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Azithromycin use and outcomes in patients with COVID-19: an observational real-world study

Open AccessPublished:September 08, 2022DOI:https://doi.org/10.1016/j.ijid.2022.09.005

      Highlights

      • The beneficial effect of azithromycin in patients with COVID-19 is debated
      • Azithromycin was associated with a 1.5-fold higher risk of hospitalization in patients with COVID-19
      • Results did not indicate any significant risk reduction in the other studied outcomes
      • Results raise concerns on risks associated with inappropriate use of azithromycin

      Abstract

      Objectives

      Previous studies ruled out the benefits of azithromycin for treatment of patients with COVID-19 who are hospitalized. However, the effects of azithromycin for treatment of patients with positive SARS-CoV-2 test results in the community remains a matter of debate. This study aimed to assess whether azithromycin, when used in subjects with positive test results for SARS-CoV-2, is associated with a reduced risk of hospitalization, in-hospital COVID-19 outcomes, and death.

      Methods

      Two study cohorts were selected. Cohort A included subjects with positive test results for SARS-CoV-2 between February 20, 2020 and December 10, 2020; cohort B included subjects infected with SARS-CoV-2 and hospitalized between February 20, 2020 and December 31, 2020. We compared the risk of hospitalization, intensive care unit access, need for mechanical ventilation, and death in azithromycin users versus nonusers. A clustered Fine-Gray analysis was employed to assess the risk of hospitalization; logistic and Cox regressions were performed to assess the risk of intensive care unit access, mechanical ventilation, and death.

      Results

      In cohort A, among 4861 azithromycin users and 4861 propensity-matched nonusers, azithromycin use was associated with higher risk of hospitalization (hazard ratio [HR] 1.59, 95% confidence interval [CI] 1.45-1.75) compared with nonuse. In cohort B, among 997 subjects selected in both groups, azithromycin use was not significantly associated with intensive care unit access (odds ratio [OR] 1.22, 95% CI 0.93-1.56), mechanical ventilation (OR 1.30, 95% CI 0.99-1.70), 14-day mortality (HR0.88, 95% CI 0.74-1.05), or 30-day mortality (HR 0.89, 95% CI 0.77-1.03).

      Conclusion

      Our findings confirm the lack of benefits of azithromycin treatment among community patients infected with SARS-CoV-2, raising concern on potential risks associated with its inappropriate use.

      Keywords

      Abbreviations:

      ATC (Anatomical Therapeutic Chemical), CI (Confidence Interval), CVD (Cardiovascular Disease), HAD (Healthcare Administrative Database), HIV (Human Immunodeficiency Virus), HPA (Health Protection Agency), HR (Hazard Ratio), ICU (Intensive Care Unit), ID (Index Date), MV (Mechanical Ventilation), NSAIDs (Nonsteroidal Anti-Inflammatory Drugs), OR (Odds Ratio), PSM (Propensity Score Matching), SD (Standard Deviation)

      Introduction

      COVID-19, caused by the new SARS-CoV-2, continues to be widespread, with nearly 600 million cases and >6 million deaths worldwide as of August 29, 2022 (

      World Health Organization. WHO coronavirus (COVID-19) dashboard. https://covid19.who.int/, 2022 (accessed 29 August 2022).

