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Learning to Address Health Inequality in the United States with a Bayesian Decision Network
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Measured effects reported in the paper
| What was changed | Where | Measured | Effect |
|---|---|---|---|
| Increase county population diversity | Bayesian Network model of US county longevity · US counties, male Q4-Q1 longevity gap > 9 years | probability: 41% -> 3% | Helped |
| Improve preventive-care quality | Bayesian Network model of US county longevity · US counties, female life expectancy beyond 85 years | probability: 43.3% (0.56 - 0.13) | Helped |
| Improve preventive-care quality | Bayesian Network model of US county longevity · US counties, male life expectancy beyond 85 years | probability: +30% | Helped |
| Choose the highest stratum of annual lipid testing for diabetics | Bayesian Decision Network · US counties, female life expectancy beyond 85 years | policy payoff: 0.52 | Helped |
| Increase mean household income from below $30,000 p.a. to above $45,000 p.a. | Bayesian Network model of US county hospital mortality · US counties, high 30-day hospital mortality index | probability: 30% less | Helped |
| Reduce smoking prevalence among males in Q4 | Bayesian Network model of US county longevity · US counties, male life expectancy beyond 82 years | probability: +30% | Helped |
| Increase exercise prevalence, especially in Q1 | Bayesian Network model of US county hospitalization · US counties, hospitalization rates in highest band | probability: 19% lower | Helped |
| Increase family stability | Bayesian Network model of US county social mobility · US counties, absolute upward mobility | mobility: 40% lowered mobility | Helped |
| Reduce Gini disparity | Bayesian Network model of US county social mobility · US counties, absolute upward mobility | mobility: 37% lowered mobility | Helped |
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