Association Between County-Level Characteristics and Eye Care Use by US Adults in 22 States After Accounting for Individual-Level Characteristics Using a Conceptual Framework

Key Points

Question  What is the association between county-level characteristics in the United States and eye care use in adults 40 years and older in 22 states surveyed between 2006 and 2010?

Findings  In this cross-sectional analysis, the odds of reporting an eye care visit in the past year or receipt of a dilated eye examination in the past year was higher among people living in counties with high percentages of black individuals or low-income households than among those living in counties with the lowest tertile of each county-level characteristic.

Meaning  These data suggest that eye care use in the United States is associated with county-level characteristics independent of individual-level characteristics.

Importance  Individual-level characteristics are associated with eye care use. The influence of contextual factors on vision and eye health, as well as health behavior, is unknown.

Objective  To examine the association between county-level characteristics and eye care use after accounting for individual-level characteristics using a conceptual framework.

Design, Setting, and Participants  This investigation was a cross-sectional study of respondents 40 years and older participating in the Behavioral Risk Factor Surveillance System surveys between 2006 and 2010 from 22 states that used the Visual Impairment and Access to Eye Care module. Multilevel regressions were used to examine the association between county-level characteristics and eye care use after adjusting for individual-level characteristics (age, sex, race/ethnicity, educational attainment, annual household income, employment status, health care insurance coverage, eye care insurance coverage, personal established physician, poor vision or eye health, and diabetes status). Data analysis was performed from March 23, 2014, to June 7, 2016.

Main Outcomes and Measures  Eye care visit and receipt of a dilated eye examination in the past year.

Results  Among 117 295 respondents who resided in 828 counties, individual-level data were obtained from the Behavioral Risk Factor Surveillance System surveys. All county-level variables were aggregated at the county level from the Behavioral Risk Factor Surveillance System surveys except for a high geographic density of eye care professionals, which was obtained from the 2010 Area Health Resource File. After controlling for individual-level characteristics, the odds of reporting an eye care visit in the past year were significantly higher among people living in counties with high percentages of black individuals (adjusted odds ratio [aOR], 1.12; 95% CI, 1.01-1.24; P = .04) or low-income households (aOR, 1.12; 95% CI, 1.00-1.25; P = .045) or with a high density of eye care professionals (aOR, 1.18; 95% CI, 1.07-1.29; P < .001) than among those living in counties with the lowest tertile of each county-level characteristic. The odds of reporting receipt of a dilated eye examination in the past year were also higher among people living in counties with the highest percentages of black individuals (aOR, 1.20; 95% CI, 1.07-1.34; P = .002) or low-income households (aOR, 1.17; 95% CI, 1.04-1.32; P = .01). However, the odds of reported receipt of a dilated eye examination in the past year were lower in counties with the highest percentages of people with poor vision and eye health compared with counties with lower percentages (aOR, 0.85; 95% CI, 0.77-0.94; P = .002).

Conclusions and Relevance  Contextual factors, measured at the county level, were associated with eye care use independent of individual-level characteristics. The findings suggest that, while individual characteristics influence health care use, it is also important to address contextual factors to improve eye care use and ultimately vision health.

Vision impairment (VI) has been recognized as a significant public health concern,¹ ranks among the top 10 disabilities, and is increasing in the US adult population.^2-5 It is associated with diminished quality of life, physical function limitations, poor mental health, and loss of productivity.^6-11 The estimated total annual cost of VI, including blindness, among individuals 40 years and older in the United States between 1996 and 2004 was $5.5 billion.^12,13 Type 2 diabetes increases the risk of VI,⁴ with diabetic retinopathy as the leading cause of new cases of VI and blindness among working age groups.^14-16 Family history of eye disease and older age are also important risk factors for vision loss. Routine eye examinations among symptomatic and high-risk adults can identify vision problems early and thereby prevent worsening vision.¹⁷ The American Academy of Ophthalmology¹⁸ recommends preventive eye checkups with pupil dilation every 2 to 4 years for persons aged 40 to 64 years old and every 1 to 2 years for persons 65 years and older. However, recommended eye care among people in need is suboptimal.^19,20

