Results from this analysis suggest that sociodemographic factors impact risk factors for development and progression of DR. Significant differences were noted in both HgbA1c and sBP among patients of different races and between patients in low- versus high-income households. Specifically, Black patients had higher HgbA1c levels and higher sBP levels compared to White patients. In contrast, those in the unknown/other/mixed race cohort had lower BMI levels compared to the White patient cohort. Despite the difference in glycemic control and blood pressure levels between the Black and White patient cohorts, there was no significant difference in the odds of having vision threatening DR among the two groups.
Disparities in rates of DR, DME, and proliferative DR between different races have been well documented in the literature, with multiple studies reporting higher likelihood of disease development and severity in Black than White patients.3,12,14−16 Our data suggest that these discrepancies may be secondary to an upstream effect of race on systemic risk factors for DR rather than race on disease alone. This is further supported by previous reports of worse glycemic and blood pressure control in Black compared to White patients.12,14 Identifying the differences in risk factor management can therefore allow for timely, targeted intervention and risk factor reduction in this group of patients.
Of the relationships examined in our study between ethnicity and systemic risk factors, only sBP showed a significant association with ethnicity. While previous reports have reported rates of DR to be about twice as high in Hispanic compared to non-Hispanic populations, we did not observe this pattern in our results.17 Similarly, a link between Hispanic ethnicity and presence of VT-DR has been suggested by other studies after controlling for other risk factors, but our results did not convey such a relationship.12 These conflicting results are likely due to low numbers of Hispanic patients included in this study. As the sample population represents patients seeking care at a large Mid-Western academic retina practice, it is likely that its ethnic composition is not representative of the national population. Despite this, it is interesting to note that we still observed a statistically significant association between Hispanic ethnicity and elevated sBP, providing another possible point of intervention to prevent worsening DR in this population.
Our results also suggest that socioeconomic status not only impacts severity of risk factors for DR but severity of disease as well. Low-income patients had higher BMI levels, higher sBP, and were more likely to have VT-DR compared to high-income patients. In fact, it is interesting to note that of the primary sociodemographic factors investigated in our analysis, only income and payor, the two modifiable factors, were significantly associated with the presence of VT diabetic retinopathy. This implies that socioeconomic status is the primary driver of risk for DR and that many of the disparities noted among different races and ethnicities are in fact due to disparities in socioeconomic standing. Signorello and colleagues came to a similar conclusion in their study, which showed that though African American adults are 50%-100% more likely compared to White adults to have diabetes, those differences in prevalence are likely due to differences in established risk factors for disease, such as socioeconomic status, which vary among the two racial groups.18 These findings suggest that disparities in rates and outcomes of DR may be further reduced by addressing broader social issues, such as income inequality and affordability of health insurance, and that systemic societal barriers may have a deep, long-standing impact on eye health and vision.
Money is a well-known barrier to healthcare. A systematic review of 77 studies reported that low income and financial concerns were most often reported as limitations by patients.19 Our findings that low income is associated with higher BMI is supported by another study investigating the impact of a one- versus two-adult family structure on BMI in 7478 children.20 Their confounder-adjusted analysis controlling for highest educational attainment and ethnicity still found income was the most significant mediating factor in BMI outcomes, reinforcing the importance of financial concerns when considering disparities in disease outcomes. Several reports have investigated potential root causes for the differences noted in disease development and progression among varying sociodemographic cohorts. Access to healthcare and financial concerns have frequently been identified as primary barriers that disproportionately impact health outcomes among certain sociodemographic groups. One report showed that patients of low socioeconomic backgrounds as well as racial and ethnic minorities are less likely to receive routine eye care, most notably an annual eye exam.21 Authors identified various structural factors responsible for this disparity, such as limited transportation options, opportunity costs associated with patient employment, and unfavorable clinical experiences. In a focus group conducted by Elam et al, clinical experiences were also cited as major contributors to healthcare disparities, namely weak patient-provider relationships, mistrust in the healthcare system to address their needs, and lack of patient-centered communication, in addition to the high copays and distant proximity to clinics.22 Awareness of these barriers is thus crucial for optimizing continuity of care and health outcomes in these populations.
There are several limitations of this study. First, the data collected is from a population sample representative of southeast Michigan, and the ethnic mix of patients in this study is not necessarily representative of other communities. However, the racial distribution of patients in our study, most notably Black and White patients, closely parallels the racial demographics in the United States Census.23 Second, the retrospective design of the study limits analysis to data already available in the electronic health record, including HgbA1c values, and relies on surrogate markers such as median household income based on zip code to approximate patient income. Finally, longitudinal analyses of the data are limited by the 40% of patients who only had one visit during our study period. Despite this, the large sample size, a racial mix similar to that of the national population, and lack of other reports on the impacts of sociodemographic factors on risk factors for DR are important strengths of this study, which can serve as a basis for further investigation.