Micropolitan Statistical Areas Research Articles

A Typology of Micropolitan Areas in the United States

In 2003, the U.S. Census Bureau created a new census designation known as micropolitan statistical areas to better differentiate between metropolitan and nonmetropolitan areas. This article uses hierarchical clustering and discriminate analysis to create a typology of micropolitan areas using U.S. census data on demographic, housing, and economic characteristics. The regional concentration ratio (RCR) is then used to determine whether a cluster type is over- or underrepresented at the census division. The analysis revealed a three-cluster solution with: (1) a middle- class cluster overrepresented in the East North Central and West North Central divisions; (2) a poor, minority cluster overrepresented in the South Atlantic, East South Central, West South Central, and Mountain divisions; and (3) a high human capital cluster overrepresented in the New England, Mountain, and Pacific divisions. Of micropolitan areas, 59.8 percent were considered middle-class; 28.1 percent were poor, minority; and 12.1 percent were high human capital. The nine-cluster solution further refined cluster categories.

Feasibility of Constructing an Interactive Mapping Tool to Visualize Sickle Cell Disease Geographic Distribution and Medical Care Utilization in the United States

Introduction: It is recognized that many people living with sickle cell disease (SCD) face significant structural obstacles to establishing and maintaining long-term hematology care. For example, some people with SCD care may lose access as a result of a geographic move; others may lose access by aging out of the pediatric care setting. These scenarios could be addressed by providing resources in areas with addressable populations and unmet medical need. However, deploying SCD treatment resources is frustrated by the lack of an established tool that can estimate the number of people living with SCD or gaps in services available in a given geography. We hypothesized that we could build a tool using a combination of publicly available demographic data combined with hospital claims data. Methods: Using CDC estimations for SCD affecting 1 in 365 African American and 1 in 16,300 Hispanic births, we estimated SCD prevalence based on data from the 2021 US Census, segmented by age, race, and county. We added a distinction between rural and urban counties using classification from the Consumer Financial Protection Bureau (CFPB), which designates rural counties as “a county that is neither in a metropolitan statistical area nor in a micropolitan statistical area that is adjacent to a metropolitan statistical area.”. PowerBI was used to generate a heat map to visualize the data at a state and county level, with the ability to filter by patient age and urban vs. rural designation. Finally, using hospital claims data for SCD “with crisis” (i.e., claims referencing ICD10 codes: D5700; D5701; D5702; D57211; D57212; D57219; D57811; D57812; D57819) and SCD “without crisis” (i.e., claims referencing ICD10 codes: D571; D5720; D5740; D5742; D5744; D5780), Pearson correlation was used to determine if an association existed between our estimated SCD prevalence and SCD related hospital claims for claim years 2021, 2022 and partial 2023. Results: Our model estimates a total of 127,297 people living with SCD in the United States, with 97% (123,454) being Black and 3% (3,843) being Hispanic (Table 1). 88% (111,977) were predicted to be under age 65, and 28% (35,675) were predicted to be under age 19. The majority (95%, 120,875) lived in urban counties. Most rural counties with SCD patients are located in the South and Southeast US (Figure 1). There was a strong positive correlation between our predictions of SCD prevalence at the state level and SCD-related hospital claims (coefficient of 0.903217 [95%CI: 0.944-0.836], p<0.001). For claim years 2021, 2022 and 2023 (partial) there were 1.6 claims per 1 SCD patient across the United States. While there were 1.6 claims per patient in urban counties, there were 0.7 claims per 1 patient in rural counties, consistent with a relative lack of access to care in rural areas compared to urban areas. Conclusions: Although not a direct measurement, and possibly overestimating the number of people >age65 living with SCD, our estimate suggests that there may be several thousand more people living with SCD than what is generally believed (100,000 per US CDC). Our estimates are partially validated by our finding that hospital claims data correlate strongly with our estimates of SCD prevalence at the state level. Although only a relatively small population of people living with SCD reside in rural counties, lower utilization of hospital services, as measured by hospital claims data, suggest that rural populations may have an unmet medical need that is particularly striking. Although working with claims data and disease estimates based on census data introduces significant limitations, it is feasible that this type of analysis could help to direct resources to areas with greatest unmet medical need.

