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Table 1. Criteria for Evaluating Adherence to Recommended Follow-up Visitsa
Table 1. Criteria for Evaluating Adherence to Recommended Follow-up Visitsa
Table 2. Demographic Characteristics of the Study Populationsa
Table 2. Demographic Characteristics of the Study Populationsa
Table 3. Predictors of Inconsistent Follow-upa
Table 3. Predictors of Inconsistent Follow-upa
Table 4. Significant Predictors of Inconsistent Adherence to Glaucoma Follow-up Visits After Multiple Logistic Regression Analysis
Table 4. Significant Predictors of Inconsistent Adherence to Glaucoma Follow-up Visits After Multiple Logistic Regression Analysis
Table 5. Breakdown of Case and Control Individuals by Race/Ethnicity and Educational Levela
Table 5. Breakdown of Case and Control Individuals by Race/Ethnicity and Educational Levela
1.
Giangiacomo AC. The epidemiology of glaucoma. In: Grehn F, Stamper R, eds. Glaucoma. Berlin, Germany: Springer; 2009:13-21
2.
Kass MA, Heuer DK, Higginbotham EJ,  et al.  The Ocular Hypertension Treatment Study: a randomized trial determines that topical ocular hypotensive medication delays or prevents the onset of primary open-angle glaucoma.  Arch Opthalmol. 2002;120(6):701-730Google ScholarCrossref
3.
Leske MC, Heijl A, Hussein M, Bengtsson B, Hyman L, Komaroff E.Early Manifest Glaucoma Trial Group.  Factors for glaucoma progression and the effect of treatment: the Early Manifest Glaucoma Trial.  Arch Ophthalmol. 2003;121(1):48-5612523884PubMedGoogle ScholarCrossref
4.
Leske MC, Heijl A, Hyman L, Bengtsson B, Dong L, Yang Z.EMGT Group.  Predictors of long-term progression in the Early Manifest Glaucoma Trial.  Ophthalmology. 2007;114(11):1965-197217628686PubMedGoogle ScholarCrossref
5.
Epstein DL, Krug JH Jr, Hertzmark E, Remis LL, Edelstein DJ. A long-term clinical trial of timolol therapy versus no treatment in the management of glaucoma suspects.  Ophthalmology. 1989;96(10):1460-14672685707PubMedGoogle ScholarCrossref
6.
Kass MA. Timolol treatment prevents or delays glaucomatous visual field loss in individuals with ocular hypertension: a five-year, randomized, double-masked, clinical trial.  Trans Am Ophthalmol Soc. 1989;87:598-6182562546PubMedGoogle Scholar
7.
Schulzer M, Drance SM, Douglas GR. A comparison of treated and untreated glaucoma suspects.  Ophthalmology. 1991;98(3):301-3072023749PubMedGoogle ScholarCrossref
8.
DiMatteo MR. Variations in patients' adherence to medical recommendations: a quantitative review of 50 years of research.  Med Care. 2004;42(3):200-20915076819PubMedGoogle ScholarCrossref
9.
Friedman DS, Hahn SR, Quigley HA,  et al.  Doctor-patient communication in glaucoma care: analysis of videotaped encounters in community-based office practice.  Ophthalmology. 2009;116(12):2277-2285Google ScholarCrossref
10.
American Academy of Ophthalmology Glaucoma Panel.  Preferred Practice Patterns: Primary Open-Angle Glaucoma, Primary Open-Angle Glaucoma Suspect, and Primary Angel Closure. San Francisco, CA: American Academy of Ophthalmology; 2005
11.
Kosoko O, Quigley HA, Vitale S, Enger C, Kerrigan L, Tielsch JM. Risk factors for noncompliance with glaucoma follow-up visits in a residents' eye clinic.  Ophthalmology. 1998;105(11):2105-21119818613PubMedGoogle ScholarCrossref
12.
Lee BW, Sathyan P, John RK, Singh K, Robin AL. Predictors of and barriers associated with poor follow-up in patients with glaucoma in South India.  Arch Ophthalmol. 2008;126(10):1448-145418852425PubMedGoogle ScholarCrossref
13.
