Health Checkup Numbers Predict Future Medical Costs in Curved, Sex-Specific Patterns

Routine health checkups generate a familiar stack of numbers: blood pressure, body mass index, waist circumference, blood sugar markers. For most people, those values are sorted into two buckets, normal or abnormal, and then filed away until the next annual visit. A new study from Japan suggests that this binary way of thinking misses much of what the numbers actually say. By tracking thousands of older adults and comparing their checkup results with real medical spending years later, researchers found that the relationship between health measurements and future healthcare costs is rarely a straight line. Instead, costs climb along curves that bend, accelerate, and in some cases differ sharply between men and women.

The research, led by a multidisciplinary team at Kanazawa University and published in Health Economics Review, set out to answer a deceptively simple question: how strongly do the measurements collected at ordinary health screenings relate to the medical bills a person generates three to four years down the road? To find out, the team linked Specific Health Checkup data, Japan’s standardized screening program for adults, with claims records from the country’s National Health Insurance Database, known as the KDB. The final analysis covered 6,757 adults aged 60 to 74, amounting to 11,148 person-years of observation, a substantial dataset for examining how today’s measurements translate into tomorrow’s expenditures.

Methodologically, the study’s most important choice was its statistical model. Rather than fitting a conventional linear regression, in which each unit increase in a health measure produces a fixed increase in predicted cost, the researchers used generalized additive models, or GAMs. These models allow the shape of the relationship itself to be estimated from the data, capturing J-shaped curves, thresholds, and plateaus that a straight line would flatten out. The team applied this approach separately to three categories of spending: outpatient costs, inpatient costs, and prescription drug costs, all incurred in the three-to-four-year window after the checkup. The result is not a single coefficient per measurement but a visualized curve showing where associations with future costs weaken, strengthen, or accelerate.

Two factors emerged as consistent predictors across both sexes: age and glycated hemoglobin, commonly abbreviated as HbA1c. HbA1c reflects average blood glucose over the preceding weeks and is a cornerstone measure for detecting diabetes risk. In this study it displayed a distinctly J-shaped association with future healthcare costs, remaining relatively flat at lower values and then bending upward, with the relationship becoming notably stronger above approximately 6.0 percent. That threshold matters because it sits near the boundary clinicians use when flagging elevated diabetes risk, and the study suggests that costs begin to diverge meaningfully from the population baseline as values cross into that territory.

The metabolic and anthropometric measures told a more layered story. Body mass index, waist circumference, and the fatty liver index, a composite marker that estimates hepatic steatosis from routine blood tests and anthropometry, were all associated with future healthcare spending. Crucially, however, the patterns were not uniform. In men, waist circumference and the fatty liver index showed relatively prominent associations with later costs. In women, the associations were stronger at higher levels of body mass index and waist circumference. In other words, the same measurement can carry different weight depending on sex, and the point at which costs begin to rise can shift accordingly. A single universal cutoff applied to everyone would obscure these differences.

This is where the study’s framing departs from conventional screening interpretation. Health checkups are typically built around reference ranges: a value falls inside the normal band or outside it, triggering either reassurance or follow-up. The Kanazawa team argues that this dichotomy discards information. What their models visualize is the continuum, the specific ranges of checkup values where the association with future healthcare costs becomes stronger, and the ranges where it barely changes. Someone whose value sits at the upper edge of the normal range and someone at the lower edge may face very different trajectories, even though both receive the same normal label. Reading the curve rather than the category restores that nuance.

The authors are careful about what the findings do and do not claim. The analysis characterizes population-level associations between checkup values and future healthcare costs; it does not provide precise predictions of any individual’s future spending. An association observed across thousands of person-years cannot tell a particular person what their bills will be, and the study did not establish causation. Still, at the level of public health planning, the distinction between linear and nonlinear association is consequential. Cost projections built on linear assumptions could underestimate the burden concentrated among people whose values sit above certain inflection points, particularly for glycemic markers like HbA1c.

The economic framing is deliberate. As populations age and healthcare expenditures continue to rise worldwide, researchers and policymakers are increasingly interested in whether routinely collected health data can help characterize future healthcare needs and costs. Japan, with one of the oldest populations on earth and a mature universal insurance system generating detailed claims records, offers an unusually good setting for this kind of work. By linking screening data to actual claims rather than to self-reported health or diagnosed disease alone, the study grounds its findings in a concrete, monetized measure of health burden, one that resonates with insurers, municipal health planners, and individuals alike.

Shigehiro Karashima, the study’s corresponding author, emphasized the human dimension of the numbers. Many health checkup results are just numbers, he noted, and that can make it difficult for people to connect them with their own health, especially for lifestyle-related diseases that may progress without noticeable symptoms. Healthcare costs, he suggested, offer a different perspective by expressing future health burden in an economic dimension. By showing how checkup values are associated with later medical spending, the team hopes to give people another way to reflect on their health. He was careful to add that the study did not directly examine behavior change, but such information may eventually help support preventive action and the design of community health programs.

The research team plans to extend the approach by integrating additional information, including disease history, prescribed medications, healthcare utilization, and lifestyle factors, to build more comprehensive tools for health-risk assessment and preventive planning. For now, the study’s central message is methodological as much as medical: the relationship between what a screening measures today and what a health system spends years later is curved, threshold-dependent, and sex-specific. Treating every value as simply normal or abnormal throws away exactly the structure that nonlinear models are designed to reveal. As health systems everywhere search for earlier and more efficient signals of future burden, the humble annual checkup, read through the right statistical lens, may contain far more predictive texture than its binary report card suggests.

Subject of Research: Nonlinear associations between routine health checkup measurements and future healthcare costs in older Japanese adults

Article Title: Beyond “normal” and “abnormal”: Health checkup values show nonlinear links to future healthcare costs

Article References: Beyond “normal” and “abnormal”: Health checkup values show nonlinear links to future healthcare costs. (n.d.). Original publication

Image Credits: AI Generated

DOI: Not provided

Keywords: health checkups, healthcare costs, HbA1c, generalized additive models, body mass index, waist circumference, fatty liver index, Japan, National Health Insurance Database, preventive medicine, health economics, nonlinear modeling