Community Prosperity/Poverty (Prospov) Maps: Development and Usefulness

ABSTRACT The number of poor people and the total number of people in United States counties are strongly and non-linearly correlated, illustrated by power laws. The total number of people and the number of enterprises in U.S. counties and micropolitan statistical areas are also non-linearly correlated. Graphs of power law data points of the latter relationship visually map the relative prosperity/poverty levels of more than 3000 counties or more than 500 micropolitans despite orders of magnitude differences in population or enterprise numbers. The utility of Prospov maps is illustrated by superimposition of different socioeconomic and entrepreneurial characteristics on them.

A Small-Town Economic Revitalisation Conundrum: Focus on Tourism, Manufacturing, or Both?

The decline in small towns is a concern in many countries. The manufacturing and tourism sectors are considered to be important in the revitalisation of towns but could be subject to ‘Dutch disease’. This is a malady in which success in one sector leads to a decline in the other. The importance of, and relationships between, the manufacturing and tourism sectors of more than 500 United States micropolitan statistical areas (micropolitans) were extensively investigated by following settlement scaling theory. Publicly available 2016 datasets were used to test a hypothesis that Dutch disease between the two sectors is important. Both sectors are present and important in virtually all of the micropolitans. Regression analyses, including log–log (power-law) analyses, were used to examine the population-based and enterprise-based orderliness in the micropolitan demographic–socioeconomic–entrepreneurial nexus. There is much orderliness, and non-linear relationships are prevalent. No evidence of the presence of Dutch disease was recorded except in one case. When the strengths of the two sectors (as a percentage of their enterprise numbers in relation to total enterprise numbers) are compared, a weak negative relationship is observed. The hypothesis that Dutch disease is important was rejected. A focus on both sectors is recommended to build resilience and to contribute to the revitalisation/development of small towns.

Macroeconomic conditions and health-related outcomes in the United States: A metropolitan and micropolitan statistical area-level analysis between 2004 and 2017.

We study the relationship between macroeconomic conditions and health in the United States using data from the Behavioral Risk Factor Surveillance System between 2004 and 2017. Unlike many existing studies that use state or county as the level of aggregation, our analysis uses a sample of metropolitan and micropolitan statistical areas. Our results suggest strong associations between worsening macroeconomic conditions and reduced access to care and health insurance coverage. While we do not detect any robust relationships between business cycles and health outcomes in the general population, we do find consistent evidence of worse self-reported health during economic downturns among minorities and less-educated individuals. In addition, there is some suggestive evidence of countercyclicality of healthier lifestyle choices. However, the findings for health behavior outcomes are not robust to adjusting for multiple hypothesis testing.

The Spatial Distribution of High Growth Entrepreneurship: Generative Mechanisms of Heavy Tails

Why does a disproportionate amount of high-growth entrepreneurship concentrate in a small number of geographical areas? Our research question is motivated by the heavy tails in the frequency-size distributions of high-growth entrepreneurship—the number of early-stage financing rounds, acquisitions among firms that received early-stage financing, and initial public offerings (IPOs)—in a metropolitan or micropolitan statistical area (MSA). The heavy tails could result from either of two distinct processes, but the existing research on entrepreneurship and agglomeration cannot tell the two processes apart. In this paper, we apply technical advances from complexity science and analyze which process is the more likely generative mechanism for the observed heavy tails. Using data from Crunchbase, our analysis covers 129,949 financing rounds of 58,517 unique firms across 657 MSAs, 10,145 acquisitions, and 4,614 IPOs. We find the multiplicative process to be the more likely mechanism, compared to the rich-get-richer process, which has been posited in the literature as a driver of regional advantage. For research, our finding changes the way we theorize and study agglomeration and entrepreneurship. For government policy, our research enhances the efficacy of interventions—in the form of tax breaks, business incubators, or grants aiming to stimulate entrepreneurial activity, spur innovation and create new jobs—by revealing the generative mechanism for the emergence of a region’s advantage in high-growth entrepreneurship. For entrepreneurs, our research informs whether and how the choice of where to locate the start-ups may have implications for the likelihood of obtaining early-stage financing, becoming acquired, and going public.

