Abstract

Climate changes have a profound effect on human health, especially when infectious diseases are concerned. Variable factors including temperature, precipitation, and relative humidity directly influence the magnitude and frequency of water-borne pathogen transfers. In this study, we determined the long-term temporal trends and seasonal patterns of shigellosis and evaluated the effects of demographic and climatic factors on its incidence in Yazd province, Iran, during 2012 through 2015. The incidence of shigellosis was highest among patients in the age group of 6–20 years and peaked in every summer of the years studied, especially during August. Furthermore, there was a significant association between climatic variables (such as monthly temperature, humidity, and atmospheric dust) and the incidence of shigellosis. However, contrary to expectations, rainfall did not affect incidence of the disease. The transmission of Shigella to humans is a complex ecological process. Socio-economic factors and lifestyle behaviours need to be addressed in future studies.

INTRODUCTION

Shigellosis continues to be a major cause of mortality and morbidity in developing countries. The burden of shigellosis has been estimated at 80–165 million cases worldwide, which results in 600,000 deaths annually. In Iran, recent studies showed that although the incidence of bacillary dysentery has decreased significantly over the past decades, the disease still remains a serious public health problem in certain parts of our country, and may reach as high as 8.8% in the north and 5.1% in the south of Iran (Ghaemi et al. 2007; Jomezadeh et al. 2014; Nadi et al. 2016; Talebreza et al. 2016; Bakhshi et al. 2017). Historically, Shigella flexneri is the most common species of Shigella genus in the developing world. However, the increasing trend of S. sonnei in some regions of these countries, especially in Iran, has been observed during recent years (MoezArdalan et al. 2003; Farshad et al. 2006; Jomezadeh et al. 2014; Soltan Dallal et al. 2015; Talebreza et al. 2016).

Shigella transmission occurs via the fecal–oral route through direct contact with infected persons, or ingestion of contaminated food and water (CDC 2016). The incidence rates (IRs) and outbreaks of shigellosis are highest where standards of living, water supply, personal hygiene, and food preparation are poor. Therefore, multiple parameters including both human and environmental factors remain worthy of study (Tang et al. 2014).

Climate changes and global warming have a profound effect on human health, especially when infectious diseases are concerned. Survival, reproduction, distribution, and transmission of pathogens and vectors require optimal weather conditions (WHO 2003). Variable parameters such as temperature, precipitation, and relative humidity can directly influence the magnitude and frequency of transfer of water-borne pathogens such as Shigella spp., Salmonella spp., and Vibrio cholerae from environmental sources (Kelly-Hope et al. 2008). For instance, rainfall can affect the transport and spread of fecal microorganisms, while temperature has an influence on their growth and survival (Wu et al. 2016).

To our knowledge, there is a scarcity of epidemiological data on the incidence of shigellosis in Yazd province, Iran (Attarpour Yazdi 2010). Given the significant morbidity and mortality of shigellosis in developing countries and the existing evidence for a role of climate forcing on bacillary dysentery, this study aims to determine the long-term temporal trends and seasonal patterns of shigellosis and to investigate the effects of climate variations (such as temperature, relative humidity, and rainfall) on its incidence in Yazd province, Iran.

MATERIALS AND METHODS

Study location

Yazd province is located in the centre of Iran (longitude 54.4342° E and latitude 32.1006° N). It has an area of 131,575 km2, and according to the 2016 census, the province had a population of about 1,138,533. Geographically, the region has an arid and dry climate because it is situated at an oasis where the Dasht-e Kavir desert and the Dasht-e Lut desert meet. Being away from the Persian Gulf, lower precipitation levels and high evaporation rates are the main causes of this dryness (Figure 1). However, certain parts of the region are suitable for residential use because they are surrounded by high mountains, therefore, these areas within the province with more than 2,500 metres altitude enjoy temperate weather.

Figure 1

The location of Yazd province inside Iran.

Figure 1

The location of Yazd province inside Iran.

