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Uncertainty in geospatial health: challenges and opportunities ahead. Ann Epidemiol 2021; 65:15-30. [PMID: 34656750 DOI: 10.1016/j.annepidem.2021.10.002] [Citation(s) in RCA: 19] [Impact Index Per Article: 6.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/11/2020] [Revised: 09/29/2021] [Accepted: 10/04/2021] [Indexed: 12/19/2022]
Abstract
PURPOSE Uncertainty is not always well captured, understood, or modeled properly, and can bias the robustness of complex relationships, such as the association between the environment and public health through exposure, estimates of geographic accessibility and cluster detection, to name a few. METHODS We review current challenges and future opportunities as geospatial data and analyses are applied to the field of public health. We are particularly interested in the sources of uncertainty in geospatial data and how this uncertainty may propagate in spatial analysis. RESULTS We present opportunities to reduce the magnitude and impact of uncertainty. Specifically, we focus on (1) the use of multiple reference data sources to reduce geocoding errors, (2) the validity of online geocoders and how confidentiality (e.g., HIPAA) may be breached, (3) use of multiple reference data sources to reduce geocoding errors, (4) the impact of geoimputation techniques on travel estimates, (5) residential mobility and how it affects accessibility metrics and clustering, and (6) modeling errors in the American Community Survey. Our paper discusses how to communicate spatial and spatiotemporal uncertainty, and high-performance computing to conduct large amounts of simulations to ultimately increase statistical robustness for studies in public health. CONCLUSIONS Our paper contributes to recent efforts to fill in knowledge gaps at the intersection of spatial uncertainty and public health.
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da Silva PP, da Silva FA, Rodrigues CAS, Souza LP, de Lima EM, Pereira MHB, Candella CN, de Oliveira Alves MZ, Lourenço ND, Tassinari WS, Barcellos C, Gomes MZR. Geographical information system and spatial-temporal statistics for monitoring infectious agents in hospital: a model using Klebsiella pneumoniae complex. Antimicrob Resist Infect Control 2021; 10:92. [PMID: 34134752 PMCID: PMC8207788 DOI: 10.1186/s13756-021-00944-5] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/16/2020] [Accepted: 04/26/2021] [Indexed: 12/25/2022] Open
Abstract
Background The emergence and spread of antimicrobial resistance and infectious agents have challenged hospitals in recent decades. Our aim was to investigate the circulation of target infectious agents using Geographic Information System (GIS) and spatial–temporal statistics to improve surveillance and control of healthcare-associated infection and of antimicrobial resistance (AMR), using Klebsiella pneumoniae complex as a model. Methods A retrospective study carried out in a 450-bed federal, tertiary hospital, located in Rio de Janeiro. All isolates of K. pneumoniae complex from clinical and surveillance cultures of hospitalized patients between 2014 and 2016, identified by the use of Vitek-2 system (BioMérieux), were extracted from the hospital's microbiology laboratory database. A basic scaled map of the hospital’s physical structure was created in AutoCAD and converted to QGis software (version 2.18). Thereafter, bacteria according to resistance profiles and patients with carbapenem-resistant K. pneumoniae (CRKp) complex were georeferenced by intensive and nonintensive care wards. Space–time permutation probability scan tests were used for cluster signals detection. Results Of the total 759 studied isolates, a significant increase in the resistance profile of K. pneumoniae complex was detected during the studied years. We also identified two space–time clusters affecting adult and paediatric patients harbouring CRKp complex on different floors, unnoticed by regular antimicrobial resistance surveillance. Conclusions In-hospital GIS with space–time statistical analysis can be applied in hospitals. This spatial methodology has the potential to expand and facilitate early detection of hospital outbreaks and may become a new tool in combating AMR or hospital-acquired infection. Supplementary Information The online version contains supplementary material available at 10.1186/s13756-021-00944-5.
