Consistently Inconsistent: A Snapshot of Across- and Within-State Disparities in the Prevalence of Childhood Overweight and Obesity

Bethell, Christina; Read, Debra; Goodman, Elizabeth; Johnson, Jessica; Besl, John; Cooper, Julie; Simpson, Lisa

doi:10.1542/peds.2008-2780f

Cited by 30 publications

(25 citation statements)

References 25 publications

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“…Since Wennberg's seminal paper in 1973, 2 research has demonstrated regional variation (including within- and across-state) for numerous health conditions and medical treatments. In pediatrics, these are as diverse as obesity, 3-5 adolescent pregnancy, 6 emergency department use, 7 hospitalizations, 8 appendicitis rupture, 9 and medical home access 10 or underinsurance 11 for children with special health care needs. Particular attention has been paid to differences seen in urban versus rural locales; the Agency for Healthcare Research and Quality (AHRQ) National Healthcare Disparities Report considers residents of rural areas to be a “priority population.” 12 They are more likely than urban residents to be in fair or poor physical health, 13 and less likely to have seen a health care provider or to have received preventive services.…”

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confidence: 99%

Geography matters: state-level variation in children's oral health care access and oral health status

et al. 2016

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Objectives To ascertain differences across states in children's oral health care access and oral health status and the factors that contribute to those differences Study Design Observational study using cross-sectional surveys Methods Using the 2007 National Survey of Children's Health, we examined state variation in parent's report of children's oral health care access (absence of a preventive dental visit) and oral health status. We assessed the unadjusted prevalences of these outcomes, then adjusted with child-, family-, and neighborhood-level variables using logistic regression; these results are presented directly and graphically. Using multilevel analysis, we then calculated the degree to which child-, family-, and community-level variables explained state variation. Finally, we quantified the influence of state-level variables on state variation. Results Unadjusted rates of no preventive dental care ranged 9.0-26.8% (mean 17.5%), with little impact of adjusting (10.3-26.7%). Almost 9% of population had fair/poor oral health; unadjusted range 4.1-14.5%. Adjusting analyses affected fair/poor oral health more than access (5.7-10.7%). Child, family and community factors explained ~¼ of the state variation in no preventive visit and ~½ of fair/poor oral health. State-level factors further contributed to explaining up to a third of residual state variation. Conclusion Geography matters: where a child lives has a large impact on his or her access to oral health care and oral health status, even after adjusting for child, family, community, and state variables. As state-level variation persists, other factors and richer data are needed to clarify the variation and drive changes for more egalitarian and overall improved oral health.

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Geography matters: state-level variation in children's oral health care access and oral health status

et al. 2016

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“…On the basis of previous findings (3,22), we used covariates related to socioeconomic and demographic status and potentially related to high BMI that are used in NHANES, the 2010 Census, and the American Community Survey (ACS). The variables considered were sex, race/ethnicity, age in months, education level of the household representative (level 1, <9th grade; 2, 9th–11th grade; 3, high school graduation or equivalent; 4, some college; 5, college graduate or above), household size (2 to ≥7 people), and family income or family poverty level (using the thresholds from ACS tables).…”

Section: Methodsmentioning

confidence: 99%

“…Because overweight or obese children are at a higher risk than normal-weight children for health problems, they are a target for intervention (2). There is evidence of disparities in pediatric obesity; Bethell et al (3) studied differences in obesity rates by race/ethnicity, insurance, and income and found within- and across-state disparities. Each of these factors can vary significantly across a city or county, so identifying small geographic areas with children at greatest risk for high body mass index (BMI) can be helpful in delivering cost-effective interventions.…”

Section: Introductionmentioning

confidence: 99%

Estimating Prevalence of Overweight or Obese Children and Adolescents in Small Geographic Areas Using Publicly Available Data

Davila-Payan¹,

DeGuzman²,

Johnson³

et al. 2015

Prev. Chronic Dis.

