Multivariate Time Series Anomaly Detection Based on Spatial-Temporal Network and Transformer in Industrial Internet of Things

In the Industrial Internet of Things(IIoT),sensors generate time series data to reflect the working state.When the systems are attacked,timely identification of outliers in time series is critical to ensure security.Although many anomaly detection methods have been proposed,the temporal correlation of the time series over the same sensor and the state(spatial)correlation between different sensors are rarely considered simultaneously in these methods.Owing to the superior capability of Transformer in learning time series features.This paper proposes a time series anomaly detection method based on a spatial-temporal network and an improved Transformer.Additionally,the methods based on graph neural networks typically include a graph structure learning module and an anomaly detection module,which are interdependent.However,in the initial phase of training,since neither of the modules has reached an optimal state,their performance may influence each other.This scenario makes the end-to-end training approach hard to effectively direct the learning trajectory of each module.This interdependence between the modules,coupled with the initial instability,may cause the model to find it hard to find the optimal solution during the training process,resulting in unsatisfactory results.We introduce an adaptive graph structure learning method to obtain the optimal model parameters and graph structure.Experiments on two publicly available datasets demonstrate that the proposed method attains higher anomaly detection results than other methods.

Spatio-Temporal Trend and Geographic Disparity of Infertility Prevalence in Burkina Faso, 2011 to 2020

Background: Infertility affected 10% to 25% of couples globally, and about half of the infertility cases were reported in sub-Saharan Africa. Infertility poses significant social, cultural, and health challenges, particularly for women who often face stigmatization. However, comprehensive and nationally representative data, including prevalence, temporal trends, and risk factors, are lacking, prompting a study in Burkina Faso to address the need for informed policies and programs in infertility care and management. Objectives: This study aims to better understand the spatiotemporal trend of infertility prevalence in Burkina Faso. Methodology: This is a retrospective population-based study of women infertility from healthcare facilities in Burkina Faso, during January 2011 to December 2020. We calculated the prevalence rates of infertility and two disparity measures, and examined the spatiotemporal trend of infertility. Results: Over the 10-year period (2011 to 2020), 143,421 infertility cases were recorded in Burkina Faso healthcare facilities, resulting of a mean prevalence rate of 3.61‰ among childbearing age women and 17.87‰ among women who consulted healthcare facilities for reproductive issues (except contraception). The findings revealed a significant increase of infertility, with the prevalence rate varied from 2.75‰ in 2011 to 4.62‰ in 2020 among childbearing age women and from 13.38‰ in 2011 to 26.28‰ in 2020 among women who consulted healthcare facilities for reproductive issues, corresponding to an estimate annual percentage change of 8.31% and 9.80% respectively. There were significant temporal and geographic variations in the prevalence of infertility. While relative geographic disparity decreased, absolute geographic disparity showed an increasing trend over time. Conclusion: The study highlights an increasing trend of infertility prevalence and significant geographic variation in Burkina Faso, underscoring the urgent necessity for etiologic research on risk factors, psychosocial implications, and economic consequences to inform effective interventions and mitigate the socioeconomic impact of infertility.

Comprehensive evaluation and spatial-temporal evolution characteristics of urban resilience in Chengdu-Chongqing Economic Circle

To clarify the connotations and extensions of urban resilience,this study focuses on the Chengdu-Chongqing Economic Circle with 16 cities as research subjects.A comprehensive evaluation index system was constructed to measure the resilience of each city from 2003 to 2020.The spatial-temporal evolution characteristics were analyzed using Kernel density estimation,standard deviation ellipse,and spatial Markov chain analysis,and the spatial Tobit model was introduced to discover the influencing factors.The results indicate the following:①Urban resilience in the Chengdu-Chongqing Economic Circle displays an upward trend,with the center of gravity moving to the southwest,and the polarization phenomenon intensifying.②The urban resilience level in a region has certain spatial and geographical dependence,while the probability of urban resilience transfer differs in adjacent cities with different resilience levels.③Urban centrality,economic scale,openness level,and financial development promote urban resilience,whereas government scale significantly inhibits it.Finally,this paper proposes countermeasures and suggestions to improve the urban resilience of the Chengdu-Chongqing Economic Circle.

