Vision-Specific Distress and Depressive Symptoms in People with Vision Impairment

Gwyneth Rees; Hui Wen Tee; Manjula Marella; Eva Fenwick; Mohamed Dirani; Ecosse L. Lamoureux

doi:10.1167/iovs.09-5080

Abstract

Purpose.: To determine the unique contribution of vision-specific distress in predicting depressive symptoms in people with vision impairment attending a tertiary eye care clinic.

Methods.: In this cross-sectional study, interview-administered surveys were conducted with 143 adult patients who had presenting visual acuity <0.3 logMAR. Depressive symptoms were assessed with the Patient Health Questionnaire-9 and vision-specific distress was assessed with the Impact of Vision Impairment (IVI) Questionnaire emotional well-being subscale. In addition, level of restriction of participation in common daily activities due to vision impairment was assessed with the IVI and measures of general physical health and social support were included.

Results.: Twenty-one (14.7%) of 143 participants reported clinically significant depressive symptoms and an additional 27.3% (n = 39) had mild depressive symptoms. Vision-specific distress was the strongest unique predictor of depressive symptoms (β = 0.37, P < 0.001), with physical health (β = −0.22, P < 0.01), age (β = −0.18, P < 0.05), and experience of a negative life event (β = 0.15, P < 0.05) also contributing significantly to depressive symptoms. Results also indicated that vision-specific distress mediates the impact of participation restriction due to vision impairment on depressive symptoms.

Conclusions.: An assessment of vision-specific distress may be a useful tool with which to identify those at risk of depression or in need of early intervention in eye care or rehabilitation settings. Depression treatment approaches or depression prevention strategies for people with vision impairment may benefit from a focus on vision-specific distress.

It is well established that a greater proportion of individuals with vision impairment experience depression than do those without.^1–4 Studies conducted largely in the United States have found that up to a third of people with vision impairment report clinically significant depressive symptoms.⁴ Although vision impairment is a risk factor for depression, it is not an inevitable consequence of vision loss, and depression can occur regardless of the level of vision impairment.^5–10 It remains unclear why some people with vision impairment experience comorbid depression and others do not.

In the general population, several risk factors for depression have been identified including the individual's sex, educational attainment, general health, functional disability, and degree of social support.^11,12 Although there is evidence to suggest a modest overlap in depression-related risk factors between individuals with vision loss and the general population,^9,13,14 little is known about independent risk factors for depression in people with vision impairment.

Research from other health areas such as diabetes suggest that disease-specific distress may be an important contributor to depression in people with chronic health conditions.^15,16 Subjective emotional reactions to vision impairment have been described in qualitative studies, and include worry, frustration, isolation, and embarrassment.^17–19 These emotional reactions are a response to the impact of vision loss on everyday activities.^17,19 A subscale to assess distress specific to vision impairment has recently been developed and undergone extensive validation as part of a vision-specific quality-of-life tool.^20,21 Vision-specific distress may be an important predictor of depressive symptoms in people with vision impairment, although no study has yet been conducted to investigate this possibility. We hypothesize that it is the emotional distress produced by functional decline that contributes to depressive symptoms in this group. Understanding the contributors to depression holds important implications for the detection, prevention, and treatment of emotional problems in people with vision impairment.

In this study, we sought to identify the predictors of depressive symptoms in individuals with vision impairment and, in particular, to determine the unique contribution of vision-specific distress. We also test the hypothesis that vision-specific distress mediates the relationship between restricted participation due to vision impairment and depressive symptoms.

Methods

Participants

Participants were recruited from public and private outpatient eye clinics in Melbourne (Australia). The eligibility criteria were best corrected visual acuity <6/12 in the better eye, age 18 years or older, ability to converse in English, adequate hearing (including the use of a hearing aid if necessary), and no cognitive impairment, as determined by the six-item Cognitive Impairment Test (6CIT).²² Ethics approval was obtained from the Royal Victorian Eye and Ear Hospital Human Research and Ethics Committee, and all participants signed a consent form. The research protocol adhered to the tenets of the Declaration of Helsinki.

