Newborn Health Data Transfer in the Cape Coast Metropolis, Ghana

  • Emmanuel Kusi Achampong University of Cape Coast
  • Godwin Adzakpah University of Cape Coast
  • Richard Okyere Boadu University of Cape Coast
  • Obed Owumbornyi Lasim University of Cape Coast
  • Richard Opoku University of Cape Coast
Keywords: newborn data, data quality, monitoring and evaluation, health information system

Abstract

Newborn data is important for decision making and planning purposes. Data transfer from facilities to district, regional and national levels must have high level of quality to benefit from its use for planning and decision making. The objective of this research was to assess the quality and accuracy of newborn health data transfer from facilities to the DHIMS II application. The study was conducted within the Cape Coast Metropolis. Four facilities (two public and two private hospitals) were randomly selected for the study. Facilities registers were compared with summary sheets as well as the data in DHIMS II. The study revealed that there were data inaccuracies across all the indicators ranging from -46.5% to 89.3%. Percentage errors 1 and 3 were extremely high due to the inability of some facilities to produce aggregated forms. Percentage error 2 was generally low for all indicators as compared to percentage errors 1 and 3 except for institutional neonatal deaths with percentage error of 89.3%. The others range from -1.4% to 4.4% which means that there is very little error in transferring the facility register data to the web-based DHIMS-II. The overall percentage errors 1, 2 and 3 in transfer of the data were 7.5% (95% CI = 6.5% to 8.6%), 43.1% (95% CI = 41.8% to 44.3%) and 3.6% (95% CI = 3.2% to 4.0%) respectively. High-quality newborn health is essential for planning and decision making to enhance service quality.

Author Biographies

Emmanuel Kusi Achampong, University of Cape Coast

Senior Lecturer, Department of Medical Education and Information Technology, School of Medical Sciences, Unviersity of Cape Coast

Godwin Adzakpah, University of Cape Coast
Department of Health Information Management, School of Allied Health Sciences
Richard Okyere Boadu, University of Cape Coast
Department of Health Information Management, School of Allied Health Sciences
Obed Owumbornyi Lasim, University of Cape Coast
Department of Health Information Management, School of Allied Health Sciences
Richard Opoku, University of Cape Coast
Department of Biomedical Sciences, School of Allied Health Sciences

References

Chen H, Hailey D, Wang N, Yu P. A review of data quality assessment methods for public health information systems. International Journal of Environmental Research and Public Health. 2014.

Wand Y, Wang R. Anchoring Data Quality Dimensions Ontological Foundations. Commun ACM. 1996;39(11):86–95.

Hahn D, Wanjala P, Marx M. Where is information quality lost at clinical level? A mixed-method study on information systems and data quality in three urban Kenyan ANC clinics. Glob Health Action. 2013;6(1):1–10.

Vaziri R, Mehran M. A Questionnaire-Based Data Quality Methodology. Int J Database Manag Syst [Internet]. 2012;4(2):55–68. Available from: http://www.airccse.org/journal/ijdms/papers/4212ijdms04.pdf

Amoakoh-Coleman M, Kayode GA, Brown-Davies C, Agyepong IA, Grobbee DE, Klipstein-Grobusch K, et al. Completeness and accuracy of data transfer of routine maternal health services data in the greater Accra region. BMC Res Notes. 2015;8(1).

Nahm M. Clinical Research Informatics. Essentials Med Genomics Second Ed. 2008;237–49.

Evaluation M. User Manual Routine Data Quality Assessment. 2015.

Abah SO. HIA practices in Nigeria. Impact Assess Proj Apprais. 2012;30(3):207–13.

Chahed MK, Bellali H, Alaya N Ben, Ali M, Mahmoudi B. Auditing the quality of immunization data in Tunisia. Asian Pacific J Trop Dis [Internet]. China Humanity Technology Publishing House; 2013 Feb [cited 2018 Jul 11];3(1):65–70. Available from: http://linkinghub.elsevier.com/retrieve/pii/S2222180813600146

Ronveaux O, Rickert D, Hadler S, Groom H, Lloyd J, Bchir A, et al. The immunization data quality audit: verifying the quality and consistency of immunization monitoring systems TT - Contrôle de la qualité des données de vaccination: vérification de la qualité et de la cohérence des systèmes de vaccinovigilance TT - Aud. Bull World Health Organ [Internet]. 2005;83(7):503–10. Available from: http://www.scielosp.org/scielo.php?script=sci_arttext&pid=S0042-96862005000700010〈=en%0Ahttp://www.scielosp.org/pdf/bwho/v83n7/v83n7a10.pdf

Glèlè Ahanhanzo Y, Ouendo E-M, Kpozèhouen A, Levêque A, Makoutodé M, Dramaix-Wilmet M. Data quality assessment in the routine health information system: an application of the Lot Quality Assurance Sampling in Benin. Health Policy Plan [Internet]. Oxford University Press; 2015 Sep 1 [cited 2018 Jul 11];30(7):837–43. Available from: https://academic.oup.com/heapol/article-lookup/doi/10.1093/heapol/czu067

Daire J, Gilson L. Does identity shape leadership and management practice? Experiences of PHC facility managers in Cape Town, South Africa. Health Policy Plan. 2014;29:ii82-ii97.

Bonenberger M, Aikins M, Akweongo P, Bosch-Capblanch X, Wyss K. What do district health managers in Ghana use their working time for? A case study of three districts. PLoS One. 2015;10(6):1–15.

Published
2018-09-21
How to Cite
Achampong, E., Adzakpah, G., Boadu, R. O., Lasim, O., & Opoku, R. (2018). Newborn Health Data Transfer in the Cape Coast Metropolis, Ghana. Journal of Information Sciences and Computing Technologies, 8(1), 744-749. Retrieved from http://scitecresearch.com/journals/index.php/jisct/article/view/1592
Section
Articles