Clive E. Sabel

Quantifying human exposure to air pollution - moving from static monitoring to spatio-temporally resolved personal exposure assessment

Quantifying human exposure to air pollutants is a challenging task. Ambient concentrations of air pollutants at potentially harmful levels are ubiquitous in urban areas and subject to high spatial and temporal variability. At the same time, every individual has unique activity-patterns. Exposure results from multifaceted relationships and interactions between environmental and human systems, adding complexity to the assessment process. Traditionally, approaches to quantify human exposure have relied on pollutant concentrations from fixed air quality network sites and static population distributions. New developments in sensor technology now enable us to monitor personal exposure to air pollutants directly while people are moving through their activity spaces and varying concentration fields. The literature review on which this paper is based on reflects recent developments in the assessment of human exposure to air pollution. This includes the discussion of methodologies and concepts, and the elaboration of approaches and study designs applied in the field. We identify shortcomings of current approaches and discuss future research needs. We close by proposing a novel conceptual model for the integrated assessment of human exposure to air pollutants taking into account latest technological capabilities and contextual information.

Personal exposure monitoring of PM2.5 in indoor and outdoor microenvironments

Adverse health effects from exposure to air pollution are a global challenge and of widespread concern. Recent high ambient concentration episodes of air pollutants in European cities highlighted the dynamic nature of human exposure and the gaps in data and knowledge about exposure patterns. In order to support health impact assessment it is essential to develop a better understanding of individual exposure pathways in peoples everyday lives by taking account of all environments in which people spend time. Here we describe the development, validation and results of an exposure method applied in a study conducted in Scotland. A low-cost particle counter based on light-scattering technology - the Dylos 1700 was used. Its performance was validated in comparison with equivalent instruments (TEOM-FDMS) at two national monitoring network sites (R(2)=0.9 at a rural background site, R(2)=0.7 at an urban background site). This validation also provided two functions to convert measured PNCs into calculated particle mass concentrations for direct comparison of concentrations with equivalent monitoring instruments and air quality limit values. This study also used contextual and time-based activity data to define six microenvironments (MEs) to assess everyday exposure of individuals to short-term PM2.5 concentrations. The Dylos was combined with a GPS receiver to track movement and exposure of individuals across the MEs. Seventeen volunteers collected 35 profiles. Profiles may have a different overall duration and structure with respect to times spent in different MEs and activities undertaken. Results indicate that due to the substantial variability across and between MEs, it is essential to measure near-complete exposure pathways to allow for a comprehensive assessment of the exposure risk a person encounters on a daily basis. Taking into account the information gained through personal exposure measurements, this work demonstrates the added value of data generated by the application of low-cost monitors.

Spatial clustering of mental disorders and associated characteristics of the neighbourhood context in Malmo, Sweden, in 2001

Study objective: Previous research provides preliminary evidence of spatial variations of mental disorders and associations between neighbourhood social context and mental health. This study expands past literature by (1) using spatial techniques, rather than multilevel models, to compare the spatial distributions of two groups of mental disorders (that is, disorders due to psychoactive substance use, and neurotic, stress related, and somatoform disorders); and (2) investigating the independent impact of contextual deprivation and neighbourhood social disorganisation on mental health, while assessing both the magnitude and the spatial scale of these effects. Design: Using different spatial techniques, the study investigated mental disorders due to psychoactive substance use, and neurotic disorders. Participants: All 89 285 persons aged 40–69 years residing in Malmö, Sweden, in 2001, geolocated to their place of residence. Main results: The spatial scan statistic identified a large cluster of increased prevalence in a similar location for the two mental disorders in the northern part of Malmö. However, hierarchical geostatistical models showed that the two groups of disorders exhibited a different spatial distribution, in terms of both magnitude and spatial scale. Mental disorders due to substance consumption showed larger neighbourhood variations, and varied in space on a larger scale, than neurotic disorders. After adjustment for individual factors, the risk of substance related disorders increased with neighbourhood deprivation and neighbourhood social disorganisation. The risk of neurotic disorders only increased with contextual deprivation. Measuring contextual factors across continuous space, it was found that these associations operated on a local scale. Conclusions: Taking space into account in the analyses permitted deeper insight into the contextual determinants of mental disorders.

