Assessing the human health impacts of exposure to disinfection by-products--a critical review of concepts and methods.

Understanding the public health implications of chemical contamination of drinking water is important for societies and their decision-makers. The possible population health impacts associated with exposure to disinfection by-products (DBPs) are of particular interest due to their potential carcinogenicity and their widespread occurrence as a result of treatments employed to control waterborne infectious disease. We searched the literature for studies that have attempted quantitatively to assess population health impacts and health risks associated with exposure to DBPs in drinking water. We summarised and evaluated these assessments in terms of their objectives, methods, treatment of uncertainties, and interpretation and communication of results. In total we identified 40 studies matching our search criteria. The vast majority of studies presented estimates of generic cancer and non-cancer risks based on toxicological data and methods that were designed with regulatory, health-protective purposes in mind, and therefore presented imprecise and biased estimates of health impacts. Many studies insufficiently addressed the numerous challenges to DBP risk assessment, failing to evaluate the evidence for a causal relationship, not appropriately addressing the complex nature of DBP occurrence as a mixture of chemicals, not adequately characterising exposure in space and time, not defining specific health outcomes, not accounting for characteristics of target populations, and not balancing potential risks of DBPs against the health benefits related with drinking water disinfection. Uncertainties were often poorly explained or insufficiently accounted for, and important limitations of data and methods frequently not discussed. Grave conceptual and methodological limitations in study design, as well as erroneous use of available dose-response data, seriously impede the extent to which many of these assessments contribute to understanding the public health implications of exposure to DBPs. In some cases, assessment results may cause unwarranted alarm among the public and potentially lead to poor decisions being made in sourcing, treatment, and provision of drinking water. We recommend that the assessment of public health impacts of DBPs should be viewed as a means of answering real world policy questions relating to drinking water quality, including microbial contaminants; that they should be conducted using the most appropriate and up-to-date data and methods, and that associated uncertainties and limitations should be accounted for using quantitative methods where appropriate.

Diagram-based Analysis of Causal Systems (DACS): elucidating inter-relationships between determinants of acute lower respiratory infections among children in sub-Saharan Africa.

BACKGROUND: Effective interventions require evidence on how individual causal pathways jointly determine disease. Based on the concept of systems epidemiology, this paper develops Diagram-based Analysis of Causal Systems (DACS) as an approach to analyze complex systems, and applies it by examining the contributions of proximal and distal determinants of childhood acute lower respiratory infections (ALRI) in sub-Saharan Africa. RESULTS: Diagram-based Analysis of Causal Systems combines the use of causal diagrams with multiple routinely available data sources, using a variety of statistical techniques. In a step-by-step process, the causal diagram evolves from conceptual based on a priori knowledge and assumptions, through operational informed by data availability which then undergoes empirical testing, to integrated which synthesizes information from multiple datasets. In our application, we apply different regression techniques to Demographic and Health Survey (DHS) datasets for Benin, Ethiopia, Kenya and Namibia and a pooled World Health Survey (WHS) dataset for sixteen African countries. Explicit strategies are employed to make decisions transparent about the inclusion/omission of arrows, the sign and strength of the relationships and homogeneity/heterogeneity across settings.Findings about the current state of evidence on the complex web of socio-economic, environmental, behavioral and healthcare factors influencing childhood ALRI, based on DHS and WHS data, are summarized in an integrated causal diagram. Notably, solid fuel use is structured by socio-economic factors and increases the risk of childhood ALRI mortality. CONCLUSIONS: Diagram-based Analysis of Causal Systems is a means of organizing the current state of knowledge about a specific area of research, and a framework for integrating statistical analyses across a whole system. This partly a priori approach is explicit about causal assumptions guiding the analysis and about researcher judgment, and wrong assumptions can be reversed following empirical testing. This approach is well-suited to dealing with complex systems, in particular where data are scarce.

Adult cancers near high-voltage overhead power lines.

