Rapid developments of telecommunication and information technology in the last decade brought along a rising field of medicine, that is, telehealth/telemedicine.1 2 teleophthalmology began to develop in rural and remote areas with the idea of facilitating health care services for underserved populations, there is also a great need for it in both urban communities and high-income regions.3,4 5 teleophthalmology is already accepted within the medical community to improve eye care adherence and access, there is still a great need for a teleophthalmologic protocol to detect the most common eye diseases.
Although the field of eye care remains stable despite its shortage of specialists, the growing population and the rise of old-age population leads to an exponential increase in follow-up visits, which indicates that the demand for teleophthalmology will continue to increase in the near future.6,7
The objective of this pilot study is to examine the benefit and feasibility of a real-time mobile teleophthalmology program for screening of undetected eye diseases in the at-risk neighborhoods of the metro area and provide an evaluation for this program. In this program, vision test results were evaluated in real time and remote consultation with an eye care professional was immediately available. Our team previously conducted urban-located, community-based screenings with teleophthalmologic models to better understand different systematic approaches.8,9 teleophthalmology program was designed in the northern Manhattan of New York City. In a previous study using data from surrounding neighborhoods of northern Manhattan, both at fixed sites and in a mobile unit, it is shown that 25% of 8547 participants screened were glaucoma suspects, 15% were deemed to need further investigation of ocular diseases other than glaucoma, and 57% had never seen an eye doctor in their lifetime.8,10
METHODS
This study adhered to the tenets of the Declaration of Helsinki and was approved by the Columbia University Institutional Review Board.
Between June 2017 and November 2018, a Wi-Fi–equipped teleophthalmology mobile unit toured neighborhoods in the metro area (Fig. 1 A). In some cases, the mobile unit was simply parked on a street; in other cases, it was parked at a community center or health fair. Flyers/handouts were distributed to announce the scheduled time and location for free vision screening. No other forms of recruitment were used. Subjects were not compensated for participating in the study. The only inclusion criterion for the study was to be 18 years of age or older.
FIGURE 1: A, Teleophthalmology mobile unit. B, Inside of the mobile van.
In an isolated part of the mobile unit or hospital-affiliated screening center, an explanation of the study was provided in English to each prospective participant on a computer screen (or in hard copy, if desired) and the information was also given verbally. Alternatively, the explanation was available in Spanish, and Spanish-speaking staff members were present in the mobile unit to explain further if necessary. Participants clicked an “I Agree” button on the computer to indicate their willingness to participate, and this action generated a unique identification number. Three staff members including ocular technicians and medical and college students screened each participant as follows:
Brief medical history taking that included self-identification of ethnicity and race, address of residence, past medical, ocular, and family histories including specific questions about diabetes mellitus, hypertension, sleep apnea, hyperlipidemia, smoking and dental examinations. This questionnaire was created to help the evaluation of risk factors for major eye diseases.
After this medical history taking process, participants were evaluated with basic systemic measurements such as:
Height and weight measurement for calculation of body mass index (BMI)
Blood pressure measurement using an electronic sphygmomanometer
Hemoglobin A1c (HbA1c) testing (Alere Afinion point-of-care assay, Abbott, Abbott Park, IL)
Next, participants were taken to different stations for various ocular screening:
Visual acuity measurement using the Snellen chart (Titmus 2s Vision Screener)
Intraocular pressure (IOP) measurement via noncontact tonometry (Reichert 7CR Auto Tonometer, Cal Coast Ophthalmic Instruments Inc., Torrance, CA)
Anterior and posterior segment optical coherence tomography (OCT) (3D OCT-1 Maestro, Topcon Medical Systems Inc., Oakland, NJ)
Nonmydriatic fundus photography (3D OCT-1 Maestro, Topcon Medical Systems Inc., Oakland, NJ)
Peripheral visual field measurement using frequency doubling technology
The data obtained was entered and transmitted via a secure Virtual Private Network connection to the Edward Harkness Eye Institute reading center. The reading center is a centralized center where all data is securely transmitted utilizing an internally built data capturing system, imaging system, and video conferencing system. An ophthalmologist or optometrist at the center analyzed the data in real time. The eye care professional then video-conferenced in real time with the participant via Skype for Business, which incorporates end-to-end encryption for voice and video, to give recommendations for follow-up care. The conversation was conducted in a private part of the mobile unit. Before leaving the screening site, each participant received a printed copy of his or her results and recommendations for follow-up care as well as a copy of the informed consent form. Each participant also received a list of eye care professionals whose offices were in or near their neighborhood. All the evaluations including anamnesis, basic systematic measurements, ocular screenings, real-time video conference with an eye care professional and information about follow-up recommendations, took about 20 minutes for each participant. All the evaluations were performed in the efficient design mobile van (Fig. 1 B) and only one participant was taken into the mobile van at a time to establish effective confidentiality.
To check follow-up rates and results within 2 to 4 months of the screening, participants who were referred to an ophthalmic examination were contacted by a patient navigator to ensure a follow-up visit with a local eye doctor. If participants failed to follow up, we inquired as to what prevented them from a follow-up to better understand the needs of the population.
The reading guidelines were prepared by an experienced glaucoma specialist for an interpretation of the findings. An experienced ophthalmologist or an optometrist analyzed the data in real time and referred to participants according to defined criteria.
Definitions
Glaucoma suspect:
IOP >21 mm Hg with corneal thickness taken into consideration, and/or glaucomatous appearance of the optic disc and/or an abnormal OCT consistent with glaucoma [deterioration of double hump appearance, asymmetry between 2 eyes in retinal nerve fiber layer (RNFL), generalized thinning in RNFL] and/or a narrow angle on anterior segment OCT and/or frequency doubling technology (FDT) abnormalities inconsistent with retinal pathologies.
Narrow-angle suspect:
Structurally assessed angles based on anterior segment OCT. Angles were defined as narrow if they were ≤20 degrees on anterior segment OCT. The narrow-angle suspect group is categorized as a glaucoma suspect as well.
