Abstract
Background: Depression and vision impairment are major public health concerns, yet the association between depressive symptom severity and severe vision difficulty remains unclear.
Methods: We conducted a cross-sectional study using data from the National Health and Nutrition Examination Survey 2013-2018. A total of 9,227 adults were included. Depressive symptoms were assessed using the Patient Health Questionnaire-9. Severe vision difficulty was defined as self-reported serious difficulty seeing or being blind. Survey-weighted logistic regression, restricted cubic spline modeling, and subgroup analyses were performed to evaluate the associations.
Results: Among 9,227 participants, 632 (6.85%) reported severe vision difficulty. In the fully adjusted model, compared with participants with no depressive symptoms, the odds ratios for severe vision difficulty were 1.74 (95% CI: 1.28-2.37) for mild depressive symptoms, 2.28 (95% CI: 1.56-3.34) for moderate depressive symptoms, 4.77 (95% CI: 2.16-10.51) for moderately severe depressive symptoms, and 5.20 (95% CI: 2.87-9.44) for severe depressive symptoms (P for trend < 0.001). When analyzed continuously, each five-point increase in PHQ-9 score was associated with higher odds of severe vision difficulty (OR: 1.60, 95% CI: 1.43-1.79, P < 0.001). Restricted cubic spline modeling showed a significant overall association (P for overall association < 0.001), whereas the test for nonlinearity was not significant (P for nonlinearity = 0.560). Exploratory subgroup analyses showed no significant interaction across major demographic and clinical strata (all P for interaction > 0.05).
Conclusions: Depressive symptom severity was independently and linearly associated with severe vision difficulty among adults in the United States. These findings suggest that greater depressive symptom burden may be linked to poorer visual health in the general population.
Keywords: cross-sectional study, depressive symptoms, nhanes, phq-9, severe vision difficulty, vision impairment
Introduction
Visual impairment and depressive symptoms are major public health challenges that substantially contribute to disability and reduced quality of life worldwide [1,2]. These conditions are common, burdensome, and often coexist, potentially compounding limitations in functional status, daily activities, and overall well-being. Accordingly, a clearer understanding of the association between depressive symptom severity and severe vision difficulty may have important clinical and public health relevance.
Previous epidemiological studies have reported a close association between visual impairment and adverse mental health outcomes [3,4]. Individuals with vision loss often experience reduced independence, social isolation, and diminished quality of life, factors that may be associated with greater depressive symptom burden. Longitudinal and population-based studies have shown that people with visual impairment are more likely to report depressive symptoms, psychological distress, and poorer mental health status [5-7].
However, most existing studies have focused on visual impairment as a predictor of depressive outcomes, emphasizing how vision loss may be associated with greater depressive symptom burden. By contrast, fewer population-based studies have examined whether increasing depressive symptom severity is associated with severe vision difficulty. In addition, many prior studies have treated depression-related measures as a binary condition, which may obscure potential gradients across levels of symptom severity. Consequently, the association between depressive symptom severity and severe vision difficulty has not been fully characterized in large population-based samples.
To address these gaps, we used data from the National Health and Nutrition Examination Survey (NHANES) 2013-2018, a nationally representative survey of adults in the United States. The primary objective of this study was to examine the association between depressive symptom severity, assessed using the Patient Health Questionnaire-9 (PHQ-9), and severe vision difficulty among U.S. adults. Secondary analyses evaluated this relationship using both categorical and continuous PHQ-9 measures and explored its functional form with restricted cubic spline modeling. We hypothesized that higher depressive symptom severity would be independently associated with greater odds of severe vision difficulty.
Materials and methods
Study population and data source
This study used data from the NHANES 2013-2018, a nationally representative cross-sectional survey of the non-institutionalized United States population conducted by the Centers for Disease Control and Prevention. A total of 29,400 participants were identified from the 2013-2018 survey cycles. Participants with missing data on severe vision difficulty were excluded (n = 1,168), leaving 28,232 participants. Individuals with missing covariate data were subsequently excluded from the base analytic sample (n = 19,005), resulting in 9,227 participants for analysis. The NHANES protocol was approved by the National Center for Health Statistics Ethics Review Board, and all participants provided written informed consent at the time of the original survey. Because the present study was a secondary analysis of de-identified, publicly available data, no additional informed consent was required.
