Colon and rectal cancer incidence and water trihalomethane concentrations in New South Wales, Australia.

BACKGROUND: There is evidence, although inconsistent, that long term exposure to disinfection by products (DBPs) increases the risk of bowel cancer. No study has been conducted in Australia to examine this association and due to difference in the methods of disinfection the risk can vary across geographical regions and. This study was conducted to analyse the association of trihalomethanes (THMs) in water with colon and rectal cancer in NSW Australia. METHODS: Average yearly concentrations of total and individual species of THMs were obtained for 50 local government areas (LGAs). Indirectly-standardized incidence rates of colon and rectal cancers in LGAs for the period 1995 to 2001 were regressed against mean THM concentrations lagged five years, adjusting for socioeconomic status, high risk drinking, smoking status, usual source of water and year of diagnosis, including local and global random effects within a Bayesian framework. The incidence rate ratios (IRRs) for an interquartile range (IQR) increase in THMs were estimated. RESULTS: Using five year lag of exposure there was a positive association between bromoform concentration and CRC in men (IRR = 1.025, 95% CI 1.010, 1.040) but not in women (IRR = 1.003, 95% CI 0.987, 1.018). The association in men was mainly found in colon cancer with bromoform (IRR = 1.035, 95% CI 1.017, 1.053). There was no appreciable association of colorectal cancer with other species of THMs. Sensitivity analyses did not materially change the associations observed. CONCLUSION: A positive association was observed between colon cancer and water bromoform concentrations in men. Given the potential population impact of such an association, further research into the relationship between THMs, particularly brominated species, and colorectal cancer is warranted.

Exposure to trihalomethanes in drinking water and small-for-gestational-age births.

BACKGROUND: Trihalomethanes in drinking water have been associated with higher occurrence of small-for-gestational-age (SGA) births, although results have been inconsistent. METHOD: We geocoded residential address for mother of live, singleton, term births to 33 water distribution systems in a large metropolitan area of New South Wales, Australia (314,982 births between 1998 and 2004) and classified births into <10th percentile and ≥ 10 percentile of weight for gestational age. Mean trihalomethane exposure was estimated by trimester and for the entire pregnancy based on monthly sampling in each of the 33 water distribution systems. We estimated the relative risk (RR) of SGA for exposure to trihalomethanes using log-binomial regression adjusting for confounding. RESULTS: SGA births increased with mother's third-trimester exposure to chloroform (RR = 1.04 [95% confidence interval = 1.02-1.06], across an interquartile range [IQR] = 25 µg/L) and bromodichloromethane (1.02 [1.01-1.04], 5 µg/L). Larger associations were found for SGA less than third percentile. Smoking modified the effects of trihalomethane exposure, with generally larger associations in births to nonsmoking mother and weaker or protective associations in births to smoking mothers. CONCLUSIONS: : Mothers' exposures during pregnancy to total trihalomethane as well as to chloroform and bromodichloromethane were associated with SGA. These associations were modified by maternal smoking during pregnancy.

Spatio-temporal variation in trihalomethanes in New South Wales.

AIM: This paper describes the spatio-temporal variation of trihalomethanes in drinking water in New South Wales, Australia from 1997 to 2007 METHOD: We obtained data on trihalomethanes (THMs) from two metropolitan and 13 rural water utilities and conducted a descriptive analysis of the spatial and temporal trends in THMs and the influence of season and drought. RESULTS: Concetrations of monthly THMs in the two metropolitan water utilities of Sydney/Illawarra (mean 66.8 µg/L) and Hunter (mean 62.7 µg/L) were similar compared to the considerable variation between rural water utilities (range in mean THMs: 14.5-330.7 µg/L). Chloroform was the predominate THM in two-thirds of the rural water utilities. Higher concentrations of THMs were found in chlorinated water distribution systems compared to chloraminated systems, and in distribution systems sourced from surface water compared to ground water or mixed surface and ground water. Ground water sourced supplies had a greater proportion of brominated THMs than surface water sourced supplies. There was substantial variation in concentration of THMs between seasons and between periods of drought or no drought. There was a moderate correlation between heavy rainfall and elevated concentrations of THMs. CONCLUSION: There is considerable spatial and temporal variation in THMs amongst New South Wales water utilities and these variations are likely related to water source, treatment processes, catchments, drought and seasonal factors.

