The potential impact of COVID-19-related disruption on tuberculosis burden.
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ABSTRACT:
SUBMITTER: McQuaid CF
PROVIDER: S-EPMC7278504 | biostudies-literature | 2020 Aug
REPOSITORIES: biostudies-literature
Publications
The potential impact of COVID-19-related disruption on tuberculosis burden.
McQuaid C Finn CF McCreesh Nicky N Read Jonathan M JM Sumner Tom T Houben Rein M G J RMGJ White Richard G RG Harris Rebecca C RC
The European respiratory journal 20200813 2
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Project description:BackgroundThe ongoing COVID-19 pandemic has greatly disrupted our everyday life, forcing the adoption of non-pharmaceutical interventions in many countries and putting public health services and healthcare systems worldwide under stress. These circumstances are leading to unintended effects such as the increase in the burden of other diseases.MethodsHere, using a data-driven epidemiological model for tuberculosis (TB) spreading, we describe the expected rise in TB incidence and mortality if COVID-associated changes in TB notification are sustained and attributable entirely to disrupted diagnosis and treatment adherence.ResultsOur calculations show that the reduction in diagnosis of new TB cases due to the COVID-19 pandemic could result in 228k (CI 187-276) excess deaths in India, 111k (CI 93-134) in Indonesia, 27k (CI 21-33) in Pakistan, and 12k (CI 9-18) in Kenya.ConclusionsWe show that it is possible to reverse these excess deaths by increasing the pre-covid diagnosis capabilities from 15 to 50% for 2 to 4 years. This would prevent almost all TB-related excess mortality that could be caused by the COVID-19 pandemic if no additional preventative measures are introduced. Our work therefore provides guidelines for mitigating the impact of COVID-19 on tuberculosis epidemic in the years to come.
| S-EPMC9243113 | biostudies-literature
Project description:Apart from the incidence and mortality caused by it, Coronavirus disease (COVID-19) has had a significant impact on other diseases. This study aimed to estimate the influences of COVID-19 pandemic on the incidence of tuberculosis (TB) and the number of TB-associated deaths in Republic of Korea. A dynamic compartment model incorporating age-structure was developed for studying TB transmission and progression using the Korean population data. After calibration with notification of incidence data from South Korea, the TB burden over 6 years (2020-2025) was predicted under the nine different scenarios. Under the scenario of strong social distancing and low-level health service disruption, new TB cases were reduced by 761 after 1 year in comparison to the baseline. However, in the elderly population, social distancing had little impact on TB incidence. On the other hand, the number of TB-related deaths mainly depends on the level of health service disruption for TB care. It was predicted that with a high degree of health service disruption, the number of TB-related deaths would increase up to 155 in 1 year and 80 percent of the TB-related deaths would be in the elderly population. The decrease of tuberculosis incidence is significantly affected by social distancing, which is owing to reduction of contacts. The impact of health service disruption is dominant on TB-related deaths, which occurs mainly in the elderly. It suggests that it is important to monitor TB-related deaths by COVID-19 because the TB burden of the elderly is high in the Republic of Korea.
| S-EPMC9374296 | biostudies-literature
Project description:BackgroundThe COVID-19 pandemic disrupted tuberculosis (TB) health services, including treatment support and access to drugs, as patients were not able to access health facilities. While the effect of this disruption on treatment outcomes has been studied in isolated treatment centres, cities and provinces, the impact of the pandemic on TB treatment outcomes at a country and regional level has not been evaluated.MethodsWe used treatment outcomes for new and relapse TB cases reported to the World Health Organization (WHO) from 49 high TB, TB/HIV and drug-resistant TB burden countries from 2012 to 2019. We developed multinomial logistic regression models for trends in TB treatment success, failure, death and loss to follow up. We predicted TB treatment outcomes for 2020 and 2021, comparing these to observations, by computing ratios between observed and predicted probabilities. We aggregated these risk ratios (RR) for six WHO-defined regions using random-effects meta-analysis.ResultsAcross 49 countries and four TB treatment outcomes, 17 (out of 196) country-outcome pairs in 2020 and 21 in 2021 had evidence of systematic differences between observed and predicted TB treatment outcome probabilities. Regionally, only four (out of 24) region-outcome pairs had evidence of systematic differences in 2020 and four in 2021, where the European region accounted for four of these in total. Globally, there was evidence of systematic differences in treatment failure in both 2020 (RR: 1.14, 95%CI: 1.01-1.28, p = 0.0381) and 2021 (RR: 1.36, 95%CI: 1.03-1.78, p = 0.0277), deaths in 2020 (RR: 1.08, 95%CI: 1.03-1.13, p = 0.0010) and losses to follow up in 2020 (RR: 0.91, 95%CI: 0.86-0.97, p = 0.0059).ConclusionsWhile for some countries and regions there were significant differences between observed and predicted treatment outcomes probabilities, there was insufficient evidence globally to identify systematic differences between observed and expected TB treatment outcome probabilities because of COVID-19-associated disruptions in general. However, larger numbers of treatment failures and deaths on treatment than expected were observed globally, suggesting a need for further investigation.
