Prevalence of dry eye disease in Ontario, Canada: A population-based survey

Prevalence of dry eye disease in Ontario, Canada: A population-based survey

The Ocular Surface 17 (2019) 526–531 Contents lists available at ScienceDirect The Ocular Surface journal homepage: www.elsevier.com/locate/jtos Or...

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The Ocular Surface 17 (2019) 526–531

Contents lists available at ScienceDirect

The Ocular Surface journal homepage: www.elsevier.com/locate/jtos

Original Research

Prevalence of dry eye disease in Ontario, Canada: A population-based survey a

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Barbara Caffery , Sruthi Srinivasan , Christopher J. Reaume , Aren Fischer , David Cappadocia , Csaba Siffele,f, Clara C. Chang,∗

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Toronto Eye Care, Toronto, ON, Canada Centre for Ocular Research & Education, School of Optometry and Vision Science, University of Waterloo, Waterloo, ON, Canada Shire Pharma Canada ULC, Now Part of Takeda Pharmaceutical Company Ltd, Toronto, ON, Canada d IQVIA, Mississauga, ON, Canada e Shire, Now Part of Takeda Pharmaceutical Company Ltd, Lexington, MA, USA f College of Allied Health Sciences, Augusta University, Augusta, GA, USA g Department of Ophthalmology & Vision Sciences, University of Toronto, Toronto, ON, Canada b c

ARTICLE INFO

ABSTRACT

Keywords: DED Dry eye disease Epidemiology Ocular surface disease Population survey Prevalence

Purpose: Population-based cross-sectional survey in Ontario to estimate the 2016 prevalence of dry eye disease (DED) and associated risk factors among adults in Canada. Methods: We emailed the 5-Item Dry Eye Questionnaire (DEQ-5) to 124,469 Ontario adults (age ≥18 years) in the IQVIA E360 database, March–April 2017. Inclusion criteria were: ≥2 visits to an Ontario based clinic, ≥1 visits in the 1 year before the study; database record with email. DED was defined as a DEQ-5 score of > 6/22. The crude prevalence by age/sex of the Ontario sample was adjusted to the 2016 Canadian population (mean age 41.0 years, 51% female). Significance of DED risk factors (age, sex, selected diseases/medical conditions and medications) was evaluated by logistic regression analysis. Results: Of the 5163 (4.1%) patients who completed the survey (59.5% female, median age, 46 years; 40.4% male, 56 years), 1135 respondents reported DED. Prevalence increased with age (p < 0.05) and was highest among those aged 55–64 years (24.7%; 95% CI, 22.1–27.3%) and lowest among those aged 25–34 years (18.4%; 95% CI, 15.9–21.0%). Prevalence was significantly higher (p < 0.001) among women (24.7%; 95% CI, 23.2–26.2%) than men (18.0%; 95% CI, 16.4–19.7%). Other risk factors were not significant. The age-/sexadjusted Canadian DED prevalence estimate from this sample was 21.3% (95% CI, 19.8–23.2%), corresponding to ∼6.3 million people. Conclusions: Based on the Ontario sample, we estimate that > 6 million Canadian adults may have DED, and that older people and females are more likely to be affected.

1. Introduction Dry eye disease (DED) is a common disorder of the tear film that leads to ocular surface damage over time [1]. It is accompanied by symptoms of ocular discomfort and visual disturbance [2]. Previous research has shown that DED reduces health- and vision-related quality of life and that it carries a considerable economic burden of disease [3,4]. Prevalence estimates vary by country and region, and the 2017 Dry Eye Workshop (DEWS) II report compiled estimates of prevalence ranging from 5 to 50% [3]. Most previous studies in North America have been US based and have variously estimated prevalence at the

following: 7.8% among women aged ≥50 years (Women's Health Study) [5], 4.3% among men aged ≥50 years (Physicians' Health Studies I and II) [6], and 6.8% among those aged ≥18 years (2013 National Health and Wellness Survey) [7]. The Beaver Dam Study reported incidence of 21.6% among those aged 48–91 years over a period of 10 years between 1993 and 2005 [8]. The most recent prevalence estimate for DED in Canada comes from the Canada Dry Eye Epidemiology Study (CANDEES) that was carried out over two decades ago. CANDEES estimated prevalence of DED symptoms at 28.7% [9]. A new estimate of DED prevalence in Canada, that takes into account our current knowledge of DED and associated risk factors, is needed. Moreover, most studies have focused on older age groups because previous

