Original research••

Socioeconomic risk factors and age-related macular degeneration in the UK Biobank study

•,,,,,.for the UK Biobank Eye and Vision Consortium

...

Abstract

Objective There is contrasting evidence on the relationship between socioeconomic status (SES) and age-related macular degeneration (AMD), the most common cause of visual impairment (VI) in developed countries. This study examines the relationship between SES, cardiovascular risk factors and self-reported AMD.

Methods and analysis Over 500000 people participated in the UK Biobank study from 2006 to 2019, with sociodemographic data and clinical measurements collected using standardised procedures. Visual acuity was measured in 117907 participants with VI defined as LogMAR ≤0.3. We used logistic regression to examine the cross-sectional associations between SES and self-reported AMD.

Results Self-reported AMD was available for 133339 participants aged 50 and older. People reporting AMD had higher academic qualifications, lower income, were unable to work due to disability, have higher BMI, diabetes and vascular heart disease after adjusting for age and sex. In a multivariable analysis, higher income was protective of AMD and economic inactivity due to disability increased the odds of AMD (2.02, 95% CI 1.13 to 3.61). Both associations were independent of cardiovascular factors, but was no longer significant after adjusting for VI.

Conclusions The association between education, employment and household income with AMD was independent of cardiovascular risk factors.

Key messages

What is already known about this subject?

Age-related macular degeneration (AMD) is a common cause of blindness globally. Lower socioeconomic status (SES) is associated with higher levels of visual impairment (VI), but the association with AMD is unclear.

What are the new findings?

People with higher income were less likely to report AMD; also, that economic inactivity due to disability was associated with AMD.

How might these results change the focus of research or clinical practice?

These results should focus greater public health efforts to address eye health inequalities, targeting preventive messages for people at greater risk of AMD.

Introduction

Age-related macular degeneration (AMD) is the leading cause of low vision and blindness in high-income countries.1 2 Early forms of the disease may be asymptomatic and are characterised by drusen deposits adjacent to the retinal pigment epithelium but AMD can progress to more advanced forms of the condition leading to loss of central vision. Advanced forms of AMD are characterised by thinning or loss of the outer retina, retinal pigment epithelium and choriocapillaris (geographic atrophy, an advanced form of ‘dry’ AMD) or through the development of choroidal neovascularisation (‘wet’ form of advanced AMD). These forms of advanced AMD damage central vision with significant impact on an individual’s quality of life and daily function. AMD causes approximately 5% of global blindness, with an estimated 71000 new cases of late AMD per year in the UK.3 This is expected to increase due to the ageing population as older age is the strongest risk factor for AMD and over 600000 people in the UK are estimated to suffer from AMD by 2020.3

The WHO Commission on Social Determinants of Health have highlighted the importance of socioeconomic risk factors in the aetiology of chronic disease and disability.4 Addressing health inequalities is a key driver in national and international health policy. People living in poorer areas, from less affluent backgrounds, have a higher risk of morbidity and mortality. There is substantial evidence that lower socioeconomic status (SES) is associated with visual impairment (VI),5–7 higher prevalence and incidence of eye disease,8–10 and ocular risk factors.11–14 There is limited information on the association between individual SES and AMD. The Beaver Dam study showed that higher SES, as measured by years of education and type of employment, but not income, was directly and independently associated with higher 5-year incidence of AMD.15 Lower educational attainment, but not lower income, was also independently associated with higher AMD prevalence in studies from Italy, Singapore and Korea.16–18 There is also evidence that people with AMD are more likely to live in more deprived areas.19 These studies indicate that lower SES may increase the risk of AMD.

Cardiovascular risk factors are associated with higher prevalence and incidence of AMD.20–25 Pooled analysis from the Beaver Dam, the Blue Mountains and Rotterdam Eye Studies showed that current smokers were more than twice as likely to develop late AMD than people who had never smoked.26 Other identified vascular associations with AMD include cardiovascular disease,23 27 stroke,28 hypertension23 and inflammatory markers such as C- reactive protein (CRP).29 30 Smoking and cardiovascular disease are more prevalent in people with lower SES and living in more deprived areas. These common risk factors could indicate common pathological mechanisms, and cardiovascular risk factors may mediate the association between SES and AMD.

In this study, we examined the relationship between socioeconomic factors, area deprivation, cardiovascular risk factors and self-reported AMD using the UK Biobank resource.

Methods

The UK Biobank (UKBB) resource is a large-scale collection of health information that includes data collected at 22 study assessment centres, from 502 656 participants in the UK registered with the National Health Service, aged 40–69 years in 2006–2010. The overarching aim of UK Biobank is to improve the prevention, diagnosis and treatment of a wide range of serious illnesses including cancer, heart diseases, stroke, diabetes, arthritis, osteoporosis, eye disorders, depression and dementia. The study protocol (http://www.ukbiobank.ac.uk/resources/) and protocols for individual tests (http://biobank.ctsu.ox.ac.uk/crystal/docs.cgi) are available online. Informed consent was obtained from all participants. Patients and the public were not involved in the design, or conduct, or reporting or dissemination plans in our research.

