Summary
Racism is a public health crisis. Black communities (including Africans, the African diaspora and people of African descent) experience worse health outcomes as demonstrated by almost any measure of health and wellbeing—e.g. life expectancy; disease prevalence; maternal mortality rates. While health promotion has its foundation in promoting equity and social justice, it is clear that however well-intended, we are not affecting meaningful change for Black communities quickly enough. Through this article, we outline the intersection of social determinants of health and anti-Black racism. We describe how in the first 8 months of 2020 Black communities around the globe have been disproportionately affected by COVID-19, while also having to respond to new instances of police brutality. We assert that the time has come for health promotion to stop neutralizing the specific needs of Black communities into unspoken ‘good intentions’. Instead, we offer some concrete ways for the field to become outspoken, intentional and honest in acknowledging what it will take to radically shift how we promote health and wellbeing for Black people.
This paper explores how the salutogenic theory can enable us to re-envision health promotion work with marginalized communities, towards an approach that acknowledges and honours their resilience. We use the three core concepts in Antonovsky’s salutogenic model of health – sense of coherence, generalized resistance resources and specific resistance resources – to explore the theory’s relevance to health equity, thus presenting new opportunities for how we might radically re-evaluate current health promotion approaches. We conclude that a more equitable health promotion requires increased participation of marginalized communities in shaping their futures and suggest a new model for historically grounded salutogenic health promotion.
IntroductionLinked population health data have the potential to inform evidence-based actions targeting serious public health concerns. However, large-scale data integration efforts can produce hundreds of population health indicators, which can overwhelm the ability of decision-makers to synthesize and interpret the information.
Objectives and ApproachOur research uses an existing semantic web application for population health surveillance, the Population Health Record (PopHR). PopHR automates a computational pipeline for linking data sources, building timely population health indicators, and uses artificial intelligence to organize indicators along a determinants of health framework. To assist users in interpreting the thousands of indicators, we developed computational algorithms combining values of multiple indicators across chronic diseases, to prioritize conditions within each region. This analytic approach can assist regional decision-makers in identifying their region’s priority conditions by facilitating the integration and analysis of multiple types of indicators (e.g. disease burden, temporal patterns).
ResultsA pilot implementation of the regional prioritization algorithm focused on indicators defined in the Public Health Agency of Canada’s Chronic Disease Indicators Framework. Within this subset of diseases, we developed a computational algorithm to integrate into a priority index regional estimates of incidence, mortality, and prevalence taking into account the relative importance of each indicators’ outlier status and statistical significance of temporal trends. Our results allowed for the development of region-specific data visualizations dashboards, emphasizing the different factors driving the rankings of indicators within and across regions. For example, regions with higher socioeconomic status having generally lower disease burden are presented with visualizations emphasizing temporal trends and other statistically compelling patterns rather than simple indicators of magnitude.
Conclusion/ImplicationsThis ranking approach represents initial stages ongoing research, expanding our methods to use machine learning strategies and additional expert knowledge. Current and future prioritization analyses within the PopHR platform offer the potential for public health to gain insights from an otherwise challenging complexity and richness of linked data.
scite is a Brooklyn-based organization that helps researchers better discover and understand research articles through Smart Citations–citations that display the context of the citation and describe whether the article provides supporting or contrasting evidence. scite is used by students and researchers from around the world and is funded in part by the National Science Foundation and the National Institute on Drug Abuse of the National Institutes of Health.