Prevalence of chronic cough, its risk factors and population attributable risk in the Burden of Obstructive Lung Disease (BOLD) study: a multinational cross-sectional study.

Background: Chronic cough is a common respiratory symptom with an impact on daily activities and quality of life. Global prevalence data are scarce and derive mainly from European and Asian countries and studies with outcomes other than chronic cough. In this study, we aimed to estimate the prevalence of chronic cough across a large number of study sites as well as to identify its main risk factors using a standardised protocol and definition. Methods: We analysed cross-sectional data from 33,983 adults (≥40 years), recruited between Jan 2, 2003 and Dec 26, 2016, in 41 sites (34 countries) from the Burden of Obstructive Lung Disease (BOLD) study. We estimated the prevalence of chronic cough for each site accounting for sampling design. To identify risk factors, we conducted multivariable logistic regression analysis within each site and then pooled estimates using random-effects meta-analysis. We also calculated the population attributable risk (PAR) associated with each of the identifed risk factors. Findings: The prevalence of chronic cough varied from 3% in India (rural Pune) to 24% in the United States of America (Lexington,KY). Chronic cough was more common among females, both current and passive smokers, those working in a dusty job, those with a history of tuberculosis, those who were obese, those with a low level of education and those with hypertension or airflow limitation. The most influential risk factors were current smoking and working in a dusty job. Interpretation: Our findings suggested that the prevalence of chronic cough varies widely across sites in different world regions. Cigarette smoking and exposure to dust in the workplace are its major risk factors. Funding: Wellcome Trust.

Investigation of the affective factors on the survival rate of patients with laryngeal cancer using Cox proportional hazards and Lin -Ying's additive hazards models.

Background: Determining the factors affecting survival and appropriate treatment methods leads to improving the survival rate and quality of life in cancer patients; therefore this study was aimed to determine the effective factors on the survival rate of patients with Laryngeal cancer in Kerman city, Iran. Methods: This retrospective cohort study included 370 patients with Laryngeal cancer who referred to the hospitals of Kerman city, Iran during 2008 to 2018. Data were analyzed using Cox Proportional Hazards and Lin-Ying's Additive Hazards models. Data analysis was done using SAS software version 9.4. The P-value of less than 0.05 was considered as statistically significant. Results: The mean age at the time of diagnosis was 58.16±10.60 years. About 92% of the patients were men. The patient's 1, 3, 5, 7 and 10-years of overall survival rates were equal to 82.38%, 60.68%, 55.98%, 49.83%, and 30.91%, respectively. Age at the diagnosis (p=0.001), radiotherapy (p=0.001), chemotherapy (p=0.015), surgery (p=0.031), and smoking (p=0.001) were found to have significant effect on the patient's survival rate in the Cox model. These variables were significant in the Lin-Ying model too. Conclusion: Treatment is an important factor in controlling the disease and survival of cancer patients, and choosing the best treatment depends on the condition of the patient and the disease level.

The relationship among primary anatomic subsite and risk and distribution of second malignant neoplasms in patients with stage I/II diffuse large B-cell lymphoma: An analysis of the surveillance, epidemiology, and end results database.

BACKGROUND: Recent studies have reported that diffuse large B-cell lymphoma (DLBCL) involving different primary extranodal sites have distinct clinicopathological characteristics and prognosis. However, the risk of secondary malignant neoplasms (SMNs) in DLBCL survivors with different primary extranodal sites are unknown. METHODS: A total of 40,714 patients diagnosed with stage I/II DLBCL were included from the Surveillance, Epidemiology, and End Results (SEER) database from 1983 to 2015.The standardized incidence ratio (SIR) and absolute excess risk (AER) were used to assess the risk of SMNs. RESULTS: The results show that the risk of SMN was significantly higher in extranodal DLBCL than in the US general population (SIR, 1.18; 95% CI, 1.11-1.26), and the risk of developing SMN remains significantly elevated with increased latency. Moreover, there were multiple site-specific risk patterns. There was a 22%, 44%, 66%, 123% and 151% increased risk of SMN 10 years after primary gastrointestinal tract, head/neck, skeletal, lung and liver/pancreas DLBCL diagnosis, respectively. There was a significant decrease risk of SMN with increasing age at diagnosis for primary gastrointestinal tract and skeletal DLBCL. In addition, DLBCL patients with primary sites in the gastrointestinal tract, thyroid and liver/pancreas had the highest incidences of secondary stomach cancer, second thyroid cancer, and second hepatobiliary cancer, respectively, which indicated that the initial site of DLBCL may predict the type of SMN. CONCLUSIONS: The strategies for cancer surveillance after extranodal DLBCL diagnosis may need to be individualized according to the subsite of extranodal DLBCL.

