Bridging the Gap in End Tuberculosis Targets in the Elderly Population in Eastern China: Observational Study From 2015 to 2020

Analyzing spatial delays of tuberculosis from surveillance and awareness surveys in Eastern China

The spatial delays of pulmonary tuberculosis (PTB) have been less explored. In this study, a total of 151,799 notified PTB cases were included, with median patient and diagnostic delays of 15 [interquartile range (IOR), 4-35] and 2 (IOR, 0-8) days, respectively. The spatial autocorrelation analysis and spatial-temporal scan statistics were used to determine the clusters, indicating that the regions in the southwestern and northeastern parts of Zhejiang Province exhibited high rates of long-term patient delay (LPD, delay ≥ 15 days) and long-term diagnostic delay (LDD, delay ≥ 2 days). Besides, the Mantel test indicated a moderately positive correlation between public awareness of suspicious symptoms and the LPD rate in 2018 (Mantel's r = 0.4, P < 0.05). These findings suggest that PTB delays can reveal deficiencies in public health education and the healthcare system. Also, it is essential to explore methods to shift PTB knowledge towards real changes in attitude and behavior to minimize patient delay. Addressing these issues will be crucial for improving public health outcomes related to PTB in Zhejiang Province.

Spatial spillover effect of environmental factors on the tuberculosis occurrence among the elderly: a surveillance analysis for nearly a dozen years in eastern China

BACKGROUND

In many areas of China, over 30% of tuberculosis cases occur among the elderly. We aimed to investigate the spatial distribution and environmental factors that predicted the occurence of tuberculosis in this group.

METHODS

Data were collected on notified pulmonary tuberculosis (PTB) cases aged ≥ 65 years in Zhejiang Province from 2010 to 2021. We performed spatial autocorrelation and spatial-temporal scan statistics to determine the clusters of epidemics. Spatial Durbin Model (SDM) analysis was used to identify significant environmental factors and their spatial spillover effects.

RESULTS

77,405 cases of PTB among the elderly were notified, showing a decreasing trend in the notification rate. Spatial-temporal analysis showed clustering of epidemics in the western area of Zhejiang Province. The results of the SDM indicated that a one-unit increase in PM2.5 led to a 0.396% increase in the local notification rate. The annual mean temperature and precipitation had direct effects and spatial spillover effects on the rate, while complexity of the shape of the greenspace (SHAPE_AM) and SO2 had negative spatial spillover effects.

CONCLUSION

Targeted interventions among the elderly in Western Zhejiang may be more efficient than broad, province-wide interventions. Low annual mean temperature and high annual mean precipitation in local and neighboring areas tend to have higher PTB onset among the elderly.

Trends and Projection of the Incidence of Active Pulmonary Tuberculosis in Southwestern China: Age-Period-Cohort Analysis

BACKGROUND

The control of pulmonary tuberculosis (PTB) is critical for achieving the vision of World Health Organization's End TB goal.

OBJECTIVE

This study analyzes the temporal trends in PTB incidence associated with age, period, and birth cohorts from 2006 to 2020 in Yunnan, China; projects the PTB burden till 2030; and explores the drivers of PTB incidence.

METHODS

The aggregated PTB incidence rates between 2005 and 2020 were obtained from the National Notifiable Disease Reporting System. We used the age-period-cohort model to evaluate the age, period, and cohort effects on PTB incidence. We applied the Bayesian age-period-cohort model to project future PTB incidence from 2021 to 2030. We applied the decomposition algorithm to attribute the incidence trends to population aging, population growth, and age-specific changes from 2006 to 2030.

RESULTS

From 2006 to 2020, the PTB incidence in Yunnan was relatively stable, although the absolute number showed an increase. The net drift was -1.56% (95% CI -2.41% to -0.70%). An M-shaped bimodal local drift and a longitudinal age curve were observed. The overall local drift was below zero for most age groups except for the age groups of 15-19 years (2.37%, 95% CI -0.28% to 5.09%) and 50-54 years (0.41%, 95% CI -1.78% to 2.64%). The highest risk of PTB incidence was observed in the age group of 65-69 years, and another peak was observed in the age group of 20-24 years. Downward trends were observed for both period and cohort effects, but the cohort effect trends were uneven. A higher risk was observed for the birth cohorts of 1961-1970 (rate ratio [RR]1961-1965=1.10, 95% CI 0.88-1.38; RR1966-1970=1.11, 95% CI 0.92-1.37) and 2001-2010 (RR2001-2005=0.92, 95% CI 0.63-1.34; RR2006-2010=0.84, 95% CI 0.45-1.58) than for the adjacent cohorts. The Bayesian age-period-cohort model projected that PTB incidence will continually increase from 2021 to 2030 and that PTB incidence in 2030 will be 2.28 times higher than that in 2006. The age-specific change was the leading cause for the growing PTB disease burden.

CONCLUSIONS

Although there are several levels and measures for PTB control, the disease burden is likely to increase in the future. To bridge the gap of TB-free vision, our study suggests that public health policies be put in place soon, including large-scale active case-finding, priority prevention policies for high-risk older adult and young adult populations, and reduction of possible grandparent-grandchildren transmission patterns.

Mass Tuberculosis Screening Among the Elderly: A Population-Based Study in a Well-Confined, Rural County in Eastern China

BACKGROUND

Mass tuberculosis (TB) screening has been recommended in certain high-risk populations. However, population-based screening interventions have rarely been implemented. Whether mass screening improves health equity is unknown.

