Factors influencing the need and willingness for presbyopic correction – A cross sectional study from south India

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It is one of the leading causes of visual impairment worldwide, especially in adults above the age of 40. If uncorrected it can significantly impair patient's quality of life. This study aims to evaluate the factors which affects patient’s need and willingness to accept presbyopic correction. Methodology: A cross-sectional analytical study was done in semiurban tertiary hospital from Jan 2021 to June 2022. Details of patients aged 40 and above who presented to OPD like whether their chief complaints were related to presbyopia or not, history of spectacle use, systemic diseases, medications and their decision regarding near vision correction were noted. Ophthalmic examination included refraction and ocular biometry. Factors that may have influenced complaints of presbyopia or willingness to accept presbyopic correction were analysed. Results: Patients with chronic ailments and on chronic medication (p=0.01), higher educational qualification (p=0.031), hypermetropia (p=0.021) and shallower AC depth (p=0.028) were more likely to present with chief complaints attributable to presbyopia. Patients who had chief complains related to presbyopia, those with higher educational qualifications (p = 0.02) and skilled workers were more likely to accept near vision glasses (p = 0.02), while those with lower Hb (p = 0.01) and myopia (p = 0.01) were less likely to accept correction for presbyopia. In patients without chief complaints related to presbyopia and were not willing to accept near vision glasses. Among them, those with higher BMI (p = 0.04) and hypermetropes (p=0.05) were more willing to accept presbyopic correction. Conclusion: Presbyopia constitutes a significant reason for patients above the age of 40 visiting eye care facility. Multiple socio-economic, systemic and ocular factors influenced both the chief complaints related to presbyopia and willingness to accept presbyopic correction. Biological sciences/Physiology/Ageing Health sciences/Anatomy INTRODUCTION Presbyopia is an age-related condition when the eye gradually losses the ability to accommodate. About the age of 40, this becomes symptomatic as a decrease in near vision, increased working distance, fatigue from near work, ocular discomfort, headaches, asthenopia and need for brighter light for reading. 1 , 2 At this time, additional converging lenses would be required to see clearly for near. According to WHO (2015), uncorrected presbyopia is the most common cause of visual impairment worldwide . 3 It has a negative impact on the quality of life of an individual by reducing the clarity of near vision and increasing dependency on glasses. 4 Educational status, occupation, corrected or uncorrected refractive errors, gender etc may influence patients’ complaints related to presbyopia. Also, a patient who requires near vision correction may not be appropriately corrected. This may attributable to various reasons. Patients may be unaware of their difficulty in near work or their lifestyle may be such that they do not perceive the need for near vision correction. They may be ignorant about the fact that it can be corrected by glasses. 5 Further they may be unable to access eye care or unable to buy glasses. 6 Prescribed glasses may be uncomfortable for the patient, compromising compliance. 4 Presbyopia can be successfully treated with spectacle prescription yet continues to remain the main contributor to visual morbidity. This warrants further investigation. Exploring patients’ perception and other factors influencing the need for presbyopic correction as well as its acceptance could shed light in this regard. Hence this study was carried out. MATERIALS AND METHODS This cross-sectional analytical study was conducted in a tertiary care hospital between January 2021 and June 2022 among patients aged 40 and above, visiting the ophthalmology outpatient clinic. After obtaining approval from Institute Human Ethics Committee (01/2020/46/IHEC/284), willing patients fulfilling the study criteria were enrolled in the study. This study was conducted in accordance to declaration of tenets of Helsinki. Based on the prevalence rate of uncorrected presbyopia (33%) in India. 7 minimum sample size was calculated to be 340 patients. Patients with acute or painful ocular conditions, structural and functional abnormalities of anterior and posterior segment and uncontrolled diabetes mellitus were excluded. From all patients, chief complaints and detailed history including past history of spectacle usage, systemic diseases, addictions, long term medications (including medications for diabetes, hypertension, hyper/hypo thyroidism and psychiatric medications), ocular trauma and ocular surgeries were elicited. Details on whether the patient’s chief complaints were related to presbyopia or not were noted. Ophthalmic examination included Visual acuity (VA) and refractive correction for distance and near, intraocular pressure (IOP) measurement by non-contact tonometer (Topcon CT-800,Topcon Medical Systems, Paramus, New-Jersey, USA), axial length (AXL), anterior chamber depth (ACD) and lens thickness (LT) measurements by A-scan (Appasamy MAX, ophthalmic ultrasound scanner, Appasamy Associates, Chennai, India), keratometry (K1/K2) by automated refractometer (URK-800, Auto Ref/Keratometer, UNICOS Co. Ltd., Daejeon, Republic Of Korea ) as well as amplitude of accommodation (AA) and near point of accommodation (NPA) by RAF ruler. Systemic investigations included random blood sugar (RBS), haemoglobin % and body mass index (BMI). Details on whether the patients were willing to accept near vision correction were also recorded. Data was entered in MS excel and statistical analysis was performed using Microsoft excel (Version 2022) (Microsoft Corporation, Redmond, WA, USA) & Statistical package for the Social Sciences (SPSS) for Windows, (version 16.0, SPSS Inc., Chicago, III., USA). Privacy and confidentiality of participants was maintained throughout the study. RESULTS Of the 342 patients included in the study, 165(48.24%) were men and 177 (51.76%) were women. The mean age of the study population was 48.55 ± 6.68 years and ranged from 40 to 80 years. About 44 (12%) participants were illiterate. Almost 1/4th (n = 85) of the participants were graduates and only 14 (4%) had received post graduate education. Almost 50% (n = 167) of the participants were skilled workers and 14% (n = 49) were professionals. Two-hundred sixty-two (77%) participants presented with chief complaints attributable to presbyopia. Only about 1/4th of the participants (n = 82) had been previously corrected for near vision. Ninety-nine (28.94%) participants had DM and 67 (19.59%) were hypertensives. About 114 (33.33%) patients were on regular long-term medications. Patients travelled a mean distance of 37.61 ± 38.57 km (range: 1- 300) in order to reach this eye care facility. The mean presenting VA for distance was 6/9 ranging from 6/6 to 6/60 and for near was N10 ranging from N 6 to N36. Among the 119 patients who required correction for distance vision, the refraction ranged from − 3.25 D to + 3 D. The additional near vision correction required ranged from nil correction to + 3 D. The mean recorded IOP was 15.09 ± 2.30 mm Hg, AC depth was 3.72 ± 1.03 mm, Lens thickness was 3.29 ± 0.87 mm, K1 was 44.41 ± 1.36 dioptre, K2 was 44.85 ± 1.30 dioptre, AA was 2.41 ± 1.17 cm, NPA was 49.79 ± 35.10 cm, NPC was 9.53 ± 1.26 cm and axial length was 23.23 ± 10.99 mm ranging from 20.05 to 24.97 mm. Based on the complaints of presbyopia, participants were divided into 2 groups – those who had complaints related to presbyopia (n = 262) and those who did not (n = 80). On comparing the two groups, it was found that those with DM (p = 0.013), HTN (p = 0.007), high RBS value (p = 0.008), requirement for chronic medication (p = 0.011), higher education qualification (p = 0.031), hypermetropia (p = 0.021). were more likely to present with chief complaints attributable to presbyopia. I n addition, it was found that those with complaints of presbyopia had a lower AC depth than those who did not had complains of presbyopia (p = 0.028) ( Table 1 and Table 2 ) Table 1 Comparison of socio-demographic parameters between patients with and without complaints related to presbyopia. Patients with complaints related to presbyopia (n = 262) Patients without complaints related to presbyopia (n = 80) p value Age (years) (mean ± SD) 48.18 ± 6.57 49.78 ± 6.95 0.062 Gender n (%) Male Female 120 (45.8%) 142 (54.2%) 45 (56.2%) 35 (43.8%) 0.102 Educational qualification n (%) Illiterate Primary School Secondary School Graduate and above 28 (10.7%) 72 (27.5%) 77 (29.4%) 86 (32.2%) 