IntroductionThe Health of Tomorrow’s SeniorsFindingsTop and Bottom StatesChange in RankFuture PerspectiveCore MeasuresBehaviorsCommunity & Environment: MacroPolicyClinical CareOutcomesSupplemental MeasuresState SummariesAlabamaAlaskaArkansasArizonaCaliforniaColoradoConnecticutDelawareFloridaGeorgiaHawaiiIdahoIllinoisIndianaIowaKansasKentuckyLouisianaMaineMarylandMassachusettsMichiganMinnesotaMississippiMissouriMontanaNebraskaNevadaNew HampshireNew JerseyNew MexicoNew YorkNorth CarolinaNorth DakotaOhioOklahomaOregonPennsylvaniaRhode IslandSouth CarolinaSouth DakotaTennesseeTexasUtahVermontVirginiaWashingtonWest VirginiaWisconsinWyomingDistrict of ColumbiaAppendixDescription of Core MeasuresDescription of Supplemental MeasuresMethodology2016 Model Development2016 Senior Health Advisory GroupThe TeamExecutive SummaryConclusionAmerica’s Health Rankings® Expansion
For each measure, the raw data are obtained from secondary sources and presented as “value.” The score for each state is based on the following formula:
This z score indicates the number of standard deviations a state is above or below the national value. A 0.00 indicates a state has the same value as the nation. States with higher values than the national value have a positive score; states below the national value have a negative score. To prevent an extreme score from exerting excessive influence, the maximum score for a measure is capped at +/- 2.00. If a US value is not available for a measure, the mean of all state values is used. Overall score is calculated by adding the scores of each measure multiplied by its assigned weight (the percentage of total overall ranking). See Table 10 for model category weights. The model category weight is distributed equally among all measures within each category.
The overall ranking is the ordering of each state according to the overall score. The ranking of individual measures is the ordering of each state according to the measure’s value. Ties in values are assigned equal ranks. Not all changes in rank are statistically significant.
Population Growth Projections Methodology
Woods and Poole projections are based on models of county population growth and migration due to economic conditions. The average absolute percent error for Woods and Poole’s ten-year total population projections has been ±4.0% for states.
Comparison of Health Estimates in the Middle-Aged Population Methodology
The prevalence of obesity, diabetes, smoking and very good or excellent health status were examined in the middle-aged population (adults aged 50–64) using 1999 and 2014 Behavioral Risk Factor Surveillance System (BRFSS) data. The 15-year relative change in these four measures was calculated. Missing data were excluded from this analysis, which includes “don’t know,” “not sure,” “refused,” and blank or missing responses.
For the measures, comparisons between estimates before and after 2011 should be approached with caution due to changes in BRFSS methodology. In 2011, BRFSS added cellular telephone-only households and a new method of weighting the data. The addition of cellular telephone-only households has disproportionately increased the numbers of certain population groups represented in the survey, and the weighting change has increased prevalence estimates of certain chronic disease estimates, such as diabetes and obesity. Thus, some of the increase seen since 1999 in diabetes and obesity prevalence and some of the decrease seen since 1999 in smoking and health status prevalence could be attributed to the new methods implemented in 2011.  Please refer to the following CDC website for more information on the 2011 methodological changes: http://www.cdc.gov/surveillancepractice/reports/brfss/brfss.html.
 Maps of Trends in Diagnosed Diabetes and Obesity. January 2015. CDC’s Division of Diabetes Translation. National Diabetes Surveillance System. http://www.cdc.gov/diabetes/statistics