LoopWHI ’26

Substance Mentions in Claims

claims
74,339 claims, ₹320.6 Cr incurred, latest 12-month window

Key takeaways

  • Substance keywords appear in about 0.8% of claims — not a prevalence figure. The survey puts drinking near 37% and nicotine near 17%; the claims book surfaces a substance mention in fewer than one claim in a hundred, because the keyword only enters the record when the admission made it relevant.
  • The handful of claims that do carry a substance mention cost more than twice the book average — ₹91,929 against ₹42,757 — and concentrate in trauma, accounting for ₹5.17 Cr of incurred expense.
  • Trauma is 36.1% of substance-flagged claims against 2.2% of the whole book — a sixteen-fold concentration. This is the severe tail of the substance-use story: alcohol-implicated road accidents, falls, head injuries, and fractures, not routine care.
  • Substance-flagged claims are 79.9% male against a 44.8% male book — the sharpest gender skew anywhere in the claims data, and the claims-side echo of the male-skewed drinking the survey pages describe in the population.
  • Almost every mention is a workflow query, not a diagnosis. 99% of substance keywords surface in the insurer's deficiency, denial, or deduction fields — the insurer asking whether an exclusion applies — not in the clinical illness field.

Substance keywords appear in roughly 0.8% of the 74,339 claims on Loop's broker book in the latest twelve months — a figure that says almost nothing about how much the workforce actually drinks or smokes. The survey side of this report puts any-alcohol consumption near 37% and nicotine use near 17%. The gap between 37% and 0.8% is the entire reason this page exists as a sidebar. A claim records a substance only when a clinician noted it as material to the admission, or when an insurer queried whether an exclusion clause applied. The vast majority of drinkers and smokers in the covered population never generate a claim where the habit is written down. What surfaces in the claims data is the narrow, expensive, male-skewed tail where substance use turned into a hospital bed.

The claims signal is one part in a hundred of the survey signal#

Signal Where it is measured Rate
Any-alcohol consumption Survey (self-report) ~37%
Nicotine use Survey (self-report) ~17%
Any substance mention in a claim Claims free-text 0.8%
— of which alcohol Claims free-text 0.7%
— of which smoking / tobacco / drug Claims free-text 0.1%

The claims free-text is not a second, harder measurement of the same thing the survey measures. It is a different thing entirely. The survey asks the whole population a question. The claims data records only the admissions where the answer became relevant to a hospital stay.

The claims signal is one part in a hundred of the survey signal

Self-reported substance use in the survey vs substance-mention rate in claims free-text — drawn to true scale

Any-alcohol consumption
survey (self-report)
~37%
Nicotine use
survey (self-report)
~17%
Any substance mention in a claim
claims free-text
0.8%
— of which alcohol
claims free-text
0.7%
— of which smoking / tobacco / drug
claims free-text
0.1%
Values in %

Not two measurements of the same thing: the survey asks the whole population a question; a claim records a substance only when an admission made it material. The thin slivers are mention rates in claim text, never workforce prevalence. The datasets are not linked at the individual level.

Substance-flagged claims cost more than twice the book average#

The few claims that carry a substance mention are expensive ones.

Metric Substance-flagged claims Rest of the book
Average incurred per claim ₹91,929 ₹42,757
90th-percentile incurred ₹2,09,500
Total incurred across the group ₹5.17 Cr

Substance-flagged claims cost more than twice the book average

Average incurred per claim, substance-flagged claims vs the rest of the book

Substance-flagged claims
2.15× the rest of the book
₹91,929
Rest of the book
₹42,757
Values in

Value axis starts at zero — the 2.15× ratio reads honestly. Flagged group: 90th-percentile incurred ₹2,09,500 · ₹5.17 Cr total incurred. The mention travels with severity, not with the substance itself.

A substance-flagged claim costs, on average, 2.15 times the typical claim on the book. That is not because alcohol or tobacco is itself expensive to treat. It is because the admissions where a clinician thought the substance worth recording are the severe ones — the trauma, the organ damage, the long stays. The mention and the severity travel together.

Trauma is sixteen times over-represented in flagged claims#

The clearest clinical pattern in the flagged group is trauma.

Ailment category Share of flagged claims Share of the whole book Over-representation
Injury / fracture / trauma 36.1% 2.2% ~16×
Gastrointestinal / liver 18.3% 11.3% ~1.6×
Cardiac / cerebrovascular / neuro 3.7% 1.5% ~2.5×

Trauma is sixteen times over-represented in flagged claims

Each category's share of substance-flagged claims (bar) against its share of the whole book (tick)

Share of the whole book
Injury / fracture / trauma
2.2% of the book · ~16× over-represented
36.1%
Gastrointestinal / liver
11.3% of the book · ~1.6×
18.3%
Cardiac / cerebrovascular / neuro
1.5% of the book · ~2.5×
3.7%
Values in %

Shares of the flagged group, not prevalence — the flagged group is roughly 0.8% of claims. Trauma's ~16× concentration is the claims-side signature of alcohol's acute harm: road accidents, falls, head injuries, fractures.