16 (20.0%) 28 (35.0%) 22 (27.5%) 33 (42.3%) 0.031 Occupation n (%) Skilled and above Semiskilled Unskilled 177 (67.6%) 74 (28.2%) 11(4.2%) 66 (82.5%) 11 (13.8%) 3 (3.8%) 0.259 Table 2 Comparison of ocular and systemic parameters between patients with and without complaints related to presbyopia. Systemic Parameters, n (%) Patients with complaints related to presbyopia (n = 262) Patients without complaints related to presbyopia (n = 80) p value Hypertensives 43 (16.4%) 24 (30.0%) 0.007 Diabetics 67 (25.6%) 32 (40.0%) 0.013 Patients taking chronic medications 78 (29.8%) 36 (45.0%) 0.011 BMI (kg/m 2 ) Under Weight Normal Weight Overweight Obese 6 (2.3%) 108 (41.2%) 94 (35.9%) 53 (20.2%) 2 (2.5%) 34 (42.5%) 26 (32.5%) 19 (22.8%) 0.943 Hb (g/dL) Anaemic 141 (53.8%) 46 (57.5%) 0.562 Ocular parameters, mean ± SD AC depth (mm) Lens thickness AA (cm) NPA (cm) NPC (cm) 3.67 ± 1.05 3.29 ± 0.89 2.46 ± 1.14 48.15 ± 28.05 9.5 ± 1.28 3.87 ± 1.01 3.30 ± 0.85 2.23 ± 1.24 55.18 ± 51.77 9.67 ± 1.18 0.028 0.913 0.092 0.651 0.229 Axial length (mm), n (%) < 22 22–26 51 (19.5%) 211 (80.5) 18 (22.5%) 62 (77.5%) 0.554 Refractive error For distance Emmetropes Myopes Hypermetropes 171 (65.3%) 29 (11.1%) 62 (23.7%) 50 (62.5%) 16 (20%) 14 (17.5%) 0.021 A regression analysis model constructed using these parameters showed only educational qualification to be statistically significant with regards to complaints of presbyopia. (R square = 0.07, p = 0.01) Those patients who had complaints related to presbyopia were further divided into 2 groups based on their willingness to accept presbyopic correction. Those with higher educational qualifications (p = 0.02) and skilled workers were more likely to accept near vision glasses (p = 0.02), while those with lower Hb (p = 0.01) and myopia (p = 0.01) were less likely to accept correction for presbyopia. (Table 3 and Table 4 ) Table 3 Comparison of socio-demographic parameters based on patient’s willingness to accept presbyopic correction. Based on patient’s willingness to wear near vision (NV) glasses Patients with complaints related to presbyopia, (n = 262) p value Patients without complaints related to presbyopia, (n = 80) p value willing for NV correction (n = 240) not willing for NV correction (n = 22) willing for NV correction (n = 45) not willing for NV correction (n = 35) Age (years), mean ± SD 48.22 ± 6.57 47.77 ± 6.67 0.95 50.87 ± 6.5 48.37 ± 7.35 0.443 Gender Male Female 112 (46.66%) 128 (53.33%) 8 (36.36%) 14 (63.64%) 0.35 22 (48.89%) 23 (51.11%) 23 (65.71%) 12(34.29%) 0.13 Educational qualification Illiterate Primary School Secondary School Above Graduate 24 (10%) 62 (25.83%) 70 (29.16%) 84 (34.99%) 4 (18.18%) 10 (45.45%) 7 (31.82%) 1 (4.55%) 0.02 9 (20.00%) 17 (37.78%) 14 (31.11%) 5 (11.09%) 7 (20.00%) 11 (31.43%) 8 (22.86%) 9 (25.71%) 0.37 Occupation Skilled Semiskilled Unskilled 192 (79.99%) 21 (8.75%) 27 (11.25%) 12 (54.55%) 4 (18.18%) 6 (27.27%) 0.02 34 (75.55%) 6 (13.33%) 5 (11.11%) 20 (57.14%) 4 (11.43%) 11 (31.43%) 0.07 Table 4: Comparison of ocular and systemic parameters parameters based on pateints' willingness to accept presbyopic correction. It was observed that almost 45%(n = 35) of patients who did not have chief complaints related to presbyopia were not willing to accept near vision glasses. Among them, patients with higher BMI (p = 0.04) and hypermetropes (p = 0.05) were more willing to accept presbyopic correction (Table 3 and Table 4 ) DISCUSSION Presbyopia is traditionally corrected with convex lens prescriptions in spectacles. Despite this non-invasive treatment, WHO has recorded the condition to be the most common cause of visual impairment worldwide. 3 The present study noted over 3/4th of the patients above the age of 40 visiting the department of ophthalmology had complaints related to presbyopia. The observations of this study suggests that patients with higher educational qualifications, those with DM, HTN and requiring chronic medications, hypermetropia and lower AC depth were more likely to present with chief complaints of presbyopia. Those with higher educational qualification, professional or skilled workers were more likely to accept presbyopic correction, while myopes and those with anaemia were less likely to accept the same. Patients who came with complaints of presbyopia were more likely to accept near vision correction. Priyambada S et al. and Mukuria M et al. observed that women had earlier onset of presbyopia, while Andhra Pradesh Eye Disease Study observed female gender to have a greater association for presbyopia. 1 , 8 , 5 The present study observed a male: female (M:F) ratio of 0.8:1 among those who presented with complaints of presbyopia. When compared with M:F ratio of 1.3:1 among those above the age of 40 who did not have complaints of presbyopia, it appears that women are more likely to present with complaints of presbyopia. However, the gender difference was not statistically significant. Patients with higher education are more likely to be involved with near work, both in professional and nonprofessional spheres. They are therefore more likely to complain of presbyopia and because of the perceived need also accept correction. Observations by Patel I et al. and Muhammad R et al. also noted that patients with higher education were more likely to be corrected for presbyopia. 9 , 10 Mukuria M et al. had a contrary observation of more severe presbyopia among those who were less literate. The authors explained it by suggesting a misinterpretation of the near vision charts, where those less literate may have preferred a magnified and therefore “clearer” correction. 5 Association of education and occupation with a perceived need for presbyopic correction is a little blurred due to the increased use of digital media. Those whose occupations do not strictly require near vision, would perceive a need for presbyopic correction in other spheres. On the other hand, the ability to change the font size in digital screen permits postponement of presbyopic correction. Skilled and professional jobs have a higher requirement of near vision acuity and therefore these patients may be more likely to accept presbyopic correction. Similar to our observations, a study in Nigeria also noted skilled professionals were more likely to procure glasses required for their near work. 10 In a study on unfulfilled need of presbyopic correction, Girum M et al. noted that those with higher education and skilled professionals were more likely to be corrected for their presbyopic needs earlier. 11 In parallel, a significant improvement in economical productivity has been observed following presbyopic correction even among those having unskilled jobs. 6 , 12 This emphasises the need for near vision correction irrespective of occupation. Some studies have tried to determine the barriers for presbyopic correction. Hutchin B et al. in United Kingdom observed comfort and convenience of handling near vision glasses to be an important factor, more so than the cost of glasses. The perception that near vision glasses were a sign of aging also contributed to reluctance for presbyopic correction. 4 Contrary to this, in resource scarce countries, cost of glasses and ease of availability were noted to be significant barriers. 6 , 10 , 12 The present study observed that patients with skilled and professional jobs were more likely to be able to afford the cost of spectacles. Once corrected, the compliance towards near vision glasses was found to be above 80% even among unskilled labourers. 12 This suggests visual impairment due to presbyopia could be significantly addressed by economical support and access to eye care. Despite chief complaints of presbyopia, some patients were unwilling to accept correction. Significant among these were patients with low Hb%. People with chronic illnesses like DM, HTN and those requiring chronic medications have the need to read and identify their medications in small prints. These patients may also present earlier with complaints of presbyopia due to compromised accommodation. 