Comparing the Implementation of different Diabetic Retinopathy Screening Models in Primary Health Care settings in Northern India: Pragmatic three arm observational study

Abstract Introduction: Diabetic retinopathy (DR) is an emerging leading cause of preventable vision impairment and blindness globally. Early screening is a recommended method for preventing avoidable blindness due to DR. We aim to examine the factors affecting the implementing of different diabetic retinopathy screening models in public health facilities and community settings. Methods and analysis: A Pragmatic three-arm observational study will be conducted in three wellness and wellness centers in Punjab, India. The study will compare three equal-sized groups of people with type 2 diabetes mellitus and those over 30 years who will be enrolled. Patients and healthcare providers will be interviewed to identify the barriers to accessing retinal examinations in primary health settings. Summary statistics will be presented as frequencies for categorical variables and mean (standard deviation) for continuous variables that are symmetrically distributed; otherwise, median and interquartile ranges will be reported. The study includes a comprehensive economic evaluation to determine the cost-effectiveness of implementing different diabetic retinopathy screening models in primary health settings. This will be done using a decision tree model. The analysis will include both deterministic and probabilistic sensitivity analysis. Discussion: A screening program should ensure early detection and referral for diabetic retinopathy treatment before it results in irreversible vision loss. However, understanding the problems of accessing diabetic retinopathy screening in public health facilities and estimating the cost-effectiveness of such a program in a public health setting in India will inform decision-makers responsible for allocating scarce public health resources. Trial registration: CTRI/2022/10/046283 Keywords: diabetic retinopathy screening, artificial intelligence, public health settings, cost-effectiveness.