Abbreviations: ATC, Anatomical Therapeutic Chemical; CANDEES, Canada Dry Eye Epidemiology Study; CI, confidence interval; DED, dry eye disease; DEQ-5, 5Item Dry Eye Questionnaire; DEWS, Dry Eye Workshop; E360, Evidence 360; EMR, electronic medical record; OHIP, Ontario Health Insurance Plan ∗ Corresponding author. Department of Ophthalmology & Vision Sciences, University of Toronto, 601-600 Sherbourne St, Toronto, ON, M4X 1W4, Canada. E-mail address: [email protected] (C.C. Chan). https://doi.org/10.1016/j.jtos.2019.02.011 Received 17 May 2018; Received in revised form 25 February 2019; Accepted 26 February 2019 1542-0124/ © 2019 The Authors. Published by Elsevier Inc. This is an open access article under the CC BY-NC-ND license (http://creativecommons.org/licenses/BY-NC-ND/4.0/).

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research has shown that prevalence increases with age, and the older demographic is more likely to be affected. There is a need for data about DED in younger age groups. Apart from age, female sex is considered the other main risk factor for DED. It is known to disproportionately affect women, with some studies estimating that prevalence among women is almost twice as high as that among men [3,5–7]. A broad range of other risk factors, with varying levels of evidence, have also been identified in different studies. These include environmental factors such as use of visual displays and low humidity, medical conditions such as Sjӧgren's syndrome, diabetes, hepatitis, conjunctival disorders, blepharitis, rheumatoid arthritis, and vitamin deficiencies, as well as certain medications for diabetes, thyroid disease, gout, and HIV [3,10–14]. This Ontario population-based survey was undertaken to estimate the current prevalence of DED and selected DED risk factors in Canada. 2. Methods

Fig. 1. Comparison of age and sex distribution in the adult population for Canada versus Ontario in the 2016 census.

2.1. Study population

substitutes (0, ≤1, 2–3, 4–5, 6–9, or ≥10 times per day) and type of tear substitutes used (none, artificial tears, lubricating ointments, or gels). To focus on over-the-counter treatments, patients were asked to exclude tear substitutes prescribed by a health care practitioner.

The study conforms to the ethical principles in the Declaration of Helsinki. Institutional Review Board Services (Aurora, ON, Canada) reviewed and approved the study protocol (approval number Pro00020380). The study population was derived from the IQVIA Evidence 360 (E360) electronic medical record (EMR) database. The E360 sources deidentified patient data from an Ontario-based group of clinics and included patients seeing general practitioners and specialists. The E360 database has access to records of > 1 million patients in Ontario, Canada's most populated province comprising ∼40% of Canada's population. To maintain patient privacy, all EMR patient-level data were passed through the patient data anonymization software, PARAT (Privacy Analytics Inc., Ottawa, ON, Canada), before making the data available to IQVIA researchers. Each patient record includes the following parameters obtained from a typical visit to the clinic within the EMR network: age, sex, Ontario Health Insurance Plan (OHIP) diagnosis codes, prescriptions (including Drug Identification Number [DIN]), and number of physician visits. Inclusion criteria were age ≥18 years as of the date of sending out the survey emails; at least two visits to the Ontario-based clinics group, with at least one visit during the 1 year before initiating the survey; and an email address linked to the patient's database identification document (ID). The survey was emailed to 124,469 Ontario patients in the E360 database from March 21 to April 29, 2017. Patients were sent an email asking them to participate in the survey, with a link to the DED questionnaire and a link allowing them to opt out of the study. Patients who clicked on the DED survey were directed to a secure online portal. After providing informed consent, they were presented with the DED questionnaire.

