Temporal Trends in Online Search Interest in Dental Implant Treatment in Türkiye: A Google Trends–Based Infodemiology Study

Abstract

ABSTRACT Objective: This study evaluated long-term trends in online search interest in dental implant treatment in Türkiye, level and slope changes around March 2020, and associations with year-on-year Consumer Price Index (CPI) change rates. Methods: Monthly relative search volume (RSV) data for five implant-related terms were obtained from Google Trends. Data from 2004–2025 were used for visual assessment and 2014–2025 for inference. Trends were evaluated using linear regression with Newey–West heteroskedasticity- and autocorrelation-consistent (HAC) standard errors and the Mann–Kendall test. Changes around March 2020 were examined using segmented regression. CPI–RSV associations were assessed using Spearman and time-adjusted partial Spearman correlations with Benjamini–Hochberg false discovery rate (FDR) correction. Results: All terms showed significant positive trends during 2014–2025 (all HAC p < 0.001); “implant diş” had the highest mean RSV and largest increase. Most terms showed no significant immediate level or slope changes. “Diş implantı” showed an immediate level decrease (β₂=−1.366; p < 0.001) followed by a positive slope change (β₃=0.0256; p < 0.001), whereas “implant fiyat” showed a negative slope change (β₃=−0.0474; p=0.028). Positive unadjusted CPI–RSV associations weakened after time adjustment; only “implant diş” remained significant after FDR correction (ρ=0.335; q < 0.001). Conclusion: Online search interest in dental implant treatment increased during 2014–2025, with patterns varying by terminology. Heterogeneous changes around March 2020 and attenuation of CPI associations after time adjustment underscore the importance of accounting for secular trends when interpreting online search behavior.

Keywords

• : Dental Implants
• ; Information Seeking Behavior
• Internet
• Inflation
• Economic; Regression Analysis

Türkiye’de Dental İmplant Tedavisine Yönelik Çevrim İçi Arama İlgisinin Zamansal Eğilimleri: Google Trends Tabanlı Bir İnfodemiyoloji Çalışması

Abstract

ÖZET Amaç: Bu çalışma, Türkiye’de dental implant tedavisine yönelik çevrim içi arama ilgisinin uzun dönemli eğilimlerini, Mart 2020 çevresindeki düzey ve eğim değişikliklerini ve yıllık Tüketici Fiyat Endeksi (TÜFE) değişim oranlarıyla ilişkilerini değerlendirdi. Yöntem: İmplantla ilişkili beş terime ait aylık göreli arama hacmi (RSV) verileri Google Trends’ten elde edildi. 2004–2025 verileri görsel değerlendirmede, 2014–2025 verileri çıkarımsal analizlerde kullanıldı. Eğilimler, Newey–West değişen varyans ve otokorelasyon tutarlı (HAC) standart hatalara dayalı doğrusal regresyon ve Mann–Kendall testiyle değerlendirildi. Mart 2020 çevresindeki değişiklikler segmentli regresyonla incelendi. TÜFE–RSV ilişkileri Spearman ve zamana göre düzeltilmiş parsiyel Spearman korelasyonlarıyla değerlendirildi; Benjamini–Hochberg yanlış keşif oranı (FDR) düzeltmesi uygulandı. Bulgular: Tüm terimler 2014–2025 döneminde anlamlı pozitif eğilim gösterdi (tüm HAC p < 0,001); “implant diş” en yüksek ortalama RSV ve en büyük artışı sergiledi. Çoğu terimde anlamlı ani düzey veya eğim değişikliği saptanmadı. “Diş implantı” ani düzey azalması (β₂=−1,366; p < 0,001) ve ardından pozitif eğim değişikliği (β₃=0,0256; p < 0,001) gösterirken, “implant fiyat” negatif eğim değişikliği gösterdi (β₃=−0,0474; p=0,028). Pozitif düzeltilmemiş TÜFE–RSV ilişkileri zamana göre düzeltme sonrasında zayıfladı; FDR düzeltmesinden sonra yalnızca “implant diş” anlamlı kaldı (ρ=0,335; q < 0,001). Sonuç: Türkiye’de dental implant tedavisine yönelik çevrim içi arama ilgisi 2014–2025 döneminde arttı ve örüntüler terminolojiye göre farklılaştı. Mart 2020 çevresindeki heterojen değişiklikler ve zamana göre düzeltme sonrasında TÜFE ilişkilerinin zayıflaması, çevrim içi arama davranışları yorumlanırken seküler eğilimlerin dikkate alınmasının önemini göstermektedir.

