1. INTRODUCTION

Antimicrobial resistance (AMR) represents one of the most urgent and complex challenges in global health today. It threatens the effectiveness of antibiotics that have been essential for decades in treating common infections, thereby increasing morbidity, mortality, and healthcare costs worldwide. Recognizing this growing threat, the World Health Organization (WHO) established the Global Antimicrobial Resistance and Use Surveillance System (GLASS) to systematically collect and monitor AMR data from participating countries.

This report explores WHO GLASS data from 2017 to 2020, focusing on trends in pathogen prevalence and antibiotic resistance across countries and regions. The analysis centers on key bacterial pathogens — Escherichia coli, Klebsiella pneumoniae, Salmonella, Acinetobacter, and Streptococcus pneumoniae — which are of major clinical and public health significance.

As a data analyst and aspiring researcher, my goal is not only to present insights from the data but also to demonstrate the analytical process behind them. Therefore, each section of this report is supported by corresponding R code blocks, showing the exact steps taken to clean, analyze, and visualize the data. This approach ensures transparency, reproducibility, and a stronger connection between the evidence and the conclusions drawn.

By integrating data-driven insights with reproducible analytical methods, this report contributes to understanding global antimicrobial resistance patterns, identifying high-risk pathogens and regions, and supporting informed decision-making for public health interventions and antimicrobial stewardship.

# Load libraries
library(dplyr)
## 
## Attaching package: 'dplyr'
## The following objects are masked from 'package:stats':
## 
##     filter, lag
## The following objects are masked from 'package:base':
## 
##     intersect, setdiff, setequal, union
library(tidyr)
library(lubridate)
## 
## Attaching package: 'lubridate'
## The following objects are masked from 'package:base':
## 
##     date, intersect, setdiff, union
library(ggplot2)
library(knitr)

WHO_GLASS <- read.csv("C:\\Users\\godfr\\OneDrive\\Documents\\WHO_GLASS.csv", stringsAsFactors = FALSE)



  1. KEY STATISTICAL SUMMARY OF RESISTANCE RATES

The analysis of antimicrobial resistance across all countries and pathogens reveals important insights into global patterns:

Mean (Average) Resistance Rate: The average resistance rate is 29%, meaning that on average, nearly 3 out of 10 infections may not respond to commonly used antibiotics. This gives a general sense of the global burden of antimicrobial resistance.

Median Resistance Rate: The median resistance rate is 20%, which indicates that half of the reported resistance rates are below 20% and half are above 20%. The difference between the mean and median suggests that some countries report extremely high resistance, which pulls the average upward.

Standard Deviation: The standard deviation of 28 highlights that resistance rates vary widely across countries and pathogens. A high variation like this indicates that while some regions have relatively low resistance, others face very high rates, emphasizing the uneven global distribution of antimicrobial resistance.

Assessment: These statistical measures provide a more nuanced understanding of AMR than averages alone. The median shows typical resistance levels, while the standard deviation underscores the variability, helping policymakers and healthcare providers identify regions where resistance is especially high and may require targeted interventions.

Resistant_Rate_mean<- round(mean(WHO_GLASS$PercentResistant))

cat(" 1. Mean_resistant_rate =", Resistant_Rate_mean, "%")
##  1. Mean_resistant_rate = 29 %
Resistant_Rate_median<- round(median(WHO_GLASS$PercentResistant))

cat(" 2. Median_resistant_rate =", Resistant_Rate_median, "%")
##  2. Median_resistant_rate = 20 %
Resistant_Rate_sd<- round(sd(WHO_GLASS$PercentResistant))

cat(" 3. sd_resistant_rate =", Resistant_Rate_sd)
##  3. sd_resistant_rate = 28



  1. TOP FIVE MOST REPORTED PATHOGENS (2017–2020)

The data reveal that the top five bacterial pathogens reported globally from 2017 to 2020 are Escherichia coli, Klebsiella pneumoniae, Salmonella, Acinetobacter, and Streptococcus pneumoniae. Among these, E. coli and K. pneumoniae dominate, accounting for a large proportion of reported isolates.

Expanded Assessment: This finding emphasizes that Gram-negative bacteria continue to drive much of the global AMR challenge. Both E. coli and K. pneumoniae are known causes of urinary tract, respiratory, and bloodstream infections. Their high frequency in surveillance data signals their widespread distribution and their significant clinical burden. The data also reflect the strong representation of pathogens commonly monitored in both community and hospital settings, underscoring the urgent need to strengthen laboratory capacity for early detection and response.

