Spatial Distribution of Obesity, Diabetes, and Hypertension in the District of Columbia

ABSTRACT

Introduction: Diabetes, hypertension, and cardiovascular diseases are among the leading cause of morbidity and mortality in the District of Columbia. These diseases are also directly associated with obesity and overweight. Objectives: To correlate the distribution of obesity with hypertension and diabetes. Methods: Data were obtained from the Behavioral risk Factors Surveillance System (BRFSS) surveys of 1997 and 2001. The distributions of obesity in the District were mapped using the Geographic Information System (GIS), ArcGIS software. Distribution of hypertension and diabetes were compared with wards in the District with high rates of obesity. Results: The highest rates of obesity were found in wards 4, 5, 7, and 8. Increase in obesity was positively correlated with increase in hypertension and diabetes. Wards with high rates of obesity also had high rates of hypertension and diabetes. Conclusion: Intervention programs for diabetes and hypertension should be directed to wards with highest rates of obesity.

BACKGROUND

Obesity has reached epidemic proportions in the United States and in the rest of the world (1). Being overweight or obese are among the most pressing new health concerns facing the United States and European Union countries (2, 3). It is estimated that 61% of US adults are overweight along with 13% children and adolescents. In 2000, the direct and indirect costs attributed to overweight and obesity were estimated to be $117 billion (2).

Overweight and obesity are expressed as body-mass index (BMI). This is calculated as weight in kilograms divided by the square of the height in meters (4). A BMI < 25 is categorized as normal, a BMI of 25.0-29.9 is overweight, and BMI > 30 is obesity. Obesity is further broken into three levels of categorization: BMI of 30.0-34.9 is obese (class I), BMI of 35.0-39.9 is severe obesity (class II), and BMI > 40 is very severe obesity (class III) (4, 5). Past studies have indicated genetic influences as a predisposing risk factor for obesity (5). Other factors for the increased prevalence of overweight and obesity can be attributed to the consumption of high-fat, high-sugar foods, less intake of fruits and vegetables, environmental and behavioral factors that discourage physical activity, low socioeconomic status, and a history of high birth weight (6, 7).

Obesity is a chronic health condition; therefore, its treatment should be assessed on a long-term basis (8). There are multiple adverse conditions that appear to be associated with overweight and obesity. Among these conditions are diabetes, hypertension, cardiovascular disease, cancer, sleep apnea, and other diseases (9, 10, 11). A recent prospective study in the US indicated that overweight and obesity contributed to the risk of dying not only from heart diseases and diabetes but accounted for 14% and 20% of all deaths from cancers in men and women respectively (9). The community-based Framingham longitudinal study established an increase of heart failure by 5% and 7% in men and women respectively for each incremental increase of 1 BMI. In the same study, there was a doubling in heart failure for obese persons compared to normal weight persons (12).

An age-related increase in total body fat is often accompanied by diabetes in the elderly. From age 75 to 84 years, the prevalence of type-2 diabetes increases progressively with age and peaking at 16.5%, and 12.8% in men and women respectively. Type-2 diabetes and obesity are both associated with a clustering of cardiovascular risk factors (13). Overweight and obesity in adulthood could be predicated by using the Centers for Disease Control (CDC) BMI age growth chart (16). Understanding the seriousness of the problem and determining areas of high risk in a community make it possible to concentrate intervention measures in areas of need. This paper therefore focuses on mapping and cluster analysis of the prevalence of obesity in the District of Columbia in relation to diabetes and high blood pressure.

METHODOLOGY

Data were obtained from the Behavioral Risk Factor Surveillance System (BRFSS), a state-based random-digit dialing telephone survey of non-institutionalized adults (aged > 18 years) conducted by the CDC and state health departments (24). Trend analysis for the period 1997 to 2001 in the District of Columbia was performed using SUDAAN and SAS software. In addition, analysis by ward (the District of Columbia is divided into 8 wards) was performed using the Geographic Information Systems (GIS) software, ArcView to display clustering and prevalence of risk factors.

