جستجوی مقالات مرتبط با کلیدواژه « تحلیل مکانی » در نشریات گروه « پزشکی »

With the spread of COVID-19 pandemic, different countries have implemented various intervention to control the disease. Providing information on how the disease spreads and local risk factors can help policymakers and managers to control the disease. The aim of this study was to analyze the spatial factors of Covid-19 disease in Hamadan province, Iran.

All cases of Covid-19 in Hamadan province from the beginning of the epidemic (Jan 2020) to the end of March 2021 with a confirmed diagnosis of Covid-19 disease based on PCR test were included in the study. The required information was collected through the Covid-19 case registration system in Hamadan University of medical science, other provincial institutions and the Statistics Center of Iran. Statistical analyzes were performed in two parts: descriptive and analytical. Zoning maps were used to show the distribution of disease cases. Getis-Ord G statistic was used in the analytical analysis to analyze the hot spots.

A total of 25197 patients suspected of Covid-19 were studied. Of these 10366 were positive based on PCR test. There were 1510 confirmed positive cases of the disease who died during the period of investigation. Population density, employment rate, development index, illiteracy rate, distance from the provincial center, air temperature, number of frosty days, average air humidity showed a significant relationship with the incidence of positive cases of Covid-19 in Hamadan province.

Based on the findings, the most important variables related to the increase in the death of covid-19 included the increase in population density and the level of development. The preparation of risk maps using GIS can help to plan a rapid response to the COVID-19 epidemic, focus on prevention programs in high-risk areas, and plan the necessary strategies appropriate to the epidemic trend to control COVID-19 and similar epidemics in the future.

Infection of birds to Highly Pathogenic Avian Influenza (HPAI) and their extinction impose heavily losses on the livestock and poultry industry along with public health. Nowadays, due to the volume and variety of data, the need of using location-based technologies and data mining sciences has become inevitable. This study aims to model the prevalence of avian influenza, using the capabilities of spatial analyses.

In this analytical-ecological study, the year 2016 is selected as the target year to prepare 17 variables (climate, environment, and man-made) and their spatial layers in Guilan province because of the high prevalence of the disease in this year. The weights of the variables were computed through combination of Boosted Regression Trees (BRT) analysis and Geographically Weighted Regression (GWR), and then prevalence of the disease was prepared and evaluated by the Receiver Operating Characteristic (ROC) curve.

The variables of wetlands, live poultry markets, and pools have the highest weights according to BRT analysis, with 18.91, 15.59, and 12.8 percent, respectively. Also, in terms of time, the month of February has the highest prevalence among the three cold months of the year.

 The disease has been observed in the areas around wetlands, pools, and live poultry markets. Therefore, the General Veterinary Administration, as a regulatory and policy-making body, and poultry producers and sellers as executive agents can play a significant role in monitoring, controlling, and preventing the spread of the disease.

Burns are tragic events with considerable mortality rates. This study was conducted to analyze the spatial pattern of burns and identify the factors associated with the burn injuries in Mashhad, Iran. 

This retrospective cross-sectional study was conducted in patients hospitalized in Imam Reza Hospital Burn Center, Mashhad, which is the only referral burn center in the northeast of Iran. Spatial generalized linear models (Poisson and negative binomial) were used to model the association between socio-economic characteristics and the incidence of burn injury. 

A total of 1044 acute burn patients (69.9% male, 30.1% female) were reviewed. Most of the burned patients were in the age group 20-29 years (41.4%). The most common cause of burn injury was flame (64.6%). The local test of spatial autocorrelation confirmed that the burns had cluster pattern in the central areas of the city and the worn-out urban texture was at higher risk of burns. The results of model showed that the percentage of worn-out urban texture (IRR =1.007; 95%CI 1.005-1.008), literacy (IRR =0.98; 95% CI 0.95-0.99), percentage of employed women (IRR =0.96; 95%CI 0.92-0.99), unemployment rate (IRR =0.92; 95%CI 0.85-0.99) and percentage of margination (IRR =1.006; 95%CI 1.005-1.008) were associated with burns. 

