Prevalence and Incidence of Dry Eye and Meibomian Gland Dysfunction in the United States: A Systematic Review and Meta-analysis

Paul McCann; Alison G Abraham; Adhuna Mukhopadhyay; Kanella Panagiotopoulou; Hongan Chen; Thanitsara Rittiphairoj; Darren G Gregory; Scott G Hauswirth; Cristos Ifantides; Riaz Qureshi; Su-Hsun Liu; Ian J Saldanha; Tianjing Li

doi:10.1001/jamaophthalmol.2022.4394

. 2022 Oct 27;140(12):1181–1192. doi: 10.1001/jamaophthalmol.2022.4394

Prevalence and Incidence of Dry Eye and Meibomian Gland Dysfunction in the United States

A Systematic Review and Meta-analysis

Paul McCann ¹, Alison G Abraham ^1,², Adhuna Mukhopadhyay ², Kanella Panagiotopoulou ³, Hongan Chen ¹, Thanitsara Rittiphairoj ¹, Darren G Gregory ¹, Scott G Hauswirth ¹, Cristos Ifantides ¹, Riaz Qureshi ^1,², Su-Hsun Liu ^1,², Ian J Saldanha ^4,⁵, Tianjing Li ^1,^2,^✉

¹Department of Ophthalmology, University of Colorado Anschutz Medical Campus, Aurora

²Department of Epidemiology, Colorado School of Public Health, Aurora

³Université Paris Cité, Research Center of Epidemiology and Statistics (CRESS-U1153), Inserm, Paris, France

⁴Center for Evidence Synthesis in Health, Department of Health Services, Policy, and Practice, Brown University School of Public Health, Providence, Rhode Island

⁵Department of Epidemiology, Brown University School of Public Health, Providence, Rhode Island

Accepted for Publication: September 1, 2022.

Published Online: October 27, 2022. doi:10.1001/jamaophthalmol.2022.4394

^✉

Corresponding Author: Tianjing Li, MD, MHS, PhD, Department of Ophthalmology, School of Medicine, University of Colorado Anschutz Medical Campus, 1675 Aurora Ct, F731, Aurora, CO 80045 (tianjing.li@cuanschutz.edu).

Author Contributions: Drs McCann and Li had full access to all of the data in the study and take responsibility for the integrity of the data and the accuracy of the data analysis.

Concept and design: McCann, Abraham, Mukhopadhyay, Chen, Gregory, Hauswirth, Ifantides, Liu, Saldanha, Li.

Acquisition, analysis, or interpretation of data: McCann, Abraham, Mukhopadhyay, Panagiotopoulou, Chen, Rittiphairoj, Qureshi, Liu, Saldanha, Li.

Drafting of the manuscript: McCann, Abraham, Li.

Critical revision of the manuscript for important intellectual content: All authors.

Statistical analysis: McCann, Abraham, Mukhopadhyay, Panagiotopoulou, Liu, Li.

Obtained funding: Li.

Administrative, technical, or material support: Abraham, Hauswirth, Saldanha.

Supervision: Gregory, Hauswirth, Ifantides, Saldanha, Li.

Conflict of Interest Disclosures: Dr Abraham reported personal fees from Implementation Group, Hirslanden Klinik, and Elsevier outside the submitted work. Dr Hauswirth reported consultant and personal fees from Allergan, Kala Pharmaceuticals, Ocular Therapeutix, Sun Pharma, Dompe, Horizon Pharma, Oyster Point, Sight Sciences, Science Based Health, and Thea Pharma; grants from Dompe; and nonfinancial support from TearRestore outside the submitted work. Dr Ifantides reported personal fees from Alcon, Johnson & Johnson, Tarsus, and Allergan outside the submitted work and having a patent pending for 3-dimensional–printed eye glasses for dry eye disease. No other disclosures were reported.

Funding/Support: This work was supported by a grant from the National Institutes of Health National Eye Institute (UG1EY020522).

Role of the Funder/Sponsor: The funder had no role in the design and conduct of the study; collection, management, analysis, and interpretation of the data; preparation, review, or approval of the manuscript; and decision to submit the manuscript for publication.

Additional Contributions: We would like to acknowledge the contribution of Kristen Desanto, MSLS, MS, RD, AHIP, information specialist at University of Colorado, who assisted with developing the search strategy for electronic databases, a library service for which a fee was paid.

^✉

Corresponding author.

PMCID: PMC9614673 PMID: 36301551

This systematic review and meta-analysis examines data from studies published since 2010 to update estimates for the prevalence and incidence of dry eye and meibomian gland dysfunction in the US population.

Key Points

Question

What is the prevalence and incidence of dry eye and meibomian gland dysfunction (MGD) in the US population from studies published after January 1, 2010?

Findings

In this systematic review and meta-analysis, the pooled prevalence of dry eye was 8.1% (95% prediction interval [PI] 0%-98.9%) in 3 studies without high risk of bias and judged to be representative of the US population. Pooled prevalence of MGD was 21.2% (95% PI, 0%-100%) in 3 studies, and incidence of dry eye ranged from 3.5% to 7.8%.

Meaning

Current epidemiological studies provide uncertain estimates of the prevalence and incidence of dry eye and MGD in the United States.

Abstract

Importance

Dry eye is a common clinical manifestation, a leading cause of eye clinic visits, and a significant societal and personal economic burden in the United States. Meibomian gland dysfunction (MGD) is a major cause of evaporative dry eye.

Objective

To conduct a systematic review and meta-analysis to obtain updated estimates of the prevalence and incidence of dry eye and MGD in the United States.

Data Sources

Ovid MEDLINE and Embase.

Study Selection

A search conducted on August 16, 2021, identified studies published between January 1, 2010, and August 16, 2021, with no restrictions regarding participant age or language of publication. Case reports, case series, case-control studies, and interventional studies were excluded.

Data Extraction and Synthesis

The conduct of review followed a protocol registered on PROSPERO (CRD42021256934). PRISMA guidelines were followed for reporting. Joanna Briggs Institute and Newcastle Ottawa Scale tools were used to assess risk of bias. Data extraction was conducted by 1 reviewer and verified by another for accuracy. Prevalence of dry eye and MGD were combined in separate meta-analyses using random-effects models.

Main Outcomes and Measures

Prevalence and incidence of dry eye and MGD in the United States. Summary estimates from meta-analysis of dry eye and MGD prevalence with 95% CI and 95% prediction intervals (95% PI).

