| Literature DB >> 29929494 |
Yu-Yen Chen1,2,3, Yun-Ju Lai1,4,5, Jen-Pang Wang6, Ying-Cheng Shen2, Chun-Yuan Wang1,2, Hsin-Hua Chen1,3,7,8,9,10, Hsiao-Yun Hu3,11, Pesus Chou12.
Abstract
BACKGROUND: Previous cross-sectional studies revealed a higher prevalence of depression among glaucoma patients. However, cohort studies were in lack to build the risk of incident depression after the diagnosis of glaucoma. The aim of our study was to investigate the association between glaucoma and the subsequent risk of developing depression and to assess risk factors associated with depression in glaucoma patients.Entities:
Keywords: Cohort study; Depression; Glaucoma; National Health Insurance Research Database; Risk factors
Mesh:
Year: 2018 PMID: 29929494 PMCID: PMC6013853 DOI: 10.1186/s12886-018-0811-5
Source DB: PubMed Journal: BMC Ophthalmol ISSN: 1471-2415 Impact factor: 2.209
Characteristics of study subjects
| Variable | Glaucoma group | Control group | |
|---|---|---|---|
| Age, year, (mean±SD) | 59.4±13.6 | 59.4±13.6 | 1.000 |
| Age, categorical | 1.000 | ||
| 30–50 | 2228 (25.4) | 8912 (25.4) | |
| 50–60 | 2012 (22.9) | 8048 (22.9) | |
| 60–70 | 2239 (25.5) | 8956 (25.5) | |
| ≥ 70 | 2298 (26.2) | 9192 (26.2) | |
| Gender | 1.000 | ||
| Male | 4376 (49.9) | 17,504 (49.9) | |
| Female | 4401 (50.1) | 17,604 (50.1) | |
| Charlson comorbidity index | < 0.0001 | ||
| < 3 | 7463 (85.0) | 309,339 (88.1) | |
| ≥3 | 1314 (15.0) | 4169 (11.9) | |
| Insurance cost | < 0.0001 | ||
| < 40,000 NTD | 7618 (86.8) | 31,052 (88.4) | |
| ≥40,000 NTD | 1159 (13.2) | 4056 (11.6) | |
| Urbanization level | < 0.0001 | ||
| Urban | 6761 (77.0) | 25,464 (72.5) | |
| Rural | 2016 (23.0) | 9644 (27.5) | |
| Living alone | 0.62 | ||
| No | 7973 (90.8) | 32,830 (90.7) | |
| Yes | 804 (9.2) | 3278 (9.3) | |
| Substance abuse | |||
| No | 8704 (99.2) | 34,786 (99.1) | 0.49 |
| Yes | 73 (0.8) | 322 (0.9) | |
| Depression during the follow-up period | 515 (5.9) | 1282 (3.7) | < 0.0001 |
SD standard deviation, NTD New Taiwan Dollar
Fig. 1Kaplan-Meier curves for depression among glaucoma patients and the control group. The black line represents the glaucoma group and the gray line represents the control group
Analyses of Risk Factors for depression in Patients with and without glaucoma
| Predictive variables | Univariate analysis | Multivariable analysis | ||
|---|---|---|---|---|
| Unadjusted HR (95% CI) | Adjusted HR (95% CI) | |||
| Glaucoma (Yes vs. No) | 1.71(1.54–1.89) | < 0.0001 | 1.71(1.54–1.89) | < 0.0001 |
| Age | ||||
| 30–50 | Reference | Reference | ||
| 50–60 | 1.39(1.21–1.60) | < 0.0001 | 1.32(1.14–1.52) | < 0.001 |
| 60–70 | 1.53(1.34–1.75) | < 0.0001 | 1.40(1.21–1.61) | < 0.0001 |
| ≥70 | 1.61(1.41–1.85) | < 0.0001 | 1.49(1.29–1.72) | < 0.0001 |
| Gender (Male vs Female) | 0.64(0.59–0.71) | < 0.0001 | 0.67(0.61–0.74) | < 0.0001 |