Top_5_pathogens <- WHO_GLASS %>%
  group_by(PathogenName) %>%
  summarise(count = n()) %>%
  arrange(desc(count)) %>%
  head(5)

print(as.data.frame(Top_5_pathogens), rows.name= FALSE)
##               PathogenName count
## 1         Escherichia coli  2353
## 2    Klebsiella pneumoniae  1945
## 3               Salmonella   758
## 4            Acinetobacter   561
## 5 Streptococcus pneumoniae   253

Table 1: Top 5 Most Reported Pathogens (2017–2020)



  1. TOP FIVE(5) ANTIBIOTICS FREQUENTLY TESTED

The analysis shows that Ciprofloxacin, Imipenem, Ceftriaxone, Meropenem, and Ceftazidime are the top five antibiotics most frequently tested across countries. Ciprofloxacin, a fluoroquinolone, stands out as the most commonly tested agent globally.

Expanded Assessment: The frequent testing of these antibiotics indicates their clinical relevance and widespread use in empirical and targeted therapy. The inclusion of carbapenems (Imipenem and Meropenem) highlights concern about last-line antibiotic effectiveness. This pattern reflects both the reliance on these drugs and the growing awareness of resistance to them. The emphasis on broad-spectrum agents suggests a reactive clinical approach where infections are treated with powerful antibiotics, a practice that may contribute to accelerated resistance if not carefully managed.

Antibiotic_frequency <- WHO_GLASS %>%
  group_by(AbTargets) %>%
  summarise(Total_tested = n()) %>%
  arrange(desc(Total_tested)) %>%
head(5)

print(as.data.frame(Antibiotic_frequency), row.names = (FALSE))
##      AbTargets Total_tested
##  Ciprofloxacin          707
##       Imipenem          644
##    Ceftriaxone          616
##      Meropenem          600
##    Ceftazidime          559

Table 2: Top 5 Most Frequently Tested Antibiotics (2017-2020)



  1. AVERAGE RESISTANCE RATE OF ESCHERICHIA COLI

Across countries, Escherichia coli shows an average resistance rate of 31.2%. This means that approximately one in every three E. coli infections may not respond effectively to commonly used antibiotics.

Expanded Assessment: This is a significant indicator of global concern. E. coli is one of the most frequent causes of community-acquired and hospital infections, and such a high resistance rate could lead to more treatment failures, prolonged illness, and increased healthcare costs. The finding reinforces the need for global antibiotic stewardship programs focusing on rational prescribing and the promotion of infection prevention measures to limit the spread of resistant strains.chia coli has an average resistance rate of 31.2% across countries. This suggests that nearly one in three E. coli infections may not respond to commonly used antibiotics, emphasizing the global challenge of antimicrobial resistance and the urgent need for continued surveillance and effective stewardship programs.

Average_resistance_E_coli <- WHO_GLASS %>%
  filter(PathogenName == "Escherichia coli") %>%
  summarise(average = round(mean(PercentResistant, na.rm = TRUE), 1))

cat("Average resistance rate =", Average_resistance_E_coli$average, "%")
## Average resistance rate = 31.2 %



  1. TREND IN RESISTANCE OF KLEBSIELLA PNEUMONIAE (2017–2020)

The resistance rate of Klebsiella pneumoniae increased gradually from 33% in 2017–2018 to 35% in 2019 and 36% in 2020.

Expanded Assessment: The steady upward trend suggests that resistance in K. pneumoniae is not an isolated event but a sustained and progressive issue. This bacterium is known for its ability to acquire resistance genes rapidly, including those conferring carbapenem resistance. The gradual increase over time implies that control measures have not yet significantly curbed its spread. Such patterns call for continuous surveillance, infection control enforcement, and the development of novel therapeutic agents.

Resistant_rate_time <- WHO_GLASS %>%
  filter(PathogenName == "Klebsiella pneumoniae") %>%
  group_by(Year) %>%
  summarise(resistant_rate = round(mean(PercentResistant, na.rm= TRUE)))

ggplot(Resistant_rate_time, aes(x= Year, y= resistant_rate)) +
  geom_line() +
  labs(title="Trend In Resistance Of Klebsiella Pneumoniae (2017–2020)")
Figure 1: Trend of Resistance in Klebsiella pneumoniae (2017–2020)

Figure 1: Trend of Resistance in Klebsiella pneumoniae (2017–2020)



  1. COUNTRY WITH THE HIGHEST RESISTANT E. COLI ISOLATES

The United Kingdom of Great Britain and Northern Ireland recorded the highest number of resistant E. coli isolates (380,972).

Expanded Assessment: This high figure likely reflects both robust surveillance systems and a high prevalence of resistance within clinical isolates. The UK’s comprehensive reporting through GLASS may contribute to higher recorded counts, but it also signals the country’s significant AMR burden. This reinforces the need for continued investment in antimicrobial stewardship, public awareness, and infection control programs within healthcare and community settings.

