Factors Contributing to Mosquito-Borne Disease: A Systematic Review in the Middle East and North Africa (MENA) Region

1. Introduction

Mosquito-borne diseases (MBDs) are a group of illnesses transmitted by mosquitoes and can be caused by bacteria, viruses, or parasites [1]. These diseases represent a significant global burden of infectious diseases, including morbidity and mortality [2]. Mosquitoes, including malaria, dengue, West Nile virus, chikungunya, yellow fever, filariasis, tularemia, dirofilariasis, Japanese encephalitis, Saint Louis encephalitis, western equine encephalitis, eastern equine encephalitis, Venezuelan equine encephalitis, Ross River fever, Barmah Forest fever, La Crosse encephalitis, and Zika fever can transmit a variety of diseases. Some diseases have recently emerged or reemerged, emphasizing the importance of effective disease control strategies [2]. It should be noted that MBDs do not occur by chance; instead, a specific interaction of agent, host, mosquitoes, and environment is necessary for the disease to occur. Any changes in this interaction or the impact of external factors such as weather, urbanization, globalization, and socioeconomic status can lead to the introduction of MBDs in a new area, the expansion of an infected area, or the re-emergence of a previously infected area [2,3]. MBDs can be severe and even fatal, making it essential to protect individuals from mosquito bites [3].

As per the Centers for Disease Control and Prevention [4], the Middle East and North Africa (MENA) region comprises 25 countries. These include Afghanistan, Algeria, Bahrain, Djibouti, Egypt, Iran, Iraq, Israel, Jordan, Kuwait, Lebanon, Libya, Morocco, Oman, Pakistan, Palestine, Qatar, Saudi Arabia, Somalia, Sudan, Syria, Tunisia, Turkiye, United Arab Emirates, and Yemen. Historically, those countries in the MENA region have experienced outbreaks of MBDs. since the 19^th century. A systematic study conducted in 2016 by Humphrey et al. [5] identified several factors influencing these outbreaks. They concluded that the epidemiology of dengue remains poorly characterized despite increasing reports of outbreaks and transmission in new areas. They found significant heterogeneity in published studies' distribution, quality, and quantity, which informs future research and surveillance priorities for the MBD in the MENA region. A more recent systematic review of MBD in North Africa was reported by Nebbak et al. [6] in 2022. They found that 26 species are involved in circulating seven MBDs in North Africa. While other reviews examine the impact of MBD on individual Arabic [7] or African [8] countries, these do not delve into the determinants of MBD spread in the whole MENA region.

Aryaprema et al. [9] conducted a recent systematic review on mosquito control worldwide in 2023. They highlighted the importance of adequate lead time to initiate control interventions and the associated surveillance characteristics, which could guide better surveillance programs to prevent the spread of MBDs. However, no systematic review has been conducted to investigate the factors influencing the spread of MBDs in the MENA region. This review could guide research studies to address data gaps and assist in developing effective surveillance programs in the MENA region.

2. Materials and Methods

2.1. Search Strategies

We adhered to the Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) guidelines in our search [10]. Our search encompassed several prominent citation and abstract databases, including PubMed/MEDLINE, Scopus, Google Scholar, Embase, and the Cochrane Library. The search focused on peer-reviewed English publications published between May 1990 and Jan 2023. The following keywords were used in the search.

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(Mosquito OR Mosquito-borne disease (MBD)) AND (MBD outbreaks OR MBD risk factors) AND (Middle East and North Africa (MENA));

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Mosquito OR Mosquito-borne disease (MBD) OR MBD outbreaks OR MBD risk factors AND MENA;

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Mosquito OR Mosquito-borne disease (MBD) AND MBD outbreaks OR MBD risk factors;

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Mosquito AND MBD outbreaks OR MBD risk factors;

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Mosquito-borne disease (MBD) AND MBD outbreaks OR MBD risk factors;

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Mosquito-borne disease (MBD) AND MBD outbreaks AND MBD risk factors;

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Mosquito OR Vector-borne disease (VBD) OR VBD outbreaks OR VBD risk factors AND MENA.

