Preventive service utilization among people who are blind or have low vision.
Visual impairment is associated with poorer quality of life, including lower perceived health, higher levels of functional limitations, higher rates of depression and anxiety, and higher ratings of emotional distress (Brody et al., 2001; Evans, Fletcher, & Wormald, 2007; Laforge, Spector, & Sternberg, 1992; Okoro et al., 2011; Scott, Schein, Feuer, Folstein, & Bandeen-Roche, 2001; Scott, Smiddy, Schiffman, Feuer, & Pappas, 1999; Slakter & Stur, 2005; Soubrane, Cruess, Lotery, et al., 2007; ZambelliWeiner, Crews, & Friedman, 2012). For people with such an impairment, there is also a link with greater dependence upon others, social isolation, and reduced access to health care (Reichard, Stolzle, & Fox, 2011;
Smith, 2008; Varma, Wu, Chong, Azen, & Hays, 2006).
There is a growing body of literature describing the receipt of clinical preventive services for individuals with disabilities, compared to those without. Results have been inconsistent in terms of whether people with one or more disability may be more or less likely to receive recommended preventive services (Andresen et al., 2013; Chevarley, Thierry, Gill, Ryerson, & Nosek, 2006; HornerJohnson, Dobbertin, Andresen, & Iezzoni, 2014; Smeltzer, 2006; Tezzoni, McCarthy, Davis, Harris-David, & O'Day, 2001). For example, one study using data from the Medical Expenditure Panel Survey (MEPS) found that women with disabilities were less likely to receive mammography and Pap testing but more likely to receive influenza immunization, cholesterol screening, and colorectal cancer screening (Wei, Findley, & Sambamoorthi, 2006). One possible explanation for these disparate findings across studies is that there are differences in study design, including who is included in the sample (for example, women only), or how a disability is identified (self-report, secondary data, or enrollment in disability services) or classified (functional status, medical diagnosis, level of severity) (Congdon, Friedman, & Lietman, 2003; Peterson-Besse et al., 2014). Regardless, there is evidence that patients with multiple chronic conditions require additional intervention for obtaining recommended services (Goodman et al., 2014).
It is possible that people with visual impairments may be at risk of not receiving recommended preventive services at the same rate as the general population. There have been few studies investigating the receipt of preventive services specifically in people with visual impairments. A recent study, also using MEPS data, compared receipt of preventive services across different categories of disability, categorized according to self-reported functional limitation, and found that people with visual impairments were most likely (compared to those with hearing, physical, or cognitive impairments) to have no usual source of medical care, to have foregone medical or dental treatment, to have received no dental care within the prior year, and to have never had screening for colorectal cancer. People with vision impairments did not appear to be less likely than those with other types of disability to receive screening for breast or cervical cancer (Horner-Johnson et al., 2014).
We hypothesized that disparities in receipt of preventive care would be greater when using more stringent criteria for vision loss, and when making the comparison to the general population, not only to those with other types of disabilities. In this study, we had two objectives: to estimate preventive service utilization among adults, by visual impairment status; and to assess how demographic factors and select comorbid conditions affect the likelihood of receiving preventive services among adults with visual impairments.
This analysis used the 2000-2011 Medical Expenditure Panel Survey (MEPS), a subset of the National Health Interview Survey (NHIS). The NHIS/MEPS is the primary survey that collects information regarding the health of the American civilian, non-institutionalized population, collecting detailed information regarding health, health behaviors, service utilization, conditions, and demographic characteristics. The survey uses a cross-sectional sampling frame, with oversampled subpopulations such as nonwhites, to create a weighted sample that is representative of the U.S. population (Centers for Disease Control and Prevention, 2015).
The MEPS respondents are drawn from the prior year's NHIS respondent pool, and are followed for two years. MEPS utilizes an overlapping panel design, with two panels overlapping annually which are then are weighted using individual level weights to ensure that analyses are reflective of the national population of noninstitutionalized adults in the United States (Medical Expenditure Panel Survey, 2014). The consolidated full-year files were merged with the medical conditions files by unique individual identifiers for each year; these files were then appended to create a single data file for analysis. Finally, the 1996-2011 pooled linkage file was used to estimate a variance structure that accounts for the pooling of the years; data was drawn from this file only for the years included in this analysis. Individual weight was divided by the number of years (12) to ensure a total sample representative of the nation (1996-2011 Pooled Linkage Variance Estimation File, 2014). Respondents with missing data for any of the analytic variables (0.9% of the study sample) were excluded. The final study sample included 323,143 total respondents (245,896,115 weighted).
