Modelling motorcycle-related head injury trends for Thailand following the 100% motorcycle helmet use campaign using log-linear model

Mena Patummasut; Nattakorn Phewchean; Jarinratn Sirirattanapa

Modelling motorcycle-related head injury trends for Thailand following the 100% motorcycle helmet use campaign using log-linear model

Mena Patummasut, Nattakorn Phewchean, Jarinratn Sirirattanapa

Research output: Contribution to journal › Article › peer-review

2 Citations (Scopus)

Abstract

The aim of this paper is to examine trends of motorcycle related head injuries in HRH Princess Maha Chakri Sirindhorn Medical Center, Nakhon Nayok province, Thailand, following the 100% Motorcycle Helmet Use campaign by using log-linear models. Since the injuries count data is overdispersion, the Poisson log-linear model is not reasonable. Consequently, the negative binomial log-linear model accounted for overdispersion is used, and it fits the data very well. The fitted model indicated the increasing trend of head injuries after the 100% Motorcycle Helmet Use campaign was lunched over the years 2011-2016. It is increasing at the rate 1.13% per month.

Original language	English
Pages (from-to)	30-40
Number of pages	11
Journal	Thailand Statistician
Volume	17
Issue number	1
Publication status	Published - Jan 2019
Externally published	Yes

Keywords

Count data
Negative binomial log-linear model
Poisson log-linear model

UN SDGs

This output contributes to the following UN Sustainable Development Goals (SDGs)

Cite this

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title = "Modelling motorcycle-related head injury trends for Thailand following the 100% motorcycle helmet use campaign using log-linear model",

abstract = "The aim of this paper is to examine trends of motorcycle related head injuries in HRH Princess Maha Chakri Sirindhorn Medical Center, Nakhon Nayok province, Thailand, following the 100% Motorcycle Helmet Use campaign by using log-linear models. Since the injuries count data is overdispersion, the Poisson log-linear model is not reasonable. Consequently, the negative binomial log-linear model accounted for overdispersion is used, and it fits the data very well. The fitted model indicated the increasing trend of head injuries after the 100% Motorcycle Helmet Use campaign was lunched over the years 2011-2016. It is increasing at the rate 1.13% per month.",

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AU - Patummasut, Mena

AU - Phewchean, Nattakorn

AU - Sirirattanapa, Jarinratn

PY - 2019/1

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N2 - The aim of this paper is to examine trends of motorcycle related head injuries in HRH Princess Maha Chakri Sirindhorn Medical Center, Nakhon Nayok province, Thailand, following the 100% Motorcycle Helmet Use campaign by using log-linear models. Since the injuries count data is overdispersion, the Poisson log-linear model is not reasonable. Consequently, the negative binomial log-linear model accounted for overdispersion is used, and it fits the data very well. The fitted model indicated the increasing trend of head injuries after the 100% Motorcycle Helmet Use campaign was lunched over the years 2011-2016. It is increasing at the rate 1.13% per month.

AB - The aim of this paper is to examine trends of motorcycle related head injuries in HRH Princess Maha Chakri Sirindhorn Medical Center, Nakhon Nayok province, Thailand, following the 100% Motorcycle Helmet Use campaign by using log-linear models. Since the injuries count data is overdispersion, the Poisson log-linear model is not reasonable. Consequently, the negative binomial log-linear model accounted for overdispersion is used, and it fits the data very well. The fitted model indicated the increasing trend of head injuries after the 100% Motorcycle Helmet Use campaign was lunched over the years 2011-2016. It is increasing at the rate 1.13% per month.

KW - Count data

KW - Negative binomial log-linear model

KW - Poisson log-linear model

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IS - 1

ER -

Modelling motorcycle-related head injury trends for Thailand following the 100% motorcycle helmet use campaign using log-linear model

Abstract

Keywords

UN SDGs

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