XII. Long Term Services and Support Model Components

Published 2022

In this module:

This module describes the data, assumptions, and methods used to adapt the Health Workforce Simulation Model (HWSM) to model the sector-specific long-term services and support (LTSS) workforce. LTSS includes nursing homes, residential care facilities, home health, hospice, and adult day services centers. Because of data limitations, home health and home-based hospice visits are combined into home care. The Medical Expenditure Panel Survey (MEPS), which is used for modeling home health visits, does not distinguish between visits associated with chronic care management and visits following hospital discharge for acute conditions. Hence, all home health visits are included under LTSS for modeling purposes.

In this module, we focus on modeling nursing assistants (NAs), home health aides (HHAs), and personal care aides (PCAs). Modeling of other health care occupations providing LTSS is described in other modules:

  • The module on the nurse workforce describes modeling of registered nurses (RNs) and licensed practical nurses (LPNs)
  • The module on physicians, advanced practice nurses (APRNs), and physician assistants (PAs) describe modeling of these respective occupations
  • The module on allied health describes various allied health occupations that provide LTSS.

As illustrated in Exhibit XII‑1, approximately 2.9 million health care workers in the 2020 American Community Survey (ACS) are in the three major LTSS settings. These workers account for approximately 17% of the estimated 16.7 million health care workers in the 2020 ACS. Nearly 2.5 million workers, or 86% of the LTSS total, are in the five largest LTSS occupations: NAs, PCAs, RNs, HHAs, and LPNs. Many of these workers work part time.

For most health care occupations, those individuals working in LTSS settings constitute a small proportion of total workers in that occupation. The nearly 421,000 RNs in LTSS constitute only 13% of the RN workforce. The small percentage of the workforce in LTSS for many occupations means that LTSS employers (e.g., nursing homes, residential care facilities, and home health agencies) are competing against other health care setting employers (e.g., hospitals) to recruit and retain workers. Further compounding recruiting and retention issues for the LTSS is the industry’s history of low pay and challenging working conditions which contributes to high turnover and attrition from the industry.1 2 3

Modeling Supply

HWSM supply projections focus on occupations with high education requirements. These requirements create time lags to train new workers. Information on future adequacy of supply for these workers can help mitigate supply inadequacies. Such occupations usually require a license. In other modules we describe the data, assumptions, and methods for modeling supply of RNs, LPNs, and various licensed therapist and other health occupations that work in LTSS settings.

Modeling supply of NAs, HHAs, and PCAs (often referred to collectively as direct care workers) poses two major challenges:

  • There are low barriers to entry into the occupation. States have either no formal training requirements or minimal requirements. Hence, there is little information on the numbers of people entering this occupation each year.
  • There are a few barriers to leaving the profession. Occupations with low pay and little formal education, such as aides and assistant, are sensitive to earnings. They can move in and out of the direct care workforce based on how earnings as a direct care worker compare to earnings from other occupations. Hence, the direct care workforce experiences high rates of turnover and attrition which presents challenges for modeling who will leave the workforce.4

Because of these challenges, we use a different approach to supply modeling than used for modeling other health occupations in HWSM. Specifically, we look at growth in the population from which the supply of direct services workers comes and model the implications if the LTSS sector can maintain its share of the candidate pool. Using the combined 2016-2020 ACS files, we see that direct care workers are disproportionately younger adults, but all ages are represented (Exhibit XII‑2). Minority populations are highly represented among direct care workers (Exhibit XII‑3). Most direct care workers are female (Exhibit XII‑4).

There are an estimated 1.65 million full time equivalent (FTE) individuals working as a direct care worker in a LTSS role, with FTE defined as 40 hours worked per week. Comparing demographics of these direct care workers in the 2016-2020 ACS with the adult population in from 2016-2020, the LTSS FTE direct care workforce comprised approximately 1.22% of adult females and 0.18% of adult males (Exhibit XII‑5). While FTEs for non-Hispanic Black females were equal to 2.83% of the total adult population for this demographic, FTEs for non-Hispanic White males were equal to only 0.12% of the total adult population for this demographic. We modeled growth in the size of the adult population by age, sex, and race/ethnicity and the ratio of FTE direct care workers for each demographic group. If the LTSS workforce can maintain these ratios, then supply of direct care workers will grow at the rate of this weighted population demographic. Supply will grow faster (or slower) if these ratios increase (or decrease)—e.g., if wages and working conditions in LTSS are more competitive and desirable (or less competitive and less desirable) than in alternative careers that might employ people in this candidate pool.

Modeling Demand

The projected demand for LTSS and workforce follows the common model estimated on the baseline population and health care usage as outlined in the other modules. This includes projected number of residents in nursing homes, number of residents in residential care facilities, and home health visits. Projections of future residents in nursing homes and residential care facilities are based on growth and aging of the population, with men and women in each age group having different probabilities of moving from community residency to an institutional setting. These probabilities are based on analysis of residency type in the 2020 ACS, and scaled to match published statistics. The total number of people living in nursing homes and residential care in the initial year, by state and age group, is constructed to match published numbers from CDC, showing nearly 1.3 million nursing home residents and 918,700 people living in residential care nationally.5 6

To model demand for adult day service centers, we use probabilities for population cohorts defined by age group estimated from the National Study of Long-Term Care Providers (NSLTCP).7 Multiplying these probabilities by the projected size of each age cohort in the HWSM population database produces projections of demand for adult day care and providers. Approximately 4,800 adult day service centers reported employing around 19,900 FTE nurses and social workers. The workforce modeled for the LTSS projections include an estimated 11,303 nurse aides, 4,080 RNs, 2,249 LPNs and 2,249 social workers working in adult day service centers.

This section focuses on the occupations working primarily in the long-term care settings: HHAs, NAs, PCAs, and social workers. HWSM uses provider staffing patterns to convert demand for LTSS into demand for the relevant occupations. HWSM multiplies these staffing patterns to the appropriate workload drivers—which include home health visits, residents in nursing homes and residential care facilities, and patient days in adult day service centers. To calculate staffing ratios (Exhibit XII‑6), we divided the workload driver for each employment setting by estimates of FTE providers from the 2020 ACS.

Demand Scenarios Modeled

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

  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 physician 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.
  2. The Reduced Barriers scenario estimates the number of health care workers 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. This scenario focuses primarily on care delivered in ambulatory and hospital settings, as well as home health care. The number of residents in nursing homes and residential care facilities and the number of people receiving care in adult day services centers under this scenario is identical to the Status Quo scenario projections.
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