Next Article in Journal
Building Community Capital—The Role of Local Area Coordinators in Disability Services: A Critical Review
Previous Article in Journal
Examining the Post-School Decision-Making and Self-Determination of Disabled Young Adults in Ireland
 
 
Search for Articles:
Title / Keyword
Author / Affiliation / Email
Journal
Article Type
 
 
Advanced Search
 
Section
Special Issue
Volume
Issue
Number
Page
 
Journals
Disabilities
Volume 4
Issue 3
10.3390/disabilities4030030
disabilities-logo
Submit to this Journal Review for this Journal Propose a Special Issue
► Article Menu

Article Menu

share Share announcement Help format_quote Cite question_answer Discuss in SciProfiles thumb_up
...
Endorse textsms
...
Comment
first_page
settings
Order Article Reprints
Font Type:
Arial Georgia Verdana
Font Size:
Aa Aa Aa
Line Spacing:
Column Width:
Background:
Article

Exploring Priority Issues among a Sample of Adults from Minority Ethnic Communities Who Are Living with Visual Impairment in the UK

by
Nikki Heinze
1,*,
Lee Jones
1,2,
Claire L. Castle
1,3 and
Renata S. M. Gomes
1,4
1
BRAVO VICTOR, 3 Queen Square, London WC1N 3AR, UK
2
Institute of Ophthalmology, University College London, 11–43 Bath Street, London EC1V 2PD, UK
3
School of Music, Faculty of Arts, Humanities and Cultures, University of Leeds, Leeds LS2 9JT, UK
4
Northern Hub for Veterans and Military Families Research, Department of Nursing, Midwifery and Health, Faculty of Health and Life Sciences, Northumbria University, Newcastle upon Tyne NE7 7XA, UK
*
Author to whom correspondence should be addressed.
Disabilities 2024, 4(3), 477-492; https://doi.org/10.3390/disabilities4030030
Submission received: 22 March 2024 / Revised: 21 June 2024 / Accepted: 26 June 2024 / Published: 3 July 2024
Browse Figure
Versions Notes

Abstract

:
Background: Despite an increased risk of visual impairment (V.I.) among adults from minority ethnic communities in the UK, limited research has explored their wider life experiences. Methods: A secondary analysis of V.I. Lives survey data explored priority issues among a sample of 46 Asian, 22 Black, and 77 White adults who have visual impairment A list of 24 issues were grouped into 10 life domains. Issue and domain mean importance scores were calculated for each to facilitate ranking of importance. Results: Kruskal–Wallis tests showed that there were statistically significant differences between the three groups for 7/10 domains and 19/24 issues. Post hoc comparisons showed that this largely reflected group differences between Asian and White participants. While there were no statistically significant differences between Asian and Black participants possibly due to small sample sizes, there were statistically significant differences between Asian and White participants in 7/10 domains and 14/24 issues. Additionally, there were significant differences between Black and White participants in 5/10 domains and 7/24 issues, specialist workplace equipment being the only issue with a significant difference between White and Black but not Asian participants. There were no group differences for confidence in ability to do everyday tasks and opportunities to take part in more sporting and leisure activities. Overall, White participants generally rated all issues as less important than Asian and Black participants. The top-three domains for Asian participants were ‘accessible environments’/‘finances’, ‘technology’, and ‘public attitudes’. The top-three issues were accessibility of public transport, employer attitudes, and reduction of street clutter. The top-three domains among Black participants were ‘employment’, ‘accessible environments’, and ‘emotional support’. The top-three issues were employer attitudes/specialised education for young people with V.I., specialist V.I. equipment in the workplace, and confidence in ability to do everyday tasks/accessibility of public transport. Conclusions: Differences in priorities between the groups suggest that the needs of individual communities may be lost when grouping culturally diverse communities together, highlighting the need for more research with different minority ethnic communities.

1. Introduction

Visual impairment (V.I.) has been associated with an adverse impact on a range of life domains. For instance, while educational attainment was better among UK children with V.I. than those with other needs/disabilities, educational attainment was poorer among children with V.I. than those without special educational needs (SEN) [1]. There is evidence that education level may impact employment outcomes for people with V.I. [2,3]. V.I. has been associated with lower employment rates [4], an increased risk of being unemployed or unable to work, and being in a lower-status job [5]. In the UK, employers are legally required to make reasonable adjustments for employees with disabilities, and the government-funded Access to Work scheme provides support for employers to make provisions. Yet, barriers such as transport issues, negative employer attitudes, and a lack of adaptive equipment in the workplace are cited as impacting the employment of people with V.I. [6,7]. People with V.I. have also been found to be at an increased risk of being in the lowest income bracket [5]. This is concerning, considering the additional everyday costs associated with having V.I., e.g., for specialist technology, such as screen readers or labelling pens; adaptations to the home; regular help with household chores to maintain independence; the additional costs associated with socialising; and travel, for those who cannot use public transport or walk [8,9].
People from minority ethnic communities (MEC) are at increased risk of V.I. [5,10] and are projected to make up an increasing proportion of adults living with V.I. in the UK [11]. Being from MEC has, similarly, been associated with inequalities in several life domains. Although the 2021 UK Census showed that Chinese, Indian, and Black African adults tended to be better educated than White adults [12], 67% of MEC adults were in employment in 2021 compared to 76% of White adults, despite a steady increase in employment among MEC adults since 2012 [13]. However, there is considerable variation between different ethnic communities. For instance, the employment rate among Indian communities (78%) exceeded that of White adults [13], and Indian households were more likely than any other ethnic group to be in the highest income brackets [14]. In contrast, just over half (58%) of Pakistani and Bangladeshi adults were in employment [13], and the prevalence of Pakistani and Bangladeshi households among the lower income brackets was high [14]. Overall, unemployment rates were highest among Black communities [12], who were also more likely to be in the lower household-income brackets than any other ethnic group [14].
People living at the intersection of disability and ethnicity may be at risk of multiple stigmatisation and discrimination. Yet, research on the wider life experiences of MEC adults with V.I. in the UK is limited [15]. A UK survey of over 1200 people who are registered as blind or partially sighted, including 703 working-age adults, found no statistically significant association between ethnicity and employment [16], but a 2007 review listed unmet needs relating to social isolation and self-esteem for MEC groups [17]. All older adults with V.I. reported difficulties with mobility outside the home, activities of daily living, maintaining control and independence, and a decreasing social network, regardless of ethnicity [18], but those from MEC may be more likely to require help from family members with activities of daily living, and less likely to have up-to-date technological devices and to leave the home than their White counterparts [18]. Neither article [17,18] provides evidence, such as quotes or statistics, to support these findings.
To ensure adequate and equitable health and social support, an understanding of the priorities and needs of the different ethnic communities is required. As part of a series that explores the wider life experiences of a small sample of MEC adults living with V.I. in the UK, the current article provides preliminary insights into the issues that are important to them, aiming to stimulate research on the experiences of individual ethnic communities with V.I., which may vary substantially from those of the majority White V.I. population.

2. Materials and Methods

This article presents findings from a secondary analysis of anonymized data collected in the V.I. Lives survey [19].