Underlying Breast Cancer Risk and Menopausal Hormone Therapy.

The recent Collaborative Group on Hormonal Factors in Breast Cancer (CGHFBC) publication calculated the attributable risk of breast cancer from use of estrogen alone and estrogen plus a synthetic progestogen for less than 5 to 15 or more years of use. This CGHFB report calculated attributable risk based on their findings of relative risk from pooled data from 58 studies. Notably, neither the CGHFBC nor other previous studies have examined the effect of underlying risk of breast cancer on attributable risk. This omission prompted us to determine the magnitude of the effect of underlying risk on attributable risk in this perspective. Meaningful communication of the potential risk of menopausal hormonal therapy requires providing women with the estimated risk above their existing underlying risk (ie, attributable risk). Therefore, we have estimated attributable risks from the data published by the CGHFBC, taking into account varying degrees of underlying risk. Based on the Endocrine Society Guideline on Menopausal Hormone Therapy (MHT), we divided groups into 3 categories of risk: low (1.5%), intermediate (3.0%), and high (6.0%) underlying risk of breast cancer over 5 years. In women taking estrogen plus a synthetic progestogen for 5 to 9 years, the attributable risks of MHT increased from 12, to 42, to 85 additional women per 1000 in the low-, intermediate-, and high-risk groups, respectively. The attributable risks for estrogen alone were lower but also increased based on underlying risk. Notably, the attributable risks were amplified with duration of MHT use, which increased both relative risk and breast cancer incidence.

Moving from two- to multi-way interactions among binary risk factors on the additive scale.

Many studies have focused on investigating deviations from additive interaction of two dichotomous risk factors on a binary outcome. There is, however, a gap in the literature with respect to interactions on the additive scale of >2 risk factors. In this paper, we present an approach for examining deviations from additive interaction among three or more binary exposures. The relative excess risk due to interaction (RERI) is used as measure of additive interaction. First, we concentrate on three risk factors - we propose to decompose the total RERI to: the RERI owned to the joint presence of all three risk factors and the RERI of any two risk factors, given that the third is absent. We then extend this approach, to >3 binary risk factors. For illustration, we use a sample from data from the Greek EPIC cohort and we investigate the association with overall mortality of Mediterranean diet, body mass index , and smoking. Our formulae enable better interpretability of any evidence for deviations from additivity owned to more than two risk factors and provide simple ways of communicating such results from a public health perspective by attributing any excess relative risk to specific combinations of these factors. Abbreviations: BMI: Body Mass Index; ERR: excess relative risk; EPIC: European Prospective Investigation into Cancer and nutrition; MD: Mediterranean diet; RERI: relative excess risk due to interaction; RR: relative risk; TotRERI: total relative excess risk due to interaction.

Establishing a valid approach for estimating familial risk of cancer explained by common genetic variants.

We critically examined existing approaches for the estimation of the excess familial risk of cancer that can be attributed to identified common genetic risk variants and propose an alternative, more straightforward approach for calculating this proportion using well-established epidemiological methodology. We applied the underlying equations of the traditional approaches and the new epidemiological approach for colorectal cancer (CRC) in a large population-based case-control study in Germany with 4,447 cases and 3,480 controls, who were recruited from 2003 to 2016 and for whom interview, medical and genomic data were available. Having a family history of CRC (FH) was associated with a 1.77-fold risk increase in our study population (95% CI 1.52-2.07). Traditional approaches yielded estimates of the FH-associated risk explained by 97 common genetics variants from 9.6% to 23.1%, depending on various assumptions. Our alternative approach resulted in smaller and more consistent estimates of this proportion, ranging from 5.4% to 14.3%. Commonly employed methods may lead to strongly divergent and possibly exaggerated estimates of excess familial risk of cancer explained by associated known common genetic variants. Our results suggest that familial risk and risk associated with known common genetic variants might reflect two complementary major sources of risk.

