Awareness and capacities of 103 countries to address antimicrobial resistance from 2017 to 2020

Abstract

Antimicrobial resistance (AMR) is a growing global concern, compromising the effectiveness of treatments for infections and being referred to as a silent pandemic. This study examines the factors associated with AMR awareness and capacities across 103 countries from 2017 to 2020. This cross-sectional study aimed to determine whether factors such as Human Development Index (HDI), Civil Liberties (CL), Gender Equality (GE), Universal Health Coverage (UHC), Healthcare Workforce Density (HWD), and State Party Self-Assessment Annual Report (SPAR) scores are significantly associated with countries’ AMR awareness and capacities. The results identified that the majority of countries had Very High HDI, Full Freedom, Fair GE, Low UHC, High HWD, and Low SPAR scores. However, despite these generally favorable profiles, the majority still lack sufficient awareness and capacity to address AMR. This underscores the importance of strengthening AMR awareness and capacities globally, regardless of a country’s characteristics. Significant associations were observed between these factors and AMR awareness and capacities (χ²). UHC emerged as the only factor significantly associated with AMR capacities, where countries with low AMR capacities are more frequently found among countries that also have poor UHC (OR = 10.49, 8.96, and 12.92 across various models; all p < 0.05). This finding highlights the potential to improve the AMR capacities of countries through the achievement of UHC targets.

Introduction

Antimicrobial Resistance (AMR) is the term used to describe the ability of bacteria, parasites, viruses, or fungi to develop resistance to drugs¹. This resistance emerges due to the improper and excessive use of antimicrobials in human health, animal health, and food production². The multifaceted nature of the AMR threat, spanning various sectors, calls for a collaborative, One Health Approach to effectively combat it^2,3.

AMR has been identified and considered as a silent pandemic⁴. This issue now stands as a leading cause of global mortality, surpassing even the impact of HIV/AIDS and Malaria^5,6. AMR threat is responsible for over 1 million deaths annually⁴ and contributes to nearly 5 million deaths^5,6. Dire projections suggest that by 2050, AMR could result in a staggering 10 million deaths each year, solidifying its status as one of the top 10 global health threats⁷.

Recognizing the urgency of addressing and preventing AMR⁸, however, countries’ capacity in AMR prevention and control is not well-addressed in the current core capacities of International Health Regulations State Party Self-Assessment Annual Report (IHR-SPAR)⁹. This lack of attention to a specific indicator measuring countries’ capabilities in addressing AMR poses a significant risk to human, animal, and environmental health, particularly within agricultural and livestock production, as well as food processing and manufacturing sectors². Neglecting the prevention and management of AMR may potentially revert the world back to a pre-antibiotic era, where common infections regain their ability to cause severe consequences⁸.

Therefore, to address AMR threat, several global strategies have been set in motion, grounded in the principles of the One Health Approach⁸. In 2015, the World Health Organization (WHO), the Food and Agriculture Organization (FAO), and the World Organization for Animal Health (WOAH/OIE) adopted the Global Action Plan on AMR. As part of this plan, countries agreed to develop National Action Plans on AMR that align with the Global Action Plan and to implement relevant policies and strategies for the prevention, control, and monitoring of AMR^10,11.

Moreover, to assess the progress of countries in implementing their national action plans, the WHO, FAO, and OIE jointly administered an annual Tripartite AMR country self-assessment survey (TrACSS) starting in 2016^10,11. TrACSS assesses and monitors the advancement of countries’ capacities to address AMR through self-assessment^10–12. Concurrently, the WHO established the Global Antimicrobial Resistance and Use Surveillance System (GLASS) to facilitate the monitoring of the status of national AMR surveillance systems. GLASS employs a standardized approach for the collection, analysis, and sharing of AMR data¹³.

Both TrACSS and SPAR employ a self-assessment approach to evaluate countries’ capacities, although they differ in their specific focuses. SPAR addresses a wide range of health issues¹⁴ that have the potential to contribute significantly to public health challenges. Conversely, TrACSS primarily focuses on assessing AMR^12,15. However, despite the comprehensive scope of SPAR and its mandatory implementation under the IHR¹⁶, there was limited participation from the countries during the initial implementation phase of SPAR¹⁷.

