Climate change and water-related infectious diseases

Background: Water-related, including waterborne, diseases remain important sources of morbidity and mortality worldwide, but particularly in developing countries. The potential for changes in disease associated with predicted anthropogenic climate changes make water-related diseases a target for prevention. Methods: We provide an overview of evidence on potential future changes in water-related disease associated with climate change. Results: A number of pathogens are likely to present risks to public health, including cholera, typhoid, dysentery, leptospirosis, diarrhoeal diseases and harmful algal blooms (HABS). The risks are greatest where the climate effects drive population movements, conflict and disruption, and where drinking water supply infrastructure is poor. The quality of evidence for water-related disease has been documented. Conclusions: We highlight the need to maintain and develop timely surveillance and rapid epidemiological responses to outbreaks and emergence of new waterborne pathogens in all countries. While the main burden of waterborne diseases is in developing countries, there needs to be both technical and financial mechanisms to ensure adequate quantities of good quality water, sewage disposal and hygiene for all. This will be essential in preventing excess morbidity and mortality in areas that will suffer from substantial changes in climate in the future

Food-borne disease and climate change in the United Kingdom

This review examined the likely impact of climate change upon food-borne disease in the UK using Campylobacter and Salmonella as example organisms. Campylobacter is an important food-borne disease and an increasing public health threat. There is a reasonable evidence base that the environment and weather play a role in its transmission to humans. However, uncertainty as to the precise mechanisms through which weather affects disease, make it difficult to assess the likely impact of climate change. There are strong positive associations between Salmonella cases and ambient temperature, and a clear understanding of the mechanisms behind this. However, because the incidence of Salmonella disease is declining in the UK, any climate change increases are likely to be small. For both Salmonella and Campylobacter the disease incidence is greatest in older adults and young children. There are many pathways through which climate change may affect food but only a few of these have been rigorously examined. This provides a high degree of uncertainty as to what the impacts of climate change will be. Food is highly controlled at the National and EU level. This provides the UK with resilience to climate change as well as potential to adapt to its consequences but it is unknown whether these are sufficient in the context of a changing climate

Recreational use of the countryside: No evidence that high nature value enhances a key ecosystem service

In Western Europe, recreational amenity is presented as an important cultural ecosystem service that, along with other values, helps justify policies to conserve biodiversity. However, whether recreational use by the public is enhanced at protected areas designated for nature conservation is unknown. This is the first study to model outdoor recreation at a national scale, examining habitat preferences with statutory designation (Site of Special Scientific Interest) as an indicator of nature conservation importance. Models were based on a massive, three year national household survey providing spatially-referenced recreational visits to the natural environment. Site characteristics including land cover were compared between these observed visit sites (n = 31,502) and randomly chosen control sites (n = 63,000). Recreationists preferred areas of coast, freshwater, broadleaved woodland and higher densities of footpaths and avoided arable, coniferous woodland and lowland heath. Although conservation designation offers similar or greater public access than undesignated areas of the same habitat, statutory designation decreased the probability of visitation to coastal and freshwater sites and gave no effect for broadleaved woodland. Thus general recreational use by the public did not represent an important ecosystem service of protected high-nature-value areas, so that intrinsic and existence values remain as the primary justifications for conservation of high nature value areas. Management of ‘green infrastructure’ sites of lower conservation value that offer desirable habitats and enhanced provision of footpaths, could mitigate recreational impacts on nearby valuable conservation areas

The effects of river flooding on dioxin and PCBs in beef

In 2008-2010, samples of meat from 40 beef cattle, along with grass, soil and commercial feed, taken from ten matched pairs of flood-prone and control farms, were analysed for PCDD/Fs and PCBs. Concentrations were higher in soil and grass from flood-prone farms. The beef samples from flood-prone farms had total TEQ levels about 20% higher than on control farms. A majority of flood-prone farms (7/10) had higher median levels in beef than on the corresponding control farm. This first controlled investigation into PCDD/F and PCB contamination in beef produced on flood-prone land, presents robust evidence that flooding is a contaminant transfer mechanism to cattle raised on river catchments with a history of urbanisation and industrialisation. PCDD/F and PCB sources in these river systems are likely to be a result of the legacy of contamination from previous industrialisation, as well as more recent combustion activity or pollution events. Crow

