Spatial Models in Econometric Research

2021 ◽  
Author(s):  
Luc Anselin
Keyword(s):  
Spatial Interaction ◽  
Asymptotic Properties ◽  
Spatial Models ◽  
Critical Element ◽  
Cross Sectional ◽  
Spatial Lag ◽  
Explanatory Variables ◽  
Spatial Weights ◽  
Spatial Weights Matrix ◽  
Definition Of

Since the late 1990s, spatial models have become a growing addition to econometric research. They are characterized by attention paid to the location of observations (i.e., ordered spatial locations) and the interaction among them. Specifically, spatial models formally express spatial interaction by including variables observed at other locations into the regression specification. This can take different forms, mostly based on an averaging of values at neighboring locations through a so-called spatially lagged variable, or spatial lag. The spatial lag can be applied to the dependent variable, to explanatory variables, and/or to the error terms. This yields a range of specifications for cross-sectional dependence, as well as for static and dynamic spatial panels. A critical element in the spatially lagged variable is the definition of neighbor relations in a so-called spatial weights matrix. Historically, the spatial weights matrix has been taken to be given and exogenous, but this has evolved into research focused on estimating the weights from the data and on accounting for potential endogeneity in the weights. Due to the uneven spacing of observations and the complex way in which asymptotic properties are obtained, results from time series analysis are not applicable, and specialized laws of large numbers and central limit theorems need to be developed. This requirement has yielded an active body of research into the asymptotics of spatial models.

scholarly journals Bias from network misspecification under spatial dependence

2019 ◽  
Author(s):  
Timm Betz ◽  
Scott J Cook ◽  
Florian M Hollenbach
Keyword(s):  
Spatial Analysis ◽  
Spatial Models ◽  
Limited Information ◽  
Network Ties ◽  
Spatial Econometric ◽  
Cross Sectional ◽  
Limit Condition ◽  
Spatial Weights Matrix ◽  
Cross Sectional Dependence ◽  
Variable Bias

The pre-specification of the network is one of the biggest hurdles for applied researchers in undertaking spatial analysis. In this letter, we demonstrate two results. First, we derive bounds for the bias in non-spatial models with omitted spatially-lagged predictors or outcomes. These bias expressions can be obtained without prior knowledge of the network, and are more informative than familiar omitted variable bias formulas. Second, we derive bounds for the bias in spatial econometric models with non-differential error in the specification of the weights matrix. Under these conditions, we demonstrate that an omitted spatial input is the limit condition of including a misspecificed spatial weights matrix. Simulated experiments further demonstrate that spatial models with a misspecified weights matrix weakly dominate non-spatial models. Our results imply that, where cross-sectional dependence is presumed, researchers should pursue spatial analysis even with limited information on network ties.

scholarly journals Bias from Network Misspecification Under Spatial Dependence

2020 ◽  
pp. 1-7
Author(s):  
Timm Betz ◽  
Scott J. Cook ◽  
Florian M. Hollenbach
Keyword(s):  
Spatial Analysis ◽  
Spatial Models ◽  
Limited Information ◽  
Network Ties ◽  
Spatial Econometric ◽  
Cross Sectional ◽  
Limit Condition ◽  
Spatial Weights Matrix ◽  
Cross Sectional Dependence ◽  
Variable Bias

Abstract The prespecification of the network is one of the biggest hurdles for applied researchers in undertaking spatial analysis. In this letter, we demonstrate two results. First, we derive bounds for the bias in nonspatial models with omitted spatially-lagged predictors or outcomes. These bias expressions can be obtained without prior knowledge of the network, and are more informative than familiar omitted variable bias formulas. Second, we derive bounds for the bias in spatial econometric models with nondifferential error in the specification of the weights matrix. Under these conditions, we demonstrate that an omitted spatial input is the limit condition of including a misspecificed spatial weights matrix. Simulated experiments further demonstrate that spatial models with a misspecified weights matrix weakly dominate nonspatial models. Our results imply that, where cross-sectional dependence is presumed, researchers should pursue spatial analysis even with limited information on network ties.

