Appendix Methodology | 2024 Annual Report

How State Rankings Were Generated
This year, 88 measures (including 49 weighted and 39 additional unweighted measures) were analyzed for the America’s Health Rankings® 2024 Annual Report, using the most recent data available as of October 31, 2024. Data years varied by measure because of the variety of data sources. Multiple data years were combined for some measures to ensure reliable state-level estimates. Measure definitions, sources and data years are available in the Appendix: Measures Table. Measure changes were based on input from the Annual Report Advisory Committee and are detailed on the 2024 Annual Report Measures Selection and Changes webpage.
Each state was ranked according to its value for each measure, with a rank of No. 1 assigned to the state with the healthiest value. Ties in value were assigned equal ranks. If a state value was unavailable for a measure in the 2024 Annual Report data, it was noted as unavailable or suppressed. 
Summations were generated overall and by model category. Summations show how a state compares with other states for a model category, such as Social and Economic Factors or Overall. 
Overall state rankings were based on 49 weighted measures that met the following criteria:
Represented issues affecting population health. 
Had state-level data available.
Used consistent measurement across the 50 states.
Were current and updated periodically.
Could be improved over time. 
The state value for each measure was normalized into a z-score, hereafter referred to as “score,” using the following formula: 
The score indicates the number of standard deviations a state value was above or below the U.S. value. Scores were capped at +/- 2.00 to prevent an extreme score from excessively influencing a state’s overall score. If a U.S. value was unavailable from the original data source for a measure, the mean of all states and the District of Columbia was used. If a value was unavailable for a state, its value from the most recent available data year was used to generate a score. 
Summation scores were calculated by adding the products of the score for each measure multiplied by that measure’s assigned model weight and association with health. Measures positively associated with population health, such as public health funding and flu vaccination, were multiplied by 1. In contrast, measures with a negative association, such as smoking and premature death, were multiplied by -1. A state that ranks No. 1 will have a higher summation score (e.g., 2.00), reflecting better health, whereas a state that ranks No. 50 will have a lower summation score (e.g., -2.00). The overall state ranks were calculated by ranking the overall summation score, which included all 49 measures with weights in the model (see Measures, Weights and Direction for model and measure weights).
Scores and ranks were not calculated for the District of Columbia because of its unique status as an entirely urban population with different governing and funding mechanisms than states. While the District of Columbia was not included in the overall state rankings, its data are available on the America’s Health Rankings website.
For additional methodology information, submit an inquiry on the America’s Health Rankings website.
Data Analysis
Each year, America’s Health Rankings identifies significant findings and disparities for the nation and each state. Public health officials, policymakers, advocates, community leaders, researchers and individuals can use these insights to guide their decisions and strategies to improve health at the state and national levels.
State Summaries
Strengths and Challenges
The top positive and negative contributors to a state’s overall health ranking are selected as strengths and challenges. Additional considerations for identifying which measures to include in this section are whether data were recently updated, how specific to the report/population the measure is and the model category as a mix of topic areas is preferred. Of note, the additional (unweighted) measures are not included in the state’s overall rankings and are therefore excluded from the selection process. 
The District of Columbia is an exception to this methodology since it is not part of the overall rankings. Strengths and challenges are written for the District by comparing its values with the healthiest and least healthy states for all ranking (weighted) measures. 
The U.S. Summary does not include strengths and challenges since it is the reference point for calculating z-scores and overall state rankings.
Key Findings
The key findings identify notable changes in measures nationally and for each state. Changes are presented as a percent change between two time periods. Only statistically significant changes based on nonoverlapping 95% confidence intervals are selected for measures with confidence intervals. For measures without confidence intervals, only measures with changes of 5% or more are selected. Priorities for selection are to: 1) include a mix of measures that are improving and worsening and 2) include a mix of model and topic areas. Some measures do not lend themselves to changes over time and are therefore excluded from this analysis (e.g., disparity measures). 
Disparities
For each demographic group within a measure, the disparity ratio is calculated by dividing the value of one group by the value of another. For example, the value of the group with the highest value is divided by that of the group with the lowest value. Only measures with significant differences between the two groups based on nonoverlapping 95% confidence intervals are included. A disparity ratio cutoff point is used if no confidence intervals are available for a measure (e.g., ≥3.0). Priorities for selection are: 1) measures with confidence intervals; 2) measures with disparity ratios above 2.0, with priority for the largest ratios within a demographic group; 3) a mix of model and topic areas; and 4) a mix of demographic groups.
