17: Estimation of Disease Incidence from Cross-Sectional Data: The CESE Method

Music City Center 

Given the wide availability of multi-wave cross-sectional studies, methods that potentially strengthen causal inference are attractive. We propose the Cross-sectional Enrichment by Sample Extrapolation (CESE) method, which matches observations from serial cross-sectional data to find proxies for longitudinal trajectories. Outcomes events from later waves can be paired with exposure data from earlier waves to estimate exposure-outcome associations. A major strength is that CESE does not require longitudinal data. In this abstract, we will describe the statistical assumptions required and demonstrate an application of CESE to estimate incident substance use disorder (SUD). Using a cross-sectional survey (n=117,590), we match individuals across two calendar years; n=10,444 participated in both years. Using a combination of Mahalanobis distance and greedy matching, CESE matched 24.48% of returning participants to themselves. Among chronic pain patients, an estimated 5.5% had incident SUD, similar to an estimate of iatrogenic opioid abuse among pain patients(4.7%). The CESE method is a potential tool for using multi-wave cross sectional data to estimate population-level incidence.

Matching

Cross-sectional data collection

Population survey

Epidemiology 

Section on Statistics in Epidemiology