Community health indicators
Indicator content and navigation
This online technical appendix provides more detailed explanations of information included in our indicators.
Each indicator includes the following information where applicable and available:
- The most recent year of data, the rate and estimated number of people affected (some indicators are currently missing this but will be updated in the future. If the estimate of affected individuals is missing, it can be calculated using Table 1 below).
- A chart and description of trends over time from 2000 onward for King County overall, King County regions, race/ethnicity as well as a few additional demographics, where available.
- A table and bar chart of multiple-year averaged estimates by all demographics (e.g. age, gender, race/ethnicity, income or neighborhood poverty level as a measure of socioeconomic status, region), where available. The average yearly number of events is also shown for each demographic characteristic.
- A table of multiple-year averaged estimates by King County Health Reporting Area (HRA).
- A map of multiple-year averaged estimates by HRA, ZIP code, or region.
- Not all demographic and sub-county data are available for all indicators.
- The following symbols are used in graphs throughout the report (*, ^, !):
* Denotes values that are significantly different from the King County average
^ There are too few cases to protect confidentiality and/or report reliable rates
! While rates are presented, there are too few cases to meet a precision standard, and results should be interpreted with caution.
- Data suppression and reliability:
At times, there may appear to be rates or data missing for specific sub-groups or special populations. These data are "suppressed" or not released due to confidentiality restrictions, agreements with data providers about what can be shared, or because rates are highly unstable in a small population. Other symbols appear by rates that have a wide standard error. Standard error is a statistical term that means the estimate is not very precise (see confidence interval below).
Data reliability and suppression guidelines document (PDF)
|Population Year||Population count in King County|
|Total King County population||General indicators||2015||2,052,800|
|Age 0-17||General indicators||2015||432,480|
|Adults 18+||General indicators||2015||1,620,320|
|Adults 18-64||General indicators||2015||1,366,101|
|Adults 65+||General indicators||2015||254,219|
|Adults 50-75||Colorectal cancer screening indicator||2015||557,978|
|Females 50-74||Mammography screening indicator||2015||280,050|
|Females 21-65||Cervical cancer screening indicator||2015||649,001|
|Men 35+||High blood cholesterol indicator||2015||537,304|
|Females 45+||High blood cholesterol indicator||2015||411,394|
|Females 15-24||Chlamydia incidence indicator||2015||125,224|
|Students in grades 6-12||HYS data source indicators||2016-2017 school year||150,691|
|Students in grades 8-12||HYS data source indicators||2016-2017 school year||107,881|
Confidence Interval (also known as error bar) is the range of values that includes the true value 95% of the time. If the confidence intervals of two groups do not overlap, the difference between groups is considered statistically significant (meaning that chance or random variation is unlikely to explain the difference). The wider or larger the confidence interval, the less precise the estimate. For example, if an indicator had a rate of 42% (CI: 18%, 66%), the 18% is the "low end" of the confidence interval, 66% is the "high end", and the estimate is 42%. That means that it is highly likely the "true value" would fall within the 18 to 66 percent range. Another sample or survey year might have a value of 32%, but because of the wide intervals, is unlikely to be a true difference. Confidence intervals are placed in a text box over each point or bar, and are presented visually as an error bar in a chart. Confidence intervals are necessary since observed health statistics (counts or rates computed from surveys, registries, or other systems), may not accurately capture the underlying risk in the population. Observed rates can vary from sample to sample or year to year. Surveys, which are based on a population sample, have "sampling error," or random variation due to having only a subset of the population. Sampling error and random variation increase when the population or number of events is small.
For indicators from the US Census Bureau's American Community Survey (ACS) tables, 90% confidence intervals are used since that is the level of error estimation provided by the Census Bureau. In some cases, the ACS data presented here are grouped differently than those presented in Census Bureau tables (for example, age groups have been collapsed). In these situations, margins of error have been calculated using the ACS Statistical Calculator designed by the New York State Data Center's staff at Empire State Development, based on equations provided in the US Census Bureau's 2006 ACS Accuracy statement.
- Counts give the estimated number of health events, which can be helpful for understanding the magnitude of the problem, or how many people are affected. In areas where there are more people, counts are often higher, so we calculate rates to allow for comparison.
- Rates are usually expressed as the number of events per population per year. When this applies to the total population (all ages), and is not adjusted, the rate is called the crude rate.
- When the measure is calculated for a specific age group (e.g., age 15-24), it is called the age-specific rate.
- Crude and age-specific rates present the actual magnitude of an event within a population or age group.
- Since diseases vary by age, a rate that is not affected by differences in the age composition of the populations allows for fair comparisons between geographies. This is the age-adjusted rate. For example, if a neighborhood with a high proportion of older people also has a higher-than-average death rate, it will be difficult to determine if that neighborhood's death rate is higher than average for residents of all ages or if it simply reflects the higher death rate that naturally occurs among older people. The age-adjusted rate mathematically removes the effect of the population's age distribution on the indicator. By convention, the rate is adjusted to the age distribution of the estimated 2000 US population. In this report, age-adjusted rates per 100,000 are presented unless otherwise noted.
Infant mortality, maternal smoking, and other maternal/child health measures are calculated with live births as the denominator and presented as a rate per 1,000 live births (infant mortality) or percent of births (preterm, low birth weight, etc.).
Prevalence rates (the number who are living with a condition) from the Behavioral Risk Factor Surveillance Survey are expressed as percent of the adult population, usually ages 18+. Exceptions to the age range are noted in the indicator title. These rates are not age-adjusted. Prevalence rates from the Healthy Youth Survey (HYS) are for public school students in the specified grades. The HYS is only asked of students in grades 6 (abbreviated version), 8, 10, and 12.