      ). Most patients with COVID-19 have flu-like syndrome with a variety of mild symptoms including rhinitis, pharyngitis, cough, and fever. However, some patients experience a more life-threatening disease characterized by respiratory failure, a proinflammatory state, and arterial thromboembolism, which may require hospitalization and intensive care unit (ICU) admission (
      • Bonaventura A
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      Since the early phase of virus diffusion, several drug classes have been repurposed as potential candidates for treatment of patients with COVID-19, including nonsteroidal anti-inflammatory drugs (NSAIDs), glucocorticoids, and heparins (
      • Perico N
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      ). Among others, azithromycin, a second-generation macrolide, received increased attention because of its antiviral and immunomodulatory activities (
      • Abdelmalek SMA
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      ;
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      ;
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      ). A body of evidence on the effect of azithromycin on viral infections such as Zika, rhinovirus, and Ebola contributed to raising of the hypothesis of potential efficacy of azithromycin against SARS-CoV-2 infection resulting from multiple possible mechanisms of action (
      • Bosseboeuf E
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      ). First, azithromycin may prevent virus entry into human cells by increasing cellular pH and consequently inhibiting endocytotic processes (
      • Echeverría-Esnal D
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      Azithromycin in the treatment of COVID-19: a review.
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      Comprehensive analysis of drugs to treat SARS-CoV-2 infection: mechanistic insights into current COVID-19 therapies (review).
      ). Another direct effect of azithromycin is driven by its ability to bind and inhibit the spike protein of SARS-CoV-2 (
      • Echeverría-Esnal D
      • Martin-Ontiyuelo C
      • Navarrete-Rouco ME
      • De-Antonio Cuscó M
      • Ferrández O
      • Horcajada JP
      • Grau S
      Azithromycin in the treatment of COVID-19: a review.
      ). Second, the drug may modulate the immune system response by reducing several inflammatory mediators such as inflammatory cytokines, tumor necrosis factor, and interleukins, which have been demonstrated to be major drivers of COVID-19 mortality (
      • Echeverría-Esnal D
      • Martin-Ontiyuelo C
      • Navarrete-Rouco ME
      • De-Antonio Cuscó M
      • Ferrández O
      • Horcajada JP
      • Grau S
      Azithromycin in the treatment of COVID-19: a review.
      ;
      • Sultana J
      • Cutroneo PM
      • Crisafulli S
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      Azithromycin in COVID-19 patients: pharmacological mechanism, clinical evidence and prescribing guidelines.
      ). Finally, some patients with viral infection may develop a secondary bacterial infection or present with a bacterial coinfection for which azithromycin could be an effective treatment.
      Previous trials investigated the potential efficacy of azithromycin in patients with COVID-19 who were admitted to the hospital, but no significant results were observed (
      • Furtado RHM
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      • Dantas VCS
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      • Soares RVP
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      • Schettino GPP
      • Rizzo LV
      • Neto AS
      • Machado FR
      • Cavalcanti AB
      COALITION COVID-19 Brazil II Investigators. Azithromycin in addition to standard of care versus standard of care alone in the treatment of patients admitted to the hospital with severe COVID-19 in Brazil (COALITION II): a randomised clinical trial.
      ;
      RECOVERY Collaborative Group
      Azithromycin in patients admitted to hospital with COVID-19 (RECOVERY): a randomised, controlled, open-label, platform trial.
      ). The potential effectiveness of azithromycin was also investigated in several observational studies selectively designed to evaluate azithromycin use in hospitalized patients with severe COVID-19; although results varied, findings from previous trials were confirmed (
      • Arshad S
      • Kilgore P
      • Chaudhry ZS
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      • O'Neill W
      • Zervos M
      Henry Ford COVID-19 Task Force. Treatment with hydroxychloroquine, azithromycin, and combination in patients hospitalized with COVID-19.
      ;
      • Albani F
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      • Granato A
      • Prezioso C
      • Divizia D
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      • Malpetti E
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      Impact of azithromycin and/or hydroxychloroquine on hospital mortality in COVID-19.
      ;
      • Kokturk N
      • Babayigit C
      • Kul S
      • Cetinkaya PD
      • Nayci SA
      • Baris SA
      • Karcioglu O
      • Aysert P
      • Irmak I
      • Yuksel AA
      • Sekibag Y
      • Toprak OB
      • Azak E
      • Mulamahmutoglu S
      • Cuhadaroglu C
      • Demirel A
      • Kerget B
      • Ketencioglu BB
      • Ozger HS
      • Ozkan G
      • Ture Z
      • Ergan B
      • Oguz VA
      • Kilinc O
      • Ercelik M
      • Ciftci TU
      • Alici O
      • Temel EN
      • Ataoglu O
      • Aydin A
      • Bahcetepe DC
      • Gullu YT
      • Fakili F
      • Deveci F
      • Kose N
      • Tor MM
      • Gunluoglu G
      • Altin S
      • Turgut T
      • Tuna T
      • Ozturk O
      • Dikensoy O
      • Gulhan PY
      • Basyigit I
      • Boyaci H
      • Oguzulgen IK
      • Borekci S
      • Gemicioglu B
      • Bayraktar F
      • Elbek O
      • Hanta I
      • Okur HK
      • Sagcan G
      • Uzun O
      • Akgun M
      • Altinisik G
      • Dursun B
      • Edis EC
      • Gulhan E
      • Eyuboglu FO
      • Gultekin O
      • Havlucu Y
      • Ozkan M
      • Coskun AS
      • Sayiner A
      • Kalyoncu AF
      • Itil O
      • Bayram H.
      The predictors of COVID-19 mortality in a nationwide cohort of Turkish patients.
      ;
      • Ip A
      • Berry DA
      • Hansen E
      • Goy AH
      • Pecora AL
      • Sinclaire BA
      • Bednarz U
      • Marafelias M
      • Berry SM
      • Berry NS
      • Mathura S
      • Sawczuk IS
      • Biran N
      • Go RC
      • Sperber S
      • Piwoz JA
      • Balani B
      • Cicogna C
      • Sebti R
      • Zuckerman J
      • Rose KM
      • Tank L
      • Jacobs LG
      • Korcak J
      • Timmapuri SL
      • Underwood JP
      • Sugalski G
      • Barsky C
      • Varga DW
      • Asif A
      • Landolfi JC
      • Goldberg SL.
      Hydroxychloroquine and tocilizumab therapy in COVID-19 patients-an observational study.
      ). However, there is little evidence regarding the effectiveness of azithromycin for treatment of individuals with suspected COVID-19 in the community, in whom earlier treatment may prevent either hospital admission or the occurrence or more severe COVID-19 outcomes. In this regard, one observational study (
      • Szente Fonseca SN
      • de Queiroz Sousa A
      • Wolkoff AG
      • Moreira MS
      • Pinto BC
      • Takeda CFV
      • Rebouças E
      • Abdon APV
      • Nascimento ALA
      • Risch HA
      Risk of hospitalization for Covid-19 outpatients treated with various drug regimens in Brazil: comparative analysis.
      ) and two trials (
      • Hinks TSC
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      • Wang A
      • Cane JL
      • Barber VS
      • Black J
      • Dutton SJ
      • Melhorn J
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      • Clarke D
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      • Underwood J
      • Lasserson D
      • Pavord ID
      • Morgan S
      • Richards D.
      Azithromycin versus standard care in patients with mild-to-moderate COVID-19 (ATOMIC2): an open-label, randomised trial.
      ;
      • Oldenburg CE
      • Pinsky BA
      • Brogdon J
      • Chen C
      • Ruder K
      • Zhong L
      • Nyatigo F
      • Cook CA
      • Hinterwirth A
      • Lebas E
      • Redd T
      • Porco TC
      • Lietman TM
      • Arnold BF
      • Doan T.
      Effect of oral azithromycin vs placebo on COVID-19 symptoms in outpatients with SARS-CoV-2 infection: a randomized clinical trial.
      ) reported no association between treatment with azithromycin in the community and need for hospital admission.
      Today, the debate on the potential beneficial effects of this medication is ongoing also because of its use in the general population after positive test results for SARS-CoV-2 infection. Recent studies indicate that azithromycin, along with other medications, is still empirically prescribed by physicians (
      • Jampani M
      • Chandy SJ.
      Increased antimicrobial use during COVID-19: the risk of advancing the threat of antimicrobial resistance.
      ) despite several statements issued by regulatory authorities outlining the lack of evidence for its beneficial effects (
      • Bartoletti M
      • Azap O
      • Barac A
      • Bussini L
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      • Krause R
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      • Tsiodras S
      • Verweij PE
      Zollner-Schwetz I, Rodríguez-Baño J. ESCMID COVID-19 living guidelines: drug treatment and clinical management.
      ).
      Therefore, this study aimed to assess whether treatment with azithromycin in patients with positive test results for SARS-CoV-2 infection reduces the risk of hospitalization, and whether use of azithromycin before hospitalization is associated with less-severe COVID-19 prognosis (indicated by need for mechanical ventilation (MV), ICU access, and death).

