Background

The SARS-CoV-2 pandemic is still burdening the healthcare system of several Western countries. Patients with mild-to-moderate COVID-19, for whom home-care treatment is requested, generally present flu-like symptoms such as fever, pharyngitis, cough, rhinitis, headache, and myalgia [1, 2]. Among the available medications, paracetamol is recommended by the World Health Organization (WHO) [3], the National Institute for Health and Care Excellence (NICE) [4] as well as other regulatory agencies [5, 6] and scientific societies [7] among the mainstays to manage coronavirus-related early symptoms.

Nevertheless, there was conflicting and misleading communication [8, 9] on the pharmacotherapy for the home care of COVID-19. Some clinicians and researchers raised concerns on the safety profile of paracetamol when used in SARS-CoV-2 infections [10, 11]. They underlined that the potential depletion of glutathione (GSH) induced by paracetamol may even favour the pulmonary endothelium damages by SARS-CoV-2 [10, 12]. The authors therefore recommended to avoid the use of paracetamol to treat the early symptoms of COVID-19, and warned on the dramatic increase of use of this drug during the pandemic [10, 13]. Consistently, Suter et al.[11] reported significant reduction of hospital admission rate when non-steroidal anti-inflammatory drugs (NSAIDs) were administered in the first 72 h [11, 13] from the infection, and indicated that nimesulide, aspirin or celecoxib as the recommended choice instead of paracetamol. However, these positions were based on pre-clinical findings [12] with no clinical proof. Second, the studies supporting a greater effectiveness for an early use of NSAIDs [11] were undersized and might suffer from methodological flaws [14]. Third, the relationship between paracetamol-induced GSH consumption and COVID-19 severity might be clinically observed because of protopathic bias [15,16,17,18].

In order to clarify the place in therapy of paracetamol for the home care of COVID-19, we investigated the prescribing patterns of paracetamol for COVID-19 in 2020 as well as for other “similar” respiratory conditions being diagnosed in pre-pandemic phase. Then, we investigated the putative risk of COVID-19-related hospitalization/death due to paracetamol use, as well as the effect of protopathic bias on the results.

Methods

Data source

We used the Health Search Database (HSD) implemented by the Italian College of General Practitioners and Primary Care. In HSD, patients’ demographic details are linked using an encrypted unique identifier with medical records (diagnoses, tests performed, tests results, hospital admissions, etc.), drug prescriptions (trade name, dosage form, Anatomical Therapeutic Chemical classification (ATC) code, ministerial code, active substances, date of filled prescription, number of days’ supply), risk factors, lifestyle variables (Body Mass Index (BMI)), smoking habits, alcohol use) and date of death. Diagnoses are coded according to the International Classification of Diseases, Ninth Revision, Clinical Modification (ICD-9-CM). The encoding of the ambulatory procedures is performed in accordance with the Nomenclatore Tariffario, a list of all outpatient specialist medical services and related tariffs, instituted by the Ministerial Decree in 1996.

At the time of the study, 747 GPs homogeneously distributed across all areas of Italy and covering almost 1,200,000 patients were selected. To be considered for participation in epidemiological studies, GPs are required to meet up-to-standard quality criteria related to the levels of coding, prevalence of well-known diseases, mortality rates, and years of recording. [19] Furthermore, a specific index (ITOT) is used to verify and check the data quality registration in HSD every semester [20]. HSD has been adopted for various clinical research topics [21,22,23,24].

Study population and design

To investigate the prescribing patterns of paracetamol, we formed two cohorts with individuals aged ≥ 15 years diagnosed with COVID-19 or other respiratory conditions (ICD-9-CM: 460* (excluding 460/30), 465.9, 485*, 480*–487* (excluding 480.9/60)) between January 1st and December 31st, 2019 and between January 1st—December 31st, 2020, respectively. The date of diagnosis was operationally defined as the index date. As no specific ICD-9-CM codes for COVID-19 were available, two new subcategories were introduced in HSD in May 2020: namely, 460/30, coding for SARS-CoV-2 infection of the upper respiratory tract, and 480.9/60 for SARS-CoV-2-related pneumonia. To capture cases that occurred before the introduction of these specific codes, we included codes for respiratory diseases (see codes mentioned above), which were likely used in 2020 to register COVID-19 cases. In this respect, the pandemic curve being obtained with HSD (see Supplementary Fig. S1), which was consistent with that revealed by national public health authorities, showed a relevant underestimation of COVID-19 cases for the first quarter of 2020 when confirmed diagnoses were included only. As such, “confirmed” cases were those using specific codes of COVID-19, while “probable” cases were formed by “confirmed” diagnoses along with those codifying other respiratory conditions being registered in the first period (up to April 2020) of the pandemic phase. We successfully adopted this approach in prior studies. [21]

