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1 ARTICLE 2 County-level correlation between adult obesity rates and prevalence of dentists Jessica Holzer, PhD, MA; Maureen Canavan, PhD, MPH; Elizabeth Bradley, PhD The United States has the highest obesity rate in the world; more than 30 percent of the population is obese, and more than $150 billion per year is spent on related health care costs and lost productivity. 1,2 Obesity s prevalence and related costs are expected to increase in the decade ahead barring a major shift in behaviors. Despite the overall high prevalence of obesity, substantial geographical variation is apparent; for instance, in Colorado, only 21 percent of the population is obese, whereas in Louisiana, 35 percent of the population is obese. 3 Investigators in previous studies 4-12 have examined several area-level correlates of obesity with mixed results. They conducted these studies in selected populations or small samples, or they focused on single factors such as access to recreation or to food stores or proximity to foreclosed homes. In a review of 20 studies in which investigators examined the effect of the built environment on obesity, the reviewers found that the results from 17 studies showed that features of the physical environment, such as lack of access to recreation facilities or presence of fast-food restaurants, were associated significantly with obesity. 13 On the other hand, investigators in three of these studies, 4,14,15 as well as those of an additional study conducted in Detroit, 7 found no relationship between built environment factors and obesity. We could find only one national study of county-level factors in which the investigators examined geographical variability in obesity rates by using multivariable analysis. 16 They concluded that higher obesity rates were associated with counties with higher percentages of the population who identified as black, higher unemployment rates, more families headed by single mothers, greater numbers of hospital outpatient visits per 1,000 population, lower educational rates and fewer adults who engaged in regular physical activity. Although the results of that study provided contemporary, national evidence, the investigators omitted a key health care factor that we hypothesize might be associated with obesity rates: the number of dentists per capita in the county. The literature also suggests the importance of primary care in addressing obesity rates via health education and prevention efforts However, investigators in empirical studies have not examined the association between the prevalence of dental care a key part of primary ABSTRACT Background. Investigators of previous studies regarding the correlation between area-level health care resources and obesity have not examined the association between the prevalence of dentists and rates of adult obesity. The authors conducted a study to address that knowledge gap. Methods. Using data compiled in the Robert Wood Johnson County Health Rankings and Roadmaps database, the authors conducted multivariable analyses of the relationship between the prevalence of dentists (from the 2011 Health Resources and Services Administration Area Resource File) and rates of obesity within counties. The authors controlled for prevalence of primary care providers, measures of the built environment (for example, number of recreational facilities per 10,000 population, the percentage of restaurants serving fast food) and county-level sociodemographic and economic factors. Results. When the authors conducted a multivariable analysis adjusted for state-level fixed effects, they found that having one additional dentist per 10,000 population was associated significantly with a 1- percentage point reduction in the rate of obesity (P <.001). This effect was significantly larger in counties in which 25 percent of children or more (versus less than 25 percent of children) lived in poverty and in counties that had more primary care physicians per 10,000 population (P.009). Conclusions. The association between the prevalence of dentists and obesity, even after adjusting for primary care resources and sociodemographic factors, was evident. Although these data could not be used to assess causality, given the strength of the ecological, cross-sectional association, additional research involving person-level, longitudinal data is warranted. Practical Implications. The correlation between the prevalence of dentists and obesity rates highlights the potential for dental professionals, as well as other primary care providers, to provide meaningful health education and support for improved nutritional behaviors, although the increased obesity rates in counties with fewer dentists per capita present challenges. Key Words. Dentists; obesity; poverty; counties; determinants of health. JADA 2014;145(9): doi: /jada JADA 145(9) September 2014

