Baseline drug treatments and long-term outcomes in COVID- 19-hospitalized patients: results of the 2020 AUTCOV study

Baseline drug treatments and long-term outcomes in COVID- 19-hospitalized patients: results of the 2020 AUTCOV study | Research Square window.SnipcartSettings = { analytics: { enabled: false } }; (function() { var accessVector = localStorage.getItem('access_vector') || ''; window.dataLayer = window.dataLayer || []; if (accessVector) { window.dataLayer.push({ user: { profile: { profileInfo: { snid: accessVector } } } }); } })(); (function(w,d,s,l,i){w[l]=w[l]||[];w[l].push({'gtm.start':new Date().getTime(),event:'gtm.js'});var f=d.getElementsByTagName(s)[0],j=d.createElement(s),dl=l!='dataLayer'?'&l='+l:'';j.async=true;j.src='https://www.googletagmanager.com/gtm.js?id='+i+dl;f.parentNode.insertBefore(j,f);})(window,document,'script','dataLayer','GTM-K279D39R'); Browse Preprints In Review Journals COVID-19 Preprints AJE Video Bytes Research Tools Research Promotion AJE Professional Editing AJE Rubriq About Preprint Platform In Review Editorial Policies Our Team Advisory Board Help Center Sign In Submit a Preprint Cite Share Download PDF Article Baseline drug treatments and long-term outcomes in COVID- 19-hospitalized patients: results of the 2020 AUTCOV study Alexandra Christine Graf, Berthold Reichardt, Christine Wagenlechner, and 8 more This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-4872684/v1 This work is licensed under a CC BY 4.0 License Status: Posted Version 1 posted You are reading this latest preprint version Abstract Limited data are available on long-term morbidity and mortality after COVID-19 hospitalization. In this population-based study, we investigated the long-term mortality and morbidity after COVID-19 hospitalization and associations with baseline drug treatments. Data were provided on hospitalized COVID-19 patients in 2020 and matched controls by the Austrian Health Insurance Funds. The primary outcome was all-cause mortality. Secondary outcomes were all-cause mortality conditional on COVID-hospital survival and re-hospitalization due to any reason. The median follow-up was 600 days. 22 571 patients aged > 18 years were hospitalized in Austria in 2020 due to COVID-19. The risk of all-cause mortality was significantly higher with polypharmacy. With the exception of the youngest age group (19–40 years), antiepileptics, antipsychotics and the medicament group of iron supplements, erythropoietic stimulating agents, Vitamin B12, and folic acid were significantly associated with a higher risk of death (all p < 0,001). For Non-steroidal anti-inflammatory drugs and other anti-inflammatory drugs, significantly increased survival was observed (all p < 0,001). Patients had a higher drug prescription load than the control population. Long-term mortality and the risk of re-hospitalization due to any reason were also significantly greater in the patients. Antipsychotics are assumed to be an underrecognized medication group linked to worse outcomes after COVID-19 hospitalization. Covid-19 hospitalization all-cause mortality polypharmacy baseline drug treatments readmission population-based observational study Figures Figure 1 Figure 2 Figure 3 Figure 4 Introduction The outbreak of Coronavirus Disease 2019 (COVID-19) marked the start of a global pandemic and has been associated with substantial morbidity and mortality. Age and gender are well-established risk factors for severe outcomes. 1–5 Numerous reports have discussed that the presence of comorbidities may increase the risk of COVID-19-related death. 1,6–9 In addition, an increased risk of readmission and mortality after hospital discharge has been observed up to 1 year after COVID-19 hospitalization. 10–13 Patients who require hospitalization for COVID-19 have a greater comorbidity burden and are expected to have worse short-term outcomes. 12 Differences in the prevalence of drug use and polypharmacy regimens were observed when compared to the general population. 14 Studies have investigated possible associations of polypharmacy with increased morbidity and mortality among patients with COVID-19. 15,16 Visser et al. 17 investigated the impact of polypharmacy on COVID-19-related mortality in nursing home residents and found a significant positive association between the total number of medications and 30-day COVID-related adjusted mortality. However, published studies focusing on the drug profiles of hospitalized COVID-19 patients and long-term outcomes are still scarce. Here, we present data on COVID-19-hospitalized patients and a matched control group provided by the Austrian Health Insurance Funds with a median follow-up of 600 days (maximum 880 days). The primary aim of this study was to analyze the long-term follow-up of patients hospitalized due to COVID-19 in Austria in 2020 in order to evaluate the association between prescribed medications and mortality or readmission after COVID-19 hospitalization. We also compared the characteristics and outcomes of the hospitalized patients with an age-, sex-, and region-matched control group, presenting a real-world picture. Methods Study Design and Cohorts This retrospective, national population-based study complied with the Declaration of Helsinki and was approved by the ethics committee of Lower Austria (GS1-EK-4/747–2021). The data on both the patient and control cohorts were available from the Austrian Health Insurance Funds. Approximately 98% of the Austrian population is registered in the public health insurance system. Health care in Austria is a national system with good access to care, is regulated by the social insurance law and mainly financed by social insurance contributions. Patients > 18 years of age hospitalized in Austria due to the main diagnosis COVID-19 (ICD-10 Codes U071, U072, U049) from 1 January 2020 to 31 December 2020 were included in this study. For all patients age, sex, region, and medication were obtained from 1 year before hospitalization until study cut-off. An age-, sex-, and region-matched control group (approximately 10 controls for each patient) consisted of individuals not hospitalized due to COVID-19 in the year 2020 were randomly chosen from the population registered in the Austrian Health Insurance Fund, representing the Austrian population. Data on the control group were available from 1 year before the first patient was hospitalized until study cut-off. The data set includes 22 571 patients and 217 295 controls. Study Outcomes The primary outcome was all-cause mortality. The secondary outcome was hospitalization due to any reason. Hospitalization was defined based on billing information (MEL codes). For patients, we used the first readmission after the index COVID-19 hospital stay. For controls, hospitalization was defined as the first hospital admission after the index COVID hospital stay of the matched patient (Table S1 ). For controls, time to death was evaluated from the index COVID-hospital admission date of the matched patient. Statistical Analysis For each patient, age, sex, region, and Anatomical Therapeutic Chemical Classification-Codes (ATC) describing prescribed medications were (for details on the statistical analyses plan see supplement). All analyses were performed separately for four age subgroups: 19–40 years, 41–64 years, 65–74 years, ≥ 75 years (Table S2). ATC codes before hospitalization were summarized in medication groups (Table S3). A binary variable was defined for each medication group, which was set to 1 if a drug of the corresponding ICD10-codes was prescribed at least once 1 year before the index COVID-19 hospitalization and 0 if no drug was prescribed. Twenty medication groups were used for statistical modeling (MG1 to MG20, see Table S3). For controls, a similar medication profile was generated using the drugs prescribed 1 year before the index COVID-hospital stay of the matched patient. These 20 medication groups were also used to define polypharmacy. Numbers and percentages were used to summarize categorical variables, medians and interquartile ranges for continuous variables. To evaluate the association between polypharmacy and all-cause death, a simple Cox regression model was calculated accounting for sex, age, half-year, and polypharmacy with clustering variable region. Polypharmacy was defined as the number of medication groups in which a patient received prescribed medication, categorized into four groups (0–1, 2–5, 6–10, > 11). To evaluate the association between polypharmacy and re-hospitalization due to any reason, competing risk models (with competing risk death) were calculated accounting for sex, age, half-year, and polypharmacy with clustering variable region. Furthermore, the association between several medication groups and all-cause death, a Cox regression model was calculated accounting for sex, age, half-year, polypharmacy, and the 20 medication groups with clustering variable region. For re-hospitalization, a competing risk model was calculated using the same co-variables as described in the model for all-cause death. Only 20 of the 32 medication groups were evaluated for the statistical models due to the underrepresentation. As patients hospitalized due to COVID-19 had potentially more serious co-morbidities compared the Austrian population, we attempted to account for this imbalance using propensity score matching for age, sex, region, and medication groups MG1 to MG20. To evaluate the difference between patients and controls in the risk of all-cause death, a Cox regression model was calculated accounting for group, sex, age, half-year, polypharmacy, and the 20 medication groups with clustering variable region. For hospitalization due to any reason, a competing risk model was calculated using the same co-variables as described in the model for all-cause death. Schönfeld residuals were used to evaluate the proportional hazard assumption and variance inflation factors to evaluate multicollinearity. For all models, hazard ratios (HR) and confidence intervals (CI) are provided. Due to the large sample size and large number of investigated co-variables (i.e., 25), p < 0,002 (= 0,05/25 applying conservative Bonferroni correction) was considered significant. All analyses were performed using R, release 4.2.2. 18 Results Characteristics of the Patients and Controls The study population included 22 571 COVID-19-hospitalized patients (Table 1 , Fig. 1 A). The median follow-up time varied from 594 to 615 days over the four age groups. The age-, sex-, and region-matched control group, representing the general Austrian population, included 217 295 controls (Table 1 ). Table 1 Demographic data for COVID-19-hospitalized patients in 2020 in Austria and corresponding age-, sex-, and region-matched controls All patients Age group 19–40 41–64 65–74 ≥ 75 n = 1201 n = 6018 n = 4502 n = 10 850 n % n % n % n % Sex Male 665 55,37 3 769 62,63 2 644 58,73 4 994 46,03 Female 536 44,63 2 249 37,37 1 858 41,27 5 856 53,97 Polypharmacy 0 364 30,31 906 15,05 253 5,62 473 4,36 0 or 1 686 57,12 1 929 32,05 619 13,75 1 129 10,41 2 to 5 454 37,80 2 991 49,70 2 261 50,22 5 106 47,06 6 to 10 57 4,75 992 16,48 1 400 31,10 4 179 38,52 ≥ 11 4 0,33 106 1,76 222 4,93 436 4,02 Follow-up time in days Median (IQR) 615 (590–673) 599 (582–637) 597 (580–615) 594 (577–611) Time to death in days Median (IQR) 47 (11–87) 29 (12–111) 23 (9–131) 16 (7–84) Patients surviving hospital stay Age group 19–40 41–64 65–74 ≥ 75 n = 1192 n = 5727 n = 3854 n = 7674 n % n % n % n % Sex Male 658 55,20 3575 62,42 2 177 56,49 3 318 43,24 Female 534 44,80 2152 37,58 1 677 43,51 4 356 56,76 Polypharmacy 0 362 30,37 822 15,40 227 5,89 344 4,48 0 or 1 682 57,21 1877 32,77 561 14,56 839 10,93 2 to 5 452 37,92 2858 49,90 1 985 51,50 3 677 47,92 6 to 10 54 4,53 903 15,77 1 130 29,32 2 874 37,45 ≥ 11 4 0,34 89 1,55 178 4,62 284 3,70 Length of hospital stay Median (IQR) 5 ( 3 – 9 ) 8 ( 5 – 13 ) 11 ( 6 – 18 ) 13 ( 8 – 21 ) Follow-up time in days Median (IQR) 615 (590–673) 599 (582–637) 597 (580–615) 594 (577–611) Time to death in days Median (IQR) 57,5 (47–278) 209 (56–377) 211 (74–389) 160 (49–371) Controls Age group 19–40 41–64 65–74 ≥ 75 n = 11 958 n = 59 057 n = 43 511 n = 102 769 n % n % n % n % Sex Male 6 616 55,32 36 870 62,43 25 353 58,27 47 038 45,77 Female 5 342 44,67 22 187 37,56 18 158 41,73 55 731 54,22 Polypharmacy 0 9 206 76,99 40 983 69,40 24 535 56,39 42 833 41,68 0 or 1 10 773 90,09 47 098 79,75 27 877 64,07 48 803 47,49 2 to 5 1139 9,53 10 153 17,19 11 322 26,02 32 629 31,75 6 to 10 45 0,38 1650 2,79 3878 8,91 19 285 18,77 ≥ 11 1 0,01 156 0,26 433 1,00 2 048 1,99 Follow-up time in days Median (IQR) 614 (590–673) 599 (582–633) 596 (578–613) 591 (574–608) Time to death in days Median (IQR) 158 (62–441) 130 (53–263) 135 (54–297) 138 (47–314) Polypharmacy of more than six medication groups was observed more often in the older age groups (65–74 years and ≥ 75 years). In the older age groups, 5,62% (65–75 years) and 4,36% (≥ 75 years) of patients and 56,39% (65–75 years) and 41,68% (≥ 75 years) of controls did not receive any drug out of the investigated medication groups (Table 1 ). Patients hospitalized due to COVID-19 are expected to have more comorbidities and, therefore, more medications than the general population. This was also observed in our Austrian population. Patients received more drugs in all investigated medication groups (Table 2 , Tables S4 to S7) across all age groups compared to controls. Table 2 Percentages of COVID-hospitalized patients and controls receiving at least one medication in the investigated medication group and corresponding 1-year mortality by age group. Age group 19–40 41–64 65–74 ≥ 75 Cohort Covid Control Covid Control Covid Control Covid Control Yes 1-year mort Yes 1-year mort Yes 1-year mort Yes 1-year mort Yes 1-year mort Yes 1-year mort Yes 1-year mort Yes 1-year mort MG1: Anticoagulants 6,08 4,11 1,42 1,76 18,79 13,53 4,61 4,45 40,18 26,81 13,05 8,63 56,97 46,59 29,03 23,47 MG2: Antibiotics, antivirals, antiprotozoals, or anthelmintics 46,88 1,60 15,74 0,16 48,31 8,81 15,84 2,02 47,71 22,72 20,19 6,06 46,59 46,96 28,03 22,15 MG3: Insulin and other antidiabetic s 3,08 5,41 0,33 0,00 14,69 12,33 2,52 3,36 26,45 26,78 7,29 7,79 21,49 44,13 9,31 23,41 MG4: Heart drugs 1,75 0,00 0,43 1,96 4,54 12,09 1,24 3,67 10,17 26,42 3,93 8,48 16,50 48,27 10,05 23,16 MG5: Antihypertensives, incl. diuretics and renin-angiotensin-aldosterone system inhibitors 6,33 3,95 0,69 3,66 33,57 11,14 8,66 2,81 60,60 22,84 23,27 5,67 68,89 43,18 39,36 19,62 MG6: Beta-blockers 3,16 5,26 0,27 6,25 14,36 14,24 3,33 4,67 31,05 26,11 10,54 7,63 35,80 45,19 19,52 20,67 MG7: Statins, fibrates, incl. proprotein convertase subtilisin/kexin type 9 inhibitors and inclisiran 2,25 7,41 0,33 0,00 23,30 9,20 6,57 1,83 42,78 21,7 18,28 4,93 38,55 39,16 24,30 14,98 MG8: Immunosuppressants and immunomodulators 3,00 2,78 0,32 0,00 3,86 9,91 0,70 2,68 4,64 24,40 1,11 5,57 2,15 40,34 1,02 15,39 MG9: Systemic steroids 6,99 4,76 1,16 0,00 12,35 10,90 3,38 4,41 16,79 28,31 6,19 9,17 14,27 43,8 8,37 18,68 MG10: Chemotherapy 0,67 0,00 0,04 20,00 1,83 30,91 0,41 17,77 3,55 41,88 1,34 20,41 3,96 49,53 2,39 28,83 MG11: Iron supplements, erythropoietic stimulating agents, vitamin B12, folic acid 6,16 2,70 1,05 0,00 5,28 22,96 0,91 10,02 9,15 34,95 1,97 17,04 13,00 54,85 5,37 34,64 MG12: Antacids, incl. antihistamines 10,82 3,08 1,75 1,44 20,41 11,89 4,03 4,58 33,05 26,55 9,07 8,09 35,91 46,79 16,07 24,43 MG13: Vitamin D and other vitamin supplements 8,74 0,95 1,16 2,16 12,94 13,74 3,05 4,28 19,90 27,12 6,70 7,34 24,33 45,95 12,89 22,03 Caplacizumab 0,08 0,00 0,00 NA 0,00 NA 0,00 NA 0,00 NA 0,00 NA 0,00 NA 0,00 NA Systemic hemostatics 0,00 NA 0,02 0,00 0,03 50,00 0,00 0,00 0,04 50,00 0,00 0,00 0,04 50,00 0,02 15,79 Hereditary angioedema therapeutics 0,00 NA 0,00 NA 0,00 NA 0,00 NA 0,00 NA 0,00 NA 0,00 NA 0,00 NA Peripheral vasodilators 0,17 0,00 0,05 0,00 0,55 6,06 0,23 0,00 1,13 27,45 0,47 4,43 1,3 38,30 1,10 20,42 Hormonal contraceptives and similar hormone preparations 4,58 0,00 1,31 0,00 3,42 6,80 2,03 0,58 3,13 12,06 2,82 1,88 2,41 30,92 2,81 10,04 Immunoglobulins 0,17 0,00 0,00 NA 0,07 0,00 0,01 33,33 0,09 25,00 0,01 0,00 0,07 100 0,01 20,00 Interferons and CSF 1,42 5,88 0,41 2,04 2,41 28,97 0,54 14,47 2,89 37,69 0,91 22,86 1,18 49,22 0,84 23,78 MG14: NSAID and other anti-inflammatory drugs 26,31 1,58 7,31 0,11 36,54 6,96 13,2 1,72 34,5 17,19 18,06 4,15 24,80 35,01 20,18 13,81 MG15: Gout medications 0,67 12,50 0,04 0,00 2,92 15,91 0,63 3,49 6,86 34,95 1,72 10,03 8,39 49,89 3,74 26,46 MG16: Antiepileptics 6,16 6,76 0,71 1,18 9,29 17,35 1,92 7,04 14,5 34,30 3,58 12,07 15,97 47,89 6,89 23,83 MG17: Antipsychotics 14,32 3,49 3,41 1,47 24,16 12,59 6,92 3,40 36,98 27,63 12,63 7,74 55,58 48,89 27,56 25,11 Rhinological and throat antiseptics 10,49 1,59 3,13 0,27 10,00 5,48 2,92 1,80 7,89 21,97 3,42 3,29 3,95 34,97 3,40 13,93 MG18: Inhaled anti-obstructive drugs 10,49 2,38 2,08 0,40 17,98 9,70 4,22 3,41 25,43 26,38 7,38 8,66 19,44 45,61 9,49 22,23 MG19: Inhaled steroids 2,75 0,00 0,62 0,00 3,62 7,34 0,98 3,63 4,40 21,21 1,50 5,97 2,56 41,37 1,60 16,87 Other COPD drugs 0,92 0,00 0,26 0,00 1,96 5,93 0,33 3,63 1,84 22,89 0,41 7,22 0,88 38,95 0,39 16,04 Cold and Cough preparations 7,24 2,30 0,93 0,90 9,27 11,47 1,43 5,34 13,24 26,01 3,06 9,30 12,65 44,97 5,62 26,93 MG20: Systemic Antihistamines 5,5 3,03 1,25 0,00 6,03 10,47 1,50 3,05 6,60 22,56 2,34 5,88 6,92 46,07 3,77 23,37 H2 Blocker 0,58 0,00 0,25 0,00 1,3 12,82 0,46 1,11 1,60 22,22 0,84 7,69 1,12 38,84 1,09 17,90 Medication groups (MG1 to MG20) used for further statistical analyses are marked in bold. Detailed numbers are shown in Supplementary Tables S4 – S7. Note that NA “not available” means that the calculation was not possible because no patient/control received a medication of this group. Interestingly, we observed that 24,2% of patients aged 41–64 years, 37% of patients aged 65–74 years, and 55,6% of patients aged ≥ 75 years received antipsychotics before COVID hospitalization. In the control group, the rate of prescribed antipsychotics was 6,9% for 41–64 years, 12,6% for 65–74 years, and 27,6% for ≥ 75 years. All-cause mortality for COVID-patients In the 19–40 years age group, 1,05% of males and 0,37% of females dies in hospital. One-year mortality rates in this age group were 1,8% in males and 0,93% in females. For older age groups, higher mortality rates were observed. In the 41–64 years age group, 5,15% of males and 4,31% of females died during the hospital stay. One-year mortality was 7,91% for males and 6.49% for females. In the 65–74 years age group, 17,66% of males and 9,97% of females died during the hospital stay. Within 1 year after hospital admission, 25,38% of males and 14,96% of females died. Furthermore, 33,56% of males and 25,61% of females aged ≥ 75 years died during the hospital stay. One-year mortality rates in this age group were 46,2% for males and 39,94% for