BACKGROUND: Extremely low-frequency magnetic fields are designated as possibly carcinogenic in humans, based on an epidemiologic association with childhood leukemia. Evidence for associations with adult cancers is weaker and inconsistent. METHODS: We conducted a case-control study to investigate risks of adult cancers in relation to distance and extremely low-frequency magnetic fields from high-voltage overhead power lines using National Cancer Registry Data in England and Wales, 1974-2008. The study included 7823 leukemia, 6781 brain/central nervous system cancers, 9153 malignant melanoma, 29,202 female breast cancer cases, and 79,507 controls frequency-matched on year and region (three controls per case except for female breast cancer, one control per case) 15-74 years of age living within 1000 m of a high-voltage overhead power line. RESULTS: There were no clear patterns of excess risk with distance from power lines. After adjustment for confounders (age, sex [except breast cancer], deprivation, rurality), for distances closest to the power lines (0-49 m) compared with distances 600-1000 m, odds ratios (ORs) ranged from 0.82 (95% confidence interval = 0.61-1.11; 66 cases) for malignant melanoma to 1.22 (0.88-1.69) for brain/central nervous system cancer. We observed no meaningful excess risks and no trends of risk with magnetic field strength for the four cancers examined. In adjusted analyses at the highest estimated field strength, ≥1000 nanotesla (nT), compared with <100 nT, ORs ranged from 0.68 (0.39-1.17) for malignant melanoma to 1.08 (0.77-1.51) for female breast cancer. CONCLUSION: Our results do not support an epidemiologic association of adult cancers with residential magnetic fields in proximity to high-voltage overhead power lines.

Delimiting affinity zones as a basis for air pollution mapping in Europe.

Affinity zones are defined as areas within which air quality displays consistent behaviour over space and time. Constructed using multivariate statistical techniques and physiographic and landscape variables reflecting underlying sources and spatial patterns of air pollution, affinity zones provide a spatial structure suited to exploring the representativity of monitoring networks and as a basis for air pollution mapping and exposure assessment. The affinity zone method is demonstrated using European air pollution monitoring sites, and environmental data compiled within a 1 km GIS. Organised into three main stages, this method involves: (i) indicator selection, using principal components analysis, (ii) zonation by cluster analysis to classify areas into distinct types, and (iii) site allocation, to confirm similarity within affinity zones in terms of monitored air pollution concentrations. Ten interpretable and coherent air pollution affinity zones were constructed for Europe, including two rural zones and eight related to different types of densely populated and built up environments. Concentrations between affinity zones differed significantly for NO(2) background and traffic sites and for PM(10) traffic sites only. Not all zones, however, were found to be sufficiently represented by monitoring sites, illustrating the importance of affinity zones in identifying deficiencies in monitoring networks. Spatial modelling within affinity zones is also demonstrated, showing that simple kriging of background NO(2) concentrations within zones (compared to kriging ignoring zones) produced a ca. 22% reduction in errors and increased R(2) by 0.25 at reserved validation monitoring sites. The affinity zone method developed here is a robust, statistical approach that can be used for evaluating the representativity of routine monitoring networks often used in continental level environmental and health risk assessments.

Traffic air pollution and other risk factors for respiratory illness in schoolchildren in the niger-delta region of Nigeria.

BACKGROUND: Association of childhood respiratory illness with traffic air pollution has been investigated largely in developed but not in developing countries, where pollution levels are often very high. OBJECTIVES: In this study we investigated associations between respiratory health and outdoor and indoor air pollution in schoolchildren 7-14 years of age in low socioeconomic status areas in the Niger Delta. METHODS: A cross-sectional survey was carried out among 1,397 schoolchildren. Exposure to home outdoor and indoor air pollution was assessed by self-report questionnaire. School air pollution exposures were assessed using traffic counts, distance of schools to major streets, and particulate matter and carbon monoxide measurements, combined using principal components analysis. Hierarchical logistic regression was used to examine associations with reported respiratory health, adjusting for potential confounders. RESULTS: Traffic disturbance at home (i.e., traffic noise and/or fumes evident inside the home vs. none) was associated with wheeze [odds ratio (OR) = 2.16; 95% confidence interval (CI), 1.28-3.64], night cough (OR = 1.37; 95% CI, 1.03-1.82), phlegm (OR = 1.49; 95% CI, 1.09-2.04), and nose symptoms (OR = 1.40; 95% CI, 1.03-1.90), whereas school exposure to a component variable indicating exposure to fine particles was associated with increased phlegm (OR = 1.38; 95% CI, 1.09-1.75). Nonsignificant positive associations were found between cooking with wood/coal (OR = 2.99; 95% CI, 0.88-10.18) or kerosene (OR = 2.83; 95% CI, 0.85-9.44) and phlegm compared with cooking with gas. CONCLUSION: Traffic pollution is associated with respiratory symptoms in schoolchildren in a deprived area of western Africa. Associations may have been underestimated because of nondifferential misclassification resulting from limitations in exposure measurement.

