Longitudinal Study of Hurricane Preparedness Behaviors: Influence of Collective Efficacy

2021 ◽  
pp. 1-7
Author(s):  
Holly B Herberman Mash ◽  
Carol S Fullerton ◽  
Joshua C Morganstein ◽  
Mary C Vance ◽  
Leming Wang ◽  
...  
Keyword(s):  
Collective Efficacy ◽  
Regression Models ◽  
Health Workers ◽  
Linear Regression Models ◽  
Community Characteristics ◽  
Community Strength ◽  
Department Of Health ◽  
Florida Department ◽  
Hurricane Season ◽  
Univariate Analyses

Abstract Objective: Community characteristics, such as collective efficacy, a measure of community strength, can affect behavioral responses following disasters. We measured collective efficacy 1 month before multiple hurricanes in 2005, and assessed its association to preparedness 9 months following the hurricane season. Methods: Participants were 631 Florida Department of Health workers who responded to multiple hurricanes in 2004 and 2005. They completed questionnaires that were distributed electronically approximately 1 month before (6.2005-T1) and 9 months after (6.2006-T2) several storms over the 2005 hurricane season. Collective efficacy, preparedness behaviors, and socio-demographics were assessed at T1, and preparedness behaviors and hurricane-related characteristics (injury, community-related damage) were assessed at T2. Participant ages ranged from 21-72 (M(SD) = 48.50 (10.15)), and the majority were female (78%). Results: In linear regression models, univariate analyses indicated that being older (B = 0.01, SE = 0.003, P < 0.001), White (B = 0.22, SE = 0.08, P < 0.01), and married (B = 0.05, SE = 0.02, p < 0.001) was associated with preparedness following the 2005 hurricanes. Multivariate analyses, adjusting for socio-demographics, preparedness (T1), and hurricane-related characteristics (T2), found that higher collective efficacy (T1) was associated with preparedness after the hurricanes (B = 0.10, SE = 0.03, P < 0.01; and B = 0.47, SE = 0.04, P < 0.001 respectively). Conclusion: Programs enhancing collective efficacy may be a significant part of prevention practices and promote preparedness efforts before disasters.

scholarly journals Florida Department of Health Workers’ Response to 2004 Hurricanes: A Qualitative Analysis

2013 ◽  
Vol 7 (2) ◽  
pp. 153-159 ◽  
Author(s):  
Holly B. Herberman Mash ◽  
Carol S. Fullerton ◽  
Kathleen Kowalski-Trakofler ◽  
Dori B. Reissman ◽  
Ted Scharf ◽  
...  
Keyword(s):  
Public Health ◽  
Qualitative Analysis ◽  
Emergency Care ◽  
Work Performance ◽  
Health Workers ◽  
Self Report ◽  
Public Health Preparedness ◽  
Department Of Health ◽  
Florida Department ◽  
Public Health Workers

AbstractObjectiveExaminations of the demands on public health workers after disaster exposure have been limited. Workers provide emergency care while simultaneously risking injury, damage to personal property, and threats to their own and their family's safety. We examined the disaster management experiences of 4323 Florida Department of Health workers 9 months after their response to 4 hurricanes and 1 tropical storm during a 7-week period in August and September of 2004.MethodsParticipants completed a self-report questionnaire focused on work performance, mental and physical health, daily functioning, sleep disturbance, physiological arousal, and injury and work demand at the time of the hurricanes, and answered open-ended questions that described their experiences in more detail.ResultsA qualitative analysis conducted from the write-in data yielded 4 domains: (1) work/life balance; (2) training for disaster response role; (3) workplace support; and (4) recovery.ConclusionsStudy findings highlighted a number of concerns that are important to public health workers who provide emergency care after a disaster and, in particular, multiple disasters such as during the 2004 hurricane season. The findings also yielded important recommendations for emergency public health preparedness. (Disaster Med Public Health Preparedness. 2013;0:1–7)

scholarly journals Predicting Health Disparities in Regions at Risk of Severe Illness to inform Healthcare Resource Allocations during Pandemics

2020 ◽  
Author(s):  
Tara Fusillo ◽  
Tara Fusillo
Keyword(s):  
New York ◽  
Regression Models ◽  
Healthcare Resource ◽  
Severe Illness ◽  
Linear Regression Models ◽  
Medical Testing ◽  
Department Of Health ◽  
Regional Population ◽  
Local Healthcare ◽  
Modelling Process

Pandemics including COVID-19 have disproportionately affected socioeconomically vulnerable populations. To create a repeatable modelling process to identify regional population centers with pandemic vulnerability, readily available COVID-19 and socioeconomic variable datasets were compiled, and linear regression models were built during the early days of the COVID-19 pandemic. The models were validated later in the pandemic timeline using actual COVID-19 mortality rates in states with high population densities, with New York, New Jersey, Connecticut, Massachusetts, Louisiana, Michigan and Pennsylvania showing the strongest predictive results. Our models have been shared with the Department of Health Commissioners of each of these states as input into a much needed pandemic playbook for local healthcare agencies in allocating medical testing and treatment resources.

