A systematic review and meta-analysis of ambient temperature and precipitation with infections from five food-borne bacterial pathogens Naveen Manchal1, Megan K. Young2,3,4, Maria Eugenia Castellanos1,5,6, Peter Leggat1,5,6,7 and Oyelola Adegboye1,5,6,8 1Public Health and Tropical Medicine, College of Public Health, Medical and Veterinary Sciences, James Cook University, Townsville, QLD, Australia; 2Metro North Public Health Unit, Metro North Hospital and Health Service, Brisbane, Australia; 3School of Medicine and Dentistry, Griffith University, Gold Coast, Australia; 4Faculty of Medicine, School of Public Health, University of Queensland, Brisbane, QLD, Australia; 5Australian Institute of Tropical Health andMedicine, James Cook University, Townsville, QLD, Australia; 6World Health Organization Collaborating Centre for Vector-Borne and Neglected Tropical Diseases, James Cook University, Townsville, QLD, Australia; 7School of Public Health, Faculty of Health Sciences, University of the Witwatersrand, Johannesburg, South Africa and 8Menzies School of Health Research, Charles Darwin University, Darwin, NT, Australia Abstract Studies on climate variables and food pathogens are either pathogen- or region-specific, necessitating a consolidated view on the subject. This study aims to systematically review all studies on the association of ambient temperature and precipitation on the incidence of gastroenteritis and bacteraemia from Salmonella, Shigella, Campylobacter, Vibrio, and Listeria species. PubMed, Ovid MEDLINE, Scopus, and Web of Science databases were searched up to 9March 2023.We screened 3,204 articles for eligibility and included 83 studies in the review and three in the meta-analysis. Except for one study on Campylobacter, all showed a positive association between temperature and Salmonella, Shigella, Vibrio sp., and Campylobacter gastroenteritis. Similarly, most of the included studies showed that precipitation was positively associated with these conditions. These positive associations were found regardless of the effect measure chosen. The pooled incidence rate ratio (IRR) for the three studies that included bacteraemia from Campylobacter and Salmonella sp. was 1.05 (95 per cent confidence interval (95% CI): 1.03, 1.06) for extreme temperature and 1.09 (95% CI: 0.99, 1.19) for extreme precipitation. If current climate trends continue, our findings suggest these pathogens would increase patient morbidity, the need for hospitalization, and prolonged antibiotic courses. Introduction Worldwide, 33 million years of healthy lives are lost each year to food-borne illness, which is underestimated [1]. Studies have shown that warmer climates and heat waves increase the incidence of Salmonellosis and Campylobacteriosis [2, 3]. However, different climate variables can affect each food-borne pathogen differently. The association between temperature rise and increased incidence of infection is more consistent with salmonellosis than with Listeria infection [4]. A meta-analysis showed the pooled relative risk (RR) for each 1-degree rise in temperature for salmonellosis was 1.05 (95% confidence interval (C):1.04–1.07) [5]. For Vibrio infections, an increase in water (not air) temperature is associated with an increased incidence of infection [4]. The intensity and rapidity of exposure to the climate variable also determine the risk of infection. In addition to the number of infections, it is also important to study the severity of the disease.Are these infections limited to gastroenteritis, or is there a trend for more invasive infections like bacteraemia? The impact of bacteraemia compared to gastroenteritis is greater, with increased morbidity and mortality, hospitalization, and health services costs [6, 7]. With climate change and more difficult conditions for environmental pathogens, bacteraemiamay reflect increased virulence from these organisms.A 10-year analysis of passive surveillance data inQueensland,Australia, noted a rise in the incidence of invasive salmonellosis, particularly in the elderly [8]. Ninety-two per cent of these invasive infections were diagnosed on blood culture [8]. Salmonella Virchow was the most common species identified [8]. The contributory factors for the increased invasiveness of these infections were unclear. It has been projected that compared to the years of life lost to disabilities (YLD) in 2000, salmonellosis would contribute to a 9–48% increase in YLD by 2030 due to temperature changes from climate change [9]. The relationship between climate change and food/waterborne disease is complex. There are temporal and regional variations across the world affected by behavioural changes in populations that increase the risk of these illnesses. While cholera, enteric fever, and bacillary dysentery Epidemiology and Infection www.cambridge.org/hyg Review Cite this article: Manchal N, Young MK, Castellanos ME, Leggat P and Adegboye O (2024). A systematic review and meta-analysis of ambient temperature and precipitation with infections from five food-borne bacterial pathogens. Epidemiology and Infection, 152, e98, 1–20 https://doi.org/10.1017/S0950268824000839 Received: 22 September 2023 Revised: 18 April 2024 Accepted: 20 May 2024 Keywords: Gastroenteritis; bacteraemia; temperature; precipitation; infectious disease Corresponding author: Oyelola Adegboye; Email: oyelola.adegboye@menzies.edu.au © The Author(s), 2024. Published by Cambridge University Press. This is an Open Access article, distributed under the terms of the Creative Commons Attribution licence (http:// creativecommons.org/licenses/by/4.0), which permits unrestricted re-use, distribution and reproduction, provided the original article is properly cited. https://doi.org/10.1017/S0950268824000839 Published online by Cambridge University Press https://orcid.org/0000-0002-9793-8024 https://doi.org/10.1017/S0950268824000839 mailto:oyelola.adegboye@menzies.edu.au http://creativecommons.org/licenses/by/4.0 http://creativecommons.org/licenses/by/4.0 https://doi.org/10.1017/S0950268824000839 predominate in the Indian subcontinent and Africa, non-cholera Vibrio species and non-typhoidal Salmonella and Campylobacter infections are prevalent in the temperate regions of theworld. There is heterogeneity in the studies reporting an association between climate variables and enteric pathogens, with varied methodologies and modelling strategies. Existing literature on the impact of climate variables on food- borne pathogens has been restricted to a particular variable [5] or pathogen [10]. We formulated the research question to determine the effect of ambient temperature (including heat waves) and pre- cipitation (including floods) on the incidence of pathogen-specific infections – gastroenteritis and bacteraemia. Methods Search strategy This systematic review followed the Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) guidelines. Searches for published literature in English on the impact of climate change on infections from Salmonella, Shigella, Campylobacter, Vibrio, and Listeria infections were conducted. MEDLINE (Ovid), Scopus, PubMed, and Web of Science electronic databases were searched without any restrictions on date range. Inclusion and exclusion criteria All published articles on the pathogens of interest and one or more climate variables were eligible for inclusion. No time frame was applied as the effect of climate variables on bacteria has not been a recent development. We excluded studies not in English and those not on selected pathogens or climate variables of interest and review articles. Conference abstracts and posters were not included. Data extraction One author (NM) screened the abstracts, shortlisted the studies for full-text assessment, and determined