https://www.nber.org/system/files/working_papers/w25018/w25018.pdf

Difference-in-Differences with Variation in Treatment Timing

https://minerva-access.unimelb.edu.au/bitstream/handle/11343/249630/PMC6227505.pdf

Forecasting life expectancy, years of life lost, and all-cause and cause-specific mortality for 250 causes of death: reference and alternative scenarios for 2016–40 for 195 countries and territories

Building on our earlier research (Case and Deaton 2015), we find that mortality and morbidity among white non-Hispanic Americans in midlife since the turn of the century continued to climb through 2015. Additional increases in drug overdoses, suicides, and alcohol-related liver mortality—particularly among those with a high school degree or less—are responsible for an overall increase in all-cause mortality among whites. We find marked differences in mortality by race and education, with mortality among white non-Hispanics (males and females) rising for those without a college degree, and falling for those with a college degree. In contrast, mortality rates among blacks and Hispanics have continued to fall, irrespective of educational attainment. Mortality rates in comparably rich countries have continued their premillennial fall at the rates that used to characterize the United States. Contemporaneous levels of resources—particularly slowly growing, stagnant, and even declining incomes—cannot provide a comprehensive explanation for poor mortality outcomes. We propose a preliminary but plausible story in which cumulative disadvantage from one birth cohort to the next—in the labor market, in marriage and child outcomes, and in health—is triggered by progressively worsening labor market opportunities at the time of entry for whites with low levels of education. This account, which fits much of the data, has the profoundly negative implication that policies—even ones that successfully improve earnings and jobs, or redistribute income—will take many years to reverse the increase in mortality and morbidity, and that those in midlife now are likely to do worse in old age than the current elderly. This is in contrast to accounts in which resources affect health contemporaneously, so that those in midlife now can expect to do better in old age as they receive Social Security and Medicare. None of this, however, implies that there are no policy levers to be pulled. For instance, reducing the overprescription of opioids should be an obvious target for policymakers.

Mortality and Morbidity in the 21st Century

Difference-in-Differences with multiple time periods

https://northumbria-test.eprints-hosting.org/id/document/266028

Future life expectancy in 35 industrialised countries: projections with a Bayesian model ensemble

Importance The relationship between income and life expectancy is well established but remains poorly understood. Objectives To measure the level, time trend, and geographic variability in the association between income and life expectancy and to identify factors related to small area variation. Design and Setting Income data for the US population were obtained from 1.4 billion deidentified tax records between 1999 and 2014. Mortality data were obtained from Social Security Administration death records. These data were used to estimate race- and ethnicity-adjusted life expectancy at 40 years of age by household income percentile, sex, and geographic area, and to evaluate factors associated with differences in life expectancy. Exposure Pretax household earnings as a measure of income. Main Outcomes and Measures Relationship between income and life expectancy; trends in life expectancy by income group; geographic variation in life expectancy levels and trends by income group; and factors associated with differences in life expectancy across areas. Results The sample consisted of 1 408 287 218 person-year observations for individuals aged 40 to 76 years (mean age, 53.0 years; median household earnings among working individuals, $61 175 per year). There were 4 114 380 deaths among men (mortality rate, 596.3 per 100 000) and 2 694 808 deaths among women (mortality rate, 375.1 per 100 000). The analysis yielded 4 results. First, higher income was associated with greater longevity throughout the income distribution. The gap in life expectancy between the richest 1% and poorest 1% of individuals was 14.6 years (95% CI, 14.4 to 14.8 years) for men and 10.1 years (95% CI, 9.9 to 10.3 years) for women. Second, inequality in life expectancy increased over time. Between 2001 and 2014, life expectancy increased by 2.34 years for men and 2.91 years for women in the top 5% of the income distribution, but by only 0.32 years for men and 0.04 years for women in the bottom 5% ( P r  = −0.69, P r  = 0.72, P r  = 0.42, P r  = 0.57, P Conclusions and Relevance In the United States between 2001 and 2014, higher income was associated with greater longevity, and differences in life expectancy across income groups increased over time. However, the association between life expectancy and income varied substantially across areas; differences in longevity across income groups decreased in some areas and increased in others. The differences in life expectancy were correlated with health behaviors and local area characteristics.

/pdf/the-association-between-income-and-life-expectancy-in-the-171foi4jgg.pdf

The Association Between Income and Life Expectancy in the United States, 2001-2014

To estimate the dynamic effects of an absorbing treatment, researchers often use two-way fixed effects regressions that include leads and lags of the treatment. We show that in settings with variation in treatment timing across units, the coefficient on a given lead or lag can be contaminated by effects from other periods, and apparent pretrends can arise solely from treatment effects heterogeneity. We propose an alternative estimator that is free of contamination, and illustrate the relative shortcomings of two-way fixed effects regressions with leads and lags through an empirical application.

Estimating Dynamic Treatment Effects in Event Studies with Heterogeneous Treatment Effects

Estimating Dynamic Treatment Effects in Event Studies With Heterogeneous Treatment Effects

There is widespread concern over the health risks and healthcare costs from potentially inappropriate high-cost imaging. As a result, the Centers for Medicare and Medicaid Services (CMS) will soon require high-cost imaging orders to be accompanied by Clinical Decision Support (CDS): software that provides appropriateness information at the time orders are placed via a best practice alert for targeted (i.e. likely inappropriate) imaging orders, although the impacts of CDS in this context are unclear. In this randomized trial of 3,511 healthcare providers at Aurora Health Care, we study the impacts of CDS on the ordering behavior of providers. We find that CDS reduced targeted imaging orders by a statistically significant 6%, however there was no statistically significant change in the total number of high-cost scans or of low-cost scans. The results suggest that the impending CMS mandate requiring healthcare systems to adopt CDS may modestly increase the appropriateness of high-cost imaging.

/pdf/clinical-decision-support-for-high-cost-imaging-a-randomized-2muxdu3njq.pdf

Sarah Abraham

Papers

The Association Between Income and Life Expectancy in the United States, 2001-2014

Estimating Dynamic Treatment Effects in Event Studies with Heterogeneous Treatment Effects

Estimating Dynamic Treatment Effects in Event Studies with Heterogeneous Treatment Effects

Estimating Dynamic Treatment Effects in Event Studies With Heterogeneous Treatment Effects

Clinical decision support for high-cost imaging: A randomized clinical trial.