Air pollution exposure estimation using dispersion modelling and continuous monitoring data in a prospective birth cohort study in the Netherlands

Background

High temperature and humidity conditions are associated with short-term elevations in the mortality rate in many United States cities. Previous research has quantified this relationship in an aggregate manner over large metropolitan areas, but within these areas the response may differ based on local-scale variability in climate, population characteristics, and socio-economic factors.

Methods

We compared the mortality response for 48 Zip Code Tabulation Areas (ZCTAs) comprising Philadelphia County, PA to determine if certain areas are associated with elevated risk during high heat stress conditions. A randomization test was used to identify mortality exceedances for various apparent temperature thresholds at both the city and local scale. We then sought to identify the environmental, demographic, and social factors associated with high-risk areas via principal components regression.

Results

Citywide mortality increases by 9.3% on days following those with apparent temperatures over 34°C observed at 7:00 p.m. local time. During these conditions, elevated mortality rates were found for 10 of the 48 ZCTAs concentrated in the west-central portion of the County. Factors related to high heat mortality risk included proximity to locally high surface temperatures, low socioeconomic status, high density residential zoning, and age.

Conclusions

Within the larger Philadelphia metropolitan area, there exists statistically significant fine-scale spatial variability in the mortality response to high apparent temperatures. Future heat warning systems and mitigation and intervention measures could target these high risk areas to reduce the burden of extreme weather on summertime morbidity and mortality.     

Gardening in the desert: a spatial optimization approach to locating gardens in rapidly expanding urban environments

Background

Food access is a global issue, and for this reason, a wealth of studies are dedicated to understanding the location of food deserts and the benefits of urban gardens. However, few studies have linked these two strands of research together to analyze whether urban gardening activity may be a step forward in addressing issues of access for food desert residents.

Methods

The Phoenix, Arizona metropolitan area is used as a case to demonstrate the utility of spatial optimization models for siting urban gardens near food deserts and on vacant land. The locations of urban gardens are derived from a list obtained from the Maricopa County Cooperative Extension office at the University of Arizona which were geo located and aggregated to Census tracts. Census tracts were then assigned to one of three categories: tracts that contain a garden, tracts that are immediately adjacent to a tract with a garden, and all other non-garden/non-adjacent census tracts. Analysis of variance is first used to ascertain whether there are statistical differences in the demographic, socio-economic, and land use profiles of these three categories of tracts. A maximal covering spatial optimization model is then used to identify potential locations for future gardening activities. A constraint of these models is that gardens be located on vacant land, which is a growing problem in rapidly urbanizing environments worldwide.

Results

The spatial analysis of garden locations reveals that they are centrally located in tracts with good food access. Thus, the current distribution of gardens does not provide an alternative food source to occupants of food deserts. The maximal covering spatial optimization model reveals that gardens could be sited in alternative locations to better serve food desert residents. In fact, 53 gardens may be located to cover 96.4% of all food deserts. This is an improvement over the current distribution of gardens where 68 active garden sites provide coverage to a scant 8.4% of food desert residents.

Conclusion

People in rapidly urbanizing environments around the globe suffer from poor food access. Rapid rates of urbanization also present an unused vacant land problem in cities around the globe. This paper highlights how spatial optimization models can be used to improve healthy food access for food desert residents, which is a critical first step in ameliorating the health problems associated with lack of healthy food access including heart disease and obesity.

     

A generic method for improving the spatial interoperability of medical and ecological databases

Background

The availability of big data in healthcare and the intensive development of data reuse and georeferencing have opened up perspectives for health spatial analysis. However, fine-scale spatial studies of ecological and medical databases are limited by the change of support problem and thus a lack of spatial unit interoperability. The use of spatial disaggregation methods to solve this problem introduces errors into the spatial estimations. Here, we present a generic, two-step method for merging medical and ecological databases that avoids the use of spatial disaggregation methods, while maximizing the spatial resolution.

Methods

Firstly, a mapping table is created after one or more transition matrices have been defined. The latter link the spatial units of the original databases to the spatial units of the final database. Secondly, the mapping table is validated by (1) comparing the covariates contained in the two original databases, and (2) checking the spatial validity with a spatial continuity criterion and a spatial resolution index.

