Spatial pattern of body mass index among adults in the diabetes study of Northern California (DISTANCE)
Tóm tắt
The role that environmental factors, such as neighborhood socioeconomics, food, and physical environment, play in the risk of obesity and chronic diseases is not well quantified. Understanding how spatial distribution of disease risk factors overlap with that of environmental (contextual) characteristics may inform health interventions and policies aimed at reducing the environment risk factors. We evaluated the extent to which spatial clustering of extreme body mass index (BMI) values among a large sample of adults with diabetes was explained by individual characteristics and contextual factors. We quantified spatial clustering of BMI among 15,854 adults with diabetes from the Diabetes Study of Northern California (DISTANCE) cohort using the Global and Local Moran’s I spatial statistic. As a null model, we assessed the amount of clustering when BMI values were randomly assigned. To evaluate predictors of spatial clustering, we estimated two linear models to estimate BMI residuals. First we included individual factors (demographic and socioeconomic characteristics). Then we added contextual factors (neighborhood deprivation, food environment) that may be associated with BMI. We assessed the amount of clustering that remained using BMI residuals. Global Moran’s I indicated significant clustering of extreme BMI values; however, after accounting for individual socioeconomic and demographic characteristics, there was no longer significant clustering. Twelve percent of the sample clustered in extreme high or low BMI clusters, whereas, only 2.67% of the sample was clustered when BMI values were randomly assigned. After accounting for individual characteristics, we found clustering of 3.8% while accounting for neighborhood characteristics resulted in 6.0% clustering of BMI. After additional adjustment of neighborhood characteristics, clustering was reduced to 3.4%, effectively accounting for spatial clustering of BMI. We found substantial clustering of extreme high and low BMI values in Northern California among adults with diabetes. Individual characteristics explained somewhat more of clustering of the BMI values than did neighborhood characteristics. These findings, although cross-sectional, may suggest that selection into neighborhoods as the primary explanation of why individuals with extreme BMI values live close to one another. Further studies are needed to assess causes of extreme BMI clustering, and to identify any community level role to influence behavior change.
Tài liệu tham khảo
Black JL, Macinko J: The changing distribution and determinants of obesity in the neighborhoods of New York City, 2003-2007. Am J Epidemiol. 2010, 171 (7): 765-775. 10.1093/aje/kwp458.
Matheson FI, Moineddin R, Glazier RH: The weight of place: a multilevel analysis of gender, neighborhood material deprivation, and body mass index among Canadian adults. Soc Sci Med. 2008, 66 (3): 675-690. 10.1016/j.socscimed.2007.10.008.
Rundle A, Neckerman KM, Freeman L, Lovasi GS, Purciel M, Quinn J, Richards C, Sircar N, Weiss C: Neighborhood food environment and walkability predict obesity in New York City. Environ Health Perspect. 2009, 117 (3): 442-447. 10.1289/ehp.11590.
Auchincloss AH, Diez Roux AV, Brown DG, O’Meara ES, Raghunathan TE: Association of insulin resistance with distance to wealthy areas: the multi-ethnic study of atherosclerosis. Am J Epidemiol. 2007, 165 (4): 389-397.
Cox M, Boyle PJ, Davey PG, Feng Z, Morris AD: Locality deprivation and Type 2 diabetes incidence: a local test of relative inequalities. Soc Sci Med. 2007, 65 (9): 1953-1964. 10.1016/j.socscimed.2007.05.043.
Ludwig J, Sanbonmatsu L, Gennetian L, Adam E, Duncan GJ, Katz LF, Kessler RC, Kling JR, Lindau ST, Whitaker RC, McDade TW: Neighborhoods, obesity, and diabetes - a randomized social experiment. N Engl J Med. 2011, 365 (16): 1509-1519. 10.1056/NEJMsa1103216.
