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Portable eye examination kit (Peek®) acuity versus tumbling E-chart in the screening for vision impairment among primary school children
*Corresponding author: Mustapha Bature, Department of Ophthalmology, Usmanu Danfodiyo University, Sokoto, Nigeria. mustaphabature25@gmail.com
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Received: ,
Accepted: ,
How to cite this article: Bature M, Kyari F, Mohammed AD, Abdulrahman AA. Portable eye examination kit (Peek®) acuity versus tumbling E-chart in the screening for vision impairment among primary school children. Global J Cataract Surg Res Ophthalmol. 2026;5:79-87. doi: 10.25259/GJCSRO_10_2026
Abstract
Objectives:
To compare the smartphone-based Peek visual acuity test and the Tumbling E-Chart in the screening for vision impairment by trained teachers among primary school pupils in Kaduna North local government area (LGA), Kaduna state.
Materials and Methods:
The study is a cross-sectional analytic study. A systematic sampling was done to select pupils in primary 1–6 from 11 schools that were randomly selected in Kaduna North LGA. Twenty-two primary school teachers were trained on the use of Tumbling E-chart and peek acuity, and they subsequently screened primary school pupils for vision impairment using both methods. Those with presenting visual acuity worse than 6/12 were referred to the principal investigator for further evaluation. The children were examined on-site, and treatment was offered. External eye examination was done with a pen torch by school teachers.
Results:
A total of 352 primary school pupils were assessed using both peek acuity and the Tumbling E-chart with a mean age of 10.3 ± 2.2 years. More females were involved in the study (54.6%). There was a good degree of agreement between the Tumbling E-chart and the Peek acuity, with Cronbach’s Alpha of 0.804. The Bland–Altman plot demonstrated majority of mean values within the limit of agreement for the two diagnostic tests. About two-thirds of the pupils preferred the Peek acuity, but there was no statistically significant difference by age and sex (p > 0.05). The prevalence of vision impairment was 2.27%, and myopia was the most common refractive error (1.4%). Conditions other than refractive error contributed 27.2% of ocular morbidity.
Conclusion:
Peek acuity demonstrates a good degree of agreement with tumbling E-chart and may be a feasible alternative for school-based screening by trained teachers.
Keywords
Peek acuity
Tumbling E-chart
Vision screening
INTRODUCTION
Of the 19 million children with vision impairment worldwide, about 12 million have refractive errors.[1] Nigeria has a reported prevalence of vision impairment due to refractive error among school children ranging from 0.7% to 8%.[2-4] This is a significant public health concern that warrants routine screening of children to identify and treat the patients appropriately. Refractive errors can be corrected if detected early, thus improving the educational attainment and quality of life of children. Training of school teachers for screening of refractive errors among school children is an effective way of bridging the gap in access to eye care.[1,5]
The standardised retro-illuminated logMAR chart for assessment of visual acuity is expensive and less accessible.[6] The Tumbling E-chart currently in use has a non-geometric progression of letter size and a variable number of letters used per line, which makes it less desirable for a school eye health screening. There are mobile health (mHealth) applications for assessment of visual acuity, such as the Acumob, Eye Chart Pro and peek acuity.[7-9] Peek acuity has been validated in previous studies in India, Ethiopia, Kenya, and Botswana.[9-13]
In our setting, the Tumbling E chart is the standard tool for school vision screening and primary care assessment. Our aim was therefore to evaluate peek acuity against the tool that is actually used in existing school screening programmes, to determine whether it could feasibly replace or complement Tumbling E within current task-shifting strategies.
