Dry Eye Disease Seasonal Pattern in Saudi Arabia Using Google Trends

Dry Eye Disease Seasonal Pattern in Saudi Arabia Using Google Trends | Research Square window.SnipcartSettings = { analytics: { enabled: false } }; (function() { var accessVector = localStorage.getItem('access_vector') || ''; window.dataLayer = window.dataLayer || []; if (accessVector) { window.dataLayer.push({ user: { profile: { profileInfo: { snid: accessVector } } } }); } })(); (function(w,d,s,l,i){w[l]=w[l]||[];w[l].push({'gtm.start':new Date().getTime(),event:'gtm.js'});var f=d.getElementsByTagName(s)[0],j=d.createElement(s),dl=l!='dataLayer'?'&l='+l:'';j.async=true;j.src='https://www.googletagmanager.com/gtm.js?id='+i+dl;f.parentNode.insertBefore(j,f);})(window,document,'script','dataLayer','GTM-K279D39R'); Browse Preprints In Review Journals COVID-19 Preprints AJE Video Bytes Research Tools Research Promotion AJE Professional Editing AJE Rubriq About Preprint Platform In Review Editorial Policies Our Team Advisory Board Help Center Sign In Submit a Preprint Cite Share Download PDF Research Article Dry Eye Disease Seasonal Pattern in Saudi Arabia Using Google Trends Abdulaziz S AlHarthi This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-5429689/v1 This work is licensed under a CC BY 4.0 License Status: Posted Version 1 posted You are reading this latest preprint version Abstract Purpose : The study aims to examine the dry eye search term pattern in Saudi Arabia using Google Trends to assess the influence of monthly effect on dry eye disease (DED). Methods : This is a Time series design study; Data were collected from Google Trends for period from January 2011 to October 2024 using Arabic term for Dry Eye with setting allocated in Saudi Arabia. The monthly specified regression with ARIMA model and Time were included to provide evidence of the seasonality. Result : The study result for dry eye disease (DED) using GTs demonstrates bimodal pattern with significant monthly/seasonal differences and increases of intent search over time. The periodic query for Arabic term dry eye is more likely to peak in late spring and in summer season mainly in month of June. A secondary peak was observed in February and March. Conclusion : The study finding provides evidence of monthly seasonality of DED using Google Trends. Emphasizing and implementing measures of recommendations to decrease risk of DED is indicated especially during seasonality. Further exploration of climate and geographic locational influence on DED is needed. Ophthalmology Dry eye disease Google Trends Seasonality Figures Figure 1 Introduction Dry Eye Disease (DED) is a common eye disorder with symptoms of ocular surface discomfort ranging from mild to more symptomatic that may have a substantial impact on individuals’ quality of life and vision 1 . DED is chronic ocular disease affecting hundreds of millions of people worldwide with reported global prevalence rate from 5% to 50% 2 . DED is a multifactorial disease cause alteration of tear film homeostasis and classified according to its pathophysiology into aqueous deficient dry eye (ADDE) and evaporative dry eye (EDE) 3 . The ocular surface is in direct contact with surrounding air; so, the environmental factors can play a role in reducing tear film by increasing evaporation rate. Previous studies showed that environments factors could affect ocular surface include low humidity, temperature, wind speed, high attitude, air pollution 4 . Tear Film & Ocular Surface Society reported in Dry Eye Workshop II the need for further research to elucidate more on the influence of climate and environmental factors on DED 3 . Google Trends (GTs) (http://google.com/trends/) is an online open source that can be used to estimate users’ interest to search for terms in different time periods. GTs is a powerful tool that has been used to observe public interest to obtain information throughout the internet, which can be used as surveillance tool to predict prevalence, assess seasonal variation or infectious disease tracking 5,6 . The Relative search volume (RSVs) In GTs is normalized and presented on scale from 0 to 100 based on popularity of all searches on all topics and duplicate searched from the same user is not included 7 . This science is known as infodemiology which use digital data to inform the public health 5 . The use of Google Trends data might reflect the specific season pattern better than hospital-based data since patients with undiagnosed DED might be difficult for them to visit the hospital, and most hospitals is not easily accessible because it requires referral letter and prior reservation. In this study we explore time-series analysis on the public internet searches of dry eye to better understand the likely evidence of seasonal variation on the prevalence of dry eye disease (DED). Such findings can help in eye care recommendation and provide reference for conducting epidemiologic research on DED. To our knowledge, this study is the first to describe seasonality of dry eye in Saudi Arabia using Google Trends tool. Materials and Methods Seasonality pattern is regular and predictable data changes which peak or trough at every certain period of time every year (month/season as in this case). While the trend is the increase or decrease in data over historical time. Meteorological season was defined as summer start from June to August, fall start from September to November, winter start from December to February, and spring start from March to May. We searched Google Trends using the term dry eye in Arabic language and set the location in Saudi Arabia during period from January 2011 to October 2024. Data were in monthly aggregated