How Much Extra Mortality Risk Does a Career on Nights Actually Carry?

An underwriter pricing a life insurance policy for a smoker doesn’t treat “smoker” as a single fact. They ask how many packs a day, for how many years, and whether the applicant has since stopped. Duration and dose turn a yes/no health question into a number a pricing model can use. Night-shift work, which now touches somewhere around one in six workers in the United States by Bureau of Labor Statistics estimates, gets none of that treatment on a typical insurance application. Carriers ask about tobacco, alcohol, and family history in detail. Almost none ask how many years an applicant has worked nights, and fewer still adjust price if they do.

That gap is not because the underlying risk is unmeasured. It’s because the industry that prices occupational mortality risk for a living has not caught up to a body of cardiovascular epidemiology that has only existed in a usable, dose-quantified form for less than a decade. This piece walks through what that evidence actually says, then builds a small exposure ladder to make the dose-response relationship concrete. The ladder is my own construction for this article. It is not a published actuarial table, not a validated clinical score, and not something any insurer currently uses; treat it as a sketch of the kind of thinking that would precede a real one.

How Actuaries Actually Price a Bad Risk

Life insurance underwriting in the US runs on a table-rating method that dates back more than a century. A “standard” applicant pays the base rate. An applicant with an elevated but manageable risk, a controlled health condition, a risky hobby, a hazardous occupation, gets assigned a table rating, typically lettered A through J or numbered on a similar scale, and each step up the table adds roughly 25% to the standard mortality charge. Table A means 25% extra mortality cost. Table B means 50%. By Table D, the applicant is paying double the standard rate for identical coverage.

There is a second, less well-known tool in the same toolbox: the flat extra. Instead of a percentage surcharge that scales with the base premium for the life of the policy, a flat extra charges a fixed dollar amount per $1,000 of coverage, and it is frequently temporary. Someone recovering from a treated cancer might carry a flat extra of a few dollars per thousand for five years and then have it removed once enough time has passed without recurrence. High-risk occupations and hobbies sometimes get flat extras that stay in place as long as the exposure continues, then drop off when it stops.

That second tool maps onto night-shift work more naturally than the permanent table rating does. Most people who work nights don’t work nights forever. A nurse might do eight years of rotating shifts early in a career and then move to a day clinic. A line cook might work closing shifts through their twenties and open a business with banker’s hours by forty. If the underlying cardiovascular research shows the risk accumulating with years of exposure and behaving, at least approximately, as a function of duration, a temporary, exposure-linked charge is a better structural fit than a permanent rating assigned once at application and never revisited. No carrier currently prices it this way. The tools to do so already exist for other risks.

The Numbers Behind the Gap

The clearest single source on how the cardiovascular risk scales with duration is Torquati, Mielke, Brown, and Kolbe-Alexander’s 2018 systematic review and meta-analysis in the Scandinavian Journal of Work, Environment & Health (44(3):229–238), run out of the University of Queensland’s Centre for Research in Exercise, Physical Activity, and Health. Pooling 21 studies, the team found shift work associated with a 26% higher rate of coronary heart disease morbidity (RR 1.26, 95% CI 1.10–1.43) and roughly a 20% increase in the risk of death from coronary heart disease or cardiovascular disease overall. The finding that matters most for this piece is buried further down the paper: cardiovascular disease event risk rose by 7.1% for every additional five years of shift-work exposure, a genuine dose-response curve rather than a flat “shift workers vs. everyone else” comparison.

A more recent and larger analysis corroborates the shape of that curve without simply restating it. Jiayu Xi and colleagues at Chengdu University and Sun Yat-sen University published a dose-response meta-analysis in Frontiers in Public Health in September 2025, pooling 23 cohort studies. Total cardiovascular event risk came in at RR 1.13 (95% CI 1.10–1.16) and total cardiovascular mortality at RR 1.27 (95% CI 1.18–1.36), with each five-year increment in shift-work duration adding roughly 7% to incident CVD risk and about 4% to CVD mortality risk. Two research groups, seven years apart, working from different cohort pools, land on close to the same dose-response slope: roughly 7% additional relative risk per five years spent on nights.

Neither paper claims the relationship stays perfectly straight across an entire working life. Both used regression modeling precisely because dose-response curves in occupational epidemiology often flatten or steepen at the extremes rather than running as a straight line; Xi’s team layered restricted cubic splines onto the data to check for that curvature directly. What both papers establish cleanly is direction, rough magnitude, and a real dependence on how long someone has been exposed, which is exactly the kind of input a table-rating or flat-extra pricing approach is built to consume.

Building the Ladder

Using 7.1% additional relative risk per five years of cumulative night or rotating-shift exposure as the working slope, and treating a worker with no night-shift history as the reference point (RR = 1.00), here is a straight-line extrapolation across a full career:

• Rung 0 (under 5 years): RR roughly 1.00 to 1.07. Close to indistinguishable from a day-shift worker on this one risk axis; other factors dominate at this exposure level.
• Rung 1 (5 to 10 years): RR roughly 1.07 to 1.14. The lower edge of what the pooled cohorts actually measured with confidence, the point where the signal starts separating from noise.
• Rung 2 (10 to 15 years): RR roughly 1.14 to 1.21. A mid-career worker here sits close to the midpoint between the two papers’ headline total-CVD-event figures of 1.13 and 1.26.
• Rung 3 (15 to 20 years): RR roughly 1.21 to 1.28. This band brackets Xi’s pooled total-mortality figure of 1.27 almost exactly, a reasonable rough proxy for someone two decades into a night-shift career.
• Rung 4 (20 to 25 years): RR roughly 1.28 to 1.36. Torquati’s coronary-heart-disease pooled RR of 1.26 lands near the bottom of this band rather than the top, which hints that a straight-line extrapolation is running slightly hot by this point in a real career.
• Rung 5 (25-plus years): RR roughly 1.36 and climbing, resting on the thinnest cohort data of any rung. Few studies feeding either meta-analysis followed workers this deep into a single night-shift career, so this rung is more extrapolation than measurement, a caveat an underwriter would flag before pricing it and one this piece is flagging for the same reason.

