Health in Human Settlements

Integrating data on health and human settlements to inform intersectoral action for health.

Health is influenced by the environments where we live such as the quality of housing, the availability of water and sanitation, and the presence of parks for physical activity. Obtaining and integrating data from multiple sectors such as health, urban planning, human settlements and education is needed to explore associations between these environmental features and health, Such efforts can help to develop appropriate built environment interventions to improve health and to measure how such interventions impact health profiles over time.

We explored factors that can either facilitate or impede the integration of data across several health and non-health government sectors. As proof-of-concept, we also merged administrative health data (health sector) and human settlements enumeration survey data (human settlements sector) for a selected informal settlement in Cape Town, South Africa. The aim was to assess whether such data integration could help to identify which areas should be prioritised for intervention based on the health profiles of the population and to explore whether this could also inform the choice of informal settlement upgrading intervention implemented in order to improve both human and environmental health.

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Using a transdisciplinary research approach, we collaborated with key government stakeholders from the health and human settlements sectors in Cape Town to explore specific urban health challenges occurring in informal settlements. 

The stakeholders contributed to our study design,  the process of integrating health and housing data, and ideas for disseminating research findings. 

The study was conducted in two parts. 

  1. Using the integrated data to explore the health profiles for an informal settlement in Cape Townand to highlight features of the living environment that may influence health.
  2. Exploring the practicalities of merging two datasets across sectors, including the factors that facilitate or impede integration, and the opportunities to improve the data merging process in future. 

Key findings / Progress

We found that merging health and human settlements data allowed for important baseline urban health evidence to be developed for a local informal settlement.

The analysis highlighted a high level of diarrhoea cases presenting at clinics surrounding the informal settlement.

Of the available environmental data, water and sanitation were identified as most linked to diarrhoea and infectious diseases, according to the available literature. We found inequality across the informal settlement in households’ self-reported utilisation of water and sanitation services. For example, not every household within 50 metres from a communal toilet made use of it, with some households instead using bucket toilets or the bush, both of which are defined as inadequate forms of sanitation. Utilisation is often used as a proxy indicator for “access”. Yet, the concept of “access” to services is more than just the physical presence or use of a service. It is also about the acceptability, affordability, and physical access to services especially for the elderly, children, and vulnerable groups including those with disabilities.

We found that qualitative information enhanced the interpretation of quantitative data for the local community setting. For example, available media reports suggested that some households claim basic services for private use, forcing others to pay out of their pocket to use what should be a public service. Additional reports highlighted crime and vandalism as other barriers to service access. These findings highlight the need to involve the community in the development of data collection tools and to include the community voice in healthy urban planning interventions.

The integration of data from more than one sector was important for informing intersectoral action for health. The use of a transdisciplinary research approach, which included government department champions in the study design process, proved to be incredibly valuable. Government department champions and designated point-persons who were familiar with the availability and validity of data, variables, data collection methods, and consolidation processes, were able to provide guidance on the selection of indicators for this study and contributed to the development of our data merging strategy that complied with data governance procedures. This study thus presents a model for data integration applying a transdisciplinary approach that may be useful for government sectors and academics seeking to improve collaboration.

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