Prevalence of Hospital-Acquired Infections in Low- and Middle-Income Countries: Systematic Review and Meta-Analysis

1. Introduction

Hospital-acquired infections (HAIs) are infections that develop over 48 hours after hospital admission or within 30 days post-discharge [1]. These infections pose considerable global risks to patient safety and well-being [2]. HAIs are the most prevalent adverse events experienced by hospitalized patients, leading to prolonged hospital stays and significant morbidity and mortality [3]. On average, 1 out of every 10 patients affected by an HAI experience mortality associated with the infection [4]. HAIs result in substantial economic burdens, with significant cost discrepancies among healthcare facilities and countries. The worldwide cost of HAIs exceeds $200 billion per year [5]. In the United States, the annual financial impact of HAIs ranges from $28-$45 billion [6], whereas in the European Union, HAI-related costs exceed €7 billion annually [7].

There are differences in the prevalence of HAIs in high-income and low- to middle-income countries. For example, in high-income nations, 7 out of every 100 hospitalized patients develop an HAI, whereas in low- and middle-income countries, this number increases to 15 per 100 patients [4]. In the United States, HAIs impact 3.2% of hospitalized patients [8], whereas in Europe, the prevalence of HAIs in acute care settings is approximately 6.5% [9], which is lower than the reported 9.0% reported in Asia [10] and the estimated 12.8% reported in Africa [11]. However, the true extent of HAIs in low- and middle-income countries (LMICs) remains underestimated or unknown, as there is no comprehensive review that has pooled data on the prevalence of HAIs in these regions. Relying on information from other regions may not accurately reflect the local situation. This review, therefore, aimed to provide insight into the prevalence of HAIs in LMICs.

2. Materials and Methods

2.1. PRISMA Guidelines

We adhered to the requirements provided by Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) [12] in order to guarantee a transparent and methodical literature search and evaluation. For the purpose of identifying, screening, and evaluating records, these recommendations offer a detailed list and flow diagram.

2.2. Search Strategy

We extensively searched Scopus, PubMed, and Web of Science for studies published between January 1, 2000, and July 15, 2024, to examine the prevalence of HAIs in LMICs. No language restrictions were applied, and we used Google Translate for non-English studies. The search terms included (“Hospital Acquired Infections” OR “Hospital-Associated Infections” OR “Nosocomial Infections”) AND (“Afghanistan” OR “Korea, Dem. People’s Rep” OR “South Sudan” OR “Burkina Faso” OR “Liberia” OR “Sudan” OR “Burundi” OR “Madagascar” OR “Syrian Arab Republic” OR “Central African Republic” OR “Malawi” OR “Togo” OR “Chad” OR “Mali” OR “Uganda” OR “Congo, Dem. Rep” OR “Mozambique” OR “Yemen, Rep” OR “Eritrea” OR “Niger” OR “Ethiopia” OR “Rwanda” OR “Gambia, The” OR “Sierra Leone” OR “Guinea-Bissau” OR “Somalia” OR “Angola” OR “Honduras” OR “Papua New Guinea” OR “Bangladesh” OR “India” OR “Philippines” OR “Benin” OR “Jordan” OR “Samoa” OR “Bhutan” OR “Kenya” OR “São Tomé and Principe” OR “Bolivia” OR “Kiribati” OR “Senegal” OR “Cabo Verde” OR “Kyrgyz Republic” OR “Solomon Islands” OR “Cambodia” OR “Lao PDR” OR “Sri Lanka” OR “Cameroon” OR “Lebanon” OR “Tajikistan” OR “Comoros” OR “Lesotho” OR “Tanzania” OR “Congo, Rep” OR “Mauritania” OR “Timor-Leste” OR “Côte d’Ivoire” OR “Micronesia, Fed. Sts” OR “Tunisia” OR “Djibouti” OR “Morocco” OR “Uzbekistan” OR “Egypt Arab Rep” OR “Myanmar” OR “Vanuatu” OR “Eswatini” OR “Nepal” OR “Vietnam” OR “Ghana” OR “Nicaragua” OR “West Bank and Gaza” OR “Guinea” OR “Nigeria” OR “Zambia” OR “Zimbabwe” OR “Albania” OR “Equatorial Guinea” OR “Moldova” OR “Algeria” OR “Fiji” OR “Mongolia” OR “Argentina” OR “Gabon” OR “Montenegro” OR “Armenia” OR “Georgia” OR “Namibia” OR “Azerbaijan” OR “Grenada” OR “North Macedonia” OR “Belarus” OR “Guatemala” OR “Paraguay” OR “Belize” OR “Indonesia” OR “Peru” OR “Bosnia and Herzegovina” OR “Iran, Islamic Rep” OR “Serbia” OR “Botswana” OR “Iraq” OR “South Africa” OR “Brazil” OR “Jamaica” OR “St. Lucia” OR “China” OR “Kazakhstan” OR “St. Vincent and the Grenadines” OR “Colombia” OR “Kosovo” OR “Suriname” OR “Costa Rica” OR “Libya” OR “Thailand” OR “Cuba” OR “Malaysia” OR “Tonga” OR “Dominica” OR “Maldives” OR “Türkiye” OR “Dominican Republic” OR “Marshall Islands” OR “Turkmenistan” OR “Ecuador” OR “Mauritius” OR “Tuvalu” OR “El Salvador” OR “Mexico” OR “Ukraine”). We included all relevant studies without restrictions on population groups or outcome measures. Furthermore, we reviewed the reference lists of relevant studies to find additional studies.

