A quest for seasonality in presentation of leukaemia and non-Hodgkin's lymphoma

Identification of seasonal variation in the diagnosis of acute myeloid leukaemia: a population-based study

Until now, the role that seasonal factors play in the aetiology of acute myeloid leukaemia (AML) has been unclear. Demonstration of seasonality in AML diagnosis would provide supportive evidence of an underlying seasonal aetiology. To investigate the potential seasonal and long‐term trends in AML diagnosis in an overall population and in subgroups according to sex and age, we used population‐based data from a Spanish hospital discharge registry. We conducted a larger study than any to date of 26 472 cases of AML diagnosed in Spain between 2004 and 2015. Using multivariable Poisson generalized linear autoregressive moving average modelling, we found an upward long‐term trend, with monthly incidence rates of AML annually increasing by 0.4% [95% confidence interval (CI), 0.2%–0.6%; p = 0.0011]. January displayed the highest incidence rate of AML, with a minimum average difference of 7% when compared to February (95% CI, 2%–12%; p = 0.0143) and a maximum average difference of 16% compared to November (95% CI, 11%–21%; p < 0.0001) and August (95% CI, 10%–21%; p < 0.0001). Such seasonal effect was consistent among subgroups according to sex and age. Our finding that AML diagnosis is seasonal strongly implies that seasonal factors, such as infectious agents or environmental triggers, influence the development and/or proliferation of disease, pointing to prevention opportunities.

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.

Hodgkin Lymphoma has a seasonal pattern of incidence and mortality that depends on latitude

Seasonal variations in incidence and mortality after a Hodgkin lymphoma (HL) diagnosis have been previously described with partly conflicting results. The goal of this analysis is to provide a comprehensive analysis of these seasonal variations. In total, 41,405 HL cases diagnosed between 1973 and 2012 in the 18 Surveillance, Epidemiology, and End Results registries were included. Cosinor analysis and Cox proportional-hazards models were employed to analyze seasonality of incidence and mortality, respectively. HL shows a sinusoid seasonal incidence pattern (p < 0.001). Estimated incidence in March is 15.4% [95%-CI: 10.8-20.0] higher than in September. This sinusoid pattern is more pronounced at higher latitudes (p = 0.023). The risk of dying within the first three years after a HL diagnosis in winter is significantly increased compared to a HL diagnosis in summer at higher latitudes (HR = 1.082 [95%-CI: 1.009-1.161], p = 0.027). Furthermore, increasing northern latitude increases the additional mortality risk conferred by a diagnosis in winter (pinteraction0.033). The seasonality patterns presented here provide epidemiological evidence that Vitamin D might play a protective role in HL. Further evidence on the direct association between Vitamin D levels and the clinical course of HL needs to be collected to advance the understanding of the role of Vitamin D in HL.

Birth seasonality in Korean Prader-Willi syndrome with chromosome 15 microdeletion

PURPOSE

Prader-Willi syndrome (PWS) is a well-known genetic disorder, and microdeletion on chromosome 15 is the most common causal mechanism. Several previous studies have suggested that various environmental factors might be related to the pathogenesis of microdeletion in PWS. In this study, we investigated birth seasonality in Korean PWS.

METHODS

A total of 211 PWS patients born from 1980 to 2014 were diagnosed by methylation polymerase chain reaction at Samsung Medical Center. Of the 211 patients, 138 were born from 2000-2013. Among them, the 74 patients of a deletion group and the 22 patients of a maternal uniparental disomy (UPD) group were compared with general populations born from 2000 using the Walter and Elwood method and cosinor analysis.

RESULTS

There was no statistical significance in seasonal variation in births of the total 211 patients with PWS (χ(2)=7.2522, P=0.2982). However, a significant difference was found in the monthly variation between PWS with the deletion group and the at-risk general population (P<0.05). In the cosinor model, the peak month of birth for PWS patients in the deletion group was January, while the nadir occurred in July, with statistical significance (amplitude=0.23, phase=1.2, low point=7.2). The UPD group showed the peak birth month in spring; however, this result was not statistically significant (χ(2)=3.39, P=0.1836).

CONCLUSION

Correlation with birth seasonality was identified in a deletion group of Korean PWS patients. Further studies are required to identify the mechanism related to seasonal effects of environmental factors on microdeletion on chromosome 15.

