• Users Online: 92
  • Print this page
  • Email this page

 Table of Contents  
Year : 2021  |  Volume : 33  |  Issue : 2  |  Page : 165-170

Economic inequality in visual impairment: A study in deprived rural population of Iran

1 School of Public Health and Safety, Shahid Beheshti University of Medical Sciences, Tehran, Iran
2 Department of Epidemiology, Faculty of Health, Ilam University of Medical sciences, Ilam, Iran
3 Refractive Errors Research Center, School of Paramedical Sciences, Mashhad University of Medical Sciences, Mashhad, Iran
4 Noor Research Center for Ophthalmic Epidemiology, Noor Eye Hospital, Tehran, Iran
5 Department of Medical Surgical Nursing, School of Nursing and Midwifery, Shahid Beheshti University of Medical Sciences, Tehran, Iran

Date of Submission05-Oct-2020
Date of Decision04-Jan-2021
Date of Acceptance05-Jan-2021
Date of Web Publication05-Jul-2021

Correspondence Address:
Masoomeh Amini
School of Public Health and Safety, Shahid Beheshti University of Medical Sciences, Tehran
Login to access the Email id

Source of Support: None, Conflict of Interest: None

DOI: 10.4103/2452-2325.288936

Rights and Permissions

Purpose: To determine economic inequality in visual impairment (VI) and its determinants in the rural population of Iran.
Methods: In this population-based, cross-sectional study, 3850 individuals, aged 3–93 years were selected from the north and southwest regions of Iran using multi-staged stratified cluster random sampling. The outcome was VI, measured in 20 feet. Economic status was constructed using principal component analysis on home assets. The concentration index (C) was used to determine inequality, and the gap between low and high economic groups was decomposed to explained and unexplained portions using the Oaxaca–Blinder decomposition method.
Results: Of the 3850 individuals that were invited, 3314 participated in the study. The data of 3095 participants were finally analyzed. The C was −0.248 (95% confidence interval [CI]: −0.347 - −0.148), indicating a pro-poor inequality (concentration of VI in low economic group). The prevalence (95% CI) of VI was 1.72% (0.92–2.52) in the high economic group and 10.66% (8.84–12.48) in the low economic group with a gap of 8.94% (6.95–10.93) between the two groups. The explained and unexplained portions comprised 67.22% and 32.77% of the gap, respectively. Among the study variables, age (13.98%) and economic status (80.70%) were significant determinants of inequality in the explained portion. The variables of education (coefficient: −4.41; P < 0.001), age (coefficient: 14.09; P < 0.001), living place (coefficient: 6.96; P: 0.006), and economic status (coefficient: −7.37; P < 0.001) had significant effects on inequality in the unexplained portion.
Conclusions: The result showed that VI had a higher concentration in the low economic group, and the major contributor of this inequality was economic status. Therefore, policymakers should formulate appropriate interventions to improve the economic status and alleviate economic inequality.

Keywords: Economic factors, Economic inequality, Iran, Visual impairment

How to cite this article:
Zarei E, Pakzad R, Yekta A, Amini M, Sardari S, Khabazkhoob M. Economic inequality in visual impairment: A study in deprived rural population of Iran. J Curr Ophthalmol 2021;33:165-70

How to cite this URL:
Zarei E, Pakzad R, Yekta A, Amini M, Sardari S, Khabazkhoob M. Economic inequality in visual impairment: A study in deprived rural population of Iran. J Curr Ophthalmol [serial online] 2021 [cited 2021 Oct 21];33:165-70. Available from: http://www.jcurrophthalmol.org/text.asp?2021/33/2/165/288936

