An adaptive spatio-temporal smoothing model for estimating trends and step changes in disease risk

Statistical models used to estimate the spatio-temporal pattern in disease risk from areal unit data often represent the risk surface for each time period in terms of known covariates and a set of spatially smooth random effects. The latter act as a proxy for unmeasured spatial confounding, whose spatial structure is often characterised by a spatially smooth evolution between some pairs of adjacent areal units while other pairs exhibit large step changes. This spatial heterogeneity is not consistent with a global smoothing model in which partial correlation exists between all pairs of adjacent spatial random effects, and a novel space-time disease model with an adaptive spatial smoothing specification that can identify step changes is therefore proposed. The new model is motivated by a new study of respiratory and circulatory disease risk across the set of Local Authorities in England, and is rigorously tested by simulation to assess its efficacy. Results from the England study show that the two diseases have similar spatial patterns in risk, and exhibit a number of common step changes in the unmeasured component of risk between neighbouring local authorities

An adaptive spatio-temporal smoothing model for estimating trends and step changes in disease risk

Statistical models used to estimate the spatio-temporal pattern in disease risk from areal unit data represent the risk surface for each time period with known covariates and a set of spatially smooth random effects. The latter act as a proxy for unmeasured spatial confounding, whose spatial structure is often characterised by a spatially smooth evolution between some pairs of adjacent areal units while other pairs exhibit large step changes. This spatial heterogeneity is not consistent with existing global smoothing models, in which partial correlation exists between all pairs of adjacent spatial random effects. Therefore we propose a novel space-time disease model with an adaptive spatial smoothing specification that can identify step changes. The model is motivated by a new study of respiratory and circulatory disease risk across the set of Local Authorities in England, and is rigorously tested by simulation to assess its efficacy. Results from the England study show that the two diseases have similar spatial patterns in risk, and exhibit a number of common step changes in the unmeasured component of risk between neighbouring local authorities

Association between Serum 25-Hydroxy Vitamin D Levels and the Prevalence of Adult-Onset Asthma

The major circulating metabolite of vitamin D (25(OH)D) has been implicated in the pathogenesis for atopic dermatitis, asthma and other allergic diseases due to downstream immunomodulatory effects. However, a consistent association between 25(OH)D and asthma during adulthood has yet to be found in observational studies. We aimed to test the association between 25(OH)D and asthma during adulthood and hypothesised that this association would be stronger in non-atopic participants. Using information collected on the participants of the 1958 birth cohort, we developed a novel measure of atopic status using total and specific IgE values and reported history of eczema and allergic rhinitis. We designed a nested case-control analysis, stratified by atopic status, and using logistic regression models investigated the association between 25(OH)D measured at age 46 years with the prevalence of asthma and wheezy bronchitis at age 50 years, excluding participants who reported ever having asthma or wheezy bronchitis before the age of 42. In the fully adjusted models, a 10 nmol/L increase in serum 25(OH)D prevalence had a significant association with asthma (aOR 0.94; 95% CI 0.88–1.00). There was some evidence of an atopic dependent trend in the association between 25(OH)D levels and asthma. Further analytical work on the operationalisation of atopy status would prove useful to uncover whether there is a role for 25(OH)D and other risk factors for asthma

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 patient's 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

What individual and neighbourhood-level factors increase the risk of heat-related mortality? A case-crossover study of over 185,000 deaths in London using high-resolution climate datasets.

OBJECTIVE: Management of the natural and built environments can help reduce the health impacts of climate change. This is particularly relevant in large cities where urban heat island makes cities warmer than the surrounding areas. We investigate how urban vegetation, housing characteristics and socio-economic factors modify the association between heat exposure and mortality in a large urban area. METHODS: We linked 185,397 death records from the Greater London area during May-Sept 2007-2016 to a high resolution daily temperature dataset. We then applied conditional logistic regression within a case-crossover design to estimate the odds of death from heat exposure by individual (age, sex) and local area factors: land-use type, natural environment (vegetation index, tree cover, domestic garden), built environment (indoor temperature, housing type, lone occupancy) and socio-economic factors (deprivation, English language, level of employment and prevalence of ill-health). RESULTS: Temperatures were higher in neighbourhoods with lower levels of urban vegetation and with higher levels of income deprivation, social-rented housing, and non-native English speakers. Heat-related mortality increased with temperature increase (Odds Ratio (OR), 95% CI?=?1.039, 1.036-1.043 per 1?°C temperature increase). Vegetation cover showed the greatest modification effect, for example the odds of heat-related mortality in quartiles with the highest and lowest tree cover were OR, 95%CI 1.033, 1.026-1.039 and 1.043, 1.037-1.050 respectively. None of the socio-economic variables were a significant modifier of heat-related mortality. CONCLUSIONS: We demonstrate that urban vegetation can modify the mortality risk associated with heat exposure. These findings make an important contribution towards informing city-level climate change adaptation and mitigation policies

Non-linear response of temperature-related mortality risk to global warming in England and Wales

Climate change is expected to lead to changes in seasonal temperature-related mortality. However, this impact on health risk does not necessarily scale linearly with increasing temperature. By examining changes in risk relative to degrees of global warming, we show that there is a delayed emergence of the increase in the summer mean mortality risk in England and Wales. Due to the relatively mild summer mean temperatures under the current climate and the non-linearity of the exposure-response relationships, minimal changes in summer mean risk are expected at lower levels of warming and an escalation in risk is projected beyond 2.5°C of global warming relative to pre-industrial levels. In contrast, a 42% increase in mortality risk during summer heat extremes is already expected by 2°C global warming. Winter attributable mortalities, on the other hand, are projected to decrease largely linearly with global warming in England and Wales

Pollen exposure and hospitalization due to asthma exacerbations: daily time series in a European city.