Low socioeconomic status is associated with lack of eye care use.^21,22 The health literature increasingly emphasizes the influence of contextual factors on health and health care behaviors. Studies^23,24 have shown that area of residence and physician density were associated with receipt of an annual dilated eye examination. To better understand these factors, a 2008 report called for studies on the association between contextual factors and use of eye care services.²⁵

According to the behavioral model of health service use by Andersen and Davidson,²⁶ there are 3 dimensions of individual or contextual factors that describe the potential to use the health care delivery system, namely, predisposing factors, enabling factors, and need factors. Individual predisposing factors include the demographic characteristics of age and sex; social factors, such as education, occupation, and race/ethnicity; and mental factors regarding health beliefs, such as values and knowledge related to health and health services. Contextual predisposing factors include the demographic and social composition of communities, collective and organizational values, cultural norms, and political perspectives. Individual enabling factors include income, wealth, health care insurance status, continuity and regular source of care, and travel time to and waiting time for health care. Contextual enabling factors include per capita community income and affluence, the prevalence of health care insurance coverage, the relative price of goods and services, methods of compensating health care professionals, and health care expenditures, locations, and structures, as well as distribution of health services facilities and personnel, such as physician and hospital density, office hours, clinician mix, and health policies. Individual need factors include health status, the need for medical care, functional state, and illness symptoms. Contextual need factors include occupational and traffic-related and crime-related injury and death rates, mortality, morbidity, and disability. Previous studies have focused mostly on individual-level associations. Therefore, we used this conceptual framework to examine whether contextual county-level characteristics influence eye care use after accounting for individual-level characteristics.

The Behavioral Risk Factor Surveillance System (BRFSS) protocol was approved by a human participants review board, and written informed consent was obtained from all participants. We used data from the BRFSS surveys between 2006 and 2010 and the 2010 Area Health Resource File (AHRF). The BRFSS and AHRF data sets have been described elsewhere.^24,27,28 The county of residence of the BRFSS respondents can be identified by the county Federal Information Processing Standards code. We used the Federal Information Processing Standards code to link the BRFSS and AHRF data sets. We analyzed the BRFSS data from the 22 states that used the Visual Impairment and Access to Eye Care module at least once between 2006 and 2010. Our sample included 117 295 respondents 40 years and older who resided in 828 counties. All individual-level data were obtained from the BRFSS. All county-level variables were aggregated at the county level from the BRFSS except for geographic density of eye care professionals, which was obtained from the AHRF.

The outcome of interest was individual-level self-reported eye care use, measured by the following indicators: (1) receipt of a dilated eye examination in the past year and (2) having 1 or more eye care visits in the past year. Respondents were classified as having had a dilated eye examination in the past year if they answered “within the past month” or “within the past year” to the following: “When was the last time you had an eye exam in which the pupils were dilated? This would have made you temporarily sensitive to bright light.” Respondents were classified as having had an eye care visit in the past year if they answered “within the past month” or “within the past year” to the following question” “When was the last time you had your eyes examined by any doctor or eye care provider?”