This limited participation reflects a global lack of awareness and commitment to enhancing capacity for infectious disease control. Furthermore, considering the focused scope of TrACSS on AMR, there is a possibility that its implementation could resemble the pattern observed during the initial stages of SPAR. This resemblance would suggest a lack of awareness among countries regarding the global threat posed by AMR.

Given AMR’s potential to contribute to future pandemics^4,18,19 and concerns about limited awareness and measurement of AMR capacities⁹, this study aimed to assess countries’ awareness and capacity to address AMR from 2017 to 2020, exploring associated factors. Additionally, with the requirement for countries to develop IHR core capacities and report them through SPAR, this study examined the relationship between SPAR and TrACSS scores to see if SPAR scores can reflect countries’ capacity to address AMR.

Results

Country characteristics based on levels of AMR awareness and capacities

Table 1 describes the characteristics of the 103 countries analyzed in this study. The majority of the sample exhibited low AMR Awareness (36.9%) and low AMR Capacities (82.5%) and represented diverse regions and characteristics. Among the 103 countries, 37.9% were located in Europe, 35% in Asia, 13.6% in Africa, 6.8% in North America, 3.9% in Latin America, and 2.9% in Oceania. Additionally, the majority of countries were characterized by a Very High Human Development Index (HDI) (51.5%), Full Freedom status (53.4%), Fair Government Effectiveness (GE) (55.3%), Low Universal Health Coverage (UHC) (66.0%), High Health Worker Density (HWD) (51.5%), and Low State Party Self-Assessment Annual Report (SPAR) capacities (57.3%).

Table 1.

Country characteristics based on levels of AMR awareness and capacities.

	N (%)	AMR awareness			AMR capacities
		Low	Medium	High	Low (%)	High (%)
	103 (100)	38 (36.9)	32 (31.1)	33 (32.0)	85 (82.5)	18 (17.5)
Region
Europe	39 (37.9)	15 (39.5)	4 (12.5)	20 (60.6)	27 (31.8)	12 (66.7)
Asia	36 (35.0)	10 (26.3)	15 (46.9)	11 (33.3)	32 (37.6)	4 (22.2)
Africa	14 (13.6)	4 (10.5)	9 (28.1)	1 (3.0)	14 (16.5)	0 (0.0)
Latin America	4 (3.9)	1 (2.6)	2 (6.3)	1 (3.0)	4 (4.7)	0 (0.0)
North America	7 (6.8)	6 (15.8)	1 (3.1)	0 (0.0)	6 (7.1)	1 (5.6)
Oceania	3 (2.9)	2 (5.3)	1 (3.1)	0 (0.0)	2 (2.4)	1 (5.6)
HDI
Low	7 (6.8)	4 (10.5)	3 (9.4)	0 (0.0)	7 (8.2)	0 (0.0)
Medium	20 (19.4)	6 (15.8)	11 (34.4)	3 (9.1)	20 (23.5)	0 (0.0)
High	23 (22.3)	10 (26.3)	10 (31.3)	3 (9.1)	22 (25.9)	1 (5.6)
Very high	53 (51.5)	18 (47.4)	8 (25.0)	27 (81.8)	36 (42.4)	17 (94.4)
CL
Not free	48 (46.6)	16 (42.1)	23 (71.9)	9 (27.3)	45 (52.9)	3 (16.7)
Free	55 (53.4)	22 (57.9)	9 (28.1)	24 (72.7)	40 (47.1)	15 (83.3)
GE
Poor	46 (44.7)	20 (52.6)	20 (62.5)	6 (18.2)	46 (54.1)	0 (0.0)
Fair	57 (55.3)	18 (47.4)	12 (37.5)	27 (81.8)	39 (45.9)	18 (100)
UHC
Low	68 (66.0)	27 (71.1)	25 (78.1)	16 (48.5)	66 (77.6)	2 (11.1)
High	35 (34.0)	11 (28.9)	7 (21.9)	17 (51.5)	19 (22.4)	16 (88.9)
HWD
Low	50 (48.5)	18 (47.4)	24 (75.0)	8 (24.2)	48 (56.5)	2 (11.1)
High	53 (51.5)	20 (52.6)	8 (25.0)	25 (75.8)	37 (43.5)	16 (88.9)
SPAR
Low	59 (57.3)	23 (60.5)	23 (71.9)	13 (39.4)	57 (67.1)	2 (11.1)
High	44 (42.7)	15 (39.5)	9 (28.1)	20 (60.6)	28 (32.9)	16 (88.9)

Italics represent the majority values for each variable.

