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Rezaee M, Tavakoli N, Tahmasbi S. Exploring the County Level Mortality Pattern Variations in Rural Areas of Iran (2006-2016). Int J Health Policy Manag 2024; 13:8032. [PMID: 38618838 PMCID: PMC11270610 DOI: 10.34172/ijhpm.2024.8032] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/18/2023] [Accepted: 03/12/2024] [Indexed: 04/16/2024] Open
Abstract
BACKGROUND Mortality rate in rural areas is a useful measure of the health of the population and the function of the health system, which varies over space and time. The objective of this research is to explore the spatial and temporal variations in the rural mortality rate in Iran at the county level in 2006, 2011, and 2016. METHODS data were gathered from the rural population and mortality statistics published by the Statistical Centre of Iran (SCI) and the National Organization for Civil Registration (NOCR). Global spatial patterns were assessed using the global Moran's I and local clusters through the local Moran's I. RESULTS Spatial distribution of rural mortality rate shows that during the years under study the number of counties with a lower rate has increased. The counties with rate of less form continuous areas in the southwest, central and east regions. The excess risk map reveals significant variations in both value and extent. Also, the values of Moran's index increased from 0.1848 in 2006 to 0.4041 in 2016, which indicates the strengthening of the cluster spatial pattern of the overall rural mortality rate. Local patterns have undergone substantial changes over space and time. CONCLUSION The findings indicate significant spatial and temporal variations in rural mortality rates in Iran. Policy-makers can use this information to plan and enhance healthcare infrastructure in specific counties. The findings serve for evaluating the effectiveness of health policies, enabling policy-makers to make informed decisions, allocate resources efficiently and design targeted interventions for improved public health outcomes.
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Affiliation(s)
| | - Nader Tavakoli
- Trauma and Injury Research Centre, Iran University of Medical Sciences, Tehran, Iran
| | - Siamak Tahmasbi
- Centre for Remote Sensing & GIS Research, Faculty of Earth Sciences, Shahid Beheshti University, Tehran, Iran
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Sharma A, Wibawa BSS, Andhikaputra G, Solanki B, Sapkota A, Chiang Hsieh LH, Iyer V, Wang YC. Spatial analysis of food and water-borne diseases in Ahmedabad, India: Implications for urban public health planning. Acta Trop 2024; 253:107170. [PMID: 38467234 DOI: 10.1016/j.actatropica.2024.107170] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/06/2023] [Revised: 02/05/2024] [Accepted: 03/03/2024] [Indexed: 03/13/2024]
Abstract
Spatial analysis of infectious diseases can play an important role in mapping the spread of diseases and can support policy making at local level. Moreover, identification of disease clusters based on local geography and landscape forms the basis for disease control and prevention. Therefore, this study aimed to examine the spatial-temporal variations, hotspot areas, and potential risk factors of infectious diseases (including Viral Hepatitis, Typhoid and Diarrhea) in Ahmedabad city of India. We used Moran's I and Local Indicators of Spatial Association (LISA) mapping to detect spatial clustering of diseases. Spatial and temporal regression analysis was used to identify the association between disease incidence and spatial risk factors. The Moran's I statistics identified presence of positive spatial autocorrelation within the considered diseases, with Moran's I from 0.09 for typhoid to 0.21 for diarrhea (p < 0.001). This indicates a clustering of affected wards for each disease, suggesting that cases were not randomly distributed across the city. LISA mapping demonstrated the clustering of hotspots in central regions of the city, especially towards the east of the river Sabarmati, highlighting key geographical areas with elevated disease risk. The spatial clusters of infectious diseases were consistently associated with slum population density and illiteracy. Furthermore, temporal analysis suggested illiteracy rates could increase risk of viral hepatitis by 13 % (95 % Confidence Interval (CI): 1.01-1.26) and of diarrhea by 18 % (95 % CI: 1.07-1.31). Significant inverse association was also seen between viral hepatitis incidence and the distance of wards from rivers. Conclusively, the study highlight the impact of socio-economic gradients, such as slum population density (indicative of poverty) and illiteracy, on the localized transmission of water and foodborne infections. The evident social stratification between impoverished and affluent households emerges as a notable contributing factor and a potential source of differences in the dynamics of infectious diseases in Ahmedabad.
