Explore the vast range of titles available.

Reduction of premature mortality is a UN Sustainable Development Goal. Unlike other high-income countries, age-adjusted mortality in the USA plateaued in 2010 and increased slightly in 2015, possibly because of rising premature mortality. We aimed to analyse trends in mortality in the USA between 1999 and 2014 in people aged 25–64 years by age group, sex, and race and ethnicity, and to identify specific causes of death underlying the temporal trends. For this analysis, we used cause-of-death and demographic data from death certificates from the US National Center for Health Statistics, and population estimates from the US Census Bureau. We estimated annual percentage changes in mortality using age-period-cohort models. Age-standardised excess deaths were estimated for 2000 to 2014 as observed deaths minus expected deaths (estimated from 1999 mortality rates). Between 1999 and 2014, premature mortality increased in white individuals and in American Indians and Alaska Natives. Increases were highest in women and those aged 25–30 years. Among 30-year-olds, annual mortality increases were 2·3% (95% CI 2·1–2·4) for white women, 0·6% (0·5–0·7) for white men, and 4·3% (3·5–5·0) and 1·9% (1·3–2·5), respectively, for American Indian and Alaska Native women and men. These increases were mainly attributable to accidental deaths (primarily drug poisonings), chronic liver disease and cirrhosis, and suicide. Among individuals aged 25–49 years, an estimated 111 000 excess premature deaths occurred in white individuals and 6600 in American Indians and Alaska Natives during 2000–14. By contrast, premature mortality decreased substantially across all age groups in Hispanic individuals (up to 3·2% per year), black individuals (up to 3·9% per year), and Asians and Pacific Islanders (up to 2·6% per year), mainly because of declines in HIV, cancer, and heart disease deaths, resulting in an estimated 112 000 fewer deaths in Hispanic individuals, 311 000 fewer deaths in black individuals, and 34 000 fewer deaths in Asians and Pacific Islanders aged 25–64 years. During 2011–14, American Indians and Alaska Natives had the highest premature mortality, followed by black individuals. Important public health successes, including HIV treatment and smoking cessation, have contributed to declining premature mortality in Hispanic individuals, black individuals, and Asians and Pacific Islanders. However, this progress has largely been negated in young and middle-aged (25–49 years) white individuals, and American Indians and Alaska Natives, primarily because of potentially avoidable causes such as drug poisonings, suicide, and chronic liver disease and cirrhosis. The magnitude of annual mortality increases in the USA is extremely unusual in high-income countries, and a rapid public health response is needed to avert further premature deaths. US National Cancer Institute Intramural Research Program.

There are several suspected environmental risk factors for non-Hodgkin lymphoma (NHL). The associations between NHL and environmental chemical exposures have typically been evaluated for individual chemicals (i.e., one-by-one). We determined the association between a mixture of 27 correlated chemicals measured in house dust and NHL risk. We conducted a population-based case-control study of NHL in four National Cancer Institute-Surveillance, Epidemiology, and End Results centers--Detroit, Michigan; Iowa; Los Angeles County, California; and Seattle, Washington--from 1998 to 2000. We used weighted quantile sum (WQS) regression to model the association of a mixture of chemicals and risk of NHL. The WQS index was a sum of weighted quartiles for 5 polychlorinated biphenyls (PCBs), 7 polycyclic aromatic hydrocarbons (PAHs), and 15 pesticides. We estimated chemical mixture weights and effects for study sites combined and for each site individually, and also for histologic subtypes of NHL. The WQS index was statistically significantly associated with NHL overall [odds ratio (OR) = 1.30; 95% CI: 1.08, 1.56; p = 0.006; for one quartile increase] and in the study sites of Detroit (OR = 1.71; 95% CI: 1.02, 2.92; p = 0.045), Los Angeles (OR = 1.44; 95% CI: 1.00, 2.08; p = 0.049), and Iowa (OR = 1.76; 95% CI: 1.23, 2.53; p = 0.002). The index was marginally statistically significant in Seattle (OR = 1.39; 95% CI: 0.97, 1.99; p = 0.071). The most highly weighted chemicals for predicting risk overall were PCB congener 180 and propoxur. Highly weighted chemicals varied by study site; PCBs were more highly weighted in Detroit, and pesticides were more highly weighted in Iowa. An index of chemical mixtures was significantly associated with NHL. Our results show the importance of evaluating chemical mixtures when studying cancer risk.

