Health Workforce Microsimulation Model Documentation · 2020-05-20 · Exhibit 5 Characteristics...

Health Workforce Microsimulation Model Documentation

Version 5.19.20

May 2020

Tim Dall Executive Director

Ryan Reynolds Senior Consultant

Ritashree Chakrabarti Senior Consultant

Will Iacobucci Senior Consultant

Kari Jones Associate Director

Life Sciences

IHS Markit | HWMM Documentation

© 2020 IHS Markit. All rights reserved. ii May 2020

Contents

Introduction .......................................................................................................................................... 1 Background ............................................................................................................................................ 1 Microsimulation model overview ............................................................................................................. 3 Healthcare Demand Microsimulation Model ....................................................................................... 5 Overview ................................................................................................................................................ 6 Population files ....................................................................................................................................... 7 Healthcare use patterns ....................................................................................................................... 10 Health workforce staffing patterns ........................................................................................................ 18 Scenarios ............................................................................................................................................. 19 Input summary ..................................................................................................................................... 21 Health Workforce Supply Model........................................................................................................ 22 Starting supply input files ..................................................................................................................... 22 New entrants ........................................................................................................................................ 23 Hours worked patterns ......................................................................................................................... 24 Labor force participation ....................................................................................................................... 27 Retirement ........................................................................................................................................... 27 Geographic migration ........................................................................................................................... 31 Scenarios ............................................................................................................................................. 32 Workforce implications of strategies to prevent or manage chronic disease ............................... 32 Model validation, strengths, and limitations .................................................................................... 35 Validation activities ............................................................................................................................... 36 Model strengths .................................................................................................................................... 36 Model limitations .................................................................................................................................. 37 References .......................................................................................................................................... 40


© 2020 IHS Markit. All rights reserved. iii May 2020

Exhibits

Exhibit 1 Integrated Health Workforce Microsimulation Model ............................................................ 4

Exhibit 2 Health occupations and specialties modeled........................................................................ 5

Exhibit 3 Schematic of Healthcare Demand Microsimulation Model ................................................... 6

Exhibit 4 Population database mapping algorithm............................................................................... 8

Exhibit 5 Characteristics available for each person in representative population sample .................. 9

Exhibit 6 Sample regressions: adult use of cardiology services ....................................................... 12

Exhibit 7 Patient characteristics on rate of primary care office visits for adults ................................ 13

Exhibit 8 Logistic regression for emergency department consultation .............................................. 15

Exhibit 9 Illustration of probability of emergency department consultation ....................................... 16

Exhibit 10 Average prescriptions per healthcare visit ........................................................................ 17

Exhibit 11 HDMM calibration: physician office visits .......................................................................... 18

Exhibit 12 Demand model input data summary ................................................................................. 22

Exhibit 13 Data sources for number and characteristics of new entrants ......................................... 24

Exhibit 14 OLS regression example: weekly patient care hours for general internal medicine ....... 25

Exhibit 15 OLS regression coefficients predicting weekly hours worked for select occupations ..... 26

Exhibit 16 Odds ratios predicting probability active ........................................................................... 27

Exhibit 17 Physician retirement patterns by age and sex .................................................................. 29

Exhibit 18 Probability male physician is still active by specialty and age ......................................... 30

Exhibit 19 Overview diagram of the Disease Prevention Microsimulation Model ............................. 34

Exhibit 20 Overview diagram of body weight component in DPMM ................................................. 35


© 2020 IHS Markit. All rights reserved. iv May 2020

Acronyms used in report

AACN American Association of Colleges of Nursing

AAPA American Academy of Physician Assistants

ACS American Community Survey

ADA American Dental Association

AMA American Medical Association

APRN Advanced practice nurse

BLS Bureau of Labor Statistics

BRFSS Behavioral Risk Factor Surveillance System

CDC Centers for Disease Control and Prevention

CMS Centers for Medicare and Medicaid Services

DPMM Disease Prevention Microsimulation Model

HDMM Healthcare Demand Microsimulation Model

HRSA Health Resources and Services Administration

HWSM Health Workforce Supply Model

IPEDS Integrated Postsecondary Education Data System

LPN/LVN Licensed practical/vocational nurse

MEPS Medical Expenditure Panel Survey

NAMCS National Ambulatory Medical Care Survey

NCLEX National Council Licensure Examination

NCSBN National Council of State Boards of Nursing

NCCPA National Commission on Certification of Physician Assistants

NHAMCS National Hospital Ambulatory Medical Care Survey

NIS National Inpatient Sample

NP Nurse practitioner

NSSRN National Sample Survey of Registered Nurses

PA Physician assistant

PCMH Patient centered medical home

RN Registered nurse

SNF Skilled Nursing Facility

Note: Earlier versions of this technical documentation are available upon request from [email protected].

mailto:[email protected]

IHS Markit | Title of Report

© 2020 IHS Markit. All rights reserved. 1 May 2020

Health Workforce Microsimulation Model Documentation

Version 5.19.2020

Introduction This report provides technical documentation of the health workforce microsimulation models developed by IHS

Markit, with contributions to model development from the various organizations for which studies have been

conducted using these models. The following section provides background information and an overview of the

workforce models. Next, we document the data, methods, assumptions and inputs for the demand model—

referred to as the Healthcare Demand Microsimulation Model (HDMM)—as well as the supply model—referred

to as the Health Workforce Supply Model (HWSM), and provide a brief overview of the Disease Prevention

Microsimulation Model (DPMM) used to model the workforce implications of strategies to prevent or manage

chronic disease.1 The final section describes work to validate the models, model strengths and limitations, and

areas of ongoing and future research. An appendix contains additional information about model inputs.

We continue to maintain and refine the models as new data and research become available; additionally, we

continue to develop new modules and scenario modeling capabilities. This documentation is intended to help

make the models transparent and provide the opportunity for feedback to improve these models. This report is

updated periodically to reflect refinements to the models and updated data sources. Hence, application of the

model to previous studies might have used earlier data sources than documented in this report.

Background

The workforce models described here are unique in their approach, breadth and complexity. Health workforce

projection models have been used for decades to assist with workforce planning and to assess whether the

workforce is sufficient to meet current and projected future demand (or need) at the local, regional, state, and

national levels. The models described here use a microsimulation approach where individual people (patients and

clinicians) are the unit of analysis. While microsimulation models have been used to study complex policy and

health issues2–6, the models described here are the first broad application of microsimulation modeling for

developing health workforce projections.

Approaches used historically in the U.S. to model the demand for health workers include: (1) convening expert

panels that consider patient epidemiological needs and provider productivity7; (2) extrapolating care use and

delivery patterns from beneficiaries in health maintenance organizations8,9; (3) extrapolating trends based on an

econometric approach of the correlation between provider-to-population and population characteristics and

economic measures10–12; and (4) developing demand models that use historical patterns of healthcare use and

delivery to create detailed provider-to-population ratios.a Such “macro” approaches that model demand at the

population level have limited ability to model policy changes or paradigm shifts in care delivery because most

coverage and treatment decisions are determined by individual patient circumstances. While approaches used

historically for modeling demand vary widely, the approach to supply modeling has been relatively similar across

studies, and models the likely workforce decisions of provider cohorts as they enter and progress through their

careers. Similar modeling approaches have been used across health professions.

Modeling approaches used in the past faced many challenges—data limitations, computing resources, and gaps in

research and understanding of health workforce issues. The use of microsimulation modeling to study the

a For example, workforce models used by the Health Resources and Services Administration from the 1990s to approximately 2012.


Confidential. © 2020 IHS Markit. All rights reserved. 2 May 2020

healthcare system was proposed in the early 1970s by Yett and colleagues, but data and computer computational

constraints prevented the full implementation of such a model.13 Improved computing power and wider access to

data and research have enabled development of more sophisticated workforce models that provide more reliable

projections and that can be forward looking in terms of a changing healthcare delivery and policy landscape. The

microsimulation models described here were designed to help address limitations of earlier models.

These microsimulation models have been adapted to model national, state and local area supply and demand for

many organizations. These include:

• Federal Bureau of Health Workforce (to model physicians, advanced practice providers, nurses, oral health

providers, behavioral health providers, and many other health occupations) at the national, state, and urban/rural

levels;14,15

• States—including Arkansas (primary care providers), Florida (physicians), Georgia (nurses, physicians, and

physician assistants), Hawaii (multiple occupations), Maryland (select physician specialties), New York (multiple

occupations), South Carolina (multiple occupations), Texas (multiple occupations), and Vermont (multiple

occupations);16–22

• Trade and professional associations;23–26

• Hospitals and health systems—including market assessment and regional planning, and the workforce implications

of strategies to restructure the healthcare delivery system;27–32 and

• Independent analyses.33,34

DPMM, which models strategies to prevent or manage chronic disease and the resulting implications for

healthcare use and provider demand, has also been used for work with:

• Life sciences companies -- to model burden of disease and strategies to prevent or delay onset of diabetes,

cardiovascular disease and other chronic conditions associated with obesity;35–38 and

• Trade associations and non-profit organizations -- to model burden of chronic disease and strategies to reduce

future burden including lifestyle interventions to promote improved diet and increased physical activity, smoking

cessation programs, improved screening and treatment, and improved medication adherence (to control blood

pressure, cholesterol, and blood glucose levels).39,40

The goals behind development and maintenance of these microsimulation models include:

• Providing the most accurate workforce supply and demand projections possible, as well as timely updates to reflect

the latest data, trends, policies, and research in the field;

• Informing strategies and policy decisions with health workforce implications;

• Integrating supply and demand across many occupations and specialties into a dynamic model; and

• Adapting the models to state and sub-state levels.



Microsimulation model overview

To provide maximum flexibility for adapting the model to different populations and to unique supply and demand

scenarios, these models use a microsimulation approach. As depicted in Exhibit 1, there are three major modeling

components: (1) modeling demand, (2) modeling supply, and (3) modeling disease management and prevention.

Consistent with recommended standards, we developed and validated self-contained modules that describe

different components of the healthcare system.41

• Demand: HDMM has three major components: (a) characteristics of each person in a representative sample of the

current and future population (demographics, socioeconomics, health-related behaviors, presence of chronic

conditions, insurance type/status, etc.), (b) healthcare use patterns that relate patient characteristics to annual use of

healthcare services by delivery setting and medical condition/provider specialty, and (c) staffing patterns that

translate demand for healthcare services into requirements for full time equivalent (FTE) providers by

occupation/specialty and by care delivery setting. Healthcare use and staffing patterns are influenced by changing

demographics and trends in care reimbursement and delivery.

• Supply: HWSM simulates workforce decisions for each person in a representative sample of providers based on

the person’s demographics, profession and specialty, and characteristics of the local or national economy and labor

market. Components include: (a) characteristics of the starting supply, (b) characteristics of new entrants to the

workforce, (c) attrition, (d) geographic mobility, and (e) work patterns.

• Disease management: DPMM simulates treatment/intervention scenarios to quantify their impact on preventing or

delaying onset of chronic disease and sequelae.

These three models are partially integrated as depicted by the dotted lines in Exhibit 1. For example, the available

supply influences staffing patterns; provider demand influences career decisions of individual providers; and

disease prevention and management strategies influence patient health outcomes and the derived demand for

services and providers. The three models are programmed in R, which is open source software.



Exhibit 1 Integrated Health Workforce Microsimulation Model

The health occupations and medical specialties included in this model are summarized in

Integrated Health Workforce Microsimulation Model

Source: IHS Markit © 2020 IHS Markit



Exhibit 2. Not all occupations are included in the supply analysis, often because of data limitations on entry and

exit from low compensated occupations with low barriers to entering the profession.



Exhibit 2 Health occupations and specialties modeled

Health occupations and specialties modeled

Occupations & Specialties Occupations & Specialties, cont.