females (Table S8). In all age groups, we found a trend of a larger risk of death for men compared to women, which was significant in the older age groups (age > 65 years) after multiplicity correction (Table S10). The number of hospital admissions per day and the cumulative number of deaths are shown in Fig. 1 A. One-year mortality rates are presented in Fig. 1 B. Simple Cox-regression models evaluated a significant higher risk of all-cause death for patients with a larger number of prescribed medication groups compared to patients receiving drugs from none of the medication groups or only one medication group. No significant relationship was observed for patients aged 19–41 years (Fig. 2 A-D and Figure S1 ). Due to the large number of medication groups and their potential interactions, polypharmacy is just one factor in a complex system and may not completely explain the associations of prescribed medications with all-cause death after COVID-19. Therefore, we included all 20 medication groups in the statistical Cox regression models to evaluate the effects of individual medication groups. Due to the small number of events in the youngest age group (19–40 years), not all 20 medication groups could be included in the model for this age group. Figure 3 summarizes the results of the Cox regression model of all-cause mortality when including the twenty medication groups (Table S11. In the youngest group (19–40 years), only prescribed vitamin D and other vitamin supplements (p < 0,001) and systemic antihistamines (p = 0,002) were significantly associated with survival. For patients receiving vitamin supplements before the COVID-19 hospital stay, we observed a significantly lower risk of death. In addition, systemic antihistamines were significantly associated with poor survival. The other medication groups did not show significant results. However, due to the small number of events in this age group, the results should be interpreted with caution. For several medication groups, the results were different between the age groups. Anticoagulants (p < 0,001 for 41–64 years and ≥ 75 years); Antibiotics, antivirals, antiprotozoals, or anthelmintics (p < 0,001 for ≥ 75 years); Insulin and other antidiabetics (p < 0,001 for age groups ≥ 65 years); Heart drugs (p = 0,001 for ≥ 75 years); Beta-blockers (p < 0,001 for ≥ 75 years); Systemic steroids (p < 0,001 for 65–74 years); Chemotherapy (p < 0,001 for 41–74 years); Antacids (p < 0,001 for ≥ 75 years); Vitamin D or other vitamin supplements (p = 0,001 for 64–74 years); and Inhaled anti-obstructive drugs (p 64 years old. In the three age groups > 40 years a significant association of antiepileptics (all p < 0,001), antipsychotics (all p < 0,001), and the group Iron supplements, erythropoietic stimulating agents (ESA), vitamin B12 (B12), and folic acid (FA) (all p 40: p < 0,001). Figure 4 shows the Kaplan-Meier curves for important prescribed medication groups associated with poor survival while Figure S6 shows the Kaplan-Meier curves of medication groups associated with poor survival in the control cohort. In the models including all investigated medication groups, the factor polypharmacy did not remain significant, indicating that polypharmacy alone may not be a predictor of all-cause death, and specific medication groups may be more important factors for outcomes in COVID-19-hospitalized patients. All-cause death after COVID-hospital survival We also evaluated all-cause death for the subgroup of patients who survived the COVID-19 hospital stay. Among these patients, 0.76% of males and 0.56% of females aged 19–40 years died within the first year after hospital discharge. The 1-year mortality rates were 2.91% and 2.28% for males and females aged 41–64 years, 9.37% and 5.78% for males and females aged 65–74 years, and 19.02% and 19.26% for males and females aged ≥ 75 years, respectively (Table S9). In the age groups > 40 years, we found significant associations with a higher risk of death after surviving the COVID hospitalization for chemotherapy (all p < 0,001) and the medicament group Iron supplements, erythropoietic stimulating agents, B12, and FA (all p < 0,001). For antiepileptics, a significantly higher risk of all-cause death was found in the 41–64 and 65–74 years age groups, with an observed trend in the oldest age group (p = 0,016). Again, antipsychotics were significantly associated with poor survival in all age groups > 40 years (all p 40 years (p 65 years (both p < 0,001). Detailed results are shown in Table S12 and Figure S2. Re-hospitalization due to any reason after COVID-hospital survival For the subgroup of patients who survived the COVID hospital stay, we evaluated the secondary outcome “hospitalization due to any cause.” In the age groups > 40 years, a significantly higher risk of readmission with anticoagulants (all p < 0,001), antiepileptics (all p < 0,001), systemic steroids (all p < 0,001), and chemotherapy (all p < 0,008) was observed. We found a trend of a higher risk for re-hospitalization for patients receiving antipsychotics (p < 0,003 for 19–64 years and ≥ 75 years, p = 0,006 for 65–74 years). Detailed results are shown in Table S13 and Figure S3. Comparison of COVID-Patients to Controls The age-, sex-, and region-matched control population received less medication than the patient population prior to the hospital stay. Interestingly, in controls without medication, remarkably good survival was observed even in the older age groups (Figure S4, S5), whereas a steep decrease in survival after hospital admission was observed in the COVID patients (Figure S1 ). As a sensitivity analysis, we evaluated the difference between COVID-19-hospitalized patients and the Austrian control population concerning all-cause death and hospitalization due to any reasons. As patients had potentially more severe co-morbidities and received more medication, we attempted to account for this imbalance using propensity score matching (PSM) with the 20 medication groups in addition to the age, sex, and region. Due to too small a number of events, this analysis could not be performed for all-cause death in the 19–40 years age group. For all other subgroups, a significant difference in the risk of all-cause death was found between COVID-19-hospitalized patients and PSM-controls (all p < 0,001). For the subgroup of patients surviving the COVID-hospital stay, reduced hazard ratios were observed for the comparison to the PSM-controls. The difference between patients and controls remained significant in the age groups 41–64 years (p < 0,001) and 65–74 years (p < 0,001), but not in the oldest age group (p = 0,078). Kaplan-Meier curves for propensity score-matched COVID patients and controls are shown in Figure S7. Concerning hospitalization due to any reason, in all four age groups, we observed a significantly greater probability of re-hospitalization among the COVID-19-hospitalized patients compared to controls (all p < 0,001). These results may indicate that polypharmacy may not completely explain the worse effect in patients with severe COVID-19 (Table S14, Figure S3). Discussion In this retrospective study, we evaluated whether baseline medication profiles may be associated with survival or hospitalization due to any reason after COVID-19-related hospitalization in an Austrian population. Hospitalized COVID-19 patients had a higher drug prescription load prior to COVID-hospitalization and increased long-term mortality, especially in patients > 75 years old. Pre-COVID prescription of antipsychotic drugs, antiepileptic drugs, chemotherapy, iron/FA/ B12, beta-blockers, and anticoagulants was significantly associated with increased mortality, whereas patients who were prescribed NSAIDs and other anti-inflammatory drugs prior to COVID-19 hospitalization had a significantly lower risk of all-cause death. Due to our study design, we were able to present the “real life” prescription and mortality rate of patients hospitalized with the diagnosis of COVID-19 in Austria, as well as in a matched control population that was followed from 2020 for up to a maximum of 880 days. Polypharmacy frequently occurs in patients with COVID-19 15 and may be associated with increased morbidity and mortality. 15,16 Analyzing the prescription data in the control population, we detected no drug prescription in the investigated medication groups in 77% (19–40 years), 69% (41–64 years), 56% (65–74 years), and 41% (≥ 75 years) of controls. In contrast, 30% (19–40 years), 15% (41–64 years), 6% (65–74 years), and 4% (≥ 75 years) of COVID-19-hospitalized patients had no drug prescription. We found that the intake of antipsychotic drugs was associated with a significant increased risk of death. The estimated mortality for patients with prescribed antipsychotic drugs was 56.3% (CI: 54.7–57.9) within a two-years follow up in the age group ≥ 75 years as compared to 40.83% (CI: 42.5–39.1) for patients without antipsychotic drugs. The mortality rate for controls with prescribed antipsychotic drugs was 32.9% (CI: 32.1–33.7) as compared to patients not receiving these drugs (11.4%, CI: 11.7–11.1) within two years follow-up for patients ≥ 75 years. The association of anti-inflammatory medication, such as NSAIDs, with lower mortality could be attributed to the pathophysiology of COVID-19, with a high pro-inflammatory state in the second phase of the disease, also referred to as the cytokine storm phase, 19,20 which could be attenuated by anti-inflammatory medication. In patients with high pro-inflammatory state in need of oxygen therapy and SARS-CoV-2-associated lung infiltration, anti-inflammatory therapy (e.g., dexamethasone) was the only pharmacological intervention to reduce mortality. 21 However, we cannot exclude the possibility of bias by indication with the selection of possibly healthier patients using NSAIDS on a regular basis because more comorbid patients (e.g., with chronic kidney disease, diabetes, cardiovascular disease, or heart failure) are regularly advised to avoid NSAIDs. The pronounced association of antipsychotic medication with higher mortality is assumed to be related to the comorbidities in a population that has high prescription rates of such medication, especially at higher ages. Antipsychotic drugs are associated with severe COVID-19 morbidity and mortality. 16 Antipsychotic medication is mainly prescribed to patients in resident or nursing homes with dementia or behavioral disorders. 22,23 The number of antipsychotic prescriptions is higher in nursing homes (57,1%) compared to residential homes (29,5%), 24 emphasizing more frequent prescriptions in a more morbid population. Therefore, chronic psychotic medication use at more advanced ages is most probably indicative of patients with severe impairments. 25–27 Among nursing home residents, a significant positive association was found between the total number of medications and 30-day COVID-related adjusted mortality. 17 After additional correction for dementia and use of Proton pump inhibitors (PPI), vitamin D, antipsychotics, and antithrombotics, this effect was no longer significant, suggesting that polypharmacy itself may not be the problem, but the type of medication. In our analyses, polypharmacy did not remain significant after correcting for several medication groups. In patients who were discharged alive from a COVID-19-related hospitalization, the risk of post discharge death of patients > 64 of age within 180 days was nearly twice that observed in historical controls admitted to the hospital with influenza. 12 Although readmission after COVID-19-related hospitalization was common, the frequency by 180 days was similar to the frequency of patients discharged alive from influenza-related hospitalization. Furthermore, crude differences in drug use between COVID-19 patients and the general population were found in antithrombotic agents, antiepileptics, anti-gout preparations, and cardiac therapy. 14 Scant data are available on clinical outcomes in patients discharged alive from COVID-19 hospitalization. Data from a large study with patients discharged after COVID-19 reported an increased risk of readmission and mortality during a follow-up of 140 days. 10 In a German cohort of hospitalized COVID-19 patients, 30-day all-cause mortality was 23,9% and 180-day all-cause mortality 29,6%. Another study after COVID-19 hospitalization among patients in Italy reported an 8% age-related overall relative increase in all-cause death after 6 months of follow-up. 13 However, age was the only independent predictor of mortality after multivariate analysis. Another report from the US investigated the 12-month mortality after recovery from the initial episode of COVID-19 and reported a significantly higher 12-month adjusted all-cause mortality risk for patients with severe COVID-19 compared to both COVID-19-negative patients and mild COVID-19 patients. A large retrospective long-term outcome cohort study indicated an overall 2-year mortality risk that was worse by day 180 among those infected with COVID-19 compared to matched uninfected comparators, but there was no excess mortality during the subsequent 1,5 years. 28 In our study, we observed increased long-term mortality and increased risk of hospitalization due to any reason after surviving COVID-19 hospitalization. Mortality was more pronounced within the first 50 days after index-hospitalization. The main strength of this study is the use of a large, representative, real-world national database. The retrospective design, however, is a limitation, which we sought to mitigate by including several potential confounding factors in the statistical models and performing propensity score matching to support meaningful comparisons. Yet, as in any observational research, even with the large sample size and long-term follow-up, unmeasured confounding leading to bias is still possible. The study population was drawn exclusively from the Austrian Health Insurance Funds, raising potential concerns about the generalizability and external validity of the findings to a broader patient population. Furthermore, no information was available from the Austrian Insurance Fund on vaccination or intensive care in hospitals. However, vaccination was first available in the very end of 2020 and therefore, it may not be an important factor for patients hospitalized with COVID-19 in 2020. In conclusion, this large Austrian cohort of COVID-19-hospitalized patients and matched controls an increased short- and long-term risk of mortality was observed. Patients hospitalized with COVID-19 had a higher drug prescription load (polypharmacy). Antipsychotics were significantly associated with poor survival in patients > 40 years old. Our findings may help identify the most vulnerable patients at higher risk of mortality after COVID-19 discharge regardless of age by screening prescribed medication groups, with implications for preventive measures. Antipsychotics are assumed to be an underrecognized medication group linked to worse patient outcomes after COVID-19 hospitalization. Declarations Acknowledgements: We thank the anonymous referees and editors for their support. This work was financed by ARGE Ankersmit of the surgical research laboratory. Author Contributions Statement : H.J.A., B.R., R.W., and A.C.G. were responsible for conceptualization. P.K., B.R., A.C.G., C.W., J.M., and H.J.A. conceived the study and curated the data. C.W., P.K., and A.C.G. cleaned, analyzed, and verified the underlying data. H.J.A., A.C.G., R.W., C.W., and P.K. wrote the paper and visualized the data. A.C.G., B.R., C.W., P.K., D.T-W., M.M., J.M., C.A., J.A., R.W., and H.J.A. commented on the paper, oversaw the analysis, and edited the final manuscript. All authors contributed to drafting the paper and revised the manuscript for important intellectual content. Conflict of Interest: The authors declare no conflict of interest. Data availability Statement: Data that support the findings of this study are available upon request from the corresponding author. References Williamson EJ., Walker AJ., Bhaskaran K., Bacon S., Bate X., Morton CE., Curtis HJ., Mehrkar A., Evans D., Inglesby P., Cockburn J., McDonald H., MacKenna B., Tomlinson L., Douglas IJ., Rentsch CT., Mathur R., Wong AYS., Grieve R., Harrisonh D., Forbes H., Schultze A., Croker R., Parry J., Hester F., Harper S., Perera R., Evans SJW., Liam Smeeth, Goldacre B. (2020). Factors associated with COVID-19-related death using OpenSAFELY, Nature : 584: 430 – 436 Posch M., Bauer P., Posch A., Koenig F. (2020). Analysis of Austrian COVID-19 deaths by age and sex. Wiener Klinische Wochenschrift 132: 685-689. Bauer P., Brugger J., Koenig F., Posch M. (2021). An international comparison of age and sex dependency of COVID-19 deaths in 2020: a descriptive analysis. Scientific reports , 11: 19143. Kautzky-Willer A., Kaleta M., Lindner SD., Leutner M., Thurner S., Klimek P. (2022) Sex Differences in Clinical Characteristics and Outcomes of Patients with SARS-CoV-2 Infection Admitted to Intensive Care Units in Austria. JPM , (12): 4. Zajic P., Hiesmayr M., Bauer P., Baron DM., Gruber A., Joannidis M., Posch M., Metnitz PHG. (2023) Nationwide analysis of hospital admissions and outcomes of patients with SARS-CoV-2 infection in Austria in 2020 and 2021. Scientific Reports : 13: 8548 Sze S., Pan D., Nevill CR., Gray LJ., Martin CA., Nazareth J., Minhas JS., Divall P., Khunti K., Abrams KR., Nellums LB., Pareek M. (2020). Ethnicity and clinical outcomes in COVID-19: A systematic review and meta-analysis. Eclinical Medicine , 29-30: 100630. Zheng Z., Peng F., Xu B., Zhao J., Liu H., Peng J., Li, Q., Jiang C., Zhou Y., Liu S., Ye C., Zhang P., Xing Y., Guo H., Tang W. (2020) Risk factors of critical & mortal COVID-19 cases: A systematic literature review and meta-analysis. Journal of Infection , 81: e16-e25 Ssentongo P., Ssentongo AE., Heilbrunn ES., Ba DM., Chinchilli VM. (2020). Association of cardiovascular disease and 10 other pre-existing comorbidities with COVID-19 mortality: A systematic review and meta-analysis. Plos One , doi: 10.1371/journal.pone.0238215 Liu S., Cao Y., Du T., Zhi Y. (2020). Prevalence of Comorbid Asthma and Related Outcomes in COVID-19: A Systematic Review and Meta-Analysis. J Allerg Clin Immunol Pract : doi: 10.1016/j.jaip.2020.11.054 Ayoubkhani D., Khunti K., Nafilyan V., Maddox T, Humberstone B., Diamond I, Banerjee A (2021). Post-covid syndrome in individuals admitted to hospital with covid-19: a retrospective cohort study. British Journal of Medicine , 372: n693 Mainous III AG., Rooks BJ., Wu V., Orlando FA. (2021). COVID-19 Post-acute Sequelae among Adults: 12 Month Mortality Risk. Frontiers in Medicine . doi: 10.3389/fmed.2021.778434 Oseran AS., Song Y., Xu J., Dahareh IJ., Wadhera RK., Lemos JA., Das SR., Sun T., Yen RW., Kazi DS. (2023) Long term risk of death and readmission after hospital admission with covid-19 among older adults: retrospective cohort study. BMJ: 382: e076222 Renda G, Ricci F, Spinoni EG, et al. (2022) Predictors of Mortality and Cardiovascular Outcome at 6 Months after Hospitalization for COVID-19. J Clin Med ., 11 (3): 729. Orlando V., Coscioni E., Guarino I., Mucherino S., Perrella A., Trama U., Limongelli G., Menditto E (2021) Drug-utilisation profiles and COVID-19, Scientific Reports , 11: 8913 Ghasemi H, Darvishi N, Salari N, Hosseinian-Far A, Akbari H, Mohammadi M. (2022). Global prevalence of polypharmacy among the COVID-19 patients: a comprehensive systematic review and meta-analysis of observational studies. Trop Med Heal ., 50 (1): 60. Iloanusi S, Mgbere O, Essien EJ. (2021)Polypharmacy among COVID-19 patients: A systematic review. J Am Pharm Assoc ., 61 (5): e14-e25. Visser AGR, Winkens B, Schols JMGA, Janknegt R, Spaetgens B. (2023) The impact of polypharmacy on 30-day COVID-related mortality in nursing home residents: a multicenter retrospective cohort study. Eur Geriatr Med .,14 (1): 51-57. R Core Team (2021). R: A language and environment for statistical computing. R Foundation for Statistical Computing, Vienna, Austria. https://www.R-project.org Fajgenbaum DC, June CH. (2020) Cytokine Storm. N Engl J Med , 383 (23): 2255-2273. Wendt R, Lingitz MT, Laggner M, et al. (2021) Clinical Relevance of Elevated Soluble ST2, HSP27 and 20S Proteasome at Hospital Admission in Patients with COVID-19. Biology . 