Magnitude of reduction in risk of second contralateral breast cancer with bilateral mastectomy in patients with breast cancer: Data from California, 1998 through 2015.

BACKGROUND: Increasingly, patients with breast cancer undergo bilateral mastectomy (BLM). To the authors' knowledge, the magnitude of benefit is unknown. METHODS: The authors used data from the Surveillance, Epidemiology, and End Results (SEER) program regarding all women diagnosed with American Joint Committee on Cancer stage 0 to stage III unilateral breast cancer in California from 1998 through 2015 and treated with BLM versus breast-conserving therapy including surgery and radiotherapy (BCT) or unilateral mastectomy (ULM). The authors measured relative risks of second contralateral breast cancer (CBC) and breast cancer death using Fine and Gray multivariable regression modeling adjusted for the competing risk of death and death from another cause, respectively, and potential confounding factors. Absolute excess risk of CBC was measured as the observed minus expected number of breast cancers in the general population divided by 10,000 person-years at risk. RESULTS: Among 245,418 patients with a median follow-up of 6.7 years, 7784 patients (3.2%) developed CBC. Relative risks were lower after BLM (hazard ratio [HR], 0.10; 95% CI, 0.07-0.14) and higher after ULM (HR, 1.07; 95% CI, 1.02-1.13) versus BCT. Absolute excess risks were higher after BCT and ULM (5.0 and 13.6 more cases, respectively) compared with BLM (28.6 fewer cases). BLM reduced risk more among older women (38.0 fewer cases for women aged ≥50 years vs 17.9 fewer cases among women aged <50 years) but provided similar risk reduction across categories of tumor grade and tumor hormone receptor status. Compared with BCT, the risk of breast cancer death was equivalent after BLM (HR, 1.03; 95% CI, 0.96-1.11) and higher after ULM (HR, 1.21; 95% CI, 1.17-1.25). CONCLUSIONS: BLM may reduce second breast cancer risk by 34 to 43 cases per 10,000 person-years compared with other surgical procedures, but is not associated with a lower risk of death. Second breast cancers are rare, and their reduction should be weighed against the harms associated with BLM.

Long-term excess risk of breast cancer after a single breast density measurement.

AIM: Breast density is a risk factor for breast cancer. As density changes across a woman's life span, we studied for how long a single density measurement taken in (post-)menopausal women remains informative. METHODS: We used data from Singaporean women who underwent a single mammography screen at age 50-64 years. For each case with breast cancer diagnosed at screening or in the subsequent 10 years, whether screen detected or diagnosed following symptoms, two age-matched controls were selected. We studied the excess risk of breast cancer, calculated as an odds ratio (OR) with conditional logistic regression and adjusted for body mass index, associated with 26-50% and with 51-100% density compared with ≤25% density by time since screening. RESULTS: In total, 490 women had breast cancer, of which 361 were diagnosed because of symptoms after screening. Women with 51-100% breast density had an excess risk of breast cancer that did not seem to attenuate with time. In 1-3 years after screening, the OR was 2.22 (95% confidence interval [CI]: 1.07-4.61); in 4-6 years after screening, the OR was 4.09 (95% CI: 2.21-7.58), and in 7-10 years after screening, the OR was 5.35 (95% CI: 2.57-11.15). Excess risk with a stable OR of about 2 was also observed for women with 26-50% breast density. These patterns were robust when the analyses were limited to post-menopausal women, non-users of hormonal replacement therapy and after stratification by age at density measurement. CONCLUSION: A single breast density measurement identifies women with an excess risk of breast cancer during at least the subsequent 10 years.

Excess risk estimation for matched cohort survival data.

