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Díez Galán MDM, Redondo-Bravo L, Gómez-Barroso D, Herrera L, Amillategui R, Gómez-Castellá J, Herrador Z. The impact of meteorological factors on tuberculosis incidence in Spain: a spatiotemporal analysis. Epidemiol Infect 2024; 152:e58. [PMID: 38505884 PMCID: PMC11022253 DOI: 10.1017/s0950268824000499] [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] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/03/2023] [Revised: 02/06/2024] [Accepted: 03/11/2024] [Indexed: 03/21/2024] Open
Abstract
Tuberculosis (TB) remains a global leading cause of death, necessitating an investigation into its unequal distribution. Sun exposure, linked to vitamin D (VD) synthesis, has been proposed as a protective factor. This study aimed to analyse TB rates in Spain over time and space and explore their relationship with sunlight exposure. An ecological study examined the associations between rainfall, sunshine hours, and TB incidence in Spain. Data from the National Epidemiological Surveillance Network (RENAVE in Spanish) and the Spanish Meteorological Agency (AEMET in Spanish) from 2012 to 2020 were utilized. Correlation and spatial regression analyses were conducted. Between 2012 and 2020, 43,419 non-imported TB cases were reported. A geographic pattern (north-south) and distinct seasonality (spring peaks and autumn troughs) were observed. Sunshine hours and rainfall displayed a strong negative correlation. Spatial regression and seasonal models identified a negative correlation between TB incidence and sunshine hours, with a four-month lag. A clear spatiotemporal association between TB incidence and sunshine hours emerged in Spain from 2012 to 2020. VD levels likely mediate this relationship, being influenced by sunlight exposure and TB development. Further research is warranted to elucidate the causal pathway and inform public health strategies for improved TB control.
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Affiliation(s)
| | - Lidia Redondo-Bravo
- Health Emergencies Department, Pan American Health Organization, Washington, DC, USA
| | - Diana Gómez-Barroso
- National Center of Epidemiology, Instituto de Salud Carlos III, Madrid, Spain
- CIBER in Epidemiology and Public Health (CIBERESP), Madrid, Spain
| | - Laura Herrera
- CIBER in Epidemiology and Public Health (CIBERESP), Madrid, Spain
- Department of Bacteriology, National Centre of Microbiology, Instituto de Salud Carlos III, Majadahonda, Spain
| | - Rocio Amillategui
- National Center of Epidemiology, Instituto de Salud Carlos III, Madrid, Spain
| | - Javier Gómez-Castellá
- División de control de VIH, ITS, Hepatitis virales y Tuberculosis. Ministerio de Sanidad, Madrid, Spain
| | - Zaida Herrador
- National Center of Epidemiology, Instituto de Salud Carlos III, Madrid, Spain
- CIBER in Epidemiology and Public Health (CIBERESP), Madrid, Spain
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De Salazar PM, Lu F, Hay JA, Gómez-Barroso D, Fernández-Navarro P, Martínez E, Astray-Mochales J, Amillategui R, García-Fulgueiras A, Chirlaque MD, Sánchez-Migallón A, Larrauri A, Sierra MJ, Lipsitch M, Simón F, Santillana M, Hernán MA. Near real-time surveillance of the SARS-CoV-2 epidemic with incomplete data. medRxiv 2021:2021.01.25.20230094. [PMID: 33532788 PMCID: PMC7852239 DOI: 10.1101/2021.01.25.20230094] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Grants] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Indexed: 11/24/2022]
Abstract
Designing public health responses to outbreaks requires close monitoring of population-level health indicators in real-time. Thus, an accurate estimation of the epidemic curve is critical. We propose an approach to reconstruct epidemic curves in near real time. We apply this approach to characterize the early SARS-CoV-2 outbreak in two Spanish regions between March and April 2020. We address two data collection problems that affected the reliability of the available real-time epidemiological data, namely, the frequent missing information documenting when a patient first experienced symptoms, and the frequent retrospective revision of historical information (including right censoring). This is done by using a novel back-calculating procedure based on imputing patients' dates of symptom onset from reported cases, according to a dynamically-estimated "backward" reporting delay conditional distribution, and adjusting for right censoring using an existing package, NobBS , to estimate in real time (nowcast) cases by date of symptom onset. This process allows us to obtain an approximation of the time-varying reproduction number ( R t ) in real-time. At each step, we evaluate how different assumptions affect the recovered epidemiological events and compare the proposed approach to the alternative procedure of merely using curves of case counts, by report day, to characterize the time-evolution of the outbreak. Finally, we assess how these real-time estimates compare with subsequently documented epidemiological information that is considered more reliable and complete that became available later in time. Our approach may help improve accuracy, quantify uncertainty, and evaluate frequently unstated assumptions when recovering the epidemic curves from limited data obtained from public health surveillance systems in other locations.