Physicians & physician assistants Advanced practice nurses

Primary Care Nurse anesthetists

Family Medicine Nurse midwives

General Internal Medicine Nurse practitioners (by specialty)

Geriatric Medicine Nursing

General Pediatrics Registered nurses

Medical Specialties Licensed practical/vocational nurses

Allergy & Immunology Nurse assistants/aides (incl. home health)

Cardiology Behavioral health (incl. psychiatrists and NPs/PAs)

Critical Care/Pulmonology Psychologists

Dermatology Addiction counselors

Endocrinology Social workers

Gastroenterology Mental health counselors

Hematology & Oncology School counselors

Infectious Disease Marriage and family therapists

Neonatal-perinatal Oral health

Nephrology General dentists

Rheumatology Specialist dentists

Surgery Dental hygienists

General Surgery Pharmacy

Colorectal Surgery Pharmacists

Neurological Surgery Pharmacy technicians

Obstetrics & Gynecology Pharmacy aides

Ophthalmology Respiratory care (therapists & technicians)

Orthopedic Surgery Rehabilitation Services

Otolaryngology Occupational therapists & assistants

Plastic Surgery Physical therapists & assistants

Thoracic Surgery Therapeutic Services

Urology Chiropractor

Vascular Surgery Podiatrists

Other Specialties Vision Services

Anesthesiology Opticians

Emergency Medicine Optometrists

Neurology Nutritionists

Pathology Select diagnostic laboratory professions

Physical Medicine & Rehabilitation Select diagnostic imaging professions

Psychiatry Long term services and support professions

Radiation Oncology

Radiology

Other Med Spec

Hospitalist


Healthcare Demand Microsimulation Model This section provides a brief overview of HDMM and describes creation of the major components: the population

file, healthcare use prediction equations, and provider staffing parameters. Data sources and methods for

producing national, state, and county demand projections are described. A description of the scenarios HDMM

was designed to model is also provided.



Overview

HDMM models demand for healthcare services and the number of providers required to meet demand for

services. Demand is defined as the level and mix of healthcare services (and providers) that are likely to be used

based on population characteristics and economic considerations such as price of services and people’s ability and

willingness to pay for services. HDMM was designed also to run a limited set of scenarios around “need” for

services. Need is defined as the healthcare services (and providers) required to provide a specified level of care

given the prevalence of disease and other health risk factors. Need is defined in the absence of economic or

cultural considerations that might preclude someone from using available services. Other scenarios model the

evolving care delivery system.

HDMM has three major components: (1) a population database with information for each person in a representative

sample of the population being modeled, (2) healthcare use patterns that reflect the relationship between patient

characteristics and healthcare use, and (3) staffing patterns that convert estimates of healthcare demand to estimates

of provider demand (Exhibit 3). Demand for services is modeled by employment or care delivery setting. Demand is

also modeled by (a) diagnosis category for hospital inpatient care and emergency department visits, and (b)

healthcare occupation or medical specialty for office, outpatient and home health visits. The services demand

projections are expressed in terms of workload measures, and demand for each health profession is tied to one or

more of these workload measures. For example, current and future demand for primary care providers is tied to

demand for primary care visits, demand for dentists is tied to projected demand for dental visits, etc. External

factors—such as trends or changes in care delivery—can influence all three major components of HDMM.

Exhibit 3 Schematic of Healthcare Demand Microsimulation Model

Schematic of Healthcare Demand Microsimulation Model




Population files

The population files used in the model contain person-level data for a representative sample of the population of

interest. The population of interest might be the entire U.S., an individual state, a county within a state, or some

other geographic unit such as a region, metropolitan area, or hospital service area defined by a set of ZIP codes.

When a population file is created for a specified area, demand estimates can be produced for subsets of the

population—e.g., subsets defined by insurance type, patient demographic, or other tracked characteristic of the

population. Prior to 2019, the population database was created at the state level and could be aggregated to the

national level. Starting in 2019, the population files were constructed for each of the 3,142 counties or county

equivalents in the U.S. The county population files can be summed to produce either state or national estimates

and by National Center for Health Statistics (NCHS) urban-rural county designation.42 The population file is

updated each November to incorporate the latest versions of the following data sources:

• American Community Survey (ACS). Each year the Census Bureau collects information on approximately three

million individuals grouped into roughly one million households. For each person, information collected includes

demographics, household income, medical insurance status, geographic location (e.g., state and sub-state [for

multi-year files]), and type of residency (e.g., community-based residence or nursing home).

• U.S. Census Bureau Population Estimates. The U.S. Census Bureau produces current population totals for each

county by demographics including five-year age groups, sex, and race/ethnicity.

• Behavioral Risk Factor Surveillance System (BRFSS). The Centers for Disease Control and Prevention (CDC)

annually collects data on a sample of over 500,000 individuals. Similar to the ACS, the BRFSS includes

demographics, household income, and medical insurance status for a stratified random sample of households in

each state. The BRFSS, however, also collects detailed information on presence of chronic conditions (e.g.,

diabetes, hypertension) and other health risk factors (e.g., overweight/obese, smoking). One limitation of BRFSS is

that as a telephone-based survey it excludes people in institutionalized settings (e.g., nursing homes) who do not

have their own telephone. We combine the latest two years of BRFSS files to provide records for approximately

one million individuals. Since BRFSS reports some variables biennially (e.g., hypertension, which is omitted from

the even year files), we used a predictive equation to estimate the probability of having those conditions in even

years based on known characteristics of the individual.

• Medicare Beneficiary Survey (MCBS). Starting in 2017, the health characteristics of the residential care

population were modeled using individuals in the MCBS living in residential care facilities (with the 2017 MCBS

data being the most recent available). Prior to 2017, individuals living in residential care were merged with the

BRFSS—thus taking on the health risk profile characteristics of a community-based population that is healthier, on

average, than the population in residential care facilities.

• CMS’s Long-Term Care Minimum Data Set (NHMDS). Starting in 2017a, we used the NHMDS to develop a

representative sample of residents in nursing homes in each state. This data source contains information on disease

prevalence and health risk factors for each person residing in a nursing home. From the NHMDS we drew a

random sample of resident records where the size of each sample was determined based on CMS published data of

the average number of nursing home residents in each state by age group.

Creation of the state population database merges information from these sources using a statistical matching

process that combines patient health information from the BRFSS, MCBS and NHMDS with the larger ACS file

a Previously, we used the 2004 National Nursing Home Survey (NNHS) combined with CMS estimates of nursing home residents in each state to develop a representative sample of the nursing home population in each state. The NNHS collected information on chronic conditions and health risk factors of this

population.



that has a representative population in each state (Exhibit 4). Creation of county-level population files uses a

similar process that is described later.

For the non-institutionalized population, each individual in the ACS was matched with someone in the BRFSS

from the same gender, age group (15 age groups), race, ethnicity, insured/uninsured status, household income

level (8 income categories), and state of residence.a Individuals categorized as residing in a residential care facility

or nursing home were randomly matched to a person in the MCBS or NHMDS, respectively, in the same state,

age group, gender, and race and ethnicity strata. Under this approach, some BRFSS, MCBS or NHMDS

individuals might be matched multiple times to similar people in the ACS, while some BRFSS or NHMDS

individuals might not be matched. The match probability for BRFSS and MCBS reflects the surveys’ sample

weights, with survey participants having higher sample weight more likely to be sampled.

Exhibit 4 Population database mapping algorithm

Exhibit 5 summarizes the population characteristics available in each source file and the characteristics used for

the statistical match process. This detailed information for each person captures systematic geographic variation in

demographics, socioeconomic characteristics, and health risk factors (e.g., obesity, smoking, diabetes and

cardiovascular disease prevalence) that reflect regional differences in diet, physical activity, and other health-

related behavior.

a The first round of BRFSS-ACS matching produced a match in the same strata for 94% of the population. To match the remaining 6%, the eight income levels were collapsed into four (1% matched), then the race/ethnicity dimension was dropped (1% matched), and then the same criteria as the first round was applied except

State was removed as a strata (remaining 4% matched), and finally for the fifth round only demographics were included (remaining 0.1% matched).

Community based

Residential care

facilities

Nursing

homes

CMS Nursing Home Minimum Data Set

Medicare Current Beneficiary Survey

Behavioral Risk Factor Surveillance

System

Population demographics Population health characteristics sourcesPopulation database mapping algorithm




Exhibit 5 Characteristics available for each person in representative population sample

Characteristics available for each person in representative population sample

Population Characteristics Match Strata Source

AC

S-B

RF

SS

AC

S-M

CB

S

AC

S-N

MM

DS

AC

S (

2018)

BR

FS

S

(2017 &

2018)

MC

BS

(2017)

NM

MD

S

(2017)

Demographics Children age groups: 0-2, 3-5, 6-13, 14-17

Adult age groups: 18-34, 35-44, 45-64, 65-74, 75+

✓b ✓ ✓ ✓ ✓ ✓ ✓

Sex: male, female ✓ ✓ ✓ ✓ ✓ ✓ ✓

Race/ethnicity: non-Hispanic white, non-Hispanic black, non-Hispanic other, Hispanic

✓ ✓ ✓ ✓ ✓ ✓ ✓

Health-related lifestyle indicators a

Body weight: normal, overweight, obese ✓ ✓ ✓

Current smoker status ✓ ✓ ✓

Socioeconomic conditions and insurance

Family income (<$10,000, $10,000 to <$15,000, $15,000 to < $20,000, $20,000 to < $25,000, $25,000 to < $35,000, $35,000 to < $50, 000, $50,000 to < $75,000, $75,000+)

✓ ✓ ✓

Medical insurance type (private, public, self-pay) ✓ ✓ ✓ ✓

In a managed care plan ✓

Chronic conditions

Diagnosed with asthma ✓ ✓ ✓

Diagnosed with arthritis, heart disease, diabetes, hypertension a ✓ ✓ ✓

History of cancer, heart attack, or stroke a ✓ ✓ ✓

Geographic location

State (or other geographic area such as county) ✓ ✓ ✓ ✓ ✓ ✓ ✓

Living in a metropolitan area ✓ Notes: a Characteristics available only for adults. b Fifteen age groups are used for the statistical match process: ages 0-19, 20-24, 25-29, 30-34, 35-39, 40-44, 45-49, 50-54, 55-59, 60-64, 65-69, 70-74, 75-79, 80-84, and 85+. Then, individual ages are used to create the nine age groups above for modeling demand for healthcare services. The smaller number of age groups used for modeling demand for healthcare services reflects smaller sample size in the data sources used for modeling patterns of healthcare use.


The ACS provides a representative sample of the population in each state for the most current year available, with

sample weights that can be aggregated to produce state (or national) totals. Developing demand forecasts for

future years requires incorporating state-specific population projections developed by state governments or other

organizations such as universities, and national population projections developed by the U.S. Census Bureau.

Using the population projections, we developed new sample weights for each individual that when aggregated

produce population estimates for each future year consistent with published population projections. The model’s

status quo demand scenario assumes that base year prevalence rates of health and health behavior characteristics

within each demographic group (by age, gender, race and ethnicity) remain the same over the projection

horizon—though HDMM can model scenarios where disease prevalence and health behavior characteristics

change within demographic strata such as modeling a population health scenario related to changes in modifiable

health risk factors.

Based on this constructed state population file, the next step is to develop the population file at the county level.

The U.S. Census Bureau produces annual data on the total population in each county by five-year age bands, sex

and race/ethnicity. We re-weight the sample weights for each metropolitan and non-metropolitan individual in a

state’s population file to match the demographics of the population characteristics in each metropolitan and non-



metropolitan county, respectively, using the published Census Bureau population data. This produces a weighted

sample that is representative of the demographics in each county. Further, county-level estimates of disease

prevalence are calibrated at the individual level to match with external published information for each county.

