Social Risk Factor Associations With Presenting Visual Acuity in Patients With Microbial Keratitis

Patrice M Hicks; Leslie M Niziol; Paula Anne Newman-Casey; Kassim Salami; Karandeep Singh; Maria A Woodward

doi:10.1001/jamaophthalmol.2023.2415

. 2023 Jun 15;141(8):727–734. doi: 10.1001/jamaophthalmol.2023.2415

Social Risk Factor Associations With Presenting Visual Acuity in Patients With Microbial Keratitis

Patrice M Hicks ¹, Leslie M Niziol ¹, Paula Anne Newman-Casey ^1,², Kassim Salami ¹, Karandeep Singh ^2,^3,⁴, Maria A Woodward ^1,^2,^✉

¹Kellogg Eye Center, Department of Ophthalmology and Visual Sciences, University of Michigan, Ann Arbor

²Institute for Healthcare Policy and Innovation, University of Michigan, Ann Arbor

³Department of Learning Health Sciences, University of Michigan Medical School, Ann Arbor

⁴Department of Internal Medicine, University of Michigan Medical School, Ann Arbor

Accepted for Publication: April 23, 2023.

Published Online: June 15, 2023. doi:10.1001/jamaophthalmol.2023.2415

^✉

Corresponding Author: Maria A. Woodward, MD, MS, Kellogg Eye Center, Department of Ophthalmology and Visual Sciences, University of Michigan, 1000 Wall St, Ann Arbor, MI 48105 (mariawoo@med.umich.edu).

Author Contributions: Dr Woodward had full access to all of the data in the study and takes responsibility for the integrity of the data and the accuracy of the data analysis.

Concept and design: Hicks, Newman-Casey, Salami, Singh, Woodward.

Acquisition, analysis, or interpretation of data: Hicks, Niziol, Woodward.

Drafting of the manuscript: Hicks, Niziol.

Critical revision of the manuscript for important intellectual content: All authors.

Statistical analysis: Niziol.

Obtained funding: Hicks, Woodward.

Administrative, technical, or material support: Niziol, Singh, Woodward.

Supervision: Hicks, Newman-Casey, Woodward.

Conflict of Interest Disclosures: Dr Hicks reported receiving funding from the National Institutes of Health and the National Eye Institute during the conduct of the study. Dr Newman-Casey reported receiving grants from Research to Prevent Blindness, the National Institutes of Health, the National Institute of Biomedical Imaging and Bioengineering, and the US Centers for Disease Control and Prevention outside the submitted work. Dr Singh reported receiving grants from the National Institute of Diabetes and Digestive and Kidney Diseases, Blue Cross Blue Shield of Michigan, Teva Pharmaceuticals, the National Institutes of Health, and the National Eye Institute and consultant/advisory board fees from Flatiron Health Past outside the submitted work. Dr Woodward reported receiving grants from the National Eye Institute, Research to Prevent Blindness, and Michigan Institute for Clinical and Translational Research during the conduct of the study. No other disclosures were reported.

Funding/Support: This research was supported in part by grant R01EY031033, from the National Eye Institute (Drs Woodward and Singh); the Research to Prevent Blindness Career Advancement Award (Dr Woodward); grant UL1TR002240 from Michigan Institute for Clinical and Translational Research (Dr Woodward); a Research to Prevent Blindness Scientist Award (Dr Newman-Casey); the National Institutes of Health (National Eye Institute grant R01EY031337-01 [Dr Newman-Casey]); National Eye Institute grant R01EY031337-03S1 [Dr Hicks]; the National Institute of General Medical Sciences grant GM111725 [Dr Hicks]; the National Institute of Diabetes and Digestive and Kidney Diseases grant R01DK133226 [Dr Singh]); and grant R01EB032328 from the National Institute of Biomedical Imaging and Bioengineering (Dr Newman-Casey).

Role of the Funder/Sponsor: The funder had no role in the design and conduct of the study; collection, management, analysis, and interpretation of the data; preparation, review, or approval of the manuscript; and decision to submit the manuscript for publication.

Data Sharing Statement: See Supplement 2.

Additional Contributions: We thank Ms Susan Lane for her financial support of personnel involved in this research study. No financial compensation was received for contributions.

^✉

Corresponding author.

PMCID: PMC10273131 PMID: 37318786

This cross-sectional study investigates the association of neighborhood-level social risk factors with worse-presenting best-corrected visual acuity in patients with microbial keratitis infections.

Key Points

Question

Individual-level factors including age and race are known to be associated with severity of microbial keratitis (MK) infections, but are neighborhood-level social risk factors associated with worse-presenting best-corrected visual acuity (BCVA) in MK?

Findings

In this cross-sectional study including 2990 patients with MK, worse-presenting BCVA was associated with neighborhood-level risk factors including worse Area Deprivation Index, increased segregation (Theil H index), higher percentage of households with no car, and lower average number of cars per household.

Meaning

Neighborhood-level factors could be considered in other patient populations when assessing presenting VA for patients with MK.

Abstract

Importance

Neighborhood-level social risk factors may contribute to health disparities in microbial keratitis (MK) disease presentation. Understanding neighborhood-level factors may identify areas for revised health policies to address inequities that impact eye health.

Objective

To investigate if social risk factors were associated with presenting best-corrected visual acuity (BCVA) for patients with MK.

Design, Setting, and Participants

This was a cross-sectional study of patients with a diagnosis of MK. Patients presenting to the University of Michigan with a diagnosis of MK between August 1, 2012, and February 28, 2021, were included in the study. Patient data were obtained from the University of Michigan electronic health record.