Based on the findings, socio-economic and environmental characteristics are associated with the incidence of burn injuries in different areas. The results of this study could be used to design burn prevention strategies, especially in the high-risk areas and groups.

The prevalence of Chronic Kidney Disease (CKD) reached about 13.4% in 2016. Among available treatment methods, hemodialysis is the most common way used for the treatment of patients with End-Stage Renal Disease (ESRD), the last stage of CKD. On average, these patients refer to hemodialysis centers three times a week. The high patients referral rate indicates the importance of proper management and planning as well as the optimal and efficient allocation of hemodialysis resources.

This overview study was conducted to investigate the applications of spatial analyses in the management and allocation of hemodialysis resources. By designing a search strategy, related articles indexed in the MEDLINE database, were reviewed.

Studies related to spatial analysis in the field of hemodialysis can be categorized into three groups including: 1) measurement of access to hemodialysis centers, 2) relationship between access to these centers and mortality as well as patients' quality of life, and 3) allocation of hemodialysis resources in a geographical area.

Spatial analysis as a geographical information system-based methodology, can be used for analyzing data associated with hemodialysis centers to optimize resources management and help policymakers to make better informed decisions in this area. Most of the studies investigated the access to hemodialysis centers and the relationship between access to these centers and mortality as well as the patient's quality of life. However, few studies have been conducted on the optimal allocation of hemodialysis resources in a geographical area.

Public health issues and spread of diseases are directly related to the regional geography. The present study was designed and conducted to investigate geographical distribution of Malaria in southern Fars province in Jahrom city using the Geographical Information System (GIS).
In the present Descriptive study, the geographical coordinates of each patient were determined for spatial analysis of malaria. For spatial analysis, Moran’s index autocorrelation and kriging interpolation method were used in GIS-10.3 software.
A total of 136 cases of Malaria had occurred during the study years. The highest annual parasite incidence related to 2006, 2007, and 2008 with incidence rate of 0.15, 0.108, and 0.116 in every 1000 people respectively, and since 2008, with a slight fluctuation the incidence of Malaria had had a descending trend, with Moran index of 0.04 and Z-score of 0.33, indicating random distribution. The results obtained from Kriging method and Jeefoo et al. classification showed that the risk of incidence is very high(0.275 per 1,000 people) on the margins of Jahrom city (P=0.73)(Moran index=0.04)(Z-score=0.33)
This study indicated that the prevalence of malaria in the margin of the city of jahrom is more than central areas. Spatial Analysis of the disease also can be a useful tool in malaria prevention and control program by showing hisk risk areas for malaria and it can help to assess environmental factors.

Leishmaniasis is a zoonosis disease. About 350 million people are at risk of developing a disease, with 1.5 to 2 million new cases every year in the world. The aim of this study was to determine the space-time clusters of cutaneous leishmaniasis in north of Khuzestan Province, Iran. In this cross-sectional study, the annual cutaneous leishmaniasis incidence per 100,000 individuals in each county was determined for the past five years. Reported from 2011 to 2015 in North of Khuzestan Province, Iran. Geographical information system (GIS) and spatial scan statistic method were used to identify spatial clusters of cutaneous leishmaniasis cases at the county level. Pure retrospective temporal analysis scanning was performed to detect the temporal clusters of cutaneous leishmaniasis cases with high rates using the discrete Poisson model. The space-time cluster was detected with high rates through the retrospective space-time analysis scanning using the discrete Poisson model. The overall cutaneous leishmaniasis incidence increased from 2011 to 2015. A total of 3 high-risk counties were determined through Local Moran’s I analysis from 2011 to 2015. Local Moran’s I enabled the detection of the spatial autocorrelation for a county with its adjacent county. The method of spatial scan statistics identified different 11 significant spatial clusters. The space-time clustering analysis determined that the most likely cluster included 11 counties, and the time frame was October 2014. The secondary cluster included one counties in October 2014. The tertiary cluster included six counties, and the time frame was from June 2014 to November 2015. Spatial and temporal clusters of cutaneous leishmaniasis have increased in the northern region of Khuzestan Province, and most clusters have occurred in November.

