Sage Journals: Discover world-class research

Abstract

Background:

Salmonella species are major pathogens responsible for gastroenteritis and typhoid fever. In 2017, Salmonella enterocolitis caused over 95 million cases of diarrhoea and 50,000 deaths globally, with India bearing more than 50% of the typhoid burden.

Objectives:

To test the association of monthly mean and maximum temperature, precipitation and absolute humidity with the incidence of Salmonella bacteraemia in a metropolitan city in South India.

Design:

A retrospective time-series analysis.

Methods:

This study employed a retrospective time-series analysis to evaluate the influence of meteorological variables, including temperature, absolute humidity and precipitation, on Salmonella bacteraemia in a metropolitan city in South India.

Results:

Between 2014 and 2019, a total of 492 blood culture-confirmed cases of Salmonella bacteraemia were identified in Mysuru, India. S. typhi was predominantly among younger patients, while non-typhoidal Salmonella was more frequent in older age groups. Resistance was highest to nalidixic acid (84%), with a rising trend in ciprofloxacin resistance. Increased mean temperature (lags 1–3 months) and absolute humidity were positively associated with Salmonella bacteraemia, while temperature variability was protective, and monsoon rainfall significantly increased the risk. Cumulative exposure–response analyses further showed elevated risks at higher humidity (>26 g/m³), temperatures (>34°C) and extreme precipitation (>250 mm), although confidence intervals were wide and most associations did not reach statistical significance.

Conclusion:

This single-centre retrospective time-series analysis has demonstrated that meteorological variables impact the incidence of Salmonella bacteraemia, which could lead to increased antibiotic use and contribute to the development of resistance.

Plain language summary

Impact of meteorological variables on Salmonella

Using a retrospective time-series approach, our study evaluates how temperature, absolute humidity, and precipitation influence Salmonella infections in a metropolitan setting in South India, where typhoid and non-typhoidal salmonellosis remain significant public health challenges. While associations between meteorological variables and Salmonella bacteraemia cases were observed, our findings underscore emerging antibiotic resistance trends, particularly high resistance (84%) to Nalidixic acid and a concerning increase in Ciprofloxacin resistance.

Keywords

bloodstream infections temperature

Introduction

Salmonella is a major pathogen that contributes significantly to the global burden of foodborne gastroenteritis and enteric fever. The Global Burden of Diseases, Injuries, and Risk Factors study estimated that Salmonella enterocolitis contributed to 95.1 million cases of diarrhoea, 50,771 deaths, and 3.1 million Disability-Adjusted Life Years (DALYs) in 2017.^1–3 Over 2500 serotypes of Salmonella have been identified, and S. enterica sub sp enterica is the commonest human pathogen.⁴ Typhoidal serovars (typhi, and paratyphi A, B and C) cause enteric fever, while non-typhoidal Salmonella (NTS) causes gastroenteritis. All species of Salmonella can cause bacteraemia, with an NTS incidence of approximately 5%.⁴ Humans are the only reservoirs for typhoidal serotypes, with the transmission occurring primarily through the faecal-oral route. By contrast, animals are the main reservoir for NTS. The emergence of multi-drug-resistant (MDR) Salmonella worldwide is a growing concern.⁵

India bears more than 50% of the global Typhoid burden, predominantly in younger age groups.⁶ The Surveillance for Enteric Fever in India (SEFI) estimated that the incidence in children up to 14 years was between 12 and 1622 per 100,000 child-years.⁶ Ninety-eight percent of these isolates were not susceptible to ciprofloxacin, highlighting escalating antimicrobial resistance.⁶ A study conducted in Bangladesh estimated that of all Salmonella bacteraemia, the prevalence of MDR Salmonella was 85%,⁷ Additionally, there is an emerging trend of NTS infections in India.⁸

The burden of Salmonella infections, particularly enteric fever, may worsen with climate change. Studies have shown that rising ambient temperatures and precipitation increase the risk of enteric infections.^9–13 In their modelling study, Chua et al.¹⁴ found that if current climate trends continue, mortality attributable to heat will mainly be caused by Salmonella typhi and Shigella sp. This trend will pose unique challenges to a country like India, which is experiencing a rising incidence of heat waves and floods.¹⁵ The impact would be greatest on the lowest socio-economic groups, who have limited access to health care and limited means of adapting to the changing climate.¹⁶ Furthermore, invasive NTS infections are increasing, with 92% diagnosed on blood culture and higher rates in the elderly population.¹⁷