MATERIALS AND METHODS
The study was a cross-sectional analytic study involving both public and private primary school pupils aged 5–15 years in Kaduna North local government of Kaduna state. It was conducted between September 2020 and February 2021. A total sample size of 352 pupils was involved in the study [Table 1]. Permission to conduct the study was obtained from the Kaduna State Schools Quality Assurance Authority and the State Universal Basic Education Board (SUBEB). Ethical approval was given by the Research and Ethics committee of the National Eye Centre, Kaduna.
| S. No. | Category of school | Name of school | Number of pupils (%) |
|---|---|---|---|
| 1 | Public | LEA Badarawa 1 | 99 (28.1) |
| 2 | Public | LEA Kawo | 159 (45.2) |
| 3 | Private | Al- Manar Academy | 7 (2.0) |
| 4 | Private | Al-Fauzan Academy | 10 (2.8) |
| 5 | Private | Betty Queen International School | 29 (8.2) |
| 6 | Private | Hammad Academy | 4 (1.1) |
| 7 | Private | Hampos Academy | 4 (1.1) |
| 8 | Private | Little Scholars | 16 (4.6) |
| 9 | Private | Taurari Trend Setters | 1 (0.3) |
| 10 | Private | Turaki International Schools | 16 (4.6) |
| 11 | Private | Zabib Schools | 7 (2.0) |
| Total | 352 (100.0) |
LEA: Local education authority
A multi-staged, stratified sampling technique was used to select the study schools and pupils [Figure 1].
Sample size calculation
Using the sample size formula for a sensitivity (or specificity) of more than one diagnostic test, the comparison in this design involved two alternative diagnostic tests (P1 and P2). To calculate the sensitivity of two alternative diagnostic tasks H0: P1 = P2 versus H1: P1 ≠ P2. The sample size needed to have 95% confidence and 80% power to detect a difference of 10% from a sensitivity of 70% (i.e., P1 = 0.70, P2 = 80% and P*= 0.75) can be calculated by:
Where:
N = Minimum sample size
Zα = Probability of falsely rejecting a true null hypothesis (α) = 0.05, Zα = 1.96
Zβ = Probability of failing to reject a false null hypothesis (β) = 0.80, Zβ = 0.84
P1 = Expected proportion of test 1 = Sensitivity/specificity of 70% = 0.70
P2 = Expected proportion of test 2 = 80% power = 0.80
P* = Average proportion of the two diagnostic tests = 0.75
N = 293 pupils
With 20% attrition rate, 20/100 × 293 = 58.6
Therefore, a total sample size of 352 was used for the study.
Stage 1
A list of all public primary schools in Kaduna North for the year 2019 enrolment was obtained from the State Universal Basic Education Board (SUBEB) while that of the private schools was from the Kaduna State Schools Quality Assurance Authority. There were 94,286 pupils in primary schools in Kaduna North local government area based on SUBEB and Kaduna State Schools Quality Assurance Authority data as of December 2019. Public schools had a population of 69,146 while the private schools had 25,140 pupils.
A proportional allocation of the calculated sample size to the public and private schools based on the total population of eligible children in each group (public/private) was done.
The sample size in each stratum was allocated as proportional allocation using the formula
(Y) = U/V × N
Y = 69,146/94,286 × 352 = 258 pupils from public school
Y = Sample size for public school
U = Total number of pupils registered in public schools
V = Total number of pupils in both public and private primary schools
N = Desired sample size for the study.
Therefore, the sample size for private schools (Z) was N-Y
Z = 352–258 = 850–623 = 94 pupils.
Stage 2
It involved the selection of 5% of all the schools in both the public (2) and private schools (9) using simple random sampling by balloting. This made a total of eleven (11) schools to ensure proportional selection of both public and private schools for the study.[3]
Stage 3
The allocation of the sample size was based on probability proportional to size among the selected schools, based on the population of eligible children in each school.
U/V × N
U = Total number of pupils registered in the selected school
V = Total number to be selected from public and private primary schools
N = Desired sample size for the study.
Each school was calculated as follows:
Stage 4
An aggregate number of the total eligible pupils in each arm of the primary 1–6 was collated from the nominal roll, which formed the sampling frame. Then, a sample size for each class arm was calculated based on probability proportional to size. A systematic sampling technique was used to determine the participants in each class arm.
The index child was selected by simple random sampling, and then the sampling interval (k) was determined by dividing the sampling frame (total number in the class arm) by the calculated sample size for the class. Every kth student in the nominal roll was included. In situations where the kth respondent was absent or parental consent could not be obtained, the next pupil on the nominal roll was included until an adequate sample size was achieved.