Measures

Each participant completed a structured interview-administered questionnaire that consisted of demographics, education level, general health, current medical history, current or previous experience of a mental health condition, and current treatment for any mental health conditions. Participants were also asked to report the two most important life events (if any) that have occurred in their lives in the past year and to rate these on a 5-point scale ranging from very negative to very positive. Details on the main cause of vision loss and visual acuity were obtained from medical records. Participants were asked to report the duration of problems with vision and receipt of low-vision services.

Depressive Symptoms.

The Patient Health Questionnaire (PHQ)-9 was used to assess depressive symptoms.²³ The PHQ-9 is based on the Diagnostic and Statistical Manual of Mental Disorders (DSM-IV-TR)²⁴ criteria for depressive disorders. It asks participants to report whether they have been bothered with nine symptoms in the past 2 weeks. Responses are rated with a four-category Likert scale from not at all to nearly every day. The PHQ-9 scores of 5, 10, 15, and 20 represent valid thresholds demarcating the lower limits of mild, moderate, moderately severe and severe depression.²³ The conventional cutoff point for the PHQ-9 is ≥10 to define levels of depressive symptoms warranting further assessment.²³ The PHQ-9 is recommended as the depression screening tool of choice in primary care and general medical settings.²⁵ We have recently validated the use of the PHQ-9 as a tool for assessing depressive symptoms in visually impaired individuals.²⁶

Vision-Specific Quality of Life.

The Impact of Vision Impairment Questionnaire (IVI) is a 28-item questionnaire developed to measure vision-specific quality of life and has undergone extensive validation.^20,27 The questionnaire includes three subscales. The Mobility and Independence subscale (11 items) and the Reading and Accessing Information subscale (9 items) assess the level of restriction of participation in common daily activities that is caused by vision impairment. The third subscale comprises eight items that assess emotional well-being (Supplementary Material).²⁰ The Emotional Well-being subscale was used as a measure of vision-specific distress. This subscale asks participants to state how they have been feeling because of their eyesight. Items refer to embarrassment, frustration and annoyance, loneliness and isolation, feeling sad or depressed, worrying about eyesight getting worse, worrying about coping with everyday life, concern about being a nuisance or burden, and interference with life in general.

Rasch analysis was used to determine the validity, reliability, and measurement characteristics of the three domains of the IVI. Rasch analysis is a form of item response theory (IRT), where the ordinal ratings of the questionnaire are transformed to estimates of interval measures. Commercial software (Winsteps, Ver. 3.67; Mesa Press, Chicago, IL) and the Andrich rating scale model were used to perform Rasch analysis.^28,29 The validity of the IVI subscales were assessed for (1) appropriate use of response categories, (2) substantial measurement precision of the scale, (3) adequate item fit statistics, (4) demonstrated unidimensionality, and (5) acceptable match of item difficulty to patients' visual ability. Details of these fit parameters for the IVI subscales have been substantially demonstrated previously.^20,21,30,31

General Physical Health.

The Short Form-12 (SF-12) Health Survey was used to assess general physical health.³² Using scoring algorithms the physical component summary (PCS) was computed. A higher score indicates better health.

Social Support.

The Medical Outcomes Study (MOS) Social Support Survey was used to assess social support in four areas: emotional/informational support (eight items), tangible support (four items), affectionate support (three items), and positive social interaction (three items).³³ Each item is rated on a 5-point scale and scores for each subscale are averaged. A high score represents greater support.

Data Analysis

Bivariate correlations and hierarchical multiple regression were conducted to examine the influence of demographic, psychosocial, and vision-specific factors on depression. A hierarchical approach enables sets of variables to be entered into the regression model in sequential steps to evaluate the change in variance explained by each variable. In this study, demographic variables and physical health measures were entered in the first step, stress and social support variables were entered in the second step, and vision-specific quality of life variables are entered in the final step, to determine their unique contribution. Because of the small sample size, we included only those variables that showed significant bivariate relationships into the models (P < 0.05). To test the mediation model that vision-specific distress mediates the impact of participation restriction on depressive symptoms we followed an established procedure.^34,35 For mediation to occur, it is necessary to identify significant relationships between (1) the independent variable (IV; i.e., Mobility and Independence subscale score) and the mediator variable (MV; i.e., vision-specific distress), (2) the MV and the dependent variable (DV; i.e., depressive symptoms), and (3) the IV and the DV. Finally, when the impact of the MV is controlled for, the strength of the relationship between the IV and the DV should be significantly reduced. The strength and significance of mediation effects can be determined by the Sobel test, which shows whether the indirect effect of the IV on the DV via the MV is significantly different from 0.^34,35

