IDEAS home Printed from https://ideas.repec.org/a/gam/jijerp/v19y2022i16p10068-d888454.html
   My bibliography  Save this article

The Association between the Burden of PM 2.5 -Related Neonatal Preterm Birth and Socio-Demographic Index from 1990 to 2019: A Global Burden Study

Author

Listed:
  • Zeyu Tang

    (Department of Biostatistics, School of Public Health, Peking University, No. 38, Xueyuan Road, Beijing 100871, China)

  • Jinzhu Jia

    (Department of Biostatistics, School of Public Health, Peking University, No. 38, Xueyuan Road, Beijing 100871, China
    Center for Statistical Science, Peking University, 5 Summer Palace Road, Beijing 100871, China)

Abstract

Background: Preterm birth (PTB) leads to short-term and long-term adverse effects on newborns. Exposure to fine particulate matter (PM 2.5 ) was positively related to PTB. However, the global annual average PM 2.5 was three times than the recommended value in 1998–2014. Socio-demographic index (SDI) is a new indicator that comprehensively reflects the overall development level of a country, partly because of “the epidemiological transition”. Among other countries with higher and similar SDI levels, policy makers have the opportunity to learn from their successful experiences and avoid their mistakes by identifying whether their burdens of disease are higher or lower than the expected. However, it is unclear about the trends of the burden of PM 2.5 -related preterm birth in different countries and different levels of SDI regions. Additionally, the relationship between the SDI and the burden in 1990–2019 is also unclear. Methods: This was a retrospective study based on the Global Burden of Disease Study 2019 (GBD2019) database from 1990 to 2019. The burden of PM 2.5 -related PTB was measured by the age-standardized mortality rate (ASMR), age-standardized disability-adjusted life years rate (ASDR), mortality rate, and the disability-adjusted life years (DALYs). The annual percentage changes (APCs) and the average annual percentage changes (AAPCs) were used to reflect the trends over the past 30 years, which were calculated using a joinpoint model. The relationships between the ASMR, ASDR, and SDI were calculated using a Gaussian process regression. Findings: In 2019, the entire burden of PM 2.5 -related PTB was relatively high, where the ASMR and the ASDR were 0.76 and 67.71, increasing by 7.04% and 7.12%, respectively. It mainly concentrated on early neonates, boys, and on low-middle SDI regions. The increase in the burden of PM 2.5 -related PTB in low and low-middle SDI regions is slightly higher than the decrease in other SDI regions. In 2019, the burden varied greatly among different levels of SDI regions where ASMRs varied from 0.13 in high SDI regions to 1.19 in low-middle regions. The relationship between the expected value of the burden of PM 2.5 -related PTB and SDI presented an inverted U-shape, and it reached the maximum when SDI is around 0.50. The burdens in four regions (South Asia, North Africa and the Middle East, western sub-Saharan Africa, and southern sub-Saharan Africa) were much higher than the mean value. Boys bore more burden that girls. The sex ratio (boys:girls) of the burden showed a dramatically increasing trend in low SDI regions and a decreasing trend in middle SDI regions and high-middle SDI regions. These differences reflect the huge inequality among regions, countries, ages, and sex in the burden of PM 2.5 -related PTB. Conclusion: The overall burden of PM 2.5 -related PTB in 2019 was relatively high, mainly concentrated on early neonates, boys, and on low-middle SDI regions. It showed an increasing trend in low-middle and low SDI regions. The association between the burden and the SDI presented an inverted U-shape. It is very necessary to promulgate policies to prevent and control air pollution in countries with large and increasing exposure to PM 2.5 pollution because it does not need action at an individual level. Focusing on public educational interventions, public and professional policies, and improving accessibility of prenatal care are other feasible ways for low and low-middle SDI countries. Policy makers should also appropriately allocate medical resources to boys and early newborns.