Quigley HA, West SK, Rodriguez J, Munoz B, Klein R, Snyder R. The prevalence of glaucoma in a population-based study of Hispanic subjects: Proyecto VER.  Arch Ophthalmol. 2001;119(12):1819-182611735794PubMedGoogle ScholarCrossref
14.
Shimmyo M, Ross AJ, Moy A, Mostafavi R. Intraocular pressure, Goldmann applanation tension, corneal thickness, and corneal curvature in Caucasians, Asians, Hispanics, and African Americans.  Am J Ophthalmol. 2003;136(4):603-61314516799PubMedGoogle ScholarCrossref
15.
Tielsch JM, Sommer A, Katz J, Royall RM, Quigley HA, Javitt J. Racial variations in the prevalence of primary open-angle glaucoma: the Baltimore Eye Survey.  JAMA. 1991;266(3):369-3742056646PubMedGoogle ScholarCrossref
16.
Kim E, Varma R. Glaucoma in Latinos/Hispanics.  Curr Opin Ophthalmol. 2009;21(2):100-10519077825PubMedGoogle ScholarCrossref
17.
Varma R, Ying-Lai M, Francis BA,  et al; Los Angeles Latino Eye Study Group.  Prevalence of open-angle glaucoma and ocular hypertension in Latinos: the Los Angeles Latino Eye Study.  Ophthalmology. 2004;111(8):1439-144815288969PubMedGoogle ScholarCrossref
18.
Martin MJ, Sommer A, Gold EB, Diamond EL. Race and primary open-angle glaucoma.  Am J Ophthalmol. 1985;99(4):383-3873985075PubMedGoogle ScholarCrossref
19.
Sommer A, Tielsch JM, Katz J,  et al.  Racial differences in the cause-specific prevalence of blindness in east Baltimore.  N Engl J Med. 1991;325(20):1412-14171922252PubMedGoogle ScholarCrossref
20.
Wormald RP, Basauri E, Wright LA, Evans JR. The African Caribbean Eye Survey: risk factors for glaucoma in a sample of African Caribbean people living in London.  Eye (Lond). 1994;8(pt 3):315-3207958037PubMedGoogle ScholarCrossref
21.
Gwira JA, Vistamehr S, Shelsta H,  et al.  Factors associated with failure to follow up after glaucoma screening: a study in an African American population.  Ophthalmology. 2006;113(8):1315-131916769119PubMedGoogle ScholarCrossref
22.
Richardson JL, Langholz B, Bernstein L, Burciaga C, Danley K, Ross RK. Stage and delay in breast cancer diagnosis by race, socioeconomic status, age and year.  Br J Cancer. 1992;65(6):922-9261616865PubMedGoogle ScholarCrossref
23.
Madison T, Schottenfeld D, James SA, Schwartz AG, Gruber SB. Endometrial cancer: socioeconomic status and racial/ethnic differences in stage at diagnosis, treatment, and survival.  Am J Public Health. 2004;94(12):2104-211115569961PubMedGoogle ScholarCrossref
24.
Rauscher GH, Ferrans CE, Kaiser K, Campbell RT, Calhoun EE, Warnecke RB. Misconceptions about breast lumps and delayed medical presentation in urban breast cancer patients.  Cancer Epidemiol Biomarkers Prev. 2010;19(3):640-64720200436PubMedGoogle ScholarCrossref
25.
Institute of Medicine.  The healthcare environment and its relation to disparities. In: Smedley BD, Stith AY, Nelson AR, eds. Unequal Treatment: Confronting Racial and Ethnic Disparities in Health Care. Washington, DC: Institute of Medicine; 2003:29-80
26.
Institute of Medicine.  Assessing potential sources of racial and ethnic disparities in care: patient- and system-level factors. In: Smedley BD, Stith AY, Nelson AR, eds. Unequal Treatment: Confronting Racial and Ethnic Disparities in Health Care. Washington, DC: Institute of Medicine; 2003:125-159
27.
Institute of Medicine.  Assessing potential sources of racial and ethnic disparities in care in the clinical encounter. In: Smedley BD, Stith AY, Nelson AR, eds. Unequal Treatment: Confronting Racial and Ethnic Disparities in Health Care. Washington, DC: Institute of Medicine; 2003:160-179
28.
US Department of Health & Human Services.  National Healthcare Disparities Report. Washington, DC: US Dept of Health and Human Services; 2003
Clinical Sciences
July 2011

