Background: Arithmetic average values about disease burden across aging adults are often used in the absence of having reliable access to real life data. Those values however assume group homogeneity in characteristics such as age, sex, disease incidence rates, or costs. The question arises about how much the overall outcome results, like total disease management costs, obtained under those homogeneity assumptions may deviate from real-world population data that may manifest non-homogeneous distributions. Without being able to have easily access to those real-world data and for getting a good approximation of the deviations in outcome results, a calculation method is proposed that should also indicate which factor may have a dominant influence on the cost difference between homogeneous and non-homogeneous results. The method should help focus the research for obtaining more accurate information from real-world data in subsequent steps that better estimate control gain of infectious diseases through new interventions. Methods: The method explores, as the outcome measure to assess, the relative deviation in overall infection management costs measured with homogeneity versus non-homogeneity design in the datasets of aging adults. Population modelling is used with an Extended Sensitivity Analysis Plan (ESAP) that simulates non-homogeneous, but realistic, approximates of age-specific distributional spread in demography, infectious disease, and its severity in people aged > 65 years old over a 1-year period in univariant and multivariant assessments. Disease management costs are adjusted for 3 infection severity levels with increased differences between them using multiplication factors up to 20 times the initial unit cost. Results: The assumed full homogenous dataset systematically overestimates up to 10% the overall disease management cost in aging adults when compared with a group simulated with non-homogeneous, but realistic distributions for age, infection, severity, and cost, mainly due to the difference in the demographic age composition. However, overall costs of a proposed homogeneous condition tend to underestimate the spending of non-homogeneous conditions when the reference case has a partially homogeneous setup instead of a full condition or when the demographic age-change in the non-homogeneous condition evolves towards age-demographic homogeneity (same number of people at each age with increasing age), a likely evolution in the coming 15 to 30 years. Conclusion: Assessing the current cost burden of infectious diseases in aging adults must consider exact age-composition of the demography, the type of infection spread with severity levels in function of age and their cost differences between severity levels to avoid unrealistic cost estimates when assuming unreal homogeneous group conditions that could currently overestimate the real costs.