Results

A total of 143 participants were recruited for the study (Table 1). The mean age of the participants was 76.2 years (SD 11.79) and 59.4% were female (n = 85/143). The majority (58.0%) of participants were born in Australia (n = 83/143), 48.3% (n = 69/143) were married, and 36.4% (n = 52/143) were widowed. Approximately half (49.7%, n = 71/143) had moderate vision impairment, with corrected visual acuity in the range of 0.48 < logMAR < 1.0 in the better eye. More than half (56.6%, n = 81/143) had age-related macular degeneration (AMD) as the main cause of vision loss.

Table 1.

View Table

The Participants' Characteristics

Table 2 shows levels of depressive symptoms in this sample. Twenty-one (14.7%) of 143 participants scored above the cutoff point for moderate depression, (≥10) on the PHQ-9. An additional 27.3% (n = 39/143) had mild depressive symptoms, scoring between 5 and 9. Twelve (8.4%) participants reported that they were currently taking antidepressants, and no participant reported to be receiving any form of professional mental health support (e.g., counseling). Only 4 of the 21 participants scoring above the PHQ-9 cutoff reported receiving any treatment for depression. Since treatment with antidepressants may obscure relationships in our data, we omitted eight participants who reported to be taking antidepressants and who had scored below the cutoff on the PHQ-9 from subsequent analysis.

Table 2.

View Table

Levels of Symptoms of Depression as Assessed by the PHQ-9 and Antidepressant Use

Table 2.

Levels of Symptoms of Depression as Assessed by the PHQ-9 and Antidepressant Use

Symptom Severity	n (%)	Reported Use of Antidepressants n (% for Each Severity Level)
No symptoms	28 (19.6)	0
Minimal (1–4)	55 (38.5)	4 (7.3)
Mild (5–9)	39 (27.3)	4 (10.3)
Moderate (10–14)*	12 (8.4)	1 (8.3)
Moderately severe (15–19)	8 (5.6)	2 (25.0)
Severe (20–27)	1 (0.7)	1 (100.0)

* The moderate cutoff point of ≥10 is the conventional point at which further evaluation is recommended.

The psychometric validation of the three subscales of the IVI showed no item misfit or disordered thresholds. The person separation reliability values of the subscales were substantial, ranging from 0.80 to 0.84. There was no evidence of multidimensionality, and the items showed good targeting, indicating acceptable match of item difficulty to patients' visual ability. There was a moderate and significant correlation between corrected visual acuity and the three subscales (0.39, 0.23, and 0.46, for the Mobility and Independence, Emotional Well-being, and Reading and Accessing Information scales, respectively) indicating satisfactory criterion validity. These results showed that three subscales of the IVI were reliable and valid to be used in this sample.

In univariate correlational analyses, both depressive symptoms and vision-specific distress were significantly associated with younger age, poor physical health, and restricted participation due to vision impairment as assessed by the IVI Mobility and Independence and Reading and Accessing Information subscales (Table 3). Depressive symptoms were also significantly associated with lower levels of social support (affectionate support, tangible support, and emotional/informational support), experience of a negative life event in the previous year, and being born outside of Australia. Vision-specific distress was associated with lower visual acuity.

Table 3.

View Table

Bivariate Correlations of Predictor Variables with PHQ-9 Scores and Vision-Specific Distress

Table 3.

Bivariate Correlations of Predictor Variables with PHQ-9 Scores and Vision-Specific Distress

Variable	PHQ-9	Vision-Specific Distress
Age, y	−.24*	−.20†
Sex	.12	−.011
Country of birth, outside Australia	.18†	.17
Marital status, married	.042	.15
Education	−.13	−.027
Visual acuity	−.039	.20†
Duration of vision impairment	.140	.053
PCS12	−.32‡	−.22†
Use of low vision services, yes	.095	−.093
Time since vision rehabilitation services (n = 75)	−.20	.062
Negative life event in past 12 months, yes	.22†	.052
MOS emotional/informational support	−.18†	−.11
MOS tangible support	−.20†	.019
MOS affectionate support	−.26*	−.052
MOS positive social interaction	−.15	−.16
IVI Reading and Accessing Information subscale	.27‡	.50‡
IVI Mobility and Independence subscale	.38‡	.60‡
Vision-specific distress	.51‡	—
PHQ-9	—	.51‡

n = 135.