Suggested Citation

  • Zeyu Tang & Jinzhu Jia, 2022. "The Association between the Burden of PM 2.5 -Related Neonatal Preterm Birth and Socio-Demographic Index from 1990 to 2019: A Global Burden Study," IJERPH, MDPI, vol. 19(16), pages 1-20, August.
  • Handle: RePEc:gam:jijerp:v:19:y:2022:i:16:p:10068-:d:888454
    as

    Download full text from publisher

    File URL: https://www.mdpi.com/1660-4601/19/16/10068/pdf
    Download Restriction: no

    File URL: https://www.mdpi.com/1660-4601/19/16/10068/
    Download Restriction: no
    ---><---

    References listed on IDEAS

    as
    1. Hwashin Hyun Shin & Aaron J. Cohen & C. Arden Pope & Majid Ezzati & Stephen S. Lim & Bryan J. Hubbell & Richard T. Burnett, 2016. "Meta‐Analysis Methods to Estimate the Shape and Uncertainty in the Association Between Long‐Term Exposure to Ambient Fine Particulate Matter and Cause‐Specific Mortality Over the Global Concentration ," Risk Analysis, John Wiley & Sons, vol. 36(9), pages 1813-1825, September.
    2. Irene C. Dedoussi & Sebastian D. Eastham & Erwan Monier & Steven R. H. Barrett, 2020. "Premature mortality related to United States cross-state air pollution," Nature, Nature, vol. 578(7794), pages 261-265, February.
    3. Diane Levin-Zamir & Isabella Bertschi, 2018. "Media Health Literacy, eHealth Literacy, and the Role of the Social Environment in Context," IJERPH, MDPI, vol. 15(8), pages 1-12, August.
    4. Karatzoglou, Alexandros & Smola, Alexandros & Hornik, Kurt & Zeileis, Achim, 2004. "kernlab - An S4 Package for Kernel Methods in R," Journal of Statistical Software, Foundation for Open Access Statistics, vol. 11(i09).
    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. Zeyu Tang & Jinzhu Jia, 2022. "PM2.5-Related Neonatal Infections: A Global Burden Study from 1990 to 2019," IJERPH, MDPI, vol. 19(9), pages 1-15, April.
    2. Tsukioka, Yasutomo & Yanagi, Junya & Takada, Teruko, 2018. "Investor sentiment extracted from internet stock message boards and IPO puzzles," International Review of Economics & Finance, Elsevier, vol. 56(C), pages 205-217.
    3. Daniel J. Luckett & Eric B. Laber & Samer S. El‐Kamary & Cheng Fan & Ravi Jhaveri & Charles M. Perou & Fatma M. Shebl & Michael R. Kosorok, 2021. "Receiver operating characteristic curves and confidence bands for support vector machines," Biometrics, The International Biometric Society, vol. 77(4), pages 1422-1430, December.
    4. Grabisch, Michel & Kojadinovic, Ivan & Meyer, Patrick, 2008. "A review of methods for capacity identification in Choquet integral based multi-attribute utility theory: Applications of the Kappalab R package," European Journal of Operational Research, Elsevier, vol. 186(2), pages 766-785, April.
    5. Bellotti, Anthony & Brigo, Damiano & Gambetti, Paolo & Vrins, Frédéric, 2021. "Forecasting recovery rates on non-performing loans with machine learning," International Journal of Forecasting, Elsevier, vol. 37(1), pages 428-444.
    6. Riza, Lala Septem & Bergmeir, Christoph & Herrera, Francisco & Benítez, José M., 2015. "frbs: Fuzzy Rule-Based Systems for Classification and Regression in R," Journal of Statistical Software, Foundation for Open Access Statistics, vol. 65(i06).
    7. Karin Wolffhechel & Amanda C Hahn & Hanne Jarmer & Claire I Fisher & Benedict C Jones & Lisa M DeBruine, 2015. "Testing the Utility of a Data-Driven Approach for Assessing BMI from Face Images," PLOS ONE, Public Library of Science, vol. 10(10), pages 1-10, October.