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Affiliation(s)
- Ayushi Sharma
- Department of Environmental Engineering, College of Engineering, Chung Yuan Christian University, 200 Chung-Pei Road, Zhongli 320, Taiwan; Department of Civil Engineering, College of Engineering, Chung Yuan Christian University, 200 Chung-Pei Road, Zhongli 320, Taiwan
| | - Bima Sakti Satria Wibawa
- Department of Environmental Engineering, College of Engineering, Chung Yuan Christian University, 200 Chung-Pei Road, Zhongli 320, Taiwan
| | - Gerry Andhikaputra
- Department of Environmental Engineering, College of Engineering, Chung Yuan Christian University, 200 Chung-Pei Road, Zhongli 320, Taiwan
| | - Bhavin Solanki
- Medical Officer of Health, Ahmedabad Municipal Corporation, Ahmedabad, Gujarat, India
| | - Amir Sapkota
- Department of Epidemiology and Biostatistics, University of Maryland, School of Public Health, College Park, MD 20742, United States
| | - Lin-Han Chiang Hsieh
- Institute of Environmental Engineering and Management, National Taipei University of Technology, Taiwan.
| | - Veena Iyer
- Indian Institute of Public Health Gandhinagar (IIPHG), Public Health Foundation of India (PHFI), Near Lekwada Bus Stop, Near Lekwada Bus Stop, Opp. New Air Force Station HQ, Palaj. Gandhinagar, 382042, Gujarat, India.
| | - Yu-Chun Wang
- Department of Environmental Engineering, College of Engineering, Chung Yuan Christian University, 200 Chung-Pei Road, Zhongli 320, Taiwan; Research Center for Environmental Changes, Academia Sinica, 128 Academia Road, Section 2, Nankang, Taipei 11529, Taiwan.
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Tapias-Rivera J, Gutiérrez JD. Environmental and socio-economic determinants of the occurrence of malaria clusters in Colombia. Acta Trop 2023; 241:106892. [PMID: 36935051 DOI: 10.1016/j.actatropica.2023.106892] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/10/2022] [Revised: 03/06/2023] [Accepted: 03/08/2023] [Indexed: 03/19/2023]
Abstract
This study identifies the environmental and socio-economic determinants of clusters of high malaria incidence in Colombia during the period of 2008-2019. The malaria cases were obtained from the National System of Surveillance in Public Health, with 798,897 cases reported in the 986 Colombian municipalities evaluated during the study period. Spatial autocorrelation of incidence was examined with global and local indices. Clusters were identified in the Amazon, Pacific, and Uraba-Bajo Cauca-Alto Sinú regions. The factors associated with a municipality belonging to a high-incidence cluster were identified using a logistic regression model with mixed effects and showed a positive association for the variables (forest coverage and minimum multi-year average rainfall). An inverse relationship was observed for aqueduct coverage and the odds of belonging to a cluster. A 1% increase in forest coverage was associated with a 4.2% increase in the odds of belonging to a malaria cluster. The association with minimum multi-year average rainfall was positive (OR = 1.0011; 95% CI 1.0005-1.0027). A 1% increase in aqueduct coverage was associated with a 4.3% decrease in the odds of belonging to malaria cluster. The identification of malaria cluster determinants in Colombia could help guide surveillance and disease control policies.
