Cancer risk in mothers of men operated for undescended testis

BACKGROUND: Undescended testis, or cryptorchidism, occurs in 2–5% of boys born at term, and by 12 months of age about 1% of all boys have manifest cryptorchidism. Several hormonal substances control this process and disruption of the foetal sex-hormones balance is a potential cause of undescended testis, however, to a great extent the aetiology of cryptorchidism is unclear. METHODOLOGY: To study risk factors involved in the aetiology of undescended testis, we assessed cancer risk in 15,885 mothers of men operated for undescended testis in Sweden. Women were followed-up for a median period of 23 years during which 811 first primary malignancies occurred. Their cancer incidence was compared with that in the general population estimating standardized incidence ratio (SIR) and corresponding 95% confidence interval (CI). PRINCIPAL FINDINGS: The overall cancer risk experienced by the mothers of cryptorchid men did not differ significantly from that of the general population (SIR = 0.94; 95% C.I. = 0.88–1.01). Specifically, there was a reduction in ovarian cancer risk (SIR = 0.72; 95% C.I. = 0.51–0.99), while the risk of lung (SIR = 1.38 95% C.I. 1.03–1.81) and biliary tract/liver cancer (SIR: 1.76, 95% CI: 1.03–2.82) were increased. CONCLUSIONS: Although we cannot rule out the role of chance, our data suggest a positive association between undescended testis and maternal lung cancer and a negative association with ovarian cancer, where the first may be partly attributable to smoking and the second to an altered hormonal milieu during pregnancy and thus both exposures may be risk factors for cryptorchidism

Risk of stomach cancer in Aotearoa/New Zealand: A Māori population based case-control study.

Māori, the indigenous people of New Zealand, experience disproportionate rates of stomach cancer, compared to non-Māori. The overall aim of the study was to better understand the reasons for the considerable excess of stomach cancer in Māori and to identify priorities for prevention. Māori stomach cancer cases from the New Zealand Cancer Registry between 1 February 2009 and 31 October 2013 and Māori controls, randomly selected from the New Zealand electoral roll were matched by 5-year age bands to cases. Logistic regression was used to estimate odd ratios (OR) and 95% confidence intervals (CI) between exposures and stomach cancer risk. Post-stratification weighting of controls was used to account for differential non-response by deprivation category. The study comprised 165 cases and 480 controls. Nearly half (47.9%) of cases were of the diffuse subtype. There were differences in the distribution of risk factors between cases and controls. Of interest were the strong relationships seen with increased stomach risk and having >2 people sharing a bedroom in childhood (OR 3.30, 95%CI 1.95-5.59), testing for H pylori (OR 12.17, 95%CI 6.15-24.08), being an ex-smoker (OR 2.26, 95%CI 1.44-3.54) and exposure to environmental tobacco smoke in adulthood (OR 3.29, 95%CI 1.94-5.59). Some results were attenuated following post-stratification weighting. This is the first national study of stomach cancer in any indigenous population and the first Māori-only population-based study of stomach cancer undertaken in New Zealand. We emphasize caution in interpreting the findings given the possibility of selection bias. Population-level strategies to reduce the incidence of stomach cancer in Māori include expanding measures to screen and treat those infected with H pylori and a continued policy focus on reducing tobacco consumption and uptake

The fecal microbiotas of women of Pacific and New Zealand European ethnicities are characterized by distinctive enterotypes that reflect dietary intakes and fecal water content

Obesity is a complex, multifactorial condition that is an important risk factor for noncommunicable diseases including cardiovascular disease and type 2 diabetes. While prevention and management require a healthy and energy balanced diet and adequate physical activity, the taxonomic composition and functional attributes of the colonic microbiota may have a supplementary role in the development of obesity. The taxonomic composition and metabolic capacity of the fecal microbiota of 286 women, resident in Auckland New Zealand, was determined by metagenomic analysis. Associations with BMI (obese, nonobese), body fat composition, and ethnicity (Pacific, n = 125; NZ European women [NZE], n = 161) were assessed using regression analyses. The fecal microbiotas were characterized by the presence of three distinctive enterotypes, with enterotype 1 represented in both Pacific and NZE women (39 and 61%, respectively), enterotype 2 mainly in Pacific women (84 and 16%) and enterotype 3 mainly in NZE women (13 and 87%). Enterotype 1 was characterized mainly by the relative abundances of butyrate producing species, Eubacterium rectale and Faecalibacterium prausnitzii, enterotype 2 by the relative abundances of lactic acid producing species, Bifidobacterium adolescentis, Bifidobacterium bifidum, and Lactobacillus ruminis, and enterotype 3 by the relative abundances of Subdoligranulum sp., Akkermansia muciniphila, Ruminococcus bromii, and Methanobrevibacter smithii. Enterotypes were also associated with BMI, visceral fat %, and blood cholesterol. Habitual food group intake was estimated using a 5 day nonconsecutive estimated food record and a 30 day, 220 item semi-quantitative Food Frequency Questionnaire. Higher intake of 'egg' and 'dairy' products was associated with enterotype 3, whereas 'non-starchy vegetables', 'nuts and seeds' and 'plant-based fats' were positively associated with enterotype 1. In contrast, these same food groups were inversely associated with enterotype 2. Fecal water content, as a proxy for stool consistency/colonic transit time, was associated with microbiota taxonomic composition and gene pools reflective of particular bacterial biochemical pathways. The fecal microbiotas of women of Pacific and New Zealand European ethnicities are characterized by distinctive enterotypes, most likely due to differential dietary intake and fecal consistency/colonic transit time. These parameters need to be considered in future analyses of human fecal microbiotas.Peer reviewe