inclusion in the review upon examination of the full text. The final list of eligible studies for meta-analysis was checked by two authors (NM and OA). The studies were tabulated by the pathogen of study and data on publication year, study location, study time period, number of cases, population number, climate variable exposure, exposure lag, quantitative estimation of risk, modelling strategy, and key findings, and reported statistics of adjusted analyses were extracted into a purpose-built database. The risk estimates that the studies reported were the correlation coefficient (r), RR, odds ratio (OR), and incidence rate ratio (IRR). Quality appraisal We used the ROBINS-E tool as a guide for assessing the risk of bias within the included studies [11]. The tool is validated for use in non-randomized ecological studies. The tool consists of seven domains: bias confounding, exposure and outcome measurements, participation selection, post-exposure intervention, missing data, and reporting bias. Each domain is assessed through signalling questions to make judgements on the risk of bias in the domain, the predicted direction of bias, and whether the risk of bias threatens conclusions regarding the exposure having an effect on the outcome. If the risk of bias was considered ‘high enough to change the direction of the outcomes’, the domain was marked as high risk. If the bias was ‘very low’, the domain was marked as low risk. Studies were considered high quality if the overall judgement suggested a low risk of bias in at most one domain. If there were ‘some concerns of bias’ in at least two domains, they were con- sidered moderate in quality, and if there were three or more domains with ‘high or very high risk of bias’, they were low in quality. Meta-analysis Studies that had included cases of bacteraemia were shortlisted for meta-analysis. We used random-effects models with inverse- variance weighting to pool the IRR estimates for each pathogen togetherwith their 95% confidence interval (CI). The between-study heterogeneity was evaluated using I2 statistics as the proportion of variability in effect estimates that is not attributed to sampling error. Following Higgins et al. 2019 [12], a threshold of p < 0.1 was used to indicate statistical significance, and I2 values of 25%, 50%, and 75% were considered to represent low, moderate, and considerable het- erogeneity, respectively. The statistical analysis was carried out in R version 4.2.2 [13] package meta and metaphor [14]. Results Characteristics of included studies A total of 3,402 studies were obtained from the databases, and after sorting duplicates, 3,204 abstracts were screened. Out of the 186 art- icles shortlisted for full-text reading and eligibility, 83were included in the qualitative review, and three were chosen for meta-analysis (Figure 1). Publication years ranged from 2007 to 2019 (the year of data extraction), with the great majority of included articles (n = 69; 73%) published since 2015 (Figure 1). The grouping of studies by countries and pathogens is summarized in Table 1. All studies in the qualitative review are tabulated. Twenty stud- ies for Campylobacter and twenty-six, nineteen, and eighteen stud- ies for Salmonella, Shigella, and Vibrio species, respectively, were identified. The maximum lagged week was 52 weeks for Vibrio sp. and 9, 12, and 4 weeks for Campylobacter, Salmonella, and Shigella species, respectively. The majority of the articles were scored as having some concerns for bias in at least two domains and were categorized as moderate in quality in the overall judgement (Table S1 in the Supplementary Material). Ten high-quality studies were identified for Campylobacter, and 11 were of moderate quality. Out of these, three had cases that included bacteraemia [15–17]. Twelve studies on Salmonellosis were high quality. Four studies included patients with bacteraemia [10, 18–20]. With shigellosis, none of the studies specifically dis- cussed bacteraemia and three studies were of high quality. With Vibrio sp., four studies were of high quality. Overview of pathogens and effect of temperature and precipitation Campylobacter species The burden of Campylobacteriosis is high in the Americas and Europe, predominantly in the temperate regions (Figure 2), with the United States of America (USA) and United Kingdom (UK) reporting age-standardized disability-adjusted life year (DALY) of 7.55 and 9.4, respectively [21]. Studies on Campylobac- teriosis were predominantly conducted in Europe, North America, and Oceania (Table 1). Campylobacteriosis had a positive 2 Naveen Manchal et al. https://doi.org/10.1017/S0950268824000839 Published online by Cambridge University Press http://doi.org/10.1017/S0950268824000839 https://doi.org/10.1017/S0950268824000839 association with ambient temperature, whether it was measured as a weekly maximum, monthly, or daily average and extreme heat (Table 2 and Figure 3a). This was true not only for gastroenteritis but also for bacteraemia. The rise in cases was mostly found in a temperature range between 10 and 25 ° C. Out of the 20 studies, 19 (95%) reported a positive association with temperature. With precipitation, six out of nine studies described a positive association (Figure 3a and Table 3). The studies by Bi et al. [22] in Australia and Carev et al. [23] in Croatia reported positive correlations. The studies used regression analyses and controlled for seasonality (using a categorical sea- sonal variable), lag effects, and long-term trends. Weekly maximum temperature had a positive impact on gastroenteritis in Brisbane but not in Adelaide in the Australian study [22]. Nei- ther these studies nor another study in Denmark [24] found any association between Campylobacter gastroenteritis and precipi- tation. A study by Kuhn et al. [11] in Nordic countries that studied 64,034 cases over 15 years included cases of bacteraemia and reported a r of 0.09. Two studies reportedOR to show the positive association between the climate variables and gastroenteritis – an international study [24] (weekly maximum temperature) and another case-crossover study on outbreaks in England (daily total rainfall) [25] reported OR of 1.3 (95% CI: 1.08, 1.55) and 2.88 (95% CI: 0.29, 28.1), Full-text articles excluded, (n=103) Id en tif ica tio n Sc re en in g El ig ib ili ty In clu de d Total n= 3402 Scopus=246 Pubmed=255 MEDLINE(ovid)=1898 Web of Science=1003 No. after duplicates =3204 Records screened n = 3204 Full-text articles assessed for eligibility n = 186 Studies included for qualitative review n=83 Records excluded after abstract screening n= 3018 Studies included for meta- analysis (reporting IRR for bacteremia cases) n=3 Studies for each pathogen Campylobacter-20 Salmonella-26 Shigella-19 Vibrio--18 Figure 1. PRISMA flow chart showing the study selection process. Epidemiology and Infection 3 https://doi.org/10.1017/S0950268824000839 Published online by Cambridge University Press https://doi.org/10.1017/S0950268824000839 respectively. The time-series study by Fleury et al. [26] in two provinces in Canada reported a 2.2% increase in gastroenteritis in Alberta and 4.5% in Newfoundland–Labrador, respectively, per degree rise in weekly mean temperature. A study in Maryland, USA [16], analysed the association with extreme heat and precipitation and found an IRR of 1.04 (95% CI: 1.01, 1.08) and 1.03 (95% CI: 1.01, 1.05), respectively. Importantly, this study included cases of bacteraemia and found that higher La Niña periods have a greater impact on the incidence of infections compared to El Niño periods (IRR = 1.09). Table 1. Grouping of studies by regions of study, pathogens, and main findings with climate variable associations Region of study (number) Time period of studies