The SAPRIN focus on long-term surveillance using high quality data to better understand health outcomes for the poor, as well as migratory patterns and their impact, is at the heart of this proposal.
However, noting the call for inter- and transdisciplinary research in the 2019 White Paper on Science, Technology and Innovation, we have proposed a team that has health experts managing every site, but locates them next to urbanists, economists, environmental scientists and others, because we believe that this kind of multidisciplinary team will generate different perspectives, and can better analyse possible urban effects on health outcomes. As our bid title suggests, we hope to use this granular data to analyse class stratification (‘the poor’ are far from an undifferentiated mass), income differentials, service access differentials and others for their impacts on health outcomes. Our goal is to provide the richest possible set of outputs for SAPRIN from this intervention. We are guided in this approach by key policy documents which point to multidisciplinarity.
Our support for covering a range of urban forms within the proposed SAPRIN node also responds to the South African Research Infrastructure Roadmap (DST, 2016:35), which notes that South Africa faces “several challenges, including high levels of inequality … an unemployment rate of 25% [now 29%] … a poverty headcount ratio of 57% [much lower in Gauteng] as well as colliding epidemics of HIV/TB and non-communicable diseases”. In other words, separating one issue from the ‘colliding’ challenges that are concentrated in Gauteng, may miss a more complex picture. Gauteng is the smallest province in South Africa, covering just 1.46% of the land mass, but with the largest population share (at 26% – 15 176 115 people), and generating 34% of national GDP. The poverty headcount ratio is far lower (at 29,3%) than nationally, for example; but the province has 1,9 million people living with HIV, second only to KwaZulu-Natal, and includes Johannesburg, the most unequal city on the planet. As Statistics South Africa noted a couple of years ago, Gauteng’s economy is roughly the size of Morocco’s national economy, and is the 7th largest in Africa (http://www.statssa.gov.za/?p=12056) – but this is far from equally shared.
Undergirding inequality in Gauteng is apartheid spatial engineering, which has proved remarkably enduring and which has hardcreted inequality into the physical landscape and the built environment. In short, apartheid meant that black, coloured and Indian townships were built far from ‘white’ suburbs and separated by physical barriers both constructed (such as the M2 which separates the CBD from Soweto) or mine dumps or industrial belts, and natural barriers such as rivers. Townships were built to embody the ‘apart-ness’ of the ideology, and were also deliberately under-resourced and under-serviced since they were ostensibly urban dormitories for temporary sojourners whose ‘real’ homes were in rural areas or Bantustans. As more and more people have moved into post-apartheid Gauteng, so they have had to use informality as a means of at least settling here – hence the vast site and service areas in the south such as Orange Farm and Weilers Farm – and to move closer to city centres with work opportunities and services, via the infiltration of garages, gardens and green spaces to use as residential spaces. If we look at Melusi, one of our proposed sites, we see the health and risk indicators for such communities, with HIV test requests at 13,2%, 3% with TB, 13% of children under 5 years of age not immunised, 6% with chronic conditions – and this from the 1906 households that have been registered and assessed. The need for the SAPRIN intervention – to understand these issues as they change over time rather than via quick snapshots, and to be able to use embedded studies to focus on specific policy or medical interventions – is clear.
In response to a growing black presence in the south of Gauteng, whites have moved further and further north into vast gated communities. (Ironically, they now suffer similarly long commutes to and from work, previously reserved for black South Africans because of spatial apartheid, but thereby add to poor air quality, traffic congestion, road accidents and the like.) As the attached maps of population distribution by race suggest, racial homogeneity in living spaces remains true – especially for the poor. Townships, informal settlements and inner cities are overwhelmingly black African; former coloured and Indian townships retain a strong element of their former racialised status, but with informality emerging on their fringes; while wealthier suburbs have deracialised as middle classes can afford to take up suburban residence, and use suburban boom gates, fully gated communities and armed response companies to keep black people out of ‘their’ spaces.
On the positive side, many middle class areas are steadily deracialising (see attached population by race map), but the barriers to moving from township to suburb are enormous. In 2011, the OECD found that an economy can be described as ‘distressed’ when median average house prices are five times the median average household income – in Gauteng, the median average suburban house was 22 times the median average household income, and even moving within Soweto was prohibitive, with house prices at 7 times the median average household income. Inequalities such as these have penetrated townships, and no longer (only) define the boundary between township and suburbia. This will be true in Atteridgeville, and the class stratifications we will find are important in helping understand issues of service access, knowledge and their impact on health outcomes.
The middle classes may be deracialising, but for the poor and working classes, the cost barriers are prohibitive. The bulk of the population continue to live in spaces deliberately located many kilometres from employment opportunities, health and education facilities, and the like. Post-apartheid build – mainly ‘RDP’ houses – has replicated the problem, by locating these often massive settlements (such as Cosmo City) far from economic hubs (primarily due to the cost of land). The result is that poor households spend an average of 20%-30% of disposable income on transport to work, or to look for work. Inner city areas and former (white) working class suburbs offer an alternative route towards the economic centres of the province, in multiple household apartments in the city centres, or via shacks built in the gardens of suburbs and townships.
These points are not for descriptive purposes, but to make the point that demographic surveillance in Gauteng has to take into account the urban, demographic, socio-economic and other characteristics of Gauteng itself. Gauteng is not simply ‘urban’ – it is a highly complex, profoundly unequal, racialised and fragmented space, comprising three metropolitan municipalities that together form a polycentric city-region, or continuous urban extent. Gauteng offers a panoply of urban forms, exacerbated by both inequality and race. In our view, a multi- or transdisciplinary approach that locates the SAPRIN longitudinal data in (at least some of) the varied contexts Gauteng offers, and analyses it through multi- or transdisciplinary teams, is vital. It may be possible to simply transplant the SAPRIN protocol from rural nodes to an urban Gauteng node and implement it – but in our view that would lose a mass of richness that a proper treatment of the province can offer. This is how we understand ‘science for the future’, as proposed by the DST (now the Department of Science and Innovation, DSI).