Nursing home quality measures impact policy decisions such as reimbursement or consumer choice. Quality indicators in the United States are collected through the federally mandated Minimum Data Set (MDS). Bias in MDS data collection or coding can thus have a negative impact on policy applications. To understand whether bias was present in coding, the authors studied 5174 pairs of MDS assessments that were independently collected by nursing home staff and study nurses from 206 nursing homes. The authors developed multivariate multilevel models to identify nursing home and resident characteristics that were significantly associated with the data quality of multiple MDS measures of nursing home quality. The outcomes were coding differences between nursing home staff and study nurses. Resident characteristics explained little of the variation in coding differences among facilities, while facilities characteristics explained 4% to 20% of the variation and state location further explained 13% to 34% of the variation. A generalized effect of nursing home state location tended to be consistent across measures. States that overidentified problems also tended to have worse quality indicators and vice versa. Comparisons of MDS-based quality indicators reflect differences in assessment practices at least as much as true quality differences. Efforts to standardize assessment practices across states are needed.
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