Outcome assessment: severe vision difficulty
Severe vision difficulty was assessed using the NHANES disability questionnaire administered during the in-home household interview by trained interviewers. Participants were asked whether they were blind or had serious difficulty seeing even when wearing glasses. Those responding “yes” were classified as having severe vision difficulty, whereas those responding “no” were classified as not having severe vision difficulty. Responses indicating refusal, uncertainty, or missing data were excluded. We retained this binary endpoint because it is the directly available vision-related functional measure in NHANES 2013-2018 and because its wording is closely aligned with the American Community Survey standard vision disability item, facilitating comparability with population-based disability surveillance. This dichotomous measure has been widely used in NHANES-based ophthalmic epidemiological studies [8-10].
Exposure assessment: depressive symptoms
Depressive symptoms were assessed using the PHQ-9, a self-reported instrument that evaluates the frequency of depressive symptoms during the previous two weeks [11-14]. For categorical analyses, PHQ-9 scores were classified as no depressive symptoms (0-4), mild depressive symptoms (5-9), moderate depressive symptoms (10-14), moderately severe depressive symptoms (15-19), and severe depressive symptoms (20-27). The no depressive symptoms group was used as the reference category. For continuous analyses, the PHQ-9 score was additionally modeled per five-point increase.
Covariates
Potential confounders were selected on the basis of clinical relevance, previous literature, and consistent availability across the NHANES 2013-2018 cycles. These covariates included age, sex, race and ethnicity, education level, poverty-income ratio, body mass index (BMI), diabetes, hypertension, smoking status, physical activity, and laboratory measures. Race and ethnicity were self-reported in NHANES and were included as sociodemographic covariates. Diabetes was defined as a self-reported physician diagnosis, current use of glucose-lowering medication, a glycated hemoglobin level of 6.5% or greater, or a fasting plasma glucose level of 126 mg/dL or greater. Hypertension was defined as a self-reported physician diagnosis, current use of antihypertensive medication, or measured blood pressure with systolic blood pressure ≥130 mmHg and/or diastolic blood pressure ≥80 mmHg. Measured blood pressure was obtained in the mobile examination center (MEC) after a five-minute seated rest, and up to three seated blood pressure readings were averaged. Smoking status was categorized as never, former, or current.
Physical activity was derived from the NHANES physical activity questionnaire, which is based on the Global Physical Activity Questionnaire framework and includes work/household, transportation, and leisure-time domains. Total physical activity was calculated as crude weekly minutes, rather than MET-minutes, by summing the products of reported days per week and minutes per day across vigorous work activity, moderate work activity, walking or bicycling for transportation, vigorous leisure-time activity, and moderate leisure-time activity. Participants were categorized as having low (<150 min/week), moderate (150-299 min/week), or high (≥300 min/week) physical activity [15].
Weighting and survey design
NHANES uses a complex, multistage probability sampling design. All analyses incorporated survey weights, strata, and primary sampling units in accordance with NHANES analytic guidance. Because the analyses included examination- and laboratory-derived variables, MEC examination weights were used. For pooled analyses across the 2013-2014, 2015-2016, and 2017-2018 cycles, six-year MEC weights were constructed by dividing the two-year MEC examination weights by three. Variance estimation incorporated SDMVSTRA and SDMVPSU using Taylor series linearization.
Statistical analysis
Baseline characteristics were compared between participants with and without severe vision difficulty using survey-weighted t-tests for continuous variables and Rao-Scott-adjusted chi-square tests for categorical variables. Continuous variables are presented as means with standard deviations, and categorical variables are presented as weighted percentages. Survey-weighted logistic regression models were used to estimate odds ratios (ORs) and 95% confidence intervals (CIs). Three models were constructed. Model 1 was unadjusted. Model 2 was adjusted for age, sex, and race and ethnicity. Model 3 was further adjusted for education level, poverty-income ratio, smoking status, BMI, hypertension, diabetes, and physical activity.
Restricted cubic spline models were fitted using the raw PHQ-9 score to examine the shape of the association between depressive symptom severity and severe vision difficulty. The spline model used internal knots at 5, 10, and 15, with boundary knots at 0 and 27. Subgroup analyses and interaction tests were performed across major demographic and clinical strata. All tests were two-sided, and P-values less than 0.05 were considered statistically significant. All statistical analyses were performed using R version 4.4.2 (R Foundation for Statistical Computing, Vienna, Austria).
Results
Participant characteristics
A total of 9,227 participants were included in the final analysis, as shown in Figure 1.
Figure 1. Flowchart of the study population selection process.
NHANES 2013-2018 participant selection process.