AMR antimicrobial resistance, HDI human development index, CL civil liberties, GE government effectiveness, UHC universal health coverage, HWD health workers density, SPAR state party self-assessment annual report.

In terms of regional distribution, low and high AMR Awareness levels were most prevalent in Europe (Low Awareness = 39.5%; High Awareness = 60.6%), while countries with medium AMR Awareness were primarily located in Asia. Among countries with low AMR Awareness, the majority were characterized by a very high HDI (47.4%), Full Freedom status (57.9%), Poor GE (52.6%), Low UHC (71.1%), High HWD (52.6%), and Low SPAR Scores (60.5%). Meanwhile, among countries with low AMR Capacities, most had a Very High HDI (42.4%), Not Free status (52.9%), Poor GE (54.1%), Low UHC (77.6%), Low HWD (56.5%), and Low SPAR Scores (67.1%). Chi-square analysis indicates significant associations between the HDI, CL, GE, UHC, HWD, and SPAR scores of these countries with their levels of AMR awareness and capacities (p < 0.05).

Changes in AMR capacities from 2017 to 2020 by countries’ AMR awareness

Figure 1 illustrates changes in countries’ Antimicrobial Resistance (AMR) capacities from 2017 to 2020. The figure shows that countries with medium AMR capacities experienced a significant annual increase [2017–2018: 48.26 to 52.69, SD = 8.47, p < 0.05; 2018–2019: 52.69 to 57.55, SD = 5.18, p < 0.05; 2019–2020: 57.55 to 61.51, SD = 5.83, p < 0.05]. In contrast, countries with low AMR capacities did not show a significant increase in 2020 [2017–2018: 52.31 to 56.36, SD = 7.71, p < 0.05; 2018–2019: 56.35 to 58.76, SD = 6.65, p < 0.05; 2019–2020: 58.76 to 60.29, SD = 6.34, p > 0.05].

Fig. 1.

Changes in antimicrobial resistance (AMR) capacities from 2017 to 2020 by countries’ AMR awareness.

For countries with high AMR capacities, a significant increase was observed only in 2018 [2017–2018: 64.97 to 72.85, SD = 6.85, p < 0.05; 2018–2019: 72.85 to 74.26, SD = 4.71, p > 0.05; 2019–2020: 74.26 to 75.61, SD = 5.49, p > 0.05].

Comparison of AMR rates for E. coli and MRSA by countries’ AMR awareness and capacities (2017–2020)

Figure 2 compares the average AMR rates for Escherichia coli (E. coli) and Methicillin-resistant Staphylococcus aureus (MRSA) across countries with different levels of AMR awareness and capacities. According to AMR awareness levels, the figure shows that countries with high AMR awareness have lower average AMR rates than those with medium awareness [E. coli = 28.36 (SD = 21.01) vs. 52.82 (SD = 27.77); MRSA = 22.61 (SD = 17.61) vs. 42.69 (SD = 27.32); p < 0.05)]. Notably, none of the countries classified as having low AMR awareness monitored their AMR rates for these pathogens. Specifically, only countries with medium or high AMR awareness reported rates for E. coli and MRSA in this study. Additionally, when examining AMR capacities, the figure demonstrates that countries with high AMR capacities had significantly lower average AMR rates than those with low capacities [E. coli = 13.64 (SD = 7.69) vs. 47.05 (SD = 26.39); MRSA = 13.71 (SD = 13.47) vs. 36.81 (SD = 24.71); p < 0.05].

Fig. 2.

