Christophe Sarran

Campylobacter epidemiology: a descriptive study reviewing 1 million cases in England and Wales between 1989 and 2011

Objectives To review Campylobacter cases in England and Wales over 2 decades and examine the main factors/mechanisms driving the changing epidemiology. Design A descriptive study of Campylobacter patients between 1989 and 2011. Cases over 3 years were linked anonymously to postcode, population density, deprivation indices and census data. Cases over 5 years were anonymously linked to local weather exposure estimates. Setting Patients were from general practice, hospital and environmental health investigations through primary diagnostic laboratories across England and Wales. Participants There were 1 109 406 cases. Outcome measures Description of changes in Campylobacter epidemiology over 23 years and how the main drivers may influence these. Results There was an increase in Campylobacter cases over the past 23 years, with the largest increase in people over 50 years. Changes in the underlying population have contributed to this, including the impacts of population increases after World War I, World War II and the ‘baby boom’ of the 1960s. A recent increase in risk or ascertainment within this population has caused an increase in cases in all age groups from 2004 to 2011. The seasonal increase in cases between weeks 18 (Early May) and 22 (Early June) was consistent across ages, years and regions and was most marked in children and in more rural regions. Campylobacter prevalence by week in each region correlated with temperature 2 weeks before. There were higher prevalences in areas with a low population density, low deprivation and lower percentage of people of ethnic origin. Data from sero–phage and multilocus sequence typing show a few common types and many uncommon types. Conclusions The drivers/mechanisms influencing seasonality, age distribution, population density, socioeconomic and long-term differences are diverse and their relative contributions remain to be established. Surveillance and typing provide insights into Campylobacter epidemiology and sources of infection, providing a sound basis for targeted interventions.

Data Mashups: Potential Contribution to Decision Support on Climate Change and Health

Linking environmental, socioeconomic and health datasets provides new insights into the potential associations between climate change and human health and wellbeing, and underpins the development of decision support tools that will promote resilience to climate change, and thus enable more effective adaptation. This paper outlines the challenges and opportunities presented by advances in data collection, storage, analysis, and access, particularly focusing on “data mashups”. These data mashups are integrations of different types and sources of data, frequently using open application programming interfaces and data sources, to produce enriched results that were not necessarily the original reason for assembling the raw source data. As an illustration of this potential, this paper describes a recently funded initiative to create such a facility in the UK for use in decision support around climate change and health, and provides examples of suitable sources of data and the purposes to which they can be directed, particularly for policy makers and public health decision makers.

Asthma Length of Stay in Hospitals in London 2001–2006: Demographic, Diagnostic and Temporal Factors

Asthma is a condition of significant public health concern associated with morbidity, mortality and healthcare utilisation. This study identifies key determinants of length of stay (LOS) associated with asthma-related hospital admissions in London, and further explores their effects on individuals. Subjects were primarily diagnosed and admitted for asthma in London between 1st January 2001 and 31st December 2006. All repeated admissions were treated uniquely as independent cases. Negative binomial regression was used to model the effect(s) of demographic, temporal and diagnostic factors on the LOS, taking into account the cluster effect of each patients hospital attendance in London. The median and mean asthma LOS over the period of study were 2 and 3 days respectively. Admissions increased over the years from 8,308 (2001) to 10,554 (2006), but LOS consistently declined within the same period. Younger individuals were more likely to be admitted than the elderly, but the latter significantly had higher LOS (p<0.001). Respiratory related secondary diagnoses, age, and gender of the patient as well as day of the week and year of admission were important predictors of LOS. Asthma LOS can be predicted by socio-demographic factors, temporal and clinical factors using count models on hospital admission data. The procedure can be a useful tool for planning and resource allocation in health service provision.

Land cover and air pollution are associated with asthma hospitalisations: A cross-sectional study

BACKGROUND There is increasing policy interest in the potential for vegetation in urban areas to mitigate harmful effects of air pollution on respiratory health. We aimed to quantify relationships between tree and green space density and asthma-related hospitalisations, and explore how these varied with exposure to background air pollution concentrations. METHODS Population standardised asthma hospitalisation rates (1997-2012) for 26,455 urban residential areas of England were merged with area-level data on vegetation and background air pollutant concentrations. We fitted negative binomial regression models using maximum likelihood estimation to obtain estimates of asthma-vegetation relationships at different levels of pollutant exposure. RESULTS Green space and gardens were associated with reductions in asthma hospitalisation when pollutant exposures were lower but had no significant association when pollutant exposures were higher. In contrast, tree density was associated with reduced asthma hospitalisation when pollutant exposures were higher but had no significant association when pollutant exposures were lower. CONCLUSIONS We found differential effects of natural environments at high and low background pollutant concentrations. These findings can provide evidence for urban planning decisions which aim to leverage health co-benefits from environmental improvements.