While the link between climate variables and Salmonella gastroenteritis is well known, there is limited evidence on bacteraemia. Understanding this relationship is crucial for predicting future disease burden and planning public health strategies. This study aimed to examine the relationship between ambient temperature, rainfall and humidity and the occurrence of Salmonella bacteraemia in a metropolitan city in South India. The results can be used to predict the incidence of severe infection with this major enteric pathogen in the coming years if the current trends in climate change continue.

Methods

Study population and study site

This retrospective time-series analysis was conducted in a tertiary centre in Mysuru, India. Mysuru is a metropolitan city in the southern Indian state of Karnataka with a population of 1.2 million as of 2020 (Figure 1). The city has a hot, semi-arid climate, characterised by summers from March to May, monsoons from June to October and winters from November to February. The city’s primary water sources are the Cauvery River and groundwater. Recently, the city has experienced significant climate change with rising ambient temperatures.¹⁸ The study site was a major private teaching hospital and university with advanced research facilities and laboratories. A large section of the population, regardless of socio-economic background, accesses the hospital.

Figure 1.

Map of India showing the study area, Mysuru district.

Time-series data on all positive Salmonella blood culture isolates from January 2014 to December 2019 were extracted from microbiology laboratory software at Jagadguru Sri Shivarathreeshwara (JSS) University. Each isolate was assigned a unique identification number to ensure patient data remained anonymous. Isolates were then organised by pathogen. The outcome measure for this study was the aggregated monthly number of Salmonella bacteraemia cases. Other variables collected include de-identified patient demographic data, such as age and sex. The reporting of this study conforms to the Strengthening the Reporting of Observational Studies in Epidemiology (STROBE) statement.¹⁹

Exposure variables

The exposure variables for this study were monthly mean and maximum temperatures (°C), monthly mean precipitation (mm), monthly relative humidity (%) and the number of days with flood waves greater than the 95th percentile of river discharge. These variables were obtained from the Indian Bureau of Meteorology for the Mysuru weather station.

Statistical analysis

We first described the study population and the characteristics of the patients with Salmonella blood isolates. Temporal trends in meteorological variables were visualised with the monthly incidence of Salmonella. To explore potential associations between these variables, we conducted a cross-correlation analysis. Univariate quasi-Poisson regression was also fitted to examine the relationship between meteorological factors and Salmonella cases at various lags, based on cross-correlation analysis.

Given the non-linear nature of the outcome and the delayed effects on Salmonella bacteraemia, we employed distributed lag non-linear models (DLNM) to examine the strength of these associations.^9,12,20–24 The association between these variables, infection incidence and lagged days was modelled via a quasi-Poisson regression framework with cross-basis parameterisation to estimate the exposure–lag–response surface. This approach allowed us to calculate the exposure–response function.

We also introduced a bi-dimensional cross-basis function between meteorological variables (temperature, humidity and precipitation) and Salmonella bacteraemia (exposure–lag–response) to capture the non-linear and delayed relationship. For absolute humidity, the exposure–response function was modelled using a natural cubic B-spline with knots placed at the 10th, 75th and 90th percentiles to capture the non-linear exposure–response relationship. By contrast, the lag response was modelled using two interior knots at equally spaced values in the log scale over a 2-month period to account for potential delayed effects. The temperature exposure–response function was modelled using a B-spline with equally spaced knots, and the lag–response used a natural cubic spline with logarithmic knots, with a maximum lag of 3 months. This allowed for flexible, non-linear and delayed temperature effects on the outcome. The exposure–response function for precipitation was modelled using a linear function with a 1-month lag period. All models account for seasons and temporal trends and were implemented in R version 4.0.3.

Results

Salmonella cases

Between 2014 and 2019, 492 positive blood culture isolates of Salmonella species were identified. S. typhi was most common in the younger age groups, while NTS was most common in the older age groups (Table 1). The cohort consisted of 67% males. Salmonella blood isolates showed resistance of >50% to a third (6/18) of antimicrobials: gentamicin, amikacin, cefuroxime axetil, cefuroxime, nalidixic acid and ciprofloxacin (Figure 2).