Racial and Ethnic Disparities in Adherence to Glaucoma Follow-up Visits in a County Hospital Population

Author Affiliations

Author Affiliations: Departments of Ophthalmology, Stanford University, Stanford, California (Drs Murakami, Lee, and Singh and Mr Duncan), University of California, San Francisco (Drs Kao and Lin), and National Taiwan University Hospital, Taipei, Taiwan (Dr Huang).

Arch Ophthalmol. 2011;129(7):872-878. doi:10.1001/archophthalmol.2011.163
Abstract

Objectives To identify predictors of inconsistent attendance at glaucoma follow-up visits in a county hospital population.

Methods Prospective recruitment from August 1, 2008, through January 31, 2009, of 152 individuals with glaucoma, with 1-to-1 matching of patients (those with inconsistent follow-up) and controls (those with consistent follow-up). Data were collected via oral questionnaire. Survey results were correlated with attendance at follow-up examinations, using the t test, χ2 test, and multivariate stepwise logistic regression analysis to calculate the odds ratios (ORs) and 95% confidence intervals.

Results After adjusting for covariates in the logistic regression analysis, factors independently associated with inconsistent follow-up included black race (adjusted OR, 7.16; 95% confidence interval, 1.64-31.24), Latino ethnicity (adjusted OR, 4.77; 1.12-20.29), unfamiliarity with necessary treatment duration (adjusted OR, 3.54; 1.26-9.94), lack of knowledge of the permanency of glaucoma-induced vision loss (adjusted OR, 3.09; 1.18-8.04), and perception that it is not important to attend all follow-up visits (adjusted OR, 3.54; 1.26-9.94).

Conclusions Demographic factors, including race and ethnicity, may directly or indirectly affect adherence to recommended glaucoma follow-up visits. Lack of information regarding irreversible vision loss from glaucoma, need for lifelong treatment, and lack of visual symptoms may be significant barriers to follow-up in this population. Targeted glaucoma education by physicians may improve follow-up, thereby decreasing the morbidity associated with glaucomatous disease.

Glaucoma is the second leading cause of blindness after cataract, accounting for 12.3% of the blindness worldwide, and is the leading cause of irreversible vision loss.1 Lowering intraocular pressure (IOP) significantly reduces disease progression and development of adverse outcomes, such as vision loss and blindness.2-4 Several clinical studies5-7 have demonstrated that the use of IOP-lowering medications decreases the likelihood of measurable disease progression, and it is postulated that patients adherent to therapy will have more favorable outcomes than those who are nonadherent.

Regular patient follow-up is critical for physicians to assess the course of glaucomatous disease and appropriately adjust therapy as needed.8,9 The American Academy of Ophthalmology suggests at least 2 follow-up visits per year in patients with primary open-angle glaucoma in the Preferred Practice Pattern guidelines.10 Inconsistent adherence to recommended follow-up hinders the ability of the physician to track disease progression, presumably increasing the likelihood of adverse disease outcomes.11 Kosoko et al11 reported that patients at a glaucoma clinic in inner-city Baltimore, Maryland, with inconsistent follow-up were more likely to have mild disease than those who demonstrated consistent follow-up. Those with inconsistent follow-up also were more likely to express displeasure with long wait times in clinics. Although those with inconsistent follow-up were less likely to have been prescribed glaucoma medications than those with consistent follow-up, it is unclear whether this was due to more severe disease in case than control individuals. A questionnaire-based study12 at the Aravind Eye Care System in Coimbatore, Tamil Nadu, India, showed that little or no formal education, not using glaucoma medications, and the perception that glaucoma follow-up is less important when patients are adherent to their prescribed glaucoma medication regimen were all independent predictors of inconsistent glaucoma follow-up.

