The Evidence Is Real
Income and sleep quality are both associated with health outcomes. The question here is narrower: does adding these measurements improve prediction beyond the variables already in each model?
Income & Mortality
Poorest income quintile: 28.0% die within 10 years. Highest: 12.3%. Data from 28,636 adults pooled across ten NHANES cycles (1999–2018).
If wealth predicts death this strongly, shouldn't it improve our models?
Sleep & Health Decline
The corrected RAND HRS coding compares five self-reported sleep-quality indicators among 24,155 people. The outcome rates are rendered directly from the promoted aggregate data below.
If sleep quality separates outcomes this clearly, shouldn't it help?
The ROC Curves Tell the Truth
ROC curves reveal how well a model distinguishes positive from negative cases across every possible threshold. If adding data helps, the curve should shift upward. Watch what actually happens.
Adding Income & Education Data
Q15: 28,636 NHANES adults · 6,626 deaths · 10-year follow-up
Adding Sleep Data
Q16: 24,155 people · 6-year health decline outcome
The Reclassification Shell Game
Net Reclassification Index (NRI) counts how many patients move to a more appropriate risk category. For every person the new data helps, another is hurt.
SDOH Reclassification
28,636 adults across 4 risk categories
Sleep Reclassification
24,155 patients across 4 risk categories
The Signal Is Already Captured
Feature importance reveals why: the new variables rank near the bottom. Age, self-rated health, and existing conditions already carry the signal that wealth and sleep correlate with.
Health + SDOH Model Features
GradientBoosting importance — SDOH features highlighted
Health + Sleep Model Features
GradientBoosting importance — sleep feature highlighted
Why SDOH Adds Little Here
Income, education, and marital status overlap predictively with measured health in this cohort. The analysis does not determine whether that overlap reflects causal pathways, reverse causation, shared causes, selection, or the chosen outcome window.
Why Sleep Doesn't Help
In this cohort, the available sleep-quality items overlap predictively with health, depression, and function measures already in the model. The comparison cannot determine whether that overlap reflects causation, reverse causation, shared causes, or measurement limits.
Two Paradoxes, One Lesson
The longevity study surfaced two symmetrical findings. Together, they define the boundaries of the right-fidelity principle.
The Biology Paradox
More computation doesn't always help. ML alone (r = 0.478) was worse than a textbook formula (r = 0.527) for biological age.
The Data Paradox
More data doesn't always help. Adding SDOH (+0.003 AUC) and sleep (+0.002 AUC) to these models changed discrimination very little.
The ADM Principle
The right model at the right fidelity. Not the most complex, not the most data-rich — the one matched to the question and the decision it supports.