Results

We used our novel method to merge a medical database (the French national diagnosis-related group database, containing 5644 spatial units) with an ecological database (produced by the French National Institute of Statistics and Economic Studies, and containing with 36,594 spatial units). The mapping table yielded 5632 final spatial units. The mapping table’s validity was evaluated by comparing the number of births in the medical database and the ecological databases in each final spatial unit. The median [interquartile range] relative difference was 2.3% [0; 5.7]. The spatial continuity criterion was low (2.4%), and the spatial resolution index was greater than for most French administrative areas.

Conclusions

Our innovative approach improves interoperability between medical and ecological databases and facilitates fine-scale spatial analyses. We have shown that disaggregation models and large aggregation techniques are not necessarily the best ways to tackle the change of support problem.

     

Control of asthma triggers in indoor air with air cleaners: a modeling analysis

Background

There is a growing body of literature linking GIS-based measures of traffic density to asthma and other respiratory outcomes. However, no consensus exists on which traffic indicators best capture variability in different pollutants or within different settings. As part of a study on childhood asthma etiology, we examined variability in outdoor concentrations of multiple traffic-related air pollutants within urban communities, using a range of GIS-based predictors and land use regression techniques.

Methods

We measured fine particulate matter (PM_2.5), nitrogen dioxide (NO₂), and elemental carbon (EC) outside 44 homes representing a range of traffic densities and neighborhoods across Boston, Massachusetts and nearby communities. Multiple three to four-day average samples were collected at each home during winters and summers from 2003 to 2005. Traffic indicators were derived using Massachusetts Highway Department data and direct traffic counts. Multivariate regression analyses were performed separately for each pollutant, using traffic indicators, land use, meteorology, site characteristics, and central site concentrations.

Results

PM_2.5 was strongly associated with the central site monitor (R² = 0.68). Additional variability was explained by total roadway length within 100 m of the home, smoking or grilling near the monitor, and block-group population density (R² = 0.76). EC showed greater spatial variability, especially during winter months, and was predicted by roadway length within 200 m of the home. The influence of traffic was greater under low wind speed conditions, and concentrations were lower during summer (R² = 0.52). NO₂ showed significant spatial variability, predicted by population density and roadway length within 50 m of the home, modified by site characteristics (obstruction), and with higher concentrations during summer (R² = 0.56).

Conclusion

Each pollutant examined displayed somewhat different spatial patterns within urban neighborhoods, and were differently related to local traffic and meteorology. Our results indicate a need for multi-pollutant exposure modeling to disentangle causal agents in epidemiological studies, and further investigation of site-specific and meteorological modification of the traffic-concentration relationship in urban neighborhoods.     

Modeling spatial patterns of traffic-related air pollutants in complex urban terrain

Background

The relationship between traffic emissions and mobile-source air pollutant concentrations is highly variable over space and time and therefore difficult to model accurately, especially in urban settings with complex terrain. Regression-based approaches using continuous real-time mobile measurements may be able to characterize spatiotemporal variability in traffic-related pollutant concentrations but require methods to incorporate temporally varying meteorology and source strength in a physically interpretable fashion.

Objective

We developed a statistical model to assess the joint impact of both meteorology and traffic on measured concentrations of mobile-source air pollutants over space and time.

Methods

In this study, traffic-related air pollutants were continuously measured in the Williamsburg neighborhood of Brooklyn, New York (USA), which is affected by traffic on a large bridge and major highway. One-minute average concentrations of ultrafine particulate matter (UFP), fine particulate matter [≤ 2.5 μm in aerodynamic diameter (PM_2.5)], and particle-bound polycyclic aromatic hydrocarbons were measured using a mobile-monitoring protocol. Regression modeling approaches to quantify the influence of meteorology, traffic volume, and proximity to major roadways on pollutant concentrations were used. These models incorporated techniques to capture spatial variability, long- and short-term temporal trends, and multiple sources.

Results

We observed spatial heterogeneity of both UFP and PM_2.5 concentrations. A variety of statistical methods consistently found a 15–20% decrease in UFP concentrations within the first 100 m from each of the two major roadways. For PM_2.5, temporal variability dominated spatial variability, but we observed a consistent linear decrease in concentrations from the roadways.

Conclusions

The combination of mobile monitoring and regression analysis was able to quantify local source contributions relative to background while accounting for physically interpretable parameters. Our results provide insight into urban exposure gradients.     

