Spatiotemporally Comparative Analysis of HIV, Pulmonary Tuberculosis, HIV-Pulmonary Tuberculosis Coinfection in Jiangsu Province, China.

Purpose: Pulmonary tuberculosis (PTB) is a severe chronic communicable disease that causes a heavy disease burden in China. Human Immunodeficiency Virus (HIV) and PTB coinfection dramatically increases the risk of death. This study analyzes the spatiotemporal dynamics of HIV, PTB and HIV-PTB coinfection in Jiangsu Province, China, and explores the impact of socioeconomic determinants. Patients and Methods: The data on all notified HIV, PTB and HIV-PTB coinfection cases were extracted from Jiangsu Provincial Center for Disease Control and Prevention. We applied the seasonal index to identify high-risk periods of the disease. Time trend, spatial autocorrelation and SaTScan were used to analyze temporal trends, hotspots and spatiotemporal clusters of diseases. The Bayesian space-time model was conducted to examine the socioeconomic determinants. Results: The case notification rate (CNR) of PTB decreased from 2011 to 2019 in Jiangsu Province, but the CNR of HIV and HIV-PTB coinfection had an upward trend. The seasonal index of PTB was the highest in March, and its hotspots were mainly distributed in the central and northern parts, such as Xuzhou, Suqian, Lianyungang and Taizhou. HIV had the highest seasonal index in July and HIV-PTB coinfection had the highest seasonal index in June, with their hotspots mainly distributed in southern Jiangsu, involving Nanjing, Suzhou, Wuxi and Changzhou. The Bayesian space-time interaction model showed that socioeconomic factor and population density were negatively correlated with the CNR of PTB, and positively associated with the CNR of HIV and HIV-PTB coinfection. Conclusion: The spatial heterogeneity and spatiotemporal clusters of PTB, HIV and HIV-PTB coinfection are exhibited obviously in Jiangsu. More comprehensive interventions should be applied to target TB in the northern part. While in southern Jiangsu, where the economic level is well-developed and the population density is high, we should strengthen the prevention and control of HIV and HIV-PTB coinfection.

A clinical indicator-based prognostic model predicting treatment outcomes of pulmonary tuberculosis: a prospective cohort study.

OBJECTIVES: Identifying prognostic factors helps optimize the treatment regimen and promote favorable outcomes. We conducted a prospective cohort study on patients with pulmonary tuberculosis to construct a clinical indicator-based model and estimate its performance. METHODS: We performed a two-stage study by recruiting 346 pulmonary tuberculosis patients diagnosed between 2016 and 2018 in Dafeng city as the training cohort and 132 patients diagnosed between 2018 and 2019 in Nanjing city as the external validation population. We generated a risk score based on blood and biochemistry examination indicators by the least absolute shrinkage and selection operator (LASSO) Cox regression. Univariate and multivariate Cox regression models were used to assess the risk score, and the strength of association was expressed as the hazard ratio (HR) and 95% confidence interval (CI). We plotted the receiver operating characteristic (ROC) curve and calculated the area under the curve (AUC). Internal validation was conducted by 10-fold cross-validation. RESULTS: Ten significant indicators (PLT, PCV, LYMPH, MONO%, NEUT, NEUT%, TBTL, ALT, UA, and Cys-C) were selected to generate the risk score. Clinical indicator-based score (HR: 10.018, 95% CI: 4.904-20.468, P < 0.001), symptom-based score (HR: 1.356, 95% CI: 1.079-1.704, P = 0.009), pulmonary cavity (HR: 0.242, 95% CI: 0.087-0.674, P = 0.007), treatment history (HR: 2.810, 95% CI: 1.137-6.948, P = 0.025), and tobacco smoking (HR: 2.499, 95% CI: 1.097-5.691, P = 0.029) were significantly related to the treatment outcomes. The AUC was 0.766 (95% CI: 0.649-0.863) in the training cohort and 0.796 (95% CI: 0.630-0.928) in the validation dataset. CONCLUSION: In addition to the traditional predictive factors, the clinical indicator-based risk score determined in this study has a good prediction effect on the prognosis of tuberculosis.

Association between the triglyceride-glucose index and carotid plaque incidence: a longitudinal study.