To investigate the safety profile of paracetamol in COVID-19 and the role of protopathic bias [17] to explain the association between paracetamol use and an increased risk of COVID-19-related hospitalization/death, we formed a cohort of COVID-19 patients who were followed up with until the occurrence of these events whichever came first: hospitalization/death (index date), end of data availability (30 June 2021). Those who cumulated less than 15 days of follow-up were excluded, so allowing a biological rationale for paracetamol putative effect. In this cohort, we nested a case–control analysis in which cases were matched up to 10 controls according to age (5 year categories), gender, calendar period (month of the cohort entry) and duration of follow-up. Controls were assigned with the same date of the respective cases. In comparison to a time-dependent survival analysis, a nested case–control analysis is computationally more efficient, while producing odds ratios (OR) that are unbiased estimators of incidence rate ratios), with little or no loss in precision. Thus, while a cohort study using a survival analysis uses the full risk set (i.e. all patients still at risk of the event at the time of the case’s event), the nested case–control analysis uses a random sample of patients (i.e. controls) from that same risk set. [25]

Outcome definition

The incidence rate and therapeutic schedules of paracetamol (ATC: N02BE01; N02BE51 excluding “codeine” and “ibuprofen” from the description of active substance) use were quantified among patients with probable SARS-CoV-2 and other respiratory infections being diagnosed in 2020 and 2019, respectively.

We identified every SARS-CoV-2-related hospitalization and/or death occurring during follow-up. As HSD provides no information on causes of hospitalization/death, we adopted an algorithm to define this outcome as in prior studies [21]. Hospitalizations were those in which the terms “SARS-CoV-2”, “COVID*”, “coron*” were reported and manually verified in the code description. Fatal cases were those occurring within 30 days of the diagnosis of SARS-CoV-2. For the case–control analysis, we identified every prescription of paracetamol being registered between entry (i.e. diagnosis of COVID-19) and index date. We classified the use of paracetamol into three mutually-exclusive categories, nominally early (within 3 of the entry date), mid-term (from 4 to 7 days of the entry date) and late (> 7 days from the entry date) use. These categories were chosen according to the proposed definition for early treatment of COVID-19-related symptoms [6, 10, 11], which were consistent with the presumptive association between paracetamol and COVID-19 progression. As potential confounders, we calculated the individual HS-CoVId [21] by identifying demographic, lifestyle and clinical features composing this vulnerability score in the overall period preceding the entry date, inclusive. This prediction score was developed and validated by us using HSD, according to TRIPOD statements [26]. Namely, the coefficients being estimated for each determinant were linearly combined to form HS-CoVId for each patient. The concurrent use of NSAIDs, antibiotics, heparin and steroids, was defined in the period preceding or on the index date [7]. The use of NSAIDs was captured in the same time windows adopted for paracetamol.

Data analysis

Continuous variables were reported as mean (SD), while categorical values as proportions. The incidence rate of paracetamol use was calculated by dividing the number of patients being diagnosed with “probable” COVID-19 and prescribed with paracetamol during follow-up (numerator) for those diagnosed with “probable” COVID-19 (denominator). The analysis was stratified by age (5 year bands) and gender. Dosage of paracetamol use was quantified for each prescription using the available strengths (i.e., 500 or 1000 mg) and therapy instructions reported by GPs. The Prescribed Daily Dose (PDD) and the overall strength (in mgs) contained in every prescription allow for the calculation of mean duration (in days). The same calculations were conducted for the other respiratory conditions being registered in 2019.

For the nested case–control analysis, a conditional logistic regression was adopted to estimate the ORs with related 95% confidence intervals (CI) to be exposed or unexposed to paracetamol among cases and related controls. The reference category was formed by non-users of paracetamol.

To verify the robustness of the results we conducted two sensitivity analyses. First, to consider the burden of misclassification for COVID-19 diagnoses, were re-ran the primary analyses by limiting cases to those classified as “confirmed”. Second, we carried out a probabilistic sensitivity analysis [27] to evaluate the effect on the results of exposure misclassification due to paracetamol use as OTC medication [28]. We assumed that younger individuals were featured by frequent use of OTC medications and less severe infection. We therefore hypothesized differential misclassification for paracetamol use by drawing the sensitivities and specificities from different uniform distributions for cases and controls [27]. For cases, we adopted distributions with an interval of equally probable values between 0.85 and 1 for both sensitivity and specificity. For controls, we adopted an interval of equally probable values ranging 0.55–1 (under-registration of OTC drug us in younger controls mainly) or 0.65–1 for sensitivity and specificity, respectively. We reported the median value (50th percentile) and 2.5th and 97.5th percentiles of the bias-adjusted OR.