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Affiliation(s)
- Priscila Pinho da Silva
- Laboratório de Genética Molecular de Microrganismos, Instituto Oswaldo Cruz - Fundação Oswaldo Cruz, Avenida Brasil, 4365, Pavilhão Leônidas Deane, 6º andar, sala 607, Rio de Janeiro, RJ, 21040-900, Brazil
| | - Fabiola A da Silva
- Department of Engineering, Hospital Federal Dos Servidores Do Estado (HFSE), Rio de Janeiro, RJ, Brazil.,Rio de Janeiro State University, Rio de Janeiro, Brazil
| | | | | | | | | | - Claudio Neder Candella
- Department of Engineering, Hospital Federal Dos Servidores Do Estado (HFSE), Rio de Janeiro, RJ, Brazil
| | | | | | - Wagner S Tassinari
- Department of Mathematics, The Federal Rural University of Rio de Janeiro, Rio de Janeiro, RJ, Brazil.,Evandro Chagas National Institute of Infectious Diseases, FIOCRUZ, Rio de Janeiro, Brazil
| | - Christovam Barcellos
- Institute of Scientific and Technological Communication and Information in Health, FIOCRUZ, Rio de Janeiro, RJ, Brazil.
| | - Marisa Zenaide Ribeiro Gomes
- Laboratório de Genética Molecular de Microrganismos, Instituto Oswaldo Cruz - Fundação Oswaldo Cruz, Avenida Brasil, 4365, Pavilhão Leônidas Deane, 6º andar, sala 607, Rio de Janeiro, RJ, 21040-900, Brazil. .,Hospital Infection Control Committee, HFSE, Rio de Janeiro, RJ, Brazil.
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Park S, Han S, Kim J, Molaie MM, Vu HD, Singh K, Han J, Lee W, Cha M. COVID-19 Discourse on Twitter in Four Asian Countries: Case Study of Risk Communication. J Med Internet Res 2021; 23:e23272. [PMID: 33684054 PMCID: PMC8108572 DOI: 10.2196/23272] [Citation(s) in RCA: 13] [Impact Index Per Article: 4.3] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/27/2020] [Revised: 12/20/2020] [Accepted: 03/03/2021] [Indexed: 01/19/2023] Open
Abstract
BACKGROUND COVID-19, caused by SARS-CoV-2, has led to a global pandemic. The World Health Organization has also declared an infodemic (ie, a plethora of information regarding COVID-19 containing both false and accurate information circulated on the internet). Hence, it has become critical to test the veracity of information shared online and analyze the evolution of discussed topics among citizens related to the pandemic. OBJECTIVE This research analyzes the public discourse on COVID-19. It characterizes risk communication patterns in four Asian countries with outbreaks at varying degrees of severity: South Korea, Iran, Vietnam, and India. METHODS We collected tweets on COVID-19 from four Asian countries in the early phase of the disease outbreak from January to March 2020. The data set was collected by relevant keywords in each language, as suggested by locals. We present a method to automatically extract a time-topic cohesive relationship in an unsupervised fashion based on natural language processing. The extracted topics were evaluated qualitatively based on their semantic meanings. RESULTS This research found that each government's official phases of the epidemic were not well aligned with the degree of public attention represented by the daily tweet counts. Inspired by the issue-attention cycle theory, the presented natural language processing model can identify meaningful transition phases in the discussed topics among citizens. The analysis revealed an inverse relationship between the tweet count and topic diversity. CONCLUSIONS This paper compares similarities and differences of pandemic-related social media discourse in Asian countries. We observed multiple prominent peaks in the daily tweet counts across all countries, indicating multiple issue-attention cycles. Our analysis identified which topics the public concentrated on; some of these topics were related to misinformation and hate speech. These findings and the ability to quickly identify key topics can empower global efforts to fight against an infodemic during a pandemic.
