Post Recovery Impact of the Second and Third Sars-Cov-2 Infection Waves on Healthcare Resource Utilization in Lombardy, Italy

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The administrative claims database of the Italian region Lombardy, the first in Europe to be hit by the SARS-CoV-2 pandemic, was employed to evaluate the impact on healthcare resource utilization following recovery from the second (mainly alpha-related variant) and third (delta-related) infection waves. Setting and design . 317.164 individuals recovered from the infection and became negative after the second wave, 271.180 after the third. Of them, 1571 (0.5%) and 1575 (0.6%) died in the first 6 post-negativization months. In the remaining cases (315.593 after the second wave and 269.605 after the third) , hospitalizations, attendances to emergency rooms and outpatient visits were compared with those recorded in the same pre-pandemic time periods in 2019. Dispensation of drugs as well as of imaging, functional and biochemical diagnostic tests were also compared as additional proxies of the healthcare impact of the second and third SARS-CoV-2 infection waves. Main results . Following both waves, hospitalizations, attendances at emergency rooms and outpatient visits were similar in number and rates to the pre-pandemic periods. However, there was an increased dispensation of drugs and diagnostic tests, particularly those addressing the cardiorespiratory and blood systems. Conclusions . In a large region such as Lombardy taken as a relevant model because early and severely hit by the SARS-CoV-2 pandemic, the post- COVID burden on healthcare facilities was mildly relevant in cases who recovered from the second and third infection waves regarding such pivotal events as deaths, hospitalizations and need for emergency room and outpatient visits, but was high regarding the dispensation of a few drug classes and types of diagnostic tests. Administrative database Lombardy region long-COVID drugs post-COVID condition polypharmacy diagnostic tests Introduction The Italian region Lombardy (10.1 million population) was the first after mainland China to be harassed by the SARS-CoV-2 pandemic and associated COVID-19 with an early dramatic toll of infections, deaths and hospitalizations that warranted a prolonged and strict lockdown [ 1 ]. As of 31 December 2022, all the infected cases have been 4.065.873 (39% of the region population) with as many as 44.688 deaths and important negative impacts on economy and quality of life [ 2 , 3 ]. At the time being in 2023 the high rate of vaccinations [ 3 ], accrued expertise on COVID-19 management, non-pharmacological interventions such as masks plus physical distancing, as well as and more effective therapeutic weapons (monoclonal antibodies, antiviral drugs) are decreasing the clinical severity of the still very high rate of new infections due to the omicron virus variants [ 4 – 7 ]. However, there is concern, both in the region and globally, for the public health and healthcare impact of cases who recovered from the viral infection [ 8 – 11 ]. We previously chose to analyze and report the data stemming from the Lombardy healthcare database in order to evaluate, after the first SARS-CoV-2 wave due to the original virus lineage, the post-COVID burden on the regional health service [ 12 , 13 ]. During the first 6 months after negativization, there was a high mortality rate, and the utilization of regional healthcare such as hospitalizations, emergency room and outpatient visits was much more prominent than in the same persons in the corresponding semester of 2019, taken for comparison because at the time the region was not yet hit by the pandemic [ 12 ]. However, in the frame of a longer post infection period, the post-COVID healthcare burden of the first pandemic wave became progressively less prominent [ 13 ]. It remained to be established whether or not the strong early impact of the post-infection period on the regional healthcare facilities differed in the frame of subsequent infection waves, which occurred following the original virus lineage. With this background and gaps of knowledge, we elected to interrogate again the regional database in order to evaluate the healthcare burden in those cases who recovered from the second infection wave in the last few months of 2020, and from the third in the first few months of 2021. Material and methods This is a retrospective cohort study of Lombardy residents who became SARS-CoV-2 positive between October 1 and December 31 2020 when the viral infection was principally due to the alpha virus variant (B.1.1.7), as well as between February 1 and May 31 2021 during the third wave principally due to the delta variant (B.1.1.617.2). In the cases who recovered and became SARS-CoV-2 negative, we obtained from the database throughout a 6-month post-negativization period the number of incident deaths. In addition, hospitalizations, visits to hospital emergency rooms without admission as well as general medicine and specialty outpatient visits were analyzed at the end of the forementioned follow-up periods. Number and types of dispensed drugs plus imaging, functional and biochemical tests dispensed for diagnostic purposes were also extracted from the database. Moreover, at the end of the follow-up periods post-recovery data were compared with those recorded before the onset of the pandemic during the corresponding 6 month 2019 periods (February 1 – July 31 for the second wave and July 1 – December 31 for the third), chosen for comparison because at that time Lombardy was free from SARS-CoV-2. The administrative healthcare database has been implemented by the Welfare Directorate of the Lombardy region since the year 2000 for claims and administrative purposes, and more details on its features have been provided [ 12 ]. In brief, it offers a comprehensive picture of all the Lombardy residents and contains their demographic data, life status, incident hospital admissions, outpatient primary care and specialty visits as well as attendances at hospital emergency rooms without subsequent admission. It also provides data on medications reimbursed by the regional health service and dispensed by pharmacies upon medical prescription, as well as on the dispensation for diagnostic purposes of imaging, functional and biochemical tests. The medication database contains drug name and anatomic therapeutic chemical (ATC) classification code [ 14 ]. However, no information is available for dispensations during hospital stay, in nursing homes nor for those purchased out-of-pocket. Moreover, results of diagnostic tests are not recorded in the database. Statistical analysis The main results are presented as means and standard deviations or medians and interquartile ranges for continuous variables and numbers with percentages for categorical ones. The events that occurred during the post-recovery semester after each wave were compared with those recorded in the same cases in the second semester of 2019 (non-COVID year in Lombardy). The McNemar test was used for comparisons between semesters, except for the total number of drugs when the Wilcoxon signed-rank for paired data was used. The generalized estimating equations logistic regression were used to fit a marginal model producing odds ratios (ORs) of change of the third wave (follow-up against before period) versus the second wave (follow-up against before period). For the number of drugs a generalized estimating equations linear regression was used. All the models were adjusted for age, sex and number of drugs (the latter was not included as a corrector when estimating the effects of number of drugs and polypharmacy). Statistical analysis was performed using SAS software, SAS Institute Inc., Cary, NC, USA. Results Cohort characteristics . Table 1 shows the number of cases who, objectively diagnosed as infected by SARS-CoV-2 during the second (N=317.164) and third (N=271.180) waves, became negative within one month after the first positive test and were followed up along six post-negativization months. The table also contains their gender and age classes, the site of infection management and incident deaths during these post-infection periods, showing that the two cohorts of recovered cases were similar pertaining to age groups, deaths and sites of infection management. Post-recovery findings . Table 2 shows the impact of the second and third waves in the 6-month post-negativization period pertaining to hospital admissions, attendances at hospital emergency rooms without subsequent admission and visits at least once at general or specialist outpatient medical settings. These data were compared with those from the same persons in the corresponding pandemic-free periods of the year 2019, even though this comparison was obviously not done for the cases who died in the post-recovery period. The table shows that the post-negativization numbers and rates of incident hospitalizations, emergency room attendances and outpatient visits were similar or lower than in the pre-pandemic period. Table 3 shows that in comparison with the pre-pandemic period polypharmacy, i.e., the chronic use of 5 or more drugs, slightly increased after both the second and third viral waves. Pertaining to the drug classes according to the ATC first level, there was a dispensation rise following both the second and third wave, for drugs of class A (alimentary tract and metabolism), B (blood and blood forming organs), C (cardiovascular system), M (musculoskeletal system) and N (nervous system), whereas for the remaining ATC drug classes dispensation rates were similar or even lower than in the corresponding pre-pandemic period, notwithstanding statistically significant differences due to the large sample size. Table 4 compares imaging, functional and biochemical diagnostic tests dispensed after recovery from the second and third infection waves with the corresponding pre-pandemic periods. The most evident increases, with rates that nearly doubled after both waves, were for tests exploring the respiratory system such as spirometry and chest CT scans as well as the cardiovascular system such as echocardiography. Pertaining to biochemical tests there was a markedly increased dispensation for C reactive protein, serum creatinine, coagulation tests and whole blood count. We calculated if changes in resource consumption from before to after COVID periods were different between the two waves (Tables 2, 3 and 4: ORs and estimated mean). A positive interaction (higher increase or lower decrease in the third wave relative to the second wave) was found for hospitalizations, emergency room attendances without hospital admission (Table 2), polypharmacy, chronic polypharmacy and for alimentary tract and metabolism drugs. blood and blood forming organs drugs, cardiovascular system drugs, systemic hormonal preparations, anti-infectives for systemic use, respiratory system drugs and sensory organs drugs (Table 3). The only larger decrease in the third wave was visible for outpatient visits. In the case of imaging, functional and biochemical tests the increase in the third wave was higher relative to the second wave only for chest CT scan (Table 4), while in almost all the other cases the increase was lower in the third wave. Mean number of drugs increased slightly more in the second wave (Table 3). Discussion The post-infection impact on health services of the SARS-Cov-2 pandemic has been the topic of several studies [ 10 , 15 – 20 ]. Among them, we recently reported data based upon an administrative database on the consequences along the first six months after recovery from the first SARS-CoV-2 infection wave in terms of deaths, hospitalizations, attendances at hospital emergency rooms, outpatient medical visits and drug dispensation [ 12 ]. However, after this early increase the post-recovery impact on the regional healthcare facilities did progressively decrease [ 13 ]. In the present analysis of the second and third infection waves a mild impact pertained to deaths, hospitalizations and outpatients visits but the healthcare impact was important regarding to a few dispensed drugs and diagnostic tests. Lombardy is an adequate model to study the post-infection healthcare resource utilization, because this Italian region (10 million inhabitants with a high density) was the first large area after China to be heavily hit by SARS-CoV-2 [ 1 – 3 ]. During the early pandemic period from March to May 2020 the total number of deaths registered in the region did increase by + 212%% in comparison with the average numbers recorded in the same months of the pre-pandemic years 2015–2019 [ 21 ]. Because scanty data are generally available on healthcare resource utilization following infection waves other than the first, we chose to evaluate 585.198 cases who did recover (315.593 after the second and 269.605 after the third wave) throughout the first six months post-negativization regarding deaths, hospitalizations, attendances at hospital emergency rooms, outpatients medical visits, dispensation of drugs and an array of imaging, instrumental and biochemical diagnostic tests. These data were compared with those obtained in the same persons before the pandemic outbreak in the corresponding months of 2019, so that each patient was his own comparator before and after the pandemic. Obviously, this comparison could not be done pertaining to deaths in the post-recovery period, but the crude mortality rates recorded after both waves were much lower than after the first wave (0.5% after the second and 0.6 after the third versus 3.9% after the first) [ 12 ]. We chose to confine our analysis to the post-infection impact of the second and third infection waves instead of directly comparing the related data with those of the first, because the epidemiological, medical and social scenarios were strikingly different regarding age, mortality, rate of hospitalisation and availability of pharmacological and non-pharmacological interventions, even though vaccination was not yet started in Lombardy at the time of the second wave and involved a small number of citizens infected during the third wave. Main findings were that after both waves hospitalizations, emergency room attendances and outpatient visits were similar or even lower than in the corresponding pre-COVID 2019 periods. Only the more compromised patients discharged from ICU, a minority in the whole cohort ranging from 0,3% to the 0,6%, needed more hospital admissions in the second and third waves than in 2019. Thus, the data reported herewith depict a scenario completely different from that of the first six months following the first dramatic wave due to the original SARS-CoV-2 lineage [ 12 ]. This scenario of relatively mild burden on the regional health service regarding pivotal events such as deaths, hospitalizations and outpatient visits is at a variance with that regarding a few dispensed drugs and diagnostic tests. A definite increase in dispensation was registered for ATC drug classes addressing the cardiorespiratory, blood and central nervous systems. In addition, diagnostic tests such as chest CT scans showed the most