Ophthalmic patients with depression faced $5894.86 in annual incremental economic expenditures because of depression, resulting in an additional $22.4 billion annually when extrapolating nationally.
Ophthalmic patients with depression also had higher expenditures for all health care service sections except inpatient and emergency room care, when adjusted for sociodemographics and comorbidities.
For ophthalmic patients with depression, depression was responsible for 6.9% of admissions (the second-leading cause).
Patients with dry eye syndrome, blindness, and retinopathy contribute to the high prevalence of depression for ophthalmic patients.
We sought to analyze the incremental economic burden of depression on adults with concurrent ophthalmic conditions in the United States.
Retrospective cross-sectional study.
Using the Medical Expenditure Panel Survey from 2016 to 2018, ophthalmic patients with ≥1 outpatient visit were identified by International Classification of Diseases, 10th revision, Clinical Modification codes and stratified based on the presence of concurrent depression. A multivariate 2-part regression model was used to determine incremental economic burden, health care sector utilization, and expenditures.
Of 7279 ophthalmic patients, 1123 (15.43%) were diagnosed with depression (mean expenditures $17,017.25 ± $2019.13) and 6156 patients (84.57%) without depression (mean expenditures $9924.50 ± $692.94). Patients with depression were more likely to be female, white, lower income, use Medicare/Medicaid, and to have comorbidities ( P < .001). These patients faced $5894.86 (95% confidence interval $4222.33-$7348.36, P < .001) in incremental economic expenditures because of depression, resulting in an additional $22.4 billion annually when extrapolating nationally. These patients had higher utilization for all health care service sectors ( P < .025 for all) and higher expenditures for outpatient ( P = .022) and prescription medications ( P = .029) when adjusted for sociodemographic variables and comorbidities. Depression was responsible for 6.9% of inpatient admissions (the second-leading cause) for this cohort of patients.
Ophthalmic patients with depression had a higher incremental economic burden and health care service sector utilization and expenditures. Patients with ophthalmic pathologies, including dry eye syndrome, blindness, and retinopathies, were more likely to be depressed. As psychiatric and ophthalmic conditions may have a bidirectional relationship, exacerbating disease severity and financial burden for patients with both, ophthalmologists may need to be more cognizant of the burden of depression among patients.
The economic burden of ophthalmic pathologies is currently estimated to be $140 billion and their prevalence is estimated to double between 2010 and 2050. , Vision loss and psychiatric illness can be a vicious cycle because vision loss can exacerbate depressive symptoms and depression can further aggravate disability from ophthalmic pathologies in a bidirectional relationship. The prevalence of depression in visually impaired elderly adults in the United States is extensive, estimated to be nearly 37%. Ophthalmic patients with depression may also be less likely to pursue care related to their ocular problems and have worse vision outcomes. Adequate screening and treatment of depression in these patients can reduce overall health care costs for patients. This presents a major avenue for improving quality of life for ophthalmic patients.
It can be revealing to examine the current state of health care expenditures and utilization for ophthalmic patients with depression. Previous studies analyzing depression in patients with ophthalmic conditions focused either on the elderly population or populations with specific disease processes, such as glaucoma, age-related macular degeneration, or dry eye diseases. Furthermore, these studies did not focus on the economic burden of depression in ophthalmic patients on the health care system, and additional evidence is needed regarding the incremental economic burden faced by these patients.
The present study uses a nationally representative data sample to investigate the economic burden of depression on adult patients with ophthalmic conditions in the United States. Analysis is performed in aggregate and by health care service sector (inpatient, outpatient, emergency room, home health, and prescription medication expenditures) to explore the cost drivers contributing to the economic burden in these patients.
Data Source and Study Population
The data compiled for this study used the Medical Expenditure Panel Survey (MEPS) database. The MEPS is federally funded by the Agency for Healthcare Research and Quality and the National Center for Health Statistics. MEPS is a nationally representative survey of the noninstitutionalized American civilian population, compiling national surveys of families and individuals, their medical providers, and employers across the United States. MEPS collects patient data, such as medical expenses, demographic characteristics, health conditions, and access to care, using a series of 5 questionnaires completed over a span of 2 years. Further information regarding MEPS designs and methods can be found herein.