2.1. The V.I. Lives Survey

The V.I. Lives survey was commissioned by the Royal National Institute of Blind People (RNIB), the Thomas Pocklington Trust (TPT), and the Guide Dogs for the Blind Association (Guide Dogs) (the data controllers), who granted access to the anonymized dataset. Quantitative survey data were collected by the market research agencies Insight Angels and Acumen Fieldwork. Consent was sought from participants at the start of the survey. Data were collected in two waves (17 December 2019 to 23 March 2020, and 14 August 2020 to 2 November 2020). Participants were recruited through Acumen’s healthcare database, partner charities, social media, radio adverts, and lists provided by RNIB and Guide Dogs. Acumen’s health database consists of individuals who have agreed to be contacted for market research and contains self-reported information about their eye condition and length of V.I. An initial call screened out non-English speakers and those without V.I. The degree of V.I. was assessed using self-reported registration status, legal ability to drive, and self-reported difficulty with near, distance, and peripheral vision when wearing glasses/contact lenses where applicable. The initial screener and the near and distance difficulty questions have previously been used in the Life Opportunities Survey (LOS), a longitudinal survey of people with disabilities conducted by the Office for National Statistics (ONS) (see D’Ardenne, Hall [20] for a review of the questions and their limitations). Potential participants were excluded from the survey if they were not registered and (1) were legally not able to drive but had no difficulties with near, distance, or peripheral vision or (2) if they were legally able to drive and had no or only mild vision difficulties. Participants who passed the screener questions were categorized as having mild, moderate or severe V.I. (Supplementary Table S1). Any inconsistencies in responses were clarified by contacting the participants. All survey interviews were conducted over the phone to ensure accessibility for those without internet access.

2.2. Materials

A questionnaire was developed for the survey and piloted with a small number of advisers. Screener and demographic questions were followed by individual sections exploring ‘health’, ‘well-being’, ‘relationships and attitudes to life’, ‘getting out of home’, ‘leisure’, ‘work’, ‘education’, ‘benefits and finances’, ‘technology and media’, ‘accessible information and reading’, ‘domestic support’, ‘charity awareness and usage’, ‘making everyday better’, and ‘coronavirus’.
As described above, V.I. status was self-reported. Despite the limitations of self-report compared to objective measures, it is routinely used in survey research, including large general population surveys [20]. Ethnicity was assessed using one question that asked participants how they would describe their ethnic background from a list of response options: White British, White other, Mixed/multiple ethnic groups, Asian/Asian British, Black/African/Caribbean/Black British, and Other ethnic group.
The section titled ‘Making everything better’ asked participants to rate the importance of a list of 24 issues relating to employment, education, technology, self-efficacy, accessibility of environments and information, public attitudes, and emotional support on a Likert-type scale ranging from extremely important to not important at all. The items were introduced as follows:
“We have discussed many things about different aspects of your life. We hope that the questions we have asked have given you a chance to express things which are important to you. The final question relates to improving the quality of your life. Please take a moment and think about what ‘quality of life’ means specifically to you and your life today. For each of the following areas, could you please tell me how much of a priority it is for you that changes and improvements are made to improve your quality of life. Would you say it extremely important, very important, somewhat important or not important at all?”.

2.3. Participants

A convenience sample of 769 participants aged 13+, including 667 adults aged 18+ from White, 46 from Asian, 22 from Black, 6 from ‘Other’ ethnic’ and 3 from mixed ethnic backgrounds took part in the survey. The White group consisted of adults who identified as White British and White other. Participants in the MEC groups were significantly younger than those in the White group (M = 40.78 vs. M = 58.72, t(103.0) = 9.36, p < 0.001). There were further statistically significant differences in the region where participants were located, Χ2 (11, 741) = 71.69, p < 0.001, Cramer’s V = 0.311, rural vs. town setting, Χ2 (2, 741) = 26.38, p < 0.001, Cramer’s V = 0.189, employment status, Χ2 (4, 743) = 35.09, p < 0.001, Cramer’s V = 0.217, highest level of education achieved, U = 28,154.5, p < 0.001, and marital status (Fisher’s exact simulated p < 0.001), but not for V.I. severity, U = 28,365.5, p = 0.092, living arrangements, Χ2 (1, 744) = 0.61, p = 0.434, nor gender, Χ2 (1, 742) = 0.63, p = 0.427.
To control for the significant differences and unequal sample sizes, previous research in this series [21,22,23,24,25] has used a matched control sample drawn using RStudio [26]. White participants were matched to MEC participants based on age, gender, region, and whether they lived in rural areas vs towns, yielding 77 participants in both groups. Age and gender are frequently used to match control samples [27,28,29,30,31,32]. In addition, there are gender differences in eye health [33,34] and age differences in the prevalence of individual eye conditions [35,36], as well as needs and priorities across the age span. Variables relating to geographical location (region and urban/rural) were selected to control for regional differences in deprivation and V.I. support [37,38,39]. One participant, who preferred to self-describe their gender, was excluded due to the potential impact of being gender-non-normative and the lack of a match.

2.4. Data Analysis

This article is exploratory. Descriptive statistics provide an overview of responses for each subgroup. Means and standard deviations were calculated for continuous variables and proportions and frequencies for categorical and ordinal variables. Invalid responses such as not stated or prefer not to say are shown in the respective tables but are excluded from statistical subgroup comparisons. Due to expected frequencies of <5 in 5 cells (27.8%), the categories student, looking after family/home, long-term sick/disabled, and unpaid work (e.g., volunteering, intern, work experiences) were collapsed into the other category for the statistical analysis of the employment variable. Statistical analysis of the education variable excludes non-UK qualifications and other. Whilst Likert scales are ordinal, and median would therefore be more appropriate, mean importance scores were created to assess the relative importance of issues by assigning a score to each response option (extremely important = 3 to not at all important = 0) and calculating the mean score for each issue. The higher the resulting mean importance score, the more people assigned the issue a higher importance, thus suggesting a greater importance. In addition, to explore priority areas, the issues were grouped into the life domains they related to (see Table 1 for an overview of the 10 domains and related issues). Mean importance scores for each domain were created by averaging the scores for all issues within the domain for each participant and then calculating the mean for each domain.
Although the survey was not specifically designed for individual subgroup analysis, and despite the relatively low subsample sizes, we undertook an exploratory analysis comparing results from the two largest MEC subgroups, Asian (n = 46) and Black (n = 22) participants, rather than a combined MEC group, to gain preliminary insights into this under-researched topic. Subgroup analysis did not include mixed and other ethnic communities due to small subsample sizes (n = 3 and 6, respectively). The matched White subsample used in previous research in this series [21,22,23,24,25] was retained to minimise sample-size differences and significant differences in demographic variables. Subgroup analyses were conducted using chi-square tests for categorical data. Where more than 20% of the cells had a cell count of less than 5, Fisher’s exact tests were calculated using R. The results of Fisher’s exact tests are presented as p-values only. A simulated p-value is presented for marital status in the full sample due to capacity issues. Kruskal–Wallis H tests were used for ordinal data, and post hoc group comparisons were conducted using Dunn’s test applying Holm correction to control for multiple testing. Age was not normally distributed among White (p = 0.002) and Asian participants (p = 0.014), and the non-parametric Kruskal–Wallis test was used to assess group differences in age. A significance level of p = 0.05 was used throughout the analysis.

3. Results

There was no association between ethnicity and gender, V.I. severity, or living arrangements (Table 2). In addition, there were no longer any statistically significant differences between ethnicity and age, region, setting, employment status, and marital status. However, there was a statistically significant group difference in level of education. Post hoc tests showed that Black participants had significantly better educational attainment than White participants (p = 0.03). Participants in all groups were mainly female, London-based, living in a city or big town, educated to undergraduate level, employed, single, living with others, and had severe V.I., but Black participants were more likely to be educated to postgraduate degree level and have moderate V.I.