Our search aimed to gather relevant studies that examined the impact of the spread of MBDs and their associated risk factors in MENA countries.

2.2. Inclusion and Exclusion Criteria

The study's selection criteria necessitated a meticulous examination of peer-reviewed papers published in English from May 1990 to Jan 2023. To maintain the rigor and integrity of the research, observational studies, including cohort, case-control, cross-sectional, longitudinal, and epidemiological studies, and experimental studies, such as quasi-experimental and randomized controlled trials, were considered in this search. The investigation focuses on the MENA region, and this systematic review has been registered in PROSPERO (CRD42022378844). However, it should be noted that the study imposes no restrictions on the setting or target population.

2.3. Study Selection

Initially, abstracts were screened by the lead author. Articles that passed the initial review underwent comprehensive full-text screening conducted independently by the initial trio of authors. In addition, this stage eliminated inaccessible or not explicitly relevant articles. Figure 1 shows a brief list of the reasons for article deletion. To ensure the quality of the reviewed articles, they were all appraised using critical appraisal tools from the Joanna Briggs Institute (JBI) [10].

2.4. Evaluation of the Quality of Reports on the Studies

The first three authors independently evaluated the initial assessment of each study included in the review, including the title, abstract, methods, results, discussion, and additional sections. The JBI guidelines were used for this process, providing checklists corresponding to the article's design type under review, each presenting a distinct set of questions [10]. The inter-rater reliability among the first three authors was strong, with an intra-class correlation coefficient (ICC) of 0.91. The employed checklists encompassed various study designs, including cross-sectional, case-control, cohort, and prevalence studies [10]. A final inclusion quality criterion was established, whereby each review article has to achieve a minimum score of 75% (Table 1).

Figure 1. The flow diagram of the systematic review.

Figure 1. The flow diagram of the systematic review.

2.5. Data Extraction

Data was extracted per PRISMA guidelines [11]. After establishing inter-rater reliability among the first three authors, duplicate articles were removed, and titles and abstracts were reviewed independently. The final inclusion of studies was based on a comprehensive full-text evaluation based on the JBI checklist [10]. Disagreements between the initial reviewers were addressed by consulting with the last two authors. There was no disagreement among the three reviewers on this review procedure. The specific data extracted included the author's name, country of origin, publication year, sample size, study duration, countries, mosquito-borne disease (MBD) events, population characteristics, and detailed information about the mosquito, including type, density, feeding, and resting behaviors, habitats, and seasonality. Other risk factors, including social and environmental, are also documented. Additionally, the correlation (r) or association (χ²) or odds ratio (OR) between outbreaks of MBD events and each risk factor were collected and are presented in Table 2.

3. Results

3.1. Description of Studies

3.1.1. Search Flow Result

A total of 12,697 and 68 records were evaluated from the journal database and other sources, respectively. After removing the duplicate records, 12,263 unique records were obtained. Eleven thousand eight hundred and sixty-three records were excluded during abstract and full-text screening, resulting in a final set of 400 records. Following the eligibility screening of these records, 31 records were included in this systematic review.

3.1.2. Study Characteristic

A comprehensive review was conducted between 2003 and 2022 and included 31 studies (Table 1, #1 to #31). The studies were primarily conducted in Sudan (n=18; #1, 7-15, 19, 20, 23-27), followed by Iran (n=5; #17, 21, 28, 30, and 31), Saudi Arabia (n=4; #2-4, and 18), Yemen (n=2; #5 and 6), Iraq (n=1; #16), Tunisia (n=1; #22), and Turkey (n=1; #29). Most studies employed a cross-sectional design (n=23; #1, 4-9, 11-17, 19, 21-28). Other study designs included prevalence (n=4; #2, 3, 29, and 31), case-control (n=3; #10, 18, and 30), and ecological studies (n=1; #20). The studies covered a range of mosquito-borne diseases, with 11 studies focused on dengue, 11 on malaria, 3 on Rift Valley fever, 4 on West Nile fever, 3 on chikungunya, and 2 on Zika.