Incidence rates (the number of new cases diagnosed in a calendar year) are presented for cancer and HIV/AIDS.Titles show the years that are included in each analysis. A dashed line indicates that all years were used (e.g. 2011-2015 includes 2011, 2012, 2013, 2014, and 2015), where a comma indicates a break in years (e.g. 2011, 2013, 2015 is only those specific years).
Are things getting worse or improving over time? This is a trend. Trends that are statistically significant (meaning that chance or random variation is unlikely to explain the difference) are noted as either "rising" or "falling"; findings that do not reach statistical significance are noted as "flat". The probability that a trend over time is simply due to random variation is calculated using statistical tests. If the probability that a trend is due to random variation is less than 5% (i.e., p<0.05), a trend is considered statistically significant. Detailed methodology of the test for trend is found in the following citation: Kim HJ, Fay MP, Feuer EJ, Midthune DN. Permutation tests for Joinpoint regression with applications to cancer rates. Stat Med 2000;19:335-51 (correction: 2001;20:655). For percents from the Healthy Youth Survey (HYS), trend over time is assessed with the svylogit command in Stata (StataCorp. 2013. Stata Statistical Software: Release 13. College Station , TX. Stata Corporation.)
Time trend graphs are shown as rolling averages (averaged rates that overlap) to visually minimize variation of unstable estimates by smoothing the trend line. For relatively small populations, small changes in the number of events will cause the rate to fluctuate substantially and create jagged lines of statistical "noise" with peaks and valleys. To help stabilize the rate and observe the time trend of an event, rates are sometimes aggregated into 3-year "rolling" averages across the total observed period. For example, for events for years 2000 to 2013, the rates are reported as three-year rolling averages: 2000-2002, 2001-2003...2011-2013. Adjacent data points will contain overlapping years of data. The overlap between time intervals makes statistical tests for trend invalid.
Geographies: Whenever possible, indicators are reported for King County as a whole, for 4 regions within the county, and by cities and neighborhoods (see HRA). Education data are reported by school district. Hospitalization data are reported by ZIP codes.
Cities and neighborhoods (Health Reporting Areas (HRAs)): City and neighborhood data are reported in a geography called Health Reporting Areas (HRAs), which were created to coincide with city boundaries in King County. HRAs are based on aggregations of US Census Bureau-defined blocks. Where possible, HRAs correspond to neighborhoods within large cities, and delineate unincorporated areas of King County. In some cases, cities and unincorporated areas need to be grouped together to present data. HRAs are designed to help cities and planners as they consider issues related to local health status or healthy policy. HRAs are used whenever we have sufficient sample size to present the data.
For additional information see our geographic definitions page.
Federal Poverty Guidelines, issued by the Department of Health and Human Services are a simplified version of the federal poverty thresholds. The guidelines are used to determine financial eligibility for various federal, state, and local assistance programs, and are based on annual household income and family size. For a family of 4, in 2010, the federal poverty guideline was a household income of $22,050 or less; in 2013 it was $23,550; in 2018 it was $25,100. When income information is collected, data are analyzed using these guidelines to examine the impact of income on health outcomes. <$15,000, $15,000-$24,999, $25,000-$34,999, $35,000-$49,999, $50,000-$74,999, and $75,000+.
Neighborhood poverty levels are based on the proportion of households in a census tract with annual household income (as reported in the US Census Bureau's American Community Survey (ACS)) below the federal poverty threshold. Neighborhood poverty is used as a proxy when data sources don't have income information for individuals. The three stratifications are:
- High poverty: 20% or more households in the tract below poverty threshold.
- Medium poverty: 5% to 19% of households below poverty threshold.
- Low poverty: fewer than 5% of households below poverty threshold.
The high-poverty area follows the definition of a Federal Poverty Area. The 5% limit for low-poverty areas was chosen to create a group markedly different from Federal Poverty Areas, and thus sensitive to differences in health outcomes that may be the result of socio-economic differences, while maintaining enough tracts in each group for robust comparisons.For area-based measures of poverty, a census tract is considered a neighborhood. Data sources where census tract information are not available use ZIP codes to designate the neighborhood. This report uses poverty information from the 2008-2012 ACS.
Race/ethnicity and discrimination: 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 because we believe it is important to be aware of racial and ethnic group disparities in these indicators. We hope this will lead to strategies that address these issues, as well as the social and economic inequities which underlie them.
Race/ethnicity terms: Federal standards mandate that race and ethnicity (Hispanic origin) are distinct concepts requiring two separate questions when collecting data from an individual. "Hispanic origin" is meant to capture the heritage, nationality group, lineage, or country of birth of an individual (or his/her parents) before arriving in the United States. Persons of Hispanic ethnicity can be of any race. 2010 Census terms: Hispanic or Latino, Not Hispanic or Latino, White alone (Not Hispanic or Latino), Black or African American, American Indian or Alaska Native, Asian, Native Hawaiian or Other Pacific Islander, White, Some Other Race, and Two or More Races. Persons of Hispanic ethnicity can be of any race and are included in other racial categories. Racial/ethnic groups are sometimes combined when sample sizes are too small for valid statistical comparisons of more discrete groups.
Some surveys collect race/ethnicity information using only one question on race. These terms are Hispanic, Non-Hispanic, white Non-Hispanic, Black, American Indian/Alaska Native (AIAN), Asian, Native Hawaiian/Pacific Islander (NHPI), white, and Multiple Race (Multiple).
Indicators include Lesbian, bi-sexual, and gay (LGB) demographics where the data are available, in the Behavioral Risk Factor Surveillance System and the Health Youth Survey data. HYS sexual orientation data are only available in 2016.