Distribution of pathogens and antibiotic resistance in post-earthquake traumatic infections: a systematic review and meta-analysis BMC infectious diseases

Systematic review and characteristics of included studies

A total of 692 records were retrieved from both English and Chinese databases. Among them, 12 studies were retrieved by forward and backward citations. Meanwhile, 14 studies conducted at the same hospital were deleted. After screening abstracts for duplicates and irrelevant articles, 16 studies were ultimately included in the analysis [8, 10,11,12,13,14,15,16,17,18,19,20,21,22,23,24,25,26,27,28,29,30,31,32,33,34,35,36,37,38] (Figure 1). Of these, 13 are in Chinese and 3 are in English.

Figure 1

Flowchart of the literature search and screening process

The studies conducted microbial assessments on various samples from earthquake victims, including wound swabs, respiratory secretions, dead tissue, blood, urine, and feces, yielding a total of 2,974 bacterial isolates. Detailed information on all included studies is presented in Table 1. The specific search strategies used are provided in the Supplementary Material (Table S1).

Full-size table: Basic characteristics of studies included in the network meta-analysis, quality assessment results

In this study, the JBI method was performed to evaluate the quality of the literature, and the quality of the included literature was higher than 50 points. Of the 16 studies included in the review, all met criteria for items 1, 2, 3, 9, and 10. Ten studies met criteria for Item 4, two studies met Item 5, and 8 studies consistently met criteria for Item 6. All 13 studies complied with Item 7, while Item 8 was not applicable to any of the studies. Baseline characteristics are shown in Table 1. Results of the quality assessment of included studies are presented in the supplementary material (Table S2).

Distribution of pathogens in earthquake victims

A meta-analysis was performed using STATA software to evaluate pathogen detection rates in earthquake victims. The results for pathogen detection rates are summarized in Table 2. The table also displays the heterogeneity of the studies included in the analysis. For cases where heterogeneity exceeded 50%, a random-effects model was used to account for heterogeneity across studies. Conversely, the fixed effects model was applied when heterogeneity was less than 50%.

Table 2: Frequency of occurrence of pathogens after earthquake

A meta-analysis identified the five most common pathogens among earthquake victims. The top five pathogens and their detection rates are as follows: Escherichia coli: 14.5% [95% CI (10.8%, 18.9%)]Acinetobacter baumannii: 12.1% [95% CI (6.4%, 19.7%)]Pseudomonas aeruginosa: 10.5% [95% CI (7.6%, 13.8%)]- Enterobacter cloacae: 9.6% [95% CI (4.6%, 16.3%)]Staphylococcus aureus (Gram-positive): 7.4% [95% CI (5.2%, 9.9%)]. Among these bacteria, Staphylococcus aureus are the only Gram-positive bacteria, while Escherichia coli, Acinetobacter baumannii, Pseudomonas aeruginosa, and Enterobacter cloacae are Gram-negative bacteria.

Analysis of pathogen distribution and resistance in earthquake victims

The analysis focused on the antibiotic resistance profiles of the top five pathogens detected, with detailed results presented in Tables 3, 4, 5, 6 and 7. Specifically, E. coli showed the highest rates of resistance. Resistance to imipenem was remarkably severe, with an overall resistance rate of 79.8%. [95% CI (29.6%, 99.5%)]. This was followed by piperacillin resistance of 70.8%. [95% CI (28.7%, 98.2%)]Cefazolin 65.2% [95% CI (39.0%, 87.2%)]Tetracycline 59.9% [95% CI (0.2%, 97.8%)]and meropenem, 59.4%. [95% CI (0.0%, 97.2%)]. The lowest rate of resistance to Escherichia coli bacteria was against Nitrofurantoin, with a total rate of 12.1%. [95% CI (1.9%, 28.7%)]Followed by ceftriaxone at 19.3%. [95% CI (0.7%, 53.5%)] Aztreonam 21.7%. [95% CI (3.0%, 51.5%)].

