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Geospatial Overlap of Undernutrition and Tuberculosis in Ethiopia

Author

Listed:
  • Fasil Wagnew

    (National Centre for Epidemiology and Population Health (NCEPH), College of Health and Medicine, The Australian National University, Canberra 2601, Australia
    College of Health Sciences, Debre Markos University, Debre Markos P.O. Box 269, Ethiopia
    Geospatial and Tuberculosis Research Team, Telethon Kids Institute, Nedlands 6009, Australia)

  • Kefyalew Addis Alene

    (Geospatial and Tuberculosis Research Team, Telethon Kids Institute, Nedlands 6009, Australia
    School of Population Health, Faculty of Health Sciences, Curtin University, Bentley 6102, Australia)

  • Matthew Kelly

    (National Centre for Epidemiology and Population Health (NCEPH), College of Health and Medicine, The Australian National University, Canberra 2601, Australia)

  • Darren Gray

    (Population Health Program, QIMR Berghofer Medical Research Institute, Brisbane 4006, Australia)

Abstract

Undernutrition is a key driver of the global tuberculosis (TB) epidemic, yet there is limited understanding regarding the spatial overlap of both diseases. This study aimed to determine the geographical co-distribution and socio-climatic factors of undernutrition and TB in Ethiopia. Data on undernutrition were found from the Ethiopian Demographic and Health Survey (EDHS). Data on TB were obtained from the Ethiopia national TB prevalence survey. We applied a geostatistical model using a Bayesian framework to predict the prevalence of undernutrition and TB. Spatial overlap of undernutrition and TB prevalence was detected in the Afar and Somali regions. Population density was associated with the spatial distribution of TB [β: 0.008; 95% CrI: 0.001, 0.014], wasting [β: −0.017; 95% CrI: −0.032, −0.004], underweight [β: −0.02; 95% CrI: −0.031, −0.011], stunting [β: −0.012; 95% CrI: −0.017, −0.006], and adult undernutrition [β: −0.007; 95% CrI: −0.01, −0.005]. Distance to a health facility was associated with the spatial distribution of stunting [β: 0.269; 95% CrI: 0.08, 0.46] and adult undernutrition [β: 0.176; 95% CrI: 0.044, 0.308]. Healthcare access and demographic factors were associated with the spatial distribution of TB and undernutrition. Therefore, geographically targeted service integration may be more effective than nationwide service integration.

Suggested Citation

  • Fasil Wagnew & Kefyalew Addis Alene & Matthew Kelly & Darren Gray, 2023. "Geospatial Overlap of Undernutrition and Tuberculosis in Ethiopia," IJERPH, MDPI, vol. 20(21), pages 1-15, October.
    • Handle: RePEc:gam:jijerp:v:20:y:2023:i:21:p:7000-:d:1271261
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    References listed on IDEAS

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    1. Kedir Y. Ahmed & Allen G. Ross & Seada M. Hussien & Kingsley E. Agho & Bolajoko O. Olusanya & Felix Akpojene Ogbo, 2023. "Mapping Local Variations and the Determinants of Childhood Stunting in Nigeria," IJERPH, MDPI, vol. 20(4), pages 1-16, February.
    2. D. J. Weiss & A. Nelson & H. S. Gibson & W. Temperley & S. Peedell & A. Lieber & M. Hancher & E. Poyart & S. Belchior & N. Fullman & B. Mappin & U. Dalrymple & J. Rozier & T. C. D. Lucas & R. E. Howes, 2018. "A global map of travel time to cities to assess inequalities in accessibility in 2015," Nature, Nature, vol. 553(7688), pages 333-336, January.
    3. Håvard Rue & Sara Martino & Nicolas Chopin, 2009. "Approximate Bayesian inference for latent Gaussian models by using integrated nested Laplace approximations," Journal of the Royal Statistical Society Series B, Royal Statistical Society, vol. 71(2), pages 319-392, April.
    4. Finn Lindgren & Håvard Rue & Johan Lindström, 2011. "An explicit link between Gaussian fields and Gaussian Markov random fields: the stochastic partial differential equation approach," Journal of the Royal Statistical Society Series B, Royal Statistical Society, vol. 73(4), pages 423-498, September.
    5. Wenjun Zhu & Si Zhu & Bruno F. Sunguya & Jiayan Huang, 2021. "Urban–Rural Disparities in the Magnitude and Determinants of Stunting among Children under Five in Tanzania: Based on Tanzania Demographic and Health Surveys 1991–2016," IJERPH, MDPI, vol. 18(10), pages 1-14, May.
    6. Hargreaves, J.R. & Boccia, D. & Evans, C.A. & Adato, M. & Petticrew, M. & Porter, J.D., 2011. "The social determinants of tuberculosis: from evidence to action," American Journal of Public Health, American Public Health Association, vol. 101(4), pages 654-662.
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