Table 1.

Profile and characteristics of patients with Salmonella bacteraemia during the study period.

Characteristic	Overall, N = 492	S. typhi N = 391	S. paratyphi A,N = 85	S. typhimurium , N = 7	Other NTS, N = 7	S. paratyphi B N = 2
Age group, n (%)
0–20	279 (56.8%)	232 (83.1%)	43 (15.4%)	2 (0.7%)	1 (0.3%)	1 (0.3%)
21–40	135 (27.4%)	101 (74.8%)	32 (23.7%)	2 (1.4%)	1 (0.3%)	(0%)
41–60	55 (11.2%)	41 (74.5%)	10 (18.1%)	0 (0%)	3 (5.4%)	1 (1.8%)
60+	22 (0.04%)	16 (72.7%)	2 (9.0%)	2 (9.0%)	2 (9.0%)	0 (0%)
Age, mean (SD)	21.8 (17)	21.2 (16.5)	21.3 (15.0)	35.1 (26.1)	48.5 (23)	28.5(27.5)
Sex, n (%)
Male	289 (59%)	223 (57%)	58 (68.2%)	5 (71.4%)	4 (57.1%)	0 (0%)
Female	202 (41%)	168 (43%)	27 (31.8%)	2 (28.6%)	3 (42.8%)	2 (100%)

One observation is missing demographic characteristics.

NTS, non-typhoidal Salmonella.

Figure 2.

Trends in antimicrobial susceptibility profiles among 492 blood Salmonella isolates in Mysuru, India, 2014–2019. The values inside each cell represent the percentage of resistant isolates for a given antimicrobial in that year.

Exploring the time series of Salmonella

The meteorological variables and the notified cases of Salmonella bacteraemia were plotted (Figure 3). The time-series decomposition reveals a clear upward trend from 2017 onwards (Figure 4), indicating a rise in Salmonella bacteraemia and a strong annual seasonal pattern. The seasonal fluctuations remained consistent throughout the years, while the random component shows some irregular variability, particularly around 2018. The cross-correlation analysis (Figure S1) between Salmonella cases and meteorological variables indicates significant relationships. Maximum and mean temperatures and mean absolute humidity show positive correlations with Salmonella bacteraemia, particularly at 1–2 lag months. In addition, precipitation shows a short-term effect, with a slight increase in Salmonella bacteraemia cases in the current or previous month. Flood events (days with flood wave exceeding the 95th percentile river discharge) exhibit significant positive correlations, with flood conditions contributing to an increase in Salmonella bacteraemia in the following 1–3 months.

Figure 3.

Distribution of monthly meteorological variables and cases of Salmonella bacteraemia during the study period, 2014–2019.

Figure 4.

Decomposition of additive time series of Salmonella.

Exposure–response–lag relationship

The preliminary investigation into the associations between meteorological variables and the incidence of Salmonella is presented in Figure 5. We observed that an increase in mean temperature with lags of 1–3 months was strongly associated with higher rates of Salmonella (Beta = 0.069, p = 0.013, R² = 0.47), (Beta = 0.103, p < .001, R² = 0.52) and (Beta = 0.085, p = 0.001, R² = 0.47), respectively. A similar result was obtained for absolute humidity (AHmean), with current absolute humidity (Beta = 0.048, p = 0.033, R² = 0.47) and lags 1–2 showing significant positive effects with increased Salmonella bacteraemia cases. On the other hand, temperature fluctuations (Beta = −0.095, p = 0.010, R² = 0.47) showed a significant negative association, while precipitation lagged by 1 month (Beta = 0.0017, p = 0.042, R² = 0.45) exhibited a small but significant positive association with Salmonella rates. There were also seasonal effects on the incidence of Salmonella, with the monsoon season (Beta = 0.429, p = 0.002, R² = 0.51) positively associated with increased cases. By contrast, the summer season was protective (Beta = −0.337, p = 0.024, R² = 0.48).

Figure 5.

