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. 2023 Dec 20;77(Suppl 7):S507-S518.
doi: 10.1093/cid/ciad634.

Methodological Approach to Identify and Expand the Volume of Antimicrobial Resistance (AMR) Data in the Human Health Sector in Low- and Middle-Income Countries in Asia: Implications for Local and Regional AMR Surveillance Systems Strengthening

Hea Sun Joh  1 , Corin Yeats  2 , Alina Shaw  3 , Nimesh Poudyal  1 , Patrick Gallagher  3 , Jong-Hoon Kim  1 , Affan Shaikh  3 , Hye Jin Seo  1 , Kyu-Young Kevin Chi  1 , Kristi Prifti  1 , Alyssa Cho  1 , Mohammad Julhas Sujan  1 , Emmanuel Eraly  1 , Kien Duc Pham  1 , Subha Shrestha  1 , Ahmed Taha Aboushady  1   4 , Gideok Pak  1 , GeunHyeog Jang  1 , Eun Lyeong Park  1 , Hyeong-Won Seo  1 , Khalil Abudahab  2 , Ben E W Taylor  2 , Adam Clark  4 , Brooke Dolabella  3 , Hyein Yoon  1 , Jihyun Han  1 , Soo Young Kwon  1 , Florian Marks  1   5   6   7 , John Stelling  4 , David M Aanensen  2 , William R MacWright  3 , Marianne Holm  1
Affiliations

Methodological Approach to Identify and Expand the Volume of Antimicrobial Resistance (AMR) Data in the Human Health Sector in Low- and Middle-Income Countries in Asia: Implications for Local and Regional AMR Surveillance Systems Strengthening

Hea Sun Joh et al. Clin Infect Dis. .

Abstract

Antimicrobial resistance (AMR) is a multifaceted global health problem disproportionately affecting low- and middle-income countries (LMICs). The Capturing data on Antimicrobial resistance Patterns and Trends in Use in Regions of Asia (CAPTURA) project was tasked to expand the volume of AMR and antimicrobial use data in Asia. The CAPTURA project used 2 data-collection streams: facility data and project metadata. Project metadata constituted information collected to map out data sources and assess data quality, while facility data referred to the retrospective data collected from healthcare facilities. A down-selection process, labelled "the funnel approach" by the project, was adopted to use the project metadata in prioritizing and selecting laboratories for retrospective AMR data collection. Moreover, the metadata served as a guide for understanding the AMR data once they were collected. The findings from CAPTURA's metadata add to the current discourse on the limitation of AMR data in LMICs. There is generally a low volume of AMR data generated as there is a lack of microbiology laboratories with sufficient antimicrobial susceptibility testing capacity. Many laboratories in Asia are still capturing data on paper, resulting in scattered or unused data not readily accessible or shareable for analyses. There is also a lack of clinical and epidemiological data captured, impeding interpretation and in-depth understanding of the AMR data. CAPTURA's experience in Asia suggests that there is a wide spectrum of capacity and capability of microbiology laboratories within a country and region. As local AMR surveillance is a crucial instrument to inform context-specific measures to combat AMR, it is important to understand and assess current capacity-building needs while implementing activities to enhance surveillance systems.

Keywords: AMR surveillance; Antimicrobial Resistance (AMR); CAPTURA; capacity building; project implementation.

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Conflict of interest statement

Potential conflicts of interest. A. Clark reports receiving funds for their institution from the CAPTURA grant. A. T. A. reports being an employee of Brigham and Women's Hospital since September 2022 and a consultant since 2021 and working as a consultant for the CAPTURA project from September 2021 to March 2022. A. S. reports receiving compensation from the Public Health Surveillance Group. W. M. reports receiving financial support from the International Vaccine Group, UKAID, and a subgrant for CAPTURA work. B. D. reports receiving compensation from the Public Health Surveillance Group. D. M. A. reports receiving financial support from the National Institute for Health and Care Research (NIHR) Global Health Research Unit on genomic surveillance of AMR and the Centre for Genomic Pathogen Surveillance. J. S. reports a subcontract for salary support and travel from the International Vaccine Institute. E. E. reports being a consultant with the International Vaccine Institute since April 2021 and staff and consulting funds from the WHO WPRO, ABD, FHI, and UNICEF. P. G. reports receiving compensation from the Public Health Surveillance Group. All other authors report no potential conflicts. All authors have submitted the ICMJE Form for Disclosure of Potential Conflicts of Interest. Conflicts that the editors consider relevant to the content of the manuscript have been disclosed.

Figures

Figure 1.
Figure 1.
Map of 12 CAPTURA countries in South and Southeast Asia. Abbreviation: CAPTURA, Capturing data on Antimicrobial resistance Patterns and Trends in Use in Regions of Asia.
Figure 2.
Figure 2.
CAPTURA's funnel approach for site selection, data collection, and analyses using project metadata. Abbreviations: AMR, antimicrobial resistance; CAPTURA, Capturing data on Antimicrobial resistance Patterns and Trends in Use in Regions of Asia.
Figure 3.
Figure 3.
Common methods of antimicrobial susceptibility testing in CAPTURA countries. Abbreviations: CAPTURA, Capturing data on Antimicrobial resistance Patterns and Trends in Use in Regions of Asia; MIC, minimum inhibitory concentration.
Figure 4.
Figure 4.
Types of variables collected in 136 laboratories where AST capacity was ascertained. Abbreviations: AST, antimicrobial susceptibility testing; R,I,S, Resistant/Intermediate/Sensitive.
Figure 5.
Figure 5.
Map of 8 CAPTURA countries where RLQAs were conducted. Location of participating laboratories is indicated with dots. Abbreviations: CAPTURA, Capturing data on Antimicrobial resistance Patterns and Trends in Use in Regions of Asia; RLQA, Rapid Laboratory Quality Assessment.
Figure 6.
Figure 6.
Distribution of total RLQA scores with the median indicated. Abbreviation: RLQA, Rapid Laboratory Quality Assessment.
Figure 7.
Figure 7.
Distribution of RLQA scores per section with median scores indicated. Abbreviation: RLQA, Rapid Laboratory Quality Assessment.
Figure 8.
Figure 8.
Distribution of total RLQA scores by country. Countries were omitted from figure if <5 assessments were conducted. Abbreviation: RLQA, Rapid Laboratory Quality Assessment.
Figure 9.
Figure 9.
Affiliation of laboratories (public/private/other) participating in RLQA. Abbreviation: RLQA, Rapid Laboratory Quality Assessment.
Figure 10.
Figure 10.
Volume of data shared with project by country. Slices on the pie charts show the number of healthcare facilities where data-sharing agreements were signed (also indicated in brackets), and each slice's size is proportional to the volume of data contributed to the total.
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