Mostafa Bondok

Background: Many factors such as declining physical function, low socioeconomic status and unemployment play a role in the complex health and social needs of older adults. While healthcare providers can assist in treating health conditions, non-clinical interventions are most likely needed to meet their social needs and improve wellbeing. Social prescribing has been introduced to help primary care providers and other connectors meet patient’s social needs that contribute to poor health and inequalities. With the increasing recognition of these complex challenges, decision-makers need to know which social prescribing interventions or social prescriptions are effective for older adults. Objectives: To map the available evidence on the effectiveness of social prescribing interventions and social prescriptions addressing a non-medical health-related need for older adults in clinical and community settings. Search Methods: We searched the following databases from inception until November 14, 2023, with no language restrictions: Ovid MEDLINE, Embase, EBM Reviews, Elsevier Scopus, Epistemonikos, CABI CAB Direct, NIHR PROSPERO, WHO Global Index Medicus, Clarivate Web of Science Social Sciences Citation Index (SSCI), Conference Proceedings, Clarivate Korean Citation Index (KCI), Clarivate SciELO Citation Index and Google Scholar via Harzing Publish or Perish. Selection Criteria: Titles and abstracts and full text of potentially eligible articles were independently screened in duplicate for systematic reviews and studies with concurrent control groups assessing the effectiveness of social prescribing and social prescription interventions on the wellbeing of older adults. Conflicts were resolved through consensus. Data Collection and Analysis: We developed and pilot-tested a data collection form in EPPI-Reviewer. Data was extracted and coded individually by reviewers based on an intervention-outcome framework which was also used to define the dimensions of the evidence and gap map. Data extraction was validated by a second reviewer for 20%. We assessed the quality of reviews using the AMSTAR2 tool. The quality of primary studies was not assessed. Main Results: We included 662 articles (430 primary studies, 232 reviews) that assessed the effectiveness of social prescribing interventions or social prescriptions targeting non-medical health-related social needs of older adults to improve their health and wellbeing. Over 65% of the articles were published in 2019 or later. AMSTAR2 quality was rated as critically low for 55%, low for 31%, moderate for 6% and high for only 4% of systematic reviews. Of the included primary studies, 48% were randomized controlled trials and 52% were non-randomized studies. Most of the evidence was from high-income countries (78% of reviews and 93% of primary studies), few from upper-middle (25% of reviews and 5% of primary studies), and lower-middle (9% of reviews and 3% of primary studies) countries, and none from low-income countries (0%). The most common intervention types identified were psychosocial (e.g. social and emotional support interventions), with 81% of reviews and 63% of primary studies including these interventions, lifestyle (exercise on prescription) with 45% of reviews and 30% of primary studies including these interventions, and arts-based (museums, galleries, arts & crafts), with 29% of reviews and 20% of primary studies including these interventions. These were followed by material support in 13% of reviews and 7% of primary studies, and nature-based interventions in 10% of reviews and 4% of primary studies. Ninety-eight percent of the reviews and 95% of the primary studies focused on assessing the impact on individual outcomes (e.g. participant wellbeing/quality of life), with 41% of reviews and 47% of primary studies assessing process outcomes (e.g. participant satisfaction), and 27% of both reviews and primary studies looking at health system outcomes (e.g. health/social service use). Eleven percent of reviews and primary studies looked at adverse effects, and no studies assessed health equity as a community outcome. Key characteristics of social prescribing interventions or social prescriptions were described in few included articles; codesign of the interventions in 6% of reviews and 15% of primary studies, while participant empowerment was described in 24% of reviews and 19% of primary studies. Most articles focused on a population at risk of or experiencing inequity (91% of reviews and 96% of primary studies). Less than 10% of both reviews and primary studies conducted any equity analysis to assess differential effects for populations experiencing inequities across factors such as age, gender or ethnicity. Authors’ Conclusions: Most of the available evidence on social prescribing and social prescription was on psychosocial and health system interventions; there is limited research on material support and nature-based interventions. Furthermore, despite the aims of social prescribing or social prescription to empower communities and reduce health inequalities, there are few studies that assess community-level outcomes or adverse effects. There is a need for more research in low-middle-income countries and for high-quality reviews. This map can help identify priorities for further research and promote the use of existing evidence in policy and practice. Abbreviations and Acronyms: EGM = evidence and gap map. GP = general practitioner. LGBTQIA2S+: lesbian, gay, bisexual, transgender, queer (or sometimes questioning), intersex, asexual, and two-spirited. PICO: population, intervention, comparison, outcome. PROGRESS-Plus: Place of residence (urban/rural), Race/ethnicity/culture and language, Occupation, Gender or sex, Religion, Occupation, Socioeconomic status, Social capital and Plus factors. WHO = World Health Organization.

