Prevalence, Correlates, and Impact of Uncorrected Presbyopia in a Multiethnic Asian Population




Purpose


To examine the prevalence, correlates, and impact of uncorrected presbyopia on vision-specific functioning (VF) in a multiethnic Asian population.


Design


Population-based cross-sectional study.


Methods


We included 7890 presbyopic subjects (3909 female; age range, 40-86 years) of Malay, Indian, and Chinese ethnicities from the Singapore Epidemiology of Eye Disease study. Presbyopia was classified as corrected and uncorrected based on self-reported near correction use. VF was assessed with the VF-11 questionnaire validated using Rasch analysis. Multivariable logistic and linear regression models were used to investigate the associations of sociodemographic and clinical parameters with uncorrected presbyopia, and its impact on VF, respectively. As myopia may mitigate the impact of noncorrection, we performed a subgroup analysis on myopic subjects only (n = 2742).


Results


In total, 2678 of 7890 subjects (33.9%) had uncorrected presbyopia. In multivariable models, younger age, male sex, Malay and Indian ethnicities, presenting distance visual impairment (any eye), and lower education and income levels were associated with higher odds of uncorrected presbyopia (all P < .05). Compared with corrected presbyopia, noncorrection was associated with worse overall VF and reduced ability to perform individual near and distance vision-specific tasks even after adjusting for distance VA and other confounders (all P < .05). Results were very similar for myopic individuals.


Conclusion


One-third of presbyopic Singaporean adults did not have near correction. Given its detrimental impact on both near and distance VF, public health strategies to increase uptake of presbyopic correction in younger individuals, male individuals, and those of Malay and Indian ethnicities are needed.


Presbyopia, an age-related inability to focus on near objects owing to a loss of accommodative amplitude, is believed to be extremely prevalent or nearly universal in individuals above the age of 65, although recent literature has suggested that this accommodative loss may be complete by as early as 50 years of age. Although not a blinding condition, presbyopia significantly reduces quality of life (QoL), especially if not corrected. However, direct estimates of the prevalence of uncorrected presbyopia, its associated risk factors, and its impact on QoL are limited, particularly in Asian populations.


In Asia, the rates of myopia are extremely high. For instance, almost half of Singaporeans aged 40 years and above have some degree of myopia. Uncorrected myopia may help mitigate the clinical impact of presbyopia, since the focal point of objects in myopic eyes falls further in front of the retina as compared to nonmyopic eyes; this consequently results in less accommodative amplitude needed to bring near objects into focus. Unfortunately, the impact, if any, of this high rate of myopia on uncorrected presbyopia is unknown.


In this study, we investigated the prevalence, correlates, and impact of uncorrected presbyopia on vision-specific functioning (VF) in a multiethnic sample of Asian adults in Singapore. A secondary aim was to determine the impact of the high rates of myopia on the determinants and impact of uncorrected presbyopia in this sample population.


Methods


Study Population


The Singapore Epidemiology of Eye Diseases (SEED) study comprises 10 033 individuals from 3 large population-based cross-sectional studies of Malay (Singapore Malay Eye Study, 2004-2006; N = 3280), Indian (Singapore Indian Eye Study, 2007-2009; N = 3400), and Chinese (Singapore Chinese Eye Study, 2009-2011; N = 3353) ethnicities. All studies followed the same study protocol, were conducted in the same study center (Singapore Eye Research Institute [SERI]), and recruited adults aged 40-80 years residing in the southwestern part of Singapore through an age-stratified random sampling method. The SEED methodology and population characteristics have been published elsewhere. The studies were conducted in accordance with the Declaration of Helsinki and written informed consent was obtained from all participants. All studies received approval from the SERI Institutional Review Board (R341/34/2003 for the Malay population and R498/47/2006 for the Indian and Chinese populations). For the current analysis, we excluded individuals who were not presbyopic (see definition below, N = 37), who had near vision impairment due to ocular morbidities unrelated to presbyopia (n=1291), and/or had missing data (N = 815), leaving a total of 7890 presbyopic participants for analyses.