Main Outcomes and Measures

Individual-level characteristics (age, self-reported sex, self-reported race and ethnicity), presenting log of the minimum angle of resolution (logMAR) BCVA, and neighborhood-level factors, including measures on deprivation, inequity, housing burden, and transportation at the census block group, were obtained. Univariate associations of presenting BCVA (< 20/40 vs ≥20/40) with individual-level characteristics were assessed with 2-sample t, Wilcoxon, and χ² tests. Logistic regression was used to test associations of neighborhood-level characteristics with the probability of presenting BCVA worse than 20/40 after adjustment for patient demographics.

Results

A total of 2990 patients with MK were identified and included in the study. Patients had a mean (SD) age of 48.6 (21.3) years, and 1723 were female (57.6%). Patients self-identified with the following race and ethnicity categories: 132 Asian (4.5%), 228 Black (7.8%), 99 Hispanic (3.5%), 2763 non-Hispanic (96.5%), 2463 White (84.4%), and 95 other (3.3%; included any race not previously listed). Presenting BCVA had a median (IQR) value of 0.40 (0.10-1.48) logMAR units (Snellen equivalent, 20/50 [20/25-20/600]), and 1508 of 2798 patients (53.9%) presented with BCVA worse than 20/40. Patients presenting with logMAR BCVA less than 20/40 were older than those who presented with 20/40 or higher (mean difference, 14.7 years; 95% CI, 13.3-16.1; P < .001). Furthermore, a larger percentage of male vs female sex patients presented with logMAR BCVA less than 20/40 (difference, 5.2%; 95% CI, 1.5-8.9; P = .04), as well as Black race (difference, 25.7%; 95% CI, 15.0%-36.5%;P < .001) and White race (difference, 22.6%; 95% CI, 13.9%-31.3%; P < .001) vs Asian race, and non-Hispanic vs Hispanic ethnicity (difference, 14.6%; 95% CI, 4.5%-24.8%; P = .04). After adjusting for age, self-reported sex, and self-reported race and ethnicity, worse Area Deprivation Index (odds ratio [OR], 1.30 per 10-unit increase; 95% CI, 1.25-1.35; P < .001), increased segregation (OR, 1.44 per 0.1-unit increase in Theil H index; 95% CI, 1.30-1.61; P < .001), higher percentage of households with no car (OR, 1.25 per 1 percentage point increase; 95% CI, 1.12-1.40; P = .001), and lower average number of cars per household (OR, 1.56 per 1 less car; 95% CI, 1.21-2.02; P = .003) were associated with increased odds of presenting BCVA worse than 20/40.

Conclusion and Relevance

Findings of this cross-sectional study suggest that in a sample of patients with MK, patient characteristics and where they live were associated with disease severity at presentation. These findings may inform future research on social risk factors and patients with MK.

Introduction

Social determinants of health (SDOH) are the conditions where people are born, play, work, learn, live, worship, and age that affect health.¹ Groups of individuals that systematically experience greater economic and social challenges have worse health outcomes.¹ The SDOH domains outlined in Healthy People 2030 include the following: education access and quality, economic stability, health care and quality, neighborhood and built environment, and social and community context.² SDOH are not negative or positive, and they affect society, whereas social risk factors (SRFs) explain how SDOH affect individuals. SRFs can be unfavorable social conditions associated with poor health³ and include high area deprivation,⁴ low income,^5,6 lower education levels,^5,6,7 and less access to health care.^5,8 Understanding the SRFs driving health inequities creates the impetus for policy change.

Evaluations of SRFs have focused on chronic eye conditions.^9,10,11 Chronic conditions require long-term care, and SRFs often inhibit consistent care. SRFs have been linked to worse outcomes and vision loss in the US^5,12 for glaucoma,¹⁰ diabetic retinopathy,¹⁰ age-related macular degeneration,¹³ cataracts,⁹ and uncorrected refractive error¹⁴ but have not been thoroughly explored for anterior segment diseases including microbial keratitis (MK). MK affects 1.5 to 2 million people globally every year causing acute, severe vision impairment and blindness.^{15,16,17,18,19} Patients need prompt treatment and frequent monitoring to mitigate the likelihood of vision loss.^19,20 A person’s SRFs may affect their ability to receive treatment given an MK diagnosis. In countries around the world, lower socioeconomic status is associated with worse health outcomes.²¹ MK treatments cannot always be obtained at local pharmacies and can be expensive.^{16,18,22,23,24,25,26} If a patient with MK has low socioeconomic status, they are at risk for not getting appropriate treatment due to the influence of SDOH on health equity through limited income and/or limited personal transportation. Further, if they reside in a neighborhood with less transportation options and limited specialized pharmacies, these neighborhood factors compound the problem. The effect of SRFs on acute conditions, such MK, has been limited.^27,28

The purpose of this study was to understand if neighborhood-level SRFs were associated with severity of MK at clinical presentation, as measured by visual acuity, after adjusting for individual-level sociodemographic factors. The goal was to identify SDOH targets for policy change to mitigate the downstream outcomes of eye health.