10 (11): 1186. Group RC, Horby P, Lim WS, et al. (2020). Dexamethasone in Hospitalized Patients with Covid-19. N Engl J Med ., 384 (8): 693-704. Ivers NM, Taljaard M, Giannakeas V, Reis C, Williams E, Bronskill S. (2019) Public reporting of antipsychotic prescribing in nursing homes: population-based interrupted time series analyses. BMJ Qual Saf ., 28 (2): 121. Rochon PA, Stukel TA, Bronskill SE, et al. (2007) Variation in Nursing Home Antipsychotic Prescribing Rates. Arch Intern Med ., 167 (7): 676-683. Chakraborty A, Linton CR. (2012). Antipsychotic prescribing in dementia patients in care homes: proactive in‐reach service improved quality of care. Int J Geriatr Psychiatry , 27 (10): 1097-1098. doi:10.1002/gps.2827 Lövheim H, Sandman PO, Kallin K, Karlsson S, Gustafson Y. (2006) Relationship between antipsychotic drug use and behavioral and psychological symptoms of dementia in old people with cognitive impairment living in geriatric care. Int Psychogeriatr ., 18 (4): 713-726. Gauthier S, Cummings J, Ballard C, et al. (2010). Management of behavioral problems in Alzheimer’s disease. Int Psychogeriatr , 22 (3): 346-372. Coon JT, Abbott R, Rogers M, et al. (2014). Interventions to Reduce Inappropriate Prescribing of Antipsychotic Medications in People With Dementia Resident in Care Homes: A Systematic Review. J Am Méd Dir Assoc , 15(10): 706-718. Iwashyna TJ, Seelye S, Berkowitz TS, et al. (2023) Late Mortality After COVID-19 Infection Among US Veterans vs Risk-Matched Comparators. JAMA Intern Med., 183 (10): 1111-1119. Additional Declarations No competing interests reported. Supplementary Files SuppMat070824.pdf Cite Share Download PDF Status: Posted Version 1 posted You are reading this latest preprint version Research Square lets you share your work early, gain feedback from the community, and start making changes to your manuscript prior to peer review in a journal. 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Also discoverable on Platform About Our Team In Review Editorial Policies Advisory Board Help Center Resources Author Services Accessibility API Access RSS feed Manage Cookie Preferences © Research Square 2026 | ISSN 2693-5015 (online) Privacy Policy Terms of Service Do Not Sell My Personal Information {"props":{"pageProps":{"initialData":{"identity":"rs-4872684","acceptedTermsAndConditions":true,"allowDirectSubmit":true,"archivedVersions":[],"articleType":"Article","associatedPublications":[],"authors":[{"id":347175678,"identity":"6ea38342-7a6e-4ce6-9cf8-10d3aad9cbbf","order_by":0,"name":"Alexandra Christine Graf","email":"","orcid":"","institution":"Medical University of Vienna","correspondingAuthor":false,"prefix":"","firstName":"Alexandra","middleName":"Christine","lastName":"Graf","suffix":""},{"id":347175679,"identity":"a9c5de9e-c61d-44c5-84f1-103821bf7fd7","order_by":1,"name":"Berthold Reichardt","email":"","orcid":"","institution":"Austrian Social Health Insurance 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07:21:52","currentVersionCode":1,"declarations":"","doi":"10.21203/rs.3.rs-4872684/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-4872684/v1","draftVersion":[],"editorialEvents":[],"editorialNote":"","failedWorkflow":false,"files":[{"id":64567883,"identity":"1bc94d12-e331-4712-9118-f10f7dd94943","added_by":"auto","created_at":"2024-09-16 00:37:38","extension":"png","order_by":1,"title":"Figure 1","display":"","copyAsset":false,"role":"figure","size":403459,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eMortality by age group.\u003c/strong\u003e (A) Timeline of hospitalization due to COVID-19 in 2020 (hospitalizations per day in blue) and timeline of death (cumulative number of deaths in red) of COVID-19 hospitalized patients. (B) 1-year mortality in age groups and by sex for COVID-19-hospitalized patients and controls. For details on mortality rates for patients and controls, as well as patients surviving the hospital stay with and without propensity score matching, see Tables S8 and S9.\u003c/p\u003e","description":"","filename":"floatimage1.png","url":"https://assets-eu.researchsquare.com/files/rs-4872684/v1/8901229fb21466d2ba4b449c.png"},{"id":64569087,"identity":"79f39562-b324-43e2-b880-f1b1cbc0a2e4","added_by":"auto","created_at":"2024-09-16 00:45:38","extension":"png","order_by":2,"title":"Figure 2","display":"","copyAsset":false,"role":"figure","size":539698,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eKaplan-Meier curves and corresponding 95% confidence intervals for polypharmacy groups within COVID-19-hospitalized patients by age group.\u003c/strong\u003e For details on hazard ratios, confidence intervals, and p-values, see Table S10. Curves are shown separately for patients with drugs in 0-1 medication groups (green), 2-5 medication groups (orange), 6-10 medication groups (blue), and \u0026gt;10 medication groups (magenta).\u003c/p\u003e","description":"","filename":"floatimage2.png","url":"https://assets-eu.researchsquare.com/files/rs-4872684/v1/b7196dcb1f120c120c3bc6a6.png"},{"id":64567887,"identity":"d83b79fb-cc74-4217-8085-826f0a9846b7","added_by":"auto","created_at":"2024-09-16 00:37:38","extension":"png","order_by":3,"title":"Figure 3","display":"","copyAsset":false,"role":"figure","size":589742,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eHazard ratios and 95% confidence intervals for medication groups for the outcome all-cause mortality in COVID-19-hospitalized patients.\u003c/strong\u003e For details, see Table S11. A hazard ratio \u0026gt;1 indicates a larger risk for all-cause death for patients receiving a drug in the corresponding medication group.\u003c/p\u003e","description":"","filename":"floatimage3.png","url":"https://assets-eu.researchsquare.com/files/rs-4872684/v1/f8912490081783d74e14f771.png"},{"id":64567885,"identity":"5f0fe2ad-60d9-4a3f-9486-fa0b3e254500","added_by":"auto","created_at":"2024-09-16 00:37:38","extension":"png","order_by":4,"title":"Figure 4","display":"","copyAsset":false,"role":"figure","size":355930,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eKaplan-Meier curves of medication groups associated with poor survival of COVID-19 hospitalized patients.\u003c/strong\u003e For details on hazard ratios, confidence interval, and p-values, see Table S11.\u003c/p\u003e","description":"","filename":"floatimage4.png","url":"https://assets-eu.researchsquare.com/files/rs-4872684/v1/319d03ee44d07889d352fb8e.png"},{"id":69225348,"identity":"cceeb543-318a-49d7-a3b0-77509c758409","added_by":"auto","created_at":"2024-11-18 07:55:12","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":3208870,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-4872684/v1/3eecef74-d835-4491-bfa7-418968d271cb.pdf"},{"id":64567884,"identity":"0cc698eb-c045-4d16-a092-031b01baffe7","added_by":"auto","created_at":"2024-09-16 00:37:38","extension":"pdf","order_by":1,"title":"","display":"","copyAsset":false,"role":"supplement","size":2212606,"visible":true,"origin":"","legend":"","description":"","filename":"SuppMat070824.pdf","url":"https://assets-eu.researchsquare.com/files/rs-4872684/v1/86b01069bbaf37256409488f.pdf"}],"financialInterests":"No competing interests reported.","formattedTitle":"Baseline drug treatments and long-term outcomes in COVID- 19-hospitalized patients: results of the 2020 AUTCOV study","fulltext":[{"header":"Introduction","content":"\u003cp\u003eThe outbreak of Coronavirus Disease 2019 (COVID-19) marked the start of a global pandemic and has been associated with substantial morbidity and mortality.\u003c/p\u003e \u003cp\u003eAge and gender are well-established risk factors for severe outcomes.\u003csup\u003e1\u0026ndash;5\u003c/sup\u003e Numerous reports have discussed that the presence of comorbidities may increase the risk of COVID-19-related death.\u003csup\u003e1,6\u0026ndash;9\u003c/sup\u003e In addition, an increased risk of readmission and mortality after hospital discharge has been observed up to 1 year after COVID-19 hospitalization.\u003csup\u003e10\u0026ndash;13\u003c/sup\u003e Patients who require hospitalization for COVID-19 have a greater comorbidity burden and are expected to have worse short-term outcomes.\u003csup\u003e12\u003c/sup\u003e Differences in the prevalence of drug use and polypharmacy regimens were observed when compared to the general population.\u003csup\u003e14\u003c/sup\u003e Studies have investigated possible associations of polypharmacy with increased morbidity and mortality among patients with COVID-19.\u003csup\u003e15,16\u003c/sup\u003e Visser et al.\u003csup\u003e17\u003c/sup\u003e investigated the impact of polypharmacy on COVID-19-related mortality in nursing home residents and found a significant positive association between the total number of medications and 30-day COVID-related adjusted mortality. However, published studies focusing on the drug profiles of hospitalized COVID-19 patients and long-term outcomes are still scarce.\u003c/p\u003e \u003cp\u003eHere, we present data on COVID-19-hospitalized patients and a matched control group provided by the Austrian Health Insurance Funds with a median follow-up of 600 days (maximum 880 days). The primary aim of this study was to analyze the long-term follow-up of patients hospitalized due to COVID-19 in Austria in 2020 in order to evaluate the association between prescribed medications and mortality or readmission after COVID-19 hospitalization. We also compared the characteristics and outcomes of the hospitalized patients with an age-, sex-, and region-matched control group, presenting a real-world picture.\u003c/p\u003e"},{"header":"Methods","content":"\u003cdiv id=\"Sec3\" class=\"Section2\"\u003e \u003ch2\u003eStudy Design and Cohorts\u003c/h2\u003e \u003cp\u003e This retrospective, national population-based study complied with the Declaration of Helsinki and was approved by the ethics committee of Lower Austria (GS1-EK-4/747\u0026ndash;2021). The data on both the patient and control cohorts were available from the Austrian Health Insurance Funds. Approximately 98% of the Austrian population is registered in the public health insurance system. Health care in Austria is a national system with good access to care, is regulated by the social insurance law and mainly financed by social insurance contributions.\u003c/p\u003e \u003cp\u003ePatients\u0026thinsp;\u0026gt;\u0026thinsp;18 years of age hospitalized in Austria due to the main diagnosis COVID-19 (ICD-10 Codes U071, U072, U049) from 1 January 2020 to 31 December 2020 were included in this study. For all patients age, sex, region, and medication were obtained from 1 year before hospitalization until study cut-off.\u003c/p\u003e \u003cp\u003eAn age-, sex-, and region-matched control group (approximately 10 controls for each patient) consisted of individuals not hospitalized due to COVID-19 in the year 2020 were randomly chosen from the population registered in the Austrian Health Insurance Fund, representing the Austrian population. Data on the control group were available from 1 year before the first patient was hospitalized until study cut-off. The data set includes 22 571 patients and 217 295 controls.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec4\" class=\"Section2\"\u003e \u003ch2\u003eStudy Outcomes\u003c/h2\u003e \u003cp\u003eThe primary outcome was all-cause mortality. The secondary outcome was hospitalization due to any reason. Hospitalization was defined based on billing information (MEL codes). For patients, we used the first readmission after the index COVID-19 hospital stay. For controls, hospitalization was defined as the first hospital admission after the index COVID hospital stay of the matched patient (Table \u003cspan refid=\"MOESM1\" class=\"InternalRef\"\u003eS1\u003c/span\u003e). For controls, time to death was evaluated from the index COVID-hospital admission date of the matched patient.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec5\" class=\"Section2\"\u003e \u003ch2\u003eStatistical Analysis\u003c/h2\u003e \u003cp\u003eFor each patient, age, sex, region, and Anatomical Therapeutic Chemical Classification-Codes (ATC) describing prescribed medications were (for details on the statistical analyses plan see supplement). All analyses were performed separately for four age subgroups: 19\u0026ndash;40 years, 41\u0026ndash;64 years, 65\u0026ndash;74 years, \u0026ge;\u0026thinsp;75 years (Table S2). ATC codes before hospitalization were summarized in medication groups (Table S3). A binary variable was defined for each medication group, which was set to 1 if a drug of the corresponding ICD10-codes was prescribed at least once 1 year before the index COVID-19 hospitalization and 0 if no drug was prescribed. Twenty medication groups were used for statistical modeling (MG1 to MG20, see Table S3). For controls, a similar medication profile was generated using the drugs prescribed 1 year before the index COVID-hospital stay of the matched patient. These 20 medication groups were also used to define polypharmacy.\u003c/p\u003e \u003cp\u003eNumbers and percentages were used to summarize categorical variables, medians and interquartile ranges for continuous variables.\u003c/p\u003e \u003cp\u003eTo evaluate the association between polypharmacy and all-cause death, a simple Cox regression model was calculated accounting for sex, age, half-year, and polypharmacy with clustering variable region. Polypharmacy was defined as the number of medication groups in which a patient received prescribed medication, categorized into four groups (0\u0026ndash;1, 2\u0026ndash;5, 6\u0026ndash;10, \u0026gt;\u0026thinsp;11). To evaluate the association between polypharmacy and re-hospitalization due to any reason, competing risk models (with competing risk death) were calculated accounting for sex, age, half-year, and polypharmacy with clustering variable region. Furthermore, the association between several medication groups and all-cause death, a Cox regression model was calculated accounting for sex, age, half-year, polypharmacy, and the 20 medication groups with clustering variable region. For re-hospitalization, a competing risk model was calculated using the same co-variables as described in the model for all-cause death.\u003c/p\u003e \u003cp\u003eOnly 20 of the 32 medication groups were evaluated for the statistical models due to the underrepresentation.\u003c/p\u003e \u003cp\u003eAs patients hospitalized due to COVID-19 had potentially more serious co-morbidities compared the Austrian population, we attempted to account for this imbalance using propensity score matching for age, sex, region, and medication groups MG1 to MG20. To evaluate the difference between patients and controls in the risk of all-cause death, a Cox regression model was calculated accounting for group, sex, age, half-year, polypharmacy, and the 20 medication groups with clustering variable region. For hospitalization due to any reason, a competing risk model was calculated using the same co-variables as described in the model for all-cause death.\u003c/p\u003e \u003cp\u003eSch\u0026ouml;nfeld residuals were used to evaluate the proportional hazard assumption and variance inflation factors to evaluate multicollinearity. For all models, hazard ratios (HR) and confidence intervals (CI) are provided. Due to the large sample size and large number of investigated co-variables (i.e., 25), p\u0026thinsp;\u0026lt;\u0026thinsp;0,002 (=\u0026thinsp;0,05/25 applying conservative Bonferroni correction) was considered significant. All analyses were performed using R, release 4.2.2.\u003csup\u003e18\u003c/sup\u003e\u003c/p\u003e \u003c/div\u003e"},{"header":"Results","content":"\u003cdiv id=\"Sec7\" class=\"Section2\"\u003e \u003ch2\u003eCharacteristics of the Patients and Controls\u003c/h2\u003e \u003cp\u003eThe study population included 22 571 COVID-19-hospitalized patients (Table\u0026nbsp;\u003cspan refid=\"Tab1\" class=\"InternalRef\"\u003e1\u003c/span\u003e, Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003eA). The median follow-up time varied from 594 to 615 days over the four age groups. The age-, sex-, and region-matched control group, representing the general Austrian population, included 217 295 controls (Table\u0026nbsp;\u003cspan refid=\"Tab1\" class=\"InternalRef\"\u003e1\u003c/span\u003e).