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- Hii, Yien Ling, 1962-
(författare)
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Climate and dengue fever : early warning based on temperature and rainfall
- 2013
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Doktorsavhandling (övrigt vetenskapligt/konstnärligt)abstract
- Background: Dengue is a viral infectious disease that is transmitted by mosquitoes. The disease causes a significant health burden in tropical countries, and has been a public health burden in Singapore for several decades. Severe complications such as hemorrhage can develop and lead to fatal outcomes. Before tetravalent vaccine and drugs are available, vector control is the key component to control dengue transmission. Vector control activities need to be guided by surveillance of outbreak and implement timely action to suppress dengue transmission and limit the risk of further spread. This study aims to explore the feasibility of developing a dengue early warning system using temperature and rainfall as main predictors. The objectives were to 1) analyze the relationship between dengue cases and weather predictors, 2) identify the optimal lead time required for a dengue early warning, 3) develop forecasting models, and 4) translate forecasts to dengue risk indices.Methods: Poisson multivariate regression models were established to analyze relative risks of dengue corresponding to each unit change of weekly mean temperature and cumulative rainfall at lag of 1-20 weeks. Duration of vector control for localized outbreaks was analyzed to identify the time required by local authority to respond to an early warning. Then, dengue forecasting models were developed using Poisson multivariate regression. Autoregression, trend, and seasonality were considered in the models to account for risk factors other than temperature and rainfall. Model selection and validation were performed using various statistical methods. Forecast precision was analyzed using cross-validation, Receiver Operating Characteristics curve, and root mean square errors. Finally, forecasts were translated into stratified dengue risk indices in time series formats.Results: Findings showed weekly mean temperature and cumulative rainfall preceded higher relative risk of dengue by 9-16 weeks and that a forecast with at least 3 months would provide sufficient time for mitigation in Singapore. Results showed possibility of predicting dengue cases 1-16 weeks using temperature and rainfall; whereas, consideration of autoregression and trend further enhance forecast precision. Sensitivity analysis showed the forecasting models could detect outbreak and non-outbreak at above 90% with less than 20% false positive. Forecasts were translated into stratified dengue risk indices using color codes and indices ranging from 1-10 in calendar or time sequence formats. Simplified risk indices interpreted forecast according to annual alert and outbreak thresholds; thus, provided uniform interpretation.Significance: A prediction model was developed that forecasted a prognosis of dengue up to 16 weeks in advance with sufficient accuracy. Such a prognosis can be used as an early warning to enhance evidence-based decision making and effective use of public health resources as well as improved effectiveness of dengue surveillance and control. Simple and clear dengue risk indices improve communications to stakeholders.
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| 2. |
- Ingole, Vijendra, 1984-
(författare)
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Too Hot! : an Epidemiological Investigation of Weather-Related Mortality in Rural India
- 2016
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Doktorsavhandling (övrigt vetenskapligt/konstnärligt)abstract
- BackgroundMost environmental epidemiological studies are conducted in high income settings. The association between ambient temperature and mortality has been studied worldwide, especially in developed countries. However, more research on the topic is necessary, particularly in India, given the limited evidence on the relationship between temperature and health in this country. The average global temperature is increasing, and it is estimated that it will go up further. The factors affecting vulnerability to heat-related mortality are not well studied. Therefore, identifying high-risk population subgroups is of particular importance given the rising temperature in India.ObjectivesThis research aimed to investigate the association of daily mean temperature and rainfall with daily deaths (Paper I), examine the relationship of hot and cold days with total and cause-specific mortality (Paper II), assess the effects of heat and cold on daily mortality among different socio-demographic groups (Paper III) and estimate the effect of maximum temperature on years of life lost (Paper IV).MethodsThe Vadu Health and Demographic Surveillance System (HDSS) monitors daily deaths, births, in-out migration and other demographic trends in 22 villages from two administrative blocks in the rural Pune district of Maharashtra state, in western India. Daily deaths from Vadu HDSS and daily weather data (temperature and rainfall) from the Indian Meteorological Department were collected from 2003 through 2013. Verbal autopsy data were used to define causes of death and classified into four groups: non-infectious diseases, infectious diseases, external causes and unspecified causes of death. Socio-demographic groups were based on education, occupation, house type and land ownership. In all papers, time series regression models were applied as the basic approach; additionally, in Paper III, a case-crossover design and, in Paper IV, a distributed lag non-linear model (DLNM) were used.ResultsThere was a significant association between daily temperature and mortality. Younger age groups (0-4 years) reported higher risk of mortality due to high and low temperature and heavy rainfall. In the working age group (20-59 years), mortality was significantly associated only with high temperature. Mortality due to non-infectious diseases was higher on hot days (>39°C), while mortality from infectious diseases and from external causes were not associated with hot or cold days. A higher heat-related total mortality was observed among men than in women. Mortality among residents with low education and those whose occupation was farming was associated with high temperature. We found a significant impact of high temperature on years of life lost, which confirms our results from the previous research (Papers I-III).ConclusionThe study findings broadened our knowledge of the health impacts of environmental exposure by providing evidence on the risks related to ambient temperature in a rural population in India. More specifically, the study identified vulnerable population groups (working age groups, those of low education and farmers) in relation to high temperature. The adverse effect of heat on population is preventable if local human and technical capacities for risk communication and promoting adaptive behavior are built. Furthermore, it is necessary to increase residents’ awareness and prevention measures to tackle this public health challenge in rural populations.
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