The main independent variable was blindness or low vision, defined as having a documented medical condition coded as ICD9 369.xx, inclusive. Diagnosis code 369.xx specifically excludes correctable impaired vision due to refractive errors, as described in greater detail below. Codes within the medical conditions file are identified via self-report as well as by medical chart review to validate the diagnosis. These data are then reviewed by medical coders to specify ICD9 codes. This collection of codes includes blindness, low vision, severe visual impairment, legal blindness, and visual loss. Those who did not have a documented medical condition noted above were considered not to have a visual impairment.
The outcome measures were the receipt of recommended preventive services, as designated by the U.S. Preventive Services Task Force (U.S. Preventive Services Task Force Recommendations, 2014). The USPSTF recommendations for receipt of certain services changed over the study period. We selected screening for colorectal, breast, and cervical cancers; cholesterol screening; and influenza immunization as the outcome measures for the following reasons:
* They represent important causes of death nationally (heart disease and malignant neoplasms were the first and second leading causes of death in 2010, and influenza and pneumonia was the ninth leading cause).
* They are considered preventive measures, recommended by the USPSTF.
* They represent different domains of prevention: cancer screening, chronic disease screening, and primary prevention of infection.
* Though nuances of the recommendations have changed over time, they are well-established preventive services that are widely adopted in the health care community so that provider-level variability should be minimal.
Biennial mammography was recommended for women aged 50 to 69 years in 2000 and 2001, for women aged 40 to 74 years in the years 2002 through 2008, and for women aged 50 to 74 years for every year after 2009. Similarly, a Pap test was recommended for women aged 18 to 65 years from 2000-2002, but only for women aged 21 to 65 years for all years after 2002. Influenza vaccination recommendations changed from only those aged 50 years and over in the years 2000 through 2009, to all ages in years 2010 and later. Table 1 summarizes the services and age groups examined.
Covariates included sex (male or female); age group (18 to 44 years, 45 to 64 years, or 65 years and older); race or ethnicity (non-Hispanic white, non-Hispanic black, Hispanic, or non-Hispanic other); residence (urban, metropolitan statistical area, versus rural, nonmetropolitan statistical area); income level (low income, less than 200% of the federal poverty level [FPL]; middle income, 200 to 400% of the FPL; or high income, greater than 400% of the FPL); educational attainment (less than high school, high school graduate, or some college and above); health insurance status (private, public, or none); having a usual source of care (yes or no); the total number of chronic conditions (none, one, or more than one); and having a mental health diagnosis.
Comorbidity status was based on having a documented condition in the respective medical conditions file, identified by ICD9 codes. These conditions were based upon the standard classification of chronic diseases developed by the U.S. Department of Health and Human Services (Goodman, Posner, Huang, Parekh, & Koh, 2013), and are included if available (see Table 2). Overweight was defined as having a body mass index greater than or equal to 25, while greater than or equal to 30 indicated obesity. Due to the limited analytic power in this sample, each individual chronic illness could not be analyzed separately, necessitating the need for the index described above.
First, select characteristics of the study population were presented, comparing those with visual impairments to those without. Next, we estimated the proportion of the visual impairment group that received each recommended service without adjusting for the other covariates, compared to those without visual impairments. All comparisons were assessed using Wald chi-square tests with a significance level of alpha = 0.05. Finally, we conducted a logistic regression analysis, one for each preventive service, to determine which factors were associated with receipt of that preventive service. This analysis produced adjusted odds ratios, adjusting for visual impairment status and the covariates described above. The final logistic regression models were estimated for the subset of the population with visual impairments, to determine the factors associated with preventive service utilization among those in this group. Only those preventive services that had a significant association with visual impairment were included in the final modeling step. All analyses were conducted using SAS 9.4. The University of South Carolina Institutional Review Board certified this analysis as exempt.
Overall, the prevalence of visual impairment was 0.38%, increasing as age increased, and was lower among Hispanics and other non-whites. Other factors associated with a higher prevalence of visual impairment included having more than one chronic condition, having a low or middle income, having public insurance and having a usual source of care (see Table 3).
Compared to the sighted population, those with visual impairments were more likely to be older, non-Hispanic white or black, have lower incomes, have public insurance coverage, have a usual source of care, have more than one chronic condition, or have any of the selected chronic conditions (see Table 4).
Compared to sighted adults, a significantly lower proportion of visually impaired adults received colorectal cancer screening (37.8% vs. 46.5%, p < 0.001), yet a significantly higher proportion obtained an influenza vaccination (63.4% vs. 45.1%, p < 0.001). A similar percentage of women with and without visual impairments received mammography and Pap tests. There was also no difference in cholesterol screening by visual impairment status (see Table 5).
