This study did not use patient data but rather publicly available data on search engine trends in conjunction with publicly available US Census data. As such, no direct patient contact or intervention was involved, and individual informed consent was not required. Therefore, our study was exempted by the University of Arkansas for Medical Sciences Institutional Review Board. Our study was conducted in accordance with the Declaration of Helsinki.

We queried Google Trends for searches originating in the United States between 2008 and 2025. Google Trends reports relative search volume (RSV) on a normalized scale from 0 to 100, with 100 representing peak popularity for a given search term for the selected time and geographical location. The searches were performed using a composite of search terms, which we grouped into 2 categories. We use the Google Trends “+” operator to combine exact syntax and capture public interest. The nonsurgical category included “floaters,” “eye floaters,” “cobwebs in vision,” “visual floaters,” and “floating specks.” The surgical category included “vitreous floaters,” “vitrectomy,” “symptomatic vitreous opacities,” “floaterectomy,” and “YAG vitreolysis.” Although “floaterectomy” is a colloquial and nonstandard medical term, it was intentionally included to capture niche public search variations that patients may use during online information seeking; the term is used to describe combined phacoemulsification and vitrectomy procedures for floaters [6]. The search terms were selected based on consensus among fellowship-trained vitreoretinal surgeons to capture both colloquial, symptom-based layperson queries and formal, treatment-based clinical inquiries. At the time of data collection (January 10, 2026), Google Trends allowed a maximum of 5 syntax inputs per query.

These terms reflect established clinical precedent, supported by previous studies evaluating vitreous floaters and prior evidence-based reviews that specifically use keywords such as “floaters,” “vitreous opacities,” “floaterectomy,” and “vitrectomy” in their search strategies [7,8].

We obtained state-level counts of vitreoretinal surgeons from the American Society of Retina Specialists (ASRS) member database [9]. We calculated retina surgeon density for each state by dividing the number of ASRS members by the 2025 US Census Bureau state population estimates and multiplying by 100,000 [10,11].

We calculated a search-to-surgeon index (SSI) to contextualize public interest relative to specialist availability [12,13]. We defined SSI as the ratio of state-level RSV to retina surgeon density (SSI=state-level RSV/retina surgeon density). Because RSV is a normalized, unitless scale rather than an absolute search count, the SSI is used strictly as an exploratory relative metric to rank geographic disparities rather than to quantify absolute clinical volume or health care demand.

Because Google Trends does not provide demographic data on users’ age, we independently obtained state-level population age distributions from the 2025 US Census Bureau. We used the proportion of residents aged 40 years or older as a proxy for populations at higher risk for symptomatic vitreous floaters. We evaluated the correlation between age distribution, RSV, and SSI using the Spearman correlation test.

We evaluated associations using the Mann-Kendall trend test with the Sen slope estimator and the Spearman correlation test performed using Microsoft Excel.

We identified 1992 US-based ASRS members. Mean retina surgeon density was 0.58 (SD 0.2; range 0.17-2.09) per 100,000 residents. Delaware (2.09), Maryland (1.04), and Vermont (0.93) had the highest densities, whereas Wyoming (0.17), Alaska (0.27), and New Hampshire (0.28) had the lowest. Census data showed the highest proportions of residents aged 40 years or older in Maine, Montana, New Hampshire, Pennsylvania, Vermont, and West Virginia, while the lowest proportions were in Alaska, Texas, Utah, Georgia, and North Dakota.

From 2008 to 2025, RSV for nonsurgical floater terms increased by 138%. Temporal trend analysis demonstrated a strong positive monotonic association between time and RSV values (Kendall τ=0.600; P<.001), with an approximate median increase of 0.172 RSV units per month (95% CI 0.153‐0.190). The minimum RSV of 42 occurred in November 2008 (Figure 1). There was a moderate positive correlation between the proportion of residents aged 40 years or older and RSV (ρ=0.45; P<.01). In 2025, the median RSV across states was 68 (IQR 61-73; range 48-100), with the highest values in Delaware (100), Alabama (77), and Tennessee (77) and the lowest in Utah (48), Wyoming (53), Montana (55), and Oregon (56) (Figure 2).

The median nonsurgical SSI across states was 123 (IQR 100-127; range 48-311). Wyoming (311), New Hampshire (254), and Alaska (244) had the highest SSI values, whereas Delaware (48), Vermont (68), and Maryland (71) had the lowest (Figure 3). There was no significant correlation between population age distribution and SSI (ρ=−0.08; P>.10).