Posttraumatic Stress Disorder and Mental Distress Following the 2004 and 2005 Florida Hurricanes

2019 ◽  
Vol 13 (1) ◽  
pp. 44-52 ◽  
Author(s):  
Carol S. Fullerton ◽  
Holly B. Herberman Mash ◽  
Leming Wang ◽  
Joshua C. Morganstein ◽  
Robert J. Ursano
Keyword(s):  
Posttraumatic Stress Disorder ◽  
Posttraumatic Stress ◽  
Collective Efficacy ◽  
Mental Distress ◽  
Stress Disorder ◽  
Health Workers ◽  
Storm Damage ◽  
Logistic Regression Models ◽  
Community Strength ◽  
Frequent Mental Distress

AbstractObjectiveCommunity characteristics, such as perceived collective efficacy, a measure of community strength, can affect mental health outcomes following disasters. We examined the association of perceived collective efficacy with posttraumatic stress disorder (PTSD) and frequent mental distress (14 or more mentally unhealthy days in the past month) following exposure to the 2004 and 2005 hurricane seasons.MethodsParticipants were 1486 Florida Department of Health workers who completed anonymous questionnaires that were distributed electronically 9 months after the 2005 hurricane season. Participant ages ranged from 20 to 79 years (mean, 48; SD, 10.7), and the majority were female (79%), white (75%), and currently married (64%). Fifty percent had a BA/BS degree or higher.ResultsIn 2 separate logistic regression models, each adjusted for individual sociodemographics, community socioeconomic characteristics, individual injury/damage, and community storm damage, lower perceived collective efficacy was significantly associated with a greater likelihood of having PTSD (OR, 0.93; 95% CI, 0.90-0.96), and lower collective efficacy was significantly associated with frequent mental distress (OR, 0.94; 95% CI, 0.92-0.96).ConclusionsPrograms enhancing community collective efficacy may be a significant part of prevention practices and possibly lead to a reduction in the rate of PTSD and persistent distress postdisaster. (Disaster Med Public Health Preparedness. 2019;13:44–52).

scholarly journals PEMODELAN INDEKS PEMBANGUNAN MANUSIA DI PROVINSI JAWA TENGAH TAHUN 2017 MENGGUNAKAN ANALISIS REGRESI SPASIAL

2020 ◽  
Vol 11 (1) ◽  
pp. 45
Author(s):  
Wahyuni Alwi ◽  
Jajang Jajang ◽  
Nunung Nurhayati
Keyword(s):  
Regression Models ◽  
Health Workers ◽  
Moving Average ◽  
Spatial Regression ◽  
Influence Factors ◽  
P Value ◽  
Linear Regression Models ◽  
Spatial Error ◽  
Central Java ◽  
Spatial Autoregressive

This research discussed about model of Human Development Index (HDI) in Central Java with spatial regression analysis. and identify  variables that give significant influence. First, analyze the influence factors based on result of p-value from t test in multiple linear regression models. Then, made spatial weight matrix with queen continguity method. After that, estimate spatial regression models, namely spatial autoregressive (SAR), Spatial error models (SEM), and spatial autoregive moving average (SARMA) and  choose the best model based on minimum AIC value. The results showed that SAR was the best spatial regression model and the significant variables was the gross enrollment rates at senior high schools, the health workers, and the district minimum wages. All of them that give positive influences. The variable that give biggest influence for HDI was the health workers. Full Article

Florida Department of Health Workers' Response to 2004 Hurricanes Exposure: A Qualitative Analysis

2011 ◽  
Author(s):  
Holly H. Mash ◽  
Carol S. Fullerton ◽  
Kathleen Kowalski-Trakofler ◽  
Dori B. Reissman ◽  
Ted Scharf ◽  
...  
Keyword(s):  
Qualitative Analysis ◽  
Health Workers ◽  
Department Of Health ◽  
Florida Department

FAKTOR DETERMINAN PENYALURAN KREDIT BANK PERSERO

2018 ◽  
Vol 23 (1) ◽  
pp. 60-71
Author(s):  
Wigiyanti Masodah
Keyword(s):  
Interest Rate ◽  
Linear Regression ◽  
Interest Rates ◽  
Regression Models ◽  
Linear Regression Models ◽  
Negative Impacts ◽  
Main Activity ◽  
The Impact ◽  
The Given ◽  
Multiple Linear Regression Models

Offering credit is the main activity of a Bank. There are some considerations when a bank offers credit, that includes Interest Rates, Inflation, and NPL. This study aims to find out the impact of Variable Interest Rates, Inflation variables and NPL variables on credit disbursed. The object in this study is state-owned banks. The method of analysis in this study uses multiple linear regression models. The results of the study have shown that Interest Rates and NPL gave some negative impacts on the given credit. Meanwhile, Inflation variable does not have a significant effect on credit given. Keywords: Interest Rate, Inflation, NPL, offered Credit.

Linear Regression Models for Interval-Valued Data using Log-transformation

2020 ◽  
Author(s):  
Nykolas Mayko Maia Barbosa ◽  
João Paulo Pordeus Gomes ◽  
César Lincoln Cavalcante Mattos ◽  
Diêgo Farias Oliveira
Keyword(s):  
Linear Regression ◽  
Regression Models ◽  
Linear Regression Models ◽  
Log Transformation ◽  
Interval Valued

THE PREDICTIVE CONTENT OF DISAGGREGATED NORMAL INCOME: An Empirical Study in the JSX

2003 ◽  
Vol 5 (3) ◽  
pp. 363 ◽  
Author(s):  
Slamet Sugiri
Keyword(s):  
Linear Regression ◽  
Regression Models ◽  
Stock Exchange ◽  
Manufacturing Firms ◽  
Single Step ◽  
Model Parameters ◽  
Linear Regression Models ◽  
Operating Income ◽  
Predictive Content ◽  
Multiple Step

The main objective of this study is to examine a hypothesis that the predictive content of normal income disaggregated into operating income and nonoperating income outperforms that of aggregated normal income in predicting future cash flow. To test the hypothesis, linear regression models are developed. The model parameters are estimated based on fifty-five manufacturing firms listed in the Jakarta Stock Exchange (JSX) up to the end of 1997.This study finds that empirical evidence supports the hypothesis. This evidence supports arguments that, in reporting income from continuing operations, multiple-step approach is preferred to single-step one.

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