NHANES: National Health and Nutrition Examination Survey; PHQ-9: Patient Health Questionnaire-9.
Table 1 summarizes the baseline characteristics of the study population based on severe vision difficulty status. The overall mean age of the participants was 46.06 ± 19.17 years. Individuals with severe vision difficulty were significantly older than those without severe vision difficulty (55.46 ± 17.91 vs. 45.49 ± 19.09 years, P < 0.001). They also had a higher prevalence of depression (21% vs. 6.7%, P < 0.001), diabetes (47% vs. 23%, P < 0.001), and hypertension (73% vs. 50%, P < 0.001). In addition, participants with severe vision difficulty were more likely to have lower educational attainment (33% vs. 19% with less than a high school education, P < 0.001), lower poverty-income ratio levels (26% vs. 15% with a poverty-income ratio < 1, P < 0.001), and lower physical activity levels (54% vs. 42% with low activity, P = 0.004).
Table 1. Baseline characteristics of the study population.
Continuous variables are presented as weighted mean ± standard deviation, and categorical variables are presented as unweighted counts with weighted percentages. P-values were calculated using survey-weighted t tests for continuous variables and Rao-Scott-adjusted chi-square tests for categorical variables.
NHANES: National Health and Nutrition Examination Survey; PIR: poverty-income ratio; BMI: body mass index; HbA1c: hemoglobin A1c.
| Variables | Overall, n = 9227 | No severe vision difficulty, n = 8595 | Severe vision difficulty, n = 632 | P-value |
| Age (years) | 46.06 ± 19.17 | 45.49 ± 19.09 | 55.46 ± 17.91 | <0.001 |
| Gender (%) | ||||
| Female | 4,705 (51) | 4,369 (51) | 336 (56) | 0.129 |
| Male | 4,522 (49) | 4,226 (49) | 296 (44) | |
| Race (%) | ||||
| Non-Hispanic White | 3,389 (65) | 3,169 (65) | 220 (61) | 0.001 |
| Mexican American | 1,459 (9.3) | 1,325 (9.1) | 134 (12) | |
| Non-Hispanic Black | 1,962 (11) | 1,833 (11) | 129 (12) | |
| Other Hispanic | 925 (5.8) | 830 (5.7) | 95 (8.6) | |
| Other Race | 1,492 (9.3) | 1,438 (9.5) | 54 (5.8) | |
| BMI | 29.34 ± 7.48 | 29.27 ± 7.45 | 30.54 ± 7.90 | 0.002 |
| Education (%) | ||||
| Below High School | 1,746 (20) | 1,498 (19) | 248 (33) | <0.001 |
| High School | 1,759 (33) | 1,613 (33) | 146 (36) | |
| Above High School | 2,412 (46) | 2,281 (48) | 131 (31) | |
| PIR Level (%) | ||||
| <1 | 2,203 (16) | 1,970 (15) | 233 (26) | <0.001 |
| ≥1 | 7,024 (84) | 6,625 (85) | 399 (74) | |
| HbA1c (%) | 5.81 ± 1.17 | 5.77 ± 1.12 | 6.46 ± 1.70 | <0.001 |
| Diabetes (%) | ||||
| No | 6,509 (76) | 6,214 (77) | 295 (53) | <0.001 |
| Yes | 2,718 (24) | 2,381 (23) | 337 (47) | |
| Hypertension (%) | ||||
| No | 3,695 (48) | 3,545 (50) | 150 (27) | <0.001 |
| Yes | 4,624 (52) | 4,168 (50) | 456 (73) | |
| Smoking (%) | ||||
| Never | 4,609 (55) | 4,335 (56) | 274 (42) | <0.001 |
| Former | 2,028 (27) | 1,844 (26) | 184 (34) | |
| Current | 1,478 (18) | 1,336 (17) | 142 (25) | |
| Activity (%) | ||||
| Low | 4,388 (42) | 4,001 (42) | 387 (54) | 0.004 |
| Moderate | 1,109 (13) | 1,048 (13) | 61 (11) | |
| High | 3,730 (45) | 3,546 (46) | 184 (35) | |
| Depression (%) | ||||
| No | 8,504 (92) | 8,012 (93) | 492 (79) | <0.001 |
| Yes | 723 (7.5) | 583 (6.7) | 140 (21) | |
Association between depressive symptom severity and severe vision difficulty
The association between depressive symptom severity and severe vision difficulty was evaluated using survey-weighted logistic regression models (Table 2). In the unadjusted model, a graded association was observed across increasing PHQ-9 categories. Compared with participants with no depressive symptoms, the odds ratios for severe vision difficulty were 2.30 (95% CI, 1.76-3.01) for mild depressive symptoms, 3.16 (95% CI, 2.21-4.52) for moderate depressive symptoms, 7.26 (95% CI, 3.88-13.57) for moderately severe depressive symptoms, and 7.03 (95% CI, 4.16-11.90) for severe depressive symptoms (P for trend < 0.001). After adjustment for age, sex, and race/ethnicity, the association remained statistically significant. In the fully adjusted model, the graded relationship persisted. Compared with participants with no depressive symptoms, the odds ratios for severe vision difficulty were 1.74 (95% CI, 1.28-2.37) for mild depressive symptoms, 2.28 (95% CI, 1.56-3.34) for moderate depressive symptoms, 4.77 (95% CI, 2.16-10.51) for moderately severe depressive symptoms, and 5.20 (95% CI, 2.87-9.44) for severe depressive symptoms (P for trend < 0.001).