  Introduction Top

Visual impairment (VI) is a public health problem, affecting a relatively large percentage of people. Estimates indicate that 43.2 million people are blind, 295.3 million have moderate-to-severe VI, 257.3 million have mild VI, and, in general, 1.1 billion people live with vision loss in 2020.[1] This is while studies suggest an increasing trend in the prevalence of VI,[1],[2] indicating that the number of blind people, individuals with moderate-to-severe VI, subjects with mild VI, and total people with vision loss will increase to 60.2 million, 473.6 million, 360.1 million, and 1.7 billion, respectively, in 2050.[1] Population growth and the proportion of the aged population underline the importance of VI increasing trend. Global estimates suggest that the costs of VI will increase from $3 trillion in 2010 to $3.6 trillion in 2020.[3]

The prevalence of VI differs in various populations, and several factors such as age distribution,[4] sex,[5] economic status, and living place[6] markedly contribute to this inequality. However, it should be noted that the importance of VI lies in its consequences such as decreased educational–economic opportunities[7] and quality of life.[8] Although different causes have been reported for VI, refractive errors, cataracts, and age-related macular degeneration are the three leading causes of VI.[2]

Due to the importance of VI, several efforts have been made to eliminate avoidable blindness by 2020 under a global initiative entitled “Vision 2020: The right to sight.”[3] Accordingly, different studies investigated the factors affecting VI.[2],[8],[9],[10],[11],[12] The results of these studies indicate an inequality in the prevalence of VI such that most VI cases occur in less developed countries.[2],[3] In other words, there is a marked economic inequality in the prevalence of VI in the world. Hence, several studies investigated economic inequality in VI and its determinants,[13],[14],[15],[16],[17] which provided health policymakers and stakeholders with valuable information in this regard. However, inequality in VI was only addressed in few studies in Iran,[9],[15],[18] indicating a lack of sufficient evidence in this regard. Our extensive search showed no information about economic inequality in VI in the Iranian rural population, while economic inequality is more prominent in the rural population.[19],[20] Lack of evidence on the one hand and the need for information for health planning in underserved areas on the other hand encouraged us to conduct a study to investigate economic inequality in VI in the rural population of Iran and its determinants using the Oaxaca–Blinder decomposition method.


This retrospective study was conducted on the data of a cross-sectional study performed in 2015. The methodological and sampling details of the present study have already been published.[21],[22] This cross-sectional, population-based study was performed in the rural parts of two underserved regions of Iran in 2015. Assuming a prevalence of 6.4% for VI (as the main objective of the study), alpha of 0.05, precision of 1%, design effect of 1.5, and loss to follow-up of 10%, 3850 individuals were selected using multistage stratified cluster random sampling. First, two underserved districts in the north (Kojur District, Mazandaran Province; 15 villages) and southwest regions of Iran (Shahyun District, Khuzestan Province; 5 villages) were selected using national data as strata. The map of Iran is presented in [Figure 1] to show the distribution of the selected rural areas.
Figure 1: The location of two villages selected in this study in the map of Iran

Click here to view

All people aged over 1 year who lived in these villages were invited to join the study. Informed consent was obtained from all participants.

Examinations included refraction and visual acuity measurement. In each village, two optometrists that had a high agreement in detection of refractive errors (myopia, hyperopia, and astigmatism) conducted the examinations in a place with standard illumination using retinoscopy and a Snellen chart. The subjects were interviewed before optometric and ophthalmic examinations to collect demographic and economic data in a researcher-made form. Finally, according to the World Health Organization definition, VI was defined as a presenting visual acuity of worse than 20/60.

Statistical analysis

To determine the economic status, the data of seven household assets (car, motorcycle, microwave oven, computer, telephone, vacuum cleaner, and washing machine) were collected and an asset index was generated using principal component analysis according to the weighting of the first component (initial Eigenvalues: 2.34; %variance: 30.01; Kaiser–Meyer–Olkin measure of sampling adequacy: 0.699).[23] Then, a concentration index (C) was used as a measure of economic inequality in VI. C is defined based on the concentration curve (CC).[24],[25] The C was calculated using the convenient covariance method with the following formula:

where yi is a variable for which the inequality is to be measured, μ is the average of this variable, Ri is the fractional rank of the ith person in the distribution of economic status, and cov is the covariance.[24],[25] In CC, the y axis shows the cumulative percentage of the health variable and the x axis presents the cumulative percentage of the population ranked by the economic status from the lowest to the highest. If every person, irrespective of his or her economic status, has exactly the same value of the health variable, the CC will be a 45° line, known as the line of equality. If, by contrast, the health variable takes a higher (or lower) value among the low economic group, the CC will lie above (or below) the line of equality. The farther the curve is from the line of equality, the more inequality exists in the distribution of the health variable between the high and low economic groups. C is defined as twice the area between the CC and the line of equality.[24],[25] Therefore, if CC falls on the line of equality, C will be equal to 0, and if CC is above or below the line of equality, it will have a negative or positive value, respectively. CC ranges from −1 to +1.[25] Then, based on the asset index, the participants were divided into two groups of below the 50th percentile (the low economic group) and above the 50th percentile (the high economic group), and the Oaxaca–Blinder decomposition method was used to decompose the gap between the two groups to its determinants. In this method, the gap in outcome between two economic groups (here the high/low economic groups) is decomposed based on determinants. In other words, this method determines that how much of the gap in outcome between two groups is 1: due to differences in the mean values of the determinants between the two groups (explained portion) and 2: due to differences in the coefficients of these determinants or determinants' effects (unexplained portion).[26],[27] Therefore, the participants were divided into two economic groups, and then a linear regression analysis was done between the outcome and determinants (gender, age, education level, living place, and economic status) to calculate the beta coefficient of each variable. Then, the mean values of the determinants were calculated in two economic groups, and finally, the mean values and beta coefficients were used to decompose the gap. Hence, in this decomposition approach, the explained portion is the differences in the mean values of the determinants between the two economic groups, and the unexplained portion is the differences in beta coefficients between the two economic groups. Because the study outcome was a binary variable, the method developed by Yun for nonlinear outcomes was used.[28] It should be emphasized that economic status was also included in the decomposition model to investigate its direct effects on economic inequality besides its indirect effects.[15] The Oaxaca command in the Stata software version 11 (StataCorp LP, College Station, Texas) was used to analyze inequality.[29] It should be noted that the “omega” command was used for decomposition. Moreover, the association between VI and the study variables was evaluated using multiple logistic regression, and the cluster sampling effect was considered for calculating the confidence intervals (CIs) in all analyses. P < 0.05 was considered statistically significant.

Ethical issues

The Ethics Committee of Shahid Beheshti University of Medical Sciences approved the study protocol, which was conducted in accordance with the tenets of the Declaration of Helsinki. All participants signed a written informed consent. For individuals below 18 years, informed consent was taken from the household head (Ethics Number: IR.SBMU.PHNS.REC.1397.060).


Of the 3851 samples, 3314 participated in the study (response rate = 86.5%). Because visual acuity was not measured in 220 participants, the data of 3095 participants were analyzed, 1747 (56.5%) of whom were women and the rest were men. The mean age of the participants was 37.6 ± 20.7 years (range, 3–93 years), and 57.4% of them (n = 1776) were from southeast villages.

The prevalence (95% CI) of VI was 6.42% (5.36–7.50) in all participants. [Table 1] presents the prevalence of VI according to demographic variables. The prevalence of VI was 6.54% (4.96–8.13) in men. According to age group, the highest prevalence of VI was seen in participants ≥65 years old (33.33%, 95% CI: 27.39–39.28). The prevalence of VI was 1.72% (0.90–2.54) in the high economic group and 10.66% (8.82–12.50) in the low economic group. The prevalence of VI according to other variables is shown in [Table 1]. According to the results, the odds of VI were lower in participants with secondary (odds ratio [OR]: 0.32, 95% CI: 0.11–0.93) and high school education (OR: 0.18, 95% CI: 0.06–0.57) compared to illiterate participants. According to the economic status, the odds of VI were significantly lower in the high economic group (OR: 0.14, 95% CI: −0.347 - −0.148 ).
Table 1: Percentage of visual impairment and its association with different independent variables based on multiple logistic regression