Visual impairment remained a nonsignificant factor in mammography and Pap tests among women in the study population when controlling for the covariates. It was negatively and statistically associated, after controlling for other factors, with the receipt of colorectal cancer screening (AOR: 0.66, 95% CI: 0.49-0.90) and the receipt of a cholesterol test (AOR: 0.69, 95% CI: 0.500.96). Visual impairment remained positively and significantly associated with influenza vaccination, controlling for other factors (AOR: 1.33, 95% CI: 1.06-1.67) (see Table 6).
Since the receipt of colorectal cancer screening, influenza vaccination, and cholesterol tests were significantly associated with visual impairment, we estimated multivariate models for the subset of those with the condition to determine what factors were related to receipt of services among this subgroup. These models indicate very few factors significantly associated with preventive service receipt (see Table 7). The factors associated with colorectal cancer screening were high income (AOR: 2.97, 95% CI: 1.17-7.51); being uninsured (AOR: 0.19, 95% CI: 0.04-0.91); and having one (AOR: 6.53, 95% CI: 1.02-41.93) or more than one (AOR: 8.51, 95% CI: 1.50-48.27) chronic condition. Having had an influenza vaccination was negatively associated with being Hispanic (AOR: 0.47, 95% CI: 0.23-0.96), yet positively associated with the age groups 65-74 and 75 and older (AOR: 3.46, CI: 1.55-7.69 and AOR: 9.45, 95% CI: 4.0721.91), having a usual source of care (AOR: 2.95, 95% CI: 1.32-6.58), and more than one chronic condition (AOR: 2.55, 95% CI: 1.04-6.22). The factors associated with receipt of a cholesterol test were rural residence (AOR: 0.45, 95% CI: 0.21-0.95), middle or high income (AOR: 2.69, 95% CI: 1.12-6.46 and AOR: 5.19, 95% CI: 1.68-16.0, respectively), having a usual source of care (AOR: 5.71, 95% CI: 2.05-15.94), and more than one chronic condition (AOR: 5.47, 95% CI: 1.87-15.97).
This analysis examined preventive service utilization among visually impaired adults in a national sample. It was important to conduct such an analysis on this subset of people with more severe visual impairments, since they have not previously been analyzed separately. The differential findings across the preventive services are important to note, so that providers can be aware of gaps in service provision.
The lower prevalence of colorectal cancer screening is of particular concern. This is consistent with previous findings (Horner-Johnson et al., 2014), in which people with visual impairments were less likely than were those with other types of disability to report no history of being screened for colorectal cancer. Since the screening percentages are low (less than 50%) for all groups, current efforts to improve screening should be continued. Programs such as the CDC's Colorectal Cancer Control Program (Centers for Disease Control and Prevention, 2014a) have been shown to be effective in increasing access to screening, but did not specifically focus on groups with disabling conditions. Interventions such as these should work to reach these disparate groups to help provide access to these services. Research to identify the most commonly reported barriers to colorectal cancer screening among visually impaired people could inform development of targeted interventions to improve adherence with colorectal cancer screening.
There was also an interesting association with cholesterol testing, where the odds of receipt were lower only after adjusting for the covariates. The drivers of the receipt of cholesterol testing, in this analysis, appear to be socioeconomic factors such as income and insurance. These factors indicate that the role of financial resources, even among those who are insured, is a substantial barrier to care receipt.
It is also worth noting the higher likelihood of receiving an influenza vaccination among the visually impaired group. This is despite controlling for other factors associated with vaccination, such as insurance coverage, income, and having a usual source of care. It is possible that visually impaired adults access services from agencies and organizations that accommodate people in group settings, and that those service providers might provide or even require immunization.
Despite evidence of reduced access to care for women with a disability, a similar percentage of women with and without visual impairments in this analysis had received mammography and Pap tests. Since these services are female specific, gender may be an intrinsic driver of receipt. In fact, female gender had a positive influence for both influenza vaccinations and cholesterol testing, indicating that this population is more likely to get services in general. Further research should be done in order to understand how this group receives such equitable care, to provide a model for outreach for other preventive services.
Implications for practitioners
Practitioners should also be aware of the particular needs of those patients with multiple chronic conditions. When serving these patients, care needs to be taken to not let their primary condition overshadow the need for other services, such as the ones explored above. In particular, these results indicate a trend for males to be less likely to receive services, which may require more intervention from the providers to increase provision. In addition to the general awareness of need for services, other factors for these individuals should be considered, including polypharmacy, patient preferences, conflicting outcomes, and uncertainty as to the appropriateness of adherence given their comorbidities (Goodman et al., 2014; Department of Health and Human Services, 2010). The U.S. Department of Health and Human Services is actively supporting research in this area, and providers should follow relevant findings closely (Parekh & Goodman, 2013).
