Ivy Chumo, Helen Elsey, Caroline Kabaria and Blessing Mberu argue that governments need to have data that can recognise the challenges that residents of informal settlements face. Data sets need to be freely available so they can be used in evidence-based policy making in Kenya and beyond.

The growth of informal settlements and poor residential neighbourhoods is a global phenomenon accompanying rapid and uncontrolled urbanisation – from small towns to mega-cities. There is a strong body of evidence to show that those living in informal settlements frequently have worse health and cannot maintain the same level of wellbeing as residents in rural areas and better parts of the city.

Despite this recognition in the academic literature, on the ground, many informal settlements remain invisible within the data available on the health and well-being of city dwellers. This phenomenon of the ‘missing millions’ has been identified and rallied-against by academics, global policy makers, data scientists and importantly, by the communities affected, for example through Slum Dwellers International’s work to map and count residents within informal settlements.

While the existence and improvement of informal settlements is highly politicised, their invisibility within available data happens both by default and by design. For example, censuses are conducted only every ten years and stick to administrative boundaries – excluding rapidly emerging informal settlements often situated on the most undesirable land at the city boundaries. Large cross-sectional surveys commonly use censuses as their sampling frame (population reference list) so by default, exclude urban poor. Even within routinely collected data from government health facilities, the poor are frequently overlooked as they are more likely to use private sector providers whose opening hours allow use by men and women working long-hours for a daily wage in the city.

The power of data

High quality data are essential to address inequities within the city and to inform decision-making across a variety of sectors. Urban data can be useful to:

• Identify areas for infrastructure support
• Pinpoint environmental and public health hazards
• Conserve biodiversity
• Deliver basic and emergency services

Many countries have made significant progress in improving systems – for example in registering births and collecting health administration data and careful collection of census data. However, these data are rarely disaggregated to enable understanding of intersecting vulnerabilities within and between informal settlements and urban neighbourhoods. The availability of such data is of vital importance for accountability, allowing marginalised urban residents and the organisations that work with them to monitor the provision of services, promotion and protection of health and to overcome inequities in the city.

Kenyan context

Kenya has seen positive economic growth in tandem with increasing rates of urbanization, though the country has yet to experience an economic transformation. Economic growth has created a growing middle class, but poverty remains stubbornly high, and most urban residents live in informal conditions, with poor access to basic networked services.

Illegal settlements are omitted from census data and as a result they continue to be geographically, economically, socially and politically disengaged from wider urban systems and excluded from urban opportunities and decision-making. Many cross-sectional surveys and case studies use the census as their sampling frame and also exclude urban poor. Routine data are limited as they do not always show the clear picture of a phenomenon, a situation described as “data shadow” because often there is information that may be missing and has direct influence on health and wellbeing.

City government attitudes to informal settlements range from opposition and eviction to reluctant tolerance and support for legalisation and upgrading. Once an informal settlement is established, clarifying land and property ownership becomes more challenging. Well-connected interest groups often take advantage of this lack of clarity to claim quasi-legal land ownership and frustrate attempts for reforms. Despite the precarious and poor living conditions with little access to rights or accountability, dense informal settlements provide essential mass housing for low-income residents that is vital to the urban economy.

Data on Nairobi’s informal settlements are often inaccurate or out of date. This renders effective planning and indeed cost-estimates for different policy options impossible. To obtain accurate physical and socio-economic data on an informal settlement, it is generally necessary to conduct a detailed mapping and surveying exercise.  African Population and Health Research Center (APHRC) research institution established in 1995 has been conducting a longitudinal Nairobi Urban Health and Demographic Surveillance System (NUHDSS) survey since 2002.

NUHDSS is a pioneering urban health demographic surveillance system in sub-Saharan Africa that has been operational in two slum communities in Nairobi (Korogocho and Viwandani) following APHRC’s 2000 Nairobi Cross-sectional Slums Survey (NCSS). The NCSS study showed that slum residents have the worst health and socio-economic outcomes of any group in Kenya, including rural residents, with limited access to water and sanitation as well as education and employment. Further, it was revealed that there was a marked absence of the public sector and law enforcement agencies as well no legal land entitlements. Not only were residents more likely to have poorer health outcomes, they were also more likely to be exposed to violence and social unrest.

Strong community involvement in the data collection has made the process both more cost-effective and less politically challenging. This is because local inhabitants know their settlement better than outsiders, and because it encourages a process of early-stage community participation, necessary for the success of nested programmes in the study sites. The Nairobi cross-sectional survey is representative of all informal settlements in Nairobi, however conducting such a survey is resource-intensive and without sufficient donor-support, the last round of data collection was 2012. It should be noted that despite these constraints, APHRC’s work has ensured that Nairobi has one of the most comprehensive assessments of health and demographic change amongst the urban poor of any low- or lower-middle-income country.

ARISE

While collecting data on health and well-being among the urban poor is challenging, ensuring that these data are used to drive improvements and reduction in health inequities is even more challenging. Decisions that affect the health and well-being of the urban poor are taken by national and local governments, as well as by donors, NGOs from multiple sectors and health providers. However, do these decision-makers have access to what data are available and can they make use of these to improve health and reduce inequities? To answer this question, the ARISE team conducted an assessment of the data sources on health and well-being that could be freely-used by decision-makers and providers, as well as by communities and community organisations to address health inequities.

Between December 2019 and May 2020, we searched government, donor, (I)NGO and academic organisation websites and open data portals to identify data sets that a) could distinguish between slum/non-slum areas and b) are freely available for subsequent analysis.

We found 24 data sets, and over half (16) of these allow for slum/non-slum analysis. However, of these, only the APHRC data sets are in the public domain and freely available for analysis, See Table 1.

This illustrates the challenges facing local governments and NGO, civil society organisations in sourcing data to identify and monitor health issues specifically in and between informal settlements.

Recommendations

Countering the negative aspects of informal settlements requires governments to have data that can recognise the challenges residents face and actively include them in wider city systems. Routine population-based information systems should include informal settlements to play fully a fundamental role for evidence-based policy. The data should be available and provide both the quantitative information – needed for setting priorities and establishing rational policies – and the real-world context for understanding how policy affects the public, including those living in informal settlements.