Bacterial contamination of Healthcare workers’ mobile phones in Africa: a systematic review and meta-analysis
1Department of Public Health, School of Health Sciences, Madda Walabu University, P.O.Box 302, Goba, Ethiopia
2Anatomy Unit, School of Medicine, Madda Walabu University, P.O.Box 302, Goba, Ethiopia
3Department of Pharmacy, School of Medicine, Madda Walabu University, P.O.Box 302, Goba, Ethiopia
4Department of Nursing, School of Health Sciences, Madda Walabu University, P.O.Box 302, Goba, Ethiopia
5Department of Medical Laboratory, School of Health Sciences, Madda Walabu University, P.O.Box 302, Goba, Ethiopia
6Center for Trans disciplinary Research, Saveetha Institute of Medical and Technical Sciences, Saveetha University, Chennai 600077, India
7Department of Community Medicine, Faculty of Medicine, Datta Meghe Institute of Medical Sciences, Wardha 442107, India
8School of Public Health, University of Alberta, Edmonton, AB T6G 1C9, Canada
*Corresponding author Email: zdemisu@gmail.com (DZ)Abstract
Background
Mobile phones are potential reservoirs for pathogens and sources of healthcare-associated illnesses. More microbes can be found on a mobile phone than on a man’s lavatory seat, the sole of a shoe, or a door handle. When examining patients, frequent handling of mobile phones can spread bacteria and provide a suitable breeding environment for numerous microorganisms. Nevertheless, evidence of bacterial contamination of mobile phones among healthcare workers in Africa was not conclusive. Thus, this meta-analysis and systematic review was conducted to estimate the pooled prevalence of bacterial contamination of mobile phones used by healthcare workers and the most frequent bacterial isolates in Africa.
Methods
We systematically retrieved relevant studies using PubMed/MEDLINE, Scopus, POPLINE, HINARI, Science Direct, Cochrane Library databases, and Google Scholar from 2009 to 2021 publication year. We included observational studies that reported the prevalence of bacterial contamination of mobile phones among healthcare workers. Two independent authors assessed the quality of the studies. The DerSimonian-random Laird’s effect model was used to calculate effect estimates for the pooled prevalence of bacterial contamination in mobile phones, as well as a 95% confidence interval (CI).
Results
Among 3882 retrieved studies, 23 eligible articles with a total sample size of 2,623 study participants were included in the meta-analysis. The pooled prevalence of mobile phones bacterial contamination among healthcare workers was 83.9% (95% CI: 80.6, 87.2%; I2 = 98%, p-value < 0.001). The most dominant type of bacteria isolated in this review was coagulase-negative staphylococci (CONS) which accounted for 44.5% of the pooled contamination rate of mobile phones used by healthcare workers, followed by Staphylococcus aureus (32.3%), and Escherichia coli (8.4%).
Conclusion
The review indicated that the contamination with a different bacterial isolate of mobile phones used by health care workers was high. The most dominant bacterial isolates were Coagulase-negative staphylococci, Staphylococcus aureus, and Escherichia coli. Hence, these findings would have implications for policymakers and resource allocation for preventive measures initiatives.
Article notes
Competing Interest Statement
The authors have declared no competing interest.
Funding Statement
No
Introduction
Currently, mobile phones have become essential accessories for healthcare workers and social life [1, 2]. Mobile phones have become an important part of the healthcare delivery system because they improve the quality of care and communication [1, 3]. It also makes interdepartmental communication easier, allowing for faster interactions within healthcare institutions and more efficient access to information for patient care [4, 5]. Despite all of the potential benefits, mobile phones play a critical role in becoming potential germ reservoirs and are known to induce healthcare-associated diseases [6-9].
A variety of bacteria, including skin flora and pathogenic bacteria, have been identified on the surface of mobile phones [10, 11]. In HICs, bacterial infections on healthcare personnel’s mobile phones range from 75 to 96% [12-19]. Coagulase-negative staphylococci (CoNS) and Micrococcus were the most commonly recovered bacteria, followed by methicillin-sensitive and methicillin-resistant Staphylococcus aureus (MRSA), Acinetobacter, and Pseudomonas species [12-19]. In low-and middle-income countries’ healthcare settings, bacterial contamination rates of mobile phones used by healthcare personnel ranged from 42% to 100%. The most prevalent bacteria isolated were coagulase-negative staphylococci, Escherichia coli, Acinetobacter species, Pseudomonas species, and MRSA bacteria [20-26]. Several infectious illnesses, including diarrhea, food poisoning, and wound infections, are caused by these bacteria [3, 27, 28].