AFSTGCN:Prediction for multivariate time series using an adaptive fused spatial-temporal graph convolutional network

The prediction for Multivariate Time Series(MTS)explores the interrelationships among variables at historical moments,extracts their relevant characteristics,and is widely used in finance,weather,complex industries and other fields.Furthermore,it is important to construct a digital twin system.However,existing methods do not take full advantage of the potential properties of variables,which results in poor predicted accuracy.In this paper,we propose the Adaptive Fused Spatial-Temporal Graph Convolutional Network(AFSTGCN).First,to address the problem of the unknown spatial-temporal structure,we construct the Adaptive Fused Spatial-Temporal Graph(AFSTG)layer.Specifically,we fuse the spatial-temporal graph based on the interrelationship of spatial graphs.Simultaneously,we construct the adaptive adjacency matrix of the spatial-temporal graph using node embedding methods.Subsequently,to overcome the insufficient extraction of disordered correlation features,we construct the Adaptive Fused Spatial-Temporal Graph Convolutional(AFSTGC)module.The module forces the reordering of disordered temporal,spatial and spatial-temporal dependencies into rule-like data.AFSTGCN dynamically and synchronously acquires potential temporal,spatial and spatial-temporal correlations,thereby fully extracting rich hierarchical feature information to enhance the predicted accuracy.Experiments on different types of MTS datasets demonstrate that the model achieves state-of-the-art single-step and multi-step performance compared with eight other deep learning models.

Disparity estimation for multi-scale multi-sensor fusion

The perception module of advanced driver assistance systems plays a vital role.Perception schemes often use a single sensor for data processing and environmental perception or adopt the information processing results of various sensors for the fusion of the detection layer.This paper proposes a multi-scale and multi-sensor data fusion strategy in the front end of perception and accomplishes a multi-sensor function disparity map generation scheme.A binocular stereo vision sensor composed of two cameras and a light deterction and ranging(LiDAR)sensor is used to jointly perceive the environment,and a multi-scale fusion scheme is employed to improve the accuracy of the disparity map.This solution not only has the advantages of dense perception of binocular stereo vision sensors but also considers the perception accuracy of LiDAR sensors.Experiments demonstrate that the multi-scale multi-sensor scheme proposed in this paper significantly improves disparity map estimation.

Spatial-temporal distribution and geochemistry of highly evolved Mesozoic granites in Great Xing’an Range,NE China:Discriminant criteria and geological significance

Highly evolved granite is an important sign of the mature continent crust and closely associated with deposits of rare metals.In this work,the authors undertake systematically zircon U-Pb ages and whole rock elemental data for highly evolved granitic intrusions from the Great Xing’an Range(GXR),NE China,to elucidate their discriminant criteria,spatial-temporal distribution,differentiation and geodynamic mecha-nism.Geochemical data of these highly evolved granites suggest that high w(SiO_(2))(>70%)and differentiation index(DI>88)could be quantified indicators,while strong Eu depletion,high TE_(1,3),lowΣREE and low Zr/Hf,Nb/Ta,K/Rb could only be qualitative indicators.Zircon U-Pb ages suggest that the highly evolved gran-ites in the GXR were mainly formed in Late Mesozoic,which can be divided into two major stages:Late Ju-rassic-early Early Cretaceous(162-136 Ma,peak at 138 Ma),and late Early Cretaceous(136-106 Ma,peak at 126 Ma).The highly evolved granites are mainly distributed in the central-southern GXR,and display a weakly trend of getting younger from northwest to southeast,meanwhile indicating the metallogenic potential of rare metals within the central GXR.The spatial-temporal distribution,combined with regional geological data,indicates the highly evolved Mesozoic granites in the GXR were emplaced in an extensional environ-ment,of which the Late Jurassic-early Early Cretaceous extension was related to the closure of the Mongol-Okhotsk Ocean and roll-back of the Paleo-Pacific Plate,while the late Early Cretaceous extension was mainly related to the roll-back of the Paleo-Pacific Plate.

