Journal of Preventive Medicine

Keywords

Quality of life; National Eye Institute-Visual Function Questionnaire (NEI VFQ-25); Visual impairment

Introduction

Humans are primarily visually motivated for survival and sight may in fact account for approximately 80% of the function of all the five senses combined [2]. Therefore, visual impairment causes restriction in all areas of life and in particular highly diminished quality of life. The leading causes of vision impairment are uncorrected refractive errors and cataracts. Almost 80% of all visual impairment in the world is considered as arising from avoidable causes. Estimates have shown that majority of people with vision impairment are above 50 years of age.

Globally, it is estimated that about 1.3 billion people live with some or the other form of vision impairment. The World Health Organization defines quality of life as “the individual’s perception of their position in life in the context of the culture and value system in which they live and in relation to their goals, standards, expectations, and concerns.” India was the first country to launch the National Programme for Control of Blindness (NPCB) in 1976, as a 100% centrally sponsored programme. Till few years ago, National Programme for Control of Blindness (NPCB) was a cataract centered programme. However, currently it is funding for management of many conditions including Diabetic Retinopathy (DR), glaucoma, ocular trauma, childhood blindness, keratoplasty, squint, low vision, Retinopathy of Prematurity (ROP) with its successful public private partrnerships.

Materials and Methods

The World Health Organization (WHO) estimates that there are 161 million visually impaired in the world, including 37 million blind, many of whom reside in less developed countries. Given the great size of population and prevalence of blindness in India, every fifth blind person lives in India resulting in an economic burden that a decade ago was estimated at $4.4 billion United States Dollars (USD) annually.

The World Health Organization (WHO) defines a person as blind if the best corrected visual acuity in the better eye is <20/400.

If a person has best corrected visual acuity <20/200 to 20/400, the visual disability is considered as Severe Visual Impairment (SVI) by WHO standards but still blind by Indian standard. Moderate Visual Impairment (MVI) is defined as the best corrected visual acuity between <20/60 and 20/200. the census 2011 collected information on eight types of disability (disability in seeing, in hearing, in speech, in movement, in mental retardation, in mental illness, any other and multiple disability) [1]. It revealed that 19% have disability in seeing. Also, among those visually disabled and non workers, 42.7% are dependents and 28% are students.

Visual impairment also causes drastic impact on daily life, including social activities and emotional functioning. Studies also note that loss of vision can cause depression, social isolation, and also falls and medication errors. However, visual acuity alone fails to capture the impact of vision disability. The national eye institute visual function questionnaire-25 (visual function questionnaire) was developed to measure self reported, visionrelated aspects of health status that are most significant to individuals with chronic eye disease. the reliability and validity of the visual function questionnaire-25 has been tested and confirmed on various group of patients with eye conditions, such as age related macular degeneration, glaucoma, cataract, viral retinitis, and diabetic retinopathy. the visual function questionnaire consists of 25 vision-targeted questions plus 1 overall health rating question. the questions fall into 11 visionspecific subscales: General vision, ocular pain, near activities, distance activities, social functioning, mental health, role difficulties, dependency, driving, colour vision, and peripheral vision. the questionnaire takes only ten minutes to complete. Visual quality of life questionnaires are more useful to assess the functional status of patients with visual handicap than general quality of life questionnaires, and can also assess the impact of low vision services provided to such patients. Data on prevalence, magnitude, causes of blindness and visual impairment and perceived quality of life among these patients are needed to better the existing preventive, medical and social rehabilitative and low visual aid services for these patients. This present study is an attempt to find out the visual quality of life perceived by patients with low vision in Mumbai and its association with age, gender, socioeconomic and educational standard.

Study design

This was a cross sectional descriptive study for determining the magnitude of visual function affected in patients with low vision. Factors like age, gender, education, occupation, comorbidities, social, economic factors were compared.

Inclusion criteria:

• Patients of both gender, and age above of 12 years.
• Patient whose visual acuity is less than 6/18 in both eyes.
• Patient or legally accepted representative willing to give voluntary informed consent.

Exclusion criteria:

• Patients who are not able to complete the questionnaire.
• Any physical or mental illness, which in the opinion of the investigator precludes.the inclusion of the participant.
• Low vision from treatable cause.