Geographical, temporal and racial disparities in late-stage prostate cancer incidence across Florida: A multiscale joinpoint regression analysis

Background

Although prostate cancer-related incidence and mortality have declined recently, striking racial/ethnic differences persist in the United States. Visualizing and modelling temporal trends of prostate cancer late-stage incidence, and how they vary according to geographic locations and race, should help explaining such disparities. Joinpoint regression is increasingly used to identify the timing and extent of changes in time series of health outcomes. Yet, most analyses of temporal trends are aspatial and conducted at the national level or for a single cancer registry.

Methods

Time series (1981-2007) of annual proportions of prostate cancer late-stage cases were analyzed for non-Hispanic Whites and non-Hispanic Blacks in each county of Florida. Noise in the data was first filtered by binomial kriging and results were modelled using joinpoint regression. A similar analysis was also conducted at the state level and for groups of metropolitan and non-metropolitan counties. Significant racial differences were detected using tests of parallelism and coincidence of time trends. A new disparity statistic was introduced to measure spatial and temporal changes in the frequency of racial disparities.

Results

State-level percentage of late-stage diagnosis decreased 50% since 1981; a decline that accelerated in the 90's when Prostate Specific Antigen (PSA) screening was introduced. Analysis at the metropolitan and non-metropolitan levels revealed that the frequency of late-stage diagnosis increased recently in urban areas, and this trend was significant for white males. The annual rate of decrease in late-stage diagnosis and the onset years for significant declines varied greatly among counties and racial groups. Most counties with non-significant average annual percent change (AAPC) were located in the Florida Panhandle for white males, whereas they clustered in South-eastern Florida for black males. The new disparity statistic indicated that the spatial extent of racial disparities reached a peak in 1990 because of an early decline in frequency of late-stage diagnosis observed for black males.

Conclusions

Analyzing temporal trends in cancer incidence and mortality rates outside a spatial framework is unsatisfactory, since it leads one to overlook significant geographical variation which can potentially generate new insights about the impact of various interventions. Differences observed among nested geographies in Florida show how the modifiable areal unit problem (MAUP) also impacts the analysis of temporal changes.     

Urban and transport planning,environmental exposures and health-new concepts,methods and tools to improve health in cities

Background

The majority of people live in cities and urbanization is continuing worldwide. Cities have long been known to be society’s predominant engine of innovation and wealth creation, yet they are also a main source of pollution and disease.

Methods

We conducted a review around the topic urban and transport planning, environmental exposures and health and describe the findings.

Results

Within cities there is considerable variation in the levels of environmental exposures such as air pollution, noise, temperature and green space. Emerging evidence suggests that urban and transport planning indicators such as road network, distance to major roads, and traffic density, household density, industry and natural and green space explain a large proportion of the variability. Personal behavior including mobility adds further variability to personal exposures, determines variability in green space and UV exposure, and can provide increased levels of physical activity.Air pollution, noise and temperature have been associated with adverse health effects including increased morbidity and premature mortality, UV and green space with both positive and negative health effects and physical activity with many health benefits. In many cities there is still scope for further improvement in environmental quality through targeted policies. Making cities ‘green and healthy’ goes far beyond simply reducing CO2 emissions. Environmental factors are highly modifiable, and environmental interventions at the community level, such as urban and transport planning, have been shown to be promising and more cost effective than interventions at the individual level. However, the urban environment is a complex interlinked system.Decision-makers need not only better data on the complexity of factors in environmental and developmental processes affecting human health, but also enhanced understanding of the linkages to be able to know at which level to target their actions. New research tools, methods and paradigms such as geographical information systems, smartphones, and other GPS devices, small sensors to measure environmental exposures, remote sensing and the exposome paradigm together with citizens observatories and science and health impact assessment can now provide this information.

Conclusion

While in cities there are often silos of urban planning, mobility and transport, parks and green space, environmental department, (public) health department that do not work together well enough, multi-sectorial approaches are needed to tackle the environmental problems. The city of the future needs to be a green city, a social city, an active city, a healthy city.

     

A Unified Spatiotemporal Modeling Approach for Predicting Concentrations of Multiple Air Pollutants in the Multi-Ethnic Study of Atherosclerosis and Air Pollution

Background:

Cohort studies of the relationship between air pollution exposure and chronic health effects require predictions of exposure over long periods of time.

