Titre : Modèles économétriques

Modèles économétriques : Questions médicales fréquentes

Termes MeSH sélectionnés :

Spatial Analysis
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risque" } ] }, { "@type": "FAQPage", "mainEntity": [ { "@type": "Question", "name": "Comment identifier un modèle économétrique approprié ?", "position": 1, "acceptedAnswer": { "@type": "Answer", "text": "Il faut analyser la nature des données et les relations entre les variables." } }, { "@type": "Question", "name": "Quels tests sont utilisés pour valider un modèle ?", "position": 2, "acceptedAnswer": { "@type": "Answer", "text": "Des tests comme le test de normalité, le test de multicolinéarité et le test de spécification." } }, { "@type": "Question", "name": "Qu'est-ce qu'un modèle de régression ?", "position": 3, "acceptedAnswer": { "@type": "Answer", "text": "C'est un modèle qui établit une relation entre une variable dépendante et une ou plusieurs variables indépendantes." } }, { "@type": "Question", "name": "Comment évaluer la performance d'un modèle ?", "position": 4, "acceptedAnswer": { "@type": "Answer", "text": "On utilise des indicateurs comme le R², l'erreur quadratique moyenne et le test de Fisher." } }, { "@type": "Question", "name": "Qu'est-ce qu'un modèle à variables instrumentales ?", "position": 5, "acceptedAnswer": { "@type": "Answer", "text": "C'est un modèle utilisé pour corriger les biais d'endogénéité en utilisant des variables externes." } }, { "@type": "Question", "name": "Quels sont les signes d'un modèle mal spécifié ?", "position": 6, "acceptedAnswer": { "@type": "Answer", "text": "Des résidus non aléatoires, des valeurs aberrantes et des relations non linéaires." } }, { "@type": "Question", "name": "Comment détecter l'hétéroscédasticité ?", "position": 7, "acceptedAnswer": { "@type": "Answer", "text": "En utilisant des tests comme le test de Breusch-Pagan ou en observant les résidus." } }, { "@type": "Question", "name": "Quels effets peut avoir la multicolinéarité ?", "position": 8, "acceptedAnswer": { "@type": "Answer", "text": "Elle peut rendre les estimations des coefficients instables et difficiles à interpréter." } }, { "@type": "Question", "name": "Qu'est-ce qu'un biais d'échantillonnage ?", "position": 9, "acceptedAnswer": { "@type": "Answer", "text": "C'est une erreur systématique due à un échantillon non représentatif de la population." } }, { "@type": "Question", "name": "Quels sont les signes d'une autocorrélation ?", "position": 10, "acceptedAnswer": { "@type": "Answer", "text": "Des résidus corrélés dans le temps, souvent détectés par le test de Durbin-Watson." } }, { "@type": "Question", "name": "Comment éviter les biais dans les modèles ?", "position": 11, "acceptedAnswer": { "@type": "Answer", "text": "En s'assurant que l'échantillon est représentatif et en utilisant des méthodes de validation." } }, { "@type": "Question", "name": "Quelles pratiques pour une bonne collecte de données ?", "position": 12, "acceptedAnswer": { "@type": "Answer", "text": "Utiliser des protocoles standardisés et s'assurer de la qualité et de la fiabilité des données." } }, { "@type": "Question", "name": "Comment choisir les bonnes variables ?", "position": 13, "acceptedAnswer": { "@type": "Answer", "text": "En se basant sur la théorie, des études antérieures et des tests de significativité." } }, { "@type": "Question", "name": "Quelles sont les bonnes pratiques de modélisation ?", "position": 14, "acceptedAnswer": { "@type": "Answer", "text": "Utiliser des diagnostics appropriés, tester les hypothèses et valider le modèle sur des données nouvelles." } }, { "@type": "Question", "name": "Comment éviter le surajustement ?", "position": 15, "acceptedAnswer": { "@type": "Answer", "text": "En utilisant des techniques de