Data resources

Monthly climatic data such as average temperature, humidity, and rainfall were obtained from the Iran Meteorological Organization (www.irimo.ir). In order to determine temporal trends and seasonal patterns of shigellosis, we quantified the monthly number of cases and IRs per 100,000 population for each city. Moreover, we collected demographic data for each city from the Statistical Center of Iran (https://www.amar.org.ir/). All of the dysentery cases which were diagnosed based on clinical symptoms were issued by the ministry of health surveillance system of Iran. In order to be eligible for participation in this study, patients had to attend health facilities with diarrhea (three or more loose stools, or at least one watery, bloody, or mucoid stool in the 24 hours prior to visiting the health facility). All of the Shigella isolates were confirmed by microscopic examination of stool specimens, routine biochemical identification, and serological tests using specific antisera, as described previously (Soltan Dallal et al. 2015). Patient questionnaires were also used to evaluate the impact of demographic factors on shigellosis.

Statistical methods

Multilevel regression models were used to estimate the association between climatic variables and shigellosis occurrences. The Poisson regression method was utilized to assess the association between IRs of shigellosis and monthly climatic variables in different cities of the province. In this multilevel analysis, the significant associations were evaluated by a likelihood ratio test. All statistical analysis was performed by STATA software (Intercooled Stata 10.0; Stata Corporation, College Station, Texas).

RESULTS

A total of 68 cases of shigellosis, which occurred in 21 outbreaks, have been diagnosed during the study period (2012–2015). Shigella sonnei was identified as the sole pathogen in all of the patients with bacillary dysentery. Other Shigella species were not detected in our study. Demographic, temporal, and climatic variables related to shigellosis have been summarized in Table 1. The incidence of shigellosis is highest (66.2%) among patients in the age group of 6–20 years. Significant differences also exist in the incidence of the disease between males and females. Notably, most of the shigellosis cases (n = 49) were reported during 2013. The IRs peaked in every summer of the years studied, especially during August.

Table 1

Demographic, temporal, and climatic variables related to patients with shigellosis

Parameter Value
Age Mean ± SD 18 ± 18 
Median (range) 11 (2 to 85) 
≤ 5 9 (13.2%) 
6–20 45 (66.2%) 
21–45 7 (10.3%) 
46–59 3 (4.4%) 
60 + 4 (5.9%) 
Gender Female 31 (45.6%) 
Male 37 (54.4%) 
Type of community Family 30 (44.1%) 
Other communities 38 (55.9%) 
Admission Hospital 53 (77.9%) 
Outpatient 15 (22.1%) 
Year 2012 10 (14.7%) 
2013 49 (72.1%) 
2014 3 (4.4%) 
2015 6 (8.8%) 
Season Spring 11 (16.2%) 
Summer 43 (63.2%) 
Autumn 13 (19.1%) 
Winter 1 (1.5%) 
Month January 1 (1.5%) 
April 7 (10.3%) 
May 3 (4.4%) 
June 1 (1.5%) 
July 4 (5.9%) 
August 39 (57.4%) 
October 6 (8.8%) 
November 6 (8.8%) 
December 1 (1.5%) 
Atmospheric dust conditions Nothing 0 (0.0%) 
Haze 0 (0.0%) 
Dust from outside of cities 8 (25.0%) 
Dust from inside of cities 24 (75.0%) 
Monthly rainfall mean Mean ± SD 3 ± 6.5 
Median (range) 0.8 (0.1 to 33.9) 
Monthly temperature mean Mean ± SD 27.9 ± 7.2 
Median (range) 32.7 (9.7 to 33.4) 
Monthly humidity mean Mean ± SD 22.5 ± 10.1 
Median (range) 20 (12 to 50) 
Parameter Value
Age Mean ± SD 18 ± 18 
Median (range) 11 (2 to 85) 
≤ 5 9 (13.2%) 
6–20 45 (66.2%) 
21–45 7 (10.3%) 
46–59 3 (4.4%) 
60 + 4 (5.9%) 
Gender Female 31 (45.6%) 
Male 37 (54.4%) 
Type of community Family 30 (44.1%) 
Other communities 38 (55.9%) 
Admission Hospital 53 (77.9%) 
Outpatient 15 (22.1%) 
Year 2012 10 (14.7%) 
2013 49 (72.1%) 
2014 3 (4.4%) 
2015 6 (8.8%) 
Season Spring 11 (16.2%) 
Summer 43 (63.2%) 
Autumn 13 (19.1%) 
Winter 1 (1.5%) 
Month January 1 (1.5%) 
April 7 (10.3%) 
May 3 (4.4%) 
June 1 (1.5%) 
July 4 (5.9%) 
August 39 (57.4%) 
October 6 (8.8%) 
November 6 (8.8%) 
December 1 (1.5%) 
Atmospheric dust conditions Nothing 0 (0.0%) 
Haze 0 (0.0%) 
Dust from outside of cities 8 (25.0%) 
Dust from inside of cities 24 (75.0%) 
Monthly rainfall mean Mean ± SD 3 ± 6.5 
Median (range) 0.8 (0.1 to 33.9) 
Monthly temperature mean Mean ± SD 27.9 ± 7.2 
Median (range) 32.7 (9.7 to 33.4) 
Monthly humidity mean Mean ± SD 22.5 ± 10.1 
Median (range) 20 (12 to 50) 