      Methods

      Study design and data source

      The study is part of an Italian regional project that assessed COVID-19 impact on the healthcare system (Valutazione dell'Impatto di COVID-19 ed Elaborazione di Strategie e Strumenti di Mitigazione del Rischio Epidemico [VICES-SMIRE]). The study was funded by the Lombardy Region and is exempt from institutional review board authorization and informed consent (according to the General Authorisation for the Processing of Personal Data for Scientific Research Purposes, issued by the Italian Data Protection Authority).
      This is a large-scale, retrospective cohort analysis based on the healthcare administrative databases (HADs) of local health protection agencies (HPAs) of Bergamo (HPA-Bergamo) and Brescia (HPA-Brescia) in Lombardy, northern Italy. The two areas covered a population of about 2.3 million inhabitants and were affected by an intense outbreak during the early stage of the pandemic (
      • Conti S
      • Ferrara P
      • Mazzaglia G
      • D'Orso MI
      • Ciampichini R
      • Fornari C
      • Madotto F
      • Magoni M
      • Sampietro G
      • Silenzi A
      • Sileo CV
      • Zucchi A
      • Cesana G
      • Manzoli L
      • Mantovani LG
      Magnitude and time-course of excess mortality during COVID-19 outbreak: population-based empirical evidence from highly impacted provinces in northern Italy.
      ).
      Italy has a universal-coverage healthcare system, and care demand is registered electronically for administrative purposes. In the study, five different administrative healthcare data sources related to the studied areas were linked through an anonymized individual code: (1) the SARS-CoV-2 swab registry, established on February 20, 2020 for monitoring individual data on SARS-CoV-2 infection; (2) the hospital discharge database, which collects information on inpatient care supplied by public or private hospitals; (3) the pharmacy claims database, which includes information on outpatient- dispensed drugs reimbursed by the national healthcare system; (4) the chronic morbidity registry, which includes information on patients’ disease based on pharmacy claims, inpatient records, and disease exemption for copayment records; and (5) the health registry, which reports data on residents with healthcare coverage, including date and reasons for entry (i.e., birth and immigration) and exit (i.e., death and emigration).

      Study population

      Starting with 64,327 individuals who had positive SARS-CoV-2 test results between February 20, 2020 and December 31, 2020 (Figure 1), two cohorts were identified. The first cohort (cohort A), to study the association between azithromycin exposure and risk of hospitalization, included individuals with swabs with positive SARS-CoV-2 test results registered between February 20, 2020 and December 10, 2020. The date of positive test result was considered the index date (ID). Each individual was then followed until the occurrence of the study outcome (i.e., hospitalization), death, or end of follow-up (i.e., 21 days), whichever came first (Figure 1).
      Figure 1
      Figure 1Study cohort selection. (a) Cohort A selection to assess the association between use of azithromycin and hospitalization. (b) cohort B selection to assess the association between previous use of azithromycin and risk of ICU access, need for MV, and death in hospitalized patients infected with SARS-CoV-2.
      ICU, intensive care unit; MV, mechanical ventilation.
      The second cohort (cohort B), to study disease outcomes (i.e., ICU access, need for MV, and death) in hospitalized patients, included subjects with positive SARS-CoV-2 test results and a hospitalization within 21 days of the test occurring between February 20, 2020 and December 31, 2020 (Figure 1). The date of hospitalization was considered the ID. Individuals were then followed up until the occurrence of each of the study outcomes or the end of the follow-up (i.e., 30 days), whichever came first. Subjects with healthcare coverage beginning after January 1, 2019 were excluded from the study.

      Exposure of interest

      Exposure to azithromycin (ATC code: J01FA10) alone was assessed in both cohorts by the presence of at least one pharmacy claim from 7 days before to 20 days after the swab with positive test results (exposure period). For hospitalized individuals, the exposure period was truncated to the day before the admission date. Subjects exposed to antibiotics other than azithromycin or to more than one antibiotic were excluded from the study cohorts. Therefore, patients exposed to azithromycin (users) were compared with those not exposed to any antibiotics (nonusers).

      Patients’ characteristics

      We collected data on sex, age, and comorbidities for each selected individual. Comorbidities were defined using the chronic morbidity registry, updated on January 1, 2020. We included the following main categories: Alzheimer's disease or dementia, respiratory disease, ischemic heart disease, peripheral vascular disease, cerebrovascular disease, hypertension, heart failure, other cardiovascular diseases (CVDs), dyslipidemia, diabetes, chronic liver disease, rheumatic disease, cancer, and infection with Human Immunodeficiency Virus (HIV). We also investigated in both cohorts the exposure to other drugs, such as anticoagulants, NSAIDs, chloroquine or hydroxychloroquine, corticosteroids for systemic use (plain), and immunosuppressants, in the 3 months preceding the ID.

      Study outcomes

      The outcome of interest for cohort A was hospitalization within 21 days after the ID. The date of hospital admission was retrieved from the hospital discharge database. In cohort B, we investigated inpatient and outpatient 14-day and 30-day mortality from the date of hospitalization (i.e., ID) and the need for MV and ICU access during hospitalization. For the need for MV and ICU access, only the occurrence of these outcomes was available; no information on the date of these events was recorded.