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Affiliation(s)
- Johanna Tapias-Rivera
- Universidad de Santander, Facultad de Ciencias Exactas, Naturales y Agropecuarias, Bucaramanga, Santander, Colombia.
| | - Juan David Gutiérrez
- Universidad de Santander, Facultad de Ingenierías y Tecnologías, Bucaramanga, Instituto Xerira, Santander, Colombia
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Shahparvari S, Hassanizadeh B, Mohammadi A, Kiani B, Lau KH, Chhetri P, Abbasi B. A decision support system for prioritised COVID-19 two-dosage vaccination allocation and distribution. TRANSPORTATION RESEARCH. PART E, LOGISTICS AND TRANSPORTATION REVIEW 2022; 159:102598. [PMID: 35185357 PMCID: PMC8843424 DOI: 10.1016/j.tre.2021.102598] [Citation(s) in RCA: 2] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Subscribe] [Scholar Register] [Received: 01/21/2021] [Revised: 08/23/2021] [Accepted: 12/23/2021] [Indexed: 05/12/2023]
Abstract
This study proposes a decision support system (DSS) that integrates GIS, analytics, and simulation methods to help develop a priority-based distribution of COVID-19 vaccines in a large urban setting. The methodology applies novel hierarchical heuristic-simulation procedures to create a holistic algorithm for prioritising the process of demand allocation and optimising vaccine distribution. The Melbourne metropolitan area in Australia with a population of over five million is used as a case study. Three vaccine supply scenarios, namely limited, excessive, and disruption, were formulated to operationalise a two-dose vaccination program. Vaccine distribution with hard constraints were simulated and then further validated with sensitivity analyses. The results show that vaccines can be prioritised to society's most vulnerable segments and distributed using the current logistics network with 10 vehicles. Compared with other vaccine distribution plans with no prioritisation, such as equal allocation of vaccines to local government areas based on population size or one on a first-come-first-serve basis, the plans generated by the proposed DSS ensure prioritised vaccination of the most needed and vulnerable population. The aim is to curb the spread of the infection and reduce mortality rate more effectively. They also achieve vaccination of the entire population with less logistical resources required. As such, this study contributes to knowledge and practice in pandemic vaccine distribution and enables governments to make real-time decisions and adjustments in daily distribution plans. In this way any unforeseen disruptions in the vaccine supply chain can be coped with.
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Affiliation(s)
- Shahrooz Shahparvari
- School of Accounting Information Systems & Supply Chain, RMIT University, Melbourne, VIC, Australia
| | | | - Alireza Mohammadi
- Department of Geography & 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
| | - Kwok Hung Lau
- School of Accounting Information Systems & Supply Chain, RMIT University, Melbourne, VIC, Australia
| | - Prem Chhetri
- School of Accounting Information Systems & Supply Chain, RMIT University, Melbourne, VIC, Australia
| | - Babak Abbasi
- School of Accounting Information Systems & Supply Chain, RMIT University, Melbourne, VIC, Australia
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Young AJ, Eaton W, Worges M, Hiruy H, Maxwell K, Audu BM, Marasciulo M, Nelson C, Tibenderana J, Abeku TA. A practical approach for geographic prioritization and targeting of insecticide-treated net distribution campaigns during public health emergencies and in resource-limited settings. Malar J 2022; 21:10. [PMID: 34983558 PMCID: PMC8724754 DOI: 10.1186/s12936-021-04028-y] [Citation(s) in RCA: 2] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/05/2021] [Accepted: 12/16/2021] [Indexed: 11/17/2022] Open
Abstract
BACKGROUND The use of data in targeting malaria control efforts is essential for optimal use of resources. This work provides a practical mechanism for prioritizing geographic areas for insecticide-treated net (ITN) distribution campaigns in settings with limited resources. METHODS A GIS-based weighted approach was adopted to categorize and rank administrative units based on data that can be applied in various country contexts where Plasmodium falciparum transmission is reported. Malaria intervention and risk factors were used to rank local government areas (LGAs) in Nigeria for prioritization during mass ITN distribution campaigns. Each factor was assigned a unique weight that was obtained through application of the analytic hierarchy process (AHP). The weight was then multiplied by a value based on natural groupings inherent in the data, or the presence or absence of a given intervention. Risk scores for each factor were then summated to generate a composite unique risk score for each LGA. This risk score was translated into a prioritization map which ranks each LGA from low to high priority in terms of timing of ITN distributions. RESULTS A case study using data from Nigeria showed that a major component that influenced the prioritization scheme was ITN access. Sensitivity analysis results indicate that changes to the methodology used to quantify ITN access did not modify outputs substantially. Some 120 LGAs were categorized as 'extremely high' or 'high' priority when a spatially interpolated ITN access layer was used. When prioritization scores were calculated using DHS-reported state level ITN access, 108 (90.0%) of the 120 LGAs were also categorized as being extremely high or high priority. The geospatial heterogeneity found among input risk factors suggests that a range of variables and covariates should be considered when using data to inform ITN distributions. CONCLUSION The authors provide a tool for prioritizing regions in terms of timing of ITN distributions. It serves as a base upon which a wider range of vector control interventions could be targeted. Its value added can be found in its potential for application in multiple country contexts, expediated timeframe for producing outputs, and its use of systematically collected malaria indicators in informing prioritization.
