Abstract
Purpose
To determine the prevalence, factors, temporal trends, and projections of glaucoma over 15 years among multiethnic older Asian adults aged 60 to 100 years.
Methods
We included 2380 participants (mean [SD] age, 73.6 [8.5] years; 55.2% female) from the baseline phase of the Population Health and Eye Disease Profile in Elderly Singaporeans (PIONEER; 2017–2022) study. Comprehensive eye examinations and standardized questionnaires assessed sociodemographic, clinical, and lifestyle factors. Glaucoma and subtypes were defined using International Society of Geographical and Epidemiological Ophthalmology guidelines, with age-standardized prevalence rates based on the 2020 Singapore census. Logistic regression using generalized estimating equations identified risk factors, temporal trends were analyzed using population-based data, and projections utilized United Nations population data.
Results
The census-adjusted glaucoma prevalence was 5.1%, comprising 3.4% primary open-angle glaucoma, 0.7% primary angle-closure glaucoma, and 1% secondary glaucoma. Prevalence was higher among Malays (6.5%) and Indians (6.2%) compared to Chinese (4.9%). Significant factors included older age (odds ratio [OR], 1.07), Malay ethnicity (OR, 2.07), higher intraocular pressure (OR, 1.14), longer axial length (OR, 1.20), cataract surgery (OR, 1.81), and polypharmacy (OR, 2.04). Over two decades, age-adjusted glaucoma prevalence in Singapore remained stable (5%–7%) but increased among Indians (3.65% in 2013 to 6.70% in 2022), likely due to the high rates of systemic diseases. Currently, ∼57,800 Singaporeans aged ≥60 years have glaucoma, projected to rise by 43%, reaching 85,800 by 2040.
Conclusions
Glaucoma is common among older Singaporeans, with notable sociodemographic and modifiable clinical factors. Rising prevalence among Indians and the projected increase in cases underscore the need for targeted screening and early interventions.
Keywords: glaucoma, risk factors, temporal trends, projections, population based, older adults
Global projections estimate that by 2050, over 2 billion people will be over the age of 65 years.1 One of the most significant challenges associated with healthy aging is visual impairment (VI),2 particularly irreversible VI caused by glaucoma, a debilitating age-related ocular condition affecting the optic nerve head. Asia, which accounts for 60% of the global aging population (those aged 60 years and older), is expected to see a 25% increase in glaucoma prevalence by 2030, impacting approximately 57 million adults in the region.3,4
Singapore is experiencing one of the fastest aging populations globally, with an average life expectancy of approximately 85 years. Although several large population-based studies on glaucoma have been conducted in Singapore, most have focused on young and middle-aged adults.5–8 For example, the Singapore Epidemiology of Eye Disease (SEED; 2004–2011) study had a mean participant age of 58.9 years and reported a glaucoma prevalence rate of 3.1%.9 The study identified key predisposing factors, including older age, increased IOP, and longer axial length (AL).9 However, as Singapore's demographic profile increasingly skews toward older adults,10 the relevance of SEED's findings to the current aging population trends is limited. Additionally, the study was conducted nearly a decade ago in the southwestern part of Singapore, further diminishing its applicability. Socioeconomic disparities in the region further restrict the generalizability of these findings to the broader older adult population.
To address these knowledge gaps, we determined the prevalence of any glaucoma, including its subtypes (primary open-angle glaucoma [POAG], primary angle-closure glaucoma [PACG], secondary glaucoma), as well as precursor conditions to PACG, including primary angle-closure suspect (PACS) and primary angle closure (PAC), in a large, geographically representative, multiethnic community-dwelling population of Asian adults aged ≥60 years participating in the PopulatION Health and Eye Disease Profile in Elderly Singaporeans (PIONEER) study.11 We also examined sociodemographic, clinical, and lifestyle determinants of any glaucoma and its subtypes and analyzed time trends in glaucoma prevalence over the past 20 years in Singapore, utilizing data from population-based studies such as the Tanjong Pagar Study (TPS), SEED, and PIONEER.9,11 Finally, we calculated projections for glaucoma prevalence up to 2040 based on the latest population growth forecasts from the United Nations Population Division (UNPD).12 We expect the prevalence of glaucoma and its subtypes in our study to be higher than current literature, reflecting both the contemporaneous nature of our data and increased disease risk with age. We also anticipate significant associations with sociodemographic (e.g., age, sex), lifestyle (e.g., smoking), and clinical factors (e.g., AL, IOP). Additionally, we foresee a rising trend in glaucoma prevalence over the past two decades and a notable increase in cases by 2040, driven by an aging population and accumulating risk factors.
Understanding current and projected trends in glaucoma epidemiology and associated factors will inform the feasibility of implementing screening programs within the community and enhance allocation of health care resources toward the management of this blinding condition among older Singaporeans.
Methods
Study Population and Design
PIONEER is a population-based cohort study of older adults aged ≥60 years from three major ethnic groups (Chinese, Malay, Indian) living across Singapore. Participants were assessed between 2017 and 2022 at baseline. A detailed study methodology is reported elsewhere.11 In short, 6377 individuals were selected using an age-, sex-, and ethnicity-stratified sampling framework from a national database. Malays, Indians, women, and older age groups were deliberately oversampled to ensure adequate precision of estimates during statistical analyses. The study protocol followed the Declaration of Helsinki and received ethics approval from Singapore's Centralized Institutional Review Board (#2016/3089). Written informed consent was obtained from all participants.
Ocular Examination
All participants in PIONEER underwent a comprehensive eye examination at the Singapore Eye Research Institute research clinic according to a standardized protocol. Participants’ presenting visual acuity (PVA) with habitual correction and best-corrected visual acuity with subjective refraction performed by study optometrists were recorded using an Early Treatment of Diabetic Retinopathy Study logMAR number chart (Lighthouse International, New York, NY, USA) at 4 m. VI and blindness were defined as PVA worse than 6/18 (logMAR >0.48 to ≤1.30) and 6/120 (logMAR >1.30), respectively, in accordance with the 2019 World Health Organization criteria for VI.13 Central corneal thickness (CCT) and AL were measured with noncontact partial optical coherence interferometry (IOL Master C3.01; Carl Zeiss Meditec AG, Göschwitzer, Jena. Germany).
Slit-lamp biomicroscopy (model BQ-900; Haag-Streit, Köniz, Switzerland) examination was performed in a standardized manner by trained study ophthalmologists to detect any abnormality of the anterior segment, signs of secondary glaucoma, and any previous ocular laser or surgery. Peripheral anterior chamber depth was assessed with the Van Herick technique at 16× magnification. IOP was measured with a Goldmann applanation tonometer (Haag-Streit). One reading was taken from each eye, and a repeated measurement was taken and used for analysis if the first reading was above 21 mm Hg.
Gonioscopy was performed in all phakic participants by trained study ophthalmologists using a Goldmann two-mirror contact lens (Ocular Instruments, Bellevue, WA, USA) under dark illumination. In addition, dynamic indentation gonioscopy using a four-mirror Sussman gonioscopy contact lens (Ocular Instruments) was used to assess the presence of peripheral anterior synechiae (PAS). Participants with open angles and those with angle closure but who had a patent peripheral laser iridotomy (LPI) underwent pupil dilation.
After pupil dilation, the optic disc was evaluated stereoscopically with a 78-diopter lens at 16× magnification. The vertical cup-to-disc ratio (VCDR) was derived, and specific morphologic features suggestive of glaucoma, such as optic disc hemorrhage, notching of the neuroretinal rim, and wedge defects of the retinal nerve fiber layer (RNFL), were documented. Additionally, a digital nonmydriatic retinal camera (Canon CR-DGi; Canon, Tokyo, Japan) was used to obtain optic disc–centered (Early Treatment Diabetic Retinopathy Study [ETDRS] standard field 1) and macular-centered (ETDRS standard field 2) photographs in each eye.
Further examinations were conducted to assess glaucomatous structural and/or functional damage in participants. This includes visual field (VF) testing, conducted on glaucoma suspects as defined below and on one in five healthy participants, using static automated perimetry with the Swedish Interactive Threshold Algorithm (SITA 24-2; Humphrey Visual Field Analyzer II [HVF]; Carl Zeiss Meditec AG) and near refractive correction. The VF test was deemed unreliable if the test reliability was unsatisfactory, defined as a fixation loss >20%, false-positive rate >33%, and/or false-negative rate >33%. Additionally, evaluation of the optic disc and RNFL was performed using optical coherence tomography (Cirrus OCT; Carl Zeiss Meditec, Dublin, CA, USA), with scans missing in at least one eye for 257 participants (9.7%) of the study population.