2.3. Statistical analysis The crude (unadjusted) prevalence was calculated using the number of patients with DED divided by the total number of survey respondents. Prevalence was estimated overall, and stratified by age and sex. Age strata used were 18–39, 40–64 and ≥ 65 years for all analyses, except for prevalence by age where the following age groups were used to observe details in trends by age: 18–24, 25–34, 35–44, 45–54, 55–64, 65–74 and ≥ 75 years. Observed stratum-specific prevalence rates (for age and sex strata) were applied to the 2016 Canadian population to obtain the expected national prevalence. The age and sex distribution of the adult population in Ontario mirrored the demographics data from all of Canada (Fig. 1) [16]. The Ontario data were also projected to estimate adjusted prevalence for each province in the same way as the national prevalence was calculated. Normal approximated confidence intervals (CIs) were calculated for crude prevalence estimates, while exact CIs were calculated for adjusted prevalence estimates. Significance testing was based on a logistic regression analysis with p < 0.05 considered significant. Average DEQ-5 scores were evaluated by age and sex. Mean response to each DEQ-5 question was evaluated for patients with DED versus those without (DEQ-5 score ≤6). Significance of DED risk factors was evaluated by logistic regression analysis which determined whether each risk factor had an effect on DED status of the participant. Risk factors were included based on previous evidence in the literature and availability of information in the E360 database [10–12]. These risk factors included (i) OHIP diagnosis codes for hepatitis (70), nutritional/ vitamin deficiencies (269), keratitis/corneal ulcer (370), conjunctiva disorders (372), blepharitis/obstruction of the lacrimal duct (373), and rheumatoid arthritis/Still's disease (714); and (ii) prescription Anatomical Therapeutic Chemical (ATC) classification codes for prescriptions related to diabetes (A10A, A10B), thyroid disease (H03), gout (M04), and HIV (J05A). Data were analyzed using the statistics software SAS (version 9.3; SAS Institute Inc., Cary, NC, USA).

2.2. DED survey Patients with DED were identified using the clinically validated 5Item Dry Eye Questionnaire (DEQ-5) [15]. Patients were asked that on a typical day: how often they feel eye discomfort (0–4 scale; 0 = never, 4 = constantly), how intense is the eye discomfort at the end of the day (0–5 scale; 0 = never had it, 5 = very intense), how often they feel eye dryness (0–4 scale; 0 = never, 4 = constantly), how intense is the eye dryness at the end of the day (0–5 scale; 0 = never had it, 5 = very intense), and how often their eyes look or feel excessively watery (0–4 scale; 0 = never, 4 = constantly). The DEQ-5 maximum total score is 22. Those with a total score of > 6 were classified as having DED [15]. The survey included additional optional treatment-related questions. Patients were asked about average frequency of use of tear

3. Results 3.1. Study population The survey was mailed to 124,469 patients. Of these, 53,820 527

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Fig. 4. Dry eye disease prevalence (unadjusted estimate) by age group and sex.

Fig. 2. Age distribution of patients who were emailed the survey (recruitment sample, n = 124,469) and patients who responded to the survey (survey respondents, n = 5163).

Prevalence was statistically higher (p < 0.001) among women (24.7%; 95% CI, 23.2–26.2%) than men (18.0%; 95% CI, 16.4–19.7%). This trend and difference remained significant for the age-adjusted prevalence of women vs men (p < 0.001). Cross tabulation with age showed statistically higher prevalence among women of each age group, although the difference between women and men was the lowest among those aged 18–39 years (Fig. 4). From projection of the Ontario data, the age- and sex-adjusted national DED prevalence in the 2016 Canadian population (29.6 million adults) was estimated at 21.3% (95% CI, 19.8–23.2%) corresponding to ∼6.3 million people. When the Ontario data were likewise projected to the other Canadian provinces, the estimated prevalence was as follows: British Columbia, 21.3% (95% CI, 19.8–23.3%); Alberta, 20.8% (95% CI, 19.3–22.8%); Saskatchewan, 21.1% (95% CI, 19.5–23.2%); Manitoba, 21.1% (95% CI, 19.5–23.3%); Ontario, 21.3% (95% CI, 19.8–23.3%); Quebec 21.4% (95% CI, 19.9–23.3%); New Brunswick, 21.6% (95% CI, 20.1–23.5%); Prince Edward Island, 21.6% (95% CI, 20.0–23.6%); Nova Scotia, 21.6% (95% CI, 20.1–23.5%); and Newfoundland and Labrador, 21.5% (95% CI, 20.1–23.4%). Evaluation of mean response to individual DEQ-5 questions (Fig. 5) indicated that DED classification was driven by how often patients experienced symptoms, and not by the intensity of these symptoms at the end of day (p < 0.05).