For more information, see Disparity Measures Methodology.
Demographic Group Definitions
Analyses were performed to illuminate disparities by age, disability status, education, gender, sexual orientation, income, metropolitan status, race/ethnicity and veteran status. Not all groups were available for all data sources and measures. Individual estimates were suppressed if they did not meet the reliability criteria laid out by the data source or internally established criteria. Some values had wide 95% confidence intervals, meaning the true value may be far from the estimate listed.
Age
Age data in this report were available for measures from the Centers for Disease Control and Prevention’s (CDC's) Behavioral Risk Factor Surveillance System (BRFSS) and CDC WONDER. BRFSS groupings included age ranges 18-44, 45-64 and 65+. CDC WONDER groupings included the following age ranges: 15-24, 25-34, 35-44, 45-54, 55-64, 65-74, 75-84 and 85+.
Disability Status
Disability status data in this report were available for measures from BRFSS. Groupings were based on responses to the questions in the core Disability section. Responses of yes to the question, “Are you deaf or do you have serious difficulty hearing?” were coded as difficulty hearing. Responses of yes to the question, “Are you blind or do you have serious difficulty seeing, even when wearing glasses?” were coded as difficulty seeing. Responses of yes to the question, “Because of a physical, mental, or emotional condition, do you have serious difficulty concentrating, remembering, or making decisions?” were coded as difficulty with cognition. Responses of yes to the question, “Do you have serious difficulty walking or climbing stairs?” were coded as difficulty with mobility. Responses of yes to the question, “Do you have difficulty dressing or bathing?” were coded as difficulty with self-care. Responses of yes to the question, “Because of a physical, mental, or emotional condition, do you have difficulty doing errands alone such as visiting a doctor's office or shopping?” were coded as independent living difficulty. Responses of no or missing to all questions, with at least one response being no, were coded as without a disability.
Education
Education data in this report were available for measures from BRFSS and the Researched Abuse, Diversion and Addiction-Related Surveillance (RADARS) System. BRFSS groupings were limited to adults age 25 and older and were based on responses to the question, “What is the highest grade or year of school you completed?” A response of grades 9 through 11 was classified as less than high school. A response of grade 12 or GED was classified as high school or GED. A response of college or technical school 1 year to 3 years was classified as some post-high school. A response of college 4 years or more was classified as college graduate. 
RADARS groupings were limited to adults age 18 and older and were based on responses to the question, “What is the highest degree or level of school you have completed? Select one.” A response of less than a high school diploma was classified as less than high school. A response of regular high school diploma, GED or alternative credential was classified as high school graduate or GED. Responses of some college credit but no degree, trade school or associate degree were classified as some college or trade school. Responses of bachelor’s degree, master’s degree, doctorate or professional degree were classified as college graduate.  
Gender
This report stratified gender as men and women for adults and female and male for data including children as available through public data sources. Data did not differentiate between assigned sex at birth and current gender identity. While sex and gender influence health, the current data collection practices of some national surveys limit the ability to describe the health of transgender or gender nonbinary individuals, especially at the state level. Healthy People 2030 has an objective to increase the number of nationally representative, population-based surveys that collect data on (or for) transgender populations.
Sexual Orientation
Sexual orientation data in this report were available for measures from BRFSS. Groupings were based on responses to the question, “Which of the following best represents how you think of yourself?” Responses of lesbian or gay, gay, bisexual or something else were summed and classified as LGBQ+. Responses of “straight, that is, not gay” were summed and classified as straight.
Income
Income data in this report were available for measures from BRFSS and RADARS. BRFSS groupings were limited to adults age 25 and older and were based on responses to the question, “[What] is your annual household income from all sources?” Responses of less than $10,000, $10,000 to less than $15,000, $15,000 to less than $20,000 and $20,000 to less than $25,000 were summed and classified as less than $25,000. Responses of $25,000 to less than $35,000 and $35,000 to less than $50,000 were summed and classified as $25,000-$49,999. Responses of $50,000 to less than $75,000 were classified as $50,000-$74,999. Responses of $75,000 or more were classified as $75,000 or more. 