BRFSS data from state BRFSS surveys is the primary source for external county-level statistics used for

calibrating prevalence of diseases and risk factors in the population files.

The resulting constructed population file contains a representative sample of adults and children in each county by

demographics, insurance type, prevalence of disease and health risk factors, with household income and residence

type (community, residential care, or nursing home) reflective of the demographics in the county.

Healthcare use patterns

Projected future use of healthcare services, based on population characteristics and patterns of health-seeking

behavior, produce workload measures used to project future demand for healthcare providers. HDMM uses

prediction equations for healthcare use based on recent patterns of care use, but also can model scenarios where

healthcare use patterns change in response to emerging care delivery models, policy changes, or other factors.

Health seeking behavior is generated from econometrically estimated equations using data from ~170,000

participants in five years (2013-2017) of pooled files of the Medical Expenditure Panel Survey (MEPS). Pooling

multiple years of data increases sample size for regression analysis for smaller health professions and lower

frequency diagnosis categories. Over time, as a new year of data becomes available and is added to the analytic

file the oldest year in the analysis file is dropped. We used the 2017 Nationwide Inpatient Sample (NIS), with ~8

million discharge records, to model the relationship between patient characteristics and length of hospitalization

by primary diagnosis category.

Many of the population characteristics such as demographics and socioeconomic circumstances are likely

correlated with cultural and other factors (e.g., access constraints) that influence use of healthcare services and are

omitted from the regressions due to data limitations. Consequently, the observed relationship between annual use

of healthcare services and observed patient characteristics reflects correlation rather than causation.

Negative Binomial regression was used to model annual office visits, annual outpatient visits, and annual home

health/hospice visits. Prior to 2019, Poisson regressiona was used to model annual visits by provider occupation or

specialty. From 2019, various regression models were evaluated in response to issues of over-dispersion in the

Poisson model and the negative binomial regression model was selected as the alternative. This change had

negligible impact on the demand projections but conceptually is more appropriate given the large percentage of

patients with no visits to certain types of providers. These regressions were estimated separately for children

versus adults. Separate regressions were estimated by physician specialty or non-physician occupations—e.g.

dentists, physical therapists, psychologists—for office-based care. Likewise, separate regressions were estimated

for occupations providing home healthcare. The dependent variable was annual visits (for office, outpatient, and

home health). The explanatory variables were the patient characteristics available in both MEPS and the

constructed population file (Exhibit 6).

Logisticb regression was used to model annual probability of hospitalization and annual probability of emergency

department visit for approximately two dozen categories of care defined by primary diagnosis code. The

a Poisson regression is often used when the dependent variable (annual visits) is a count variable with a skewed distribution—i.e., many people have 0, 1, or 2,

visits, but the number of people with higher volume of visits (3, 4, 5, etc.) declines at the higher volume levels.

b Logistic regression is often used when the dependent variable is binary (yes/no). The sample size of MEPS is too small to accurately model patients with multiple

hospitalizations and multiple emergency department visits—especially when modeling at the diagnosis category level.



dependent variable for each regression is whether the patient had a hospitalization (or ED visit) during the year for

each of the condition categories (these categories were defined using the ICD-9 and ICD-10 codes). For

hospitalized patients, we used Poisson regression with NIS data to model hospital length of stay given the

condition category and patient information (age, sex, race/ethnicity, insurance type, presence of diabetes, and

urban-rural residency).

The model contains several hundred prediction equations for healthcare use, with examples of the regression

output for cardiology care presented in Exhibit 6 and for primary care presented in Exhibit 7. The numbers in

Exhibit 6 reflect either rate ratios (for office and outpatient visits, or inpatient days) or odds ratios (for ED visits

and hospitalizations). For all types of cardiology-related care there is a strong correlation with patient age

(controlling for other patient characteristics modeled). For example, relative to patients age 75 or older, patients

age 65-74 have only 80% as many office visits but have 18% more outpatient visits, although only the office visits

estimate is statistically different from 1.0 (where a ratio of 1.0 would indicate no statistical difference with the

comparison category). Patients age 65-74 have lower odds of a cardiology-related ED visit (i.e., primary diagnosis

was cardiology-related), and lower odds of a cardiology-related hospitalization. However, the length of

hospitalization averages 94% as long as the hospitalization for the age 75 or older patient.

Blacks tend to have fewer office visits than whites, but higher odds of ED visits or hospitalizations and longer

average length of hospital stay. Obesity is associated with increased use of cardiology-related services. Smoking

is associated with fewer office and outpatient visits to a cardiologist but higher rates of ED visits (likely reflecting

correlation rather than causality in the case of ambulatory care, as smoking is a risk factor for heart disease but

could be correlated with aversion to visit a doctor). Lower income is associated with less use of ambulatory care

and more use of ED visits and hospitalization. Having any medical insurance is associated with much greater use

of ambulatory care, and if the insurance is Medicaid then there is even greater use of cardiology services across all

care delivery settings. The presence of chronic medical conditions—and especially heart disease, hypertension,

and history of heart attack—are associated with much greater use of cardiology services across care delivery

settings. In general patients living in either small/medium metro or suburban large metro fringe areas tend to have

fewer ambulatory visits compared to those living in a large core metro area. Regression equations for other types

of care (whether by medical specialty or condition category) exhibit similar patterns that are consistent with

expectations and the health research literature.



Exhibit 6 Sample regressions: adult use of cardiology services

Sample regressions: adult use of cardiology services

Parameter a Office visitsb Outpatient visitsb Emergency visitsc Hospitalizationsc Inpatient daysd

Age

18-34 years 0.10** 0.35** 0.44** 0.19** 0.80**

35-44 years 0.20** 0.49** 0.69** 0.47** 0.74**

45-64 years 0.38** 0.83 0.67** 0.57** 0.84**

65-74 years 0.80** 1.18 0.84* 0.85 0.94**

75+ years 1.00 1.00 1.00 1.00 1.00

Male 1.09** 1.18* 0.77** 1.04 1.00**

Race- Ethnicity

Non-Hispanic White 1.00 1.00 1.00 1.00 1.00

Non-Hispanic Black 0.77** 1.08 1.20** 1.19* 1.11**

Non-Hispanic Other 0.96 0.78 1.02 0.96 1.01**

Hispanic 0.90* 0.57** 0.88 0.90 0.98**

Body Weight

Normal 1.00 1.00 1.00 1.00

Overweight 1.04 1.00 1.06 1.08

Obese 1.10* 1.04 1.27** 1.04

Current Smoker 0.80** 0.76* 1.22** 1.13

Household Income

<$10,000 0.96 1.63** 1.35** 1.37**

$10,000 to <$15,000 0.95 1.21 1.38** 1.26*

$15,000 to < $20,000 0.95 0.89 1.26* 1.29*

$20,000 to < $25,000 0.91 1.02 1.44** 1.24

$25,000 to < $35,000 0.92 1.11 1.26** 1.21

$35,000 to < $50,000 0.85** 1.08 1.20* 1.09

$50,000 to < $75,000 0.93 1.16 1.16 1.02

$75,000 or higher 1.00 1.00 1.00 1.00

Insurance

Has insurance 2.58** 2.87** 0.71** 1.05** 1.04**

In Medicaid 1.22** 1.38** 1.58** 1.49** 1.10**

In managed care plan 1.01 0.80** 1.08 0.96

Diagnosed with

Arthritis 1.21** 1.54** 1.08 1.04

Asthma 1.14** 1.23 1.28** 1.17*

Diabetes 1.20** 1.15 1.12* 1.51** 1.14**

Heart disease 7.73** 9.58** 2.49** 3.62**

Hypertension 2.00** 1.43** 5.22** 2.81**

History of cancer 1.23** 1.46** 1.07 1.07

History of heart attack 1.73** 2.05** 2.31** 2.73**

History of stroke 1.18** 1.12 2.07** 2.33**

Urban-Rural Areas e

Non-core 1.07 1.12 1.06 1.03

Micropolitan 0.93 0.83 0.95 1.00

Small metro 0.83** 0.88 0.93 0.98 1.03** Medium metro 0.72** 0.78 1.06 0.90

Suburban 0.77** 0.85 0.88 1.10

Large metro core 1.00 1.00 1.00 1.00

Notes: Statistically different from 1.00 at the 0.05 (*) or 0.01 (**) level. a For children the age categories are 0-2, 3-5, 6-12, and 13-17). The adult regressions include everyone age 18 and older. Variables not available for use in the regression equations for children are body weight, smoking status, and diagnoses of the chronic conditions listed (except for asthma which is included). b Rate ratios based on negative binomial regression of MEPS data. Dependent variable is annual visits to cardiologist. c Odds ratios based on logistic regression of MEPS data. Dependent variable is whether a patient had an emergency visit or hospitalization with a cardiology-related primary diagnosis code. d Rate ratios based on Poisson regression of NIS data. Dependent variable is length of stay conditional on hospitalization for cardiology-related primary diagnosis. e NCHS urban-rural categories can be aggregated to metro and non-metro areas; non-core and micropolitan are mapped to non-metropolitan area and the rest are mapped to metropolitan area. The reference population for comparison is age 75 or older, female, non-Hispanic white, normal body weight, non-smoker, household income of $75,000 or higher, uninsured, without the diagnosed conditions listed, residing in large metro core areas.




Office visits by adults to a family medicine (FM) or general internal medicine (GIM) provider are presented for

comparison (Exhibit 7). The bars represent the percent difference in annual office visits contributed by each

characteristic controlling for other patient characteristics and relative to the reference population. Many of the

patient characteristics correlated with use of primary care services are similar to characteristics associated with

greater use of cardiologist services—e.g., the presence of chronic conditions like cardiovascular disease and

diabetes. Higher family income and residing in a metropolitan are associated with greater use of GIM services but

lower use of FM services.

Exhibit 7 Patient characteristics on rate of primary care office visits for adults



For care provided in the emergency department we link demand for emergency physicians to total demand for

emergency visits (so 10% growth in visits would translate to 10% growth in demand for emergency physicians

under the status quo scenario). Specialist physicians sometimes provide consults for emergency visits, and the mix

of patients and their diagnoses are expected to change over time. Using the 2015 and 2016 NHAMCS we

estimated a logistic regression where the dependent variable was whether during the visit a second physician was

seen. As summarized in Exhibit 8, the explanatory variables include specialty category (defined by visit primary

diagnosis), patient demographics (age, sex, and race), insurance status and whether insured through Medicaid, and

whether the patient lives in a metropolitan or non-metropolitan location. As illustrated by the odds ratios, the

likelihood that a specialist physician will be consulted during the visit differs by condition category, but in general

a second physician is most likely to be consulted if the patient’s primary diagnosis is related to nephrology,

neonatal medicine, vascular surgery, or cardiology. Patients with a primary diagnosis related to dermatology,

otolaryngology, or rheumatology are much less likely to see a second physician during their ED visit. Consults are

more likely for older patients, males, insured, not on Medicaid, and living in a metropolitan area.

For illustration, applying the logistic regression results to a female patient age 65-74, non-Hispanic white, and

living in a metropolitan area produces the following probabilities of having a consult tied to the primary diagnosis

for the emergency visit (Exhibit 9). The probabilities range from a high of 34% if the primary diagnosis is in the

category of nephrology, to a low of 8% is the primary diagnosis is in the category of otolaryngology or

rheumatology.



