Area disease estimation based on sentinel hospital records

Prevalence of Respiratory Viruses in Children With Acute Respiratory Infections in Shanghai, China, From 2013 to 2022

BACKGROUND

A variety of viruses can cause acute respiratory infections (ARIs), resulting in a high disease burden worldwide. To explore the dominant viruses and their prevalence characteristics in children with ARIs, comprehensive surveillance was carried out in the Pudong New Area of Shanghai.

METHODS

Between January 2013 and December 2022, the basic and clinical information, and respiratory tract specimens of 0-14 years old children with ARIs were collected in five sentinel hospitals in Shanghai Pudong. Each specimen was tested for eight respiratory viruses, and the positive rates of different age groups, case types (inpatient or outpatient) were analyzed.

RESULTS

In our study, 30.67% (1294/4219) children with ARIs were positive for at least one virus. Influenza virus (IFV) was the most commonly detected respiratory virus (349/4219, 8.27%), followed by respiratory syncytial virus (RSV) (217/4219, 5.14%), para-influenza virus (PIV) (215/4219, 5.10%), and human coronavirus (HCoV, including 229E, OC43, NL63, and HKU1) (184/4219, 4.36%). IFV was the leading respiratory virus in outpatients aged 5-14 years (201/1673, 12.01%); RSV was the most prevalent respiratory virus in both inpatients (61/238, 25.63%) and outpatients (4/50, 8.00%) for ARI patients aged <6 months old. For PIV, HMPV, HCoV, and HRV, the risk of infection usually was higher among young children. Co-infection with more than two viruses was seen in 3.25% (137/4219).

CONCLUSIONS

IFV and RSV played important roles in ARIs among children, but the risk populations were different. There are needs for targeted diagnosis and treatment and necessary immunization and non-pharmaceutical interventions.

Applicability of Grassland Production Estimation Using Remote Sensing for the Mongolian Plateau by Comparing Typical Regions in China and Mongolia

Grasslands on the Mongolian Plateau are critical for supporting local sustainable development. Sufficient measured sample information is the basis of remote sensing modeling and estimation of grassland production. Limited by field inventory costs, it is difficult to collect sufficient and widely distributed samples in the Mongolian Plateau, especially in transboundary areas, which affects the results of grassland production estimation. Here, considering that the measured sample points are sparse, this study took Xilingol League of Inner Mongolia Autonomous Region in China and Dornogovi Province in Mongolia as the study areas, introduced multiple interpolation methods for interpolation experiments, established a statistical regression model based on the above measured and interpolated samples combined with the normalized differential vegetation index, and discussed the applicability of grassland production estimation. The comparison results revealed that the point estimation biased sample hospital-based area disease estimation method and radial basis function showed the best interpolation results for grassland production in Xilingol League and Dornogovi Province, respectively. The power function model was suitable for grassland production estimation in both regions. By inversion, we obtained annual grassland production for 2010–2021 and the uneven spatial distribution of grassland production in both regions. In these two regions, the spatial change in grassland production showed a decreasing trend from northeast to southwest, and the interannual change generally showed a dynamic upward trend. The growth rate of grassland output was faster in Xilingol League than in Dornogovi Province with similar physical geography and climate conditions, indicating that the animal husbandry regulation policies play important roles beyond the influence of climate change. The study recommended grassland estimation methods for an area with sparse samples and the results can be used to support decision making for sustainable animal husbandry and grassland succession management.

Spatio-temporal variations of typhoid and paratyphoid fevers in Zhejiang Province, China from 2005 to 2015

Typhoid and paratyphoid are two common enteric infectious diseases with serious gastrointestinal symptoms. Data was collected of the registered cases in Zhejiang Province from 2005 to 2015. The epidemiological characteristics were investigated and high-risk regions were detected with descriptive epidemiological methods and in-depth spatio-temporal statistics. A sharp decline in the incidences of both diseases was observed. The seasonal patterns were identified with typhoid and paratyphoid, one in summer from May to September was observed from 2005 to 2010 and the other lesser one in spring from January to March only observed from 2005 to 2007. The men were more susceptible and the adults aged 20 to 60 constituted the major infected population. The farmers were more likely to get infected, especially to typhoid. The Wilcoxon sum rank test proved that the incidences in the coastal counties were significantly higher than the inland. Besides, a positive autocorrelation was obtained with typhoid fever in global autocorrelation analysis but not with paratyphoid fever. Local autocorrelation analysis and spatio-temporal scan statistics revealed that high-risk clusters were located mainly in the coastal regions with typhoid fever but scattered across the province with paratyphoid fever. The spatial risks were evaluated quantitatively with hierarchical Bayesian models.

