@article{
 title = {Knowledge synthesis to support risk assessment of climate change impacts on food and water safety: A case study of the effects of water temperature and salinity on Vibrio parahaemolyticus in raw oysters and harvest waters},
 type = {article},
 year = {2015},
 identifiers = {[object Object]},
 keywords = {Climate change,Food safety,Meta-analysis,Oysters,Systematic review,Vibrio parahaemolyticus},
 pages = {86-93},
 volume = {68},
 websites = {http://www.sciencedirect.com/science/article/pii/S096399691400430X},
 month = {2},
 id = {55f8a91d-1e33-30cb-82b7-09fc287f4455},
 created = {2015-06-19T12:52:57.000Z},
 accessed = {2015-04-08},
 file_attached = {true},
 profile_id = {a0759483-03fa-3e0b-88ee-8f55266b3657},
 group_id = {0e532975-1a47-38a4-ace8-4fe5968bcd72},
 last_modified = {2015-06-19T12:53:15.000Z},
 read = {false},
 starred = {false},
 authored = {false},
 confirmed = {true},
 hidden = {false},
 abstract = {Global climate change is expected to have multiple impacts on food and water safety. Knowledge synthesis methods can provide a credible and robust assessment of the evidence on these potential impacts to inform risk assessments. To illustrate the utility of these methods to synthesize data for a case study scenario of one of the potential impacts of climate change on food safety, and to inform a complementary risk assessment model, we conducted a systematic review of the effects of water temperature and salinity on Vibrio parahaemolyticus in raw oysters and harvest waters. We searched four bibliographic databases for literature then two independent reviewers screened 953 citations for relevance and extracted data from 120 relevant articles. Sufficient data for meta-analysis were provided in 19 articles. Random-effects meta-analysis, a quality-of-evidence assessment, and meta-regression (where applicable) were conducted on unique data subsets reporting correlation coefficients for the relationships of interest. A positive correlation was identified between water temperature and V. parahaemolyticus in oysters (r=0.58; 95% CI: 0.47 to 0.68) and water (r=0.60; 95% CI: 0.47 to 0.70). However, both analyses were significantly heterogeneous (I2=74% and I2=75%, respectively), which was not explained by the variables evaluated in meta-regression. No consistent relationship was identified for water salinity. A low and very low quality-of-evidence was identified for the water temperature and salinity relationships, irrespective of sampling media. Due to insufficient reporting and availability, we were unable to perform meta-analysis on regression coefficients for direct inclusion in the risk assessment model. However, the synthesis process provided a structured and transparent evaluation of the evidence to confirm associations between water temperature and V. parahaemolyticus densities, indicate no consistent association between salinity and V. parahaemolyticus densities, and compile available regression coefficients. Future synthesis research could evaluate other effects of climate on food and water safety to inform additional risk assessments.},
 bibtype = {article},
 author = {Young, Ian and Gropp, Kathleen and Fazil, Aamir and Smith, Ben A.},
 journal = {Food Research International}
}

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Knowledge synthesis methods can provide a credible and robust assessment of the evidence on these potential impacts to inform risk assessments. To illustrate the utility of these methods to synthesize data for a case study scenario of one of the potential impacts of climate change on food safety, and to inform a complementary risk assessment model, we conducted a systematic review of the effects of water temperature and salinity on Vibrio parahaemolyticus in raw oysters and harvest waters. We searched four bibliographic databases for literature then two independent reviewers screened 953 citations for relevance and extracted data from 120 relevant articles. Sufficient data for meta-analysis were provided in 19 articles. Random-effects meta-analysis, a quality-of-evidence assessment, and meta-regression (where applicable) were conducted on unique data subsets reporting correlation coefficients for the relationships of interest. A positive correlation was identified between water temperature and V. parahaemolyticus in oysters (r=0.58; 95% CI: 0.47 to 0.68) and water (r=0.60; 95% CI: 0.47 to 0.70). However, both analyses were significantly heterogeneous (I2=74% and I2=75%, respectively), which was not explained by the variables evaluated in meta-regression. No consistent relationship was identified for water salinity. A low and very low quality-of-evidence was identified for the water temperature and salinity relationships, irrespective of sampling media. Due to insufficient reporting and availability, we were unable to perform meta-analysis on regression coefficients for direct inclusion in the risk assessment model. However, the synthesis process provided a structured and transparent evaluation of the evidence to confirm associations between water temperature and V. parahaemolyticus densities, indicate no consistent association between salinity and V. parahaemolyticus densities, and compile available regression coefficients. Future synthesis research could evaluate other effects of climate on food and water safety to inform additional risk assessments.","bibtype":"article","author":"Young, Ian and Gropp, Kathleen and Fazil, Aamir and Smith, Ben A.","journal":"Food Research International","bibtex":"@article{\n title = {Knowledge synthesis to support risk assessment of climate change impacts on food and water safety: A case study of the effects of water temperature and salinity on Vibrio parahaemolyticus in raw oysters and harvest waters},\n type = {article},\n year = {2015},\n identifiers = {[object Object]},\n keywords = {Climate change,Food safety,Meta-analysis,Oysters,Systematic review,Vibrio parahaemolyticus},\n pages = {86-93},\n volume = {68},\n websites = {http://www.sciencedirect.com/science/article/pii/S096399691400430X},\n month = {2},\n id = {55f8a91d-1e33-30cb-82b7-09fc287f4455},\n created = {2015-06-19T12:52:57.000Z},\n accessed = {2015-04-08},\n file_attached = {true},\n profile_id = {a0759483-03fa-3e0b-88ee-8f55266b3657},\n group_id = {0e532975-1a47-38a4-ace8-4fe5968bcd72},\n last_modified = {2015-06-19T12:53:15.000Z},\n read = {false},\n starred = {false},\n authored = {false},\n confirmed = {true},\n hidden = {false},\n abstract = {Global climate change is expected to have multiple impacts on food and water safety. Knowledge synthesis methods can provide a credible and robust assessment of the evidence on these potential impacts to inform risk assessments. To illustrate the utility of these methods to synthesize data for a case study scenario of one of the potential impacts of climate change on food safety, and to inform a complementary risk assessment model, we conducted a systematic review of the effects of water temperature and salinity on Vibrio parahaemolyticus in raw oysters and harvest waters. We searched four bibliographic databases for literature then two independent reviewers screened 953 citations for relevance and extracted data from 120 relevant articles. Sufficient data for meta-analysis were provided in 19 articles. Random-effects meta-analysis, a quality-of-evidence assessment, and meta-regression (where applicable) were conducted on unique data subsets reporting correlation coefficients for the relationships of interest. A positive correlation was identified between water temperature and V. parahaemolyticus in oysters (r=0.58; 95% CI: 0.47 to 0.68) and water (r=0.60; 95% CI: 0.47 to 0.70). However, both analyses were significantly heterogeneous (I2=74% and I2=75%, respectively), which was not explained by the variables evaluated in meta-regression. No consistent relationship was identified for water salinity. A low and very low quality-of-evidence was identified for the water temperature and salinity relationships, irrespective of sampling media. Due to insufficient reporting and availability, we were unable to perform meta-analysis on regression coefficients for direct inclusion in the risk assessment model. 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