Continuity of TB services during the COVID-19 pandemic in China

Development, assessment and validation of a novel prediction nomogram model for risk identification of tracheobronchial tuberculosis in patients with pulmonary tuberculosis

OBJECTIVE

Tracheobronchial tuberculosis (TBTB), a specific subtype of pulmonary tuberculosis (PTB), can lead to bronchial stenosis or bronchial occlusion if not identified early. However, there is currently no available means for predicting the risk of associated TBTB in PTB patients. The objective of this study was to establish a risk prediction nomogram model for estimating the associated TBTB risk in every PTB patient.

METHODS

A retrospective cohort study was conducted with 2153 PTB patients. Optimised characteristics were selected using least absolute shrinkage and selection operator regression. Multivariate logistic regression was applied to build a predictive nomogram model. Discrimination, calibration and clinical usefulness of the prediction model were assessed using C-statistics, receiver operator characteristic curves, calibration plots and decision analysis. The developed model was validated both internally and externally.

RESULTS

Among all PTB patients who underwent bronchoscopies (n=2153), 40.36% (n=869) were diagnosed with TBTB. A nomogram model incorporating 11 predictors was developed and displayed good discrimination with a C-statistics of 0.782, a sensitivity of 0.661 and a specificity of 0.762 and good calibration with a calibration-in-the-large of 0.052 and a calibration slope of 0.957. Model's discrimination was favourable in both internal (C-statistics, 0.782) and external (C-statistics, 0.806) validation. External validation showed satisfactory accuracy (sensitivity, 0.690; specificity, 0.804) in independent cohort. Decision curve analysis showed that the model was clinically useful when intervention was decided on at the exacerbation possibility threshold of 2.3%-99.2%. A clinical impact curve demonstrated that our model predicted high-risk estimates and true positives.

CONCLUSION

We developed a novel and convenient risk prediction nomogram model that enhances the risk assessment of associated TBTB in PTB patients. This nomogram can help identify high-risk PTB patients who may benefit from early bronchoscopy and aggressive treatment to prevent disease progression.

Epidemiology and factors associated with Extra-pulmonary tuberculosis in a Low-prevalence area

Background

Tuberculosis is a global public health problem. Extra-pulmonary tuberculosis accounts for an increasing proportion of cases worldwide, although information about epidemiological, clinical, or microbiological factors is lacking.

Methods

We conducted a retrospective observational study of tuberculosis cases diagnosed between 2016 and 2021, classified into Pulmonary and Extra-pulmonary tuberculosis. Univariable and multivariable logistic regression models were used to investigate risk factors of Extra-pulmonary tuberculosis.

Results

20.9% of overall cases were classified as Extra-pulmonary tuberculosis, with a rising trend from 22.6% in 2016 to 27.9% in 2021. Lymphatic tuberculosis accounted for 50.6% of cases, followed by pleural tuberculosis (24.1%). 55.4% of cases belonged to foreign-born patients. Microbiological culture tested positive in 92.8% of Extra-pulmonary cases. Logistic regression analysis showed that women were more predisposed to develop Extra-pulmonary tuberculosis (aOR 2.46, 95% CI 1.45-4.20) as well as elderly patients (aged ≥ 65 years) (aOR 2.47, 95% CI 1.19-5.13) and persons with previous history of tuberculosis (4.99, 95% CI 1.40-17.82).

Conclusions

Extra-pulmonary Tuberculosis have increased within our study period. A profound decline occurred in 2021 tuberculosis cases, probably due to COVID-19. Women, elderly population, and persons with previous history of tuberculosis are at higher risk of developing Extra-pulmonary tuberculosis in our setting.

Impact of COVID-19 pandemic and anti-pandemic measures on tuberculosis, viral hepatitis, HIV/AIDS and malaria-A systematic review

COVID-19 pandemic puts an enormous strain on health care systems worldwide and may have a detrimental effect on prevention, treatment and outcomes of tuberculosis (TB), viral hepatitis, HIV/AIDS and malaria, whose ending is part of the United Nations 2030 Agenda for Sustainable Development. We conducted a systematic review of scientific and grey literature in order to collect wide-ranging evidence with emphasis on quantification of the projected and actual indirect impacts of COVID-19 on the four infectious diseases with a global focus. We followed PRISMA guidelines and the protocol registered for malaria (CRD42021234974). We searched PubMed, Scopus, preView (last search: January 13, 2021) and websites of main (medical) societies and leading NGOs related to each of the four considered infectious diseases. From modelling studies, we identified the most impactful disruptions; from surveys and other quantitative studies (based e.g. on surveillance or program data), we assessed the actual size of the disruptions. The identified modelling studies warned about under-diagnosis (TB), anti-retroviral therapy interruption/decrease in viral load suppression (HIV), disruptions of insecticide-treated nets (ITN) distribution and access to effective treatment (malaria), and treatment delays and vaccination interruptions (viral hepatitis). The reported disruptions were very heterogeneous both between and within countries. If observed at several points in time, the initial drops (partly dramatic, e.g. TB notifications/cases, or HIV testing volumes decreased up to -80%) were followed by a gradual recovery. However, the often-missing assessment of the changes against the usual pre-pandemic fluctuations hampered the interpretation of less severe ones. Given the recurring waves of the pandemic and the unknown mid- to long-term effects of adaptation and normalisation, the real consequences for the fight against leading infectious diseases will only manifest over the coming years.

