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Soltan Dallal M M, Ehrampoush M H, Mazaheri Nezhad Fard R, Aminharati F, Dehghani Tafti A A, Yaseri M. The Ecological Risk Assessment of Foodborne Disease Outbreaks in Yazd Province, Iran. JNFS 2024; 9 (2) :343-351
URL: http://jnfs.ssu.ac.ir/article-1-682-en.html
The Ecological Risk Assessment of Foodborne Disease Outbreaks in Yazd Province, Iran
Mohammad Mehdi Soltan Dallal , Mohammad Hassan Ehrampoush , Ramin Mazaheri Nezhad Fard , Farzaneh Aminharati * , Abbas Ali Dehghani Tafti , Mehdi Yaseri
Department of Pathobiology, School of Public Health, Tehran University of Medical Sciences, Tehran, Iran
Keywords: Climate, Environment, Disease outbreaks, Risk, Iran.
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The Ecological Risk Assessment of Foodborne Disease Outbreaks in Yazd Province, Iran

Mohammad Mehdi Soltan Dallal; PhD1, 2, Mohammad Hassan Ehrampoush; PhD3, Ramin Mazaheri Nezhad Fard; PhD1, 2, Farzaneh Aminharati; PhD*1, 4, Abbas Ali Dehghani Tafti; PhD4 & Mehdi Yaseri; PhD5

1 Department of Pathobiology, School of Public Health, Tehran University of Medical Sciences, Tehran, Iran; 2 Food Microbiology Research Center, School of Public Health, Tehran University of Medical Science, Tehran, Iran;
3 Department of Environmental Health Engineering, School of Public Health, Yazd Shahid Sadoughi University Medical Science, Yazd, Iran; 4 Department of Health in Disaster and Emergency, School Public of health, Yazd Shahid Sadoughi University Medical Science, Yazd, Iran; 5 Department of Epidemiology and Biostatistics, School of Public Health, Tehran University of Medical Sciences, Tehran, Iran.
ARTICLE INFO ABSTRACT
ORIGINAL ARTICLE
Background: Ecological conditions can change infection routes and increase the risks of outbreaks. The aim of this study is risk assessment of foodborne disease outbreaks based on dispersed regional climatic and demographic variables in Yazd Province, Iran. Methods: In this cross-sectional study, data of temporal climatic parameters and regional demographic factors linked to bacterial foodborne diseases were addressed. A multi-level regression analysis model was used to detect associations between the risk of outbreaks and ecological risk factors; the relationships were verified using (P<0.05). Results: Significant associations were observed between the outbreaks and age )P<0.001(, community type (P<0.001), temperature (P=0.04), rainfall (P=0.03) and dust pollution (P<0.001) in scattered parts of the province. The maximum rate of outbreaks was seen in spring, while the frequency of the outbreaks increased during April and October, compared to other months of the year (2012–2016). Conclusion: Consequences have revealed interventions of the environmental exposures in transmissions of microbial agents by complex ecological processes that caused the outbreaks.

Keywords: Climate; Environment; Disease outbreaks; Risk; Iran.
Article history:
Received: 19 Jul 2022
Revised:12 Oct 2022
Accepted: 12 Nov 2022
*Corresponding author
shahrood2005@yahoo.com
Department of Pathobiology, School of Public Health, Tehran University of Medical Sciences, Tehran, Iran.