Individual-level predisposing factors were age group (40-49, 50-64, or ≥65 years), sex, race/ethnicity (non-Hispanic white [white], non-Hispanic black [black], Hispanic, or other), and educational attainment (less than high school, high school or general equivalency diploma, or more than high school). Enabling factors were annual household income (<$15 000, $15 000-$24 999, $25 000-$34 999, $35 000-$49 999, or ≥$50 000), employment status (employed, retired, or unemployed), health care insurance coverage (yes or no), eye care insurance coverage (yes or no), and personal established physician (yes or no). Need factors were poor vision or eye health (yes or no) and diabetes status (yes or no). Vision or eye health status was measured by self-reports of VI or any eye disease (cataract, glaucoma, age-related macular degeneration, or diabetic retinopathy). Vision impairment was defined based on responses to the following 2 questions about visual function: “How much difficulty, if any, do you have in recognizing a friend across the street?” and “How much difficulty, if any, do you have reading print in newspapers, magazines, recipes, menus, or numbers on the telephone?” We classified those who answered “moderate difficulty,” “extreme difficulty,” “unable to do because of eyesight,” or “blind” to either of the questions as having VI, while those responding “no difficulty” or “little difficulty” were classified as not having VI. Any eye diseases were confirmed by the respondents, who indicated that they “had been told by an eye doctor or other health care professional” that they had cataract, glaucoma, age-related macular degeneration, or diabetic retinopathy.

County-level predisposing factors were median age, percentage of the population that was of black race/ethnicity, and percentage of the population with less than a high school education. County-level enabling factors were percentage of the population with an annual household income of less than $35 000, percentage of the population who are unemployed, percentage of the population without health care insurance coverage, percentage of the population without eye care insurance coverage, and geographic density of eye care professionals (number per 10 000 population). County-level need factors were percentage of the population with poor vision or eye health and county-specific diabetes prevalence.²⁹ These county-level variables were categorized into tertiles (low, middle, or high).

Before beginning the analyses, which were performed from March 23, 2014, to June 7, 2016, we used the variance inflation factor³⁰ to identify possible multicollinearity by examining correlations between all variables of interest. No high collinearity was found (<10 for all). We assessed frequency distributions of individual factors and contextual factors according to each indicator of eye care use. We used the delta method based on Taylor series expansions³¹ to estimate the variance of functions of random variables and calculate 95% CIs for the prevalence of eye care use by tertile of county characteristics. To examine whether contextual factors are associated with eye care use independent of individual-level characteristics, we used multilevel regression analyses. First, to assess associations, we developed a series of models to estimate the odds of reporting eye care use by a resident of a county with each county-level characteristic of interest. Then, to control for individual-level characteristics, we entered each group of individual-level predisposing, enabling, and need variables into the model sequentially. The BRFSS is a complex survey with unequal sampling probabilities and stratification, and single overall weights are provided for each sampled person. We standardized these level 1 (individual) scaled sampling weights to sum to the effect cluster size (method 1)³² and left level 2 (county) as unweighted. We repeated the multilevel analyses using the scaled weight of method 2, which used the scaling factor as the apparent cluster size equal to the actual cluster size, and unweighted level 2 data.³² We found no conclusive differences between these methods; therefore, we present results from the weighted models because we were primarily interested in point estimates.³³ All analyses were conducted using statistical software (Stata, version 14.0; StataCorp LP) for data management accounting for the complex sampling design and multilevel structure. We used Taylor linearization³⁴ to calculate standard errors. We considered results significant at P < .05.

Table 1 lists the sample characteristics at the individual level. Most individuals were younger than 65 years, were of white race/ethnicity, had more than a high school education, and were employed. Almost half had an annual household income of at least $50 000. Almost 90% of the respondents reported having health care insurance coverage, and 58.3% reported having eye care insurance coverage. More than one-third (37.8%) reported poor vision or eye health, and 12.4% reported having diabetes.

Table 2 lists percentages of people who used eye care services by tertile of county characteristics. Overall, eye care use was fairly uniform across counties. Percentages with an eye care visit in the past year ranged from 58.3% to 64.5%, while percentages with a dilated eye examination in the past year ranged from 48.1% to 53.8%. Respondents were slightly more likely to report having an eye care visit if they lived in a county with a high percentage of black individuals, a low percentage of people with less than a high school education, a low percentage of people with an annual household income of less than $35 000, and a low percentage of unemployed. They were also more likely to report having an eye care visit if they lived in a county with a low percentage of people with no health care or eye care insurance coverage, a high density of eye care professionals, a low percentage of people with poor vision or eye health, and a low diabetes prevalence. Respondents who reported receipt of a dilated eye examination in the past year had a similar pattern for percentage of the population that was of black race/ethnicity, less than high school educated, unemployed, and insured, as well as counties that had a high density of eye care professionals; however, the association with county-level annual household income, poor vision or eye health, and diabetes was inconsistent.

