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. 2025 Aug 21;20(8):e0329474. doi: 10.1371/journal.pone.0329474

The influence of pneumococcal positivity on clinical outcomes among patients hospitalized with COVID-19: A retrospective cohort study

Chang-Seok Yoon 1, Ha-Young Park 1, Hwa-Kyung Park 1, Jae-Kyeong Lee 1, Bo-Gun Kho 1,2, Hong-Joon Shin 1,2, Yong-Soo Kwon 1,2, Yu-Il Kim 1,2, Sung-Chul Lim 1,2, Tae-Ok Kim 1,2,*
Editor: Clinton Moodley3
PMCID: PMC12370052  PMID: 40839694

Abstract

Background

Bacterial co-infection has been associated with adverse outcomes in patients with COVID-19. Streptococcus pneumoniae is a common cause of community-acquired pneumonia and may contribute to poor clinical outcomes when co-detected in COVID-19 patients. This study aimed to investigate the clinical significance of pneumococcal positivity in hospitalized patients with COVID-19.

Methods

We conducted a retrospective analysis of adult patients hospitalized with COVID-19 at two tertiary care centers. Pneumococcal positivity was defined by either a positive urinary antigen test or multiplex real-time polymerase chain reaction. Disease severity of COVID-19 pneumonia was assessed using the pneumonia severity index and CURB-65 scoring systems. Propensity score matching and multivariable logistic regression were used to adjust for confounders and identify independent risk factors for mortality.

Results

Among 280 patients, 65 pneumococcus-positive patients were matched with 65 pneumococcus-negative patients after propensity score matching. In the overall matched cohort, pneumococcal positivity was not significantly associated with in-hospital mortality. However, in patients with severe disease (n = 156), defined as pneumonia severity index >130 or CURB-65 ≥ 3, mortality was significantly higher in pneumococcus-positive patients (n = 39) than in pneumococcus-negative patients (53.8% vs. 29.1%, p = 0.009). In the multivariable analysis of this subgroup, pneumococcal positivity (odds ratio, 4.050; 95% confidence interval, 1.285–12.765; p = 0.017) and high-flow oxygen therapy (odds ratio, 6.510; 95% confidence interval, 1.847–22.944; p = 0.004) were independently associated with mortality.

Conclusion

Detection of S. pneumoniae by urinary antigen test or multiplex polymerase chain reaction was associated with increased mortality in patients hospitalized with severe COVID-19.

Introduction

COVID-19, caused by SARS-CoV-2, was declared a global pandemic in March 2020. Since its emergence, over 65.8 million cases have been reported worldwide, with mortality exceeding 1.5 million [1]. The clinical presentation of COVID-19 varies widely, ranging from asymptomatic or mild upper respiratory symptoms to severe pneumonia, acute respiratory distress syndrome, multi-organ failure, and death. Several prognostic factors have been associated with disease severity and poor clinical outcomes in patients with COVID-19. Advanced age, male sex, and underlying comorbidities such as hypertension, diabetes mellitus, cardiovascular disease, chronic lung disease, obesity, and immunosuppression are significant independent factors [2].

Bacterial co-infections have also been recognized as factors contributing to adverse clinical outcomes in COVID-19 patients. Such co-infections are associated with higher mortality rates, a greater likelihood of intensive care unit (ICU) admission, and an increased need for invasive mechanical ventilation (IMV) [3,4]. Common bacterial pathogens identified in COVID-19 patients include Streptococcus pneumoniae, Staphylococcus aureus, Haemophilus influenzae, and Klebsiella pneumoniae [5].

Streptococcus pneumoniae, one of the most common pathogens in community-acquired pneumonia (CAP), is known to cause lower respiratory tract infections through interactions with respiratory viruses, including SARS-CoV-2 [6,7]. To facilitate early and accurate identification of Streptococcus pneumoniae, diagnostic methods such as urinary antigen tests (UATs) and multiplex polymerase chain reaction (PCR) assays are frequently used. However, these assays may demonstrate relatively low specificity, particularly due to their inability to differentiate colonization from true infection. Despite these limitations, both UAT and multiplex PCR have become important diagnostic tools in routine clinical practice, aiding in the early identification of co-infections and guiding the timely initiation of appropriate antimicrobial therapy.

In this context, we aimed to evaluate whether Streptococcus pneumoniae positivity is associated with adverse clinical outcomes by comparing COVID-19 patients with and without detection of Streptococcus pneumoniae.

Materials and methods

Population

This retrospective study was conducted at Chonnam National University Hospital and Chonnam National University Hwasun Hospital from January 2020 to June 2023. Inclusion criteria were as follows: patients who were [1] older than 18 years, [2] diagnosed with COVID-19 via rapid antigen test or real-time reverse transcription PCR test, and [3] underwent streptococcus UAT and multiplex PCR testing for respiratory pathogens at the time of COVID-19 diagnosis. Patients who were diagnosed with COVID-19 but did not require hospitalization, as well as those diagnosed with COVID-19 during hospitalization for unrelated medical conditions, were excluded from the analysis.

To analyze the impact of pneumococcal positivity on in-hospital outcomes among patients with COVID-19, we collected demographic and clinical data, including age, sex, and underlying medical conditions. Laboratory parameters such as white blood cell count, neutrophil count, lymphocyte count, C-reactive protein, and procalcitonin levels were also recorded. Pneumonia severity was assessed using validated scoring systems, including the pneumonia severity index (PSI) and the CURB-65 score. Details of administered treatments, including supplemental oxygen, organ support, antibiotic therapy, and antiviral agents, were documented. The outcome measures analyzed included in-hospital mortality, length of hospital stay, and duration of ICU admission.

Definition of pneumococcus-positive

The term “pneumococcus-positive” was defined as fulfillment of at least one of the following criteria:

  1. A positive result from multiplex real-time PCR using the Allplex PneumoBacter Assay (Seegene, Seoul, Korea) or

  2. A positive result from a rapid UAT for S. pneumoniae using the BinaxNOW assay (Binax, Portland, ME, USA).

Definition of severe COVID-19 pneumonia

Severe COVID-19 pneumonia was defined based on the PSI and CURB-65 scoring systems. Patients with a PSI greater than 130 or a CURB-65 score greater than 3 were categorized as having severe disease.

