Spatial analysis of health inequalities in Dakar, Senegal

    Student thesis: Doc typesDocteur en Sciences


    Sub-Saharan African cities are highly heterogeneous, which makes the analysis of spatial health inequalities particularly interesting and challenging. Indeed, cities offer both the best and the worst environments for health and well-being. Multiple determinants converge to influence the health status of city dwellers, and positive and negative influences tend to cluster according to the specific neighbourhood or place within the city. The objective of this doctoral research is to analyse the spatial health inequalities in Dakar. In a health geography perspective, a systemic approach is developed to understand these spatial inequalities. Based on this approach, states of health are conceived as the result of the interplay between population, habitat and behaviour.
    Available data sources were first examined to assess their potential for spatial analyses. The usefulness of civil registration and DHIS 2 data for spatial analyses is hampered by several deficiencies. Such deficiencies are overcome by census data, which can provide spatially representative health indicators that cover the whole region of Dakar. Efforts were made to ingrate the 2013’s census data into a GIS system in order to conduct spatial analyses. In addition to census data, survey data and Land Cover/Land Use data derived from very-high resolution satellite imagery were used.
    A typology of neighbourhoods was carried out and revealed the extreme heterogeneity of Dakar in terms of living conditions. These heterogeneities are reflected in health conditions, with higher crude mortality rates in spontaneous settlements, and lower crude mortality rates in residential neighbourhoods. A deeper understanding of these inequalities is gained through the decomposition of the crude mortality rate into age-specific mortality rates. Spatial autocorrelation analyses revealed presence of clusters in the spatial distribution of mortality for both child-adolescent, adult and the elderly age groups. A geographically weighted regression was used to model the impact of contextual risk factors in spatial variations of age-specific mortality. Determinants of mortality vary both across age-groups and across space, except for population density that is consistently positively associated with age-specific mortality rates.
    The spatial distribution of COVID-19 infections is also clustered, with higher incidence rates in both western and eastern neighbourhoods. Different models highlighted population density as the most influential variable in explaining spatial variations in COVID-19 infection, followed by the highly connected administrative, commercial and service areas. The predicted spatial distribution of COVID-19 infections revealed a higher probability of infection in the western parts of Dakar, especially in areas characterized by a high population density and a high connectivity.
    The influence of contextual risk factors on health operates through many indirect and complex mechanisms, and thus caution is needed when drawing conclusions about mortality or morbidity determinants. Above all, these results are useful indicators of mortality differentials and differentials in their determinants. From a public health perspective, results can help to develop geographically targeted interventions.
    la date de réponse31 janv. 2022
    langue originaleAnglais
    L'institution diplômante
    • Universite de Namur
    SuperviseurCatherine Linard (Promoteur), Mouhamadou Diallo (Copromoteur), Sabine Henry (Président), Bruno Masquelier (Jury), Niko Speybroeck (Jury) & Gérard Salem (Jury)

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