Table 2. Association between depressive symptom severity and severe vision difficulty.
Model 1 was unadjusted. Model 2 was adjusted for age, sex, and race and ethnicity. Model 3 was additionally adjusted for education level, poverty-income ratio, smoking status, body mass index, hypertension, diabetes, and physical activity. All models accounted for the complex survey design of NHANES.
NHANES: National Health and Nutrition Examination Survey; OR: odds ratio; CI: confidence interval.
| Model | No depressive symptoms (0–4) | Mild depressive symptoms (5–9) | Moderate depressive symptoms (10–14) | Moderately severe depressive symptoms (15–19) | Severe depressive symptoms (20–27) | P for trend |
| Model 1 | 1 | 2.30 (1.76–3.01) | 3.16 (2.21–4.52) | 7.26 (3.88–13.57) | 7.03 (4.16–11.90) | <0.001 |
| Model 2 | 1 | 2.01 (1.55–2.61) | 2.85 (1.96–4.14) | 6.32 (3.35–11.93) | 6.80 (4.03–11.47) | <0.001 |
| Model 3 | 1 | 1.74 (1.28–2.37) | 2.28 (1.56–3.34) | 4.77 (2.16–10.51) | 5.20 (2.87–9.44) | <0.001 |
When the PHQ-9 score was analyzed as a continuous variable, each five-point increase was associated with higher odds of severe vision difficulty in the fully adjusted model (OR, 1.60; 95% CI, 1.43-1.79; P < 0.001), as shown in Table 3.
Table 3. Association between Patient Health Questionnaire-9 score and severe vision difficulty per five-point increase.
Associations are presented per five-point increase in the Patient Health Questionnaire-9 score. Model 1 was unadjusted. Model 2 was adjusted for age, sex, and race and ethnicity. Model 3 was additionally adjusted for education level, poverty-income ratio, smoking status, body mass index, hypertension, diabetes, and physical activity. All models accounted for the complex survey design of NHANES.
OR: odds ratio; CI: confidence interval; NHANES: National Health and Nutrition Examination Survey.
| Model | OR (95% CI) | P for trend |
| Model 1 | 1.78 (1.63–1.94) | <0.001 |
| Model 2 | 1.72 (1.58–1.88) | <0.001 |
| Model 3 | 1.60 (1.43–1.79) | <0.001 |
Restricted cubic spline analysis
Restricted cubic spline modeling was performed to further examine the association between PHQ-9 score and severe vision difficulty (Figure 2). A statistically significant overall association was observed (P overall < 0.001). However, the test for non-linearity was not significant (P for non-linearity = 0.560), indicating limited evidence of meaningful curvature and suggesting that the association was largely close to linear across the observed PHQ-9 range.
Figure 2. Association between Patient Health Questionnaire-9 score and severe vision difficulty based on restricted cubic spline modeling.
Restricted cubic spline model showing the association between Patient Health Questionnaire-9 score and severe vision difficulty. The solid line represents adjusted odds ratios, and the shaded area represents 95% confidence intervals. The model was adjusted for age, sex, race and ethnicity, education level, poverty-income ratio, smoking status, body mass index, hypertension, diabetes, and physical activity.
PHQ-9: Patient Health Questionnaire-9; CI: confidence interval.