Click here to view

Economic inequality in visual impairment and decomposition results

[Figure 2] presents the CC for economic inequality in VI. CC was above the line of equity, indicating a pro-poor inequality. In other words, VI was concentrated in the low economic group. The value of C was −0.248 (95% CI: −0.347 - −0.148).
Figure 2: Concentration curve (CC) of visual impairment and economic status; the CC is above the line of equity, showing pro-poor inequality

Click here to view

The Oaxaca–Blinder decomposition method was applied to determine the factors affecting economic inequality in VI and their share in developing inequality. The decomposition results are presented in [Table 2]. The prevalence of VI was 1.72% (95% CI: 0.92–2.52) in the high economic group and 10.66% (95% CI: 8.84–12.48) in the low economic group with a statistically significant gap (8.94%) between the two groups (P < 0.001). The share of explained and unexplained portions was 6.01% (95% CI: 4.25–7.78) and 2.93% (95% CI: 1.63–4.23), respectively; age (b: 0.84; P: 0.019) and economic status (b: 4.85; P < 0.001) in the explained portion and education level (b: −4.41; P < 0.001), age (b: 14.09; P < 0.001), living place (b: 6.96; P: 0.006), and economic status (b: −7.37; P < 0.001) in the unexplained portion.
Table 2: Decomposition result of visual impairment gap between economic groups by Oaxaca-Blinder decomposition

Click here to view

  Discussion Top

The results of this study showed a significant pro-poor inequality in VI in people living in underserved regions of Iran. In other words, VI was seen in about 6.42% of the study population. However, according to the economic group, the prevalence of VI was about 8.94% higher in the low economic group, that is, the low economic group suffered from VI 6.2 (10.66/1.72) times more than the high economic group. Emamian et al.[9] and Gilbert et al.[30] showed inequality in VI in Shahroud (Iran) and Pakistan, respectively. Moreover, the Vision 2020 initiative report showed that VI and blindness were more common in less developed societies.[31]

Comparison of inequality between that of our research and similar studies indicates a gap of 8.94% between the high and low economic groups that was higher than similar domestic studies. For example, Emamian et al.[9] and Mansouri et al.[15] reported a gap of 7.49% and 7.05%, respectively, suggesting that the gap is more prominent in rural areas. Several studies have shown the unfavorable condition of the rural population and a high prevalence of VI in this population,[30] indicating their need for more attention.

Decomposition of the gap between the two groups demonstrated that about 67% of the gap was related to the explained portion. In other words, if the two groups became similar, a marked portion of the economic inequality would be eliminated.[29] In the explained portion, age had a significant effect, accounting for 14% of inequality in the explained portion, which was lower than the results of the studies by Emamian et al.[9] (19%) and Mansouri et al.[15] (15%). Aging increased the difference in the prevalence of VI between the low and high economic groups (gap). On an average, the mean age of the low economic group was 2 years more than that of the high economic group, and younger people had a better visual status in the low economic group. Therefore, supportive programs for low economic group should especially focus on older age groups.[9],[16] The fact that older people do not usually have a job or income underlines the importance of attention to this age group. In line with our results, Emamian et al.[9] and Mansouri et al.[15] also found that age was effective in generating economic inequality in VI and presenting vision. Several studies have shown the effect of age on inequality in other diseases such as diabetes[14] and the high prevalence of VI.[11],[15],[17],[32],[33]

An important finding of the present study was the effect of economic status in generating economic inequality in the prevalence of VI. In other words, 54% of the gap between the high and low economic groups was caused by the direct effect of economic status, and if the economic status of the two groups became similar, more than half of the inequality would be eliminated. It seems that people with a better economic status utilize more eye care services and follow preventive programs more seriously, resulting in the decreased prevalence of different diseases in this population. Although Emamian et al.[9] rejected the role of economic status in their study, Mansouri et al.[15] found that the economic variable was an effective contributor to economic inequality in presenting vision, accounting for 19% of inequality. It should be noted that the percent contribution of economic status in this study was much lower than that of our results, indicating a worse status in the rural population. Therefore, priority should be given to improving low economic people's access to eye care services.[34]