2.3. Study Selection

We implemented a two-step screening procedure to find pertinent studies. Initially, we reviewed titles and abstracts to exclude books, case–control studies, commentaries, interventional studies, letters to the editor, reports, and reviews. We subsequently evaluated the full-text of the remaining studies to ascertain their eligibility. The studies were assessed using predetermined inclusion and exclusion criteria. Eligible studies reported the prevalence of HAIs in LMICs, while those conducted outside LMICs or focusing on community-acquired infections were excluded. Studies without patient populations or with inaccuracies in HAI prevalence were also excluded. The screening process involved two independent reviewers who adhered to the criteria, with discrepancies resolved by a third reviewer. To handle the search results and find duplicate studies, we used Mendeley Desktop version 1.19.8.

2.4. Data Extraction

The data from the reviewed studies were organized and managed using Microsoft Excel 2019, version 2406. The extracted information included details from articles such as author(s) and year, the country of the study setting, guidelines used for defining HAIs, study period, patient population, prevalence of HAIs, and types of HAIs. The two reviewers independently employed a data abstraction format, to guarantee accurate data extraction.

2.5. Quality Assessment

The study quality was assessed using the JBI manual of Evidence Synthesis for prevalence studies [13]. This tool comprises 9 questions, each answered with “Y” for YES, “N” for NO, or “U” for Unclear. Studies scoring 50% or less were deemed “high risk of bias” or low quality, those between 50% and 70% were considered “moderate risk of bias” or medium quality, and those above 70% were labelled “low risk of bias” or high quality. A third reviewer arbitrated differences between the two independent reviewers who carried out the quality evaluation (Table S3).

2.6. Statistical Analysis

The meta and metafor packages in the R software (version 4.3.1) were used to perform statistical data analyses. The pooled prevalence of HAIs was estimated using a random-effects model, which generated an effect size with a 95% confidence interval (CI). Forest plots were used to visualize the meta-analysis results. The I² test quantified statistical heterogeneity, with a p value <0.05 considered significant. A funnel plot was used to illustrate the assessment of publication bias through Egger’s test. Subgroup analyses were performed to address variability in estimates on the basis of regional differences and income levels.

3. Results

3.1. Search Results

The initial database search on platforms such as PubMed (n = 3942), Scopus (n = 3885), and Web of Science (n = 1316) produced a total of 9143 publications. An additional 150 articles were identified through manual searches, resulting in a total of 9293. After removing duplicates (n = 818), 8475 records remained and were screened on the basis of their titles and abstracts. After excluding 8341 articles that failed to meet the inclusion criteria, 134 full-text articles were assessed for eligibility. Of these, 87 articles met the criteria and were included in the review.

