Demographic data encompasses all static aspects of a population: age, economic characteristics, race, and sex . The importance of addressing demographic data boils down to the relationship between health equity, health disparities, and health literacy. The World Health Organization encourages the practice of health inequality monitoring to use demographic data to identify disadvantaged groups within populations and inform equity-oriented policies, practices, and programs . Reducing inequalities is one of the Sustainable Development Goals of the UN and COVID-19 has deepened the existing inequalities, even more with vulnerable populations affected by systematic and structural discriminations .
Addressing demographic data allows public health professionals to study health disparities in a population. Identifying these health disparities in a broad range of social dimensions can expose patterns of health literacy between populations and how they contribute to health inequities. Disparities in health literacy parallels with disparities in health outcomes, public health research has shown a significant overlap in demographic characteristics of those who are at risk for health disparities and low health literacy. Observing where those differences lie in economic and social inequalities allow for further understanding on how to address them. The disproportions in income and education contribute to disparities in health literacy and the range of disparities within these demographic characteristics show populations that could benefit from tailored interventions to improve their health literacy .
With the raging wave of the COVID-19 cases in the US, gathering demographic data is as crucial as ever. Therefore, this poster includes the research of gathering and analyzing demographic data on age, sex/gender, and race/ethnicity throughout all 50 states and the District of Columbia, to better understand the impact COVID-19 has on the US population.
Currently, BroadStreet is developing a State Grades webpage. The intention is to focus on how dashboards present information and provide a full grading schema analyzing representation, accuracy, and consistency within dashboard reporting. Please come back soon to learn more!
The need to create a more uniform reporting structure for demographic information is imperative, as it reduces health inequities in research and healthcare treatments for specific populations. In addition, proper data can provide greater accountability and better treatment in healthcare settings so that when it comes to disease and illness, patients of different genders, ethnic backgrounds, or age groups, are treated with the specific demographic risk factors in mind [2,7]. Furthermore, the more accessible demographic information is, the higher capacity for health literacy in the general population. Increasing the populations health literacy allows individuals to make sense of health information and services available to them, understand their choices, and communicate their needs .
Ultimately we urge state dashboards to incorporate reliable demographic data on their webpages in a more accessible format with accurate definitions and clear categorizations so to better serve the community during the current COVID-19 pandemic. The ability to reach a data transparent world is evident, however, further research and competency training is required to best determine a clear reporting format that is representative and standardized.
The racial categories included in the census questionnaire generally reflect a social definition of race recognized in this country and not an attempt to define race biologically, anthropologically, or genetically.
According to the Census, origin refers to Hispanic, Latino, or Spanish ethnicity. Though many respondents expect to see a Hispanic, Latino, or Spanish category on the race question, this question is asked separately because people of Hispanic ethnic origin may be of any race(s).
An individual’s response to the race question is based upon self-identification. The 1997 OMB standards permit the reporting of more than one race. OMB requires five minimum categories: White, Black or African American, American Indian or Alaska Native, Asian, and Native Hawaiian or Other Pacific Islander.
To see a deep dive into racial and ethnic demographic data across the US, check out BroadStreets Health Equity dataset. The Heath Equity track identified which U.S. counties are providing COVID-19 confirmed case counts by race and ethnicity using state health department websites, county websites, or county public health department websites.
The Census definition of sex is determined by biological aspects of men and women (anatomy, hormones, chromosomes).
The Census describes gender as a social construct determined by a society of culture. These assignments may differ across cultures and among people within a culture. Depending on the culture, gender may or may not correspond directly to sex.
In the Census, one question is asked about the individuals sex to mark either male or female. They do not ask about gender in the Census.
The purpose of having set definitions across the country is to provide consistency in the data. Ambiguity in these different topics interferes with accuracy of the data.
We decided to use the US Census since it is widely used for state departments to format their reporting of demographics. The Census Bureau has a long history of conducting research to improve questions and data on race and ethnicity. Since the 1970s, the Census Bureau has conducted content tests to research and improve the design and function of different questions, including questions on race and ethnicity. Census has been using the OMB standards in partnership with the NIH since 1977 and have revisited and altered their definitions each year.