volume. The monthly time series data were decomposed into trend, season, and errors. stationarity test was performed using Augmented Dickey-Fuller (ADF). Time series analysis by utilizing regression model using monthly season variables with Autoregressive Integrated Moving Average (ARIMA) model and trend was included to fit the data structure and evaluate the significant effect of monthly variation. The model was evaluated by assessing the residual normality, ACF plot, and Ljung-Box test. The R software, version 4.4.1 (R Development Core Team) was used to make analysis on the GT collected data with forecast package using auto.arima() function. Across all analysis, the significant p value was set < 0.05. Ethical Consideration No institutional Review Board (IRB) was needed as data is open public-source. Results Figure 1 shows the finding of data decomposition which suggests the occurrence of DED using GTs have obvious seasonal components, and the trend of search is increased throughout the study period and height of cycles appears to be increasing. The Augmented Dickey-Fuller (ADF) test result was (-6.7372) indicating the data in this study is stationary. The determined ARIMA model was (1,0,0). The model passed the box Ljung test ( X 2 = 8.7971, p = 0.4562), the residual was normally distributed and no spikes on autocorrelation, indicates that the fitting residual is white noise. The summery results of the ARIMA model are presented in Table 1 . Table 1 The Result of ARIMA (1,0,0) Model of the Different Month of the Year for Dry Eye Search Term in Saudi Arabia Variable Coefficient Std. Error t-Statistic Prob January (C) 10.364 3.284 3.156 0.002* February 7.081 1.871 3.785 0.000* March 6.308 2.383 2.647 0.009* April 4.109 2.713 1.515 0.132 May 6.911 2.504 2.760 0.007* June 10.002 2.650 3.774 0.000* July 6.595 3.003 2.196 0.030* August 6.336 2.699 2.347 0.020* September 4.586 2.521 1.819 0.071 October 4.498 2.829 1.590 0.114 November 1.187 2.666 0.445 0.657 December 0.070 2.109 0.033 0.973 Time 0.475 0.071 6.650 0.000* Time^2 -0.000 0.000 -0.954 0.342 AR (1) 0.471 0.073 6.491 0.000* The regression coefficient for February, March, May, June, July, and August were positive and statistically significant. The intent search volume exhibited increases in prevalence starting from May and continued to be high in summer season mainly in month of June. The decline of search volume is observed from the beginning of fall in September with lowest point mainly in month of January. Secondary peak was observed in late winter and beginning of spring in the month of February and March. The ranking of search volume based on GTs for different administrative regions in Saudi Arabia were as follow: Al-Qaseem (100), Hayel (95), Aseer (93), Al-Madinah (87), Al-Baha (86), Tabouk (85), Jazan (80), Al-Riyadh (79), Makkah (76), Northern Border (75), Al-Jouf (75), Najran (73), Eastern Region (66). Discussion The widespread use of smartphones and availability of internet as it become part of daily life activity, and the intent search data can reflect on particularly public interest. The Google search engine is one of the top used to search for information in Saudi Arabia 8 . The Google Trends is powerful infodemiology tools; and data analysis can be used to monitor diseases and identify public interest pattern of disease to make healthcare care policy 5 . In this study, we evaluated the utility of internet search data through Google Trends (GTs). The search term for dry eye showed monthly variation according to monthly season. Our study demonstrates the bimodal season pattern in which the primary peak was in late spring, and during summer (warm) season mainly in month of June and the secondary peak of search volume was in February. Since the Coefficient for June is the highest and statistically significant is an indication for seasonality of dry eye in Saudi Arabia. Previous studies have concluded the presence of seasonal pattern in DED. Study in US reported significant higher internet search volume in spring and being lowest in fall season 9 . However, in China the prevalence of dry eye was slightly higher in warm season than cold season 10 . On the other hand, Van Stten et al 11 have described the seasonality for dry eye complaints to be during winter and summer. Another study in China indicated seasonal peak in winter 12 . Other investigations, did not support the evidence for seasonality in DED except for seasonal variation in Schirmer test and tear meniscus height parameters 13 . Variability of the reported findings concerning the seasonal pattern in DED between the studies or not finding association with DED could be linked to different geographical location. Geographical variation in climate and environments can be associated with variation in the prevalence of DED. Factors that play a rule include temperature, humidity, rainfall, and air pollution. The overall higher volume internet searches in specific months might be explained by Saudi Arabia climate. The Arab peninsula does experience nearly 15 heat waves lasting 2 to 5 days annually mainly during summer months 14 . The drought based on the rainfall threshold occur in dry season (mainly in June and September) with the exception of February being higher than January and March 15 . In regard to storms events, Autumn months have the lowest storms events due to stable winds condition 16 . The air quality is also affected by climate change as some air pollution is worse in summer and others worse in winter. Hence, the climate changes may contribute to the increased internet search volume of dry eye during summer months and February. The ongoing climate temperature rise, and climate warming is also a