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
Bibliographic record
Abstract
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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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.003 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".