AI Article Synopsis
- Aging, GDP, obesity, low fertility, natural selection opportunities, and urbanization were examined as potential risk factors for ovarian cancer worldwide.
- Bivariate analysis showed a strong correlation between these factors and ovarian cancer incidence, but partial correlation analysis revealed that low fertility and aging were the only significant contributors when controlling for other variables.
- Ultimately, low fertility emerged as the most important predictor of ovarian cancer incidence, with findings indicating that fertility's impact eclipses that of aging and other socioeconomic factors.
Article Abstract
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) 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 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.
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|---|---|
| http://www.ncbi.nlm.nih.gov/pmc/articles/PMC6097201 | PMC |
| http://dx.doi.org/10.1186/s13048-018-0441-9 | DOI Listing |
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