Methods

The University of Michigan institutional review board reviewed and exempted this study because of the use of electronic health record data. All patients with a diagnosis code for MK between August 1, 2012, and February 28, 2021, were identified through the Epic electronic health record (EHR) at the University of Michigan (Epic Systems). The EHR includes patients seen at the Kellogg Eye Center and satellite clinics. International Classification of Diseases, Ninth (ICD-9) and Tenth (ICD-10) Revision codes were used to identify those with MK (eTable in Supplement 1). Diagnoses were obtained from encounters that originated in the ophthalmology department at an in-person office visit. Laterality of MK was determined by ICD-10 codes when available. If ICD-10 codes were not sufficiently specific or if ICD-9 codes were used, then natural language processing of the note was applied to classify MK laterality, as has been previously described by our group.²⁹ For each patient, the first date of MK diagnosis identified in the EHR was designated as the date of MK presentation. Best-corrected visual acuity (BCVA) at presentation was extracted for the affected eye or the worse eye in patients with bilateral MK. We aimed to minimize missing data in the sample; therefore, we selected distance BCVA measures according to the following order: BCVA by manifest refraction, BCVA with the patient’s current eyeglasses, and uncorrected VA if no BCVA measures were taken. BCVA measured as count fingers, hand motions, light perception, and no light perception were approximated with a Snellen equivalent of 20/1000. Patient demographics (age, self-reported sex, and self-reported race and ethnicity) and address were also extracted from the EHR. Patients reported their primary race and ethnicity, which included the following categories: African American or Black, American Indian or Alaskan Native, Asian, Hispanic, Native Hawaiian or Other Pacific Islander, Non-Hispanic, White, and other (included any race not previously listed). This study followed the Strengthening the Reporting of Observational Studies in Epidemiology (STROBE) reporting guidelines.

The patients’ address information was obtained from DataDirect (a tool to query the EHR at University of Michigan) and mapped to the 12-digit Federal Information Processing Standards (FIPS) codes. The FIPS code contains census block group (BG) number, which was then used to map individuals to neighborhood-level measures. Neighborhood-level socioeconomic measures were obtained from the 2015 to 2019 American Community Survey (ACS) 5-year BG estimates via PolicyMap.^30,31 These estimates were used because most patient encounters (61%) occurred during this time frame. However, if the BG sample is too small to protect individuals’ privacy, estimates are not published. Thus, some neighborhood-level data were not available for some patients.

The neighborhood-level (BG) socioeconomic variables obtained included the Area Deprivation Index (ADI), which was extracted from the Neighborhood Atlas. The ADI is a measure of neighborhood socioeconomic disadvantage, which is a composite measure combining income, employment, housing quality, socioeconomic status, and education.³² ADI scores are a national percentile ranking that range from 1% to 100%, with higher percentiles indicating worse deprivation. The following additional neighborhood-level measures were obtained from PolicyMap using ACS data: median percentage renter cost burden (gross rent plus the estimated average monthly cost of utilities and fuels as a percentage of total household income),³³ percentage of renters burdened by cost (gross rents that are 30% or more of household income),³³ median percentage homeowner cost burden (homeownership cost, including the principal and interest payments, real estate taxes, property insurance, homeowner fees, condominium or cooperative organization fees, and utilities, as a percentage of the total household income),³³ percentage of homeowners burdened by cost (homeowners’ costs that are 30% or more of their household income),³³ percentage of households with no car,³³ and average number of cars per household.³³ These measures are aggregated from ACS survey respondents over a BG and were merged into our MK sample by patient address. Thus, although percentage measures are based on binary proportions at the BG, they are analyzed as continuous measures in the MK sample of patients in many different BGs. Lastly, the 2010 Theil H Index, also obtained from PolicyMap, is a measure of racial segregation ranging from 0 to 1 with larger values representing a more segregated neighborhood.³⁴ The Theil H index was calculated by PolicyMap using the methodology from the Multigroup Entropy Index.³⁵ The 2010 Theil H index is the newest metric reported by PolicyMap.

Statistical Methods

Individual-level and neighborhood-level characteristics of the MK sample were summarized with descriptive statistics including means, SDs, medians, and IQRs. Visual acuity was converted to logMAR for analyses.³⁶ Univariate associations with presenting BCVA (worse than [<20/40] vs better than or equal to [≥20/40]) were investigated with 2-sample t tests and Wilcoxon tests for continuous variables and χ² tests for categorical variables. Significant χ² tests were followed by post hoc pairwise comparisons with Holm multiple comparison adjustment, for categorical variables with more than 2 groups. Separate logistic regression models were used to test the association of each neighborhood-level characteristic with the probability of presenting BCVA worse than 20/40, after adjusting for individual-level factors (age, sex, race, and ethnicity). Model estimates are presented as odds ratios (ORs) with 95% CIs and displayed with forest plots. All tests performed were 2-sided, P values were adjusted for multiple comparisons using the Holm procedure, and a P value < .05 was considered statistically significant.³⁷ SAS software, version 9.4 (SAS Institute) was used for statistical analysis.

Results

A total of 2990 patients with MK were identified and included in the study. Sample sizes ranged from 2174 to 2990 depending on the demographic and social risk factor. Patients had a mean (SD) age of 48.6 (21.3) years when they presented with MK; 1723 patients were female (57.6%), and 1267 patients were male (42.4%). Patients self-identified with the following race and ethnicity categories: 132 Asian (4.5%), 228 Black (7.8%), 99 Hispanic (3.5%), 2763 non-Hispanic (96.5%), 2463 White (84.4%), and 95 other (3.3%) (Table 1). Most patients had unilateral infections (1406 of 2990 [47.0%] right eye only, 1489 of 2990 [49.8%] left eye only, 95 of 2990 [3.2%] both eyes). Of the 2990 patients with MK, 2798 (93.6%) had BCVA available at presentation. BCVA was based on manifest refraction in 309 of 2798 (11.0%), the patient’s current eyeglasses in 1441 of 2798 patients (51.5%), and uncorrected VA in 1048 of 2789 (37.5%). Presenting BCVA had a median (IQR) value of 0.40 (0.10-1.48) logMAR units (Snellen equivalent, 20/50 [20/25-20/600]), and 1508 of 2798 patients (53.9%) presented with BCVA worse than 20/40.

