Modelling the Effects of Mindfulness Based Stress on Breast Cancer Survival Rate among Women in Meru and Nyeri Counties, Kenya, using Cox Proportional Hazard Model

Abstract

Breast cancer remains the most commonly diagnosed cancer among women, affecting 34 women per every 100,000. This has led to high number of fatalities annually, which need to be mitigated. The main interest among many cancer survivors and their families is establishing other conventional therapies they can engage in to improve their prognosis and survival. Among some of the key therapies is the interest in working on mindfulness-based stress (MBS) that patients undergo after diagnosis as complementary and alternate measures. Regardless of this, there is little that is known about the effects of MBS factors on breast cancer survival. Management of breast cancer can be enhanced through modelling the effects of MBS on breast cancer survival rate. However, there is limited information on accuracy of existing models. This study focused on developing a model to predict the effect MBS factors have on breast cancer survival rate among women in Meru and Nyeri Counties. Both Primary data and Secondary data were used. Primary data was obtained using a structured questionnaire from the breast cancer survivors and the medical practioners while secondary data was obtained from records at Meru teaching and referral hospital and Nyeri level five hospital on the MBS variables (cost burden of treatment, stress on diagnosis, prolonged time taken to access treatment, poor diet, alcohol use, physical activity and lack of awareness) among breast cancer patients for the period 2012 to 2017. Mixed method research design was used in the study. Both quantitative and qualitative data used in the study was analysed using R software. Cox proportional hazard model was used in establishing the survival rates, with the breast cancer survival rate being dependent variable while MBS factors were the independent variables. Kaplain-Meier estimators were used in determining the varying effects which the MBS factors have on survival rate. Log-rank test was used to perform comparisons of survival curves using hypothesis tests on the patients‘ survival rate considering age. The likelihood ratio test showed that MBS factors are significant in predicting hazard rates ( = 66.7, p = 0.0000119). Treatment period was highly statistically significant (p = 0.00014) as compared to other covariates. Lack of awareness (p = 0.0010124), ease of coping with stress (p = 0.000514) and observing the right diet (p = 0.04092) were also found to significantly affect survival rate. Access of treatment immediately after diagnosis, availing the right information to the patients, helping patients to cope easily with stress and observing the right diet were found to be the best estimators in increasing breast cancer survival rate. The study therefore recommends use of the model in predicting breast cancer survival rates which can greatly improve breast cancer prognosis.