Objectives:

We developed a unified modeling approach for predicting fine particulate matter, nitrogen dioxide, oxides of nitrogen, and black carbon (as measured by light absorption coefficient) in six U.S. metropolitan regions from 1999 through early 2012 as part of the Multi-Ethnic Study of Atherosclerosis and Air Pollution (MESA Air).

Methods:

We obtained monitoring data from regulatory networks and supplemented those data with study-specific measurements collected from MESA Air community locations and participants’ homes. In each region, we applied a spatiotemporal model that included a long-term spatial mean, time trends with spatially varying coefficients, and a spatiotemporal residual. The mean structure was derived from a large set of geographic covariates that was reduced using partial least-squares regression. We estimated time trends from observed time series and used spatial smoothing methods to borrow strength between observations.

Results:

Prediction accuracy was high for most models, with cross-validation R² (R²_CV) > 0.80 at regulatory and fixed sites for most regions and pollutants. At home sites, overall R²_CV ranged from 0.45 to 0.92, and temporally adjusted R²_CV ranged from 0.23 to 0.92.

Conclusions:

This novel spatiotemporal modeling approach provides accurate fine-scale predictions in multiple regions for four pollutants. We have generated participant-specific predictions for MESA Air to investigate health effects of long-term air pollution exposures. These successes highlight modeling advances that can be adopted more widely in modern cohort studies.

Citation:

Keller JP, Olives C, Kim SY, Sheppard L, Sampson PD, Szpiro AA, Oron AP, Lindström J, Vedal S, Kaufman JD. 2015. A unified spatiotemporal modeling approach for predicting concentrations of multiple air pollutants in the Multi-Ethnic Study of Atherosclerosis and Air Pollution. Environ Health Perspect 123:301–309; http://dx.doi.org/10.1289/ehp.1408145     

Capturing the spatial variability of HIV epidemics in South Africa and Tanzania using routine healthcare facility data

Background

Large geographical variations in the intensity of the HIV epidemic in sub-Saharan Africa call for geographically targeted resource allocation where burdens are greatest. However, data available for mapping the geographic variability of HIV prevalence and detecting HIV ‘hotspots’ is scarce, and population-based surveillance data are not always available. Here, we evaluated the viability of using clinic-based HIV prevalence data to measure the spatial variability of HIV in South Africa and Tanzania.

Methods

Population-based and clinic-based HIV data from a small HIV hyper-endemic rural community in South Africa as well as for the country of Tanzania were used to map smoothed HIV prevalence using kernel interpolation techniques. Spatial variables were included in clinic-based models using co-kriging methods to assess whether cofactors improve clinic-based spatial HIV prevalence predictions. Clinic- and population-based smoothed prevalence maps were compared using partial rank correlation coefficients and residual local indicators of spatial autocorrelation.

Results

Routinely-collected clinic-based data captured most of the geographical heterogeneity described by population-based data but failed to detect some pockets of high prevalence. Analyses indicated that clinic-based data could accurately predict the spatial location of so-called HIV ‘hotspots’ in?>?50% of the high HIV burden areas.

Conclusion

Clinic-based data can be used to accurately map the broad spatial structure of HIV prevalence and to identify most of the areas where the burden of the infection is concentrated (HIV ‘hotspots’). Where population-based data are not available, HIV data collected from health facilities may provide a second-best option to generate valid spatial prevalence estimates for geographical targeting and resource allocation.

     

Terra and Aqua satellites track tiger mosquito invasion: modelling the potential distribution of <Emphasis Type="Italic">Aedes albopictus</Emphasis> in north-eastern Italy

Background

The continuing spread of the Asian tiger mosquito Aedes albopictus in Europe is of increasing public health concern due to the potential risk of new outbreaks of exotic vector-borne diseases that this species can transmit as competent vector. We predicted the most favorable areas for a short term invasion of Ae. albopictus in north-eastern Italy using reconstructed daily satellite data time series (MODIS Land Surface Temperature maps, LST). We reconstructed more than 11,000 daily MODIS LST maps for the period 2001-09 (i.e. performed spatial and temporal gap-filling) in an Open Source GIS framework. We aggregated these LST maps over time and identified the potential distribution areas of Ae. albopictus by adapting published temperature threshold values using three variables as predictors (0°C for mean January temperatures, 11°C for annual mean temperatures and 1350 growing degree days filtered for areas with autumnal mean temperatures > 11°C). The resulting maps were integrated into the final potential distribution map and this was compared with the known current distribution of Ae. albopictus in north-eastern Italy.