We present an excess risk regression model for matched cohort data, where the occurrence of some events for individuals with a disease is compared to that of healthy controls that are matched at the onset-of-disease by various factors. By using the matched structure, we show how to estimate the excess risk and its dependence on covariates on both proportional and additive form. We remove the individual effects on background mortality related to matching factors by considering differences. The model handles two different time scales, namely attained age and follow-up time. First, we solve estimating equations for the non-parametric and parametric components of the excess risk model, providing large sample properties for the suggested estimators. Next, we report results from a simulation study. Lastly, we describe an application of the method on childhood cancer data, to study the excess risk of cardiovascular events in adults' life among childhood cancer survivors.

Patient, tumor, and healthcare factors associated with regional variability in lung cancer survival: a Spanish high-resolution population-based study.

PURPOSE: The third most frequently diagnosed cancer in Europe in 2018 was lung cancer; it is also the leading cause of cancer death in Europe. We studied patient and tumor characteristics, and patterns of healthcare provision explaining regional variability in lung cancer survival in southern Spain. METHODS: A population-based cohort study included all 1196 incident first invasive primary lung cancer (C33-C34 according to ICD-10) cases diagnosed between 2010 and 2011 with follow-up until April 2015. Data were drawn from local population-based cancer registries and patients' hospital medical records from all public and private hospitals from two regions in southern Spain. RESULTS: There was evidence of regional differences in lung cancer late diagnosis (58% stage IV in Granada vs. 65% in Huelva, p value < 0.001). Among patients with stage I, only 67% received surgery compared with 0.6% of patients with stage IV. Patients treated with a combination of radiotherapy, chemotherapy, and surgery had a 2-year mortality risk reduction of 94% compared with patients who did not receive any treatment (excess mortality risk 0.06; 95% CI 0.02-0.16). Geographical differences in survival were observed between the two regions: 35% vs. 26% at 1-year since diagnosis. CONCLUSIONS: The observed geographic differences in survival between regions are due in part to the late cancer diagnosis which determines the use of less effective therapeutic options. Results from our study justify the need for promoting lung cancer early detection strategies and the harmonization of the best practice in lung cancer management and treatment.

Estimation of causal effect measures with the R-package stdReg.

Measures of causal effects play a central role in epidemiology. A wide range of measures exist, which are designed to give relevant answers to substantive epidemiological research questions. However, due to mathematical convenience and software limitations most studies only report odds ratios for binary outcomes and hazard ratios for time-to-event outcomes. In this paper we show how logistic regression models and Cox proportional hazards regression models can be used to estimate a wide range of causal effect measures, with the R-package stdReg. For illustration we focus on the attributable fraction, the number needed to treat and the relative excess risk due to interaction. We use two publicly available data sets, so that the reader can easily replicate and elaborate on the analyses. The first dataset includes information on 487 births among 188 women, and the second dataset includes information on 2982 women diagnosed with primary breast cancer.

An observational study on risk of secondary cancers in chronic myeloid leukemia patients in the TKI era in the United States.

INTRODUCTION: The treatment with tyrosine kinase inhibitors (TKIs) has drastically improved the outcome of chronic myeloid leukemia (CML) patients. This study was conducted to examine the risk of secondary cancers (SCs) in the CML patients who were diagnosed and treated in the TKI era in the United States. METHODS: The surveillance epidemiology and end results (SEER) database was used to identify CML patients who were diagnosed and received treatment during January 2002-December 2014. Standardized incidence ratios (SIRs) and absolute excess risks (AER) were calculated. RESULTS: Overall, 511 SCs (excluding acute leukemia) developed in 9,200 CML patients followed for 38,433 person-years. The risk of developing SCs in the CML patients was 30% higher than the age, sex and race matched standard population (SIR 1.30, 95% CI: 1.2-1.40; p < 0.001). The SIRs for CLL (SIR 3.4, 95% CI: 2-5.5; p < 0.001), thyroid (SIR 2.2, 95% CI: 1.2-3.5; p < 0.001), small intestine (SIR 3.1, 95% CI: 1.1-7; p = 0.004), gingiva (SIR 3.7, 95% CI: 1.2-8.7; p = 0.002), stomach (SIR 2.1, 95% CI: 1.1-3.5; p = 0.005), lung (SIR 1.4, 95% CI: 1.1-1.7; p = 0.006) and prostate (SIR 1.3, 95% CI: 1.02-1.6; p = 0.026) cancer among CML patients were significantly higher than the general population. The risk of SCs was higher irrespective of age and it was highest in the period 2-12 months after the diagnosis of CML. The risk of SCs in women was similar to that of the general population. CONCLUSION: CML patients diagnosed and treated in the TKI era in the United States are at an increased risk of developing a second malignancy. The increased risk of SCs in the early period after CML diagnosis suggests that the risk of SCs may be increased due to the factors other than TKIs treatment.