Comparison of antimicrobial resistance (AMR) rates for Escherichia coli and methicillin-resistant Staphylococcus aureus (MRSA) by countries’ AMR awareness and capacities (2017–2020).

Factors associated with countries’ AMR capacities

Moreover, in further analysis, we developed 3 Logistic regression models (Table 2) to identify the factors that are significantly associated with the countries’ capacities. Among the independent variables, we did not find any multicollinearity. We found that countries’ AMR awareness is not significantly associated with their AMR capacities (Model 1, OR = 2.96, p > 0.05). Our 3 Models identified that UHC is the only factor that significantly associated with countries AMR capacities. The models revealed that the odds of countries to have Low AMR capacities is higher in the countries with Poor UHC (Model (1) OR = 10.49; Model 2, OR = 8.96; Model 3, OR = 12.92; p < 0.05). Additionally, our models also showed that the odds of having Low AMR capacities is higher in the countries with Low SPAR scores (Model 1, OR = 5.66; Model 2, OR = 5.51; p > 0.05). However, the association between countries’ SPAR scores and their AMR capacities is not significantly associated (Model (2) OR = 5.51; Model 3, OR = 5.66; p > 0.05).

Table 2.

Factors associated with countries’ AMR capacities.

	AMR capacities (low)
	MODEL-1			MODEL-2			MODEL-3			VIF
	OR	Sig	95% CI	OR	Sig	95% CI	OR	Sig	95% CI
HDI
Low	1.72	1.00	0.00; –	0.45	1.00	0.00; –	0.24	1.00	0.00; –	2.75
Medium	1.85 × 10−7	0.99	0.00; –	1.62 × 10−7	0.99	0.00; –	1.65 × 10−7	0.99	0.00; –
High	1.35	0.85	0.06; 30.05	0.86	0.94	0.02; 32.56	0.56	0.75	0.02; 19.89
Very high	Ref	Ref		Ref	Ref		Ref	Ref
CL
Not free	0.54	0.54	0.07; 3.95	0.66	0.70	0.08; 5.73	0.68	0.75	0.06; 7.24	1.65
Free	Ref	Ref		Ref	Ref		Ref	Ref
GE
Poor	1.31 × 10−8	0.99	0.00; –	1.82 × 10−8	0.99	0.00; –	2.34 × 10−8	0.99	0.00; –	2.31
Fair	Ref	Ref		Ref	Ref		Ref	Ref
UHC
Poor	12.92	0.01	1.93; 86.52	8.96	0.04	1.11; 72.43	10.49	0.04	1.15; 95.94	1.79
Fair	Ref	Ref		Ref	Ref		Ref	Ref
HWD
Lack	0.79	0.85	0.08; 8.30	0.68	0.77	0.05; 8.75	0.73	0.81	0.06; 9.30	2.07
Sufficient	Ref	Ref		Ref	Ref		Ref	Ref
SPAR
Low	–	–		5.51	0.05	0.97; 31.39	5.66	0.06	0.94; 34.05	1.51
High	–	–		Ref	Ref		Ref	Ref
AMR awareness
Low	–	–		–	–		2.56	0.27	0.48; 13.55	1.10
Medium	–	–		–	–		0.99	0.99	0.15; 6.42
High	–	–		–	–		Ref	Ref

AMR antimicrobial resistance, HDI human development index, CL civil liberties, GE government effectiveness, UHC universal health coverage, HWD health workers density, SPAR state party self-assessment annual report.

In Table 2, entries marked with “–” denote variables that were excluded from specific models. Model 3 includes all factors, Model 2 excludes AMR awareness, and Model 1 considers only the independent variables, excluding both AMR awareness and SPAR scores.

Discussion

Our research indicates that, among the 103 countries analyzed, a significant majority still lack adequate awareness and capacity to address the threat of AMR. Notably, even countries with substantial resources and high development levels are predominantly in this group. For instance, despite their high HDI²⁰, such as Canada, Iceland, and New Zealand, these high-income countries face AMR threats similarly to low-resource countries like Benin, Guinea, and Sierra Leone.