The global burden of healthcare-associated infections (HAIs) is increasing, resulting in increased morbidity and death among patients and significant challenges for the healthcare system [7, 29, 30]. The cumulative incidence of HAIs ranges from 5.7 to 48.5% within African countries [31]. Contamination of inanimate gadgets used by healthcare professionals, such as cell phones, is one of the many sources of healthcare-acquired infections [30, 32]. More bacteria can be found on a mobile phone than on a man’s lavatory seat, the sole of a shoe, or a door handle [31, 33, 34]. Drug-resistant organisms such as MRSA and vancomycin-resistant enterococci (VRE) have also been found in hospital settings using mobile phones. The drug-resistant bacterium that can cause HAIs and is a public health issue was found to be responsible for 40% to 70% of the contamination of healthcare professionals’ mobile phones [14, 33].
When examining patients, frequent handling of mobile phones can spread bacteria and provide a suitable breeding environment for numerous microorganisms [10, 35, 36]. Although there has been some small-scale research on the bacterial contamination of mobile phones among healthcare workers, a comprehensive review and meta-analysis have not been conducted for Africa. Therefore, this systematic review and meta-analysis aimed to estimate the pooled prevalence of bacterial contamination of mobile phones used by healthcare workers and the most common bacterial isolates in Africa. Besides, we anticipated descriptively summarizing bacterial isolates’ antimicrobial susceptibility and multidrug resistance patterns.
Methods
Registration and protocol
This systematic review and meta-analysis (SRMA) was conducted to estimate the pooled prevalence of bacterial contamination of mobile phones among HCWs in Africa. To ensure the usefulness of this SRMA to the readers, we developed a transparent, complete, and accurate report of the purpose of this review, using the Preferred Reporting Items for Systematic Reviews and Meta-Analysis (PRISMA) criteria (Additional file 1 Table). This systematic review was carried out following the Joanna Briggs Institute (JBI) methodology for systematic reviews of a proportion of evidence [37]. The systematic review and meta-analysis were prospectively registered in PROSPERO (record ID: CRD42022306250, February 22, 2022).
Search strategy
We systematically retrieved relevant studies using PubMed/MEDLINE, Scopus, POPLINE, HINARI, Science Direct, Cochrane Library databases, and Google Scholar from inception to March 25, 2022. All the databases were comprehensively searched to find potentially relevant papers published and unpublished between 2009 and 2021. All searches were limited to English-language papers. In addition to the electronic database search, Google was used to look for grey literature. We also looked for related studies in the reference lists of included studies. For the PubMed/MEDLINE search, the following phrases and keywords were used:[“Bacterial Contamination” OR “microbial contamination” OR “Contamination, equipment” AND “Cell Phones” OR “Mobile Phone” OR “Mobile Phones” OR “Smart Phones” OR “cellular Phones” AND Health Personnel” OR “HealthCare Providers” OR “Health Care Provider” OR “Provider, Health Care” OR “Healthcare Providers” OR “Healthcare Provider” OR “Provider, Healthcare” OR “Healthcare Workers” OR “Healthcare Worker” OR “Health Care Professionals” OR “Health Care Professional” OR “Professional, Health Care” AND Africa). We used database-specific subject headings linked with the above terms and keywords used in PubMed for the other electronic databases (Additional file 2).
Eligibility criteria
Inclusion Criteria
The review process included all studies that met the following criteria: (1) studies that reported the magnitude of bacterial contamination from healthcare workers’ mobile phones surfaces, (2) studies published in English but conducted only in Africa at any given time, and (3) studies conducted using standard bacteriological techniques (i.e., swab method or settle plate sampling method) [32, 38, 39]. (4) Studies that accurately reported the swab culture growth rate for bacterial isolates, (5) all relevant free-of-charge full-text original research articles, and (6) all observational study designs, including published and unpublished studies, were all taken into account.
Exclusion Criteria
The study was excluded for the following reasons: inaccessible or irretrievable full-text articles after contacting the corresponding authors via email at least two times; reviews, commentaries, letters to the editor, conference proceedings, and abstracts; studies with unclear methods; reports from inanimate objects other than mobile phones (such as Stethoscopes, BP apparatus, and patient beds); studies conducted on non-healthcare workers; and studies that did not report the outcome of interest.
Assessment of outcome variables
The primary outcome variable was the prevalence of bacterial contamination of mobile phones used by healthcare workers, as defined by the included studies’ operational definition. The prevalence of mobile phone bacterial contamination was calculated by dividing the total number of swabs collected from the mobile phones of healthcare professionals by the total number of swabs taken from mobile phones of healthcare workers and multiplying by 100. The second objective of this study was to descriptively characterize the most common types of bacteria isolated from healthcare workers’ mobile phones and their drug sensitivity and resistance patterns, utilizing studies that were included.
