Geographical location of health and laboratory facilities in South Africa.
Map to reveal geographic location of ~4756 health facilities (as at 2011/2012); including primary care, community centers and hospital-based clinics (black dots) and 260 NHLS routine pathology service laboratories, across nine provinces and the related 52 districts. Insert reveals the proportions of different category of health facilities requesting CD4 testing (also see Table 1).

Geographical location of health and laboratory facilities in South Africa. Map to reveal geographic location of ~4756 health facilities (as at 2011/2012); including primary care, community centers and hospital-based clinics (black dots) and 260 NHLS routine pathology service laboratories, across nine provinces and the related 52 districts. Insert reveals the proportions of different category of health facilities requesting CD4 testing (also see Table 1).

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Background: The South African National Health Laboratory Service (NHLS) responded to HIV treatment initiatives with two-tiered CD4 laboratory services in 2004. Increasing programmatic burden, as more patients access anti-retroviral therapy (ART), has demanded extending CD4 services to meet increasing clinical needs. The aim of this study was to re...

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... Another factor that hindered the implementation of EuroFlow was the unit's heavy workload. The unit is a 24-h CD4 laboratory processing up to 12 000 samples per month 39,40 ; the technical staff managing the CD4 and HIV immunology bench also support the leukaemia bench. Thus, although the staff are competent to run the CD4 services using pre-titrated and pipetted reagents and standardised, automated testing procedures, 40,41 they are not primarily trained in flow cytometry. ...
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Background: Flow cytometric immunophenotyping is well established for the diagnosis of haematological neoplasms. New commercially available systems offer fixed, pre-aliquoted multi-parameter analysis to simplify sample preparation and standardise data analysis. Objective: The Beckman Coulter (BC) ClearLLab™ 10C (4-tube) system was evaluated against an existing laboratory developed test (LDT). Methods: Peripheral blood and bone marrow aspirates (n = 101), tested between August 2019 and November 2019 at an academic pathology laboratory in Johannesburg, South Africa, were analysed. Following daily instrument quality control, samples were prepared for LDT (using 20 2–4-colour in-house panels and an extensive liquid monoclonal reagent repertoire) or ClearLLab 10C, and respectively analysed using in-house protocols on a Becton Dickinson FACSCalibur, or manufacturer-directed protocols on a BC Navios. Becton Dickinson Paint-a-Gate or BC Kaluza C software facilitated data interpretation. Diagnostic accuracy (concordance) was established by calculating sensitivity and specificity outcomes. Results: Excellent agreement (clinical diagnostic concordance) with 100% specificity and sensitivity was established between LDT and ClearLLab 10C in 67 patients with a haematological neoplasm and 34 participants with no haematological disease. Similar acceptable diagnostic concordance (97%) was noted when comparing ClearLLab 10C to clinicopathological outcomes. Additionally, the ClearLLab 10C panels, analysed with Kaluza C software, enabled simultaneous discrimination of disease and concurrent background myeloid and lymphoid haematological populations, including assessing stages of maturation or sub-populations. Conclusion: ClearLLab 10C panels provide excellent agreement to existing LDTs and may reliably be used for immunophenotyping of haematological neoplasms, simplifying and standardising sample preparation and data acquisition.
... This study supports the findings of previous studies 24,28,31,43 and demonstrates how decentralising CD4 testing services to district or community facilities according to a tiered service delivery model 25 can improve the TAT at the decentralised site. Smaller community laboratories with existing laboratory infrastructure offering more generalised pathology services can reliably implement CD4 services, effectively eliminating delays due to the transport of samples historically sent to centralised testing facilities. ...
... NHLS community laboratories are usually located in the seat of the respective districts and generally serve large service precincts, typically supporting up to 40 or more health facilities. With minimal training effort and cost 25,43 (benchtop installation, installation of LIS interface with instruments, staff training, and testing platforms on national service level agreement), laboratories can be upgraded to include CD4 testing in their repertoire of services. Decentralised services also improve coordinated technical efforts across the parent province (the Northern Cape in this instance), where CD4 laboratories can, in line with the integrated service delivery model service plan, 25 operate as a consolidated network with two-way support between the main reference laboratory (in Kimberley) and sister community laboratories, thereby ensuring uninterrupted self-sustaining service. ...
