Abstract PO-090: The impacts of depression and socioeconomic factors on cognitive function among low-income Asian and African American elderly aged 65 and above
Notice bibliographique
Résumé
Abstract Background: Alzheimer's disease (AD) and Alzheimer's disease and related dementias (ADRD) have been a public health problem in the United States for a long time, and its adverse impact on the health of minority elderly population has been increasing since 2000. Nearly 40% of ADRD patients suffer from depression. However, the burden of ADRD and depression comorbidity among Asian and African Americans elderly was understudied. Methods: Participants aged 65 and older were recruited from Chinese, Vietnamese, and African American community-based organizations in the Greater Philadelphia Region and New York City. We conducted a cross sectional survey to assess their ADRD-related knowledge, depression symptoms, sociodemographic and health related factors. Cognitive function was assessed with the Montreal Cognitive Assessment (MoCA), depression severity was tested with Patient Health Questionnaire (PHQ-9), and stressful life events (SLE) impact score was measured with a 6-item scale with excellent internal consistency (Cronbach's alpha=0.94). A p value that is smaller than 0.05 is considered statistically significant, while a p value that is smaller than 0.1 indicates marginally significant level. Results: Overall, the participants (n=306, 12.21% African Americans, 54.79% Chinese Americans, and 33% Vietnamese Americans) had an average age of 73.57; 89.56% of them had < $20k annual household income, and 62.13% did not have a college degree. The average MoCA score was 21.24, which was significantly lower than the normal criteria 26, indicating mild cognitive impairment. Bivariate analysis showed that depression (r=-0.51, p<0.001) and SLE impact (r=-0.33, p<0.001) were significantly negatively correlated to MoCA scores. After controlling for demographics, depression severity (Coef. =-0.42, p<0.001) remained a significant predictor of cognitive function. Multivariate analysis also found that age (Coef. =-0.15, p=0.016) and education levels (Coef. =2.11, p<0.001) were significant predictors of MoCA score. Compared with those who did not speak English at all, participants who speak some English (Coef. =3.10, p<0.001) and good at English speaking (Coef. =4.54, p=0.034) were more likely to have higher cognitive scores. Moreover, being retired (Coef. =-3.23, p=0.054) and having >$40k annual household income (Coef. =-3.73, p=0.091) showed marginally negative associations with cognitive function level. Conclusion: The preliminary findings demonstrate an association between depression and mild cognitive impairment among Asian and African American elderly. With the remaining experimental work, targeted interventions will be identified in improving ADRD knowledge, cognitive performance, and mental health among understudied older Asian and African Americans. Citation Format: Wenyue Lu, Lin Zhu, Michael Coronado, Guercie E. Guerrier, Yin Tan, Grace X. Ma. The impacts of depression and socioeconomic factors on cognitive function among low-income Asian and African American elderly aged 65 and above [abstract]. In: Proceedings of the AACR Virtual Conference: 14th AACR Conference on the Science of Cancer Health Disparities in Racial/Ethnic Minorities and the Medically Underserved; 2021 Oct 6-8. Philadelphia (PA): AACR; Cancer Epidemiol Biomarkers Prev 2022;31(1 Suppl):Abstract nr PO-090.
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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 | 0,000 | 0,001 |
| Méta-épidémiologie (sens strict) | 0,000 | 0,000 |
| Méta-épidémiologie (sens large) | 0,000 | 0,000 |
| Bibliométrie | 0,001 | 0,001 |
| Études des sciences et des technologies | 0,001 | 0,000 |
| Communication savante | 0,000 | 0,000 |
| Science ouverte | 0,000 | 0,000 |
| Intégrité de la recherche | 0,000 | 0,000 |
| Charge utile insuffisante (le modèle a refusé de juger) | 0,003 | 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 ».