Leisure time physical activity reduces the risk of premature mortality, but the years of life expectancy gained at different levels remains unclear. Our objective was to determine the years of life gained after age 40 associated with various levels of physical activity, both overall and according to body mass index (BMI) groups, in a large pooled analysis. We examined the association of leisure time physical activity with mortality during follow-up in pooled data from six prospective cohort studies in the National Cancer Institute Cohort Consortium, comprising 654,827 individuals, 21-90 y of age. Physical activity was categorized by metabolic equivalent hours per week (MET-h/wk). Life expectancies and years of life gained/lost were calculated using direct adjusted survival curves (for participants 40+ years of age), with 95% confidence intervals (CIs) derived by bootstrap. The study includes a median 10 y of follow-up and 82,465 deaths. A physical activity level of 0.1-3.74 MET-h/wk, equivalent to brisk walking for up to 75 min/wk, was associated with a gain of 1.8 (95% CI: 1.6-2.0) y in life expectancy relative to no leisure time activity (0 MET-h/wk). Higher levels of physical activity were associated with greater gains in life expectancy, with a gain of 4.5 (95% CI: 4.3-4.7) y at the highest level (22.5+ MET-h/wk, equivalent to brisk walking for 450+ min/wk). Substantial gains were also observed in each BMI group. In joint analyses, being active (7.5+ MET-h/wk) and normal weight (BMI 18.5-24.9) was associated with a gain of 7.2 (95% CI: 6.5-7.9) y of life compared to being inactive (0 MET-h/wk) and obese (BMI 35.0+). A limitation was that physical activity and BMI were ascertained by self report. More leisure time physical activity was associated with longer life expectancy across a range of activity levels and BMI groups. Please see later in the article for the Editors' Summary.

Breast, endometrial, and ovarian cancers share some hormonal and epidemiologic risk factors. While several models predict absolute risk of breast cancer, there are few models for ovarian cancer in the general population, and none for endometrial cancer. Using data on white, non-Hispanic women aged 50+ y from two large population-based cohorts (the Prostate, Lung, Colorectal, and Ovarian Cancer Screening Trial [PLCO] and the National Institutes of Health-AARP Diet and Health Study [NIH-AARP]), we estimated relative and attributable risks and combined them with age-specific US-population incidence and competing mortality rates. All models included parity. The breast cancer model additionally included estrogen and progestin menopausal hormone therapy (MHT) use, other MHT use, age at first live birth, menopausal status, age at menopause, family history of breast or ovarian cancer, benign breast disease/biopsies, alcohol consumption, and body mass index (BMI); the endometrial model included menopausal status, age at menopause, BMI, smoking, oral contraceptive use, MHT use, and an interaction term between BMI and MHT use; the ovarian model included oral contraceptive use, MHT use, and family history or breast or ovarian cancer. In independent validation data (Nurses' Health Study cohort) the breast and ovarian cancer models were well calibrated; expected to observed cancer ratios were 1.00 (95% confidence interval [CI]: 0.96-1.04) for breast cancer and 1.08 (95% CI: 0.97-1.19) for ovarian cancer. The number of endometrial cancers was significantly overestimated, expected/observed = 1.20 (95% CI: 1.11-1.29). The areas under the receiver operating characteristic curves (AUCs; discriminatory power) were 0.58 (95% CI: 0.57-0.59), 0.59 (95% CI: 0.56-0.63), and 0.68 (95% CI: 0.66-0.70) for the breast, ovarian, and endometrial models, respectively. These models predict absolute risks for breast, endometrial, and ovarian cancers from easily obtainable risk factors and may assist in clinical decision-making. Limitations are the modest discriminatory ability of the breast and ovarian models and that these models may not generalize to women of other races. Please see later in the article for the Editors' Summary.

The principal tool used to estimate a woman's risk of breast cancer is the Breast Cancer Risk Assessment Tool, or the Gail model, which includes the number of first-degree relatives with breast cancer, age at menarche, age at first live birth, and number of previous breast biopsies. In this study, the addition of data on genetic variants associated with breast cancer yielded only a minor improvement in the performance of the model. The Breast Cancer Risk Assessment Tool includes the number of first-degree relatives with breast cancer, age at menarche, age at first live birth, and number of previous breast biopsies. In this study, the addition of data on genetic variants associated with breast cancer yielded only a minor improvement in the performance of the model. Personalized medicine, the assignment of preventive measures or treatment interventions on the basis of individual characteristics, can result in better outcomes than the use of the same strategy for everyone. Recent changes in the U.S. Preventive Services Task Force guidelines 1 for mammographic screening raise the question of whether recommendations about age at the onset of screening and the frequency of screening can be calibrated to an individual woman's risk of breast cancer. Clinicians already use guidelines in making decisions about assessments to identify carriers of rare BRCA1 and BRCA2 mutations, which confer very high risks of breast cancer and ovarian . . .

Quantitative measurements of environmental factors greatly improve the quality of epidemiologic studies but can pose challenges because of the presence of upper or lower detection limits or interfering compounds, which do not allow for precise measured values. We consider the regression of an environmental measurement (dependent variable) on several covariates (independent variables). Various strategies are commonly employed to impute values for interval-measured data, including assignment of one-half the detection limit to nondetected values or of \"fill-in\" values randomly selected from an appropriate distribution. On the basis of a limited simulation study, we found that the former approach can be biased unless the percentage of measurements below detection limits is small (5-10%). The fill-in approach generally produces unbiased parameter estimates but may produce biased variance estimates and thereby distort inference when 30% or more of the data are below detection limits. Truncated data methods (e.g., Tobit regression) and multiple imputation offer two unbiased approaches for analyzing measurement data with detection limits. If interest resides solely on regression parameters, then Tobit regression can be used. If individualized values for measurements below detection limits are needed for additional analysis, such as relative risk regression or graphical display, then multiple imputation produces unbiased estimates and nominal confidence intervals unless the proportion of missing data is extreme. We illustrate various approaches using measurements of pesticide residues in carpet dust in control subjects from a case-control study of non-Hodgkin lymphoma.

