Background/aims: Low socio-economic status is increasingly being identified as a risk marker for chronic diseases, but few studies have investigated the link between socio-economic factors and age-related macular degeneration (AMD). The present study aimed to assess the association between socio-economic status and the prevalence of AMD.
Methods: A population-based cross-sectional study of 3280 (78.7% response rate) Malay adults aged 40–80 years residing in 15 south-western districts of Singapore. AMD was graded from retinal photographs at a central reading centre using the modified Wisconsin AMD scale. Early and late AMD signs were graded from retinal photographs following the Wisconsin grading system. Socio-economic status including education, housing type and income were determined from a detailed interview.
Results: Of the participants, 3265 had photographs of sufficient quality for grading of AMD. Early AMD was present in 168 (5.1%) and late AMD in 21 (0.6%). After adjusting for age, gender, smoking, hypertension, diabetes and body mass index, participants with lower educational levels were significantly more likely to have early AMD (multivariate OR 2.2, 95% CI 1.2 to 4.0). This association was stronger in persons who had never smoked (multivariate OR 3.6, 95% confidence CI 1.4 to 9.4). However, no association with housing type or income was seen.
Conclusions: Low educational level is associated with a higher prevalence of early AMD signs in our Asian population, independent of age, cardiovascular risk factors and cigarette smoking.
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Age-related macular degeneration (AMD) is the main cause of blindness in elderly patients not only in the Western World, but also increasingly in Asian countries.1 2 The pathogenesis of AMD is likely a result of combined influences from both genetic and environmental risk factors.3 Recently genetic susceptibility to AMD has been confirmed.4 5 Despite this, some studies suggest that genetic determinants are responsible for only between 25% and 50% of AMD cases,6 so that further identifying other risk factors for AMD continues to be important. It also seems likely that genetic factors will interact with established (modifiable) risk factors, such as cigarette smoking, to increase an individual’s susceptibility.
There is increasing recognition that socio-economic status (SES) variables are important markers or surrogates of many factors associated with major chronic diseases, such as coronary heart disease and certain cancers.7 8 Only a few studies have examined the relationship of SES with AMD. In the Age-Related Eye Disease Study (AREDS), education level was found to be inversely related to the presence of one or more large drusen or extensive intermediate drusen, geographic atrophy and choroidal neovascularisation.9 Other studies, however, have not found any relationship between education level and AMD.10–12 Importantly, these studies have all been conducted in Caucasian white populations. Whether a similar lack of association is present in Asians and other racial/ethnic groups remains unclear and requires investigation especially as the prevalence and genetic risk factors for AMD are known to differ between Asians and Caucasians.13 14 In this paper, we assessed the associations between SES, specifically education level, housing type and income, and the prevalence of AMD in an Asian Malay population.
MATERIALS AND METHODS
The Singapore Malay Eye Study (SiMES) is a new population-based cross-sectional study of Malay adults, conducted to assess prevalence, and risk factors and the public health impact of common age-related eye diseases in an urban Asian population. Singapore has a population of approximately 4 million people, comprises three main ethnic groups, namely Chinese (77%), Malay (14%) and Indian (8%), and is located in South-East Asia. Detailed population selection and methodology are reported elsewhere.15 16 An age-stratified (by 10-year age group) random sample of the Malay population residing in 15 residential districts aged 40–80 years was drawn from the computer-generated random list of 16 069 Malay names provided by the Ministry of Home Affairs. In total, 3280 individuals participated in the study (overall response rate 78.7%).
Measures of SES
A structured interviewer administered questionnaire was used to collect socio-economic, demographic and medical information, including age, sex, marital status, level of education obtained, occupation, income and family history of diabetes and hypertension. We assess SES variables based on education, income and housing. These variables have previously been strongly correlated with myopia in an adult Singapore population.17 Education level was recorded as the highest number of years of schooling completed and was categorised into two groups: (1) primary school level or lower (⩽6 years) and (2) secondary school level or higher (⩾7 years, including university education). Income level was defined as individual monthly income in Singapore dollars (S$) (S$1.00 = £0.37) and was divided into two categories: (1) low (<1000) and (2) middle and high (⩾1000). Housing was categorised into two categories: (1) 1–2 room public housing and (2) 3–5 room public or private housing. Questions on lifestyle included smoking habits and alcohol consumption.
