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Mohammadi A, Bergquist R, Fathi G, Pishgar E, de Melo SN, Sharifi A, Kiani B. Homicide rates are spatially associated with built environment and socio-economic factors: a study in the neighbourhoods of Toronto, Canada. BMC Public Health 2022; 22:1482. [PMID: 35927698 PMCID: PMC9351166 DOI: 10.1186/s12889-022-13807-4] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/22/2021] [Accepted: 07/14/2022] [Indexed: 11/26/2022] Open
Abstract
Objectives Homicide rate is associated with a large variety of factors and therefore unevenly distributed over time and space. This study aims to explore homicide patterns and their spatial associations with different socioeconomic and built-environment conditions in 140 neighbourhoods of the city of Toronto, Canada. Methods A homicide dataset covering the years 2012 to 2021 and neighbourhood-based indicators were analysed using spatial techniques such as Kernel Density Estimation, Global/Local Moran’s I and Kulldorff’s SatScan spatio-temporal methodology. Geographically weighted regression (GWR) and multi-scale GWR (MGWR) were used to analyse the spatially varying correlations between the homicide rate and independent variables. The latter was particularly suitable for manifested spatial variations between explanatory variables and the homicide rate and it also identified spatial non-stationarities in this connection. Results The adjusted R2 of the MGWR was 0.53, representing a 4.35 and 3.74% increase from that in the linear regression and GWR models, respectively. Spatial and spatio-temporal high-risk areas were found to be significantly clustered in downtown and the north-western parts of the city. Some variables (e.g., the population density, material deprivation, the density of commercial establishments and the density of large buildings) were significantly associated with the homicide rate in different spatial ways. Conclusion The findings of this study showed that homicide rates were clustered over time and space in certain areas of the city. Socioeconomic and the built environment characteristics of some neighbourhoods were found to be associated with high homicide rates but these factors were different for each neighbourhood. Supplementary Information The online version contains supplementary material available at 10.1186/s12889-022-13807-4.
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Affiliation(s)
- Alireza Mohammadi
- Department of Geography and Urban Planning, Faculty of Social Sciences, University of MohagheghArdabili, Ardabil, Iran.
| | - Robert Bergquist
- Ingerod, Brastad, SE-454 94 Sweden. Formerly UNICEF/UNDP/World Bank/WHO Special Programme for Research and Training in Tropical Diseases (TDR), World Health Organization, Geneva, Switzerland
| | - Ghasem Fathi
- Department of Geography and Urban Planning, Faculty of Social Sciences, University of MohagheghArdabili, Ardabil, Iran
| | - Elahe Pishgar
- Department of Human Geography, Faculty of Earth Sciences, Shahid Beheshti University, Tehran, Iran
| | - Silas Nogueira de Melo
- Department of Geography, State University of Maranhão, CidadeUniversitária Paulo VI, São Luís, 65055-000, Brazil
| | - Ayyoob Sharifi
- Graduate School of Humanities and Social Sciences, and Network for Education and Research on Peace and Sustainability, Hiroshima University, Higashi-Hiroshima, 739-8530, Japan
| | - Behzad Kiani
- Centre de Recherche en Santé Publique, Université de Montréal, 7101, Avenue du Parc, Montréal, Canada.
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Mohammadi A, Pishgar E, Salari Z, Kiani B. Geospatial analysis of cesarean section in Iran (2016-2020): exploring clustered patterns and measuring spatial interactions of available health services. BMC Pregnancy Childbirth 2022; 22:582. [PMID: 35864462 PMCID: PMC9302231 DOI: 10.1186/s12884-022-04856-z] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/04/2022] [Accepted: 06/17/2022] [Indexed: 11/10/2022] Open
Abstract
BACKGROUND The lives of babies and mothers are at risk due to the uneven distribution of healthcare facilities required for emergency cesarean sections (CS). However, CS without medical indications might cause complications for mothers and babies, which is a global health problem. Identifying spatiotemporal variations of CS rates in each geographical area could provide helpful information to understand the status of using CS services. METHODS This cross-sectional study explored spatiotemporal patterns of CS in northeast Iran from 2016 to 2020. Space-time scan statistics and spatial interaction analysis were conducted using geographical information systems to visualize and explore patterns of CS services. RESULTS The temporal analysis identified 2017 and 2018 as the statistically significant high clustered times in terms of CS rate. Five purely spatial clusters were identified that were distributed heterogeneously in the study region and included 14 counties. The spatiotemporal analysis identified four clusters that included 13 counties as high-rate areas in different periods. According to spatial interaction analysis, there was a solid spatial concentration of hospital facilities in the political center of the study area. Moreover, a high degree of inequity was observed in spatial accessibility to CS hospitals in the study area. CONCLUSIONS CS Spatiotemporal clusters in the study area reveal that CS use in different counties among women of childbearing age is significantly different in terms of location and time. This difference might be studied in future research to identify any overutilization of CS or lack of appropriate CS in clustered counties, as both put women at risk. Hospital capacity and distance from population centers to hospitals might play an essential role in CS rate variations and spatial interactions among people and CS facilities. As a result, some healthcare strategies, e.g., building new hospitals and empowering the existing local hospitals to perform CS in areas out of service, might be developed to decline spatial inequity.