scholarly journals Incidence Correction Factors for Moderate and Severe Acute Child Malnutrition From 2 Longitudinal Cohorts in Mali and Burkina Faso

2020 ◽  
Vol 189 (12) ◽  
pp. 1623-1627
Author(s):  
Francisco M Barba ◽  
Lieven Huybregts ◽  
Jef L Leroy
Keyword(s):  
Burkina Faso ◽  
Low Income ◽  
Severe Acute Malnutrition ◽  
Acute Malnutrition ◽  
Cross Sectional ◽  
Incidence Data ◽  
Moderate Acute Malnutrition ◽  
Cluster Randomized ◽  
Definition Of ◽  
Mid Upper Arm Circumference

Abstract Child acute malnutrition (AM) is an important cause of child mortality. Accurately estimating its burden requires cumulative incidence data from longitudinal studies, which are rarely available in low-income settings. In the absence of such data, the AM burden is approximated using prevalence estimates from cross-sectional surveys and the incidence correction factor $K$, obtained from the few available cohorts that measured AM. We estimated $K$ factors for severe acute malnutrition (SAM) and moderate acute malnutrition (MAM) from AM incidence and prevalence using representative cross-sectional baseline and longitudinal data from 2 cluster-randomized controlled trials (Innovative Approaches for the Prevention of Childhood Malnutrition—PROMIS) conducted between 2014 and 2017 in Burkina Faso and Mali. We compared K estimates using complete (weight-for-length z score, mid-upper arm circumference (MUAC), and edema) and partial (MUAC, edema) definitions of SAM and MAM. $K$ estimates for SAM were 9.4 and 5.7 in Burkina Faso and in Mali, respectively; K estimates for MAM were 4.7 in Burkina Faso and 5.1 in Mali. The MUAC and edema–based definition of AM did not lead to different $K$ estimates. Our results suggest that $K$ can be reliably estimated when only MUAC and edema-based data are available. Additional studies, however, are required to confirm this finding in different settings.

scholarly journals Knowledge and Practice of Health Professionals in the Management of Dysphagia

2021 ◽  
Vol 18 (4) ◽  
pp. 2139
Author(s):  
Eduardo Sánchez-Sánchez ◽  
Ylenia Avellaneda-López ◽  
Esperanza García-Marín ◽  
Guillermo Ramírez-Vargas ◽  
Jara Díaz-Jimenez ◽  
...  
Keyword(s):  
Healthcare Providers ◽  
Cross Sectional Study ◽  
Clinical Settings ◽  
Sectional Study ◽  
Swallowing Disorder ◽  
Cross Sectional ◽  
Anonymous Questionnaire ◽  
Knowledge And Practice ◽  
Definition Of ◽  
Management Of Dysphagia

The aim of this study was to determine healthcare providers’ knowledge and practices about dysphagia. A descriptive cross-sectional study was carried out based on a self-administered and anonymous questionnaire addressed to healthcare providers in Spain. A total of 396 healthcare providers participated in the study. Of these, 62.3% knew the definition of dysphagia as a swallowing disorder. In addition, up to 39.2% of the participants reported that they did not know whether the EatingAssessmentTool (EAT-10) dysphagia screening test was usedin their own clinical settings. Similarly, up to 49.1% of them did not know the ClinicalExaminationVolume-Viscosity (MECV-V) method. Nearly all participants (98.8%) reported that thickeners must be used forall liquids administered to patients. A higher percentage of respondents based the choice of texture on patient’s tolerance (78.2%) rather than on the MECV-V result (17.3%). In addition,76.4% of the professionals had witnessed a bronchoaspiration; after it, 44.4% (n = 175) of them reported the appearance of pneumonia, and 14.5% (n = 57) the death of the patient (p = 0.005). The participants revealeda moderate/low knowledge ofthe definition, diagnosis, and clinical management of liquid dysphagia, which indicates some room for improvements.

