Spatial modeling of cutaneous leishmaniasis in Iranian army units during 2014-2017 using a hierarchical Bayesian method and the spatial scan statistic

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 significant 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, five space-time clusters were found. The geographical displacement and spread of the disease followed a moving pattern over the 9-year study period affecting many regions of the country.

CONCLUSIONS

Our study has revealed significant regional, temporal, and spatiotemporal patterns of CL distribution in Iran. Over the years, there have been multiple shifts in spatiotemporal clusters, encompassing many different 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 finer geographical scale, such as county level, might provide more precise results than analyses at the scale of the province.

Modeling County-Level Rare Disease Prevalence Using Bayesian Hierarchical Sampling Weighted Zero-Inflated Regression

Estimates of county-level disease prevalence have a variety of applications. Such estimation is often done via model-based small-area estimation using survey data. However, for conditions with low prevalence (i.e., rare diseases or newly diagnosed diseases), counties with a high fraction of zero counts in surveys are common. They are often more common than the model used would lead one to expect; such zeros are called 'excess zeros'. The excess zeros can be structural (there are no cases to find) or sampling (there are cases, but none were selected for sampling). These issues are often addressed by combining multiple years of data. However, this approach can obscure trends in annual estimates and prevent estimates from being timely. Using single-year survey data, we proposed a Bayesian weighted Binomial Zero-inflated (BBZ) model to estimate county-level rare diseases prevalence. The BBZ model accounts for excess zero counts, the sampling weights and uses a power prior. We evaluated BBZ with American Community Survey results and simulated data. We showed that BBZ yielded less bias and smaller variance than estimates based on the binomial distribution, a common approach to this problem. Since BBZ uses only a single year of survey data, BBZ produces more timely county-level incidence estimates. These timely estimates help pinpoint the special areas of county-level needs and help medical researchers and public health practitioners promptly evaluate rare diseases trends and associations with other health conditions.

Cutaneous Leishmaniasis Based on Climate Regions in Iran (1998-2021): A Systematic Review and Meta-Analysis

BACKGROUND

Climate change can facilitate the expansion of leishmaniasis and create the suitable habitat for vector and reservoir species. The objective of this study was to estimate the prevalence of cutaneous leishmaniasis (CL) at the climatic regions of Iran.

MATERIALS AND METHODS

The literature search was conducted to identify all published studies reporting the prevalence or incidence of CL in humans in Iran. A total of 350 articles that reported leishmaniasis in Iran were retrieved, due to eligibility criteria, only 42 studies were selected to the final systematic review and meta-analysis procedure. Random effects meta-analysis was done with the estimate of heterogeneity being taken from an inverse-variance model. Subgroup analysis was conducted and it stratified the studies according to climatic regions. Between-study heterogeneity was assessed by using I 2 and Cochran's Q method I 2 value of heterogeneity. Meta regression was used to investigate factors potentially contributed the between-study heterogeneity.

RESULTS

Individual studies showed that prevalence per 100,000 population estimated the range from 1.5 to 318.7 with the overall random pooled prevalence of 83.3 (95% confidence interval 74.5-92.1). Subgroup analysis by climatic regions showed that many studies were conducted in the desert areas and also, it has more prevalent than the other climatic regions.

CONCLUSIONS

Leishmaniasis was more prevalent in regions with dry and desert climates than the other climatic regions. One of the advantages of this work is that the majority of selected studies have been conducted on population-base. However, some of the studies have been designed poorly or have had a lack of internal validity.

Spatial patterning of occupational stress and its related factors in Iranian critical care nurses using a hierarchical Bayesian technique

BACKGROUND: In each community, health problems’ patterns and geographical changes are of prime importance to determine high and low-risk areas. OBJECTIVES: This study aimed to investigate the Spatial patterning of occupational stress and its related factors in Iranian critical care nurses using a hierarchical Bayesian technique METHODS: The current research was a cross-sectional descriptive-analytical study. The data includes the number of critical care unit nurses who show a high stress level based on a questionnaire. We used variables such as age, gender, collaboration status, working time, marital status, clinical experience, education, supervisor support, stress score, and working on holiday days for this study. The survey participants had to be at least 18 years old, a registered nurse, and working in the intensive care unit (ICU). OpenBUGS version 3.2.3 was used to implement the Bayesian hierarchical Poisson model and find partial patterning of occupational stress and its related factors. RESULTS: The final sample size was 17414 nurses. The overall prevalence of occupational stress in ICU nurses was estimated at 70%. The lowest and highest prevalence was 65.8% in the North Khorasan province and 75.2% in Golestan province. Occupational stress had a statistically significant association with collaboration status, but with demographic variables, shift work, supportive supervisor, and working on holidays had no statistically significant association. CONCLUSIONS: According to the findings, it is necessary to eliminate or reduce job stress and increase efficiency in Iranian nurses, encourage teamwork and collaboration as an essential element of a healthy workplace environment.

Spatio-temporal visualisation of cutaneous leishmaniasis in an endemic, urban area in Iran

INTRODUCTION

Cutaneous Leishmaniasis (CL) is a significant public health concern worldwide. Iran is among the most CL-affected countries, being one of the six most endemic countries in the world. This study aimed to provide a spatio-temporal visualisation of CL cases in an endemic urban area in north-eastern Iran identifying high-risk and low-risk areas during the period 2016-2019.

METHODS

This ecological study was conducted in the city of Mashhad, north-eastern Iran. All cases (n=2425) were diagnosed based on clinical findings and parasitological tests. The patient data were aggregated at the census tract level (the highest resolution available). CL incidence rates were subjected to Empirical Bayesian smoothing across the census tracts followed by spatial autocorrelation analysis to identify clusters and outliers. Spatial scan statistic was used to explore the purely temporal, purely spatial and spatio-temporal trend of the disease. In all instances, the null hypothesis of no clusters was rejected at p ≤0.05.

RESULTS

The overall crude incidence rate decreased from 34.6 per 100,000 individuals in 2016 to 19.9 per 100,000 in 2019. Cluster analysis identified high-risk areas in south-western Mashhad and low-risk areas in the north-eastern areas. Purely time scan statistics identified March to July as the time period with highest risk for CL occurrence. One most likely purely high-risk spatial cluster and six secondary purely high-risk spatial clusters were identified. Further, two spatio-temporal high-risk clusters, one in the north of the city from April to August and a second in the south-western part from March to September were observed.

CONCLUSIONS

Significant spatial, temporal and spatio-temporal patterns of CL distribution were observed in the study area, which should be considered when designing tailored interventions, such as effective resource allocation models, informed control plans and implementation of efficient surveillance systems. Furthermore, this study generated new hypotheses to test potential relationships between socio-economic and environmental risk factors and incidence of CL in high-risk areas.