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Mrs X was eight months pregnant when she died. There are many reasons for her death, and any one could have tipped the balance. Health education. Family planning advice. Better access to antenatal and facility-based care. It is a familiar story, and one that must still be told*. 

The fact is, that even in today’s era of Sustainable Development, 50% of the world’s population live without full access to essential health services. It’s a complex issue. About more than just the availability of health centres and facilities, there are countless variables to consider, including cultural acceptability, affordability and physical accessibility. Focussing on the latter, if the world wants to make universal health coverage a reality, it must build an accurate picture of the situation on the ground. The ‘haves’ and ‘have-nots’ of healthcare access. Experts in health data science and technology, at Bluesquare Hub we believe that geospatial data has the power to unlock this information – and we want to take this chance to explain why.

The limitations of current accessibility models

Traditional measures of physical healthcare access – Euclidean distances and 5km catchment zones – simplify what is, in reality, a far more complicated situation. Evidence shows that service uptake decreases with distance, travel time and other logistical barriers. An expectant mother might live within 5km of her local health centre, but if it’s too far to walk, there is no car, affordable public transport, or the road becomes impassable, then she cannot get there. 

It is a reality compounded by the fact that current geospatial models do not give policy and decision-makers the information they need to effectively improve healthcare accessibility. Produced on a per-project basis, methods are rooted in regional population estimates and siloed datasets that are static and imprecise. The results are paper-based reports that simply cannot account for the variables that define healthcare access as experienced on the ground.  

In this situation, accessibility measures become theoretical. There is little space for local validation of resulting models, with real-time variations of service availability rarely examined. Yet if ambition towards universal health coverage is to be achieved, we must understand and account for reality. Only then will governments and global health actors have the information they need to drive development forward.

Combining geospatial modelling and Big Earth Data to better understand healthcare accessibility

Leveraging recent advances in geospatial modelling, the team at Bluesquare Hub is working to build digital tools that allow for the automatic and rapid computation of accessibility metrics from multiple data sources. For example, in recent years we have seen the development of several open-access libraries providing essential, high-quality data. These include, but are not limited to:

• The high-resolution land cover maps provided by Sentinel mission.
• Spatial demographic/population data maps developed by WorldPop.
• Crowd-sourced geographic information from OpenStreetMap. 

Capturing variables that influence healthcare access in real-time (topography, road networks, population distribution and demographics), together these sources create a newly available set of ‘Big Earth’ data that can be easily read, updated and integrated into modern accessibility models.  

Combining this information with routine health data, BlueSquare Hub joins the Spatial Epidemiology Lab from the University of Brussels and the University of Geneva in new research to ‘productivise’ this process – using machine learning and artificial intelligence to automate near-continuous updates. This combination of advanced geospatial modelling, ‘Big Earth’ and real world data allows users to develop digital accessibility maps that empower them to:  

• Understand healthcare accessibility down to a local, village level.  
• Calculate key accessibility metrics in line with local public health priorities.
• Accurately map facility catchment areas, population access and travel time.
• Focus on specific population subsets and demographics (e.g. women of childbearing age). 
• Account for socio-economic variables, including ethnicity, religion and household income. 
• Tailor input data to account for multiple scenarios, including seasonal changes.

With pilot models currently focussed on the Democratic Republic of Congo, Niger and Senegal, for the first time in history, policy and decision-makers can have an up-to-date picture of health service access as it happens on the ground. Not only does this process allow for enhanced accessibility mapping, it makes it dynamic – allowing global health actors to track healthcare accessibility in evolving situations and tailor their response accordingly.

There will be some hard truths to face. With preliminary work in the Republic of Senegal showing 80% of women living with reduced, or no, access to family planning, current understanding around healthcare access will almost certainly shift. It is important that it does. By improving the way physical healthcare accessibility is defined and measured, we can create new opportunities for a data-driven future. One that allows for the smarter allocation of health resources and has the power and potential to revolutionise access to healthcare.  

*The story of Mrs X was first told by the WHO in 1988 and retold in 2012. Click here for more information.