At the study clinic, participants underwent an extensive and standardised examination procedure, which included visual acuity (VA) testing, a detailed clinical slit-lamp and fundus examination before and after pupil dilation. Colour digital fundus photography of the macular region was also performed. Images were examined for the presence of AMD and were graded following the Wisconsin Age Related Maculopathy grading system by the same grader who also performed the AMD grading in the Blue Mountains Eye Study at the Centre for Vision Research, University of Sydney.18
Age was defined as the age at the time of examination. Age was categorised into 10-year age groups: 40–49, 50–59, 60–69 and 70–80 years. In the current study, we examined education, income and housing and created a low-SES group variable combining the observed high-risk categories of primary or lower education and income <S$1000. Although occupation was also assessed, it was not included as an SES indicator in our study, as it was not available for all individuals, and the data collected on homemakers and retirees were imprecise. Furthermore, the employment rate of this older population, and in particular of the women in Singapore, is lower (only 20% of Malay women and 49% of Malay men in the 60–64-year-old group were employed in 2005), compared with other developed countries.19 Smoking status was categorised into ever smoked and never smoked. Alcohol consumption was divided into drinker (irrespective of quantity) and non-drinker.
As previous studies indicated gender differences in the association between SES and AMD, all analyses were stratified by gender. Descriptive analyses were performed for all variables, and differences between men and women were initially assessed using the t test or chi square test, as appropriate. The prevalence of AMD and associated 95% CIs were calculated stratified by SES and other risk factors. The multivariable logistic regression analysis was used to evaluate associations between indicators of SES and the prevalence of AMD. In order to obtain sufficient sample size within different strata in multivariable models, SES indicators were categorised as dichotomous variables using the highest class as reference and simultaneously controlling for age and smoking status. The statistical interaction between smoking status and education was examined in the corresponding multivariable logistic regression model by including cross-product interaction terms. All reported p values were based on two-sided tests and compared with a significance level of 5%. The statistical interaction was deemed significant if the p interaction was <0.10. All analyses were performed using SPSS version 15 (SPSS, Chicago).
In our study, of the 3280 participants, retinal photographs were available in 3265 right and 3263 left eyes, giving a total of 6528 photographs. Of these, 5989 (91.7%) were gradable for AMD. Characteristics of the SiMES participants are shown in table 1.
The mean age of participants was 58.7 years (SD 11.07), and 1576 (48.0%) were men. The proportion of participants with primary or lower education level was 75.2%, and 24.8% achieved secondary or higher levels. A significantly higher proportion of men achieved a higher educational level than women (29.8% in men vs 20.2% in women, p<0.001). The proportions of participants with monthly incomes <S$1000 (low income) was 64.4%. The remaining proportion reported middle or high income levels (⩾S$1000). The average income for men was significantly higher than for women (p<0.001). The proportion of participants living in a 1–2 room public flat was 15.4%, and proportion living in 3–5 room public/private housing was 84.6%. Men had a significantly higher standard of housing conditions than women (p = 0.004).
The low SES category included 61.2% of participants, and women were significantly more likely to be in this SES category (p<0.001). The proportion of participants who smoked was 20.2%, and men were significantly more likely to smoke than women (p<0.001). Only 1.6% of participants drank alcohol, and again men were significantly more likely to drink alcohol than women (p<0.001).
Early AMD was present in 168 participants (5.1%) and late AMD in 21 (0.6%). Table 2 shows the prevalence of AMD by SES factors.