3.1. Subgroup Differences in Priorities

Kruskal–Wallis H tests showed that there were statistically significant differences between the three groups for all domains except for ‘public attitudes’, ‘emotional support’ and ‘accessible information’, and for all issues except for cost/availability of specialist equipment, accessibility features of mainstream tech, help with job applications/interview preparation, support and training for mainstream education staff to adapt courses for people with V.I., and reduction of street clutter (Table 3), although the latter was approaching statistical significance. White participants generally rated all issues as less important than Asian and Black participants.
Post hoc comparisons found no statistically significant differences between Asian and Black participants. But, there were statistically significant differences between White and Asian and White and Black participants in the domains of education (p = 0.003 and p = 0.033) including specialist education for young people with V.I. (p = 0.035 and p = 0.002), employment (p = 0.015 and p = 0.006) including employer attitudes (p = 0.007 and p = 0.012), technology (p < 0.001 and p = 0.006) including internet use (p = 0.001 and p = 0.043) and tech training (p = 0.001 and p = 0.01), accessible environments (p = 0.010 and p = 0.039) including accessibility of public buildings (p = 0.010 and p = 0.010), and efficacy (p = 0.003 and p = 0.010) including support to look after self and one’s home (p = 0.002 and p = 0.017).
There were further statistically significant differences between White and Asian but not Black participants in the finance (p = 0.006), including benefits (p = 0.017), and social domains (p = 0.007), including meeting like-minded people (p = 0.007), as well as the issues of online learning (p < 0.001), new tech (p = 0.001), better navigation aids (p = 0.001), public transport (p = 0.041), information about eye conditions (p = 0.006), and street clutter (p = 0.045).
In contrast, there were statistically significant differences between White and Black but not Asian participants in the issue of specialist workplace equipment (p = 0.007).
There were no statistically significant differences between any groups in the importance of confidence in ability to do everyday tasks and opportunities to take part in more sporting and leisure activities.

3.2. Priority Issues for Each Group

There was relatively little variation in mean importance scores among Asian participants. Domain mean importance scores in this group ranged from 2.08 to 2.33 and from 1.91 to 2.46 for issues, equating to an assessment of all domains as very important. Domain mean importance scores were highest for ‘accessible environments’ (M = 2.33, 95% CI: 2.12, 2.54) and ‘finances’ (M = 2.33, 95% CI: 2.11, 2.54), ‘technology’ (M = 2.26, 95% CI: 2.08, 2.45) and ‘public attitudes’ (M = 2.24, 95% CI: 2.00, 2.49) (Figure 1). Accessibility of public transport (M = 2.46, 95% CI: 2.20, 2.71), employer attitudes, (M = 2.42, 95% CI: 2.14, 2.70), and reduction of obstacles and street clutter (M = 2.40, 95% CI: 2.17, 2.63) were the most important issues. Domain mean scores were lowest for ‘employment’ (M = 2.12, 95% CI: 1.84, 2.40), ‘emotional support’ (M = 2.11, 95% CI: 1.82, 2.40), and ‘social participation’ (M = 2.08, 95% CI: 1.87, 2.29). The three least important issues were support with job applications (M = 1.91, 95% CI: 1.57, 2.25), opportunities to participate in more sporting and/or leisure activities (M = 1.98, 95% CI: 1.69, 2.26), and availability of remote learning courses (M = 2.02, 95% CI: 1.72, 2.32).
In contrast, there was slightly more variation in mean importance scores of participants from Black communities. Domain mean importance scores ranged from 1.98 to 2.39 (very important), but issue mean importance scores ranged from 1.77 (very important) to 2.59 (extremely important). Domain mean importance scores were highest for ‘employment’ (M = 2.39, 95% CI: 2.15, 2.64), ‘accessible environments’ (M = 2.38, 95% CI: 2.18, 2.58), and ‘emotional support’ (M = 2.32, 95% CI: 1.95, 2.69). The most important issues were employer attitudes (M = 2.59, 95% CI: 2.27, 2.92) as well as access to specialised education and support for children and young people with V.I. (M = 2.59, 95% CI: 2.27, 2.92), availability of specialist V.I. equipment in the workplace (M = 2.55, 95% CI: 2.25, 2.84), and confidence in their ability to do everyday tasks (M = 2.45, 95% CI: 2.19, 2.72), as well as accessibility of public transport (M = 2.45, 95% CI: 2.07, 2.83). Domain mean importance scores were lowest for ‘public attitudes’ (M = 2.09, 95% CI: 1.71, 2.48), ‘accessible information’ (M = 2.05, 95% CI: 1.70, 2.39), and ‘social participation’ (M = 1.98, 95% CI: 1.63, 2.33). The least important issues were, again, availability of remote learning courses (M = 1.77, 95% CI: 1.36, 2.18) and opportunities to participate in more sporting and/or leisure activities (M = 1.95, 95% CI: 1.51, 2.40), as well as the ability to connect to like-minded people (M = 2.00, 95% CI: 1.59, 2.41).