important increase with nearly tripled numbers, but also spirometry, electrocardiogram, echocardiography and a number of blood tests (i.e., complete blood count and coagulation tests) were performed more frequently than in 2019, particularly after the second wave. The finding of less hospitalizations, emergency room and outpatient visits contrasting with the increased dispensation of a number of drugs classes and diagnostic tests might derive from various causes including logistic factors, health management habits by family doctors and self-medication. Another potential cause is a rebound effect after the strict lockdown and subsequent containment measures. However, a rebound effect was not actually recorded in the region, that in general witnessed a continuing low dispensation of all resources related to healthcare. In addition, the increase of drug and diagnostic test dispensation was not uniform as it would be expected in the frame of a rebound effect, but preferentially confined to drugs and tests related to organs and body systems such as the heart, lung, blood and the central nervous system, that are frequently involved in the post-infection sequelae. The important growth of CT scan and spirometry dispensation is perhaps due to the persistence over time of respiratory discomfort and other subjective symptoms, as well as to monitoring the risk of fibrotic lung evolution. The increase of blood and coagulation drugs and diagnostic tests may be linked to the post-recovery persistence of the abnormalities recorded during the acute infection, but also to the reluctant deprescribing by family doctors. This study has the limitations of those based upon administrative databases, because data could not be audited and a number of details are not available from the database. For instance, we have no direct information on the main signs and symptoms in the post-recovery period, so that for instance the persistence of respiratory discomfort explains only hypothetically the important increase in the use of chest CT scans and spirometry. On the other hand, administrative data indeed offer an opportunity to monitor the access to pivotal health care resources of large populations, that in this study are represented by an important and densely populated European region. The epidemiology of the second and third wave were quite similar in different European regions and Lombardy mirrors this pattern [ 22 , 23 ]. Other strengths of this study are the large number of cases, the real-life approach and particularly that infected cases were their own comparator owing to the availability of the corresponding data in the year before the onset of the pandemic. In conclusion this report, describing the post- infection healthcare burden of 585.198 cases who recovered from the second and third infection waves, indicates that during the periods considered the post-COVID burden was much smaller than following the early post-recovery period after the first wave due to the original Wuhan SARS-CoV-2 lineage. Declarations Acknowledgements We thank Giuseppe Preziosi, Monica Arivetti and Giovanna Rigotti from ARIA S.p.A, Alfredo Bevilacqua from Laife Reply S.p.A., Marco D. Forlani from T Bridge - BV Tech S.p.A and Igor Monti from Istituto di Ricerche Farmacologiche Mario Negri IRCCS who kindly assisted us with data collection. We also thank Regione Lombardia, Grant/Award Number: DGR n. 3375 - July 14, 2020; Health Ministry of the Lombardy, Grant/Award Number: EPIFARM-Pharmaco-epidemiology. Author contributions SH, PMM, AN, MT, GR, BDA, AAG, study concept and design; IF and OL acquisition of data; SH, PMM, AN, MT, BDA, AAG, analysis and interpretation of data; SH, PMM, AN, BDA, MT drafting of the manuscript. All authors were involved in the critical revision of the manuscript for important intellectual content. All authors approved the final version of the manuscript. Conflict of interest None of the authors declares to have conflict of interest. Human and animal rights and informed consent This study was conducted according to the principles expressed in the Declaration of Helsinki and its later amendments, and approved by local Institutional Review Board (0051814/19). Being a retrospective study performed on an administrative database of anonymized patients, patient informed consent was waived. Funding Regione Lombardia, Grant/Award Number: DGR n. 3375 - July 14, 2020; Health Ministry of the Lombardy, Grant/Award Number: EPIFARM-Pharmaco-epidemiology. References Consolandi E (2021) Chapter 1.3—Evolution and intensity of infection in Lombardy. In: Casti E, Adobati F, Negri I, editors. Mapping the epidemic: a systemic geography of COVID-19 in Italy, volume 9. https://www.sciencedirect.com/science/article/abs/pii/B9780323910613000089. 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J Rehabil Med. 54:jrm00339. https://doi:10.2340/jrm.v54.4363. https://www.istat.it/it/files/2020/03/tabella-regionale-decessi-totali-14022023-1.xlsx Accessed March, 2023 WHO Coronavirus (COVID-19) Dashboard https://covid19.who.int/?adgroupsurvey={adgroupsurvey}&gclid=Cj0KCQiA9YugBhCZARIsAACXxeJkPe1pSdC-swv1pFpfwArZO3t0XPc_C1bASCfeS08cdK8jQUJZm9gaAkbjEALw_wcB. Accessed March, 2023 WHO Coronavirus (COVID-19) ITALY: https://covid19.who.int/region/euro/country/it Accessed March, 2023 Tables Table1. Characteristics of SARS-CoV-2 infected cases who recovered from the second and third infection waves and were followed-up for a 6 month period Variables 2nd wave follow-up period 01/02/2021 – 31/07/2021 3rd wave follow-up period (01/07/2021 – 31/12/2021) Overall cohort included in the analysis N=317164 N=271180 Sex, females, N (%) 161739 (51.0) 135733 (50.1) Age, mean (SD) 43.1 (20.0) 43.6 (21.3) Age groups <=29 87579 (27.6) 76718 (28.3) 30-49 102156 (32.2) 81240 (30.0) 50-69 94705 (29.9) 79995 (29.5) 70+ 32724 (10.3) 33227 (12.3) Site of management - Home 290117 (91.5) 242627 (89.5) - Hospital medical ward 26193 (8.3) 27024 (10.0) - ICU 854 (0.3) 1529 (0.6) Alive at the end of follow-up 315.593 (99.5) 269.605 (99.4) Died during follow-up 1571 (0.5) 1575 (0.6) Died during the follow-up period in relation to the original site of management - Home 868 (0.3) 762 (0.3) - Hospital medical wards 688 (2.6) 785 (2.9) - ICU 15 (1.8) 28 (1.8) Table 2. Hospitalizations, emergency room attendances and outpatient visits after recovery from the second and third infection waves. Barring for deaths, data were compared with those obtained in the same persons in the corresponding time periods before the pandemic in 2019 2nd wave 3rd wave OR (95% C.I.) Before the pandemic Follow-up period Before the pandemic Follow-up period Follow-up period after recovery 01/02/2019 – 31/07/2019 01/02/2021 – 31/07/2021 change p-value 01/07/2019 – 31/12/2019 01/07/2021 – 31/12/2021 change p-value Alive at the end of follow-up period N=585 198 N=315593 N=315593 N= 269605 N= 269605 Number and rate of patients with at least one hospitalization 14115 (4.5) 12506 (4.0) <0.0001 11168 (4.1) 11152 (4.1) 0.9096 1.09 (1.05;1.13) Number and rate of patients with at least one emergency room attendance without subsequent hospitalization 42462 (13.5) 33546 (10.6) <0.0001 35031 (13.0) 31960 (11.9) <0.0001 1.15 (1.12; 1.17) Number and rate of patients who underwent at least one outpatient visit 120734 (38.3) 118207 (37.5) <0.0001 101557 (37.7) 100322 (37.2) <0.0001 0.97 (0.96; 0.99) Change p-value: McNemar test OR (95% C.I.): adjusted for age, sex and number of drugs. Generalized Estimate Equation model (Interaction between 3rd vs. 2nd wave and follow-up vs. before period) Table 3. Comparison of drugs dispensed before the pandemic ( 2019) and after recovery from the second and third infection waves 2nd wave 3rd wave Before the pandemic Follow-up period Before the pandemic Follow-up period 01/02/2019 – 31/07/2019 01/02/2021 – 31/07/2021 change p-value 01/07/2019 – 31/12/2019 01/07/2021 – 31/12/2021 change p-value Alive at the end of follow-up period N=585 198 N=315593 N=315593 N=269605 N=269605 Mean (SD) Median (IQR) Mean (SD) Median (IQR) Mean (SD) Median (IQR) Mean (SD) Median (IQR) estimated mean (95% C.I.) Number of prescribed drugs 1.5 (2.4) 0 (0-2) 1.6 (2.6) 0 (0-2) <0.0001 1.4 (2.4) 0 (0-2) 1.6 (2.6) 0 (0-2) <0.0001 0.12 (0.11; 0.13) N (%) N (%) N (%) N (%) OR (95% C.I.) Polypharmacy (5 or more drugs) 33125 (10.5) 36525 (11.6) <0.0001 27093 (10.0) 32352 (12.0) <0.0001 1.13 (1.11; 1.15) Chronic polypharmacy 14443 (4.6) 18316 (5.8) <0.0001 11843 (4.4) 15419 (5.7) <0.0001 1.03 (1.01; 1.06) Drug classes by ATC 1st level classification, N (%) (number of cases with at least one drug in the class) - A 61887 (19.6) 68735 (21.8) <0.0001 50452 (18.7) 56923 (21.1) <0.0001 1.03 (1.01; 1.04) - B 27130 (8.6) 32232 (10.2) <0.0001 23119 (8.6) 28286 (10.5) <0.0001 1.04 (1.02; 1.06) - C 59351 (18.8) 68061 (21.6) <0.0001 52726 (19.6) 60389 (22.4) <0.0001 1.01 (1.00; 1.02) - D 2165 (0.7) 2375 (0.8) 0.0002 1559 (0.6) 1613 (0.6) 0.2624 0.94 (0.87; 1.02) - G 10611 (3.4) 11920 (3.8) <0.0001 8917 (3.3) 10027 (3.7) <0.0001 1.00 (0.98; 1.03) - H 19542 (6.2) 20412 (6.5) <0.0001 15813 (5.9) 17391 (6.5) <0.0001 1.06 (1.03; 1.08) - J 67155 (21.3) 42416 (13.4) <0.0001 51259 (19.0) 46308 (17.2) <0.0001 1.54 (1.51; 1.57) - L 4882 (1.5) 6219 (2.0) <0.0001 4006 (1.5) 5048 (1.9) <0.0001 0.99 (0.96; 1.02) - M 22228 (7.0) 24138 (7.6) <0.0001 18516 (6.9) 20300 (7.5) <0.0001 1.01 (0.99; 1.04) - N 25910 (8.2) 30238 (9.6) <0.0001 21996 (8.2) 25419 (9.4) <0.0001 0.99 (0.97; 1.01) - P 1799 (0.6) 1766 (0.6) 0.4848 1745 (0.6) 1633 (0.6) 0.0144 0.95 (0.88; 1.03) - R 28774 (9.1) 25102 (8.0) <0.0001 18857 (7.0) 20123 (7.5) <0.0001 1.25 (1.22; 1.27) - S 3611 (1.1) 3838 (1.2) <0.0001 2955 (1.1) 3264 (1.2) <0.0001 1.04 (1.01; 1.08) - V 1004 (0.3) 1929 (0.6) <0.0001 657 (0.2) 1515 (0.6) <0.0001 1.20 (1.10; 1.32) Legend : A =Alimentary tract and metabolism. B =Blood and blood forming organs. C =Cardiovascular system. D =Dermatologicals. G =Genito-urinary system and sex hormones. H =Systemic hormonal preparations, excluding sex hormones and insulins. J =Anti-infectives for systemic use. L =Antineoplastic and immunomodulating agents. M =Musculo-skeletal system, N =Nervous system. P =Antiparasitic products, insecticides and repellents. R =Respiratory system. S =Sensory organs. V =Various. Change p-value: McNemar test, except for number of dispensed drugs, (Wilcoxon signed-rank for paired data). OR (95% C.I.): adjusted for age and sex. Generalized Estimate Equation model (Interaction between 3rd vs. 2nd wave and follow-up vs. before period) Table 4. Comparison of imaging, functional and biochemical tests dispensed before the pandemic ( 2019) and after recovery from the second and third infection waves ( 2021) 2nd wave 3rd wave OR (95% C.I.) Before the pandemic Follow-up period Before the pandemic Follow-up period 01/02/2019 – 31/07/2019 01/02/2021 – 31/07/2021 change p-value 01/07/2019 – 31/12/2019 01/07/2021 – 31/12/2021 change p-value Alive at the end of follow-up period N=585 198 N=315593 N=315593 N= 269605 N= 269605 Number of patients with at least one exam N (%) N (%) N (%) N (%) Spirometry 6643 (2.1) 11002 (3.5) <0.0001 10590 (3.9) 12946 (4.8) <0.0001 0.71 (0.69; 0.74) Chest CT scan 3143 (1.0) 10480 (3.3) <0.0001 2301 (0.9) 8654 (3.2) <0.0001 1.10 (1.03; 1.16) Electrocardiogram 20701 (6.6) 23506 (7.4) <0.0001 16678 (6.2) 18638 (6.9) <0.0001 0.95 (0.92; 0.97) Echocardiogram 8.67 (2.6) 13652 (4.3) <0.0001 6495 (2.4) 10784 (4.0) <0.0001 0.97 (0.93; 1.01) C-reactive protein 28308 (9.0) 35957 (11.4) <0.0001 21081 (7.8) 26906 (10.0) <0.0001 0.96 (0.94; 0.99) Whole blood count 81575 (25.8) 98291 (31.1) <0.0001 63079 (23.4) 73731 (27.3) <0.0001 0.91 (0.90; 0.93) Creatinine 69664 (22.1) 86400 (27.4) <0.0001 53277 (19.8) 65071 (24.1) <0.0001 0.93 (0.91; 0.95) Glycemia 68388 (21.7) 82760 (26.2) <0.0001 52384 (19.4) 62006 (23.0) <0.0001 0.93 (0.91; 0.95) Prothrombin time 15780 (5.0) 20957 (6.6) <0.0001 12754 (4.7) 16461 (6.1) <0.0001 0.93 (0.91; 0.96) D-dimer 1832 (0.6) 8081 (2.6) <0.0001 1420 (0.5) 5693 (2.1) <0.0001 0.88 (0.82; 0.95) AST 56388 (17.9) 72463 (23.0) <0.0001 42711 (15.8) 53618 (19.9) <0.0001 0.93 (0.91; 0.95) ALT 61613 (19.5) 78045 (24.7) <0.0001 46882 (17.4) 58405 (21.7) <0.0001 0.94 (0.92; 0.95) LDH 11443 (3.6) 15559 (4.9) <0.0001 8302 (3.1) 11204 (4.2) <0.0001 0.96 (0.92; 0.99) Ferritin 18482 (5.9) 25739 (8.2) <0.0001 13521 (5.0) 18051 (6.7) <0.0001 0.93 (0.90; 0.95) Potassium 37466 (11.9) 46717 (14.8) <0.0001 28382 (10.5) 35351 (13.1) <0.0001 0.96 (0.93; 0.98) Sodium 32764 (10.4) 41161 (13.0) <0.0001 24880 (9.2) 31527 (11.7) <0.0001 0.97 (0.95; 0.99) Change p-value: McNemar test OR (95% C.I.): adjusted for age, sex and number of drugs. Generalized Estimate Equation model (Interection wave and before/follow-up period – 3 rd wave vs 2 nd wave) Cite Share Download PDF Status: Published Journal Publication published 31 Aug, 2023 Read the published version in Internal and Emergency Medicine → Version 1 posted Editorial decision: Minor Revisions Needed 06 Jul, 2023 Reviewers agreed at journal 10 Jun, 2023 Reviewers invited by journal 10 Jun, 2023 Editor assigned by journal 06 Jun, 2023 First submitted to journal 05 Jun, 2023 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. As a division of Research Square Company, we’re committed to making research communication faster, fairer, and more useful. We do this by developing innovative software and high quality services for the global research community. 