Subject recruitment plans and consent process

When the patient came to ophthalmology out patient department for routine ophthalmology examination after their screening has been done having best corrected visual acuity less than 6/18 in both eyes and if the patient fulfilled the inclusion criteria, he/she was approached and counselled about the study. Subjects who were willing to participate and sign the written informed consent, were enrolled in the study and their doubts were cleared regarding the study. Subjects for this study were ages 12 and above, who visited the out patient department of a tertiary care hospital in India. For routine ophthalmological examination, he/she was tested for visual acuity using Snellen’s chart. If the best corrected visual acuity of the patient was found to be less than 6/18 in both eyes, he/she was informed about the study and a consent form provided. Informed consents were obtained from each subject before enrollment. Detailed ocular and systemic history was taken. Details like age, gender, educational status, occupation, diagnosis, past history, history of known co-morbidities were also documented [2]. The visual function questionnaire (visual function questionnaire-25) was given to the patient on the same day in the language best understood by the patient.

Assessments

• Best corrected visual acuity was documented with the help of using a Snellen’s chart
• (annexure 2) placed 6 meters away from the participant in a well illuminated area. Near
• vision was also documented.
• Anterior segment evaluation was done using slit lamp, to look for anterior segment
• conditions like corneal opacities, anterior uveitis, cataract.
• Intraocular pressure was measured for patients using Goldmann applanation tonometer for patients with glaucoma.
• Posterior segment evaluation was done using direct and indirect ophthalmoscopy.
• In cases that required optical coherence tomography for diagnosis, it was done free of cost for the patient.
• The researcher read out the questionnaire in the language that the participant can understand and filled it according to the participant’s responses. Once filled, the data was used for scoring and assessment based on guidelines given as per the national eye institute-visual function questionnaire 25 manual itself.
• To ease the implementation of the national eye institute visual function questionnaire in the study, the questionnaire was self-administered. Subscales, scored 0 to 100 (with 100 indicating highest function), were generated for overall health, overall vision, difficulty with near vision activities, difficulty with distance vision activities, limitations in social functioning due to vision, role limitations due to vision, dependency due to vision, mental health symptoms due to vision, future expectations for vision, driving difficulties, limitations with peripheral and color vision, and pain or discomfort in or around eyes. For analysis, when one or more items from a subscale were missing, the subscale was considered missing. Analyses using all available data (, a subscale score was generated when the subject responded to one or more items from the subscale) provided similar results [3].

Statistical analysis used

Continuous variables were presented as mean ± Standard Deviation (SD). The data obtained from visual function questionnaire were analyzed and compared in relation to age, gender, educational status, occupation and co-morbidities. Scores of the Visual function questionnaire were calculated as total scores as well as sub scores and presented as numbers and as range. Statistical tests were used to correlate the above data, including T test for two means, Chi square test, and Pearson correlation coefficient to find the association between two variables. A p value of <0.05 was considered statistically significant. The data was entered in and analysed using Microsoft excel. Statistical analysis was performed using the Microsoft Excel program.

Results

Age and gender

A total of 733 participants ages 12 years and above with bilateral visual acuity lower than 6/18 on Snellen visual acuity chart participated in the study. The mean age of participants was 56.38 ± 13.49 years (mean ± standard deviation) [4]. Minimum age of the participant recruited was 12 years and maximum age was 89 years. The participants were divided into 8 groups for data analysis according to age group 12 to 18 years, 19 to 30 years, 31 to 40 years, 41 to 50 years, 51 to 60 years, 61 to 70 years, 71 to 80 years, and 81 to 90 years (Table 1 and Figure 1).

Table 1: Age distribution of the study population.

Figure 1: Age distribution of the study population.

81.56% of the patients in study were between 41-70 years of age, among whom 32.60% of patients b elong to age group of 51-60 years, 26.73% being 61-70 years old, 22.23% patients were between 41-50 years old. It was found that maximum percentage of patients with low vision were in the age group of 51 to 60 years (32.60%) followed by age group of 61 to 70 years (22.23%) and the least in the age group of 31 to 40 years (1.63%).

The relation between age of patient and mean score was a weak negative correlation, not statistically significant. (The P-value is 0.21. The result is not significant at p<0.05). 81.56%of the patients in study were between 41-70 years of age [5]. It was found that among this most common age group, most patients with better visual function scores were between age groups 41-50, followed by other age groups (Table 2 and Figure 2).

Table 2: Relation between age group and mean score.

Figure 2: Relation between age of patient and mean score.

