Global prevalence of congenital hypothyroidism among neonates from 1969 to 2020: a systematic review and meta-analysis.

Little is known about the global prevalence of congenital hypothyroidism (CH), though it is known to vary across countries and time periods. This meta-analysis aims to estimate the global and regional prevalence of CH among births between 1969 and 2020. PubMed, Web of Sciences, and Embase databases were searched for relevant studies between January 1, 1975, and March 2, 2020. Pooled prevalence was calculated using a generalized linear mixed model, and expressed as a rate per 10,000 neonates. The meta-analysis involved 116 studies, which analyzed 330,210,785 neonates, among whom 174,543 were diagnosed with CH. The pooled global prevalence of CH from 1969 to 2020 was 4.25 (95% confidence interval (CI) 3.96-4.57). The geographic region with highest prevalence was the Eastern Mediterranean (7.91, 95% CI 6.09-10.26), where the prevalence was 2.48-fold (95% CI 2.04-3.01) that in Europe. The national income level with the highest prevalence was upper-middle (6.76, 95% CI 5.66-8.06), which was 1.91-fold (95% CI 1.65-2.22) that in high-income countries. Global prevalence of CH was 52% (95% CI 4-122%) higher in 2011-2020 than in 1969-1980, after adjusting for geographic region, national income level, and screening strategy.  Conclusion: The global prevalence of CH increased from 1969 to 2020, which may reflect the implementation of national neonatal screening, neonatal testing for thyroid-stimulating hormone, and a lowering of the diagnostic level of this hormone. Additional factors are likely to be driving the increase, which should be identified in future research. What is Known: • Cumulated evidence had suggested that the occurrences of congenital hypothyroidism (CH) among newborns were varied in different countries.. • Up-trends of the birth prevalence of CH were observed in many European and American countries. What is New: • This is the first meta-analysis to estimate global and regional prevalence of CH among newborns. • The global prevalence of CH has increased by 127% since 1969. The Eastern Mediterranean has the highest prevalence and stands out with the most pronounced escalation in the prevalence of CH.

Comparison of cardiovascular magnetic resonance characteristics and clinical prognosis in left ventricular noncompaction patients with and without arrhythmia.

BACKGROUND: Left ventricular noncompaction (LVNC) is a rare type of cardiomyopathy, and one of its clinical manifestations is arrhythmia. Cardiovascular magnetic resonance (CMR) is valuable for the diagnosis and prognosis of LVNC. However, studies are lacking on the use of CMR for LVNC patients with arrhythmia. This study aimed to characterize and compare CMR features and prognosis in LVNC patients with and without arrhythmia. METHODS: Eighty-four LVNC patients diagnosed by CMR were enrolled retrospectively in this study. Clinical data, arrhythmia characteristics, and CMR parameters were collected. Patients were divided into different groups according to the arrhythmia characteristics and CMR manifestations for statistical analysis and comparison. Ventricular tachycardia (VT), ventricular fibrillation (Vf), ventricular flutter (VFL), III° atrioventricular block (III° AVB), Wolff-Parkinson-White syndrome (WPW) and ventricular escape (VE) were defined as malignant arrhythmias and benign arrhythmias included premature ventricular contraction, atrial premature beats, atrial fibrillation, supraventricular tachycardia, supraventricular premature beat, bundle branch block, atrial flutter and sinus tachycardia. The outcome events were defined as a composition event of cardiac death, rehospitalization for heart failure, heart transplantation, and implantation of an implantable cardioverter defibrillator (ICD). RESULTS: Sixty-seven LVNC patients (79.76%) mainly presented with arrhythmia, including premature ventricular beat (33 patients [27.73%]), bundle branch block (14 patients [11.77%]), electrocardiogram waveform changes (18 patients [15.13%]), and ventricular tachycardia (11 patients [9.24%]). The cardiac function and structure parameters had no significant difference among the nonarrhythmia group, benign arrhythmia group, and malignant arrhythmia group. However, the presence of late gadolinium enhancement (LGE) was higher in the malignant arrhythmia group than in the other two groups (p = 0.023). At a mean follow-up of 46 months, cardiac events occurred in twenty-three patients (46.94%). Kaplan-Meier analysis showed that there was no statistically significant difference in prognosis among the nonarrhythmia, benign, and malignant arrhythmia groups, but the patients with arrhythmia and association with LGE + or left ventricular ejection fraction (LVEF) < 30% had a higher risk than patients with LGE- or LVEF > 30% (LGE +, HR = 4.035, 95% CI 1.475-11.035; LVEF < 30%, HR = 8.131, 95% CI 1.805-36.636; P < 0.05). CONCLUSIONS: In LVNC patients, the types of arrhythmias are numerous and unrepresentative, and arrhythmia is not the prognostic factor. Arrhythmia combined with presence of LGE or LVEF < 30% is associated with poor prognosis in LVNC patients.

