Assessment of Strategies and Epidemiological Characteristics of Tuberculosis in Henan Province, China: Observational Study

Pulmonary Tuberculosis Notification Rate Within Shenzhen, China, 2010-2019: Spatial-Temporal Analysis

BACKGROUND

Pulmonary tuberculosis (PTB) is a chronic communicable disease of major public health and social concern. Although spatial-temporal analysis has been widely used to describe distribution characteristics and transmission patterns, few studies have revealed the changes in the small-scale clustering of PTB at the street level.

OBJECTIVE

The aim of this study was to analyze the temporal and spatial distribution characteristics and clusters of PTB at the street level in the Shenzhen municipality of China to provide a reference for PTB prevention and control.

METHODS

Data of reported PTB cases in Shenzhen from January 2010 to December 2019 were extracted from the China Information System for Disease Control and Prevention to describe the epidemiological characteristics. Time-series, spatial-autocorrelation, and spatial-temporal scanning analyses were performed to identify the spatial and temporal patterns and high-risk areas at the street level.

RESULTS

A total of 58,122 PTB cases from 2010 to 2019 were notified in Shenzhen. The annual notification rate of PTB decreased significantly from 64.97 per 100,000 population in 2010 to 43.43 per 100,000 population in 2019. PTB cases exhibited seasonal variations with peaks in late spring and summer each year. The PTB notification rate was nonrandomly distributed and spatially clustered with a Moran I value of 0.134 (P=.02). One most-likely cluster and 10 secondary clusters were detected, and the most-likely clustering area was centered at Nanshan Street of Nanshan District covering 6 streets, with the clustering time spanning from January 2010 to November 2012.

CONCLUSIONS

This study identified seasonal patterns and spatial-temporal clusters of PTB cases at the street level in the Shenzhen municipality of China. Resources should be prioritized to the identified high-risk areas for PTB prevention and control.

Changes in tuberculosis burden and its associated risk factors in Guizhou Province of China during 2006-2020: an observational study

BACKGROUND

Understanding the trends of tuberculosis (TB) burden and its risk factors at the provincial level in the context of global End TB targets is crucial to identify the progress and challenges in TB control. We aimed to estimate the burden of TB and risk factors for death from 2006 to 2020 for the first time in Guizhou Province, China.

METHODS

Data were collected from the national TB surveillance system. Four indicators of TB burden and their corresponding age-standardized rates (ASRs), including incidence (ASIR), prevalence (ASPR), mortality (ASMR) and disability-adjusted life years (DALYs) (ASDR), were estimated and stratified by year, age, gender and prefecture. Temporal trends of ASRs were presented by locally weighted regression, and the annual percentage change was calculated. The correlation between gross domestic product (GDP) per capita and ASRs was evaluated by Pearson correlation analysis. The associated risk factors for death in PTB patients were determined using logistic regression models.

RESULTS

A total of 557,476 pulmonary TB (PTB) cases and 11,234 deaths were reported, including 2233 (19.9%) TB specific deaths and 9001 (80.1%) deaths from other causes. The 15-year average incidence, prevalence and mortality rates were 94.6, 102.6 and 2.1 per 100,000 population, respectively. The average DALY rate was 0.60 per 1000 population. The ASIR and ASPR have shown downward trends since 2012, with the largest percentage decrease in 2020 (ASIR: -29.8%; ASPR: -30.5%). The number in TB specific deaths consistently decreased during the study period (P<0.001), while the increase in deaths from other causes drove the overall upward trend in ASMR and ASDR. Four ASRs remained high in males and 5 prefectures. GDP per capita was negatively associated with the ASIR, ASPR and ASDR (P<0.05). Among PTB patients, men, patients with no fixed job, those with a low GDP level, patients with increasing age, those previously treated, those with severe symptoms, those transferred in and those receiving directly observed treatment were more likely to suffer death.

CONCLUSION

Guizhou has made progress in reducing PTB cases and TB specific deaths over the last 15 years. Targeted interventions are needed to address these risk factors for death in PTB patients and high-risk areas.

An investigation of the effects of meteorological factors on the incidence of tuberculosis

