Sociodemographic characteristics of community eye screening participants: protocol for cross-sectional equity analyses in Botswana, Kenya, and Nepal

Abstract

Background: Attendance rates for eye clinics are low across low- and middle-income countries (LMICs) and exhibit marked sociodemographic (SD) inequalities. We aimed to quantify the association between a range of SD domains and attendance rates from vision screening in programmes launching in Botswana, Kenya and Nepal. Methods: We will develop a set of sociodemographic questions and introduce them into routine community-based eye screening programmes in Kenya, Botswana and Nepal, targeting children aged 5-18 years and adults. Our study design is a rolling survey, embedded within the Peek screening programme. The sociodemographic questions will be asked of 10% of all those presenting to be screened, and 100% of those identified with an eye problem. We will also collect data on whether people referred to ophthalmic clinic for treatment or further assessment attended, and we will use logistic regression to report odds ratios for this outcome attendance) for each socioeconomic domain in each country. We hypothesise that attendance rates will be lowest among marginalised sociodemographic groups such as older, less educated, less wealthy women. To identify the most appropriate sociodemographic items we will perform a literature review, and then hold workshops with researchers, academics, programme implementers, and programme designers in each country to tailor the domains and response options to the national context. We will report outcome data at 6 and 12 months, identifying the groups facing the highest barriers to access. Discussion: This low-risk, embedded, pragmatic, observational data collection will enable eye screening programme managers to accurately identify which sociodemographic groups are facing the highest systematic barriers to accessing care at any point in time. This information will be used to inform the development of service improvements to improve equity.

Identifying barriers and potential solutions to improve equitable access to community eye services in central Kenya: a rapid exploratory sequential mixed methods study

“…6 Our first set of findings from a cross-sectional equity analysis of over 4,000 people in Kenya's Meru county found that only 46% of those found to have an eye need were able to access their free local treatment outreach clinic. 7 We found that younger adults, males, and those working in sales, services, or manual jobs were the least likely to receive the care they need. Age was the strongest predictor of poor access, with less than a third of people aged 18-44 years receiving care compared to two thirds of those aged >45 years, even after controlling for severity of eye condition and a wide range of other factors.…”

Comparison of telephone and in-person interview modalities: duration, richness, and costs in the context of exploring determinants of equitable access to community health services in Meru, Kenya

“…A previous equity analysis conducted by our team in Meru had found that younger adults (aged 18-44 years old) were the least likely to receive eye care in the county's communitybased screening programme. 4 We obtained a list of all of the younger adults who did not receive care from Peek Vision, a partner organisation that provides the screening and patient flow management software for the programme. 3 Peek also record contact numbers for all participants.…”

“…In Meru County we have found that younger adults (aged 18-44 years) are the least likely to receive the care they need. 4 We wanted to explore these peoples' experiences and perceptions of specific barriers to accessing care, as well as their ideas around any changes we could make to the eye care services to make it easier to access care.…”

Identifying barriers and potential solutions to improve equitable access to community eye services in Botswana, India, Kenya, and Nepal: a rapid exploratory sequential mixed methods study protocol

“…Recruitment and sampling Participants in Peek-powered screening programmes operating in Botswana, Inda, Kenya, and Nepal provide their name, a contact number and -if they consent -data on approximately ten sociodemographic domains including age, sex, education, income, assets, and health status (the unique lists for each national programme and selection processes have been detailed in a previous IM-SEEN publication 90 ). Peek has consent procedures and agreements that enable these data to be shared with our embedded research team.…”