Results

Thirteen studies were included in the systematic review. Dry eye prevalence was reported by 10 studies, dry eye incidence by 2 studies, and MGD prevalence by 3 studies. Meta-analysis estimated a dry eye prevalence of 8.1% (95% CI, 4.9%-13.1%; 95% PI, 0%-98.9%; 3 studies; 9 808 758 participants) and MGD prevalence of 21.2% (95% CI, 7.2%-48.3%; 95% PI, 0%-100%; 3 studies; 19 648 participants). Dry eye incidence was 3.5% in a population 18 years and older and 7.8% in a population aged 68 years and older. No studies reported MGD incidence.

Conclusions and Relevance

This systematic review and meta-analysis demonstrated uncertainty about the prevalence and incidence of dry eye and MGD in the United States. Population-based epidemiological studies that use consistent and validated definitions of dry eye and MGD are needed for higher-certainty estimates of dry eye and MGD prevalence and incidence in the United States.

Introduction

The Tear Film and Ocular Surface Society (TFOS) Dry Eye Workshop II (DEWS-II) defines dry eye disease (DED) as “a multifactorial disease of the ocular surface characterized by a loss of homeostasis of the tear film, and accompanied by ocular symptoms, in which tear film instability and hyperosmolarity, ocular surface inflammation and damage, and neurosensory abnormalities play etiological roles.”¹ Because there is no gold standard diagnostic test for DED,¹ we use the term dry eye to describe various presentations of ocular discomfort and visual disturbance caused by tear film abnormalities.

Dry eye can be categorized as aqueous deficient, evaporative, or mixed mechanisms. Aqueous deficiency arises from reduced lacrimal gland secretion, and evaporative disease arises from abnormalities in the tear film lipid layer.^2,3 Meibomian gland dysfunction (MGD) is a major cause of disruption of the tear film lipid layer and consequently of DED.⁴

Dry eye is a common clinical manifestation, a leading driver of eye clinic visits, and a considerable burden to patients and society. In 2011, the estimated direct economic cost to the US health care system for dry eye therapy was $3.8 billion per year and the estimated total societal cost was $55.4 billion per year.⁵ More recently, total US per capita expenditure for all ophthalmic medication classes has been shown to be highest for dry eye medications.⁶

Global burden of disease estimates for blindness and vision impairment do not include dry eye prevalence.⁷ The TFOS DEWS-II report estimated dry eye to be prevalent in 5% to 50% of populations worldwide, depending on disease definition and other contextual factors.⁸ The US prevalence of symptomatic dry eye was reported by the TFOS DEWS-II report to range from 6.8% in the Physicians’ Health Study to 21.6% in the Beaver Dam Eye Study.^8,9,10 The prevalence of MGD has been reported to vary widely, from 3.5% to 69.3% depending on study design and setting, population characteristics, and definition of MGD.¹¹ The objective of the current systematic review and meta-analysis is to provide updated estimates of the prevalence and incidence of dry eye and MGD in the United States.

Methods

We registered the review prospectively on PROSPERO (CRD42021256934) and reported it in accordance with the Meta-analysis of Observational Studies in Epidemiology (MOOSE)¹² and the Preferred Reporting Items for Systematic Reviews and Meta-analyses (PRISMA) guidelines.¹³ We followed the methods established in the published protocol.¹⁴

Eligibility Criteria

We included population-based, clinic-based, and secondary health care database studies that reported prevalence or incidence of dry eye or MGD in the United States. We included studies using both population-based and clinic-based samples. As there remains variability in the diagnostic criteria for dry eye, we did not place restrictions on the diagnostic criteria used to define dry eye in the included studies. We followed guidance from the TFOS DEWS-II report to categorize case definitions, including: (1) Women’s Health Study criteria (ie, self-reported physician diagnosis and/or self-reported constant or often symptoms),¹⁵ (2) dry eye symptoms when signs were not measured (eg, measured by the 5-item Dry Eye Questionnaire), (3) dry eye clinical signs when symptoms were not measured (eg, tear breakup time), (4) combination of dry eye signs and symptoms (distinct from Women’s Health Study criteria), and (5) MGD (eg, meiboscore).⁸ We also accepted dry eye and MGD definitions based on relevant Current Procedural Terminology and International Classification of Disease (ICD) codes.

We excluded case reports, case series, case-control studies, and interventional studies. We excluded studies with fewer than 73 total participants so their 95% CIs around estimates would not be wider than our anticipated minimum population prevalence.¹⁶

Search Strategies

In collaboration with a University of Colorado School of Medicine librarian, we searched Ovid MEDLINE and Embase on August 16, 2021, limiting the search to studies published on or after January 1, 2010, to provide current estimates of dry eye and MGD frequency. We included relevant controlled vocabulary terms (ie, medical subject headings in MEDLINE, Emtree terms in Embase) and text words (eTable 1 in the Supplement). We hand-searched the reference lists of included studies.

We searched the Cochrane Eyes and Vision US Satellite (CEV@US) database of systematic reviews on April 28, 2022, for systematic reviews tagged with condition: dry eye and review type: epidemiology – prevalence/incidence and reviewed available reference lists of relevant systematic reviews.¹⁷ We also searched the World Health Organization site on April 28, 2022, for the keywords dry eye and meibomian gland dysfunction. We retrieved the World Health Organization World Report on Vision and reviewed its reference lists.¹⁸ Further, we searched the reference list of the American Academy of Ophthalmology Dry Eye Syndrome Preferred Practice Pattern 2018 guidance.¹⁹

Study Selection

Two investigators independently screened each record for eligibility at both the title/abstract and full-text stages in Covidence.²⁰ We contacted authors for further information and clarifications when required. If the authors did not respond within 4 weeks, we used the reported information to determine eligibility. We resolved discrepancies during the title/abstract and full-text screening stages by discussion or adjudication by a third investigator where needed.

Data Extraction and Risk-of-Bias Assessment

One investigator extracted all relevant study characteristics, methods, and results from each included study using the Systematic Review Data Repository Plus.²¹ An independent investigator verified all extracted data. We resolved discrepancies during data extraction by discussion or adjudication by a third investigator where needed. Data only available in graphical format were extracted by 2 investigators, independently, using an online graph digitizer.²² We analyzed the mean of the 2 independent investigators’ extracted values.