\u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab1\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 1\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eDemographic data for COVID-19-hospitalized patients in 2020 in Austria and corresponding age-, sex-, and region-matched controls\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"12\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c6\" colnum=\"6\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c7\" colnum=\"7\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c8\" colnum=\"8\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c9\" colnum=\"9\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c10\" colnum=\"10\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c11\" colnum=\"11\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c12\" colnum=\"12\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colspan=\"12\" nameend=\"c12\" namest=\"c1\"\u003e \u003cp\u003eAll patients\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e \u003cp\u003eAge group\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/th\u003e \u003cth align=\"left\" colspan=\"2\" nameend=\"c4\" namest=\"c3\"\u003e \u003cp\u003e19\u0026ndash;40\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colspan=\"2\" nameend=\"c6\" namest=\"c5\"\u003e \u003cp\u003e41\u0026ndash;64\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colspan=\"3\" nameend=\"c9\" namest=\"c7\"\u003e \u003cp\u003e65\u0026ndash;74\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colspan=\"3\" nameend=\"c12\" namest=\"c10\"\u003e \u003cp\u003e\u0026ge;\u0026thinsp;75\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/th\u003e \u003cth align=\"left\" colspan=\"2\" nameend=\"c4\" namest=\"c3\"\u003e \u003cp\u003en\u0026thinsp;=\u0026thinsp;1201\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colspan=\"2\" nameend=\"c6\" namest=\"c5\"\u003e \u003cp\u003en\u0026thinsp;=\u0026thinsp;6018\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colspan=\"3\" nameend=\"c9\" namest=\"c7\"\u003e \u003cp\u003en\u0026thinsp;=\u0026thinsp;4502\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colspan=\"3\" nameend=\"c12\" namest=\"c10\"\u003e \u003cp\u003en\u0026thinsp;=\u0026thinsp;10 850\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e\u003cb\u003en\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e\u003cb\u003e%\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e\u003cb\u003en\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e\u003cb\u003e%\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e\u003cb\u003en\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c9\" namest=\"c8\"\u003e \u003cp\u003e\u003cb\u003e%\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003e\u003cb\u003en\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c12\" namest=\"c11\"\u003e \u003cp\u003e\u003cb\u003e%\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eSex\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e\u003cb\u003eMale\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e665\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e55,37\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e3 769\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e62,63\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e2 644\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c9\" namest=\"c8\"\u003e \u003cp\u003e58,73\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003e4 994\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c12\" namest=\"c11\"\u003e \u003cp\u003e46,03\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e\u003cb\u003eFemale\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e536\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e44,63\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e2 249\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e37,37\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e1 858\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c9\" namest=\"c8\"\u003e \u003cp\u003e41,27\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003e5 856\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c12\" namest=\"c11\"\u003e \u003cp\u003e53,97\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003ePolypharmacy\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e\u003cb\u003e0\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e364\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e30,31\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e906\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e15,05\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e253\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c9\" namest=\"c8\"\u003e \u003cp\u003e5,62\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003e473\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c12\" namest=\"c11\"\u003e \u003cp\u003e4,36\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e\u003cb\u003e0 or 1\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e686\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e57,12\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e1 929\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e32,05\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e619\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c9\" namest=\"c8\"\u003e \u003cp\u003e13,75\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003e1 129\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c12\" namest=\"c11\"\u003e \u003cp\u003e10,41\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e\u003cb\u003e2 to 5\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e454\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e37,80\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e2 991\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e49,70\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e2 261\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c9\" namest=\"c8\"\u003e \u003cp\u003e50,22\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003e5 106\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c12\" namest=\"c11\"\u003e \u003cp\u003e47,06\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e\u003cb\u003e6 to 10\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e57\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e4,75\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e992\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e16,48\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e1 400\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c9\" namest=\"c8\"\u003e \u003cp\u003e31,10\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003e4 179\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c12\" namest=\"c11\"\u003e \u003cp\u003e38,52\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e\u003cb\u003e\u0026ge;\u0026thinsp;11\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e4\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0,33\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e106\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e1,76\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e222\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c9\" namest=\"c8\"\u003e \u003cp\u003e4,93\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003e436\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c12\" namest=\"c11\"\u003e \u003cp\u003e4,02\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eFollow-up time in days\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e\u003cb\u003eMedian (IQR)\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c4\" namest=\"c3\"\u003e \u003cp\u003e615 (590\u0026ndash;673)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c6\" namest=\"c5\"\u003e \u003cp\u003e599 (582\u0026ndash;637)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"3\" nameend=\"c9\" namest=\"c7\"\u003e \u003cp\u003e597 (580\u0026ndash;615)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"3\" nameend=\"c12\" namest=\"c10\"\u003e \u003cp\u003e594 (577\u0026ndash;611)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eTime to death in days\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e\u003cb\u003eMedian (IQR)\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c4\" namest=\"c3\"\u003e \u003cp\u003e47 (11\u0026ndash;87)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c6\" namest=\"c5\"\u003e \u003cp\u003e29 (12\u0026ndash;111)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"3\" nameend=\"c9\" namest=\"c7\"\u003e \u003cp\u003e23 (9\u0026ndash;131)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"3\" nameend=\"c12\" namest=\"c10\"\u003e \u003cp\u003e16 (7\u0026ndash;84)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colspan=\"12\" nameend=\"c12\" namest=\"c1\"\u003e \u003cp\u003e\u003cb\u003ePatients surviving hospital stay\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eAge group\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c4\" namest=\"c3\"\u003e \u003cp\u003e\u003cb\u003e19\u0026ndash;40\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c6\" namest=\"c5\"\u003e \u003cp\u003e\u003cb\u003e41\u0026ndash;64\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"3\" nameend=\"c9\" namest=\"c7\"\u003e \u003cp\u003e\u003cb\u003e65\u0026ndash;74\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"3\" nameend=\"c12\" namest=\"c10\"\u003e \u003cp\u003e\u003cb\u003e\u0026ge;\u0026thinsp;75\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c4\" namest=\"c3\"\u003e \u003cp\u003e\u003cb\u003en\u0026thinsp;=\u0026thinsp;1192\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c6\" namest=\"c5\"\u003e \u003cp\u003e\u003cb\u003en\u0026thinsp;=\u0026thinsp;5727\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"3\" nameend=\"c9\" namest=\"c7\"\u003e \u003cp\u003e\u003cb\u003en\u0026thinsp;=\u0026thinsp;3854\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"3\" nameend=\"c12\" namest=\"c10\"\u003e \u003cp\u003e\u003cb\u003en\u0026thinsp;=\u0026thinsp;7674\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e\u003cb\u003en\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e\u003cb\u003e%\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e\u003cb\u003en\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e\u003cb\u003e%\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e\u003cb\u003en\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c9\" namest=\"c8\"\u003e \u003cp\u003e\u003cb\u003e%\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003e\u003cb\u003en\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c12\" namest=\"c11\"\u003e \u003cp\u003e\u003cb\u003e%\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eSex\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e\u003cb\u003eMale\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e658\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e55,20\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e3575\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e62,42\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e2 177\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c9\" namest=\"c8\"\u003e \u003cp\u003e56,49\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003e3 318\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c12\" namest=\"c11\"\u003e \u003cp\u003e43,24\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e\u003cb\u003eFemale\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e534\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e44,80\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e2152\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e37,58\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e1 677\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c9\" namest=\"c8\"\u003e \u003cp\u003e43,51\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003e4 356\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c12\" namest=\"c11\"\u003e \u003cp\u003e56,76\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003ePolypharmacy\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e\u003cb\u003e0\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e362\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e30,37\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e822\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e15,40\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e227\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c9\" namest=\"c8\"\u003e \u003cp\u003e5,89\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003e344\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c12\" namest=\"c11\"\u003e \u003cp\u003e4,48\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e\u003cb\u003e0 or 1\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e682\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e57,21\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e1877\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e32,77\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e561\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c9\" namest=\"c8\"\u003e \u003cp\u003e14,56\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003e839\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c12\" namest=\"c11\"\u003e \u003cp\u003e10,93\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e\u003cb\u003e2 to 5\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e452\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e37,92\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e2858\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e49,90\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e1 985\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c9\" namest=\"c8\"\u003e \u003cp\u003e51,50\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003e3 677\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c12\" namest=\"c11\"\u003e \u003cp\u003e47,92\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e\u003cb\u003e6 to 10\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e54\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e4,53\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e903\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e15,77\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e1 130\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c9\" namest=\"c8\"\u003e \u003cp\u003e29,32\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003e2 874\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c12\" namest=\"c11\"\u003e \u003cp\u003e37,45\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e\u003cb\u003e\u0026ge;\u0026thinsp;11\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e4\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0,34\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e89\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e1,55\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e178\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c9\" namest=\"c8\"\u003e \u003cp\u003e4,62\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003e284\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c12\" namest=\"c11\"\u003e \u003cp\u003e3,70\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eLength of hospital stay\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e\u003cb\u003eMedian (IQR)\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c4\" namest=\"c3\"\u003e \u003cp\u003e5 (\u003cspan additionalcitationids=\"CR4 CR5 CR6 CR7 CR8\" citationid=\"CR3\" class=\"CitationRef\"\u003e3\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR9\" class=\"CitationRef\"\u003e9\u003c/span\u003e)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c6\" namest=\"c5\"\u003e \u003cp\u003e8 (\u003cspan additionalcitationids=\"CR6 CR7 CR8 CR9 CR10 CR11 CR12\" citationid=\"CR5\" class=\"CitationRef\"\u003e5\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR13\" class=\"CitationRef\"\u003e13\u003c/span\u003e)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"3\" nameend=\"c9\" namest=\"c7\"\u003e \u003cp\u003e11 (\u003cspan additionalcitationids=\"CR7 CR8 CR9 CR10 CR11 CR12 CR13 CR14 CR15 CR16 CR17\" citationid=\"CR6\" class=\"CitationRef\"\u003e6\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR18\" class=\"CitationRef\"\u003e18\u003c/span\u003e)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"3\" nameend=\"c12\" namest=\"c10\"\u003e \u003cp\u003e13 (\u003cspan additionalcitationids=\"CR9 CR10 CR11 CR12 CR13 CR14 CR15 CR16 CR17 CR18 CR19 CR20\" citationid=\"CR8\" class=\"CitationRef\"\u003e8\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR21\" class=\"CitationRef\"\u003e21\u003c/span\u003e)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eFollow-up time in days\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e\u003cb\u003eMedian (IQR)\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c4\" namest=\"c3\"\u003e \u003cp\u003e615 (590\u0026ndash;673)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c6\" namest=\"c5\"\u003e \u003cp\u003e599 (582\u0026ndash;637)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"3\" nameend=\"c9\" namest=\"c7\"\u003e \u003cp\u003e597 (580\u0026ndash;615)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"3\" nameend=\"c12\" namest=\"c10\"\u003e \u003cp\u003e594 (577\u0026ndash;611)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eTime to death in days\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e\u003cb\u003eMedian (IQR)\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c4\" namest=\"c3\"\u003e \u003cp\u003e57,5 (47\u0026ndash;278)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c6\" namest=\"c5\"\u003e \u003cp\u003e209 (56\u0026ndash;377)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"3\" nameend=\"c9\" namest=\"c7\"\u003e \u003cp\u003e211 (74\u0026ndash;389)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"3\" nameend=\"c12\" namest=\"c10\"\u003e \u003cp\u003e160 (49\u0026ndash;371)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colspan=\"12\" nameend=\"c12\" namest=\"c1\"\u003e \u003cp\u003e\u003cb\u003eControls\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eAge group\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c4\" namest=\"c3\"\u003e \u003cp\u003e\u003cb\u003e19\u0026ndash;40\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c6\" namest=\"c5\"\u003e \u003cp\u003e\u003cb\u003e41\u0026ndash;64\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"3\" nameend=\"c9\" namest=\"c7\"\u003e \u003cp\u003e\u003cb\u003e65\u0026ndash;74\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"3\" nameend=\"c12\" namest=\"c10\"\u003e \u003cp\u003e\u003cb\u003e\u0026ge;\u0026thinsp;75\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c4\" namest=\"c3\"\u003e \u003cp\u003e\u003cb\u003en\u0026thinsp;=\u0026thinsp;11 958\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c6\" namest=\"c5\"\u003e \u003cp\u003e\u003cb\u003en\u0026thinsp;=\u0026thinsp;59 057\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"3\" nameend=\"c9\" namest=\"c7\"\u003e \u003cp\u003e\u003cb\u003en\u0026thinsp;=\u0026thinsp;43 511\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"3\" nameend=\"c12\" namest=\"c10\"\u003e \u003cp\u003e\u003cb\u003en\u0026thinsp;=\u0026thinsp;102 769\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e\u003cb\u003en\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e\u003cb\u003e%\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e\u003cb\u003en\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e\u003cb\u003e%\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c8\" namest=\"c7\"\u003e \u003cp\u003e\u003cb\u003en\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e\u003cb\u003e%\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c11\" namest=\"c10\"\u003e \u003cp\u003e\u003cb\u003en\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c12\"\u003e \u003cp\u003e\u003cb\u003e%\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eSex\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e\u003cb\u003eMale\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e6 616\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e55,32\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e36 870\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e62,43\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c8\" namest=\"c7\"\u003e \u003cp\u003e25 353\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e58,27\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c11\" namest=\"c10\"\u003e \u003cp\u003e47 038\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c12\"\u003e \u003cp\u003e45,77\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e\u003cb\u003eFemale\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e5 342\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e44,67\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e22 187\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e37,56\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c8\" namest=\"c7\"\u003e \u003cp\u003e18 158\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e41,73\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c11\" namest=\"c10\"\u003e \u003cp\u003e55 731\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c12\"\u003e \u003cp\u003e54,22\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003ePolypharmacy\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e\u003cb\u003e0\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e9 206\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e76,99\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e40 983\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e69,40\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c8\" namest=\"c7\"\u003e \u003cp\u003e24 535\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e56,39\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c11\" namest=\"c10\"\u003e \u003cp\u003e42 833\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c12\"\u003e \u003cp\u003e41,68\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e\u003cb\u003e0 or 1\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e10 773\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e90,09\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e47 098\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e79,75\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c8\" namest=\"c7\"\u003e \u003cp\u003e27 877\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e64,07\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c11\" namest=\"c10\"\u003e \u003cp\u003e48 803\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c12\"\u003e \u003cp\u003e47,49\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e\u003cb\u003e2 to 5\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e1139\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e9,53\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e10 153\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e17,19\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c8\" namest=\"c7\"\u003e \u003cp\u003e11 322\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e26,02\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c11\" namest=\"c10\"\u003e \u003cp\u003e32 629\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c12\"\u003e \u003cp\u003e31,75\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e\u003cb\u003e6 to 10\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e45\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0,38\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e1650\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e2,79\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c8\" namest=\"c7\"\u003e \u003cp\u003e3878\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e8,91\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c11\" namest=\"c10\"\u003e \u003cp\u003e19 285\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c12\"\u003e \u003cp\u003e18,77\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e\u003cb\u003e\u0026ge;\u0026thinsp;11\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0,01\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e156\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0,26\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c8\" namest=\"c7\"\u003e \u003cp\u003e433\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e1,00\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c11\" namest=\"c10\"\u003e \u003cp\u003e2 048\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c12\"\u003e \u003cp\u003e1,99\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eFollow-up time in days\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e\u003cb\u003eMedian (IQR)\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c4\" namest=\"c3\"\u003e \u003cp\u003e614 (590\u0026ndash;673)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c6\" namest=\"c5\"\u003e \u003cp\u003e599 (582\u0026ndash;633)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"3\" nameend=\"c9\" namest=\"c7\"\u003e \u003cp\u003e596 (578\u0026ndash;613)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"3\" nameend=\"c12\" namest=\"c10\"\u003e \u003cp\u003e591 (574\u0026ndash;608)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eTime to death in days\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e\u003cb\u003eMedian (IQR)\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c4\" namest=\"c3\"\u003e \u003cp\u003e158 (62\u0026ndash;441)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c6\" namest=\"c5\"\u003e \u003cp\u003e130 (53\u0026ndash;263)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"3\" nameend=\"c9\" namest=\"c7\"\u003e \u003cp\u003e135 (54\u0026ndash;297)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"3\" nameend=\"c12\" namest=\"c10\"\u003e \u003cp\u003e138 (47\u0026ndash;314)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003ePolypharmacy of more than six medication groups was observed more often in the older age groups (65\u0026ndash;74 years and \u0026ge;\u0026thinsp;75 years). In the older age groups, 5,62% (65\u0026ndash;75 years) and 4,36% (\u0026ge;\u0026thinsp;75 years) of patients and 56,39% (65\u0026ndash;75 years) and 41,68% (\u0026ge;\u0026thinsp;75 years) of controls did not receive any drug out of the investigated medication groups (Table\u0026nbsp;\u003cspan refid=\"Tab1\" class=\"InternalRef\"\u003e1\u003c/span\u003e).\u003c/p\u003e \u003cp\u003ePatients hospitalized due to COVID-19 are expected to have more comorbidities and, therefore, more medications than the general population. This was also observed in our Austrian population. Patients received more drugs in all investigated medication groups (Table\u0026nbsp;\u003cspan refid=\"Tab2\" class=\"InternalRef\"\u003e2\u003c/span\u003e, Tables S4 to S7) across all age groups compared to controls.\u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab2\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 2\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003ePercentages of COVID-hospitalized patients and controls receiving at least one medication in the investigated medication group and corresponding 1-year mortality by age group.\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"17\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c6\" colnum=\"6\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c7\" colnum=\"7\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c8\" colnum=\"8\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c9\" colnum=\"9\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c10\" colnum=\"10\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c11\" colnum=\"11\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c12\" colnum=\"12\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c13\" colnum=\"13\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c14\" colnum=\"14\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c15\" colnum=\"15\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c16\" colnum=\"16\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c17\" colnum=\"17\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e \u003cp\u003eAge group\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colspan=\"4\" nameend=\"c5\" namest=\"c2\"\u003e \u003cp\u003e19\u0026ndash;40\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colspan=\"4\" nameend=\"c9\" namest=\"c6\"\u003e \u003cp\u003e41\u0026ndash;64\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colspan=\"4\" nameend=\"c13\" namest=\"c10\"\u003e \u003cp\u003e65\u0026ndash;74\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colspan=\"4\" nameend=\"c17\" namest=\"c14\"\u003e \u003cp\u003e\u0026ge;\u0026thinsp;75\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e \u003cp\u003eCohort\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colspan=\"2\" nameend=\"c3\" namest=\"c2\"\u003e \u003cp\u003eCovid\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colspan=\"2\" nameend=\"c5\" namest=\"c4\"\u003e \u003cp\u003eControl\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colspan=\"2\" nameend=\"c7\" namest=\"c6\"\u003e \u003cp\u003eCovid\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colspan=\"2\" nameend=\"c9\" namest=\"c8\"\u003e \u003cp\u003eControl\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colspan=\"2\" nameend=\"c11\" namest=\"c10\"\u003e \u003cp\u003eCovid\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colspan=\"2\" nameend=\"c13\" namest=\"c12\"\u003e \u003cp\u003eControl\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colspan=\"2\" nameend=\"c15\" namest=\"c14\"\u003e \u003cp\u003eCovid\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colspan=\"2\" nameend=\"c17\" namest=\"c16\"\u003e \u003cp\u003eControl\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eYes\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003e1-year mort\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003eYes\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c5\"\u003e \u003cp\u003e1-year mort\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c6\"\u003e \u003cp\u003eYes\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c7\"\u003e \u003cp\u003e1-year mort\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c8\"\u003e \u003cp\u003eYes\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c9\"\u003e \u003cp\u003e1-year mort\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c10\"\u003e \u003cp\u003eYes\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c11\"\u003e \u003cp\u003e1-year mort\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c12\"\u003e \u003cp\u003eYes\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c13\"\u003e \u003cp\u003e1-year mort\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c14\"\u003e \u003cp\u003eYes\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c15\"\u003e \u003cp\u003e1-year mort\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c16\"\u003e \u003cp\u003eYes\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c17\"\u003e \u003cp\u003e1-year mort\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eMG1: Anticoagulants\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e6,08\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e4,11\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e1,42\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e1,76\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e18,79\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e13,53\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e4,61\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e4,45\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003e40,18\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c11\"\u003e \u003cp\u003e26,81\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c12\"\u003e \u003cp\u003e13,05\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c13\"\u003e \u003cp\u003e8,63\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c14\"\u003e \u003cp\u003e56,97\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c15\"\u003e \u003cp\u003e46,59\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c16\"\u003e \u003cp\u003e29,03\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c17\"\u003e \u003cp\u003e23,47\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eMG2: Antibiotics, antivirals, antiprotozoals, or anthelmintics\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e46,88\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e1,60\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e15,74\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0,16\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e48,31\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e8,81\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e15,84\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e2,02\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003e47,71\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c11\"\u003e \u003cp\u003e22,72\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c12\"\u003e \u003cp\u003e20,19\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c13\"\u003e \u003cp\u003e6,06\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c14\"\u003e \u003cp\u003e46,59\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c15\"\u003e \u003cp\u003e46,96\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c16\"\u003e \u003cp\u003e28,03\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c17\"\u003e \u003cp\u003e22,15\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eMG3: Insulin and other antidiabetic\u003c/b\u003es\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e3,08\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e5,41\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0,33\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0,00\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e14,69\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e12,33\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e2,52\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e3,36\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003e26,45\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c11\"\u003e \u003cp\u003e26,78\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c12\"\u003e \u003cp\u003e7,29\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c13\"\u003e \u003cp\u003e7,79\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c14\"\u003e \u003cp\u003e21,49\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c15\"\u003e \u003cp\u003e44,13\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c16\"\u003e \u003cp\u003e9,31\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c17\"\u003e \u003cp\u003e23,41\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eMG4: Heart drugs\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e1,75\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0,00\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0,43\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e1,96\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e4,54\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e12,09\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e1,24\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e3,67\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003e10,17\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c11\"\u003e \u003cp\u003e26,42\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c12\"\u003e \u003cp\u003e3,93\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c13\"\u003e \u003cp\u003e8,48\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c14\"\u003e \u003cp\u003e16,50\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c15\"\u003e \u003cp\u003e48,27\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c16\"\u003e \u003cp\u003e10,05\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c17\"\u003e \u003cp\u003e23,16\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eMG5: Antihypertensives, incl. diuretics and renin-angiotensin-aldosterone system inhibitors\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e6,33\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e3,95\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0,69\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e3,66\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e33,57\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e11,14\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e8,66\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e2,81\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003e60,60\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c11\"\u003e \u003cp\u003e22,84\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c12\"\u003e \u003cp\u003e23,27\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c13\"\u003e \u003cp\u003e5,67\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c14\"\u003e \u003cp\u003e68,89\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c15\"\u003e \u003cp\u003e43,18\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c16\"\u003e \u003cp\u003e39,36\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c17\"\u003e \u003cp\u003e19,62\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eMG6: Beta-blockers\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e3,16\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e5,26\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0,27\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e6,25\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e14,36\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e14,24\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e3,33\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e4,67\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003e31,05\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c11\"\u003e \u003cp\u003e26,11\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c12\"\u003e \u003cp\u003e10,54\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c13\"\u003e \u003cp\u003e7,63\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c14\"\u003e \u003cp\u003e35,80\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c15\"\u003e \u003cp\u003e45,19\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c16\"\u003e \u003cp\u003e19,52\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c17\"\u003e \u003cp\u003e20,67\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eMG7: Statins, fibrates, incl. proprotein convertase subtilisin/kexin type 9 inhibitors and inclisiran\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e2,25\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e7,41\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0,33\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0,00\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e23,30\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e9,20\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e6,57\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e1,83\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003e42,78\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c11\"\u003e \u003cp\u003e21,7\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c12\"\u003e \u003cp\u003e18,28\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c13\"\u003e \u003cp\u003e4,93\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c14\"\u003e \u003cp\u003e38,55\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c15\"\u003e \u003cp\u003e39,16\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c16\"\u003e \u003cp\u003e24,30\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c17\"\u003e \u003cp\u003e14,98\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eMG8: Immunosuppressants and immunomodulators\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e3,00\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e2,78\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0,32\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0,00\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e3,86\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e9,91\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e0,70\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e2,68\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003e4,64\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c11\"\u003e \u003cp\u003e24,40\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c12\"\u003e \u003cp\u003e1,11\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c13\"\u003e \u003cp\u003e5,57\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c14\"\u003e \u003cp\u003e2,15\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c15\"\u003e \u003cp\u003e40,34\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c16\"\u003e \u003cp\u003e1,02\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c17\"\u003e \u003cp\u003e15,39\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eMG9: Systemic steroids\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e6,99\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e4,76\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e1,16\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0,00\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e12,35\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e10,90\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e3,38\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e4,41\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003e16,79\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c11\"\u003e \u003cp\u003e28,31\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c12\"\u003e \u003cp\u003e6,19\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c13\"\u003e \u003cp\u003e9,17\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c14\"\u003e \u003cp\u003e14,27\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c15\"\u003e \u003cp\u003e43,8\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c16\"\u003e \u003cp\u003e8,37\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c17\"\u003e \u003cp\u003e18,68\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eMG10: Chemotherapy\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0,67\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0,00\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0,04\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e20,00\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e1,83\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e30,91\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e0,41\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e17,77\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003e3,55\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c11\"\u003e \u003cp\u003e41,88\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c12\"\u003e \u003cp\u003e1,34\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c13\"\u003e \u003cp\u003e20,41\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c14\"\u003e \u003cp\u003e3,96\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c15\"\u003e \u003cp\u003e49,53\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c16\"\u003e \u003cp\u003e2,39\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c17\"\u003e \u003cp\u003e28,83\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eMG11: Iron supplements, erythropoietic stimulating agents, vitamin B12, folic acid\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e6,16\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e2,70\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e1,05\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0,00\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e5,28\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e22,96\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e0,91\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e10,02\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003e9,15\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c11\"\u003e \u003cp\u003e34,95\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c12\"\u003e \u003cp\u003e1,97\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c13\"\u003e \u003cp\u003e17,04\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c14\"\u003e \u003cp\u003e13,00\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c15\"\u003e \u003cp\u003e54,85\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c16\"\u003e \u003cp\u003e5,37\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c17\"\u003e \u003cp\u003e34,64\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eMG12: Antacids, incl. antihistamines\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e10,82\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e3,08\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e1,75\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e1,44\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e20,41\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e11,89\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e4,03\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e4,58\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003e33,05\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c11\"\u003e \u003cp\u003e26,55\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c12\"\u003e \u003cp\u003e9,07\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c13\"\u003e \u003cp\u003e8,09\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c14\"\u003e \u003cp\u003e35,91\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c15\"\u003e \u003cp\u003e46,79\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c16\"\u003e \u003cp\u003e16,07\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c17\"\u003e \u003cp\u003e24,43\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eMG13: Vitamin D and other vitamin supplements\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e8,74\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0,95\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e1,16\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e2,16\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e12,94\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e13,74\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e3,05\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e4,28\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003e19,90\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c11\"\u003e \u003cp\u003e27,12\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c12\"\u003e \u003cp\u003e6,70\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c13\"\u003e \u003cp\u003e7,34\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c14\"\u003e \u003cp\u003e24,33\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c15\"\u003e \u003cp\u003e45,95\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c16\"\u003e \u003cp\u003e12,89\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c17\"\u003e \u003cp\u003e22,03\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eCaplacizumab\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0,08\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0,00\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0,00\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003eNA\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0,00\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003eNA\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e0,00\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003eNA\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003e0,00\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c11\"\u003e \u003cp\u003eNA\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c12\"\u003e \u003cp\u003e0,00\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c13\"\u003e \u003cp\u003eNA\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c14\"\u003e \u003cp\u003e0,00\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c15\"\u003e \u003cp\u003eNA\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c16\"\u003e \u003cp\u003e0,00\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c17\"\u003e \u003cp\u003eNA\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eSystemic hemostatics\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0,00\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eNA\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0,02\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0,00\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0,03\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e50,00\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e0,00\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e0,00\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003e0,04\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c11\"\u003e \u003cp\u003e50,00\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c12\"\u003e \u003cp\u003e0,00\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c13\"\u003e \u003cp\u003e0,00\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c14\"\u003e \u003cp\u003e0,04\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c15\"\u003e \u003cp\u003e50,00\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c16\"\u003e \u003cp\u003e0,02\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c17\"\u003e \u003cp\u003e15,79\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eHereditary angioedema therapeutics\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0,00\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eNA\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0,00\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003eNA\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0,00\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003eNA\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e0,00\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003eNA\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003e0,00\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c11\"\u003e \u003cp\u003eNA\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c12\"\u003e \u003cp\u003e0,00\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c13\"\u003e \u003cp\u003eNA\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c14\"\u003e \u003cp\u003e0,00\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c15\"\u003e \u003cp\u003eNA\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c16\"\u003e \u003cp\u003e0,00\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c17\"\u003e \u003cp\u003eNA\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003ePeripheral vasodilators\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0,17\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0,00\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0,05\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0,00\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0,55\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e6,06\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e0,23\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e0,00\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003e1,13\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c11\"\u003e \u003cp\u003e27,45\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c12\"\u003e \u003cp\u003e0,47\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c13\"\u003e \u003cp\u003e4,43\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c14\"\u003e \u003cp\u003e1,3\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c15\"\u003e \u003cp\u003e38,30\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c16\"\u003e \u003cp\u003e1,10\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c17\"\u003e \u003cp\u003e20,42\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eHormonal contraceptives and similar hormone preparations\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e4,58\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0,00\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e1,31\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0,00\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e3,42\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e6,80\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e2,03\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e0,58\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003e3,13\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c11\"\u003e \u003cp\u003e12,06\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c12\"\u003e \u003cp\u003e2,82\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c13\"\u003e \u003cp\u003e1,88\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c14\"\u003e \u003cp\u003e2,41\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c15\"\u003e \u003cp\u003e30,92\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c16\"\u003e \u003cp\u003e2,81\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c17\"\u003e \u003cp\u003e10,04\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eImmunoglobulins\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0,17\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0,00\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0,00\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003eNA\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0,07\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e0,00\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e0,01\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e33,33\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003e0,09\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c11\"\u003e \u003cp\u003e25,00\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c12\"\u003e \u003cp\u003e0,01\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c13\"\u003e \u003cp\u003e0,00\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c14\"\u003e \u003cp\u003e0,07\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c15\"\u003e \u003cp\u003e100\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c16\"\u003e \u003cp\u003e0,01\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c17\"\u003e \u003cp\u003e20,00\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eInterferons and CSF\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e1,42\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e5,88\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0,41\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e2,04\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e2,41\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e28,97\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e0,54\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e14,47\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003e2,89\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c11\"\u003e \u003cp\u003e37,69\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c12\"\u003e \u003cp\u003e0,91\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c13\"\u003e \u003cp\u003e22,86\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c14\"\u003e \u003cp\u003e1,18\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c15\"\u003e \u003cp\u003e49,22\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c16\"\u003e \u003cp\u003e0,84\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c17\"\u003e \u003cp\u003e23,78\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eMG14: NSAID and other anti-inflammatory drugs\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e26,31\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e1,58\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e7,31\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0,11\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e36,54\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e6,96\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e13,2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e1,72\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003e34,5\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c11\"\u003e \u003cp\u003e17,19\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c12\"\u003e \u003cp\u003e18,06\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c13\"\u003e \u003cp\u003e4,15\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c14\"\u003e \u003cp\u003e24,80\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c15\"\u003e \u003cp\u003e35,01\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c16\"\u003e \u003cp\u003e20,18\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c17\"\u003e \u003cp\u003e13,81\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eMG15: Gout medications\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0,67\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e12,50\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0,04\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0,00\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e2,92\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e15,91\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e0,63\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e3,49\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003e6,86\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c11\"\u003e \u003cp\u003e34,95\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c12\"\u003e \u003cp\u003e1,72\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c13\"\u003e \u003cp\u003e10,03\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c14\"\u003e \u003cp\u003e8,39\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c15\"\u003e \u003cp\u003e49,89\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c16\"\u003e \u003cp\u003e3,74\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c17\"\u003e \u003cp\u003e26,46\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eMG16: Antiepileptics\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e6,16\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e6,76\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0,71\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e1,18\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e9,29\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e17,35\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e1,92\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e7,04\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003e14,5\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c11\"\u003e \u003cp\u003e34,30\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c12\"\u003e \u003cp\u003e3,58\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c13\"\u003e \u003cp\u003e12,07\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c14\"\u003e \u003cp\u003e15,97\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c15\"\u003e \u003cp\u003e47,89\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c16\"\u003e \u003cp\u003e6,89\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c17\"\u003e \u003cp\u003e23,83\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eMG17: Antipsychotics\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e14,32\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e3,49\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e3,41\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e1,47\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e24,16\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e12,59\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e6,92\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e3,40\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003e36,98\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c11\"\u003e \u003cp\u003e27,63\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c12\"\u003e \u003cp\u003e12,63\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c13\"\u003e \u003cp\u003e7,74\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c14\"\u003e \u003cp\u003e55,58\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c15\"\u003e \u003cp\u003e48,89\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c16\"\u003e \u003cp\u003e27,56\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c17\"\u003e \u003cp\u003e25,11\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eRhinological and throat antiseptics\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e10,49\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e1,59\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e3,13\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0,27\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e10,00\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e5,48\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e2,92\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e1,80\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003e7,89\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c11\"\u003e \u003cp\u003e21,97\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c12\"\u003e \u003cp\u003e3,42\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c13\"\u003e \u003cp\u003e3,29\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c14\"\u003e \u003cp\u003e3,95\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c15\"\u003e \u003cp\u003e34,97\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c16\"\u003e \u003cp\u003e3,40\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c17\"\u003e \u003cp\u003e13,93\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eMG18: Inhaled anti-obstructive drugs\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e10,49\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e2,38\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e2,08\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0,40\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e17,98\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e9,70\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e4,22\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e3,41\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003e25,43\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c11\"\u003e \u003cp\u003e26,38\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c12\"\u003e \u003cp\u003e7,38\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c13\"\u003e \u003cp\u003e8,66\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c14\"\u003e \u003cp\u003e19,44\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c15\"\u003e \u003cp\u003e45,61\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c16\"\u003e \u003cp\u003e9,49\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c17\"\u003e \u003cp\u003e22,23\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eMG19: Inhaled steroids\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e2,75\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0,00\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0,62\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0,00\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e3,62\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e7,34\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e0,98\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e3,63\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003e4,40\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c11\"\u003e \u003cp\u003e21,21\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c12\"\u003e \u003cp\u003e1,50\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c13\"\u003e \u003cp\u003e5,97\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c14\"\u003e \u003cp\u003e2,56\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c15\"\u003e \u003cp\u003e41,37\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c16\"\u003e \u003cp\u003e1,60\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c17\"\u003e \u003cp\u003e16,87\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eOther COPD drugs\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0,92\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0,00\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0,26\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0,00\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e1,96\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e5,93\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e0,33\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e3,63\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003e1,84\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c11\"\u003e \u003cp\u003e22,89\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c12\"\u003e \u003cp\u003e0,41\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c13\"\u003e \u003cp\u003e7,22\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c14\"\u003e \u003cp\u003e0,88\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c15\"\u003e \u003cp\u003e38,95\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c16\"\u003e \u003cp\u003e0,39\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c17\"\u003e \u003cp\u003e16,04\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eCold and Cough preparations\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e7,24\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e2,30\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0,93\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0,90\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e9,27\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e11,47\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e1,43\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e5,34\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003e13,24\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c11\"\u003e \u003cp\u003e26,01\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c12\"\u003e \u003cp\u003e3,06\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c13\"\u003e \u003cp\u003e9,30\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c14\"\u003e \u003cp\u003e12,65\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c15\"\u003e \u003cp\u003e44,97\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c16\"\u003e \u003cp\u003e5,62\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c17\"\u003e \u003cp\u003e26,93\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eMG20: Systemic Antihistamines\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e5,5\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e3,03\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e1,25\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0,00\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e6,03\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e10,47\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e1,50\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e3,05\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003e6,60\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c11\"\u003e \u003cp\u003e22,56\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c12\"\u003e \u003cp\u003e2,34\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c13\"\u003e \u003cp\u003e5,88\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c14\"\u003e \u003cp\u003e6,92\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c15\"\u003e \u003cp\u003e46,07\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c16\"\u003e \u003cp\u003e3,77\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c17\"\u003e \u003cp\u003e23,37\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eH2 Blocker\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0,58\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0,00\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0,25\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0,00\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e1,3\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e12,82\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e0,46\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e1,11\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003e1,60\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c11\"\u003e \u003cp\u003e22,22\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c12\"\u003e \u003cp\u003e0,84\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c13\"\u003e \u003cp\u003e7,69\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c14\"\u003e \u003cp\u003e1,12\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c15\"\u003e \u003cp\u003e38,84\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c16\"\u003e \u003cp\u003e1,09\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c17\"\u003e \u003cp\u003e17,90\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003ctfoot\u003e \u003ctr\u003e\u003ctd colspan=\"17\"\u003eMedication groups (MG1 to MG20) used for further statistical analyses are marked in bold. Detailed numbers are shown in Supplementary Tables S4 \u0026ndash; S7. Note that NA \u0026ldquo;not available\u0026rdquo; means that the calculation was not possible because no patient/control received a medication of this group.\u003c/td\u003e\u003c/tr\u003e \u003c/tfoot\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003cp\u003eInterestingly, we observed that 24,2% of patients aged 41\u0026ndash;64 years, 37% of patients aged 65\u0026ndash;74 years, and 55,6% of patients aged\u0026thinsp;\u0026ge;\u0026thinsp;75 years received antipsychotics before COVID hospitalization. In the control group, the rate of prescribed antipsychotics was 6,9% for 41\u0026ndash;64 years, 12,6% for 65\u0026ndash;74 years, and 27,6% for \u0026ge;\u0026thinsp;75 years.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec8\" class=\"Section2\"\u003e \u003ch2\u003eAll-cause mortality for COVID-patients\u003c/h2\u003e \u003cp\u003eIn the 19\u0026ndash;40 years age group, 1,05% of males and 0,37% of females dies in hospital. One-year mortality rates in this age group were 1,8% in males and 0,93% in females. For older age groups, higher mortality rates were observed. In the 41\u0026ndash;64 years age group, 5,15% of males and 4,31% of females died during the hospital stay. One-year mortality was 7,91% for males and 6.49% for females. In the 65\u0026ndash;74 years age group, 17,66% of males and 9,97% of females died during the hospital stay. Within 1 year after hospital admission, 25,38% of males and 14,96% of females died. Furthermore, 33,56% of males and 25,61% of females aged\u0026thinsp;\u0026ge;\u0026thinsp;75 years died during the hospital stay. One-year mortality rates in this age group were 46,2% for males and 39,94% for females (Table S8).\u003c/p\u003e \u003cp\u003eIn all age groups, we found a trend of a larger risk of death for men compared to women, which was significant in the older age groups (age\u0026thinsp;\u0026gt;\u0026thinsp;65 years) after multiplicity correction (Table S10). The number of hospital admissions per day and the cumulative number of deaths are shown in Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003eA. One-year mortality rates are presented in Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003eB.\u003c/p\u003e \u003cp\u003eSimple Cox-regression models evaluated a significant higher risk of all-cause death for patients with a larger number of prescribed medication groups compared to patients receiving drugs from none of the medication groups or only one medication group. No significant relationship was observed for patients aged 19\u0026ndash;41 years (Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003eA-D and Figure \u003cspan refid=\"MOESM1\" class=\"InternalRef\"\u003eS1\u003c/span\u003e).\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003eDue to the large number of medication groups and their potential interactions, polypharmacy is just one factor in a complex system and may not completely explain the associations of prescribed medications with all-cause death after COVID-19. Therefore, we included all 20 medication groups in the statistical Cox regression models to evaluate the effects of individual medication groups. Due to the small number of events in the youngest age group (19\u0026ndash;40 years), not all 20 medication groups could be included in the model for this age group.\u003c/p\u003e \u003cp\u003eFigure \u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003e summarizes the results of the Cox regression model of all-cause mortality when including the twenty medication groups (Table S11. In the youngest group (19\u0026ndash;40 years), only prescribed vitamin D and other vitamin supplements (p\u0026thinsp;\u0026lt;\u0026thinsp;0,001) and systemic antihistamines (p\u0026thinsp;=\u0026thinsp;0,002) were significantly associated with survival. For patients receiving vitamin supplements before the COVID-19 hospital stay, we observed a significantly lower risk of death. In addition, systemic antihistamines were significantly associated with poor survival. The other medication groups did not show significant results. However, due to the small number of events in this age group, the results should be interpreted with caution.\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003eFor several medication groups, the results were different between the age groups. Anticoagulants (p\u0026thinsp;\u0026lt;\u0026thinsp;0,001 for 41\u0026ndash;64 years and \u0026ge;\u0026thinsp;75 years); Antibiotics, antivirals, antiprotozoals, or anthelmintics (p\u0026thinsp;\u0026lt;\u0026thinsp;0,001 for \u0026ge;\u0026thinsp;75 years); Insulin and other antidiabetics (p\u0026thinsp;\u0026lt;\u0026thinsp;0,001 for age groups\u0026thinsp;\u0026ge;\u0026thinsp;65 years); Heart drugs (p\u0026thinsp;=\u0026thinsp;0,001 for \u0026ge;\u0026thinsp;75 years); Beta-blockers (p\u0026thinsp;\u0026lt;\u0026thinsp;0,001 for \u0026ge;\u0026thinsp;75 years); Systemic steroids (p\u0026thinsp;\u0026lt;\u0026thinsp;0,001 for 65\u0026ndash;74 years); Chemotherapy (p\u0026thinsp;\u0026lt;\u0026thinsp;0,001 for 41\u0026ndash;74 years); Antacids (p\u0026thinsp;\u0026lt;\u0026thinsp;0,001 for \u0026ge;\u0026thinsp;75 years); Vitamin D or other vitamin supplements (p\u0026thinsp;=\u0026thinsp;0,001 for 64\u0026ndash;74 years); and Inhaled anti-obstructive drugs (p\u0026thinsp;\u0026lt;\u0026thinsp;0,001 for 64\u0026ndash;74 years) were significantly associated with poor survival. Statins and fibrates were significantly associated with a lower risk of death in patients\u0026thinsp;\u0026gt;\u0026thinsp;64 years old.\u003c/p\u003e \u003cp\u003eIn the three age groups\u0026thinsp;\u0026gt;\u0026thinsp;40 years a significant association of antiepileptics (all p\u0026thinsp;\u0026lt;\u0026thinsp;0,001), antipsychotics (all p\u0026thinsp;\u0026lt;\u0026thinsp;0,001), and the group Iron supplements, erythropoietic stimulating agents (ESA), vitamin B12 (B12), and folic acid (FA) (all p\u0026thinsp;\u0026lt;\u0026thinsp;0,001) with a higher risk of all-cause death was observed. Furthermore, for NSAID and other anti-inflammatory drugs, a significant larger probability for survival was observed (all age groups\u0026thinsp;\u0026gt;\u0026thinsp;40: p\u0026thinsp;\u0026lt;\u0026thinsp;0,001). Figure\u0026nbsp;\u003cspan refid=\"Fig4\" class=\"InternalRef\"\u003e4\u003c/span\u003e shows the Kaplan-Meier curves for important prescribed medication groups associated with poor survival while Figure S6 shows the Kaplan-Meier curves of medication groups associated with poor survival in the control cohort.\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003eIn the models including all investigated medication groups, the factor polypharmacy did not remain significant, indicating that polypharmacy alone may not be a predictor of all-cause death, and specific medication groups may be more important factors for outcomes in COVID-19-hospitalized patients.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec9\" class=\"Section2\"\u003e \u003ch2\u003eAll-cause death after COVID-hospital survival\u003c/h2\u003e \u003cp\u003eWe also evaluated all-cause death for the subgroup of patients who survived the COVID-19 hospital stay. Among these patients, 0.76% of males and 0.56% of females aged 19\u0026ndash;40 years died within the first year after hospital discharge. The 1-year mortality rates were 2.91% and 2.28% for males and females aged 41\u0026ndash;64 years, 9.37% and 5.78% for males and females aged 65\u0026ndash;74 years, and 19.02% and 19.26% for males and females aged\u0026thinsp;\u0026ge;\u0026thinsp;75 years, respectively (Table S9).\u003c/p\u003e \u003cp\u003eIn the age groups\u0026thinsp;\u0026gt;\u0026thinsp;40 years, we found significant associations with a higher risk of death after surviving the COVID hospitalization for chemotherapy (all p\u0026thinsp;\u0026lt;\u0026thinsp;0,001) and the medicament group Iron supplements, erythropoietic stimulating agents, B12, and FA (all p\u0026thinsp;\u0026lt;\u0026thinsp;0,001). For antiepileptics, a significantly higher risk of all-cause death was found in the 41\u0026ndash;64 and 65\u0026ndash;74 years age groups, with an observed trend in the oldest age group (p\u0026thinsp;=\u0026thinsp;0,016). Again, antipsychotics were significantly associated with poor survival in all age groups\u0026thinsp;\u0026gt;\u0026thinsp;40 years (all p\u0026thinsp;\u0026lt;\u0026thinsp;0,001).\u003c/p\u003e \u003cp\u003eNSAID and other anti-inflammatory drugs had a significant association with a lower risk of all-cause death after hospital survival in the age groups\u0026thinsp;\u0026gt;\u0026thinsp;40 years (p\u0026thinsp;\u0026lt;\u0026thinsp;0,001) and statins and fibrates in the two age groups\u0026thinsp;\u0026gt;\u0026thinsp;65 years (both p\u0026thinsp;\u0026lt;\u0026thinsp;0,001). Detailed results are shown in Table S12 and Figure S2.