In our study, the climatic parameters including temperature, relative humidity, rainfall, and dust origination status were used as independent variables, while Shigella outbreaks were treated as a dependent variable. Interestingly, multilevel regression model analysis showed strong positive relation between suspended-dust conditions and Shigella outbreaks. In this respect compared to normal weather conditions, the outbreak IRs reached to 4.09 and 4.29 times by suspended dust originated from outside of the cities and by suspended dust originated from inside of the cities, respectively. Furthermore, there was a significant correlation between monthly temperature and the IR of Shigella outbreaks. That is to say, one Celsius degree (°C) temperature rise increased the IR of shigellosis occurrence by 1.249 times with regard to the 95% confidence interval (CI: 1.077–1.450 and P value = 0.003). Similarly, a one percent rise in relative humidity increased the IR of outbreaks by 1.103 times (CI: 1.007–1.208, and P value = 0.033). However, there was no obvious relationship between the average monthly rainfall and the IR of the outbreaks. The analysis also showed that the IR of outbreaks significantly decreased in winter (IRR = 0.18, P value = 0.027) and in autumn (IRR = 0.27, P value = 0.046) in comparison with the IR of outbreaks in summer. Likewise, we observed significant positive associations between certain months and outbreaks. For instance, the IR of outbreaks in August was nine times (P value = 0.034) higher than that in January, as shown in Table 2. Regarding spatial variables, the IRs of outbreaks in some cities are significantly higher than that of Yazd city, as evidenced in Table 3. According to the results of this study, the IR of shigellosis in women was higher than men and the IR of the disease in children aged ≤5 years was higher than other age groups. We observed no significant relation between the incidence rates of shigellosis and type of community per 100,000 population. The IRs of shigellosis based on demographic parameters are also shown in Table 4. Notably, no significant association was observed between family societies and other communities (including soldiers living in barracks, residents of school dormitories, and hotels), as shown in Table 4.

Table 2

The IRR of Shigella outbreaks based on climatic and temporal variables

Climatic variablesParameterIRRa95% CI
P value
LowerUpper
Temperature  1.249 1.077 1.450 0.003 
Humidity  1.103 1.007 1.208 0.033 
Rainfall  1.043 0.928 1.172 0.47 
Dust condition      
 1 No dust Ref    
 2 Suspended dust originated from outside of the cities 4.096 1.242 13.500 0.020 
 3 Suspended dust originated from inside of the cities 4.291 1.387 13.273 0.011 
Seasons      
 2 Summer Ref    
 1 Spring 0.73 0.29 1.81 0.493 
 3 Fall 0.27 0.08 0.98 0.046 
 4 Winter 0.18 0.04 0.82 0.027 
Months 
 1 January Ref    
 2 February >1,000 0.994 
 3 March 0.06 15.99 1.000 
 4 April 4.00 0.45 35.79 0.215 
 5 May 3.00 0.31 28.85 0.341 
 6 June 0.06 15.99 1.000 
 7 July 2.00 0.18 22.06 0.571 
 8 August 9.00 1.14 71.6 0. 037 
 9 September >1,000 0.994 
 10 October 2.000 0.18 22.06 0.571 
 11 November >1,000 0.994 
 12 December 0.06 15.99 1.000 
Climatic variablesParameterIRRa95% CI
P value
LowerUpper
Temperature  1.249 1.077 1.450 0.003 
Humidity  1.103 1.007 1.208 0.033 
Rainfall  1.043 0.928 1.172 0.47 
Dust condition      
 1 No dust Ref    
 2 Suspended dust originated from outside of the cities 4.096 1.242 13.500 0.020 
 3 Suspended dust originated from inside of the cities 4.291 1.387 13.273 0.011 
Seasons      
 2 Summer Ref    
 1 Spring 0.73 0.29 1.81 0.493 
 3 Fall 0.27 0.08 0.98 0.046 
 4 Winter 0.18 0.04 0.82 0.027 
Months 
 1 January Ref    
 2 February >1,000 0.994 
 3 March 0.06 15.99 1.000 
 4 April 4.00 0.45 35.79 0.215 
 5 May 3.00 0.31 28.85 0.341 
 6 June 0.06 15.99 1.000 
 7 July 2.00 0.18 22.06 0.571 
 8 August 9.00 1.14 71.6 0. 037 
 9 September >1,000 0.994 
 10 October 2.000 0.18 22.06 0.571 
 11 November >1,000 0.994 
 12 December 0.06 15.99 1.000 