All participants were examined using standardised procedures across sites. A touch screen self-administered questionnaire was used to collect information on their general health, SES and lifestyle factors. Ethnicity was recorded by the participant as white, Chinese, Asian, black or mixed/other. Smoking status, both past and current, was determined through questionnaire. Diabetes status was determined as those who answered yes to ‘Has a doctor ever told you that you have diabetes?’ Socioeconomic information included education (qualifications attained, grouped into three categories: higher academic/professional (college or university, A levels or above, professional qualifications), lower academic/vocational (O levels or equivalent, CSEs or equivalence, NVQ, HND or equivalent) and other (none of the above)), employment (in paid employment, retired, looking after home/family, unable to work because of sickness/disability, unemployed, unpaid/voluntary work, student, or none of the above), and annual household income (less than £18 000, 18000–30 999, 31000–51 999, 52000–100000, greater than 100 000). The Townsend Deprivation Index is a marker of material deprivation, where higher scores represent higher levels of deprivation. The index is based on census data from the participants’ postcode, and aggregated from measures of unemployment, car ownership, home ownership and household overcrowding.

Glaucoma and macular degeneration statuses were determined as those who selected ‘glaucoma’ or ‘macular degeneration’ from a list of eye disorders to the question ‘Has a doctor told you that you have any of the following problems with your eyes?’ Summary data of the whole cohort were obtained from the online data showcase (http://www.ukbiobank.ac.uk/data-showcase/).

In 2009, an additional ophthalmic examination was appended to the main protocol and we obtained LogMAR visual acuity on 117 907 participants. Presenting visual acuity was measured, with the participant wearing optical correction as prescribed. Visual acuity was measured using a bespoke UK Biobank testing software on a display screen with standard illumination with the room lights turned off. More information is available in the UK Biobank Visual Acuity testing manual on the Biobank website. Visual impairment was defined in this study as VA worse than LogMAR 0.3 in the better eye (Snellen equivalent of cannot see 6/12 in better eye).

Physical measurements

Blood pressure and heart rate were measured using the Omron HEM-70151T digital blood pressure monitor. Two measurements of each were taken and the mean was used in subsequent analysis. Weight was measured with the Tanita BV-418 MA body composition analyser. Height was measured using a Seca 202 height measure. Body mass index (BMI) was calculated as weight/height². Waist circumference at the level of the umbilicus was measured using a Wessex non-stretchable sprung tape measure. Baseline visual acuity was measured using a computerised semiautomated LogMAR system at 3 m, with best available correction.

Statistical analysis

Analyses were conducted on data from participants with complete data for all variables of interest. χ² tests and t-tests were used to examine the relationship between categorical variables and quantitative variables, respectively.

Logistic regression was used to explore the univariable associations with AMD, and multivariable regression was used to estimate adjusted OR for AMD. Confounders were identified a priori (age, sex and smoking) or detected through observed associations with both the exposure (socioeconomic variables) and outcome of interest. Indicators were used to relax underlying assumptions on the distributions of categorical variables. We first explored multivariable associations adjusting for age and sex to gain a better understanding of the underlying associations independent of age and sex. A stepwise forward fitting model was built using the Wald (binary or quantitative variable) or likelihood ratio test (multilevel categorical variable) to determine if each new variable included in the model was independently associated with the outcome, adjusting for previously included variables. All variables were then checked for significance against the final proposed model. A separate analysis was conducted on the participants with visual acuity measurement due to the smaller sample size.

Results

Of 502 656 participants in the baseline Biobank study, we excluded those who were under 50 years old (117 906), those who did not respond to the self-reported eye diseases question (247 411) or reported null answers (3985), and a further 15 with missing data, leaving 133 339 participants with responses to the self-reported AMD questions.

In this study of 133 339 people, 3634 people aged 55 and older reported a diagnosis of AMD, with study prevalence of 2.72% (95% CI 2.63 to 2.81). The mean age was 61.01 (95% CI 69.98 to 61.04) with median age of 61.66 (IQR 56.75–65.50). There were 72 906 women (54.7%), and 60 433 men (45.3%) who responded to the question on whether they had received a diagnosis of AMD. A large proportion of the study participants were white (93.4%), with blacks and South Asians forming 2.1% and 2.5%, respectively, 1.1% reported ‘other’, 0.6% mixed and 0.4% East Asian.

Table 1 shows the characteristics of the study population by age groups. There were higher proportions of women in younger age groups compared with men. There were also higher proportions of ethnic minorities, current smokers, people in paid employment, higher levels of educational attainment and with higher household incomes in the younger age groups. Older people were more likely to have higher BMI, systolic BP, but lower diastolic BP and pulse rate, and live in more affluent areas. The majority of people in the 65–70 year age group were retired, as expected, with income in the lower brackets, and more likely to be previous smokers.

Table 1

Distribution of exposure and confounder variables by age groups

Table 2 shows the associations between sociodemographic variables, cardiovascular variables and AMD. There were higher proportions of people with AMD in older age groups, and who were male. There were greater proportions of people who were white, East Asian, mixed and other who reported AMD. People who reported AMD were also more likely to have VI, to be previous smokers, higher systolic but lower diastolic BP, to report diabetes, vascular, heart disease and have higher pulse rate. People with AMD were also more likely to be retire, or unable to work because of sickness or disability, and have lower levels of household income. There were higher proportions of people who reported AMD with VI.

