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 2022. The period from the base year through the last year for which projections are made is the “projection period.” The projection period is 2022-2037.
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. This survey 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 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 represents twice as many nurses in the starting supply based on their survey response. 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. Estimates of total weighted counts of starting supply were provided. Therefore, the samples of RNs drawn from the NSSRN for these states reflect the demographics of the Census Division to which the state belongs. For example, nurses in 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” are based on responses from all nurses in the NSSRN employed in that state. However, nurses with the characteristic “unemployed” are from all nurses in the NSSRN living in that state.
Data to create the starting supply samples of LPNs by state come from the 2022 Occupational Employment and Wage Statistics (OEWS) survey and 2018-2022 American Community Survey (ACS). State-level estimates of the size of LPN supply in 2022 come from the OEWS. The ACS sample size in the 2022 file was too small to accurately estimate the size of the LPN supply by state. We created the starting supply of LPNs in each state by sampling the state’s LPNs in the 2018-2022 ACS data using totals from the 2022 OWES. 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.
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
Prior to 2023, HWSM excluded data on 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 listed the Philippines as country of origin. 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 108,987 RNs (baccalaureate level), 84,085 RNs (associate or diploma level), and 43,876 LPNs. The counts of new RNs passing the NCLEX-RN include LPNs who become RNs. The totals differ from the sum of U.S.-trained and internationally-trained RNs due to an adjustment in the number of RN graduates in Florida, based on an analysis of Florida licensure data, which indicated that fewer RNs were obtaining licenses in the state.
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 2018-2022 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 LPNs who become RNs, and departures from the RN workforce for RNs 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 RNs in 2023 if they were still working in the nursing position that they held on December 31st, 2021. An annualized 1.53% of RNs 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. Analysis of the 2011-2023 Current Population Survey found that 2.21% of LPNs under age 50 were working in a different occupation one year later. Therefore, 1.53% of RNs under 50 and 2.21% of LPNs under 50 are removed from the model each year to simulate nurses below retirement age who leave nursing for a different career.
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. The attrition patterns used in these projections are based on the most recent data available—which is the 2022 NSSRN for RNs and the 2018-2022 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. Instead, the technical documentation for modeling APRNs is included alongside that for 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 2019 ACS are combined with 2022 NSSRN data on registered nurses whose highest degree was awarded in 2019 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 2019 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 only NSSRN data is used. We created a subset of responses from the NSSRN including RNs active in 2019 and APRNs who achieved their highest education level in 2019. We created an attrition pattern by each individual age using the nurses’ age in 2019. Though ACS data later than 2019 are available, we used 2019 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 RNs is modeled using prediction equations derived from the 2022 NSSRN and 2018-2022 ACS. For LPNs, only the 2018-2022 ACS is used. 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 11% higher odds of being active at ages 45-49 compared to those 30 and under. Non-Hispanic Black LPNs have 61% 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 2018-2022 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 2-3 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 RNs 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 (2018-2022) ACS file and for the distribution of state destinations, combined that with the 5-year (2013-2017) ACS file. Multiple 5-year files were used to increase sample size for state destinations, 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 127,978 baccalaureate-level RNs in the 10-year file (with different nurses surveyed each year), 4,471 (3.4%) indicated working in a different state compared to a year ago. Of the 69,055 RNs at the diploma or associate degree level in this file, there were 1,330 (1.9%) who indicated working in a different state compared to a year ago. Of the 46,491 LPNs in this file, there were 992 (2.1%) 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 RNs are more likely to move than female RNs.
- Hispanic RNs are less likely to relocate than non-Hispanic White RNs. (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 2013 and 2022, of the estimated 24,000 LPNs who moved to another state, approximately 1.7% moved to Alabama and 4.5% 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 demographics 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 projected 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 shortage estimates based on analysis of the literature. For RNs, an assumed overall starting shortage of 196,503 FTEs is distributed across inpatient, outpatient, academia, nursing home and residential care settings. The 196,503 FTE shortfall estimate for RNs recalibrates the RN shortage estimates to a 40-hour definition.
To begin, three settings retain a direct and robust analogue to existing literature, specifically regarding accurate vacancy rates in certain practice settings. A Nursing Solutions Inc. (NSI) report shows that RN vacancy rates across hospitals remained relatively constant between 8.0 and 9.0 percent from 2020 to 2021, only jumping to 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 17.0% vacancy rate is indicative of about a 9.0% headcount shortfall of RNs in hospitals.5 This 9.0% headcount shortfall indicates an inpatient FTE shortfall of 9.0% and an outpatient FTE shortfall of 9.0%. Additionally, the American Association of Colleges of Nursing (AACN) reports 1,977 full-time faculty vacancies for nursing programs, or a 7.8% vacancy rate. 6 We subtract out the natural rate of unemployment (~4.4%) to yield a national demand shortfall of 895 FTEs (3.3%).7
Across most settings, we cannot establish clear demand shortfalls on account of insufficient or inadequate literature, despite a preponderance of anecdotal and small-scale evidence. A nurse shortage is clearly felt throughout the workforce, but the nursing home and residential care settings do not yield readily available shortfall patterns. Therefore, to model unfilled capacity in the market for nurses brought about by preference shifts and a sticky labor market, we assume that demand for nurses in the nursing home and residential care settings follows its respective population to provider ratios from before COVID. In 2019, 1 nurse worked in the nursing home setting for every 9 nursing home residents or so; likewise, the pre-COVID nurse-to-patient ratio in residential care stood around 1 nurse for every nearly 15 patients. Since 2019, despite nursing home and residential care populations either holding or increasing, ever fewer nurses work in the nursing home and residential care settings. The percent change in nurse-to-patient ratio from the pre-COVID era through 2022 represents a nursing shortfall that will continue to persist as long as the nurse-to-patient ratio remains depressed. We accordingly estimate an RN shortfall of 18,519 FTEs (9.2%) in the nursing home setting and 6,123 FTEs (15.3%) in the residential care setting.8
LPN shortages were calculated using a similar methodology to their nursing home and residential care RN counterparts. We compared a pre-Covid patient to provider ratio against the present patient to provider ratio and identified the difference between the two ratios as a shortfall. HWSM therefore incorporates a 37,429 FTE (42.1%) shortfall in the office setting, a 10,003 FTE (15.3%) shortfall in the home health setting, a 17,264 FTE (10.6%) shortfall in the nursing home setting, and a 12,643 FTE (28.4%) shortfall in the residential care setting. 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. 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 2022 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 2022 across the projection period. Nurses working in adult day service centers are isolated from nurses working in the ‘other’ settings via the 2020 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 (2021 and 2022) 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:
- The Status Quo scenario models a continuation of (2017-2021) national patterns of how people seek health care services plus estimates of demand for acute and Long COVID care as COVID-19 becomes endemic. Care seeking patterns are based on peoples’ characteristics and are projected to continue over the projection period. 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 health care 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 nursing workforce is sufficient to provide at least the current level of care.
- 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.