Bayesian spatial modelling of childhood cancer incidence in Switzerland using exact point data: A nationwide study during 1985-2015 (original) (raw)
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Geospatial health, 2013
Tobacco smoking is a main cause of disease in Switzerland; lung cancer being the most common cancer mortality in men and the second most common in women. Although disease-specific mortality is decreasing in men, it is steadily increasing in women. The four language regions in this country might play a role in this context as they are influenced in different ways by the cultural and social behaviour of neighbouring countries. Bayesian hierarchical spatio-temporal, negative binomial models were fitted on subgroup-specific death rates indirectly standardized by national references to explore age-and gender-specific spatio-temporal patterns of mortality due to lung cancer and other tobacco-related cancers in Switzerland for the time period 1969-2002. Differences influenced by linguistic region and life in rural or urban areas were also accounted for. Male lung cancer mortality was found to be rather homogeneous in space, whereas women were confirmed to be more affected in urban regions. Compared to the German-speaking part, female mortality was higher in the French-speaking part of the country, a result contradicting other reports of similar comparisons between France and Germany. The spatio-temporal patterns of mortality were similar for lung cancer and other tobacco-related cancers. The estimated mortality maps can support the planning in health care services and evaluation of a national tobacco control programme. Better understanding of spatial and temporal variation of cancer of the lung and other tobacco-related cancers may help in allocating resources for more effective screening, diagnosis and therapy. The methodology can be applied to similar studies in other settings.
Hierarchical Bayesian Spatiotemporal Analysis of Childhood Cancer Trends
Geographical Analysis, 2012
In this article, generalized additive mixed models are constructed for the analysis of geographical and temporal variability of cancer ratios. In this class of models, spatially correlated random effects and temporal components are adopted. Spatio-temporal models that use intrinsic conditionally autoregressive smoothing across the spatial dimension and B-spline smoothing over the temporal dimension are considered. We study the patterns of incidence ratios over time and identify areas with consistently high ratio estimates as areas for further investigation. A hierarchical Bayesian approach using Markov chain Monte Carlo techniques is employed for the analysis of the childhood cancer diagnoses in the province of Alberta, Canada during 1995Canada during -2004. We also evaluate the sensitivity of such analyses to prior assumptions in the Poisson context.
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Statistics for Industry and Technology, 2007
Generalized linear models with a Poisson distribution are often used to model cancer registry data stratified by sex, age, year, and little geographical units. We compare three different approaches which take into account possible spatial correlation among neighbouring units, using lung cancer incidence data. Inference is fully Bayesian and uses Markov Chain Monte Carlo techniques. Comparison between models is based on the Deviance Information Criterion (DIC).
Quantitative Geology and Geostatistics, 2005
This paper presents a geostatistical methodology that accounts for spatially varying population sizes and spatial patterns in the processing of cancer mortality data. The binomial cokriging approach is adapted to the situation where the variance of observed rates is smaller than expected under the binomial model, thereby avoiding negative estimates for the semivariogram of the risk. Simulation studies are conducted using lung cancer mortality rates measured over two different geographies: New England counties and US State Economic Areas. For both datasets and different spatial patterns for the risk (i.e. random, spatially structured with and without nugget effect) the proposed approach generally leads to more accurate risk estimates than traditional binomial cokriging, empirical Bayes smothers or local means. 787
38 - Spatial variation of multiple diseases in relation to an environmental risk source
Working Papers Graspa, 2010
The analysis of the spatial variation of disease risk is crucial in Environmental Epidemiology studies. In this context, the effects of the presence of a source of pollution on the population health can be evaluated by models that consider distance from the source as a possible risk factor. We introduce an hierarchical Bayesian model in order to investigate the association between the risk of multiple pathologies and the presence of the risk source in the context of spatial case-control studies. Our approach extends some previous works based on spatial point patterns, concerning the risk variation of a single pathology and provides the possibility to incorporate spatial effects and other confounding factors within a logistic regression model. Moreover, spatial effects are decomposed into the sum of a disease-specific parametric component accounting for the distance from the point source and a common semi-parametric component that can be interpreted as a residual spatial variation. The proposed model is estimated by MCMC and is applied to data from a spatial case-control study in order to evaluate the association of the incidence of different cancers typologies with the residential location in the neighbourhood of a petrochemical plant in the Brindisi area (South-eastern Italy).