Postal code: 1471613151
Tel: +98 2142933047
Introduction
Ecological conditions such as environmental parameters and human behaviors affect public health and contribute to multiple hazards (Cao et al., 2017, Jenerette, 2018, Siziba, 2017). The effects of environmental variations on disease transmissions linked to seasonal warming of sea-surface temperature are significant reasons for enhancing the growth of microorganisms and their transfer routes (Shi et al., 2020, Wei et al., 2020). Severe annual flooding of extreme weather plays an important role in shigellosis outbreaks in some regions, as the seasonal surveillance data of dysentery in Shandong Province of China were associated with El Nino-Southern Oscillation (ENSO) (Chen et al., 2019, Wu et al., 2020). Seasonal climatic patterns and ecological conditions have affected foodborne disease outbreaks among Vietnamese and Australian young children and females during the summer of the warmest states (Ghazani et al., 2018, Vo et al., 2014). Temporal climatic patterns is one of the important reasons for the spread of microbial contaminants via vegetables and herbs (Pradhan et al., 2018, Ssemanda, 2018). Lack of rainfall in drought seasons can cause dust storms which decrease infectious particles in farms and increase infectious diseases by carrier insects in warm and dry weather (Aik et al., 2020, Soleimani et al., 2020). Another reason for pathogen preservation is the humidity that affects environmental contents and wildlife habitats such as the roles of raccoons in Salmonella outbreaks in Ontario, Canada, 2011–2013 (Bondo et al., 2016). The rise in ambient temperature associated with monthly non-typhoidal Salmonella outbreaks in international travelers is known as significant evidence for ecological risk factors in developed countries (Dallal et al., 2020, Savage et al., 2019, Wong et al., 2019). Other effective ecological factors include population density, human lifestyle and the community’s inadequate sanitation facilities such as low volumes of water supplies, which are known as important reasons for enteric disease transmissions and foodborne outbreaks (Brouwer et al., 2018). Due to patient sensitivity and private nature of specific communities, Shigella outbreaks spread in Orthodox Jewish community of Antwerp, Belgium, from the beginning of April 2008 caused endemic shigelloses in Israel (De Schrijver et al., 2011).
The increased population density in high-temperature provinces of China was associated with global warming and irregular climatic variations that affected transmission of communicable diseases in high population densities (Deb, 2018, Levy et al., 2018). Moreover, spatial circumstances may change during human land use and settlement, road construction, and water sanitation plants such as dams, canals, irrigations, and reservoirs which can affect transmissions of etiologic agents (Mas-Coma et al., 2018). Unsanitary conditions after disasters can increase the rate of enteric diseases, especially in the hot weather of desert areas, as marine gastroenteritis outbreaks increased in Iraq, in 2003, and Shigella caused serious health concerns by non-sanitary facility controls (DuPont and Whichard, 2017, Williams et al., 2017). Yazd Province, with cold humid winters and arid warm summers, is located in central desert of Iran. In this desert province, a particular lifestyle can be seen due to the specific climate as the climatic conditions affect people’s eating habits. Therefore, this study was carried out in Yazd Province (Iran) with more than 1,138,533 populations and 131,575 km² area; the natural geomorphologic phenomena in Yazd have caused climatic diversities between its various regions. This ecological study investigates possible associations between foodborne disease outbreaks and environmental risk factors such as temporal patterns of climatic parameters and demographic factors in various ecosystems of Yazd Province, Iran.
Materials and Methods
Study design and setting: This study was based on the most important goals of national development in the Fourth Economic Social and Cultural Development Program in Iran. Therefore, the criteria were used to ensure that the collected data were prepared based on the standardized World Health Organization (WHO) questionnaire guidelines on foodborne disease outbreaks by all the personnel at all times, places, and levels. Furthermore, data on foodborne disease outbreaks from Yazd province climatic database were compared with those from Iran Meteorological Organization (IRIMO). A multivariate regression analysis model was used for surveillance data, and hypothesis records were formulated based on the nature of foodborne disease distributions in various cities of the province. Moreover, foodborne disease incidence rates (IR) were assessed in various regions of Yazd province, which were at high risk of outbreaks.
This cross-sectional study was carried out based on two essential elements of effects, and thus, exposures and available information on sources, stressors, effects, ecosystems, and the risk characteristics used in the risk assessment were integrated. Sources included water or food, and climatic parameters acted as stressors and foodborne diseases known as effects during the outbreaks. While exposures were known as microbial infectious agents, cities of the Yazd province were considered as various ecosystems, and outbreak rates were considered as risk characteristics.