... With minimal training effort and cost 25,43 (benchtop installation, installation of LIS interface with instruments, staff training, and testing platforms on national service level agreement), laboratories can be upgraded to include CD4 testing in their repertoire of services. Decentralised services also improve coordinated technical efforts across the parent province (the Northern Cape in this instance), where CD4 laboratories can, in line with the integrated service delivery model service plan, 25 operate as a consolidated network with two-way support between the main reference laboratory (in Kimberley) and sister community laboratories, thereby ensuring uninterrupted self-sustaining service. ...
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Background: The Northern Cape is South Africa’s largest province with an HIV prevalence of 7.1% versus a 13.5% national prevalence. CD4 testing is provided at three of five National Health Laboratory Service district laboratories, each covering a 250 km precinct radius. Districts without a local service report prolonged CD4 turn-around times (TAT). Objective: This study documented the impact of a new CD4 laboratory in Tshwaragano in the remote John Taolo Gaetsewe district of the Northern Cape, South Africa. Methods: CD4 test volumes and TAT (total, pre-analytical, analytical, and post-analytical) data for the Northern Cape province were extracted for June 2018 to October 2019. The percentage of CD4 results within the stipulated 40-h TAT cut-off and the median and 75th percentiles of all TAT parameters were calculated. Pre-implementation, samples collected at Tshwaragano were referred to Kimberley or Upington, Northern Cape, South Africa. Results: Pre-implementation, 95.4% of samples at Tshwaragano were referred to Kimberley for CD4 testing (36.3% of Kimberley’s test volumes). Only 7.5% of Tshwaragano’s total samples were referred post-implementation. The Tshwaragano laboratory’s CD4 median total TAT decreased from 24.7 h pre-implementation to 12 h post-implementation (p = 0.003), with 95.0% of results reported within 40 h. The Kimberley laboratory workload decreased by 29.0%, and testing time significantly decreased from 10 h to 4.3 h. Conclusion: The new Tshwaragano CD4 service significantly decreased local TAT. Upgrading existing community laboratories to include CD4 testing can alleviate provincial service load and improve local access, TAT and efficiency in the centralised reference laboratory.
... The studies targeted seven different countries including Ghana [18], Zambia [8,19], Lesotho [20], South Africa [21], Democratic Republic of the Congo, and Angola [22]. Finally, the only study based on a high-income country was conducted in the UK. ...
... Finally, the only study based on a high-income country was conducted in the UK. Four of these studies were conducted at national level [8,[19][20][21]. ...
... Three types of geographic accessibility measures were identified: those using travel time [8,19], those using distance [20][21][22][23], and one combining these two measures [18]. The studies used different strategies to measure physical accessibility: using ArcGIS Costdistance function (ESRI, Redlands, CA, USA), for a unique transport mode and a mean travel speed [18]; using ArcGIS ModelBuilder to solve a Vehicle Routing Problem, considering several transport modes and speeds [19], or only a single transport mode [8]; using the Open-Source Routing Machine to compute walking distance [23]; using distance data along transport routes where known, otherwise using a distance adjustment factor to define them [20,22]; and finally, using Euclidean distances to determine the coverage of a health center [21]. ...
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... The objective of network optimization is to balance the need for increased access to services with cost efficiency and feasibility of implementation in resource-constrained settings, and to help Ministries of Health identify gaps and misalignments in diagnostic service delivery that can be addressed through laboratory strengthening interventions. This approach has previously been applied to inform country-led decision-making processes for tuberculosis and HIV diagnosis (18,19). ...