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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-3024426","acceptedTermsAndConditions":true,"allowDirectSubmit":false,"archivedVersions":[],"articleType":"Research Article","associatedPublications":[],"authors":[{"id":208631184,"identity":"5eba32d0-a073-4e99-a198-2105c49dd405","order_by":0,"name":"Sergio Harari","email":"","orcid":"","institution":"IRCCS MultiMedica","correspondingAuthor":false,"submittingAuthor":false,"prefix":"","firstName":"Sergio","middleName":"","lastName":"Harari","suffix":""},{"id":208631185,"identity":"500aaa1e-ca58-4f77-ba70-7bac18ad18cf","order_by":1,"name":"Pier Mannuccio Mannucci","email":"data:image/png;base64,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","orcid":"https://orcid.org/0000-0002-1915-3897","institution":"University of Milan, Fondazione IRCCS Ca' Granda Ospedale Maggiore Policlinico","correspondingAuthor":true,"submittingAuthor":false,"prefix":"","firstName":"Pier","middleName":"Mannuccio","lastName":"Mannucci","suffix":""},{"id":208631186,"identity":"8071bbb6-188e-45d7-b637-a74640d299c5","order_by":2,"name":"Alessandro Nobili","email":"","orcid":"","institution":"Istituto di Ricerche Farmacologiche Mario Negri","correspondingAuthor":false,"submittingAuthor":false,"prefix":"","firstName":"Alessandro","middleName":"","lastName":"Nobili","suffix":""},{"id":208631187,"identity":"7df7c067-66ed-4670-9c90-486a801073e7","order_by":3,"name":"Alessia Antonella Galbussera","email":"","orcid":"","institution":"Istituto di Ricerche Farmacologiche Mario Negri","correspondingAuthor":false,"submittingAuthor":false,"prefix":"","firstName":"Alessia","middleName":"Antonella","lastName":"Galbussera","suffix":""},{"id":208631188,"identity":"d0678e3a-4f45-4ac8-aa44-fc95039d8ead","order_by":4,"name":"Ida Fortino","email":"","orcid":"","institution":"Regione Lombardia","correspondingAuthor":false,"submittingAuthor":false,"prefix":"","firstName":"Ida","middleName":"","lastName":"Fortino","suffix":""},{"id":208631189,"identity":"ba12c1b4-0d58-4fd8-8287-8505d689d697","order_by":5,"name":"Olivia Leoni","email":"","orcid":"","institution":"Regione Lombardia","correspondingAuthor":false,"submittingAuthor":false,"prefix":"","firstName":"Olivia","middleName":"","lastName":"Leoni","suffix":""},{"id":208631190,"identity":"aa307965-0e29-4ed4-8bfd-efaf5432da97","order_by":6,"name":"Giuseppe Remuzzi","email":"","orcid":"","institution":"Istituto di Ricerche Farmacologiche Mario Negri","correspondingAuthor":false,"submittingAuthor":false,"prefix":"","firstName":"Giuseppe","middleName":"","lastName":"Remuzzi","suffix":""},{"id":208631191,"identity":"53135369-1ca5-4f40-b752-45387b615647","order_by":7,"name":"Barbara D’Avanzo","email":"","orcid":"","institution":"Istituto di Ricerche Farmacologiche Mario Negri","correspondingAuthor":false,"submittingAuthor":false,"prefix":"","firstName":"Barbara","middleName":"","lastName":"D’Avanzo","suffix":""},{"id":208631192,"identity":"2e7e3b4b-1e99-4d53-900c-656b84d79d99","order_by":8,"name":"Mauro Tettamanti","email":"","orcid":"","institution":"Istituto di Ricerche Farmacologiche Mario Negri","correspondingAuthor":false,"submittingAuthor":false,"prefix":"","firstName":"Mauro","middleName":"","lastName":"Tettamanti","suffix":""}],"badges":[],"createdAt":"2023-06-05 12:07:50","currentVersionCode":1,"declarations":"","doi":"10.21203/rs.3.rs-3024426/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-3024426/v1","draftVersion":[],"editorialEvents":[{"content":"https://doi.org/10.1007/s11739-023-03396-4","type":"published","date":"2023-08-31T15:08:54+00:00"}],"editorialNote":"","failedWorkflow":false,"files":[{"id":42782677,"identity":"9b50da5c-fcdd-4b33-8183-7fda9db977e7","added_by":"auto","created_at":"2023-09-07 15:17:31","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":710003,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-3024426/v1/059b76a4-937b-40e5-8cc5-550f891256df.pdf"}],"financialInterests":"","formattedTitle":"\u003cp\u003ePost Recovery Impact of the Second and Third Sars-Cov-2 Infection Waves on Healthcare Resource Utilization in Lombardy, Italy\u003c/p\u003e","fulltext":[{"header":"Introduction","content":"\u003cp\u003eThe Italian region Lombardy (10.1\u0026nbsp;million population) was the first after mainland China to be harassed by the SARS-CoV-2 pandemic and associated COVID-19 with an early dramatic toll of infections, deaths and hospitalizations that warranted a prolonged and strict lockdown [\u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e1\u003c/span\u003e]. As of 31 December 2022, all the infected cases have been 4.065.873 (39% of the region population) with as many as 44.688 deaths and important negative impacts on economy and quality of life [\u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2\u003c/span\u003e, \u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e3\u003c/span\u003e]. At the time being in 2023 the high rate of vaccinations [\u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e3\u003c/span\u003e], accrued expertise on COVID-19 management, non-pharmacological interventions such as masks plus physical distancing, as well as and more effective therapeutic weapons (monoclonal antibodies, antiviral drugs) are decreasing the clinical severity of the still very high rate of new infections due to the omicron virus variants [\u003cspan additionalcitationids=\"CR5 CR6\" citationid=\"CR4\" class=\"CitationRef\"\u003e4\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR7\" class=\"CitationRef\"\u003e7\u003c/span\u003e]. However, there is concern, both in the region and globally, for the public health and healthcare impact of cases who recovered from the viral infection [\u003cspan additionalcitationids=\"CR9 CR10\" citationid=\"CR8\" class=\"CitationRef\"\u003e8\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR11\" class=\"CitationRef\"\u003e11\u003c/span\u003e]. We previously chose to analyze and report the data stemming from the Lombardy healthcare database in order to evaluate, after the first SARS-CoV-2 wave due to the original virus lineage, the post-COVID burden on the regional health service [\u003cspan citationid=\"CR12\" class=\"CitationRef\"\u003e12\u003c/span\u003e, \u003cspan citationid=\"CR13\" class=\"CitationRef\"\u003e13\u003c/span\u003e]. During the first 6 months after negativization, there was a high mortality rate, and the utilization of regional healthcare such as hospitalizations, emergency room and outpatient visits was much more prominent than in the same persons in the corresponding semester of 2019, taken for comparison because at the time the region was not yet hit by the pandemic [\u003cspan citationid=\"CR12\" class=\"CitationRef\"\u003e12\u003c/span\u003e]. However, in the frame of a longer post infection period, the post-COVID healthcare burden of the first pandemic wave became progressively less prominent [\u003cspan citationid=\"CR13\" class=\"CitationRef\"\u003e13\u003c/span\u003e].\u003c/p\u003e \u003cp\u003eIt remained to be established whether or not the strong early impact of the post-infection period on the regional healthcare facilities differed in the frame of subsequent infection waves, which occurred following the original virus lineage. With this background and gaps of knowledge, we elected to interrogate again the regional database in order to evaluate the healthcare burden in those cases who recovered from the second infection wave in the last few months of 2020, and from the third in the first few months of 2021.\u003c/p\u003e"},{"header":"Material and methods","content":"\u003cp\u003eThis is a retrospective cohort study of Lombardy residents who became SARS-CoV-2 positive between October 1 and December 31 2020 when the viral infection was principally due to the alpha virus variant (B.1.1.7), as well as between February 1 and May 31 2021 during the third wave principally due to the delta variant (B.1.1.617.2). In the cases who recovered and became SARS-CoV-2 negative, we obtained from the database throughout a 6-month post-negativization period the number of incident deaths. In addition, hospitalizations, visits to hospital emergency rooms without admission as well as general medicine and specialty outpatient visits were analyzed at the end of the forementioned follow-up periods. Number and types of dispensed drugs plus imaging, functional and biochemical tests dispensed for diagnostic purposes were also extracted from the database. Moreover, at the end of the follow-up periods post-recovery data were compared with those recorded before the onset of the pandemic during the corresponding 6 month 2019 periods (February 1 \u0026ndash; July 31 for the second wave and July 1 \u0026ndash; December 31 for the third), chosen for comparison because at that time Lombardy was free from SARS-CoV-2.\u003c/p\u003e \u003cp\u003eThe administrative healthcare database has been implemented by the Welfare Directorate of the Lombardy region since the year 2000 for claims and administrative purposes, and more details on its features have been provided [\u003cspan citationid=\"CR12\" class=\"CitationRef\"\u003e12\u003c/span\u003e]. In brief, it offers a comprehensive picture of all the Lombardy residents and contains their demographic data, life status, incident hospital admissions, outpatient primary care and specialty visits as well as attendances at hospital emergency rooms without subsequent admission. It also provides data on medications reimbursed by the regional health service and dispensed by pharmacies upon medical prescription, as well as on the dispensation for diagnostic purposes of imaging, functional and biochemical tests. The medication database contains drug name and anatomic therapeutic chemical (ATC) classification code [\u003cspan citationid=\"CR14\" class=\"CitationRef\"\u003e14\u003c/span\u003e]. However, no information is available for dispensations during hospital stay, in nursing homes nor for those purchased out-of-pocket. Moreover, results of diagnostic tests are not recorded in the database.\u003c/p\u003e \u003cdiv id=\"Sec3\" class=\"Section2\"\u003e \u003ch2\u003eStatistical analysis\u003c/h2\u003e \u003cp\u003eThe main results are presented as means and standard deviations or medians and interquartile ranges for continuous variables and numbers with percentages for categorical ones. The events that occurred during the post-recovery semester after each wave were compared with those recorded in the same cases in the second semester of 2019 (non-COVID year in Lombardy). The McNemar test was used for comparisons between semesters, except for the total number of drugs when the Wilcoxon signed-rank for paired data was used.\u003c/p\u003e \u003cp\u003eThe generalized estimating equations logistic regression were used to fit a marginal model producing odds ratios (ORs) of change of the third wave (follow-up against before period) versus the second wave (follow-up against before period). For the number of drugs a generalized estimating equations linear regression was used. All the models were adjusted for age, sex and number of drugs (the latter was not included as a corrector when estimating the effects of number of drugs and polypharmacy). Statistical analysis was performed using SAS software, SAS Institute Inc., Cary, NC, USA.\u003c/p\u003e \u003c/div\u003e"},{"header":"Results","content":"\u003cp\u003e\u003cu\u003eCohort characteristics\u003c/u\u003e. Table 1 shows the number of cases who, objectively diagnosed as infected by SARS-CoV-2 during the second (N=317.164) and third (N=271.180) waves, became negative within one month after the first positive test and were followed up along six post-negativization months. The table also contains their gender and age classes, the site of infection management and incident deaths during these post-infection periods, showing that the two cohorts of recovered cases were similar pertaining to age groups, deaths and sites of infection management. \u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u003cu\u003ePost-recovery findings\u003c/u\u003e. Table 2 shows the impact of the second and third waves in the 6-month post-negativization period pertaining to hospital admissions, attendances at hospital emergency rooms without subsequent admission and visits at least once at general or specialist outpatient medical settings. These data were compared with those from the same persons in the corresponding pandemic-free periods of the year 2019, even though this comparison was obviously not done for the cases who died in the post-recovery period. The table shows that the post-negativization numbers and rates of incident hospitalizations, emergency room attendances and outpatient visits were similar or lower than in the pre-pandemic period.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eTable 3 shows that in comparison with the pre-pandemic period polypharmacy, i.e., the chronic use of 5 or more drugs, slightly increased after both the second and third viral waves. Pertaining to the drug classes according to the ATC first level, there was a dispensation rise following both the second and third wave, for drugs of class A (alimentary tract and metabolism), B (blood and blood forming organs), C (cardiovascular system), M (musculoskeletal system) and N (nervous system), whereas for the remaining ATC drug classes dispensation rates were similar or even lower than in the corresponding pre-pandemic period, notwithstanding statistically significant differences due to the large sample size.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eTable 4 compares imaging, functional and biochemical diagnostic tests dispensed after recovery from the second and third infection waves with the corresponding pre-pandemic periods. The most evident increases, with rates that nearly doubled after both waves, were for tests exploring the respiratory system such as spirometry and chest CT scans as well as the cardiovascular system such as echocardiography. Pertaining to biochemical tests there was a markedly increased dispensation for C reactive protein, serum creatinine, coagulation tests and whole blood count.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eWe calculated if changes in resource consumption from before to after COVID periods were different between the two waves (Tables 2, 3 and 4: ORs and estimated mean). A positive interaction (higher increase or lower decrease in the third wave relative to the second wave) was found for hospitalizations, emergency room attendances without hospital admission (Table 2), polypharmacy, chronic polypharmacy and for alimentary tract and metabolism drugs. blood and blood forming organs drugs, cardiovascular system drugs, systemic hormonal preparations, anti-infectives for systemic use, respiratory system drugs and sensory organs drugs (Table 3). The only larger decrease in the third wave was visible for outpatient visits. In the case of imaging, functional and biochemical tests the increase in the third wave was higher relative to the second wave only for chest CT scan (Table 4), while in almost all the other cases the increase was lower in the third wave. Mean number of drugs increased slightly more in the second wave (Table 3).\u003c/p\u003e"},{"header":"Discussion","content":"\u003cp\u003eThe post-infection impact on health services of the SARS-Cov-2 pandemic has been the topic of several studies [\u003cspan citationid=\"CR10\" class=\"CitationRef\"\u003e10\u003c/span\u003e, \u003cspan additionalcitationids=\"CR16 CR17 CR18 CR19\" citationid=\"CR15\" class=\"CitationRef\"\u003e15\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR20\" class=\"CitationRef\"\u003e20\u003c/span\u003e]. Among them, we recently reported data based upon an administrative database on the consequences along the first six months after recovery from the first SARS-CoV-2 infection wave in terms of deaths, hospitalizations, attendances at hospital emergency rooms, outpatient medical visits and drug dispensation [\u003cspan citationid=\"CR12\" class=\"CitationRef\"\u003e12\u003c/span\u003e]. However, after this early increase the post-recovery impact on the regional healthcare facilities did progressively decrease [\u003cspan citationid=\"CR13\" class=\"CitationRef\"\u003e13\u003c/span\u003e]. In the present analysis of the second and third infection waves a mild impact pertained to deaths, hospitalizations and outpatients visits but the healthcare impact was important regarding to a few dispensed drugs and diagnostic tests.