(43.2%) were male, with a median age of 40 years (range 18–101 years), and 70,598 (56.7%) were female with a median age of 36 years (range 18–106 years, Fig. 2). Sex information was not recorded in the database for 51 participants. A total of 5163 (4.1%) patients completed the survey. Of these, 2088 (40.4%) were male, with a median age of 56 years (range 18–101 years), and 3071 (59.5%) were female, with a median age of 46 years (range 18–95 years, p < 0.05; Fig. 2). Four did not provide their sex. The respondents represented 0.05% of the 2016 population of Ontario. The mean and median age of survey respondents was numerically higher than the mean and median age of all patients emailed the survey. Mean ± SD [median] age of survey responders was 49.74 ± 16.88 [50] years, and for all the patients who were emailed the survey, these numbers were 40.82 ± 15.63 [37] years. 3.2. DED prevalence The study identified 1135 patients with DED, resulting in an unadjusted prevalence of 22.0% (95% CI, 20.8–23.1%). Prevalence was highest among those aged 55–64 years (24.7%; 95% CI, 22.1–27.3%) and lowest among those aged 25–34 years (18.4%; 95% CI, 15.9–21.0%). Prevalence among those aged 18–24 years (22.6%; 95% CI, 17.7–27.5%) was higher than among those aged 25–34 years, 35–44 years (19.9%; 95% CI, 17.3–22.5%), and ≥75 years (21.3%; 95% CI, 17.3–25.2%; Fig. 3). Age was significantly associated with DED prevalence (p = 0.0142).

Fig. 5. Mean (SD) scores for each DEQ-5 question for patients with versus those without DED (non-DED). p < 0.05 for all DED vs non-DED. DED, dry eye disease; DEQ-5, 5-Item Dry Eye Questionnaire.

Fig. 3. Dry eye disease prevalence (unadjusted estimate) by age group. CI, confidence interval. p = 0.0142 for association of age with DED prevalence.

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Table 1 Differences between patients with DED and those without (non-DED). Risk factor

Age, years 18–39 40–64 ≥65 Sex Male Female Diagnosis, OHIP code Hepatitis Nutritional and vitamin deficiencies Keratitis, corneal ulcer Conjunctiva disorders Blepharitis, chalazion, stye Dacryocystitis, obstruction of lacrimal duct Rheumatoid arthritis, Still's disease Prescription for disease, ATC prescription Diabetes Thyroid disease Gout HIV

OHIP code/ATC prescription

Patient count

P valuea

Patients, %

DED (n = 1135)

Non-DED (n = 4028)

DED (22%)

Non-DED (78%)

334 555 245

1330 1856 842

20.12 23.02 22.54

79.88 76.98 77.46

reference 0.0277 0.1284

375 759

1713 2312

17.96 24.72

82.04 75.28

reference < 0.001

70 269 370 372 373 375 714

3 30 2 18 3 0 5

7 88 2 40 14 0 13

0.26 2.64 0.18 1.59 0.26 0 0.44

0.17 2.18 0.05 0.99 0.35 0 0.32

0.590 0.127 0.223 0.136 0.469 – 0.445

A10A, A10B H03 M04 J05A

35 37 7 19

126 142 34 67

3.08 3.26 0.62 1.67

3.13 3.53 0.84 1.66

0.688 0.339 0.463 0.572

ATC, Anatomical Therapeutic Chemical; DED, dry eye disease; OHIP, Ontario Health Insurance Plan. a Logistic regression analysis was used to determine the significance of differences between groups.

3.3. Risk factors for DED

4. Discussion

Patients with DED were significantly older than those without DED (p < 0.05), and significantly more likely to be female (p < 0.001). Differences between the groups with and without DED were not significant for any of the other risk factors analyzed (Table 1). Risk factors were based on diagnosis codes and prescriptions written by the doctors in the Ontario-based clinics. Diagnoses or prescriptions outside this setting would not be included in our analysis.

