Afshar Parya Jangipour, Bahrampour Abbas, Shahesmaeili Armita
Student Research Committee, Kerman University of Medical Sciences, Kerman, Iran, Department of Biostatistics and Epidemiology, Faculty of Public Health, Kerman University of Medical Sciences, Kerman, Iran.
Modeling in Health Research Center, Institute for Futures Studies in Health, Department of Biostatistics and Epidemiology, Faculty of Health, Kerman University of Medical Sciences, Kerman Iran.
PLoS Negl Trop Dis. 2022 Apr 11;16(4):e0010250. doi: 10.1371/journal.pntd.0010250. eCollection 2022 Apr.
Cutaneous leishmaniasis (CL) is currently a health problem in several parts of Iran, particularly Kerman. This study was conducted to determine the incidence and trend of CL in Kerman during 2014-2020 and its forecast up to 2023. The effects of meteorological variables on incidence was also evaluated.
4993 definite cases of CL recorded from January 2014 to December 2020 by the Vice-Chancellor for Health at Kerman University of Medical Sciences were entered. Meteorological variables were obtained from the national meteorological site. The time series SARIMA methods were used to evaluate the effects of meteorological variables on CL.
Monthly rainfall at the lag 0 (β = -0.507, 95% confidence interval:-0.955,-0.058) and monthly sunny hours at the lag 0 (β = -0.214, 95% confidence interval:-0.308,-0.119) negatively associated with the incidence of CL. Based on the Akaike information criterion (AIC) the multivariable model (AIC = 613) was more suitable than univariable model (AIC = 690.66) to estimate the trend and forecast the incidence up to 36 months.
The decreasing pattern of CL in Kerman province highlights the success of preventive, diagnostic and therapeutic interventions during the recent years. However, due to endemicity of disease, extension and continuation of such interventions especially before and during the time periods with higher incidence is essential.
皮肤利什曼病(CL)目前是伊朗多个地区,尤其是克尔曼的一个健康问题。本研究旨在确定2014 - 2020年克尔曼地区CL的发病率和趋势,并预测至2023年的情况。还评估了气象变量对发病率的影响。
录入了克尔曼医科大学卫生副校长记录的2014年1月至2020年12月期间4993例确诊的CL病例。气象变量数据来自国家气象站点。采用时间序列SARIMA方法评估气象变量对CL的影响。
滞后0个月的月降雨量(β = -0.507,95%置信区间:-0.955,-0.058)和滞后0个月的月日照时数(β = -0.214,95%置信区间:-0.308,-0.119)与CL发病率呈负相关。基于赤池信息准则(AIC),多变量模型(AIC = 613)比单变量模型(AIC = 690.66)更适合估计趋势并预测长达36个月的发病率。
克尔曼省CL发病率的下降模式凸显了近年来预防、诊断和治疗干预措施的成功。然而,由于该疾病的地方性,尤其是在发病率较高的时期之前和期间,继续扩大并持续实施此类干预措施至关重要。