Gap to End-TB targets in eastern China: A joinpoint analysis from population-based notification data in Zhejiang Province, China, 2005-2018

Trends in the Notification Rates and Treatment Outcome of Tuberculosis in Shandong Province, China, 2005-2021

Purpose

To analyze the time trends in the notification rates of registered tuberculosis (TB) and bacteriologically confirmed TB in Shandong Province. And analyze the changes in TB treatment outcomes during 2005-2021.

Patients and Methods

The information of TB patients registered in the Shandong Information Center for Disease Control and Prevention (CDC) was collected during 2005-2021. We calculated the notification rates of registered TB and bacteriologically confirmed TB. Moreover, we calculated the year-to-year change rate of TB in treatment outcomes before and after COVID-19. The time trends were analyzed using the joinpoint regression method and illustrated as the annual percentage change (APC) of notification rates.

Results

A total of 236,898 cases of TB were diagnosed during 2005-2021, of which 51.11% were bacteriologically confirmed cases. Since 2008, the notification rates of registered TB have declined. The notification rates of bacteriologically confirmed TB had been declining during 2005-2016, then remained stable after 2016. In subgroup, the notification rates of both registered TB and bacteriologically confirmed TB were higher among men, rural residents, and people aged ≥ 60 years. Compared with clinically confirmed TB, bacteriologically confirmed TB has shown higher rates of poor outcomes since 2008 and higher case fatality rate since 2005. The rate of poor outcomes remained stable during 2008-2019. However, after the COVID-19 outbreak, the rate of poor outcomes and case fatality rate of TB has risen significantly.

Conclusion

After unremitting efforts to fight against TB, the notification rates of registered TB and bacteriologically confirmed TB declined in Shandong Province. The rate of poor outcomes remained stable during 2008-2019, then rise significantly after the COVID-19 outbreak. In the context of the long-term existence of COVID-19, further efforts should be made in TB diagnosis and treatment among high-risk population, especially with regard to males, rural residents and older adults.

Active screening for tuberculosis among high-risk populations in high-burden areas in Zhejiang province, China

OBJECTIVES

Tuberculosis (TB) is a major global public health concern. Although the incidence of TB in China is declining, the country continues to face many challenges regarding TB control. This study aimed to develop an active case finding (ACF) strategy for high-risk populations in areas with high TB burden and evaluate the effectiveness of the ACF strategy for early TB detection in patients to reduce TB transmission.

STUDY DESIGN

This was a descriptive study.

METHODS

From May to October 2019, active TB screening was conducted in Zhejiang Province, China. Overall, 24 high-burden townships were chosen as study sites. Residents aged ≥65 years, suffering from diabetes, diagnosed with HIV/AIDS, or with a history of TB were mobilized for screening. Chest radiography was performed for all participants in the community. Sputum specimens were collected for sputum smear tests and cultures at county-level TB-designed hospitals. A professional medical team performed the final diagnoses.

RESULTS

Overall, 130,643 residents were included, accounting for 8.85% of the total population in the selected areas. After screening, 89 confirmed cases and 419 suspected cases were identified. The detection rates for suspected and confirmed cases were 320.72/100,000 and 68.12/100,000, respectively. Individuals with a history of TB accounted for a large proportion of detected cases, and the detection rate was higher among males than in females. This study identified 10.5% of reported cases in the selected areas in 2019. In Zhejiang province, compared with the previous year, the rates of TB notification in 2019 and 2020 declined by 7.0% and 7.4%, respectively, compared with the previous year. However, the TB notification rate in 2019 was almost the same as that in 2018 (a decline of 2.5%) but sharply declined in 2020 (14.4%) in the screened areas.

CONCLUSIONS

Our findings suggest that the ACF strategy may have helped to maintain the downward trends in TB notification rates by detecting patients with TB and suspected cases in the short term.

The effect of diabetes mellitus on tuberculosis in eastern China: A decision-tree analysis based on a real-world study

OBJECTIVES

The public health system faces major challenges due to the double burden of diabetes mellitus (DM) and tuberculosis (TB) in China. We aimed to investigate the prevalence and impact of diabetes on patients with TB.

METHODS

Stratified cluster sampling was used to select 13 counties as study sites in the Zhejiang province. Patients who visited designated TB hospitals in these areas participated in this study between 1 January 2017 and 28 February 2019. Multiple logistic regression models were performed to investigate the association between DM and bacteriological and imaging results. A decision tree was used to predict the bacteriology and imaging results under the influence of DM.

