Background: Perfluorooctanoic acid (PFOA) is a synthetic chemical ubiquitous in the serum of U.S. residents. It causes liver, testicular, and pancreatic tumors in rats. Human studies are sparse.Objective: We examined cancer incidence in Mid-Ohio Valley residents exposed to PFOA in drinking water due to chemical plant emissions.Methods: The cohort consisted of adult community residents who resided in contaminated water districts or worked at a local chemical plant. Most participated in a 2005–2006 baseline survey in which serum PFOA was measured. We interviewed the cohort in 2008–2011 to obtain further medical history. Retrospective yearly PFOA serum concentrations were estimated for each participant from 1952 through 2011. Self-reported cancers were validated through medical records and cancer registry review. We estimated the association between cancer and cumulative PFOA serum concentration using proportional hazards models.Results: Participants (n = 32,254) reported 2,507 validated cancers (21 different cancer types). Estimated cumulative serum PFOA concentrations were positively associated with kidney and testicular cancer [hazard ratio (HR) = 1.10; 95% CI: 0.98, 1.24 and HR = 1.34; 95% CI: 1.00, 1.79, respectively, for 1-unit increases in ln-transformed serum PFOA]. Categorical analyses also indicated positive trends with increasing exposures for both cancers: for kidney cancer HRs for increasing exposure quartiles were 1.0, 1.23, 1.48, and 1.58 (linear trend test p = 0.18) and for testicular cancer, HRs were 1.0, 1.04, 1.91, 3.17 (linear trend test p = 0.04).Conclusions: PFOA exposure was associated with kidney and testicular cancer in this population. Because this is largely a survivor cohort, findings must be interpreted with caution, especially for highly fatal cancers such as pancreatic and lung cancer.Citation: Barry V, Winquist A, Steenland K. 2013. Perfluorooctanoic acid (PFOA) exposures and incident cancers among adults living near a chemical plant. Environ Health Perspect 121:1313–1318; http://dx.doi.org/10.1289/ehp.1306615

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Purpose-To examine the relationship between two sedentary behaviors (riding in car and watching television) and cardiovascular disease (CVD) mortality in men in the Aerobics Center Longitudinal Study.Methods-Participants were 7,744 men (20-89 yr) initially free of CVD who returned a mailback survey during 1982. Time spent watching TV and riding in a car were reported. Mortality data were ascertained through the National Death Index till Dec 31, 2003. Cox regression analysis quantified the association between sedentary behaviors (hr/wk watching television, hr/wk riding in car, total hr/wk in these two behaviors) and CVD mortality rates.Results-377 CVD deaths occurred during 21 years of follow-up. After age-adjustment, time riding in a car and combined time spent in these two sedentary behaviors were positively (p trend <.001) associated with CVD death. Men who reported >10 hrs/wk riding in a car or >23 hr/wk of combined sedentary behavior had 82% and 64% greater risk of dying from CVD than those who reported <4 hr/wk or <11 hr/wk, respectively. The pattern of the association did not materially change after multivariate adjustment. Regardless of the amount of sedentary activity reported by these men, being older, normal weight, normotensive, and physically active was associated with a reduced risk of CVD death.Conclusion-In men, riding in a car and combined time spent in these two sedentary behaviors were significant CVD mortality predictors. Additionally, high levels of physical activity were related to notably lower rates of CVD death even in the presence of high levels of sedentary behavior. Health promotion efforts targeting physically inactive men should emphasize both reducing sedentary activity and increasing regular physical activity for optimal cardiovascular health.

