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Chen L, Wang L, Xing Y, Xie J, Su B, Geng M, Ren X, Zhang Y, Liu J, Ma T, Chen M, Miller JE, Dong Y, Song Y, Ma J, Sawyer S. Persistence and Variation of the Indirect Effects of COVID-19 Restrictions on the Spectrum of Notifiable Infectious Diseases in China: Analysis of National Surveillance Among Children and Adolescents From 2018 to 2021. JMIR Public Health Surveill 2024; 10:e47626. [PMID: 38748469 PMCID: PMC11137434 DOI: 10.2196/47626] [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] [Received: 03/27/2023] [Revised: 02/21/2024] [Accepted: 02/25/2024] [Indexed: 06/01/2024] Open
Abstract
BACKGROUND Beyond the direct effect of COVID-19 infection on young people, the wider impact of the pandemic on other infectious diseases remains unknown. OBJECTIVE This study aims to assess changes in the incidence and mortality of 42 notifiable infectious diseases during the pandemic among children and adolescents in China, compared with prepandemic levels. METHODS The Notifiable Infectious Disease Surveillance System of China was used to detect new cases and fatalities among individuals aged 5-22 years across 42 notifiable infectious diseases spanning from 2018 to 2021. These infectious diseases were categorized into 5 groups: respiratory, gastrointestinal and enterovirus, sexually transmitted and blood-borne, zoonotic, and vector-borne diseases. Each year (2018-2021) was segmented into 4 phases: phase 1 (January 1-22), phase 2 (January 23-April 7), phase 3 (April 8-August 31), and phase 4 (September 1-December 31) according to the varying intensities of pandemic restrictive measures in 2020. Generalized linear models were applied to assess the change in the incidence and mortality within each disease category, using 2018 and 2019 as the reference. RESULTS A total of 4,898,260 incident cases and 3701 deaths were included. The overall incidence of notifiable infectious diseases decreased sharply during the first year of the COVID-19 pandemic (2020) compared with prepandemic levels (2018 and 2019), and then rebounded in 2021, particularly in South China. Across the past 4 years, the number of deaths steadily decreased. The incidence of diseases rebounded differentially by the pandemic phase. For instance, although seasonal influenza dominated respiratory diseases in 2019, it showed a substantial decline during the pandemic (percent change in phase 2 2020: 0.21, 95% CI 0.09-0.50), which persisted until 2021 (percent change in phase 4 2021: 1.02, 95% CI 0.74-1.41). The incidence of gastrointestinal and enterovirus diseases decreased by 33.6% during 2020 but rebounded by 56.9% in 2021, mainly driven by hand, foot, and mouth disease (percent change in phase 3 2021: 1.28, 95% CI 1.17-1.41) and infectious diarrhea (percent change in phase 3 2020: 1.22, 95% CI 1.17-1.28). Sexually transmitted and blood-borne diseases were restrained during the first year of 2021 but rebounded quickly in 2021, mainly driven by syphilis (percent change in phase 3 2020: 1.31, 95% CI 1.23-1.40) and gonorrhea (percent change in phase 3 2020: 1.10, 95% CI 1.05-1.16). Zoonotic diseases were not dampened by the pandemic but continued to increase across the study period, mainly due to brucellosis (percent change in phase 2 2020: 0.94, 95% CI 0.75-1.16). Vector-borne diseases showed a continuous decline during 2020, dominated by hemorrhagic fever (percent change in phase 2 2020: 0.68, 95% CI 0.53-0.87), but rebounded in 2021. CONCLUSIONS The COVID-19 pandemic was associated with a marked decline in notifiable infectious diseases in Chinese children and adolescents. These effects were not sustained, with evidence of a rebound to prepandemic levels by late 2021. To effectively address the postpandemic resurgence of infectious diseases in children and adolescents, it will be essential to maintain disease surveillance and strengthen the implementation of various initiatives. These include extending immunization programs, prioritizing the management of sexually transmitted infections, continuing feasible nonpharmaceutical intervention projects, and effectively managing imported infections.