Pathogen Infections reported Association with rise in ambient temperature Association with rise in precipitation Europe (30), UK (9), Germany (6), Italy (4), Sweden (4), Denmark (2), France (2), Georgia (1), Finland (1) 1981–2015 Campylobacter Salmonella Vibrio sp. Gastroenteritis and bacteraemia Gastroenteritis Gastroenteritis, wound infections Positive associations Positive association in all studies Positive association with sea temperature 6 out of 9 studies reported a positive association No association in temperate regions Australia (24) 1990–2019 Campylobacter Salmonella Gastroenteritis Gastroenteritis Positive association in Brisbane not Adelaide Positive association in all studies No association Asia (21), China (11), Iran (4), India (1), Jordan (1), Korea (1), Nepal (1), Philippines (1), Taiwan (1) 1984–2018 Salmonella Shigella Vibrio sp. Gastroenteritis Gastroenteritis Gastroenteritis Positive association in all studies Positive association in all studies Positive association 1990 studies out of 16 reported positive association 9 out of 10 studies found positive association with floods 8 out of 9 studies had a positive association North and South America (17), USA (15), Canada (1), Brazil (1) 1992–2018 Campylobacter Salmonella Shigella Vibrio parahaemolyticus Gastroenteritis and bacteraemia Gastroenteritis and bacteraemia Gastroenteritis Gastroenteritis Positive association Positive association Positive association Positive in one study Positive association for extreme precipitation Positive Africa (3), Ethiopia (1), Ghana (1) 2002–2008 Vibrio Gastroenteritis Positive association Positive association Figure 2. Global distribution of the burden of Campylobacter, cholera, non-typhoid Salmonella, and Shigella. Source: GBD Results tool: Global Burden of Disease Collaborative Network. Global Burden of Disease Study 2019 (GBD 2019) Results, Seattle, United States: Institute for Health Metrics and Evaluation (IHME), 2020. 4 Naveen Manchal et al. https://doi.org/10.1017/S0950268824000839 Published online by Cambridge University Press https://doi.org/10.1017/S0950268824000839 Table 2. Studies on Campylobacter with temperature as the climate variable, stratified by type of temperature measurement Number Study Region Time period Infection studied Case number Population number Analytical method Key findings and reported statistica Weekly maximum temperature 1 Bi 2008 [22] Brisbane, Australia 1990–2005 Weekly lab–confirmed gastroenteritis 14,697 1,600,000 Time–series Poisson regression R = 0.01 at lag 6 weeks 2 Bi 2008 [22] Adelaide, Australia 1990–2006 Weekly lab–confirmed gastroenteritis 20,211 n/a Time–series Poisson regression B = 0.01 at lag 9 weeks 3 Kovats 2005 [24] Czech Republic, England and Wales, Scotland, Spain, Switzerland, Denmark 1991–2002 Weekly lab–confirmed infections 75,312 n/a Logistic regression Maximum OR 1.3 after lag 14 weeks Temperature range (TR) 10–30 °C Weekly mean temperature 4 Fleury et al. 2006 [26] Alberta, Canada 1992–2000 Weekly lab–confirmed infections 1743 2,696,826 Generalized linear model RR = 1.025 (1.02, 1.03) at lag 3 weeks TR 0–20 °C 5 Lake et al. 2009 [35] UK 1989–2006 Weekly lab–confirmed infections n/a n/a Regression analysis RR = 1.0534 (1.03, 1.08) at lag 0 week. TR 10–200 C 6 Kuhn 2020 [3] Nordic countries (2000–2015) 2000–2015 Weekly mean temperature, heat wave gastroenteritis and bacteraemia 64,034 26,000,000 Poisson regression B = 0.09 for temperature and � 0.1 for heat wave TR �35.3–32.8 °C 7 Patrick et al. 2004 [73] Denmark 1998–2001 Weekly lab–confirmed infections 16,305 4,400,000 Linear regression Max. temperature 4 weeks prior had 68% variance TR 13–20 °C 8 Rosenberg et al. 2018 [74] Israel 1999–2010 Weekly lab–confirmed gastroenteritis 29,762 n/a Poisson generalized additive 1–deg. rise associated with 16.1% increase in Campylobacter jejuni and 18.8% increase in Campylobacter coli TR 15–30 °C 9 Tam et al. 2006 [77] UK 1989–1999 Weekly lab–confirmed gastroenteritis 623,817 n/a Negative binomial regression RR = 1.05 (1.03, 1.06) at lag 6weeks up to a threshold of 14 deg. TR 0–140 C 10 White et al. 2009 [80] Philadelphia, USA 1994–2007 Weekly lab–confirmed gastroenteritis 1,477 1,517,550 Poisson regression IRR = 1.041, warm humid weather increases risk 11 Yun et al. 2016 [81] Germany 2004–2007 Weekly clinical and lab– confirmed cases n/a n/a Regression analysis Positive correlation at lag 5 weeks TR 10–25 °C (Continued) Epidem iology and Infection 5 https://doi.org/10.1017/S0950268824000839 Published online by Cam bridge U niversity Press https://doi.org/10.1017/S0950268824000839 Table 2. (Continued) Number Study Region Time period Infection studied Case number Population number Analytical method Key findings and reported statistica Monthly mean temperature 12 Kim et al. 2015 [94] South Korea 2003–2012 Monthly temperature and outbreaks of gastroenteritis n/a n/a Pearson correlation r = 0.66 Incidence calculated by dividing pathogen–specific outbreak by total food–borne outbreaks 13 Carev et al. 2018 [23] Croatia 2007–2012 Monthly counts of lab– confirmed infections 2,658 454,798 Linear regression r = 0.58 TR 10–25 °C 14 Vucković et al. 2011 [76] Croatia 2003–2007 Annual counts of gastroenteritis 1,242 305,505 Multiple regression B = 0.83 in 2003 TR 10–250 C 15 Sanderson et al. 2018 [75] UK 2004–2009 Monthly lab–confirmed infections n/a n/a Autoregressive moving average B = 0.07 at lag 4 weeks Extreme heat and daily temperature 16 Soneja et al. 2016 [16] USA 2002–2012 Extreme heat and gastroenteritis and bacteraemia 4,804 5,900,000 Multivariate binomial regression IRR =1.04 ETT95 17 Djennad et al. 2019 [72] UK 2005–2009 Weekly lab–confirmed gastroenteritis, daily mean temperature n/a n/a Generalized time series B = 7.32 accounting for 33.3.% of cases at lag 2 weeks TR 10–25 °C 18 Milazzo et al. 2017 [78] Adelaide, Australia 1990–2012 Lab–confirmed infections 35,601 n/a Poisson regression model IRR = 0.906 with heat waves, no effect of temperature in warm season, and no lag effect 19 Spencer et al. 2012 [79] New Zealand 2001–2007 Lab–confirmed infections n/a n/a Poisson regression model Spatial and temporal risk factors studied and no temporal risk factors identified ar = correlation coefficient, B = beta coefficient, RR = relative risk, OR = odds ratio, IRR = incidence rate ratio, n/a = not available. 6 N aveen M anchalet al. https://doi.org/10.1017/S0950268824000839 Published online by Cam bridge U niversity Press https://doi.org/10.1017/S0950268824000839 Salmonella species The majority of typhoid and non-typhoidal salmonella infections are found in Africa and Asia. Salmonellosis is the most common cause of bacteraemia in African children [27]. This pathogen also contributes to significant DALY in developed countries (Figure 2) [27]. Studies on Salmonellosis were conducted in North America, Asia, Europe, and Australia. All studies on Salmonella with increases in monthly, weekly, daily, and extreme temperatures showed an association with a rise in cases regardless of the outcome measure used (Tables 4 and 5, Figure 3b). However, precipitation had different effects in temperate and tropical regions of the world. Four out of the 16 studies (25%) did not find a positive association with precipitation. Both climate variables had a positive association with bacteraemia in the USA (Figure 4). Four studies measured monthly average temperature and reported a positive correlation of Salmonella gastroenteritis with ambient temperature. Cherrie et al. [28] performed a time-series analysis in England reporting r = 0.37 for temperature. A surveil- lance study in Ontario, Canada, by Ravel et al. [29] found monthly cases peaking in the summer months, while there was no associ- ation with precipitation. Similar seasonality