Browsing by Author "Pearce NE"

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    Characteristics of ovarian cancer in women residing in Aotearoa, New Zealand: 1993-2004
    (BMJ Journals, 2009) Firestone RT; Wong KC; Ellison Loschmann EA; Pearce NE; Jeffreys M
    Background: Few studies have compared ovarian cancer rates between different ethnic groups in the same country. The aim of this study was to describe ethnic patterns in the incidence and mortality of ovarian cancer in New Zealand, and to investigate ethnic and socioeconomic differences in the grade and stage of ovarian cancer. Methods: Data on all women registered with ovarian cancer on the New Zealand Cancer Registry (1993-2004) were analysed. Population data were taken from the 1996 and 2001 census. Logistic regression was used to estimate associations between ethnicity, deprivation and tumour characteristics. Results: Age-standardised incidence rates were highest in Pacific women, intermediate in Māori women, and lowest in non-Māori, non-Pacific women. Age-standardised mortality rates showed the same pattern. Ovarian cancer subtypes differed by ethnic group. There was no significant association between socioeconomic deprivation and tumour grade or stage. Age-adjusted models showed that Māori women were more likely to have well-differentiated tumours and less likely to present at a later stage compared to non-Māori, non-Pacific women. These patterns were partly explained by socioeconomic deprivation, and were not apparent for Pacific women. Conclusions: Pacific and Māori women experience higher incidence of ovarian cancer and mortality, compared to non-Māori, non-Pacific women. Māori women seemed to have better prognostic factors (local stage and well-differentiated tumours) than non-Māori, non-Pacific women. More work is needed to improve current cancer prevention strategies, particularly in Pacific women.
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    Heirarchical regression for multiple comparisons in a case-control study of occupational risks for lung cancer.
    (Public Library of Science, 11/06/2012) Corbin M; Richiardi L; Vermeulen R; Kromhout H; Merletti F; Peters S; Simonato L; Steenland K; Pearce NE; Maule M
    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.