Five genome-wide association studies of the timing of menarche and menopause have now taken us beyond the range of candidate gene and linkage studies. The list of new genetic associations identified for these two traits should shed light on the mechanisms of ovarian aging, as well as breast cancer and other diseases associated with reproductive lifespan.

Background Multidisciplinary attempts to understand the etiology of breast cancer are expanding to increasingly include new potential markers of disease risk. Those efforts may have maximal scientific and practical influence if new findings are placed in context of the well-understood lifestyle and reproductive risk factors or existing risk prediction models for breast cancer. We therefore evaluated known risk factors for breast cancer in a cancer screening trial that does not have breast cancer as a study endpoint but is large enough to provide numerous analytic opportunities for breast cancer. Methods We evaluated risk factors for breast cancer (N = 2085) among 70,575 women who were randomized in the Prostate, Lung, Colorectal and Ovarian Cancer Screening Trial. Using Poisson regression, we calculated adjusted relative risks [RRs, with 95% confidence intervals (CIs)] for lifestyle and reproductive factors during an average of 5 years of follow-up from date of randomization. Results As expected, increasing age, nulliparity, positive family history of breast cancer, and use of menopausal hormone therapy were positively associated with breast cancer. Later age at menarche (16 years or older vs. < 12: RR = 0.81, 95% CI, 0.65–1.02) or menopause (55 years or older vs. < 45: RR = 1.29, 95% CI, 1.03–1.62) were less strongly associated with breast cancer than was expected. There were weak positive associations between taller height and heavier weight, and only severe obesity [body mass index (BMI; kg/m 2 ) 35 or more vs. 18.5–24.9: RR = 1.21, 95% CI, 1.02–1.43] was statistically significantly associated with breast cancer. Conclusion The ongoing PLCO trial offers continued opportunities for new breast cancer investigations, but these analyses suggest that the associations between breast cancer and age at menarche, age at menopause, and obesity might be changing as the underlying demographics of these factors change. Clinical Trials Registration http://www.clinicaltrials.gov , NCT00002540.

Background A promoter polymorphism in the pro-inflammatory cytokine tumor necrosis factor (TNF) (TNF G-308A) is associated with increased non-Hodgkin lymphoma (NHL) risk. The protein product, TNF-[alpha], activates the nuclear factor kappa beta (NF-[kappa]B) transcription factor, and is critical for inflammatory and apoptotic responses in cancer progression. We hypothesized that the TNF and NF-[kappa]B pathways are important for NHL and that gene variations across the pathways may alter NHL risk. Methodology/Principal Findings We genotyped 500 tag single nucleotide polymorphisms (SNPs) from 48 candidate gene regions (defined as 20 kb 5', 10 kb 3') in the TNF and TNF receptor superfamilies and the NF-[kappa]B and related transcription factors, in 1946 NHL cases and 1808 controls pooled from three independent population-based case-control studies. We obtaineded a gene region-level summary of association by computing the minimum p-value (\"minP test\"). We used logistic regression to compute odds ratios and 95% confidence intervals for NHL and four major NHL subtypes in relation to SNP genotypes and haplotypes. For NHL, the tail strength statistic supported an overall relationship between the TNF/NF-[kappa]B pathway and NHL (p = 0.02). We confirmed the association between TNF/LTA on chromosome 6p21.3 with NHL and found the LTA rs2844484 SNP most significantly and specifically associated with the major subtype, diffuse large B-cell lymphoma (DLBCL) (p-trend = 0.001). We also implicated for the first time, variants in NFKBIL1 on chromosome 6p21.3, associated with NHL. Other gene regions identified as statistically significantly associated with NHL included FAS, IRF4, TNFSF13B, TANK, TNFSF7 and TNFRSF13C. Accordingly, the single most significant SNPs associated with NHL were FAS rs4934436 (p-trend = 0.0024), IRF4 rs12211228 (p-trend = 0.0026), TNFSF13B rs2582869 (p-trend = 0.0055), TANK rs1921310 (p-trend = 0.0025), TNFSF7 rs16994592 (p-trend = 0.0024), and TNFRSF13C rs6002551 (p-trend = 0.0074). All associations were consistent in each study with no apparent specificity for NHL subtype. Conclusions/Significance Our results provide consistent evidence that variation in the TNF superfamily of genes and specifically within chromosome 6p21.3 impacts lymphomagenesis. Further characterization of these susceptibility loci and identification of functional variants are warranted.