Attributable Causes of Breast Cancer and Ovarian Cancer in China:Reproductive Factors,Oral Contraceptives and Hormone Replacement Therapy

Objective： To provide an evidence-based, consistent assessment of the burden of breast cancer attributable to reproductive factors （RFs, including nulliparity, mean number of children, age at first birth and breastfeeding）, use of oral contraceptives （OCs, restricted to the age group of 15-49 years）, and hormone replacement therapy （HRT）, as well as of the burden of ovarian cancer attributable to the mean number of children in China in 2005. Methods： We derived the prevalence of these risk factors and the relative risk of breast and ovarian cancer from national surveys or large-scale studies conducted in China. In the case of RFs, we compared the exposure distributions in 2001 and counterfactual exposure. Results： Exposure of RFs in 2002 was found to account for 6.74% of breast cancer, corresponding to 9,617 cases and 2,769 deaths, and for 2.78% of ovarian cancer （712 cases, 294 deaths）. The decrease in mean number of children alone was responsible for 1.47% of breast cancer and 2.78% of ovarian cancer. The prevalence of OC use was 1.74% and the population attributable fraction （PAF） of breast cancer was 0.71%, corresponding to 310 cases and 90 deaths. The PAF of breast cancer due to HRT was 0.31%, resulting in 297 cases and 85 deaths. Conclusion： RFs changes in China contributed to a sizable fraction of breast and ovarian cancer incidence and mortality, whereas HRT and OCs accounted for relatively low incidence of breast cancer in China.

Protective effects of female reproductive factors on gastric signetring cell carcinoma

BACKGROUND The overall incidence of gastric cancer is higher in men than women worldwide.However,gastric signet-ring cell carcinoma(GSRC)is more frequently observed in younger female patients.AIM To analyze clinicopathological differences between sexes in GSRC,because of the limited evidence regarding association between sex-specific differences and survival.METHODS We reviewed medical records for 1431 patients who received treatment for GSRC at the Cancer Hospital,Chinese Academy of Medical Sciences between January 2011 and December 2018 and surveyed reproductive factors.Clinicopathological characteristics were compared between female and male patients.Cox multivariable model was used to compare the mortality risks of GSRC among men,menstrual women,and menopausal women.RESULTS Of 1431 patients,935 patients were male and 496 were female(181 menstrual and 315 menopausal).The 5-year overall survival in male,menstrual female and menopausal female groups was 65.6%,76.5% and 65%,respectively(P<0.01).Menstruation was found to be a protective factor(hazard ratio=0.58,95% confidence interval:0.42–0.82).CONCLUSION The mortality risk of GSRC in menstrual women was lower than that in men.This study identified the protective effects of female reproductive factors in GSRC.

Meta-analysis of the risk factors of breast cancer concerning reproductive factors and oral contraceptive use

The authors performed a meta-analysis of case-control studies that addressed whether reproductive factors and oral contraceptive use were associated with breast cancer by searching the MEDLINE,PubMed,Proquest,Embase,ScienceDirect,African Healthline,BMJ Health Intelligence and Chinese Periodical net databases for all English-language and Chinese-language papers published from January 1,1997 to December 31,2007.A total of 15 studies calculating pool ORs indicated that menopausal age>50 yr[odds ratio(OR),1.39;95%confidence interval(CI),1.22–1.57]and oral contraceptive use(OR,2.12*,“*”:summary OR was adjusted;95%CI,1.24–3.62)were correlated with the increase in breast cancer risk while the summary OR based on number of full-term pregnancies≥1(OR,0.63*;95%CI,0.60–0.68)and breast-feeding(OR,0.76;95%CI,0.64–0.90)indicated no association with breast cancer risk.The correlation was statistically significant.Menopausal age>50 yr and oral contraceptive use are positively correlated with an increase in breast cancer risk while breast-feeding and number of full-term pregnancies≥1 are protective factors.

Statistical data driven approach of COVID-19 in Ecuador: R_(0) and R_(t) estimation via new method

The growth of COVID-19 pandemic throughout more than 213 countries around the world have put a lot of pressures on governments and health services to try to stop the rapid expansion of the pandemic.During 2009,H1N1 Influenza pandemic,statistical and mathematical methods were used to track how the virus spreads around countries.Most of these models that were developed at the beginning of the XXI century are based on the classical susceptible-infected-recovered(SIR)model developed almost a hundred years ago.The evolution of this model allows us to forecast and compute basic and effective reproduction numbers(R_(t) and R_(0)),measures that quantify the epidemic potential of a pathogen and estimates different scenarios.In this study,we present a traditional estimation technique for R_(0) with statistical distributions by best fitting and a Bayesian approach based on continuous feed of prior distributions to obtain posterior distributions and computing real time R_(t).We use data from COVID-19 officially reported cases in Ecuador since the first confirmed case on February 29th.Because of the lack of data,in the case of R_(0) we compare two methods for the estimation of these parameters below exponential growth and maximum likelihood estimation.We do not make any assumption about the evolution of cases due to limited information and we use previous methods to compare scenarios about R_(0) and in the case of R_(t) we used Bayesian inference to model uncertainty in contagious proposing a new modification to the well-known model of Bettencourt and Ribeiro based on a time window of m days to improve estimations.Ecuadorian R_(0) with exponential growth criteria was 3.45 and with the maximum likelihood estimation method was 2.93.The results show that Guayas,Pichincha and Manabíwere the provinces with the highest number of cases due to COVID-19.Some reasons explain the increased transmissibility in these localities:massive events,population density,cities dispersion patterns,and the delayed time of public health actions to contain pandemic.In conclusion,this is a novel approach that allow us to measure infection dynamics and outbreak distribution when not enough detailed data is available.The use of this model can be used to predict pandemic distribution and to implement data-based effective measures.