3.2. Population Factors

3.2.1. Age

Seventeen studies examined the relationship between age and MBDs. Al-Nefaie et al. [12] reported a significant association between age and MBDs (χ²=75.05, p<0.001), as did Al-Quhaiti et al. [13] (OR=8.2, p<0.001), Eldigail et al. [14] (OR=3.24, p=0.001), and Pouriayevali et al. [15] (p=0.001). Elaagip et al. [16] found a significant association between individuals aged > 60 years and dengue (OR=6.31, p=0.04), while Kadir et al. [17] identified a significant difference between individuals aged 21-30 years and other age groups (p<0.001). Soghaier et al. [18] observed significant associations in age groups, with those under 15 years compared to those aged 15-39 years (OR=2.1, p=0.01) and those aged 40-65 years (OR=2.1, p=0.01) in Zika disease. However, no significant association was found in the age group > 65 years (OR=2.2, p=0.07). Conversely, the remaining 11 out of 17 articles [14,19,20,21,22,23,24] found no significant association between age and MBDs (p>0.05). In the context of malaria in pregnant women, Elghazali et al. [24] also reported no significant correlation with age (p=0.215). However, Kalantari et al. [25] (p=0.001) and Kholedi et al. [26] (χ²=12.34, p=0.03) reported a significant association between age and the presence of dengue. Vasmehjani et al. [27] reported significant associations in different age groups, specifically between those aged 1-24 years compared to those aged 45-54 years (OR=1.82, p=0.04) and those aged 55 years and older (OR=3.52, p<0.001) in West Nile disease. However, no significant differences were found in other age groups for West Nile disease, dengue, or chikungunya. Ziyaeyan et al. [28] reported significant differences between the age group 0-25 years and those over 45 years (OR=4.1, p=0.00), but no significant differences were observed between the age group 0-25 years and those aged 26-45 years (OR=1.3, p=0.416) in West Nile and Zika diseases.

3.2.2. Gender

Seventeen studies analyzed the association between gender and MBDs. Al Azraqi et al. [29] (χ²=3.98, p=0.048) and Al-Nefaie et al. [12] (χ²=14.7, p<0.001) both found a statistically significant association between sex and Rift Valley fever and dengue, respectively. Riabi et al. [22] (p=0.01), Saeed and Ahmed [23] (adjusted OR=2.02, p<0.05), Elmardi et al. [30] (p=0.035), and Soghaier et al. [18] (p=0.03) showed similar significant associations. Ziyaeyan et al. [28] supported these findings with a significant association (OR=2.0, p=0.005). Conversely, Al-Quhaiti et al. [13] (OR=1.4, p=0.33), Bamaga et al. [19] (OR=1.04, 95% CI 0.98-1.12), Elaagip et al. [16] (OR=0.73, p = 0.43), Eldigail et al. [31] (p=0.123), Kadir et al. [17] (OR=1.07, p=0.137), Kholedi et al. [26] (χ²=0.146, p=0.703), Mahdi et al. [32] (OR=0.5, p=0.052), Pouriayevali et al. [15] (p=0.584), Seidahmed et al. [33] (χ²=0.168, p=0.400), Soghaier et al. [34] (OR=1.55, p=0.240), and Vasmehjani et al. [27] (West Nile virus: p=0.53; dengue: p=0.310; chikungunya: p=0.36) reported no statistically significant association between sex and MBDs.