| S-EPMC11288071 | biostudies-literature
Project description:During COVID-19 pandemic, a lot of diseases suffered from a limited access to health care services, owing to the use of resources, both technical and financial, mainly directed towards such a dramatic outbreak. Among these, tuberculosis (TB) has been one of the most penalized, with a huge delay both in diagnosis and in start of treatment, with a consequential dramatic increase in morbidity and mortality. COVID-19 and tuberculosis share similar common pathogenetic pathways, and both diseases affect primarily the lungs. About the impact of TB on COVID-19 severity and mortality, data are unclear and literature reports are often conflicting. Certainly, considering the management of coinfected patients, there are pharmacokinetic interactions between several drugs used for the therapy of SARS-CoV-2 infection and the treatment of TB.
| S-EPMC9715002 | biostudies-literature
Project description:BackgroundTuberculosis (TB) is a major cause of death globally. India carries the highest share of the global TB burden. The COVID-19 pandemic has severely impacted diagnosis of TB in India, yet there is limited data on how TB case reporting has changed since the pandemic began and which factors determine differences in case notification.MethodsWe utilized publicly available data on TB case reporting through the Indian Central TB Division from January 2017 through April of 2021 (prior to the first COVID-19 related lockdown). Using a Poisson model, we estimated seasonal and yearly patterns in TB case notification in India from January 2017 through February 2020 and extended this estimate as the counterfactual expected TB cases notified from March 2020 through April 2021. We characterized the differences in case notification observed and those expected in the absence of the pandemic by State and Territory. We then performed a linear regression to examine the relationship between the logit ratio of reported TB to counterfactual cases and mask use, mobility, daily hospitalizations/100,000 population, and public/total TB case reporting.ResultsWe found 1,320,203 expected cases of TB (95% uncertainty interval (UI) 1,309,612 to 1,330,693) were not reported during the period from March 2020 through April 2021. This represents a 63.3% difference (95% UI 62.8 to 63.8) in reporting. We found that mobility data and average hospital admissions per month per population were correlated with differences in TB case notification, compared to the counterfactual in the absence of the pandemic (p > 0.001).ConclusionThere was a large difference between reported TB cases in India and those expected in the absence of the pandemic. This information can help inform the Indian TB program as they consider interventions to accelerate case finding and notification once the pandemic related TB service disruptions improve. Mobility data and hospital admissions are surrogate measures that correlate with a greater difference in reported/expected TB cases and may correlate with a disruption in TB diagnostic services. However, further research is needed to clarify this association and identify other key contributors to gaps in TB case notifications in India.
| S-EPMC8792515 | biostudies-literature
Project description:BackgroundRoutine services for tuberculosis (TB) are being disrupted by stringent lockdowns against the novel SARS-CoV-2 virus. We sought to estimate the potential long-term epidemiological impact of such disruptions on TB burden in high-burden countries, and how this negative impact could be mitigated.MethodsWe adapted mathematical models of TB transmission in three high-burden countries (India, Kenya and Ukraine) to incorporate lockdown-associated disruptions in the TB care cascade. The anticipated level of disruption reflected consensus from a rapid expert consultation. We modelled the impact of these disruptions on TB incidence and mortality over the next five years, and also considered potential interventions to curtail this impact.FindingsEven temporary disruptions can cause long-term increases in TB incidence and mortality. If lockdown-related disruptions cause a temporary 50% reduction in TB transmission, we estimated that a 3-month suspension of TB services, followed by 10 months to restore to normal, would cause, over the next 5 years, an additional 1⋅19 million TB cases (Crl 1⋅06-1⋅33) and 361,000 TB deaths (CrI 333-394 thousand) in India, 24,700 (16,100-44,700) TB cases and 12,500 deaths (8.8-17.8 thousand) in Kenya, and 4,350 (826-6,540) cases and 1,340 deaths (815-1,980) in Ukraine. The principal driver of these adverse impacts is the accumulation of undetected TB during a lockdown. We demonstrate how long term increases in TB burden could be averted in the short term through supplementary "catch-up" TB case detection and treatment, once restrictions are eased.InterpretationLockdown-related disruptions can cause long-lasting increases in TB burden, but these negative effects can be mitigated with rapid restoration of TB services, and targeted interventions that are implemented as soon as restrictions are lifted.FundingUSAID and Stop TB Partnership.