Exposure to anti-Black Lives Matter movement and obesity of the Black population

RationaleBlack Lives Matter (BLM) is a social movement against systematic injustice and police violence toward Black people whose goal is to ensure their safety and the expression of their culture. As BLM gained momentum, counter-movements emerged, such as All Lives Matter (ALM), White Lives Matter (WLM), and Blue Lives Matter (BlueLM). Because they undermine support for Black people's safety and culture, exposure to stances against BLM can be a race-related stressor. Although the perception of racial discrimination has been associated with negative health outcomes in Black people, it is not clear whether exposure to negative stances on a race-related social issue is associated with worse health outcomes. ObjectiveWe investigated whether living in areas of the United States with a high prevalence of negative stances on BLM is associated with worse health outcomes, such as higher body mass index (BMI) and prevalence of obesity. MethodsWe scraped geo-coded tweets (N = 51,020) that contained #BLM, #ALM, #WLM, and #BlueLM from 2014 to 2016. We determined the stances of the tweets on BLM using machine learning algorithms and aggregated stances at the metropolitan or micropolitan statistical area (MMSA) levels. Participants’ BMI and obesity status were derived from the 2017 BRFSS SMART data in 76 MMSAs, as compiled by the Centers for Disease Control and Prevention (N = 20,530). ResultsAfter controlling for individual- and regional-level covariates, regional measures of racism and police brutality rate, and baseline BMI in 2014 aggregated on MMSA level, Black people had a higher BMI and prevalence of obesity in areas that showed higher negative stances on BLM. Stances against BLM were positively associated with implicit racism against Black people and can be an acute race-related stressor associated with negative downstream health outcomes. ConclusionNegative societal sentiments around race-related issues may be detrimental to the health outcomes of minority populations.

Orderliness in Tourism Enterprise Dynamics in United States Micropolitan Statistical Areas

Micropolitan statistical areas (micropolitans) are important elements in understanding the small-town economic forces operating in the United States. This study focuses on the tourism enterprise dynamics of micropolitans. These dynamics are an oft-neglected element in tourism analyses and reports. Power law (log-log) regression analyses are central to the examination of complex socio-economic systems, and have been used here. Micropolitan tourism enterprises are ubiquitous and there is much non-linear orderliness in the interplay between their demographic and entrepreneurial characteristics. The dynamics of the orderliness result in important differences. For instance, total micropolitan employment increases sub-linearly (more slowly) than increases in tourism enterprise numbers, while tourism employment increases super-linearly (more rapidly). This difference could be important in considerations of micropolitan employment. The relationship of tourism and poverty has often been debated. Here, a statistically significant negative correlation was observed between the number of tourism enterprises and a measure of community prosperity/wealth of the micropolitans. Expansion of the tourism sector apparently reduces community poverty in micropolitans. However, community poverty is also lower in larger micropolitans. Therefore, further analyses are needed to examine the potentially spurious correlation. Overall, future decision-making could be supported by the quantified information about the tourism dynamics of micropolitans.

Cancer deaths attributable to cigarette smoking in 152 U.S. metropolitan or micropolitan statistical areas, 2013-2017.

There are limited data on the burden of cancer attributable to cigarette smoking by metropolitan areas to inform local tobacco control policies in the USA. We estimated the proportion of cancer deaths attributable to cigarette smoking (or population attributable fraction [PAF]) in 152 U.S. metropolitan or micropolitan statistical areas (MMSAs). Smoking-related PAFs for cancer mortality in ages ≥ 30years in 2013-2017 were estimated using cross-sectional age-, sex-, and MMSA-specific cigarette smoking prevalence and cancer mortality data obtained from the Behavioral Risk Factor Surveillance System and the U.S. Cancer Statistics Database, respectively. Overall smoking-related PAFs of cancer ranged from 8.8% (95% CI, 6.3-11.9%) to 35.7% (33.3-37.9%); MMSAs with the highest PAFs were in the South region and Appalachia. PAFs also substantially varied across MMSAs within regions or states. In the Northeast, for example, the PAF ranged from 24.2% (23.7-24.7%) to 33.7% (31.3-36.2%). The proportion of cancer deaths attributable to cigarette smoking is considerable in each MMSA, with as many as 4 in 10 cancer deaths attributable to smoking in the South region and Appalachia. Broad and equitable implementation and enforcement of proven tobacco control interventions at all government levels could avert many cancer deaths across the USA.