3.2.3. Occupation

Seven studies examined the relationship between occupation and MBDs. Notably, Al-Nefaie et al. [12] (χ²=23.04, p<0.001) and Bamaga et al. [19] found significant associations with different occupational categories (government employees vs. not working: OR=4.84, p<0.05; government employees vs. farmers: OR=1.33, p<0.05). Al-Nefaie et al. [12] identified a significant correlation between occupation types (not working, health worker, and non-health worker) and MBDs. Eldigail et al. [14,31] reported a significant association between low-income levels and MBDs (OR=1.61, p = 0.039) in 2018 and (OR=3.75, p=0.027) in 2020. Ziyaeyan et al. [28] highlighted significant differences between individuals who primarily work outdoors and those who work mostly indoors in the context of Zika and West Nile diseases (OR=3.7, p<0.001). However, no significant difference was observed between those who usually work indoors (OR=1.70, p=0.085). Conversely, Al-Quhaiti et al. [13] (OR=1.0, p=0.999) and Hassanain et al. [20] (OR=1.9, p=0.19) reported no significant association between occupation and MBDs, specifically malaria and Rift Valley fever, respectively.

3.2.4. Socioeconomic Status

Al-Quhaiti et al. [13] found no significant association between parents' educational status and MBDs for fathers (p=0.370) and 0.253 for mothers (p=0.253). Eldigail et al. [31] (p=0.732) and Hassanain et al. [20] (p=0.1) also reported no significant association between educational status and MBD. Similarly, a study by Saeed and Ahmed [23] revealed no significant association between educational status and the presence of malaria (p>0.05). Al-Quhaiti et al. [13] reported a significant association in individuals who had slept under a mosquito net the previous night of the survey, with a significant association with malaria (OR=4.5, p<0.001), compared to those who had not (OR=11.8, p<0.001). The study also found that malaria incidence was significantly higher in households without indoor residual spraying the previous year (OR=3.4, p<0.001). However, no significant association was observed between malaria and residence proximity to garbage collection and screened windows (p>0.05).

3.2.5. Demography

Alkhaldy and Barnett [35] reported that high-density populations and large non-Saudi migrant populations were significantly associated with MBD (p<0.001). Elmardi et al. [30] reported that people in urban areas have significantly higher MBDs than in rural (p=0.003). Soghaier et al. [36] reported no significant difference in Zika virus infection between urban and rural areas (OR=1.4, p=0.09). Tezcan-Ulger et al. [37] reported no significant difference in Rift Valley fever between rural and urban areas (p=0.933). Ziyaeyan et al. [28] reported no significant difference between urban and rural areas with Zika and West Nile disease (OR=1.5, p=0.056).

3.2.6. Blood Group

Only one study considered the effect of blood group on MBDs, which was by Adam et al. [38]. No significant correlation was reported between the blood group and MBDs (p>0.05).

3.2.7. Skin Type

Regarding the possible effect that skin type may have on MBDs, Ziyaeyan et al. [28] reported there is a significant difference in Zika and West Nile disease between skin type I/II vs III/IV (OR=2.9, p=0.003) and type V/VI (OR=3.8, p=0.003).

3.2.8. Number of households

Elaagip et al. [16] reported that there is no significant correlation between the number of individuals living in the house or the number of children under five years living in the house with MBDs (p>0.05)

3.3. Environmental Factors

3.3.1. Climate

Noureldin and Shaffer [39] identified a significant correlation between rainfall and the outbreak of dengue (p<0.05) and the minimum temperature on the spread of dengue (p<0.05). Pouriayevali et al. [15] reported a significant correlation between the season of symptom onset and chikungunya (p=0.042). Seidahmed et al. [33] found significant correlations between dengue and minimum temperature (r=0.67, p=0.03) and maximum temperature (r=-0.83, p=0.027). Soleimani-Ahmadi et al. [40] demonstrated a significant correlation between malaria and various environmental conditions, including water temperature (r=0.17, p<0.01), sulfate ions in water (r=0.23, p<0.04), chloride ions in water (r=0.19, p<0.02), alkalinity of water (r=0.16, p<0.01), conductivity of water (r=0.29, p<0.03), permanence of water (p<0.001), water current (p=0.001), light intensity (p=0.041), and turbidity (p=0.002) except the substrate type (p=0.581).