* P ≤ 0.01.

† P ≤ 0.05.

‡ P ≤ 0.001.

Table 4 outlines the regression model that predicts depressive symptoms. The overall model was statistically significant and accounted for 37% of the variance in depressive symptoms (F ₍ _10,121 ₎= 8.525, P < 0.001). In the final model, vision-specific distress was the strongest unique predictor (β = 0.37, P < 0.001), with physical health (β = −0.22, P < 0.01), age (β = −0.18, P < 0.05), and experience of a negative life event (β = 0.15, P < 0.05) also contributing significantly to depressive symptoms. The factors added in each step resulted in a significant R ² change (P ≤ 0.05), although the change in step 2 was small (6% of additional variance), and none of the social support models were significant predictors. In the final step in which the IVI subscales were included, only the vision-specific distress measure contributed significantly to the model, whereas the subscales reflecting participation restrictions did not. At this step, the vision-specific distress measure added an additional 14% of the variance in the model. The pattern of significant correlations and output from the regression models were similar when they were recalculated including the eight participants currently being successfully treated with antidepressants.

Table 4.

View Table

Hierarchical Regression Analysis of Variables Predicting PHQ-9 Scores

Table 4.

Hierarchical Regression Analysis of Variables Predicting PHQ-9 Scores

Variable	Step 1 (t-Value)	Step 2 β (t-Value)	Step 3 β (t-Value)
Age	−.27 (−3.40)*	−.26 (−3.37)*	−.18 (−.23)†
PCS12	−.36 (−4.49)‡	−.33 (−4.16)‡	−.22 (−.29)*
Country of birth	.18 (2.29)†	.16 (2.09)†	.10 (1.39)
Negative life event in last 12 months		.18 (2.29)†	.15 (2.04)†
MOS emotional/informational support		−.003 (−.027)	.077 (.69)
MOS tangible support		−.023 (−.20)	−.10 (−.95)
MOS affectionate support		−.13 (−1.16)	−.15 (−1.34)
IVI Reading and accessing information			−.004 (−.033)
IVI Mobility and independence subscale			.058 (.48)
Vision-specific distress			.37 (3.79)‡
R ² Change	.21‡	.063†	.14‡
Total adjusted R ²			.36‡

n = 135. Model significance F _(10,121) = 8.525, P = 0.000.

* P ≤ 0.01.

† P ≤ 0.05.

‡ P ≤ 0.001.

The univariate correlations (Table 3) revealed that the data met the criteria for mediation, given that significant correlations were found with both of the independent variables (Reading and Accessing Information and Mobility and Independence) with vision-specific distress (mediating variable) and depressive symptoms (dependent variable) (all P < 0.001). Mediation analysis revealed that when adjustment was made for vision-specific distress, the direct association between Mobility and Independence and depressive symptoms became nonsignificant (β = 0.33 P = 0.25). The Sobel test also indicated a significant mediation effect (z = 4.12, P < 0.001). A similar pattern was found for Reading and Accessing Information. When adjustment was made for vision-specific distress, the direct association between Reading and Accessing Information and depressive symptoms became nonsignificant (β = 0.04, P = 0.84) and the Sobel test was significant (z = 4.32, P < 0.001). These results suggest that vision-specific distress mediates the impact of participation restriction due to vision impairment on depressive symptoms.

Discussion

Vision-specific distress was found to be the strongest unique predictor of depressive symptoms in our study. Our results suggest that it is the emotional consequence of vision impairment that contributes most significantly to depressive symptoms regardless of other characteristics such as the degree or duration of vision loss, vision-specific functioning, and participation in daily living. Our mediation analysis also confirmed our hypothesis that restriction in participation due to vision impairment contributes to depressive symptoms via vision-specific distress. This explanation may help to account for the inconsistent findings regarding functional impairment and depression in people with vision impairment.^9,13,14,36