Table 2

Distribution of exposure and confounder variables by AMD status

The age and sex-adjusted associations with AMD are shown in table 3 as the associations shown in table 2 indicated that the direction of effect between SES variables were aligned with that for age and sex. After adjusting for age and sex, East Asians were more likely to report AMD. People with lower academic or vocational qualification, or reported ‘none of the above’ had lower odds of reporting AMD after adjusting for age and sex. An association was also observed with those unable to work because of sickness or disability. People living in less deprived areas, with higher levels of income, those reporting diabetes, vascular/heart disease (VHD) and higher BMI also had higher odds of AMD. There was also a strong direct association between AMD and VI (OR=2.34, 95% CI 1.90 to 2.87, p<0.01).

Table 3

Age and sex-adjusted associations with age-related macular degeneration

The multivariable associations with AMD in the final model are shown in table 4. Older age, being male, East Asian and having VHD were all independently associated with increased odds of AMD. Any qualifications, employment and household income were socioeconomic indicators independently associated with AMD, whereas Townsend index was not a significant risk factor for AMD after adjusting for other SES variables, and therefore not included in the final model. Those reporting any qualifications had increased odds of AMD compared with those stating ‘none of the above’. There was evidence of a linear trend in the association between household income and odds of AMD, with those with higher income having lower odds of AMD, with the most affluent households earning greater than 100 000K per year with a 24% reduced odds of AMD (OR 0.76, 95%CI 0.61 to 0.96, p=0.02). People who were unable to work because of sickness and disability were twice as likely to report AMD (OR 2.20, 95% CI 1.13 to 3.61, p=0.02). We included VI in the final model to determine whether VI played a role in the relationship between SES and AMD with complete data on 73 748 participants. After adjusting for VI, only educational attainment, but not employment or income, was associated with AMD.

Table 4

Multivariable associations with age-related macular degeneration

Discussion

AMD is an important public health problem worldwide, with incidence and prevalence increasing due to an ageing population,3 and no treatment for a majority of cases.25 The adverse impact of AMD extends beyond loss of visual function with increased risk of poor mental and physical health from depression and falls.31–33

In this large community-based cohort study, we have shown that people living in the most affluent households have 24% reduced odds of AMD compared with those living in the poorest households, after adjusting for age, sex, education, employment and smoking. Our findings support the study from Zhang et al, examining data from the National Health and Nutrition Examination Surveys (NHANES III and NHANES 2005–2008). They found higher age and sex-standardised prevalence of AMD in people with lowest levels of income compared with those with the highest income level in NHANES III (17.9% vs 11.5% p=0.03), but not in NHANES 2005–2008 (10.4% vs 6.8%, p=0.06).8

Although we detected an association with qualifications, we found that people with higher academic qualifications or having professional qualifications had increased risk of AMD. This finding appears to contrast with other studies which report an increased risk of AMD with lower lever levels of educational attainment16 and several studies that have failed to detect an association between AMD and education, including results from India34 and the USA.8 35 In this study, we used those who stated ‘none of the above’ as the reference group. This group had lower household income, were predominantly in the oldest age groups (41.9% vs 23.5% in higher academic and 25.6% in lower academic qualifications) and more likely to be retired. This could indicate a selection bias with a relatively healthy older group, who have lower levels of educational attainment and live in poorer households, but were sufficiently healthy and interested to participate in a research study. Another explanation could be reporting bias, given the self-reported nature of the outcome, as people with higher levels of education may be more aware of the presence of early disease, possibly identified in routine eye checks.

People with the lowest income were older and more likely to report AMD. After adjusting for age, sex, ethnicity, and other SES and clinical factors, we showed that people with higher incomes were less likely to report AMD, which suggests that this relationship is independent of other SES factors. Although other studies have not reported an independent effect of income once education was accounted for,16–18 this study had greater power to detect smaller effects through a larger sample size. Similar to other SES factor assessed, the relationship of income and AMD was no longer significant after adjusting for VI. This suggests that it is impaired vision, and possibly disability as discussed next, that is key to the relationship between low income and AMD.

We found that people who were unable to work due to sickness or disability had twice the odds of reporting AMD. There could be a bidirectional relationship in this association, those with cardiovascular risk factors may have pre-existing disease and disability in addition to increased risk of AMD, and people with AMD can experience and register disability which limits their ability to work and gain income. AMD is the most common cause of registered sight loss in the UK. We cannot determine direction of association with this cross-sectional study. Nonetheless, that AMD is associated with twice the risk of economic inactivity due to sickness and disability is an important consideration for health and social care, and reinforces the need for greater focus on treatment and prevention of this condition.

