| Literature DB >> 24808720 |
Jan C Semenza1, Susanne Herbst2, Andrea Rechenburg2, Jonathan E Suk1, Christoph Höser2, Christiane Schreiber2, Thomas Kistemann2.
Abstract
The PubMed and ScienceDirect bibliographic databases were searched for the period of 1998-2009 to evaluate the impact of climatic and environmental determinants on food- and waterborne diseases. The authors assessed 1,642 short and concise sentences (key facts), which were extracted from 722 relevant articles and stored in a climate change knowledge base. Key facts pertaining to temperature, precipitation, water, and food for 6 selected pathogens were scrutinized, evaluated, and compiled according to exposure pathways. These key facts (corresponding to approximately 50,000 words) were mapped to 275 terminology terms identified in the literature, which generated 6,341 connections. These relationships were plotted on semantic network maps to examine the interconnections between variables. The risk of campylobacteriosis is associated with mean weekly temperatures, although this link is shown more strongly in the literature relating to salmonellosis. Irregular and severe rain events are associated with Cryptosporidium sp. outbreaks, while noncholera Vibrio sp. displays increased growth rates in coastal waters during hot summers. In contrast, for Norovirus and Listeria sp. the association with climatic variables was relatively weak, but much stronger for food determinants. Electronic data mining to assess the impact of climate change on food- and waterborne diseases assured a methodical appraisal of the field. This climate change knowledge base can support national climate change vulnerability, impact, and adaptation assessments and facilitate the management of future threats from infectious diseases. In the light of diminishing resources for public health this approach can help balance different climate change adaptation options.Entities:
Keywords: Campylobacter sp.; Cryptosporidium sp.; Listeria sp.; Norovirus; Salmonella sp.; Vibrio sp.; climate change; climate variability; environment; food; food- and waterborne diseases; ontology; precipitation; rain; reservoir
Year: 2012 PMID: 24808720 PMCID: PMC3996521 DOI: 10.1080/10643389.2010.534706
Source DB: PubMed Journal: Crit Rev Environ Sci Technol ISSN: 1064-3389 Impact factor: 12.561
FIGURE 1.Semantic network maps of thematic attributes for Cryptosporidium sp. and Listeria sp. Climate change knowledgebase for food- and water-borne diseases, 1998–2009. Note: Maps to be read clockwise: thematic aspects are arranged concentrically and colour coded (food = yellow; water = blue; pathogens = pink; climate/environment = green). Cryptosporidium and Listeria pathogens on the bottom left of the circle are coloured blue and initiate connections with other terms, represented with red stings linking to red terms. Gray strings in the background represent the network of all connections not activated in this view. The list of 275 terms in the ontology has been reduced for this map to app. 140 terms due to space restrictions. The 4th level of the hierarchy was deleted for this map but the link was retained by moving it to its ancestor items at the third level (Color figure available online).
FIGURE 2.Annual notifications of campylobacteriosis, cryptosporidiosis, listeriosis, and salmonellosis in the EU and EEA/EFTA countries from 1995 to 2007. These data reflect incomplete reporting by member states. The 2007 data for campylobacteriosis were reported from 25 EU member states, plus Iceland, Lichtenstein, and Norway (Greece and Portugal did not report). Salmonellosis was reported by all EU countries plus Iceland, Liechtenstein and Norway. Cryptosporidiosis notifications are based on 10 of the 19 countries providing data (9 countries reported zero cases). Listeriosis was reported by 29 countries, with the exception of Portugal. Please note the different scales on the y axes in Figure 4B (Color figure available online).
