IDEAS home Printed from https://ideas.repec.org/a/plo/pone00/0279483.html
   My bibliography  Save this article

AIDS-related mortality in Pará Province, Brazilian Amazon region: Spatial and temporal analysis

Author

Listed:
  • Taymara Barbosa Rodrigues
  • Bruna Rafaela Leite Dias
  • Dulce Gomes
  • Ricardo Alexandre Arcêncio
  • Jorge Alberto Azevedo Andrade
  • Glenda Roberta Oliveira Naiff Ferreira
  • Lucia Hisako Takase Gonçalves
  • Eliã Pinheiro Botelho

Abstract

Despite considerable therapeutic advances in the care of people living with human immunodeficiency virus (HIV) and with the acquired immunodeficiency syndrome (AIDS) and an overall reduction of 47% in the AIDS mortality rate in the last decade, the AIDS-mortality rates remains high. The social determinants of health (SDH) have a direct influence on the dynamics of this phenomenon. However, changes in SDH caused by the implemented policies against HIV have been poorly investigated. Moreover, the Brazilian rainforest has had the highest and continuously increasing AIDS mortality rate in Brazil since the 1980s. In this study, AIDS mortality in a province of the Brazilian rainforest was examined by using temporal and spatial analyses. Methods. In this ecological study, data from 2007 to 2018 were extracted from the Mortality Information System provided by the State Department of Public Health of Pará. For the temporal analysis, the integrated autoregressive model of moving average (ARIMA) and locally weighted polynomial regression (STLF) were used to forecast AIDS mortality from 2019 to 2022. For the spatial analysis, spatial autocorrelation and geographically weighted regression (GWR) analyses were employed. Results. The samples consisted of 6,498 notifications for AIDS-related deaths. From 2007 to 2013, the AIDS mortality rates showed an upward trend, followed by a stabilization until 2018 and an upward forecasted trend from 2019 to 2022. High mortality rates and high-high clusters were found in economic pole municipalities. Furthermore, AIDS mortality risk was directly associated with per capita income and demographic density, except in the southwestern region of Pará, which exhibited an inverse association with population density. Conclusion. Although the policies against HIV may have contributed to the stabilization of AIDS mortality rates from 2013 in Pará, the upward forecasted trend until 2022 raises an alert and concern to health authorities to provide reinforcement of the policies. The geographic variability of AIDS mortality promoted by SDH provides subsidies to health authorities to implement SDH-focused strategies for AIDS mortality reduction.

Suggested Citation

  • Taymara Barbosa Rodrigues & Bruna Rafaela Leite Dias & Dulce Gomes & Ricardo Alexandre Arcêncio & Jorge Alberto Azevedo Andrade & Glenda Roberta Oliveira Naiff Ferreira & Lucia Hisako Takase Gonçalves, 2023. "AIDS-related mortality in Pará Province, Brazilian Amazon region: Spatial and temporal analysis," PLOS ONE, Public Library of Science, vol. 18(1), pages 1-15, January.
    • Handle: RePEc:plo:pone00:0279483
    DOI: 10.1371/journal.pone.0279483
    as

    Download full text from publisher

    File URL: https://journals.plos.org/plosone/article?id=10.1371/journal.pone.0279483
    Download Restriction: no
    File URL: https://journals.plos.org/plosone/article/file?id=10.1371/journal.pone.0279483&type=printable
    Download Restriction: no
    File URL: https://libkey.io/10.1371/journal.pone.0279483?utm_source=ideas
    LibKey link: if access is restricted and if your library uses this service, LibKey will redirect you to where you can use your library subscription to access this item
    ---><---