Results from the Quasi-Poisson regression model assessing the impact of various meteorological variables on monthly salmonellosis cases. Each row displays the estimate (beta) and 95% confidence interval (CI) for each covariate, along with the p-value and pseudo-R².

Figure 6 illustrates the cumulative association between meteorological variables, absolute humidity (g/m³), temperature (°C), precipitation (mm) and the incidence of Salmonella bacteraemia. Figure 6(a) shows that the relative risk (RR) of Salmonella bacteraemia is lower at absolute humidity levels below 22 g/m³, with a sharp increase observed after 26 g/m³. For example, at the 2.5th percentile of absolute humidity (16 g/m³), the RR of Salmonella bacteraemia was 0.76 (95% CI: 0.13, 4.56), while at the 97.5th percentile (26.8 g/m³), the RR was 1.85 (95% CI: 0.65, 5.23). The RR remains relatively constant across lag periods (Figure S2). Similarly, in Figure 6(b), temperature–Salmonella associations follow a comparable pattern. At lower temperatures (28°C), the RR was 0.48 (95% CI: 0.08, 2.74), and at higher temperatures (97.5th percentile, 35.6°C), the RR increased to 1.49 (95% CI: 0.41, 5.41). However, it is important to note that none of these results are statistically significant, as indicated by the wide confidence intervals.

Figure 6.

Overall cumulative association between (a) absolute humidity (g/m³), (b) temperature (in °C), (c) precipitation and the incidence of Salmonella bacteraemia. Top plots: RR with 95% confidence interval. The dark represents the RR, whereas the shaded area represents the 95% confidence interval. Bottom plots: Frequency of Salmonella bacteraemia cases. The middle-dotted lines represent the mean values (reference), while the two dashed lines represent the 2.5th and 95th percentile values. The shaded area represents the 95% confidence interval.

The exposure–lag–response relationship (Figure S2) indicates that higher temperatures (above 34°C) are associated with an increased risk, particularly with longer lag periods. The risk of Salmonella bacteraemia remains stable at lower precipitation levels but increases significantly beyond 100 mm, with a more pronounced rise after 250 mm (Figure 6(c)). While extreme precipitation is associated with significantly increased risk, such as at the 97.5th percentile (224 mm), where the RR of Salmonella bacteraemia was 1.44 (95% CI: 1.01, 2.05), lower precipitation levels are linked to a significantly reduced risk. For instance, at a low precipitation level of 10 mm, the RR was 0.87 (95% CI: 0.76, 0.99). The RR shows a slight upward trend, increasing to about 1.2 at a 1-month lag (Figure S2).

Discussion

This retrospective study assessed the association between climatic variables and the incidence of Salmonella bacteraemia in Mysuru, India. Our findings suggest that extreme climate conditions, particularly absolute humidity and elevated temperatures, may influence the risk of Salmonella bacteraemia in this region. Previous studies have documented this positive association primarily in relation to gastroenteritis—four of them measured monthly average temperature and reported a rise in gastroenteritis in England,²⁰ Canada,²⁵ Macedonia¹² and China.²⁵ Four other studies reported the same association, measuring weekly average temperatures.^21,26–28 The magnitude of the effect varies depending on the method used to measure meteorological variables, the lag period employed and the region of the world. Only two studies in the United States of America (USA) included positive blood culture cases and reported a positive association with extreme temperature and precipitation events.¹³

There are several explanations for the positive correlation of Salmonella bacteraemia and climate variables. Salmonella species colonise crop plants when manure is used as an organic fertiliser. Pigs and poultry are typical hosts of Salmonella, and their manure is often used as a fertiliser.²⁹ Another source of contamination in agriculture is contaminated irrigation water. During extreme climatic events, such as heatwaves and flooding, the scarcity of clean, potable water increases the risk of microbial contamination. A study in Mysore found that although water from the treatment plant met microbiological standards, there was significant isolation of Escherichia coli and Salmonella when water was stored in containers for an extended period.³⁰ A rise in ambient temperature not only increases the risk of exposure to Salmonella but also leads to increased virulence and bacteraemia, as we have shown in this study. Genetic analysis of virulence factors in Salmonella has shown the differential expression of virulence genes when the bacteria are exposed to heat stress.³¹ These virulence factors enable intracellular entry and survival, colonising the liver and spleen, which are defence organs for bacteremia.^32–34 Heat stress also compromises gut integrity, facilitating the inflammation and invasiveness of Salmonella.³⁵

