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Utilizing the household expenditure survey data from ABS (2006), household income and consumption data were disaggregated into ten household groups according to income level.
With the use of ratios from (Sherbourne 2009) and (Mendelsohn et al. 2006), the data were disaggregated into those attributable to commercial land livestock, communal land livestock, commercial land crop production and communal land crop production.
Data were disaggregated into 12 single racial/ethnic groups, including 7 distinct Asian subgroups.
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Even when the data is disaggregated into urban and rural areas, health facilities and assisted deliveries continue to show gradual increases, except for the large gap between rural and urban areas.
The summary data was disaggregated into smaller areas using numbers of population and jobs in each area [ 17].
Feeding data were disaggregated by season based on farmer recall.
For England, census population data were disaggregated to postcode areas (mean 41, sd 37).
Where relevant, data are disaggregated by age and sex, to provide insights into risk and protective factors.
Where available, data is disaggregated down to island level.
However, even when data are disaggregated by sex, they are rarely also disaggregated by urban and rural locations.
To the extent possible, data are disaggregated by gender, geographic location and socio-economic status.
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Since I tried Ludwig back in 2017, I have been constantly using it in both editing and translation. Ever since, I suggest it to my translators at ProSciEditing.

Justyna Jupowicz-Kozak
CEO of Professional Science Editing for Scientists @ prosciediting.com