Predicting non-communicable disease based on behavioral risk factors and social determinants of health -- A Canadian study
Notice bibliographique
Résumé
In Canada, the concern over non-communicable diseases (NCDs), also known as chronic diseases, has become a major issue. With the significant increase of NCDs risk factors, Canadians are facing NCDs challenges. Two out of five Canadians, above the age of 12 years, have at least one NCD and 80% are at risk of developing a NCD. NCDs rates are affected by a complex interaction of factors including the underlying biological, behavioral, social, physical conditions, and health service related. This study only focused on behavioral and social conditions. The impact of social determinants of health (SDOH) and behavioral risk factors (BRFs) on individualsâ probability of getting a NCD in Canada were assessed in this study. Both the independent effect of each risk factor and their multi-function effects were assessed. BRFs include unhealthy diet (low fruit and vegetable consumption), physical inactivity, tobacco use and harmful use of alcohol. The SDOH considered in this thesis include income, education level, marital status, age and work stress. The Canadian Community Health Survey (2010) data set was used in the analysis of this study. A sample of 62,909 individuals were investigated in the CCHS 2010 survey. Univariable and multivariable logistic regression models were used in the analysis. These results indicate that the socio-economic status is related to an individualâs probability of getting a NCD. Higher socio-economic status is associated with better health, and people with less NCDs. Respondents who reported higher levels of education and income experienced fewer NCDs than respondents with lower education and income levels. Respondents with the highest work stress levels were more likely to have NCDs than those not so stressed. A healthy lifestyle, i.e. more fruit and vegetable consumption, being physically active, less smoking, is important to maintain better physical health in order to reduce the risk of having a NCD. Respondents who were obese and overweight were more likely to have NCD than those of normal weight. However, due to limitations of the data, the results regarding marital status and alcohol consumption were not clear and needs further research. Recommendations from both institutional level and community involvement policies were made in the conclusion chapter of the thesis.
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Comment cette classification a été obtenuedéplier
Prédiction distillée sur la base complète
Imitation des enseignantsNi prévalence calibrée, ni vérité terrain. Validation humaine à venir. Apprise à partir de 10 348 étiquettes directes de Codex et de 10 348 étiquettes directes de Gemma. Le mode candidate est l'union des têtes enseignantes seuillées; le consensus est leur intersection. Ces sorties portent le statut machine_predicted_unvalidated et ne sont ni des étiquettes humaines ni des étiquettes directes de modèles de pointe.
Scores Codex et Gemma par catégorie
| Catégorie | Codex | Gemma |
|---|---|---|
| Métarecherche | 0,001 | 0,000 |
| Méta-épidémiologie (sens strict) | 0,000 | 0,000 |
| Méta-épidémiologie (sens large) | 0,001 | 0,000 |
| Bibliométrie | 0,000 | 0,000 |
| Études des sciences et des technologies | 0,004 | 0,000 |
| Communication savante | 0,000 | 0,000 |
| Science ouverte | 0,000 | 0,000 |
| Intégrité de la recherche | 0,000 | 0,001 |
| Charge utile insuffisante (le modèle a refusé de juger) | 0,000 | 0,000 |
Scores machine (provisoires)
Les deux têtes enseignantes du modèle étudiant, lues sur ce travail. Un score ordonne la base pour la relecture; il n'affirme jamais une catégorie, et le statut de validation accompagne chaque rangée tel quel.
Scores de référence d'un modèle non mature (critères de maturité non atteints, 7 itérations). Un score ordonne; il n'affirme jamais une catégorie.
score_only:v0-immature-baseline · tel quel depuis la passe de notation : score_only signifie que le nombre peut ordonner les travaux, et qu'aucune étiquette de catégorie n'en découleClassification
machine, non validéePrédiction automatique; un appel candidat d’une seule tête enseignante, pas un consensus.
Le détail, modèle par modèle et score par score, se trouve en fin de page sous « Comment cette classification a été obtenue ».