Evaluation of seasonality in the diagnosis of acute myeloid leukaemia among adults in the United States, 1992-2008

Recent studies have suggested seasonal variation in the diagnosis of acute myeloid leukaemia (AML), and the aetiological role seasonal factors may play in this group of haematological neoplasms remains unclear. We evaluated potential seasonality of AML diagnosis among adults. Cases included were ascertained from the Surveillance, Epidemiology, and End Results (SEER) 13 registries from 1992-2008. Chi-square analysis for heterogeneity and multiple Poisson regression using parametric harmonic modelling and bootstrap testing were used to detect possible monthly variation. Months of peak diagnoses were December and January, although some variation was present by sex and age. Heterogeneity across months was statistically significant (P < 0·001). In stratified analyses, monthly variation was detected only among males (P = 0·009) and in cases aged 65 years and older (P = 0·031). Poisson regression found no seasonal effect among all cases when fit to the sinusoidal model (P = 0·110). However, similar variation among males (P = 0·009) and cases aged 65 years and older (P = 0·018) was present. There is growing evidence of seasonality in AML diagnosis, particularly among older persons and men. Investigation of specific seasonal risk factors would be informative in explaining the aetiology behind the observed variation.

Season of birth and diagnosis for childhood cancer in Northern England, 1968-2005

The aim of this study was to investigate seasonal variation in the incidence of cancer in children aged 0-14 years. Details of 2959 primary malignant cases (1659 males, 1300 females), diagnosed during the period 1968-2005, were extracted from a specialist registry (the Northern Region Young Persons' Malignant Disease Registry). Seasonal variation was analysed with respect to month of birth and diagnosis. The chi-squared heterogeneity test was used to test for non-uniform variation. Poisson regression analysis was used to fit sinusoidal (harmonic) models to the data, using month of birth and month of diagnosis, respectively, as covariates in separate models. There was significant sinusoidal variation based on month of birth for acute lymphoblastic leukaemia (ALL) aged 1-6 years (P = 0.04; peak in March). For 0- to 14-year-old boys, there was statistically significant sinusoidal variation in month of birth for acute non-lymphocytic leukaemia (P = 0.04; peak in September) and astrocytoma (P = 0.03; peak in October). Based on month of diagnosis, there was statistically significant sinusoidal variation in girls for all lymphomas (P = 0.048; peak in March) and Hodgkin lymphoma (HL) (P = 0.005; peak in January), and in boys for osteosarcoma (P = 0.049; peak in October). This study confirms previous findings of seasonal variation around the month of birth for childhood ALL (at the peak ages) and provides further evidence of seasonal variation around month of birth for astrocytoma and around month of diagnosis for HL. The results are consistent with a role for environmental factors in the aetiology of these diagnostic groups. Further studies are needed to examine putative candidate agents.

Survival outcome in childhood ALL: experience from a tertiary care centre in North India

BACKGROUND

Survival of children with ALL, in developing nations has not kept pace with cure rates in developed countries. Our study was designed to assess survival data and identify risk factors.

PROCEDURE

Data of 762 patients with ALL were analyzed. Information regarding the clinical-demographic profile, therapy and course of illness were recorded. Status and duration at last follow-up were utilized to generate Kaplan-Meier survival curves.

RESULTS

The mean age was 5.7 +/- 0.23 years (M/F, 3.2:1). Parents of 230 (30.2%) patients opted for no therapy. There were 68 and 60 deaths in induction and remission phases respectively. Besides these, 111 children either defaulted therapy or were lost to follow up. Relapsed disease was documented in 125 cases. The 5-year OS and EFS was 46% and 43% respectively. Survival analysis, using the Cox multivariate regression, for gender (P = 0.659, CI: 0.852-1.161), age (P = 0.943, CI: 0.725-1.225), symptom-diagnosis interval (P = 0.002, CI: 1.116-1.668), WCC (P < 0.001, CI: 1.353-1.814) and platelet count (P = 0.001, CI: 0.546-0.849) was performed. Bulk disease (P = 0.049, CI: 0.428-0.986), mediastinal adenopathy (P = 0.045, CI: 1.040-3.697), WCC (P = 0.016, CI: 1.395-2.691), platelet count (P = 0.031, CI: 0.431-0.967) and administration of 2 intensification blocks (P = 0.012, CI: 0.624-0.940) were found to be significant predictors of outcome by multivariate analysis.