The unexplained portion comprised 23% of the gap in VI prevalence between the low and high economic groups, indicating that 23% of the gap was caused by factors that were not included in the model or due to the different effects of the study variables on the low and high economic groups, making the low economic group more vulnerable to inequality determinants.[29]

Several studies have investigated the roles of other variables such as education level and sex in inequality. Some studies found a higher percentage of VI in people with lower education[10],[35] and in females,[36] whereas our results showed that sex and education level had no effects on the explained and unexplained portions.

One of the variables that could be evaluated in the model used in this study was insurance because the coverage of rural insurance (as a governmental insurance) is very high in rural areas (about 100%), and the coverage of private insurance is very low (about 1% of the total population) due to its high costs. Therefore, because there was no variation in this variable, it was not possible to evaluate its contribution in this study. Studies have shown the effect of insurance on inequality;[10],[20] in other words, lack of insurance coverage is associated with decreased utilization of eye care services and increased prevalence of VI[34] because eye care services are usually expensive[37] and low-income people cannot afford the out-of-pocket costs of ophthalmic services.[38]

It should be noted that one of the major limitations of the present study is that it was conducted in a deprived area, so it does not reflect all areas of the country, even other deprived areas. Moreover, measuring a few asset variables may increase sampling bias and may not be sufficient to generate a valid socioeconomic status variable.

Despite several limitations, the results of the present study can be used to address economic inequality in VI to achieve the objectives of “Vision 2020: The right to sight,” including decreased avoidable blindness. Our study benefited from a large sample size, a high participation rate, a methodologically correct population-based design, and quality control to decrease any errors during data collection and analysis, which were the strong points of this research. However, it should be noted that the observed inequality and the decomposition results do not indicate a causal direction between the relationships. On the other hand, the Oaxaca–Blinder decomposition approach is a deterministic method that decomposes a gap according to the variables included in the model and is unable to determine the role of other variables.

In general, the results showed a significant pro-poor (VI concentration in the low economic group) inequality in VI in the Iranian rural population. A major part of this inequality was due to differences in the economic status and age between the two groups. Economic status had direct and indirect effects on inequality. It also had a significant effect on the explained and unexplained portions. To eliminate the gap, health policymakers should direct their efforts on improving the economic status.

Financial support and sponsorship

This project was supported by the Deputy of Research of Shahid Beheshti University of Medical Sciences, Tehran, Iran (grant code: 9733).

Conflicts of interest

There are no conflicts of interest.