Disparities in the prevalence of glaucoma by race and ethnicity have been well documented in several large population-based studies.13-17 Previous studies15,18-20 have found that blacks and Latinos are at greater risk for glaucoma than whites. Black race is a known risk factor for glaucoma, evidenced by increased disease prevalence, earlier disease development, and increased progression to blindness. Latinos also experience an exponentially greater incidence of disease development with age compared with whites.13,16,17 Several such studies were conducted at academic medical centers serving patient populations predominantly of 1 minority racial/ethnic group who were then compared with whites. No large prevalence surveys, however, have directly compared glaucoma prevalence in blacks and Latinos in 1 city or county population, to our knowledge.

We conducted a case-control study of individuals with definitive or suspected glaucoma to assess differences between those with inconsistent follow-up (ie, cases) and those with consistent follow-up (ie, controls) at San Francisco General Hospital (SFGH), a county hospital serving a patient population that is diverse with regard to race, ethnicity, and language. Data were examined for independent predictors of inconsistent glaucoma follow-up, and patient-reported barriers to such follow-up were assessed.

Methods

We recruited 152 individuals diagnosed as having glaucoma or suspected of having glaucoma examined at the SFGH Glaucoma Clinic in San Francisco, California, from August 1, 2008, through January 31, 2009, with 1-to-1 matching of cases (those with inconsistent follow-up) and controls (those with consistent follow-up). This clinic receives referrals from within SFGH and its satellite community centers throughout San Francisco. The clinic is located in an inner-city county hospital that provides comprehensive health care services as the safety net system throughout the San Francisco area for patients covered by Medi-Cal government-sponsored health insurance and those who are homeless, uninsured, and/or immigrants.

Table 1 summarizes criteria for classifying adherence to recommended follow-up patterns at SFGH based on disease severity. Classification according to these guidelines was based on data gathered from medical records. Eligibility criteria included having a medical record documenting the dates of all glaucoma follow-up visits scheduled and attended in the past 12 months, age of 40 years or older, and 1 of the following diagnoses made at SFGH: primary open-angle glaucoma, primary angle-closure glaucoma, exfoliative glaucoma, pigmentary glaucoma, low-tension glaucoma, or ocular hypertension. These selection criteria were designed to exclude patients who would not require ongoing follow-up at the SFGH Glaucoma Clinic, such as those seeking a second opinion or for urgent care. The chief of the SFGH Glaucoma Service evaluated disease severity (ie, mild, moderate, or severe) according to the American Academy of Ophthalmology Preferred Practice Patterns guidelines for primary open-angle glaucoma based on visual field testing, cup-to-disc ratios, and applanation tonometry.10

All study participants were interviewed in their preferred language (ie, English, Spanish, Mandarin, Cantonese, Vietnamese, or Tagalog) by a trained member of the multilingual research team. Data were collected by oral questionnaire regarding patient demographics, perceived barriers to follow-up, and reasons for nonattendance. Patients self-reported their racial/ethnic classifications. In the case of multiethnic individuals, patients were asked with which classification they most identified. Oral informed consent was obtained from all patients before the interview. Questions were standardized across languages. The questions were based on a questionnaire used at the Aravind Eye Hospital Glaucoma Clinic in Coimbatore, Tamil Nadu, India, and adapted to the present study population.12 The questionnaire was validated in a pilot study with a randomly selected cohort of 14 patients who met the aforementioned eligibility criteria. Questions were adapted to the SFGH patient population, response coding classifications were calibrated, and protocol feasibility was tested. The participants in the pilot study were not included among the 152 cases and controls who comprised the study group. On the basis of pilot study results, it was determined that 150 patients would be adequate to identify predictors of inconsistent adherence with an odds ratio (OR) of 2 or greater with a power of 80% and an α of .05. Human subjects approval for this study was obtained from the institutional review boards of the following organizations: SFGH; University of California, San Francisco; and the Stanford University School of Medicine. The study followed the tenets of the Treaty of Helsinki.

