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Part 1: Document Description
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Citation |
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Title: |
Replication Data for: Identifying and Quantifying Initial Post-Discharge Needs for Clinical Review of Sick, Newborns in Kenya based on a large multi-site, retrospective cohort study |
Identification Number: |
doi:10.7910/DVN/ZX7VQK |
Distributor: |
Harvard Dataverse |
Date of Distribution: |
2024-04-04 |
Version: |
2 |
Bibliographic Citation: |
Wainaina, John; Irimu, Grace; Aluvaala, Jalemba; English, Mike, 2024, "Replication Data for: Identifying and Quantifying Initial Post-Discharge Needs for Clinical Review of Sick, Newborns in Kenya based on a large multi-site, retrospective cohort study", https://doi.org/10.7910/DVN/ZX7VQK, Harvard Dataverse, V2 |
Citation |
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Title: |
Replication Data for: Identifying and Quantifying Initial Post-Discharge Needs for Clinical Review of Sick, Newborns in Kenya based on a large multi-site, retrospective cohort study |
Identification Number: |
doi:10.7910/DVN/ZX7VQK |
Authoring Entity: |
Wainaina, John (Health Services Unit, KEMRI-Wellcome Trust Research Programme, Nairobi, Kenya) |
Irimu, Grace (Health Services Unit, KEMRI-Wellcome Trust Research Programme, Nairobi, Kenya; Department of Paediatrics and Child Health, University of Nairobi, Nairobi, Kenya) |
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Aluvaala, Jalemba (Health Services Unit, KEMRI-Wellcome Trust Research Programme, Nairobi, Kenya; Department of Paediatrics and Child Health, University of Nairobi, Nairobi, Kenya) |
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English, Mike (Health Services Unit, KEMRI-Wellcome Trust Research Programme, Nairobi, Kenya; Nuffield Department of Clinical Medicine, Oxford, Oxfordshire, UK) |
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Distributor: |
Harvard Dataverse |
Access Authority: |
Wainaina, John |
Access Authority: |
The Data Governance Committee |
Depositor: |
Wainaina, John |
Date of Deposit: |
2024-01-18 |
Holdings Information: |
https://doi.org/10.7910/DVN/ZX7VQK |
Study Scope |
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Keywords: |
Medicine, Health and Life Sciences |
Abstract: |
<p>This is a replication dataset for the manuscript titled: "<a href="">Identifying and Quantifying Initial Post-Discharge Needs for Clinical Review of Sick, Newborns in Kenya based on a large multi-site, retrospective cohort study</a>."</p> <p>We present a retrospective cohort study dataset of newborns discharged from 23 public hospital neonatal units (NBUs) in Kenya between January 2018 and June 2023. The data was used to identify likely minimal, initial follow-up needs. Comprehensive information on data collection and methodology is presented within the manuscript.</p> |
Kind of Data: |
Restricted Access |
Notes: |
<p><strong>Data Access: </strong>Restricted</p> <p>Access to these data requires submission of a formal request for consideration by our Data Governance Committee. </p> <strong>How to request</strong> <ol> <li>Download and complete the <a href="https://kemri-wellcome.org/zp-content/uploads/2021/02/KWTRP_Dataverse_Data_Request_Form_2019.docx"><strong>data request form</strong></a></li> <li> Email completed data request form to the Data Governance Committee at <a href="mailto:dgc@kemri-wellcome.org?cc=lmwango@kemri-wellcome.org?Subject=Data%20Access%20Request"><strong>dgc@kemri-wellcome.org</strong></a> </li> </ol> |
Methodology and Processing |
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Sources Statement |
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Data Access |
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Notes: |
For access to restricted files and more detailed information beyond the metadata/documentation provided, please send a signed data request form to [<a href="mailto:dgc@kemri-wellcome.org">dgc@kemri-wellcome.org</a> ]. <strong> <a href="https://kemri-wellcome.org/zp-content/uploads/2021/02/KWTRP_Dataverse_Data_Request_Form_2019.docx">Click to download Data Request Form</a></strong> |
Other Study Description Materials |
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Label: |
01_data_manipulation.R |
Notes: |
type/x-r-syntax |
Label: |
02_data_analysis.R |
Notes: |
type/x-r-syntax |
Label: |
CIN_Neonatal_Data Dictionary.pdf |
Notes: |
application/pdf |
Label: |
CIN_Neonatal_Dataset_Descriptor.pdf |
Notes: |
application/pdf |
Label: |
Discharge Phenotypes and Post Discharge Specialist Needs Study - Data_Readme.txt |
Notes: |
text/plain |
Label: |
phenotypes_data_anon.csv |
Notes: |
text/csv |