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

Document Type : Original Article

Authors

1 1. Psychiatry and Behavioral Sciences Research Center, Addiction Institute, Mazandaran University of Medical Sciences, Sari, Iran 2. Department of Psychiatry, Faculty of Medicine, Mazandaran University of Medical Sciences, Sari, Iran

2 Department of Statistics, Faculty of Mathematical Sciences, Vali-e-Asr University of Rafsanjan, Rafsanjan, Iran

3 Community Medicine Department, Medical Faculty, Mazandaran University of Medical Sciences, Sari, Iran

4 1.Psychiatry and Behavioral Sciences Research Center, Addiction Institute, Mazandaran University of Medical Sciences, Sari, Iran 2.Social Determinants of Health Research Center, Semnan University of Medical Sciences, Semnan, Iran

5 Deputy for Public Health, Ministry of Health and Medical Education, Tehran, Iran

6 Legal Medicine Research Center, Legal Medicine Organization, Tehran, Iran

10.34172/ahj.2023.1277

Abstract

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

Highlights

Mehran Zarghami: (Google Scholar) (PubMed)

Omid Kharazmi: (Google Scholar) (PubMed)

Abbas Alipour: (Google Scholar) (PubMed)

Masoudeh Babakhanian: (Google Scholar) (PubMed)

Ardeshr Khosravi: (Google Scholar) (PubMed)

Seyyed Davood Mirtorabi: (Google Scholar) (PubMed)

Keywords

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Addiction & Health
Volume 15, Issue 3
July 2023
Pages 149-155

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