Disagreement Between Self-Reporting and Objective Diagnosis in Chronic Diseases Among Omanis 2008
Notice bibliographique
Résumé
Background: Health specialists and researchers usually collect information about chronic diseases from self-reports. However, the accuracy of self-reports has been questioned as it relies on the respondents’ understanding of pathological conditions and their ability to recall information. Accordingly, an objective diagnosis is generally regarded as a more precise indication of the presence of disease.Objective: The study objectives were to determine the extent of disagreement between self-reporting and objective diagnosis, identify contributory factors to the discrepancy, and examine the effects of the incongruity on quality of healthcare services and health status.Methods: Secondary data from the most recent Oman World Health Survey (OWHS), for which data were readily available (2008), were analysed in the current study. This was the most recent survey conducted in Oman to date as collection of the data for the subsequent survey only commenced in February 2017 and is still in progress. Agreement between the self-reporting of chronic disease (diabetes mellitus and hypertension) and the results of medical examinations was calculated using kappa (ϰ) statistics. Sociodemographic risk factors for the self-reported and objective measurement of disease were identified (second objective). Univariate analysis was measured initially to determine associations between the variables and the outcome. Thereafter, significant variables were included in multivariate analysis performed using logistic regression. The impact of disagreement on quality of healthcare service and health status (third objective) was also examined using the chi-square test in relation to health service quality and health status variables.Results: Of 3524 Oman adults, aged ≥ 20 years (48% males), agreement between the self-reported and objective measurement of chronic disease was found to be poor to moderate (ϰ = 0.001-0.141). The highest agreement was observed for diabetes mellitus (ϰ = 0.402) and the lowest was found for asthma (ϰ = 0.000). Socioeconomic or demographic characteristics were not significantly associated with the degree of agreement attained between the methods used to measure chronic disease (p = > 0.050), except for sex, age and region. The discrepancy did not significantly impact on familial support (i.e., financial, social, health, physical and personal), the responsiveness of the health system, and household income or expenditure. However, the disagreement was associated with significant effects for other healthcare service and health status variables, i.e., quality of life and health service utilisation (p = < 0.050). It was found that people with the chronic disease and aware of their health status (positive agreement), and those with negative objective measure but positive self-reported disease (negative disagreement), were more likely to access healthcare services (83% of who had a positive agreement for chronic lung disease) and to be satisfied with the quality of care provided (82% of who had a negative disagreement for hypertension), compared to those who assumed they were healthy but had a chronic disease.Conclusions and Recommendations: Although agreement between the self-reported and objective measurement of chronic disease was found to be poor to moderate, we found that some socioeconomic demographic characteristics, such as educational and economic level, did not affect the agreement of measure tools for hypertension and diabetes, except for sex, age and region. Contrary to our expectations, disagreement between objective and self-reported measures in chronic diseases appears not to significantly impact on the quality of healthcare services and health status. The high use of health care services in participants with positive disagreement may result in unnecessary healthcare service costs required to treat chronic diseases. The implications on health services use and planning of this disagreement in the diagnosis of chronic diseases have been scarcely addressed in the literature, therefore, the results from our study need to be taken as a first approximation to this issue. Provided the unexpected results, we recommend examining closely the integrity of the dataset before giving full value about the validity of them.
Récupéré en direct depuis OpenAlex et désinversé. Les résumés ne sont pas conservés dans cette base de données : les index inversés représentent 8,6 Go des 9,3 Go de texte de la base, et le serveur dispose de 13 Go libres.
Comment cette classification a été obtenuedéplier
Prédiction machine sur la base complète
Imitation des enseignantsNi prévalence calibrée, ni vérité terrain. Validation humaine à venir. Le volet Gemma est une étiquette directe du modèle pour chaque travail de la base, lue sur la notice réduite au titre. Le volet Codex est un classifieur appris des 10 348 étiquettes directes de Codex et calibré sur les taux pondérés de l'échantillon; les champs sans appui suffisant ne portent aucun appel Codex. Le mode candidate est l'union des deux volets; le consensus est leur intersection. Ces sorties portent le statut machine_predicted_unvalidated et ne sont pas des étiquettes humaines.
Scores du classifieur distillé par catégorie (deux têtes)
| Catégorie | Codex | Gemma |
|---|---|---|
| Métarecherche | 0,012 | 0,022 |
| Méta-épidémiologie (sens strict) | 0,000 | 0,000 |
| Méta-épidémiologie (sens large) | 0,001 | 0,000 |
| Bibliométrie | 0,003 | 0,002 |
| Études des sciences et des technologies | 0,001 | 0,001 |
| Communication savante | 0,001 | 0,001 |
| Science ouverte | 0,001 | 0,002 |
| Intégrité de la recherche | 0,001 | 0,000 |
| Charge utile insuffisante (le modèle a refusé de juger) | 0,001 | 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 source (Gemma direct ou Codex distillé), 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 ».