Abstract
Introduction
Understanding the proportion of patients with COVID-19 who have respiratory bacterial co-infections and the responsible pathogens is important for managing COVID-19 effectively while ensuring responsible antibiotic use.
Objective
To estimate the frequency of bacterial co-infection in COVID-19 hospitalized patients and of antibiotic prescribing during the early pandemic period and to appraise the use of antibiotic stewardship criteria.
Methods
Systematic review and meta-analysis was performed using major databases up to May 5, 2021. We included studies that reported proportion/prevalence of bacterial co-infection in hospitalized COVID-19 patients and use of antibiotics. Where available, data on duration and type of antibiotics, adverse events, and any information about antibiotic stewardship policies were also collected.
Results
We retrieved 6,798 studies and included 85 studies with data from more than 30,000 patients. The overall prevalence of bacterial co-infection was 11% (95% CI 8% to 16%; 70 studies). When only confirmed bacterial co-infections were included the prevalence was 4% (95% CI 3% to 6%; 20 studies). Overall antibiotic use was 60% (95% CI 52% to 68%; 52 studies). Empirical antibiotic use rate was 62% (95% CI 55% to 69%; 11 studies). Few studies described criteria for stopping antibiotics.
Conclusion
There is currently insufficient evidence to support widespread empirical use of antibiotics in most hospitalised patients with COVID-19, as the overall proportion of bacterial co-infection is low. Furthermore, as the use of antibiotics during the study period appears to have been largely empirical, clinical guidelines to promote and support more targeted administration of antibiotics in patients admitted to hospital with COVID-19 are required.
Supplementary Information
The online version contains supplementary material available at 10.1186/s12879-022-07942-x.
Keywords: Bacterial co-infection, COVID-19, Antibiotic stewardship
Introduction
The COVID-19 pandemic has impacted health systems worldwide, with SARS-CoV-2 infection being implicated in more than 6 million deaths to date [1, 2]. Some clinical guidelines have recommended empirical antibiotic therapy to treat suspected bacterial respiratory co-infection in COVID-19 patients, and tools to support and promote antibiotic stewardship in this population are therefore needed [3, 4].
Distinguishing between viral pneumonia and bacterial co-infection at presentation and during the course of COVID-19 disease can be challenging due to various similarities, including characteristically high inflammatory markers and the frequent presence of pulmonary infiltrates on chest X-ray or computed tomography (CT) imaging [5]. There is therefore potential for considerable overuse of antibiotics in the management of COVID-19 pneumonia, with the attendant risk of an increase in the prevalence of antimicrobial resistance in affected populations. Given the current pandemic context, the implications of this for public health and health systems are likely to be considerable. Clinical guidelines to support the most effective treatment for patients while promoting the responsible use of antibiotics should be informed by an understanding of what proportion of patients admitted to hospital with COVID-19 pneumonia have confirmed acute respiratory bacterial co-infection and of the commonly associated pathogens.
We performed a systematic review to estimate the frequency of confirmed bacterial co-infection in patients admitted to hospital with COVID-19 pneumonitis, the frequency of empirical antibiotic use in this patient group, and to identify any antibiotic stewardship criteria that have been used during the COVID-19 pandemic to date.
Methods
We registered the review protocol at the PROSPERO international prospective register of systematic reviews (CRD 42020181215). We followed the method for the elaboration of systematic reviews recommended by the Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) statement [6]. Although the PRISMA statement is mainly used in systematic reviews of intervention studies, several domains are also applicable to systematic reviews of prevalence [7]. As PRISMA is the most widely used tool for the reporting of systematic reviews, we used it in the present work. The PRISMA checklist for this study is presented in Additional file 1: Material S1.
Selection criteria and search strategy
We included studies with patients admitted to a hospital setting with suspected lower respiratory tract infection (LRTI) and with SARS-CoV-2 infection confirmed by PCR. Due to the high number of publications, we only included original studies with at least 10 participants and which provided enough information to appraise the methods used. Randomised and non-randomised studies that presented at least one of the following outcomes of interest were included: (a) prevalence of bacterial co-infection in patients with confirmed SARS-CoV-2 infection; (b) the proportion of patients with confirmed SARS-CoV-2 infection that were commenced on empirical antibiotic treatment. Where available, we collected information on the duration and type of antibiotics and on any related adverse events. In cases receiving specific treatment for COVID-19 as part of a clinical trial, we only included standard-of-care comparator arms. We excluded antibiotic use for indications other than bacterial LRTI (e.g., azithromycin used as specific therapy for SARS-CoV-2 at the beginning of the pandemic was excluded). In order for our findings to be readily generalisable, we excluded pregnant women and patients with chronic immunosuppressive conditions, these being specific populations with different and increased infection risk profiles. We also excluded studies that mentioned bacterial co-infection rates but did not provide clinical details (e.g., cost-effectiveness analyses or modelling studies). Given that many authors provided only limited descriptions of antibiotic use, we performed two sub-analyses: one of studies clearly stating bacterial co-infection confirmed by cultures taken less than 48 h from point of admission, and another including only studies that clearly stated the empirical use of antibiotics. In the latter, we also describe any antibiotic stewardship strategies.
We also performed sub-group analyses of any available data on critically ill patients, defined as those patients identified by study authors as requiring admission to high-dependency or intensive care. Definitions of bacterial co-infection provided by study authors were accepted.
We searched the following databases up to May 5, 2021: Pubmed, LILACS, Embase, Web of Science and Cochrane Library. Our search strategy is given in Additional file 1: Material S2. Searches were limited to papers written in English, German, Russian, French, Spanish, or Portuguese. Reference lists from all included articles were also scrutinised to identify additional studies of potential interest.
Screening and data extraction
We used a two-stage screening process to identify publications that would be eligible for inclusion: title and abstract, followed by full text review. Any original manuscripts referenced by systematic reviews but not identified by the initial search were also included if they were eligible. All publications were then screened in duplicate and independently by reviewers working in pairs (MC, GG, AG, LK, AM, DC); any disagreements in screening were resolved by a third, independent reviewer (EH or BP). Data from eligible papers were extracted by two independent reviewers into separate, piloted and standardised Microsoft Excel spreadsheets; the third reviewer was then asked to resolve any discrepancies and a single consensus dataset was produced after discussion.
Data analysis
We present the results of all included studies according to the selected outcomes of interest. We analysed our data using a proportion meta-analysis. We applied an arc-sine transformation to stabilise the variance of proportions (Freeman-Tukey variant of the arc-sine square-root of transformed proportions method), where y = arcsine[√(r/(n + 1))] + arcsine[√(r/(n + 1)/(n + 1)], with a variance of 1/(n + 1), with n being the population size. The pooled proportion was calculated as the back-transformation of the weighted mean of the transformed proportions, using inverse arcsine variance weights for the fixed and random effects models. Where heterogeneity between studies was found we applied DerSimonian-Laird weights for the random effects model. We calculated the I2 statistic as a measure of the overall variation in the proportion that was attributable to between-study heterogeneity. STATA 17.0 was used for all analyses.
Study quality assessment
To describe the quality of the prevalence data extracted from the included studies, we used The Joanna Briggs Institute (JBI) Critical Appraisal Checklist for cross-sectional/prevalence data [8]. This is a tool that has been developed acknowledging that prevalence data can come from different study designs, as in our case.
Quality of included studies were assessed independently by two investigators; any disagreements were resolved by a third senior investigator.
Results
Database searches identified 6798 studies. After removing duplicates and reviewing the secondary reference lists from included papers we screened a total of 4,132 studies for title and abstract. Of these, 162 (3.9%) went to full text review and 85 (2.1%) were selected for data extraction (Fig. 1).
Fig. 1.
Flowchart of included studies. Moher D, Liberati A, Tetzlaff J, Altman DG, The PRISMA Group (2009). Preferred Reporting Items for Systematic Reviews and Meta-Analyses: The PRISMA Statement. PLoS Med 6(7): e1000097. https://doi.org/10.1371/journal.pmed1000097. For more information, visit www.prisma-statement.org
The independent assessment of the quality of included papers is described in the Additional file 1: Material S3. Using the selected quality assessment tool, we identified that the majority of included studies had appropriate samples for their specific objectives, adequate description of participants and diagnosis of condition.
Data derived from a total of 31,123 individuals were included for analysis. The study designs of all included papers comprised case series, cohorts, registries, and clinical trials. The majority of papers were from China (29, 34.1%) and USA (16, 18.8%). The main characteristics of the included studies are shown in Table 1. Full references of included studies are provided in Additional file 1: Material S4.
Table 1.