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Affiliation(s)
- Alireza Mohammadi
- Department of Geography and Urban Planning, Faculty of Social Sciences, University of Mohaghegh Ardabili, Ardabil, Iran.
| | - Elahe Pishgar
- Department of Human Geography, Faculty of Earth Sciences, Shahid Beheshti University, Tehran, Iran
| | - Zahra Salari
- Jahrom University of Medical Sciences, Jahrom, Iran
| | - Behzad Kiani
- Department of Medical Informatics, School of Medicine, Mashhad University of Medical Sciences, Mashhad, Iran. .,Centre de Recherche en Santé Publique, Université de Montréal, 7101, Avenue du Parc, Montréal, Canada.
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MohammadEbrahimi S, Mohammadi A, Bergquist R, Akbarian M, Arian M, Pishgar E, Kiani B. A spatial-epidemiological dataset of subjects infected by SARS-CoV-2 during the first wave of the pandemic in Mashhad, second-most populous city in Iran. BMC Res Notes 2021; 14:292. [PMID: 34315517 PMCID: PMC8313880 DOI: 10.1186/s13104-021-05710-9] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.7] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/19/2021] [Accepted: 07/21/2021] [Indexed: 12/24/2022] Open
Abstract
OBJECTIVE In March 2020, Iran tackled the first national wave of COVID-19 that was particularly felt in Mashhad, Iran's second-most populous city. Accordingly, we performed a spatio-temporal study in this city to investigate the epidemiological aspects of the disease in an urban area and now wish to release a comprehensive dataset resulting from this study. DATA DESCRIPTION These data include two data files and a help file. Data file 1: "COVID-19_Patients_Data" contains the patient sex and age + time from symptoms onset to hospital admission; hospitalization time; co-morbidities; manifest symptoms; exposure up to 14 days before admission; disease severity; diagnosis (with or without RT-PCR assay); and outcome (recovery vs. death). The data covers 4000 COVID-19 patients diagnosed between 14 Feb 2020 and 11 May 2020 in Khorasan-Razavi Province. Data file 2: "COVID-19_Spatiotemporal_Data" is a digital map of census tract divisions of Mashhad, the capital of the province, and their population by gender along with the number of COVID-19 cases and deaths including the calculated rates per 100,000 persons. This dataset can be a valuable resource for epidemiologists and health policymakers to identify potential risk factors, control and prevent pandemics, and optimally allocate health resources.
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Affiliation(s)
- Shahab MohammadEbrahimi
- Department of Medical Informatics, School of Medicine, Mashhad University of Medical Sciences, Mashhad, Iran
- Student Research Committee, Mashhad University of Medical Sciences, Mashhad, Iran
| | - Alireza Mohammadi
- Department of Geography and Urban Planning, Faculty of Social Sciences, University of Mohaghegh Ardabili, Ardabil, Iran
| | - Robert Bergquist
- Ingerod, Brastad, Sweden (formerly with the UNICEF/UNDP/World Bank/WHO Special Program for Research and Training in Tropical Diseases, World Health Organization), Geneva, Switzerland
| | - Mahsan Akbarian
- Department of Physiology, School of Medicine, Mashhad University of Medical Sciences, Mashhad, Iran
| | - Mahnaz Arian
- Department of Infectious Diseases, School of Medicine, Mashhad University of Medical Sciences, Mashhad, Iran
| | - Elahe Pishgar
- Department of Human Geography, Faculty of Earth Science, Shahid Beheshti University, Tehran, Iran
| | - Behzad Kiani
- Department of Medical Informatics, School of Medicine, Mashhad University of Medical Sciences, Mashhad, Iran
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MohammadEbrahimi S, Mohammadi A, Bergquist R, Dolatkhah F, Olia M, Tavakolian A, Pishgar E, Kiani B. Epidemiological characteristics and initial spatiotemporal visualisation of COVID-19 in a major city in the Middle East. BMC Public Health 2021; 21:1373. [PMID: 34247616 PMCID: PMC8272989 DOI: 10.1186/s12889-021-11326-2] [Citation(s) in RCA: 20] [Impact Index Per Article: 6.7] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/23/2020] [Accepted: 06/18/2021] [Indexed: 12/24/2022] Open
