Adult obesity prevalence at the county level in the United States, 2000–2010: Downscaling public health survey data using a spatial microsimulation approach

2018 ◽  
Vol 26 ◽  
pp. 153-164 ◽  
Author(s):  
Keumseok Koh ◽  
Sue C. Grady ◽  
Joe T. Darden ◽  
Igor Vojnovic
2014 ◽  
Vol 134 ◽  
pp. 435-452 ◽  
Author(s):  
Alberto M. Ortega Hinojosa ◽  
Molly M. Davies ◽  
Sarah Jarjour ◽  
Richard T. Burnett ◽  
Jennifer K. Mann ◽  
...  

2018 ◽  
Vol 133 (2) ◽  
pp. 169-176
Author(s):  
Keumseok Koh ◽  
Sue C. Grady ◽  
Igor Vojnovic ◽  
Joe T. Darden

Objectives: From 2000 to 2010, the Division of Nutrition, Physical Activity, and Obesity (DNPAO) at the Centers for Disease Control and Prevention (CDC) funded 37 state health departments to address the obesity epidemic in their states through various interventions. The objective of this study was to investigate the overall impacts of CDC-DNPAO statewide intervention programs on adult obesity prevalence in the United States. Methods: We used a set of an individual-level, interrupted time-series regression and a quasi-experimental analysis to evaluate the overall effect of CDC-DNPAO intervention programs before (1998-1999) and after (2010) their implementation by using data from CDC’s Behavioral Risk Factor Surveillance System. Results: States that implemented the CDC-DNPAO program had a 2.4% to 3.8% reduction in the odds of obesity during 2000-2010 compared with states without the program. The effect of the CDC-DNPAO program varied by length of program implementation. A quasi-experimental analysis found that states with longer program implementation did not necessarily have lower odds of obesity than states with shorter program implementation. Conclusions: Statewide obesity interventions can contribute to reduced odds of obesity in the United States. Future research should evaluate the CDC-DNPAO programs in relation to their goals, objectives, and other environmental obesity risk factors to inform future interventions.


2013 ◽  
Vol 50 (4) ◽  
pp. 565-574 ◽  
Author(s):  
Sabrina Jones Niggel ◽  
Scott B. Robinson ◽  
Ian Hewer ◽  
Joshua Noone ◽  
Shweta Shah ◽  
...  

2013 ◽  
Vol 10 (7) ◽  
pp. 1032-1038 ◽  
Author(s):  
Stephanie B. Jilcott Pitts ◽  
Michael B. Edwards ◽  
Justin B. Moore ◽  
Kindal A. Shores ◽  
Katrina Drowatzky DuBose ◽  
...  

Background:Little is known about the associations between natural amenities, recreation facility density, and obesity, at a national level. Therefore, the purpose of this paper was to examine associations between county-level natural amenities, density of recreation facilities, and obesity prevalence among United States counties.Methods:Data were obtained from a compilation of sources within the United States Department of Agriculture Economic Research Service Food Environment Atlas. Independent variables of interest were the natural amenities scale and recreation facilities per capita. The dependent variable was county-level obesity prevalence. Potential covariates included a measure of county-level percent Black residents, percent Hispanic residents, median age, and median household income. All models were stratified by population loss, persistent poverty, and metro status. Multilevel linear regression models were used to examine the association between obesity and natural amenities and recreation facilities, with “state” as a random effects second level variable.Results:There were statistically significant negative associations between percent obesity and 1) natural amenities and 2) recreation facilities per capita.Conclusions:Future research should examine environmental and policy changes to increase recreation facilities and enhance accessible natural amenities to decrease obesity rates.