This study is a retrospective analysis of 3 years of data from the MEPS database (2016-2018), which was used to assess health care system expenditures associated with patients with underlying ophthalmic conditions. To analyze the expenditures specifically related to medical diagnoses and corresponding demographic differences, the MEPS database household component and medical condition files were linked. Expenditures are recorded as part of the billing data shared by the providers to the MEPS survey and accounts for costs to the health care system including the insurance component of the costs. Patients with ophthalmic conditions with ≥1 outpatient visit were identified with complete International Classification of Diseases, 10th revision, Clinical Modification (ICD-10-CM) codes as part of this database. ICD-10-CM codes are recorded in this database primarily from billing data recorded by providers and supplemented by telephone surveys where patients can self-identify diagnoses. The medical condition file is updated with diagnoses and carries over for every subsequent year if a patient is diagnosed with any conditions (eg, depression diagnosed in 2013 will be stored in the 2016 condition file). MEPS clinical classification codes, which correlate with ICD-10-CM codes, considered as ophthalmic conditions included hordeolum and chalazion, other disorders of eyelid, disorders of lacrimal system, conjunctivitis, other disorders of cornea, retinal detachment and breaks, other retinal disorders, glaucoma, disorders of vitreous body, disorders of globe, other disorders of optic nerve and visual pathways, disorders of refraction and accommodation, visual disturbances, blindness and low vision, and other disorders of eye and adnexa. Patients with ophthalmic conditions without outpatient visits were excluded to better include patients receiving care for chronic ophthalmic conditions. Ophthalmic patients were further stratified by the presence of depression (ICD-10-CM code F32.x) as a medical diagnosis. This methodology was used to stratify patients with depression in previous studies based upon further communication with Agency for Healthcare Research and Quality personnel. , As this information is publicly available, a waiver of approval was obtained from the institutional review board. Data collection was in conformity with all federal and state laws.
Primary outcome measures used for this study were health care utilization and expenditures. All expenditures were adjusted to 2018 U.S. dollars using the urban Consumer Price Index of the U.S. Bureau of Labor Statistics. The study accounted for the complex survey design (sampling weights, clustering, and stratification) to more accurately estimate aggregate health care expenditures. For each patient, total health care system expenditures and number of visits both in aggregate and according to the health care service sectors, including inpatient, outpatient, emergency room, home health visits, prescription medications, and all payments by third-party payers and out-of-pocket costs, were recorded. Per capita expenditures and health care utilization were assessed for patients with ophthalmic conditions with and without the presence of depression and secondarily stratified based on the presence of comorbidities and certain sociodemographic characteristics.
Sociodemographic information included self-identified race/ethnicity, age, educational attainment, income, insurance status, and geographic location of the patient. Self-identified race/ethnicity were categorized as non-Hispanic white, Hispanic, Black, Native American, or Asian. Educational attainment was categorized as individuals with no degree obtained, high school diploma or equivalent, or a college degree. Income was calculated in relation to the 2018 federal poverty level (FPL). Individuals were categorized as poor (<100% of FPL), near poor (100%-124% of FPL), low income (125%-199% of FPL), middle income (200%-399% of FPL), or high income (>399% of FPL). Insurance status was categorized as private (includes patients with concurrent Medicare coverage), public (Medicare or Medicaid patients with no private insurance), or uninsured. For regional analysis, patients were categorized as living in the Northeast, South, Midwest, or West based on census regions.
Comorbidities were scored based on a modified comorbidity index based heavily on the Charlson Comorbidity Index. The Charlson Comorbidity Index is calculated as a sum of various comorbidities of an individual, such as congestive heart failure, myocardial infarction, and diabetes, because these comorbidities independently contribute to costs and health outcomes and defined by ICD-10-CM diagnosis codes. Additional comorbidities were included in this index based on the most common causes for inpatient admissions for these patients, such as pregnancy, hypertension, and pneumonia that is not included in the Charlson Comorbidity Index (Supplemental Table 1; Supplemental Material available at AJO.com). The total number of inpatient admissions along with pathologies that were directly associated with the most (defined by ICD-10-CM diagnosis codes) admissions (but not necessarily the primary reason for admission) were recorded for ophthalmic patients and for ophthalmic patients with and without depression. In addition, the frequency of depression for each aforementioned ophthalmic condition (as defined by the first 3 characters of the ICD-10 code) were recorded.
Rao–Scott χ 2 tests were used to compare sociodemographic characteristics and comorbidity indices of ophthalmic patients that had outpatient visits with and without depression. Incremental burden, otherwise known as the individual economic burden faced caused by patients on the health care system solely because of their diagnosis of depression and not confounded by other variables, such as age, race, income, and our modified Comorbidity Index, was calculated using a 2-part model consisting of logistic regression and gamma distribution with log-link function to address health care expenditure data. , Health care expenditure data contain a high concentration of zero expenditures and positive skewing of expenditures. Therefore, we used a 2-part model rather than log ordinary least squares models because of reduced normality and homoscedasticity assumptions. , Logistic regression modeling was used to estimate the health care expenditure ratio for ophthalmic patients with and without depression over the pooled period (2016-2018), adjusting for sociodemographic characteristics (age, sex, race/ethnicity, insurance coverage, education, and geographic region), modified comorbidity indices, and year of care received.
