Development and Validation of a Conceptual Epidemiological and Economic Model for Predicting Obesity in Low- and Middle-Income Countries: A Systematic Review and Meta-Synthesis–Informed Approach

Obesity is no longer just a problem for wealthy nations; it is rising rapidly in low- and middle-income countries, where people are facing a double burden of undernutrition and increasing rates of excess weight. This shift brings a heavy toll on health, driving up cases of diabetes, heart disease, and stroke, while also straining healthcare systems that already have limited resources. To make smart decisions about how to spend money and design health programs, officials need a way to look into the future. They need tools that can estimate how many people will become obese, what diseases will follow, and how much it will all cost. In high-income countries, scientists have long used computer simulations to answer these questions, but these tools were built using data and assumptions from wealthier nations, making them unreliable for places with different diets, lifestyles, and healthcare access.

A team of researchers set out to build a new kind of map specifically for these resource-limited settings. Their goal was to create a conceptual model—a structured way of thinking about how obesity spreads and costs money—that could be used to predict the future of the condition in low- and middle-income countries. To do this, they did not start from scratch. Instead, they gathered every available study that had ever tried to model obesity and its economic impact, looking for patterns in how experts had previously described the disease. They sifted through hundreds of papers, keeping only those that used computer simulations to track how people move from being a normal weight to overweight, and then to obesity, and finally to serious health complications. They found 31 studies that met their strict criteria, published between 1997 and 2024. Most of these studies used a specific type of computer simulation called a Markov model, which tracks groups of people as they move through different health stages over time, while others used decision trees or other simulation methods.

From this collection of past work, the researchers identified six key health states that form the backbone of their new model. These states represent the journey a person might take: starting at a normal weight, moving to overweight, then to obesity, and potentially developing diabetes, stroke, or cardiovascular disease. By combining the evidence from the 31 studies, they synthesized these six states into a single, unified framework. This framework acts as a skeleton for a new tool, showing exactly how the different parts of the problem connect. For instance, it illustrates how moving from a normal weight to obesity increases the risk of developing diabetes, and how that disease, in turn, adds to the economic burden on a country. The researchers then took this draft model to a panel of three experts who specialize in obesity, economics, and public health in low- and middle-income countries. These experts reviewed the structure to ensure it made sense in the real world, checking if it captured the right risks and outcomes for these specific regions. The experts agreed that the model was valid, complete, and ready to be used as a foundation for future, more detailed studies.

The result is the first conceptual model designed specifically to predict obesity trends and their economic costs in low- and middle-income countries. The researchers emphasize that this is a foundational step, not a final answer. The model provides a clear structure that policymakers can use to plug in local data, allowing them to simulate different scenarios. For example, a government could use this framework to test how a new diet program might change the number of people with diabetes over the next ten years and how much money that would save. The study highlights that while the model is a powerful tool, its accuracy depends entirely on the quality of the local data fed into it. Without accurate numbers on how many people are currently overweight or how much treatment costs in a specific country, the predictions will not be precise. However, by establishing a shared language and structure for these problems, the researchers have provided a vital starting point. This work allows leaders in resource-limited settings to move away from guessing and toward evidence-based planning, helping them allocate scarce funds to the prevention and management strategies that will have the greatest impact on their populations.