\u003c/p\u003e \u003cp\u003eLombardy is an adequate model to study the post-infection healthcare resource utilization, because this Italian region (10\u0026nbsp;million inhabitants with a high density) was the first large area after China to be heavily hit by SARS-CoV-2 [\u003cspan additionalcitationids=\"CR2\" citationid=\"CR1\" class=\"CitationRef\"\u003e1\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e3\u003c/span\u003e]. During the early pandemic period from March to May 2020 the total number of deaths registered in the region did increase by +\u0026thinsp;212%% in comparison with the average numbers recorded in the same months of the pre-pandemic years 2015\u0026ndash;2019 [\u003cspan citationid=\"CR21\" class=\"CitationRef\"\u003e21\u003c/span\u003e]. Because scanty data are generally available on healthcare resource utilization following infection waves other than the first, we chose to evaluate 585.198 cases who did recover (315.593 after the second and 269.605 after the third wave) throughout the first six months post-negativization regarding deaths, hospitalizations, attendances at hospital emergency rooms, outpatients medical visits, dispensation of drugs and an array of imaging, instrumental and biochemical diagnostic tests. These data were compared with those obtained in the same persons before the pandemic outbreak in the corresponding months of 2019, so that each patient was his own comparator before and after the pandemic. Obviously, this comparison could not be done pertaining to deaths in the post-recovery period, but the crude mortality rates recorded after both waves were much lower than after the first wave (0.5% after the second and 0.6 after the third versus 3.9% after the first) [\u003cspan citationid=\"CR12\" class=\"CitationRef\"\u003e12\u003c/span\u003e].\u003c/p\u003e \u003cp\u003eWe chose to confine our analysis to the post-infection impact of the second and third infection waves instead of directly comparing the related data with those of the first, because the epidemiological, medical and social scenarios were strikingly different regarding age, mortality, rate of hospitalisation and availability of pharmacological and non-pharmacological interventions, even though vaccination was not yet started in Lombardy at the time of the second wave and involved a small number of citizens infected during the third wave. Main findings were that after both waves hospitalizations, emergency room attendances and outpatient visits were similar or even lower than in the corresponding pre-COVID 2019 periods. Only the more compromised patients discharged from ICU, a minority in the whole cohort ranging from 0,3% to the 0,6%, needed more hospital admissions in the second and third waves than in 2019. Thus, the data reported herewith depict a scenario completely different from that of the first six months following the first dramatic wave due to the original SARS-CoV-2 lineage [\u003cspan citationid=\"CR12\" class=\"CitationRef\"\u003e12\u003c/span\u003e].\u003c/p\u003e \u003cp\u003eThis scenario of relatively mild burden on the regional health service regarding pivotal events such as deaths, hospitalizations and outpatient visits is at a variance with that regarding a few dispensed drugs and diagnostic tests. A definite increase in dispensation was registered for ATC drug classes addressing the cardiorespiratory, blood and central nervous systems. In addition, diagnostic tests such as chest CT scans showed the most important increase with nearly tripled numbers, but also spirometry, electrocardiogram, echocardiography and a number of blood tests (i.e., complete blood count and coagulation tests) were performed more frequently than in 2019, particularly after the second wave.\u003c/p\u003e \u003cp\u003eThe finding of less hospitalizations, emergency room and outpatient visits contrasting with the increased dispensation of a number of drugs classes and diagnostic tests might derive from various causes including logistic factors, health management habits by family doctors and self-medication. Another potential cause is a rebound effect after the strict lockdown and subsequent containment measures. However, a rebound effect was not actually recorded in the region, that in general witnessed a continuing low dispensation of all resources related to healthcare. In addition, the increase of drug and diagnostic test dispensation was not uniform as it would be expected in the frame of a rebound effect, but preferentially confined to drugs and tests related to organs and body systems such as the heart, lung, blood and the central nervous system, that are frequently involved in the post-infection sequelae. The important growth of CT scan and spirometry dispensation is perhaps due to the persistence over time of respiratory discomfort and other subjective symptoms, as well as to monitoring the risk of fibrotic lung evolution. The increase of blood and coagulation drugs and diagnostic tests may be linked to the post-recovery persistence of the abnormalities recorded during the acute infection, but also to the reluctant deprescribing by family doctors.\u003c/p\u003e \u003cp\u003eThis study has the limitations of those based upon administrative databases, because data could not be audited and a number of details are not available from the database. For instance, we have no direct information on the main signs and symptoms in the post-recovery period, so that for instance the persistence of respiratory discomfort explains only hypothetically the important increase in the use of chest CT scans and spirometry. On the other hand, administrative data indeed offer an opportunity to monitor the access to pivotal health care resources of large populations, that in this study are represented by an important and densely populated European region. The epidemiology of the second and third wave were quite similar in different European regions and Lombardy mirrors this pattern [\u003cspan citationid=\"CR22\" class=\"CitationRef\"\u003e22\u003c/span\u003e, \u003cspan citationid=\"CR23\" class=\"CitationRef\"\u003e23\u003c/span\u003e]. Other strengths of this study are the large number of cases, the real-life approach and particularly that infected cases were their own comparator owing to the availability of the corresponding data in the year before the onset of the pandemic.\u003c/p\u003e \u003cp\u003eIn conclusion this report, describing the post- infection healthcare burden of 585.198 cases who recovered from the second and third infection waves, indicates that during the periods considered the post-COVID burden was much smaller than following the early post-recovery period after the first wave due to the original Wuhan SARS-CoV-2 lineage.\u003c/p\u003e"},{"header":"Declarations","content":"\u003cp\u003e\u003cstrong\u003eAcknowledgements\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eWe thank Giuseppe Preziosi, Monica Arivetti and Giovanna Rigotti from ARIA S.p.A, Alfredo Bevilacqua from Laife Reply S.p.A., Marco D. Forlani from T Bridge - BV Tech S.p.A and Igor Monti from Istituto di Ricerche Farmacologiche Mario Negri IRCCS who kindly assisted us with data collection. We also thank Regione Lombardia, Grant/Award Number: DGR n. 3375 - July 14, 2020; Health Ministry of the Lombardy, Grant/Award Number: EPIFARM-Pharmaco-epidemiology.\u003c/p\u003e\n\n\u003cp\u003e\u003cstrong\u003eAuthor contributions\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eSH, PMM, AN, MT, GR, BDA, AAG, study concept and design; IF and OL acquisition of data; SH, PMM, AN, MT, BDA, AAG, analysis and interpretation of data; SH, PMM, AN, BDA, MT drafting of the manuscript. All authors were involved in the critical revision of the manuscript for important intellectual content. All authors approved the final version of the manuscript.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eConflict of interest\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eNone of the authors declares to have conflict of interest. \u003c/p\u003e\n\n\u003cp\u003e\u003cstrong\u003eHuman and animal rights and informed consent\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThis study was conducted according to the principles expressed in the Declaration of Helsinki and its later amendments, and approved by local Institutional Review Board (0051814/19). Being a retrospective study performed on an administrative database of anonymized patients, patient informed consent was waived.\u003c/p\u003e\n\n\u003cp\u003e\u003cstrong\u003eFunding\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eRegione Lombardia, Grant/Award Number: DGR n. 3375 - July 14, 2020; Health Ministry of the Lombardy, Grant/Award Number: EPIFARM-Pharmaco-epidemiology.\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\n \u003cli\u003eConsolandi E (2021) Chapter 1.3\u0026mdash;Evolution and intensity of infection in Lombardy. In: Casti E, Adobati F, Negri I, editors. Mapping the epidemic: a systemic geography of COVID-19 in Italy, volume 9. https://www.sciencedirect.com/science/article/abs/pii/B9780323910613000089. Accessed March, 2023.\u003c/li\u003e\n \u003cli\u003ehttps://www.regione.lombardia.it/wps/portal/istituzionale/HP/vaccinazionicovid Accessed March, 2023.\u003c/li\u003e\n \u003cli\u003ehttps://opendatamds.maps.arcgis.com/apps/dashboards/0f1c9a02467b45a7b4ca12d8ba296596 Accessed March, 2023.\u003c/li\u003e\n \u003cli\u003eCaramello V, Catalano A, Macciotta A, Dansero L, Sacerdote C, Costa G, et al (2022) Improvements throughout the three waves of COVID-19 pandemic: results from 4 million inhabitants of North-West Italy. J Clin Med. 11:4304. https://doi: 10.3390/jcm11154304.\u003c/li\u003e\n \u003cli\u003eHavers FP, Pham H, Taylor CA, Whitaker M, Patel K, Anglin O, et al (2022) COVID-19-associated hospitalizations among vaccinated and unvaccinated adults 18 years or older in 13 US States, January 2021 to April 2022. JAMA Intern Med. 182:1071-1081. https:// doi: 10.1001/jamainternmed.2022.4299.\u003c/li\u003e\n \u003cli\u003eTaylor CA, Whitaker M, Anglin O, Milucky J, Patel K, Pham H, et al (2022) COVID-19-associated hospitalizations among adults during SARS-CoV-2 delta and omicron variant predominance, by race/ethnicity and vaccination status - COVID-NET, 14 states, July 2021-January 2022. MMWR Morb Mortal Wkly Rep. 71:466-473. https:// doi: 10.15585/mmwr.mm7112e2.\u003c/li\u003e\n \u003cli\u003eBobrovitz N, Ware H, Ma X, Li Z, Hosseini R, Cao C, et al (2023) Protective effectiveness of previous SARS-CoV-2 infection and hybrid immunity against the omicron variant and severe disease: a systematic review and meta-regression. Lancet Infect Dis.S1473-3099(22)00801-5. https:// doi: 10.1016/S1473-3099(22)00801-5.\u003c/li\u003e\n \u003cli\u003eParums DV (2021) Editorial: long-COVID, or post-COVID syndrome, and the global impact on health care. Med Sci Monit. 27:e933446. https:// doi: 10.12659/MSM.933446.\u003c/li\u003e\n \u003cli\u003eSanchez-Ramirez DC, Normand K, Zhaoyun Y, Torres-Castro R (2021). Long-term impact of COVID-19: a systematic review of the literature and meta-analysis. Biomedicines. 9:900. https:// doi:10.3390/biomedicines9080900\u003c/li\u003e\n \u003cli\u003eAlkodaymi MS, Omrani OA, Fawzy NA, Shaar BA, Almamlouk R, Riaz M, et al (2022) Prevalence of post-acute COVID-19 syndrome symptoms at different follow-up periods: a systematic review and meta-analysis. Clin Microbiol Infect. 28:657-666. https:// doi: 10.1016/j.cmi.2022.01.014.\u003c/li\u003e\n \u003cli\u003eBussani R, Zentilin L, Correa R, Colliva A, Silvestri F, Zacchigna S, et al (2023). Persistent SARS-CoV-2 infection in patients seemingly recovered from COVID-19. J Pathol. 259:254-263. https:// doi: 10.1002/path.6035.\u003c/li\u003e\n \u003cli\u003eMannucci PM, Nobili A, Tettamanti M, D\u0026apos;Avanzo B, Galbussera AA, Remuzzi G, et al (2022) Impact of the post-COVID-19 condition on health care after the first disease wave in Lombardy. J Intern Med. 292:450-462. https:// doi: 10.1111/joim.13493.\u003c/li\u003e\n \u003cli\u003eMannucci PM, Nobili A, Tettamanti M, D\u0026apos;Avanzo B, Galbussera AA, Remuzzi G, et al (2022). Progressive decrease of the healthcare impact of the post-COVID condition after the first disease wave in Lombardy, Italy. J Intern Med. 292:961-464. https: doi: 10.1111/joim.13554.\u003c/li\u003e\n \u003cli\u003eATC/DDD Index 2021 https://www.whocc.no/atc_ddd_index/. Accessed March, 2023\u003c/li\u003e\n \u003cli\u003eMcNaughton CD, Austin PC, Sivaswamy A, Fang J, Abdel-Qadir H, Daneman N, et al (2022). Post-acute health care burden after SARS-CoV-2 infection: a retrospective cohort study. CMAJ. 194:E1368-E1376. https:// doi: 10.1503/cmaj.220728.\u003c/li\u003e\n \u003cli\u003ePeter RS, Nieters A, Kr\u0026auml;usslich HG, Brockmann SO, G\u0026ouml;pel S, Kindle G, et al (2022) Post-acute sequelae of covid-19 six to 12 months after infection: population based study. BMJ. 379:e071050. https:// doi: 10.1136/bmj-2022-071050.\u003c/li\u003e\n \u003cli\u003eTsioutis C, Tofarides A, Spernovasilis N (2022) Transition beyond the acute phase of the COVID-19 pandemic: Need to address the long-term health impacts of COVID-19. World J Clin Cases. 10:9967-9969. https://doi: 10.12998/wjcc.v10.i27.9967\u003c/li\u003e\n \u003cli\u003ePaterson C, Davis D, Roche M, Bissett B, Roberts C, Turner M, et al (2022) What are the long-term holistic health consequences of COVID-19 among survivors? An umbrella systematic review. J Med Virol. 94:5653-5668. https:// doi: 10.1002/jmv.28086.\u003c/li\u003e\n \u003cli\u003eGlobal Burden of Disease Long COVID Collaborators (2022) Estimated global proportions of individuals with persistent fatigue, cognitive, and respiratory symptom clusters following symptomatic COVID-19 in 2020 and 2021. JAMA. 328:1604-1605. https:// doi: 10.1001/jama.2022.18931.\u003c/li\u003e\n \u003cli\u003eRapin A, Boyer FC, Mourvillier B, Giordano Orsini G, Launois C, et al (2022) Post-intensive care syndrome prevalence six months after critical COVID-19: comparison between first and second waves. J Rehabil Med. 54:jrm00339. https://doi:10.2340/jrm.v54.4363.