Assessment of Near Vision and Definition of Presbyopia


Near vision was tested unilaterally by adding increments of +0.25 diopters (D) to a participant’s best-corrected distance vision correction while having them read a near logarithm of the minimal angle of resolution (logMAR) chart (Lighthouse International, New York, New York, USA) at standard reading distance (40 cm) until no further improvement in number of lines read could be observed. Presbyopia was defined as requiring a near correction of ≥+1.00 D added to a participant’s best-corrected distance vision correction to obtain a near visual acuity (VA) of ≤N8 (equivalent to 0.2 logMAR units) on the near logMAR chart in either eye (ie, objective presbyopia). We further confirmed that all eyes meeting the above definition had a best-corrected distance VA ≤0.2 logMAR units, hence minimizing the possibility of near vision impairment (VI) due to nonpresbyopic causes. Presbyopic individuals were then further categorized as corrected and uncorrected based on self-reported use of near correction (ie, reading glasses, bifocals, or multifocal glasses) obtained from standardized questionnaires.


Visual Functioning


VF was assessed with the VF-11, a modified version of VF-14 that has been changed to suit the local cultural context. The administration and Rasch validation of the VF-11 in an Asian population has been comprehensively described previously. Briefly, 11 VF questions were used to assess the level of difficulty in performing the following activities: seeing stairs, seeing street or shop signs, recognizing people, watching television, cooking, playing cards (or mahjong), reading newspapers, completing lottery forms, reading small print, driving in the day, and driving at night. The first 9 items are rated on a numeric scale ranging from 0 “no difficulty” to 4 “unable to perform activity” and the 2 driving items are rated on a 3-point scale ranging from 0 “no difficulty” to 3 “a great deal of difficulty.”


Psychometric Assessment of the VF-11


Rasch analysis was undertaken to determine the validity and measurement characteristics of the VF-11 using Winsteps software (version 3.91.2; Chicago, Illinois, USA) and the Andrich rating scale model. In brief, Rasch analysis is a form of item response theory, where ordinal ratings of the questionnaire are transformed into estimates of measures on an interval scale in logits, which improves measurement precision and limits measurement “noise.” Rasch analysis also provides extensive insight into the psychometric properties of the scale, including response category functioning, measurement precision, item “fit” to the underlying construct (eg, visual functioning), unidimensionality (ie, measurement of a single construct), targeting of item difficulty to subjects’ ability, and differential item functioning (DIF)/item bias. Rasch analysis is important for studies using rating scales, as loss of measurement quality owing to participants’ poor understanding of questions or underutilization of response categories can reduce the value of clinical research.


During Rasch analysis, coding of the VF-11 was reversed so that a higher score indicates that a person possesses better visual functioning and vice versa. The VF-11 demonstrated satisfactory fit to the Rasch model, with ordered thresholds, good range-based precision, no evidence of multidimensionality, and no DIF. However, item 6 “ playing games such as chess or cards” displayed substantial misfit (infit mean square = 1.89) and had to be removed. Following this, item 11 “driving at night” misfit slightly (1.37); however, removal of this item did not improve other fit parameters and it was therefore retained. Targeting of the composite VF-11 score was suboptimal (difference between person and item means 3.99 logits, meaning that the participants’ mean VF levels were higher than what was required to complete the VF tasks), which is not unexpected in a population-based sample where most participants were not visually impaired. In addition to the composite VF-11 score, we generated transformed individual person scores for each of the 10 items remaining after Rasch analysis.