Statistical analysis

All data were expressed as means and standard deviations or as numbers (%) in the text and tables. Intergroup comparisons were performed using the independent t-test for continuous variables following a normal distribution, the Wilcoxon rank-sum test for continuous variables not following a normal distribution, and Pearson’s χ2 or Fisher’s exact tests for categorical variables. To identify risk factors associated with mortality, logistic regression analysis was conducted. Univariable logistic regression was first performed using demographic characteristics, comorbidities, and clinical variables. Variables with a p-value < 0.2 in the univariable analysis were subsequently included in a multivariable logistic regression model using the backward selection method. Throughout the analysis, a p-value < 0.05 was considered statistically significant.

Propensity score matching (PSM) was applied in the comparative analysis to adjust for confounders and rigorously evaluate the impact of pneumococcal positivity on clinical outcomes. The matching process included demographic variables, such as age, sex, and comorbidities, as well as severity scores like the PSI and CURB-65. Additionally, initial treatments—including the use of antiviral agents, corticosteroids, antibiotics, and oxygen delivery methods—were incorporated into the matching procedure. PSM was performed using the nearest-neighbor approach with a 1:1 matching ratio. To maintain the matching ratio and ensure inclusion of all treated patients, the caliper width was set to 0.2 standard deviations of the logit of the propensity score. To confirm the appropriateness of the matching, we evaluated the balance of matched variables between the two groups using standardized mean differences (SMDs) as the primary measure. Overall, covariate balance was relatively well achieved through propensity score matching, with the majority of variables showing SMDs below 0.1, although some imbalance remained in a few variables (S1 Table). In addition, t-tests and chi-square tests were also performed, where appropriate, to provide supplementary comparisons. All analyses were conducted using SPSS Statistics for Windows, version 26.0 (IBM Corp., Armonk, NY, USA).

Ethics statement

The study was approved by the Chonnam National University Hospital Institutional Review Board (IRB approval number: CNUH-2024–025) and was performed in accordance with the principles of the Declaration of Helsinki. The requirement for informed consent was waived by the Chonnam National University Hospital Institutional Review Board due to the retrospective nature of the study. The clinical data were accessed for research purposes on 1 February 2024. All data were fully anonymized before analysis, and the investigators did not have access to any personally identifiable information during or after data collection.

Results

In total, 507 patients with COVID-19 underwent a UAT or sputum multiplex PCR for Streptococcus pneumoniae during the study period (Fig 1). Among these patients, 280 were ultimately included in the study, after the exclusion of 42 patients younger than 18 years, 40 patients who were not admitted to the hospital, 102 patients diagnosed with COVID-19 during hospitalization, and 43 patients without pneumonia. Of these included patients, 65 tested positive for Streptococcus pneumoniae by UAT or multiplex PCR, while 215 tested negative.

Fig 1. Study enrollment flow chart.

Fig 1

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Comparisons of basal characteristics between pneumococcus-positive group and pneumococcus-negative group

Table 1 presents a detailed comparison of clinical characteristics and outcomes between pneumococcus-positive and pneumococcus-negative COVID-19 patients. In the pneumococcus-positive group, the mean age was 76.14 ± 13.38 years, and 63.1% were male (n = 41). Overall, baseline demographic characteristics were not significantly different between the two groups. However, hypertension was more prevalent in the pneumococcus-positive group than in the pneumococcus-negative group (61.5% vs. 46.5%, p = 0.047). In contrast, hematologic malignancies were observed more frequently in the pneumococcus-negative group than in the pneumococcus-positive group (17.7% vs. 4.6%, p = 0.008). High-flow oxygen therapy (HFOT) was administered more frequently to patients in the pneumococcus-positive group (50.8% vs. 35.3%, p = 0.03), whereas no significant differences were observed between groups regarding the use of non-invasive ventilation (NIV) or IMV. Among laboratory results, procalcitonin levels were significantly higher in patients who tested positive for pneumococcus compared to those who tested negative (13.98 vs. 4.00 ng/mL, p = 0.007). Similarly, patients in the pneumococcus-positive group had significantly higher CURB-65 scores (2.45 vs. 2.03, p = 0.022). However, no significant difference was noted in PSI between the two groups. Regarding clinical outcomes, the in-hospital mortality rate was significantly higher among pneumococcus-positive group compared to pneumococcus-negative group (33.8% vs. 19.1%, p = 0.017). There were no significant differences between the two groups in terms of secondary bacteremia or length of hospital stay.

Table 1. Characteristics and clinical outcomes in patients with COVID-19 with and without pneumococcal positivity.

