Evidence of seasonality in the diagnosis of monocytic leukaemia

Eatough, J P

doi:10.1038/sj.bjc.6600497

Cited by 16 publications

(23 citation statements)

References 15 publications

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“…Although childhood studies of seasonality in leukaemia have shown peaks of diagnosis in summer months, [2] another study for adults has shown they have peaks in winter months [4] which is similar to our finding. Differences between the seasonality amongst childhood leukaemia compared to TYA leukaemia are not necessarily unexpected, due to the rapidly progressive nature of leukaemia amongst TYAs compared to children and the changes in disease epidemiology and tumour biology such that the incidence of ALL decreases and AML increases when moving through the childhood and young adult age range.…”

Section: Discussionsupporting

confidence: 89%

“…Seasonal variation in month of diagnosis amongst children and adolescents for Hodgkin’s disease (HD) has been observed in Denmark with a peak in March [3]. Amongst adults, there is evidence of seasonality of diagnosis in England of monocytic leukaemia with a peak in February and March, and in Sweden melanoma diagnosis peak in May/June and September/October, prostate cancer diagnosis peak in October and breast cancer diagnosis peak in November [4,5]. …”

Section: Introductionmentioning

confidence: 99%

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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

et al. 2013

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BackgroundWe 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.MethodsCases 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.ResultsThere 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).ConclusionOur 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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Section: Discussionsupporting

confidence: 89%

Section: Introductionmentioning

confidence: 99%

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

et al. 2013

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“…Somatic genetic alterations, deletions, and translocations, mainly in chromosomes 8 and 11, are common in monocytic leukemia, similar to other acute myeloid leukemias (58). Seasonal variations in the diagnosis of monocytic leukemia have been reported in England and Wales, suggesting a potential role for infections (3). However, no specific infectious agent has yet been identified.…”

Section: Discussionmentioning

confidence: 99%

“…Several indirect markers of exposures to infectious agents, including birth order, day care attendance, and socially unprivileged environments, have been found to be inversely associated with leukemia and Hodgkin's lymphoma (1)(2)(3)(4). Large families and number of older siblings are possible indicators of early-life exposure to infections because children come in close contact with each other, thereby sharing exposures to many infectious agents.…”

Section: Introductionmentioning

confidence: 99%

Number of Siblings and the Risk of Lymphoma, Leukemia, and Myeloma by Histopathology

Altieri

Castro

Bermejo

et al. 2006

Cancer Epidemiology, Biomarkers &Amp; Prevention

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Epidemiologic evidence indicates that several markers of exposure to childhood infections are inversely associated with the risk of childhood leukemia and lymphomas. We used the Swedish Family-Cancer Database to assess the effects of number of siblings on the risk of nonHodgkin's (n = 7,007) and Hodgkin's lymphomas (n = 3,115), leukemias (n = 7,650), and multiple myeloma (n = 1,492) by histopathology. Poisson regression models included terms for age, sex, family history, period, and socioeconomic index. Having four or more siblings compared with none was associated with an excess risk of childhood acute lymphoblastic leukemia [ALL; rate ratio (RR), 2.11; P trend = 0.001], acute monocytic leukemia (RR, 2.51; P trend = 0.002), and multiple myeloma (RR, 1.34; P trend = 0.006). Having three or more older siblings compared with none decreased the risk of acute monocytic leukemia (RR, 0.35; P trend = 0.001) and childhood ALL (RR, 0.69; P trend = 0.01). The risk of Hodgkin's lymphoma for five or more older siblings compared with none was 0.41 (P trend = 0.003). Acute myeloid leukemia, chronic lymphocytic leukemia, and other lymphoproliferative malignancies were not associated with number of siblings. In conclusion, we found an excess risk of childhood ALL and acute monocytic leukemia in large families. However, for ALL, acute monocytic leukemia, and Hodgkin's lymphoma, younger siblings were strongly protected compared with older siblings. The remarkable protective effect of number of older siblings on acute monocytic leukemia is a novel finding of potential interest. Possible interpretations of our findings in the context of a putative infectious etiology are discussed. (Cancer Epidemiol Biomarkers Prev 2006; 15(7):1281 -6)

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“…Although seasonal patterns, or seasonality, have been observed in many areas of medicine (e.g., Anderak et al, 2000;Basta et al, 2011;Bound et al, 1989;Castilla et al, 1990;Eatough, 2002;Fitzpatrick et al, 1994;Kershenbaum et al, 2011;Ordookhani et al, 2010;Seretakis et al, 1997;Yamaguchi et al, 2002), the occurrence of seasonal variation in errors in the clinical care of hospitalized patients events has received less analysis, possibly due to the limited availability of sufficiently large or enduring data sets. One of the most widely studied sources of variability over the annual scale is associated with the academic year and the commencement of new graduate doctors working in health care facilities.…”

Section: Introductionmentioning

confidence: 97%

Trends and Weekly and Seasonal Cycles in the Rate of Errors in the Clinical Management of Hospitalized Patients

Buckley

Bulger

2012

Chronobiology International

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Studies on the rate of adverse events in hospitalized patients seldom examine temporal patterns. This study presents evidence of both weekly and annual cycles. The study is based on a large and diverse data set, with nearly 5 yrs of data from a voluntary staff-incident reporting system of a large public health care provider in rural southeastern Australia. The data of 63 health care facilities were included, ranging from large non-metropolitan hospitals to small community and aged health care facilities. Poisson regression incorporating an observation-driven autoregressive effect using the GLARMA framework was used to explain daily error counts with respect to long-term trend and weekly and annual effects, with procedural volume as an offset. The annual pattern was modeled using a first-order sinusoidal effect. The rate of errors reported demonstrated an increasing annual trend of 13.4% (95% confidence interval [CI] 10.6% to 16.3%); however, this trend was only significant for errors of minor or no harm to the patient. A strong "weekend effect" was observed. The incident rate ratio for the weekend versus weekdays was 2.74 (95% CI 2.55 to 2.93). The weekly pattern was consistent for incidents of all levels of severity, but it was more pronounced for less severe incidents. There was an annual cycle in the rate of incidents, the number of incidents peaking in October, on the 282 nd day of the year (spring in Australia), with an incident rate ratio 1.09 (95% CI 1.05 to 1.14) compared to the annual mean. There was no so-called "killing season" or "July effect," as the peak in incident rate was not related to the commencement of work by new medical school graduates. The major finding of this study is the rate of adverse events is greater on weekends and during spring. The annual pattern appears to be unrelated to the commencement of new graduates and potentially results from seasonal variation in the case mix of patients or the health of the medical workforce that alters health care performance. These mechanisms will need to be elucidated with further research.

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Evidence of seasonality in the diagnosis of monocytic leukaemia

Cited by 16 publications

References 15 publications

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

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

Number of Siblings and the Risk of Lymphoma, Leukemia, and Myeloma by Histopathology

Trends and Weekly and Seasonal Cycles in the Rate of Errors in the Clinical Management of Hospitalized Patients

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