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Estimating prevalence of overweight or obese children and adolescents in small geographic areas using publicly available data.

Davila-Payan C, DeGuzman M, Johnson K, Serban N, Swann J - Prev Chronic Dis (2015)

Bottom Line: 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.Prevalence 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.

View Article: PubMed Central - PubMed

Affiliation: Georgia Institute of Technology, Atlanta, Georgia.

ABSTRACT

Introduction: Interventions 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.

Methods: We 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.

Results: In 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.

Conclusion: Prevalence 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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Related in: MedlinePlus

The prevalence of high body mass index (BMI) in the 25% of counties (n = 39) in Georgia with the greatest estimated number of children and adolescents with high BMI. Of all children and adolescents with high BMI in Georgia, 77% reside in these 39 counties. These counties are strongly correlated with population centers. Gray areas indicate the other 75% of counties.CountyCounty No.Estimated Prevalence of High Body Mass Index, %Estimated Number of Children with High Body Mass IndexGwinnett1313533.1210,789Fulton1312133.4194,766Cobb1306732.9157,838Dekalb1308934.6144,462Clayton1306335.966,450Henry1315133.654,486Chatham1305134.552,468Cherokee1305731.753,061Hall1313935.344,691Richmond1324535.543,417Muscogee1321534.942,900Forsyth1311730.548,578Paulding1322332.838,912Bibb1302135.835,524Douglas1309734.333,807Houston1315333.733,529Coweta1307733.031,206Columbia1307332.230,681Whitfield1331336.726,179Newton1321734.426,057Fayette1311332.626,414Carroll1304534.025,047Bartow1301534.124,150Lowndes1318534.423,613Dougherty1309536.421,498Floyd1311534.821,004Rockdale1324735.120,669Walton1329733.520,417Clarke1305935.017,569Glynn1312734.217,119Barrow1301333.017,340Liberty1317933.816,466Troup1328534.815,975Spalding1325535.314,403Walker1329533.714,607Jackson1315733.414,410Catoosa1304732.614,422Gordon1312934.913,281Effingham1310333.313,551Bulloch1303133.812,779
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Figure 2: The prevalence of high body mass index (BMI) in the 25% of counties (n = 39) in Georgia with the greatest estimated number of children and adolescents with high BMI. Of all children and adolescents with high BMI in Georgia, 77% reside in these 39 counties. These counties are strongly correlated with population centers. Gray areas indicate the other 75% of counties.CountyCounty No.Estimated Prevalence of High Body Mass Index, %Estimated Number of Children with High Body Mass IndexGwinnett1313533.1210,789Fulton1312133.4194,766Cobb1306732.9157,838Dekalb1308934.6144,462Clayton1306335.966,450Henry1315133.654,486Chatham1305134.552,468Cherokee1305731.753,061Hall1313935.344,691Richmond1324535.543,417Muscogee1321534.942,900Forsyth1311730.548,578Paulding1322332.838,912Bibb1302135.835,524Douglas1309734.333,807Houston1315333.733,529Coweta1307733.031,206Columbia1307332.230,681Whitfield1331336.726,179Newton1321734.426,057Fayette1311332.626,414Carroll1304534.025,047Bartow1301534.124,150Lowndes1318534.423,613Dougherty1309536.421,498Floyd1311534.821,004Rockdale1324735.120,669Walton1329733.520,417Clarke1305935.017,569Glynn1312734.217,119Barrow1301333.017,340Liberty1317933.816,466Troup1328534.815,975Spalding1325535.314,403Walker1329533.714,607Jackson1315733.414,410Catoosa1304732.614,422Gordon1312934.913,281Effingham1310333.313,551Bulloch1303133.812,779

Mentions: Approximately 77% of children with high BMI resided in 39 counties (25% of counties) in Georgia (Figure 2). Areas of high BMI included densely populated areas, such as metropolitan Atlanta, smaller cities such as Augusta, Macon, Savannah, and Rome, as well as rural areas.


Estimating prevalence of overweight or obese children and adolescents in small geographic areas using publicly available data.