\u003c/li\u003e\n \u003cli\u003ehttps://www.istat.it/it/files/2020/03/tabella-regionale-decessi-totali-14022023-1.xlsx Accessed March, 2023\u003c/li\u003e\n \u003cli\u003eWHO Coronavirus (COVID-19) Dashboard https://covid19.who.int/?adgroupsurvey={adgroupsurvey}\u0026amp;gclid=Cj0KCQiA9YugBhCZARIsAACXxeJkPe1pSdC-swv1pFpfwArZO3t0XPc_C1bASCfeS08cdK8jQUJZm9gaAkbjEALw_wcB. Accessed March, 2023\u003c/li\u003e\n \u003cli\u003eWHO Coronavirus (COVID-19) ITALY: https://covid19.who.int/region/euro/country/it Accessed March, 2023\u003c/li\u003e\n\u003c/ol\u003e"},{"header":"Tables","content":"\u003cp\u003e\u003cstrong\u003eTable1. Characteristics of SARS-CoV-2 infected cases who recovered from the second\u0026nbsp;\u003c/strong\u003e\u003cstrong\u003eand third\u0026nbsp;\u003c/strong\u003e\u003cstrong\u003einfection waves and were followed-up for a 6 month period\u003c/strong\u003e\u003c/p\u003e\n\u003ctable border=\"0\" cellspacing=\"0\" cellpadding=\"0\" width=\"993\"\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd width=\"43.202416918429%\"\u003e\n \u003cp\u003e\u003cstrong\u003eVariables\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"31.017119838872105%\" valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003e2nd wave follow-up period\u003c/strong\u003e\u003c/p\u003e\n \u003cp\u003e\u003cstrong\u003e01/02/2021 \u0026ndash; 31/07/2021\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"25.780463242698893%\"\u003e\n \u003cp\u003e\u003cstrong\u003e3rd wave\u0026nbsp;\u003c/strong\u003e\u003cstrong\u003efollow-up period\u003c/strong\u003e\u003c/p\u003e\n \u003cp\u003e\u003cstrong\u003e(01/07/2021 \u0026ndash; 31/12/2021)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"43.202416918429%\"\u003e\n \u003cp\u003e\u003cstrong\u003eOverall cohort included in the analysis\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"31.017119838872105%\" valign=\"bottom\"\u003e\n \u003cp\u003e\u003cstrong\u003eN=317164\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"25.780463242698893%\" valign=\"bottom\"\u003e\n \u003cp\u003e\u003cstrong\u003eN=271180\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"43.202416918429%\"\u003e\n \u003cp\u003e\u003cstrong\u003eSex, females, N (%)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"31.017119838872105%\" valign=\"top\"\u003e\n \u003cp\u003e161739 (51.0)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"25.780463242698893%\" valign=\"top\"\u003e\n \u003cp\u003e135733 (50.1)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"43.202416918429%\"\u003e\n \u003cp\u003e\u003cstrong\u003eAge, mean (SD)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"31.017119838872105%\" valign=\"bottom\"\u003e\n \u003cp\u003e43.1 (20.0)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"25.780463242698893%\" valign=\"bottom\"\u003e\n \u003cp\u003e43.6 (21.3)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"43.202416918429%\"\u003e\n \u003cp\u003e\u003cstrong\u003eAge groups\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"31.017119838872105%\" valign=\"bottom\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd width=\"25.780463242698893%\" valign=\"bottom\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"43.202416918429%\"\u003e\n \u003cp\u003e\u0026lt;=29\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"31.017119838872105%\" valign=\"top\"\u003e\n \u003cp\u003e87579 (27.6)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"25.780463242698893%\" valign=\"top\"\u003e\n \u003cp\u003e76718 (28.3)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"43.202416918429%\"\u003e\n \u003cp\u003e30-49\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"31.017119838872105%\" valign=\"top\"\u003e\n \u003cp\u003e102156 (32.2)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"25.780463242698893%\" valign=\"top\"\u003e\n \u003cp\u003e81240 (30.0)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"43.202416918429%\"\u003e\n \u003cp\u003e50-69\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"31.017119838872105%\" valign=\"top\"\u003e\n \u003cp\u003e94705 (29.9)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"25.780463242698893%\" valign=\"top\"\u003e\n \u003cp\u003e79995 (29.5)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"43.202416918429%\"\u003e\n \u003cp\u003e70+\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"31.017119838872105%\" valign=\"top\"\u003e\n \u003cp\u003e32724 (10.3)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"25.780463242698893%\" valign=\"top\"\u003e\n \u003cp\u003e33227 (12.3)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"43.202416918429%\"\u003e\n \u003cp\u003e\u003cstrong\u003eSite of management\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"31.017119838872105%\" valign=\"bottom\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd width=\"25.780463242698893%\" valign=\"bottom\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"43.202416918429%\"\u003e\n \u003cp\u003e-\u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp;\u0026nbsp;\u003cstrong\u003eHome\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"31.017119838872105%\" valign=\"top\"\u003e\n \u003cp\u003e290117 (91.5)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"25.780463242698893%\" valign=\"top\"\u003e\n \u003cp\u003e242627 (89.5)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"43.202416918429%\"\u003e\n \u003cp\u003e-\u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp;\u0026nbsp;\u003cstrong\u003eHospital medical ward\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"31.017119838872105%\" valign=\"top\"\u003e\n \u003cp\u003e26193 (8.3)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"25.780463242698893%\" valign=\"top\"\u003e\n \u003cp\u003e27024 (10.0)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"43.202416918429%\"\u003e\n \u003cp\u003e-\u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp;\u0026nbsp;\u003cstrong\u003eICU\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"31.017119838872105%\" valign=\"top\"\u003e\n \u003cp\u003e854 (0.3)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"25.780463242698893%\" valign=\"top\"\u003e\n \u003cp\u003e1529 (0.6)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"43.202416918429%\"\u003e\n \u003cp\u003e\u003cstrong\u003eAlive at\u0026nbsp;\u003c/strong\u003e\u003cstrong\u003ethe end of follow-up\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"31.017119838872105%\" valign=\"bottom\"\u003e\n \u003cp\u003e315.593 (99.5)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"25.780463242698893%\" valign=\"bottom\"\u003e\n \u003cp\u003e269.605 (99.4)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"43.202416918429%\"\u003e\n \u003cp\u003e\u003cstrong\u003eDied during follow-up\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"31.017119838872105%\" valign=\"bottom\"\u003e\n \u003cp\u003e1571 (0.5)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"25.780463242698893%\" valign=\"bottom\"\u003e\n \u003cp\u003e1575 (0.6)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"43.202416918429%\"\u003e\n \u003cp\u003e\u003cstrong\u003eDied during the follow-up period in relation to the original site of management\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"31.017119838872105%\" valign=\"bottom\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"25.780463242698893%\" valign=\"bottom\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"43.202416918429%\"\u003e\n \u003cp\u003e\u003cstrong\u003e- \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp;Home\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"31.017119838872105%\" valign=\"top\"\u003e\n \u003cp\u003e868 (0.3)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"25.780463242698893%\" valign=\"top\"\u003e\n \u003cp\u003e762 (0.3)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"43.202416918429%\"\u003e\n \u003cp\u003e\u003cstrong\u003e- \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp;Hospital medical wards\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"31.017119838872105%\" valign=\"top\"\u003e\n \u003cp\u003e688 (2.6)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"25.780463242698893%\" valign=\"top\"\u003e\n \u003cp\u003e785 (2.9)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"43.202416918429%\"\u003e\n \u003cp\u003e\u003cstrong\u003e- \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp;ICU\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"31.017119838872105%\" valign=\"top\"\u003e\n \u003cp\u003e15 (1.8)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"25.780463242698893%\" valign=\"top\"\u003e\n \u003cp\u003e28 (1.8)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n\u003c/table\u003e\n\u003cp\u003e\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eTable 2. Hospitalizations, emergency room attendances and outpatient visits after recovery from the second\u0026nbsp;\u003c/strong\u003e\u003cstrong\u003eand third\u0026nbsp;\u003c/strong\u003e\u003cstrong\u003einfection waves. Barring for deaths, data were compared with those obtained in the same persons in the corresponding time periods before the pandemic in 2019\u003c/strong\u003e\u003c/p\u003e\n\u003ctable border=\"1\" cellspacing=\"0\" cellpadding=\"0\" width=\"100%\"\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd width=\"30.612244897959183%\" valign=\"top\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd width=\"29.591836734693878%\" colspan=\"3\" valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp;\u003c/strong\u003e\u003cstrong\u003e2nd \u0026nbsp;wave\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"28.571428571428573%\" colspan=\"3\" valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; 3rd \u0026nbsp;wave\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"11.224489795918368%\" valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003eOR (95% C.I.)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"31.57894736842105%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"12.631578947368421%\" valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003eBefore the pandemic\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"10.526315789473685%\" valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003eFollow-up period\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"6.315789473684211%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"10.526315789473685%\" valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003eBefore the pandemic\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"10.526315789473685%\" valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003eFollow-up period\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"6.315789473684211%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"11.578947368421053%\" valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"31.57894736842105%\" valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003eFollow-up period after recovery\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"12.631578947368421%\" valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003e01/02/2019 \u0026ndash; 31/07/2019\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"10.526315789473685%\" valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003e01/02/2021 \u0026ndash; 31/07/2021\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"6.315789473684211%\" rowspan=\"2\" valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003echange p-value\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"10.526315789473685%\" valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003e01/07/2019 \u0026ndash; 31/12/2019\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"10.526315789473685%\" valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003e01/07/2021 \u0026ndash; 31/12/2021\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"6.315789473684211%\" rowspan=\"2\" valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003echange p-value\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"11.578947368421053%\" valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"36.144578313253014%\" valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003eAlive at the end of follow-up period N=585 198\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"14.457831325301205%\" valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003eN=315593\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"12.048192771084338%\" valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003eN=315593\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"12.048192771084338%\" valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003eN=\u003c/strong\u003e\u003cstrong\u003e269605\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"12.048192771084338%\" valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003eN=\u003c/strong\u003e\u003cstrong\u003e269605\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"13.25301204819277%\" valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"31.57894736842105%\" valign=\"top\"\u003e\n \u003cp\u003eNumber and rate of patients with at least one hospitalization\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"12.631578947368421%\" valign=\"top\"\u003e\n \u003cp\u003e14115 (4.5)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"10.526315789473685%\" valign=\"top\"\u003e\n \u003cp\u003e12506 (4.0)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"6.315789473684211%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026lt;0.0001\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"10.526315789473685%\" valign=\"top\"\u003e\n \u003cp\u003e11168 (4.1)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"10.526315789473685%\" valign=\"top\"\u003e\n \u003cp\u003e11152 (4.1)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"6.315789473684211%\" valign=\"top\"\u003e\n \u003cp\u003e0.9096\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"11.578947368421053%\" valign=\"top\"\u003e\n \u003cp\u003e1.09 (1.05;1.13)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"31.57894736842105%\" valign=\"top\"\u003e\n \u003cp\u003eNumber and rate of patients with at least one emergency room attendance without subsequent hospitalization\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"12.631578947368421%\" valign=\"top\"\u003e\n \u003cp\u003e42462 (13.5)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"10.526315789473685%\" valign=\"top\"\u003e\n \u003cp\u003e33546 (10.6)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"6.315789473684211%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026lt;0.0001\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"10.526315789473685%\" valign=\"top\"\u003e\n \u003cp\u003e35031 (13.0)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"10.526315789473685%\" valign=\"top\"\u003e\n \u003cp\u003e31960 (11.9)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"6.315789473684211%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026lt;0.0001\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"11.578947368421053%\" valign=\"top\"\u003e\n \u003cp\u003e1.15 (1.12; 1.17)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"31.57894736842105%\" valign=\"top\"\u003e\n \u003cp\u003eNumber and rate of patients who underwent at least one outpatient visit\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"12.631578947368421%\" valign=\"top\"\u003e\n \u003cp\u003e120734 (38.3)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"10.526315789473685%\" valign=\"top\"\u003e\n \u003cp\u003e118207 (37.5)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"6.315789473684211%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026lt;0.0001\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"10.526315789473685%\" valign=\"top\"\u003e\n \u003cp\u003e101557 (37.7)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"10.526315789473685%\" valign=\"top\"\u003e\n \u003cp\u003e100322 (37.2)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"6.315789473684211%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026lt;0.0001\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"11.578947368421053%\" valign=\"top\"\u003e\n \u003cp\u003e0.97 (0.96; 0.99)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n\u003c/table\u003e\n\u003cp\u003e\u003cstrong\u003eChange p-value: McNemar test\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eOR (95% C.I.): adjusted for age, sex and number of drugs. Generalized Estimate Equation model (Interaction between 3rd vs. 2nd wave and follow-up vs. before period)\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eTable 3. Comparison of drugs dispensed before the pandemic (\u003c/strong\u003e\u003cstrong\u003e2019) and after recovery from