Cataract suspect:
Visual acuity ≤20/40 with evidence of cataract on anterior segment OCT
Diabetic retinopathy:
Hemorrhages or exudates on 45∞ fundus photography
Diabetic state:
Prediabetic state: HbA1c between 5.7% and 6.4%
Diabetes: HbA1c 6.5% or above11
Blood pressure guidelines:
Low blood pressure: Systolic ≤90 mm Hg or diastolic ≤60 mm Hg
Normal: <120/80 mm Hg
Elevated: Systolic 120–129 mm Hg and diastolic <80 mm Hg
Stage 1 hypertension: Systolic 130–139 mm Hg or diastolic 80–89 mm Hg
Stage 2 hypertension: Systolic ≥140 mm Hg or diastolic ≥90 mm Hg 12
BMI state:
Obesity: BMI ≥30
Above morbid obesity: BMI >40
The usual equation for calculating BMI (weight in pounds / height in inches2 ) was adjusted to account for the fact that the participants were clothed (−2.65 for males; −1.76 for females).13
Data Analysis
Relative risk ratios (RRR) for each predictor and their corresponding p-values were used for statistical inference. A multivariate Bayesian logistic regression was performed to identify patient characteristics associated with possible abnormal ophthalmic findings. A Bayesian approach to logistic regression was chosen to specify the prior distribution of each continuous variable and to avoid model overfitting. The regression model was built from the full set of candidate predictor variables, that is, all of the patient demographics and characteristics, and their pairwise interactions. Computerized statistical analyses were conducted using STATA software (version 14, StataCorp, College Station, TX). The alpha level (type 1 error) was set to be 0.05.
RESULTS
Of 957 adults screened with 3828 total images, 3744 (97.81%) were readable images. No participant who wished to be screened was turned away, and no one refused screening once the study was explained. We eliminated 9 participants from the analysis because they did not finish the screening, or did not have images taken (fundus photographs and OCT images).
The median age of the participants was 58 years and 54% were female as shown in Table 1 . The vast majority (93%) were ethnic/racial minorities (nonwhite Hispanic, 46%; African American, 31%; Asian, 10%; Caucasian, 6%; others, 7%). Evidently, the non-Hispanic and non–African American percentages in our study group are relatively small. That is why even though the large size of our study group would still allow some (but not strong) statistical comparison across different ethnicities, we refrain from doing so in the paper. Nevertheless, we should yield that this is a limitation for our study. Sizeable percentages had other risk factors for eye disease; close to one-third of the participants reported dyslipidemia, close to one-fifth were current smokers, and about 10% reported sleep apnea, which is a risk factor for glaucoma.14
TABLE 1 -
Demographics and Patient Characteristics
Characteristics
Total (n = 957)
Healthy Ocular Findings (n = 499)
Ophthalmology Referred Patients (n = 458)
Glaucoma Suspects (n = 305)
Anatomic Narrow Angle Suspects (n = 136)
Cataract Suspects (n = 124)
AMD Suspects (n = 9)
Retinal Disorders Suspects (n = 29)
Other Ocular Findings (n = 97)
Age, mean ± std
57.72 ± 12. 92
54.49 ± 13.01
61.24 ± 11.87
61.6 ± 11.25
59.74 ± 9.61
67.77 ± 10.9
67.89 ± 11.49
62.31 ± 9.55
60.20 ± 12.11
18≤ age ≤44, n (%)
142 (15%)
104 (21%)
38 (8%)
17 (6%)
6 (4%)
3 (2%)
0 (0%)
1 (3%)
12 (12%)
45≤ age ≤54, n (%)
217 (23%)
133 (27%)
84 (18%)
60 (20%)
34 (25%)
12 (10%)
1 (11%)
7 (24%)
16 (16%)
55≤ age ≤64
318 (33%)
136 (27%)
148 (32%)
104 (34%)
53 (39%)
36 (29%)
3 (33%)
9 (31%)
30 (31%)
Age ≥65
280 (29%)
126 (25%)
188 (41%)
124 (41%)
43 (32%)
73 (59%)
5 (56%)
12 (41%)
39 (40%)
Gender, n (%)
Female
519 (54%)
278 (56%)
241 (%)
162 (53%)
85 (63%)
64 (52%)
7 (78%)
11 (38%)
51 (53%)
Male
438 (46%)
221 (44%)
217 (%)
143 (47%)
51 (38%)
60 (48%)
2 (22%)
18 (62%)
46 (47%)
Ethnicity, n (%)
African American
300 (31%)
142 (28%)
158 (34%)
107 (35%)
43 (32%)
34 (27%)
4 (44%)
7 (24%)
27 (28%)
Asian
94 (10%)
52 (10%)
42 (9%)
26 (9%)
11 (8%)
12 (10%)
4 (44%)
1 (3%)
10 (10%)
Caucasian
55 (6%)
35 (7%)
20 (4%)
14 (5%)
3 (2%)
14 (11%)
0 (0%)
1 (3%)
3 (3%)
Hispanic
443 (46%)
235 (47%)
208 (45%)
140 (46%)
68 (50%)
59 (48%)
1 (11%)
18 (62%)
46 (47%)
Other
65 (7%)
35 (7%)
28 (6%)
17 (6%)
11 (8%)
5 (4%)
0 (0%)
2 (7%)
9 (9%)
Self-declaration, n (%)
Insurance
738 (77%)
383 (77%)
355 (78%)
231 (76%)
100 (74%)
21 (72%)
9 (100%)
21 (72%)
86 (89%)
Current smoker
182 (19%)
89 (18%)
93 (20%)
57 (19%)
27 (20%)
23 (19%)
2 (22%)
7 (24%)
17 (18%)
Sleep apnea
93 (10%)
49 (10%)
44 (10%)
26 (9%)
9 (7%)
14 (11%)
1 (11%)
2 (7%)
10 (10%)
Hypertension
383 (40%)
21 (4%)
219 (48%)
145 (48%)
52 (38%)
70 (56%)
1 (11%)
18 (62%)
44 (45%)
Diabetes mellitus
213 (22%)
13 (3%)
119 (26%)
71 (23%)
29 (21%)
33 (27%)
1 (11%)
28 (97%)
31 (32%)
Dyslipidemia
302 (32%)
20 (4%)
163 (36%)
113 (37%)
46 (34%)
45 (36%)
1 (11%)
14 (48%)
32 (33%)
Glaucoma (all)
62 (6%)
12 (2%)
50 (11%)
44 (14%)
10 (7%)
14 (11%)
0 (0%)
6 (21%)
5 (5%)
Cataracts
113 (12%)
41 (8%)
72 (16%)
48 (16%)
12 (9%)
30 (24%)
3 (33%)
14 (48%)
12 (12%)
Macular degeneration
6 (<1%)
3 (<1%)
3 (<1%)
3 (1%)
2 (1%)
1 (1%)
0 (0%)
0 (0%)
1 (1%)
Retinal disorders
18 (2%)
6 (1%)
12 (3%)
9 (3%)
3 (2%)
2 (2%)
0 (0%)
4 (14%)
5 (5%)
Last dental exam
<1 year
622 (65%)
319 (64%)
303 (66%)
196 (64%)
87 (64%)
75 (60%)
8 (89%)
16 (55%)
66 (68%)
>2 years
213 (22%)
117 (23%)
96 (21%)
70 (23%)
32 (24%)
31 (25%)
1 (11%)
9 (31%)
14 (14%)
>5 years
108 (11%)
54 (11%)
54 (11%)
36 (12%)
16 (12%)
15 (12%)
0 (0%)
3 (10%)
17 (18%)
Never
14 (1%)
9 (2%)
5 (1%)
3 (1%)
1 (<1%)
3 (2%)
0 (0%)
1 (3%)
0 (0%)
Last eye exam
<1 year
407 (43%)
178 (36%)
200 (44%)
154 (50%)
65 (48%)
19 (66%)
4 (44%)
19 (66%)
54 (56%)
>2 years
317 (33%)
180 (36%)
108 (24%)
98 (32%)
43 (32%)
4 (14%)
2 (22%)
4 (14%)
21 (22%)
>5 years
171 (18%)
97 (19%)
41 (9%)
41 (13%)
22 (16%)
5 (17%)
3 (33%)
5 (17%)
20 (21%)
Never
61 (6%)
43 (9%)
6 (1%)
12 (4%)
6 (4%)
1 (3%)
0 (0%)
1 (3%)
2 (2%)
Of the 957 participants whose data was analyzed, 380 participants (40%) were newly diagnosed and 458 (48%) were referred for further ophthalmic evaluation. Of those, 305 (52%) were glaucoma suspects, 124 (25%) cataracts, 29 (6%) diabetic retinopathy, and 9 (1.8%) participants were macular degeneration suspects.