One of the most important issues in disaster management is road accidents, of which its essential indicators are site selection of relief bases on the roads. In Iran, due to the multiple and separation of tasks and duties, many organizations are legally responsible for road accidents including Red Crescent society, EMS, Road Maintenance & Transportation Organization (Rahdari), and Iranian Traffic Police. Given the social, economic and geographical characteristics, the spatial analysis of relief bases along the roads is considered as an important factor in reducing the vulnerability of road accidents. The purpose of this study is to answer the question of whether the location of the road relief bases is based on appropriate criteria.
In this applied research, the methods of documentary, analytical and survey are used. In addition, data is gathered from internet, map, statistical and library resources based on combined methods using applied models such as AHP and GIS.
Findings and The results of this research indicate that the most important and priority areas in the case studies have not covered with any relief services. According to the prioritization process of this study, apart from Tehran-Haraz axis, it is necessary to develop rescue and relief bases in two axes of Firuzkuh and Varamin. Also, all three axes especially Haraz and Firuzkuh have not been covered with Emergency Road Services. Moreover, the end part of Firuzkuh axis in Tehran province requires Rahdari and the area of ​​ Firuzkuh to the border of Mazandaran province and the axis of Varamin need to study the construction of a police station.

One of the applications of population attributable risk percent (PAR%) is to estimate the disease burden in a population exposed to several risk factors. Therefore, this study was conducted to estimates the PAR% of the space-time clusters of pulmonary tuberculosis.
In this study, the data of pulmonary TB cases were obtained from the health department of Hamadan University of Medical Sciences. After detecting significant clusters using the spatiotemporal scan statistic, PAR% was used to analyze the clusters and to detect the location of clusters more accurately.
Four primary space-time clusters and three secondary spatiotemporal clusters were detected for patients with pulmonary tuberculosis and 92% of the patients who did not have a clear state in term of HIV infection. Despite differences in the location of clusters and PAR% attributed to them, the second primary cluster of pulmonary tuberculosis cases (consisting of the cities of Razan, Famenain and Kabudrahang) had the highest amount of PAR% that needs more attention.
The presence of significance spatiotemporal clusters in Hamadan highlights the necessity of the use of PAR% to distinguish cluster areas and to implement prevention and control policy. However, more analytical studies are needed to detect the on the determinants of the occurrence of pulmonary TB.

Street dust is consisting of solid particles with complex compositions which can be an appropriate indicator to determine urban environmental contamination. Therefore, the purpose of this study was to determine spatial patterns and heavy metals contamination in the street dusts of Kashan.
A total of 48 urban street dust samples were collected and total concentrations of Pb, Cu, Zn, Fe, Cr, Ni, and Cd were determined in the dust samples. The geoaccumulation index was applied to assess heavy metals contamination. In addition, the spatial patterns of heavy metals concentrations in the street dust were determined using GIS.
The results showed that the average concentrations of Zn, Cd, Cr, Fe, Ni, Pb and Cu in the street dust samples were 237.21, 0.43, 37.12, 16589.77, 13.62, 45.18 and 45.58 mg/kg, respectively. The average concentrations of Pb, Cu, Zn, and Fe were higher than their local background values. Based on geoaccumulation index, the heavy metals in the street dust were in the following decreasing order: Pb > Zn > Cu > Ni > Cd > Cr. According to the spatial analysis results, higher concentrations of heavy metals were observed in the city center and Kashan-Qom highway; whereas, lower concentrations were found in the residential regions.
Based on the obtained results, it seems the high concentrations of Pb, Zn and Cu and to a lesser extent Fe and Cd concentrations in the dust samples was derived from anthropogenic activities; whereas the concentration of Cr and Ni has been mostly affected by natural sources. The high concentrations of heavy metals in the street dusts of Kashan could be attributed to vehicle emissions and industrial activities.

