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First description of seasonality of birth and diagnosis amongst teenagers and young adults with cancer aged 15-24 years in England, 1996-2005.

van Laar M, Kinsey SE, Picton SV, Feltbower RG - BMC Cancer (2013)

Bottom Line: There were 6251 cases diagnosed with leukaemia (n = 1299), lymphoma (n = 3070) and CNS tumours (n = 1882), the overall IR was 92 (95% CI 89-96) per 1,000,000 15-24 year olds per year.There was significant evidence of seasonality around the time of diagnosis for Hodgkin's lymphoma (P < 0.001) with a peak in February, and for 'other CNS tumours' (P = 0.010) with peaks in December and June.Birth peaks for those with 'other Gliomas' (Gliomas other than Astrocytoma and Ependymoma) were observed in May and November (P = 0.015).Further work will examine correlation with specific infections occurring around the time of birth and diagnosis within certain diagnostic groups.

View Article: PubMed Central - HTML - PubMed

Affiliation: Paediatric Epidemiology Group, Room 8,49 Worsley Building, Clarendon Way, University of Leeds, Leeds LS2 9JT, UK.

ABSTRACT

Background: We aimed to examine evidence for an infectious aetiology among teenagers and young adults (TYA) by analysing monthly seasonality of diagnosis and birth amongst 15-24 year olds diagnosed with cancer in England.

Methods: Cases of leukaemia, lymphoma and central nervous system (CNS) tumours were derived from the national TYA cancer register (1996-2005). Incidence rates (IR) and trends were assessed using Poisson regression. Seasonality of diagnosis and birth was assessed using Poisson and logistic regression respectively with cosine functions of varying periods.

Results: There were 6251 cases diagnosed with leukaemia (n = 1299), lymphoma (n = 3070) and CNS tumours (n = 1882), the overall IR was 92 (95% CI 89-96) per 1,000,000 15-24 year olds per year.There was significant evidence of seasonality around the time of diagnosis for Hodgkin's lymphoma (P < 0.001) with a peak in February, and for 'other CNS tumours' (P = 0.010) with peaks in December and June. Birth peaks for those with 'other Gliomas' (Gliomas other than Astrocytoma and Ependymoma) were observed in May and November (P = 0.015).

Conclusion: Our novel findings support an infectious aetiological hypothesis for certain subgroups of TYA cancer in England. Further work will examine correlation with specific infections occurring around the time of birth and diagnosis within certain diagnostic groups.

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Seasonality in month of diagnosis for male 15–24 year olds with cancer, 1996–2005.
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Figure 2: Seasonality in month of diagnosis for male 15–24 year olds with cancer, 1996–2005.

Mentions: Table 2 gives the results of the Poisson regression models to assess seasonality around the time of cancer diagnosis. The best fitting model is given in each case, with either 12 or 6 month periods. We observed significant evidence of a 12 monthly seasonal effect in those diagnosed with lymphoma overall (P = 0.008) which was driven by Hodgkin’s lymphoma (P < 0.001) with peaks in February, and a 6 monthly seasonal effect in those diagnosed with other CNS tumours (P = 0.010) with peaks in December and June (Figure 1). The goodness of fit test gave P-values of 0.716, 0.580 and 0.305 respectively, indicating no evidence of any lack of model fit. When stratifying the analysis by sex, we observed significant seasonal effects of diagnosis with a 12 monthly cycle amongst males diagnosed with leukaemia (P = 0.020; peak in October) and Hodgkin’s lymphoma (P = 0.005; peak in February) and seasonality with a 6 month cycle for male diagnosis of astrocytoma (P = 0.043; peaks in April and October) and other CNS tumours (P = 0.018; peaks in January and July) (Figure 2). Amongst females, there was evidence of seasonality in month of diagnosis of lymphoma (P = 0.017; peak in February) and Hodgkin’s lymphoma (P = 0.013; peak in February), and evidence of a 6-monthly seasonal effect of medulloblastoma diagnoses (P = 0.032; peaks in March and September) (Figure 3). All goodness of fit statistics showed adequate model fit (P > 0.05)


First description of seasonality of birth and diagnosis amongst teenagers and young adults with cancer aged 15-24 years in England, 1996-2005.