2 care and obesity rates. Although 2010 data indicate that only a minority of dentists provide counseling related to obesity, 20 the prevalence of dentists may be a marker for stronger primary care resources in an area and may highlight an opportunity for greater engagement of dentists in nutritional education and obesity prevention efforts. Given calls in 2012 within the dental profession for contributing to systemic health 21 and to become more involved in health care for people who are obese, 22,23 understanding the correlation between the prevalence of dentists and obesity rates can underscore areas of greater need and opportunities for improvement. Accordingly, we sought to examine the association between the number of dentists per capita and adult obesity rates by using county-level data for health care resources, measures of the built environment and sociodemographic and economic factors. Because poverty was associated with obesity rates in previous studies, 24,25 we also examined whether the effect of having more dentists per 10,000 population differed significantly for counties with higher rates of children living in poverty and those with lower rates. The findings may be useful in identifying the largely neglected but potentially important role dentists play in addressing obesity in the United States. METHODS Study design and sample. We conducted a crosssectional analysis by using data from the 2013 County Health Rankings and Roadmaps program, 26 a database that integrated county-level data from the Centers for Disease Control and Prevention s (CDC) Behavioral Risk Factor Surveillance System, the CDC s National Center for Health Statistics, the Health Resources and Services Administration, the U.S. Census Bureau, the U.S. Department of Agriculture Economic Research service, the U.S. Bureau of Labor Statistics and the Dartmouth Atlas for Health Care for any county or county equivalent that had its own Federal Information Processing Standard. The etable (shown in the supplemental data to the online version of this article [found at org/content/145/9/932/suppl/dc1]) presents a complete list of the data sources and years for the variables we included in our sample. Overall, we compiled data for 3,141 counties across the United States. We excluded 300 counties because their data did not include our independent variables, resulting in a final analytic sample of 2,841 counties (inclusion rate, 90.4 percent). Measures. Dependent variable. Our primary dependent variable was percentage of adults who were obese (body mass index [BMI] of 30 or greater) within a county. The CDC calculated BMI from self-reported height and weight estimates obtained as part of its Behavioral Risk Factor Surveillance System, and the CDC s Division of Diabetes Translation, National Center for Chronic Disease Prevention and Health Promotion, compiled the BMI data into county-level estimates. Independent variables. We included several independent variables that on the basis of the literature 5,13,27-31 we hypothesized were related to obesity rates. These variables included health care resources, measures of the built environment and sociodemographic and economic factors. We assessed health care resources, according to the number of primary care physicians (PCPs) per 10,000 population and the number of dentists per 10,000 population. We obtained data regarding the number of dentists and the number of PCPs in 2011 from the Health Resources and Services Administration Area Health Resource Files, 32 as we expected greater primary care resources to be associated with lower obesity rates. Measures of the built environment included the number of recreational facilities (expected to be associated with lower obesity rates) per 10,000 population, the percentage of adults who reported having no leisure-time physical activity, the percentage of fast-food restaurants from among the total number of restaurants in the county and the percentage of the population that reported having limited access to healthy food (all expected to be associated with higher obesity rates). Sociodemographic and economic factors included the percentage of the population 65 years or older, the percentage of adults aged from 25 through 44 years who had some postsecondary education, racial and ethnic composition (percentage black, percentage Hispanic), the percentage of children living below the poverty threshold, the percentage of children living in a single-parent household, the percentage of people younger than 65 years who were uninsured, the percentage of the labor force that was unemployed (calculated as people 16 years or older who were unemployed as a percentage of the civilian labor force). We expected all of these sociodemographic and economic factors to be associated with higher obesity rates except for the percentage of the population 65 years or older, which we expected to be associated with lower obesity rates. We also adjusted the analysis for the percentage of the population living in a rural area (defined as all population, housing and territory not included within an urban area) and overall county population size (per 10,000 population), both of which we anticipated would be associated with either higher or lower obesity rates. Data analysis. We described the sample characteristics by using standard frequency analysis. We conducted bivariate and multivariable linear regression analyses to estimate unadjusted and adjusted associations between independent variables and the percentage of adults who were obese within a county. We included state-level fixed effects in our bivariate and multivariable models to account for clustering of observations within states. Before ABBREVIATION KEY. BMI: Body mass index. CDC: Centers for Disease Control and Prevention. PCP: Primary care physician. JADA 145(9) September

