Nicolau Martin-Bassols, Pietro Biroli, Elisabetta De Cao, Massimo Anelli, Stephanie von Hinke, Silvia Mendolia
7 min
Abstract
The establishment of the UK National Health Service (NHS) in July 1948 was one of the most consequential health policy interventions of the twentieth century, providing universal and free access to medical care and substantially expanding maternal and infant health services. In this paper, we estimate the causal effect of the NHS introduction on early-life mortality and we test whether survival is selective. We adopt a regression discontinuity design under local randomization, comparing individuals born just before and just after July 1948. Leveraging newly digitized weekly death records, we document a significant decline in stillbirths and infant mortality following the introduction of the NHS, the latter driven primarily by reductions in deaths from congenital conditions and diarrhea. We then use polygenic indexes (PGIs), fixed at conception, to track changes in population composition, showing that cohorts born at or after the NHS introduction exhibit higher PGIs associated with contextually-adverse traits (e.g., depression, COPD, and preterm birth) and lower PGIs associated with contextually-valued traits (e.g., educational attainment, self-rated health, and pregnancy length), with effect sizes as large as 7.5% of a standard deviation. These results based on the UK Biobank data are robust to family-based designs and replicate in the English Longitudinal Study of Ageing and the UK Household Longitudinal Study. Effects are strongest in socioeconomically disadvantaged areas and among males. This novel evidence on the existence and magnitude of selective survival highlights how large-scale public policies can leave a persistent imprint on population composition and generate long-term survival biases.
Sam: The study checked that directly—no shifts in birth numbers, family sizes, or birth order around the cutoff. This rules out fertility changes; the genetic makeup only shifted because more vulnerable babies lived.
Alex: Okay, so hospital births and free treatments likely helped the weakest—especially poor boys—pass through to adulthood, raising average genetic risks in survivors. What did the death records show exactly about which babies made it through?
Sam: The researchers digitized weekly reports from large towns and cities, tracking births and deaths right around July 1948. These showed clear drops in stillbirths and infant deaths from causes like birth defects or diarrhea. Diarrhea deaths especially fell because free hospital care meant quick treatment for dehydration, something poor families couldn't afford before. Congenital conditions, like heart or organ problems present from birth, also declined as hospitals provided surgeries or monitoring unavailable earlier.
Alex: Interesting—these were mostly things needing immediate medical help. And it hit harder in poorer spots?
Sam: Yes. They linked 1951 census data on parents' jobs to classify neighborhoods by social class. Effects on genetic shifts were largest in low-status areas with high pre-NHS death rates, where access mattered most. Males showed stronger patterns too. Sibling pairs from the same family—one born just before and one after the cutoff—confirmed this: the post-NHS sibling had higher adverse genetic scores, even controlling for shared home life.
Alex: So free care filtered in more vulnerable boys from tough areas, especially those with treatable birth issues or gut problems. You've mentioned sibling comparisons holding up the findings. But how exactly do they use those to rule out differences between families?
Sam: They look inside families with siblings born close to the 1948 cutoff—one just before NHS, one just after. Full siblings share the same parents, home, and basic genes on average, so any difference in their genetic risk scores must come from the policy letting the frailer one survive. They use family fixed effects, which compares only within each family, wiping out all shared traits like parental genes or neighborhood. The post-NHS siblings still show higher risks for issues like depression—about the same shift as the bigger study.
Alex: Huh. So even matching siblings from the exact same setup, the later one has those extra genetic vulnerabilities. What about other big events right then, like food shortages?
Sam: Food rationing was ongoing, but surveys show no jumps in nutrition tied to July 1948. WWII was long over, no major wars or strikes hit infant survival then. These checks strengthen the case that NHS access alone drove the mortality drop and genetic shifts.
Alex: So the evidence stacks up across families, births, and history. Did they test the pattern holds under different ways of slicing the data?
Sam: They did extensive robustness checks, varying the time window around the 1948 cutoff and using different math for weighting data near it. The pattern stayed consistent: post-NHS groups with higher genetic risks for issues like depression. They also adjusted for testing many traits at once, and results held across smaller datasets.
Alex: So changing the math doesn't flip the direction. You've laid out a clear case. But I wonder—did the effects vary by gender?
Sam: They did. Boys showed consistently larger shifts toward higher genetic risks for adverse traits, fitting the male frailty hypothesis. Baby boys grow faster in the womb and have less flexible support from the placenta, making them more prone to problems like stillbirths or defects under stress, without medical help. This pattern was strongest in poorer districts with high death rates before 1948.
Alex: Huh. So boys, being naturally more at risk early on, got a bigger survival edge from the free care.
Sam: Yes. The paper highlights that universal health policies like the NHS cut early deaths and shifted population genetics. It traces selective survival using fixed genetic markers. This suggests modern expansions could leave similar traces. There are limits: the genetic scores come from studies mostly on Europeans, post-1948 samples are smaller, and they can't pinpoint exact biology. The paper stresses these are group patterns, not individual predictions.
Alex: So researchers can now measure and correct for who survives policies. A practical tool.
Sam: Precisely. This work grounds how health systems subtly alter populations over time, with methods to track it accurately.
Alex: Well put. Thanks for breaking it down, Sam—that's our look at the NHS's genetic legacy. Thanks for listening to ResearchPod.