was noted in a study in Macedonia [30] with a rise of 5.2% incidence per month with maximum monthly mean temperature. Lastly, Wang et al. [31] found an r = 0.55 formonthly temperature and r = 0.48 formonthly precipitation in Guizhou, China. Studies by Akil et al. [2] and Mun et al. [32] reported a positive correlation with an outbreak of infections. However, the association was tested with an actual number of infections in the study period in the former study. Using RR as an outcomemeasure and weeklymean temperature for exposure, four studies reported a positive association with salmonella gastroenteritis. Three of these were time-series analyses. The first [26] in Alberta, Canada, showed a log RR increase of 1.2%; the second [33], in Dhaka, Bangladesh, reported an increase of 14.2% with a 1° rise in temperature for typhoid cases; and the third [34] in Melbourne, Australia, estimated a twofold increase at 33 °C compared to average weekly temperature. Lastly, Lake et al. [35] reported a RR of 1.05 for S. typhimurium and S. enteritidis infec- tions in England. In contrast to the temperate regions of the world, four studies in Asian countries reported a positive association with precipitation. Three of these reported a rise in typhoid cases with increased rainfall and floods [33, 36, 37]. The study by Wang et al. [38] reported a rise in Salmonella hospitalizations in Hong Kong, along with a rise in daily precipitation. For bacteraemia, two studies in the USA that included positive blood culture cases reported a positive association with extreme temperature and precipitation events. Firstly, the study byMorgado et al. [19] reported an IRR of 1.06 (95%CI: 1.04, 1.09). Similarly, the study by Jiang et al. [18] in Maryland, USA, reported an IRR of 1.041 (95% CI: 1.013, 1.069). Another study using IRR was a time- series analysis in Singapore [39] that examined weekly temperature (1 °C rise) and precipitation (10 mm rise) and reported a 4.3% increase and 0.8% increase in gastroenteritis, respectively. Figure 3. Graphs summarizing the estimated effects (r, beta, RR, IRR, and OR) of temperature and precipitation on specific pathogens. (a) Campylobacter, (b) Salmonella, (c) Shigella, and (d) Vibrio. Epidemiology and Infection 7 https://doi.org/10.1017/S0950268824000839 Published online by Cambridge University Press https://doi.org/10.1017/S0950268824000839 Table 3. Studies on Campylobacter with precipitation as the climate variable, stratified by type of precipitation measurement Number Study on organism Region Time period Infection studied Case number Population number Analytical method Key findings and reported statistica Weekly total precipitation 1 Bi 2008 [22] Adelaide, Australia 1990–2006 Weekly lab–confirmed gastroenteritis 20,211 n/a Time–series Poisson regression B = 0.01 at lag 1 week, r = 0.05 2 Kuhn et al. 2020 [15] Nordic countries (2000–2015) 2000–2015 Gastroenteritis and bacteraemia 64,034 2,600,0000 Poisson regression B = 0.3 Precipitation range (PR) 0–105 mm 3 Patrick et al. 2004 [73] Denmark 1998–2001 Weekly lab–confirmed infections 16,305 4,400,000 Linear regression r = 0.06 at lag 4 weeks 4 Djennad et al. 2019 [72] UK 2005–2009 Weekly lab–confirmed gastroenteritis n/a n/a Generalized time series B = 9.36 at lag 1 week PR 0–80 mm Monthly mean precipitation 5 Carev et al. 2018 [23] Croatia 2007–2012 Monthly counts of lab– confirmed infections 2,658 454,798 Linear regression r = 0.04 PR 0–25 mm 6 Sanderson et al. 2018 [75] UK (2004–2009) 2004–2009 Monthly lab–confirmed infections n/a n/a Autoregressive moving average B = 0.01 at lag 4 weeks Daily and extreme precipitation 7 Nichols et al. 2009 [25] England 1910–1999 Lab–confirmed outbreaks (2 or more cases) and daily rainfall n/a n/a Conditional logistic regression OR 2.88 2 weeks prior to outbreak with rainfall>40 mm. (Outbreaks vs. control years were 7 vs. 2) 8 Soneja et al. 2016 [16] USA 2002–2012 Gastroenteritis and bacteraemia, extreme precipitation 4,804 5,900,000 Multivariate binomial regression IRR =1.03 (95% CI: 1.01, 1.05) at 1 day EPT90 9 Colston et al. 2020 [53] Peru 2011–2012 Monthly lab–confirmed cases and floods 1,386 n/a Interrupted time series RR = 1.41 (95% CI: 1.01, 1.07) ar = correlation coefficient, B = beta coefficient, RR = relative risk, OR = odds ratio, IRR = incidence rate ratio, n/a = not available, PR = precipitation range. 8 N aveen M anchalet al. https://doi.org/10.1017/S0950268824000839 Published online by Cam bridge U niversity Press https://doi.org/10.1017/S0950268824000839 Table 4. Studies on Salmonella sp. with temperature as the climate variable, stratified by type of temperature measurement Number Study Region Time period Infection studied Cases number Population number Analytical method Key findings and reported statistica Monthly mean temperature 1 Kim et al. 2015 South Korea 2003–2012 Monthly outbreaks of gastroenteritis n/a n/a Pearson correlation r = 0.75 Incidence calculated by dividing pathogen– specific outbreaks by total food–borne outbreaks 2 Akil et al. 2014 [2] USA 2002–2011 Monthly outbreaks, analysis reported with cases n/a n/a Regression analysis and neural network modelling r = 0.76 10 F rise in temperature led to 4 new cases. TR 35–95 °F. 3 Mun 2020 [32] USA 2009–2016 Monthly lab–confirmed outbreaks compared with outbreaks in restaurants n/a n/a Linear regression analysis B = 0.01 with lag 4 weeks 4 Britton et al. 2010 [82] NZ 1965–2006 Monthly lab–confirmed cases n/a n/a Negative binomial regression IRR = 1.15 (95% CI: 1.07, 1.24),15% rise in cases per degree rise in monthly average temp 5 Cherrie et al. 2018 [28] England 1989–2014 Monthly lab–confirmed cases n/a n/a ARMA r = 0.37, Salmonella enteritidis and Salmonella typhimurium strongest correlation at 4weeks 6 Ravel et al. 2010 [9] Ontario, Canada 2005–2008 Monthly lab–confirmed cases 216 500,000 Poisson regression r = 0.04 7 Grjibovski et al. 2013 [83] Arkhangelsk, Russia 1992–2008 Monthly lab–confirmed cases 4,585 348,000 Negative binomial regression B = 2.04 at lag 4 weeks, 2.04% rise per degree rise in temperature TR �20–20 °C 8 Kendrovski et al. 2011 [30] Macedonia 1998–2008 Monthly lab–confirmed cases 3,890 2,052,722 Pearson correlation r = 0.51 at 4–week lag TR 4–24 °C 9 Wang et al. 2012 [31] Guizhou, China 1984–2007 Monthly lab–confirmed cases n/a n/a Spearman rank correlation and wavelet analysis r = 0.55 at 4–week lag TR 1.8–25.8 °C 10 Zhang et al. 2010 [86] Townsville,A ustralia 1990–2005 Monthly lab–confirmed cases 1,170 186,000 Spearman correlation B = 0.04 with max temperature (TR 24–34 ° C) B = 0.06 withmin temp. (0–25 ° C) at lag 4 weeks Weekly mean temperature 11 Aik et al. 2018 [39] Singapore 2005–2015 Weekly lab–confirmed infections 11,324 5,500,000 Multivariable regression analysis IRR = 1.06 (95% CI: 1.02, 1.11) 6.3% increase per degree after 3 weeks. TR 25.3–30.1 12 Lake et al. 2009 [35] UK 1981–2006 Weekly lab–confirmed gastroenteritis Regression analysis RR = 1.05 (95% CI: 1.03, 1.08) 13 Dewan et al. 2013 [33] Bangladesh 2005–2009 Weekly lab–confirmed cases n/a n/a Spatial and time series RR = 1.8 (95% CI: 1.2, 2.8) at 4 weeks, 14.2% rise with 1–degree rise in temp. TR 20–300 C 14 Fleury et al. 2006 [26] Alberta, Canada 1992–2000 Weekly lab–confirmed cases 6,282 2,696,826 Generalized linear and additive model Max RR = 1.02 (95% CI: 1.01, 1.02) at lag 2 weeks. Positive association with temperature in Alberta but not Newfoundland TR 15–400 C (Continued) Epidem iology and Infection 9 https://doi.org/10.1017/S0950268824000839 Published online by Cam bridge U niversity Press https://doi.org/10.1017/S0950268824000839 Table 4. (Continued) Number Study Region Time period Infection studied Cases number Population number Analytical method Key findings and reported statistica 