We then estimated the mortality differentials, using a cohort and a period approach, to control for mortality improvement over time.
The effect estimates were also not sensitive to whether the models controlled for mortality trends in the rest of the United States, eastern U.S. states, or neighboring states, but were consistently smaller when counts for the bordering states were included.
In summary, our data add to a growing literature suggesting that once SES and other risk factors are controlled for, mortality is no higher among African American than white patients with diabetes.
Bach et al found that after controlling for population mortality (non-cancer related death), the difference in cancer-related mortality between blacks and white was diminished [ 24].
After controlling for demographics, mortality risk and comorbidities in the mixed logistic model, we detected a significant reduction in readmission rate (adjusted OR (AOR) 0.73, 95% CI 0.58 to 0.93, p=0.01) in the post-intervention period.
The standard Cox survival model assumes that the survival time of each subject is statistically independent from that of other subjects after controlling for the mortality risk factors included in the model.
Available mortality data indicate a significant strike-period decrease in mortality, even while statistically controlling for time trends, mortality counts in bordering states, and nationwide mortality counts for influenza/pneumonia, cardiovascular, and respiratory deaths (Pope et al 2007).
The effect estimates were reasonably stable and robust to controlling for time trends, mortality trends in other areas of the United States, and nationwide monthly mortality counts for influenza/pneumonia, cardiovascular, and other respiratory deaths.
