The Three-Day Blind Spot: Can Pocket Sequencers Help Zambia See Drug Resistance in Time?

Drug-resistant infections have become one of the leading causes of death worldwide. In 2019 alone, bacterial resistance was directly responsible for an estimated 1.27 million deaths and associated with almost five million more. The latest global forecasts suggest that, without stronger action, more than 39 million people could die from resistant infections between 2025 and 2050. Governments have noticed: at the 2024 United Nations high-level meeting, member states committed to cutting deaths associated with antimicrobial resistance (AMR) by 10% by 2030.

Yet the tools we use to see resistance are surprisingly old. To identify a bacterium and the drugs it resists, a laboratory must first grow it, a method little changed since the nineteenth century. Culture takes two to four days, fails for slow-growing organisms and for patients already given antibiotics, and exists only where microbiology laboratories exist. Everywhere else, clinicians prescribe blind.

The cost of waiting

The delay is not harmless. A landmark study of septic shock found that each hour's delay in effective antibiotic therapy was associated with a measurable fall in survival. When laboratory results arrive days later, doctors fall back on broad-spectrum drugs, and that empirical over-treatment itself breeds further resistance.

Surveillance suffers as well. Culture records only what happens to grow, so viruses, slow-growers and organisms suppressed by earlier treatment drop out of the picture. The World Health Organization's global surveillance system still depends on routine laboratory data that many countries cannot yet produce in sufficient volume or quality.

Sequencing leaves the reference laboratory

Genomic sequencing offers a way around culture, and it no longer needs a large laboratory. Pocket-sized nanopore devices read DNA in real time and run from a laptop. In Brazil, researchers drove one through the country's north-east in a mobile laboratory to trace the hidden spread of Zika virus. In Norway, a hospital team showed that nanopore sequencing could identify pathogens and resistance genes from positive blood cultures within hours rather than days. Applied directly to urine or respiratory samples, clinical metagenomics can detect bacteria and their resistance genes without growing anything at all.

The caveat is accuracy. Europe's antimicrobial susceptibility testing committee concluded that predicting resistance from genetic data was promising, but that the evidence was not yet strong enough to replace laboratory testing for most bacteria. A gene's presence is not always proof that a drug will fail.

Africa: the highest burden, the thinnest coverage

The case for decentralised sequencing is strongest in Africa, where western sub-Saharan Africa recorded the highest AMR death rate of any region in 2019. Yet the Mapping AMR and AMU Partnership (MAAP) study across 14 African countries found that only 1.3% of some 50,000 medical laboratories conducted bacteriology testing, and that in eight of those countries more than half the population lived beyond reach of any bacteriology laboratory. An earlier systematic review had warned that resistance data were scarcest precisely where resistance was high.

The continent has nonetheless shown what is possible. During the West African Ebola epidemic, scientists sequenced virus genomes in Guinea within days of sampling, using equipment carried in airline luggage. COVID-19 accelerated investment further, and African laboratories sequenced thousands of SARS-CoV-2 genomes, tracking the pandemic as it spread. Analysts caution, however, that this capacity remains concentrated in a few national hubs and has yet to be directed systematically at bacterial resistance.

Zambia: building on what exists

Zambia was one of the MAAP countries, and its own evidence points to the same gap. The Multi-sectoral National Action Plan on AMR 2017–2027, coordinated by the Zambia National Public Health Institute, already records multidrug-resistant tuberculosis, ESBL-producing Klebsiella pneumoniae and MRSA in the country. A study at the University Teaching Hospital in Lusaka found high resistance among bacteria causing diarrhoea in young children, while research in community pharmacies showed that antibiotics were frequently sold without a prescription. Beyond the main referral hospitals, few facilities can culture bacteria at all, so most prescribing decisions are made without any resistance result.

Zambia also has genomic foundations. In December 2020, national scientists detected the B.1.351 SARS-CoV-2 variant through local sequencing. That capacity, built for a virus, could be turned towards bacteria.

What it would take

Portable sequencing will not replace culture overnight, and four conditions should decide where it is used. First, predictions from DNA must be validated against laboratory results for the pathogens common in Zambian hospitals. Second, the cost per sample must fall to a level district budgets can bear. Third, reagents need a reliable supply and storage that can cope with heat and power cuts. Fourth, the analysis must be simplified so that results reach clinicians as clear treatment guidance rather than raw data. Quality assurance must run through all four: each site needs external proficiency testing, and results should flow to the Zambia National Public Health Institute so that individual findings add up to national intelligence.

The stakes justify the effort. The World Bank estimates that drug-resistant infections could reduce annual global GDP by up to 3.8% by 2050, with low-income countries hit hardest. A practical first step would be a pilot linking two or three provincial hospitals to the national reference laboratory, running sequencing and culture side by side on the same samples and feeding the results into national surveillance.

The question is no longer whether resistance can be read from DNA. It is whether Zambia can create the conditions to trust what it reads, close enough to the patient to change the prescription.

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**RIS Contributors are researchers and practitioners writing for CCET's Research Intelligence Service; views are the author's own.

Sources

Primary sources

•         Government of the Republic of Zambia (2017). Multi-sectoral National Action Plan on Antimicrobial Resistance 2017–2027. Lusaka: Zambia National Public Health Institute

•         African Society for Laboratory Medicine (2022). MAAP: multi-country research on antimicrobial resistance in Africa. Addis Ababa: ASLM

•         World Health Organization (2022). Global Antimicrobial Resistance and Use Surveillance System (GLASS) Report 2022. Geneva: WHO

•         World Health Organization (2024). World leaders commit to decisive action on antimicrobial resistance. News release on the UN General Assembly political declaration, 26 September 2024

•         World Bank (2017). Drug-resistant infections: A threat to our economic future. Washington, DC: World Bank

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•         Faria, N.R. et al. (2017). Establishment and cryptic transmission of Zika virus in Brazil and the Americas. Nature, 546(7658), pp. 406–410

•         GBD 2021 Antimicrobial Resistance Collaborators (2024). Global burden of bacterial antimicrobial resistance 1990–2021: A systematic analysis with forecasts to 2050. The Lancet, 404(10459), pp. 1199–1226

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