| S-EPMC7584493 | biostudies-literature
Project description:The COVID-19 pandemic and public health "lockdown" responses in sub-Saharan Africa, including Uganda, are now widely reported. Although the impact of COVID-19 on African populations has been relatively light, it is feared that redirecting focus and prioritization of health systems to fight COVID-19 may have an impact on access to non-COVID-19 diseases. We applied age-based COVID-19 mortality data from China to the population structures of Uganda and non-African countries with previously established outbreaks, comparing theoretical mortality and disability-adjusted life years (DALYs) lost. We then predicted the impact of possible scenarios of the COVID-19 public health response on morbidity and mortality for HIV/AIDS, malaria, and maternal health in Uganda. Based on population age structure alone, Uganda is predicted to have a relatively low COVID-19 burden compared with an equivalent transmission in comparison countries, with 12% of the mortality and 19% of the lost DALYs predicted for an equivalent transmission in Italy. By contrast, scenarios of the impact of the public health response on malaria and HIV/AIDS predict additional disease burdens outweighing that predicted from extensive SARS-CoV-2 transmission. Emerging disease data from Uganda suggest that such deterioration may already be occurring. The results predict a relatively low COVID-19 impact on Uganda associated with its young population, with a high risk of negative impact on non-COVID-19 disease burden from a prolonged lockdown response. This may reverse hard-won gains in addressing fundamental vulnerabilities in women and children's health, and underlines the importance of tailoring COVID-19 responses according to population structure and local disease vulnerabilities.
| S-EPMC7470592 | biostudies-literature
Project description:On 11 March 2020, the World Health Organization announced the Corona Virus Disease-2019 (COVID-19) as a global pandemic, which originated in China. At the host level, COVID-19, caused by the Severe Acute Respiratory Syndrome Coronavirus-2 (SARS-CoV-2), affects the respiratory system, with the clinical symptoms ranging from mild to severe or critical illness that often requires hospitalization and oxygen support. There is no specific therapy for COVID-19, as is the case for any common viral disease except drugs to reduce the viral load and alleviate the inflammatory symptoms. Tuberculosis (TB), an infectious disease caused by Mycobacterium tuberculosis (Mtb), also primarily affects the lungs and has clinical signs similar to pulmonary SARS-CoV-2 infection. Active TB is a leading killer among infectious diseases and adds to the burden of the COVID-19 pandemic worldwide. In immunocompetent individuals, primary Mtb infection can also lead to a non-progressive, asymptomatic latency. However, latent Mtb infection (LTBI) can reactivate symptomatic TB disease upon host immune-suppressing conditions. Importantly, the diagnosis and treatment of TB are hampered and admixed with COVID-19 control measures. The US-Center for Disease Control (US-CDC) recommends using antiviral drugs, Remdesivir or corticosteroid (CST), such as dexamethasone either alone or in-combination with specific recommendations for COVID-19 patients requiring hospitalization or oxygen support. However, CSTs can cause immunosuppression, besides their anti-inflammatory properties. The altered host immunity during COVID-19, combined with CST therapy, poses a significant risk for new secondary infections and/or reactivation of existing quiescent infections, such as LTBI. This review highlights CST therapy recommendations for COVID-19, various types and mechanisms of action of CSTs, the deadly combination of two respiratory infectious diseases COVID-19 and TB. It also discusses the importance of screening for LTBI to prevent TB reactivation during corticosteroid therapy for COVID-19.
| S-EPMC8038708 | biostudies-literature
Project description: Not available
| S-EPMC8809899 | biostudies-literature
Project description:BackgroundDespite extensive knowledge of tuberculosis (TB) and its control, there remains a significant gap in understanding the comprehensive impact of the COVID-19 pandemic on TB incidence patterns. This study aims to explore the impact of COVID-19 on the pattern of pulmonary tuberculosis in China and examine the application of time series models in the analysis of these patterns, providing valuable insights for TB prevention and control.MethodsWe used pre-COVID-19 pulmonary tuberculosis (PTB) data (2007-2018) to fit SARIMA, Prophet, and LSTM models, assessing their ability to predict PTB incidence trends. These models were then applied to compare the predicted PTB incidence patterns with actual reported cases during the COVID-19 pandemic (2020-2023), using deviations between predicted and actual values to reflect the impact of COVID-19 countermeasures on PTB incidence.ResultsPrior to the COVID-19 outbreak, PTB incidence in China exhibited a steady decline with strong seasonal fluctuations, characterized by two annual peaks-one in March and another in December. These seasonal trends persisted until 2019. During the COVID-19 pandemic, there was a significant reduction in PTB cases, with actual reported cases falling below the predicted values. The disruption in PTB incidence appears to be temporary, as 2023 data indicate a gradual return to pre-pandemic trends, though the incidence rate remains slightly lower than pre-COVID levels. Additionally, we compared the fitting and forecasting performance of the SARIMA, Prophet, and LSTM models using RMSE (root mean squared error), MAE (mean absolute error), and MAPE (mean absolute percentage error) indexes prior to the COVID-19 outbreak. We found that the Prophet model had the lowest values for all three indexes, demonstrating the best fitting and prediction performance.ConclusionsThe COVID-19 pandemic has had a temporary but significant impact on PTB incidence in China, leading to a reduction in reported cases during the pandemic. However, as pandemic control measures relax and the healthcare system stabilizes, PTB incidence patterns are expected to return to pre-COVID-19 levels. The Prophet model demonstrated the best predictive performance and proves to be a valuable tool for analyzing PTB trends and guiding public health planning in the post-pandemic era.
| S-EPMC11648691 | biostudies-literature