Approximately 46 million persons (14%) in the United States live in nonmetropolitan counties.* Compared with metropolitan residents, nonmetropolitan residents have a higher prevalence of obesity-associated chronic diseases such as diabetes (1), coronary heart disease (1), and arthritis (2). The 2005–2008 National Health and Nutrition Examination Survey (NHANES) found a significantly higher obesity prevalence among adults in nonmetropolitan (39.6%) than in metropolitan (33.4%) counties (3). However, this difference has not been examined by state. Therefore, CDC examined state-level 2016 Behavioral Risk Factor Surveillance System (BRFSS) data and found that the prevalence of obesity (body mass index [BMI] ≥30 kg/m2) was 34.2% among U.S. adults living in nonmetropolitan counties and 28.7% among those living in metropolitan counties (p<0.001). Obesity prevalence was significantly higher among nonmetropolitan county residents than among metropolitan county residents in all U.S. Census regions, with the largest absolute difference in the South (5.6 percentage points) and Northeast (5.4 percentage points). In 24 of 47 states, obesity prevalence was significantly higher among persons in nonmetropolitan counties than among those in metropolitan counties; only in Wyoming was obesity prevalence higher among metropolitan county residents than among nonmetropolitan county residents. Both metropolitan and nonmetropolitan counties can address obesity through a variety of policy and environmental strategies to increase access to healthier foods and opportunities for physical activity (4).
BRFSS is a state-based, random-digit–dialed telephone survey of U.S. adults aged ≥18 years, conducted annually by CDC and state and territorial health departments to monitor health conditions and related behaviors.† BRFSS uses multistage, stratified sampling to select a representative sample of the noninstitutionalized adult population in 50 states, the District of Columbia (DC), and selected U.S. territories. In 2016, using combined landline and cell phone data across all states, the median response rate was 47.0%, which was calculated using rates from the American Association of Public Opinion Research.§ Self-reported weight and height were used to calculate BMI (weight [kg]/height [m]2); obesity was defined as BMI ≥30 kg/m2.¶ Among 477,665 respondents, 39,186 (8.2%) were excluded, including 36,848 with missing BMI values and 2,338 with implausible BMI values, leaving a final analytic sample of 438,479 adults from 50 states and DC. Unadjusted obesity prevalence is presented overall and by sociodemographic characteristics (age, sex, race/ethnicity, education, income, and employment status), state, and four U.S. Census regions and nine divisions: Northeast region (New England and Middle Atlantic divisions), Midwest region (East North Central and West North Central divisions), South region (South Atlantic, East South Central, and West South Central divisions), and West region (Mountain and Pacific divisions).**
Using 2010 Census data, CDC’s National Center for Health Statistics (NCHS) developed an Urban-Rural Classification Scheme for Counties,†† which specified six county types; for this analysis, to ensure sufficient sample size for regional and state-level comparisons, counties were collapsed into two categories: metropolitan (large central metro, large fringe metro, medium metro, and small metro) and nonmetropolitan (micropolitan and noncore). In this analysis, the nonmetropolitan designation was used to classify counties with small populations (<50,000). Rhode Island, New Jersey, Delaware, and DC do not have nonmetropolitan counties; for these jurisdictions, obesity prevalence was calculated for adults living in metropolitan counties only. U.S. territories were excluded because the NCHS classification scheme does not include them. Unadjusted obesity prevalence was stratified by metropolitan and nonmetropolitan status. Differences in obesity prevalence between adults living in metropolitan and nonmetropolitan counties were examined using multivariable logistic regression, controlling for age, sex, and race/ethnicity within levels of the sociodemographic characteristics, states, and Census regions and divisions (statistically significant at p<0.05). All analyses accounted for complex survey design and sampling weights.
In 2016, overall obesity prevalence was 29.6% and was highest among persons residing in the South (32.0%) and Midwest (31.4%) regions and the East South Central (35.3%) and West South Central (33.9%) divisions (Table 1). Overall, obesity prevalence was significantly higher among adults living in nonmetropolitan counties (34.2%) than among those living metropolitan counties (28.7%) (p<0.001), and the same was found in all Census regions and Census divisions. Among Census regions, the largest difference in obesity prevalence between persons living in nonmetropolitan and metropolitan counties was in the South (5.6 percentage points) and Northeast (5.4 percentage points); among Census divisions, the largest difference in obesity prevalence between nonmetropolitan and metropolitan residents was in the Middle Atlantic division (6.6 percentage points). Obesity prevalence was also significantly higher among nonmetropolitan county residents than among metropolitan county residents for all sociodemographic categories except Hispanics and persons with less than a high school education.