Main characteristics of included studies
| Author, Year (Ref) | Country | Centre(s) | Study period | N | Age (years) Mean/SD(range) or *Median/IQR |
Female (%) | Ventilatory support | ITU admission | Mortality (%) | |
|---|---|---|---|---|---|---|---|---|---|---|
| NIV | MV | |||||||||
| Alharty et al., 2020 (1) | SaudiArabia | Single | 20/03/20–31/05/20 | 352 | 50.6/13.3 | 45/352 (12.8%) | 0/352 (0%) | 352/352 (100%) | 352/352 (100%) | 113/352 (32.1%) |
| Allou et al., 2021 (2) | France | Single | 01/03/20–30/04/20 | 36 | 66/56–77* | 11/36 (30.5%) | 3/36 (8.3%) | 2/36(5.6%) | 10/36 (27.8%) | 0/36 (0%) |
| Amit et al., 2020 (3) | Israel | Multi | 05/03/20–27/04/20 | 156 | 72/60–82* | 48/156 (30.8%) | 39/156 (25%) | 93/156(59.7%) | 156/156 (100%) | 87/156 (55.8%) |
| Asmarawati et al., 2021 (4) | Indonesia | Single | 14/03/20–30/09/20 | 218 | 52.4/14.4 | 98/218 (44.9%) | NR | 23/218 (10.5%) | 52/218 (23.8%) | 21/218 (9.6%) |
| Ayding Bahat et al., 2020 (5) | Turkey | Single | 11/03/20–24/04/20 | 25 | 60.5/15 | 15/25 (60%) | NR | NR | 8/25(32%) | 5/25(20%) |
| Balena et al., 2020 (6) | Italy | Single | 01/03/20–15/06/20 | 148 | 80/72–86* | 83/148 (66%) | 31/148 (21%) | 4/148 (4%) | NR | 34/128 (23%) |
| Baraboutis et al., 2020 (7) | Greece | Single | 16/03/20–12/04/20 | 49 | 63/20–95* | 19/49 (38.8%) | NR | 8/49 (16.3%) | NR | 6/49 (12.2%) |
| Bardi et al., 2021 (8) | Spain | Single | 01/03/20–01/06/20 | 140 | 61/57–67* | 32/140 (23%) | NR | 134/140 (96%) | 140/140 (100%) | 51/140 (36%) |
| Barrasa et al., 2020 (9) | Spain | Single | 04/03/20–31/03/20 | 48 | 67/53–74* | 21/48 (43.8%) | 0/48 (0%) | 45/48 (94%) | 48/48(100%) | 16/48 (36%) |
| Barry et al., 2020 (10) | Saudi | Single | 22/03/20–31/05/20 | 99 | 44.0/(19–87) | 33/99 (33.3%) | 9/99 (9.1%) | 13/99 (13.1%) | 22/99(22.2%) | 12/99 (12%) |
| Basakaran et al., 2021 (11) | UK | Multi | 21/02/20–01/05/20 | 254 | 59/49–69* | 90/254 (35.4%) | NR | 151/254 (59.5%) | 254/254 (100%) | 2/254 (0.8%) |
| Bhatt et al., 2021 (12) | USA | Multi | 01/03/20–07/05/20 | 375 | 63.2/16.2 | 146/375 (38.9%) | 7/375 (1.8%) | 17/375 (4.5%) | 175/375 (46.7%) | 149/375 (39.7%) |
| Buckner et al., 2020 (13) | USA | Multi | 02/03/20–26/03/20 | 105 | 69/23–97* | 52/105 (49.5%) | 1/105 (1.0%) | 17/105 (16.2%) | 51/105 (48.6%) | 35/105(33.3%) |
| Chen et al., 2020 (14) | China | Single | 01/01/20–10/02/20 | 203 | 54/(20–91) | 95/203 (46.8%) | NR | 39/203 (19.2%) | 39/203 (19.2%) | 26/203 (12.8%) |
| Chen et al., 2021 (15) | China | Single | 11/01/20–31/03/20 | 408 | 48/34–60* | 212/408 (51.9%) | NR | NR | NR | 3/408 (0.7%) |
| Cheng et al., 2020 (16) | China | Single | 08/01/20–08/05/20 | 147 | 36/24–54* | 62/147 (42.1%) | NR | NR | 3/147 (2%) | 0/147 (0%) |
| Chengy et al., 2020 (17) | China | Single | 01/01/20–18/03/20 | 62 | 61/49.3–67.5* | 44/62 (70.9%) | NR | NR | NR | 0/62 (0%) |
| Chong et al., 2021 (18) | USA | Single | 8/03/20–22/06/20 | 244 | 63/51–75* | 96/244 (40.6%) | 18/244 (7.4%) | 71/244 (29.1%) | 118/244 (48.4%) | NR |
| Choubey et al., 2021 (19) | UK | Single | 01/03/20–31/05/20 | 209 | NR | NR | NR | NR | NR | NR |
| Contou et al., 2020 (20) | France | Single | 01/03/20–30/02/20 | 92 | NR/(55–70) | 19/92 (20.6%) | NR | 83/92 (90%) | 92/92 (100%) | 45/92 (49%) |
| D’Onofrio et al., 2020 (21) | Belgium | Single | 12/03/20–12/04/20 | 110 | 73/60–82* | 62/110 (56.4%) | NE | NE | 29/110(26.4%) | 34/110 (30.9%) |
| Desai et al., 2020 (22) | Italy | Single | 01/04/20–30/09/20 | 536 | 62.9/12.8 | 5268/536 (50%) | NR | NR | NR | 116/536 (21.6%) |
| Dolci et al., 2020 (23) | Italy | Single | 01/02/20–31/03/20 | 83 | 61/(49–67) | 11/83 (13.3%) | NR | NR | NR | 44/83 (53.0%) |
| Ekadashi et al., 2021 (24) | India | Single | 23/03/20–23/08/20 | 158 | NR | NR | NR | NR | NR | NR |
| Elabbadi et al., 2021 (25) | France | Single | 01/02/20–31/05/20 | 101 | 61/53–69* | 22/101 (10.8%) | NR | 83/101(82.2%) | 101/101 (100%) | 21/101 (20.8%) |
| Falcone et al., 2021 (26) | Italy | Single | 04/03/20–30/04/20 | 315 | NR | 105/315 (33.3%) | 68/315 (21.6%) | 55/315 (17.5%) | 85/315 (26.9%) | 70/315 (22.2%) |
| Fan et al., 2021 (27) | China | Single | 01/01/20–31/01/20 | 55 | 46.46/14.41 | 25/55 (45.5%) | NR | NR | NR | 0/55 (0%) |
| Garcia-Vidal et al., 2021 (28) | Spain | Single | 01/02/20–30/04/20 | 989 | 62/48–74* | 437/989 (44.2%) | NR | NR | 146/989 (14.8%) | 99/989 (10.0%) |
| Gayam et al., 2020 (29) | USA | Single | 01/03/20–30/04/20 | 350 | NR | NR | NR | NR | NR | NR |
| Goncalves et al., 2021 (30) | USA | Single | 01/03/20–20/04/20 | 242 | 66/14.75 | 119/242 (49.2%) | NR | NR | NR | 52/242 (22.7%) |
| Guan et al., 2020 (31) | China | Single | NR | 61 | 56.8/15.1 | NR | NR | NR | NR | 1/61 (1.6%) |
| He et al., 2020 (32) | China | Single | 01/02/20–28/02/20 | 192 | 45/NR | 93/192(48.4%) | NR | NR | NR | 5/192 (2.6%) |
| He et al., 2021 (33) | China | Multi | 01/01/20–28/02/20 | 905 | 47/35–57* | 463/905 (51.2%) | NR | NR | NR | 57/905 (6.2%) |
| Huang et al., 2020(34) | China | Single | 16/12/19–02/01/20 | 41 | 49.0/41.0–58.0* | 11/41 (26.8%) | 10/41 (24%) | 2/41(5%) | NR | 6/41(14.6%) |
| Huang et al., 2020 (34) | China | Single | 16/12/19–02/01/20 | 13 | 49/41–61* | 2/13 (15%) | 8/13 (62%) | 2/13 (15%) | 13/13 (100%) | 5/13(38%) |
| Huang et al., 2021 (35) | USA | Single | 01/03/20–31/05/20 | 41 | 66.6/19.1 | 21/41(51.2%) | 26/41(63.4%) | 15/41(37%) | 15/41(36.6%) | 15/41(36.6%) |
| Hughes et al., 2020 (36) | UK | Multi | 20/02/20–20/04/20 | 836 | 69/55–81* | 317/836 (37.9%) | NR | NR | NR | NR |
| Humières et al., 2021(37) | France | Multi | 29/01/20–31/05/20 | 197 | 59/(50–68) | 48/197 (24.8%) | NR | 129/197(67%) | 197/197 (100%) | 71/197 (26.0%) |
| Karaba et al., 2021 (38) | USA | Multi | 01/03/20–31/05/20 | 1016 | 62/48–74* | 473/1016(46.5%) | NR | NR | NR | NR |
| Karami et al., 2021 (39) | Netherlands | Multi | 01/03/20–31/05/20 | 925 | 70/59–77* | 334/925 (36.1%) | NR | NR | NR | 214/925 (23.1%) |
| Kimming et al., 2020(40) | USA | Single | 01/03/20–27/04/20 | 111 | NR | 49/111 (44.1%) | NR | NR | NR | 30/111 (27%) |
| Kolenda et al., 2020 (41) | France | Multi | 01/03/20–15/04/20 | 99 | NR | NR | NR | NR | NR | NR |
| Lardaro et al., 2021 (42) | USA | Multi | 01/03/20–30/04/20 | 542 | 64.8/16.5 | 273/542 (50.4%) | NR | 162/542 (29.9%) | 86/542 (15.9%) | 78/542 (14.4%) |
| Li et al., 2020 (43) | China | Single | 20/01/20–14/02/20 | 225 | 50.0/14.0 | 105/225 (46.7%) | NR | NR | NR | 2/225 (0.9%) |
| Li et al., 2020 (44) | China | Single | 27/01/20–17/03/20 | 1495 | NR | NR | NR | NR | NR | NR |
| Liu et al., 2020 (45) | China | Single | 18/01/20–12/03/20 | 140 | 65.5/54.3–73.0* | 91/140 (65.0%) | NR | NR | NR | NR |
