Antimicrobial Resistance Situational Analysis 2019-2020: Design and Performance for Human Health Surveillance in Uganda

Abstract

Antibiotic resistance and its mechanisms have been known for over six decades, but global efforts to characterize its routine drivers have only gained momentum in the recent past. Drivers of clinical and community resistance go beyond just clinical practice, which is why one-health approaches offer the most realistic option for controlling antibiotic resistance. It is noteworthy that the emergence of resistance occurs naturally in the environment, but akin to climate change, the current accelerated emergence and spread bears hallmarks of anthropomorphic influence. If left unchecked, this can undo the medical and agricultural advancements of the last century. The WHO recommends that nations develop, adopt, and implement strategies that track the changing trends in antibiotic resistance levels to tackle this problem. This article examines efforts and progress in developing and implementing a human health antimicrobial resistance surveillance strategy in Uganda. We do so within the context of the National Action Plan for tackling antimicrobial resistance (AMR-NAP) launched in 2018. We discuss the technical milestones and progress in implementing surveillance of GLASS priority pathogens under this framework. The preliminary output of the framework examines the performance and compares AMR and AMU surveillance data to explain observed trends. We conclude that Uganda is making progress in developing and implementing a functional AMR surveillance strategy for human health.

Keywords: antimicrobial resistance (AMR); national action plan (NAP); one health approach; sentinel surveillance.

Figure 1

The design of Uganda’s AMR governance structure at national level. On top is the One health platform, under which is the committee that oversees activities on AMR in Uganda. The oversee the pillars on which the National action plan is implemented todate.

Figure 2

Uganda’s design of AMR Sentinel Surveillance system based on the already-existing Ministry of Health structure of regional referral hospitals and how functional linkages for conducting AMR surveillance from the facility level are extended to the national level.

Figure 3

Distribution of the 1209 samples cultured from the four sentinel sites whose results are presented in this report. The X-axis represents the age brackets of the patients from whom samples were collected. The Y-axis shows the number of samples.

Figure 4

Illustration of the data sharing architecture with Human Health data linkages to One Health. This is colour coded according to the human health (green), animal health (brown), water and environment as a light brown shade.

Figure 5

Microbes recovered from the cultured samples in Figure 3. Here, (A) has two panels, the first showing distribution by specimen and the second by district of origin. (B) shows the GLASS priority pathogens recovered from each of the district sentinel sites.

Figure 6

Preliminary analysis of microbes recovered from specific samples. (A) shows the distribution of these organisms by district, age group and gender. (B,C) show the same distribution by specimen collected and source, i.e., community or hospital.

Figure 7

Stacked column graph for E. coli is for Arua Health Region isolates for AST patterns validated by the National Microbiology Ref. Lab for 2019. Data source and analysis by NMRL-AMR NCC-Biostatician.

Figure 8

Distribution of antimicrobial susceptibility profiles of S. aureus across different available antibiotics.

Figure 9

Antibiotic consumption performance among four Sentinel Sites, Data Collection by AMU/C Technical Working Group with support from IDI-GHSA Project.

Figure 10

Antibiotic prescription at the outpatient department of Arua regional referral hospital for Quarter 1& Quarter 2 of 2019. The figure is a bar plot whose axes were flipped to improve visualisation. The y-axis shows the cumulative number of days the patients at this department were on any given antibiotics while the x-axis shows the type of antibiotics. The color fill shows the system to which the disease under management clusters. For example, a Urinary tract infections would cluster under Urogenital system. Note that majority of the “others” are malaria cases.