3 fitting the multivariable model, we assessed potential multicollinearity among independent variables by using tolerance and variance inflation factor cutoff points of 0.1 and 4.0, respectively, as recommended by Belsley and colleagues. 33 We fit the model with all hypothesized variables, and then we eliminated the variables that were nonsignificant one at a time to determine how their removal affected the overall model. We retained nonsignificant variables as confounding factors if their removal changed any remaining parameter estimates by more than 20 percent. We tested whether poverty or the prevalence of PCPs moderated the relationship between the prevalence of dentists and percentage of adults who were obese by adding interaction effects separately to the multivariable model. First, we assessed the interaction of high-poverty counties defined as having 25 percent of children or more living below the poverty threshold and the prevalence of dentists in the county. Because this interaction was significant (P =.001), we reported results stratified into highpoverty and low-poverty counties. We then evaluated the interaction of low PCP prevalence, defined as being in the lowest quintile for prevalence of PCPs ( 0.3 PCP per 10,000 population), and the prevalence of dentists in the county. Because this interaction was significant (P <.001), we also reported results stratified according to the prevalence of PCPs. We tested for spatial autocorrelation of the percentage of adults who were obese by using the Moran I method, 34 which we used to measure linear association between these values in a specific county and the percentage of adults who were obese in neighboring counties. We found that spatial autocorrelation was present within our sample and, therefore, included a spatial lag term in our multivariable models to account for the spatial autocorrelation of the percentage of adults who were obese in our sample. We performed all analyses by using statistical software (SAS, Version 9.2, SAS Institute, Cary, N.C.), and we mapped county-level distribution by using mapping software (ArcGIS, Version 10.2, ESRI, Redlands, Calif.). RESULTS Sample characteristics. A mean of percent of adults were obese across the counties (Table 1), although this varied on the basis of geographical location (Figure 1). TABLE 1 Sample characteristics (N = 2,841). COUNTY-LEVEL VARIABLE MEAN STANDARD DEVIATION Adults Who Are Obese, % Dentist Rate (per 10,000 Population), No Primary Care Physician Rate (per 10,000 Population), No Adults With No Leisure-Time Physical Activity, % Access to Recreational Facilities Rate (per 10,000 Population), No Restaurants Serving Fast Food, % Population With Limited Access to Healthy Food, % Adults With at Least Some College Education, % Population 65 Years or Older, % Population Black, % Population Hispanic, % Children Living in Poverty, % Children Living in Single-Parent Households, % Population That Is Uninsured, % Labor Force That Is Unemployed, % County Classified as Rural, % County Population Size (per 10,000 Population), No The average number of dentists per 10,000 population in a county was 0.38, with a range of 0 to 2.88 (Figure 2). Together, the figures show several counties that have high obesity rates and a low number of dentists. The average percentage of children living in poverty in a county was percent, percent of people in counties were uninsured, and percent of adults reported having no leisure-time physical activity. Counties had a mean of 78 recreational facilities per 10,000 population and a mean of 0.56 PCPs per 10,000 population. Unadjusted associations between obesity and independent variables. The results of unadjusted analyses showed that higher obesity rates were associated with a lower prevalence of dentists per 10,000 population in the county (P <.001) (Table 2, page 936). Higher obesity also was associated with lower prevalence of PCPs per 10,000 population in the county (P <.001) and a lower percentage of the population that was 65 years or older (P =.01). A higher percentage of adults who reported no leisure-time physical activity, a higher percentage of fast-food restaurants as a proportion of all restaurants, a higher percentage of the population who reported limited access to healthy food and a lower prevalence of recreational facilities per 10,000 population each also were associated independently with an increased percentage of adults who were obese (P <.001). In addition, sociodemographic and economic variables (that is, higher percentages of children living in poverty, unemployment, people who were uninsured, children living in single-parent households, people who were black and counties classified as rural) each were associated with a higher percentage of adults who were obese (P.001). The global Moran I was 0.09, which 934 JADA 145(9) September 2014

