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ECHO-T2D – economic, clinical epidemiologic, and humanistic profiling of diabetic retinopathy, diabetic kidney disease, and painful diabetic peripheral neuropathy in India: A systematic review protocol

AI Summary
  • Microvascular complications of type 2 diabetes, DR, DKD, and pDPN, cause substantial morbidity, reduced quality of life, and increased healthcare costs in India.
  • Evidence on clinical course, economic burden, and humanistic impact is fragmented, necessitating an integrated synthesis of these outcomes for T2D microvascular complications in India.
  • Study will generate model-ready, context-specific estimates of clinical trajectories, costs, and health related quality of life to inform economic evaluations and policy.
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MethodsX. 2026 May 12;16:103953. doi: 10.1016/j.mex.2026.103953. eCollection 2026 Jun.

ABSTRACT

Microvascular complications of type 2 diabetes (T2D) like diabetic retinopathy (DR), diabetic kidney disease (DKD), and painful diabetic peripheral neuropathy (pDPN) contribute to morbidity, impaired quality of life, and healthcare costs. Evidence describing their clinical course, economic burden, and humanistic impact remains fragmented, limiting its utility for policy and health economic modelling. We aim to carry out an integrated synthesis of the economic, clinical epidemiologic, and humanistic outcomes associated with DR, DKD, and pDPN among individuals with T2D in India. All primary studies on our research question are eligible. Economic outcomes include both costs and resource consumption. Clinical epidemiologic outcomes cover disease staging, severity markers, and progression. Humanistic outcomes include health-related quality of life with utility and disutility values. Our results will provide consolidated and methodologically justified, model-ready estimates of clinical trajectories, economic burden, and health-related quality of life that are specific to a local context and can be adapted elsewhere. These outputs will strengthen health economic evaluations by reducing reliance on non-contextual data. In turn, the evidence generated will support the updating of clinical practice guidance, identify key gaps in the existing evidence base to guide future research, and inform priority setting for resource allocation within constrained healthcare resources.

PMID:42233051 | PMC:PMC13224113 | DOI:10.1016/j.mex.2026.103953

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