Operational definitions
Study selection and data extraction
All the retrieved citations were imported into EndNote version X8 and duplicates were removed. The JBI data extraction format was used to extract the data [40]. Based on the established inclusion criteria, two authors (DZ and BS) independently assessed and identified papers by their titles, abstracts, and full texts. Any disagreements that arose were resolved by consensus or with the additional author/s (DZ and GB). The data extraction format included the primary author, publication year, country, study area, bacteria isolation method, optimum temperature, incubation period, the most prevalent types of bacteria isolated, isolated bacteria drug sensitivity, isolated bacteria drug resistance, sample size, and prevalence of mobile phone bacterial contamination.
Assessment of risk of bias
The quality of the appended studies was assessed using the JBI meta-analysis of statistics assessment and review instrument (MAStARI) quality rating tool [40, 41]. An appropriate sampling frame, proper sampling technique, study subject and setting description, sufficient data analysis, use of valid methods for the identified conditions, a valid measurement for all participants, using appropriate statistical analysis, in a valid and reliable outcome measure, with a 50% or higher overall score considered low risk of bias, are among the JBI parameters. As a result, bias risks were classified as low (total score of 2), moderate (total score of 3-4), or high (total score of > 5) [41]. Two independent authors rated the quality of the included studies (DZ and BS). Any disagreements that arose were addressed through consensus. Finally, papers with a score of 5 or higher were ruled out as having a significant risk of bias (Additional file 3).
Data synthesis
Before being evaluated, the data was extracted into a Microsoft Excel file. The data were analyzed using STATA software version 16. The standard errors of the included studies were determined using the formula (SE = p (1p)/n). To investigate heterogeneity in the stated proportion, the I2 statistics and p-values of the Cochrane Q-test were utilized. The Cochrane Q-test p-values are less than 0.1 and are deemed to indicate the presence of heterogeneity among studies. To assess the percentage of total variance owing to heterogeneity across trials, we used the Higgins I2 test statistics [41]. Although no specific criterion exists for when heterogeneity becomes substantial, some researchers suggest low heterogeneity when I2 values are between (25–50%), moderate (50–75%), and high (> 75%) [41]. Because the test statistic revealed significant heterogeneity among the research (I2 = 98%, p-value 0.001), the DerSimonian-influence Laird’s was analyzed using a random-effects model. The effect sizes were calculated as a percentage with a 95% confidence interval (CI). There was a lot of variation in the included studies in this review according to the I2 category. We used subgroup analysis by sub-region, study area, bacteria isolation method, sample size, and publication year to find the source of variation. The meta-analysis findings were displayed using a forest plot. A funnel plot was employed in conjunction with meta-regression to assess publication bias. The plot resembles an asymmetrical, huge, inverted funnel in the absence of publication bias. Egger’s weighted regression and Begg’s rank correlation tests (p-value < 0.05) were used to objectively assess publication bias; however, only Egger’s test was shown to be statistically significant (p-value = 0.001). To test the findings’ robustness, a leave-one-out sensitivity meta-analysis was used.
Results
A total of 3,882 articles were identified after a thorough search of the literature. Of these articles, 2,951 duplicates were removed, and 931 were screened only based on their titles and abstracts. Following the exclusion of 844 articles, a total of 87 full-text papers were verified for eligibility using the pre-determined criteria, with 64 articles being excluded. Finally, 23 articles [21–25, 42-59] that satisfied the criteria were included in the meta-analysis (Fig. 1).
Descriptions of the included studies
All included studies were cross-sectional by design and were published between 2009 and 2021. The current meta-analysis used 2,623 mobile phones from healthcare professionals to estimate the pooled proportion of bacterial contamination. In terms of sub-regional distribution, nine studies were from Eastern Africa [22, 24, 25, 42, 44–46, 58], four studies from Western [21, 51, 52, 55], seven studies from Northern [23, 47–50, 54, 57], one study from Southern [56], and two studies from middle African countries [53, 59] (Table 1).
Prevalence and types of bacterial isolates
The pooled prevalence of bacterial contamination of mobile phones used by healthcare professionals in Africa was 83.9%; 95% CI: (80.6, 87.2%) (Fig.2). the high heterogeneity was showed among included studies (I2 = 98%, p = 0.001). As a result, a random effect model was used to estimate the pooled prevalence of bacterial contamination of mobile phones of healthcare personnel. A univariate meta-regression analysis was performed using variables such as year of publication, quality score, and sample size to identify credible sources of heterogeneity. Accordingly, the year of publication was a significant source of variability among the variables included in the studies (Table 2).