Based on a survey of Ontario patients in the E360 database, which found an unadjusted prevalence of 22.0%, this study estimates that the age-/sex-adjusted prevalence of DED among adults (aged ≥18 years) in Canada is 21.3%, which is the equivalent of ∼6.3 million people. Prevalence is higher among women (24.7%) than men (18.0%) in the Ontario sample, and there was a general trend towards increasing prevalence in the older age groups, with the highest prevalence among those aged 55–64 years. These data provide the most recent estimate for DED prevalence in Canada and help identify and characterize the population subgroups most at risk for this common condition. Consistent with previous studies, these results describe a higher prevalence among women and older age groups [3]. Using average DEQ-5 scores as a proxy for DED severity, women report more severe disease compared with men. This observation is consistent with data from the US Women's Health Study and Physician's Health Studies, which demonstrated that women generally experience more severe DED [4]. It should be noted, however, that no objective assessment was performed in our survey to confirm disease severity. A number of risk factors were evaluated, but only increasing age and female sex emerged as significant risk factors for DED in our analysis. These data also indicate that nearly 50% of patients with DED do not utilize any over-the-counter medications to treat their symptoms. The E360 database did not capture any information on the use of overthe-counter tear substitutes among the non-dry eye respondents. Direct comparison between DED prevalence estimates is challenging due to the different survey methodologies and DED definitions employed in different studies. These prevalence estimates are broadly consistent with numbers from previous US/Canadian studies that used symptom-based DED definitions [3]. The 13-point CANDEES questionnaire results from 1994 estimated prevalence in Canada at 28.7% among patients aged 1–80 years or older [9]. In the mid-1990s in the United States, the Salisbury Eye Study estimated prevalence at 14.6% (patients aged 65–84 years, one or more of six dry eye symptoms often/ all the time) [17], and the Beaver Dam Offspring Study, 14.5% (patients aged 21–84 years, DED symptoms or usage of dry eye rescue medications) [18]. The Beaver Dam study estimated incidence at 21.6% among patients aged 48–91 years (sensation of dry eye or dry eye symptoms)

3.4. DED severity and treatments The average DEQ-5 score by age and sex was calculated as a nonvalidated proxy for disease severity. Those aged 40–64 years had the highest mean ± SD DEQ-5 score (8.8 ± 3.0). Mean ± SD score for those aged < 40 and ≥ 65 years was 8.2 ± 2.1 and 8.3 ± 2.5, respectively. Compared with those aged < 40 years, the mean DEQ-5 score was significantly higher among those aged 40–64 years (p < 0.01) but not among those aged ≥65 years (p = 0.06). Females had a higher (p < 0.01) mean ± SD score (8.7 ± 2.9) compared with males (8.1 ± 2.2). Questions about frequency of use of over-the-counter tear substitutes were answered by 1129 patients with DED. Nearly half the patients (550 [48.7%]) never used tear substitutes, 317 (28.1%) patients used tear substitutes once a day or less, and 262 (23.2%) patients used tear substitutes two or more times a day. There was a trend for increased tear substitute use in the older age groups and among females. Among those aged < 40 years, 42.2% of patients used tear substitutes (43.0% of all females and 39.5% of all males). Among those aged 40–64 and ≥ 65 years, 50.8% (55.2% of females and 42.3% of males) and 63.7% (67.4% of females and 59.1% of males) of patients used tear substitutes, respectively. Overall, 53.2% of females and 46.7% of males used tear substitutes. Of the patients with DED, 1064 provided an answer about the type of tear substitutes they use. Tear substitute use was reported by 506 (47.6%) of the patients while 558 (52.4%) patients responded that they did not use any tear substitutes. Of those who used tear substitutes, the most commonly used were artificial tears (408 [80.6%]), followed by lubricating ointments (68 [13.4%]) and gels (30 [5.9%]). 529