15 Robinson et al. 2022 [34] Melbourne, Australia 2000–2019 Weekly lab–confirmed cases 29,421 5,000,000 Quasi–Poisson generalized linear model RR = 1.1 (95% CI: 1.05, 1.2) at lag 4 weeks TR 15–350 C 16 Zhang et al. 2008 [85] Adelaide, Australia 1990–2004 Weekly lab–confirmed cases 4,740 1,100,000 Spearman correlation B = 0.04 at lag 1 week, increase in cases up to 4 weeks 17 Zhang et al. 2010 [86] Brisbane, Australia 1990–2005 Weekly lab–confirmed cases 5,294 1,600,000 Spearman correlation B = 0.09 with max temp. (15–350 C) and B = 0.06 with min temperature (5–250 C) at lag 2 weeks 18 Nili et al. 2021 [21] Iran 2008–2018 Stool, blood weekly lab– confirmed cases 569 1,952,435 Negative binomial generalized linear model IRR = 1.04 (95% CI: 1.02, 1.06) Daily, annual and extreme temperature 19 Wang et al. 2018 [38] Hong Kong 2002–2011 Daily admissions, daily mean temperature 4,828 7,340,000 DLNM and GAM RR =6.13 (95% CI: 3.52, 10.67) TR 15–300 C 20 Milazzo et al. 2016 [40] Adelaide, Australia 1990–2012 Daily lab–confirmed cases, daily maximum temperature 7,845 n/a Time–series Poisson regression IRR 1.034 in summer months in Adelaide at lag 2 weeks, risk varied with serotypes TR 10–400 C 21 Simpson et al. 2019 [84] NSW, Australia 2001–2015 Annual lab–confirmed cases, mean annual temperature 514 n/a CAR RR = 1.31 (95% CI: 1.01, 1.68), more with S. wangata compared to typhimurium 22 Jiang et al. 2015 [18] Maryland, USA 2000–2012 Extreme temperature, blood, stool 9,529 5,980,000 Negative binomial GEE IRR = 1.04 (95% CI: 1.01, 1.07) ETT95 23 Morgado et al. 2021 [19] Connecticut, USA 2004–2014 Extreme temperature, blood, stool 32,951 n/a Negative binomial GEE IRR =1.06 (95% CI: 1.04, 1.09) ETT95 24 Iyer et al. 2021 [36] Gujarat, India 1995–2017 Monthly lab–confirmed enteric fever cases, extreme temperature > 95th percentile 29,236 10,400,000 Negative binomial generalized linear model RR = 1.01 (95% CI: 0.98, 1.04) TR 15–350 C Note: TR: 0–35 °C. ar = correlation coefficient, B = beta coefficient, RR = relative risk, OR = odds ratio, IRR = incidence rate ratio, n/a = not available, DLNM = distributed lag non-linear model, GAM = generalized additive model, CAR = conditional autoregressive model, GEE = generalized estimating equation. 10 N aveen M anchalet al. https://doi.org/10.1017/S0950268824000839 Published online by Cam bridge U niversity Press https://doi.org/10.1017/S0950268824000839 Table 5. Studies on Salmonella sp. with precipitation as the climate variable, stratified by type of precipitation measurement Number Study Region Time period Infection studied Cases number Population number Analytical method Key findings and reported statistica Weekly total precipitation 1 Aik et al. 2018 [39] Singapore 2005–2015 Weekly lab–confirmed infections 11,324 5,500,000 Multivariable regression analysis IRR = 1.01 (95% CI: 1.02, 1.02) at lag 2 weeks PR 0–440 mm 2 Dewan et al. 2013 [33] Bangladesh 2005–2009 Weekly lab–confirmed cases Spatial and time series RR = 1.5 (95% CI: 1.2, 2.2) at 3 weeks PR 50–200 mm 3 Liu et al. 2018 [37] Hunan, China 2005–2012 Weekly lab–confirmed cases 1,682 n/a DLNM RR = 1.46 (95% CI: 1.10, 1.92) after lag 1 week PR 0–200 mm 4 Robinson et al. 2022 [34] Melbourne, Australia 2000–2019 Weekly lab–confirmed cases 29,421 5,000,000 Quasi–Poisson generalized linear model No association PR 0–40 mm 5 Zhang et al. 2010 [86] Brisbane, Australia 1990–2005 Weekly lab–confirmed cases 5,294 1,600,000 Spearman correlation B = 0.002 with lag 2 weeks Monthly total precipitation 6 Zhang et al. 2010 [86] Townsville, Australia Monthly lab–confirmed cases 1,170 186,000 Poisson regression B = 0.0006 at lag 12 weeks 7 Akil et al. 2014 [2] USA 2002–2011 Monthly outbreaks n/a n/a Regression analysis and neural network modelling No correlation 8 Mun 2020 [32] 2009–2016 USA Monthly lab–confirmed outbreaks compared with outbreaks in restaurants n/a n/a Linear regression analysis B = �0.02 with lag 4 weeks 9 Ravel et al. 2010 [29] Ontario, Canada 2005–2008 Monthly lab–confirmed cases 216 500,000 Poisson regression No association 10 Grjibovski et al. 2013 [83] Arkhangelsk, Russia 1992–2008 Monthly lab–confirmed cases 4,585 348,000 Negative binomial regression Uncertain association PR 0–150 mm 11 Wang et al. 2012 [31] Guizhou, China 1984–2007 Monthly lab–confirmed cases n/a n/a Spearman rank correlation and wavelet analysis r = 0.48 at 4–week lag PR 5–437 mm Extreme and daily precipitation 12 Jiang et al. 2015 [18] Maryland, USA 2000–2012 Blood, stool, extreme precipitation 9,529 5,980,000 Negative binomial GEE IRR = 1.06 (95% CI: 1.04, 1.08) EPT90 13 Morgado et al. 2021 [19] Connecticut, USA 2004–2014 Blood, stool, extreme precipitation 32,951 n/a Negative binomial GEE IRR = 1.22 (95% CI: 1.10, 1.35) EPT95 14 Iyer et al. 2021 [36] Gujarat, India 1995–2017 Monthly lab–confirmed enteric fever cases, extreme precipitation 29,236 10,400,000 Negative binomial generalized linear model RR = 1.01 (95% CI: 0.97, 1.05) PR 250–450 mm 15 Wang et al. 2018 [38] Hong Kong 2002–2011 Daily admissions, daily precipitation 4,828 7,340,000 DLNM and GAM RR =1.34 (95% CI: 0.98, 1.84) PR 0–100 mm 16 Zhang et al. 2008 [85] Adelaide, Australia 1990–2004 Weekly lab–confirmed cases, daily rainfall 4,740 1,100,000 Spearman correlation r = �0.02 (95% CI: �0.04, �0.003) ar = correlation coefficient, B = beta coefficient, RR = relative risk, OR = odds ratio, IRR = incidence rate ratio, n/a = not available, DLNM = distributed lag non-linear model, GEE = generalized estimating equation, GAM = generalized additive model, PR = precipitation range. Epidem iology and Infection 11 https://doi.org/10.1017/S0950268824000839 Published online by Cam bridge U niversity Press https://doi.org/10.1017/S0950268824000839 Lastly, Milazzo et al. [40] found an increased risk of Salmonella cases varies with serotypes in Adelaide, and Britton et al. reported an IRR of 1.15 (95% CI: 1.07, 1.24) in New Zealand with a rise in monthly average temperature. Shigella species Studies on Shigellosis [41–48] were predominantly from China, and all nine studies on temperature showed a positive association (Figure 3c). Nine of ten studies (90%) on extreme precipitation events like floods showed a positive association (Tables 6 and 7). (Figure 3c). Most studies found a rise in the incidence of gastro- enteritis between 10 and 30 ° C temperature range. All the included studies had gastroenteritis as the predominant clinical manifest- ation, and no studies specified bacteraemia as an outcome. Three studies reported a correlation (r). Lee et al. [44] reported r = 0.65 for monthly average temperature and r = 0.17 for monthly precipitation in their study in Kon Tum Province, Vietnam. Two other studies [49, 50] found a rise in gastroenteritis cases in China after a lag of 2 weeks. Other Chinese studies [41, 43, 45, 47, 51, 52] reported an RR rise in Shigellosis with a rise in daily temperature. Li et al. [45] noted that each degree rise led to an increase of 1.6%, and children aged 0–5 years were largely affected.Wang et al. [51] noted that ambient temperature was the most important factor regardless of the climate zone studied. Also, temperate cities in China were more affected than subtropical cities. Further, studies in China [49, 53–55] revealed a positive association between Shigellosis cases and floods, with an increased incidence for up to three weeks. The risk was increased with short-term and severe floods and reduced with flood duration. Vibrio species – cholera and non-cholera strains Cholera is a major public health burden in Africa and Asia (Figure 2), and a majority of the studies on cholera were conducted in these continents. All nine studies on temperature and seven out of eight studies on precipitation showed a positive association