Table 1. Descriptive Statistics for the Microbial Keratitis Sample Including Patient Demographics, Clinical Characteristics, and Neighborhood-Level Social Risk Factors at the Census Block Group.

Variable	No.	Mean (SD)	Median (IQR)
Continuous variable^a
Age, y	2990	48.6 (21.3)	49.1 (30.6-65.1)
Presenting logMAR VA [Snellen equivalent]	2798	0.72 (0.73) [20/105 (7.3 lines)]	0.40 (0.10-1.48) [20/50 (20/25-20/600)]
ADI, national rank	2633	49.6 (25.6)	46 (29-70)
Theil H Index	2629	0.19 (0.09)	0.17 (0.13-0.23)
Median renter cost burden	2049	29.8 (10.4)	27.7 (22.7-35.4)
Renters cost burdened	2494	38.8 (25.1)	39.2 (21.8-54.4)
Median owner cost burden	2595	17.4 (3.6)	17.1 (15.3-18.6)
Owners cost burdened	2632	19.7 (10.5)	18.3 (13.4-24.5)
Households with no car	2673	5.7 (8.3)	2.8 (0.7-7.2)
Average No. cars per household	2174	1.8 (0.4)	1.9 (1.6-2.1)
Categorical variable
Sex^b
Female	1723/2990 (57.6)	NA	NA
Male	1267/2990 (42.4)	NA	NA
Primary race^b
Asian	132/2918 (4.5)	NA	NA
Black	228/2918 (7.8)
White	2463/2918 (84.4)
Other^c	95/2918 (3.3)
Ethnicity^a
Non-Hispanic	2763/2862 (96.5)	NA	NA
Hispanic	99/2862 (3.5)	NA	NA

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Abbreviations: ADI, Area Deprivation Index; NA, not applicable; VA, visual acuity.

^{^a}

No. represents the number in the sample that could be mapped to each social determinants of health variable studied.

^{^b}

Variable self-reported.

^{^c}

Other race includes any race not previously listed.

Residential address information could be mapped to a BG for 2747 of 2798 patients [98.2%] who had a BCVA for evaluation of neighborhood-level measures (eFigure in Supplement 1). Patients with MK lived in neighborhoods where the mean (SD) National ADI rank was 49.6 (25.6), with a median (IQR) rank of 46 (29-70). Median renter cost burden was a mean (SD) of 29.8% (10.4%) of income (median [IQR], 27.7% [22.7%-35.4%]). A mean (SD) of 38.8% (25.1%) renters (median [IQR], 39.2% [21.8%-54.4%]) were considered cost burdened such that 30% or more of their household income went to gross rent of their home. Median owner cost burden was a mean (SD) of 17.4% (3.6%) of income (median [IQR], 17.1% [15.3%-18.6%]). A mean (SD) of 19.7% (10.5%) of owners (median [IQR], 18.3% [13.4%-24.5%]) were cost burdened such that 30% or more of their household income went to their homeowner’s costs. Patients with MK also lived in neighborhoods where a mean (SD) of 5.7% (8.3%) of households reported that they did not own a car (median [IQR], 2.8% [0.7%-7.2%]), and the mean (SD) number of cars per household was 1.8 (0.4) with a median (IQR) of 1.9 (1.6-2.1) cars. The Theil H index had a mean (SD) score of 0.19 (0.09) with a median (IQR) score of 0.17 (0.13-0.23).

In univariate analysis, presenting BCVA (<20/40 vs ≥20/40) of patients with MK was associated with demographic characteristics (Table 2). Specifically, a larger percentage of male patients with MK presented with BCVA worse than 20/40 than female patients with MK (673 of 1183 [56.9%] vs 835 of 1615 [51.7%], respectively; difference = 5.2%; 95% CI, 1.5%-8.9%; Holm-adjusted P = .04). Additionally, White and Black patients showed higher percentages of presenting BCVA worse than 20/40 than Asian patients (White vs Asian: 1267 of 2312 [54.8%] vs 38 of 118 [32.2%]; difference = 22.6%; 95% CI, 13.9%-31.3%; Black vs Asian: 124 of 214 [57.9%] vs 38 of 118 [32.2%]; difference = 25.7%; 95% CI, 15.0%-36.5%; both Holm-adjusted P < .001). A larger percentage on non-Hispanic patients presented with BCVA worse than 20/40 than Hispanic patients (1407 of 2585 [54.4%] vs 37 of 93 [39.8%], respectively; difference = 14.6%; 95% CI, 4.5%-24.8%; Holm-adjusted P = .04). Lastly, those patients with presenting BCVA worse than 20/40 were older than those who presented with better BCVA (≥20/40; mean [SD] of 56.0 [20.8] years vs 41.3 [18.0] years, respectively; difference = 14.7; 95% CI, 13.3-16.1; Holm-adjusted P < .001).

Table 2. Univariate Tests of Association for Presenting Best-Corrected Visual Acuity (VA) With Patient Demographic Characteristics and Neighborhood-Level Social Risk Factors.

