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P. P. Chiang, J. Xie, R. T. Le Mesurier, J. E. Keeffe; Critical Success Factors in the Delivery of Low Vision Services and Implications for Health Policy. Invest. Ophthalmol. Vis. Sci. 2009;50(13):5209.
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© ARVO (1962-2015); The Authors (2016-present)
To map the provision of low vision services globally and identify the critical success factors (CSF) for low vision service coverage.
Data were generated from a survey distributed to Vision 2020 contacts, government, and non-government organizations (NGOs) in 195 countries during 2006-2007 and also from key informants. Themes from the survey were: policies and guidelines on low vision; provision of services; infrastructure and equipment; coverage and barriers; and human resources. These formed the service-related variables examined for associations with service coverage. External variables comprised socio-economic and demographic data of the countries. Coverage, defined as better (>10%) and poorer (≤10%), was the outcome measure. The classification and regression tree (CART), using the Chi-squared Automatic Interaction Detection (CHAID) growing method, were used to identify and prioritize the CSFs of low vision services.
Data from primary and secondary sources were obtained from 178/195 of the countries surveyed. Of the 131 who responded regarding the coverage of low vision services, 101 (77.1%) countries have ≤10% coverage; 30 (22.9%) countries have >10%. The risk estimate of 8.4% indicated that the category predicted by the model (better or poorer coverage) is correct for 91.6% of the countries. The results revealed that countries where >50% of children obtain low vision devices when prescribed (Χ2=39.58, p=0.000); presence of national referral guidelines (Χ2 =10.19, p=0.001); having >3 rehabilitation officers per 10 million of population (Χ2 =7.99, p=0.005) and availability of government funding (Χ2 =4.13, p=0.042) are critical factors associated with better coverage of low vision services. When socio-economic and demographic data of the countries were added into the CART model - the higher the percentage of population urbanized (Χ2 =13.77, p=0.004) and at least some private expenditure on health as percentage of total expenditure on health (Χ2 =14.19, p=0.017) were shown to impact on greater coverage.
Having access to equipment, adequate numbers of trained rehabilitation staff, establishing referral networks, and government support are critical factors in the better coverage of low vision services.
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