RESULTS

From 1997-2001, the overall trend in the United States as well as the District of Columbia showed an increase in obesity (figure 1). In a five-year period, the rate of obesity increased for the District of Columbia and United States by 37.9% and 26.5% respectively. In the District of Columbia, in 1997, wards 4, 5, 6, 7, and 8 showed higher rates of obesity than the District's overall prevalence of 14.5% (figure 2). In 2001, wards 1, 4, 5, 6, 7, and 8 had obesity rates higher than the District's 20.1% prevalence ( figure 3). Between 1997 and 2001, the wards show significant mean percent difference of 7.9% (95% CI 3.8-11.8, p<0.05) ( figure 4).

In 1997, one out of five Blacks and one out of sixteen Whites were considered obese. In 2001, all races had shown an increase in obesity with a ratio of one out of three in Blacks, one out of thirteen in Whites, one out of two in Hispanics, and one out of five in all other races. Males showed an increase in obesity by 48% (11.0% to 16.2% in 1997 and 2001, respectively) and females an increase of 34% (17.5% to 23.6% in 1997 and 2001, respectively) (table 1).

Table 1. Prevalence of obesity, high blood pressure, and diabetes among adults aged 18 years and above in the District of Columbia by sex and race (BRFSS Survey, 1997 and 2001)

*Inadequate data, denominator less than 50

	1997			2001
	Obesity % SE	Obesity % SE	Obesity % SE	Obesity % SE	Obesity % SE	Obesity % SE
Total DC	14.5 (1.00)	19.4 (1.20)	4.6 (0.60)	20.1 (1.08)	29.0 (1.27)	8.3 (0.78)
Sex:
Men	11.0 (2.26)	16.7 (1.71)	3.9 (0.88)	16.2 (1.59)	26.6 (1.93)	9.6 (1.28)
Women	17.5 (1.63)	21.8 (1.57)	5.9 (0.88)	23.6 (1.59)	31.3 (1.69)	8.8 (0.98)
Race:
Black	20.3 (1.48)	25.3 (1.62)	7.7 (0.87)	30.3 (1.88)	40.4 (1.97)	14.1 (1.36)
Hispanic	9.0 (4.70)*	6.3 (3.87)*	0.0	42.1 (3.96)	14.5 (3.99)	1.4 (0.84)
White	5.8 (1.23)	11.0 (1.65)	0.9 (0.48)	7.5 (1.05)	17.8 (1.62)	4.2 (0.87)
Other	2.3 (2.34)*	9.8 (5.87)*	0.0	20.3 (3.53)	16.8 (4.33)	5.5 (2.27)

Increase in obesity by ward is significantly associated with high blood pressure and diabetes. (figure 5, 6). The association of obesity to high blood pressure was significant for 1997 and 2001 (r=0.9, p<0.05 and r=0.86, p<0.05 in 1997and 2001 respectively). Wards with higher rates of obesity are associated with wards having higher rates of hypertension and diabetes.

( Figure 7) shows the distribution of obesity by ward in relation to regular sustained physical activity, physical inactivity, and intake of five or more fruits and vegetables servings/day. The wards having higher rates of obesity were associated with wards showing higher rates of physical inactivity, lower rates of regular sustained physical activity, and lower rates of consumption of five or more fruits and vegetables servings/day.

DISCUSSION

Obesity is now a growing epidemic in the United States and unless steps are taken to arrest this trend, mortality from diseases associated with this condition will continue to increase. Hence the need for behavioral changes in the population as well as a determined effort to address other associated causes that may be socioeconomic. Behavioral changes may include weight loss through dietary fat and calorie control or reduction, appropriate exercise, variety in food choices, and consumption of fruits and vegetables (18). For some extreme obese cases surgical procedures such as gastric bypass, inducing some malabsorption is taken as a choice. However, once weight loss is achieved, the biggest challenge is to maintain it. Walking and vigorous exercise are associated with substantial reductions in the incidences of cardiovascular diseases among post-menopausal women irrespective of race/ethnic groups, age, and body-mass-index (12). Obesity and type-2 diabetes are preventable by engaging in physical activities and eating five or more servings of fruits and vegetables every day. Longitudinal studies that combine intervention on BMI and fitness in obese children and adolescents resulted in decreased BMI and improved fitness (20). A Canadian study on treatment of childhood obesity suggests that family-based behavioral intervention treatment for obesity is a cost-effective approach (21).