Results

LST maps show the microclimatic characteristics peculiar to complex terrains, which would not be visible in maps commonly derived from interpolated meteorological station data. The patterns of the three indicator variables partially differ from each other, while winter temperature is the determining limiting factor for the distribution of Ae. albopictus. All three variables show a similar spatial pattern with some local differences, in particular in the northern part of the study area (upper Adige valley).

Conclusions

Reconstructed daily land surface temperature data from satellites can be used to predict areas of short term invasion of the tiger mosquito with sufficient accuracy (200 m pixel resolution size). Furthermore, they may be applied to other species of arthropod of medical interest for which temperature is a relevant limiting factor. The results indicate that, during the next few years, the tiger mosquito will probably spread toward northern latitudes and higher altitudes in north-eastern Italy, which will considerably expand the range of the current distribution of this species.

     

Local determinants of road traffic noise levels versus determinants of air pollution levels in a Mediterranean city

Background

Both traffic-related noise and air pollution have been associated with cardiovascular disease (CVD). Spatial correlations between these environmental stressors may entail mutual confounding in epidemiological studies investigating their long-term effects. Few studies have investigated their correlation – none in Spain – and results differ among cities.

Objectives

We assessed the contribution of urban land-use and traffic variables to the noise–air pollution correlation in Girona town, where an investigation of the chronic effects of air pollution and noise on CVD takes place (REGICOR-AIR).

Methodology

Outdoor annual mean concentrations of nitrogen dioxide (NO₂) derived from monthly passive sampler measurements were obtained at 83 residential locations. Long-term average traffic-related noise levels from a validated model were assigned to each residence. Linear regression models were fitted both for NO₂ and noise.

Results

The correlation between NO₂ and noise (L_{24 h}) was 0.62. However, the correlation differed across the urban space, with lower correlations at sites with higher traffic density and in the modern downtown. Traffic density, distance from the location to the sidewalk and building density nearby explained 35.6% and 73.2% of the variability of NO₂ and noise levels, respectively. The correlation between the residuals of the two models suggested the presence of other unmeasured common variables.

Conclusions

The substantial correlation between traffic-related noise and NO₂, endorsed by common determinants, and the dependence of this correlation on complex local characteristics call for careful evaluations of both factors to ultimately assess their cardiovascular effects.     

Developing GIS-based eastern equine encephalitis vector-host models in Tuskegee,Alabama

Background

A site near Tuskegee, Alabama was examined for vector-host activities of eastern equine encephalomyelitis virus (EEEV). Land cover maps of the study site were created in ArcInfo 9.2^® from QuickBird data encompassing visible and near-infrared (NIR) band information (0.45 to 0.72 μm) acquired July 15, 2008. Georeferenced mosquito and bird sampling sites, and their associated land cover attributes from the study site, were overlaid onto the satellite data. SAS 9.1.4^® was used to explore univariate statistics and to generate regression models using the field and remote-sampled mosquito and bird data. Regression models indicated that Culex erracticus and Northern Cardinals were the most abundant mosquito and bird species, respectively. Spatial linear prediction models were then generated in Geostatistical Analyst Extension of ArcGIS 9.2^®. Additionally, a model of the study site was generated, based on a Digital Elevation Model (DEM), using ArcScene extension of ArcGIS 9.2^®.

Results

For total mosquito count data, a first-order trend ordinary kriging process was fitted to the semivariogram at a partial sill of 5.041 km, nugget of 6.325 km, lag size of 7.076 km, and range of 31.43 km, using 12 lags. For total adult Cx. erracticus count, a first-order trend ordinary kriging process was fitted to the semivariogram at a partial sill of 5.764 km, nugget of 6.114 km, lag size of 7.472 km, and range of 32.62 km, using 12 lags. For the total bird count data, a first-order trend ordinary kriging process was fitted to the semivariogram at a partial sill of 4.998 km, nugget of 5.413 km, lag size of 7.549 km and range of 35.27 km, using 12 lags. For the Northern Cardinal count data, a first-order trend ordinary kriging process was fitted to the semivariogram at a partial sill of 6.387 km, nugget of 5.935 km, lag size of 8.549 km and a range of 41.38 km, using 12 lags. Results of the DEM analyses indicated a statistically significant inverse linear relationship between total sampled mosquito data and elevation (R² = -.4262; p < .0001), with a standard deviation (SD) of 10.46, and total sampled bird data and elevation (R² = -.5111; p < .0001), with a SD of 22.97. DEM statistics also indicated a significant inverse linear relationship between total sampled Cx. erracticus data and elevation (R² = -.4711; p < .0001), with a SD of 11.16, and the total sampled Northern Cardinal data and elevation (R² = -.5831; p < .0001), SD of 11.42.