| Liu et al., 2021 (46) | China | Single | 01/01/20–28/02/20 | 53 | 38/28–47* | 27/53 (50.9%) | 32/53 (63.4%) | 1/53(1.9%) | 1/53 (1.9%) | 0/53 (0%) |
| Liu et al., 2021 (47) | China | Single | 26/01/20–18/03/20 | 1123 | 61/50–69* | 563/1123 (50.1%) | NR | NR | NR | 111/1123 (9.9%) |
| Mady et al., 2020 (48) | Saudi Arabia | Single | 12/08/20–12/09/20 | 61 | 51/42.5–58.8* | 7/61 (11.5%) | 32/61 (52.5%) | 29/61 (47.5%) | 61/61 (100%) | 19/61 (31.1%) |
| Mahmoudi et al., 2020 (49) | Iran | Single | 17/02/20–20/10/20 | 340 | NR | NR | NR | NR | NR | NR |
| Mason et al., 2021 (50) | UK | Multi | 01/03/20–31/05/20 | 800 | NR/(18–100) | 310/800 (38.7%) | NR | NR | NR | NR |
| M. Movahed et al., 2021 (51) | Iran | Single | 22/02/20–19/04/20 | 854 | 55.6/17.63 | 382/854 (44.7%) | NR | NR | 183/854(21/4%) | 119/854 (13.9%) |
| Nassir et al., 2021 (52) | Pakistan | Single | 01/02/20–30/06/20 | 100 | 58/49–57* | 11/100 (11%) | NR | 35/100 (35%) | 79/100 (79%) | 30/100 (30%) |
| Nebreda et al., 2020 (53) | Spain | Single | 08/03/20–31/05/20 | 712 | NR | NR | NR | NR | NR | NR |
| Pulia et al., 2021 (54) | USA | Multi | 15/03/20–18/05/20 | 73 | NR | 38/73 (52.1%) | NR | 8/73 (10.9%) | NR | NR |
| Quartuccio et al., 2020(55) | Italy | Single | 01/02/20–30/04/20 | 69 | 56.2/14.2 | 25/69 (36.2%) | 0/69 (0%) | 0/69 (0%) | 0/69 (0%) | 0/69 (0%) |
| Richardson et al., 2020 (56) | USAe | Multi | 01/03/20–04/04/20 | 5700 | 63.0/52.0–75.0 | 2263/5700 (39.7%) | NR | 320/5700 (5.6%) | 373/5700 (6.5%) | 553/5700 (9.7%) |
| Rippa et al., 2021 (57) | Italy | Single | 25/02/20– 6/04/20 | 731 | 64/(55–76) | 235/731 (32.1%) | NR | NR | 45/731(6.1%) | NR |
| Rothe et al. 2020 (58) | Germany | Single | 01/02/20–30/04/20 | 140 | 63.5/(17–99) | 50/140 (35.7%) | NR | 41/14 0(29.3%) | 56/140 (40%) | 18/140 (12.8%) |
| Seaton et al., 2020 (59) | Scotland | Multi | 20/04/20–30/04/20 | 531 | 72/61–82* | 257/531 (48.4%) | NR | NR | 110/531 (20.7%) | NR |
| Shah et al., 2020 (60) | USA | Single | 03/02/20–31/03/20 | 33 | 63/50–75* | 12/33 (36.4%) | 0/33(0%) | 6/11 (55%) | 11/26 (42%) | 1/26 (4%) |
| Shao et al., 2020 (61) | China | Multi | 23/01/20–23/03/20 | 126 | NR/(19–91) | 58/126 (46.0%) | NR | NR | NR | 1/126 (0.1%) |
| Sharifipour et al., 2020 (62) | Iran | Multi | NR | 19 | 67/4.6 | 8/19 (42.1%) | NR | NR | 19/19(100%) | 18/19(94.7%) |
| Silva et al., 2021 (63) | Brazil | Single | 01/05/20–30/11/20 | 212 | NR | 86/212 (40.5%) | NR | NR | 212/212(100%) | 107/212(52.9%) |
| Singh et al., 2021 (64) | USA | Single | 16/03/20–01/08/20 | 4259 | 45.2/20–43* | 2513/4259 (55.5%) | NR | NR | NR | NR |
| Soogard et al., 2021 (65) | Switzerland | Single | 25/02/20–31/05/20 | 162 | 64.4/50.4–74.2* | 63/162(38.9%) | NR | 34/162(20.9%) | 41/162(25.3%) | 17/162(10.5%) |
| Staub et al., 2021 (66) | USA | Single | 01/03/20–15/05/20 | 131 | 56/17.4 | 53/131(39.7%) | NR | NR | NR | 13/131 (9.9%) |
| Stevens et al., 2021 (67) | USA | Single | 01/03/20–28/04/20 | 346 | 45/18 | 176/346 (51%) | NR | NR | 0/346 (0%) | 0/346 (0%) |
| Tang et al., 2021 (68) | China | Single | 28/01/20–15/03/20 | 78 | 47.7/17.2 | 37/78(47.4%) | NR | NR | 8/78 (10.3%) | NR |
| Thelen et al., 2021 (69) | Netherlands | Multi | 28/02/20–02/06/20 | 678 | 70/58–78* | 235/678 (34.7%) | NR | NR | 6/678 (0.9%) | 191/678 (28.3%) |
| Townsend et al., 2020 (70) | Ireland | Multi | 01/03/20–31/04/20 | 117 | NR | 43/117 (36.8%) | NR | NR | 34/117 (29.1%) | 17/117 (14.5%) |
| Vanhomwegen et al., 2021 (71) | Belgium | Single | 03/03/20–02/05/20 | 66 | 61/49–71* | 25/66 (38%) | NR | NR | 66/66 (100%) | 20/66 (30.3%) |
| Vaughn et al., 2021 (72) | USA | Multi | 01/03/20–01/06/20 | 1705 | 64.7/53.0–76.7* | 820/1705 (48.1%) | 13/1705 (0.8%) | 116 /1705 (6.8%) | NR | 325/1705 (19.1%) |
| Wan et al., 2020 (73) | China | Single | 23/01/20–08/02/20 | 135 | 47.0/36.0–55.0* | 63/135 (46/7%) | 34/135 (19.4%) | 1/135 (0.7%) | 40/135 (29.6%) | 1/135 (0.7%) |
| Wan et al., 2020 (73) | China | Single | 23/01/20–08/02/20 | 40 | 56.0/52.0–73.0* | 19/40 (47.5%) | 27/40 (67.5%) | 1/40 (2.5%) | 40/40 | 1/40 (2.5%) |
| Wang et al., 2020 (74) | China | Single | 29/01/20–10/02/20 | 28 | 68.6/9.0 (53–82) | 7/28 (25%) | 11/28 (39.3%) | 7/28 (25%) | 14/28 (50%) | 12/28 (42.9%) |
| Wang et al., 2020 (74) | China | Single | 29/01/20–10/02/20 | 14 | 71.4/7.9 | 4/14(71.4%) | 11/14 (79.6%) | 7/14 (50%) | 14/14 (100%) | 12/14 (85.7%) |
| Wang et al., 2020 (75) | China | Single | 01/01/20–06/02/20 | 339 | 69.0/65.0–76.0* | 173/339(51.0%) | NR | NR | 65/339 (19.2%) | 65/339 (19.2%) |
| Wang et al., 2021 (76) | UK | Multi | 01/03/20–30/04/20 | 1396 | 67.4/16.2 | NR | NR | NR | 226/1396 (16.2%) | 420/1396 (30.1%) |
| Xu et al., 2021 (77) | China | Single | Up to 12/03/20 | 62 | 56.5/45.3–74.8* | 27/62 (44%) | 24/62 (45%) | 15/62 (24%) | 62/62 (100%) | 7/62 (11.3%) |
| Yang et al., 2020 (78) | China | Single | 05/01/20–22/02/20 | 251 | NR | 128/251 (51.0%) | NR | NR | NR | 21/251 (8.4%) |
| Zhang et al., 2020 (79) | China | Single | 02/01/20–10/02/20 | 221 | 55.0/39.0–66.5* | 113/221 (51.1%) | 26/221 (12.2%) | 16/221 (7.2%) | NR | 12/221 (5.4%) |
| Zhang et al., 2020 (80) | China | Single | 16/01/20–03/02/20 | 140 | 57.0/25.0–87.0* | 69/140 (49.3%) | NR | NR | NR | NR |
| Zhang et al., 2020 (81) | China | Single | 22/01/20–30/04/20 | 38 | 64.7/13.7 | 6/38 (15.8%) | NR | NR | NR | NR |
| Zhang et al., 2020 (82) | China | Single | 10/12/19–20/02/20 | 134 | 60.8/12.9 | 47/134 (35.1%) | 91/134 (67.9%) | 79/134 (58.9%) | 134/134 (100%) | 101/134 (75.4%) |
| Zhang et al., 2021 (83) | China | Single | 01/01/20–28/02/20 | 91 | 74.9/68–82* | 52/91 (57.1%) | 3/91 (3.3%) | 11/91 (12.1%) | NR | 5/91 (55.6%) |
| Zhang et al., 2020 (84) | China | Single | 01/01/20–31/03/20 | 365 | 46.8/15.5 | 189/365(51.8%) | NR | NR | NR | 2/365(0.5%) |
| Zhao et al., 2020 (85) | China | Single | 01/01/20–28/02/20 | 1000 | 61/46–70* | 534/1000 (53.4%) | 147/1000 (14.7%) | 43/1000 (4.3%) | 63/1000 (6.3%) | 119/1000 (11.9%) |
N: number. NR: not reported. IQR: interquartile range. SD: standard deviation, SOCa: standard of care
NIV: Non-invasive ventilation, MV: mechanical ventilation, ITU: intensive treatment unit
Full list of references are in Additional file 1: Material S4
Bacterial co-infection prevalence
We included 70 studies that reported on the prevalence of bacterial co-infection (including critically ill patients and not critically ill patients) (Table 2). Meta-analysis of these studies showed an overall prevalence of bacterial co-infection of 12% (51 studies, 95% CI 8% to 16%; I2 99.2%) (Fig. 2A); subgroup meta-analysis of critically ill patients showed a prevalence of 23% (21 studies, 95% CI 16 to 31%; I2 94.6%) (Fig. 2B).
