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Open Access Research article

The promise of record linkage for assessing the uptake of health services in resource constrained settings: a pilot study from South Africa

Chodziwadziwa W Kabudula1*, Benjamin D Clark2, Francesc Xavier Gómez-Olivé1, Stephen Tollman134, Jane Menken15 and Georges Reniers6

Author Affiliations

1 MRC/Wits Rural Public Health and Health Transitions Research Unit (Agincourt), School of Public Health, Faculty of Health Sciences, University of the Witwatersrand, Johannesburg, South Africa

2 Department of Ecology, Evolution and Environmental Biology, Columbia University, New York, USA

3 Umeå Centre for Global Health Research, Division of Epidemiology and Global Health, Department of Public Health and Clinical Medicine, Umeå University, Umeå, Sweden

4 INDEPTH Network, Accra, Ghana

5 Institute of Behavioral Science, University of Colorado, Boulder, Colorado, USA

6 Department of Population Health, London School of Hygiene and Tropical Medicine, London, UK

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BMC Medical Research Methodology 2014, 14:71  doi:10.1186/1471-2288-14-71

Published: 24 May 2014

Abstract

Background

Health and Demographic Surveillance Systems (HDSS) have been instrumental in advancing population and health research in low- and middle- income countries where vital registration systems are often weak. However, the utility of HDSS would be enhanced if their databases could be linked with those of local health facilities. We assess the feasibility of record linkage in rural South Africa using data from the Agincourt HDSS and a local health facility.

Methods

Using a gold standard dataset of 623 record pairs matched by means of fingerprints, we evaluate twenty record linkage scenarios (involving different identifiers, string comparison techniques and with and without clerical review) based on the Fellegi-Sunter probabilistic record linkage model. Matching rates and quality are measured by their sensitivity and positive predictive value (PPV). Background characteristics of matched and unmatched cases are compared to assess systematic bias in the resulting record-linked dataset.

Results

A hybrid approach of deterministic followed by probabilistic record linkage, and scenarios that use an extended set of identifiers including another household member’s first name yield the best results. The best fully automated record linkage scenario has a sensitivity of 83.6% and PPV of 95.1%. The sensitivity and PPV increase to 84.3% and 96.9%, respectively, when clerical review is undertaken on 10% of the record pairs. The likelihood of being linked is significantly lower for females, non-South Africans and the elderly.

Conclusion

Using records matched by means of fingerprints as the gold standard, we have demonstrated the feasibility of fully automated probabilistic record linkage using identifiers that are routinely collected in health facilities in South Africa. Our study also shows that matching statistics can be improved if other identifiers (e.g., another household member’s first name) are added to the set of matching variables, and, to a lesser extent, with clerical review. Matching success is, however, correlated with background characteristics that are indicative of the instability of personal attributes over time (e.g., surname in the case of women) or with misreporting (e.g., age).

Keywords:
Health and Demographic Surveillance System (HDSS); Record linkage; Health facilities; South Africa; Population surveillance