Why Accurately Counting Type 1 Diabetes Matters—and Why It's Harder Than You'd Think

Diabetes affects approximately 537 million people worldwide. Yet despite its scale, we don't have accurate counts of how many people have Type 1 diabetes versus Type 2 diabetes versus rarer forms. The reason? The computer systems that hospitals and clinics use to classify patients don't work well enough.

When a doctor enters a patient's diabetes diagnosis into a medical record, that information flows into large administrative databases used for public health planning, research, and surveillance. But those databases rely on algorithms—sets of rules coded into software—that struggle to tell different types of diabetes apart. This misclassification hides the true burden of Type 1 diabetes and makes it nearly impossible to track trends or plan care effectively.

Type 1 diabetes is fundamentally different from Type 2 diabetes. It's an autoimmune disease where the body attacks the cells that produce insulin. Type 2 develops when the body can't use insulin effectively. Yet current medical billing and administrative systems often lump them together or misclassify them entirely.

The problem becomes even more complex with LADA—latent autoimmune diabetes in adults. LADA develops slowly and appears later in life, sometimes leading doctors to initially diagnose it as Type 2. Without a standardized clinical definition, LADA remains one of the most under-recognized diabetes phenotypes. As a result, we simply don't know how many people have it or how it affects public health.

Researchers in Quebec are launching a long-term study to fix this problem. They'll examine 30 years of medical and pharmacy records—from 1997 through 2027—to test whether a new algorithm can accurately sort patients into the correct diabetes categories: Type 1, Type 2, LADA, and other specific types.

The study will use three independent groups as reference standards. One group will provide self-reported Type 1 and LADA diagnoses. Another will provide physician-confirmed Type 2 and other diabetes types. A third will be drawn from the general population. By linking these reference groups to real medical records, researchers can calculate how accurate the new algorithm truly is and identify where it works well and where it needs improvement.

Getting these numbers right isn't just academic. Accurate diabetes classification is the foundation for effective prevention, surveillance, and clinical strategies. When we know how many people have Type 1 diabetes and where they are, we can target screening and early intervention efforts. We can study what causes the disease and how to delay or prevent it. We can also make sure patients receive appropriate care tailored to their specific type of diabetes.

This study represents an important step toward understanding the true landscape of diabetes in Quebec—and by extension, in populations with similar healthcare systems. The hope is that better classification tools will eventually be adopted widely, giving patients, families, researchers, and public health officials a clearer picture of Type 1 diabetes and its impact.

Source: BMJ open. Evidence type: PubMed indexed literature. Type1Cure is an information and intelligence hub, not a medical advice service. This article summarizes published research and does not provide diagnosis, treatment, or personal medical guidance. Always talk to your own care team before changing anything about your Type 1 diabetes management.