Diabetic Retinopathy Risk, Severity Show Socioeconomic Links
Can someone share some details about socioeconomic effect on eye related complications of diabetes ?
@gayatri Key Takeaways
Low income, but not nonwhite race or Hispanic ethnicity, was significantly linked with increased odds for having known clinical factors for developing diabetic retinopathy (DR) — high A1c, high systolic blood pressure (BP), and high body mass index (BMI) — as well as greater odds of developing vision-threatening DR in a large study of patients with diabetes managed at a Michigan eye clinic.
Despite significant differences in risk factors for development of DR among people from varying racial and ethnic groups, the only sociodemographic factors associated with susceptibility to DR were two modifiable risk factors: income and the specific type of health insurance coverage that people had.
Why This Matters
The findings suggest that socioeconomic status rather than race or ethnicity is the primary driver of risk for DR, and that systemic societal barriers can have a meaningful impact on eye health and vision.
Addressing income inequality and barriers to routine eye care and annual eye examinations such as transportation hurdles and mistrust of the healthcare system among low-income patients with diabetes could help reduce disparities in eye health and progression to vision-threatening DR, a preventable cause of blindness.
The results also highlight the importance of further research on the interactions between sociodemographic factors and DR, and the need for earlier and more targeted interventions for patients in these higher-risk groups.
This was a retrospective review of 3470 patients with diabetes who made 11,437 visits to the WK Kellogg Eye Center in Michigan during July 2016 to June 2018. This Center operates as a collaboration between ophthalmologists and diabetologists at the University of Michigan in Ann Arbor.
The researchers obtained information about patient age, sex, race, ethnicity, and type of health insurance, as well A1c, BMI, and systolic BP levels. They also estimated mean household income based on where each subject lived.
Clinicians classified patients as having vision-threatening DR based on whether they had received a diagnosis of diabetic macular edema, proliferative DR, or both.
Patients averaged 62 years old; 47% were women; and 74% were white, 15% were Black, 90% were non-Hispanic, and 3% were Hispanic.
The researchers identified 27% of patients as having a low income, 49% with medium income, and 24% with high income.
The most common health insurance was Medicare (37%), followed by Blue Cross Blue Shield (24%), other commercial insurance (21%), Blue Care Network (17%), and Medicaid (less than 2%).
The results showed significant differences in both A1c and systolic BP among patients of different races and between patients in low- vs high-income households.
Specifically, Black patients had higher A1c levels and higher systolic BP levels compared to white patients. In contrast, people in the unknown/other/mixed race subgroup had lower BMI levels compared with white patients.
Despite differences in glycemic control and blood pressure levels between the Black and white patients, the results documented no significant difference in the odds of having vision-threatening DR among these two groups.
In multivariable analysis, the odds of having vision-threatening DR were significantly greater among low-income patients compared with middle-income patients (odds ratio [OR], 1.57), those with higher A1c (OR, 1.17), and those with higher systolic BP (OR, 1.01).
Low-income patients had higher BMI levels, higher systolic BP, and were more likely to have vision-threatening DR compared with high-income patients.
In multivariable analysis, only having Medicaid insurance was linked with an increased rate of vision-threatening DR (OR, 2.55; but this was of borderline significance, P = .07).
Having Medicare, Blue Cross/Blue Shield, or Blue Care Network health insurance was linked with reduced rates of developing vision-threatening DR, but none of the relationships were significant.
The distribution of Black and white patients in this southeast Michigan cohort resembled the general US pattern, but other communities may have distinct racial and ethnic makeups and so the findings may not be generalizable.
This was a retrospective study based on electronic health record data and used several surrogate markers, such as ZIP codes to estimate patient income.
Longitudinal analysis of the data is limited because 40% of patients only had one visit during the study period.