Small area estimation of sparse disease counts using shared component models-application to birth defect registry data in New South Wales, Australia.

In the field of disease mapping, little has been done to address the issue of analysing sparse health datasets. We hypothesised that by modelling two outcomes simultaneously, one would be able to better estimate the outcome with a sparse count. We tested this hypothesis utilising Bayesian models, studying both birth defects and caesarean sections using data from two large, linked birth registries in New South Wales from 1990 to 2004. We compared four spatial models across seven birth defects: spina bifida, ventricular septal defect, OS atrial septal defect, patent ductus arteriosus, cleft lip and or palate, trisomy 21 and hypospadias. For three of the birth defects, the shared component model with a zero-inflated Poisson (ZIP) extension performed better than other simpler models, having a lower deviance information criteria (DIC). With spina bifida, the ratio of relative risk associated with the shared component was 2.82 (95% CI: 1.46-5.67). We found that shared component models are potentially beneficial, but only if there is a reasonably strong spatial correlation in effect for the study and referent outcomes.

Socioeconomic and maternal determinants of small-for-gestational age births: patterns of increasing disparity.

OBJECTIVE: To better characterize the relation between socioeconomic disadvantage and small-for-gestational age births (SGA). DESIGN: Analysis of data from a mandatory population-based surveillance system. SETTING: Public or private hospitals and at home. POPULATION: All 877,951 singleton births occurring in New South Wales, Australia, between 1994 and 2004. METHODS: Multilevel models were developed to determine the factors associated with babies weighing less than the 3rd percentile for gestation and gender. MAIN OUTCOME MEASURES: Odds of SGA. RESULTS: The risk of SGA increased with increasing socioeconomic disadvantage. Smoking accounted for approximately 40% of the increased risk associated with socioeconomic disadvantage, and delayed antenatal care approximately 5%. While the absolute rate of SGA remained stable over the study period, the odds of SGA in mothers living in the most disadvantaged areas compared to those in the least disadvantaged areas increased from approximately 1.7 to 2.2. This trend persisted after accounting for maternal smoking. The risk of SGA over this period also increased in mothers commencing antenatal care after the first trimester. After accounting for smoking, socioeconomic disadvantage and clinical conditions, mothers under 21 years of age were at reduced risk of SGA, but mothers over 35 were at increased risk. CONCLUSIONS: Socioeconomic disadvantage remains one of the dominant determinants of SGA, even in a developed country with universal insurance. This relation appears to be strengthening. Smoking patterns, inadequate antenatal care and clinical conditions partially account for this association and trend, however, most is mediated by other factors.

Influence of socioeconomic and cultural factors on rural health.

OBJECTIVE: To provide a framework for investigating the influence of socioeconomic and cultural factors on rural health. DESIGN: Discussion paper. RESULTS: Socioeconomic and cultural factors have long been thought to influence an individual's health. We suggest a framework for characterising these factors that comprises individual-level (e.g. individual socioeconomic status, sex, race) and neighbourhood-level dimensions (population composition, social environment, physical environment) operating both independently and through interaction. Recent spatial research suggests that in rural communities, socioeconomic disadvantage and indigenous status are two of the greatest underlying influences on health status. However, rural communities also face additional challenges associated with access to, and utilisation of, health care. The example is given of procedural angiography for individuals with an acute coronary event. CONCLUSIONS: Socioeconomic and cultural factors specific to rural Australia are key influences on the health of residents. These range from individual-level factors, such as rural stoicism, poverty and substance use norms, to neighbourhood-level social characteristics, such as lack of services, migration out of rural areas of younger community members weakening traditionally high levels of social cohesion, and to environmental factors, such as climate change and access to services.