Variables Without propensity score matching With propensity score matching
Pneumococcus-negative
(n = 215)		 Pneumococcus-positive
(n = 65)		 p-value Pneumococcus negative
(n = 65)		 Pneumococcus positive
(n = 65)		 p-value
Sex, male (%) 142 (66.0) 41 (63.1) 0.658 45 (69.2) 41 (63.1) 0.458
Age, years, mean ± SD 73.16 ± 13.75 76.14 ± 13.38 0.121 75.97 ± 11.74 76.14 ± 13.38 0.939
Underlying diseases
Hypertension, n (%) 100 (46.5) 40 (61.5) 0.047 37 (56.9) 40 (61.5) 0.592
Diabetes mellitus, n (%) 76 (35.3) 29 (44.6) 0.190 31 (47.7) 29 (44.6) 0.725
Chronic lung disease, n (%) 73 (34.0) 21 (32.3) 0.881 20 (30.8) 21 (32.3) 0.850
Chronic heart disease, n (%) 56 (26.0) 25 (38.5) 0.062 29 (44.6) 25 (38.5) 0.477
Chronic kidney disease, n (%) 50 (23.3) 16 (24.6) 0.868 23 (35.4) 16 (24.6) 0.251
Chronic liver disease, n (%) 8 (3.7) 1 (1.5) 0.382 2 (3.1) 1 (1.5) 0.559
Solid cancer, n (%) 52 (24.2) 11 (16.9) 0.240 11 (16.9) 11 (16.9) >0.999
Hematologic disease, n (%) 38 (17.7) 3 (4.6) 0.008 1 (1.5) 3 (4.6) 0.310
Cerebrovascular disease, n (%) 50 (23.3) 19 (29.2) 0.329 18 (27.7) 19 (29.2) 0.846
Oxygen therapy
High-flow oxygen therapy, n (%) 76 (35.3) 33 (50.8) 0.030 33 (50.8) 33 (50.8) >0.999
Non-invasive ventilation, n (%) 1 (0.5) 2 (3.1) 0.136 0 (0.0) 2 (3.1) 0.156
Mechanical ventilation, n (%) 43 (20.0) 11 (16.9) 0.720 10 (15.4) 11 (16.9) 0.812
Laboratory findings
WBC, x103/μl, mean ± SD 8.90 ± 6.24 8.85 ± 5.60 0.957 9.13 ± 5.31 8.86 ± 5.60 0.777
Neutrophil, x103/μl, mean ± SD 7.28 ± 5.70 7.41 ± 5.37 0.870 7.61 ± 5.16 7.41 ± 5.37 0.825
Lymphocyte, x103/μl, mean ± SD 1.02 ± 1.43 0.98 ± 0.64 0.746 0.89 ± 0.63 0.98 ± 0.64 0.396
CRP, mg/dL, mean ± SD 12.01 ± 8.94 12.13 ± 9.96 0.932 13.36 ± 8.91 12.12 ± 9.96 0.459
Procalcitonin, ng/mL, mean ± SD 4.00 ± 16.19 13.98 ± 41.30 0.007 6.32 ± 26.16 12.26 ± 38.91 0.310
Severity scores
CURB-65, mean ± SD 2.03 ± 1.22 2.45 ± 1.29 0.022 2.34 ± 1.09 2.45 ± 1.29 0.608
PSI, mean ± SD 130.90 ± 43.15 141.40 ± 46.90 0.113 137.37 ± 41.27 141.35 ± 46.97 0.608
Treatment
Antiviral agentsʰ, n (%) 183 (85.1) 59 (90.8) 0.244 56 (86.2) 57 (87.7) 0.795
Corticosteroids‡, n (%) 147 (68.4) 48 (73.8) 0.400 42 (64.6) 48 (73.8) 0.342
Antibiotics§, n (%) 167 (77.7) 50 (76.9) 0.899 52 (80.0) 49 (75.4) 0.527
Clinical outcomes
Length of hospital stay, days, mean ± SD 14.93 ± 13.40 13.62 ± 13.27 0.485 13.29 ± 10.83 13.62 ± 13.27 0.879
Mortality, n (%) 41 (19.1) 22 (33.8) 0.017 13 (20.0) 22 (33.8) 0.075
Secondary bacteremia, n (%) 19 (8.8) 11 (16.9) 0.140 9 (13.9) 11 (16.9) 0.627
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Abbreviations: SD, standard deviation; WBC, white blood cells; CRP, C-reactive protein; PSI, pneumonia severity index

h Antiviral agents such as remdesivir, molnupiravir, nirmatrelvir/ritonavir, and others used in the treatment of COVID-19.

‡ Refers to all corticosteroids, including dexamethasone and methylprednisolone, used in the treatment of COVID-19.

§ Refers to empirical or targeted antibiotic therapy for confirmed or suspected bacterial infection.

Among the 280 patients included in the analysis, 156 (55.7%) were classified as having severe disease, defined as a PSI > 130 or a CURB-65 score≥3. Of these severely ill patients, 39 (25.0%) belonged to the pneumococcus-positive group. The overall mortality rate among patients with severe disease was 35.3%. Table 2 presents a detailed comparison of clinical characteristics and outcomes between pneumococcus-positive and pneumococcus-negative COVID-19 patients. Among severe COVID-19 patients, those positive for pneumococcus had a higher prevalence of hypertension (74.4% vs. 53.8%, p = 0.024) and chronic heart disease (51.3% vs. 31.6%, p = 0.027) but a lower prevalence of solid cancer (12.8% vs. 29.1%, p = 0.043). HFOT was also administered more often in the pneumococcus-positive group (66.7% vs. 44.4%, p = 0.016). However, there was no significant difference between the groups in the use of NIV or IMV. The administration of therapeutic agents—including antivirals, corticosteroids, and antibiotics—was comparable between the groups. Patients in the pneumococcus-positive group had higher CURB-65 scores than those in the pneumococcus-negative group (3.23 vs. 2.79, p = 0.014). In contrast, no significant difference was observed in PSI between the two groups. Notably, patient mortality was significantly higher in the pneumococcus-positive group compared to the pneumococcus-negative group (53.8% vs. 29.1%, p = 0.009).

Table 2. Characteristics and clinical outcomes of patients with severe COVID-19, with and without pneumococcal positivity.