Assessment of Other Covariates


An interviewer-administered questionnaire, standardized across all 3 studies, was used to obtain information on sociodemographic and lifestyle factors. Ethnicities were defined by the Singapore census and as indicated on the National Registration Identity Card. Participants were given the choice to be interviewed in English, Chinese, Malay, or Tamil. Variables collected include age (years), sex, ethnicity (Malay/Indian/Chinese), smoking (current/ex-smoker or nonsmoker), alcohol consumption (yes/no), highest education levels attained (<6 years/≥6 years), income (<SGD1000/≥SGD1000), and medical history (self-reported history of angina, myocardial infarction, hypertension, hyperlipidemia, thyroid problems, stroke, and diabetes).


Clinical covariates were obtained via a standardized clinical examination. Presenting distance VA was checked monocularly with participant wearing current refractive correction (if any) using a logMAR number chart at a distance of 4 meters under standard lighting by a trained optometrist. Subjective refraction, followed by assessment of best-corrected distance and near VA, was then conducted. Undercorrected refractive error was defined as an improvement of ≥2 lines on the logMAR chart after subjective refraction. Myopia was defined as spherical equivalent (SE) <-0.50 D, hyperopia was defined as SE >+1.00 D, and emmetropia as SE −0.5 to +1.00 D.


A comprehensive eye examination was conducted by an ophthalmologist, including assessment of pupillary reaction and slit-lamp biomicroscopy (Haag-Streit model BQ-900; Haag-Streit, Bern, Switzerland) for anterior segment abnormalities; measurement of intraocular pressure (IOP) with a Goldmann applanation tonometer (Haag-Streit); and gonioscopy with a Goldmann 2-mirror lens (Ocular Instruments, Inc, Bellevue, Washington, USA) under standard dark illumination for angles of the anterior chamber. All participants with IOP >21 mm Hg or narrow anterior chamber angles (temporal peripheral Van Herick grade 2 or less) underwent static automated perimetry (Swedish Interactive Threshold Algorithm standard 24-2, Humphrey Field Analyzer II; Carl Zeiss Meditec, Dublin, California, USA). A detailed examination of the lens, vitreous, and posterior segment was then conducted after pupillary dilation with 1% tropicamide and 2.5% phenylephrine hydrochloride. Two-field fundus photographs were taken using a Canon DGI nonmydriatic fundus camera and graded by trained graders at SERI for presence of any retinal abnormalities. Presence of ocular morbidities was determined by the ophthalmologist based on standardized definitions; glaucoma was diagnosed and classified using the International Society of Geographical and Epidemiological Ophthalmology Scheme, based on gonioscopy, optic disc characteristics, and visual fields results. Age-related macular degeneration (AMD) was graded from retinal photographs according to the Wisconsin Age-related Maculopathy grading system. Diabetic retinopathy (DR) was graded from retinal photographs according to a modification of the Airlie House classification system as used in the Early Treatment Diabetic Retinopathy Study.


Systolic and diastolic blood pressure (BP) was measured twice using a digital automatic blood pressure monitor (Dinamap Pro Series DP110X-RW; GE Medical Systems Information Technologies, Inc, Buckinghamshire, United Kingdom), with the average value for each parameter used in analyses. A third measurement was obtained if the 2 previous systolic BP readings differed by more than 10 mm Hg or if diastolic BP readings differed by more than 5 mm Hg. Blood samples were collected for biochemistry analysis (HbA1c, random glucose, total and low-density lipoprotein- and high-density lipoprotein-cholesterol), and DNA extraction. Based on biochemistry analyses and BP measurements, diabetes mellitus was defined as having a random glucose level of at least 200 mg/dL (to convert glucose level to millimoles per liter, multiply by 0.0555); hypertension as having a systolic BP of ≥140 mm Hg or a diastolic BP of ≥90 mm Hg; and hyperlipidemia as a total cholesterol level of at least 239 mg/dL (to convert cholesterol level to millimoles per liter, multiply by 0.0259).


