Continuous versus Intermittent Data Collection of Health Care Utilization

Abstract

In economic evaluations, participants have to report their health care utilization continuously during follow-up. To unburden participants, researchers often collect data intermittently (i.e., in at least 3 months a year). However, comparability of intermittent v. continuous data collection is unknown. Therefore, this study aimed to compare costs estimated with intermittent data collection of health care utilization with those based on continuous data collection. We used continuous health care utilization data from a trial with 12 months of follow-up and simulated several intermittent data collection patterns. Then 3 imputation techniques--individual mean (IM), last observation carried forward (LOCF) and next observation carried backward (NOCB)--were used to estimate total annual costs. Estimated annual costs were compared with observed annual costs from continuous data collection both in the original sample and in 1000 bootstrap samples. Analyses showed that intermittent data collection using cost diaries may offer good estimates of the actual total annual health expenditures. However, estimations of groups of costs differ between data collection patterns and imputation methods. The best estimations of annual total costs and groups of costs were obtained by random cohort data collection, using 3 random cohorts, ensuring that at least a third of the participants were measuring costs each month, combined with IM imputation. Intermittent data collection of health expenditures carries a small risk of missing infrequent expensive events. Continuous cost data collection remains the first choice. However, if intermittent measurement is chosen, we recommend calculating annual costs from intermittent data collection in random cohorts, combined with IM imputation.