The situation is even more concerning for countries with limited resources and lower development levels. Out of nearly 200 WHO member countries, our study was only able to analyze 103. Among these, the majority (53 countries) had Very High HDI levels²⁰. Conversely, only 27 countries with low or medium HDI (7 with low HDI and 20 with medium HDI) were included from the 75 countries in these categories²⁰. Given that this study determines a country’s awareness level based on whether they regularly report AMR-related data, we infer that countries not reporting any data are at the highest risk from AMR threats, potentially even more so than those classified as having low awareness.

It’s expected that countries with strong awareness and capacities to address AMR threats should exhibit lower AMR rates. However, this study found no significant difference in AMR rates for E. coli and MRSA among countries with varying levels of awareness and capacity. This lack of differentiation is likely because most countries in this study were categorized as having low awareness and capacities to address AMR threats.

In this study, countries classified as having low awareness were those lacking in reporting data on AMR rates for E. coli and MRSA, and also reporting their TrACSS scores. The fact that the majority of countries in this study fall into this low-awareness category is particularly concerning, given that monitoring AMR rates for these pathogens is a crucial indicator^21,22 for achieving the Sustainable Development Goals (SDGs) by 2030.

Literature^23,24 suggests that drug misuse and abuse are major contributors to the incidence of AMR. Therefore, monitoring antimicrobial use is crucial for identifying and mitigating the acceleration of AMR incidents²⁵. In this context, healthcare professionals play a vital role²⁶. However, our study found that the availability of doctors is not a significant factor related to a country’s capacity to address AMR threats; rather, UHC is. Our study indicates that countries with low AMR capacities are at least nine times more likely to be found in countries with low UHC coverage. This implies that both the quantity and quality of other healthcare workers and services, not just the number of doctors, are critical in determining countries’ capacities to prevent and manage AMR threats. A report²⁷ highlighted that nurses and midwives are the guardians of the health system and play crucial role as in preventing the emergence and spread of AMR through antibiotic stewardship and infection prevention and control programs.

Furthermore, considering that every WHO member state is required to report their IHR core capacities through SPAR annually¹⁶, we also analyzed whether SPAR scores reflect countries’ capacities to address AMR threats. It’s expected that countries with high SPAR scores (80 and above) would be better prepared to handle health issues, including those with pandemic potential²⁸. Our study found that better SPAR scores are indeed associated with stronger AMR mitigation capacities, although this relationship was not statistically significant.

Next, we hypothesize that the lack of a significant relationship may be due to several factors, which also relate to the limitations of this research. Firstly, the SPAR scores used in this study were average scores from various indicators related to infectious disease control, which may affect the study’s results. Secondly, SPAR does not specifically or sufficiently monitor a country’s capacity to address AMR threats. In SPAR, the issue of AMR is solely highlighted in capacity which focuses on Infection Prevention and Control (IPC) from a human health perspective, while AMR is generally understood to be an interdisciplinary health issue. A more explicit monitoring of AMR capacities is conducted through the Joint External Evaluation (JEE)^29,30. JEE reports are organized into four areas: ‘prevent,’ ‘detect,’ ‘respond,’ and ‘International Health Regulations (IHR) related hazards and points of entry,’ with 19 sub-areas within these. One of these sub-areas is ‘AMR,’ which is further divided into four categories to better identify a country’s AMR indicators^29,30.

Limitation

In the context of study limitation, it lies in the data used, which primarily comes from the human health perspective. It is increasingly recognized that AMR is a cross-sectoral issue involving not only human health but also animal health and the environment³¹. Thus, the magnitude of the problem shown by this research reflects the threat from a human health perspective. Nonetheless, this research highlights the current gaps in efforts to prevent and tackle AMR threats.

Conclusion

This study highlights a critical global issue: a significant number of countries, regardless of their development status, lack sufficient awareness and capacity to combat AMR. The findings reveal that even high-resource countries face similar AMR threats to those in low-resource countries. The lack of differentiation in AMR rates between countries with varying levels of awareness and capacity underscores the pervasive nature of AMR threat.