Given the scale of this population-based study and the real-world challenges associated with testing an elderly cohort, such as variability in compliance and testing fatigue, standard automated perimetry was generally performed once. However, to ensure diagnostic accuracy, the study implemented a comprehensive glaucoma assessment protocol, which included IOP measurement, optic nerve head evaluation, and imaging modalities such as fundus photography and OCT, where available. Crucially, in alignment with the International Society of Geographical and Epidemiological Ophthalmology (ISGEO) guidelines (see details below), participants classified as glaucoma suspects based on structural or other functional criteria underwent a second VF test to confirm or refute the presence of glaucomatous VF loss. This two-step approach allowed for accurate categorization using ISGEO Category 1, where both reliable structural and functional evidence were available. In cases where reliable VF results could not be obtained, Category 2 and Category 3 definitions were applied based on optic disc appearance and visual acuity, respectively. Despite the logistical constraints of repeat VF testing in a large, elderly population, this protocol ensured a robust, standardized, and reliable classification of glaucoma cases and suspects.
Definition of Glaucoma and Subtypes
In this study, each participant was assessed for glaucoma using a combination of clinical examination, perimetry, and imaging, as previously described.14,15 Participants were classified as a glaucoma suspect if any of the following was present: (1) VCDR >0.60 or VCDR asymmetry >0.20, (2) VCDR <0.60 with glaucomatous disc features, (3) IOP >21 mm Hg, (4) signs in the anterior segment consistent with pseudo‐exfoliation or pigment dispersion syndrome, (5) PAS or any findings suggestive of secondary glaucoma, (6) closed or occludable anterior chamber angle, and (7) known history of glaucoma from available clinical records or on IOP-lowering medications.6,7
When reliable results of structural and functional examinations were both unavailable, principles of glaucoma diagnosis followed the ISGEO classification.16 The ISGEO classification comprises three levels of evidence and emphasizes diagnosis based on structural and functional abnormalities (Category 1); however, provisions are made to allow diagnosis based on a severely damaged optic disc in the absence of a reliable VF test (Category 2) or on a combination of VA, IOP, and history of having undergone glaucoma surgery in cases with no optic disc and VF data (Category 3). For Category 1 (structural and functional evidence), glaucoma is diagnosed when an eye has a VCDR or VCDR asymmetry ≥97.5th percentile for the healthy population, or a neuroretinal rim width reduced to ≤0.1 CDR (between 11- and 1-o’clock or 5- to 7-o’clock), along with a definite VF defect consistent with glaucoma. A VF defect was consistent with glaucoma if the following were found: (1) glaucoma hemifield test result outside normal limits and (2) a cluster of three or more nonedge, contiguous points, not crossing the horizontal meridian, with probability of <5% of the age-matched healthy group on the pattern deviation plot on two separate tests. For Category 2 (advanced structural damage with unproven field loss), if reliable VF testing is not possible, glaucoma is diagnosed based solely on structural evidence—specifically, a VCDR or VCDR asymmetry ≥99.5th percentile for the healthy population. For Category 3 (optic disc not visible, field test not possible), if the optic disc cannot be assessed and VF testing is not feasible (e.g., due to corneal opacity or dense cataract), glaucoma is diagnosed if (1) visual acuity is <3/60 and IOP is >99.5th percentile, or (2) visual acuity is <3/60 and there is evidence of prior glaucoma surgery or medical records confirming glaucomatous visual morbidity. Therefore, the definition of glaucoma remains IOP-independent for Categories 1 and 2. Although IOP is included as a criterion in Category 3, it is applied only when both optic disc evaluation and VF testing are not feasible, as previously described. Accordingly, the overall definition of glaucoma can be considered IOP-independent.
Patients with glaucoma and an open, normal drainage angle with no identifiable secondary pathologic processes were classified as having POAG. Participants with elevated IOP but absence of glaucomatous damage on structural or functional examination were classified as having ocular hypertension (OHT). As CCT is known to influence IOP measurements, the upper limits of normal IOP, defined as 20.4, 21.5, and 22.6 mm Hg for the Chinese, Indian, and Malay cohort, respectively, were based on previously published normative data from the SEED study that accounted for interethnic differences, including CCT.17
Primary angle closure disease refers to PACS, PAC, and PACG combined. Precursor conditions to PACG, such as PACS and PAC, refer to early stages that can lead to PACG if not treated. First, PACS was diagnosed when two or more quadrants of posterior trabecular meshwork were not visualized on gonioscopy. Second, PAC was diagnosed in PACS eyes with additional signs of PAS on indentation gonioscopy, Glaukomflecken, or IOP above the 97.5th percentile for the ethnic-specific healthy population evaluated. Third, PACG was diagnosed in PACS eyes with evidence of glaucomatous optic neuropathy. Other cases in which it was difficult to accurately assess the underlying cause of the glaucoma were termed unclassifiable.
Secondary glaucoma was diagnosed when the following criteria were met: a positive history and ocular findings of pathologies such as trauma, lens-related issues, uveitis, neovascularization, intraocular surgery, or any other abnormal ocular or systemic findings that could have caused prior or current IOP elevation leading to optic nerve damage. Patients with unilateral glaucoma were included as having secondary glaucoma only if the other eye had no evidence of primary glaucoma.18 Pseudoexfoliation and pigment dispersion were also included in secondary glaucoma.7 Developmental/childhood glaucoma was classified as per the childhood glaucoma research network (CGRN) definitions.19 Specifically, cases were classified as developmental glaucoma if there was a documented history of glaucoma diagnosis or treatment from childhood or if clinical findings were consistent with anterior segment dysgenesis, indicating a longstanding origin of the disease.
An experienced glaucoma fellowship-trained ophthalmologist (ST, OH, JRL) reviewed the final identification, adjudication, and classification of glaucoma cases. In cases where the diagnosis of glaucoma was less certain, disc photographs, imaging, and VFs were assessed by a panel of three glaucoma specialists to reach a consensus.
Assessment of Covariables, Risk Factors, and Associated Definitions
Participants’ sociodemographic details, including age, sex, ethnicity, and socioeconomic status (comprising income and education); self-reported medical history (presence of diabetes, hypertension, hyperlipidemia, cardiovascular disease [CVD, including ischemic heart disease and stroke]; chronic kidney disease [CKD]; number of medications); and lifestyle factors (smoking status, alcohol consumption status, and weekly duration spent on moderate-to-vigorous physical activity levels, e.g., gardening, brisk walking, dancing, jogging) were collected via an in-house questionnaire. Low socioeconomic status (SES) was defined as having primary or lower education and a household monthly income <SGD$2000. Low moderate-to-vigorous levels of physical activity were defined as the sex-specific lowest quintile of total self-reported duration of moderate-to-vigorous physical activity levels. Dietary (nutrition) data were collected using an electronic food frequency questionnaire developed and validated in the local multiethnic Singaporean population.20 Participants’ weekly food intake was categorized into 146 separate food groups, and the corresponding total caloric values were extracted for analyses.
Blood pressure (BP) was taken using a digital automatic BP monitor (Dinamap Pro Series DP110X-RW; GE HealthCare Technologies, Chicago, IL, USA). Hypertension was defined as systolic BP ≥ 140 mm Hg, diastolic BP ≥ 90 mm Hg, self-reported use of antihypertensive medications, or self-reported history of physician-diagnosed hypertension. Body mass index (BMI) was calculated as weight in kilograms divided by height in meters squared (Wt [kg]/Ht [m]2), and obesity was defined as BMI > 27.5 kg/m2 according to the Asian cutoffs.21
Blood samples were collected for HbA1c, random glucose, total cholesterol, high-density lipoprotein cholesterol, low-density lipoprotein cholesterol, triglycerides, and serum creatinine measurements. Diabetes was defined as random glucose ≥ 11.1 mmol/L, HbA1c ≥ 6.5%, self-reported use of diabetic medication, or reported history of physician-diagnosed diabetes.22 Hyperlipidemia was defined as total cholesterol ≥ 6.2 mmol/L, self-reported use of lipid-lowering medications, or reported history of physician-diagnosed hyperlipidemia. CKD was defined as an estimated glomerular filtration rate < 60 mL/min/1.73 m2.23 CVD was defined as self-reported history of stroke, myocardial infarction, or angina. Polypharmacy was considered present if the patient was taking more than five medications primarily for systemic conditions, including diabetes mellitus, hypertension, high cholesterol, high blood pressure, and heart conditions, while excluding short-term medication (e.g., pain relievers), supplements, or vitamins.
