Feasibility of Using a Nationally Representative Telephone Survey to Monitor Multiple Sclerosis Prevalence in the United States
Bibliographic record
Abstract
BACKGROUND: Multiple sclerosis (MS) is the most common chronic neurologic disease of young adults, placing a heavy burden on patients, families, and the healthcare system. Ongoing surveillance of the incidence and prevalence of MS is critical for health policy and research, but feasible options are limited in the United States and many other countries. We investigated the feasibility of monitoring the prevalence of MS using a large national telephone survey of the adult US population. METHODS: We developed questions to estimate the lifetime prevalence and age of onset of MS using the US-based Behavioral Risk Factor Surveillance System (BRFSS) and piloted these questions in 4 states (MN, RI, MD, and TX). There was a total of 45,198 respondents aged 18 years and above. Analyses investigated individual state and combined prevalence estimates along with health-related comorbidities and limitations. MS prevalence estimates from the BRFSS were compared to estimates from multi-source administrative claims and traditional population-based methods. RESULTS: The estimated lifetime prevalence of self-reported MS (per 100,000 adults) was 682 (95% CI 528-836); 384 (95% CI 239-529) among males and 957 (95% CI 694-1,220) among females. Estimates were consistent across the 4 states but much higher than recently published estimates using population-based administrative claims data. This was observed for both national results and for MS prevalence estimates from other studies within specific states (MN, RI, and TX). Prevalence estimates for Caucasian, African American, and Hispanic respondents were 824, 741, and 349 per 100,000 respectively. Age and sex distributions were consistent with prior epidemiologic reports. Comorbidity and functional limitations were more pronounced among female than male respondents. CONCLUSIONS: While yielding higher overall MS prevalence estimates compared to recent studies, this large-scale self-report telephone method yielded relative prevalence estimates (e.g., prevalence patterns of MS by sex, age, and race-ethnicity) that were generally comparable to other surveillance approaches. With certain caveats, population-based telephone surveys may eventually offer the ability to investigate novel disease correlates and are relatively feasible, and affordable. Further work is needed to create a valid question set and methodology for case ascertainment before this approach could be adopted to accurately estimate MS prevalence.
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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 | 0.003 | 0.086 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| 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".