Introduction
Mass displacement alters skin health through crowding, hygiene disruption, vector exposure, and barriers to care; clinical series in Syrian asylum seekers have suggested a prominent burden of infections and infestations.Reference Burke, Yang and Beer 1 Türkiye hosted approximately 3.2 million Syrians under temporary protection during much of the study period. 2 We tested whether multilingual search behavior could identify convergent and divergent dermatological signals in refugees and host populations without equating search volume with disease incidence. Digital search data have been proposed as complementary surveillance signals,Reference Eysenbach 3 and are increasingly used in dermatology.Reference Sivesind, Szeto and Kim 4
Methods
We analyzed monthly Google Health Trends queries from January 2010 to December 2024 for 30 dermatological conditions in three cohorts: Turkish-language searches in Türkiye as host population (TUR–TUR), Arabic-language searches in Türkiye as Syrian refugee proxy (TUR–SYR), and Arabic-language searches in Syria as origin comparator (SYR–SYR). For each condition, composite query strings combined clinical terminology and colloquial expressions; term sets were refined by dermatologist review, lexical normalization, and frequency filtering to reduce cross-language semantic loss. The prespecified main comparison contrasted the settlement period (2016-2019) with the post-pandemic period (2022-2024). For each cohort and condition, we compared monthly relative search interest using Mann–Whitney U tests and effect size r. Sentinel signals required both P < 0.05 and |r| ≥ 0.50. The resulting matrix comprised 16,200 monthly observations (Supplementary File 1).
Results
Eleven of 30 conditions met sentinel criteria. Three patterns emerged (Figure 1). First, scabies showed a synchronized post-pandemic surge across all cohorts: refugees r ≈ +1.00 (rank 7 to 1), hosts r = +0.98 (rank 15 to 3), and origin r ≈ +1.00 (rank 5 to 2) (Figure 2). Eczema, urticaria, hair loss, and warts also increased in both Türkiye cohorts, although their public-health implications are less specific. Second, pediculosis diverged sharply: it declined in the host cohort (r = −0.82; rank 1 to 4) but remained the leading refugee signal (r = +0.94; rank 1 to 1), suggesting persistence of conditions linked to crowding or differential care-seeking despite broader host-community improvement. Third, leishmaniasis provided an ecological contrast: it remained stable in the origin comparator (r = +0.35; rank 20 to 20) but showed little post-pandemic increase among Syrian refugees in Türkiye (r = +0.08; rank 26 to 29), compatible with reduced vector exposure after relocation into more urbanized host settings. STI-related search signals rose among refugees (syphilis r = +0.89; HIV r = +0.55) and paralleled the origin comparator, but these findings warrant cautious interpretation because symptom-based Arabic search strings may overlap across genital ulceration, stigma-driven queries, and non-specific health anxiety.
Divergent and shared dermatological trends between host and refugee populations based on effect size (r).
Comparison of effect sizes (r) for dermatological conditions between host and refugee communities. Quadrants defined by the r = 0.50 threshold highlight shared epidemiological burdens (red), divergent trends (yellow/green), and weak effects across both groups (blue).
Figure 1. Long description
The scatter plot uses an X-axis representing Host Community Effect Size r and a Y-axis representing Refugee Community Effect Size r. Both axes range from negative 1.00 to 1.00. A threshold at r equals 0.50 on both axes divides the chart into four colored zones.
* Top-Right Quadrant (Red): High effect sizes for both groups. Points include Scabies at approximately (0.98, 1.0), Eczema at (0.95, 0.95), Urticaria at (0.98, 0.56), Hair Loss at (0.70, 0.78), and Warts at (0.60, 0.76).
* Bottom-Right Quadrant (Green): High effect in host but lower in refugees. The only point is H P V at (0.98, 0.0).
* Bottom-Left Quadrant (Blue): Low effect sizes for both groups. A single point for Leishmaniasis is located near the center at (0.0, 0.08).
* Top-Left Quadrant (Yellow): High effect in refugees but lower in host. Points include Syphilis at (0.05, 0.89), H I V at (minus 0.12, 0.55), Varicella at (minus 0.23, 0.56), and Pediculosis at (minus 0.90, 0.94).
Longitudinal correlation patterns of dermatological conditions across host, refugee, and origin populations.
Heatmap of longitudinal dermatological disease trends across host, refugee, and origin populations. Cells display correlation values (r) with corresponding rank shifts between time points. Color intensity represents the strength and direction of correlation, with red indicating strong positive trends and blue indicating negative trends.
