Anemia

Of 4,375 women Loop tested for haemoglobin in the latest 12-month window, 34.6% fall below the WHO anemia threshold of 12 g/dL — down 2.5 points from the 37.1% reported in WHI 2025, on a female sample that grew from 1,214 to 4,375. The national reference is far higher: NFHS-5 finds 57% of Indian women aged 15–49 anemic (IIPS & ICF, National Family Health Survey-5, 2021). Loop's audience — salaried, urban, insured, with diagnostic access most Indian women never get — sits 22 points below that line and still lands one in three women in clinical anemia.

The distribution is gendered to a degree few biomarkers on this report match.

Women are anemic at 3.9 times the male rate. The average woman in the sample carries 2.5 g/dL less haemoglobin than the average man — 12.30 against 14.82 — and the female average sits only 0.30 above the anemia threshold itself. Half the female distribution is within a rounding error of clinical anemia. The male average, by contrast, sits 1.82 above its threshold, with most of the distribution comfortably clear. The two sexes are not at different points on the same curve; they are on different curves, and the female one is centred almost on the line that defines disease.

The headline rate eased, but only because the sample got bigger#

The thresholds match exactly across years. The female rate fell 2.5 points; the male rate held. Both average haemoglobin values are unchanged to within a tenth of a gram. The most defensible read is that the larger 2026 sample is the more accurate estimate, and that the underlying biology of the audience did not shift in twelve months. The story is not improvement. The story is that the rate is durable: a third of working women, measured twice, two years apart, on different samples.

The female disadvantage is flat across age, not concentrated in mid-career#

WHI 2025 framed anemia as a condition that "peaks during peak careers" — worst in the 30s and 40s. The larger 2026 sample does not support that shape. The female rate is high from the first working band and climbs only modestly with age.

Nearly a third of women are already anemic in their 20s — before pregnancy and perimenopause, the two life stages most associated with iron loss, have done their heaviest work. The rate rises about seven points across the next two decades and then sits in the high 30s to low 40s. There is no sharp mid-career spike. The condition is established at workforce entry and persists.

The male line is the inverse. Men stay in low single digits through the working years and only rise after 50 — at 60-plus, the male rate (32.2%) finally approaches the female (42.0%). Late-life male anemia is a different mechanism: less menstrual loss, more chronic disease, occult gastrointestinal bleeding, and declining marrow function.

Mumbai women are twice as anemic as Bengaluru women#

The biomarker file records a six-digit pincode for every report. Mapping the first three digits to a metro via the India Post allocation scheme resolves the five WHI cities cleanly. Split by gender, the city gap is the largest single inter-city finding for any biomarker on this report.

More than half of Mumbai women tested are anemic — the worst female rate in the dataset, and one of the markers the Mumbai city page builds its "uniformly hostile to women's biomarkers" finding on. Fewer than a quarter of Bengaluru women are. The 29-point gap between the two metros is wider than the 26-point gap between the youngest and oldest female age bands — geography moves a woman's anemia risk more than three decades of ageing does. The male ordering tracks the female one (Mumbai worst at 16.2%, Bengaluru best at 3.8%), which argues the driver is environmental or dietary rather than a quirk of female-specific sampling in one city.

The pattern is not new. WHI 2025 found Mumbai female anemia at 54.4% and Bengaluru at 23.8% on much smaller city samples (158 and roughly 500 women respectively). The 2026 numbers — 52.3% and 23.2%, on 621 and 1,131 — replicate the 2025 finding almost exactly on samples three to four times larger. This is one of the most stable city-level signals across both editions of the report.

What the data cannot do is isolate the cause. Three candidate explanations all point the same direction and cannot be separated with a biomarker file alone. Diet and food culture differ across the two cities. Bengaluru's audience skews younger and more tech-sector, though the city × age table above shows the gap holds within every band, which weakens age as the explanation. And chronic inflammation — from air quality, commute stress, and population density — raises hepcidin, the hormone that locks iron away from red-cell production, producing a functional iron deficiency on top of any dietary one. Mumbai scores worse on the lifestyle and environmental measures elsewhere in this report. The honest statement is that the gap is real, replicated, and large, and that the mechanism is a composite the data points toward but cannot resolve.