Figure 1. PRISMA flow diagram for articles screening.

Figure 1. PRISMA flow diagram for articles screening.

3.2. Study Distribution

These 87 articles [14,15,16,17,18,19,20,21,22,23,24,25,26,27,28,29,30,31,32,33,34,35,36,37,38,39,40,41,42,43,44,45,46,47,48,49,50,51,52,53,54,55,56,57,58,59,60,61,62,63,64,65,66,67,68,69,70,71,72,73,74,75,76,77,78,79,80,81,82,83,84,85,86,87,88,89,90,91,92,93,94,95,96,97,98,99,100] presented data on the prevalence of HAIs in LMICs (Table S1). According to the WHO region classification, 35 studies were from the African Region (AFR), 17 from the Eastern Mediterranean Region (EMR), 11 from the South East Asian Region (SEAR), 7 from the European Region (EUR), 9 from the Western Pacific Region (WPR), 6 from the Region of the Americas (AMR), and 2 were unclassified.

Concerning the income level of the countries based on the World Bank income level for 2024-2025 [101], 20 studies were from low-income countries, 36 were from lower-middle-income countries, and 31 were from upper-middle-income countries.

Figure 2. Distribution of studies according to WHO region.

Figure 2. Distribution of studies according to WHO region.

Figure 3. Distribution of studies according to income level.

Figure 3. Distribution of studies according to income level.

3.2. Pool Prevalence of HAIs in LMICs

In the studies reviewed, out of 17,221,026 hospitalized patients, 246,190 cases were identified as HAIs. The meta-analysis estimated that the overall prevalence of HAIs in LMICs was 22% (95% CI: 17-28) (Figure 4). Subgroup analysis by WHO region revealed a prevalence of 14% (95% CI: 5-23) in the European Region (EUR) and 37% (95% CI:13-62) in the Southeast Asian Region (SEAR). In terms of income levels, low-income countries had a prevalence of 37% (95% CI: 23-52), whereas upper-middle-income countries presented a lower prevalence of 13% (95% CI: 6-20) (Table 1).

3.3. HAI Type

Among the 246,190 HAI cases analysed, surgical site infections (SSIs) had the highest prevalence at 27% (95% CI: 23-31), followed by pneumonia at 0.23 (95% CI: 18-28) and urinary tract infections (UTIs) at 22% (95% CI: 19-26). Gastrointestinal infections (GIs) had the lowest number of cases at 174, with a prevalence of 6% (95% CI: 3-9) (Table 2). Notably, UTIs were predominant, identified in 77 studies across 46 countries, whereas bloodstream infections (BSIs) were documented in 65 studies across 40 countries. In contrast, gastrointestinal infections (GIs) were observed in only 19 studies across 16 countries.

3.4. Quality of the Studies

After the JBI critical appraisal questions were assessed, only 3 studies (3.4%) were identified as medium quality or moderate risk of bias. In contrast, the majority, comprising 84 studies (96.6%), were classified as high quality or low risk of bias (Table S3).

4. Discussion

HAIs impose a significant burden on healthcare systems globally, with varying prevalence and types across regions. This review examines HAIs in LMICs, highlighting major infection types. The review involved 17,273,166 hospitalized patients, with an overall HAI prevalence of 22% (95% CI: 17-28) in LMICs, which is significantly greater than that reported in high-income countries. In Europe, the prevalence was 6.5% [102], and in the United States, it was 4.0% [103]. The prevalence of HAIs varied across WHO regions, with LMICs in the Southeast Asian Region (SEAR) having the highest at 37% (95% CI: 13-62), followed by those in the African Region at 25% (95% CI: 16-33). This contrasts with a global study where the African Region had the highest prevalence, followed by the Southeast Asian Region (SEAR) [104]. The estimated prevalence reported in this review surpasses that reported in previous reviews in LMICs across different regions. For example, a systematic review in 2015 reported a prevalence of 9.0% in Southeast Asia [10], whereas a recent study by Goh et al. [105] reported a prevalence of 21.6% in the same region. In Africa, the prevalence was 12.8% in 2022 [11], whereas a recent review reported a prevalence of 15% [106]. These trends suggest a growing prevalence of HAIs, supported by the increasing prevalence observed in LMICs in this review. Compared with upper-middle-income countries, low-income countries presented a greater prevalence, which aligns with the findings of a global study by Raoofi et al. [104]. Low-income countries, primarily in the Southeast Asian Region (SEAR) and the African Region (AFR), often face challenges such as limited healthcare infrastructure, fewer healthcare facilities, and a shortage of healthcare professionals. These factors can contribute to higher rates of HAIs due to inadequate infection control measures and overcrowding hospitals.

