{"id":8289,"date":"2021-11-12T10:44:31","date_gmt":"2021-11-12T10:44:31","guid":{"rendered":"https:\/\/www.bluesquarehub.com\/?p=8289"},"modified":"2025-10-15T12:16:52","modified_gmt":"2025-10-15T12:16:52","slug":"geohealthaccess-making-health-services-accessibility-analysis-accessible","status":"publish","type":"post","link":"https:\/\/www.bluesquarehub.com\/fr\/geohealthaccess-making-health-services-accessibility-analysis-accessible\/","title":{"rendered":"GeoHealthAccess : making health services accessibility analysis accessible"},"content":{"rendered":"\n

In 2019, it was estimated that 50% of the world\u2019s population lived without full access to essential health services<\/a>. A key issue driving inaccessibility of health services is the time it takes to reach a health facility. The longer the travel time to health services, the lower the probability of seeking treatment.\u00a0<\/p>\n\n\n\n

To tackle this issue, it is essential to gather insights on how long it takes to get to health facilities. While this may seem like an easy question in the days of ubiquitous geolocation and navigation services, in many countries the travel time to health services is hard to estimate due to lack of data on road and health infrastructure. Computational approaches to estimating access to care have progressed significantly during the last decade<\/a>. However, the amount of human intervention needed to tweak and run these models prevents up-to-date analyses from reaching decision makers on a regular basis. <\/p>\n\n\n\n

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Bringing insights on accessibility to health facilities through geospatial data<\/h2>\n\n\n\n

In 2021, Bluesquare and the Spatial Epidemiology Lab (SpELL<\/a>), an academic research group from Universit\u00e9 libre de Bruxelles with funding from Innoviris<\/a>, collaborated to develop an open source<\/em> solution that brings advanced insights on accessibility to health facilities in a regularly updated<\/em> and interactive format<\/em>: GeoHealthAccess (GHA). <\/p>\n\n\n\n

GeoHealthAccess enables healthcare decision makers such as governments, NGOs, global donors, community-based organisations and local institutions to access up-to-date estimates regarding the accessibility of health facilities. Through interactive maps, users can look at the percentage of the population having access to a given health service in less than a certain amount of time.<\/p>\n\n\n\n

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Travel time to the nearest health center in Benin<\/figcaption><\/figure><\/div>\n\n\n\n

The estimation of travel time is made using widely disseminated models. The innovation of GHA lies in its automated data acquisition through direct connection to routine health information systems and big earth data catalogs (see below). This means that accessibility calculations can be updated on a regular basis to reflect the availability of certain drugs, the seasonal variations of water levels or the opening of new facilities. Moreover, GHA provides interactive and easy to use visualisation to help non-specialists understand its results.<\/p>\n\n\n\n

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% of the population with access to a health center within 30minutes in DRC<\/figcaption><\/figure><\/div>\n\n\n\n

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Making use of recent advancement on Combining geospatial modelling and Big Earth Data <\/h2>\n\n\n\n

GHA relies on recent advancement in geospatial modelling (digital simulation of the real-world using spatial relationships of geographic features) that can now be combined with Big Earth Data (big data associated with the Earth sciences). <\/p>\n\n\n\n

For example, in recent years, we have seen the development of several open-access resources providing essential, high quality and detailed data including : <\/p>\n\n\n\n