Variable	Baseline VA <20/40 (n = 1508)			Baseline VA ≥20/40 (n = 1290)			Difference, mean (95% CI)	P value^a	Holm P value
Variable	Nonmissing, No.	Mean (SD)	Median	Nonmissing, No.	Mean (SD)	Median	Difference, mean (95% CI)	P value^a	Holm P value
Continuous variables
Age, y	1508	56.0 (20.8)	57.5	1290	41.3 (18.0)	39.4	14.7 (13.3-16.1)	<.001	<.001
ADI, national rank	1406	54.2 (24.7)	54.0	1180	40.4 (25.6)	35.0	13.8 (11.9-15.8)	<.001	<.001
Theil Index	1343	0.20 (0.10)	0.19	1125	0.17 (0.08)	0.16	0.03 (0.02-0.04)	<.001	<.001
Median renter cost burden, %	1068	29.9 (10.1)	27.7	850	29.6 (10.6)	27.5	0.3 (−0.7 to 1.2)	.39	>.99
% Renters cost burdened	1279	38.8 (24.5)	39.1	1060	38.5 (25.7)	39.2	0.3 (−1.8 to 2.3)	.69	>.99
Median owner cost burden, %	1339	17.4 (4.0)	17.0	1099	17.4 (3.2)	17.3	0.0 (−0.2 to 0.2)	.79	>.99
% Owners cost burdened	1347	19.7 (10.1)	18.5	1125	19.5 (10.8)	18.1	0.2 (−0.6 to 1.0)	.62	>.99
% Households with no car	1357	6.1 (8.5)	3.2	1152	5.0 (7.6)	2.4	1.1 (0.5 to 1.7)	<.001	<.001
Average No. of cars per household, %	1131	1.83 (0.39)	1.90	906	1.88 (0.39)	1.90	−0.05 (−0.08 to −0.01)	.005	.04
Categorical variables	No./Non-missing total No. (row %) ^b			No./Non-missing total No. (row %) ^b			Difference,% (95% CI) ^c	P value ^d	Holm P value
Sex^e
Female	835/1615 (51.7)			780/1615 (48.3)			5.2 (1.5-8.9)	.007	.04
Male	673/1183 (56.9)			510/1183 (43.1)			5.2 (1.5-8.9)	.007	.04
Race^e
Asian	38/118 (32.2)			80/118 (67.8)			[Reference]	<.001^f	.001
Black	124/214 (57.9)			90/214 (42.1)			25.7 (15.0-36.5)
White	1267/2312 (54.8)			1045/2312 (45.2)			22.6 (13.9-31.3)
Other^g	44/90 (48.9)			46/90 (51.1)			16.7 (3.4-30.0)
Ethnicity^e
Hispanic	37/93 (39.8)			56/93 (60.2)			14.6 (4.5-24.8)	.005	.04
Non-Hispanic	1407/2585 (54.4)			1178/2585 (45.6)			14.6 (4.5-24.8)	.005	.04

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Abbreviations: ADI, Area Deprivation Index; VA, visual acuity.

^{^a}

2-Sample t test (age, ADI, Theil Index, median owner cost burden, % owner cost burden, average cars per household) or 2-sample Wilcoxon test (median renter cost burden, % renters cost burdened, % households with no car).

^{^b}

Percentages displayed are row percentages, not column percentages. For example, there are 1615 females, of which 835 females had vision that was less than 20/40, therefore 835 of 1615 = 51.7%. There are 780 that presented with best-corrected visual acuity greater than or equal to 20/40 of the 1615 females patients, therefore 780 of 1615 = 48.3%.

^{^c}

Difference in percentage of patients with baseline visual acuity less than 20/40.

^{^d}

χ² Test.

^{^e}

Variable self-reported.

^{^f}

Post hoc pairwise comparisons with Holm multiple comparison adjustment showed that White race was significantly different than Asian (Holm P < .001) and Black race was significantly different than Asian (Holm P < .001).

^{^g}

Other race includes any race not previously listed.

Presenting BCVA worse than 20/40 was also associated with neighborhood-level SDOH in univariate analyses (Table 2). Specifically, those patients presenting with BCVA worse than 20/40 vs 20/40 or better had worse neighborhood deprivation (mean [SD] ADI, 54.2 [24.7] vs 40.4 [25.6], respectively; difference = 13.8; 95% CI, 11.9-15.8; Holm-adjusted P < .001) and experienced more segregation (mean [SD] Theil index, 0.20 [0.10] vs 0.17 [0.08], respectively; difference = 0.03; 95% CI, 0.02-0.04; Holm-adjusted P < .001). Additionally, those patients presenting with worse BCVA lived in neighborhoods with higher percentages of households with no car (mean [SD] of 6.1% [8.5%] vs 5.0% [7.6%], respectively; difference = 1.1%; 95% CI, 0.5%-1.7%; Holm-adjusted P < .001) and with lower average number of cars per household (mean [SD], 1.83 [0.39] vs 1.88 [0.39], respectively; difference = −0.05; 95% CI, −0.08 to −0.01; Holm-adjusted P = .04).

After adjusting for age, sex, race, and ethnicity, neighborhood-level SDOH variables that remained independently associated with the probability of presenting logMAR BCVA worse than 20/40 were ADI, Theil H index, percentage of household with no car, and the average number of cars per household (Figure). Specifically, a 10-unit higher ADI score (worse deprivation) was associated with 30% increased odds of presenting logMAR BCVA worse than 20/40 (OR, 1.30; 95% CI, 1.25-1.35; Holm-adjusted P < .001). A 0.1-unit increase in the Theil H index (more segregation) was associated with 44% increased odds of presenting logMAR BCVA worse than 20/40 (OR, 1.44; 95% CI, 1.30-1.61; Holm-adjusted P < .001). A 10 percentage-point increase in the percentage of households with no cars was associated with 25% increased odds of presenting logMAR BCVA worse than 20/40 (OR, 1.25; 95% CI, 1.12-1.40; Holm-adjusted P = .001). A decrease of 1 car for the average number of cars per household was associated with 56% increased odds of presenting logMAR BCVA worse than 20/40 (OR,1.56; 95% CI, 1.21-2.02; Holm-adjusted P = .003).

