Study design and population
The study design was a cross-sectional survey of patients attending eye health outreach camps in the district of Kasungu, central Malawi (figure 1).
Study participants were recruited from those who attended the outreach camps, providing they were over the age of 18 years, or accompanied by an adult if below this age and gave consent.
The study was conducted between April and September 2017. During this period eye camps were organised monthly across the district and lasted between two and four days each (Figure 1). Comprehensive eye health activities were delivered alongside outreach camps targeting trachoma trachomatous (TT) and organised as part of the trachoma elimination programme funded by the Queen Elizabeth Diamond Jubilee Trust (QEDJT) (13).
The study population was all individuals who presented at outreach camps who met the inclusion criteria stated above. Study participants were sampled using systematic (interval–based) sampling until the required minimum sample size was reached.
The sample size was calculated based on the formula for proportions’ comparison (14). The following assumptions from an earlier pilot and from Demographic and Health Surveys (DHS) 2015/2016 were used: i) an estimated prevalence of non-visual disability of 10%; ii) 8% of those with a disability would belong to the wealthiest quintile; and iii) a power of 80% to detect a 10% difference in the prevalence of disability between attendees belonging to the poorest group (quintile 1) and the wealthiest group (quintile 5) at alpha 0.05, with a ratio of 2. The minimum sample size required was 1,275 participants, and 1,358 observations were collected in total (15). In this study household and dwelling data were self-reported. Therefore, a number of household visits were organized for randomly selected patients participating in the study in order to verify the validity of these data. The sample size needed for the household visits was calculated to be 102, using a 7.5% margin of error (alpha 0.05).
Measuring participants’ socio-economic status
Income is commonly used as a measure of socio-economic status. However, it is extremely difficult to measure income in low- and middle-income contexts with large informal sectors and where income does not include in-kind payments and/or fluctuates according to seasonality and migration (16). Household ownership of assets can be used as an alternative to estimate wealth and poverty levels in such contexts (17-19). In this study we used two validated asset-based tools, the EquityTool and the Simple Poverty Scorecard (20, 21).
The Simple Poverty Scorecard, developed by Mark Schreiner of Microfinance Risk Management L.L.C, is a country specific tool, which estimates the likelihood of a household’s consumption to be below a certain poverty line, based on their characteristics and asset possession. The Malawi tool available at the time of this study consisted of 10 key indicators (Appendix 1), based on the Malawi’s 2010/2011 Integrated Household Survey (IHS) (22). Each response to an indicator has a given number of points, and the total “poverty score” for a household ranges between 0 and 100 points. The calculated poverty score for each household is then compared to a matrix to estimate the poverty rate, or the proportion of a group to be below four government defined poverty lines: i) food or ultra-poverty line; ii) national poverty line (includes food and non-food components); iii) $1.90 per day line; and iv) $3.10 per day line (Appendix 2) (21, 22).
The EquityTool, developed by Metrics for Management, measures relative wealth based on household characteristics and possession of durable assets. The Malawi EquityTool 2015 is composed of 17 indicators (Appendix 3), and the corresponding scores are computed through a principal component analysis based on the Malaria Indicator Survey from 2012 (20). Each DHS participant is given a wealth index according to the 17 indicators generated, and are then ordered and split into five equal quintiles based on their score. , Quintile one represents the poorest segment of the population, and quintile five the wealthiest. Using national quintiles to determine cut-off points (based on the score of the 17 indicators), our study participants are allocated to their corresponding quintile, which allows a comparison of their socio-economic status with the rest of the national population. Indeed, if study participants’ level of wealth is the same as the national population, each quintile would have 20 percent of respondents. If the level of wealth is different, the five quintiles would be unequally split. The study used the latest version of the tool available at the time of the study which was validated for Malawi based on the Malawi MIS 2012 (20, 23).
Both national surveys (IHS and MIS), used for deriving tools’ indicators, allow for disaggregating data at the regional and district level. Therefore, in our analysis for both tools we compared wealth of our programme participants with the national population and the population of Kasungu district. In addition, by the time of the data analysis two new national surveys (DHS from 2015/16 and IHS 2016/17) had been released (15, 24). We therefore used the same indicators for both tools with these more recent datasets.