TABLE 1. Prevalence of self-reported obesity among adults (aged ≥18 years) by respondent characteristics and metropolitan/nonmetropolitan status — Behavioral Risk Factor Surveillance System, 50 states and the District of Columbia, 2016. Characteristic No. of respondents Unadjusted adult obesity prevalence–weighted % (95% CI)* Total Metropolitan† Nonmetropolitan† Total 438,479 29.6 (29.3–29.8) 28.7 (28.4–29.0)§ 34.2 (33.6–34.8)§ Age group (yrs)¶ 18–24 23,734 17.3 (16.5–18.1) 16.5 (15.6–17.4)§ 22.2 (20.3–24.2)§ 25–34 42,706 27.2 (26.5–27.9) 26.4 (25.6–27.2)§ 32.5 (30.8–34.3)§ 35–44 48,951 33.1 (32.3–33.8) 32.0 (31.2–32.9)§ 39.6 (38.0–41.2)§ 45–54 68,854 35.1 (34.4–35.8) 34.0 (33.2–34.8)§ 40.8 (39.4–42.3)§ 55–64 96,566 34.2 (33.6–34.8) 33.4 (32.7–34.1)§ 38.0 (36.9–39.2)§ ≥65 157,668 28.0 (27.5–28.5) 27.5 (26.9–28.1)§ 30.1 (29.3–31.0)§ Sex** Male 198,440 29.6 (29.2–30.0) 28.8 (28.3–29.2)§ 34.4 (33.6–35.2)§ Female 240,000 29.5 (29.1–29.9) 28.7 (28.2–29.1)§ 34.0 (33.2–34.8)§ Race/Ethnicity¶,** White, non-Hispanic 341,192 28.6 (28.3–28.9) 27.5 (27.2–27.9)§ 33.2 (32.6–33.8)§ Black, non-Hispanic 35,091 38.3 (37.4–39.3) 37.7 (36.7–38.7)§ 44.2 (41.7–46.7)§ Hispanic, any race 28,666 33.1 (32.1–34.1) 32.9 (31.9–33.9) 36.0 (32.6–39.5) Other, non-Hispanic 26,954 18.2 (17.3–19.2) 16.8 (15.8–17.8)§ 33.2 (31.2–35.3)§ Education¶,** <High school 32,325 35.5 (34.5–36.5) 35.4 (34.3–36.6) 35.9 (34.0–37.8) High school 123,241 32.3 (31.8–32.8) 31.5 (30.9–32.1)§ 35.6 (34.7–36.5)§ Some college 120,735 31.0 (30.5–31.5) 30.3 (29.7–30.9)§ 34.7 (33.7–35.7)§ College graduate 161,309 22.2 (21.9–22.6) 21.5 (21.1–21.9)§ 28.8 (27.9–29.7)§ Annual household income¶,** <$25,000 99,244 34.1 (33.5–34.7) 33.4 (32.7–34.2)§ 37.1 (35.9–38.2)§ $25,000–49,999 95,553 31.9 (31.3–32.6) 31.1 (30.3–31.8)§ 35.9 (34.7–37.1)§ $50,000–74,999 61,211 31.1 (30.3–31.8) 30.2 (29.4–31.1)§ 35.4 (34.0–36.8)§ ≥$75,000 120,901 25.4 (24.9–25.9) 24.8 (24.3–25.3)§ 30.9 (29.8–32.1)§ Employment status¶,** Employed 215,226 29.0 (28.6–29.4) 28.2 (27.8–28.6)§ 34.1 (33.3–34.9)§ Out of work 17,009 32.9 (31.6–34.3) 32.4 (30.9–34.0)§ 35.8 (33.1–38.7)§ Homemaker 22,372 29.0 (27.7–30.3) 28.4 (27.0–29.9)§ 32.0 (29.5–34.7)§ Student 11,277 15.2 (14.1–16.3) 14.8 (13.6–16.0)§ 18.8 (16.2–21.7)§ Retired 136,638 29.1 (28.5–29.6) 28.6 (28.0–29.2)§ 31.2 (30.3–32.2)§ Unable to work 33,534 45.8 (44.8–46.9) 45.5 (44.2–46.8)§ 47.1 (45.2–49.1)§ Census region¶,†† Northeast 88,335 26.9 (26.3–27.5) 26.4 (25.8–27.0)§ 31.8 (30.4–33.2)§ Midwest 106,697 31.4 (30.9–31.9) 30.5 (29.9–31.2)§ 34.2 (33.3–35.1)§ South 146,919 32.0 (31.5–32.5) 31.0 (30.4–31.6)§ 36.6 (35.6–37.6)§ West 96,528 26.0 (25.4–26.6) 25.7 (25.1–26.4)§ 28.6 (27.5–29.7)§ Census division¶,†† New England 43,889 25.4 (24.7–26.1) 25.0 (24.2–25.8)§ 28.7 (27.4–30.0)§ Middle Atlantic 44,446 27.4 (26.7–28.2) 26.9 (26.1–27.7)§ 33.5 (31.5–35.6)§ East North Central 42,215 31.8 (31.1–32.5) 31.0 (30.2–31.8)§ 34.9 (33.5–36.3)§ West North Central 64,482 30.6 (30.0–31.2) 29.3 (28.5–30.1)§ 33.3 (32.4–34.2)§ South Atlantic 93,367 29.9 (29.3–30.4) 29.1 (28.5–29.7)§ 35.3 (33.9–36.7)§ East South Central 26,587 35.3 (34.4–36.2) 34.5 (33.3–35.6)§ 36.9 (35.6–38.1)§ West South Central 26,965 33.9 (32.7–35.2) 33.1 (31.7–34.5)§ 37.8 (35.4–40.3)§ Mountain 57,788 26.2 (25.6–26.8) 26.0 (25.3–26.7)§ 27.2 (26.3–28.1)§ Pacific 38,740 25.9 (25.0–26.7) 25.6 (24.7–26.4)§ 30.3 (28.1–32.6)§Among adults living in nonmetropolitan counties, obesity prevalence ranged from 20.8% in Colorado to 39.1% in Louisiana; among those living in metropolitan counties, prevalence ranged from 22.5% in Colorado to 36.9% in West Virginia. (Table 2). In 24 (51%) of the 47 states with both metropolitan and nonmetropolitan counties, obesity prevalence was significantly higher among adults living in nonmetropolitan counties than among those living in metropolitan counties; in 22 (47%) states, no difference was observed. Wyoming was the only state where obesity prevalence was significantly higher among metropolitan county residents (32.8%) than among nonmetropolitan residents (25.4%; p = 0.002).
TABLE 2. Prevalence of self-reported obesity among adults (aged ≥18 years) by state and metropolitan/nonmetropolitan status — Behavioral Risk Factor Surveillance System, 50 states and the District of Columbia, 2016. Census division†/State No. of respondents Unadjusted adult obesity prevalence–weighted % (95% CI)* Metropolitan§ Nonmetropolitan§ New England Connecticut 9,960 25.9 (24.7–27.1) 28.1 (22.7–34.2) Maine 9,554 29.3 (27.3–31.3) 30.9 (29.1–32.7) Massachusetts 7,480 23.6 (22.2–24.9) 24.4 (16.9–34.0) New Hampshire 5,888 26.0 (23.8–28.2) 27.6 (25.4–29.9) Rhode Island 4,936 26.6 (24.9–28.4) —¶ Vermont 6,071 24.1 (21.3–27.1)** 28.7 (26.9–30.6)** Middle Atlantic New Jersey 6,810 27.4 (25.7–29.1) —¶ New York 31,269 24.9 (23.9–26.0)** 33.0 (31.6–34.5)** Pennsylvania 6,367 29.7 (28.1–31.4)** 33.9 (30.4–37.5)** East North Central Illinois 4,518 31.0 (29.2–32.9)** 35.7 (31.0–40.6)** Indiana 10,319 32.0 (30.6–33.5) 33.9 (31.3–36.7) Michigan 11,130 31.6 (30.4–32.9)** 36.0 (33.7–38.5)** Ohio 11,455 30.7 (29.2–32.3)** 34.4 (32.1–36.8)** Wisconsin 4,793 29.1 (27.0–31.3)** 34.4 (31.6–37.3)** West