Figure. — Logistics regression model odds ratio estimates for the association of neighborhood-level social risk factors with presenting logMAR visual acuity worse than 20/40. Model estimates are adjusted for patient age, self-reported sex, and self-reported race and ethnicity. Each variable is included in a separate model. ADI indicates Area Deprivation Index.

Discussion

This cross-sectional study of patients with MK found that neighborhood-level measures of SDOH were associated with presenting BCVA worse than 20/40. Patients from neighborhoods with worse area deprivation, larger percentages of households with no car, lower average number of cars per household, and more segregation were shown to have higher odds of presenting BCVA worse than 20/40, after adjusting for individual-level risk factors of age, sex, race, and ethnicity. Findings suggest that where a patient with MK lives was associated with VA at presentation. Expanding the research lens beyond individual demographics identified broader factors that were associated with poor outcomes for an acute eye condition.

The analysis confirms univariate individual-level characteristic associations with MK outcomes in patients.^{18,27,38,39,40} Specifically, patients with MK with presenting BCVA worse than 20/40 were older compared with those with better BCVA (≥20/40). Parmar and colleagues³⁹ found that a larger percentage of patients 65 years or older with MK presented with more severe ulcers compared with patients 17 to 64 years old (29.1% vs. 17.0%; P = .04), more centrally located ulcers (60.0% vs. 44.7%; P = .04), and with worse presenting BCVA less than 20/200 (65.2% vs. 48.4%; P = .003).In addition, we found an association with race. A smaller percentage of Asian patients with MK (32.2%) had presenting BCVA worse than 20/40 than White (54.8%) or Black (57.9%) patients (P < .001). A study by Lim and colleagues⁴⁰ found an association with race and contact-related MK in Singapore. Other race participants had a 7-times increased risk of MK in contact lens wearers compared with Chinese participants (OR, 7.02; 95% CI, 2.32-21.29). In that study, Chinese, Malay, Indian, and other categories (any race other than Chinese, Malay, and Indian) were included as races; therefore, different race categorizations are used for stratification depending on the clinical setting. Our group previously reported¹⁸ an association of non-White race (Asian, Black, and other races) with decreased count of diagnostic and therapeutic procedures for MK (difference in the logs of expected counts,−1.3; 95% CI, −2.60 to 0.05; P = 0.05) and decreased count of visits, emails, and phone calls for MK (difference in the logs of expected counts, −0.58; 95% CI, −1.1 to −0.04; P = .03) compared with White race in a sample of 69 patients with MK. Another study²⁸ found that race was not associated with final BCVA (measurement on the visit where epithelial defect was stated to be healed or if the patient was lost to follow-up, their final visit) in patients with MK. Our study also found a smaller percentage of Hispanic patients had presenting BCVA worse than 20/40 as compared with non-Hispanic patients (39.8% vs. 54.4%; P = .04). This finding was unexpected as Hispanic patients have a higher risk of several eye conditions, including diabetic retinopathy and glaucoma, compared with non-Hispanic White patients.^41,42 Further research is needed to explore if the patients in our study were immigrants or native-born US citizens because of the possible immigrant initial health advantages.^43,44 Even after adjusting for individual-level characteristics previously identified as risk factors for more severe MK, we found that neighborhood still was associated with the severity of BCVA at presentation.

Neighborhood-level factors identified in this study have been associated with eye health for chronic ophthalmic conditions. In a case series⁴⁵ of 18 patients with viral retinitis associated retinal detachment, those who lived in areas with high ADI had worse final BCVA (average logMAR of 2.6; Snellen equivalent, 20/8000) as compared with patients living in neighborhoods with low ADI (logMAR 0.7; Snellen equivalent, 20/100; P = .004); however, the groups had no difference in baseline VA although this may be limited by the small sample size. Using the Theil H index, our study sample was not highly segregated, but there was still an association of more neighborhood segregation with worse presenting BCVA. Although our group has previously reported on the vision implications of segregation through redlining,⁴⁶ the Theil H index had not been used, but other measures of residential segregation through the isolation index have been studied in association with health care service utilization.⁴⁷ Researchers analyzing national data for rural residents found that segregation was negatively associated with a usual source of health care.⁴⁷ If patients with MK do not use health care services, then they would be at risk for worse vision outcomes. Furthermore, the association with transportation and presenting BCVA requires additional studies. A study of over 290 000 older individuals found that individuals with no car access had a 14% less likelihood of going to an eye examination as compared with individuals with a car (relative risk, 0.86; 95% CI, 0.86-0.87).⁴⁸

The Maslow hierarchy of needs states that individuals must meet their basic needs (water, food, shelter, safety, and security) before they can attend to additional needs such as health problems.^{49,50,51,52,53} Thus, to ensure that individuals with eye disease seek care, modifiable factors beyond access to care must be addressed.⁵⁴ Research has demonstrated that SDOH account for 80% to 90% of a population’s modifiable contributors to healthy outcomes.⁵⁴ Frameworks to study and improve unmet social needs exist. The Outcomes from Addressing SDOH in Systems (OASIS) framework proposes that social needs should be evaluated in health care systems and research so that patients in need can be connected to specific resources.⁵⁵ Individuals with social needs may not seek health care; therefore, implementing a framework in settings such as schools or community centers should also be considered to enable them to have the capacity to seek health care. For patients with MK, the SRF of transportation access should be addressed in conjunction with social workers for patients to receive optimal health care.