Socioeconomic disadvantage and acute coronary events: a spatiotemporal analysis.

BACKGROUND: The associations between socioeconomic disadvantage and ischemic heart disease are not well understood. We explore the relationship between socioeconomic factors and acute coronary events using spatiotemporal analysis. METHODS: We studied all deaths from acute myocardial infarction and hospital admissions for acute coronary syndrome and related revascularization procedures for the state of New South Wales, Australia, from 1996 through 2002. We used conditional autoregressive models to describe how characteristics of subjects' place of residence (socioeconomic disadvantage, proportion of the population of indigenous background, and metropolitan versus nonmetropolitan area) influenced admissions and mortality. RESULTS: There were 32,534 deaths due to acute myocardial infarction and 129,045 admissions for acute coronary syndrome. We found a relationship between increasing socioeconomic disadvantage and mortality (unadjusted relative risk for highest quartile of disadvantage relative to lowest = 1.40; 95% confidence interval = 1.27-1.54) as well as admissions (1.41; 1.28-1.55). After accounting for admission rates, socioeconomic disadvantage was associated with lower rates of angiography (0.75; 0.63-0.88) and interventional angiography (0.70; 0.56-0.85). After adjusting for socioeconomic disadvantage, areas with higher proportions of the population identified as indigenous had higher rates of admission and mortality, while residency in the state capital was associated with higher admission rates and more interventional angiography. After accounting for admission rates, the association of socioeconomic disadvantage with mortality was reduced. CONCLUSIONS: Socioeconomic disadvantage increases both the risk of acute coronary syndrome and related mortality. A contributing factor appears to be a reduced chance of receiving appropriate care. Regions with a higher proportion of indigenous residents show risk beyond the effects of general socioeconomic disadvantage, while residents of metropolitan communities had increased utilization of more recent interventions.

Evaluating the effect of neighbourhood weight matrices on smoothing properties of Conditional Autoregressive (CAR) models.

BACKGROUND: The Conditional Autoregressive (CAR) model is widely used in many small-area ecological studies to analyse outcomes measured at an areal level. There has been little evaluation of the influence of different neighbourhood weight matrix structures on the amount of smoothing performed by the CAR model. We examined this issue in detail. METHODS: We created several neighbourhood weight matrices and applied them to a large dataset of births and birth defects in New South Wales (NSW), Australia within 198 Statistical Local Areas. Between the years 1995-2003, there were 17,595 geocoded birth defects and 770,638 geocoded birth records with available data. Spatio-temporal models were developed with data from 1995-2000 and their fit evaluated within the following time period: 2001-2003. RESULTS: We were able to create four adjacency-based weight matrices, seven distance-based weight matrices and one matrix based on similarity in terms of a key covariate (i.e. maternal age). In terms of agreement between observed and predicted relative risks, categorised in epidemiologically relevant groups, generally the distance-based matrices performed better than the adjacency-based neighbourhoods. In terms of recovering the underlying risk structure, the weight-7 model (smoothing by maternal-age 'Covariate model') was able to correctly classify 35/47 high-risk areas (sensitivity 74%) with a specificity of 47%, and the 'Gravity' model had sensitivity and specificity values of 74% and 39% respectively. CONCLUSION: We found considerable differences in the smoothing properties of the CAR model, depending on the type of neighbours specified. This in turn had an effect on the models' ability to recover the observed risk in an area. Prior to risk mapping or ecological modelling, an exploratory analysis of the neighbourhood weight matrix to guide the choice of a suitable weight matrix is recommended. Alternatively, the weight matrix can be chosen a priori based on decision-theoretic considerations including loss, cost and inferential aims.