Statistical Analysis
All statistical evaluations were conducted using R software version 4.3.1 (R Core Team, Vienna, Austria) and assumed a two-sided test at the 5% significance level. Participant sociodemographic, medical (systemic and ocular), clinical, and lifestyle characteristics were summarized using means (SD) for continuous variables and n (%) for categorical variables. As we oversampled minority races, women, and older participants, the overall, age-, sex-, and ethnicity-stratified prevalence rates for any glaucoma and subtypes were determined by weighting individuals according to their sampling probabilities and standardizing to Singapore's 2020 population census.
To investigate the sociodemographic, clinical, and lifestyle factors associated with glaucoma, we conducted a logistic regression analysis (assuming a linear relationship at the logit scale) using generalized estimating equations (GEEs) to account for the intracorrelation of ocular measurements from each participant’s pair of eyes. We fitted a comprehensive model that included variables such as age, sex, ethnicity, SES, smoking status, alcohol consumption, caloric intake, polypharmacy, and the presence of systemic comorbidities (including diabetes, hypertension, hyperlipidemia, CVD, and CKD), as well as AL, CCT, IOP, and history of cataract surgery. Furthermore, in the analysis of patients with unilateral glaucoma, we treated the unaffected eye as a reference for comparison with the glaucoma-affected eye. This approach, commonly used in glaucoma studies, offers a practical framework for understanding the impact of glaucoma. The use of GEE models further accounts for intereye correlation, thereby reducing potential bias from this method.
We also examined temporal trends in glaucoma prevalence by plotting time-series graphs of age-standardized glaucoma rates among individuals aged 60 and older from selected studies, including the TPS, SEED, and PIONEER. For projections of the number of people with glaucoma from 2025 to 2040, we assumed a constant prevalence using the overall weighted prevalence observed in the PIONEER study based on the clinical observations and existing evidence, suggesting that the proportion of glaucoma cases remains relatively stable over time.3,24 Using this assumption, we estimated the number of individuals with glaucoma by multiplying the projected prevalence with the anticipated population figures for Singapore, derived from the UNPD 2024 prospects,12 which account for mortality and fertility rates in population projections.
Results
Of the 3720 eligible participants, 2643 participated in the baseline study (response rate 71.1%). Of these, 7 (0.3%) were recruited due to an administrative oversight despite not meeting the age and ethnicity inclusion criteria and hence were excluded, leaving 2636 individuals (mean age [SD], 73.6 [8.5] years; 55.2% female) for analysis.
Of the included participants, 1319 (50.0%), 661 (25.1%), and 656 (24.9%) were of Chinese, Malay, and Indian ethnicities, respectively. In the univariate analysis, participants with glaucoma were older, male, more likely to be Malays or Indians, and more likely to have polypharmacy and systemic diseases, including hypertension, hyperlipidemia, CVD, and CKD (Table 1) compared to those without glaucoma. In terms of ocular profile, participants with glaucoma had longer AL (24.2 mm), higher baseline IOP (14.8 mm Hg), and thinner corneas (542.2 µm), and nearly 80% had cataract, with 58.5% having undergone cataract surgery in any eye (Table 2) compared to those with no glaucoma.
Table 1.
Demographic, Socioeconomic, and Systemic Characteristics of Participants in the PIONEER Study, Stratified by Glaucoma Status (Person Level)
| Characteristic | No Glaucoma (n = 2442) | Glaucoma (n = 194) | Overall (n = 2636) |
|---|---|---|---|
| Age, mean (SD), y | 73.3 (8.5) | 77.6 (7.9) | 73.6 (8.5) |
| Age group | |||
| 60–69 | 928 (38.0) | 39 (20.1) | 967 (36.7) |
| 70–79 | 796 (32.6) | 52 (26.8) | 848 (32.2) |
| ≥80 | 718 (29.4) | 103 (53.1) | 821 (31.1) |
| Female sex | 1362 (55.8) | 92 (47.4) | 1454 (55.2) |
| Ethnicity | |||
| Chinese | 1234 (50.5) | 85 (43.8) | 1319 (50.0) |
| Malay | 607 (24.9) | 54 (27.8) | 661 (25.1) |
| Indian | 601 (24.6) | 55 (28.4) | 656 (24.9) |
| Low socioeconomic status | 411 (16.8) | 28 (14.4) | 439 (16.7) |
| Obesity | 669 (27.4) | 48 (24.7) | 717 (27.2) |
| Smoking status | |||
| Never smoked or past smoker | 2107 (86.3) | 170 (87.6) | 2277 (86.4) |
| Current smoker | 197 (8.1) | 12 (6.2) | 209 (7.9) |
| Alcohol consumption | |||
| Never drank or past drinker | 2037 (83.4) | 160 (82.5) | 2197 (83.3) |
| Current drinker | 264 (10.8) | 21 (10.8) | 285 (10.8) |
| Low caloric intake (<1789 kcal/d) | 885 (36.2) | 62 (32.0) | 947 (35.9) |
| Low MVPA level | 1349 (55.2) | 105 (54.1) | 1454 (55.2) |
| Polypharmacy | 461 (18.9) | 55 (28.4) | 516 (19.6) |
| Systemic conditions | |||
| Diabetes | 842 (34.5) | 69 (35.6) | 911 (34.6) |
| Hypertension | 2085 (85.4) | 178 (91.8) | 2263 (85.8) |
| Hyperlipidemia | 1301 (53.3) | 111 (57.2) | 1412 (53.6) |
| CVD | 393 (16.1) | 43 (22.2) | 436 (16.5) |
| CKD | 462 (18.9) | 52 (26.8) | 514 (19.5) |
| Visual impairment | 584 (23.9) | 66 (34.0) | 650 (24.7) |
| Blindness | 105 (4.3) | 21 (10.8) | 126 (4.8) |
CKD, chronic kidney disease; CVD, cardiovascular disease; MVPA, moderate-vigorous physical activity; SD, standard deviation.
Values are presented as n (%) unless otherwise indicated. Total column percentage for a categorical variable may not add up to 100% due to missing data.
Table 2.
Ocular Characteristics of Participants in the PIONEER Study, Stratified by Glaucoma Status (Eye Level)
| Characteristic | No Glaucoma (n = 4916) | Glaucoma (n = 299) | Overall (n = 5272) |
|---|---|---|---|
| Axial length, mean (SD), mm | 23.8 (1.4) | 24.2 (1.7) | 23.8 (1.4) |
| Central corneal thickness, mean (SD), µm | 546.9 (35.2) | 542.2 (45.9) | 546.6 (35.9) |
| Intraocular pressure, mean (SD), mm Hg | 14.0 (2.8) | 14.8 (5.0) | 14.1 (3.0) |
| Cataract status | |||
| No cataract | 1515 (30.8) | 54 (18.1) | 1582 (30.0) |
| Cataract | 1413 (28.7) | 64 (21.4) | 1483 (28.1) |
| Cataract surgery (pseudophakic lens) | 1851 (37.7) | 175 (58.5) | 2063 (39.1) |
| Missing data | 137 (2.8) | 6 (2.0) | 144 (2.7) |
Values are presented as n (%) unless otherwise indicated. The status of some eyes (n = 57) remains undetermined, which results in the counts for nonglaucoma and glaucoma eyes not adding up to the overall total.
A total of 657 (24.9%) individuals were identified as glaucoma suspects. The crude prevalence of any glaucoma was 194 individuals (7.4%), including 127 (4.8%) with POAG, 26 (1.0%) with PACG, and 40 (1.5%) with secondary glaucoma. The disease was bilateral in 105 (54.12%) participants (Supplementary Table S1). Additionally, 66 (34.02%) participants with glaucoma had VI and 21 (10.82%) were blind (Supplementary Table S2). The ISGEO categorization of glaucoma types (Table 3) revealed a predominance of Category 1 (78.35%). A total of 42 cases were missing structural and/or VF data, with 32 participants classified under Category 2 and 10 under Category 3. Individuals with secondary angle-closure glaucoma due to anterior synechiae, such as that occurring after complicated cataract surgery, were classified under the category of glaucoma with pseudophakia. In total, 10 such cases were identified: 8 in Category 1 and 2 in Category 2, comprising approximately 5% of the overall glaucoma cases. Among the remaining 30 participants classified as having secondary glaucoma, which included pseudoexfoliation glaucoma, neovascular glaucoma, pigment dispersion glaucoma, unspecified glaucoma, and steroid-induced glaucoma, only one patient was diagnosed with neovascular glaucoma, and three had pseudoexfoliation glaucoma.
Table 3.