Since this was a community-based screening study, the only inclusion criterion for the study was to be 18 years of age or older. All participants who may have an ocular disease were evaluated with the same screening protocol as those who do not. Although some of the participants had self-reported eye conditions or were being monitored for a previously detected eye condition and disease, the newly identified disease rate is quite high as shown in Table 2 ; 261 of the glaucoma and 94 of the cataract suspects were referred to an ophthalmic examination with a novel pre-diagnosis. In addition, from the mobile screening 244 (25%) participants learned that they had diabetes or prediabetes and 183 (19%) of the study participants learned that they were hypertensive.
TABLE 2 -
Comparison of Self-Reported Diseases and Newly Detected Diseases
Condition
Disease Detected by Screening (n = 567)
Self-Reported Within Condition
Newly Identified Disease
Self-Reported Disease, Not Identified by Screening
Ocular conditions (all)
n = 567
n = 129
n = 438
n = 107
Glaucoma (all)
305
44
261
18
Cataract∗
124
30
94
83
Macular degeneration
9
0
9
6
Retinal disorders (DRP and the others)
29
4
25
14
Diabetes or prediabetes†
433
213
220
24
Hypertension
344
123
221
200
DRP indicates diabetic retinopathy.
∗ Patients who self-reported a history of cataracts. No differentiation between being preop or postop.
† Prediabetic state was defined as HbA1c 5.7%–6.4% and diabetes was defined as HbA1c ≥ 6.5%.
Table 3 shows various systemic examination findings among study participants, including HbA1c, systolic and diastolic blood pressure, and BMI measurements. As expected, the highest value of HbA1c, systolic blood pressure, and BMI were measured in the diabetic retinopathy group.
TABLE 3 -
Results of Vision Testing from the Telemedicine Protocol
Systematic Variables
Total (n = 957)
Healthy Ocular Findings (n = 499)
Ophthalmology Referred Patients (n = 458)
Glaucoma Suspects (n = 305)
Anatomic Narrow-Angle Suspects (n = 136)
Cataract Suspects (n = 124)
AMD Suspects (n = 9)
Retinal Disorders Suspects (n = 29)
Other Ocular Findings (n = 97)
HbA1c, mean ± std
5.96 ± 1.26
5.79 ± 1.03
6.14 ± 1.44
6.15 ± 1.48
5.94 ± 1.18
6.12 ± 1.25
5.74 ± 0.36
8.72 ± 2.14
6.03 ± 1.45
Healthy A1C (Under 5.3), n (%)
243 (25%)
135 (27%)
108 (24%)
66 (22%)
27 (20%)
25 (20%)
1 (11%)
0 (0%)
27 (28%)
Treading towards prediabetes (5.4–5.6), n (%)
279 (29%)
167 (33%)
112 (24%)
86 (28%)
41 (30%)
23 (19%)
4 (44%)
2 (7%)
22 (23%)
Prediabetic state (5.7–6.4), n (%)
289 (30%)
142 (28%)
147 (32%)
92 (30%)
47 (35%)
50 (40%)
4 (44%)
4 (14%)
31 (32%)
Diabetic (≥6.5), n (%)
144 (15%)
54 (11%)
90 (20%)
60 (20%)
21 (15%)
26 (21%)
0 (0%)
23 (79%)
17 (18%)
Systolic BP, mean ± std
128.8 ± 19.66
127.02 ± 18.47
130.74 ± 20.72
130.50 ± 20.13
128.93 ± 20.32
134.97 ± 21.48
125.56 ± 23.67
137.69 ± 24.75
126.48 ± 19.79
Diastolic BP, mean ± std
78.81 ± 12.02
78.39 ± 12.33
79.27 ± 11.67
79.47 ± 11.77
78.10 ± 11.01
78.77 ± 11.35
73.67 ± 12.13
79.76 ± 10.58
76.18 ± 10.32
Low BP, n (%)
10 (1%)
5 (1%)
5 (1%)
3 (1%)
2 (1%)
3 (2%)
0 (0%)
0 (0%)
2 (2%)
Normal BP, (%)
314 (33%)
184 (37%)
131 (29%)
85 (28%)
41 (30%)
26 (21%)
5 (56%)
8 (28%)
35 (36%)
Pre-HT, n (%)
266 (28%)
130 (26%)
136 (30%)
88 (29%)
45 (33%)
44 (35%)
0 (0%)
5 (17%)
32 (33%)
Stage 1 HT, n (%)
115 (12%)
62 (12%)
53 (12%)
40 (13%)
15 (11%)
12 (10%)
2 (22%)
3 (10%)
13 (13%)
Stage 2 HT, n (%)
243 (25%)
115 (23%)
128 (28%)
87 (29%)
33 (24%)
36 (29%)
2 (22%)
11 (38%)
14 (14%)
Hypertensive urgency, n (%)
8 (<1%)
3 (<1%)
5 (1%)
2 (1%)
0 (0%)
3 (2%)
0 (0%)
2 (7%)
1 (1%)
BMI, mean ± std
28.36 ± 5.86
28.64 ± 6.03
28.05 ± 5.66
27.89 ± 5.46
28.16 ± 6.29
28.38 ± 6.68
28.36 ± 8.76
30.58 ± 6.76
27.71 ± 5.71
Normal range (18.5–24.9), n (%)
265 (28%)
127 (25%)
138 (30%)
94 (31%)
39 (29%)
40 (32%)
5 (56%)
4 (14%)
30 (31%)
Elevated (25–29.9), n (%)
393 (42%)
206 (41%)
187 (41%)
123 (40%)
55 (40%)
45 (36%)
1 (11%)
11 (38%)
42 (43%)
High (≥30), n (%)
299 (31%)
166 (33%)
133 (29%)
88 (29%)
42 (31%)
39 (31%)
3 (33%)
14 (48%)
25 (26%)
AMD indicates age-related macular degeneration; BMI, body mass index; BP, blood pressure; HbA1c, hemoglobin A1c; HT, hypertension.