Two investigators independently assessed the risk of bias in each included study. For prevalence studies, we used the tool proposed by Hoy et al.²³ For incidence studies, we used items from the Joanna Briggs Institute Checklist for Cohort Studies and the Newcastle Ottawa Scale for Cohort Studies.^24,25 We resolved discrepancies during the risk-of-bias assessment by discussion or adjudication by a third investigator where needed.

Evaluation of Heterogeneity and Data Synthesis

We summarized data from each study in evidence tables. We investigated clinical heterogeneity by assessing diversity in population (eg, age, sex distribution) and disease characteristics (eg, DED, MGD). We assessed methodological heterogeneity by evaluating study designs and risk of bias. We assessed statistical heterogeneity by calculating the amount of heterogeneity (τ²) and the contribution of heterogeneity to the total variability across studies (I²).²⁶

We meta-analyzed prevalence proportions using a generalized linear mixed model²⁷ through the metaprop package in R version 4.1.2.^28,29 We reported 95% CIs and 95% prediction intervals (PIs) for summary estimates. Prediction intervals predict the range in which the next sampled prevalence estimate will lie and are suggested to be more appropriate to evaluate and incorporate uncertainty in proportional meta-analyses of large prevalence studies compared with attempting to minimize the I² statistic via sensitivity analyses.³⁰ Our primary analysis excluded dry eye prevalence estimates derived from single-institution clinic-based studies, studies judged to be unrepresentative of the US population, and studies with high risk of bias.

Deviations From Protocol

We were unable to cross-walk prevalence from studies with diverse definitions because we did not find a methodologically robust study with a satisfactory reference standard to demonstrate the bias associated with each definition.^14,31 We could not perform meta-regression on geo-environmental variables (eg, latitude, humidity) because of the lack of reported data.^14,31

Results

Our search yielded 11 133 unique titles and abstracts. After screening 128 full-text reports, we included 13 studies (14 reports) (eFigure 1 in the Supplement)^{32,33,34,35,36,37,38,39,40,41,42,43,44,45} and provided reasons for excluding 14 studies that might have been expected to be included (eTable 2 in the Supplement).

Characteristics of Included Studies

The included studies covered 3 topics: dry eye prevalence (n = 10), dry eye incidence (n = 2), and MGD prevalence (n = 3) (Table 1).^{32,33,34,35,36,37,38,39,40,41,42,43,44,45} Data sources varied across the 13 studies (Table 1): health care system, multi-institutional, or nationwide studies using secondary data (n = 3)^32,37,40; population-based epidemiological studies (n = 3)^33,42,45; and single-institution clinic or hospital-based studies (n = 7).^{34,35,36,38,41,43,44} Definitions of dry eye varied across prevalence (Table 2) and incidence studies (Table 3): relevant ICD codes and/or data extracted from medical records (n = 6),^{32,38,39,40,41,43} symptom questionnaires without signs and/or self-reported diagnosis (n = 4),^33,35,42,44 and signs alone (n = 1).⁴⁵ Definitions of MGD also varied across studies (Table 2): relevant ICD codes and/or data extracted from medical records (n = 1)³⁴ and signs alone (n = 2).^35,36

Table 1. Study Characteristics.

Design					Characteristics of study population
Source	Etiology (DED and/or MGD)	Source population (Census Bureau division)	Data source (national, regional, local)	Sampling scheme (response rate)	Female, %	Age, y	Race and ethnicity, %
Prevalence
Dana et al,³² 2019	DED	DOD military and civilian facilities (nationwide)^a	Health care system, multi-institutional, or nationwide study using secondary data (national)	Full census or registry (NA)	48.1	2-17 y: 20.8%; 18-39 y: 34.6%; 40-49 y: 11.2%; ≥50 y: 33.4%	NR
Paulsen et al,³³ 2014	DED	Beaver Dam, Wisconsin, (East North Central)	Population-based epidemiological study (regional)	Unspecified (99.7%)^b	54.6	Mean (range): 49 (21-84)^c	NR
Davis et al,³⁸ 2015	DED	University of North Carolina outpatient clinics (South Atlantic)	Single-institution clinic- or hospital-based study (local)	Full census or registry (NA)	NR	NR	NR
Lee et al, 2018³⁹ and 2017⁴⁰	DED	Veteran Affairs health care system (nationwide)	Health care system, multi-institutional, or nationwide study using secondary data (national)	Full census or registry (NA)	4.9	Mean (SD): 65.0 (15.0)	White: 75.3; Black: 19.7
Yalamanchili et al,³⁴ 2018	MGD	University of North Carolina ophthalmology system (South Atlantic)	Single-institution clinic- or hospital-based study (local)	Full census or registry (NA)	NR	NR	NR
Russo and Bass,⁴¹ 2021	DED	Boston Health Care for the Homeless Program, Boston, Massachusetts (New England)	Single-institution clinic- or hospital-based study (local)	Consecutive enrollment (NR)	25.9	Mean (range) [IQR]: 52.7 (18-84) [40-60]	White: 40.8; Black: 33.3; Asian: 1.9; Hispanic and/or Latino: 13.9; American Indian or Alaska Native: 0.5; other: 3.3
Farrand et al,⁴² 2017	DED	US census regions: Northeast, Midwest, South, West (nationwide)	Population-based epidemiological study (national)	Random (from panel of self-selected online volunteers) (NR)	51.6	18-49 y: 52.8%; ≥50 y: 47.2%	White: 72.4; Black: 11.5; Asian: 5.0; Hispanic: 8.2; other 2.9
Stull et al,⁴³ 2017	DED	Temple University ophthalmology and optometry outpatient clinics, Philadelphia, Pennsylvania (Middle Atlantic)	Single-institution clinic- or hospital-based study (local)	Unspecified (86.9%)	63.0	Mean (SD) [range]: 58.0 (16.1) [18-93]	White: 22.8; African American: 60.5; Asian: 3.8; Hispanic/Latino: 11.0; other: 2.0
Chang et al,⁴⁴ 2018	DED	Bascom Palmer Eye Institute, Miami, Florida (South Atlantic)	Single-institution clinic- or hospital-based study (local)	Consecutive enrollment (95.5%)	67.8	Mean (SD) [range]: 46.3 (13) [19-77]	White: 75.5; Black: 18.0; Hispanic: 51.5
Yeh and Lin,³⁵ 2017	DED, MGD	University of California, Berkeley, Clinical Research Center (Pacific)	Single-institution clinic- or hospital-based study (local)	Unspecified (NR)	70.3	Mean (SD) [range]: 22.3 (4) [18-41]	Asian: 54.5; Non-Asian (White/Hispanic): 28.7; other: 16.8
Manoj,⁴⁵ 2016	DED	NR	Population-based epidemiological study	Unspecified (NR)	NR	NR	NR
Alghamdi et al,³⁶ 2016	MGD	Miami Veteran Affairs Medical Center, Miami, Florida (South Atlantic)	Single-institution clinic- or hospital-based study (local)	Unspecified (NR)	9.0	Mean (SD) [range]: 63 (11) [27-89]	NR
Incidence
Chi et al,³⁷ 2012	DED	NR	Health care system, multi-institutional, or nationwide study using secondary data (national)	5% Sample of Medicare beneficiaries’ Part B claims to identify beneficiaries with DED diagnoses (NR)	NR	≥68	NR
Dana et al,³² 2019	DED	DOD military and civilian facilities (nationwide)^a	Health care system, multi-institutional, or nationwide study using secondary data (national)	Full census or registry (NA)	NR	≥18	NR

