The following are steps undertaken for deidentifying NDMA data. The data is dis aggregated per county for all 23 counties - ASAL. The information covers the years of 2000 - 2020, where data prior to 2016 was stored in a different database (REWAS) and data from 2016 henceforth in the new database (DEWS). In each county data set workbook there are 6 different sheets: HHA REWAS, HHA DEWS, KIA REWAS, KIA DEWS, MUAC REWAS, MUAC DEWS The process involves inspecting individual sheets for each data set, dropping P.I.I columns, and then writing all the sheets to a single workbook - Kitui.

The following are steps undertaken for deidentifying NDMA data. The data is dis aggregated per county for all 23 counties - ASAL. The information covers the years of 2000 - 2020, where data prior to 2016 was stored in a different database (REWAS) and data from 2016 henceforth in the new database (DEWS). In each county data set workbook there are 6 different sheets: HHA REWAS, HHA DEWS, KIA REWAS, KIA DEWS, MUAC REWAS, MUAC DEWS The process involves inspecting individual sheets for each data set, dropping P.I.I columns, and then writing all the sheets to a single workbook - Kajiado.

The following are steps undertaken for deidentifying NDMA data. The data is dis aggregated per county for all 23 counties - ASAL. The information covers the years of 2000 - 2020, where data prior to 2016 was stored in a different database (REWAS) and data from 2016 henceforth in the new database (DEWS). In each county data set workbook there are 6 different sheets: HHA REWAS, HHA DEWS, KIA REWAS, KIA DEWS, MUAC REWAS, MUAC DEWS The process involves inspecting individual sheets for each data set, dropping P.I.I columns, and then writing all the sheets to a single workbook - Isiolo.

The following are steps undertaken for deidentifying NDMA data. The data is dis aggregated per county for all 23 counties - ASAL. The information covers the years of 2000 - 2020, where data prior to 2016 was stored in a different database (REWAS) and data from 2016 henceforth in the new database (DEWS). In each county data set workbook there are 6 different sheets: HHA REWAS, HHA DEWS, KIA REWAS, KIA DEWS, MUAC REWAS, MUAC DEWS The process involves inspecting individual sheets for each data set, dropping P.I.I columns, and then writing all the sheets to a single workbook - Garissa.

The following are steps undertaken for deidentifying NDMA data. The data is dis aggregated per county for all 23 counties - ASAL. The information covers the years of 2000 - 2020, where data prior to 2016 was stored in a different database (REWAS) and data from 2016 henceforth in the new database (DEWS). In each county data set workbook there are 6 different sheets: HHA REWAS, HHA DEWS, KIA REWAS, KIA DEWS, MUAC REWAS, MUAC DEWS The process involves inspecting individual sheets for each data set, dropping P.I.I columns, and then writing all the sheets to a single workbook - Embu.

The following are steps undertaken for deidentifying NDMA data. The data is dis aggregated per county for all 23 counties - ASAL. The information covers the years of 2000 - 2020, where data prior to 2016 was stored in a different database (REWAS) and data from 2016 henceforth in the new database (DEWS). In each county data set workbook there are 6 different sheets: HHA REWAS, HHA DEWS, KIA REWAS, KIA DEWS, MUAC REWAS, MUAC DEWS The process involves inspecting individual sheets for each data set, dropping P.I.I columns, and then writing all the sheets to a single workbook - Baringo.

The following are steps undertaken for deidentifying NDMA data. This dataset covers 2020 through to June 2024 for MUAC.

The following are steps undertaken for deidentifying NDMA data. This dataset covers the month of August 2024 for HHA, KIA and MUAC. Since the geocoordinates in the HHA dataset represent household coordinates, we will mask them (random displacement) using the Haversine Formula to randomly distribute a point around a central coordinate within a radius of 2.5 KM and drop other P.I.I.s in the relevant sheets.

The following are steps undertaken for deidentifying NDMA data. This dataset covers the month of July 2024 for HHA, KIA and MUAC. Since the geocoordinates in the HHA dataset represent household coordinates, we will mask them (random displacement) using the Haversine Formula to randomly distribute a point around a central coordinate within a radius of 2.5 KM and drop other P.I.I.s in the relevant sheets.

The following are steps undertaken for deidentifying NDMA data. This dataset covers the month of September 2024 for HHA, KIA and MUAC. Since the geocoordinates in the HHA dataset represent household coordinates, we will mask them (random displacement) using the Haversine Formula to randomly distribute a point around a central coordinate within a radius of 2.5 KM and drop other P.I.I.s in the relevant sheets.

The following are steps undertaken for deidentifying NDMA data. This dataset covers 2020 through to June 2024 for KIA.

The following are steps undertaken for deidentifying NDMA data. This dataset covers 2020 through to June 2024 for HHA Since the geocoordinates in the HHA dataset represent household coordinates, we will mask them (random displacement) using the Haversine Formula to randomly distribute a point around a central coordinate within a radius of 2.5 KM and drop other P.I.I.s.