Exhibit 8 Logistic regression for emergency department consultation

Logistic regression for emergency department consultation

Parameter Odds Ratio 95% Confidence Interval

Diagnosis category (General Surgery comparison group) a

Cardiology 2.67 2.18 3.26

Dermatology 0.78 0.61 0.99

Endocrinology 1.44 1.10 1.88

Gastroenterology 1.14 0.94 1.37

Hematology 2.57 1.92 3.43

Infectious Disease 1.00 0.76 1.30

Neonatal Medicine 2.98 1.35 5.89

Nephrology 3.55 2.21 5.60

Neurological Surgery 1.50 0.94 2.31

Neurology 1.15 0.92 1.43

Obstetrics & Gynecology 2.21 1.76 2.77

Ophthalmology 1.14 0.76 1.66

Orthopedic Surgery 0.95 0.80 1.14

Otolaryngology 0.64 0.42 0.93

Other Specialties 1.21 1.00 1.47

Plastic Surgery 0.86 0.38 1.70

Psychiatry 2.36 1.96 2.86

Pulmonology 1.36 1.15 1.60

Rheumatology 0.64 0.45 0.89

Thoracic Surgery 1.85 1.55 2.22

Urology 1.07 0.90 1.29

Vascular Surgery 2.74 1.05 6.38

Female 0.90 0.84 0.97

Age (45-64 comparison group)

0-2 0.34 0.27 0.41

3-5 0.44 0.34 0.55

6-12 0.47 0.39 0.57

13-17 0.67 0.56 0.80

18-34 0.62 0.56 0.69

35-44 0.69 0.60 0.78

65-74 1.32 1.16 1.49

75+ 1.67 1.49 1.87

Race/ethnicity (non-Hispanic white comparison group)

Hispanic 1.46 1.33 1.61

Non-Hispanic black 1.03 0.94 1.13

Non-Hispanic other 1.29 1.07 1.55

Has medical insurance 1.35 1.18 1.54

Insurance is Medicaid 0.83 0.76 0.91

Lives in metropolitan area 3.09 2.72 3.53

2015 (vs 2016) 0.88 0.82 0.94

Source: Logistic regression analysis of the 2015 and 2016 NHAMCS. a Diagnosis categories defined by ICD-9 diagnosis and procedure codes to reflect types of care most likely provided by a physician specialty.




Exhibit 9 Illustration of probability of emergency department consultation

Demand for medications is the workload driver to model demand for pharmacy-related health occupations. The

NAMCS and NHAMCS indicate prescription medications ordered by a health provider, though this is used as a

proxy for number of prescriptions filled (under the assumption that the ratio of prescribed-to-filled remains

relatively constant over time). Patients who visit a cardiologist in an office setting average 6.11 prescriptions per

visit, for example, while for primary care visits the average is 3.82 prescriptions per visit (Exhibit 10). To model

projected growth in demand for pharmacy-related occupations, under the status quo scenario, provider demand is

tied to projected growth in number of prescriptions.



Exhibit 10 Average prescriptions per healthcare visit

Average prescriptions per healthcare visit

Physician Specialty Office Outpatient Emergency

Nephrology - 5.43 3.58

Cardiology 6.11 4.20 2.76

Vascular Surgery - 3.02 2.98

Endocrinology - 4.03 2.75

Thoracic Surgery - 3.18 2.01

Pulmonology - 2.95 2.65

Neurology 3.72 2.90 2.59

Gastroenterology - 2.94 2.66

Hematology & Oncology - 3.58 2.67

Psychiatry 2.30 2.16 1.62

Rheumatology - 2.66 1.76

Urology 3.36 2.42 3.01

Orthopedic Surgery 2.46 2.49 2.07

Allergy & Immunology - 2.70 1.98

Dermatology 2.38 2.64 2.23

Plastic Surgery - 1.79 2.28

Ophthalmology 2.80 1.78 1.68

Otolaryngology 2.75 2.19 2.12

Primary Care 3.82 - -

General Surgery 2.22 1.91 1.76

OBGYN 1.80 1.83 1.96

Neurological Surgery - 1.67 1.81

Neonatal-perinatal - 1.15 1.04

Other Med Spec 3.78 1.77 1.45

Note: Average prescriptions per visit based on analysis of 2013-2015 combined NAMCS and 2011-2015 combined NHAMCS files.


To model demand for oral health services we analyzed the MEPS Dental Visits File for the period 2012-2016. The

combined file was used to model annual visits to dental hygienists, and annual visits to each type of dentist

including general or pediatric dentist, endodontist, orthodontist, periodontist and other type of dentist. The

regressions were estimated separately for adults and children. MEPS does not identify pediatric dentists as a

unique specialty, and so using MEPS we cannot indicate whether dental services provided to children were by a

pediatric dentist or a general dentist. Information from ADA’s survey of dental practices allowed us to model the

proportion of dental visits by children and adolescents that likely were to general dentists and pediatric

dentists.26,43

These regressions quantify the relationship between patient characteristics and annual oral health visits similar to

the regression output summarized in Exhibit 6. The regression results show that use of oral health services is

highly correlated with insurance status (where medical insurance is used as a proxy for dental insurance),

household income, living in a metropolitan area, patient age, and race/ethnicity.

MEPS is a representative sample of the non-institutionalized population, and although the healthcare use

prediction equations are applied to a representative sample of the entire U.S. population, parts of the model

require calibration to ensure that at the national level the predicted healthcare use equals actual use. Applying the

prediction equations to the population for 2016 through 2017 creates predicted values of healthcare use in those

years (e.g., total hospitalizations, inpatient days, and ED visits by specialty category, and total office visits by

physician specialty). For model calibration, we compared predicted national totals to estimates of national total

hospitalizations and inpatient days, by diagnosis category, derived from the 2017 NIS. Comparative national

estimates of ED visits and office visits came from the 2016 NHAMCS and 2016 NAMCS, respectively.



Multiplicative scalars were then created by dividing national estimates by predicted estimates. For example, if the

model under-predicted ED visits for a particular diagnosis category by 10% then a scalar of 1.1 was added to the

prediction equation for that diagnosis category.

Applying this approach to diagnosis/specialty categories, the model’s predicted healthcare use was consistent with

national totals for most settings (see Exhibit 11 for calibration scalars for physician office visits). Setting/category

combinations where the model predicted less accurately (and therefore required larger scalars) tended to cluster

around diagnosis categories in the ED characterized by lower frequency of visits likely due to a combination of

small sample size in both MEPS and NAMCS.

Exhibit 11 HDMM calibration: physician office visits

HDMM calibration: physician office visits

Specialty

NAMCS Visits (in thousands), 2016 a

HDMM Initial Visits Pre-Scalar (in thousands), 2018 Scalar

Family Medicine 202,494 411,955 0.492

Pediatrics 136,119 81,775 1.665

Internal Medicine 81,701 72,292 1.130

Obstetrics & Gynecology 73,198 80,804 0.906

Orthopedic Surgery 30,114 124,001 0.243

Ophthalmology 46,289 127,436 0.363

Dermatology 49,947 90,870 0.550

Psychiatry 29,993 110,045 0.273

Cardiovascular Diseases 27,783 32,945 0.843

Otolaryngology 28,965 27,495 1.053

Urology 26,153 35,925 0.728

General Surgery 15,685 16,282 0.963

Neurology 14,407 29,811 0.483

All other specialties 120,875 96,173 1.257

Note: a https://www.cdc.gov/nchs/data/ahcd/namcs_summary/2016_namcs_web_tables.pdf


Health workforce staffing patterns

Demand for healthcare workers is derived from the demand for healthcare services. The status quo scenario in

HDMM extrapolates current staffing levels as reflected by national healthcare use-to-provider ratios. For example,

demand for registered nurses (RNs) under the status quo is modeled based on the current national ratio of

inpatient days-to-RNs to model RNs in hospital inpatient settings, the national ratio of ED visits-to-RNs to model

demand for RNs in emergency departments, the national ratio of office visits-to-RNs to model demand for RNs in

office settings, etc.

The national number of health workers comes from many different sources, as described in the chapter describing

supply modeling, including associations’ Master Files (e.g., AMA Master File for physicians, ADA Master File

for dentists), the Health Resources and Services Administration’s (HRSA’s) National Sample Survey of

Registered Nurses for RNs and advanced practice registered nurses (APRNs), association publications such as

NCCPA reports for number of licensed physician assistants (PAs), and ACS and Occupational Employment

Statistics (OES) survey data collected from employers by the Bureau of Labor Statistics for select health

occupations.

The distribution of health workers across care delivery settings comes from multiple sources—including

published data collected by specialty associations via surveys of their members (e.g., NCCPA data on physician

assistants); specialty surveys (e.g., HRSA’s National Sample Survey of Registered Nurses); and OES data from

https://www.cdc.gov/nchs/data/ahcd/namcs_summary/2016_namcs_web_tables.pdf



employer surveys reported by detailed health occupation, industry sector, and state. Limitations of OES data

include (1) it counts job positions, which may produce overcounting in occupations that have a high proportion of

part time workers, and (2) the data are for employed individuals, which can undercount the workforce in

occupations with a high proportion of self-employed individuals such as dentists or physicians.

For many occupations, demand is tied to one workload measure—e.g., demand for dentists is tied to demand for

dental visits (excluding dental cleaning visits), and demand for dental hygienists is tied to demand for dental

cleanings. For nurses, physicians, APRNs, PAs, and health occupations that work in multiple care delivery

settings there are multiple workload measures specific to each occupation and employment setting. The use of

multiple workload measures reflects that demand in each setting will grow at different rates.

In addition to using current staffing ratios to model a status quo scenario, HDMM was designed to model possible

changes in staffing patterns to reflect emerging care delivery models as informed by the literature. These scenarios

are discussed in more detail later and are also areas of ongoing research. Population health risk factors affect the

demand for healthcare services, but HDMM staffing currently does not account for variation across geographic

areas or over time in average patient acuity level for those who seek care. This is also an area of ongoing research.

Scenarios

The capabilities of HDMM to model alternative demand scenarios continue to evolve, and scenarios previously

modeled continue to be refined as new information becomes available. Many of these scenarios have been

described and the demand implications summarized in previous publications.25,44

• Status quo. This scenario models the implications of changing demographics as the population grows, ages,

and becomes more racially and ethnically diverse. Under this scenario healthcare use and delivery patterns are

modeled as remaining consistent with current patterns (i.e., observed during the 2013-2017 as reflected in the

MEPs and the 2017 NIS). Prevalence of disease and other health risk factors (e.g., smoking and obesity)

remain constant within each demographic group, but do change in the aggregate level as population

demographics change. For example, prevalence of diabetes and heart disease will rise as the population ages

but do not change independent of changing demographics. This scenario models the future demand for health

workers to provide a level of care consist with current levels.

• Increased medical insurance coverage. Earlier workforce studies modeled the implications of expanded

medical insurance coverage under the Affordable Care Act (ACA), but because recent patterns of healthcare

use and delivery largely have incorporated the effects of ACA this scenario is no longer modeled. However,

HDMM has been used to model hypothetical scenarios of insuring the uninsured to estimate the potential

impact of goals to improve access to care. This scenario assumes that a person who gains insurance will have

healthcare use patterns similar to his or her commercially insured counterpart with the same demographics and

risk factors. Although there may be an initial uptick in care sought, the scenario captures what happens when

the care sought by the newly insured settle into patterns of the currently insured. In HDMM this is essentially

done by switching the insurance status of a person from uninsured to insured and holding all other patient

characteristics constant.