validation croisée et en limitant la complexité du modèle." } }, { "@type": "Question", "name": "Comment corriger l'hétéroscédasticité ?", "position": 16, "acceptedAnswer": { "@type": "Answer", "text": "En utilisant des transformations de données ou des modèles de régression robustes." } }, { "@type": "Question", "name": "Quelles méthodes pour traiter la multicolinéarité ?", "position": 17, "acceptedAnswer": { "@type": "Answer", "text": "On peut utiliser la sélection de variables, la régularisation ou l'analyse en composantes principales." } }, { "@type": "Question", "name": "Comment améliorer un modèle économétrique ?", "position": 18, "acceptedAnswer": { "@type": "Answer", "text": "En ajoutant des variables pertinentes, en transformant les données ou en utilisant des modèles non linéaires." } }, { "@type": "Question", "name": "Qu'est-ce que la régularisation ?", "position": 19, "acceptedAnswer": { "@type": "Answer", "text": "C'est une technique pour prévenir le surajustement en ajoutant une pénalité aux coefficients." } }, { "@type": "Question", "name": "Comment utiliser des modèles de séries temporelles ?", "position": 20, "acceptedAnswer": { "@type": "Answer", "text": "Pour analyser des données chronologiques et prévoir des tendances futures à l'aide de lissage." } }, { "@type": "Question", "name": "Quelles sont les conséquences d'un modèle mal spécifié ?", "position": 21, "acceptedAnswer": { "@type": "Answer", "text": "Des prévisions inexactes, des décisions erronées et une mauvaise interprétation des résultats." } }, { "@type": "Question", "name": "Quels risques d'une autocorrélation non traitée ?", "position": 22, "acceptedAnswer": { "@type": "Answer", "text": "Elle peut conduire à des estimations biaisées et à des tests statistiques non fiables." } }, { "@type": "Question", "name": "Comment la multicolinéarité affecte-t-elle les résultats ?", "position": 23, "acceptedAnswer": { "@type": "Answer", "text": "Elle rend difficile l'évaluation de l'impact individuel des variables sur la variable dépendante." } }, { "@type": "Question", "name": "Quelles erreurs peuvent survenir dans l'interprétation des résultats ?", "position": 24, "acceptedAnswer": { "@type": "Answer", "text": "Des conclusions hâtives, des généralisations inappropriées et des politiques mal orientées." } }, { "@type": "Question", "name": "Quels effets d'un échantillonnage biaisé ?", "position": 25, "acceptedAnswer": { "@type": "Answer", "text": "Il peut fausser les résultats et mener à des recommandations inappropriées." } }, { "@type": "Question", "name": "Quels facteurs influencent la sélection des variables ?", "position": 26, "acceptedAnswer": { "@type": "Answer", "text": "La théorie économique, la disponibilité des données et les objectifs de recherche." } }, { "@type": "Question", "name": "Comment la taille de l'échantillon affecte-t-elle les résultats ?", "position": 27, "acceptedAnswer": { "@type": "Answer", "text": "Un échantillon trop petit peut entraîner des estimations instables et des biais." } }, { "@type": "Question", "name": "Quels sont les risques d'une mauvaise collecte de données ?", "position": 28, "acceptedAnswer": { "@type": "Answer", "text": "Des données inexactes peuvent fausser les résultats et compromettre la validité du modèle." } }, { "@type": "Question", "name": "Comment les variables omises affectent-elles le modèle ?", "position": 29, "acceptedAnswer": { "@type": "Answer", "text": "Elles peuvent introduire un biais et fausser les relations estimées entre les variables." } }, { "@type": "Question", "name": "Quels sont les impacts d'une mauvaise spécification du modèle ?", "position": 30, "acceptedAnswer": { "@type": "Answer", "text": "Des prévisions erronées et des décisions basées sur des analyses incorrectes." } } ] } ] }

