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

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.

The relationship between extreme weather and low crop yields

2020 ◽  
Author(s):  
Matias Heino ◽  
Weston Anderson ◽  
Michael Puma ◽  
Matti Kummu
Keyword(s):  
Soil Moisture ◽  
Crop Yield ◽  
Crop Yields ◽  
Explanatory Power ◽  
The United States ◽  
Extreme Weather ◽  
Extreme Weather Events ◽  
Weather Data ◽  
Yield Variability ◽  
Weather Events

<p>It is well known that climate extremes and variability have strong implications for crop productivity. Previous research has estimated that annual weather conditions explain a third of global crop yield variability, with explanatory power above 50% in several important crop producing regions. Further, compared to average conditions, extreme events contribute a major fraction of weather induced crop yield variations. Here we aim to analyse how extreme weather events are related to the likelihood of very low crop yields at the global scale. We investigate not only the impacts of heat and drought on crop yields but also excess soil moisture and abnormally cool temperatures, as these extremes can be detrimental to crops as well. In this study, we combine reanalysis weather data with national and sub-national crop production statistics and assess relationships using statistical copulas methods, which are especially suitable for analysing extremes. Further, because irrigation can decrease crop yield variability, we assess how the observed signals differ in irrigated and rainfed cropping systems. We also analyse whether the strength of the observed statistical relationships could be explained by socio-economic factors, such as GDP, social stability, and poverty rates. Our preliminary results indicate that extreme heat and cold as well as soil moisture abundance and excess have a noticeable effect on crop yields in many areas around the globe, including several global bread baskets such as the United States and Australia. This study will increase understanding of extreme weather-related implications on global food production, which is relevant also in the context of climate change, as the frequency of extreme weather events is likely to increase in many regions worldwide.</p>

scholarly journals Impact of the COVID-19 Vaccine on Asymptomatic Infection Among Patients Undergoing Pre-Procedural COVID-19 Molecular Screening

2021 ◽  
Author(s):  
Aaron J Tande ◽  
Benjamin D Pollock ◽  
Nilay D Shah ◽  
Gianrico Farrugia ◽  
Abinash Virk ◽  
...  
Keyword(s):  
United States ◽  
Relative Risk ◽  
Healthcare System ◽  
The United States ◽  
Asymptomatic Infection ◽  
Screening Tests ◽  
Molecular Screening ◽  
Molecular Tests ◽  
Asymptomatic Individuals ◽  
The Impact

Abstract Background Several vaccines are now clinically available under emergency use authorization in the United States and have demonstrated efficacy against symptomatic COVID-19. The impact of vaccines on asymptomatic SARS-CoV-2 infection is largely unknown. Methods We conducted a retrospective cohort study of consecutive, asymptomatic adult patients (n = 39,156) within a large United States healthcare system who underwent 48,333 pre-procedural SARS-CoV-2 molecular screening tests between December 17, 2020 and February 8, 2021. The primary exposure of interest was vaccination with at least one dose of an mRNA COVID-19 vaccine. The primary outcome was relative risk of a positive SARS-CoV-2 molecular test among those asymptomatic persons who had received at least one dose of vaccine, as compared to persons who had not received vaccine during the same time period. Relative risk was adjusted for age, sex, race/ethnicity, patient residence relative to the hospital (local vs. non-local), healthcare system regions, and repeated screenings among patients using mixed effects log-binomial regression. Results Positive molecular tests in asymptomatic individuals were reported in 42 (1.4%) of 3,006 tests performed on vaccinated patients and 1,436 (3.2%) of 45,327 tests performed on unvaccinated patients (RR=0.44 95% CI: 0.33-0.60; p<.0001). Compared to unvaccinated patients, the risk of asymptomatic SARS-CoV-2 infection was lower among those >10 days after 1 st dose (RR=0.21; 95% CI: 0.12-0.37; p<.0001) and >0 days after 2 nd dose (RR=0.20; 95% CI: 0.09-0.44; p<.0001) in the adjusted analysis. Conclusions COVID-19 vaccination with an mRNA-based vaccine showed a significant association with a reduced risk of asymptomatic SARS-CoV-2 infection as measured during pre-procedural molecular screening. The results of this study demonstrate the impact of the vaccines on reduction in asymptomatic infections supplementing the randomized trial results on symptomatic patients.

scholarly journals A Mental Models Study of Hurricane Forecast and Warning Production, Communication, and Decision-Making*

2016 ◽  
Vol 8 (2) ◽  
pp. 111-129 ◽  
Author(s):  
Ann Bostrom ◽  
Rebecca E. Morss ◽  
Jeffrey K. Lazo ◽  
Julie L. Demuth ◽  
Heather Lazrus ◽  
...  
Keyword(s):  
Risk Communication ◽  
Mental Models ◽  
Warning System ◽  
Weather Forecast ◽  
Extreme Weather ◽  
South Florida ◽  
Extreme Weather Events ◽  
Research Approach ◽  
Action Context ◽  
Risk Decision