Glaucoma Diagnosis Based on ISGEO Categorization Among 194 Patients With Confirmed Glaucoma
| Glaucoma Type | Category 1 | Category 2 | Category 3 |
|---|---|---|---|
| Primary | 124 (81.6) | 24 (75.0) | 6 (60.0) |
| POAG | 105 (69.1) | 19 (59.4) | 3 (30.0) |
| PACG | 19 (12.5) | 4 (12.5) | 3 (30.0) |
| Developmental glaucoma | 0 (0.0) | 1 (3.1) | 0 (0.0) |
| Secondary | 28 (18.4) | 8 (25.0) | 4 (40.0) |
| Glaucoma with pseudophakia | 8 (5.3) | 2 (6.2) | 0 (0.0) |
| Others* | 20 (13.2) | 6 (18.8) | 4 (40.0) |
ISGEO, international society of geographical and epidemiologic ophthalmology; PACG, primary angle-closure glaucoma; POAG, primary open-angle glaucoma.
Values are presented as n (%). Total column percentage for a categorical variable may not add up to 100% due to missing data.
Others include pseudoexfoliation glaucoma, neovascular glaucoma, pigment dispersion glaucoma, unspecified glaucoma, and steroid-induced glaucoma. Due to the low counts for these subtypes, they have been grouped together.
Prevalence of Glaucoma Stratified by Age, Sex, and Ethnicity
Table 4 shows the overall weighted prevalence of any glaucoma, primary glaucoma (including POAG and PACG), and secondary glaucoma. The national census-adjusted prevalence (95% confidence interval [CI]) rate of any glaucoma, POAG, PACG, and secondary glaucoma was 5.1% (4.2%–6.2%), 3.4% (2.6%–4.4%), 0.7% (0.4%–1.1%), and 1.0% (0.6%–1.6%), respectively.
Table 4.
Weighted Prevalence of Glaucoma, Stratified by Age, Sex, and Ethnicity in the PIONEER Study
| Sex | Ethnicity | |||||||||||
|---|---|---|---|---|---|---|---|---|---|---|---|---|
| All (N = 2636) | Male (n = 1182) | Female (n = 1454) | Chinese (n = 1319) | Malay (n = 661) | Indian (n = 656) | |||||||
| n | Weighted, % (95% CI) | n | Weighted, % (95% CI) | n | Weighted, % (95% CI) | n | Weighted, % (95% CI) | n | Weighted, % (95% CI) | n | Weighted, % (95% CI) | |
| Any glaucoma | ||||||||||||
| 60–69 | 39 | 3.5 (2.2–5.1) | 23 | 4.8 (2.7–7.7) | 16 | 2.2 (1.0–4.3) | 14 | 3.2 (1.7–5.2) | 15 | 5.4 (3.0–8.7) | 10 | 4.1 (2.0–7.5) |
| 70–79 | 52 | 5.6 (3.9–7.7) | 25 | 4.9 (2.7–8.1) | 27 | 6.3 (3.9–9.4) | 25 | 5.4 (3.5–7.9) | 12 | 6.0 (3.1–10.2) | 15 | 7.9 (4.5–12.8) |
| ≥80 | 103 | 10.9 (8.4–13.9) | 54 | 15.1 (10.9–20.1) | 49 | 8.3 (5.3–12.3) | 46 | 10.4 (7.6–13.8) | 27 | 15.2 (9.9–21.8) | 30 | 13.0 (8.8–18.1) |
| Total | 194 | 5.1 (4.2–6.2) | 102 | 6.0 (4.4–7.8) | 92 | 4.4 (3.2–5.8) | 85 | 4.9 (3.8–6.2) | 54 | 6.5 (4.7–8.9) | 55 | 6.2 (4.4–8.4) |
| Primary glaucoma | ||||||||||||
| 60–69 | 31 | 2.8 (1.7–4.3) | 19 | 4.1 (2.2–6.9) | 12 | 1.4 (0.5–3.0) | 11 | 2.5 (1.2–4.4) | 13 | 4.6 (2.5–7.8) | 7 | 2.9 (1.2–5.9) |
| 70–79 | 47 | 5.0 (3.4–7.0) | 22 | 4.3 (2.3–7.3) | 25 | 5.6 (3.4–8.6) | 22 | 4.8 (3.0–7.2) | 12 | 6.0 (3.1–10.2) | 13 | 6.8 (3.6–11.4) |
| ≥80 | 76 | 8.1 (6.0–10.7) | 38 | 11.4 (7.7–16.1) | 38 | 6.1 (3.6–9.5) | 35 | 7.8 (5.4–10.8) | 17 | 9.7 (5.5–15.5) | 24 | 10.6 (6.8–15.5) |
| Total | 154 | 4.1 (3.3–5.2) | 79 | 5.0 (3.6–6.8) | 75 | 3.4 (2.4–4.6) | 68 | 3.9 (2.9–5.1) | 42 | 5.5 (3.8–7.7) | 44 | 4.8 (3.3–6.8) |
| POAG | ||||||||||||
| 60–69 | 26 | 2.4 (1.4–3.9) | 17 | 3.7 (1.9–6.4) | 9 | 1.2 (0.4–2.8) | 10 | 2.2 (1.1–4.1) | 10 | 3.7 (1.8–6.6) | 6 | 2.5 (0.9–5.3) |
| 70–79 | 40 | 4.2 (2.8–6.1) | 20 | 4.2 (2.1–7.2) | 20 | 4.2 (2.3–6.9) | 18 | 4.0 (2.4–6.2) | 11 | 5.5 (2.8–9.6) | 11 | 5.8 (2.9–10.1) |
| ≥80 | 61 | 6.0 (4.2–8.3) | 32 | 8.7 (5.6–13.0) | 29 | 4.3 (2.3–7.3) | 26 | 5.7 (3.7–8.3) | 14 | 7.7 (4.1–13.1) | 21 | 9.3 (5.7–14.0) |
| Total | 127 | 3.4 (2.6–4.4) | 69 | 4.4 (3.1–6.1) | 58 | 2.6 (1.7–3.7) | 54 | 3.3 (2.3–4.4) | 35 | 4.5 (3.0–6.6) | 38 | 4.1 (2.7–6.0) |
| PACG | ||||||||||||
| 60–69 | 5 | 0.3 (0.1–1.0) | 2 | 0.4 (0.0–2.0) | 3 | 0.2 (0.0–0.6) | 1 | 0.2 (0.0–1.2) | 3 | 1.0 (0.2–2.8) | 1 | 0.4 (0.0–2.5) |
| 70–79 | 7 | 0.8 (0.3–1.9) | 2 | 0.2 (0.0–0.6) | 5 | 1.4 (0.4–3.4) | 4 | 0.8 (0.2–2.1) | 1 | 0.5 (0.0–2.7) | 2 | 1.0 (0.1–3.7) |
| ≥80 | 14 | 1.9 (0.9–3.6) | 5 | 2.3 (0.7–5.2) | 9 | 1.7 (0.5–4.2) | 8 | 2.0 (0.8–4.0) | 3 | 2.0 (0.3–6.0) | 3 | 1.3 (0.3–3.8) |
| Total | 26 | 0.7 (0.4–1.1) | 9 | 0.6 (0.2–1.3) | 17 | 0.8 (0.4–1.4) | 13 | 0.7 (0.3–1.2) | 7 | 0.9 (0.3–2.1) | 6 | 0.7 (0.2–1.8) |
| Secondary glaucoma | ||||||||||||
| 60–69 | 8 | 0.7 (0.2–1.7) | 4 | 0.6 (0.1–2.0) | 4 | 0.8 (0.1–2.7) | 3 | 0.7 (0.1–2.0) | 2 | 0.8 (0.1–2.8) | 3 | 1.2 (0.3–3.6) |
| 70–79 | 5 | 0.6 (0.2–1.7) | 3 | 0.6 (0.1–2.3) | 2 | 0.7 (0.1–2.4) | 3 | 0.7 (0.1–1.9) | 0 | — | 2 | 1.2 (0.1–4.1) |
| ≥80 | 27 | 2.8 (1.6–4.6) | 16 | 3.7 (1.9–6.5) | 11 | 2.3 (0.8–5.1) | 11 | 2.6 (1.2–4.8) | 10 | 5.5 (2.6–10.2) | 6 | 2.4 (0.9–5.1) |
| Total | 40 | 1.0 (0.6–1.6) | 23 | 1.0 (0.5–1.7) | 17 | 1.0 (0.4–2.0) | 17 | 1.0 (0.5–1.7) | 12 | 1.1 (0.4–2.2) | 11 | 1.3 (0.5–2.7) |
CI, confidence interval.
Weighted prevalences are calculated with sampling weights specific to each age group, sex, and ethnicity to adjust for oversampling and poststratification weights to align with the population distribution based on the 2020 Singapore Census.