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Background Onchocerciasis (river blindness) is a filarial disease targeted for elimination of transmission. However, challenges exist to the implementation of effective diagnostic and surveillance strategies at various stages of elimination programs. To address these challenges, we used a network data analytics approach to identify optimal diagnostic scenarios for onchocerciasis elimination mapping (OEM). Methods The diagnostic network optimization (DNO) method was used to model the implementation of the old Ov16 rapid diagnostic test (RDT) and of new RDTs in development for OEM under different testing strategy scenarios with varying testing locations, test performance and disease prevalence. Environmental suitability scores (ESS) based on machine learning algorithms were developed to identify areas at risk of transmission and used to select sites for OEM in Bandundu region in the Democratic Republic of Congo (DRC) and Uige province in Angola. Test sensitivity and specificity ranges were obtained from the literature for the existing RDT, and from characteristics defined in the target product profile for the new RDTs. Sourcing and transportation policies were defined, and costing information was obtained from onchocerciasis programs. Various scenarios were created to test various state configurations. The actual demand scenarios represented the disease prevalence at IUs according to the ESS, while the counterfactual scenarios (conducted only in the DRC) are based on adapted prevalence estimates to generate prevalence close to the statistical decision thresholds (5% and 2%), to account for variability in field observations. The number of correctly classified implementation units (IUs) per scenario were estimated and key cost drivers were identified. Results In both Bandundu and Uige, the sites selected based on ESS had high predicted onchocerciasis prevalence >10%. Thus, in the actual demand scenarios in both Bandundu and Uige, the old Ov16 RDT correctly classified all 13 and 11 IUs, respectively, as requiring CDTi. In the counterfactual scenarios in Bandundu, the new RDTs with higher specificity correctly classified IUs more cost effectively. The new RDT with highest specificity (99.8%) correctly classified all 13 IUs. However, very high specificity (e.g., 99.8%) when coupled with imperfect sensitivity, can result in many false negative results (missing decisions to start MDA) at the 5% statistical decision threshold (the decision rule to start MDA). This effect can be negated by reducing the statistical decision threshold to 2%. Across all scenarios, the need for second stage sampling significantly drove program costs upwards. The best performing testing strategies with new RDTs were more expensive than testing with existing tests due to need for second stage sampling, but this was offset by the cost of incorrect classification of IUs. Conclusion The new RDTs modelled added most value in areas with variable disease prevalence, with most benefit in IUs that are near the statistical decision thresholds. Based on the evaluations in this study, DNO could be used to guide the development of new RDTs based on defined sensitivities and specificities. While test sensitivity is a minor driver of whether an IU is identified as positive, higher specificities are essential. Further, these models could be used to explore the development and optimization of new tools for other neglected tropical diseases.
... While a network optimization approach has been used in improving supply chains in the corporate sector, application to optimizing the design of diagnostic networks and specimen referral is relatively new in LMICs [25]. Some examples of the use of DNO in the field include the use of mathematical modeling linked with GIS software in Zambia and South Africa, as well as the use of specific software (Llamasoft's Supply Chain Guru) in Lesotho, Kenya, and the Philippines [20][21][22][26][27][28][29][30][31]. The focus of DNO in Zambia has been on the optimization of viral-load access and has seen the number of viral tests performed double within a year [21,22,27,32]. ...
... The focus of DNO in Zambia has been on the optimization of viral-load access and has seen the number of viral tests performed double within a year [21,22,27,32]. DNO exercises in South Africa have optimized the diagnostic network for CD4 testing, placement of viral-load point-of-care, and also optimized the specimen routing for multiple diagnostics [26,33,34]. In Lesotho, Kenya, and the Philippines, DNO has informed instrument placement and specimen referral within the TB diagnostic network [20,[28][29][30]. ...
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Diagnostics services are an essential component of healthcare systems, advancing universal health coverage and ensuring global health security, but are often unavailable or under-resourced in low- and middle-income (LMIC) countries. Typically, diagnostics are delivered at various tiers of the laboratory network based on population needs, and resource and infrastructure constraints. A diagnostic network additionally incorporates screening and includes point-of-care testing that may occur outside of a laboratory in the community and clinic settings; it also emphasizes the importance of supportive network elements, including specimen referral systems, as being critical for the functioning of the diagnostic network. To date, design and planning of diagnostic networks in LMICs has largely been driven by infectious diseases such as TB and HIV, relying on manual methods and expert consensus, with a limited application of data analytics. Recently, there have been efforts to improve diagnostic network planning, including diagnostic network optimization (DNO). The DNO process involves the collection, mapping, and spatial analysis of baseline data; selection and development of scenarios to model and optimize; and lastly, implementing changes and measuring impact. This review outlines the goals of DNO and steps in the process, and provides clarity on commonly used terms.