Figure 2. Long description
A heatmap with three columns and eleven rows. The X-axis labels are Host (T U R-T U R), Refugee (T U R-S Y R), and Origin (S Y R-S Y R). The Y-axis lists conditions: Scabies, Eczema, Urticaria, Hair Loss, Warts, Pediculosis, Syphilis, H I V, Varicella, H P V, and Leishmaniasis. A vertical color bar on the right indicates Correlation (r) from -1.00 (dark blue) to +1.00 (dark red), with 0.00 in white.
Data points include correlation values and rank shifts (start rank arrow end rank):
* Scabies: Host +0.98 (15 to 3); Refugee +1.00 (7 to 1); Origin +1.00 (5 to 2).
* Eczema: Host +0.92 (3 to 5); Refugee +0.94 (9 to 9); Origin +0.74 (11 to 10).
* Urticaria: Host +0.96 (13 to 10); Refugee +0.56 (21 to 20); Origin +0.89 (22 to 18).
* Hair Loss: Host +0.69 (5 to 11); Refugee +0.78 (3 to 4); Origin -0.10 (3 to 4).
* Warts: Host +0.60 (9 to 8); Refugee +0.76 (19 to 16); Origin +0.24 (19 to 19).
* Pediculosis: Host -0.82 (1 to 4); Refugee +0.94 (1 to 1); Origin +0.91 (1 to 1).
* Syphilis: Host +0.04 (23 to 24); Refugee +0.89 (6 to 3); Origin +0.33 (6 to 3).
* H I V: Host -0.12 (4 to 6); Refugee +0.55 (5 to 7); Origin +0.99 (4 to 8).
* Varicella: Host -0.23 (16 to 16); Refugee +0.56 (10 to 12); Origin +0.44 (8 to 6).
* H P V: Host +0.97 (7 to 4); Refugee 0.00 (27 to 26); Origin +0.16 (24 to 25).
* Leishmaniasis: Host 0.00 (27 to 27); Refugee +0.08 (26 to 29); Origin +0.35 (20 to 20).
Discussion
These results support digital epidemiology as a complement, not a substitute, for clinical surveillance. The synchronized scabies signal is biologically plausible and operationally important: scabies is intensified by close contact and is repeatedly reported in displaced or overcrowded settings.Reference Yitageasu, Asnake and Gebrehana 5 In contrast, the discordant pediculosis and leishmaniasis signals show why refugee dermatology should not be inferred from host-population averages. A single national trend can conceal opposite directions of change in groups sharing the same geography but not the same language, household density, occupational exposure, or care pathway.
The study has limitations. Google Health Trends gives relative, not absolute, interest; results cannot estimate prevalence, incidence, or unmet need. The refugee cohort is a language-and-location proxy and may include Arabic-speaking non-refugees or bilingual users. We could not stratify by age, sex, socioeconomic status, province, device access, or migration history. Query intensity is influenced by media, outbreaks, stigma, and care availability. Nonetheless, use of an origin comparator, disease-specific composite queries, and a prespecified effect-size threshold improved internal consistency and limited purely significance-driven interpretation.
Multilingual search data can therefore serve as a low-cost sentinel for under-represented groups. In Türkiye, the key pattern was transnational scabies convergence, persistent pediculosis among refugees, and dampened leishmaniasis; these signals call for targeted screening, clinical confirmation, and multilingual risk communication rather than undifferentiated national surveillance.
Supplementary material
The supplementary material for this article can be found at http://doi.org/10.1017/dmp.2026.10437.
Data availability statement
All data analyzed in this study are publicly available. Google Trends data are freely accessible through https://trends.google.com. The processed dataset and R analysis scripts are available from the corresponding author upon reasonable request.
Author contribution
Ahmet Uğur Atılan: Conception, design, supervision, data collection and/or processing, analysis and/or interpretation, literature review, writing, critical review.
Niyazi Çetin: Conception, design, supervision, data collection and/or processing, analysis and/or interpretation, literature review, writing, critical review.
Funding statement
This research received no specific grant from any funding agency in the public, commercial, or not-for-profit sectors.
Competing interests
None.
Ethical approval
This study did not involve human participants, patient data, or animal experiments. Therefore, institutional ethical approval was not required.
Ethics statement
No human participants, patient-level records, or sensitive personal information were collected, accessed, or analyzed. All analyses were conducted at the ecological level using publicly accessible datasets.
Consent to participate
Not applicable.
Prior publication
This study’s preliminary findings were presented as a poster at the EADV 2026 Symposium in Athens.
Permissions
No permissions were required for this study.
Use of artificial intelligence-enhanced tools
During the preparation of this manuscript, the authors used the free version of Grammarly AI to improve language clarity and grammar. No artificial intelligence system was involved in data analysis, data interpretation, or decision-making processes. The authors reviewed and edited all content and take full responsibility for the accuracy and integrity of the work.
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