Robust Tests for Additive Gene-Environment Interaction in Case-Control Studies Using Gene-Environment Independence.

There have been recent proposals advocating the use of additive gene-environment interaction instead of the widely used multiplicative scale, as a more relevant public health measure. Using gene-environment independence enhances statistical power for testing multiplicative interaction in case-control studies. However, under departure from this assumption, substantial bias in the estimates and inflated type I error in the corresponding tests can occur. In this paper, we extend the empirical Bayes (EB) approach previously developed for multiplicative interaction, which trades off between bias and efficiency in a data-adaptive way, to the additive scale. An EB estimator of the relative excess risk due to interaction is derived, and the corresponding Wald test is proposed with a general regression setting under a retrospective likelihood framework. We study the impact of gene-environment association on the resultant test with case-control data. Our simulation studies suggest that the EB approach uses the gene-environment independence assumption in a data-adaptive way and provides a gain in power compared with the standard logistic regression analysis and better control of type I error when compared with the analysis assuming gene-environment independence. We illustrate the methods with data from the Ovarian Cancer Association Consortium.

Relative versus absolute risk of comorbidities in patients with psoriasis.

BACKGROUND: Psoriasis is associated with numerous comorbidities, often reported in terms of relative risk. Both doctors and the general population tend to overestimate the effects of exposures when presented in relative terms, leading to anxiety and potentially poor treatment decisions. Absolute risks might provide a better basis for risk assessment. OBJECTIVE: To characterize and compare relative and absolute risks of comorbidities in patients with psoriasis. METHODS: A systematic review using Medline identified comorbidities associated with psoriasis, their relative risks, and information for calculating absolute risks. RESULTS: The comorbidities associated with psoriasis with the highest relative risk were nonmelanoma skin cancer, melanoma, and lymphoma, with relative risks of 7.5, 6.12, and 3.61, respectively; the attributable risk for these 3 conditions were 0.64, 0.05, and 0.17 per 1000 person-years, respectively. To attribute 1 event of these conditions to psoriasis would require seeing 1551; 20,135; and 5823 patients, respectively. LIMITATIONS: Database studies might not fully account for confounders, resulting in overestimates of the risk impact of comorbidities. CONCLUSIONS: Presenting attributable risk in the form of the number needed to harm provides a clearer picture of the magnitude of risk and a basis for wiser medical decision making and patient education.

Differences in cancer survival in Canada by sex.

BACKGROUND: Research in the United States and Europe has found that women have an advantage over men in surviving a diagnosis of cancer, but the issue has not been systematically studied in Canada. DATA AND METHODS: Data are from the Canadian Cancer Registry, with mortality follow-up through record linkage to the Canadian Vital Statistics Death Database. The percentage unit difference in five-year relative survival ratios (RSRs) between women and men and the relative excess risk (RER) of death for women compared with men were used as measures of differences in cancer survival. RESULTS: A significant advantage for women compared with men was observed in 13 of the 18 cancers studied. Point estimates of RER were almost uniformly lower among those diagnosed at younger ages (15 to 54). For all cancers combined, women had a 13% lower excess risk of death-23% lower among women younger than 55. The overall advantage was greatest for thyroid cancer (RER = 0.31), skin melanoma (0.52) and Hodgkin lymphoma (0.65). The advantage for thyroid cancer was somewhat attenuated, though still significant, in earlier time periods. Bladder cancer was the only cancer for which women had a significant disadvantage (RER = 1.23); this excess risk seemed to be restricted to the first 12 to 18 months after diagnosis. INTERPRETATION: The reasons behind sex-specific differences in cancer survival are not well understood. Many explanations are possible, and differences are best explored on a cancer-by-cancer basis. The pronounced advantage for women at younger ages lends indirect support to a hypothesized hormonal influence.