... A network of more than 266 laboratories are strategically placed around the country to optimally accommodate the needs of local communities (urban and rural). 1,2 Routine laboratory tests have a predetermined total turn-around time (TAT) cut-off that ensures that tests are processed within the required timeframe to effect the appropriate clinical intervention. Total TAT is defined as the time from first registration of a sample on the laboratory information system (LIS) to the time a result is reviewed and released to the requesting physician. ...
... Across the NHLS, TAT information typically remains the jurisdiction of the testing laboratory where the laboratory manager uses this data to identify problems and initiate corrective action; the individual laboratory has sole and direct access to its own daily or weekly TAT data. 3,4,5 TAT monitoring is however critical for priority programmes, such as HIV and tuberculosis, 2,12 where individual laboratories monitor their respective test TAT, while the organisation is responsible for reporting performance of the network of laboratories. Relevant updated information on the efficiency of service delivery is vital in this context for risk assessment and timely intervention to ensure the continued excellence of service delivery 10 and meeting dire local HIV and tuberculosis programme needs. ...
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Background: High-level monthly, quarterly and annual turn-around time (TAT) reports are used to assess laboratory performance across the National Health Laboratory Service in South Africa. Individual laboratory performances are masked by aggregate TAT reporting across network of testing facilities. Objective: This study investigated weekly TAT reporting to identify laboratory inefficiencies for intervention. Methods: CD4 TAT data were extracted for 46 laboratories from the corporate data warehouse for the 2016/2017 financial period. The total TAT median, 75th percentile and percentage of samples meeting organisational TAT cut-off (90% within 40 hours) were calculated. Total TAT was reported at national, provincial and laboratory levels. Provincial TAT performance was classified as markedly or moderately poor, satisfactory and good based on the percentage of samples that met the cut-off. The pre-analytical, testing and result review TAT component times were calculated. Results: Median annual TAT was 18.8 h, 75th percentile was 25 h and percentage within cut-off was 92% (n = 3 332 599). Corresponding 75th percentiles of component TAT were 10 h (pre-analytical), 22 h testing and 1.6 h review. Provincial 75th percentile TAT varied from 17.6 h to 34.1 h, with three good (n = 13 laboratories), four satisfactory (n = 24 laboratories) and two poor performers (n = 9 laboratories) provinces. Weekly TAT analysis showed 12/46 laboratories (28.6%) without outlier weeks, 31/46 (73.8%) with 1-10 outlier weeks and 3/46 (6.5%) with more than 10 (highest of 20/52 weeks) outlier weeks. Conclusion: Masked TAT under-performances were revealed by weekly TAT analyses, identifying poorly performing laboratories needing immediate intervention; TAT component analyses identified specific areas for improvement.
... 1 Testing is typically offered using a tiered laboratory service approach. 2 In the lower tiers, a basket of eight standard pathology tests are performed locally to provide emergency services and include full blood count, urea and electrolytes, cardiac enzymes, glucose, liver enzymes, among others. Specialised assays and tests supporting local HIV/AIDS and tuberculosis testing regarded as high-volume workloads are transferred through the laboratory referral network to appropriate specialist and dedicated testing laboratories, depending on the nature of the test. ...
... 2 CD4 testing, for example, is centralised into 43 laboratories located strategically across the country to ensure widespread full-service coverage. 2,3 In South Africa, samples are collected daily from health facilities through a network of couriers and transported to the local source laboratory. These laboratories perform a basic repertoire of tests. ...
... Previous work revealed that the placement of CD4 testing equipment and testing capacity, within an area identified with longer TAT attributable to pre-analytical causes, led to a marked shortening of the associated pre-analytical TAT and noticeably shorter overall TAT. 9 Glencross et al. reported using a service radius around existing CD4 testing laboratories to determine healthcare facilities and clinics that lay outside of existing service precincts. 2 This study facilitated the identification of additional testing sites required to improve local TAT. 2 To achieve prompt sample referral within the CD4 network to address pre-analytic TAT, the NHLS employs a 'hub and spoke' approach whereby each testing CD4 laboratory receives referred samples from multiple source laboratories where the samples are first accepted into the laboratory network (but where there are no facilities to perform the testing). 10 At the referring (source) laboratory, the CD4 sample is registered (as a referral) onto the laboratory information system (LIS). ...
