High-risk spatiotemporal patterns of cutaneous leishmaniasis:a nationwide study in Iran from 2011 to 2020

Background Cutaneous leishmaniasis(CL)is a wide-reaching infection of major public health concern.Iran is one of the six most endemic countries in the world.This study aims to provide a spatiotemporal visualization of CL cases in Iran at the county level from 2011 to 2020,detecting high-risk zones,while also noting the movement of high-risk clusters.Methods On the basis of clinical observations and parasitological tests,data of 154,378 diagnosed patients were obtained from the Iran Ministry of Health and Medical Education.Utilizing spatial scan statistics,we investigated the disease’s purely temporal,purely spatial,spatial variation in temporal trends and spatiotemporal patterns.At P=0.05 level,the null hypothesis was rejected in every instance.Results In general,the number of new CL cases decreased over the course of the 9-year research period.From 2011 to 2020,a regular seasonal pattern,with peaks in the fall and troughs in the spring,was found.The period of September–February of 2014–2015 was found to hold the highest risk in terms of CL incidence rate in the whole country[relative risk(RR)=2.24,P<0.001)].In terms of location,six signifcant high-risk CL clusters covering 40.6%of the total area of the country were observed,with the RR ranging from 1.87 to 9.69.In addition,spatial variation in the temporal trend analysis found 11 clusters as potential high-risk areas that highlighted certain regions with an increasing tendency.Finally,fve space-time clusters were found.The geographical displacement and spread of the disease followed a moving pattern over the 9-year study period afecting many regions of the country.Conclusions Our study has revealed signifcant regional,temporal,and spatiotemporal patterns of CL distribution in Iran.Over the years,there have been multiple shifts in spatiotemporal clusters,encompassing many diferent parts of the country from 2011 to 2020.The results reveal the formation of clusters across counties that cover certain parts of provinces,indicating the importance of conducting spatiotemporal analyses at the county level for studies that encompass entire countries.Such analyses,at a fner geographical scale,such as county level,might provide more precise results than analyses at the scale of the province.

Measuring COVID-19 vaccination coverage: an enhanced age-adjusted two-step foating catchment area model

Background:There are only limited studies on access to COVID-19 vaccines and identifying the most appropriate health centres for performing vaccination in metropolitan areas.This study aimed to measure potential spatial access to COVID-19 vaccination centres in Mashhad,the second-most populous city in Iran.Methods:The 2021 age structure of the urban census tracts was integrated into the enhanced two-step foating catchment area model to improve accuracy.The model was developed based on three diferent access scenarios:only public hospitals,only public healthcare centres and both(either hospitals or healthcare centres)as potential vaccination facilities.The weighted decision-matrix and analytic hierarchy process,based on four criteria(i.e.service area,accessibility index,capacity of vaccination centres and distance to main roads),were used to choose potential vaccination centres looking for the highest suitability for residents.Global Moran’s index(GMI)was used to measure the spatial autocorrelation of the accessibility index in diferent scenarios and the proposed model.Results:There were 26 public hospitals and 271 public healthcare centres in the study area.Although the exclusive use of public healthcare centres for vaccination can provide the highest accessibility in the eastern and north-eastern parts of the study area,our fndings indicate that including both public hospitals and public healthcare centres provide high accessibility to vaccination in central urban part.Therefore,a combination of public hospitals and public healthcare centres is recommended for efcient vaccination coverage.The value of GMI for the proposed model(accessibility to selected vaccination centres)was calculated as 0.53(Z=162.42,P<0.01).Both GMI and Z-score values decreased in the proposed model,suggesting an enhancement in accessibility to COVID-19 vaccination services.Conclusions:The periphery and poor areas of the city had the least access to COVID-19 vaccination centres.Measuring spatial access to COVID-19 vaccination centres can provide valuable insights for urban public health decisionmakers.Our model,coupled with geographical information systems,provides more efcient vaccination coverage by identifying the most suitable healthcare centres,which is of special importance when only few centres are available.