Drug-utilisation Proles and COVID-19: Retrospective Cohort Study in Italy

Coronavirus disease 2019 (COVID-19) has wrought havoc on healthcare systems worldwide. Age, chronic diseases, use of drugs acting on the renin-angiotensin system (RAS), male sex and genetic predisposition have been postulated as risk factors for adverse outcomes in COVID-19 cases. A retrospective drug-utilisation study was carried out using information collected routinely in a healthcare database (CaReDB) in Campania (Southern Italy). We wished to discover the prevalence of drug utilisation (monotherapy and polytherapy) in COVID-19 vs. non-COVID-19 patients in Campania (~6 million inhabitants). The study cohort was 1,532 individuals who tested positive for COVID-19. Drugs were grouped according to the Anatomical Therapeutic Chemical (ATC) classication system. We noted a higher prevalence of use of drugs in the ATC category C01, B01 and M04, and this was probably linked to related comorbidities (i.e., cardiovascular, metabolic). Nevertheless, the prevalence of use of drugs acting on the RAS, such as antihypertensive drugs, was not higher among COVID-19 patients compared with that in non-COVID-19 patients. These results highlight the need for further case–control studies to dene the effect of medications and comorbidities on susceptibility to, and associated mortality from, COVID-19.


Introduction
As of 24 April 2020, coronavirus disease 2019 (COVID-19) has been responsible for ~3,000,000 cases and >200,000 deaths worldwide 1 . COVID-19 is very contagious and has a wide spectrum of presentation.
COVID-19 can range from an absence of symptoms to severe illness, and includes three phases (i.e., viral infection, pulmonary, hyperin ammation/systemic) 2 . Aging and underlying disease (e.g., heart disease, diabetes mellitus) have been reported to be risk factors for adverse outcomes, but, being male and a genetic predisposition to infection are under investigation as potential contributors [3][4][5][6][7] . Moreover, initial reports have suggested a potential pro-infective effect of drugs. Two classes of drugs that have been implicated are angiotensin-converting enzyme inhibitors (ACEIs) and angiotensin II receptor blockers.
This action may be due to interaction between the virus that causes COVID-19, severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2), and ACE-2 receptors in the lungs, though this theory is controversial 8-12 . However, there is a lack of data on drug use (monotherapy and polytherapy) in COVID-19 patients. The main aims of this study were to: (i) discover the prevalence of drug utilisation (monotherapy and polytherapy) in COVID-19 vs. non-COVID-19 patients in Campania, Southern Italy; (ii) ascertain the epidemiology and pro les of patients affected in relation to drug utilisation.

Study design
A retrospective drug-utilisation study was carried out using information collected routinely in healthcare databases in Campania. The Campania Region Database (CaReDB) includes information on patient demographics, and the electronic records of outpatient pharmacy dispensing for ~6 million residents of a well-de ned population in Italy (~10% of the population of Italy). CaReDB is complete and includes validated data in previous drug-utilisation studies [13][14][15][16][17][18][19][20] . The characteristics of CaReDB are described in Supplemental Table S1.
From the beginning of the COVID-19 epidemic, a surveillance system was implemented to collect all cases identi ed by reverse transcription-polymerase chain reaction (RT-PCR) testing for SARS-CoV-2.
These archives can be linked together by a unique anonymous identi er that is encrypted to protect patient privacy. Our research protocol adhered to the tenets of the Declaration of Helsinki 1975 and its later amendments. Permission use anonymized data to this study was granted to the researchers of the Centro di Ricerca in Farmacoeconomia e Farmacoutilizzazione (CIRFF) by the governance board of Unità del Farmaco della Regione Campania. The research does not contain clinical studies, and all patients' data were fully anonymized and were analysed retrospectively. For this type of study, formal consent is not required according to current national law from Italian Medicines Agency and according to the Italian Data Protection Authority, neither Ethical Committee approval nor informed consent were required for our study 21 . Our research protocol adhered to the tenets of the Declaration of Helsinki 1975 and its later amendments.

Study population
People who had been dispensed medication according to CaReDB during 2019 formed the study cohort. From the regional surveillance system, we obtained information on con rmed cases of COVID-19 from the beginning of the epidemic (26 February 2020) to 30 March 2020 who were linked to the population identi ed in CaReDB. For the purposes of our investigation, the study population diagnosed as having SARS-CoV-2 infection at the date of the analysis was referred to as the "COVID-19 group" (C19G). The remaining individuals were used as a comparator group for the analysis and were referred to as the "general population group" (GPG).

Patient characteristics
The study population was categorised by sex and subdivided into four age groups in years; 0-39; 40-59; 60-79; ≥80. The number of drug prescriptions, prevalence of drug use and polypharmacy regimens (classi ed as 'no-polypharmacy'; 'polypharmacy'; 'excessive polypharmacy') were ascertained in 2019. Drugs were grouped according to the Anatomical Therapeutic Chemical (ATC) classi cation system. ATC II and ATC IV codes with a prevalence ≥3% in the C19G were included in the analysis.