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Background: The South African National Health Laboratory Service provides laboratory services for public sector health facilities, utilising a tiered laboratory model to refer samples for CD4 testing from 255 source laboratories into 43 testing laboratories. Objective: The aim of this study was to determine the impact of distance on inter-laboratory referral time for public sector testing in South Africa in 2018. Methods: A retrospective cross-sectional study design analysed CD4 testing inter-laboratory turn-around time (TAT) data for 2018, that is laboratory-to-laboratory TAT from registration at the source to referral receipt at the testing laboratory. Google Maps was used to calculate inter-laboratory distances and travel times. Distances were categorised into four buckets, with the median and 75th percentile reported. Wilcoxon scores were used to assess significant differences in laboratory-to-laboratory TAT across the four distance categories. Results: CD4 referrals from off-site source laboratories comprised 49% (n = 1 390 510) of national reporting. A positively skewed distribution of laboratory-to-laboratory TAT was noted, with a median travel time of 11 h (interquartile range: 7-17), within the stipulated 12 h target. Inter-laboratory distance categories of less than 100 km, 101-200 km, 201-300 km and more than 300 km (p < 0.0001) had 75th percentiles of 8 h, 17 h, 14 h and 27 h. Conclusion: Variability in inter-laboratory TAT was noted for all inter-laboratory distances, especially those exceeding 300 km. The correlation between distance and laboratory-to-laboratory TAT suggests that interventions are required for distant laboratories.
... In South Africa, effectively interfacing clinics and laboratories remains a programmatic issue for CrAg screening and treatment. The South African NHLS conducts routine CD4 testing on over 3 million samples per year with a typical turnaround time of 24-48 hours [46,74]. However, how to effectively translate rapidly available laboratory results to clinical action remains poorly understood, especially in the context of a reflex testing system where providers do not directly order the CrAg test. ...
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Introduction Cryptococcal meningitis remains a significant contributor to AIDS-related mortality despite widened access to antiretroviral therapy. Cryptococcal antigen (CrAg) can be detected in the blood prior to the development of meningitis. The development of highly sensitive and specific rapid diagnostic CrAg tests has helped facilitate the adoption of CrAg screening programs in 19 African countries. Areas Covered The biological rationale for CrAg screening and the programmatic strategies that have been adopted are reviewed. We describe the approach to the investigation of patients with cryptococcal antigenemia and the importance of lumbar puncture to identify individuals who may have cryptococcal meningitis in the absence of symptoms. The limitations of current treatment recommendations and the potential role of newly defined combination antifungal therapies are discussed. A literature review was conducted using a broad database search for cryptococcal antigen screening and related terms in published journal articles dating up to December 2019. Conference abstracts, publicly available guidelines and project descriptions were also incorporated. Expert Opinion As we learn more about the risks of cryptococcal antigenemia, it has become clear that the current management paradigm is inadequate. More intensive investigation and management are required to prevent the development of cryptococcal meningitis and reduce mortality associated with cryptococcal antigenemia.
... Therefore, CD4 count remains an essential tool for HIV management for many low-to-middle income countries, mainly in Sub Saharan African countries, that have adopted "test and treat" approach, but its implementation has been challenging [16][17][18]. Conventional methods are cumbersome due to manual pipetting and longer incubation periods, but they are expensive, because of a high sample throughput compared to the POC PIMA or FACSPresto, as they can perform up to 32 blood samples per carousel [19]. However, the turnaround times for these results from the central laboratory to the health facility can take from a couple of hours to days to be received by the clinic. ...
