Conducting a secondary data analysis to estimate the incidence of congenital syphilis in South Africa, 2010 - 2016
Lazarus Rugare Kuonza, Tuyakula Nakale, Sinesia Lucinda Jose Sitao, Casey Daniel Hall
The Pan African Medical Journal. 2018;30 (Supp 1):16. doi:10.11604/pamj.supp.2018.30.1.15272

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Case study

Conducting a secondary data analysis to estimate the incidence of congenital syphilis in South Africa, 2010 - 2016

Cite this: The Pan African Medical Journal. 2018;30 (Supp 1):16. doi:10.11604/pamj.supp.2018.30.1.15272

Received: 21/02/2018 - Accepted: 05/04/2018 - Published: 18/05/2018

Key words: Data management, data analysis, secondary data analysis, congenital syphilis, South Africa

© Lazarus Rugare Kuonza et al. The Pan African Medical Journal - ISSN 1937-8688. This is an Open Access article distributed under the terms of the Creative Commons Attribution License (http://creativecommons.org/licenses/by/2.0), which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.

Available online at: http://www.panafrican-med-journal.com/content/series/30/1/16/full

Corresponding author: Lazarus Rugare Kuonza, South African Field Epidemiology Training Programme, National Institute for Communicable Diseases, National Health Laboratory Services, Johannesburg, South Africa (Lkuonza@gmail.com)

This article is published as part of the supplement “African Case Studies for Public Health Volume 2” sponsored by African Field Epidemiology Network, (Case Study Design and Development, Rollins School of Public Health, Emory University

Guest editors: Scott JN McNabb, Ghada N Farhat, Casey Daniel Hall, Joseph Asamoah Frimpong, Richard Dicker


Conducting a secondary data analysis to estimate the incidence of congenital syphilis in South Africa, 2010 - 2016

Lazarus Rugare Kuonza1,2,3,&, Tuyakula Nakale4, Sinesia Lucinda Jose Sitao5, Casey Daniel Hall6

 

1South African Field Epidemiology Training Programme, National Institute for Communicable Diseases, National Health Laboratory Services, Johannesburg, South Africa, 2School of Public Health, University of Witwatersrand, Johannesburg, South Africa, 3School of Health Systems and Public Health, University of Pretoria, South Africa, 4Namibia Institute of Pathology, Windhoek, Namibia, 5Mozambican Field Epidemiology and Laboratory Training Programme, 6Rollins School of Public Health, Emory University, Atlanta, USA

 

 

&Corresponding author
Lazarus Rugare Kuonza, South African Field Epidemiology Training Programme, National Institute for Communicable Diseases, National Health Laboratory Services, Johannesburg, South Africa

 

 

Abstract

Analysis of existing health data is often used as a cost-effective and time-efficient meansto provide evidence to inform important public health decisions. However, the accuracy of the resultant decisions largely depends on the quality of the accessible data, and how the data are processed, analyzed, interpreted and reported. This case study, based on an actual secondary data analysis that was conducted by a trainee of the South African Field Epidemiology Training Programme during April 2017, was designed to provide a classroom simulation of practicalconsiderations that should be taken into account when planning an analysis of a secondary dataset. The case study is ideally suited to reinforce principles already covered in lectures or in background reading assignments.

 

 

How to use this case study    Down

General instructions: the case study is suited for a class of 15-20 trainees per session. Trainees should preferably be seated in a U-shaped setup to encourageparticipation and interaction. The instructor facilitating the session should direct participants to read a paragraph aloud, going around the room to give each participant a chance to read. When a participant reads a question, the instructor maychoose to engage the class in large group discussion of the answer, randomly identify a participant to respond to the question, or divide the class into smaller groups for exercises, depending on the type of question. The role of the instructor is largely to coordinate the session such that participants learn from each other, and not just from the instructor.

 

Audience: public health practitioners involved in the analysis and interpretation of surveillance data, and others who are interested in the topic.

 

Prerequisites: before using this case study, case study participants should have received lectures or other instruction on data quality, basic descriptive epidemiology, and analysis of secondary datasets.

 

Materials needed: flip chart or white board with markers, graph paper, and calculator.

 

Level of training and associated public health activity: data management and analysis.

 

Time required: 3 hours

 

Language: English

 

 

Case study material Up    Down

 

 

Competing interests Up    Down

The authors declare no competing interests.

 

 

Acknowledgments Up    Down

We wish to thank the African Field Epidemiology Network and Emory University for organizing a case study development workshop through which this case study was conceptualized and developed. We also acknowledge Ms. Rudzani Mathebula, a resident of the South African Field Epidemiology Training Programme during 2017, for conducting the secondary data analysis on which this case study is based.

 

 

References Up    Down

  1. Wikipedia. Notifiable disease. 2017. Accessed on 22 May 2018.

  2. Statistics South Africa. Publication | Statistics South Africa [Internet]. Statistics South Africa - Recorded Live births. Accessed on 22 May 2017.

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 


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