4 Figure 1. Percentage of adults who are obese within U.S. counties. Figure 2. Quintile distribution in rate of dentists, according to county. JADA 145(9) September

5 indicated the presence of positive spatial autocorrelation (that is, clustering of high- and low-obesity prevalence across counties) at a statistically significant level (P <.001) (data not shown). Adjusted associations between the percentage of adults who are obese and county-level factors. The results of the multivariable analysis, adjusted for state-level fixed effects, showed that having one additional dentist per 10,000 population was associated with an almost 1-percentage point reduction in the percentage of adults who were obese (P <.001) (Table 3). Measures of the built environment TABLE 2 Unadjusted fixed effect linear regression models examining the associations between county-level variables and percentage of adults who are obese (N = 2,841). COUNTY-LEVEL VARIABLE ESTIMATE P VALUE (STANDARD ERROR) Dentist Rate (per 10,000 Population), No (0.26) <.001 Primary Care Physician Rate (per 10,000 Population), No (0.17) <.001 Adults With No Leisure-Time Physical Activity, % 0.47 (0.01) <.001 Access to Recreational Facilities Rate (per 10,000 Population), No (0.01) <.001 Restaurants Serving Fast Food, % 0.03 (0.01) <.001 Population With Limited Access to Healthy Food, % 0.06 (0.01) <.001 Adults With at Least Some College Education, % 0.13 (0.01) <.001 Population 65 Years or Older, % 0.04 (0.02).01 Population Black, % 0.11 (0.01) <.001 Population Hispanic, % 0.01 (0.01).14 Children Living in Poverty, % 0.17 (0.01) <.001 Children Living in Single-Parent Households, % 0.14 (0.01) <.001 Population That Is Uninsured, % 0.11 (0.02).001 Labor Force That Is Unemployed, % 0.44 (0.03) <.001 County Classified as Rural, % 0.02 (0.01) <.001 County Population Size (per 10,000 Population), No (0.61) <.001 (that is, a higher percentage of adults who reported no leisure-time physical activity, a higher percentage of fast-food restaurants as a proportion of all restaurants and a lower number of recreational facilities per 10,000 population) remained significantly associated with a higher percentage of adults who were obese in the multivariable model (P.005). Similarly, a lower percentage of adults with at least some (versus no) college education, a lower percentage of adults 65 years or older, a lower percentage of people who were of Hispanic ethnicity, a higher percentage of people who were black and a higher percentage of children living in single-parent households remained significantly associated with higher rates of obesity in multivariable analysis (P <.005). The results of the multivariable model showed that the spatial lag term (4.63) was associated significantly with the percentage of adults who were obese (P <.001). The percentage of people who were uninsured remained associated significantly with obesity; however, the direction of the effect shifted, with a lower percentage of people who were uninsured being associated with higher rates of obesity (P <.001). The results of the multivariate analysis showed that the following factors, which had been significant in the unadjusted analysis, no longer were significant: percentage of the population who reported limited access to healthy food, percentage of children living in poverty, percentage of the labor force that was unemployed and the percentage of the population living in a rural area. Effect modification. The association between the prevalence of dentists and obesity rates was modified significantly by county-level poverty rates (interaction, P =.001); the magnitude of the effect of dentists was magnified in counties in which 25 percent of children or more lived in poverty compared with counties in which less than 25 percent of the children lived in poverty. In counties in which at least 25 percent of children lived in poverty, one additional dentist per 10,000 population was associated with a 1.23 percent decrease in the percentage of adults who were obese (P =.009) (Table 3), whereas in counties in which less than 25 percent of children lived in poverty, one additional dentist per 10,000 population was associated with only a 0.55 percent decrease in the percentage of adults who were obese (P =.048). The association between the prevalence of dentists and obesity rates also was modified significantly by the low prevalence of PCPs within a county (interaction, P <.001); the magnitude of the effect of dentists was amplified in counties with a higher prevalence of PCPs but was not significant in counties that had a low prevalence of PCPs. In counties with more than 0.3 PCPs per 10,000 population, one additional dentist per 10,000 population was associated with decrease of 1.73 percent or more in the percentage of adults who were obese (P <.001) (Table 4, page 938), whereas in counties with 0.3 or fewer PCPs per 10,000 population, there was no statistically significant association between the prevalence of dentists and adults who were obese. 936 JADA 145(9) September 2014

