Modeling Demand for Health Care Services and Providers

In this module:

The health workforce demand component of HWSM consists of three major elements:  

  • Population databases that contain demographic, socioeconomic, health status, and health behavior information for a representative sample of the current and projected future population in a geographic area (county, state, national).
  • Demand for health care services modeled using prediction equations that relate an individual’s demographic, socioeconomic, health status, and health risk factors to annual health service use by care delivery setting and by health profession seen or diagnosis category.
  • Staffing patterns that convert demand for services into demand for providers.

Exhibit 4 presents a flow diagram for the demand component of HWSM. Not all care delivery sites pertain to every health occupation modeled.

Exhibit 4: Flow Diagram for the Demand Component of HWSM

This diagram shows how various inputs are synthesized into demand projections. It gives a high-level view of the model. The individual components are explained in more detail in this module. The basic equation is that combining Utilization Patterns and Population Data translates into a Demand for Services. Demand for Services is then translated into Demand for Health Workers using Staffing Ratios.

Constructing the Population Databases

The microsimulation approach is where demand for health care services is modeled separately for individual people. This approach requires individual level (micro) data on the predictors of health care use for each person in a representative sample of a designated geographic region (national, state, or county-equivalent). 

Prior to 2019, HWSM reported projections at the state and national levels. State-level population databases were created which could also be aggregated to the national level. Starting in 2019, population files were constructed for each of the 3,142 counties or county equivalents (e.g., parishes, boroughs, independent cities) in the U.S (excluding U.S. territories). Modeling at the county level facilitates evaluation of supply and demand by rurality across states and the nation. This allows for better modeling of health workforce supply for underserved communities and populations. County population files can be combined to produce state files, which in turn can produce the national file.

County level population files start with combining data from multiple sources, as specified later, to create preliminary state population files. These files contain a representative sample of the population in each state by:

  • demographic information
  • household income
  • medical insurance
  • residency institution status (resides in community, in residential care facility, in nursing home) 

Then, the population data are re-calibrated to produce a representative sample of the population in each county with the prevalence of health care use demand determinants (demographics, disease, lifestyle choices, and medical insurance) benchmarked to external sources.

The core micro data file on which HWSM’s baseline population databases are built is the most recent year of ACS.1 The ACS provides the demographic and socioeconomic characteristics of a representative sample of the population in each state. It also has information on medical insurance type, household income, and whether the person lives in a community or institutional setting. To add health risk factors and information on disease presence, using a statistical matching process described later, we match each person in ACS with a similar person in the Behavioral Risk Factor Surveillance System (BRFSS), the Medicare Beneficiary Survey (MCBS)2 , or CMS’s Long-Term Care Minimum Data Set (MDS)3 using random sampling with replacement. 

This process preserves the number of records from the ACS file as well as each record’s ACS sample weight, and thus produces a preliminary population file for each state with population characteristics representative of that state. Each record has a person’s demographics, health-related lifestyle indicators, health conditions, socioeconomic and insurance characteristics, and residency setting (Exhibit 5).

  • 1The 2018 ACS file is used to model primary care workforce. The 2017 ACS file is used to model oral health, updated behavioral health, and the general surgeon workforce. The 2016 ACS file was used to model behavioral health demand and allied health occupations. The 2015 ACS file was used to model demand for long term services and support occupations. The 2014 ACS file was used to model demand for nurses. Earlier versions of ACS were used for previous studies of physicians, nurses, and other health occupations.
  • 2The 2017 Medicare Beneficiary Survey, which is the most recent data, is used for modeling the primary care workforce.
  • 3The 2017 MDS file is the most recent data provided by HRSA for modeling the primary care workforce.

As illustrated in Exhibit 6, for the non-institutionalized population, each individual in the ACS file is matched with someone in the BRFSS from the same:

  • sex
  •  age group (17 age groups)
  •  race
  •  ethnicity
  •  insurance type
  •  household income level (8 income categories)
  •  state of residence4  

Individuals residing in a residential care facility or nursing home are randomly matched to a person in the MCBS or Nursing Home MDS, respectively, in the same state, age group, sex, and race and ethnicity strata. The total number of people living in nursing homes and residential care, by state and age groups, is constructed to match published numbers from CDC. CDC figures show approximately 1.3 million nursing home residents and 687,000 people living in residential care.5

  • 4The 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), then the same criteria as the first round was applied except State was removed as a stratum (remaining 4% matched), and finally for the fifth round, only demographics was included (remaining 0.1% matched).
  • 5National Center for Health Statistics. 2016 National Study of Long-Term Care Providers Web Tables of State Estimates on Residential Care Community Residents (PDF - 165 KB).; 2018.

