IDEAS home Printed from https://ideas.repec.org/a/gam/jijerp/v19y2022i8p4427-d788546.html
   My bibliography  Save this article

The Scale of Intoxications with New Psychoactive Substances over the Period 2014–2020—Characteristics of the Trends and Impacts of the COVID-19 Pandemic on the Example of Łódź Province, Poland

Author

Listed:
  • Anna Garus-Pakowska

    (Department of Nutrition and Epidemiology, Medical University of Lodz, 90-752 Lodz, Poland)

  • Agnieszka Kolmaga

    (Department of Nutrition and Epidemiology, Medical University of Lodz, 90-752 Lodz, Poland)

  • Ewelina Gaszyńska

    (Department of Nutrition and Epidemiology, Medical University of Lodz, 90-752 Lodz, Poland)

  • Magdalena Ulrichs

    (Department of Econometrics, Faculty of Economics and Sociology, University of Lodz, 90-255 Lodz, Poland)

Abstract

Legal highs are new psychoactive substances (NPSs) which pose a high risk for human health, and the spread of the SARS-CoV-2 pandemic has changed peoples’ behaviours, including the demand for NPS. The aim of the study was to assess both the frequency of intoxication with NPS in Łódź province over the period 2014–2020, and the impact of the COVID-19 pandemic on developing this trend. An analysis was carried out of data on intoxications in Łódź province in the years 2014–2020 reported by hospitals. The medical interventions rate (MI) per 100,000 people in the population was calculated. The frequency of intoxications was compared taking sociodemographic variables into account, and the effect of seasonal influence on intoxications was calculated using the Holt–Winter multiplicative seasonal method. In the period considered, there were 7175 acute NPS poisonings in the Łódź province and 25,495 in Poland. The averaged MI rate between 2014–2020 was 9.45 for Poland and 38.53 for the Łódź province, and the lowest value was found during the COVID pandemic in the year 2020 (respectively, 2.1 vs. 16.94). NPS users were mainly young men of 19–24 years old from a big city. Most cases were registered at weekends and in summer months. The majority of intoxications were caused by unidentified psychoactive substances of legal highs (chi 2 = 513.98, p < 0.05). The actual number of NPS-related poisonings in the Łódź province in 2020 was lower than the value extrapolated from trend analysis of data between 2014–2019. NPS use in Poland decreased during the pandemic. It should be noted that a decrease in the number of drug-related incidents can have more than one reason, e.g., preventive programs, increased awareness, or changes in the law. This paper advocates that, in addition to monitoring NPS-related intoxications, there is further investigation into the social, cultural, and behavioural determinants of NPS to facilitate targeted prevention programmes and the development of new medical treatments.

Suggested Citation

  • Anna Garus-Pakowska & Agnieszka Kolmaga & Ewelina Gaszyńska & Magdalena Ulrichs, 2022. "The Scale of Intoxications with New Psychoactive Substances over the Period 2014–2020—Characteristics of the Trends and Impacts of the COVID-19 Pandemic on the Example of Łódź Province, Poland," IJERPH, MDPI, vol. 19(8), pages 1-12, April.
    • Handle: RePEc:gam:jijerp:v:19:y:2022:i:8:p:4427-:d:788546
    as

    Download full text from publisher

    File URL: https://www.mdpi.com/1660-4601/19/8/4427/pdf
    Download Restriction: no
    File URL: https://www.mdpi.com/1660-4601/19/8/4427/
    Download Restriction: no
    ---><---

    References listed on IDEAS

    as
    1. Hyndman, Rob J. & Koehler, Anne B. & Snyder, Ralph D. & Grose, Simone, 2002. "A state space framework for automatic forecasting using exponential smoothing methods," International Journal of Forecasting, Elsevier, vol. 18(3), pages 439-454.
    Full references (including those not matched with items on IDEAS)