There are limitations to this analysis. First, the previously reported visual impairment prevalence of 3% in those 40 years of age and older was not observed in this study (Centers for Disease Control and Prevention, 2014b). We believe this is the case in part because people living in nursing homes were excluded from the sample. Second, the identification of visual impairment in MEPS is primarily dependent upon respondent self-reports, which are then analyzed by trained coders. To be classified as being visually impaired, an individual had to first report a vision limitation, and then be verified as having a diagnosis of uncorrectable vision loss (369.xx). Therefore, we are confident that those classified as being visually impaired in our study truly do have substantial vision impairment and that the findings should be generalizable to noninstitutionalized adults with severe vision impairment across the country. However, there is the possibility that visually impaired individuals were unable to be confirmed by way of medical record verification due to the lack of a consistent provider of medical care. Third, it is unknown how long each respondent has had this condition, and what its origin was, which may also slant the results in some unknown way. Finally, 12 years of data were pooled to gain adequate statistical power to conduct the analysis. Even with a larger data set, some of the estimates were unstable due to small sample sizes. Excluding 1% of the sample, although that was small, did not help in this regard. Caution also needs to be taken in extrapolating these findings to the entire population of people with visual impairments. MEPS is derived from noninstitutionalized adults in the United States, and it is not generalizable to that entire population. It is very possible that excluding those in institutional or other care systematically biases the results in some unknown way.
Andresen, E., Peterson-Besse, J., Krahn, G., Walsh, E., Horner-Johnson, W., & Iezzoni, L. (2013). Pap, mammography, and clinical breast examination screening among women with disabilities: A systematic review. Women's Health Issues, 23(4), e205-e214.
Brody, B., Gamst, A., Williams, R., Smith, A., Lau, P., Dolnak, D., & Brown, S. (2001). Depression, visual acuity, comorbidity, and disability associated with agerelated macular degeneration. Ophthalmology, 108(10), 1893-1900.
Burrows, N., Hora, I., Li, Y., & Saaddine, J. (2011). Self-reported visual impairment among persons with diagnosed diabetes-- United States, 1997-2010. Morbidity and Mortality Weekly Report (MMWR), 60(45), 1549-1553.
Centers for Disease Control and Prevention. (2015). About the NHIS. Retrieved from http:// www.cdc.gov/nchs/nhis/about_nhis.htm
Centers for Disease Control and Prevention. (2014a). Colorectal cancer control program. Retrieved from http://www.cdc.gov/ cancer/crccp
Centers for Disease Control and Prevention. (2014b). Vision health initiative. Retrieved from http://www.cdc.gov/visionhealth/ index.htm
Chevarley, F., Thierry, J., Gill, C., Ryerson, A., & Nosek, M. (2006). Health, preventive health care, and health care access among women with disabilities in the 1994-1995 National Health Interview Survey, Supplement on Disability. Women's Health Issues, 16(6), 297-312.
Congdon, N., Friedman, D., & Lietman, T. (2003). Important causes of visual impairment in the world today. JAMA, 290(15), 2057-2060.
Department of Health and Human Services. (2010). Multiple chronic conditions--A strategic framework: Optimum health and quality of life for individuals with multiple chronic conditions. Retrieved from http:// www.hhs.gov/ash/initiatives/mcc/mcc_ framework.pdf
Evans, J., Fletcher, A., & Wormald, R. (2007). Depression and anxiety in visually impaired older people. Ophthalmology, 114(2), 283-288.
Goodman, R., Boyd, C., Tinetti, M., Von Kohorn, I., Parekh, A., & McGinnis, J. (2014). IOM and DHHS meeting on making clinical practice guidelines appropriate for patients with multiple chronic conditions. Annals of Family Medicine, 12(3), 256-259.
Goodman, R., Posner, S., Huang, E., Parekh, A., & Koh, H. (2013). Defining and measuring chronic conditions: Imperatives for research, policy, program, and practice. Preventing Chronic Disease, 10, E66.
Horner-Johnson, W., Dobbertin, K., Andresen, E., & Iezzoni, L. (2014). Breast and cervical cancer screening disparities associated with disability severity. Women's Health Issues, 24(1), e147-e153.
Horner-Johnson, W., Dobbertin, K., Lee, J., & Andresen, E. (2014). Disparities in health care access and receipt of preventive services by disability type: Analysis of the Medical Expenditure Panel Survey. Health Services Research, 49(6), 1980-1999.
Laforge, R., Spector, W., & Sternberg, J. (1992). The relationship of vision and hearing impairment to one-year mortality and functional decline. Journal of Aging and Health, 4(1), 126-148.