      Statistical analysis

      Patients’ demographic and clinical characteristics were summarized using frequency and percentage for categorical variables and mean and standard deviation (SD) or median and interquartile range for count variables. Characteristics were compared between the study groups using Pearson chi-square or Fisher's exact test for categorical variables and Student t-test or Wilcoxon test for continuous variables.
      Propensity score matching (PSM) was then applied to reduce possible bias due to confounding factors between azithromycin users and nonusers. Propensity scores were computed by age, sex, comorbidities, and concomitant use of one of the drugs listed above. All variables were imputed into the model in a nonparsimonious way. PSM was performed using a 1:1 nearest-neighbor-matching algorithm without replacement and with a caliper width equal to 0.2 of the SD of the propensity scores. PSM balance was tested using standardized differences and variance ratios for all variables included in the propensity score computation.
      In cohort A, the association between use of azithromycin and risk of hospitalization was analyzed using a clustered Fine-Gray regression model, with death as competing risk. Results were expressed as hazard ratio (HR) with 95% confidence interval (CI). In cohort B, the association between previous azithromycin exposure and 14-day and 30-day mortality was assessed using Cox proportional hazards regression with matched pairs. Patients experiencing ICU access and need for MV were not censored, and stratified analyses were carried out to evaluate their effects on mortality. Results were expressed as HR with 95%CI. The association between use of azithromycin and need for MV or ICU access was assessed using a logistic regression model with matched pairs. Results were expressed as odds ratio (OR) with 95% CI.
      Subgroup analyses were built by running multiple PSMs according to baseline patients’ characteristics, such as age (<65 or ≥65 years), sex, and presence of CVD, diabetes, and cancer, which are proven to be associated with poorer COVID-19 prognosis (
      • Bae S
      • Kim SR
      • Kim MN
      • Shim WJ
      • Park SM.
      Impact of cardiovascular disease and risk factors on fatal outcomes in patients with COVID-19 according to age: a systematic review and meta-analysis.
      ). Furthermore, we explored the risk of each study outcome by calendar date to account for potential changes in COVID-19 management with time. All statistical analyses were performed using R 4.0.5 (R Foundation for Statistical Computing, Vienna, Austria) and SAS 9.4 (SAS Institute, Cary, North Carolina, USA).