Highest_resistant_Ecoli_country <- WHO_GLASS %>%
  filter(PathogenName == "Escherichia coli") %>%
  group_by(CountryTerritoryArea) %>%
  summarise(resistant_count = max(Resistant)) %>%
  arrange(desc(resistant_count)) %>%
  head(1)

print(as.data.frame(Highest_resistant_Ecoli_country), row.names = TRUE) 
##                                   CountryTerritoryArea resistant_count
## 1 United Kingdom of Great Britain and Northern Ireland          380972



  1. HEATMAP OF PATHOGEN–ANTIBIOTIC RESISTANCE

The heatmap visualizes the mean resistance percentages of different pathogen–antibiotic pairs. Lighter colors denote higher resistance rates. Notably, E. coli shows high resistance to Ciprofloxacin, Ceftriaxone, and Ceftazidime, while K. pneumoniae displays elevated resistance to multiple cephalosporins and carbapenems.

Expanded Assessment: This multidimensional view of resistance highlights cross-resistance patterns, suggesting that certain antibiotics may no longer be effective against major pathogens. The high resistance in Acinetobacter and Staphylococcus aureus further underscores the multidrug-resistant organism (MDRO) challenge. Heatmaps such as this can guide antibiotic selection in clinical practice and help policymakers identify priority drug–pathogen combinations requiring urgent attention.

ggplot(WHO_GLASS, aes(x = PathogenName, y = AbTargets, fill = PercentResistant)) +
  geom_tile() +
  labs(title = "A Heatmap of Pathogens & Antibitoics resistance", x = "Pathogens", y = "Anitbiotics") +
  theme(axis.text.x = element_text(angle = 30, hjust = 1))
Figure 2:A Heatmap of Pathogens & Antibitoics resistance

Figure 2:A Heatmap of Pathogens & Antibitoics resistance



  1. TIME TREND OF E. COLI RESISTANCE TO CIPROFLOXACIN

The time-trend analysis of E. coli resistance to Ciprofloxacin shows fluctuations across regions, indicating variable but persistent resistance levels over time.

Expanded Assessment: Resistance to Ciprofloxacin — a widely used first-line antibiotic — signals a major threat to community health. Persistent resistance trends imply that existing interventions have not significantly reversed the trend. This calls for improved surveillance integration and regional cooperation to share data and implement coordinated response strategies.

WHO_GLASS %>%
  filter(PathogenName == "Escherichia coli") %>%
  group_by(Year) %>%
  summarise(resistant_count = round(mean(Resistant))) %>%
  ggplot(aes(x = Year, y = resistant_count, color = "red" )) +
  geom_line()+
  labs(title="Time Trend of E.coli resistance to Ciprofloxacin")
Figure 3:Time Trend of E.coli resistance to Ciprofloxacin

Figure 3:Time Trend of E.coli resistance to Ciprofloxacin



  1. GLOBAL MAP OF CIPROFLOXACIN RESISTANCE IN SALMONELLA

The choropleth map displays global variation in Salmonella resistance to Ciprofloxacin, with darker regions representing higher resistance levels.

Expanded Assessment: This spatial visualization reveals clear geographical disparities in resistance distribution. Regions with darker shades may correspond to countries with overuse or misuse of antibiotics in both humans and livestock. It also highlights potential surveillance gaps where data are incomplete or absent. Identifying such hotspots is essential for prioritizing regional interventions and tailoring national action plans.

World <- WHO_GLASS %>%
  filter(PathogenName == "Salmonella", AbTargets == "Ciprofloxacin") %>%
  group_by(CountryTerritoryArea) %>%
  summarise(resistant_rate = round(mean(PercentResistant, na.rm= TRUE)))

world_map <- map_data("world")

Joined_data <- (left_join(World, world_map, by = c("CountryTerritoryArea" = "region"), keep = TRUE)) 


ggplot(Joined_data, aes(x = long, y = lat, group = group, fill = resistant_rate)) +
  geom_polygon(color = "white") +
  scale_fill_gradient(low = "lightyellow", high = "darkred", na.value = "grey90") +
  theme_minimal() +
  labs(
    title = "Average Ciprofloxacin Resistance in Salmonella",
    fill = "Resistance (%)"
  )
Figure 4:Average Ciprofloxacin Resistance in Salmonella

Figure 4:Average Ciprofloxacin Resistance in Salmonella



  1. TOP COUNTRIES BY OVERALL RESISTANCE

The bar chart shows the top countries with the highest recorded resistance across bacteria–antibiotic pairs. The United Kingdom, followed by Japan and Germany, report the greatest numbers of resistant isolates.

Expanded Assessment: These results suggest that high-income countries with advanced diagnostic systems may capture more data, but they also face substantial AMR burdens due to complex healthcare networks and antibiotic usage patterns. The findings emphasize that AMR is not limited to developing regions — it is a universal challenge requiring context-specific strategies.