For governments to effectively manage the health crisis caused by COVID-19, they must have the ability to make decisions based on reliable and consolidated data.
Today, the data that are key in this decision making – specifically in low- and middle-income countries – are those related to the availability and location of the infrastructure and sanitary facilities necessary to meet the needs of their populations. Identifying the infrastructure facilities will allow governments to allocate the necessary funding to the essential health services.
During a crisis, time is critical. Governments must be able to rely on what has already been accomplished to improve the information system and health surveillance. Those who started this work before the crisis will have a head start. They will already have critical data at their disposal to manage the crisis. This kind of data management and collection should not stop during the crisis. On the contrary, it is one of the crucial elements of health risk management and mitigation.
In the below, based on Bluesquare experience in the DRC and Niger, we describe the approach to leverage Digital Health Master Plan (Carte sanitaire in French) to tackle COVID-19.

Initial and ongoing work to build the Digital Health Master Plan

We have been supporting the DRC and the Niger Ministries of Health in the implementation of an interactive and dynamic health map of public and private health facilities (pharmacies, clinics and health centres).

The results of this work provide routine information, taking into account the health pyramid and population, to the health authorities (the Minister, Technical and Regional Directorates of the Ministry, Health Districts, Projects and Programmes, Partners, external agencies, NGOs, hospitals and health workers). With this information, they can better allocate resources and target underserved regions and populations. 

Bluesquare has specifically focused on identifying databases containing GPS coordinates of health structures, with precise information, such as photos and features that are specific to the DRC and Niger. In Niger, we identified 14 different databases, including the one from the SNIS (HMIS).

We also identified new sources of data to help increase the number of GPS coordinates on the health map, based on existing data. In the DRC, we have identified the following databases: Geolocation of health facilities in the Ebola response, a database from MSF/common geographical repository, a database from the PDSS containing the GPS coordinates of about 500 health facilities in Kinshasa and Kasai.

These multiple data sources, combined with a constant change of data (population estimates from remote sensing, settlement location, names of villages and settlements, health areas, health districts, health  facilities and “découpage communal”), inconsistency between sources (for example, the health layer does not always correspond to the administrative layer) require continuous integration.

Thanks to our visualisation tool, Dataviz, we have set up public portals to visualise the ongoing work on the health map (carte sanitaire) in Niger and the DRC.

Work that facilitates a quick response to COVID-19

In the framework of the COVID-19 response, countries must quickly identify the infrastructures and services they have at their disposal to address the disease and to target funds to acquire the ones still needed. 

In the DRC, where we have been working with the Health Ministry, supporting health information systems and helping the government to create the “carte sanitaire”, we are currently working with the national COVID-19 Task Force to help them tackle the disease.

We are providing support for the geolocalisation of the COVID capacity response (screening devices, in-patient beds, ventilator beds, critical care beds, laboratories, oxygen production units, etc.).

The work that has been done, and which is continuing with the health map (“carte sanitaire”), supports the establishment of an  “état des lieux” of the existing infrastructures and services for a COVID response.  We are putting in place an android app for the “carte sanitaire” update.

We are also investigating other databases that provide integrated information on the COVID Capacity response in order to consolidate data into a single digital database.

This work, while seemingly low-impact, reveals its importance with the COVID-19 pandemic. Ministries of Health and different stakeholders are even more tempted to work with non-centralised databases, gathering data through excel files and sharing them by email, creating different versions of the same database. More than ever, a centralised digital database, to which the different actors can contribute, is critical.

In the first phase, Bluesquare, in partnership with the Agence Nationale d’Ingénierie Clinique d’Information et d’Informatique de Santé or ANICiiS, is identifying the different existing data flows and databases to have a clear view of the full process (what kind of data exist, how data are gathered, by who, and how often etc..) and inviting the different partners to share any complementary existing databases. When complete, the second phase is to propose a set of tools that take into consideration the actual flow of information, while switching from a decentralised multi-source database to one central tool. 

This information will be made publicly available via the public data visualisation interface: “STOP COVID“

To get more information about Bluesquare’s support to COVID-19 response.

The first impression you get when you meet Romain is his discreet nature, attention to detail, commitment to quality and a dedication to the work he does. So when we asked him to tell us about himself, it was clear we wanted to understand how he got where he is today.  

While his journey was not linear we immediately notice a central theme: his commitment to understanding complex problems and his desire to make an impact.