Application of diagnostic network optimization in Kenya and Nepal to design integrated, sustainable and efficient bacteriology and antimicrobial resistance surveillance networks.

Antimicrobial resistance (AMR) is a major global public health concern, particularly in low- and middle-income countries, which experience the highest burden of AMR. Critical to combatting AMR is ensuring there are effective, accessible diagnostic networks in place to diagnose, monitor and prevent AMR, but many low- and middle-income countries lack such networks. Consequently, there is substantial need for approaches that can inform the design of efficient AMR laboratory networks and sample referral systems in lower-resource countries. Diagnostic network optimization (DNO) is a geospatial network analytics approach to plan diagnostic networks and ensure greatest access to and coverage of services, while maximizing the overall efficiency of the system. In this intervention, DNO was applied to strengthen bacteriology and AMR surveillance network design in Kenya and Nepal for human and animal health, by informing linkages between health facilities and bacteriology testing services and sample referral routes between farms, health facilities and laboratories. Data collected from the target settings in each country were entered into the open-access DNO tool OptiDx, to generate baseline scenarios, which depicted the current state of AMR laboratory networks and sample referral systems in the countries. Subsequently, baselines were adjusted to evaluate changing factors such as samples flows, transport frequency, transport costs, and service distances. Country stakeholders then compared resulting future scenarios to identify the most feasible solution for their context. The DNO analyses enabled a wealth of insights that will facilitate strengthening of AMR laboratory and surveillance networks in both countries. Overall, the project highlights the benefits of using a data-driven approach for designing efficient diagnostic networks, to ensure better health resource allocation while maximizing the impact and equity of health interventions. Given the critical need to strengthen AMR laboratory and surveillance capacity, DNO should be considered an integral part of diagnostic strategic planning in the future.

Leveraging tuberculosis case relative locations to enhance case detection and linkage to care in Swaziland.

BACKGROUND: In Swaziland, as in many high HIV/TB burden settings, there is not information available regarding the household location of TB cases for identifying areas of increased TB incidence, limiting the development of targeted interventions. Data from "Butimba", a TB REACH active case finding project, was re-analyzed to provide insight into the location of TB cases surrounding Mbabane, Swaziland. OBJECTIVE: The project aimed to identify geographical areas with high TB burdens to inform active case finding efforts. METHODS: Butimba implemented household contact tracing; obtaining landmark based, informal directions, to index case homes, defined here as relative locations. The relative locations were matched to census enumeration areas (known location reference areas) using the Microsoft Excel Fuzzy Lookup function. Of 403 relative locations, an enumeration area reference was detected in 388 (96%). TB cases in each census enumeration area and the active case finders in each Tinkhundla, a local governmental region, were mapped using the geographic information system, QGIS 2.16. RESULTS: Urban Tinkhundla predictably accounted for most cases; however, after adjusting for population, the highest density of cases was found in rural Tinkhundla. There was no correlation between the number of active case finders currently assigned to the 7 Tinkhundla surrounding Mbabane and the total number of TB cases (Spearman rho = -0.57, p = 0.17) or the population adjusted TB cases (Spearman rho = 0.14, p = 0.75) per Tinkhundla. DISCUSSION: Reducing TB incidence in high-burden settings demands novel analytic approaches to study TB case locations. We demonstrated the feasibility of linking relative locations to more precise geographical areas, enabling data-driven guidance for National Tuberculosis Programs' resource allocation. In collaboration with the Swazi National Tuberculosis Control Program, this analysis highlighted opportunities to better align the active case finding national strategy with the TB disease burden.