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[8]. Although there is less research on prevalence in younger age groups (aged < 40 years), recently available data (up to 2013) shows a trend for increasing prevalence with age [3,7]. The DEWS II metaanalysis shows that prevalence of DED increases linearly with age, with a gradual increase over the age of 50 years, and only modest changes for younger age groups [3]. In contrast, these results showed a prevalence among individuals aged 18–24 years that was comparable to prevalence in those aged ≥45 years, and higher than in those aged 25–44 years. This could be attributable to lower sample size in the 18–24 years age stratum (n = 279 vs: 25–44 years, n = 1810; ≥45 years, n = 3074) and consequent random error (as reflected in the larger CIs) or a true increase in prevalence among this age group. Use of visual displays has been implicated in increased risk of DED [3,19], and the widespread use of computers and mobile devices among the younger age groups may contribute to this. Previous studies have identified a number of diseases/conditions and medications as risk factors for DED. These include hepatitis, vitamin deficiencies, keratitis, conjunctival disorders, blepharitis, dacryocystitis, rheumatoid arthritis, and Still's disease, as well as medications for diabetes, thyroid disease, gout, and HIV [3,10–14]. The results of this study found no significant difference between patients with and without DED for these risk factors. This may be due to the small number of patients in each group with these risk factors. The DEWS II report has identified several other risk factors for DED that were not included in the current analysis due to lack of information in the patient records. These risk factors include Sjӧgren's syndrome, contact lens wear, and Asian race [3]. Strengths of the study include a large sample size relative to the Ontario population, covering a broad age range across both sexes, as well as use of a clinically validated DED questionnaire to estimate prevalence. Also, the symptom-based evaluation of DED in this study maximizes sensitivity, ensuring that patients with even mild DED would be captured in the estimate. This study needs to be interpreted in light of some limitations. Results of the study were based on a sampled population from Ontario and were extrapolated to the entire Canadian population. It is noteworthy that the sex composition and mean age of Ontario closely matches that of all of Canada for the 2016 census data. However, to the extent that the Ontario population may differ from the rest of Canada in other demographic composition characteristics (for example, by ethnic/ racial makeup or environmental factors) and clinical practice patterns, it is possible that this may have a modest impact on the extrapolated Canada estimate. Measurement of DED prevalence independent of diagnosis may be another limitation. While a symptom-based DED evaluation maximizes analysis sensitivity, it is also likely to reduce specificity, potentially including patients with medical conditions that have symptoms overlapping with DED. As with any survey, possible sampling biases should also be considered. The sample used in this study was limited to recently treated patients who had an email account linked to their database record. Moreover, self-selection of participants may also introduce possible sampling biases based on participants’ access and ability to complete online surveys. It is notable that the median age of survey responders was higher than the median age of all patients emailed the survey, suggesting that an age bias may have been introduced in the overall prevalence results if either the DED or non-DED cohort was subject to more of an age increase as compared with the other cohort.

maintaining awareness of this common condition. Role of the funding source This study was sponsored by Shire Development LLC, now a member of the Takeda group of companies. The sponsor was involved in all aspects of the study, including study design, analysis and interpretation of data, writing of the report, and the decision to submit the article for publication. Two employees of the sponsor are also authors on this manuscript. Disclosure Dr. Caffery has been a consultant for Allergan, Santen, and Shire, a Takeda company. Dr. Srinivasan has been a consultant for Shire, a Takeda company, and has received research funding/honoraria from Advanced Vision Research, Alcon, Allergan, Contamac, CooperVision, Essilor, GL Chemtec, Inflamax Research, Johnson & Johnson Vision, Nature's Way, Novartis, Ocular Dynamics, Oculus, Safilens, Santen, Shire, a Takeda company, TearLab, and TearScience. Dr. Reaume is an employee of and owns stock/stock options in Shire Pharma Canada ULC, a Takeda company. Mr. Fischer and Dr. Cappadocia are employees of IQVIA, which is a paid consultant to Shire, a Takeda company, in relation to this study. Dr. Siffel is an employee of and owns stock/stock options in Shire, a Takeda company. Dr. Chan has been a consultant for Alcon, Allergan, Bausch & Lomb, Santen, Shire, a Takeda company, and TearScience, and has received research support from Allergan, Bausch & Lomb, and TearLab. All authors have made substantial contributions to all of the following: (1) the conception and design of the study, or acquisition of data, or analysis and interpretation of data; (2) drafting the article or revising it critically for important intellectual content; and (3) final approval of the version to be submitted. Acknowledgments The authors thank Mirek Wilmer, PhD, of IQVIA, who provided statistical support, and Ira Probodh, PhD, of Excel Scientific Solutions, who provided medical writing assistance, funded by Shire, now a member of the Takeda group of companies. References [1] Bron AJ, de Paiva CS, Chauhan SK, Bonini S, Gabison EE, Jain S, et al. TFOS DEWS II pathophysiology report. Ocul Surf 2017;15:438–510. [2] Craig JP, Nichols KK, Akpek EK, Caffery B, Dua HS, Joo CK, et al. TFOS DEWS II definition and classification report. Ocul Surf 2017;15:276–83. [3] Stapleton F, Alves M, Bunya VY, Jalbert I, Lekhanont K, Malet F, et al. TFOS DEWS II epidemiology report. Ocul Surf 2017;15:334–65. [4] Schaumberg DA, Uchino M, Christen WG, Semba RD, Buring JE, Li JZ. Patient reported differences in dry eye disease between men and women: impact, management, and patient satisfaction. PLoS One 2013;8:e76121. [5] Schaumberg DA, Sullivan DA, Buring JE, Dana MR. Prevalence of dry eye syndrome among US women. Am J Ophthalmol 2003;136:318–26. [6] Schaumberg DA, Dana R, Buring JE, Sullivan DA. Prevalence of dry eye disease among US men: estimates from the Physicians' Health Studies. Arch Ophthalmol 2009;127:763–8. [7] Farrand KF, Fridman M, Stillman IÖ, Schaumberg DA. Prevalence of diagnosed dry eye disease in the United States among adults aged 18 years and older. Am J Ophthalmol 2017;182:90–8. [8] Moss SE, Klein R, Klein BE. Long-term incidence of dry eye in an older population. Optom Vis Sci 2008;85:668–74. [9] Doughty MJ, Fonn D, Richter D, Simpson T, Caffery B, Gordon K. A patient questionnaire approach to estimating the prevalence of dry eye symptoms in patients presenting to optometric practices across Canada. Optom Vis Sci 1997;74:624–31. [10] Moss SE, Klein R, Klein BE. Prevalence of and risk factors for dry eye syndrome. Arch Ophthalmol 2000;118:1264–8. [11] The epidemiology of dry eye disease: report of the epidemiology subcommittee of the international Dry Eye Workshop (2007). Ocul Surf 2007;5:93–107. [12] Yildirim P, Garip Y, Karci AA, Guler T. Dry eye in vitamin D deficiency: more than an incidental association. Int J Rheum Dis 2016;19:49–54. [13] Roh HC, Lee JK, Kim M, Oh JH, Chang M-W, Chuck RS, et al. Systemic