public health concern for increased number of DED. Wearing sunglasses and avoiding going out during bad weather should be emphasized. In comparison to different administrative regions in Saudi Arabia, the Eastern region in Saudi Arabia which located by Arabian Gulf Sea was the lowest in ranking of search volume. This considerable variability across different locations could be implicated by the influence of high humidity climate in Eastern region to reduce tear evaporation giving protective effect from DED. It has been shown that higher humidity has a strong correlation to improve corneal fluorescein staining and tear breakup time (TBUT) 17 . The prevalence of DED in Saudi Arabia demonstrated by studies using survey from different regions revealed variable prevalence rate 18 . Due to divergence in climate and humidity in different regions in Saudi Arabia, further research for the prevalence might elaborate more on the climate effects. Most people in Saudi Arabia tend to stay indoor during daylight in summer, so indoor temperatures, humidity, air pollution, and reliance on air conditioners can adversely contribute to dry eye. Individuals can perceive dry air by dry skin and dry eyes because of the direct contact with environmental air. Modifications of environment have also been demonstrated to decrease the alteration in tear film stability, for instance, it has been shown that elevation of low indoor humidity ,within set points, is beneficial in decreasing the perception of dry air 19 . Excess screen staring might also be another reason for DED 20 . Emphasizing for more behavioural adaptation such as regular blinking and eyelid closure, especially during unavoidable risk of higher tear evaporation rate during the seasonal peak should be advised. During COVID-19 (coronavirus) pandemic, it has been demonstrated that DED increased due to excess digital screen exposure. However, in this study there was no significant shift in the trend of search volume in Google Trends during COVID-19 lockdown. Limitation This study has some limitations. The result is for the current period. The reliability of Google Trends can be affected by media influence. Google Trends does not differentiate between distinct groups of patients, severity of dryness or if the symptoms were only transient feeling of dryness that does not meet the criteria for DED diagnosis. Conclusion Based on this study, there was seasonal fluctuation with higher number of searches on the internet for dry eye during late spring and summer months. Understanding the disease pattern can help in recommending eye care measures to modify the environmental factors and to reduce the impact on the eye and therefore reducing the prevalence of this disease. Further studies to elaborate on the parameters of climate changes and indoor environment, and to compare the prevalence between regions needs to be explored in Saudi Arabia. References Dry Eye Disease: Impact on Quality of Life and Vision - PubMed. Accessed October 15, 2024. https://pubmed.ncbi.nlm.nih.gov/23710423/ Stapleton F, Alves M, Bunya VY, et al. TFOS DEWS II Epidemiology Report. Ocul Surf . 2017;15(3):334-365. doi:10.1016/j.jtos.2017.05.003 Jp C, Jd N, Dt A, et al. TFOS DEWS II Report Executive Summary. Ocul Surf . 2017;15(4). doi:10.1016/j.jtos.2017.08.003 Alves M, Asbell P, Dogru M, et al. TFOS Lifestyle Report: Impact of environmental conditions on the ocular surface. Ocul Surf . 2023;29:1-52. doi:10.1016/j.jtos.2023.04.007 G E. Infodemiology and infoveillance: framework for an emerging set of public health informatics methods to analyze search, communication and publication behavior on the Internet. J Med Internet Res . 2009;11(1). doi:10.2196/jmir.1157 Pradeep T, Ravipati A, Melachuri S, Fu R. More than just a stye: identifying seasonal patterns using google trends, and a review of infodemiological literature in ophthalmology. Orbit Amst Neth . 2023;42(2):130-137. doi:10.1080/01676830.2022.2040542 FAQ about Google Trends data - Trends Help. Accessed October 15, 2024. https://support.google.com/trends/answer/4365533?hl=en Saudi Arabia: search engine market share 2022. Statista. Accessed October 24, 2024. https://www.statista.com/statistics/1318431/saudi-arabia-search-engine-market-share/ Azzam DB, Nag N, Tran J, et al. A Novel Epidemiological Approach to Geographically Mapping Population Dry Eye Disease in the United States Through Google Trends. Cornea . 2021;40(3):282-291. doi:10.1097/ICO.0000000000002579 J M, D Z, J F, et al. Associations Between Air Pollution Exposure and Daily Pediatric Outpatient Visits for Dry Eye Disease: A Time-Series Study in Shenzhen, China. Int J Public Health . 2021;66. doi:10.3389/ijph.2021.1604235 van Setten G, Labetoulle M, Baudouin C, Rolando M. Evidence of seasonality and effects of psychrometry in dry eye disease. Acta Ophthalmol (Copenh) . 2016;94(5):499-506. doi:10.1111/aos.12985 Yu H, Zeng W, Zhang M, Zhao G, Wu W, Feng Y. Utilizing Baidu Index to Investigate Seasonality, Spatial Distribution and Public Attention of Dry Eye Diseases in Chinese Mainland. Front Public Health . 2022;10:834926. doi:10.3389/fpubh.2022.834926 Eidet JR, Chen X, Ræder S, Badian RA, Utheim TP. Seasonal variations in presenting symptoms and signs of dry eye disease in Norway. Sci Rep . 2022;12(1):21046. doi:10.1038/s41598-022-25557-9 Regional climate modelling outputs for Saudi Arabia: Key findings - United Nations Economic and Social Commission for Western Asia. Accessed October 15, 2024. http://www.unescwa.org/publications/regional-climate-modelling-outputs-saudi-arabia Xia M, Yang Y, Sun J, et al. Time-series analysis of the association between air pollution exposure and outpatient visits for dry eye disease: a case study in Zhengzhou, China. Front Public Health . 