In recognition of the seriousness of obesity as a disease and public health problem, the US Internal Revenue Service has issued a policy regarding how taxpayers may deduct the cost of weight loss with control programs and set guidelines as to who may benefit from this policy. Intervention programs in the District of Columbia should focus on the reduction of obesity in wards 4 to 8 which have been shown to have the highest rates of obesity. Wards 1-3 should also be given attention so that gains already made could be maintained. Ward 1 showed a slight increase in 2001, which clearly means that even those wards where obesity has been lowest, the trend is now slowly changing. Ward 1 showed a slight in 2001 compared to 1997. An aggressive intervention program should therefore be put in place. As mentioned above, while considering appropriate intervention measures, the root-causes of the problem should also be studied. For example, wards 1-3 are relatively affluent parts of the city compared with wards 7and 8 which are of lower socioeconomic status. It is clear that to put the appropriate policies and intervention programs together, it will be necessary to examine why people, especially in the most affected wards, eat less than five servings of fruit and vegetables, and do not always engage in regular sustained physical activity.

Although the survey is based on telephone interview responses, it reflects similar outcomes as shown in many studies that obesity is associated with diabetes, hypertension, less fiber containing food intake, and physical inactivity. It is likely that rates provided could be underestimated because individuals without telephones are not included in BRFSS. In validation studies of self-reported weight and height, overweight participants tend to underestimate their weight, and all participants tend to overestimate their height (23). Undiagnosed diabetes was not counted; recent estimates indicate that about 35% of all persons with diabetes have not been diagnosed. However, estimates of reliability and validity derived from specific studies of data quality and from comparisons with reference data collected in other surveys indicate that, risk behaviors are captured in the BRFSS data with considerable accuracy (25).

ACKNOWLEDGMENTS

We acknowledge Dr. Joy Philips and Edward Dewitt for their assistance with GIS analysis, and all those with whom we have had many discussions on this subject. Their valuable suggestions have contributed to the writing of this paper and will continue to inspire us as we strive to address this problem.