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Abbreviations: DED, dry eye disease; DOD, US Department of Defense; MGD, meibomian gland dysfunction; NA, not applicable; NR, not reported.

^{^a}

US Department of Defense Military Health System data.

^{^b}

Adult offspring of the participants in the Epidemiology of Hearing Loss study were invited to participate.

^{^c}

Participants by age group: 21-34 years: 179 (5.5%); 35-44 years: 931 (28.4%); 45-54 years: 1227 (37.5%); 55-64 years: 710 (21.7%); 65-84 years: 228 (7.0%).

Table 2. Prevalence of Dry Eye and Meibomian Gland Dysfunction Results.

Source	Etiology (DED and/or MGD)	Measurement method: components contributing to diagnostic standards	Diagnostic standards	Prevalence denominator, No.	Prevalence numerator, No.	Prevalence, % (95% CI)	Point or period of data collection	Characteristics used to stratify prevalence or report an association	Overall risk of bias
Dana et al,³² 2019	DED	Relevant ICD codes^a; CPT codes for procedures and prescriptions^b; prescription fills^c	(1) Overall DED	(1) 9 732 272	(1) 513 988	(1) 5.3 (5.3-5.3)^g	Period: 2003-2015 (8 y)	Age, sex, year of prevalence estimate (annual prevalence)^h; NRⁱ; NR^j	M
			(2) Unspecified DED^d	(2) 9 732 272	(2) 207 727	(2) 2.1 (2.1-2.1)^g
			(3) Specified DED^e	(3) 9 732 272	(3) 24 809	(3) 0.3 (0.3-0.3)^g
			(4) Sjögren-type DED^f	(4) 9 732 272	(4) 17 403	(4) 0.2 (0.2-0.2)^g
Paulsen et al,³³ 2014	DED	Symptoms^k	Symptoms (questionnaire)^k	3275	475	14.5 (13.3-15.8)^g	Period: 2005-2008 (3 y)	NR^h; NRⁱ; age, sex, medical comorbidities^l, medication use^j^,^m	L
Davis et al,³⁸ 2015	DED	Relevant ICD codes	ICD-9 codeⁿ	460 611	7207	1.6 (1.5-1.6)^g	Period: 2008-2013 (5 y)	NR^h; medical comorbiditiesⁱ^,^o; NR^j	H
Lee et al, 2018³⁹ and 2017⁴⁰	DED	Relevant ICD codes	(1) ICD-9 code^p	(1) 3 265 894	(1) 854 480	(1) 26.2 (26.1-26.2)^g	Period: 2010-2014 (4 y)	NR^h; age, sex, race and ethnicity, medical comorbidities, medication useⁱ; NR^j	M
			(2) ICD-9 code^q	(2) 3 265 894	(2) 843 194	(2) 25.8 (25.8-25.9)^g
			(3) ICD-9 codes^r	(3) 3 265 894	(3) 9315	(3) 0.3 (0.3-0.3)^g
			(4) ICD-9 codes^s	(4) 3 265 894	(4) 28 407	(4) 0.9 (0.9-0.9)^g
			(5) ICD-9 codes^t	(5) 3 265 894	(5) 959 881	(5) 29.4 (29.3-29.4)
			(6) ICD-9 codes^u	(6) 3 265 894	(6) 186 299	(6) 5.7 (5.7-5.7)^g
Russo and Bass,⁴¹ 2021	DED	Relevant ICD codes; other	ICD-10 codes and other (diagnoses contained in the attending optometrist’s assessment and plan)	424	121	28.5 (24.3-33.1)^g	Period: 2016-2017 (1-2 y)	NR^h; NRⁱ; NR^j	H
Farrand et al,⁴² 2017	DED	Self-reported diagnosis^v; symptoms^v	(1) Self-reported diagnosis with symptoms	(1) 73 211	(1) 5051	(1) 6.9 (6.7-7.1)^g	Point: 2013	Age, sex, race and ethnicity, US region, dry eye disease severity^h; NRⁱ; age, sex, race/ethnicity, US region^j	M
			(2) Self-reported diagnosis with symptoms^w	(2) 73 211	(2) NR	(2) 6.8 (6.6-7.0)
			(3) Symptoms without self-reported diagnosis	(3) 75 000	(3) 1789	(3) 2.4 (2.3-2.5)^g
			(4) Symptoms without self-reported diagnosis^w	(4) 75 000	(4) NR	(4) 2.5 (2.3-2.6)
Stull et al,⁴³ 2017	DED	Other	Other (extracted from patient medical records)	400	187	46.8 (41.8-51.8)^g	Point: 2014/2015	NR^h; NRⁱ; NR^j	H
Chang et al,⁴⁴ 2018	DED	Symptoms^x	(1) DEQ-5 ≥6	(1) 233	(1) 94	(1) 40.3 (34.0-47.0)^g	Point: 2016	Dry eye disease severity^h; age, sex, race and ethnicity, medical comorbidities, medication useⁱ; NR^j	M
			(2) DEQ-5 ≥12	(2) 233	(2) 24	(2) 10.3 (6.7-14.9)^g
			(3) Chief concern dry eye symptoms	(3) 233	(3) 60	(3) 25.8 (20.3-31.9)^g
Manoj,⁴⁵ 2016	DED	Signs	(1) Tear hyperosmolarity	(1) 9947	(1) 4605^g	(1) 46.3 (45.3-47.3)^g	NR	Sex, dry eye disease severity^h; medical comorbiditiesⁱ; NR^j	H
Manoj,⁴⁵ 2016	DED	Signs	(2) Tear osmolarity ≥316 mOsm/L	(2) 9947	(2) 2885^g	(2) 29.0 (28.1-29.9)^g	NR		H
Yeh and Lin,³⁵ 2017^y	DED; MGD	Symptoms^z; signs^a ^a	(1) SPEED ≥9	(1) 101	(1) 19	(1) 18.8 (11.7-27.8)^g	Point: NR	Dry eye disease severity^h; age, sex, race and ethnicity, medication useⁱ; NR^j	H
			(2) SPEED ≥6	(2) 101	(2) 43	(2) 42.6 (32.8-52.8)^g
			(3) Meiboscore = 3	(3) 101	(3) 12	(3) 11.9 (6.2-19.8)^g
Alghamdi et al,³⁶ 2016	MGD	Signs^b ^b^,^c ^c	(1) Eyelid vascularity score ≥2	(1) 233	(1) 39	(1) 16.7 (12.2-22.2)^g	Point: NR	Age, sex, race and ethnicity, medical comorbidities, medication use^h; NRⁱ; NR^j	H
Alghamdi et al,³⁶ 2016	MGD	Signs^b ^b^,^c ^c	(2) Meibum quality score ≥2	(2) 233	(2) 129	(2) 55.4 (48.7-61.9)^g	Point: NR		H
Yalamanchili et al,³⁴ 2018	MGD	Relevant ICD codes	(1) ICD and EMR codes for MGD and MGD spectrum disorders	(1) 19 314	(1) 2002	(1) 10.4 (9.9-10.8)^g	Period: 2014-2017 (3 y)	Age, race and ethnicity^h; NRⁱ; NR^j	H