Results

Overall, 46,522 probable cases of COVID-19 (mean age: 50.3 (SD: 17.7) years; 50.8% females) were identified in HSD in 2020. Among them, 1554 (33.4 per 1000) were newly prescribed with paracetamol to treat virus-related symptomatology. In 2019, we identified 32797 patients (mean age: 51.1 (SD: 19.1) years; 51.2% females) who suffered from other respiratory conditions. Among them, 2566 cases (78.3 per 1000) were prescribed with paracetamol.

Figure 1 depicts the incidence rates of paracetamol use among probable cases of COVID-19 being identified in HSD in 2020, taken as a whole and broken down by age and gender. Those aged 35–44 showed the highest prevalence rate (44.7 per 1000), while the oldest category reported the lowest value (17.8 per 1000). Males showed a higher use of paracetamol than females in almost all age categories except for those aged 15–24 and 65–74 years old. There was no age-related trend. Only those aged >  = 85 years old reported lower incident use of paracetamol when compared with other age categories. This is indeed the only age category showing a significant difference versus other categories given the absence of overlapped CIs with other subgroups.

Fig. 1
figure 1

Incidence rate of paracetamol use among ‘probable’ cases of COVID-19 according to age and gender

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Figure 2 depicts the incidence rates of paracetamol use for other respiratory infections in 2019. The incident users of paracetamol for these indications were higher than those observed for COVID-19, although the patterns of use appeared similar across all age categories. In specific, the highest rate of paracetamol use was observed among those aged 15–24 (99.0 per 1000) years, while the lowest incident use (39.8 per 1000) was seen among individuals aged >  = 85 years. No age-related trend was observed for the other age categories. We calculated a mean number of prescriptions per patient equal to 1.03 (SD: 0.2) for ‘probable’ cases, respectively. Paracetamol strength of 500 or 1000 mg was prescribed in 8.9 and 46% of SARS-CoV-2 cases, respectively. For both dosages, the most frequent schedule was of 3 time daily (27.5 and 17.2% for 500 and 1000 mg, respectively). When the same analysis was run in 2019 using ICD9CM codes related to other respiratory conditions, the prevalence of use for paracetamol strength of 500 and 1000 mg was equal to 7.5 and 45.6%, respectively. The most frequent schedule was the three-time daily (27.5 and 17.2% for 500 and 1000 mg, respectively).

Fig. 2
figure 2

Incidence rate of paracetamol use by age and gender among cases of other respiratory infective conditions

Full size image

For the safety study, we identified a cohort of 78,117 cases of SARS-CoV-2 infections (mean age: 50.7 (SD: 17.8), 52.4% females) cumulating 22,918 person-months. Among them, we captured 2756 cases of COVID-19-related hospitalizations/deaths (incidence rate = 4 (95% CI: 3.9–4.2) per 1000 person-months). Table 1 depicts the association between paracetamol use, whose exposure categories were operationally defined at different times points following the date of COVID-19 diagnoses, and the risk of COVID-19-related hospitalization/death (see Supplementary Table S1 for case–control characteristics). There was no statistically significant association for early (OR = 1.15; 95% CI: 0.92–1.43) or mid-term (OR = 1.29; 95% CI: 0.61–2.73) users of paracetamol versus non- users. The late users of paracetamol showed a statistically significant increased risk of hospitalization/death (OR = 1.75; 95% CI: 1.40–2.18).

Table 1 Paracetamol use and risk of COVID-19-related hospitalization/death
Full size table

For what concerns the sensitivity analyses, the incidence rates of paracetamol use among ‘confirmed’ cases (n = 154 out of 22655) of COVID-19 being identified in 2020 was higher in males than females for almost all age categories. The youngest subgroup showed the lowest incidence rate (4.0 per 1000), while those aged 65–74 reported the highest rate of paracetamol use (11.2 per 1000) (Supplementary Fig. S2). For the nested case–control analysis, when COVID-19 cases were limited to confirmed diagnoses, the results were consistent with those obtained for the primary analysis (Supplementary Table S2). The probabilistic sensitivity analysis reported a median bias-adjusted OR for early use of paracetamol equal to 1.12 (2.5th and 97.5th percentile: 0.87 and 5.86) or 1.13 (2.5th and 97.5th percentile: 0.80 and 5.72) where systematic and systematic and random error were considered, respectively.