Data collection: Demographic information regarding Yazd province was provided by the Statistical Centre of Iran (SCI) (https://www.amar.org.ir/) and was classified based on age, sex, and the community’s type as instituted social groups and household groups. Climatic data, such as mean monthly temperature, rainfall, relative humidity, and dust conditions were provided by IRIMO (www.irimo.ir). In this study, the inclusion criteria were demographic information, clinical symptoms, climatic variables, and geographic characteristics. The exclusion criteria were the participants who were involved with non-infectious gastroenteritis diseases that could interfere with the success output.
Sampling: People suspected of foodborne diseases were diagnosed based on clinical symptoms described by the surveillance system of the Iran Ministry of Health and foodborne disease outbreak guidelines. Totally, 729 rectal swabs from patients living in Yazd Province were sent to the Reference Laboratory of the School of Public Health, in Tehran University of Medical Sciences, Tehran, Iran, 2012–2016. The patients’ questionnaires were assessed based on WHO’s foodborne disease outbreak tools; and the patients verbally agreed to participate in the study (World Health Organization, 2008). Questionnaires included patients’ age, sex, address, job, visiting care centers or admission types, and clinical symptoms and laboratory-diagnosed etiological agents (Dewey-Mattia et al., 2018). In this study, bias was decreased by skilled questioners in surveillance systems, and hence, the over-representation of unknown gastroenteritis or non-foodborne disease diarrheal cases that might underestimate the true contributions of sporadic illnesses, were omitted. Furthermore, travelling diarrheal patients who referred to clinics and laboratories were suggested as the confounders, and the patients who suffered from foodborne diseases and lived in various cities of the province, were considered as the target populations.
Ethical considerations: This study was ethically approved by the Department of Disaster and Emergency Health, from the International Campus of Yazd Shahid Sadoughi University
of Medical Sciences, in Yazd, Iran (IR.SSU.MEDICINE.REC.1395.127) and was a part of a research project from Food Microbiology Research Center in Tehran University of Medical Sciences, Tehran, Iran.
Data analysis: Collected information, including climatic variables, clinical symptoms, patients’ age, sex, and the community type regarding the outbreaks, were coded using Excel 2007 spreadsheets. Multilevel regression analysis method was used to quantify the magnitude and extent of the links between the foodborne disease outbreaks, climatic parameters (temperature, humidity and dust condition), demographic factors (age, genus and community type), and regional cities. Poisson regression analysis was used to assess the associations between the IR of outbreaks and the environmental conditions of the province. Factors involved with all the applicable levels of a hierarchy were assessed using multivariable random intercept models by identical sequences to the one described for province-level analyses. In this statistical method, significant associations were tested using likelihood ratio tests, and analyses were carried out using Stata Software v.14 (Stata, USA). The multilevel regression analysis model was used to identify the relationships between the environmental stressors as independent variables and the foodborne disease outbreak risk characteristics as dependent variables. Relations were reported when (P-value < 0.05).
Results
From 2012 to 2016, 161 outbreaks have been reported in Yazd Province, from which, 664 bacterial foodborne diseases were identified within 729 samples using differential assays. Most foodborne diseases have been associated with environmental stressors such as temperature, humidity, rainfall, air dust condition, and temporal pattern of exposure to infectious agents in various regional cities. The most frequent outbreaks occurred at temperatures higher than 24.51 ˚C, the humidity of lower than 22%, and 0 ml of rainfall in summer. In isolated areas of the province, statistically significant correlations were seen between the outbreaks and age (P<0.001), community type (P<0.001), temperature (P=0.04), rainfall (P=0.03), and dust pollution (P<0.001). In comparison to other months of the year (2012–2016), the rate of outbreaks was maximum in the spring and increased between April and October. Moreover, a high frequency of outbreak risk characteristics was observed in Yazd City of the province in May and October (Table 1). In addition, significant associations of dust, temperature and rainfall with bacterial exposures were reported during outbreak events using approaches of various risk characteristics. Statistical analysis revealed that the inner dust was more effective than external dust in outbreaks. Furthermore, temperature and rainfall demonstrated similar relationships with the IR of outbreaks since the IR of outbreaks in Ashkezar, Mehriz, and Behabad increased in May and October 2013, compared to that of the other regional ecosystems (Table 2).