RESULTS

Of 5920 patients with newly diagnosed pulmonary tuberculosis, 643 (12.16%) had DM. Patients with pulmonary TB and DM were more likely to have pulmonary cavities (adjusted odds ratio [aOR], 2.81; 95% confidence intervals [95% CI]: 2.35-3.37) and higher rates of positive bacteriological tests (aOR, 2.32; 95% CI:1.87-2.87). Decision-tree analysis showed similar results.

CONCLUSIONS

Concurrence of DM and pulmonary TB makes patients more likely to have positive bacteriological results and pulmonary cavities. Therefore, appropriate measures are necessary to promptly identify and manage patients with TB and DM.

Clinical assistant decision-making model of tuberculosis based on electronic health records

BACKGROUND

Tuberculosis is a dangerous infectious disease with the largest number of reported cases in China every year. Preventing missed diagnosis has an important impact on the prevention, treatment, and recovery of tuberculosis. The earliest pulmonary tuberculosis prediction models mainly used traditional image data combined with neural network models. However, a single data source tends to miss important information, such as primary symptoms and laboratory test results, that is available in multi-source data like medical records and tests. In this study, we propose a multi-stream integrated pulmonary tuberculosis diagnosis model based on structured and unstructured multi-source data from electronic health records. With the limited number of lung specialists and the high prevalence of tuberculosis, the application of this auxiliary diagnosis model can make substantial contributions to clinical settings.

METHODS

The subjects were patients at the respiratory department and infectious cases department of a large comprehensive hospital in China between 2015 to 2020. A total of 95,294 medical records were selected through a quality control process. Each record contains structured and unstructured data. First, numerical expressions of features for structured data were created. Then, feature engineering was performed through decision tree model, random forest, and GBDT. Features were included in the feature exclusion set as per their weights in descending order. When the importance of the set was higher than 0.7, this process was concluded. Finally, the contained features were used for model training. In addition, the unstructured free-text data was segmented at the character level and input into the model after indexing. Tuberculosis prediction was conducted through a multi-stream integration tuberculosis diagnosis model (MSI-PTDM), and the evaluation indices of accuracy, AUC, sensitivity, and specificity were compared against the prediction results of XGBoost, Text-CNN, Random Forest, SVM, and so on.

RESULTS

Through a variety of characteristic engineering methods, 20 characteristic factors, such as main complaint hemoptysis, cough, and test erythrocyte sedimentation rate, were selected, and the influencing factors were analyzed using the Chinese diagnostic standard of pulmonary tuberculosis. The area under the curve values for MSI-PTDM, XGBoost, Text-CNN, RF, and SVM were 0.9858, 0.9571, 0.9486, 0.9428, and 0.9429, respectively. The sensitivity, specificity, and accuracy of MSI-PTDM were 93.18%, 96.96%, and 96.96%, respectively. The MSI-PTDM prediction model was installed at a doctor workstation and operated in a real clinic environment for 4 months. A total of 692,949 patients were monitored, including 484 patients with confirmed pulmonary tuberculosis. The model predicted 440 cases of pulmonary tuberculosis. The positive sample recognition rate was 90.91%, the false-positive rate was 9.09%, the negative sample recognition rate was 96.17%, and the false-negative rate was 3.83%.

CONCLUSIONS

MSI-PTDM can process sparse data, dense data, and unstructured text data concurrently. The model adds a feature domain vector embedding the medical sparse features, and the single-valued sparse vectors are represented by multi-dimensional dense hidden vectors, which not only enhances the feature expression but also alleviates the side effects of sparsity on the model training. However, there may be information loss when features are extracted from text, and adding the processing of original unstructured text makes up for the error within the above process to a certain extent, so that the model can learn data more comprehensively and effectively. In addition, MSI-PTDM also allows interaction between features, considers the combination effect between patient features, adds more complex nonlinear calculation considerations, and improves the learning ability of the model. It has been verified using a test set and via deployment within an actual outpatient environment.

Spatiotemporal Changes in Pulmonary Tuberculosis Incidence in a Low-Epidemic Area of China in 2005-2020: Retrospective Spatiotemporal Analysis

BACKGROUND

In China, tuberculosis (TB) is still a major public health problem, and the incidence of TB has significant spatial heterogeneity.

