State-level adjusted ESRD incident rates: Use of observed vs model-predicted category-specific rates

Abstract

Because of differences in case-mix across states, state-level case-mix-adjusted end-stage renal disease (ESRD) incident rates are reported in each United States Renal Data System Annual Data Report to make the across-state comparisons valid. The adjusted rates were estimated by the direct adjustment method, a widely used method for adjusted event rate calculation, based on observed category-specific ESRD incident rates in each state (called the observation-based method). However, when some adjusting categories in a state are small, the adjusted rate and the standard error for this state as estimated by this method may be inaccurate. This report proposes a model-based method that can overcome the disadvantages of the observation-based method and can be extended to continuous adjusting variables. National ESRD incident data and national population data from 1990 to 1999 were used. State-level adjusted ESRD incident rates were estimated by both the observation- and the model-based methods. For the model-based method, a Poisson regression model was used to estimate category-specific ESRD incident rates. For large-population states, both observation- and model-based methods produced similar estimates for adjusted ESRD incident rates. For small-population states, however, the observation-based method produced year-to-year estimates of adjusted ESRD incident rates that varied considerably and also had very large standard errors. In contrast, the model-based method produced stable estimates. The model-based method can overcome the disadvantages of the observation-based method for estimating state-level adjusted ESRD incident rates, especially for small states.

Trends in Mortality Following Acute Myocardial Infarction Among Dialysis Patients in the United States Over 15 Years

“…The cumulative probability of death during the follow‐up period was estimated by the Kaplan–Meier method; differences were tested using the log‐rank test. The cumulative probability of death was further adjusted for age, sex, race, ESRD duration, ESRD etiology, dialysis modality type at AMI admission, baseline coronary revascularization, and comorbid conditions using a model‐based direct adjustment method with the 2008 cohort as the reference 17. The differences in unadjusted and adjusted cumulative probability of death across cohort years at 1 and 2 years of follow‐up were assessed using the bootstrap method.…”

“…The cumulative probability of death was further adjusted for age, sex, race, ESRD duration, ESRD etiology, dialysis modality type at AMI admission, baseline coronary revascularization, and comorbid conditions using a model-based direct adjustment method with the 2008 cohort as the reference. 17 The differences in unadjusted and adjusted cumulative probability of death across cohort years at 1 and 2 years of follow-up were assessed using the bootstrap method. Visual inspection revealed that the proportionality of the risk of death over time across cohort years was questionable, and a piece-wise Cox regression model was used to address the time dependency of risk of death with cutoffs of ≤1 month and >1 to ≤24 months after AMI admission and included factors listed in Table 1.…”

Prevalence of chronic kidney disease in a population in southern Brazil (Pro-Renal Study)

“…An attractive alternative is a model-based approach, in which we find a good model to calculate category-specific estimated rates for each group, and then calculate direct adjusted rates using these estimates with a given reference population. This method can also be extended to adjustments with continuous adjusting variables (Liu et al, 2006). As in previous ADRs, standard errors of the adjusted rates are calculated using a bootstrap approach.…”

Trends in Anemia Management Practices in Patients Receiving Hemodialysis and Peritoneal Dialysis: A Retrospective Cohort Analysis

“…As such, adjusted rates and associated standard errors can be inaccurate or misleading. For this reason, the predicted outcome was directly standardized to patient characteristics from a reference cohort (Q4 2011) [21] . Finally, we evaluated whether the observed changes in each of the quarterly outcomes over time persisted after adjustment for case-mix differences (age, sex, race, dialysis duration, and primary cause of ESRD).…”