The incidence and mortality of ovarian cancer, its association with Body Mass Index and Human Development Index: an ecological study

Objective: Epidemiologic studies link obesity with a wide range of cancers, and ovarian cancer is one of the most common malignancies associated with BMI. The aim of this study was to investigate the relationship between Body Mass Index (BMI), Human Developmental Index (HDI) and ovarian cancer. Materials and Methods: The study was based on World Cancer Information and World Bank information (including HDI and its components). In this population-based study, we estimated the Population Attribution Frequency (PAF) by using BMI in adult communities. The prevalence and mortality rates and distribution maps for ovarian cancer were extracted for different countries. To analyze the data, correlation and regression tests were used to examine the relationship between prevalence and mortality with HDI. Statistical analysis of data was performed by stata-14 and the significance level was considered as 0.05. Results: The results showed that there was a positive and significant correlation between incidence, mortality, ovarian cancer and BMI and HDI ratio (p <0.05). Linear regression model showed that the increment of HDI, MYS and EYS increases the incidence of ovarian cancer. This increase was statistically significant only in MYS (p > 0.05). Analyzing the degree of mortality, regression analysis showed that the increase in HDI reduced motility. However, this decrease was not statistically significant, and the increase in MYS significantly increased mortality (B = 0.24). High HDI significantly increased BMI-related cancers (B = 11.7, p <0.05). Also, the results showed that high HDI was associated with an increase in the risk of cancer (B = 4.9, p <0.05). Conclusions: Overweight and obesity are risk factors for ovarian cancer, which is associated with the HDI. Therefore, to prevent this cancer, implementing an intervention program to control obesity is important for each country's developmental indicator.

Abstract licence: CC BY-NC-SA

A person counts once a day per paper. We keep no cookie and no address: a reader is a hash that changes every day. Any month under five reads shows no number.