Delays in the diagnosis and treatment of tuberculosis during the COVID-19 outbreak in the Republic of Korea in 2020

Prevalence and associated factors of diabetes mellitus among patients with tuberculosis in South Korea from 2011 to 2018: a nationwide cohort study

OBJECTIVES

This study aimed to identify the prevalence of diabetes mellitus (DM) among patients with tuberculosis (TB) using a nationwide cohort in South Korea.

DESIGN

A retrospective cohort study.

SETTING

This study used the Korean Tuberculosis and Post-Tuberculosis cohort, which was constructed by linking the Korean National Tuberculosis Surveillance, National Health Information Database (NHID) and Statistics Korea data for the causes of death.

PARTICIPANTS

During the study period, all notified patients with TB with at least one claim in the NHID were included. Exclusion criteria were age less than 20 years, drug resistance, initiation of TB treatment before the study period and missing values in covariates.

OUTCOME MEASURES

DM was defined as having at least two claims of the International Classification of Diseases (ICD) code for DM or at least one claim of the ICD code for DM and prescription of any antidiabetic drugs. Newly diagnosed DM (nDM) and previously diagnosed DM (pDM) were defined as DM diagnosed after and before TB diagnosis, respectively.

RESULTS

A total of 26.8% (70 119) of patients were diagnosed with DM. The age-standardised prevalence increased as age increased or income decreased. Patients with DM were more likely to be men, older, had the lowest income group, had more acid-fast bacilli smear and culture positivity, had a higher Charlson Comorbidity Index score and had more comorbidities compared with patients without DM. Approximately 12.5% (8823) patients had nDM and 87.4% (61 296) had pDM among those with TB-DM.

CONCLUSIONS

The prevalence of DM among patients with TB was considerably high in Korea. To achieve the goal of TB control and improve the health outcomes of both TB and DM, integrated screening of TB and DM and care delivery in clinical practice are necessary.

The Impact of the COVID-19 Pandemic on Tuberculosis Case Notification and Treatment Outcomes in Eswatini

Objectives: We investigated the impact of COVID-19 on tuberculosis (TB) case notification and treatment outcomes in Eswatini.

Methods: A comparative retrospective cohort study was conducted using TB data from eight facilities. An interrupted time series analysis, using segmented Poisson regression was done to assess the impact of COVID-19 on TB case notification comparing period before (December 2018-February 2020, n = 1,560) and during the pandemic (March 2020–May 2021, n = 840). Case notification was defined as number of TB cases registered in the TB treatment register. Treatment outcomes was result assigned to patients at the end of treatment according to WHO rules.

Results: There was a significant decrease in TB case notification (IRR 0.71, 95% CI: 0.60–0.83) and a significant increase in death rate among registrants during the pandemic (21.3%) compared to pre-pandemic (10.8%, p < 0.01). Logistic regression indicated higher odds of unfavorable outcomes (death, lost-to-follow-up, and not evaluated) during the pandemic than pre-pandemic (aOR 2.91, 95% CI: 2.17–3.89).

Conclusion: COVID-19 negatively impacted TB services in Eswatini. Eswatini should invest in strategies to safe-guard the health system against similar pandemics.

Real-world association of adherence with outcomes and economic burden in patients with tuberculosis from South Korea claims data

Objectives: We analyzed tuberculosis (TB)-related costs according to treatment adherence, as well as the association between treatment adherence, treatment outcomes, and costs related to drug-susceptible TB in South Korea. Methods: Patients who had newly treated TB in South Korea between 2006 and 2015 were selected from nationwide sample claims data and categorized into adherent and non-adherent groups using the proportion of days TB drugs covered. Patients were followed-up from the initiation of TB treatment. The mean five-year cumulative costs per patient were estimated according to adherence. Moreover, we evaluated the relative ratios to identify cost drivers such as adherence, treatment outcomes, and baseline characteristics using generalized linear models. Four treatment outcomes were included: treatment completion, loss to follow-up, death, and the initiation of multidrug-resistant TB treatment. Results: Out of the 3,799 new patients with TB, 2,662 were adherent, and 1,137 were non-adherent. Five years after initiating TB treatment, the mean TB-related costs were USD 2,270 and USD 2,694 in the adherent and non-adherent groups, respectively. The TB-related monthly cost per patient was also lower in the adherent than in the non-adherent (relative ratio = 0.89, 95% CI 0.92-0.98), while patients who were lost to follow-up spent more on TB-related costs (2.52, 2.24-2.83) compared to those who completed the treatment. Conclusion: Non-adherent patients with TB spend more on treatment costs while they have poorer outcomes compared to adherent patients with TB. Improving patient adherence may lead to effective treatment outcomes and reduce the economic burden of TB. Policymakers and providers should consider commitment programs to improve patient's adherence.

