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Low fertility may be a significant determinant of ovarian cancer worldwide: an ecological analysis of cross- sectional data from 182 countries

BACKGROUND: Ageing, socioeconomic level, obesity, fertility, relaxed natural selection and urbanization have been postulated as the risk factors of ovarian cancer (OC56). We sought to identify which factor plays the most significant role in predicting OC56 incidence rate worldwide. METHODS: Bivariat...

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Detalles Bibliográficos
Autores principales: You, Wenpeng, Symonds, Ian, Henneberg, Maciej
Formato: Online Artículo Texto
Lenguaje:English
Publicado: BioMed Central 2018
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC6097201/
https://www.ncbi.nlm.nih.gov/pubmed/30115095
http://dx.doi.org/10.1186/s13048-018-0441-9
Descripción
Sumario:BACKGROUND: Ageing, socioeconomic level, obesity, fertility, relaxed natural selection and urbanization have been postulated as the risk factors of ovarian cancer (OC56). We sought to identify which factor plays the most significant role in predicting OC56 incidence rate worldwide. METHODS: Bivariate correlation analysis was performed to assess the relationships between country-specific estimates of ageing (measured by life expectancy), GDP PPP (Purchasing power parity), obesity prevalence, fertility (indexed by the crude birth rate), opportunity for natural selection (I(bs)) and urbanization. Partial correlation was used to compare contribution of different variables. Fisher A-to-Z was used to compare the correlation coefficients. Multiple linear regression (Enter and Stepwise) was conducted to identify significant determinants of OC56 incidence. ANOVA with post hoc Bonferroni analysis was performed to compare differences between the means of OC56 incidence rate and residuals of OC56 standardised on fertility and GDP respectively between the six WHO regions. RESULTS: Bivariate analyses revealed that OC56 was significantly and strongly correlated to ageing, GDP, obesity, low fertility, I(bs) and urbanization. However, partial correlation analysis identified that fertility and ageing were the only variables that had a significant correlation to OC56 incidence when the other five variables were kept statistically constant. Fisher A-to-Z revealed that OC56 had a significantly stronger correlation to low fertility than to ageing. Stepwise linear regression analysis only identified fertility as the significant predictor of OC56. ANOVA showed that, between the six WHO regions, multiple mean differences of OC56 incidence were significant, but all disappeared when the contributing effect of fertility on OC56 incidence rate was removed. CONCLUSIONS: Low fertility may be the most significant determining predictor of OC56 incidence worldwide. ELECTRONIC SUPPLEMENTARY MATERIAL: The online version of this article (10.1186/s13048-018-0441-9) contains supplementary material, which is available to authorized users.