    8. Jessica Liu & Donghee N. Lee & Elise M. Stevens, 2023. "Characteristics Associated with Young Adults’ Intentions to Engage with Anti-Vaping Instagram Posts," IJERPH, MDPI, vol. 20(11), pages 1-13, June.
    9. Andrea S Martinez-Vernon & James A Covington & Ramesh P Arasaradnam & Siavash Esfahani & Nicola O’Connell & Ioannis Kyrou & Richard S Savage, 2018. "An improved machine learning pipeline for urinary volatiles disease detection: Diagnosing diabetes," PLOS ONE, Public Library of Science, vol. 13(9), pages 1-20, September.
    10. Khamma, Thulasi Ram & Zhang, Yuming & Guerrier, Stéphane & Boubekri, Mohamed, 2020. "Generalized additive models: An efficient method for short-term energy prediction in office buildings," Energy, Elsevier, vol. 213(C).
    11. Madhumita Sahoo & Aman Kasot & Anirban Dhar & Amlanjyoti Kar, 2018. "On Predictability of Groundwater Level in Shallow Wells Using Satellite Observations," Water Resources Management: An International Journal, Published for the European Water Resources Association (EWRA), Springer;European Water Resources Association (EWRA), vol. 32(4), pages 1225-1244, March.
    12. P. J. Zarco-Tejada & T. Poblete & C. Camino & V. Gonzalez-Dugo & R. Calderon & A. Hornero & R. Hernandez-Clemente & M. Román-Écija & M. P. Velasco-Amo & B. B. Landa & P. S. A. Beck & M. Saponari & D. , 2021. "Divergent abiotic spectral pathways unravel pathogen stress signals across species," Nature Communications, Nature, vol. 12(1), pages 1-11, December.
    13. Grubinger, Thomas & Zeileis, Achim & Pfeiffer, Karl-Peter, 2014. "evtree: Evolutionary Learning of Globally Optimal Classification and Regression Trees in R," Journal of Statistical Software, Foundation for Open Access Statistics, vol. 61(i01).
    14. Uwe Ligges & Sebastian Krey, 2011. "Feature clustering for instrument classification," Computational Statistics, Springer, vol. 26(2), pages 279-291, June.
    15. Arnout Van Messem & Andreas Christmann, 2010. "A review on consistency and robustness properties of support vector machines for heavy-tailed distributions," Advances in Data Analysis and Classification, Springer;German Classification Society - Gesellschaft für Klassifikation (GfKl);Japanese Classification Society (JCS);Classification and Data Analysis Group of the Italian Statistical Society (CLADAG);International Federation of Classification Societies (IFCS), vol. 4(2), pages 199-220, September.
    16. Don Nutbeam & Diane Levin-Zamir & Gill Rowlands, 2018. "Health Literacy in Context," IJERPH, MDPI, vol. 15(12), pages 1-3, November.
    17. Jacqueline Adelowo & Mathias Mier & Christoph Weissbart, 2021. "Taxation of Carbon Emissions and Air Pollution in Intertemporal Optimization Frameworks with Social and Private Discount Rates," ifo Working Paper Series 360, ifo Institute - Leibniz Institute for Economic Research at the University of Munich.
    18. Nunes, Matthew, 2015. "Statistical Analysis of Network Data with R," Journal of Statistical Software, Foundation for Open Access Statistics, vol. 66(b01).
    19. Ana Patrícia Rocha & Hugo Miguel Pereira Choupina & Maria do Carmo Vilas-Boas & José Maria Fernandes & João Paulo Silva Cunha, 2018. "System for automatic gait analysis based on a single RGB-D camera," PLOS ONE, Public Library of Science, vol. 13(8), pages 1-24, August.
    20. John S. Evans, 2016. "Characterizing Uncertainty in Estimates of Mortality Risk from Exposure to Ambient Fine Particulate Matter," Risk Analysis, John Wiley & Sons, vol. 36(9), pages 1748-1750, September.

    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:16:p:10068-:d:888454. 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.