First, missing data on demographic, socio-economic and self-reported medical conditions in MCBS and NHANES are imputed using sequential regression multiple-imputation procedure.

Covariates listed in Table 1 and self-reported medical conditions were missing for some subjects in both NHANES and MCBS data files. A sequential regression multiple imputation procedure (Kennickel (1992), Van Buuren and Oudshoom (2000) and Raghunathan et al (2001)) as implemented in the software package IVEWARE (Raghunathan et al (2002)) was used multiply impute the missing values. These are iterative procedures in which the missing values in each variable are imputed conditional on all other variables using appropriate regression models. Random draws from an approximate predictive distribution of the missing values under these models are then used as imputations.

Surveys for community dwellers and institutionalized populations differ, hence the sets of common MCBS-NHANES variables were different for institutionalized and community populations. We separately imputed missing values of common covariates in NHANES, MCBS-community, and MCBS-institutionalized populations. The rates of missing values for varied by survey ranging between [0%-81%] and were generally highest in the MCBS institutionalized population. Table shows percent of missing values in each survey for year missing values for year 2009. Some variables with the highest rate of missingness in MCBS were imputed by combining the community and institutionalized MCBS populations to increase stability.