Spatial-temporal Divergence Characteristics and Driving Factors of Green Economic Efficiency in the Yangtze River Economic Belt of China

The spatial and temporal variation of green economic efficiency and its driving factors are of great significance for the con-struction of high-efficiency and low-consumption green development model and sustainable socio-economic development.The research focused on the Yangtze River Economic Belt(YREB)and employed the miniumum distance to strong efficient frontier DEA(MinDs)model to measure the green economic efficiency of the municipalities in the region between 2008 and 2020.Then,the spatial autocorrel-ation model was used to analyze the evolution characteristics of its spatial pattern.Finally,Geodetector was applied to reveal the drivers and their interactions on green economic efficiency.It is found that:1)the overall green economic efficiency of the YREB from 2008 to 2020 shows a W-shaped fluctuating upward trend,green economic efficiency is greater in the downstream and smallest in the upstream;2)the spatial distribution of green economic efficiency shows clustering characteristics,with multi-core clustering based on‘city clusters-central cities'becoming more obvious over time;the High-High agglomeration type is mainly clustered in Jiangsu and Zheji-ang,while the Low-Low agglomeration type is clustered in the western Sichuan Plateau area and southwestern Yunnan;3)from input-output factors,whether it is the YREB as a whole or the upper,middle and lower reaches regions,the economic development level,labor input,and capital investment are the leading factors in the spatial-temporal evolution of green economic efficiency,with the com-prehensive influence of economic development level and pollution index being the most important interactive driving factor;4)from so-cio-economic factors,information technology drivers such as government intervention,transportation accessibility,information infra-structure,and Internet penetration are always high impact influencers and dominant interaction factors for green economic efficiency in the YREB and the three major regions in the upper,middle and lower reaches.Accordingly,the article puts forward relevant policy re-commendations in terms of formulating differentiated green transformation strategies,strengthening network leadership and informa-tion technology construction and coordinating multi-factor integrated development,which could provide useful reference for promoting synergistic green economic efficiency in the YREB.

Adaptive spatial-temporal graph attention network for traffic speed prediction

Considering the nonlinear structure and spatial-temporal correlation of traffic network,and the influence of potential correlation between nodes of traffic network on the spatial features,this paper proposes a traffic speed prediction model based on the combination of graph attention network with self-adaptive adjacency matrix(SAdpGAT)and bidirectional gated recurrent unit(BiGRU).First-ly,the model introduces graph attention network(GAT)to extract the spatial features of real road network and potential road network respectively in spatial dimension.Secondly,the spatial features are input into BiGRU to extract the time series features.Finally,the prediction results of the real road network and the potential road network are connected to generate the final prediction results of the model.The experimental results show that the prediction accuracy of the proposed model is im-proved obviously on METR-LA and PEMS-BAY datasets,which proves the advantages of the pro-posed spatial-temporal model in traffic speed prediction.

Spatial-temporal Variation Characteristics of Water Quality in the Lower Reaches of the Nenjiang River

As an important river in the western part of Jilin Province,the lower reach of the Nenjiang River is an important wetland water source conservation area in Jilin Province.Within the watershed,it governs the Momoge Wetland,the Xianghai Wetland,and the Danjiang Wetland in Jilin Province.The main problem in the lower reaches of the Nenjiang River is the uneven distribution of water resources in time and space,and the intensification of land salinization.Zhenlai County and Da an City in the Nenjiang River Basin have sufficient surface water resources,with surface water as the drinking water source.Baicheng City and Tongyu County have scarce surface water resources,and both use groundwater as their domestic water source.The main polluted section in the basin is the Xianghai Reservoir,and the annual water quality evaluation is Class V.However,the water quality of the Tao er River,the main stream of the Nenjiang River,is significantly better than that of the Xianghai Reservoir.In order to better study the water environmental pollution situation in the Nenjiang River basin,monitoring data from five sections of non seasonal rivers in the basin from 2012 to 2021 were selected for studying water quality.This in-depth exploration of the water pollution status and river water quality change trends in the Nenjiang River basin is of great significance for future rural development,agricultural pattern transformation,and the promotion of water ecological civilization construction.