Simple random sampling via balloting was used to enrol two teachers from each selected school. Only those teachers willing to participate in the study formed the sampling frame. A total of 22 school teachers were selected from the eleven schools.
The peek acuity and Tumbling E-charts were pre-tested at LEA primary school, Mahuta, in Igabi local government, which was outside the Kaduna North LGA. Two teachers were trained on the methods of assessing visual acuity using both peek acuity and Tumbling E-charts, how to identify common ocular conditions with a pen torch and fill out the questionnaire on-site. The teachers assessed the visual acuity of 10 randomly selected pupils, performed external eye examination and recorded the findings immediately in the questionnaire during the break time period.
The 22 teachers recruited from the primary schools for the study had a 1 week training on the use of Tumbling E-chart and peek acuity app by the principal investigator at LEA Badarawa 1. They were provided with the peek acuity app on Android devices and Tumbling E-charts for the study. A leaflet describing the study objectives and a consent form were given to each of the teachers. They had training on how to: (a) Correctly measure distance visual acuity, apply the occluder, conduct visual acuity assessment with both Tumbling E-chart and peek acuity, and record the visual acuity on the questionnaire. (b) Identify common external/visible eye abnormalities using images from a computer. Subsequently, the clinical signs such as redness, abnormal eye deviation, abnormal whitish reflex and discharge were demonstrated with primary school pupils at Badarawa LEA using a pen torch. (c) How to fill the referral forms.
The teachers were trained to achieve a substantial level of inter-rater reliability. A set of 5 school pupils at the training centre was used to establish the competence of the teachers in the training and the kappa score.[14] Following the completion of the training, the school teachers were assessed for the peek acuity and Tumbling E-chart measurement while pairing them with the principal investigator (standard) in order to calculate the kappa score. The teachers identified pupils with vision impairment and or external eye abnormality out of the 5 pupils as depicted by the principal investigator. A Kappa coefficient was used to verify the presence of the themes that were presented.
The results were tabulated and interpreted as follows: Values ≤0 as indicating no agreement, 0.01–0.20 as none to slight, 0.21–0.40 as fair, 0.41–0.60 as moderate, 0.61–0.80 as substantial and 0.81–1.00 as almost perfect agreement. A kappa score of >0.7 was considered significant for the level of competence in screening for vision impairment and/or external eye abnormality. Those teachers with a score <70% had to retake the test on the next training day.
Study procedure
The recommended guidelines of the World Health Organization were adhered to, minimising risk of transmission for COVID-19 using personal protective equipment such as the face shield, face mask and gloves.[15]
The study was conducted on the school premises, and data were recorded. Children with vision impairment and/or external eye abnormality were evaluated, treated and refraction was also done on-site, while those with ocular abnormalities that required specialist care were referred to the neck for review and management.
Biodata
The pupil’s identification number, date of examination, age, sex and class were recorded.
Visual acuity measurement
The teachers were randomly assigned to either peek acuity or tumbling E-chart groups in each school through balloting. The teachers assessed the pupils independently during the break time. In the Tumbling E-chart, the assessments were done outside the classroom in the corridor, where ambient illumination was the best, while the peek acuity test was done in the classroom with 100% maximum screen illumination and to reduce glare. The chart was taken close to the child, and an explanation of optotypes was given before commencement of the test. Each child was given a unique identification (ID) number and had a visual acuity test using peek acuity and Tumbling E-chart consecutively or the reverse. In the first half of the study, pupils in each school had the Tumbling E-chart before peek acuity, and the remaining half had peek acuity before the Tumbling E-chart. However, the only study child at Taurari Trendsetters had Peek before the Tumbling E-chart by balloting. The unaided visual acuity of the pupils was recorded against their unique ID number in both groups by the school teachers.
