Cluster Analysis Methods to Support Population Health Improvement Among US Counties.

CONTEXT: Population health rankings can be a catalyst for the improvement of health by drawing attention to areas in need of relative improvement and summarizing complex information in a manner understood by almost everyone. However, ranks also have unintended consequences, such as being interpreted as "hard truths," where variations may not be significant. There is a need to improve communication about uncertainty in ranks, with accurate interpretation. The most common solutions discussed in the literature have included modeling approaches to minimize statistical noise or borrow strength from covariates. However, the use of complex models can limit communication and implementation, especially for broad audiences. OBJECTIVES: Explore data-informed grouping (cluster analysis) as an easier-to-understand, empirical technique to account for rank imprecision that can be effectively communicated both numerically and visually. DESIGN: Cluster analysis, specifically k-means clustering with Wasserstein (earth mover's) distance, was explored as an approach to identify natural and meaningful groupings and gaps in the data distribution for the County Health Rankings' (CHR) health outcomes ranks. SETTING: County-level health outcomes from the 2022 CHR. PARTICIPANTS: 3082 counties that were ranked in the 2022 CHR. MAIN OUTCOME MEASURE: Data-informed health groups. RESULTS: Cluster analysis identified 30 health groupings among counties nationwide, with cluster size ranging from 9 to 184 counties. On average, states had 16 identified clusters, ranging from 3 in Delaware and Hawaii to 27 in Virginia. Number of clusters per state was associated with number of counties per state and population of the state. The method helped address many of the issues that arise from providing rank estimates alone. CONCLUSIONS: Public health practitioners can use this information to understand uncertainty in ranks, visualize distances between county ranks, have context around which counties are not meaningfully different from one another, and compare county performance to peer counties.

Discounting Health Grades for Disparity: The 2021 Wisconsin Population Health and Equity Report Card.

We describe updates to the University of Wisconsin Population Health Institute's methodology for a state health report card, first described in Preventing Chronic Disease in 2010, and the considerations that were weighed in making those updates. These methods have been used since 2006 to issue a periodic report entitled Health of Wisconsin Report Card. The report highlights Wisconsin's standing among other states and serves as an example for others seeking to measure and improve their population's health. For 2021, we revisited our approach with an increased emphasis on disparities and health equity, which required many choices about data, analysis, and reporting methods. In this article, we outline the decisions, rationale, and implications of several choices we made in assessing Wisconsin's health by answering several questions, among them: Who is the intended audience and which measures of length (eg, mortality rate, years of potential life lost) and quality of life (eg, self-reported health, quality-adjusted life years) are most relevant to them? Which subgroups should we report disparities about, and which metric is most easily understood? Should disparities be summarized with overall health or reported separately? Although these decisions are applicable to 1 state, the rationale for our choices could be applied to other states, communities, and nations. Consideration of the purpose, audience, and context for health and equity policy making is important in developing report cards and other tools that can improve the health of all people and places.

Trends in infants born at low birthweight and disparities by maternal race and education from 2003 to 2018 in the United States.

BACKGROUND: Understanding current levels, as well as past and future trends, of the percentage of infants born at low birthweight (LBW) in the United States is imperative to improving the health of our nation. The purpose of this study, therefore, was to examine recent trends in percentage of LBW, both overall and by maternal race and education subgroups. Studying disparities in percentage of LBW by these subgroups can help to further understand the health needs of the population and can inform policies that can close race and class disparities in poor birth outcomes. METHODS: Trends of percentage of LBW in the U.S. from 2003 to 2018, both overall and by race/ethnicity, and from 2007 to 2018 by education and race by education subgroups were analyzed using CDC WONDER Natality data. Disparities were analyzed using between group variance methods. RESULTS: Percentage of LBW experienced a significant worsening in the most recent 5 years of data, negating nearly a decade of prior improvement. Stark differences were observed by race/ethnicity and by education, with all subgroups experiencing increasing rates in recent years. Disparities also worsened over the course of study. Most notably, all disparities increased significantly from 2014 to 2018, with annual changes near 2-5%. CONCLUSIONS: Recent reversals in progress in percentage of LBW, as well as increasing disparities particularly by race, are troubling. Future study is needed to continue monitoring these trends and analyzing these issues at additional levels. Targets must be set and solutions must be tailored to population subgroups to effectively make progress towards equitable birth outcomes and maternal health.

