Chapter 6. Comparison of neighborhood deprivation index and food desert status as environmental predictors of early childhood obesity


Shannon C Conrey1,2, PhD, Allison R Burrell1,2, BSN, Cole Brokamp1,3, PhD, Rachel M Burke4, PhD, Sarah C Couch5, PhD, RD, Liang Niu1, PhD, Claire P Mattison4,6, MPH, Daniel C Payne7, PhD, Mary A Staat1,2, MD, MPH, and Ardythe L Morrow1,2, PhD
1University of Cincinnati College of Medicine, Department of Environmental and Public Health Sciences, Cincinnati, Ohio, United States of America
2Cincinnati Children’s Hospital Medical Center, Department of Infectious Disease, Cincinnati, Ohio, United States of America
3Cincinnati Children’s Hospital Medical Center, Department of Biostatistics and Epidemiology, Cincinnati, Ohio, United States of America
4Centers for Disease Control and Prevention, Division of Viral Diseases, Atlanta, Georgia, United States of America
5University of Cincinnati College of Allied Health Sciences, Department of Rehabilitation, Exercise and Nutrition Science, Cincinnati, Ohio, United States of America
6Cherokee Nation Assurance, Arlington, Virginia, United States of America
7Centers for Disease Control and Prevention, Division of Foodborne, Waterborne, and Environmental Diseases, Atlanta, Georgia, United States of America

Part of the book: Environmental Health: Poverty, Race and Child Health in the Time of COVID-19


Nearly 14% of American children aged 2–5 have obesity, with higher rates in children from lower-income and Black families. While evidence connects neighborhood socio-economic environment (SEE) and obesity in adults and adolescents, little is known of this relationship in young children. We compared measures of SEE and family-level socio[1]demographic factors as predictors of obesity at age two. Methods: Family-level data from the PREVAIL Cohort, a CDC-funded birth cohort in Cincinnati, Ohio, were collected prenatally from the mothers. Residential addresses were geocoded and assigned validated measures of census tract-level SEE, including USDA food desert indicators and the Deprivation Index. Family-level and ecological SEE were compared as predictors of obesity (BMIz ≥1.65) at age two in terms of proportional differences, relative risk, and model fit statistics. Results: Residing outside of Deprivation Index High SEE neighborhoods was significantly associated with higher proportion (20.0% vs 5.9%; χ2=4.36, p=0.037) and increased risk of obesity in univariable (RR = 3.4, 95%CI: 1.26–13.86) and multivariable models (RR = 3.5, 95%CI: 1.06–11.71). There were no differences in proportion or risk of obesity by USDA food desert indicators or family-level factors. Models using categorical Deprivation Index performed better than the family-level and the USDA food desert variables in terms of model fit. Conclusion: In the PREVAIL Cohort, only category of Deprivation Index was a significant predictor of obesity in two-year-old children. Future studies are needed to evaluate the Deprivation Index as a generalizable tool to identify neighborhoods at higher risk for obesity.


