How much do four risks explain US life-expectancy gaps?
Smoking, high blood pressure, high glucose, and adiposity help explain large US life-expectancy disparities across race–place groups (Eight Americas).
Source
The promise of prevention: the effects of four preventable risk factors on national life expectancy and life expectancy disparities by race and county in the United States
Study at a glance
- Design
- Computational / modelling — Population-level attribution of smoking, blood pressure, glucose, and adiposity to US life-expectancy disparities (Eight Americas)
- N
- National mortality and risk-factor modeling rather than a recruited sample
- Population
- US race–county population groupings used to describe life-expectancy inequality
- Outcome
- Contribution of four preventable risks to national life expectancy and geographic/racial disparities
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Inspect source passages
How much do four risks explain US life-expectancy gaps?
These passages occur verbatim in the stored paper text. They support the points listed below; they do not verify every statement in the explanation.
Optimal risk distributions would lower life-expectancy SD by 0.50 y (18%) in men
Had risk factor levels been at their optimal distributions in all Americas, the population weighted SD of life expectancies would be lowered by 0.50 y (18%) in men and 0.45 y (21%) in women; life expectancy gaps between the best- and worst-off Americas would be lowered by 1.9 y (13%; America 1 versus America 8) in men and 2.0 y (17%; America 1 versus America 7) in women.
What population framework captures US longevity inequality?
Based on these findings, we used eight subgroups of the US race–county combinations, referred to as the Eight Americas in a previous work and defined in , as our units of analysis.
What they did
Using risk-factor and mortality data across the Eight Americas, investigators estimated how smoking, SBP, fasting glucose, and BMI contribute to national life expectancy and cross-group disparities.
What they found
These preventable risks are leading mortality drivers and contribute to disparities; bringing them to optimal levels would raise life expectancy and shrink disparity spread (e.g., lowering population-weighted SD of life expectancies).
The limits
What it doesn't show
Modeling attributable effects is not a randomized test of a specific prevention program’s real-world delivery.
Key terms
- Eight Americas
- Race–geography population groupings that capture much of US life-expectancy inequality.
- Preventable risk factors
- Modifiable exposures such as smoking, high BP, high glucose, and adiposity.
- Life expectancy disparity
- Gaps in expected lifespan across social or geographic groups.
- SBP
- Systolic blood pressure.
- FPG
- Fasting plasma glucose, a glycaemia marker.
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The four risks include all except:
Common questions
Which four risks?
Smoking, high blood pressure, high blood glucose, and adiposity.
What are Eight Americas?
Defined race–place population units used to study US longevity gaps.
Main equity claim?
These risks contribute to mortality disparities, not only average deaths.
If risks were optimal?
Models suggest higher life expectancy and smaller cross-group spread.
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