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Incidence and trend of leishmaniasis and its related factors in Golestan province, northeastern Iran: time series analysis

  • Mostafa Majidnia ORCID logo , Ali Hosseinzadeh ORCID logo and Ahmad Khosravi ORCID logo EMAIL logo
Published/Copyright: May 3, 2023
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Epidemiologic Methods
From the journal Epidemiologic Methods Volume 12 Issue 1

Abstract

Objectives

Leishmaniasis is a parasitic disease whose transmission depends on climatic conditions and is more important in northeast Iran. This study aimed to investigate the time trend of leishmaniasis and present a prediction model using meteorological variables in Golestan province.

Methods

The 10-year data on leishmaniasis (2010–2019) were collected from the portal of the Ministry of Health and the National Meteorological Organization. First, the disease incidence (per 100,000 population) in different cities of the Golestan province was estimated. Then, the geographical distribution and disease clusters map were prepared at the province level. Finally, by using the Jenkins box model time series analysis method, the disease incidence was predicted for the period 2020 to 2023 of the total province.

Results

From 2010 to 2019, 8,871 patients with leishmaniasis were identified. The mean age of patients was 21.0 ± 18.4 years. The highest mean annual incidence was in Maravah-Tappeh city (188 per 100,000 population). The highest and lowest annual incidence was in 2018 and 2017, respectively. The average 10-year incidence was 48 per 100,000 population. The daily meteorological variables like monthly average wind speed, sunshine, temperature, and mean soil temperature at depth of 50 cm were significantly associated with the incidence of the disease. The estimated threshold for an epidemic was 40.3 per 100,000 population.

Conclusions

According to the results, leishmaniasis incidence cases apears in July and with a peak in November. The incidence rate was highest in Maravah-Tapeh and Gonbad-Kavous, and lowest in Kordkoy counties. The study showed that there were two peaks in 2010 and 2018 and also identified a downward trend in the disease between 2010 and 2013 with a clear decrease in the incidence. Climate conditions had an important effect on leishmaniasis incidence. These climate and epidemiological factors such as migration and overcrowding could provide important input to monitor and predict disease for control strategies.

Keywords: ARIMA; incidence; Iran; leishmaniasis; meteorological variables; time trend
Corresponding author: Ahmad Khosravi, PhD of Epidemiology, Ophthalmic Epidemiology Research Center, Shahroud University of Medical Sciences, Shahroud, Iran, Phone: 0989125731774, E-mail:

Acknowledgments

We would like to thank the Deputy Minister of Health of Golestan University of Medical Sciences and Health Services and the Iran Meteorological Organization for providing the data.

  1. Research funding: Not applicable.

  2. Author contributions: MM and AH designed the study and performed the analyses. AKh wrote the original draft. All authors contributed to review and editing. All authors read and approved the final manuscript.

  3. Competing interests: The authors declare that they have no competing interests.

  4. Informed consent: Informed consent was obtained from all individuals included in this study.

  5. Ethical approval: This study is the result of the first author’s thesis and approved by the Vice Chancellor for Research and Technology of Shahroud University of Medical Sciences with the ethical code IR.SHMU.REC.1399.034. Consent was also sought from the Golestan University of Medical Sciences administration before gathering the data.

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Received: 2022-07-02
Accepted: 2023-04-08
Published Online: 2023-05-03

© 2023 Walter de Gruyter GmbH, Berlin/Boston

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https://doi.org/10.1515/em-2022-0124
Keywords for this article
ARIMA; incidence; Iran; leishmaniasis; meteorological variables; time trend

Articles in the same Issue

  1. Research Articles
  2. Outliers in nutrient intake data for U.S. adults: national health and nutrition examination survey 2017–2018
  3. Using repeated antibody testing to minimize bias in estimates of prevalence and incidence of SARS-CoV-2 infection
  4. A compartmental model of the COVID-19 pandemic course in Germany
  5. Energy-efficient model “DenseNet201 based on deep convolutional neural network” using cloud platform for detection of COVID-19 infected patients
  6. Identification of time delays in COVID-19 data
  7. A country-specific COVID-19 model
  8. Incidence and trend of leishmaniasis and its related factors in Golestan province, northeastern Iran: time series analysis
  9. Application of machine learning tools for feature selection in the identification of prognostic markers in COVID-19
  10. A study of the impact of policy interventions on daily COVID scenario in India using interrupted time series analysis
  11. Measuring COVID-19 spreading speed through the mean time between infections indicator
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  13. Energy- efficient model “Inception V3 based on deep convolutional neural network” using cloud platform for detection of COVID-19 infected patients
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