van Laar M, Kinsey SE, Picton SV, Feltbower RG - BMC Cancer (2013)

Seasonality in month of diagnosis for male 15–24 year olds with cancer, 1996–2005.
© Copyright Policy - open-access
Related In: Results  -  Collection

License
Show All Figures
getmorefigures.php?uid=PMC3750867&req=5

Figure 2: Seasonality in month of diagnosis for male 15–24 year olds with cancer, 1996–2005.
Mentions: Table 2 gives the results of the Poisson regression models to assess seasonality around the time of cancer diagnosis. The best fitting model is given in each case, with either 12 or 6 month periods. We observed significant evidence of a 12 monthly seasonal effect in those diagnosed with lymphoma overall (P = 0.008) which was driven by Hodgkin’s lymphoma (P < 0.001) with peaks in February, and a 6 monthly seasonal effect in those diagnosed with other CNS tumours (P = 0.010) with peaks in December and June (Figure 1). The goodness of fit test gave P-values of 0.716, 0.580 and 0.305 respectively, indicating no evidence of any lack of model fit. When stratifying the analysis by sex, we observed significant seasonal effects of diagnosis with a 12 monthly cycle amongst males diagnosed with leukaemia (P = 0.020; peak in October) and Hodgkin’s lymphoma (P = 0.005; peak in February) and seasonality with a 6 month cycle for male diagnosis of astrocytoma (P = 0.043; peaks in April and October) and other CNS tumours (P = 0.018; peaks in January and July) (Figure 2). Amongst females, there was evidence of seasonality in month of diagnosis of lymphoma (P = 0.017; peak in February) and Hodgkin’s lymphoma (P = 0.013; peak in February), and evidence of a 6-monthly seasonal effect of medulloblastoma diagnoses (P = 0.032; peaks in March and September) (Figure 3). All goodness of fit statistics showed adequate model fit (P > 0.05)

Bottom Line: There were 6251 cases diagnosed with leukaemia (n = 1299), lymphoma (n = 3070) and CNS tumours (n = 1882), the overall IR was 92 (95% CI 89-96) per 1,000,000 15-24 year olds per year.There was significant evidence of seasonality around the time of diagnosis for Hodgkin's lymphoma (P < 0.001) with a peak in February, and for 'other CNS tumours' (P = 0.010) with peaks in December and June.Birth peaks for those with 'other Gliomas' (Gliomas other than Astrocytoma and Ependymoma) were observed in May and November (P = 0.015).Further work will examine correlation with specific infections occurring around the time of birth and diagnosis within certain diagnostic groups.

View Article: PubMed Central - HTML - PubMed

Affiliation: Paediatric Epidemiology Group, Room 8,49 Worsley Building, Clarendon Way, University of Leeds, Leeds LS2 9JT, UK.

ABSTRACT

Background: We aimed to examine evidence for an infectious aetiology among teenagers and young adults (TYA) by analysing monthly seasonality of diagnosis and birth amongst 15-24 year olds diagnosed with cancer in England.

Methods: Cases of leukaemia, lymphoma and central nervous system (CNS) tumours were derived from the national TYA cancer register (1996-2005). Incidence rates (IR) and trends were assessed using Poisson regression. Seasonality of diagnosis and birth was assessed using Poisson and logistic regression respectively with cosine functions of varying periods.

Results: There were 6251 cases diagnosed with leukaemia (n = 1299), lymphoma (n = 3070) and CNS tumours (n = 1882), the overall IR was 92 (95% CI 89-96) per 1,000,000 15-24 year olds per year.There was significant evidence of seasonality around the time of diagnosis for Hodgkin's lymphoma (P < 0.001) with a peak in February, and for 'other CNS tumours' (P = 0.010) with peaks in December and June. Birth peaks for those with 'other Gliomas' (Gliomas other than Astrocytoma and Ependymoma) were observed in May and November (P = 0.015).

Conclusion: Our novel findings support an infectious aetiological hypothesis for certain subgroups of TYA cancer in England. Further work will examine correlation with specific infections occurring around the time of birth and diagnosis within certain diagnostic groups.

Show MeSH
Related in: MedlinePlus