The intersecting pandemics of tuberculosis and COVID-19: population-level and patient-level impact, clinical presentation, and corrective interventions

The global tuberculosis burden remains substantial, with more than 10 million people newly ill per year. Nevertheless, tuberculosis incidence has slowly declined over the past decade, and mortality has decreased by almost a third in tandem. This positive trend was abruptly reversed by the COVID-19 pandemic, which in many parts of the world has resulted in a substantial reduction in tuberculosis testing and case notifications, with an associated increase in mortality, taking global tuberculosis control back by roughly 10 years. Here, we consider points of intersection between the tuberculosis and COVID-19 pandemics, identifying wide-ranging approaches that could be taken to reverse the devastating effects of COVID-19 on tuberculosis control. We review the impact of COVID-19 at the population level on tuberculosis case detection, morbidity and mortality, and the patient-level impact, including susceptibility to disease, clinical presentation, diagnosis, management, and prognosis. We propose strategies to reverse or mitigate the deleterious effects of COVID-19 and restore tuberculosis services. Finally, we highlight research priorities and major challenges and controversies that need to be addressed to restore and advance the global response to tuberculosis.

COVID-19-related healthcare impacts: an uncontrolled, segmented time-series analysis of tuberculosis diagnosis services in Mozambique, 2017-2020

Introduction

Currently, COVID-19 dominates the public health agenda and poses a permanent threat, leading to health systems’ exhaustion and unprecedented service disruption. Primary healthcare services, including tuberculosis services, are at increased risk of facing severe disruptions, particularly in low-income and middle-income countries. Indeed, corroborating model-based forecasts, there is increasing evidence of the COVID-19 pandemic’s negative impact on tuberculosis case detection.

Methods

Applying a segmented time-series analysis, we assessed the effects of COVID-19-related measures on tuberculosis diagnosis service across districts in Mozambique. Ministry health information system data were used from the first quarter of 2017 to the end of 2020. The model, performed under the Bayesian premises, was estimated as a negative binomial with random effects for districts and provinces.

Results

A total of 154 districts were followed for 16 consecutive quarters. Together, these districts reported 96 182 cases of all forms of tuberculosis in 2020. At baseline (first quarter of 2017), Mozambique had an estimated incidence rate of 283 (95% CI 200 to 406) tuberculosis cases per 100 000 people and this increased at a 5% annual rate through the end of 2019. We estimated that 17 147 new tuberculosis cases were potentially missed 9 months after COVID-19 onset, resulting in a 15.1% (95% CI 5.9 to 24.0) relative loss in 2020. The greatest impact was observed in the southern region at 40.0% (95% CI 30.1 to 49.0) and among men at 15% (95% CI 4.0 to 25.0). The incidence of pulmonary tuberculosis increased at an average rate of 6.6% annually; however, an abrupt drop (15%) was also observed immediately after COVID-19 onset in March 2020.

Conclusion

The most significant impact of the state of emergency was observed between April and June 2020, the quarter after COVID-19 onset. Encouragingly, by the end of 2020, clear signs of health system recovery were visible despite the initial shock.

Research capacity training for surveillance and response in the Indo-Pacific: a case study of implementation

SETTING

Surveillance and response workforce in the Indo-Pacific region, including Papua New Guinea (PNG), Solomon Islands, Fiji, Eastern Indonesia and Timor-Leste.

OBJECTIVE

To evaluate the implementation of a modified WHO SORT IT research training programme which included a workplace-based research project. The training was designed for surveillance and response frontline workforce in the Indo-Pacific region.

DESIGN

This was a programme evaluation using mixed methods. Fifty-three health and biosecurity workers from Fiji, Indonesia, PNG, Solomon Islands and Timor-Leste participated in the research training programme.

RESULTS

Implementation of the programme was modified to reflect the context of participant countries. Work-place research projects focused on priority issues identified by local policy makers and in-country stakeholders. Self-reported research skills showed a significant increase (P < 0.01) after the completion of training. Participants reported high scores for satisfaction with training.

CONCLUSIONS

This case study provides lessons learnt for future research training, and demonstrates that the SORT IT model can be modified to reflect the context of implementation without compromising purpose or outcomes.

The impact of COVID-19 on TB: a review of the data

Early in the COVID-19 pandemic, models predicted hundreds of thousands of additional TB deaths as a result of health service disruption. To date, empirical evidence on the effects of COVID-19 on TB outcomes has been limited. Here we summarise the evidence available at a country level, identifying broad mechanisms by which COVID-19 may modify TB burden and mitigation efforts. From the data, it is clear that there have been substantial disruptions to TB health services and an increase in vulnerability to TB. Evidence for changes in Mycobacterium tuberculosis transmission is limited, and it remains unclear how the resources required and available for the TB response have changed. To advocate for additional funding to mitigate the impact of COVID-19 on the global TB burden, and to efficiently allocate resources for the TB response, requires a significant improvement in the TB data available.