India is urbanising rapidly. The current projection is that an estimated 534 million urban residents will make up 38% of the population in 2026.1,2
One of India’s three megacities, Mumbai’s population is over 16 million and will rise to 24 million over the next decade. Land is at a premium, existing infrastructure is overburdened and rich and poor from diverse backgrounds coexist intimately. The 2001 census identified 1,959 slum settlements, home to 54% of Mumbai’s people but covering only 6% of its land area.3
Localities were categorised as slums if they had been notified as such by state or local government, or recognised even if not formally notified. They were characteristically compact areas with populations of at least 300 (60–70 households), living in poorly built, congested dwellings in an unhygienic environment, usually with insufficient infrastructure, sanitary, and drinking water provision.
The loose nature of this definition reflects the diversity of informal settlements. The UN provisional operational definition of a slum (UN Expert Group Meeting, Nairobi 2002) includes five dimensions of vulnerability,4
and there is substantial heterogeneity both between and within communities.5,6
All of Mumbai’s slums share one characteristic—density—but “…slum pockets are highly differentiated by type, size and location, and occupy land held under a variety of ownership structures, including central government, state, municipal and private…”.7
Localities with durable housing, metered electricity, piped water, well-maintained public toilets, enclosed drainage, thriving businesses, ubiquitous television, and (relative) security of tenure contrast with localities with rudimentary shelter built by polluted creeks, by railway lines or garbage dumps, with poor access to water and electricity, non-existent sanitation and drainage, and high population transience.
Our growing understanding of the social determinants of health underpins an emphasis on inequalities,8
of which Mumbai’s slums are a manifestation. Until relatively recently, health indicators have been collected and viewed as aggregates comparing urban with rural processes and outcomes. However, since cities are engines of demographic and epidemiologic change, and because vast inequalities exist within them, there is a consensus that health interventions should promote urban equity.9,10
We need to disaggregate urban data both to understand the determinants of inequality and to try to address them.11–14
One would assume that women and children who live in urban slum areas are less healthy than those who live in non-slum areas, and several studies confirm that this is the case.15–22
For example, India’s National Family Health Survey (NFHS-3, 2005–6) found that women living in Mumbai informal settlements were less likely than women living in non-slum areas to make first trimester antenatal care visits (64% compared with 76%) and deliver their babies at health facilities (83% compared with 91%).23
Unfortunately, in trying to plan interventions, we have been limited by a lack of disaggregated information and by the difficulty of untangling the knot of risk factors around life in informal settlements. Take, for example, a young married woman living in a Mumbai slum with her husband and his parents. Her health could be compromised by a wide range of factors. She may have been exposed to a heritage of poverty, societal marginalisation, the monetization of subsistence needs, and dietary insufficiency; limited education, early marriage and conception; hard work and gendered discrimination; infection from substandard water supply and sanitation, hazardous location and household crowding; harmful effects of home and local industry, noise, damp and insubstantial housing; and hazards associated with early sexual activity, spousal alcohol or drug dependency, domestic violence, informal sector livelihoods, identity politics, accidents, and crime.
A minimum conceptual framework for these determinants would include her physical environment, her social environment, and her access to and use of health and social services.24
However, since risk factors cluster together and are mutually reinforcing, understanding their individual influences is difficult.25
An experience in the planning phase for a major health project made us think about this. The Society for Nutrition, Education and Health Action (SNEHA), a Mumbai-based non-government organisation, works to improve the health of women and children in informal settlements. In partnership with the Municipal Corporation of Greater Mumbai and with community members themselves, we undertook the City Initiative for Newborn Health.26
One component of the Initiative was a cluster randomised controlled trial of community mobilisation activities, which required us to identify slum localities at higher health risk. Data on health outcomes were not available at this level, so we undertook an extensive vulnerability assessment. The Environmental Health Program had carried out a model assessment in Indore, which began with municipal lists of slum areas, expanded them through participatory mapping, and classified localities on the basis of economic, social, and environmental conditions, access to and usage of public health services, disease incidence, and collective community efforts.27
The views of staff of public sector, non-governmental and community-based organisations were solicited and validated through site visits.
We took an approach with a similar ethos. We used an iterative process to collect and triangulate information. In the first step, we discussed potential criteria for health risk with a range of local informants (women’s group leaders, health workers, ration shopkeepers, community and political leaders, members of community-based and non-government organisations, private practitioners, and preschool teachers). This yielded three sets of risk indicators: social (unemployment, groups in difficult circumstances, substandard housing), environmental (open drainage, informal water supply, informal electricity supply, sanitation), and healthcare utilisation (infrequent interaction with community health volunteers, home deliveries). The key informant process generated a provisional list of 117 slum localities. The second step was to undertake triangulation and transect walks in each area to classify vulnerability systematically. Vulnerability criteria were confirmed with municipal community health volunteers. They accompanied our team members on walks around locality boundaries and in diagonal transects, meeting, and verifying vulnerability indices with local people such as groups of women, Integrated Child Development Services anganwadi teachers, ration shopkeepers, doctors, tea stall owners, members of community-based organisations, local social workers, pharmacists, political party workers, and industry owners. The process suggested that 92 informal settlements in six municipal wards were at high risk of poorer maternal and child health outcomes. Of these, 48 were randomly selected for inclusion in our trial.
The vulnerability assessment raised three issues. First, since health outcomes in individual communities were unquantified, we assumed (reasonably, we thought) that a multidimensional risk assessment would correlate with the health outcomes of women and children. Second, although the process was crucial to understanding the community environment, and although it was essentially a first step in engagement, it was time-consuming and more than half of the informal settlements surveyed were not involved in the subsequent program. Third, it was possible that our evaluation had been over-comprehensive. Several commentators suggested that deprivation is obvious and we wondered if a rapid look at an informal settlement might provide enough information to rate its degree of vulnerability.
This paper describes our responses to these concerns, based on subsequent data collection. First, we examined the associations of presumptive indicators of vulnerability with selected maternal and newborn healthcare and outcomes. Second, we tried to develop a tool for rapid triage that used ‘obvious’ characteristics and could be applied during a brief visit to a slum locality. Third, we evaluated the performance of the tool.