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Abbreviations: CPT, Current Procedural Terminology; DED, dry eye disease; DEQ-5, 5-item dry eye questionnaire; EMR, electronic medical record; H, high; ICD-9, International Classification of Diseases, Ninth Revision; ICD-10, International Statistical Classification of Diseases and Related Health Problems, Tenth Revision; L, low; M, moderate; MGD, meibomian gland dysfunction; NR, not reported; SPEED, Standardized Patient Evaluation of Eye Dryness.

^{^a}

Driving indicators: 370.33, keratoconjunctivitis sicca; 370.34, exposure keratoconjunctivitis; 372.53, conjunctival xerosis; 375.15, tear film insufficiency, unspecified; 710.20, sicca syndrome, Sjögren; nondriving indicators: 370.20, superficial keratoconjunctivitis; 370.21, punctate keratitis; 714.0, rheumatoid arthritis; 695.4, discoid lupus erythematosus; 710.0, systemic lupus erythematosus; 373.34, discoid lupus erythematosus of eyelid.

^{^b}

Driving indicators: 68 760, closure of the lacrimal punctum (by thermocauterization, ligation, or laser surgery); 68 761, punctal plugs; 09.91, obliteration of lacrimal punctum.

^{^c}

Driving indicator: restasis prescription fill for cyclosporine ophthalmic emulsion.

^{^d}

Using ICD-9 codes 375.15 and 370.33, at least 1 of each or 2 of the same.

^{^e}

Using ICD-9 codes nondriving indicators 370.21 and 370.20, at least 1 of 370.21 or 370.20 and 1 driving indicator (375.15 or 370.33).

^{^f}

Using ICD-9 code driving indicator 710.2, at least 2 of 710.2 OR 1 of 710.2 and 1 driving indicator (375.15 or 370.33).

^{^g}

Derived by binomial “exact” calculation.

^{^h}

Stratified by characteristic.

^ⁱ

Characteristic included in a univariate analysis.

^{^j}

Characteristic included in a multivariate analysis.

^{^k}

Participants were asked “How often do you have dry eyes, a dry, gritty, or burning feeling?”; “How much does the dryness in your eyes bother you?”; “Is there a season of the year when the dryness in your eyes is the worst?”; and “Are you currently using eye drops at least once a day for dry eyes?” Participants who reported that symptoms were present sometimes or more often and that they were moderately bothersome or greater, or those who reported currently using eye drops at least once a day for dry eyes, were considered to be cases.

^{^l}

Arthritis, osteoporosis, allergies, thyroid disease, migraine headache, history of head injury (no loss of consciousness, loss of consciousness <5 minutes, loss of consciousness ≥5 minutes).

^{^m}

Antihistamines, acetaminophen, benzodiazepine, antidepressants, steroids, multivitamin, hormones (women).

^ⁿ

ICD-9 code 375.15.

^{^o}

Diabetes (ICD-9 codes 250.00-250.93).

^{^p}

ICD-9 code indicative of tear film dysfunction (sicca syndrome [710.2] or keratoconjunctivitis sicca [370.33] or tear film insufficiency [375.15]).

^{^q}

ICD-9 code 375.15.

^{^r}

ICD-9 code 710.2.

^{^s}

ICD-9 code 370.33.

^{^t}

“Any dry eye” (tear film insufficiency [375.15] or visual discomfort [368.13] or pain in or around eye [379.91] or sicca syndrome [710.2] or keratoconjunctivitis sicca [370.33]).

^{^u}

ICD-9 code indicative of ocular pain (visual discomfort [368.13] or pain in or around eye [379.91]).

^{^v}

Participants were asked whether they had ever experienced dry eye. Possible responses were yes or no. Those who answered no to ever experiencing dry eye were classified as non-DED and were not asked any other DED-related questions. All participants who said yes were asked a series of dry eye–related questions. The first was whether their dry eye had ever been diagnosed by a physician. Possible responses were yes or no. Those who answered yes to experiencing dry eye but no to being diagnosed by a physician were classified as symptomatic–undiagnosed. Those who confirmed both experiencing dry eye and physician diagnosis were classified as diagnosed-DED and were given a list of symptoms (pain, light sensitivity, gritty sensation, feeling of a foreign body or sand in the eye, itching, redness, and blurring of vision) and asked to select all that applied.