Tumbling E-chart testing
Each child was asked to stand at 6 m measured using a tape measure as a guide, then wear the trial frame adjusted to the child’s inter-pupillary distance (IPD). One eye was measured at a time. The child was shown how to identify the direction of the optotypes at close range. An occluder was then placed into the trial frame to occlude the non-tested eye. The teacher stood beside the Tumbling E-chart, pointed to the 6/12 optotype and asked the child to identify the direction it was facing. Looking at the letter on a chart, the child points in the direction the letter was facing: up, down, left or right. The child who could identify four of the five optotypes of the 6/12 was asked to proceed to the next line of 6/9 and 6/6. However, any child who failed to read the 6/12 line (could not identify four out of five optotypes) was asked to identify the 6/60 E optotype. If they failed to identify the 6/60 E optotype, the visual acuity was recorded as <6/60. Children who could not see 6/12 were referred to the PI for review and refraction. All children with an ocular abnormality were also referred for evaluation, regardless of their level of visual acuity. The data were coded and recorded on an Excel sheet and Stata MP (version 14) for evaluation.
Peek acuity testing
Each child was asked to stand at 2 m using a tape measure as a guide; then wear the trial frame adjusted to the child’s IPD. The teacher used the peek acuity vision screening app on a smartphone at 2 m, and each eye was tested separately, with the fellow eye covered with an occluder. A series of up to five Tumbling-E optotypes was presented randomly in one of four orientations. The child identified the direction he/she perceived the arms of the letter E to be pointing, and the teacher used the phone’s touch screen to swipe in the same direction to enter the child’s response without looking at the phone’s screen. One optotype was presented at a time. The test was automatically concluded when the threshold number of passes (four of five) or fails (two of five) at the 6/12 optotype size was reached. If the child fails the 6/12 level, the app will automatically present a 6/60 sized optotype, and the test will be repeated to determine whether or not 6/60 could be seen. The final visual acuity was displayed on the screen of the smartphone. Children who had a visual acuity worse than 6/12 in either eye or an interocular difference of more than two lines of visual acuity were referred to the PI. The data were coded and recorded on an Excel sheet and Stata MP (version 14) for evaluation.
Eye examination and refraction
After the visual acuity measurement, the teachers asked the child if he/she has itching and then used a pen torch to inspect the child’s eye for redness, abnormal eye deviation, abnormal whitish reflex and discharge. All children with visual acuity worse than 6/12 or external ocular abnormality were referred to the principal investigator and research team.
The principal investigator and the research assistant examined the children on-site. External inspection, corneal light reflex test and pupil testing were done with a pen torch. Motility testing and undilated fundoscopy with a direct ophthalmoscope (Heine, Germany) were done for children with a decrease in vision as prescribed in the WHO guidelines due to the COVID-19 pandemic. Those with refractive error had an on-site cycloplegic refraction and dilated fundoscopic examination in their respective schools. Dilated fundoscopy was done with a direct ophthalmoscope (Heine, Germany) while cycloplegic refraction was done with 1% cyclopentolate drops instilled at 15-min intervals over 1 h. A streak retinoscope (Heine, Germany) was used for objective refraction, and subsequently a subjective refraction was done with trial lenses. Spectacles were issued within a week of screening.
At the end of VA testing by both methods, the children were asked which of the two tests they preferred, based on the ease of use and the ease of the VA assessment.
As a service for the survey, other children in the school with ocular problems and not involved in the study were evaluated at a separate desk. Those with eye conditions were treated with medications such as topical anti-allergies, antibiotic drops, etc. and other conditions such as corneal scar, ptosis, cataract, etc. were appropriately referred.
Referral
Children with conditions such as cataract, ptosis, and squint that warranted further evaluation were referred to the National Eye Centre, Kaduna, by the PI.
RESULTS
The age range of the pupils was 5–15 years, with a mean age of 10.3 years ± 2.2 years. About half of the pupils (51.1%) were between the age range 6–10 years. Females constituted 54.6% with an M: F ratio of 1.2:1. All pupils in primary 1 to 6 were involved in the study. Majority were in primary 3 (19.3%), while the least number were in primary 5 (15.9%). However, the sex distribution of pupils by age and class was not statistically significant [Table 2].