MS prevalence in New Zealand, an ethnically and latitudinally diverse country

Background: The prevalence of multiple sclerosis (MS) is not uniform, with a latitudinal gradient of prevalence present in most studies. Understanding the drivers of this gradient may allow a better understanding of the environmental factors involved in MS pathogenesis. Method: The New Zealand national MS prevalence study (NZMSPS) is a cross-sectional study of people with definite MS (DMS) (McDonald criteria 2005) resident in New Zealand on census night, 7 March 2006, utilizing multiple sources of notification. Capture—recapture analysis (CRA) was used to estimate missing cases. Results: Of 2917 people with DMS identified, the crude prevalence was 72.4 per 100,000 population, and 73.1 per 100,000 when age-standardized to the European population. CRA estimated that 96.7% of cases were identified. A latitudinal gradient was seen with MS prevalence increasing three-fold from the North (35°S) to the South (48°S). The gradient was non-uniform; females with relapsing—remitting/secondary-progressive (RRMS/SPMS) disease have a gradient 11 times greater than males with primary-progressive MS (p < 1 × 10-7). DMS was significantly less common among those of Māori ethnicity. Conclusions: This study confirms the presence of a robust latitudinal gradient of MS prevalence in New Zealand. This gradient is largely driven by European females with the RRMS/SPMS phenotype. These results indicate that the environmental factors that underlie the latitudinal gradient act differentially by gender, ethnicity and MS phenotype. A better understanding of these factors may allow more targeted MS therapies aimed at modifiable environmental triggers at the population level.

Beyond greenspace: an ecological study of population general health and indicators of natural environment type and quality

BackgroundMany studies suggest that exposure to natural environments (‘greenspace’) enhances human health and wellbeing. Benefits potentially arise via several mechanisms including stress reduction, opportunity and motivation for physical activity, and reduced air pollution exposure. However, the evidence is mixed and sometimes inconclusive. One explanation may be that “greenspace” is typically treated as a homogenous environment type. However, recent research has revealed that different types and qualities of natural environments may influence health and wellbeing to different extents.MethodsThis ecological study explores this issue further using data on land cover type, bird species richness, water quality and protected or designated status to create small-area environmental indicators across Great Britain. Associations between these indicators and age/sex standardised prevalence of both good and bad health from the 2011 Census were assessed using linear regression models. Models were adjusted for indicators of socio-economic deprivation and rurality, and also investigated effect modification by these contextual characteristics.ResultsPositive associations were observed between good health prevalence and the density of the greenspace types, “broadleaf woodland”, “arable and horticulture”, “improved grassland”, “saltwater” and “coastal”, after adjusting for potential confounders. Inverse associations with bad health prevalence were observed for the same greenspace types, with the exception of “saltwater”. Land cover diversity and density of protected/designated areas were also associated with good and bad health in the predicted manner. Bird species richness (an indicator of local biodiversity) was only associated with good health prevalence. Surface water quality, an indicator of general local environmental condition, was associated with good and bad health prevalence contrary to the manner expected, with poorer water quality associated with better population health. Effect modification by income deprivation and urban/rural status was observed for several of the indicators.ConclusionsThe findings indicate that the type, quality and context of ‘greenspace’ should be considered in the assessment of relationships between greenspace and human health and wellbeing. Opportunities exist to further integrate approaches from ecosystem services and public health perspectives to maximise opportunities to inform policies for health and environmental improvement and protection.

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: conceptualising 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 conceptualisation 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.

The influence of road curvature on fatal crashes in New Zealand.