^{^w}

Adjusted using the Horvitz-Thompson estimator and inverse probability weighting.

^{^x}

Using the DEQ-5.

^{^y}

Individual patient data are available.

^{^z}

Using the SPEED.

^{^aa}

Using the Meiboscore.

^{^bb}

Using the eyelid vascularity score (0-3).

^{^cc}

Using the meibum quality score (0-4).

Table 3. Incidence of Dry Eye Results.

Source	Etiology (DED and/or MGD)	Measurement method: components contributing to diagnostic standards	Diagnostic standards	Incidence denominator	Incidence numerator	Incidence, % (95% CI)	Cumulative incidence or incidence rate (period of data collection)	Cumulative incidence normalized to 5-y incidence	Characteristics used to stratify incidence or report an association	Annual incidence, %	Overall risk of bias
Chi et al,³⁷ 2012	DED	Relevant ICD codes^a	ICD-9 codes	NR	NR	23.3 (NR)	Cumulative incidence (1994-2008, 15 y)	7.8%	Year of incidence estimate (annual incidence)^b; NR^c; NR^d	1994: 1.17; 1995: 1.21; 1996: 1.24; 1997: 1.33; 1998: 1.35; 1999: 1.35; 2000: 1.40; 2001: 1.49; 2002: 1.58; 2003: 1.59; 2004: 1.62; 2005: 1.63; 2006: 2.11; 2007: 1.81; 2008: 1.83^e	H
Dana et al,³² 2019	DED	Relevant ICD codes^f; CPT codes for procedures and prescriptions^g; prescription fills^h	(1) Overall DED	(1) NRⁱ	(1) 58 126^j	(1) NR	Cumulative incidence (2008-2012, 5 y)	3.5% (All ages); 0.96% (18-39 y); 2.60% (40-49 y); 6.48% (≥50 y)	Age, sex, year of incidence estimate (annual incidence)^b; NR^c; NR^d	All ages: 2008: 0.55; 2009: 0.61; 2010: 0.68; 2011: 0.74; 2012: 0.87	M
			(2) Unspecified DED^k	(2) NRⁱ	(2) 32 341^j	(2) NR				18-39 y: 2008: 0.15; 2009: 0.17; 2010: 0.18; 2011: 0.20; 2012: 0.26
			(3) Specified DED^l	(3) NRⁱ	(3) 2705^j	(3) NR				40-49 y: 2008: 0.42; 2009: 0.46; 2010: 0.51; 2011: 0.56; 2012: 0.65
			(4) Sjögren-type DED^m	(4) NRⁱ	(4) 1716^j	(4) NR				≥50 y: 2008: 1.03; 2009: 1.15; 2010: 1.28; 2011: 1.40; 2012: 1.62

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Abbreviations: CPT, Current Procedural Terminology; DED, dry eye disease; H, high; ICD-9, International Classification of Diseases, Ninth Revision; M, moderate; NR, not reported.

^{^a}

ICD-9 codes 375.15 or 370.20 or 370.21 or 370.33 or 370.34 or 372.53 or 710.2.

^{^b}

Stratified by characteristic.

^{^c}

Characteristic included in univariate analysis.

^{^d}

Characteristic included in multivariate analysis.

^{^e}

Extracted using an online graph digitizer.

^{^f}

^{^g}

Driving indicators: 68 760, closure of the lacrimal punctum (by thermocauterization, ligation, or laser surgery); 68 761, punctal plugs; 09.91, obliteration of lacrimal punctum.

^{^h}

Driving indicator: restasis prescription fill for cyclosporine ophthalmic emulsion.

^ⁱ

Reported as approximately 7.4 million.

^{^j}

2012 Annual incidence numerator.

^{^k}

Using ICD-9 codes 375.15 and 370.33, at least 1 of each or 2 of the same.

^{^l}

Using ICD-9 codes nondriving indicators 370.21 and 370.20, at least 1 of 370.21 or 370.20 and 1 driving indicator (375.15 or 370.33).

^{^m}

Using ICD-9 codes driving indicator 710.2, at least 2 of 710.2 or 1 of 710.2 and 1 driving indicator (375.15 or 370.33).

Risk-of-Bias Assessment

Summaries of risk-of-bias assessments for the dry eye and MGD prevalence studies are presented in eTable 3 in the Supplement. Of the 10 studies that reported prevalence of DED, we judged overall risk of bias to be high in 5 studies (50%),^{35,38,41,43,45} moderate in 4 studies (40%),^32,40,42,44 and low in 1 study (10%).³³ We judged all 3 studies that reported prevalence of MGD to be at overall high risk of bias.^34,35,36 Of the 2 studies that reported incidence of dry eye, we judged overall risk of bias to be high in Chi et al³⁷ and moderate in Dana et al.³²

Prevalence of Dry Eye

In our primary analysis, prevalence of dry eye ranged from 5.3% to 14.5%, with a summary estimate of 8.1% (95% CI, 4.9%-13.1%; τ² = 0.22; I² = 100%; 95% PI, 0%-98.9%; 3 studies, 9 808 758 participants) (Figure 1A). When we added the prevalence (26.2%) from Lee et al,⁴⁰ the summary estimate increased to 11.1% (95% CI, 5.8%-20.2%; τ² = 0.53; I² = 100%; 95% PI, 0.4%-80.3%; 4 studies, 13 074 652 participants) (Figure 1B). When we added the prevalence (40.6%) from Chang et al,⁴⁴ the summary estimate increased to 14.9% (95% CI, 7.2%-28.2%; τ² = 0.86; I² = 100%; 95% PI, 0.7%-81.6%; 5 studies, 13 074 885 participants) (Figure 1C). When all studies were included, prevalence ranged from 1.6% to 46.8%, with a summary estimate of 17.4% (95% CI, 8.9%-31.4%; τ² = 1.57; I² = 100%; 95% PI, 1.0%-81.4%; 10 studies, 13 546 368 participants) (eFigure 2 in the Supplement).

Figure 1. — A, Prevalence reported in studies without high risk of bias and judged to be representative of the general population. B, Prevalence reported in studies without high risk of bias and judged to be representative of the general population plus Lee et al⁴⁰ (a nationwide Veterans Affairs health system database study with a 95.1% male population). C, Prevalence reported in studies without high risk of bias and judged to be representative of the general population plus Lee et al⁴⁰ (a nationwide Veterans Affairs health system database study with a 95.1% male population) and Chang et al⁴⁴ (a single-institution clinic-based study that used consecutive enrollment from a comprehensive eye clinic in Miami, Florida).