The public health implications of a potential rise in Salmonella bacteraemia associated with increasing ambient temperatures due to climate change are enormous in India. As noted in previous studies, S. typhi was the most common blood isolate, predominantly affecting males in the 0–10-year age group. Nalidixic acid resistance was 84%, which is similar to the findings reported in the published literature.⁷ A rising trend in ciprofloxacin resistance was also noted. The misuse of antibiotics in animals, the inadequate use of antibiotics in humans and climate change contribute to the increasing resistance to antibiotics in Salmonella.

Our study has several strengths. All cases were blood culture-positive, which is the gold standard test for diagnosing Salmonella bacteraemia. This is the first study to demonstrate a positive correlation between climate variables and Salmonella bacteraemia as a specific outcome. This has been shown through robust statistical modelling using DLNM and generalised additive models, which account for seasonal trends in infections.

We acknowledged the following limitations in this study. Firstly, we did not have data on the residential address of the patients in this study; therefore, we cannot rule out referrals from neighbouring areas. However, the clinician believes that most of the patients attending the hospital reside in Mysore, with very few visiting from outside the district. For those who did, climate variables were unlikely to have significant variations in the surrounding areas. Secondly, water supply quality and distribution can vary seasonally; monsoon periods may increase water contamination, which can confound the association. Thirdly, food safety practices and cold chain systems can break down during extreme weather events, which can contribute to the development of Salmonella sepsis. Lastly, this was a single-centre hospital-based study, and the true incidence may be higher, and different effect sizes may be obtained in different climatic conditions. Further studies are needed to explore a causal relationship between climate and Salmonella bacteraemia.

Conclusion

This retrospective, single-centre time-series analysis has shown that maximum monthly ambient temperature, absolute humidity and precipitation are associated with an increase in Salmonella bacteraemia. With current trends in climate change in Mysuru, this would result in increased hospitalisation, morbidity and increased use of antibiotics, leading to the risk of resistance. Addressing these needs requires a ‘One-health’ approach to monitoring food and water quality, agricultural practices and disaster management in the event of extreme climatic conditions.

Supplemental Material

sj-doc-2-tai-10.1177_20499361251389056 – Supplemental material for The impact of meteorological variables on Salmonella bacteraemia in Mysuru District, Karnataka State, India: a retrospective time-series analysis

Supplemental material, sj-doc-2-tai-10.1177_20499361251389056 for The impact of meteorological variables on Salmonella bacteraemia in Mysuru District, Karnataka State, India: a retrospective time-series analysis by Naveen Manchal, Mahadevaiah N. Sumana, Megan K. Young, Maria Eugenia Castellanos, Peter Leggat and Oyelola A. Adegboye in Therapeutic Advances in Infectious Disease

Supplemental Material

sj-docx-1-tai-10.1177_20499361251389056 – Supplemental material for The impact of meteorological variables on Salmonella bacteraemia in Mysuru District, Karnataka State, India: a retrospective time-series analysis

Supplemental material, sj-docx-1-tai-10.1177_20499361251389056 for The impact of meteorological variables on Salmonella bacteraemia in Mysuru District, Karnataka State, India: a retrospective time-series analysis by Naveen Manchal, Mahadevaiah N. Sumana, Megan K. Young, Maria Eugenia Castellanos, Peter Leggat and Oyelola A. Adegboye in Therapeutic Advances in Infectious Disease

Footnotes

Acknowledgements

The first author would like to acknowledge the support of the Australian government through the fellowship under the Australia-India Research Students Fellowship program.

Declarations

ORCID iDs

Naveen Manchal

Oyelola A. Adegboye

Supplemental material

Supplemental material for this article is available online.

References

GBD 2017 DALYs and HALE Collaborators. Global, regional, and national disability-adjusted life-years (DALYs) for 359 diseases and injuries and healthy life expectancy (HALE) for 195 countries and territories, 1990–2017: a systematic analysis for the Global Burden of Disease Study 2017. Lancet 2018; 392: P1859–P1922.

