Medical care and genetics both matter for health, but neither is the dominant factor explaining why health outcomes vary so much across a population. Social determinants of health — a person's income, housing quality, education level, employment conditions and the broader characteristics of the neighbourhood they live in — collectively explain a larger share of that variation than genetics or access to medical care alone, which is a large part of why a person's postal code, standing in for the cluster of social and economic conditions typically associated with a given area, can predict their health outcomes surprisingly well.
Health happens mostly outside the doctor's office
Medical care matters enormously once someone is already sick or injured, but a large share of what actually determines whether someone becomes sick or injured in the first place happens well outside any doctor's office or hospital — in the conditions of daily life that shape a person's exposure to stress, pollution, poor nutrition, unsafe housing or unstable income, none of which a single medical visit can fully reverse the cumulative effects of. Public health researchers estimate that social and environmental factors of this kind, combined with individual health behaviours themselves shaped substantially by those same underlying social and economic conditions, account for a considerably larger share of health outcome variation across a population than clinical medical care does on its own.
Neighbourhood conditions cluster together, producing a genuinely predictive pattern
What makes a specific geographic area, like a postal code, such a strong statistical predictor of health outcomes is that the relevant social determinants tend to cluster together within the same area rather than varying independently — a neighbourhood's income level, housing quality, access to fresh food, exposure to environmental pollutants and quality of local schools are all typically correlated with each other in the same geographic area, meaning postal code ends up functioning as a genuinely informative statistical proxy for a whole bundle of interrelated social and economic conditions at once, not for any single isolated cause. This clustering is exactly why public health researchers and policymakers treat geographic health disparities as a real signal pointing toward underlying social and economic conditions worth addressing directly, rather than dismissing the pattern as mere statistical coincidence.
What we're still unsure about
The broad finding that social determinants explain more population-level health variation than medical care or genetics alone is well established across a large body of public health research. What remains more genuinely contested is exactly how to disentangle the relative contribution of specific individual social determinants from each other, given how tightly they tend to cluster together in the real world — since income, housing, education and local environmental quality are all so strongly correlated within the same population and area, isolating precisely how much each individual factor independently contributes, as opposed to simply reflecting the same broader underlying pattern of disadvantage, remains a genuinely difficult methodological challenge public health researchers continue actively working on.
This sits inside Health Disparities & Social Determinants, one of seven topics in Demographics, one of four domains in Sociology, one of seventeen subjects the app can quiz you on.