Table 2.
Studies reporting bacterial co-infection in patients with COVID-19
| Study | n | N | Patient group | Definition of bacterial co-infection | Microorganism identified |
|---|---|---|---|---|---|
| Alharty et al., 2020 (1) | 25 | 352 | Critically ill | Nosocomial acquired bacterial infection by culture (15 VAP + 10 CLI) | Most common: Acinetobacter baumannii, and MRSA |
| Allou et al., 202 1(2) | 3 | 36 | Not specific: General including critically ill | Co-infections. Method: Measured by multiplex PCR, pneumococcal and Legionella urinary antigen tests, cytobacteriological examination of sputum cultures, and serology of atypical respiratory pathogens |
Branhamella catarrhalis = 1 Streptococcus pneumoniae and Haemophilus influenza = 1 MSSA = 1 |
| Amit et al., 2020 (3) | 27 | 156 | Critically ill | Secondary infection. Method: NR | NR |
| Asmarawati et al., 2021 (4) | 43 | 218 | Moderate to Critically ill |
Expressed as co-infection and secondary infections Method: Blood and sputum and urine cultures |
Most common presented Blood culture: ESBL-producing Klebsiella pneumoniae, Pseudomonas spp. Sputum: Acinetobacter baumannii, Klebsiella pneumoniae |
| Ayding Bahat et al., 2020 (5) | 7 | 25 | Haemodialysis | Secondary infection. Method: NR | NR |
| Balena et al., 2020 (6) | 32 | 128 | Elderly | At least one secondary infection by authors. Method: NR | NR |
| Baraboutis et al., 2020 (7) | 0 | 49 | Overall | Blood and sputum cultures, urine pneumococcal and legionella antigen | NR |
| Bardi et al., 2021 (8) | 57 | 140 | Not specific: General |
Nosocomial infection (30 LRTI, 21 VAP, 28 BSI, 24 CRBSI, 7 UTI, 2 soft tissue infections) Method: Cultures |
Most common presented: BSI: Enterococcus faecium (43%), followed by Enterococcus faecalis (21%) CRBSI: coagulase-negative staphylococci (54%), Enterococus faecium (17%) VAP: Staphylococcus aureus (24%) |
| Barrasa et al., 2020 (9) | 3 | 48 | Critically ill | NR | NR |
| Barry et al., 2020 (10) | 9 | 99 | Not specific: General including critically ill | Method: Sputum culture and blood culture |
Sputum culture: Stenotrophomonas maltophilia = 1, Klebsiella pneumoniae = 1 Blood culture: Staphylococcus epidermidis = 4; Enterococcus faecalis = 1; Corynebacterium amycolatum = 1; Bacillus pumilus = 1 |
| Basakaran et al., 2021 (11)* | 14 | 254 | Critically ill | Method: Standard culture (blood, sputum, tracheal-aspirate, bronchoalveolar lavage, urine) and validated culture-independent tests such as respiratory viral PCR and urinary antigens | The most common potential co-pathogens identified were Gram negative bacteria, including Klebsiella spp. (23) and Escherichia coli (20) |
| Bhatt et al., 2021 (12) | 128 | 375 | Not specific: General including critically ill | Method: Blood culture |
Most common: Staphylococcus epidermidis, MSSA Enterococcus faecalis, Escherichia coli, MRSA |
| Chen et al., 2020 (14) | 2 | 203 | Elderly | Method: PCR | Mycoplasma pneumoniae = 2 |
| Chen et al., 2021 (15)* | 25 | 408 | Not specific: General | Method: Blood culture, respiratory culture, serology, PCR and metagenomic next-generation sequencing |
Mycoplasma pneumoniae = 3 Haemophilus influenzae = 6 Klebsiella pneumoniae = 2 Streptococcus pneumoniae = 1 Staphylococcus aureus + Streptococcus pneumoniae = 1 Staphilococcus aureus + Haemophilus influenzae = 1 MRSA + Haemophilis influenzae + Streptococcus pneumoniae = 1 |
| Cheng et al., 2020 (16)* | 12 | 147 | Not specific: General including critically ill | Method: Blood culture, respiratory culture, serology, PCR | MSSA + Haemophilus influenzae = 1, MSSA = 8, Pseudomonas aeruginosa = 1, Haemophilus influenza = 2 |
| Chengy et al., 2020 (17) | 43 | 64 | Not specific: General | Method: Positive culture or clinical/laboratory suspicion | NR |
| Chong et al., 2021 (18)* | 13 | 244 | Not specific: General including critically ill | Method: Respiratory tract cultures ± concurrent positive blood culture |
Haemophilus influenzae = 3, Klebsiella penumoniae = 3 Pseudomona aeruginosa + MSSA = 3, Corynebacterium striatum = 2 MRSA = 2 Others = Citrobacter freundii, Moraxella catarrhalis, Enterobacter aerogenes, Klebsiella aerogenes (respiratory culture) |
| Choubey et al., 2021 (19) | 8 | 209 | Not specific: General | Method: Mycoplasma pneumoniae serology | Mycoplasma pneumoniae = 8 |
| Contou et al., 2020 (20)* | 26 | 92 | Critically ill | Method: Cultures, PCR, antigen | Most common: MSSA (10/32, 31%), Haemophilus influenzae (7/32, 22%), Streptococcus pneumoniae (6/32, 19%), Enterobacteriaceae spp. (5/32, 16%) |
| D’Onofrio et al., 2020 (21)* | 3 | 10 | Not specific: General including critically ill | Method: Cultures, PCR, antigen | Staphylococus hominis = 1, Corynebacterium aurimucosum = 1, Streptococcus pyogenes = 1 |
| Desai et al., 2020 (22) | 68 | 536 | Not specific: General | Method: Streptococcus pneumoniae urinary antigen (u-Ag) | Streptococcus pneumoniae = 68 |
| Dolci et al., 2020 (23) | 33 | 83 | Not specific: General | Definition/Method: positivity of blood cultures and/or of cultures of lower respiratory tract specimens (bronchoalveolar lavage fluid or bronchial aspirate) | NR |
| Ekadashi et al., 2021 (24) | 15 | 158 | Not specific: General including critically ill | Blood culture | Coagulase negative Staphylococcus spp. (11, 73.3%) |
| Elabbadi et al., 2021 (25)* | 20 | 101 | Critically ill | Method: Culture (respiratory, blood), urinary antigen | Gram positive = 11, Gram negative = 13 |
| Falcone et al., 2021 (26)* | 69 | 315 | Not specific: General including critically ill |
Definition: Hospital acquired > 48 h Method: Blood culture |
Enterobacterales (44.9%), non-fermenting Gram negative bacilli (15.6%), Gram positive bacteria (15.6%)** |
| Garcia-Vidal., 2021 (28)* | 21 | 989 | Not specific: General including critically ill | Method: Culture (respiratory, blood), urinary antigen. < 24 h |
Streptococcus pneumoniae + Moraxella catarrhalis = 1 Staphylococcus aureus + Haemophilus influenzae = 1 |
| Gayam et al., 2020 (29)* | 6 | 350 | Not specific: General | Method: Mycoplasma PCR | Mycoplasma pneumoniae = 6 |
| Goncalves et al., 2021 (30)* | 46 | 242 | Not specific: General | Definition/Method: Clinical features and positive blood, sputum, urine, or tissue culture results | NR |
| Guan et al., 2020 (31) | 5 | 61 | Not specific: General | Method: Blood and respiratory culture |
Gram negative bacteria = 2 Gram negative + Gram positive bacteria = 3 |
| He et al., 2020 (32) | 125 | 192 | Not specific: General | Method: PCR | Streptococcus pneumoniae = 14, Bordetella pertussis = 19, Streptococcus.pyogenes = 3, Staphylococcus aureus = 1, Mycobacterium tuberculosis = 7, Neisseria meningitidis = 7, Haemophilus influenzae = 17, Pseudomonas aeruginosa = 57 |
| He et al., 2021 (33)* | 86 | 905 | Not specific: General | Definition/Method: Clinical diagnosis based on clinical findings combined with laboratory and radiology findings | NR |
| Huang et al., 2020 (34) | 4 | 41 | Not specific: General including critically ill | Definition/Method: Positive culture of a new pathogen from a lower respiratory tract specimen | NR |
| Huang et al., 2020 (34) | 4 | 13 | Critically illl | Definition/Method: Positive culture of a new pathogen from a lower respiratory tract specimen | NR |
| Huang et al., 2021 (35) | 7 | 41 | Critically illl |
Method: Culture NB: Majority of infections were considered nosocomial |
NR |
| Hughes et al., 2020 (36) | 21 | 643 | Not specific: General | Method: Blood culture, respiratory culture, pneumococcal antigen, Legionella antigen |
CRBSI: Klebsiella pneumoniae = 1, VAP: Enterobacter cloacae = 1 CLI: Enterococcus spp. = 2, Pseudomonas aeruginosa = 1 |
| Humières et al., 2021 (37) | 88 | 197 | Critically ill | Definition/Method: Nosocomial infections. Clinical features and positive blood, sputum, urine, or tissue culture results | NR |
| Karaba et al., 2021 (38)* | 12 | 1016 | Not specific: General | Definition/Method: Clinical, laboratory, and radiographic criteria plus microbiologic diagnosis |