This study also showed that the association between individual SES indicators and AMD was independent of Townsend index, age, sex and smoking. The Townsend index is based on four questions and has been superseded by the index of multiple deprivation (IMD) in the UK, which aggregates 38 markers of deprivation. A study from Norfolk, UK, has shown that those living in more affluent areas, as measured by IMD, had lower risk of AMD, independent of individual SES including education and social class.19 It is likely that Townsend is a closer proxy to individual SES compared with IMD, given that IMD includes additional measures of area crime, and the living environment; and the effect of area deprivation as measured by Townsend may have been mediated by the markers of individual SES in this study.

We included VI as an additional covariable in the final model to determine whether this would affect the detected relationships between SES indicators and AMD. Higher income no longer held a protective effect on AMD after adjusting for VI. Similarly, employment status, in particular, disability was also no longer associated with AMD after adjusting for VI. This suggests that it is visual function that determines the relationship between income, disability and AMD. As this is a cross-sectional analysis, we cannot determine direction of association, and it is possible that people with AMD and poorer vision and more likely to report disability and have lower levels of household income. This contrasts to our previous analysis examining the association between SES and self-reported glaucoma, where higher income conferred lower odds of reported glaucoma, independent of VI.36 These findings could indicate different mechanisms through which social determinant may impact on different eye diseases.

Smoking is a strong risk factor for AMD,26 and people with lower SES are more likely to smoke.37 However, in our study population, despite people living in poorer households being more likely to be current smokers, we did not detect an association between cigarette smoking and AMD, likely due to low power in a healthy population with low prevalence of current smokers (9.15%, 95% CI 8.99% to 9.30%). Therefore, although our results indicate that the association between individual SES and AMD is independent of smoking, we cannot rule out this mechanism in other populations. The relationship between SES and AMD was also independent of diabetes and vascular/heart disease. Other potential downstream mechanisms of SES include lifestyle risk factors such as diet, as people in more affluent social groups have diets with higher levels of serum carotenoids,38 and lutein and zeaxanthin are principal components of macular pigment with important roles in visual function.39

The UK Biobank is a large prospective population-based cohort, and the large sample size and extensive information allow for examination of a wide range of exposures. Here, we have focused on socioeconomic determinants, commonly considered as the ‘causes of causes’ in the aetiology of disease.40 Although large in size, Biobank UK had a response rate of around 5.5%, which will limit its generalisability. However, provided there is sufficient variation in the distribution of exposures and outcomes in a large sample, the detected associations can be generalisable, although the point estimates may differ.41 We used self-reported measures for ascertainment of both exposure and outcome, which can lead to measurement bias. Studies have shown that self-reported measures have high levels of accuracy (>80%) in well-defined chronic conditions such as ischaemic heart disease, diabetes and hypertension, but lower for stroke.42–44 However, non-differential measurement bias can remain and result in inaccurate estimates. The accuracy of self-reported estimates for AMD, a primarily asymptomatic disease, is poorer, with likely underestimation of prevalence.45–47 The impact of this limitation may have been reduced power of the study to detect an association. Our sample included people aged under 70, which would exclude people at highest risk and with more severe disease. This would have led to lower estimated frequency of AMD, and possibly underestimated the association between SES and AMD, as older people were also more likely to be retired and report lower household incomes. Although we did detect an association with the key SES variables, the effect of these limitations cannot be quantified and may have resulted in an underestimate or overestimate of the main relationship examined.

This is the largest analysis of the relationship between SES and AMD to date, which showed higher levels of educational attainment and unemployment due to disability were associated with higher levels of AMD, and higher household income was associated with lower prevalence of AMD. These associations with SES were independent of cardiovascular risk factors.