GBD 2017 Causes of Death Collaborators. Global, regional, and national age-sex-specific mortality for 282 causes of death in 195 countries and territories, 1980–2017: a systematic analysis for the Global Burden of Disease Study 2017. Lancet 2018; 392: P1736–P1788.

GBD 2017 Disease and Injury Incidence and Prevalence Collaborators. Global, regional, and national incidence, prevalence, and years lived with disability for 354 diseases and injuries for 195 countries and territories, 1990–2017: a systematic analysis for the Global Burden of Disease Study 2017. Lancet 2018; 392: 10–16.

Eng

S-K

Pusparajah

Ab Mutalib

N-S

, et al. Salmonella: a review on pathogenesis, epidemiology and antibiotic resistance. Front Life Sci 2015; 8: 284–293.

Alvarez

Barrón-Montenegro

Conejeros

, et al. A review of the global emergence of multidrug-resistant Salmonella enterica subsp. enterica Serovar Infantis. Int J Food Microbiol 2023; 403: 110297.

John

Bavdekar

Rongsen-Chandola

, et al. Burden of typhoid and paratyphoid fever in India. N Engl J Med 2023; 388: 1491–1500.

Mina

Hasan

Hossain

, et al. The prevalence of multi-drug resistant Salmonella typhi isolated from blood sample. Microbiol Insights 2023: 16: 11786361221150760.

Kumar

, et al. Non-typhoidal Salmonella infections across India: emergence of a neglected group of enteric pathogens. J Taibah Univ Med Sci 2022; 17(5): 747–754.

Aik

Heywood

Newall

, et al. Climate variability and salmonellosis in Singapore - a time series analysis. Sci Total Environ 2018; 639: 1261–1267.

10.

Zhang

Hiller

, et al. Climate variability and Salmonella infection in an Australian temperate climate city. Epidemiology 2009; 20: S98.

11.

Chua

PLC

CFS

Tobias

, et al. Associations between ambient temperature and enteric infections by pathogen: a systematic review and meta-analysis. Lancet Planetary Health 2022; 6: E202–E218.

12.

Kendrovski

Karadzovski

Spasenovska

Ambient maximum temperature as a function of Salmonella food poisoning cases in the Republic of Macedonia. North Am J Med Sci 2011; 3: 264–267.

13.

Morgado

Jiang

Zambrana

, et al. Climate change, extreme events, and increased risk of salmonellosis: foodborne diseases active surveillance network (FoodNet), 2004–2014. Environ Health 2021; 20(1): 105.

14.

Chua

PLC

Huber

CFS

, et al. Global projections of temperature-attributable mortality due to enteric infections: a modelling study. Lancet Planetary Health 2021; 5: e436–e445.

15.

World Meteorological Organisation. Climate change made heatwaves in India and Pakistan “30 times more likely,” https://public.wmo.int/en/media/news/climate-change-made-heatwaves-india-and-pakistan-30-times-more-likely (2022, accessed 10/8/2023).

16.

Dhara

Schramm

Luber

Climate change & infectious diseases in India: implications for health care providers. Ind J Med Res 2013; 138: 847–852.

17.

Parisi

Crump

Stafford

, et al. Increasing incidence of invasive nontyphoidal Salmonella infections in Queensland, Australia, 2007–2016. PLoS Neglected Trop Dis 2019; 13: e0007187.

18.

Climate change in urban areas: a case study of Mysuru City. Biosci Biotechnol Res Commun 2021; 14: 277–282.

19.

von Elm

Altman

Egger

, et al. The Strengthening the Reporting of Observational Studies in Epidemiology (STROBE) statement: guidelines for reporting observational studies. BMJ 2007; 335: 806–808.

20.

Cherrie

MPC

Nichols

Iacono

, et al. Pathogen seasonality and links with weather in England and Wales: a big data time series analysis. BMC Public Health 2018; 18: 1067.

21.

Dewan

Corner

Hashizume

, et al. Typhoid fever and its association with environmental factors in the Dhaka Metropolitan area of Bangladesh: a spatial and time-series approach. PLoS Neglected Trop Dis 2013; 7: e1998.

22.

Liu

Lao

Zhang

, et al. Association between floods and typhoid fever in Yongzhou, China: effects and vulnerable groups. Environm Res 2018; 167: 718–724.