Variables Without propensity score matching With propensity score matching
Pneumococcus negative n = 117 Pneumococcus positive n = 39 p-value Pneumococcus negative n = 39 Pneumococcus positive n = 39 p-value
Sex, male (%) 86 (73.5) 27 (69.2) 0.605 30 (76.9) 27 (69.2) 0.444
Age, years, mean ± SD 77.62 ± 9.29 80.56 ± 8.13 0.079 78.49 ± 7.88 80.56 ± 8.13 0.255
Underlying diseases
 Hypertension, n (%) 63 (53.8) 29 (74.4) 0.024 25 (64.1) 29 (74.4) 0.326
 Diabetes mellitus, n (%) 51 (43.6) 20 (51.3) 0.403 24 (61.5) 20 (51.3) 0.361
 Chronic lung disease, n (%) 41 (35.0) 13 (33.3) 0.846 12 (30.8) 13 (33.3) 0.808
 Chronic heart disease, n (%) 37 (31.6) 20 (51.3) 0.027 22 (56.4) 20 (51.3) 0.650
 Chronic kidney disease, n (%) 38 (32.5) 13 (33.3) 0.922 17 (43.6) 13 (33.3) 0.352
 Chronic liver disease, n (%) 5 (4.30) 1 (2.60) 0.631 2 (5.1) 1 (2.6) 0.556
 Solid cancer, n (%) 34 (29.1) 5 (12.8) 0.043 8 (20.5) 5 (12.8) 0.362
 Hematologic disease, n (%) 16 (13.7) 1 (2.6) 0.054 0 (0.0) 1 (2.6) 0.314
 Cerebrovascular disease, n (%) 39 (33.3) 17 (43.6) 0.248 14 (35.9) 17 (43.6) 0.488
Oxygen therapy
 High flow oxygen therapy, n (%) 52 (44.4) 26 (66.7) 0.016 20 (51.3) 26 (66.7) 0.250
 Non-invasive mechanical ventilation, n (%) 0 (0.0) 0 (0.0) >0.999 0 (0.0) 0 (0.0) >0.999
 Invasive mechanical ventilation, n (%) 36 (30.8) 10 (25.6) 0.543 7 (17.9) 10 (25.6) 0.411
Laboratory findings
 WBC, x103/μl, mean ± SD 10.06 ± 5.98 9.79 ± 6.47 0.814 10.22 ± 5.58 9.79 ± 6.47 0.754
 Neutrophil, x103/μl, mean ± SD 8.58 ± 5.74 8.56 ± 6.20 0.981 8.75 ± 5.39 8.56 ± 6.20 0.887
 Lymphocyte, x103/μl, mean ± SD 0.96 ± 1.42 0.88 ± 0.50 0.696 0.83 ± 0.58 0.87 ± 0.50 0.769
 CRP, mg/dL, mean ± SD 15.13 ± 8.69 14.66 ± 11.18 0.784 14.77 ± 9.44 14.66 ± 11.18 0.961
 Procalcitonin, ng/mL, mean ± SD 5.72 ± 20.69 16.06 ± 39.90 0.046 9.94 ± 33.39 14.82 ± 38.54 0.551
Severity score
 CURB-65, mean ± SD 2.79 ± 0.97 3.23 ± 0.87 0.014 2.95 ± 0.86 3.23 ± 0.87 0.154
 PSI, mean ± SD 162.81 ± 25.82 170.79 ± 30.00 0.111 165.05 ± 22.23 170.79 ± 30.00 0.340
Treatment
 Antiviral agentsh, n (%) 108 (92.3) 37 (94.9) 0.588 35 (89.7) 36 (92.3) 0.692
 Corticosteroid‡, n (%) 95 (81.2) 34 (87.2) 0.392 27 (69.2) 34 (87.2) 0.100
 Antibiotics§, n (%) 100 (85.5) 34 (87.2) 0.791 34 (87.2) 33 (84.6) 0.745
Clinical outcomes
 Length of hospital stay, days, mean ± SD 16.62 ± 15.31 15.77 ± 15.87 0.765 13.44 ± 11.87 15.77 ± 15.87 0.464
 Mortality, n (%) 34 (29.1) 21 (53.8) 0.009 9 (23.1) 21 (53.8) 0.005
 Bacteremia, n (%) 15 (12.8) 9 (23.1) 0.124 6 (15.4) 9 (23.1) 0.389
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Abbreviations: SD, standard deviation; WBC, white blood cell; CRP, C-reactive protein; PSI, pneumonia severity index.

h Antiviral agents such as remdesivir, molnupiravir, nirmatrelvir/ritonavir, and others used in the treatment of COVID-19.

‡ Refers to all corticosteroids, including dexamethasone and methylprednisolone, used in the treatment of COVID-19.

§ Refers to empirical or targeted antibiotic therapy for confirmed or suspected bacterial infection.

Relationship between pneumococcal positivity and mortality in COVID-19 patients

In the multivariable analysis (Table 3), the PSI (odds ratio [OR], 1.028; 95% confidence interval [CI], 1.017–1.039; p < 0.001), HFOT (OR, 6.524; 95% CI, 2.892–14.718; p < 0.001), and IMV (OR, 9.994; 95% CI, 4.423–22.583; p < 0.001) were independently associated with in-hospital mortality. However, pneumococcal positivity was not significantly associated with mortality in the overall cohort.

Table 3. Risk factors for mortality in patients with COVID-19.

Without propensity score matching With propensity score matching
Univariable analysis Multivariable analysis Univariable analysis Multivariable analysis
Variables OR p-value OR 95% CI p-value OR p-value OR 95% CI p-value
Sex, male 1.580 0.149 1.685 0.237
Age 1.024 0.052 1.020 0.271
Pneumococcal positivity† 2.171 0.014 2.047 0.078
Hypertension 1.575 0.117 1.231 0.610
Diabetes 1.228 0.483 1.142 0.737
Chronic lung disease 0.818 0.515 1.187 0.683
Chronic heart disease 1.891 0.034 2.382 0.030
Chronic kidney disease 1.562 0.164 1.097 0.829
Chronic liver disease 2.875 0.124 1.368 0.801
Solid tumor 1.100 0.777 1.022 0.968
CVA 1.053 0.875 0.828 0.674
Hematologic disorder 1.525 0.264 0.902 0.930
WBC 1.000 0.068 1.000 0.154
Neutrophil 1.000 0.029 1.000 0.104
Lymphocyte 1.000 0.282 0.999 0.033
CRP 1.060 <0.001 1.041 0.049
Procalcitonin 0.998 0.772 0.997 0.687
CURB-65 2.614 <0.001 2.774 <0.001
PSI 1.029 <0.001 1.028 1.017–1.039 <0.001 1.027 <0.001 1.025 1.010–1.039 0.001
HFNO 5.415 <0.001 6.524 2.892–14.718 <0.001 9.833 <0.001 9.173 2.720–30.933 <0.001
IMV 9.150 <0.001 9.994 4.423–22.583 <0.001 4.986 0.001 3.601 1.084–11.963 0.036
Secondary bacteremia 8.008 <0.001 5.674 0.001 2.882 0.848–9.796 0.090
Antiviral agentsʰ 6.000 0.016 3.094 0.148
Corticosteroids‡ 8.785 <0.001 5.418 0.008
Antibiotics§ 7.219 0.001 3.048 0.035
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Abbreviations: OR, odds ratio; CI, confidence interval; CVA, cerebrovascular accident; WBC, white blood cells; CRP, C-reactive protein; PSI, pneumonia severity index; HFNO, high flow nasal oxygen; IMV, invasive mechanical ventilation.

† Positive result of pneumococcal urinary antigen test or multiplex polymerase chain reaction from sputum samples.

h Antiviral agents such as remdesivir, molnupiravir, nirmatrelvir/ritonavir, and others used in the treatment of COVID-19.

‡ Refers to all corticosteroids, including dexamethasone and methylprednisolone, used in the treatment of COVID-19.

§ Refers to empirical or targeted antibiotic therapy for confirmed or suspected bacterial infection.

PSM was performed to adjust for baseline differences between pneumococcus-positive and pneumococcus-negative COVID-19 patients. After matching, 65 pairs of patients were identified. In the matched cohort, there was no significant difference in in-hospital mortality between pneumococcus-positive and pneumococcus-negative groups (Table 1). In the multivariable analysis of the matched cohort, PSI (OR, 1.025; 95% CI, 1.010–1.039; p = 0.001), HFOT (OR, 9.173; 95% CI, 2.720–30.933; p < 0.001), and IMV (OR, 3.601; 95% CI, 1.084–11.963; p = 0.036) were significantly associated with mortality (Table 3).