scholarly journals Comparison of predicted psychological distress among workers between artificial intelligence and psychiatrists: a cross-sectional study in Tsukuba Science City, Japan

BMJ Open ◽  
2021 ◽  
Vol 11 (6) ◽  
pp. e046265
Author(s):  
Shotaro Doki ◽  
Shinichiro Sasahara ◽  
Daisuke Hori ◽  
Yuichi Oi ◽  
Tsukasa Takahashi ◽  
...  
Keyword(s):  
Neural Network ◽  
Artificial Intelligence ◽  
Psychological Distress ◽  
Cross Sectional Study ◽  
Sectional Study ◽  
Cross Sectional ◽  
Explanatory Variables ◽  
The Neural Network ◽  
Significant Difference ◽  
Total Data

ObjectivesPsychological distress is a worldwide problem and a serious problem that needs to be addressed in the field of occupational health. This study aimed to use artificial intelligence (AI) to predict psychological distress among workers using sociodemographic, lifestyle and sleep factors, not subjective information such as mood and emotion, and to examine the performance of the AI models through a comparison with psychiatrists.DesignCross-sectional study.SettingWe conducted a survey on psychological distress and living conditions among workers. An AI model for predicting psychological distress was created and then the results were compared in terms of accuracy with predictions made by psychiatrists.ParticipantsAn AI model of the neural network and six psychiatrists.Primary outcomeThe accuracies of the AI model and psychiatrists for predicting psychological distress.MethodsIn total, data from 7251 workers were analysed to predict moderate and severe psychological distress. An AI model of the neural network was created and accuracy, sensitivity and specificity were calculated. Six psychiatrists used the same data as the AI model to predict psychological distress and conduct a comparison with the AI model.ResultsThe accuracies of the AI model and psychiatrists for predicting moderate psychological distress were 65.2% and 64.4%, respectively, showing no significant difference. The accuracies of the AI model and psychiatrists for predicting severe psychological distress were 89.9% and 85.5%, respectively, indicating that the AI model had significantly higher accuracy.ConclusionsA machine learning model was successfully developed to screen workers with depressed mood. The explanatory variables used for the predictions did not directly ask about mood. Therefore, this newly developed model appears to be able to predict psychological distress among workers easily, regardless of their subjective views.

scholarly journals Food Consumption Determinants and Barriers for Healthy Eating at the Workplace—A University Setting

Foods ◽  
2021 ◽  
Vol 10 (4) ◽  
pp. 695
Author(s):  
João P. M. Lima ◽  
Sofia A. Costa ◽  
Teresa R. S. Brandão ◽  
Ada Rocha
Keyword(s):  
Food Consumption ◽  
Healthy Eating ◽  
Nutritional Value ◽  
Perceived Barriers ◽  
Cross Sectional ◽  
Meal Quality ◽  
Main Determinants ◽  
Definition Of ◽  
Self Administered Questionnaire ◽  
University Setting

Background: A wide variety of social, cultural and economic factors may influence dietary patterns. This work aims to identify the main determinants of food consumption and barriers for healthy eating at the workplace, in a university setting. Methods: A cross-sectional observational study was conducted with 533 participants. Data were obtained through the application of a self-administered questionnaire that included socio-demographic information, food consumption determinants and the main perceived barriers for healthy eating at the workplace. Results: The respondents identified “price” (22.5%), “meal quality” (20.7%), and “location/distance” (16.5%). For women, the determinant “availability of healthy food options” was more important than for men (p < 0.001). The food consumption determinants at the workplace most referred to by respondents were related to the nutritional value. Smell, taste, appearance and texture, and good value for money, were also considered important for choosing food at the workplace. Respondents referred to work commitments and lack of time as the main barriers for healthy eating at the workplace. Conclusions: Identification of determinants involved in food consumption, and the barriers for healthy eating, may contribute to a better definition of health promotion initiatives at the workplace aiming to improve nutritional intake.

