COVID-19, the escalation of diabetes mellitus and the repercussions on tuberculosis

Interaction between type 2 diabetes and past COVID-19 on active tuberculosis

BACKGROUND

The global setback in tuberculosis (TB) prevalence and mortality in the post-COVID-19 era has been partially attributed to pandemic-related disruptions in healthcare systems. The additional biological contribution of COVID-19 to TB is less clear. The goal of this study was to determine if there is an association between COVID-19 in the past 18 months and a new TB episode, and the role played by type 2 diabetes mellitus (DM) comorbidity in this relationship.

METHODS

A cross-sectional study was conducted among 112 new active TB patients and 373 non-TB controls, identified between June 2020 and November 2021 in communities along the Mexican border with Texas. Past COVID-19 was based on self-report or positive serology. Bivariable/multivariable analysis were used to evaluate the odds of new TB in hosts with past COVID-19 and/or DM status.

RESULTS

The odds of new TB were higher among past COVID-19 cases vs. controls, but only significant among DM patients (aOR 2.3). The odds of TB in people with DM was 2.7-fold higher among participants without past COVID-19 and increased to 7.9-fold among those with past COVID-19.

CONCLUSION

DM interacts with past COVID-19 synergistically to magnify the risk of TB. Latent TB screening and prophylactic treatment, if positive, is recommended in past COVID-19 persons with DM. Future studies are warranted with a longitudinal design and larger sample size to confirm our findings.

Interaction between type 2 diabetes and past COVID-19 on active tuberculosis

BACKGROUND

The global setback in tuberculosis (TB) prevalence and mortality in the post-COVID-19 era have been partially attributed to pandemic-related disruptions in healthcare systems. The additional biological contribution of COVID-19 to TB is less clear. The goal of this study was to determine if there is an association between COVID-19 in the past 18 months and a new TB episode, and the role played by type 2 diabetes mellitus (DM) comorbidity in this relationship.

METHODS

A cross-sectional study was conducted among 112 new active TB patients and 373 non-TB controls, identified between June 2020 and November 2021 in communities along the Mexican border with Texas. Past COVID-19 was based on self-report or positive serology. Bivariable/multivariable analysis were used to evaluate the odds of new TB in hosts with past COVID-19 and/or DM status.

RESULTS

The odds of new TB were higher among past COVID-19 cases vs. controls, but only significant among DM patients (aOR 2.3). The odds of TB given DM was 2.7-fold among participants without past COVID-19 and increased to 7.9-fold among those with past COVID-19.

CONCLUSION

DM interacts with past COVID-19 synergistically to magnify the risk of TB. Latent TB screening and prophylactic treatment, if positive, is recommended in this COVID-19/DM/latent TB high-risk group.

COVID-19 pandemic and other factors associated with unfavorable tuberculosis treatment outcomes-Almaty, Kazakhstan, 2018-2021

Introduction

The COVID-19 pandemic negatively influenced the availability of tuberculosis (TB) services, such as detection, diagnosis and treatment, around the world, including Kazakhstan. We set out to estimate the COVID-19 pandemic influence on TB treatment outcomes by comparing outcomes among people starting treatment before the pandemic (2018-2019) and during the pandemic (2020-2021) and to determine risk factors associated with unfavorable outcomes.

Methods

We conducted a retrospective cohort study among all people newly diagnosed with drug-sensitive pulmonary or extrapulmonary TB at least 18 years old who initiated treatment from 2018 to 2021 in Almaty. We abstracted data from the national electronic TB register. Unfavorable treatment outcomes were ineffective treatment, death, loss to follow-up, results not evaluated, and transferred. We used multivariable Poisson regression to calculate adjusted relative risk (aRR) and 95% confidence intervals (95%CI).

Results

Among 1548 people newly diagnosed with TB during the study period, average age was 43 years (range 18-93) and 52% were male. The number of people initiating treatment was higher before than the pandemic (935 vs. 613, respectively). There was significantly different proportions before compared to during the pandemic for people diagnosed through routine screening (39% vs. 31%, p < 0.001), 60 years and older (16% vs. 22%, p = 0.005), and with diabetes (5% vs. 8%, p = 0.017). There was no difference in the proportion of HIV (8% in both periods). Unfavorable outcomes increased from 11 to 20% during the pandemic (aRR = 1.83; 95% CI: 1.44-2.31). Case fatality rose from 6 to 9% (p = 0.038). Risk factors for unfavorable TB treatment outcomes among all participants were being male (aRR = 1.44, 95%CI = 1.12-1.85), having HIV (aRR = 2.72, 95%CI = 1.99-3.72), having alcohol use disorder (aRR = 2.58, 95%CI = 1.83-3.62) and experiencing homelessness (aRR = 2.94, 95%CI = 1.80-4.80). Protective factors were being 18-39 years old (aRR = 0.33, 95%CI = 0.24-0.44) and 40-59 years old (aRR = 0.56, 95%CI = 0.41-0.75) compared to 60 years old and up.

Conclusion

COVID-19 pandemic was associated with unfavorable treatment outcomes for people newly diagnosed with drug-sensitive TB in Almaty, Kazakhstan. People with fewer comorbidities were at increased risk. Results point to the need to maintain continuity of care for persons on TB treatment, especially those at higher risk for poor outcomes during periods of healthcare service disruption.

World Tuberculosis Day 2023 theme "Yes! We Can End TB!"

Intro

Viruses, including SARS-CoV-2, which causes COVID-19, are constantly changing. These genetic changes (aka mutations) occur over time and can lead to the emergence of new variants that may have different characteristics. After the first SARS-CoV-2 genome was published in early 2020, scientists all over the world soon realized the immediate need to obtain as much genetic information from as many strains as possible. However, understanding the functional significance of the mutations harbored by a variant is important to assess its impact on transmissibility, disease severity, immune escape, and the effectiveness of vaccines and therapeutics.

Methods

Here in Canada, we have developed an interactive framework for visualizing and reporting mutations in SARS-CoV-2 variants. This framework is composed of three stand-alone yet connected components; an interactive visualization (COVID-MVP), a manually curated functional annotation database (pokay), and a genomic analysis workflow (nf-ncov-voc). Findings: COVID-MVP provides (i) an interactive heatmap to visualize and compare mutations in SARS-CoV-2 lineages classified across different VOCs, VOIs, and VUMs; (ii) mutation profiles including the type, impact, and contextual information; (iii) annotation of biological impacts for mutations where functional data is available in the literature; (iv) summarized information for each variant and/or lineage in the form of a surveillance report; and (v) the ability to upload raw genomic sequence(s) for rapid processing and annotating for real-time classification.

Discussion

This comprehensive comparison allows microbiologists and public health practitioners to better predict how the mutations in emerging variants will impact factors such as infection severity, vaccine resistance, hospitalization rates, etc.

Conclusion

This framework is cloud-compatible & standalone, which makes it easier to integrate into other genomic surveillance tools as well. COVID-MVP is integrated into the Canadian VirusSeq data portal (https://virusseqdataportal.ca ) - a national data hub for SARS-COV-2 genomic data. COVID-MVP is also used by the CanCOGeN and CoVaRR networks in national COVID-19 genomic surveillance.