Relationship between pneumococcal positivity and mortality in severe COVID-19 patients

In the multivariable analysis limited to patients with severe disease (Table 4), the following variables were independently associated with mortality: PSI (OR, 1.031; 95% CI, 1.015–1.047; p < 0.001), HFOT (OR, 4.530; 95% CI, 1.812–11.330; p = 0.001), IMV (OR, 7.537; 95% CI, 2.961–19.186; p < 0.001), and hematologic malignancies (OR, 4.998; 95% CI, 1.456–17.157; p = 0.011). Notably, pneumococcal positivity was also independently associated with mortality in this subgroup (OR, 3.286; 95% CI, 1.299–8.308; p = 0.012).

Table 4. Risk factors for mortality in severe COVID-19 patients (PSI > 130 or CURB-65 ≥ 3).

Without propensity score matching With propensity score matching
Univariable analysis Multivariable analysis Univariable analysis Multivariable analysis
Variables OR p-value OR 95% CI p-value OR p-value OR 95% CI p-value
Sex, male 1.365 0.419 1.353 0.573
Age 0.982 0.333 0.436 0.977
Pneumococcal positivity† 2.848 0.006 3.286 1.299–8.308 0.012 3.889 0.006 4.050 1.285–12.765 0.017
Hypertension 1.068 0.848 0.824 0.698
Diabetes 0.707 0.308 0.525 0.172
Chronic lung disease 0.774 0.473 1.100 0.848
Chronic heart disease 1.115 0.753 1.500 0.390
Chronic kidney disease 0.882 0.726 0.551 0.227
Chronic liver disease 3.882 0.125 0.793 0.853
Malignancy 1.038 0.923 1.464 0.534
CVA 0.626 0.193 0.506 0.167 0.372 0.114–1.216 0.102
Hematological disorder 2.984 0.037 4.998 1.456–17.157 0.011 NA >1.00
WBC 1.000 0.315 1.000 0.730
Neutrophil count 1.000 0.389 1.000 0.759
Lymphocyte count 1.000 0.795 0.999 0.156
CRP 1.021 0.250 1.005 0.832
Procalcitonin 0.995 0.483 0.994 0.465
CURB-65 score 2.267 <0.001 2.530 0.004
PSI score 1.028 <0.001 1.031 1.015–1.047 <0.001 1.030 0.003 1.028 1.005–1.051 0.018
HFNO 2.662 0.005 4.530 1.812–11.330 0.001 6.429 0.001 6.510 1.847–22.944 0.004
IMV 4.162 <0.001 7.537 2.961–19.186 <0.001 2.143 0.170
Secondary bacteremia 6.008 <0.001 4.300 0.017
Antiviral agentsʰ 2.592 0.234 1.628 0.576
Corticosteroid‡ 3.760 0.020 3.706 0.056
Antibiotics§ 4.016 0.031 1.800 0.415
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Abbreviations: OR, odds ratio; CI, confidence interval; CVA, cerebrovascular accident; WBC, white blood cells; CRP, C-reactive protein; PSI, pneumonia severity index; HFNO, high flow nasal oxygen; IMV, invasive mechanical ventilation.

† Positive result of pneumococcal urinary antigen test or multiplex polymerase chain reaction from sputum samples.

h Antiviral agents such as remdesivir, molnupiravir, nirmatrelvir/ritonavir, and others used in the treatment of COVID-19.

‡ Refers to all corticosteroids, including dexamethasone and methylprednisolone, used in the treatment of COVID-19.

§ Refers to empirical or targeted antibiotic therapy for confirmed or suspected bacterial infection.

We also conducted a subgroup analysis for severe disease among matched patients. In this subgroup analysis, 39 pairs of pneumococcus-positive and pneumococcus-negative patients were matched. Among matched patients with severe COVID-19, the pneumococcus-positive group had a significantly higher mortality rate compared to the pneumococcus-negative group (53.8% vs. 23.1%; p = 0.005) (Table 2). In the multivariable analysis, HFOT (OR, 6.510; 95% CI, 1.847–22.944; p = 0.004), PSI score (OR, 1.028; 95% CI, 1.005–1.051; p = 0.018), and pneumococcal positivity (OR, 4.050; 95% CI, 1.285–12.765; p = 0.017) were independently associated with in-hospital mortality (Table 4).

Discussion

The prevalence of bacterial co-infection in patients with COVID-19 was reported to range from 5.6% to 19% across various studies [3,8–11]. Regardless of the specific prevalence rates, several studies have consistently demonstrated that bacterial co-infection in COVID-19 patients is significantly associated with adverse outcomes, including increased mortality [3,4,12]. Bacterial co-infections in COVID-19 have been attributed to a variety of pathogens, including Haemophilus influenzae, Staphylococcus aureus, Streptococcus pneumoniae, and Klebsiella pneumoniae [5].

S. pneumoniae, one of the most common causative organisms of CAP, was known to interact with respiratory viruses, including SARS-CoV-2, to induce lower respiratory tract infections [6,7,13,14]. Pneumococcal colonization occurs in approximately 5–20% of healthy adults, predominantly in the upper respiratory tract [15]. This colonization may progress to invasive disease under conditions involving compromised host immunity, such as advanced age, chronic lung disease, immunodeficiency, or co-infection with respiratory viruses—including influenza, respiratory syncytial virus, rhinovirus, and SARS-CoV-2 [6,15,16]. A previous study demonstrated that pneumococcal colonization in COVID-19 patients was associated with reduced salivary IgA (immunoglobulin A) levels specific to SARS-CoV-2 antigens, potentially impairing mucosal immunity. This immunological alteration correlated with higher hospital readmission rates and mortality [6]. These findings are consistent with our study, which observed higher mortality rates among patients with pneumococcal positivity.

In our study, pneumococcal positivity was identified as an independent risk factor for increased mortality among patients with severe COVID-19 compared to those without pneumococcal positivity. This finding likely reflects a greater probability of true pneumococcal co-infection among patients with more severe disease. Consistent with this observation, previous studies have reported increased rates of bacterial co-infection in critically ill patients. A meta-analysis published in 2020 identified bacterial co-infection in 4.9% of COVID-19 patients at the time of hospital admission, rising to 16.0% among those admitted to the ICU [17]. Similarly, another meta-analysis reported a bacterial co-infection rate of 7% in hospitalized patients with COVID-19, increasing to 14% in ICU settings [18]. Furthermore, the association between bacterial co-infection and poor prognosis is supported by the findings of Patton et al., who reported a mortality rate of 24% among COVID-19 patients with bacterial co-infection—substantially higher than the 5.9% mortality observed in patients with community-acquired bacteremia before the pandemic [19].