Bayesian spatial modeling of disease risk in relation to multivariate environmental risk fields
Statistics in Medicine, 2009
The relationship between exposure to environmental chemicals during pregnancy and early childhood development is an important issue which has a spatial risk component. In this context, we have examined mental retardation and developmental delay (MRDD) outcome measures for children in a Medicaid population in South Carolina and sampled measures of soil chemistry (e.g. As, Hg, etc.) on a network of sites which are misaligned to the outcome residential addresses during pregnancy. The true chemical concentration at the residential addresses is not observed directly and must be interpolated from soil samples. In this study, we have developed a Bayesian joint model which interpolates soil chemical fields and estimates the associated MRDD risk simultaneously. Having multiple spatial fields to interpolate, we have considered a low-rank Kriging method for the interpolation which requires less computation than Bayesian Kriging. We performed a sensitivity analysis for a bivariate smoothing, changing the number of knots and the smoothing parameter. These analyses show that a low-rank Kriging method can be used as an alternative to a full-rank Kriging, reducing computational burden. However, the number of knots for the low-rank Kriging model need to be selected with caution as a bivariate surface estimation can be sensitive to the choice of the number of knots.
Spatial Analysis of Childhood Cancer: A Case/Control Study
PLOS ONE, 2015
Childhood cancer was the leading cause of death among children aged 1-14 years for 2012 in Spain. Leukemia has the highest incidence, followed by tumors of the central nervous system (CNS) and lymphomas (Hodgkin lymphoma, HL, and Non-Hodgkin's lymphoma, NHL). Spatial distribution of childhood cancer cases has been under concern with the aim of identifying potential risk factors.
Journal of preventive medicine and public health = Yebang Uihakhoe chi, 2018
The aim of this study was to determine the factors associated with the spatial distribution of the incidence of colorectal cancer (CRC) in the neighborhoods of Tehran, Iran using Bayesian spatial models. This ecological study was implemented in Tehran on the neighborhood level. Socioeconomic variables, risk factors, and health costs were extracted from the Equity Assessment Study conducted in Tehran. The data on CRC incidence were extracted from the Iranian population-based cancer registry. The Besag-York-Mollié (BYM) model was used to identify factors associated with the spatial distribution of CRC incidence. The software programs OpenBUGS version 3.2.3, ArcGIS 10.3, and GeoDa were used for the analysis. The Moran index was statistically significant for all the variables studied (p<0.05). The BYM model showed that having a women head of household (median standardized incidence ratio [SIR], 1.63; 95% confidence interval [CI], 1.06 to 2.53), living in a rental house (median SIR, 0...
Spatial analysis of air pollution and cancer incidence rates in Haifa Bay, Israel
Science of the Total …, 2010
The Israel National Cancer Registry reported in 2001 that cancer incidence rates in the Haifa area are roughly 20% above the national average. Since Haifa has been the major industrial center in Israel since 1930, concern has been raised that the elevated cancer rates may be associated with historically high air pollution levels. This work tests whether persistent spatial patterns of metrics of chronic exposure to air pollutants are associated with the observed patterns of cancer incidence rates. Risk metrics of chronic exposure to PM 10 , emitted both by industry and traffic, and to SO 2 , a marker of industrial emissions, was developed. Wardbased maps of standardized incidence rates of three prevalent cancers: Non-Hodgkin's lymphoma, lung cancer and bladder cancer were also produced. Global clustering tests were employed to filter out those cancers that show sufficiently random spatial distribution to have a nil probability of being related to the spatial non-random risk maps. A Bayesian method was employed to assess possible associations between the morbidity and risk patterns, accounting for the ward-based socioeconomic status ranking. Lung cancer in males and bladder cancer in both genders showed non-random spatial patterns. No significant associations between the SO 2 -based risk maps and any of the cancers were found. Lung cancer in males was found to be associated with PM 10 , with the relative risk associated with an increase of 1 μg/m 3 of PM 10 being 12%. Special consideration of wards with expected rates b 1 improved the results by decreasing the variance of the spatially correlated residual log-relative risk.
SPATIAL STATISTICAL METHODS IN THE ANALYSIS OF PUBLIC HEALTH DATA
Various studies claim that cancer is likely to be caused by the diverse environmental pollutants; lifestyle, i.e., poor diet, smoking, alcohol, stress, sun exposure, lack of physical activity, non-healthy weight; genetic inheritance; some kind of infections, etc. In most cases, around 90-95% of cancers are due to lifestyle and ecological factors that influence the living organisms. This implies that people affected by cancer most probably are clustered around the most polluted regions, meaning that the geographical location has an effect in the chances of contracting the disease. We use spatial statistical methods to support this and to point out the contrasts in rates that come out from different geographical distributions of the population. After the heart disease, cancer is the second cause of the worldwide deaths, in spite of the intensive research done in the last years. Based on the information on the causes of deaths by group diseases, provided by the Albanian Institute of Statistics INSTAT for the last two decades, this fact is also true in the case of Albania, where on the first place we have the circulatory system diseases with an average of 242 deaths per 100.000 inhabitants a year, followed by an average of 78 deaths per 100.000 inhabitants a year caused by neoplasm. In this study we take into consideration some specific geographical areas and see how the critical points do influence in the higher chance of being affected by cancer. We use correlation to show the relation between number of sick people and air pollution rates.