with gastroenteritis (Figure 3d). The temperature range of rise in cases was 15–40 ° C. The study by Ruiz-Moreno et al. [56] extensively investigated the rainfall–cholera relationship in Madras and explained the dual peak in annual cases by the differential effects of rainfall in endemic and epidemic areas. Generally, a complex relationship between rainfall and ambient temperature and cholera varies across regions (Table 8 and Table 9). The study by Ali et al. in matrix laboratory (MATLAB), Bangladesh, found that for an increase in sea surface temperature by 1 °C, there was a 25% increase in cholera incidence in the current month and a 6% increase in incidence with per degree Celsius rise in ambient temperature [57]. Two other studies reported a correlation of 0.204 for daily temperature [58] and 0.42 for monthly precipitation [59]. Only two studies reported the relationship between Vibrio infections and precipitation using RR as the measure of effect: one for cholera [60] and the other for non-Vibrio cholera infections [61]. The cholera study reported a RR of 1.05 (1.04, 1.06) at lag 6weeks, and the study onVibrio Vulnificus infections reported a RR of 5.06 (95% CI: 2.41, 10.64) at lag 2 weeks. Non-cholera strains are predominantly associated with wound infections and septicaemia. These infections rise with sea surface temperature (Table 6). A case–control analysis for V. parahaemolyticus infections in Washington, USA, reported an OR of 2.16 (95% CI: 1.15, 4.05) with yearly temperature [62], while a modelling study in Haiti showed an OR of 1.46 (95% CI: 1.32, 1.16) for daily precipitation [63]. An observational German study by Brehm et al. [64] noted an association between heat waves and increased Vibrio cases. Of 63 cases, 38 with wound infections and one with septicaemia were found in cases who had recreational exposure to the Baltic Sea or consumed shrimp from the sea after heatwave events. Listeria species Only one study by Chersich et al. [65] addressed the possibility of climate factors and Listeriosis. This discussed an outbreak of inva- sive Listeriosis in South Africa that resulted in 180 deaths. The source was traced to a food production facility that processed Figure 4. Pooled studies including bacteraemia climate estimated risk IRR. Pooled IRR indicating the health impacts associated with one unit increase in exceedance days for extreme temperature threshold 95th percentile (ETT95) and extreme precipitation threshold 90th percentile (EPT90), with 95% CIs. 12 Naveen Manchal et al. https://doi.org/10.1017/S0950268824000839 Published online by Cambridge University Press https://doi.org/10.1017/S0950268824000839 Table 6. Studies on Shigella sp. with temperature as the climate variable, stratified by type of temperature measurement Number Study Region Time period Infection studied Cases number Population number Analytical method Key findings and reported statisticsa Daily mean temperature 1 Ai et al. 2022 [41] China 2010–2018 Daily lab–confirmed cases and maximum temperature n/a n/a Distributed lag non–linear model RR = 1.15 (95% CI: 1.04, 1.28) Hot nights more associated than hot days. Short lag period of up to 7 days in China. TR 19.7–280 C 2 Cheng et al. 2017 [43] Hefei, China 2006–2012 Daily lab–confirmed gastroenteritis n/a n/a Distributed lag non–linear model RR = 1.03 (95% CI: 1.02, 1.05) at lag 1 week. Acute effects due to short incubation period. Effect sizes varied in different provinces in China. TR 10–340 C 3 Li et al. 2016 [45] Hefei, China 2006–2012 Daily lab–confirmed gastroenteritis 6,511 76,100,000 Poisson generalized linear regression RR = 1.01 (95% CI: 1.00, 1.01) at lag 6 days TR 15–300 C 4 Liu et al. 2020 [52] China 2014–2016 Daily clinical and lab– confirmed gastroenteritis 396,134 n/a DLNM RR = 1.02 (95% CI: 1.01, 1.02) at lag 2 weeks. TR 15–300 C 5 Wang y et al. 2021 [46] Jilin, China 2008–2018 Daily clinical and lab– confirmed gastroenteritis 26,971 26,907,300 DLNM RR = 1.88 (95% CI: 1.51, 2.34). Positive association for temperature up to 26 degrees. Reinforced by humidity and precipitation 6 Wen et al. 2016 [47] Hefei, China 2006–2012 Daily clinical and lab– confirmed gastroenteritis 5,544 7,611,000 DLNM RR = 1.08 (95% CI: 1.03, 1.13) diurnal temperature range above 8 degrees increased childhood dysentery cases Monthly mean temperature 7 Zhang et al. 2007 [48] Jinan, China (temperate) Baoan (subtropical) 1987–2000 Monthly lab–confirmed gastroenteritis and maximum temperature 60,905 4,300,000 SARIMA B = 0.11. Lag 4 weeks. Both monthly max (15–350 C) andminmean temperature (8– 25 ° C) related to rise in cases. 1–deg. rise leads to 12% rise in cases in Jinan B = 0.16 in Baoan 8 Lee et al. 2017 [44] Vietnam 1999–2013 Monthly gastroenteritis 596,343 90,700,000 Negative binomial regression r = 0.65, IRR = 1.06 (95% CI: 1.04, 1.09) 9 Aminharati et al. 2018 [42] Yazd, Iran 2012–2015 Total lab–confirmed cases 68 1,138,533 Poisson regression IRR = 1.25 (1.08, 1.45) ar = correlation coefficient, B = beta coefficient, RR = relative risk, OR = odds ratio, IRR = incidence rate ratio, n/a = not available, TR = temperature ranges. Epidem iology and Infection 13 https://doi.org/10.1017/S0950268824000839 Published online by Cam bridge U niversity Press https://doi.org/10.1017/S0950268824000839 Table 7. Studies on Shigella sp. with precipitation as the climate variable, stratified by type of precipitation measurement Number Study Region Time period Infection studied Cases number Population number Analytical method Key findings and reported statisticsa Floods 1 Gao et al. 2016 [87] Anhui, China 2007 Clinical and lab–confirmed cases 1,148 61,200,000 Poisson regression OR = 1.04 (95% CI: 0.97, 1.12) 2 Liu et al. 2016 [55] Huaihua, China 2005–2011 Weekly lab–confirmed cases 3,709 4,740,000 DLNM RR = 1.32 (95% CI: 1.12, 1.56) with lag 1 week 3 Xu et al. 2017 [49] Dalian, China 2004–2010 Weekly lab–confirmed cases 18,976 6,690,000 Generalized additive mixed model r = 0.182 at lag 2 weeks RR = 1.17 (95% CI: 1.03, 1.33) 4 Liu et al. 2017 [50] Baise, China 2004–2012 Monthly lab–confirmed cases 9,255 3,780,000 Mixed generalized additive model r = 0.34 at lag 4 week RR = 1.40 (95% CI: 1.16, 1.69) r = 0.58 at lag 2 weeks RR = 1.78 (95% CI: 1.61, 1.77) 5 Liu et al. 2017 [54] Guangxi, China 2004–2010 Monthly lab–confirmed cases 78,794 46,026,600 Poisson regression with generalized additive model r = 0.34, lag 4 weeks r = 0.58, lag 2 weeks 6 Colston 2020 [53] 2011–2012 Peru Total lab–confirmed cases 606 n/a Modified Poisson regression RR = 2.86 (95% CI: 1.81, 4.52) Monthly and weekly total precipitation 7 Hines et al. 2018 [88] Oregon, USA 2015–2016 Total lab–confirmed cases, total precipitation in a week 105 4,000,000 Poisson regression RR = 1.18 (95% CI: 1.06, 1.33) at lag 1 week 8 Lee et al. 2017 [44] Vietnam 1999–2013 Monthly gastroenteritis 596,343 90,700,000 Negative binomial regression r = 0.17 IRR = 1.04 (95% CI: 1.01, 1.07) 9 Aminharati et al. 2018 [42] Yazd, Iran 2012–2015 Total lab–confirmed cases 68 1,138,533 Poisson regression Not associated 10 Na et al. 2016 [61] South Korea 2001–2009 Clinical and lab–confirmed cases n/a n/a Multivariate log–linear model RR = 3.1 (95% CI: 1.21, 7.92) at lag 2 weeks Cumulative precipitation of 209 mm ar = correlation coefficient, B = beta coefficient, RR = relative risk, OR = odds ratio, IRR = incidence rate ratio, n/a = not available. 