Cargando…
Linking health facility data from young adults aged 18-24 years to longitudinal demographic data: Experience from The Kilifi Health and Demographic Surveillance System
Background: In 2014, a pilot study was conducted to test the feasibility of linking clinic attendance data for young adults at two health facilities to the population register of the Kilifi Health and Demographic Surveillance System (KHDSS). This was part of a cross-sectional survey of health proble...
Autores principales: | , , , , , , , , , , , |
---|---|
Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
F1000 Research Limited
2020
|
Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7059845/ https://www.ncbi.nlm.nih.gov/pubmed/32175477 http://dx.doi.org/10.12688/wellcomeopenres.11302.2 |
_version_ | 1783504129579024384 |
---|---|
author | Nyundo, Christopher Doyle, Aoife M. Walumbe, David Otiende, Mark Kinuthia, Michael Amadi, David Jibendi, Boniface Mochamah, George Kihuha, Norbert Williams, Thomas N. Ross, David A. Bauni, Evasius |
author_facet | Nyundo, Christopher Doyle, Aoife M. Walumbe, David Otiende, Mark Kinuthia, Michael Amadi, David Jibendi, Boniface Mochamah, George Kihuha, Norbert Williams, Thomas N. Ross, David A. Bauni, Evasius |
author_sort | Nyundo, Christopher |
collection | PubMed |
description | Background: In 2014, a pilot study was conducted to test the feasibility of linking clinic attendance data for young adults at two health facilities to the population register of the Kilifi Health and Demographic Surveillance System (KHDSS). This was part of a cross-sectional survey of health problems of young people, and we tested the feasibility of using the KHDSS platform for the monitoring of future interventions. Methods: Two facilities were used for this study. Clinical data from consenting participants aged 18-24 years were matched to KHDSS records. Data matching was achieved using national identity card numbers or otherwise using a matching algorithm based on names, sex, date of birth, location of residence and the names of other homestead members. A study form was administered to all matched patients to capture reasons for their visits and time taken to access the services. Distance to health facility from a participants’ homestead was also computed. Results: 628 participated in the study: 386 (61%) at Matsangoni Health Centre, and 242 (39%) at Pingilikani Dispensary. 610 (97%) records were matched to the KHDSS register. Most records (605; 96%) were matched within these health facilities, while 5 (1%) were matched during homestead follow-up visits. 463 (75.9%) of those matched were women. Antenatal care (25%), family planning (13%), respiratory infections (9%) and malaria (9%) were the main reasons for seeking care. Antenatal clinic visits (n=175) and malaria (n=27) were the commonest reasons among women and men, respectively. Participants took 1-1.5 hours to access the services; 490 (81.0%) participants lived within 5 kilometres of a facility. Conclusions: With a full-time research clerk at each health facility, linking health-facility attendance data to a longitudinal HDSS platform was feasible and could be used to monitor and evaluate the impact of health interventions on health care outcomes among young people. |
format | Online Article Text |
id | pubmed-7059845 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2020 |
publisher | F1000 Research Limited |
record_format | MEDLINE/PubMed |
spelling | pubmed-70598452020-03-12 Linking health facility data from young adults aged 18-24 years to longitudinal demographic data: Experience from The Kilifi Health and Demographic Surveillance System Nyundo, Christopher Doyle, Aoife M. Walumbe, David Otiende, Mark Kinuthia, Michael Amadi, David Jibendi, Boniface Mochamah, George Kihuha, Norbert Williams, Thomas N. Ross, David A. Bauni, Evasius Wellcome Open Res Research Article Background: In 2014, a pilot study was conducted to test the feasibility of linking clinic attendance data for young adults at two health facilities to the population register of the Kilifi Health and Demographic Surveillance System (KHDSS). This was part of a cross-sectional survey of health problems of young people, and we tested the feasibility of using the KHDSS platform for the monitoring of future interventions. Methods: Two facilities were used for this study. Clinical data from consenting participants aged 18-24 years were matched to KHDSS records. Data matching was achieved using national identity card numbers or otherwise using a matching algorithm based on names, sex, date of birth, location of residence and the names of other homestead members. A study form was administered to all matched patients to capture reasons for their visits and time taken to access the services. Distance to health facility from a participants’ homestead was also computed. Results: 628 participated in the study: 386 (61%) at Matsangoni Health Centre, and 242 (39%) at Pingilikani Dispensary. 