Regional variations in cancer survival: Impact of tumour stage, socioeconomic status, comorbidity and type of treatment in Norway.

Cancer survival varies by place of residence, but it remains uncertain whether this reflects differences in tumour, patient and treatment characteristics (including tumour stage, indicators of socioeconomic status (SES), comorbidity and information on received surgery and radiotherapy) or possibly regional differences in the quality of delivered health care. National population-based data from the Cancer Registry of Norway were used to identify cancer patients diagnosed in 2002-2011 (n = 258,675). We investigated survival from any type of cancer (all cancer sites combined), as well as for the six most common cancers. The effect of adjusting for prognostic factors on regional variations in cancer survival was examined by calculating the mean deviation, defined by the mean absolute deviation of the relative excess risks across health services regions. For prostate cancer, the mean deviation across regions was 1.78 when adjusting for age and sex only, but decreased to 1.27 after further adjustment for tumour stage. For breast cancer, the corresponding mean deviations were 1.34 and 1.27. Additional adjustment for other prognostic factors did not materially change the regional variation in any of the other sites. Adjustment for tumour stage explained most of the regional variations in prostate cancer survival, but had little impact for other sites. Unexplained regional variations after adjusting for tumour stage, SES indicators, comorbidity and type of treatment in Norway may be related to regional inequalities in the quality of cancer care.

Prognostic and Predictive Values and Statistical Interactions in the Era of Targeted Treatment.

The current era of targeted treatment has accelerated the interest in studying gene-treatment, gene-gene, and gene-environment interactions using statistical models in the health sciences. Interactions are incorporated into models as product terms of risk factors. The statistical significance of interactions is traditionally examined using a likelihood ratio test (LRT). Epidemiological and clinical studies also evaluate interactions in order to understand the prognostic and predictive values of genetic factors. However, it is not clear how different types and magnitudes of interaction effects are related to prognostic and predictive values. The contribution of interaction to prognostic values can be examined via improvements in the area under the receiver operating characteristic curve due to the inclusion of interaction terms in the model (ΔAUC). We develop a resampling based approach to test the significance of this improvement and show that it is equivalent to LRT. Predictive values provide insights into whether carriers of genetic factors benefit from specific treatment or preventive interventions relative to noncarriers, under some definition of treatment benefit. However, there is no unique definition of the term treatment benefit. We show that ΔAUC and relative excess risk due to interaction (RERI) measure predictive values under two specific definitions of treatment benefit. We investigate the properties of LRT, ΔAUC, and RERI using simulations. We illustrate these approaches using published melanoma data to understand the benefits of possible intervention on sun exposure in relation to the MC1R gene. The goal is to evaluate possible interventions on sun exposure in relation to MC1R.

A comprehensive review of occupational and general population cancer risk: 1,3-Butadiene exposure-response modeling for all leukemia, acute myelogenous leukemia, chronic lymphocytic leukemia, chronic myelogenous leukemia, myeloid neoplasm and lymphoid neoplasm.

