Journal of Medical Statistics and Informatics

Journal of Medical Statistics and Informatics

ISSN 2053-7662
Original Research

Cancer occurrence among women resident in the Gorizia province (North-Eastern Italy). May ecological data be used for adjusted epidemiological measures? Deterministic and probabilistic sensitivity analysis

Luigi Castriotta1,2*, Valentina Rosolen2†, Ettore Bidoli3†, Paolo Collarile3†, Diego Serraino3† and Fabio Barbone4†

*Correspondence: Luigi Castriotta

†These authors contributed equally this work.

1. Istituto di Igiene ed Epidemiologia Clinica, Azienda Sanitaria Universitaria Integrata di Udine.

Author Affiliations

2. Dipartimento di Area Medica, Università degli Studi di Udine.

3. SOC Epidemiologia Oncologica, IRCCS Centro di Riferimento Oncologico, Aviano.

4. Istituto di Ricerca e Cura a Carattere Scientifico materno infantile “Burlo Garofolo”, Trieste.


Background: The evaluation of the confounders is crucial to accurately estimate the association between environmental factors and diseases. The deterministic sensitivity analysis permits an external adjustment of the observed measures of effect. The probabilistic sensitivity analysis allows to define several probability density functions for the bias parameters and to use these prior distributions tocalculate limits for the biasadjusted exposure-disease relative risks. The confounding effect of known risk factors, for lung, breast and bladder cancer, in women residing in the Gorizia Province (GP),was estimated.

Methods: The deterministic sensitivity analysis was performed by calculating confounding risk ratios (CRRs) under different scenarios of confounding. Several parameterswere defined, as the population being studied; the prevalence of women residing in the GP and in the southern and northern Gorizia province (SGP and NGP respectively); the prevalence of the different risk factors in the selected areas;and the risk factordisease risk ratios per specific type of cancer. The CRRs were finally used to adjust theage-standardised rate ratios. Probabilistic sensitivity analysis allowed to specifyprior probability distributions for the lognormal risk factors-disease risk ratio with 95% lognormal confidence limits ln(RR1) and ln(RR2), mean and standard deviation equal to {ln(RR1)+ln(RR1)}/2 and {ln(RR2)*ln(RR1)}/(2*1.96) respectively.

Results: In women resident in the GP, the age-standardisedIRR for bladder cancer is equal to 1.18 but, adjusting for smoking through deterministic analysis, the new IRR ranges from 1.13 to 1.25. In SGP the same age-standardised IRR is equal to 1.45 while after adjustment for smoking it ranges from 1.31 to 1.67. Adjusting for the deprivation index in the areas of interest, the IRRs for bladder cancer in GP range from 1.17 to 1.18 while in SGP resident from 1.45 to 1.47. Results from the probabilistic analysis show that the RR for bladder cancer inGP women, when adjusted for smoking results to be equal to 1.20 (2.5th – 97.5th: 0.99 – 1.44) when adjusted for deprivation, appears to be equal to 1.19 (2.5th – 97.5th: 0.99 – 1.43). In the SGPthe IRR for bladder cancer, when adjusted for smoking, results equal to 1.58 (2.5th – 97.5th: 1.09 – 2.25) when adjusted for deprivation, 1.57 (2.5th – 97.5th: 1.10 – 2.23). Regarding breast and lung cancers, no risk excess arises in the studied areas, even after adjustment in both deterministic and probabilistic analysis.

Conclusions: Data on bladder cancer would seem to suggest a higher incidence of this cancer among the women residing in GP and, in particular, in the SGP. This excess risk isconfirmedafter adjustment for smoking and deprivation.No risk excesses in the studied areas for lung and breast cancers.

Keywords: Confounding risk ratio, deterministic analysis, probabilistic analysis, cancer

ISSN 2053-7662
Volume 6
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