title: The use of quantile regression to forecast higher than expected respiratory deaths in a daily time series: a study of New York City data 1987-2000.
abstract: Forecasting higher than expected numbers of health events provides potentially valuable insights in its own right, and may contribute to health services management and syndromic surveillance. This study investigates the use of quantile regression to predict higher than expected respiratory deaths. Data taken from 70,830 deaths occurring in New York were used. Temporal, weather and air quality measures were fitted using quantile regression at the 90th-percentile with half the data (in-sample). Four QR models were fitted: an unconditional model predicting the 90th-percentile of deaths (Model 1), a seasonal/temporal (Model 2), a seasonal, temporal plus lags of weather and air quality (Model 3), and a seasonal, temporal model with 7-day moving averages of weather and air quality. Models were cross-validated with the out of sample data. Performance was measured as proportionate reduction in weighted sum of absolute deviations by a conditional, over unconditional models; i.e., the coefficient of determination (R1). The coefficient of determination showed an improvement over the unconditional model between 0.16 and 0.19. The greatest improvement in predictive and forecasting accuracy of daily mortality was associated with the inclusion of seasonal and temporal predictors (Model 2). No gains were made in the predictive models with the addition of weather and air quality predictors (Models 3 and 4). However, forecasting models that included weather and air quality predictors performed slightly better than the seasonal and temporal model alone (i.e., Model 3 > Model 4 > Model 2) This study provided a new approach to predict higher than expected numbers of respiratory related-deaths. The approach, while promising, has limitations and should be treated at this stage as a proof of concept.
authors.affiliation: South East Asia Community Observatory (SEACO), School of Medicine and Health Sciences, Monash University, Kuala Lumpur, Malaysia ; Global Public Health, School of Medicine and Health Sciences, Monash University, Kuala Lumpur, Malaysia.
authors.forename: Ireneous N
authors.initials: IN
authors.lastname: Soyiri
authors.forename: Daniel D
authors.initials: DD
authors.lastname: Reidpath
mesh_terms: D000393:Air Pollutants / Q000633:toxicity; D000397:Air Pollution / Q000009:adverse effects; D004781:Environmental Exposure / Q000009:adverse effects; D005544:Forecasting; D006801:Humans; D008962:Models, Theoretical; D009519:New York City / Q000453:epidemiology; D052638:Particulate Matter / Q000633:toxicity; D012140:Respiratory Tract Diseases / Q000453:epidemiology* / Q000401:mortality*
chemical_list: D000393:Air Pollutants; D052638:Particulate Matter