Forecasting asthma-related hospital admissions in London using negative binomial models

Abstract

Health forecasting can improve health service provision and individual patient outcomes. Environmental factors are known to impact chronic respiratory conditions such as asthma, but little is known about the extent to which these factors can be used for forecasting. Using weather, air quality and hospital asthma admissions, in London (2005London ( -2006, two related negative binomial models were developed and compared with a naive seasonal model. In the first approach, predictive forecasting models were fitted with 7-day averages of each potential predictor, and then a subsequent multivariable model is constructed. In the second strategy, an exhaustive search of the best fitting models between possible combinations of lags (0-14 days) of all the environmental effects on asthma admission was conducted. Three models were considered: a base model (seasonal effects), contrasted with a 7-day average model and a selected lags model (weather and air quality effects). Season is the best predictor of asthma admissions. The 7-day average and seasonal models were trivial to implement. The selected lags model was computationally intensive, but of no real value over much more easily implemented models. Seasonal factors can predict daily hospital asthma admissions in London, and there is a little evidence that additional weather and air quality information would add to forecast accuracy.

Seasonal asthma in Melbourne, Australia, and some observations on the occurrence of thunderstorm asthma and its predictability

“…Although multivariate statistical regression was successful in simulating seasonal variation in asthma admissions, it explained only a small fraction of the variation around the seasonal average. These findings echo those of Soyiri et al [ 43 ], who used similar regression techniques to examine two years of asthma admissions data in London. The statistical classification of HAADs vs. non-HAADs showed similarly poor skill, which is partly a result of the rare nature of HAADs.…”

Can syndromic surveillance help forecast winter hospital bed pressures in England?

“…Chan et al found that the greatest lag and therefore potential for early warning of changes in hospital admissions was from children aged 5-17 years with ILI [24], whilst, Schanzer et al found that the relationship between ED ILI attendances and hospital admissions was not consistent enough for forecasting [25]. However, it is often more useful to planners to predict peaks when demand is at its greatest, rather than these temporal associations [26]. Therefore, our study focussed on the timing of peak demand for hospital admissions and the intensity Can syndromic surveillance help forecast winter hospital bed pressures in England?…”

“…For example, Elliot et al found that although the timing of peaks in GP ILI consultations varied widely by season, between 1990 and 2005 all but one of the 15 seasons had a peak in elderly respiratory hospital admissions occurring between weeks 52 and 02. Therefore, studies into associations between syndromic data and hospital admissions usually incorporate seasonality into their models [3], and seasonality is often found to have a stronger association than syndromic data [2,13,23,26]. By contrast, Fuhrman et al found that the variation in timing of peaks in hospital admissions in France was strongly associated with peak timing for GP ILI consultations [21].…”

Applicability of internet search index for asthma admission forecast using machine learning

“…This study found that given more corresponding input data, an artificial neural network could provide a more reliable forecast for childhood asthma admissions. In order to predict daily hospital asthma admissions in London, Soyiri et al demonstrated a multi‐stage quantile regression approach and also developed 2 related negative binomial models compared with a naïve seasonal model to predict excess demand for health care services, using retrospective data from the Hospital Episode Statistics, weather, and air quality. A negative binomial regression model was also used to measure the association between daily asthma hospital admissions and ambient air pollution concentrations .…”

“…In all existing asthma admissions forecast researches, historical asthma admissions, weather, and air pollutants are the common predictors for asthma admissions forecast. Some researchers have proved that the number of asthma admissions today can be predicted based on historical admissions . In addition, other studies have also proved that asthma admissions are related to weather and air pollutants .…”