Underlying factors affecting death due to flood in Iran: A qualitative content analysis

Yari, Arezoo; Ardalan, Ali; Ostadtaghizadeh, Abbas; Zarezadeh, Yadolah; Boubakran, Mohsen Soufi; Bidarpoor, Farzam; Rahimiforoushani, Abbas

doi:10.1016/j.ijdrr.2019.101258

Cited by 44 publications

(26 citation statements)

References 46 publications

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“…It is a systematic and rigorous method widely used in qualitative research to extract themes and subthemes (underlying factors) from interviews e.g. [ 20 , 27 , 34 , 35 ]. Hence, this study employed content analysis to analyse the results of the interviews.…”

Section: Methodsmentioning

confidence: 99%

“…After that, they held a meeting with the third researcher to make a consensus on the possible inconsistencies in the coding schemes and finalise the final themes and sub-themes. Finally, researchers counted the frequencies of the sub-themes to examine whether the sub-themes were common (“Yes” and “No” were used to code the presence or absence of each sub-theme) [ 35 ].…”

Section: Methodsmentioning

confidence: 99%

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What shapes people's willingness to wear a face mask at the beginning of a public health disaster? A qualitative study based on COVID-19 in China

Kwok

Yan

Huang

et al. 2021

International Journal of Disaster Risk Reduction

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COVID-19 should not be the world's last public health disaster, so there is an urgent need to learn from COVID-19 to prepare better for the next public health disaster. This study aims to understand the factors that make people wear a face mask at the beginning of an outbreak of public health disaster. Semi-structured interviews were conducted during April 2020 in China, one month after the COVID-19 was declared a pandemic. The respondents were members of the public living in China, covering two age groups: young adults and older adults. They were recruited using a convenient sample and snowball sampling strategy. The results were analysed using content analysis. Seventeen subjects were recruited, among which nine were young adults (average age = 26.4; SD = 10.5), and eight were older adults (average age = 60.4; SD = 12.1). This study found that environmental factors, personal factors, factors concerning wearing masks, specific circumstances, and development of the pandemic were the common factors considered by both young adults and older adults. This study should help the authority formulate prevention policies better to reduce the risk of an outbreak if there is a new virus outbreak in the future, unfortunately.

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Section: Methodsmentioning

confidence: 99%

Section: Methodsmentioning

confidence: 99%

What shapes people's willingness to wear a face mask at the beginning of a public health disaster? A qualitative study based on COVID-19 in China

Kwok

Yan

Huang

et al. 2021

International Journal of Disaster Risk Reduction

View full text Add to dashboard Cite

show abstract

“…Non-flash floods can likewise be dangerous to human life, especially in densely populated areas and where floods are primarily caused by tropical cyclone activity and monsoon rain. Paulikas and Rahman (2015) presented a list of the 31 deadliest floods (Yari et al, 2019).…”

Section: Flood Speed and Bed Loadmentioning

confidence: 99%

“…Most likely, they did this after the flood warning had been issued 308 and had managed to save their cars on several previous occasions, but lost their lives during the last attempt. People between 18 and 35 years old seem prone to cross floods on foot because (a) they do not take flood warnings seriously, (b) they believe they are good swimmers, and (c) they do not have previous experience with floods (Shabanikiya et al 2014;Yari et al, 2019). In contrast, it appears that individuals who know how 312 to avoid floods (i.e., through personal experiences or by asking others for advice), are less likely to enter floodwaters (Coles and Hirschboeck, 2020).…”

Section: Victims' Hazardous Behaviorsmentioning

confidence: 99%

Review article. Factors leading to the occurrence of flood fatalities: a systematic review of research papers published between 2010 and 2020

Petrucci

2021

Preprint

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Abstract. Floods kill several people every year in both developed and developing countries. The transfer of research findings from the academic community to practitioners, policy-makers and citizens may reduce the impact of floods on mortality. This systematic review analyzes 44 scientific articles extracted from WOS and SCOPUS databases written in English, published between 2010 and 2020, and focuses on flood fatalities. The first main finding of this review is the classification of drivers of flood mortality into two groups: the first group relates to the environment and the second group relates to the victims. The second main finding is the identification of strategies to practically cope with the identified drivers of flood fatalities. The main lacks of the review concern: a) the unavailability of papers based on flood fatality occurrence in developing countries and b) the absence of data focusing on people who have survived floods. This review amplifies useful findings, best practices, and lessons learned that can be useful for administrators, risk managers, and teachers of primary and secondary schools to mitigate the impact of future floods on human life.

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“…Flooding= 0.151 * slope + 0.054 * lithology + 0.181* drainage density + 0.001 * curvature + 0.044 * elevation + 0.058 * DR river + 0.069 * TWI + 0.078 * SPI + 0.199 * Rainfall+ 0.191 * Land use + 0.083 * NDVI + 0.048 * hydrologic soil groups + 0.033 * runoff + 0.083 * NDVI By performing the above equation on the layers, the flood-prone map with 5, 15, 25, and 50 year return periods of the basin was obtained(Figures 25,26,27, …”

mentioning

confidence: 99%

Evaluating and Zoning Flood Susceptibility Using Curve Number (CN) Logistic and Hydrological Regression Model (Case Study of Kalateh Qanbar Drainage Basin, Nishabur)

Naemitabar¹,

Asadi²,

Amirahmadi³

et al. 2020

Preprint

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Spatial evaluation of flood-prone areas at the drainage basins is one of the basic strategies in the field of flood risk management. The present study aims to investigate the efficiency of the CN logistic and hydrological regression model for predicting and zoning floods. In the first stage, 13 runoff parameters, hydrologic soil groups (HSGs), slope, lithology, drainage density (DD), land curvature, elevation, distance to waterways/rivers, topographic wetness index (TWI), stream power index (SPI), rainfall, land use, and NDVI were employed. In the SCS-CN model of the drainage basin, the infiltration rate (S) and runoff amount (Q) were determined. The weights of the used layers were weighted by the AHP. Also, a flood zoning map of the drainage basin with different 5, 15, 25, and 50 year return periods was drawn by applying the weights of the layers. To ensure the accuracy of the zoning map with the logistic regression model, the ROC curve, and the area below the curve were used. The results showed that for the prediction rate, the AUC is 0.81%, indicating that the model has acceptable accuracy. The most important factors affecting flood are geological index; distance to waterways/rivers; and NDVI in the logistic regression model, and slope, DD, rainfall, and land use in the SCS-CN model respectively. 30 to 46% of the drainage basin area during 5 to 50 year periods has moderate flood potential, and 28 to 34% has high potential.

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Underlying factors affecting death due to flood in Iran: A qualitative content analysis

Cited by 44 publications

References 46 publications

What shapes people's willingness to wear a face mask at the beginning of a public health disaster? A qualitative study based on COVID-19 in China

What shapes people's willingness to wear a face mask at the beginning of a public health disaster? A qualitative study based on COVID-19 in China

Review article. Factors leading to the occurrence of flood fatalities: a systematic review of research papers published between 2010 and 2020

Evaluating and Zoning Flood Susceptibility Using Curve Number (CN) Logistic and Hydrological Regression Model (Case Study of Kalateh Qanbar Drainage Basin, Nishabur)

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