Subgroup analyses
Exploratory subgroup analyses were performed to assess whether the association between PHQ-9 score and severe vision difficulty differed across major demographic and clinical strata (Table 4). Positive associations were observed across most subgroups. However, no statistically significant interaction was detected for age, BMI, diabetes, education level, sex, hypertension, poverty-income ratio, physical activity, race/ethnicity, or smoking status (all P for interaction > 0.05). These findings suggest that the direction of association was generally similar across strata, although the subgroup analyses should be interpreted as exploratory.
Table 4. Subgroup analyses of the association between Patient Health Questionnaire-9 score.
Models were adjusted as appropriate within each subgroup. P for interaction represents the significance of interaction across subgroups. Age is expressed in years, and BMI is expressed in kg/m².
OR: odds ratio; CI: confidence interval; PHQ-9: Patient Health Questionnaire-9; BMI: body mass index; PIR: poverty-income ratio.
| Subgroup | Sample size | OR (95% CI) | P-value | P for interaction |
| Age (years) | ||||
| <60 | 6235 | 1.60 (1.40–1.84) | <0.001 | 0.583 |
| ≥60 | 2992 | 1.59 (1.30–1.95) | <0.001 | |
| BMI (kg/m²) | ||||
| <30 | 5676 | 1.75 (1.48–2.06) | <0.001 | 0.177 |
| ≥30 | 3551 | 1.47 (1.29–1.68) | <0.001 | |
| Diabetes | ||||
| No | 6509 | 1.60 (1.39–1.85) | <0.001 | 0.935 |
| Yes | 2718 | 1.57 (1.37–1.80) | <0.001 | |
| Education Level | ||||
| Below High School | 1746 | 1.72 (1.48–2.00) | <0.001 | 0.11 |
| Above High School | 2412 | 1.76 (1.44–2.16) | <0.001 | |
| High School | 1759 | 1.35 (1.08–1.69) | 0.01 | |
| Gender | ||||
| Female | 4705 | 1.65 (1.44–1.89) | <0.001 | 0.867 |
| Male | 4522 | 1.55 (1.21–1.98) | 0.001 | |
| Hypertension | ||||
| No | 3695 | 1.68 (1.28–2.19) | <0.001 | 0.569 |
| Yes | 4624 | 1.55 (1.38–1.74) | <0.001 | |
| PIR Level | ||||
| <1 | 2203 | 1.39 (1.19–1.62) | <0.001 | 0.067 |
| ≥1 | 7024 | 1.70 (1.48–1.96) | <0.001 | |
| Physical Activity | ||||
| High | 3730 | 1.45 (1.21–1.74) | <0.001 | 0.795 |
| Low | 4388 | 1.71 (1.51–1.93) | <0.001 | |
| Moderate | 1109 | 1.60 (1.09–2.36) | 0.019 | |
| Race/Ethnicity | ||||
| Mexican American | 1459 | 1.65 (1.30–2.11) | <0.001 | 0.992 |
| Non-Hispanic Black | 1962 | 1.53 (1.23–1.90) | <0.001 | |
| Non-Hispanic White | 3389 | 1.62 (1.41–1.87) | <0.001 | |
| Other Hispanic | 925 | 1.80 (1.48–2.20) | <0.001 | |
| Other Race | 1492 | 1.30 (0.99–1.70) | 0.056 | |
| Smoking Status | ||||
| Current | 1478 | 1.65 (1.35–2.00) | <0.001 | 0.157 |
| Former | 2028 | 1.67 (1.33–2.10) | <0.001 | |
| Never | 4609 | 1.43 (1.21–1.69) | <0.001 | |
Discussion
In this nationally representative study of U.S. adults, we observed a significant and graded association between depressive symptom severity and severe vision difficulty. Participants with higher PHQ-9 scores had progressively greater odds of reporting severe vision difficulty, even after adjustment for a broad set of sociodemographic factors, lifestyle behaviors, and chronic conditions. In the fully adjusted model, compared with participants with no depressive symptoms, those with mild, moderate, moderately severe, and severe depressive symptoms had progressively higher odds of severe vision difficulty. When the PHQ-9 score was analyzed as a continuous variable, each five-point increase was associated with 60% higher odds of severe vision difficulty. In addition, restricted cubic spline analysis showed a statistically significant overall association, whereas the test for non-linearity was not significant, suggesting that the association was largely close to linear across the observed PHQ-9 range.