Table 4 shows various ophthalmologic parameters in study participants, including visual acuity (VA), intraocular pressure (IOP), central corneal thickness (CCT), vertical cup-to-disc ratio (VCDR, measured by reader and OCT), RNFL thickness, and FDT visual field (FDT-VF). Compared to those without any eye disease, the mean IOP was higher in patients with suspected glaucoma (14.56 mm Hg vs 16.89 mm Hg for the right eye, P value = 0.000, similar for the left eye). The mean CCT in patients with suspected glaucoma was similar to the mean CCT in healthy individuals (P = 0.55). The mean RNFL was notably thinner in patients with suspected glaucoma compared to the mean of the routine follow-up cohort (97.71 vs 108.58 for the left eye, P = 0.000, similar for the right eye). The cup-to-disc ratio in glaucoma suspects was larger on average by 0.13 mm compared to the healthy individuals (P = 0.00 for both eyes). Overall, and in all subgroups of participants by eye condition, the cup-to-disc ratio was greater when assessed by OCT than when assessed by the readers.
TABLE 4 -
Results of Vision Testing from the Telemedicine Protocol
Ocular Variables
Total (n = 957)
Healthy Ocular Findings (n = 499)
Ophthalmology Referred Patients (n = 458)
Glaucoma Suspects (n = 305)
Anatomic Narrow-Angle Suspects
Cataract Suspects (n = 124)
AMD Suspects (n = 9)
Retinal Disorders Suspects (n = 29)
Other Ocular Findings (n = 97)
VA (logMAR), mean ± std
Right eye
0.31 ± 0.32
0.26 ± 0.28
0.37 ± 0.35
0.34 ± 0.32
0.29 ± 0.20
0.52 ± 0.47
0.34 ± 0.19
0.67 ± 0.52
0.35 ± 0.36
Left eye
0.35 ± 0.42
0.27 ± 0.29
0.43 ± 0.51
0.41 ± 0.50
0.33 ± 0.40
0.55 ± 0.53
0.36 ± 0.28
0.74 ± 0.73
0.34 ± 0.31
IOP (mm Hg), mean ± std
Right eye
15.13 ± 4.23
14.46 ± 3.11
15.86 ± 5.09
16.79 ± 5.50
15.65 ± 4.46
15.34 ± 4.34
16.33 ± 3.77
15.10 ± 4.62
13.80 ± 3.45
Left eye
15.27 ± 4.46
14.56 ± 3.16
16.04 ± 5.45
16.89 ± 6.00
15.77 ± 4.33
15.57 ± 4.74
15.89 ± 4.86
15.46 ± 5.31
14.07 ± 3.49
CCT (μm), mean ± std
Right eye
544.56 ± 40.43
543.31 ± 39.14
545.86 ± 41.73
545.89 ± 40.61
546.45 ± 39.84
544.26 ± 42.61
550.00 ± 36.90
559.31 ± 32.04
548.77 ± 38.17
Left eye
545.19 ± 40.41
544.54 ± 38.96
545.90 ± 41.97
547.07 ± 41.12
547.95 ± 39.50
546.46 ± 46.45
552.00 ± 43.50
545.70 ± 27.19
550.68 ± 34.97
Reader: VCDR, mean ± std
Right eye
0.37 ± 0.18
0.33 ± 0.14
0.42 ± 0.20
0.46 ± 0.21
0.38 ± 0.18
0.38 ± 0.19
0.40 ± 0.13
0.42 ± 0.20
0.35 ± 0.16
Left eye
0.38 ± 0.18
0.34 ± 0.14
0.42 ± 0.21
0.47 ± 0.22
0.38 ± 0.18
0.37 ± 0.22
0.39 ± 0.10
0.46 ± 0.21
0.36 ± 0.17
OCT: VCDR, mean ± std
Right eye
0.56 ± 0.20
0.53 ± 0.18
0.58 ± 0.21
0.62 ± 0.21
0.57 ± 0.19
0.56 ± 0.23
0.58 ± 0.19
0.50 ± 0.25
0.52 ± 0.22
Left eye
0.56 ± 0.21
0.54 ± 0.19
0.58 ± 0.22
0.63 ± 0.20
0.58 ± 0.18
0.52 ± 0.26
0.58 ± 0.11
0.54 ± 0.24
0.53 ± 0.23
RNFL (μm), mean ± std
Right eye
105.17 ± 16.70
109.99 ± 11.61
99.88 ± 19.60
98.82 ± 20.28
105.40 ± 16.79
97.78 ± 20.52
109.56 ± 9.77
97.59 ± 23.18
100.63 ± 20.21
Left eye
104.19 ± 16.94
108.58 ± 11.72
99.30 ± 20.23
97.71 ± 20.51
105.57 ± 16.64
98.56 ± 17.26
112.89 ± 7.37
92.17 ± 25.00
100.03 ± 20.21
FDT-VF (μm), mean ± std
Right eye, avg misses
1.56 ± 3.23
0.86 ± 2.23
2.32 ± 3.92
1.79 ± 3.32
1.15 ± 2.76
2.38 ± 4.02
2.50 ± 5.90
6.14 ± 6.05
3.79 ± 4.34
Left eye, avg misses
1.69 ± 3.68
0.81 ± 2.31
2.68 ± 4.57
2.12 ± 4.05
1.23 ± 3.08
2.55 ± 4.66
3.13 ± 6.27
5.96 ± 6.43
4.46 ± 5.37
AMD indicates age-related macular degeneration; CCT, central corneal thickness; FDT-VF, frequency doubling technology visual field; IOP, intraocular pressure; OCT, optical coherence tomography; RNFL, retinal nerve fiber layer; VA, visual acuity; VCDR, vertical cup-to-disc ratio.