Bayesian modelling of household solid fuel use: insights towards designing effective interventions to promote fuel switching in Africa.

Indoor air pollution from solid fuel use is a significant risk factor for acute lower respiratory infections among children in sub-Saharan Africa. Interventions that promote a switch to modern fuels hold a large health promise, but their effective design and implementation require an understanding of the web of upstream and proximal determinants of household fuel use. Using Demographic and Health Survey data for Benin, Kenya and Ethiopia together with Bayesian hierarchical and spatial modelling, this paper quantifies the impact of household-level factors on cooking fuel choice, assesses variation between communities and districts and discusses the likely nature of contextual effects. Household- and area-level characteristics appear to interact as determinants of cooking fuel choice. In all three countries, wealth and the educational attainment of women and men emerge as important; the nature of area-level factors varies between countries. In Benin, a two-level model with spatial community random effects best explains the data, pointing to an environmental explanation. In Ethiopia and Kenya, a three-level model with unstructured community and district random effects is selected, implying relatively autonomous economic and social areas. Area-level heterogeneity, indicated by large median odds ratios, appears to be responsible for a greater share of variation in the data than household-level factors. This may be an indication that fuel choice is to a considerable extent supply-driven rather than demand-driven. Consequently, interventions to promote fuel switching will carefully need to assess supply-side limitations and devise appropriate policy and programmatic approaches to overcome them. To our knowledge, this paper represents the first attempt to model the determinants of solid fuel use, highlighting socio-economic differences between households and, notably, the dramatic influence of contextual effects. It illustrates the potential that multilevel and spatial modelling approaches hold for understanding determinants of major public health problems in the developing world.

Assessment of complex environmental health problems: framing the structures and structuring the frameworks.

Many environmental risks are multi-faceted and their health consequences can be far-ranging in both time and space. It can be a challenging task to develop informed policies for such risks. Integrated environmental health impact assessment aims to support policy by assessing environmental health effects in ways that take into account the complexities and uncertainties involved. For such assessment to be successful, a clear and agreed conceptual framework is needed, which defines the issue under consideration and sets out the principles on which the assessment is based. Conceptual frameworks facilitate involvement of stakeholders, support harmonized discussions, help to make assumptions explicit, and provide a framework for data analysis and interpretation. Various conceptual frameworks have been developed for different purposes, but as yet no clear taxonomy exists. We propose a three-level taxonomy of conceptual frameworks for use in environmental health impact assessment. At the first level of the taxonomy, structural frameworks show the wide context of the issues at hand. At the second level, relational frameworks describe how the assessment variables are causally related. At the third level, this causal structure is translated into an operational model, which serves as a basis for analysis. The different types of frameworks are complementary and all play a role in the assessment process. The taxonomy is illustrated using a hypothetical assessment of urban brownfield development for residential uses. We suggest that a better understanding of types of conceptual frameworks and their potential roles in the different phases of assessment will contribute to more informed assessments and policies.

Exposure to disinfection by-products, fetal growth, and prematurity: a systematic review and meta-analysis.

BACKGROUND: Exposure to total trihalomethanes in drinking water has been associated with several adverse birth outcomes relating to fetal growth and prematurity. METHODS: We carried out a systematic review and meta-analysis of epidemiologic studies featuring original peer-reviewed data on the association of total trihalomethane exposure and health outcomes related to fetal growth and prematurity. RESULTS: A comprehensive literature search yielded 37 studies, 15 of which were selected for the extraction of relative risks relating adverse birth outcomes to trihalomethane exposure. Sufficient data were available for meta-analyses to be carried out for 4 adverse birth outcomes: low birth weight (LBW), term low birth weight (term LBW), preterm delivery, and small for gestational age (SGA) (including intra uterine growth retardation). We found little or no evidence for associations between third trimester trihalomethane exposure and LBW (odds ratio per 10 microg total trihalomethane/L = 1.00 [95% confidence interval = 0.97-1.03]), term LBW (1.03 [0.93-1.15]), or preterm delivery (0.99 [0.98-1.00]), but some evidence for SGA (1.01 [1.00-1.02]). CONCLUSIONS: There was little or no evidence for associations between total trihalomethane concentration and adverse birth outcomes relating to fetal growth and prematurity, with the possible exception of SGA. We discuss these findings and the uncertainties-relating particularly to exposure-that may have affected them.