We assessed disease severity using the PSI and CURB-65 scoring systems. Although these tools were originally developed for bacterial pneumonia, they have demonstrated strong predictive value for 30-day mortality in patients with COVID-19. Prior studies reported area under the receiver operating characteristic curve (AUC) values of 0.83 and 0.91 for PSI and 0.78 and 0.88 for CURB-65, respectively [20,21], indicating excellent discriminatory performance.

In our study, pneumococcal positivity was defined by either a positive UAT or multiplex PCR result. In clinical practice, timely and accurate identification of the causative pathogen is essential for guiding antimicrobial therapy and improving patient outcomes. However, conventional diagnostic methods such as blood and sputum cultures often fail to identify the pathogen, with detection rates as low as 30–50% in CAP [22–24]. To improve diagnostic yield, adjunctive methods such as UAT and multiplex PCR assays have been increasingly employed. While these methods offer high sensitivity, they are limited by the inability to distinguish colonization from true infection and by the potential for false-positive results. This limitation may have led to misclassification, particularly among patients with mild or moderate COVID-19. Nonetheless, previous evidence suggests that both colonization and active infection with Streptococcus pneumoniae are associated with adverse clinical outcomes in patients with COVID-19 [3,4,6,12], indicating that the presence of this pathogen is clinically relevant regardless of its pathogenic state. Furthermore, bacterial co-infections have been reported more frequently in patients with severe COVID-19 [17,18], likely due to factors such as immune dysregulation, impaired mucociliary clearance, and epithelial barrier disruption [25]. Consistent with this, our findings demonstrated a significant association between pneumococcal positivity and mortality only in patients with severe disease, supporting the interpretation that these cases more likely reflect true infection rather than incidental colonization.

This study had several limitations. First, as this was a dual-center retrospective study, the limited number of participating hospitals may affect the generalizability of our findings. Regional and international differences in patient populations, COVID-19 prevalence, and healthcare systems may influence clinical outcomes and limit the applicability of our results. Second, although propensity score matching improved covariate balance, it remains prone to residual confounding and selection bias. This may be partly attributable to variable selection in the matching process, which can omit important confounders. Some variables showed increased imbalance, likely due to the wide caliper used to retain all treated patients. Despite subsequent multivariable adjustment, unmeasured confounding cannot be fully excluded. Therefore, the observed associations should be interpreted with caution. Nevertheless, our data provide valuable insights into the clinical relevance of pneumococcal positivity in patients with severe COVID-19. Third, as noted above, we were unable to distinguish colonization from true infection, potentially resulting in an overestimation of the clinical impact of S. pneumoniae. However, since diagnostic tests were performed in patients presenting with respiratory symptoms and radiographic evidence of pneumonia, most cases likely represented clinically meaningful disease. Lastly, the final matched cohorts were composed predominantly of elderly patients, which may reflect the clinical characteristics of severe pneumococcal co-infection. However, this limited age range reduces the generalizability of our findings, particularly to younger populations, and may introduce age-related confounding factors such as comorbidities and baseline mortality risk.

Conclusion

The detection of Streptococcus pneumoniae using urinary antigen testing or multiplex PCR was associated with increased mortality among patients with severe COVID-19. These findings highlight the potential clinical importance of pneumococcal positivity in patients with COVID-19.

Supporting information

S1 Table. Covariate balance before and after propensity score matching assessed by standardized mean differences (SMDs) comparing COVID-19 patients with and without pneumococcal positivity.

(DOCX)

pone.0329474.s001.docx (21KB, docx)
S1 Data. Pneumococcusin COVID19_rawdata_V2.2.

(XLSX)

pone.0329474.s002.xlsx (105KB, xlsx)

Data Availability

The raw data have been provided as Supporting Information.

Funding Statement

This research was supported by the National Research Foundation of Korea (2022R1F1A1069623) and the Chonnam National University Hospital Biomedical Research Institute (BCRI22033).

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Decision Letter 0

Clinton Moodley

26 Jun 2025

The influence of pneumococcal positivity on clinical outcomes among patients hospitalized with COVID-19: A retrospective cohort study

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1. Is the manuscript technically sound, and do the data support the conclusions?

Reviewer #1: Yes

Reviewer #2: Partly

**********

2. Has the statistical analysis been performed appropriately and rigorously? -->?>

Reviewer #1: Yes

Reviewer #2: I Don't Know

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Reviewer #2: Yes

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Reviewer #1: Clarify the distinction between colonization vs. true infection:

The definition of “pneumococcal positivity” includes UAT and PCR, which do not differentiate between colonization and active infection.

Please elaborate in the Discussion how this limitation may have affected the results, and justify your interpretation that these findings likely reflect clinically significant infection.

Overstatement in Abstract and Conclusion:

Statements such as “was independently associated with increased mortality” may imply causality.

Please revise to state that pneumococcal positivity was “associated with” increased mortality, particularly in severe COVID-19 patients.

Generalizability and Study Limitations:

The study is limited to two hospitals in South Korea. Please emphasize this and potential healthcare system differences in the Discussion.

Clearly state that residual confounding is possible, even after PSM and multivariable adjustments.

Details on Propensity Score Matching:

Include a table of pre- and post-matching covariate balance (standardized mean differences).

Confirm whether caliper width and matching ratio were pre-specified or exploratory.

Data Availability Statement:

Please revise your data availability to comply with PLOS policy. If data are not publicly available, the contact method, specific restrictions, and IRB process should be transparently described.

Reviewer #2: 1. Formatting corrections: Lines 142 and 146 contain formatting inconsistencies regarding Streptococcus pneumoniae. These should be corrected to reflect microbial convention (italicized with the genus capitalized).