the second and third infection waves\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n\u003ctable border=\"1\" cellspacing=\"0\" cellpadding=\"0\" width=\"105%\"\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd width=\"21.649484536082475%\" valign=\"top\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd width=\"32.98969072164948%\" colspan=\"3\" valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp;\u003c/strong\u003e\u003cstrong\u003e2nd wave\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"34.02061855670103%\" colspan=\"3\" valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; 3rd wave\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"11.34020618556701%\" valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"21.875%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"13.541666666666666%\" valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003eBefore the pandemic\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"12.5%\" valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003eFollow-up period\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"6.25%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"13.541666666666666%\" valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003eBefore the pandemic\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"14.583333333333334%\" valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003eFollow-up period\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"6.25%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"11.458333333333334%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"21.875%\" valign=\"top\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd width=\"13.541666666666666%\" valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003e01/02/2019 \u0026ndash; 31/07/2019\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"12.5%\" valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003e01/02/2021 \u0026ndash; 31/07/2021\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"6.25%\" rowspan=\"2\" valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003echange p-value\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"13.541666666666666%\" valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003e01/07/2019 \u0026ndash; 31/12/2019\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"14.583333333333334%\" valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003e01/07/2021 \u0026ndash; 31/12/2021\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"6.25%\" rowspan=\"2\" valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003echange p-value\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"11.458333333333334%\" valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"25%\" valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003eAlive at the end of follow-up period N=585 198\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"15.476190476190476%\" valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003eN=315593\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"14.285714285714286%\" valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003eN=315593\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"15.476190476190476%\" valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003eN=269605\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"16.666666666666668%\" valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003eN=269605\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"13.095238095238095%\" valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"21.875%\" valign=\"top\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd width=\"13.541666666666666%\" valign=\"top\"\u003e\n \u003cp\u003eMean (SD)\u003cbr\u003e\u0026nbsp;Median (IQR)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"12.5%\" valign=\"top\"\u003e\n \u003cp\u003eMean (SD)\u003cbr\u003e\u0026nbsp;Median (IQR)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"6.25%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"13.541666666666666%\" valign=\"top\"\u003e\n \u003cp\u003eMean (SD)\u003cbr\u003e\u0026nbsp;Median (IQR)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"14.583333333333334%\" valign=\"top\"\u003e\n \u003cp\u003eMean (SD)\u003cbr\u003e\u0026nbsp;Median (IQR)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"6.25%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"11.458333333333334%\" valign=\"top\"\u003e\n \u003cp\u003eestimated mean (95% C.I.)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"21.875%\" valign=\"top\"\u003e\n \u003cp\u003eNumber of prescribed drugs\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"13.541666666666666%\" valign=\"top\"\u003e\n \u003cp\u003e1.5 (2.4)\u003c/p\u003e\n \u003cp\u003e0 (0-2)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"12.5%\" valign=\"top\"\u003e\n \u003cp\u003e1.6 (2.6)\u003c/p\u003e\n \u003cp\u003e0 (0-2)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"6.25%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026lt;0.0001\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"13.541666666666666%\" valign=\"top\"\u003e\n \u003cp\u003e1.4 (2.4)\u003c/p\u003e\n \u003cp\u003e0 (0-2)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"14.583333333333334%\" valign=\"top\"\u003e\n \u003cp\u003e1.6 (2.6)\u003c/p\u003e\n \u003cp\u003e0 (0-2)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"6.25%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026lt;0.0001\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"11.458333333333334%\" valign=\"top\"\u003e\n \u003cp\u003e0.12 (0.11; 0.13)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"21.875%\" valign=\"top\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd width=\"13.541666666666666%\" valign=\"top\"\u003e\n \u003cp\u003eN (%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"12.5%\" valign=\"top\"\u003e\n \u003cp\u003eN (%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"6.25%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"13.541666666666666%\" valign=\"top\"\u003e\n \u003cp\u003eN (%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"14.583333333333334%\" valign=\"top\"\u003e\n \u003cp\u003eN (%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"6.25%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"11.458333333333334%\" valign=\"top\"\u003e\n \u003cp\u003eOR (95% C.I.)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"21.875%\" valign=\"top\"\u003e\n \u003cp\u003ePolypharmacy (5 or more drugs)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"13.541666666666666%\" valign=\"bottom\"\u003e\n \u003cp\u003e33125 (10.5)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"12.5%\" valign=\"bottom\"\u003e\n \u003cp\u003e36525 (11.6)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"6.25%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026lt;0.0001\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"13.541666666666666%\" valign=\"top\"\u003e\n \u003cp\u003e27093 (10.0)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"14.583333333333334%\" valign=\"top\"\u003e\n \u003cp\u003e32352 (12.0)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"6.25%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026lt;0.0001\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"11.458333333333334%\" valign=\"bottom\"\u003e\n \u003cp\u003e1.13 (1.11; 1.15)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"21.875%\" valign=\"top\"\u003e\n \u003cp\u003eChronic polypharmacy\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"13.541666666666666%\" valign=\"bottom\"\u003e\n \u003cp\u003e14443 (4.6)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"12.5%\" valign=\"bottom\"\u003e\n \u003cp\u003e18316 (5.8)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"6.25%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026lt;0.0001\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"13.541666666666666%\" valign=\"top\"\u003e\n \u003cp\u003e11843 (4.4)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"14.583333333333334%\" valign=\"top\"\u003e\n \u003cp\u003e15419 (5.7)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"6.25%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026lt;0.0001\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"11.458333333333334%\" valign=\"bottom\"\u003e\n \u003cp\u003e1.03 (1.01; 1.06)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"88.88888888888889%\" colspan=\"7\" valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003eDrug classes by ATC 1st level classification, N (%) (number of cases with at least one drug in the class)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"11.11111111111111%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"21.875%\" valign=\"top\"\u003e\n \u003cp\u003e- \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp;A\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"13.541666666666666%\" valign=\"bottom\"\u003e\n \u003cp\u003e61887 (19.6)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"12.5%\" valign=\"bottom\"\u003e\n \u003cp\u003e68735 (21.8)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"6.25%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026lt;0.0001\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"13.541666666666666%\" valign=\"top\"\u003e\n \u003cp\u003e50452 (18.7)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"14.583333333333334%\" valign=\"top\"\u003e\n \u003cp\u003e56923 (21.1)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"6.25%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026lt;0.0001\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"11.458333333333334%\" valign=\"bottom\"\u003e\n \u003cp\u003e1.03 (1.01; 1.04)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"21.875%\" valign=\"top\"\u003e\n \u003cp\u003e- \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp;B\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"13.541666666666666%\" valign=\"bottom\"\u003e\n \u003cp\u003e27130 (8.6)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"12.5%\" valign=\"bottom\"\u003e\n \u003cp\u003e32232 (10.2)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"6.25%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026lt;0.0001\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"13.541666666666666%\" valign=\"top\"\u003e\n \u003cp\u003e23119 (8.6)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"14.583333333333334%\" valign=\"top\"\u003e\n \u003cp\u003e28286 (10.5)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"6.25%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026lt;0.0001\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"11.458333333333334%\" valign=\"bottom\"\u003e\n \u003cp\u003e1.04 (1.02; 1.06)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"21.875%\" valign=\"top\"\u003e\n \u003cp\u003e- \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp;C\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"13.541666666666666%\" valign=\"bottom\"\u003e\n \u003cp\u003e59351 (18.8)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"12.5%\" valign=\"bottom\"\u003e\n \u003cp\u003e68061 (21.6)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"6.25%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026lt;0.0001\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"13.541666666666666%\" valign=\"top\"\u003e\n \u003cp\u003e52726 (19.6)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"14.583333333333334%\" valign=\"top\"\u003e\n \u003cp\u003e60389 (22.4)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"6.25%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026lt;0.0001\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"11.458333333333334%\" valign=\"bottom\"\u003e\n \u003cp\u003e1.01 (1.00; 1.02)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"21.875%\" valign=\"top\"\u003e\n \u003cp\u003e- \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp;D\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"13.541666666666666%\" valign=\"bottom\"\u003e\n \u003cp\u003e2165 (0.7)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"12.5%\" valign=\"bottom\"\u003e\n \u003cp\u003e2375 (0.8)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"6.25%\" valign=\"top\"\u003e\n \u003cp\u003e0.0002\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"13.541666666666666%\" valign=\"top\"\u003e\n \u003cp\u003e1559 (0.6)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"14.583333333333334%\" valign=\"top\"\u003e\n \u003cp\u003e1613 (0.6)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"6.25%\" valign=\"top\"\u003e\n \u003cp\u003e0.2624\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"11.458333333333334%\" valign=\"bottom\"\u003e\n \u003cp\u003e0.94 (0.87; 1.02)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"21.875%\" valign=\"top\"\u003e\n \u003cp\u003e- \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp;G\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"13.541666666666666%\" valign=\"bottom\"\u003e\n \u003cp\u003e10611 (3.4)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"12.5%\" valign=\"bottom\"\u003e\n \u003cp\u003e11920 (3.8)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"6.25%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026lt;0.0001\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"13.541666666666666%\" valign=\"top\"\u003e\n \u003cp\u003e8917 (3.3)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"14.583333333333334%\" valign=\"top\"\u003e\n \u003cp\u003e10027 (3.7)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"6.25%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026lt;0.0001\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"11.458333333333334%\" valign=\"bottom\"\u003e\n \u003cp\u003e1.00 (0.98; 1.03)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"21.875%\" valign=\"top\"\u003e\n \u003cp\u003e- \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp;H\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"13.541666666666666%\" valign=\"bottom\"\u003e\n \u003cp\u003e19542 (6.2)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"12.5%\" valign=\"bottom\"\u003e\n \u003cp\u003e20412 (6.5)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"6.25%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026lt;0.0001\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"13.541666666666666%\" valign=\"top\"\u003e\n \u003cp\u003e15813 (5.9)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"14.583333333333334%\" valign=\"top\"\u003e\n \u003cp\u003e17391 (6.5)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"6.25%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026lt;0.0001\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"11.458333333333334%\" valign=\"bottom\"\u003e\n \u003cp\u003e1.06 (1.03; 1.08)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"21.875%\" valign=\"top\"\u003e\n \u003cp\u003e- \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp;J\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"13.541666666666666%\" valign=\"bottom\"\u003e\n \u003cp\u003e67155 (21.3)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"12.5%\" valign=\"bottom\"\u003e\n \u003cp\u003e42416 (13.4)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"6.25%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026lt;0.0001\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"13.541666666666666%\" valign=\"top\"\u003e\n \u003cp\u003e51259 (19.0)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"14.583333333333334%\" valign=\"top\"\u003e\n \u003cp\u003e46308 (17.2)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"6.25%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026lt;0.0001\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"11.458333333333334%\" valign=\"bottom\"\u003e\n \u003cp\u003e1.54 (1.51; 1.57)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"21.875%\" valign=\"top\"\u003e\n \u003cp\u003e- \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp;L\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"13.541666666666666%\" valign=\"bottom\"\u003e\n \u003cp\u003e4882 (1.5)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"12.5%\" valign=\"bottom\"\u003e\n \u003cp\u003e6219 (2.0)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"6.25%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026lt;0.0001\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"13.541666666666666%\" valign=\"top\"\u003e\n \u003cp\u003e4006 (1.5)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"14.583333333333334%\" valign=\"top\"\u003e\n \u003cp\u003e5048 (1.9)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"6.25%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026lt;0.0001\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"11.458333333333334%\" valign=\"bottom\"\u003e\n \u003cp\u003e0.99 (0.96; 1.02)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"21.875%\" valign=\"top\"\u003e\n \u003cp\u003e- \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp;M\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"13.541666666666666%\" valign=\"bottom\"\u003e\n \u003cp\u003e22228 (7.0)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"12.5%\" valign=\"bottom\"\u003e\n \u003cp\u003e24138 (7.6)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"6.25%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026lt;0.0001\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"13.541666666666666%\" valign=\"top\"\u003e\n \u003cp\u003e18516 (6.9)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"14.583333333333334%\" valign=\"top\"\u003e\n \u003cp\u003e20300 (7.5)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"6.25%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026lt;0.0001\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"11.458333333333334%\" valign=\"bottom\"\u003e\n \u003cp\u003e1.01 (0.99; 1.04)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"21.875%\" valign=\"top\"\u003e\n \u003cp\u003e- \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp;N\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"13.541666666666666%\" valign=\"bottom\"\u003e\n \u003cp\u003e25910 (8.2)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"12.5%\" valign=\"bottom\"\u003e\n \u003cp\u003e30238 (9.6)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"6.25%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026lt;0.0001\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"13.541666666666666%\" valign=\"top\"\u003e\n \u003cp\u003e21996 (8.2)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"14.583333333333334%\" valign=\"top\"\u003e\n \u003cp\u003e25419 (9.4)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"6.25%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026lt;0.0001\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"11.458333333333334%\" valign=\"bottom\"\u003e\n \u003cp\u003e0.99 (0.97; 1.01)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"21.875%\" valign=\"top\"\u003e\n \u003cp\u003e- \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp;P\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"13.541666666666666%\" valign=\"bottom\"\u003e\n \u003cp\u003e1799 (0.6)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"12.5%\" valign=\"bottom\"\u003e\n \u003cp\u003e1766 (0.6)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"6.25%\" valign=\"top\"\u003e\n \u003cp\u003e0.4848\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"13.541666666666666%\" valign=\"top\"\u003e\n \u003cp\u003e1745 (0.6)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"14.583333333333334%\" valign=\"top\"\u003e\n \u003cp\u003e1633 (0.6)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"6.25%\" valign=\"top\"\u003e\n \u003cp\u003e0.0144\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"11.458333333333334%\" valign=\"bottom\"\u003e\n \u003cp\u003e0.95 (0.88; 1.03)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"21.875%\" valign=\"top\"\u003e\n \u003cp\u003e- \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp;R\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"13.541666666666666%\" valign=\"bottom\"\u003e\n \u003cp\u003e28774 (9.1)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"12.5%\" valign=\"bottom\"\u003e\n \u003cp\u003e25102 (8.0)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"6.25%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026lt;0.0001\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"13.541666666666666%\" valign=\"top\"\u003e\n \u003cp\u003e18857 (7.0)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"14.583333333333334%\" valign=\"top\"\u003e\n \u003cp\u003e20123 (7.5)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"6.25%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026lt;0.0001\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"11.458333333333334%\" valign=\"bottom\"\u003e\n \u003cp\u003e1.25 (1.22; 1.27)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"21.875%\" valign=\"top\"\u003e\n \u003cp\u003e- \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp;S\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"13.541666666666666%\" valign=\"bottom\"\u003e\n \u003cp\u003e3611 (1.1)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"12.5%\" valign=\"bottom\"\u003e\n \u003cp\u003e3838 (1.2)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"6.25%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026lt;0.0001\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"13.541666666666666%\" valign=\"top\"\u003e\n \u003cp\u003e2955 (1.1)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"14.583333333333334%\" valign=\"top\"\u003e\n \u003cp\u003e3264 (1.2)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"6.25%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026lt;0.0001\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"11.458333333333334%\" valign=\"bottom\"\u003e\n \u003cp\u003e1.04 (1.01; 1.08)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"21.875%\" valign=\"top\"\u003e\n \u003cp\u003e- \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp;V\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"13.541666666666666%\" valign=\"bottom\"\u003e\n \u003cp\u003e1004 (0.3)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"12.5%\" valign=\"bottom\"\u003e\n \u003cp\u003e1929 (0.6)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"6.25%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026lt;0.0001\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"13.541666666666666%\" valign=\"top\"\u003e\n \u003cp\u003e657 (0.2)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"14.583333333333334%\" valign=\"top\"\u003e\n \u003cp\u003e1515 (0.6)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"6.25%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026lt;0.0001\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"11.458333333333334%\" valign=\"bottom\"\u003e\n \u003cp\u003e1.20 (1.10; 1.32)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n\u003c/table\u003e\n\u003cp\u003e\u003cstrong\u003eLegend\u003c/strong\u003e: \u003cstrong\u003eA\u003c/strong\u003e=Alimentary tract and metabolism. \u003cstrong\u003eB\u003c/strong\u003e=Blood and blood forming organs. \u003cstrong\u003eC\u003c/strong\u003e=Cardiovascular system. \u003cstrong\u003eD\u003c/strong\u003e=Dermatologicals. \u003cstrong\u003eG\u003c/strong\u003e=Genito-urinary system and sex hormones. \u003cstrong\u003eH\u003c/strong\u003e=Systemic hormonal preparations, excluding sex hormones and insulins. \u003cstrong\u003eJ\u003c/strong\u003e=Anti-infectives for systemic use. \u003cstrong\u003eL\u003c/strong\u003e=Antineoplastic and immunomodulating agents. \u003cstrong\u003eM\u003c/strong\u003e=Musculo-skeletal system, \u003cstrong\u003eN\u003c/strong\u003e=Nervous system. \u003cstrong\u003eP\u003c/strong\u003e=Antiparasitic products, insecticides and repellents. \u003cstrong\u003eR\u003c/strong\u003e=Respiratory system. \u003cstrong\u003eS\u003c/strong\u003e=Sensory organs. \u003cstrong\u003eV\u003c/strong\u003e=Various.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eChange p-value: McNemar test, except for number of dispensed drugs, (Wilcoxon signed-rank for paired data).\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eOR (95% C.I.): adjusted for age and sex. Generalized Estimate Equation model (Interaction between 3rd vs. 2nd wave and follow-up vs. before period)\u003cbr\u003e\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eTable 4. Comparison of imaging, functional and biochemical tests dispensed\u0026nbsp;\u003c/strong\u003e\u003cstrong\u003ebefore the pandemic (\u003c/strong\u003e\u003cstrong\u003e2019) and after recovery from the second and third infection waves\u0026nbsp;\u003c/strong\u003e\u003cstrong\u003e(\u003c/strong\u003e\u003cstrong\u003e2021)\u003c/strong\u003e\u003c/p\u003e\n\u003ctable border=\"1\" cellspacing=\"0\" cellpadding=\"0\" width=\"108%\"\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd width=\"23.46938775510204%\" valign=\"top\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd width=\"31.632653061224488%\" colspan=\"3\" valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp;\u003c/strong\u003e\u003cstrong\u003e2nd wave\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"33.673469387755105%\" colspan=\"3\" valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; 3rd \u0026nbsp;wave\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"11.224489795918368%\" valign=\"top\"\u003e\n \u003cp\u003eOR (95% C.I.)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"24.210526315789473%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"13.68421052631579%\" valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003eBefore the pandemic\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"11.578947368421053%\" valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003eFollow-up period\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"6.315789473684211%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"12.631578947368421%\" valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003eBefore the pandemic\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"13.68421052631579%\" valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003eFollow-up period\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"6.315789473684211%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"11.578947368421053%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"24.210526315789473%\" valign=\"top\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd width=\"13.68421052631579%\" valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003e01/02/2019 \u0026ndash; 31/07/2019\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"11.578947368421053%\" valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003e01/02/2021 \u0026ndash; 31/07/2021\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"6.315789473684211%\" rowspan=\"2\" valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003echange p-value\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"12.631578947368421%\" valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003e01/07/2019 \u0026ndash; 31/12/2019\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"13.68421052631579%\" valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003e01/07/2021 \u0026ndash; 31/12/2021\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"6.315789473684211%\" rowspan=\"2\" valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003echange p-value\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"11.578947368421053%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"27.710843373493976%\" valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003eAlive at the end of follow-up period N=585 198\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"15.662650602409638%\" valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003eN=315593\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"13.25301204819277%\" valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003eN=315593\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"14.457831325301205%\" valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003eN=\u003c/strong\u003e\u003cstrong\u003e269605\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"15.662650602409638%\" valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003eN=\u003c/strong\u003e\u003cstrong\u003e269605\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"13.25301204819277%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"24.210526315789473%\" valign=\"top\"\u003e\n \u003cp\u003eNumber of patients with at least one exam\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"13.68421052631579%\" valign=\"top\"\u003e\n \u003cp\u003eN (%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"11.578947368421053%\" valign=\"top\"\u003e\n \u003cp\u003eN (%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"6.315789473684211%\" valign=\"top\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd width=\"12.631578947368421%\" valign=\"top\"\u003e\n \u003cp\u003eN (%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"13.68421052631579%\" valign=\"top\"\u003e\n \u003cp\u003eN (%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"6.315789473684211%\" valign=\"top\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd width=\"11.578947368421053%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"24.210526315789473%\" valign=\"top\"\u003e\n \u003cp\u003eSpirometry\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"13.68421052631579%\" valign=\"top\"\u003e\n \u003cp\u003e6643 (2.1)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"11.578947368421053%\" valign=\"top\"\u003e\n \u003cp\u003e11002 (3.5)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"6.315789473684211%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026lt;0.0001\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"12.631578947368421%\" valign=\"top\"\u003e\n \u003cp\u003e10590 (3.9)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"13.68421052631579%\" valign=\"top\"\u003e\n \u003cp\u003e12946 (4.8)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"6.315789473684211%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026lt;0.0001\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"11.578947368421053%\" valign=\"bottom\"\u003e\n \u003cp\u003e0.71 (0.69; 0.74)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"24.210526315789473%\" valign=\"top\"\u003e\n \u003cp\u003eChest CT scan\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"13.68421052631579%\" valign=\"top\"\u003e\n \u003cp\u003e3143 (1.0)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"11.578947368421053%\" valign=\"top\"\u003e\n \u003cp\u003e10480 (3.3)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"6.315789473684211%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026lt;0.0001\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"12.631578947368421%\" valign=\"top\"\u003e\n \u003cp\u003e2301 (0.9)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"13.68421052631579%\" valign=\"top\"\u003e\n \u003cp\u003e8654 (3.2)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"6.315789473684211%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026lt;0.0001\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"11.578947368421053%\" valign=\"bottom\"\u003e\n \u003cp\u003e1.10 (1.03; 1.16)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"24.210526315789473%\" valign=\"top\"\u003e\n \u003cp\u003eElectrocardiogram\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"13.68421052631579%\" valign=\"top\"\u003e\n \u003cp\u003e20701 (6.6)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"11.578947368421053%\" valign=\"top\"\u003e\n \u003cp\u003e23506 (7.4)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"6.315789473684211%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026lt;0.0001\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"12.631578947368421%\" valign=\"top\"\u003e\n \u003cp\u003e16678 (6.2)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"13.68421052631579%\" valign=\"top\"\u003e\n \u003cp\u003e18638 (6.9)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"6.315789473684211%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026lt;0.0001\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"11.578947368421053%\" valign=\"bottom\"\u003e\n \u003cp\u003e0.95 (0.92; 0.97)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"24.210526315789473%\" valign=\"top\"\u003e\n \u003cp\u003eEchocardiogram\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"13.68421052631579%\" valign=\"top\"\u003e\n \u003cp\u003e8.67 (2.6)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"11.578947368421053%\" valign=\"top\"\u003e\n \u003cp\u003e13652 (4.3)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"6.315789473684211%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026lt;0.0001\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"12.631578947368421%\" valign=\"top\"\u003e\n \u003cp\u003e6495 (2.4)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"13.68421052631579%\" valign=\"top\"\u003e\n \u003cp\u003e10784 (4.0)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"6.315789473684211%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026lt;0.0001\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"11.578947368421053%\" valign=\"bottom\"\u003e\n \u003cp\u003e0.97 (0.93; 1.01)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"24.210526315789473%\" valign=\"top\"\u003e\n \u003cp\u003eC-reactive protein\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"13.68421052631579%\" valign=\"top\"\u003e\n \u003cp\u003e28308 (9.0)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"11.578947368421053%\" valign=\"top\"\u003e\n \u003cp\u003e35957 (11.4)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"6.315789473684211%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026lt;0.0001\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"12.631578947368421%\" valign=\"top\"\u003e\n \u003cp\u003e21081 (7.8)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"13.68421052631579%\" valign=\"top\"\u003e\n \u003cp\u003e26906 (10.0)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"6.315789473684211%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026lt;0.0001\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"11.578947368421053%\" valign=\"bottom\"\u003e\n \u003cp\u003e0.96 (0.94; 0.99)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"24.210526315789473%\" valign=\"top\"\u003e\n \u003cp\u003eWhole blood count\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"13.68421052631579%\" valign=\"top\"\u003e\n \u003cp\u003e81575 (25.8)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"11.578947368421053%\" valign=\"top\"\u003e\n \u003cp\u003e98291 (31.1)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"6.315789473684211%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026lt;0.0001\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"12.631578947368421%\" valign=\"top\"\u003e\n \u003cp\u003e63079 (23.4)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"13.68421052631579%\" valign=\"top\"\u003e\n \u003cp\u003e73731 (27.3)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"6.315789473684211%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026lt;0.0001\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"11.578947368421053%\" valign=\"bottom\"\u003e\n \u003cp\u003e0.91 (0.90; 0.93)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"24.210526315789473%\" valign=\"top\"\u003e\n \u003cp\u003eCreatinine\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"13.68421052631579%\" valign=\"top\"\u003e\n \u003cp\u003e69664 (22.1)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"11.578947368421053%\" valign=\"top\"\u003e\n \u003cp\u003e86400 (27.4)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"6.315789473684211%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026lt;0.0001\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"12.631578947368421%\" valign=\"top\"\u003e\n \u003cp\u003e53277 (19.8)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"13.68421052631579%\" valign=\"top\"\u003e\n \u003cp\u003e65071 (24.1)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"6.315789473684211%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026lt;0.0001\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"11.578947368421053%\" valign=\"bottom\"\u003e\n \u003cp\u003e0.93 (0.91; 0.95)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"24.210526315789473%\" valign=\"top\"\u003e\n \u003cp\u003eGlycemia\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"13.68421052631579%\" valign=\"top\"\u003e\n \u003cp\u003e68388 (21.7)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"11.578947368421053%\" valign=\"top\"\u003e\n \u003cp\u003e82760 (26.2)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"6.315789473684211%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026lt;0.0001\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"12.631578947368421%\" valign=\"top\"\u003e\n \u003cp\u003e52384 (19.4)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"13.68421052631579%\" valign=\"top\"\u003e\n \u003cp\u003e62006 (23.0)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"6.315789473684211%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026lt;0.0001\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"11.578947368421053%\" valign=\"bottom\"\u003e\n \u003cp\u003e0.93 (0.91; 0.95)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"24.210526315789473%\" valign=\"top\"\u003e\n \u003cp\u003eProthrombin time\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"13.68421052631579%\" valign=\"top\"\u003e\n \u003cp\u003e15780 (5.0)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"11.578947368421053%\" valign=\"top\"\u003e\n \u003cp\u003e20957 (6.6)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"6.315789473684211%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026lt;0.0001\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"12.631578947368421%\" valign=\"top\"\u003e\n \u003cp\u003e12754 (4.7)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"13.68421052631579%\" valign=\"top\"\u003e\n \u003cp\u003e16461 (6.1)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"6.315789473684211%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026lt;0.0001\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"11.578947368421053%\" valign=\"bottom\"\u003e\n \u003cp\u003e0.93 (0.91; 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0.98)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"24.210526315789473%\" valign=\"top\"\u003e\n \u003cp\u003eSodium\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"13.68421052631579%\" valign=\"top\"\u003e\n \u003cp\u003e32764 (10.4)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"11.578947368421053%\" valign=\"top\"\u003e\n \u003cp\u003e41161 (13.0)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"6.315789473684211%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026lt;0.0001\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"12.631578947368421%\" valign=\"top\"\u003e\n \u003cp\u003e24880 (9.2)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"13.68421052631579%\" valign=\"top\"\u003e\n \u003cp\u003e31527 (11.7)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"6.315789473684211%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026lt;0.0001\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"11.578947368421053%\" valign=\"bottom\"\u003e\n \u003cp\u003e0.97 (0.95; 0.99)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n\u003c/table\u003e\n\u003cp\u003eChange p-value: McNemar test\u003c/p\u003e\n\u003cp\u003eOR (95% C.I.): adjusted for age, sex and number of drugs. Generalized Estimate Equation model (Interection wave and before/follow-up period \u0026ndash; 3\u003csup\u003erd\u003c/sup\u003e wave vs 2\u003csup\u003end\u003c/sup\u003e wave)\u003c/p\u003e"}],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":true,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":false,"hideJournal":false,"highlight":"","institution":"","isAcceptedByJournal":true,"isAuthorSuppliedPdf":false,"isDeskRejected":"","isHiddenFromSearch":false,"isInQc":false,"isInWorkflow":true,"isPdf":false,"isPdfUpToDate":true,"isWithdrawnOrRetracted":false,"journal":{"display":true,"email":"[email protected]","identity":"internal-and-emergency-medicine","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"iaem","sideBox":"Learn more about [Internal and Emergency Medicine](http://link.springer.com/journal/11739)","snPcode":"11739","submissionUrl":"https://www.editorialmanager.com/iaem/default.aspx","title":"Internal and Emergency Medicine","twitterHandle":"","acdcEnabled":true,"dfaEnabled":true,"editorialSystem":"em","reportingPortfolio":"Springer Hybrid","inReviewEnabled":true,"inReviewRevisionsEnabled":false},"keywords":"Administrative database, Lombardy region, long-COVID, drugs, post-COVID condition, polypharmacy, diagnostic tests","lastPublishedDoi":"10.21203/rs.3.rs-3024426/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-3024426/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003cp\u003e\u003cu\u003eBackground\u003c/u\u003e. The administrative claims database of the Italian region Lombardy, the first in Europe to be hit by the SARS-CoV-2 pandemic, was employed to evaluate the impact on healthcare resource utilization following recovery from the second (mainly alpha-related variant) and third (delta-related) infection waves.\u003c/p\u003e\n\u003cp\u003e\u003cu\u003eSetting and design\u003c/u\u003e. 317.164 individuals recovered from the infection and became negative after the second wave, 271.180 after the third. Of them, 1571 (0.5%) and 1575 (0.6%) died in the first 6 post-negativization months. In the remaining cases (315.593 after the second wave and 269.605 after the third)\u003cstrong\u003e,\u003c/strong\u003ehospitalizations, attendances to emergency rooms and outpatient visits were compared with those recorded in the same pre-pandemic time periods in 2019. Dispensation of drugs as well as of imaging, functional and biochemical diagnostic tests were also compared as additional proxies of the healthcare impact of the second and third SARS-CoV-2 infection waves.\u003c/p\u003e\n\u003cp\u003e\u003cu\u003eMain results\u003c/u\u003e. Following both waves, hospitalizations, attendances at emergency rooms and outpatient visits were similar in number and rates to the pre-pandemic periods. However, there was an increased dispensation of drugs and diagnostic tests, particularly those addressing the cardiorespiratory and blood systems.\u003c/p\u003e\n\u003cp\u003e\u003cu\u003eConclusions\u003c/u\u003e. In a large region such as Lombardy taken as a relevant model because early and severely hit by the SARS-CoV-2 pandemic, the post- COVID burden on healthcare facilities was mildly relevant in cases who recovered from the second and third infection waves regarding such pivotal events as deaths, hospitalizations and need for emergency room and outpatient visits, but was high regarding the dispensation of a few drug classes \u0026nbsp;and types of diagnostic tests.\u003c/p\u003e","manuscriptTitle":"Post Recovery Impact of the Second and Third Sars-Cov-2 Infection Waves on Healthcare Resource Utilization in Lombardy, Italy","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2023-06-13 11:22:21","doi":"10.21203/rs.3.rs-3024426/v1","editorialEvents":[{"type":"communityComments","content":0},{"type":"decision","content":"Minor Revisions Needed","date":"2023-07-06T09:22:45+00:00","index":"","fulltext":""},{"type":"reviewerAgreed","content":"","date":"2023-06-10T17:09:15+00:00","index":0,"fulltext":""},{"type":"reviewersInvited","content":"","date":"2023-06-10T14:29:45+00:00","index":"","fulltext":""},{"type":"editorAssigned","content":"","date":"2023-06-06T10:43:51+00:00","index":"","fulltext":""},{"type":"submitted","content":"Internal and Emergency Medicine","date":"2023-06-05T08:07:31+00:00","index":"","fulltext":""}],"status":"published","journal":{"display":true,"email":"[email protected]","identity":"internal-and-emergency-medicine","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"iaem","sideBox":"Learn more about [Internal and Emergency Medicine](http://link.springer.com/journal/11739)","snPcode":"11739","submissionUrl":"https://www.editorialmanager.com/iaem/default.aspx","title":"Internal and Emergency Medicine","twitterHandle":"","acdcEnabled":true,"dfaEnabled":true,"editorialSystem":"em","reportingPortfolio":"Springer Hybrid","inReviewEnabled":true,"inReviewRevisionsEnabled":false}}],"origin":"","ownerIdentity":"10a73870-7570-4f6f-a7ad-154c167d857e","owner":[],"postedDate":"June 13th, 2023","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"published-in-journal","subjectAreas":[],"tags":[],"updatedAt":"2023-09-07T15:17:05+00:00","versionOfRecord":{"articleIdentity":"rs-3024426","link":"https://doi.org/10.1007/s11739-023-03396-4","journal":{"identity":"internal-and-emergency-medicine","isVorOnly":false,"title":"Internal and Emergency Medicine"},"publishedOn":"2023-08-31 15:08:54","publishedOnDateReadable":"August 31st, 2023"},"versionCreatedAt":"2023-06-13 11:22:21","video":"","vorDoi":"10.1007/s11739-023-03396-4","vorDoiUrl":"https://doi.org/10.1007/s11739-023-03396-4","workflowStages":[]},"version":"v1","identity":"rs-3024426","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-3024426","identity":"rs-3024426","version":["v1"]},"buildId":"WrCJVZZCHTDjtuVLN7oU0","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}