Statistical analysis was conducted using IBM SPSS Statistics statistical software, version 18.0 (SPSS Inc, Chicago, Illinois). Proportions were compared using the t and χ2 tests, and adjusted ORs and 95% confidence intervals (CIs) were calculated using a stepwise multivariate logistic regression model. Variables with P <.20 were initially included in the multivariate regression model with age and sex then successively eliminated based on higher P values. All variables except age and sex had P <.05 in the final regression model.

Results

Characteristics of the patients with inconsistent follow-up (cases) and consistent follow-up (controls) are summarized in Table 2. Of the 186 patients recruited for the study, 14 were involved in the pilot study, an additional 16 were unreachable, and 4 declined to participate, citing time constraints. A total of 152 patients completed the oral questionnaire in a 1:1 matched ratio and were included in the final analysis.

The follow-up patterns of the 152 study patients varied across ethnic groups (Table 2). The number (percentage) of black, Latino, Asian, and white patients classified as cases was 21 (27.6%), 31 (40.8%), 18 (23.7%), and 6 (7.9%), respectively. The proportion of cases within each race/ethnicity was 61.8% black, 54.4% Latino, 42.9% Asian, and 31.6% white.

Table 3 shows the univariate analysis for the potential predictors of inconsistent follow-up. A stepwise multivariate logistic regression model identified independent predictors of inconsistent follow-up after adjusting for covariates (ie, age, sex, disease severity, employment status, marital status, and health insurance coverage status). These predictors included black race, Latino ethnicity, having little or no recollection of receiving glaucoma-related education from clinic staff, receiving information regarding glaucoma from family members, being unaware of the risk of irreversible blindness due to glaucoma and the need for lifelong treatment, and perceiving follow-up visits not to be important if patients were regularly using medications and did not notice any visual symptoms (Table 4).

Black race and Latino ethnicity were strong predictors of inconsistent follow-up even after adjustment for age and sex (OR, 7.16 [95% CI, 1.64-31.24]; P = .009; and 4.77 [1.12-20.29]; P = .04, respectively). A breakdown of cases and controls by race/ethnicity and educational background (used as a proxy for socioeconomic status) is given in Table 5. Latino patients having a low level of education (ie, no formal education through completion of primary school) and black patients with a medium level of education (ie, any secondary school through completion of a secondary school degree or a general educational development certificate) were more likely to have inconsistent follow-up patterns compared with their white counterparts with similar educational backgrounds (P = .04 for each). Although not statistically significant, a trend was observed of black patients with high educational level (ie, having any undergraduate university or community college coursework or beyond) and Asian/Pacific Islander patients of low educational background demonstrating consistent follow-up patterns (P = .07 and P = .08, respectively).

Factors not found to be predictive of follow-up patterns included employment status, marital status, health insurance coverage status, self-reported ability to pay for medications, history of laser treatment for glaucoma, and self-reported inconvenience of transportation to clinic (Table 3 and Table 4).

Comment

Disease severity at the time of diagnosis and rate of disease progression are well-established risk factors for vision loss in patients with glaucoma.3,4 Recently, much attention has been given to nonadherence to prescribed glaucoma medication regimens as an important predictor of adverse outcomes.11,21 Remarkably little attention, however, has been given to inconsistent adherence to recommended follow-up visits as a predictive factor for vision loss. The physician treating glaucoma has many treatment options, including medications, laser trabeculoplasty, and incisional glaucoma surgery, all of which can lower IOP. Insufficient IOP lowering with 1 approach because of lack of efficacy of or inconsistent adherence to glaucoma therapy generally results in advancement to the next step in this treatment algorithm. Such assessment and advancement of therapy can only take place, however, if patients are seen on a timely basis for follow-up. Although high-quality confirmatory evidence is lacking, one can hypothesize that inconsistent follow-up is a risk factor for inconsistent outcomes regarding any chronic degenerative disease for which multiple potentially effective therapeutic options exist.