Time trend prediction and spatial-temporal analysis of multidrug-resistant tuberculosis in Guizhou Province, China, during 2014-2020

Background

Guizhou is located in the southwest of China with high multidrug-resistant tuberculosis (MDR-TB) epidemic. To fight this disease, Guizhou provincial authorities have made efforts to establish MDR-TB service system and perform the strategies for active case finding since 2014. The expanded case finding starting from 2019 and COVID-19 pandemic may affect the cases distribution. Thus, this study aims to analyze MDR-TB epidemic status from 2014 to 2020 for the first time in Guizhou in order to guide control strategies.

Methods

Data of notified MDR-TB cases were extracted from the National TB Surveillance System correspond to population information for each county of Guizhou from 2014 to 2020. The percentage change was calculated to quantify the change of cases from 2014 to 2020. Time trend and seasonality of case series were analyzed by a seasonal autoregressive integrated moving average (SARIMA) model. Spatial–temporal distribution at county-level was explored by spatial autocorrelation analysis and spatial–temporal scan statistic.

Results

Guizhou has 9 prefectures and 88 counties. In this study, 1,666 notified MDR-TB cases were included from 2014–2020. The number of cases increased yearly. Between 2014 and 2019, the percentage increase ranged from 6.7 to 21.0%. From 2019 to 2020, the percentage increase was 62.1%. The seasonal trend illustrated that most cases were observed during the autumn with the trough in February. Only in 2020, a peak admission was observed in June. This may be caused by COVID-19 pandemic restrictions being lifted until May 2020. The spatial–temporal heterogeneity revealed that over the years, most MDR-TB cases stably aggregated over four prefectures in the northwest, covering Bijie, Guiyang, Liupanshui and Zunyi. Three prefectures (Anshun, Tongren and Qiandongnan) only exhibited case clusters in 2020.

Conclusion

This study identified the upward trend with seasonality and spatial−temporal clusters of MDR-TB cases in Guizhou from 2014 to 2020. The fast rising of cases and different distribution from the past in 2020 were affected by the expanded case finding from 2019 and COVID-19. The results suggest that control efforts should target at high-risk periods and areas by prioritizing resources allocation to increase cases detection capacity and better access to treatment.

Increased Healthcare Delays in Tuberculosis Patients During the First Wave of COVID-19 Pandemic in Korea: A Nationwide Cross-Sectional Study

BACKGROUND

The coronavirus disease 2019 (COVID-19) pandemic caused disruptions to healthcare systems, consequently endangering tuberculosis (TB) control. We investigated delays in TB treatment among notified patients during the first wave of the COVID-19 pandemic in Korea.

METHODS

We systemically collected and analyzed data from the Korea TB cohort database from January to May 2020. Groups were categorized as 'before-pandemic' and 'during-pandemic' based on TB notification period. Presentation delay was defined as the period between initial onset of symptoms and the first hospital visit, and healthcare delay as the period between the first hospital visit and anti-TB treatment initiation. A multivariate logistic regression analysis was performed to evaluate factors associated with delays in TB treatment.

RESULTS

Proportion of presentation delay > 14 days was not significantly different between two groups (48.3% vs. 43.7%, P = 0.067); however, proportion of healthcare delay > 5 days was significantly higher in the during-pandemic group (48.6% vs. 42.3%, P = 0.012). In multivariate analysis, the during-pandemic group was significantly associated with healthcare delay > 5 days (adjusted odds ratio = 0.884, 95% confidence interval = 0.715-1.094).

CONCLUSION

The COVID-19 pandemic was associated with healthcare delay of > 5 days in Korea. Public health interventions are necessary to minimize the pandemic's impact on the national TB control project.