Associations of residential greenness exposure and ambient air pollutants with newly-diagnosed drug-resistant tuberculosis cases.

Growing evidence has found the health protective effects of greenness exposure on tuberculosis (TB) and the impact of ambient air pollutants on TB drug-resistance. However, it remains unclear whether residential greenness is also beneficial to reduce TB drug-resistance, and whether air pollution modify the greenness-TB resistance relationship. We enrolled 5006 newly-diagnosed TB patients from Shandong, China, during 2014 to 2021. Normalized Difference Vegetation Index (NDVI) in 250 m and 500 m buffer around individuals' residential zone was used to assess greenness exposure. All patients were divided by quartiles of NDVI250-m and NDVI500-m (from low to high: Q1, Q2, Q3, Q4) respectively. Six logistic regression models (NDVI, NDVI + PM2.5/PM10/SO2/NO2/O3) were used to estimate the association of NDVI and TB drug-resistance when adjusting different air pollutants or not. All models were adjusted for age, gender, body mass index, complications, smoking, drinking, population density, nighttime light index, road density. Compared with participants in NDVI250-m Q1 and NDVI500-m Q1, other groups had lower rates of MDR-TB, PDR-TB, RFP-resistance, SM-resistance, RFP + SM resistance, INH + RFP + EMB + SM resistance. NDVI500-m reduced the risk of multidrug resistant tuberculosis (MDR-TB) and the adjusted odds ratio (aOR, 95% confidence interval, CI) compared with NDVI500-m Q1 were 0.736 (0.547-0.991) in NDVI + PM10 model, 0.733 (0.544-0.986) in NDVI + PM2.5 model, 0.735(0.546-0.99) in NDVI + SO2 model, 0.736 (0.546-0.991) in NDVI + NO2 model, respectively, P < 0.05. NDVI500-m contributed to a decreased risk of streptomycin (SM)-resistance. The aOR of rifampicin (RFP) + SM resistance were 0.132 (NDVI250-m, Q4 vs Q1, 95% CI: 0.03-0.578), 0.199 (NDVI500-m, Q3 vs. Q1, 95% CI: 0.057-0.688) and 0.264 (NDVI500-m, Q4 vs. Q1, 95% CI: 0.087-0.799). The adjusted ORs (Q2 vs. Q1, 95% CI) of isoniazid (INH) + RFP + ethambutol (EMB) + SM resistance in 500 m buffer were 0.276 (0.119-0.639) in NDVI model, 0.279 (0.11-0.705) in NDVI + PM10 model, 0.281 (0.111-0.713) in NDVI + PM2.5 model, 0.279 (0.11-0.709) in NDVI + SO2 model, 0.296 (0.117-0.754) in NDVI + NO2 model, 0.294 (0.116-0.748) in NDVI + O3 model, respectively. The study showed, for the first time, that residential greenness exposure in 500 m buffer is beneficial for reducing newly-diagnosed DR-TB (including PDR-RB, MDR-TB, MR-TB), and ambient air pollutants may partially mediate this association.

Impact of alcohol drinking and tobacco smoking on the drug-resistance of newly diagnosed tuberculosis: a retrospective cohort study in Shandong, China, during 2004-2020.

OBJECTIVES: To investigate the independent and collective impact of alcohol drinking and tobacco smoking on the drug-resistance of newly diagnosed tuberculosis (TB). DESIGN: This was a retrospective cohort study. SETTING: Shandong, China. PARTICIPANTS: Patients with newly diagnosed TB from 1 January 2004 to 31 December 2020 were collected. Exclusive criteria: retreated cases; extrapulmonary tuberculosis; without information on drug susceptibility testing results, smoking or drinking habits; bacteriological identification as non-tuberculous mycobacteria. PRIMARY AND SECONDARY OUTCOME MEASURES: Patients were classified into four groups including smokers only (G1), drinker only (G2), smoker +drinker (G3), non-smoker +non-drinker group (G0). We described the drug-resistant profiles, clinical factors and calculated the ORs of different drug-resistance among G1, G2, G3, compared with G0 through univariate and multivariate logistics regression models. RESULTS: Of the 7996 TB cases enrolled, the proportions of G1, G2, G3 and G0 were 8.25%, 3.89%, 16.46% and 71.40%, respectively. The rates of drug-resistant (DR)-TB, mono-resistant TB, multidrug resistant (MDR)-TB, polydrug resistant TB in G1, G2, G3 and G0 were 19.24%/16.4%/17.33%/19.08%, 11.52%/8.68%/10.94%/11.63%, 3.03%/2.57%/2.96%/3.66% and 4.70%/4.82%/3.34%/ 4.08%, respectively. G3 had a higher risk of MDR1: isoniazid +rifampin (adjusted OR (aOR)=1.91, 95% CI: 1.036 to 3.532), but had a lower risk of DR-TB (aOR=0.84, 95% CI: 0.71 to 0.99), rifampin-related resistance (aOR=0.68, 95% CI: 0.49 to 0.93), streptomycin-related resistance (aOR=0.82, 95% CI: 0.68 to 0.99), ethambutol-related resistance (aOR=0.57, 95% CI: 0.34 to 0.95), MDR3: isoniazid +rifampin+streptomycin (aOR=0.41, 95% CI: 0.19 to 0.85), any isoniazid +streptomycin resistance (aOR=0.85, 95% CI: 0.71 to 1.00). However, there were no significant differences between G1 and G0, G2 and G0 in all drug-resistant subtypes. Those patients with cavity had a higher risk of DR-TB among G3 (OR=1.35, 95% CI: 1.01 to 1.81). CONCLUSION: Although we did not found an independent impact of alcohol drinking or tobacco smoking on TB drug-resistance, respectively, these two habits had a combined effect on TB drug-resistance.