Abstract
Background The Severe Acute Respiratory Syndrome Coronavirus-2 (SARS-CoV-2) emerged initially in China in December 2019 causing the COVID-19 disease, which quickly spread worldwide. Iran was one of the first countries outside China to be affected in a major way and is now under the spell of a fourth wave. This study aims to investigate the epidemiological characteristics of COVID-19 cases in north-eastern Iran through mapping the spatiotemporal trend of the disease. Methods The study comprises data of 4000 patients diagnosed by laboratory assays or clinical investigation from the beginning of the disease on Feb 14, 2020, until May 11, 2020. Epidemiological features and spatiotemporal trends of the disease in the study area were explored by classical statistical approaches and Geographic Information Systems. Results Most common symptoms were dyspnoea (69.4%), cough (59.4%), fever (54.4%) and weakness (19.5%). Approximately 82% of those who did not survive suffered from dyspnoea. The highest Case Fatality Rate (CFR) was related to those with cardiovascular disease (27.9%) and/or diabetes (18.1%). Old age (≥60 years) was associated with an almost five-fold increased CFR. Odds Ratio (OR) showed malignancy (3.8), nervous diseases (2.2), and respiratory diseases (2.2) to be significantly associated with increased CFR with developments, such as hospitalization at the ICU (2.9) and LOS (1.1) also having high correlations. Furthermore, spatial analyses revealed a geographical pattern in terms of both incidence and mortality rates, with COVID-19 first being observed in suburban areas from where the disease swiftly spread into downtown reaching a peak between 25 February to 06 March (4 incidences per km2). Mortality peaked 3 weeks later after which the infection gradually decreased. Out of patients investigated by the spatiotemporal approach (n = 727), 205 (28.2%) did not survive and 66.8% of them were men. Conclusions Older adults and people with severe co-morbidities were at higher risk for developing serious complications due to COVID-19. Applying spatiotemporal methods to identify the transmission trends and high-risk areas can rapidly be documented, thereby assisting policymakers in designing and implementing tailored interventions to control and prevent not only COVID-19 but also other rapidly spreading epidemics/pandemics.
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Affiliation(s)
- Shahab MohammadEbrahimi
- Department of Medical Informatics, School of Medicine, Mashhad University of Medical Sciences, Mashhad, Iran.,Student Research Committee, Mashhad University of Medical Sciences, Mashhad, Iran
| | - Alireza Mohammadi
- Department of Geography and Urban Planning, Faculty of Social Sciences, University of Mohaghegh Ardabili, Ardabil, Iran.
| | - Robert Bergquist
- Ingerod, Brastad, Sweden.,(Formerly with the UNICEF/UNDP/World Bank/WHO Special Program for Research and Training in Tropical Diseases, World Health Organization), Geneva, Switzerland
| | - Fatemeh Dolatkhah
- Student Research Committee, Mashhad University of Medical Sciences, Mashhad, Iran.,Department of Microbiology and Virology, School of Medicine, Mashhad University of Medical Sciences, Mashhad, Iran
| | - Mahsa Olia
- Department of Anaesthesiology, School of Medicine, Shahid Sadoughi University of Medical Sciences, Yazd, Iran
| | - Ayoub Tavakolian
- Department of Emergency Medicine, Faculty of Medicine, Sabzevar University of Medical Sciences, Sabzevar, Iran
| | - Elahe Pishgar
- Department of Human Geography and Logistics, Faculty of Earth Science, Shahid Beheshti University, Tehran, Iran
| | - Behzad Kiani
- Department of Medical Informatics, School of Medicine, Mashhad University of Medical Sciences, Mashhad, Iran.
