Modeling risks from natural hazards with generalized additive models for location, scale and shape

Pitt, David; Trück, Stefan; Honert, Rob van den; Wong, Wan Wah

doi:10.1016/j.jenvman.2020.111075

Cited by 10 publications

(8 citation statements)

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“…Our findings add to an emerging literature which has investigated associations between risk factors and outcome variability. Studies [11][12][13][14][15][16][17] have reported that risk factors associated with higher means are also associated with higher outcome variability. For example, Beyerlein et al (2008) 17 found that multiple risk factors for high childhood BMI (such as more frequent television viewing and greater rapid infant weight gain) were related to both higher mean BMI and greater variability in BMI.…”

Section: Discussionmentioning

confidence: 99%

“…The copyright holder for this this version posted March 31, 2021. ; https://doi.org/10.1101/2021.03.30.21254645 doi: medRxiv preprint matched by increases in BMI variability indicates that there may be differential susceptibility to the obesogenic environment. 9 10 Recent studies in biological, 11 12 environmental 13 and economic science 14 15 16 have begun to examine how risk factors relate to the distribution of the outcome of interest. However, there have been few epidemiological applications of this approach to date; 17 and fewer still that provide explanations for such findings, which are essential if such methods are to have utility.…”

Section: Introductionmentioning

confidence: 99%

“…Recent studies in biological, 11 12 environmental 13 and economic science 14 15 16 have begun to examine how risk factors relate to the distribution of the outcome of interest. However, there have been few epidemiological applications of this approach to date; 17 and fewer still that provide explanations for such findings, which are essential if such methods are to have utility.…”

Section: Introductionmentioning

confidence: 99%

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What if risk factors influenced the variability of health outcomes as well as the mean? Evidence and implications

Bann

Cole

2021

Preprint

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Risk factors may affect the variability as well as the mean of health outcomes. Understanding this can aid aetiological understanding and public health translation, in that interventions which shift the outcome mean and reduce variability are preferable to those which affect only the mean. However, few statistical tools routinely test for differences in variability. We used GAMLSS (Generalised Additive Models for Location, Scale and Shape) to investigate how multiple risk factors (sex, childhood social class and midlife physical inactivity) related to differences in health outcome mean and variability. The 1970 British birth cohort study was used, with body mass index (BMI; N = 6,025) and mental wellbeing (Warwick-Edinburgh Mental Wellbeing Scale; N = 7,128) as outcomes. For BMI, males had a 2% higher mean than females yet 28% lower variability. Lower social class and physical inactivity were associated with higher mean and higher variability (6% and 13% respectively). For mental wellbeing, gender was not associated with the mean while males had 4% lower variability. Lower social class and physical inactivity were associated with lower mean yet higher variability (-7% and 11% respectively). This provides empirical support for the notion that risk factors can reduce or increase variability in health outcomes. Such findings may be explained by heterogeneity in the causal effect of each exposure, by the influence of other (typically unmeasured) variables, and/or by measurement error. This underutilised approach to the analysis of continuously distributed outcomes may have broader utility in epidemiological, medical, and psychological sciences.

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Section: Discussionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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What if risk factors influenced the variability of health outcomes as well as the mean? Evidence and implications

Bann

Cole

2021

Preprint

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“…Recent studies in biological ( Sun et al, 2020 ; Nakagawa et al, 2014 ), environmental ( Pitt et al, 2020 ), and economic science ( Hohberg et al, 2020 ; Silbersdorff and Schneider, 2019 ; Silbersdorff et al, 2018 ) have begun to examine how risk factors relate to the distribution of the outcome of interest. However, there have been few epidemiological applications of this approach to date; ( Beyerlein et al, 2008a ) and fewer still that provide explanations for such findings, which are essential if such methods are to have utility.…”

Section: Introductionmentioning

confidence: 99%

Risk factors relate to the variability of health outcomes as well as the mean: A GAMLSS tutorial