In Table 5 , the effect of all the ophthalmologic parameters was evaluated using an RRR calculation for 5 different groups. Besides the demographic characteristics, the risk factors were grouped in 3 categories as: self-reported conditions, systemic findings, and ocular findings. These risk factors were further subdivided to 5 groups, ie, the groups with healthy ocular findings, and patients referred for a further ophthalmic evaluation, glaucoma, narrow-angle, and cataract suspects.
TABLE 5 -
Results of Relative Risk Ratio from the Telemedicine Protocol
Healthy Ocular Findings RRR (95%)
P value
Ophthalmology Referred Patients RRR (95%)
P value
Glaucoma Referred
P value
Narrow-Angle Referred
P value
Cataract Referred
P value
Age (relative to group age 18–44)
45≤ age ≤54
0.84 (0.72–0.97)
0.02
1.45 (1.05–1.99)
0.02
2.31 (1.41–3.79)
0.00
3.71 (1.60- 8.60)
0.00
2.62 (0.75–9.11)
0.13
55≤ age ≤64
0.65 (0.56–0.76)
0.00
1.97 (1.45–2.61)
0.00
3.06 (1.91–4.90)
0.00
4.42 (1.95–10.03)
0.00
5.17 (1.61–16.61)
0.01
Age ≥65
0.54 (0.46–0.65)
0.00
2.24 (1.68–2.98)
0.00
3.30 (2.07–5.26)
0.00
3.24 (1.41- 7.44)
0.01
11.76 (3.78–36.62)
0.00
Gender (relative to male)
Female
1.06 (0.94–1.20)
0.34
0,94 (0.82–1.07)
0.34
0.96 (0.79–1.15)
0.64
1.41 (1.02–1.94)
0.04
0.90 (0.65–1.25)
0.53
Ethnicity (relative to Caucasian)
Hispanic
0.83 (0.67–1.04)
0.10
1.29 (0.90- 1.86)
0.17
1.24 (0.77–1.99)
0.37
2.81 (0.92–8.64)
0.07
0.52 (0.31– 0.87)
0.01
African American
0.74 (0.59–0.94)
0.01
1.45 (1.01–2.09)
0.05
1.40 (0.87–2.26)
0.17
2.63 (0.85–8.17)
0.10
0.45 (0.26– 0.77)
0.00
Asian
0.87 (0.66–1.14)
0.31
1.23 (0.81–1.86)
0.33
1.09 (0.62–1.90)
0.77
2.15 (0.63–7.36)
0.23
0.50 (0.25–1.00)
0.05
Others
0.85 (0.63–1.14)
0.28
1.27 (0.82- 1.97)
0.29
1.09 (0.60- 1.98)
0.78
3.10 (0.91–10.56)
0.07
0.30 (0.12–0.79)
0.01
Self-report Conditions
Presence of insurance
0.98 (0.85–1.13)
0.77
1.02 (0.87–1.20)
0.78
0.93 (0.75- 1.15)
0.51
0.84 (0.59- 1.20)
0.34
0.92 (0.63–1.35)
0.67
Smoking
0.92 (0.79–1.09)
0.34
1.08 (0.92–1.27)
0.32
0.98 (0.77–1.24)
0.86
1.05 (0.71–1.56)
0.79
0.97 (0.64–1.48)
0.89
Sleep apnea
1.01 (0.83–1.24)
0.91
0.99 (0.79–1.24)
0.91
0.87 (0.62–1.22)
0.41
0.66 (0.35–1.25)
0.20
1.18 (0.71–1.98)
0.52
Personal history of hypertension
0.73 (0.64–0.84)
0.00
1.37 (1.21–1.56)
0.00
1.36 (1.13–1.63)
0.00
0.93 (0.67–1.28)
0.65
1.94 (1.40–2.70)
0.00
Personal history of DM
0.81 (0.69–0.96)
0.01
1.23 (1.06–1.41)
0.00
1.06 (0.85–1.32)
0.60
0.95 (0.65–1.39)
0.78
1.27 (0.88–1.83)
0.21
Personal history of dyslipidemia
0.84 (0.73–0.96)
0.01
1.19 (1.05–1.37)
0.01
1.28 (1.06–1.54)
0.01
1.11 (0.80–1.54)
0.54
1.24 (0.88–1.74)
0.22
Personal history of glaucoma
0.35 (0.21–0.59)
0.00
1.77 (1.53–2.04)
0.00
2.43 (2.01–2–94)
0.00
1.14 (0.63–2.06)
0.65
1.84 (1.12–3.00)
0.02
Personal history of cataract
0.67 (0.52–0.86)
0.00
1.39 (1.19–1.63)
0.00
1.39 (1.10–1.77)
0.01
0.72 (0.41–1.26)
0.25
2.38 (1.66–3.42)
0.00
Last dental exam (related to less than 1 year)
Last dental exam >2 years
1.07 (0.92–1.24)
0.35
0.93 (0.78–1.10)
0.37
1.04 (0.83–1.31)
0.77
1.07 (0.74–1.56)
0.71
1.21 (0.82–1.78)
0.34
Last dental exam >5 years
0.97 (0.80–1.20)
0.81
1.03 (0.84–1.26)
0.80
1.06 (0.79–1.41)
0.71
1.06 (0.65–1.73)
0.82
1.15 (0.69–1.93)
0.59
Last dental exam never
1.25 (0.84–1.87)
0.27
0.73 (0.36–1.49)
0.39
0.68 (0.25–1.87)
0.45
0.51 (0.08–3.41)
0.49
1.78 (0.64–4.95)
0.27
Last eye exam (related to last eye exam less than 1 year)
Last eye exam >2 years
1.30 (1.12–1.50)
0.00
0.77 (0.66–0.89)
0.00
0.82 (0.66–1.00)
0.06
0.85 (0.59–1.21)
0.37
0.55 (0.36–0.82)
0.00
Last eye exam >5 years
1.30 (1.09–1.54)
0.00
0.77 (0.63–0.93)
0.01
0.63 (0.47–0.85)
0.00
0.81 (0.51–1.26)
0.35
0.74 (0.47–1.16)
0.19
