Background: Drug-resistant tuberculosis (DR-TB) is a major public health problem caused by various factors. It is essential to systematically investigate the epidemiological and, in particular, the ecological factors of DR-TB for its prevention and control. Studies of the ecological factors can provide information on etiology, and assist in the effective prevention and control of disease. So it is of great significance for public health to explore the ecological factors of DR-TB, which can provide guidance for formulating regional prevention and control strategies. Methods: Anti-TB drug resistance data were obtained from the World Health Organization/International Union Against Tuberculosis and Lung Disease (WHO/UNION) Global Project on Anti-Tuberculosis Drug Resistance Surveillance, and data on ecological factors were collected to explore the ecological factors for DR-TB. Partial least square path modeling (PLS-PM), in combination with ordinary least squares (OLS) regression, as well as geographically weighted regression (GWR), were used to build a global and local spatial regression model between the latent synthetic DR-TB factor ("DR-TB") and latent synthetic risk factors. Results: OLS regression and PLS-PM indicated a significant globally linear spatial association between "DR-TB" and its latent synthetic risk factors. However, the GWR model showed marked spatial variability across the study regions. The "TB Epidemic", "Health Service" and "DOTS (directly-observed treatment strategy) Effect" factors were all positively related to "DR-TB" in most regions of the world, while "Health Expenditure" and "Temperature" factors were negatively related in most areas of the world, and the "Humidity" factor had a negative influence on "DR-TB" in all regions of the world. Conclusions: In summary, the influences of the latent synthetic risk factors on DR-TB presented spatial variability. We should formulate regional DR-TB monitoring planning and prevention and control strategies, based on the spatial characteristics of the latent synthetic risk factors and spatial variability of the local relationship between DR-TB and latent synthetic risk factors.

Hand, foot and mouth disease (HFMD) has been a substantial burden throughout the Asia-Pacific countries over the past decades. For the purposes of disease prevention and climate change health impact assessment, it is important to understand the temperature–disease association for HFMD in different geographical locations. This study aims to assess the impact of temperature on HFMD incidence in an inland city and a coastal city and investigate the heterogeneity of temperature–disease associations. Daily morbidity data and meteorological variables of the study areas were collected for the period from 2007 to 2012. A total of 108,377 HFMD cases were included in this study. A distributed lag non-linear model (DLNM) with Poisson distribution was used to examine the nonlinear lagged effects of daily mean temperature on HFMD incidence. After controlling potential confounders, temperature showed significant association with HFMD incidence and the two cities demonstrated different impact modes ( I²= 96.1%; p < 0.01). The results highlight the effect of temperature on HFMD incidence and the impact pattern may be modified by geographical localities. Our findings can be a practical reference for the early warning and intervention strategies of HFMD.

Hand, foot, and mouth disease (HFMD) is the most common communicable disease in China. Shandong Province is one of the most seriously affected areas. The distribution of HFMD had spatial heterogeneity and seasonal characteristic in this setting. The aim of this study was to explore the associations between climate and HFMD by a Bayesian approach from spatio-temporal interactions perspective. The HFMD data of Shandong Province during 2008–2012 were derived from the China National Disease Surveillance Reporting and Management System. And six climatic indicators were obtained from the Meteorological Bureau of Shandong Province. The global spatial autocorrelation statistic (Moran’s I) was used to detect the spatial autocorrelation of HFMD cases in each year. The optimal one among four Bayesian models was further adopted to estimate the relative risk of the occurrence of HFMD via Markov chain Monte Carlo. The annual average incidence rate of HFMD was 104.40 per 100,000 in Shandong Province. Positive spatial autocorrelation appeared at county level (Moran’s I ≥0.30, P < 0.001). The best fitting Spatio-temporal interactive model showed that annual average temperature, annual average pressure, annual average relative humidity, annual average wind speed and annual sunshine hours were significantly positive related to the occurrence of HFMD. The estimated relative risk of 36, 87, 91, 79, 65 out of 140 counties for 2008–2012 respectively were significantly more than 1. There were obvious spatio-temporal heterogeneity of HFMD in Shandong Province, and the climatic indicators were associated with the epidemic of HFMD. Bayesian approach should be recommended to capture the spatial-temporal pattern of HFMD.