      Results

      Descriptive statistics

      Cohort A included 5089 azithromycin users and 37,751 nonusers (Figure 1). Azithromycin users were more likely to be male (51% vs 46%) and older (mean age 54.5 vs 48.8 years) compared with nonusers (Table 1). Azithromycin users also reported significantly higher prevalence of comorbidities and were more likely to use anticoagulants, NSAIDs, chloroquine/hydroxychloroquine, and corticosteroids for systemic use. The PSM led to the selection of 4861 azithromycin users and 4861 nonusers. The study groups showed similar distributions of the main demographic and clinical characteristics; no statistically significant differences were observed (Table 1; Figure 1; Supplementary Table 1; Supplementary Figure 1). Cohort B included 1100 azithromycin users and 6169 nonusers (Figure 1). Nonusers showed a higher prevalence of comorbidities, whereas users were more likely to be treated with anticoagulants (23% vs 15%), chloroquine/hydroxychloroquine (7% vs 2%), and corticosteroids for systemic use (20% vs 9%) (Table 1). The PSM led to the selection of 987 azithromycin users and 987 nonusers; study groups showed no statistically significant differences in main demographic and clinical characteristics (Table 1; Figure 1; Supplementary Table 1; Supplementary Figure 1).
      Table 1Demographic and clinical characteristics of patients with positive SARS-CoV-2 test results (cohort A) and of individuals hospitalized with COVID-19 (cohort B), before and after PSM
      Cohort ACohort B
      CharacteristicsBefore PSMAfter PSMBefore PSMAfter PSM
      AzithromycinusersNonusersAzithromycinusersNonusersAzithromycinusersNonusersAzithromycinusersNonusers
      Total5,08937,7514,8614,8611,1006,169997997
      Sex, N (%)
      Female2,500 (49.1%)20,446 (54.2%)
      P-value <0.05 PSM, propensity score matching.
      2,409 (49.6%)2,447 (50.3%)352 (32%)2,543 (41.2%)
      P-value <0.05 PSM, propensity score matching.
      323 (32.4%)322 (32.3%)
      Age
      Age mean ±SD54.5 (17.3)48.8 (22.7)
      P-value <0.05 PSM, propensity score matching.
      54.6 (17.2)54.6 (17.2)66.9 (13.2)69.2 (15.6)
      P-value <0.05 PSM, propensity score matching.
      67.9 (12.6)67.9 (12.6)
      Age groups
      0-441,308 (25.7%)15,531 (41.1%)
      P-value <0.05 PSM, propensity score matching.
      1,227 (25.2%)1,227 (25.2%)54 (4.9%)433 (7%)
      P-value <0.05 PSM, propensity score matching.
      33 (3.3%)33 (3.3%)
      45-642,367 (46.5%)13,038 (34.5%)
      P-value <0.05 PSM, propensity score matching.
      2,292 (47.2%)2,292 (47.2%)417 (37.9%)1,652 (26.8%)
      P-value <0.05 PSM, propensity score matching.
      367 (36.8%)367 (36.8%)
      ≥651,414 (27.8%)9,182 (24.3%)
      P-value <0.05 PSM, propensity score matching.
      1,342 (27.6%)1,342 (27.6%)629 (57.2%)4,084 (66.2%)
      P-value <0.05 PSM, propensity score matching.
      597 (59.9%)597 (59.9%)
      Comorbidity, N (%)
      Alzheimer/dementia24 (0.5%)510 (1.4%)
      P-value <0.05 PSM, propensity score matching.
      22 (0.5%)17 (0.3%)9 (0.8%)125 (2%)
      P-value <0.05 PSM, propensity score matching.
      6 (0.6%)4 (0.4%)
      Respiratory disease273 (5.4%)1,422 (3.8%)
      P-value <0.05 PSM, propensity score matching.
      251 (5.2%)251 (5.2%)63 (5.7%)423 (6.9%)52 (5.2%)61 (6.1%)
      Ischemic heart disease210 (4.1%)1,218 (3.2%)
      P-value <0.05 PSM, propensity score matching.
      201 (4.1%)145 (3%)84 (7.6%)626 (10.1%)
      P-value <0.05 PSM, propensity score matching.
      79 (7.9%)66 (6.6%)
      Other cardiovascular diseases385 (7.6%)2,508 (6.6%)
      P-value <0.05 PSM, propensity score matching.
      365 (7.5%)299 (6.2%)131 (11.9%)1,059 (17.2%)
      P-value <0.05 PSM, propensity score matching.
      124 (12.4%)106 (10.6%)
      Peripheral vascular disease81 (1.6%)516 (1.4%)73 (1.5%)58 (1.2%)24 (2.2%)234 (3.8%)
      P-value <0.05 PSM, propensity score matching.
      23 (2.3%)20 (2%)
      Cerebrovascular disease88 (1.7%)880 (2.3%)
      P-value <0.05 PSM, propensity score matching.
      81 (1.7%)60 (1.2%)29 (2.6%)341 (5.5%)
      P-value <0.05 PSM, propensity score matching.
      24 (2.4%)23 (2.3%)
      Hypertension1,286 (25.3%)7,000 (18.5%)
      P-value <0.05 PSM, propensity score matching.
      1,212 (24.9%)1,169 (24%)428 (38.9%)2,667 (43.2%)
      P-value <0.05 PSM, propensity score matching.
      390 (39.1%)375 (37.6%)
      Heart failure103 (2%)750 (2%)97 (2%)58 (1.2%)45 (4.1%)414 (6.7%)
      P-value <0.05 PSM, propensity score matching.
      43 (4.3%)43 (4.3%)
      Dyslipidemia475 (9.3%)2,412 (6.4%)
      P-value <0.05 PSM, propensity score matching.
      447 (9.2%)386 (7.9%)180 (16.4%)1124 (18.2%)166 (16.6%)146 (14.6%)
      Diabetes430 (8.4%)2,402 (6.4%)
      P-value <0.05 PSM, propensity score matching.
      413 (8.5%)360 (7.4%)160 (14.5%)1063 (17.2%)
      P-value <0.05 PSM, propensity score matching.
      151 (15.1%)123 (12.3%)
      Chronic liver disease141 (2.8%)954 (2.5%)132 (2.7%)114 (2.3%)45 (4.1%)360 (5.8%)
      P-value <0.05 PSM, propensity score matching.
      39 (3.9%)34 (3.4%)
      Rheumatic disease53 (1%)304 (0.8%)50 (1%)52 (1.1%)15 (1.4%)96 (1.6%)14 (1.4%)8 (0.8%)
      Cancer285 (5.6%)1,687 (4.5%)
      P-value <0.05 PSM, propensity score matching.
      273 (5.6%)284 (5.8%)91 (8.3%)568 (9.2%)84 (8.4%)100 (10%)
      HIV6 (0.1%)68 (0.2%)6 (0.1%)7 (0.1%)2 (0.2%)20 (0.3%)2 (0.2%)2 (0.2%)
      Concomitant therapies within 3 months, N (%)
      Anticoagulants560 (11%)1,523 (4%)
      P-value <0.05 PSM, propensity score matching.
      440 (9.1%)389 (8%)248 (22.5%)900 (14.6%)
      P-value <0.05 PSM, propensity score matching.
      187 (18.8%)178 (17.9%)
      Nonsteroidal anti-inflammatory drugs275 (5.4%)1,066 (2.8%)
      P-value <0.05 PSM, propensity score matching.
      238 (4.9%)261 (5.4%)81 (7.4%)409 (6.6%)68 (6.8%)61 (6.1%)
      Chloroquine, hydroxychloroquine210 (4.1%)337 (0.9%)
      P-value <0.05 PSM, propensity score matching.
      132 (2.7%)119 (2.4%)73 (6.6%)106 (1.7%)
      P-value <0.05 PSM, propensity score matching.
      37 (3.7%)28 (2.8%)
      Corticosteroids for systemic use579-11.4%)1,320 (3.5%)
      P-value <0.05 PSM, propensity score matching.
      430 (8.8%)443 (9.1%)220 (20%)577 (9.4%)156 (15.6%)
      P-value <0.05 PSM, propensity score matching.
      160 (16%)
      Immunosuppressants45 (0.9%)263 (0.7%)39 (0.8%)41 (0.8%)16 (1.5%)110 (1.8%)14 (1.4%)9 (0.9%)
      a P-value <0.05PSM, propensity score matching.

