Modeling the impact of climate variability on diarrhea-associated diseases in Taiwan (1996–2007)

Abstract

Diarrhea is an important public health problem in Taiwan. Climatic changes and an increase in extreme weather events (extreme heat, drought or rainfalls) have been strongly linked to the incidence of diarrhea-associated disease.

This study investigated and quantified the relationship between climate variations and diarrhea-associated morbidity in subtropical Taiwan. Specifically, this study analyzed the local climatic variables and the number of diarrhea-associated infection cases from 1996 to 2007. This study applied a climate variation-guided Poisson regression model to predict the dynamics of diarrhea-associated morbidity. The proposed model allows for climate factors (relative humidity, maximum temperature and the numbers of extreme rainfall), autoregression, long-term trends and seasonality, and a lag-time effect. Results indicated that the maximum temperature and extreme rainfall days were strongly related to diarrhea-associated morbidity. The impact of maximum temperature on diarrhea-associated morbidity appeared primarily among children (0–14 years) and older adults (40–64 years), and had less of an effect on adults (15–39 years). Otherwise, relative humidity and extreme rainfall days significantly contributed to the diarrhea-associated morbidity in adult. This suggested that children and older adults were the most susceptible to diarrhea-associated morbidity caused by climatic variation. Because climatic variation contributed to diarrhea morbidity in Taiwan, it is necessary to develop an early warning system based on the climatic variation information for disease control management.

Introduction

Due to anthropogenic climate changes, the global average temperature is continuing to increase, and extreme hydrologic cycles (such as floods and droughts) are projected to increase as the ambient temperature increases. Extreme weather events indicate that global climate continues to change, damaging human activity, and health. Increasing evidence shows that such changes in the global-scale climate system may already pose a threat to humans through increased morbidity and mortality caused by heat, cold, drought or rainfalls, changes in air and water quality, and the ecology of infectious diseases (Stott et al., 2004, Gregory et al., 2009, Semenza & Menne, 2009). Several prevalent human diseases have been linked to climate-mediated changes for susceptible populations such as infants and the elderly, who often have relatively poor immunity (Patz et al., 2005). Therefore, an understanding of the impact of climate change on disease patterns is critical to control efforts.

Infectious (bacterial, viral and parasites) and non-infectious (food intolerances or intestinal diseases) diarrhea remains a major public health problem around the world. Diarrhea is one of the primary causes of morbidity and mortality on a global scale, leading to 1 billion disease episodes and 1.8 million deaths each year (WHO, 2008). Previous studies have showed that climate factors significantly affect seasonal diarrhea in susceptible populations (Gajadhar & Allen, 2004, Emch et al., 2008, de Magny et al., 2008). Checkley et al. (2003) presented that higher temperatures increase bacterial and parasitic diarrhea, and extend the survival of enterogastritis-causing bacteria, such as Escherichia coli, in contaminated food. Higher temperatures may also indirectly affect behavior patterns, such as increased consumption of water, lax hygiene, which may promote diarrhea transmission. Checkley et al. (2000) observed that daily hospital admissions for diarrhea exhibited a twofold increase per 5 °C increase in the mean ambient temperature. Diarrhea outbreaks are related to periods of heavy rainfall and runoff when subsequent turbidity compromises the efficiency of the drinking water treatment plants (Kramer et al., 1996). For example, Auld et al. (2004) found that heavy rainfall increases diarrhea outbreaks due to contamination of the water distribution systems. Zhang et al. (2010) revealed a strong correlation between heavy rainfall events and gastroenteritis (Salmonella infection) in Australia. These studies suggested that temperature/precipitation factors have a strong effect on triggering diarrhea.

Previous researchers have used time-series analysis to analyze the correlation between diarrhea epidemics and climatic factors (Pascual et al., 2000, Rodo et al., 2002). A time-series regression model has been applied to assess the impact of long-term climate change, especially for extreme diarrhea epidemics (Kale et al., 2004, Hashizume et al., 2007, de Magny et al., 2008). This weather variation-guided modeling approach employs a Poisson regression model to fit hospital surveillance and mortality data for diarrhea diseases to estimate the temporal pattern of diarrhea in susceptible populations. This approach provides support for decisions about the prevention and control of this disease. Fernandez et al. (2009) appropriately applied the Poisson regression model to estimate the impact of daily maximum temperature and rainfall on the number of hospitalizations for cholera diarrhea in Zambia.

Hashizume et al. (2007) indicated that high temperature and heavy rainfall are associated with an increased number of diarrhea cases. This suggests that rainfall and temperature have a sufficient force to forecast the epidemics of diarrhea, and implies that these weather factors provide valuable insights into the seasonality of diarrhea. de Magny et al. (2008) adopted a generalized linear model with Poisson distribution to identify how environmental signatures (chlorophyll a concentration) and climatic factors (rainfall anomalies) can significantly influence the dynamics of the cholera epidemics in India and Bangladesh. Therefore, surveillance data is useful to predict disease occurrence through regional climatic factors such as temperature or rainfall.

Global warming has directly affected the weather in Taiwan. Hsu and Chen (2002) indicated a 0.9–2.7 °C temperature raise from 1961–1999 relative to the past 100 years, and significant changes in precipitation. Typhoon Morakot struck Taiwan, bringing nearly 9 ft (around 2.5 m) of rain and the island's worst floods in over 50 years. Such extreme weather events increase the number of waterborne disease cases, especially diarrhea. Hence, a robust early warning system that considers how climatic factors affect diarrhea diseases in Taiwan is necessary for decision-making in policy and public health.

This study investigated the correlation between climatic variables and diarrhea cases in Taiwan from 1996 to 2007. The time-resolved meteorological data in this analysis included temperature, humidity, and rainfall during the entire 12-year study period. The purpose of this study was (1) to estimate the relationship between climate variations and occurrence of diarrhea cases and predict the impact of diarrhea-associated morbidity in Taiwan, and (2) to predict the dynamics of diarrhea epidemics by a best-fit Poisson regression model.