Population-based cancer registries generate estimates of incidence and survival that are essential for cancer surveillance, research, and control strategies. Although data on cancer stage allow meaningful assessments of changes in cancer incidence and outcomes, stage is not recorded by most population-based cancer registries. The main method of staging adult cancers is the TNM classification. The criteria for staging paediatric cancers, however, vary by diagnosis, have evolved over time, and sometimes vary by cooperative trial group. Consistency in the collection of staging data has therefore been challenging for population-based cancer registries. We assembled key experts and stakeholders (oncologists, cancer registrars, epidemiologists) and used a modified Delphi approach to establish principles for paediatric cancer stage collection. In this Review, we make recommendations on which staging systems should be adopted by population-based cancer registries for the major childhood cancers, including adaptations for low-income countries. Wide adoption of these guidelines in registries will ease international comparative incidence and outcome studies.

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Background: In low-and middle-income countries (LMICs), inconsistent or delayed management of fever contributes to poor outcomes among pediatric patients with cancer. We hypothesized that standardizing practice with a clinical algorithm adapted to local resources would improve outcomes. Therefore, we developed a resource-specific algorithm for fever management in Davao City, Philippines. The primary objective of this study was to evaluate adherence to the algorithm.Procedure: This was a prospective cohort study of algorithm adherence to assess the types of deviation, reasons for deviation, and pathogens isolated. All pediatric oncology patients who were admitted with fever (defined as an axillary temperature >37.7 • C on one occasion or ≥37.4 • C on two occasions 1 hr apart) or who developed fever within 48 hr of admission were included. Univariate and multiple linear regression analyses were used to determine the relation between clinical predictors and length of hospitalization.Results: During the study, 93 patients had 141 qualifying febrile episodes. Even though the algorithm was designed locally, deviations occurred in 70 (50%) of 141 febrile episodes on day 0, reflecting implementation barriers at the patient, provider, and institutional levels. There were 259 deviations during the first 7 days of admission in 92 (65%) of 141 patient episodes. Failure to identify high-risk patients, missed antimicrobial doses, and pathogen isolation were associated with prolonged hospitalization. Conclusions:Monitoring algorithm adherence helps in assessing the quality of pediatric oncology care in LMICs and identifying opportunities for improvement. Measures that decrease highfrequency/high-impact algorithm deviations may shorten hospitalizations and improve healthcare use in LMICs.

AWT is complex and multifactorial; it may be reduced by educating parents and care providers about infection and infection control and improving the availability of antibiotics and associated supplies. These interventions will most likely reduce ICU admissions and possibly LOS and increase the survival of pediatric oncology patients at SPMC.

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