      Outcomes

      Results reported in Table 2 indicate a significantly higher risk of hospitalization in azithromycin users compared with nonusers (HR 1.59, 95 % CI 1.45-1.75). Results from the subgroup analyses were consistent with the main results, with higher risks observed in males (HR 1.37, 95% CI 1.22-1.54), in those aged <65 years (HR 1.45, 95 %CI 1.24-1.69), and in patients without CVDs (HR 1.43, 95% CI 1.26-1.62), diabetes (HR 1.28, 95% CI 1.15-1.41), and cancer (HR 1.22, 95% CI 1.11-1.35) (Figure 2).
      Table 2Azithromycin use and hospitalization, ICU access, MV, and mortality in patients with COVID-19
      OutcomeRelative risk before PSMRelative risk after PSM
      Data presented as HR for hospitalization and mortality and as OR for ICU access and MV. HR, hazard ratio; ICU, intensive care unit; MV, mechanical ventilation; OR, odds ratio; PSM, propensity score matching.
      Cohort A – patients tested positive to SARS-CoV-2
      Hospitalization
      Azithromycin nonuser3,711 (9.8%)Reference527 (10.8%)Reference
      Azithromycin user854 (16.8%)1.74 (1.62-1.87)820 (16.9%)1.59 (1.45-1.75)
      Cohort B – patients hospitalized and tested positive to SARS-CoV-2
      ICU access
      Azithromycin nonuser576 (9.3%)Reference104 (10.4%)Reference
      Azithromycin user132 (12.0%)1.32 (1.08-1.62)124 (12.4%)1.22 (0.93-1.56)
      MV
      Azithromycin nonuser549 (8.9%)Reference101 (10.1%)Reference
      Azithromycin user133 (12.1%)1.41 (1.15-1.72)127 (12.7%)1.30 (0.99-1.70)
      Mortality at 14 days
      Azithromycin nonuser894 (14.5%)Reference167 (16.8%)Reference
      Azithromycin user153 (13.9%)0.96 (0.81-1.14)148 (14.8%)0.88 (0.74-1.05)
      Mortality at 30 days
      Azithromycin nonuser1,136 (18.4%)Reference213 (21.4%)Reference
      Azithromycin user199 (18.1%)0.98 (0.84-1.14)191 (19.2%)0.89 (0.77-1.03)
      a Data presented as HR for hospitalization and mortality and as OR for ICU access and MV.HR, hazard ratio; ICU, intensive care unit; MV, mechanical ventilation; OR, odds ratio; PSM, propensity score matching.
      Figure 2
      Figure 2Subgroup analyses of hospitalization risk in individuals with positive SARS-CoV-2 test results.
      CVDs, cardiovascular diseases; Pts, patients.
      In cohort B, results showed no statistically significant associations between ICU access (OR 1.22, 95% CI 0.93-1.56) or need for MV (OR 1.30, 95% CI 0.99-1.70) and azithromycin use compared with nonuse (Table 2). Similarly, the Cox regression model did not show a statistically significant effect of azithromycin use on 14-day (HR 0.88, 95% CI 0.74-1.05) and 30-day mortality (HR 0.89, 95% CI 0.77-1.03) (Table 2). However, lower 30-day mortality (HR 0.65, 95% CI 0.45-0.92) was observed in azithromycin users with ICU access or subjected to MV. Subgroup analyses showed a significantly increased risk of ICU access (OR 1.40, 95% CI 1.04, 1.86) and MV (OR 1.35, 95% CI 1.02-1.79) in azithromycin users without diabetes (Figure 3). Conversely, previous use of azithromycin was associated with decreased risk of 30-day mortality in males (HR 0.78, 95% CI 0.63-0.96), in patients aged ≥65 years (HR 0.83, 95% CI 0.71, 0.98), and in those without cancer (HR 0.84, 95% CI 0.72-0.98) (Figure 3).
      Figure 3
      Figure 3Subgroup analyses of (a) risk of intensive care unit access; (b) risk of need for mechanical ventilation; (c) 14-day all-cause mortality; and (d) 30-day all-cause mortality in individuals hospitalized with COVID-19.
      CVDs, cardiovascular diseases; Pts, patients.

      Discussion

      This population-based study explored the effects of treatment with azithromycin in a community setting on major COVID-19-related outcomes—namely, hospitalization, ICU access, need for MV, and death. Our findings do not support the hypothesis of a protective role of azithromycin in any observed endpoint. Instead, our data outline the concern of potential inappropriate use of this medication, which was empirically prescribed in subjects infected with SARS-CoV-2 during the pandemic period (

      European Centre for Disease Prevention and Control (ECDC). Treatment and pharmaceutical prophylaxis of COVID-19. https://www.ecdc.europa.eu/en/covid-19/latest-evidence/treatment#:~:text=Use%20of%20antibiotics%20in%20patients,COVID%2D19%20%5B10%5D, 2021 (accessed 25 August 2022).

      ;
      • Gonzalez-Zorn B.
      Antibiotic use in the COVID-19 crisis in Spain.
      ;
      • Huang JT
      • Ran RX
      • Lv ZH
      • Feng LN
      • Ran CY
      • Tong YQ
      • Li D
      • Su HW
      • Zhu CL
      • Qiu SL
      • Yang J
      • Xiao MY
      • Liu MJ
      • Yang YT
      • Liu SM
      • Li Y.
      Chronological changes of viral shedding in adult inpatients with COVID-19 in Wuhan, China.
      ;
      • Sharma S
      • Singh A
      • Banerjee T.
      Antibacterial agents used in COVID-19: a systematic review and meta-analysis.
      ). In fact, despite the massive use of antibiotics observed in subjects who were developing early symptoms of COVID-19 (
      • Khan S
      • Hasan SS
      • Bond SE
      • Conway BR
      • Aldeyab MA.
      Antimicrobial consumption in patients with COVID-19: a systematic review and meta-analysis.
      ;
      • Sharma S
      • Singh A
      • Banerjee T.
      Antibacterial agents used in COVID-19: a systematic review and meta-analysis.
      ), several studies have shown that coinfection and secondary bacterial infection occurred only in 3.5%-14.3% of patients with COVID-19 (
      • Chedid M
      • Waked R
      • Haddad E
      • Chetata N
      • Saliba G
      • Choucair J.
      Antibiotics in treatment of COVID-19 complications: a review of frequency, indications, and efficacy.
      ;

      European Centre for Disease Prevention and Control (ECDC). Treatment and pharmaceutical prophylaxis of COVID-19. https://www.ecdc.europa.eu/en/covid-19/latest-evidence/treatment#:~:text=Use%20of%20antibiotics%20in%20patients,COVID%2D19%20%5B10%5D, 2021 (accessed 25 August 2022).