Evidence that collaborative action between local health departments and nonprofit hospitals helps foster healthy behaviors in communities: a multilevel study

BackgroundThe Patient Protection and Affordable Care Act of 2010 (ACA) encouraged nonprofit hospitals to collaborate with local public health experts in the conduct of community health needs assessments (CHNAs) for the larger goal of improving community health. Yet, little is known about whether collaborations between local health departments and hospitals may be beneficial to community health. In this study, we investigated whether individuals residing in communities with stronger collaboration between nonprofit hospitals and local public health departments (LHDs) reported healthier behaviors. We further explored whether social capital acts as a moderating factor of these relationships.MethodsWe used multilevel cross-sectional models, controlling for both individual and community-level factors to explore LHD-hospital collaboration (measured in the National Association of County and City Health Officials (NACCHO) Forces of Change Survey), in relation to individual-level health behaviors in 56,826 adults living in 32 metropolitan and micropolitan statistical areas, captured through the 2015 Behavioral Risk Factor Surveillance System (BRFSS) SMART dataset. Nine health behaviors were examined including vigorous exercise, eating fruits and vegetables, smoking and binge drinking. Social capital, measured using an index developed by the Northeast Regional Center for Rural Development, was also explored as an effect modifier of these relationships.ResultsStronger collaboration between nonprofit hospitals and LHDs was associated with not smoking (odds ratio, OR 1.32, 95% CI 1.11 to 1.58), eating vegetables daily (OR 1.29; 95% CI 1.06 to 1.57), and vigorous exercise (OR 1.17; 95% CI 1.05 to 1.30). The presence of higher social capital also strengthened the relationships between LHD-hospital collaborations and wearing a seatbelt (p for interaction = 0.01) and general exercise (p for interaction = 0.03).ConclusionsStronger collaboration between nonprofit hospitals and LHDs was positively associated with healthier individual-level behaviors. Social capital may also play a moderating role in improving individual and population health.

Identification of “Hot Spots” of COVID-19 in the United States (January 2020-January 2021)

Objective To examine the sociodemographic factors associated with US metropolitan or micropolitan statistical areas (MSAs) most impacted by the COVID-19 pandemic and how the sociodemographic profiles of these highest-risk MSAs have changed across two timepoints during the pandemic. Methods This study compares data on the Top 20 MSAs with the highest cumulative COVID-19 case rate at two timepoints (October 13, 2020 and January 1, 2021). The means of CDC’s Social Vulnerability Index (SVI) variables for the highest risk MSAs were compared with the Welch Two Sample t-test to the means of the SVI variables for the rest of the U.S. Results The Top 20 MSAs on October 13, 2020 (Time 1) tended to be located around the US-Mexico border and the South, and the Top 20 MSAs on January 1, 2021 (Time 2) tended to located in the Southwest and Midwest regions. At Time 1, more vulnerable MSAs were disproportionately affected. Conclusion Our findings highlight the excessively high rates of COVID-19 and how it is important for clinicians and public health practitioners to consider these “hot spots” when planning interventions.

Economic Development Effects of Major and Minor League Teams and Stadiums

Professional teams and leagues claim new stadiums lead to economic development. To test this, we utilize data from the Census Bureau on net establishment and employment changes across 871 Metropolitan and Micropolitan Statistical Areas from 2004 to 2012. Difference-in-differences and panel data techniques allow for a cross-sectional and time series comparison for both teams and new stadia in both professional and development leagues. Nearly all results from hundreds of models are insignificantly different from zero. Results from between- and random-effects models suggest that teams move into markets that already have higher employment and establishment growth. (JEL R58, H71, L83, Z28)

Geographical analysis of the distribution of certified genetic counselors in the United States.