Mapping of background air pollution at a fine spatial scale across the European Union.

BACKGROUND: There is a need to understand much more about the geographic variation of air pollutants. This requires the ability to extrapolate from monitoring stations to unsampled locations. The aim was to assess methods to develop accurate and high resolution maps of background air pollution across the EU. METHODS: We compared the validity of ordinary kriging, universal kriging and regression mapping in developing EU-wide maps of air pollution on a 1x1 km resolution. Predictions were made for the year 2001 for nitrogen dioxide (NO(2)), fine particles <10 microm (PM(10)), ozone (O(3)), sulphur dioxide (SO(2)) and carbon monoxide (CO) using routine monitoring data in Airbase. Predictor variables from EU-wide databases were land use, road traffic, population density, meteorology, altitude, topography and distance to sea. Models were developed for the global, rural and urban scale separately. The best method to model concentrations was selected on the basis of predefined performance measures (R(2), Root Mean Square Error (RMSE)). RESULTS: For NO(2), PM(10) and O(3) universal kriging performed better than regression mapping and ordinary kriging. Validation of the final universal kriging estimates with results from all validation sites gave R(2)-values and RMSE-values of 0.61 and 6.73 microg/m(3) for NO(2); 0.45 and 5.19 microg/m(3) for PM(10); and 0.70 and 7.69 microg/m(3) for O(3). For SO(2) and CO none of the three methods was able to provide a satisfactory prediction. CONCLUSION: Reasonable prediction models were developed for NO(2), PM(10) and O(3) on an EU-wide scale. Our study illustrates that it is possible to develop detailed maps of background air pollution using EU-wide databases.

Uncertainty in epidemiology and health risk and impact assessment.

Environmental epidemiology and health risk and impact assessment have long grappled with problems of uncertainty in data and their relationships. These uncertainties have become more challenging because of the complex, systemic nature of many of the risks. A clear framework defining and quantifying uncertainty is needed. Three dimensions characterise uncertainty: its nature, its location and its level. In terms of its nature, uncertainty can be both intrinsic and extrinsic. The former reflects the effects of complexity, sparseness and nonlinearity; the latter arises through inadequacies in available observational data, measurement methods, sampling regimes and models. Uncertainty occurs in three locations: conceptualizing the problem, analysis and communicating the results. Most attention has been devoted to characterising and quantifying the analysis--a wide range of statistical methods has been developed to estimate analytical uncertainties and model their propagation through the analysis. In complex systemic risks, larger uncertainties may be associated with conceptualization of the problem and communication of the analytical results, both of which depend on the perspective and viewpoint of the observer. These imply using more participatory approaches to investigation, and more qualitative measures of uncertainty, not only to define uncertainty more inclusively and completely, but also to help those involved better understand the nature of the uncertainties and their practical implications.

A framework for integrated environmental health impact assessment of systemic risks.