• Reducing barriers to accessing care. This scenario builds on the increased medical insurance coverage

scenario to model the impact on health workforce demand if historically underserved populations had

improved access to care. Populations identified as underserved include minority populations and people living

in non-metropolitan areas—as well as people without medical insurance.45–48 When modeling this scenario for

oral health, lower household income is also identified as a barrier to receiving care (whereas for most other

healthcare services household income has only a small correlation with use of healthcare services controlling



for insurance status). In some studies this scenario has been referred to as a “health care utilization equity”

scenario.25

• Increased use of managed care principles. A variety of integrated care delivery models are being

implemented for both publicly and privately insured populations in an effort to both control rising medical

expenditures and improve delivery of care. Risk-bearing entities such as accountable care organizations

(ACOs) and Health Maintenance Organizations (HMOs) incorporate financial incentives for patients and

providers to better manage utilization by creating incentives for providers to collaborate in providing and

coordinating patient care across settings. ACOs have been promoted under ACA, but because they are a

relatively new care delivery model there is still limited data on their impact on patient use of services, how

care is delivered, and the demand implications for the health professions. Looking historically at the effect of

HMOs and other risk-bearing delivery models on use of services provides insights on what might happen if

ACOs gain greater prominence. One aspect of managed care is promotion of primary care and preventive care

to reduce need for expensive, hospital-based care and need for specialist care. One of the explanatory

variables in HDMM is the MEPS variable of whether the person is in an HMO-type managed care plan. By

changing people’s status from non-HMO to HMO, while holding all other characteristics constant, we model

the demand implications of increasing the proportion of the population in managed care plans. In general,

scenario findings are an increase in demand for primary care services and providers with a decrease in demand

for many types of specialist services and their providers.

• Expanded use of retail clinics. Retail clinics provide a convenient, cost-effective option for patients with

minor acute conditions. The number of retail clinics has grown rapidly over the past decade and is projected to

reach about 5,600 clinics by 2022.49–51 Retail clinics appear to be servicing demand for some types of services

historically provided in other settings, and also appear to be creating a net increase in healthcare utilization for

services provided to populations historically underserved and who would not otherwise receive care.52,53 For

example, an estimated 39% of visits to retail clinics replace physician visits, 3% replace emergency

department visits, and 58% are new visits that would not otherwise have occurred.52 This scenario explores

the demand implications of shifting care from primary care physician offices to retail clinics for 10 conditions

typically treated at retail clinics: upper respiratory infection, sinusitis, bronchitis, otitis media (middle ear

infection) and otitis externa (external ear infection), pharyngitis, conjunctivitis, urinary tract infection,

immunization, blood pressure check or lab test, and other preventive visit.51,53

In this scenario, patient visits to specialist physician are unaffected, and patients with modeled chronic

conditions in HDMM (i.e., cardiovascular, diabetes, asthma, hypertension or history of stroke) will continue

to be seen by their regular primary care provider even for non-complex health issues that could be treated in a

retail clinic. The scenario models a shift in demand from primary care physician offices to retail clinics,

incorporating into the workforce demand implications that 83% of visits to a pediatrician’s office are handled

primarily by a physician (reflecting that between NPs and physicians, 83% of the pediatric workforce are

physicians) and 71% of adult primary care office visits will be handled primarily by a physician. Care in retail

clinics is provided mostly by nurse practitioners.

• Increased use of APRNs and PAs. Studies conducted for the Association of American Medical Colleges

(AAMC) have modeled the implications on demand for physicians of the rapid growth in supply of APRNs

and PAs. This scenario, described elsewhere, uses different assumptions of the degree to which demand for

physicians might decrease as a result of growing supply of APRNs and PAs.25 The scenario assumes that a

portion of the increased supply of APRNs and PAs will replace some physician demand, a portion will expand

overall patient access to care but not replace physician demand, and a portion will increase the

comprehensiveness of care provided to patients but not replace physician demand. A 2012 study, for example,

estimated that patients receiving care from primary care physicians working alone received only 55% of



recommended chronic and preventive services.54 The gap between services recommended and services

provided was attributed to physicians being overworked with unmanageable patient panel sizes, but that much

of the care for recommended services could be delegated to other team members such as NPs and PAs, thus

delivering more comprehensive care while also reducing provider burnout.

• Achieving select population health goals: This scenario models the healthcare services and workforce

demand implications of achieving select population health goals. Consistent with Healthy People goals and

objectives, many programs and interventions target modifiable lifestyle behaviors and health risk factors

known to contribute to chronic disease—including efforts to reduce excess body weight, hypertension,

dyslipidemia, hyperglycemia, and smoking.55–58 This scenario is modeled using the Disease Prevention

Microsimulation Model (DPMM), discussed later, to simulate the healthcare demand implications of (a) a

modest 5% sustained reduction in excess body weight among adults who are overweight or obese; (b)

reductions in blood pressure, cholesterol, and blood glucose levels among adults with elevated levels with the

magnitude of reductions reflecting what can be achieved through appropriate medication and counseling as

reported in published clinical trials; and (c) 25% of smokers quit smoking—though with high recidivism.59–65

The mechanisms by which this hypothetical scenario could be achieved included increased use of medical

homes, value-based insurance design, and increased emphasis on preventive care to provide patients with

testing and counseling to improve patient adherence to treatment regimens.66–72 Research shows that people

who stop smoking can lower their risk for various cancers, diabetes, cardiovascular disease and other

morbidity and also reduce mortality.73–75

This scenario produces three impacts on provider demand:

(1) improved population health delays or prevents onset of adverse patient conditions thereby reducing

demand for some types of healthcare services and providers;

(2) shifts between different types of providers and care delivery settings—e.g., lower demand for

endocrinologists but higher demand for geriatricians, and shifts from hospital to ambulatory-based care; and

(3) people living to an older age due to met population health goals with chronic conditions will require more

healthcare services.

• Evolving care delivery system. While each of the above scenarios are modeled in isolation to quantify their

individual effect on demand, the healthcare system continues to evolve along multiple fronts—often with the

above scenarios overlapping. For example, to achieve the modeled population health goals requires more

comprehensive preventive services and improved access to care, as well as team-based care that involves

greater use of NPs, PAs, and other providers. Improved access to counseling and medications to achieve these

modeled population health goals is consistent with managed care principles, patient centered medical home

(PCMH) models of care delivery, and value-based insurance design (VBID). Managed care principles are also

consistent with interventions to divert costly hospital-based care to appropriate ambulatory settings, and

improve integration of care delivery. Policy initiatives try to advance national goals of increasing equity in

health outcomes and improving access to high quality, affordable care. This scenario, therefore, combines

several of the above scenarios with attention paid to not double counting the effects that overlapping scenarios

might have on demand for healthcare services and providers.

Input summary

HDMM uses data from a variety of public data sources, which are summarized in Exhibit 12. The model

undergoes a major update in November of each year—reflecting that many of the government sponsored annual

surveys and data sources used in the model are often released to the public approximately July – October each

year.



Exhibit 12 Demand model input data summary

Demand model input data summary

Data Source Use Latest Data

Used Last Updated

Population File

American Community Survey (ACS) Create representative sample of population in each state (demographics, insurance, household income)

2018 November 2019

U.S. Census Bureau Population estimates by County

Create representative sample of population in each county (demographics)

2018 November 2019

Behavioral Risk Factor Surveillance System (BRFSS)

Create representative sample of community-based population in each state (heath risk factors and disease prevalence)

2018 November 2019

CMS Nursing Home Minimum Data Set (MDS)

Create representative sample of nursing home population in each state (heath risk factors and disease prevalence)

2017 November 2019

CMS Medicare Beneficiary Survey (MCBS)

Create representative sample of population in residential care facilities in each state (heath risk factors and disease prevalence)

2017 November 2019

U.S. Census Population Projections National population projections 2016 November 2017

State Population Projections Individual state population projections Various November 2019

County Population Projections Individual county population projections and IHS Markit’s regional forecast

Various November 2019

Healthcare Use

Medical Expenditure Panel Survey (MEPS)

Estimate health seeking behavior by care delivery setting and provider type

2017 November 2019

National Inpatient Sample (NIS) Estimate hospital length of stay; model calibration for annual hospital visits

2017 November 2019

National Ambulatory Medical Care Survey (NAMCS)

Model use of non-physician services during office visits; model calibration for annual office visits

2016 November 2019

National Hospital Ambulatory Medical Care Survey (NHAMCS)

Model use of non-physician services and physician consults during ED visits; model calibration for annual ED visits

2016 November 2019

Healthcare Provider Staffing

Bureau of Labor Statistics, Occupational Employment Statistics

Estimate provider staffing ratios by health occupation and delivery setting (excluding physicians)

2018 November 2019

Individual profession association surveys

Estimating staffing across care delivery settings Various November 2019


Health Workforce Supply Model HWSM is designed to project future supply of health professionals under alternative forecasting scenarios using a

microsimulation approach. Supply projections account for characteristics of the current and projected future

workforce and other external factors (e.g., training capacity, demand for services) that might affect career choices

of health professionals. Below, we describe the logic, data, methods, and assumptions for modeling health

workforce supply, as well as the major components of the model and the scenarios that can be modeled.

Starting supply input files

The microsimulation model projects future supply by simulating likely workforce decisions of individual, de-

identified healthcare providers. This approach requires developing a starting supply file of all providers

(preferred approach) or a representative sample of providers from survey data. When modeling supply for

individual states and at the sub-state level the primary data source of de-identified, individual-level provider data

is state licensure files. These files typically contain the providers’ occupation/specialty, active/inactive status,

geographic area where working, and demographics. Age is the most important demographic information used to



model workforce decisions as hours worked patterns and retirement probabilities are highly correlated with age.

Workforce decisions (especially hours worked patterns) also vary systematically by sex. Race/ethnicity is added

for some occupations based on availability, but it is a less significant predictor of workforce decisions than age

and sex. State licensure files sometimes contain information collected via survey at time of re-licensure—such as

weekly patient care hours worked, employment setting, and retirement intentions (as discussed later).

Other data sources that have been used to develop a file for starting supply when state licensure data is

unavailable include national surveys, national certification data, and association membership and registration

databases:

• National databases (licensure, membership, or registration)

o American Medical Association (AMA) Master File: continuously updated with a record for each

physician who has been licensed in the U.S.

o American Dental Association (ADA) Master File: continuously updated with a record for each dentist

who has been licensed in the U.S.

o American Academy of Physician Assistants (AAPA) Master File: includes a record for physician

assistants by specialty, we combine this with NCCPA publications on total number of PAs

o Membership files created by individual professional associations

o National Plan and Provider Enumeration System (NPPES), continuously updated to provide a unique

identifier for providers who bill CMS for services

• Surveys

o American Community Survey (ACS), updated annually by the U.S. Census Bureau, contains a stratified

random sample of the population in each state and lists occupation and employment status

o Occupational Employment Statistics (OES), updated by the U.S. Bureau of Labor Statistics, collects data

on employed individuals via an employer-based survey

o Occupation/specialty surveys

▪ HRSA National Sample Survey of Registered Nurses (NSSRN), last updated in 2018, includes an

oversample of APRNs

Each of the data sources contains different types of data and comprehensiveness of health workers—ranging from

licensure files that contain a complete census of providers in the geographic area of interest, to association

membership files that contain data on members and limited data on non-members, to employer or population-

based surveys that use sample weights to scale to the population in the geographic area. State licensure files are

usually the most accurate source of data to create the starting supply files, and some of the above data sources are

derived from and updated periodically using state licensure data.

New entrants

When modeling at the national level the new entrants are those individuals entering the workforce after

completing appropriate training and licensure. When modeling at the state or sub-state level the new entrants

reflect both those individuals newly entering the workforce for the first time, as well as individuals who might be

migrating mid-career from one geographic area to another.

Each year new entrants are added to the supply file via creation of a “synthetic” population based on the number

and characteristics of new entrants to the workforce. For example, if 100 new providers in a given occupation or

specialty entered the workforce in a particular year then the model creates 100 new records—one for each person.

The age and sex of each new person is generated based on the estimated distribution from recent entrants to the

workforce. If, for example, 90% of new entrants to the RN workforce were female then the model generates a

random number for each new person using a uniform (0, 1) distribution. The person is designated as male if the

random number for that person is less than or equal to 0.1, and otherwise designated as female. A similar process



is used to designate each new person’s age and race/ethnicity (for those occupations were this dimension has been

added to the supply model).