Both early and late AMD were significantly more prevalent in persons who achieved either a primary or lower educational level (p<0.001) and in those with income levels in the lower category (all p<0.05). Persons living in the 1–2 room HDB flat category were also more likely to have early AMD (p = 0.005). The prevalence of both early and late AMD was significantly higher in participants in the low SES category (early p = 0.028, late p = 0.062).
After adjusting either for age, gender and smoking, or for age, gender, smoking, hypertension, diabetes and body mass index, participants with the lowest educational level category were significantly more likely (p = 0.008) to have early AMD, adjusted multivariate OR 2.2 (95% CI 1.2 to 4.0, table 3).
Association with late AMD was in the same direction but not statistically significant, adjusted multivariate OR 1.9 (95% CI 0.2 to 15.0). There were no significant gender differences in the association between early AMD and education. The association of low education and early AMD was stronger in persons who had never smoked, although the interaction with smoking was not statistically significant. After adjusting for multiple variables, monthly income level, housing types and SES category were not significantly associated with either early or late AMD (data not shown).
In this study we examined the link between SES and AMD in an Asian population. We found that early AMD prevalence was significantly higher in participants with education only to primary school or a lower standard. Lower education was associated with a twofold higher odds of early AMD, independent of multiple confounding variables including age, gender, smoking, hypertension, diabetes and body mass index. This association was somewhat stronger in persons who have never smoked. Other SES factors, however, were not associated with AMD.
There are few studies for direct comparison. Our finding is consistent with that from another population study: the Beijing eye study, which also reported an association between early AMD and lower education level.20 These findings, however, are in contrast to those from other population studies including the Visual Impairment Project,12 the Framingham Eye Study10 and the National Health and Nutrition Examination Survey,11 which did not find any association of AMD with socio-economic status.
The underlying reasons for this association are unclear. Although no definite link between sunlight exposure and AMD has been demonstrated,21 22 it was thought that the association of AMD with low education standard could be related to the degree of excessive sunlight exposure in the outdoor occupations associated low educational levels. When we investigated the association between indoor and outdoor occupations and AMD, however, we found no association of either early or late AMD with outdoor occupations (data not shown). We speculate that the known differences in AMD related genes between Asians and Caucasians23 could contribute to individuals’ different responses to the environmental exposures associated with low SES, so that different susceptibilities to these exposures may explain the difference in findings of such an association between Asian and Caucasian samples.
We could also infer that the association of lower educational levels and AMD may be related to poor diet. It is well established that people of low SES are more likely to develop heart disease, stroke and some cancers.24 25 A link has been established between this health inequality and poor diet.25 Diets consumed by persons with lower SES status tend to be higher in fat and lower in other healthy nutrients such as magnesium, vitamin C, folate, calcium and iron.25 An increased risk of developing AMD has previously been found with high fat intake, and protective effects shown from dietary omega-3 fatty acids, antioxidants, fish and nuts.26–28 We therefore postulate that participants in the low-education group may have had a poorer diet quality, which increased their risk of AMD. Unfortunately, we do not have detailed dietary data in this study to confirm this.
Strengths of our study include its population-based sample, the use of standardised retinal photography to assess AMD signs, and detailed information on a range of risk factors and potential confounders. Limitations include its cross-sectional nature and the need to exclude the possibility of a chance finding.
In summary, our study showed a significant association between low education and the prevalence of early AMD, even after adjusting for multiple variables including age, sex, smoking, hypertension, diabetes and body mass index. We demonstrate this association even in persons who have never smoked. We hypothesise that this increased risk may have resulted from risk factor processes associated with lower education, such as poorer diet quality. Future studies, including the collection of longitudinal data in this sample, and studies in other populations are needed to confirm these findings.
Funding: This study was funded by Funded by the National Medical Research Council (NMRC), 0796/2003 & the Biomedical Research Council (BMRC), 501/1/25-5, with support from the Singapore Prospective Study Program and the Singapore Tissue Network, A*STAR.
Competing interests: None.
Ethics approval: The study was approved by the Singapore Eye Research Institute Ethics Committee.
Patient consent: Obtained.
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