An Exploration of Temperature Metrics for Further Developing the Heat-Health Weather Warning System in Hong Kong

Background. The current weather warning system aims to reduce mortality from heat and cold stress but still has room to be improved in terms of incorporating other temperature metrics. The aim of this study is to determine how extreme temperature affects mortality in Hong Kong. Methods. An ecological study was used; daily weather data were subdivided into seven temperature metrics. Daily detrended mortality data were stratified by disease groups and analysed using seven different metrics for temperature. The temperature metrics were then compared. Results. A diurnal temperature range (DTR) of ≥8°C leading to an increase in median mortality of up to 16% and a mean temperature change between neighbouring days of ≥4°C leading to an increase in median mortality of up to 6% were the critical thresholds for excess mortality in Hong Kong. Conclusions. This study reveals that mean net effective temperature, DTR, and temperature change between neighbouring days are effective to predict excess mortality in Hong Kong.

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Real-time estimation of the influenza-associated excess mortality in Hong Kong

Epidemiology and Infection ◽

10.1017/s0950268819001067 ◽

2019 ◽

Vol 147 ◽

Cited By ~ 1

Author(s):

Jessica Y. Wong ◽

Edward Goldstein ◽

Vicky J. Fang ◽

Benjamin J. Cowling ◽

Peng Wu

Keyword(s):

Hong Kong ◽

Real Time ◽

Excess Mortality ◽

Time Estimation ◽

Surveillance Data ◽

Control Measures ◽

Influenza Surveillance ◽

Mortality Data ◽

Real Time Estimation ◽

The Impact

Abstract Statistical models are commonly employed in the estimation of influenza-associated excess mortality that, due to various reasons, is often underestimated by laboratory-confirmed influenza deaths reported by healthcare facilities. However, methodology for timely and reliable estimation of that impact remains limited because of the delay in mortality data reporting. We explored real-time estimation of influenza-associated excess mortality by types/subtypes in each year between 2012 and 2018 in Hong Kong using linear regression models fitted to historical mortality and influenza surveillance data. We could predict that during the winter of 2017/2018, there were ~634 (95% confidence interval (CI): (190, 1033)) influenza-associated excess all-cause deaths in Hong Kong in population ⩾18 years, compared to 259 reported laboratory-confirmed deaths. We estimated that influenza was associated with substantial excess deaths in older adults, suggesting the implementation of control measures, such as administration of antivirals and vaccination, in that age group. The approach that we developed appears to provide robust real-time estimates of the impact of influenza circulation and complement surveillance data on laboratory-confirmed deaths. These results improve our understanding of the impact of influenza epidemics and provide a practical approach for a timely estimation of the mortality burden of influenza circulation during an ongoing epidemic.

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Prolonged diagnostic delay in acromegaly is associated with long-term morbidity and excess mortality: data from a nationwide study

Endocrine Abstracts ◽

10.1530/endoabs.63.gp48 ◽

2019 ◽

Author(s):

Daniela Esposito ◽

Oskar Ragnarsson ◽

Gudmundur Johannsson ◽

Daniel S Olsson

Keyword(s):

Excess Mortality ◽

Diagnostic Delay ◽

Mortality Data ◽

Nationwide Study

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Evaluation of Weather Data asInputsto a Disease-Warning System for Control of Sooty Blotch and Flyspeck

10.31274/farmprogressreports-180814-2542 ◽

2008 ◽

Author(s):

Nenad Tatalovic ◽

Mark L. Gleason ◽

Jean C. Batzer

Keyword(s):

Warning System ◽

Weather Data ◽

Sooty Blotch ◽

Sooty Blotch And Flyspeck

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Distribution and incidence of viruses in Irish seed potato crops

Irish Journal of Agricultural and Food Research ◽

10.1515/ijafr-2015-0011 ◽

2015 ◽

Vol 54 (2) ◽

pp. 98-106 ◽

Cited By ~ 4

Author(s):

F. Hutton ◽

J.H. Spink ◽

D. Griffin ◽

S. Kildea ◽

D. Bonner ◽

...