Generating Subcounty Health Data Products: Methods and Recommendations From a Multistate Pilot Initiative.

BACKGROUND: County Health Rankings & Roadmaps (CHR&R) makes data on health determinants and outcomes available at the county level, but health data at subcounty levels are needed. Three pilot projects in California, Missouri, and New York explored multiple approaches for defining measures and producing data at subcounty geographic and demographic levels based on the CHR&R model. This article summarizes the collective technical and implementation considerations from the projects, challenges inherent in analyzing subcounty health data, and lessons learned to inform future subcounty health data projects. METHODS: The research teams used 12 data sources to produce 40 subcounty measures that replicate or approximate county-level measures from the CHR&R model. Using varying technical methods, the pilot projects followed similar stages: (1) conceptual development of data sources and measures; (2) analysis and presentation of small-area and subpopulation measures for public health, health care, and lay audiences; and (3) positioning the subcounty data initiatives for growth and sustainability. Unique technical considerations, such as degree of data suppression or data stability, arose during the project implementation. A compendium of technical resources, including samples of automated programs for analyzing and reporting subcounty data, was also developed. RESULTS: The teams summarized the common themes shared by all projects as well as unique technical considerations arising during the project implementation. Furthermore, technical challenges and implementation challenges involved in subcounty data analyses are discussed. Lessons learned and proposed recommendations for prospective analysts of subcounty data are provided on the basis of project experiences, successes, and challenges. CONCLUSIONS: This multistate pilot project offers 3 successful approaches for creating and disseminating subcounty data products to communities. Subcounty data often are more difficult to obtain than county-level data and require additional considerations such as estimate stability, validating accuracy, and protecting individual confidentiality. We encourage future projects to further refine techniques for addressing these critical considerations.

Joinpoint Trend Analysis of Infant Mortality Disparities in Wisconsin, 1999-2016.

OBJECTIVES: To address shortcomings of previous research exploring trends in racial, educational, and race by educational disparities in infant mortality rates (IMRs) by using nonlinear methods to compare improvement within and between disparity domains. METHODS: We used joinpoint regression modeling to perform a cross-sectional analysis of IMR trends from linked birth and death certificates in Wisconsin between 1999 and 2016. RESULTS: In the race and education domains, IMR decreased by 1.9% per year for infants of White mothers and 1.1% per year for infants of less-educated mothers. Further analysis showed these IMR reductions to be among infants of White mothers with more education (-0.6%/year) and Black mothers with less education (-2.0%/year). CONCLUSIONS: As previously reported, gaps in IMR by race and education in Wisconsin appear to be closing; however, only the change by education is statistically significant. Evidence suggests the racial divide in IMR might soon widen after years of progress in reducing IMR among infants of Black mothers. Public Health Implications. Those advancing strategies to address IMR disparities should pursue data and methods that provide the most accurate and refined information about the challenges that persist and progress that has been realized.

Separate and Sick: Residential Segregation and the Health of Children and Youth in Metropolitan Statistical Areas.

The purpose of this study was to better understand residential segregation and child/youth health by examining the relationship between a measure of Black-White residential segregation, the index of dissimilarity, and a suite of child and youth health measures in 235 U.S. metropolitan statistical areas (MSAs). MSAs are urban areas with a population of 50,000 or more and adjacent communities that share a high degree of economic and social integration. MSAs are defined by the Office of Management and Budget. Health-related measures included child mortality (CDC WONDER), teen births (NCHS natality data), children in poverty (SAIPE program), and disconnected youth (Measure of America). Simple linear regression and two-level hierarchical linear regression models, controlling for income, total population, % Black, and census region, examined the association between segregation and Black health, White health, and Black-White disparities in health. As segregation increased, Black children and youth had worse health across all four measures, regardless of MSA total and Black population size. White children and youth in small MSAs with large Black populations had worse levels of disconnected youth and teen births with increasing segregation, but no associations were found for White children and youth in other MSAs. Segregation worsened Black-White health disparities across all four measures, regardless of MSA total and Black population size. Segregation adversely affects the health of Black children in all MSAs and White children in smaller MSAs with large Black populations, and these effects are seen in measures that span all of childhood. Residential segregation may be an important target to consider in efforts to improve neighborhood conditions that influence the health of families and children.

The Epidemic of Despair Among White Americans: Trends in the Leading Causes of Premature Death, 1999-2015.