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Affiliation(s)
- Sungkyu Park
- Data Science Group, Institute for Basic Science, Daejeon, Republic of Korea
| | - Sungwon Han
- Korea Advanced Institute of Science and Technology, Daejeon, Republic of Korea
| | - Jeongwook Kim
- Korea Advanced Institute of Science and Technology, Daejeon, Republic of Korea
| | - Mir Majid Molaie
- Korea Advanced Institute of Science and Technology, Daejeon, Republic of Korea
| | - Hoang Dieu Vu
- Electrical and Electronic Engineering, Phenikaa University, Hanoi, Vietnam
| | - Karandeep Singh
- Data Science Group, Institute for Basic Science, Daejeon, Republic of Korea
| | - Jiyoung Han
- Korea Advanced Institute of Science and Technology, Daejeon, Republic of Korea
| | - Wonjae Lee
- Korea Advanced Institute of Science and Technology, Daejeon, Republic of Korea
| | - Meeyoung Cha
- Data Science Group, Institute for Basic Science, Daejeon, Republic of Korea
- Korea Advanced Institute of Science and Technology, Daejeon, Republic of Korea
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Yeng PK, Woldaregay AZ, Solvoll T, Hartvigsen G. Cluster Detection Mechanisms for Syndromic Surveillance Systems: Systematic Review and Framework Development. JMIR Public Health Surveill 2020; 6:e11512. [PMID: 32357126 PMCID: PMC7284413 DOI: 10.2196/11512] [Citation(s) in RCA: 5] [Impact Index Per Article: 1.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/06/2018] [Revised: 10/29/2018] [Accepted: 02/06/2020] [Indexed: 12/26/2022] Open
Abstract
Background The time lag in detecting disease outbreaks remains a threat to global health security. The advancement of technology has made health-related data and other indicator activities easily accessible for syndromic surveillance of various datasets. At the heart of disease surveillance lies the clustering algorithm, which groups data with similar characteristics (spatial, temporal, or both) to uncover significant disease outbreak. Despite these developments, there is a lack of updated reviews of trends and modelling options in cluster detection algorithms. Objective Our purpose was to systematically review practically implemented disease surveillance clustering algorithms relating to temporal, spatial, and spatiotemporal clustering mechanisms for their usage and performance efficacies, and to develop an efficient cluster detection mechanism framework. Methods We conducted a systematic review exploring Google Scholar, ScienceDirect, PubMed, IEEE Xplore, ACM Digital Library, and Scopus. Between January and March 2018, we conducted the literature search for articles published to date in English in peer-reviewed journals. The main eligibility criteria were studies that (1) examined a practically implemented syndromic surveillance system with cluster detection mechanisms, including over-the-counter medication, school and work absenteeism, and disease surveillance relating to the presymptomatic stage; and (2) focused on surveillance of infectious diseases. We identified relevant articles using the title, keywords, and abstracts as a preliminary filter with the inclusion criteria, and then conducted a full-text review of the relevant articles. We then developed a framework for cluster detection mechanisms for various syndromic surveillance systems based on the review. Results The search identified a total of 5936 articles. Removal of duplicates resulted in 5839 articles. After an initial review of the titles, we excluded 4165 articles, with 1674 remaining. Reading of abstracts and keywords eliminated 1549 further records. An in-depth assessment of the remaining 125 articles resulted in a total of 27 articles for inclusion in the review. The result indicated that various clustering and aberration detection algorithms have been empirically implemented or assessed with real data and tested. Based on the findings of the review, we subsequently developed a framework to include data processing, clustering and aberration detection, visualization, and alerts and alarms. Conclusions The review identified various algorithms that have been practically implemented and tested. These results might foster the development of effective and efficient cluster detection mechanisms in empirical syndromic surveillance systems relating to a broad spectrum of space, time, or space-time.
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Affiliation(s)
- Prosper Kandabongee Yeng
- Department of Computer Science, University of Tromsø, The Arctic University of Norway, Gjøvik, Norway.,Department of Information Security and Communication Technology, Norwegian University of Science and Technology, Gjøvik, Norway
| | | | - Terje Solvoll
- Norwegian Centre for E-health Research, University Hospital, Tromsø, Norway
| | - Gunnar Hartvigsen
- Department of Computer Science, University of Tromsø, The Arctic University of Norway, Gjøvik, Norway
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Goldenholz DM, Goldenholz SR, Krishnamurthy KB, Halamka J, Karp B, Tyburski M, Wendler D, Moss R, Preston KL, Theodore W. Using mobile location data in biomedical research while preserving privacy. J Am Med Inform Assoc 2019; 25:1402-1406. [PMID: 29889279 DOI: 10.1093/jamia/ocy071] [Citation(s) in RCA: 5] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/02/2018] [Accepted: 05/16/2018] [Indexed: 01/18/2023] Open
Abstract
Location data are becoming easier to obtain and are now bundled with other metadata in a variety of biomedical research applications. At the same time, the level of sophistication required to protect patient privacy is also increasing. In this article, we provide guidance for institutional review boards (IRBs) to make informed decisions about privacy protections in protocols involving location data. We provide an overview of some of the major categories of technical algorithms and medical-legal tools at the disposal of investigators, as well as the shortcomings of each. Although there is no "one size fits all" approach to privacy protection, this article attempts to describe a set of practical considerations that can be used by investigators, journal editors, and IRBs.
