Modeling repeated count measures with excess zeros in an epidemiological study

Abstract

Purpose: Highly skewed count data with excess zeros challenge the application of conventional statistical methods. Additional problems arise from repeated zero-inflated measures. Longitudinal zero-inflated Poisson (ZIP-mixed) models are mixtures of logistic and Poisson models that accommodate excess zeros and repeated counts. We compared a ZIP-mixed model with traditional Poisson and negative binomial models using data on problems with female condom use reported by women at high risk of sexually transmitted diseases.

Methods: The follow-up experience of this cohort represents a mixture of "perfect use" (no opportunity to report problems), represented by the structural zeros, and use experience that bears the risk of condom use problems, represented by a Poisson distribution.

Results: The ZIP-mixed model provided better fit and richer results than other models. The odds of being in the zero problem category increased with age (odds ratio [OR] = 1.1 per additional year, 95% confidence interval [CI]: 1.0-1.3) and with follow-up (OR = 3.0 per additional month, 95% CI: 1.4-6.0).The nonzero problem rate was lower among women who believed in the benefits of condom use (rate ratio [RR] = 0.9, 95% CI: 0.7-1.0) and had no sexually transmitted diseases at baseline (RR = 0.7, 95% CI: 0.6-0.9), and it decreased during follow-up (RR = 0.8 per additional month, 95% CI: 0.7-0.9).

Conclusions: Using ZIP-mixed model provided further insights into the determinants of condom failure.

Keywords: Generalized linear model; Longitudinal zero-inflated Poisson; Zero-inflated Poisson mixed.