IDEAS home Printed from https://ideas.repec.org/a/plo/pone00/0240879.html
   My bibliography  Save this article

Small-area level socio-economic deprivation and tuberculosis rates in England: An ecological analysis of tuberculosis notifications between 2008 and 2012

Author

Listed:
  • Patrick Nguipdop-Djomo
  • Laura C Rodrigues
  • Ibrahim Abubakar
  • Punam Mangtani

Abstract

Background: Tuberculosis (TB) rates in England are among the highest in high-income countries. Poverty and historic and current immigration from high TB incidence parts of the world are two major drivers of tuberculosis in England. However, little has been done in recent years to examine socio-economic trends in TB rates in England, and to disentangle the role of deprivation from that of place of birth in the current TB epidemiology. Objectives: To assess the association between England’s 2008–2012 TB notification rates and small area-level deprivation, together and separately in the UK-born and foreign-born populations. Methods: Ecological analysis of the association between quintiles of England’s 2010 Index of Multiple Deprivation (IMD) and TB rates at the Lower-layer Super Output Area (LSOA; average population ~1500) level, using negative binomial and zero-inflated negative binomial regression models, adjusting for age, sex, urban/rural area classification, and area-level percentage of non-White residents. Results: There was a log-linear gradient between area-deprivation levels and TB rates, with overall TB rates in the most deprived quintile areas three times higher than the least deprived quintile after adjustment for age and sex (IRR = 3.35; 95%CI: 3.16 to 3.55). The association and gradient were stronger in the UK-born than the foreign-born population, with UK-born TB rates in the most deprived quintiles about two-and-a-half times higher than the least deprived quintile (IRR = 2.39; 95%CI: 2.19 to 2.61) after controlling for age, sex, urban/rural classification and percentage of non-White residents; whereas the comparable figure for foreign-born persons was 80% higher (IRR = 1.78; 95%CI: 1.66 to 1.91). Conclusions: Socio-economic deprivation continues to play a substantial role in sustaining the TB epidemic in England, especially in the UK-born population. This supports the case for further investigations of the underlying social- determinants of TB.

Suggested Citation

  • Patrick Nguipdop-Djomo & Laura C Rodrigues & Ibrahim Abubakar & Punam Mangtani, 2020. "Small-area level socio-economic deprivation and tuberculosis rates in England: An ecological analysis of tuberculosis notifications between 2008 and 2012," PLOS ONE, Public Library of Science, vol. 15(10), pages 1-15, October.
  • Handle: RePEc:plo:pone00:0240879
    DOI: 10.1371/journal.pone.0240879
    as

    Download full text from publisher

    File URL: https://journals.plos.org/plosone/article?id=10.1371/journal.pone.0240879
    Download Restriction: no

    File URL: https://journals.plos.org/plosone/article/file?id=10.1371/journal.pone.0240879&type=printable
    Download Restriction: no