From 2008 to 2025, RSV increased by 133%. Temporal trend analysis demonstrated a moderate positive monotonic association between time and RSV values (Kendall τ=0.443; P<.001) with an approximate median increase of 0.101 RSV units per month (95% CI 0.084‐0.118) (Figure 4). In 2025, the median RSV across states was 70 (IQR 62-79; range 50-100). West Virginia (100), South Dakota (100), Montana (95), and Nebraska (95) demonstrated the highest RSV values, while Alaska (50), Georgia (58), California (58), Wyoming (58), and Mississippi (58) demonstrated the lowest (Figure 5). The proportion of residents aged 40 years and older showed a low but statistically significant positive correlation with surgical RSV (ρ=0.39; P<.01).

The median surgical SSI across states was 133 (IQR 107-164; range 34-341), with the highest values in Wyoming (341), New Hampshire (278), and Iowa (243) and the lowest in Delaware (34), Maryland (72), and Vermont (81) (Figure 6). Population age distribution did not significantly correlate with surgical SSI (ρ=−0.02; P>.10).

Public search interest in vitreous floaters and related interventions increased substantially across the US over the study period. These findings suggest symptomatic floaters are an increasingly important topic for online health information seeking.

Notably, our data demonstrated a significant upward inflection in RSV around March 2018, which coincided with the publication of a randomized clinical trial demonstrating improvement in floater symptoms after yttrium-aluminum-garnet (YAG) laser vitreolysis, and subsequent media coverage [14,15]. However, this correlation does not prove causality; multiple factors, including broader media coverage, social media activity, and changes in search engine algorithms, could have simultaneously influenced general search behavior. Similarly, a temporary decline in search growth in early 2020 coincided with the COVID-19 pandemic. While this mirrors reported reductions in elective ophthalmic procedures, it remains a speculative association without direct data on changes in individual health search patterns [16].

When public interest was interpreted relative to local specialist availability via the SSI, regional variations emerged. For instance, Delaware’s high surgeon density and SSI reflect its smaller population and proximity to major metropolitan referral centers, which act as cross-state catchments. Due to its extreme positive skew in surgeon density (2.09 per 100,000), Delaware was excluded from select visual analyses to preserve the interpretability of the scatterplots for the remaining states. Conversely, states with high SSI values and low surgeon densities such as Wyoming and Alaska may be indicative of regions where public interest outpaces local capacity, raising the possibility of regional disparities in subspecialty access [17,18].

Our study has several limitations. First, due to the inherent nature of internet search data, state-level RSV is an indirect indicator that cannot be used to infer individual-level care-seeking behavior, true clinical demand, or disease prevalence. Online search behavior is influenced by multiple confounding factors—such as educational campaigns, baseline internet access, and digital health literacy—which were not empirically adjusted for in this study. Furthermore, failure to adjust for state-level confounding variables, such as socioeconomic status, health insurance rates, and local referral patterns, increases the possibility of spurious relationships.

Second, our search strategy was constrained by Google Trends, which restricts exact-match term combinations (using the “+” operator) to a maximum of 5 inputs per query. While our composite categories used the 5 most representative terms determined by expert consensus, this platform limitation inherently omits less common and niche search variations, potentially underestimating the true totality of public interest. In the absence of published data on the exact usage rates of these search terms, our query strategy relied on expert consensus and historical literature precedent, which may not capture all relevant patient searches.

Third, using ASRS membership as a proxy for the total retinal workforce likely underestimates the true density of treating providers, as many patients with floater symptoms are initially evaluated by general ophthalmologists and optometrists. ASRS density is a crude proxy that does not account for an individual surgeon’s clinical capacity, surgical volume, or interstate referral dynamics.

Finally, without external validation against actual patient attendance data, referral rates, or surgical volume, the operational relevance of the SSI to workforce policy decisions remains strictly exploratory.

Public search interest in vitreous floaters and related interventions has increased substantially across the United States from 2008 to 2025. Integrating Google Trends data with surgeon distribution provides an exploratory framework for evaluating relative public interest in vitreoretinal care. Future studies incorporating more granular workforce and referral data are required to validate whether these search patterns translate into measurable gaps in clinical access.