Public Interest in Dry Eye Disease and Its Association With Environmental Parameters in Taiwan: Google Trends Infodemiology Study
Bibliographic record
Abstract
Background: A high prevalence of dry eye disease (DED) has intensified public health concerns in Taiwan. With the growing reliance on online resources for health information, platforms such as Google Trends (GT) provide a valuable method for capturing public interest. This approach also allows for the exploration of potential associations between public interest in DED and environmental parameters, which may further elucidate underlying factors contributing to the disease's rising prevalence. Objective: This study aims to (1) analyze public interest in DED in Taiwan using GT data, (2) investigate correlations between search interest and environmental parameters, and (3) identify shifts in the focus of search over time. Methods: We analyzed GT data from December 2018 to July 2024, focusing on relative search volume (RSV) for DED across Taiwan and its 6 special municipalities. Temporal trends in RSV were assessed using spline regression models, and monthly variations were assessed using the Kruskal-Wallis test. The Spearman correlation analysis was used to evaluate the association between RSV and environmental parameters, while dynamic time warping analysis clarified the temporal alignment of RSV with these parameters. Rising search queries were analyzed to identify shifts in public interest over time. Furthermore, top Google search results for DED-related keywords were assessed for topic coverage, quality, and readability. Results: A significant rising trend in RSV for DED was observed over the study period in Taiwan (mean instantaneous derivative=0.445; P<.001) and across all 6 special municipalities. Environmental parameters such as methane (CH4), total hydrocarbons, and nonmethane hydrocarbons were identified as novel pollutants strongly correlated with RSV (P<.001), along with known pollutants such as nitric oxide (NO), nitrogen dioxide (NO2), sulfur dioxide (SO2), nitrogen oxides (NOx), and carbon monoxide (CO). Dynamic time warping analysis revealed the strongest temporal alignment was between RSV and hydrocarbons, including CH4 and total hydrocarbons, further emphasizing their potential role in influencing public interest. Assessment of web-based DED information of 80 websites revealed generally low quality (DISCERN score: mean 2.14, SD 0.40), and the average readability corresponded to a college reading level (grade: mean 21.1, SD 4.5). Rising search queries shifted from diagnostic and treatment methods before the COVID-19 pandemic to natural remedies during the COVID-19 lockdown and self-diagnosis and treatment options after the pandemic. Gaps were also identified between public interest and the availability of online information. Conclusions: Public interest in DED has increased significantly in Taiwan from 2018 to 2024, with hydrocarbons identified as strongly associated environmental parameters. The shifts in related queries reflect changing public interest, accentuating the need for health care information that aligns with public interest and addresses gaps in available resources.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 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 | 0.001 |
| 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".