Few studies have investigated the associations between outdoor air pollution and outpatient visits for respiratory diseases (RDs) in general population. We collected daily outpatient data of primary RDs from five hospitals in Jinan during January 2012 and December 2016, as well as daily measurements of air pollutants from the Jinan Environmental Monitoring Center and daily meteorological variables from the China Meteorological Data Sharing Service System. A generalized additive model (GAM) with quasi-Poisson regression was constructed to estimate the associations between daily average concentrations of outdoor air pollutants (PM2.5,PM10, SO2, NO2, CO and O3) and daily outpatient visits of RDs after adjusting for long-time trends, seasonality, the “day of the week” effect, and weather conditions. Subgroup analysis stratified by gender, age group and the type of RDs was conducted. A total of 1,373,658 outpatient visits for RDs were identified. Increases of 10 μg/m3 in PM2.5, PM10, NO2, CO and O3 were associated with0.168% (95% CI, 0.072–0.265%), 0.149% (95% CI, 0.082–0.215%), 0.527% (95% CI, 0.211–0.843%), 0.013% (95% CI, 0.003–0.023%), and 0.189% (95% CI, 0.032–0.347%) increases in daily outpatient visits for RDs, respectively. PM2.5 and PM10 showed instant and continuous effects, while NO2, CO and O3 showed delayed effects on outpatient visits for RDs. In stratification analysis, PM2.5 and PM10 were associated with acute RDs only. Exposure to outdoor air pollutants including PM2.5, PM10, NO2, CO and O3 associated with increased risk of outpatient visits for RDs.

The influence of meteorological factors on hand, foot, and mouth disease (HFMD) is not on the same scale, it’s rare for previous studies to measure and recognize the independent regression relationship between each variable in space and time scale. This study used a multiscale geographically and temporally weighted regression (MGTWR) model to explore the relationship between the incidence of HFMD and related meteorological factors in Shandong Province, China, during 2015–2019 and attempted to quantify the influence of meteorological factors on HFMD under different spatiotemporal effects. Meanwhile, we used the Global Moran’s I statistic and Local Moran’s I statistic to test the spatial autocorrelation of the incidence of HFMD. HFMD had spatial autocorrelation at the county level in Shandong Province. The MGTWR model outperformed the OLS and GTWR models in determining the relationship between meteorological factors and HFMD. The study highlights significant spatiotemporal non-stationarity in the relationship between meteorological factors and HFMD. Temperature was predominantly positively correlated with HFMD, especially in the peninsula region during spring and summer. Humidity exhibited a predominantly positive correlation, especially in the Shandong Peninsula. Precipitation also showed a positive correlation with HFMD, particularly in western regions and during the winter months. Wind speed had a predominantly negative correlation with HFMD in the central and southwestern regions. The results might help public health authorities set priorities for targeted prevention and control measures in different regions and weather conditions, and provide guidance for the government to rationally allocate public health resources.

Objective: Type 2 diabetes is more common in adults, but is becoming the major concern in children and adolescent recently. This study aimed to provide additional pharmaceutical management for children and adolescents with type 2 diabetes by assessing the efficacy and safety of several glucose-lowering drugs.Methods: Searches were performed in PubMed, Medline, Ovid, Cochrane Controlled Register of Trials (CENTRAL), and ClinicalTrials.gov that reported the efficacy and safety of drugs for children and adolescents with type 2 diabetes. Pooled effects were calculated by frequentist fixed effects network meta-analyses and additive network meta-analyses.Results: A total of 12 trials assessing eight glucose-lowering drugs were included, which compose of seven trials with monotherapy and five trials with combination therapies. Network meta-analysis results showed compared to placebo, saxagliptin+metformin (mean difference (MD) -1.91% [-2.85%, -0.97%]), liraglutide+metformin (MD -1.45% [-1.65%, -1.26%]), and liraglutide (MD -0.90% [-1.35%, -0.45%]) were the top 3 drugs that significantly reduced hemoglobin A1c (HbA1c). Sitagliptin+metformin, dapagliflozin, exenatide-2mcg, linagliptin-5mg, metformin, exenatide-5/10mcg, glimepiride, and sitagliptin also showed significant reduction in HbA1c. There were no significant differences between treatments in the incidence of adverse events, except that liraglutide+metformin had significant adverse effect such as abdominal pain. In addition, dapagliflozin, sitagliptin+metformin, and saxagliptin+metformin showed better efficacy compared with FDA-approved drugs.Conclusions: The top 10 treatments of type 2 diabetes in children and adolescents aged 10–17 years were saxagliptin+metformin, liraglutide+metformin, liraglutide, dapagliflozin, exenatide–2 mcg, sitagliptin+metformin, linagliptin–5 mg, linagliptin–1 mg, metformin, and exenatide–5/10 mcg.Systematic Review Registration: https://www.crd.york.ac.uk/prospero/display_record.php?RecordID=284897, identifier CRD42021284897.