Forecasting Asthma Hospital Admissions from Remotely Sensed Environmental Data

Rute Almeida, Ana Cláudia Teodoro, Hernâni Gonçalves, Alberto Freitas, Ana Sa-Sousa, Cristina Jácome, João Fonseca

2017

Abstract

Asthma has a major social impact and is prone to exacerbations. It is known that environmental factors, such as meteorological conditions and air pollutants, have a role over their occurrence. In a previous work, positive associations were found between hospital admissions due to asthma exacerbation at highly urbanized regions of Portugal and higher atmospheric NO2 levels, lower vegetation density and higher air temperatures, estimated using remote sensing. In this study we propose the use of georeferenced environmental factors to forecast the risk of hospital admissions due to asthma exacerbation. We applied linear discriminant analysis using monthly averages based in 2003–2007 environmental data to forecast positive monthly admission rates in municipalities of Lisboa district (Portugal) during 2008. Space-time estimates of nitrogen dioxide (NO2), vegetation density from MODIS Normalized Difference Vegetation Index (NDV I) and near-surface air temperature (Ta) were considered as independent variables. We identified over 65% of the combinations months/municipalities having hospital admissions in the testing set, with less than 10% of false positives. These results confirm that NO2, NDV I and Ta levels obtained from remotely sensed data can be used to predict hospital admissions due to asthma exacerbation, and may be helpful if applied in warning systems for patients in the future.

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Paper Citation


in Harvard Style

Almeida R., Cláudia Teodoro A., Gonçalves H., Freitas A., Sa-Sousa A., Jácome C. and Fonseca J. (2017). Forecasting Asthma Hospital Admissions from Remotely Sensed Environmental Data . In Proceedings of the 3rd International Conference on Geographical Information Systems Theory, Applications and Management - Volume 1: GISTAM, ISBN 978-989-758-252-3, pages 124-130. DOI: 10.5220/0006385201240130


in Bibtex Style

@conference{gistam17,
author={Rute Almeida and Ana Cláudia Teodoro and Hernâni Gonçalves and Alberto Freitas and Ana Sa-Sousa and Cristina Jácome and João Fonseca},
title={Forecasting Asthma Hospital Admissions from Remotely Sensed Environmental Data},
booktitle={Proceedings of the 3rd International Conference on Geographical Information Systems Theory, Applications and Management - Volume 1: GISTAM,},
year={2017},
pages={124-130},
publisher={SciTePress},
organization={INSTICC},
doi={10.5220/0006385201240130},
isbn={978-989-758-252-3},
}


in EndNote Style

TY - CONF
JO - Proceedings of the 3rd International Conference on Geographical Information Systems Theory, Applications and Management - Volume 1: GISTAM,
TI - Forecasting Asthma Hospital Admissions from Remotely Sensed Environmental Data
SN - 978-989-758-252-3
AU - Almeida R.
AU - Cláudia Teodoro A.
AU - Gonçalves H.
AU - Freitas A.
AU - Sa-Sousa A.
AU - Jácome C.
AU - Fonseca J.
PY - 2017
SP - 124
EP - 130
DO - 10.5220/0006385201240130