Our findings are broadly consistent with previous epidemiological studies reporting a close relationship between visual impairment and adverse mental health outcomes, while extending the literature in several ways. First, using NHANES 2013-2018, we examined this association in a large nationally representative sample of U.S. adults. Second, by classifying PHQ-9 scores into five severity groups, we demonstrated a graded pattern across increasing levels of depressive symptom severity rather than relying on a simple dichotomous classification. Third, the combined use of categorical, continuous, and spline-based analyses allowed us to characterize the association more comprehensively. Notably, the spline analysis suggested that the relationship was primarily near-linear rather than strongly non-linear across the observed score range.
Several biologically and behaviorally plausible mechanisms may underlie the observed association, although these pathways were not directly examined in the present study. Prior literature has suggested that depression-related processes may be linked to systemic inflammation, endothelial dysfunction, oxidative stress, and microvascular abnormalities, all of which have also been implicated in ocular disease [16-19]. Individuals with depressive symptoms often exhibit elevated levels of pro-inflammatory cytokines such as interleukin-6 and tumor necrosis factor-α [20-22], and inflammatory pathways have been implicated in the development of vision-threatening ocular conditions, including age-related macular degeneration, diabetic retinopathy, and glaucoma [23,24]. In addition, depression-related processes have been associated with endothelial dysfunction, oxidative stress, and impaired microvascular circulation, which may further affect retinal perfusion and visual function [25-27]. Behavioral pathways may likewise be relevant, as greater depressive symptom burden may be associated with lower physical activity, poorer treatment adherence, and reduced health-seeking behavior. At the same time, psychosocial processes may also contribute, including the possibility that individuals with more severe depressive symptoms perceive or report functional visual limitations more negatively. Accordingly, these mechanisms should be regarded as tentative hypotheses rather than explanations directly supported by the current data.
This study has several strengths. The use of NHANES data provided a large and nationally representative sample of U.S. adults. All analyses accounted for the complex survey design, allowing population-level estimation. In addition, the use of both categorical and continuous PHQ-9 measures, together with restricted cubic spline modeling, enabled a more detailed characterization of the association between depressive symptom severity and severe vision difficulty. The subgroup analyses also suggested that the direction of association was generally similar across the examined strata; however, these analyses should be interpreted as exploratory.
Several limitations should be acknowledged. First, the cross-sectional design precludes causal inference, and the temporal ordering of depressive symptoms and severe vision difficulty cannot be determined. The observed association may be bidirectional: severe vision difficulty may contribute to psychological distress through reduced independence and social participation, whereas greater depressive symptom burden may amplify the perception or reporting of functional visual limitations. Second, severe vision difficulty was assessed using a self-reported questionnaire item asking whether the participant was blind or had serious difficulty seeing even when wearing glasses. Although this measure captures real-world functional vision, it combines heterogeneous visual states and may not map directly onto clinical ophthalmic diagnoses. However, self-reported functional vision has been widely used in population-based research [28]. Third, despite adjustment for multiple covariates, residual confounding remains possible, particularly from factors not fully captured in the available data, such as specific ocular diseases, psychotropic medication use, anxiety-related symptoms, sleep problems, and other behavioral or clinical characteristics. Finally, substantial exclusions due to missing covariate data may have introduced selection bias and reduced precision. Therefore, the findings should be interpreted cautiously, and future longitudinal studies using more clinically specific ophthalmic measures are needed to clarify temporality and underlying mechanisms.
Conclusions
In this nationally representative cross-sectional study of U.S. adults, greater depressive symptom severity was significantly associated with higher odds of severe vision difficulty. This association showed a graded pattern across PHQ-9 categories and was also observed when the PHQ-9 score was analyzed as a continuous variable. Restricted cubic spline analysis further suggested that the association was largely close to linear across the observed PHQ-9 range. Given the cross-sectional design, these findings should be interpreted as evidence of association rather than causation. Future longitudinal studies incorporating objective ophthalmologic measures are needed to clarify temporality and the mechanisms underlying this relationship.
Disclosures
Human subjects: Informed consent for treatment and open access publication was obtained or waived by all participants in this study. National Center for Health Statistics (NCHS) Ethics Review Board issued approval NHANES 2013–2016: continuation of Protocol #2011-17; NHANES 2017–2018: Protocol #2018-01 (effective beginning October 26, 2017), continuation of Protocol #2011-17 effective through October 26, 2017.
Animal subjects: All authors have confirmed that this study did not involve animal subjects or tissue.
Conflicts of interest: In compliance with the ICMJE uniform disclosure form, all authors declare the following:
Payment/services info: All authors have declared that no financial support was received from any organization for the submitted work.