Moreover, the study emphasizes the importance of UHC in enhancing countries’ capacities to manage AMR threats, more so than merely the availability of doctors. The association between better SPAR scores and stronger AMR mitigation capacities, although not statistically significant, suggests the need for more targeted and interdisciplinary monitoring approaches, such as those offered by the JEE.

While this research provides valuable insights, it also underscores the gaps in current global efforts to prevent and tackle AMR threats, particularly the need for a more comprehensive approach that includes data from animal health and environmental sectors. Addressing these gaps is essential for strengthening global AMR capacities and achieving the SDGs by 2030.

Methods

Study design and data sources

This cross-sectional study aimed to explore factors associated with antimicrobial resistance (AMR) awareness and capacities across 103 countries. Data were collected from national health reports, global databases, and country-specific assessments for the years 2017 to 2020. The inclusion of these 103 countries was based on their availability of complete data for all variables used in the study, ensuring robust analysis. The four-year timeframe was chosen because one key variable was only accessible from 2017, and data for all countries were available up to 2020. Although some countries had data extending to 2021, including it would have reduced the overall number of countries analyzed, potentially compromising the study’s insights and conclusions. Therefore, the selected timeframe and country criteria were essential for maintaining the integrity of the research.

Dependent variables

Countries’ AMR awareness

In this study, countries’ awareness to address AMR is classified into three levels: High, Medium, and Low. This classification is based on the consistency and comprehensiveness of countries in reporting data on AMR rates^21,22 and TrACSS scores^12,15. Referring to Sustainable Development Goals Indicators 3.d.2, this study only focus on the comprehensiveness of countries in reporting data on AMR rate on E. coli against third-generation cephalosporins²² and Methicillin-Resistant Staphylococcus aureus (MRSA)²¹.

Countries classified as having High Awareness are those that consistently and comprehensively reported both AMR rate data and TrACSS scores for four consecutive years (2017 to 2020). On the other hand, countries possessing AMR rate data and TrACSS scores but not reporting them consistently are categorized as Medium Awareness. Low Awareness countries are those lacking AMR data and displaying inconsistent reporting of their TrACSS scores during the same period. The data pertaining to these variables were sourced from the WHO-GLASS³², ensuring a reliable foundation for our classification.

Countries’ AMR capacities

Countries’ AMR capacities refer to countries’ ability to implement their national action plans to combat AMR¹² which is measured using TrACSS scores. It is generated from the self-assessment of countries in implementing their national action plans to address AMR¹². TrACSS encompasses various capacities that are reported on an annual basis. However, the specific capacities reported may vary slightly each year. For this study, only the capacities that were consistently reported from 2017 to 2020 were included for analysis. A total of 12 questions were summarized in Table 1 to evaluate countries’ capacities in addressing AMR. The original scoring system ranged from A to E; however, for this study, following the scoring system in SPAR, the scores were converted to a scale of 0 to 100. The lowest score, A (0 to 20), represents the lowest capacity, while the highest score, E (80 to 100), indicates the highest capacity. Countries with an average score of 80 and above are considered to have a high capacity in addressing AMR, while countries with an average score below 80 are classified as having a low capacity. This variable used data from 2017 to 2021 that was collected from Global Database for Tracking AMR¹⁵, a website compiled by WHO, FAO, WOAH, and UNEP.

Independent variables

Referring to the SYSRA Framework³³, this research incorporates the Human Development Index (HDI), Freedom, Government Effectiveness (GE), Universal Health Coverage (UHC), Health Workers Density (HWD), and SPAR scores of the countries as the independent variables.

Human development index (HDI)

HDI data were sourced from the United Nations Development Program (UNDP)²⁰. Countries were categorized into four groups—Low, Medium, High, and Very High—according to the UNDP’s classification criteria²⁰.

Freedom

For the Freedom variable, it’s generated from Civil Liberties (CL) scores ranging from 1 (Free) to 7 (Not Free) that were obtained from the Freedom House website^34,35. The mean CL score across the 103 countries was used to determine the classification threshold, dividing countries into Free and Not Free categories based on this cutoff.

Universal health coverage (UHC)

Next, UHC data acquired from the World Health Organization (WHO)³⁶, were classified using the mean score across all countries. Those with UHC coverage exceeding this mean were classified as High UHC, while countries falling below this threshold were designated as Low UHC.