Exposure to pollen can contribute to increased hospital admissions for asthma exacerbation. This study applied an ecological time series analysis to examine associations between atmospheric concentrations of different pollen types and the risk of hospitalization for asthma in London from 2005 to 2011. The analysis examined short-term associations between daily pollen counts and hospital admissions in the presence of seasonal and long-term patterns, and allowed for time lags between exposure and admission. Models were adjusted for temperature, precipitation, humidity, day of week, and air pollutants. Analyses revealed an association between daily counts (continuous) of grass pollen and adult hospital admissions for asthma in London, with a 4-5-day lag. When grass pollen concentrations were categorized into Met Office pollen 'alert' levels, 'very high' days (vs. 'low') were associated with increased admissions 2-5 days later, peaking at an incidence rate ratio of 1.46 (95%, CI 1.20-1.78) at 3 days. Increased admissions were also associated with 'high' versus 'low' pollen days at a 3-day lag. Results from tree pollen models were inconclusive and likely to have been affected by the shorter pollen seasons and consequent limited number of observation days with higher tree pollen concentrations. Future reductions in asthma hospitalizations may be achieved by better understanding of environmental risks, informing improved alert systems and supporting patients to take preventive measures

Weather regimes and patterns associated with temperature-related excess mortality in the UK: a pathway to sub-seasonal risk forecasting

Non-optimal temperatures, both warm and cold, are associated with enhanced mortality in the United Kingdom (UK). In this study we demonstrate a pathway to sub-seasonal and medium range forecasting of temperature-related mortality risk by quantifying the impact of large-scale weather regimes and synoptic scale weather patterns on temperature-associated excess deaths in 12 regions across the UK. We find a clear dominance of the NAO− regime in leading to high wintertime excess mortality across all regions. In summer, we note that cold spells lead to comparable cumulative excess mortality as moderate hot days, with cold days accounting for 11 (London) to 100% (Northern Ireland) of the summer days with the highest 5% cumulative excess mortality. However, exposure to high temperatures is typically associated with an immediate but short lived spike in mortality, while the impact of cold weather tends to be more delayed and spread out over a longer period. Weather patterns with a Scandinavian high component are most likely to be associated with summer hot extremes, while a strong zonal jet stream weather pattern which rarely occurs in summer is most likely to be associated with summer cold spells

Data mashups: potential contribution to decision support on climate change and health.

notes: PMCID: PMC3945564This is a freely-available open access publication. Please cite the published version which is available via the DOI link in this record.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.UK Medical Research CouncilUK Natural Environment Research CouncilEuropean Regional Development Fund Programme 2007 to 2013European Social Fund Convergence Programme for Cornwall and the Isles of Scill

A comparison of weather variables linked to infectious disease patterns using laboratory addresses and patient residence addresses

Background: To understand the impact of weather on infectious diseases, information on weather parameters at patient locations is needed, but this is not always accessible due to confidentiality or data availability. Weather parameters at nearby locations are often used as a proxy, but the accuracy of this practice is not known. Methods: Daily Campylobacter and Cryptosporidium cases across England and Wales were linked to local temperature and rainfall at the residence postcodes of the patients and at the corresponding postcodes of the laboratory where the patient’s specimen was tested. The paired values of daily rainfall and temperature for the laboratory versus residence postcodes were interpolated from weather station data, and the results were analysed for agreement using linear regression. We also assessed potential dependency of the findings on the relative geographic distance between the patient’s residence and the laboratory. Results: There was significant and strong agreement between the daily values of rainfall and temperature at diagnostic laboratories with the values at the patient residence postcodes for samples containing the pathogens Campylobacter or Cryptosporidium. For rainfall, the R-squared was 0.96 for the former and 0.97 for the latter, and for maximum daily temperature, the R-squared was 0.99 for both. The overall mean distance between the patient residence and the laboratory was 11.9 km; however, the distribution of these distances exhibited a heavy tail, with some rare situations where the distance between the patient residence and the laboratory was larger than 500 km. These large distances impact the distributions of the weather variable discrepancies (i.e. the differences between weather parameters estimated at patient residence postcodes and those at laboratory postcodes), with discrepancies up to ±10 °C for the minimum and maximum temperature and 20 mm for rainfall. Nevertheless, the distributions of discrepancies (estimated separately for minimum and maximum temperature and rainfall), based on the cases where the distance between the patient residence and the laboratory was within 20 km, still exhibited tails somewhat longer than the corresponding exponential fits suggesting modest small scale variations in temperature and rainfall. Conclusion: The findings confirm that, for the purposes of studying the relationships between meteorological variables and infectious diseases using data based on laboratory postcodes, the weather results are sufficiently similar to justify the use of laboratory postcode as a surrogate for domestic postcode. Exclusion of the small percentage of cases where there is a large distance between the residence and the laboratory could increase the precision of estimates, but there are generally strong associations between daily weather parameters at residence and laboratory