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Spatiotemporal Patterns of Cholera Hospitalization in Vellore, India

Author

Listed:
  • Aishwarya Venkat

    (Friedman School of Nutrition Science and Policy, Tufts University, Boston, MA 02111, USA)

  • Tania M. Alarcon Falconi

    (Friedman School of Nutrition Science and Policy, Tufts University, Boston, MA 02111, USA)

  • Melissa Cruz

    (Sackler School of Graduate Biomedical Sciences, Tufts University, Boston, MA 02111, USA)

  • Meghan A. Hartwick

    (School of Marine Science and Ocean Engineering, University of New Hampshire, Durham, NH 03824, USA)

  • Shalini Anandan

    (Christian Medical College, Vellore, Tamil Nadu 632004, India)

  • Naveen Kumar

    (Christian Medical College, Vellore, Tamil Nadu 632004, India)

  • Honorine Ward

    (Sackler School of Graduate Biomedical Sciences, Tufts University, Boston, MA 02111, USA
    Christian Medical College, Vellore, Tamil Nadu 632004, India
    Tufts Medical Center, Boston, MA 02111, USA)

  • Balaji Veeraraghavan

    (Christian Medical College, Vellore, Tamil Nadu 632004, India)

  • Elena N. Naumova

    (Friedman School of Nutrition Science and Policy, Tufts University, Boston, MA 02111, USA
    Christian Medical College, Vellore, Tamil Nadu 632004, India)

Abstract

Systematically collected hospitalization records provide valuable insight into disease patterns and support comprehensive national infectious disease surveillance networks. Hospitalization records detailing patient’s place of residence (PoR) can be utilized to better understand a hospital’s case load and strengthen surveillance among mobile populations. This study examined geographic patterns of patients treated for cholera at a major hospital in south India. We abstracted 1401 laboratory-confirmed cases of cholera between 2000–2014 from logbooks and electronic health records (EHRs) maintained by the Christian Medical College (CMC) in Vellore, Tamil Nadu, India. We constructed spatial trend models and identified two distinct clusters of patient residence—one around Vellore (836 records (61.2%)) and one in Bengal (294 records (21.5%)). We further characterized differences in peak timing and disease trend among these clusters to identify differences in cholera exposure among local and visiting populations. We found that the two clusters differ by their patient profiles, with patients in the Bengal cluster being most likely older males traveling to Vellore. Both clusters show well-aligned seasonal peaks in mid-July, only one week apart, with similar downward trend and proportion of predominant O1 serotype. Large hospitals can thus harness EHRs for surveillance by utilizing patients’ PoRs to study disease patterns among resident and visitor populations.

Suggested Citation

  • Aishwarya Venkat & Tania M. Alarcon Falconi & Melissa Cruz & Meghan A. Hartwick & Shalini Anandan & Naveen Kumar & Honorine Ward & Balaji Veeraraghavan & Elena N. Naumova, 2019. "Spatiotemporal Patterns of Cholera Hospitalization in Vellore, India," IJERPH, MDPI, vol. 16(21), pages 1-14, November.
    • Handle: RePEc:gam:jijerp:v:16:y:2019:i:21:p:4257-:d:282889
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    References listed on IDEAS

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    1. Klompas, M. & Cocoros, N.M. & Menchaca, J.T. & Erani, D. & Hafer, E. & Herrick, B. & Josephson, M. & Lee, M. & Weiss, M.D.P. & Zambarano, B. & Eberhardt, K.R. & Malenfant, J. & Nasuti, L. & Land, T., 2017. "State & local chronic disease surveillance using electronic health record systems," American Journal of Public Health, American Public Health Association, vol. 107(9), pages 1406-1412.
    2. Siobhan M Mor & Alfred DeMaria Jr. & Elena N Naumova, 2014. "Hospitalization Records as a Tool for Evaluating Performance of Food- and Water-Borne Disease Surveillance Systems: A Massachusetts Case Study," PLOS ONE, Public Library of Science, vol. 9(4), pages 1-8, April.
    3. Birgit Nikolay & Henrik Salje & Katharine Sturm-Ramirez & Eduardo Azziz-Baumgartner & Nusrat Homaira & Makhdum Ahmed & A Danielle Iuliano & Repon C Paul & Mahmudur Rahman & M Jahangir Hossain & Stephe, 2017. "Evaluating Hospital-Based Surveillance for Outbreak Detection in Bangladesh: Analysis of Healthcare Utilization Data," PLOS Medicine, Public Library of Science, vol. 14(1), pages 1-18, January.
    4. Melissa S Cruz & Tania M AlarconFalconi & Meghan A Hartwick & Aishwarya Venkat & Hanna Y Ehrlich & Shalini Anandan & Honorine D Ward & Balaji Veeraraghavan & Elena N Naumova, 2017. "From hospitalization records to surveillance: The use of local patient profiles to characterize cholera in Vellore, India," PLOS ONE, Public Library of Science, vol. 12(8), pages 1-21, August.
    5. repec:aph:ajpbhl:10.2105/ajph.2017.303874_8 is not listed on IDEAS
    6. Pavel S. Stashevsky & Irina N. Yakovina & Tania M. Alarcon Falconi & Elena N. Naumova, 2019. "Agglomerative Clustering of Enteric Infections and Weather Parameters to Identify Seasonal Outbreaks in Cold Climates," IJERPH, MDPI, vol. 16(12), pages 1-19, June.
    7. Peter Diggle, 1985. "A Kernel Method for Smoothing Point Process Data," Journal of the Royal Statistical Society Series C, Royal Statistical Society, vol. 34(2), pages 138-147, June.
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    Cited by:

    1. Anastasia Marshak & Aishwarya Venkat & Helen Young & Elena N. Naumova, 2021. "How Seasonality of Malnutrition Is Measured and Analyzed," IJERPH, MDPI, vol. 18(4), pages 1-12, February.

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