The following technical terms are used in Public Health data publications.
Age-adjusted rate: Rates are useful for comparing indicators across different populations. Since age is a factor that influences health, comparisons are more representative when we account for different age distributions of populations. Age-adjustment is a method used to compare populations allowing for an “apples-to-apples” comparison between different geographic or demographic populations with different age profiles.
Community of color: In some instances, we combine all of the minority racial ethnic populations together to display a rate for all “communities of color” when there are too few data points to display individual racial ethnic categories. We report on communities of color to emphasize common experiences of social and economic discrimination and other forms of racism that can negatively affect the health and well-being of these communities.
Confidence interval: Confidence intervals tell us how certain we are that a statistic is accurate. The width of the confidence interval is determined by the margin of error. The smaller the confidence interval, the more confident we are that the statistic is accurate (the true value). Large confidence intervals can be a sign that the data is based on a small sample size and the statistic may not be a stable estimate.
Demographic information - The "person" characteristics — age, sex, race, and occupation — of descriptive epidemiology used to characterize the populations at risk.
Health: We use the World Health Organization definition of health, which is a state of complete physical, mental, spiritual, cultural and social well-being, not merely the absence of disease or illness.
Health disparity: A difference in health between different populations, neighborhoods, or communities.
Health equity: 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, 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.1
Health inequity: A health disparity that is rooted in unfair opportunities for education, employment, housing, income, self-determination, and other elements needed to attain full health. These differences in health are not only unnecessary and avoidable but, in addition, are considered unfair and unjust.2
Indicator: A measure that reflects, or indicates, the state of health of persons in a defined population.
Protective factor: An aspect of personal behavior or lifestyle, an environmental exposure, or an inherited characteristic that is associated with a decreased occurrence of an adverse health outcome.
Risk factor: An aspect of personal behavior or lifestyle, an environmental exposure, or an inborn or inherited characteristic that is associated with an increased occurrence of disease or other health-related event or condition.
Significant difference: An analysis to determine whether two data points (for example, rates in Clark County and Washington state) are truly different or different due to chance.
Structurally disadvantaged people or populations: People who face systemic barriers to health and prosperity due to discrimination based on social class, race or ethnicity, gender, educational attainment, and neighborhood of residence. More recent efforts have expanded these attributes to include sexual orientation, gender identity (cis vs. transgender), indigeneity, and disability status.
Trend: A long-term movement or change in frequency, usually upwards or downwards. Influencing changes in trends takes time - typically at least 3 or more data points are needed before we can assess significant changes in trends.
Underserved and disinvested neighborhood or community: A neighborhood or community that has historically received scarce or insufficient public-sector and private-sector investment and services relative to their needs, due to structural racism and other factors linked to power and influence.
- “What is Healthy Equity?” Robert Wood Johnson Foundation, 21, July 2021, https://www.rwjf.org/en/library/research/2017/05/what-is-health-equity-.html
- “Health Disparities.” Robert Wood Johnson Foundation, 4 Nov. 2020, https://www.rwjf.org/en/our-focus-areas/topics/health-disparities.html
Frequently Asked Questions
Monitoring health status to identify and solve community health problems is a foundational public health service. Data and research serve as the foundation for public health action to improve the health of people and communities.
To access data at the population level, we rely on public health surveillance, which is the ongoing, systematic collection, analysis, and interpretation of health-related data. This information collected by more than 3,000 federal, state, and local agency partners, and many of these data systems are included on our webpage. The data are used to plan, implement, and evaluate public health programs and policies.
No single school district, agency or neighborhood association is responsible for the health and wellbeing of Clark County – we all are. We publish data hoping they spark important conversations about collective community priorities and our vision for Clark County. Our intention is that data serves as a call to action and that community efforts are driven by both data and by the experiences, observations, and deep collective wisdom from day-to-day life across Clark County.
When reporting data among smaller populations within Clark County, such as demographic or geographic sub-groups, there are not enough data points in a single year to either maintain confidentiality or produce statistically reliable estimates. Therefore, we combine multiple years of data to increase the data points available for specific sub-populations, allowing us to maintain confidentiality and produce reliable estimates.
The data sources utilized in public health undergo rigorous processing and quality assurance at the state and/or national levels, which can take up to two years after data collection is complete. In addition, some data are collected only every other year (such as Healthy Youth Survey) or less frequently. We make every effort to provide the most recent data available at the time of publishing.
For birth certificate and death certificate data, race and ethnicity categories follow the federal standards for reporting on race and ethnicity and are presented as seven mutually exclusive categories:
- non-Hispanic White only
- Hispanic (of any race)
- non-Hispanic Black only
- non-Hispanic American Indian/Alaska Native only
- non-Hispanic Asian only
- non-Hispanic Native Hawaiian/Pacific Islander only
For the Behavioral Risk Factor Surveillance System (BRFSS) and the Healthy Youth Survey (HYS) data, self-identified racial/ethnic disparity data are reported but categories are not mutually exclusive. A higher proportion of people identifying as American Indian/Alaska Native and Black identify with multiple races and are excluded when race categories only count people of a single race. In order to improve our ability to include more data for minority racial/ethnic groups, we disaggregate the multi-racial category and present on seven categories that are not mutually exclusive:
- American Indian/Alaska Native
- Native Hawaiian/Pacific Islander
Some indicators only display a subset of the racial/ethnic groups listed above when there are too few data points to maintain confidentiality and/or produce statistically reliable estimates. In these cases, we combine all of the minority racial ethnic categories to display a rate for all “communities of color.”
Race and ethnicity are markers for complex social, economic, and political factors that can influence community and individual health in important ways. Many communities of color have experienced social and economic discrimination and other forms of racism that can negatively affect the health and well-being of these communities. We continue to analyze and present data by race/ethnicity (when possible) or combined as communities of color because we believe it is important to be aware of racial and ethnic disparities in these indicators.
In mapping public health data, we are able to identify areas or groups of people that are important to support because 1) they have a higher risk of experiencing health inequities than the rest of the community; 2) they have a high percentage of structurally disadvantaged people; and/or 3) they have been historically underserved and deprived of investment.
We take care to recognize and explain the caveats of the data sets that are available in Clark County. Data are suppressed (not included) when there are not enough data points to maintain confidentiality or produce statistically reliable estimates among a population group or a geographic area.
Zip codes and census tracts are different “geographies” or representations of a geographic area. Geography is central to the work of many data collection efforts, providing the framework for survey design and sampling, data collection, and reporting. Geographies like the zip code, census tract, school boundaries, or neighborhood boundaries, provide meaning and context to statistical data. Sometimes the boundaries are meaningful (e.g. how you identify with your neighborhood, or your school district) while others are purely administrative.
Census tracts were drawn by the U.S. Census Bureau and typically encompass an area with about 4,000 people in it. Tracts tend to follow existing boundaries (including city, county, and road boundaries). Click on this link to learn more on the U.S. Census website.
Zip codes are typically larger areas and therefore encompass many census tracts or parts of various tracts. They were developed by the U.S. postal service to assist with the processing of mail.
It is important to understand that a relationship between two data points does not mean statistical causation. Just because patterns in the data look similar between two indicators does not mean that one causes the other – or even that one is related to the other (correlation). Therefore, we urge you to explore the data and have conversations about the patterns that you may see – but not to draw conclusions about causes.
We always look forward to your feedback, ideas, and questions. Let us know how you are using public health data or send us a question at email@example.com.
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