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This paper reviews the literature regarding the aggregation of benefit value estimates for environmental resources. The paper is prompted by the UK Environment Agency 'political jurisdiction' approach to aggregation of values for a single site as used in their study for the River Kennet tribunal. Two case studies are presented through which an alternative approach to aggregation is developed that applies the spatial analytic capabilities of a geographical information system to combine geo-referenced physical, census and survey data to estimate a spatially sensitive valuation function. These functions highlight the fact that resource values are expected to decline with increasing distance of households from the resource. The case studies show that the reliance upon political jurisdictions and the use of sample mean values within the aggregation process are liable to lead to significant errors in resultant values. The paper concludes with some limitations of the approach used as well as recommendations for future work in this area.

One of the results of new road construction is often a reduction in the price of nearby properties. In the United Kingdom property owners can be compensated for this loss through the Land Compensation Act. The appropriate level of compensation is currently determined by valuers and is mainly based upon their expertise and skill. This study aims to determine what the correct level of compensation should be. It has been specifically designed to fulfil the requirements of current legislation and can be integrated into existing compensation procedures. This was achieved through a hedonic pricing study that relates current property prices to a wide range of factors. These variables include the structure, neighbourhood, accessibility, and environment of the property, in addition to the impact of nearby roads. These were all created through GIS and large-scale digital data. The study, which is based on over 3500 property sales in Glasgow, Scotland, suggests that property prices were depressed by 0.202% for each decibel increase in road noise. This result has enabled a more streamlined compensation procedure to be developed and demonstrates that compensation claims can be estimated at the road-development stage. This would allow any compensation claims to be assessed prior to road construction and inform the design of noise-reduction measures.

The spatio-temporal dynamics of an outbreak provide important insights to help direct public health resources intended to control transmission. They also provide a focus for detailed epidemiological studies and allow the timing and impact of interventions to be assessed.A common approach is to aggregate case data to administrative regions. Whilst providing a good visual impression of change over space, this method masks spatial variation and assumes that disease risk is constant across space. Risk factors for COVID-19 (e.g. population density, deprivation and ethnicity) vary from place to place across England so it follows that risk will also vary spatially. Kernel density estimation compares the spatial distribution of cases relative to the underlying population, unfettered by arbitrary geographical boundaries, to produce a continuous estimate of spatially varying risk.Using test results from healthcare settings in England (Pillar 1 of the UK Government testing strategy) and freely available methods and software, we estimated the spatial and spatio-temporal risk of COVID-19 infection across England for the first 6 months of 2020. Widespread transmission was underway when partial lockdown measures were introduced on 23 March 2020 and the greatest risk erred towards large urban areas. The rapid growth phase of the outbreak coincided with multiple introductions to England from the European mainland. The spatio-temporal risk was highly labile throughout.In terms of controlling transmission, the most important practical application of our results is the accurate identification of areas within regions that may require tailored intervention strategies. We recommend that this approach is absorbed into routine surveillance outputs in England. Further risk characterisation using widespread community testing (Pillar 2) data is needed as is the increased use of predictive spatial models at fine spatial scales.

IntroductionThere is a lack of evidence of the adverse effects of air pollution and pollen on cognition for people with air quality related health conditions. This study explored the effects of air quality and respiratory health conditions on educational attainment for 18,241 pupils across the city of Cardiff, United Kingdom.
Objectives and ApproachAnonymised, routinely collected health and education data were linked at the household and school level with modelled high spatial resolution pollution data, and daily pollen measurements using the Secure Anonymised Information Linkage (SAIL) databank. This created 7 repeated cross-sectional cohorts (2009-2015). Multilevel linear regression analysis examined whether exam performance was associated with health status and/or air quality levels averaged at school and home locations during revision and examination periods. We also investigated the combined effects of air quality and associations with educational attainment for pupils who were treated for asthma and/or Severe Allergic Rhinitis (SAR), and those who were not.
ResultsThe cohort contained 9337 males and 8904 female pupils. There were 871 treated for asthma, 2091 for SAR, and 634 treated for both. Asthma was not associated with exam performance (p=0.700). However, SAR was positively associated with exam performance (p 2) was negatively associated with educational attainment (p = 0.002). Other indicators of air quality (pollutants: Ozone, Particulate Matter - PM2.5, and pollen) were not associated with educational attainment (p> 0.05). Exposure to NO2 was negatively associated with educational attainment irrespective of treatment for asthma or SAR. There was no combined effect of air quality on the variation in educational attainment between those who are treated for asthma and/or SAR and those who were not.
Conclusion/ImplicationsIrrespective of health status, exposure to NO2 was negatively associated with educational attainment. Treatment seeking behaviour may be a possible explanation for the positive association between SAR and educational attainment. For a more accurate reflection of health status, health outcomes not subject to treatment seeking behaviour should be investigated.