Overall_resistance_countries <- WHO_GLASS %>%
  group_by(CountryTerritoryArea) %>%
  summarise(resistant_count = max(Resistant)) %>%
  arrange(desc(resistant_count)) %>%
  head(10)


ggplot(Overall_resistance_countries, aes(x = reorder(CountryTerritoryArea, -resistant_count), y = resistant_count)) +
  geom_bar(stat = "identity", fill = "steelblue") +   # use your precomputed values
  theme_minimal() +
  labs(
    title = "Top  Countries by Overall Resistance Rate",
    x = "Country",
    y = "Resistance Count"
  ) +
  theme(axis.text.x = element_text(angle = 20, hjust = 1)) +
   scale_y_continuous(labels = scales::comma) # tilt x labels for readability
Figure 4:Top Countries by Overall Resistance Rate

Figure 4:Top Countries by Overall Resistance Rate



  1. MOST RESISTANT BACTERIA–ANTIBIOTIC PAIRS

The analysis of bacteria–antibiotic pairs identifies Escherichia coli resistant to Ampicillin, Levofloxacin, and Ciprofloxacin as the most concerning combinations. Staphylococcus aureus resistance to Meticillin also features among the top cases.

Expanded Assessment: This reflects classic resistance mechanisms such as β-lactamase production and fluoroquinolone resistance, which limit the effectiveness of essential antibiotics. The prominence of E. coli across multiple resistant pairs underscores its role as a sentinel organism in AMR surveillance. These results highlight the urgent need for alternative treatment regimens and continued research into new antimicrobial compounds.

Bacteria_Antibiotic_Pairs <- WHO_GLASS %>%
  group_by(PathogenName, AbTargets) %>%
  summarise(resistant_count = sum(Resistant, na.rm = TRUE), .groups = "drop") %>%
  arrange(desc(resistant_count)) %>%
  head(5)

print(as.data.frame(Bacteria_Antibiotic_Pairs))
##       PathogenName                     AbTargets resistant_count
## 1 Escherichia coli                    Ampicillin         2754016
## 2 Escherichia coli                 Ciprofloxacin         1162748
## 3 Escherichia coli                  Levofloxacin          751976
## 4 Escherichia coli Trimethoprim/sulfamethoxazole          650024
## 5 Escherichia coli                    Cefotaxime          581798

Table 3: Top 5 Most most resistant bacteria-antibiotic pairs



  1. TOP FIVE COUNTRIES WITH HIGHEST RESISTANCE LEVELS

The top five countries — United Kingdom, Japan, Germany, and two others in Europe and Asia — show the highest concentrations of resistant isolates.

Expanded Assessment: This clustering pattern suggests that AMR is most pronounced in regions with high antibiotic consumption, dense healthcare utilization, and strong reporting systems. These findings support the need for transnational initiatives such as data-sharing frameworks, joint research, and standardized stewardship policies to reduce the global AMR load.

Overall_resistance_countries <- WHO_GLASS %>%
  group_by(CountryTerritoryArea) %>%
  summarise(resistant_count = max(Resistant)) %>%
  arrange(desc(resistant_count)) %>%
  head(5)

print(as.data.frame(Overall_resistance_countries))
##                                   CountryTerritoryArea resistant_count
## 1 United Kingdom of Great Britain and Northern Ireland          380972
## 2                                                Japan          165534
## 3                                              Germany          123927
## 4                                               Sweden           79286
## 5                                          Switzerland           27237

Table 4: Top 5 countries with highest resistant levels.



  1. RESULTS IMPLICATIONS AND CONCLUSION

The analysis of WHO GLASS data from 2017–2020 provides strong evidence that antimicrobial resistance is widespread, persistent, and unevenly distributed across regions and pathogens. Escherichia coli and Klebsiella pneumoniae remain dominant global threats, with rising resistance to key antibiotics such as Ciprofloxacin, Ceftriaxone, and Carbapenems.

Countries like the United Kingdom, Japan, and Germany report high resistance levels, reflecting both extensive surveillance and significant local burdens. The combination of rising resistance trends, multidrug resistance, and strong regional disparities underscores the urgency of a unified global response.

From the data, it is clear that:

  1. Resistance is increasing over time, especially in major Gram-negative pathogens.

  2. Critical antibiotics such as Ciprofloxacin and Carbapenems are losing efficacy.

  3. High-resistance regions require targeted interventions and resource support.

In conclusion, AMR remains a global health emergency. Strengthening surveillance systems, enforcing antibiotic stewardship, and investing in innovative therapies and vaccines are essential to preserve the effectiveness of existing drugs. The WHO GLASS data serve as both a warning and a guide for global collective action to safeguard future generations from the devastating impacts of untreatable infections.



  1. DATA SOURCE Data extracted from the WHO Global Antimicrobial Resistance Surveillance System (GLASS), 2017–2020 reporting period.