Forecasting asthma-related hospital admissions in London using negative binomial models

Health forecasting can improve health service provision and individual patient outcomes. Environmental factors are known to impact chronic respiratory conditions such as asthma, but little is known about the extent to which these factors can be used for forecasting. Using weather, air quality and hospital asthma admissions, in London (2005–2006), two related negative binomial models were developed and compared with a naive seasonal model. In the first approach, predictive forecasting models were fitted with 7-day averages of each potential predictor, and then a subsequent multivariable model is constructed. In the second strategy, an exhaustive search of the best fitting models between possible combinations of lags (0–14 days) of all the environmental effects on asthma admission was conducted. Three models were considered: a base model (seasonal effects), contrasted with a 7-day average model and a selected lags model (weather and air quality effects). Season is the best predictor of asthma admissions. The 7-day average and seasonal models were trivial to implement. The selected lags model was computationally intensive, but of no real value over much more easily implemented models. Seasonal factors can predict daily hospital asthma admissions in London, and there is a little evidence that additional weather and air quality information would add to forecast accuracy.

Quantifying the impact of current and future concentrations of air pollutants on respiratory disease risk in England

BackgroundEstimating the long-term health impact of air pollution in a spatio-temporal ecological study requires representative concentrations of air pollutants to be constructed for each geographical unit and time period. Averaging concentrations in space and time is commonly carried out, but little is known about how robust the estimated health effects are to different aggregation functions. A second under researched question is what impact air pollution is likely to have in the future.MethodsWe conducted a study for England between 2007 and 2011, investigating the relationship between respiratory hospital admissions and different pollutants: nitrogen dioxide (NO2); ozone (O3); particulate matter, the latter including particles with an aerodynamic diameter less than 2.5 micrometers (PM2.5), and less than 10 micrometers (PM10); and sulphur dioxide (SO2). Bayesian Poisson regression models accounting for localised spatio-temporal autocorrelation were used to estimate the relative risks (RRs) of pollution on disease risk, and for each pollutant four representative concentrations were constructed using combinations of spatial and temporal averages and maximums. The estimated RRs were then used to make projections of the numbers of likely respiratory hospital admissions in the 2050s attributable to air pollution, based on emission projections from a number of Representative Concentration Pathways (RCP).ResultsNO2 exhibited the largest association with respiratory hospital admissions out of the pollutants considered, with estimated increased risks of between 0.9 and 1.6% for a one standard deviation increase in concentrations. In the future the projected numbers of respiratory hospital admissions attributable to NO2 in the 2050s are lower than present day rates under 3 Representative Concentration Pathways (RCPs): 2.6, 6.0, and 8.5, which is due to projected reductions in future NO2 emissions and concentrations.ConclusionsNO2 concentrations exhibit consistent substantial present-day health effects regardless of how a representative concentration is constructed in space and time. Thus as concentrations are predicted to remain above limits set by European Union Legislation until the 2030s in parts of urban England, it will remain a substantial health risk for some time.

Coastal climate is associated with elevated solar irradiance and higher 25(OH)D level.

INTRODUCTION There is evidence that populations living close to the coast have improved health and wellbeing. Coastal environments are linked to promotion of physical activity through provision of safe, opportune, aesthetic and accessible spaces for recreation. Exposure to coastal environments may also reduce stress and induce positive mood. We hypothesised that coastal climate may influence the vitamin D status of residents and thus partly explain benefits to health. MATERIALS AND METHODS Ecological and cross-sectional analyses were designed to elucidate the connection between coastal residence and vitamin D status. We divided residential data, from developed land use areas and the Lower Super Output Areas or Data Zones (Scotland) of the 1958 Birth Cohort participants, into the following coastal bands: <1 km, 1-5 km, 5-20 km, 20-50 km and over 50 km. In the ecological analysis we used a multiple regression model to describe the relationship between UV vitd and coastal proximity adjusted for latitude. Subsequently, using the residential information of the participants of the 1958 Birth Cohort we developed a multiple regression model to understand the relationship between serum 25(OH)D (a marker of vitamin D status) and coastal proximity adjusted for several factors related to vitamin D status (e.g. diet, outdoor activity). RESULTS We found that coastal proximity was associated with solar irradiance; on average a 99.6 (96.1-103.3)J/m(2)/day regression coefficient was recorded for settlements <1 km from the coast compared with those at >50 km. This relationship was modified by latitude with settlements at a lower latitude exhibiting a greater effect. Individuals living closer to the coast in England had higher vitamin D levels than those inland, particularly in autumn. CONCLUSION Geographic location may influence biochemistry and health outcomes due to environmental factors. This can provide benefits in terms of vitamin D status but may also pose a risk due to higher skin cancer risk. We provide further evidence in support of the claim that coastal environments can provide opportunities for health and wellbeing.