Table 3 summarizes results of the multilevel logistic regression models for the effect of contextual factors on having an eye care visit in the past year, controlling for various groups of individual-level characteristics. In the full model (model 4), the odds of visiting an eye physician were significantly higher among people living in counties in the highest tertile of percentage of black individuals (adjusted odds ratio [aOR], 1.12; 95% CI, 1.01-1.24; P = .04), percentage of low-income households (aOR, 1.12; 95% CI, 1.00-1.25; P = .045), and density of eye care professionals (aOR, 1.18; 95% CI, 1.07-1.29; P < .001) compared with their counterparts in counties in the lowest tertiles. The odds of reporting having an eye care visit were significantly lower for respondents who lived in counties with the highest tertile of population with less than a high school education after adjusting for other county-level characteristics and individual-level predisposing and enabling factors (models 1-3), but a likely association was no longer identified (aOR, 0.91; 95% CI, 0.81-1.02; P = .10) (model 4).

We observed a similar pattern among those reporting receipt of a dilated eye examination for high percentages of black individuals (aOR, 1.20; 95% CI, 1.07-1.34; P = .002) and low income (aOR, 1.17; 95% CI, 1.04-1.32; P < .01) (Table 4, model 4). However, this pattern was not seen for the density of eye care professionals (aOR, 1.02; 95% CI, 0.92-1.13; P = .77), whereas the odds of reporting receipt of a dilated eye examination were lower for persons who lived in counties in the highest tertile of poor vision or eye health (aOR, 0.85; 95% CI, 0.77-0.94; P = .002) compared with their counterparts in counties in the lowest tertile.

We examined whether contextual predisposing, enabling, and need factors influence eye care use by Americans 40 years and older and found that several county-level characteristics predict eye care use independent of individual-level characteristics. In particular, residence in counties with the highest proportions of black individuals or low-income households or with the highest density of eye care professionals or lowest proportion of the population with poor vision or eye health was associated with a 12.1% to 20.3% greater likelihood of having an eye care visit or dilated eye examination in the previous year. These associations have policy rather than clinical relevance because they describe the settings that enable or inhibit access to health care services, regardless of an individual’s agency. They also suggest areas in which improvement or development could benefit the eye health of residents.

The result that those living in counties with the highest percentages of black individuals were more likely to use eye care than residents of counties with low percentages of black individuals is similar to the findings that have been reported for use of preventive services by black and Hispanic individuals.^35-37 A previous study³⁸ found that black individuals use health care services more than their white counterparts. Our individual-level analysis was also consistent with that report, with eye care use significantly more likely among black than white individuals. Moreover, even after controlling for this individual-level effect, counties with more black individuals had 12.1% to 20.3% higher odds of use than those with fewer black individuals, suggesting a synergistic effect of cultural norms. These findings suggest that there may be a culture of health care use in black communities. The finding may also reflect the effect of intensive health promotion efforts aimed at improving awareness and quality of eye care among groups known to have high risk for diabetes and its complications, including minority populations.^39,40

