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      Effect of flood on the cutaneous leishmaniasis incidence in northeast of Iran: an interrupted time series study

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          Abstract

          Introduction

          Cutaneous Leishmaniasis (CL) is a zoonosis infection which is endemic in more than 100 countries in Asia, Africa, Europe and America. It was estimated that nearly 20 thousand of new cases are reported in Iran annually. This study aimed to investigate the impact of floods on the incidence of leishmaniasis in Golestan province (northeast of Iran) over nine years, from 2015 to 2023.

          Methods

          Interrupted Time Series Analysis (ITSA) is a study design used to study the effects of an intervention, disaster, or natural event on occurrence of a disease over time. In March 2019, a major flood occurred in Golestan Province, and it had a particularly strong effect on the Gonbad Kavous county (region 1). The trend of CL incidence before and after the flood was assessed using ITSA. In addition, the flood impact on CL incidence was compared between Gonbad Kavous county (region 1) with other counties (13 counties, as the region 2).

          Results

          Throughout the study, a total of 8953 cases were identified with 2148 (24%) infected with leishmaniasis before the flood and 6805 (76%) after that. A comparison of leishmaniasis between the two regions before and after the flood revealed a significant increase in Gonbad Kavous County following the flood. Also, in the multivariate regression analysis, the average difference in the baseline occurrence level before the flood in regions 1 and 2 was 30.3 per 10 5 population, which was statistically significant. Additionally, the average difference in the occurrence of leishmaniasis after the flood between the two regions was 37.3 cases per 10 5 population. The difference in trend of incidence between the two regions increased to 30.4 per 10 5 population after the flood, compared to 5.5 per 10 5 before the flood. Also, the long-term trend difference after the flood between the two regions has reached 27.3 per 10 5 population.

          Discussion

          Natural disasters such as floods, earthquakes, and climate change can increase the spread of diseases such as leishmaniasis. Some interventional strategies are needed to decrease the risk of leishmaniasis outbreaks in flooded areas. Besides informing the community, allocating more financial resources for healthcare activities is essential. Environmental and individual protective activities, regular waste collection and disposal, and combating reservoirs and vectors are particularly crucial.

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          Most cited references28

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          Interrupted time series regression for the evaluation of public health interventions: a tutorial

          Abstract Interrupted time series (ITS) analysis is a valuable study design for evaluating the effectiveness of population-level health interventions that have been implemented at a clearly defined point in time. It is increasingly being used to evaluate the effectiveness of interventions ranging from clinical therapy to national public health legislation. Whereas the design shares many properties of regression-based approaches in other epidemiological studies, there are a range of unique features of time series data that require additional methodological considerations. In this tutorial we use a worked example to demonstrate a robust approach to ITS analysis using segmented regression. We begin by describing the design and considering when ITS is an appropriate design choice. We then discuss the essential, yet often omitted, step of proposing the impact model a priori. Subsequently, we demonstrate the approach to statistical analysis including the main segmented regression model. Finally we describe the main methodological issues associated with ITS analysis: over-dispersion of time series data, autocorrelation, adjusting for seasonal trends and controlling for time-varying confounders, and we also outline some of the more complex design adaptations that can be used to strengthen the basic ITS design.
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            Leishmaniasis Worldwide and Global Estimates of Its Incidence

