Metrics and definitions

Age-adjusted ratios

Health equity significance

Adjusting for age allows for fairer comparison between populations, where age might be a confounding risk factor and the studied groups have different distributions of individuals per age group. By normalizing for age, we can paint a more accurate picture of undue burden of disease and death between populations.

Measurement definition

A statistical process applied to rates of disease, death, or other health outcomes that correlate with an individual's age.

Percent rate

Measurement definition

A percent rate, in health equity data visualization, is a measure that expresses a particular metric as a percentage of a given population.

Percent share

Health equity significance

Measuring percent share helps identify disproportionate impacts on different demographic groups by comparing their share of a health outcome to their share of the total population.

Measurement definition

Percent share represents the proportion of a specific subgroup's contribution to a total metric, often expressed as a percentage. When compared to population share, it reveals whether certain groups are experiencing outcomes at rates higher or lower than expected.

Example: COVID-19 cases

In the example below, we use COVID-19 cases as the variable, and race and ethnicity as the demographic breakdown for simplicity; the definitions apply to all topics and demographic breakdowns.

As an example, if in a certain month, White (Non-Hispanic) people in Georgia had 65.7% share of all COVID-19 deaths, but only 52.7% share of their population, their disproportionate percent share would be +13%:

Formula: 65.7% − 52.7% = +13.0%

This value is then divided by the population percent share to give a percent relative inequity of +24.7%:

Formula: (+13% ÷ 52.7%) = +24.7%

In plain language, this would be interpreted as,

"Deaths of individuals identifying as White (Non Hispanic) in Georgia from COVID-19 were almost 25% higher than expected, based on their share of Georgia's overall population."

Population share

Health equity significance

Measuring population share helps policymakers and healthcare organizations allocate resources more equitably. When certain demographic groups are underrepresented in healthcare access or have worse health outcomes, it may indicate a need to invest in programs and services that address these disparities.

Measurement definition

The percentage of the total population that identified as a particular race/ethnicity in the ACS (American Community Survey). This metric is rounded to one decimal place. In instances where this would round to 0%, two decimal places are used.

Relative inequity

Health equity significance

Highlighting the relative disadvantage experienced by specific communities or individuals helps identify and address disparities to achieve greater equity in health.

Measurement definition

Relative inequity refers to disparities in health outcomes, access to healthcare, or resource allocation between different demographic groups within a population. It measures the extent to which one group's health status or healthcare access deviates from the overall population's.

Share of total cases

Health equity significance

This calculation is done for every point in time for which we have data, allowing visualization of inequity relative to population, over time.

Measurement definition

To demonstrate the often inequitable distribution of a condition or disease, we calculate each demographic group's relative inequity using the formula: (OBSERVED - EXPECTED) / EXPECTED. OBSERVED is each group's percent share of the condition, and EXPECTED is that group's share of the reference population. For most metrics this is the total population; for adult-only metrics (such as those from America's Health Rankings), EXPECTED uses the adult (18+) population share so numerator and denominator are measured on the same population basis.

Share of total cases with unknown races

Health equity significance

Measuring the share of total cases with unknown race and ethnicity is significant for health equity because it signals potential underreporting or lack of data collection in specific demographic groups, making it challenging to address health inequities effectively. Addressing these gaps is crucial to ensure that healthcare resources and interventions are targeted to the most vulnerable populations, promoting equitable health outcomes.

Measurement definition

Within a locale, the percentage of cases that reported unknown race/ethnicity.

Social Vulnerability Index

Measurement definition

A Social Vulnerability Index (SVI) score refers to the "demographic and socioeconomic factors (such as poverty, lack of access to transportation, and crowded housing) that adversely affect communities that encounter hazards and other community-level stressors. These stressors can include natural or human-caused disasters (such as tornadoes or chemical spills) or disease outbreaks (such as COVID-19)." (Source: Agency for Toxic Substances and Disease Registry)

To provide context when viewing county-level reports, SVI rankings are obtained from the CDC and incorporated into our alerting system where available. We use the most recent SVI data which is from 2022.

The Social Vulnerability Index uses percentile ranking values ranging from 0 to 1 to assess the resilience of communities when confronted by external stresses. Scores between 0-0.33 indicate a low level of vulnerability, 0.34-0.66 signify a medium level, and 0.67-1 designate a high level of vulnerability. Specifically, areas that fall in the top 10%, or at the 90th percentile of values, are marked as highly vulnerable, while those below this threshold have comparatively lower vulnerability. From a health equity perspective, understanding these vulnerability scores is crucial. It enables policymakers and health officials to allocate resources effectively, ensuring that the most vulnerable communities receive the necessary support during crises, thus promoting equitable health outcomes.

Time-series

Health equity significance

Time-series data allows for the visualization of health inequities over time, enabling researchers and policymakers to track the evolution of disparities and assess the effectiveness of interventions. By examining trends across different demographic groups, we can identify persistent patterns of inequity and understand how external factors may disproportionately impact certain populations.

Measurement definition

Time-series data represents health outcomes tracked chronologically, typically using the earliest available date for each case (such as symptom onset, positive test date, or initial report date). Each data point represents the incidence rate - the number of new cases reported within a specific time period, usually monthly intervals.

Data collection notes

Time-series analysis uses the earliest reliable date field available for each case to categorize when health events occurred. This approach provides the most accurate temporal distribution possible while acknowledging that for complex outcomes like deaths or hospitalizations, the plotted date may represent when the case was first identified rather than when the specific outcome occurred.

Methodology

Only confirmed cases are included in rate calculations and inequitable distribution metrics. When data shows zero cases for a demographic group in a time period, unconfirmed cases may still exist but have not been officially reported. Geographic jurisdictions that report zero cases for an entire demographic group across all time periods are excluded from visualizations, as this typically indicates lack of data collection for that population.

Total cases per 100k people

Health equity significance

Measuring total cases per 100,000 people allows for the standardized comparison of disease burden across different populations. This metric accounts for differences in population size and provides a clearer understanding of the prevalence and impact of a health condition on communities, making it valuable for identifying disparities.

Measurement definition

This metric normalizes for population size, allowing for comparisons across demographic groups. This metric is rounded to the nearest integer in the tracker. The total rate of occurrence of cases expressed per 100,000 people (i.e. 10,000 per 100k implies a 10% occurrence rate).

Population

Unless otherwise noted throughout this methodology, population counts and rates are obtained from the American Community Survey (ACS) 5-year estimates. Wherever possible, we utilize source data population counts, and when those are unavailable we match historical condition numerators with population denominators from the same years. For current, single-year reports, we utilize the ACS 5-year estimates from 2024.

Some sources report on a specific sub-population rather than everyone living in a place. For measures that describe adults only, such as most of those from America's Health Rankings, we use the adult (18+) population as the denominator for percent share and relative inequity calculations. This keeps the numerator and denominator on the same population basis, and avoids overstating disadvantage for groups with younger age distributions.

Visualizations

Please consider the impact of under-reporting and data gaps when exploring the visualizations. These issues may lead to incorrect conclusions, e.g. low rates in a given location may be due to under-reporting rather than absence of impact.