Spatial Disparity in Availability of Tuberculosis Diagnostic Services Based on Sector and Level of Care in Nigeria

Background: Delay in Tuberculosis (TB) diagnosis can contribute to late presentation, severe disease, and continued transmission. KNCV TB Foundation Nigeria through the United States Agency for International Development (USAID) funded the TB Local Organization Network (LON) 1 and 2 projects that explored the availability of Tuberculosis services based on sector and levels of care. Methods: TB Patient Pathway Analysis was carried out in 14 states comprising 92 facilities. It involved primary, secondary, and tertiary levels of health care in both the public and private sectors. This was a cross-sectional study under program implementation. Proforma was used to collect data on the available TB diagnostic services. Results: In public health facilities, GeneXpert was available at 100% in tertiary facilities in 8 (57%) states;up to 82% in 4 (33%) states, 50% available at secondary facilities in 2 states, and There is none at the primary facilities. Smear microscopy was available at 100% in tertiary facilities in 9 (64%) states and 3 (25%) states have 50% to 82%;secondary -10 (71%) states have > 70% at facilities;primary 1 (7%) state has it in 61% of facilities. Loop-mediated isothermal amplification (TB-LAMP) in tertiary 2 (17%) states have 20% and 100% respectively;secondary 4 (<30%) states have in 1 or 2 facilities;none for primary facilities. In private health facilities, 79% of states have Smear microscopy at both primary and secondary facilities, and only 2 states (14%) at tertiary facilities. Only 1 (7%) state has GeneXpert in all tertiary facilities, 2 (14%) states have secondary facilities, and 4 states in about 1% of facilities. TB LAMP was not available in any tertiary facility, one (7%) state at secondary with coverage of 1%, and 2 (14%) states at primary both with 4% overall facility coverage. Conclusions: There is an inequitable distribution of TB diagnostic services in both sectors and levels of care in Nigeria. TB care and control will improve with enhanced equitable distribution of TB diagnostic services across the health system.

Spatial-temporal difference between nitrate in groundwater and nitrogen in soil based on geostatistical analysis

The study of temporal and spatial variations of nitrate in groundwater under different soil nitrogen environments is helpful to the security of groundwater resources in agricultural areas.In this paper,based on 320 groups of soil and groundwater samples collected at the same time,geostatistical analysis and multiple regression analysis were comprehensively used to conduct the evaluation of nitrogen contents in both groundwater and soil.From May to August,as the nitrification of groundwater is dominant,the average concentration of nitrate nitrogen is 34.80 mg/L;The variation of soil ammonia nitrogen and nitrate nitrogen is moderate from May to July,and the variation coefficient decreased sharply and then increased in August.There is a high correlation between the nitrate nitrogen in groundwater and soil in July,and there is a high correlation between the nitrate nitrogen in groundwater and ammonium nitrogen in soil in August and nitrate nitrogen in soil in July.From May to August,the area of low groundwater nitrate nitrogen in 0-5 mg/L and 5-10 mg/L decreased from 10.97%to 0,and the proportion of high-value area(greater than 70 mg/L)increased from 21.19%to 27.29%.Nitrate nitrogen is the main factor affecting the quality of groundwater.The correlation analysis of nitrate nitrogen in groundwater,nitrate nitrogen in soil and ammonium nitrogen shows that they have a certain period of delay.The areas with high concentration of nitrate in groundwater are mainly concentrated in the western part of the study area,which has a high consistency with the high value areas of soil nitrate distribution from July to August,and a high difference with the spatial position of soil ammonia nitrogen distribution in August.

STGSA:A Novel Spatial-Temporal Graph Synchronous Aggregation Model for Traffic Prediction

The success of intelligent transportation systems relies heavily on accurate traffic prediction,in which how to model the underlying spatial-temporal information from traffic data has come under the spotlight.Most existing frameworks typically utilize separate modules for spatial and temporal correlations modeling.However,this stepwise pattern may limit the effectiveness and efficiency in spatial-temporal feature extraction and cause the overlook of important information in some steps.Furthermore,it is lacking sufficient guidance from prior information while modeling based on a given spatial adjacency graph(e.g.,deriving from the geodesic distance or approximate connectivity),and may not reflect the actual interaction between nodes.To overcome those limitations,our paper proposes a spatial-temporal graph synchronous aggregation(STGSA)model to extract the localized and long-term spatial-temporal dependencies simultaneously.Specifically,a tailored graph aggregation method in the vertex domain is designed to extract spatial and temporal features in one graph convolution process.In each STGSA block,we devise a directed temporal correlation graph to represent the localized and long-term dependencies between nodes,and the potential temporal dependence is further fine-tuned by an adaptive weighting operation.Meanwhile,we construct an elaborated spatial adjacency matrix to represent the road sensor graph by considering both physical distance and node similarity in a datadriven manner.Then,inspired by the multi-head attention mechanism which can jointly emphasize information from different r epresentation subspaces,we construct a multi-stream module based on the STGSA blocks to capture global information.It projects the embedding input repeatedly with multiple different channels.Finally,the predicted values are generated by stacking several multi-stream modules.Extensive experiments are constructed on six real-world datasets,and numerical results show that the proposed STGSA model significantly outperforms the benchmarks.