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Affiliation(s)
- PM De Salazar
- Center for Communicable Disease Dynamics, Department of Epidemiology, Harvard TH Chan School of Public Health, Boston, United States
| | - F Lu
- Machine Intelligence Lab, Boston Children’s Hospital, Boston, United States
- Computational Health Informatics Program, Boston Children’s Hospital, Boston, United States
| | - JA Hay
- Center for Communicable Disease Dynamics, Department of Epidemiology, Harvard TH Chan School of Public Health, Boston, United States
| | - D Gómez-Barroso
- Centro Nacional de Epidemiología, Carlos III Health Institute, Madrid, Spain
- Consorcio de Investigación Biomédica en Red de Epidemiología y Salud Pública (CIBERESP)
| | - P Fernández-Navarro
- Centro Nacional de Epidemiología, Carlos III Health Institute, Madrid, Spain
- Consorcio de Investigación Biomédica en Red de Epidemiología y Salud Pública (CIBERESP)
| | - E Martínez
- Consorcio de Investigación Biomédica en Red de Epidemiología y Salud Pública (CIBERESP)
- Centro de Coordinación de Alertas y Emergencias Sanitarias, Ministry of Health, Madrid, Spain
| | - J Astray-Mochales
- Directorate-General for Public Health, Madrid General Health Authority, Spain
| | - R Amillategui
- Centro Nacional de Epidemiología, Carlos III Health Institute, Madrid, Spain
| | - A García-Fulgueiras
- Department of Epidemiology, Regional Health Council, IMIB-Arrixaca, Murcia, Spain CIBER in Epidemiology and Public Health (CIBERESP), Madrid, Spain
| | - MD Chirlaque
- Department of Epidemiology, Regional Health Council, IMIB-Arrixaca, Murcia, Spain CIBER in Epidemiology and Public Health (CIBERESP), Madrid, Spain
| | - A Sánchez-Migallón
- Directorate-General for Public Health, Madrid General Health Authority, Spain
| | - A Larrauri
- Computational Health Informatics Program, Boston Children’s Hospital, Boston, United States
- Centro Nacional de Epidemiología, Carlos III Health Institute, Madrid, Spain
| | - MJ Sierra
- Centro de Coordinación de Alertas y Emergencias Sanitarias, Ministry of Health, Madrid, Spain
| | - M Lipsitch
- Center for Communicable Disease Dynamics, Department of Epidemiology, Harvard TH Chan School of Public Health, Boston, United States
| | - F Simón
- Consorcio de Investigación Biomédica en Red de Epidemiología y Salud Pública (CIBERESP)
- Centro de Coordinación de Alertas y Emergencias Sanitarias, Ministry of Health, Madrid, Spain
| | - M Santillana
- Center for Communicable Disease Dynamics, Department of Epidemiology, Harvard TH Chan School of Public Health, Boston, United States
- Machine Intelligence Lab, Boston Children’s Hospital, Boston, United States
- Computational Health Informatics Program, Boston Children’s Hospital, Boston, United States
- Department of Pediatrics, Harvard Medical School, Harvard University, Boston, United States
| | - MA Hernán
- Department of Epidemiology and Department of Biostatistics, Harvard T.H. Chan School of Public Health; Harvard-MIT Division of Health Sciences and Technology, Boston, United States
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