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GLOBAL ADVANCED RESEARCH JOURNAL OF MEDICINE AND MEDICAL SCIENCES (GARJMMS) ISSN: 2315-5159

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March 2015 Vol. 4 Issue 3
 

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Global Advanced Research Journal of Medicine and Medical Sciences (GARJMMS) ISSN: 2315-5159

March 2015 Vol. 4(3), pp. 110-116

Copyright © 2015 Global Advanced Research Journals

 

 

Full Length Research Paper

 

Temperature, hospital admissions in Campo Grande, MS, Brazil: A time-series analysis

 

Amaury de Souza1, Flavio Aristone1, Ismail Sabbah2 and Débora A. da Silva Santos3

 

1Institute of Physics – Federal University of South Mato Grosso, PO Box 549, 79070-900 Campo Grande, Mato Grosso do Sul, Brazil.

2Department of Natural Sciences, College of Health Sciences, the Public Authority for Applied Education and Training, Kuwait

3Nursing Department, Federal University of Mato Grosso, Campus Rondonopolis

 

Accepted 16 February, 2015

 

*Corresponding Author E-mail: amaury.de@uol.com.br

 

Abstract

 

The association between temperature and morbity has been examined mainly in Campo Grande , MS, Brazil. However, less evidence is available in developing countries, especially in Brazil. In this study, we examined the relationship between temperature and morbity Campo Grande city, Brazil, during 2008–2014. A time series model was used to examine the effects of temperature on cause-specific morbity (non-external and respiratory) and age-specific non-external mortality (0–4, 5–60, and > =65 years), while controlling for relative humidity, air pollution, day of the week, season and long- term trend. We used a distributed lag non-linear model to examine the delayed effects of temperature on morbity up to 21 days. We found non-linear effects of temperature on all morbity types and age groups. Both hot and cold temperatures resulted in immediate increase in all morbity types and age groups. Generally, the hot effects on all morbity types and age groups were short-term, while the cold effects lasted longer. The relative risk of non- external morbity associated with cold temperature with 25th percentile of temperature (20.0°C) was 1.30 (95% confidence interval (CI): 1.04, 1.34) for lags 0–21. The relative risk of non- external mortality associated with high temperature with 75th percentile of temperature (35.4°C) was 1.14 (95% CI: 1.04, 1.34) for lags 0–21. This study indicates that exposure to both hot and cold temperatures were related to increased mortality. Both cold and hot effects occurred immediately but cold effects lasted longer than hot effects. This study provides useful data for policy makers to better prepare local responses to manage the impact of hot and cold temperatures on population health.

 

Keywords: respiratory diseases, temperatures, climate change, global change, environmental health surveillance; predictive models, Hospital admissions, Morbidity, Vulnerability