Life expectancy has nearly doubled over the past century, transforming aging from a relatively uncommon experience into a defining feature of modern societies. But living longer also creates more opportunities for chronic diseases to accumulate. Many older adults now live with several conditions at once, including heart disease, diabetes, cancer, lung disease, kidney disease, and neurological disorders. This phenomenon, known as multimorbidity, is linked to higher rates of hospitalization, emergency department visits, disability, and healthcare costs. A new analysis of medical records from more than 238,000 adults suggests that the health problems of older people become not only more numerous but also dramatically more different from one person to another.
The study, led by Joel Cohen of Rockefeller University’s Laboratory of Populations and Jonathan Tobin, director of Community-Engaged Research at Rockefeller’s Center for Clinical and Translational Science, identifies a mathematical pattern in the way multimorbidity varies with age. The researchers found that as the average burden of chronic disease rises, the differences between individuals also expand. In practical terms, two people of the same age are likely to have increasingly dissimilar combinations of illnesses as they grow older. The finding challenges the widespread use of chronological age as a simple guide for medical screening and treatment decisions and points toward a more individualized approach based on each patient’s actual health profile.
The analysis emerged from the Tipping Points clinical trial, a large effort focused on patients who are frequently missing from conventional medical research. These patients often receive care through Federally Qualified Health Centers, which provide primary and preventive services to millions of people in low-income communities across the United States. Because people with multiple serious conditions can introduce numerous confounding variables into a clinical trial, they are often excluded from studies that eventually shape medical guidelines. Tobin and his colleagues instead placed multimorbid patients at the center of their research, seeking to understand how their health changes and whether targeted support can prevent hospitalizations.
The research team examined de-identified electronic health records from 238,156 people receiving care through 16 Federally Qualified Health Centers in New York City and Chicago. The data were assembled with the help of clinical research networks and health information exchanges, including INSIGHT, CAPriCORN, Healthix, BronxRHIO, and AllianceChicago. The records included each individual’s age, location, and score on the enhanced Charlson Comorbidity Index, or eCCI. Nearly 2,000 patients were subsequently enrolled in the Tipping Points trial, in which health coaches helped participants manage their conditions and recognize problems before they escalated into emergency visits or hospital admissions.
The eCCI provided the mathematical foundation for the new analysis. The index assigns weights to chronic conditions according to their association with hospitalization risk and healthcare costs. Less severe conditions, such as myocardial infarction, congestive heart failure, and peripheral vascular disease, receive lower scores, while conditions including metastatic solid tumors, AIDS, and organ transplants receive higher weights. Most of the 39 categories included in the index fall between these extremes. By combining the scores, researchers can estimate the overall burden of disease carried by an individual rather than simply counting the number of diagnoses.
Cohen examined how the average eCCI score changed across age groups and, crucially, how widely individual scores were scattered around each age-specific average. That second measurement—statistical variance—proved to be the key result. The average burden of multimorbidity increased with age, as expected, but the variance increased as well. Older age groups therefore contained a wider range of health profiles. While some older adults had relatively limited chronic disease, others had extensive and severe multimorbidity, producing a much broader spread than was seen among younger adults.
The pattern resembles Taylor’s law, a mathematical relationship Cohen has identified in diverse biological and social systems, from infectious diseases and wildlife populations to human censuses and weather. Taylor’s law generally describes a power-law relationship between the mean of a population and its variance: as the average level of a phenomenon changes, the amount of variation around that average changes in a predictable way. In this study, the researchers found that the variability of eCCI scores rose in a mathematically consistent relationship with the mean. The result indicates that aging is not simply associated with a steadily increasing number of diseases; it is associated with a widening divergence in the kinds and severity of diseases experienced by different people.
The contrast can be illustrated by comparing patients in their forties with patients in their seventies. Two 40-year-olds may differ in their health, but their overall chronic disease profiles tend to be more alike than those of two 70-year-olds. By the time people reach older age, their medical histories have been shaped by different genetics, environmental exposures, behaviors, social conditions, access to care, treatments, and chance events. One person may have accumulated cardiovascular disease and diabetes, while another may have cancer and chronic lung disease, and a third may have relatively few serious diagnoses. Chronological age alone cannot capture those differences.
That finding has direct implications for clinical guidelines, many of which use age thresholds to determine when screening or preventive care should begin or end. The U.S. Preventive Services Task Force, for example, recommends colorectal cancer screening for adults beginning at age 45 and continuing through age 75, while biennial mammography is recommended for many women between ages 40 and 74. Such recommendations are essential population-level tools, but the new analysis suggests that their application may need to account more explicitly for the medical complexity of individual patients. A treatment or screening strategy that is appropriate for one 70-year-old may be ineffective, burdensome, or even inappropriate for another with a very different combination of conditions.
The researchers say the mathematical relationship could also help identify patients approaching a “tipping point,” when the accumulation or interaction of chronic conditions sharply increases the likelihood of hospitalization or disability. If future studies confirm that certain multimorbidity patterns predict rapid deterioration, clinicians might be able to intervene earlier with medication adjustments, health coaching, social support, or closer monitoring. The study does not establish that the mathematical pattern itself can predict an individual hospitalization, and the eCCI is an aggregate measure rather than a detailed map of disease interactions. Even so, the results provide a framework for moving beyond age-based assumptions. As Cohen puts it, the central lesson is that medical care cannot be one-size-fits-all: the older people become, the more important it is to understand the particular constellation of conditions carried by each person.
Subject of Research: Multimorbidity, aging, chronic disease variation, population health, and personalized medicine.
Web References: Rockefeller University; Journal of Population Ageing article DOI: https://doi.org/10.1007/s12062-026-09571-7
References: Journal of Population Ageing, DOI 10.1007/s12062-026-09571-7.
Keywords: Multimorbidity, chronic diseases, aging, life expectancy, healthcare, hospitalization, Charlson Comorbidity Index, eCCI, Taylor’s law, mathematical modeling, personalized medicine, public health, clinical guidelines, health disparities.