  References Top

The International Agency for the Prevention of Blindness (IAPB), Projected Change in Vision Loss 2020 to 2050. Available from: https://www.iapb.org/learn/vision-atlas/magnitude-and-projections/projected-change. [Last accessed on 2020 Nov 20].  Back to cited text no. 1
Flaxman SR, Bourne RR, Resnikoff S, Ackland P, Braithwaite T, Cicinelli MV, et al. Global causes of blindness and distance vision impairment 1990-2020: A systematic review and meta-analysis. Lancet Glob Health 2017;5:e1221-34.  Back to cited text no. 2
Gordois A, Cutler H, Pezzullo L, Gordon K, Cruess A, Winyard S, et al. An estimation of the worldwide economic and health burden of visual impairment. Glob Public Health 2012;7:465-81.  Back to cited text no. 3
Global Data on Visual Impairments 2010. Geneva: World Health Organization; 2012. Available from: https://www.who.int/blindness/publications/globaldata/en. [Last accessed on2019 May 27].  Back to cited text no. 4
Mohammadi SF, Saeedi-Anari G, Ashrafi E, Mohammadi SM, Farzadfar F, Lashay A, et al. Prevalence and major causes of visual impairment in Iranian adults: A systematic review. Middle East Afr J Ophthalmol 2017;24:148-55.  Back to cited text no. 5
[PUBMED]  [Full text]  
Gakidou EE, Murray CJ, Frenk J. Defining and measuring health inequality: An approach based on the distribution of health expectancy. Bull World Health Organ 2000;78:42-54.  Back to cited text no. 6
Eckert KA, Carter MJ, Lansingh VC, Wilson DA, Furtado JM, Frick KD, et al. A simple method for estimating the economic cost of productivity loss due to blindness and moderate to severe visual impairment. Ophthalmic Epidemiol 2015;22:349-55.  Back to cited text no. 7
Ramrattan RS, Wolfs RC, Panda-Jonas S, Jonas JB, Bakker D, Pols HA, et al. Prevalence and causes of visual field loss in the elderly and associations with impairment in daily functioning: The Rotterdam Study. Arch Ophthalmol 2001;119:1788-94.  Back to cited text no. 8
Emamian MH, Zeraati H, Majdzadeh R, Shariati M, Hashemi H, Fotouhi A. The gap of visual impairment between economic groups in Shahroud, Iran: A Blinder-Oaxaca decomposition. Am J Epidemiol 2011;173:1463-7.  Back to cited text no. 9
Freeman EE, Roy-Gagnon MH, Samson E, Haddad S, Aubin MJ, Vela C, et al. The global burden of visual difficulty in low, middle, and high income countries. PLoS One 2013;8:e63315.  Back to cited text no. 10
Hashemi H, Khabazkhoob M, Saatchi M, Ostadimoghaddam H, Yekta A. Visual impairment and blindness in a population-based study of Mashhad, Iran. J Curr Ophthalmol 2018;30:161-8.  Back to cited text no. 11
Hashemi H, Yekta A, Jafarzadehpur E, Doostdar A, Ostadimoghaddam H, Khabazkhoob M. The prevalence of visual impairment and blindness in underserved rural areas: A crucial issue for future. Eye (Lond) 2017;31:1221-8.  Back to cited text no. 12
Hong H, M ngia OJ, Anaya J, Lansingh VC, L nsi E, Silva JC. The challenge of universal eye health in Latin America: Distributive inequality of ophthalmologists in 14 countries. BMJ Open 2016;6:e012819.  Back to cited text no. 13
Hwang J, Rudnisky C, Bowen S, Johnson JA. Income-related inequalities in visual impairment and eye screening services in patients with type 2 diabetes. J Public Health (Oxf) 2016;38:e571-9.  Back to cited text no. 14
Mansouri A, Emamian MH, Zeraati H, Hashemi H, Fotouhi A. Economic inequality in presenting vision in Shahroud, Iran: Two decomposition methods. Int J Health Policy Manag 2018;7:59-69.  Back to cited text no. 15
Morasae EK, Forouzan AS, Majdzadeh R, Asadi-Lari M, Noorbala AA, Hosseinpoor AR. Understanding determinants of socioeconomic inequality in mental health in Iran's capital, Tehran: A concentration index decomposition approach. Int J Equity Health 2012;11:18.  Back to cited text no. 16
Ulldemolins AR, Lansingh VC, Valencia LG, Carter MJ, Eckert KA. Social inequalities in blindness and visual impairment: A review of social determinants. Indian J Ophthalmol 2012;60:368-75.  Back to cited text no. 17