            As part of a World Health Organization-led effort to update the empirical evidence base for the leishmaniases, national experts provided leishmaniasis case data for the last 5 years and information regarding treatment and control in their respective countries and a comprehensive literature review was conducted covering publications on leishmaniasis in 98 countries and three territories (see ‘Leishmaniasis Country Profiles Text S1, S2, S3, S4, S5, S6, S7, S8, S9, S10, S11, S12, S13, S14, S15, S16, S17, S18, S19, S20, S21, S22, S23, S24, S25, S26, S27, S28, S29, S30, S31, S32, S33, S34, S35, S36, S37, S38, S39, S40, S41, S42, S43, S44, S45, S46, S47, S48, S49, S50, S51, S52, S53, S54, S55, S56, S57, S58, S59, S60, S61, S62, S63, S64, S65, S66, S67, S68, S69, S70, S71, S72, S73, S74, S75, S76, S77, S78, S79, S80, S81, S82, S83, S84, S85, S86, S87, S88, S89, S90, S91, S92, S93, S94, S95, S96, S97, S98, S99, S100, S101’). Additional information was collated during meetings conducted at WHO regional level between 2007 and 2011. Two questionnaires regarding epidemiology and drug access were completed by experts and national program managers. Visceral and cutaneous leishmaniasis incidence ranges were estimated by country and epidemiological region based on reported incidence, underreporting rates if available, and the judgment of national and international experts. Based on these estimates, approximately 0.2 to 0.4 cases and 0.7 to 1.2 million VL and CL cases, respectively, occur each year. More than 90% of global VL cases occur in six countries: India, Bangladesh, Sudan, South Sudan, Ethiopia and Brazil. Cutaneous leishmaniasis is more widely distributed, with about one-third of cases occurring in each of three epidemiological regions, the Americas, the Mediterranean basin, and western Asia from the Middle East to Central Asia. The ten countries with the highest estimated case counts, Afghanistan, Algeria, Colombia, Brazil, Iran, Syria, Ethiopia, North Sudan, Costa Rica and Peru, together account for 70 to 75% of global estimated CL incidence. Mortality data were extremely sparse and generally represent hospital-based deaths only. Using an overall case-fatality rate of 10%, we reach a tentative estimate of 20,000 to 40,000 leishmaniasis deaths per year. Although the information is very poor in a number of countries, this is the first in-depth exercise to better estimate the real impact of leishmaniasis. These data should help to define control strategies and reinforce leishmaniasis advocacy.
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              Time series regression studies in environmental epidemiology

              Time series regression studies have been widely used in environmental epidemiology, notably in investigating the short-term associations between exposures such as air pollution, weather variables or pollen, and health outcomes such as mortality, myocardial infarction or disease-specific hospital admissions. Typically, for both exposure and outcome, data are available at regular time intervals (e.g. daily pollution levels and daily mortality counts) and the aim is to explore short-term associations between them. In this article, we describe the general features of time series data, and we outline the analysis process, beginning with descriptive analysis, then focusing on issues in time series regression that differ from other regression methods: modelling short-term fluctuations in the presence of seasonal and long-term patterns, dealing with time varying confounding factors and modelling delayed (‘lagged’) associations between exposure and outcome. We finish with advice on model checking and sensitivity analysis, and some common extensions to the basic model.
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                Author and article information

                Contributors
                khosravi2000us@yahoo.com
                Journal
                BMC Infect Dis
                BMC Infect Dis
                BMC Infectious Diseases
                BioMed Central (London )
                1471-2334
                3 January 2025
                3 January 2025
                2025
                : 25
                : 15
                Affiliations
                [1 ]Department of Epidemiology and Biostatistics, School of Medicine, Kurdistan University of Medical Sciences, ( https://ror.org/01ntx4j68) Kurdistan, Iran
                [2 ]Student Research Committee, School of Public Health, Isfahan University of Medical Sciences, ( https://ror.org/04waqzz56) Isfahan, Iran
                [3 ]Department of Epidemiology, School of Public Health, Shahroud University of Medical Sciences, ( https://ror.org/023crty50) Shahroud, Iran
                Author information
                https://orcid.org/0000-0002-8271-954X
                https://orcid.org/0000-0003-3435-8708
                https://orcid.org/0000-0002-1106-3782
                Article
                10436
                10.1186/s12879-024-10436-7
                11697918
                39754068
                bd9ab076-fff9-4ea7-8464-7d653d0cbb69
                © The Author(s) 2025

                Open Access This article is licensed under a Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 International License, which permits any non-commercial use, sharing, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons licence, and indicate if you modified the licensed material. You do not have permission under this licence to share adapted material derived from this article or parts of it. The images or other third party material in this article are included in the article’s Creative Commons licence, unless indicated otherwise in a credit line to the material. If material is not included in the article’s Creative Commons licence and your intended use is not permitted by statutory regulation or exceeds the permitted use, you will need to obtain permission directly from the copyright holder. To view a copy of this licence, visit http://creativecommons.org/licenses/by-nc-nd/4.0/.

                History
                : 2 August 2024
                : 31 December 2024
                Categories
                Research
                Custom metadata
                © BioMed Central Ltd., part of Springer Nature 2025

                Infectious disease & Microbiology
                cutaneous leishmaniasis,floods,interrupted time series analysis,incidence,golestan

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