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Affiliation(s)
- Daniel M Goldenholz
- Clinical Epilepsy Section, NINDS, NIH
- Epilepsy Division, Beth Israel Deaconess Medical Center
| | | | | | - John Halamka
- Department of Emergency Medicine, Beth Israel Deaconess Medical Center
| | - Barbara Karp
- Combined NeuroScience IRB, Office of Clinical Director, NINDS, NIH
| | - Matthew Tyburski
- Intramural Research Program, National Institute on Drug Abuse, NIH
| | - David Wendler
- Section on Research Ethics, Department of Bioethics, NIH
| | | | - Kenzie L Preston
- Intramural Research Program, National Institute on Drug Abuse, NIH
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Packer C, Shivakumar S, Athreya V, Craft ME, Dhanwatey H, Dhanwatey P, Gurung B, Joshi A, Kushnir H, Linnell JDC, Fountain‐Jones NM. Species‐specific spatiotemporal patterns of leopard, lion and tiger attacks on humans. J Appl Ecol 2019. [DOI: 10.1111/1365-2664.13311] [Citation(s) in RCA: 12] [Impact Index Per Article: 2.4] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 10/27/2022]
Affiliation(s)
- Craig Packer
- Department of Ecology, Evolution and Behavior University of Minnesota St. Paul Minnesota
- School of Life Sciences University of KwaZulu‐Natal Pietermaritzburg South Africa
| | | | | | - Meggan E. Craft
- Department of Veterinary Population Medicine University of Minnesota St. Paul Minnesota
| | | | | | | | - Anup Joshi
- Conservation Sciences Program University of Minnesota St. Paul Minnesota
| | - Hadas Kushnir
- United States Agency for International Development Washington DC
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Sloan CD, Gebretsadik T, Wu P, Carroll KN, Mitchel E, Hartert TV. Spatiotemporal patterns of infant bronchiolitis in a Tennessee Medicaid population. Spat Spatiotemporal Epidemiol 2013; 6:17-23. [PMID: 23956809 PMCID: PMC3742041 DOI: 10.1016/j.sste.2013.05.001] [Citation(s) in RCA: 5] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Indexed: 11/28/2022]
Abstract
Respiratory syncytial virus (RSV) is a major cause of worldwide morbidity and mortality in infants, primarily through the induction of bronchiolitis. RSV epidemics are highly seasonal, occurring in the winter months in the northern hemisphere. Within the United States, RSV epidemic dynamics vary both spatially and temporally. This analysis employs a retrospective space–time scan statistic to locate spatiotemporal clustering of infant bronchiolitis in a very large Tennessee (TN) Medicaid cohort. We studied infants less than 6 months of age (N = 52,468 infants) who had an outpatient visit, emergency department visit, or hospitalization for bronchiolitis between 1995 and 2008. The scan statistic revealed distinctive and consistent patterns of deviation in epidemic timing. Eastern TN (Knoxville area) showed clustering in January and February, and Central TN (Nashville area) in November and December. This is likely due to local variation in geography-associated factors which should be taken into consideration in future modeling of RSV epidemics.
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Affiliation(s)
- Chantel D. Sloan
- Vanderbilt Center for Asthma & Environmental Health Sciences Research, Vanderbilt University Medical Center, Nashville, TN
| | | | - Pingsheng Wu
- Vanderbilt University Department of Biostatistics
| | - Kecia N. Carroll
- Vanderbilt Center for Asthma & Environmental Health Sciences Research, Vanderbilt University Medical Center, Nashville, TN
| | | | - Tina V. Hartert
- Vanderbilt Center for Asthma & Environmental Health Sciences Research, Vanderbilt University Medical Center, Nashville, TN
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