Exhibit 6: Population Database Mapping Algorithm

This diagram shows how microdata from the American Community Survey (ACS) are matched with other datasets to incorporate health risk factors and information on disease presence. It gives a high-level view of the model. The individual components are explained in more detail in this module.

After creating the preliminary state population file, we construct and calibrate the county level population files. The U.S. Census Bureau reports data on the aggregate number of people in each county in 2018 by five-year age group, sex, and race/ethnicity.6  Using the NCHS urban-rural classifications, we categorize each county as metropolitan or nonmetropolitan.7  In the constructed preliminary state population file, we first divide the population into metropolitan/ nonmetropolitan location using the metropolitan designation in BRFSS. In this file, the number of people in nonmetropolitan areas is understated relative to published sources.

We then re-weight sample weights for people identified as metropolitan to match the demographics of the population in each metropolitan county. We also re-weight sample weights for people identified as nonmetropolitan to match the demographics in each nonmetropolitan county. This produces a weighted sample for each county that is representative of the demographics in each county. The other variables (e.g., household income, insurance coverage, disease prevalence, and prevalence of health risk factors) in this weighted sample are representative of the demographically-adjusted metropolitan and nonmetropolitan populations. We calibrate the county population files to match data from external sources on disease prevalence, lifestyle choices, and medical insurance status.

County-level estimates of disease prevalence are calibrated at the individual level so that the population prevalence numbers exactly match published statistics. Calibration is achieved by first estimating a series of logistic regression equations using BRFSS data.  

The dependent variable is whether the person has the modeled condition or risk factor, with separate regressions used to model:

  • arthritis
  • asthma
  • hypertension
  • cardiovascular disease
  • diabetes
  • history of cancer
  • history of heart attack
  • history of stroke
  • obesity
  • current smoker.8

Independent variables in the regressions are:

  • demographic variables used for demand modeling (age group, sex, race/ethnicity)
  • dichotomous variable indicating whether the person has exercised or participated in physical activity other than their regular job in the past 30 days
  • body weight status—normal, overweight, obese (except for the obesity regression)
  • current smoker status (except for the smoker regression)

Hypertension is included as an independent variable for modeling cardiovascular disease, history of cancer, history of heart attack, and history of stroke. 

Applying the prediction equation to each person in the constructed population file creates a probability that the person has the condition or risk factor. This probability is then compared to a random number generated from a uniform distribution from 0-1. The population file prevalence for a specific condition or risk factor is then adjusted (if needed) until the population prevalence exactly matches the published statistic.

Combined years of BRFSS data are the primary source for external county-level statistics used for calibrating the population file. These statistics are obtained from health departments from individual states (though county-level data availability varies by state). Many states report county-level data for at least some of the disease characteristics and risk factors. Other states report prevalence at the region-level (with counties comprising a region). In these cases, we use the region prevalence for the prevalence for each county in the region. In other cases, states may report prevalence statistics for a subset of the conditions that are calibrated, or for only a subset of the counties within the state. For example, a state might only report prevalence statistics for counties with a sufficiently large population to provide reliable estimates.

  • 6The base year differs for each profession modeled depending on when that profession was last modeled, but each year HWSM is updated with the most current Census Bureau population data.
  • 7Centers for Disease Control and Prevention. 2013 NCHS Urban-Rural Classification Scheme for Counties.
  • 8The logistic regression equations are unweighted because the independent variables included in the regressions (e.g., demographics) are the same variables used in the development of the BRFSS sample weights.

Developing demand forecasts requires creating population databases for future populations. We adjust sample weights of the starting year population to match population demographics (age group, sex, race and ethnicity) in the projections. States report projections at the state and county level, and that the U.S. Census Bureau reports at the national level.9

Except for the “Population Health” scenario, all demand scenarios assume that baseline prevalence rates of health and health behavior characteristics remain the same within each demographic strata (by age, sex, race and ethnicity) into the future. Any scenario that models achieving select population health goals does model changes in disease prevalence and health risk factors within demographic strata.