5. Conclusions The analysis of a sampled patient population in Ontario demonstrated that 22% of adults are affected by DED. When these results are projected to all of Canada, the estimated DED prevalence is > 21% of adults in Canada, with older people and females more likely to have symptoms consistent with the condition. These data provide a current estimate of DED prevalence in Canada and highlight the importance of 530

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B. Caffery, et al. comorbidities of dry eye syndrome: the Korean National Health and Nutrition Examination Survey V, 2010 to 2012. Cornea 2016;35:187–92. [14] Gomes JAP, Azar DT, Baudouin C, Efron N, Hirayama M, Horwath-Winter J, et al. TFOS DEWS II iatrogenic report. Ocul Surf 2017;15:511–38. [15] Chalmers RL, Begley CG, Caffery B. Validation of the 5-Item Dry Eye Questionnaire (DEQ-5): discrimination across self-assessed severity and aqueous tear deficient dry eye diagnoses. Contact Lens Anterior Eye 2010;33:55–60. [16] Age (in single years) and average age (127) and sex (3) for the population of Canada, provinces and territories, census metropolitan areas and census agglomerations, 2016 and 2011 censuses - 100% data. 2016 Available from: https:// www12.statcan.gc.ca/census-recensement/2016/dp-pd/dt-td/Rp-eng.cfm?LANG= E&APATH=3&DETAIL=0&DIM=0&FL=A&FREE=0&GC=0&GID=0&GK=0&

GRP=1&PID=109523&PRID=0&PTYPE=109445&S=0&SHOWALL=0&SUB=0 &Temporal=2016&THEME=115&VID=0&VNAMEE=&VNAMEF=, Accessed date: 9 July 2018. [17] Schein OD, Muñoz B, Tielsch JM, Bandeen-Roche K, West S. Prevalence of dry eye among the elderly. Am J Ophthalmol 1997;124:723–8. [18] Paulsen AJ, Cruickshanks KJ, Fischer ME, Huang GH, Klein BE, Klein R, et al. Dry eye in the Beaver Dam Offspring Study: prevalence, risk factors, and health-related quality of life. Am J Ophthalmol 2014;157:799–806. [19] Uchino M, Yokoi N, Uchino Y, Dogru M, Kawashima M, Komuro A, et al. Prevalence of dry eye disease and its risk factors in visual display terminal users: the Osaka study. Am J Ophthalmol 2013;156:759–66.

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