Keyword(s):

Potato Virus ◽

Significant Proportion ◽

Potato Production ◽

Weather Data ◽

Management Approach ◽

Potato Seed ◽

Virus Diseases ◽

Aphid Control ◽

Daily Weather Data ◽

Potato Crops

Abstract Virus diseases are of key importance in potato production and in particular for the production of disease-free potato seed. However, there is little known about the frequency and distribution of potato virus diseases in Ireland. Despite a large number of samples being tested each year, the data has never been collated either within or across years. Information from all known potato virus testing carried out in the years 2006–2012 by the Department of Agriculture Food and Marine was collated to give an indication of the distribution and incidence of potato virus in Ireland. It was found that there was significant variation between regions, varieties, years and seed classes. A definition of daily weather data suitable for aphid flight was developed, which accounted for a significant proportion of the variation in virus incidence between years. This use of weather data to predict virus risk could be developed to form the basis of an integrated pest management approach for aphid control in Irish potato crops.

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Local mortality estimates during the COVID-19 pandemic in Italy

Journal of Population Economics ◽

10.1007/s00148-021-00857-y ◽

2021 ◽

Author(s):

Augusto Cerqua ◽

Roberta Di Stefano ◽

Marco Letta ◽

Sara Miccoli

Keyword(s):

Machine Learning ◽

Excess Mortality ◽

Control Method ◽

Local Level ◽

Supervised Machine Learning ◽

Machine Learning Techniques ◽

Mortality Data ◽

Official Method ◽

Learning Techniques ◽

Mortality Estimates

AbstractEstimates of the real death toll of the COVID-19 pandemic have proven to be problematic in many countries, Italy being no exception. Mortality estimates at the local level are even more uncertain as they require stringent conditions, such as granularity and accuracy of the data at hand, which are rarely met. The “official” approach adopted by public institutions to estimate the “excess mortality” during the pandemic draws on a comparison between observed all-cause mortality data for 2020 and averages of mortality figures in the past years for the same period. In this paper, we apply the recently developed machine learning control method to build a more realistic counterfactual scenario of mortality in the absence of COVID-19. We demonstrate that supervised machine learning techniques outperform the official method by substantially improving the prediction accuracy of the local mortality in “ordinary” years, especially in small- and medium-sized municipalities. We then apply the best-performing algorithms to derive estimates of local excess mortality for the period between February and September 2020. Such estimates allow us to provide insights about the demographic evolution of the first wave of the pandemic throughout the country. To help improve diagnostic and monitoring efforts, our dataset is freely available to the research community.

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A method for simulating risk profiles of wheat yield in data-sparse conditions

The Journal of Agricultural Science ◽

10.1017/s0021859621000253 ◽

2021 ◽

pp. 1-12

Author(s):

G. Bracho-Mujica ◽

P.T. Hayman ◽

V.O. Sadras ◽

B. Ostendorf

Keyword(s):

Reference Site ◽

Climate Impacts ◽

Weather Data ◽

Climate Data ◽

High Quality ◽

Risk Profiles ◽

Record Length ◽

Daily Weather Data ◽

Study Sites

Abstract Process-based crop models are a robust approach to assess climate impacts on crop productivity and long-term viability of cropping systems. However, these models require high-quality climate data that cannot always be met. To overcome this issue, the current research tested a simple method for scaling daily data and extrapolating long-term risk profiles of modelled crop yields. An extreme situation was tested, in which high-quality weather data was only available at one single location (reference site: Snowtown, South Australia, 33.78°S, 138.21°E), and limited weather data was available for 49 study sites within the Australian grain belt (spanning from 26.67 to 38.02°S of latitude, and 115.44 to 151.85°E of longitude). Daily weather data were perturbed with a delta factor calculated as the difference between averaged climate data from the reference site and the study sites. Risk profiles were built using a step-wise combination of adjustments from the most simple (adjusted series of precipitation only) to the most detailed (adjusted series of precipitation, temperatures and solar radiation), and a variable record length (from 10 to 100 years). The simplest adjustment and shortest record length produced bias of modelled yield grain risk profiles between −10 and 10% in 41% of the sites, which increased to 86% of the study sites with the most detailed adjustment and longest record (100 years). Results indicate that the quality of the extrapolation of risk profiles was more sensitive to the number of adjustments applied rather than the record length per se.