In various ecosystems of the province, relationships were shown between the IR values of foodborne diseases, beginning with types of microbial exposures, and demographic factors of various cities. Therefore, the effects of risk factors such as age, sex, and community type on various exposures such as Salmonella, Shigella, Citrobacter freundii and enterotoxigenic Escherichia coli (ETEC) were assessed. In all foodborne diseases except ETEC, the most vulnerable age groups were ≤5, and sex only was linked to shigellosis IR, while the community type



was an important risk factor only in Salmonella foodborne diseases (Table 3).
Table 1. Frequency of the outbreaks based on temporal patterns, regions, and climatic parameters.
Variables Outbreak 95% CI
Lower Upper
Temperature(C°)
   ≤ 13.90
14.00
5.86
22.14
   13.91–24.50 66.00 44.50 87.50
   24.51≤ 81.00 56.91 105.09
Humidity(%)
   ≤ 22.00
91.00
65.89
116.11
   22.01–38.00 53.00 32.93 73.07
   38.01≤ 17.00 8.29 25.71
Rainfall(mm)
   0.00
88.00
63.73
112.27
   0.01–3.70 37.00 20.65 53.35
   3.71≤ 36.00 19.12 52.88
Climate
   Normal air 
57.00
38.67
75.33
   External dust 41.00 25.57 56.43
   Inner dust 63.00 41.35 84.65
Season
   Spring
60.00
38.50
81.50
   Summer 54.00 34.51 73.49
   Fall 36.00 20.57 51.43
   Winter 11.00 4.09 17.91
Month
   January
3.00
0.00
6.41
   February 3.00 0.00 6.41
   March 5.00 0.00 10.17
   April 18.00 4.36 31.64
   May 23.00 9.60 36.40
   June 19.00 8.03 29.97
   July 21.00 7.60 34.40
   August 17.00 5.37 28.63
   September 16.00 6.81 25.19
   October 23.00 10.23 35.77
   November 6.00 0.53 11.47
   December 7.00 0.58 13.42
City
   Yazd
88.00
67.33
108.67
   Ashkezar 14.00 2.46 25.54
   Mehriz 13.00 4.88 21.12
   Taft 11.00 3.73 18.27
   Meybod 7.00 1.23 12.77
   Abarkooh 8.00 2.69 13.31
   Khatam 7.00 1.23 12.77
   Ardekan 11.00 4.95 17.05
   Bahabad 2.00 0.00 6.00


Table 2. Incidence rate ratios (IRR) of the outbreaks based on the temporal and regional patterns
of the climatic stressors.
Variables IRR 95% CI P-valuea
Lower Upper
Climate
   Normal air 
Ref.b
   External dust 2.22 1.43 3.46 < 0.001
   Inner dust 3.79 2.48 5.78 < 0.001
Temperature 1.08 1.00 1.17 0.04
Humidity 0.99 0.95 1.03 0.63
Rainfall 1.06 1.00 1.11 0.04
Season
   Spring
1.55
0.65
3.71		 0.32
   Summer 1.05 0.37 2.97 0.93
   Fall 1.90 0.89 4.03 0.10
   Winter Ref.
Year
   2012
Ref.
   2013 3.49 1.86 6.53 < 0.001
   2014 2.91 1.56 5.43 < 0.001
   2015 2.45 1.33 4.55 < 0.001
   2016 1.80 0.94 3.45 0.08
Month
   January
Ref.
   February 1.00 0.20 4.95 1.00
   March 1.67 0.40 6.97 0.48
   April 6.00 1.77 20.37 < 0.001
   May 7.67 2.30 25.53 < 0.001
   June 6.33 1.87 21.40 < 0.001
   July 7.00 2.09 23.47 < 0.001
   August 5.67 1.66 19.34 0.01
   September 5.33 1.55 18.30 0.01
   October 7.67 2.30 25.53 < 0.001
   November 2.00 0.50 8.00 0.33
   December 2.33 0.60 9.02 0.22
City
   Yazd
Ref.