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec10\" class=\"Section2\"\u003e \u003ch2\u003eRe-hospitalization due to any reason after COVID-hospital survival\u003c/h2\u003e \u003cp\u003eFor the subgroup of patients who survived the COVID hospital stay, we evaluated the secondary outcome \u0026ldquo;hospitalization due to any cause.\u0026rdquo; In the age groups\u0026thinsp;\u0026gt;\u0026thinsp;40 years, a significantly higher risk of readmission with anticoagulants (all p\u0026thinsp;\u0026lt;\u0026thinsp;0,001), antiepileptics (all p\u0026thinsp;\u0026lt;\u0026thinsp;0,001), systemic steroids (all p\u0026thinsp;\u0026lt;\u0026thinsp;0,001), and chemotherapy (all p\u0026thinsp;\u0026lt;\u0026thinsp;0,008) was observed.\u003c/p\u003e \u003cp\u003eWe found a trend of a higher risk for re-hospitalization for patients receiving antipsychotics (p\u0026thinsp;\u0026lt;\u0026thinsp;0,003 for 19\u0026ndash;64 years and \u0026ge;\u0026thinsp;75 years, p\u0026thinsp;=\u0026thinsp;0,006 for 65\u0026ndash;74 years). Detailed results are shown in Table S13 and Figure S3.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec11\" class=\"Section2\"\u003e \u003ch2\u003eComparison of COVID-Patients to Controls\u003c/h2\u003e \u003cp\u003eThe age-, sex-, and region-matched control population received less medication than the patient population prior to the hospital stay. Interestingly, in controls without medication, remarkably good survival was observed even in the older age groups (Figure S4, S5), whereas a steep decrease in survival after hospital admission was observed in the COVID patients (Figure \u003cspan refid=\"MOESM1\" class=\"InternalRef\"\u003eS1\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eAs a sensitivity analysis, we evaluated the difference between COVID-19-hospitalized patients and the Austrian control population concerning all-cause death and hospitalization due to any reasons. As patients had potentially more severe co-morbidities and received more medication, we attempted to account for this imbalance using propensity score matching (PSM) with the 20 medication groups in addition to the age, sex, and region. Due to too small a number of events, this analysis could not be performed for all-cause death in the 19\u0026ndash;40 years age group.\u003c/p\u003e \u003cp\u003eFor all other subgroups, a significant difference in the risk of all-cause death was found between COVID-19-hospitalized patients and PSM-controls (all p\u0026thinsp;\u0026lt;\u0026thinsp;0,001). For the subgroup of patients surviving the COVID-hospital stay, reduced hazard ratios were observed for the comparison to the PSM-controls. The difference between patients and controls remained significant in the age groups 41\u0026ndash;64 years (p\u0026thinsp;\u0026lt;\u0026thinsp;0,001) and 65\u0026ndash;74 years (p\u0026thinsp;\u0026lt;\u0026thinsp;0,001), but not in the oldest age group (p\u0026thinsp;=\u0026thinsp;0,078). Kaplan-Meier curves for propensity score-matched COVID patients and controls are shown in Figure S7.\u003c/p\u003e \u003cp\u003eConcerning hospitalization due to any reason, in all four age groups, we observed a significantly greater probability of re-hospitalization among the COVID-19-hospitalized patients compared to controls (all p\u0026thinsp;\u0026lt;\u0026thinsp;0,001). These results may indicate that polypharmacy may not completely explain the worse effect in patients with severe COVID-19 (Table S14, Figure S3).\u003c/p\u003e \u003c/div\u003e"},{"header":"Discussion","content":"\u003cp\u003eIn this retrospective study, we evaluated whether baseline medication profiles may be associated with survival or hospitalization due to any reason after COVID-19-related hospitalization in an Austrian population. Hospitalized COVID-19 patients had a higher drug prescription load prior to COVID-hospitalization and increased long-term mortality, especially in patients\u0026thinsp;\u0026gt;\u0026thinsp;75 years old. Pre-COVID prescription of antipsychotic drugs, antiepileptic drugs, chemotherapy, iron/FA/ B12, beta-blockers, and anticoagulants was significantly associated with increased mortality, whereas patients who were prescribed NSAIDs and other anti-inflammatory drugs prior to COVID-19 hospitalization had a significantly lower risk of all-cause death. Due to our study design, we were able to present the \u0026ldquo;real life\u0026rdquo; prescription and mortality rate of patients hospitalized with the diagnosis of COVID-19 in Austria, as well as in a matched control population that was followed from 2020 for up to a maximum of 880 days.\u003c/p\u003e \u003cp\u003ePolypharmacy frequently occurs in patients with COVID-19\u003csup\u003e15\u003c/sup\u003e and may be associated with increased morbidity and mortality.\u003csup\u003e15,16\u003c/sup\u003e Analyzing the prescription data in the control population, we detected no drug prescription in the investigated medication groups in 77% (19\u0026ndash;40 years), 69% (41\u0026ndash;64 years), 56% (65\u0026ndash;74 years), and 41% (\u0026ge;\u0026thinsp;75 years) of controls. In contrast, 30% (19\u0026ndash;40 years), 15% (41\u0026ndash;64 years), 6% (65\u0026ndash;74 years), and 4% (\u0026ge;\u0026thinsp;75 years) of COVID-19-hospitalized patients had no drug prescription.\u003c/p\u003e \u003cp\u003eWe found that the intake of antipsychotic drugs was associated with a significant increased risk of death. The estimated mortality for patients with prescribed antipsychotic drugs was 56.3% (CI: 54.7\u0026ndash;57.9) within a two-years follow up in the age group\u0026thinsp;\u0026ge;\u0026thinsp;75 years as compared to 40.83% (CI: 42.5\u0026ndash;39.1) for patients without antipsychotic drugs. The mortality rate for controls with prescribed antipsychotic drugs was 32.9% (CI: 32.1\u0026ndash;33.7) as compared to patients not receiving these drugs (11.4%, CI: 11.7\u0026ndash;11.1) within two years follow-up for patients\u0026thinsp;\u0026ge;\u0026thinsp;75 years.\u003c/p\u003e \u003cp\u003eThe association of anti-inflammatory medication, such as NSAIDs, with lower mortality could be attributed to the pathophysiology of COVID-19, with a high pro-inflammatory state in the second phase of the disease, also referred to as the cytokine storm phase,\u003csup\u003e19,20\u003c/sup\u003e which could be attenuated by anti-inflammatory medication. In patients with high pro-inflammatory state in need of oxygen therapy and SARS-CoV-2-associated lung infiltration, anti-inflammatory therapy (e.g., dexamethasone) was the only pharmacological intervention to reduce mortality.\u003csup\u003e21\u003c/sup\u003e However, we cannot exclude the possibility of bias by indication with the selection of possibly healthier patients using NSAIDS on a regular basis because more comorbid patients (e.g., with chronic kidney disease, diabetes, cardiovascular disease, or heart failure) are regularly advised to avoid NSAIDs.\u003c/p\u003e \u003cp\u003eThe pronounced association of antipsychotic medication with higher mortality is assumed to be related to the comorbidities in a population that has high prescription rates of such medication, especially at higher ages. Antipsychotic drugs are associated with severe COVID-19 morbidity and mortality.\u003csup\u003e16\u003c/sup\u003e Antipsychotic medication is mainly prescribed to patients in resident or nursing homes with dementia or behavioral disorders.\u003csup\u003e22,23\u003c/sup\u003e The number of antipsychotic prescriptions is higher in nursing homes (57,1%) compared to residential homes (29,5%),\u003csup\u003e24\u003c/sup\u003e emphasizing more frequent prescriptions in a more morbid population. Therefore, chronic psychotic medication use at more advanced ages is most probably indicative of patients with severe impairments.\u003csup\u003e25\u0026ndash;27\u003c/sup\u003e\u003c/p\u003e \u003cp\u003eAmong nursing home residents, a significant positive association was found between the total number of medications and 30-day COVID-related adjusted mortality.\u003csup\u003e17\u003c/sup\u003e After additional correction for dementia and use of Proton pump inhibitors (PPI), vitamin D, antipsychotics, and antithrombotics, this effect was no longer significant, suggesting that polypharmacy itself may not be the problem, but the type of medication. In our analyses, polypharmacy did not remain significant after correcting for several medication groups.\u003c/p\u003e \u003cp\u003eIn patients who were discharged alive from a COVID-19-related hospitalization, the risk of post discharge death of patients\u0026thinsp;\u0026gt;\u0026thinsp;64 of age within 180 days was nearly twice that observed in historical controls admitted to the hospital with influenza. \u003csup\u003e12\u003c/sup\u003e Although readmission after COVID-19-related hospitalization was common, the frequency by 180 days was similar to the frequency of patients discharged alive from influenza-related hospitalization. Furthermore, crude differences in drug use between COVID-19 patients and the general population were found in antithrombotic agents, antiepileptics, anti-gout preparations, and cardiac therapy. \u003csup\u003e14\u003c/sup\u003e\u003c/p\u003e \u003cp\u003eScant data are available on clinical outcomes in patients discharged alive from COVID-19 hospitalization. Data from a large study with patients discharged after COVID-19 reported an increased risk of readmission and mortality during a follow-up of 140 days.\u003csup\u003e10\u003c/sup\u003e In a German cohort of hospitalized COVID-19 patients, 30-day all-cause mortality was 23,9% and 180-day all-cause mortality 29,6%. Another study after COVID-19 hospitalization among patients in Italy reported an 8% age-related overall relative increase in all-cause death after 6 months of follow-up.\u003csup\u003e13\u003c/sup\u003e However, age was the only independent predictor of mortality after multivariate analysis. Another report from the US investigated the 12-month mortality after recovery from the initial episode of COVID-19 and reported a significantly higher 12-month adjusted all-cause mortality risk for patients with severe COVID-19 compared to both COVID-19-negative patients and mild COVID-19 patients. A large retrospective long-term outcome cohort study indicated an overall 2-year mortality risk that was worse by day 180 among those infected with COVID-19 compared to matched uninfected comparators, but there was no excess mortality during the subsequent 1,5 years.\u003csup\u003e28\u003c/sup\u003e In our study, we observed increased long-term mortality and increased risk of hospitalization due to any reason after surviving COVID-19 hospitalization. Mortality was more pronounced within the first 50 days after index-hospitalization.\u003c/p\u003e \u003cp\u003eThe main strength of this study is the use of a large, representative, real-world national database. The retrospective design, however, is a limitation, which we sought to mitigate by including several potential confounding factors in the statistical models and performing propensity score matching to support meaningful comparisons. Yet, as in any observational research, even with the large sample size and long-term follow-up, unmeasured confounding leading to bias is still possible. The study population was drawn exclusively from the Austrian Health Insurance Funds, raising potential concerns about the generalizability and external validity of the findings to a broader patient population. Furthermore, no information was available from the Austrian Insurance Fund on vaccination or intensive care in hospitals. However, vaccination was first available in the very end of 2020 and therefore, it may not be an important factor for patients hospitalized with COVID-19 in 2020.\u003c/p\u003e \u003cp\u003eIn conclusion, this large Austrian cohort of COVID-19-hospitalized patients and matched controls an increased short- and long-term risk of mortality was observed. Patients hospitalized with COVID-19 had a higher drug prescription load (polypharmacy). Antipsychotics were significantly associated with poor survival in patients\u0026thinsp;\u0026gt;\u0026thinsp;40 years old. Our findings may help identify the most vulnerable patients at higher risk of mortality after COVID-19 discharge regardless of age by screening prescribed medication groups, with implications for preventive measures. Antipsychotics are assumed to be an underrecognized medication group linked to worse patient outcomes after COVID-19 hospitalization.\u003c/p\u003e"},{"header":"Declarations","content":"\u003cp\u003e\u003cstrong\u003eAcknowledgements:\u003c/strong\u003e\u003cstrong\u003e\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eWe thank the anonymous referees and editors for their support. This work was financed by ARGE Ankersmit of the surgical research laboratory.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAuthor Contributions Statement\u003c/strong\u003e\u003cstrong\u003e:\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eH.J.A., B.R., R.W., and A.C.G. were responsible for conceptualization. P.K., B.R., A.C.G., C.W., J.M., and H.J.A. conceived the study and curated the data. C.W., P.K., and A.C.G. cleaned, analyzed, and verified the underlying data. H.J.A., A.C.G., R.W., C.W., and P.K. wrote the paper and visualized the data. A.C.G., B.R., C.W., P.K., D.T-W., M.M., J.M., C.A., J.A., R.W., and H.J.A. commented on the paper, oversaw the analysis, and edited the final manuscript. All authors contributed to drafting the paper and revised the manuscript for important intellectual content.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eConflict of Interest:\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe authors declare no conflict of interest.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eData availability Statement:\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eData that support the findings of this study are available upon request from the corresponding author.\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\n\u003cli\u003eWilliamson EJ., Walker AJ., Bhaskaran K., Bacon S., Bate X., Morton CE., Curtis HJ., Mehrkar A., Evans D., Inglesby P., Cockburn J., McDonald H., MacKenna B., Tomlinson L., Douglas IJ., Rentsch CT., Mathur R., Wong AYS., Grieve R., Harrisonh D., Forbes H., Schultze A., Croker R., Parry J., Hester F., Harper S., Perera R., Evans SJW., Liam Smeeth, Goldacre B. (2020). 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(2023) Late Mortality After COVID-19 Infection Among US Veterans vs Risk-Matched Comparators. \u003cem\u003eJAMA Intern Med., \u003c/em\u003e183 (10): 1111-1119. \u003c/li\u003e\n\u003c/ol\u003e"}],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":true,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":false,"hideJournal":true,"highlight":"","institution":"","isAcceptedByJournal":false,"isAuthorSuppliedPdf":false,"isDeskRejected":"","isHiddenFromSearch":false,"isInQc":false,"isInWorkflow":false,"isPdf":false,"isPdfUpToDate":true,"isWithdrawnOrRetracted":false,"journal":{"display":true,"email":"[email protected]","identity":"researchsquare","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":true,"externalIdentity":"","sideBox":"","snPcode":"","submissionUrl":"/submission","title":"Research Square","twitterHandle":"researchsquare","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"","reportingPortfolio":"","inReviewEnabled":false,"inReviewRevisionsEnabled":true},"keywords":"Covid-19 hospitalization, all-cause mortality, polypharmacy, baseline drug treatments, readmission, population-based observational study","lastPublishedDoi":"10.21203/rs.3.rs-4872684/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-4872684/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003cp\u003eLimited data are available on long-term morbidity and mortality after COVID-19 hospitalization. In this population-based study, we investigated the long-term mortality and morbidity after COVID-19 hospitalization and associations with baseline drug treatments. Data were provided on hospitalized COVID-19 patients in 2020 and matched controls by the Austrian Health Insurance Funds. The primary outcome was all-cause mortality. Secondary outcomes were all-cause mortality conditional on COVID-hospital survival and re-hospitalization due to any reason. The median follow-up was 600 days. 22 571 patients aged\u0026thinsp;\u0026gt;\u0026thinsp;18 years were hospitalized in Austria in 2020 due to COVID-19. The risk of all-cause mortality was significantly higher with polypharmacy. With the exception of the youngest age group (19\u0026ndash;40 years), antiepileptics, antipsychotics and the medicament group of iron supplements, erythropoietic stimulating agents, Vitamin B12, and folic acid were significantly associated with a higher risk of death (all p\u0026thinsp;\u0026lt;\u0026thinsp;0,001). For Non-steroidal anti-inflammatory drugs and other anti-inflammatory drugs, significantly increased survival was observed (all p\u0026thinsp;\u0026lt;\u0026thinsp;0,001). Patients had a higher drug prescription load than the control population. Long-term mortality and the risk of re-hospitalization due to any reason were also significantly greater in the patients. Antipsychotics are assumed to be an underrecognized medication group linked to worse outcomes after COVID-19 hospitalization.\u003c/p\u003e","manuscriptTitle":"Baseline drug treatments and long-term outcomes in COVID- 19-hospitalized patients: results of the 2020 AUTCOV study","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2024-09-16 00:37:33","doi":"10.21203/rs.3.rs-4872684/v1","editorialEvents":[{"type":"communityComments","content":0}],"status":"published","journal":{"display":true,"email":"[email protected]","identity":"researchsquare","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":true,"externalIdentity":"","sideBox":"","snPcode":"","submissionUrl":"/submission","title":"Research Square","twitterHandle":"researchsquare","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"","reportingPortfolio":"","inReviewEnabled":false,"inReviewRevisionsEnabled":true}}],"origin":"","ownerIdentity":"fe8d7dd3-1e80-482a-9f9c-6f93367a1acf","owner":[],"postedDate":"September 16th, 2024","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"posted","subjectAreas":[],"tags":[],"updatedAt":"2024-11-18T07:54:43+00:00","versionOfRecord":[],"versionCreatedAt":"2024-09-16 00:37:33","video":"","vorDoi":"","vorDoiUrl":"","workflowStages":[]},"version":"v1","identity":"rs-4872684","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-4872684","identity":"rs-4872684","version":["v1"]},"buildId":"qtupq5eGEP_6zYnWcrvyt","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}