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Background: The aetiology of bone cancers is poorly understood. This study examined geographical patterning in incidence of primary bone cancers diagnosed in 0-49 year olds in Great Britain during 1980-2005 to provide information on factors linked with disease development. We investigated putative associations with deprivation and population density. Methods: Data on osteosarcoma and Ewing sarcoma were obtained from national population-based registries. Negative binomial regression was used to examine the relationship between incidence rates and the Townsend deprivation score (and its component variables) and small-area population density. Results: The study analyzed 2566 osteosarcoma and 1650 Ewing sarcoma cases. For females with osteosarcoma, statistically significant decreased risk was associated with higher levels of deprivation (relative risk [RR] per unit increase in deprivation score = 0.969; 95% confidence interval [CI] 0.946-0.993). For all Ewing sarcoma combined, statistically significant decreased risk was associated with greater area-level population density and higher levels of non-car ownership (RR per person per hectare increase = 0.984; 95% CI 0.976-0.993, RR per 1% increase in non-car ownership = 0.994; 95% CI 0.991-0.998). Conclusions: Higher incidence of osteosarcoma was observed for females in areas with lower deprivation levels indicating increased risk is linked to some aspect of affluent living. Higher incidence of Ewing sarcoma occurred in areas of low population density and where more people owned cars, both characteristic of rural environments. The study adds substantially to evidence associating Ewing sarcoma risk with rural environmental exposures. Putative risk factors include agricultural exposures, such as pesticides and zoonotic agents [ABSTRACT FROM AUTHOR]

Basta NO, James PW, Craft AW, McNally RJQ. Season of birth and diagnosis for childhood cancer in Northern England, 1968–2005. Paediatric and Perinatal Epidemiology 2010; 24: 309–318. 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. [ABSTRACT FROM AUTHOR]

Background: The aetiology of type 1 diabetes in children is uncertain. A number of recent studies have suggested an infectious aetiology. It has been postulated that an infectious agent may be involved. Support for this hypothesis may be provided by a finding of space-time clustering. The aims of this study were: (i) to determine whether there was space-time clustering in cases of childhood diabetes from north-east England; and to test for differences in space-time clustering: (ii) due to age at diagnosis; (iii) between the sexes and (iv) between levels of residential population density. Methods: We studied incidence of type 1 diabetes diagnosed in children aged 0-14 years and diagnosed during the period 1990-2007. All cases were resident in a defined geographical region of north-east England (Northumberland, Newcastle upon Tyne and North Tyneside). We applied a second-order procedure based on K-functions to test for global clustering. Locations were residential addresses at time of diagnosis. Tests were repeated using nearest neighbour thresholds to allow for variable population density, providing the primary result for each analysis. Differences between sexes and between levels of population density were assessed. Results: We analysed 457 cases of type 1 diabetes. Overall, there was marginally significant evidence of global space-time clustering (P = 0.089). There was statistically significant clustering amongst pairs of cases that contained at least one female (P = 0.017), but not amongst pairs of cases that contained at least one male (P = 0.190). Furthermore, there was significant clustering amongst pairs of cases that contained at least one from a more densely populated area (P = 0.044), but not amongst pairs of cases that contained at least one from a less densely populated area (P = 0.226). Conclusion: Although the analyses have only found marginally significant evidence of global spacetime clustering for cases of type 1 diabetes diagnosed in north-east England, there were two notable findings. First, there was evidence of clustering amongst females and secondly clustering was confined to cases from more densely populated areas. These findings are consistent with a possible aetiological involvement of an infectious agent. [ABSTRACT FROM AUTHOR]

This review considers recent studies regarding the role of environmental factors in the etiology of childhood leukemia and lymphoma. Potential environmental risk factors identified for childhood leukemia include exposure to magnetic fields of more than 0.4 micro Tessla, exposure to pesticides, solvents, benzene and other hydrocarbons, maternal alcohol consumption (but only for certain genotypes), contaminated drinking water, infections, and high birth weight. The finding of space-time clustering and seasonal variation also supports a role for infections. There is little evidence linking childhood leukemia with lifetime exposure to ionizing radiation although fetal exposures to X-rays are associated with increased risk. Breast-feeding, consumption of fresh fruit and vegetables and having allergies all appear to be protective. Burkitt lymphoma (BL) is confined to areas of the world where malaria is endemic, with the additional involvement of the Epstein-Barr virus (EBV) as a co-factor. Environmental risk factors suggested for other types of non-Hodgkin lymphoma (NHL) include exposure to ionizing radiation (both lifetime and antenatal), pesticides, and, in utero exposure to cigarette smoke, benzene and nitrogen dioxide (via the mother). Hodgkin lymphoma (HL) is especially associated with higher levels of socioeconomic deprivation, but breast-feeding seems to confer lower risk. This is consistent with an infection or immune-response mediated etiology for HL. [ABSTRACT FROM AUTHOR]

There are three current hypotheses concerning infectious mechanisms in the aetiology of childhood leukaemia: exposure in utero or around the time of birth, delayed exposure beyond the first year of life to common infections and unusual population mixing. No specific virus has been definitively linked with childhood leukaemia and there is no evidence to date of viral genomic inclusions within leukaemic cells. The case–control and cohort studies have revealed equivocal results. Maternal infection during pregnancy has been linked with increased risk whilst breast feeding and day care attendance in the first year of life appear to be protective. There is inconclusive evidence from studies on early childhood infectious exposures, vaccination and social mixing. Some supportive evidence for an infectious aetiology is provided by the findings of space-time clustering and seasonal variation. Spatial clustering suggests that higher incidence is confined to specific areas with increased levels of population mixing, particularly in previously isolated populations. Ecological studies have also shown excess incidence with higher population mixing. The marked childhood peak in resource-rich countries and an increased incidence of the childhood peak in acute lymphoblastic leukaemia (ALL) (occurring at ages 2–6 years predominantly with precursor B-cell ALL) is supportive of the concept that reduced early infection may play a role. Genetically determined individual response to infection may be critical in the proliferation of preleukaemic clones as evidenced by the human leucocyte antigen class II polymorphic variant association with precursor B-cell and T-cell ALL. [ABSTRACT FROM AUTHOR]

The nearest neighbour analysis method has been developed to determine whether a disease case may be regarded as being unusually close to other neighbouring cases of the same disease. Using this method, each disease case is classified as spatially 'clustered' or 'nonclustered'. The method is also used to provide a test for global clustering. 'Clusters' are constructed by amalgamating geographically neighbouring clustered cases into one contiguous 'cluster area'. This paper describes a method for studying differences between clustered and non-clustered cases, in terms of case 'attributes'. These attributes may be person related, such as age and sex, or area based, such as geographical isolation. The area-based variables are subject to geographical correlation. The comparison of clustered and non-clustered cases may reveal similarities or differences, which may, in turn, give clues to disease aetiology. A method for studying 'linkage' or similarities in attributes between cases that occur in the same clusters is also described. The methods are illustrated by application to incidence data for leukaemias and lymphomas. [ABSTRACT FROM AUTHOR]