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Pishgar E, Mohammadi A, Bagheri N, Kiani B. A spatio-temporal geodatabase of mortalities due to respiratory tract diseases in Tehran, Iran between 2008 and 2018: a data note. BMC Res Notes 2020; 13:469. [PMID: 33028414 PMCID: PMC7539274 DOI: 10.1186/s13104-020-05319-4] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/03/2020] [Accepted: 09/30/2020] [Indexed: 12/25/2022] Open
Abstract
Objectives Respiratory tract diseases (RTDs) are among the top five leading causes of death worldwide. Mortality rates due to respiratory tract diseases (MRRTDs) follow a spatial pattern and this may suggest a potential link between environmental risk factors and MRRTDs. Spatial analysis of RTDs mortality data in an urban setting can provide new knowledge on spatial variation of potential risk factors for RTDs. This will enable health professionals and urban planners to design tailored interventions. We aim to release the datasets of MRRTDs in the city of Tehran, Iran, between 2008 and 2018. Data description The Research data include four datasets; (a) mortality dataset which includes records of deaths and their attributes (age, gender, date of death and district name where death occurred), (b) population data for 22 districts (age groups with 5 years interval and gender by each district). Furthermore, two spatial datasets about the city are introduced; (c) the digital boundaries of districts and (d) urban suburbs of Tehran.
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Affiliation(s)
- Elahe Pishgar
- Department of Human Geography and Logistics, Faculty of Earth Science, Shahid Beheshti University, Tehran, Iran
| | - Alireza Mohammadi
- Department of Geography & Planning, Faculty of Social Sciences, University of Mohaghegh Ardabili, Ardabil, Iran
| | - Nasser Bagheri
- Visualization and Decision Analytics (VIDEA) Lab, Centre for Mental Health Research, Research School of Population Health, College of Health and Medicine, The Australian National University, Canberra, Australia
| | - Behzad Kiani
- Department of Medical Informatics, School of Medicine, Mashhad University of Medical Sciences, Mashhad, Iran.
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Pishgar E, Fanni Z, Tavakkolinia J, Mohammadi A, Kiani B, Bergquist R. Mortality rates due to respiratory tract diseases in Tehran, Iran during 2008-2018: a spatiotemporal, cross-sectional study. BMC Public Health 2020; 20:1414. [PMID: 32943045 PMCID: PMC7495408 DOI: 10.1186/s12889-020-09495-7] [Citation(s) in RCA: 10] [Impact Index Per Article: 2.5] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/14/2020] [Accepted: 09/03/2020] [Indexed: 01/04/2023] Open
Abstract
BACKGROUND Tehran, the 22nd most populous city in the world, has the highest mortality rate due to respiratory system diseases (RSDs) in Iran. This study aimed to investigate spatiotemporal patterns of mortality due to these diseases in Tehran between 2008 and 2018. METHODS We used a dataset available from Tehran Municipality including all cases deceased due RSDs in this city between 2008 and 2018. Global Moran's I was performed to test whether the age-adjusted mortality rates were randomly distributed or had a spatial pattern. Furthermore, Anselin Local Moran's I was conducted to identify potential clusters and outliers. RESULTS During the 10-year study, 519,312 people died in Tehran, 43,177 because of RSDs, which corresponds to 831.1 per 10,000 deaths and 5.0 per 10,000 population. The death rate was much higher in men (56.8%) than in women (43.2%) and the highest occurred in the > 65 age group (71.2%). Overall, three diseases dominated the mortality data: respiratory failure (44.2%), pneumonia (15.9%) and lung cancer (10.2%). The rates were significantly higher in the central and southeastern parts of the city and lower in the western areas. It increased during the period 2008-2018 and showed a clustered spatial pattern between 2008 and 2013 but presented a random geographical pattern afterwards. CONCLUSIONS This study provides a first report of the spatial distribution of mortality due to RSDs in Tehran and shows a significant increase in respiratory disease mortality in the last ten years. Effective control of the excess fatality rates would warrant a combination of urban prevention and treatment strategies including environmental health plans.
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Affiliation(s)
- Elahe Pishgar
- Department of Human Geography and Logistics, Faculty of Earth Science, Shahid Beheshti University, Tehran, Iran
| | - Zohre Fanni
- Department of Human Geography and Logistics, Faculty of Earth Science, Shahid Beheshti University, Tehran, Iran.
| | - Jamileh Tavakkolinia
- Department of Human Geography and Logistics, Faculty of Earth Science, Shahid Beheshti University, Tehran, Iran
| | - Alireza Mohammadi
- Department of Geography and Urban Planning, Faculty of Social Sciences, University of Mohaghegh Ardabili, Ardabil, Iran
| | - Behzad Kiani
- Department of Medical Informatics, School of Medicine, Mashhad University of Medical Sciences, Mashhad, Iran
| | - Robert Bergquist
- Ingerod, Brastad, Sweden (formerly with the UNICEF/UNDP/World Bank/WHO Special Programme for Research and Training in Tropical Diseases, World Health Organization), Geneva, Switzerland
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