610 (97%) records were matched to the KHDSS register. Most records (605; 96%) were matched within these health facilities, while 5 (1%) were matched during homestead follow-up visits. 463 (75.9%) of those matched were women. Antenatal care (25%), family planning (13%), respiratory infections (9%) and malaria (9%) were the main reasons for seeking care. Antenatal clinic visits (n=175) and malaria (n=27) were the commonest reasons among women and men, respectively. Participants took 1-1.5 hours to access the services; 490 (81.0%) participants lived within 5 kilometres of a facility. Conclusions: With a full-time research clerk at each health facility, linking health-facility attendance data to a longitudinal HDSS platform was feasible and could be used to monitor and evaluate the impact of health interventions on health care outcomes among young people. F1000 Research Limited 2020-02-27 /pmc/articles/PMC7059845/ /pubmed/32175477 http://dx.doi.org/10.12688/wellcomeopenres.11302.2 Text en Copyright: © 2020 Nyundo C et al. http://creativecommons.org/licenses/by/4.0/ This is an open access article distributed under the terms of the Creative Commons Attribution Licence, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited. |
spellingShingle | Research Article Nyundo, Christopher Doyle, Aoife M. Walumbe, David Otiende, Mark Kinuthia, Michael Amadi, David Jibendi, Boniface Mochamah, George Kihuha, Norbert Williams, Thomas N. Ross, David A. Bauni, Evasius Linking health facility data from young adults aged 18-24 years to longitudinal demographic data: Experience from The Kilifi Health and Demographic Surveillance System |
title | Linking health facility data from young adults aged 18-24 years to longitudinal demographic data: Experience from The Kilifi Health and Demographic Surveillance System |
title_full | Linking health facility data from young adults aged 18-24 years to longitudinal demographic data: Experience from The Kilifi Health and Demographic Surveillance System |
title_fullStr | Linking health facility data from young adults aged 18-24 years to longitudinal demographic data: Experience from The Kilifi Health and Demographic Surveillance System |
title_full_unstemmed | Linking health facility data from young adults aged 18-24 years to longitudinal demographic data: Experience from The Kilifi Health and Demographic Surveillance System |
title_short | Linking health facility data from young adults aged 18-24 years to longitudinal demographic data: Experience from The Kilifi Health and Demographic Surveillance System |
title_sort | linking health facility data from young adults aged 18-24 years to longitudinal demographic data: experience from the kilifi health and demographic surveillance system |
topic | Research Article |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7059845/ https://www.ncbi.nlm.nih.gov/pubmed/32175477 http://dx.doi.org/10.12688/wellcomeopenres.11302.2 |
work_keys_str_mv | AT nyundochristopher linkinghealthfacilitydatafromyoungadultsaged1824yearstolongitudinaldemographicdataexperiencefromthekilifihealthanddemographicsurveillancesystem AT doyleaoifem linkinghealthfacilitydatafromyoungadultsaged1824yearstolongitudinaldemographicdataexperiencefromthekilifihealthanddemographicsurveillancesystem AT walumbedavid linkinghealthfacilitydatafromyoungadultsaged1824yearstolongitudinaldemographicdataexperiencefromthekilifihealthanddemographicsurveillancesystem AT otiendemark linkinghealthfacilitydatafromyoungadultsaged1824yearstolongitudinaldemographicdataexperiencefromthekilifihealthanddemographicsurveillancesystem AT kinuthiamichael linkinghealthfacilitydatafromyoungadultsaged1824yearstolongitudinaldemographicdataexperiencefromthekilifihealthanddemographicsurveillancesystem AT amadidavid linkinghealthfacilitydatafromyoungadultsaged1824yearstolongitudinaldemographicdataexperiencefromthekilifihealthanddemographicsurveillancesystem AT jibendiboniface linkinghealthfacilitydatafromyoungadultsaged1824yearstolongitudinaldemographicdataexperiencefromthekilifihealthanddemographicsurveillancesystem AT mochamahgeorge linkinghealthfacilitydatafromyoungadultsaged1824yearstolongitudinaldemographicdataexperiencefromthekilifihealthanddemographicsurveillancesystem AT kihuhanorbert linkinghealthfacilitydatafromyoungadultsaged1824yearstolongitudinaldemographicdataexperiencefromthekilifihealthanddemographicsurveillancesystem AT williamsthomasn linkinghealthfacilitydatafromyoungadultsaged1824yearstolongitudinaldemographicdataexperiencefromthekilifihealthanddemographicsurveillancesystem AT rossdavida linkinghealthfacilitydatafromyoungadultsaged1824yearstolongitudinaldemographicdataexperiencefromthekilifihealthanddemographicsurveillancesystem AT baunievasius linkinghealthfacilitydatafromyoungadultsaged1824yearstolongitudinaldemographicdataexperiencefromthekilifihealthanddemographicsurveillancesystem |