Only confirmed: Sputum culture: MSSA = 1 |
| Karami et al., 2021 (39)* | 12 | 925 | Not specific: General | Method: Respiratory cultures, pneumococcal antigen, Legionella antigen | Most common: Staphylococcus.aureus, Escherichia coli, Stenotrophomonas maltophilia |
| Kimming et al., 2020 (40) | 16 | 58 | Critically ill. Soc |
Definition: Including hospital acquired infections Method: Cultures |
NR |
| Kolenda et al., 2020 (41) | 15 | 99 | Critically ill | Method: PCR and culture | Most common: Staphylococcus.aureus,and Haemophilus influenzae |
| Lardaro et al., 2021 (42)* | 6 | 542 | Not specific: General including critically ill | Method: Blood cultures | NR |
| Li et al., 2020 (a) (44) | 102 | 1495 | Not specific: General | Method: Cultures | Acinetobacter baumannii (57/159, 35.8%), Klebsiella. pneumoniae (49/159, 30.8%,), Stenotrophomonas maltophilia (10/159, 6.3%) |
| Mady et al., 2020 (48) | 11 | 61 | Critically ill |
Definition: Including hospital acquired infections Method: Blood and respiratory cultures |
Blood culture: Staphylococcus aureus = NR, Vancomycin resistant enterococcus (sensitive to tigecycline) = NR, Acinetobacter baumannii = NR VAP: Pseudomonas spp. = 3, Acinetobacter baumannii = 3 |
| Mahmoudi et al., 2020 (49) | 36 | 340 | Not specific: General | Method: Endotracheal and blood cultures | Klebsiella spp. (11, 25.59%), MSSA (9, 20.93%), Escherichia.coli (7, 16.28%), MRSA (6, 13.95%), Enterobacter spp. (5, 11.63%), Streptococcus pneumoniae (1, 2.32%), Pseudomonas aeruginosa (4, 9.30%) |
| Mason et al., 2021 (50) | 40 | 800 | Not specific: General | Method: Sputum, blood, urine ag, Mycoplasma PCR | NR |
| Nassir et al., 2021 (52) | 50 | 100 | Not specific: General including critically ill | Method: Blood and respiratory cultures | NR |
| Nebreda et al., 2020 (53)* | 39* | 712 | Not specific: General including critically ill | Method: Blood and respiratory culture | Most common: Gram negative bacilli (59%), Escherichia.coli (47%) Enterococcus faecalis (21%), Streptococcus pneumoniae (33%) and Staphylococcus aureus (33%) |
| Quartuccio et al., 2020 (55) | 0 | 69 | SOCa | Method: Respiratory and blood cultures | NR |
| Richardson et al., 2020 (56) | 3 | 5700 | Not specific: General including critically ill | Method: PCR for extensive respiratory panel including atypical bacteria |
Chlamydia pneumoniae = 2 Mycoplasma pneumoniae = 1 |
| Rippa et al., 2021 (57) | 68 | 731 | Not specific: General including critically ill | Definition: Clearly stated as secondary co-infection within > 48 h of admission |
BSIs: Gram positive bacteria (76/106, 71.7%), of which 53/76, 69.7% were coagulase-negative staphylococci BSIs: Gram negative bacteria (23/106, 21.7%), of which 7/23, 30.4% Acinetobacter baumannii, and 5/23, 21.7% were Escherichia coli LRTIs: Gram-negative bacteria (14/26, 53.8%) |
| Rothe et al., 2020 (58) | 10 | 118 | Not specific: General including critically ill | Method: Blood cultures, Legionella pneumophila and Streptococcus pneumoniae urinary antigens | Only blood cultures were positive (n = 10) |
| Shah et al., 2020 (60) | 1 | 33 | Overall | Method: Blood and respiratory cultures |
Blood culture: Enterococcus faecium = 1 Respiratory culture: Stenotrophomonas maltophilia = 1 |
| Shao et al., 2020 (61) | 56 | 126 | Not specific: General including critically ill | NR | NR |
| Sharifipour et al., 2020 (62) | 19 | 19 | Critically ill |
Definition: nosocomial infections Method: Cultures |
Acinetobacter baumannii (17, 90%) Staphylococcus aureus (2, 10%) |
| Silva et al., 2021 (63) | 64 | 212 | Critically ill | Method: Cultures | Staphylococcus spp. (29, 45.3%), Acinetobacter spp. (21, 32.8%), Pseudomonas spp. (21, 32.8%), Stenotrophomonas spp. (9, 14.06%), Klebsiella spp. (8, 12.5%), Enterobacter spp. (6, 9.4%), Enterococcus spp. (6, 9.4%), and Escherichia coli (4, 6%) |
| Singh et al., 2021 (64) | 1413 | 4259 | Overall | Method: Cultures, serology and PCR | Heamophilus influenzae (9.27%), Staphylococcus aureus (13.17%), Streptococcus pneumoniae (1.94%) |
| Soogard et al., 2021 (65)* | 1 | 162 | Not specific: General including critically ill | Definition/Method: Community-acquired bacterial pneumonia was defined as a microbiology-confirmed pneumonia diagnosed concurrent with SARS-CoV-2 infection or within < 48 h of hospital admission | NR |
| Tang et al., 2021 (68) | 5 | 78 | Not specific: General including critically ill | Method: Mycoplasma pneumonia PCR | Mycoplasma pneumoniae = 5 |
| Thelen et al., 2021 (69)* | 7 | 678 | Not specific: General including critically ill | Method: Blood cultures | Escherichia coli = 2, Klebsiella pneumoniae = 1, Pseudomonas aeruginosa = 1, Streptococcus pneumoniae = 2, Staphylococcus aureus = 1 |
| Towsend et al., 2020 (70)* | 7 | 117 | Not specific: General including critically ill | Method: Cultures and urinary antigen | Pseudomonas aeruginosa, Escherichia.coli, Klebsiella pneumoniae,. Oxytoca, Klebsiella aerogenes, MSSA, Streptococcus pneumoniae |
| Vanhomwegen et al., 2021 (71)* | 7 | 66 | Critically ill | Method: Respiratory or blood cultures | NR |
| Vaughn et al., 2021 (72)* | 59 | 1705 | Not specific: general | Method: Blood and sputum cultures, urine pneumococcal and legionella antigen, Mycoplasma pneumonia or Chlamydophila pneumonia PCR | NR |
| Wan et al., 2020 (73) | 7 | 135 | Not specific: General including critically ill | NR | NR |
| Wan et al., 2020 (73) | 7 | 40 | Severe and critically ill | NR | NR |
| Wang et al., 2020 (75) | 143 | 339 | Elderly | NR | NR |
| Wang et al., 2021 (76)* | 37 | 1396 | Not specific: General including critically ill | Method: Blood, lower respiratory tract, urine and other cultures |
Blood cultures: Escherichia coli, Klebsiella pneumoniae, Klebsiella variicola, Proteus mirabilis, MRSA, MSSA and Staphylococcus epidermidis Respiratory cultures: Escherichia coli (ESBL-producing), group A streptococcus, Haemophilus influenzae, Pseudomonas aeruginosa, MSSA |
| Zhang et al., 2020 (79) | 17 | 221 | Not specific: General including critically ill | Definition/Method NR. Likely nosocomial |
Mons common: A.baumannii, Escherichia coli = NR Pseudomonas aeruginosa, Enterococcus = NR |
| Zhang et al., 2020 (80) | 5 | 58 | Not specific: general | Method: IgM | Mycoplasma pneumoniae = 5 |
| Zhang et al., 2020 (81) | 22 | 38 | Critically ill |
Definition: VAP Method: Cultures |
Gram negative bacteria (26, 50.00%), Gram positive bacteria (14, 26.92%), virus (6, 11.54%), fungi (4, 7.69%), and others (2, 3.85%) |
| Zhang et al., 2021 (83) | 12 | 91 | Elderly | NR | NR |
| Zhang et al., 2020 (84) | 228 | 365 | Not specific: general | NR | NR |
n: number of patients with reported bacterial co-infection. N: total number of patients. NR: not reported. MRSA: methicillin resistant staphylococcus aureus. MSSA: methicillin susceptible staphylococcus aureus. VAP: ventilator associated pneumonia, CLI: central line infection. ESBL: extended spectrum beta-lactamase. LRTI: lower respiratory tract infection. BSI: bloodstream infection. CRBSI: catheter-related bloodstream infection. UTI: urinary tract infection. PCR: polymerase chain reaction. SOCa: standard of care
*Specifically reported as bacterial co-infections detected 48 h after admission
Full list of references are in Additional file 1: Material S4
Fig. 2.
Meta-analysis of bacterial prevalence in patients with SARS-Cov-2: a overall population, b critically ill patients
Twenty studies (31.4%) gave a clear definition of bacterial co-infection, stating that this was diagnosed within 48 h from admission. All of them included cultures, urinary antigen and PCR for definitions of bacterial co-infection. We performed a meta-analysis of this subgroup that showed a prevalence of 4% (15 studies, 95% CI 3% to 6%; I2 94.2%) in the overall population (Fig. 3A) and a bacterial coinfection prevalence of 12% (5 studies, 95% CI 4% to 22%; I2 91.2%) in critically ill patients. (Fig. 3B).