Financial relationships: All authors have declared that they have no financial relationships at present or within the previous three years with any organizations that might have an interest in the submitted work.
Other relationships: All authors have declared that there are no other relationships or activities that could appear to have influenced the submitted work.
Author Contributions
Concept and design: Jingwen Wang, Ying Li, Jia Qu
Acquisition, analysis, or interpretation of data: Jingwen Wang, Ying Li, Jia Qu
Drafting of the manuscript: Jingwen Wang, Ying Li, Jia Qu
Critical review of the manuscript for important intellectual content: Jingwen Wang, Ying Li, Jia Qu
Supervision: Jingwen Wang, Ying Li, Jia Qu
References
- 1.Global causes of blindness and distance vision impairment 1990-2020: a systematic review and meta-analysis. Flaxman SR, Bourne RRA, Resnikoff S, et al. Lancet Glob Health. 2017;5:1221–1234. doi: 10.1016/S2214-109X(17)30393-5. [DOI] [PubMed] [Google Scholar]
- 2.Global, regional, and national burden of 12 mental disorders in 204 countries and territories, 1990-2019: a systematic analysis for the Global Burden of Disease Study 2019. GBD 2019 Mental Disorders Collaborators. Lancet Psychiatry. 2022;9:137–150. doi: 10.1016/S2215-0366(21)00395-3. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 3.The association between visual impairment and mental disorders. Hashemi A, Hashemi H, Jamali A, Ghasemi H, Ghazizadeh Hashemi F, Khabazkhoob M. Sci Rep. 2024;14:2301. doi: 10.1038/s41598-024-52389-6. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 4.Association between visual impairment and suicide: a systematic review and meta-analysis. Palbo A, Roed Rasmussen ML, Hansen MS, Subhi Y. Acta Ophthalmol. 2024;102:263–273. doi: 10.1111/aos.15730. [DOI] [PubMed] [Google Scholar]
- 5.Combined effect of vision and hearing impairment on depression in older adults: evidence from the English Longitudinal Study of Ageing. Chou KL. J Affect Disord. 2008;106:191–196. doi: 10.1016/j.jad.2007.05.028. [DOI] [PubMed] [Google Scholar]
- 6.Well-being, mental health and sleep in children and young people with vision impairment: a narrative review. Canavan RF, Hayton J, Tibber MS, Dekker TM, Wood LA, Crossland MD. Prog Brain Res. 2025;292:231–263. doi: 10.1016/bs.pbr.2025.04.001. [DOI] [PubMed] [Google Scholar]
- 7.Association of socioeconomic, demographic, and health care access disparities with severe visual impairment in the US. Besagar S, Yonekawa Y, Sridhar J, et al. JAMA Ophthalmol. 2022;140:1219–1226. doi: 10.1001/jamaophthalmol.2022.4566. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 8.Vision and hearing loss associated with lifetime drug use: NHANES 2013-2018. Hinson-Enslin AM, Nahhas RW, McClintock HF. Disabil Health J. 2022;15:101286. doi: 10.1016/j.dhjo.2022.101286. [DOI] [PubMed] [Google Scholar]
- 9.The association between depressive symptoms and limitations in disability domains among US adults. Meshkat S, Lin Q, Tassone VK, Janssen-Aguilar R, Lou W, Bhat V. J Mood Anxiety Disord. 2025;9:100103. doi: 10.1016/j.xjmad.2024.100103. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 10.A study on the relationship between recreational physical activity and audiovisual difficulty for older adults. Zhang J, Feng R, Cao Y, Mo H. Sci Rep. 2024;14:7059. doi: 10.1038/s41598-024-55209-z. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 11.Improving PHQ9 utilization rates in a primary care-mental health integration setting. Brown MJ, Adams SM, Vanderhoef D, Schipani R, Taylor A. J Am Psychiatr Nurses Assoc. 2020;26:206–211. doi: 10.1177/1078390319865331. [DOI] [PubMed] [Google Scholar]
- 12.Screening for depression in primary care with Patient Health Questionnaire-9 (PHQ-9): a systematic review. Costantini L, Pasquarella C, Odone A, et al. J Affect Disord. 2021;279:473–483. doi: 10.1016/j.jad.2020.09.131. [DOI] [PubMed] [Google Scholar]