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IntroductionInterventions for pediatric obesity can be geographically targeted if high-risk populations can be identified. We developed an approach to estimate the percentage of overweight or obese children aged 2 to 17 years in small geographic areas using publicly available data. We piloted our approach for Georgia.MethodsWe created a logistic regression model to estimate the individual probability of high body mass index (BMI), given data on the characteristics of the survey participants. We combined the regression model with a simulation to sample subpopulations and obtain prevalence estimates. The models used information from the 2001–2010 National Health and Nutrition Examination Survey, the 2010 Census, and the 2010 American Community Survey. We validated our results by comparing 1) estimates for adults in Georgia produced by using our approach with estimates from the Centers for Disease Control and Prevention (CDC) and 2) estimates for children in Arkansas produced by using our approach with school examination data. We generated prevalence estimates for census tracts in Georgia and prioritized areas for interventions.ResultsIn DeKalb County, the mean prevalence among census tracts varied from 27% to 40%. For adults, the median difference between our estimates and CDC estimates was 1.3 percentage points; for Arkansas children, the median difference between our estimates and examination-based estimates data was 1.7 percentage points.ConclusionPrevalence estimates for census tracts can be different from estimates for the county, so small-area estimates are crucial for designing effective interventions. Our approach validates well against external data, and it can be a relevant aid for planning local interventions for children.

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“…The National Survey for Children’s Health (NSCH) includes a large enough sample size for state-specific estimates and has shown substantial variation across states in the prevalence and disparities in overweight and obesity among 10- to 17-year-old children. 5 NSCH, however, is based on parent-reported data, as opposed to measured data as can be found in NHANES. Parent-reported data are not recommended for estimating overweight prevalence in elementary school–aged children 6 because parents tend to overestimate height and underestimate weight.…”

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confidence: 99%

Trends in Body Mass Index among Ohio's Third-Grade Children: 2004-2005 to 2009-2010

Oza-Frank

Hade²,

Norton³

et al. 2013

Journal of the Academy of Nutrition and Dietetics

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Substantial variation across states in the prevalence and trends in childhood overweight and obesity indicate a need for state-specific surveillance to make state comparisons to national estimates and identify high-risk populations. The purpose of this study was to examine body mass index (BMI) trends among third-grade children in Ohio between the 2004–2005 and 2009–2010 school years and examine changes in prevalence of obesity by specific demographic subgroups. Third-grade children (n = 33,672) were directly weighed and measured throughout the school years by trained health care professionals. Trends in overweight/obesity (≥85th percentile of BMI by age/sex), obesity (≥95th percentile), and obesity level 2 (≥97th percentile) over five time periods (2004–2005, 2006–2007, 2007–2008, 2008–2009, 2009–2010) were modeled using logistic regression, accounting for the survey design and adjusting for sex, race/ethnicity, National School Lunch Program (NSLP) participation, and age. Differences in these BMI categories were also examined by these subgroups. BMI estimates did not demonstrate a statistically significant trend over the five time periods for overweight/obesity (34% to 36%), obesity (18% to 20%), or obesity level 2 (12% to 14%). However, increases in overweight/obesity prevalence were found in Hispanic children (37.8% vs 53.1%; P<0.01). Decreases in obesity (16.6% vs 14.1%; P=0.02) and obesity level 2 (11.3% vs 9.3%; P=0.02) were found among children not participating in NSLP and residing in suburban counties (obesity [17.3% vs 14.7%; P=0.03] and obesity level 2 [11.8% vs 9.8%; P=0.05]). Finally, decreases in overweight/obesity and obesity level 2 among boys were observed (15% vs 12.9%; P=0.02). Despite no significant overall trends in overweight/obesity, obesity, or obesity level 2 between 2004 and 2010, prevalence changed among specific subgroups. Obesity prevention efforts should be widespread and include special emphasis on groups experiencing increases or no change in prevalence.

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Consistently Inconsistent: A Snapshot of Across- and Within-State Disparities in the Prevalence of Childhood Overweight and Obesity

Abstract: BACKGROUND. The epidemic of childhood overweight and obesity is characterized by known disparities. Less is known about how these disparities vary across and within the state in which a child lives.

Cited by 30 publications

References 25 publications

Geography matters: state-level variation in children's oral health care access and oral health status

Geography matters: state-level variation in children's oral health care access and oral health status

Estimating Prevalence of Overweight or Obese Children and Adolescents in Small Geographic Areas Using Publicly Available Data

Trends in Body Mass Index among Ohio's Third-Grade Children: 2004-2005 to 2009-2010

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