Original Research

Programmatic implications of implementing the relational algebraic capacitated location (RACL) algorithm outcomes on the allocation of laboratory sites, test volumes, platform distribution and space requirements

Naseem Cassim, Honora Smith, Lindi M. Coetzee, Deborah K. Glencross
African Journal of Laboratory Medicine | Vol 6, No 1 | a545 | DOI: https://doi.org/10.4102/ajlm.v6i1.545 | © 2017 Naseem Cassim, Honora Smith, Lindi M. Coetzee, Deborah K. Glencross | This work is licensed under CC Attribution 4.0
Submitted: 18 August 2016 | Published: 28 February 2017

About the author(s)

Naseem Cassim, National Health Laboratory Service (NHLS), National Priority Programmes, Johannesburg and Department of Molecular Medicine and Haematology, Faculty of Health Sciences, University of Witwatersrand, Johannesburg, South Africa
Honora Smith, Department of Mathematical Sciences, University of Southampton, Southampton, United Kingdom
Lindi M. Coetzee, National Health Laboratory Service (NHLS), National Priority Programmes, Johannesburg and Department of Molecular Medicine and Haematology, Faculty of Health Sciences, University of Witwatersrand, Johannesburg, South Africa
Deborah K. Glencross, National Health Laboratory Service (NHLS), National Priority Programmes, Johannesburg and Department of Molecular Medicine and Haematology, Faculty of Health Sciences, University of Witwatersrand, Johannesburg, South Africa


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Abstract

Introduction: CD4 testing in South Africa is based on an integrated tiered service delivery model that matches testing demand with capacity. The National Health Laboratory Service has predominantly implemented laboratory-based CD4 testing. Coverage gaps, over-/under-capacitation and optimal placement of point-of-care (POC) testing sites need investigation.

Objectives: We assessed the impact of relational algebraic capacitated location (RACL) algorithm outcomes on the allocation of laboratory and POC testing sites.

Methods: The RACL algorithm was developed to allocate laboratories and POC sites to ensure coverage using a set coverage approach for a defined travel time (T). The algorithm was repeated for three scenarios (A: T = 4; B: T = 3; C: T = 2 hours). Drive times for a representative sample of health facility clusters were used to approximate T. Outcomes included allocation of testing sites, Euclidian distances and test volumes. Additional analysis included platform distribution and space requirement assessment. Scenarios were reported as fusion table maps.

Results: Scenario A would offer a fully-centralised approach with 15 CD4 laboratories without any POC testing. A significant increase in volumes would result in a four-fold increase at busier laboratories. CD4 laboratories would increase to 41 in scenario B and 61 in scenario C. POC testing would be offered at two sites in scenario B and 20 sites in scenario C.

Conclusion: The RACL algorithm provides an objective methodology to address coverage gaps through the allocation of CD4 laboratories and POC sites for a given T. The algorithm outcomes need to be assessed in the context of local conditions.


Keywords

CD4; Coverage; Modelling

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