Traditional methods of risk assessment have provided good service in support of policy, mainly in relation to standard setting and regulation of hazardous chemicals or practices. In recent years, however, it has become apparent that many of the risks facing society are systemic in nature - complex risks, set within wider social, economic and environmental contexts. Reflecting this, policy-making too has become more wide-ranging in scope, more collaborative and more precautionary in approach. In order to inform such policies, more integrated methods of assessment are needed. Based on work undertaken in two large EU-funded projects (INTARESE and HEIMTSA), this paper reviews the range of approaches to assessment now in used, proposes a framework for integrated environmental health impact assessment (both as a basis for bringing together and choosing between different methods of assessment, and extending these to more complex problems), and discusses some of the challenges involved in conducting integrated assessments to support policy. Integrated environmental health impact assessment is defined as a means of assessing health-related problems deriving from the environment, and health-related impacts of policies and other interventions that affect the environment, in ways that take account of the complexities, interdependencies and uncertainties of the real world. As such, it depends heavily on how issues are selected and framed, and implies the involvement of stakeholders both in issue-framing and design of the assessment, and to help interpret and evaluate the results. It is also a comparative process, which involves evaluating and comparing different scenarios. It consequently requires the ability to model the way in which the influences of exogenous factors, such as policies or other interventions, feed through the environment to affect health. Major challenges thus arise. Chief amongst these are the difficulties in ensuring effective stakeholder participation, in dealing with the multicausal and non-linear nature of many of the relationships between environment and health, and in taking account of adaptive and behavioural changes that characterise the systems concerned.

Effects of travel mode on exposures to particulate air pollution.

Monitoring was carried out of particulate concentrations whilst simultaneously walking and driving 48 routes in London, UK. Monitoring was undertaken during May and June 2005. Route lengths ranged from 601 to 1351 m, and most routes were travelled in both directions. Individual journey times ranged from 1.5 to 15 min by car (average 3.7 min) and 7.3 to 30 min (average 12.8 min) whilst walking; car trips were therefore repeated up to 5 times for each single walking trip and the results averaged for the route. Car trips were made with windows closed and the ventilation system on a moderate setting. Results show that mean exposures while walking are greatly in excess of those while driving, by a factor 4.7 for the coarse particle mass (PM10-PM2.5), 2.2 for the fine particle mass (PM2.5-PM1), 1.9 for the very fine particle mass (

Long-term associations of outdoor air pollution with mortality in Great Britain.

BACKGROUND: Recent studies have indicated long-term effects on mortality of particulate and sulphur dioxide (SO(2)) pollution, but uncertainties remain over the size of any effects, potential latency and generalisability. METHODS: A small area study was performed across electoral wards in Great Britain of mean annual black smoke (BS) and SO(2) concentrations (from 1966) and subsequent all-cause and cause-specific mortality using random effect models within a Bayesian framework adjusted for social deprivation and urban/rural classification. Different latencies and changes in associations over time were assessed. RESULTS: Significant associations were found between BS and SO(2) concentrations and mortality. The effects were stronger for respiratory illness than other causes of mortality for the most recent exposure periods (shorter latency times) and most recent mortality period (lower pollutant concentrations). In pooled analysis across four sequential 4 year mortality periods (1982-98), adjusted excess relative risk for respiratory mortality was 3.6% (95% CI 2.6% to 4.5%) per 10 microg/m(3) BS and 13.2% (95% CI 11.5% to 14.9%) per 10 ppb SO(2), and in the most recent period (1994-8) it was 19.3% (95% CI 5.1% to 35.7%) and 21.7% (95% CI 2.9% to 38.5%), respectively. CONCLUSIONS: These findings add to the evidence that air pollution has long-term effects on mortality and point to continuing public health risks even at the relatively lower levels of BS and SO(2) that now occur. They therefore have importance for policies on public health protection through regulation and control of air pollution.

Time-space modeling of journey-time exposure to traffic-related air pollution using GIS.

Journey-time exposures represent an important, though as yet little-studied, component of human exposure to traffic-related air pollution, potentially with important health effects. Methods for assessing journey-time exposures, either as part of epidemiological studies or for policy assessment, are, however, poorly developed. This paper describes the development and testing of a GIS-based system for modeling human journey-time exposures to traffic-related air pollution: STEMS (Space-Time Exposure Modeling System). The model integrates data on source activity, pollutant dispersion, and travel behavior to derive individual- or group-level exposure measures to atmospheric pollution. The model, which is designed to simulate exposures of people as they move through a changing air pollution field, was developed, validated, and trialed in Northampton, UK. The system currently uses ArcInfo to couple four separate submodels: a source activity/emission model (SATURN), a proprietary atmospheric dispersion model (ADMS-Urban), an empirically derived background air pollution model, and a purposely designed time-activity-based exposure model (TOTEM). This paper describes the structure of the modeling system; presents results of field calibration, validation, and sensitivity analysis; and illustrates the use of the model to analyze journey-time exposures of schoolchildren.