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Background: In the era of "test and treat strategy", CD4 testing remains an important tool for monitoring HIV-infected individuals. Since conventional methods of CD4 count measurement are costly and cumbersome, POC CD4 counting technique are more affordable and practical for countries with limited resources. Before introducing such methods in Morocco, we decided to assess their reliability. Methods: In this study 92 blood samples from HIV-infected patients, were tested by PIMA and FACSPresto to derive CD4 count. Flow cytometry using FacsCalibur, was used as reference method for CD4 count comparison. Linear regression, Bland-Altman analysis were performed to assess correlation and agreement between these POC methods and the reference method. In addition, sensitivity and specificity, positive predictive value (PPV), negative predictive value (NPV) and misclassification percentage at 350 and 200 CD4 count thresholds; were also determined. Finally, because FACSPresto can also measure hemoglobin (Hb) concentration, 52 samples were used to compare FACSPresto against an automated hematology analyzer. Results: The coefficient of determination R2 was 0.93 for both methods. Bland-Altman analysis displayed a mean bias of - 32.3 and - 8.1 cells/µl for PIMA and FACSPresto, respectively. Moreover, with a threshold of 350 CD4 count, PIMA displayed a sensitivity, specificity, PPV, NPV, were 88.57%, 94.12%, 91.18%, 92.31%; respectively. FACSPresto showed 88.23%, 96.23%, 93.75% and 92.73%; respectively. Furthermore, the upward misclassification percentage was 8.57 and 5.88%, for PIMA and FACSPresto, respectively; whereas the downward misclassification percentage was 7.84% and 7.54%; respectively. With 200 cells/µl threshold, PIMA had a sensitivity, specificity, PPV and NPV of 83.33%, 98.53%, 93.75% and 95.71%, respectively. Regarding FACSPresto, sensitivity, specificity, PPV and NPV was 82.35%, 98.57%, 88.57% and 95.83%; respectively. Upward misclassification percentage was 5.56% and 5.88%, for PIMA and FACSPresto, respectively; whereas downward misclassification percentage was 4.41% and 4.29%; respectively. Finally, the hemoglobin measurement evaluation displayed an R2 of 0.80 and a mean bias of - 0.12 with a LOA between - 1.75 and 1.51. Conclusion: When compared to the reference method, PIMA and FACSPresto have shown good performance, for CD4 counting. The introduction of such POC technology will speed up the uptake of patients in the continuum of HIV care, in our country.
... Network optimization, the selection of a best network configuration from a set of available alternatives based on selected criteria, can be used to inform instrument placement, sample transportation and referral mechanisms, staffing, geographical prioritization, quality assurance and integration of testing to meet the priority needs of a disease programme [8]. Network optimization and strategic supply chain management using specialized software and modelling approaches is common practice in the commercial sector [9]; while examples in the public health sector are somewhat limited, and mostly restricted to supply chain and procurement applications, modelling approaches have previously been used to inform placement of TB diagnostics in Tanzania [10], and of CD4 testing facilities in South Africa [11]. The latter included assessment of testing site workload, coverage areas and turnaround time to optimize service provision. ...
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Background To reach WHO End tuberculosis (TB) targets, countries need a quality-assured laboratory network equipped with rapid diagnostics for tuberculosis diagnosis and drug susceptibility testing. Diagnostic network analysis aims to inform instrument placement, sample referral, staffing, geographical prioritization, integration of testing enabling targeted investments and programming to meet priority needs. Methods Supply chain modelling and optimization software was used to map Lesotho’s TB diagnostic network using available data sources, including laboratory and programme reports and health and demographic surveys. Various scenarios were analysed, including current network configuration and inclusion of additional GeneXpert and/or point of care instruments. Different levels of estimated demand for testing services were modelled (current [30,000 tests/year], intermediate [41,000 tests/year] and total demand needed to find all TB cases [88,000 tests/year]). Results Lesotho’s GeneXpert capacity is largely well-located but under-utilized (19/24 sites use under 50% capacity). The network has sufficient capacity to meet current and near-future demand and 70% of estimated total demand. Relocation of 13 existing instruments would deliver equivalent access to services, maintain turnaround time and reduce costs compared with planned procurement of 7 more instruments. Gaps exist in linking people with positive symptom screens to testing; closing this gap would require extra 11,000 tests per year and result in 1000 additional TB patients being treated. Closing the gap in linking diagnosed patients to treatment would result in a further 629 patients being treated. Scale up of capacity to meet total demand will be best achieved using a point-of-care platform in addition to the existing GeneXpert footprint. Conclusions Analysis of TB diagnostic networks highlighted key gaps and opportunities to optimize services. Network mapping and optimization should be considered an integral part of strategic planning. By building efficient and patient-centred diagnostic networks, countries will be better equipped to meet End TB targets.