This study demonstrates that race/ethnicity and educational background are important factors that affect the follow-up patterns of patients with glaucoma in an ethnically diverse, resource-limited county hospital population. Black and Latino patients were found, on average, to show less consistent follow-up relative to whites. The proportion of black patients with inconsistent follow-up (61.8%) was approximately double that observed among white patients (31.6%), resulting in a calculated OR of 7.16 (P = .009) even after adjustment for covariates (ie, age, sex, disease severity, employment status, marital status, and health insurance coverage status). Latino patients had a calculated OR of 4.77 (P = .04) compared with their white counterparts after similar covariate adjustment. These results are consistent with the trends of lower follow-up and screening rates observed in black and Latino patients with breast and cervical cancer compared with their white counterparts.22,23

Patients' understanding of glaucoma disease mechanisms greatly influences their adherence to recommended follow-up visits. In particular, those unaware of the chronic and insidious nature of glaucoma and the need for permanent treatment despite regular use of prescribed medications and lack of symptoms were more likely to have inconsistent follow-up in this study. Uncertainty regarding these issues was highly correlated with patient perceptions regarding insufficient glaucoma-related education from clinic staff. This finding dovetails with the observation in the breast cancer literature that misconceptions regarding breast lumps and appropriate follow-up care were more common among women of ethnic minority backgrounds, those with lower socioeconomic status, and those with less access to health care.24

The identification of modifiable risk factors for inconsistent follow-up may allow assessment of targeted solutions, such as increasing physician-initiated patient education in an effort to eliminate the knowledge gap and to decrease inconsistent adherence to follow-up. Given our results, targeted interventions aimed at patients with multiple predictors of inconsistent follow-up, such as minority race/ethnicity, low educational background, and those with limited knowledge of glaucoma, have the greatest potential of increasing rates of glaucoma disease follow-up. Additional larger studies in the future can help refine recommendations regarding groups to target for improvement in follow-up.

Several prior studies11,12 have reported that mild glaucomatous disease and lack of glaucoma symptoms are predictors of inconsistent adherence to follow-up. In contrast, individuals with severe disease and those with ocular pain and/or vision changes at the time of their initial glaucoma diagnosis were more likely to show inconsistent adherence to recommended follow-up in this study. Although causality is difficult to assess in such a study, a possible explanation for these findings is that inconsistent adherence to appointments in the past led to greater disease severity and also predicted future follow-up behavior. This hypothesis is supported by reports from the Institute of Medicine25-27 and the US Department of Health and Human Services28 documenting that racial/ethnic minorities and patients of lower socioeconomic status are more likely to have limited access to care, to have frequently missed opportunities for preventive care, and to report insufficient patient-physician communication. These trends are believed to contribute to the high rates of late-stage breast, endometrial, cervical, and colorectal cancer diagnoses in those patients.25-28 Whether similar patterns are evident in patients with glaucoma, thus allowing for greater disease progression and adverse symptoms at the initial office visit, is an area for future investigation.

This study suggests that blacks and Latinos, 2 racial/ethnic groups previously identified as being at high risk for glaucomatous disease, may benefit from specialized education and counseling to improve follow-up. Targeted physician-initiated educational interventions might primarily focus on 3 variables: the risk of irreversible blindness from glaucoma, the long-term nature of treatment, and the importance of attending follow-up visits despite the ongoing use of medications and lack of visual symptoms. The effect of such interventions should be assessed, with the primary outcome being visual preservation. Glaucoma education by physicians may be important in correcting the pervasive lack of awareness regarding the insidious nature of disease progression, which is predictive of inconsistent follow-up. Our work suggests that advocating for patient-centered education is important when caring for a multilingual patient population in a resource-limited setting. With health system reform in full swing, pressure is growing to deliver high-quality, cost-effective care. An understanding of the population-specific pattern of each practice is likely a necessary prerequisite to providing high-yield interventions in an effort to prevent avoidable blindness.

