Where Is Obesity Prevention on the Map?: Distribution and Predictors of Local Health Department Prevention Activities in Relation to County-Level Obesity Prevalence in the United States
The system of local health departments (LHDs) in the United States has the potential to advance a locally oriented public health response in obesity control and reduce geographic disparities. However, the extent to which obesity prevention programs correspond to local obesity levels is unknown.
This study examines the extent to which LHDs across the United States have responded to local levels of obesity by examining the association between jurisdiction-level obesity prevalence and the existence of obesity prevention programs.
Data on LHD organizational characteristics from the Profile Study of Local Health Departments and county-level estimates of obesity from the Behavioral Risk Factor Surveillance System were analyzed (n = 2300). Since local public health systems are nested within state infrastructure, multilevel models were used to examine the relationship between county-level obesity prevalence and LHD obesity prevention programming and to assess the impact of state-level clustering.
Two thousand three hundred local health department jurisdictions defined with respect to county boundaries.
Practitioners in local health departments who responded to the 2005 Profile Study of Local Health Departments.
Likelihood of having obesity prevention activities and association with area-level obesity prevalence.
The existence of obesity prevention activities was not associated with the prevalence of obesity in the jurisdiction. A substantial portion of the variance in LHD activities was explained by state-level clustering.
This article identified a gap in the local public health response to the obesity epidemic and underscores the importance of multilevel modeling in examining predictors of LHD performance.
Available from: Shahida Bibi
- "According to recent studies, the estimated annual health care costs of obesity-related illness are incredible, $190.2 billion or nearly 21% of annual medical spending in the United States . As a result, there are multiple national and local programs aimed at the prevention of obesity      . Yet despite all this, obesity continues to be a problem  . "
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Despite much effort, obesity remains a significant public health problem. One of the main contributing factors is patients' perception of their target ideal body weight. This study aimed to assess this perception.
The study took place in an urban area, with the majority of participants in the study being Hispanic (65.7%) or African-American (28.0%). Patients presented to an outpatient clinic were surveyed regarding their ideal body weight and their ideal BMI calculated. Subsequently they were classified into different categories based on their actual measured BMI. Their responses for ideal BMI were compared.
In 254 surveys, mean measured BMI was 31.71 ± 8.01. Responses to ideal BMI had a range of 18.89-38.15 with a mean of 25.96 ± 3.25. Mean (±SD) ideal BMI for patients with a measured BMI of <18.5, 18.5-24.9, 25-29.9, and ≥30 was 20.14 ± 1.46, 23.11 ± 1.68, 25.69 ± 2.19, and 27.22 ± 3.31, respectively. These differences were highly significant (P < 0.001, ANOVA).
Most patients had an inflated sense of their target ideal body weight. Patients with higher measured BMI had higher target numbers for their ideal BMI. Better education of patients is critical for obesity prevention programs.
Journal of obesity 12/2014; 2014:491280. DOI:10.1155/2014/491280
Available from: Alexandre Lebel
- "Additionally, between and within countries comparisons provide an opportunity to study the moderating effect of social context on the relationship between SES and obesity with respect to public health interventions and/or social policies . Although some research has reported geographic differences in mean BMI between regional contexts for both countries , , few studies have attempted to explore these aspects , , and none have modeled the heterogeneity of the SES-BMI associations at the individual-level. Analyzing the variability of the mean BMI by socioeconomic status at multiple geographical levels can help to disentangle the individual effect (who we are) from the contextual effect (where we are); a time-honored conundrum that is widely recognized, but not well understood –. "
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Reported associations between socioeconomic status (SES) and obesity are inconsistent depending on gender and geographic location. Globally, these inconsistent observations may hide a variation in the contextual effect on individuals' risk of obesity for subgroups of the population. This study explored the regional variability in the association between SES and BMI in the USA and in Canada, and describes the geographical variance patterns by SES category.
The 2009–2010 samples of the Behavioral Risk Factor Surveillance System (BRFSS) and the Canadian Community Health Survey (CCHS) were used for this comparison study. Three-level random intercept and differential variance multilevel models were built separately for women and men to assess region-specific BMI by SES category and their variance bounds.
Associations between individual SES and BMI differed importantly by gender and countries. At the regional-level, the mean BMI variation was significantly different between SES categories in the USA, but not in Canada. In the USA, whereas the county-specific mean BMI of higher SES individuals remained close to the mean, its variation grown as SES decreased. At the county level, variation of mean BMI around the regional mean was 5 kg/m2 in the high SES group, and reached 8.8 kg/m2 in the low SES group.
This study underlines how BMI varies by country, region, gender and SES. Lower socioeconomic groups within some regions show a much higher variation in BMI than in other regions. Above the BMI regional mean, important variation patterns of BMI by SES and place of residence were found in the USA. No such pattern was found in Canada. This study suggests that a change in the mean does not necessarily reflect the change in the variance. Analyzing the variance by SES may be a good way to detect subtle influences of social forces underlying social inequalities.
PLoS ONE 06/2014; 9(6):e99158. DOI:10.1371/journal.pone.0099158 · 3.23 Impact Factor
Available from: Peter J Embi
- "However, since information on key community-level factors is not contained in the EHR, current studies on obesity in the context of only the EHR are limited. As we try to shift the focus of healthcare towards wellness and prevention, these community data that exist beyond the confines of clinics and hospitals are increasingly important [32,33]. We hypothesized that adding community-level data on socioeconomic and obesogenic environmental factors would enrich EHR-derived data and enable us to better study overweight and obesity in a patient population. "
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Obesity and overweight are multifactorial diseases that affect two thirds of Americans, lead to numerous health conditions and deeply strain our healthcare system. With the increasing prevalence and dangers associated with higher body weight, there is great impetus to focus on public health strategies to prevent or curb the disease. Electronic health records (EHRs) are a powerful source for retrospective health data, but they lack important community-level information known to be associated with obesity. We explored linking EHR and community data to study factors associated with overweight and obesity in a systematic and rigorous way.
We augmented EHR-derived data on 62,701 patients with zip code-level socioeconomic and obesogenic data. Using a multinomial logistic regression model, we estimated odds ratios and 95% confidence intervals (OR, 95% CI) for community-level factors associated with overweight and obese body mass index (BMI), accounting for the clustering of patients within zip codes.
33, 31 and 35 percent of individuals had BMIs corresponding to normal, overweight and obese, respectively. Models adjusted for age, race and gender showed more farmers’ markets/1,000 people (0.19, 0.10-0.36), more grocery stores/1,000 people (0.58, 0.36-0.93) and a 10% increase in percentage of college graduates (0.80, 0.77-0.84) were associated with lower odds of obesity. The same factors yielded odds ratios of smaller magnitudes for overweight. Our results also indicate that larger grocery stores may be inversely associated with obesity.
Integrating community data into the EHR maximizes the potential of secondary use of EHR data to study and impact obesity prevention and other significant public health issues.
BMC Medical Informatics and Decision Making 05/2014; 14(1):36. DOI:10.1186/1472-6947-14-36 · 1.83 Impact Factor
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