Bends in roads can cause crashes but a recent study in the UK found that areas with mostly curved roads had lower crash rates than areas with straighter roads. This present study aimed to replicate the previous research in a different country. Variations in the number of fatal road crashes occurring between 1996 and 2005 in 73 territorial local authorities across New Zealand were modelled against possible predictors. The predictors were traffic flow, population counts and characteristics, car use, socio-economic deprivation, climate, altitude and road characteristics including four measures of average road curvature. The best predictors of the number of fatal crashes on urban roads, rural state highways and other rural roads were traffic flow, speed limitation and socio-economic deprivation. Holding significant factors constant, there was no evidence that TLAs with the most curved roads had more crashes than elsewhere. Fatal crashes on urban roads were significantly and negatively related to two measures of road curvature: the ratio of road length to straight distance and the cumulative angle turned per kilometre. Weaker negative associations on rural state highways could have occurred by chance. These results offer limited support to the suggestion that frequently occurring road bends might be protective.

Mortality from amyotrophic lateral sclerosis in Finland, 1986-1995

Objective– To study the possible changes, between 1986 and 1995, in the mortality due to amyotrophic lateral sclerosis (ALS) among Finnish patients. Materials and methods– A total of 1000 deaths from ALS were extracted from the Finnish Death Certificate Register for the study years. General population data were obtained from the Statistical Yearbooks of Finland. Results– From a death rate of 1.54/100,000 in 1986 an increase to 2.27/100,000 in 1995 was observed. Since 1963 the number of ALS deaths has tripled. The documented increased life‐expectancy in Finland correlates with the ALS death rate, at least partly explaining the increase. Contrary to other countries, on the whole equal numbers of men and women died of ALS. Women tended to be older than men when they died of ALS. Conclusion– In accordance with other countries ALS mortality in Finland is steadily increasing.

Modelling individual space–time exposure opportunities: A novel approach to unravelling the genetic or environment disease causation debate

The aetiology of Amyotrophic Lateral Sclerosis (ALS) is uncertain. While around 10% is assumed to be inherited, the relative influence of genetic versus physical or social environmental factors (or some combination of the two) has yet to be determined. A previous study identified significant clustering of ALS at the time of birth in south-east Finland and this could support either a genetic or an environmental hypothesis. We know that south-east Finland is an environmentally degraded area, but the population in this region may also be genetically susceptible to this condition. We therefore extend this research by comparing the lifetime residential histories of 1000 ALS cases and 1000 controls matched by birth date, sex and municipality of birth. By focusing on those who originated in the south-east, and comparing the subsequent residential mobility of these two groups, we test whether remaining in south-east Finland is more common among cases than controls and, hence, whether there may be an environmental or genetic influence on ALS associated with that region. Our results indeed suggest that the cases were more likely to remain in south-east Finland after birth, compared to the geographically matched controls. This suggests that moving away is protective, and points towards a risk factor after birth being implicated in the aetiology of the disease.

Long-term trend and spatial pattern of PM2.5 induced premature mortality in China

With rapid economic growth, China has witnessed increasingly frequent and severe haze and smog episodes over the past decade, posing serious health impacts to the Chinese population, especially those in densely populated city clusters. Quantification of the spatial and temporal variation of health impacts attributable to ambient fine particulate matter (PM2.5) has important implications for Chinas policies on air pollution control. In this study, we evaluated the spatial distribution of premature deaths in China between 2000 and 2010 attributable to ambient PM2.5 in accord with the Global Burden of Disease based on a high resolution population density map of China, satellite retrieved PM2.5 concentrations, and provincial health data. Our results suggest that Chinas anthropogenic ambient PM2.5 led to 1,255,400 premature deaths in 2010, 42% higher than the level in 2000. Besides increased PM2.5 concentration, rapid urbanization has attracted large population migration into the more developed eastern coastal urban areas, intensifying the overall health impact. In addition, our analysis implies that health burdens were exacerbated in some developing inner provinces with high population density (e.g. Henan, Anhui, Sichuan) because of the relocation of more polluting and resource-intensive industries into these regions. In order to avoid such national level environmental inequities, Chinas regulations on PM2.5 should not be loosened in inner provinces. Furthermore policies should create incentive mechanisms that can promote transfer of advanced production and emissions control technologies from the coastal regions to the interior regions.