2018 ◽  
Author(s):  
Sam Liu ◽  
Brian Chen ◽  
Alex Kuo

BACKGROUND Social media technology such as Twitter allows users to share their thoughts, feelings, and opinions online. The growing body of social media data is becoming a central part of infodemiology research as these data can be combined with other public health datasets (eg, physical activity levels) to provide real-time monitoring of psychological and behavior outcomes that inform health behaviors. Currently, it is unclear whether Twitter data can be used to monitor physical activity levels. OBJECTIVE The aim of this study was to establish the feasibility of using Twitter data to monitor physical activity levels by assessing whether the frequency and sentiment of physical activity–related tweets were associated with physical activity levels across the United States. METHODS Tweets were collected from Twitter’s application programming interface (API) between January 10, 2017 and January 2, 2018. We used Twitter's garden hose method of collecting tweets, which provided a random sample of approximately 1% of all tweets with location metadata falling within the United States. Geotagged tweets were filtered. A list of physical activity–related hashtags was collected and used to further classify these geolocated tweets. Twitter data were merged with physical activity data collected as part of the Behavioral Risk Factor Surveillance System. Multiple linear regression models were fit to assess the relationship between physical activity–related tweets and physical activity levels by county while controlling for population and socioeconomic status measures. RESULTS During the study period, 442,959,789 unique tweets were collected, of which 64,005,336 (14.44%) were geotagged with latitude and longitude coordinates. Aggregated data were obtained for a total of 3138 counties in the United States. The mean county-level percentage of physically active individuals was 74.05% (SD 5.2) and 75.30% (SD 4.96) after adjusting for age. The model showed that the percentage of physical activity–related tweets was significantly associated with physical activity levels (beta=.11; SE 0.2; P<.001) and age-adjusted physical activity (beta=.10; SE 0.20; P<.001) on a county level while adjusting for both Gini index and education level. However, the overall explained variance of the model was low (R2=.11). The sentiment of the physical activity–related tweets was not a significant predictor of physical activity level and age-adjusted physical activity on a county level after including the Gini index and education level in the model (P>.05). CONCLUSIONS Social media data may be a valuable tool for public health organizations to monitor physical activity levels, as it can overcome the time lag in the reporting of physical activity epidemiology data faced by traditional research methods (eg, surveys and observational studies). Consequently, this tool may have the potential to help public health organizations better mobilize and target physical activity interventions.


Circulation ◽  
2020 ◽  
Vol 141 (Suppl_1) ◽  
Author(s):  
Joseph Granato ◽  
Nicole Zhang ◽  
Mike Hughes

Intro: Obesity is well established as a cause of multiple diseases that put excessive strain on healthcare resources. This is particularly true in the United States where obesity levels are among the highest in the world. However, forecasting future trends in obesity prevalence can be problematic given the difficulty associated with accurately quantifying the effect of the many risk factors that have been documented for obesity. In this study, a model is presented to forecast future adult obesity prevalence based on the current childhood obesity prevalence and the conditional probability of adult obesity given childhood obesity. Hypothesis: Adult obesity prevalence can be forecast based on current childhood obesity and the likelihood of the former given the latter. Methods: The annual change in historical (1975-2016) childhood (ages 5-19) obesity was calculated to ascertain a gender-specific trend. To forecast the prevalence of adult obesity (ages 20-59) in coming decades the model relied upon published age-specific probabilities of adult obesity given childhood obesity. To forecast the annual change in ten-year age-groups of obese adults these probabilities were then applied to the annual change figures derived from the historical childhood obesity data. The model used the linear regression of childhood obesity, from 1996-2016, to extend the forecast and determine a year in which the annual change in adult obesity became negative. Such a forecast provided an age-and gender-specific year in which the obesity epidemic in adult Americans comes to an end and prevalence begins to decrease. Results: By using historical childhood obesity data and the probability of adult obesity associated with childhood obesity the model forecasts the American obesity epidemic in males ages 20-29, 30-39, 40-49, and 50-59 to stop increasing and begin decreasing in 2048, 2054, 2059, and 2064 respectively. Likewise, the model estimated obesity prevalence will cease to rise in adult American females about a decade earlier with forecasts for ages 20-29, 30-39, 40-49, and 50-59 to be 2037, 2043, 2048, and 2053 respectively. Conclusions: In conclusion, adult obesity in the United States, like most documented disease epidemics will reach a point, beyond which the prevalence is expected to fall. The model was built to handle the difficulty associated with quantifying the effect of the multiple risk factors and the well documented period effects linked to obesity. The proposed model successfully used historical childhood obesity data and the correlation between childhood and adult obesity to forecast when obesity in the United States will cease to grow and start to decline.


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