OBJECTIVES: To evaluate trends in premature death rates by cause of death, age, race, and urbanization level in the United States. METHODS: We calculated cause-specific death rates using the Compressed Mortality File, National Center for Health Statistics data for adults aged 25 to 64 years in 2 time periods: 1999 to 2001 and 2013 to 2015. We defined 48 subpopulations by 10-year age groups, race/ethnicity, and county urbanization level (large urban, suburban, small or medium metropolitan, and rural). RESULTS: The age-adjusted premature death rates for all adults declined by 8% between 1999 to 2001 and 2013 to 2015, with decreases in 39 of the 48 subpopulations. Most decreases in death rates were attributable to HIV, cardiovascular disease, and cancer. All 9 subpopulations with increased death rates were non-Hispanic Whites, largely outside large urban areas. Most increases in death rates were attributable to suicide, poisoning, and liver disease. CONCLUSIONS: The unfavorable recent trends in premature death rate among non-Hispanic Whites outside large urban areas were primarily caused by self-destructive health behaviors likely related to underlying social and economic factors in these communities.

Patterns of sedentary behavior and physical function in older adults.

BACKGROUND/AIMS: The purposes of this study were to examine the relationship between various objectively measured sedentary behavior (SB) variables and physical function in older adults, examine the measurement properties of an SB questionnaire, and describe the domains of SB in our sample. METHODS: Forty-four older adults (70 ± 8 years, 64 % female) had their SB measured via activPAL activity monitor and SB questionnaire for 1 week followed by performance-based tests of physical function. RESULTS: The pattern of SB was more important than total SB time. Where a gender by SB interaction was found, increasing time in SB and fewer breaks were associated with worse function in the males only. The SB questionnaire had acceptable test-retest reliability but poor validity compared to activPAL-measured SB. The majority of SB time was spent watching television, using the computer and reading. DISCUSSION/CONCLUSIONS: This study provides further evidence for the association between SB and physical function and describes where older adults are spending their sedentary time. This information can be used in the design of future intervention to reduce sedentary time and improve function in older adults.

Assessment of Factors Contributing to Health Outcomes in the Eight States of the Mississippi Delta Region.

INTRODUCTION: The objective of this observational study was to examine the key contributors to health outcomes and to better understand the health disparities between Delta and non-Delta counties in 8 states in the Mississippi River Delta Region. We hypothesized that a unique set of contributors to health outcomes in the Delta counties could explain the disparities between Delta and non-Delta counties. METHODS: Data were from the 2014 County Health Rankings for counties in 8 states (Alabama, Arkansas, Illinois, Kentucky, Louisiana, Mississippi, Missouri, and Tennessee). We used the Delta Regional Authority definition to identify the 252 Delta counties and 468 non-Delta counties or county equivalents. Information on health factors (eg, health behaviors, clinical care) and outcomes (eg, mortality) were derived from 38 measures from the 2014 County Health Rankings. The contributions of health factors to health outcomes in Delta and non-Delta counties were examined using path analysis. RESULTS: We found similarities between Delta counties and non-Delta counties in the health factors (eg, tobacco use, diet and exercise) that significantly predicted the health outcomes of self-rated health and low birthweight. The most variation was seen in predictors of mortality; however, Delta counties shared 2 of the 3 significant predictors (ie, community safety and income) of mortality with non-Delta counties. On average across all measures, values in the Delta were 16% worse than in the non-Delta and 22% worse than in the rest of the United States. CONCLUSION: The health status of Delta counties is poorer than that of non-Delta counties because the health factors that contribute to health outcomes in the entire region are worse in the Delta counties, not because of a unique set of health predictors.

Dose-response relationships between sedentary behaviour and the metabolic syndrome and its components.