3.3.2. Sanitation

Al-Nefaie et al. [12] reported a significant correlation between the presence of water containers and MBDs (χ²=20.91, p<0.001). In contrast, other factors, including air conditioning, cement pools, infiltration, sewage systems, water surfaces, vases, water coolers, open tanks, water company supply, and stream water sources, were not significantly correlated (p>0.05). Elaagip et al. [16] identified a significant association between the type of bathroom used in households (OR=3.52, p=0.01) and the use of water-based air conditioners (OR=6.9, p=0.01) with MBDs. However, no significant association was observed between MBDs and the other household condition factors (p>0.05).

3.3.3. Breeding Habitats

Eldigail et al. [31] reported no significant association between clean water and MBDs (p = 0.308). Conversely, Kholedi et al. [26] found a significant association between MBDs and the presence of uncovered water containers (χ²=4.09, p=0.04) and nearby buildings under construction (χ²=8.22, p=0.004). In contrast, Saeed and Ahmed [23] reported no significant association between potential breeding habitats and malaria (adjusted OR=2.25, p>0.05).

3.4. Disease Factors

3.4.1. Pathogen

Mahdi et al. [32] reported a significant association between the Plasmodium falciparum variant MSP2 gene and malaria severity (OR=0.119, p=0.008).

3.4.2. Clinical Symptoms

Bamaga et al. [19] identified a significant correlation between various symptoms, including fever, shivering, headache, jaundice, and anemia, and MBDs (p<0.05). Elghazali et al. [24] reported a significant correlation between birth weight and MBDs (p<0.001). However, there was no significant correlation between hemoglobin levels and MBDs (p>0.05) in this study. Elkhalifa et al. [41] observed significant correlations between MBDs and various hematological parameters, including hemoglobin levels (p<0.001), red blood cell (RBC) count (p<0.001), mean corpuscular hemoglobin (MCH) levels (p<0.001), mean corpuscular hemoglobin concentration (MCHC) (p=0.037), red cell distribution width (RDW) (p<0.001), neutrophil count (p<0.001), lymphocyte count (p=0.004), monocyte count (p=0.021), and platelet count (p<0.001). Ibrahim et al. [21] reported a significant correlation between hemoglobin levels and MBDs (p=0.02), but no significant correlations were found for weight (p=0.57), urea (p=0.88), creatinine (p=0.4), and total cortisol (p=0.12). Pouriayevali et al. [15] reported a significant correlation between chills (p<0.001) and headache (p=0.02) with chikungunya. In contrast, no significant correlations were observed between myalgia, rash, conjunctivitis, retroorbital pain, stomachache, nausea, vomiting, diarrhea, white blood cell (WBC) count, and platelet count with chikungunya. Riabi et al. [22] identified a significant correlation between meningitis and West Nile disease (p=0.001). In contrast, Al-Quhaiti et al. [13] reported no significant correlation between MBDs and symptoms such as fever, sweating, chills, vomiting, and jaundice (p>0.05).

3.4.3. Mosquito

Kholedi et al. [26] discovered a noteworthy association between indoor Aedes aegypti mosquitoes and the incidence of dengue fever cases from Jeddah in Saudi Arabia. Their findings revealed a statistically significant correlation (p=0.027) for adult mosquitoes and an even stronger correlation (p=0.001) for mosquito larvae. Meanwhile, in a study by Seidahmed et al. [33], a significant correlation was observed between the pupae/person index and the IgM seroprevalence of dengue fever in Port Sudan. This correlation suggests a notable relationship between the density of mosquito pupae and the prevalence of dengue fever in the area.

4. Discussion

The results of this study highlight several key findings regarding population, environmental, disease, and mosquito factors in the context of MBDs. Concerning population-related factors, the study findings suggest that the relationship between age and MBDs is complex. While some studies reported significant correlations between age and diseases such as dengue, Zika, and West Nile fever, with individuals over 60 at a higher risk for dengue, others observed no significant age-related differences. This variation could be attributed to differences in disease dynamics, mosquito vector behavior, and population demographics across different regions.