2024;12:1352057. doi:10.3389/fpubh.2024.1352057 Labban AH, Butt MJ. Analysis of sand and dust storm events over Saudi Arabia in relation with meteorological parameters and ENSO. Arab J Geosci . 2021;14(1):22. doi:10.1007/s12517-020-06291-w Berg EJ, Ying GS, Maguire MG, et al. Climatic and Environmental Correlates of Dry Eye Disease Severity: A Report From the Dry Eye Assessment and Management (DREAM) Study. Transl Vis Sci Technol . 2020;9(5):25. doi:10.1167/tvst.9.5.25 AlSomali AI, Alsaad MA, Alshammary AA, et al. Awareness About Dry Eye Symptoms and Risk Factors Among Eastern Province Population in Saudi Arabia. Cureus . 2023;15(11):e48197. doi:10.7759/cureus.48197 Wolkoff P. The mystery of dry indoor air - An overview. Environ Int . 2018;121(Pt 2):1058-1065. doi:10.1016/j.envint.2018.10.053 Kaur K, Gurnani B, Nayak S, et al. Digital Eye Strain- A Comprehensive Review. Ophthalmol Ther . 2022;11(5):1655. doi:10.1007/s40123-022-00540-9 Additional Declarations The authors declare no competing interests. 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DED is chronic ocular disease affecting hundreds of millions of people worldwide with reported global prevalence rate from 5% to 50%\u003csup\u003e2\u003c/sup\u003e. DED is a multifactorial disease cause alteration of tear film homeostasis and classified according to its pathophysiology into aqueous deficient dry eye (ADDE) and evaporative dry eye (EDE)\u003csup\u003e3\u003c/sup\u003e. The ocular surface is in direct contact with surrounding air; so, the environmental factors can play a role in reducing tear film by increasing evaporation rate. Previous studies showed that environments factors could affect ocular surface include low humidity, temperature, wind speed, high attitude, air pollution\u003csup\u003e4\u003c/sup\u003e. Tear Film \u0026amp; Ocular Surface Society reported in Dry Eye Workshop II the need for further research to elucidate more on the influence of climate and environmental factors on DED\u003csup\u003e3\u003c/sup\u003e.\u003c/p\u003e\n\u003cp\u003eGoogle Trends (GTs) (http://google.com/trends/) is an online open source that can be used to estimate users\u0026rsquo; interest to search for terms in different time periods. GTs is a powerful tool that has been used to observe public interest to obtain information throughout the internet, which can be used as surveillance tool to predict prevalence, assess seasonal variation or infectious disease tracking\u003csup\u003e5,6\u003c/sup\u003e. The Relative search volume (RSVs) In GTs is normalized and presented on scale from 0 to 100 based on popularity of all searches on all topics and duplicate searched from the same user is not included\u003csup\u003e7\u003c/sup\u003e. This science is known as infodemiology which use digital data to inform the public health\u003csup\u003e5\u003c/sup\u003e. The use of Google Trends data might reflect the specific season pattern better than hospital-based data since patients with undiagnosed DED might be difficult for them to visit the hospital, and most hospitals is not easily accessible because it requires referral letter and prior reservation.\u003c/p\u003e\n\u003cp\u003eIn this study we explore time-series analysis on the public internet searches of dry eye to better understand the likely evidence of seasonal variation on the prevalence of dry eye disease (DED). Such findings can help in eye care recommendation and provide reference for conducting epidemiologic research on DED. To our knowledge, this study is the first to describe seasonality of dry eye in Saudi Arabia using Google Trends tool.\u003c/p\u003e"},{"header":"Materials and Methods","content":"\u003cp\u003eSeasonality pattern is regular and predictable data changes which peak or trough at every certain period of time every year (month/season as in this case). While the trend is the increase or decrease in data over historical time. Meteorological season was defined as summer start from June to August, fall start from September to November, winter start from December to February, and spring start from March to May.\u003c/p\u003e \u003cp\u003eWe searched Google Trends using the term dry eye in Arabic language and set the location in Saudi Arabia during period from January 2011 to October 2024. Data were in monthly aggregated volume. The monthly time series data were decomposed into trend, season, and errors. stationarity test was performed using Augmented Dickey-Fuller (ADF). Time series analysis by utilizing regression model using monthly season variables with Autoregressive Integrated Moving Average (ARIMA) model and trend was included to fit the data structure and evaluate the significant effect of monthly variation. The model was evaluated by assessing the residual normality, ACF plot, and Ljung-Box test. The R software, version 4.4.1 (R Development Core Team) was used to make analysis on the GT collected data with forecast package using auto.arima() function. Across all analysis, the significant p value was set\u0026thinsp;\u0026lt;\u0026thinsp;0.05.\u003c/p\u003e \u003cp\u003eEthical Consideration\u003c/p\u003e \u003cp\u003eNo institutional Review Board (IRB) was needed as data is open public-source.