The most prevalent bacteria in this review were coagulase-negative staphylococci (CONS), which accounted for 44.5% of the pooled contamination rate (95% CI: 34.3, 54.8%) of mobile phones used by healthcare workers, followed by Staphylococcus aureus, which accounted for 32.3% of the pooled contamination rate (95% CI: (34.3, 54.8%) of mobile phones used by healthcare workers (24.3, 40.2%). On the other hand, the gram-negative bacterium Escherichia coli was found in 8.4% of mobile phones used by healthcare personnel (95% CI: (5.1, 11.7%)) (Fig. 3-5).
Sensitivity analysis
The findings were put to the test using a leave-one-out sensitivity analysis. The random-effects model was shown to be robust, and according to the sensitivity analyses, no single study had an effect on the pooled rate of bacterial contamination of mobile phones used by healthcare personnel. The pooled mobile phone bacterial contamination was nearly equal to the real effect magnitude when a single study was eliminated from a meta-analysis (Fig. 6).
Publication bias
The funnel plot was used to examine the publication bias. The funnel plot demonstrated that the item distribution was consistent. We employed Begg’s and Egger’s tests to objectively confirm the asymmetry. In the prevalence of bacterial contamination of mobile phones used by healthcare workers, Egger’s test indicated evidence of publishing bias (p 0.001). However, Begg’s test revealed no indication of publication bias (p = 1.999). (Fig. 7). As a result, a non-parametric trim-and-fill analysis of the publication bias linear estimator was performed on the left, with the pooled prevalence of bacterial contamination set to 79.8% (Fig.8).
Subgroup analysis
This meta-analysis used subgroup analysis based on the country’s sub-regions, study setting, and sample size. As a result, the northern African countries had the greatest pooled prevalence of bacterial contamination of mobile phones, at 87.3% (95% CI: (81.6, 93.0%), followed by the eastern African countries, at 83.62 %(95 % CI: 74.40, 92.84%). A subgroup analysis depending on the year of publication was also performed. The combined percentage of bacterial contamination in mobile phones among studies conducted from 2009 to 2014 and 2015 to 2021 was 62.5% and 88% respectively. The prevalence of bacterial contamination on mobile phones was 95.2% in studies that used a selective bacterial isolation method. However, it was found to be 70.4% in studies that used a non-selective bacterial isolation method, and 86.3% among studies that used both (selective + non-selective) bacterial isolation methods. A substantial source of variability was observed across the country’s sub-regions, year of publication, types of healthcare facilities, and bacterial isolation methods of included studies in all subgroup analyses (Table 3).
Narrative review
Antimicrobial susceptibility and multidrug resistance patterns
We descriptively explained the antimicrobial susceptibility and multidrug resistance of bacterial isolates using 14 studies [21, 22, 24, 25, 42–46, 53–56, 58]. According to an Ethiopian study, bacterial isolates had a greater rate of resistance to penicillin (84%), ampicillin (81%), and tetracycline (81%). Nevertheless, a study conducted in Nigeria revealed that over 75% of bacterial isolates were sensitive to fluoroquinolone and ceftriaxone (Table 4).
Discussion
Mobile phones (MPs) are now widely used worldwide and are regarded as one of the essential gadgets. This technology is thought to be one of the most significant threats to human health [60, 61]. Healthcare workers’ (HCWs’) continuous handlings of MPs promote the spread of healthcare-associated illnesses [62]. In addition, pathogenic organisms colonizing mobile phones may increase antibiotic resistance [1]. The objective of this systematic review and meta-analysis was to estimate the pooled prevalence of bacterial contamination of mobile phones used by healthcare workers in Africa. As a result, 83.9% of mobile phones were found to be contaminated with bacteria. Mobile phone bacterial contamination is responsible for different infectious illnesses and increases the burden of nosocomial infections unless standard guidelines for using and cleaning mobile phones in health care settings are established [1, 3, 27, 28]. On the other hand, bacterial contamination of MPs could be a significant concern influencing the execution of efficient infection prevention measures, thus jeopardizing efforts to limit cross-contamination [63]. This review’s result was somewhat higher than that of a meta-analysis in Egypt, which reported a pooled prevalence of bacterial contamination of mobile phones of 78% [26]. Similarly, this review finding was consistent with findings from a systematic review published elsewhere [64]. The variation in bacterial contamination of mobile phones could be due to the varying levels of hand hygiene practiced by healthcare staff, the different models of mobile phones utilized, and bacterial isolation method [17, 65]. Furthermore, the type and load of bacterial contamination are known to be influenced by the design of touchscreen phones and the type of keypad surface [66-68].
