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Lancet Reg Health Southeast Asia ; : 100023, 2022 Jun 06.
Artigo em Inglês | MEDLINE | ID: mdl-35769163


Background: Surge of SARS CoV-2 infections ascribed to omicron variant began in December 2021 in New Delhi. We determined the infection and reinfection density in a cohort of health care workers (HCWs) along with vaccine effectiveness (VE) against symptomatic infection within omicron transmission period (considered from December 01, 2021 to February 25, 2022. Methods: This is an observational study from the All India Institute of Medical Sciences, New Delhi. Data were collected telephonically. Person-time at risk was counted from November 30, 2021 till date of infection/ reinfection, or date of interview. Comparison of clinical features and severity was done with previous pandemic periods. VE was estimated using test-negative case-control design [matched pairs (for age and sex)]. Vaccination status was compared and adjusted odds ratios (OR) were computed by conditional logistic regression. VE was estimated as (1-adjusted OR)X100-. Findings: 11474 HCWs participated in this study. The mean age was 36⋅2 (±10⋅7) years. Complete vaccination with two doses were reported by 9522 (83%) HCWs [8394 (88%) Covaxin and 1072 Covishield (11%)]. The incidence density of all infections and reinfection during the omicron transmission period was 34⋅8 [95% Confidence Interval (CI): 33⋅5-36⋅2] and 45⋅6 [95% CI: 42⋅9-48⋅5] per 10000 person days respectively. The infection was milder as compared to previous periods. VE was 52⋅5% (95% CI: 3⋅9-76⋅5, p = 0⋅036) for those who were tested within 14-60 days of receiving second dose and beyond this period (61-180 days), modest effect was observed. Interpretation: Almost one-fifth of HCWs were infected with SARS CoV-2 during omicron transmission period, with predominant mild spectrum of COVID-19 disease. Waning effects of vaccine protection were noted with increase in time intervals since vaccination. Funding: None.

Artigo em Inglês | MEDLINE | ID: mdl-35484727


AIMS: The goal of this research is to propose a simpler and more efficient model for evaluating healthcare establishments (HCEs). With this motivation, this study aims to discover key performance indicators (KPIs) that affect HCE performance, present a ranking model for KPIs in Indian HCEs, and evaluate Indian HCEs using the identified and prioritised KPIs. MATERIAL AND METHODS: Through extensive literature review and expert opinions, this research identifies the various KPIs in HCEs, classifies them into six main categories, and prioritises them using the full consistency method (FUCOM). Further, well-known HCEs across northern India were evaluated and ranked using Measurement Alternatives and Ranking according to Compromise Solution. RESULTS: The 'technology adoption related indicators' is found as the most important main KPIs, whereas 'adequate number of hospital beds and bathrooms (IE5)' as the most dominating sub-category KPIs. Also, amongst the 20 evaluated Indian HCEs 'healthcare establishment-1 (HCE1)' was found to be the best performing HCE while 'healthcare establishment-12 (HCE12)' was found to be the worst-performing HCE. The stability and consistency of the results are ascertained by performing sensitivity analysis and comparing the results with other existing methodologies. CONCLUSION: The findings of this study are quite important for HCEs management to fully comprehend the key areas to improve upon so that managers can improve medical standards in a targeted manner. The developed prioritisation model and methodology shown in this paper will help and motivate managers and intellectuals of HCEs to evaluate and improve the HCE's performance.

JAMA Netw Open ; 5(1): e2142210, 2022 01 04.
Artigo em Inglês | MEDLINE | ID: mdl-34994793