    File URL: https://libkey.io/10.1371/journal.pone.0240879?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. Vuong, Quang H, 1989. "Likelihood Ratio Tests for Model Selection and Non-nested Hypotheses," Econometrica, Econometric Society, vol. 57(2), pages 307-333, March.
    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. Fabrice Gilles & Sabina Issehnane & Florent Sari, 2022. "Using short-term jobs as a way to find a regular job. What kind of role for local context?," TEPP Working Paper 2022-07, TEPP.
    2. Paulo M. D. C. Parente & Richard J. Smith, 2021. "Quasi‐maximum likelihood and the kernel block bootstrap for nonlinear dynamic models," Journal of Time Series Analysis, Wiley Blackwell, vol. 42(4), pages 377-405, July.
    3. Cornelia Lawson, 2013. "Academic Inventions Outside the University: Investigating Patent Ownership in the UK," Industry and Innovation, Taylor & Francis Journals, vol. 20(5), pages 385-398, July.
    4. Vipin Arora & Shuping Shi, 2016. "Nonlinearities and tests of asset price bubbles," Empirical Economics, Springer, vol. 50(4), pages 1421-1433, June.
    5. Luiz Paulo Fávero & Joseph F. Hair & Rafael de Freitas Souza & Matheus Albergaria & Talles V. Brugni, 2021. "Zero-Inflated Generalized Linear Mixed Models: A Better Way to Understand Data Relationships," Mathematics, MDPI, vol. 9(10), pages 1-28, May.
    6. Da Fonseca José & Grasselli Martino & Ielpo Florian, 2014. "Estimating the Wishart Affine Stochastic Correlation Model using the empirical characteristic function," Studies in Nonlinear Dynamics & Econometrics, De Gruyter, vol. 18(3), pages 253-289, May.
    7. Hansen, Lars Peter & Heaton, John & Luttmer, Erzo G J, 1995. "Econometric Evaluation of Asset Pricing Models," The Review of Financial Studies, Society for Financial Studies, vol. 8(2), pages 237-274.
    8. Das, Marcel & van Soest, Arthur, 1999. "A panel data model for subjective information on household income growth," Journal of Economic Behavior & Organization, Elsevier, vol. 40(4), pages 409-426, December.
    9. Gillespie, Colin S., 2015. "Fitting Heavy Tailed Distributions: The poweRlaw Package," Journal of Statistical Software, Foundation for Open Access Statistics, vol. 64(i02).
    10. Luis Garicano & Thomas N. Hubbard, 2016. "The Returns to Knowledge Hierarchies," The Journal of Law, Economics, and Organization, Oxford University Press, vol. 32(4), pages 653-684.
    11. Yen, Steven T. & Chern, Wen S. & Lee, Hwang-Jaw, 1991. "Effects Of Income Sources On Household Food Expenditures," 1991 Annual Meeting, August 4-7, Manhattan, Kansas 271167, American Agricultural Economics Association (New Name 2008: Agricultural and Applied Economics Association).
    12. Adrian Bruhin & Ernst Fehr & Daniel Schunk, 2019. "The many Faces of Human Sociality: Uncovering the Distribution and Stability of Social Preferences," Journal of the European Economic Association, European Economic Association, vol. 17(4), pages 1025-1069.
    13. Bel, K. & Paap, R., 2013. "Modeling the impact of forecast-based regime switches on macroeconomic time series," Econometric Institute Research Papers EI 2013-25, Erasmus University Rotterdam, Erasmus School of Economics (ESE), Econometric Institute.
    14. Seok, Sang Ik & Cho, Hoon & Ryu, Doojin, 2020. "The information content of funds from operations and net income in real estate investment trusts," The North American Journal of Economics and Finance, Elsevier, vol. 51(C).
    15. Downward, Paul & Rasciute, Simona, 2015. "Assessing the impact of the National Cycle Network and physical activity lifestyle on cycling behaviour in England," Transportation Research Part A: Policy and Practice, Elsevier, vol. 78(C), pages 425-437.
    16. Filiz-Ozbay, Emel & Guryan, Jonathan & Hyndman, Kyle & Kearney, Melissa & Ozbay, Erkut Y., 2015. "Do lottery payments induce savings behavior? Evidence from the lab," Journal of Public Economics, Elsevier, vol. 126(C), pages 1-24.
    17. Arthur Caplan & John Gilbert, 2010. "Can fighting grade inflation help the bottom line?," Applied Economics Letters, Taylor & Francis Journals, vol. 17(17), pages 1663-1667.
    18. Mozhaeva, Irina, 2022. "Inequalities in utilization of institutional care among older people in Estonia," Health Policy, Elsevier, vol. 126(7), pages 704-714.
    19. Magnus, Jan R., 2007. "The Asymptotic Variance Of The Pseudo Maximum Likelihood Estimator," Econometric Theory, Cambridge University Press, vol. 23(5), pages 1022-1032, October.
    20. repec:lan:wpaper:2935 is not listed on IDEAS
    21. Ronelle Burger & Canh Thien Dang & Trudy Owens, 2017. "Better performing NGOs do report more accurately: Evidence from investigating Ugandan NGO financial accounts," Discussion Papers 2017-10, University of Nottingham, CREDIT.

    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:pone00:0240879. 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.