Our study also showed the expected inverse association between individual-level income and use of eye care services. However, after controlling for individual-level income, residents of counties with the highest percentages of low-income households had 12.2% to 17.1% increased odds of reporting having an eye care visit or having a dilated eye examination compared with residents of counties with the lowest percentages of low-income households. This finding is not consistent with previous studies^39-41 on access to health care. For example, one study⁴² that used earlier data from a single state showed that a 1% rise in county-level poverty was associated with a 5% increase in reporting an unmet need for health care. However, that study did not control for all of the variables in our models and had a narrower geographic focus, which could explain the differences. In addition, recent targeted public health programs designed to increase access to care for underserved populations may have had an effect. It is also possible that residents of affluent counties with diabetes are less likely to use eye care services because they do not perceive the need to do so. A study²⁰ of US residents with diabetes in 22 states found that those who would be expected to live in affluent areas (older, male, best educated, higher income, and with health or eye care insurance coverage) were most likely to report “no need” as their main reason for not seeking eye care in the past 12 months. Our results also revealed that the density of eye care professionals was a predictor of having an eye care visit, which is consistent with previous studies^20,43 finding that the availability of ophthalmologists was positively associated with eye care use for people with diabetes. Another study⁴⁴ showed that living in a county with a low density of eye care professionals reduced the odds of having a dilated eye examination among persons with insurance, although our results for this outcome were not statistically significant. Similar results were found in other studies of use of preventive care recommended for other specialties or diseases. For instance, a study⁴⁵ found that increased density of primary care physicians at the county level was associated with greater use of screening and vaccinations by women.

While county-level insurance coverage was an important factor in the bivariate analysis (and multivariable analyses, controlling for other county-level characteristics), it was no longer significant after controlling for individual-level characteristics. Previous studies have found that people living in counties with a high percentage of uninsured residents were less likely to obtain health care services. For example, Luo and colleagues⁴⁶ found that residents with diabetes living in a county with a high percentage of uninsured individuals were less likely to use preventive diabetes care services.

Our study has some limitations. The 2006 to 2010 BRFSS surveys were landline telephone surveys; therefore, respondents may not be representative of those without a telephone or of cell phone users. In addition, all data were self-reported and may be affected by recall or social desirability bias because respondents may claim that their vision is better or worse than it is or that they visit eye care professionals more or less often than they actually do. However, our findings are not confounded by ecological fallacy because we make no inferences about individuals from the county-level associations that we found. Finally, our results may not be representative of the entire population of visually impaired US adults 40 years and older because the survey excludes institutionalized adults and our study was based only on the 22 states that administered the Visual Impairment and Access to Eye Care module during the study period. However, the percentage observed for health insurance coverage is consistent with national estimates for the age group.⁴⁷ We have not been able to find estimates for eye care or vision care insurance from any source other than the BRFSS data. Our estimate is consistent with the findings from other studies that used the BRFSS data.

In our study, the characteristics of the counties in which people live influence eye care use independent of the characteristics of the individuals themselves. The findings suggest that, while individual characteristics influence health care use, it is also important to address contextual factors to assure effective access to eye care services and ultimately vision health. The study results may further our understanding of the association between social context and eye care use and contribute to the development of future public health interventions at various levels (states, systems, and communities). However, additional research is needed to replicate our findings in a representative sample of the adult US population.

Accepted for Publication: June 26, 2016.

Corresponding Author: Chiu-Fang Chou, DrPH, Division of Diabetes Translation, National Center for Chronic Disease Prevention and Health Promotion, Centers for Disease Control and Prevention, 4770 Buford Hwy NE, Mailstop F-75, Atlanta, GA 30341 (cchou@cdc.gov).

Published Online: August 25, 2016. doi:10.1001/jamaophthalmol.2016.3007

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

Study concept and design: All authors.

Acquisition, analysis, or interpretation of data: Chou, Beckles, Cheng.

Drafting of the manuscript: Chou.

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

Statistical analysis: Chou, Cheng.

Administrative, technical, or material support: Chou, Beckles, Cheng.

Study supervision: Chou, Beckles, Saaddine.

Conflict of Interest Disclosures: All authors have completed and submitted the ICMJE Form for Disclosure of Potential Conflicts of Interest and none were reported.

Disclaimer: The findings and conclusions in this article are those of the authors and do not necessarily represent the official position of the Centers for Disease Control and Prevention.