BACKGROUND: Carotid plaque and triglyceride-glucose (TyG) index are associated with insulin resistance. However, a highly debated question is whether there is an association between the TyG index and carotid plaque incidence. Thus we performed an in-depth longitudinal study to investigate the relationship between carotid plaque occurrence and the TyG index among Chinese individuals. METHODS: Two thousand and three hundred seventy subjects (1381 males and 989 females) were enrolled and followed up for three years. The subjects were stratified into four groups based on the quartile of the TyG index at baseline. Univariate and multivariate Cox proportional hazard models were conducted to examine the role of TyG played in the carotid plaque. The strength of association was expressed as hazard ratio (HR) and 95% confidence interval (CI). RESULTS: After three years of follow-up, 444 subjects were detected with newly formed carotid plaque. The overall 3-year cumulative carotid plaque incidence was 18.7%, and the risk of carotid plaque increased with elevated TyG index (p < 0.001). The Cox regression analysis showed that males (HR: 1.33, 95% CI: 1.10-1.61), and people with higher systolic blood pressure (HR:1.01, 95% CI: 1.01-1.02), lower high-density lipoprotein cholesterol (HR: 0.68, 95% CI: 0.50-0.93), diabetes (HR: 2.21, 95% CI: 1.64-2.97), and hypertension (HR:1.49, 95% CI: 1.23-1.81) had a significantly increased risk for the carotid plaque formation. Similar results remained in the sensitivity analysis. CONCLUSIONS: The TyG index can be used as a dose-responsive indicator of carotid plaque in the Chinese population. Elderly males with dyslipidemia, diabetes, or hypertension should be more vigilant about their TyG index since they are susceptible to developing carotid plaque. Physicians are encouraged to monitor the TyG index to help identify and treat patients with carotid plaque at an early stage.

Serum LDL-C/HDL-C ratio and the risk of carotid plaques: a longitudinal study.

BACKGROUND: Dyslipidemia contributes to an increased risk of carotid atherosclerosis. However, the association between the ratio of low-density lipoprotein cholesterol (LDL-C) to high-density lipoprotein cholesterol (HDL-C) and carotid plaque formation has not been well documented. This study aims to assess the role of LDL-C/HDL-C in the risk of carotid plaque formation in a Chinese population. METHODS: We followed 2,191 participants who attended the annual routine health examination. Cox proportional hazards regression, restricted cubic spline (RCS), and subgroup analysis were applied to evaluate the association between the LDL-C/HDL-C ratio and carotid plaques. The hazard ratio (HR) and 95% confidence interval (CI) were used to estimate the strength of the association. RESULTS: Among 2,191 participants, 388 had incident carotid plaques detected, with a median follow-up time of 1.05 years. Compared with subjects younger than 45 years, those aged 45 to 59 years (HR: 2.00, 95% CI: 1.55-2.58) and over 60 years (HR: 3.36, 95% CI: 2.47-4.58) had an increased risk of carotid plaque formation. Males (HR: 1.26, 95% CI: 1.01-1.56), diabetes (HR: 1.46, 95% CI: 1.06-2.01) and a high LDL-C/HDL-C ratio (HR: 1.22, 95% CI: 1.07-1.38) were significantly linked with the occurrence of carotid plaques. After adjusting for potential confounding factors, we observed that a high LDL-C/HDL-C ratio promoted carotid plaque events (HR: 1.30, 95% CI: 1.12-1.50). The RCS analysis revealed a significant nonlinear association. The association was stronger among females (P-interaction < 0.05). CONCLUSION: A high LDL-C/HDL-C ratio could accelerate the occurrence of carotid plaques. Older men with diabetes and dyslipidemia are the critical target population. Women may be more likely to benefit from lipid-lowering interventions and thus avoid carotid plaque formation.

Risk factors for types of recurrent tuberculosis (reactivation versus reinfection): A global systematic review and meta-analysis.

BACKGROUND: The purpose of this meta-analysis (PROSPERO number: CRD42021243204) is to perform extensive and penetrating analyses on the risk factors associated with reactivation or reinfection. METHODS: We searched PubMed and Embase using search terms. Risk factors (including gender, length of time between first onset and recurrent diagnosis, extrapulmonary tuberculosis, sputum smear, pulmonary cavity, Beijing family strains, diabetes, HIV infection, history of imprisonment, and immigration) were analyzed. The pooled risk ratio (RR) and 95% confidence interval (CI) were calculated with STATA 15.1. Heterogeneity was evaluated by I2 and P values. RESULTS: The meta-analysis included 25 studies with a total of 1,477 recurrent patients. After subgroup analysis, sensitivity analysis, and testing for publication bias, it was concluded that time spanning less than two years (RR = 1.56, 95% CI: 1.33-1.85) was a risk factor for endogenous reactivation, whereas coinfection with HIV (RR = 0.72, 95% CI: 0.63-0.83), Beijing family genotype (RR = 0.46, 95% CI: 0.32-0.67), history of imprisonment (RR = 0.36, 95% CI: 0.16-0.81), and immigration (RR = 0.66, 95% CI: 0.53-0.82) were associated with exogenous reinfection. CONCLUSIONS: The recurrence interval is a risk factor for the endogenous reactivation of tuberculosis. Infection with Beijing family strains, coinfection with HIV, imprisonment, and immigration contribute to the risk of exogenous reinfection.