For state-level analyses, licensure files are the most useful source of information on the number and

characteristics of providers entering the workforce. Analyzing several years’ data helps provide a sufficient

sample size to estimate the annual number and demographics of new entrants. In addition to state licensure files,

additional national data sources for information on the number and characteristics of newly trained health

providers entering the workforce are listed in Exhibit 13.

Data limitations regarding new entrants presents challenges for modeling future supply of some health

occupations. This includes some aide/assistant/paraprofessional occupations where new entrants might enter the

workforce through formal or on-the-job training, or where there is no formal licensure process.

Exhibit 13 Data sources for number and characteristics of new entrants

Data sources for number and characteristics of new entrants

Profession Number and Characteristics of New Entrants

All licensed professions State licensure files (where available)

Registered nurses NCLEX; National League for Nursing, http://www.nln.org/researchgrants/slides/topic_nursing_stud_demographics.htm

Licensed practical nurses National Council Licensure Examination (NCLEX)

Dentists American Dental Association Master File

Dental hygienists Integrated Postsecondary Education Data System (IPEDS)

Physicians American Medical Association (publications76 and Master File)

Advanced practice nurses American Association of Colleges of Nursing (AACN)

Physician assistants National Commission on Certification of Physician Assistants; Physician Assistant Education Association

Therapeutic service providers IPEDS

Rehabilitation service providers IPEDS

Respiratory care providers IPEDS

Vision and hearing care providers IPEDS

Dietitians & nutritionists IPEDS

Pharmacy professions IPEDS

Non-physician behavioral health providers IPEDS

Diagnostic laboratory providers IPEDS


Hours worked patterns

The model simulates weekly hours worked for each health worker and captures changing demographics of the

workforce over time. Hours worked patterns vary by occupation/specialty, provider age and sex, and for some

occupations by economic conditions and geographic location. Hours worked is converted to FTE levels by

dividing the hours worked for each provider by current average hours worked in the profession. Patterns of hours

worked were calculated differently by occupation based on data availability. Where possible, we used regression

analysis (with Ordinary Least Squares regression) to estimate the effect of workforce determinants on weekly

hours worked.

http://www.nln.org/researchgrants/slides/topic_nursing_stud_demographics.htm



Physicians

For physicians, the prediction equation for weekly hours worked in initial workforce studies was based on OLS

regression using Florida’s 2012-2013 bi-annual Physician Licensure Workforce Survey (n=17,782), restricted to

physicians who reported working at least 8 hours per week in professional activities, where patient care hours was

the dependent variable and explanatory variables consisted of the following class variables: specialty, age group,

sex, and age-by-sex interaction terms. Subsequently, we added similar data from South Carolina (2014, n= 8,924),

New York (2014, n = 44,181), and Maryland (2016-2017, n=24,668) for a total of 95,555 total physician

responses. This increase in sample size allowed for OLS regressions by individual specialty.

Exhibit 14 illustrates regression results and presents information for general internal medicine. Starting with 46.29

hours/week for the reference group (male, age<35, working in Florida), we find that females work 3.53 fewer

patient care hours/week, on average, relative to their male colleagues with older females working even fewer

hours than their male colleagues; hours/week drops slightly around age 60 and then continues to decline more

rapidly for older physicians; and patient care hours/week are lower in New York and Maryland as compared to

Florida and South Carolina.

Exhibit 14 OLS regression example: weekly patient care hours for general internal medicine

OLS regression example: weekly patient care hours for general internal medicine

Parameter Patient hours

Intercept 46.29

Age 35 to 44 0.52

Age 45 to 54 0.30

Age 55 to 59 0.17

Age 60 to 64 -2.04 **

Age 65 to 69 -5.65 **

Age 70-74 -8.37 **

Age 75+ -14.91 **

Female -3.53 **

Age 25 to 34 Female 0.75

Age 35 to 44 Female -2.20 **

Age 45 to 54 Female -2.03 **

Maryland -4.01 **

New York -7.13 **

South Carolina 0.50

Note: Notes: Statistically significant at the 1% (**) or 5% (*) level. Comparison groups are age <35, male, Florida. R2=0.09.


Our goal is to use a nationally representative data source to create the OLS regressions; to that end, IHS Markit

teamed with AAMC to estimate physician hours worked patterns based on the AAMC National Sample Survey of

Physicians (2019) for the 2020 update of AAMC’s physician workforce projections report. Where we have

conducted workforce studies for individual specialties or other professional occupations, we use survey data

collected by the associations sponsoring such studies (e.g., American Academy of Neurology [2016 AAN Career

Satisfaction Survey, n=910], American Academy of Pediatric Dentistry [2017 AAPD Survey of Pediatric

Dentists, n=2,546], Association of Academic Physiatrists [2019 AAP Survey of Physiatrists, n=616]).



Other health occupations

The hours worked regressions for other health occupations modeled analyze ACS data for employed clinicians

who reported at least 8 hours worked per week. Dependent variables include clinician characteristics such as age,

sex, and race. We also include the year the clinician responded to the ACS to control for changes in hours worked

over time, but that variable is not included in the supply simulation. Multiple years of ACS data were combined to

increase sample size.

Exhibit 15 summarizes regression output for select occupations using ACS data from 2013-2017. For all

occupations, weekly hours worked decline rapidly from age 65 onward. Using RNs as an example, we find that on

average, male RNs work 2.85 more hours than their female counterparts, Hispanic RNs work 1.25 hours more

than non-Hispanic white RNs, and RNs in 2017 worked 0.39 more hours than in 2013. The low R2 values for

occupation-specific regressions suggest that demographics alone explain only a small portion of the variation

across providers in weekly hours worked. Other factors that might explain variation in weekly hours worked are

household characteristics (e.g., number and age of children, health status, marital status and earnings potential of

spouse or significant other). However, these variables are unavailable to be included in the microsimulation model

for supply. Still, at the occupation level the regression results do find statistically significant and substantial

variation in weekly hours worked that can be explained by provider demographics

Exhibit 15 OLS regression coefficients predicting weekly hours worked for select occupations

OLS regression coefficients predicting weekly hours worked for select occupations

Parameter RN LPN Dental hygienist Physical therapist Pharmacist

Intercept 39.30 ** 39.05 ** 38.08 ** 43.39 ** 45.67 **

Female -2.85 ** -2.13 ** -6.31 ** -5.97 ** -5.28 **

Hispanic 1.25 ** 0.85 * 0.77 -1.05 1.87

Non-Hispanic black 2.00 ** 0.49 ** 4.43 ** 2.32 ** -1.18

Non-Hispanic other 1.12 ** 0.17 1.51 ** 0.74 ** -0.25

Age 35 to 44 0.32 ** 0.84 ** -1.37 ** -2.59 ** -1.07

Age 45 to 54 1.35 ** 1.09 ** -1.40 ** -1.80 ** -0.22

Age 55 to 59 1.14 ** 0.77 ** -2.37 ** -1.07 ** -1.09

Age 60 to 64 0.30 ** -0.18 -2.82 ** -2.20 ** -2.00 *

Age 65 to 69 -3.20 ** -3.93 ** -5.12 ** -4.44 ** -6.81 **

Age 70+ -7.10 ** -7.43 ** -10.49 ** -11.93 ** -10.29 **

Year 2014 0.07 -0.03 0.59 0.35 -0.46

Year 2015 0.30 ** 0.35 * 0.21 0.61 * 0.07

Year 2016 0.40 ** 0.56 ** 0.75 * 0.45 -0.33

Year 2017 0.39 ** 0.72 ** 0.92 ** 0.53 -0.42

Sample size 171,635 44,641 9,752 12,573 16,035

R-squared 0.03 0.02 0.05 0.09 0.05

Notes: Analysis of American Community Survey. Statistically significant at the 0.01 (**) or 0.05 (*) level. Comparison groups are age <35, male, non-Hispanic white, ACS year=2013.


One limitation of ACS as a data source for modeling hours worked is that ACS does not collect data on specialty

area or clinical setting. Therefore, to model hours worked for physicians by individual specialty category we used

data collected by states or professional associations. Similarly, for ongoing work with HRSA the prediction

equations for primary care PAs is based on data from the 2019 AAPA Salary Survey. Prediction equations for

RNs by delivery setting and for APRNs by specialty are based on HRSA’s National Sample Survey of Registered

Nurses (2018).



Labor force participation

Labor force participation decisions encompasses whether a provider joins the workforce as well as his/her level of

participation. Providers might temporarily leave the labor force due to familial, educational, economic or other

considerations. Labor force participation status for some occupations is modeled using prediction equations

derived from ACS data, with multiple years of data combined to increase sample size. This analysis focuses on

clinicians under age 50 (as the HWSM switches to permanent retirement as the activity status changes for

clinicians age 50 and over). The dependent variable was whether the nurse was employed or not employed) with

explanatory variables listed in Exhibit 16. The estimated odds of being employed for three occupations by

clinician demographics—in particular age. Registered nurses and licensed practical nurses are less likely to be

active in the 30-34 and 35-39 age groups compared to RNs under age 30, but pharmacists are much more likely to

be active in their thirties and forties. Female RNs, LPNs, and pharmacists are all less likely to be active than males

in their occupation. Race has an effect on labor force participation that differs by the three occupations illustrated

here.

Exhibit 16 Odds ratios predicting probability active

Odds ratios predicting probability active

Parameter RN (n=90,696)

Odds ratio and CI LPN (n=22,836)

Odds ratio and CI Pharmacist (n=9,773)

Odds ratio and CI

Female b 0.77 0.69 0.86 0.66 0.55 0.78 0.62 0.49 0.79

Non-Hispanic black b 1.45 1.27 1.65 1.72 1.50 1.97 1.00 0.62 1.61

Non-Hispanic other b 1.22 1.10 1.34 1.01 0.87 1.17 0.84 0.66 1.06

Hispanic b 0.90 0.66 1.23 0.68 0.49 0.94 0.56 0.24 1.33

Age 30-34 0.81 0.73 0.89 0.93 0.80 1.08 1.37 1.04 1.82

Age 35-39 0.91 0.83 1.01 0.97 0.84 1.13 1.49 1.10 2.03

Age 40 to 44 1.08 0.98 1.20 1.02 0.87 1.18 1.66 1.20 2.29

Age 45 to 49 1.15 1.04 1.28 0.97 0.84 1.13 1.71 1.22 2.40

Year 2014 b 1.01 0.92 1.12 0.89 0.76 1.03 1.07 0.75 1.53

Year 2015 b 1.08 0.98 1.19 0.89 0.76 1.04 0.94 0.67 1.33

Year 2016 b 1.07 0.97 1.18 0.94 0.80 1.10 0.78 0.57 1.08

Year 2017 b 1.09 0.99 1.20 0.97 0.83 1.14 0.72 0.52 1.00

Notes: Odds ratios and 95% confidence interval (CI) from logistic regression. Comparison groups are male, non-Hispanic white, age <30, and ACS year=2013. Labor force participation regressions are based only on clinicians under age 50.


Retirement

In addition to temporary departures from the workforce, clinicians can also leave permanently. The approach to

modeling retirement differs by occupation depending on data availability. The supply model assigns each person

an attrition probability based on age, sex, and occupation/specialty. However, surveys asking about retirement

intention are rare and often have few retirements, so in many cases the responses from male and female clinicians

are combined before creating the retirement pattern. Alternatively, some specialties or occupations may be

combined into groups such as all primary care physicians or all counselors and therapists. Once calculated, this

probability is then compared with a random number between 0 and 1 (using a uniform distribution) generated for

each observation in the supply input file to simulate whether the person leaves the workforce each year. For

example, if an active clinician age 66 has a 20% probability of retiring by age 67, then if the random number is

below 0.2 the person will be removed from the simulated workforce. Otherwise, that person is considered still

active at age 67 and the simulation will consider them for retirement at age 68 during the next iteration. Each

occupation has a maximum age at which the retirement probability is set to 1 and all providers are removed upon

reaching that age. This is necessary because the retirement patterns are calculated based on age and at a



sufficiently advanced age there is not enough data to predict retirement at that age. In general, this maximum age

is 75 for most occupations and 90 for physicians and dentists.