2. Inclusion criteria and age representation: Although the study's inclusion criteria specify patients aged over 18 years, the reported mean age of final group of participants analysed is above 70 (e.g., Age (yrs), mean ± SD: 73.16 ± 13.75 and 76.14 ± 13.38; for the PSM group: 75.97 ± 11.74 and 76.14 ± 13.38, p-values 0.121 and 0.93). This raises questions about the absence of younger adults in the final dataset used for analysis. Given that COVID-19 affects all age groups and although community-acquired pneumonia (CAP) disproportionately impacts the elderly, the exclusion of individuals below 70 years is not explained in the manuscript. Moreover, pneumococcal carriage tends to be higher in individuals aged 65 and younger, which challenges the rationale for including only pneumococcus-positive patients aged above 75 (was this the only age group that fitted the matching (PSM)?). The limited age range of patients in the final matched group limits generalizability of results. It further introduces confounding variables such as age-associated comorbidities and inherent mortality risk. It is therefore recommended that the authors mention/discuss this age limitation and its implications for interpretation.

3. Data availability and transparency: While ethical considerations are paramount, the manuscript lacks a clear explanation as to why the deidentified raw data cannot be shared. The authors state they themselved did not access patient identifiers, implying minimal risk to breaking confidentiality. Making deidentified datasets available would allow for independent validation (and confirmation of statistical robustness and workflow) and may help clarify the absence of younger age groups. If published, those wishing to critically appraise the study should be able to access such data upon request.

In summary, although the manuscript requires only minor revisions in formatting and generally reads well, the age-related limitation (point 2) and the absence of transparent data sharing (point 3) are major concerns. The authors may wish to consider revising the manuscript title to reflect the age-specific focus—possibly including the term geriatric—given the lack of data representation from younger cohorts.

Statistical note: The statistical approach appears sound; however, the fact that all matched, included participants were above 70, despite higher pneumococcal carriage rates in younger individuals (as per published data in general), may bias the conclusions. This cohort inherently has a higher mortality risk, which could skew outcome interpretation.

**********

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Reviewer #2: No

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PLoS One. 2025 Aug 21;20(8):e0329474. doi: 10.1371/journal.pone.0329474.r002

Author response to Decision Letter 1

11 Jul 2025

Review 1

1.Clarify the distinction between colonization vs. true infection: The definition of “pneumococcal positivity” includes UAT and PCR, which do not differentiate between colonization and active infection. Please elaborate in the Discussion how this limitation may have affected the results, and justify your interpretation that these findings likely reflect clinically significant infection.

� Thank you for this insightful comment. We agree that the use of UAT and PCR cannot definitively distinguish between colonization and true infection, which constitutes a potential limitation of our study. The interpretation of pneumococcal positivity must therefore be contextualized according to the clinical setting and disease severity. As mentioned in the manuscript and supported by prior meta-analyses, the prevalence of bacterial co-infection increases with the severity of COVID-19. Several mechanisms have been proposed to explain this, including immune dysregulation, impaired mucociliary clearance, and disruption of epithelial barriers in severe cases. These changes can facilitate the transition of colonizing microbiota into invasive pathogens, thereby increasing the likelihood of active infection in severe COVID-19 patients. Thus, in the context of severe COVID-19, pneumococcal positivity is more likely to indicate clinically meaningful co-infection rather than mere colonization. In contrast, in patients with mild to moderate disease, similar test results may more plausibly represent colonization without pathogenic significance. In our study as well, pneumococcal positivity should be interpreted in a context-dependent manner according to disease severity. Importantly, in patients with severe disease, pneumococcal positivity is more likely to indicate active infection, which may in turn contribute to increased mortality.

According to your comment, we added the sentences in 5th paragraph of discussion. Follow as; This limitation may have led to misclassification, particularly among patients with mild or moderate COVID-19. Nonetheless, previous evidence suggests that both colonization and active infection with Streptococcus pneumoniae are associated with adverse clinical outcomes in patients with COVID-19, indicating that the presence of this pathogen is clinically relevant regardless of its pathogenic state. Furthermore, bacterial co-infections have been reported more frequently in patients with severe COVID-19, likely due to factors such as immune dysregulation, impaired mucociliary clearance, and epithelial barrier disruption. Consistent with this, our findings demonstrated a significant association between pneumococcal positivity and mortality only in patients with severe disease, supporting the interpretation that these cases more likely reflect true infection rather than incidental colonization.

2. Overstatement in Abstract and Conclusion:

-Statements such as “was independently associated with increased mortality” may imply causality. Please revise to state that pneumococcal positivity was “associated with” increased mortality, particularly in severe COVID-19 patients.

� Thank you for your insightful comment. In accordance with your suggestion, we have revised the Abstract and Introduction to remove the term “independently” in order to avoid implying causality. The revised wording now emphasizes that pneumococcal positivity was associated with, rather than causally linked to, increased mortality—particularly among patients with severe COVID-19. However, we retained the term “independently associated” when reporting multivariable regression results, as it reflects the statistical adjustment for potential confounders.

3.Generalizability and Study Limitations:

-The study is limited to two hospitals in South Korea. Please emphasize this and potential healthcare system differences in the Discussion.

� Thank you for your insightful comment. We agree that the limited number of participating hospitals may affect the generalizability of our findings. As noted, the two hospitals included in this study are both tertiary care centers affiliated with Chonnam National University Medical School and are located within the same region of South Korea. This geographical concentration introduces a limitation in terms of regional and institutional diversity.

As you rightly pointed out, differences in patient populations, COVID-19 prevalence, and healthcare systems across regions and countries may influence clinical outcomes and model applicability. Therefore, the generalizability of our findings may be limited, and caution is warranted when applying them to other populations or healthcare settings.

According to your comment, we changed the sentences in 6th paragraph of discussion. Follow as; First, as this was a dual-center retrospective study, the limited number of participating hospitals may affect the generalizability of our findings. Regional and international differences in patient populations, COVID-19 prevalence, and healthcare systems may influence clinical outcomes and limit the applicability of our results.

-Clearly state that residual confounding is possible, even after PSM and multivariable adjustments.

� Thank you for your important comment. We agree that, despite the use of propensity score matching and multivariable adjustments, the possibility of residual confounding cannot be entirely excluded due to the observational nature of the study and the potential influence of unmeasured or unknown variables. Accordingly, we have revised and expanded the limitations in discussion of the original manuscript to highlight potential selection bias and residual confounding that may arise from the use of propensity score matching (PSM) and multivariable adjustment.

According to your comment, we changed the sentences in 6th paragraph of discussion. Follow as; Second, although propensity score matching improved covariate balance, it remains prone to residual confounding and selection bias. This may be partly attributable to variable selection in the matching process, which can omit important confounders. Some variables showed increased imbalance, likely due to the wide caliper used to retain all treated patients. Despite subsequent multivariable adjustment, unmeasured confounding cannot be fully excluded. Therefore, the observed associations should be interpreted with caution.