Collaborators: Additional UK Biobank Eye and Vision Consortium Membership: Professor Tariq ASLAM (Manchester University, Manchester, United Kingdom); Professor Sarah BARMAN (Kingston University, London, United Kingdom); Professor Jenny BARRETT (University of Leeds, Yorkshire, United Kingdom); Professor Paul BISHOP (Manchester University, Manchester, United Kingdom); Mr Peter BLOWS (NIHR Biomedical Research Centre at Moorfields Eye Hospital and UCL Institute of Ophthalmology, London, UK); Dr Roxana CARARE (Faculty of Medicine, University Of Southampton, UK); Dr Catey BUNCE (King’s College London, London, United Kingdom); Professor Usha CHAKRAVARTHY (Queens University Belfast, Belfast, Ireland); Miss Michelle CHAN (NIHR Biomedical Research Centre at Moorfields Eye Hospital and UCL Institute of Ophthalmology, London, UK); Dr Sharon CHUA (NIHR Biomedical Research Centre at Moorfields Eye Hospital and UCL Institute of Ophthalmology, London, UK); Professor David CRABB (City, University of London, London, United Kingdom); Mrs Philippa CUMBERLAND (UCL Great Ormond Street Institute of Child Health, London, United Kingdom); Dr Alexander DAY (NIHR Biomedical Research Centre at Moorfields Eye Hospital and UCL Institute of Ophthalmology, London, UK); Miss Parul DESAI (NIHR Biomedical Research Centre at Moorfields Eye Hospital and UCL Institute of Ophthalmology, London, UK); Professor Bal DHILLON (University of Edinburgh, Scotland, United Kingdom); Professor Andrew DICK (University of Bristol, Bristol, United Kingdom); Dr Cathy EGAN (NIHR Biomedical Research Centre at Moorfields Eye Hospital and UCL Institute of Ophthalmology, London, UK); Professor Sarah ENNIS (Faculty of Medicine, University Of Southampton, UK); Dr Marcus FRUTTIGER (NIHR Biomedical Research Centre at Moorfields Eye Hospital and UCL Institute of Ophthalmology, London, UK); Dr John GALLACHER (University of Oxford, Oxford, United Kingdom); Prof David (Ted) GARWAY-HEATH (NIHR Biomedical Research Centre at Moorfields Eye Hospital and UCL Institute of Ophthalmology, London, UK); Dr Jane GIBSON (Faculty of Medicine, University Of Southampton, UK); Mr Dan GORE (NIHR Biomedical Research Centre at Moorfields Eye Hospital and UCL Institute of Ophthalmology, London, UK); Professor Jeremy GUGGENHEIM (Cardiff University, Wales, United Kingdom); Professor Chris HAMMOND (King’s College London, London, United Kingdom); Professor Alison HARDCASTLE (NIHR Biomedical Research Centre at Moorfields Eye Hospital and UCL Institute of Ophthalmology, London, UK); Professor Simon HARDING (University of Liverpool, London, United Kingdom); Dr Ruth HOGG (Queens University Belfast, Belfast, Ireland); Dr Pirro HYSI (King’s College London, London, United Kingdom); Mr Pearse A KEANE (NIHR Biomedical Research Centre at Moorfields Eye Hospital and UCL Institute of Ophthalmology, London, UK); Professor Sir Peng Tee KHAW (NIHR Biomedical Research Centre at Moorfields Eye Hospital and UCL Institute of Ophthalmology, London, UK); Mr Anthony KHAWAJA (NIHR Biomedical Research Centre at Moorfields Eye Hospital and UCL Institute of Ophthalmology, London, UK); Mr Gerassimos LASCARATOS (NIHR Biomedical Research Centre at Moorfields Eye Hospital and UCL Institute of Ophthalmology, London, UK); Professor Phil LUTHERT (NIHR Biomedical Research Centre at Moorfields Eye Hospital and UCL Institute of Ophthalmology, London, UK); Dr Tom MACGILLIVRAY (University of Edinburgh, Scotland, United Kingdom); Dr Sarah MACKIE (University of Leeds, Yorkshire, United Kingdom); Professor Keith MARTIN (University of Cambridge, Cambridge, United Kingdom); Ms Michelle MCGAUGHEY (Queens University Belfast, Belfast, Ireland); Dr Bernadette MCGUINNESS (Queens University Belfast, Belfast, Ireland); Mr Martin MCKIBBIN (University of Leeds, Yorkshire, United Kingdom); Dr Danny MITRY (NIHR Biomedical Research Centre at Moorfields Eye Hospital and UCL Institute of Ophthalmology, London, UK; Royal Free Hospital, London, United Kingdom); Professor Tony MOORE (NIHR Biomedical Research Centre at Moorfields Eye Hospital and UCL Institute of Ophthalmology, London, UK); Professor James MORGAN (Cardiff University, Wales, United Kingdom); Ms Zaynah MUTHY (NIHR Biomedical Research Centre at Moorfields Eye Hospital and UCL Institute of Ophthalmology, London, UK); Mr Eoin O'SULLIVAN (King’s College London, London, United Kingdom); Dr Chris OWEN (St George's, University of London, London, United Kingdom); Mr Praveen PATEL (NIHR Biomedical Research Centre