CONCLUSIONS

The management of ALL requires financial resources and access to quality supportive care. One third of our patients opted for no therapy. The other problem areas were a high proportion of therapy defaulters, lost to follow up and infection related deaths during induction and remission phases. The introduction of remedial measures for resolving the difficulties identified would hopefully improve cure rates in ALL in developing nations.

Excess diagnosis of non-Hodgkin's lymphoma during spring in the USA

A seasonal peak in hematologic malignancies may support hypotheses of infection-related precipitating events. Moderately increased incidence rates have been observed during the spring for leukemias and Hodgkin's disease but few studies have been conducted of non-Hodgkin's lymphoma (NHL). Our study consisted of 77,173 NHL patients in the Surveillance, Epidemiology, and End Results database diagnosed during 1973 - 99. Chi-square analyses showed excess observed-vs.-expected diagnoses during March, April, and June (P < 0.0001). B-cell origin subtype, but not T-cell/NK, was diagnosed more frequently in March. Controlling for age, sex, geographical location, and diagnosis year, multivariate Poisson regression revealed peaks in both March and April (P < 0.0001). Excluding cases in December, due to consistent troughs, regression uncovered greater-than-expected incidence during spring months for patients aged 20 - 39 years and 40 - 64 years (P = 0.043, P = 0.0001) but not among patients >or= 65 years. Future studies are needed to discern if a spring peak is due to diagnostic bias or other uncontrolled factors.

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

Inconsistent seasonal patterns in peak presentation of acute lymphoblastic leukemia have been reported but no formal synthesis of published reports has been attempted to date. We extracted monthly tabulations of cases from publications and reanalysed using an angular methodology and the von Mises distribution. Twenty-four articles from 11 countries in locations ranging from 35.05 degrees S to 65.01 degrees N were identified. Formal synthesis established weak evidence for seasonality in adults and children. Were seasonality present, and arising from a climatic determinant, one might anticipate peaks of an increasing magnitude as the associated latitude moved away from the equator. No such pattern was apparent.

On the application of the von Mises distribution and angular regression methods to investigate the seasonality of disease onset

This paper describes an approach to summarize the data arising from studies investigating the pattern of disease onset within a calendar year. Such data have been traditionally summarized into monthly counts summated over the complete years studied and patterns often examined by use of Pearson's chi(2) tests with 11 degrees of freedom. This test and others commonly used in practice are reviewed. As an alternative, we suggest that by first representing the date of onset for an individual as a point on a unit circle that the von Mises distribution with a single peak may provide a useful description of such data. Further an extension to angular regression including covariates, analogous to that used routinely in other areas of clinical research, potentially allows a more systematic and detailed investigation of possible seasonal patterns in patient subgroups. The methodology is applied to examples from the date of onset of primary angle-closure glaucoma and date of diagnosis of acute lymphoblastic leukaemia and examines in both situations how the peak onset varies with covariates. Difficulties associated with convergence to the maximum likelihood estimates of the associated parameters are described. Finally, we emphasize the need for individualized (rather than grouped) patient data to be available for study, a clear specification of the particular 'onset' time studied, and suggest that further case studies are required to evaluate the approach.

Variation in the seasonal diagnosis of acute lymphoblastic leukemia: evidence from Singapore, the United States, and Sweden

This study investigated, by summing data over successive years, the evidence for the seasonal diagnosis of acute lymphoblastic leukemia. To do so, the authors estimated the dates of peak diagnosis over a range of geographic locations including Singapore (1968-1999), Hawaii and mainland United States (1973-1999), and western Sweden (1977-1994) at latitudes of 1.16 degrees N to 58.24 degrees N. In contrast to other studies, the authors used case-by-case information on dates, gender, and age rather than grouped data for analysis. The seasonal pattern was estimated by fitting a von Mises distribution to the data from each location. No seasonal pattern was found in Singapore, which is close to the equator and does not have marked climatic changes. Likewise, seasonality was not demonstrated in Hawaii or mainland United States despite a 26.18 degrees range of latitudes. In contrast, a significant peak (early January) was observed for western Sweden that appeared strongest for males (December 22, 95% confidence interval: November 16, January 16) and those less than age 20 years (January 14, 95% confidence interval: December 8, March 27). Thus, despite a wide geographic range of localities, there is little evidence of any seasonality in the diagnosis of acute lymphoblastic leukemia in most populations studied and no strong evidence of any influence of climate (as expressed by latitude).

