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Time Series Modeling and Forecasting of Drug-Related Deaths in Iran (2014-2016)

BACKGROUND: Investigating the temporal variations and forecasting the trends in drug-related deaths can help prevent health problems and develop intervention programs. The recent policy in Iran is strongly focused on deterring drug use and replacing illicit drugs with legal ones. This study aimed to...

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Autores principales: Zarghami, Mehran, Kharazmi, Omid, Alipour, Abbas, Babakhanian, Masoudeh, Khosravi, Ardeshr, Mirtorabi, Seyyed Davood
Formato: Online Artículo Texto
Lenguaje:English
Publicado: Kerman University of Medical Sciences and Health Services 2023
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10658097/
https://www.ncbi.nlm.nih.gov/pubmed/38026723
http://dx.doi.org/10.34172/ahj.2023.1277
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author Zarghami, Mehran
Kharazmi, Omid
Alipour, Abbas
Babakhanian, Masoudeh
Khosravi, Ardeshr
Mirtorabi, Seyyed Davood
author_facet Zarghami, Mehran
Kharazmi, Omid
Alipour, Abbas
Babakhanian, Masoudeh
Khosravi, Ardeshr
Mirtorabi, Seyyed Davood
author_sort Zarghami, Mehran
collection PubMed
description BACKGROUND: Investigating the temporal variations and forecasting the trends in drug-related deaths can help prevent health problems and develop intervention programs. The recent policy in Iran is strongly focused on deterring drug use and replacing illicit drugs with legal ones. This study aimed to investigate drug-related deaths in Iran in 2014-2016 and forecast the death toll by 2019. METHODS: In this longitudinal study, Box-Jenkins time series analysis was used to forecast drug-related deaths. To this end, monthly counts of drug-related deaths were obtained from March 2014 to March 2017. After data processing, to obtain stationary time series and examine the stability assumption with the Dickey-Fuller test, the parameters of the Autoregressive Integrated Moving Averages (ARIMA) model were determined using autocorrelation function (ACF) and partial autocorrelation function (PACF) graphs. Based on Akaike statistics, ARIMA (0, 1, 1) was selected as the best-fit model. Moreover, the Dickey-Fuller test was used to confirm the stationarity of the time series of transformed observations. The forecasts were made for the next 36 months using the ARIMA (0,1,2) model and the same confidence intervals were applied to all months. The final extracted data were analyzed using R software, Minitab, and SPSS-23. FINDINGS: According to the Iranian Ministry of Health and the Legal Medicine Organization, there were 8883 drug-related deaths in Iran from March 2014 to March 2017. According to the time series findings, this count had an upward trend and did not show any seasonal pattern. It was forecasted that the mean drug-related mortality rate in Iran would be 245.8 cases per month until 2019. CONCLUSION: This study showed a rising trend in drug-related mortality rates during the study period, and the modeling process for forecasting suggested this trend would continue until 2019 if proper interventions were not instituted.
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spelling pubmed-106580972023-07-01 Time Series Modeling and Forecasting of Drug-Related Deaths in Iran (2014-2016) Zarghami, Mehran Kharazmi, Omid Alipour, Abbas Babakhanian, Masoudeh Khosravi, Ardeshr Mirtorabi, Seyyed Davood Addict Health Original Article BACKGROUND: Investigating the temporal variations and forecasting the trends in drug-related deaths can help prevent health problems and develop intervention programs. The recent policy in Iran is strongly focused on deterring drug use and replacing illicit drugs with legal ones. This study aimed to investigate drug-related deaths in Iran in 2014-2016 and forecast the death toll by 2019. METHODS: In this longitudinal study, Box-Jenkins time series analysis was used to forecast drug-related deaths. To this end, monthly counts of drug-related deaths were obtained from March 2014 to March 2017. After data processing, to obtain stationary time series and examine the stability assumption with the Dickey-Fuller test, the parameters of the Autoregressive Integrated Moving Averages (ARIMA) model were determined using autocorrelation function (ACF) and partial autocorrelation function (PACF) graphs. Based on Akaike statistics, ARIMA (0, 1, 1) was selected as the best-fit model. Moreover, the Dickey-Fuller test was used to confirm the stationarity of the time series of transformed observations. The forecasts were made for the next 36 months using the ARIMA (0,1,2) model and the same confidence intervals were applied to all months. The final extracted data were analyzed using R software, Minitab, and SPSS-23. FINDINGS: According to the Iranian Ministry of Health and the Legal Medicine Organization, there were 8883 drug-related deaths in Iran from March 2014 to March 2017. According to the time series findings, this count had an upward trend and did not show any seasonal pattern. It was forecasted that the mean drug-related mortality rate in Iran would be 245.8 cases per month until 2019. CONCLUSION: This study showed a rising trend in drug-related mortality rates during the study period, and the modeling process for forecasting suggested this trend would continue until 2019 if proper interventions were not instituted. Kerman University of Medical Sciences and Health Services 2023-07 2023-07-29 /pmc/articles/PMC10658097/ /pubmed/38026723 http://dx.doi.org/10.34172/ahj.2023.1277 Text en © 2023 Kerman University of Medical Sciences https://creativecommons.org/licenses/by-nc/3.0/This work is licensed under a Creative Commons Attribution-Non Commercial 3.0 Unported License which allows users to read, copy, distribute and make derivative works for non-commercial purposes from the material, as long as the author of the original work is cited properly.
spellingShingle Original Article
Zarghami, Mehran
Kharazmi, Omid
Alipour, Abbas
Babakhanian, Masoudeh
Khosravi, Ardeshr
Mirtorabi, Seyyed Davood
Time Series Modeling and Forecasting of Drug-Related Deaths in Iran (2014-2016)
title Time Series Modeling and Forecasting of Drug-Related Deaths in Iran (2014-2016)
title_full Time Series Modeling and Forecasting of Drug-Related Deaths in Iran (2014-2016)
title_fullStr Time Series Modeling and Forecasting of Drug-Related Deaths in Iran (2014-2016)
title_full_unstemmed Time Series Modeling and Forecasting of Drug-Related Deaths in Iran (2014-2016)
title_short Time Series Modeling and Forecasting of Drug-Related Deaths in Iran (2014-2016)
title_sort time series modeling and forecasting of drug-related deaths in iran (2014-2016)
topic Original Article
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10658097/
https://www.ncbi.nlm.nih.gov/pubmed/38026723
http://dx.doi.org/10.34172/ahj.2023.1277
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