Hierarchical Regression for Multiple Comparisons in a Case-Control Study of Occupational Risks for Lung Cancer

BACKGROUND Occupational studies often involve multiple comparisons and therefore suffer from false positive findings. Semi-Bayes adjustment methods have sometimes been used to address this issue. Hierarchical regression is a more general approach, including Semi-Bayes adjustment as a special case, that aims at improving the validity of standard maximum-likelihood estimates in the presence of multiple comparisons by incorporating similarities between the exposures of interest in a second-stage model. METHODOLOGY/PRINCIPAL FINDINGS We re-analysed data from an occupational case-control study of lung cancer, applying hierarchical regression. In the second-stage model, we included the exposure to three known lung carcinogens (asbestos, chromium and silica) for each occupation, under the assumption that occupations entailing similar carcinogenic exposures are associated with similar risks of lung cancer. Hierarchical regression estimates had smaller confidence intervals than maximum-likelihood estimates. The shrinkage toward the null was stronger for extreme, less stable estimates (e.g., "specialised farmers": maximum-likelihood OR: 3.44, 95%CI 0.90-13.17; hierarchical regression OR: 1.53, 95%CI 0.63-3.68). Unlike Semi-Bayes adjustment toward the global mean, hierarchical regression did not shrink all the ORs towards the null (e.g., "Metal smelting, converting and refining furnacemen": maximum-likelihood OR: 1.07, Semi-Bayes OR: 1.06, hierarchical regression OR: 1.26). CONCLUSIONS/SIGNIFICANCE Hierarchical regression could be a valuable tool in occupational studies in which disease risk is estimated for a large amount of occupations when we have information available on the key carcinogenic exposures involved in each occupation. With the constant progress in exposure assessment methods in occupational settings and the availability of Job Exposure Matrices, it should become easier to apply this approach

Bayesian methods to address multiple comparisons and misclassification bias in studies of occupational and environmental risks of cancer : a thesis by publications presented in partial fulfilment of the requirements for the degree of Doctor of Philosophy in Public Health, Massey University, Wellington, New Zealand

In this thesis I explore the application of several Bayesian approaches, implemented with standard statistical software, in environmental and occupational epidemiology. These methods are applied to case-control studies of occupational risks for lung and upper aerodigestive tract cancers conducted in New Zealand and Europe. The findings are of interest in themselves, but the focus of the thesis is on the application of Bayesian methods to produce these findings. It is not intended to represent a comprehensive overview of all Bayesian methods, but rather to explore Bayesian methods which are most appropriate for the studies which are presented here. In the first section, I review the underlying theory involved in such analyses. In the second section, I use Bayesian methods to address the problem of multiple comparisons. In occupational case-control studies, we may collect information on hundreds of occupations/exposures for which there is little or no prior evidence. For those occupations/exposures, we get a false positive finding by chance about 5% of the time. This means that if we repeat the study in a new population, these chance associations are likely to exhibit ‘regression to the mean’ and will not show such extreme risks again. Bayesian methods can be used to ‘shrink’ effect estimates based on how strong the regression to the mean is likely to be. In the third section, I use Bayesian methods for assessing and correcting systematic error. Although the methods I use can be applied to several situations (selection bias, misclassification, residual confounding), I apply them to the specific situation of misclassification of the main exposure. In particular, I apply four different methods for such sensitivity analyses: multiple imputation for measurement error (MIME); imputation based on specifying the sensitivity and specificity (SS), Direct Imputation (DI) of the ‘true’ exposure using a regression model for the predictive values and imputation based on a fully Bayesian analysis. I conclude by summarising the strengths, limitations, and areas of future development for the use of these methods. It is anticipated that, in 5-10 years time, such analyses may become standard supplements to ‘traditional’ forms of analysis, i.e. that Bayesian methods may be routinely used, and may form part of the ‘epidemiological toolkit’ for assessing and correcting for both random and systematic error