Mumbai women are the most anemic in every age band#

The Mumbai excess is not an age-composition artefact. Splitting Mumbai's female rate by age band against Bengaluru's shows the gap holds at every stage of working life.

A Mumbai woman in her 20s (48.3%) is more anemic than a Bengaluru woman in any age band. The lowest Mumbai female rate, in the 50s, still sits twice as high as Bengaluru's. Whatever is driving Mumbai's anemia load reaches every cohort of women in the city at once — it is not a problem that arrives with age, and it is not diluted by the city's younger working population. Bengaluru, for its part, holds women between 18% and 25% across all five bands, the flattest and lowest female line of any metro.

Most anemic women are iron-deficient, and a sixth are past the mild stage#

Anemia at this scale is not a borderline finding for most of the women carrying it. Split the female distribution into clinical severity bands and the moderate-and-worse share is substantial.

Two-thirds of women clear the threshold. Of the third who don't, the majority are not marginal: 14.7% of all women tested sit below 11 g/dL, in the moderate-or-severe range where fatigue, breathlessness on exertion, and reduced work capacity are clinically expected, not subtle. The mild band (11–11.9) is the one where a woman feels tired and is told she is fine.

The deficiency is specifically an iron problem. Among anemic women with a red-cell-volume reading, 55% are microcytic — MCV below 80 fL — the morphological signature of iron-deficiency anemia rather than the macrocytic pattern of B12 or folate deficiency. This matters for what an employer does about it. The remaining anemic women are normocytic or macrocytic, a mix of early iron deficiency before the cells shrink, anemia of chronic disease, and B12 or folate shortfalls — but iron is the dominant single cause, and the population-level intervention follows the dominant cause.

Dependents run more than twice the employee anemia rate#

The biomarker file separates the employee from the dependents covered on the same policy — spouses, parents, parents-in-law. The split is wide, and it is not only a female story.

Dependents are anemic at more than twice the employee rate. Some of that is composition — the dependent layer skews female and older, both of which raise anemia risk. But the split survives within each sex — one of the few biomarker gaps that holds up after age and gender controls, as the Self vs Dependent page documents. Among men, employees run 5.5% anemic and dependents 21.7% — a four-fold jump that the male-employee average completely hides. Among women, employees run 31.9% and dependents 37.2%. The dependent layer is where the policy's quietest health risk sits, and the male dependents in it — fathers and fathers-in-law — are the ones the employee-centric view of workplace health never sees.

The women who are most anemic also eat the least heme iron#

The survey, run on the same broad audience, lets us look at diet by sex. Women in Loop's audience are more often vegetarian and less often non-vegetarian than men — a small gap, but it runs in the direction that raises anemia risk.

Just over a third of women report a vegetarian diet, and another tenth eggetarian — close to half the female audience eating little or no heme iron, against a daily menstrual iron loss men do not carry. The diet gap alone is too small to produce a 3.9× anemia gap. But it compounds with the physiological iron demand and the absorption inhibitors common in Indian eating patterns — tea and coffee with meals — into a deficit that diet awareness does not close on its own. The biomarker and survey datasets are not linked at the individual level, so this is an audience-level pattern, not a claim that the vegetarian women in the sample are the anemic ones. It is the population-level coincidence of higher iron demand and lower heme intake in the same group.

Anemia is a work-capacity problem, not only a health one#

Iron-deficiency anemia is not silent. At the moderate levels carried by one in seven women in this sample, the consequences are measurable in exactly the domains an employer cares about.

The clinical fix is among the cheapest in medicine. Oral iron supplementation is inexpensive and, for the microcytic iron-deficiency pattern that describes 55% of anemic women here, directly corrective. The gap is not the treatment. It is the diagnosis — a complete blood count that gets read, and a follow-up that gets booked.

What the anemia data says#

Anemia is the biomarker where Loop's audience looks healthiest against the national benchmark and where the gender gap is widest within it. A third of working women are anemic; most of them are iron-deficient; the rate is set by the time they enter the workforce and barely moves for decades. The single largest lever on a woman's risk in this dataset is not her age or her seniority. It is the city she works in — a Mumbai woman is more than twice as likely to be anemic as a Bengaluru woman, at every stage of her working life. That gap held across two editions of this report and two independent samples.