1921
Volume 101, Issue 1
  • ISSN: 0002-9637
  • E-ISSN: 1476-1645

Abstract

Abstract.

The large number of activities contributing to zoonoses surveillance and control capability, on both human and animal domains, and their likely heterogeneous implementation across administrative units make assessment and comparisons of capability performance between such units a complex task. Such comparisons are important to identify gaps in capability development, which could lead to clusters of vulnerable areas, and to rank and subsequently prioritize resource allocation toward the least capable administrative units. Area-level preparedness is a multidimensional entity and, to the best of our knowledge, there is no consensus on a single comprehensive indicator, or combination of indicators, in a summary metric. We use Bayesian spatial factor analysis models to jointly estimate and rank disease control and surveillance capabilities against visceral leishmaniasis (VL) at the municipality level in Brazil. The latent level of joint capability is informed by four variables at each municipality, three reflecting efforts to monitor and control the disease in humans, and one variable informing surveillance capability on the reservoir, the domestic dog. Because of the large volume of missing data, we applied imputation techniques to allow production of comprehensive rankings. We were able to show the application of these models to this sparse dataset and present a ranked list of municipalities based on their overall VL capability. We discuss improvements to our models, and additional applications.

[open-access] This is an open-access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited.

Loading

Article metrics loading...

The graphs shown below represent data from March 2017
/content/journals/10.4269/ajtmh.18-0327
2019-06-03
2019-09-16
Loading full text...

Full text loading...

/deliver/fulltext/14761645/101/1/tpmd180327.html?itemId=/content/journals/10.4269/ajtmh.18-0327&mimeType=html&fmt=ahah

References

  1. Vinhaes MC, de Oliveira SV, Reis PO, de Lacerda Sousa AC, Silva RA, Obara MT, Bezerra CM, da Costa VM, Alves RV, Gurgel-Goncales R, , 2014. Assessing the vulnerability of Brazilian municipalities to the vectorial transmission of Trypanosoma cruzi using multi-criteria-decision analysis. Acta Trop 137: 105110. [Google Scholar]
  2. Hogan JW, Tchernis R, , 2004. Bayesian factor analysis for spatially correlated data, with application to summarizing area-level material deprivation from cesus data. J Am Stat Assoc 99: 314324. [Google Scholar]
  3. Courtemanche C, Soneji S, Tchernis R, , 2015. Modelling area-level health rankings. Health Serv Res 50: 14131431. [Google Scholar]
  4. WHO, 2005. International Health Regulations, 2nd edition. Geneva, Switzerland: World Health Organization. Available at: http://www.who.int/topics/international_health_regulations/en/. Accessed March 1, 2018. [Google Scholar]
  5. Briand S, Beresniak A, Nguyen T, Yonli T, Duru G, Kambire C, Perea W, , 2009. Assessment of yellow fever epidemic risk: an original multi-criteria modelling approach. PLoS Negl Trop Dis 3: e483. [Google Scholar]
  6. Hagenlocher M, Castro MC, , 2015. Mapping malaria risk and vulnerability in the United Republic of Tanzania: a spatial explicit model. Popul Health Metr 13: 2. [Google Scholar]
  7. Servadio JL, Convertino M, , 2018. Optimal information networks: application for data-driven integrated health in populations. Sci Adv 4: e1701088. [Google Scholar]
  8. Pan American Health Organization (PAHO), 2017. Epidemiological Bulletin, Number 5. Available at: http://iris.paho.org/xmlui/bitstream/handle/123456789/34112/leishmaniases_report_5_eng.pdf?sequence=5&isAllowed=y. Accessed November 11, 2017. [Google Scholar]
  9. Werneck GL, , 2014. Visceral leishmaniasis in Brazil: rationale and concerns related to reservoir control. Rev Saude Publica 48: 851856. [Google Scholar]
  10. Boaza R, Corberán-Vallet A, Lawson A, de Ferreira Lima Jr. FE, Edel Donato L, Vieira Alves R, Machado G, de Carvalho FM, Pompei J, Del RioVilas VJ, , 2019. Integration of animal health and public health surveillance sources to exhaustively inform the risk of zoonosis: an application to visceral leishmaniasis data in Brazil. Spat Spatiotemporal Epidemiol 29: 177185. [Google Scholar]
  11. Knorr-Held L, Besag J, , 1998. Modelling risk from a disease in time and space. Stat Med 17: 20452060. [Google Scholar]
  12. Gelman A, Rubin DB, , 1992. Inference from iterative simulation using multiple sequences. Stat Sci, 7, 457511. [Google Scholar]
  13. Arndt S, , 2015. Just how useful are health rankings? Health Serv Res 50: 14031406. [Google Scholar]
  14. Del Rio Vilas VJ, Burgueno A, Montibeller G, Clavijo A, Vigilato MA, Cosivi O, , 2013. Prioritization of capacities for dog-mediated human rabies elimination in the Americas: building the framework. Pathog Glob Health 107: 340345. [Google Scholar]
  15. Montibeller G, Carreras A, Del Rio Vilas VJ, Franco A, , 2017. Evaluating Capabilities of Health Systems with Multi-Criteria Decision Analysis. Operational Research Society Conference (OR59), September 2017, Loughborough, England. [Google Scholar]
  16. Del Rio Vilas VJ, 2017. A value-driven framework for the evaluation of biosurveillance systems. Online J Public Health Inform 9: e083. [Google Scholar]
http://instance.metastore.ingenta.com/content/journals/10.4269/ajtmh.18-0327
Loading
/content/journals/10.4269/ajtmh.18-0327
Loading

Data & Media loading...

  • Received : 17 Apr 2018
  • Accepted : 13 Mar 2019
  • Published online : 03 Jun 2019

Most Cited This Month

This is a required field
Please enter a valid email address
Approval was a Success
Invalid data
An Error Occurred
Approval was partially successful, following selected items could not be processed due to error