When the data was analyzed gender wise, there was a male: female ratio of 0.898:1. 347 patients were males and 386 were females. The percentage of male participants was 47.33% and that of female participants was 52.66% (Tables 3 and 4). There was a symmetric distribution in the scores on visual function questionnaire found among males and females (Figures 3 and 4).

There was no statistical difference between the scores of males and females, as the p-value is 0.12 (p<0.05).

Table 3: Gender distribution of the study population.

Figure 3: Gender distribution of the study population.

Table 4: Relation between gender and mean total score.

Figure 4: Relation between gender and score.

Visual acuity

Patients with visual acuity lesser than 6/18 on Snellen visual acuity chart in both eyes were taken, thus belonging to category III a/b/c/d/e or IV a/b (low vision/blindness) as per disability affairs, government of India (Table 5 and Figure 5).

Table 5: Percentage of study patients in each category of disability.

Figure 5: Percentage of study patients in each category of disability.

1.50% of patients were 100% blind/category IV b/blind with bilateral visual acuity hand movement close to face or only perception of light or no light perception. 21.82% of patients were 90% blind/category IV a/blind with better eye visual acuity of less than 3/60 to 1/60 and worse eye less than 3/60 to No Light Perception [6]. 18.55% of patients were 80 % blind/ category III e/low vision with better eye visual acuity of Less than 6/60 to 3/60 and worse eye less than 3/60 to no light perception. 12.27% of patients were 70% blind/ category III d/low vision with better eye visual acuity of Less than 6/60 to 3/60 and worse eye Less than 6/60 to 3/60. 10.23% of patients were 60 % blind/category III c/low vision with better eye visual acuity of 6/24 to 6/60 and worse eye less than 3/60 to no light perception 9.00% of patients were 50% blind/category III b/low vision with better eye visual acuity of 6/24 to 6/60 and worse eye less than 6/60 to 3/60. 26.60% of patients were 40 % blind/ category III a/low vision with better eye visual acuity of 6/24 to 6/60 and worse eye 6/24 to 6/60.

Although patients with better visual acuity were expected to have better scores than patients with lower visual acuity, there was a variation of scores within patients with the same category of low vision, indicating that factors other than visual acuity also played a role in quality of life for these patients (Table 6 and Figure 6).

Figure 6: Showing scores of patients in different categories.

Table 6: Comparing the visual score with disability category.

There is a strong negative correlation between mean score and category of visual disability. As expected, patients with lower visual acuity and higher categories of visual impairment had poorer final scores [7]. There were however some exceptions indicating other factors involved like social support, and use of rehabilitative services.

Relation of sub scale score to category of disability

The following table shows the mean sub scale scores of patients with different categories to visual disability (Table 7 and Figure 7).

Table 7: Sub scale scores of patients in different categories of visual disability.

Figure 7: Relation of sub scale scores to category of visual disability.

C Mean subscale scores were worse for patients in category IV a and IV b than those compared to categories III a, b, c, d, e (Table 8 and Figure 8). It was also noted that lower visual acuity had a significant impact on social function as shown in this graph [8]. Mental health scores were also extremely low in patients with lower visual acuity (Table 9 and Figure 9).

Table 8: Social function scores of patients in different categories of visual disability.

Figure 8: Social function scores of patients in different categories of visual disability.

Table 9: Mental health scores of patients in different categories of visual disability.

Figure 9: Mental health scores of patients in different categories of visual disability in various sub scales.

Mean scores the scores were found to be worse for role difficulties, near activities, peripheral vision and mental health (Table 10 and Figure 10).

Table 10: Mean subscale scores of all patients.

Figure 10: Mean subscale scores of all patients.

Educational status and visual function score 27.01% of patients surveyed were illiterate. 25.38% patients had primary school certificate, 14.19% had middle school certificate, 13.92% were literate but with no schooling, 9.14% were graduate or postgraduate, with 5.32% having high school certificate [9]. Intermediate or post high school diploma holders constituted only 2.59% of the participants (Table 12 and Figure 12).

Patients in different educational groups had different range of scores as shown in Table 12 and Figure 12.

Table 11: Educational status of study population.

Figure 11: Educational status of study population.

Table 12: Relation between education and score.

Figure 12: Relation between education and score.

The study found a mostly positive relation between education and visual function score. Most patients with better education had better scores and those with lower or no education had poorer scores [10]. This may be due to better accessibility to low vision aids and knowledge of rehabilitative services available among the educated patients (Table 13 and Figure 13).