\u003c/p\u003e"},{"header":"Results","content":"\u003cp\u003eFigure \u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003e shows the finding of data decomposition which suggests the occurrence of DED using GTs have obvious seasonal components, and the trend of search is increased throughout the study period and height of cycles appears to be increasing. The Augmented Dickey-Fuller (ADF) test result was (-6.7372) indicating the data in this study is stationary. The determined ARIMA model was (1,0,0). The model passed the box Ljung test (\u003cem\u003eX\u003c/em\u003e\u003csup\u003e\u003cem\u003e2\u003c/em\u003e\u003c/sup\u003e\u0026thinsp;=\u0026thinsp;8.7971, \u003cem\u003ep\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.4562), the residual was normally distributed and no spikes on autocorrelation, indicates that the fitting residual is white noise. The summery results of the ARIMA model are presented in Table\u0026nbsp;\u003cspan refid=\"Tab1\" class=\"InternalRef\"\u003e1\u003c/span\u003e.\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab1\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 1\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eThe Result of ARIMA (1,0,0) Model of the Different Month of the Year for Dry Eye Search Term in Saudi Arabia\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"5\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e \u003cp\u003eVariable\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eCoefficient\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003eStd. Error\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003et-Statistic\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c5\"\u003e \u003cp\u003eProb\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eJanuary (C)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e10.364\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e3.284\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e3.156\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.002*\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eFebruary\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e7.081\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e1.871\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e3.785\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.000*\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eMarch\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e6.308\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e2.383\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e2.647\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.009*\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eApril\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e4.109\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e2.713\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e1.515\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.132\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eMay\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e6.911\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e2.504\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e2.760\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.007*\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eJune\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e10.002\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e2.650\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e3.774\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.000*\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eJuly\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e6.595\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e3.003\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e2.196\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.030*\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eAugust\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e6.336\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e2.699\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e2.347\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.020*\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eSeptember\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e4.586\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e2.521\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e1.819\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.071\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eOctober\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e4.498\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e2.829\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e1.590\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.114\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eNovember\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e1.187\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e2.666\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.445\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.657\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eDecember\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e0.070\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e2.109\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.033\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.973\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eTime\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e0.475\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.071\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e6.650\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.000*\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eTime^2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e-0.000\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.000\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e-0.954\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.342\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eAR (1)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e0.471\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.073\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e6.491\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.000*\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003cp\u003eThe regression coefficient for February, March, May, June, July, and August were positive and statistically significant. The intent search volume exhibited increases in prevalence starting from May and continued to be high in summer season mainly in month of June. The decline of search volume is observed from the beginning of fall in September with lowest point mainly in month of January. Secondary peak was observed in late winter and beginning of spring in the month of February and March.