Many factors such as declining physical function, low socioeconomic status and unemployment play a role in the complex health and social needs of older adults. While healthcare providers can assist in treating health conditions, non-clinical interventions are most likely needed to meet their social needs and improve wellbeing. Social prescribing has been introduced to help primary care providers and other connectors meet patient’s social needs that contribute to poor health and inequalities. With the increasing recognition of these complex challenges, decision-makers need to know which social prescribing interventions or social prescriptions are effective for older adults. To map the available evidence on the effectiveness of social prescribing interventions and social prescriptions addressing a non-medical health-related need for older adults in clinical and community settings. We searched the following databases from inception until November 14, 2023, with no language restrictions: Ovid MEDLINE, Embase, EBM Reviews, Elsevier Scopus, Epistemonikos, CABI CAB Direct, NIHR PROSPERO, WHO Global Index Medicus, Clarivate Web of Science Social Sciences Citation Index (SSCI), Conference Proceedings, Clarivate Korean Citation Index (KCI), Clarivate SciELO Citation Index and Google Scholar via Harzing Publish or Perish. Titles and abstracts and full text of potentially eligible articles were independently screened in duplicate for systematic reviews and studies with concurrent control groups assessing the effectiveness of social prescribing and social prescription interventions on the wellbeing of older adults. Conflicts were resolved through consensus. We developed and pilot-tested a data collection form in EPPI-Reviewer. Data was extracted and coded individually by reviewers based on an intervention-outcome framework which was also used to define the dimensions of the evidence and gap map. Data extraction was validated by a second reviewer for 20%. We assessed the quality of reviews using the AMSTAR2 tool. The quality of primary studies was not assessed. We included 662 articles (430 primary studies, 232 reviews) that assessed the effectiveness of social prescribing interventions or social prescriptions targeting non-medical health-related social needs of older adults to improve their health and wellbeing. Over 65% of the articles were published in 2019 or later. AMSTAR2 quality was rated as critically low for 55%, low for 31%, moderate for 6% and high for only 4% of systematic reviews. Of the included primary studies, 48% were randomized controlled trials and 52% were non-randomized studies. Most of the evidence was from high-income countries (78% of reviews and 93% of primary studies), few from upper-middle (25% of reviews and 5% of primary studies), and lower-middle (9% of reviews and 3% of primary studies) countries, and none from low-income countries (0%). The most common intervention types identified were psychosocial (e.g. social and emotional support interventions), with 81% of reviews and 63% of primary studies including these interventions, lifestyle (exercise on prescription) with 45% of reviews and 30% of primary studies including these interventions, and arts-based (museums, galleries, arts & crafts), with 29% of reviews and 20% of primary studies including these interventions. These were followed by material support in 13% of reviews and 7% of primary studies, and nature-based interventions in 10% of reviews and 4% of primary studies. Ninety-eight percent of the reviews and 95% of the primary studies focused on assessing the impact on individual outcomes (e.g. participant wellbeing/quality of life), with 41% of reviews and 47% of primary studies assessing process outcomes (e.g. participant