23.

Milazzo

Giles

Zhang

, et al. The effect of temperature on different Salmonella serotypes during warm seasons in a Mediterranean climate city, Adelaide, Australia. Epidemiol Infect 2016; 144: 1231–1240.

24.

Wang

Goggins

Chan

EYY

. Associations of Salmonella hospitalizations with ambient temperature, humidity and rainfall in Hong Kong. Environ Int 2018; 120: 223–230.

25.

Ravel

Smolina

Sargeant

, et al. Seasonality in human salmonellosis: assessment of human activities and chicken contamination as driving factors. Foodborne Pathogens Dis 2010; 7: 785–794.

26.

Fleury

Charron

Holt

, et al. A time series analysis of the relationship of ambient temperature and common bacterial enteric infections in two Canadian provinces. Int J Biometeorol 2006; 50: 385–391.

27.

Robinson

Gregory

Mulvenna

, et al. Effect of temperature and rainfall on sporadic Salmonellosis notifications in Melbourne, Australia 2000–2019: a time-series analysis. Foodborne Pathogens Dis 2022; 19(5): 341–348.

28.

Lake

Colón-González

Takkinen

, et al. Exploring campylobacter seasonality across Europe using The European Surveillance System (TESSy), 2008 to 2016. Euro Surveill 2019; 24(13): 1800028.

29.

Jechalke

Schierstaedt

Becker

, et al. Salmonella establishment in agricultural soil and colonization of crop plants depend on soil type and plant species. Front Microbiol 2019; 10: 967.

30.

University of Mysore. Application of novel methods for routine testing of potable water samples for bacterial contamination, https://uni-mysore.ac.in/sites/default/files/content/ugc_new_report.pdf (2023, accessed 15 Sep 2023).

31.

Kumar

Datta

Lalitha

. Salmonella grows vigorously on seafood and expresses its virulence and stress genes at different temperature exposure. BMC Microbiol 2015; 15(254): 1–10.

32.

Ibarra

Steele-Mortimer

. Salmonella–the ultimate insider. Salmonella virulence factors that modulate intracellular survival. Cell Microbiol 2009; 11(11): 1579–1586.

33.

Worley

. Salmonella bloodstream infections. Trop Med Infect Dis 2023; 8(11): 487.

34.

Christaki

Giamarellos-Bourboulis

. The complex pathogenesis of bacteremia: from antimicrobial clearance mechanisms to the genetic background of the host. Virulence 2014; 5(1): 57–65.

35.

Ghulam Mohyuddin

SA-O

Khan

IA-O

Zada

AA-O

, et al. Influence of heat stress on intestinal epithelial barrier function, tight junction protein, and immune and reproductive physiology. Biomed Res Int 2022; 2022(1): 8547379.

Supplementary Material

Please find the following supplemental material available below.

For Open Access articles published under a Creative Commons License, all supplemental material carries the same license as the article it is associated with.

For non-Open Access articles published, all supplemental material carries a non-exclusive license, and permission requests for re-use of supplemental material or any part of supplemental material shall be sent directly to the copyright owner as specified in the copyright notice associated with the article.

0.00 MB

0.08 MB

0.23 MB

The impact of meteorological variables on Salmonella bacteraemia in Mysuru District,Karnataka State,India: a retrospective time-series analysis

Abstract

Background:

Objectives:

Design:

Methods:

Results:

Conclusion:

Plain language summary

Keywords

Introduction

Methods

Study population and study site

Exposure variables

Statistical analysis

Results

Salmonella cases

Exploring the time series of Salmonella

Exposure–response–lag relationship

Discussion

Conclusion

Supplemental Material

sj-doc-2-tai-10.1177_20499361251389056 – Supplemental material for The impact of meteorological variables on Salmonella bacteraemia in Mysuru District, Karnataka State, India: a retrospective time-series analysis

Supplemental Material

sj-docx-1-tai-10.1177_20499361251389056 – Supplemental material for The impact of meteorological variables on Salmonella bacteraemia in Mysuru District, Karnataka State, India: a retrospective time-series analysis

Footnotes

Acknowledgements

Declarations

ORCID iDs

Supplemental material

References

Supplementary Material