Physician attrition patterns

Historically there has been a paucity of information on physician retirement patterns. Few surveys collect

information on retirement intentions or retirement age, and state licensure files often have insufficient sample

sizes for older physicians in individual specialties to adequately estimate retirement patterns. National surveys like

ACS do not indicate physician specialty.

Many of the retirement rates for individual specialties used in HWSM were estimated using survey data from the

Florida bi-annual physician survey (2012-2013 data) that asks about intention to retire in the upcoming five years.

Derived retirement patterns from this survey are similar to estimates derived from analysis of the AAMC’s 2006

Survey of Physicians over Age 50 (which collected information on actual retirement age of retired physicians, or

age those physicians still active were expecting to retire). For select physician specialties and other health

professionals the retirement patterns were estimated using survey data collected by the sponsoring associations

(e.g., American Academy of Neurology [2016 AAN Career Satisfaction Survey, n=910], American Academy of

Pediatric Dentistry [2017 AAPD Survey of Pediatric Dentists, n=2,546], and Association of Academic

Physiatrists [2019 AAP Survey of Physiatrists, n=616]).

IHS Markit has worked with AAMC to create physician retirement patterns based on the AAMC National Sample

Survey of Physicians (2019), and those retirement rates are used in HWSM to produce the projections in AAMC’s

2020 report on the physician workforce.

Based on data from the Florida survey, female physicians intend to retire slightly earlier than their male

colleagues (Exhibit 17). Overall, among 100 physicians active in the workforce at age 50, by age 60

approximately 80 will still be active. By age 70 approximately 30 will still be active. When taking into

consideration that average hours worked declines with age, the number of FTE physicians above age 70 is lower

than indicated by retirement patterns alone.



Exhibit 17 Physician retirement patterns by age and sex

Exhibit 18 shows estimated overall attrition patterns for male physicians by specialty, with some specialties such

as emergency medicine experiencing earlier attrition relative to other specialties. For example, by age 65

approximately 65% of allergists & immunologists are still active, while only 50% of emergency physicians are

still active.



Exhibit 18 Probability male physician is still active by specialty and age

Nurse retirement patterns

HRSA’s 2018 National Sample Survey of Registered Nurses (released in late 2019), which collected information

on age respondents intended to retire, now is used to model retirement patterns for RNs and for APRNs by

specialty.

Different approaches were explored and used to estimate nurse retirement patterns in prior studies of the nurse

workforce. One approach used ACS data and state licensure data to estimate attrition by comparing the number of

nurses in each age cohort across years. For example, the number of active nurses age 60 in a particular year (Y)

are compared to those still active at age 61 in the subsequent year (Y+1) to estimate retirement during age 60. The

advantages of this approach are that a cohort comparison estimates net attrition with some people leaving the

workforce and others re-entering. Disadvantages of this approach are (a) data sources such as ACS survey

different groups of people each year so the number of people in a particular age group and occupation might differ

from year-to-year due to sampling issues, and (b) in both national surveys and state licensure files the number of

people of a specific age and occupation might be small—especially when sub-setting the data to estimate separate



retirement patterns for men versus women. Still, estimated retirement patterns for RNs and for LPNs were similar

using this approach with ACS and state licensure data.

Retirement patterns of other health providers

For other health occupations, HWSM uses retirement patterns estimated from the most recent ACS 5-year public

use microdata sample (PUMS) data. Using the 5-year file instead of the 1-year file allows for a larger sample size

of recent retirees. The ACS includes questions that asks respondents both if they are currently working and if they

were working one year previously. The retirement pattern is created using the assumption that respondents who

are not working currently, were working one year ago, and are 50 or more years old have retired in the last year.

Thus, these retirement patterns are created from observed retirements instead of survey responses about intention

to retire.

Geographic migration

Migration patterns of clinicians across states is an ongoing area of research for HWSM. Cross-state migration can

happen at the start of one’s career upon completion of training or can occur mid-career. The probability of cross-

state migration and the factors influencing such migration vary by occupation, specialty and by state. Higher-

paying occupations like physicians are more likely to be in a national labor market relative to lower-paying health

occupations (from which recruiters might look locally). However, providers in occupations with high rates of self-

employment (e.g., dentists or physicians) are probably less likely to move mid-career--after establishing a

practice—relative to those in occupations with higher proportions of employees who tend to be more mobile.

One scenario models that areas of the country experiencing faster growth in demand for healthcare services will

also experience faster growth in provider supply relative to areas of the country experiencing slower growth in

demand for services. That is, health workers will migrate to those geographic areas where there is greater demand

for their services. This approach has been applied when modeling demand for physicians, dentists, and RNs. The

approach produces the following for the occupation or medical specialty of interest:

1. Projected growth in demand in each state over the forecast time horizon;

2. Projected retirements in each state over the same time horizon;

3. Add projected growth and projected retirements to estimate total new workers required to meet future

demand for services;

4. Sums of total new requirements across states and estimates of each state’s share of total requirements; and

5. How new workers will be distributed across states using this distribution of requirements as a proxy.

Each new entrant to the workforce is assigned a state using this calculated distribution under the assumption that

new graduates will migrate to those geographic locations where growth in demand or retirements creates

opportunities for employment (but allowing current mal-distribution of health professionals to persist). For

example, faster growing states are anticipated to attract a growing proportion of the nation’s new health

professionals while slower growing states are likely to attract a smaller proportion than historical patterns. This

topic is an area for continued research.

Migration patterns for select occupations for which workforce studies have been conducted have used de-

identified records from association membership files to assess change-in-address information for modeling cross-

state migration patterns.



Scenarios

HWSM is designed to model a status quo scenario as well as alternative scenarios based on changes in supply

drivers—namely, number of new entrants to the workforce, changes in labor force participation or hours worked

patterns, and changes in retirement patterns.

• Status quo. This scenario uses the most recent data on number and characteristics of health workers being

trained and entering the workforce, and current data on labor force participation, hours worked, and retirement

patterns.

• Change in number of new graduates. The status quo supply projections model the current annual number of

workers trained, or in the case of occupations with rapid growth model the increase in training capacity as

announced new programs start graduating new workers. High growth scenarios might model, for example, the

implications of training 10% more providers. Low growth scenarios might model the implications of training fewer

providers. In situations where specific data on future changes to the training pipeline is available, a scenario can be

created that compares the potential future change to the status quo. For example, HWSM has been used to evaluate

the workforce implications of proposed state or national legislation to modify the number of physicians being

trained.

• Delayed and Early Retirement. A common concern in the field of health workforce analysis is that retirement

patterns can evolve over time. In particular is concern that rising levels of provider burnout could contribute to

earlier retirement.77–83 Scenarios simulating a one- or two-year shift in retirement patterns can make it easier to

understand the effect this may have on the overall supply of a health profession.

• Hours Worked Cohort Effects. Work-life balance expectations and hours work patterns for health workers

newly entering the workforce could be systematically different from current patterns. That is, in 10 years the

typical physician currently 30-year old might not work the same number of hours as a typical physician currently

40 years old. Analysis of ACS data indicates that average hours worked by physicians has declined over the past

two decades across all age groups, though in recent years the hours worked patterns appear to be stabilizing.25

Likewise, declines in the number of clinicians who are self-employed and changes in reimbursement schemes could

contribute to physicians working fewer hours.84 A scenario which models shifts in hours worked patterns explores

the potential effects of declining hours worked.

Workforce implications of strategies to prevent or manage chronic disease The Disease Prevention Microsimulation Model (DPMM) is designed to model the health and economic

implications of interventions to improve population health. Population health management plays an important role

in modeling future demand for healthcare services and providers—using lifestyle indicators and health-related

behavioral variables regarding smoking, diet, physical activity, and other activities (e.g., preventative screenings,

vaccinations, and early treatment) linked to patient health. Improved lifestyle choices and other preventative care

can help prevent, delay onset, or reduce severity of many chronic conditions such as asthma, diabetes, heart

disease, and cancer.

DPMM has been used to model the implications of lifestyle counseling among overweight and obese adults with

risk factors for cardiovascular disease and diabetes; improved control of blood pressure, cholesterol, and blood

glucose levels through medication; tobacco cessation; and screening and early treatment for select preventable

conditions.25,35–39 Detailed documentation of DPMM is available elsewhere.1

An interdependent relationship exists between the health workforce and prevention efforts to improve health.



• Many prevention interventions are provided by health workers (e.g., screening, counseling, and vaccinations) thus

increasing demand for the occupations that provide such services.

• Reducing prevalence or severity of chronic conditions and adverse medical events through prevention reduces

demand for clinicians who provide those services (and can shift demand to lower-acuity care delivery settings).

• Preventing or delaying onset of chronic disease can increase life expectancy thus increasing patient use of other

healthcare services over the lifespan.

DPMM uses a Markov Chain Monte Carlo simulation approach to model likelihood and timing of disease onset

for each person in a representative sample of the population of interest. Earlier we described creation of a

representative sample of the population in each U.S. county for modeling with HDMM. The population file for

simulation using DPMM requires additional information not needed for HDMM modeling which we obtained

from the National Health and Nutrition Examination Survey (NHANES). In addition to the variables used in the

HDMM population files, DPMM requires body mass index, systolic blood pressure, cholesterol levels, blood

glucose levels, and the presence of other diseases. We constructed a nationally representative sample of 50,000

adults combining multiple years of NHANES data for running the DPMM simulations. Outcomes from DPMM

where then extrapolated to the population employed in HDMM using propensity matching for the demographics,

health behaviors, socioeconomic characteristics, and disease presence information available in both the HDMM

and DPMM population files.

Exhibit 19 provides an overview of DPMM. In a particular year (y), a person’s health risk factors and biometric

readings can affect how biometric levels change over the year as the person ages (to year y+1). Changing

biometrics (as well as the other risk factors) are linked to the probability of various health states (e.g., onset of

diabetes or heart disease). The health states are also linked to each other—e.g., diabetes is an independent risk

factor for heart disease in addition to sharing common risk factors such as obesity and smoking. The presence and

severity of chronic disease affect patient mortality, medical expenses, and other economic and quality of life

outcomes.



Exhibit 19 Overview diagram of the Disease Prevention Microsimulation Model

Similarly, Exhibit 20 illustrates how a biometric variable like BMI is linked to various cancers and endocrine,

cardiovascular, respiratory, and other medical conditions. Many of these medical conditions have independent

effects on disease onset risk for other medical conditions. Each arrow represents a prediction equation in DPMM

that connects disease states.

Risk FactorsBiometrics and Health Inputs

Health States Outcomes

Demographics

Disease history

Biometrics

Smoking

Alcohol misuse

BMI

A1c

SBP

DBP

HDL-C

Total Cholesterol

LVH

Atrial fibrillation

Diabetes & Sequelae

Cardiovascular

Cancers• Breast• Cervical• Colorectal• Endometrial• Esophageal• Gallbladder• Kidney• Leukemia

• Liver• Ovarian• Pancreatic• Prostate• Stomach• Thyroid• Lung• Non-Hodgkin's

Mental & Cognitive

• Diabetes• Prediabetes

• Amputation• Retinopathy

• CHF• IHD• Hypertension

• Dyslipidemia• Stroke• MI

• Depression• Alzheimer’s

• Bipolar • Schizophrenia

Pulmonary• Pneumonia• Asthma• COPD

• Pulmonary embolism

Others• Osteoporosis• Chronic back pain

• GERD• NAFLD

Year y

Year y+1

Mortality

Employment

Absenteeism

Social Security Cost

QALY

Medical Expenses

Personal Income

Long Term Care

Note: Connecting lines show the items in the model that are linked

Abbreviations: BMI=body mass index, CHF=congestive heart failure, CKD=chronic kidney disease, DBP=diastolic blood pressure, HbA1c=hemoglobin A1c, HDL=high-density lipoprotein, IHD=ischemic heart disease, LVH=left ventricular hypertrophy, PVD=peripheral vascular disease, SBP=systolic blood pressure.