    References listed on IDEAS

    as
    1. 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).
    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. Gkillas, Konstantinos & Gupta, Rangan & Pierdzioch, Christian, 2020. "Forecasting realized oil-price volatility: The role of financial stress and asymmetric loss," Journal of International Money and Finance, Elsevier, vol. 104(C).
    2. Rob Hyndman & Heather Booth & Farah Yasmeen, 2013. "Coherent Mortality Forecasting: The Product-Ratio Method With Functional Time Series Models," Demography, Springer;Population Association of America (PAA), vol. 50(1), pages 261-283, February.
    3. Nahapetyan Yervand, 2019. "The benefits of the Velvet Revolution in Armenia: Estimation of the short-term economic gains using deep neural networks," Central European Economic Journal, Sciendo, vol. 6(53), pages 286-303, January.
    4. 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.
    5. Dombi, József & Jónás, Tamás & Tóth, Zsuzsanna Eszter, 2018. "Modeling and long-term forecasting demand in spare parts logistics businesses," International Journal of Production Economics, Elsevier, vol. 201(C), pages 1-17.
    6. Amita Gajewar & Gagan Bansal, 2016. "Revenue Forecasting for Enterprise Products," Papers 1701.06624, arXiv.org.
    7. Tao XIONG & Chongguang LI & Yukun BAO, 2017. "An improved EEMD-based hybrid approach for the short-term forecasting of hog price in China," Agricultural Economics, Czech Academy of Agricultural Sciences, vol. 63(3), pages 136-148.
    8. Pieter van der Spek & Chris Verhoef, 2014. "Balancing Time‐to‐Market and Quality in Embedded Systems," Systems Engineering, John Wiley & Sons, vol. 17(2), pages 166-192, June.
    9. Hewamalage, Hansika & Bergmeir, Christoph & Bandara, Kasun, 2021. "Recurrent Neural Networks for Time Series Forecasting: Current status and future directions," International Journal of Forecasting, Elsevier, vol. 37(1), pages 388-427.
    10. 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.
    11. 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.
    12. Hossein Hassani & Emmanuel Sirimal Silva & Rangan Gupta & Mawuli K. Segnon, 2015. "Forecasting the price of gold," Applied Economics, Taylor & Francis Journals, vol. 47(39), pages 4141-4152, August.
    13. Thomas Horvath & Peter Huber & Ulrike Huemer & Helmut Mahringer & Philipp Piribauer & Mark Sommer & Stefan Weingärtner, 2022. "Mittelfristige Beschäftigungsprognose für Österreich und die Bundesländer. Berufliche und sektorale Veränderungen 2021 bis 2028," WIFO Studies, WIFO, number 70720.
    14. Sasikiran Kandula & Jeffrey Shaman, 2019. "Reappraising the utility of Google Flu Trends," PLOS Computational Biology, Public Library of Science, vol. 15(8), pages 1-16, August.
    15. de Silva, Ashton J, 2010. "Forecasting Australian Macroeconomic variables, evaluating innovations state space approaches," MPRA Paper 27411, University Library of Munich, Germany.
    16. Kyungsub Lee, 2022. "Application of Hawkes volatility in the observation of filtered high-frequency price process in tick structures," Papers 2207.05939, arXiv.org, revised Sep 2024.
    17. Pawlikowski, Maciej & Chorowska, Agata, 2020. "Weighted ensemble of statistical models," International Journal of Forecasting, Elsevier, vol. 36(1), pages 93-97.
    18. Tendai Makoni & Delson Chikobvu, 2023. "Assessing and Forecasting the Long-Term Impact of the Global Financial Crisis on Manufacturing Sales in South Africa," Economies, MDPI, vol. 11(6), pages 1-17, May.
    19. Fijorek Kamil & Leśniewska Agnieszka, 2012. "Statistical Forecasting of the Indicators of Polish Airport’s Operations," Folia Oeconomica Stetinensia, Sciendo, vol. 11(1), pages 7-7, January.
    20. 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.

    More about this item

    Statistics

    Access and download statistics

    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:plo:pone00:0279483. 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: plosone (email available below). General contact details of provider: https://journals.plos.org/plosone/ .

    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.