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Impact of Extreme Weather on Healthcare Utilization by People with HIV in Metropolitan Miami

International Journal of Environmental Research and Public Health ◽

10.3390/ijerph18052442 ◽

2021 ◽

Vol 18 (5) ◽

pp. 2442

Author(s):

Daniel Samano ◽

Shubhayu Saha ◽

Taylor Corbin Kot ◽

JoNell E. Potter ◽

Lunthita M. Duthely

Keyword(s):

Relative Risk ◽

The United States ◽

Extreme Weather ◽

South Florida ◽

Heat Index ◽

Extreme Weather Events ◽

Weather Data ◽

Steady Increase ◽

Daily Weather Data ◽

The Impact

Extreme weather events (EWE) are expected to increase as climate change intensifies, leaving coastal regions exposed to higher risks. South Florida has the highest HIV infection rate in the United States, and disruptions in clinic utilization due to extreme weather conditions could affect adherence to treatment and increase community transmission. The objective of this study was to identify the association between EWE and HIV-clinic attendance rates at a large academic medical system serving the Miami-Dade communities. The following methods were utilized: (1) Extreme heat index (EHI) and extreme precipitation (EP) were identified using daily observations from 1990–2019 that were collected at the Miami International Airport weather station located 3.6 miles from the studied HIV clinics. Data on hurricanes, coastal storms and flooding were collected from the National Oceanic and Atmospheric Administration Storms Database (NOAA) for Miami-Dade County. (2) An all-HIV clinic registry identified scheduled daily visits during the study period (hurricane seasons from 2017–2019). (3) Daily weather data were linked to the all-HIV clinic registry, where patients’ ‘no-show’ status was the variable of interest. (4) A time-stratified, case crossover model was used to estimate the relative risk of no-show on days with a high heat index, precipitation, and/or an extreme natural event. A total of 26,444 scheduled visits were analyzed during the 383-day study period. A steady increase in the relative risk of ‘no-show’ was observed in successive categories, with a 14% increase observed on days when the heat index was extreme compared to days with a relatively low EHI, 13% on days with EP compared to days with no EP, and 10% higher on days with a reported extreme weather event compared to days without such incident. This study represents a novel approach to improving local understanding of the impacts of EWE on the HIV-population’s utilization of healthcare, particularly when the frequency and intensity of EWE is expected to increase and disproportionately affect vulnerable populations. More studies are needed to understand the impact of EWE on routine outpatient settings.

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A heat-health watch and warning system with extended season and evolving thresholds

BMC Public Health ◽

10.1186/s12889-021-10982-8 ◽

2021 ◽

Vol 21 (1) ◽

Author(s):

Mahamat Abdelkerim Issa ◽

Fateh Chebana ◽

Pierre Masselot ◽

Céline Campagna ◽

Éric Lavigne ◽

...

Keyword(s):

Excess Mortality ◽

Heat Waves ◽

Meteorological Data ◽

Warm Season ◽

Warning System ◽

Early Warning Systems ◽

Climate Projections ◽

Warning Systems ◽

Extreme Heat Events ◽

Watch And Warning System

Abstract Background Many countries have developed heat-health watch and warning systems (HHWWS) or early-warning systems to mitigate the health consequences of extreme heat events. HHWWS usually focuses on the four hottest months of the year and imposes the same threshold over these months. However, according to climate projections, the warm season is expected to extend and/or shift. Some studies demonstrated that health impacts of heat waves are more severe when the human body is not acclimatized to the heat. In order to adapt those systems to potential heat waves occurring outside the hottest months of the season, this study proposes specific health-based monthly heat indicators and thresholds over an extended season from April to October in the northern hemisphere. Methods The proposed approach, an adoption and extension of the HHWWS methodology currently implemented in Quebec (Canada). The latter is developed and applied to the Greater Montreal area (current population 4.3 million) based on historical health and meteorological data over the years. This approach consists of determining excess mortality episodes and then choosing monthly indicators and thresholds that may involve excess mortality. Results We obtain thresholds for the maximum and minimum temperature couple (in °C) that range from (respectively, 23 and 12) in April, to (32 and 21) in July and back to (25 and 13) in October. The resulting HHWWS is flexible, with health-related thresholds taking into account the seasonality and the monthly variability of temperatures over an extended summer season. Conclusions This adaptive and more realistic system has the potential to prevent, by data-driven health alerts, heat-related mortality outside the typical July–August months of heat waves. The proposed methodology is general and can be applied to other regions and situations based on their characteristics.

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