aIR ratio of outbreak based on climatic parameter variations.

Table 3

The IRR of Shigella outbreaks based on cities inside the Yazd province

CityIRRa95% CI
P value
LowerUpper
Ref    
2.821 0.364 21.854 0.321 
0.000 >1,000 0.997 
2.812 0.3630 21.779 0.322 
3.302 1.147 9.505 0.027 
1.842 0.237 14.271 0.558 
4.828 1.070 21.782 0.041 
0.7916 0.102 6.131 0.823 
0.000 >1,000 0.997 
CityIRRa95% CI
P value
LowerUpper
Ref    
2.821 0.364 21.854 0.321 
0.000 >1,000 0.997 
2.812 0.3630 21.779 0.322 
3.302 1.147 9.505 0.027 
1.842 0.237 14.271 0.558 
4.828 1.070 21.782 0.041 
0.7916 0.102 6.131 0.823 
0.000 >1,000 0.997 

aIncident rate ratios of outbreaks based on counties.

1: Yazd, 2: Ashkezar, 3: Mehriz, 4: Taft, 5: Meybod, 6: Abarkuh, 7: Khatam, 8: Ardekan, 9: Behabad.

Table 4

IRs of shigellosis based on some demographic parameters

VariableLevelnIRa95% CI
P value
LowerUpper
Gender Female 31 1.13 1.2 2.52 0.018 
Male 37 1.77 0.796 1.56  
Age ≤ 5 15.64 7.15 29.7 0.005 
6–20 45 2.40 1.75 3.21  
21–45 0.69 0.276 1.41  
46–59 3.09 0.636 9.02  
60 + 4.26 1.16 10.9  
Type of community Family 31 0.90 0.614 1.28 0.333 
Other communitiesb 37 1.43 0.636 9.02  
VariableLevelnIRa95% CI
P value
LowerUpper
Gender Female 31 1.13 1.2 2.52 0.018 
Male 37 1.77 0.796 1.56  
Age ≤ 5 15.64 7.15 29.7 0.005 
6–20 45 2.40 1.75 3.21  
21–45 0.69 0.276 1.41  
46–59 3.09 0.636 9.02  
60 + 4.26 1.16 10.9  
Type of community Family 31 0.90 0.614 1.28 0.333 
Other communitiesb 37 1.43 0.636 9.02  

aShigellosis IR per 100,000 population.

bOther communities include residents of school dormitories, hotels, and soldiers living in barracks.