Our results confirm that some generic determinants of depression identified in the general population—namely, poor physical health and a negative life event in the past year—are also predictors of depressive symptoms in people with vision impairment, whereas other demographic factors such as the sex and educational level of the individual appear not to be associated with depression in this group.^9,13,14 However, in contrast to other studies focused on people with vision impairment, we did not find social support variables or the other IVI subscales representing restricted participation to be significant predictors in the multivariate model.^9,13 Younger age was an independent predictor of depressive symptoms in this group and was also significantly associated with greater vision-specific distress. The role of age in depression in the general population is unclear, with some studies finding age to be a risk factor and others finding age to be protective against depression.^11,37,38 Our findings suggested that it was the age at which individuals deal with vision impairment that is critical, rather than the duration of vision loss. People dealing with vision impairment at a relatively young age may experience greater distress due to a greater disruption in life roles (such as work, family commitment) or due to less well-developed coping strategies than those of older counterparts.

Although our mediation hypothesis was confirmed, the cross-sectional nature of this study means that we are unable to make conclusions about causality. The relations between disability, vision-specific distress and depression are likely to be complex and reciprocal. One of the main assumptions of our mediation testing approach is that the dependent variable does not cause the mediator.³⁴ However, it is possible that depression influences vision-specific distress or that both are confounded by personality traits such as negative affectivity, trait anxiety, and neuroticism. Such dispositional tendencies have been shown to reflect differences in negative emotionality.³⁹ Individuals with high levels of these traits tend to be distressed, upset, and pessimistic and may therefore experience higher levels of both depressive symptoms and vision-specific distress. Many depression screening tools include a range of affective symptoms that are not part of the diagnostic criteria for depression and may tap negative affectivity rather than clinical depression. To avoid capturing general emotional distress in this study and to ensure that our measure of depression was distinct from our measure of vision-specific distress, we chose to use the PHQ-9, which is specific to the diagnostic criteria for major depression according to the DSM-IV,²⁴ and includes cognitive and physical items rather than items that focus solely on affective symptoms.

The strong association between vision-specific distress and depression holds important implications. First, identifying patients with high levels of vision-specific distress measures could be a way to determine those at risk of depression and in need of early intervention. Researchers in other health care areas such as diabetes have argued that condition-specific distress measures, which have undergone validation as screening tools, may be more appropriate for use in clinical settings than measures of depression.¹⁶ Second, there are implications for treatment. Treatments for depression administered to people with vision impairment may benefit from integrating a focus on vision-specific distress.^15,40 Third, a proportion of people may benefit from early interventions focused on coping and dealing with vision-specific distress rather than being treated for depression. Indeed, self-management approaches show promise for reducing distress and preventing depression in people with vision impairment.^41,42

Consistent with previous research in population-based samples, we found in this study that a greater proportion of people with vision impairment recruited from tertiary eye care settings scored above the cutoff point for moderate depression (≥10) on the PHQ-9, than was reported in normative data.⁴³ In this study we found that the majority of individuals with vision impairment and clinically significant depressive symptoms were not being treated. This finding suggests that clinicians and eye health professionals should take a more active role in identifying clinical depression or high levels of vision-specific distress and providing treatment options for people with vision impairment. Given that patients with vision impairment have been identified as a group in which depression is least likely to be recognized by their primary physician,⁴⁴ other health professionals in contact with people with vision impairment, such as eye health professionals and rehabilitation staff, may be best placed to do this.^45,46

In conclusion, this study adds to the evidence base on depression in people with vision impairment by focusing on predictors in a clinical sample and, for the first time, highlights the unique impact of vision-specific distress. However, the relatively small sample size and cross-sectional design are clear limitations. Further work of longitudinal design is needed to prospectively explore the relationships between vision impairment, functional ability, vision-specific distress, and depression.

Studies with larger sample sizes sufficient to support the use of structural equation modeling techniques will be useful for exploring interrelationships between these variables. In particular, there is a need to examine the distinction or overlap between depression and vision-specific distress and to prospectively investigate the development, progression, persistence, and responsiveness of these conditions to treatment.

