by the base year estimate of the workload measure. Workload measures include office and outpatient visits in ambulatory settings, inpatient days in hospital inpatient settings, emergency visits, home health visits, and population size metrics. The workload measures for 2021 are modeled as expected values in health care use patterns based on 2015-2019 data that predates COVID-19. Applying these patterns to the 2021 population provides estimates of what health care use would have been in 2021 absent COVID-19. As described in the module detailing the demand component of HWSM, the health care use patterns incorporate assumptions about increasing demand for hospital and ambulatory care associated with COVID-19 becoming endemic and thus increasing the annual number of inpatient days and outpatient visits. This increase in demand for health care services will have a proportionate increase in the demand for nurses in these settings.

In the Status Quo demand scenario, these ratios are constant over time. For example, demand for RNs under the Status Quo scenario is based on the 2021 ratio of inpatient days to RNs for hospital inpatient settings for every year of the projection period. Demand for nurses in academia is based on the estimated population of college graduates and applies the ratio of nurse educators to students in 2021 across the projection period. Nurses working in adult day service centers are isolated from nurses working in the ‘other’ settings via the 2017-2018 National Center for Health Statistics report estimates of long-term care providers.10 Demand for nurses in adult day service center is based on the national ratio of day service center users to nurses. In the HWSM framework, probabilities weighted by age group are assigned to estimate how many people use adult day care services daily.

Estimates of the distribution of RNs and LPNs across employment settings came from analysis of the detailed industry classification of the combined (2019 and 2021) ACS.11

National staffing ratios by employment setting at baseline were applied to the projected service use to generate staffing requirements by setting. Demand projections were calculated at the county level and summed to produce state and national estimates for reporting.

Demand Scenarios Modeled

As with other health occupations modeled, HWSM models demand for nurses under two scenarios as described in the other modules:

  1. The Status Quo scenario models a continuation of pre-COVID-19 (2015-2019) national patterns of care use extrapolated to the future population plus estimated demand implications of COVID-19 becoming endemic. 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 nursing 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 assumes national demand equal to national supply plus the estimated vacancies in 2021. The scenario evaluates whether the nation’s future nursing workforce is sufficient to provide at least the current level of care.
  2. The Reduced Barriers scenario estimates the number of nurse FTEs required if populations that historically faced barriers to accessing health care 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 on nurse 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. Reduced Barrier Demand projections for nursing services delivered in nursing homes, residential care facilities, school-based settings, nurse education, and public health settings equal those of the Status Quo scenario.
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