4. Discussion

Limited research has explored the experiences of MEC adults with V.I. in the UK [15]. This article set out to explore priority issues among Asian and Black adults with V.I. and compare these to a subgroup of White participants. Findings for the UK V.I. population tend to relate predominantly (e.g., Refs. [40,41]), and sometimes exclusively, to White participants (e.g., Refs. [42,43]). This may reflect recruitment issues [44] and a lack of reporting of ethnicity as part of sample demographics (e.g., Refs. [45,46]), resulting in small subsample sizes that do not allow for subgroup analysis. However, as a consequence, the needs and priorities of MEC participants may be lost. For instance, ‘public attitudes’ is the domain with the highest priority among White but not Asian and Black participants. It is also the domain with the highest priority in the full sample (M = 2.18).
Although the findings are based on a small convenience sample and can, therefore, not be extrapolated to the wider V.I. population, post hoc analyses showed that the two MEC groups in this sample rated all domains as significantly more important than White participants, except for ‘accessible information’ and ‘public attitudes’, which appeared to be of similar importance. In addition, ‘finances’ and ‘social participation’ related issues were significantly more important to Asian but not Black participants. It is unclear if this reflects greater need or response style, whereby participants from the two MEC groups tended to rate all issues as important rather than select issues with the biggest impact on their QoL. In contrast, there were no statistically significant differences between Asian and Black participants. This may be due to a lack of statistical power resulting from small subsample sizes. Instead of conducting subgroup analysis for a combined MEC group, subgroup analyses comparing Asian and Black participants were included to account for inherent cultural differences between communities that are routinely grouped together in a BAME category [47]. Indeed, descriptive analyses show that there were some non-significant differences between the two groups in the relative importance of different issues that can impact QoL.
‘Accessible environments’, particularly accessibility of public transport, emerged as a top priority across all groups (Figure 1). Indeed, it was the most important priority (alongside ‘finances’) among Asian participants. In addition, reduction of street clutter ranked among the most important issues for Asian and White participants. Collisions with street obstacles are a common problem among people with V.I. A survey of 500 people with V.I. found that 95% had collided with street obstacles in the past 3 months, most commonly with cars parked on pavements followed by bins, permanent and temporary street furniture, and advertising boards [48]. Even though Black participants (50.0%) were slightly more likely than Asian (45.7%) and White participants (41.6%) to have been injured in the past year by an obstacle on the pavement, such as parked cars, advertising boards, rubbish or bikes, reduction of street clutter was of comparatively less importance to this group. A further similarity between all groups was the low importance of support with job applications and interview preparations (possibly because a majority in all groups were self-/employed) and ‘social participation’, despite the impact of V.I. on participation in sports and leisure activities [49,50]. The latter may reflect good access to social activities and networks or greater needs related to daily functioning.
‘Finances’, ‘technology’, and ‘public attitudes’ were additional top priorities for Asian participants. Concern about finances may be expected considering the additional costs associated with V.I., which has been found to increase the weekly budget (excluding rent) required to achieve a minimum, socially acceptable standard of living outside of London [8]. Technology can play an important role in the QoL of people with V.I. through remotely delivered vision rehabilitation and support [51] and by facilitating social contact, access to information, entertainment, and route planning, [52]. Issues, such as the development of new smart technology and apps, were highly important among Asian participants in this sample. Notably, the least important technology issue, access and support to use the internet, ranked higher than issues such as emotional support to come to terms with V.I., despite significantly poorer mental well-being among Asian compared to Black participants in this sample [21]. Noteworthy, also, is the importance of employer attitudes, despite the low priority of employment overall.
In contrast, Black participants prioritised ‘employment’ and ‘emotional support’ to come to terms with their V.I. alongside ‘accessible environments’. Employer attitudes and availability of specialist V.I. equipment in the workplace were priority issues for Black participants, as was specialised education and support for children with V.I. The importance of these issues may reflect lower employment rates among adults with V.I. [4], and the barriers to employment posed by negative employer attitudes and the lack of adjustments made in the workplace [3,6]. However, as indicated earlier, the role of ethnicity in employment status is unclear [12,16]. In the current sample, there were no statistically significant group differences in employment status. As such, the perceived importance of employment-related issues did not reflect differences in status. Instead, it may reflect dissatisfaction with the roles, career prospects, and/or workplace support available to Black participants.
Confidence in their ability to do everyday tasks was a further priority issue among Black and White but not Asian participants. This relates to functioning and activities of daily living. There is evidence of a negative impact of sight loss on activities of daily living [53,54], which, in turn, can impact the extent to which people feel independent and are able to live independently. This finding may reflect differences in the extent to which participants in these groups are limited by their V.I. and/or comorbid conditions and the extent to which they have received vision rehabilitation to help them live independently. Future research could explore other factors that may impact self-efficacy and the perceived importance of it. For instance, Cross and colleagues [55] describe ingroup stereotypes of Afro-Caribbean people, particularly women, as being proud, stoic, and independent. These contrast associations of blindness with victimhood, helplessness, and social isolation. It is possible that the importance of confidence in their ability to do everyday tasks and employment issues among Black participants reflect a desire to increase independence to fit cultural norms. The importance assigned to emotional support among Black participants in this sample is encouraging considering the barriers to help-seeking, particularly for mental and emotional health support, identified within these communities These include practical barriers such as the cost and availability of support, fear of stigmatisation, associations of help-seeking with weakness, and distrust and concerns about confidentiality of the information shared with practitioners [56,57,58]. This may be particularly pertinent among African-Caribbean men [58] and some Asian communities. For instance, attitudes towards seeking psychological help were similar among Chinese and British university students [59], but generally negative among South Asian students, with more negative attitudes being associated with being male, greater identification with one’s ethnic identity, greater cultural mistrust, and greater adherence to Asian values [60]. A higher prevalence of depression has been found among Pakistani compared to White women [61], but there is no evidence relating to the prevalence of distress after sight loss in different communities. It is unclear if Asian participants in this sample, among whom ‘emotional support’ was one of the least important priorities, had access to better emotional support, or prioritised other, more functional, support. Despite the benefits of technology and the higher prevalence of households from Black communities in the lower household-income brackets [14], ‘technology’ and ‘finances’ were of comparatively low importance among Black participants in this sample. Future research could explore the technical literacy among different MEC groups to identify support needs.
Future research will also need to explore the reasons for differences in priorities in this sample. While high importance may reflect a greater support need, it may equally reflect cultural and individual differences in the value attached to a specific area of life, such as finances and technology for Asian participants and employment for Black participants. Similarly, the comparatively low importance of issues may reflect a lower impact of V.I. in this area, better support available, a greater need in other areas of life, or less value attached to this area of life. A detailed exploration of differences in status was outside the scope of this article. Perceived importance and status relating to health and comorbidity [24], mental and emotional well-being [21], prejudice and discrimination [21], accessibility [25], social participation and relationship [23], and the use of eye health and support services [22] are discussed elsewhere in this series. The observed differences may also be an artefact of the question used. Although mean or median scores on Likert scales are routinely used to assess group differences and within-group changes, their real-world significance has been questioned. For instance, Ogden and Lo [62] found that responses on Likert scales are impacted by how participants understand the question (frame of reference) and who they use as a reference group (method of comparison). Elsewhere, changes in the visual field in early-stage glaucoma patients were accompanied by a statistically significant deterioration in vision-related QoL, as measured by a Likert scale [63]. However, on average, this translated into changes from ‘no difficulty’ to ‘a little bit of difficulty’ on only 4 of the 15 scale items. In the current study, for example, it is difficult to assess the real-world difference between a score of 2.42 and of 1.97, which indicates that, on average, employer attitudes were very important for both Asian and White adults.

Limitations

The findings relate to a small, non-probability-based sample and can, therefore, not be extrapolated to the wider V.I. population. Indeed, the sample, particularly the matched White subgroup, is younger than would be expected, although the UK does not monitor V.I. in the population. Asian and Black participants were not matched. While there were no statistically significant differences in the demographics between these groups, small response differences may reflect factors other than ethnicity. The small subsample sizes resulted in a lack of statistical power. Future research will need to confirm findings in a larger, representative sample. This may also enable more detailed subgroup analysis based on different ethnic subgroups, age groups, degree of V.I., and eye conditions. The latter can have different patterns of visual loss and may affect daily life in different ways.
A full and objective clinical profile of participants’ eye conditions and degree of V.I. was not available. Instead, V.I. severity was based on self-report, with its associated limitations. Furthermore, the 24 issues covered a range of life domains, but important issues may be missing from this list. There is also considerable variation in the number of issues relating to the individual domains and some crossover. For instance, better route planning and navigation aids were categorised as a technology-related issue but could have been categorized as an independent-mobility-related issue. Some issues would have benefited from an explanation, as was given for the issue accessibility of public transport (signage, announcement, training of drivers/staff), to ensure participants used the same ‘frame of reference’ [62]. For instance, participants may have very different ideas of what “specialised education” means based on when they completed their education and if they had a V.I. during their education. Responses to this issue may, therefore, have qualitatively different meanings. There is the further possibility that cultural differences impacted upon the interpretation of question and/or response wording. Lee, Jones [64], for example, found differences in how people who self-identified as Chinese, Japanese, and American responded to a Likert scale.
The question about the importance of issues was asked at the end of the survey. Responses to this question could have been influenced by preceding questions (question order effect), e.g., Refs. [65,66,67]. For example, preceding questions about charity awareness and usage, and domestic support could have increased the perceived importance of confidence in my ability to do everyday tasks.
Finally, the question required participants to define QoL, identify areas in which changes would impact their QoL, identify priorities, and rate the importance of issues, making it cognitively burdensome. Although participants were prompted to think about the impact on their own lives, most of the questions related to ‘other people’; only one or two referred to ‘my’ or ‘myself’. This makes it difficult to interpret the findings with certainty. For instance, an extremely important response may reflect the importance to people with V.I. in general or the participant. Similarly, it may reflect how important employer attitudes are to well-being or how important improvements in employer attitudes are to well-being. Despite these limitations, the question provides a useful, albeit preliminary, insight into the importance of different issues among a sample population about whom very little is known.

5. Conclusions

This article provides a preliminary insight into the issues that are important to a small convenience sample of adults with V.I. who are from different ethnic communities in the UK. The findings show that there are statistically significant differences with majority White participants. Although not statistically significant, differences between Asian and Black participants indicate slightly different priorities and needs among two groups that are routinely grouped together. While Asian participants may prefer support relating to their finances, Black participants may benefit from employment-related support. This highlights the risk that community-specific needs and issues are missed, especially if there are unequal sample sizes, and diverse groups are treated as one group and the need for more research which addresses priorities and needs in individual ethnic groups. Practitioners may also need to take into account ethnic group differences in needs when developing and providing support to beneficiaries from different ethnic backgrounds.

Supplementary Materials

The following supporting information can be downloaded at: https://www.mdpi.com/article/10.3390/disabilities4030030/s1, Table S1: V.I. severity classification based on survey responses.

Author Contributions

Conceptualization, N.H.; methodology, N.H.; data curation, N.H.; formal analysis, N.H.; writing—original draft preparation, N.H.; writing—review and editing, N.H., L.J., C.L.C. and R.S.M.G.; visualization, N.H. and L.J.; supervision, N.H. and R.S.M.G.; project administration, N.H.; funding acquisition, R.S.M.G. All authors have read and agreed to the published version of the manuscript.