Strengths and Limitations

The strengths of this study include its large sample, socioeconomically diverse neighborhoods, and neighborhood-level analysis adjusting for individual-level risk factors. Limitations exist, including the fact that patients with MK were diagnosed in 2012 to 2021, but only neighborhood-level data from 2015 to 2019 estimates were available; therefore, the data do not perfectly align. Second, the Theil H Index is different than the standard Theil Index, a measure of economic inequality, making comparisons with other studies more complex. Third, the ACS does not provide BG data if the sample is too small, which could lessen generalizability. Desirability bias and reporting bias are possible for ACS data; however, the ACS has been validated to examine census-level variables.⁵⁶ Fourth, primary race is reported which may limit the findings for multiracial patients. Fifth, the findings are limited to 1 eye care center. Future research should expand across populations. Sixth, the majority of our cohort participants identified with White race (84.4%) and non-Hispanic ethnicity (96.5%), limiting generalizability. Finally, this study did not provide final VA. We were, therefore, unable to explore how these factors may impact final outcomes. Future studies could examine the social risk factors investigated in this study controlling for MK treatment and duration.

Conclusions

In conclusion, findings of this cross-sectional study suggest that where an MK patient lives was associated with the severity of vision impairment at presentation after accounting for individual-level factors. There are many factors that impact a patient’s ability to obtain care. Future research is needed to examine why neighborhood-level measures affect the severity of presenting MK and explore potential policy and support changes. For example, researchers and clinicians could use the OASIS framework to implement and evaluate screening and referral interventions for SRFs. Neighborhood-level SRFs impact chronic and acute eye conditions. Addressing the underlying inequities and repairing neighborhood infrastructure may improve eye health outcomes.

Supplement 1.

eTable. International Classification of Disease (ICD) Codes Used to Identify Patients With Microbial Keratitis Within the Electronic Health Record

eFigure. Flow Chart That Displays the Study Cohort Selected for Analysis Including Selection Criteria and Neighborhood Social Determinants of Health Measures Available

Click here for additional data file.^{(504.9KB, pdf)}

Supplement 2.

Data Sharing Statement

Click here for additional data file.^{(14.4KB, pdf)}

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Associated Data

This section collects any data citations, data availability statements, or supplementary materials included in this article.

Supplementary Materials

Supplement 1.

eTable. International Classification of Disease (ICD) Codes Used to Identify Patients With Microbial Keratitis Within the Electronic Health Record

eFigure. Flow Chart That Displays the Study Cohort Selected for Analysis Including Selection Criteria and Neighborhood Social Determinants of Health Measures Available

Click here for additional data file.^{(504.9KB, pdf)}

Supplement 2.

Data Sharing Statement

Click here for additional data file.^{(14.4KB, pdf)}

[eoi230034r1] 1.Braveman P. What are health disparities and health equity? we need to be clear. Public Health Rep. 2014;129 suppl 2(suppl 2):5-8. doi: 10.1177/00333549141291S203 [DOI] [PMC free article] [PubMed] [Google Scholar]

[eoi230034r2] 2.US Department of Health and Human Services . Healthy people 2030—what are social determinants of health? Accessed September 14, 2022. https://health.gov/healthypeople/objectives-and-data/social-determinants-health

[eoi230034r3] 3.Alderwick H, Gottlieb LM. Meanings and misunderstandings: a social determinants of health lexicon for health care systems. Milbank Q. 2019;97(2):407-419. doi: 10.1111/1468-0009.12390 [DOI] [PMC free article] [PubMed] [Google Scholar]

[eoi230034r4] 4.Yip JL, Luben R, Hayat S, et al. Area deprivation, individual socioeconomic status and low vision in the EPIC-Norfolk Eye Study. J Epidemiol Community Health. 2014;68(3):204-210. doi: 10.1136/jech-2013-203265 [DOI] [PMC free article] [PubMed] [Google Scholar]

[eoi230034r5] 5.Williams AM, Sahel JA. Addressing social determinants of vision health. Ophthalmol Ther. 2022;11(4):1371-1382. doi: 10.1007/s40123-022-00531-w [DOI] [PMC free article] [PubMed] [Google Scholar]

[eoi230034r6] 6.Rho CR, Kim H, Kim MS, Kim EC. Income and education are independently associated with visual impairment: the Korean National Health and Nutrition Examination Survey 2010-2012. Semin Ophthalmol. 2019;34(3):131-136. doi: 10.1080/08820538.2019.1597133 [DOI] [PubMed] [Google Scholar]

[eoi230034r7] 7.Zhang X, Cotch MF, Ryskulova A, et al. Vision health disparities in the US by race/ethnicity, education, and economic status: findings from 2 nationally representative surveys. Am J Ophthalmol. 2012;154(6)(suppl):S53-62.e1. doi: 10.1016/j.ajo.2011.08.045 [DOI] [PMC free article] [PubMed] [Google Scholar]

[eoi230034r8] 8.Zaback T, Lam S, Randall J, Field T, Brinks MV. Access to eye care before and after vision loss: a qualitative study investigating eye care among persons who have become blind. Qual Rep. 2020;25(6):1473-1488. doi: 10.46743/2160-3715/2020.4180 [DOI] [PMC free article] [PubMed] [Google Scholar]

[eoi230034r9] 9.Moxon NR, Wang A, Margo CE, Greenberg PB, French DD. The association between complex cataract surgery and social determinants of health in Florida. Ophthalmic Epidemiol. 2022;29(3):279-285. doi: 10.1080/09286586.2021.1939888 [DOI] [PubMed] [Google Scholar]