[1] Kelsey MM, Zaepfel A, Bjornstad P, Nadeau KJ. Age-related consequences of
childhood obesity. Gerontology 2014;60(3):222-8.
[2] Llewellyn A, Simmonds M, Owen CG, Woolacott N. Childhood obesity as a
predictor of morbidity in adulthood: a systematic review and meta-analysis. Obes
Rev 2016;17(1):56-67.
[3] Ogden C, Fryar C, Martin C, Freedman D, Carroll M, Gu Q, et al. Trends in obesity
prevalence by race and Hispanic origin—1999–2000 to 2017–2018. JAMA
[4] Hales C, Carroll M, Fryar C, Ogden C. Prevalence of obesity and severe obesity
among adults: United States, 2017–2018. NCHS Data Brief. Hyattsville, MD:
National Center for Health Statistics, 2020:360.
[5] Ogden C, Carroll M, Fakhouri T, Hales C, Fryar C, Li X, et al. Prevalence of obesity
among youths by household income and education level of head of household —
United States 2011–2014. MMWR 2018;67:186-9.
[6] Vargas CM, Stines EM, Granado HS. Health-equity issues related to childhood
obesity: a scoping review. J Public Health Dent 2017;77(S1):S32-42.
[7] Duncan DT, Sharifi M, Melly SJ, Marshall R, Sequist TD, Rifas-Shiman SL, et al.
Characteristics of walkable built environments and BMI z-scores in children:
evidence from a large electronic health record database. Environ Health Perspect
[8] Carroll-Scott A, Gilstad-Hayden K, Rosenthal L, Peters SM, McCaslin C, Joyce R,
et al. Disentangling neighborhood contextual associations with child body mass
index, diet, and physical activity: the role of built, socioeconomic, and social
environments. Soc Sci Med 2013;95:106-14.
[9] Johnson KA, Showell NN, Flessa S, Janssen M, Reid N, Cheskin LJ, et al. Do
neighborhoods matter? A systematic review of modifiable risk factors for obesity
among low socio-economic status Black and Hispanic children. Child Obes
[10] Fiechtner L, Sharifi M, Sequist T, Block J, Duncan DT, Melly SJ, et al. Food
environments and childhood weight status: effects of neighborhood median income.
Child Obes 2015;11(3):260.
[11] Do DP, Frank R, Iceland J. Black-white metropolitan segregation and self-rated
health: Investigating the role of neighborhood poverty. Soc Sci Med 2017;187:85-92.
[12] Davison KK, Birch LL. Childhood overweight: A contextual model and
recommendations for future research. Obes Rev 2001;2(3):159-71.
[13] Saelens BE, Sallis JF, Frank LD, Couch SC, Zhou C, Colburn T, et al. Obesogenic
neighborhood environments, child and parent obesity: The Neighborhood Impact on
Kids study. Am J Prev Med 2012;42(5):e57.
[14] Kang Sim DE, Strong DR, Manzano MA, Rhee KE, Boutelle KN. Evaluation of
dyadic changes of parent-child weight loss patterns during a family-based
behavioral treatment for obesity. Pediatr Obes 2020;15(6):e12622.
[15] Quillian L. Segregation and poverty concentration: The role of three segregations.
Am Sociol Rev 2012;77(3):354-79.
[16] Massey DS, Denton NA. American apartheid: segregation and the making of the
underclass. Cambridge, MA: Harvard University Press,1993.
[17] Cobb LK, Appel LJ, Franco M, Jones-Smith JC, Nur A, Anderson CAM. The
relationship of the local food environment with obesity: A systematic review of
methods, study quality, and results. Obesity 2015;23(7):1331-44.
[18] Mei K, Huang H, Xia F, Hong A, Chen X, Zhang C, et al. State-of-the-art of
measures of the obesogenic environment for children. Obes Rev 2020 Jul 28.
[19] American Community Survey. URL:
[20] Food Access Research Atlas. URL:
[21] Brokamp C, Beck AF, Goyal NK, Ryan P, Greenberg JM, Hall ES. Material
community deprivation and hospital utilization during the first year of life: an urban
population–based cohort study. Ann Epidemiol 2019;30:37-43.
[22] Woodruff RC, Haardörfer R, Raskind IG, Hermstad A, Kegler MC. Comparing food
desert residents with non-food desert residents on grocery shopping behaviours, diet
and BMI: results from a propensity score analysis. Public Health Nutr 2020;23(5):806-11.
[23] Thomsen MR, Nayga RM, Alviola PA, Rouse HL. The effect of food deserts on the
body mass index of elementary schoolchildren. Am J Agric Econ 2016;98(1):1-18.
[24] Briggs AC, Black AW, Lucas FL, Siewers AE, Fairfield KM. Association between
the food and physical activity environment, obesity, and cardiovascular health
across Maine counties. BMC Public Health 2019;19(1):374-9.
[25] MacNell L. A geo-ethnographic analysis of low-income rural and urban women’s
food shopping behaviors. Appetite 2018;128:311-20.
[26] Santorelli ML, Okeke JO. Evaluating community measures of healthy food access.
J Commun Health 2017;42(5):991-7.
[27] Block JP, Subramanian SV. Moving beyond “food deserts”: Reorienting United
States policies to reduce disparities in diet quality. PLoS Med 2015;12(12):e1001914.
[28] Kimbro RT, Denney JT. Neighborhood context and racial/ethnic differences in
young children’s obesity: structural barriers to interventions. Soc Sci Med 2013;95:97.
[29] Phillips RL, Liaw W, Crampton P, Exeter DJ, Bazemore A, Vickery KD, et al. How
other countries use deprivation indices-and why the United States desperately needs
one. Health Aff (Millwood) 2016;35(11):1991-8.
[30] Brokamp C, Jandarov R, Rao MB, LeMasters G, Ryan P. Exposure assessment
models for elemental components of particulate matter in an urban environment: A
comparison of regression and random forest approaches. Atmos Environ 2017;151:1-11.
[31] Brokamp C, LeMasters GK, Ryan PH. Residential mobility impacts exposure
assessment and community socioeconomic characteristics in longitudinal
epidemiology studies. J Expo Sci Environ Epidemiol 2016;26(4):428-34.
[32] Morrow AL, Staat MA, DeFranco EA, McNeal MM, Cline AR, Conrey SC, et al.
Pediatric respiratory and enteric virus acquisition and immunogenesis in US
mothers and children aged 0-2: PREVAIL Cohort Study. JMIR Res Protoc 2021;12;10(2):e22222.
[33] Overweight and obesity. URL:
[34] Harris PA, Taylor R, Thielke R, Payne J, Gonzalez N, Conde JG. Research
electronic data capture (REDCap) – A metadata-driven methodology and workflow
process for providing translational research informatics support. J Biomed Inform 2009;42(2):377-81.
[35] Brokamp C. DeGAUSS: Decentralized Geomarker Assessment for Multi-Site
Studies. J Open Source Software 2018;3(30):812.
[36] Growth charts-Z score data files. URL:
[37] Data, trend and maps. URL:
[38] R Core Team. R: A language and environment for statistical computing. Vienna,
Austria: R Foundation for Statistical Computing, 2018. URL:
[39] Anderson KF. Racial residential segregation and the distribution of health-related
organizations in urban neighborhoods. Social Problems 2017;64(2):256-76.
[40] Metallinos-Katsaras, Elizabeth, PhD, RD, Must A, PhD, Gorman K, PhD. A
longitudinal study of food insecurity on obesity in preschool children. J Acad Nutr Diet 2012;112(12):1949-58.
[41] Kim Y, Cubbin C, Oh S. A systematic review of neighbourhood economic context
on child obesity and obesity-related behaviours. Obes Rev 2019;20(3):420-31


Publish with Nova Science Publishers

We publish over 800 titles annually by leading researchers from around the world. Submit a Book Proposal Now!

See some of our Authors and Editors