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PLoS Med. 2015 May 19;12(5):e1001827; discussion e1001827. doi: 10.1371/journal.pmed.1001827. eCollection 2015 May.

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

Author information

1
Prevention 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.
2
School of Public Health, Imperial College London, London, United Kingdom; Public Health Foundation of India, Delhi, India.
3
Center 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.
4
Department of Epidemiology and Population Health, London School of Hygiene and Tropical Medicine, London, United Kingdom.
5
Prevention 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.
6
Division of Medicine, University College London, London, United Kingdom.

Abstract

BACKGROUND:

Like 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 FINDINGS:

We 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.

CONCLUSIONS:

Large-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.

PMID:
25992895
PMCID:
PMC4437977
DOI:
10.1371/journal.pmed.1001827
[Indexed for MEDLINE]
Free PMC Article

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