Conclusion

These data demonstrate that GIS/remote sensing models and spatial statistics can capture space-varying functional relationships between field-sampled mosquito and bird parameters for determining risk for EEEV transmission.

     

Short-term effects of air temperature on blood pressure and pulse pressure in potentially susceptible individuals

Background

Only few epidemiological studies have investigated the association between air temperature and blood pressure (BP) or pulse pressure (PP), with inconsistent findings. We examined whether short-term changes in air temperature were associated with changes in BP or PP in three different populations.

Methods

Between March 2007 and December 2008, 371 systolic and diastolic BP measurements were collected in 30 individuals with type-2 diabetes mellitus (T2D), 30 persons with impaired glucose tolerance and 42 healthy individuals without a metabolic disorder from Augsburg, Germany. Hourly means of ambient meteorological data were obtained from a fixed measurement station. Personal temperature measurements were conducted using data loggers. Temperature effects were evaluated using additive mixed models adjusting for time trend and relative humidity.

Results

Decreases in air temperature were associated with an increase in systolic BP, diastolic BP and PP in individuals with T2D. For example, a 1 °C decrease in ambient temperature was associated with an immediate increase in systolic BP of 1.0 mmHg (95%-confidence interval: [0.5;1.4] mmHg). Effects of personally measured air temperature were similar. Temperature effects were modified by age, body mass index, gender, antihypertensive medication and whereabouts, such as being indoors.

Conclusions

We observed associations between decreases in air temperature and increases in BP as well as PP in persons with T2D indicating that these people might be potentially more susceptible to changes in air temperature. Our findings may provide a hypothesis for a mechanism between air temperature decreases and short-term increases of cardiovascular events.     

Does the edge effect impact on the measure of spatial accessibility to healthcare providers?

Background

Spatial accessibility indices are increasingly applied when investigating inequalities in health. Although most studies are making mentions of potential errors caused by the edge effect, many acknowledge having neglected to consider this concern by establishing spatial analyses within a finite region, settling for hypothesizing that accessibility to facilities will be under-reported. Our study seeks to assess the effect of edge on the accuracy of defining healthcare provider access by comparing healthcare provider accessibility accounting or not for the edge effect, in a real-world application.

Methods

This study was carried out in the department of Nord, France. The statistical unit we use is the French census block known as ‘IRIS’ (Ilot Regroupé pour l’Information Statistique), defined by the National Institute of Statistics and Economic Studies. The geographical accessibility indicator used is the “Index of Spatial Accessibility” (ISA), based on the E2SFCA algorithm. We calculated ISA for the pregnant women population by selecting three types of healthcare providers: general practitioners, gynecologists and midwives. We compared ISA variation when accounting or not edge effect in urban and rural zones. The GIS method was then employed to determine global and local autocorrelation. Lastly, we compared the relationship between socioeconomic distress index and ISA, when accounting or not for the edge effect, to fully evaluate its impact.

Results

The results revealed that on average ISA when offer and demand beyond the boundary were included is slightly below ISA when not accounting for the edge effect, and we found that the IRIS value was more likely to deteriorate than improve. Moreover, edge effect impact can vary widely by health provider type. There is greater variability within the rural IRIS group than within the urban IRIS group. We found a positive correlation between socioeconomic distress variables and composite ISA. Spatial analysis results (such as Moran’s spatial autocorrelation index and local indicators of spatial autocorrelation) are not really impacted.

Conclusion

Our research has revealed minor accessibility variation when edge effect has been considered in a French context. No general statement can be set up because intensity of impact varies according to healthcare provider type, territorial organization and methodology used to measure the accessibility to healthcare. Additional researches are required in order to distinguish what findings are specific to a territory and others common to different countries. It constitute a promising direction to determine more precisely healthcare shortage areas and then to fight against social health inequalities.