   Ashkezar 2.93 1.66 5.14 < 0.001
   Mehriz 1.92 1.07 3.45 0.03
   Taft 1.70 0.91 3.17 0.10
   Meybod 0.55 0.25 1.18 0.12
   Abarkooh 1.14 0.55 2.36 0.72
   Khatam 1.35 0.63 2.92 0.44
   Ardekan 0.90 0.48 1.69 0.75
   Bahabad 0.16 0.04 0.67 0.01
a: Multilevel regression analysis method ; b: Reference

Discussion
This study was the first ecological risk assessment regarding the effects of regional climatic and demographic parameters on IR of foodborne disease outbreaks in Iran. Adverse outcomes of the risks were identified in target populations to complete problem formulation by defining outbreak problems and solving the problems based on ecological assessment frameworks (Suter II, 2016). Effects of temporal climatic variables on the exposing effects of foodborne disease agents were assessed with respect to the regional characteristics of the populations. A similar quantitative study was carried out by Semenza and Smith on the roles of environmental conditions and climate changes on the growth of foodborne disease agents and their outbreak incidences (Semenza et al., 2017, Smith and Fazil, 2019). In the planning stage, the regional scope in outbreak complexity extents was estimated using IRs of the outbreaks in various cities of Yazd Province and multilevel regression analysis (Table 2). Regarding problem formulation, at first, information was collected to show at-risk populations such as old people (> 60 years old) and children (≤ 5 years old) after contact with contaminated foods or water (Table 3). A similar quantitative microbial study regarding risk assessment focused on aspiration and inhalation of contaminated water in Legionella spp. outbreaks as the role of exposure in enteric diseases during foodborne and waterborne disease outbreaks (Bentham and Whiley, 2018). At the end of problem formulation stage, potential areas of disease outbreaks were identified in regional cities, and Ashkezar was reported as an important hot spot for the outbreaks (Suter II, 2016).
In all the regions of the world, effects of foodborne diseases on communities vary significantly based on demographic parameters; however, IR of shigellosis and salmonellosis and ETEC foodborne diseases have been reported to be higher in children ≤ 5 years of age compared with other age groups. The current study showed significant relationships between ≤ 5-year-old groups and outbreak rates regarding Salmonella and Shigella outbreaks using multilevel regression analysis models, which was in line with studies from NZ did (On et al., 2011, Sukted et al., 2017). The IR of ETEC foodborne diseases in patients ≥ 60 years of age were significantly linked to the outbreaks in Yazd Province
(Table 3).
Table 3. Incidence rates of various foodborne diseases based on the demographic factors.
Foodborne disease agent IR 95% CI P-valuea
Lower Upper
Salmonella
  Age ≤ 5 y 8.37 3.07 18.21 < 0.001
  Family 0.31 0.201 0.45 < 0.001
  Socialb 72.20 51.09 99.1 ˃ 0.05
Shigella
  Female 1.77 1.2 2.52 0.018
  Male 1.13 0.796 1.56 ˃ 0.05
  Age ≤ 5 y 15.64 7.15 29.7 0.005
  Age ≤ 60 y 4.78 1.55 11.15 < 0.001
Enterotoxigenic Escherichia coli (ETEC)
   Age ≥ 60 y 4.78 1.55 11.15 < 0.001
Citrobacter
  Age ≤ 5 y 3.06 1.23 6.3 0.002
IR: the incidence rate of foodborne diseases per 100,000 populations; a: Multilevel regression analysis method; b: A community that used prepared food from restaurants or organizational foods

Collected information and outcomes of multilevel regression revealed that the older people in ETEC outbreaks who lived in Mehriz Sanatorium were further vulnerable to outbreak incidents. In the analysis step, associations of various infectious agents and harmful ecological stressors with foodborne diseases were assessed using the framework of data to show effective relationships between the stressors, infectious agent exposures, and various regional ecosystems of outbreaks (Tables 1 and 3). Therefore, critical modes of stressor effects were investigated as climatic variables on endpoints of the microbial contaminant effects during outbreaks. Most investigations on climate change revealed climatic parameters such as temperature, humidity, and dust pollution as risk factors in IR of disease outbreaks, showing significant associations with disease outbreaks worldwide (Wu et al., 2020). Due to the roles of dust conditions in the spread and survival of pathogens in dry weather, increased rates of outbreaks in falls might be linked to such environmental conditions in Yazd Province. Based on the analysis results, atmospheric dust condition was significantly associated with foodborne diseases and IR of the outbreaks increased in various cities of the province in summer.