| Socio-demographic characteristic | Sex | Total (%) | χ2 | p-value | |
|---|---|---|---|---|---|
| Female (%) | Male (%) | ||||
| Age group in years | |||||
| 1–5 | 1 (0.5) | 1 (0.6) | 2 (0.6) | 0.50 | 0.78 |
| 6–10 | 95 (49.5) | 85 (53.1) | 180 (51.1) | ||
| 11–15 | 96 (50.0) | 74 (46.3) | 170 (48.3) | ||
| Total | 192 (100.0) | 160 (100.0) | 352 (100.0) | ||
| Class | |||||
| 1 | 29 (15.1) | 29 (18.1) | 58 (16.5) | 7.83 | 0.17 |
| 2 | 24 (12.5) | 25 (15.6) | 49 (13.9) | ||
| 3 | 43 (22.4) | 25 (15.6) | 68 (19.3) | ||
| 4 | 32 (16.7) | 31 (19.4) | 63 (17.9) | ||
| 5 | 26 (13.5) | 30 (18.8) | 56 (15.9) | ||
| 6 | 38 (19.8) | 20 (12.5) | 58 (16.5) | ||
| Total | 192 (100.0) | 160 (100.0) | 352 (100.0) | ||
p(<0.05) is statistically significant
Majority of the pupils had visual acuity of 6/6 or better using both methods. Eighteen pupils in the peek acuity group (2.6%) and fourteen pupils in the Tumbling E-chart group (2.0%) had vision worse than 6/12 from the teachers’ vision assessment.
About one-third of the students had other eye abnormalities (27.3%), while the majority had normal. Itching was more common in females (n = 33) than males (n = 26), while males had more cases of red eye. However, the difference in eye conditions between males and females was not statistically significant [Table 3].
| Eye conditions noted by teachers | Sex | Total (%) | χ2 | p-value | |
|---|---|---|---|---|---|
| Female (%) | Male (%) | ||||
| None | 143 (74.5) | 113 (70.6) | 256 (72.7) | 5.79 | 0.22 |
| Itching | 33 (17.1) | 26 (16.3) | 59 (16.8) | ||
| Red eye | 5 (2.6) | 11 (6.9) | 16 (4.6) | ||
| Abnormal whitish reflex | 2 (1.0) | 0 (0.0) | 2 (0.6) | ||
| Discharge | 9 (4.9) | 10 (6.3) | 19 (5.4) | ||
| Total | 192 (100.0) | 160 (100.0) | 352 (100.0) | ||
p(<0.05) is statistically significant
Among the 352 pupils screened by the school teachers for vision impairment and ocular conditions, 31.3% were referred for further review by the principal investigator on-site. The ratio of pupils referred to those not referred was 1:2.2, with an equal number of male and female (Chi-square statistic with Yates correction = 1.08, p = 0.30)
The sample t-test showed no statistically significant difference (p = 0.72) in the mean difference of the visual acuity measured by the peek acuity and Tumbling E-chart by the teachers.
When compared with the Tumbling E chart, peek acuity classified 653 of the 672 children who had normal vision (6/12 or better) on Tumbling E as having normal vision, giving a sensitivity of 97.17%. Nineteen children (2.83%) with normal vision on Tumbling E were incorrectly classified by peek acuity as having vision worse than 6/12 (false positives) [Table 4].
| Comparator to peek acuity | Peek acuity | |||
|---|---|---|---|---|
| 6/12 or better | <6/12 | Total | ||
| Tumbling | 6/12 or | 653 (97.17%) | 15 (46.88%) | 668 (94.89%) |
| E chart | better | |||
| < 6/12 | 19 (2.83%) | 17 (53.13%) | 36 (5.11%) | |
| Total | 672 (100.00%) | 32 (100.00%) | 704 (100.00%) | |
Among the 32 children classified by peek acuity as having vision worse than 6/12, 17 were truly impaired according to the Tumbling E chart, and 15 had normal vision. Thus, the specificity of peek acuity for detecting visual impairment (<6/12) was 53.13%, with a false-negative rate (Type II error) of 46.88% for impaired vision.