Abstract The study reported here explores how to enhance the public value of hurricane forecast and warning information by examining the entire warning process. A mental models research approach is applied to address three risk management tasks critical to warnings for extreme weather events: 1) understanding the risk decision and action context for hurricane warnings, 2) understanding the commonalities and conflicts in interpretations of that context and associated risks, and 3) exploring the practical implications of these insights for hurricane risk communication and management. To understand the risk decision and action context, the study develops a decision-focused model of the hurricane forecast and warning system on the basis of results from individual mental models interviews with forecasters from the National Hurricane Center (n = 4) and the Miami–South Florida Weather Forecast Office (n = 4), media broadcasters (n = 5), and public officials (n = 6), as well as a group decision-modeling session with a subset of the forecasters. Comparisons across professionals reveal numerous shared perceptions, as well as some critical differences. Implications for improving extreme weather event forecast and warning systems and risk communication are threefold: 1) promote thinking about forecast and warning decisions as a system, with informal as well as formal elements; 2) evaluate, coordinate, and consider controlling the proliferation of forecast and warning information products; and 3) further examine the interpretation and representation of uncertainty within the hurricane forecast and warning system as well as for users.

scholarly journals Statistical Weather-Impact Models: An Application of Neural Networks and Mixed Effects for Corn Production over the United States

2016 ◽  
Vol 55 (11) ◽  
pp. 2509-2527 ◽  
Author(s):  
Jordane A. Mathieu ◽  
Filipe Aires
Keyword(s):  
United States ◽  
The United States ◽  
Weather Data ◽  
Corn Yield ◽  
Mixed Effect ◽  
Local Conditions ◽  
Weather Impact ◽  
Yield Forecasting ◽  
The Impact ◽  
Impact Models

AbstractStatistical meteorological impact models are intended to represent the impact of weather on socioeconomic activities, using a statistical approach. The calibration of such models is difficult because relationships are complex and historical records are limited. Often, such models succeed in reproducing past data but perform poorly on unseen new data (a problem known as overfitting). This difficulty emphasizes the need for regularization techniques and reliable assessment of the model quality. This study illustrates, in a general way, how to extract pertinent information from weather data and exploit it in impact models that are designed to help decision-making. For a given socioeconomic activity, this type of impact model can be used to 1) study its sensitivity to weather anomalies (e.g., corn sensitivity to water stress), 2) perform seasonal forecasting (yield forecasting) for it, and 3) quantify the longer-term (several decades) impact of weather on it. The size of the training database can be increased by pooling data from various locations, but this requires statistical models that are able to use the localization information—for example, mixed-effect (ME) models. Linear, neural-network, and ME models are compared, using a real-world application: corn-yield forecasting over the United States. Many challenges faced in this paper may be encountered in many weather-impact analyses: these results show that much care is required when using space–time data because they are often highly spatially correlated. In addition, the forecast quality is strongly influenced by the training spatial scale. For the application that is described herein, learning at the state scale is a good trade-off: it is specific to local conditions while keeping enough data for the calibration.

scholarly journals Integrated assessment of impact of water resources of important river basins in Eastern India under projected climate conditions

2015 ◽  
Vol 17 (3) ◽  
pp. 594-606 ◽  
Keyword(s):  
Climate Change ◽  
Water Resources ◽  
Catchment Area ◽  
River Basins ◽  
Weather Data ◽  
Distributed Hydrological Model ◽  
Eastern India ◽  
Impact Of Climate Change ◽  
Daily Weather Data ◽  
The Impact

<div> <p>The impact of climate change on water resources through increased evaporation combined with regional changes in precipitation characteristics has the potential to affect mean runoff, frequency and intensity of floods and droughts, soil moisture and water supply for irrigation and hydroelectric power generation. The Ganga-Brahmaputra-Meghna (GBM) system is the largest in India with a catchment area of about 110Mha, which is more than 43% of the cumulative catchment area of all the major rivers in the country. The river Damodar is an important sub catchment of GBM basin and its three tributaries- the Bokaro, the Konar and the Barakar form one important tributary of the Bhagirathi-Hughli (a tributary of Ganga) in its lower reaches. The present study is an attempt to assess the impacts of climate change on water resources of the four important Eastern River Basins namely Damodar, Subarnarekha, Mahanadi and Ajoy, which have immense importance in industrial and agricultural scenarios in eastern India. A distributed hydrological model (HEC-HMS) has been used on the four river basins using HadRM2 daily weather data for the period from 2041 to 2060 to predict the impact of climate change on water resources of these river systems.&nbsp;</p> </div> <p>&nbsp;</p>

Determining the Long-Term Effectiveness of a Group-Based Physical Activity Program