Bann

Wright

Cole

2022

eLife

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Background: Risk factors or interventions may affect the variability as well as the mean of health outcomes. Understanding this can aid aetiological understanding and public health translation, in that interventions which shift the outcome mean and reduce variability are typically preferable to those which affect only the mean. However, most commonly used statistical tools do not test for differences in variability. Tools that do have few epidemiological applications to date, and fewer applications still have attempted to explain their resulting findings. We thus provide a tutorial for investigating this using GAMLSS (Generalised Additive Models for Location, Scale and Shape). Methods: The 1970 British birth cohort study was used, with body mass index (BMI; N=6,007) and mental wellbeing (Warwick-Edinburgh Mental Wellbeing Scale; N=7,104) measured in midlife (42-46 years) as outcomes. We used GAMLSS to investigate how multiple risk factors (sex, childhood social class and midlife physical inactivity) related to differences in health outcome mean and variability. Results: Risk factors were related to sizable differences in outcome variability-for example males had marginally higher mean BMI yet 28% lower variability; lower social class and physical inactivity were each associated with higher mean and higher variability (6.1% and 13.5% higher variability, respectively). For mental wellbeing, gender was not associated with the mean while males had lower variability (-3.9%); lower social class and physical inactivity were each associated with lower mean yet higher variability (7.2% and 10.9% higher variability, respectively). Conclusions: The results highlight how GAMLSS can be used to investigate how risk factors or interventions may influence the variability in health outcomes. This underutilised approach to the analysis of continuously distributed outcomes may have broader utility in epidemiologic, medical, and psychological sciences. A tutorial and replication syntax is provided online to facilitate this (https://osf.io/5tvz6/). Funding: DB is supported by the Economic and Social Research Council (grant number ES/M001660/1), The Academy of Medical Sciences / Wellcome Trust ('Springboard Health of the Public in 2040' award: HOP001/1025); DB and LW are supported by the Medical Research Council (MR/V002147/1). The funders had no role in study design, data collection and analysis, decision to publish, or preparation of the manuscript.

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“…It becomes a main alarm that with the global warming, and also weather structure drive more inconsistent and the occurrence and cruelty of calamitous proceedings may rise in future. Recently, this alarm has established gradually, added care and it was recommended that natural hazards have converted further expensive in admiration of victims triggered over in past 2 eras [1]. Earthquakes, hot cyclones, landslides, floods and forest-fires are most of the causes and create highest loss event and source substantial harm to the community.…”

Section: Introductionmentioning

confidence: 99%

Hybrid Natural Disaster mapping and Mitigation Plans

Amuthadevi¹,

MonicaSubarnan²,

Vijayan³

2021

Proceedings of the First International Conference on Computing, Communication and Control System, I3CAC 2021, 7-8 June 2021, Bh

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Many of the natural disasters are threatens the human lives and also assets of the people. Hazard mapping is one way to connect the particular location into kind of hazardous based on the history of the events in the specific geographic zone. The weather changes may trigger into many natural disasters. In this chapter, Multi-hazard is focused. All kinds of natural disasters happened in particular zone like floods, earthquakes, tsunami and landslides for the past years are used for prediction. It will be useful to save many lives and reduce the damages. This chapter attentions on  Hazard category and Cruelty  Hazard-mapping  Detection of Risk and mapping  Awareness Engendering  Reserve plan  Approaches and plan for evacuation The hybrid approaches are used by using history of information, prediction strategy by automated learning strategy and real time data collection from satellite images and sensors. Different mitigation plans will be analysed and summarize the useful solutions in the plan.

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Modeling risks from natural hazards with generalized additive models for location, scale and shape

Cited by 10 publications

References 57 publications

What if risk factors influenced the variability of health outcomes as well as the mean? Evidence and implications

What if risk factors influenced the variability of health outcomes as well as the mean? Evidence and implications

Risk factors relate to the variability of health outcomes as well as the mean: A GAMLSS tutorial

Hybrid Natural Disaster mapping and Mitigation Plans

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