satisfaction), and 27% of both reviews and primary studies looking at health system outcomes (e.g. health/social service use). Eleven percent of reviews and primary studies looked at adverse effects, and no studies assessed health equity as a community outcome. Key characteristics of social prescribing interventions or social prescriptions were described in few included articles; codesign of the interventions in 6% of reviews and 15% of primary studies, while participant empowerment was described in 24% of reviews and 19% of primary studies. Most articles focused on a population at risk of or experiencing inequity (91% of reviews and 96% of primary studies). Less than 10% of both reviews and primary studies conducted any equity analysis to assess differential effects for populations experiencing inequities across factors such as age, gender or ethnicity. Most of the available evidence on social prescribing and social prescription was on psychosocial and health system interventions; there is limited research on material support and nature-based interventions. Furthermore, despite the aims of social prescribing or social prescription to empower communities and reduce health inequalities, there are few studies that assess community-level outcomes or adverse effects. There is a need for more research in low-middle-income countries and for high-quality reviews. This map can help identify priorities for further research and promote the use of existing evidence in policy and practice. EGM = evidence and gap map. GP = general practitioner. LGBTQIA2S+: lesbian, gay, bisexual, transgender, queer (or sometimes questioning), intersex, asexual, and two-spirited. PICO: population, intervention, comparison, outcome. PROGRESS-Plus: Place of residence (urban/rural), Race/ethnicity/culture and language, Occupation, Gender or sex, Religion, Occupation, Socioeconomic status, Social capital and Plus factors. WHO = World Health Organization.

Background: To assess financial disclosures of American ophthalmology society board members by comparing self‐reported disclosures with industry‐reported payments and examining characteristics linked to larger financial relationships. Methods: In this retrospective, cross‐sectional study, we assessed all governance board members from American ophthalmology societies in December 2022. Board composition was identified from society websites, payment data from the Open Payments database, and conflict of interest (COI) policies from IRS Form 990 filings. Outcomes included concordance between self‐ and industry‐reported disclosures, payment values, gender and subspecialty differences and academic characteristics. Results: Among 871 board members from 66 societies, 566 (65.0%) had industry‐reported relationships, yet only 22 (2.5%) disclosed COIs on society websites. In 2022, 13 187 payments totaling $57.8 million were reported, with 79.5% related to research. Most societies reported internal COI policies (77.8%) and annual disclosure requirements (75.6%) via IRS filings. Men received significantly higher median payments than women ($217.5 vs. $43.3; p < 0.001). Retina specialists accounted for the largest share of payment value (55.3%), while paediatric ophthalmologists received the least (0.4%). Board members with research payments had higher academic productivity (median h ‐index: 19 vs. 8; p < 0.001). Conclusions: Public reporting of board members' financial relationships on ophthalmology society websites was uncommon, likely reflecting differences in society‐level disclosure practices rather than individual nondisclosure. These findings underscore an opportunity for societies to enhance transparency by adopting more consistent, transparent COI reporting practices in ophthalmology governance.