\u003c/p\u003e \u003cp\u003eThe ranking of search volume based on GTs for different administrative regions in Saudi Arabia were as follow: Al-Qaseem (100), Hayel (95), Aseer (93), Al-Madinah (87), Al-Baha (86), Tabouk (85), Jazan (80), Al-Riyadh (79), Makkah (76), Northern Border (75), Al-Jouf (75), Najran (73), Eastern Region (66).\u003c/p\u003e"},{"header":"Discussion","content":"\u003cp\u003eThe widespread use of smartphones and availability of internet as it become part of daily life activity, and the intent search data can reflect on particularly public interest. The Google search engine is one of the top used to search for information in Saudi Arabia\u003csup\u003e\u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e8\u003c/span\u003e\u003c/sup\u003e. The Google Trends is powerful infodemiology tools; and data analysis can be used to monitor diseases and identify public interest pattern of disease to make healthcare care policy\u003csup\u003e\u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e5\u003c/span\u003e\u003c/sup\u003e.\u003c/p\u003e \u003cp\u003eIn this study, we evaluated the utility of internet search data through Google Trends (GTs). The search term for dry eye showed monthly variation according to monthly season. Our study demonstrates the bimodal season pattern in which the primary peak was in late spring, and during summer (warm) season mainly in month of June and the secondary peak of search volume was in February. Since the Coefficient for June is the highest and statistically significant is an indication for seasonality of dry eye in Saudi Arabia.\u003c/p\u003e \u003cp\u003ePrevious studies have concluded the presence of seasonal pattern in DED. Study in US reported significant higher internet search volume in spring and being lowest in fall season\u003csup\u003e\u003cspan citationid=\"CR9\" class=\"CitationRef\"\u003e9\u003c/span\u003e\u003c/sup\u003e. However, in China the prevalence of dry eye was slightly higher in warm season than cold season\u003csup\u003e\u003cspan citationid=\"CR10\" class=\"CitationRef\"\u003e10\u003c/span\u003e\u003c/sup\u003e. On the other hand, Van Stten et al\u003csup\u003e\u003cspan citationid=\"CR11\" class=\"CitationRef\"\u003e11\u003c/span\u003e\u003c/sup\u003e have described the seasonality for dry eye complaints to be during winter and summer. Another study in China indicated seasonal peak in winter\u003csup\u003e\u003cspan citationid=\"CR12\" class=\"CitationRef\"\u003e12\u003c/span\u003e\u003c/sup\u003e. Other investigations, did not support the evidence for seasonality in DED except for seasonal variation in Schirmer test and tear meniscus height parameters\u003csup\u003e\u003cspan citationid=\"CR13\" class=\"CitationRef\"\u003e13\u003c/span\u003e\u003c/sup\u003e. Variability of the reported findings concerning the seasonal pattern in DED between the studies or not finding association with DED could be linked to different geographical location.\u003c/p\u003e \u003cp\u003eGeographical variation in climate and environments can be associated with variation in the prevalence of DED. Factors that play a rule include temperature, humidity, rainfall, and air pollution. The overall higher volume internet searches in specific months might be explained by Saudi Arabia climate. The Arab peninsula does experience nearly 15 heat waves lasting 2 to 5 days annually mainly during summer months\u003csup\u003e\u003cspan citationid=\"CR14\" class=\"CitationRef\"\u003e14\u003c/span\u003e\u003c/sup\u003e. The drought based on the rainfall threshold occur in dry season (mainly in June and September) with the exception of February being higher than January and March\u003csup\u003e\u003cspan citationid=\"CR15\" class=\"CitationRef\"\u003e15\u003c/span\u003e\u003c/sup\u003e. In regard to storms events, Autumn months have the lowest storms events due to stable winds condition\u003csup\u003e\u003cspan citationid=\"CR16\" class=\"CitationRef\"\u003e16\u003c/span\u003e\u003c/sup\u003e. The air quality is also affected by climate change as some air pollution is worse in summer and others worse in winter. Hence, the climate changes may contribute to the increased internet search volume of dry eye during summer months and February. The ongoing climate temperature rise, and climate warming is also a public health concern for increased number of DED. Wearing sunglasses and avoiding going out during bad weather should be emphasized.\u003c/p\u003e \u003cp\u003eIn comparison to different administrative regions in Saudi Arabia, the Eastern region in Saudi Arabia which located by Arabian Gulf Sea was the lowest in ranking of search volume. This considerable variability across different locations could be implicated by the influence of high humidity climate in Eastern region to reduce tear evaporation giving protective effect from DED. It has been shown that higher humidity has a strong correlation to improve corneal fluorescein staining and tear breakup time (TBUT)\u003csup\u003e\u003cspan citationid=\"CR17\" class=\"CitationRef\"\u003e17\u003c/span\u003e\u003c/sup\u003e. The prevalence of DED in Saudi Arabia demonstrated by studies using survey from different regions revealed variable prevalence rate\u003csup\u003e\u003cspan citationid=\"CR18\" class=\"CitationRef\"\u003e18\u003c/span\u003e\u003c/sup\u003e. Due to divergence in climate and humidity in different regions in Saudi Arabia, further research for the prevalence might elaborate more on the climate effects.