Davila-Payan C, DeGuzman M, Johnson K, Serban N, Swann J - Prev Chronic Dis (2015)

The prevalence of high body mass index (BMI) in the 25% of counties (n = 39) in Georgia with the greatest estimated number of children and adolescents with high BMI. Of all children and adolescents with high BMI in Georgia, 77% reside in these 39 counties. These counties are strongly correlated with population centers. Gray areas indicate the other 75% of counties.CountyCounty No.Estimated Prevalence of High Body Mass Index, %Estimated Number of Children with High Body Mass IndexGwinnett1313533.1210,789Fulton1312133.4194,766Cobb1306732.9157,838Dekalb1308934.6144,462Clayton1306335.966,450Henry1315133.654,486Chatham1305134.552,468Cherokee1305731.753,061Hall1313935.344,691Richmond1324535.543,417Muscogee1321534.942,900Forsyth1311730.548,578Paulding1322332.838,912Bibb1302135.835,524Douglas1309734.333,807Houston1315333.733,529Coweta1307733.031,206Columbia1307332.230,681Whitfield1331336.726,179Newton1321734.426,057Fayette1311332.626,414Carroll1304534.025,047Bartow1301534.124,150Lowndes1318534.423,613Dougherty1309536.421,498Floyd1311534.821,004Rockdale1324735.120,669Walton1329733.520,417Clarke1305935.017,569Glynn1312734.217,119Barrow1301333.017,340Liberty1317933.816,466Troup1328534.815,975Spalding1325535.314,403Walker1329533.714,607Jackson1315733.414,410Catoosa1304732.614,422Gordon1312934.913,281Effingham1310333.313,551Bulloch1303133.812,779
© Copyright Policy
Related In: Results  -  Collection

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getmorefigures.php?uid=PMC4362446&req=5

Figure 2: The prevalence of high body mass index (BMI) in the 25% of counties (n = 39) in Georgia with the greatest estimated number of children and adolescents with high BMI. Of all children and adolescents with high BMI in Georgia, 77% reside in these 39 counties. These counties are strongly correlated with population centers. Gray areas indicate the other 75% of counties.CountyCounty No.Estimated Prevalence of High Body Mass Index, %Estimated Number of Children with High Body Mass IndexGwinnett1313533.1210,789Fulton1312133.4194,766Cobb1306732.9157,838Dekalb1308934.6144,462Clayton1306335.966,450Henry1315133.654,486Chatham1305134.552,468Cherokee1305731.753,061Hall1313935.344,691Richmond1324535.543,417Muscogee1321534.942,900Forsyth1311730.548,578Paulding1322332.838,912Bibb1302135.835,524Douglas1309734.333,807Houston1315333.733,529Coweta1307733.031,206Columbia1307332.230,681Whitfield1331336.726,179Newton1321734.426,057Fayette1311332.626,414Carroll1304534.025,047Bartow1301534.124,150Lowndes1318534.423,613Dougherty1309536.421,498Floyd1311534.821,004Rockdale1324735.120,669Walton1329733.520,417Clarke1305935.017,569Glynn1312734.217,119Barrow1301333.017,340Liberty1317933.816,466Troup1328534.815,975Spalding1325535.314,403Walker1329533.714,607Jackson1315733.414,410Catoosa1304732.614,422Gordon1312934.913,281Effingham1310333.313,551Bulloch1303133.812,779
Mentions: Approximately 77% of children with high BMI resided in 39 counties (25% of counties) in Georgia (Figure 2). Areas of high BMI included densely populated areas, such as metropolitan Atlanta, smaller cities such as Augusta, Macon, Savannah, and Rome, as well as rural areas.

Bottom Line: 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.Prevalence 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.

View Article: PubMed Central - PubMed

Affiliation: Georgia Institute of Technology, Atlanta, Georgia.

ABSTRACT

Introduction: Interventions 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.

Methods: We 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.

Results: In 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.

Conclusion: Prevalence 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.

Show MeSH
Related in: MedlinePlus