at Moorfields Eye Hospital and UCL Institute of Ophthalmology, London, UK); Mr Euan PATERSON (Queens University Belfast, Belfast, Ireland); Dr Tunde PETO (Queens University Belfast, Belfast, Ireland); Dr Axel PETZOLD (UCL Institute of Neurology, London, United Kingdom); Professor Jugnoo RAHI (UCL Great Ormond Street Institute of Child Health, London, United Kingdom); Dr Alicja RUDNICKA (St George's, University of London, London, United Kingdom); Mr Jay SELF (Faculty of Medicine, University Of Southampton, UK); Professor Sobha SIVAPRASAD (NIHR Biomedical Research Centre at Moorfields Eye Hospital and UCL Institute of Ophthalmology, London, UK); Mr David STEEL (Newcastle University, Newcastle, United Kingdom); Mrs Irene STRATTON (Gloucestershire Hospitals NHS Foundation Trust); Mr Nicholas STROUTHIDIS (NIHR Biomedical Research Centre at Moorfields Eye Hospital and UCL Institute of Ophthalmology, London, UK); Professor Cathie SUDLOW12 Dr Caroline THAUNG (NIHR Biomedical Research Centre at Moorfields Eye Hospital and UCL Institute of Ophthalmology, London, UK); Miss Dhanes THOMAS (NIHR Biomedical Research Centre at Moorfields Eye Hospital and UCL Institute of Ophthalmology, London, UK); Professor Emanuele TRUCCO (University of Dundee, Scotland, United Kingdom); Professor Adnan TUFAIL (NIHR Biomedical Research Centre at Moorfields Eye Hospital and UCL Institute of Ophthalmology, London, UK); Dr Veronique VITART (University of Edinburgh, Scotland, United Kingdom); Professor Stephen VERNON (Nottingham University Hospitals NHS Trust, Nottingham, United Kingdom); Mr Ananth VISWANATHAN (NIHR Biomedical Research Centre at Moorfields Eye Hospital and UCL Institute of Ophthalmology, London, UK); Miss Cathy WILLIAMS (University of Bristol, Bristol, United Kingdom); Dr Katie WILLIAMS (King’s College London, London, United Kingdom); Professor Jayne WOODSIDE (Queens University Belfast, Belfast, Ireland); Dr Max YATES (University of East Anglia, Norwich, United Kingdom); Dr Yalin ZHENG (University of Liverpool, London, United Kingdom).
Contributors: JLYY and PP conceived the study; PF developed data collection; JLYY and ZM conducted the analysis; JLYY drafted the manuscript JLYY and ZM drafted the tables; All authors made intellectual contributions and approved the final draft. 'The Consortium works together to raise enabling funding, determine research directions and priorities, and comments on ongoing analyses, so we do think there is justification for group authorship.'
Funding: The author(s) have made the following disclosure(s): PF: Consultant: Zeiss (Oberkochen, Germany); Financial support: Alcon (Fort Worth, TX); Allergan (Marlow, Buckinghamshire, UK); Lecturer: Allergan; Board membership: Allergan; Expert testimony: Alcon, Allergan, Zeiss. PP: Consultant e Thrombogenics (to institution) (Leuven, Belgium); Bayer; Novartis; Merck (Kenilworth, NJ); Financial support e Bayer UK (Newbury, Berkshire, UK); Bayer; Salutaris MD (Tuscan, AZ); Lecturer e Bayer; Heidelberg Engineering; Topcon; Allergan; Alcon. AL is a NIHR Senior Investigator. The UK Biobank was established by the Wellcome Trust (London, UK), medical charity; the Medical Research Council (London, UK); the Department of Health (London, UK); Scottish Government (Edinburgh, UK); and the Northwest Regional Development Agency (Warrington, UK). It also received funding from the Welsh Assembly Government (Cardiff, UK); the British Heart Foundation (London, UK); and Diabetes UK (London, UK). Supported by the Richard Desmond Charitable Trust (London, UK) via Fight for Sight (London, UK) (grant no.: 1956 (PF)); the Special Trustees of Moorfields Eye Hospital, London, United Kingdom (grant no.: ST 12 09); and the Department for Health through the award made by the NIHR Biomedical Research Centre at Moorfields Eye Hospital NHS Foundation Trust, London, United Kingdom (grant no.: BRC2_009).
Competing interests: None declared.
Patient and public involvement: Patients and/or the public were not involved in the design, or conduct, or reporting or dissemination plans of this research.
Patient consent for publication: Not required.
Ethics approval: The work was carried out with the approval of the North West Research Ethics Committee (REC Reference Number: 06/MRE08/65), in accordance with the principles of the Declaration of Helsinki.
Provenance and peer review: Not commissioned; externally peer reviewed.
Data availability statement: Data are available in a public, open access repository.