scholarly journals Rurality and Dropout in Virtual Higher Education Programmes in Colombia

2021 ◽  
Vol 13 (9) ◽  
pp. 4953
Author(s):  
Alfredo Guzmán Rincón ◽  
Sandra Barragán ◽  
Favio Cala Vitery
Keyword(s):  
Higher Education ◽  
Rural Areas ◽  
Hierarchical Cluster ◽  
Cross Sectional Study ◽  
Evaluation Instrument ◽  
Cross Sectional ◽  
Social Stratum ◽  
Level Of Education ◽  
Explanatory Variables ◽  
Rural Student

As part of the 2030 Agenda, higher education has been conceptualised as one of the ways to overcome the social disparities experienced in rural areas in Colombia. Thus, in concordance with the benefits of this level of education, the state has been designing public policies during the last few years, in order to facilitate access to undergraduate programmes to these populations, focusing mainly on the implementation of the virtual modality. In this context, it is recognised that access itself is not enough, but that continuance and timely graduation are required to materialise the benefits obtained along with a higher education degree; hence, dropout is a subject of interest for study, especially due to the high rates existing in the rural student population. Therefore, the event of dropout becomes an obstacle to social change and transformation in rural areas. Thus, this article aimed to identify which individual, institutional, academic and socio-economic characteristics influence rural student dropout in virtual undergraduate programmes in Colombia. For this purpose, an exploratory, quantitative and cross-sectional study was proposed, with a sample of 291 students to whom a student characterisation instrument and a classroom evaluation instrument were applied. With these data, it was proceeded to establish which of them had deserted, constituting the extraction of the sample of the study, which were 168. With the information, an exploratory factor analysis, hierarchical cluster analysis and descriptive statistics were used to establish which explanatory variables are involved in the dropout of this type of student. The results showed that the academic variables analysed do not have an impact on the event, while marital status (associated with family obligations), age, social stratum, work obligations, parents’ level of education and type of work, income and type of employment relationship of the student, and, finally, the number of people who depend on the family’s income do.

Ex Vivo assessment of competent strut coverage after coronary stenting by optical coherence tomography

2020 ◽  
Vol 41 (Supplement_2) ◽  
Author(s):  
R Bhoite ◽  
H Jinnouchi ◽  
F Otsuka ◽  
Y Sato ◽  
A Sakamoto ◽  
...  
Keyword(s):  
Optical Coherence Tomography ◽  
Predictive Value ◽  
Ex Vivo ◽  
High Sensitivity ◽  
Optical Coherence ◽  
Cross Sectional ◽  
Neointimal Thickness ◽  
Stent Strut ◽  
Oct Imaging ◽  
Definition Of

Abstract Background In many studies, struts coverage is defined as &gt;0 mm of tissue overlying the stent struts by optical coherence tomography (OCT). However, this definition has never been validated using histology as the “gold standard”. The present study sought to assess the appropriate cut-off value of neointimal thickness of stent strut coverage by OCT using histology. Methods OCT imaging was performed on 39 human coronary arteries with stents from 25 patients at autopsy. A total of 165 cross-sectional images from 46 stents were co-registered with histology. The optimal cut-off value of strut coverage by OCT was determined. Strut coverage by histology was defined as endothelial cells with at least underlying two layers of smooth muscle cells. Considering the resolution of OCT is 10–20 μm, 3 different cut-off values (i.e. at ≥20, ≥40, and ≥60 μm) were assessed. Results A total of 2235 struts were evaluated by histology. Eventually, 1216 struts which were well-matched struts were analyzed in this study. By histology, uncovered struts were observed in 160 struts and covered struts were observed in 1056 struts. The broadly used definition of OCT-coverage which does not consider neointimal thickness yielded a poor specificity of 37.5% and high sensitivity 100%. Of 3 cut-off values, the cut-off value of &gt;40 μm was more accurate as compared to &gt;20 and &gt;60 mm [sensitivity (99.3%), specificity (91.0%), positive predictive value (98.6%), and negative predictive value (95.6%)] Conclusion The most accurate cut-off value was ≥40 μm neointimal thickness by OCT in order to identify stent strut coverage validated by histology. Funding Acknowledgement Type of funding source: None

scholarly journals Definitions and Prevalence of Multimorbidity in Large Database Studies: A Scoping Review