The Washington Group Short Set of Questions on Disability (WGSS) was used to measure self-reported disability. The tool was developed by the United Nations Statistical Commission for use in national censuses and surveys. The tool assesses functional difficulties when conducting basic activities in six domains: seeing, hearing, walking/climbing; remembering/concentrating; self-care and communicating. The answers are given on a four-point scale from ‘no difficulty’ to ‘cannot do at all’. Disability status is defined when participants report having a lot of difficulty or cannot do at all in at least one domain (25). As we expected, a significant number of participants coming to the outreach camps had difficulty in seeing. We also used a measure of “non-visual disability”, i.e. a functional difficulty (a lot of difficulty or cannot do at all) in any domain except seeing.
Upon arrival at the screening site, all attendees were provided information about the study by the data collection team. Information was provided in local languages and people had an opportunity to ask questions.
All attendees first undertook visual acuity test using a tumbling E chart. Those who failed the test or had other visible eye problems (such as red eye) were examined by the Ophthalmic Clinical Officer, who made a diagnosis and provided a treatment or a referral. Patients selected by the interval random sampling (see above) for a further interview were again informed about the study, its purpose and how the data will be used and were asked to provide their consent. If provided, they were asked questions from the EquityTool, Simple Poverty Scorecard and WGSS questionnaires. A subset of participants was then randomly selected for home visits to verify their household asset scores (Figure 2).
The data was collected electronically using the mobile phone survey software KOBO ToolBox. Five data collectors with experience of collecting mobile data were recruited and trained ahead of the camp. The questionnaires were administered in local languages, Chichewa and Tumbuka.
Household visits to verify self-reported wealth were attempted on the same day as the camp visit when possible; if not, they were carried out the following day. Community volunteers assisted the data collectors to trace the respondents’ homes.
Personal identifiable information collected was separated from the rest of the data before the analysis. Data cleaning and analysis was undertaken using STATA 14 (26).
Kappa statistics were used to measure the inter-rater reliability of self-responses at the camp compared to the household survey on dwelling characteristics and ownership of assets, following the methodology described in the literature (27). The guidelines from Landis & Koch (1977) were used to interpret the level of agreement as follows: 0.0 -0.20: slight; 0.21 -0.40: fair; 0.41 -0.60: moderate; 0.61 -0.90: substantial; 0.81 -1.00: almost perfect (28).
To compare the level of wealth of camp participants to the rest of the Kasungu district, both MIS 2012 and DHS 2015/2016 data sets were retrieved from the Demographic and Health Surveys programme website and were compared against our study sample. For the analysis using DHS 2015/16 data, the EquityTool 2010 wealth index was used, similar to methods used by Pitchforth et al. and Wilunda et al. (20, 29-31). The choice of applying the EquityTool proxy variables 2010 on the DHS 2015/16 data was justified by the fact that there was an important time gap between MIS 2012 and DHS 2015/16, and that the updated Malawi EquityTool 2017 contained different questions that our survey did not cover (Appendix 4) (32). The same procedure was applied to the Simple Poverty Scorecard score (based on 10 indicators as seen above) using the IHS 2011 and IHS 2017 at national and district levels as references (22, 24).
STATA complex design-based F-test of independence was used to test our hypothesis that the proportion of study participants belonging to the lowest relative wealth quintile was different from the Kasungu residents, the MIS, and from the DHS full sample (33).
STATA 14 was also used to assess participants’ disability status using the recommended cut off (a lot of difficulty or cannot do at all) in at least one domain. Non-visual disability was determined by using the same cut offs but excluding the seeing domain. Disability data from the SINTEF national survey, conducted in 2017 and using the same WGSS tool, were compared to CATCH camps participants (34). A chi-square test of independence was performed for 10-year age groups (except for the 0 to 20 years old) in order to have homogenic, large enough sub-groups. This also corrected for the age distribution difference given that disability is positively associated with age (34-36).
Ethical approval was obtained from the Malawi National Health Sciences Research Committee (NHSRC) [protocol #16/11/1685]. Informed consent was obtained from all study participants. In the case of minors, their carer provided the consent. Additional verbal consent was requested prior to the household visits; and at the household level, a consent was sought from both the head of the household and the study participant. All information collected was anonymised and kept confidential. All study participants with eye problems were either treated at the camp or referred to the district hospital for further management.