Evidence mapPaperPMID 42334557Full record

ReviewDiabetologia2026

Impacting lives of people with diabetes: lessons learnt in India may be applicable to other low- and middle-income countries.

Viswanathan Mohan

Abstract readReview
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In one paragraph

Review in Diabetologia, 2026. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.

0numbers the graph read from it
0cells of the map it votes in
0citing papers in PubMed
field-weighted citation impact
1 · What the graph read from it

What it found

Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.

The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.

2 · The registry

The trial behind it

Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.

Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.

3 · Its place in the literature

Who cites it

0 citing papers in PubMed.

No citing paper in PubMed yet.

4 · The record

Corrections and comments

PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.

5 · Who and what money

Authors and funding

1 author.

Viswanathan MohanMadras Diabetes Research Foundation (ICMR - Collaborating Centre of Excellence), Chennai, Tamil Nadu, India. drmohans@diabetes.ind.in.ORCID http://orcid.org/0000-0001-5038-6210

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Delivering optimal care to people with diabetes in low- and middle-income countries (LMICs) such as India presents several challenges. The magnitude of the population affected by diabetes is considerable. Providing quality diabetes care to all people with diabetes is therefore not easy given the resource constraints, including inadequate numbers of trained specialists, diabetes educators and nurses, and lack of specialist clinics. Moreover, a large percentage of people with diabetes have to pay 'out of pocket' for their treatment, as the majority of healthcare in LMICs, especially in urban areas, is privately managed. Thus, tackling the problem of diabetes requires multiple approaches and local solutions. In this review, I provide an overview of our work in India over the last four decades and its impact. This includes capacity building, such as training of doctors in basic diabetes care, and task-shifting, by producing a cadre of diabetes educators or community health workers who can assist physicians. Setting up of low-cost models of care is essential if care is to reach remote underserved areas. In this context, use of telemedicine, digital technology and various apps have been found to be of great value. Using mobile vans fitted with laboratory and other equipment to screen for diabetes and its complications has helped deliver diabetes care even to remote villages, where specialist care is unavailable. More recently, home care models have also been successfully implemented. Research into the heterogeneity of diabetes has enabled development of local solutions that are low-cost and scalable. Lessons learnt from the management of diabetes in India can potentially be applied to other LMICs with local adaptation.

Indexed as

Diabetes MellitusDelivery of Health CareDeveloping CountriesHumansIndiaResource-Limited SettingsTask ShiftingTelemedicineDiabetesHeterogeneityIndiaLMICsLow-cost models of careReviewTelemedicine

Identifiers

What Socratic holds

Textmetadata
Read underepoch 390

Registered trials

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Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the Socratic graph.