    Most related items

    These are the items that most often cite the same works as this one and are cited by the same works as this one.
    1. Barrow, Devon & Kourentzes, Nikolaos, 2018. "The impact of special days in call arrivals forecasting: A neural network approach to modelling special days," European Journal of Operational Research, Elsevier, vol. 264(3), pages 967-977.
    2. Hyndman, Rob J. & Ahmed, Roman A. & Athanasopoulos, George & Shang, Han Lin, 2011. "Optimal combination forecasts for hierarchical time series," Computational Statistics & Data Analysis, Elsevier, vol. 55(9), pages 2579-2589, September.
    3. Kourentzes, Nikolaos & Petropoulos, Fotios & Trapero, Juan R., 2014. "Improving forecasting by estimating time series structural components across multiple frequencies," International Journal of Forecasting, Elsevier, vol. 30(2), pages 291-302.
    4. de Silva, Ashton J, 2010. "Forecasting Australian Macroeconomic variables, evaluating innovations state space approaches," MPRA Paper 27411, University Library of Munich, Germany.
    5. Pawlikowski, Maciej & Chorowska, Agata, 2020. "Weighted ensemble of statistical models," International Journal of Forecasting, Elsevier, vol. 36(1), pages 93-97.
    6. Alysha M De Livera, 2010. "Automatic forecasting with a modified exponential smoothing state space framework," Monash Econometrics and Business Statistics Working Papers 10/10, Monash University, Department of Econometrics and Business Statistics.
    7. Spiliotis, Evangelos & Makridakis, Spyros & Kaltsounis, Anastasios & Assimakopoulos, Vassilios, 2021. "Product sales probabilistic forecasting: An empirical evaluation using the M5 competition data," International Journal of Production Economics, Elsevier, vol. 240(C).
    8. Barrow, Devon K., 2016. "Forecasting intraday call arrivals using the seasonal moving average method," Journal of Business Research, Elsevier, vol. 69(12), pages 6088-6096.
    9. Marlon Mesquita Lopes Cabreira & Felipe Leite Coelho da Silva & Josiane da Silva Cordeiro & Ronald Miguel Serrano Hernández & Paulo Canas Rodrigues & Javier Linkolk López-Gonzales, 2024. "A Hybrid Approach for Hierarchical Forecasting of Industrial Electricity Consumption in Brazil," Energies, MDPI, vol. 17(13), pages 1-15, June.
    10. Aye, Goodness C. & Balcilar, Mehmet & Gupta, Rangan & Majumdar, Anandamayee, 2015. "Forecasting aggregate retail sales: The case of South Africa," International Journal of Production Economics, Elsevier, vol. 160(C), pages 66-79.
    11. Niematallah Elamin & Mototsugu Fukushige, 2016. "Forecasting extreme seasonal tourism demand," Discussion Papers in Economics and Business 16-23, Osaka University, Graduate School of Economics.
    12. Hyndman, Rob J. & Khandakar, Yeasmin, 2008. "Automatic Time Series Forecasting: The forecast Package for R," Journal of Statistical Software, Foundation for Open Access Statistics, vol. 27(i03).
    13. Aviral Kumar Tiwari & Claudiu T Albulescu & Phouphet Kyophilavong, 2014. "A comparison of different forecasting models of the international trade in India," Economics Bulletin, AccessEcon, vol. 34(1), pages 420-429.
    14. Meira, Erick & Cyrino Oliveira, Fernando Luiz & de Menezes, Lilian M., 2022. "Forecasting natural gas consumption using Bagging and modified regularization techniques," Energy Economics, Elsevier, vol. 106(C).
    15. Kang, Yanfei & Hyndman, Rob J. & Smith-Miles, Kate, 2017. "Visualising forecasting algorithm performance using time series instance spaces," International Journal of Forecasting, Elsevier, vol. 33(2), pages 345-358.
    16. Fildes, Robert & Petropoulos, Fotios, 2015. "Simple versus complex selection rules for forecasting many time series," Journal of Business Research, Elsevier, vol. 68(8), pages 1692-1701.
    17. Huber, Jakob & Stuckenschmidt, Heiner, 2020. "Daily retail demand forecasting using machine learning with emphasis on calendric special days," International Journal of Forecasting, Elsevier, vol. 36(4), pages 1420-1438.
    18. Bergmeir, Christoph & Hyndman, Rob J. & Benítez, José M., 2016. "Bagging exponential smoothing methods using STL decomposition and Box–Cox transformation," International Journal of Forecasting, Elsevier, vol. 32(2), pages 303-312.
    19. E. Vercher & A. Corberán-Vallet & J. Segura & J. Bermúdez, 2012. "Initial conditions estimation for improving forecast accuracy in exponential smoothing," TOP: An Official Journal of the Spanish Society of Statistics and Operations Research, Springer;Sociedad de Estadística e Investigación Operativa, vol. 20(2), pages 517-533, July.
    20. Spiliotis, Evangelos & Petropoulos, Fotios, 2024. "On the update frequency of univariate forecasting models," European Journal of Operational Research, Elsevier, vol. 314(1), pages 111-121.

    Corrections

    All material on this site has been provided by the respective publishers and authors. You can help correct errors and omissions. When requesting a correction, please mention this item's handle: RePEc:gam:jijerp:v:19:y:2022:i:8:p:4427-:d:788546. See general information about how to correct material in RePEc.

    If you have authored this item and are not yet registered with RePEc, we encourage you to do it here. This allows to link your profile to this item. It also allows you to accept potential citations to this item that we are uncertain about.

    If CitEc recognized a bibliographic reference but did not link an item in RePEc to it, you can help with this form .

    If you know of missing items citing this one, you can help us creating those links by adding the relevant references in the same way as above, for each refering item. If you are a registered author of this item, you may also want to check the "citations" tab in your RePEc Author Service profile, as there may be some citations waiting for confirmation.

    For technical questions regarding this item, or to correct its authors, title, abstract, bibliographic or download information, contact: MDPI Indexing Manager (email available below). General contact details of provider: https://www.mdpi.com .

    Please note that corrections may take a couple of weeks to filter through the various RePEc services.

    IDEAS is a RePEc service. RePEc uses bibliographic data supplied by the respective publishers.