Viral pathogens among elderly people with acute respiratory infections in Shanghai, China: Preliminary results from a laboratory-based surveillance, 2012-2015

Acute respiratory infections (ARIs), with viral pathogens as the major contributors, are the most common illnesses worldwide, and increase the morbidity and mortality among the elderly population. The clinical and pathological features of elderly people with ARIs need to be identified for disease intervention. From January 1, 2012 through December 31, 2015, respiratory specimens from patients above 60 years old with ARIs were collected from the outpatient and inpatient settings of six sentinel hospitals in Pudong New Area. Each specimen was tested via multiplex polymerase chain reaction (PCR) for eight target viral etiologies including influenza, human rhinovirus (HRV), human para‐influenza virus (PIV), adenovirus (ADV), respiratory syncytial virus (RSV), human metapneumovirus (hMPV), human coronavirus (hCoVs), and human bocavirus (hBoV). A total of 967 elderly patients with ARIs were enrolled, including 589 (60.91%) males, and the median age was 73 years old. 306 (31.64%) patients were tested positive for any one of the eight viruses, including 276 single infections and 30 co‐infections. Influenza was the predominant virus (14.17%, 137/967), detected from 21.35% (76/356) of the outpatients and 9.98% (61/611) of the inpatients. Influenza infections presented two annual seasonal peaks during winter and summer. Compared with non‐influenza patients, those with influenza were more likely to have fever, cough, sore throat, and fatigue. This study identified influenza as the leading viral pathogen among elderly with ARIs, and two seasonal epidemic peaks were observed in Shanghai. An influenza vaccination strategy needs to be advocated for the elderly population.

SCM: a practical tool to implement hospital-based syndromic surveillance

BACKGROUND

Syndromic surveillance has been widely used for the early warning of infectious disease outbreaks, especially in mass gatherings, but the collection of electronic data on symptoms in hospitals is one of the fundamental challenges that must be overcome during operating a syndromic surveillance system. The objective of our study is to describe and evaluate the implementation of a symptom-clicking-module (SCM) as a part of the enhanced hospital-based syndromic surveillance during the 41st World Exposition in Shanghai, China, 2010.

METHODS

The SCM, including 25 targeted symptoms, was embedded in the sentinels' Hospital Information Systems (HIS). The clinicians used SCM to record these information of all the visiting patients, and data were collated and transmitted automatically in daily batches. The symptoms were categorized into seven targeted syndromes using pre-defined criteria, and statistical algorithms were applied to detect temporal aberrations in the data series.

RESULTS

SCM was deployed successfully in each sentinel hospital and was operated during the 184-day surveillance period. A total of 1,730,797 patient encounters were recorded by SCM, and 6.1 % (105,352 visits) met the criteria of the seven targeted syndromes. Acute respiratory and gastrointestinal syndromes were reported most frequently, accounted for 92.1 % of reports in all syndromes, and the aggregated time-series presented an obvious day-of-week variation over the study period. In total, 191 aberration signals were triggered, and none of them were identified as outbreaks after verification and field investigation.

CONCLUSIONS

SCM has acted as a practical tool for recording symptoms in the hospital-based enhanced syndromic surveillance system during the 41st World Exposition in Shanghai, in the context of without a preexisting electronic tool to collect syndromic data in the HIS of the sentinel hospitals.

Towards Identifying and Reducing the Bias of Disease Information Extracted from Search Engine Data

The estimation of disease prevalence in online search engine data (e.g., Google Flu Trends (GFT)) has received a considerable amount of scholarly and public attention in recent years. While the utility of search engine data for disease surveillance has been demonstrated, the scientific community still seeks ways to identify and reduce biases that are embedded in search engine data. The primary goal of this study is to explore new ways of improving the accuracy of disease prevalence estimations by combining traditional disease data with search engine data. A novel method, Biased Sentinel Hospital-based Area Disease Estimation (B-SHADE), is introduced to reduce search engine data bias from a geographical perspective. To monitor search trends on Hand, Foot and Mouth Disease (HFMD) in Guangdong Province, China, we tested our approach by selecting 11 keywords from the Baidu index platform, a Chinese big data analyst similar to GFT. The correlation between the number of real cases and the composite index was 0.8. After decomposing the composite index at the city level, we found that only 10 cities presented a correlation of close to 0.8 or higher. These cities were found to be more stable with respect to search volume, and they were selected as sample cities in order to estimate the search volume of the entire province. After the estimation, the correlation improved from 0.8 to 0.864. After fitting the revised search volume with historical cases, the mean absolute error was 11.19% lower than it was when the original search volume and historical cases were combined. To our knowledge, this is the first study to reduce search engine data bias levels through the use of rigorous spatial sampling strategies.

