Uncertainty in geospatial health: challenges and opportunities ahead

Delmelle, Eric; Desjardins, Michael R.; Jung, Paul H.; Owusu, Claudio; Lan, Yu; Hohl, Alexander; Dony, Coline

doi:10.1016/j.annepidem.2021.10.002

Cited by 28 publications

(10 citation statements)

References 195 publications

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“…The risk of geoprivacy breaches also increases when the dataset containing information on individuals, such as addresses and daily activities and trips, is collected or processed in smartphones connected to multiple sensors (e.g., cameras, microphones) and by online service providers such as commercial smartphone applications (apps) or online geocoding services ( 13 , 17 ). Reviews of popular health apps concluded that frequently a written privacy policy is lacking, policies regarding third party transmission are omitted, or the legal jurisdictions that would handle data are not specified ( 18 , 19 ).…”

Section: Resultsmentioning

confidence: 99%

Geoprivacy in Neighbourhoods and Health Research: A Mini-Review of the Challenges and Best Practices in Epidemiological Studies

Ribeiro

Dias²,

Ribeiro³

et al. 2022

Public Health Rev

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Neighbourhood and health research often relies on personal location data (e.g., home address, daily itineraries), despite the risks of geoprivacy breaches. Thus, geoprivacy is an important emerging topic, contemplated in international regulations such as the General Data Protection Regulation. In this mini-review, we briefly assess the potential risks associated with the usage of personal location data and provide geoprivacy-preserving recommendations to be considered in epidemiological research. Risks include inference of personal information that the individual does not wish to disclose, reverse-identification and security breaches. Various measures should be implemented at different stages of a project (pre-data collection, data processing, data analysis/publication and data sharing) such as informed consent, pseudo-anonymization and geographical methods.

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Section: Resultsmentioning

confidence: 99%

Geoprivacy in Neighbourhoods and Health Research: A Mini-Review of the Challenges and Best Practices in Epidemiological Studies

Ribeiro

Dias²,

Ribeiro³

et al. 2022

Public Health Rev

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“…To account for these aspects, better data will be needed. This will also aid in testing model assumptions, and deal with the perennial incompleteness of spatial data, poor record keeping and geocoding inadequacies [92]. With the advancement in data science (such as machine learning) and data collection techniques (such as remote sensing), a range of climatic and environmental data (e.g., land use, rainfall patterns), road networks and traffic patterns can now be easily collected [93][94][95].…”

Section: Discussionmentioning

confidence: 99%

Approaches to defining health facility catchment areas in sub-Saharan Africa

Macharia

Odhiambo

Mumo

et al. 2022

Preprint

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The geographical area around a health facility characterizing the population that utilizes some or all of its services, — a health facility catchment area (HFCA) —, forms the fundamental basis of estimating reliable population denominator for disease mapping and routine healthcare planning. Consequently, the approaches used to delineate the catchment area have a direct impact on the health of a population. To date, there is no systematic literature review documenting different approaches that have been used to define HFCAs while elucidating the implications on derived population denominators. To fill this gap, we systematically reviewed literature and documented approaches that have been used to define HFCA in sub —Saharan Africa (SSA). Simple to complex approaches have been used to define catchment areas in SSA with varying degrees of complexity and limitations in the last four decades. These approaches are mainly driven by lack of geocoded data on the residential address of care seekers and their care—seeking behaviour. To generate closer—to— reality HFCA, for robust disease mapping and healthcare planning, additional data and innovative approaches balancing between model complexity and routine programmatic use are required.

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“…The data include information on 645 municipalities in the state of São Paulo. In addition, we used records between February 25 and May 5, 2020, and a geographic information system (GIS) to geocode the locations 13 of COVID-19 cases and aggregated them to the municipality level. Municipality-level GIS files were retrieved from the Instituto Brasileiro de Geografia e Estatística (IBGE).…”

Section: Methodsmentioning

confidence: 99%

Utilizing prospective space-time scan statistics to discover the dynamics of coronavirus disease 2019 clusters in the State of São Paulo, Brazil

Ferreira¹,

Martines²,

Toppa³

et al. 2022

Rev. Soc. Bras. Med. Trop

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Background: The number of deaths and people infected with coronavirus disease 2019 (COVID-19) in Brazil has steadily increased in the first few months of the pandemic. Despite the underreporting of coronavirus cases by government agencies across the country, São Paulo has the highest rate among all Brazilian states. Methods: To identify the highest-risk municipalities during the initial outbreak, we utilized daily confirmed case data from official reports between February 25 and May 5, 2020, which were aggregated to the municipality level. A prospective space-time scan statistic was conducted to detect active clusters in three different time periods. Results: Our findings suggest that approximately 4.6 times more municipalities belong to a significant space-time cluster with a relative risk (RR) > 1 on May 5, 2020. Conclusions: Our study demonstrated the applicability of the space-time scan statistic for the detection of emerging clusters of COVID-19. In particular, we identified the clusters and RR of municipalities in the initial months of the pandemic, explaining the spatiotemporal patterns of COVID-19 transmission in the state of São Paulo. These results can be used to improve disease monitoring and facilitate targeted interventions.

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Uncertainty in geospatial health: challenges and opportunities ahead

Cited by 28 publications

References 195 publications

Geoprivacy in Neighbourhoods and Health Research: A Mini-Review of the Challenges and Best Practices in Epidemiological Studies

Geoprivacy in Neighbourhoods and Health Research: A Mini-Review of the Challenges and Best Practices in Epidemiological Studies

Approaches to defining health facility catchment areas in sub-Saharan Africa

Utilizing prospective space-time scan statistics to discover the dynamics of coronavirus disease 2019 clusters in the State of São Paulo, Brazil

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