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Affiliation(s)
- Li Chen
- Institute of Child and Adolescent Health, School of Public Health, National Health Commission Key Laboratory of Reproductive Health, Peking University, Beijing, China
| | - Liping Wang
- Division of Infectious Disease Control and Prevention, Key Laboratory of Surveillance and Early Warning on Infectious Disease, Chinese Center for Disease Control and Prevention, Beijing, China
| | - Yi Xing
- Institute of Child and Adolescent Health, School of Public Health, National Health Commission Key Laboratory of Reproductive Health, Peking University, Beijing, China
| | - Junqing Xie
- Centre for Statistics in Medicine, Nuffield Department of Orthopaedics Rheumatology and Musculoskeletal Sciences, University of Oxford, Oxford, United Kingdom
| | - Binbin Su
- Institute of Population Research, Peking University Asia-Pacific Economic Cooperation Health Sciences Academy, Beijing, China
| | - Mengjie Geng
- Division of Infectious Disease Control and Prevention, Key Laboratory of Surveillance and Early Warning on Infectious Disease, Chinese Center for Disease Control and Prevention, Beijing, China
| | - Xiang Ren
- Division of Infectious Disease Control and Prevention, Key Laboratory of Surveillance and Early Warning on Infectious Disease, Chinese Center for Disease Control and Prevention, Beijing, China
| | - Yi Zhang
- Institute of Child and Adolescent Health, School of Public Health, National Health Commission Key Laboratory of Reproductive Health, Peking University, Beijing, China
| | - Jieyu Liu
- Institute of Child and Adolescent Health, School of Public Health, National Health Commission Key Laboratory of Reproductive Health, Peking University, Beijing, China
| | - Tao Ma
- Institute of Child and Adolescent Health, School of Public Health, National Health Commission Key Laboratory of Reproductive Health, Peking University, Beijing, China
| | - Manman Chen
- Institute of Child and Adolescent Health, School of Public Health, National Health Commission Key Laboratory of Reproductive Health, Peking University, Beijing, China
| | - Jessica E Miller
- Department of Paediatrics, University of Melbourne, Melbourne, Victoria, Australia
- Murdoch Children's Research Institute, Royal Children's Hospital, Parkville, Victoria, Australia
| | - Yanhui Dong
- Institute of Child and Adolescent Health, School of Public Health, National Health Commission Key Laboratory of Reproductive Health, Peking University, Beijing, China
| | - Yi Song
- Institute of Child and Adolescent Health, School of Public Health, National Health Commission Key Laboratory of Reproductive Health, Peking University, Beijing, China
| | - Jun Ma
- Institute of Child and Adolescent Health, School of Public Health, National Health Commission Key Laboratory of Reproductive Health, Peking University, Beijing, China
| | - Susan Sawyer
- Department of Paediatrics, University of Melbourne, Melbourne, Victoria, Australia
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Singh AK, Panigrahi MK, Pradhan SK, Pal D, Subba SH, Patro BK, Behera BK, Mishra B, Behera B, Mohapatra PR, Bhuniya S, Bal SK, Sarkar S, Pillai JSK, Mohanty S, Gitanjali B. Clinico-Epidemiological Characteristics of Healthcare Workers with SARS-CoV-2 Infection during the First and Second Waves in a Teaching Hospital from Eastern India: A Comparative Analysis. Hosp Top 2024; 102:84-95. [PMID: 35852422 DOI: 10.1080/00185868.2022.2096523] [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] [Indexed: 06/15/2023]
Abstract
In this retrospective observational study, we have performed a comparative analysis of the demographic, clinical and epidemiological characteristics of the HCWs affected with SARS-CoV-2 infection during first two waves in India. The overall prevalence of SARS-CoV-2 infection among HCWs was found to be 15.24% (14.20-16.33) and 23.38% (22.14-25.65) during first and second waves respectively. The second wave showed an adjusted odds ratio of 0.04(0.02-0.07) and 2.09(1.49-2.93) for hospitalization and being symptomatic, respectively. We detected significantly higher level of C-reactive protein (CRP) among admitted HCWs during the second wave (5.10 -14.60 mg/dl) as compared to the first wave (2.00 - 2.80 mg/dl). Our study found the relative risk of SARS-CoV-2 reinfection among HCWs during the second wave to be 0.68 [0.57-0.82, p < 0.001)]. Although, the prevalence of SARS CoV-2 infection and risk of being symptomatic was higher during second wave, the risk of hospitalization was less when compared with the first wave.
