Economic costing methodologies for drug-resistant bacterial infections in humans in low-and middle-income countries: a systematic review

Background This review examined methodologies used to cost the impact of antimicrobial resistance (AMR) infections in humans from household and health system perspectives. Although extensive research has been conducted on the clinical AMR burden in low- and middle-income countries (LMICs) in terms of prevalence and other drivers of antimicrobial resistance, there is increased misuse and overuse of antibiotics which increases the risk of AMR infections compared to high-income countries. Lack of comprehensive estimates on economic costs of AMR in LMICs due to lack of standard methodologies that incorporate time biases and inference for instance, may negatively affect accuracy and robustness of results needed for reliable and actionable policies. Methods We conducted a systematic review of studies searched in PubMed and other electronic databases. Only studies from LMICs were included. Data were extracted via a modified Covidence template and a Joanna Briggs Institute (JBI) assessment tool for economic evaluations to assess the quality of the papers. Results Using PRISMA, 2542 papers were screened at the title and abstract levels, of which 148 were retrieved for full-text review. Of these, 62 articles met the inclusion criteria. The articles had a quality assessment score averaging 85%, ranging from 63 to 100%. Most studies, 13, were from China (21%), followed by 8 from South Africa (13%). Tuberculosis (TB), general bacterial, and nosocomial infection costs are the most studied, accounting for 40%, 39%, and 6%, respectively with TB common in South Africa than the rest of the countries. The majority of the papers used a microcosting approach (71%), followed by gross costing (27%), while the remainder used both. Most studies analyzed costs descriptively (61%), followed by studies using regression-based techniques (17%) and propensity score matching (5%), among others. Conclusion Overall, the use of descriptive statistics without justification, limited consideration for potential data challenges, including confounders, and short-term horizons suggest that the full AMR cost burden in humans in LMICs has not been well accounted for. Given the limited data available for these studies, the use of a combination of methodologies may help triangulate more accurate and policy-relevant estimates. While the resources to conduct such cost studies are limited, the use of modeling costs via regression techniques while adjusting for cofounding could help maximize robustness and better estimate the vast and varied burden derived directly and indirectly from AMR.