4. Details on Propensity Score Matching:

-Include a table of pre- and post-matching covariate balance (standardized mean differences). Confirm whether caliper width and matching ratio were pre-specified or exploratory.

� Thank you for your insightful comment. We have included a new Supplementary Table (Table S3) showing the standardized mean differences before and after PSM. We also clarified in the Methods section that the caliper width (0.2) and 1:1 matching ratio were pre-specified based on previous literature and methodological standards.

According to your comment, we changed the sentences in 2nd paragraph of Methods. Follow as; PSM was performed using the nearest-neighbor approach with a 1:1 matching ratio. To maintain the matching ratio and ensure inclusion of all treated patients, the caliper width was set to 0.2 standard deviations of the logit of the propensity score. To confirm the appropriateness of the matching, we evaluated the balance of matched variables between the two groups using standardized mean differences (SMDs) as the primary measure. Overall, covariate balance was relatively well achieved through propensity score matching, with the majority of variables showing SMDs below 0.1, although some imbalance remained in a few variables. (Table S3). In addition, t-tests and chi-square tests were also performed, where appropriate, to provide supplementary comparisons.

Data Availability Statement:

-Please revise your data availability to comply with PLOS policy. If data are not publicly available, the contact method, specific restrictions, and IRB process should be transparently described.

� Thank you for raising this important point. We fully acknowledge the value of data transparency and independent validation. The raw data have been provided as Supporting Information. These data include all relevant variables used in the analysis and are available to facilitate independent verification of the results.

According to your comment, we changed the sentences in Data Availability statement. Follow as; To ensure transparency and reproducibility, the raw data underlying the findings of this study have been included in the Supporting Information files.

Reviewer 2

1. Formatting corrections: Lines 142 and 146 contain formatting inconsistencies regarding Streptococcus pneumoniae. These should be corrected to reflect microbial convention (italicized with the genus capitalized).

� Thank you very much for your valuable comments. We have corrected all instances of Streptococcus pneumoniae in the manuscript to follow appropriate microbial formatting conventions in lines 142 and 146

2. Inclusion criteria and age representation: Although the study's inclusion criteria specify patients aged over 18 years, the reported mean age of final group of participants analysed is above 70 (e.g., Age (yrs), mean ± SD: 73.16 ± 13.75 and 76.14 ± 13.38; for the PSM group: 75.97 ± 11.74 and 76.14 ± 13.38, p-values 0.121 and 0.93). This raises questions about the absence of younger adults in the final dataset used for analysis. Given that COVID-19 affects all age groups and although community-acquired pneumonia (CAP) disproportionately impacts the elderly, the exclusion of individuals below 70 years is not explained in the manuscript. Moreover, pneumococcal carriage tends to be higher in individuals aged 65 and younger, which challenges the rationale for including only pneumococcus-positive patients aged above 75 (was this the only age group that fitted the matching (PSM)?). The limited age range of patients in the final matched group limits generalizability of results. It further introduces confounding variables such as age-associated comorbidities and inherent mortality risk. It is therefore recommended that the authors mention/discuss this age limitation and its implications for interpretation.

� Thank you for your thoughtful and important comment. Although the inclusion criteria permitted enrollment of patients aged over 18 years, the final matched cohorts were predominantly composed of elderly individuals. This reflects the underlying age distribution of hospitalized patients with severe COVID-19 and pneumococcal positivity during the study period. Younger patients were less likely to meet both the disease severity and pathogen positivity criteria, resulting in limited overlap for propensity score matching.

We agree that this age distribution limits the generalizability of our findings and introduces the potential for age-related confounding. In response, we have added a statement in the Discussion acknowledging the restricted age range of the matched cohorts and its implications for the interpretation of our results.

According to your comment, we added the sentences in last paragraph of Discussions. Follow as; The final matched cohorts were composed predominantly of elderly patients, which may reflect the clinical characteristics of severe pneumococcal co-infection. However, this limited age range reduces the generalizability of our findings, particularly to younger populations, and may introduce age-related confounding factors such as comorbidities and baseline mortality risk.

3. Data availability and transparency: While ethical considerations are paramount, the manuscript lacks a clear explanation as to why the deidentified raw data cannot be shared. The authors state they themselved did not access patient identifiers, implying minimal risk to breaking confidentiality. Making deidentified datasets available would allow for independent validation (and confirmation of statistical robustness and workflow) and may help clarify the absence of younger age groups. If published, those wishing to critically appraise the study should be able to access such data upon request.

� Thank you for raising this important point. We fully acknowledge the value of data transparency and independent validation. The raw data have been provided as Supporting Information. These data include all relevant variables used in the analysis and are available to facilitate independent verification of the results.

According to your comment, we changed the sentences in Data Availability statement. Follow as; To ensure transparency and reproducibility, the raw data underlying the findings of this study have been included in the Supporting Information files.

Attachment

Submitted filename: Response to reviewer V1.1.docx

pone.0329474.s003.docx (27.1KB, docx)

Decision Letter 1

Clinton Moodley

17 Jul 2025

The influence of pneumococcal positivity on clinical outcomes among patients hospitalized with COVID-19: A retrospective cohort study

PONE-D-25-19958R1

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Additional Editor Comments (optional):

The authors have addressed the reviewers comments and concerns adequately, and the manuscript more accurately reflects the data and analyses presented.

Reviewers' comments:

Acceptance letter

Clinton Moodley

PONE-D-25-19958R1

PLOS ONE

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Associated Data

    This section collects any data citations, data availability statements, or supplementary materials included in this article.

    Supplementary Materials

    S1 Table. Covariate balance before and after propensity score matching assessed by standardized mean differences (SMDs) comparing COVID-19 patients with and without pneumococcal positivity.

    (DOCX)

    pone.0329474.s001.docx (21KB, docx)
    S1 Data. Pneumococcusin COVID19_rawdata_V2.2.

    (XLSX)

    pone.0329474.s002.xlsx (105KB, xlsx)
    Attachment

    Submitted filename: Response to reviewer V1.1.docx

    pone.0329474.s003.docx (27.1KB, docx)

    Data Availability Statement

    The raw data have been provided as Supporting Information.

    Articles from PLOS One are provided here courtesy of PLOS

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