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Affiliation(s)
- Arvind Kumar Singh
- Department of Community Medicine and Family Medicine, All India Institute of Medical Sciences, Bhubaneswar, India
| | - Manoj Kumar Panigrahi
- Department of Pulmonary Medicine & Critical Care, All India Institute of Medical Sciences, Bhubaneswar, India
| | - Somen Kumar Pradhan
- Department of Community Medicine and Family Medicine, All India Institute of Medical Sciences, Bhubaneswar, India
| | - Debkumar Pal
- Department of Community Medicine and Family Medicine, All India Institute of Medical Sciences, Bhubaneswar, India
| | - Sonu H Subba
- Department of Community Medicine and Family Medicine, All India Institute of Medical Sciences, Bhubaneswar, India
| | - Binod Kumar Patro
- Department of Community Medicine and Family Medicine, All India Institute of Medical Sciences, Bhubaneswar, India
| | - Binod Kumar Behera
- Department of Community Medicine and Family Medicine, All India Institute of Medical Sciences, Bhubaneswar, India
| | - Baijayantimala Mishra
- Department of Microbiology, All India Institute of Medical Sciences, Bhubaneswar, India
| | - Bijayini Behera
- Department of Microbiology, All India Institute of Medical Sciences, Bhubaneswar, India
| | - Prasanta Raghab Mohapatra
- Department of Pulmonary Medicine & Critical Care, All India Institute of Medical Sciences, Bhubaneswar, India
| | - Sourin Bhuniya
- Department of Pulmonary Medicine & Critical Care, All India Institute of Medical Sciences, Bhubaneswar, India
| | - Shakti Kumar Bal
- Department of Pulmonary Medicine & Critical Care, All India Institute of Medical Sciences, Bhubaneswar, India
| | - Saurav Sarkar
- Department of Ear Nose Throat, All India Institute of Medical Sciences, Bhubaneswar, India
| | - Jawahar S K Pillai
- Department of Hospital Administration, All India Institute of Medical Sciences, Bhubaneswar, India
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Kieltyka J, Ghattas J, Ruppol S, Nicaise P, Raymenants J, Speybroeck N. COVID-19 contact tracing at work in Belgium - how tracers tweak guidelines for the better. BMC Public Health 2023; 23:2148. [PMID: 37924055 PMCID: PMC10623756 DOI: 10.1186/s12889-023-16911-1] [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] [Received: 01/18/2023] [Accepted: 10/05/2023] [Indexed: 11/06/2023] Open
Abstract
BACKGROUND When conducting COVID-19 contact tracing, pre-defined criteria allow differentiating high-risk contacts (HRC) from low-risk contacts (LRC). Our study aimed to evaluate whether contact tracers in Belgium followed these criteria in practice and whether their deviations improved the infection risk assessment. METHOD We conducted a retrospective cohort study in Belgium, through an anonymous online survey, sent to 111,763 workers by email. First, we evaluated the concordance between the guideline-based classification of HRC or LRC and the tracer's classification. We computed positive and negative agreements between both. Second, we used a multivariate Poisson regression to calculate the risk ratio (RR) of testing positive depending on the risk classification by the contact tracer and by the guideline-based risk classification. RESULTS For our first research question, we included 1105 participants. The positive agreement between the guideline-based classification in HRC or LRC and the tracer's classification was 0.53 (95% CI 0.49-0.57) and the negative agreement 0.70 (95% CI: 0.67-0.72). The type of contact tracer (occupational doctors, internal tracer, general practitioner, other) did not significantly influence the results. For the second research question, we included 589 participants. The RR of testing positive after an HRC compared to an LRC was 3.10 (95% CI: 2.71-3.56) when classified by the contact tracer and 2.24 (95% CI: 1.94-2.60) when classified by the guideline-based criteria. CONCLUSION Our study indicates that contact tracers did not apply pre-defined criteria for classifying high and low risk contacts. Risk stratification by contact tracers predicts who is at risk of infection better than guidelines only. This result indicates that a knowledgeable tracer can target testing better than a general guideline, asking for a debate on how to adapt the guidelines.