AIMS/HYPOTHESIS: The aim of this study was to examine the relationship among sedentary behaviour (SB) and the metabolic syndrome and its components by age, moderate-to-vigorous physical activity (MVPA) and sex. METHODS: A cross-sectional analysis was performed on 2003-2006 National Health and Nutrition Examination Survey data from 5,076 adults aged ≥18 years (mean ± SD = 43.8 ± 19.5). SB was measured using ActiGraph accelerometers worn for 1 week and defined as <100 counts/min. Metabolic syndrome was defined using the Adult Treatment Panel III criteria. Natural cubic spline logistic regression models were used to estimate the odds of meeting criteria for the metabolic syndrome and its components by total daily SB time and breaks in SB. Statistical interactions between SB and age, sex and MVPA were explored. RESULTS: The prevalence of the metabolic syndrome was 19% and the average daily SB time was 8.1 ± 2.8 h, with 90 ± 25 breaks/day. The relationship between daily SB time and the metabolic syndrome was linear and characterised by an OR of 1.09 (95% CI 1.01, 1.18) for each hour of SB. Total SB was associated with the following components: high triacylglycerol, low HDL-cholesterol and high fasting glucose. All three associations were modified by MVPA level. No relationship between breaks in SB and the metabolic syndrome was found. CONCLUSIONS/INTERPRETATION: There appears to be no SB threshold at which the risk of the metabolic syndrome is elevated. Therefore, an effort should be made to maintain low levels of total time spent in SB and so lessen the risk of the metabolic syndrome.

The County Health Rankings: rationale and methods.

BACKGROUND: Annually since 2010, the University of Wisconsin Population Health Institute and the Robert Wood Johnson Foundation have produced the County Health Rankings-a "population health checkup" for the nation's over 3,000 counties. The purpose of this paper is to review the background and rationale for the Rankings, explain in detail the methods we use to create the health rankings in each state, and discuss the strengths and limitations associated with ranking the health of communities. METHODS: We base the Rankings on a conceptual model of population health that includes both health outcomes (mortality and morbidity) and health factors (health behaviors, clinical care, social and economic factors, and the physical environment). Data for over 30 measures available at the county level are assembled from a number of national sources. Z-scores are calculated for each measure, multiplied by their assigned weights, and summed to create composite measure scores. Composite scores are then ordered and counties are ranked from best to worst health within each state. RESULTS: Health outcomes and related health factors vary significantly within states, with over two-fold differences between the least healthy counties versus the healthiest counties for measures such as premature mortality, teen birth rates, and percent of children living in poverty. Ranking within each state depicts disparities that are not apparent when counties are ranked across the entire nation. DISCUSSION: The County Health Rankings can be used to clearly demonstrate differences in health by place, raise awareness of the many factors that influence health, and stimulate community health improvement efforts. The Rankings draws upon the human instinct to compete by facilitating comparisons between neighboring or peer counties within states. Since no population health model, or rankings based off such models, will ever perfectly describe the health of its population, we encourage users to look to local sources of data to understand more about the health of their community.

Development of a nationally representative built environment measure of access to exercise opportunities.

We sought to develop a county-level measure to evaluate residents' access to exercise opportunities. Data were acquired from Esri, DeLorme World Vector (MapMart), and OneSource Global Business Browser (Avention). Using ArcGIS (Esri), we considered census blocks to have access to exercise opportunities if the census block fell within a buffer area around at least 1 park or recreational facility. The percentage of county residents with access to exercise opportunities was reported. Measure validity was examined through correlations with other County Health Rankings & Roadmaps' measures. Included were 3,114 of 3,141 US counties. The average population with access to exercise opportunities was 52% (range, 0%-100%) with large regional variation. Access to exercise opportunities was most notably associated with no leisure-time physical activity (r = -0.47), premature death (r = -0.38), and obesity (r = -0.36). The measure uses multiple sources to create a valid county-level measure of exercise access. We highlight geographic disparities in access to exercise opportunities and call for improved data.

Dose-response walking activity and physical function in older adults.

The aim of this study was to examine the dose-response relationship between walking activity and physical function (PF) in community-dwelling older adults. Physical activity (PA, pedometry) and PF (self-report [SF-36] and 6-minute walk test [6MWT]) were assessed in 836 individuals. Accumulated PA was categorized into four groups (1 = ≤ 2,500; 2 = 2,501-5,000; 3 = 5,001-7,500; and 4 = ≥ 7,501 steps/day). Across individual groups 1-4, SF-36 scores increased from 66.9 ± 25.0% to 73.5 ± 23.2% to 78.8 ± 19.7% to 81.3 ± 20.6%, and 6MWT increased from 941.7 ± 265.4 ft to 1,154.1 ± 248.2 ft to 1,260.1 ± 226.3 ft to 1,294.0 ± 257.9 ft. Both SF-36 and 6MWT scores were statistically different across all groups, apart from groups 3 and 4. PA and ranks of groups were highly significant predictors (p < .0001) for both SF-36 and 6MWT. There was a positive dose-response relationship evident for both SF-36 and 6MWT with increasing levels of PA. Low levels of PA appear to be an important indicator of poor functionality in older adults.

