OBJECTIVE: To refine and revise previous air pollution, climate and health time series analysis in Christchurch, New Zealand, introducing viral identification data (positive identification count and outbreak, defined as two of more positive tests). METHOD: The effects on daily respiratory admissions for five years (1998-2002) of air pollution (PM(10) ), climate and virology (incorporating actual counts and outbreaks of influenza A and B (INF), para influenza virus type 3 (PIV) and respiratory syncytial virus (RSV) were examined using generalised additive models (GAMs), which are one of semiparametric models. Results were also compared with a model that included climate and air pollution parameters but without the inclusion of virology data. The data were analysed aggregately and then stratified by age group and season. RESULTS: Different virology data detected various association levels. The highest estimates were a 3.93% (CI: 2.69-5.17) and a 3.88% (CI: 2.65-5.12) rise in respiratory admissions for a rise of 10 µg/m(3) annual PM(10) with outbreak and actual counts of PIV respectively for 0-19 years old with a three-day lag. CONCLUSION: Refining a statistical model with the addition of virology data gives a similar estimation of the association between PM(10) levels and respiratory admissions to previous research. Use of the indicator of an outbreak of viral infection appears to be similar to actual count of viruses detected.
OBJECTIVE: To refine and revise previous air pollution, climate and health time series analysis in Christchurch, New Zealand, introducing viral identification data (positive identification count and outbreak, defined as two of more positive tests). METHOD: The effects on daily respiratory admissions for five years (1998-2002) of air pollution (PM(10) ), climate and virology (incorporating actual counts and outbreaks of influenza A and B (INF), para influenza virus type 3 (PIV) and respiratory syncytial virus (RSV) were examined using generalised additive models (GAMs), which are one of semiparametric models. Results were also compared with a model that included climate and air pollution parameters but without the inclusion of virology data. The data were analysed aggregately and then stratified by age group and season. RESULTS: Different virology data detected various association levels. The highest estimates were a 3.93% (CI: 2.69-5.17) and a 3.88% (CI: 2.65-5.12) rise in respiratory admissions for a rise of 10 µg/m(3) annual PM(10) with outbreak and actual counts of PIV respectively for 0-19 years old with a three-day lag. CONCLUSION: Refining a statistical model with the addition of virology data gives a similar estimation of the association between PM(10) levels and respiratory admissions to previous research. Use of the indicator of an outbreak of viral infection appears to be similar to actual count of viruses detected.