Improving Public Health Information: A Data Quality Intervention in KwaZulu-Natal, South Africa/ Amelioration Des Informations Sur la Sante Publique: Une Intervention Sur la Qualite Des Donnees Dans le KwaZulu-Natal (Afrique Du Sud)/Mejorar la Informacion Acerca De la Salud Publica: Intervencion Para Mejorar la Calidad De Los Datos En KwaZulu-Natal, Sudafrica
Mphatswe, W., Mate, K. S., Bennett, B., Ngidi, H., Reddy, J., Barker, P. M., Rollins, N., Bulletin of the World Health Organization
Reliable and accurate public health information is essential for monitoring health and for evaluating and improving the delivery of health-care services and programmes. (1-4) As countries report their progress towards achieving the United Nations Millennium Development Goals, the need for high-quality data has never been greater. (5,6) Furthermore, funding and support for public health activities, such as immunization programmes, remain contingent on demonstrating coverage using routine statistics. (7) However, assuring the quality of health information systems remains a challenge.
Studies of public health information systems in resource-poor countries frequently document problems with data quality, such as incomplete records and untimely reporting. (8,9) Yet these systems are often the only data sources available for the continuous, routine monitoring of health programmes. (10,11) Efforts have been made to improve the quality and management of public health information systems in developing countries. Two examples are the Health Metrics Network, an international network that seeks to improve the quality of health information from various sources, (12) and the Performance of Routine Information System Management (PRISM) framework, which was developed as a method for assessing the strengths and weaknesses of routine health information systems. (13,14) Other initiatives, such as the Data Quality Audit, have been used by the GAVI Alliance to improve the monitoring of immunization coverage. (7) However, the complex nature of health information systems and the demands placed upon them have complicated efforts to improve the quality of routine data. (15)
In South Africa, the effect of human immunodeficiency virus (HIV) infection on maternal and child health has raised considerable concern. The reported prevalence of HIV infection among women attending antenatal clinics in the province of KwaZulu-Natal, for example, is 38.7%, the highest in the country. (16) This led South Africa to set a target of reducing the rate of mother-to-child HIV transmission to below 5% by 2011. (17) Although HIV testing is essential for delivering interventions for the prevention of mother-to-child transmission (PMTCT) and thereby for reducing maternal and infant mortality, (18) routinely available health information indicates that the HIV testing rate is highly variable. (19,20) The questionable accuracy and reliability of these data compromise efforts to improve the health-care systems that provide PMTCT interventions. (20)
Routine health data from the primary health-care system in South Africa, which is organized in districts, are collected and stored in the District Health Information System (DHIS). (21) Recent studies of this system, however, have reported that the quality of the data, including those used to track PMTCT care, is suboptimal and is hindering efforts to strengthen service delivery. (20,22) As a result, in 2008 the KwaZulu-Natal Department of Health, the University of KwaZulu-Natal and the Institute for Health care Improvement launched a large-scale effort, entitled the 20000+ Partnership, to improve the quality of PMTCT services in three health districts in the province. This programme included an intervention to increase the completeness and accuracy of the public health data routinely recorded in the DHIS. The aim of this paper is to report on the effect of that intervention.
Routine data collection for the DHIS in South Africa starts with information being collected in registers at each point where clinical care is provided. Every month the staff at the different primary health-care facilities collate the data and send monthly summaries on paper to a clinic supervisor who, on average, oversees six facilities. The monthly summaries are then converted into electronic format by an information officer based either at the facility or centrally within the district health office. …