Nesting or resolution? Facility proxies for variance estimation versus confounding adjustment in claims data without facility identifiers
Abstract
In claims databases, facility identifiers are often removed during de-identification, leaving only coarse attributes such as bed size. Yet facility information is needed for variance estimation, confounding adjustment and assessment of treatment availability. We examined whether accreditation-gated billing items can build strata standing for hospitals, and how to construct a facility proxy for each use. In 1,730,736 adults undergoing surgery (Medical Data Vision database, Japan, 2019-2023), we built marker-pattern strata from 66 accreditation-gated receipt codes, assumed to be observed with imperfect sensitivity and near-perfect specificity, and evaluated them, alongside supplied-attribute cells, against a true facility identifier in an insurer-based database (DeSC Healthcare) and in simulation. The variance explained by the strata fell as variables became more patient- than hospital-determined ($R^2$ 0.921 for an accreditation item, 0.036 for sex). For variance estimation, the supplied-attribute cells, which can merge but never split hospitals, kept the type I error rate at 4.8% with a wild cluster bootstrap, against 5.4% for the true identifier and 39% when clustering was ignored; every finer marker-based grouping did worse. For adjustment, with accreditation for a withheld code as the confounder, the marker pattern removed 51% of the confounding, against 8% for the cells. For treatment availability, 49% of patients were in groups mixing accredited and non-accredited hospitals under the marker pattern, against 98% under the cells. No single facility proxy serves every purpose: nesting governs variance estimation, and resolution governs adjustment and assessment of treatment availability. Without a facility identifier, the supplied-attribute cells can carry the standard errors, and marker-pattern strata are worth considering for the other two uses.