A new methodology of spatial cross-correlation analysis

Spatial correlation modeling comprises both spatial autocorrelation and spatial cross-correlation processes. The spatial autocorrelation theory has been well-developed. It is necessary to advance the method of spatial cross-correlation analysis to supplement the autocorrelation analysis. This paper presents a set of models and analytical procedures for spatial cross-correlation analysis. By analogy with Moran's index newly expressed in a spatial quadratic form, a theoretical framework is derived for geographical cross-correlation modeling. First, two sets of spatial cross-correlation coefficients are defined, including a global spatial cross-correlation coefficient and local spatial cross-correlation coefficients. Second, a pair of scatterplots of spatial cross-correlation is proposed, and the plots can be used to visually reveal the causality behind spatial systems. Based on the global cross-correlation coefficient, Pearson's correlation coefficient can be decomposed into two parts: direct correlation (partial correlation) and indirect correlation (spatial cross-correlation). As an example, the methodology is applied to the relationships between China's urbanization and economic development to illustrate how to model spatial cross-correlation phenomena. This study is an introduction to developing the theory of spatial cross-correlation, and future geographical spatial analysis might benefit from these models and indexes.

Seizure detection, seizure prediction, and closed-loop warning systems in epilepsy

Nearly one-third of patients with epilepsy continue to have seizures despite optimal medication management. Systems employed to detect seizures may have the potential to improve outcomes in these patients by allowing more tailored therapies and might, additionally, have a role in accident and SUDEP prevention. Automated seizure detection and prediction require algorithms which employ feature computation and subsequent classification. Over the last few decades, methods have been developed to detect seizures utilizing scalp and intracranial EEG, electrocardiography, accelerometry and motion sensors, electrodermal activity, and audio/video captures. To date, it is unclear which combination of detection technologies yields the best results, and approaches may ultimately need to be individualized. This review presents an overview of seizure detection and related prediction methods and discusses their potential uses in closed-loop warning systems in epilepsy.

Estimation of citywide air pollution in Beijing

There has been discrepancies between the daily air quality reports of the Beijing municipal government, observations recorded at the U.S. Embassy in Beijing, and Beijing residents’ perceptions of air quality. This study estimates Beijing’s daily area PM_2.5 mass concentration by means of a novel technique SPA (Single Point Areal Estimation) that uses data from the single PM_2.5 observation station of the U.S Embassy and the 18 PM₁₀ observation stations of the Beijing Municipal Environmental Protection Bureau. The proposed technique accounts for empirical relationships between different types of observations, and generates best linear unbiased pollution estimates (in a statistical sense). The technique extends the daily PM_2.5 mass concentrations obtained at a single station (U.S. Embassy) to a citywide scale using physical relations between pollutant concentrations at the embassy PM_2.5 monitoring station and at the 18 official PM₁₀ stations that are evenly distributed across the city. Insight about the technique’s spatial estimation accuracy (uncertainty) is gained by means of theoretical considerations and numerical validations involving real data. The technique was used to study citywide PM_2.5 pollution during the 423-day period of interest (May 10, 2010 to December 6, 2011). Finally, a freely downloadable software library is provided that performs all relevant calculations of pollution estimation.

European outbreaks of atypical myopathy in grazing equids (2006-2009): spatiotemporal distribution, history and clinical features

REASONS FOR PERFORMING STUDY

Improved understanding of the epidemiology of atypical myopathy (AM) will help to define the environmental factors that permit or support the causal agent(s) to exert toxicity.

OBJECTIVES

This European survey of AM aimed to describe spatiotemporal distribution, survival, clinical signs, circumstances in which AM develops and its different expressions between countries and over time.

METHODS

The spatiotemporal distribution, history and clinical features of AM cases reported to the Atypical Myopathy Alert Group from 2006 to 2009 were described. Comparisons of data from the most severely affected countries and from the large outbreaks were made with Fisher's exact and Welch's tests with Bonferroni correction.

RESULTS

Of 600 suspected cases, 354 met the diagnostic criteria for confirmed or highly probable AM. The largest outbreaks occurred during the autumns of 2006 and 2009 in Belgium, France and Germany. For the first time, donkeys, zebras and old horses were affected, and clinical signs such as gastrointestinal impaction, diarrhoea, penile prolapse, buccal ulceration and renal dysfunction were observed. Affected horses spent >6 h/day on pastures that almost always contained or were surrounded by trees. The latency period was estimated at up to 4 days. Overall survival rate was 26%. Although differences between countries in affected breeds, body condition, horse management and pasture characteristics were recognised, the common presenting clinical signs and mortality were similar between countries.

CONCLUSIONS AND POTENTIAL RELEVANCE

This study describes new data on case details, history and clinical course of AM that is of preventive, diagnostic and therapeutic value. However, the true impact of the findings of this study on the development of or severity of AM should be tested with case-control studies.