US State Policies, Politics, and Life Expectancy
Bibliographic record
Abstract
Policy Points Changes in US state policies since the 1970s, particularly after 2010, have played an important role in the stagnation and recent decline in US life expectancy. Some US state policies appear to be key levers for improving life expectancy, such as policies on tobacco, labor, immigration, civil rights, and the environment. US life expectancy is estimated to be 2.8 years longer among women and 2.1 years longer among men if all US states enjoyed the health advantages of states with more liberal policies, which would put US life expectancy on par with other high-income countries. CONTEXT: Life expectancy in the United States has increased little in previous decades, declined in recent years, and become more unequal across US states. Those trends were accompanied by substantial changes in the US policy environment, particularly at the state level. State policies affect nearly every aspect of people's lives, including economic well-being, social relationships, education, housing, lifestyles, and access to medical care. This study examines the extent to which the state policy environment may have contributed to the troubling trends in US life expectancy. METHODS: We merged annual data on life expectancy for US states from 1970 to 2014 with annual data on 18 state-level policy domains such as tobacco, environment, tax, and labor. Using the 45 years of data and controlling for differences in the characteristics of states and their populations, we modeled the association between state policies and life expectancy, and assessed how changes in those policies may have contributed to trends in US life expectancy from 1970 through 2014. FINDINGS: Results show that changes in life expectancy during 1970-2014 were associated with changes in state policies on a conservative-liberal continuum, where more liberal policies expand economic regulations and protect marginalized groups. States that implemented more conservative policies were more likely to experience a reduction in life expectancy. We estimated that the shallow upward trend in US life expectancy from 2010 to 2014 would have been 25% steeper for women and 13% steeper for men had state policies not changed as they did. We also estimated that US life expectancy would be 2.8 years longer among women and 2.1 years longer among men if all states enjoyed the health advantages of states with more liberal policies. CONCLUSIONS: Understanding and reversing the troubling trends and growing inequalities in US life expectancy requires attention to US state policy contexts, their dynamic changes in recent decades, and the forces behind those changes. Changes in US political and policy contexts since the 1970s may undergird the deterioration of Americans' health and longevity.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.006 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".