Medical Expenditure Panel Survey. (2014). Medical Expenditure Panel Survey: 19962011 Pooled Linkage Variance Estimation File. (2014). Retrieved from http://meps. ahrq.gov/mepsweb/data_stats/download_ data/pufs/h36/h36u11doc.shtml
National Eye Institute. (2014). Statistics and data. Retrieved from http://www.nei.nih. gov/eyedata
Okoro, C., McKnight-Eily, L., Strine, T., Crews, J., Holt, J., & Balluz, L. (2011). State and local area estimates of depression and anxiety among adults with disabilities in 2006. Disability and Health Journal, 4(2), 78-90.
Parekh, A., & Goodman, R. (2013). The HHS Strategic Framework on multiple chronic conditions: Genesis and focus on research. Journal of Comorbidity, 3(2), 22-29.
Peterson-Besse, J., O'Brien, M., Walsh, E., Monroe-Gulick, A., White, G., & Drum, C. (2014). Clinical preventive service use disparities among subgroups of people with disabilities: A scoping review. Disability and Health Journal, 7(4), 373-393.
Reichard, A., Stolzle, H., & Fox, M. (2011). Health disparities among adults with physical disabilities or cognitive limitations compared to individuals with no disabilities in the United States. Disability and Health Journal, 4(2), 59-67.
Scott, I., Schein, O., Feuer, W., Folstein, M., & Bandeen-Roche, K. (2001). Emotional distress in patients with retinal disease. American Journal of Ophthalmology, 131(5), 584-589.
Scott, I. U., Smiddy, W., Schiffman, J., Feuer, W., & Pappas, C. (1999). Quality of life of low-vision patients and the impact of lowvision services. American Journal of Ophthalmology, 128(1), 54-62.
Slakter, J., & Stur, M. (2005). Quality of life in patients with age-related macular degeneration: Impact of the condition and benefits of treatment. Survey of Ophthalmology, 50(3), 263-273.
Smeltzer, S. (2006). Preventive health screening for breast and cervical cancer and osteoporosis in women with physical disabilities. Family & Community Health, 29(1), 35S-43S.
Smith, D. L. (2008). Disparities in health care access for women with disabilities in the United States from the 2006 National Health Interview Survey. Disability and Health Journal, 1(2), 79-88.
Soubrane, G., Cruess, A., Lotery, A. et al. (2007). Burden and health care resource utilization in neovascular age-related macular degeneration: Findings of a multicountry study. Archives of Ophthalmology, 125(9), 1249-1254.
Tezzoni, L., McCarthy, E., Davis, R., HarrisDavid, L., & O'Day, B. (2001). Use of screening and preventive services among women with disabilities. American Journal of Medical Quality, 16(4), 135-144.
U.S. Preventive Services Task Force. (2014). Recommendations. Retrieved from http:// www.uspreventiveservicestaskforce.org/ recommendations.htm
Varma, R., Wu, J., Chong, K., Azen, S., & Hays, R. (2006). Impact of severity and bilaterality of visual impairment on health-related quality of life. Ophthalmology, 113(10), 1846-1853.
Wei, W., Findley, P., & Sambamoorthi, U. (2006). Disability and receipt of clinical preventive services among women. Women's Health Issues, 16(6), 286-296. Zambelli-Weiner, A., Crews, J., & Friedman, D. (2012). Disparities in adult vision health in the United States. American Journal of Ophthalmology, 154(6 Suppl.), S23-S30.
The findings and conclusions in this report are those of the authors and do not necessarily represent the official position of the Centers for Disease Control and Prevention.
Kevin J. Bennett, Ph.D., associate professor, Department of Family and Preventive Medicine, University of South Carolina School of Medicine, 3209 Colonial Drive, Columbia, SC 29203; email: <email@example.com>. Suzanne McDermott, Ph.D., professor, Department of Epidemiology and Biostatistics, University of South Carolina, Arnold School of Public Health, Discovery 1, 915 Greene Street, Room 417, Columbia, SC 29208; email: <firstname.lastname@example.org>. Joshua R. Mann, M.D., professor and chair, Department of Preventive Medicine, University of Mississippi Medical Center, 2500 North State Street, Jackson, MS 39216; e-mail: <email@example.com>. James W. Hardin, Ph.D., associate professor, biostatistics division head, and director, Biostatistics Collaborative Unit, Discovery 445, 948 Greene Street, Room 445, Columbia, SC 29201; e-mail: <firstname.lastname@example.org>.