Importance: A surge of COVID-19 occurred from March to June 2021, in New Delhi, India, linked to the B.1.617.2 (Delta) variant of SARS-CoV-2. COVID-19 vaccines were rolled out for health care workers (HCWs) starting in January 2021. Objective: To assess the incidence density of reinfection among a cohort of HCWs and estimate the effectiveness of the inactivated whole virion vaccine BBV152 against reinfection. Design, Setting, and Participants: This was a retrospective cohort study among HCWs working at a tertiary care center in New Delhi, India. Exposures: Vaccination with 0, 1, or 2 doses of BBV152. Main Outcomes and Measures: The HCWs were categorized as fully vaccinated (with 2 doses and ≥15 days after the second dose), partially vaccinated (with 1 dose or 2 doses with <15 days after the second dose), or unvaccinated. The incidence density of COVID-19 reinfection per 100 person-years was computed, and events from March 3, 2020, to June 18, 2021, were included for analysis. Unadjusted and adjusted hazard ratios (HRs) were estimated using a Cox proportional hazards model. Estimated vaccine effectiveness (1 - adjusted HR) was reported. Results: Among 15 244 HCWs who participated in the study, 4978 (32.7%) were diagnosed with COVID-19. The mean (SD) age was 36.6 (10.3) years, and 55.0% were male. The reinfection incidence density was 7.26 (95% CI: 6.09-8.66) per 100 person-years (124 HCWs [2.5%], total person follow-up period of 1696 person-years as time at risk). Fully vaccinated HCWs had lower risk of reinfection (HR, 0.14 [95% CI, 0.08-0.23]), symptomatic reinfection (HR, 0.13 [95% CI, 0.07-0.24]), and asymptomatic reinfection (HR, 0.16 [95% CI, 0.05-0.53]) compared with unvaccinated HCWs. Accordingly, among the 3 vaccine categories, reinfection was observed in 60 of 472 (12.7%) of unvaccinated (incidence density, 18.05 per 100 person-years; 95% CI, 14.02-23.25), 39 of 356 (11.0%) of partially vaccinated (incidence density 15.62 per 100 person-years; 95% CI, 11.42-21.38), and 17 of 1089 (1.6%) fully vaccinated (incidence density 2.18 per 100 person-years; 95% CI, 1.35-3.51) HCWs. The estimated effectiveness of BBV152 against reinfection was 86% (95% CI, 77%-92%); symptomatic reinfection, 87% (95% CI, 76%-93%); and asymptomatic reinfection, 84% (95% CI, 47%-95%) among fully vaccinated HCWs. Partial vaccination was not associated with reduced risk of reinfection. Conclusions and Relevance: These findings suggest that BBV152 was associated with protection against both symptomatic and asymptomatic reinfection in HCWs after a complete vaccination schedule, when the predominant circulating variant was B.1.617.2.

COVID-19/epidemiologia , Pessoal de Saúde , Reinfecção , SARS-CoV-2 , Adulto , COVID-19/etiologia , COVID-19/prevenção & controle , Vacinas contra COVID-19/administração & dosagem , Estudos de Coortes , Feminino , Humanos , Imunogenicidade da Vacina , Índia/epidemiologia , Masculino , Pessoa de Meia-Idade , Inquéritos e Questionários , Centros de Atenção Terciária , Vacinas de Produtos Inativados/administração & dosagem , Vírion/imunologia , Adulto Jovem
Curr Med Imaging ; 2021 Oct 04.
Artigo em Inglês | MEDLINE | ID: mdl-34607548


COVID-19 is a pandemic initially identified in Wuhan, China, which is caused by a novel coronavirus, also recognized as the Severe Acute Respiratory Syndrome (SARS-nCoV-2). Unlike other coronaviruses, this novel pathogen may cause unusual contagious pain which results in viral pneumonia, serious heart problems, and even death. Researchers worldwide are continuously striving to develop a cure for this highly infective disease, yet there are no well-defined absolute treatments available at present. Several vaccination drives with emergency use authorisation vaccines are being done across many countries, however, their long term efficacy and side-effects study are yet to be done. The research community is analysing the situation by collecting the datasets from various sources. Healthcare professionals must thoroughly analyse the situation, devise preventive measures for this pandemic, and even develop possible drug combinations. Various analytical and statistical models have been developed, however, their outcome rate is prolonged. Thus, modern science stresses on the application of state-of-the-art methods in this combat against COVID-19. The application of Artificial intelligence (AI), and AI-driven tools are emerging as effective tools, especially with X-Ray and CT-Scan imaging data of infected subjects, infection trend predictions etc. The high efficacy of these AI systems can be observed in terms of highly accurate results, i.e. >95%, as reported in various studies. AI-driven tools are being used in COVID diagnostic, therapeutics, trend prediction, drug design and prevention to help fight against this pandemic. This paper aims to provide a deep insight into the comprehensive literature about AI and AI-driven tools in this battle against the COVID-19 pandemic. The extensive literature is divided into five sections, each describing the application of AI against COVID-19 viz. COVID-19 Prevention, diagnostic, infection spread trend prediction, therapeutic and drug repurposing.