2021 ◽  
Vol 18 (4) ◽  
pp. 1673
Author(s):  
Ying Pin Chua ◽  
Ying Xie ◽  
Poay Sian Sabrina Lee ◽  
Eng Sing Lee
Keyword(s):  
Scoping Review ◽  
Chronic Conditions ◽  
Meta Analysis ◽  
Epidemiological Studies ◽  
Cross Sectional ◽  
Coding Systems ◽  
Wide Range ◽  
Scoping Reviews ◽  
Retrospective Cohort Studies ◽  
Definition Of

Background: Multimorbidity presents a key challenge to healthcare systems globally. However, heterogeneity in the definition of multimorbidity and design of epidemiological studies results in difficulty in comparing multimorbidity studies. This scoping review aimed to describe multimorbidity prevalence in studies using large datasets and report the differences in multimorbidity definition and study design. Methods: We conducted a systematic search of MEDLINE, EMBASE, and CINAHL databases to identify large epidemiological studies on multimorbidity. We used the Preferred Reporting Items for Systematic Reviews and Meta-analysis Extension for Scoping Reviews (PRISMA-ScR) protocol for reporting the results. Results: Twenty articles were identified. We found two key definitions of multimorbidity: at least two (MM2+) or at least three (MM3+) chronic conditions. The prevalence of multimorbidity MM2+ ranged from 15.3% to 93.1%, and 11.8% to 89.7% in MM3+. The number of chronic conditions used by the articles ranged from 15 to 147, which were organized into 21 body system categories. There were seventeen cross-sectional studies and three retrospective cohort studies, and four diagnosis coding systems were used. Conclusions: We found a wide range in reported prevalence, definition, and conduct of multimorbidity studies. Obtaining consensus in these areas will facilitate better understanding of the magnitude and epidemiology of multimorbidity.

Influence of measurement error on assessment of response to anticancer chemotherapy: proposal for new criteria of tumor response.

1984 ◽  
Vol 2 (9) ◽  
pp. 1040-1046 ◽  
Author(s):  
D Warr ◽  
S McKinney ◽  
I Tannock
Keyword(s):  
Partial Response ◽  
Tumor Response ◽  
Costal Margin ◽  
Cross Sectional ◽  
Anticancer Chemotherapy ◽  
Inappropriate Treatment ◽  
Metastatic Lung ◽  
Hepatic Lesions ◽  
Definition Of ◽  
New Criteria

The decision to use a given type of chemotherapy to treat cancer patients is often based on the prior demonstration that a proportion of similar patients has "responded" in a clinical trial. Most responses are recorded as a partial shrinkage of tumor, defined usually as a greater than 50% shrinkage of the sum of cross-sectional areas of index lesions for at least one month. The errors in categorization of response have been estimated by comparing measurements of several physicians on real or simulated malignant lesions. False categorization of partial response based on a comparison of two measurements of the same lesion was 1.3% and 12.6% for large and small simulated nodules, respectively, 13.1% for malignant neck nodes, and 0.8% for metastatic lung nodules. Partial response for hepatic lesions has been defined by a 50% or 30% decrease in liver span below the costal margin; these definitions led to a false categorization of partial response of 8.5% and 18.4%, respectively. Larger errors are evident when using the current definition of disease progression that requires only a 25% increase in area. False categorization of response is increased by comparing any of serial measurements with the initial lesions, as is usually done clinically. Many published trials have used criteria for response that are subject to large errors; an uncritical interpretation of their results may lead to inappropriate treatment of patients. Based on the results, new criteria for evaluating tumor response are proposed.

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