text: Planned sensitivity analyses for missing data 
  The electronic OPT-IN data collection forms required 
  complete data from respondents before they were 
  allowed to proceed to subsequent survey pages. There- 
  fore, the occurrence of missing data for independent 
  predictor variables was minimal (< 1% of sample). How- 
  ever, for subjects who were lost to follow-up, we planned 
  two approaches to assess the potential influence of miss- 
  ing data on study outcomes. First, demographic and 
  baseline health variables would be compared between 
  those with complete follow-up at 1 year and those with- 
  out follow-up at 1 year to identify any potential group 
  differences related to completion of follow-up. Second, 
  sensitivity analyses would be conducted by repeating 
  each analysis using inverse probability of attrition 
  weighting (IPAW). This propensity scoring approach 
  accounts for attrition-related selection bias in longitu- 
  dinal studies by more heavily weighting observations 
  associated with a lower probability of study completion 
  [40]. Thus, the resulting analysis is compensated for 
  under-representation of subjects who are more likely to 
  be lost to follow-up. IPAW produces smaller effect esti- 
  mate biases than more conventional methods that adjust 
  for baseline predictors of attrition [41]. Briefly, logistic 
  regression will be performed to identify predictors of 
  attrition using an opportunistic approach that optimizes 
  model fit, with an area under the curve (AUC) target 
  value of > 0.7. Demographic and baseline health variables 
  that differ between follow-up status cohorts will be used 
  as candidate variables for the regression model to derive 
  weights. Then, 
  inverse of predicted probabilities for 
  remaining in the study will be used to weight observa- 
  tions, and all analyses will be repeated. Regression re- 
  sults using IPAW will be compared with those obtained 
  from complete case only analyses to assess the potential 
  influence of missing data on the findings and identify ro- 
  bust predictors. We will focus our interpretation on pre- 
  dictors that are consistent across complete case and 
  IPAW models for each type of healthcare service as they 
  are more robust and most likely to be reproduced in fu- 
  ture studies. 