North Central Iowa 6,645 31.4 (29.4–33.5) 32.7 (30.7–34.8) Kansas 10,947 29.9 (28.5–31.3)** 33.7 (32.0–35.5)** Minnesota 15,420 26.5 (25.6–27.5)** 31.7 (30.1–33.2)** Missouri 6,578 30.5 (28.4–32.6)** 34.9 (32.1–37.9)** Nebraska 14,173 30.8 (29.1–32.6)** 34.2 (32.9–35.5)** North Dakota 5,348 30.5 (28.2–32.9) 33.4 (31.2–35.6) South Dakota 5,371 27.0 (23.9–30.5)** 31.8 (29.2–34.5)** South Atlantic Delaware 3,702 30.7 (28.7–32.8) —¶ District of Columbia 3,479 22.6 (20.9–24.3) —¶ Florida 33,186 27.2 (26.1–28.2)** 34.9 (32.6–37.2)** Georgia 4,884 30.8 (28.9–32.8) 34.0 (30.3–37.9) Maryland 16,701 29.8 (28.7–30.9)** 35.1 (32.0–38.3)** North Carolina 5,984 31.1 (29.5–32.9) 34.1 (31.4–37.0) South Carolina 10,503 31.2 (29.8–32.7)** 37.8 (35.1–40.6)** Virginia 8,293 27.7 (26.3–29.1)** 36.1 (33.2–39.1)** West Virginia 6,635 36.9 (35.2–38.7) 38.8 (36.6–41.0) East South Central Alabama 6,526 35.6 (33.8–37.5) 36.0 (33.1–38.9) Kentucky 9,583 32.1 (30.2–34.0)** 36.9 (34.7–39.2)** Mississippi 4,821 36.5 (33.4–39.7) 37.9 (35.7–40.1) Tennessee 5,657 34.3 (32.1–36.6) 36.4 (33.6–39.3) West South Central Arkansas 4,859 35.4 (32.2–38.8) 36.1 (32.6–39.7) Louisiana 4,868 34.8 (32.5–37.3) 39.1 (34.7–43.7) Oklahoma 6,449 30.8 (28.8–32.8)** 36.3 (33.9–38.8)** Texas 10,789 32.9 (31.0–34.8)** 38.7 (34.3–43.2)** Mountain Arizona 10,033 28.8 (27.2–30.4) 33.6 (29.1–38.4) Colorado 13,637 22.5 (21.5–23.5) 20.8 (19.0–22.8) Idaho 4,880 26.3 (23.9–28.8) 29.6 (27.0–32.4) Montana 5,483 25.9 (23.1–29.0) 25.3 (23.3–27.3) Nevada 3,981 25.1 (23.1–27.3)** 32.1 (28.6–35.9)** New Mexico 5,531 27.0 (24.7–29.4)** 31.1 (28.7–33.6)** Utah 10,043 25.4 (24.2–26.7) 24.9 (22.7–27.2) Wyoming 4,200 32.8 (29.0–36.9)** 25.4 (23.1–27.8)** Pacific Alaska 2,739 30.9 (27.1–35.0) 32.4 (28.8–36.4) California 10,352 25.0 (24.0–26.1) 24.2 (19.2–30.0) Hawaii 7,659 23.3 (21.8–24.9)** 26.1 (23.5–28.8)** Oregon 5,000 27.4 (25.8–29.1)** 35.1 (31.5–38.8)** Washington 12,990 27.8 (26.8–28.9)** 35.3 (32.3–38.4)** DiscussionIn this study, obesity prevalence was significantly higher among adults living in nonmetropolitan counties than among those living in metropolitan counties, overall, in all Census regions, all Census divisions, and in approximately half of states with both county types. Across regions and divisions, this disparity in obesity prevalence was highest in the South and Northeast regions and the Middle Atlantic division. With the exception of Hispanics and persons with less than a high school education, the higher obesity prevalence among nonmetropolitan residents was observed in all sociodemographic groups.
