Preventive Care Behavior

Of 7,048 urban working Indians who answered the preventive-care question in WHI 2026, only 24.3% had a full-body diagnostic test in the year just ended. Almost all of them are covered by an employer health policy that includes or subsidises exactly this test. The Loop biomarker panel on the Blood Sugar page finds 38.7% of tested professionals already in the glucose-dysfunction band against 5.8% who self-report a diabetes diagnosis — detected risk running roughly 7× ahead of diagnosed cases. The screening gap on this page is the mechanism behind that number. Most of the risk is invisible because most of the population never looks.

The single most common answer to "what have you done for your health this year" is nothing. Routine consults and full-body tests sit close behind at roughly a quarter each, and the two overlap heavily — the people who screen are largely the people who also see a doctor. Company-organised camps reach one in ten. Fitness assessment, the most active form of self-monitoring on the list, is a rounding error at 2.6%.

Screening tracks income, not need — a 3.5× gap on identical cover#

The cleanest pattern in the data is also the most uncomfortable. The employer policy that pays for an annual full-body panel is, in broad terms, the same across the salary band — a junior analyst and a director on the same plan have the same entitlement. The behaviour is not the same.

Full-body testing climbs from 15.6% at the bottom of the income distribution to 54.3% at the top — a 3.5× spread. The obvious objection is that income is a proxy for age, and older, better-paid employees screen more because they are older. It isn't. Hold age constant at the 20s, where the cohort is large, and the gradient survives almost intact: 13.4% of under-₹3-lakh earners in their twenties tested, against 36.8% of the ₹15–25 lakh band in the same decade. A 2.7× gap inside a single ten-year age window, on cover that costs the employee nothing.

The people screening is built to catch screen the least#

Annual screening earns its place in medicine on one premise: it finds disease in the window before symptoms, when the condition is cheapest to treat and most reversible. That premise only pays off if the people without a diagnosis are the ones getting tested. In this sample they are the ones who aren't.

The split is clean. Respondents already carrying a diagnosis — hypertension, diabetes, thyroid, a heart event — screen at 36.0%, because once you have a condition, the test becomes follow-up. Respondents with no diagnosis screen at 21.4%, and nearly half of them, 47.6%, took no preventive action of any kind. Screening has become a confirmatory tool, not an early-warning one. It follows the diagnosis instead of producing it.

Inaction is highest in the 20s and falls with every decade#

Inaction is a young person's behaviour here. Nearly half of professionals in their twenties did nothing preventive; that share falls by roughly ten points a decade, to under a quarter in the fifties. The intuition behind the pattern is straightforward — younger workers feel well, carry no diagnosis, and see no reason to test. The problem is that the Blood Sugar data shows glucose dysfunction already doubling between the 20s and the 30s. Engagement with prevention and the onset of metabolic risk move in opposite directions across the same decade. The cohort with the most to gain from a baseline reading is the cohort least likely to take one.

The full-body-test view of the same age structure tells the constructive half of the story: testing rises from 18.0% among men in their twenties to 35.3% in their fifties, and from 23.6% to 38.0% among women across the same span. People do eventually screen. They just start a decade or two after the biology does.

Women screen more and skip less than men at every age#

Women are nine points less likely than men to have done nothing (36.1% vs 45.2%) and modestly more likely to have run a full-body test. The gap is widest where it matters most — among the youngest, least-diagnosed cohort. In their twenties, 52.2% of men took no preventive action against 41.5% of women; the male disadvantage narrows with age but never closes. This is one of the few lifestyle pages in the report where the female cohort comes out ahead, and it runs counter to the gendered pattern in the biomarker data, where women carry the heavier anemia and micronutrient load. Women here engage more with the system; the system, on several markers, still finds them in worse shape.

Work-from-office staff screen least, hybrid staff most#

The full-time-office cohort screens least, at 21.5%. Hybrid staff screen most, at 30.1%. The likely reading isn't that the office is bad for screening but that the office cohort skews younger and lower-paid — the two groups already shown to test least — while hybrid arrangements concentrate among older, senior, higher-earning employees who screen most on every cut. The work-mode pattern is real but largely downstream of the income and age gradients already on this page. It earns a line, not a thesis.

City and industry vary little; the structural gaps are demographic, not geographic#

Inaction by metro city sits in a narrow band — Kolkata lowest at 35.7%, Hyderabad highest among the majors at 44.8%, with Bengaluru, Pune, Delhi NCR, and Mumbai clustered between 39.5% and 42.1%. Industry is similarly flat: most sectors with a usable sample fall between 37% and 46% inaction, with no outlier large enough to clear the anomaly bar. The variation that matters on this page is not where you work or what sector you are in. It is what you earn, how old you are, and whether you already carry a diagnosis — the same income-and-engagement gradient that decides who supplements a deficiency, as the Supplementation page finds. Geography and industry are noise around those three signals.

What the screening gap costs downstream#

The preventive-care gap is not a wellness footnote. It is the upstream cause of the claims pattern that runs through the rest of this report. When the early-warning window is missed at scale, the condition presents later, harder, and more expensively — as a hospitalisation rather than a prescription. The claims data shows ₹320.6 Cr incurred across the broker book in twelve months, much of it on conditions that an annual panel flags years before they reach a hospital. A 24.3% screening rate is the behavioural input to that cost. Raise the rate and you move detection upstream of the admission; leave it where it is and the policy keeps paying for the late presentation instead of the early test.