Keywords

• Dental İmplantlar
• ; Bilgi Arama Davranışı
• İnternet;
• Ekonomik Enflasyon
• Regresyon Analizi

References

1. 1. Buser D, Sennerby L, De Bruyn H. Modern implant dentistry based on osseointegration: 50 years of progress, current trends and open questions. Periodontol 2000. 2017;73(1):7-21. doi:10.1111/prd.12185.
2. 2. Howe MS, Keys W, Richards D. Long-term (10-year) dental implant survival: a systematic review and sensitivity meta-analysis. J Dent. 2019;84:9-21. doi:10.1016/j.jdent.2019.03.008.
3. 3. Elani HW, Starr JR, Da Silva JD, et al. Trends in dental implant use in the U.S., 1999-2016, and projections to 2026. J Dent Res. 2018;97(13):1424-1430. doi:10.1177/0022034518792567.
4. 4. Tepper G, Haas R, Mailath G, et al. Representative marketing-oriented study on implants in the Austrian population. I. Level of information, sources of information and need for patient information. Clin Oral Implants Res. 2003;14(5):621-633. doi:10.1034/j.1600-0501.2003.00916.x.
5. 5. Pommer B, Zechner W, Watzak G, et al. Progress and trends in patients’ mindset on dental implants. I: Level of information, sources of information and need for patient information. Clin Oral Implants Res. 2011;22(2):223-229. doi:10.1111/j.1600-0501.2010.02035.x.
6. 6. Tomruk CÖ, Özkurt-Kayahan Z, Şençift K. Patients’ knowledge and awareness of dental implants in a Turkish subpopulation. J Adv Prosthodont. 2014;6(2):133-137. doi:10.4047/jap.2014.6.2.133.
7. 7. Jia X, Pang Y, Liu LS. Online health information seeking behavior: a systematic review. Healthcare (Basel). 2021;9(12):1740. doi:10.3390/healthcare9121740.
8. 8. Özkan S, Tüzün H, Uğraş Dikmen A, et al. The relationship between health literacy level and media used as a source of health-related information. Health Lit Res Pract. 2021;5(2):e109-e117. doi:10.3928/24748307-20210330-01.