One limitation of this study is the small number of patients classified as white cases. Despite the aim of the study to interview more patients in this reference category, limited numbers of white patients with inconsistent follow-up were available. Such a small sample size in 1 group is clearly suboptimal for many reasons, including the resultant wide CIs, which limit the race/ethnicity analysis. Future studies will aim for larger sample sizes to address this issue.

Generalizing the current findings to other practices must be done with caution given site-specific variability in demographics, patient counseling, and availability of care. However, this study demonstrates the adaptability of our research method, originally applied at Aravind Eye Hospital in India, to other geographic locations. We believe this survey will be a useful tool in determining predictors of and barriers associated with inconsistent patient adherence to glaucoma follow-up visits in a variety of settings. The application of these findings in examining the influence of educational interventions on adherence to follow-up should be the focus of future studies.

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Article Information

Correspondence: Shan C. Lin, MD, University of California, San Francisco, 10 Koret Way, Room K-325, San Francisco, CA 94143-0730 (LinS@vision.ucsf.edu).

Submitted for Publication: April 20, 2010; final revision received October 5, 2010; accepted October 11, 2010.

Financial Disclosure: None reported.

Additional Contributions: We thank Rita Popat, PhD, at Stanford University; Bennie Jeng, MD, and Dandan Wang, MD, at the University of California, San Francisco; and the San Francisco General Hospital staff for their invaluable help with this project.