Smokers' physical activity and weight gain one year after a successful versus unsuccessful quit attempt.

OBJECTIVE: To examine whether smokers' physical activity is related to weight change following a quit attempt. METHOD: Data were analyzed for participants (n=683) of a randomized controlled trial comparing the efficacy of different smoking cessation pharmacotherapies (Wisconsin, 2005-2008). Activity (assessed via pedometry) and body weight were measured in the days surrounding the quit day and again one year later, at which time 7-day point-prevalence abstinence from smoking was assessed. We examined the effects of quitting, physical activity, and their interaction, on a one-year weight change with relevant covariate adjustment. RESULTS: Participants were predominantly female (57%), 46 ± 11 years of age (mean ± SD), and took 7544 ± 3606 steps/day at baseline. Of those who quit, 87% gained weight. A main effect was found for quitting (p<0.001), but not physical activity (p=0.06). When pattern of activity was examined across the 1-year study period, quitters who decreased their physical activity had significantly greater weight gain than quitters who increased their physical activity (p<0.01) or maintained a high level of activity (p=0.02). CONCLUSION: Physical activity is associated with an attenuation of the weight gain that often occurs after quitting smoking.

Comparative Methodologic and Practical Considerations for Life Expectancy as a Public Health Mortality Measure.

INTRODUCTION: Life expectancy is a public health metric used to assess mortality. We describe life expectancy calculations for US counties and present methodologic considerations compared with years of potential life lost before age 75 (YPLL-75) and premature age-adjusted mortality (PAAM), 2 commonly used length-of-life metrics. METHODS: We used death data from the National Center for Health Statistics for 2015-2017 and other health measures from the 2019 County Health Rankings & Roadmaps. We calculated life expectancy from birth at the county level using an abridged life table and the Chiang method of variance. Studentized residuals identified counties with discordant life expectancy and YPLL-75 or PAAM values. Correlations tested associations of life expectancy with key health measures (eg, smoking, child poverty, uninsured). RESULTS: Among 3073 US counties, life expectancy ranged from 62.4 to 98.0 years, with a mean of 77.4 years. Life expectancy was strongly and negatively correlated with YPLL-75 (r = -0.91) and PAAM (r = -0.95) at the county level. Life expectancy was also associated with other key health metrics, such as smoking, employment, and education rates, where an improvement in the health factor indicated improvement in the respective length-of-life measure. Counties with discordant life expectancy and YPLL-75 or PAAM values had differing age structures. PRACTICE IMPLICATIONS: Commonly used length-of-life metrics in population health settings are differentiated by methodological matters, such as computation complexity, data availability, and differential risk among age groups, especially among the very old or very young. The choice of metric should consider these factors, in addition to practical concerns, such as the communication needs of the audience.

Life Expectancy At The US-Mexico Border: Evidence Of Disparities By Place, Race, And Ethnicity.

The mortality experience for the cluster of US counties in the US-Mexico border region has not been well described. We calculated 2016-18 life expectancy for the border region (counties within 100 kilometers of the border), making key comparisons to the US overall and to nonborder counties in border states. Life expectancy from birth for the border region was 81.1 years, which was greater than for the US and for the nonborder counties of border states. However, the disparity in life expectancy between racial/ethnic subgroups in the border region was also greater, within a range of more than thirteen years. Although White, Black, and Asian residents of the border region could expect to live significantly longer than residents of the US and nonborder counties of border states, Hispanic and American Indian residents could not. Understanding the mortality experience via life expectancy can help public health professionals and leaders prioritize efforts to ensure that all border residents have an equal opportunity to live a long, healthy life.

Deaths of Despair(ity) in Early 21st Century America: The Rise of Mortality and Racial/Ethnic Disparities.