[A comparative study of time series models in predicting COVID-19 cases].

Objective: To compare the performances of different time series models in predicting COVID-19 in different countries. Methods: We collected the daily confirmed case numbers of COVID-19 in the USA, India, and Brazil from April 1 to September 30, 2020, and then constructed an autoregressive integrated moving average (ARIMA) model and a recurrent neural network (RNN) model, respectively. We applied the mean absolute percentage error (MAPE) and root mean square error (RMSE) to compare the performances of the two models in predicting the case numbers from September 21 to September 30, 2020. Results: For the ARIMA models applied in the USA, India, and Brazil, the MAPEs were 13.18%, 9.18%, and 17.30%, respectively, and the RMSEs were 6 542.32, 8 069.50, and 3 954.59, respectively. For the RNN models applied in the USA, India, and Brazil, the MAPEs were 15.27%, 7.23% and 26.02%, respectively, and the RMSEs were 6 877.71, 6 457.07, and 5 950.88, respectively. Conclusions: The performance of the prediction models varied with country. The ARIMA model had a better prediction performance for COVID-19 in the USA and Brazil, while the RNN model was more suitable in India.

A Prospective Cohort Study on the Prevalent and Recurrent Tuberculosis Isolates Using the MIRU-VNTR Typing.

The study aims to describe the clustering characteristics of Mycobacterium tuberculosis (M.tb) strains circulating in eastern China and determine the ratio of relapse and reinfection in recurrent patients. We recruited sputum smear-positive pulmonary tuberculosis cases from five cities of Jiangsu Province, China, during August 2013 and December 2015. Patients were followed for the treatment outcomes and recurrence based on a cohort design. M.tb strains were isolated and genotyped using the 12-locus MIRU-VNTR. The Beijing family was identified by the extended Region of Difference (RD) analysis. The Hunter-Gaston Discriminatory Index (HGDI) was used to judge the resolution ability of MIRU-VNTR. The odds ratio (OR) together with 95% confidence interval (CI) were used to estimate the strength of association. We performed a cluster analysis on 2098 M.tb isolates and classified them into 545 genotypes and five categories (I, 0.19%; II, 0.43%; III, 3.34%; IV, 77.46%; V, 18.59%). After adjusting for potential confounders, the Beijing family genotype (OR = 118.63, 95% CI: 79.61-176.79, P = 0.001) was significantly related to the dominant strain infections. Patients infected with non-dominant strains had a higher risk of the pulmonary cavity (OR = 1.39, 95% CI: 1.01-1.91, P = 0.046). Among 37 paired recurrent cases, 22 (59.46%) were determined as endogenous reactivation, and 15 (40.54%) were exogenous reinfection. The type of M.tb strains prevalent in Jiangsu Province is relatively single. Beijing family strains infection is dominant in local tuberculosis cases. Endogenous reactivation appears to be a major cause of recurrent tuberculosis in Eastern China. This finding emphasizes the importance of case follow-up and monitoring after the completion of antituberculosis treatment.

Predicting the outbreak of hand, foot, and mouth disease in Nanjing, China: a time-series model based on weather variability.

Hand, foot, and mouth disease (HFMD) is a significant public health issue in China and an accurate prediction of epidemic can improve the effectiveness of HFMD control. This study aims to develop a weather-based forecasting model for HFMD using the information on climatic variables and HFMD surveillance in Nanjing, China. Daily data on HFMD cases and meteorological variables between 2010 and 2015 were acquired from the Nanjing Center for Disease Control and Prevention, and China Meteorological Data Sharing Service System, respectively. A multivariate seasonal autoregressive integrated moving average (SARIMA) model was developed and validated by dividing HFMD infection data into two datasets: the data from 2010 to 2013 were used to construct a model and those from 2014 to 2015 were used to validate it. Moreover, we used weekly prediction for the data between 1 January 2014 and 31 December 2015 and leave-1-week-out prediction was used to validate the performance of model prediction. SARIMA (2,0,0)52 associated with the average temperature at lag of 1 week appeared to be the best model (R 2 = 0.936, BIC = 8.465), which also showed non-significant autocorrelations in the residuals of the model. In the validation of the constructed model, the predicted values matched the observed values reasonably well between 2014 and 2015. There was a high agreement rate between the predicted values and the observed values (sensitivity 80%, specificity 96.63%). This study suggests that the SARIMA model with average temperature could be used as an important tool for early detection and prediction of HFMD outbreaks in Nanjing, China.