     

How does Poisson kriging compare to the popular BYM model for mapping disease risks?

Background

Geostatistical techniques are now available to account for spatially varying population sizes and spatial patterns in the mapping of disease rates. At first glance, Poisson kriging represents an attractive alternative to increasingly popular Bayesian spatial models in that: 1) it is easier to implement and less CPU intensive, and 2) it accounts for the size and shape of geographical units, avoiding the limitations of conditional auto-regressive (CAR) models commonly used in Bayesian algorithms while allowing for the creation of isopleth risk maps. Both approaches, however, have never been compared in simulation studies, and there is a need to better understand their merits in terms of accuracy and precision of disease risk estimates.

Results

Besag, York and Mollie's (BYM) model and Poisson kriging (point and area-to-area implementations) were applied to age-adjusted lung and cervix cancer mortality rates recorded for white females in two contrasted county geographies: 1) state of Indiana that consists of 92 counties of fairly similar size and shape, and 2) four states in the Western US (Arizona, California, Nevada and Utah) forming a set of 118 counties that are vastly different geographical units. The spatial support (i.e. point versus area) has a much smaller impact on the results than the statistical methodology (i.e. geostatistical versus Bayesian models). Differences between methods are particularly pronounced in the Western US dataset: BYM model yields smoother risk surface and prediction variance that changes mainly as a function of the predicted risk, while the Poisson kriging variance increases in large sparsely populated counties. Simulation studies showed that the geostatistical approach yields smaller prediction errors, more precise and accurate probability intervals, and allows a better discrimination between counties with high and low mortality risks. The benefit of area-to-area Poisson kriging increases as the county geography becomes more heterogeneous and when data beyond the adjacent counties are used in the estimation. The trade-off cost for the easier implementation of point Poisson kriging is slightly larger kriging variances, which reduces the precision of the model of uncertainty.

Conclusion

Bayesian spatial models are increasingly used by public health officials to map mortality risk from observed rates, a preliminary step towards the identification of areas of excess. More attention should however be paid to the spatial and distributional assumptions underlying the popular BYM model. Poisson kriging offers more flexibility in modeling the spatial structure of the risk and generates less smoothing, reducing the likelihood of missing areas of high risk.     

Geospatial analyses to prioritize public health interventions: a case study of pedestrian and pedal cycle injuries in New South Wales, Australia

Objectives

Pedestrian and pedal cycle injuries are important causes of child morbidity and mortality. The combination of Bayesian methods and geographical distribution maps may assist public health practitioners to identify communities at high risk of injury.

Methods

Data were obtained on all hospitalizations of children from NSW (Australia), for pedestrian and pedal cycle injuries, from 2000?C2001 to 2004?C2005. Using Bayesian methods, posterior expected rate ratios (as an estimate of smoothed standardized hospitalization ratios for each injury mechanism) were mapped by local government area (LGA) across the state.

Results

There were over 7,000 hospitalizations for pedestrian and pedal cycle injuries. High risk LGAs accounted for more than one third of hospitalized pedestrian and pedal cycle injuries in NSW.

Conclusions

LGAs at high risk for pedestrian injury tended to be urbanized metropolitan areas with a high population density, while high risk LGAs for pedal cycle injury tended to be either in urban regional areas, or on the margin of urbanized metropolitan areas. Geospatial analyses can assist policymakers and practitioners to identify high risk communities for which public health interventions can be prioritized.     

Mutagenic and DNA repair activity in traffic policemen: a case-crossover study

Background

Emissions from vehicles are composed of heterogeneous mixtures of hazardous substances; several pollutants such as Polycyclic Aromatic Hydrocarbons (PAHs) are amongst the most dangerous substances detected in urban monitoring. A cohort of traffic policemen usually occupationally exposed to PAHs present in the urban environment were examined in order to assess the mutagenicity and DNA capacity repair.

Methods

Seventy-two urban traffic policemen working in Catania’s metropolitan area were enrolled in the study. Two spot urine samples were collected from each subject during the whole working cycle as follows: sample 1 (S1), pre-shift on day 1; sample 2 (S2) post-shift on day 6. 1-hydroxypyrene (1-OHP) was measured to serve as an indirect exposure indicator. Urinary mutagenic activity was assessed through the plate incorporation pre-incubation technique with S9, using YG1024 Salmonella typhimurium strain over-sensitive to PAH metabolite. Concentrations of urinary 8-oxodG were measured using liquid chromatography tandem mass spectrometry.