Spatial-temporal variations and driving factors of soil organic carbon in forest ecosystems of Northeast China

Forest soil carbon is a major carbon pool of terrestrial ecosystems,and accurate estimation of soil organic carbon(SOC)stocks in forest ecosystems is rather challenging.This study compared the prediction performance of three empirical model approaches namely,regression kriging(RK),multiple stepwise regression(MSR),random forest(RF),and boosted regression trees(BRT)to predict SOC stocks in Northeast China for 1990 and 2015.Furthermore,the spatial variation of SOC stocks and the main controlling environmental factors during the past 25 years were identified.A total of 82(in 1990)and 157(in 2015)topsoil(0–20 cm)samples with 12 environmental factors(soil property,climate,topography and biology)were selected for model construction.Randomly selected80%of the soil sample data were used to train the models and the other 20%data for model verification using mean absolute error,root mean square error,coefficient of determination and Lin's consistency correlation coefficient indices.We found BRT model as the best prediction model and it could explain 67%and 60%spatial variation of SOC stocks,in 1990,and 2015,respectively.Predicted maps of all models in both periods showed similar spatial distribution characteristics,with the lower SOC in northeast and higher SOC in southwest.Mean annual temperature and elevation were the key environmental factors influencing the spatial variation of SOC stock in both periods.SOC stocks were mainly stored under Cambosols,Gleyosols and Isohumosols,accounting for 95.6%(1990)and 95.9%(2015).Overall,SOC stocks increased by 471 Tg C during the past 25 years.Our study found that the BRT model employing common environmental factors was the most robust method for forest topsoil SOC stocks inventories.The spatial resolution of BRT model enabled us to pinpoint in which areas of Northeast China that new forest tree planting would be most effective for enhancing forest C stocks.Overall,our approach is likely to be useful in forestry management and ecological restoration at and beyond the regional scale.

Sex and racial disparities in non-alcoholic fatty liver disease-related cardiovascular events: National inpatient sample analysis (2019)

BACKGROUND Non-alcoholic fatty liver disease(NAFLD)increases cardiovascular disease(CVD)risk irrespective of other risk factors.However,large-scale cardiovascular sex and race differences are poorly understood.AIM To investigate the relationship between NAFLD and major cardiovascular and cerebrovascular events(MACCE)in subgroups using a nationally representative United States inpatient sample.METHODS We examined National Inpatient Sample(2019)to identify adult hospitalizations with NAFLD by age,sex,and race using ICD-10-CM codes.Clinical and demographic characteristics,comorbidities,and MACCE-related mortality,acute myocardial infarction(AMI),cardiac arrest,and stroke were compared in NAFLD cohorts by sex and race.Multivariable regression analyses were adjusted for sociodemographic characteristics,hospitalization features,and comorbidities.RESULTS We examined 409130 hospitalizations[median 55(IQR 43-66)years]with NFALD.NAFLD was more common in females(1.2%),Hispanics(2%),and Native Americans(1.9%)than whites.Females often reported non-elective admissions,Medicare enrolment,the median age of 55(IQR 42-67),and poor income.Females had higher obesity and uncomplicated diabetes but lower hypertension,hyperlipidemia,and complicated diabetes than males.Hispanics had a median age of 48(IQR 37-60),were Medicaid enrollees,and had non-elective admissions.Hispanics had greater diabetes and obesity rates than whites but lower hypertension and hyperlipidemia.MACCE,all-cause mortality,AMI,cardiac arrest,and stroke were all greater in elderly individuals(P<0.001).MACCE,AMI,and cardiac arrest were more common in men(P<0.001).Native Americans(aOR 1.64)and Asian Pacific Islanders(aOR 1.18)had higher all-cause death risks than whites.CONCLUSION Increasing age and male sex link NAFLD with adverse MACCE outcomes;Native Americans and Asian Pacific Islanders face higher mortality,highlighting a need for tailored interventions and care.