- 13.Psychometric comparison of the PHQ-9 and BDI-II for measuring response during treatment of depression. Titov N, Dear BF, McMillan D, Anderson T, Zou J, Sunderland M. Cogn Behav Ther. 2011;40:126–136. doi: 10.1080/16506073.2010.550059. [DOI] [PubMed] [Google Scholar]
- 14.The PHQ-9: validity of a brief depression severity measure. Kroenke K, Spitzer RL, Williams JB. J Gen Intern Med. 2001;16:606–613. doi: 10.1046/j.1525-1497.2001.016009606.x. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 15.US physical activity guidelines: current state, impact and future directions. Singh R, Pattisapu A, Emery MS. Trends Cardiovasc Med. 2020;30:407–412. doi: 10.1016/j.tcm.2019.10.002. [DOI] [PubMed] [Google Scholar]
- 16.Systemic inflammation and eye diseases. The Beijing Eye Study. Jonas JB, Wei WB, Xu L, Wang YX. PLoS One. 2018;13:0. doi: 10.1371/journal.pone.0204263. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 17.Depression and visual functioning in patients with ocular inflammatory disease. Qian Y, Glaser T, Esterberg E, Acharya NR. Am J Ophthalmol. 2012;153:370–378. doi: 10.1016/j.ajo.2011.06.028. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 18.Depression and eye disease—a narrative review of common underlying pathophysiological mechanisms and their potential applications. Tang WS, Lau NX, Krishnan MN, Chin YC, Ho CS. J Clin Med. 2024;13 doi: 10.3390/jcm13113081. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 19.The prevalence of depression and depressive symptoms among eye disease patients: a systematic review and meta-analysis. Zheng Y, Wu X, Lin X, Lin H. Sci Rep. 2017;7:46453. doi: 10.1038/srep46453. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 20.Pro-inflammatory cytokines IL-1α, IL-6 and TNF-α in major depressive disorder: sex-specific associations with psychological symptoms. Elgellaie A, Thomas SJ, Kaelle J, Bartschi J, Larkin T. Eur J Neurosci. 2023;57:1913–1928. doi: 10.1111/ejn.15992. [DOI] [PubMed] [Google Scholar]
- 21.Altered serum levels of TNF-α, IL-6, and IL-18 in depressive disorder patients. Fan N, Luo Y, Ou Y, He H. Hum Psychopharmacol. 2017;32 doi: 10.1002/hup.2588. [DOI] [PubMed] [Google Scholar]
- 22.Higher plasma interleukin-6 (IL-6) level is associated with SSRI- or SNRI-refractory depression. Yoshimura R, Hori H, Ikenouchi-Sugita A, Umene-Nakano W, Ueda N, Nakamura J. Prog Neuropsychopharmacol Biol Psychiatry. 2009;33:722–726. doi: 10.1016/j.pnpbp.2009.03.020. [DOI] [PubMed] [Google Scholar]
- 23.The role of inflammation in diabetic eye disease. Mesquida M, Drawnel F, Fauser S. Semin Immunopathol. 2019;41:427–445. doi: 10.1007/s00281-019-00750-7. [DOI] [PubMed] [Google Scholar]
- 24.Mechanism of inflammation in age-related macular degeneration. Parmeggiani F, Romano MR, Costagliola C, et al. Mediators Inflamm. 2012;2012:546786. doi: 10.1155/2012/546786. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 25.Association of microvascular dysfunction with late-life depression: a systematic review and meta-analysis. van Agtmaal MJ, Houben AJ, Pouwer F, Stehouwer CD, Schram MT. JAMA Psychiatry. 2017;74:729–739. doi: 10.1001/jamapsychiatry.2017.0984. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 26.Endothelial dysfunction in people with depressive disorders: a systematic review and meta-analysis. Waclawovsky AJ, de Brito E, Smith L, Vancampfort D, da Silva AM, Schuch FB. J Psychiatr Res. 2021;141:152–159. doi: 10.1016/j.jpsychires.2021.06.045. [DOI] [PubMed] [Google Scholar]
- 27.Immune mechanisms linked to depression via oxidative stress and neuroprogression. Bakunina N, Pariante CM, Zunszain PA. Immunology. 2015;144:365–373. doi: 10.1111/imm.12443. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 28.The validity of self-report of eye diseases in participants with vision loss in the National Eye Health Survey. Foreman J, Xie J, Keel S, van Wijngaarden P, Taylor HR, Dirani M. Sci Rep. 2017;7:8757. doi: 10.1038/s41598-017-09421-9. [DOI] [PMC free article] [PubMed] [Google Scholar]