14 N aveen M anchalet al. https://doi.org/10.1017/S0950268824000839 Published online by Cam bridge U niversity Press https://doi.org/10.1017/S0950268824000839 Table 8. Studies on Vibrio sp. with temperature as the climate variable, stratified by type of temperature measurement Number Study Region Time period Infection studied Cases number Population number Analytical method Key findings and reported statisticsa Monthly mean temperature 1 Kim et al. 2015 South Korea 2003–2012 Monthly mean temperature and outbreaks of V. parahaemolyticus n/a n/a Correlation analysis r = 0.69 Incidence calculated by dividing pathogen–specific outbreak by total food–borne outbreaks 2 Ali et al. 2013 [57] Bangladesh 1988–2001 Monthly lab–confirmed gastroenteritis and cholera 4,157 210,000 SARIMA B = 0.41, r = 0.04 at lag 4 weeks. Minimum temperature increases of one degree Celsius in the current month led to 6% increase in cases 3 Baker et al. 2013 [89] Baltic countries 1982–2010 Monthly sea temperature and all Vibrio infections 280 n/a ARIMA RR = 1.93 at lag 52 weeks. Highest mortality with Vibrio vulnificus infections 4 Reyburn et al. 2011 [93] Zanzibar 2002–2008 Monthly lab–confirmed cholera cases 3,245 1,100,000 SARIMA B = 2.21 at lag 16 weeks. Temperature and rainfall interacted significantly at 1 month lag. 1–degree rise in temp led to twofold rise in cases at 4 months. TR 0–220 C Daily and weekly temperature 5 Hsiao et al. 2016 [91] Taiwan 2000–2011 Monthly lab–confirmed V. parahaemolyticus 3,870 outbreaks n/a ARIMA r = 1. Average temperature, ocean temperature, and salinity had a significant impact but not rainfall. TR 15–300 C 6 Islam et al. 2009 [92] MATLAB, Bangladesh cholera n/a n/a Regression and principal component analysis Synergistic effect of temperature and sunshine hrs TR 18–300 C 7 Asadgol et al. 2019 [58] Qom, Iran 1998–2016 Daily lab–confirmed cholera cases 1,243 1,000,000 Artificial neural network modelling and gamma test r = 0.20 at lag 4 weeks. Warm and dry environments increased the incidence TR 18–400 C 8 Davis et al. 2021 [62] Washington 2013–2018 Annual lab–confirmed V. parahaemolyticus cases 112 n/a Multivariate logistic regression OR = 2.16 (95% CI: 1.15, 4.05) Regional variations in the association. Also studied oyster tissue temperature 9 Fernandez et al. 2009 [60] Lusaka, Zambia 2003–2006 Weekly lab–confirmed cholera (Ogawa) 13,069 1,284,642 Poisson autoregressive RR = 1.05 (95% CI: 1.04, 1.06) at lag 6 weeks. 1–deg. rise in temp explained 5.2% rise in cholera cases. Favours the growth of algae and copepods. TR 21–260 C ar = correlation coefficient, B = beta coefficient, RR = relative risk, OR = odds ratio, IRR = incidence rate ratio, n/a = not available, TR = temperature ranges. Epidem iology and Infection 15 https://doi.org/10.1017/S0950268824000839 Published online by Cam bridge U niversity Press https://doi.org/10.1017/S0950268824000839 Table 9. Studies on Vibrio sp. with precipitation as the climate variable, stratified by type of precipitation measurement Number Study Region Time period Infection studied Cases number Population number Analytical method Key findings and reported statisticsa Monthly total rainfall 1 de Magny et al. 2008 [90] Kolkata, India 1998–2006 Monthly lab–confirmed cases n/a n/a Wavelet analyses r = 0.06 Surface runoff into rivers floods water supply increases algal bloom promoting Vibrio 2 Reyburn et al. 2011 [93] Zanzibar 2002–2008 Monthly lab–confirmed cholera cases 3,245 1,100,000 SARIMA B = 0.01 at lag 8 weeks. Temperature and rainfall interacted significantly at 1 month lag. PR 3–705 mm 3 Na et al. 2016 [61] South Korea 2001–2009 Clinical and lab–confirmed cases n/a n/a Multivariate log–linear model RR = 5.06 (95% CI: 2.41, 10.64) at lag 2 weeks Cumulative precipitation of 209 mm Daily and weekly precipitation and floods 4 Eisenberg et al. 2013 [63] Haiti 2010–2011 Lab–confirmed cases 4,662 n/a DLNM OR = 1.46 (95% CI: 1.32, 1.61) at lag 1 week PR 0–216 mm 5 Hsiao et al. 2016 [91] Taiwan 2000–2011 Monthly lab–confirmed V. parahaemolyticus, daily maximum rainfall 3,870 outbreaks ARIMA R = 0. 56. Average temp, ocean temp, and salinity had a significant impact but not rainfall 6 Asadgol et al. 2019 [58] Qom, Iran 1998–2016 Daily lab–confirmed cholera cases 1,243 1,000,000 Artificial neural network modelling and gamma test r = �0.23 at lag 4 weeks. PR 9–35 mm 7 Fernandez et al. 2009 [60] Lusaka, Zambia 2003–2006 Weekly lab–confirmed cholera (Ogawa), weekly total precipitation 13,069 1,284,642 Poisson autoregressive RR = 1.02 (95% CI: 1.01, 1.04) at lag 3 weeks. PR 0–307 mm 8 Cash et al. 2014 [59] Matlab, Bangladesh 1983–2010 Monthly lab–confirmed cases, floods n/a n/a Pearson correlation r = 0.42 ar = correlation coefficient, B = beta coefficient, RR = relative risk, OR = odds ratio, IRR = incidence rate ratio, n/a = not available, PR = precipitation range. 16 N aveen M anchalet al. https://doi.org/10.1017/S0950268824000839 Published online by Cam bridge U niversity Press https://doi.org/10.1017/S0950268824000839 ‘ready-to-eat’meat products. The risks identified were the impacts of temperature augmenting replication cycles of the bacterium, hot climate leading to breakdown in the food cooling chain, and the increased use of contaminated surface water. Pooled estimates for bacteraemia Four studies reported IRR for cases that included bacteraemia with ambient temperature and precipitation as climate variables [16, 18–20]. Our meta-analysis combined three of these studies as they included extreme heat and precipitation as exposure variables (Figure 4). These studies used extreme temperature threshold 95% percentile (ETT95) and extreme precipitation threshold (EPT90) and showed a pooled IRR of 1.05 (95% CI: 1.03, 1.06) associated with a unit increase in ETT95 exceedance days and 1.09 (95%CI: 0.99, 1.19) associated with a unit increase in EPT90 exceedance days (Figure 4). Discussion In this systematic review and meta-analysis, we conducted a com- prehensive synthesis of the impact of ambient temperature and precipitation on five food-borne pathogens based on published data between 2001 and 2021. In Europe, Australia, and North America, where Campylobacteriosis is predominant, a positive association was found with a rise in ambient temperature. Similarly, Salmon- ellosis incidence rose worldwide with temperature, with all studies showing a positive association. In contrast, the association with precipitation for both pathogens was less evident in temperate regions of the world. Shigellosis and Vibrio infections, more pre- dominant in Africa and Asia, had a positive association with both temperature and excess precipitation. The positive association between these climate variables and illness was also consistent among studies where bacteraemia cases were included. The findings of our review andmeta-analysis are consistent with prior reviews on ambient temperature rise and infections from Campylobacter, Salmonella, and Shigella species [66, 67]. The majority of the studies for Campylobacter and Salmonella were either of high or moderate quality, which increased the reliability of the outcome measures, particularly for the two pathogens. This is the first review to demonstrate a positive association for studies including bacteraemia fromCampylobacter and Salmonella species as an outcome. The variable effect of climate variables on bacterial food patho- gens in different regions of the world needs an understanding of not only the pathogen’s multiplication risks but also the modes of transmission and human behavioural factors. Campylobacter stud- ies mostly found a lag period of 4–5 weeks, suggesting food con- tamination as the likely reason for the rise in incidence. The increase in cases in summer, particularly in Europe, seems to be related to the changes in behaviour among the people, for example, having more barbecues, outdoor parties, and contact with infected animals. The rise in temperature also increases the risk of infection in broiler flocks, and any errors in the cold chain of food transport can increase the risk in humans [4]. With projected rises in ambient temperature, Campylobacter