Dense Spatial-Temporal Graph Convolutional Network Based on Lightweight OpenPose for Detecting Falls

Fall behavior is closely related to high mortality in the elderly,so fall detection becomes an important and urgent research area.However,the existing fall detection methods are difficult to be applied in daily life due to a large amount of calculation and poor detection accuracy.To solve the above problems,this paper proposes a dense spatial-temporal graph convolutional network based on lightweight OpenPose.Lightweight OpenPose uses MobileNet as a feature extraction network,and the prediction layer uses bottleneck-asymmetric structure,thus reducing the amount of the network.The bottleneck-asymmetrical structure compresses the number of input channels of feature maps by 1×1 convolution and replaces the 7×7 convolution structure with the asymmetric structure of 1×7 convolution,7×1 convolution,and 7×7 convolution in parallel.The spatial-temporal graph convolutional network divides the multi-layer convolution into dense blocks,and the convolutional layers in each dense block are connected,thus improving the feature transitivity,enhancing the network’s ability to extract features,thus improving the detection accuracy.Two representative datasets,Multiple Cameras Fall dataset(MCF),and Nanyang Technological University Red Green Blue+Depth Action Recognition dataset(NTU RGB+D),are selected for our experiments,among which NTU RGB+D has two evaluation benchmarks.The results show that the proposed model is superior to the current fall detection models.The accuracy of this network on the MCF dataset is 96.3%,and the accuracies on the two evaluation benchmarks of the NTU RGB+D dataset are 85.6%and 93.5%,respectively.

Are there sex-based disparities in cataract surgery?

AIM:To investigate sex-based differences in the occurrence of intra-operative and post-operative complications and associated visual outcomes following cataract surgery.METHODS:This was a retrospective study of patients who had phacoemulsification cataract surgery at the University of Colorado School of Medicine.Data collected included the patient’s health history,ocular comorbidities,operative and post-operative complications,and the post-operative best corrected visual acuity(BCVA).The data were analyzed using univariate and multivariable logistic regression with generalized estimating equations to account for the correlation of some patients having two eyes included in the study.RESULTS:A total of 11977 eyes from 7253 patients were included in the study.Ocular comorbidities differed by sex,with males having significantly higher percentages of traumatic cataracts(males 0.7%vs females 0.1%),prior ocular surgery(6.7%vs 5.5%),and mature cataracts(2.8%vs 1.9%).Conversely,females had significantly higher rates of pseudoexfoliation(2.0%vs 3.2%).In unadjusted analysis,males had higher rates of posterior capsular rupture(0.8%vs 0.4%)and vitreous loss(1.0%vs 0.6%),but this difference was not significant after adjustment for confounders.Males had a significantly increased risk of post-operative retinal detachment,but in multivariable analysis this was no longer significant.Males were significantly less likely to undergo post-operative neodymium-doped yttrium aluminum garnet(Nd:YAG)laser capsulotomy for posterior capsule opacification(OR=0.8,95%CI=0.7-0.9,P=0.0005).The BCVA was slightly worse for males pre-operatively;but post-operatively,both sexes exhibited similar visual acuity of Snellen equivalent 20/25.CONCLUSION:The study finds that in a cohort of patients presenting for cataract surgery,sex differences exist in pre-operative comorbidities and surgical characteristics that contribute to higher rates of some complications for males.However,observed surgical complication rates exhibit almost no difference by sex after adjusting for pre-operative differences and post-operative BCVA is similar between sexes.

Gastric Cancer Disparities in the United States: Overcoming the Barriers

In this narrative review, we highlight the disparities in the incidence and mortality of gastric cancer across various racial and ethnic populations in the United States (US). Despite the low and decreasing trend in the incidence of gastric cancer in the US, the incidence remains significantly high among Asian and Hispanic Americans, showing a striking racial and ethnic disparity. The low survival rate of gastric cancer further accentuates the magnitude of this disparity. In addition, there is a marked funding disparity among different cancers in the US, reflecting the significantly lower level of support for cancers, such as gastric cancer, which are more prevalent in minority populations, compared to the cancers more prevalent among non-Hispanic Whites (NHW). Moreover, the economic burden from health disparities remains high. Although studies from the US and Asia suggest that screening for stomach cancer may be cost-effective, there is no currently available guideline for scree-ning high-risk populations in the US. A multidimensional framework involving the community, physicians, and policymakers is proposed to tackle these gastric cancer disparities and to develop population-based screening and surveillance programs to reduce the burden of gastric cancer.