Peek acuity showed very high sensitivity but only modest specificity when compared with the Tumbling E chart. One possible explanation is that the Peek application presents a single optotype at a time, thereby reducing the crowding effect seen in linebased charts. Reduced crowding may make optotypes easier to recognise and could lead to a slight overestimation of acuity relative to the Tumbling E chart, especially in children with mild visual impairment. This difference in test design may partly account for the discrepancy in specificity observed in our study.
The Bland–Altman scatter plot showed the difference between the peek acuity and Tumbling E-chart on the y-axis, while the mean values on the x-axis [Figure 2]. Majority of the mean values were within the limit of agreement at 95% confidence interval (–0.214–0.211).
The intraclass correlation demonstrated good correlation between the two test methods with Cronbach’s Alpha of 0.804 at 95% confidence interval between the peek acuity and the Tumbling E-chart [Table 5].
| Intraclass Correlation | 95% Confidence interval | F Test with True Value 0 | |||||
|---|---|---|---|---|---|---|---|
| Lower bound | Upper bound | Value | df1 | df2 | p-value | ||
| Averae measures | 0.804 | 0.77 | 0.83 | 5.11 | 703 | 703 | 0.001 |
Cronbach’s Alpha=0.804, p (<0.05) is statistically significant, df: Degree of freedom
About two-thirds (63.1%) of the primary school pupils preferred the use of peek acuity for the visual acuity measurement [Figure 3]. This distribution is similar in both males and females [Figure 3].
Out of the 352 primary school pupils examined by the principal investigator and assistant, 258 (73%) were normal [Figure 4]. Conjunctivitis accounted for 91 cases (25.9%) of pupils reviewed. The pupils with conjunctivitis were predominantly females, but no statistically significant difference in the sex distribution of ocular morbidities.
Only eight pupils (2.3%) out of the 352 had refractive error with visual acuity worse than 6/12. Myopia accounted for 1.4% (n = 5), astigmatism for 0.6% (n = 2) and hyperopia 0.3% (n = 1) [Figure 5]. There were more cases of refractive error in the age group 6–10 years, but the distribution was not statistically significant.
DISCUSSION
The study showed a good degree of agreement between the peek acuity and the Tumbling E-chart when used for the assessment of vision impairment among primary school pupils. This result is similar to the findings of Bastawrous et al.[12] when the peek acuity was compared with the illuminated LogMAR chart to measure visual acuity during school screening. However, in our current study, the peek acuity overestimated visual acuity when compared to the Tumbling E-chart as reported by the teachers. This finding is in contrast to the results of de Venecia et al.[16] where they showed higher specificity (83%) but lower sensitivity (48%) following the validation of peek acuity in paediatric screening programmes.
The study has demonstrated the potential of using the peek acuity as a screening tool for vision impairment by school teachers in Nigeria due to the good level of agreement with the Tumbling E-chart in Kaduna North LGA. This is similar to the finding of Rono et al.[1] on the usability of peek acuity for task shifting to school teachers in screening primary school children for vision impairment. However, there are chances of over-referral of children screened with peek acuity due to the modest specificity demonstrated in this study.
The study demonstrated the usability of both methods for the assessment of vision impairment among primary school pupils. However, the pupils preferred the use of smartphone-based visual acuity to the Tumbling E-chart method. This finding was substantiated in an online survey involving the adult population by Keel et al.,[17] which showed that 72% of participants would prefer an automated smartphone-based visual acuity assessment tool. The factors that may have contributed to such could be the advent of mHealth services, especially with the advent of the COVID-19 pandemic. Furthermore, the availability of mobile gadgets for video games, education, etc., may have influenced the preference for the smartphone-based acuity in this group of research subjects.