Seasonal variation in month of birth and diagnosis in children and adolescents with Hodgkin disease and non-Hodgkin lymphoma

PURPOSE

Infections have been hypothesized to be a risk factor for Hodgkin disease and non-Hodgkin lymphoma. Because infections usually occur seasonally, the authors looked at the seasonal pattern of occurrence of these diseases, since seasonal variation in occurrence would lend support to the hypothesis of an etiologic link between infection and lymphoma.

METHODS

The authors identified 575 patients with Hodgkin disease and 498 with non-Hodgkin lymphoma under the age of 20 years in the Danish Cancer Registry from 1943 to 1994. A periodic regression model was used to evaluate the seasonal distribution of month of diagnosis and month of birth.

RESULTS

For the month of diagnosis of Hodgkin disease, the peak month of occurrence was March. The peak-to-trough ratio (the estimated incidence in the month of the peak divided by the estimated incidence in the month of the trough) was 1.57. For birth date, the ratio of case births at the peak month to case births at the trough month was 1.21, with a peak in July. There was no meaningful seasonal variation in the diagnosis or month of birth of children with non-Hodgkin lymphoma.

CONCLUSIONS

These data corroborate a previously reported March peak in the diagnosis of Hodgkin disease.

Seasonal variations in the onset of childhood leukemia/lymphoma: April 1996 to March 2000, Shiraz, Iran

Infection has long been suspected as a possible factor in the aetiology of leukemia and lymphoma, one of the most common malignancies in children. Since most viral infections have seasonal variations of onset, if seasonal trends in 1 month of diagnosis of leukemia and lymphoma could be proved, this would be supportive evidence for an infectious aetiology. A total of 367 cases in the Hospitals of Shiraz University of Medical Sciences, from April 1996 through March 2000, who were diagnosed as having acute lymphocytic leukemia (ALL), acute myeloblastic leukemia (AML), Burkitt's lymphoma (BL) chronic myeloblastic lymphoma (CML), Hodgkin's disease (HD) or non-Burkitt's type non-Hodgkin's lymphoma (NBNHL) were analysed. The month of appearance of the first symptom and the date of diagnosis were recorded. ALL demonstrated statistically significant monthly variation in the date of appearance of the first symptom (p < 0.05; peak in October) and the date of diagnosis (p < 0.05; peak in November). Seasonal variation was demonstrated in the date of the first appearance of symptoms in BL (p < 0.042), and in the date of diagnosis in AML (p < 0.049). There was no statistically significant seasonal variation in the month of diagnosis for other groups. Analysis based on the date of the first symptoms and the date of diagnosis for ALL patients, using summer-winter ratios, also showed a significant winter excess (p < 0.001). Our data provide modest support for an autumn-winter peak in the diagnosis of childhood ALL, underlying mechanisms that account for these patterns are likely to be complex and need more definitive studies.

Evidence of seasonality in the diagnosis of monocytic leukaemia

Evidence of seasonality in the diagnosis of monocytic leukaemia in England and Wales is presented, with a maximum diagnosis rate in February/March and a minimum in August/September. Previous published results for monocytic leukaemia are of small sample size yet appear consistent with this finding.

Season of birth and diagnosis of children with leukaemia: an analysis of over 15 000 UK cases occurring from 1953-95

If infections are involved in the aetiology of childhood leukaemia then seasonal variation in the birth or onset dates of the malignancy may be apparent. Previous studies that have examined seasonality of these dates have produced conflicting results. Using population-based data from the National Registry of Childhood Tumours we conducted a larger study than any to date of 15 835 cases of childhood leukaemia born and diagnosed in the UK between 1953-95. We found no evidence of seasonality in either month of birth or month of diagnosis overall or in any subgroups by age, sex, histology or immunophenotype. We did however find a significant (P = 0.01) February peak in month of birth for cases born before 1960 and a significant (P = 0.02) August peak in month of diagnosis for those diagnosed before 1962. Whilst these findings may be due to chance they are also consistent with changes over time in the seasonality of exposure, or immunological response, to a relevant infection. Changes in the seasonal variation in the fatality rate of a pre-leukaemic illness, such as pneumonia, could be another explanation.