Discussion

This is the first study investigating the place in therapy of paracetamol in the treatment of SARS-CoV-2-related symptoms. We found a lower incident use of paracetamol for COVID-19 when compared with other respiratory conditions in the pre-pandemic period. However, similar prescribing patterns were observed in pandemic and pre-pandemic phases. Younger and male patients were those majorly receiving paracetamol and the incident users decreased moving towards older-age categories. The early and mid-term use of paracetamol after COVID-19 diagnoses were not associated with an increased risk of COVID-19-related hospitalization and/or death, so providing further evidence that the significant association observed for late users was likely due to protopathic bias.

Recently, some works [13, 29] raised concerns on the safety profile of paracetamol when used to treat COVID-19-related symptoms. These statements were based on pre-clinical studies on paracetamol capacity to deplete GSH in pneumocytes and alveolar macrophages [12], thus limiting protection towards oxidative stress exerted by viral infections. Such a mechanism should explain the association between paracetamol use and the increased risk of severe pneumonia in COVID-19 patients [30]. Nevertheless, a residual amount of paracetamol (up to 15% at most) is usually undergone to the oxidative metabolism involving GSH, and the daily dosage able to cause a relevant depletion of GSH should reach 15 g/daily (for an adult of 65–70 kgs), which is fivefold higher than the maximum allowed daily dose [31]. It is well known that paracetamol toxicity occurs with a dose-dependent mechanism and that cellular GSH depletion follows the excess production of the main toxic intermediate (NAPQI) when paracetamol doses exceed cellular GSH reserve. Such a mechanism has been proven as cause of acute liver injury, where NAPQI generation from paracetamol metabolism predominantly occurs [32]. In addition, there are no clinical studies supporting the association between paracetamol use and an increased risk of COVID-19-related complications.

The same authors [10, 29], supported the early use of some NSAIDs or COXIBs [30] in place of paracetamol. This position was based on an explorative study whose results have been largely discussed because of methodological concerns [14]. In this respect, Bonferroni-adjusted p-values were adopted for dependent (secondary) outcomes, so violating the main assumption of Bonferroni’s correction [33]; the exposure categories were matched by propensity score but there had details on matching procedures and related performance [34]. In specific, the small sample size (n = 90) of the study cohort could have led to unbalanced confounders between treatment and control group, as observed for dyspnoea proportions (20 vs. 36.7% p value = 0.020). As such, the control group artificially showed a greater incidence rate of hospitalization because it featured more severe SARS-CoV-2 infections at the baseline than treatment group.

Then, as demonstrated by us for late users, the hypothetical relationship between paracetamol and COVID-19-related progression might have been clinically observed because of protopathic bias [17, 18]. A significant proportion of patients were prescribed with paracetamol because of increasing symptomatology in the time-window closely preceding the hospital admission. Thus, medications use to relieve fever and/or pain is erroneously associated with the occurrence of negative outcomes.

With the present study, we attempted to clarify the misleading messages stemming from the aforementioned publications, which are clearly inconsistent with what is recommended by WHO [3], and other public health authorities [4,5,6]. As a whole, GPs seem to prescribe paracetamol for COVID-19 as in other respiratory conditions, and we found no association with early use of paracetamol in COVID-19 and an increased risk hospitalization/death.

This study suffers from limitations as well. First, in the first quarter of 2020, GPs contributing to the HSD could not formally register COVID-19 patients as such. When we limited the analysis to confirmed cases of COVID-19, we still obtained consistent results. Second, this analysis was conducted using data of 2020 and 2021 (first semester). As such, they cannot be immediately applied to the Omicron variant. Nevertheless, there are no different patterns of early symptoms between previous and current variants given the high vaccine coverage [35]. As such, there is no reason to imagine a different safety profile for paracetamol when used with the Omicron variant. Third, prescriptions of paracetamol for COVID-19 might be underestimated because it is not fully reimbursed. Nevertheless, the exposure misclassification due to paracetamol use as an OTC drug [28] might have caused biased results towards the null. When we conducted probabilistic sensitivity analysis [27], the bias-adjusted OR were consistent with those obtained for the primary analysis. Finally, the analysis was not fully adjusted for the anti-COVID vaccine effect, which was incepted among older adults in 2021. We found only eight cases exposed to paracetamol in 2021, demonstrating a negligible effect likely due to vaccine exposure.

In conclusion, our findings provide reassuring evidence on the safety profile of paracetamol to treat early symptoms of COVID-19 as in other respiratory infections. This result is particularly relevant given the characteristics of last Omicron variant, whose infectious spread is currently responsible for a quick and sensible increased number of COVID-19 patients needing home care.