Background: When fitting statistical models for complex health outcome data; zero inflation, autocorrelation, confounding, and seasonality play an important role in accurately assessing air pollution risk, especially when using such model estimates for national air quality health indices (NAQHI) formation. NAQHI generalizes model estimates across all geographies and seasons and neglects area and season specific variations. The aim here is to develop complex statistical models, specific to the data structures and to demonstrate effectiveness of these model estimates in public health message delivery using NAQHI. Methods: I fitted zero inflated, auto regressive, Poisson and Negative Binomial models with lagged effects for sparse asthma admissions and ambient air pollution data and compared the model risk estimates with that of the NAQHI. Data came from two sites, Halifax, an urban, traffic and industry polluted site and Sydney, a rural waste disposal polluted site, in the province of Nova Scotia, Canada. Data complexity structure was assessed by comparing the estimates with and without each structure. Results: NAQHI used three pollutants, Nitrogen Dioxide, Ozone and particulate matter. I found Carbon monoxide in the urban site and lead in the waste disposal site as prominent pollutants with significant seasonal differences. The findings demonstrated severe under-assessment of asthma admission relative risk by NAQHI, when auto correlation and zero inflation are ignored whereby prominent pollutant effects are omitted. Conclusion: This study demonstrated the importance of complex statistical model use and the consequences of not consideration of specific data structures in public health risk assessments.