Water, sanitation and hygiene risk factors for the transmission of cholera in a changing climate: using a systematic review to develop a causal process diagram

Cholera is a severe diarrhoeal disease affecting vulnerable communities. A long-term solution to cholera transmission is improved access to and uptake of water, sanitation and hygiene (WASH). Climate change threatens WASH. A systematic review and meta-analysis determined five overarching WASH factors incorporating 17 specific WASH factors associated with cholera transmission, focussing upon community cases. Eight WASH factors showed lower odds and six showed higher odds for cholera transmission. These results were combined with findings in the climate change and WASH literature, to propose a health impact pathway illustrating potential routes through which climate change dynamics (e.g. drought, flooding) impact on WASH and cholera transmission. A causal process diagram visualising links between climate change dynamics, WASH factors, and cholera transmission was developed. Climate change dynamics can potentially affect multiple WASH factors (e.g. drought-induced reductions in handwashing and rainwater use). Multiple climate change dynamics can influence WASH factors (e.g. flooding and sea-level rise affect piped water usage). The influence of climate change dynamics on WASH factors can be negative or positive for cholera transmission (e.g. drought could increase pathogen desiccation but reduce rainwater harvesting). Identifying risk pathways helps policymakers focus on cholera risk mitigation, now and in the future

Regional Differences in Presence of Shiga toxin-producing Escherichia coli Virulence-Associated Genes in the Environment in the North West and East Anglian regions of England

Shiga toxin-producing Escherichia coli is carried in the intestine of ruminant animals, and outbreaks have occurred after contact with ruminant animals or their environment. The presence of STEC virulence genes in the environment was investigated along recreational walking paths in the North West and East Anglia regions of England. In all, 720 boot sock samples from walkers’ shoes were collected between April 2013 and July 2014. Multiplex PCR was used to detect E. coli based on the amplification of the uidA gene and investigate STEC-associated virulence genes eaeA, stx1 and stx2. The eaeA virulence gene was detected in 45·5% of the samples, where stx1 and/or stx2 was detected in 12·4% of samples. There was a difference between the two regions sampled, with the North West exhibiting a higher proportion of positive boot socks for stx compared to East Anglia. In univariate analysis, ground conditions, river flow and temperature were associated with positive boot socks. The detection of stx genes in the soil samples suggests that STEC is present in the English countryside and individuals may be at risk for infection after outdoor activities even if there is no direct contact with animals. Significance and Impact of the Study: Several outbreaks within the UK have highlighted the danger of contracting Shiga toxin-producing Escherichia coli from contact with areas recently vacated by livestock. This is more likely to occur for STEC infections compared to other zoonotic bacteria given the low infectious dose required. While studies have determined the prevalence of STEC within farms and petting zoos, determining the risk to individuals enjoying recreational outdoor activities that occur near where livestock may be present is less researched. This study describes the prevalence with which stx genes, indicative of STEC bacteria, were found in the environment in the English countryside

Deep Learning for Relevance Filtering in Syndromic Surveillance: A Case Study in Asthma/Difficulty Breathing

