Disparities in pediatric leukemia early survival in Argentina: a population-based study

Rev Panam Salud Publica. 2014 Oct;36(4):248-56.

Abstract

Objective: To identify disparities-using recursive partitioning (RP)-in early survival for children with leukemias treated in Argentina, and to depict the main characteristics of the most vulnerable groups.

Methods: This secondary data analysis evaluated 12-month survival (12-ms) in 3 987 children diagnosed between 2000 and 2008 with lymphoid leukemia (LL) and myeloid leukemia (ML) and registered in Argentina's population-based oncopediatric registry. Prognostic groups based on age at diagnosis, gender, socioeconomic index of the province of residence, and migration to a different province to receive health care were identified using the RP method.

Results: Overall 12-ms for LL and ML cases was 83.7% and 59.9% respectively. RP detected major gaps in 12-ms. Among 1-10-year-old LL patients from poorer provinces, 12-ms for those who did and did not migrate was 87.0% and 78.2% respectively. Survival of ML patients < 2 years old from provinces with a low/medium socioeconomic index was 38.9% compared to 62.1% for those in the same age group from richer provinces. For 2-14-year-old ML patients living in poor provinces, patient migration was associated with a 30% increase in 12-ms.

Conclusions: Major disparities in leukemia survival among Argentine children were found. Patient migration and socioeconomic index of residence province were associated with survival. The RP method was instrumental in identifying and characterizing vulnerable groups.

Publication types

  • Research Support, Non-U.S. Gov't

MeSH terms

  • Adolescent
  • Age Factors
  • Argentina / epidemiology
  • Child
  • Child, Preschool
  • Developing Countries
  • Female
  • Healthcare Disparities*
  • Humans
  • Infant
  • Infant, Newborn
  • Kaplan-Meier Estimate
  • Leukemia, Lymphoid / mortality*
  • Leukemia, Myeloid / mortality*
  • Male
  • Prognosis
  • Proportional Hazards Models
  • Registries
  • Sex Factors
  • Socioeconomic Factors
  • Survival Rate