Results

As regards the exposure to PAHs, results highlighted a statistically significant difference (p?<?0.001) between pre-shift on day 1 and post-shift on day 6 levels. Mutagenic activity was detected in 38 (66%) workers on S1 and in 47 (81%) on S2. Also 8-oxodG analysis showed a statistically significant difference between S1 and S2 sampling.

Conclusions

This study demonstrated that occupational exposure to pollutants from traffic emission, assessed via 1-OHP measurements in urine, may lead to DNA repair and mutagenic activity, in line with other studies.

     

Peer Reviewed: Investment in Safe Routes to School Projects: Public Health Benefits for the Larger Community

Introduction

The Safe Routes to School (SRTS) program is designed to encourage active and safe transportation for children to school. This report examines the potential broader impact of these programs on communities within 0.5 mile (0.8 km) of schools.

Methods

We used a geographic information system to generate estimates of the land area within 0.5 mile of public schools in 4 U.S. Census-defined categories: 37 large urban areas, 428 small urban areas, 1088 metropolitan counties (counties in metropolitan statistical areas excluding the urban areas), and 2048 nonmetropolitan counties. We estimated population at the county level or at the U.S. Census-defined urban-area level using data from the 2000 U.S. Census.

Results

In large urban areas, 39.0% of the land area was within 0.5 mile of a public school, and in small urban areas, 26.5% of the land area was within 0.5 mile of a public school. An estimated 65.5 million people in urban areas could benefit from SRTS projects. In nonurban areas, 1% or less of land is within 0.5 mile of a public school.

Conclusion

Results suggest that SRTS projects in urban areas can improve the walking and bicycling environment for adults as well as for children, the target users. Investment in SRTS can contribute to increased physical activity among children and adults.     

Mapping Community Determinants of Heat Vulnerability

Background

The evidence that heat waves can result in both increased deaths and illness is substantial, and concern over this issue is rising because of climate change. Adverse health impacts from heat waves can be avoided, and epidemiologic studies have identified specific population and community characteristics that mark vulnerability to heat waves.

Objectives

We situated vulnerability to heat in geographic space and identified potential areas for intervention and further research.

Methods

We mapped and analyzed 10 vulnerability factors for heat-related morbidity/mortality in the United States: six demographic characteristics and two household air conditioning variables from the U.S. Census Bureau, vegetation cover from satellite images, and diabetes prevalence from a national survey. We performed a factor analysis of these 10 variables and assigned values of increasing vulnerability for the four resulting factors to each of 39,794 census tracts. We added the four factor scores to obtain a cumulative heat vulnerability index value.

Results

Four factors explained > 75% of the total variance in the original 10 vulnerability variables: a) social/environmental vulnerability (combined education/poverty/race/green space), b) social isolation, c) air conditioning prevalence, and d) proportion elderly/diabetes. We found substantial spatial variability of heat vulnerability nationally, with generally higher vulnerability in the Northeast and Pacific Coast and the lowest in the Southeast. In urban areas, inner cities showed the highest vulnerability to heat.

Conclusions

These methods provide a template for making local and regional heat vulnerability maps. After validation using health outcome data, interventions can be targeted at the most vulnerable populations.     

Rural and urban differences in the associations between characteristics of the community food environment and fruit and vegetable intake

Objective

To examine the relationship between measures of the household and retail food environments and fruit and vegetable (FV) intake in both urban and rural environmental contexts.

Design

A cross-sectional design was used. Data for FV intake and other characteristics were collected via survey instrument and geocoded to the objective food environment based on a ground-truthed (windshield audit) survey of the retail food environment.

Setting

One urban and 6 contiguous rural counties.

Participants

This study involved 2,556 residents of the Brazos Valley, Texas, who were selected through random-digit dialing.

Main Outcome Measure

Two-item scale of FV intake.

Analysis

Data were analyzed using chi-square analysis, 2-sample t tests, and linear regression.

Results

Distance to supermarket or supercenter was insignificant in the urban model, but significant in the rural model (β = -.014, P < .010, confidence interval = -.024, -.003).

Conclusions and Implications

Retail food environments have different impacts on FV intake in urban and rural settings. Interventions to improve FV intake in these settings should account for the importance of distance to the retail food environment in rural settings.