The social determinants of health are well-known and are firmly part health research, planning, and practice. Research on the social determinants seeks to understand and change how divisions based on social class, ethnicity, gender, and age affect the public’s health. The World Health Organization defines social determinants as:

The conditions in which people are born, grow, work, live, and age, and the wider set of forces and systems shaping the conditions of daily life. These forces and systems include economic policies and systems, development agendas, social norms, social policies and political systems. (WHO)

There are two aspects of this definition that are often under-looked within health research. First, is the recognition of the structural determinants that contribute to inequities in population health, including:

  • the compounded effects of neoliberalism and public divestment in health on health inequity;
  • urban planning policies (segregation, auto-dominated development, gentrification) and the consequences on health; and,
  • the colonial policies that have led to long-term inequities in health.

Second, are the spatial determinants that reinforce and amplify observed health inequalities, including geographic access to timely and appropriate health care, exposure to adverse environmental exposures, the spatial segregation of poverty, inequities in urban planning, and the geographic compartmentalization of health service delivery.

Underlying the analysis of health process and patterns is a need for reliable data at multiple levels of aggregation. On the one hand, researchers require quantitative measures that reflect the issues under study, are scalable over time and place, and sensitive enough to evaluate underlying health inequalities. On the other, there is also a need for data to be grounded in the qualitative, lived experiences of the individuals and communities under study. The recognition of this duality guides the focus of our Lab, where data-informed analysis is used to expose the structural changes can promote health equity and social justice.

Perhaps counterintuitively, the patterns and processes of these inequalities at the local level can be measured using large data sources, sometimes termed “big data”. These data sets provide for a range of health measures, including morbidity and mortality, and encompass enough of the local population to allow for representation of standard rates within small, sub-regional areas. These quantitative data can then act as starting point from where more nuanced, qualitatively-informed research can guide analysis and inform our interpretation. Alternately, these data can be used to augment deep, qualitative experience gained from immersive research.

Whether analysing problems manifest in small urban areas (neighbourhoods), or in small rural places (villages), our research seeks to engage the communities under study. This research process, described as ‘Dirt Research,’ is an attempt to produce thick description of phenomenon that might be typically represented by quantitative data. This is not only the process of providing detail via qualitative data, but is about seeing beyond the obvious, and making links between different sources of data and different observations.

Research in small spaces and small places presents challenges for both quantitative and qualitative methods. The quantitative record tends to be incomplete at smaller levels, and the impact of otherwise minimal changes on quantitative measures (such as disease or hospitalization rates) mean quantitative description alone provides a poor understanding of the health dynamics of small neighbourhoods and villages. At the same time, qualitative research methods have difficulty in ‘scaling-up’ results and aligning findings with quantitative measures. Our research works between these areas, by informing our quantitative analysis with engaged participants and grounding our qualitative work with key ‘data points.’