Statistical modeling of complex health outcomes and air pollution data: Application of air quality health indexing for asthma risk assessment

Abstract


 Background: Statistical models for complex health outcome data analyses with; zero inflation, autocorrelation, confounding, seasonality and delayed exposures, play an important role in accurately assessing air pollution risk, especially in public health warning using national air quality health indices (NAQHI). NAQHI assessment generalizes model estimates across all geographies and seasons and neglects area and season specific variations. The aim is to develop complex statistical models, specific to the complex data structures and to demonstrate effectiveness of the model estimates in public health massage delivery.
 Methods: A time series model was fitted to asthma hospital admissions and ambient air pollution data 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. I fitted zero inflated, auto regressive, Poisson and Negative Binomial models with lagged effects for sparse asthma admissions and air pollution data and compared the model risk estimates with that of the NAQHI.
 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 and there were seasonal differences. The findings demonstrated severe under-assessment of asthma relative risk by NAQHI, when prominent pollutants are neglected and also when auto correlation and zero inflation are ignored. 
 Conclusion: This study demonstrated the importance of complex statistical model use and the consequences of lack of such modelling that accounted for data structures in public health risk assessments.