Table 13: Relation between education and mean score.

Figure 13: Relation between education and mean score.

Correlation between education and mean score was statistically signi icant (P<0.5). Employment and per capita monthly family income. Table 14 shows distribution of the study patients according to their occupation [11]. Almost 20% of the study population was unemployed (Table 14 and Figure 14).

Table 14: Occupation of study population.

Figure 14: Occupation of study population.

Patients who were unemployed had lower scores ranging from 0-40. 85 of the unemployed patients had a score between 11-20, 27 patients scored between 21-30, 23 patients between 0-10, and only 9 patients scored between 31-40. On the other end, two of the patients with professional employment had higher scores [12]. Semi professionals had scores between 31-90, with most of the patients (37 patients) having scores between 81-90 and least (2 patients) having 41-50.

As expected, patients with better employment opportunities, most probably tailored to their needs with regards to visual handicap, were likely to have better quality of life scores due to better inancial and social status, and it would also help at a psychological level [13]. The following table shows the relation of scores and number of patients in different occupations who had participated in the study (Table 15 and Figure 15).

Table 15: Scores of study participants in various occupations.

Figure 15: Scores of patients in various occupations.

As shown in Table 16, it was found that there was a strong correlation between occupation and score (p<0.5). Patients with better occupational opportunities had better social, mental and inancial scores and lower dependency, while patients who were unemployed had worse inancial, emotional, social scores and greater dependency [14].

Table 16: Relation between occupation and score.

Figure 16: Relation between occupation and mean score.

Capita income

The number of participants in different groups of per capita income is as shown in the Table 17. 25.37% of the patients had per capita income lower than 20,000 per month (Figure 17).

Table 17: Distribution of study population according to income.

Figure 17: Distribution of study population according to income.

Relation of per capita income with score

A statistically signi icant positive relation is noted between per capita income and score (signi icant at p<.05), indicating that patients with more inancial resources may also have better access to different rehabilitative services that others may not be able to afford. It was also noted however that in some cases better scores were seen in patients with lower incomes, and vice versa, which may result from other factors, such and social support and other co-morbidities (Table 18 and Figure 18).

Table 18: Relation between per capita income and mean score.

Figure 18: Relation between per capita income and score.

Positive relation is noted between per capita income and score, indicating that patients with more financial resources may also have better access to different rehabilitative services that others may not be able to afford (Table 19 and Figure 19). It was also noted however that in some cases better scores were seen in patients with lower incomes, and vice versa, which may result from other factors, such and social support and other comorbidities (Table 20 and Figure 20).

The following chart shows how patients with lower score belonged to the lower income groups, with 13 patients in 5001-10,000 income group, and 12 patients in <5000 group. The chart shows how patients who scored between 81-100 had higher incomes, with 9 patients in 80,001 to 90,000 group, 8 patients in 90,001 to 100,000 group (Figure 20).

Table 19: Patients with score 0-10.

Figure 19: Income of patients with lower scores.

Table 20: Per capita income of patients with higher scores.

Figure 20: Patients with score 81-100.

Ocular conditions causing low vision and blindness seen in the study. Out of the 1466 eyes (733 patients) studied, 414 had Retinal vascular disorders including diabetic retinopathy, hypertensive retinopathy, anterior ischaemic optic neuropathy, central retinal artery or vein occlusion, most common being diabetic retinopathy associated with complications such as maculopathy, retinal detachment, etc [15]. 380 eyes had diabetic retinopathy. 369 eyes had age related macular degeneration including CNVM complex.

188 eyes had other retinal conditions including chronic vitreous hemorrhage, retinal detachment, colobomas, Best, Stargardt’s, other maculopathies, myopic maculopathy, retinitis pigmetosa, pachychoroid disease spectrum, cystiod macular edema, sympathetic ophthalmitis, foveal hypoplasia, posterior uveitis 127 eyes were affected with glaucoma including congenital glaucoma (Table 21 and Figure 21).

Table 21: Ocular conditions in the study population.

Figure 21: Ocular conditions in the study eyes.

The following chart shows the distribution of various ocular conditions in patients who scored 0-10. Most of the eyes in this group had diabetic retinopathy (Table 22 and Figure 22). The following chart shows the distribution of ocular conditions among patients with higher scores (81-100) (Table 23 and Figure 23).

Table 22: Ocular conditions associated with lowest scores (0-10).

Figure 22: Ocular conditions associated with lowest scores (0-10).