In sex-stratified analysis, we observed similar rates overall, for any glaucoma (6% in males vs. 4.4% in females), POAG (4.4% in males vs. 2.6 % in females), and PACG (0.6% in males vs. 0.8% in females).
In ethnicity-stratified results, the rates were similar across ethnic groups for any glaucoma (6.5% in Malays, 6.2% in Indians, and 4.9% in Chinese), POAG (4.5% in Malays, 4.1% in Indians, and 3.3% in Chinese), PACG (0.9% in Malays, 0.7% in Indians, and 0.7% in Chinese), and secondary glaucoma (1.3% in Indians, 1.1% in Malays, and 1% in Chinese).
Prevalence of Precursor Conditions to PACG: PACS and PAC
The prevalence of precursor conditions to glaucoma, including OHT and PACG (PACS and PAC), stratified by age, sex, and ethnicity, is shown in Table 5. The overall weighted prevalence was 0.9% for OHT and 6.5% (PACS: 5.4% and PAC: 1.1%) for precursor conditions to PACG.
Table 5.
Weighted Prevalence of Precursor Conditions to PACG (PACS and PAC) Stratified by Age, Sex, and Ethnicity in the PIONEER Study
| Sex | Ethnicity | |||||||||||
|---|---|---|---|---|---|---|---|---|---|---|---|---|
| All (N = 2636) | Male (n = 1182) | Female (n = 1454) | Chinese (n = 1319) | Malay (n = 661) | Indian (n = 656) | |||||||
| n | Weighted, % (95% CI) | n | Weighted, % (95% CI) | n | Weighted, % (95% CI) | n | Weighted, % (95% CI) | n | Weighted, % (95% CI) | n | Weighted, % (95% CI) | |
| Conditions progressing to PACG (PACS and PAC) | ||||||||||||
| 60–69 | 72 | 7.4 (5.5–9.7) | 24 | 4.2 (2.3–6.8) | 48 | 10.5 (7.3–14.5) | 32 | 7.3 (5.1–10.1) | 25 | 8.5 (5.5–12.2) | 15 | 6.4 (3.6–10.3) |
| 70–79 | 58 | 5.7 (4.0–7.8) | 19 | 4.1 (2.1–7.2) | 39 | 7.1 (4.7–10.3) | 24 | 5.2 (3.4–7.6) | 19 | 9.6 (5.9–14.7) | 15 | 7.3 (4.1–11.9) |
| ≥80 | 31 | 4.6 (3.0–6.8) | 11 | 3.3 (1.4–6.4) | 20 | 5.5 (3.1–8.7) | 21 | 4.8 (2.9–7.4) | 8 | 5.0 (2.0–10.1) | 2 | 0.8 (0.1–2.8) |
| Total | 161 | 6.5 (5.3–7.9) | 54 | 4.0 (2.7–5.8) | 107 | 8.7 (6.7–11.0) | 77 | 6.3 (4.9–8.0) | 52 | 8.4 (6.2–11.0) | 32 | 6.0 (4.0–8.5) |
| PAC | ||||||||||||
| 60–69 | 12 | 1.2 (0.5–2.4) | 5 | 1.0 (0.2–2.7) | 7 | 1.4 (0.4–3.4) | 5 | 1.1 (0.4–2.6) | 6 | 2.0 (0.7–4.4) | 1 | 0.4 (0.0–2.5) |
| 70–79 | 14 | 0.8 (0.3–1.6) | 6 | 0.8 (0.2–2.4) | 8 | 0.8 (0.2–1.9) | 2 | 0.4 (0.1–1.6) | 5 | 2.5 (0.8–5.9) | 7 | 3.6 (1.4–7.3) |
| ≥80 | 8 | 1.1 (0.4–2.6) | 4 | 1.1 (0.2–3.4) | 4 | 1.2 (0.2–3.4) | 5 | 1.2 (0.4–2.9) | 2 | 0.9 (0.1–3.3) | 1 | 0.4 (0.0–2.2) |
| Total | 34 | 1.1 (0.6–1.7) | 15 | 1.0 (0.4–2.0) | 19 | 1.2 (0.5–2.2) | 12 | 0.9 (0.4–1.8) | 13 | 2.1 (1.0–3.6) | 9 | 1.3 (0.5–2.5) |
| PACS | ||||||||||||
| 60–69 | 60 | 6.2 (4.5–8.3) | 19 | 3.2 (1.6–5.6) | 41 | 9.1 (6.1–12.9) | 27 | 6.2 (4.1–8.8) | 19 | 6.4 (3.9–9.8) | 14 | 5.9 (3.3–9.8) |
| 70–79 | 44 | 4.9 (3.3–6.9) | 13 | 3.3 (1.5–6.2) | 31 | 6.3 (4.0–9.4) | 22 | 4.8 (3.0–7.1) | 14 | 7.1 (3.9–11.7) | 8 | 3.8 (1.6–7.3) |
| ≥80 | 23 | 3.5 (2.1–5.5) | 7 | 2.2 (0.7–4.9) | 16 | 4.3 (2.3–7.2) | 16 | 3.6 (2.0–5.9) | 6 | 4.1 (1.4–9.1) | 1 | 0.4 (0.0–2.2) |
| Total | 127 | 5.4 (4.3–6.8) | 39 | 3.1 (1.9–4.6) | 88 | 7.5 (5.7–9.7) | 65 | 5.4 (4.1–7.0) | 39 | 6.3 (4.4–8.7) | 23 | 4.7 (2.9–7.2) |
PAC, primary angle-closure; PACS, primary angle-closure suspect.
Weighted prevalences are calculated with sampling weights specific to each age group, sex, and ethnicity to adjust for oversampling and poststratification weights to align with the population distribution based on the 2020 Singapore Census.
Factors Associated With Any Glaucoma
In multivariable models exploring the factors associated with any glaucoma representing a broad-level analysis (Table 6), we found that older age (per year increase: odds ratio [OR], 1.07; 95% confidence interval [CI], 1.03–1.11; P < 0.001), Malay ethnicity (OR, 2.07; 95% CI, 1.09–3.91; P = 0.026), longer AL (OR, 1.20; 95% CI, 1.04–1.39; P = 0.015), higher IOP (OR, 1.14; 95% CI, 1.06–1.22; P < 0.001), history of cataract surgery (OR, 1.81; 95% CI, 1.09–3.02; P = 0.022), and polypharmacy (OR, 2.04; 95% CI, 1.06–3.92; P = 0.033) were independently associated with higher odds of any glaucoma.
Table 6.
Factors Associated With Any Glaucoma
| Variable | OR (95% CI) | P Value |
|---|---|---|
| Age | 1.07 (1.03–1.11) | <0.001 |
| Sex | ||
| Male | Reference | NA |
| Female | 0.83 (0.49–1.42) | 0.50 |
| Ethnicity | ||
| Chinese | Reference | NA |
| Malay | 2.07 (1.09–3.91) | 0.026 |
| Indian | 0.86 (0.40–1.86) | 0.70 |
| Low socioeconomic status | ||
| No | Reference | NA |
| Yes | 0.81 (0.41–1.62) | 0.56 |
| Obesity | ||
| No | Reference | NA |
| Yes | 0.94 (0.54–1.63) | 0.83 |
| Smoking status | ||
| Never or past smoker | Reference | NA |
| Current smoker | 0.76 (0.28–2.05) | 0.59 |
| Alcohol consumption | ||
| Never drank or past drinker | Reference | NA |
| Current drinker | 1.04 (0.51–2.13) | 0.91 |
| Caloric intake (kcal/d) | ||
| Normal caloric intake | Reference | NA |
| Low caloric intake (<1789 kcal/d) | 0.77 (0.47–1.28) | 0.31 |
| Low MVPA level | ||
| No | Reference | NA |
| Yes | 0.89 (0.53–1.50) | 0.66 |
| Polypharmacy | ||
| No | Reference | NA |
| Yes | 2.04 (1.06–3.92) | 0.033 |
| Diabetes | ||
| No | Reference | NA |
| Yes | 1.03 (0.61–1.74) | 0.91 |
| Hypertension | ||
| No | Reference | NA |
| Yes | 0.98 (0.47–2.01) | 0.95 |
| Hyperlipidemia | ||
| No | Reference | NA |
| Yes | 1.28 (0.75–2.17) | 0.37 |
| CVD | ||
| No | Reference | NA |
| Yes | 0.53 (0.26–1.09) | 0.083 |
| CKD | ||
| No | Reference | NA |
| Yes | 0.65 (0.34–1.25) | 0.20 |
| Axial length (mm) | 1.20 (1.04–1.39) | 0.015 |
| CCT (per 100 µm decrease) | 1.81 (0.81–4.05) | 0.15 |
| IOP (mm Hg) | 1.14 (1.06–1.22) | <0.001 |
| Cataract surgery | ||
| No | Reference | NA |
| Yes | 1.81 (1.09–3.02) | 0.022 |
CCT, central corneal thickness; IOP, intraocular pressure; NA, not applicable.
