Asian Pacific Journal of Tropical Biomedicine (Jan 2018)

Does prospective permutation scan statistics work well with cutaneous leishmaniais as a high-frequency or malaria as a low-frequency infection in Fars province, Iran?

  • Abbas Rezaianzadeh,
  • Marjan Zare,
  • Hamidreza Tabatabaee,
  • Mohsen Ali-Akbarpour,
  • Hossain Faramarzi,
  • Mostafa Ebrahimi

DOI
https://doi.org/10.4103/2221-1691.244138
Journal volume & issue
Vol. 8, no. 10
pp. 478 – 484

Abstract

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Objective: To determine whether permutation scan statistics was more efficient in finding prospective spatial-temporal outbreaks for cutaneous leishmaniasis (CL) or for malaria in Fars province, Iran in 2016. Methods: Using time-series data including 29 177 CL cases recorded during 2010-2015 and 357 malaria cases recorded during 2010-2015, CL and malaria cases were predicted in 2016. Predicted cases were used to verify if they followed uniform distribution over time and space using space-time analysis. To testify the uniformity of distributions, permutation scan statistics was applied prospectively to detect statistically significant and non-significant outbreaks. Finally, the findings were compared to determine whether permutation scan statistics worked better for CL or for malaria in the area. Prospective permutation scan modeling was performed using SatScan software. Results: A total of 5 359 CL and 23 malaria cases were predicted in 2016 using time-series models. Applied time-series models were well-fitted regarding auto correlation function, partial auto correlation function sample/model, and residual analysis criteria (Pv was set to 0.1). The results indicated two significant prospective spatial-temporal outbreaks for CL (P0.5) in the area. Conclusions: Both CL and malaria follow a space-time trend in the area, but prospective permutation scan modeling works better for detecting CL spatial-temporal outbreaks. It is not far away from expectation since clusters are defined as accumulation of cases in specified times and places. Although this method seems to work better with finding the outbreaks of a high-frequency disease; i.e., CL, it is able to find non-significant outbreaks. This is clinically important for both high- and low-frequency infections; i.e., CL and malaria.

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