Using the WHO cut-off mark of vision impairment of visual acuity worse than 6/12, this study showed that the prevalence of refractive error among primary school pupils in Kaduna North LGA is low. This is somewhat similar to the prevalence of 2.2% reported by Opubiri and Pedro-Egbe, in primary schools in Bayelsa state, South-south Nigeria.[18]
However, this was lower than the 7.3% reported by Faderin and Ajaiyeoba.[2] in Lagos, South-western Nigeria, 7.7% obtained by Ezinne et al.[19] in Anambra, South-eastern Nigeria and the 6.9% obtained by Ayanniyi et al.[20] in Ilorin, North-central Nigeria. Furthermore, Barasa et al., reported a high prevalence of vision impairment due to refractive error in Kitale, Kenya.[21] The prevalence reported by our study at the primary school level was probably low because of the visual acuity cut-off of worse than 6/12 as set by the WHO, while some of these studies considered visual acuity of 6/9 or worse as an operational definition of vision impairment and had a higher prevalence of refractive error.[2,19] Rono et al.[1] used a visual acuity of <6/12 and reported vision impairment of 5% in the peek acuity group and 4% in the Early treatment diabetic retinopathy study (ETDRS) LogMAR chart group, which was higher than in our study. However, the peek acuity and the Tumbling E-chart groups were independently assessed for the detection of vision impairment in the two different groups, which is in contrast to our study, where the children had sequential visual acuity with both methods to assess the level of agreement of the two diagnostic tests.
Some studies that involved children from post-primary education reported a higher prevalence of vision impairment due to refractive errors.[4] Other factors, such as age range, since the prevalence of refractive error in children increases with age and types of examination, such as non-cycloplegic refraction in children, may lead to misclassification of refractive error in a significant population of children. These may have contributed to the different prevalence among studies.
Myopia was the most common refractive error from the study. This is similar to the findings of Ezinne et al.[19] in Anambra and Obajolowo et al.[3] in Kwara. However, this is in contrast to the findings of Faderin and Ajaiyeoba.[2] where hyperopia formed 52.2% of all refractive errors in the primary schools in Lagos.
Other eye conditions contributed about one-third to the ocular morbidity and vision impairment among the primary school pupils. Similarly, most studies reported a higher prevalence of normal children in the primary schools.[1,18] More females had ocular abnormalities compared to the males. These findings correspond to the findings by Faderin and Ajaiyeoba.[2] This may be because there were more females from the systematic sampling in the study, or a greater proportion of enrolment. The data from the United Nations Educational, Scientific and Cultural Organization shows that more countries are achieving gender parity in girl child enrolment into primary education.[22] Moreover, there are numerous campaigns promoting girls’ education in Nigeria.
Four pupils with vision impairment that included corneal scar, cataract, ptosis and severe vernal keratoconjunctivitis were referred to the neck. There was 100% uptake of referral. This could probably be because the pupils and parents were adequately counselled on the need for the referral, or could be because the parents of the identified pupils were given transport fare and contact with the principal investigator to facilitate clinical review at the centre. The Child with ptosis was diagnosed as a case of myasthenia gravis and referred to Neurologist at Barau Dikko Teaching Hospital. The other three (severe vernal keratoconjunctivitis, cataract and corneal scar) were reviewed and managed appropriately.
The limitations of this study include a small sample size, and cluster randomised sampling could provide more data for comparison of the two diagnostic tests. In addition, a school-based study may not reflect the prevalence of vision impairment in the community. A qualitative study in the form of group discussion may have been better to ascertain the factors affecting the preference for peek acuity against the Tumbling E-chart. The peek acuity application is not readily available on all mobile devices, which will limit its use by School teachers compared to the Tumbling E-chart.
CONCLUSION
Peek acuity demonstrates a good degree of agreement with tumbling E-chart and may be a feasible alternative for school-based screening by trained teachers.
Ethical approval:
The research/study was approved by the Institutional Review Board at West African College of Surgeons, number EXM/PR/OPH/61/VOL 20, dated 20th August 2020.
Declaration of patient consent:
The authors certify that they have obtained all appropriate patient consent forms. In the form, the patient has given consent for clinical information to be reported in the journal. The patient understands that the patient’s names and initials will not be published and due efforts will be made to conceal their identity, but anonymity cannot be guaranteed.
Conflicts of interest:
There are no conflicts of interest.
Use of artificial intelligence (AI)-assisted technology for manuscript preparation:
The authors confirm that there was no use of artificial intelligence (AI)-assisted technology for assisting in the writing or editing of the manuscript, and no images were manipulated using AI.
Financial support and sponsorship: Nil.
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