Pascolini D, Mariotti SP. Global estimates of visual impairment: 2010. Br J Ophthalmol 2012; 96:614–8.
doi:10.1136/bjophthalmol-2011-300539•Google Scholar •PubMed
Bourne RRA, Flaxman SR, Braithwaite T, et al. Magnitude, temporal trends, and projections of the global prevalence of blindness and distance and near vision impairment: a systematic review and meta-analysis. Lancet Glob Health 2017; 5:e888–97.
doi:10.1016/S2214-109X(17)30293-0•Google Scholar •PubMed
Owen CG, Jarrar Z, Wormald R, et al. The estimated prevalence and incidence of late stage age related macular degeneration in the UK. Br J Ophthalmol 2012; 96:752–6.
doi:10.1136/bjophthalmol-2011-301109•Google Scholar •PubMed
World Health Organisation. Commission on Social Determinants of Health - final report. Geneva 2008;
Google Scholar
Varma R. Prevalence and risk indicators of visual impairment and blindness in Latinos*1the Los Angeles Latino eye study. Ophthalmology 2004; 111:1132–40.
doi:10.1016/j.ophtha.2004.02.002•Google Scholar •PubMed
Tielsch JM, Sommer A, Katz J, et al. Socioeconomic status and visual impairment among urban Americans. Baltimore eye survey Research Group. Arch Ophthalmol 1991; 109:637–41.
doi:10.1001/archopht.1991.01080050051027•Google Scholar •PubMed
Dandona L, Dandona R, Srinivas M, et al. Blindness in the Indian state of Andhra Pradesh. Invest Ophthalmol Vis Sci 2001; 42:908–16.
Google Scholar •PubMed
Zhang X, Cotch MF, Ryskulova A, et al. Vision health disparities in the United States by race/ethnicity, education, and economic status: findings from two nationally representative surveys. Am J Ophthalmol 2012; 154:S53–62.
doi:10.1016/j.ajo.2011.08.045•Google Scholar •PubMed
Yip JLY, Nolan WP, Davaatseren U, et al. Primary angle closure glaucoma in East Asia: educational attainment as a protective factor. Ophthalmic Epidemiol 2011; 18:217–25.
doi:10.3109/09286586.2011.602507•Google Scholar •PubMed
Lane M, Lane V, Abbott J, et al. Multiple deprivation, vision loss, and ophthalmic disease in adults: global perspectives. Surv Ophthalmol 2018; 63:406–36.
doi:10.1016/j.survophthal.2017.10.009•Google Scholar •PubMed
Yip JLY, Aung T, Wong T-Y, et al. Socioeconomic status, systolic blood pressure and intraocular pressure: the Tanjong Pagar study. Br J Ophthalmol 2007; 91:56–61.
doi:10.1136/bjo.2006.099549•Google Scholar •PubMed
Wong TY, Foster PJ, Johnson GJ, et al. Education, socioeconomic status, and ocular dimensions in Chinese adults: the Tanjong Pagar survey. Br J Ophthalmol 2002; 86:963–8.
doi:10.1136/bjo.86.9.963•Google Scholar •PubMed
Ng WS, Agarwal PK, Sidiki S, et al. The effect of socio-economic deprivation on severity of glaucoma at presentation. Br J Ophthalmol 2010; 94:85–7.
doi:10.1136/bjo.2008.153312•Google Scholar •PubMed
Fraser S, Bunce C, Wormald R, et al. Deprivation and late presentation of glaucoma: case-control study. BMJ 2001; 322:639–43.
doi:10.1136/bmj.322.7287.639•Google Scholar •PubMed
Klein R, Klein BE, Jensen SC, et al. The relation of socioeconomic factors to the incidence of early age-related maculopathy: the Beaver dam eye study. Am J Ophthalmol 2001; 132:128–31.
doi:10.1016/S0002-9394(00)00931-4•Google Scholar •PubMed
Cackett P, Tay WT, Aung T, et al. Education, socio-economic status and age-related macular degeneration in Asians: the Singapore Malay eye study. Br J Ophthalmol 2008; 92:1312–5.
doi:10.1136/bjo.2007.136077•Google Scholar •PubMed
Piermarocchi S, Segato T, Scopa P, et al. The prevalence of age-related macular degeneration in Italy (PAMDI) study: report 1. Ophthalmic Epidemiol 2011; 18:129–36.
doi:10.3109/09286586.2011.574334•Google Scholar •PubMed
Park SJ, Lee JH, Woo SJ, et al. Age-Related macular degeneration: prevalence and risk factors from Korean National health and nutrition examination survey, 2008 through 2011. Ophthalmology 2014; 121:1756–65.
doi:10.1016/j.ophtha.2014.03.022•Google Scholar •PubMed
Yip JLY, Khawaja AP, Chan MPY, et al. Area deprivation and age related macular degeneration in the EPIC-Norfolk eye study. Public Health 2015; 129:103–9.
doi:10.1016/j.puhe.2014.10.012•Google Scholar •PubMed
Klein R, Deng Y, Klein BEK, et al. Cardiovascular disease, its risk factors and treatment, and age-related macular degeneration: women's health Initiative sight exam ancillary study. Am J Ophthalmol 2007; 143:473–83.
doi:10.1016/j.ajo.2006.11.058•Google Scholar •PubMed
Cackett P, Wong TY, Aung T, et al. Smoking, cardiovascular risk factors, and age-related macular degeneration in Asians: the Singapore Malay eye study. Am J Ophthalmol 2008; 146:960–7.
doi:10.1016/j.ajo.2008.06.026•Google Scholar •PubMed
Fraser-Bell S, Wu J, Klein R, et al. Cardiovascular risk factors and age-related macular degeneration: the Los Angeles Latino eye study. Am J Ophthalmol 2008; 145:308–16.
doi:10.1016/j.ajo.2007.10.007•Google Scholar •PubMed
Hogg RE, Woodside JV, Gilchrist SECM, et al. Cardiovascular disease and hypertension are strong risk factors for choroidal neovascularization. Ophthalmology 2008; 115:1046–52.
doi:10.1016/j.ophtha.2007.07.031•Google Scholar •PubMed
