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Comparing exposure metrics for classifying 'dangerous heat' in heat wave and health warning systems.

ABSTRACT: Heat waves have been linked to excess mortality and morbidity, and are projected to increase in frequency and intensity with a warming climate. This study compares exposure metrics to trigger heat wave and health warning systems (HHWS), and introduces a novel multi-level hybrid clustering method to identify potential dangerously hot days. Two-level and three-level hybrid clustering analysis as well as common indices used to trigger HHWS, including spatial synoptic classification (SSC), and the 90th, 95th, and 99th percentiles of minimum and relative minimum temperature (using a 10 day reference period), were calculated using a summertime weather dataset in Detroit from 1976 to 2006. The days classified as 'hot' with hybrid clustering analysis, SSC, minimum and relative minimum temperature methods differed by method type. SSC tended to include the days with, on average, 2.5 °C lower daily minimum temperature and 5.3 °C lower dew point than days identified by other methods. These metrics were evaluated by comparing their performance in predicting excess daily mortality. The 99th percentile of minimum temperature was generally the most predictive, followed by the three-level hybrid clustering method, the 95th percentile of minimum temperature, SSC and others. Our proposed clustering framework has more flexibility and requires less substantial meteorological prior information than the synoptic classification methods. Comparison of these metrics in predicting excess daily mortality suggests that metrics thought to better characterize physiological heat stress by considering several weather conditions simultaneously may not be the same metrics that are better at predicting heat-related mortality, which has significant implications in HHWSs.

SUBMITTER: Zhang K

PROVIDER: S-EPMC3401591 | biostudies-literature | 2012 Oct

REPOSITORIES: biostudies-literature

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Comparing exposure metrics for classifying 'dangerous heat' in heat wave and health warning systems.

Zhang Kai K Rood Richard B RB Michailidis George G Oswald Evan M EM Schwartz Joel D JD Zanobetti Antonella A Ebi Kristie L KL O'Neill Marie S MS

Environment international 20120605

Heat waves have been linked to excess mortality and morbidity, and are projected to increase in frequency and intensity with a warming climate. This study compares exposure metrics to trigger heat wave and health warning systems (HHWS), and introduces a novel multi-level hybrid clustering method to identify potential dangerously hot days. Two-level and three-level hybrid clustering analysis as well as common indices used to trigger HHWS, including spatial synoptic classification (SSC), and the 9 ...[more]

PMID: 22673187

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Project description:BackgroundEarly warning systems (EWS) are management tools to predict the occurrence of epidemics of infectious diseases. While climate-based EWS have been developed for malaria, no standard protocol to evaluate and compare EWS has been proposed. Additionally, there are several neglected tropical diseases whose transmission is sensitive to environmental conditions, for which no EWS have been proposed, though they represent a large burden for the affected populations.Methodology/principal findingsIn the present paper, an overview of the available linear and non-linear tools to predict seasonal time series of diseases is presented. Also, a general methodology to compare and evaluate models for prediction is presented and illustrated using American cutaneous leishmaniasis, a neglected tropical disease, as an example. The comparison of the different models using the predictive R(2) for forecasts of "out-of-fit" data (data that has not been used to fit the models) shows that for the several linear and non-linear models tested, the best results were obtained for seasonal autoregressive (SAR) models that incorporate climatic covariates. An additional bootstrapping experiment shows that the relationship of the disease time series with the climatic covariates is strong and consistent for the SAR modeling approach. While the autoregressive part of the model is not significant, the exogenous forcing due to climate is always statistically significant. Prediction accuracy can vary from 50% to over 80% for disease burden at time scales of one year or shorter.Conclusions/significancethis study illustrates a protocol for the development of EWS that includes three main steps: (i) the fitting of different models using several methodologies, (ii) the comparison of models based on the predictability of "out-of-fit" data, and (iii) the assessment of the robustness of the relationship between the disease and the variables in the model selected as best with an objective criterion.

Project description:BackgroundThe introduction of the determinants of health has caused a shift towards understanding health from a holistic perspective as well as increased recognition of public health's contributions to the health of the population. Several frameworks exist to conceptualise healthcare systems, highlighting the stark contrast of frameworks unique to public health systems. The objectives of this study were to define public health systems and assess differences between healthcare systems and public health systems within established health systems frameworks.MethodsA critical interpretive synthesis was conducted. Databases searched included EBSCOhost, OVID, Scholars Portal, Web of Science, Cochrane Library and Health Systems Evidence. Data extraction, coding and analysis followed a best-fit framework analysis method. Initial codes were based on a current leading health systems and policy classification scheme - health systems arrangements (governance, financial and delivery arrangements).ResultsA total of 5933 unique documents were identified and 67 were included in the analysis. Definitions of public health and public health systems varied significantly as did their roles and functions across jurisdictions. Public health systems arrangements generally followed those of health systems, with the addition of partnerships (community and inter-sectoral) and communication playing a larger role in public health. A public health systems framework and conceptualisation of how public health currently fits within health systems are presented.ConclusionsPublic health systems are unique and vital entities within health systems. In addition to examining how public health and public health systems have been defined within the literature, this review suggests that establishing the scope of public health is crucial to understanding its role within the larger health system and adds to the discourse around the relationship between public health, healthcare and population health. More broadly, this study addresses an important gap in understanding public health systems and provides conceptual and practical contributions as well as areas for future research.

Project description:Exposure to heatwaves may result in adverse human health impacts. Heat alerts in South Africa are currently based on defined temperature-fixed threshold values for large towns and cities. However, heat-health warning systems (HHWS) should incorporate metrics that have been shown to be effective predictors of negative heat-related health outcomes. This study contributes to the development of a HHWS for South Africa that can potentially minimize heat-related mortality. Distributed lag nonlinear models (DLNM) were used to assess the association between maximum and minimum temperature and diurnal temperature range (DTR) and population-adjusted mortality during summer months, and the effects were presented as incidence rate ratios (IRR). District-level thresholds for the best predictor from these three metrics were estimated with threshold regression. The mortality dataset contained records of daily registered deaths (n = 8,476,532) from 1997 to 2013 and data for the temperature indices were for the same period. Maximum temperature appeared to be the most statistically significant predictor of all-cause mortality with strong associations observed in 40 out of 52 districts. Maximum temperature was associated with increased risk of mortality in all but three of the districts. Our results also found that heat-related mortality was influenced by regional climate because the spatial distribution of the thresholds varied according to the climate zones across the country. On average, districts located in the hot, arid interior provinces of the Northern Cape and North West experienced some of the highest thresholds compared to districts located in temperate interior or coastal provinces. As the effects of climate change become more significant, population exposure to heat is increasing. Therefore, evidence-based HHWS are required to reduce heat-related mortality and morbidity. The exceedance of the maximum temperature thresholds provided in this study could be used to issue heat alerts as part of effective heat health action plans.

Dataset Information

Comparing exposure metrics for classifying 'dangerous heat' in heat wave and health warning systems.

Publications

Comparing exposure metrics for classifying 'dangerous heat' in heat wave and health warning systems.

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