Outcome
The drug-utilisation pro le was evaluated as the prevalence of drug use. Prevalent users were estimated as individuals dispensed ≥1 drug prescription per 100 inhabitants in 2019. The prevalence of drug use was evaluated in the C19G and GPG. Prevalence was strati ed by age group and sex. Prevalence was probably in uenced by the heterogeneous demographic distribution among the age groups, so we provide to use direct standardization.

Statistical methods
Baseline characteristics of the study population were analysed using descriptive statistics. Quantitative variables are described by the mean ± standard deviation. Categorical variables are described by counts and percentages. Crude and age-adjusted prevalence was calculated. Differences in prevalence between the C19G and GPG are expressed as risk ratios (RRs) adjusted for sex and age with 95% con dence intervals (CIs). Standardisation was done using a direct method whereby the Italian population up to 1 January 2019 was used as the standard population (available on the Demo Istat website 22 ).

Drug-utilisation pro les of the C19G
Twenty-three pharmacological ATC II groups and 39 ATC IV groups resulted had a prevalence >3% in the C19G. The highest unadjusted and adjusted prevalence of drug use in ATC II groups was for drug category J01, A02, C09, M01, B01 and R03 in the C19G and GPG ( Figure 1).
Crude differences (in terms of at least ±20% in the overall prevalence of drug use between the C19G and GPG) were found in all 23 pharmacological ATC II groups and in 30 of 39 ATC IV groups included in the analysis ( Figure 1, Table 2). After adjustment, differences remained in six ATC II groups and eight ATC IV groups. With respect to Drugs Acting on the Renin-Angiotensin System (RAS) (C09), Beta-Blockers (C07), Antibacterial Drugs for Systemic Use (J01) and Anti-in ammatory and Antirheumatic Drugs (M01), the differences disappeared after adjustment. The large differences in Antithrombotic Agents (B01), Cardiac Therapy (C01) and Antiepileptics (N03) diminished after adjustment, even though they were more common in the C19G after adjustment.

ATC C: Drugs for the cardiovascular system
Among drugs for cardiovascular system, Cardiac Therapy (ATC II: C01) showed the highest adjusted difference in prevalence between the C19G and GPG overall and by age group, and decreased with age  Table S3).
The other ATC II therapeutic group, which pertained to the cardiovascular system, did not show relevant differences in the overall adjusted prevalence between the C19G and GPG (Figure 1). Nevertheless, looking at values strati ed by age group, a higher RR (C19G/GPG) in people aged <60 years was noted. Focusing on people older than 80 years, differences disappeared or reversed, such as for Agents acting on the RAS (ATC II: C09) and Lipid-modifying Agents (ATC II: C10) (65.6% vs. 71.2% and 34.6 % vs. 42.7% in the C19G vs. GPG, respectively) ( Figure 2).
With regard to Antimycotics for Systemic Use (ATC IV: J02AC), greater sex differences in overall adjusted prevalence in the C19G was noted (male RR: 1.41) (Supplementary Tables S5). ATC M: Drugs for the musculoskeletal system Among Anti-in ammatory and Antirheumatic Drugs (ATC II: M01), no signi cant differences were observed in overall adjusted prevalence between the C10G and CPG (Figure 1). Focusing on the Chemical Subgroup (ATC IV), Acetic Acid Derivatives and Related Substances (M01AB; RR, 2.07) and Propionic Acid Derivatives (M01AE; RR, 1.75) showed a higher prevalence in those aged >40 years (Figure 3).