OBJECTIVE

This study aimed to investigate the temporal trends and spatial patterns of pulmonary tuberculosis (PTB) in a low-epidemic area of eastern China, Wuxi city, from 2005 to 2020.

METHODS

The data of PTB cases from 2005 to 2020 were obtained from the Tuberculosis Information Management System. The joinpoint regression model was used to identify the changes in the secular temporal trend. Kernel density analysis and hot spot analysis were used to explore the spatial distribution characteristics and clusters of the PTB incidence rate.

RESULTS

A total of 37,592 cases were registered during 2005-2020, with an average annual incidence rate of 34.6 per 100,000 population. The population older than 60 years had the highest incidence rate of 59.0 per 100,000 population. In the study period, the incidence rate decreased from 50.4 to 23.9 per 100,000 population, with an average annual percent change of -4.9% (95% CI -6.8% to -2.9%). The incidence rate of pathogen-positive patients increased during 2017-2020, with an annual percent change of 13.4% (95% CI 4.3%-23.2%). The TB cases were mainly concentrated in the city center, and the incidence of hot spots areas gradually changed from rural areas to urban areas during the study period.

CONCLUSIONS

The PTB incidence rate in Wuxi city has been declining rapidly with the effective implementation of strategies and projects. The populated urban centers will become key areas of TB prevention and control, especially in the older population.

Spatial-temporal analysis of pulmonary tuberculosis among students in the Zhejiang Province of China from 2007-2020

Background

Pulmonary tuberculosis (PTB) is a serious chronic communicable disease that causes a significant disease burden in China; however, few studies have described its spatial epidemiological features in students.

Methods

Data of all notified PTB cases from 2007 to 2020 in the student population were collected in the Zhejiang Province, China using the available TB Management Information System. Analyses including time trend, spatial autocorrelation, and spatial-temporal analysis were performed to identify temporal trends, hotspots, and clustering, respectively.

Results

A total of 17,500 PTB cases were identified among students in the Zhejiang Province during the study period, accounting for 3.75% of all notified PTB cases. The health-seeking delay rate was 45.32%. There was a decreasing trend in PTB notifications throughout the period; clustering of cases was seen in the western area of Zhejiang Province. Additionally, one most likely cluster along with three secondary clusters were identified by spatial-temporal analysis.

Conclusion

Although was a downward trend in PTB notifications among students during the time period, an upward trend was seen in bacteriologically confirmed cases since 2017. The risk of PTB was higher among senior high school and above than of junior high school. The western area of Zhejiang Province was the highest PTB risk settings for students, and more comprehensive interventions should be strengthened such as admission screening and routine health monitoring to improve early identification of PTB.

Musculoskeletal Disorder Burden and Its Attributable Risk Factors in China: Estimates and Predicts from 1990 to 2044

Musculoskeletal disorders are one of the three major disabling diseases in the world. However, the current disease burden in China is not well-known. This study aimed to explore the burden and risk factors of musculoskeletal disorders in China from 1990 to 2019, predicting the incidence trend from 2020 to 2044. All data were extracted from the Global Burden of Disease Study 2019 (GBD 2019). Joinpoint regression and age-period-cohort (APC) models were selected to analyze the epidemic trend, and descriptive analyses of the time trends and age distributions of risk factors were performed. The Bayesian APC model was used to foresee the incidence trend from 2020 to 2044. The results indicated that the burden of musculoskeletal disorders is higher in women and older adults. Its attributable risk factors were found to be tobacco, a high body mass index, kidney dysfunction and occupational risks. In 2044, musculoskeletal disorders in China showed a downward trend for 35-59-year-olds and a slight upward trend for 30-34- and 65-84-year-olds. The 70-74 year age group saw the largest increase in incidence at 4.66%. Overall, the incidence increased with age. Therefore, prevention and control policies should focus on women and the elderly, and health interventions should be carried out based on risk factors.