Last eye exam never
1.61 (1.32–1.96)
0.00
0.52 (0.35–0.78)
0.00
0.52 (0.30–0.88)
0.01
0.62 (0.28–1.36)
0.23
0.59 (0.27–1.30)
0.19
Presence of regular eye doctor
0.77 (0.67–0.89)
0.00
1.30 (1.14–1.49)
0.00
1.22 (1.00–1.48)
0.05
1.10 (0.79–1.54)
0.58
1.03 (0.72–1.47)
0.89
Reported vision change
0.92 (0.82–1.04)
0.20
1.09 (0.95–1.25)
0.20
0.97 (0.81–1.17)
0.77
1.18 (0.85–1.62)
0.33
1.73 (1.20–2.49)
0.00
Reported eye injury / surgery
0.80 (0.68–0.95)
0.01
1.24 (1.07–1.43)
0.00
1.24 (1.01–1.53)
0.04
1.02 (0.70–1.49)
0.92
1.04 (0.70–1.54)
0.86
Systemic Findings
BMI (related to normal range 18.5–24.9)
BMI elevated (25–29.9)
1.09 (0.93–1.28)
0.26
0.91 (0.78–1.07)
0.26
0.88 (0.71–1.10)
0.26
0.95 (0.65–1.39)
0.80
0.76 (0.51–1.13)
0.17
BMI high (≥30)
1.16 (0.99–1.36)
0.07
0.85 (0.72 -1.01)
0.07
0.83 (0.65–1.05)
0.13
0.95 (0.64–1.43)
0.82
0.86 (0.57–1.30)
0.48
HbA1c (related to healthy A1C level <5.3)
HbA1c: Treading towards prediabetes (5.4–5.6)
1.08 (0.93–1.25)
0.32
0.90 (0.74–1.10)
0.32
1.13 (0.87–1.49)
0.36
1.32 (0.84–2.08)
0.23
0.80 (0.47–1.37)
0.42
HbA1c: Prediabetic state (5.7–6.4)
0.88 (0.25–1.04)
0.14
1.14 (0.96–1.37)
0.14
1.17 (0.90–1.53)
0.24
1.46 (0.94–2.28)
0.09
1.68 (1.07–2.63)
0.02
HbA1c: Diabetic (6.5 and higher)
0.67 (0.53–0.87)
0.00
1.41 (1.16–1.70)
0.00
1.53 (1.16–2.03)
0.00
1.31 (0.77–2.23)
0.32
1.76 (1.06–2.92)
0.03
Blood pressure (related to normal blood pressure)
Prehypertension
0.84 (0.72–0.98)
0.02
1.23 (1.03–1.47)
0.02
1.23 (0.96–1.57)
0.11
1.29 (0.88–1.92)
0.19
2.00 (1.27–3.16)
0.00
Stage 1 hypertesion
0.92 (0.76–1.12)
0.42
1.11 (0.87–1.40)
0.40
1.29 (0.95–1.76)
0.11
1.00 (0.58–1.74)
0.99
1.26 (0.66–2.42)
0.48
Stage 2 hypertesion
0.81 (0.69–0.95)
0.01
1.27 (1.06–1.51)
0.01
1.33 (1.04–1.70)
0.03
1.04 (0.68–1.60)
0.85
1.79 (1.12–2.89)
0.02
Hypertensive urgency
0.64 (0.26–1.58)
0.33
1.50 (0.86–2.61)
0.15
0.93 (0.28–3.12)
0.90
4.54 (1.73–11.95)
0.00
Low blood pressure (hypoTA)
0.86 (0.46–1.60)
0.63
1.20 (0.64–2.27)
0.57
1.11 (0.42–2.92)
0.83
1.54 (0.43–5.48)
0.51
3.63 (1.32–10.04)
0.01
Ocular Findings (in either eye)
IOP >21 mm Hg
0.27 (0.17–0.41)
0.00
1.99 (1.78–2.22)
0.00
2.70 (2.30–3.18)
0.00
1.37 (0.91–2.08)
0.13
1.89 (1.28–2.78)
0.00
VCDR ≥0.5 according to specialist
0.60 (0.51–0.70)
0.00
1.60 (1.41–1.82)
0.00
2.50 (2.09–3.01)
0.00
1.11 (0.80–1.53)
0.54
1.24 (0.89–1.73)
0.21
VCDR ≥0.7 according to specialist
0.21 (0.11–0.39)
0.00
1.99 (1.78–2.22
0.00
3.08 (2.66–3.56)
0.00
0.92 (0.51–1.68)
0.79
1.72 (1.07–2.75)
0.02
VCDR ≥0.5 according to OCT
0.82 (0.72–0.94)
0.01
1.27 (1.05–1.53)
0.01
1.89 (1.38–2.57)
0.00
1.31 (0.85–2.01)
0.22
1.00 (0.66–1.51)
0.99
VCDR ≥0.7 according to OCT
1.68 (0.58–0.79)
0.00
1.45 (1.27–1.64)
0.00
2.26 (1.89–2.69)
0.00
0.99 (0.71–1.38)
0.94
1.13 (0.80–1.60)
0.48
VF (FDT) abnormality
0.66 (0.58–0.75)
0.00
1.36 (1.28–1.46)
0.00
1.22 (1.10–1.36)
0.00
0.83 (0.66–1.06)
0.13
1.20 (0.98–1.47)
0.07
RNFL <75 μm
0.07 (0.02–0.21)
0.00
2.04 (1.84–2.21)
0.00
2.76 (2.35–3.24)
0.00
1.29 (0.78–2.14)
0.32
2.57 (1.74–3.79)
0.00
RNFL <95 μm
0.44 (0.35–0.54)
0.00
1.88 (1.67–2.12)
0.00
2.46 (2.07–2.93)
0.00
1.06 (0.75–1.50)
0.76
1.85 (1.33–2.57)
0.00
CCT <535 μm
1.06 (0.94–1.20)
0.34
0.94 (0.82–1.07)
0.35
0.91 (0.75–1.09)
0.31
0.78 (0.57–1.07)
0.13
1.07 (0.77–1.49)
0.68
CCT <510 μm
0.98 (0.85–1.14)
0.81
1.02 (0.87–1.19)
0.81
1.07 (0.86–1.33)
0.56
1.08 (0.75–1.56)
0.68
0.98 (0.66–1.46)
0.91
BMI indicates body mass index; CCT, central corneal thickness; FDT, frequency doubling technology; HbA1c, hemoglobin A1c; IOP, intraocular pressure; OCT, optical coherence tomography; RNFL, retinal nerve fiber layer; RRR, relative risk ratios; VA, visual acuity; VF, visual field; VCDR, vertical cup-to-disc ratio.