Previous years’ analyses accounted for projected insurance expansion under the Affordable Care Act (ACA) and state-level reform estimates. By 2018, much of the expanded coverage provisions of ACA had been implemented, which is reflected in the baseline demand estimates. Projections for health occupations updated in 2019 (oral health, general surgery, and behavioral health) did model small increases in medical insurance coverage. This reflected ongoing efforts to expand coverage in ID, ME, NE, UT and VA using published estimates of expanded Medicaid coverage.10  At the national level, the projected expansion is smaller than modeled in previous reports. Thirteen states did not expand Medicaid coverage and have no current plans to do so. This analysis was dropped from the 2020 projections for primary care, women’s health, and behavioral health. It was dropped due to the uncertainty and marginal impact at the national level from expanding insurance coverage in individual states.

Modeling Demand for Health Care Services

This section documents the regression equations employed to estimate health care usage by settings. It also details the health care usage measures that constitute the dependent variables in the regression equations. Also included are the workload measures for care delivery settings like nursing homes and residential care facilities not modeled using regression analysis (Exhibit 9). Exhibit 10 lists the population groups used to estimate the demand for health care services that depend on the population size of potential users.

General Approach to Estimating Health Care Utilization

Healthcare-seeking behavior is generated using data from the Medical Expenditure Panel Surveys (MEPS).11  Five years of data were pooled to provide a sufficient sample size for regression analysis. Regression analyses yielded predicted probabilities and intensity of healthcare use by care delivery setting and type of services, based on a person’s:

  • demographics
  • income
  • insurance status
  • health conditions and risk factors
  • rurality of their place of residence

Predicted probabilities are then applied to the relevant population databases to estimate market demand in the given year.

The Status Quo demand scenario assumes current patterns of care use continue, controlling for changing demographics. Alternative scenarios, described later, make different assumptions regarding care use patterns reflected in emerging care delivery models.

Regression coefficients were generated to calculate the estimated annual amount of healthcare use by each representative person in the population database for:

  • medical office visits
  • hospital outpatient visits
  • emergency department visits
  • hospitalizations
  • home health visits
  • hospice visits
  • oral health visits

Office/clinic visits

MEPS data were used to quantify the relationship between patient characteristics and number of annual office/clinic visits or hospital outpatient visits with a provider. MEPS contains data on visits to many types of providers, including physicians, psychologists, dentists, optometrists, opticians, physical therapists, occupational therapists, and other types of providers.

Prior to 2019, the prediction equations modeled annual visits by provider using Poisson regression to reflect the skewed nature of annual visits. In response to inquiries about issues of over dispersion, potential alternative regression models were evaluated. Negative binomial regression was chosen to replace Poisson regression as discussed further in HWSM Improvement. The change had minimal impact on demand projections.

Explanatory variables in the regressions were variables available in both the constructed population file and in MEPS. These variables are:

  • age group
  • race/ethnicity
  • smoking status
  • body weight category (normal, overweight, obese)
  • presence of chronic conditions (diagnosed with arthritis, asthma, coronary heart disease, diabetes, or hypertension; history of cancer, heart attack, or stroke)
  • insurance type
  • enrollment in a managed care plan
  • household income level
  • rurality of residence
  • MEPS survey year (included to test for systematic changes in utilization over the 5 years of MEPS data analyzed)

MEPS reports the highest-trained person seen during an ambulatory visit. If a patient visited with a physician, the MEPS survey did not indicate whether the patient also saw other health professionals during the visit. Predictive equations were developed from the National Ambulatory Medical Care Survey (NAMCS) to determine the likelihood that a patient would see additional health professionals (e.g., registered nurse) during a clinical visit. Data from NAMCS were also used to estimate the number of prescriptions that were generated during an ambulatory care visit. This number was used in the demand projections for pharmacy-related professions.

Hospital-Related Services

Regressions predicting demand for hospital inpatient and emergency services employ the five latest years of MEPS files, along with the latest National Inpatient Sample (NIS) and National Hospital Ambulatory Medical Care Survey (NHAMCS) files.12  Multiple years of MEPS data were used to increase the sample size and provide reliable estimates for hospitalization and emergency department (ED) visits by medical and surgical conditions.

Hospital Inpatient Services

Utilization patterns of inpatient services by individual characteristics were modeled in three parts:  

  • Annual probability that an individual would experience at least one hospitalization for each of 28 broad diagnosis categories (with categories defined using ICD-9 and ICD-10 codes)
  • The expected length of stay (LOS) for the hospitalization
  • Specialty services and prescriptions received during the hospitalization

The probability of hospitalization in general, acute care, long term, or specialty hospitals for each of the 28 diagnosis categories is modeled with logistic regression using MEPS data. Explanatory variables were the same explanatory variables described previously for modeling office and outpatient visits to providers.  