DISCUSSION

Bacillary dysentery still remains an important cause of morbidity and mortality in developing countries. Shigella spp. are endemic in temperate and tropical regions of the world (Kahsay & Muthupandian 2016). To our knowledge, this is the first study to examine the effects of spatial, temporal, and climatic variables on the incidence of Shigella spp. outbreaks in Iran. In our study, Shigella sonnei was isolated as the sole pathogen from 68 cases of shigellosis during the years 2012–2015. The prevalence of Shigella species varies geographically both within countries and between countries. In this respect, S. sonnei is more commonly found in industrialized countries, whereas S. flexneri prevails in the developing world (Goldberg et al. 2013). Our finding differs from those of studies performed in other developing countries, where S. flexneri was the predominant isolated species (Sousa et al. 2013; Kahsay & Muthupandian 2016; Taneja & Mewara 2016). Although some previous studies from our country showed that S. sonnei was the predominant species in Tehran (Tajbakhsh et al. 2012) and Shiraz (Farshad et al. 2006), other surveys reported that S. flexneri was the most frequent species in less developed regions of Iran (MoezArdalan et al. 2003; Jomezadeh et al. 2014). In general, this may suggest the possible replacement of S. flexneri by S. sonnei in some regions of Iran, as can be observed in the current study. Furthermore, we showed that the incidence rate of shigellosis was highest in people aged ≤5 years, which is consistent with the findings of similar studies (Chompook et al. 2006; Liu et al. 2015; Valcour et al. 2016). The reason for a higher burden of shigellosis in adolescents may be due the fact that young people have more contact with the etiological agent during outdoor activities and lack attention to hygienic practices.

Many researchers have confirmed climatic variations such as ambient temperatures and relative humidity act as a driving force for propagation of enteric diseases (Huang et al. 2008; Akil et al. 2014; Valcour et al. 2016; Wu et al. 2016). Our study indicated that an increase in temperature was positively related with Shigella outbreaks. A seasonal trend was also observed in the current study, with the highest outbreaks during the summer period, which is in accordance with the results of other studies (Huang et al. 2008; Tang et al. 2014; Liu et al. 2015; Valcour et al. 2016). Rainfall and relative humidity are other primary factors which affect the transmission of water-borne diseases by influencing the pathogens. In this study, more humid conditions were significantly associated with an increased incidence of Shigella outbreak, which is in agreement with the findings of other studies (Huang et al. 2008; Akil et al. 2014; Cash et al. 2014; Liu et al. 2015; Valcour et al. 2016). However, contrary to expectations, rainfall did not affect the incidence of Shigella outbreaks. Interestingly, suspended atmospheric dust was significantly associated with an increased risk of bacillary dysentery. The survival of aerosolized gram-negative bacteria such as Escherichia coli and Salmonella spp. in dust has been reported in some earlier studies (Rosas et al. 1997; Fernstrom & Goldblatt 2013; Rosselli et al. 2015; Schulz et al. 2016; Kumar et al. 2017). As for spatial analysis, the IR of Shigella outbreaks in some cities was significantly higher than that in Yazd city. For instance, Khatam and Meybod cities had the greatest accumulated IRs among all cities.

One limitation of our study was that we only focused on the relationship between the climatic variables and the incidence of Shigella outbreaks. Indeed, climate is only one aspect of a multitude of complex interactions that cause bacillary dysentery. Changes in socio-economic status including economic development, health development, medical development and humans' own conditions need to be addressed in future studies (Chompook et al. 2006; Nie et al. 2014). On the other hand, the low number of patients in our study is due to the fact that the majority of the notified cases were those with severe symptoms, who attended hospitals or clinics. Some patients with mild clinical symptoms and self-treated cases might not seek medical help. Therefore, some cases went unreported, resulting in an underestimation of the disease incidence (Von Seidlein et al. 2006). Moreover, future surveillance studies should use molecular typing methods such as pulsed-field gel electrophoresis (PFGE), repetitive element sequence-based polymerase chain reaction (REP-PCR), and multiple-locus variable number tandem repeat analysis (MLVA) to assess whether these bacterial isolates are epidemiologically related (Bakhshi et al. 2017).

In conclusion, our study showed that meteorological parameters such as relative humidity, temperature, and suspended dust in the air can significantly increase the risk of shigellosis in the study area. Without any doubt, the increasing significance of water scarcity in the region and the drinking of poor quality water from different sources including qanats, flumes, and wells play an important role in the dissemination of water-borne diseases. The transmission of Shigella to humans is a complex ecological process. Further research is needed to determine whether socio-economic factors and lifestyle behaviours are associated with spatial and temporal variations in IRs of bacillary dysentery.

ACKNOWLEDGEMENTS

This paper is a part of a research project (Grant No. 35508) approved by the Food Microbiology Research Centre, Tehran University of Medical Sciences, Tehran, Iran.

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