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Spatial and temporal projections of the prevalence of active tuberculosis in Cambodia
  1. Kiesha Prem1,
  2. Sok Heng Pheng2,
  3. Alvin Kuo Jing Teo1,
  4. Konstantin Evdokimov1,
  5. Ei Ei Khaing Nang1,
  6. Li Yang Hsu1,
  7. Vonthanak Saphonn3,
  8. Sivanna Tieng2,
  9. Tan Eang Mao2,
  10. Alex R Cook1
  1. 1 Saw Swee Hock School of Public Health, National University of Singapore and National University Health System, Singapore
  2. 2 National Center for Tuberculosis and Leprosy Control (CENAT), Ministry of Health, Phnom Penh, Cambodia
  3. 3 University of Health Sciences, Phnom Penh, Cambodia
  1. Correspondence to Dr Alex R Cook; alex.richard.cook{at}gmail.com

Abstract

Introduction Cambodia is among the 30 highest burden of tuberculosis (TB) countries. Active TB prevalence has been estimated using nationally representative multistage sampling that represents urban, rural and remote parts of the country, but the prevalence in non-sampled communes remains unknown. This study uses geospatial Bayesian statistics to estimate point prevalence across Cambodia, and demographic modelling that accounts for secular trends in fertility, mortality, urbanisation and prevalence rates to project the future burden of active TB.

Methods A Bayesian hierarchical model was developed for the 2011 National Tuberculosis Prevalence survey to estimate the differential effect of age, sex and geographic stratum on active TB prevalence; these estimates were then married with high-resolution geographic information system layers to project prevalence across Cambodia. Future TB projections under alternative scenarios were then derived by interfacing these estimates with an individual-based demographic model.

Results Strong differences in risk by age and sex, together with geographically varying population structures, yielded the first estimated prevalence map at a 1 km scale. The projected number of active TB cases within the catchment area of each existing government healthcare facility was derived, together with projections to the year 2030 under three scenarios: no future improvement, c ontinual r eduction and GDP projection.

Conclusion Synthesis of health and geographic data allows likely disease rates to be mapped at a high resolution to facilitate resource planning, while demographic modelling allows scenarios to be projected, demonstrating the need for the acceleration of control efforts to achieve a substantive impact on the future burden of TB in Cambodia.

  • tuberculosis
  • mathematical modelling
  • geographic information systems

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Footnotes

  • KP and SHP contributed equally.

  • Handling editor Alberto L Garcia-Basteiro

  • Contributors ARC, LYH, KE, EEKN and TEM conceptualised the study. ARC, LYH, KE, EEKN, SHP, VS, ST and TEM contributed to study design. SHP, ST, TEM and CENAT were involved in data collection. KP and ARC did the statistical and modelling analyses. KP made the figures. KP, AKJT and KE did the literature review. KP, AKJT and ARC wrote the initial draft. All authors contributed equally to data interpretation, critically reviewed the manuscript and approved the final version.

  • Funding This work was funded by USIRP under the Infectious Diseases Programme at the Saw Swee Hock School of Public Health at the National University of Singapore. ARC was supported by the Singapore Ministry of Health’s National Medical Research Council under the Centre Grant Programme - Singapore Population Health Improvement Centre (NMRC/CG/C026/2017_NUHS).

  • Disclaimer This article was prepared while EEKN was employed at the National University of Singapore. She is currently employed at the National Institutes of Health. The opinions expressed in this article are the authors' own and do not reflect the view of the National Institutes of Health, the Department of Health and Human Services, or the United States government.

  • Competing interests None declared.

  • Patient consent Not required.

  • Provenance and peer review Not commissioned; externally peer reviewed.

  • Data sharing statement No additional data are available.

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