Factors Associated With Precursor Conditions to PACG: PACS and PAC
Given the limited number of OHT cases (n = 22), we could not run multivariable analyses. However, in the multivariable models exploring type-specific factors such as those associated with precursor conditions to PACG (PACS and PAC; Table 7), we found that a longer axial length (OR, 0.38; 95% CI, 0.29–0.51; P < 0.001) and a history of cataract surgery (OR, 0.34; 95% CI, 0.16–0.69; P = 0.003) were associated with lower odds of having PACG precursor conditions. Conversely, higher IOP was associated with greater odds of having these precursor conditions (OR, 1.16; 95% CI, 1.05–1.27; P = 0.004).
Table 7.
Factors Associated With Precursor Conditions to PACG (PACS and PAC)
| Variable | OR (95% CI) | P Value |
|---|---|---|
| Age | 0.98 (0.94–1.02) | 0.30 |
| Sex | ||
| Male | Reference | NA |
| Female | 1.06 (0.59–1.90) | 0.85 |
| Ethnicity | ||
| Chinese | Reference | NA |
| Malay | 1.15 (0.61–2.16) | 0.66 |
| Indian | 0.49 (0.24–1.04) | 0.063 |
| Low socioeconomic status | ||
| No | Reference | NA |
| Yes | 1.03 (0.55–1.93) | 0.91 |
| Obesity | ||
| No | Reference | NA |
| Yes | 0.93 (0.53–1.63) | 0.81 |
| Smoking status | ||
| Never or past smoker | Reference | NA |
| Current smoker | 0.71 (0.28–1.78) | 0.46 |
| Alcohol consumption | ||
| Never drank or past drinker | Reference | NA |
| Current drinker | 0.91 (0.41–2.02) | 0.82 |
| Caloric intake (kcal/d) | ||
| Normal caloric intake | Reference | NA |
| Low caloric intake (<1789 kcal/d) | 0.79 (0.46–1.35) | 0.39 |
| Low MVPA level | ||
| No | Reference | NA |
| Yes | 0.93 (0.57–1.50) | 0.76 |
| Polypharmacy | ||
| No | Reference | NA |
| Yes | 1.08 (0.47–2.46) | 0.86 |
| Diabetes | ||
| No | Reference | NA |
| Yes | 0.67 (0.39–1.13) | 0.13 |
| Hypertension | ||
| No | Reference | NA |
| Yes | 0.57 (0.31–1.02) | 0.059 |
| Hyperlipidemia | ||
| No | Reference | NA |
| Yes | 0.82 (0.50–1.35) | 0.44 |
| CVD | ||
| No | Reference | NA |
| Yes | 1.21 (0.55–2.67) | 0.64 |
| CKD | ||
| No | Reference | NA |
| Yes | 0.86 (0.37–2.00) | 0.72 |
| Axial length (mm) | 0.38 (0.29–0.51) | <0.001 |
| CCT (per 100 µm decrease) | 1.20 (0.60–2.38) | 0.61 |
| IOP (mm Hg) | 1.16 (1.05–1.27) | 0.004 |
| Cataract surgery | ||
| No | Reference | NA |
| Yes | 0.34 (0.16–0.69) | 0.003 |
Analysis of Temporal Trends and Projections From 2024 to 2040 for Glaucoma
Analysis of age-standardized prevalence rates of glaucoma over the past two decades (2000–2022), based on data from three major population-based studies in Singapore—the TPS, SEED, and the current PIONEER study—shows a slight increase in overall age-adjusted prevalence, rising from 5.80% to 6.29% over the 20-year period (Fig. 1A). This suggests a gradual upward trend that warrants continued surveillance. When stratified by ethnicity, the data reveal a statistically significant rise in glaucoma prevalence among Indians. Specifically, the prevalence in the current PIONEER study is 6.7%, which is 3% higher than the 3.65% prevalence observed in the SEED study (Fig. 1B).
Figure 1.
(a) Age-standardized prevalence of glaucoma over time in 3 major studies in Singapore. (b) Age-standardized prevalence of glaucoma over time by ethnicity (Chinese, Malay, Indian) in 3 major studies in Singapore.
In terms of projected prevalence rates for any glaucoma until 2040, the total number of cases in those aged 60 years and older is expected to rise from approximately 57,800 people to 85,800 by 2040, representing a ∼43% increase in glaucoma cases. When stratified by sex, the trend indicates a projected rise in glaucoma cases for both males and females, with males consistently having higher numbers compared to females (Fig. 2).
Figure 2.
Projected number of individuals aged 60 and above with glaucoma by sex from 2025 to 2040.
Discussion
In our contemporary population-based study of multiethnic Singaporean older adults, we found that 5.1% had any glaucoma (3.4% POAG, 0.7% PACG, and 1% secondary glaucoma). The age-specific prevalence of glaucoma increased systematically with age across all sex and ethnic groups. Notably, the oldest old (those aged 80 years and above) had a nearly three times higher prevalence of glaucoma compared with their younger counterparts aged 60 to 69 years, reinforcing the crucial role of age in the pathogenesis of glaucoma. More than half of those with glaucoma had bilateral disease, and nearly half experienced visually debilitating glaucoma. Various sociodemographic and modifiable clinical factors were associated with increased odds of developing the condition. While the overall age-adjusted prevalence of glaucoma in Singapore has remained relatively stable over the past 20 years, there has been a significant increase in prevalence among individuals of Indian ethnicity. Our projections also suggest a growing trend in glaucoma cases, with an anticipated ∼43% increase by the year 2040. Overall, our findings suggest that glaucoma is a significant health concern in older Asians, particularly among the oldest old, and in people of Indian and Malay descent. Targeted glaucoma screening and intervention programs for at-risk individuals to prevent and/or delay the development of glaucoma are warranted to reduce the burden of glaucoma and associated VI on patients and society.
The national age-standardized prevalence of any glaucoma in our study is 6.3%, surpassing the 5.8% prevalence reported in the SEED study among multiethnic Singaporeans.9 This difference may be partly due to the higher average age of participants in our study (mean age, 73.6 years) compared to the SEED study (mean age, 58.9 years). Additionally, the SEED study collected baseline data between 2004 and 2011, which may not accurately reflect recent demographic trends in population aging. Therefore, while these prevalence rates provide a snapshot in time, the higher rate observed in our study may better represent current aging trends in the general Singaporean population. Notably, our age-standardized prevalence for POAG is 4.13%, which exceeds SEED’s reported rate of 2.94%. Conversely, our prevalence rate for PACG is 0.84%, slightly lower than SEED’s rate of 1.17%. These variations may stem from differences in population characteristics between the two studies.
Consistently, our findings showed that the prevalence of any glaucoma and subtypes systematically increased with age, a trend supported by prior research and systematic review on glaucoma.3,6–8,24,25 Importantly, the highest prevalence of glaucoma and subtypes across the sex and ethnicity spectrum was observed in the oldest age group (i.e., those ≥80 years). Given the recent focus on healthy and meaningful aging, our findings support the allocation of additional public health resources to address the increasing burden of glaucoma, particularly among the oldest old. As previous studies on the prevalence of glaucoma in Singapore have only reported age-standardized prevalence, we are not able to compare our results for sex or ethnicity.
Similar to other studies, we also observed that higher IOP was associated with a higher likelihood of glaucoma, which corroborates available data in the literature.6–8 Although elevated IOP is no longer a defining criterion for the diagnosis of glaucoma, it remains an important factor and the only modifiable one found in our study. Importantly, effective treatments to lower IOP can play a crucial role in preventing the development and progression of glaucoma, highlighting the need for proactive management in at-risk populations. Longer eyeball length was associated with increased odds of any glaucoma, likely due to increased scleral stress in axially elongated eyes.26 However, longer AL was found to be protective against precursor conditions to PACG (i.e., PACS and PAC) likely because it reduces the likelihood of anterior chamber crowding, thereby lowering the risk of angle closure.27 A history of cataract surgery was associated with a higher likelihood of any glaucoma, possibly due to surgical trauma and postsurgical inflammation, which can obstruct aqueous humor outflow. It could also be because pre- and postoperative care for individuals with cataracts offers increased monitoring opportunities for the detection of glaucoma.28 Additionally, as cataract surgery is often the first-line surgical treatment option for glaucoma, there may be selection bias at play—namely, those with glaucoma are more likely to have had cataract surgery. However, cataract surgery was also found to be protective in our study against precursor conditions to PACG. Indeed, following the EAGLE trial, cataract surgery has become a therapeutic option in those with narrow angles who prefer not to undergo LPI.29 This helps prevent angle closure by increasing anterior chamber depth and reducing irido-trabecular contact. Interestingly, we observed that polypharmacy was associated with statistically significantly higher odds of having any glaucoma. Previous studies have linked certain systemic medications to glaucoma,30 and evidence suggests that multiple medications can influence glaucoma pathophysiology (e.g., corticosteroids can increase IOP, while certain antihypertensives might have protective effects).30 However, the specific effects of polypharmacy on glaucoma remain largely unexplored, highlighting the need for further validation in larger, multiethnic Asian cohorts.