9. 9. Eysenbach G. Infodemiology: the epidemiology of (mis)information. Am J Med. 2002;113(9):763-765. doi:10.1016/S0002-9343(02)01473-0.
10. 10. Nuti SV, Wayda B, Ranasinghe I, et al. The use of Google Trends in health care research: a systematic review. PLoS One. 2014;9(10):e109583. doi:10.1371/journal.pone.0109583.
11. 11. Mavragani A, Ochoa G. Google Trends in infodemiology and infoveillance: methodology framework. JMIR Public Health Surveill. 2019;5(2):e13439. doi:10.2196/13439.
12. 12. Arora VS, McKee M, Stuckler D. Google Trends: opportunities and limitations in health and health policy research. Health Policy. 2019;123(3):338-341. doi:10.1016/j.healthpol.2019.01.001.
13. 13. Rovetta A. Reliability of Google Trends: analysis of the limits and potential of web infoveillance during COVID-19 pandemic and for future research. Front Res Metr Anal. 2021;6:670226. doi:10.3389/frma.2021.670226.
14. 14. Alibudbud R. Google Trends for health research: its advantages, application, methodological considerations, and limitations in psychiatric and mental health infodemiology. Front Big Data. 2023;6:1132764. doi:10.3389/fdata.2023.1132764.
15. 15. Patthi B, Kumar JK, Singla A, et al. Global search trends of oral problems using Google Trends from 2004 to 2016: an exploratory analysis. J Clin Diagn Res. 2017;11(9):ZC12-ZC16. doi:10.7860/JCDR/2017/26658.10564.
16. 16. Sarı ÖF, Büyükçavuş MH, Kale B, et al. Türkiye’de ortodonti ile ilgili internet verilerinin değerlendirilmesi: bir Google Trends analizi. Aydın Dental Journal. 2022;8(1):10-25. doi:10.17932/IAU.DENTAL.2015.009/dental_v08i1002.
17. 17. Kanmodi KK, Abolade O, Amzat J, et al. Analysis of global search and research interests on dentists using infoveillance and bibliometric approaches. Oral. 2023;3:11-30. doi:10.3390/oral3010002.
18. 18. Fidan M, Olgar İE. Türkiye’de diş hekimliği ile ilgili internet verilerinin Google Trends ile değerlendirilmesi: metodolojik çalışma. Turkiye Klinikleri Journal of Dental Sciences. 2023;29(4):605-612. doi:10.5336/dentalsci.2023-97357.
19. 19. İyilikci B. Effects of the pandemic and economic crisis on public search trends related to oral and maxillofacial surgery in Türkiye: a Google Trends time-series analysis (2020-2025). BMC Oral Health. 2026;26:503. doi:10.1186/s12903-026-07878-7.
20. 20. Newey WK, West KD. A simple, positive semi-definite, heteroskedasticity and autocorrelation consistent covariance matrix. Econometrica. 1987;55(3):703-708. doi:10.2307/1913610.
21. 21. Shen JK, Every J, Morrison SD, et al. Global interest in oral and maxillofacial surgery: analysis of Google Trends data. J Oral Maxillofac Surg. 2020;78(9):1484-1491. doi:10.1016/j.joms.2020.05.017.
22. 22. McMullan M. Patients using the Internet to obtain health information: how this affects the patient-health professional relationship. Patient Educ Couns. 2006;63(1-2):24-28. doi:10.1016/j.pec.2005.10.006.
23. 23. Carneiro HA, Mylonakis E. Google Trends: a web-based tool for real-time surveillance of disease outbreaks. Clin Infect Dis. 2009;49(10):1557-1564. doi:10.1086/630200.
24. 24. Sycińska-Dziarnowska M, Szyszka-Sommerfeld L, Woźniak K, et al. Predicting interest in orthodontic aligners: a Google Trends data analysis. Int J Environ Res Public Health. 2022;19(5):3105. doi:10.3390/ijerph19053105.
25. 25. Sabitha V, Santhoshi P, Ganesh R, et al. Analysis of interest of Google users (Web, Google Image, and YouTube search) on oral health information among South Indian states. Journal of Academy of Dental Education. 2024;10(1):15-19. doi:10.25259/JADE_62_2023.
26. 26. Akay A, Erkan Akay S. Evaluation of treatment and dentist options for patients with toothache in Turkey using Google Trends. HRU International Journal of Dentistry and Oral Research. 2025;5(1):20-23. doi:10.61139/ijdor.1588499.
27. 27. Sycińska-Dziarnowska M, Bielawska-Victorini H, Budzyńska A, et al. The implications of the COVID-19 pandemic on the interest in orthodontic treatment and perspectives for the future: real-time surveillance using Google Trends. Int J Environ Res Public Health. 2021;18(11):5647. doi:10.3390/ijerph18115647.
28. 28. Güneş G, Sadık E, Ünal Erzurumlu Z. “Dr. Google, I have a toothache”—assessment of the impact of COVID-19 pandemic on toothache-related internet searches with Google Trends data. Current Research in Dental Sciences. 2025;35(2):117-120. doi:10.17567/currresdentsci.1416263.
29. 29. Taşkaldıran ES, Meşeli SE. An infodemiological study on Google search trends for “gum bleeding”: demographic, economic, and temporal perspectives. European Annals of Dental Sciences. 2023;50(3):133-137. doi:10.52037/eads.2023.0030.
30. 30. Alqutub AW, Nassar AA, Alzahrani AA, et al. Factors affecting demand for dental implant treatment: a population-based study in the Western Region of Saudi Arabia. Open Dent J. 2025;19:e18742106370233. doi:10.2174/0118742106370233250306074925.
31. 31. Najafi Shahkoohi A, Alipour V, Arabloo J, et al. Patient preferences and willingness to pay for dental services: a systematic review. BMC Oral Health. 2025;25:227. doi:10.1186/s12903-025-05520-6.