Bayesian mapping of neural tube defects prevalence in Heshun County, Shanxi Province, China during 1998～2001

Objective: To estimate the prevalence rates of neural tube defects (NTDs) in Heshun County, Shanxi Province, China by Bayesian smoothing technique. Methods: A total of 80 infants in the study area who were diagnosed with NTDs were analyzed. Two mapping techniques were then used. Firstly, the GIS software ArcGIS was used to map the crude prevalence rates. Secondly, the data were smoothed by the method of empirical Bayes estimation. Results: The classical statistical approach produced an extremely dishomogeneous map, while the Bayesian map was much smoother and more interpretable. The maps produced by the Bayesian technique indicate the tendency of villages in the southeastern region to produce higher prevalence or risk values. Conclusions: The Bayesian smoothing technique addresses the issue of heterogeneity in the population at risk and it is therefore recommended for use in explorative mapping of birth defects. This approach provides procedures to identify spatial health risk levels and assists in generating hypothesis that will be investigated in further detail.

Typology of the Main Fungal Diseases Affecting Winter Wheat in the Grand Duchy of Luxembourg

Over the 2003-2009 period, field campaigns were carried out in order to identify the main fungal diseases of winter wheat （Triticum aestivum） in the Grand-Duchy of Luxembourg. Four fungal diseases （septoria leaf blotch （SLB）, wheat leaf rust （WLR）, wheat powdery mildew （WPM） and fusarium head blight （FHB）） were observed and a regional-based typology was established according to their severity and prevalence. In the Gutland （South）, SLB severity was strong （about 51% on average） and higher than the severity （about 16%） prevailing in the Oesling （North）. Similar typology was observed with the WLR： high severity in the Gutland （66% and 57% for the years 2003 and 2007, respectively） and low severity （〈 1%） in the Oesling. The FHB was also present in the Eastern part of the Gutland, with a prevalence and severity significantly higher （P = 0.049 and P = 0.012, respectively, Tukey＇s test） compared with their values in the Oesling. On the other hand, the WPM severity was high in the Oesling （15% to 40%） while less than 1% in the Gutland. Such a study is important for the spatial mapping of wheat fungal diseases risk based on agroclimatic parameters and for defining optimal frequencies and dates of chemical treatments.

Narrative minireview of the spatial epidemiology of substance use disorder in the United States:Who is at risk and where?

Drug overdose is the leading cause of death by injury in the United States.The incidence of substance use disorder(SUD)in the United States has increased steadily over the past two decades,becoming a major public health problem for the country.The drivers of the SUD epidemic in the United States have changed over time,characterized by an initial heroin outbreak between 1970 and 1999,followed by a painkiller outbreak,and finally by an ongoing synthetic opioid outbreak.The nature and sources of these abused substances reveal striking differences in the socioeconomic and behavioral factors that shape the drug epidemic.Moreover,the geospatial distribution of the SUD epidemic is not homogeneous.The United States has specific locations where vulnerable communities at high risk of SUD are concentrated,reaffirming the multifactorial socioeconomic nature of this epidemic.A better understanding of the SUD epidemic under a spatial epidemiology framework is necessary to determine the factors that have shaped its spread and how these patterns can be used to predict new outbreaks and create effective mitigation policies.This narrative minireview summarizes the current records of the spatial distribution of the SUD epidemic in the United States across different periods,revealing some spatiotemporal patterns that have preceded the occurrence of outbreaks.By analyzing the epidemic of SUD-related deaths,we also describe the epidemic behavior in areas with high incidence of cases.Finally,we describe public health interventions that can be effective for demographic groups,and we discuss future challenges in the study and control of the SUD epidemic in the country.

Spatial epidemiology of diabetes: Methods and insights

Diabetes mellitus(DM)is a growing epidemic with global proportions.It is estimated that in 2019,463 million adults aged 20-79 years were living with DM.The latest evidence shows that DM continues to be a significant global health challenge and is likely to continue to grow substantially in the next decades,which would have major implications for healthcare expenditures,particularly in developing countries.Hence,new conceptual and methodological approaches to tackle the epidemic are long overdue.Spatial epidemiology has been a successful approach to control infectious disease epidemics like malaria and human immunodeficiency virus.The implementation of this approach has been expanded to include the study of non-communicable diseases like cancer and cardiovascular diseases.In this review,we discussed the implementation and use of spatial epidemiology and Geographic Information Systems to the study of DM.We reviewed several spatial methods used to understand the spatial structure of the disease and identify the potential geographical drivers of the spatial distribution of DM.Finally,we discussed the use of spatial epidemiology on the design and implementation of geographically targeted prevention and treatment interventions against DM.

Graph-based multivariate conditional autoregressive models

The conditional autoregressive model is a routinely used statistical model for areal data thatarise from, for instances, epidemiological, socio-economic or ecological studies. Various multivariate conditional autoregressive models have also been extensively studied in the literatureand it has been shown that extending from the univariate case to the multivariate case is nottrivial. The difficulties lie in many aspects, including validity, interpretability, flexibility and computational feasibility of the model. In this paper, we approach the multivariate modelling froman element-based perspective instead of the traditional vector-based perspective. We focus onthe joint adjacency structure of elements and discuss graphical structures for both the spatialand non-spatial domains. We assume that the graph for the spatial domain is generally knownand fixed while the graph for the non-spatial domain can be unknown and random. We proposea very general specification for the multivariate conditional modelling and then focus on threespecial cases, which are linked to well-known models in the literature. Bayesian inference forparameter learning and graph learning is provided for the focused cases, and finally, an examplewith public health data is illustrated.

Spatio-temporal clustering analysis of COVID-19 cases in Johor

At the end of the year 2019,a virus named SARS-CoV-2 induced the coronavirus disease,which is very contagious and quickly spread around the world.This new infectious disease is called COVID-19.Numerous areas,such as the economy,social services,education,and healthcare system,have suffered grave consequences from the invasion of this deadly virus.Thus,a thorough understanding of the spread of COVID-19 is required in order to deal with this outbreak before it becomes an infectious disaster.In this research,the daily reported COVID-19 cases in 92 sub-districts in Johor state,Malaysia,as well as the population size associated to each sub-district,are used to study the propagation of COVID-19 disease across space and time in Johor.The time frame of this research is about 190 days,which started from August 5,2021,until February 10,2022.The clustering technique known as spatio-temporal clustering,which considers the spatio-temporal metric was adapted to determine the hot-spot areas of the COVID-19 disease in Johor at the sub-district level.The results indicated that COVID-19 disease does spike in the dynamic populated sub-districts such as the state's economic centre(Bandar Johor Bahru),and during the festive season.These findings empirically prove that the transmission rate of COVID-19 is directly proportional to human mobility and the presence of holidays.On the other hand,the result of this study will help the authority in charge in stopping and preventing COVID-19 from spreading and become worsen at the national level.