An Infodemiological Study to Estimate Risk Factors of Urinary Incontinence Using Elastic Net Regression

Öz

Urinary incontinence is a health problem of having a higher prevalence in the society which is observed widely and affecting the life quality negatively. In this study, it is hereby aimed to determine the risk factors which lead to urinary incontinence and to assess the seasonality of it.This study was a cross-sectional research. In the study, Infodemiologic procedures were used. Persons’ nationwide, relative search volume for the words relevant with urinary incontinence and risk factors were obtained from the internet search engines during 2016-2021. Relative search volume values were received from Google Trends. Urinary incontinence reasonability was determined through seasonal decomposition. Risk factors’ effect on urinary incontinence was evaluated through Elastic.net regression procedure. Search volume of the words relevant to urinary incontinence in internet search engines displays seasonality. Search volume of urinary incontinence in internet search engines between November and February is hereby determined to be higher than other months. Risk factors on urinary incontinence are; prostate, diabetes, kidney pain, constipation and menopause. In the study, infodemiologic procedures were being applied and it was determined that urinary incontinence is showing seasonality. Risk factors of urinary incontinence were estimated successfully. Relative search volumes obtained from Google Trends are able to be used for the estimation of the epidemiologic parameters of urinary incontinence in the society successfully. In researches, enabling to include more countries and more variables to the models may provide a contribution for the generalization of the results

Anahtar Kelimeler

• Infodemiologic analysis
• Urinary Incontinence
• Google Trends
• Seasonality

Kaynakça

1. [1] Y. Ahn and Y. Jun, “Measurement of pain-like response to various NICU stimulants for high-risk infants,” Early Hum. Dev., vol. 83, no. 4, pp. 255–262, 2007.
2. [2] B. T. Haylen et al., “An international urogynecological association (IUGA)/international continence society (ICS) joint report on the terminology for female pelvic floor dysfunction,” Neurourology and Urodynamics, vol. 29, no. 1. pp. 4–20, 2010.
3. [3] S. Hunskaar, K. Burgio, A. Diokno, A. R. Herzog, K. Hjälmås, and M. C. Lapitan, “Epidemiology and natural history of urinary incontinence in women,” in Urology, 2003, vol. 62, no. 4 SUPPL. 1, pp. 16–23.
4. [4] DeMaagd, G.A. and Davenport, T.C, “Management of urinary incontinence”, Pharmacy and Therapeutics, vol.37, no.6, pp.345, 2012.
5. [5] R. Onur, S. E. Deveci, S. Rahman, F. Sevindik, and Y. Acik, “Prevalence and risk factors of female urinary incontinence in eastern Turkey,” Int. J. Urol., vol. 16, no. 6, pp. 566–569, 2009.
6. [6] I. Maral, H. Özkardeş, L. Peşkircioǧlu, and M. Ali Bumin, “Prevalence of stress urinary incontinence in both sexes at or after age 15 years: A cross-sectional study,” J. Urol., vol. 165, no. 2, pp. 408–412, 2001.
7. [7] J. Edgley, “The psychosocial impact of incontinence on older people: a review.,” Nursing older people, vol. 14, no. 1. pp. 17–19, 2002.
8. [8] F. Güngör Uğurlucan, C. Comba, Ş. Emegil, and Ö. Yalçin, “Türkiye’de Üri̇ne Inkonti̇nans İlgi̇li̇ Düşünce ve Tutumlar,” İstanbul Tıp Fakültesi Derg., vol. 79, no. 4, pp. 141–141, 2016.