6 TABLE 3 Multivariable fixed effects linear regression models examining the associations between county-level variables and percentage of adults who are obese, stratified* according to the percentage of children living in poverty. COUNTY-LEVEL VARIABLE ALL COUNTIES (N = 2,841) COUNTIES WITH LESS THAN 25 PERCENT OF CHILDREN LIVING IN POVERTY (n = 1,531) COUNTIES WITH 25 PERCENT OF CHILDREN OR MORE LIVING IN POVERTY (n = 1,310) Estimate (SE ) P Value Estimate (SE) P Value Estimate (SE) P Value Dentist Rate (per 10,000 Population), No (0.26) < (0.30) (0.47).009 Primary Care Physician Rate (per 10, (0.16) < (0.18) < (0.27).022 Population), No. Adults With No Leisure-Time Physical Activity, % 0.33 (0.02) < (0.02) < (0.02) <.001 Access to Recreational Facilities Rate (per 10, (0.01) (0.08).004 Population), No. Restaurants Serving Fast Food, % 0.01 (0.01) < (0.01) <.001 Adults With at Least Some College Education, % 0.06 (0.01) < (0.01) < (0.01) <.001 Population 65 Years or Older, % 0.16 (0.01) < (0.02) < (0.02) <.001 Population Black, % 0.06 (0.01) < (0.01) < (0.01) <.001 Population Hispanic, % 0.02 (0.01) (0.01).038 Children Living in Single-Parent Households, % 0.05 (0.01) < (0.01) < (0.01).010 Population That Is Uninsured, % 0.06 (0.02) < (0.02) < (0.03).008 County Population Size (per 10,000 Population), No (0.49) < (0.93) < (0.58) <.001 Spatial Lag Term 4.63 (0.12) < (0.16) < (0.19) <.001 * Interaction between having 25 percent of children or more living in poverty and prevalence of dentists (P =.001). SE: Standard error. An em-dash indicates that the variable was dropped from the model because it was not significant. For each county, the spatial lag term refers to the weighted average of values for adult obesity in neighboring counties. DISCUSSION The association between the prevalence of dentists and obesity is evident and to our knowledge has not been reported previously in the literature. Although provision of dental care has been associated with important outcomes among homeless veterans (for example, increased transition to permanent housing and financial stability), 35 as well as with better diabetes control and nutritional intake in people with periodontal disease 36,37 and with fewer missing teeth, 38 respectively, investigators in previous studies have not reported links between dental care and obesity rates. A higher prevalence of dentists is a marker of better health behavior, and lower rates of obesity are an outcome of better health behavior; therefore, the association we found may be an artifact of better health behaviors in a county, and not a direct causal relationship between dentists and obesity. We included measures of leisuretime physical activity, as well as PCPs per capita, to adjust for health behavior and primary care resources, but residual confounding factors associated with the number of dentists per capita may explain our empirical result. Although the study design we used did not allow us to establish causal links between the prevalence of dentists and obesity, it highlighted the potential role dentists could play in providing nutrition and obesity counseling services. In line with the Health Belief Model, 39,40 dentists may be a source for cues that can influence patients perceptions of their risks of developing obesity, and they may have multiple opportunities to influence patients behavior because of the regular nature of preventive dental visits in addition to occasional treatment visits. Although evidence from 2010 indicates that less than 5 percent of dentists actively provide obesity counseling, 20 the potential effect of their expanding into this role may be substantial. Our findings also highlight the complexity of the obesity epidemic. As indicated in the county-level maps (Figures 1 and 2), several counties in the highest quintile for obesity were in the lowest quintile for prevalence of dentists; many of these counties have higher levels of children living in poverty and adults with lower education levels. The lack of dentists may contribute to an overall lack of primary care available in these counties. Although an association between the prevalence of dentists and the percentage of adults who are obese persists even in high-poverty areas, the interplay between PCPs and dentists also is relevant. Our results indicated that an association between the prevalence of dentists and obesity was fully attenuated in counties with 0.3 or fewer PCPs per 10,000 population, whereas the effect of dentists was prominent in counties with larger numbers of PCPs, suggesting that JADA 145(9) September