Funding

This research was funded by the Thomas Pocklington Trust, grant number: FR-00380.

Institutional Review Board Statement

This study is based on a secondary analysis of anonymized survey data. Ethical approval was therefore not required.

Informed Consent Statement

Informed consent was obtained from all subjects involved in the study during data collection.

Data Availability Statement

The original contributions presented in the study are included in the article/Supplementary Materials; further inquiries can be directed to the corresponding author.

Acknowledgments

We would like to thank the Royal National Institute of Blind People (RNIB), Thomas Pocklington Trust (TPT), and Guide Dogs for the Blind Association (Guide Dogs) for sharing ‘V.I. Lives’ survey data with us for the purpose of this secondary analysis. A particular thank you to Hilary Ingleton at the RNIB for providing the data set and answering questions about the survey methods, data collection and individual variables. Thank you also to Nicolas Bulois at Insight Angels for clarifying the sample, methodology, and data-related queries.

Conflicts of Interest

Authors N.H., L.J., C.L.C. and R.S.M.G. were employed by BRAVO VICTOR. The authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest. While the funder was involved in the original data collection, they were not involved in the design of the current study, data analysis, interpretation of data, the writing of this article, or the decision to submit it for publication.

References

  1. Bassett, P. Educational Progress of Young Blind and Partially Sighted Pupils; RNIB: London, UK, 2010. [Google Scholar]
  2. Lund, E.M.; Cmar, J.L. A Systematic Review of Factors Related to Employment Outcomes for Adults with Visual Impairments. J. Vis. Impair. Blind. 2019, 113, 493–517. [Google Scholar] [CrossRef]
  3. Castle, C.L.; Chan, S.; Tang, E. A Plain Language Summary of Research and Evidence Relating to Adults with Visual Impairment and Employment in the United Kingdom; VI Insight Hub: London, UK, 2023. [Google Scholar]
  4. McDonnall, M.C.; Sui, Z. Employment and unemployment rates of people who are blind or visually impaired: Estimates from multiple sources. J. Vis. Impair. Blind. 2019, 113, 481–492. [Google Scholar] [CrossRef]
  5. Cumberland, P.M.; Rahi, J.S. Visual Function, Social Position, and Health and Life Chances: The UK Biobank Study. JAMA Ophthalmol. 2016, 134, 959–966. [Google Scholar] [CrossRef] [PubMed]
  6. Crudden, A.; McBroom, L.W. Barriers to employment: A survey of employed persons who are visually impaired. J. Vis. Impair. Blind. 1999, 93, 341–350. [Google Scholar] [CrossRef]
  7. Coffey, M.; Coufopoulos, A.; Kinghorn, K. Barriers to employment for visually impaired women. Int. J. Workplace Health Manag. 2014, 7, 171–185. [Google Scholar] [CrossRef]
  8. Hill, K.; Horsley, N.; Hirsch, D.; Padley, M. Sight Loss and Minimum Income Standards: The Additional Costs of Severity and Age; Centre for Research in Social Policy, Loughborough University: Loughborough, UK, 2017. [Google Scholar]
  9. Hill, K.; Marshall, L.; Hirsch, D.; Padley, M. Sight Loss and Minimum Living Standards: The Additional Costs of Living for People of Working Age Who Are Severely Sight Impaired and for People of Pension Age with Acquired Sight Impairment; Centre for Research in Social Policy, Loughborough University: Loughborough, UK, 2016. [Google Scholar]
  10. Dawes, P.; Dickinson, C.; Emsley, R.; Bishop, P.N.; Cruickshanks, K.J.; Edmondson-Jones, M.; McCormack, A.; Fortnum, H.; Moore, D.R.; Norman, P.; et al. Vision impairment and dual sensory problems in middle age. Ophthalmic Physiol. Opt. 2014, 34, 479–488. [Google Scholar] [CrossRef]
  11. Deloitte Access Economics. The Economic Impact of Sight Loss and Blindness in the UK Adult Population, 2013; RNIB: London, UK, 2014. [Google Scholar]
  12. ONS. Ethnic Group Differences in Health, Employment, Education and Housing Shown in England and Wales’ Census. 2021. Available online: https://www.ons.gov.uk/peoplepopulationandcommunity/culturalidentity/ethnicity/articles/ethnicgroupdifferencesinhealthemploymenteducationandhousingshowninenglandandwalescensus2021/latest (accessed on 17 April 2023).
  13. ONS. Employment. Ethnicity Facts and Figures. 2022. Available online: https://www.ethnicity-facts-figures.service.gov.uk/work-pay-and-benefits/employment/employment/latest#by-ethnicity (accessed on 5 April 2023).
  14. ONS. Household Income. Ethnicity Facts and Figures. 2022. Available online: https://www.ethnicity-facts-figures.service.gov.uk/work-pay-and-benefits/pay-and-income/household-income/latest (accessed on 29 March 2023).
  15. Heinze, N.; Jones, L.; Makwana, B. A rapid review of evidence relating to service use, experiences, and support needs of adults from minority ethnic communities along the eyecare pathway in the United Kingdom. Front. Public Health 2023, 11, 1119540. [Google Scholar] [CrossRef] [PubMed]
  16. Slade, J.; Edwards, E.; White, A. Employment Status and Sight Loss; RNIB: London, UK, 2017. [Google Scholar]
  17. Johnson, M.R.D.; Morjaria-Keval, A. Ethnicity, sight loss and invisibility. Br. J. Vis. Impair. 2007, 25, 21–31. [Google Scholar] [CrossRef]
  18. Peace, S.; Katz, J.; Holland, C.; Jones, R. The Needs and Aspirations of Older People with Vision Impairment; TPT: London, UK, 2016. [Google Scholar]
  19. RNIB; Guide Dogs; TPT. VI Lives—An In-Depth Understanding of the Experiences of People Living with Vision Impairment (VI) in the UK; RNIB: London, UK; Guide Dogs: London, UK; TPT: London, UK, 2022. [Google Scholar]
  20. D’Ardenne, J.; Hall, M.; McManus, S. Measurement of Visual Impairment in National Surveys: A Review of Available Data Sources; NatCen Social Research: London, UK, 2012. [Google Scholar]
  21. Heinze, N.; Castle, C.L. Exploring mental well-being, the emotional impact of visual impairment and experiences of prejudice and discrimination among adults from minority ethnic communities in the UK. Front. Public Health 2023, 11, 1277341. [Google Scholar] [CrossRef]
  22. Heinze, N.; Jones, L. Access to eye care and support services among adults from minority ethnic communities living with visual impairment in the UK. Front. Public Health 2023, 11, 1277519. [Google Scholar]
  23. Heinze, N.; Jones, L. Social functioning in adults with visual impairment from minority ethnic communities in the UK. Front. Public Health 2024, 12, 1277472. [Google Scholar] [CrossRef]
