The influence of environmental and health indicators on premature mortality: An empirical analysis of the City of Toronto's 140 neighborhoods

The objective of this paper was to assess the link between premature mortality and a combination of neighbourhood contextual (environmental and health) and compositional (socioeconomic and demographic) characteristics. We statistically and spatially examined six environmental variables (ultrafine particles, carcinogenic and non-carcinogenic pollutants, pollution released to air, tree cover, and walkability index), six health service indicators (number health providers, breast, colorectal and cervical cancer screening uptake rates, student nutrition program uptake rates, and healthy food index), and eight socioeconomic indicators (total income, Gini coefficient, two age categories – below and above 40 years, proportion of females to males, visible minorities, Indigenous peoples, education, less than grade 9) among 140 neighbourhoods of the City of Toronto in Ontario (Canada). We applied principal component analysis to identify patterns and to reduce the number of explanatory variables into combined component axes that represent unique variation in these confounded and overlapping factors. We then applied regression analysis to model the relationship between the indices of enviro-health and socioeconomics and their potential relationship with premature mortality. Residual spatial analysis was used to investigate any remaining spatial structure (such as neighbourhoods with higher residual premature mortality rates). Neighbourhood Equity Index was correlated with our enviro-health and socioeconomic indices. Premature mortality within neighbourhoods was predicted by poor cancer screenings, pollution, lack of tree canopy, increased uptake of student nutrition programs and high walkability index. A negative association between premature mortality and pollution was associated low walkability index and presence of visible minorities within neighbourhoods. There was some unexplained residual spatial variation in our model of premature mortality - especially along the shores of Lake Ontario and in neighbourhoods with major highways or road corridors: premature mortality in Toronto neighbourhoods was higher than expected along highway-corridor neighbourhoods and shorelines. Our analysis revealed a significant relationship between neighbourhood contextual features – both environmental and health – and premature mortality, suggesting that these contextual components of neighbourhoods can predict rates of urban premature mortality in Toronto.     

Evaluating spatial methods for investigating global clustering and cluster detection of cancer cases

There have been articles on comparing methods for global clustering evaluation and cluster detection in disease surveillance, but power and sample size (SS) requirements have not been explored for spatially correlated data in this area. We are developing such requirements for tests of spatial clustering and cluster detection for regional cancer cases. We compared global clustering methods including Moran's I, Tango's and Besag-Newell's R statistics, and cluster detection methods including circular and elliptic spatial scan statistics (SaTScan), flexibly shaped spatial scan statistics, Turnbull's cluster evaluation permutation procedure, local indicators of spatial association, and upper-level set scan statistics. We identified eight geographic patterns that are representative of patterns of mortality due to various types of cancer in the U.S. from 1998 to 2002. We then evaluated the selected spatial methods based on state- and county-level data simulated from these different spatial patterns in terms of geographic locations and relative risks, and varying SSs using the 2000 population in each county. The comparison provides insight into the performance of the spatial methods when applied to varying cancer count data in terms of power and precision of cluster detection.     

Spatio-temporal interaction with disease mapping

Markov chain Monte Carlo methods are used to estimate mortality rates under a Bayesian hierarchical model. Spatial correlations are introduced to examine spatial effects relative to both regional and regional changes over time by groups. A special feature of the models is the inclusion of longitudinal variables which will describe temporal trends in mortality or incidences for different population groups. Disease maps are used to illustrate the role of different parameters in the model and pinpointing areas of interesting patterns. The methods are demonstrated by male cancer mortality data from the state of Missouri during 1973-1992. Of special interest will be the geographic variations in the trend of lung cancer mortality over the recent past. Marginal posterior distributions are used to examine effects due to spatial correlations and age difference in temporal trends. Numerical results from the Missouri data show that although spatial correlations exist, they do not have a large effect on the estimated mortality rates.     

Geostatistical analysis of disease data: accounting for spatial support and population density in the isopleth mapping of cancer mortality risk using area-to-point Poisson kriging

Background  

Geostatistical techniques that account for spatially varying population sizes and spatial patterns in the filtering of choropleth maps of cancer mortality were recently developed. Their implementation was facilitated by the initial assumption that all geographical units are the same size and shape, which allowed the use of geographic centroids in semivariogram estimation and kriging. Another implicit assumption was that the population at risk is uniformly distributed within each unit. This paper presents a generalization of Poisson kriging whereby the size and shape of administrative units, as well as the population density, is incorporated into the filtering of noisy mortality rates and the creation of isopleth risk maps. An innovative procedure to infer the point-support semivariogram of the risk from aggregated rates (i.e. areal data) is also proposed.     

