On the evidence for seasonal variation in the onset of acute lymphoblastic leukemia (ALL)

Seasonal variations in childhood leukaemia incidence in France, 1990-2014

BACKGROUND

Several studies have addressed the potential seasonality of childhood acute leukaemia (AL) without conclusive results. Using data from the National Registry of Childhood Cancers over 1990-2014 in mainland France, we investigated the seasonal variations in childhood AL taken together, and lymphoblastic (ALL) and myeloid (AML) leukaemia separately.

METHODS

Assuming constant variations over 1990-2014, we used a Poisson regression model to evaluate variations in standardized incidence ratios (SIRs) by month of birth or diagnosis. A scan method for temporal cluster detection was used to identify windows of several consecutive months with high or low SIR. The yearly reproducibility of the observed monthly variations was then evaluated.

RESULTS

We included 11,528 AL, of which 9493 ALL and 1,843 AML. No seasonal variation was detected for ALL. With a clear seasonal pattern, differences in AML incidence rates were evidenced between January-April and May-December birth periods (SIR = 0.85, 95% CI 0.77-0.94 and SIR = 1.07, 95% CI 1.01-1.14, respectively). AML incidence variations by month of diagnosis were less clear-cut.

CONCLUSION

Based on a large number of cases from a high-quality registry, we did not evidence any seasonality in ALL incidence rates but evidenced seasonal variations in AML incidence rates by month of birth.

Monthly variation in diagnosis of acute lymphoblastic leukemia and survival outcome in children and adults: 15-year trends at a single center

BACKGROUND

The date of acute lymphoblastic leukemia (ALL) diagnosis has been studied regarding potential etiologic roles with contrasting results and the issue remains controversial. The principal aim of this study was to analyze monthly variation of ALL diagnosis in a large homogenous Hispanic Latin American cohort over 15 years; its association with survival rates was also assessed.

METHODS

Clinical files and electronic records of 501 consecutive patients of all ages with ALL in northeastern Mexico over the years of 2004-2018 were scrutinized. Patients were divided into children ≤18 and adults >18 years. The Chi-square heterogeneity analysis was used to test for non-uniform variation. The Poisson regression analysis was used to fit sinusoidal (harmonic) models to the data, using the month of diagnosis as a covariate in a separate model.

RESULTS

During the study period 363 children (72.5%) and 138 adults (27.5%) (p < 0.001) were diagnosed with ALL. Heterogeneity across the months of diagnosis was confirmed (p = 0.019) and the Poisson regression analysis confirmed a significant monthly variation (p < 0.001) (95% CI, 3.024-3.745), a higher annual peak being observed in the month of March (p = 0.002), followed by a second peak in October (p = 0.026). The five-year OS for children was 68.2% (95% CI, 67.64-68.74) and for adults, 43.7% (95% CI, 42.67-44.71) (p < 0.001). No significant association between the month of diagnosis and OS was found (p = 0.789).

CONCLUSION

The monthly variation of ALL diagnosis was documented; these results confirm the heterogeneous behavior of the disease and appear to be consistent with an interplay of environmental and biologic factors. Further studies are needed to examine putative candidate agents.

Seasonality in Pediatric Cancer

Although seasonal trends in incidence and diagnosis of pediatric cancers have been widely investigated, the results have been inconclusive. A consistent seasonal trend may possibly provide etiological insights into pediatric cancers. This study aims to determine if there is a seasonal variation in cancer diagnoses in the pediatric population at the IWK Health Centre, a tertiary care center serving three Canadian provinces: Nova Scotia, New Brunswick, and Prince Edward Island. All pediatric cancer patients aged 0-20 y diagnosed from 1995 to 2015 at the center were included in this study. The annual data was divided into four seasonal periods (December to February, March to May, June to August, and September to November). The cancer diagnoses were categorized as leukemia, lymphoma, sarcoma, brain tumors, and miscellaneous. Seasonal variation was assessed by a harmonic function in a Poisson regression model. The amplitude of multiplicative change in the incidence rate caused by the seasonal variation is expressed as the incidence rate ratio (IRR). For all cancer diagnoses for the entire cohort of 1200 patients, the IRR was 1.03 [95% confidence interval (CI) 0.96-1.13]. None of the IRRs for the cancer groups indicated a statistically significant seasonality of cancer diagnosis: Leukemia 1.11 (95% CI 0.96-1.28); Lymphoma 1.17 (95% CI 0.93-1.47); Sarcoma 1.29 (95% CI 0.99-1.69); Brain tumors 1.16 (95% CI 0.97-1.38); Miscellaneous 1.09 (95% CI 0.93-1.27). The present study did not show a seasonal variation in the various cancer types in the pediatric population at the IWK.

Cancer cluster investigations: review of the past and proposals for the future

Residential clusters of non-communicable diseases are a source of enduring public concern, and at times, controversy. Many clusters reported to public health agencies by concerned citizens are accompanied by expectations that investigations will uncover a cause of disease. While goals, methods and conclusions of cluster studies are debated in the scientific literature and popular press, investigations of reported residential clusters rarely provide definitive answers about disease etiology. Further, it is inherently difficult to study a cluster for diseases with complex etiology and long latency (e.g., most cancers). Regardless, cluster investigations remain an important function of local, state and federal public health agencies. Challenges limiting the ability of cluster investigations to uncover causes for disease include the need to consider long latency, low statistical power of most analyses, uncertain definitions of cluster boundaries and population of interest, and in- and out-migration. A multi-disciplinary Workshop was held to discuss innovative and/or under-explored approaches to investigate cancer clusters. Several potentially fruitful paths forward are described, including modern methods of reconstructing residential history, improved approaches to analyzing spatial data, improved utilization of electronic data sources, advances using biomarkers of carcinogenesis, novel concepts for grouping cases, investigations of infectious etiology of cancer, and "omics" approaches.