13 , 14 surprisingly we did not find a higher rate of acceptance for near vision correction among them. Systemic illness and cost of its treatment would have played a role in this and therefore their inability to afford the cost of near vision glasses. At the same time, participants with higher BMI (overweight and above) were more likely to accept near vision correction even when they did not have chief complains related to presbyopia. This could be because of their better socioeconomic status and therefore ability to afford near vision glasses. Higher BMI may also signify lesser physical activity and more time spent in near work. Therefore, their need for near vision glasses may be higher. Myopes were less likely to present with complains of presbyopia and also less willing to wear near vision glasses. Hypermetropes have lesser AA. 15 So they may present earlier and also perceive greater need for presbyopic correction. The observation that patients with lower AC depth were more likely to present with complaints related to presbyopia may be attributed to hypermetropic refraction in them. 16 The present study highlights the fact that even among the patients who have access to health care facility uncorrected presbyopia is widely prevalent. Not many studies so far have explored patients’ need and willingness to accept presbyopic correction even though presbyopia is an important cause for visual impairment worldwide. There were few limitations to this study. It was a single centre study. Studies involving multiple centres may be better able to address factors like socio-economic and geographical variations which would yield a deeper understanding into the needs and acceptance of presbyopic correction. Further, grading of lens opacification was not done for participants. This could have affected NPA, thereby influencing their complaints related to presbyopia and decision for wearing glasses. Also, only patients’ willingness to accept presbyopic correction at the time of prescription was noted. Longitudinal study would be better to determine the patient compliance. CONCLUSION Presbyopia is a significant cause for hospital visit in patients above the age of 40. Patients with higher educational qualifications, requiring chronic medications and hypermetropes were more likely to present with complaints of presbyopia. Those with higher education, skilled profession hypermetropes and higher BMI were more likely to accept presbyopic correction, while those with anaemia were less likely to accept near vision correction. References Priyambada S. Premature Presbyopia and its Risk Factors - A Hospital based Study. International Journal of Contemporary Medical Research [IJCMR]. 2019 Mar;6(3). Abraham LM, Kuriakose T, Sivanandam V, Venkatesan N, Thomas R, Muliyil J. Amplitude of accommodation and its relation to refractive errors. Indian Journal of Ophthalmology. 2005;53(2):105–8. Holden BA, Tahhan N, Jong M, Wilson DA, Fricke TR, Bourne R, et al. Towards better estimates of uncorrected presbyopia. Bulletin of the World Health Organization. 2015;93(10):667. Hutchins B, Huntjens B. Patients’ attitudes and beliefs to presbyopia and its correction. Journal of Optometry. 2021;14(2):127–32. Mukuria M, Kariuki M, Kollmann M, Al. E. Magnitude and pattern of presbyopia among patients seen on outreach with Lions SightFirst Eye Hospital, Loressho, Nairobi. Joecsa. 2012;16(1):42–7. Wubben TJ, Guerrero CM, Salum M, Wolfe GS, Giovannelli GP, Ramsey DJ. Presbyopia: A pilot investigation of the barriers and benefits of near visual acuity correction among a rural Filipino population. BMC Ophthalmology. 2014;14(1). Sheeladevi S, Seelam B, Nukella P, Borah R, Ali R, Keay L. Prevalence of refractive errors, uncorrected refractive error, and presbyopia in adults in India: A systematic review. Indian Journal of Ophthalmology. 2019;67(5):583. Nirmalan PK, Krishnaiah S, Shamanna BR, Rao GN, Thomas R. A population-based assessment of presbyopia in the state of Andhra Pradesh, south India: The Andhra Pradesh eye disease study. Investigative Ophthalmology and Visual Science. 2006;47(6):2324–8. Patel I, Munoz B, Burke AG, Kayongoya A, Mchiwa W, Schwarzwalder AW, et al. Impact of Presbyopia on Quality of Life in a Rural African Setting. Ophthalmology. 2006;113(5):728–34. Muhammad R, Jamda M. Presbyopic correction coverage and barriers to the use of near vision spectacles in rural Abuja, Nigeria. Sub-Saharan African Journal of Medicine. 2016;3(1):20. Girum M, Gudeta AD, Alemu DS. Determinants of high unmet need for presbyopia correction: A Community-Based study in Northwest Ethiopia. Clinical Optometry. 2017;9:25–31. Reddy PA, Congdon N, MacKenzie G, Gogate P, Wen Q, Jan C, et al. Effect of providing near glasses on productivity among rural Indian tea workers with presbyopia (PROSPER): a randomised trial. The Lancet Global Health. 2018;6(9):e1019–27. Venugopal DM. A Study of Clinical Profile of Premature Presbyopia in A Tertiary Care Hospital. journal of medical science and clinical research. 2017 Jul 30;5(7):85–9. Sridhar S, Ramachandra S. Accommodative parameter assessment in peri-presbyopic early onset diabetics with age matched healthy individuals - A case control study. Indian Journal of Clinical and Experimental Ophthalmology. 2020;6(3):422–8. Abraham LM, Kuriakose T, Sivanandam V, Venkatesan N, Thomas R, Muliyil J. Correlation between ocular parameters and amplitude of accommodation. Indian Journal of Ophthalmology. 2010;58(6):483–5. Maheshwari R, Sukul RR, Gupta Y, Gupta M, Phougat A, Dey M, et al. Accommodation: its relation to refractive errors, amblyopia and biometric parameters. Nepalese journal of ophthalmology : a biannual peer-reviewed academic journal of the Nepal Ophthalmic Society : NEPJOPH. 2011;3(2):146–50. Additional Declarations There is NO Competing Interest. 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Also discoverable on Platform About Our Team In Review Editorial Policies Advisory Board Help Center Resources Author Services Accessibility API Access RSS feed Manage Cookie Preferences © Research Square 2026 | ISSN 2693-5015 (online) Privacy Policy Terms of Service Do Not Sell My Personal Information {"props":{"pageProps":{"initialData":{"identity":"rs-3172918","acceptedTermsAndConditions":true,"allowDirectSubmit":true,"archivedVersions":[],"articleType":"Article","associatedPublications":[],"authors":[{"id":219349767,"identity":"f4bab264-a8a1-4d37-817f-9c6d0a5efbf8","order_by":0,"name":"Dhruval Khurana","email":"data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAZAAAAAyAQMAAABI0h/eAAAABlBMVEX///8AAABVwtN+AAAACXBIWXMAAA7EAAAOxAGVKw4bAAABEUlEQVRIiWNgGAWjYBACewkQeeAAhJdQYSPHD2YU4NZiOANZy4MzacaSDSAtBri1GNxA0sL4sO1wogGYjU/L7d5n0jxn7iRubz978ENi2+EE4/OrEz88MGCQ5xc7gF3LneNm0jw3niXOOZOXLJFwLj3P7MbbzRJAhxnOnJ2Aw2FpbJIzPhxOnMGQYyCRUGZdbHbj7AaQlgSD24S08L8x/pHAxpy4ecbZzT8IaZH4cAOoRSLHTCKhzTlxA3/vNry2GM5IY7b4cOaw8QyJN2YWCcBAlrjBu80iwUACp1/sJdIYbyQcOyw7gz/H+OYPUFT2n90MYsjzS2PXggVIgFVKEKscBPgPkKJ6FIyCUTAKRgAAAN1+bVLTLLS+AAAAAElFTkSuQmCC","orcid":"","institution":"MGMCRI","correspondingAuthor":true,"submittingAuthor":false,"prefix":"","firstName":"Dhruval","middleName":"","lastName":"Khurana","suffix":""},{"id":219349768,"identity":"00a76170-165d-47f9-a986-b802c6c39565","order_by":1,"name":"Swathi N","email":"","orcid":"","institution":"MGMCRI","correspondingAuthor":false,"submittingAuthor":false,"prefix":"","firstName":"Swathi","middleName":"","lastName":"N","suffix":""},{"id":219349769,"identity":"79ff2639-de85-4d56-a01d-dcd1d57afc4f","order_by":2,"name":"A.R Rajalakshmi","email":"","orcid":"","institution":"MGMCRI","correspondingAuthor":false,"submittingAuthor":false,"prefix":"","firstName":"A.R","middleName":"","lastName":"Rajalakshmi","suffix":""}],"badges":[],"createdAt":"2023-07-15 11:00:42","currentVersionCode":1,"declarations":"","doi":"10.21203/rs.3.rs-3172918/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-3172918/v1","draftVersion":[],"editorialEvents":[],"editorialNote":"","failedWorkflow":false,"files":[{"id":40386740,"identity":"90ae14f3-ce5b-474c-9319-3380dde4a3cd","added_by":"auto","created_at":"2023-07-21 15:06:35","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":363924,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-3172918/v1/bc65a6ce-156a-490d-a8df-4c9eb7b9ce9a.pdf"}],"financialInterests":"There is \u003cb\u003eNO\u003c/b\u003e Competing Interest.","formattedTitle":"Factors influencing the need and willingness for presbyopic correction – A cross sectional study from south India","fulltext":[{"header":"INTRODUCTION","content":"\u003cp\u003ePresbyopia is an age-related condition when the eye gradually losses the ability to accommodate. About the age of 40, this becomes symptomatic as a decrease in near vision, increased working distance, fatigue from near work, ocular discomfort, headaches, asthenopia and need for brighter light for reading.\u003csup\u003e\u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e1\u003c/span\u003e,\u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2\u003c/span\u003e\u003c/sup\u003e At this time, additional converging lenses would be required to see clearly for near. According to WHO (2015), uncorrected presbyopia is the most common cause of visual impairment worldwide .\u003csup\u003e\u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e3\u003c/span\u003e\u003c/sup\u003e It has a negative impact on the quality of life of an individual by reducing the clarity of near vision and increasing dependency on glasses.