Background: Campylobacteriosis is the most commonly reported food-borne infection in the European Union, with an annual number of cases estimated at around 9 million. In many countries, campylobacteriosis has a striking seasonal peak during early/ mid-summer. In the early 2000s, several publications reported on campylobacteriosis seasonality across Europe and associations with temperature and precipitation. Subsequently, many European countries have introduced new measures against this foodborne disease. Aim: To examine how the seasonality of campylobacteriosis varied across Europe from 2008–16, to explore associations with temperature and precipitation, and to compare these results with previous studies. We also sought to assess the utility of the European Surveillance System TESSy for cross-European seasonal analysis of campylobacteriosis. Methods: Ward's Minimum Variance Clustering was used to group countries with similar seasonal patterns of campylobacteriosis. A two-stage multivariate meta-analysis methodology was used to explore associations with temperature and precipitation. Results: Nordic countries had a pronounced seasonal campylobacteriosis peak in mid-to late summer (weeks 29–32), while most other European countries had a less pronounced peak earlier in the year. The United Kingdom, Ireland, Hungary and Slovakia had a slightly earlier peak (week 24). Campylobacteriosis cases were positively associated with temperature and, to a lesser degree, precipitation. Conclusion: Across Europe, the strength and timing of campylobacteriosis peaks have remained similar to those observed previously. In addition, TESSy is a useful resource for cross-Euro-pean seasonal analysis of infectious diseases such as campylobacteriosis, but its utility depends upon each country's reporting infrastructure.

ABSTRACTBackground Campylobacteriosis is a major public health concern. Despite evidence that climate factors influence the spatio-temporal patterns of the infections; their impact is not fully described and understood.
ObjectivesTo examine methods for determining the impact of rainfall and temperature on Campylobacter cases in England and Wales.
MethodsReported cases for England and Wales were linked to local temperature and rainfall at laboratory postcode locations in the 30 days before the specimen date. Descriptive, statistical and spatial methods included a novel Comparative Conditional Incidence (CCI), wavelet analysis, hierarchical clustering, generalized additive model (GAM) and generalized structural time series model (GEST).ResultsThe Campylobacter increase in late spring was linked to temperature two weeks prior, with an increase in CCI of 0.175 cases per 100,000 per week for weeks 17 to 24; the relationship was non-linear and changed through the year. GEST with penalized varying temperature coefficient found 33% of the seasonal change was attributable to temperature, while with a fixed temperature coefficient found 8%. Wavelet analysis showed a strong annual cycle, with harmonics at six and four months and no simple association with temperature or rainfall. Geographic clustering showed three clusters with geographic similarities, representing metropolitan, rural, and other areas.
ConclusionsOur analyses provide more robust and convincing associations than simple regression analysis. The association with temperature is likely to be indirect and the primary driver remains to be determined. Local-temporal linkage of weather parameters and cases is important in improving the resolution of climate associations with infectious diseases and provides methods which can improve disease predictions. Further examination of data from a wider geographic area and longer time series should improve the understanding of the epidemiology and drivers of human Campylobacter infections.

