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Data's increasing role in society and high profile reproduction of inequalities is in tension with traditional methods of using social data for social justice. Alongside this, ‘intersectionality’ has increased in prominence as a critical social theory and praxis to address inequalities. Yet, there is not a comprehensive review of how intersectionality is operationalized in research data practice. In this study, we examined how intersectionality researchers across a range of disciplines conduct intersectional analysis as a means of unpacking how intersectional praxis may advance an intersectional data science agenda. To explore how intersectionality researchers collect and analyze data, we conducted a critical discourse analysis approach in a review of 172 articles that stated using an intersectional approach in some way. We contemplated whether and how Collins’ three frames of relationality were evident in their approach. We found an over-reliance on the additive thinking frame in quantitative research, which poses limits on the potential for this research to address structural inequality. We suggest ways in which intersectional data science could adopt an articulation mindset to improve on this tendency.

BackgroundShigella is a leading cause of bacterial diarrhea morbidity and mortality affecting mainly children under five in the developing world. In Zambia, Shigella has a high prevalence of 34.7% in children with diarrhea and an attributable fraction of 6.7% in Zambian children with moderate to severe diarrhea. Zambian diarrhea management guidelines and the health ministry reporting tool Health Management Information System (HMIS) heavily rely on the WHO clinical classification of dysentery to potentially identify and estimate the burden of Shigella in children. This reliance on clinical dysentery as a proxy to shigellosis in under five children may be resulting in gross under-estimation of shigella disease burden in Zambia.MethodsWe used existing laboratory and clinical data to examine the sensitivity and predictive value of dysentery to correctly identify Shigella infection in under five children with PCR confirmed Shigella infection in Lusaka and Ndola districts, Zambia.ResultsClinical dysentery had a sensitivity of 8.5% (34/401) in identifying under five children with Shigella by stool PCR. Dysentery was able to correctly classify Shigella in 34 of 68 bloody stool samples giving a corresponding positive predictive value of 50%. Of the 1087 with non-bloody diarrhea, 720 did not have Shigella giving a negative predictive value of 66.2%.ConclusionsUse of clinical dysentery as a screening symptom for Shigella infection in children under five presenting with moderate to severe diarrhea has low sensitivity and low positive predictive value respectively. Clinical dysentery as a screening symptom for Shigella contributes to gross under diagnosis and reporting of Shigella infection among under five children in Zambia. Further research is required to better inform practice on more accurate methods or tools to use in support of routine diagnosis, particularly in low middle-income settings where laboratory diagnosis remains a challenge.