The number of certified genetic counselors (CGCs) in the genetic counseling workforce has increased over the past few decades as the number of training programs increases and CGCs expand into new patient-facing and non-patient-facing roles. Few studies have explored the distribution of CGCs across the United States. We sought to identify the U.S. geographical regions with the highest number of CGCs and those regions where the physical presence of CGCs is sparser. Deidentified city, state, and ZIP code information for each CGC in the United States were obtained from the American Board of Genetic Counseling (ABGC) database. A countrywide analysis of the distribution of CGCs was completed using geographic information system (GIS) mapping software. The data were organized into U.S. metropolitan or micropolitan statistical areas, if applicable, and analyzed by CGC per capita. We included a total of 4,554 data points (92.2%) in the analysis. Results showed there is one CGC for every 71,842 people nationwide. Of 3,141 total counties (or county equivalents) in the United States, 535 counties had at least one CGC (17.0%). The majority (98.7%) of CGCs live or work within metropolitan statistical areas (MSAs), which are defined by this study as geographical areas with greater than 50,000 people. Of the MSAs with a CGC, approximately half have more than one CGC per 100,000 people. These results are consistent with the overall distribution of the U.S. population. We believe that the MSAs with the most CGCs per capita are due to associations with specific institutions, that is, genetic counseling training programs, health system headquarters, or genetic laboratories. Although the present study cannot draw definite conclusions regarding direct patient care services provided by CGCs, it does provide a snapshot of current CGC distribution within the country. Knowing the distribution of CGCs provides a tool to conduct further workforce analyses to determine the number of CGCs needed to serve the U.S. population.

Macroeconomic Conditions and Health-Related Outcomes in the United States: A City-Level Analysis between 2004-2017

We study the relationship between macroeconomic conditions and health in the U.S. using data from the Behavioral Risk Factor Surveillance System (BRFSS) between 2004-2017. Unlike many existing studies that use state or county as the level of aggregation, our analysis uses a balanced sample of metropolitan and micropolitan statistical areas (MMSAs). We find consistent evidence of adverse health effects of economic downturns among minorities and less-educated individuals. Our results also suggest strong and negative associations between macroeconomic conditions and access to care and health insurance coverage. In addition, there is some suggestive evidence of counter-cyclicality for smoking among men. However, the estimates for health behavior outcomes are not robust to adjusting for multiple hypothesis testing.

Scaling in words on Twitter.

Scaling properties of language are a useful tool for understanding generative processes in texts. We investigate the scaling relations in citywise Twitter corpora coming from the metropolitan and micropolitan statistical areas of the United States. We observe a slightly superlinear urban scaling with the city population for the total volume of the tweets and words created in a city. We then find that a certain core vocabulary follows the scaling relationship of that of the bulk text, but most words are sensitive to city size, exhibiting a super- or a sublinear urban scaling. For both regimes, we can offer a plausible explanation based on the meaning of the words. We also show that the parameters for Zipf’s Law and Heaps' Law differ on Twitter from that of other texts, and that the exponent of Zipf’s Law changes with city size.

A Typology of U.S. Shrinking Cities

The literature on shrinking cities has significantly grown in recent years. Little work, however, has been done toward the development of a shrinking city typology, which could help inform solutions and strategies to address problems associated with population loss. With a focus on central cities, this article identifies 367 shrinking cities within the United States and categorizes them using a geographic information system to identify seven types of shrinking cities in the United States: (1) large shrinking central cities, (2) inner-ring suburbs of shrinking central cities, (3) outer-ring suburbs of shrinking central cities, (4) inner-ring suburbs of growing central cities, (5) outer-ring suburbs of growing central cities, (6) small shrinking central cities in small Metropolitan Statistical Areas, and (7) small shrinking cities in small Micropolitan Statistical Areas. The empirically generated clusters, combined with associated social and demographic information, identify separate but sometimes interrelated shrinking city types and provide a new perspective for addressing the problems faced by U.S. shrinking cities. Key Words: population decline, shrinking cities, typology, urban population.

Light pollution and insufficient sleep: Evidence from the United States.

The objective of this study was to test whether light pollution is associated with lower or insufficient sleep. The American Medical Association recently issued a public notice cautioning against the effects of nighttime light on sleep quality and quantity. Light pollution, through the suprachiasmatic nucleus, disrupts circadian rhythm by reducing the secretion of melatonin, a sleep-inducing hormone. I used 282 403 individual self-reports of sleep hours and insufficient sleep from the 2014 and 2016 metropolitan and micropolitan statistical area (MMSA) Behavioral Risk Factor Surveillance System (BRFSS) and the prevalence of insufficient sleep during 2014 in 2823 US counties from the County Health Rankings. The nighttime artificial light data are from the cloud-free Visible Infrared Imaging Radiometer Suite available from the National Oceanic and Atmospheric Administration in the US. At the MMSA level, for a 10-unit increase in nighttime light (nW/[cm2 sr]) the estimated decline in sleep was about 5.59 minutes per day and the odds of reporting insufficient sleep (<7 hours) increased by 13.77%. At the county-level, for a 10-unit increase in nighttime light, the prevalence of insufficient sleep increased by 2.19%. Although light pollution was negatively associated with sleep outcomes, the practical effect sizes were small. The small effects suggest that the effects at the population level are negligible, and the effect of nighttime light pollution is more idiosyncratic.

