Effects of COVID-19 on Autoimmune Disease Incidence and DMARD Utilization: Evidence from Japanese Insurance Claims Data

Effects of COVID-19 on Autoimmune Disease Incidence and DMARD Utilization: Evidence from Japanese Insurance Claims Data | Research Square window.SnipcartSettings = { analytics: { enabled: false } }; (function() { var accessVector = localStorage.getItem('access_vector') || ''; window.dataLayer = window.dataLayer || []; if (accessVector) { window.dataLayer.push({ user: { profile: { profileInfo: { snid: accessVector } } } }); } })(); (function(w,d,s,l,i){w[l]=w[l]||[];w[l].push({'gtm.start':new Date().getTime(),event:'gtm.js'});var f=d.getElementsByTagName(s)[0],j=d.createElement(s),dl=l!='dataLayer'?'&l='+l:'';j.async=true;j.src='https://www.googletagmanager.com/gtm.js?id='+i+dl;f.parentNode.insertBefore(j,f);})(window,document,'script','dataLayer','GTM-K279D39R'); Browse Preprints In Review Journals COVID-19 Preprints AJE Video Bytes Research Tools Research Promotion AJE Professional Editing AJE Rubriq About Preprint Platform In Review Editorial Policies Our Team Advisory Board Help Center Sign In Submit a Preprint Cite Share Download PDF Research Article Effects of COVID-19 on Autoimmune Disease Incidence and DMARD Utilization: Evidence from Japanese Insurance Claims Data Daisuke Miyamori, Masanori Ito This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-7104550/v1 This work is licensed under a CC BY 4.0 License Status: Posted Version 1 posted You are reading this latest preprint version Abstract Background This matched cohort study utilized a nationwide insurance claims database from Japan, covering approximately 16% of the population across five prefectures. Methods Propensity score matching was employed to create 3,098,948 matched pairs based on age, sex, the Charlson Comorbidity Index, and individual comorbidities. The primary outcome was a composite endpoint of disease-modifying antirheumatic drug (DMARD) prescriptions, including biological, conventional synthetic, targeted synthetic, and COVID-specific drugs. The secondary outcomes included the incidence of various autoimmune and inflammatory conditions. Participants were required to have continuous healthcare access from January 2020 to December 2022, with a 1-year look-back period. The follow-up period was extended from the COVID-19 index month to December 31, 2022. Incidence rate ratios were calculated to assess the association between COVID-19 and subsequent autoimmune conditions or DMARD use. Results COVID-19 patients had a 33% higher incidence rate of DMARD prescriptions than controls (IRR: 1.33, 95% CI: 1.28–1.38). Subgroup analyses revealed stronger associations among males, younger age groups, and those with multiple comorbidities. COVID-19 infection is also associated with an increased risk of arthritis (IRR: 1.15, 95% CI: 1.13–1.18), glomerulonephritis (IRR: 1.08, 95% CI: 1.05–1.12), respiratory disorders (IRR: 1.49, 95% CI: 1.45–1.53), and other autoimmune conditions. These findings suggest a significant long-term impact of COVID-19 on healthcare utilization for managing autoimmune and inflammatory diseases. Conclusions This study highlights the need for the early identification and management of individuals at risk for post-COVID inflammatory sequelae and ensuring equitable access to DMARD therapies. Further research is warranted to elucidate the underlying mechanisms and develop targeted interventions to mitigate the long-term burden of COVID-19 on the healthcare system. COVID-19 National Insurance Claims Database Matched Cohort Study SARS-CoV-2 DMARD Autoimmune disease Rheumatoid arthritis Figures Figure 1 Figure 2 Introduction The increased risk of new-onset autoinflammatory and autoimmune conditions in individuals who have recovered from COVID-19 is of particular concern. Recent studies have reported cases of adult-onset Still's disease, systemic lupus erythematosus, and inflammatory arthritis in patients with no prior history of these conditions following COVID-19 [1] [2]. A large-scale study using electronic health records found significantly higher rates of new autoimmune diagnoses, including rheumatoid arthritis and psoriasis, in the months following COVID-19 than in other infections [3]. These emerging autoinflammatory sequelae of COVID-19 have led to increased focus on the prescription of disease-modifying antirheumatic drugs (DMARDs) in post-COVID patients. However, the effects of COVID-19 on the initiation of DMARDs and the specific burden of each newly diagnosed autoinflammatory disease remain poorly understood [4] [5]. Further research is needed to elucidate the mechanisms underlying these post-COVID inflammatory conditions, their prevalence across different populations, and optimal management strategies to improve outcomes for affected individuals. These emerging autoinflammatory sequelae of COVID-19 have led to an increased focus on the prescription of DMARDs in post-COVID patients [6] [7]. DMARDs are a class of medications that are commonly used to treat various autoimmune and inflammatory conditions [8]. The potential increase in DMARD prescriptions following COVID-19 warrants careful investigation to understand the burden of autoinflammatory diseases on healthcare systems and patient outcomes. This study aimed to investigate the relationship between COVID-19 and subsequent DMARD prescriptions using a nationwide insurance claims database in Japan. By elucidating the specific autoinflammatory conditions leading to increased DMARD use, we can develop more effective strategies for monitoring, diagnosing, and treating post-COVID inflammatory sequelae, ultimately improving outcomes in affected individuals. Methods Study design and data source This research utilized a matched cohort approach, analyzing data from a Japanese insurance claims database. The research analyzed records from the National Insurance Claims Database and Specific Health Check-ups (NDB) spanning from January 2015 to December 2022. This dataset includes comprehensive individual-level information for both inpatient and outpatient services, allowing for the monitoring of patient visits and treatments. The claims database provides details such as age, sex, diagnoses coded according to the International Statistical Classification of Diseases and Related Health Problems, Tenth Revision (ICD-10), and medications dispensed based on the Anatomical Therapeutic Chemical (ATC) Classification System [9, 10]. Study population and eligibility criteria The research cohort was drawn from five Japanese prefectures—Hiroshima, Osaka, Kyoto, Hyogo, and Okayama—which had access to the NDB database as of January 2020. These areas encompass roughly 20.5 million individuals, constituting approximately 16% of Japan's overall population. Participants were required to have health insurance coverage and continuous healthcare access from January 2020, when COVID-19 was widespread, until December 2022, the study's conclusion. However, individuals were omitted if they lacked a one-year observation period before the index month or if they had been administered any DMARDs during this time frame. The research methodology is illustrated in Supplemental Figure 1. Exposure, and outcome definition Identification of Exposure: The COVID-19 group comprised individuals who received a COVID-19 diagnosis within the study's timeframe. During the observation period, the government covered medical costs for those diagnosed with COVID-19. This study determined exposure status by utilizing the public expense number used for payment [11]. Outcome Assessment: The DMARDs included in Supplemental Table 1 are categorized into four distinct classes with the ATC Classification System based on their mechanism of action and therapeutic application: biologic DMARDs (bDMARDs), conventional synthetic DMARDs (csDMARDs), targeted synthetic DMARDs (tsDMARDs), and DMARDs used specifically for COVID-19 management [12] [13]. COVID DMARDs (e.g., baricitinib, tocilizumab) have been repurposed or developed to treat severe inflammatory responses in COVID-19 [14]. The composite endpoint in this study is defined as the initiation of at least one DMARD across these categories: bDMARDs, csDMARDs, tsDMARDs, or COVID DMARDs. The composite outcome includes the prescription of one of these DMARD within the post-exposure period, reflecting the new-onset autoimmune diseases or exacerbation of preexisting conditions necessitating such treatment. We also assess secondary outcomes as autoinflammatory diseases related to the prescription of DMARDS. Supplementary Table2 shows the disease category and associated ICD-10 codes [15] [16]. Statistical Analyses We conducted a matched cohort study employing propensity score matching to ensure balance between the COVID-19 and control groups. Matching variables included age, sex, the Charlson Comorbidity Index (CCI), and individual comorbidities encompassed within the CCI. Propensity scores were generated using logistic regression, and nearest-neighbor matching was applied with a caliper width set at 0.2 standard deviations of the logit of the propensity score to minimize bias. As shown in Supplementary Figure 1, the study timeline integrates a one-year look-back period for baseline assessment prior to the COVID-19 index month, followed by a longitudinal follow-up phase extending through December 31, 2022. This design ensures a robust comparison of health outcomes between the matched groups while accounting for preexisting conditions and medication history. Incidence rate ratios (IRRs) and incidence rate differences (IRDs) were calculated to evaluate the association between COVID-19 infection and the composite endpoint of DMARD prescription. The analysis also examined the secondary endpoints, assessing whether COVID-19 infection was associated with an increased incidence of autoimmune and autoinflammatory diseases linked to DMARD prescriptions. We assessed subgroup interactions within the comorbidities included in the CCI to evaluate whether the effect of COVID-19 infection on new DMARD prescriptions differed across these conditions. Subgroup analyses were performed by stratifying the matched cohort according to the age, sex, and CCI score and presence or absence of individual comorbidities (e.g., diabetes, cardiovascular disease, chronic kidney disease). To formally test for interaction between COVID-19 infection and each comorbidity, we included interaction terms applied likelihood ratio tests (LRTs) to compare models with and without these interaction terms. Statistical significance was defined as a p-value < 0.05. As sensitivity analyses, we conducted three analyses: 1) those in which the period of follow-up was divided into 1 year or less and 1-2 years; 2) those in which the outcome was not the initiation of new DMARDs but the continuation of DMARDs for 6 months or 1 year after the initiation of DMARDs; and 3) those in which Participants with a history of Rheumatoid diseases were excluded. These three sensitivity analyses were performed to confirm the robustness of the study. The incidence rate ratios (IRR)All statistical analyses were performed using Stata version 18.0 (StataCorp, College Station, TX, USA). Ethical Consideration and Data Availability The research protocol was reviewed and approved by the Epidemiological Research Committee of Hiroshima University (approval number E2022-0024-01). In accordance with Japanese data protection laws, individual-level data cannot be made publicly available. The data used in this study were obtained with ethical approval from the Ministry of Health, Labour and Welfare of Japan (approval number 1502). Researchers seeking access to the data must submit an application to the primary data owners, including the Ministry, and adhere to Japanese regulatory requirements. Informed consent was not required for this study, as it utilized registry data with all personal information encrypted to ensure privacy. Results Figure 1 illustrates the flow chart of study participants for this matched cohort study utilizing a national insurance claims database. Among 16,056,476 subjects registered as of January 2020, 6,106,346 individuals were diagnosed with COVID-19 during the study period. After excluding ineligible patients, 5,317,163 individuals were potentially eligible for the COVID-19 group. Propensity score matching was conducted, and 3,018,180 matched pairs were established for COVID-19-infected and non-infected individuals. Baseline Characteristics of Study Participants Table 1 presents the baseline characteristics of the study participants at the index month, comprising equal-sized cohorts of COVID-19-infected individuals (N=3,018,180) and non-infected controls (N=3,018,180). Age distribution was well-balanced between the two groups, with similar percentages across all other categories. Overall, the baseline characteristics were well-balanced across the two groups, confirming the robustness of the propensity score matching process. Table 2 presents the IRRs and IRDs for both primary and secondary outcomes. For the composite endpoint of DMARD prescription, COVID-19 patients had an incidence rate of 289.7 events per 1,000,000 person-months (95% CI: 283.5–296.1), compared to 217.7 events in the control group (95% CI: 212.3–223.2). The IRR for COVID-19 was 1.33 (95% CI: 1.28–1.38), indicating a 33% higher rate of DMARD prescription in the COVID-19 group, with an IRD of 72.1 (95% CI: 63.8–80.3). For individual DMARD categories, bDMARDs showed an IRR of 1.48 (95% CI: 1.31–1.67) and an IRD of 7.74 (95% CI: 5.41–10.06), while csDMARDs had an IRR of 1.40 (95% CI: 1.35–1.46) and an IRD of 66.99 (95% CI: 59.64–74.35). Similarly, tsDMARDs and COVID DMARDs showed elevated IRRs of 1.26 (95% CI: 1.09–1.46) and 1.29 (95% CI: 1.12–1.49), respectively. For secondary endpoints, COVID-19 infection was associated with an increased incidence of several conditions. Arthritis had an IRR of 1.15 (95% CI: 1.13–1.18) and an IRD of 73.02 (95% CI: 61.26–84.78). Bullous disorders demonstrated an IRR of 1.28 (95% CI: 1.20–1.36) with an IRD of 17.23 (95% CI: 12.87–21.59), while central nervous system disorders had the highest IRR of 1.55 (95% CI: 1.42–1.69) and an IRD of 16.1 (95% CI: 12.92–19.29). Other notable findings include glomerulonephritis with an IRR of 1.08 (95% CI: 1.05–1.12) and an IRD of 21.1 (95% CI: 12.68–29.53), and inflammatory bowel disease with an IRR of 1.19 (95% CI: 1.11–1.27) and an IRD of 10.94 (95% CI: 6.79–15.09). Immunodeficiency disorders also showed a marked increase with an IRR of 1.56 (95% CI: 1.47–1.66) and an IRD of 34.63 (95% CI: 30.02–39.25). In addition, respiratory disorders and systemic connective tissue disorders showed significant increases in incidence. Respiratory disorders had an IRR of 1.49 (95% CI: 1.45–1.53) and an IRD of 159.91 (95% CI: 149.43–170.39), while systemic connective tissue disorders demonstrated an IRR of 1.23 (95% CI: 1.20–1.26) and an IRD of 87.43 (95% CI: 76.62–98.24). These findings indicate that COVID-19 infection significantly increased the risk of both DMARD prescriptions and a variety of autoimmune and inflammatory conditions, highlighting its substantial impact on long-term health outcomes. The sensitivity analysis confirms that the increased incidence of DMARD prescriptions is most pronounced within the first year following COVID-19 infection (IRR: 1.39, 95% CI: 1.34–1.45) but persists, albeit to a lesser extent, into the second year (IRR: 1.12, 95% CI: 1.04–1.20). This highlights the long-term implications of COVID-19 on healthcare utilization for autoimmune and inflammatory conditions. To account for potential bias from short-term DMARD use or discontinuation due to misdiagnosis, we conducted additional sensitivity analyses using more stringent definitions of the primary outcome. The IRR for outcomes treated with DMARDs for durations exceeding six months and one year are 1.45 (95% CI 1.39-1.52) and 1.67 (95% CI 1.56-1.79), respectively. Sensitivity analyses conducted excluding individuals with a history of rheumatoid diseases demonstrated a trend consistent with the primary analysis (see supplementary Table 3). The subgroup analysis revealed significant variations in the association between COVID-19 infection and DMARD prescriptions across demographic and clinical subgroups (Figure 2). COVID-19 patients had an significant risk of amputation on any subgroup. For the demographic factors, male sex and younger age had significant interaction. The association was stronger in males (IRR: 1.44, 95% CI: 1.36–1.52) than females (IRR: 1.26, 95% CI: 1.21–1.32). Younger age groups showed higher IRRs, with those aged 0–19 years having an IRR of 1.46 (95% CI: 1.22–1.69), while adults aged 20–64 years and older adults (≥65 years) had IRRs of 1.25 (95% CI: 1.20–1.30) and 1.42 (95% CI: 1.35–1.49), respectively. For the subgroup of comorbidities. Significant interactions were found on CCI score, chronic heart failure (CHF), Rheumatoid diseases, diabetes mellitus (DM), cancer, hemiplegia/paraplegia (HP/PAPL), and renal disease (RD). Patients having lower CCI scores (0–1) showed an IRR of 1.23 (95% CI: 1.15–1.30), while those with higher scores (≥2) exhibited a stronger association (IRR: 1.38, 95% CI: 1.33–1.43). Patients with diabetes (IRR: 1.53, 95% CI: 1.38–1.67) and renal disease (IRR: 1.70, 95% CI: 1.53–1.87) had markedly elevated risks. Notably, patients with HP/PAPL demonstrated the highest IRR (2.14, 95% CI: 1.49–2.78). On the other hand, IRR for patients with rheumatoid diseases showed lower risk than those without such conditions. Other conditions, such as chronic pulmonary disease (CPD), peptic ulcer disease (PUD), and liver disease (LD), showed slightly elevated IRRs, ranging from 1.30 to 1.36. Patients with cancer had an IRR of 1.42 (95% CI: 1.32–1.53). Interestingly, conditions like dementia had lower IRRs (1.23, 95% CI: 1.02–1.44) compared to other subgroups. These findings underscore the heterogeneous impact of COVID-19 on DMARD prescriptions, with stronger associations observed in males, younger patients, those with multiple comorbidities, and individuals with autoimmune or hematologic conditions. Discussion This study highlights the substantial impact of COVID-19 on the prescription of DMARDs, with a 33% higher incidence rate observed in COVID-19 patients compared to matched controls. The findings were consistent across subgroups, with males, younger age groups, and those with multiple comorbidities showing the strongest associations. COVID-19 patients demonstrated significantly increased risks for DMARD prescriptions across all categories—bDMARDs, csDMARDs, tsDMARDs, and COVID-specific DMARDs. Secondary outcomes also revealed an increased incidence of autoimmune and inflammatory conditions, such as arthritis, glomerulonephritis, and respiratory disorders. These findings suggest a long-term impact of COVID-19 on healthcare utilization, particularly in managing autoimmune and inflammatory diseases. Comparing these results to previous studies, the findings align with reports of elevated risks for autoimmune conditions following COVID-19. Previous population level study showed the decreased prescription of bDMARDS during COVID-19 pandemic [17]. Earlier research has demonstrated increased rates of rheumatoid arthritis, psoriasis, and systemic inflammatory conditions in COVID-19 survivors [18] [19]. The observed 33% increase in DMARD prescriptions is consistent with the heightened risk of autoimmune diseases, as noted in a large-scale electronic health record study. Moreover, the associations with subgroups, such as higher risks in younger individuals and males, reflect similar trends reported in prior research [20]. However, this study uniquely captures the broader population-level impact of COVID-19 on DMARD prescriptions, providing a more comprehensive perspective. The observed increase in DMARD prescriptions following COVID-19 infection can be explained through several potential mechanisms, such as, pro-inflammatory and immune dysregulation effects of SARS-CoV-2 infection [21]. SARS-CoV-2 infection may trigger autoimmunity through molecular mimicry, where viral antigens resemble host tissue antigens, leading to cross-reactive immune responses. This can result in the production of autoantibodies and activation of autoreactive T cells, initiating or exacerbating autoimmune conditions [22]. COVID-19 is known to trigger a cytokine storm and persistent immune activation, characterized by excessive release of pro-inflammatory cytokines like IL-6, TNF-α, and IL-1β. This hyperinflammatory state can disrupt immune homeostasis, potentially breaking self-tolerance and promoting autoimmune reactions, which can lead to the onset of autoinflammatory and autoimmune conditions [23]. The prolonged endothelial damage and thrombotic events associated with COVID-19 may exacerbate underlying inflammatory processes [24]. Furthermore, COVID-19 can lead to dysregulation of the innate immune system, including aberrant activation of neutrophils and complement cascades, which may contribute to autoimmune pathogenesis. [25] [26] These mechanisms collectively increase the incidence of autoimmune and inflammatory conditions, thereby driving up the demand for DMARDs to manage these emerging or exacerbated autoimmune sequelae. The clinical implications of these findings are significant. Early identification and management of individuals at risk for post-COVID inflammatory sequelae are crucial. The results underscore the need for healthcare providers to closely monitor COVID-19 survivors, particularly those with preexisting conditions, for signs of emerging autoimmune diseases. Moreover, the elevated prescription rates for DMARDs highlight the importance of ensuring equitable access to these therapies and addressing the long-term burden on healthcare systems. The primary strength of this study is the use of a nationwide insurance claims database, which allowed for the analysis of a large, population-based cohort with robust matching techniques to minimize confounding. The inclusion of both primary and secondary outcomes adds depth to the findings, providing a comprehensive assessment of the post-COVID burden of autoimmune diseases and DMARD prescriptions. The longitudinal follow-up and subgroup analyses further enhance the study’s validity and generalizability. However, the study has several limitations. A key limitation of this study is the relatively short maximum follow-up period of approximately 2 years (until December 2022). This timeframe may be insufficient to fully assess the long-term autoimmune risks associated with COVID-19 infection. Autoimmune conditions can develop over extended periods, sometimes taking several years to manifest clinically. Therefore, our findings primarily reflect the short to medium-term impacts of COVID-19 on autoimmune disease incidence and DMARD utilization. Longer follow-up periods in future studies will be crucial to elucidate the full spectrum of autoimmune sequelae that may emerge in COVID-19 survivors over time. The reliance on administrative data may result in misclassification of diagnoses or exposure status. In this study, the incidence of diagnoses such as arthritis was notably higher than the incidence of DMARDs prescriptions. Relying solely on ICD10 codes for new diagnoses may result in biased conclusions. Consequently, we designated DMARDs prescription as the primary outcome and validated its robustness through three sensitivity analyses. The database lacks detailed clinical information, such as the severity of COVID-19 or disease-specific biomarkers, which could influence the results. Additionally, residual confounding from unmeasured factors, such as vaccination status, socioeconomic status or lifestyle, cannot be entirely ruled out. In the future analysis, incorporating data on COVID-19 variants and vaccination status would enhance our understanding of how these factors influence the development of autoimmune conditions following infection. Despite these limitations, the large sample size and rigorous statistical methods provide robust and reliable findings. In conclusion, this study demonstrates a significant association between COVID-19 and increased DMARD prescriptions, reflecting a higher burden of autoimmune and inflammatory conditions in COVID-19 survivors. These findings underscore the importance of long-term monitoring and tailored interventions for individuals at risk, as well as the need for further research to elucidate the mechanisms driving these associations. Addressing these challenges will be critical to improving outcomes for COVID-19 survivors and mitigating the long-term impact on healthcare systems. Abbreviations AMI – Acute Myocardial Infarction APC – Article Processing Charge ATC – Anatomical Therapeutic Chemical Classification System bDMARD – Biologic Disease-Modifying Antirheumatic Drug CCI – Charlson Comorbidity Index CEVD – Cerebrovascular Disease CI – Confidence Interval CHF – Congestive Heart Failure COPD – Chronic Obstructive Pulmonary Disease COVID DMARDs – Disease-Modifying Antirheumatic Drugs repurposed for COVID-19 treatment CS-DMARD – Conventional Synthetic Disease-Modifying Antirheumatic Drug DMARD – Disease-Modifying Antirheumatic Drug GN – Glomerulonephritis HP/PAPL – Hemiplegia/Paraplegia IBD – Inflammatory Bowel Disease ICD-10 – International Classification of Diseases, 10th Revision ID – Immunodeficiency Disorder IRD – Incidence Rate Difference IRR – Incidence Rate Ratio LD – Liver Disease PUD – Peptic Ulcer Disease PVD – Peripheral Vascular Disease RD – Renal Disease Rheum Dis – Rheumatoid Disease SCTD – Systemic Connective Tissue Disorders tsDMARD – Targeted Synthetic Disease-Modifying Antirheumatic Drug Declarations Ethics approval and consent to participate The research protocol was reviewed and approved by the Epidemiological Research Committee of Hiroshima University (approval number E2022-0024-01). In accordance with Japanese data protection laws, individual-level data cannot be made publicly available. The data used in this study were obtained with ethical approval from the Ministry of Health, Labour and Welfare of Japan (approval number 1502). Availability of data and materials Researchers seeking access to the data must submit an application to the primary data owners, including the Ministry, and adhere to Japanese regulatory requirements. Informed consent was not required for this study, as it utilized registry data with all personal information encrypted to ensure privacy. Consent for publication : Not applicable. Conflict of Interest: The researchers state that they have no connections to or engagement with any organizations or entities that have financial stakes in the topic or materials addressed in this study. Funding Financial support for this research was provided by two sources: the Innovative Research Program on Suicide Countermeasures (Grant Number JPSCIRS20220305), and JSPS KAKENHI (Grant Number 21K17227). Author Contributions: DM was involved in designing and executing the study, examining the outcomes, and drafting the manuscript. MI developed the idea for the project and oversaw its implementation. Acknowledgements Not applicable References Li J, Liu H-H, Yin X-D, Li C-C, Wang J, Li J, Liu H-H, Yin X-D, Li C-C, Wang J: COVID-19 illness and autoimmune diseases: recent insights. Inflammation Research 2021 70 : 4 2021-02-28, 70(4). MC S, S T, P S, L A, P DG, A S, EC L, A R, R T, R G et al : SARS-CoV-2 infection as a trigger of autoimmune response - PubMed. Clinical and translational science 2021 May, 14(3). Sacchi MC, Tamiazzo S, Stobbione P, Agatea L, De Gaspari P, Stecca A, Lauritano EC, Roveta A, Tozzoli R, Guaschino R et al : SARS-CoV-2 infection as a trigger of autoimmune response. Clin Transl Sci 2021, 14(3):898–907. Ruscitti P, Conforti A, Cipriani P, Giacomelli R, Tasso M, Costa L, Caso F: Pathogenic implications, incidence, and outcomes of COVID-19 in autoimmune inflammatory joint diseases and autoinflammatory disorders. Advances in Rheumatology 2021, 61(1):45. Pope JE: What Does the COVID-19 Pandemic Mean for Rheumatology Patients? Current Treatment Options in Rheumatology 2020, 6(2):71–74. Venkat RK, Wang X, Patel NJ, Kawano Y, Schiff A, Kowalski EN, Cook CE, Vanni KMM, Qian G, Bade KJ et al : Associations of DMARDs with post-acute sequelae of COVID-19 in patients with systemic autoimmune rheumatic diseases: a prospective study. Rheumatology 2023, 63(10):2828–2837. D’Silva KM, Wallace ZS: COVID-19 and Disease-Modifying Anti-rheumatic Drugs. Current rheumatology reports 2021, 23(5):28. Venkat R, Wallace ZS, Sparks JA: Considerations for Pharmacologic Management of Rheumatoid Arthritis in the COVID-19 Era: a Narrative Review. Current rheumatology reports 2023, 25(11):236–245. Charlson ME, Pompei P, Ales KL, MacKenzie CR: A new method of classifying prognostic comorbidity in longitudinal studies: development and validation. J Chronic Dis 1987, 40(5):373–383. Quan H, Sundararajan V, Halfon P, Fong A, Burnand B, Luthi JC, Saunders LD, Beck CA, Feasby TE, Ghali WA: Coding algorithms for defining comorbidities in ICD-9-CM and ICD-10 administrative data. Medical care 2005, 43(11):1130–1139. Resonse to COVID-19 after the classification change [ https://www.mhlw.go.jp/stf/seisakunitsuite/bunya/0000164708_00079.html] Smolen JS, Landewé R, Bijlsma J, Burmester G, Chatzidionysiou K, Dougados M, Nam J, Ramiro S, Voshaar M, van Vollenhoven R et al : EULAR recommendations for the management of rheumatoid arthritis with synthetic and biological disease-modifying antirheumatic drugs: 2016 update. Annals of the rheumatic diseases 2017, 76(6):960–977. Knaus K, Pearcey W: The SAGE Encyclopedia of Pharmacology and Society. In. Alattar R, Ibrahim TBH, Shaar SH, Abdalla S, Shukri K, Daghfal JN, Khatib MY, Aboukamar M, Abukhattab M, Alsoub HA et al : Tocilizumab for the treatment of severe coronavirus disease 2019. Journal of medical virology 2020, 92(10):2042–2049. Benjamin O, Bansal P, Goyal A, Lappin SL: Disease Modifying Anti-Rheumatic Drugs (DMARD). In: 2019 ; 2019. Allen PB, Peyrin-Biroulet L: Moving towards disease modification in inflammatory bowel disease therapy. Current Opinion in Gastroenterology 2013, 29(4). Russell M, Balachandran S, Norton S, Alveyn E, Nagra D, Adas M, Galloway J: POS1228 IMPACT OF COVID-19 ON THE PRESCRIPTION OF BIOLOGIC DMARDs: A POPULATION-LEVEL STUDY IN ENGLAND. Annals of the rheumatic diseases 2022, 81(Suppl 1):946–946. Syed U, Subramanian A, Wraith DC, Lord JM, McGee K, Ghokale K, Nirantharakumar K, Haroon S: Incidence of immune-mediated inflammatory diseases following COVID-19: a matched cohort study in UK primary care. BMC Medicine 2023, 21(1):363. Gracia-Ramos AE, Martin-Nares E, Hernández-Molina G: New Onset of Autoimmune Diseases Following COVID-19 Diagnosis. Cells 2021, 10(12):3592. Heo Y-W, Jeon JJ, Ha MC, Kim YH, Lee S: Long-Term Risk of Autoimmune and Autoinflammatory Connective Tissue Disorders Following COVID-19. JAMA Dermatology 2024, 160(12):1278–1287. Wong L-YR, Perlman S: Immune dysregulation and immunopathology induced by SARS-CoV-2 and related coronaviruses — are we our own worst enemy? Nature Reviews Immunology 2022, 22(1):47–56. Arévalo-Cortés A, Rodriguez-Pinto D, Aguilar-Ayala L: Evidence for Molecular Mimicry between SARS-CoV-2 and Human Antigens: Implications for Autoimmunity in COVID-19. Autoimmune Diseases 2024, 2024(1):8359683. Zebardast A, Hasanzadeh A, Ebrahimian Shiadeh SA, Tourani M, Yahyapour Y: COVID-19: A trigger of autoimmune diseases. Cell Biol Int 2023, 47(5):848–858. Bikdeli B, Madhavan MV, Jimenez D, Chuich T, Dreyfus I, Driggin E, Nigoghossian C, Ageno W, Madjid M, Guo Y et al : COVID-19 and Thrombotic or Thromboembolic Disease: Implications for Prevention, Antithrombotic Therapy, and Follow-Up: JACC State-of-the-Art Review. Journal of the American College of Cardiology 2020, 75(23):2950–2973. Gardiman E, Bianchetto-Aguilera F, Gasperini S, Tiberio L, Scandola M, Lotti V, Gibellini D, Salvi V, Bosisio D, Cassatella MA: SARS-CoV-2-Associated ssRNAs activate human neutrophils in a TLR8-dependent fashion. Cells 2022, 11(23):3785. Zhou T, Su TT, Mudianto T, Wang J: Immune asynchrony in COVID-19 pathogenesis and potential immunotherapies. Journal of Experimental Medicine 2020, 217(10):e20200674. Tables Table 1 Baseline characteristics of study participants at the index month Non Infected COVID-19 SMD N=3,018,180 N=3,018,180 Age <0.001 0–4 199,553 ( 7%) 199,539 ( 7%) 5–9 159,673 ( 5%) 159,646 ( 5%) 10–14 126,040 ( 4%) 125,954 ( 4%) 15–19 122,887 ( 4%) 122,853 ( 4%) 20–24 135,614 ( 4%) 135,617 ( 4%) 25–29 137,658 ( 5%) 137,731 ( 5%) 30-34 145,425 ( 5%) 145,507 ( 5%) 35–39 149,081 ( 5%) 149,203 ( 5%) 40–44 153,016 ( 5%) 153,199 ( 5%) 45–49 179,676 ( 6%) 179,783 ( 6%) 50–54 176,179 ( 6%) 176,233 ( 6%) 55–59 163,627 ( 5%) 163,697 ( 5%) 60–64 157,523 ( 5%) 157,511 ( 5%) 65–69 167,582 ( 6%) 167,462 ( 6%) 70–74 241,333 ( 8%) 240,911 ( 8%) 75–79 208,158 ( 7%) 207,591 ( 7%) 80 or over 395,155 (13%) 395,743 (13%) SEX <0.001 Female 1,681,931 (55.7%) 1,681,759 (55.7%) CCI <0.001 0 523,041 (17%) 523,028 (17%) 1 1,012,293 (34%) 1,012,203 (34%) 2-3 765,198 (25%) 764,389 (25%) 4 or over 717,648 (24%) 718,560 (24%) Past Medical History AMI 70,253 ( 2.3%) 71,929 ( 2.4%) 0.004 CHF 519,599 (17.2%) 519,798 (17.2%) <0.001 PVD 407,806 (13.5%) 408,607 (13.5%) 0.001 CEVD 488,021 (16.2%) 488,259 (16.2%) <0.001 Dementia 162,164 ( 5.4%) 161,936 ( 5.4%) <0.001 CPD 1,767,461 (58.6%) 1,765,979 (58.5%) -0.001 Rheum Dis 104,707 ( 3.5%) 106,749 ( 3.5%) 0.004 PUD 850,334 (28.2%) 849,487 (28.1%) -0.001 LD 802,239 (26.6%) 802,814 (26.6%) <0.001 Diabetes <0.001 HP/PAPL 45,228 ( 1.5%) 47,258 ( 1.6%) 0.005 RD 160,611 ( 5.3%) 162,275 ( 5.4%) 0.002 Cancer 395,154 (13.1%) 395,178 (13.1%) <0.001 DMARD, Disease-Modifying Antirheumatic Drug; CCI, Charlson Comorbidity Index; IRR, Incidence Rate Ratio; CI, Confidence Interval; PI, P-value for Interaction; AMI, Acute Myocardial Infarction; CHF, Congestive Heart Failure; PVD, Peripheral Vascular Disease; CEVD, Cerebrovascular Disease; CPD, Chronic Pulmonary Disease; Rheum Dis, Rheumatoid Disease; PUD, Peptic Ulcer Disease; DM, Diabetes Mellitus; LD, Liver Disease; HP/PAPL, Hemiplegia or Paraplegia; RD, Renal Disease. Table2 IRRs and IRDs for Primary and Secondary outcome No. of Individuals No. of Events Cumulative Incidence (No. of Events per 1 000 000 Person months) Rate (95% CI) Difference (95% CI) Ratio (95% CI) Composite endpoint COVID-19 3018180 8219 289.7 (283.5-296.1) 72.1 (63.8-80.3) 1.33 (1.28 - 1.38) Control 3018180 6194 217.7 (212.3-223.2) Primary outcome bDMARDs COVID-19 3018180 675 23.80 (22.07–25.66) 7.74 (5.41–10.06) 1.48 (1.31–1.67) Control 3018180 457 16.06 (14.66–17.61) COVID DMARDs COVID-19 3018180 451 15.90 (14.50–17.44) 3.60 (1.65–5.55) 1.29 (1.12–1.49) Control 3018180 350 12.30 (11.08–13.66) csDMARDs COVID-19 3018180 6585 232.99 (227.43–238.69) 66.99 (59.64–74.35) 1.40 (1.35–1.46) Control 3018180 4706 166.00 (161.32–170.81) tsDMARDs COVID-19 3018180 440 15.51 (14.13–17.03) 3.21 (1.27–5.15) 1.26 (1.09–1.46) Control 3018180 350 12.30 (11.08–13.66) Sensitivity Analysis Within 1 yr COVID-19 3018180 6639 287.24 (280.42–294.24) 81.25 (72.20–90.29) 1.39 (1.34–1.45) Control 3018180 4779 206.00 (200.24–211.92) 1-2 yr COVID-19 912553 1580 300.71 (286.25–315.91) 31.43 (11.01–51.84) 1.12 (1.04–1.20) Control 917280 1415 269.29 (255.62–283.69) Rx > half yr COVID-19 2560704 4535 167.85 (163.04–172.81) 52.44 (46.10–58.78) 1.45(1.39–1.52) Control 2560704 3128 115.41 (111.44–119.53) Rx > 1 yr COVID-19 1539640 2202 105.42 (101.11–109.91) 42.45(36.89–48.01) 1.67(1.56–1.79) Control 1539640 1321 62.967 (59.66–66.46) Secondary endpoints Arthritis COVID-19 3018180 15454 546.45 (537.91–555.14) 73.02 (61.26–84.78) 1.15 (1.13–1.18) Control 3018180 13440 473.43 (465.5–481.51) Bullous Disorders COVID-19 3018180 2238 78.93 (75.72–82.27) 17.23 (12.87–21.59) 1.28 (1.2–1.36) Control 3018180 1755 61.7 (58.88–64.65) Central Nervous System Disorders COVID-19 3018180 1292 45.56 (43.14–48.11) 16.1 (12.92–19.29) 1.55 (1.42–1.69) Control 3018180 838 29.46 (27.53–31.52) Glomerulonephritis COVID-19 3018180 7720 272.59 (266.57–278.74) 21.1 (12.68–29.53) 1.08 (1.05–1.12) Control 3018180 7146 251.48 (245.72–257.38) Inflammatory Bowel Disease COVID-19 3018180 1959 69.08 (66.09–72.21) 10.94 (6.79–15.09) 1.19 (1.11–1.27) Control 3018180 1654 58.14 (55.41–61.02) Immunodeficiency Disorders COVID-19 3018180 2720 95.94 (92.41–99.62) 34.63 (30.02–39.25) 1.56 (1.47–1.66) Control 3018180 1744 61.31 (58.5–64.26) Psoriasis COVID-19 3018180 10423 368.26 (361.26–375.4) 34.72 (24.97–44.48) 1.10 (1.07–1.14) Control 3018180 9473 333.54 (326.89–340.32) Respiratory Disorders COVID-19 3018180 13696 484.68 (476.63–492.87) 159.91 (149.43–170.39) 1.49 (1.45–1.53) Control 3018180 9226 324.77 (318.21–331.46) Systemic Connective Tissue Disorders COVID-19 3018180 13420 474.61 (466.65–482.71) 87.43 (76.62–98.24) 1.23 (1.20–1.26) Control 3018180 10994 387.19 (380.01–394.49) No, number: CI, confidence interval: bDMARD, Biologic Disease-Modifying Antirheumatic Drugs; csDMARD, Conventional Synthetic Disease-Modifying Antirheumatic Drugs; tsDMARD, Targeted Synthetic Disease-Modifying Anti rheumatic Drugs; COVID DMARDs, Disease-Modifying Antirheumatic Drugs repurposed or developed for the treatment of severe inflammatory responses in COVID-19; Rx; Treatment duration. 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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-7104550","acceptedTermsAndConditions":true,"allowDirectSubmit":true,"archivedVersions":[],"articleType":"Research Article","associatedPublications":[],"authors":[{"id":485235252,"identity":"234e3754-bdb4-4f7c-9a33-2275f3a12135","order_by":0,"name":"Daisuke Miyamori","email":"data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAZAAAAAyAQMAAABI0h/eAAAABlBMVEX///8AAABVwtN+AAAACXBIWXMAAA7EAAAOxAGVKw4bAAABAklEQVRIiWNgGAWjYJCCAwwGDAz8EowNIDYPXBjMx6dFcgYpWsDA4AZUO0Gg29778NCNgsPyxreb2z58+HNHRrf9AJsEQ40dA/Ns7NaYnTlucDjH4LDhtjsHm2fObHvGY3YmAajlWDID4xzsVprdSGMAaWHcdiOxmZm34TCP2YH8bxIMbAcYGGck4NViv3kGUAvPH6CW8w+AtvwjrCVxgwRICxtQyw2gwxjb8Gg5cwykJT15BtBhjDPbQFoeMFsk9iXz4PTL8Tbmzzl/rG37Z6Q/Zvjw57C92fkExhsfvtnJGeIIMShoRuMDncRjOAOfDoY6LGLyEni1jIJRMApGwcgBADe+ZeAAyJ6qAAAAAElFTkSuQmCC","orcid":"","institution":"Hiroshima University Hospital","correspondingAuthor":true,"prefix":"","firstName":"Daisuke","middleName":"","lastName":"Miyamori","suffix":""},{"id":485235253,"identity":"4d70b582-b241-49dd-a7c3-230a059e6996","order_by":1,"name":"Masanori Ito","email":"","orcid":"","institution":"Hiroshima University Hospital","correspondingAuthor":false,"prefix":"","firstName":"Masanori","middleName":"","lastName":"Ito","suffix":""}],"badges":[],"createdAt":"2025-07-11 21:23:24","currentVersionCode":1,"declarations":"","doi":"10.21203/rs.3.rs-7104550/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-7104550/v1","draftVersion":[],"editorialEvents":[],"editorialNote":"","failedWorkflow":false,"files":[{"id":87042794,"identity":"f7c21beb-175d-4da6-be1d-1cc7fa2b5679","added_by":"auto","created_at":"2025-07-18 14:12:41","extension":"jpeg","order_by":1,"title":"Figure 1","display":"","copyAsset":false,"role":"figure","size":254361,"visible":true,"origin":"","legend":"\u003cp\u003eFlow chart of Study Participants\u003c/p\u003e\n\u003cp\u003eThe flowchart illustrates the selection process for the study population from the national insurance claims database. Initially, 16,056,476 subjects were registered in the database as of January 2020. Among them, 6,106,346 individuals were diagnosed with COVID-19 during the study period. A total of 719,877 subjects were excluded due to the lack of a 1-year look-back period, and an additional 69,306 subjects were excluded for having initiated DMARDs during the assessment period. After exclusions, 5,317,163 individuals were identified as potentially eligible for the COVID-19 infected group. Propensity score matching was conducted based on age category, sex, and Charlson comorbidity index at the enrollment month, resulting in 3,018,180 matched pairs of COVID-19-infected and non-infected individuals.\u003c/p\u003e","description":"","filename":"floatimage1.jpeg","url":"https://assets-eu.researchsquare.com/files/rs-7104550/v1/60a601d3b9f2b4b5c0c9fb7a.jpeg"},{"id":87045050,"identity":"9275e8c0-059e-452e-8556-699ff6c493e2","added_by":"auto","created_at":"2025-07-18 14:28:41","extension":"jpeg","order_by":2,"title":"Figure 2","display":"","copyAsset":false,"role":"figure","size":199623,"visible":true,"origin":"","legend":"\u003cp\u003eSubgroup Analysis of Incidence Rate Ratios and Differences for New DMARDs Prescriptions\u003c/p\u003e\n\u003cp\u003eThis figure presents the subgroup analysis results comparing the incidence of disease-modifying antirheumatic drug (DMARD) prescriptions between COVID-19 patients and matched controls. The analysis is stratified by sex, age groups, Charlson Comorbidity Index (CCI) scores, and various comorbidities. The left panel provides the number at risk and events in each subgroup, while the right panel displays the incidence rate ratios (IRRs) with 95% confidence intervals. An IRR greater than 1 indicates a higher incidence of DMARD prescriptions in the COVID-19 group compared to the controls. Subgroup-specific p-values for interaction (PI) are also reported.\u003c/p\u003e\n\u003cp\u003eDMARD, Disease-Modifying Antirheumatic Drug; CCI, Charlson Comorbidity Index; IRR, Incidence Rate Ratio; CI, Confidence Interval; PI, P-value for Interaction; AMI, Acute Myocardial Infarction; CHF, Congestive Heart Failure; PVD, Peripheral Vascular Disease; CEVD, Cerebrovascular Disease; CPD, Chronic Pulmonary Disease; Rheum Dis, Rheumatoid Disease; PUD, Peptic Ulcer Disease; DM, Diabetes Mellitus; LD, Liver Disease; HP/PAPL, Hemiplegia or Paraplegia; RD, Renal Disease.\u003c/p\u003e","description":"","filename":"floatimage2.jpeg","url":"https://assets-eu.researchsquare.com/files/rs-7104550/v1/94ac8da79947767cc67d5426.jpeg"},{"id":90156215,"identity":"08c3a107-f92f-4b71-84e6-350087c2bc4c","added_by":"auto","created_at":"2025-08-29 08:17:13","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":5849825,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-7104550/v1/a5bd41a9-633a-4b6b-af5f-485bee0eb2f1.pdf"},{"id":87042798,"identity":"c3bd022c-5b14-435e-8c46-20908debad07","added_by":"auto","created_at":"2025-07-18 14:12:41","extension":"docx","order_by":0,"title":"","display":"","copyAsset":false,"role":"supplement","size":108009,"visible":true,"origin":"","legend":"","description":"","filename":"Supplemental.docx","url":"https://assets-eu.researchsquare.com/files/rs-7104550/v1/c1e3e5933813f1e2a711a8ea.docx"}],"financialInterests":"No competing interests reported.","formattedTitle":"Effects of COVID-19 on Autoimmune Disease Incidence and DMARD Utilization: Evidence from Japanese Insurance Claims Data","fulltext":[{"header":"Introduction","content":"\u003cp\u003eThe increased risk of new-onset autoinflammatory and autoimmune conditions in individuals who have recovered from COVID-19 is of particular concern. Recent studies have reported cases of adult-onset Still's disease, systemic lupus erythematosus, and inflammatory arthritis in patients with no prior history of these conditions following COVID-19 [1] [2]. A large-scale study using electronic health records found significantly higher rates of new autoimmune diagnoses, including rheumatoid arthritis and psoriasis, in the months following COVID-19 than in other infections [3]. These emerging autoinflammatory sequelae of COVID-19 have led to increased focus on the prescription of disease-modifying antirheumatic drugs (DMARDs) in post-COVID patients.\u003c/p\u003e\n\u003cp\u003eHowever, the effects of COVID-19 on the initiation of DMARDs and the specific burden of each newly diagnosed autoinflammatory disease remain poorly understood [4] [5]. Further research is needed to elucidate the mechanisms underlying these post-COVID inflammatory conditions, their prevalence across different populations, and optimal management strategies to improve outcomes for affected individuals.\u003c/p\u003e\n\u003cp\u003eThese emerging autoinflammatory sequelae of COVID-19 have led to an increased focus on the prescription of DMARDs in post-COVID patients [6] [7]. DMARDs are a class of medications that are commonly used to treat various autoimmune and inflammatory conditions [8]. The potential increase in DMARD prescriptions following COVID-19 warrants careful investigation to understand the burden of autoinflammatory diseases on healthcare systems and patient outcomes.\u003c/p\u003e\n\u003cp\u003eThis study aimed to investigate the relationship between COVID-19 and subsequent DMARD prescriptions using a nationwide insurance claims database in Japan. By elucidating the specific autoinflammatory conditions leading to increased DMARD use, we can develop more effective strategies for monitoring, diagnosing, and treating post-COVID inflammatory sequelae, ultimately improving outcomes in affected individuals.\u003c/p\u003e"},{"header":"Methods","content":"\u003cp\u003e\u003cstrong\u003e\u003cem\u003eStudy design and data source\u003c/em\u003e\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThis research utilized a matched cohort approach, analyzing data from a Japanese insurance claims database.\u003c/p\u003e\n\u003cp\u003eThe research analyzed records from the National Insurance Claims Database and Specific Health Check-ups (NDB) spanning from January 2015 to December 2022. This dataset includes comprehensive individual-level information for both inpatient and outpatient services, allowing for the monitoring of patient visits and treatments. The claims database provides details such as age, sex, diagnoses coded according to the International Statistical Classification of Diseases and Related Health Problems, Tenth Revision (ICD-10), and medications dispensed based on the Anatomical Therapeutic Chemical (ATC) Classification System [9, 10].\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003e\u003cem\u003eStudy population and eligibility criteria\u003c/em\u003e\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe research cohort was drawn from five Japanese prefectures\u0026mdash;Hiroshima, Osaka, Kyoto, Hyogo, and Okayama\u0026mdash;which had access to the NDB database as of January 2020. These areas encompass roughly 20.5 million individuals, constituting approximately 16% of Japan's overall population. Participants were required to have health insurance coverage and continuous healthcare access from January 2020, when COVID-19 was widespread, until December 2022, the study's conclusion. However, individuals were omitted if they lacked a one-year observation period before the index month or if they had been administered any DMARDs during this time frame. The research methodology is illustrated in Supplemental Figure 1.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003e\u003cem\u003eExposure, and outcome definition\u003c/em\u003e\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eIdentification of Exposure: The COVID-19 group comprised individuals who received a COVID-19 diagnosis within the study's timeframe. During the observation period, the government covered medical costs for those diagnosed with COVID-19. This study determined exposure status by utilizing the public expense number used for payment [11].\u003c/p\u003e\n\u003cp\u003eOutcome Assessment: The DMARDs included in Supplemental Table 1 are categorized into four distinct classes with the ATC Classification System based on their mechanism of action and therapeutic application: biologic DMARDs (bDMARDs), conventional synthetic DMARDs (csDMARDs), targeted synthetic DMARDs (tsDMARDs), and DMARDs used specifically for COVID-19 management [12] [13]. COVID DMARDs (e.g., baricitinib, tocilizumab) have been repurposed or developed to treat severe inflammatory responses in COVID-19 [14]. The composite endpoint in this study is defined as the initiation of at least one DMARD across these categories: bDMARDs, csDMARDs, tsDMARDs, or COVID DMARDs. The composite outcome includes the prescription of one of these DMARD within the post-exposure period, reflecting the new-onset autoimmune diseases or exacerbation of preexisting conditions necessitating such treatment.\u003c/p\u003e\n\u003cp\u003eWe also assess secondary outcomes as autoinflammatory diseases related to the prescription of DMARDS. Supplementary Table2 shows the disease category and associated ICD-10 codes [15] [16].\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003e\u003cem\u003eStatistical Analyses\u003c/em\u003e\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eWe conducted a matched cohort study employing propensity score matching to ensure balance between the COVID-19 and control groups. Matching variables included age, sex, the Charlson Comorbidity Index (CCI), and individual comorbidities encompassed within the CCI. Propensity scores were generated using logistic regression, and nearest-neighbor matching was applied with a caliper width set at 0.2 standard deviations of the logit of the propensity score to minimize bias.\u003c/p\u003e\n\u003cp\u003eAs shown in Supplementary Figure 1, the study timeline integrates a one-year look-back period for baseline assessment prior to the COVID-19 index month, followed by a longitudinal follow-up phase extending through December 31, 2022. This design ensures a robust comparison of health outcomes between the matched groups while accounting for preexisting conditions and medication history.\u003c/p\u003e\n\u003cp\u003eIncidence rate ratios (IRRs) and incidence rate differences (IRDs) were calculated to evaluate the association between COVID-19 infection and the composite endpoint of DMARD prescription. The analysis also examined the secondary endpoints, assessing whether COVID-19 infection was associated with an increased incidence of autoimmune and autoinflammatory diseases linked to DMARD prescriptions.\u003c/p\u003e\n\u003cp\u003eWe assessed subgroup interactions within the comorbidities included in the CCI to evaluate whether the effect of COVID-19 infection on new DMARD prescriptions differed across these conditions. Subgroup analyses were performed by stratifying the matched cohort according to the age, sex, and CCI score and presence or absence of individual comorbidities (e.g., diabetes, cardiovascular disease, chronic kidney disease). To formally test for interaction between COVID-19 infection and each comorbidity, we included interaction terms applied likelihood ratio tests (LRTs) to compare models with and without these interaction terms. Statistical significance was defined as a p-value \u0026lt; 0.05.\u003c/p\u003e\n\u003cp\u003eAs sensitivity analyses, we conducted three analyses: 1) those in which the period of follow-up was divided into 1 year or less and 1-2 years; 2) those in which the outcome was not the initiation of new DMARDs but the continuation of DMARDs for 6 months or 1 year after the initiation of DMARDs; and 3) those in which Participants with a history of Rheumatoid diseases were excluded. These three sensitivity analyses were performed to confirm the robustness of the study. The incidence rate ratios (IRR)All statistical analyses were performed using Stata version 18.0 (StataCorp, College Station, TX, USA).\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003e\u003cem\u003eEthical Consideration and Data Availability\u003c/em\u003e\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe research protocol was reviewed and approved by the Epidemiological Research Committee of Hiroshima University (approval number E2022-0024-01). In accordance with Japanese data protection laws, individual-level data cannot be made publicly available. The data used in this study were obtained with ethical approval from the Ministry of Health, Labour and Welfare of Japan (approval number 1502). Researchers seeking access to the data must submit an application to the primary data owners, including the Ministry, and adhere to Japanese regulatory requirements. Informed consent was not required for this study, as it utilized registry data with all personal information encrypted to ensure privacy.\u003c/p\u003e"},{"header":"Results","content":"\u003cp\u003eFigure 1 illustrates the flow chart of study participants for this matched cohort study utilizing a national insurance claims database. Among 16,056,476 subjects registered as of January 2020, 6,106,346 individuals were diagnosed with COVID-19 during the study period. After excluding ineligible patients, 5,317,163 individuals were potentially eligible for the COVID-19 group. Propensity score matching was conducted, and 3,018,180 matched pairs were established for COVID-19-infected and non-infected individuals.\u003c/p\u003e\n\u003cp\u003eBaseline Characteristics of Study Participants\u003c/p\u003e\n\u003cp\u003eTable 1 presents the baseline characteristics of the study participants at the index month, comprising equal-sized cohorts of COVID-19-infected individuals (N=3,018,180) and non-infected controls (N=3,018,180). Age distribution was well-balanced between the two groups, with similar percentages across all other categories. Overall, the baseline characteristics were well-balanced across the two groups, confirming the robustness of the propensity score matching process.\u003c/p\u003e\n\u003cp\u003eTable 2 presents the IRRs and IRDs for both primary and secondary outcomes. For the composite endpoint of DMARD prescription, COVID-19 patients had an incidence rate of 289.7 events per 1,000,000 person-months (95% CI: 283.5\u0026ndash;296.1), compared to 217.7 events in the control group (95% CI: 212.3\u0026ndash;223.2). The IRR for COVID-19 was 1.33 (95% CI: 1.28\u0026ndash;1.38), indicating a 33% higher rate of DMARD prescription in the COVID-19 group, with an IRD of 72.1 (95% CI: 63.8\u0026ndash;80.3). For individual DMARD categories, bDMARDs showed an IRR of 1.48 (95% CI: 1.31\u0026ndash;1.67) and an IRD of 7.74 (95% CI: 5.41\u0026ndash;10.06), while csDMARDs had an IRR of 1.40 (95% CI: 1.35\u0026ndash;1.46) and an IRD of 66.99 (95% CI: 59.64\u0026ndash;74.35). Similarly, tsDMARDs and COVID DMARDs showed elevated IRRs of 1.26 (95% CI: 1.09\u0026ndash;1.46) and 1.29 (95% CI: 1.12\u0026ndash;1.49), respectively.\u003c/p\u003e\n\u003cp\u003eFor secondary endpoints, COVID-19 infection was associated with an increased incidence of several conditions. Arthritis had an IRR of 1.15 (95% CI: 1.13\u0026ndash;1.18) and an IRD of 73.02 (95% CI: 61.26\u0026ndash;84.78). Bullous disorders demonstrated an IRR of 1.28 (95% CI: 1.20\u0026ndash;1.36) with an IRD of 17.23 (95% CI: 12.87\u0026ndash;21.59), while central nervous system disorders had the highest IRR of 1.55 (95% CI: 1.42\u0026ndash;1.69) and an IRD of 16.1 (95% CI: 12.92\u0026ndash;19.29). Other notable findings include glomerulonephritis with an IRR of 1.08 (95% CI: 1.05\u0026ndash;1.12) and an IRD of 21.1 (95% CI: 12.68\u0026ndash;29.53), and inflammatory bowel disease with an IRR of 1.19 (95% CI: 1.11\u0026ndash;1.27) and an IRD of 10.94 (95% CI: 6.79\u0026ndash;15.09). Immunodeficiency disorders also showed a marked increase with an IRR of 1.56 (95% CI: 1.47\u0026ndash;1.66) and an IRD of 34.63 (95% CI: 30.02\u0026ndash;39.25). In addition, respiratory disorders and systemic connective tissue disorders showed significant increases in incidence. Respiratory disorders had an IRR of 1.49 (95% CI: 1.45\u0026ndash;1.53) and an IRD of 159.91 (95% CI: 149.43\u0026ndash;170.39), while systemic connective tissue disorders demonstrated an IRR of 1.23 (95% CI: 1.20\u0026ndash;1.26) and an IRD of 87.43 (95% CI: 76.62\u0026ndash;98.24). These findings indicate that COVID-19 infection significantly increased the risk of both DMARD prescriptions and a variety of autoimmune and inflammatory conditions, highlighting its substantial impact on long-term health outcomes.\u003c/p\u003e\n\u003cp\u003eThe sensitivity analysis confirms that the increased incidence of DMARD prescriptions is most pronounced within the first year following COVID-19 infection (IRR: 1.39, 95% CI: 1.34\u0026ndash;1.45) but persists, albeit to a lesser extent, into the second year (IRR: 1.12, 95% CI: 1.04\u0026ndash;1.20). This highlights the long-term implications of COVID-19 on healthcare utilization for autoimmune and inflammatory conditions. To account for potential bias from short-term DMARD use or discontinuation due to misdiagnosis, we conducted additional sensitivity analyses using more stringent definitions of the primary outcome. \u0026nbsp;The IRR for outcomes treated with DMARDs for durations exceeding six months and one year are 1.45 (95% CI 1.39-1.52) and 1.67 (95% CI 1.56-1.79), respectively. Sensitivity analyses conducted excluding individuals with a history of rheumatoid diseases demonstrated a trend consistent with the primary analysis (see supplementary Table 3).\u003c/p\u003e\n\u003cp\u003eThe subgroup analysis revealed significant variations in the association between COVID-19 infection and DMARD prescriptions across demographic and clinical subgroups (Figure 2). COVID-19 patients had an significant risk of amputation on any subgroup. For the demographic factors, male sex and younger age had significant interaction. The association was stronger in males (IRR: 1.44, 95% CI: 1.36\u0026ndash;1.52) than females (IRR: 1.26, 95% CI: 1.21\u0026ndash;1.32). Younger age groups showed higher IRRs, with those aged 0\u0026ndash;19 years having an IRR of 1.46 (95% CI: 1.22\u0026ndash;1.69), while adults aged 20\u0026ndash;64 years and older adults (\u0026ge;65 years) had IRRs of 1.25 (95% CI: 1.20\u0026ndash;1.30) and 1.42 (95% CI: 1.35\u0026ndash;1.49), respectively.\u003c/p\u003e\n\u003cp\u003eFor the subgroup of comorbidities. Significant interactions were found on CCI score, chronic heart failure (CHF), Rheumatoid diseases, diabetes mellitus (DM), cancer, hemiplegia/paraplegia (HP/PAPL), and renal disease (RD). Patients having lower CCI scores (0\u0026ndash;1) showed an IRR of 1.23 (95% CI: 1.15\u0026ndash;1.30), while those with higher scores (\u0026ge;2) exhibited a stronger association (IRR: 1.38, 95% CI: 1.33\u0026ndash;1.43). Patients with diabetes (IRR: 1.53, 95% CI: 1.38\u0026ndash;1.67) and renal disease (IRR: 1.70, 95% CI: 1.53\u0026ndash;1.87) had markedly elevated risks. Notably, patients with HP/PAPL demonstrated the highest IRR (2.14, 95% CI: 1.49\u0026ndash;2.78). On the other hand, IRR for patients with rheumatoid diseases showed lower risk than those without such conditions.\u003c/p\u003e\n\u003cp\u003eOther conditions, such as chronic pulmonary disease (CPD), peptic ulcer disease (PUD), and liver disease (LD), showed slightly elevated IRRs, ranging from 1.30 to 1.36. Patients with cancer had an IRR of 1.42 (95% CI: 1.32\u0026ndash;1.53). Interestingly, conditions like dementia had lower IRRs (1.23, 95% CI: 1.02\u0026ndash;1.44) compared to other subgroups. These findings underscore the heterogeneous impact of COVID-19 on DMARD prescriptions, with stronger associations observed in males, younger patients, those with multiple comorbidities, and individuals with autoimmune or hematologic conditions.\u003c/p\u003e"},{"header":"Discussion","content":"\u003cp\u003eThis study highlights the substantial impact of COVID-19 on the prescription of DMARDs, with a 33% higher incidence rate observed in COVID-19 patients compared to matched controls. The findings were consistent across subgroups, with males, younger age groups, and those with multiple comorbidities showing the strongest associations. COVID-19 patients demonstrated significantly increased risks for DMARD prescriptions across all categories\u0026mdash;bDMARDs, csDMARDs, tsDMARDs, and COVID-specific DMARDs. Secondary outcomes also revealed an increased incidence of autoimmune and inflammatory conditions, such as arthritis, glomerulonephritis, and respiratory disorders. These findings suggest a long-term impact of COVID-19 on healthcare utilization, particularly in managing autoimmune and inflammatory diseases.\u003c/p\u003e\n\u003cp\u003eComparing these results to previous studies, the findings align with reports of elevated risks for autoimmune conditions following COVID-19. Previous population level study showed the decreased prescription of bDMARDS during COVID-19 pandemic [17]. Earlier research has demonstrated increased rates of rheumatoid arthritis, psoriasis, and systemic inflammatory conditions in COVID-19 survivors [18] [19]. The observed 33% increase in DMARD prescriptions is consistent with the heightened risk of autoimmune diseases, as noted in a large-scale electronic health record study. Moreover, the associations with subgroups, such as higher risks in younger individuals and males, reflect similar trends reported in prior research [20]. However, this study uniquely captures the broader population-level impact of COVID-19 on DMARD prescriptions, providing a more comprehensive perspective.\u003c/p\u003e\n\u003cp\u003eThe observed increase in DMARD prescriptions following COVID-19 infection can be explained through several potential mechanisms, such as, pro-inflammatory and immune dysregulation effects of SARS-CoV-2 infection [21]. SARS-CoV-2 infection may trigger autoimmunity through molecular mimicry, where viral antigens resemble host tissue antigens, leading to cross-reactive immune responses. This can result in the production of autoantibodies and activation of autoreactive T cells, initiating or exacerbating autoimmune conditions [22]. COVID-19 is known to trigger a cytokine storm and persistent immune activation, characterized by excessive release of pro-inflammatory cytokines like IL-6, TNF-\u0026alpha;, and IL-1\u0026beta;. This hyperinflammatory state can disrupt immune homeostasis, potentially breaking self-tolerance and promoting autoimmune reactions, which can lead to the onset of autoinflammatory and autoimmune conditions [23]. The prolonged endothelial damage and thrombotic events associated with COVID-19 may exacerbate underlying inflammatory processes [24]. Furthermore, COVID-19 can lead to dysregulation of the innate immune system, including aberrant activation of neutrophils and complement cascades, which may contribute to autoimmune pathogenesis. [25] [26] These mechanisms collectively increase the incidence of autoimmune and inflammatory conditions, thereby driving up the demand for DMARDs to manage these emerging or exacerbated autoimmune sequelae.\u003c/p\u003e\n\u003cp\u003eThe clinical implications of these findings are significant. Early identification and management of individuals at risk for post-COVID inflammatory sequelae are crucial. The results underscore the need for healthcare providers to closely monitor COVID-19 survivors, particularly those with preexisting conditions, for signs of emerging autoimmune diseases. Moreover, the elevated prescription rates for DMARDs highlight the importance of ensuring equitable access to these therapies and addressing the long-term burden on healthcare systems.\u003c/p\u003e\n\u003cp\u003eThe primary strength of this study is the use of a nationwide insurance claims database, which allowed for the analysis of a large, population-based cohort with robust matching techniques to minimize confounding. The inclusion of both primary and secondary outcomes adds depth to the findings, providing a comprehensive assessment of the post-COVID burden of autoimmune diseases and DMARD prescriptions. The longitudinal follow-up and subgroup analyses further enhance the study\u0026rsquo;s validity and generalizability.\u003c/p\u003e\n\u003cp\u003eHowever, the study has several limitations. A key limitation of this study is the relatively short maximum follow-up period of approximately 2 years (until December 2022). This timeframe may be insufficient to fully assess the long-term autoimmune risks associated with COVID-19 infection. Autoimmune conditions can develop over extended periods, sometimes taking several years to manifest clinically. Therefore, our findings primarily reflect the short to medium-term impacts of COVID-19 on autoimmune disease incidence and DMARD utilization. Longer follow-up periods in future studies will be crucial to elucidate the full spectrum of autoimmune sequelae that may emerge in COVID-19 survivors over time. The reliance on administrative data may result in misclassification of diagnoses or exposure status. In this study, the incidence of diagnoses such as arthritis was notably higher than the incidence of DMARDs prescriptions. Relying solely on ICD10 codes for new diagnoses may result in biased conclusions. Consequently, we designated DMARDs prescription as the primary outcome and validated its robustness through three sensitivity analyses. The database lacks detailed clinical information, such as the severity of COVID-19 or disease-specific biomarkers, which could influence the results. Additionally, residual confounding from unmeasured factors, such as vaccination status, socioeconomic status or lifestyle, cannot be entirely ruled out. In the future analysis, incorporating data on COVID-19 variants and vaccination status would enhance our understanding of how these factors influence the development of autoimmune conditions following infection. Despite these limitations, the large sample size and rigorous statistical methods provide robust and reliable findings.\u003c/p\u003e\n\u003cp\u003eIn conclusion, this study demonstrates a significant association between COVID-19 and increased DMARD prescriptions, reflecting a higher burden of autoimmune and inflammatory conditions in COVID-19 survivors. These findings underscore the importance of long-term monitoring and tailored interventions for individuals at risk, as well as the need for further research to elucidate the mechanisms driving these associations. Addressing these challenges will be critical to improving outcomes for COVID-19 survivors and mitigating the long-term impact on healthcare systems.\u003c/p\u003e"},{"header":"Abbreviations","content":"\u003cul\u003e\n\u003cli\u003e\u003cstrong\u003eAMI\u003c/strong\u003e \u0026ndash; Acute Myocardial Infarction\u003c/li\u003e\n\u003cli\u003e\u003cstrong\u003eAPC\u003c/strong\u003e \u0026ndash; Article Processing Charge\u003c/li\u003e\n\u003cli\u003e\u003cstrong\u003eATC\u003c/strong\u003e \u0026ndash; Anatomical Therapeutic Chemical Classification System\u003c/li\u003e\n\u003cli\u003e\u003cstrong\u003ebDMARD\u003c/strong\u003e \u0026ndash; Biologic Disease-Modifying Antirheumatic Drug\u003c/li\u003e\n\u003cli\u003e\u003cstrong\u003eCCI\u003c/strong\u003e \u0026ndash; Charlson Comorbidity Index\u003c/li\u003e\n\u003cli\u003e\u003cstrong\u003eCEVD\u003c/strong\u003e \u0026ndash; Cerebrovascular Disease\u003c/li\u003e\n\u003cli\u003e\u003cstrong\u003eCI\u003c/strong\u003e \u0026ndash; Confidence Interval\u003c/li\u003e\n\u003cli\u003e\u003cstrong\u003eCHF\u003c/strong\u003e \u0026ndash; Congestive Heart Failure\u003c/li\u003e\n\u003cli\u003e\u003cstrong\u003eCOPD\u003c/strong\u003e \u0026ndash; Chronic Obstructive Pulmonary Disease\u003c/li\u003e\n\u003cli\u003e\u003cstrong\u003eCOVID DMARDs\u003c/strong\u003e \u0026ndash; Disease-Modifying Antirheumatic Drugs repurposed for COVID-19 treatment\u003c/li\u003e\n\u003cli\u003e\u003cstrong\u003eCS-DMARD\u003c/strong\u003e \u0026ndash; Conventional Synthetic Disease-Modifying Antirheumatic Drug\u003c/li\u003e\n\u003cli\u003e\u003cstrong\u003eDMARD\u003c/strong\u003e \u0026ndash; Disease-Modifying Antirheumatic Drug\u003c/li\u003e\n\u003cli\u003e\u003cstrong\u003eGN\u003c/strong\u003e \u0026ndash; Glomerulonephritis\u003c/li\u003e\n\u003cli\u003e\u003cstrong\u003eHP/PAPL\u003c/strong\u003e \u0026ndash; Hemiplegia/Paraplegia\u003c/li\u003e\n\u003cli\u003e\u003cstrong\u003eIBD\u003c/strong\u003e \u0026ndash; Inflammatory Bowel Disease\u003c/li\u003e\n\u003cli\u003e\u003cstrong\u003eICD-10\u003c/strong\u003e \u0026ndash; International Classification of Diseases, 10th Revision\u003c/li\u003e\n\u003cli\u003e\u003cstrong\u003eID\u003c/strong\u003e \u0026ndash; Immunodeficiency Disorder\u003c/li\u003e\n\u003cli\u003e\u003cstrong\u003eIRD\u003c/strong\u003e \u0026ndash; Incidence Rate Difference\u003c/li\u003e\n\u003cli\u003e\u003cstrong\u003eIRR\u003c/strong\u003e \u0026ndash; Incidence Rate Ratio\u003c/li\u003e\n\u003cli\u003e\u003cstrong\u003eLD\u003c/strong\u003e \u0026ndash; Liver Disease\u003c/li\u003e\n\u003cli\u003e\u003cstrong\u003ePUD\u003c/strong\u003e \u0026ndash; Peptic Ulcer Disease\u003c/li\u003e\n\u003cli\u003e\u003cstrong\u003ePVD\u003c/strong\u003e \u0026ndash; Peripheral Vascular Disease\u003c/li\u003e\n\u003cli\u003e\u003cstrong\u003eRD\u003c/strong\u003e \u0026ndash; Renal Disease\u003c/li\u003e\n\u003cli\u003e\u003cstrong\u003eRheum Dis\u003c/strong\u003e \u0026ndash; Rheumatoid Disease\u003c/li\u003e\n\u003cli\u003e\u003cstrong\u003eSCTD\u003c/strong\u003e \u0026ndash; Systemic Connective Tissue Disorders\u003c/li\u003e\n\u003cli\u003e\u003cstrong\u003etsDMARD\u003c/strong\u003e \u0026ndash; Targeted Synthetic Disease-Modifying Antirheumatic Drug\u003c/li\u003e\n\u003c/ul\u003e"},{"header":"Declarations","content":"\u003cp\u003e\u003cstrong\u003eEthics approval and consent to participate \u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe research protocol was reviewed and approved by the Epidemiological Research Committee of Hiroshima University (approval number E2022-0024-01). In accordance with Japanese data protection laws, individual-level data cannot be made publicly available. The data used in this study were obtained with ethical approval from the Ministry of Health, Labour and Welfare of Japan (approval number 1502).\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAvailability of data and materials\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eResearchers seeking access to the data must submit an application to the primary data owners, including the Ministry, and adhere to Japanese regulatory requirements. Informed consent was not required for this study, as it utilized registry data with all personal information encrypted to ensure privacy.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eConsent for publication\u003c/strong\u003e: Not applicable.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eConflict of Interest: \u003c/strong\u003eThe researchers state that they have no connections to or engagement with any organizations or entities that have financial stakes in the topic or materials addressed in this study.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eFunding\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eFinancial support for this research was provided by two sources: the Innovative Research Program on Suicide Countermeasures (Grant Number JPSCIRS20220305), and JSPS KAKENHI (Grant Number 21K17227).\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAuthor Contributions: \u003c/strong\u003eDM was involved in designing and executing the study, examining the outcomes, and drafting the manuscript. MI developed the idea for the project and oversaw its implementation.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAcknowledgements\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eNot applicable\u003cstrong\u003e\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\u003cli\u003e\u003cspan\u003eLi J, Liu H-H, Yin X-D, Li C-C, Wang J, Li J, Liu H-H, Yin X-D, Li C-C, Wang J: COVID-19 illness and autoimmune diseases: recent insights. \u003cem\u003eInflammation Research 2021 70\u003c/em\u003e:\u003cem\u003e4\u003c/em\u003e 2021-02-28, 70(4).\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eMC S, S T, P S, L A, P DG, A S, EC L, A R, R T, R G \u003cem\u003eet al\u003c/em\u003e: SARS-CoV-2 infection as a trigger of autoimmune response - PubMed. \u003cem\u003eClinical and translational science\u003c/em\u003e 2021 May, 14(3).\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eSacchi MC, Tamiazzo S, Stobbione P, Agatea L, De Gaspari P, Stecca A, Lauritano EC, Roveta A, Tozzoli R, Guaschino R \u003cem\u003eet al\u003c/em\u003e: SARS-CoV-2 infection as a trigger of autoimmune response. \u003cem\u003eClin Transl Sci\u003c/em\u003e 2021, 14(3):898\u0026ndash;907.\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eRuscitti P, Conforti A, Cipriani P, Giacomelli R, Tasso M, Costa L, Caso F: Pathogenic implications, incidence, and outcomes of COVID-19 in autoimmune inflammatory joint diseases and autoinflammatory disorders. \u003cem\u003eAdvances in Rheumatology\u003c/em\u003e 2021, 61(1):45.\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003ePope JE: What Does the COVID-19 Pandemic Mean for Rheumatology Patients? \u003cem\u003eCurrent Treatment Options in Rheumatology\u003c/em\u003e 2020, 6(2):71\u0026ndash;74.\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eVenkat RK, Wang X, Patel NJ, Kawano Y, Schiff A, Kowalski EN, Cook CE, Vanni KMM, Qian G, Bade KJ \u003cem\u003eet al\u003c/em\u003e: Associations of DMARDs with post-acute sequelae of COVID-19 in patients with systemic autoimmune rheumatic diseases: a prospective study. \u003cem\u003eRheumatology\u003c/em\u003e 2023, 63(10):2828\u0026ndash;2837.\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eD\u0026rsquo;Silva KM, Wallace ZS: COVID-19 and Disease-Modifying Anti-rheumatic Drugs. \u003cem\u003eCurrent rheumatology reports\u003c/em\u003e 2021, 23(5):28.\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eVenkat R, Wallace ZS, Sparks JA: Considerations for Pharmacologic Management of Rheumatoid Arthritis in the COVID-19 Era: a Narrative Review. \u003cem\u003eCurrent rheumatology reports\u003c/em\u003e 2023, 25(11):236\u0026ndash;245.\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eCharlson ME, Pompei P, Ales KL, MacKenzie CR: A new method of classifying prognostic comorbidity in longitudinal studies: development and validation. \u003cem\u003eJ Chronic Dis\u003c/em\u003e 1987, 40(5):373\u0026ndash;383.\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eQuan H, Sundararajan V, Halfon P, Fong A, Burnand B, Luthi JC, Saunders LD, Beck CA, Feasby TE, Ghali WA: Coding algorithms for defining comorbidities in ICD-9-CM and ICD-10 administrative data. \u003cem\u003eMedical care\u003c/em\u003e 2005, 43(11):1130\u0026ndash;1139.\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eResonse to COVID-19 after the classification change [\u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://www.mhlw.go.jp/stf/seisakunitsuite/bunya/0000164708_00079.html]\u003c/span\u003e\u003cspan address=\"https://www.mhlw.go.jp/stf/seisakunitsuite/bunya/0000164708_00079.html]\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eSmolen JS, Landew\u0026eacute; R, Bijlsma J, Burmester G, Chatzidionysiou K, Dougados M, Nam J, Ramiro S, Voshaar M, van Vollenhoven R \u003cem\u003eet al\u003c/em\u003e: EULAR recommendations for the management of rheumatoid arthritis with synthetic and biological disease-modifying antirheumatic drugs: 2016 update. \u003cem\u003eAnnals of the rheumatic diseases\u003c/em\u003e 2017, 76(6):960\u0026ndash;977.\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eKnaus K, Pearcey W: The SAGE Encyclopedia of Pharmacology and Society. In.\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eAlattar R, Ibrahim TBH, Shaar SH, Abdalla S, Shukri K, Daghfal JN, Khatib MY, Aboukamar M, Abukhattab M, Alsoub HA \u003cem\u003eet al\u003c/em\u003e: Tocilizumab for the treatment of severe coronavirus disease 2019. \u003cem\u003eJournal of medical virology\u003c/em\u003e 2020, 92(10):2042\u0026ndash;2049.\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eBenjamin O, Bansal P, Goyal A, Lappin SL: Disease Modifying Anti-Rheumatic Drugs (DMARD). In: \u003cem\u003e2019\u003c/em\u003e; 2019.\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eAllen PB, Peyrin-Biroulet L: Moving towards disease modification in inflammatory bowel disease therapy. \u003cem\u003eCurrent Opinion in Gastroenterology\u003c/em\u003e 2013, 29(4).\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eRussell M, Balachandran S, Norton S, Alveyn E, Nagra D, Adas M, Galloway J: POS1228 IMPACT OF COVID-19 ON THE PRESCRIPTION OF BIOLOGIC DMARDs: A POPULATION-LEVEL STUDY IN ENGLAND. \u003cem\u003eAnnals of the rheumatic diseases\u003c/em\u003e 2022, 81(Suppl 1):946\u0026ndash;946.\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eSyed U, Subramanian A, Wraith DC, Lord JM, McGee K, Ghokale K, Nirantharakumar K, Haroon S: Incidence of immune-mediated inflammatory diseases following COVID-19: a matched cohort study in UK primary care. \u003cem\u003eBMC Medicine\u003c/em\u003e 2023, 21(1):363.\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eGracia-Ramos AE, Martin-Nares E, Hern\u0026aacute;ndez-Molina G: New Onset of Autoimmune Diseases Following COVID-19 Diagnosis. \u003cem\u003eCells\u003c/em\u003e 2021, 10(12):3592.\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eHeo Y-W, Jeon JJ, Ha MC, Kim YH, Lee S: Long-Term Risk of Autoimmune and Autoinflammatory Connective Tissue Disorders Following COVID-19. \u003cem\u003eJAMA Dermatology\u003c/em\u003e 2024, 160(12):1278\u0026ndash;1287.\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eWong L-YR, Perlman S: Immune dysregulation and immunopathology induced by SARS-CoV-2 and related coronaviruses \u0026mdash; are we our own worst enemy? \u003cem\u003eNature Reviews Immunology\u003c/em\u003e 2022, 22(1):47\u0026ndash;56.\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eAr\u0026eacute;valo-Cort\u0026eacute;s A, Rodriguez-Pinto D, Aguilar-Ayala L: Evidence for Molecular Mimicry between SARS-CoV-2 and Human Antigens: Implications for Autoimmunity in COVID-19. \u003cem\u003eAutoimmune Diseases\u003c/em\u003e 2024, 2024(1):8359683.\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eZebardast A, Hasanzadeh A, Ebrahimian Shiadeh SA, Tourani M, Yahyapour Y: COVID-19: A trigger of autoimmune diseases. \u003cem\u003eCell Biol Int\u003c/em\u003e 2023, 47(5):848\u0026ndash;858.\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eBikdeli B, Madhavan MV, Jimenez D, Chuich T, Dreyfus I, Driggin E, Nigoghossian C, Ageno W, Madjid M, Guo Y \u003cem\u003eet al\u003c/em\u003e: COVID-19 and Thrombotic or Thromboembolic Disease: Implications for Prevention, Antithrombotic Therapy, and Follow-Up: JACC State-of-the-Art Review. \u003cem\u003eJournal of the American College of Cardiology\u003c/em\u003e 2020, 75(23):2950\u0026ndash;2973.\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eGardiman E, Bianchetto-Aguilera F, Gasperini S, Tiberio L, Scandola M, Lotti V, Gibellini D, Salvi V, Bosisio D, Cassatella MA: SARS-CoV-2-Associated ssRNAs activate human neutrophils in a TLR8-dependent fashion. \u003cem\u003eCells\u003c/em\u003e 2022, 11(23):3785.\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eZhou T, Su TT, Mudianto T, Wang J: Immune asynchrony in COVID-19 pathogenesis and potential immunotherapies. \u003cem\u003eJournal of Experimental Medicine\u003c/em\u003e 2020, 217(10):e20200674.\u003c/span\u003e\u003c/li\u003e\u003c/ol\u003e"},{"header":"Tables","content":"\u003ctable width=\"558\"\u003e\n\u003ctbody\u003e\n\u003ctr\u003e\n\u003ctd colspan=\"4\" width=\"558\"\u003e\n\u003cp\u003eTable 1 Baseline characteristics of study participants at the index month\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd width=\"151\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003ctd width=\"161\"\u003e\n\u003cp\u003eNon Infected\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"142\"\u003e\n\u003cp\u003eCOVID-19\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"104\"\u003e\n\u003cp\u003eSMD\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd width=\"151\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003ctd width=\"161\"\u003e\n\u003cp\u003eN=3,018,180\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"142\"\u003e\n\u003cp\u003eN=3,018,180\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"104\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd width=\"151\"\u003e\n\u003cp\u003e\u003cstrong\u003eAge\u003c/strong\u003e\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"161\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003ctd width=\"142\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003ctd width=\"104\"\u003e\n\u003cp\u003e\u0026lt;0.001\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd width=\"151\"\u003e\n\u003cp\u003e\u0026nbsp;\u0026nbsp; 0\u0026ndash;4\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"161\"\u003e\n\u003cp\u003e199,553 ( 7%)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"142\"\u003e\n\u003cp\u003e199,539 ( 7%)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"104\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd width=\"151\"\u003e\n\u003cp\u003e\u0026nbsp;\u0026nbsp; 5\u0026ndash;9\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"161\"\u003e\n\u003cp\u003e159,673 ( 5%)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"142\"\u003e\n\u003cp\u003e159,646 ( 5%)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"104\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd width=\"151\"\u003e\n\u003cp\u003e\u0026nbsp;\u0026nbsp; 10\u0026ndash;14\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"161\"\u003e\n\u003cp\u003e126,040 ( 4%)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"142\"\u003e\n\u003cp\u003e125,954 ( 4%)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"104\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd width=\"151\"\u003e\n\u003cp\u003e\u0026nbsp;\u0026nbsp; 15\u0026ndash;19\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"161\"\u003e\n\u003cp\u003e122,887 ( 4%)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"142\"\u003e\n\u003cp\u003e122,853 ( 4%)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"104\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd width=\"151\"\u003e\n\u003cp\u003e\u0026nbsp;\u0026nbsp; 20\u0026ndash;24\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"161\"\u003e\n\u003cp\u003e135,614 ( 4%)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"142\"\u003e\n\u003cp\u003e135,617 ( 4%)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"104\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd width=\"151\"\u003e\n\u003cp\u003e\u0026nbsp;\u0026nbsp; 25\u0026ndash;29\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"161\"\u003e\n\u003cp\u003e137,658 ( 5%)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"142\"\u003e\n\u003cp\u003e137,731 ( 5%)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"104\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd width=\"151\"\u003e\n\u003cp\u003e\u0026nbsp;\u0026nbsp; 30-34\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"161\"\u003e\n\u003cp\u003e145,425 ( 5%)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"142\"\u003e\n\u003cp\u003e145,507 ( 5%)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"104\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd width=\"151\"\u003e\n\u003cp\u003e\u0026nbsp;\u0026nbsp; 35\u0026ndash;39\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"161\"\u003e\n\u003cp\u003e149,081 ( 5%)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"142\"\u003e\n\u003cp\u003e149,203 ( 5%)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"104\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd width=\"151\"\u003e\n\u003cp\u003e\u0026nbsp;\u0026nbsp; 40\u0026ndash;44\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"161\"\u003e\n\u003cp\u003e153,016 ( 5%)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"142\"\u003e\n\u003cp\u003e153,199 ( 5%)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"104\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd width=\"151\"\u003e\n\u003cp\u003e\u0026nbsp;\u0026nbsp; 45\u0026ndash;49\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"161\"\u003e\n\u003cp\u003e179,676 ( 6%)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"142\"\u003e\n\u003cp\u003e179,783 ( 6%)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"104\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd width=\"151\"\u003e\n\u003cp\u003e\u0026nbsp;\u0026nbsp; 50\u0026ndash;54\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"161\"\u003e\n\u003cp\u003e176,179 ( 6%)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"142\"\u003e\n\u003cp\u003e176,233 ( 6%)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"104\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd width=\"151\"\u003e\n\u003cp\u003e\u0026nbsp;\u0026nbsp; 55\u0026ndash;59\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"161\"\u003e\n\u003cp\u003e163,627 ( 5%)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"142\"\u003e\n\u003cp\u003e163,697 ( 5%)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"104\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd width=\"151\"\u003e\n\u003cp\u003e\u0026nbsp;\u0026nbsp; 60\u0026ndash;64\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"161\"\u003e\n\u003cp\u003e157,523 ( 5%)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"142\"\u003e\n\u003cp\u003e157,511 ( 5%)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"104\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd width=\"151\"\u003e\n\u003cp\u003e\u0026nbsp;\u0026nbsp; 65\u0026ndash;69\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"161\"\u003e\n\u003cp\u003e167,582 ( 6%)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"142\"\u003e\n\u003cp\u003e167,462 ( 6%)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"104\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd width=\"151\"\u003e\n\u003cp\u003e\u0026nbsp;\u0026nbsp; 70\u0026ndash;74\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"161\"\u003e\n\u003cp\u003e241,333 ( 8%)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"142\"\u003e\n\u003cp\u003e240,911 ( 8%)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"104\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd width=\"151\"\u003e\n\u003cp\u003e\u0026nbsp;\u0026nbsp; 75\u0026ndash;79\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"161\"\u003e\n\u003cp\u003e208,158 ( 7%)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"142\"\u003e\n\u003cp\u003e207,591 ( 7%)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"104\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd width=\"151\"\u003e\n\u003cp\u003e\u0026nbsp;\u0026nbsp; 80 or over\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"161\"\u003e\n\u003cp\u003e395,155 (13%)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"142\"\u003e\n\u003cp\u003e395,743 (13%)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"104\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd width=\"151\"\u003e\n\u003cp\u003e\u003cstrong\u003eSEX\u003c/strong\u003e\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"161\"\u003e\n\u003cp\u003e\u0026nbsp;\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"142\"\u003e\n\u003cp\u003e\u0026nbsp;\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"104\"\u003e\n\u003cp\u003e\u0026lt;0.001\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd width=\"151\"\u003e\n\u003cp\u003eFemale\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"161\"\u003e\n\u003cp\u003e1,681,931 (55.7%)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"142\"\u003e\n\u003cp\u003e1,681,759 (55.7%)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"104\"\u003e\n\u003cp\u003e\u0026nbsp;\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd width=\"151\"\u003e\n\u003cp\u003e\u003cstrong\u003eCCI\u003c/strong\u003e\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"161\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003ctd width=\"142\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003ctd width=\"104\"\u003e\n\u003cp\u003e\u0026lt;0.001\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd width=\"151\"\u003e\n\u003cp\u003e\u0026nbsp;\u0026nbsp; 0\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"161\"\u003e\n\u003cp\u003e523,041 (17%)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"142\"\u003e\n\u003cp\u003e523,028 (17%)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"104\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd width=\"151\"\u003e\n\u003cp\u003e\u0026nbsp;\u0026nbsp; 1\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"161\"\u003e\n\u003cp\u003e1,012,293 (34%)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"142\"\u003e\n\u003cp\u003e1,012,203 (34%)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"104\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd width=\"151\"\u003e\n\u003cp\u003e\u0026nbsp;\u0026nbsp; 2-3\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"161\"\u003e\n\u003cp\u003e765,198 (25%)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"142\"\u003e\n\u003cp\u003e764,389 (25%)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"104\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd width=\"151\"\u003e\n\u003cp\u003e\u0026nbsp;\u0026nbsp; 4 or over\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"161\"\u003e\n\u003cp\u003e717,648 (24%)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"142\"\u003e\n\u003cp\u003e718,560 (24%)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"104\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd width=\"151\"\u003e\n\u003cp\u003e\u003cstrong\u003ePast Medical History\u003c/strong\u003e\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"161\"\u003e\n\u003cp\u003e\u0026nbsp;\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"142\"\u003e\n\u003cp\u003e\u0026nbsp;\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"104\"\u003e\n\u003cp\u003e\u0026nbsp;\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd width=\"151\"\u003e\n\u003cp\u003eAMI\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"161\"\u003e\n\u003cp\u003e70,253 ( 2.3%)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"142\"\u003e\n\u003cp\u003e71,929 ( 2.4%)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"104\"\u003e\n\u003cp\u003e0.004\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd width=\"151\"\u003e\n\u003cp\u003eCHF\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"161\"\u003e\n\u003cp\u003e519,599 (17.2%)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"142\"\u003e\n\u003cp\u003e519,798 (17.2%)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"104\"\u003e\n\u003cp\u003e\u0026lt;0.001\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd width=\"151\"\u003e\n\u003cp\u003ePVD\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"161\"\u003e\n\u003cp\u003e407,806 (13.5%)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"142\"\u003e\n\u003cp\u003e408,607 (13.5%)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"104\"\u003e\n\u003cp\u003e0.001\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd width=\"151\"\u003e\n\u003cp\u003eCEVD\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"161\"\u003e\n\u003cp\u003e488,021 (16.2%)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"142\"\u003e\n\u003cp\u003e488,259 (16.2%)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"104\"\u003e\n\u003cp\u003e\u0026lt;0.001\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd width=\"151\"\u003e\n\u003cp\u003eDementia\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"161\"\u003e\n\u003cp\u003e162,164 ( 5.4%)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"142\"\u003e\n\u003cp\u003e161,936 ( 5.4%)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"104\"\u003e\n\u003cp\u003e\u0026lt;0.001\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd width=\"151\"\u003e\n\u003cp\u003eCPD\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"161\"\u003e\n\u003cp\u003e1,767,461 (58.6%)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"142\"\u003e\n\u003cp\u003e1,765,979 (58.5%)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"104\"\u003e\n\u003cp\u003e-0.001\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd width=\"151\"\u003e\n\u003cp\u003eRheum Dis\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"161\"\u003e\n\u003cp\u003e104,707 ( 3.5%)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"142\"\u003e\n\u003cp\u003e106,749 ( 3.5%)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"104\"\u003e\n\u003cp\u003e0.004\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd width=\"151\"\u003e\n\u003cp\u003ePUD\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"161\"\u003e\n\u003cp\u003e850,334 (28.2%)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"142\"\u003e\n\u003cp\u003e849,487 (28.1%)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"104\"\u003e\n\u003cp\u003e-0.001\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd width=\"151\"\u003e\n\u003cp\u003eLD\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"161\"\u003e\n\u003cp\u003e802,239 (26.6%)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"142\"\u003e\n\u003cp\u003e802,814 (26.6%)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"104\"\u003e\n\u003cp\u003e\u0026lt;0.001\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd width=\"151\"\u003e\n\u003cp\u003eDiabetes\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"161\"\u003e\n\u003cp\u003e\u0026nbsp;\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"142\"\u003e\n\u003cp\u003e\u0026nbsp;\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"104\"\u003e\n\u003cp\u003e\u0026lt;0.001\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd width=\"151\"\u003e\n\u003cp\u003eHP/PAPL\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"161\"\u003e\n\u003cp\u003e45,228 ( 1.5%)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"142\"\u003e\n\u003cp\u003e47,258 ( 1.6%)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"104\"\u003e\n\u003cp\u003e0.005\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd width=\"151\"\u003e\n\u003cp\u003eRD\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"161\"\u003e\n\u003cp\u003e160,611 ( 5.3%)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"142\"\u003e\n\u003cp\u003e162,275 ( 5.4%)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"104\"\u003e\n\u003cp\u003e0.002\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd width=\"151\"\u003e\n\u003cp\u003eCancer\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"161\"\u003e\n\u003cp\u003e395,154 (13.1%)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"142\"\u003e\n\u003cp\u003e395,178 (13.1%)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"104\"\u003e\n\u003cp\u003e\u0026lt;0.001\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003c/tbody\u003e\n\u003c/table\u003e\n\u003cp\u003eDMARD, Disease-Modifying Antirheumatic Drug; CCI, Charlson Comorbidity Index; IRR, Incidence Rate Ratio; CI, Confidence Interval; PI, P-value for Interaction; AMI, Acute Myocardial Infarction; CHF, Congestive Heart Failure; PVD, Peripheral Vascular Disease; CEVD, Cerebrovascular Disease; CPD, Chronic Pulmonary Disease; Rheum Dis, Rheumatoid Disease; PUD, Peptic Ulcer Disease; DM, Diabetes Mellitus; LD, Liver Disease; HP/PAPL, Hemiplegia or Paraplegia; RD, Renal Disease.\u003c/p\u003e\n\u003cp\u003e\u0026nbsp;\u003c/p\u003e\n\u003ctable width=\"879\"\u003e\n\u003ctbody\u003e\n\u003ctr\u003e\n\u003ctd colspan=\"3\" width=\"331\"\u003e\n\u003cp\u003eTable2 IRRs and IRDs for Primary and Secondary outcome\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd colspan=\"4\" width=\"548\"\u003e\n\u003cp\u003e\u0026nbsp;\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd width=\"142\"\u003e\n\u003cp\u003e\u003cstrong\u003e\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"85\"\u003e\n\u003cp\u003e\u0026nbsp;\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"104\"\u003e\n\u003cp\u003e\u003cstrong\u003eNo. of Individuals\u003c/strong\u003e\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"76\"\u003e\n\u003cp\u003e\u003cstrong\u003eNo. of Events\u003c/strong\u003e\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd colspan=\"3\" width=\"472\"\u003e\n\u003cp\u003e\u003cstrong\u003eCumulative Incidence\u003cbr /\u003e \u0026nbsp;(No. of Events per 1 000 000 Person months)\u003c/strong\u003e\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd width=\"142\"\u003e\n\u003cp\u003e\u003cstrong\u003e\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"85\"\u003e\n\u003cp\u003e\u0026nbsp;\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"104\"\u003e\n\u003cp\u003e\u0026nbsp;\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"76\"\u003e\n\u003cp\u003e\u0026nbsp;\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"151\"\u003e\n\u003cp\u003e\u003cstrong\u003e\u003cem\u003eRate (95% CI)\u003c/em\u003e\u003c/strong\u003e\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"151\"\u003e\n\u003cp\u003e\u003cstrong\u003e\u003cem\u003eDifference (95% CI)\u003c/em\u003e\u003c/strong\u003e\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"170\"\u003e\n\u003cp\u003e\u003cstrong\u003e\u003cem\u003eRatio (95% CI)\u003c/em\u003e\u003c/strong\u003e\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd rowspan=\"2\" width=\"142\"\u003e\n\u003cp\u003e\u003cstrong\u003eComposite endpoint\u003c/strong\u003e\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"85\"\u003e\n\u003cp\u003eCOVID-19\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"104\"\u003e\n\u003cp\u003e3018180\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"76\"\u003e\n\u003cp\u003e8219\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"151\"\u003e\n\u003cp\u003e289.7 (283.5-296.1)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"151\"\u003e\n\u003cp\u003e72.1 (63.8-80.3)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"170\"\u003e\n\u003cp\u003e1.33 (1.28 - 1.38)\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd width=\"85\"\u003e\n\u003cp\u003eControl\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"104\"\u003e\n\u003cp\u003e3018180\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"76\"\u003e\n\u003cp\u003e6194\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"151\"\u003e\n\u003cp\u003e217.7 (212.3-223.2)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"151\"\u003e\n\u003cp\u003e\u0026nbsp;\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"170\"\u003e\n\u003cp\u003e\u0026nbsp;\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd width=\"142\"\u003e\n\u003cp\u003e\u003cstrong\u003ePrimary outcome\u003c/strong\u003e\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"85\"\u003e\n\u003cp\u003e\u0026nbsp;\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"104\"\u003e\n\u003cp\u003e\u0026nbsp;\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"76\"\u003e\n\u003cp\u003e\u0026nbsp;\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"151\"\u003e\n\u003cp\u003e\u0026nbsp;\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"151\"\u003e\n\u003cp\u003e\u0026nbsp;\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"170\"\u003e\n\u003cp\u003e\u0026nbsp;\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd width=\"142\"\u003e\n\u003cp\u003ebDMARDs\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"85\"\u003e\n\u003cp\u003eCOVID-19\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"104\"\u003e\n\u003cp\u003e3018180\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"76\"\u003e\n\u003cp\u003e675\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"151\"\u003e\n\u003cp\u003e23.80 (22.07\u0026ndash;25.66)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"151\"\u003e\n\u003cp\u003e7.74 (5.41\u0026ndash;10.06)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"170\"\u003e\n\u003cp\u003e1.48 (1.31\u0026ndash;1.67)\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd width=\"142\"\u003e\n\u003cp\u003e\u0026nbsp;\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"85\"\u003e\n\u003cp\u003eControl\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"104\"\u003e\n\u003cp\u003e3018180\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"76\"\u003e\n\u003cp\u003e457\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"151\"\u003e\n\u003cp\u003e16.06 (14.66\u0026ndash;17.61)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"151\"\u003e\n\u003cp\u003e\u0026nbsp;\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"170\"\u003e\n\u003cp\u003e\u0026nbsp;\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd width=\"142\"\u003e\n\u003cp\u003eCOVID DMARDs\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"85\"\u003e\n\u003cp\u003eCOVID-19\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"104\"\u003e\n\u003cp\u003e3018180\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"76\"\u003e\n\u003cp\u003e451\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"151\"\u003e\n\u003cp\u003e15.90 (14.50\u0026ndash;17.44)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"151\"\u003e\n\u003cp\u003e3.60 (1.65\u0026ndash;5.55)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"170\"\u003e\n\u003cp\u003e1.29 (1.12\u0026ndash;1.49)\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd width=\"142\"\u003e\n\u003cp\u003e\u0026nbsp;\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"85\"\u003e\n\u003cp\u003eControl\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"104\"\u003e\n\u003cp\u003e3018180\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"76\"\u003e\n\u003cp\u003e350\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"151\"\u003e\n\u003cp\u003e12.30 (11.08\u0026ndash;13.66)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"151\"\u003e\n\u003cp\u003e\u0026nbsp;\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"170\"\u003e\n\u003cp\u003e\u0026nbsp;\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd width=\"142\"\u003e\n\u003cp\u003ecsDMARDs\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"85\"\u003e\n\u003cp\u003eCOVID-19\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"104\"\u003e\n\u003cp\u003e3018180\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"76\"\u003e\n\u003cp\u003e6585\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"151\"\u003e\n\u003cp\u003e232.99 (227.43\u0026ndash;238.69)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"151\"\u003e\n\u003cp\u003e66.99 (59.64\u0026ndash;74.35)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"170\"\u003e\n\u003cp\u003e1.40 (1.35\u0026ndash;1.46)\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd width=\"142\"\u003e\n\u003cp\u003e\u0026nbsp;\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"85\"\u003e\n\u003cp\u003eControl\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"104\"\u003e\n\u003cp\u003e3018180\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"76\"\u003e\n\u003cp\u003e4706\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"151\"\u003e\n\u003cp\u003e166.00 (161.32\u0026ndash;170.81)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"151\"\u003e\n\u003cp\u003e\u0026nbsp;\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"170\"\u003e\n\u003cp\u003e\u0026nbsp;\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd width=\"142\"\u003e\n\u003cp\u003etsDMARDs\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"85\"\u003e\n\u003cp\u003eCOVID-19\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"104\"\u003e\n\u003cp\u003e3018180\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"76\"\u003e\n\u003cp\u003e440\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"151\"\u003e\n\u003cp\u003e15.51 (14.13\u0026ndash;17.03)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"151\"\u003e\n\u003cp\u003e3.21 (1.27\u0026ndash;5.15)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"170\"\u003e\n\u003cp\u003e1.26 (1.09\u0026ndash;1.46)\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd width=\"142\"\u003e\n\u003cp\u003e\u003cstrong\u003e\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"85\"\u003e\n\u003cp\u003eControl\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"104\"\u003e\n\u003cp\u003e3018180\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"76\"\u003e\n\u003cp\u003e350\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"151\"\u003e\n\u003cp\u003e12.30 (11.08\u0026ndash;13.66)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"151\"\u003e\n\u003cp\u003e\u0026nbsp;\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"170\"\u003e\n\u003cp\u003e\u0026nbsp;\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd colspan=\"2\" width=\"227\"\u003e\n\u003cp\u003e\u003cstrong\u003eSensitivity Analysis\u003c/strong\u003e\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"104\"\u003e\n\u003cp\u003e\u0026nbsp;\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"76\"\u003e\n\u003cp\u003e\u0026nbsp;\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"151\"\u003e\n\u003cp\u003e\u0026nbsp;\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"151\"\u003e\n\u003cp\u003e\u0026nbsp;\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"170\"\u003e\n\u003cp\u003e\u0026nbsp;\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd width=\"142\"\u003e\n\u003cp\u003eWithin 1 yr\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"85\"\u003e\n\u003cp\u003eCOVID-19\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"104\"\u003e\n\u003cp\u003e3018180\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"76\"\u003e\n\u003cp\u003e6639\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"151\"\u003e\n\u003cp\u003e287.24 (280.42\u0026ndash;294.24)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"151\"\u003e\n\u003cp\u003e81.25 (72.20\u0026ndash;90.29)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"170\"\u003e\n\u003cp\u003e1.39 (1.34\u0026ndash;1.45)\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd width=\"142\"\u003e\n\u003cp\u003e\u0026nbsp;\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"85\"\u003e\n\u003cp\u003eControl\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"104\"\u003e\n\u003cp\u003e3018180\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"76\"\u003e\n\u003cp\u003e4779\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"151\"\u003e\n\u003cp\u003e206.00 (200.24\u0026ndash;211.92)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"151\"\u003e\n\u003cp\u003e\u0026nbsp;\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"170\"\u003e\n\u003cp\u003e\u0026nbsp;\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd width=\"142\"\u003e\n\u003cp\u003e1-2 yr\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"85\"\u003e\n\u003cp\u003eCOVID-19\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"104\"\u003e\n\u003cp\u003e912553\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"76\"\u003e\n\u003cp\u003e1580\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"151\"\u003e\n\u003cp\u003e300.71 (286.25\u0026ndash;315.91)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"151\"\u003e\n\u003cp\u003e31.43 (11.01\u0026ndash;51.84)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"170\"\u003e\n\u003cp\u003e1.12 (1.04\u0026ndash;1.20)\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd width=\"142\"\u003e\n\u003cp\u003e\u003cstrong\u003e\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"85\"\u003e\n\u003cp\u003eControl\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"104\"\u003e\n\u003cp\u003e917280\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"76\"\u003e\n\u003cp\u003e1415\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"151\"\u003e\n\u003cp\u003e269.29 (255.62\u0026ndash;283.69)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"151\"\u003e\n\u003cp\u003e\u0026nbsp;\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"170\"\u003e\n\u003cp\u003e\u0026nbsp;\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd width=\"142\"\u003e\n\u003cp\u003e\u003cstrong\u003e\u0026nbsp;\u003c/strong\u003eRx \u0026gt; half yr\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"85\"\u003e\n\u003cp\u003eCOVID-19\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"104\"\u003e\n\u003cp\u003e2560704\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"76\"\u003e\n\u003cp\u003e4535\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"151\"\u003e\n\u003cp\u003e167.85 (163.04\u0026ndash;172.81)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"151\"\u003e\n\u003cp\u003e52.44 (46.10\u0026ndash;58.78)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"170\"\u003e\n\u003cp\u003e1.45(1.39\u0026ndash;1.52)\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd width=\"142\"\u003e\n\u003cp\u003e\u003cstrong\u003e\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"85\"\u003e\n\u003cp\u003eControl\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"104\"\u003e\n\u003cp\u003e2560704\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"76\"\u003e\n\u003cp\u003e3128\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"151\"\u003e\n\u003cp\u003e115.41 (111.44\u0026ndash;119.53)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"151\"\u003e\n\u003cp\u003e\u0026nbsp;\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"170\"\u003e\n\u003cp\u003e\u0026nbsp;\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd width=\"142\"\u003e\n\u003cp\u003e\u003cstrong\u003e\u0026nbsp;\u003c/strong\u003eRx \u0026gt; 1 yr\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"85\"\u003e\n\u003cp\u003eCOVID-19\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"104\"\u003e\n\u003cp\u003e1539640\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"76\"\u003e\n\u003cp\u003e2202\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"151\"\u003e\n\u003cp\u003e105.42 (101.11\u0026ndash;109.91)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"151\"\u003e\n\u003cp\u003e42.45(36.89\u0026ndash;48.01)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"170\"\u003e\n\u003cp\u003e1.67(1.56\u0026ndash;1.79)\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd width=\"142\"\u003e\n\u003cp\u003e\u003cstrong\u003e\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"85\"\u003e\n\u003cp\u003eControl\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"104\"\u003e\n\u003cp\u003e1539640\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"76\"\u003e\n\u003cp\u003e1321\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"151\"\u003e\n\u003cp\u003e62.967 (59.66\u0026ndash;66.46)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"151\"\u003e\n\u003cp\u003e\u0026nbsp;\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"170\"\u003e\n\u003cp\u003e\u0026nbsp;\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd colspan=\"2\" width=\"227\"\u003e\n\u003cp\u003e\u003cstrong\u003eSecondary endpoints\u003c/strong\u003e\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"104\"\u003e\n\u003cp\u003e\u0026nbsp;\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"76\"\u003e\n\u003cp\u003e\u0026nbsp;\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"151\"\u003e\n\u003cp\u003e\u0026nbsp;\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"151\"\u003e\n\u003cp\u003e\u0026nbsp;\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"170\"\u003e\n\u003cp\u003e\u0026nbsp;\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd width=\"142\"\u003e\n\u003cp\u003eArthritis\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"85\"\u003e\n\u003cp\u003eCOVID-19\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"104\"\u003e\n\u003cp\u003e3018180\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"76\"\u003e\n\u003cp\u003e15454\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"151\"\u003e\n\u003cp\u003e546.45 (537.91\u0026ndash;555.14)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"151\"\u003e\n\u003cp\u003e73.02 (61.26\u0026ndash;84.78)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"170\"\u003e\n\u003cp\u003e1.15 (1.13\u0026ndash;1.18)\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd width=\"142\"\u003e\n\u003cp\u003e\u0026nbsp;\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"85\"\u003e\n\u003cp\u003eControl\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"104\"\u003e\n\u003cp\u003e3018180\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"76\"\u003e\n\u003cp\u003e13440\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"151\"\u003e\n\u003cp\u003e473.43 (465.5\u0026ndash;481.51)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"151\"\u003e\n\u003cp\u003e\u0026nbsp;\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"170\"\u003e\n\u003cp\u003e\u0026nbsp;\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd width=\"142\"\u003e\n\u003cp\u003eBullous Disorders\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"85\"\u003e\n\u003cp\u003eCOVID-19\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"104\"\u003e\n\u003cp\u003e3018180\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"76\"\u003e\n\u003cp\u003e2238\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"151\"\u003e\n\u003cp\u003e78.93 (75.72\u0026ndash;82.27)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"151\"\u003e\n\u003cp\u003e17.23 (12.87\u0026ndash;21.59)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"170\"\u003e\n\u003cp\u003e1.28 (1.2\u0026ndash;1.36)\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd width=\"142\"\u003e\n\u003cp\u003e\u0026nbsp;\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"85\"\u003e\n\u003cp\u003eControl\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"104\"\u003e\n\u003cp\u003e3018180\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"76\"\u003e\n\u003cp\u003e1755\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"151\"\u003e\n\u003cp\u003e61.7 (58.88\u0026ndash;64.65)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"151\"\u003e\n\u003cp\u003e\u0026nbsp;\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"170\"\u003e\n\u003cp\u003e\u0026nbsp;\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd rowspan=\"2\" width=\"142\"\u003e\n\u003cp\u003eCentral Nervous System Disorders\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"85\"\u003e\n\u003cp\u003eCOVID-19\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"104\"\u003e\n\u003cp\u003e3018180\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"76\"\u003e\n\u003cp\u003e1292\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"151\"\u003e\n\u003cp\u003e45.56 (43.14\u0026ndash;48.11)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"151\"\u003e\n\u003cp\u003e16.1 (12.92\u0026ndash;19.29)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"170\"\u003e\n\u003cp\u003e1.55 (1.42\u0026ndash;1.69)\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd width=\"85\"\u003e\n\u003cp\u003eControl\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"104\"\u003e\n\u003cp\u003e3018180\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"76\"\u003e\n\u003cp\u003e838\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"151\"\u003e\n\u003cp\u003e29.46 (27.53\u0026ndash;31.52)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"151\"\u003e\n\u003cp\u003e\u0026nbsp;\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"170\"\u003e\n\u003cp\u003e\u0026nbsp;\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd width=\"142\"\u003e\n\u003cp\u003eGlomerulonephritis\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"85\"\u003e\n\u003cp\u003eCOVID-19\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"104\"\u003e\n\u003cp\u003e3018180\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"76\"\u003e\n\u003cp\u003e7720\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"151\"\u003e\n\u003cp\u003e272.59 (266.57\u0026ndash;278.74)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"151\"\u003e\n\u003cp\u003e21.1 (12.68\u0026ndash;29.53)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"170\"\u003e\n\u003cp\u003e1.08 (1.05\u0026ndash;1.12)\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd width=\"142\"\u003e\n\u003cp\u003e\u0026nbsp;\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"85\"\u003e\n\u003cp\u003eControl\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"104\"\u003e\n\u003cp\u003e3018180\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"76\"\u003e\n\u003cp\u003e7146\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"151\"\u003e\n\u003cp\u003e251.48 (245.72\u0026ndash;257.38)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"151\"\u003e\n\u003cp\u003e\u0026nbsp;\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"170\"\u003e\n\u003cp\u003e\u0026nbsp;\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd rowspan=\"2\" width=\"142\"\u003e\n\u003cp\u003eInflammatory Bowel Disease\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"85\"\u003e\n\u003cp\u003eCOVID-19\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"104\"\u003e\n\u003cp\u003e3018180\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"76\"\u003e\n\u003cp\u003e1959\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"151\"\u003e\n\u003cp\u003e69.08 (66.09\u0026ndash;72.21)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"151\"\u003e\n\u003cp\u003e10.94 (6.79\u0026ndash;15.09)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"170\"\u003e\n\u003cp\u003e1.19 (1.11\u0026ndash;1.27)\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd width=\"85\"\u003e\n\u003cp\u003eControl\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"104\"\u003e\n\u003cp\u003e3018180\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"76\"\u003e\n\u003cp\u003e1654\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"151\"\u003e\n\u003cp\u003e58.14 (55.41\u0026ndash;61.02)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"151\"\u003e\n\u003cp\u003e\u0026nbsp;\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"170\"\u003e\n\u003cp\u003e\u0026nbsp;\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd rowspan=\"2\" width=\"142\"\u003e\n\u003cp\u003eImmunodeficiency Disorders\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"85\"\u003e\n\u003cp\u003eCOVID-19\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"104\"\u003e\n\u003cp\u003e3018180\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"76\"\u003e\n\u003cp\u003e2720\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"151\"\u003e\n\u003cp\u003e95.94 (92.41\u0026ndash;99.62)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"151\"\u003e\n\u003cp\u003e34.63 (30.02\u0026ndash;39.25)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"170\"\u003e\n\u003cp\u003e1.56 (1.47\u0026ndash;1.66)\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd width=\"85\"\u003e\n\u003cp\u003eControl\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"104\"\u003e\n\u003cp\u003e3018180\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"76\"\u003e\n\u003cp\u003e1744\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"151\"\u003e\n\u003cp\u003e61.31 (58.5\u0026ndash;64.26)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"151\"\u003e\n\u003cp\u003e\u0026nbsp;\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"170\"\u003e\n\u003cp\u003e\u0026nbsp;\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd width=\"142\"\u003e\n\u003cp\u003ePsoriasis\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"85\"\u003e\n\u003cp\u003eCOVID-19\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"104\"\u003e\n\u003cp\u003e3018180\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"76\"\u003e\n\u003cp\u003e10423\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"151\"\u003e\n\u003cp\u003e368.26 (361.26\u0026ndash;375.4)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"151\"\u003e\n\u003cp\u003e34.72 (24.97\u0026ndash;44.48)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"170\"\u003e\n\u003cp\u003e1.10 (1.07\u0026ndash;1.14)\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd width=\"142\"\u003e\n\u003cp\u003e\u0026nbsp;\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"85\"\u003e\n\u003cp\u003eControl\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"104\"\u003e\n\u003cp\u003e3018180\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"76\"\u003e\n\u003cp\u003e9473\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"151\"\u003e\n\u003cp\u003e333.54 (326.89\u0026ndash;340.32)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"151\"\u003e\n\u003cp\u003e\u0026nbsp;\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"170\"\u003e\n\u003cp\u003e\u0026nbsp;\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd rowspan=\"2\" width=\"142\"\u003e\n\u003cp\u003eRespiratory Disorders\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"85\"\u003e\n\u003cp\u003eCOVID-19\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"104\"\u003e\n\u003cp\u003e3018180\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"76\"\u003e\n\u003cp\u003e13696\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"151\"\u003e\n\u003cp\u003e484.68 (476.63\u0026ndash;492.87)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"151\"\u003e\n\u003cp\u003e159.91 (149.43\u0026ndash;170.39)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"170\"\u003e\n\u003cp\u003e1.49 (1.45\u0026ndash;1.53)\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd width=\"85\"\u003e\n\u003cp\u003eControl\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"104\"\u003e\n\u003cp\u003e3018180\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"76\"\u003e\n\u003cp\u003e9226\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"151\"\u003e\n\u003cp\u003e324.77 (318.21\u0026ndash;331.46)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"151\"\u003e\n\u003cp\u003e\u0026nbsp;\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"170\"\u003e\n\u003cp\u003e\u0026nbsp;\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd rowspan=\"2\" width=\"142\"\u003e\n\u003cp\u003eSystemic Connective Tissue Disorders\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"85\"\u003e\n\u003cp\u003eCOVID-19\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"104\"\u003e\n\u003cp\u003e3018180\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"76\"\u003e\n\u003cp\u003e13420\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"151\"\u003e\n\u003cp\u003e474.61 (466.65\u0026ndash;482.71)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"151\"\u003e\n\u003cp\u003e87.43 (76.62\u0026ndash;98.24)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"170\"\u003e\n\u003cp\u003e1.23 (1.20\u0026ndash;1.26)\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd width=\"85\"\u003e\n\u003cp\u003eControl\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"104\"\u003e\n\u003cp\u003e3018180\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"76\"\u003e\n\u003cp\u003e10994\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"151\"\u003e\n\u003cp\u003e387.19 (380.01\u0026ndash;394.49)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"151\"\u003e\n\u003cp\u003e\u0026nbsp;\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"170\"\u003e\n\u003cp\u003e\u0026nbsp;\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003c/tbody\u003e\n\u003c/table\u003e\n\u003cp\u003eNo, number: CI, confidence interval: bDMARD, Biologic Disease-Modifying Antirheumatic Drugs; csDMARD, Conventional Synthetic Disease-Modifying Antirheumatic Drugs; tsDMARD, Targeted Synthetic Disease-Modifying Anti rheumatic Drugs; COVID DMARDs, Disease-Modifying Antirheumatic Drugs repurposed or developed for the treatment of severe inflammatory responses in COVID-19; Rx; Treatment duration.\u003c/p\u003e"}],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":true,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":false,"hideJournal":true,"highlight":"","institution":"","isAcceptedByJournal":false,"isAuthorSuppliedPdf":false,"isDeskRejected":"","isHiddenFromSearch":false,"isInQc":false,"isInWorkflow":false,"isPdf":false,"isPdfUpToDate":true,"isWithdrawnOrRetracted":false,"journal":{"display":true,"email":"[email protected]","identity":"researchsquare","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":true,"externalIdentity":"","sideBox":"","snPcode":"","submissionUrl":"/submission","title":"Research Square","twitterHandle":"researchsquare","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"","reportingPortfolio":"","inReviewEnabled":false,"inReviewRevisionsEnabled":true},"keywords":"COVID-19, National Insurance Claims Database, Matched Cohort Study, SARS-CoV-2, DMARD, Autoimmune disease, Rheumatoid arthritis","lastPublishedDoi":"10.21203/rs.3.rs-7104550/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-7104550/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003cp\u003eBackground\u003c/p\u003e\n\u003cp\u003eThis matched cohort study utilized a nationwide insurance claims database from Japan, covering approximately 16% of the population across five prefectures.\u003c/p\u003e\n\u003cp\u003eMethods\u003c/p\u003e\n\u003cp\u003ePropensity score matching was employed to create 3,098,948 matched pairs based on age, sex, the Charlson Comorbidity Index, and individual comorbidities. The primary outcome was a composite endpoint of disease-modifying antirheumatic drug (DMARD) prescriptions, including biological, conventional synthetic, targeted synthetic, and COVID-specific drugs. The secondary outcomes included the incidence of various autoimmune and inflammatory conditions. Participants were required to have continuous healthcare access from January 2020 to December 2022, with a 1-year look-back period. The follow-up period was extended from the COVID-19 index month to December 31, 2022. Incidence rate ratios were calculated to assess the association between COVID-19 and subsequent autoimmune conditions or DMARD use.\u003c/p\u003e\n\u003cp\u003eResults\u003cbr\u003e\nCOVID-19 patients had a 33% higher incidence rate of DMARD prescriptions than controls (IRR: 1.33, 95% CI: 1.28–1.38). Subgroup analyses revealed stronger associations among males, younger age groups, and those with multiple comorbidities. COVID-19 infection is also associated with an increased risk of arthritis (IRR: 1.15, 95% CI: 1.13–1.18), glomerulonephritis (IRR: 1.08, 95% CI: 1.05–1.12), respiratory disorders (IRR: 1.49, 95% CI: 1.45–1.53), and other autoimmune conditions. These findings suggest a significant long-term impact of COVID-19 on healthcare utilization for managing autoimmune and inflammatory diseases.\u003c/p\u003e\n\u003cp\u003eConclusions\u003c/p\u003e\n\u003cp\u003eThis study highlights the need for the early identification and management of individuals at risk for post-COVID inflammatory sequelae and ensuring equitable access to DMARD therapies. Further research is warranted to elucidate the underlying mechanisms and develop targeted interventions to mitigate the long-term burden of COVID-19 on the healthcare system.\u003c/p\u003e","manuscriptTitle":"Effects of COVID-19 on Autoimmune Disease Incidence and DMARD Utilization: Evidence from Japanese Insurance Claims Data","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2025-07-18 14:12:37","doi":"10.21203/rs.3.rs-7104550/v1","editorialEvents":[{"type":"communityComments","content":0}],"status":"published","journal":{"display":true,"email":"[email protected]","identity":"researchsquare","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":true,"externalIdentity":"","sideBox":"","snPcode":"","submissionUrl":"/submission","title":"Research Square","twitterHandle":"researchsquare","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"","reportingPortfolio":"","inReviewEnabled":false,"inReviewRevisionsEnabled":true}}],"origin":"","ownerIdentity":"e82d60b8-130e-4e2b-94d3-1a911f77c3ab","owner":[],"postedDate":"July 18th, 2025","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"posted","subjectAreas":[],"tags":[],"updatedAt":"2025-08-29T08:08:58+00:00","versionOfRecord":[],"versionCreatedAt":"2025-07-18 14:12:37","video":"","vorDoi":"","vorDoiUrl":"","workflowStages":[]},"version":"v1","identity":"rs-7104550","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-7104550","identity":"rs-7104550","version":["v1"]},"buildId":"8U1c8b4HqxoKbykW_rLl7","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}