Associations With Prevalence of Dry Eye

Summaries of the association with age, sex, race and ethnicity, medical comorbidities, medication use, and region of the United States are outlined in eTables 4-6 in the Supplement. Prevalence of dry eye appears to increase with age,^32,33,42 female sex,^{32,33,39,40,42,44,45} diabetes and diabetic complications,^38,39,40,44 and southern US regions.⁴² There is inconsistent evidence regarding the association between dry eye prevalence and race and ethnicity or medication use.^39,40,42,44

Incidence of Dry Eye

Only 2 studies contributed to dry eye incidence estimates, and after normalization, the 5-year incidence was 3.5% in the population 18 years and older and 7.8% in the population 68 years and older (Table 3).^32,37 The age-stratified and annual incidence, reported by Chi et al³⁷ and Dana et al,³² are presented in Table 3. Dana et al³² reported the incidence in the population 50 years and older as 6.5%, which is comparable with 7.8% in the population 68 years and older reported by Chi et al.³⁷ Because of the lack of well-defined incidence denominators and the heterogeneity between the 2 studies, we did not meta-analyze the incidence data.

Prevalence of MGD

Prevalence of MGD ranged from 10.4% to 55.4% in the 3 included studies.^34,35,36 The pooled estimate of MGD prevalence was 21.2% (95% CI, 7.2%-48.3%; τ² = 1.18; I² = 99%; 95% PI, 0%-100%; 3 studies, 19 648 participants) (Figure 2). There is inconsistent evidence whether prevalence of MGD increases with age. Yalamanchili et al³⁴ reported that prevalence was 7.7% in the group aged 50 to 59 years and 13.7% in the group aged 80 to 89 years (eTable 4 in the Supplement)³⁴; however, Yeh et al³⁵ reported a nonsignificant association with age in a univariate regression model (eTable 5 in the Supplement). We found no study that reported incidence of MGD.

Discussion

In this systematic review and meta-analysis, we searched for studies that estimated the prevalence or incidence of DED or MGD in the US population after 2010. Our meta-analysis showed that the pooled prevalence of dry eye was 8.1% and the pooled prevalence of MGD was 21.2%. Incidence of dry eye was 3.5% (population aged ≥18 years) and 7.8% (population aged ≥68 years).^32,37

The wide range of prevalence estimates for dry eye reflect the notable clinical and methodological heterogeneity across studies. Studies included in the meta-analysis had diverse population characteristics, variations in study designs and settings, and heterogenous definitions of dry eye, which increase our uncertainty in the summary prevalence estimate. Dana et al³² analyzed a nationwide database of US Department of Defense staff and their families. They defined dry eye with relevant diagnostic codes and reported prevalence of dry eye as 5.3%,³² whereas Paulsen et al³³ and Farrand et al⁴² were both population-based epidemiological studies and defined dry eye with self-reported diagnosis and/or symptoms. Farrand et al⁴² (prevalence estimate of 6.9%) was a nationwide study that randomly sampled from a self-selected panel of online volunteers, and Paulsen et al³³ (prevalence estimate of 14.5%) was a regional study sampled from offspring from the Beaver Dam Study, Wisconsin. Population age differences may, at least in part, explain the heterogeneity in the prevalence estimates between studies.^33,42

The inclusion of unrepresentative studies and studies at high risk of bias in the meta-analysis appears to inflate the pooled prevalence of dry eye in the US population. Lee et al⁴⁰ used a nationwide database and defined dry eye with relevant diagnostic codes; however, the diagnostic codes were not the same as those used by Dana et al.³² Furthermore, although Lee et al⁴⁰ may be representative of the Veterans Affairs health care system population, given the predominantly male population and the unique exposures in the veteran population, the prevalence estimate of 26.2% is unlikely to adequately represent the US population.⁴⁰ Chang et al⁴⁴ used consecutive enrollment of patients from a single-institution comprehensive eye clinic in Miami, Florida, and defined dry eye as a score of 6 or higher on the 5-item Dry Eye Questionnaire. The prevalence of 40.3% is unlikely to adequately represent the US population because of the enriched eye clinic–derived population and the specifics of the environment and climate in Miami.

A set of working diagnostic criteria for DED is necessary to allow standardization and comparisons in dry eye epidemiological studies. Such efforts have been successfully implemented in the fields of glaucoma, age-related macular degeneration, and uveitis.^46,47,48

Understanding of the pathophysiology of dry eye has evolved considerably over the past decade, and this is reflected in the updated definition of the disease provided by the TFOS DEWS-II report.¹ However, although consensus on a battery of diagnostic tests has been published, these have not been implemented in any epidemiological studies in the United States.⁴⁹

Several studies included in the current systematic review reported dry eye prevalence according to several diagnostic criteria.^32,40,42 However, because these multiple criteria were not replicated across studies,^32,40 this only compounded uncertainty regarding the underlying population prevalence.

Similar methodological challenges have been encountered by other systematic reviews of dry eye burden, and they have been handled differently across reviews. A systematic review of the burden of DED in Arab populations in the Middle East, which reported prevalence ranging from 6.8% in Egypt to 69.0% in Palestine, did not attempt to combine results across studies in a meta-analysis.⁵⁰ However, a systematic review of the burden of DED in Africa reported that prevalence ranged from 6.6% to 78.4% and the pooled prevalence was 42.0%. A systematic review of burden of DED in China reported that the pooled prevalence of dry eye by symptoms and signs was 18.5% and by symptoms only was 38.9%.^51,52

In other large population-based studies, the prevalence of dry eye has been reported as 22.0% in Canada using the 5-item Dry Eye Questionnaire⁵³ and 9.1% in Germany using the Women’s Health Study questionnaire,⁵⁴ and the 6-year incidence of dry eye has been reported as 6.1% in Singapore.⁵⁵ However, dry eye prevalence estimates from global populations cannot be easily applied to their respective US-based ethnic groups (eg, estimates from China cannot be applied to Chinese American individuals) because of the reported effect of local and regional geo-environmental influences.⁵⁶

In the US population, dry eye epidemiology studies published between 1997 and 2009 reported prevalence and incidence of dry eye and MGD.^15,57,58,59 Prevalence of dry eye was 4.3% in the Physicians’ Health Study, 7.8% in the Women’s Health Study, 14.4% in the Beaver Dam Study, and 14.6% in the Salisbury Eye Study. Although we excluded these studies (because of our intent to obtain recent estimates), their reported estimates are consistent with our summary estimate of 8.1%. The Beaver Dam Study reported a 5-year cumulative incidence of 13.3% and a 10-year cumulative incidence of 21.6%.^9,10 However, these estimates are higher than reported in the 2 incidence studies included in our review, which may be due to diversity in study designs and contexts.