Multilevel Analysis of Socio-Demographic Disparities in Adulthood Obesity Across the United States Geographic Regions.

ObjectivesThe objective of this study was to examine the socio-demographic disparities in obesity among US adults across 130 metropolitan and micropolitan statistical areas.MethodsThis study used data from the 2015 Behavioral Risk Factor Surveillance System and Selected Metropolitan/Micropolitan Area Risk Trend of 159,827 US adults aged 18 years and older. Data were analyzed using the multilevel linear regression models.ResultsAccording to individual level analyses, socio-demographic disparities in obesity exist in the United States. Individuals with low socioeconomic status were associated with a higher body mass index. The participants from the Midwest United States tend to have higher body mass index than those who from the South. According to metropolitan and micropolitan statistical area level analyses, secondly, there were significant differences in obesity status between different areas and the relation of obesity with 5 socio-demographic factors varied across different areas. According to geospatial mapping analyses, even though obesity status by metropolitan and micropolitan statistical area level has improved overtime, differences in body mass index between United States regions are increasing from 2007 to 2015.ConclusionSocio-demographic and regional disparities in obesity status persist among US adults. Hence, these findings underscore the need to take socio-environmental factors into account when planning obesity prevention on vulnerable populations and areas.

The residential segregation of the American Indian and Alaska Native population in US metropolitan and micropolitan areas, 2010.

Racial/ethnic residential segregation has been studied extensively, but few studies have focused on the growing population of American Indians and Alaska Natives (AIANs). An examination of the group's residential patterns will contribute to an understanding of the social position of AIANs and the overall pattern of racial/ethnic residential segregation in the United States. What is the average level of residential segregation in 2010 across the metropolitan and micropolitan areas with a substantial AIAN presence? What are the most- and least-segregated areas for this group? What are the causes and correlates of residential segregation for this group? I use the index of dissimilarity to measure the residential segregation of single-race and multiracial AIANs from non-Hispanic Whites in 264 metropolitan and micropolitan statistical areas, using data from the 2010 census. I also use data from the 2010 census and 2006-2010 American Community Survey to estimate OLS regression models examining the causes and correlates of segregation levels across metropolitan and micropolitan areas. The index of dissimilarity for single-race AIANs is 31.8, while for multiracial AIANs it is 23.6. For both single-race and multiracial AIANs, higher segregation levels are found in metropolitan areas with larger populations, a higher proportion of AIANs, more female-headed households, and a lower relative education level for AIANs. Relative to other racial/ethnic groups, AIANs - especially multiracial AIANs - experience low levels of residential segregation from non-Hispanic Whites. This is the first study that reports and examines residential segregation levels for AIANs using 2010 census data.

Surveillance for Certain Health Behaviors and Conditions Among States and Selected Local Areas - Behavioral Risk Factor Surveillance System, United States, 2015.