Sources (10000 au total)

Spatial analysis to evaluate risk of malaria in Northern Sumatera, Indonesia.

As Indonesia aims for malaria elimination by 2030, provisional malaria epidemiology and risk factors evaluation are important in pursue of this national goal. Therefore, this study aimed to understand... Malaria cases from 2019 to 2020 were obtained from the Indonesian Ministry of Health Electronic Database. Climatic variables were provided by the Center for Meteorology and Geophysics Medan branch off... A total of 2208 (indigenous: 76.0% [1679] and imported: 17.8% [392]) were reported during the study period. Risk factors of imported malaria were: ages 19-30 (adjusted odds ratio [AOR] = 3.31; 95% con... Both indigenous and imported malaria is limited to a few regencies and cities in Northern Sumatera. The control measures should focus on these risk factors to achieve elimination in Indonesia....

Spatial analysis of the epidemiological risk of leprosy in the municipalities of Minas Gerais.

Leprosy remains a significant public health problem of high importance. This investigation aims to analyze the spatial distribution of the leprosy epidemiological risk in the municipalities of Minas G... This ecological study was conducted with new leprosy cases diagnosed from 2004 to 2019 in the municipalities of the state of Minas Gerais. Based on the epidemiological indicators, a composite indicato... Although leprosy is declining in the state of Minas Gerais, the Global Moran Index confirmed the spatial dependence between municipalities for the two analyzed periods, characterizing the formation of... Leprosy has a heterogeneous spatial pattern and remains concentrated in historically endemic areas of the state. It underscores the importance of intensifying actions to combat leprosy in these munici...

Spatial Analysis of Access to Psychiatrists for US Military Personnel and Their Families.

Military service members and their families have greater mental health care needs compared with their civilian counterparts. Some communities have inadequate access to psychiatrists for this populatio... To identify geographic variations in the availability of military and civilian psychiatrists within a 30-minute driving time of TRICARE (the US military's health care program) beneficiaries' communiti... This retrospective cohort study of all zip code communities in the continental US, Hawaii, and Alaska with at least one TRICARE beneficiary between January 1, 2016, and September 30, 2020, combines da... A community's likelihood of having a shortage of military and civilian psychiatrists within a 30-minute driving time and a community's likelihood of having no psychiatrists. Odds ratios were calculate... This study includes 39 487 unique communities where 13% of the population is Black and 14% of the population is Hispanic. During the study period, 35% of TRICARE beneficiaries lived in communities wit... In this cohort study of US communities, 35% of TRICARE beneficiaries lived in communities with inadequate access to psychiatrists. Psychiatric capacity was structurally inequitable along 2 separate di...

Spatial Analysis of Breast Cancer Mortality Rates in a Rural State.

Breast cancer affects 1 in 8 women in the US and is the most frequently diagnosed cancer in women. In South Dakota, 102 women die from breast cancer each year. We assessed which sociodemographic facto... We computed standardized incidence ratios (SIRs) of all counties in South Dakota by using the age-adjusted mortality rates, the 2000 US standard population, and the South Dakota estimated population. ... Educational level and breast cancer incidence rates were significantly associated with breast cancer mortality rates at the county level. The SIR values based on age-adjusted counts showed which count... The regression model helped identify factors associated with mortality and provided insights into which risk factors are at play in South Dakota. This information, in combination with the spatial dist...

Spatial analysis of cardiovascular mortality and associated factors around the world.

Cardiovascular disease (CVD) is one of the most serious health issues and the leading cause of death worldwide in both developed and developing countries. The risk factors for CVD include demographic,... We present the spatial distribution of the age-standardized crude mortality rate from cardiovascular disease, as well as conduct an exploratory data analysis (EDA) to obtain a basic understanding of t... Our empirical findings show that the relationship between CVD and income, as well as other socioeconomic variables, are important. In addition, we highlight the importance of understanding how changes... We argue that this study provides useful clues for policymakers establishing effective public health planning and measures for the prevention of deaths from cardiovascular disease. The reduction of CV...

Spatial pattern and determinants of institutional delivery in Ethiopia: Spatial and multilevel analysis using 2019 Ethiopian demographic and health survey.

In Ethiopia, despite the progress that has been made to improve maternal and child health, the proportion of births occurring at health institutions is still very low (26%), Which significantly contri... Data from 2019 Ethiopian demographic and health survey were used. Taking into account the nested structure of the data, multilevel logistic regression analysis has been employed to a nationally repres... A significant heterogeneity was observed between clusters for institutional delivery which explains about 57% of the total variation. Individual-level variables: primary education (OR = 1.8: 95% CI: 1... A clustered pattern of areas with low institutional delivery was observed in Ethiopia. Both individual and community level factors found significantly associated with institutional delivery theses sho...