Emamian MH, Zeraati H, Majdzadeh R, Shariati M, Hashemi H, Jafarzadehpur E, et al. Economic inequality in presenting near vision acuity in a middle-aged population: A Blinder-Oaxaca decomposition. Br J Ophthalmol 2013;97:1100-3.  Back to cited text no. 18
Keeffe JE, Weih LM, McCarty CA, Taylor HR. Utilisation of eye care services by urban and rural Australians. Br J Ophthalmol 2002;86:24-7.  Back to cited text no. 19
Xie E. Income-related inequalities of health and health care utilization. Front Econ China 2011;6:131-56.  Back to cited text no. 20
Hashemi H, Abbastabar H, Yekta A, Heydarian S, Khabazkhoob M. The prevalence of uncorrected refractive errors in underserved rural areas. J Curr Ophthalmol 2017;29:305-9.  Back to cited text no. 21
Hashemi H, Pakzad R, Yekta A, Khabazkhoob M. The prevalence of corneal opacity in rural areas in Iran: A population-based study. Ophthalmic Epidemiol 2018;25:21-6.  Back to cited text no. 22
Vyas S, Kumaranayake L. Constructing socio-economic status indices: How to use principal components analysis. Health Policy Plan 2006;21:459-68.  Back to cited text no. 23
Kakwani N, Wagstaff A, van Doorslaer E. Socioeconomic inequalities in health: Measurement, computation, and statistical inference. J Econom 1997;77:87-103.  Back to cited text no. 24
O'donnell O, van Doorslaer E, Wagstaff A, Lindelow M. Analyzing Health Equity Using Household Survey Data: A Guide to Techniques and their Implementation. Washington, DC: The World Bank; 2007.  Back to cited text no. 25
Blinder AS. Wage discrimination: Reduced form and structural estimates. J Hum Resour 1973;8:436-55.  Back to cited text no. 26
Oaxaca R. Male-female wage differentials in urban labor markets. Int Econ Rev 1973;14:693-709.  Back to cited text no. 27
Yun MS. Decomposing differences in the first moment. Econ Lett 2004;82:275-80.  Back to cited text no. 28
Jann B. A Stata implementation of the Blinder-Oaxaca decomposition. Stata J 2008;8:453-79.  Back to cited text no. 29
Gilbert CE, Shah SP, Jadoon MZ, Bourne R, Dineen B, Khan MA, et al. Poverty and blindness in Pakistan: Results from the Pakistan national blindness and visual impairment survey. BMJ 2008;336:29-32.  Back to cited text no. 30
World Health Organization. State of the World's Sight: VISION 2020: The Right to Sight: 1999-2005. Geneva: World Health Organization; 2005. Available from: http://www.who.int/iris/handle/10665/43300. [Last accessed on 2019 May 28].  Back to cited text no. 31
Horowitz A, Brennan M, Reinhardt JP. Prevalence and risk factors for self-reported visual impairment among middle-aged and older adults. Res Aging 2005;27:307-26.  Back to cited text no. 32
Balegamire S, Aubin MJ, Curcio CL, Alvarado B, Guerra RO, Ylli A, et al. Factors associated with visual impairment and eye care utilization: The international mobility in aging study. J Aging Health 2018;30:1369-88.  Back to cited text no. 33
Emamian MH, Zeraati H, Majdzadeh R, Shariati M, Hashemi H, Fotouhi A. Economic inequality in eye care utilization and its determinants: A Blinder-Oaxaca decomposition. Int J Health Policy Manag 2014;3:307-13.  Back to cited text no. 34
Cockburn N, Steven D, Lecuona K, Joubert F, Rogers G, Cook C, et al. Prevalence, causes and socio-economic determinants of vision loss in Cape Town, South Africa. PLoS One 2012;7:e30718.  Back to cited text no. 35
Zetterberg M. Age-related eye disease and gender. Maturitas 2016;83:19-26.  Back to cited text no. 36
Fotouhi A, Hashemi H, Mohammad K. Eye care utilization patterns in Tehran population: A population based cross-sectional study. BMC Ophthalmol 2006;6:4.  Back to cited text no. 37
McWilliams JM, Zaslavsky AM, Meara E, Ayanian JZ. Health insurance coverage and mortality among the near-elderly. Health Aff (Millwood) 2004;23:223-33.  Back to cited text no. 38


  [Figure 1], [Figure 2]

  [Table 1], [Table 2]


Similar in PUBMED
   Search Pubmed for
   Search in Google Scholar for
 Related articles
Access Statistics
Email Alert *
Add to My List *
* Registration required (free)

  In this article
Case Report
Article Figures
Article Tables

 Article Access Statistics
    PDF Downloaded37    
    Comments [Add]    

Recommend this journal