For the participants, age older than 65, personal history of hypertension, diabetes mellitus, glaucoma and cataract, presence of regular eye doctor, reported eye injury/surgery, a HbA1c measurement of 6.5, stage 2 hypertension, and as for the ocular parameters, all but CCT; including an IOP ≥21 mm Hg, VCDR ≥0.5, VF abnormalities, and RNFL thinning, negatively impacted the health status. All these parameters are associated with an increase in the incidence of ocular finding.
Not surprisingly, these parameters are all associated with a higher risk of being referred for a further ophthalmic evaluation, as well as for being classified as a glaucoma suspect. Interestingly, narrow-angle suspects who include the participants who have anatomically narrow angle at 3 and 9 o’clock on anterior segment OCT, have the only significant risk factors as age and female gender. None of the ocular findings, systemic findings, or self-reported conditions were significant.
Significant factors associated with a higher risk for cataract suspicion include age, personal history of hypertension-glaucoma-cataract, reported vision change, a HbA1c measurement of ≥5.7, both low and high blood pressure measurement, VCDR ≥0.5 and RNFL thinning. However, ethnic groups relative to Caucasians were found to be less at risk for cataract.
Table 6 presents the results of the Bayesian logistic regressions. The first 3 columns of Table 6 show the results of the regressions of being referred for further evaluation and the remaining 3 columns do the same for glaucoma suspicion. According to the presented results, aging, HbA1c, being previously treated for glaucoma, high IOP and low RNFL are all associated with a higher probability of being referred. The same factors are also significant for glaucoma suspicion.
TABLE 6 -
Bayesian Regression of Ophthalmological Referral (1,2,3) and Glaucoma Referral (4,5,6)
(1)
(2)
(3)
(4)
(5)
(6)
Variables
Oph. Ref.
Oph. Ref.
Oph. Ref.
Glauc. Ref.
Glauc. Ref.
Glauc. Ref.
Age
0.04∗
0.04∗
0.04∗
0.04∗
0.02∗
0.03∗
(0.01)
(0.01)
(0.01)
(0.01)
(0.01)
(0.01)
Hispanic
0.63∗∗∗
0.71∗∗
0.70∗∗∗
0.53
0.61
0.60
(0.33)
(0.34)
(0.40)
(0.40)
(0.41)
(0.48)
African American
0.97∗
1.02∗
1.00∗∗
0.82∗∗
0.84∗∗
0.86∗∗∗
(0.33)
(0.35)
(0.40)
(0.40)
(0.42)
(0.48)
Asian
0.57
0.60
0.65
0.38
0.42
0.43
(0.38)
(0.40)
(0.46)
(0.45)
(0.47)
(0.53)
Others
0.94∗∗
1.00∗∗
1.07∗∗
0.62
0.68
0.79
(0.42)
(0.43)
(0.48)
(0.49)
(0.50)
(0.56)
HbA1c
0.61∗
0.51∗∗
0.58∗∗
0.41∗∗∗
0.27
0.48∗∗∗
(0.21)
(0.22)
(0.26)
(0.21)
(0.22)
(0.24)
Treated glaucoma
1.26∗
1.04∗
0.66∗∗∗
1.54∗
1.30∗
1.17∗
(0.32)
(0.32)
(0.37)
(0.31)
(0.32)
(0.35)
IOP >21mm Hg
2.10∗
2.04∗
2.39∗
1.98∗
1.92∗
2.27v
RNFL<95 μm
(0.28)
(0.29) 1.05∗ (0.18)
(0.35) 0.80∗ (0.21)
(0.23)
(0.24) 1.12∗ (0.18)
(0.28) 0.89∗ (0.20)
VF Abnormal
0.66∗
0.15
Constant
-3.76∗
-3.53∗
(0.11) -3.96∗
-3.92∗
-3.60∗
(0.10) -3.86∗
(0.49)
(0.49)
(0.55)
(0.55)
(0.54)
(0.60)
Observations
956
956
784
956
956
784
Robust standard errors in parentheses.HbA1c indicates hemoglobin A1c; IOP, intraocular pressure; RNFL, retinal nerve fiber layer; VF, visual field.
∗ P < 0.01
∗∗ P < 0.05
∗∗∗ P < 0.1
As shown in Table 7 , telephone follow-up was successful for only 38% of participants with cataracts, 38% with glaucoma, 30% narrow angles, 48% with retinal disorders, and none with macular degeneration. Besides, detected additional eye problems and disease confirmation rates were shown in Table 7 .
TABLE 7 -
Individual Follow-up
Disease, n
Reached, n (%)
Followed Up With Ophthalmologist, n (%)∗
Disease Confirmed, n (%)∗
Additional Eye Problem Detected, n (%)∗
Required Rx, n (%)∗
Cataract, n = 124
47 (38%)
39 (83%)
16 (34%)
7 (15%)
3 (6%)
Glaucoma, n = 305
117 (38%)
82 (70%)
50 (42%)
20 (17%)
0 (0%)
Narrow angle, n = 136
41 (30%)
41 (100%)
18 (44%)
25 (61%)
0 (0%)
Retinal disorders, n = 29
14 (48%)
10 (71%)
11 (79%)
6 (43%)
0 (0%)
Macular degeneration, n = 9
0 (0%)
0 (0%)
0 (0%)
NA
NA
NA indicates not applicable; Rx = Prescription for glasses.
∗ Percentage based on patients reached.