An underwriter would immediately ask what happens between the rungs. Nobody has published a curve fine-grained enough to say with any confidence. Torquati’s team reported the slope in five-year blocks because that’s how their pooled cohorts recorded exposure, not because cardiovascular risk actually jumps in five-year steps. A real pricing model would need the raw person-year data behind both meta-analyses to fit a continuous curve; this piece only has the published summary statistics to work with, which is exactly the kind of gap that separates a working sketch like this one from something an actuarial committee could sign off on.

A worked example makes the ladder concrete. Someone who spent 9 years on rotating nights as a young nurse, then transferred to a day unit at 31 and stayed there for the next two decades, locked in a Rung 1 exposure years ago. Their cardiovascular risk on this one axis stopped climbing the day the schedule changed, even though plenty of other risk factors kept moving in the meantime. Thinking in exposure-years rather than a lifetime “shift worker, yes or no” checkbox captures something a binary label can’t: the risk is tied to a bounded period on a schedule, and it stops accruing once that period ends, the same way a smoker’s excess mortality risk gradually declines after they quit rather than staying frozen at whatever level it reached.

What It Would Actually Take to Price This

Building a real rating table isn’t just a matter of an actuary reading these two papers and drawing a line. New rating classes at most carriers get vetted by reinsurance treaties before a direct writer can offer them, since reinsurers absorb a share of the mortality risk on larger policies and won’t sign off on a pricing category without their own review of the underlying studies. That review process typically wants more than two meta-analyses; it wants the individual cohort studies broken out, replication across different countries and industries, and ideally a purpose-built cohort rather than pooled data assembled from cohorts that were designed to answer other questions.

There’s also a verification problem here that most rated conditions don’t have. A carrier can confirm a heart condition with a medical exam and lab work on the day of underwriting. Shift-work history has no equivalent point-in-time test. The Bureau of Labor Statistics collects data on shift schedules through supplemental questions to the Current Population Survey, which is useful for population-level estimates but isn’t something an individual applicant’s insurer can pull to verify one person’s work history. Until that verification gap closes, self-reported exposure years would need some kind of employer attestation or payroll record to count as underwriting-grade evidence rather than an honor-system answer on an application form, and building that pipeline is a bigger project than running the epidemiology.

What the Ladder Leaves Out

A relative risk of 1.27 does not mean 27% of night-shift workers develop cardiovascular disease who otherwise wouldn’t have. It means the rate in the exposed group runs 1.27 times the rate in the reference group, and if the reference group’s absolute risk is low to begin with, a large-looking relative increase can still translate into a modest absolute number. Neither paper published the underlying baseline rates this piece would need to convert the ladder into “extra deaths per thousand workers,” and building that conversion without their raw cohort data would mean inventing a precision the evidence doesn’t actually support.

The ladder also can’t separate night-shift work from what tends to travel alongside it. Cohort studies in this literature have consistently struggled with confounding from smoking prevalence, socioeconomic status, and a phenomenon epidemiologists call the healthy-worker effect: people who start developing symptoms tend to self-select out of night-shift jobs before their decline shows up in a cohort’s outcome data, which can make the surviving night-shift population look artificially resilient in the numbers. Both meta-analyses adjusted for some of these factors in their pooled estimates. Neither could remove them entirely, and neither paper claims to have.

There’s a third gap, separate from confounding: the two meta-analyses pool cohorts from Europe, East Asia, Australia, and North America under a single set of definitions for “night shift” and “rotating shift” that don’t always mean the same schedule in every country’s labor data. A twelve-hour rotating shift in a UK hospital cohort and an eight-hour fixed-night shift in a US manufacturing cohort both get folded into the same pooled estimate, which is standard practice in meta-analysis and also means the pooled RR is an average across schedule types that plausibly carry somewhat different risk on their own. Nobody has yet published a breakdown fine enough to separate them, which limits how much weight any single rung of this ladder should carry for a reader on one particular schedule type rather than the pooled average.

What This Framework Is Actually For

This ladder isn’t a diagnostic tool, and it isn’t a reason to quit a night-shift job on the spot. It’s a way to replace a vague sense that night shift is “probably bad for you” with a duration-scaled model that has two independent, real meta-analyses behind its slope, which is a meaningfully different footing than most shift-work advice circulating without any citation attached at all.

The biology underneath these numbers helps explain why duration matters this much: circadian disruption to inflammatory signaling and insulin regulation that doesn’t fully reverse with better sleep habits alone means each additional year on a misaligned schedule is another year those pathways run on the wrong clock, well beyond whatever fatigue shows up the next morning. For anyone currently on a rotating or permanent night schedule, the practical lever isn’t quitting nights on the strength of a relative-risk figure. It’s the handful of duration-independent habits, sleep-window anchoring, light timing, and pre-rotation adjustment, that reduce the physiological cost of each individual year even though none of them can move a worker down a rung on the exposure ladder itself. The ladder tracks how many years someone has been exposed. It has nothing to say about how well any single one of those years was managed, and that turns out to be the question an individual worker actually has some control over.

Insurance underwriting is one of the few industries built entirely around pricing exactly how much a confirmed fact about a person’s condition is worth, which is a more rigorous version of the same instinct behind a term like “proof of life,” borrowed from a much higher-stakes field than either insurance or wake-up apps: both worlds have learned that a confirmed state is worth real money or real risk, and an assumed one isn’t.