A systematic review of the global burden of MGD reported that the pooled global prevalence was 35.8%, and before 2010, the prevalence in the United States ranged from 3.5% in a population-based study to 38.9% in a hospital-based study.⁶⁰ In Japan, the Hirado-Takushima Study reported the prevalence of MGD as 32.9%, the prevalence of DED as 33.4%, and the coexistence with of MGD and DED in 12.4%.⁶¹ We were unable to report the prevalence of coexisting MGD and DED.

We report that prevalence of MGD ranged from 10.4% to 55.4%, with a summary prevalence of 21.2%, in studies published since 2010. Our MGD prevalence estimates were derived from hospital-based studies using MGD-specific ICD codes or objective signs of MGD to define cases. Differences in the clinical signs used to diagnose MGD may explain the heterogeneity, but the very wide confidence intervals make the prevalence estimates very uncertain. We did not identify any MGD incidence studies, which is a similar finding to that of the International Workshop on Meibomian Gland Dysfunction in 2011.¹¹ Given the lack of population-based prevalence studies for MGD and the lack of incidence studies for MGD, contemporary longitudinal population-based studies are needed.

Limitations

There are limitations to our meta-analysis that should be acknowledged. As mentioned above, studies included in the meta-analysis had diverse population characteristics, variations in designs and settings, and heterogenous definitions of dry eye. Diagnostic codes and criteria differed across studies. Future epidemiological studies would benefit from the development of a standard set of diagnostic criteria for DED. In addition, a lack of MGD incidence studies and differences in the clinical signs used to diagnose MGD undermined the certainty of the prevalence estimates in this review. However, our pooled estimated of dry eye prevalence was consistent with those reported by previous studies conducted in the time frame prior to that for our review.

Conclusions

In this systematic review and meta-analysis, the prevalence of dry eye ranges from 5.3% to 14.5%, and the prevalence of MGD ranges from 10.4% to 55.4%. The 5-year incidence of dry eye was 3.5% in a population 18 years and older and 7.8% in a population 68 years and older. This study is the first to our knowledge to provide a pooled estimate of dry eye prevalence in the United States. However, the evidence provides uncertain estimates of dry eye burden and highlights the need for further studies that minimize risk of bias and use validated diagnostic criteria.

Supplement.

eFigure 1. PRISMA search flow diagram

eFigure 2. Forest plot of dry eye prevalence including all studies

eTable 1. MEDLINE and Embase search strategies

eTable 2. Excluded full texts

eTable 3. Risk of bias assessments for prevalence and incidence studies

eTable 4. Stratified associations with dry eye and meibomian gland dysfunction

eTable 5. Univariable model associations with dry eye and meibomian gland dysfunction

eTable 6. Multivariable model associations with dry eye and meibomian gland dysfunction

Click here for additional data file.^{(1.2MB, pdf)}

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57.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(6):763-768. doi: 10.1001/archophthalmol.2009.103 [DOI] [PMC free article] [PubMed] [Google Scholar]
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59.Moss SE, Klein R, Klein BEK. Prevalence of and risk factors for dry eye syndrome. Arch Ophthalmol. 2000;118(9):1264-1268. doi: 10.1001/archopht.118.9.1264 [DOI] [PubMed] [Google Scholar]
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Associated Data

This section collects any data citations, data availability statements, or supplementary materials included in this article.

Supplementary Materials

Supplement.

eFigure 1. PRISMA search flow diagram

eFigure 2. Forest plot of dry eye prevalence including all studies

eTable 1. MEDLINE and Embase search strategies

eTable 2. Excluded full texts

eTable 3. Risk of bias assessments for prevalence and incidence studies

eTable 4. Stratified associations with dry eye and meibomian gland dysfunction

eTable 5. Univariable model associations with dry eye and meibomian gland dysfunction

eTable 6. Multivariable model associations with dry eye and meibomian gland dysfunction

Click here for additional data file.^{(1.2MB, pdf)}

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PERMALINK

Prevalence and Incidence of Dry Eye and Meibomian Gland Dysfunction in the United States

Paul McCann, MBChB, MRes, PhD

Alison G Abraham, MHS, MS, PhD

Adhuna Mukhopadhyay, MBBS, MPH

Kanella Panagiotopoulou, MSc

Hongan Chen, MD

Thanitsara Rittiphairoj, MD, MPH

Darren G Gregory, MD

Scott G Hauswirth, OD

Cristos Ifantides, MD, MBA

Riaz Qureshi, MSc, PhD

Su-Hsun Liu, MD, MPH, PhD

Ian J Saldanha, MBBS, MPH, PhD

Tianjing Li, MD, MHS, PhD

Key Points

Question

Findings

Meaning

Abstract

Importance

Objective

Data Sources

Study Selection

Data Extraction and Synthesis

Main Outcomes and Measures

Results

Conclusions and Relevance

Introduction

Methods

Eligibility Criteria

Search Strategies

Study Selection

Data Extraction and Risk-of-Bias Assessment

Evaluation of Heterogeneity and Data Synthesis

Deviations From Protocol

Results

Characteristics of Included Studies

Table 1. Study Characteristics.

Table 2. Prevalence of Dry Eye and Meibomian Gland Dysfunction Results.

Table 3. Incidence of Dry Eye Results.

Risk-of-Bias Assessment

Prevalence of Dry Eye

Figure 1. Meta-analysis of the Prevalence of Dry Eye in the US Population.

Associations With Prevalence of Dry Eye

Incidence of Dry Eye

Prevalence of MGD

Figure 2. Meta-analysis of the Prevalence of Meibomian Gland Dysfunction in the US Population.

Discussion

Limitations

Conclusions

References

Associated Data

Supplementary Materials

ACTIONS

PERMALINK

RESOURCES

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