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Affiliation(s)
- Jerome Kieltyka
- CESI ASBL, Avenue Konrad Adenauer 8, 1200, Woluwe-Saint-Lambert, Belgium.
| | - Jinane Ghattas
- Institute of Health and Society (IRSS), Université Catholique de Louvain, Brussels, Belgium
| | - Sandrine Ruppol
- CESI ASBL, Avenue Konrad Adenauer 8, 1200, Woluwe-Saint-Lambert, Belgium
| | - Pablo Nicaise
- Institute of Health and Society (IRSS), Université Catholique de Louvain, Brussels, Belgium
| | - Joren Raymenants
- Laboratory of Clinical Microbiology, KU Leuven, Herestraat 49, 3000, Louvain, Belgium
| | - Niko Speybroeck
- Institute of Health and Society (IRSS), Université Catholique de Louvain, Brussels, Belgium
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Velhal GD, Shah AK, Dhanusu S. Contact Tracing for COVID-19 among Health-Care Workers of a Tertiary Care Hospital in Mumbai. Indian J Community Med 2022; 47:420-424. [PMID: 36438541 PMCID: PMC9693958 DOI: 10.4103/ijcm.ijcm_1178_21] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/03/2021] [Accepted: 12/27/2021] [Indexed: 06/16/2023] Open
Abstract
Background Contact tracing (CT) is an effective tool for breaking the chains of transmission in infectious disease outbreaks. This study was conducted to observe the trend of isolation and quarantine, assess the source of infection and contacts, and assess the effectiveness of CT in the early detection of infection among health-care workers (HCWs). Methods This study was conducted using secondary analysis of routine CT records of HCWs of a tertiary care hospital in Mumbai from April 9, 2020, to December 31, 2020. Details of all HCWs exposed or infected with COVID-19 were collected in a standard format developed for this purpose telephonically. The exposed HCWs were further divided into high-risk (HR)/low-risk (LR) contacts and quarantined. Results A total of 744 HCWs were isolated during this period and 1486 contacts were quarantined against them. Majority of the HCWs affected from COVID-19 were resident doctors, interns, and nursing staff. More than 81% of the positive HCWs were symptomatic. The overall ratio between isolated HCWs and quarantined HCWs is 1:2. A total of 88 (6%) HCWs tested positive from quarantine. The test positivity rate among HR contacts was 9.01% and among LR contacts was 2.72%. Conclusions Effective CT of positive HCWs greatly aids in the early identification of contacts and timely quarantine. Over a period of time, the number of HCWs getting isolated or quarantined is found to decrease. This is the true success of CT. This strategy can be implemented among other medical colleges and hospitals too.
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Affiliation(s)
- Gajanan D. Velhal
- Department of Community Medicine, Seth G S Medical College and KEM Hospital, Mumbai, Maharashtra, India
| | - Anuradha Kunal Shah
- Department of Community Medicine, Seth G S Medical College and KEM Hospital, Mumbai, Maharashtra, India
| | - Subasri Dhanusu
- Department of Community Medicine, Seth G S Medical College and KEM Hospital, Mumbai, Maharashtra, India
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