A comparison of sensitivity-specificity imputation, direct imputation and fully Bayesian analysis to adjust for exposure misclassification when validation data are unavailable

Purpose: Measurement error is an important source of bias in epidemiological studies. We illustrate three approaches to sensitivity analysis for the effect of measurement error: imputation of the ‘true’ exposure based on specifying the sensitivity and specificity of the measured exposure (SS); direct imputation (DI) using a regression model for the predictive values; and adjustment based on a fully Bayesian analysis. Methods: We deliberately misclassify smoking status in data from a case-control study of lung cancer. We then implement the SS and DI methods using fixed-parameter (FBA) and probabilistic (PBA) bias analyses, and Bayesian analysis using the Markov-Chain Monte-Carlo program WinBUGS to show how well each recovers the original association. Results: The ‘true’ smoking-lung cancer odds ratio (OR), adjusted for sex in the original dataset, was OR = 8.18 [95% confidence limits (CL): 5.86, 11.43]; after misclassification, it decreased to OR = 3.08 (nominal 95% CL: 2.40, 3.96). The adjusted point estimates from all three approaches were always closer to the ‘true’ OR than the OR estimated from the unadjusted misclassified smoking data, and the adjusted interval estimates were always wider than the unadjusted interval estimate. When imputed misclassification parameters departed much from the actual misclassification, the ‘true’ OR was often omitted in the FBA intervals whereas it was always included in the PBA and Bayesian intervals. Conclusions: These results illustrate how PBA and Bayesian analyses can be used to better account for uncertainty and bias due to measurement error

Semi-Bayes and empirical Bayes adjustment methods for multiple comparisons.

Epidemiological studies often involve multiple comparisons, and may therefore report many "false positive" statistically significant findings simply because of the large number of statistical tests involved. Traditional methods ofadjustment for multiple comparisons, such as the Bonferroni method, may induce investigators to ignore potentially important findings, because they do not take account of the fact that some variables are of greater a priori interest than others. The Bonferroni method involves "adjustings all of the findings to take account of the number of comparisons involved even though the a priori evidence may be very strong for some exposures, but may be much weaker (or non-existent)for the other exposures being considered. Furthermore, the Bonferroni method only "adjusts" for estimates of statistical signficance (p-values) and does not "adjust" the effect estimates themselves (e.g. odds ratios and 95% CI). Empirical Bayes and semi-Bayes methods can enable the avoidance of numerous false positive associations, and can produce effect estimates that are, on the average, more valid. In this paper, we report on a research in which we applied these methods to a case-control study of occupational risk factors for lung cancer and tested their performance

Sweet Taste Perception in Pacific and NZ European Women is Associated with Dietary Intake and Eating Behaviour

Background: Taste perception may influence long-term dietary preferences, potentiallycontributing to the development of obesity [...

Respiratory health in professional cleaners: Symptoms, lung function, and risk factors.

BACKGROUND: Cleaning is associated with an increased risk of asthma symptoms, but few studies have measured functional characteristics of airway disease in cleaners. AIMS: To assess and characterize respiratory symptoms and lung function in professional cleaners, and determine potential risk factors for adverse respiratory outcomes. METHODS: Symptoms, pre-/post-bronchodilator lung function, atopy, and cleaning exposures were assessed in 425 cleaners and 281 reference workers in Wellington, New Zealand between 2008 and 2010. RESULTS: Cleaners had an increased risk of current asthma (past 12 months), defined as: woken by shortness of breath, asthma attack, or asthma medication (OR = 1.83, 95% CI = 1.18-2.85). Despite this, they had similar rates of current wheezing (OR = 0.93, 95% CI = 0.65-1.32) and were less likely to have a doctor diagnosis of asthma ever (OR = 0.62, 95% CI = 0.42-0.92). Cleaners overall had lower lung function (FEV1 , FVC; P 3 vs ≤3 times/year), and reduced bronchodilator response (6% vs 9% mean FEV1 -%-predicted change, P < .05) compared to asthma in reference workers. Cleaning of cafes/restaurants/kitchens and using upholstery sprays or liquid multi-use cleaner was associated with symptoms, whilst several exposures were also associated with lung function deficits (P < .05). CONCLUSIONS AND CLINICAL RELEVANCE: Cleaners are at risk of some asthma-associated symptoms and reduced lung function. However, as it was not strongly associated with wheeze and atopy, and airway obstruction was less reversible, asthma in some cleaners may represent a distinct phenotype