Journals / Journal of Health Sciences and Medicine / 2026 / Volume: 9 Issue: 3

Global digital epidemiology of carpal tunnel syndrome: 15-year trends and predictive insights

Osman Görkem Muratoğlu(ISTANBUL ATLAS UNIVERSITY, SCHOOL OF MEDICINE)0000-0003-0049-7937
Ecem Esma Yeğin(ISTINYE UNIVERSITY, SCHOOL OF MEDICINE)0000-0003-4385-472X
Mehmet Emre Yeğin(ISTINYE UNIVERSITY, SCHOOL OF MEDICINE)0000-0001-5788-0705
Pages
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Publication Date
2026-05-19
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AI Summary & Key Takeaways

AI Insight
⚡ TL;DR(Too Long; Didn't Read)

Digital epidemiology provides valuable insights for public health planning, highlighting the need for targeted awareness strategies rather than reliance on macro-level indicators.

📌 3 Key Takeaways

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Objective & Hypothesis:Aims: Carpal tunnel syndrome (CTS) is a leading cause of neuropathic pain and disability worldwide.
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Key Findings & Data:This study investigates global trends in CTS-related search interest over 15 years (2010-2025) and explores its association with demographic and economic indicators.
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Conclusion & Impact:Digital epidemiology provides valuable insights for public health planning, highlighting the need for targeted awareness strategies rather than reliance on macro-level indicators.
🔬Design:Modeling & Simulation
📍Scope:2010-2025 Verileri
⚙️Variables:Carpal tunnel syndrome · digital epidemiology · internet searching

Abstract

Aims: Carpal tunnel syndrome (CTS) is a leading cause of neuropathic pain and disability worldwide. In the digital era, online search behavior offers a unique lens to gauge public health awareness. This study investigates global trends in CTS-related search interest over 15 years (2010-2025) and explores its association with demographic and economic indicators. Methods: Using Google Trends data combined with United Nations and World Bank statistics; we analyzed temporal patterns and cross-national correlations. Time-series modeling was performed with ARIMA to forecast future trends. Results: Global CTS search interest remained stable despite population growth, showing negligible correlations with GDP per capita (r=+0.02), population size (r=-0.09), and median age (r=-0.27). ARIMA forecasting for 2024-2028 suggested a gradual decline in search interest, with widening prediction intervals over time. Conclusion: Public interest in CTS does not align strongly with demographic or economic factors, suggesting that awareness is shaped by other determinants such as occupational risk and healthcare accessibility. Digital epidemiology provides valuable insights for public health planning, highlighting the need for targeted awareness strategies rather than reliance on macro-level indicators.

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Cite This Article

Muratoğlu, O. G., Yeğin, E. E., Yeğin, M. E. (2026). Global digital epidemiology of carpal tunnel syndrome: 15-year trends and predictive insights. Journal of Health Sciences and Medicine, Volume: 9 Issue: 3. https://doi.org/10.32322/jhsm.1885942

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References (15)

  1. Padua L, Coraci D, Erra C, et al. Carpal tunnel syndrome: clinical features, diagnosis, and management. Lancet Neurol. 2016;15(12):1273-1284. doi:10.1016/S1474-4422(16)30231-9
  2. Atroshi I. Prevalence of carpal tunnel syndrome in a general population. JAMA. 1999;282(2):153. doi:10.1001/jama.282.2.153
  3. Pourmemari MH, Shiri R. Diabetes as a risk factor for carpal tunnel syndrome: a systematic review and meta-analysis. Diabet Med. 2016; 33(1):10-16. doi:10.1111/dme.12855
  4. Dale AM, Harris-Adamson C, Rempel D, et al. Prevalence and incidence of carpal tunnel syndrome in US working populations: pooled analysis of six prospective studies. Scand J Work Environ Health. 2013;39(5):495-505. doi:10.5271/sjweh.3351
  5. Shiri R, Pourmemari MH, Falah-Hassani K, Viikari-Juntura E. The effect of excess body mass on the risk of carpal tunnel syndrome: a meta-analysis of 58 studies. Obes Rev. 2015;16(12):1094-1104. doi:10.1111/obr. 12324
  6. Brinkman JC, Moore ML, Lai C, et al. Patient interest in quadriceps autograft anterior cruciate ligament reconstruction is increasing over other autograft options: a 12-year Google trends analysis. Arthrosc Sports Med Rehabil. 2024;6(3):100942. doi:10.1016/j.asmr.2024.100942
  7. Casciato DJ, Thompson J, Rushing CJ, McKenna B, Hyer C. Consumer interest in total ankle replacements over the last 10 years: a Google TrendsTM analysis from 2009 to 2019. J Foot Ankle Surg. 2023;62(3):492-497. doi:10.1053/j.jfas.2022.11.016
  8. Cohen SA, Zhuang T, Xiao M, Michaud JB, Amanatullah DF, Kamal RN. Google trends analysis shows increasing public interest in platelet-rich plasma injections for hip and knee osteoarthritis. J Arthroplasty. 2021;36(10):3616-3622. doi:10.1016/j.arth.2021.05.040
  9. Dupley L, Atwan Y, Viswanath A. Trends in shoulder arthroplasty research over the decades. J Clin Orthop Trauma. 2025;62:102882. doi: 10.1016/j.jcot.2024.102882
  10. Mohty KM, Lashkari N, Gittings DJ, Bell JA, Stevanovic M, Nicholson LT. Utilizing Google trends to track online interest in elective hand surgery during the COVID-19 pandemic. Cureus. 2021. doi:10.7759/cureus.17313
  11. Google trends as a tool for evaluating public interest in total knee arthroplasty and total hip arthroplasty. J Clin Transl Res. 2021. doi:10. 18053/jctres.07.202104.006
  12. Rotaru-Zavaleanu AD, Lungulescu CV, Bunescu MG, et al. Occupational carpal tunnel syndrome: a scoping review of causes, mechanisms, diagnosis, and intervention strategies. Front Public Health. 2024;12:1407302. doi:10.3389/fpubh.2024.1407302
  13. Cazares-Manríquez MA, Wilson CC, Vardasca R, et al. A review of carpal tunnel syndrome and its association with age, body-mass index, cardiovascular risk factors, hand dominance, and sex. Appl Sci. 2020; 10(10):3488. doi:10.3390/app10103488
  14. Cervellin G, Comelli I, Lippi G. Is Google trends a reliable tool for digital epidemiology? Insights from different clinical settings. J Epidemiol Glob Health. 2017;7(3):185. doi:10.1016/j.jegh.2017.06.001
  15. Amusa LB, Twinomurinzi H, Okonkwo CW. Modeling COVID-19 incidence with Google trends. Front Res Metr Anal. 2022;7:1003972. doi: 10.3389/frma.2022.1003972