\u003csup\u003e\u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e4\u003c/span\u003e\u003c/sup\u003e\u003c/p\u003e \u003cp\u003eEducational status, occupation, corrected or uncorrected refractive errors, gender etc may influence patients\u0026rsquo; complaints related to presbyopia. Also, a patient who requires near vision correction may not be appropriately corrected. This may attributable to various reasons. Patients may be unaware of their difficulty in near work or their lifestyle may be such that they do not perceive the need for near vision correction. They may be ignorant about the fact that it can be corrected by glasses.\u003csup\u003e\u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e5\u003c/span\u003e\u003c/sup\u003e Further they may be unable to access eye care or unable to buy glasses.\u003csup\u003e\u003cspan citationid=\"CR6\" class=\"CitationRef\"\u003e6\u003c/span\u003e\u003c/sup\u003e Prescribed glasses may be uncomfortable for the patient, compromising compliance.\u003csup\u003e\u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e4\u003c/span\u003e\u003c/sup\u003e\u003c/p\u003e \u003cp\u003ePresbyopia can be successfully treated with spectacle prescription yet continues to remain the main contributor to visual morbidity. This warrants further investigation. Exploring patients\u0026rsquo; perception and other factors influencing the need for presbyopic correction as well as its acceptance could shed light in this regard. Hence this study was carried out.\u003c/p\u003e"},{"header":"MATERIALS AND METHODS","content":"\u003cp\u003e This cross-sectional analytical study was conducted in a tertiary care hospital between January 2021 and June 2022 among patients aged 40 and above, visiting the ophthalmology outpatient clinic. After obtaining approval from Institute Human Ethics Committee (01/2020/46/IHEC/284), willing patients fulfilling the study criteria were enrolled in the study. This study was conducted in accordance to declaration of tenets of Helsinki. Based on the prevalence rate of uncorrected presbyopia (33%) in India.\u003csup\u003e7\u003c/sup\u003eminimum sample size was calculated to be 340 patients. Patients with acute or painful ocular conditions, structural and functional abnormalities of anterior and posterior segment and uncontrolled diabetes mellitus were excluded.\u003c/p\u003e \u003cp\u003eFrom all patients, chief complaints and detailed history including past history of spectacle usage, systemic diseases, addictions, long term medications (including medications for diabetes, hypertension, hyper/hypo thyroidism and psychiatric medications), ocular trauma and ocular surgeries were elicited. Details on whether the patient\u0026rsquo;s chief complaints were related to presbyopia or not were noted. Ophthalmic examination included Visual acuity (VA) and refractive correction for distance and near, intraocular pressure (IOP) measurement by non-contact tonometer (Topcon CT-800,Topcon Medical Systems, Paramus, New-Jersey, USA), axial length (AXL), anterior chamber depth (ACD) and lens thickness (LT) measurements by A-scan (Appasamy MAX, ophthalmic ultrasound scanner, Appasamy Associates, Chennai, India), keratometry (K1/K2) by automated refractometer (URK-800, Auto Ref/Keratometer, UNICOS Co. Ltd., Daejeon, Republic Of Korea ) as well as amplitude of accommodation (AA) and near point of accommodation (NPA) by RAF ruler. Systemic investigations included random blood sugar (RBS), haemoglobin % and body mass index (BMI). Details on whether the patients were willing to accept near vision correction were also recorded.\u003c/p\u003e \u003cp\u003eData was entered in MS excel and statistical analysis was performed using Microsoft excel (Version 2022) (Microsoft Corporation, Redmond, WA, USA) \u0026amp; Statistical package for the Social Sciences (SPSS) for Windows, (version 16.0, SPSS Inc., Chicago, III., USA). Privacy and confidentiality of participants was maintained throughout the study.\u003c/p\u003e"},{"header":"RESULTS","content":"\u003cp\u003eOf the 342 patients included in the study, 165(48.24%) were men and 177 (51.76%) were women. The mean age of the study population was 48.55\u0026thinsp;\u0026plusmn;\u0026thinsp;6.68 years and ranged from 40 to 80 years. About 44 (12%) participants were illiterate. Almost 1/4th (n\u0026thinsp;=\u0026thinsp;85) of the participants were graduates and only 14 (4%) had received post graduate education. Almost 50% (n\u0026thinsp;=\u0026thinsp;167) of the participants were skilled workers and 14% (n\u0026thinsp;=\u0026thinsp;49) were professionals. Two-hundred sixty-two (77%) participants presented with chief complaints attributable to presbyopia. Only about 1/4th of the participants (n\u0026thinsp;=\u0026thinsp;82) had been previously corrected for near vision. Ninety-nine (28.94%) participants had DM and 67 (19.59%) were hypertensives. About 114 (33.33%) patients were on regular long-term medications. Patients travelled a mean distance of 37.61\u0026thinsp;\u0026plusmn;\u0026thinsp;38.57 km (range: 1- 300) in order to reach this eye care facility.\u003c/p\u003e\n\u003cp\u003eThe mean presenting VA for distance was 6/9 ranging from 6/6 to 6/60 and for near was N10 ranging from N 6 to N36. Among the 119 patients who required correction for distance vision, the refraction ranged from \u0026minus;\u0026thinsp;3.25 D to +\u0026thinsp;3 D. The additional near vision correction required ranged from nil correction to +\u0026thinsp;3 D. The mean recorded IOP was 15.09\u0026thinsp;\u0026plusmn;\u0026thinsp;2.30 mm Hg, AC depth was 3.72\u0026thinsp;\u0026plusmn;\u0026thinsp;1.03 mm, Lens thickness was 3.29\u0026thinsp;\u0026plusmn;\u0026thinsp;0.87 mm, K1 was 44.41\u0026thinsp;\u0026plusmn;\u0026thinsp;1.36 dioptre, K2 was 44.85\u0026thinsp;\u0026plusmn;\u0026thinsp;1.30 dioptre, AA was 2.41\u0026thinsp;\u0026plusmn;\u0026thinsp;1.17 cm, NPA was 49.79\u0026thinsp;\u0026plusmn;\u0026thinsp;35.10 cm, NPC was 9.53\u0026thinsp;\u0026plusmn;\u0026thinsp;1.26 cm and axial length was 23.23\u0026thinsp;\u0026plusmn;\u0026thinsp;10.99 mm ranging from 20.05 to 24.97 mm.\u003c/p\u003e\n\u003cp\u003eBased on the complaints of presbyopia, participants were divided into 2 groups \u0026ndash; those who had complaints related to presbyopia (n\u0026thinsp;=\u0026thinsp;262) and those who did not (n\u0026thinsp;=\u0026thinsp;80). On comparing the two groups, it was found that those with DM (p\u0026thinsp;=\u0026thinsp;0.013), HTN (p\u0026thinsp;=\u0026thinsp;0.007), high RBS value (p\u0026thinsp;=\u0026thinsp;0.008), requirement for chronic medication (p\u0026thinsp;=\u0026thinsp;0.011), higher education qualification (p\u0026thinsp;=\u0026thinsp;0.031), hypermetropia (p\u0026thinsp;=\u0026thinsp;0.021). were more likely to present with chief complaints attributable to presbyopia. \u003cstrong\u003eI\u003c/strong\u003en addition, it was found that those with complaints of presbyopia had a lower AC depth than those who did not had complains of presbyopia (p\u0026thinsp;=\u0026thinsp;0.028) \u003cstrong\u003e(\u003c/strong\u003eTable\u0026nbsp;\u003cspan class=\"InternalRef\"\u003e1\u003c/span\u003e and Table\u0026nbsp;\u003cspan class=\"InternalRef\"\u003e2\u003c/span\u003e)\u003c/p\u003e\n\u003cdiv class=\"gridtable\"\u003e\n\u003ctable id=\"Tab1\" border=\"1\"\u003e\u003ccaption\u003e\n\u003cdiv class=\"CaptionNumber\"\u003eTable 1\u003c/div\u003e\n\u003cdiv class=\"CaptionContent\"\u003e\n\u003cp\u003eComparison of socio-demographic parameters between patients with and without complaints related to presbyopia.\u003c/p\u003e\n\u003c/div\u003e\n\u003c/caption\u003e\n\u003cthead\u003e\n\u003ctr\u003e\n\u003cth align=\"left\"\u003e\u0026nbsp;\u003c/th\u003e\n\u003cth align=\"left\"\u003e\n\u003cp\u003ePatients with complaints related to presbyopia (n\u0026thinsp;=\u0026thinsp;262)\u003c/p\u003e\n\u003c/th\u003e\n\u003cth align=\"left\"\u003e\n\u003cp\u003ePatients without complaints related to presbyopia (n\u0026thinsp;=\u0026thinsp;80)\u003c/p\u003e\n\u003c/th\u003e\n\u003cth align=\"left\"\u003e\n\u003cp\u003ep value\u003c/p\u003e\n\u003c/th\u003e\n\u003c/tr\u003e\n\u003c/thead\u003e\n\u003ctbody\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eAge (years)\u003c/p\u003e\n\u003cp\u003e(mean\u0026thinsp;\u0026plusmn;\u0026thinsp;SD)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e48.18\u0026thinsp;\u0026plusmn;\u0026thinsp;6.57\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e49.78\u0026thinsp;\u0026plusmn;\u0026thinsp;6.95\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\"\u003e\n\u003cp\u003e0.062\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eGender n (%)\u003c/p\u003e\n\u003cp\u003eMale\u003c/p\u003e\n\u003cp\u003eFemale\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e120 (45.8%)\u003c/p\u003e\n\u003cp\u003e142 (54.2%)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e45 (56.2%)\u003c/p\u003e\n\u003cp\u003e35 (43.8%)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\"\u003e\n\u003cp\u003e0.102\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e\u003cstrong\u003eEducational qualification n (%)\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eIlliterate\u003c/p\u003e\n\u003cp\u003ePrimary School\u003c/p\u003e\n\u003cp\u003eSecondary School\u003c/p\u003e\n\u003cp\u003eGraduate and above\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e28 (10.7%)\u003c/p\u003e\n\u003cp\u003e72 (27.5%)\u003c/p\u003e\n\u003cp\u003e77 (29.4%)\u003c/p\u003e\n\u003cp\u003e86 (32.2%)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e16 (20.0%)\u003c/p\u003e\n\u003cp\u003e28 (35.0%)\u003c/p\u003e\n\u003cp\u003e22 (27.5%)\u003c/p\u003e\n\u003cp\u003e33 (42.3%)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\"\u003e\n\u003cp\u003e\u003cstrong\u003e0.031\u003c/strong\u003e\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eOccupation n (%)\u003c/p\u003e\n\u003cp\u003eSkilled and above\u003c/p\u003e\n\u003cp\u003eSemiskilled\u003c/p\u003e\n\u003cp\u003eUnskilled\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e177 (67.6%)\u003c/p\u003e\n\u003cp\u003e74 (28.2%)\u003c/p\u003e\n\u003cp\u003e11(4.2%)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e66 (82.5%)\u003c/p\u003e\n\u003cp\u003e11 (13.8%)\u003c/p\u003e\n\u003cp\u003e3 (3.8%)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\"\u003e\n\u003cp\u003e0.259\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003c/tbody\u003e\n\u003c/table\u003e\n\u003c/div\u003e\n\u003cdiv class=\"gridtable\"\u003e\n\u003cdiv class=\"colspec\" align=\"left\"\u003e\u0026nbsp;\u003c/div\u003e\n\u003ctable id=\"Tab2\" border=\"1\"\u003e\u003ccaption\u003e\n\u003cdiv class=\"CaptionNumber\"\u003eTable 2\u003c/div\u003e\n\u003cdiv class=\"CaptionContent\"\u003e\n\u003cp\u003eComparison of ocular and systemic parameters between patients with and without complaints related to presbyopia.\u003c/p\u003e\n\u003c/div\u003e\n\u003c/caption\u003e\n\u003cthead\u003e\n\u003ctr\u003e\n\u003cth align=\"left\"\u003e\n\u003cp\u003eSystemic Parameters,\u003c/p\u003e\n\u003cp\u003en (%)\u003c/p\u003e\n\u003c/th\u003e\n\u003cth align=\"left\"\u003e\n\u003cp\u003ePatients with complaints related to presbyopia (n\u0026thinsp;=\u0026thinsp;262)\u003c/p\u003e\n\u003c/th\u003e\n\u003cth align=\"left\"\u003e\n\u003cp\u003ePatients without complaints related to presbyopia (n\u0026thinsp;=\u0026thinsp;80)\u003c/p\u003e\n\u003c/th\u003e\n\u003cth align=\"left\"\u003e\n\u003cp\u003ep value\u003c/p\u003e\n\u003c/th\u003e\n\u003c/tr\u003e\n\u003c/thead\u003e\n\u003ctbody\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e\u003cstrong\u003eHypertensives\u003c/strong\u003e\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\"\u003e\n\u003cp\u003e43 (16.4%)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\"\u003e\n\u003cp\u003e24 (30.0%)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\"\u003e\n\u003cp\u003e\u003cstrong\u003e0.007\u003c/strong\u003e\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e\u003cstrong\u003eDiabetics\u003c/strong\u003e\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\"\u003e\n\u003cp\u003e67 (25.6%)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\"\u003e\n\u003cp\u003e32 (40.0%)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\"\u003e\n\u003cp\u003e\u003cstrong\u003e0.013\u003c/strong\u003e\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e\u003cstrong\u003ePatients taking chronic medications\u003c/strong\u003e\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\"\u003e\n\u003cp\u003e78 (29.8%)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\"\u003e\n\u003cp\u003e36 (45.0%)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\"\u003e\n\u003cp\u003e\u003cstrong\u003e0.011\u003c/strong\u003e\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eBMI (kg/m\u003csup\u003e2\u003c/sup\u003e)\u003c/p\u003e\n\u003cp\u003eUnder Weight\u003c/p\u003e\n\u003cp\u003eNormal Weight\u003c/p\u003e\n\u003cp\u003eOverweight\u003c/p\u003e\n\u003cp\u003eObese\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\"\u003e\n\u003cp\u003e\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e6 (2.3%)\u003c/p\u003e\n\u003cp\u003e108 (41.2%)\u003c/p\u003e\n\u003cp\u003e94 (35.9%)\u003c/p\u003e\n\u003cp\u003e53 (20.2%)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\"\u003e\n\u003cp\u003e\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e2 (2.5%)\u003c/p\u003e\n\u003cp\u003e34 (42.5%)\u003c/p\u003e\n\u003cp\u003e26 (32.5%)\u003c/p\u003e\n\u003cp\u003e19 (22.8%)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\"\u003e\n\u003cp\u003e0.943\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eHb (g/dL) Anaemic\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\"\u003e\n\u003cp\u003e141 (53.8%)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\"\u003e\n\u003cp\u003e46 (57.5%)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\"\u003e\n\u003cp\u003e0.562\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e\u003cstrong\u003eOcular parameters, mean\u0026thinsp;\u0026plusmn;\u0026thinsp;SD\u003c/strong\u003e\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e\u003cstrong\u003eAC depth (mm)\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eLens thickness\u003c/p\u003e\n\u003cp\u003eAA (cm)\u003c/p\u003e\n\u003cp\u003eNPA (cm)\u003c/p\u003e\n\u003cp\u003eNPC (cm)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\"\u003e\n\u003cp\u003e3.67\u0026thinsp;\u0026plusmn;\u0026thinsp;1.05\u003c/p\u003e\n\u003cp\u003e3.29\u0026thinsp;\u0026plusmn;\u0026thinsp;0.89\u003c/p\u003e\n\u003cp\u003e2.46\u0026thinsp;\u0026plusmn;\u0026thinsp;1.14\u003c/p\u003e\n\u003cp\u003e48.15\u0026thinsp;\u0026plusmn;\u0026thinsp;28.05\u003c/p\u003e\n\u003cp\u003e9.5\u0026thinsp;\u0026plusmn;\u0026thinsp;1.28\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\"\u003e\n\u003cp\u003e3.87\u0026thinsp;\u0026plusmn;\u0026thinsp;1.01\u003c/p\u003e\n\u003cp\u003e3.30\u0026thinsp;\u0026plusmn;\u0026thinsp;0.85\u003c/p\u003e\n\u003cp\u003e2.23\u0026thinsp;\u0026plusmn;\u0026thinsp;1.24\u003c/p\u003e\n\u003cp\u003e55.18\u0026thinsp;\u0026plusmn;\u0026thinsp;51.77\u003c/p\u003e\n\u003cp\u003e9.67\u0026thinsp;\u0026plusmn;\u0026thinsp;1.18\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\"\u003e\n\u003cp\u003e\u003cstrong\u003e0.028\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003e0.913\u003c/p\u003e\n\u003cp\u003e0.092\u003c/p\u003e\n\u003cp\u003e0.651\u003c/p\u003e\n\u003cp\u003e0.229\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eAxial length (mm), n (%)\u003c/p\u003e\n\u003cp\u003e\u0026lt;\u0026thinsp;22\u003c/p\u003e\n\u003cp\u003e22\u0026ndash;26\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\"\u003e\n\u003cp\u003e\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e51 (19.5%)\u003c/p\u003e\n\u003cp\u003e211 (80.5)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\"\u003e\n\u003cp\u003e\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e18 (22.5%)\u003c/p\u003e\n\u003cp\u003e62 (77.5%)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\"\u003e\n\u003cp\u003e0.554\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e\u003cstrong\u003eRefractive error\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eFor distance\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eEmmetropes\u003c/p\u003e\n\u003cp\u003eMyopes\u003c/p\u003e\n\u003cp\u003eHypermetropes\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\"\u003e\n\u003cp\u003e\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e171 (65.3%)\u003c/p\u003e\n\u003cp\u003e29 (11.1%)\u003c/p\u003e\n\u003cp\u003e62 (23.7%)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\"\u003e\n\u003cp\u003e\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e50 (62.5%)\u003c/p\u003e\n\u003cp\u003e16 (20%)\u003c/p\u003e\n\u003cp\u003e14 (17.5%)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\"\u003e\n\u003cp\u003e\u003cstrong\u003e0.021\u003c/strong\u003e\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003c/tbody\u003e\n\u003c/table\u003e\n\u003c/div\u003e\n\u003cp\u003eA regression analysis model constructed using these parameters showed only educational qualification to be statistically significant with regards to complaints of presbyopia. (R square\u0026thinsp;=\u0026thinsp;0.07, p\u0026thinsp;=\u0026thinsp;0.01)\u003c/p\u003e\n\u003cp\u003eThose patients who had complaints related to presbyopia were further divided into 2 groups based on their willingness to accept presbyopic correction. Those with higher educational qualifications (p\u0026thinsp;=\u0026thinsp;0.02) and skilled workers were more likely to accept near vision glasses (p\u0026thinsp;=\u0026thinsp;0.02), while those with lower Hb (p\u0026thinsp;=\u0026thinsp;0.01) and myopia (p\u0026thinsp;=\u0026thinsp;0.01) were less likely to accept correction for presbyopia. (Table\u0026nbsp;\u003cspan class=\"InternalRef\"\u003e3\u003c/span\u003e and Table\u0026nbsp;\u003cspan class=\"InternalRef\"\u003e4\u003c/span\u003e)\u003c/p\u003e\n\u003cdiv class=\"gridtable\"\u003e\n\u003ctable id=\"Tab3\" style=\"width: 950px;\" border=\"1\"\u003e\u003ccaption\u003e\n\u003cdiv class=\"CaptionNumber\"\u003eTable 3\u003c/div\u003e\n\u003cdiv class=\"CaptionContent\"\u003e\n\u003cp\u003eComparison of socio-demographic parameters based on patient\u0026rsquo;s willingness to accept presbyopic correction.