ABSTRACT
Background & objectives: Robust forecasting of malaria cases is desirable as we are approaching towards malaria elimination in India. Methods enabling robust forecasting and timely case detection in unstable transmission areas are the need of the hour. Methods: Forecasting efficacy of the eight most prominent statistical models that are based on three statistical methods: Generalized linear model (Model A and Model B), Smoothing method (Model C), and SARIMA (Model D to model H) were compared using last twelve years (2008–19) monthly malaria data of two districts (Kheda and Anand) of Gujarat state of India. Results: The SARIMA Model F was found the most appropriate when forecasted for 2017 and 2018 using modelbuilding data sets 1 and 2, respectively, for both the districts: Kheda and Anand. Model H followed by model C were the two models found appropriate in terms of point estimates for 2019. Still, we regretted these two because confidence intervals from these models are wider that they do not have any forecasting utility. Model F is the third one in terms of point prediction but gives a relatively better confidence interval. Therefore, model F was considered the most appropriate for the year 2019 for both districts. Interpretation & conclusion: Model F was found relatively more appropriate than others and can be used to forecast malaria cases in both districts.
ABSTRACT
Background: Limited data are available on the epidemiology of breast cancer (BC) in India. Objective: To study the epidemiological characteristics of BC patients attending a tertiary care hospital in National Capital Territory of India. Materials and Methods: A cross-sectional study was conducted and information from 320 women with confirmed BC was collected on a questionnaire for demographic profile, socioeconomic status (SES), reproductive risk factors, and family history of BC. Information on clinical presentation and staging of BC was recorded. Anthropometric assessment for body mass index (BMI) was done. Data were analyzed and presented as mean ± standard deviation and frequency tables. Results: The mean age at diagnosis of BC was 47 ± 10 years. Fifty-three percent of patients were illiterate or only primary school education. About 74% of patients were from urban areas. Only 11% of patients were from upper SES and 26% from lower SES. Forty-seven percent of patients had stage II followed by 36% with stage III BC. About 15% patients had experienced early menarche (<13 years of age) and 15% of women had attained late menopause (>51 years of age). About 42% of patients had <3 children and 15% patients had a family history of BC. About 38% patients were overweight and 21% were obese. Conclusion: Other than the established risk factors, other factors such as lack of education, SES, and higher BMI were present in our study. A higher percentage of women were diagnosed with BC at later stages. There is a need for educating women about BC, self-examination of breast, and screening programs for early detection of BC