2020 State Report Technical Notes and Glossary of Terms
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What is health equity? What are health disparities? And how do they relate?
Health equity means that everyone has a fair and just opportunity to be as healthy as possible. This requires removing obstacles to health such as poverty and discrimination, and their consequences, including powerlessness and lack of access to good jobs with fair pay, quality education and housing, safe environments, and health care.
Health disparities are differences in health or in the key determinants of health such as education, safe housing, and discrimination, which adversely affect marginalized or excluded groups.
Health equity and health disparities are closely related to each other. Health equity is the ethical and human rights principle or value that motivates us to eliminate health disparities. Reducing and ultimately eliminating disparities in health and its determinants of health is how we measure progress toward health equity.
Braveman P, Arkin E, Orleans T, Proctor D, and Plough A. What is Health Equity? And What Difference Does a Definition Make? Robert Wood Johnson Foundation. May 2017
How do we define racial/ethnic groups?
In our analyses by race/ethnicity we define each category as follows:
- Hispanic includes those who identify themselves as Mexican, Puerto Rican, Cuban, Central or South American, other Hispanic, or Hispanic of unknown origin.
- American Indian & Alaska Native includes people who identify themselves as American Indian or Alaska Native and do not identify as Hispanic.
- Asian & Pacific includes people who identify themselves as Asian or Pacific Islander and do not identify as Hispanic.
- Black includes people who identify themselves as black/African American and do not identify as Hispanic.
- White includes people who identify themselves as white and do not identify as Hispanic.
All racial/ethnic categories are exclusive so that one person fits into only one category. Our analyses do not include people reporting more than one race, as this category was not measured uniformly across our data sources.
We recognize that “race” is a social category, meaning the way society may identify individuals based on their cultural ancestry, not a way of characterizing individuals based on biology or genetics. A strong and growing body of empirical research provides support for the notion that genetic factors are not responsible for racial differences in health factors and very rarely for health outcomes.
How do we rank counties?
To calculate the ranks, we first standardize each of the measures using z-scores. Z-scores allow us to combine multiple measures because the measures are now on the same scale. The ranks are then calculated based on weighted sums of the measure z-scores within each state to create an aggregate z-score. The county with the best aggregate z-score (best health) gets a rank of #1 for that state. The aggregate z-scores are graphed next to the maps for health outcomes and health factors in the state report to show the distribution of the values that contribute to the rank. To see more detailed information on rank calculation please visit our website: https://www.countyhealthrankings.org/explore-health-rankings/our-methods/calculating-ranks
- In this report, we use the terms disparities, differences, and gaps interchangeably.
- We follow basic design principles for cartography in displaying color spectrums with less intensity for lower values and increasing color intensity for higher values. We do not intend to elicit implicit biases that “darker is bad”.
- Overall county level values of children in poverty are obtained from one-year modeled estimates from the Small Area Income and Poverty Estimates (SAIPE) Program. Because SAIPE does not provide estimates by racial/ethnic groups, data from the 5-year American Community Survey (ACS) was used to quantify children living in poverty by racial/ethnic groups.
- County-level data for race- and ethnicity specific Children in Poverty values are not shown if the estimate was considered to be unreliable (confidence interval width was greater than 40% or value was 0% or 100%). This most often happens when estimates are based on a very small sample size.
- Given the suppression of data for small sample sizes particularly for county data by race, there may be a gap between the state value and the data for the county data that are available.
- In many of the images using one circle to depict a county the values are very close causing overlapping circles. In these cases, greater color intensity indicates overlapping of multiple counties.