METHODS

We implemented a mass TB screening intervention among elderly persons (>60 years old) in Lanxi County, China. Standardized questionnaires, physical examinations, and chest radiographs (CXRs) were administered to all participants. Systematic testing with computed tomography, smear, culture, or Xpert was performed among persons with an abnormal CXR. We assessed TB prevalence per 100 000 persons and constructed multivariable regression models among subgroups that were and were not screened. Medical insurance was categorized as participation in either a basic program with limited coverage or a more comprehensive coverage program.

RESULTS

In total, 49 339 individuals (32% of the elderly population in Lanxi) participated in the screening. One hundred fifteen screened persons were diagnosed with TB (233 cases per 100 000 persons), significantly higher than persons not screened (168 cases among 103 979 person-years; prevalence-to-case notification ratio, 1.44 [95% confidence interval {CI}, 1.14-1.83]). This increase was largely driven by diagnosis of asymptomatic disease during mass screening (n = 57 [50% of participants with TB]). Participants with basic medical insurance were much more likely to be diagnosed through mass screening than by passive detection (adjusted odds ratio, 4.52 [95% CI, 1.35-21.28]).

CONCLUSIONS

In a population-based, mass TB screening intervention encompassing >30% of the elderly population in a county in rural China, case finding was 44% higher than background detection, driven by diagnosis of TB without recognized symptoms. Importantly, mass screening identified TB in people with limited healthcare options who were less likely to be found through background case detection.

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.

Assessment of Effective Anti-TB Regimens and Adverse Outcomes Related Risk Factors in the Elderly and Senile-Aged TB Patients

Objective

Compared to younger patients with tuberculosis (TB), elderly and senile-aged patients with TB had a higher incidence of adverse outcomes particularly in terms of lost to follow-up and deaths. Our study aimed to gain insight into the effectiveness of anti-tuberculosis (anti-TB) treatment in the elderly or senile-aged patients and identify the risk factors for adverse outcomes.

Methods

The case information was obtained from the "Tuberculosis Management Information System". From January 2011 to December 2021, this retrospective analysis was conducted in Lishui City, Zhejiang Province to observe and record the outcomes of elderly patients diagnosed with TB who agreed to receive anti-TB and(or) traditional Chinese medicine(TCM) treatment. We also employed a logistic regression model to analyze the risk factors for adverse outcomes.

Results

Among the 1191 elderly or senile-aged patients with TB who received the treatment, the success rate was 84.80% (1010/1191). Using logistic regression analysis, several risk factors for adverse outcomes (failure, death, loss to follow-up) were identified, including age ≥ 80 years (OR 2.186, 95% CI 1.517~3.152, P<0.001), lesion area ≥ 3 lung fields (OR 0.410, 95% CI 0.260~0.648, P<0.001), radiographic lesions failing to improve after 2 months of treatment (OR 2.048, 95% CI 1.302~3.223, P=0.002), sputum bacteriology failing to turn negative after 2 months of treatment (OR 2.213, 95% CI 1.227~3.990, P=0.008), lack of a standardized treatment plan (OR 2.095, 95% CI 1.398~3.139, P<0.001), and non-involvement of traditional Chinese medicine (OR 2.589, 95% CI 1.589~4.216, P<0.001).

Conclusion

The anti-TB treatment success rate in the elderly and senile-aged patients is suboptimal. Contributing factors include advanced age, extensive lesions, and low sputum negative conversion rate during the intensive treatment phase. The results will informative and could be useful for policy maker for to control of reemergence of TB in big cities.

Machine Learning Prediction Model of Tuberculosis Incidence Based on Meteorological Factors and Air Pollutants

BACKGROUND

Tuberculosis (TB) is a public health problem worldwide, and the influence of meteorological and air pollutants on the incidence of tuberculosis have been attracting interest from researchers. It is of great importance to use machine learning to build a prediction model of tuberculosis incidence influenced by meteorological and air pollutants for timely and applicable measures of both prevention and control.

METHODS

The data of daily TB notifications, meteorological factors and air pollutants in Changde City, Hunan Province ranging from 2010 to 2021 were collected. Spearman rank correlation analysis was conducted to analyze the correlation between the daily TB notifications and the meteorological factors or air pollutants. Based on the correlation analysis results, machine learning methods, including support vector regression, random forest regression and a BP neural network model, were utilized to construct the incidence prediction model of tuberculosis. RMSE, MAE and MAPE were performed to evaluate the constructed model for selecting the best prediction model.

RESULTS

(1) From the year 2010 to 2021, the overall incidence of tuberculosis in Changde City showed a downward trend. (2) The daily TB notifications was positively correlated with average temperature (r = 0.231), maximum temperature (r = 0.194), minimum temperature (r = 0.165), sunshine duration (r = 0.329), PM_2.5 (r = 0.097), PM₁₀ (r = 0.215) and O₃ (r = 0.084) (p < 0.05). However, there was a significant negative correlation between the daily TB notifications and mean air pressure (r = -0.119), precipitation (r = -0.063), relative humidity (r = -0.084), CO (r = -0.038) and SO₂ (r = -0.034) (p < 0.05). (3) The random forest regression model had the best fitting effect, while the BP neural network model exhibited the best prediction. (4) The validation set of the BP neural network model, including average daily temperature, sunshine hours and PM₁₀, showed the lowest root mean square error, mean absolute error and mean absolute percentage error, followed by support vector regression.

CONCLUSIONS

The prediction trend of the BP neural network model, including average daily temperature, sunshine hours and PM₁₀, successfully mimics the actual incidence, and the peak incidence highly coincides with the actual aggregation time, with a high accuracy and a minimum error. Taken together, these data suggest that the BP neural network model can predict the incidence trend of tuberculosis in Changde City.

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.