Public health issues and the spread of disease correlate with geography and always have locational aspects.. Therfore, in this study the spatial analysis of malaria disease reports between 2004-2014 in Bam were investigated using geographic information system (GIS).
In this cross-sectional study, information on positive cases of malaria were obtained from the health center in theBam city for 11 years from 2004 to 2014 Then Based on the postal address of each case, the geographical coordinates of each patient were determined for spatial analysis of malaria. For spatial analysis, Moran’s index autocorrelation and kriging interpolation method were used in GIS software.
During the 11-year period, 112 cases of malaria disease occurred in Bam, among which 38 cases were women (33.9%) and 74 cases were men (66.1%). From a total of 112 patients, 102 (91.08%) had vivax malaria and 10 patients (8.92%) were infected with Plasmodium falciparum. The spatial distribution of disease was random (Z-score = -0.04) in the study area and most outbreaks risk of disease was in the peripheral areas of the city.
This study indicated that the prevalence of malaria in the Bam countryside is more than the central areas. Spatial Analysis can be a useful tool in malaria prevention and control program by showing high risk areas for malaria and it can help to assess environmental factors.

Spatial epidemiology is the description and analysis of geographic variations of diseases with respect to demographic, environmental, behavioral, socioeconomic, genetic, and infectious risk factors. Disease mapping includes a set of statistical techniques that lead to providing clean maps based on estimation of the incidence, prevalence and mortality rates for the users to enable them to reliably estimate the distribution of the diseases. In this study, the spatial epidemiology of diarrhea was evaluated in Mazandaran.
In the present study, statistical methods like the Moran’s I spatial autocorrelation, Anselin local Moran’s I, K function and Ripley index were used to evaluate 4924 cases between 1385-1390, based on certain factors detected in diarrheal diseases.
The results of the global Moran index showed that this disease provided adherence to a cluster pattern in the province. The results of the K function also showed that apart from the western regions of the province, the diarrheal disease followed the above-mentioned cluster pattern. The results of Anselin local Moran and sensitive indicators revealed that the central region of the province, including the cities of Sari, Qaymshmhr, and Babol had the highest prevalence of the disease.
The results showed that the prevalence of syndrome diarrhea follows the pattern cluster and the use of spatial analysis methods in a specific geographic area is appropriate for programs to reduce health risks. And in epidemiological studies, analysis and risk assessment diarrhea syndrome are very important.

Background and So far, no spatial analysis of in-hospital mortality due to myocardial infarction (MI) has been conducted in Iran. The present study was performed to determine the geographical pattern of mortality due to myocardial infarction in Iran. In a prospective hospital-based study, we used the data of cases with MI registered by Myocardial Infarction Registry of Iran, 2012. For spatial analysis, LISA, Global Moran’s I, and Getis-Ord’s statistics were used. Data was analyzed using Stata software and ArcGIS 9.3 A total of 20,750 patients was admitted to hospital due to MI and 2511 (12.1%) deaths occurred (median age at death: 65.2 ± 15.2). The incidence of death from MI did not follow a particular pattern (Moran's Index: 0.141, P-value = 0.126). The highest standardized incidence rates of in-hospital mortality from MI in 100,000 population were observed in Ardebil (31), North Khorasan (23.7), Yazd (20.2), West Azerbaijan (19.1) and Khuzestan (18.4). The lowest incidence rates were found in Sistan-Baluchestan and Khorasan provinces. This study supports the hypothesis of random pattern of mortality due to myocardial infarction in Iran.

This article reviews how geospatial analysis capabilities، remote sensing techniques، as well as geographical information system approaches could be applicable in individuals'' and environment’s health observation. The investigation of spatial factors related to individuals'' health has been the most important aspect of efforts to identify and control environmental factors. Spatial analysis and geographical information could benefit researches in a variety of ways in this field. This article considers the carried out studies worldwide on integration of remote sensing، GIS with health and diseases related notions، i. e. medical geography. GIS، as a central core of location-based disasters، can play a significant role in discovering the influence of location on inception، existence، and distribution of diseases and infections. This article aims to introduce geospatial techniques and space-based information to researchers who study and monitor health systems، e. g.، epidemiologists، demographers and geographers. Thanks to the information technology advancements، GI Systems ease and facilitate temporal، geospatial analysis through data mining، data visualization، and crowd sourcing. More importantly، geospatial techniques provide more precise and valuable information at lower prices and time. Involving location and time into some studies on health system will certainly inspire new visions and creativities to explore influential variables on diseases inception and dissemination، which is very beneficial in controlling and isolating diseases، patients، and environmental impacts.