RADARS groupings were limited to adults age 18 and older and were based on responses to the question, “What was your combined household income during the last 12 months? Select one.” A response of less than $25,000 was classified as less than $25,000. Responses of between $25,000 and $49,999 and between $50,000 and $74,999 were summed and classified as $25,000-$74,999. Responses of between $75,000 and $99,999 and $100,000 or more were summed and classified as $75,000 or more.
Metropolitan Status
Metropolitan status data in this report were available for measures from BRFSS. Groupings were coded based on residence geography. Identification as large central metro, large fringe metro, medium metro and small metro were classified as metropolitan, and identification as micropolitan and noncore were classified as nonmetropolitan. 
Race/Ethnicity
Data were provided where available for the following racial and ethnic groups: American Indian/Alaska Native, Asian, Black or African American (classified in this report as Black), Hispanic or Latino (classified as Hispanic), Native Hawaiian or Other Pacific Islander (classified as Hawaiian/Pacific Islander), white, multiracial, and those who identify as other race. Hispanic ethnicity includes members of all racial groups. Racial/ethnic groups were defined differently across data sources (details below). 
In summary, while American Community Survey data were collected and calculated as Hispanic-inclusive (except for white, which is non-Hispanic), all other sources collected race data as non-Hispanic. Those include BRFSS; CDC National Center for HIV, Viral Hepatitis, STD, and TB Prevention (NCHHSTP); CDC WONDER; the U.S. Department of Housing and Urban Development (HUD); and RADARS.
Race and ethnicity categories by source:
American Community Survey: American Indian and Alaska Native; Asian; Black or African American; Hispanic or Latino Origin (any race); Native Hawaiian or Other Pacific Islander; white (non-Hispanic); two or more races; and some other race.
BRFSS: American Indian/Alaskan Native (non-Hispanic); Asian (non-Hispanic); Black or African American (non-Hispanic); Hispanic, Latino/a or Spanish origin (any race); Native Hawaiian or Other Pacific Islander (non-Hispanic); white (non-Hispanic); multiracial (non-Hispanic); and other race (non-Hispanic).
CDC NCHHSTP: American Indian or Alaska Native (non-Hispanic); Asian (non-Hispanic); Black or African American (non-Hispanic); Hispanic or Latino/a (any race); Native Hawaiian or Other Pacific Islander (non-Hispanic); white (non-Hispanic); and more than one race (non-Hispanic).
CDC WONDER: American Indian or Alaska Native (non-Hispanic); Asian (non-Hispanic); Black or African American (non-Hispanic); Hispanic (any race); Native Hawaiian or Other Pacific Islander (non-Hispanic); white (non-Hispanic); and more than one race (non-Hispanic).
HUD: American Indian or Alaska Native (non-Hispanic); Asian (non-Hispanic); Black or African American (non-Hispanic); Hispanic (any race); Pacific Islander (non-Hispanic); white (non-Hispanic); and other race, including multiple races (non-Hispanic).
RADARS: American Indian/Alaska Native (non-Hispanic); Asian (non-Hispanic); Black (non-Hispanic); Hispanic (any race); Hawaiian/ Pacific Islander (non-Hispanic); white (non-Hispanic); and other race (non-Hispanic).
Veteran Status
Veteran status data in this report were available for measures from BRFSS. Groupings were based on responses to the question, “Have you ever served on active duty in the United States Armed Forces, either in the regular military or in a National Guard or military reserve unit?” Responses of yes were summed and classified as served. Responses of no were summed and classified as not served.
Limitations
Rankings are a relative measure of health. Not all changes in rank translate into actual declines or improvements in health. Data presented in this report were aggregated at the state level and cannot be used to make inferences at the individual level. Additionally, estimates cannot be extrapolated beyond the population upon which they were created. Values and ranks from prior years were updated on the America’s Health Rankings website to reflect known errors and updates from the reporting source. 
Use caution when interpreting data on certain health and behavioral measures. Many are self-reported and rely on an individual’s perception of health and behaviors. Additionally, some health outcome measures are based on respondents being told by a health care professional that they have a disease and may exclude those who have not received a diagnosis or sought or obtained treatment. 
This report provides health disparity data by various demographic group characteristics. To address health inequities, health disparities must be examined alongside the underlying drivers that create and perpetuate health inequities, such as socioeconomic factors, environmental influences and systemic and structural inequality. Relying solely on health disparity data may lead to misinterpretations of health outcomes, as they do not account for the social drivers that significantly impact individuals’ access to care, quality of life and overall well-being. 
Inclusivity in data collection is essential to documenting, analyzing and addressing the disparities people experience. Equitable systems must accurately represent diverse populations throughout the data life cycle, including data collection, analysis and interpretation. Inadequate representation of populations may hinder the identification of trends and patterns within different demographic groups and limit the ability to tailor public health interventions to specific populations. 
Methodology

How State Rankings Were Generated

Data Analysis

State Summaries

Demographic Group Definitions

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