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The ethnicity variable was constructed using a 'prioritised' definition.
We defined a generic ethnicity variable, and whenever that generic ethnicity variable appears in a model it was replaced by one of the above four variables.
The "ethnicity" variable was categorised into 2 groups: Estonians and non-Estonians (the latter consisted of mainly Russian speaking population).
This indicates that the machine-learning techniques are capturing information from the ethnicity variable when included in the data set.
16 As such, we explicitly state how we interpreted ethnicity and our subsequent choice of ethnicity variable.
The 'modified total ethnicity' variable has categories 'Māori', 'Pacific', 'Asian', and 'non-Māori, non-Pacific, non-Asian' (non-MPA).
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For example, while some studies control for race and ethnicity variables, others do not.
Self-reported race and ethnicity variables were combined into a single race/ethnicity variable.
Coefficients on our area-level ethnicity variables may therefore have explanatory power in our models because they serve, in part, as proxies of individual-level ethnicity.
A combined race-ethnicity variable was created using the separate race and Hispanic ethnicity variables found in the hospital discharge dataset: White Hispanic, Black Non-Hispanic, Asian, Other and White Non-Hispanic (referent).
The first step included only the ethnicity variables, separated by immigration and language status.
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Justyna Jupowicz-Kozak
CEO of Professional Science Editing for Scientists @ prosciediting.com