[eoi230034r10] 10.Elam AR, Andrews C, Musch DC, Lee PP, Stein JD. Large disparities in receipt of glaucoma care between enrollees in Medicaid and those with commercial health insurance. Ophthalmology. 2017;124(10):1442-1448. doi: 10.1016/j.ophtha.2017.05.003 [DOI] [PMC free article] [PubMed] [Google Scholar]

[eoi230034r11] 11.De Moraes CG, Hark LA, Saaddine J. Screening and Interventions for Glaucoma and Eye Health Through Telemedicine (SIGHT) studies. J Glaucoma. 2021;30(5):369-370. doi: 10.1097/IJG.0000000000001782 [DOI] [PMC free article] [PubMed] [Google Scholar]

[eoi230034r12] 12.Lee TC, Saseendrakumar BR, Nayak M, et al. Social determinants of health data availability for patients with eye conditions. Ophthalmol Sci. 2022;2(2):100151. doi: 10.1016/j.xops.2022.100151 [DOI] [PMC free article] [PubMed] [Google Scholar]

[eoi230034r13] 13.More P, Almuhtaseb H, Smith D, Fraser S, Lotery AJ. Socioeconomic status and outcomes for patients with age-related macular degeneration. Eye (Lond). 2019;33(8):1224-1231. doi: 10.1038/s41433-019-0393-3 [DOI] [PMC free article] [PubMed] [Google Scholar]

[eoi230034r14] 14.Jeganathan VSE, Robin AL, Woodward MA. Refractive error in underserved adults: causes and potential solutions. Curr Opin Ophthalmol. 2017;28(4):299-304. doi: 10.1097/ICU.0000000000000376 [DOI] [PMC free article] [PubMed] [Google Scholar]

[eoi230034r15] 15.Ung L, Bispo PJM, Shanbhag SS, Gilmore MS, Chodosh J. The persistent dilemma of microbial keratitis: global burden, diagnosis, and antimicrobial resistance. Surv Ophthalmol. 2019;64(3):255-271. doi: 10.1016/j.survophthal.2018.12.003 [DOI] [PMC free article] [PubMed] [Google Scholar]

[eoi230034r16] 16.Ballouz D, Maganti N, Tuohy M, Errickson J, Woodward MA. Medication burden for patients with bacterial keratitis. Cornea. 2019;38(8):933-937. doi: 10.1097/ICO.0000000000001942 [DOI] [PMC free article] [PubMed] [Google Scholar]

[eoi230034r17] 17.Whitcher JP, Srinivasan M, Upadhyay MP. Corneal blindness: a global perspective. Bull World Health Organ. 2001;79(3):214-221. [PMC free article] [PubMed] [Google Scholar]

[eoi230034r18] 18.Ashfaq H, Maganti N, Ballouz D, Feng Y, Woodward MA. Procedures, visits, and procedure costs in the management of microbial keratitis. Cornea. 2021;40(4):472-476. doi: 10.1097/ICO.0000000000002534 [DOI] [PMC free article] [PubMed] [Google Scholar]

[eoi230034r19] 19.Byrd LB, Martin N. Corneal ulcer. In: StatPearls. StatPearls Publishing; 2023. [PubMed] [Google Scholar]

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PERMALINK

Social Risk Factor Associations With Presenting Visual Acuity in Patients With Microbial Keratitis

Patrice M Hicks, PhD, MPH

Leslie M Niziol, MS

Paula Anne Newman-Casey, MD, MS

Kassim Salami, BS

Karandeep Singh, MD, MMSc

Maria A Woodward, MD, MSc

Key Points

Question

Findings

Meaning

Abstract

Importance

Objective

Design, Setting, and Participants

Main Outcomes and Measures

Results

Conclusion and Relevance

Introduction

Methods

Statistical Methods

Results

Table 1. Descriptive Statistics for the Microbial Keratitis Sample Including Patient Demographics, Clinical Characteristics, and Neighborhood-Level Social Risk Factors at the Census Block Group.

Table 2. Univariate Tests of Association for Presenting Best-Corrected Visual Acuity (VA) With Patient Demographic Characteristics and Neighborhood-Level Social Risk Factors.

Figure. Logistics Regression Model Odds Ratio Estimates for the Association of Neighborhood-Level Social Risk Factors With Presenting LogMAR Visual Acuity Worse Than 20/40.

Discussion

Strengths and Limitations

Conclusions

References

Associated Data

Supplementary Materials

ACTIONS

PERMALINK

RESOURCES

Cite

Add to Collections

PERMALINK

Social Risk Factor Associations With Presenting Visual Acuity in Patients With Microbial Keratitis

Patrice M Hicks, PhD, MPH

Leslie M Niziol, MS

Paula Anne Newman-Casey, MD, MS

Kassim Salami, BS

Karandeep Singh, MD, MMSc

Maria A Woodward, MD, MSc

Key Points

Question

Findings

Meaning

Abstract

Importance

Objective

Design, Setting, and Participants

Main Outcomes and Measures

Results

Conclusion and Relevance

Introduction

Methods

Statistical Methods

Results

Table 1. Descriptive Statistics for the Microbial Keratitis Sample Including Patient Demographics, Clinical Characteristics, and Neighborhood-Level Social Risk Factors at the Census Block Group.

Table 2. Univariate Tests of Association for Presenting Best-Corrected Visual Acuity (VA) With Patient Demographic Characteristics and Neighborhood-Level Social Risk Factors.

Figure. Logistics Regression Model Odds Ratio Estimates for the Association of Neighborhood-Level Social Risk Factors With Presenting LogMAR Visual Acuity Worse Than 20/40.

Discussion

Strengths and Limitations

Conclusions

References

Associated Data

Supplementary Materials

ACTIONS

PERMALINK

RESOURCES

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