The large-scale screening for diabetes in India has been predicted to be ineffective, as per current available survey.
Conducted by Sanjay Basu of Stanford University and colleagues, the predictions of this simulation, suggest that large numbers of false positive results would waste financial resources, and that focusing on symptom-based screening and on improvements to diabetes treatment might better serve India’s population.
The researchers develop a microsimulation model (a computer model that operates at the level of individuals) to investigate the implications of using alternative screening instruments to identify people with undetected diabetes across India. Depending on which approach was used for screening, between 158 million and 306 million of the 567 million Indians eligible for screening would be referred for confirmatory testing.
However, between 126 million and 273 million of these high-risk individuals would be false positives; only between 26 million and 37 million would meet the international diagnostic criteria for diabetes.
The researchers estimate that the cost per case found would vary from 5.28 dollars (for random glucose screening) to 17.06 dollars (for a survey-based screening instrument). Finally, they estimate that the total cost for screening the eligible population would be between 169 and 567 million dollars.
The findings of poor test specificity are consistent with results from small, regional trials in India. Here, study limitations include the uncertainty of modeling a large, diverse population and of forecasting costs.
The authors state, “Improving instruments to reduce false positive screens, preparing the health system for very substantial confirmatory testing demands, and identifying how to deliver efficacious treatment, are three priority areas that require urgent attention before rapidly-developing countries implement large-scale community-based diabetes screening programs.”