7 TABLE 4 Multivariable fixed effects linear regression models examining the associations between county-level variables and percentage of adults who are obese, stratified* according to prevalence of primary care physicians (PCPs). COUNTY-LEVEL VARIABLE COUNTIES WITH 0.3 PCPS OR FEWER PER 10,000 POPULATION (n = 554) COUNTIES WITH MORE THAN 0.3 PCPS PER 10,000 POPULATION (n = 2,287) Estimate (SE ) P Value Estimate (SE) P Value Dentist Rate (per 10,000 Population), No (0.27) <.001 PCP Rate (per 10,000 Population), No. Adults With No Leisure-Time Physical Activity, % 0.14 (0.03) < (0.02) <.001 Access to Recreational Facilities Rate (per 10,000 Population), No (0.08).003 Restaurants Serving Fast Food, % 0.02 (0.01) <.001 Adults With at Least Some College Education, % 0.06 (0.01) <.001 Population 65 Years or Older, % 0.15 (0.02) < (0.02) <.001 Population Black,% 0.08 (0.01) < (0.01) <.001 Population Hispanic, % 0.02 (0.01).001 Children Living in Single-Parent Households, % 0.04 (0.01) < (0.01) <.001 Population That Is Uninsured, % 0.05 (0.02).013 County Population Size (per 10,000 Population), No (0.50) <.001 Spatial Lag Term 3.50 (0.22) < (0.14) <.001 * Interaction between having 0.3 or fewer PCPs per 10,000 population and prevalence of dentists (P <.001). Counties that had 0.3 PCPs or fewer per 10,000 population were in the bottom quintile for PCP prevalence. SE: Standard error. An em-dash indicates that the variable was dropped from the model because it was not significant. For each county, the spatial lag term refers to the weighted average of values for adult obesity in neighboring counties. their combined efforts may be most effective. Further studies are needed to understand the potential interplay between dentists and PCPs to determine potential intervention strategies to reduce obesity involving primary health care providers. Our results should be considered in light of some limitations. We conducted a cross-sectional, ecological study in which we used data from the county level rather than the person level. Studies such as ours are useful for generating hypotheses but are not effective for establishing causal inferences. Person-level longitudinal data for both dental care use and BMI would be helpful in examining causal effects. Furthermore, data were missing for about 10 percent of counties, and results may have differed among those counties, although counties with missing data did not differ significantly from the remaining counties in regard to education or ethnicity. Although the prevalence of dentists is an important variable, we lacked more in-depth data regarding their type of practice (for example, private practice or public clinic) and the frequency and types of dental visits, which may be important in subsequent investigations. CONCLUSIONS Our findings highlight the need for further investigations regarding the association between dentists and obesity. Future work involving longitudinal data regarding the prevalence of dentists and subsequent rates of obesity may address whether a causal link exists and in what direction. In addition, researchers seeking to adjust for health care context when analyzing factors that contribute to obesity may wish to include numbers of dentists per capita in their analyses. Dr. Holzer was a postdoctoral fellow, Department of Health Policy and Management, Yale School of Public Health, Yale University, New Haven, Conn., when this article was written. She now is an assistant professor, Department of Health Professions, Hofstra University, Hempstead, N.Y. Address correspondence to Dr. Holzer at 435 E. Market St., Floor 2, Long Beach, N.Y , Dr. Canavan is an associate research scientist, Department of Health Policy and Management, Yale School of Public Health, New Haven, Conn. Dr. Bradley is a professor, Department of Health Policy and Management, Yale School of Public Health, New Haven, Conn., and is the executive director, Global Health Leadership Institute, Yale School of Public Health. Disclosure. None of the authors reported any disclosures. Dr. Holzer was supported by T32 postdoctoral fellowship HS from the Agency for Healthcare Research and Quality, Rockville, Md. The authors thank Dr. Douglas Vaughn for contributing his dental expertise to identifying potential explanations for their findings. 1. Cawley J, Meyerhoefer C. The medical care costs of obesity: an instrumental variables approach (published online ahead of print Oct. 20, 2011). J Health Econ 2012;31(1): Finkelstein EA, Trogdon JG, Cohen JW, Dietz W. Annual medical spending attributable to obesity: payer-and service-specific estimates. Health Aff (Millwood) 2009;28(5):w822-w JADA 145(9) September 2014

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