References
1.
Giangiacomo AC. The epidemiology of glaucoma. In: Grehn F, Stamper R, eds. Glaucoma. Berlin, Germany: Springer; 2009:13-21
2.
Kass MA, Heuer DK, Higginbotham EJ,  et al.  The Ocular Hypertension Treatment Study: a randomized trial determines that topical ocular hypotensive medication delays or prevents the onset of primary open-angle glaucoma.  Arch Opthalmol. 2002;120(6):701-730Google ScholarCrossref
3.
Leske MC, Heijl A, Hussein M, Bengtsson B, Hyman L, Komaroff E.Early Manifest Glaucoma Trial Group.  Factors for glaucoma progression and the effect of treatment: the Early Manifest Glaucoma Trial.  Arch Ophthalmol. 2003;121(1):48-5612523884PubMedGoogle ScholarCrossref
4.
Leske MC, Heijl A, Hyman L, Bengtsson B, Dong L, Yang Z.EMGT Group.  Predictors of long-term progression in the Early Manifest Glaucoma Trial.  Ophthalmology. 2007;114(11):1965-197217628686PubMedGoogle ScholarCrossref
5.
Epstein DL, Krug JH Jr, Hertzmark E, Remis LL, Edelstein DJ. A long-term clinical trial of timolol therapy versus no treatment in the management of glaucoma suspects.  Ophthalmology. 1989;96(10):1460-14672685707PubMedGoogle ScholarCrossref
6.
Kass MA. Timolol treatment prevents or delays glaucomatous visual field loss in individuals with ocular hypertension: a five-year, randomized, double-masked, clinical trial.  Trans Am Ophthalmol Soc. 1989;87:598-6182562546PubMedGoogle Scholar
7.
Schulzer M, Drance SM, Douglas GR. A comparison of treated and untreated glaucoma suspects.  Ophthalmology. 1991;98(3):301-3072023749PubMedGoogle ScholarCrossref
8.
DiMatteo MR. Variations in patients' adherence to medical recommendations: a quantitative review of 50 years of research.  Med Care. 2004;42(3):200-20915076819PubMedGoogle ScholarCrossref
9.
Friedman DS, Hahn SR, Quigley HA,  et al.  Doctor-patient communication in glaucoma care: analysis of videotaped encounters in community-based office practice.  Ophthalmology. 2009;116(12):2277-2285Google ScholarCrossref
10.
American Academy of Ophthalmology Glaucoma Panel.  Preferred Practice Patterns: Primary Open-Angle Glaucoma, Primary Open-Angle Glaucoma Suspect, and Primary Angel Closure. San Francisco, CA: American Academy of Ophthalmology; 2005
11.
Kosoko O, Quigley HA, Vitale S, Enger C, Kerrigan L, Tielsch JM. Risk factors for noncompliance with glaucoma follow-up visits in a residents' eye clinic.  Ophthalmology. 1998;105(11):2105-21119818613PubMedGoogle ScholarCrossref
12.
Lee BW, Sathyan P, John RK, Singh K, Robin AL. Predictors of and barriers associated with poor follow-up in patients with glaucoma in South India.  Arch Ophthalmol. 2008;126(10):1448-145418852425PubMedGoogle ScholarCrossref
13.
Quigley HA, West SK, Rodriguez J, Munoz B, Klein R, Snyder R. The prevalence of glaucoma in a population-based study of Hispanic subjects: Proyecto VER.  Arch Ophthalmol. 2001;119(12):1819-182611735794PubMedGoogle ScholarCrossref
14.
Shimmyo M, Ross AJ, Moy A, Mostafavi R. Intraocular pressure, Goldmann applanation tension, corneal thickness, and corneal curvature in Caucasians, Asians, Hispanics, and African Americans.  Am J Ophthalmol. 2003;136(4):603-61314516799PubMedGoogle ScholarCrossref
15.
Tielsch JM, Sommer A, Katz J, Royall RM, Quigley HA, Javitt J. Racial variations in the prevalence of primary open-angle glaucoma: the Baltimore Eye Survey.  JAMA. 1991;266(3):369-3742056646PubMedGoogle ScholarCrossref
16.
Kim E, Varma R. Glaucoma in Latinos/Hispanics.  Curr Opin Ophthalmol. 2009;21(2):100-10519077825PubMedGoogle ScholarCrossref
17.
Varma R, Ying-Lai M, Francis BA,  et al; Los Angeles Latino Eye Study Group.  Prevalence of open-angle glaucoma and ocular hypertension in Latinos: the Los Angeles Latino Eye Study.  Ophthalmology. 2004;111(8):1439-144815288969PubMedGoogle ScholarCrossref
18.
Martin MJ, Sommer A, Gold EB, Diamond EL. Race and primary open-angle glaucoma.  Am J Ophthalmol. 1985;99(4):383-3873985075PubMedGoogle ScholarCrossref
19.
Sommer A, Tielsch JM, Katz J,  et al.  Racial differences in the cause-specific prevalence of blindness in east Baltimore.  N Engl J Med. 1991;325(20):1412-14171922252PubMedGoogle ScholarCrossref
20.
Wormald RP, Basauri E, Wright LA, Evans JR. The African Caribbean Eye Survey: risk factors for glaucoma in a sample of African Caribbean people living in London.  Eye (Lond). 1994;8(pt 3):315-3207958037PubMedGoogle ScholarCrossref
21.
Gwira JA, Vistamehr S, Shelsta H,  et al.  Factors associated with failure to follow up after glaucoma screening: a study in an African American population.  Ophthalmology. 2006;113(8):1315-131916769119PubMedGoogle ScholarCrossref
22.
Richardson JL, Langholz B, Bernstein L, Burciaga C, Danley K, Ross RK. Stage and delay in breast cancer diagnosis by race, socioeconomic status, age and year.  Br J Cancer. 1992;65(6):922-9261616865PubMedGoogle ScholarCrossref
23.
Madison T, Schottenfeld D, James SA, Schwartz AG, Gruber SB. Endometrial cancer: socioeconomic status and racial/ethnic differences in stage at diagnosis, treatment, and survival.  Am J Public Health. 2004;94(12):2104-211115569961PubMedGoogle ScholarCrossref
24.
Rauscher GH, Ferrans CE, Kaiser K, Campbell RT, Calhoun EE, Warnecke RB. Misconceptions about breast lumps and delayed medical presentation in urban breast cancer patients.  Cancer Epidemiol Biomarkers Prev. 2010;19(3):640-64720200436PubMedGoogle ScholarCrossref
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