Résumé
OBJECTIVES@#The aim of this study was to provide an overview of published mathematical estimation approaches to quantify the duration of the preclinical detectable phase (PCDP) using data from cancer screening programs. @*METHODS@#A systematic search of PubMed and Embase was conducted for original studies presenting mathematical approaches using screening data. The studies were categorized by mathematical approach, data source, and assumptions made. Furthermore, estimates of the duration of the PCDP of breast and colorectal cancer were reported per study population. @*RESULTS@#From 689 publications, 34 estimation methods were included. Five distinct types of mathematical estimation approaches were identified: prevalence-to-incidence ratio (n=8), maximum likelihood estimation (n=16), expectation-maximization algorithm (n=1), regression of observed on expected (n=6) and Bayesian Markov-chain Monte Carlo estimation (n=5). Fourteen studies used data from both screened and unscreened populations, whereas 19 studies included only information from a screened population. Estimates of the duration of the PCDP varied between 2 years and 7 years for breast cancer in the Health Insurance Plan study (annual mammography and clinical breast examinations in women aged 40-64 years) and 2 years and 5 years for colorectal cancer in the Calvados study (a guaiac fecal occult blood test in men and women aged 45-74 years). @*CONCLUSIONS@#Different types of mathematical approaches lead to different estimates of the PCDP duration. We advise researchers to use the method that matches the data available, and to use multiple methods for estimation when possible, since no method is perfect.
Résumé
Objective: To understand the dynamic variation of BMI and influencing factors among HIV/AIDS patients receiving highly active anti-retroviral therapy (HAART) in Liuzhou, Guangxi Zhuang Autonomous Region (Guangxi). Methods: HIV/AIDS patients receiving HAART for the first time since 1 January 2013 were selected. Data on BMI was analyzed among patients receiving HAART at baseline,6 months and 12 months after treatment. By using the general linear model repeated measures of analysis of variance, BMI dynamic variations and influencing factors were described and analyzed. Results: The average BMI of 2 871 patients at baseline, 6th months and 12th months appeared as (20.65±3.32), (20.87±3.22) and (21.18±3.20), respectively, with differences all statistically significant (F=18.86, P<0.001). BMI were increasing over time with treatments (F=37.25, P<0.001). Main influencing factors were noticed as: age, sex, marital status, baseline data of CD(4)(+)T cells and the WHO classification on clinical stages. Conclusions: Higher proportion of BMI malnutrition counts was seen among patients before receiving HAART in Liuzhou. BMI of the patients that were on HAART seemed being influenced by many factors. It is necessary to select appropriate treatment protocols on different patients so as to improve the nutritional status of the patients.