      ;
      • Hughes S
      • Troise O
      • Donaldson H
      • Mughal N
      • Moore LSP.
      Bacterial and fungal coinfection among hospitalized patients with COVID-19: a retrospective cohort study in a UK secondary-care setting.
      ;
      • Langford BJ
      • So M
      • Raybardhan S
      • Leung V
      • Westwood D
      • MacFadden DR
      • Soucy JPR
      • Daneman N.
      Bacterial co-infection and secondary infection in patients with COVID-19: a living rapid review and meta-analysis.
      ;
      • Lansbury L
      • Lim B
      • Baskaran V
      • Lim WS.
      Co-infections in people with COVID-19: a systematic review and meta-analysis.
      ;
      • Sharma S
      • Singh A
      • Banerjee T.
      Antibacterial agents used in COVID-19: a systematic review and meta-analysis.
      ).
      Among patients with positive test results for SARS-CoV-2, azithromycin was associated with a significantly increased risk of hospitalization during 21 days of follow-up. A possible mechanistic explanation for these results may be related to the effects of this drug on microbiota and on the immune system (
      • Langford BJ
      • So M
      • Raybardhan S
      • Leung V
      • Soucy JPR
      • Westwood D
      • Daneman N
      • MacFadden DR.
      Antibiotic prescribing in patients with COVID-19: rapid review and meta-analysis.
      ;
      • Wypych TP
      • Marsland BJ.
      Antibiotics as instigators of microbial dysbiosis: implications for asthma and allergy.
      ;
      • Yin X
      • Xu X
      • Li H
      • Jiang N
      • Wang J
      • Lu Z
      • Xiong N
      • Gong Y.
      Evaluation of early antibiotic use in patients with non-severe COVID-19 without bacterial infection.
      ). In fact, azithromycin may cause a temporary dysbiosis that can result in the inability of lungs to clear pathogens and can make them more vulnerable to viral infections. The improper use of antibiotics in patients without bacterial infections may also cause a cytokine-mediated overactivation of the immune system and a septic-shock-like phenomenon, thus worsening the hyperinflammation stimulated by COVID-19 (
      • Marsland BJ
      • Trompette A
      • Gollwitzer ES.
      The gut-lung axis in respiratory disease.
      ;
      • McAleer JP
      • Kolls JK.
      Contributions of the intestinal microbiome in lung immunity.
      ;
      • Wypych TP
      • Marsland BJ.
      Antibiotics as instigators of microbial dysbiosis: implications for asthma and allergy.
      ). Another possible explanation may be related to patients’ and clinicians’ risk perception and to differences in disease management over time (
      • Arefi MF
      • Babaei AP
      • Barzanouni S
      • Ebrahimi S
      • Salehi AR
      • Khajehnasiri F
      • Poursadeghian M.
      Risk perception in the COVID-19 pandemic; a health promotion approach.
      ;
      • Hayat K
      • Mustafa ZU
      • Ikram MN
      • Ijaz-Ul-Haq M
      • Noor I
      • Rasool MF
      • Ishaq HM
      • Rehman AU
      • Hasan SS
      • Fang Y.
      Perception, attitude, and confidence of physicians about antimicrobial resistance and antimicrobial prescribing among COVID-19 patients: a cross-sectional study from Punjab, Pakistan.
      ). In fact, during the early phases of the pandemic, the intensity of care of patients with COVID-19 was based mainly on empirical evidence such as disease severity, clinical symptoms, and risk of disease progression (
      • Jampani M
      • Chandy SJ.
      Increased antimicrobial use during COVID-19: the risk of advancing the threat of antimicrobial resistance.
      ). Therefore, clinicians were more likely to use azithromycin in patients with COVID-19 with mild symptoms, with consequent decreased use of hospital care and potential increased risk of disease worsening (
      • Yin X
      • Xu X
      • Li H
      • Jiang N
      • Wang J
      • Lu Z
      • Xiong N
      • Gong Y.
      Evaluation of early antibiotic use in patients with non-severe COVID-19 without bacterial infection.
      ). This hypothesis is supported by our findings, which showed higher risk of hospitalization among younger and healthier users compared with older users and those with diabetes, CVDs, and cancer.
      Findings from cohort B confirm the evidence from previous studies that found no beneficial effect of in- and out-of-hospital treatment with azithromycin (
      • Arshad S
      • Kilgore P
      • Chaudhry ZS
      • Jacobsen G
      • Wang DD
      • Huitsing K
      • Brar I
      • Alangaden GJ
      • Ramesh MS
      • McKinnon JE
      • O'Neill W
      • Zervos M
      Henry Ford COVID-19 Task Force. Treatment with hydroxychloroquine, azithromycin, and combination in patients hospitalized with COVID-19.
      ;
      • Albani F
      • Fusina F
      • Giovannini A
      • Ferretti P
      • Granato A
      • Prezioso C
      • Divizia D
      • Sabaini A
      • Marri M
      • Malpetti E
      • Natalini G.
      Impact of azithromycin and/or hydroxychloroquine on hospital mortality in COVID-19.
      ;
      • Kokturk N
      • Babayigit C
      • Kul S
      • Cetinkaya PD
      • Nayci SA
      • Baris SA
      • Karcioglu O
      • Aysert P
      • Irmak I
      • Yuksel AA
      • Sekibag Y
      • Toprak OB
      • Azak E
      • Mulamahmutoglu S
      • Cuhadaroglu C
      • Demirel A
      • Kerget B
      • Ketencioglu BB
      • Ozger HS
      • Ozkan G
      • Ture Z
      • Ergan B
      • Oguz VA
      • Kilinc O
      • Ercelik M
      • Ciftci TU
      • Alici O
      • Temel EN
      • Ataoglu O
      • Aydin A
      • Bahcetepe DC
      • Gullu YT
      • Fakili F
      • Deveci F
      • Kose N
      • Tor MM
      • Gunluoglu G
      • Altin S
      • Turgut T
      • Tuna T
      • Ozturk O
      • Dikensoy O
      • Gulhan PY
      • Basyigit I
      • Boyaci H
      • Oguzulgen IK
      • Borekci S
      • Gemicioglu B
      • Bayraktar F
      • Elbek O
      • Hanta I
      • Okur HK
      • Sagcan G
      • Uzun O
      • Akgun M
      • Altinisik G
      • Dursun B
      • Edis EC
      • Gulhan E
      • Eyuboglu FO
      • Gultekin O
      • Havlucu Y
      • Ozkan M
      • Coskun AS
      • Sayiner A
      • Kalyoncu AF
      • Itil O
      • Bayram H.
      The predictors of COVID-19 mortality in a nationwide cohort of Turkish patients.
      ;
      • Ip A
      • Berry DA
      • Hansen E
      • Goy AH
      • Pecora AL
      • Sinclaire BA
      • Bednarz U
      • Marafelias M
      • Berry SM
      • Berry NS
      • Mathura S
      • Sawczuk IS
      • Biran N
      • Go RC
      • Sperber S
      • Piwoz JA
      • Balani B
      • Cicogna C
      • Sebti R
      • Zuckerman J
      • Rose KM
      • Tank L
      • Jacobs LG
      • Korcak J
      • Timmapuri SL
      • Underwood JP
      • Sugalski G
      • Barsky C
      • Varga DW
      • Asif A
      • Landolfi JC
      • Goldberg SL.
      Hydroxychloroquine and tocilizumab therapy in COVID-19 patients-an observational study.
      ;
      RECOVERY Collaborative Group
      Azithromycin in patients admitted to hospital with COVID-19 (RECOVERY): a randomised, controlled, open-label, platform trial.
      ). Additionally, we were able to provide powerful new evidence on the potential role of this drug in patients with COVID-19 who are admitted to the hospital but were treated before hospitalization. Although not statistically significant, results on ICU access and need for MV mirrored what was observed in cohort A, thus suggesting that perception of lower risk among azithromycin users may have influenced not only the risk of hospitalization but also hospital-based outcomes such as ICU access and need for MV. Conversely, the intensity of care within the hospitals may have influenced the data on mortality, as demonstrated by the statistically significant lower risk observed in azithromycin users who accessed ICU or were subjected to MV.
      This study has limitations. First, the Italian healthcare administrative databases does not include data on in-hospital patient management or clinical data such as severity of COVID-19 disease or results from biological samples (i.e., microbiological tests). Second, the use of PSM analysis (
      • Austin PC.
      An introduction to propensity score methods for reducing the effects of confounding in observational studies.
      ) allowed us to balance measured baseline demographic and clinical characteristics between exposed and unexposed groups, as already proposed in similar studies (
      • Trifirò G
      • Massari M
      • Da Cas R
      • Ippolito FM
      • Sultana J
      • Crisafulli S
      • Rossi PG
      • Marino M
      • Zorzi M
      • Bovo E
      • Leoni O
      • Ludergnani M
      • Alegiani SS
      ITA-COVID-19: RAAS inhibitor group. Renin-angiotensin-aldosterone system inhibitors and risk of death in patients hospitalised with COVID-19: a retrospective Italian cohort study of 43,000 patients.
      ), and thus to minimize potential confounding by indication. However, we were unable to completely rule out the presence of residual unmeasured confounders because of the lack of some relevant information in the Italian healthcare administrative databases, such as COVID-19 disease severity (