Impacts of land use on spatial distribution of mortality rates of cancers caused by naturally occurring asbestos

This study investigated the spatial distributions of mortality rates of six cancers: mesothelioma, lung cancer, intestinal cancer, nasopharyngeal and laryngeal cancer, liver cancer, and stomach cancer in Dayao using Geographic Information Systems. Relationships between the mortality rates of the six cancers and land use patterns were investigated by Pearson Correlation Coefficients. The results indicated that the mortality rates of nasopharyngeal and laryngeal cancer, lung cancer, intestinal cancer, and mesothelioma were significantly associated with outcropped asbestos. Both the proportions of farmland and urban area were positively related to the mortality rates of nasopharyngeal and laryngeal cancer, lung cancer, intestinal cancer, and mesothelioma, and significant negative correlations were found between the proportion of forestland and nasopharyngeal and laryngeal cancer and intestinal cancer. It can be concluded that naturally occurring asbestos may significantly elevate the mortality rates of nasopharyngeal and laryngeal cancer, intestinal cancer, lung cancer, and mesothelioma. Moreover, higher proportions of farmland, urban area, and lower proportions of forested land may elevate the mortality rate of the four cancers.     

Relation of occupations to the regional differences of lung cancer motality in Fukuoka Prefecture

Geographic pattern of lung cancer mortality in Fukuoka Prefecture showed elevated mortalities among males in the Chikuho district where many coal-mines had long been operated as one of the biggest coal-mining areas in Japan. The analysis in relations of occupations to lung cancer mortality revealed that consistently significant correlations exist between lung cancer mortality, and mining and quarrying occupations in every census year after World War II. No other occupations showed consistent relations to lung cancer though a few significant correlations were found only in the recent years. The results appear to suggest that elevated risk of lung cancer among coal-mining workers may exist and deserve further analytical study.     

Mapping under-five mortality in the Wenchuan earthquake using hierarchical Bayesian modeling

More than two years after the 2008 earthquake in Wenchuan, China, the total number of lives lost remains unclear, particularly for children under five years old. Mortality for this age group can be estimated using a variety of techniques, but sample proportion estimates may be unreliable in areas with low populations of children under five. To address this problem, we propose a hierarchical Bayesian model to map the distribution of under-five mortality in Wenchuan at the township scale. This model is based on conditional distributions for data conditioned on a spatial process and parameters to capture uncertainties usually identified as either spatially-correlated effects or heterogeneity effects. The method was adapted to obtain reliable estimates of the under-five mortality rate in townships with low under-five populations. The approach was compared to other models and, despite some limitations, was found to outperform other methods in its smoothing effect as well as in exploration of other aspects of spatial patterns.     

Calculating expected mortality

The widely used 'person-years method' of calculating expected mortality has been discussed recently by several authors. In studies where mortality is either lower or higher than the standard mortality of some reference population, the use of exposure to death as an estimator of the expected number of deaths will generally lead to bias, always exaggerating the difference between study and standard mortality. This bias is examined in a proportional hazards model. The recent suggestion by Hartz et al.1 of calculating the mortalities of individuals during their 'potential follow-up time' is claimed to be only rarely feasible in practice.     

广东省翁源县居民恶性肿瘤死亡资料分析

目的了解广东省翁源县恶性肿瘤死亡的流行特征,为防制工作提供科学依据。方法按照全国第3次死因回顾性调查方案,对2004m2005年翁源县主要死因进行回顾性调查,分析主要恶性肿瘤死亡率、标化率及对预期寿命的影响。结果2004--2005年翁源县恶性肿瘤的粗死亡率为141．9／10万,标化率为138．3／10万,死亡数居全死因的第2位;男女标化率分别为183．5／10万和98．0／10万,男性明显高于女性（x2=26．163,P=0．000）,肝癌、胃癌和肺癌的男性标化率也显著性高于女性;且无论在男性和女性还是总体,胃癌的标化率均显著高于广东省农村地区。恶性肿瘤死亡率随年龄增大而增高（x。=3375．0,P=0．000）,75岁～组的死亡率最高。居前5位的恶性肿瘤依次为肝癌、胃癌、肺癌、食道癌和其他恶性肿瘤;对期望寿命影响前3位的是肝癌、胃癌和肺癌。结论翁源县的恶性肿瘤、肝癌、胃癌和肺癌死亡水平男性高于女性,且胃癌显著高于广东省农村地区。因此,恶性肿瘤仍是危害居民健康的常见病和多发病,尤其是胃癌和肝癌等消化系统肿瘤在该地区死亡率较高,应有针对性地开展综合防制工作。