Urinary tract infections (UTIs), surgical site infections (SSIs), and bloodstream infections (BSIs), and were the most commonly reported HAIs in more than 74% of the studies included in our review. SSIs had the highest prevalence, which is consistent with findings in the African review [11]. In contrast, the study in the United States by Magill et al. [103] identified pneumonia as the most prevalent HAI. Other significant HAIs included pneumonia, UTIs, and BSIs, whereas gastrointestinal infections were less common. Rigorous hand hygiene among healthcare workers likely reduces the transmission of gastrointestinal pathogens, often resulting from inadequate food handling and hygiene practices, thereby contributing to a lower prevalence. SSIs, which are particularly prevalent in low-resource settings, may result from inadequate hygiene practices before, during, and after surgery. The incidence of SSIs varies from 3% to 15% across different surgical procedures and is influenced by patient health and surgery type [107]. Factors such as high air contamination in operating rooms, frequent door openings and crowded surgery settings increase the risk of SSI [108,109]. The reported SSI rates in studies may underestimate the actual rates, as some infections occur post discharge. Establishing a surveillance system with active follow-up post discharge is essential to identify SSIs that develop after hospital stays.

HAIs present a global concern, with LMICs imposing a disproportionate burden, as outlined in this review. The ECDC suggests that up to 50% of HAIs are preventable [110]. Several studies have indicated that relying solely on manual cleaning is insufficient to completely eliminate contamination and ensure the safety of patients and medical staff [111,112,113]. To improve outcomes and reduce infection rates, a comprehensive infection control strategy that integrates automated disinfection methods such as UV-C light and hydrogen peroxide vapour with manual cleaning techniques is crucial [114,115]. Active surveillance is essential for gathering data on HAI incidence, identifying risk factors, evaluating the effectiveness of infection control programs, and highlighting areas requiring further interventions and resources. However, information on surveillance activities for HAI prevention and control in LMICs is limited. Critical care medicine in LMICs is still in its early stages compared with that in more developed countries with established systems. Surveillance systems such as the European Center for Disease Prevention and Control (ECDC) in Europe [110] and the National Health Safety Network (NHSN) in the United States [116] continuously monitor HAIs. Most of the studies in our review followed the criteria of these established surveillance systems for identifying and classifying HAIs. However, some studies used criteria set by the authors, which may not accurately determine HAIs.

It is important to acknowledge some limitations of our review. This review focused primarily on quantitative data, limiting the exploration of qualitative factors contributing to HAIs in LMICs. The analysis included studies from only 50 out of the total 132 LMICs defined by the World Bank, representing approximately 17.4% of the entire LMIC population with available HAI prevalence data. Encouraging studies in countries without existing HAI research is vital. Variations in study designs and methodologies may lead to disparities in results. While some studies have used single-center and retrospective designs, prospective active surveillance is considered the preferred method for accurate data collection and analysis. Future studies in LMICs should involve multiple centers and adopt a prospective approach to address this limitation.

5. Conclusions

The review revealed that HAIs pose a significant issue in LMICs, with prevalence rates varying across regions and income levels. Surgical site infections and pneumonia had the highest prevalence, whereas urinary tract infections were the most commonly reported HAIs. These results emphasize the importance of implementing specific interventions and infection control strategies to address HAIs in LMICs.