These findings are consistent with those previously reported using 2005–2008 NHANES data, which documented higher overall obesity prevalence among adults living in nonmetropolitan versus metropolitan counties of the United States (3), and expand the understanding of this health disparity by highlighting differences across states and regions. Research has documented differences between adults living in nonmetropolitan and metropolitan counties in health behaviors and community factors, which could influence obesity risk (5–7). An analysis of 2013 BRFSS data found that adults living in U.S. nonmetropolitan counties were less physically active and less likely to meet physical activity recommendations than their metropolitan counterparts (5). Data from 2011 indicated that across all regions, adults living in rural areas were less likely to have access to healthier food retailers (supermarkets, large grocery stores, and fruit/vegetable specialty stores) than were those living in urban areas (6). In addition, several social determinants of health that are risk factors for obesity, such as persistent poverty and food insecurity (7), are more prevalent in rural than in urban areas.§§,¶¶
In this analysis, the highest obesity prevalence and the greatest disparity in prevalence between persons living in nonmetropolitan and metropolitan counties were in the South Census region. One possible contributing factor is the high rate of persistent poverty in the South, which also is affected by the largest difference in poverty rate between metropolitan and nonmetropolitan county residents.¶¶
The findings in this report are subject to at least two limitations. First, data are self-reported, and self-reported weight and height data underestimate BMI values, particularly among persons with a higher BMI (8). It is not known whether self-reporting bias is comparable across regions and between metropolitan and nonmetropolitan counties. Second, to ensure sufficient sample size for regional and state-level comparisons, the nonmetropolitan classification was used to designate counties with small populations (<50,000 persons). The literature on rural obesity disparities and prevention strategies uses various methods to define rural areas, some of which might differ in population size from the nonmetropolitan designation used in this paper.
CDC recommends 24 obesity-prevention policy and environmental strategies (4). Two systematic reviews summarized the relevance and effectiveness of these strategies in rural areas and identified how these strategies could be adapted for rural settings (9,10). One nutrition-related obesity prevention strategy, increasing the availability of healthier food and beverage choices, is challenging to implement in rural areas because of the long distances between food suppliers and retailers and between retailers and consumers, which can influence food cost and the availability of fresh foods. Approaches to overcoming this challenge include strengthening networks between food producers, distributors, and retail food outlets, as well as reducing the distance customers need to travel, for example, by increasing access to nearby farmers’ markets (9). The 2018 CDC State Indicator Report on Fruits and Vegetables also highlights approaches to increase the purchase, supply, and demand of fruits and vegetables in states and communities across the United States.*** Other approaches include working with schools and worksites to develop nutrition-related policies and forming strong partnerships with groups such as the Cooperative Extension Service to promote federal food and nutrition assistance program benefits (9).
Strategies to increase physical activity in rural areas should take into consideration geographic dispersion, transportation challenges, and limitations on community resources that might not be present in urban areas (10). Strategies that have been implemented in rural settings include improving community access to public buildings (e.g., school facilities) after hours for physical activity purposes; improving infrastructure and land use design to support walking and other physical activity (e.g., bicycle paths, paved sidewalks, and outdoor public recreation facilities); promoting existing places for physical activity with improved signage; enhancing physical education in schools; and implementing worksite policies to promote physical activity (10). The data in this report can serve as a resource for states seeking to reduce obesity disparities in nonmetropolitan counties through strategies to increase physical activity and healthier eating.
Summary. What is already known about this topic?National estimates from a decade ago found a higher prevalence of obesity among adults living in nonmetropolitan counties than among those living in metropolitan counties.
What is added by this report?Analysis of 2016 Behavioral Risk Factor Surveillance System data found a higher obesity prevalence among adults in nonmetropolitan counties than among those in metropolitan counties. The greatest differences in obesity prevalence between nonmetropolitan and metropolitan residents were in the South (5.6 percentage points) and Northeast (5.4 percentage points).
What are the implications for public health practice?Both nonmetropolitan and metropolitan counties can address obesity through a variety of policy and environmental strategies to increase access to healthier foods and opportunities for physical activity.
AcknowledgmentsWilliam Garvin, Division of Population Health, National Center for Chronic Disease Prevention and Health Promotion, CDC; state and DC BRFSS staff members.
Conflict of Interest: No conflicts of interest were reported.
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