LOS for the hospitalization is analyzed with Poisson regression using discharge records in NIS. Separate regressions were modeled for each of the 28 diagnosis categories. The dependent variable is total days in the hospital, and the explanatory variables were:

  • patient age group
  • sex
  • race
  • ethnicity
  • insurance type
  • presence of diabetes among the diagnosis codes.

Because NIS contains over 8 million hospital stays, estimates derived from NIS were stable even for hospitalizations for the condition categories with fewer hospitalizations. Expected LOS calculated from NIS is applied to the individuals in the population database and multiplied by hospitalization probability.  This estimates each person’s expected number of inpatient days during the year for the modeled medical or surgical condition categories.

NIS also is used to determine the expected number of prescriptions for hospitalized individuals (which is a component to model demand for pharmacists).

Hospital Emergency Department Services

Utilization patterns of emergency department (ED) services were modeled in two parts:  

  • Probability that a person with given characteristics would have at least one emergency visit during the year for each of 20 categories of services defined by ICD-10 (with earlier studies using ICD-9 codes in older NHAMCS files).
  • During the ED visit, did the patient see a physician, NP, PA and second physician (with the first physician encountered assumed to be an emergency physician and the second physician encountered assumed to be a specialist consultant)? Also modeled are medications prescribed during the visit.

Logistic regression with MEPS data estimates the likelihood that a person with given characteristics would have at least one ED visit during the year for each of the 20 condition categories. MEPS does not identify the medical specialty of providers and lists only the highest level of provider seen. Therefore, the NHAMCS is used to identify the probability that another provider is seen as well as the types of services that typically accompany an emergency visit for a particular category of services. These include medications prescribed and lab tests/exams performed. This information is used to model demand for pharmacists and various allied health occupations.

Post-Acute Care Services

Demand for post-acute care in hospitals and SNFs that are a part of a hospital is modeled as inpatient services. Demand for nursing home care in free-standing nursing homes is linked to the size of the population in nursing homes.

Home Health and Hospice Services

The pooled 5-year MEPS files (n~22,000) were used to model home visits. The files contain annual use of home health services, including information on the type of provider seen during the visit (home health aide, physical therapist, etc.). Prior to 2019, Poisson regression modeled annual visits by each provider type. Starting in 2019, negative binomial regression is used. The dependent variable is annual visits from a specific provider type. Explanatory variables consisted of the same variables used to model demand for office, outpatient, hospital inpatient, and emergency department care.

Utilization of Healthcare Worker Resources Not Captured in MEPS

Some health workers provide services that are not captured in MEPS or not in traditional clinical settings. HWSM models demand for these workers as a provider-to-population ratio (see Exhibit 10). This includes occupations such as nurses, counselors, and physicians who are employed by schools, work in public health departments, or are involved in teaching or research.  

Demand is modeled based on the size of the population who might use such services. For example, the demand for school-based services is derived by HWSM directly from the projected size of the population of school-aged children. Under the Status Quo demand scenario, if the size of the population of school-aged children increased by 5%, then demand for school-based health care would increase by 5%.

  • 11HWSM prediction equations are based on combined five years of MEP data, which combined contains data for approximately 170,000 individuals. The 2013-2017 combined MEPS files were used to model demand for primary care providers. The 2012-2016 combined MEPS files were used to model demand for oral health providers, updated projections for behavioral health, and general surgeons. The 2011-2015 combined MEPS files were used to model demand for behavioral health workers and allied health occupations. The 2010-2014 MEPS files were used to model demand for long term care workers. Earlier years of MEPS data were used to model demand for physicians, nurses, and other health occupations.
  • 12The model currently uses the 2017 NIS and 2015 & 2016 NHAMCS files.

Staffing to Meet Demand for Health Care Services

By applying information on staffing patterns, HWSM converts demand for visits and other utilization measures (described previously) into demand for FTEs by occupation or specialty.

The base year staffing ratio is calculated by dividing the national volume of service used by the number of health care professionals employed in each setting. (This assumes the base year demand for services in each setting is fully met by the available professionals in that setting.) For occupations that provide services in a single setting, base year utilization is divided by the base year supply to derive the staffing ratio for that occupation. The staffing ratio is then applied to the projected volume of services to obtain the projected demand for providers in every year after the base year.

For occupations that provide services across multiple settings (e.g., nurses and therapists), information from the Bureau of Labor Statistics (BLS) on the employment distribution of the care providers in the base year determines the number of individuals working in each setting. The modeled staffing ratios for many health occupations are summarized in an Appendix. Other staffing ratios are summarized in occupation-specific modules of this report.

