Department of Mathématiques Informatique et Télécommunications, Université Toulouse III, Paul Sabatier, INSERM, UMR 1027, 31000, Toulouse, France.
INSPECT-LB, Institut National de Santé Publique, d'Épidémiologie Clinique et de Toxicologie-Liban, Beirut, 6573-14, Lebanon.
Antimicrob Resist Infect Control. 2021 Mar 31;10(1):63. doi: 10.1186/s13756-021-00931-w.
Data on comprehensive population-based surveillance of antimicrobial resistance is lacking. In low- and middle-income countries, the challenges are high due to weak laboratory capacity, poor health systems governance, lack of health information systems, and limited resources. Developing countries struggle with political and social dilemma, and bear a high health and economic burden of communicable diseases. Available data are fragmented and lack representativeness which limits their use to advice health policy makers and orientate the efficient allocation of funding and financial resources on programs to mitigate resistance. Low-quality data means soaring rates of antimicrobial resistance and the inability to track and map the spread of resistance, detect early outbreaks, and set national health policy to tackle resistance. Here, we review the barriers and limitations of conducting effective antimicrobial resistance surveillance, and we highlight multiple incremental approaches that may offer opportunities to strengthen population-based surveillance if tailored to the context of each country.
缺乏关于综合人群抗菌药物耐药性监测的数据。在中低收入国家，由于实验室能力薄弱、卫生系统治理不善、缺乏卫生信息系统以及资源有限，这些挑战更为严峻。发展中国家面临着政治和社会困境，承受着传染病带来的高健康和经济负担。现有数据分散且缺乏代表性，限制了其用于为卫生政策制定者提供建议和指导资金和财政资源的有效分配，以制定方案来减轻耐药性。低质量的数据意味着抗菌药物耐药率飙升，且无法跟踪和绘制耐药性传播图、发现早期疫情，并制定国家卫生政策来应对耐药性。在这里，我们回顾了开展有效抗菌药物耐药性监测的障碍和限制，并强调了多种渐进式方法，如果根据每个国家的情况进行调整，可能为加强基于人群的监测提供机会。