Disparities in Prevalence and Determinants of Hypertension amongst Bamiléké Adults Residing in Two Different Agroecological Zones of Cameroon

Background: Cardiovascular diseases such as hypertension (HTN) are one of the main causes of death in Cameroon. This study aimed at assessing prevalence disparities and determinants of hypertension amongst Bamiléké adults residing in two different agroecological zones of Cameroon. Methods: A cross-sectional and descriptive survey was conducted among Bamiléké population living in the Highlands zone (Western region) and in the Monomodal Rainforest zone (Littoral region) of Cameroon from August 2016 to August 2017. Participants (962) were aged at least 20 years old. Data on sociodemographic, hemodynamic, anthropometric, and biochemical parameters and lifestyle of the participants were collected. Results: Results obtained revealed that 34.2% were hypertensive and those residing in the highland zone were more affected than those living in the monomodal rainforest zone (44.5% vs 22.9%). The different subtypes of HTN (Isolated systolic hypertension (14.1%), isolated diastolic hypertension (7.2%) and Systo-diastolic hypertension (23.3%)) were also more prevalent in the Highlands Zone. The most prevalent stage of HTN was pre-HTN (31.5%). However, people living in the monomodal rainforest zone were more affected by pre-HTN compared to Bamiléké living in the highland zone (33.6% vs. 29.6%). Results also showed that high consumption (≥ 3 times/week) of carbohydrate- and fat-rich foods, ageing, obesity, and marital status were associated with high blood pressure in both agroecological zones. Besides, secondary education (OR = 0.68;95% CI: 0.42 - 0.99) in the Highlands Zone and high (≥3 times/week) vegetable consumption (OR = 0.66;95% CI: 0.44 - 0.98) in the Monomodal Rainforest Zone had a protective effect on elevated blood pressure of population. Conclusion: There is a disparity in the prevalence of hypertension and some of its determinants among Bamiléké adults residing in different agroecological zones. This work highlights the need to advocate for local and ethno-cultural health policies to prevent, diagnose and manage hypertension.

Disparities in the impact of economic well-being on self-esteem in adulthood:Race and ethnicity

BACKGROUND Most studies have defined economic well-being as socioeconomic status,with little attention given to whether other indicators influence self-esteem.Little is known about racial/ethnic disparities in the relationship between economic wellbeing and self-esteem during adulthood.AIM To explore the impact of economic well-being on self-esteem in adulthood and differences in the association across race/ethnicity.METHODS The current study used data from the National Longitudinal Survey of Youth 1979.The final sample consisted of 2267 African Americans,1425 Hispanics,and 3678 non-Hispanic Whites.Ordinary linear regression analyses and logistic regression analyses were conducted.RESULTS African Americans and Hispanics were more likely to be in poverty in comparison with non-Hispanic Whites.More African Americans were unemployed than Whites.Those who received fringe benefits,were more satisfied with jobs,and were employed were more likely to have higher levels of self-esteem.Poverty was negatively associated with self-esteem.Interaction effects were found between African Americans and job satisfaction predicting self-esteem.CONCLUSION The role of employers is important in cultivating employees’self-esteem.Satisfactory outcomes or feelings of happiness from the workplace may be more important to non-Hispanic Whites compared to African Americans and Hispanics.

Bridging the gap: Addressing disparities in hepatitis C screening, access to care, and treatment outcomes

Hepatitis C virus(HCV)is a significant public health challenge globally,with substantial morbidity and mortality due to chronic liver disease.Despite the availability of highly effective and well-tolerated direct-acting antiviral therapies,widespread disparities remain in hepatitis C screening,access to treatment,linkage to care,and therapeutic outcomes.This review article synthesizes evi-dence from various studies to highlight the multifactorial nature of these dispari-ties,which affects ethnic minorities,people with lower socioeconomic status,in-dividuals with substance use disorders,and those within correctional facilities.The review also discusses policy implications and targeted strategies needed to overcome barriers and ensure equitable care for all individuals with HCV.Recom-mendations for future research to address gaps in knowledge and evaluation of the effectiveness of interventions designed to reduce disparities are provided.