The hidden complexity of measuring number of chronic conditions using administrative and self-report data: A short report.

OBJECTIVE: To examine agreement between administrative and self-reported data on the number of and constituent chronic conditions (CCs) used to measure multimorbidity. STUDY DESIGN AND SETTING: Cross-sectional self-reported survey data from four Canadian Community Health Survey waves were linked to administrative data for residents of Ontario, Canada. Agreement for each of 12 CCs was assessed using kappa (κ) statistics. For the overall number of CCs, perfect agreement was defined as agreement on both the number and constituent CCs. Jackknife methods were used to assess the impact of individual CCs on perfect agreement. RESULTS: The level of chance-adjusted agreement between self-report and administrative data for individual CCs varied widely, from κ = 5.5% (inflammatory bowel disease) to κ = 77.5% (diabetes), and there was no clear pattern on whether using administrative data or self-reported data led to higher prevalence estimates. Only 26.9% of participants had perfect agreement on the number and constituent CCs; 10.6% agreed on the number but not constituent CCs. The impact of each CC on perfect agreement depended on both the level of agreement and the prevalence of the individual CC. CONCLUSION: Our results show that measuring agreement on multimorbidity is more complex than for individual CCs and that even small levels of individual condition disagreement can have a large impact on the agreement on the number of CCs.