Bachelor of Science in Psychology | University of Minnesota College of Liberal Arts
Master of Public Health-Epidemiology and Biostatistics | University of Minnesota School of Public Health
1. Beauchamp, A., Buchbinder, R., Dodson, S., Batterham, R. W., Elsworth, G. R., McPhee, C., . . . Osborne, R. H. (2015). Distribution of health literacy strengths and weaknesses across socio-demographic groups: A cross-sectional survey using the health literacy questionnaire (HLQ). BMC Public Health, 15 Retrieved from https://ezproxy.elon.edu/login?url=https://www-proquest-com.ezproxy.elon.edu/docview/1780735275?accountid=10730
2. Berg, S. (2018, May 15). Improve health equity by collecting patient demographic data. Retrieved September 17, 2020, from https://www.ama-assn.org/delivering-care/population-care/improve-health-equity-collecting-patient-demographic-data
3. Bureau, U. (2020, April 22). About Race. Retrieved September 18, 2020, from https://www.census.gov/topics/population/race/about.html
4. Bureau, U. (n.d.). Why We Ask About... Sex. Retrieved September 18, 2020, from https://www.census.gov/acs/www/about/why-we-ask-each-question/sex/
5. Bureau, U. (2020, September 11). Age and Sex. Retrieved September 18, 2020, from https://www.census.gov/topics/population/age-and-sex.html
6. Demographic Data. (2019, March 14). Retrieved September 18, 2020, from https://www.nihlibrary.nih.gov/resources/subject-guides/health-data-resources/demographic-data
7. Ensocare. (2017, June 15). How Demographics Impact Healthcare Delivery. Retrieved September 17, 2020, from https://www.ensocare.com/knowledge-center/how-demographics-impact-health-care-delivery
8. Fleary, S. A., & Ettienne, R. (2019). Social disparities in health literacy in the united states. Health Literacy Research and Practice, 3(1), 47-52. doi:http://dx.doi.org.ezproxy.elon.edu/10.3928/24748307-20190131-01
9. Howden, L. M., & Meyer, J. A. (2011, May). Age and Sex Composition: 2010. Retrieved September 18, 2020, from https://www.census.gov/prod/cen2010/briefs/c2010br-03.pdf
10. Introduction. (2018, April). Retrieved September 18, 2020, from https://www.ahrq.gov/research/findings/final-reports/iomracereport/reldata1.html
11. Reduce inequality within and among countries – United Nations Sustainable Development. (n.d.). Retrieved September 18, 2020, from https://www.un.org/sustainabledevelopment/inequality/
12. Rikard, R. V., Thompson, M. S., McKinney, J., & Beauchamp, A. (2016). Examining health literacy disparities in the united states: A third look at the national assessment of adult literacy (NAAL). BMC Public Health, 16 doi:http://dx.doi.org.ezproxy.elon.edu/10.1186/s12889-016-3621-9
13. Thomas, B. (2014). Health and health care disparities: The effect of social and environmental factors on individual and population health. International Journal of Environmental Research and Public Health, 11(7), 7492-507. Retrieved from https://ezproxy.elon.edu/login?url=https://www-proquest-com.ezproxy.elon.edu/docview/1555275005?accountid=10730
14. Hosseinpoor, A. R., Bergen, N., & Schlotheuber, A. (2015). Promoting health equity: WHO health inequality monitoring at global and national levels. Global Health Action, 8(1) doi:http://dx.doi.org.ezproxy.elon.edu/10.3402/gha.v8.29034
All references to state health departments retrieved September 18th, 2020.
Alaska | Alabama | Arkansas | American Samoa | California | Colorado | Connecticut | District of Columbia | Delaware | Florida | Georgia | Guam | Hawaii | Iowa | Idaho | Illinois | Indiana | Kansas | Kentucky | Louisiana | Massachusetts | Maryland | Maine | Michigan | Minnesota | Missouri | Northern Mariana Islands | Mississippi | Montana | North Carolina | North Dakota | Nebraska | New Hampshire | New Jersey | New Mexico | Nevada | New York | Ohio | Oklahoma | Oregon | Pennsylvania | Puerto Rico | Rhode Island | South Carolina | South Dakota | Tennessee | Texas | Utah | Virginia | US Virgin Islands | Vermont | Washington | Wisconsin | West Virginia | Wyoming