  24. Hussain, S.F.; Heinze, N.; Gomes, R.S.M. Health and Comorbidities in Minority Ethnic Adults Living with Visual Impairment in the UK. Disabilities 2024, 4, 79–100. [Google Scholar] [CrossRef]
  25. Kempapidis, T.; Heinze, N.; Green, A.; Gomes, R.S.M. Accessibility, Functioning, and Activities of Daily Living with Visual Impairment amongst Adults from Minority Ethnic Communities in the UK. Disabilities 2024, 4, 163–182. [Google Scholar] [CrossRef]
  26. R Core Team. R: A Language and Environment for Statistical Computing; R Foundation for Statistical Computing: Vienna, Austria, 2021. [Google Scholar]
  27. Ardeljan, A.D.; Polisetty, T.S.; Palmer, J.; Vakharia, R.M.; Roche, M.W. Comparative analysis on the effects of sarcopenia following primary total knee arthroplasty: A retrospective matched-control analysis. J. Knee Surg. 2020, 35, 128–134. [Google Scholar] [CrossRef]
  28. Wormser, G.P.; McKenna, D.; Karmen, C.L.; Shaffer, K.D.; Silverman, J.H.; Nowakowski, J.; Scavarda, C.; Shapiro, E.D.; Visintainer, P. Prospective evaluation of the frequency and severity of symptoms in Lyme disease patients with erythema migrans compared with matched controls at baseline, 6 months, and 12 months. Clin. Infect. Dis. 2020, 71, 3118–3124. [Google Scholar] [CrossRef] [PubMed]
  29. Perets, I.; Walsh, J.P.; Mu, B.H.; Mansor, Y.; Rosinsky, P.J.; Maldonado, D.R.; Lall, A.C.; Domb, B.G. Short-term clinical outcomes of robotic-arm assisted total hip arthroplasty: A pair-matched controlled study. Orthopedics 2021, 44, e236–e242. [Google Scholar] [CrossRef] [PubMed]
  30. Hunfalvay, M.; Murray, N.P.; Carrick, F.R. Fixation stability as a biomarker for differentiating mild traumatic brain injury from age matched controls in pediatrics. Brain Inj. 2021, 35, 209–214. [Google Scholar] [CrossRef] [PubMed]
  31. Miarons, M.; Larrosa-García, M.; Garcia-Garcia, S.; Los-Arcos, I.; Moreso, F.; Berastegui, C.; Castells, L.; Perez-Hoyos, S.; Varela, J.; Pau-Parra, A. COVID-19 in solid organ transplantation: A matched retrospective cohort study and evaluation of immunosuppression management. Transplantation 2021, 105, 138–150. [Google Scholar] [CrossRef]
  32. Ayoubkhani, D.; Khunti, K.; Nafilyan, V.; Maddox, T.; Humberstone, B.; Diamond, I.; Banerjee, A. Post-covid syndrome in individuals admitted to hospital with COVID-19: Retrospective cohort study. BMJ 2021, 372, n693. [Google Scholar] [CrossRef]
  33. Clayton, J.A.; Davis, A.F. Sex/Gender Disparities and Women’s Eye Health. Curr. Eye Res. 2015, 40, 102–109. [Google Scholar] [CrossRef]
  34. Rius Ulldemolins, A.; Benach, J.; Guisasola, L.; Artazcoz, L. Why are there gender inequalities in visual impairment? Eur. J. Public Health 2019, 29, 661–666. [Google Scholar] [CrossRef] [PubMed]
  35. Weih, L.M.; VanNewkirk, M.R.; McCarty, C.A.; Taylor, H.R. Age-specific causes of bilateral visual impairment. Arch. Ophthalmol. 2000, 118, 264–269. [Google Scholar] [CrossRef] [PubMed]
  36. Buch, H.; Vinding, T.; La Cour, M.; Appleyard, M.; Jensen, G.B.; Nielsen, N.V. Prevalence and causes of visual impairment and blindness among 9980 Scandinavian adults: The Copenhagen City Eye Study. Ophthalmology 2004, 111, 53–61. [Google Scholar] [CrossRef] [PubMed]
  37. Noble, S.; McLennan, D.; Noble, M.; Plunkett, E.; Gutacker, N.; Silk, M.; Wright, G. The English Indices of Deprivation 2019; Ministry of Housing, Communities and Local Government: London, UK, 2019. [Google Scholar]
  38. Slade, J. Early Intervention Support in Eye Clinics—An Overview of Emotional and Practical Support in UK Eye Clinics for the Year 2012/13; RNIB: London, UK, 2014. [Google Scholar]
  39. Lane, M.; Lane, V.; Abbott, J.; Braithwaite, T.; Shah, P.; Denniston, A.K. Multiple deprivation, vision loss, and ophthalmic disease in adults: Global perspectives. Surv. Ophthalmol. 2018, 63, 406–436. [Google Scholar] [CrossRef] [PubMed]
  40. Hodge, S.; Thetford, C.; Knox, P.; Robinson, J. Finding your own way around: Experiences of health and social care provision for people with a visual impairment in the United Kingdom. Br. J. Vis. Impair. 2015, 33, 200–211. [Google Scholar] [CrossRef]
  41. Frost, A.; Eachus, J.; Sparrow, J.; Peters, T.J.; Hopper, C.; Davey-Smith, G.; Frankel, S. Vision-related quality of life impairment in an elderly UK population: Associations with age, sex, social class and material deprivation. Eye 2001, 15, 739–744. [Google Scholar] [CrossRef] [PubMed]
  42. Lotery, A.; Xu, X.; Zlatava, G.; Loftus, J. Burden of illness, visual impairment and health resource utilisation of patients with neovascular age-related macular degeneration: Results from the UK cohort of a five-country cross-sectional study. Br. J. Ophthalmol. 2007, 91, 1303–1307. [Google Scholar] [CrossRef] [PubMed]
  43. De Sousa Peixoto, R.; Krstic, L.; Hill, S.C.L.; Foss, A.J.E. Predicting quality of life in AMD patients-insights on the new NICE classification and on a bolt-on vision dimension for the EQ-5D. Eye 2021, 35, 3333–3341. [Google Scholar] [CrossRef]
  44. Jones, N.; Bartlett, H.E.; Cooke, R. An analysis of the impact of visual impairment on activities of daily living and vision-related quality of life in a visually impaired adult population. Br. J. Vis. Impair. 2019, 37, 50–63. [Google Scholar] [CrossRef]
  45. Rahi, J.S.; Cumberland, P.M.; Peckham, C.S. Visual Impairment and Vision-Related Quality of Life in Working-Age Adults: Findings in the 1958 British Birth Cohort. Ophthalmology 2009, 116, 270–274. [Google Scholar] [CrossRef]
  46. Tabrett, D.R.; Latham, K. Factors Influencing Self-reported Vision-Related Activity Limitation in the Visually Impaired. Investig. Ophthalmol. Vis. Sci. 2011, 52, 5293–5302. [Google Scholar] [CrossRef]
  47. King’s Fund. Access to Health Care and Minority Ethnic Groups; King’s Fund: London, UK, 2006. [Google Scholar]
  48. Wilson, M. Who Put That There: The Barrier to Blind and Partially Sighted People Getting Out and About; RNIB: London, UK, 2015. [Google Scholar]
  49. Jaarsma, E.A.; Dekker, R.; Koopmans, S.A.; Dijkstra, P.U.; Geertzen, J.H. Barriers to and facilitators of sports participation in people with visual impairments. Adapt. Phys. Act. Q. 2014, 31, 240–264. [Google Scholar]
  50. Phoenix, C.; Griffin, M.; Smith, B. Physical activity among older people with sight loss: A qualitative research study to inform policy and practice. Public Health 2015, 129, 124–130. [Google Scholar] [CrossRef]
  51. Jones, L.; Lee, M.; Castle, C.L.; Heinze, N.; Gomes, R.S. Scoping review of remote rehabilitation (telerehabilitation) services to support people with vision impairment. BMJ Open 2022, 12, e059985. [Google Scholar] [CrossRef]