Status Quo and Alternative Scenarios

Status Quo Scenario

The Status Quo demand scenario in HWSM assumes current national patterns of care use and delivery to the modeled population remain relatively unchanged over time. This scenario models demand considering population demographics, health risk factors, disease prevalence, and economic factors correlated with demand for health care services. It captures population growth and aging over time, as well as geographic variation in demand determinants. When compared against supply projections, this scenario helps inform whether there will be sufficient supply to provide a level of care at least consistent with current levels. The main demand drivers of this scenario are population growth and aging. Changing racial/ethnic diversity also affects demand.

Reduced Barriers Scenario

A hypothetical reduced barriers/improved access scenario was added to HWSM in 2019. National and state goals, as described in initiatives such as Healthy People 2020, are to remove barriers that contribute to inequities in use of services and health outcomes. This will improve access to affordable, high quality care — especially preventive services.13 14 15  This is also part of HRSA’s strategic plan: To improve health outcomes and address health disparities through access to quality services, a skilled health workforce, and innovative, high-value programs.16  

This scenario is modeled for primary care providers, behavioral health providers, women’s health providers, and oral health providers. This scenario first identifies a population that likely faces few access barriers to care. For modeling, we assume this is non-Hispanic white, with insurance, living in a metropolitan area. For oral health, this scenario also includes people in the top income level modeled in HWSM—household income of $75,000 or greater.

Then, using the health care use prediction equations estimated with MEPS data, we simulate if people not in this group had care utilization rates similar to the population likely experiencing fewer access barriers. Examples of people outside the likely group are racial or ethnic minorities, those without insurance, and people living in a nonmetropolitan area.

For women’s health, the metropolitan/nonmetropolitan component of the Reduced Barriers scenario was omitted. This scenario only models the additional demand for providers associated with gaining insurance and if minority populations had care use patterns like those of non-Hispanic white women and adolescent girls.

Although HWSM structure is consistent across occupations, some input data or assumptions vary by occupation. Subsequent modules present occupation-specific information about the estimation process.

Evolving Care Delivery System Scenario

For general surgeons discussed HWSM Improvement, Validation, Strengths, and Limitations and for the allied health and select other occupations discussed in Allied Health & Select Other Occupations Model Components, HWSM modeled an evolving care delivery system scenario that builds on the Status Quo scenario. This scenario was later replaced by the Reduced Barriers scenario for two reasons. First, there were data limitations on the potential workforce impact of evolving trends in care delivery. Also, the Reduced Barriers scenario more clearly and rigorously models key national goals around improving equity in health care access for vulnerable populations.

The Evolving Care Delivery System scenario models that the health care system continues to evolve reflecting:  

  • innovation and evidence-based medicine
  • economic considerations, including payment reform and aligning patient incentives and health plan incentives
  • growing use of team-based care with each occupation contributing based on their specialization and evolving scope of practice
  • public expectations and policies around population health, care access, and quality

This scenario attempts to model the health care system the nation is striving to achieve and the trends that are moving it in that direction. 

This scenario is based on the principles of a high performing health care system striving to:

  • achieve population health goals and provide better preventive care
  • provide a continuum of care across care delivery settings and coordinating multidisciplinary care
  • identify and manage high risk populations using evidence-based strategies and information technology
  • improve efficiency of care delivery—including reducing unnecessary or duplicative diagnostics and treatments
  • measure and improve quality of care.17 18

Mechanisms for achieving these principles include policy and payment reform such as value-based insurance design (VBID) and risk sharing arrangements such as Accountable Care Organizations (ACOs). Patient-centered medical homes (PCMH), team-based care, technological advances, and cost control and other economic considerations are also used. These principles and mechanisms are inter-related, and some mechanisms accomplish multiple principles simultaneously.