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Affiliation(s)
- Alyssa J Young
- Tulane University School of Public Health and Tropical Medicine, New Orleans, LA, USA.
| | - Will Eaton
- Tulane University School of Public Health and Tropical Medicine, New Orleans, LA, USA
| | - Matt Worges
- Tulane University School of Public Health and Tropical Medicine, New Orleans, LA, USA
| | - Honelgn Hiruy
- Tulane University School of Public Health and Tropical Medicine, New Orleans, LA, USA
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Odhiambo JN, Kalinda C, Macharia PM, Snow RW, Sartorius B. Spatial and spatio-temporal methods for mapping malaria risk: a systematic review. BMJ Glob Health 2021; 5:bmjgh-2020-002919. [PMID: 33023880 PMCID: PMC7537142 DOI: 10.1136/bmjgh-2020-002919] [Citation(s) in RCA: 11] [Impact Index Per Article: 3.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/16/2020] [Revised: 08/23/2020] [Accepted: 08/24/2020] [Indexed: 12/21/2022] Open
Abstract
Background Approaches in malaria risk mapping continue to advance in scope with the advent of geostatistical techniques spanning both the spatial and temporal domains. A substantive review of the merits of the methods and covariates used to map malaria risk has not been undertaken. Therefore, this review aimed to systematically retrieve, summarise methods and examine covariates that have been used for mapping malaria risk in sub-Saharan Africa (SSA). Methods A systematic search of malaria risk mapping studies was conducted using PubMed, EBSCOhost, Web of Science and Scopus databases. The search was restricted to refereed studies published in English from January 1968 to April 2020. To ensure completeness, a manual search through the reference lists of selected studies was also undertaken. Two independent reviewers completed each of the review phases namely: identification of relevant studies based on the Preferred Reporting Items for Systematic Reviews and Meta-Analyses guidelines, data extraction and methodological quality assessment using a validated scoring criterion. Results One hundred and seven studies met the inclusion criteria. The median quality score across studies was 12/16 (range: 7–16). Approximately half (44%) of the studies employed variable selection techniques prior to mapping with rainfall and temperature selected in over 50% of the studies. Malaria incidence (47%) and prevalence (35%) were the most commonly mapped outcomes, with Bayesian geostatistical models often (31%) the preferred approach to risk mapping. Additionally, 29% of the studies employed various spatial clustering methods to explore the geographical variation of malaria patterns, with Kulldorf scan statistic being the most common. Model validation was specified in 53 (50%) studies, with partitioning data into training and validation sets being the common approach. Conclusions Our review highlights the methodological diversity prominent in malaria risk mapping across SSA. To ensure reproducibility and quality science, best practices and transparent approaches should be adopted when selecting the statistical framework and covariates for malaria risk mapping. Findings underscore the need to periodically assess methods and covariates used in malaria risk mapping; to accommodate changes in data availability, data quality and innovation in statistical methodology.