REFERENCE LIST

1. Heljic B.Cengic M.Dilic M. Obesity as a risk factor for the onset of type 2 diabetes mellitus. Med Arh 2002;56(1):35-7.
2. Jackson County, Floridan. Obesity problem getting worse in USA. Jackson County Floridan, 1-2. 6-28-2002.
3. Bamegas KR. Prerrez-Garcoa E. Gitoerrez-Fosac K. Gia, aar-Castillon P.Rodriguez-Artalejo F. A simple estimate of mortality attributable to excess weight in the European Union. Eur J Clin Nutr 2003;Feb; 57(2): 201-8.
4. Flegal KM.Trolano RP. Changes in the distribution of body mass index of adults and children in the US population. Int J Obes Relat Metab Diord 2000;Jul; 24(7): 807-18.
5. Todd M.S. Effective management of obesity. The Journal of Family Practice 2003;Vol. 52, No.1: 1-10.
6. Walcott-McQuigg JA.Chen SP.Davis K.Stevenson E.Choi A.Wangsrikhun S. Weight loss and weight loss maintenance in African-American women. J Natl Med Assoc Aug; 94(8): 686-694. 2002.
7. Parsonss TJ.Power C.Logan S.Summerbell CD. Childhood predictors of adult obesity: a systematic review. Int J.Obes Relat Metab Disord 1999;8(suppl):SI-107.
8. Scheen AJ. Resultts of obesity treatment. Ann Endocrinol (Paris) Apr; 633(2 Pt): 163-170. 2002.
9. Eugenia EC.Carmen R.Kimberly Walker-Thurmond.Michael JT. Overweight, obesity, and mortality from cancer in a prospectively studied cohort of U.S. adults. N Engl J Med 2003;348:1625-38.
10. Young T.POeppard PE.Gottlieb DJ. Epidemiology of obstructive sleep apnea: a population health perspective. Am J Respir Crit Care Med 2002; 165 (9): 1217-1239.
11. Rogers RG.Hummer RA.Krueeger PM. The effect of obesity on overall, circulatory diseaqse - and diabetes-specific mortality. Biosoc Sci 2003;Jan;35(1):107-29.
12. Kenchaian S.Evans J.C.Levy D.Wilson P.W.F.Benjamin E.J.Larson M.G.Kannel W.B.Vasan R.S. Obesity and the risk of heart failure. N Engl J Med 2002;347 (5):305-13.
13. Wilson PW.Kannel WB. Obesity, diabetes, and risk of cardiovascular disease in the elderly. Am J Geriatr Cardiol 2002;11(2): 119-125.
14. Fairfield KM.Willett WC.Rosner BA.Manson JE.Speizer FE.Hankinson SE. Obesity, weight gain, and ovarian cancer. Obstet Gynecol 2002; Aug;100(2): 288-296.
15. Terry PD.Miller AB.Rohan TE. Obesity and colorectal cancer risk in women. Gut 2002;Aug;(51(2): 191-194.
16. Guo SS.Wu W.Chumlea WC.Roche AF. Predicting overweight and obesity in adulthood from body mass index values in childhood and adolescence. Am J Clin Nutr 2002;Sep;76(3): 653-658.
17. Shavers VL.Shankar S. Trend in the prevalence of overweight and obesity among uran African American hospital employees and public housing residents. J Natl Med Assoc 2002; Jul;94(7): 566-576.
18. Davis RB.Turner LW. A review off current weight management: research and recommendations. J Am Acad Nurse Pract 200; Jan;13(1): 15-19.
19. Kimm SY.S.Glynn N.W.Kriska A.M.Barton B.A.Kronsberg S.S.Daniels S.R.Crawrofd P.B.Sabry Z.I.Liu K. Decline in Physical Activity in Black Girls and White Girls During Adolescence. N Engl J Med 2002 Sep 5; 347(10): 709-15
20. Eliakim A.Kaven G.Berger I.Friedland O.Wolach B.Nemet D. The effect of a combined intervention on bodymass index and fitness in obese children and adolescents - a clinical experience. Eur J Pediatr 2002 Aug;161(8): 449-454.
21. Goldfield GS.Epstein LH.Kilanowski CK.Paluch RA.Kogut-Bossler B. Cost-effectiveness of group and mixed family-based treatment for childhood obesity. Int J Obes Relat Metab Disord 2001 Dec;25(12): 1843-1849.
22. A Taxpayer's Guide on IRS Policy to Deduct Weight Control Treatment. Obesity Treatments/Tax Deduction. The Hoffman Center (212) 779-1744, 40 East 30th Street 10th Floor, New York, NY 10016 , 1-5. 9-16 2002.
23. Mokdad AH, Ford ES, Bowman BA, Dietz WH, Vinicor F, Bales VS, Marks JS. Prevalence of obesity, diabetes, and obesity-related health risk factors. JAMA, January 1, 2003: 289(1): 76-79.
24. Behavioral Risk Factor Surveillance System. Available at: http://www.cdc.gov/brfss. Accessed May 2003.
25. Nelson DE, Holtzman D, Bolen J, Stanwyck CA, Mack KA. Reliability and validity of measures from the Behavioral Risk factor Surveillance System (BRFSS). International Journal of Public Health; 2001; 46 (suppl 1): S3 �" S 42.

John O. Davies-Cole, PhD, MPH; Gebreyesus Kidane, PhD, MPH; Garret Lum, MPH; Michael Richardson, MD, FACP
District of Columbia Department of Health
825 N. Capitol Street, NE, 3rd Floor
Washington, DC 20002
(202) 442-9138; (202) 442-4796 fax