FAQ

These Workforce Dashboards originated with a comparison of public data sources available to monitor allied health workforce supply and their demographic characteristics with a national report published in 2017. We followed on with a report comparing state estimates of health workforce supply, which complements the Workforce Dashboard on Supply Estimates. We will continue to leverage and add years of data from multiple public data sources across multiple allied health professions in the future, to the extent funding allows.

UW CHWS conducts health workforce research on allied health professionals, among other occupations. These dashboards were supported by the Health Resources and Services Administration (HRSA) of the U.S. Department of Health and Human Services (HHS) as part of an award totaling $619,272 with zero percentage financed with non-governmental sources. The contents are those of the authors and do not necessarily represent the official views of, nor an endorsement by, HRSA, HHS or the U.S. Government. For more information, please visit HRSA.gov. https://www.hrsa.gov/grants/manage/acknowledge-hrsa-funding 

Researchers, policymakers, health workforce planners, and health professional organizations will likely be interested in the findings presented in these dashboards. The information is intended to improve understanding of the local, state and national supply of health care workers, particularly those working in allied health occupations, and how those estimates may vary based on the data source.

For each of our dashboards, you will find a tab titled “Examples” that will walk through example statements that will help you interpret the findings presented in each of the figures. We also recommend that you read through the entire FAQ section to understand the limitations to each dataset as well as read our national and state reports.

If specific figures or data are needed, please contact us at uwchws@uw.edu with subject: Workforce Dashboards.

When using data from these dashboards, we suggest using an accepted bibliographic citation style such as APA or AMA. You can find a suggested citation at the bottom of each dashboard.

We provide detailed descriptions of the data sources used in the dashboards in our national and state health workforce supply reports. In brief:

The American Community Survey (ACS) is an annual survey administered to a nationally representative sample of approximately three million U.S. households by the U.S. Census Bureau. We extracted ACS data from IPUMS USA, which is a free online database that harmonizes variables across time and multiple data sources that is easily accessible to the public.

The Occupational Employment and Wage Statistics (OEWS) were directly obtained for free from the U.S. Bureau of Labor Statistics (BLS). The BLS collects employment and earnings data through a survey administered semi-annually on approximately 200,000 non-farm businesses (800 occupations) across the U.S.. A sampling process combines the current sample with the immediate five prior samples to obtain a combined sample of about 1.2 million businesses.

The years shown in our reports and on the dashboard reflect the most recent data available at the time of production. We expect to update data over time as funding allows, so check back  with our Workforce Dashboards regularly. 

To learn more about how often the underlying data sources are collected and released to the public, please read our national and state reports. 

The data representing the workforce in these dashboards were restricted to individuals age 18 and older and residing in the 50 states and the District of Columbia. 

For dashboards using the American Community Survey (ACS), we included individuals who reported being employed in selected health care occupations defined by 2010 Census occupation codes. 

For dashboards using the Occupation Employment and Wage Statistics (OEWS), formerly known as the Occupational Employment Statistics (OES), we included employer-reported counts of health care workers as defined by the 2010 Standard Occupational Classification (SOC) system (which crosswalks to the Census codes).

Our analyses were funded by a federal grant focused on the allied health workforce, which we broadly define. We selected occupations that reflect a mix of educational and training requirements, relevance, and size. The occupations currently covered across the Workforce Dashboards are occupational therapists, physical therapists, respiratory therapists, speech-language pathologists, clinical laboratory technologists and technicians, dental hygienists, diagnostic-related technologists and technicians, medical assistants, and social workers. We plan to expand the list of occupations that we analyze as funding allows
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To learn more about how specific occupations are mapped to both Census occupation codes and the Standard Occupational Classification (SOC) system, and revisions made over time, please visit the Census website. To learn more about the limitations in comparing counts by occupation across data sources, please read our national and state reports. More information about the health workforce occupations covered by our analyses can be found at the U.S. Department of Labor’s O*Net Online website. 

Data from the American Community Survey (ACS) are based on surveys of individuals while the Occupational Employment and Wages Statistics (OEWS) are based on surveys of employers. Both data sources rely on survey weights to make the data nationally representative. Our research team applied survey weights provided with the ACS data extract while the publicly available OEWS data are already weighted by the U.S. Bureau of Labor Statistics.

To learn more about comparability of counts across data sources, please read our state report. 

Both ACS and OEWS estimates include some amount of error because they are based on a sample of the population. The relative standard error (RSE) is the quantitative measure of the variability of an estimate due to sampling and reflects the uncertainty associated with likely differences in estimates between samples as well as between a sample and the population. We use the stoplight icons on our dashboards to indicate where error may be large: a yellow light means “use with caution” (25%≤ RSE ≤ 49.9%) and a red light means “unreliable” (RSE≥50.0%). To learn more about RSEs, please read our state report. 

Our dashboards do not restrict based on health care setting, sector, or industry so the dashboards represent health care workers across settings. While not used in these dashboards, the American Community Survey (ACS) collects self-report data on individuals’ sector/industry of employment as defined by Census industry codes, which can be crosswalked to the North American Industry Classification System (NAICS). Learn more about these codes here.

Our dashboards do not attempt to address missing data. Missing data in the American Community Survey (ACS) are managed (e.g., imputations) by the U.S. Census Bureau and IPUMS as described here. Missing data in the Occupational Employment and Wage Statistics (OEWS) are managed by the U.S. Bureau of Labor Statistics as described here.

We obtained the annual estimates of the population published by the U.S. Census Bureau for the respective years of health care occupation counts. These population data were used to calculate the supply of each occupation per 100,000 population estimates at the national and state levels. 

Public Use Microdata Areas (PUMAs) are a geographic boundary developed by the U.S. Census Bureau. PUMAs consist of about 100,000 individuals within states, and reflect parts or groups of counties. PUMAs are designed so as not to disclose identifiable respondent information. PUMAs are the smallest geographic regions available in the American Community Survey (ACS). 

The American Community Survey (ACS) collects information on the location of an individual’s residence and the location of an individual’s primary workplace. While both locations are coded using the same PUMA coding system, PUMAs may be aggregated differently for work and residence due to differences in confidentiality concerns. For example, work PUMAs are larger than residence PUMAs. Read more here.

Data reported in the commute maps are based on self-reported place of residence and place of primary work. Examples of reasons why this occurs include that some individuals may have multiple residences that are not captured by the survey, and some individuals may not be commuting on a daily basis and may travel to a place of work where they have temporary residence.