Using primary care electronic medical record data to establish a case definition and describe the burden of young-adult onset metabolic syndrome in Northern Alberta
Notice bibliographique
Résumé
Background: There is little evidence on the prevalence of metabolic syndrome (MetS) in the younger adult Canadian population. Moreover, MetS is even less studied within the primary care setting due to multiple barriers including difficulty for providers to identify patients given the multitude of definitions used in practice, varying electronic medical record systems (EMRs) used, and the presumption that younger people are generally healthier. With the growing prevalence of preventable chronic diseases worldwide, the need to expand our understanding of MetS in younger adults is critical to preventing its long-term sequelae. Objectives: 1. Develop a case definition and case-finding algorithm for MetS using primary care electronic medical record data. 2. Describe the patterns and prevalence of MetS in younger adults, aged 18-40 years old. 3. Describe the patterns and prevalence of MetS between sexes, aged 18-40 years old. Methods: Using a cross-sectional study design, we developed a case definition and casefinding algorithm for the identification of MetS. Electronic medical record (EMR) data from the Northern Alberta Primary Care Research Network (NAPCReN), a part of the Canadian Primary Care Sentinel Surveillance Network (CPCSSN), was used with a focus on younger adults who were 18-40 years of age. Both studies for this thesis used data including anthropometric measurements, laboratory investigations, and CPCSSN-validated disease diagnoses to establish prevalence and patterns of MetS. The first study outlines the case definition and casefinding algorithm and describes the patterns of MetS in the NAPCReN younger adult population who attend primary care clinics in Northern Alberta. The second study aims to describe the patterns of young-adult onset MetS stratified by sex. The analysis was performed in RStudio (version 1.1.453) and includes descriptive statistics, multiple comparisons (p < .05), and a linear search (case-finding) algorithm development. iii Results: According to the MetS case-finding algorithm, the prevalence of MetS in younger adults was 4.4%. Nearly all individuals with MetS were overweight and obese (91.2%). The most frequent 3-factor combination of MetS consisted of being overweight or obese, having elevated blood pressure (BP), and hypertriglyceridemia (41.4% of cases). Half of the individuals with MetS were missing measures for FBG, and one-fifth were missing a HbA1c measure. The proportions of missing laboratory data were even greater for all individuals who were overweight and obese. Of the CPCSSN validated diseases among individuals with MetS, depression (16.5%) had the highest prevalence followed by diabetes (15.2%), hypertension (14.2%), and osteoarthritis (2.6%). When assessing the differences in sex, there were more females than males in this sample with females having more favourable metabolic profiles compared to males. In those with MetS, the reverse was found where males had better measures for BMI and HDL-C compared to females. The most prevalent 3-factor MetS combination among males consisted of being overweight, having elevated BP, and hypertriglyceridemia. The most prevalent 3-factor MetS combination among females consisted of being overweight, having elevated BP, and low HDL-C. Being overweight as defined by a BMI ≥25 kg/m2, was the most common factor among both sexes with MetS. The prevalence of chronic diseases such as depression and diabetes were higher in females compared to males however, hypertension was higher among males. Conclusion: We found that one in twenty-five younger adults attending a primary care clinic had MetS, which is likely an underestimate given the high levels of missing data for those noted to be overweight and obese. In those with MetS, women appear to have more metabolic dysfunction than men. The large proportion of missing data, especially amongst those who are overweight and obese, calls for exploration of whether levels of missed testing are appropriate and sets the stage for future quality improvement to do earlier risk stratification and prevention of metabolic syndrome sequelae.
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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 | 0,003 | 0,008 |
| Méta-épidémiologie (sens strict) | 0,000 | 0,000 |
| Méta-épidémiologie (sens large) | 0,000 | 0,000 |
| Bibliométrie | 0,004 | 0,005 |
| Études des sciences et des technologies | 0,002 | 0,001 |
| Communication savante | 0,001 | 0,000 |
| Science ouverte | 0,002 | 0,001 |
| Intégrité de la recherche | 0,000 | 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 ».