Modeling repeated count measures with excess zeros in an epidemiological study

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.

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.

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).

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