Risk of dry eye in headache patients: a systematic review and meta-analysis

Figures & data

Figure 1. Literature screening flowchart.

Table 1. Characteristics of studies included in the review.

Author	Year	Country	Study type	Sample Size	Study period	Age (years)	Diagnosis of DED	Diagnosis of headache
Tsung-Jen Wang	2010	China (Taiwan)	Case–control	Total: 48,028; DED: 12,007; No DED: 36,021	2005–2006	52.4 ± 17.5	ICD-9-CM	ICD-9-CM
Adam J. Paulsen	2014	America	Cohort	Total: 3275; DED: 475; No DED: 2800	2005–2008	49 (range 21–84)	Questionnaire	Questionnaire
Jelle Vehof	2014	England	Cross-sectional	Total: 3538; DED: 367; No DED: 3171	2012	57.1 ± 13.1 (20–87)	Questionnaire	Structured postal questionnaire
Asuman Celikbilek	2014	Turkey	Cross-sectional	Total: 99; DED: 42; No DED: 57	2013	18–50	A complete ophthalmologic examination	International Classification of Headache Disorders II diagnostic criteria
Soonwon Yang	2016	Korea	Cross-sectional	Total: 14,329; DED: 1390; No DED: 12,939	2010–2012	Over 19	Full ocular examinations	Questionnaire
Victoria S. Chang	2018	America	Case–control	Total: 233; DED: 94; No DED: 139	2016	46.3 ± 13.0 (19–77)	DED questionnaire 5 (DEQ5)	A numerical rating scale questionnaire
Charity J. Lee	2017	America	Cross-sectional	Total: 3,265,894; DED (tear film dysfunction, ocular pain): 959,881; No DED: 2,306,013	2010–2014	DED: 69.4 ± 12.9, 63.4 ± 15.3, respectively; No DED: 64.5 ± 14.3	ICD-9	ICD-9
Omar M. Ismail	2019	America	Case–control	Total: 72,969; DED: 9638; No DED: 63,331	2008–2018	Over 18	ICD-9 and10	ICD-9 and 10
Karel Kostev	2019	Germany	Case–control	Total: 87,354; DED: 102; No DED: 87,252	2018	Over 18	ICD-10	ICD-10
Michael T.M. Wang	2020	New Zealand	Cross-sectional	Total: 372; DED: 109; No DED: 263	2018–2019	39 ± 22 (21–85)	TFOS DEWS II	Not reported
Jelle Vehof	2020	England	Cross-sectional	Total: 79,866; DED: 7230; No DED: 72,636	2014–2017	50.4 (20–94)	Women’s Health Study dry eye questionnaire	A self-administered questionnaire
Author	Headache type	Confounders adjusted	Adjusted OR	Scores
Tsung-Jen Wang	Headache Migraine	Gender, age, monthly income and level of the community	Headaches: 1.30 (1.24–1.36) Migraine: 1.76 (1.57–1.98)	9
Adam J. Paulsen	Migraine headache	Sex and age	Migraine headache: 1.44 (1.10–1.90)	7
Jelle Vehof	Migraine	Sex and age	Migraine: 1.47 (1.15–1.88)	6
Asuman Celikbilek	Migraine	Sex and age	Migraine: 1.56 (0.69–3.50)	8
Soonwon Yang	Migraine	Age, sex, BMI, current smoking, heavy drinking, regular exercise, metabolic syndrome, dyslipidemia, stress perception, and diagnosis of depression	Migraine: 1.58 (1.34–1.86)	9
Victoria S. Chang	Headache Migraine	Diet, environmental factors	Headaches: 2.14 (1.16–3.95) Migraine: 1.70 (0.84–3.45)	6
Charity J. Lee	Headaches Tension headache Migraine	Not reported	Headaches: 1.60 (1.59–1.61) Tension headache: 1.61 (1.58–1.63) Migraine: 1.31 (1.30–1.32)	6
Omar M. Ismail	Migraine headache	Age, sex, use of specific medications, a history of rheumatoid arthritis, Sjögren disease or lupus, a history of cataract or refractive surgery	Migraine headache: 1.42 (1.20–1.68)	8
Karel Kostev	Migraine	Age, sex and relevant codiagnoses (rheumatoid arthritis, Sjögren syndrome, lupus and cataract)	Migraine: 3.45 (2.16–5.47)	8
Michael T.M. Wang	Migraine headaches	Not reported	Migraine headache: 2.96 (1.38–6.37)	6
Jelle Vehof	Cluster headache Migraine	Sex and age	Cluster headache: 2.12 (1.10–4.06) Migraine: 1.27 (1.20–1.34)	8

Figure 2. Forest plot showing the effect of all-cause headache on DED.

Figure 3. Funnel figure showing the effect of all-cause headache on DED.

Figure 4. Forest plot showing the types of headache and risk of DED.

Figure 5. Forest plot showing the study type and risk of DED.

Figure 6. Forest plot showing the types of territory and risk of DED.

Data availability statement

The data sets in the study are presented in the article or supplementary material, and further information can be directed to the authors.