Overview diagram of the Disease Prevention Microsimulation Model



Exhibit 20 Overview diagram of body weight component in DPMM

The patient-level output from DPMM can then be run through HDMM to simulate how the presence of chronic

conditions affects patient use of healthcare services and demand for providers.

Model validation, strengths, and limitations The models described in this report continue to be updated and refined to incorporate new data, changes in trends

or care delivery, and improvements to the underlying methods and assumptions. In this chapter we describe model

validation activities, and strengths and limitations of these models.

Endocrine

Diabetes (HbA1c)

Prediabetes (HbA1c)

Cardiovascular

LVH

Hypertension (SBP, DBP)

Dyslipidemia (HDL, Total cholesterol)

IHD

CHF

Direct Effect Disease States Indirect Effect Disease States

Atrial fibrillation

Amputation

PVD

Renal failure

CKD

Stroke

Myocardial infarction

Blindness

Body weight(BMI)

Respiratory

PneumoniaPulmonary embolism

Other

Chronic back pain

Osteoarthritis

Gallstones & gallbladder

GERDMajor depression

NAFLDOSA

CancersBreast

Cervical

Endometrial

Esophageal

Gallbladder

Kidney

Leukemia

Liver

NHL

Multiple Myeloma

Ovarian

Pancreatic

Prostate

Stomach

Thyroid

Colorectal

Note: Connecting lines show the items in the model that are linked

Abbreviations: BMI=body mass index, CHF=congestive heart failure, CKD=chronic kidney disease, DBP=diastolic blood pressure, GERD= gastroesophageal reflux disease, HbA1c=hemoglobin A1c, HDL=high-density lipoprotein, IHD=ischemic heart disease, LVH=left ventricular hypertrophy, NAFLD=non-alcoholic fatty liver disease, OSA=obstructive sleep apnea, PVD=peripheral vascular disease, SBP=systolic blood pressure.

Overview diagram of body weight component in DPMM




Validation activities

Validation activities continue on an ongoing basis during model development and refinement as a long-term

process of evaluating the accuracy of the model and making refinements as needed. For each of four primary

types of validation deployed, key short term and long-term activities include the following:

• Conceptual validation: Through reports, presentations at professional conferences and submission of peer-

reviewed manuscripts the three models described here (HDMM, HWSM, and DPMM) continue to undergo a peer-

review evaluation of their theoretical framework. Contributors to these models include health economists,

statisticians and others with substantial modeling experience; physicians, nurses, behavioral health providers and

other clinicians; health policy experts; and professionals in management positions within health systems.

Conceptual validation requires transparency of the data and methods to allow health workforce researchers and

modelers to critique the model. This technical documentation is an attempt to increase the transparency of these

complex workforce projection models and facilitate improvements to the theoretical underpinnings, methods,

assumptions, and other model inputs. Additional technical documentation for various health occupations is

published elsewhere.24,85,86

• Internal validation: The models run using R, which is open source software. As new capabilities are added to the

models and data sources updated, substantial effort is made to ensure the integrity of the programming code.

Internal validation activities include: (1) generating results for comparison to published statistics used to generate

the models to ensure that population statistics for the input files are consistent with published statistics, (2)

checking for consistency with earlier versions of the models, and (3) stress-testing the models for comparison

against a priori expectations.

• External validation: Presenting findings to subject matter experts for their critique is one approach to externally

validate the model. Intermediate outputs from the model also can be validated. For example, HDMM has been used

to project demand for healthcare services for comparison to external sources not used to generate model inputs.

Results of such comparisons across geographic areas indicate that more geographic variation in use of healthcare

services occurs than is reflected in geographic variation in demographics, presence of chronic disease, and health

risk factors such as obesity and smoking.

• Data validation: Extensive analyses and quality review have been conducted to ensure data accuracy as model data

inputs were prepared. Most of the model inputs come from publicly available sources (e.g., MEPS, BRFSS, ACS,

or published studies)—with the exception that licensure data used in the model is often proprietary to each state

licensure board and purchased data from the American Medical Association and other groups has sometimes been

used for certain studies.

Model strengths

The main strengths of the three models are (1) use of recent data sources and efforts to continuously update and

maintain the models; (2) use of a sophisticated microsimulation approach that has substantial flexibility for

modeling changes in care use and delivery by individuals or by the healthcare system, as well as flexibility to

model a wide range of scenarios; and (3) its development and use across a broad range of different health

occupations, specialties, and stakeholder groups.

Compared to population-based modeling approaches used historically, these microsimulation models incorporate

more detailed information on population characteristics and health risk factors when making demand projections

across geographic areas and over time. For example, rates of disease prevalence and health related risk factors and

household income vary substantially by geographic area. Such additional population data can provide more



precise estimates of service demand at State and county levels compared to models that assume all people within a

demographic group have the same risk factors or use the same level of services.

HDMM simulates care use patterns by delivery setting. Certain populations have disproportionately high use of

specific care delivery settings (e.g., emergency care) and lower use of other settings. Setting-specific information

on patient characteristics and use rates provides insights for informing policies that influence the way care is

delivered. Because the microsimulation approach uses individuals as the unit of analysis, HDMM can simulate

demand for healthcare services and providers of care for sub-populations of particular interest such as low income

populations, populations in select underserved areas, or populations with certain chronic conditions. Additionally,

using individuals as the unit of analysis creates flexibility for incorporating evidence-based research on the

implications of changes in technology and care delivery models that disproportionately affect subsets of the

population with certain chronic conditions or health-related behaviors and risk factors. This information leads to

presumably more accurate projections at state and local levels.

DPMM models the implications for patient health and mortality of changes in health risk factors or health-related

behavior. Combining DPMM with HDMM creates the ability to model scenarios of how changes in population

health can affect demand for healthcare services and providers capturing factors that decrease demand (e.g.,

improved health) and factors that increase demand (e.g., extending longevity so there are more people still living).

Model limitations

Many limitations of these microsimulation models stem from data constraints, as well as uncertainties associated

with an evolving care delivery system and medical technology. Conceptual limitations of the model include how

to define whether the size and mix of the health workforce is adequate, distinguishing between demand versus

need for healthcare services and providers, and quantifying the overlapping scope of services provided by

clinicians in different occupations and specialties.

• Data limitations: These include small sample size associated with some surveys, time lags between when data are

collected via surveys or medical claims files and when information becomes available to researchers, and data not

collected or nor reported in ways that are ideal for modeling.

o Supply data: Supply data for many health professions comes from proprietary data sources such

as association master files (e.g., American Medical Association Master File), state licensure files,

and association-based surveys collecting information on their member practices. Other supply data

comes from the U.S. Census Bureau’s American Community Survey, the Department of Labor’s

Occupational Employment Statistics, data collected and reported by CMS, and other published

sources such as association publications. Each of these data sources has limitations. For example,

surveys might have small sample sizes for certain geographic areas and subsets of the workforce

(e.g., older workers, which data are needed to model retirement patterns), or surveys will differ in

the wording of questions they ask regarding workforce decisions such as retirement intentions.

OES data are collected on filled positions that do not distinguish between part-time and fulltime

employment, and under-report employment in occupations where people are self-employed.

Estimates of workforce supply can differ by source—e.g., estimates from the AMA Master File

data can differ from numbers in state licensure files.

o Demand data: One of the major sources of data on healthcare use patterns is MEPS, which

although is a rich source of data has several limitations. The MEPS sample contains approximately

30,700 to 35,100 individuals per year with the sample size varying from year to year. While for

most healthcare use this sample is large, for less frequent events such as hospitalizations and

emergency visits by diagnosis area the sample is relatively small. Therefore, we combine the latest



five years of survey data to increase sample size for analyses that estimate the correlation between

patient characteristics and annual use of healthcare services. Furthermore, base year utilization

estimates based on MEPS are calibrated to estimates from other sources such as NIS and NAMCS

which have large sample size and/or are weighted to more accurately reflect national totals for

specific types of healthcare use.

A limitation of BRFSS as a data source for disease prevalence and health-related behavior among

the population is that as a telephone-based survey it tends to exclude people in institutionalized

settings who typically do not own telephones. Hence, when creating the population files that

underlie the demand projections BRFSS data is supplemented with CMS data containing

information on a representative sample of people in residential care facilities and nursing homes.

• Evolving care delivery system uncertainties: The healthcare system continues to evolve, with

substantial uncertainty in how advances in medicine and technology might affect demand for services.

Some advances might cure disease or reduce the time to perform certain procedures or patient recovery

time (e.g., laparoscopic surgery) thus reducing demand for healthcare providers; however, these and other

advances can make some services more accessible and improve patient outcomes thereby increasing

demand for these services and providers. To address uncertainty in evolving care delivery, we use

sensitivity analysis and model a variety of scenarios involving potential changes in care delivery.

• Conceptual limitations: Conceptual limitations with the workforce models occur, in part, due to lack of

data to clearly define certain aspects of the modeling and thus the reliance on assumptions.

o Supply adequacy: Most workforce studies start with the assumption that at the national level

supply is adequate to meet demand for services unless there is clear evidence of a supply shortfall

or surplus. This assumption of base year national equilibrium essentially presents future adequacy

relative to current levels, and geographic differences in adequacy compared to the national

average. To the extent that there are current gaps between national supply and demand, then such

base year imbalances persist into the projections of future supply adequacy. One approach used to

quantify current national imbalances is to use estimates from HRSA of the number of additional

providers required to remove Health Professional Shortage Area (HPSA) designations—for

primary care, dental care, and mental health.25,44 Another approach is to survey practices on their

“busyness” to determine if they have excess capacity and would prefer to have more work, or if

they are running at or above capacity and would prefer to have less work or turn some new

patients away because of capacity constraints.26,87

o Demand versus need: HDMM models use of healthcare services, with demand modeled as use

patterns under current prices, economic conditions, care delivery patterns and policies, and current

social expectations. The concept of “need” for services implies a clinical judgement, with need for

health providers also based on a specified care delivery model. Need for services does not take

into consideration economic realities or social expectations (e.g., stigma associated with seeking

behavioral health services) that might cause some people with a clinical need to not seek services.

Likewise, demand for services includes any inefficiencies in care delivery—such as provider-

induced demand, the practice of defensive medicine, or patients seeking care that is not needed.

While the model does not capture demand based on need, some of the modeled scenarios address

the topic of inequity in access to receiving services by modeling the demand implications if

historically underserved populations used care at the same levels as populations that historically

have had fewer barriers to accessing care.



o Overlapping scope of practice: The U.S. healthcare system has substantial overlap in capabilities

across health occupations and medical specialties. For example, much of primary care could be

provided by a physician (primary care or specialist physician), NP or PA. The goal of optimizing

the health workforce is to have providers practicing at the top of their abilities in a cost-effective

manner. While analysis of medical claims files might indicate what types of services are being

provided by different types of providers, there is insufficient information in such claims files to

know if the patients’ needs were effectively being met by the provider seen.

These workforce models were developed using a microsimulation approach in part with the goal of reflecting

evolving standards of care, newly enacted policies, and changing economic factors. To date, data limitations have

limited the ability to model some emerging care delivery models. However, increasingly data is becoming

available to model trends in care use and delivery. This “research in progress” is part of ongoing efforts to

continue to refine and improve the microsimulation models.



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doi:10.2460/javma.242.11.1507



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