Fig. 3.
Studies clearly describing bacterial coinfection with microorganism identification in samples taken < 48 h from admission
Antibiotic use
Fifty-two (61.2%) studies were included in the analysis of antibiotic use (Table 3). Meta-analysis showed an overall prevalence of antibiotic use of 60% (38 studies, 95% CI 52% to 76%; I2 98.8%) (Fig. 4A); sub-group analysis restricted to critically ill patients identified a prevalence of antibiotic usage of 86% (19 studies, 95% CI 78% to 92%; I2 93.2%) (Fig. 4B).
Table 3.
Antibiotic use in patients with COVID-19
| Author | n | N | Patient groups | Antibiotics used |
|---|---|---|---|---|
| Amit et al., 2020 (3) | 131 | 156 | Critically ill | NR |
| Asmarawati et al., 2021 (4)* | 164 | 218 | Moderate to critically ill | Quinolones (60.1%), cephalosporins (28.4%), carbapenem (23.8%), and aminoglycosides (5.6%) |
| Baraboutis et al., 2020 (7) | 33 | 49 | Not specific: General | NR |
| Bardi et al., 2021 (8) | 105 | 140 | Critically ill | Ceftriaxone (120, 86%) and/or azithromycin (118, 84%) |
| Barrasa et al., 2020 (9) | 42 | 48 | Critically ill | Ceftriaxone, levofloxacin, beta-lactams, azithromycin, linezolid |
| Barry et al., 2020 (10) | 53 | 99 | Not specific: General including critically ill | NR |
| Basakaran et al., 2021 (11) | 241 | 254 | Critically ill | NR |
| Bhatt et al., 2021 (12) | 301 | 375 | Not specific: General including critically ill | Most common: ceftriaxone, azithromycin, and piperacillin-tazobactam |
| Buckner et al., 2020 (13) | 51 | 105 | Not specific: General including critically ill | NR |
| Chen et al., 2021 (15) | 60 | 408 | Not specific: General | NR |
| Cheng et al., 2020 (16)* | 52 | 147 | Not specific: General | Penicillin & cephalosporins = 46, tetracyclines = 14, quinolones = 3, macrolides = 3 |
| Chengy et al., 2020 (17) | 45 | 64 | Not specific: General | NR |
| Chong et al., 2021 (18) | 205 | 244 | Not specific: General including critically ill | NR |
| D’Onofrio et al., 2020 (21)* | 93 | 110 | Not specific: General including critically ill | NR |
| Desai et al., 2020 (22) | 494 | 536 | Not specific: General | Included a combination of ceftriaxone 2 g intramuscular/intravenous twice daily for 7–10 days and azithromycin 500 mg oral once daily for 3 consecutive days. levofloxacin 750 mg oral/intravenous once daily for 5 days was administered when contraindication |
| Elabbadi et al., 2021 (25)* | 58 | 101 | Critically ill | NR |
| Fan et al., 2021 (27) | 29 | 55 | Not specific: General | Moxifloxacin (19/29, 65.52%), Linezolid (3/29, 10.34%) |
| Goncalves et al., 2021 (30)* | 162 | 242 | Not specific: General | NR |
| Huang et al., 2020 (34) | 41 | 41 | Not specific: General including critically ill | NR |
| Huang et al., 2020 (34) | 13 | 13 | Critically ill | NR |
| Hunieres et al., 2021 (37) | 88 | 197 | Critically ill | NR |
| Karaba et al., 2021 (38)* | 717 | 1016 | Not specific: General | NR |
| Karami et al., 2021 (39)* | 556 | 925 | Not specific: General |
Amoxicillin/benzylpenicillin (34, 6.1%), Ceftriaxone (95, 17.1%), Cefuroxime (350, 62.9%) Other antibiotics (48, 8.6%) |
| Kolenda et al., 2020 (41) | 15 | 99 | Critically ill | Mainly amoxicillin and clavulanic acid or third generation cephalosporins associated with macrolides |
| Li et al., 2020 (43) | 148 | 225 | Not specific: General | Moxifloxacin and others |
| Liu et al., 2020 (45) | 128 | 140 | Not specific: General | NR |
| Liu et al., 2021 (47) | 792 | 1123 | Not specific: General |
Fluoroquinolones (59.3%) Moxifloxacin (36.4%) |
| Mousav Movahed et al., 2021 (51) | 243 | 854 | Not specific: General including critically ill | NR |
| Nassir et al., 2021 (52) | 82 | 100 | Not specific: General including critically ill | NR |
| Nebreda et al., 2020 (53) | 84 | 712 | Not specific: General including critically ill | NR |
| Pulia et al., 2021 (54) | 27 | 73 | Not specific: General | NR |
| Quatuccio et al., 2020 (55) | 9 | 69 | SOC overall | NR |
| Rothe et al., 2020 (58)* | 22 | 56 | critically ill | Various mentioned: most common piperacillin-tazobactam |
| Rothe et al., 2020 (58)* | 109 | 135 | Not specific: General including critically ill | Various mentioned: most common ampicillin/sulbactam |
| Seaton et al., 2020 (59)* | 219 | 421 | Not specific: General including critically ill | Various antibiotics, most common including: doxycycline, amoxicillin, co-amoxiclav, piperacillin-tazobactam and vancomycin among others |
| Seaton et al., 2020 (59)* | 71 | 110 | Critically ill | Various antibiotics, most common including: meropenem, piperacillin-tazobactam and co-amoxiclav |
| Shah et al., 2020 (60) | 17 | 26 | Not specific: General including critically ill | Majority received vancomycin, tazocin, cefepime, or ceftriaxone |
| Shao et al., 2020 (61) | 81 | 126 | Not specific: General l | NR |
| Sharifipour et al., 2020 (62) | 19 | 19 | Critically ill | NR |
| Soogard et al., 2021 (65) | 71 | 162 | Not specific: General including critically ill | Antibiotics or antifungals |
| Soogard et al., 2021 (65) | 36 | 41 | Critically ill | Antibiotics or antifungals |
| Staub et al., 2021 (66) | 86 | 131 | Not specific: General | NR |
| Stevens et al., 2021 (67) | 33 | 346 | Not specific: General including critically ill | Most common: piperacillin-tazobactam, ceftriaxone and azithromycin |
| Tang et al., 2021 (68) | 58 | 72 | Not specific: General including critically ill | Levofloxacin = 21, moxifloxacin = 22, levofloxacin swapped to moxifloxacin = 5, among others |
| Towsend et al., 2020 (70)* | 84 | 117 | Not specific: General including critically ill | Treated as lower respiratory tract infection |
| Vanhomwegen et al., 2021 (71)* | 54 | 66 | Critically ill | NR |
| Vaughn et al., 2021 (72) | 965 | 1705 | Not specific: General | The most commonly prescribed empirical antibiotics were ceftriaxone (663/1705, 38.9%), vancomycin (235/1705, 13.8%), doxycycline (185/1705, 10.9%), and cefepime (177/1705, 10.4%) |
| Wan et al., 2020 (73) | 59 | 135 | Not specific: General including critically ill | NR |
| Wan et al., 2020 (73) | 35 | 40 | Severe and critically ill | NR |
| Wang et al., 2020 (74) | 27 | 28 | Not specific: General including critically ill | NR |
| Wang et al., 2020 (74) | 14 | 14 | Critically ill | NR |
| Yang et al., 2020 (78) | 172 | 251 | Not specific: General | NR |
| Zhang et al., 2020 (82) | 131 | 134 | Critically ill | NR |
| Zhang et al., 2021 (83) | 21 | 91 | Elderly | NR |
| Zhang et al., 2020 (84) | 251 | 365 | Not specific: General | NR |
| Zhao et al., 2020 (85) | 783 | 1000 | Not specific: General including critically ill | NR |
| Xu et al., 2021 (77) | 58 | 62 | Critically ill | NR |
n: number of patients prescribed antibiotics. N: total number of patients. ITU: intensive treatment unit
**Clearly referred to as empirical use
Full list of references are in Additional file 1: Material S4
Fig. 4.
Antibiotic usage in: a overall population, b critically ill patients
Eleven studies (12.9%) clearly described empirical antibiotic use. A sub-analysis of these papers found that overall empirical antibiotic use was 62% (eight studies, 95% CI 55 to 69%; I2 95.1%) (Fig. 5A) and in critically ill patients was 66% (six studies, 95% CI 58 to 73%; I2 96.6%) (Fig. 5B).
Fig. 5.
Studies clearly describing empirical antibiotic use
Antibiotic stewardship
Eleven studies specifically stated that empirical antibiotics were commenced of which five described decision-making processes regarding antibiotics.
Cheng et al. [9] mentioned that 52/147 (35%) patients received empirical antibiotics and that 19 (37%) received antibiotics for more than a week despite negative cultures. The median length of course of empirical antibiotics was seven (IQR = 5 to 12) days.
Rothe et al. [10] described the implementation of an antibiotic stewardship standard operational procedure in their institution in which initiation of antibiotic therapy was recommended only in cases of clinically suspected infection (narrow spectrum aminopenicillin/beta-lactamase inhibitor combination). However, decisions regarding stewardship were at the clinician’s discretion. The most used antibiotic scheme during the observation period were ampicillin/sulbactam (41.5%) and piperacillin/ tazobactam (19.3%) with or without azithromycin. Median duration of were variable being longer in the case of piperacillin/ tazobactam 10 (range 3 to 26) days. Interestingly, azithromycin was not included in the guidelines, although it was used in 43 patients (31.9%) as combination therapy.