Fusing remote sensing data with spatiotemporal in situ samples for red tide (Karenia brevis) detection.

We present a novel method for detecting red tide (Karenia brevis) blooms off the west coast of Florida, driven by a neural network classifier that combines remote sensing data with spatiotemporally distributed in situ sample data. The network detects blooms over a 1-km grid, using seven ocean color features from the MODIS-Aqua satellite platform (2002-2021) and in situ sample data collected by the Florida Fish and Wildlife Conservation Commission and its partners. Model performance was demonstrably enhanced by two key innovations: depth normalization of satellite features and encoding of an in situ feature. The satellite features were normalized to adjust for depth-dependent bottom reflection effects in shallow coastal waters. The in situ data were used to engineer a feature that contextualizes recent nearby ground truth of K. brevis concentrations through a K-nearest neighbor spatiotemporal proximity weighting scheme. A rigorous experimental comparison revealed that our model outperforms existing remote detection methods presented in the literature and applied in practice. This classifier has strong potential to be operationalized to support more efficient monitoring and mitigation of future blooms, more accurate communication about their spatial extent and distribution, and a deeper scientific understanding of bloom dynamics, transport, drivers, and impacts in the region. This approach also has the potential to be adapted for the detection of other algal blooms in coastal waters. Integr Environ Assess Manag 2024;00:1-15. © 2024 SETAC.

A Hierarchical Spatial Transformer for Massive Point Samples in Continuous Space.

Transformers are widely used deep learning architectures. Existing transformers are mostly designed for sequences (texts or time series), images or videos, and graphs. This paper proposes a novel transformer model for massive (up to a million) point samples in continuous space. Such data are ubiquitous in environment sciences (e.g., sensor observations), numerical simulations (e.g., particle-laden flow, astrophysics), and location-based services (e.g., POIs and trajectories). However, designing a transformer for massive spatial points is non-trivial due to several challenges, including implicit long-range and multi-scale dependency on irregular points in continuous space, a non-uniform point distribution, the potential high computational costs of calculating all-pair attention across massive points, and the risks of over-confident predictions due to varying point density. To address these challenges, we propose a new hierarchical spatial transformer model, which includes multi-resolution representation learning within a quad-tree hierarchy and efficient spatial attention via coarse approximation. We also design an uncertainty quantification branch to estimate prediction confidence related to input feature noise and point sparsity. We provide a theoretical analysis of computational time complexity and memory costs. Extensive experiments on both real-world and synthetic datasets show that our method outperforms multiple baselines in prediction accuracy and our model can scale up to one million points on one NVIDIA A100 GPU. The code is available at https://github.com/spatialdatasciencegroup/HST.

Assessing the causal effects of long-term exposure to PM2.5 during pregnancy on cognitive function in the adolescence: Evidence from a nationwide cohort in China.