Compared to people of Chinese descent, individuals of Malay ethnicity had increased odds of having any and most subtypes of glaucoma in our study, independent of other sociodemographic, medical, clinical, or lifestyle factors. Our findings align with ethnicity-related trends reported in the SEED study, with the prevalence of glaucoma being the highest in Malays (3.8%).9 Indeed, the SEED studies have also reported a higher proportion of Malays compared to Chinese or Indians having undiagnosed eye diseases such as cataract,31 glaucoma,32 diabetic retinopathy,33 and undercorrected refractive errors.34 These results could be attributed to ethnic disparities in health awareness, cultural preferences, and eye care utilization despite residing within the same health care system with equal access to health care.35–37 It is possible that targeted screening programs for Malays rather than community-wide screening efforts may be a more practicable and cost-effective approach to address the growing problem of glaucoma in older Asian adults.
In our study, nearly one-third of patients with glaucoma experienced VI, which is relatively high. The reasons for this are likely multifactorial. For example, the presence of age-related eye conditions like cataracts was high in our patients with glaucoma, with 33% of our patients with glaucoma with VI also having cataracts, compared to 23% of those without VI. Additionally, glaucoma is a progressive disease, and in older individuals, long-term damage from elevated IOP often results in more advanced stages of the condition. In the PIONEER study, 47% of those with VI had severe glaucoma, compared to just 29% of those without VI. Older patients also tend to have more comorbidities, which can complicate the management of glaucoma and potentially contribute to a higher prevalence of VI. This is despite the availability of easily accessible and affordable health care services in Singapore, with public health insurance packages like Medisave, Pioneer Generation, and Merdeka benefits covering up to 80% of the cost of medical procedures in both public and private sectors. These findings suggest a need for improving eye health awareness and ensuring that elderly patients get regular ophthalmic assessments to prevent or delay the development and progression of glaucoma and associated vision loss in this growing segment of our population.
While the overall age-adjusted prevalence of glaucoma in Singapore has remained relatively stable, our study observed a notable increase in age-standardized prevalence rates of glaucoma among Indians over the past two decades. This rise may be linked to the high rates of diabetes, hypertension, and CVD observed in this ethnic group in the PIONEER study (Supplementary Tables S3 and S4). As a result, Indians may have increased opportunities for eye examinations, including screenings for diabetic retinopathy. This increased frequency of eye examinations could lead to more cases of glaucoma being detected, contributing to the higher observed prevalence. Looking forward, our projections suggest a growing trend in glaucoma cases, with an estimated overall increase of approximately 43% over the next 15 years.
Our study has several strengths, including its large, geographically representative cohort of older adults that is well characterized and ethnically diverse, enhancing the generalizability of our findings to the broader Singaporean community. We employed robust grading and definitions of glaucoma, along with comprehensive multivariable adjustments for various relevant confounders. Notably, unlike other studies such as SEED, where gonioscopy was limited to glaucoma suspects, our study included gonioscopy for all phakic participants, allowing for a more thorough assessment of angle anatomy and improving the reliability of our results. However, some important limitations should be noted. We did not collect data on family history of glaucoma, an important risk factor for the condition. In our study, IOP was assessed with a single measurement, while trials like the ocular hypertension study (OHTS) used multiple Goldmann applanation tonometr (GAT) readings. Future research should consider using at least two measurements,6,38 adding a third if the first two differ by over 2 mm Hg, or using the median of three consecutive readings.39 It is important to note that this limitation does not affect the glaucoma estimates reported, as glaucoma was not defined using IOP in our study. While the majority of glaucoma diagnoses in our study are IOP-independent, there are specific clinical scenarios where IOP contributes to the classification. For example, IOP is incorporated into the diagnostic criteria for certain categories, such as PAC and ISGEO Category 3. This may partially influence its observed association with glaucoma. The substantial proportion of missing VCDR data (∼38%) may have introduced bias in estimating the prevalence of VCDR > 0.60 and could limit the robustness of conclusions about its association with glaucoma risk in our population. We assessed only CCT and socioeconomic status. Other potentially relevant factors, such as disc size, parapapillary zones, and anterior chamber depth (ACD), were not included and should be explored in future research. We also did not perform multiple-testing corrections in our analyses, meaning some associations could be spurious, and thus the findings may need to be interpreted with caution. Lastly, our data are cross-sectional, which limits our ability to make cause-and-effect inferences. To address this gap, we are conducting a 4-year follow-up study of PIONEER participants (PIONEER-2), which will help us better understand the risk factors and underlying mechanisms related to glaucoma.
In conclusion, the prevalence of glaucoma in community-dwelling Singaporean adults aged above 60 years in our study was 5.1%, with various sociodemographic and modifiable clinical risk factors associated with increased likelihood of developing the condition. While the overall prevalence of glaucoma in Singapore has remained stable over the past 20 years, there has been a significant increase among the Indian population. Projections over the next 15 years suggest an approximate 43% rise in the total number of glaucoma cases. These findings highlight the importance of targeted screening programs to detect and manage glaucoma in high-risk individuals, to prevent the development of this debilitating disease in older community-dwelling adults.
Supplementary Material
Acknowledgments
Supported by the National Medical Research Council Senior Clinician Scientist Award (NMRC-CSA-SI #JRNMRR140601 and JRNMRR197001; ELL) and also supported by the Singapore Ministry of Health's National Medical Research Council (NMRC/HCSAINV/MOH-001019-00; CS).
Author Contributions: P.G., E.L.L. had full access to all the data in the study and take responsibility for the integrity of the data and the accuracy of the data analysis; Study concept and design: P.G., E.L.L.; Acquisition, analysis, or interpretation of data: P.G., Wong, E.K.F., R.E.K.M., S.T., E.L.L.; Drafting of manuscript: P.G., E.K.F., R.E.K.M., S.T., E.L.L.; Critical revision of the manuscript for important intellectual content: P.G., E.K.F., R.E.K.M., Wong, C.S., O.H., J.R.L., S.A.P., Wong, S.T., E.L.L.; Obtained funding: E.L.L. (Study PI); Statistical analysis: P.G., Wong; Administrative, technical, or material support: P.G.; Study Supervision: P.G., E.L.L.
The funding sources had no role in design and conduct of the study; collection, management, analysis, and interpretation of the data; preparation, review, or approval of the manuscript; or in the decision to submit the manuscript for publication.