\u003c/p\u003e\n\u003c/div\u003e\n\u003c/caption\u003e\n\u003cthead\u003e\n\u003ctr\u003e\n\u003cth colspan=\"7\" align=\"left\"\u003e\n\u003cp\u003eBased on patient\u0026rsquo;s willingness to wear near vision (NV) glasses\u003c/p\u003e\n\u003c/th\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003cth style=\"width: 17.568%;\" align=\"left\"\u003e\u0026nbsp;\u003c/th\u003e\n\u003cth style=\"width: 32.9151%;\" colspan=\"2\" align=\"left\"\u003e\n\u003cp\u003ePatients with complaints related to presbyopia, (n\u0026thinsp;=\u0026thinsp;262)\u003c/p\u003e\n\u003c/th\u003e\n\u003cth style=\"width: 4.4575%;\" align=\"left\"\u003e\n\u003cp\u003ep value\u003c/p\u003e\n\u003c/th\u003e\n\u003cth colspan=\"2\" align=\"left\"\u003e\n\u003cp\u003ePatients without complaints related to presbyopia, (n\u0026thinsp;=\u0026thinsp;80)\u003c/p\u003e\n\u003c/th\u003e\n\u003cth align=\"left\"\u003e\n\u003cp\u003ep value\u003c/p\u003e\n\u003c/th\u003e\n\u003c/tr\u003e\n\u003c/thead\u003e\n\u003ctbody\u003e\n\u003ctr\u003e\n\u003ctd style=\"width: 17.568%;\" align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003ctd style=\"width: 18.1058%;\" align=\"left\"\u003e\n\u003cp\u003ewilling for NV correction\u003c/p\u003e\n\u003cp\u003e(n\u0026thinsp;=\u0026thinsp;240)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd style=\"width: 14.7951%;\" align=\"left\"\u003e\n\u003cp\u003enot willing for NV correction\u003c/p\u003e\n\u003cp\u003e(n\u0026thinsp;=\u0026thinsp;22)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd style=\"width: 4.4574%;\" align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003ewilling for NV correction (n\u0026thinsp;=\u0026thinsp;45)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003enot willing for NV correction (n\u0026thinsp;=\u0026thinsp;35)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd style=\"width: 17.568%;\" align=\"left\"\u003e\n\u003cp\u003eAge (years), mean\u0026thinsp;\u0026plusmn;\u0026thinsp;SD\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd style=\"width: 18.1058%;\" align=\"left\"\u003e\n\u003cp\u003e48.22\u0026thinsp;\u003cstrong\u003e\u0026plusmn;\u0026thinsp;6.57\u003c/strong\u003e\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd style=\"width: 14.7951%;\" align=\"left\"\u003e\n\u003cp\u003e47.77\u0026thinsp;\u003cstrong\u003e\u0026plusmn;\u0026thinsp;6.67\u003c/strong\u003e\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd style=\"width: 4.4574%;\" align=\"char\"\u003e\n\u003cp\u003e0.95\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e50.87\u0026thinsp;\u003cstrong\u003e\u0026plusmn;\u0026thinsp;6.5\u003c/strong\u003e\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e48.37\u0026thinsp;\u003cstrong\u003e\u0026plusmn;\u0026thinsp;7.35\u003c/strong\u003e\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\"\u003e\n\u003cp\u003e0.443\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd style=\"width: 17.568%;\" align=\"left\"\u003e\n\u003cp\u003eGender\u003c/p\u003e\n\u003cp\u003eMale\u003c/p\u003e\n\u003cp\u003eFemale\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd style=\"width: 18.1058%;\" align=\"left\"\u003e\n\u003cp\u003e\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e112 (46.66%)\u003c/p\u003e\n\u003cp\u003e128 (53.33%)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd style=\"width: 14.7951%;\" align=\"left\"\u003e\n\u003cp\u003e\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e8 (36.36%)\u003c/p\u003e\n\u003cp\u003e14 (63.64%)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd style=\"width: 4.4574%;\" align=\"char\"\u003e\n\u003cp\u003e0.35\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e22 (48.89%)\u003c/p\u003e\n\u003cp\u003e23 (51.11%)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e23 (65.71%)\u003c/p\u003e\n\u003cp\u003e12(34.29%)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\"\u003e\n\u003cp\u003e0.13\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd style=\"width: 17.568%;\" align=\"left\"\u003e\n\u003cp\u003e\u003cstrong\u003eEducational qualification\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eIlliterate\u003c/p\u003e\n\u003cp\u003ePrimary School\u003c/p\u003e\n\u003cp\u003eSecondary School\u003c/p\u003e\n\u003cp\u003eAbove Graduate\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd style=\"width: 18.1058%;\" align=\"left\"\u003e\n\u003cp\u003e\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e24 (10%)\u003c/p\u003e\n\u003cp\u003e62 (25.83%)\u003c/p\u003e\n\u003cp\u003e70 (29.16%)\u003c/p\u003e\n\u003cp\u003e84 (34.99%)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd style=\"width: 14.7951%;\" align=\"left\"\u003e\n\u003cp\u003e\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e4 (18.18%)\u003c/p\u003e\n\u003cp\u003e10 (45.45%)\u003c/p\u003e\n\u003cp\u003e7 (31.82%)\u003c/p\u003e\n\u003cp\u003e1 (4.55%)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd style=\"width: 4.4574%;\" align=\"char\"\u003e\n\u003cp\u003e\u003cstrong\u003e0.02\u003c/strong\u003e\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e9 (20.00%)\u003c/p\u003e\n\u003cp\u003e17 (37.78%)\u003c/p\u003e\n\u003cp\u003e14 (31.11%)\u003c/p\u003e\n\u003cp\u003e5 (11.09%)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e7 (20.00%)\u003c/p\u003e\n\u003cp\u003e11 (31.43%)\u003c/p\u003e\n\u003cp\u003e8 (22.86%)\u003c/p\u003e\n\u003cp\u003e9 (25.71%)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\"\u003e\n\u003cp\u003e0.37\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd style=\"width: 17.568%;\" align=\"left\"\u003e\n\u003cp\u003e\u003cstrong\u003eOccupation\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eSkilled\u003c/p\u003e\n\u003cp\u003eSemiskilled\u003c/p\u003e\n\u003cp\u003eUnskilled\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd style=\"width: 18.1058%;\" align=\"left\"\u003e\n\u003cp\u003e\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e192 (79.99%)\u003c/p\u003e\n\u003cp\u003e21 (8.75%)\u003c/p\u003e\n\u003cp\u003e27 (11.25%)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd style=\"width: 14.7951%;\" align=\"left\"\u003e\n\u003cp\u003e\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e12 (54.55%)\u003c/p\u003e\n\u003cp\u003e4 (18.18%)\u003c/p\u003e\n\u003cp\u003e6 (27.27%)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd style=\"width: 4.4574%;\" align=\"char\"\u003e\n\u003cp\u003e\u003cstrong\u003e0.02\u003c/strong\u003e\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e34 (75.55%)\u003c/p\u003e\n\u003cp\u003e6 (13.33%)\u003c/p\u003e\n\u003cp\u003e5 (11.11%)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e20 (57.14%)\u003c/p\u003e\n\u003cp\u003e4 (11.43%)\u003c/p\u003e\n\u003cp\u003e11 (31.43%)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\"\u003e\n\u003cp\u003e0.07\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003c/tbody\u003e\n\u003c/table\u003e\n\u003c/div\u003e\n\u003cdiv class=\"gridtable\"\u003e\n\u003cdiv class=\"colspec\" align=\"left\"\u003e\u0026nbsp;\u003c/div\u003e\n\u003cp\u003eTable 4: Comparison of ocular and systemic parameters parameters based on pateints' willingness to accept presbyopic correction.\u003c/p\u003e\n\u003cp\u003e\u003cimg src=\"data:image/png;base64,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\" alt=\"\" width=\"724\" height=\"599\" /\u003e\u003c/p\u003e\n\u003c/div\u003e\n\u003cp\u003eIt was observed that almost 45%(n\u0026thinsp;=\u0026thinsp;35) of patients who did not have chief complaints related to presbyopia were not willing to accept near vision glasses. Among them, patients with higher BMI (p\u0026thinsp;=\u0026thinsp;0.04) and hypermetropes (p\u0026thinsp;=\u0026thinsp;0.05) were more willing to accept presbyopic correction (Table\u0026nbsp;\u003cspan class=\"InternalRef\"\u003e3\u003c/span\u003e and Table\u0026nbsp;\u003cspan class=\"InternalRef\"\u003e4\u003c/span\u003e)\u003c/p\u003e"},{"header":"DISCUSSION","content":"\u003cp\u003ePresbyopia is traditionally corrected with convex lens prescriptions in spectacles. Despite this non-invasive treatment, WHO has recorded the condition to be the most common cause of visual impairment worldwide.