| Charlson comorbidity index | ||||
| < 3 | Reference | Reference | ||
| ≥3 | 1.16(1.02–1.32) | < 0.05 | 1.04(0.91–1.19) | 0.59 |
| Insurance cost | ||||
| < 40,000 NTD | Reference | Reference | ||
| ≥40,000 NTD | 0.61(0.51–0.73) | < 0.0001 | 0.77(0.64–0.92) | < 0.01 |
| Urbanization level | ||||
| Urban | Reference | Reference | ||
| Rural | 1.03(0.93–1.14) | 0.56 | 0.99(0.89–1.10) | 0.85 |
| Living alone | ||||
| No | Reference | Reference | ||
| Yes | 1.22(1.06–1.42) | < 0.01 | 1.20(1.03–1.41) | < 0.05 |
| Substance abuse | ||||
| No | Reference | Reference | ||
| Yes | 1.54(1.09–2.18) | < 0.05 | 1.44(1.03–1.82) | < 0.05 |
NTD New Taiwan Dollar, HR hazard ratio, CI confidence interval,
In the multivariable analysis, all the other variables in the Table are included for adjustment
Analyses of Risk Factors for depression among Patients with glaucoma
| Predictive variables | Univariate analysis | Multivariable analysis | ||
|---|---|---|---|---|
| Unadjusted HR | Adjusted HR | |||
| (95% CI) | (95% CI) | |||
| Age | ||||
| 30–50 | Reference | Reference | ||
| 50–60 | 1.56(1.23–1.99) | < 0.001 | 1.39(1.09–1.77) | < 0.001 |
| 60–70 | 1.31(1.03–1.68) | < 0.05 | 1.27(1.03–1.58) | < 0.05 |
| ≥ 70 | 1.56(1.22–1.99) | < 0.001 | 1.35(1.06–1.78) | < 0.05 |
| Gender (Male vs. Female) | 0.64(0.54–0.76) | < 0.0001 | 0.71(0.60–0.84) | < 0.0001 |
| Charlson comorbidity index | ||||
| < 3 | Reference | Reference | ||
| ≥ 3 | 1.18(0.94–1.49) | 0.15 | 1.16(0.92–1.47) | 0.23 |
| Insurance cost | ||||
| < 40,000 NTD | Reference | Reference | ||
| ≥ 40,000 NTD | 0.44(0.32–0.61) | < 0.0001 | 0.53(0.38–0.75) | < 0.001 |
| Urbanization level | ||||
| Urban | Reference | Reference | ||
| Rural | 1.07(0.88–1.30) | 0.49 | 0.97(0.80–1.18) | 0.77 |
| Living alone | ||||
| No | Reference | Reference | ||
| Yes | 1.36(1.06–1.73) | < 0.05 | 1.34(1.03–1.73) | < 0.05 |
| Substance abuse | ||||
| No | Reference | Reference | ||
| Yes | 1.62(1.08–2.87) | < 0.05 | 1.51(1.02–2.57) | < 0.05 |
| Number of glaucoma medications | ||||
| < 3 | Reference | Reference | ||
| ≥ 3 | 0.88(0.70–1.11) | 0.28 | 0.83(0.60–1.16) | 0.28 |
| Types of glaucoma medications | ||||
| Sympathomimetics (Yes vs. No) | 0.91(0.76–1.08) | 0.28 | 0.68(0.36–1.28) | 0.23 |
| Pilocarpine (Yes vs. No) | 1.52(0.92–2.30) | 0.11 | 1.57(0.93–2.10) | 0.10 |
| Carbonic anhydrase inhibitors (Yes vs. No) | 0.82(0.67–1.01) | 0.07 | 0.89(0.69–1.14) | 0.35 |
| β-blocker (Yes vs. No) | 1.00(0.85–1.19) | 0.97 | 1.07(0.88–1.29) | 0.51 |
| Prostaglandin analogs (Yes vs. No) | 0.94(0.79–1.14) | 0.54 | 1.40(0.74–2.68) | 0.31 |
NTD indicates New Taiwan Dollar, HR indicates hazard ratio, CI indicates confidence interval, In the multivariable analysis, all the other variables in the Table are included for adjustment