\u003c/p\u003e \u003cp\u003eMost people in Saudi Arabia tend to stay indoor during daylight in summer, so indoor temperatures, humidity, air pollution, and reliance on air conditioners can adversely contribute to dry eye. Individuals can perceive dry air by dry skin and dry eyes because of the direct contact with environmental air. Modifications of environment have also been demonstrated to decrease the alteration in tear film stability, for instance, it has been shown that elevation of low indoor humidity ,within set points, is beneficial in decreasing the perception of dry air\u003csup\u003e\u003cspan citationid=\"CR19\" class=\"CitationRef\"\u003e19\u003c/span\u003e\u003c/sup\u003e. Excess screen staring might also be another reason for DED\u003csup\u003e\u003cspan citationid=\"CR20\" class=\"CitationRef\"\u003e20\u003c/span\u003e\u003c/sup\u003e. Emphasizing for more behavioural adaptation such as regular blinking and eyelid closure, especially during unavoidable risk of higher tear evaporation rate during the seasonal peak should be advised.\u003c/p\u003e \u003cp\u003eDuring COVID-19 (coronavirus) pandemic, it has been demonstrated that DED increased due to excess digital screen exposure. However, in this study there was no significant shift in the trend of search volume in Google Trends during COVID-19 lockdown.\u003c/p\u003e \u003cp\u003eLimitation\u003c/p\u003e \u003cp\u003eThis study has some limitations. The result is for the current period. The reliability of Google Trends can be affected by media influence. Google Trends does not differentiate between distinct groups of patients, severity of dryness or if the symptoms were only transient feeling of dryness that does not meet the criteria for DED diagnosis.\u003c/p\u003e"},{"header":"Conclusion","content":"\u003cp\u003eBased on this study, there was seasonal fluctuation with higher number of searches on the internet for dry eye during late spring and summer months. Understanding the disease pattern can help in recommending eye care measures to modify the environmental factors and to reduce the impact on the eye and therefore reducing the prevalence of this disease. Further studies to elaborate on the parameters of climate changes and indoor environment, and to compare the prevalence between regions needs to be explored in Saudi Arabia.\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\n \u003cli\u003eDry Eye Disease: Impact on Quality of Life and Vision - PubMed. Accessed October 15, 2024. https://pubmed.ncbi.nlm.nih.gov/23710423/\u003c/li\u003e\n \u003cli\u003eStapleton F, Alves M, Bunya VY, et al. TFOS DEWS II Epidemiology Report. \u003cem\u003eOcul Surf\u003c/em\u003e. 2017;15(3):334-365. doi:10.1016/j.jtos.2017.05.003\u003c/li\u003e\n \u003cli\u003eJp C, Jd N, Dt A, et al. TFOS DEWS II Report Executive Summary. \u003cem\u003eOcul Surf\u003c/em\u003e. 2017;15(4). doi:10.1016/j.jtos.2017.08.003\u003c/li\u003e\n \u003cli\u003eAlves M, Asbell P, Dogru M, et al. TFOS Lifestyle Report: Impact of environmental conditions on the ocular surface. \u003cem\u003eOcul Surf\u003c/em\u003e. 2023;29:1-52. doi:10.1016/j.jtos.2023.04.007\u003c/li\u003e\n \u003cli\u003eG E. Infodemiology and infoveillance: framework for an emerging set of public health informatics methods to analyze search, communication and publication behavior on the Internet. \u003cem\u003eJ Med Internet Res\u003c/em\u003e. 2009;11(1). doi:10.2196/jmir.1157\u003c/li\u003e\n \u003cli\u003ePradeep T, Ravipati A, Melachuri S, Fu R. More than just a stye: identifying seasonal patterns using google trends, and a review of infodemiological literature in ophthalmology. \u003cem\u003eOrbit Amst Neth\u003c/em\u003e. 2023;42(2):130-137. doi:10.1080/01676830.2022.2040542\u003c/li\u003e\n \u003cli\u003eFAQ about Google Trends data - Trends Help. Accessed October 15, 2024. https://support.google.com/trends/answer/4365533?hl=en\u003c/li\u003e\n \u003cli\u003eSaudi Arabia: search engine market share 2022. Statista. Accessed October 24, 2024. https://www.statista.com/statistics/1318431/saudi-arabia-search-engine-market-share/\u003c/li\u003e\n \u003cli\u003eAzzam DB, Nag N, Tran J, et al. A Novel Epidemiological Approach to Geographically Mapping Population Dry Eye Disease in the United States Through Google Trends. \u003cem\u003eCornea\u003c/em\u003e. 2021;40(3):282-291. doi:10.1097/ICO.0000000000002579\u003c/li\u003e\n \u003cli\u003eJ M, D Z, J F, et al. Associations Between Air Pollution Exposure and Daily Pediatric Outpatient Visits for Dry Eye Disease: A Time-Series Study in Shenzhen, China. \u003cem\u003eInt J Public Health\u003c/em\u003e. 2021;66. doi:10.3389/ijph.2021.1604235\u003c/li\u003e\n \u003cli\u003evan Setten G, Labetoulle M, Baudouin C, Rolando M. Evidence of seasonality and effects of psychrometry in dry eye disease. \u003cem\u003eActa Ophthalmol (Copenh)\u003c/em\u003e. 2016;94(5):499-506. doi:10.1111/aos.12985\u003c/li\u003e\n \u003cli\u003eYu H, Zeng W, Zhang M, Zhao G, Wu W, Feng Y. Utilizing Baidu Index to Investigate Seasonality, Spatial Distribution and Public Attention of Dry Eye Diseases in Chinese Mainland. \u003cem\u003eFront Public Health\u003c/em\u003e. 2022;10:834926. doi:10.3389/fpubh.2022.834926\u003c/li\u003e\n \u003cli\u003eEidet JR, Chen X, R\u0026aelig;der S, Badian RA, Utheim TP. Seasonal variations in presenting