The expenditure ratio was then used to estimate the direct incremental economic burden of depression for ophthalmic patients in the United States, using population-level weights assigned to each patient by MEPS. Mean expenditures for these patients were extrapolated to the civilian noninstitutionalized U.S. population. Odds and expenditure ratios were generated to compare health care service sector (inpatient, outpatient, emergency room, home health, and prescription medications) utilization and expenditures respectively between ophthalmic patients with and without depression, adjusted for comorbidities and sociodemographic characteristics. The odds ratio compares utilization between the 2 groups. An adjusted odds ratio of 3 for utilization for inpatients with depression indicates 3 times the inpatient admissions for this cohort. The expenditure ratio is an odds ratio that is used specifically to compare expenditures.
P < .05 was considered statistically significant and all statistical analyses were performed using R software (R Foundation, Vienna, Austria).
The survey sample included a total of 7279 patients >18 years of age with an ophthalmic condition and ≥1 outpatient visit (mean expenditure $10,986.00 ± $678.20; Table 1 ). Of these patients, 1123 (15.43%) had a diagnosis of depression (mean expenditure $17,017.25 ± $2019.13). There were 6156 patients (84.57%) without depression (mean expenditure $9924.50 ± $692.94). There were 48,738 patients without visual pathologies in the cohort (mean expenditure $7549.00 ± $235.47). Of these patients, 5722 (11.74%) had a diagnosis of depression (mean expenditure $11,696.22 ± $761.57). Overall, ophthalmic patients with ≥1 outpatient visit had a mean age of 58.7 ± 18.1 years, and when stratified by the presence of depression there was no difference in age between the 2 cohorts ( Table 2 ). Ophthalmic patients with depression were more likely to be female (72% vs 60%), white (65% vs 56%), reside in the Midwest (28% vs 23%), poor (<100% of FPL; 22% vs 13%), use public insurance (45% vs 32%), less likely to have a college degree (42% vs 49%), and have a higher modified comorbidity index (1.87 ± 2.00 vs 1.28 ± 1.60; P < .001 for all comparisons).
|n (%)||Mean Expenditures ($) (95% CI)|
|Patients with ophthalmic conditions||7279 (100)||10,986.00 (10,307.80-11,664.20)|
|With depression||1123 (15.43)||17,017.25 (14,998.12-19,036.37)|
|Without depression||6156 (84.57)||9924.50 (9231.57-10,617.44)|
|Patients without ophthalmic conditions||48,738 (100)||7549.00 (7313.53-7784.47)|
|With depression||5722 (11.74)||11,696.22 (10,934.65-12,457.79)|
|Without depression||43,016 (88.26)||6998.77 (6761.89-7235.65)|
a Within each cohort, patients were further stratified based on whether they had a diagnosis of depression. Prevalence of these conditions in patients with ophthalmic conditions with ≥1 outpatient visit indicated as percent. Patients were considered to have ophthalmic conditions if they had ≥1 outpatient visit.
|Characteristic||Depression, n = 1123||Without Depression, n = 6156||P Value a|
|Sex, n (%)||<.001|
|Female||813 (72)||3696 (60)|
|Male||310 (28)||2460 (40)|
|Age (y), mean ± SD||58 ± 17||59 ± 18||.093|
|Race, n (%)||<.001|
|White||725 (65)||3432 (56)|
|Hispanic||219 (20)||1143 (19)|
|Black||117 (10)||948 (15)|
|Native American||36 (3.2)||146 (2.4)|
|Asian||26 (2.3)||487 (7.9)|
|Geographic region, n (%)||<.001|
|Northeast||260 (23)||1248 (20)|
|Midwest||313 (28)||1422 (23)|
|South||282 (25)||2039 (33)|
|West||262 (23)||1414 (23)|
|Income level, n (%)||<.001|
|Poor (<100% of FPL)||246 (22)||794 (13)|
|Near poor (100%–124% of FPL)||68 (6.1)||279 (4.5)|
|Low income (125%–199% of FPL)||164 (15)||764 (12)|
|Middle income (200%–399% of FPL)||304 (27)||1691 (27)|
|High income (>399% of FPL)||341 (30)||2628 (43)|
|Insurance coverage, n (%)||<.001|
|Private||591 (53)||3949 (64)|
|Public||505 (45)||1996 (32)|
|Uninsured||27 (2.4)||211 (3.4)|
|Educational attainment, n (%)||<.001|
|No degree||180 (20)||866 (17)|
|High school degree||336 (38)||1650 (33)|
|College degree||372 (42)||2438 (49)|
|Year, n (%)||<.001|
|2016||502 (45)||2317 (38)|
|2017||408 (36)||2152 (35)|
|2018||213 (19)||1687 (27)|
|Modified comorbidity index, mean ± SD||1.87 ± 2.00||1.28 ± 1.60||<.001|
a Statistical tests included the χ 2 test of independence and the t test. The P value indicates if the distribution of characteristics is statistically significantly different between the 2 groups.