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Affiliation(s)
| | - Chester Kalinda
- Discipline of Public Health Medicine, University of KwaZulu-Natal, Durban, South Africa.,Faculty of Agriculture and Natural Resources, University of Namibia, Windhoek, Namibia
| | - Peter M Macharia
- Population Health Unit, Kenya Medical Research Institute-Wellcome Trust Research Programme, Nairobi, Kenya
| | - Robert W Snow
- Population Health Unit, Kenya Medical Research Institute-Wellcome Trust Research Programme, Nairobi, Kenya.,Centre for Tropical Medicine and Global Health, Nuffield Department of Clinical Medicine, University of Oxford, Oxford, UK
| | - Benn Sartorius
- Discipline of Public Health Medicine, University of KwaZulu-Natal, Durban, South Africa.,Department of Disease Control, Faculty of Infectious and Tropical Diseases, London School of Hygiene and Tropical Medicine, London, UK
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Nasr M, Pourmirzaei M, Esmaeil Motlagh M, Heshmat R, Qorbani M, Kelishadi R. Mapping the relative risk of weight disorders in children and adolescents across provinces of Iran: the CASPIAN-V study. Health Promot Perspect 2020; 10:238-243. [PMID: 32802760 PMCID: PMC7420165 DOI: 10.34172/hpp.2020.37] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/08/2019] [Accepted: 03/30/2020] [Indexed: 11/30/2022] Open
Abstract
Background: This study aimed to find possible spatial variation in children’s weight disorders and in predicting the spatial distribution. Methods: The study population of this ecological study consisted of 7-18-year-old students living in 30 provinces of Iran. We used Besag, York and Mollie (BYM) model, a Bayesian model, to study the relative risk (RR) of underweight and excess weight (overweight and obese). The model was fitted to data using OpenBUGS (3.2.1) software. Results: The highest RR of underweight was found in southeastern provinces. Whereas, the highest RR of excess weight was documented in northern, northwestern and capital provinces.Sistan-Balouchestan (RR=1.973; Bayesian confidence interval [BCI]: 1.682, 2.289), Hormozgan(RR=1.482; BCI: 1.239, 1.749), South Khorasan (RR=1.422; BCI: 1.18, 1.687) and Kerman(RR=1.413; BCI: 1.18, 1.669) had the highest RR of underweight. Mazandaran (RR=1.366; BCI:1.172,1.581), Gilan (RR=1.346; BCI: 1.15,1.562), Tehran (RR=1.271; BCI: 1.086,1.472) and Alborz (RR=1.268; BCI: 1.079,1.475) provinces are high risk regions for excess weight. Conclusion: The significant variations in geographical distribution of weight disorders are because of various sociodemographic and ethnic differences. The current findings should be considered in health policy making in different regions of the country.
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Affiliation(s)
- Marzieh Nasr
- Child Growth and Development Research Center, Research Institute for Primordial Prevention of Non-Communicable Disease, Isfahan University of Medical Sciences, Isfahan, Iran
| | - Mohammadali Pourmirzaei
- Pediatrics Department, Child Growth and Development Research Center, Research Institute for Primordial Prevention of Non-Communicable Disease, Isfahan University of Medical Sciences, Isfahan, Iran
| | | | - Ramin Heshmat
- Chronic Diseases Research Center, Endocrinology and Metabolism Population Sciences Institute, Tehran University of Medical Sciences, Tehran, Iran
| | - Mostafa Qorbani
- Non-communicable Diseases Research Center, Alborz University of Medical Sciences, Karaj, Iran
| | - Roya Kelishadi
- Pediatrics Department, Child Growth and Development Research Center, Research Institute for Primordial Prevention of Non-Communicable Disease, Isfahan University of Medical Sciences, Isfahan, Iran
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