Low-resource settings are disproportionately burdened by infectious diseases and antimicrobial resistance. Good quality clinical bacteriology through a well functioning reference laboratory network is necessary for effective resistance control, but low-resource settings face infrastructural, technical, and behavioural challenges in the implementation of clinical bacteriology. In this Personal View, we explore what constitutes successful implementation of clinical bacteriology in low-resource settings and describe a framework for implementation that is suitable for general referral hospitals in low-income and middle-income countries with a moderate infrastructure. Most microbiological techniques and equipment are not developed for the specific needs of such settings. Pending the arrival of a new generation diagnostics for these settings, we suggest focus on improving, adapting, and implementing conventional, culture-based techniques. Priorities in low-resource settings include harmonised, quality assured, and tropicalised equipment, consumables, and techniques, and rationalised bacterial identification and testing for antimicrobial resistance. Diagnostics should be integrated into clinical care and patient management; clinically relevant specimens must be appropriately selected and prioritised. Open-access training materials and information management tools should be developed. Also important is the need for onsite validation and field adoption of diagnostics in low-resource settings, with considerable shortening of the time between development and implementation of diagnostics. We argue that the implementation of clinical bacteriology in low-resource settings improves patient management, provides valuable surveillance for local antibiotic treatment guidelines and national policies, and supports containment of antimicrobial resistance and the prevention and control of hospital-acquired infections.

Bloodstream infections (BSI) have a substantial impact on morbidity and mortality worldwide. Despite scarcity of data from many low- and middle-income countries (LMICs), there is increasing awareness of the importance of BSI in these countries. For example, it is estimated that the global mortality of non-typhoidal Salmonella bloodstream infection in children under 5 already exceeds that of malaria. Reliable and accurate diagnosis of these infections is therefore of utmost importance. Blood cultures are the reference method for diagnosis of BSI. LMICs face many challenges when implementing blood cultures, due to financial, logistical, and infrastructure-related constraints. This review aims to provide an overview of the state-of-the-art of sampling and processing of blood cultures, with emphasis on its use in LMICs. Laboratory processing of blood cultures is relatively straightforward and can be done without the need for expensive and complicated equipment. Automates for incubation and growth monitoring have become the standard in high-income countries (HICs), but they are still too expensive and not sufficiently robust for imminent implementation in most LMICs. Therefore, this review focuses on “manual” methods of blood culture, not involving automated equipment. In manual blood cultures, a bottle consisting of a broth medium supporting bacterial growth is incubated in a normal incubator and inspected daily for signs of growth. The collection of blood for blood culture is a crucial step in the process, as the sensitivity of blood cultures depends on the volume sampled; furthermore, contamination of the blood culture (accidental inoculation of environmental and skin bacteria) can be avoided by appropriate antisepsis. In this review, we give recommendations regarding appropriate blood culture sampling and processing in LMICs. We present feasible methods to detect and speed up growth and discuss some challenges in implementing blood cultures in LMICs, such as the biosafety aspects, supply chain and waste management.

Growing data suggest that antimicrobial-resistant bacterial infections are common in low- and middle-income countries. This review summarises the microbiology of key bacterial syndromes encountered in West Africa and estimates the prevalence of antimicrobial resistance (AMR) that could compromise first-line empirical treatment. We systematically searched for studies reporting on the epidemiology of bacterial infection and prevalence of AMR in West Africa within key clinical syndromes. Within each syndrome, the pooled proportion and 95% confidence interval were calculated for each pathogen-antibiotic pair using random-effects models. Among 281 full-text articles reviewed, 120 met the eligibility criteria. The majority of studies originated from Nigeria (70; 58.3%), Ghana (15; 12.5%) and Senegal (15; 12.5%). Overall, 43 studies (35.8%) focused on urinary tract infections (UTI), 38 (31.7%) on bloodstream infections (BSI), 27 (22.5%) on meningitis, 7 (5.8%) on diarrhoea and 5 (4.2%) on pneumonia. Children comprised the majority of subjects. Studies of UTI reported moderate to high rates of AMR to commonly used antibiotics including evidence of the emergence of cephalosporin resistance. We found moderate rates of AMR among common bloodstream pathogens to typical first-line antibiotics including ampicillin, cotrimoxazole, gentamicin and amoxicillin/clavulanate. Among S. pneumoniae strains isolated in patients with meningitis, levels of penicillin resistance were low to moderate with no significant resistance noted to ceftriaxone or cefotaxime. AMR was common in this region, particularly in hospitalized patients with BSI and both outpatient and hospitalized patients with UTI. This raises concern given the limited diagnostic capability and second-line treatment options in the public sector in West Africa.

Serological rapid diagnostic tests (RDTs) are widely used across pathologies, often providing users a simple, binary result (positive or negative) in as little as 5 to 20 min. Since the beginning of the COVID-19 pandemic, new RDTs for identifying SARS-CoV-2 have rapidly proliferated. However, these seemingly easy-to-read tests can be highly subjective, and interpretations of the visible “bands” of color that appear (or not) in a test window may vary between users, test models, and brands. We developed and evaluated the accuracy/performance of a smartphone application (xRCovid) that uses machine learning to classify SARS-CoV-2 serological RDT results and reduce reading ambiguities. Across 11 COVID-19 RDT models, the app yielded 99.3% precision compared to reading by eye. Using the app replaces the uncertainty from visual RDT interpretation with a smaller uncertainty of the image classifier, thereby increasing confidence of clinicians and laboratory staff when using RDTs, and creating opportunities for patient self-testing.