The effects of air pollution on adolescents need further consideration. Although there is evidence that maternal exposure to air pollution may affect the cognitive function of offspring, relevant studies remain limited and inconsistent, with a lack of studies assessing the causal effects and evidence from developing countries. Using data from Chinese Family Panel Studies, a representative Chinese nationwide cohort study, OLS combined with instrumental variable + two-stage least square (IV+2SLS) was used to explore the causal effects of exposure to PM2.5 concentrations during pregnancy on the cognitive function of offspring when they become adolescents. After detailed argumentation and multiple testing, Planetary Boundary Layer Height (PBLH) and Surface Pressure (SP) were selected as the instrumental variables for this study. One thousand five hundred fifty-five adolescents participated in this study, with a mean age of 13.3 years (sd = 2.3). There were 706 females (45.4%), the mean maternal PM2.5 exposure concentration was 64.9 µg/m3, and recorded a mean cognitive function score of 38.1 (sd = 9.4). The OLS results found that maternal exposure to air pollution increased cognitive function in offspring adolescents, corroborating the presence of endogeneity. Multi-domain knowledge, the results of the weak instrumental variable assessments of F-tests (F = 237 > 10) and Stock-yogo tests (minimum eigenvalue statistic = 153.16 > 16.38), and the results of the Hansen J overidentification test (p > 0.05) verified the plausibility and validity of the instrumental variables. The IV+2SLS results, following causal modeling, showed that PM2.5 exposure during pregnancy impairs the cognitive ability of offspring adolescents (ß = -0.040, p < 0.05). Robustness tests also validated the results. This study provides important policy implications for developing countries on protecting their adolescents and reminds parents that the protection of adolescents from air pollution should begin from conception.

Flood Inundation Mapping with Limited Observations Based on Physics-Aware Topography Constraint.

Spatial classification with limited observations is important in geographical applications where only a subset of sensors are deployed at certain spots or partial responses are collected in field surveys. For example, in observation-based flood inundation mapping, there is a need to map the full flood extent on geographic terrains based on earth imagery that partially covers a region. Existing research mostly focuses on addressing incomplete or missing data through data cleaning and imputation or modeling missing values as hidden variables in the EM algorithm. These methods, however, assume that missing feature observations are rare and thus are ineffective in problems whereby the vast majority of feature observations are missing. To address this issue, we recently proposed a new approach that incorporates physics-aware structural constraint into the model representation. We design efficient learning and inference algorithms. This paper extends our recent approach by allowing feature values of samples in each class to follow a multi-modal distribution. Evaluations on real-world flood mapping applications show that our approach significantly outperforms baseline methods in classification accuracy, and the multi-modal extension is more robust than our early single-modal version. Computational experiments show that the proposed solution is computationally efficient on large datasets.

Spatial inequality, characteristics of internal migration, and pulmonary tuberculosis in China, 2011-2017: a spatial analysis.

BACKGROUND: Human migration facilitate the spread of tuberculosis (TB). Migrants face an increased risk of TB infection. In this study, we aim to explore the spatial inequity of sputum smear-positive pulmonary TB (SS + PTB) in China; and the spatial heterogeneity between SS + PTB and internal migration. METHODS: Notified SS + PTB cases in 31 provinces in mainland China were obtained from the national web-based PTB surveillance system database. Internal migrant data were extracted from the report on China's migrant population development. Spatial autocorrelations were explored using the global Moran's statistic and local indicators of spatial association. The spatial variation in temporal trends was performed using Kulldorff's scan statistic. Fixed effect and spatial autoregressive models were used to explore the spatial inequity between SS + PTB and internal migration. RESULTS: A total of 2 380 233 SS + PTB cases were reported in China between 2011 and 2017, of which, 1 716 382 (72.11%) were male and 663 851 (27.89%) were female. Over 70% of internal migrants were from rural households and had lower income and less education. The spatial variation in temporal trend results showed that there was an 9.9% average annual decrease in the notification rate of SS + PTB from 2011 to 2017; and spatial clustering of SS + PTB cases was mainly located in western and southern China. The spatial autocorrelation results revealed spatial clustering of internal migration each year (2011-2017), and the clusters were stable within most provinces. Internal emigration, urban-to-rural migration and GDP per capita were significantly associated with SS + PTB, further, internal emigration could explain more variation in SS + PTB in the eastern region in mainland. However, internal immigration and rural-to-urban migration were not significantly associated with SS + PTB across China. CONCLUSIONS: Our study found the spatial inequity between SS + PTB and internal migration. Internal emigration, urban-to-rural migration and GDP per capita were statistically associated with SS + PTB; the negative association was identified between internal emigration, urban-to-rural migration and SS + PTB. Further, we found those migrants with lower income and less education, and most of them were from rural households. These findings can help stakeholders to implement effective PTB control strategies for areas at high risk of PTB and those with high rates of internal migration.