2018 ◽  
Vol 20 (3) ◽  
pp. 401-408
Author(s):  
Anamica Batra ◽  
Richard C. Palmer ◽  
Elena Bastida ◽  
H. Virginia McCoy ◽  
Hafiz M. R. Khan
Keyword(s):  
Physical Activity ◽  
Older Adults ◽  
Healthy Aging ◽  
Population Level ◽  
The United States ◽  
South Florida ◽  
Community Based ◽  
Evidence Based Programs ◽  
The Impact

Objective. In 2015, only half (48%) of older adults in the United States (≥60 years) reported engaging in any kind of physical activity. Few studies examine the impact of evidence-based programs when adopted in community-based settings. The purpose of this study is to assess the effectiveness of EnhanceFitness (EF) upto 12-months. Method. EF was offered to older adults in South Florida. A total of 222 EF classes were offered between October 2008 and December 2014. Program consisted of a 1-hour session held three times a week. Even though participation was required for 4 months, 1,295 participants continued the program for at least 1 year. Results. All participants showed significant improvement in outcome measures. A mean change of 1.5, 1.7, and 1.9 was seen in number of chair stands at 4, 8, and 12 months (p < .001), respectively. The number of arm curls performed improved from 16.8 at baseline to 18.8, 18.8, and 19.2 at 4-, 8-, and 12-months, respectively. Participants improved their up-and-go time by decreasing from 9.1 (baseline) to 8.7 (4 months) to 8.6 (12 months; p = .001). Discussion. Randomized controlled trials are commonly used to determine the efficacy of an intervention. These interventions when disseminated at the population level have the potential to benefit large masses. EF is currently offered at more than 700 locations. This tremendous success of EF brings attention to an important question of continuous monitoring of these programs to ensure program consistency and intended outcomes. The model used by the Healthy Aging Regional Collaborative could be replicated by other communities.

scholarly journals Seasonal Time Series Forecasting by F1-Fuzzy Transform

Sensors ◽  
2019 ◽  
Vol 19 (16) ◽  
pp. 3611 ◽  
Author(s):  
Di Martino ◽  
Sessa
Keyword(s):  
Time Series ◽  
Moving Average ◽  
Prediction Method ◽  
Heat Index ◽  
Weather Data ◽  
Fuzzy Transform ◽  
Campania Region ◽  
Seasonal Time Series ◽  
Forecasting Method ◽  
Daily Weather Data

We present a new seasonal forecasting method based on F1-transform (fuzzy transform of order 1) applied on weather datasets. The objective of this research is to improve the performances of the fuzzy transform-based prediction method applied to seasonal time series. The time series’ trend is obtained via polynomial fitting: then, the dataset is partitioned in S seasonal subsets and the direct F1-transform components for each seasonal subset are calculated as well. The inverse F1-transforms are used to predict the value of the weather parameter in the future. We test our method on heat index datasets obtained from daily weather data measured from weather stations of the Campania Region (Italy) during the months of July and August from 2003 to 2017. We compare the results obtained with the statistics Autoregressive Integrated Moving Average (ARIMA), Automatic Design of Artificial Neural Networks (ADANN), and the seasonal F-transform methods, showing that the best results are just given by our approach.

Does extreme rainfall lead to heavy losses in the food industry?

2019 ◽  
Vol 32 (2) ◽  
pp. 244-266
Author(s):  
Edimilson Costa Lucas ◽  
Wesley Mendes-Da-Silva ◽  
Gustavo Silva Araujo
Keyword(s):  
Stock Returns ◽  
Food Production ◽  
Food Industry ◽  
Extreme Rainfall ◽  
Extreme Weather ◽  
Extreme Weather Events ◽  
Extreme Rainfall Events ◽  
Content Type ◽  
Weather Events ◽  
The Impact

Purpose Managing the risks associated to world food production is an important challenge for governments. A range of factors, among them extreme weather events, has threatened food production in recent years. The purpose of this paper is to analyse the impact of extreme rainfall events on the food industry in Brazil, a prominent player in this industry. Design/methodology/approach The authors use the AR-GARCH-GPD hybrid methodology to identify whether extreme rainfall affects the stock price of food companies. To do so, the authors collected the daily closing price of the 16 food industry companies listed on the Brazilian stock exchange (B3), in January 2015. Findings The results indicate that these events have a significant impact on stock returns: on more than half of the days immediately following the heavy rain that fell between 28 February 2005 and 30 December 2014, returns were significantly low, leading to average daily losses of 1.97 per cent. These results point to the relevance of the need for instruments to hedge against weather risk, particularly in the food industry. Originality/value Given that extreme weather events have been occurring more and more frequently, financial literature has documented attempts at assessing the economic impacts of weather changes. There is little research, however, into assessing the impacts of these events at corporate level.

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