Discussion
The COVID-19 pandemic has wrought havoc on healthcare systems worldwide. A body of literature has been produced on the clinical aspects, possible treatments and risk factors of patients with COVID-19 [23][24][25][26] . Nevertheless, apart from a few studies, the epidemiology and pro le of drug use in patients with COVID-19 has not been studied. To our knowledge, this is the rst study dealing with this topic.
In general, from our results we can describe four pro les. The rst is an age range of 0-39 (median age, 27±9) years, male, half of patients with no exposure to any drug and a very low prevalence of polytherapy. The second is an age range of 40-59 (median age, 51±5) years, male, nearly half of patients taking 1-4 drugs and a low prevalence of polytherapy (<25%). The third is an age range of 60-79 (median age, 68±6) years, male, 90% of patients taking ≥1 drug and more than half of patients having polytherapy. The nal pro le is age >80 (median age 85±4) years, female, 94% of patients taking ≥1 drug, including 78% taking polytherapy.
Analyses of drug-utilisation pro les highlighted differences between the C19G and GPG in terms of prevalence of drug exposure. Drug categories showing a variation of ≥30% were Antithrombotic Agents (B01), Antiepileptics (N03), Anti-hyperuricemics/Anti-gout (M04) and cardiac therapy (C01). The higher prevalence of use of drug category C01, B01 and M04 is a proxy of a more frequent pattern of cardiovascular and metabolic comorbidity in COVID-19 populations, as reported from other studies 4,5,8 . It is of some relevance that B01 drugs showed the highest difference in drug exposure between COVID -19 and General population. This therapeutic pro le can be a proxy for cardiovascular complications (including venous thromboembolism), supporting the hypothesis of an increased risk associated with COVID -19 infection in these patients 8 .
With regard to greater exposure to drugs in the M04 category, a retrospective cohort study on 131,565 patients and 252,763 controls, using data from the UK Clinical Practice Research Datalink, reported an increased risk of pneumonia (hazard ratio, 1.27; 95%CI 1.18-1.36) in patients with gout 27 .
There is no clear association between epilepsy and the risk of developing COVID-19. Nevertheless, epilepsy may be associated with other comorbidities, or as part of congenital/inherited syndromes that may affect the immune system. Also, antiepileptic agents can be used in association with other medications that can in uence the immune system (e.g., adrenocorticotropic hormone, corticosteroids, everolimus, immunotherapy), and this may increase the infection risk 28 . Moreover, these patients may require frequent clinical evaluation, which may explain (at least in part) greater exposure to potential healthcare infections.
Notably, the adjusted prevalence of Drugs Acting on the RAS (C09) did not show differences between the C19G and GPG (RR, 1.02; 95%CI, 1.01-1.02). This result is in accordance with evidence from a retrospective study undertaken on a COVID-19 cohort in Italy 29 , and supports the position of the European Society of Cardiology 30 . Furthermore, no major differences were noted for any category of antihypertensive drugs.
Strati cation by age showed a higher prevalence of exposure to drugs of category B01, B03, C09 and C10 in people aged <40 years. This evidence should be interpreted with caution because the number of such patients was very small. Nevertheless, a similar morbidity pattern to that for older patients could be hypothesised for these patients. Conversely, in patients aged >60 years, there was no signi cant difference in use of drugs for cardiometabolic diseases compared with that in the CPG, but the prevalence of use of drugs for respiratory disease and drugs for neurological disease increased in the C19G.
A high number of males took Analgesics (N02) and drugs for Cardiac Therapy (C01). A high number of females took Anti-anemia Agents (B03) and Anti-epileptic Agents (N03). Early descriptions of COVID-19 suggested a male preponderance for this disease 23,24,31 . Sex-based immunological, genetic, or lifestyle differences (e.g., tobacco smoking) have been postulated for the male preponderance for COVID-19 32 . In a population of 507 patients with COVID-19 reported between 13 January and 31 January 2020 (including 364 from mainland China), 281 patients were male (55%) and the median age was 46 (IQR, 35-60) years 33 . Zhou and colleagues described 191 COVID-19 patients from Wuhan (Hubei Province, China) during the rst month of the outbreak. That cohort had a median age of 56 (IQR, 46.0-67.0) years, with 62% of men and 48% of patients with comorbidities 23 . Also, data from Italy have shown a higher prevalence of males vs. females with COVID-19 34,35 . However, sex-and age-disaggregated data revealed the opposite to be true for women aged >80 years in Campania. National data for Italy reveals that, in those aged 20-29 years, 56.5% of diagnosed cases are female, and only after the age of 50 years does the male preponderance for COVID-19 increase. Thus, caution should be employed regarding the male preponderance for COVID-19 because sex-disaggregated data are incomplete, and evidence that is more robust is needed.
Our study was not designed to de ne the association between drug use, comorbidities risk of adverse outcome and outcome in COVID-19 patients. The association between use of certain drugs and susceptibility to SARS-CoV-2 infection (e.g., predictive factors for poor outcome) must be studied using a large cohort, a control group and robust clinical data. This was a retrospective study of health records. More detailed patient information (mainly regarding clinical outcomes) was not available at the time of analyses. Despite these limitations, we delineated the drug, epidemiological and demographic characteristics of 1,532 Italian patients with COVID-19. This information delineates the rst picture of the association between drug utilization and Covid-19 risk, giving us a solid background for further analysis and interpretation using upcoming data.

Conclusions
In conclusion, the current data provide a picture of baseline complexity of patients affected by COVID- 19 showing frequencies and differences of drug utilization pro les in COVID-19 patients compared with the general population. The higher prevalence of C01, B01 and M04 is probably linked to related comorbidities (i.e. cardiovascular, metabolic). Nevertheless, prevalence of drugs acting on RAS, such as other antihypertensive drugs, didn't show higher prevalence among COVID-19 patients than observed in the general population. Since these pilot data derived from the rst month of documented COVID-19 cases in Campania Region (Southern Italy), our results highlight the need for further case-control studies to de ne the effect of medications and comorbidities on susceptibility to, and associated mortality from, COVID-19 infection. Finally, to better understand the global epidemiology of COVID-19, reproducible and comparable results are needed from cohorts of multiple countries and multiple regions for further investigation and metanalysis.

Declarations
Funding Figure 1 Differences in prevalence of drug use between the C19G and GPG according to Therapeutic Group (ATC II).

Figure 2
Prevalence of drug use between the C19G and GPG strati ed by age group.