  52. Ali, Z.C.; Shakir, S.; Aslam, T.M. Perceptions and use of technology in older people with ophthalmic conditions. F1000Res 2019, 8, 86. [Google Scholar] [CrossRef] [PubMed]
  53. Alma, M.A. Participation of the elderly after vision loss. Disabil. Rehabil. 2011, 33, 63–72. [Google Scholar] [CrossRef] [PubMed]
  54. Gopinath, B.; Liew, G.; Burlutsky, G.; Mitchell, P. Age-related macular degeneration and 5-year incidence of impaired activities of daily living. Maturitas 2014, 77, 263–266. [Google Scholar] [CrossRef] [PubMed]
  55. Cross, V.; Shah, P.; Bativala, R.; Spurgeon, P. Glaucoma awareness and perceptions of risk among African–Caribbeans in Birmingham, UK. Divers. Health Soc. Care 2005, 2, 81–90. [Google Scholar]
  56. Ogueji, I.A.; Okoloba, M.M. Seeking Professional Help for Mental Illness: A Mixed-Methods Study of Black Family Members in the UK and Nigeria. Psychol. Stud. 2022, 67, 164–177. [Google Scholar] [CrossRef]
  57. Mantovani, N.; Pizzolati, M.; Edge, D. Exploring the relationship between stigma and help-seeking for mental illness in African-descended faith communities in the UK. Health Expect. 2017, 20, 373–384. [Google Scholar] [CrossRef]
  58. Memon, A.; Taylor, K.; Mohebati, L.M.; Sundin, J.; Cooper, M.; Scanlon, T.; De Visser, R. Perceived barriers to accessing mental health services among black and minority ethnic (BME) communities: A qualitative study in Southeast England. BMJ Open 2016, 6, e012337. [Google Scholar] [CrossRef] [PubMed]
  59. Tang, T.T.T.; Reilly, J.; Dickson, J.M. Attitudes toward seeking professional psychological help among Chinese students at a UK university. Couns. Psychother. Res. 2012, 12, 287–293. [Google Scholar] [CrossRef]
  60. Soorkia, R.; Snelgar, R.; Swami, V. Factors influencing attitudes towards seeking professional psychological help among South Asian students in Britain. Ment. Health Relig. Cult. 2011, 14, 613–623. [Google Scholar] [CrossRef]
  61. Fazil, Q.; Cochrane, R. The prevalence of depression in Pakistani women living in the West Midlands. Pak. J. Women’s Stud. 2003, 10, 21–30. [Google Scholar]
  62. Ogden, J.; Lo, J. How meaningful are data from Likert scales? An evaluation of how ratings are made and the role of the response shift in the socially disadvantaged. J. Health Psychol. 2012, 17, 350–361. [Google Scholar] [CrossRef]
  63. Jones, L.; Garway-Heath, D.F.; Azuara-Blanco, A.; Crabb, D.P.; Bunce, C.; Lascaratos, G.; Amalfitano, F.; Anand, N.; Bourne, R.R.; Broadway, D.C. Are patient self-reported outcome measures sensitive enough to be used as end points in clinical trials?: Evidence from the United Kingdom Glaucoma Treatment Study. Ophthalmology 2019, 126, 682–689. [Google Scholar] [CrossRef] [PubMed]
  64. Lee, J.W.; Jones, P.S.; Mineyama, Y.; Zhang, X.E. Cultural differences in responses to a Likert scale. Res. Nurs. Health 2002, 25, 295–306. [Google Scholar] [CrossRef]
  65. Strack, F. “Order effects” in survey research: Activation and information functions of preceding questions. In Context Effects in Social and Psychological Research; Springer: Berlin/Heidelberg, Germany, 1992; pp. 23–34. [Google Scholar]
  66. Stark, T.H.; Silber, H.; Krosnick, J.A.; Blom, A.G.; Aoyagi, M.; Belchior, A.; Bosnjak, M.; Clement, S.L.; John, M.; Jónsdóttir, G.A. Generalization of classic question order effects across cultures. Sociol. Methods Res. 2020, 49, 567–602. [Google Scholar] [CrossRef]
  67. Schwarz, N.; Strack, F.; Hippler, H.J.; Bishop, G. The impact of administration mode on response effects in survey measurement. Appl. Cogn. Psychol. 1991, 5, 193–212. [Google Scholar] [CrossRef]
Disabilities 04 00030 g001
Figure 1. Top-3 domains and issues by subgroup (Asian, Black, White).
Figure 1. Top-3 domains and issues by subgroup (Asian, Black, White).
Disabilities 04 00030 g001
Table 1. Overview of domains and associated issues.
Table 1. Overview of domains and associated issues.
DomainNumber of IssuesIssues
‘Public attitudes’1
  • Understanding amongst the general public about how they can help people with V.I.
‘Emotional support’1
  • Emotional support to come to terms with V.I.
‘Self-efficacy’3
  • Ongoing information and support to look after a sight condition;
  • Confidence in my ability to do everyday tasks;
  • Availability of help and support to take care of myself and my home.
‘Finances’2
  • Availability of benefits to maintain a decent income;
  • Cost and availability of the specialist equipment I need.
‘Technology’5
  • Access and support to use the internet;
  • Training to use technology to its full potential;
  • Accessibility features of mainstream technology;
  • Development of new smart technology and apps to support people with V.I.;
  • Availability of better route planning and navigation aids.
‘Employment’3
  • Support in applying for jobs and preparing for interviews;
  • Attitudes and understanding of employers about employing someone with a V.I.;
  • Availability of specialist equipment in the workplace for people with V.I.
‘Education’3
  • Access to specialised education and support for children/young people with V.I.;
  • Remotely/internet-based learning courses/training;
  • Support/training of mainstream education staff to adapt courses for people with V.I.
‘Accessible information’1
  • Format of information provided by service providers.
‘Accessible environments’3
  • Accessibility of public transport (signage, announcements, training of drivers/staff);
  • Reduction of obstacles and street clutter;
  • Design and accessibility of public buildings.
‘Social participation’2
  • Ability to connect to other like-minded people;
  • Opportunity to participate in more sporting and/or leisure activities.
V.I. = visual impairment.
Table 2. Participant characteristics by subgroup.
Table 2. Participant characteristics by subgroup.
Asian (n = 46)Black (n = 22)White (n = 77)
% (n)% (n)% (n)
AgeH(2) = 0.18, p = 0.916
   M (SD)40.17 (14.61)39.18 (14.70)41.09 (15.62)
   Range18–7418–7518–85
GenderΧ2 = 0.51, p = 0.776
   Female50.0 (23)59.1 (13)51.9 (40)
   Male50.0 (23)40.9 (9)48.1 (37)
Regionp = 0.813
   England89.1 (41)90.9 (20)80.5 (62)
   Scotland4.3 (2)9.1 (2)9.1 (7)
   Wales4.3 (2)-7.8 (6)
   Northern Ireland2.2 (1)-2.6 (2)
Settingp = 0.073
   City/big town67.4 (31)77.3 (17)55.8 (43)
   Small town26.1 (12)9.1 (2)37.7 (29)
   Rural area6.5 (3)13.6 (3)6.5 (5)
Education 1H(2) = 6.67, p = 0.036
   No formal qualifications--5.2 (4)
   GCSE/O-Level15.2 (7)4.5 (1)14.3 (11)
   A-Level /Advanced Highers15.2 (7)9.1 (2)18.2 (14)
   Apprenticeship, vocational qualif., NVQ or HND17.4 (8)18.2 (4)11.7 (9)