Trauma is 2.2% of all claims on the book. Among substance-flagged claims it is 36.1% — a sixteen-fold concentration, and the dominant category of the flagged group. The underlying illness text confirms what the category implies: head injuries, road accidents, ACL ruptures, humerus and ulna fractures, falls. This is the claims-side signature of alcohol's best-documented acute harm — not the slow organ damage of chronic use, but the sudden injury of a single night.

The gastrointestinal and liver group is the second concentration, at 18.3% against an 11.3% book share. Buried inside it is chronic pancreatitis, which appears repeatedly in the flagged illness text and is, in the absence of gallstones, one of the most specific markers of sustained heavy drinking in clinical medicine.

The flagged claims are overwhelmingly male#

Group Share of substance-flagged claims Share of the whole book
Male 79.9% 44.8%
Female 18.9% 48.2%

The flagged claims are overwhelmingly male

Each gender's share of substance-flagged claims against its share of the whole book

Share of substance-flagged claimsShare of the whole book
Male
79.9% / 44.8%
Female
18.9% / 48.2%
Share of substance-flagged claims versus Share of the whole book, values in %

The sharpest gender skew anywhere in the claims data — 79.9% of flagged claims are male against a 44.8% male book. A share of the flagged group, not a substance-use rate; the survey pages carry the prevalence numbers.

The book is split roughly evenly between male and female claimants. The flagged group is 79.9% male — the sharpest gender skew of any cut in the claims data. By relationship, the flagged group is just over half employees and another fifth fathers; spouses and mothers together account for well under a fifth. This tracks the survey pages precisely: drinking and nicotine use in the covered population skew heavily male, and the severe, hospitalising tail of that behaviour skews more heavily still. The claims data does not contradict the survey here. It is the same population, seen at its most severe edge.

The mention is a query, not a diagnosis#

The most important methodological fact on this page is where the keywords actually live.

Field carrying the mention Share of flagged claims
Deficiency Reason (insurer requesting clarification) 71.0%
Deduction Reason (amount disallowed) 22.1%
Denial Reason 6.2%
Illness / Ailment (clinical diagnosis fields) 0.7%

99% of substance mentions are a workflow query, not a diagnosis

Share of flagged claims by the field carrying the mention — insurer workflow fields in green, clinical fields in brass

71.0%
22.1%
Deficiency Reason (insurer requesting clarification)71.0%Deduction Reason (amount disallowed)22.1%Denial Reason6.2%Illness / Ailment (clinical diagnosis fields)0.7%

The keyword is the paper trail of an adjudication — the insurer asking whether an exclusion applies — not the paper trail of a disease. The large majority of flagged claims were ultimately paid; the exclusion was checked far more often than it was applied.

Ninety-nine percent of substance keywords appear in the insurer's workflow fields — deficiency, deduction, denial — and barely any in the clinical illness field, the same workflow-versus-diagnosis split the Claims Free-Text page finds when it mines those notes at scale. The substance is rarely the recorded diagnosis. It is the insurer asking a question: was alcohol involved, and if so, does the policy's intoxication or substance-abuse exclusion apply to this claim? The keyword is the paper trail of an adjudication, not the paper trail of a disease.

That distinction matters for how the number is read. The 0.8% of flagged claims are not admissions for alcoholism. They are admissions — mostly trauma — where someone in the claims chain raised the substance question. And in most cases the answer cleared the claim: the large majority of flagged claims were ultimately paid, with only a small minority repudiated outright. The exclusion was checked far more often than it was applied.

Methodology and limits#

The figures above come from a keyword scan across five claim fields — three insurer workflow fields (Deficiency Reason, Deduction Reason, Denial Reason) and two clinical fields (Illness, Ailment Group). Common false positives such as "alcohol swab" and "alcohol prep" were excluded. Each claim is counted once, against the first field in which a keyword appears.

References

  1. 1Rehm J, Gmel GE, Gmel G, et al. The relationship between different dimensions of alcohol use and the burden of disease — an update. Addiction, 2017;112(6):968–1001. https://doi.org/10.1111/add.13757
  2. 2Roerecke M. Alcohol's impact on the cardiovascular system. Nutrients, 2021;13(10):3419. https://doi.org/10.3390/nu13103419
  3. 3Cherpitel CJ, Ye Y, Bond J, et al. Multi-level analysis of alcohol-related injury and drinking pattern: emergency department data from 19 countries. Addiction, 2012;107(7):1263–1272. https://doi.org/10.1111/j.1360-0443.2012.03793.x
  4. 4Roerecke M, Vafaei A, Hasan OSM, et al. Alcohol consumption and risk of liver cirrhosis: a systematic review and meta-analysis. American Journal of Gastroenterology, 2019;114(10):1574–1586. https://doi.org/10.14309/ajg.0000000000000340