FIGURE 3.Seasonal distribution of campylobacteriosis, cryptosporidiosis, listeriosis, and salmonellosis in the EU and EEA/EFTA countries, in 2007. Source: Country reports. Campylobacteriosis: Austria, Belgium, Cyprus, Czech Republic, Denmark, Estonia, Finland, France, Germany, Hungary, Ireland, Italy, Luxembourg, Malta, Netherlands, Poland, Slovakia, Slovenia, Spain, Sweden, United Kingdom, Iceland, and Norway. Latvia reported zero cases. Cryptosporidiosis: Belgium, Bulgaria, Finland, Germany, Ireland, Luxembourg, Malta, Slovenia, Spain, Sweden, United Kingdom. Cyprus, Czech Republic, Estonia, Finland, Hungary, Latvia, Lithuania, Poland, and Slovakia reported zero cases. Listeriosis: Austria, Belgium, Bulgaria, Cyprus, Denmark, Finland, France, Germany, Greece, Hungary, Ireland, Italy, Latvia, Luxembourg, Netherlands, Poland, Slovakia, Slovenia, Spain, Sweden, United Kingdom, and Norway. Malta and Iceland reported zero cases. Salmonellosis: Austria, Bulgaria, Cyprus, Czech Republic, Denmark, Estonia, Finland, Germany, Greece, Hungary, Ireland, Italy, Latvia, Luxembourg, Netherlands, Portugal, Romania, Slovakia, Slovenia, Spain, Sweden, United Kingdom, Iceland, and Norway (Color figure available online).
FIGURE 4.Radar diagram of thematic aspects for food, water, climate/environment, reservoir, air temperature, water temperature, precipitation, and heavy rainfall event, by pathogen from the climate change knowledgebase for food- and waterborne diseases, 1998–2009. Axes (spokes) with different scales. A radar diagram is a graphical method of plotting multivariate data on a two-dimensional chart, on multiple axes originating from the same pole (Color figure available online).
Selected pathogens with environmental/climatic variables (and factors associated with climate) from the climate change knowledge base for food- and waterborne diseases, 1998–2009
Reviews of articles by predefined categories: data quality, study design and data source, causal inference from the climate change knowledge base for food- and waterborne diseases, 1998–2009
| Reviews | Campylobacter | Salmonella | Listeria | Vibrio | Cryptosporidium | Norovirus |
|---|---|---|---|---|---|---|
| Data quality | ||||||
| High | 81 | 65 | 17 | 16 | 27 | 48 |
| Moderate | 85 | 98 | 34 | 33 | 53 | 33 |
| Low | 2 | 7 | 3 | 6 | 7 | 3 |
| Very low | __ | 1 | __ | __ | __ | 1 |
| Not classified | 4 | 20 | 2 | 3 | 3 | 9 |
| Study design | ||||||
| Meta-analyses | 19 | 8 | 2 | 3 | 11 | 15 |
| Review | 40 | 40 | 11 | 22 | 36 | 8 |
| Randomized controlled trial | 2 | 1 | __ | __ | 2 | 2 |
| Nonrandomized intervention study | __ | __ | __ | 1 | __ | __ |
| Cohort study | 9 | 6 | 1 | __ | 1 | 6 |
| Case-control study | 11 | 14 | 2 | __ | 3 | 2 |
| Cross-sectional (survey) | 3 | 2 | 1 | __ | 1 | 4 |
| Ecological study | 13 | 10 | 1 | 10 | 2 | 2 |
| Case study | 31 | 15 | 2 | 4 | 12 | 11 |
| Expert opinion | __ | 2 | __ | 1 | __ | 1 |
| In vivo experiment | 10 | 13 | 2 | 1 | 2 | 3 |
| In vitro experiment | 4 | 14 | 18 | __ | 3 | 1 |
| Molecular evidence | 2 | 1 | 2 | __ | 2 | 9 |
| Outbreak | 11 | 21 | 2 | 1 | 4 | 6 |
| Data source | __ | __ | __ | __ | __ | 4 |
| Other | 15 | 24 | 10 | 15 | 7 | 11 |
| Not classified | 2 | 20 | 2 | __ | 4 | 9 |
| Casual inference | ||||||
| Direct | 84 | 56 | 16 | 22 | 22 | 40 |
| Moderate | 57 | 58 | 10 | 19 | 39 | 31 |
| Indirect | 29 | 57 | 28 | 17 | 26 | 15 |
| Not classified | 2 | 20 | 2 | __ | 3 | 8 |
Note. The numbers represent reviews conducted by independent reviewers and not the number of peer reviewed articles; categories may overlap (e.g., multiple pathogens per study).