Disclosure: P. Gupta, None; S. Thakur, None; C.M.J. Wong, None; R.E.K. Man, None; E.K. Fenwick, None; C. Sabanayagam, None; O. Huang, None; J.R. Low, None; S.A. Perera, None; T.T. Wong, None; E.L. Lamoureux, None
References
- 1. United Nations. World Population Prospects 2019: Data Booklet. New York, NY: United Nations; 2019. [Google Scholar]
- 2. GBD 2019 Blindness and Vision Impairment Collaborators. Trends in prevalence of blindness and distance and near vision impairment over 30 years: an analysis for the Global Burden of Disease Study. Lancet Glob Health. 2021; 9: e130–e143. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 3. Tham YC, Li X, Wong TY, Quigley HA, Aung T, Cheng C-Y. Global prevalence of glaucoma and projections of glaucoma burden through 2040: a systematic review and meta-analysis. Ophthalmology. 2014; 121(11): 2081–2090. [DOI] [PubMed] [Google Scholar]
- 4. Chan EW, Li X, Tham YC, et al.. Glaucoma in Asia: regional prevalence variations and future projections. Br J Ophthalmol. 2016; 100(1): 78–85. [DOI] [PubMed] [Google Scholar]
- 5. Foster PJ, Oen FT, Machin D, et al.. The prevalence of glaucoma in Chinese residents of Singapore: a cross-sectional population survey of the Tanjong Pagar district. Arch Ophthalmol. 2000; 118(8): 1105–1111. [DOI] [PubMed] [Google Scholar]
- 6. Baskaran M,, Foo RC, Cheng CY, et al.. The prevalence and types of glaucoma in an urban Chinese population: the Singapore Chinese Eye Study. JAMA Ophthalmol. 2015; 133(8): 874–880. [DOI] [PubMed] [Google Scholar]
- 7. Narayanaswamy A, Baskaran M, Zheng Y, et al.. The prevalence and types of glaucoma in an urban Indian population: the Singapore Indian Eye Study. Invest Ophthalmol Vis Sci. 2013; 54(7): 4621–4627. [DOI] [PubMed] [Google Scholar]
- 8. Shen SY, Wong TY, Foster PJ, et al.. The prevalence and types of glaucoma in Malay people: the Singapore Malay eye study. Invest Ophthalmol Vis Sci. 2008; 49(9): 3846–3851. [DOI] [PubMed] [Google Scholar]
- 9. Majithia S, Tham YC, Chee ML, et al.. Cohort profile: the Singapore Epidemiology of Eye Diseases study (SEED). Int J Epidemiol. 2021; 50(1): 41–52. [DOI] [PubMed] [Google Scholar]
- 10. Report of the Inter-Ministerial Committee on the Ageing Population, Ministry of Community Development. Singapore; 1999. [Google Scholar]
- 11. Gupta P, Man REK, Fenwick EK, et al.. Rationale and methodology of the PopulatION HEalth and Eye Disease PRofile in Elderly Singaporeans Study [PIONEER]. Aging Dis. 2020; 11(6): 1444–1458. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 12. United Nations, Department of Economic and Social Affairs, Population Division. World Population Prospects. 2024, https://population.un.org/wpp/. Accessed August 21, 2024.
- 13. World Health Organization. International classification of diseases for mortality and morbidity statistics (11th revision). 2020, https://icd.who.int/browse11/l-m/en. Accessed May 25, 2020.
- 14. Thakur S, Soh ZD, Tham YC, et al.. Six-year incidence and risk factors for primary open-angle glaucoma and ocular hypertension: the Singapore Epidemiology of Eye Diseases Study. Ophthalmol Glaucoma. 2024; 7(2): 157–167. [DOI] [PubMed] [Google Scholar]
- 15. Teo ZL, Soh ZD, Tham YC, et al.. Six-year incidence and risk factors for primary angle-closure disease: the Singapore Epidemiology of Eye Diseases Study. Ophthalmology. 2022; 129(7): 792–802. [DOI] [PubMed] [Google Scholar]
- 16. Foster PJ, Buhrmann R, Quigley HA, Johnson GJ. The definition and classification of glaucoma in prevalence surveys. Br J Ophthalmol. 2002; 86(2): 238–242. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 17. Chua J, Tham YC, Liao J, et al.. Ethnic differences of intraocular pressure and central corneal thickness: the Singapore Epidemiology of Eye Diseases study. Ophthalmology. 2014; 121(10): 2013–2022. [DOI] [PubMed] [Google Scholar]
- 18. Liu Q, Liu C, Cheng W, et al.. Clinical analysis of secondary glaucoma in Central China. Sci Rep. 2023; 13(1): 8439. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 19. Thau A, Lloyd M, Freedman S, Beck A, Grajewski A, Levin AV. New classification system for pediatric glaucoma: implications for clinical care and a research registry. Curr Opin Ophthalmol. 2018; 29(5): 385–394. [DOI] [PubMed] [Google Scholar]
- 20. Neelakantan N, Whitton C, Seah S, et al.. Development of a semi-quantitative food frequency questionnaire to assess the dietary intake of a multi-ethnic urban Asian population. Nutrients. 2016; 8(9)528. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 21. Tan KHX, Tan LWL, Sim X, et al.. Cohort profile: the Singapore Multi-Ethnic Cohort (MEC) study. Int J Epidemiol. 2018; 47(3): 699–699j. [DOI] [PubMed] [Google Scholar]
- 22. American Diabetes Association. Standards of medical care in diabetes—2010. Diabetes Care. 2010; 33(suppl 1): S11–S61. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 23. Shankar A, Leng C, Chia KS, et al.. Association between body mass index and chronic kidney disease in men and women: population-based study of Malay adults in Singapore. Nephrol Dial Transplant. 2008; 23(6): 1910–1918. [DOI] [PubMed] [Google Scholar]
- 24. Zhang N, Wang J, Li Y, Jiang B, Prevalence of primary open angle glaucoma in the last 20 years: a meta-analysis and systematic review. Sci Rep. 2021; 11(1): 13762. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 25. Rudnicka AR, Mt-Isa S, Owen CG, Cook DG, Ashby D. Variations in primary open-angle glaucoma prevalence by age, gender, and race: a Bayesian meta-analysis. Invest Ophthalmol Vis Sci. 2006; 47(10): 4254–4261. [DOI] [PubMed] [Google Scholar]
- 26. Markov PP, Eliasy A, Pijanka JK, et al.. Bulk changes in posterior scleral collagen microstructure in human high myopia. Mol Vis. 2018; 24: 818–833. [PMC free article] [PubMed] [Google Scholar]
- 27. George R, Paul PG, Baskaran M, et al.. Ocular biometry in occludable angles and angle closure glaucoma: a population based survey. Br J Ophthalmol. 2003; 87(4): 399–402. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 28. Arvind H, George R, Raju P, et al.. Glaucoma in aphakia and pseudophakia in the Chennai Glaucoma Study. Br J Ophthalmol. 2005; 89(6): 699–703. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 29. Azuara-Blanco A, Burr J, Ramsay C, et al.. Effectiveness of early lens extraction for the treatment of primary angle-closure glaucoma (EAGLE): a randomised controlled trial. Lancet. 2016; 388(10052): 1389–1397. [DOI] [PubMed] [Google Scholar]
- 30. Vergroesen JE, Schuster AK, Stuart KV, et al.. Association of systemic medication use with glaucoma and intraocular pressure: the European Eye Epidemiology Consortium. Ophthalmology. 2023; 130(9): 893–906. [DOI] [PubMed] [Google Scholar]
- 31. Chua J, Lim B, Fenwick EK, et al.. Prevalence, risk factors, and impact of undiagnosed visually significant cataract: the Singapore Epidemiology of Eye Diseases Study. PLoS One. 2017; 12(1): e0170804. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 32. Chua J, Baskaran M, Ong PG, et al.. Prevalence, risk factors, and visual features of undiagnosed glaucoma: the Singapore Epidemiology of Eye Diseases Study. JAMA Ophthalmol. 2015; 133(8): 938–946. [DOI] [PubMed] [Google Scholar]
- 33. Huang OS, Tay WT, Ong PG, et al.. Prevalence and determinants of undiagnosed diabetic retinopathy and vision-threatening retinopathy in a multiethnic Asian cohort: the Singapore Epidemiology of Eye Diseases (SEED) study. Br J Ophthalmol. 2015; 99(12): 1614–1621. [DOI] [PubMed] [Google Scholar]
- 34. Rosman M, Wong TY, Tay WT, Tong L, Saw SM. Prevalence and risk factors of undercorrected refractive errors among Singaporean Malay adults: the Singapore Malay Eye Study. Invest Ophthalmol Vis Sci. 2009; 50(8): 3621–3628. [DOI] [PubMed] [Google Scholar]
- 35. Gupta P, Majithia S, Fenwick EK, et al.. Rates and determinants of eyecare utilization and eyeglass affordability among individuals with visual impairment in a multi-ethnic population-based study in Singapore. Transl Vis Sci Technol. 2020; 9(5): 11. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 36. Gomersall T, Madill A, Summers LK. A metasynthesis of the self-management of type 2 diabetes. Qual Health Res. 2011; 21(6): 853–871. [DOI] [PubMed] [Google Scholar]
- 37. Muhamad M, Merriam S, Suhami N. Why breast cancer patients seek traditional healers. Int J Breast Cancer. 2012; 2012: 689168. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 38. Kass MA, Heuer DK, Higginbotham EJ, et al.. Assessment of cumulative incidence and severity of primary open-angle glaucoma among participants in the ocular hypertension treatment study after 20 years of follow-up. JAMA Ophthalmol. 2021; 139(5): 1–9. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 39. Dielemans I, Vingerling JR, Hofman A, Grobbee DE, de Jong PT, Reliability of intraocular pressure measurement with the Goldmann applanation tonometer in epidemiological studies. Graefes Arch Clin Exp Ophthalmol. 1994; 232(3): 141–144. [DOI] [PubMed] [Google Scholar]
Associated Data
This section collects any data citations, data availability statements, or supplementary materials included in this article.