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BackgroundThe SenseWear™ Armband (SWA) (BodyMedia, Inc. Pittsburgh, PA) is a physical activity and lifestyle monitor that objectively and accurately measures free-living energy balance and sleep and includes software for self-monitoring of daily energy expenditure and energy intake. The real-time feedback of the SWA can improve individual self-monitoring and, therefore, enhance weight loss outcomes.MethodsWe recruited 197 sedentary overweight or obese adults (age, 46.8 ± 10.8 y; body mass index (BMI), 33.3 ± 5.2 kg/m2; 81% women, 32% African-American) from the greater Columbia, South Carolina area. Participants were randomized into 1 of 4 groups, a self-directed weight loss program via an evidence-based weight loss manual (Standard Care, n = 50), a group-based behavioral weight loss program (GWL, n = 49), the armband alone (SWA-alone, n = 49), or the GWL plus the armband (GWL+SWA, n = 49), during the 9-month intervention. The primary outcome was change in body weight and waist circumference. A mixed-model repeated-measures analysis compared change in the intervention groups to the standard care group on weight and waist circumference status after adjusting for age, sex, race, education, energy expenditure, and recruitment wave.ResultsBody weight was available for 62% of participants at 9 months (52% standard care, 70% intervention). There was significant weight loss in all 3 intervention groups (GWL, 1.86 kg, P = 0.05; SWA-alone, 3.55 kg, P = 0.0002; GWL+SWA, 6.59 kg, P < 0.0001) but not in the Standard Care group (0.89 kg, P = 0.39) at month 9. Only the GWL+SWA group achieved significant weight loss at month 9 compared to the Standard Care group (P = 0.04). Significant waist circumference reductions were achieved in all 4 groups at month 9 (Standard Care, 3.49 cm, P = 0.0004; GWL, 2.42 cm, P = 0.008; SWA-alone, 3.59 cm, P < 0.0001; GWL+SWA, 6.77 cm, P < 0.0001), but no intervention group had significantly reduced waist circumference compared to the Standard Care group.ConclusionsContinuous self-monitoring from wearable technology with real-time feedback may be particularly useful to enhance lifestyle changes that promote weight loss in sedentary overweight or obese adults. This strategy, combined with a group-based behavioral intervention, may yield optimal weight loss.Trial RegistrationClinicalTrials.gov: NCT00957008

Disparities in vaccination coverage by social vulnerability, defined as social and structural factors associated with adverse health outcomes, were noted during the first 2.5 months of the U.S. COVID-19 vaccination campaign, which began during mid-December 2020 (1). As vaccine eligibility and availability continue to expand, assuring equitable coverage for disproportionately affected communities remains a priority. CDC examined COVID-19 vaccine administration and 2018 CDC social vulnerability index (SVI) data to ascertain whether inequities in COVID-19 vaccination coverage with respect to county-level SVI have persisted, overall and by urbanicity. Vaccination coverage was defined as the number of persons aged ≥18 years (adults) who had received ≥1 dose of any Food and Drug Administration (FDA)-authorized COVID-19 vaccine divided by the total adult population in a specified SVI category. † SVI was examined overall and by its four themes (socioeconomic status, household composition and disability, racial/ethnic minority status and language, and housing type and transportation). Counties were categorized into SVI quartiles, in which quartile 1 (Q1) represented the lowest level of vulnerability and quartile 4 (Q4), the highest. Trends in vaccination coverage were assessed by SVI quartile and urbanicity, which was categorized as large central metropolitan, large fringe metropolitan (areas surrounding large cities, e.g., suburban), medium and small metropolitan, and nonmetropolitan counties. § During December 14, 2020-May 1, 2021, disparities in vaccination coverage by SVI increased, especially in large † Vaccination coverage was calculated by summing the number of vaccinated adults in each SVI category and dividing by the total adult population in the specified SVI category. Population denominators were obtained from the U.S. Census Bureau. § Urbanicity was defined on the basis of the 2013 National Center for Health Statistics urban-rural classification scheme. For this analysis, categories included large central metropolitan counties, large fringe metropolitan counties, medium and small metropolitan counties, and nonmetropolitan counties. Large central metropolitan counties are counties in metropolitan statistical areas (MSAs) with ≥1 million population; large fringe metropolitan counties are counties in MSAs with ≥1 million population that did not qualify as large central metropolitan counties; medium metropolitan counties are counties in MSAs with populations of 250,000-999,999; small metropolitan counties are counties in MSAs with populations <250,000; nonmetropolitan counties are all micropolitan and noncore counties. https://www.cdc.gov/nchs/data_access/urban_rural.htm fringe metropolitan (e.g., suburban) and nonmetropolitan counties. By May 1, 2021, vaccination coverage was lower among adults living in counties with the highest overall SVI; differences were most pronounced in large fringe metropolitan (Q4 coverage = 45.0% versus Q1 coverage = 61.7%) and nonmetropolitan (Q4 = 40.6% versus Q1 = 52.9%) counties. ...

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