      National Institutes of Health (NIH). Clinical spectrum of SARS-CoV-2 infection. https://www.covid19treatmentguidelines.nih.gov/overview/clinical-spectrum/, 2022 (accessed 24 August 2022).

      ) and possible risk factors for disease outcomes and death in patients with COVID-19, such as obesity and smoking. Lastly, we were not able to identify the reasons for antibiotic use, as the Italian pharmacy claims database does not include information on drug indications.

      Conclusion

      Our findings are in line with available evidence that does not recommend the use of azithromycin as effective treatment for patients with SARS-CoV-2 infection and raise concern on risks associated with inappropriate use of this drug. This highlights the importance of following antibiotic stewardship principles—even during challenging times such as COVID-19 pandemic—because the use of antibiotics without solid microbiological evidence on bacterial coinfection may result in both no beneficial effects for patients with COVID-19 and increased risk of adverse events (
      • Chedid M
      • Waked R
      • Haddad E
      • Chetata N
      • Saliba G
      • Choucair J.
      Antibiotics in treatment of COVID-19 complications: a review of frequency, indications, and efficacy.
      ;
      • Sharma S
      • Singh A
      • Banerjee T.
      Antibacterial agents used in COVID-19: a systematic review and meta-analysis.
      ). These aspects are crucial for preserving the effectiveness of these medications and preventing the spread of drug-resistant organisms.

      Funding

      The VICES-SMIRE project was funded by Lombardy Region (DGR n. XI/3017/2020 and D.G. Welfare nr. 7082/2020).

      Ethical approval statement

      The study is exempt from institutional review board authorization and informed consent (according to General Authorisation for the Processing of Personal Data for Scientific Research Purposes, issued by the Italian Data Protection Authority).

      Author contributions

      Conceptualization: ICA, CF, LGM, and GM; methodology: ICA, CF, GM; data curation: CF, DR, SC, and SK; writing (original draft preparation): ICA, CF and GM; writing (review and editing): all authors. All authors have read and agreed to the published version of the manuscript.

      Data availability statement

      Data sharing is not applicable to this article.

      Declarations of competing interest

      ICA, CF, DR, SC, PF, PC, RDP, SK, AZ, GM, AS, GC and GM have no conflicts of interest to declare. LGM reported receiving grants from Bayer, Daiiki-Sankyo, and Boehringer Ingelheim outside the submitted work and speaker fees from Pfizer and Bayer.

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