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Information sources and search strategy: We performed systematic search in six electronic databases, including PubMed, Embase (OVID), Chinese BioMedical Database (CBM), China National Knowledge Infrastructure (CNKI), VIP Chinese Science and Technique Journals Database, and Wanfang Database, to identify the relevant studies. Since the focus in the review was on the epidemiological characteristics of S. aureus and MRSA in SSIs during recent years, search was limited to the publication date from January 2007 to November 2012. A combination of Mesh words and free text words applied to PubMed, Embase and CBM, and free text words were used to search CNKI, VIP and Wanfang database. The following search terms were mainly used: “surgery”, “wound infection*”, “postoperative wound infection*”, “surgical site infection*”, “S. aureus.”, “Staphylococcus aureus”, “methicillin”, “MSSA” and “MRSA”. Details of the search strategies for each database were summarized in S1 Table.
Patients: those with SSIs regardless of other characteristics;Outcomes: S. aureus and MRSA isolates identified from SSIs;Study types: observational studies including cross-sectional, monitoring, prospective, ambispective and retrospective study.Criteria of exclusion: Duplicate studies;Involvement of studied population from outside mainland China;Therapeutic study including randomized controlled trial and observational research for comparative effectiveness;Studies with data from the China Nosocomial Infection Surveillance System.
According to the criteria of inclusion and exclusion, two reviewers independently screened each record by the title, keywords and abstract. The eligibility was determined further through the full texts if selection cannot be made only based on the screening. Any disagreement was resolved by the third reviewer.
An original extraction form was designed and then modified following a pilot test. The revised extraction form encompassed three parts: general information, clinical characteristics and numbers for calculating proportions of S. aureus and MRSA isolates. Two reviewers extracted information from each study independently. Any disagreement was also resolved by the third reviewer.
Assessment of risk of bias: As there were no acknowledged or standardized quality assessment tools for the included study designs, we used a checklist with 8 items adapted from a scale for case series [15], which was originally developed by the National Institute for Health and Care Excellence (NICE), a special health authority in the UK which is committed to providing national guidance and advice to improve health and social care. Low, high or unclear risk of bias for each item was determined according to the pre-specified criteria (S2 Table) and the graph of summary of risk of bias was developed with Revman 5.1. One point was scored if an item was judged low risk of bias. We defined study of higher quality with a total of at least 4 points.
When information of the variables for analysis was missing from publications, the correspondent authors were contacted by email every one week. If the authors did not reply to the emails after our second contact attempt, the publications were excluded when the related variables were analyzed.
We conducted all the data analyses using R (Version 3.1.2, The R Foundation for Statistical Computing).
Calculation formula for the proportions of S. aureus and MRSA: Proportions of S. aureus and MRSA isolates were calculated by the following formula for each related study: Proportion of S. aureus=Number of S. aureus isolates detectedNumber of all the detected isolates×100% Proportion of MRSA=Number of MRSA isolates detectedNumber of S.aureus isolates detected×100% Proportion of antibiotics-resistant MRSA=Number of detected MRSA isolates resistant to a given antibioticNumber of MRSA isolates detected×100% Incremental 0.5 was added to both the numerator and denominator in studies with zero or all events. 95% CI for the proportion in each study was calculated based on the logit-transformed metric.
Meta-analysis was conducted for the pooled estimates, followed by comparison between our overall estimate of S. aureus and MRSA and the corresponding proportions in the US and in the China Nosocomial Infection Surveillance System. Statistical difference between the proportions in such comparisons was tested by Q statistic for heterogeneity [16]. P-value of less than 0.05 indicated statistical significance. Considering probable heterogeneity across all the observational studies, random-effects model with Der-Simonian Laird method was used a priori throughout the data analyses.
Q test and I2 statistic were used to examine and quantify the heterogeneity of the logit-transformed proportion across the studies. P-value of less than 0.05 or I2 statistic of more than 50% were regarded as substantial heterogeneity [17]. Subgroup analysis was conducted to explore the possible sources of heterogeneity based on the pre-defined variables including study quality, sample size, region, level of hospital, provincial economic condition, types of surgeries. A map for the distribution of S. aureus was drawn through MapInfo Professional 11.0 according to the subgroup analysis by provinces. We determined small sample size if at most 20 bacteria isolates or S.aureus isolates were included in analysis for primary studies respectively reporting the proportion of S. aureus or MRSA. Based on whether the annual Gross Domestic Product (GDP) per capita of each province in 2011 was higher or lower than the national average (35,181RMB) in China, provinces were categorized into higher or lower provincial economic condition [18]. Informal comparisons were made between subgroups for the proportions of S. aureus and MRSA by directly comparing the magnitudes of proportions between different subgroups instead of significance tests which tend to be misleading for the comparison in subgroup analysis. Statistical significance was defined as non-overlap of the confidence intervals of the proportions between the subgroups [19].
Meta-regression for the proportion of S. aureus isolates: Meta-regression was used to explore the impact of pre-defined factors on the proportion of S. aureus isolates. We defined logit(P) as the dependent variable where P referred to proportion of S. aureus isolates. All the independent factors were initially selected based on the expertise in clinical microbiology and the availability of related information in the included articles, including study quality, sample size, region, level of hospital, provincial economic condition and type of surgery, all of which were defined as dummy variables. The factors without colinearity indicated by no correlation to each other (P-value≥0.10) were finally included into the random-effects meta-regression model with restricted maximum likelihood (REML) method. The statistical significance of any single coefficient was tested by Z-test and 0.05 was used as the threshold of P-value for statistically significant difference.
Egger’s test served to assess the probability of publication bias for the overall S. aureus and MRSA proportion [20]. The test was based on the logit-transformed proportion and corresponding standard error. A P-value of less than 0.10 was regarded as statistical significance, indicating probable publication bias.
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