   Undergraduate degree30.4 (14)22.7 (5)31.2 (24)
   Masters, PhD15.2 (7)31.8 (7)16.9 (13)
   Non-UK qualifications4.3 (2)--
   Other2.2 (1)13.6 (3)2.6 (2)
Employment 2p = 0.903
   Employed (including part-time)41.3 (19)54.5 (12)40.3 (31)
   Self-employed8.7 (4)4.5 (1)5.2 (4)
   Unemployed19.6 (9)9.1 (2)14.3 (11)
   Retired6.5 (3)9.1 (2)11.7 (9)
   Other 223.9 (11)22.7 (5)28.6 (22)
Marital statusp = 0.673
   Single37.0 (17)54.5 (12)37.7 (29)
   In a relationship10.9 (5)-9.1 (7)
   Cohabiting8.7 (4)4.5 (1)10.4 (8)
   Married34.8 (16)27.3 (6)36.4 (28)
   Civil partnership2.2 (1)--
   Separated-4.5 (1)1.3 (1)
   Divorced6.5 (3)9.1 (2)3.9 (3)
   Widowed--1.3 (1)
Living arrangementsΧ2 = 0.30, p = 0.860
   Living alone28.3 (13)22.7 (5)24.7 (19)
   Living with others71.7 (33)77.3 (17)75.3 (58)
V.I. severity 3H(2)= 0.38, p = 0.826
   Severe41.3 (19)31.8 (7)44.2 (34)
   Moderate34.8 (16)40.9 (9)23.4 (18)
   Mild23.9 (11)27.3 (6)31.2 (24)
   Could not be classified--1.3 (1)
1 Statistical analysis excludes non-UK qualifications and other. 2 Due to expected frequencies of less than 5 in 5 cells (27.8%), the categories looking after family/home, student, long-term sick/disabled, and Unpaid work (e.g., volunteering, intern, work experiences) were collapsed into the other category for the statistical analysis. 3 Statistical analysis excludes could not be classified. Statistically significant results are shown in bold. Results for Fisher’s exact test are shown as p-values only. GCSE = General Certificate of Secondary Education; NVQ = National Vocational Qualification; HND = Higher National Diploma. Reproduced with permission from Ref. [21], 2023, Heinze and Castle.
Table 3. Mean importance scores and 95% confidence interval for 10 domains and 24 issues by subgroup.
Table 3. Mean importance scores and 95% confidence interval for 10 domains and 24 issues by subgroup.
Domains and IssuesAsian n = 46Black n = 22White n = 77Kruskal–Wallis
H(2) =
M (95% CI)M (95% CI)M (95% CI)
PUBLIC ATTITUDES2.24 (2.00, 2.49)2.09 (1.71, 2.48)2.17 (1.99, 2.35)1.11, p = 0.574
EMOTIONAL SUPPORT2.11 (1.82, 2.40)2.32 (1.95, 2.69)1.91 (1.69, 2.13)3.73, p = 0.155
SELF-EFFICACY2.22 (2.00, 2.44)2.26 (1.96, 2.56)1.80 (1.64, 1.96)14.86, p < 0.001
   Ongoing info/support to look after eye condition2.30 (2.07, 2.54)2.14 (1.70, 2.58)1.78 (1.57, 1.99)10.44, p = 0.005
   Confidence in ability to do everyday tasks2.26 (2.01, 2.51)2.45 (2.19, 2.72)2.04 (1.86, 2.21)6.41, p = 0.040
   Help/support to take care of self and home2.14 (1.86, 2.41)2.18 (1.74, 2.63)1.57 (1.35, 1.79)14.58, p < 0.001
FINANCES2.33 (2.11, 2.54)2.16 (1.79, 2.52)1.87 (1.68, 2.06)10.42, p = 0.005
   Benefits to maintain a decent income2.33 (2.07, 2.58)2.27 (1.86, 2.69)1.83 (1.60, 2.06)9.10, p = 0.011
   Cost/availability of specialist equipment2.33 (2.07, 2.58)2.05 (1.60, 2.49)1.91 (1.68, 2.14)5.38, p = 0.068
TECHNOLOGY2.26 (2.08, 2.45)2.16 (1.86, 2.46)1.73 (1.58, 1.88)22.54, p < 0.001
   Access and support to use the internet2.15 (1.89, 2.41)2.05 (1.62, 2.47)1.51 (1.28, 1.73)14.36, p < 0.001
   Training to use tech to its full potential2.26 (2.02, 2.50)2.27 (1.88, 2.66)1.68 (1.47, 1.88)15.98, p < 0.001
   Accessibility features of mainstream tech2.24 (2.00, 2.47)2.27 (1.96, 2.58)1.99 (1.79, 2.19)3.46, p = 0.177
   New smart tech/apps for people with V.I.2.37 (2.11, 2.63)2.18 (1.78, 2.58)1.81 (1.61, 2.00)14.45, p < 0.001
   Better route planning and navigation aids2.29 (2.03, 2.55)2.05 (1.62, 2.47)1.69 (1.48, 1.90)14.45, p < 0.001
EMPLOYMENT2.12 (1.84, 2.40)2.39 (2.15, 2.64)1.76 (1.55, 1.96)13.05, p = 0.001
   Help with job applications/interview preparation1.91 (1.57, 2.25)2.05 (1.62, 2.47)1.51 (1.27, 1.76)5.89, p = 0.053
   Employer attitudes2.42 (2.14, 2.70)2.59 (2.27, 2.92)1.97 (1.74, 2.21)13.04, p = 0.001
   Specialist V.I. equipment in the workplace 2.04 (1.71, 2.37)2.55 (2.25, 2.84)1.79 (1.55, 2.03)9.92, p = 0.007
EDUCATION2.15 (1.91, 2.40)2.17 (1.92, 2.41)1.64 (1.44, 1.84)13.23, p = 0.001
   Specialised education/support for children/young people with V.I.2.24 (1.96, 2.51)2.59 (2.27, 2.92)1.71 (1.45, 1.97)13.87, p < 0.001
   Distance/online learning courses/training2.02 (1.72, 2.32)1.77 (1.36, 2.18)1.30 (1.08, 1.52)15.48, p < 0.001
   Support/training of mainstream education staff to adapt courses for people with V.I.2.20 (1.91, 2.48)2.14 (1.74, 2.53)1.91 (1.66, 2.16)1.86, p = 0.395
ACCESSIBLE INFORMATION FROM SERVICE PROVIDERS2.17 (1.92, 2.43)2.05 (1.70, 2.39)1.90 (1.69, 2.10)3.02, p = 0.221
ACCESSIBLE ENVIRONMENTS2.33 (2.12, 2.54)2.38 (2.18, 2.58)2.00 (1.84, 2.15)11.07, p = 0.004
   Accessibility of public transport2.46 (2.20, 2.71)2.45 (2.07, 2.83)2.17 (1.99, 2.35)7.54, p = 0.023
   Reduction of obstacles and street clutter2.40 (2.17, 2.63)2.27 (1.96, 2.58)2.05 (1.85, 2.25)5.96, p = 0.051
   Design and accessibility of public buildings2.15 (1.84, 2.46)2.41 (2.15, 2.67)1.77 (1.57, 1.96)12.82, p = 0.002
SOCIAL2.08 (1.87, 2.29)1.98 (1.63, 2.33)1.64 (1.47, 1.81)10.37, p = 0.006
   Ability to connect to like-minded people2.17 (1.92, 2.43)2.00 (1.59, 2.41)1.68 (1.47, 1.88)9.60, p = 0.008
   Participate in more sporting/leisure activities1.98 (1.69, 2.26)1.95 (1.51, 2.40)1.61 (1.40, 1.82)6.32, p = 0.042
Statistically significant results are shown in bold.
Disclaimer/Publisher’s Note: The statements, opinions and data contained in all publications are solely those of the individual author(s) and contributor(s) and not of MDPI and/or the editor(s). MDPI and/or the editor(s) disclaim responsibility for any injury to people or property resulting from any ideas, methods, instructions or products referred to in the content.

Share and Cite

MDPI and ACS Style

Heinze, N.; Jones, L.; Castle, C.L.; Gomes, R.S.M. Exploring Priority Issues among a Sample of Adults from Minority Ethnic Communities Who Are Living with Visual Impairment in the UK. Disabilities 2024, 4, 477-492. https://doi.org/10.3390/disabilities4030030

AMA Style

Heinze N, Jones L, Castle CL, Gomes RSM. Exploring Priority Issues among a Sample of Adults from Minority Ethnic Communities Who Are Living with Visual Impairment in the UK. Disabilities. 2024; 4(3):477-492. https://doi.org/10.3390/disabilities4030030

Chicago/Turabian Style

Heinze, Nikki, Lee Jones, Claire L. Castle, and Renata S. M. Gomes. 2024. "Exploring Priority Issues among a Sample of Adults from Minority Ethnic Communities Who Are Living with Visual Impairment in the UK" Disabilities 4, no. 3: 477-492. https://doi.org/10.3390/disabilities4030030

Article Metrics

Back to TopTop