AbstractThe COVID‐19 pandemic has disrupted economies and societies throughout the world since early 2020. Education is especially affected, with schools and universities widely closed for long periods. People under 25 years have the lowest risk of severe disease but their activities can be key to persistent ongoing community transmission. A challenge arose for how to provide education, including university level, without the activities of students increasing wider community SARS‐CoV‐2 infections. We used a Hazard Analysis of Critical Control Points (HACCP) framework to assess the risks associated with university student activity and recommend how to mitigate these risks. This tool appealed because it relies on multiagency collaboration and interdisciplinary expertise and yet is low cost, allowing rapid generation of evidence‐based recommendations. We identified key critical control points associated with university student' activities, lifestyle, and interaction patterns both on‐and‐off campus. Unacceptable contact thresholds and the most up‐to‐date guidance were used to identify levels of risk for potential SARS‐CoV‐2 transmission, as well as recommendations based on existing research and emerging evidence for strategies that can reduce the risks of transmission. Employing the preventative measures we suggest can reduce the risks of SARS‐CoV‐2 transmission among and from university students. Reduction of infectious disease transmission in this demographic will reduce overall community transmission, lower demands on health services and reduce risk of harm to clinically vulnerable individuals while allowing vital education activity to continue. HACCP assessment proved a flexible tool for risk analysis in a specific setting in response to an emerging infectious disease threat. Systematic approaches to assessing hazards and risk critical control points (#HACCP) enable robust strategies for protecting students and staff in HE settings during #COVID19 pandemic.

IntroductionThe Secure Anonymised Information Linkage (SAIL) databank facilitated linkage of routinely collected health and education data, high spatial resolution pollution modelling and daily pollen measurements for 18,241 pupils in 7 cross-sectional cohorts across Cardiff city, UK, to investigate effects of air quality and respiratory health conditions on education attainment.
Objectives and ApproachAn urban atmospheric dispersion and chemistry modelling system (ADMS-Urban) simulated modelled hourly concentrations of air pollutants: PM2.5, PM10, NO2 and ozone levels. These were summarised into minimum, average and maximum daily readings for 4 time periods (e.g. school hours 9am-3pm) for all home and school locations across Cardiff between 2009 and 2015. The combination of different pollutants, measurements and time-periods created a comprehensive multi-row dataset per location. We transformed the dimensionality of this high-resolution data to create one row of summarised data per pupil per cohort, in preparation for statistical analysis.
Results157,361 school and home locations across Cardiff were anonymised and household linkage fields were appended to combine pollution estimates at the household/school to individual health data. The pollution dataset contained 369 columns, 472,083 rows per year with one column per location, pollutant type, pollutant measurement, daily time-period, and day of year. Dataset transformation reduced algorithm computation by creating a single date column, producing a five column, 3,446,205,900-row matrix per year dataset. The algorithm adjusted for weekends, school/bank holidays and allowed location to vary 3pm-5pm on school days when pupil location was uncertain. The algorithm calculated tailored pollution exposures per pupil for revision and examination periods, creating one row per pupil and reducing 7 years of data and 24 billion rows to 18,241.
Conclusion/ImplicationsWe successfully linked 95% of the cohorts' household/school pollution data to their corresponding health and education data. This demonstrates data linking retrospective exposures for total populations using multiple daily locations, and extends our analysis platform for natural experiments to include daily exposure. Future work includes adding modelled route exposures.

Background and ObjectivesThere is a lack of evidence of the adverse effects of air pollution and pollen on cognition for people with air quality-related health conditions. The CORTEX project combined routinely collected health and education data, high spatial resolution air pollution modelling, and daily pollen measurements for 18,241 pupils living in Cardiff, UK, between 2009 and 2015, to investigate the acute effects of air quality and respiratory conditions on education attainment.
DatasetsAir pollutants PM2.5, PM10, NO2, and ozone levels were modelled for 157,361 home and school locations, anonymised into the Secure Anonymised Information Linkage (SAIL) Databank, and summarised into minimum, average and maximum readings for 4 daily time periods reflecting pupil home/school exposure. Adding a unique Residential Anonymised Linking Field (RALF) allowed linkage of pollution estimates to individual level data. Annual pollution datasets contained 369 columns and 472,083-rows, with one column per location, pollutant, daily time-period and day of year. Dataset transformation produced a 5 column, 3,446,205,900-row matrix per year.
Methods and ConclusionsAn algorithm using Structured Query Language (SQL) to manage data held within a relational database management system, was designed to reduce dimensionality from 24 billion to 18,241 rows of data. The algorithm calculated average means for each pollutant (PM2.5, PM10, NO2, and ozone levels) over the revision and examination periods, and summarised data into one row per pupil. The algorithm adjusted for weekends, school, and bank holidays, it calculated daily pollutant exposure for each pupil, and successfully linked 95% of pupil pollution exposures to their health and education data.