ProblemChronic conditions and disorders (e.g., diabetes, cardiovascular diseases, arthritis, and depression) are leading causes of morbidity and mortality in the United States. Healthy behaviors (e.g., physical activity, avoiding cigarette use, and refraining from binge drinking) and preventive practices (e.g., visiting a doctor for a routine check-up, tracking blood pressure, and monitoring blood cholesterol) might help prevent or successfully manage these chronic conditions. Monitoring chronic diseases, health-risk behaviors, and access to and use of health care are fundamental to the development of effective public health programs and policies at the state and local levels.Reporting PeriodJanuary–December 2015.Description of the SystemThe Behavioral Risk Factor Surveillance System (BRFSS) is an ongoing, state-based, random-digit–dialed landline- and cellular-telephone survey of noninstitutionalized adults aged ≥18 years residing in the United States. BRFSS collects data on health-risk behaviors, chronic diseases and conditions, access to and use of health care, and use of preventive health services related to the leading causes of death and disability. This report presents results for all 50 states, the District of Columbia, the Commonwealth of Puerto Rico (Puerto Rico), and Guam and for 130 metropolitan and micropolitan statistical areas (MMSAs) (N = 441,456 respondents) for 2015.ResultsThe age-adjusted prevalence estimates of health-risk behaviors, self-reported chronic health conditions, access to and use of health care, and use of preventive health services varied substantially by state, territory, and MMSA in 2015. Results are summarized for selected BRFSS measures. Each set of proportions refers to the median (range) of age-adjusted prevalence estimates for health-risk behaviors, self-reported chronic diseases or conditions, or use of preventive health care services by geographic jurisdiction, as reported by survey respondents. Adults with good or better health: 84.6% (65.9%–88.8%) for states and territories and 85.2% (66.9%–91.3%) for MMSAs. Adults with ≥14 days of poor physical health in the past 30 days: 10.9% (8.2%–17.2%) for states and territories and 10.9% (6.6%–19.1%) for MMSAs. Adults with ≥14 days of poor mental health in the past 30 days: 11.3% (7.3%–15.8%) for states and territories and 11.4% (5.6%–20.5%) for MMSAs. Adults aged 18–64 years with health care coverage: 86.8% (72.0%–93.8%) for states and territories and 86.8% (63.2%–95.7%) for MMSAs. Adults who received a routine physical checkup during the preceding 12 months: 69.0% (58.1%–79.8%) for states and territories and 69.4% (57.1%–81.1%) for MMSAs. Adults who ever had their blood cholesterol checked: 79.1% (73.3%–86.7%) for states and territories and 79.5% (65.1%–87.3%) for MMSAs. Current cigarette smoking among adults: 17.7% (9.0%–27.2%) for states and territories and 17.3% (4.5%–29.5%) for MMSAs. Binge drinking among adults during the preceding 30 days: 17.2% (11.2%–26.0%) for states and territories and 17.4% (5.5%–24.5%) for MMSAs. Adults who reported no leisure-time physical activity during the preceding month: 25.5% (17.6%–47.1%) for states and territories and 24.5% (16.1%–47.3%) for MMSAs. Adults who reported consuming fruit less than once per day during the preceding month: 40.5% (33.3%–55.5%) for states and territories and 40.3% (30.1%–57.3%) for MMSAs. Adults who reported consuming vegetables less than once per day during the preceding month: 22.4% (16.6%–31.3%) for states and territories and 22.3% (13.6%–32.0%) for MMSAs. Adults who have obesity: 29.5% (19.9%–36.0%) for states and territories and 28.5% (17.8%–41.6%) for MMSAs. Adults aged ≥45 years with diagnosed diabetes: 15.9% (11.2%–26.8%) for states and territories and 15.7% (10.5%–27.6%) for MMSAs. Adults aged ≥18 years with a form of arthritis: 22.7% (17.2%–33.6%) for states and territories and 23.2% (12.3%–33.9%) for MMSAs. Adults having had a depressive disorder: 19.0% (9.6%–27.0%) for states and territories and 19.2% (9.9%–27.2%) for MMSAs. Adults with high blood pressure: 29.1% (24.2%–39.9%) for states and territories and 29.0% (19.7%–41.0%) for MMSAs. Adults with high blood cholesterol: 31.8% (27.1%–37.3%) for states and territories and 31.4% (23.2%–42.0%) for MMSAs. Adults aged ≥45 years who have had coronary heart disease: 10.3% (7.2%–16.8%) for states and territories and 10.1% (4.7%–17.8%) for MMSAs. Adults aged ≥45 years who have had a stroke: 4.9% (2.5%–7.5%) for states and territories and 4.7% (2.1%–8.4%) for MMSAs.InterpretationThe prevalence of health care access and use, health-risk behaviors, and chronic health conditions varied by state, territory, and MMSA. The data in this report underline the importance of continuing to monitor chronic diseases, health-risk behaviors, and access to and use of health care in order to assist in the planning and evaluation of public health programs and policies at the state, territory, and MMSA level.Public Health ActionState and local health departments and agencies and others interested in health and health care can continue to use BRFSS data to identify groups with or at high risk for chronic conditions, unhealthy behaviors, and limited health care access and use. BRFSS data also can be used to help design, implement, monitor, and evaluate health-related programs and policies.