Epidemiological Insights into Erectile Dysfunction in the United States: A Google Trends Analysis
Bibliographic record
Abstract
Background/Objectives: Erectile dysfunction (ED) significantly impacts the well-being and quality of life of millions of men. Understanding geographic patterns and associated factors influencing ED search trends can offer valuable insights for healthcare improvement and advocacy. This study investigated the correlation between Google search trends for ED and various factors across the US. Methods: Google search trends for “erectile dysfunction” were collected over a 6-year period between March 2018 and August 2024. The Google search trends provided data for individual states on a scale from 1 to 100. Search volumes were analyzed alongside the urologist-to-population ratio, percentage of the population aged 65 and older, median household income, and percentage of state residents with a bachelor’s degree or higher. Pearson correlation coefficients were used to examine the relationships between ED search volumes and these factors. Results: Higher ED search volumes were associated with a higher percentage of older adults (r = 0.4332, p = 0.001676). A negative correlation was found between ED search volume and higher education (r = −0.482, p = 0.000394). No significant correlation was found between median household income and ED search volume (r = −0.201, p = 0.164) or a greater urologist density (r = 0.0612, p = 0.6729). Conclusions: This study highlights how healthcare access and demographics influence ED search trends. States with older, less educated populations showed higher interest, while wealthier areas with more urologists had no significant correlation. These findings can guide targeted interventions to improve sexual care in underserved regions.
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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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".