symptoms and signs of dry eye disease in Norway. \u003cem\u003eSci Rep\u003c/em\u003e. 2022;12(1):21046. doi:10.1038/s41598-022-25557-9\u003c/li\u003e\n \u003cli\u003eRegional climate modelling outputs for Saudi Arabia: Key findings - United Nations Economic and Social Commission for Western Asia. Accessed October 15, 2024. http://www.unescwa.org/publications/regional-climate-modelling-outputs-saudi-arabia\u003c/li\u003e\n \u003cli\u003eXia M, Yang Y, Sun J, et al. Time-series analysis of the association between air pollution exposure and outpatient visits for dry eye disease: a case study in Zhengzhou, China. \u003cem\u003eFront Public Health\u003c/em\u003e. 2024;12:1352057. doi:10.3389/fpubh.2024.1352057\u003c/li\u003e\n \u003cli\u003eLabban AH, Butt MJ. Analysis of sand and dust storm events over Saudi Arabia in relation with meteorological parameters and ENSO. \u003cem\u003eArab J Geosci\u003c/em\u003e. 2021;14(1):22. doi:10.1007/s12517-020-06291-w\u003c/li\u003e\n \u003cli\u003eBerg EJ, Ying GS, Maguire MG, et al. Climatic and Environmental Correlates of Dry Eye Disease Severity: A Report From the Dry Eye Assessment and Management (DREAM) Study. \u003cem\u003eTransl Vis Sci Technol\u003c/em\u003e. 2020;9(5):25. doi:10.1167/tvst.9.5.25\u003c/li\u003e\n \u003cli\u003eAlSomali AI, Alsaad MA, Alshammary AA, et al. Awareness About Dry Eye Symptoms and Risk Factors Among Eastern Province Population in Saudi Arabia. \u003cem\u003eCureus\u003c/em\u003e. 2023;15(11):e48197. doi:10.7759/cureus.48197\u003c/li\u003e\n \u003cli\u003eWolkoff P. The mystery of dry indoor air - An overview. \u003cem\u003eEnviron Int\u003c/em\u003e. 2018;121(Pt 2):1058-1065. doi:10.1016/j.envint.2018.10.053\u003c/li\u003e\n \u003cli\u003eKaur K, Gurnani B, Nayak S, et al. Digital Eye Strain- A Comprehensive Review. \u003cem\u003eOphthalmol Ther\u003c/em\u003e. 2022;11(5):1655. doi:10.1007/s40123-022-00540-9\u003c/li\u003e\n\u003c/ol\u003e"}],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":true,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":true,"hideJournal":true,"highlight":"","institution":"","isAcceptedByJournal":false,"isAuthorSuppliedPdf":false,"isDeskRejected":"","isHiddenFromSearch":false,"isInQc":false,"isInWorkflow":false,"isPdf":false,"isPdfUpToDate":true,"isWithdrawnOrRetracted":false,"journal":{"display":true,"email":"[email protected]","identity":"researchsquare","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":true,"externalIdentity":"","sideBox":"","snPcode":"","submissionUrl":"/submission","title":"Research Square","twitterHandle":"researchsquare","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"","reportingPortfolio":"","inReviewEnabled":false,"inReviewRevisionsEnabled":true},"keywords":"Dry eye disease, Google Trends, Seasonality","lastPublishedDoi":"10.21203/rs.3.rs-5429689/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-5429689/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003cp\u003e\u003cstrong\u003ePurpose\u003c/strong\u003e: The study aims to examine the dry eye search term pattern in Saudi Arabia using Google Trends to assess the influence of monthly effect on dry eye disease (DED).\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eMethods\u003c/strong\u003e: This is a Time series design study; Data were collected from Google Trends for period from January 2011 to October 2024 using Arabic term for Dry Eye with setting allocated in Saudi Arabia. The monthly specified regression with ARIMA model and Time were included to provide evidence of the seasonality.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eResult\u003c/strong\u003e: The study result for dry eye disease (DED) using GTs demonstrates bimodal pattern with significant monthly/seasonal differences and increases of intent search over time. The periodic query for Arabic term dry eye is more likely to peak in late spring and in summer season mainly in month of June. A secondary peak was observed in February and March.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eConclusion\u003c/strong\u003e: The study finding provides evidence of monthly seasonality of DED using Google Trends. Emphasizing and implementing measures of recommendations to decrease risk of DED is indicated especially during seasonality. Further exploration of climate and geographic locational influence on DED is needed.\u003c/p\u003e","manuscriptTitle":"Dry Eye Disease Seasonal Pattern in Saudi Arabia Using Google Trends","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2024-11-12 01:42:40","doi":"10.21203/rs.3.rs-5429689/v1","editorialEvents":[{"type":"communityComments","content":0}],"status":"published","journal":{"display":true,"email":"[email protected]","identity":"researchsquare","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":true,"externalIdentity":"","sideBox":"","snPcode":"","submissionUrl":"/submission","title":"Research Square","twitterHandle":"researchsquare","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"","reportingPortfolio":"","inReviewEnabled":false,"inReviewRevisionsEnabled":true}}],"origin":"","ownerIdentity":"3510698e-7a23-4350-9459-ba4be0fcb6ed","owner":[],"postedDate":"November 12th, 2024","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"posted","subjectAreas":[{"id":40073595,"name":"Ophthalmology"}],"tags":[],"updatedAt":"2024-11-12T01:42:40+00:00","versionOfRecord":[],"versionCreatedAt":"2024-11-12 01:42:40","video":"","vorDoi":"","vorDoiUrl":"","workflowStages":[]},"version":"v1","identity":"rs-5429689","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-5429689","identity":"rs-5429689","version":["v1"]},"buildId":"8U1c8b4HqxoKbykW_rLl7","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}