Risk factors for drug-resistant tuberculosis, the association between comorbidity status and drug-resistant patterns: a retrospective study of previously treated pulmonary tuberculosis in Shandong, China, during 2004-2019.

OBJECTIVE: This study was designed to identify the risk factors for drug-resistant tuberculosis (DR-TB) and the association between comorbidity and drug resistance among retreated pulmonary tuberculosis (PTB). DESIGN: A retrospective study was conducted among all the 36 monitoring sites in Shandong, China, over a 16-year period. Baseline characteristics were collected from the TB Surveillance System. Categorical variables were compared by Fisher's exact or Pearson's χ2 test. The risk factors for drug resistance were identified using univariable analysis and multivariable logistic models. The influence of comorbidity on different types of drug resistance was evaluated by performing multivariable logistic models with the covariates adjusted by age, sex, body mass index, drinking/smoking history and cavity. RESULTS: A total of 10 975 patients with PTB were recorded during 2004-2019, and of these 1924 retreated PTB were finally included. Among retreated PTB, 26.2% were DR-TB and 12.5% had comorbidity. Smoking (adjusted OR (aOR): 1.69, 95% CI 1.19 to 2.39), cavity (aOR: 1.55, 95% CI 1.22 to 1.97) and comorbidity (aOR: 1.44, 95% CI 1.02 to 2.02) were risk factors for DR-TB. Of 504 DR-TB, 9.5% had diabetes mellitus, followed by hypertension (2.0%) and chronic obstructive pulmonary disease (1.8%). Patients with retreated PTB with comorbidity were more likely to be older, have more bad habits (smoking, alcohol abuse) and have clinical symptoms (expectoration, haemoptysis, weight loss). Comorbidity was significantly associated with DR-TB (aOR: 1.44, 95% CI 1.02 to 2.02), overall rifampin resistance (aOR: 2.17, 95% CI 1.41 to 3.36), overall streptomycin resistance (aOR: 1.51, 95% CI 1.00 to 2.27) and multidrug resistance (aOR: 1.96, 95% CI 1.17 to 3.27) compared with pan-susceptible patients (p<0.05). CONCLUSION: Smoking, cavity and comorbidity lead to an increased risk of drug resistance among retreated PTB. Strategies to improve the host's health, including smoking cessation, screening and treatment of comorbidity, might contribute to the control of tuberculosis, especially DR-TB, in China.

Using a risk model for probability of cancer in pulmonary nodules.

BACKGROUND: Considering the high morbidity and mortality of lung cancer and the high incidence of pulmonary nodules, clearly distinguishing benign from malignant lung nodules at an early stage is of great significance. However, determining the kind of lung nodule which is more prone to lung cancer remains a problem worldwide. METHODS: A total of 480 patients with pulmonary nodule data were collected from Shandong, China. We assessed the clinical characteristics and computed tomography (CT) imaging features among pulmonary nodules in patients who had undergone video-assisted thoracoscopic surgery (VATS) lobectomy from 2013 to 2018. Preliminary selection of features was based on a statistical analysis using SPSS. We used WEKA to assess the machine learning models using its multiple algorithms and selected the best decision tree model using its optimization algorithm. RESULTS: The combination of decision tree and logistics regression optimized the decision tree without affecting its AUC. The decision tree structure showed that lobulation was the most important feature, followed by spiculation, vessel convergence sign, nodule type, satellite nodule, nodule size and age of patient. CONCLUSIONS: Our study shows that decision tree analyses can be applied to screen individuals for early lung cancer with CT. Our decision tree provides a new way to help clinicians establish a logical diagnosis by a stepwise progression method, but still needs to be validated for prospective trials in a larger patient population.