Statistical Analyses


All analyses were conducted using Stata 12 for Windows (StataCorp LC, College Station, Texas, USA). To evaluate the associations of clinical and sociodemographic characteristics with uncorrected presbyopia, participants’ characteristics with and without near correction were first compared using χ 2 statistic for proportions and a t test/Mann-Whitney U test for means or median as appropriate. Age-sex and multivariable logistic regression models adjusted for variables found to be significantly different between the 2 groups of participants ( Table 1 ), as well as confounders of uncorrected presbyopia reported in previous research, were used to determine the independent determinants of uncorrected presbyopia in our sample population. These confounders include age, sex, ethnicity, smoking status, history of systemic diseases (angina, myocardial infarction, thyroid problems, hypertension, hyperlipidemia, stroke, hypertension, diabetes), presence of eye conditions (undercorrected refractive error, glaucoma, AMD, cataract, DR), education, income, and presence of presenting VI in either eye. Interactions were checked for age, sex, income status, education, and ethnicity, with a P value <.1 deemed statistically significant.



Table 1

Clinical and Demographic Characteristics of 7890 Presbyopic Participants Stratified by Self-Reported Presence of Near Vision Correction























































































































Characteristics Uncorrected (n = 2678) Corrected (n = 5212) P Value
Age (y) 55.6 (10.2) 58.0 (9.0) <.001*
Age (%)
40-49 38.2 22.4 <.001*
50-59 29.3 37.5
60-69 19.7 28.1
≥70 12.7 12.0
Sex (%)
Male 50.4 50.5 .95
Race (%)
Chinese 29.2 36.7 <.001*
Malay 37.2 27.4
Indian 33.6 35.9
Smoking (%)
Yes 18.4 15.5 .001*
Alcohol consumption (%)
Yes 8.7 9.5 .22
Presence of systemic diseases a (%)
Yes 73.7 77.5 <.001*
Presence of eye diseases b (%)
Yes 43.6 46.1 .06
Education (%)
> Primary school 41.1 44.6 .003*
≤ Primary school 58.9 55.4
Income (SGD)
≥1000 49.2 51.0 .13
<1000 50.8 49.0
Presenting distance visual impairment (%)
Yes 41.0 34.8 <.001*

Asterisk indicates statistically significant results.

a Angina, heart attack, stroke, hypertension, hyperlipidemia, thyroid problems.


b Undercorrected refractive error, glaucoma, age-related macular degeneration, cataract, diabetic retinopathy.



To assess the impact of uncorrected presbyopia on VF, linear regression models were first used to determine the unadjusted associations of uncorrected presbyopia with the composite VF-11 score. The independent associations of uncorrected presbyopia on overall VF and individual visual tasks were determined using multiple linear regression models adjusted for age, sex, ethnicity, education, income, presenting VA in the better eye (in log units), presence of eye diseases, and comorbidities. These confounders were found to be associated with VF in unadjusted analyses ( Table 3 ) and/or have been associated with VF in previous research conducted by our group in this population.


Because most myopic individuals may have objective, but not functional, presbyopia (defined as an improvement of ≥1 line on the near logMAR chart with near correction added to presenting distance visual correction), owing to the mitigating effect of myopia on uncorrected presbyopia (as explained in the introduction), we conducted the same analyses on myopic participants only to evaluate if there was a difference in determinants and impact of uncorrected presbyopia as compared to individuals without myopia.




Results


A total of 7890 participants with presbyopia (age range, 40-86 years) were included in the analyses. The mean age (SD) was 57.2 (9.5) years and 3981 subjects (50.4%) were male. Of the 2742 (34.7%) individuals with presbyopia who also had myopia, the mean (SD) refractive error was −2.65 (2.34) D for the right eye (range −18.25 to −0.75 D) and −2.59 (2.25) D for the left eye (range −20.5 to −0.75 D). In total, there were 2678 (33.9%) participants with uncorrected presbyopia, of which 1522 (56.8%) were myopic. Those with uncorrected presbyopia tended to be younger, of Malay and Indian ethnicities, current smokers, less likely to have any systemic diseases, more likely to have primary school education or below, and more likely to have presenting distance VI ( Table 1 , all P < .05).