Table 1 USPSTF recommended services, by sex and age. Service Met recommendations if: Mammography 2000-2001--Women 50-69 2002-2008--Women 40-74 2009-2011--Women 50-74 Received within the prior 2 years Pap test 2000-2002--Women 18-65 2003-2011--Women 21-65 Received within the prior 3 years CRC screening Men and women >50-75 If colonoscopy within the prior 10 years OR sigmoidoscopy within the prior 5 years OR blood stool test within the prior year Influenza vaccination 2000-2009--Men and women 50 + 2010-2011--All Received within the prior year Cholesterol test Men > 35 and women > 45 Received within the prior 5 years Table 2 ICD9 codes utilized to identify comorbid conditions. Comorbidity ICD9 code Diabetes 250.xx Congestive heart failure 428.xx Cardiovascular disease 410-414.xx, 430-438.xx Cancer 140-239.xx Any mental health condition 290-319.xx Severe mental health 290-296.xx, 298.xx, condition 303-305.xx, 311.xx Chronic obstructive 490.xx, 491.xx, 492.xx, pulmonary disease 494.xx, 496.xx Hypertension 401.##, 402.##, 403.#3, 404.##, 405.## Hyperlipidemia 272.## Stroke 430, 431, 433##, 434##, 435.## Arthritis 714.##, 715, 720, 721 Asthma 493.## CKD 581.##-588.##, 591.##, 753.## Osteoporosis 733.## Overweight or obese BMI [greater than or equal to] 25 Table 3 Prevalence of blindness or low vision, by selected characteristics. % 95% CI Total population prevalence 0.38 0.35-0.42 Sex Male * 0.38 0.34-0.43 Female 0.37 0.33-0.42 Age group (in years) < 21 * 0.21 0.16-0.25 21-50 0.20 0.16-0.24 51-75 0.39 ([dagger]) 0.33-0.45 75 + 1.21 ([dagger]) 1.05-1.38 Race/ethnicity White, non-Hispanic * 0.41 0.37-0.45 Black, non-Hispanic 0.44 0.37-0.52 Hispanic 0.25 ([dagger]) 0.20-0.30 Other, non-Hispanic 0.28 ([dagger]) 0.19-0.36 Rurality Urban * 0.38 0.34-0.41 Rural 0.39 0.32-0.47 Income level (% federal poverty level) < 200% * 0.50 0.44-0.56 200-400% 0.36 ([dagger]) 0.30-0.41 > 400% 0.29 ([dagger]) 0.24-0.34 Educational attainment <High school * 0.36 0.31-0.40 High school diploma 0.44 0.37-0.51 Some college or more 0.37 0.31-0.42 Insurance coverage Private insurance * 0.29 0.25-0.33 Public insurance 0.78 ([dagger]) 0.69-0.88 Uninsured 0.21 ([dagger]) 0.14-0.28 Usual source of care Yes * 0.42 0.38-0.46 No 0.21 ([dagger]) 0.16-0.26 Number of chronic conditions 0 * 0.19 0.16-0.22 1 0.27 ([dagger]) 0.24-0.30 >1 0.72 ([dagger]) 0.66-0.78 Any mental health diagnosis 0.67 0.57-0.77 * Referent level; ([dagger]) Significantly different from the referent level, [alpha] = 0.05. Table 4 Weighted percentage of demographic characteristics of those with blindness or low vision vs. those without blindness or low vision. BLV Non-BLV N (weighted) 1,116 (874,753) 322,027 (245,021,362) Sex Male 49.6 49.0 Female 50.4 51.0 Age group (in years) < 21 13.4 24.6 21-50 19.3 36.8 51-75 26.8 26.1 75 + 40.5 12.5 Race/ethnicity White, non-Hispanic 70.3 65.1 Black, non-Hispanic 14.2 12.1 Hispanic 10.3 15.8 Other, non-Hispanic 5.2 7.0 Rurality Urban 83.4 84.1 Rural 16.6 15.9 Income level (% federal poverty level) <200% 42.0 31.9 200-400% 29.0 30.9 >400% 28.9 37.2 Educational attainment <High school 35.8 38.1 High school diploma 26.3 22.8 Some college or more 38.0 39.1 Insurance coverage Private insurance 51.4 67.2 Public insurance 41.4 19.9 Uninsured 7.2 13.0 Usual source of care Yes 89.0 80.2 No 11.0 19.8 Number of chronic conditions 0 18.4 37.7 1 22.1 31.1 >1 59.4 31.3 Any