Townsend et al. [11] described 84 patients treated empirically for respiratory bacterial co-infection of which 78 (92.9%) received monotherapy. All treatment was initially intravenous, and an oral switch took place in only 34 (40.5%) cases. The median durations of intravenous and oral therapies were five days (range 1 to 14) and three days (range 1 to 4) respectively.
Karami et al. [12] described the adherence to local guidelines on empiric antibiotic therapy in their institution. Mean adherence was 60.3% (range 45.3% to 74.7%) on the first day of admission showing that 556 of 925 (60.1%) patients were prescribed empirical antibiotics. However, the rate of antibiotic prescribing increased after seven days of admission to 669 (72.3%). Confirmed bacterial co-infection was confirmed only in 12/925 (1.2%) patients. Regarding length of antibiotics use, 467 of 555 (84.1%) had five days of antibiotics. Intravenous antibiotics exceeded 48 h in 413 patients who started antibiotic treatment on the first day of admission and oral switched were performed in 9.9% of those.
Vaugh et al. [13] described that of the patients who received empiric antibiotic therapy (N = 965), the majority (612, 63.4%) received antibiotics targeting community-acquired microorganism. The median of duration of inpatient antibiotic was three days (IQR, 2 to 6 days) in the patients receiving antibiotics. Total days of inpatient antibiotic therapy was 4158 days/1000 patients.
The remainder of these studies (Seaton et al. [14], Baskaraban et al. [15], Goncalves et al. [16], Karaba et al. [17], Asmarawati et al. [18], D’onofrio et al. [19] and Elabbadi [20]) did not describe empirical antibiotic duration or any specific criteria for stopping treatment, although they do state that local guidelines for empirical antibiotic use in COVID-19 pneumonitis should be applied.
Discussion
In the absence of clear guidance on when to give empirical antibiotic therapy to patients admitted to hospital with COVID-19 pneumonia, clinicians face a dilemma. In the context of a global pandemic and given the potential risks of antibiotic treatment to patients and to public health, it is essential that the best available evidence is used to support clinicians on the front line to appropriately balance risks to patients and to the wider public.
We analysed bacterial co-infection in different ways in order to evaluate how estimates may vary depending on authors’ definitions. Based only on author descriptions, we found a prevalence of bacterial co-infection of 12% (95% CI 8 to 16%) in the overall population. Interestingly, we observed that bacterial co-infection was lower when including only studies with clear definitions of bacterial co-infection (overall population 4% (95% CI 3 to 6%), critically ill patients 12% (95% CI 4 to 22%)). Our results are similar to that found by other authors who have evaluated co-infections in patients with COVID-19. For example, Rawson et al. [21] conducted a meta-analysis which found a prevalence of bacterial and fungal coinfection of 8%. Langford et al. evaluated bacterial co-infection at presentation and after presentation of COVID-19, finding a prevalence of 3.5% (95% CI 0.4 to 6.7%) for primary co-infection and 14.3% (95% CI 9.6 to 18.9%) for secondary (nosocomial) co-infection [22].
It is important to acknowledge that our estimates of the prevalence of bacterial co-infection prevalence were derived from a number of different definitions, as provided by the authors of the source papers. This is relevant, as although microbiological cultures are the gold standard for diagnosis, these are neither quick nor universally available tools on which to base prescribing decisions, particularly in patients with severe disease.
Our study also finds that, as expected, the overall use of antibiotics in patients with COVID-19 is high compared to the estimated prevalence of bacterial co-infection. We identified a prevalence of empirical antibiotic use of 62% (95%CI 55 to 29%). These estimates are similar to those of Langford et al. [22], who found an overall prevalence of antibiotic use of 71.9% (95% CI 56.1 to 87.7%). Our slightly lower estimates may be explained by having retrieved studies nearly one and a half years after the start of the pandemic. This could reflect changes in empirical practice through increased experience in managing COVID-19, coupled with more data being available to inform evidence-based practice regarding antibiotic use. Furthermore, the previous study provided estimates of antibiotic use based only on patients with culture confirmed bacterial co-infections, while we included all COVID-19 patients that were considered to have an infection in our estimate, regardless of whether bacterial co-infections were ultimately confirmed. In doing so, we have sought to reflect real world practice, and we suggest that estimates of overall empirical antibiotic use that are not restricted to patients with confirmed infections are important to understanding the need for, and potential impact of, antimicrobial stewardship tools and strategies as part of the response to the COVID-19 pandemic.
The final aim of our study was to identify to what degree decisions to stop empirically prescribed antibiotics were being made according to any defined criteria. This aspect has not been addressed previously in published systematic reviews and meta-analyses. Despite terms related to stewardship being specifically included in our search strategy and despite meticulously reading all included citations in full, including discussion sections, we found very little information on stewardship measures. We found this absence of information particularly notable given that antimicrobial resistance is widely acknowledged as being one of the most serious public health challenges of our times [23–25]. Whilst we acknowledge that case reports and series are generally more concerned with describing the clinical and demographic characteristics of their patients, it is nevertheless disappointing that the large observed differences between confirmed bacterial co-infection and frequency of antibiotic use does not prompt authors to consider this matter more prominently in their discussions. Despite these deficits in the current literature, we assert that it is of fundamental importance to preserve any goals and achievements relating to antibiotic stewardship established prior to the COVID-19 pandemic. Several antibiotic stewardship programs such as ARK (Antibiotic Review Kit) and TARGET (Treat Antibiotics Responsibly, Guidance, Education, Tools) have been shown to be both feasible and acceptable in supporting the safe discontinuation of antibiotics post-prescription in acute hospital settings [26, 27]. These are just two examples of efforts that must be continued, particularly in the current climate of highly prevalent empirical use of antibiotics during a viral pandemic in which the prevalence of confirmed bacterial co-infection appears to be low.
Our study has some important limitations. The most important being that the COVID-19 pandemic has given rise to an unprecedented situation in the scientific world in terms of a seemingly exponential increase in the volume of related publications over a very short time. Thus, at the time of writing there are likely to be additional studies that would have qualified for inclusion. This rapidity of publication would necessitate updating searches and analysis on as much as a weekly basis, which we suggest would be unrealistic for a piece of peer-reviewed work such as this. It is reassuring to know, however, that other groups pursuing similar research questions [21, 22, 28] have found similar results despite not having included the same studies or conducting searches that cover the same dates. To the best of our knowledge, the present systematic review is the currently most up to date systematic review of this subject, presenting data from more than 30,000 patients from studies identified through an exhaustive search strategy. Another important point to highlight is that most of the included studies in this systematic review are from high income countries, and caution should therefore be exercised when generalising from our results to other settings. Further studies should analyse how COVID-19 has affected antibiotic use in low- and middle-income countries, where the burden of drug-resistant infections is greatest [24].
Another important limitation is one that is inherent to this type of analysis. There is a consensus that the methodology for systematic reviews of prevalence data is not well developed, with a notable lack of methodological and reporting guidance for systematic reviews of prevalence data [29, 30]. Thus, in most cases authors present adapted or de novo tools to assess the quality of the prevalence data that will be included in the analysis, regardless of the study design [22, 28, 31]. In our case, we used a tool that has been developed acknowledging that prevalence data can come from different study designs, however we cannot make an overall assessment of risk of bias [8]. Prevalence metanalysis have also the risk of presenting high level of heterogeneity. We have sought to address the high level of heterogeneity by using statistical correction as well as performing subgroup analyses. Nevertheless, caution should be exercised with extrapolation to specific contexts.
Conclusion
In this study we have reported bacterial co-infection and antibiotic use during the first 18 months of the SARS-CoV-2 pandemic. This work can help clinicians to reflect on and understand the initial response to a global pandemic of a novel respiratory virus. Our results show that there is currently insufficient evidence to support the use of empirical use of antibiotics in most hospitalised patients with COVID-19, as the overall proportion of bacterial co-infection in these patients is low. Furthermore, as the use of antibiotics in COVID-19 appears to have been largely empirical, it is necessary to identify clinical and laboratory markers and to formulate guidelines to promote more targeted administration of antibiotics in patients admitted to hospital with COVID-19.
Supplementary Information
Additional file 1. Material S1. PRISMA check list. Material S2. Search Strategy. Material S3. Quality Assessment of included studies. Material S4. Reference list of included studies.
Acknowledgements
None.
Author contributions
MC, GG and EH participated in the conception and design of the study. MC, GG, AG, AM, LK, EH participated in the acquisition of data and analysis. MC, GG, BP and EH participated in the interpretation of data. MC and GG participated drafting the article. All authors read and approved the final manuscript.
Funding
BP was funded in part by the Wellcome Trust [109975/Z/15/Z]. For the purpose of open access, the authors have applied a CC BY public copyright licence to any Author Accepted Manuscript version arising from this submission. All other authors—none to declare.
Availability of data and materials
All data generated or analysed during this study are included in this published article [and its supplementary information files].
Declarations
Ethics approval and consent to participate
Not applicable to the current study.
Consent for publication
Not applicable to the current study.
Competing interests
Authors declare no conflict of interest.
Footnotes
Publisher's Note
Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.
References
- 1.Ibn-Mohammed T, Mustapha KB, Godsell J, Adamu Z, Babatunde KA, Akintade DD, et al. A critical analysis of the impacts of COVID-19 on the global economy and ecosystems and opportunities for circular economy strategies. Resour Conserv Recycl. 2021;164:105169. doi: 10.1016/j.resconrec.2020.105169. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 2.WHO Coronavirus (COVID-19) Dashboard. Geneva: World Health Organization; 2022. https://covid19.who.int.