Cox M, Boyle PJ, Davey P, Morris A: Does health-selective migration following diagnosis strengthen the relationship between Type 2 diabetes and deprivation?. Soc Sci Med. 2007, 65 (1): 32-42. 10.1016/j.socscimed.2007.02.045.
Devine DJ, Gray RW, Rubin L, Taghavi LB: Housing choice voucher location patterns: Implications for participant and neighborhood welfare. Edited by: Development USDoHaU. 2003, Washington, DC: HUD USER
Klein S, Sheard NF, Pi-Sunyer X, Daly A, Wylie-Rosett J, Kulkarni K, Clark NG, American Diabetes Association; North American Association for the Study of Obesity; American Society for Clinical Nutrition: Weight management through lifestyle modification for the prevention and management of type 2 diabetes: rationale and strategies: a statement of the American Diabetes Association, the North American Association for the Study of Obesity, and the American Society for Clinical Nutrition. Diabetes Care. 2004, 27 (8): 2067-2073. 10.2337/diacare.27.8.2067.
Bosello O, Armellini F, Zamboni M, Fitchet M: The benefits of modest weight loss in type II diabetes. Int J Obes Relat Metab Disord. 1997, 21 (Suppl 1): S10-S13.
Wing RR, Look AHEAD Research Group: Long-term effects of a lifestyle intervention on weight and cardiovascular risk factors in individuals with type 2 diabetes mellitus: four-year results of the Look AHEAD trial. Arch Intern Med. 2010, 170 (17): 1566-1575.
Wing RR, Bolin P, Brancati FL, Bray GA, Clark JM, Coday M, Crow RS, Curtis JM, Egan CM, Espeland MA, Evans M, Foreyt JP, Ghazarian S, Gregg EW, Harrison B, Hazuda HP, Hill JO, Horton ES, Hubbard VS, Jakicic JM, Jeffery RW, Johnson KC, Kahn SE, Kitabchi AE, Knowler WC, Lewis CE, Maschak-Carey BJ, Montez MG, Murillo A, Look AHEAD Research Group: Cardiovascular effects of intensive lifestyle intervention in type 2 diabetes. N Engl J Med. 2013, 369 (2): 145-154.
Doehner W, Erdmann E, Cairns R, Clark AL, Dormandy JA, Ferrannini E, Anker SD: Inverse relation of body weight and weight change with mortality and morbidity in patients with type 2 diabetes and cardiovascular co-morbidity: an analysis of the PROactive study population. Intl J Cardiol. 2012, 162 (1): 20-26. 10.1016/j.ijcard.2011.09.039.
Auchincloss AH, Gebreab SY, Mair C, Diez Roux AV: A review of spatial methods in epidemiology, 2000-2010. Annu Rev Public Health. 2012, 33: 107-122. 10.1146/annurev-publhealth-031811-124655.
Cook AJ, Li Y, Arterburn D, Tiwari RC: Spatial cluster detection for weighted outcomes using cumulative geographic residuals. Biometrics. 2010, 66 (3): 783-792. 10.1111/j.1541-0420.2009.01323.x.
Gordon NP: How does the adult Kaiser Permanente Membership in Northern California compare with the larger community? Brief Report. 2006, Oakland, CA: Kaiser Premanente Northern California Division of Research
Krieger N: Overcoming the absence of socioeconomic data in medical records: validation and application of a census-based methodology. Am J Public Health. 1992, 82 (5): 703-710. 10.2105/AJPH.82.5.703.
Moffet HH, Adler N, Schillinger D, Ahmed AT, Laraia B, Selby JV, Neugebauer R, Liu JY, Parker MM, Warton M, Karter AJ: Cohort Profile: The Diabetes Study of Northern California (DISTANCE)–objectives and design of a survey follow-up study of social health disparities in a managed care population. Int J Epidemiol. 2009, 38 (1): 38-47. 10.1093/ije/dyn040.
Jerrett M, Gale S, Kontgis C: Spatial modeling in environmental and public health research. Int J Environ Res Public Health. 2010, 7 (4): 1302-1329. 10.3390/ijerph7041302.