Excess cancer risks associated with 1,3-butadiene (BD) inhalation exposures are calculated using an extensive data set developed by the University of Alabama at Birmingham (UAB) from an epidemiology study of North American workers in the styrene butadiene rubber (SBR) industry. While the UAB study followed SBR workers, risk calculations can be adapted to estimate both occupational and general population risks. The data from the UAB SBR study offer an opportunity to quantitatively evaluate the association between cumulative exposure to BD and different types of cancer, accounting for the number of tasks involving high-intensity exposures to BD as well as confounding associated with the exposures to the multiple other chemicals in the SBR industry. Quantitative associations of BD exposure and cancer, specifically leukemia, can be further characterized by leukemia type, including potential associations with acute myelogenous leukemia (AML), chronic lymphocytic leukemia (CLL), and chronic myelogenous leukemia (CML), and the groups of lymphoid and myeloid neoplasms. Collectively, these multiple evaluations lead to a comprehensive analysis that makes use of all of the available information and is consistent with the risk assessment goals of the USEPA and other regulatory agencies, and in line with the recommendations of the USEPA Science Advisory Board. While a range of cancer risk values can result from these multiple factors, a preferred case for occupational and general population risk is highlighted. Cox proportional hazards models are used to fit exposure-response models to the most recent UAB data. The slope of the model with cumulative BD ppm-years as the predictor variable is not statistically significantly greater than zero for CML, AML, or, when any one of eight exposure covariates is added to the model, for all leukemias combined. The slope for CLL is statistically significantly different from zero. The slope for myeloid neoplasms is not statistically significantly greater than zero while the slope for lymphoid neoplasms is statistically significantly greater than zero. The excess risk for the general population is largest for lymphoid neoplasms. The best estimates of the environmental concentrations (ECs) associated with an excess risk of 1/100,000 by age 70 years for lymphoid neoplasms, all leukemias, and CLL are EC(1/100,000)'s equal to 0.06, 0.16 and 0.38 ppm, respectively. The best estimates of the occupational BD exposure from 20 to 65 years of age associated with an excess risk of 1/10,000 by age 70 years for lymphoid neoplasms, all leukemias, and CLL are the EC(1/10,000)'s of 2.7, 7.3 and 15.1 ppm, respectively.

Physical activity and dietary behavior in US adults and their combined influence on health.

OBJECTIVE: To examine the association between objectively measured physical activity and dietary behavior and their combined effect on health. PATIENTS AND METHODS: Data for this study were obtained from the 2003-2006 National Health and Nutrition Examination Survey cycles. The data were evaluated between September 9, 2012, and August 14, 2013. As part of the national survey, participants wore an accelerometer for 4 or more days to assess physical activity, blood samples were obtained to assess various biological markers, and interviews were conducted to assess dietary behavior. We selected a sample of 5211 participants and categorized them into 4 groups: (1) healthy diet and active, (2) unhealthy diet and active, (3) healthy diet and inactive, and (4) unhealthy diet and inactive. RESULTS: A total of 16.5% of participants (weighted proportions) were classified as consuming a healthy diet and being sufficiently active. After adjustments, participants were 32% more likely to consume a healthy diet if they met physical activity guidelines. For nearly all biomarkers, those who consumed a healthy diet and were sufficiently active had the most favorable biomarker levels. Compared with those who consumed a healthy diet and were active, participants who consumed an unhealthy diet and were inactive were 2.4 times more likely to have metabolic syndrome. CONCLUSION: Our findings indicate a relationship between objectively measured physical activity and dietary behavior and that participating in regular physical activity and eating a healthy diet are associated with better health outcomes when compared with diet or physical activity alone.

Mediterranean diet and hepatocellular carcinoma.

BACKGROUND & AIMS: Hepatocellular carcinoma (HCC) has a very poor prognosis and any effort to identify additional risk factors, besides those already established, would be important for the prevention of the disease. Data on the role of diet on HCC risk are still controversial. METHODS: We have evaluated the association of adherence to the Mediterranean diet with HCC risk, as well as the interaction of this dietary pattern with chronic hepatitis infection, by combining two case-control studies undertaken in Italy and Greece, including overall 518 cases of HCC and 772 controls. Adherence to the traditional Mediterranean diet was assessed through the Mediterranean diet score (MDS), which ranges between 0 (lowest adherence) and 9 (highest adherence). Odds ratios (OR) for HCC were obtained through multiple logistic regression models, controlling for potentially confounding factors, including chronic infection with hepatitis B/C viruses. RESULTS: Compared to MDS of 0-3, the ORs for HCC were 0.66 (95% confidence interval (CI), 0.41-1.04) for MDS equal to 4 and 0.51 (95% CI, 0.34-0.75) for MDS ⩾ 5, with a significant trend (p<0.001). The detrimental effect of poor adherence to Mediterranean diet on HCC risk was disproportionally high among those chronically infected with hepatitis B and/or C viruses, with a suggestion of super-additive interaction, albeit statistically non-significant. CONCLUSIONS: Closer adherence to the Mediterranean diet appears to be protective against HCC. Our results also point to potential benefits from adhering to a Mediterranean dietary pattern for patients chronically infected with hepatitis viruses.