Increased IR could be associated with etiologic agents of diseases since most of the infectious disease agents were reported as clinically-relevant bacteria with warning-worth occurrences in dust particles (Wei et al., 2020). Recent studies demonstrated that the effects of high temperature and low humidity could be controlled by dietary habits, which affected the IR indirectly (Clark et al., 2019, Rushton et al., 2019). Assessed exposures in the analysis stage of risk assessment revealed that high contingency of outbreaks might be associated with tourism attractions or potentials of specific cities, compared to other cities of the province (Aminharati et al., 2018). Furthermore, waterfalls that caused prosperity of the city regions could provide opportunities for microbial survival and transmissions (Wang et al., 2018, Weiskerger et al., 2019). In the ecological stage of risk assessment, the roles of the precipitation on IRs of outbreaks were unclear or reversed since rainfalls had various effects on foodborne diseases such as salmonellosis in various regions of the world (Lee et al., 2019, Wang et al., 2018). In risk characterization stage, the weight of exposing effects on the most sensitive variables of the host such as age, sex and community type was assessed based on spatial/temporal trends of climatic variables using P-values of ≤ 0.05 (Table 3). In the present study, females were reported as a sensitive group with respect to shigellosis, which was different from other studies.
In a social community, ≤ 5-year-old groups were more vulnerable with regard to Salmonella outbreaks. These different results could be linked to demographic conditions in different communities or environmental variations, nutrition recourses, and food habits of populations in scattered geographical regions. Moreover, the IR of foodborne disease outbreak regarding salmonellosis increased in social groups compared to the family groups, possibly due to Salmonella’s latent carriers or infection spread by employees (Bonardi, 2017). A similar study was carried out in US Carolina State (2008–2015) to find the effects of environmental exposures on foodborne disease outbreaks. Relevant exposures were reported using logistic regression models, which showed that most outbreaks occurred in urban areas in females of ≥45 in August and those of ≤45 in May (Alianell, 2017). In the current study, significant relationships were observed between the outbreaks and demographic parameters of scattered regional cities, while females were more vulnerable to the risk of shigellosis, possibly due to cooking traditions in Yazd province. Similar to other cross-sectional studies, this study included multiple variables at the time of data snapshots which was used to prove or disprove assumptions; however, these could not be used to analyze behaviors such as traditional cooking over a while and did not help constantly determine causes and effects. Larger sample sizes and longer study periods could lead to a better understanding of the problem.
Conclusion
Weather variations can affect local vulnerabilities, surveillance activities, and community’s resiliencies, which need to be considered in preparedness activities and anticipating response plans. Furthermore, spatial/temporal trends of climatic variations can affect functions of health infrastructures by changes in magnitude and frequency of the outbreaks. Therefore, healthcare facility infrastructures must be maintained and activated in the response and recovery phases. In general, understanding possible climatic risks and the way they can be most efficiently managed are critically necessary.
Acknowledgments
The authors would like to thank the Department of Disaster and Emergency Health in International Campus of Shahid Sadoughi University of Medical Sciences, Yazd, Iran, and Food Microbiology Research Center affiliated with Tehran University of Medical Sciences, Tehran, Iran (Contract No. 35508).
Authors' contributions
Ehrampoush MH designed the study; Aminharati F carried out the research; Soltan Dallal MM, Dehghani Tafti AA, and Yaseri M provided the materials and reagents; and, Mazaheri Nezhad Fard R revised the manuscript.
Conflict of interests
The authors declared no conflict of interests.
Funding
This study did not receive any grants from funding agencies in public, commercial, or not-for-profit sectors.
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Received: 2022/07/19 | Published: 2024/05/21 | ePublished: 2024/05/21
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