mental health diagnosis 27.3 15.5 BLV = blind/low vision. Table 5 Preventive service utilization prevalence estimates, by blindness or low vision status. BLV % (95% CI) Non-BLV % (95% CI) Mammography 2000-2001--women 50-69 2002-2008--women 40-74 2009-2011--women 50-74 72.0 (64-79.9) 75.9 (75.4-76.4) Pap test 2000-2002--women 18-65 2003-2011--women 21-65 86.3 (80.2-92.5) 88.2 (87.9-88.6) CRC Screening ([dagger]) Men and women >50-75 37.8 (31.2-44.3) 46.5 (46.0-47.0) Influenza vaccination ([dagger]) 2000-2009-50 ([dagger]) 2010-2011--all 63.4 (58.5-68.2) 45.1 (44.7-45.5) Cholesterol test Men >35 and women >45 87.2 (83.8-90.6) 85.8 (85.5-86.1) ([dagger]) Significantly different across blindness or low vision status, a = 0.05; BLV = blind/low vision. Table 6 Adjusted odds ratio estimates of factors associated with receipt of selected preventive services. Mammography Pap test AOR AOR (95% CI) (95% CI) Blindness or low vision 0.83(0.53-1.29) 0.98(0.56-1.73) Male Female White, non-Hispanic Ref. Ref. Black, non-Hispanic 1.45(1.33-1.58) 1.83(1.65-2.02) Hispanic 1.50(1.36-1.65) 1.54 (1.41-1.69) Other, non-Hispanic 0.89(0.80-1.01) 0.53 (0.47-0.58) Age group 1 * Ref. Ref. Age group 2 1.28(1.19-1.37) 0.83 (0.76-0.91) Age group 3 1.29(1.17-1.41) 0.57 (0.52-0.62) Age group 4 0.39 (0.35-0.44) Private insurance Ref. Ref. Public insurance 0.69 (0.63-0.76) 0.73 (0.65-0.82) Uninsured 0.39 (0.36-0.43) 0.39 (0.36-0.43) Rural residence 0.88 (0.81-0.95) 0.78 (0.71-0.85) < 200% FPL Ref. Ref. 200-400% FPL 1.29(1.19-1.39) 1.02 (0.93-1.11) > 400% FPL 2.34 (2.14-2.55) 1.66(1.49-1.84) < High school 0.65 (0.59-0.71) 0.64 (0.57-0.70) High school diploma 0.78 (0.73-0.84) 0.64 (0.59-0.69) Some college or more Ref. Ref. Usual source of care 0.65 (0.59-0.71) 0.64 (0.57-0.70) 0 Chronic conditions Ref. Ref. 1 Chronic condition 1.10(1.01-1.21) 1.10(1.01-1.19) > 1 Chronic condition 1.85(1.68-2.04) 1.27(1.14-1.41) Any mental health diagnosis 0.78 (0.73-0.84) 0.64 (0.59-0.69) CRC screening Influenza vacc. AOR (95% CI) AOR (95% CI) Blindness or low vision 0.66 (0.49-0.90) 1.33(1.06-1.67) Male Ref. Ref. Female 1.01 (0.96-1.05) 1.31 (1.26-1.35) White, non-Hispanic Ref. Ref. Black, non-Hispanic 0.96 (0.90-1.01) 0.67 (0.64-0.70) Hispanic 0.69 (0.64-0.74) 0.95 (0.90-0.99) Other, non-Hispanic 0.69 (0.63-0.76) 1.18 (1.10-1.26) Age group 1 * Ref. Ref. Age group 2 1.64 (1.55-1.73) 1.27 (1.21-1.33) Age group 3 2.72 (2.56-2.89) Age group 4 4.82 (4.50-5.16) Private insurance Ref. Ref. Public insurance 0.80 (0.75-0.85) 0.92 (0.88-0.97) Uninsured 0.54 (0.49-0.59) 0.51 (0.48-0.55) Rural residence 0.89 (0.84-0.94) 0.99 (0.95-1.04) < 200% FPL Ref. Ref. 200-400% FPL 1.10 (1.03-1.17) 0.98 (0.94-1.03) > 400% FPL 1.41 (1.33-1.51) 1.16 (1.11-1.22) < High school 0.75 (0.71-0.81) 0.75 (0.71-0.79) High school diploma 0.82 (0.78-0.86) 0.80 (0.77-0.84) Some college or more Ref. Ref. Usual source of care 0.75 (0.71-0.81) 0.75 (0.71-0.79) 0 Chronic conditions Ref. Ref. 1 Chronic condition 1.25 (1.15-1.36) 1.20 (1.13-1.27) > 1 Chronic condition 1.97 (1.82-2.13) 2.25 (2.12-2.39) Any mental health diagnosis 0.82 (0.78-0.86) 0.80 (0.77-0.84) Cholesterol test AOR (95% CI) Blindness or low vision 0.69 (0.50-0.96) Male Ref. Female 1.43 (1.36-1.51) White, non-Hispanic Ref. Black, non-Hispanic 1.21 (1.13-1.30) Hispanic 1.44 (1.34-1.54) Other, non-Hispanic 1.10 (1.01-1.21) Age group 1 * Ref. Age group 2 