Anselin L: Local Indicators of Spatial Association - Lisa. Geogr Anal. 1995, 27 (2): 93-115.
Moran PAP: Notes on continuous stochastic phenomena. Biometrika. 1950, 37 (1–2): 17-23.
ESRI: ArcGIS Desktop Help 10.1. 2013
US Department of Health and Human Services (US DHHS): The Poverty Guidelines Updated Periodically in the Federal Register. 2005, Washington, DC: U.S. Department of Health and Human Services under the authority of 42 U.S.C. 9902(2)
Messer LC, Kaufman JS, Dole N, Savitz DA, Laraia BA: Neighborhood crime, deprivation, and preterm birth. Ann Epidemiol. 2006, 16 (6): 455-462. 10.1016/j.annepidem.2005.08.006.
Walls and Associates: NETS Database by Walls & Associates. 2013
Silverman BW: Density Estimation for Statistics and Data Analysis. 1986, London; New York: Chapman and Hall
Spence JC, Cutumisu N, Edwards J, Raine KD, Smoyer-Tomic K: Relation between local food environments and obesity among adults. BMC Public Health. 2009, 9: 192-10.1186/1471-2458-9-192.
Jones-Smith JC, Karter AJ, Warton EM, Kelly M, Kersten E, Moffet HH, Adler N, Schillinger D, Laraia BA: Obesity and the food environment: income and ethnicity differences among people with diabetes: the Diabetes Study of Northern California (DISTANCE). Diabetes Care. 2013, 36 (9): 2697-2705. 10.2337/dc12-2190.
Deyo RA, Cherkin DC, Ciol MA: Adapting a clinical comorbidity index for use with ICD-9-CM administrative databases. J Clin Epidemiol. 1992, 45 (6): 613-619. 10.1016/0895-4356(92)90133-8.
Rogers W: Regression standard errors in clustered samples. Stata Tech Bull. 1993, 13: 5-
Williams RL: A note on robust variance estimation for cluster-correlated data. Biometrics. 2000, 56 (2): 645-646. 10.1111/j.0006-341X.2000.00645.x.
Ver Ploeg M, Breneman V, Farrigan T, Hamrick K, Hopkins D, Kaufman P, Lin B, Nord M, Smith T, Williams R, Kinnison K, Olander C, Singh A, Tuckermanty E, Krantz-Kent R, Polen C, McGowan H, Kim S: Report to Congress. Access to Affordable and Nutritious Food: Measuring and Understanding Food Deserts and Their Consequences. 2009, Washington DC: USDA Economic Research Service
National Heart Lung and Blood Institute (NHLBI) Health Information Center: The Practical Guide: Identification, Evaluation, and Treatment of Overweight and Obesity in Adults. 2000
Frank LD, Schmid TL, Sallis JF, Chapman J, Saelens BE: Linking objectively measured physical activity with objectively measured urban form - Findings from SMARTRAQ. Am J Prev Med. 2005, 28 (2): 117-125. 10.1016/j.amepre.2004.11.001.
Cervero R, Kockelman K: Travel demand and the 3Ds: Density, diversity, and design. Transport Res D-TR E. 1997, 2 (3): 199-219. 10.1016/S1361-9209(97)00009-6.
Mobley LR, Finkelstein EA, Khavjou OA, Will JC: Spatial analysis of body mass index and smoking behavior among WISEWOMAN participants. J Womens Health. 2004, 13 (5): 519-528. 10.1089/1540999041281034.
Schuurman N, Peters PA, Oliver LN: Are obesity and physical activity clustered? A spatial analysis linked to residential density. Obesity. 2009, 17 (12): 2202-2209. 10.1038/oby.2009.119.
Curtis AJ, Lee WA: Spatial patterns of diabetes related health problems for vulnerable populations in Los Angeles. Int J Health Geogr. 2010, 9: 43-10.1186/1476-072X-9-43.