Sun C, Klein R, Wong TY, et al. Age-Related macular degeneration and risk of coronary heart disease and stroke: the cardiovascular health study. Ophthalmology 2009; 116:1913–9.
doi:10.1016/j.ophtha.2009.03.046•Google Scholar •PubMed
Lim LS, Mitchell P, Seddon JM, et al. Age-Related macular degeneration. Lancet 2012; 379:1728–38.
doi:10.1016/S0140-6736(12)60282-7•Google Scholar •PubMed
Tomany SC, Wang JJ, Van Leeuwen R, et al. Risk factors for incident age-related macular degeneration: pooled findings from 3 continents. Ophthalmology 2004; 111:1280–7.
doi:10.1016/j.ophtha.2003.11.010•Google Scholar •PubMed
Chakravarthy U, Wong TY, Fletcher A, et al. Clinical risk factors for age-related macular degeneration: a systematic review and meta-analysis. BMC Ophthalmol 2010; 10.
doi:10.1186/1471-2415-10-31•Google Scholar •PubMed
Wong TY, Klein R, Sun C, et al. Age-Related macular degeneration and risk for stroke. Ann Intern Med 2006; 145:98–106.
doi:10.7326/0003-4819-145-2-200607180-00007•Google Scholar •PubMed
Mitta VP, Christen WG, Glynn RJ, et al. C-Reactive protein and the incidence of macular degeneration: pooled analysis of 5 cohorts. JAMA Ophthalmol 2013; 131:507–13.
doi:10.1001/jamaophthalmol.2013.2303•Google Scholar •PubMed
Hong T, Tan AG, Mitchell P, et al. A review and meta-analysis of the association between C-reactive protein and age-related macular degeneration. Surv Ophthalmol 2011; 56:184–94.
doi:10.1016/j.survophthal.2010.08.007•Google Scholar •PubMed
Tinetti ME, Speechley M, Ginter SF, et al. Risk factors for falls among elderly persons living in the community. N Engl J Med 1988; 319:1701–7.
doi:10.1056/NEJM198812293192604•Google Scholar •PubMed
Deandrea S, Lucenteforte E, Bravi F, et al. Risk factors for falls in community-dwelling older people: a systematic review and meta-analysis. Epidemiology 2010; 21:658–68.
doi:10.1097/EDE.0b013e3181e89905•Google Scholar •PubMed
Zhang X, Bullard KM, Cotch MF, et al. Association between depression and functional vision loss in persons 20 years of age or older in the United States, NHANES 2005-2008. JAMA Ophthalmol 2013; 131:573–81.
doi:10.1001/jamaophthalmol.2013.2597•Google Scholar •PubMed
Krishnaiah S, Das T, Nirmalan PK, et al. Risk factors for age-related macular degeneration: findings from the Andhra Pradesh eye disease study in South India. Invest Ophthalmol Vis Sci 2005; 46:4442–9.
doi:10.1167/iovs.05-0853•Google Scholar •PubMed
Fraser-Bell S, Donofrio J, Wu J, et al. Sociodemographic factors and age-related macular degeneration in Latinos: the Los Angeles Latino eye study. Am J Ophthalmol 2005; 139:30–8.
doi:10.1016/j.ajo.2004.08.029•Google Scholar •PubMed
Shweikh Y, Ko F, Chan MPY, et al. Measures of socioeconomic status and self-reported glaucoma in the U.K. Biobank cohort. Eye 2015; 29:1360–7.
doi:10.1038/eye.2015.157•Google Scholar •PubMed
Office of National Statistics. Do smoking rates vary between more and less advantaged areas? 2014;
Available: here
Google Scholar
Darmon N, Drewnowski A. Does social class predict diet quality? Am J Clin Nutr 2008; 87:1107–17.
doi:10.1093/ajcn/87.5.1107•Google Scholar •PubMed
Bernstein PS, Delori FC, Richer S, et al. The value of measurement of macular carotenoid pigment optical densities and distributions in age-related macular degeneration and other retinal disorders. Vision Res 2010; 50:716–28.
doi:10.1016/j.visres.2009.10.014•Google Scholar •PubMed
Marmot M. Social determinants of health inequalities. Lancet 2005; 365:1099–104.
doi:10.1016/S0140-6736(05)71146-6•Google Scholar •PubMed
Manolio TA, Collins R. Enhancing the feasibility of large cohort studies. JAMA 2010; 304:2290–1.
doi:10.1001/jama.2010.1686•Google Scholar •PubMed
Martin LM, Leff M, Calonge N, et al. Validation of self-reported chronic conditions and health services in a managed care population. Am J Prev Med 2000; 18:215–8.
doi:10.1016/s0749-3797(99)00158-0•Google Scholar •PubMed
Kehoe R, Wu SY, Leske MC, et al. Comparing self-reported and physician-reported medical history. Am J Epidemiol 1994; 139:813–8.
doi:10.1093/oxfordjournals.aje.a117078•Google Scholar •PubMed
Bergmann MM, Byers T, Freedman DS, et al. Validity of self-reported diagnoses leading to hospitalization: a comparison of self-reports with hospital records in a prospective study of American adults. Am J Epidemiol 1998; 147:969–77.
doi:10.1093/oxfordjournals.aje.a009387•Google Scholar •PubMed
Patty L, Wu C, Torres M, et al. Validity of self-reported eye disease and treatment in a population-based study: the Los Angeles Latino eye study. Ophthalmology 2012; 119:1725–30.
doi:10.1016/j.ophtha.2012.02.029•Google Scholar •PubMed
MacLennan PA, McGwin G, Searcey K, et al. Medical record validation of self-reported eye diseases and eye care utilization among older adults. Curr Eye Res 2013; 38:1–8.
doi:10.3109/02713683.2012.733054•Google Scholar •PubMed
Foreman J, Xie J, Keel S, et al. The validity of self-report of eye diseases in participants with vision loss in the National eye health survey. Sci Rep 2017; 7.
doi:10.1038/s41598-017-09421-9•Google Scholar •PubMed

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