1.47 (1.39-1.57) Age group 3 1.92 (1.75-2.10) Age group 4 1.73 (1.57-1.92) Private insurance Ref. Public insurance 0.83 (0.77-0.90) Uninsured 0.50 (0.47-0.54) Rural residence 0.70 (0.66-0.75) < 200% FPL Ref. 200-400% FPL 1.05 (0.98-1.12) > 400% FPL 1.42 (1.33-1.53) < High school 0.55 (0.51-0.59) High school diploma 0.64 (0.61-0.68) Some college or more Ref. Usual source of care 0.55 (0.51-0.59) 0 Chronic conditions Ref. 1 Chronic condition 1.42 (1.33-1.51) > 1 Chronic condition 5.18 (4.79-5.60) Any mental health diagnosis 0.64 (0.61-0.68) * Age groups: mammography: 40-54, 55-64, 65-75; pap test: 18-34, 35-44, 45-54, 55-65; CRC screening: 50-64, 65-75; influenza vaccination: 18-49, 50-64, 65-74, 75 + ; cholesterol test: 35-54, 55-64, 65-74, 75 + ; BLV = blind- low vision; CRC = colorectal cancer; FPL = federal poverty level. Table 7 Adjusted odds ratio estimates for receiving selected preventive services among those with blindness or low vision. Influenza CRC screening vaccination AOR (95% CI) AOR (95% CI) Male Ref. Ref. Female 1.38 (0.76-2.48) 0.66 (0.42-1.04) White, non-Hispanic Ref. Ref. Black, non-Hispanic 0.85 (0.38-1.86) 0.60 (0.33-1.10) Hispanic 0.47 (0.17-1.28) 0.47 (0.23-0.96) Other-non-Hispanic 0.41 (0.10-1.72) 1.85 (0.64-5.29) Age group 1 * Ref. Ref. Age group 2 1.18 (0.63-2.19) 1.84 (0.88-3.89) Age group 3 3.46 (1.55-7.69) Age group 4 9.45(4.07-21.91) Private insurance Ref. Ref. Public insurance 1.59 (0.71-3.53) 0.87 (0.48-1.58) Uninsured 0.19(0.04-0.91) 2.43 (0.86-6.87) Rural residence 1.27 (0.62-2.6) 0.67 (0.37-1.21) < 200% FPL Ref. Ref. 200-400% FPL 1.05 (0.48-2.29) 0.81 (0.45-1.46) > 400% FPL 2.97 (1.17-7.51) 0.74 (0.38-1.46) < High school 0.64 (0.28-1.47) 0.81 (0.43-1.55) High school diploma 0.58 (0.27-1.22) 1.67 (0.97-2.86) Some college or more Ref. Ref. Usual source of care 0.50 (0.19-1.31) 2.95 (1.32-6.58) 0 Chronic conditions Ref. Ref. 1 Chronic condition 6.53 (1.02-41.93) 1.17 (0.44-3.11) > 1 Chronic condition 8.51 (1.50-48.27) 2.55 (1.04-6.22) Any mental health 0.78 (0.41-1.48) 0.86 (0.50-1.50) diagnosis Cholesterol test AOR (95% CI) Male Ref. Female 1.88 (0.90-3.94) White, non-Hispanic Ref. Black, non-Hispanic 1.70 (0.77-3.75) Hispanic 0.99 (0.37-2.65) Other-non-Hispanic 3.29 (0.65-16.7) Age group 1 * Ref. Age group 2 1.01 (0.40-2.55) Age group 3 0.77 (0.31-1.89) Age group 4 0.90 (0.35-2.30) Private insurance Ref. Public insurance 0.79 (0.33-1.93) Uninsured 0.78 (0.23-2.68) Rural residence 0.45 (0.21-0.95) < 200% FPL Ref. 200-400% FPL 2.69 (1.12-6.46) > 400% FPL 5.19 (1.68-16.0) < High school 0.86 (0.35-2.11) High school diploma 0.52 (0.22-1.25) Some college or more Ref. Usual source of care 5.71 (2.05-15.94) 0 Chronic conditions Ref. 1 Chronic condition 3.07 (0.98-9.64) > 1 Chronic condition 5.47 (1.87-15.97) Any mental health 0.80 (0.37-1.71) diagnosis * Age groups: mammography: 40-54, 55-64, 65-75; pap test: 18-34, 35-44, 45-54, 55-65; CRC screening: 50-64, 65-75; influenza vaccination: 18-49, 50-64, 65-74, 75 + ; cholesterol test: 35-54, 55-64, 65-74, 75 + ; BLV = blind- low vision; CRC = colorectal cancer; FPL = federal poverty level.
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|Author:||Bennett, Kevin J.; McDermott, Suzanne; Mann, Joshua R.; Hardin, James W.|
|Publication:||Journal of Visual Impairment & Blindness|
|Date:||Mar 1, 2016|
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