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Application of Data Science for Cluster Analysis of COVID-19 Mortality According to Sociodemographic Factors at Municipal Level in Mexico. MATHEMATICS 2022. [DOI: 10.3390/math10132167] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 12/16/2022]
Abstract
Mexico is among the five countries with the largest number of reported deaths from COVID-19 disease, and the mortality rates associated to infections are heterogeneous in the country due to structural factors concerning population. This study aims at the analysis of clusters related to mortality rate from COVID-19 at the municipal level in Mexico from the perspective of Data Science. In this sense, a new application is presented that uses a machine learning hybrid algorithm for generating clusters of municipalities with similar values of sociodemographic indicators and mortality rates. To provide a systematic framework, we applied an extension of the International Business Machines Corporation (IBM) methodology called Batch Foundation Methodology for Data Science (FMDS). For the study, 1,086,743 death certificates corresponding to the year 2020 were used, among other official data. As a result of the analysis, two key indicators related to mortality from COVID-19 at the municipal level were identified: one is population density and the other is percentage of population in poverty. Based on these indicators, 16 municipality clusters were determined. Among the main results of this research, it was found that clusters with high values of mortality rate had high values of population density and low poverty levels. In contrast, clusters with low density values and high poverty levels had low mortality rates. Finally, we think that the patterns found, expressed as municipality clusters with similar characteristics, can be useful for decision making by health authorities regarding disease prevention and control for reinforcing public health measures and optimizing resource distribution for reducing hospitalizations and mortality.
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Marateb HR, von Cube M, Sami R, Haghjooy Javanmard S, Mansourian M, Amra B, Soltaninejad F, Mortazavi M, Adibi P, Khademi N, Sadat Hosseini N, Toghyani A, Hassannejad R, Mañanas MA, Binder H, Wolkewitz M. Absolute mortality risk assessment of COVID-19 patients: the Khorshid COVID Cohort (KCC) study. BMC Med Res Methodol 2021; 21:146. [PMID: 34261439 PMCID: PMC8278186 DOI: 10.1186/s12874-021-01340-8] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.7] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/17/2021] [Accepted: 06/17/2021] [Indexed: 12/28/2022] Open
Abstract
BACKGROUND Already at hospital admission, clinicians require simple tools to identify hospitalized COVID-19 patients at high risk of mortality. Such tools can significantly improve resource allocation and patient management within hospitals. From the statistical point of view, extended time-to-event models are required to account for competing risks (discharge from hospital) and censoring so that active cases can also contribute to the analysis. METHODS We used the hospital-based open Khorshid COVID Cohort (KCC) study with 630 COVID-19 patients from Isfahan, Iran. Competing risk methods are used to develop a death risk chart based on the following variables, which can simply be measured at hospital admission: sex, age, hypertension, oxygen saturation, and Charlson Comorbidity Index. The area under the receiver operator curve was used to assess accuracy concerning discrimination between patients discharged alive and dead. RESULTS Cause-specific hazard regression models show that these baseline variables are associated with both death, and discharge hazards. The risk chart reflects the combined results of the two cause-specific hazard regression models. The proposed risk assessment method had a very good accuracy (AUC = 0.872 [CI 95%: 0.835-0.910]). CONCLUSIONS This study aims to improve and validate a personalized mortality risk calculator based on hospitalized COVID-19 patients. The risk assessment of patient mortality provides physicians with additional guidance for making tough decisions.
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Affiliation(s)
- Hamid Reza Marateb
- Biomedical Engineering Department, Engineering Faculty, University of Isfahan, Isfahan, Iran
- Biomedical Engineering Research Centre (CREB), Automatic Control Department (ESAII), Universitat Politècnica de Catalunya-Barcelona Tech (UPC)Building H, Floor 4, Av. Diagonal 647, 08028 Barcelona, Spain
| | - Maja von Cube
- Institute of Medical Biometry and Statistics, Faculty of Medicine and Medical Center, University of Freiburg, Freiburg, Germany
| | - Ramin Sami
- Department of Internal Medicine, School of Medicine, Isfahan University of Medical Sciences, Isfahan, Iran
| | - Shaghayegh Haghjooy Javanmard
- Applied Physiology Research Center, Cardiovascular Research Institute, Isfahan University of Medical Sciences, Isfahan, Iran
| | - Marjan Mansourian
- Biomedical Engineering Research Centre (CREB), Automatic Control Department (ESAII), Universitat Politècnica de Catalunya-Barcelona Tech (UPC)Building H, Floor 4, Av. Diagonal 647, 08028 Barcelona, Spain
- Department of Epidemiology and Biostatistics, School of Health, Isfahan University of Medical Sciences, Isfahan, Iran
| | - Babak Amra
- Bamdad Respiratory Research Center, Isfahan University of Medical Sciences, Isfahan, Iran
| | - Forogh Soltaninejad
- The Respiratory Research Center, Pulmonary Division, Department of Internal Medicine, School of Medicine, Isfahan University of Medical Sciences, Isfahan, Iran
| | - Mojgan Mortazavi
- Isfahan Kidney Diseases Research Center, Isfahan University of Medical Sciences, Isfahan, Iran
| | - Peyman Adibi
- Isfahan Gastroenterology and Hepatology Research Center (lGHRC), Isfahan University of Medical Sciences, Isfahan, Iran
| | - Nilufar Khademi
- School of Medicine, Isfahan University of Medical Sciences, Isfahan, Iran
| | | | - Arash Toghyani
- School of Medicine, Isfahan University of Medical Sciences, Isfahan, Iran
| | - Razieh Hassannejad
- Isfahan Cardiovascular Research Center, Cardiovascular Research Institute, Isfahan University of Medical Sciences, Isfahan, Iran
| | - Miquel Angel Mañanas
- Biomedical Engineering Research Centre (CREB), Automatic Control Department (ESAII), Universitat Politècnica de Catalunya-Barcelona Tech (UPC)Building H, Floor 4, Av. Diagonal 647, 08028 Barcelona, Spain
- Biomedical Research Networking Center in Bioengineering, Biomaterials, and Nanomedicine (CIBER-BBN), Madrid, Spain
| | - Harald Binder
- Institute of Medical Biometry and Statistics, Faculty of Medicine and Medical Center, University of Freiburg, Freiburg, Germany
| | - Martin Wolkewitz
- Institute of Medical Biometry and Statistics, Faculty of Medicine and Medical Center, University of Freiburg, Freiburg, Germany
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