In this paper, we investigate deep learning methods that may extract some word context for Twitter mining for syndromic surveillance. Most of the work on syndromic surveillance has been done on the flu or Influenza- Like Illnesses (ILIs). For this reason, we decided to look at a different but equally important syndrome, asthma/difficulty breathing, as this is quite topical given global concerns about the impact of air pollution. We also compare deep learning algorithms for the purpose of filtering Tweets relevant to our syndrome of interest, asthma/difficulty breathing. We make our comparisons using different variants of the F-measure as our evaluation metric because they allow us to emphasise recall over precision, which is important in the context of syndromic surveillance so that we do not lose relevant Tweets in the classification. We then apply our relevance filtering systems based on deep learning algorithms, to the task of syndromic surveillance and compare the results with real-world syndromic surveillance data provided by Public Health England (PHE).We find that the RNN performs best at relevance filtering but can also be slower than other architectures which is important for consideration in real-time application. We also found that the correlation between Twitter and the real-world asthma syndromic surveillance data was positive and improved with the use of the deep- learning-powered relevance filtering. Finally, the deep learning methods enabled us to gather context and word similarity information which we can use to fine tune the vocabulary we employ to extract relevant Tweets in the first place

Community use of facemasks and similar barriers to prevent respiratory illness such as COVID-19: A rapid scoping review

Background: Evidence for face-mask wearing in the community to protect against respiratory disease is unclear. Aim: To assess effectiveness of wearing face masks in the community to prevent respiratory disease, and recommend improvements to this evidence base. Methods: We systematically searched Scopus, Embase and MEDLINE for studies evaluating respiratory disease incidence after face-mask wearing (or not). Narrative synthesis and random-effects meta-analysis of attack rates for primary and secondary prevention were performed, subgrouped by design, setting, face barrier type, and who wore the mask. Preferred outcome was influenza-like illness. Grading of Recommendations, Assessment, Development and Evaluations (GRADE) quality assessment was undertaken and evidence base deficits described. Results: 33 studies (12 randomised control trials (RCTs)) were included. Mask wearing reduced primary infection by 6% (odds ratio (OR): 0.94; 95% CI: 0.75–1.19 for RCTs) to 61% (OR: 0.85; 95% CI: 0.32–2.27; OR: 0.39; 95% CI: 0.18–0.84 and OR: 0.61; 95% CI: 0.45–0.85 for cohort, case–control and cross-sectional studies respectively). RCTs suggested lowest secondary attack rates when both well and ill household members wore masks (OR: 0.81; 95% CI: 0.48–1.37). While RCTs might underestimate effects due to poor compliance and controls wearing masks, observational studies likely overestimate effects, as mask wearing might be associated with other risk-averse behaviours. GRADE was low or very low quality. Conclusion: Wearing face masks may reduce primary respiratory infection risk, probably by 6–15%. It is important to balance evidence from RCTs and observational studies when their conclusions widely differ and both are at risk of significant bias. COVID-19-specific studies are required

Twitter mining using semi-supervised classification for relevance filtering in syndromic surveillance

We investigate the use of Twitter data to deliver signals for syndromic surveillance in order to assess its ability to augment existing syndromic surveillance efforts and give a better understanding of symptomatic people who do not seek healthcare advice directly. We focus on a specific syndrome—asthma/difficulty breathing. We outline data collection using the Twitter streaming API as well as analysis and pre-processing of the collected data. Even with keyword-based data collection, many of the tweets collected are not be relevant because they represent chatter, or talk of awareness instead of an individual suffering a particular condition. In light of this, we set out to identify relevant tweets to collect a strong and reliable signal. For this, we investigate text classification techniques, and in particular we focus on semi-supervised classification techniques since they enable us to use more of the Twitter data collected while only doing very minimal labelling. In this paper, we propose a semi-supervised approach to symptomatic tweet classification and relevance filtering. We also propose alternative techniques to popular deep learning approaches. Additionally, we highlight the use of emojis and other special features capturing the tweet’s tone to improve the classification performance. Our results show that negative emojis and those that denote laughter provide the best classification performance in conjunction with a simple word-level n-gram approach. We obtain good performance in classifying symptomatic tweets with both supervised and semi-supervised algorithms and found that the proposed semi-supervised algorithms preserve more of the relevant tweets and may be advantageous in the context of a weak signal. Finally, we found some correlation (r = 0.414, p = 0.0004) between the Twitter signal generated with the semi-supervised system and data from consultations for related health conditions