COVID-19 and tuberculosis coinfection: An overview of case reports/case series and meta-analysis of prevalence studies

Daneshvar, Parham; Hajikhani, Bahareh; Sameni, Fatemeh; Noorisepehr, Negin; Zare, Fereshteh; Bostanshirin, Nazila; Yazdani, Shahrooz; Goudarzi, Mehdi; Sayyari, Saba; Dadashi, Masoud

doi:10.1016/j.heliyon.2023.e13637

Cited by 18 publications

(15 citation statements)

References 69 publications

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“…Recent reports showed that the prevalence of G1 infection (past or current) was about 1.1%% in COVID-19 patients [17]. Our results included the following: likelihood ratio in the CNM model is 311; the post-test probability is 77.3% in a prevalence of 1.1% of hospitalized patients with underlying diseases having the impact on the outcome of the COVID-19.…”

Section: Strengthmentioning

confidence: 65%

Predictors of active pulmonary tuberculosis among hospitalized patients with atypical symptom and sign and underlying diseases having impact on the outcome of the COVID-19

Chen,

Kuo,

Huang

et al. 2024

Preprint

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Background This study aimed to focus on the diagnostic use of high-resolution computed tomography (HRCT) to identify active pulmonary tuberculosis (aPTB) with atypical symptom and sign among the hospitalized patients with the underlying diseases having the impact on the outcome of the Coronavirus disease 2019 (COVID-19). Methods Within the study period (2018.01.01-2021.12.31), for patients with underlying diseases having the impact on the outcome of the COVID-19, chest –x-ray (CXR) / HRCT scans along with their patients’ charts were reviewed. These patients (n = 4,380) were classified into the [aPTB] group I (G1, n = 277) and pulmonary disease without aPTB (G2, n = 4103). Lung morphology, and lobar (segmental) distribution using CXR/HRCT, the underlying diseases and clinical symptom/sign were analyzed. To identify independent variables associated with G1, multivariate analysis was performed. Independent variables were used to generate prediction scores, which were used to develop models for predicting G1. Results For the HRCT model, multivariate analysis revealed cavitation, clusters nodules/mass (CNM) of the right/left upper lobe or ground-glass opacity were useful predictors for the G1. The negative predictive value of the HRCT model, and the CNM model for the GI were 99.3%, and 97.5%, respectively. However, the CNM model has the highest positive predictive value of 95.4%. Conclusions The CNM model may play an auxiliary role for the identification of G1 with atypical symptom and sign among the patients with underlying diseases having the impact on the outcome of the COVID-19.

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Section: Strengthmentioning

confidence: 65%

Predictors of active pulmonary tuberculosis among hospitalized patients with atypical symptom and sign and underlying diseases having impact on the outcome of the COVID-19

Chen,

Kuo,

Huang

et al. 2024

Preprint

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“…Mechanisms may include increased lung damage in TB patients with COVID-19, resulting in impaired lung function ( 202 ) or higher risk of TB reactivation after COVID-19 infection due to depletion of CD4 T cells and excessive lung fibrosis. Worse outcomes of co-infection may also be because of shared clinical, immunological, and social determinants ( 203 – 206 ), as well as compromised linkage to care for HIV and TB in a pandemic environment ( 207 ). In our own South Africa based cohort of SARS-CoV-2 infected individuals, we did not observe a clear enrichment of active TB disease ( 208 ) relative to the observed incidence in the South African population ( 209 ).…”

Section: Mtb and Sars-cov-2 Co-infectionmentioning

confidence: 99%

Tuberculosis and COVID-19 in the elderly: factors driving a higher burden of disease

Allué-Guardia,

Torrelles,

Sigal

2023

Front. Immunol.

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Mycobacterium tuberculosis (M.tb) and SARS-CoV-2 are both infections that can lead to severe disease in the lower lung. However, these two infections are caused by very different pathogens (Mycobacterium vs. virus), they have different mechanisms of pathogenesis and immune response, and differ in how long the infection lasts. Despite the differences, SARS-CoV-2 and M.tb share a common feature, which is also frequently observed in other respiratory infections: the burden of disease in the elderly is greater. Here, we discuss possible reasons for the higher burden in older adults, including the effect of co-morbidities, deterioration of the lung environment, auto-immunity, and a reduced antibody response. While the answer is likely to be multifactorial, understanding the main drivers across different infections may allow us to design broader interventions that increase the health-span of older people.

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“…Studies from Turkey described the clinical features of 16 patients with co‐infection, highlighting the need for careful management and monitoring of these individuals (Gül et al., 2022 ). However, the overall prevalence of TB and COVID‐19 co‐infection appears to be relatively low (Daneshvar et al., 2023 ). Impact on TB mortality and adverse outcomes: the association between TB and COVID‐19 mortality/adverse outcomes is still not fully understood.…”

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Spatio‐Temporal Pattern of Tuberculosis Distribution in Romania and Particulate Matter Pollution Associated With Risk of Infection

Peptenatu,

Băloi,

Andronic

et al. 2024

GeoHealth

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The study proposes a dynamic spatio‐temporal profile of the distribution of tuberculosis incidence and air pollution in Romania, where this infectious disease induces more than 8,000 new cases annually. The descriptive analysis for the years 2012–2021 assumes an identification of the structuring patterns of mycobacterium tuberculosis risk in the Romanian population, according to gender and age, exploiting spatial modeling techniques of time series data. Through spatial autocorrelation, the degree of similarity between the analyzed territorial systems was highlighted and the relationships that are built between the analysis units in spatial proximity were investigated. By modeling the geographical distribution of tuberculosis, the spatial correlation with particulate matter (PM2.5) pollution was revealed. The identification of clusters of infected persons is an indispensable step in the construction of efficient tuberculosis management systems. The results highlight the link between the distribution of tuberculosis, air pollution and socio‐economic development, which requires a detailed analysis of the epidemiological data obtained in the national tuberculosis surveillance and control program from the perspective of geographical distribution.

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COVID-19 and tuberculosis coinfection: An overview of case reports/case series and meta-analysis of prevalence studies

Cited by 18 publications

References 69 publications

Predictors of active pulmonary tuberculosis among hospitalized patients with atypical symptom and sign and underlying diseases having impact on the outcome of the COVID-19

Predictors of active pulmonary tuberculosis among hospitalized patients with atypical symptom and sign and underlying diseases having impact on the outcome of the COVID-19

Tuberculosis and COVID-19 in the elderly: factors driving a higher burden of disease

Spatio‐Temporal Pattern of Tuberculosis Distribution in Romania and Particulate Matter Pollution Associated With Risk of Infection

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