INTRODUCTION: Recent media coverage and research have emphasized increasing mortality rates for middle-aged white Americans. A concern is that this has shifted focus away from the health burden of other population subgroups. This cross-sectional study compares the magnitude of racial/ethnic mortality disparities across age groups and investigates how changing mortality trends have affected these disparities. METHODS: Mortality data from 2007 to 2016 by race/ethnicity and age were obtained from the Centers for Disease Control and Prevention Wide-Ranging Online Data for Epidemiologic Research database in 2018‒2019. Absolute and relative racial/ethnic mortality disparities by age groups were determined by calculating between-group variance and mortality rate-adjusted between-group variance, respectively. Trends in disparities were analyzed using joinpoint regression modeling. Annual percentage change in rate-adjusted between-group variance was calculated for each trend segment as well as the relative contribution of each racial/ethnic group to the change. RESULTS: The largest relative and absolute disparities were found in the youngest and oldest age groups, respectively. Trend analysis detected an inflection point between 2009 and 2012 for most age groups where a period of decreasing disparities changed to one of increasing disparities. Three quarters of the decreasing disparities in Period 1 were resultant of lowering mortality among the black subgroup. During Period 2, the increase in child disparities were due to increased mortality among blacks, whereas increased adult disparities were due to increased mortality among whites shifting the overall mean away from subgroups with lower rates. CONCLUSIONS: Racial/ethnic mortality disparities persist and are widening for some age groups. It is imperative to maintain focus on the age groups where those with historically poorer health are contributing most to the increase.

Predictors of discordance in self-report versus device-measured physical activity measurement.

PURPOSE: Accurate measurement of free-living physical activity is challenging in population-based research, whether using device-based or reported methods. Our purpose was to identify demographic predictors of discordance between physical activity assessment methods and to determine how these predictors modify the discordance between device-based and reported physical activity measurement methods. METHODS: Three hundred forty-seven adults from the Survey of the Health of Wisconsin wore the ActiGraph accelerometer for 7 days and completed the Global Physical Activity Questionnaire. Multivariate linear regression was conducted to assess predictors of discordance including gender, education, body mass index, marital status, and other individual level characteristics in physical activity reporting. RESULTS: Seventy-seven percent of men and 72% of women self-reported meeting the U.S. Centers for Disease Control and Prevention guidelines for aerobic activity but when measured by accelerometer, only 21% of men and 17% of women met guidelines. Demographic characteristics that predicted discordance between methods in multivariate regression included greater educational attainment (P < .001) and partnered status (P = .003). CONCLUSIONS: These varying levels of discordance imply that comparisons of self-reported activity among groups defined by (or substantially varying by) educational attainment or marital status should be done with considerable caution as observed differences may be due, in part, to systematic, differential measurement biases among groups.

Accelerometer-derived physical activity and sedentary time by cancer type in the United States.

The 2003-2004 and 2005-2006 cycles of the National Health and Nutrition Examination Survey (NHANES) were among the first population-level studies to incorporate objectively measured physical activity and sedentary behavior, allowing for greater understanding of these behaviors. However, there has yet to be a comprehensive examination of these data in cancer survivors, including short- and long-term survivors of all cancer types. Therefore, the purpose of this analysis was to use these data to describe activity behaviors in short- and long-term cancer survivors of various types. A secondary aim was to compare activity patterns of cancer survivors to that of the general population. Cancer survivors (n = 508) and age-matched individuals not diagnosed with cancer (n = 1,016) were identified from a subsample of adults with activity measured by accelerometer. Physical activity and sedentary behavior were summarized across cancer type and demographics; multivariate regression was used to evaluate differences between survivors and those not diagnosed with cancer. On average, cancer survivors were 61.4 (95% CI: 59.6, 63.2) years of age; 57% were female. Physical activity and sedentary behavior patterns varied by cancer diagnosis, demographic variables, and time since diagnosis. Survivors performed 307 min/day of light-intensity physical activity (95% CI: 295, 319), 16 min/day of moderate-vigorous intensity activity (95% CI: 14, 17); only 8% met physical activity recommendations. These individuals also reported 519 (CI: 506, 532) minutes of sedentary time, with 86 (CI: 84, 88) breaks in sedentary behavior per day. Compared to non-cancer survivors, after adjustment for potential confounders, survivors performed less light-intensity activity (P = 0.01), were more sedentary (P = 0.01), and took fewer breaks in sedentary time (P = 0.04), though there were no differences in any other activity variables. These results suggest that cancer survivors are insufficiently active. Relative to adults of similar age not diagnosed with cancer, they engage in more sedentary time with fewer breaks. As such, sedentary behavior and light-intensity activity may be important intervention targets, particularly for those for whom moderate-to-vigorous activity is not well accepted.