4.1. Impacts of Population Factors on MBD

When considering the population's occupation, this study's findings suggest that occupational factors play a significant role in some studies, with certain occupations, such as farming, showing a higher risk of MBDs [12,28]. This could be attributed to increased outdoor exposure and proximity to mosquito breeding sites for individuals with specific occupations [7]. However, not all studies have identified a significant correlation, indicating that occupational risk varies across contexts.

Regarding gender, the study's findings suggest that the association between gender and MBDs yielded mixed results. While some studies have found significant correlations, others have not. This suggests that gender may not consistently predict disease risk, with factors such as exposure patterns, immune responses, and behavioral differences between genders contributing to these variations [19,27].

Finally, regarding the population's socioeconomic status, the study's findings suggest that the influence of socioeconomic status on MBDs did not show significant correlations in most studies [6]. This suggests that other factors, such as environmental conditions and healthcare access, could be more influential in determining the disease risk [13].

4.2. Impact of Environmental Factors on MBD

Another aspect of MBDs is highlighted by findings related to environmental factors. First, several studies have emphasized the significance of climate, such as rainfall, temperature, and water quality, in transmitting MBDs [39]. Climate changes can impact mosquito breeding and survival rates, leading to fluctuations in disease prevalence [9]. The correlation between seasonality and disease incidence underscores the need for targeted interventions at specific times of the year [15].

Second, this study noted the influence of sanitation practices on MBDs. Water containers were a significant risk factor for MBDs, and proper sanitation practices, such as eliminating potential mosquito breeding sites, are essential for disease prevention [12]. However, other sanitation-related factors did not consistently demonstrate significant correlations, indicating that specific practices and their impacts could vary widely [16].

The impact of breeding habitats also plays an important role in the spread of MBD. In some studies, uncovered water containers and construction sites near residences were associated with a higher risk of MBDs (Humphrey et al., 2016). This highlights the importance of mosquito breeding site management and construction site sanitation in disease prevention [26]. However, not all studies found significant correlations, suggesting that local environmental factors may influence the significance of these risk factors [31].

4.3. Impact of Disease Factors on MBD

The last aspect of note illustrated by the findings of this study was the impact of different disease factors on MBDS. First, the pathogen itself was found to be necessary, as specific pathogen variants have been linked to disease severity in some studies [32,42]. Understanding the genetic diversity of mosquito-borne pathogens can aid in predicting disease outcomes and developing targeted interventions [9].

The role of clinical conditions in affecting MBDs was noted. Various clinical symptoms, such as fever, anemia, and hematological parameters, have been associated with MBDs in different studies [6,15,41,42]. These findings underscore the multifaceted nature of disease manifestations and their potential implications in diagnosis and patient care.

The results also highlighted the significance of the type and species of mosquito affecting the incidence of MBDs in MENA countries [5]. The indoor presence of Aedes aegypti mosquitoes was significantly associated with dengue fever cases from Jeddah, in Saudi Arabia, as reported by Kholedi et al. [26]. This underscores the importance of implementing vector control measures, such as indoor mosquito control, to prevent dengue transmission. Moreover, the correlation between mosquito pupae density and dengue seroprevalence in Port Sudan, as reported by Seidahmed et al. [33], highlights the crucial role of mosquito breeding site management for disease prevention.

4.4. Limitations

Considering the finite time and resources at our disposal, it is essential to recognize the inherent limitations of this systematic review. These limitations include confining the literature search solely to the MENA countries, restricting the search timeframe to 1990-2023, and limiting the inclusion of literature to the English language only. These limitations, while imperative for the feasibility of the study, may have resulted in the inadvertent exclusion of noteworthy publications beyond the findings from this study.

5. Conclusions

The findings of these studies emphasize the need for region-specific strategies and interventions to effectively control and prevent these diseases. Targeted public health measures, such as vector control, sanitation improvement, and education campaigns, can play a crucial role in reducing the burden of these diseases in affected regions. Further research and surveillance are essential to understanding these diseases better and developing evidence-based interventions.