We conducted a sub-group analysis based on country sub-region, finding that research from northern African countries had the highest incidence of bacterial contamination in mobile phones. In contrast, studies from southern African countries had the lowest prevalence. Compared to research conducted in other countries in sub-regions, most of the papers included in this review were from eastern and northern African countries, and differences in healthcare facilities could explain regional heterogeneity. Another reason for the disparities could be variances in healthcare personnel’s hand hygiene standards, mobile phone cleaning methods, and phone types (touchscreen versus Keypad surface). As a result of our findings, it may be necessary to encourage all African countries to achieve a zero prevalence of bacterial contamination in mobile phones.
A subgroup analysis was also performed using the year of publication and the method of bacterial isolation. As a result, studies conducted from 2015 to 2021 found a higher incidence of bacterial contamination in mobile phones than studies conducted from 2009 to 2014, which demonstrated a lower frequency of bacterial contamination. This disparity could be because smartphones or screen touch mobile phones, which have a high contamination rate and have been used by healthcare personnel in recent years, have a high contamination rate. In terms of bacterial isolation methods, research using selective bacterial isolation methods like MacConkey had the highest frequency of bacterial contamination on mobile phones compared to studies using non-selective and both (selective & non-selective) bacterial isolation methods. These differences could be related to competition among bacteria as selective media inhibit other contaminating organisms.
Coagulase-negative staphylococci (CONS) were the most common bacteria isolated in this review, followed by gram-positive bacteria such as Staphylococcus aureus. However, Staphylococcus aureus is the most common bacterial infection in most countries and is responsible for over 1 million worldwide deaths, with no focus on global public health expenditure [10]. Escherichia coli were the most isolated gram-negative bacteria from mobile phones used by healthcare personnel. The possible reason for the high isolation of Staphylococci species might be related to their residence in mucosal systems and the isolation of E. coli, possibly due to cross-contamination with gastrointestinal samples. This finding was in line with findings from other studies [1, 2, 10, 16, 69].
The review’s second objective was to describe medication sensitivity and resistance patterns among African bacterial isolates. In a study conducted in Ethiopia, ceftriaxone and ciprofloxacin were effective against 71.7% and 89.1% of gram-positive bacterial isolates such as CONs and S. aureus, respectively, while E. coli was 100% sensitive to ciprofloxacin, gentamycin, and trimethoprim-sulfamethoxazole [42]. However, a study conducted in Nigeria found substantial resistance levels to cotrimoxazole, tetracycline, ampicillin, gentamicin, ceftriaxone, and ciprofloxacin [21]. Most patients who are treated at home are resistant to one or more antimicrobials [66]. Different bacterial strains, hospital environment, empirical treatment practice, use of antibacterial as a prophylactic, easy availability of some drugs without a prescription, drug dose, and indiscriminate/prolonged use of common antibiotics could all contribute to discrepancies in antimicrobial susceptibility in the included studies [70].
Data Availability
The manuscript contains all pertinent information.
Limitations
This research has certain limitations. Since all of the studies examined were cross-sectional studies in design, it could be difficult to establish a cause-effect relationship. The study’s findings were only generalizable to the included country’s sub-regions. In the end, only articles written in English were considered. Finally, future researchers should concentrate on observational studies with well-designed designs, such as cohort and interventional studies.
Conclusion
The contamination of mobile phones used by HCWs with various bacterial isolates was found to be high in this review. The most common bacteria isolated were coagulase-negative staphylococci, Staphylococcus aureus, and Escherichia coli. The prevalence of bacterial contamination in mobile phones varies by country and sub-region. Healthcare workers should be required to practice proper hand hygiene and clean after using their phones in healthcare facilities. Thus, healthcare planners and policymakers should adopt guidelines to govern healthcare workers’ hand hygiene, disinfection, sterilization, and cleansing after using mobile phones in healthcare facilities.
Ethics approval and consent to participate
Not applicable.
Consent for publication
Not applicable.
Availability of data and materials
The manuscript contains all pertinent information.
Competing interests
There are no competing interests declared by the authors.
Funding
There was no funding for this work from any entity.
Additional files
Additional file 1: PRISMA checklist (DOC)
Additional file 2: Search results of electronic databases (Screenshot)
Additional file 3: Risk of bias assessment of included studies (XLSX).