Government effectiveness (GE)

GE scores, ranging from − 2.5 (Poor) to 2.5 (Fair), were obtained from the Worldwide Governance Indicators (WGI) website³⁷. A zero score was employed as the cutoff for classification, distinguishing countries with scores above zero as Fair GE and those with scores at or below zero as Poor GE.

Healthcare workforce density (HWD)

HWD data were obtained from the WHO³⁸, with a cutoff point of 23 based on WHO recommendations. Countries with a HWD of 23 or more were classified as having Sufficient HWD³⁹, whereas those below this threshold were categorized as having a Lack of HWD.

SPAR scores

In the context of SPAR scores of the countries in this study, it is generated from calculating the average scores of 11 of 13 IHR capacities reported through SPAR¹⁴. This study excluding capacities related to Chemical contamination and Radiation events, as it focuses specifically on infectious diseases. Each SPAR capacity was assigned a score ranging from 20 (lowest) to 100 (highest) that was determined by averaging the scores of all indicators within each capacity. A score of 20 indicates that the capacity has not yet been established in a country, while a score of 100 represents sustainable implementation of the capacity. Data for this study were collected from the WHO-SPAR website¹⁴, and the analysis was conducted using data spanning from 2017 to 2021. In this study, countries with SPAR scores of 80 and above are categorized as having High SPAR capacities, indicating a strong level of preparedness and implementation of the IHR core capacities. Conversely, countries with SPAR scores below 80 are classified as having Low SPAR capacities, suggesting a need for further improvement in their preparedness and implementation efforts.

Statistical analysis

Descriptive statistics were computed to summarize the distribution of the variables across the countries. Chi-square tests were performed to evaluate the significant associations between AMR awareness and AMR capacities with the independent variables.

Next, changes in AMR capacities from 2017 to 2020 relative to AMR awareness levels were analyzed, as shown in Fig. 1. Meanwhile, the comparison of AMR rates for Escherichia coli (E. coli) and Methicillin-resistant Staphylococcus aureus (MRSA) across different AMR awareness and capacities categories was conducted, with results displayed in Fig. 2. Mean AMR rates were compared using statistical tests.

Furthermore, three logistic regression models were developed to identify factors associated with AMR capacities. Model 1 included AMR awareness, HDI, and Freedom as predictors. Model 2 expanded on this by adding GE and UHC. Model 3 further included HWD and SPAR scores. Odds ratios (OR) and significance levels (p-values) for each variable across the models are detailed in Table 2. All statistical analyses were performed using R version 4.3.1 with a significance level set at p < 0.05.

Abbreviations

AIDS

Acquired immunodeficiency syndrome

AMR

Antimicrobial resistance

CI

Confidence interval

CL

Civil liberties

FAO

Food and Agriculture Organization

GLASS

Global antimicrobial resistance and use surveillance system

GE

Government effectiveness

HWD

Health workers density

HDI

Human development index

HIV

Human immunodeficiency virus

IPC

Infection prevention and control

IHR

International health regulations

JEE

Joint external evaluation

MRSA

Methicillin-resistant Staphylococcus aureus

OR

Odds ratio

SD

Standard deviation

SDGs

Sustainable development goals

SPAR

State party self-assessment annual report

TrACSS

Tripartite AMR country self-assessment survey

UNDP

United Nations Development Program

UHC

Universal health coverage

WHO

World Health Organization

WOAH/OIE

World Organization for Animal Health

Author contributions

FBS and FJT contributed equally to the study’s conception, design, data analysis, and manuscript drafting. Additionally, both authors were involved in data interpretation, manuscript revisions, and have approved the final version of the manuscript.

Funding

This research received no specific grant from any funding agency in the public, commercial, or not-for-profit sectors.

Data availability

The datasets used and/or analyzed during the current study are available from the corresponding author on reasonable request.

Declarations

Competing interests

The authors declare no competing interests.

Ethics approval and consent to participate

Not applicable as this study did not involve direct use of human subjects and utilized publicly available data.