infection seasonality will be longer and not restricted just to summer months. This could translate to an increase of infections by 200% in the Nordic countries by the end of the century [3]. Although Campylobacter sp. replicates in humid conditions, a positive association with precipitation has not been consistently found. Possible explanations are, firstly, a paucity of studies and, secondly, heterogeneity in using the time of exposure of precipitation. A significant impact may not be found when daily total precipitation is averaged out to weekly estimates. Studies using excessive precipitation in a day showed a significant association [16, 25]. Although the studies on Campylobacter bacteraemia do not mention the incidence of bacteraemia separately, the proportion of bacteraemia would also be expected to rise with current climate trends. Salmonella replication is enhanced with the rise in temperature, which explains the cyclical rise in cases in late summer in temperate regions of the world. The variation in temperature in equatorial regions is less pronounced, which could explain the lesser impact of temperature in these areas [10]. However, seasonal monsoons in these regions lead to a rise in enteric fever every year, as flooding is a risk for transmission of enteric fever [10]. This can explain the positive association with lagged effects in Asian countries. In con- trast, only excessive daily precipitation positively affected temper- ate regions of the world. The review by Saad [10] includes 16 datasets with Salmonella bacteraemia and showed a positive association with temperature and rainfall. An increase in ambient temperature over the coming years would significantly impact the incidence of salmonellosis worldwide, particularly in the non- equatorial regions, which would also translate to an increase in hospitalizations. Bacillary dysentery cases from Shigella species also rise with temperature as the bacterium replicates more and food-borne transmission rises. The difference compared to the other bacterial food pathogens is the short lag period, as the incubation period is short. With precipitation, the most consistent association is with floods. This is more obvious in low socio-economic areas in China, where poor access to clean drinking water during floods increases the risk of transmission [51]. Given that most cases are diagnosed with stool specimens, our review found no studies specific to bacteraemia. Only three studies were of high quality, as most other studies had biases with confounding and exposure. Ambient temperature promotes Vibrio species growth, and increases in algal blooms contribute as well [68]. For non-cholera species, water temperature and salinity are the two most important risk factors for growth. With the warming of the oceans, coastal regions will face increased sepsis cases from these species [69]. Heat waves have helped spread Vibrio sp. to higher latitudes, and a mini- review predicted that infections might quadruple in the coming years [70]. Cholera cases in Africa and India have a complex relationship with climate variables. In the dry season, the rise in cases is chiefly due to increased ambient temperature. During the monsoon, the dilutional effect of rainfall on water salinity leads to a reduction in the number of cases. After a lag period, due to increased contact with contaminated water, there is another peak of cases [56]. A study by Koelle et al. [71] inMATLAB, Bangladesh, demonstrated an association of outbreaks with monsoons and a lag period as long as eight months. The authors also noted that if herd immunity is high after a recent outbreak, climate variables had a limited impact on cholera transmission. Four studies were of high quality, while the rest had confounding and selection biases. In the future, the prediction is that Vibrio infections will rise and expand geographically with current climate trends [69]. This would include cases of bacteraemia and lead to high mortality. We acknowledge the following limitations in this review and meta-analysis. First, some studies conducted in non-English lan- guages were excluded. Second, the absence of data on the propor- tion of cases of bacteraemia in the studies prevented an accurate prediction of the genuine impact of climate on this severe outcome. Thirdly, only one author did the screening, and onlyMEDLINEwas updated in March 2023 to capture any missing recent studies. The Epidemiology and Infection 17 https://doi.org/10.1017/S0950268824000839 Published online by Cambridge University Press https://doi.org/10.1017/S0950268824000839 maximum number of publications was obtained from the data- bases, and the potential for missed publications, although possible, is low. Many studies reported outcomes for multiple pathogens but reported outcome measures separately, reducing the chance of any reporting bias. Lastly, although we developed our study protocol a priori (available on request from the corresponding author upon request), time constraints prevented us from registration or publi- cation before this was completed. Despite these acknowledged limitations, the findings in this study are important and valid. In summary, this is the first review that provides a comprehen- sive overview of the complex interactions between the intensity and timing of climate variable exposure and the incidence of pathogen- specific infections. Studies that included cases of Campylobacter and Salmonella bacteraemia reported a rise in incidence with ambient temperature and precipitation. Further research is needed to study the impact of a surge in food pathogen bacteraemia with current trends in climate change. Supplementary material. The supplementary material for this article can be found at http://doi.org/10.1017/S0950268824000839. Data availability statement. All relevant data are presented in the manuscript. Author contribution. N.M. conceived the study, conducted the systematic search, extracted the data, analysed the data, interpreted the results, and wrote the first draft of the paper; M.Y.K. assisted with interpreting the results and the first draft of the paper; MEC assisted with interpreting the results and the first draft of the paper; P.L. assisted with interpreting the results and the first draft of the paper; and O.A. assisted with data extraction, data analysis, interpretation of the results, and the first draft of the paper. All authors contributed to drafting and revising the manuscript. Competing interest. The authors declare no competing interests. References [1] World Health Organisation (2020) Estimating the burden of food borne diseases. [2] Akil L, Ahmad HA and Reddy RS (2014) Effects of climate change on salmonella infections. Foodborne Pathogens and Disease 11(12), 974–980. [3] Kuhn KG, et al. (2020) Campylobacter infections expected to increase due to climate change in northern Europe. 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Food Research International 68, 24–30. 20 Naveen Manchal et al. https://doi.org/10.1017/S0950268824000839 Published online by Cambridge University Press https://doi.org/10.1017/S0950268824000839 A systematic review and meta-analysis of ambient temperature and precipitation with infections from five food-borne bacterial pathogens Introduction Methods Search strategy Inclusion and exclusion criteria Data extraction Quality appraisal Meta-analysis Results Characteristics of included studies Overview of pathogens and effect of temperature and precipitation Campylobacter species Salmonella species Shigella species Vibrio species - cholera and non-cholera strains Listeria species Pooled estimates for bacteraemia Discussion Supplementary material Data availability statement Author contribution Competing interest References