Supplementary Materials

Supplementary Material - (PDF)

Footnotes

Disclosure: G. Rees, None; H.W. Tee, None; M. Marella, None; E. Fenwick, None; M. Dirani, None; E.L. Lamoureux, None

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Age, y	Mean ± SD (range)	76.20 ± 11.79 (24–97)
Sex	Female	85 (59.4%)
Marital status	Married/defacto	69 (48.3%)
Country of birth	Australia	83 (58%)
Educational level	Primary School or Less	21 (14.7%)
	Some secondary education	62 (43.4%)
	Secondary education complete	29 (20.3%)
	Trade/Apprenticeship	6 (4.2%)
	Some University/TAFE	10 (7.0%)
	University Degree or Higher	15 (10.5%)
Best corrected visual acuity*	0.3 < LogMAR ≤ 0.48	45 (31.7%)
	0.48 < LogMAR ≤ 1.0	71 (49.7%)
	< 1.0 LogMAR	26 (18.2%)
Main cause of vision loss	Age-related macular degeneration	81 (56.6%)
	Diabetic retinopathy	35 (24.5%)
	Glaucoma	14 (9.8%)
	Other	13 (9.1%)
Duration of vision impairment, y	Mean ± SD (range)	8.46 ± 12.97 (0–81)
Previous use of vision rehabilitation services	Yes	75 (52.4%)
Time since use of vision rehabilitation services, y (n = 75)	Mean ± SD (range)	2.43 ± 2.44 (0.2–12)
PCSI2 score	Mean ± SD (range)	39.96 ± 11.84 (13.60–59.58)
Negative life event in last 12 months	Yes	38 (26.6%)
MOS emotional/informational support	Mean ± SD (range)	4.13 ± 99 (1.0–5.0)
MOS tangible support	Mean ± SD (range)	4.46 ± 96 (1.0–5.0)
MOS affectionate support	Mean ± SD (range)	4.34 ± 1.03 (1.0–5.0)
MOS positive social interaction	Mean ± SD (range)	4.01 ± 1.17 (1.0–5.0)
IVI Reading and accessing information	Mean ± SD (range)	.98 ± 2.14 (−5.52–5.62)
IVI Mobility and independence subscale	Mean ± SD (range)	−.11 ± 1.60 (−4.82–5.09)
Vision-specific distress	Mean ± SD (range)	−.46 ± 1.68 (−4.66–4.82)
PHQ-9 score	Mean ± SD (range)	4.78 ± 4.79 (0–20)

Age, y	Mean ± SD (range)	76.20 ± 11.79 (24–97)
Sex	Female	85 (59.4%)
Marital status	Married/defacto	69 (48.3%)
Country of birth	Australia	83 (58%)
Educational level	Primary School or Less	21 (14.7%)
	Some secondary education	62 (43.4%)
	Secondary education complete	29 (20.3%)
	Trade/Apprenticeship	6 (4.2%)
	Some University/TAFE	10 (7.0%)
	University Degree or Higher	15 (10.5%)
Best corrected visual acuity*	0.3 < LogMAR ≤ 0.48	45 (31.7%)
	0.48 < LogMAR ≤ 1.0	71 (49.7%)
	< 1.0 LogMAR	26 (18.2%)
Main cause of vision loss	Age-related macular degeneration	81 (56.6%)
	Diabetic retinopathy	35 (24.5%)
	Glaucoma	14 (9.8%)
	Other	13 (9.1%)
Duration of vision impairment, y	Mean ± SD (range)	8.46 ± 12.97 (0–81)
Previous use of vision rehabilitation services	Yes	75 (52.4%)
Time since use of vision rehabilitation services, y (n = 75)	Mean ± SD (range)	2.43 ± 2.44 (0.2–12)
PCSI2 score	Mean ± SD (range)	39.96 ± 11.84 (13.60–59.58)
Negative life event in last 12 months	Yes	38 (26.6%)
MOS emotional/informational support	Mean ± SD (range)	4.13 ± 99 (1.0–5.0)
MOS tangible support	Mean ± SD (range)	4.46 ± 96 (1.0–5.0)
MOS affectionate support	Mean ± SD (range)	4.34 ± 1.03 (1.0–5.0)
MOS positive social interaction	Mean ± SD (range)	4.01 ± 1.17 (1.0–5.0)
IVI Reading and accessing information	Mean ± SD (range)	.98 ± 2.14 (−5.52–5.62)
IVI Mobility and independence subscale	Mean ± SD (range)	−.11 ± 1.60 (−4.82–5.09)
Vision-specific distress	Mean ± SD (range)	−.46 ± 1.68 (−4.66–4.82)
PHQ-9 score	Mean ± SD (range)	4.78 ± 4.79 (0–20)

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