Purpose: We evaluated the ability of ChatGPT, an Artificial Intelligence (AI) Chatbot, to respond to patient eye health queries. Methods: A retrospective, cross-sectional analysis of eye health questions and physician responses posted on the American Academy of Ophthalmology (AAO) “Ask an Ophthalmologist” forum was performed on a random sample from January 2016 to December 2022. We compared board-certified ophthalmologists’ responses to ChatGPT (version GPT-4o, OpenAI) responses in September 2024. Primary outcomes included ophthalmologist-rated accuracy of ChatGPT and AAO responses using a 7-point Likert scale, as well as ophthalmologists’ preferences between the two responses. Secondary outcomes assessed differences in readability, empathy, and response length between ChatGPT and ophthalmologists. Results: A random sample 250 questions and responses from 41 board-certified ophthalmologists were evaluated. ChatGPT and AAO responses had similar mean accuracy ratings (5.8 [SD=1.1] vs 5.5 [SD=1.1], p=0.07). Evaluators preferred ChatGPT over physician responses in half (49.5%) the cases. Ophthalmologist responses were easier to understand, with a lower mean Flesch-Kincaid Grade Level (Grade 11.0 [SD=2.7] vs Grade 12.7 [SD=1.9], p< 0.001). Ophthalmologist responses were also significantly shorter than ChatGPT responses (80.6 [SD=56.4] words vs (337.8 [SD=141.6] words, p< 0.001). Empathy ratings did not differ significantly between ChatGPT and ophthalmologists (4.4 [SD=0.6] vs 4.4 [SD=0.6], p=0.5). Conclusion: Our findings suggest that Chatbot responses were as frequently preferred as physician responses, rated with higher accuracy, and demonstrated comparable empathy in addressing online patient eye health queries. AI chatbots may assist in drafting initial responses to patient concerns, potentially improving efficiency and reducing physician workload.

Purpose: To determine the prevalence and causes of blindness and vision impairment (VI) among adults aged ≥50 years in Western Uganda. Methods: A population-based cross-sectional survey was conducted in Western Uganda (July-August 2023) using RAAB7. Adults aged ≥50 years who had resided in the study districts for at least six months in the past year were eligible. Participants were identified through door-to-door household visits using a two-stage cluster sampling approach. Primary outcomes include prevalence of blindness and VI and its causes. Secondary outcomes include cataract surgical coverage (CSC), effective CSC (eCSC), refractive error coverage (REC), and effective REC (eREC). Results: A total of 3,125 participants were examined (54.1% female). The adjusted prevalence of blindness (presenting visual acuity (PVA) <3/60) was 0.9% (95% CI: 0.5–1.3%). Severe, moderate, and mild VI were found in 0.9% (95% CI: 0.4–1.3%), 4.5% (95% CI: 3.3–5.8%), and 3.8% (95% CI: 3.0–4.6%), respectively. Untreated cataract was the leading cause of bilateral blindness (49.4%). The CSC and eCSC at the < 6/12 threshold were 19.7% and 7.3%, respectively. Only 19.4% of 108 operated eyes achieved good outcomes (PVA ≥ 6/12). The main barriers to cataract surgery included lack of awareness (32.8%), cost (23.9%), and perceived lack of need (20.9%). The adjusted prevalence of uncorrected refractive error as a cause of moderate VI was 1.6% (95% CI: 1.1–2.0%), and mild VI was 2.8% (95% CI: 2.2–3.5%). REC was 1.0%, while eREC was 0.6% (95% CI: 0.0–1.4%). Conclusion: Blindness and vision impairment remain major public health issues in Western Uganda, primarily due to untreated cataract and uncorrected refractive error. Poor post-operative outcomes highlight the urgent need to improve surgical quality. These findings may guide targeted interventions and policy to strengthen eye care services.

Artificial intelligence (AI) and augmented intelligence have significant potential in oculoplastics, offering tools for diagnosis, treatment recommendations, and administrative efficiency. This article discusses current and potential applications of AI in ptosis, eyelid and conjunctival cancer, thyroid-associated orbitopathy (TAO), giant cell arteritis (GCA), and orbital fractures. AI-based programs can assist in screening, predicting surgical outcomes, and improving patient care through data-driven decisions. Privacy concerns, particularly with the use of facial and ocular photographs, require robust solutions, including blockchain, federated learning and steganography. Large generalizable datasets with adequate validation are crucial for future AI development. While AI can assist in clinical decision-making and administrative tasks, physician oversight remains critical to prevent potential errors. Large language models like ChatGPT also have the potential to counsel patients, although further validation is needed to ensure accuracy and patient safety. Ultimately, AI should be regarded as an augmentative tool that supports, rather than replaces, physician expertise in oculoplastic care.