Diagnostic delay in pediatric Cancer: social determinants and impact on survival

Abstract

A significant improvement in the survival of children with cancer has been observed in the last 20 years. In high-income countries since the end of the 90s, the survival rates at 5 years are close to 80%. Unfortunately, despite advances in diagnosis and treatment, survival rates are still low- in low and middle-income countries of. This difference between the results observed in the high-and low or medium income may be related to socioeconomic factors and also the delay in diagnosis, factors that are interrelated in some way, as social inequalities may influence access to health services and can somehow cause a delay in diagnosis. The identification of the determinants of delay in diagnosis and their possible impact on the prognosis of children and adolescents with cancer will enable the development of effective strategies to reduce this delay, increase survival rates and thus achieve a more significant reduction in mortality. Objectives: This study aims to evaluate the influence of socioeconomic conditions in the intervals between the first consultation and diagnosis and between diagnosis and initiation of treatment in children with cancer living in São Paulo, as well as the impact of delayed diagnosis in overall survival. Methods: this is a retrospective cohort study that will include all cases of cancer (all groups of the International Classification of Childhood Cancer - ICCC) diagnosed in children and adolescents under 20 years of age and registered in the Hospital Cancer Registry the state of São Paulo (RHC-FOSP) as analytic cases (diagnosed outside but treated in the institution that registered the case, or diagnosed and treated at the institution that registered the case), in the period January 2000 to December 2008, residents in São Paulo (approximately 2,600 cases). Data for the following variables will be collected: sex, age, province of residence, socioeconomic, method for confirming diagnosis, date of first consultation, date of diagnosis, topography, morphology, ICCC classification, staging, treatment, date of initiation of treatment, recurrence, date of recurrence, status at last follow-up, date of last follow-up or death. Measures of central tendency and dispersion for quantitative variables and as absolute and relative frequencies for categorical variables will be calculated. Student's t test for independent samples and analysis of variance (ANOVA), or equivalent non parametric test (Mann-Whitney or Kruskal-Wallis) will be used to assess the differences in the avearge time first consultation-diagnosis and diagnosis-treatment by sex, tumor type, age, stage and socioeconomic indicators. Follow-up time for overall survival estimation will be defined as time interval between the date of diagnosis and the date of last follow-up or death. All death will be considered events. For survival analysis, Kaplan-Meier analysis will be utilized and curves will be compared using the log-rank test. For all statistical tests, results will be considered statistically significant when p <0.05. All analyzes will be performed with the aid of the software Stata version 11.0. (AU)