Evidence map›Paper›PMID 25992895›Full record

ArticlePLoS medicine2015

The health system and population health implications of large-scale diabetes screening in India: a microsimulation model of alternative approaches.

Sanjay Basu, Christopher Millett, Sandeep Vijan, Rodney A Hayward, Sanjay Kinra, Rahoul Ahuja, John S Yudkin

Open access · goldAbstract read
In one paragraph

Article in PLoS medicine, 2015. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 9 papers, 2 of them syntheses that pooled it.

0numbers the graph read from it
0cells of the map it votes in
9citing papers in PubMed, 2 pooled it
2.7field-weighted citation impact, top 10% of its field
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

9 citing papers in PubMed, 2 syntheses or guidelines pooled it, 33 citations in OpenAlex.

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4 · The record

Corrections and comments

5 · Who and what money

Authors and funding

7 authors at 6 institutions in 3 countries.

Sanjay BasuPrevention Research Center, Centers for Health Policy, Primary Care and Outcomes Research, Center on Poverty and Inequality, and Cardiovascular Institute, Stanford University, Stanford, California, United States of America; Department of Public Health and Policy, London School of Hygiene and Tropical Medicine, London, United Kingdom.
Christopher MillettSchool of Public Health, Imperial College London, London, United Kingdom; Public Health Foundation of India, Delhi, India.
Sandeep VijanCenter for Clinical Management Research, Ann Arbor Veterans Affairs Hospital, Ann Arbor, Michigan, United States of America; Department of Internal Medicine, University of Michigan, Ann Arbor, Michigan, United States of America.
Rodney A HaywardCenter for Clinical Management Research, Ann Arbor Veterans Affairs Hospital, Ann Arbor, Michigan, United States of America; Department of Internal Medicine, University of Michigan, Ann Arbor, Michigan, United States of America.
Sanjay KinraDepartment of Epidemiology and Population Health, London School of Hygiene and Tropical Medicine, London, United Kingdom.
Rahoul AhujaPrevention Research Center, Centers for Health Policy, Primary Care and Outcomes Research, Center on Poverty and Inequality, and Cardiovascular Institute, Stanford University, Stanford, California, United States of America.
John S YudkinDivision of Medicine, University College London, London, United Kingdom.
Stanford University · USLondon School of Hygiene & Tropical Medicine · GBMichigan United · USPublic Health Foundation of India · INUniversity College London · GBVA Center for Clinical Management Research · US

Funding

PUBLIC HEALTH DEMONSTRATIONP60DK020572 · NIDDK · UNIVERSITY OF MICHIGAN AT ANN ARBOR · PI MYERS, MARTIN G · 1985 to 2012
$26.5M
Research BaseP30DK092926 · NIDDK · UNIVERSITY OF MICHIGAN AT ANN ARBOR · PI MARY ELLEN MICHELE HEISLER, ADESUWA B OLOMU · 2011 to 2026
$10.0M
CORE--PROGRAM DEVELOPMENTP30AG017253 · NIA · STANFORD UNIVERSITY · PI BHATTACHARYA, JAY · 1999 to 2019
$5.8M
NIA NIH HHS P30 AG017253NIDDK NIH HHS P30 DK092926NIDDK NIH HHS P60 DK020572NIDDK NIH HHS P60 DK-20572Wellcome Trust 084674
6 · The paper itself

Abstract

backgroundLike a growing number of rapidly developing countries, India has begun to develop a system for large-scale community-based screening for diabetes. We sought to identify the implications of using alternative screening instruments to detect people with undiagnosed type 2 diabetes among diverse populations across India. METHODS AND

findingsWe developed and validated a microsimulation model that incorporated data from 58 studies from across the country into a nationally representative sample of Indians aged 25-65 y old. We estimated the diagnostic and health system implications of three major survey-based screening instruments and random glucometer-based screening. Of the 567 million Indians eligible for screening, depending on which of four screening approaches is utilized, between 158 and 306 million would be expected to screen as "high risk" for type 2 diabetes, and be referred for confirmatory testing. Between 26 million and 37 million of these people would be expected to meet international diagnostic criteria for diabetes, but between 126 million and 273 million would be "false positives." The ratio of false positives to true positives varied from 3.9 (when using random glucose screening) to 8.2 (when using a survey-based screening instrument) in our model. The cost per case found would be expected to be from US$5.28 (when using random glucose screening) to US$17.06 (when using a survey-based screening instrument), presenting a total cost of between US$169 and US$567 million. The major limitation of our analysis is its dependence on published cohort studies that are unlikely fully to capture the poorest and most rural areas of the country. Because these areas are thought to have the lowest diabetes prevalence, this may result in overestimation of the efficacy and health benefits of screening.

conclusionsLarge-scale community-based screening is anticipated to produce a large number of false-positive results, particularly if using currently available survey-based screening instruments. Resource allocators should consider the health system burden of screening and confirmatory testing when instituting large-scale community-based screening for diabetes.

Indexed as

Computer SimulationAdultAgedDiabetes Mellitus, Type 2HumansIndiaMass ScreeningMiddle AgedPrevalenceRisk AssessmentRisk FactorsSensitivity and Specificity

Identifiers

PMID25992895
PMCPMC4437977
OpenAlexW1570393364

What OpenQuestion holds

Textmetadata
LicenceCC BY
Read underepoch 390

Registered trials

None linked

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 OpenQuestion graph.