Background: Overweight and obesity prevalence are commonly used for public and policy communication of the extent of the obesity epidemic, yet comparable estimates of trends in overweight and obesity prevalence by country are not available. Methods: We estimated trends between 1980 and 2008 in overweight and obesity prevalence and their uncertainty for adults 20 years of age and older in 199 countries and territories. Data were from a previous study, which used a Bayesian hierarchical model to estimate mean body mass index (BMI) based on published and unpublished health examination surveys and epidemiologic studies. Here, we used the estimated mean BMIs in a regression model to predict overweight and obesity prevalence by age, country, year, and sex. The uncertainty of the estimates included both those of the Bayesian hierarchical model and the uncertainty due to cross-walking from mean BMI to overweight and obesity prevalence. Results: The global age-standardized prevalence of obesity nearly doubled from 6.4% (95% uncertainty interval 5.7-7.2%) in 1980 to 12.0% (11.5-12.5%) in 2008. Half of this rise occurred in the 20 years between 1980 and 2000, and half occurred in the 8 years between 2000 and 2008. The age-standardized prevalence of overweight increased from 24.6% (22.7-26.7%) to 34.4% (33.2-35.5%) during the same 28-year period. In 2008, female obesity prevalence ranged from 1.4% (0.7-2.2%) in Bangladesh and 1.5% (0.9-2.4%) in Madagascar to 70.4% (61.9-78.9%) in Tonga and 74.8% (66.7-82.1%) in Nauru. Male obesity was below 1% in Bangladesh, Democratic Republic of the Congo, and Ethiopia, and was highest in Cook Islands (60.1%, 52.6-67.6%) and Nauru (67.9%, 60.5-75.0%). Conclusions: Globally, the prevalence of overweight and obesity has increased since 1980, and the increase has accelerated. Although obesity increased in most countries, levels and trends varied substantially. These data on trends in overweight and obesity may be used to set targets for obesity prevalence as requested at the United Nations high-level meeting on Prevention and Control of NCDs.

Background: The inverse relationship between BMI and lung cancer diagnosis is well defined. However, few studies have examined the racial differences in these relationships. The purpose of this paper is to explore the relationships amongst race, BMI, and lung cancer diagnosis using the National Lung Screening Trial (NLST) data. Methods: Multivariate regression analysis was used to analyze the BMI, race, and lung cancer diagnosis relationships. Results: Among 53,452 participants in the NLST cohort, 3.9% were diagnosed with lung cancer, 43% were overweight, and 28% were obese. BMI was inversely related to lung cancer diagnosis among Whites: those overweight (aOR = .83, 95%CI = .75-.93), obese (aOR = .64, 95%CI = .56-.73) were less likely to develop lung cancer, compared to those with normal weight. These relationships were not found among African-Americans. Conclusion: Our findings indicate that the inverse relationship of BMI and lung cancer risk among Whites is consistent, whereas this relationship is not significant for African-Americans. In consideration of higher lung cancer incidence among African Americans, we need to explore other unknown mechanisms explaining this racial difference.

This study presents a novel geo-based metric to identify neighborhoods with high burdens of prostate cancer, and compares this metric to other methods to prioritize neighborhoods for prostate cancer interventions. We geocoded prostate cancer patient data (n = 10,750) from the Pennsylvania cancer registry from 2005 to 2014 by Philadelphia census tract (CT) to create standardized incidence ratios (SIRs), mortality ratios (SMRs), and mean prostate cancer aggressiveness. We created a prostate cancer composite (PCa composite) variable to describe CTs by mean-centering and standard deviation-scaling the SMR, SIR, and mean aggressiveness variables and summing them. We mapped CTs with the 25 highest PCa composite scores and compared these neighborhoods to CTs with the 25 highest percent African American residents and the 25 lowest median household incomes. The mean PCa composite score among the 25 highest CTs was 4.65. Only seven CTs in Philadelphia had both one of the highest PCa composite scores and the highest percent African American residents. Only five CTs had both the highest PCa composites and the lowest median incomes. Mean PCa composite scores among CTs with the highest percent African American residents and lowest median incomes were 2.08 and 1.19, respectively. The PCa composite score is an accurate metric for prioritizing neighborhoods based on burden. If neighborhoods were prioritized based on percent African American or median income, priority neighborhoods would have been very different and not based on PCa burden. These methods can be utilized by public health decision-makers when tasked to prioritize and select neighborhoods for cancer interventions.