A key component of this modeled scenario is improving population health. This component builds on the population health scenario modeled for HRSA for the allied health workforce, the long-term services and support workforce and the nursing workforce. It builds on IHS Markit’s modeling demand for physicians.19  New policy guidelines, provisions in the ACA, and new reimbursement models promote preventive care. These changes have the potential to improve the health of the entire population (beyond just high risk, high utilization subpopulations). Examples include guidelines and reimbursement for counseling and treatment to promote a healthful diet and physical activity to individuals at high risk for developing cardiovascular disease or diabetes, for smoking cessation, and to improve control of blood pressure, cholesterol levels, and hemoglobin A1c levels.20 21 22 23  

The PCMH model is associated with improved adherence to medication for chronic disease. Adherence rose from 59% to 64% among PCMH patients compared to a matched control group in a study.24  Pharmacist-led interventions within the PCMH model have better controlled glycosylated hemoglobin for patients with diabetes, and improved adherence to medication to control high cholesterol and blood pressure.25

The long-term health and mortality implications from achieving population health goals were modeled using a Markov-based microsimulation approach. The long-term findings were then incorporated into HWSM to model the long-term implications for health care use by delivery setting and workforce demand.26 27  The microsimulation model’s prediction equations came from published clinical trials and observational studies. The simulation was conducted using a nationally representative sample of adults constructed using the National Health and Nutrition Examination Survey (NHANES) combined with Census Bureau population projections.

We modeled potential long-term health impacts and health workforce demand implications of achieving the following population health goals. The modeled assumption is that individuals could reach these goals over a short period of time. Nationally, it would take longer to achieve such goals. Many people eligible to participate in programs to achieve such goals (such as Medicare’s Diabetes Prevention Program) might choose not to participate. Hence, this is a hypothetical scenario modeling if the nation could achieve desired goals. It does not attempt to model a specific set of policies or interventions.

  • Sustained 5% body weight loss for overweight and obese adults: Numerous lifestyle interventions have achieved 5% or more body weight loss, on average.28 29  Insurers increasingly are trying to reduce obesity in the US to reduce cardiovascular disease, diabetes, various cancers, and other chronic conditions where obesity contributes. An example intervention is the new Medicare Diabetes Prevention Program (MDPP). Here, patients at high risk for developing diabetes can receive counseling to improve nutritional intake and increase level of physical activity. Patients also receive other intervention to improve body weight and reduce blood glucose levels. Even though patients often regain some body weight after an intervention formally ends, sustained weight loss is possible through a PCMH model or other mechanism that provides long-term counseling and pharmacotherapy.30 31
  • Improved blood pressure, cholesterol, and blood glucose levels for adults with elevated levels: Controlling these key vital signs is part of disease management programs for cardiovascular disease, diabetes, and other chronic diseases. In addition, improving these vital signs is part of the Healthy People goals.15 32  These goals can be achieved with appropriate screening, lifestyle counseling to improve nutritional intake and increased physical activity, pharmacotherapy, and other interventions or policies (e.g., VBID) to improve adherence to medications. Clinical trials show that patients with hypercholesterolemia can reduce total blood cholesterol by 34.42 mg/dL (CI, 22.04-46.40) by using statins33 . Patients with uncontrolled hypertension can reduce systolic blood pressure by 14.5 mm Hg (CI, 14.2-14.8) and diastolic blood pressure by 10.7 mm Hg (CI, 10.5-10.8) by using anti-hypertensives34 . Patients with elevated hemoglobin A1c levels can reduce A1c by 1 percentage point (CI, 0.5-1.25) annually. Modeled improvements then occur gradually until diabetes control is reached at A1c of 7.5%.35  We modeled the above reduction in blood pressure, cholesterol levels, and blood glucose levels for people with elevated levels.
  • Smoking cessation: Smoking cessation is for disease management programs, preventive care, and the nation’s population health strategy. Patients who stop smoking can lower their risk for various cancers, diabetes, cardiovascular disease and other diseases.36  We model that 25% of smokers quit smoking. Our modeling work reflects high levels of recidivism.

Model findings indicate that achieving these health outcomes results in a healthier population. This population requires slightly less per capita use of many types of health care services over time. However, achieving these outcomes reduces mortality. Decreased mortality means increased future demand for health care services to support a larger and older population from delayed mortality.  

In addition to modeled long-term clinical outcomes for health workforce demand, population health strategies can impact changing care use patterns in the short term. One study reports that a population health strategy implemented among a predominantly uninsured, minority, and lower income population reduced ED visits by 21.4% and reduced inpatient care by 36.7% over the subsequent 12 months.37  Other aspects of evolving care delivery are described in later modules discussing specific health occupations and care delivery settings.

Population File Validation

A key demand component of HWSM is the constructed population files containing person-level data for a representative sample of the current and projected future population. Within this population file, the variables most highly correlated with health care services are age, having medical insurance, whether the medical insurance is Medicaid, and presence of chronic diseases. Other variables correlated with use of many health care services are race/ethnicity, rurality of the county in which the person resides, and whether the insured person is in a managed care plan.  