9. [9] B. K. Agarwal and N. Agarwal, “Urinary incontinence: prevalence, risk factors, impact on quality of life and treatment seeking behaviour among middle aged women,” Int. Surg. J., vol. 4, no. 6, p. 1953, 2017.
10. [10] A. Dinç, “Prevalence of Urinary Incontinence During Pregnancy and Associated Risk Factors,” LUTS Low. Urin. Tract Symptoms, vol. 10, no. 3, pp. 303–307, 2018.
11. [11] G. Eysenbach, “Infodemiology: The epidemiology of (mis)information,” American Journal of Medicine, vol. 113, no. 9. pp. 763–765, 2002.
12. [12] Google Trends. URL: https://trends.google.com/trends/?geo=TR. (visited on Feb 24-26, 2021).
13. [13] A. Mavragani and G. Ochoa, “Google Trends in Infodemiology and Infoveillance: Methodology Framework,” JMIR Public Heal. Surveill., vol. 5, no. 2, p. e13439, 2019.
14. [14] A. Mavragani and K. Gkillas, “COVID-19 predictability in the United States using Google Trends time series,” Sci. Rep., vol. 10, no. 1, 2020.
15. [15] S. Morsy et al., “Prediction of Zika-confirmed cases in Brazil and Colombia using Google Trends,” Epidemiol. Infect., vol. 146, no. 13, pp. 1625–1627, 2018.
16. [16] L. Samaras, E. García-Barriocanal, and M.-A. Sicilia, “Syndromic Surveillance Models Using Web Data: The Case of Influenza in Greece and Italy Using Google Trends,” JMIR Public Heal. Surveill., vol. 3, no. 4, p. e90, 2017.
17. [17] J. Parker, C. Cuthbertson, S. Loveridge, M. Skidmore, and W. Dyar, “Forecasting state-level premature deaths from alcohol, drugs, and suicides using Google Trends data,” J. Affect. Disord., vol. 213, pp. 9–15, 2017.
18. [18] M. Moccia, R. Palladino, A. Falco, F. Saccà, R. Lanzillo, and V. Brescia Morra, “Google Trends: New evidence for seasonality of multiple sclerosis,” Journal of Neurology, Neurosurgery and Psychiatry, vol. 87, no. 9. pp. 1028–1029, 2016.
19. [19] L. Rossignol, C. Pelat, B. Lambert, A. Flahault, E. Chartier-Kastler, and T. Hanslik, “A Method to Assess Seasonality of Urinary Tract Infections Based on Medication Sales and Google Trends,” PLoS One, vol. 8, no. 10, 2013.
20. [20] X. Zhang et al., “Seasonality of cellulitis: Evidence from Google Trends,” Infect. Drug Resist., vol. 11, pp. 689–693, 2018.
21. [21] J. Friedman, T. Hastie, and R. Tibshirani, “Regularization paths for generalized linear models via coordinate descent,” J. Stat. Softw., vol. 33, no. 1, pp. 1–22, 2010.
22. [22] H. Zou and T. Hastie, “Regularization and variable selection via the elastic net,” J. R. Stat. Soc. Ser. B Stat. Methodol., vol. 67, no. 2, pp. 301–320, 2005.
23. [23] V. A. Minassian, H. P. Drutz, and A. Al-Badr, “Urinary incontinence as a worldwide problem,” Int. J. Gynecol. Obstet., vol. 82, no. 3, pp. 327–338, 2003.
24. [24] S. Horrocks, M. Somerset, H. Stoddart, and T. J. Peters, “What prevents older people from seeking treatment for urinary incontinence? A qualitative exploration of barriers to the use of community continence services,” Fam. Pract., vol. 21, no. 6, pp. 689–696, 2004.
25. [25] D. Fisman, “Seasonality of viral infections: Mechanisms and unknowns,” Clinical Microbiology and Infection, vol. 18, no. 10. pp. 946–954, 2012.
26. [26] K. Levy, A. E. Hubbard, and J. N. S. Eisenberg, “Seasonality of rotavirus disease in the tropics: A systematic review and meta-analysis,” International Journal of Epidemiology, vol. 38, no. 6. pp. 1487–1496, 2008.
27. [27] A. Watad et al., “Seasonality and autoimmune diseases: The contribution of the four seasons to the mosaic of autoimmunity,” Journal of Autoimmunity, vol. 82. pp. 13–30, 2017.
28. [28] A. Rosello et al., “Seasonality of urinary tract infections in the United Kingdom in different age groups: Longitudinal analysis of The Health Improvement Network (THIN),” Epidemiol. Infect., vol. 146, no. 1, pp. 37–45, 2018.
29. [29] Y. Aoki, H. W. Brown, L. Brubaker, J. N. Cornu, J. O. Daly, and R. Cartwright, “Urinary incontinence in women,” Nat. Rev. Dis. Prim., vol. 3, 2017.
30. [30] P. Norton and L. Brubaker, “Urinary incontinence in women,” in Lancet, 2006, vol. 367, no. 9504, pp. 57–67.
31. [31] L. A. Møller, G. Lose, and T. Jørgensen, “The prevalence and bothersomeness of lower urinary tract symptoms in women 40-60 years of age,” Acta Obstet. Gynecol. Scand., vol. 79, no. 4, pp. 298–305, 2000.
32. [32] B. S. Buckley and M. C. M. Lapitan, “Prevalence of urinary incontinence in men, women, and children-current evidence: Findings of the fourth international consultation on incontinence,” Urology, vol. 76, no. 2. pp. 265–270, 2010.