\u003csup\u003e\u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e3\u003c/span\u003e\u003c/sup\u003e The present study noted over 3/4th of the patients above the age of 40 visiting the department of ophthalmology had complaints related to presbyopia. The observations of this study suggests that patients with higher educational qualifications, those with DM, HTN and requiring chronic medications, hypermetropia and lower AC depth were more likely to present with chief complaints of presbyopia. Those with higher educational qualification, professional or skilled workers were more likely to accept presbyopic correction, while myopes and those with anaemia were less likely to accept the same. Patients who came with complaints of presbyopia were more likely to accept near vision correction.\u003c/p\u003e \u003cp\u003ePriyambada S et al. and Mukuria M et al. observed that women had earlier onset of presbyopia, while Andhra Pradesh Eye Disease Study observed female gender to have a greater association for presbyopia.\u003csup\u003e\u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e1\u003c/span\u003e,\u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e8\u003c/span\u003e,\u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e5\u003c/span\u003e\u003c/sup\u003e The present study observed a male: female (M:F) ratio of 0.8:1 among those who presented with complaints of presbyopia. When compared with M:F ratio of 1.3:1 among those above the age of 40 who did not have complaints of presbyopia, it appears that women are more likely to present with complaints of presbyopia. However, the gender difference was not statistically significant.\u003c/p\u003e \u003cp\u003ePatients with higher education are more likely to be involved with near work, both in professional and nonprofessional spheres. They are therefore more likely to complain of presbyopia and because of the perceived need also accept correction. Observations by Patel I et al. and Muhammad R et al. also noted that patients with higher education were more likely to be corrected for presbyopia.\u003csup\u003e\u003cspan citationid=\"CR9\" class=\"CitationRef\"\u003e9\u003c/span\u003e,\u003cspan citationid=\"CR10\" class=\"CitationRef\"\u003e10\u003c/span\u003e\u003c/sup\u003e Mukuria M et al. had a contrary observation of more severe presbyopia among those who were less literate. The authors explained it by suggesting a misinterpretation of the near vision charts, where those less literate may have preferred a magnified and therefore \u0026ldquo;clearer\u0026rdquo; correction.\u003csup\u003e\u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e5\u003c/span\u003e\u003c/sup\u003e\u003c/p\u003e \u003cp\u003eAssociation of education and occupation with a perceived need for presbyopic correction is a little blurred due to the increased use of digital media. Those whose occupations do not strictly require near vision, would perceive a need for presbyopic correction in other spheres. On the other hand, the ability to change the font size in digital screen permits postponement of presbyopic correction.\u003c/p\u003e \u003cp\u003eSkilled and professional jobs have a higher requirement of near vision acuity and therefore these patients may be more likely to accept presbyopic correction. Similar to our observations, a study in Nigeria also noted skilled professionals were more likely to procure glasses required for their near work.\u003csup\u003e\u003cspan citationid=\"CR10\" class=\"CitationRef\"\u003e10\u003c/span\u003e\u003c/sup\u003e In a study on unfulfilled need of presbyopic correction, Girum M et al. noted that those with higher education and skilled professionals were more likely to be corrected for their presbyopic needs earlier.\u003csup\u003e\u003cspan citationid=\"CR11\" class=\"CitationRef\"\u003e11\u003c/span\u003e\u003c/sup\u003e In parallel, a significant improvement in economical productivity has been observed following presbyopic correction even among those having unskilled jobs.\u003csup\u003e\u003cspan citationid=\"CR6\" class=\"CitationRef\"\u003e6\u003c/span\u003e,\u003cspan citationid=\"CR12\" class=\"CitationRef\"\u003e12\u003c/span\u003e\u003c/sup\u003e This emphasises the need for near vision correction irrespective of occupation.\u003c/p\u003e \u003cp\u003eSome studies have tried to determine the barriers for presbyopic correction. Hutchin B et al. in United Kingdom observed comfort and convenience of handling near vision glasses to be an important factor, more so than the cost of glasses. The perception that near vision glasses were a sign of aging also contributed to reluctance for presbyopic correction.\u003csup\u003e\u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e4\u003c/span\u003e\u003c/sup\u003e Contrary to this, in resource scarce countries, cost of glasses and ease of availability were noted to be significant barriers.\u003csup\u003e\u003cspan citationid=\"CR6\" class=\"CitationRef\"\u003e6\u003c/span\u003e,\u003cspan citationid=\"CR10\" class=\"CitationRef\"\u003e10\u003c/span\u003e,\u003cspan citationid=\"CR12\" class=\"CitationRef\"\u003e12\u003c/span\u003e\u003c/sup\u003e The present study observed that patients with skilled and professional jobs were more likely to be able to afford the cost of spectacles. Once corrected, the compliance towards near vision glasses was found to be above 80% even among unskilled labourers.\u003csup\u003e\u003cspan citationid=\"CR12\" class=\"CitationRef\"\u003e12\u003c/span\u003e\u003c/sup\u003e This suggests visual impairment due to presbyopia could be significantly addressed by economical support and access to eye care.\u003c/p\u003e \u003cp\u003eDespite chief complaints of presbyopia, some patients were unwilling to accept correction. Significant among these were patients with low Hb%. People with chronic illnesses like DM, HTN and those requiring chronic medications have the need to read and identify their medications in small prints. These patients may also present earlier with complaints of presbyopia due to compromised accommodation.\u003csup\u003e\u003cspan citationid=\"CR13\" class=\"CitationRef\"\u003e13\u003c/span\u003e,\u003cspan citationid=\"CR14\" class=\"CitationRef\"\u003e14\u003c/span\u003e\u003c/sup\u003e surprisingly we did not find a higher rate of acceptance for near vision correction among them. Systemic illness and cost of its treatment would have played a role in this and therefore their inability to afford the cost of near vision glasses.\u003c/p\u003e \u003cp\u003eAt the same time, participants with higher BMI (overweight and above) were more likely to accept near vision correction even when they did not have chief complains related to presbyopia. This could be because of their better socioeconomic status and therefore ability to afford near vision glasses. Higher BMI may also signify lesser physical activity and more time spent in near work. Therefore, their need for near vision glasses may be higher.\u003c/p\u003e \u003cp\u003eMyopes were less likely to present with complains of presbyopia and also less willing to wear near vision glasses. Hypermetropes have lesser AA.\u003csup\u003e\u003cspan citationid=\"CR15\" class=\"CitationRef\"\u003e15\u003c/span\u003e\u003c/sup\u003e So they may present earlier and also perceive greater need for presbyopic correction. The observation that patients with lower AC depth were more likely to present with complaints related to presbyopia may be attributed to hypermetropic refraction in them.\u003csup\u003e\u003cspan citationid=\"CR16\" class=\"CitationRef\"\u003e16\u003c/span\u003e\u003c/sup\u003e\u003c/p\u003e \u003cp\u003eThe present study highlights the fact that even among the patients who have access to health care facility uncorrected presbyopia is widely prevalent. Not many studies so far have explored patients\u0026rsquo; need and willingness to accept presbyopic correction even though presbyopia is an important cause for visual impairment worldwide.\u003c/p\u003e \u003cp\u003eThere were few limitations to this study. It was a single centre study. Studies involving multiple centres may be better able to address factors like socio-economic and geographical variations which would yield a deeper understanding into the needs and acceptance of presbyopic correction. Further, grading of lens opacification was not done for participants. This could have affected NPA, thereby influencing their complaints related to presbyopia and decision for wearing glasses. Also, only patients\u0026rsquo; willingness to accept presbyopic correction at the time of prescription was noted. Longitudinal study would be better to determine the patient compliance.