Gender is correlated with use of some health care services, as are current smoking status and body weight status. Household income is correlated with use of oral health services. For most health care services, after controlling for whether the person has medical insurance, the correlation between household income and care utilization diminishes. To more precisely model demand for health care services, some patient characteristics appear to be more important than others when ensuring they’re accurately reflected in the population file.

Prior to 2019, population files were constructed at the state level. One challenge is that ACS does not have a metropolitan/nonmetropolitan variable, unlike BRFSS. So metropolitan could not be a strata when statistically matching a person in ACS with a similar person in BRFSS (to add the health-related variables in BRFSS that were absent in ACS). This match process understated the size of the population in nonmetropolitan areas. States generally do not provide data to calibrate/validate the population characteristics by metropolitan/nonmetropolitan location. Another challenge is that states generally do not produce population projections by metropolitan/nonmetropolitan designation. However, population projections and characteristics that can be used to calibrate/validate the population file are available at the county level for most states.

Starting in 2019, there was increased policy interest in modeling at the sub-state level. Therefore, the population files used to model demand were constructed to be representative of each county. This allows aggregation by level of rurality based on each county’s urban-rural designation.38  The approach used to construct the population files ensures that demographics of the state’s population is identical whether one constructs state-level files or constructs county-level files and then aggregates to the state level.

However, sampling issues with surveys such as BRFSS and ACS can result in slightly different estimates of prevalence for population characteristics (e.g., disease prevalence). This occurs when constructing state population files versus constructing county population files and aggregating to the state level. This is particularly true when projecting into future years because some counties within a state are growing faster than other counties. The characteristics of faster-growing counties will have a larger impact on the state-wide prevalence of select characteristics.  

Two approaches were explored to develop the county population files. In addition to the approach described earlier, an alternative approach would use Public Use Microdata Areas (PUMA) as the sampling unit from ACS and build county-level population files up from each PUMA. This approach to develop county level population files conceptually is an improvement on the current approach used. The county sample would be drawn from a geographic area that is narrower than using the state-wide data files. However, there are drawbacks to using this approach.  

  • Multiple years of data are required (e.g., 3-year or 5-year files) to increase sample size
    • Instead of using the most recent available data, the population file would be constructed with slightly older data. For example, 2016-2018 data for some PUMAs and 2013-2018 data for PUMAs in less densely populated areas
  • The ACS sample might be small for some demographic groups even after combining multiple years of ACS data
  • The contiguous counties that constitute some PUMAs can cross urban-rural designations

We conducted extensive validation exercises on the county-level files to determine if use of PUMA designations improved the county-level population files. Both approaches produce almost identical counts of population demographics (age, sex, race/ethnicity) because demographic characteristics are calibrated to Census Bureau county population statistics. Both approaches required that disease prevalence, medical insurance prevalence, and prevalence of health risk factors (obesity and smoking) be calibrated to match estimates from published sources.

Published data on household income by county do not lend itself to validating whether one of the two approaches performs better. Household income for each person in the sample is reported in ranges that cannot be averaged. In summary, there is no strong evidence that one approach performed better than the other to construct the population files. The current approach takes advantage of more recent data while the PUMA approach might better capture intra-state variation in household income. After controlling for medical insurance, household income appears to have only a small impact on annual use of healthcare services.

These evaluations revealed that the constructed county population files are representative of the counties’ characteristics described by published statistics.