To explore the influence of meteorological factors on the incidence of tuberculosis (TB) in Yingjisha County, Kashgar Region, Xinjiang, and to provide a scientific basis for the prevention and control of TB. The Spearman correlation analysis and distribution lag nonlinear model analysis were conducted on the number of daily reported cases of TB from 2016 to 2023 to study the association effect of various meteorological factors and the daily incidence number of TB in Yingjisha County. A total of 13,288 TB cases were reported from January 2016 to June 2023, and June to October is the peak period of annual TB incidence. Spearman correlation analysis revealed that average daily temperature (AT) and average daily wind speed (WS) were positively correlated with TB incidence (rAT = 0.110, rWS = 0.090); and average daily relative humidity (RH) and TB incidence was negatively correlated (rRH = - 0.093). When AT was - 15 °C, the RR reached a maximum of 2.20 (95% CI: 0.77-6.29) at a lag of 21 days. When RH was 92%, the RR reached a maximum of 1.05 (95% CI: 0.92-1.19) at a lag of 6 days. When WS was 5.2 m/s, the RR reached a maximum of 1.30 (95% CI: 0.78-2.16) at a lag of 16 days. There is a non-linearity and a certain lag between meteorological factors and the occurrence and prevalence of TB in the population, which is mainly manifested in the fact that the risk of incidence of TB decreases with the increase of the daily AT, has a hazardous effect within a certain range of humidity as the average daily RH rises, and gradually increases with the increase of the average daily WS. Local residents are advised to pay attention to climate change so as to take appropriate preventive measures, especially women and middle and old age group should pay close attention to climate change and add more clothes in time, minimise travelling in hazy weather and windy and sandy weather, maintain good nutrition, adequate sleep and moderate exercise in daily life to enhance their immunity, wash hands frequently and ventilate the air, and try to avoid staying in humid and confined spaces in order to reduce the risk of latent TB patients developing the disease.

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.

The unbalanced risk of pulmonary tuberculosis in China at subnational scale: A spatio-temporal analysis (Preprint)

Background

China has one of the highest tuberculosis (TB) burdens in the world. However, the unbalanced spatial and temporal trends of TB risk at a fine level remain unclear.

Objective

We aimed to investigate the unbalanced risks of pulmonary tuberculosis (PTB) at different levels and how they evolved from both temporal and spatial aspects using PTB notification data from 2851 counties over a decade in China.

Methods

County-level notified PTB case data were collected from 2009 to 2018 in mainland China. A Bayesian hierarchical model was constructed to analyze the unbalanced spatiotemporal patterns of PTB notification rates during this period at subnational scales. The Gini coefficient was calculated to assess the inequality of the relative risk (RR) of PTB across counties.

Results

From 2009 to 2018, the number of notified PTB cases in mainland China decreased from 946,086 to 747,700. The average number of PTB cases in counties was 301 (SD 26) and the overall average notification rate was 60 (SD 6) per 100,000 people. There were obvious regional differences in the RRs for PTB (Gini coefficient 0.32, 95% CI 0.31-0.33). Xinjiang had the highest PTB notification rate, with a multiyear average of 155/100,000 (RR 2.3, 95% CI 1.6-2.8; P<.001), followed by Guizhou (117/100,000; RR 1.8, 95% CI 1.3-1.9; P<.001) and Tibet (108/100,000; RR 1.7, 95% CI 1.3-2.1; P<.001). The RR for PTB showed a steady downward trend. Gansu (local trend [LT] 0.95, 95% CI 0.93-0.96; P<.001) and Shanxi (LT 0.94, 95% CI 0.92-0.96; P<.001) experienced the fastest declines. However, the RRs for PTB in the western region (such as counties in Xinjiang, Guizhou, and Tibet) were significantly higher than those in the eastern and central regions (P<.001), and the decline rate of the RR for PTB was lower than the overall level (P<.001).

Conclusions

PTB risk showed significant regional inequality among counties in China, and western China presented a high plateau of disease burden. Improvements in economic and medical service levels are required to boost PTB case detection and eventually reduce PTB risk in the whole country.

The Spatial Distribution of Pulmonary Tuberculosis in Kabanjahe District, Karo Regency, Indonesia

BACKGROUND: Tuberculosis is an infectious disease and global concern today. AIM: This study aims to map the incidence of pulmonary tuberculosis risk factors in Kabanjahe District, Karo Regency. METHOD: This research is an ecological study with a case-control study design. This research was conducted in Kabanjahe District in January - October 2020. All people who checked and declared to have tuberculosis based on clinical symptoms to be the population in this study. The sample size was calculated with a minimum sample size of 58 for the case group and 58 for the control group with a ratio of 1:1. The distribution pattern of pulmonary tuberculosis and environmental risk factors with the incidence of tuberculosis was carried out using a Geographic Information System (GIS) to determine the distribution of cases. Spatial analysis used average nearest neighbor, overlay and buffer followed with logistic regression as multivariate statistical analysis. RESULT:  The distribution pattern of pulmonary tuberculosis in Kabanjahe District tends to group (clusters). GeoDa software found the relationship between population density and tuberculosis incidence in Kabanjahe District with p values 0.04. There is a relationship between income, ventilation, floor conditions, humidity, and lighting with the incidence of tuberculosis. Humidity is the most dominant variable associated with the incidence of tuberculosis.   CONCLUSION: The incidence of tuberculosis cases in Kabanjahe District is dominantly influenced by the humidity factor of the house which is increasingly at risk due to poor ventilation, unstable room temperature, and bad circulation.