Table 3 Antibiotic resistance of Escherichia coliTable 4 Antibiotic resistance of Acinetobacter baumanniiTable 5 Antibiotic resistance of Pseudomonas aeruginosaTable 6 Antibiotic resistance of Enterobacter cloacaeTable 7 Antibiotic resistance of Staphylococcus aureus

For pathogens with heterogeneity greater than 50%, as detailed in Table 3, a random effects model was used to analyze the data. Conversely, a fixed effects model was used when heterogeneity was less than 50%.

Acinetobacter baumannii showed significant antibiotic resistance, with the most severe resistance observed against cefazolin, showing an overall resistance rate of 85.1%. [95% CI (30.0%, 95.9%)]. Other notable resistance rates include ceftriaxone at 62.3% [95% CI (7.9%, 99.9%)]Meropenem, 59.4%. [95% CI (23.4%, 90.1%)]Ceftazidime 58.9% [95% CI (18.5%, 93.1%)]and piperacillin at 57.4%. [95% CI (6.9%, 98.7%)]. The lowest resistance rate was found against cefoperazone/sulbactam at only 19.7%. [95% CI (3.0%, 79.4%)]Followed by ciprofloxacin at 31.0%. [95% CI (1.3%, 76.1%)] Ampicillin/sulbactam alone at 32.8%. [95% CI (0.1%, 90.4%)].

Given the observed heterogeneity in these results, as detailed in Table 4, a random effects model was applied for analyzes with heterogeneity greater than 50%, while a fixed effects model was used otherwise.

Pseudomonas aeruginosa has shown significant resistance to various antibiotics. The most pronounced resistance was observed against piperacillin, with an overall resistance rate of 45.5%. [95% CI (25.1%, 66.2%)]. Other antibiotics with high resistance rates include piperacillin/tazobactam at 42.0%. [95% CI (14.8%, 71.7%)]- Imipenem 37.6% [95% CI (1.9%, 85.5%)]Levofloxacin 35.7% [95% CI (14.5%, 60.9%)]And nitrofurantoin by 35.2%. [95% CI (23.8%, 95.5%)]. Conversely, less resistance was observed against cefoxitin, cefuroxime sodium, and ampicillin, both at 0.5%. [95% CI (0.2%, 3.4%)].

As detailed in Table 5, the heterogeneity analysis indicated that for pathogens with heterogeneity greater than 50%, a random-effects model was applied, while a fixed-effects model was used for less heterogeneity, ensuring the reliability and validity of the analysis results.

Enterobacter cloacae showed significant resistance to antibiotics, with the highest resistance observed against cefazolin, showing an overall resistance rate of 85.8%. [95% CI (30.0%, 95.5%)]. A significantly high resistance to ciprofloxacin was also found at 82.9%. [95% CI (51.0%, 99.4%)]Levofloxacin 64.3% [95% CI (30.5%, 91.8%)]Piperacillin 63.8% [95% CI (20.9%, 96.4%)]and ceftriaxone, 61.8%. [95% CI (27.8%, 90.1%)]. The lowest resistance rates were observed against tetracycline, at 17.8%. [95% CI (0.9%, 49.5%)]Followed by meropenem at 31.4%. [95% CI (18.1%, 99.6%)] and ampicillin at 31.9%. [95% CI (2.9%, 96.0%)].

Given the high heterogeneity as documented in Table 6, a random-effects model was used to meta-analyze pathogens with heterogeneity exceeding 50%. Conversely, a fixed effects model was applied where heterogeneity was below this threshold.

Staphylococcus aureus showed the highest rates of resistance to ceftriaxone, with an overall rate of 87.9%. [95% CI (55.0%, 99.9%)]. This was closely followed by Linezolid resistance at 69.4%. [95% CI (4.3%, 96.3%)]Cefotaxime 69.4% [95% CI (43.0%, 90.6%)]Clindamycin 67.1% [95% CI (55.4%, 77.8%)]and ampicillin by 65.7% [95% CI (10.5%, 100.0%)]. The least observed resistance was against tetracycline at 23.0%. [95% CI (6.4%, 46.0%)]Followed by levofloxacin at 25.1%. [95% CI (15.2%, 36.6%)] and gatifloxacin by 28.2%. [95% CI (2.4%, 67.6%)].

Table 7 presents details of the heterogeneity of the studies analysed, with a random effects model applied in cases where heterogeneity exceeds 50% and a fixed effects model used otherwise.

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