\u003c/p\u003e"},{"header":"CONCLUSION","content":"\u003cp\u003ePresbyopia is a significant cause for hospital visit in patients above the age of 40. Patients with higher educational qualifications, requiring chronic medications and hypermetropes were more likely to present with complaints of presbyopia. Those with higher education, skilled profession hypermetropes and higher BMI were more likely to accept presbyopic correction, while those with anaemia were less likely to accept near vision correction.\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\n\u003cli\u003ePriyambada S. Premature Presbyopia and its Risk Factors - A Hospital based Study. International Journal of Contemporary Medical Research [IJCMR]. 2019 Mar;6(3). \u003c/li\u003e\n\u003cli\u003eAbraham LM, Kuriakose T, Sivanandam V, Venkatesan N, Thomas R, Muliyil J. Amplitude of accommodation and its relation to refractive errors. Indian Journal of Ophthalmology. 2005;53(2):105\u0026ndash;8. \u003c/li\u003e\n\u003cli\u003eHolden BA, Tahhan N, Jong M, Wilson DA, Fricke TR, Bourne R, et al. Towards better estimates of uncorrected presbyopia. Bulletin of the World Health Organization. 2015;93(10):667. \u003c/li\u003e\n\u003cli\u003eHutchins B, Huntjens B. Patients\u0026rsquo; attitudes and beliefs to presbyopia and its correction. Journal of Optometry. 2021;14(2):127\u0026ndash;32. \u003c/li\u003e\n\u003cli\u003eMukuria M, Kariuki M, Kollmann M, Al. E. Magnitude and pattern of presbyopia among patients seen on outreach with Lions SightFirst Eye Hospital, Loressho, Nairobi. Joecsa. 2012;16(1):42\u0026ndash;7. \u003c/li\u003e\n\u003cli\u003eWubben TJ, Guerrero CM, Salum M, Wolfe GS, Giovannelli GP, Ramsey DJ. Presbyopia: A pilot investigation of the barriers and benefits of near visual acuity correction among a rural Filipino population. BMC Ophthalmology. 2014;14(1). \u003c/li\u003e\n\u003cli\u003eSheeladevi S, Seelam B, Nukella P, Borah R, Ali R, Keay L. Prevalence of refractive errors, uncorrected refractive error, and presbyopia in adults in India: A systematic review. Indian Journal of Ophthalmology. 2019;67(5):583. \u003c/li\u003e\n\u003cli\u003eNirmalan PK, Krishnaiah S, Shamanna BR, Rao GN, Thomas R. A population-based assessment of presbyopia in the state of Andhra Pradesh, south India: The Andhra Pradesh eye disease study. Investigative Ophthalmology and Visual Science. 2006;47(6):2324\u0026ndash;8. \u003c/li\u003e\n\u003cli\u003ePatel I, Munoz B, Burke AG, Kayongoya A, Mchiwa W, Schwarzwalder AW, et al. Impact of Presbyopia on Quality of Life in a Rural African Setting. Ophthalmology. 2006;113(5):728\u0026ndash;34. \u003c/li\u003e\n\u003cli\u003eMuhammad R, Jamda M. Presbyopic correction coverage and barriers to the use of near vision spectacles in rural Abuja, Nigeria. Sub-Saharan African Journal of Medicine. 2016;3(1):20. \u003c/li\u003e\n\u003cli\u003eGirum M, Gudeta AD, Alemu DS. Determinants of high unmet need for presbyopia correction: A Community-Based study in Northwest Ethiopia. Clinical Optometry. 2017;9:25\u0026ndash;31. \u003c/li\u003e\n\u003cli\u003eReddy PA, Congdon N, MacKenzie G, Gogate P, Wen Q, Jan C, et al. Effect of providing near glasses on productivity among rural Indian tea workers with presbyopia (PROSPER): a randomised trial. The Lancet Global Health. 2018;6(9):e1019\u0026ndash;27. \u003c/li\u003e\n\u003cli\u003eVenugopal DM. A Study of Clinical Profile of Premature Presbyopia in A Tertiary Care Hospital. journal of medical science and clinical research. 2017 Jul 30;5(7):85\u0026ndash;9. \u003c/li\u003e\n\u003cli\u003eSridhar S, Ramachandra S. Accommodative parameter assessment in peri-presbyopic early onset diabetics with age matched healthy individuals - A case control study. Indian Journal of Clinical and Experimental Ophthalmology. 2020;6(3):422\u0026ndash;8. \u003c/li\u003e\n\u003cli\u003eAbraham LM, Kuriakose T, Sivanandam V, Venkatesan N, Thomas R, Muliyil J. Correlation between ocular parameters and amplitude of accommodation. Indian Journal of Ophthalmology. 2010;58(6):483\u0026ndash;5. \u003c/li\u003e\n\u003cli\u003eMaheshwari R, Sukul RR, Gupta Y, Gupta M, Phougat A, Dey M, et al. Accommodation: its relation to refractive errors, amblyopia and biometric parameters. Nepalese journal of ophthalmology : a biannual peer-reviewed academic journal of the Nepal Ophthalmic Society : NEPJOPH. 2011;3(2):146\u0026ndash;50. \u003c/li\u003e\n\u003c/ol\u003e"}],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":true,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":true,"hideJournal":true,"highlight":"","institution":"","isAcceptedByJournal":false,"isAuthorSuppliedPdf":false,"isDeskRejected":"","isHiddenFromSearch":false,"isInQc":false,"isInWorkflow":false,"isPdf":false,"isPdfUpToDate":true,"isWithdrawnOrRetracted":false,"journal":{"display":true,"email":"[email protected]","identity":"researchsquare","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":true,"externalIdentity":"","sideBox":"","snPcode":"","submissionUrl":"/submission","title":"Research Square","twitterHandle":"researchsquare","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"","reportingPortfolio":"","inReviewEnabled":false,"inReviewRevisionsEnabled":true},"keywords":"","lastPublishedDoi":"10.21203/rs.3.rs-3172918/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-3172918/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003cp\u003eBackground: Presbyopia is an age-related physiological phenomenon in which eye gradually losses its ability to accommodate. It is one of the leading causes of visual impairment worldwide, especially in adults above the age of 40. If uncorrected it can significantly impair patient's quality of life. This study aims to evaluate the factors which affects patient’s need and willingness to accept presbyopic correction.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eMethodology: A cross-sectional analytical study was done in semiurban tertiary hospital from Jan 2021 to June 2022. Details of patients aged 40 and above who presented to OPD like whether their chief complaints were related to presbyopia or not, history of spectacle use, systemic diseases, medications and their decision regarding near vision correction were noted. Ophthalmic examination included refraction and ocular biometry. Factors that may have influenced complaints of presbyopia or willingness to accept presbyopic correction were analysed.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eResults: Patients with chronic ailments and on chronic medication (p=0.01), higher educational qualification (p=0.031), hypermetropia (p=0.021) and shallower AC depth (p=0.028) were more likely to present with chief complaints attributable to presbyopia. Patients who had chief complains related to presbyopia, those with higher educational qualifications (p = 0.02) and skilled workers were more likely to accept near vision glasses (p = 0.02), while those with lower Hb (p = 0.01) and myopia (p = 0.01) were less likely to accept correction for presbyopia. In patients without chief complaints related to presbyopia and were not willing to accept near vision glasses. Among them, those with higher BMI (p = 0.04) and hypermetropes (p=0.05) were more willing to accept presbyopic correction.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eConclusion: Presbyopia constitutes a significant reason for patients above the age of 40 visiting eye care facility. Multiple socio-economic, systemic and ocular factors influenced both the chief complaints related to presbyopia and willingness to accept presbyopic correction.\u003c/p\u003e","manuscriptTitle":"Factors influencing the need and willingness for presbyopic correction – A cross sectional study from south India","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2023-07-18 09:39:27","doi":"10.21203/rs.3.rs-3172918/v1","editorialEvents":[{"type":"communityComments","content":0}],"status":"published","journal":{"display":true,"email":"[email protected]","identity":"researchsquare","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":true,"externalIdentity":"","sideBox":"","snPcode":"","submissionUrl":"/submission","title":"Research Square","twitterHandle":"researchsquare","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"","reportingPortfolio":"","inReviewEnabled":false,"inReviewRevisionsEnabled":true}}],"origin":"","ownerIdentity":"2ea8e0e1-9502-494d-a820-4c9ac8060783","owner":[],"postedDate":"July 18th, 2023","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"posted","subjectAreas":[{"id":23332024,"name":"Biological sciences/Physiology/Ageing"},{"id":23332025,"name":"Health sciences/Anatomy"}],"tags":[],"updatedAt":"2023-08-27T16:44:12+00:00","versionOfRecord":[],"versionCreatedAt":"2023-07-18 09:39:27","video":"","vorDoi":"","vorDoiUrl":"","workflowStages":[]},"version":"v1","identity":"rs-3172918","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-3172918","identity":"rs-3172918","version":["v1"]},"buildId":"cBFmMYwuxLRRLfASyISRj","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}