Association between Migraine and Quality of Life, Mental Health, Sleeping Disorders, and Health Care Utilization Among Older African American Adults
Bibliographic record
Abstract
PURPOSE: This study examines the associations between migraine headaches, well-being, and health care use among a sample of underserved older African American adults. Controlling for relevant variables, the association between migraine headaches and (1) health care utilization, (2) health-related quality of life (HRQoL), and (3) physical and mental health outcomes was examined. METHODS: Our sample included 760 older African American adults from South Los Angeles recruited through convenience and snowball sampling. In addition to demographic variables, our survey included validated instruments, such as the SF-12 QoL, Short-Form McGill Pain Questionnaire, and the Geriatric Depression Scale. Data analysis included 12 independent multivariate models using multiple linear regression, log transferred linear regression, binary and multinomial logistic regression, and generalized linear regression with Poisson distribution. RESULTS: Having migraine was associated with three categories of outcomes: (1) higher level of health care utilization measured by (i) emergency department admissions and (ii) number of medication use; (2) lower level of HRQoL and health status measured by (i) lower self-rated health (ii) physical QoL, and (iii) mental QoL; and (3) worse physical and mental health outcomes measured by (i) higher number of depressive symptoms, (ii) higher level of pain, (iii) sleep disorder, and (iv) being disabled. CONCLUSIONS: Migraine headache significantly was associated with quality of life, health care utilization, and many health outcomes of underserved African American middle-aged and older adults. Diagnoses and treatments of migraine among underserved older African American adults require multi-faceted and culturally sensitive interventional studies.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.002 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 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) | 0.000 | 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 teacher head, 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".