- 3.COVID-19 rapid guideline: antibiotics for pneumonia in adults in hospital. United Kingdom: National Institute of Clinical Excellence; 2020. https://www.nice.org.uk/guidance/ng173/chapter/3-Initial-approach-to-antibiotic-treatment-choices. [PubMed]
- 4.Gautret P, Lagier JC, Parola P, Hoang VT, Meddeb L, Mailhe M, et al. Hydroxychloroquine and azithromycin as a treatment of COVID-19: results of an open-label non-randomized clinical trial. Int J Antimicrob Agents. 2020;56:105949. doi: 10.1016/j.ijantimicag.2020.105949. [DOI] [PMC free article] [PubMed] [Google Scholar] [Retracted]
- 5.Feldman C, Anderson R. The role of co-infections and secondary infections in patients with COVID-19. Pneumonia. 2021;13(1):5. doi: 10.1186/s41479-021-00083-w. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 6.Liberati A, Altman DG, Tetzlaff J, Mulrow C, Gøtzsche PC, Ioannidis JP, et al. The PRISMA statement for reporting systematic reviews and meta-analyses of studies that evaluate healthcare interventions: explanation and elaboration. BMJ. 2009;339:b2700. doi: 10.1136/bmj.b2700. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 7.Page MJ, McKenzie JE, Bossuyt PM, Boutron I, Hoffmann TC, Mulrow CD, et al. The PRISMA 2020 statement: an updated guideline for reporting systematic reviews. Syst Rev. 2021;10(1):89. doi: 10.1186/s13643-021-01626-4. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 8.Munn Z, Moola S, Riitano D, Lisy K. The development of a critical appraisal tool for use in systematic reviews addressing questions of prevalence. Int J Health Policy Manag. 2014;3(3):123–128. doi: 10.15171/ijhpm.2014.71. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 9.Cheng LS-K, Chau SK-Y, Tso EY-K, Tsang SW-C, Li IY-F, Wong BK-C, et al. Bacterial co-infections and antibiotic prescribing practice in adults with COVID-19: experience from a single hospital cluster. Ther Adv Infect Dis. 2020;7:20499. doi: 10.1177/2049936120978095. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 10.Rothe K, Feihl S, Schneider J, Wallnöfer F, Wurst M, Lukas M, et al. Rates of bacterial co-infections and antimicrobial use in COVID-19 patients: a retrospective cohort study in light of antibiotic stewardship. Eur J Clin Microbiol Infect Dis. 2021;40(4):859–869. doi: 10.1007/s10096-020-04063-8. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 11.Townsend L, Hughes G, Kerr C, Kelly M, O'Connor R, Sweeney E, et al. Bacterial pneumonia coinfection and antimicrobial therapy duration in SARS-CoV-2 (COVID-19) infection. JAC Antimicrob Resist. 2020;2(3):dlaa071. doi: 10.1093/jacamr/dlaa071. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 12.Karami Z, Knoop BT, Dofferhoff ASM, Blaauw MJT, Janssen NA, van Apeldoorn M, et al. Few bacterial co-infections but frequent empiric antibiotic use in the early phase of hospitalized patients with COVID-19: results from a multicentre retrospective cohort study in The Netherlands. Infect Dis. 2021;53(2):102–110. doi: 10.1080/23744235.2020.1839672. [DOI] [PubMed] [Google Scholar]
- 13.Vaughn VM, Gandhi TN, Petty LA, Patel PK, Prescott HC, Malani AN, et al. Empiric antibacterial therapy and community-onset bacterial coinfection in patients hospitalized with coronavirus disease 2019 (COVID-19): a multi-hospital cohort study. Clin Infect Dis. 2021;72(10):e533–e541. doi: 10.1093/cid/ciaa1239. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 14.Seaton RA, Gibbons CL, Cooper L, Malcolm W, McKinney R, Dundas S, et al. Survey of antibiotic and antifungal prescribing in patients with suspected and confirmed COVID-19 in Scottish hospitals. J Infect. 2020;81(6):952–960. doi: 10.1016/j.jinf.2020.09.024. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 15.Baskaran V, Lawrence H, Lansbury LE, Webb K, Safavi S, Zainuddin NI, et al. Co-infection in critically ill patients with COVID-19: an observational cohort study from England. J Med Microbiol. 2021;70(4):2068. doi: 10.1099/jmm.0.001350. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 16.Goncalves Mendes Neto A, Lo KB, Wattoo A, Salacup G, Pelayo J, DeJoy R, 3rd, et al. Bacterial infections and patterns of antibiotic use in patients with COVID-19. J Med Virol. 2021;93(3):1489–1495. doi: 10.1002/jmv.26441. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 17.Karaba SM, Jones G, Helsel T, Smith LL, Avery R, Dzintars K, et al. Prevalence of Co-infection at the time of hospital admission in COVID-19 patients, a multicenter study. Open Forum Infect Dis. 2021;8(1):ofaa578. doi: 10.1093/ofid/ofaa578. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 18.Asmarawati TP, Rosyid AN, Suryantoro SD, Mahdi BA, Windradi C, Wulaningrum PA, et al. The clinical impact of bacterial co-infection among moderate, severe and critically ill COVID-19 patients in the second referral hospital in Surabaya. F1000Res. 2021;10:113. doi: 10.12688/f1000research.31645.1. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 19.D'Onofrio V, Van Steenkiste E, Meersman A, Waumans L, Cartuyvels R, Van Halem K, et al. Differentiating influenza from COVID-19 in patients presenting with suspected sepsis. Eur J Clin Microbiol Infect Dis. 2021;40(5):987–995. doi: 10.1007/s10096-020-04109-x. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 20.Elabbadi A, Turpin M, Gerotziafas GT, Teulier M, Voiriot G, Fartoukh M. Bacterial coinfection in critically ill COVID-19 patients with severe pneumonia. Infection. 2021;49(3):559–562. doi: 10.1007/s15010-020-01553-x. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 21.Rawson TM, Moore LSP, Zhu N, Ranganathan N, Skolimowska K, Gilchrist M, et al. Bacterial and fungal coinfection in individuals with coronavirus: a rapid review to support COVID-19 antimicrobial prescribing. Clin Infect Dis. 2020;71(9):2459–2468. doi: 10.1093/cid/ciaa530. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 22.Langford BJ, So M, Raybardhan S, Leung V, Westwood D, MacFadden DR, et al. Bacterial co-infection and secondary infection in patients with COVID-19: a living rapid review and meta-analysis. Clin Microbiol Infect. 2020;26(12):1622–1629. doi: 10.1016/j.cmi.2020.07.016. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 23.Antibiotic resistance. Geneva: World Health Organization; 2020. https://www.who.int/news-room/fact-sheets/detail/antibiotic-resistance.
- 24.O’Neill J. Review on Antimicrobial resistance: tackling a crisis for the health and wealth of nations. London, UK2014. https://amr-review.org/sites/default/files/AMR%20Review%20Paper%20-%20Tackling%20a%20crisis%20for%20the%20health%20and%20wealth%20of%20nations_1.pdf.
- 25.Antibiotic/Antimicrobial Resistance (AR/AMR). USA: Center of Disease Control and Prevention; 2020. https://www.cdc.gov/drugresistance/index.html.
- 26.Cross ELA, Sivyer K, Islam J, Santillo M, Mowbray F, Peto TEA, et al. Adaptation and implementation of the ARK (Antibiotic Review Kit) intervention to safely and substantially reduce antibiotic use in hospitals: a feasibility study. J Hosp Infect. 2019;103(3):268–275. doi: 10.1016/j.jhin.2019.07.017. [DOI] [PubMed] [Google Scholar]
- 27.Jones LF, Hawking MKD, Owens R, Lecky D, Francis NA, Butler C, et al. An evaluation of the TARGET (Treat Antibiotics Responsibly; Guidance, Education, Tools) Antibiotics Toolkit to improve antimicrobial stewardship in primary care-is it fit for purpose? Fam Pract. 2018;35(4):461–467. doi: 10.1093/fampra/cmx131. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 28.Lansbury L, Lim B, Baskaran V, Lim WS. Co-infections in people with COVID-19: a systematic review and meta-analysis. J Infect. 2020;81(2):266–275. doi: 10.1016/j.jinf.2020.05.046. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 29.Borges Migliavaca C, Stein C, Colpani V, Barker TH, Munn Z, Falavigna M. How are systematic reviews of prevalence conducted? A methodological study. BMC Med Res Methodol. 2020;20(1):96. doi: 10.1186/s12874-020-00975-3. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 30.Migliavaca CB, Stein C, Colpani V, Munn Z, Falavigna M. Quality assessment of prevalence studies: a systematic review. J Clin Epidemiol. 2020;127:59–68. doi: 10.1016/j.jclinepi.2020.06.039. [DOI] [PubMed] [Google Scholar]
- 31.Joanna Briggs Institute Reviewers Manual 2014. The Systematic Review of Prevalence and Incidence Data. Australia: Joanna Briggs Institute. University of Adelaide; 2015. https://nursing.lsuhsc.edu/JBI/docs/ReviewersManuals/Prevalence-and-Incidence-Data.pdf.
Associated Data
This section collects any data citations, data availability statements, or supplementary materials included in this article.
Supplementary Materials
Additional file 1. Material S1. PRISMA check list. Material S2. Search Strategy. Material S3. Quality Assessment of included studies. Material S4. Reference list of included studies.
Data Availability Statement
All data generated or analysed during this study are included in this published article [and its supplementary information files].