The burden and predictors of latent tuberculosis infection among elder adults in high epidemic rural area of tuberculosis in Zhejiang, China

Diagnosis and treatment of latent tuberculosis infection (LTBI) is critical to tuberculosis (TB) control. Identifying the risk factors associated with LTBI can contribute to developing an optimized strategy for LTBI management. We conducted a survey of adults aged 65 years and older living in rural areas in Zhejiang Province during July 2021, followed by a one-year follow-up period to determine TB incidence. Participants underwent a physical examination and 5–6 mL of blood was drawn to test for Mycobacterium tuberculosis infection A total of 1856 individuals participated in the study, of whom 50.5% were men and 80.1% were married. Most participants (96.8%) often opened windows for ventilation at home. One-third (33.4%) of participants had abnormal chest radiographs and 34.9% had LTBI. Nine participants (0.5%) developed active TB patients during the one-year follow-up period. People who frequented closed entertainment places such as chess and card rooms had a relatively high percentage of LTBI (39.5%). Factors associated with a higher risk of LTBI in multivariable logistic regression analysis included being male (odds ratio [OR]:1.32; 95% confidence interval [CI] =:1.01-1.72), smoking (OR: 1.43; 95% CI:1.04-1.97), not opening windows for ventilation at home frequently (OR: 1.88; 95% CI: 1.10–3.22), and abnormal chest radiographs (OR; 1.48; 95% CI; 1.20–1.81). LTBI was prevalent among the elder adults living in high-epidemic rural areas of TB in Zhejiang province. Men, people who smoke, and people without the habit of ventilating at home should be targeted for LTBI screening to accelerate the decline of the TB epidemic in Zhejiang Province.

Lip and Oral Cavity Cancer Burden and Related Risk Factors in China: Estimates and Forecasts from 1990 to 2049

Lip and oral cavity cancer is a common malignancy faced by many developing countries, and the disease burden is high in China. This study explored this cancer burden and its risk factors using data from China in the GBD 2019, along with predicting the incidence trends in 2020-2049. Data on age-standardized rates (ASR), incidence, death and disability-adjusted life years (DALY), by sex, age and risk factors were collected from the Institute for Health Metrics and Evaluation (IHME). Joinpoint regression and Age-Period-Cohort (APC) models were selected to analyze the epidemic trend of this cancer in China, and descriptive analysis was used for the time trend and age distribution of risk factors. The Bayesian APC model was selected to foresee the incidence trend in 2020-2049. This cancer burden was found to be in an upward trend in China in 1990-2019. The upward trend was more pronounced among men than among women. These cancer deaths and DALYs are overwhelmingly attributable to smoking and drinking. On APC analysis, the younger generation in China demonstrated a lower cancer risk. In 2049, the incidence of this cancer is projected to be 3.99/100,000, 6.07/100,000, 7.37/100,000, 10.49/100,000, 14.82/100,000, 19.19/100,000, 20.71/100,000, 23.64/100,000, 16.42/100,000 and 9.91/100,000 among those aged 50-54, 55-59, 60-64, 65-69, 70-74, 75-79, 80-84, 85-89, 85-89 and over 95 years, respectively. Disease control policies and early screening should focus on men and the elderly and target different risk factors.

Temporal Trends in Notification and Mortality of Tuberculosis in China, 2004-2019: A Joinpoint and Age-Period-Cohort Analysis

Tuberculosis (TB) remains a major public health problem in China and worldwide. In this article, we used a joinpoint regression model to calculate the average annual percent change (AAPC) of TB notification and mortality in China from 2004 to 2019. We also used an age–period–cohort (APC) model based on the intrinsic estimator (IE) method to simultaneously distinguish the age, period and cohort effects on TB notification and mortality in China. A statistically downward trend was observed in TB notification and mortality over the period, with AAPCs of −4.2% * (−4.9%, −3.4%) and −5.8% (−7.5%, −4.0%), respectively. A bimodal pattern of the age effect was observed, peaking in the young adult (aged 15–34) and elderly (aged 50–84) groups. More specifically, the TB notification risk populations were people aged 20–24 years and 70–74 years; the TB mortality risk population was adults over the age of 60. The period effect suggested that TB notification and mortality risks were nearly stable over the past 15 years. The cohort effect on both TB notification and mortality presented a continuously decreasing trend, and it was no longer a risk factor after 1978. All in all, the age effect should be paid more attention.

Implementation Performance of Tuberculosis Control in China: 2011-2020

What is already known about this topic?

World Health Organization (WHO) launched END TB Strategy but performance of tuberculosis (TB) control in China hasn't been systematically evaluated after 2010.

What is added by this report?

All five key indicators monitorred in national TB program (NTP) have kept with high level or got impressively improved from 2011 to 2020. There were some differences in the performance of indicators among different regions.

What are the implications for public health practice?

NTP indicators should be readapted to new strategies and requirements in future plan.