A retrospective study of the impact of a telephone alert service (Healthy Outlook) on hospital admissions for patients with chronic obstructive pulmonary disease

Background:Healthy Outlook is a service delivered by the UK Met Office directly to patients with chronic obstructive pulmonary disease (COPD) that has been in place since 2006. Its objective is to reduce the severity and length of COPD exacerbations, hence improving the quality of life and life expectancy.Aims:To assess the effect of the Healthy Outlook service on hospital admission rates of all general practitioners that have used the service.Methods:Control practices were selected for each of the 661 participating practices. The number of hospital admissions for each practice was extracted from the Hospital Episode Statistics database. The differences in admission rates per practice between the first year of use of the Healthy Outlook service and the previous year were compared by paired t-test analyses.Results:For admissions with a primary diagnosis of COPD, the difference between participating and control practices was −0.8% (95% confidence interval (CI)=−1.8 to 0.2%; P=0.13). For admissions with a primary or co-morbid diagnosis of COPD, the difference was −2.3% (95% CI=−4.2 to −0.4%; P=0.02).Conclusions:Participation in the Healthy Outlook service reduces hospital admission rates for patients coded on discharge with COPD (including co-morbid).

Meteorological analysis of symptom data for people with seasonal affective disorder

It is thought that variation in natural light levels affect people with Seasonal Affective Disorder (SAD). Several meteorological factors related to luminance can be forecast but little is known about which factors are most indicative of worsening SAD symptoms. The aim of this meteorological analysis is to determine which factors are linked to SAD symptoms. The symptoms of 291 individuals with SAD in and near Groningen have been evaluated over the period 2003-2009. Meteorological factors linked to periods of low natural light (sunshine, global radiation, horizontal visibility, cloud cover and mist) and others (temperature, humidity and pressure) were obtained from weather observation stations. A Bayesian zero adjusted auto-correlated multilevel Poisson model was carried out to assess which variables influence the SAD symptom score BDI-II. The outcome of the study suggests that the variable sunshine duration, for both the current and previous week, and global radiation for the previous week, are significantly linked to SAD symptoms.

The Weather and Ménière's Disease: A Longitudinal Analysis in the UK.

HYPOTHESIS Changes in the weather influence symptom severity in Ménières disease (MD). BACKGROUND MD is an unpredictable condition that significantly impacts on quality of life. It is suggested that fluctuations in the weather, especially atmospheric pressure may influence the symptoms of MD. However, to date, limited research has investigated the impact of the weather on MD. METHODS In a longitudinal study, a mobile phone application collected data from 397 individuals (277 females and 120 males with an average age of 50 yr) from the UK reporting consultant-diagnosed MD. Daily symptoms (vertigo, aural fullness, tinnitus, hearing loss, and attack prevalence) and GPS locations were collected; these data were linked with Met Office weather data (including atmospheric pressure, humidity, temperature, visibility, and wind speed). RESULTS Symptom severity and attack prevalence were reduced on days when atmospheric pressure was higher. When atmospheric pressure was below 1,013 hectopascals, the risk of an attack was 1.30 (95% confidence interval: 1.10, 1.54); when the humidity was above 90%, the risk of an attack was 1.26 (95% confidence interval 1.06, 1.49). CONCLUSION This study provides the strongest evidence to date that changes in atmospheric pressure and humidity are associated with symptom exacerbation in MD. Improving our understanding of the role of weather and other environmental triggers in Ménières may reduce the uncertainty associated with living with this condition, significantly contributing to improved quality of life.