In age- and sex-adjusted models ( Table 2 ), older age both continuously (odds ratio [OR]: 0.97, 95% confidence interval [CI]: 0.96-0.97, per year increase) and categorically ( P trend: <.001) was associated with lower odds of having uncorrected presbyopia, while Malay (OR: 1.67, 95% CI: 1.49-1.88) and Indian ethnicities (OR: 1.12, 95% CI: 1.00-1.26), having primary school education and below (OR: 1.36, 95% CI: 1.23-1.50, compared to high school education and above) and income levels <SG$1000 (OR: 1.44, 95% CI: 1.29-1.60, compared to income ≥SG$1000), current smokers (OR: 1.15, 95% CI: 1.01-1.32, compared to nonsmokers), and having presenting distance VI (OR: 1.59, 95% CI: 1.44-1.76) were all associated with higher likelihood of uncorrected presbyopia (Model 1). These associations remained after multivariable adjustments, with the exception of smoking, which became nonsignificant, and male sex, which turned out to be significantly associated with greater odds of uncorrected presbyopia (OR: 1.14, 95% CI: 1.01-1.27, compared to female sex; Model 2). No significant interactions were found between age, sex, income status, education, and ethnicity (all P > .1).



Table 2

Factors Associated With Uncorrected Presbyopia in Presbyopic Participants (n = 7890)






































































































































































Parameters (N) Uncorrected (%) Model 1 a
OR (95% CI)
Model 2 b
OR (95% CI)
Age (y)
40-49 2192 46.7 Reference Reference
50-59 2741 28.6 0.45 (0.40-0.51)* 0.42 (0.37-0.48)*
60-69 1991 26.5 0.41 (0.36-0.46)* 0.34 (0.29-0.40)*
70+ 966 35.3 0.62 (0.53-0.72)* 0.46 (0.38-0.56)*
P trend <.001* P trend <.001*
Age (per year increase) 7890 0.97 (0.96-0.97)* 0.96 (0.95-0.96)*
Sex
Female 3909 33.9 Reference Reference
Male 3981 33.9 1.03 (0.94-1.13) 1.14 (1.01-1.27)*
Race
Chinese 2422 29.0 Reference Reference
Malay 2771 41.1 1.67 (1.49-1.88)* 1.58 (1.40-1.78)*
Indian 2697 32.4 1.12 (1.00-1.26)* 1.11 (1.00-1.25)*
Smoking
No 6590 33.2 Reference Reference
Yes 1300 37.8 1.15 (1.01-1.32)* 1.01 (0.88-1.16)
History of systemic diseases c
No 1876 37.6 Reference Reference
Yes 6014 32.8 0.97 (0.87-1.09) 0.93 (0.82-1.04)
Presence of eye diseases d
No 5167 35.2 Reference Reference
Yes 2723 31.5 1.08 (0.98-1.20) 0.96 (0.87-1.07)
Education
> Primary school 3424 32.1 Reference Reference
≤ Primary school 4466 35.3 1.36 (1.23-1.50)* 1.16 (1.04-1.29)*
Presence of presenting VI
No 4978 31.8 Reference Reference
Yes 2912 37.7 1.59 (1.44-1.76)* 1.59 (1.43-1.77)*
Income (SGD)
≥1000 3976 34.7 Reference Reference
<1000 3914 32.8 1.44 (1.29-1.60)* 1.22 (1.08-1.37)*

Asterisk indicates statistically significant results.

CI = confidence interval; OR = odds ratio; VI = vision impairment.

Interactions checked for age, sex, income status, education, and ethnicity. All P > .1.

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Jan 6, 2017 | Posted by in OPHTHALMOLOGY | Comments Off on Prevalence, Correlates, and Impact of Uncorrected Presbyopia in a Multiethnic Asian Population
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