The CIN group conducts multidisciplinary research, both basic and applied, that examine the use, costs, quality, accessibility, delivery, organization, financing, and outcomes of health care services to increase knowledge and understanding of the structure, processes, and effects of health services for individuals and populations. The key focus at HSU is the understanding and acquisition of knowledge on how to improve quality and coverage of effective interventions to achieve desired health outcomes. Research at HSU includes pragmatic clinical trials, measuring effectiveness, clinical policy, measuring and understanding outcomes, implementation health research, quality improvement, Service delivery innovation, health governance, human resource for health, patient and family experiences, and health information systems. The group is affiliated to the KEMRI-Wellcome Trust Research Programme. Each dataset has a citation and DOI, facilitating attribution and connection to research publications. For more information on our research work, visit our page via Health Systems Research @ KEMRI-Wellcome Trust Research Programme
Featured Dataverses

In order to use this feature you must have at least one published or linked dataverse.

Publish Dataverse

Are you sure you want to publish your dataverse? Once you do so it must remain published.

Publish Dataverse

This dataverse cannot be published because the dataverse it is in has not been published.

Delete Dataverse

Are you sure you want to delete your dataverse? You cannot undelete this dataverse.

Advanced Search

11 to 20 of 127 Results
Replication Data for: External validation of an admission risk score for predicting inpatient paediatric mortality in two Kenyan public hospitals
Nov 25, 2024
Stephen Kamau; Joyce Kigo; Michuki Maina; John Gachohi, 2024, "Replication Data for: External validation of an admission risk score for predicting inpatient paediatric mortality in two Kenyan public hospitals", https://doi.org/10.7910/DVN/O0J6DD, Harvard Dataverse, V1, UNF:6:aKpth0JGkkDu4XBTh2laLQ== [fileUNF]
The data was collected as part of the Clinical Information Network (CIN) project which collects routine admission data from participating hospitals. The data used for this analysis were collected between January 2017 and October 2023. These data were abstracted from patient files at discharge by trained Health Records and Information Officer into a...
Admission risk score validation.R
Nov 25, 2024 - Replication Data for: External validation of an admission risk score for predicting inpatient paediatric mortality in two Kenyan public hospitals
R Syntax - 32.2 KB - MD5: 7ca4aa105727bdd59dce7e018f560c45
Code
Admission_riskscore_validation_Readme_SK.txt
Nov 25, 2024 - Replication Data for: External validation of an admission risk score for predicting inpatient paediatric mortality in two Kenyan public hospitals
Plain Text - 4.1 KB - MD5: 9a6b90cd8ce7d6a6cfe6b71f64908d4b
Documentation
codebook.tab
Nov 25, 2024 - Replication Data for: External validation of an admission risk score for predicting inpatient paediatric mortality in two Kenyan public hospitals
Tabular Data - 4.5 KB - 10 Variables, 38 Observations - UNF:6:aKpth0JGkkDu4XBTh2laLQ==
Documentation
Data_2017-2023.csv
Nov 25, 2024 - Replication Data for: External validation of an admission risk score for predicting inpatient paediatric mortality in two Kenyan public hospitals
Comma Separated Values - 3.8 MB - MD5: 53ee934e6c6ca115e1094bbdc88a2882
Data
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
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
Sep 17, 2024
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
This is a replication dataset for the manuscript titled: "Identifying and Quantifying Initial Post-Discharge Needs for Clinical Review of Sick, Newborns in Kenya based on a large multi-site, retrospective cohort study." We present a retrospective cohort study dataset of newborns discharged from 23 public hospital neonatal units (NBUs) in Kenya betw...
01_data_manipulation.R
Apr 4, 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
R Syntax - 22.8 KB - MD5: 96e4e0af72e08463e2aaf555636d6200
CodeR script
02_data_analysis.R
Apr 4, 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
R Syntax - 28.9 KB - MD5: eee2d6ff303890992b70c4162bd91817
CodeR script
CIN_Neonatal_Data Dictionary.pdf
Apr 4, 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
Adobe PDF - 1.3 MB - MD5: 3236cdde29cc63ad4a7223f502df04ca
DocumentationVariable Codebook/Data Dictionary
CIN_Neonatal_Dataset_Descriptor.pdf
CIN_Neonatal_Dataset_Descriptor.pdf
Apr 4, 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
Adobe PDF - 66.6 KB - MD5: 28bad92056f923c442c41fe797abde15
Data DescriptorDocumentation
Add Data

Sign up or log in to create a dataverse or add a dataset.

Sign Up Log In
Share Dataverse

Share this dataverse on your favorite social media networks.

Link Dataverse
Reset Modifications

Are you sure you want to reset the selected metadata fields? If you do this, any customizations (hidden, required, optional) you have done will no longer appear.