DISCUSSION
Our data indicate that 380 participants (40%) were newly diagnosed. 458 (48%) participants were detected for having at least 1 eye condition requiring treatment or control by an eye care specialist. It is worth emphasizing that this is not in an underserved neighborhood. Our team's previous studies that were done in the same area showed that this ratio is 40.79% for an abnormal eye condition,9 8
In this study, both the prevalence of detected ophthalmologic pathologies (48%) and newly diagnosed ocular conditions (40%) are absolutely higher compared to the results of similar studies. The prevalence of asymptomatic, newly diagnosed eye disease is 14% to 33% of patients in a great majority of the studies.15–18 15–17 18 19
The highest prevalence of abnormal ocular condition in literature is seen in our study. There might be several reasons that might contribute to this high rate. The high-risk populations in our subject pool have inadequate health insurance, irregular eye care, and ethnic and systemic risk factors. The participants screened for eye diseases in this pilot study, of whom 93% are racial or ethnic minority group, were confirmed to be considered underserved. 23% of these individuals did not have health insurance and more than half of them (57%) had not had an eye examination within the past year, including 18% who could not recall an eye examination within the past 5 years. An additional 6% said they had never had an eye examination. A similar report was published by Wang et al,18 20
Another possible reason for the high rate of detection of the abnormal ocular conditions might be the comprehensiveness of our screening. Although a comprehensive screening generally assists clinicians in catching asymptomatic, mild diseases, it can also lead to false positivits.21,22 23–25
In our study, glaucoma suspects are the largest group among all referrals. Similar to our study, the largest referred group (38.7%) in Maa et al26 27 28
We used glaucoma suspect as an umbrella term to include all glaucoma possibilities (probable-definite, possible, likely) in a group of healthy participants who face risk factors for glaucoma (IOP >21mm Hg, narrow angle on anterior segment OCT, etc) or mimic glaucomatous appearance. Diagnosing glaucoma still remains a challenge as there is no single litmus test that can reliably tell whether glaucomatous changes are present. Even in the clinic, a definitive examination and the diagnosis of glaucoma is multifactorial and includes IOP, CCT, VF, disc appearance, OCT-RNFL findings, all of which were performed in this study. Despite all these factors, we should also yield that there is no single and definite consensus on glaucoma diagnosis parameters. Luckily, for most patients, these definitive parameters make the condition obvious that they either have or do not have glaucoma and they display objective documentation of the disease status. However, the detection of early glaucoma still remains challenging, as there is a significant overlap between normal variants and factors leading to an early detection of disease. Therefore, multiple diagnostic tests may play a massive role to overcome uncertainty. On the other hand, healthy subjects may still be categorized as glaucoma suspects due to “statistical abnormality” of outputs of diagnostic devices. Clinician affinity and over-reliance on newer diagnostic devices may lead to overdiagnosing glaucoma if findings are interpreted in isolation without taking into consideration the complete clinical scenario.22,29 30–32 29 33 34
In summary, the inability to clearly distinguish between early glaucoma and normal variants is one of the major issues in glaucoma and it has persisted despite technological improvements, including teleophthalmologic innovations that may further aggravate the problem.
In the modeling phase of our study, definition criteria and reference conditions were aimed to be as sensitive as possible to cover all possible diseases. As the prevalence of open-angle glaucoma among adults in the US was stated to be 1.8% over the age of 40,35
False positives generate direct costs due to unneeded further clinical examinations and excessive testing which may affect the participants’ quality of life. Even in face-to-face examination, it is possible to come across the unneeded initiation of treatment due to the slightest hint in ophthalmic screening. Therefore, mass ophthalmic screening and over-reliance of these techniques in teleophthalmology might lead to overtreatment, unnecessary medication costs, follow-up visits, and the risks of side effects without much gain. We believe the problems related to underdiagnosis and overtreatment can be tackled by creating a proper, well-designed teleophthalmologic model. For our future studies, we plan to continuously modify our algorithm based on lesson learned to decrease the false positive to create more accurate references.
Danish teleophthalmology platform is a good example to overcome this challenge.36
Undoubtedly, devices cannot diagnose the patients and each condition requires a comprehensive assessment of personal history, risk factors, examination, and screening findings. In traditional clinical practice, information gained from the examination leads to ordering diagnostic tests where the clinician can then decide the probability of disease being present. The study we designed with this teleophthalmologic model is quite the opposite, beginning with a comprehensive screening which then leads to face-to-face examination for definite diagnosis. Despite the existence of potential issues, we believe technology-based teleophthalmologic approach will be the inevitable method in the near future. Teleophthalmology is an expanding domain that could mitigate resource-incentive aspects of image analysis, nonmydriatic fundus photographs and remote interpretation, which have been used smoothly in both rural and urban settings.3 37 38–46
The effective, applicable technology-based eye screening models require not only comprehensive screening devices, but also detailed questionnaire forms to detect personal risk factors effectively. Thus, the questionnaire form was prepared to take the necessary information from participants as fast and as effectively as possible. It included specific questions which might be risk factors for major eye diseases. Dental health and glaucoma were also found to be related in a prospective cohort study of men with glaucoma. Pasquale et al also identified poor oral health and recent tooth loss as risk factors for primary open-angle glaucoma in men.47
In addition to the questionnaire form, basic systemic examination findings are also assessed as risk factors for eye diseases. Substantial proportions of participants had newly detected diabetes, hypertension, and conditions that predisposed them to ocular disease.48,49 Table 5 , both self-reported systemic diseases and systemic evaluations that are done by our mobile unit are among the significant risk factors in this group. The systemic risk factors were found more than literature-based anticipations.50–52
In Table 5 , the effects of all parameters (demographic characteristics as well as systemic and ocular findings) were evaluated using an RRR calculation. This analysis was also complemented with a Bayesian logistic regression as presented in Table 6 . As it was a pilot study, the deficiencies were detected—detailed statistical evaluation of the parameters will help improve our models. Different thresholds for risk groups, giving different weights to each technique instead of the either positive rule, modeling combined testing for diagnosis, and priority queuing for reference, shall further be investigated. All these possibilities for future research require a clinically integrated model to see the coherent match between the teleophthalmologic model and clinical diagnosis. These future studies may shed a further light on the clinical significance of our results beyond the statistically significant findings we presented in the current paper.
One major limitation of our study is that it was not designed with a consecutive clinical and comprehensive eye examination. Even though we aimed to understand the true-false-positive-negative diagnosis rates with the follow-up calls, results presented in Table 7 suggest that this was not really feasible. As seen in Table 7 , first, the rate of answered phone calls was quite limited. The rate of follow-up examinations done by ophthalmologists was considerably limited. Follow-up visit results were obtained from the participants through phone conversations by using special follow-up questionnaire forms. Therefore, we had to rely on participants’ self-reports about disease confirmation or contradiction. All these factors were the barriers to report accurate true-false positive-negative diagnosis. This would constitute a further motivation to construct a health record integrated teleophthalmologic model. We believe such a potentially integrated system would provide a well-settled diagnostic prediction model and risk analysis with demographics and past medical history.
CONCLUSION
In conclusion, our study demonstrates the feasibility of real-time, ophthalmology screening in high risk and low socioeconomics minorities of New York City. Additionally, it has the potential to drastically improve access to ophthalmic care while presenting an opportunity to share health information with the community. We obtained important results that are relevant to the population; however, we also believe that our study has room to improve upon the sensitivity and specificity. We plan to perform further research studies that will be guided by this pilot study.
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