  • 13Centers for Disease Control and Prevention, Division of Oral Health. Healthy People 2020: Oral Health Objectives. Published 2014. Accessed June 11, 2019.
  • 14Koh HK. A 2020 Vision for Healthy People. New England Journal of Medicine. 2010;362(18):1653-1656.
  • 15 a b Office of Disease Prevention and Health Promotion. Diabetes | Healthy People 2020. Accessed July 18, 2019.
  • 16Health Resources & Services Administration. Strategic Plan FY 2019-2022. Department of Health and Human Services; 2019. Accessed June 8, 2020.
  • 17Ahluwalia SC, Damberg CL, Silverman M, Motala A, Shekelle PG. What Defines a High-Performing Health Care Delivery System: A Systematic Review. The Joint Commission Journal on Quality and Patient Safety. 2017;43(9):450-459. doi:10.1016/j.jcjq.2017.03.010.
  • 18Cathy Schoen. The Path to a High Performance U.S. Health System: A 2020 Vision and the Policies to Pave the Way. The Commonwealth Fund; 2009.
  • 19Dall TM, Reynolds R, Chakrabarti R, Jones K, Iacobucci W. 2020 Update, The Complexities of Physician Supply and Demand: Projections from 2018 to 2033 (PDF - 3 MB). IHS Markit report prepared for the Association of American Medical Colleges; 2020.
  • 20Moyer VA. Screening for and Management of Obesity in Adults: U.S. Preventive Services Task Force Recommendation Statement. Ann Intern Med. Published online June 26, 2012. doi:10.7326/0003-4819-157-5-201209040-00475.
  • 21U.S. Preventive Services Task Force. Healthy Diet and Physical Activity for Cardiovascular Disease Prevention in Adults With Cardiovascular Risk Factors: Behavioral Counseling.; 2016.
  • 22Centers for Medicare and Medicaid Services. Medicare Preventive Services. Accessed July 18, 2019.
  • 23U.S. Department of Health & Human Services. Preventive Care. Published June 10, 2013. Accessed July 18, 2019.
  • 24Lauffenburger JC, Shrank WH, Bitton A, et al. Association Between Patient-Centered Medical Homes and Adherence to Chronic Disease Medications: A Cohort Study. Annals of Internal Medicine. 2017;166(2):81.
  • 25Hawes EM, Lambert E, Reid A, Tong G, Gwynne M. Implementation and evaluation of a pharmacist-led electronic visit program for diabetes and anticoagulation care in a patient-centered medical home. American Journal of Health-System Pharmacy. 2018;75(12):901-910.
  • 26Dall TM, Storm MV, Semilla AP, Wintfeld N, O’Grady M, Narayan KM. Value of lifestyle intervention to prevent diabetes and sequelae. AmJPrevMed. 2015;48(1873-2607 (Electronic)):271-280.
  • 27Su W, Huang J, Chen F, et al. Modeling the clinical and economic implications of obesity using microsimulation. JMedEcon. 2015;18(1941-837X (Electronic)):886-897.
  • 28Su W, Chen F, Dall TM, Iacobucci W, Perreault L. Return on Investment for Digital Behavioral Counseling in Patients With Prediabetes and Cardiovascular Disease. Preventing Chronic Disease. 2016;13. doi:10.5888/pcd13.150357.
  • 29Long-term effects of lifestyle intervention or metformin on diabetes development and microvascular complications over 15-year follow-up: the Diabetes Prevention Program Outcomes Study. The Lancet Diabetes & Endocrinology. 2015;3(11):866-875. doi:10.1016/S2213-8587(15)00291-0.
  • 30Costello T, Dorrell M, Kellams T, Kraska K. Review of Pharmacologic Weight Loss Medications in a Patient-Centered Medical Home. Journal of Pharmacy Technology. 2016;32(1):37-41.
  • 31Quattrin T, Cao Y, Paluch RA, Roemmich JN, Ecker MA, Epstein LH. Cost-effectiveness of Family-Based Obesity Treatment. Pediatrics. 2017;140(3):e20162755. doi:10.1542/peds.2016-2755.
  • 32Office of Disease Prevention and Health Promotion. Heart Disease and Stroke | Healthy People 2020. Accessed July 18, 2019.
  • 33Taylor F, Huffman MD, Macedo AF, et al. Statins for the primary prevention of cardiovascular disease. CochraneDatabaseSystRev. 2013;(1469-493X (Electronic)):CD004816.
  • 34Baguet JP, Legallicier B, Auquier P, Robitail S. Updated meta-analytical approach to the efficacy of antihypertensive drugs in reducing blood pressure. ClinDrug Investig. 2007;27(1173-2563 (Print)):735-753.
  • 35Sherifali D, Nerenberg K, Pullenayegum E, Cheng JE, Gerstein HC. The effect of oral antidiabetic agents on A1C levels: a systematic review and meta-analysis. Diabetes Care. 2010;33(1935-5548 (Electronic)):1859-1864.
  • 36Yang W, Dall TM, Zhang Y, et al. Simulation of quitting smoking in the military shows higher lifetime medical spending more than offset by productivity gains. Health Aff(Millwood). 2012;31(1544-5208 (Electronic)):2717-2726.
  • 37Wesson D, Kitzman H, Halloran KH, Tecson K. Innovative Population Health Model Associated With Reduced Emergency Department Use And Inpatient Hospitalizations. Health Affairs. 2018;37(4):543-550. doi:10.1377/hlthaff.2017.1099.
  • 38Centers for Disease Control and Prevention. 2013 NCHS Urban-Rural Classification Scheme for Counties.
Date Last Reviewed: