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Steinmann M, Lampe D, Grosser J, Schmidt J, Hohoff ML, Fischer A, Greiner W. Risk factors for herpes zoster infections: a systematic review and meta-analysis unveiling common trends and heterogeneity patterns. Infection 2024; 52:1009-1026. [PMID: 38236326 PMCID: PMC11142967 DOI: 10.1007/s15010-023-02156-y] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/24/2023] [Accepted: 12/04/2023] [Indexed: 01/19/2024]
Abstract
PURPOSE The burden of herpes zoster (HZ) is substantial and numerous chronic underlying conditions are known as predisposing risk factors for HZ onset. Thus, a comprehensive study is needed to synthesize existing evidence. This study aims to comprehensively identify these risk factors. METHODS A systematic literature search was done using MEDLINE via PubMed, EMBASE and Web of Science for studies published from January 1, 2003 to January 1, 2023. A random-effects model was used to estimate pooled Odds Ratios (OR). Heterogeneity was assessed using the I2 statistic. For sensitivity analyses basic outlier removal, leave-one-out validation and Graphic Display of Heterogeneity (GOSH) plots with different algorithms were employed to further analyze heterogeneity patterns. Finally, a multiple meta-regression was conducted. RESULTS Of 6392 considered records, 80 were included in the meta-analysis. 21 different conditions were identified as potential risk factors for HZ: asthma, autoimmune disorders, cancer, cardiovascular disorders, chronic heart failure (CHF), chronic obstructive pulmonary disorder (COPD), depression, diabetes, digestive disorders, endocrine and metabolic disorders, hematological disorders, HIV, inflammatory bowel disease (IBD), mental health conditions, musculoskeletal disorders, neurological disorders, psoriasis, renal disorders, rheumatoid arthritis (RA), systemic lupus erythematosus (SLE) and transplantation. Transplantation was associated with the highest risk of HZ (OR = 4.51 (95% CI [1.9-10.7])). Other risk factors ranged from OR = 1.17-2.87, indicating an increased risk for all underlying conditions. Heterogeneity was substantial in all provided analyses. Sensitivity analyses showed comparable results regarding the pooled effects and heterogeneity. CONCLUSIONS This study showed an increased risk of HZ infections for all identified factors.
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Affiliation(s)
- Maren Steinmann
- Department of Health Economics and Health Care Management, School of Public Health, Bielefeld University, Bielefeld, Germany.
| | - David Lampe
- Department of Health Economics and Health Care Management, School of Public Health, Bielefeld University, Bielefeld, Germany
| | - John Grosser
- Department of Health Economics and Health Care Management, School of Public Health, Bielefeld University, Bielefeld, Germany
| | - Juliana Schmidt
- Department of Health Economics and Health Care Management, School of Public Health, Bielefeld University, Bielefeld, Germany
| | - Marla Louise Hohoff
- Department of Health Economics and Health Care Management, School of Public Health, Bielefeld University, Bielefeld, Germany
| | - Anita Fischer
- Department of Health Economics and Health Care Management, School of Public Health, Bielefeld University, Bielefeld, Germany
| | - Wolfgang Greiner
- Department of Health Economics and Health Care Management, School of Public Health, Bielefeld University, Bielefeld, Germany
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Furer V, Weil C, Chodik G, Slav SA, Blonder SN, Fisher-Shoval Y, Barak M, Elkayam O. Real-World Coverage With Influenza, Pneumococcal, and Herpes Zoster Vaccines Among Patients With Rheumatic Diseases in a Nationwide Healthcare Plan. J Rheumatol 2024; 51:505-516. [PMID: 38302167 DOI: 10.3899/jrheum.2023-0867] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Accepted: 01/23/2024] [Indexed: 02/03/2024]
Abstract
OBJECTIVE Vaccination against preventable infections is important for the management of rheumatic diseases (RDs). This study assessed the vaccination coverage and predictors among patients with RDs using real-world data from Israel. METHODS This retrospective cross-sectional study, based on a Maccabi Healthcare Services database, included adult patients diagnosed with rheumatoid arthritis (RA), psoriatic arthritis (PsA), and systemic lupus erythematosus (SLE), as of April 30, 2019. Age-specific vaccination coverage for influenza (past year), pneumococcal (23-valent pneumococcal polysaccharide vaccine [PPSV23] and/or 13-valent pneumococcal conjugate vaccine [PCV13]), and live-attenuated herpes zoster (HZ) vaccines (past 5 years) was reported. Logistic regression was used to investigate predictors of vaccination. RESULTS The study included 14,528 patients (RA: n = 6932; PsA: n = 4395; SLE: n = 1951; > 1 condition: n = 1250). Influenza vaccine coverage among patients with RA, PsA, and SLE was 45.1%, 36.2%, and 33.7%, respectively. For PPSV23, corresponding rates were 19.6%, 16.2%, and 12.6%, respectively. In the elderly population (≥ 65 years), 63.2% had influenza vaccine in the past year and 83.4% had a PPSV23 vaccine in the past 5 years or at age ≥ 65. For PCV13 and HZ, coverage in the overall study population was low at 4.8% and 3.6%, respectively. Central residence and treatment with corticosteroids and biologic or targeted synthetic disease-modifying antirheumatic drugs within the past 5 years were significant predictors of vaccination coverage across all vaccines (P < 0.05). Other predictors varied by vaccine, including female sex (influenza, PPSV23, PCV13), age (influenza, PPSV23), chronic comorbidities (influenza, PPSV23, PCV13), shorter disease duration (PCV13), and high socioeconomic status (PCV13, HZ). CONCLUSION This study demonstrated suboptimal coverage of influenza, pneumococcal, and HZ vaccination in patients with RA, PsA, and SLE, in particular among younger adults in Israel.
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Affiliation(s)
- Victoria Furer
- V. Furer, MD, O. Elkayam, MD, Department of Rheumatology, Tel Aviv Sourasky Medical Center, and Faculty of Medicine, Tel Aviv University, Tel Aviv;
| | - Clara Weil
- C. Weil, MSc, Maccabi Institute for Research and Innovation, Maccabi Healthcare Services, Tel Aviv
| | - Gabriel Chodik
- G. Chodik, MD, Faculty of Medicine, Tel Aviv University, and Maccabi Institute for Research and Innovation, Maccabi Healthcare Services, Tel Aviv
| | - Shera Ann Slav
- S.A. Slav, DMD, S.N. Blonder, PhD, Y. Fisher-Shoval, PhD, M. Barak, MD, AbbVie Inc., Hod HaSharon, Israel
| | - Shiran Naftelberg Blonder
- S.A. Slav, DMD, S.N. Blonder, PhD, Y. Fisher-Shoval, PhD, M. Barak, MD, AbbVie Inc., Hod HaSharon, Israel
| | - Yonit Fisher-Shoval
- S.A. Slav, DMD, S.N. Blonder, PhD, Y. Fisher-Shoval, PhD, M. Barak, MD, AbbVie Inc., Hod HaSharon, Israel
| | - Moran Barak
- S.A. Slav, DMD, S.N. Blonder, PhD, Y. Fisher-Shoval, PhD, M. Barak, MD, AbbVie Inc., Hod HaSharon, Israel
| | - Ori Elkayam
- V. Furer, MD, O. Elkayam, MD, Department of Rheumatology, Tel Aviv Sourasky Medical Center, and Faculty of Medicine, Tel Aviv University, Tel Aviv
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Wang DC, Tang YY, He CS, Fu L, Liu XY, Xu WD. Exploring machine learning methods for predicting systemic lupus erythematosus with herpes. Int J Rheum Dis 2023; 26:2047-2054. [PMID: 37578132 DOI: 10.1111/1756-185x.14869] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/27/2023] [Revised: 07/03/2023] [Accepted: 08/02/2023] [Indexed: 08/15/2023]
Abstract
OBJECTIVES To investigate whether machine learning, which is widely used in disease prediction and diagnosis based on demographic data and serological markers, can predict herpes occurrence in patients with systemic lupus erythematosus (SLE). METHODS A total of 286 SLE patients were included in this study, including 200 SLE patients without herpes and 86 SLE patients with herpes. SLE patients were randomly divided into a training group and a test group, and 18 demographic characteristics and serological indicators were compared between the two groups. RESULTS We selected basophil, monocyte, white blood cell, age, immunoglobulin E, SLE Disease Activity Index, complement 4, neutrophil, and immunoglobulin G as the basic features of modeling. A random forest model had the best performance, but logistic and decision tree analyses had better clinical decision-making benefits. Random forest had a good consistency between feature importance judgment and feature selection. The 10-fold cross-validation showed the optimization of five model parameters. CONCLUSION The random forest model may be an excellently performing model, which may help clinicians to identify SLE patients whose disease is complicated by herpes early.
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Affiliation(s)
- Da-Cheng Wang
- Department of Evidence-Based Medicine, Southwest Medical University, Luzhou, Sichuan, China
| | - Yang-Yang Tang
- Department of Evidence-Based Medicine, Southwest Medical University, Luzhou, Sichuan, China
| | - Cheng-Song He
- Department of Rheumatology and Immunology, Affiliated Hospital of Southwest Medical University, Luzhou, Sichuan, China
| | - Lu Fu
- Laboratory Animal Center, Southwest Medical University, Luzhou, Sichuan, China
| | - Xiao-Yan Liu
- Department of Evidence-Based Medicine, Southwest Medical University, Luzhou, Sichuan, China
| | - Wang-Dong Xu
- Department of Evidence-Based Medicine, Southwest Medical University, Luzhou, Sichuan, China
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