Abstract

Microbial resistance represents one of the most significant challenges to global public health today, especially in developing countries. Therefore, this project has focused on creating an interactive web map of bacterial resistance in Latin America, integrating critical data from institutions and reference projects to analyze spatial patterns. This project aims to develop a valuable tool for understanding these relationships, which could be helpful for evidence-based public health decision-making.

If you wish to explore the map, visit: Mapa de Resistencia Bacteriana en América Latina 


Acknowledgement Note:
This project was carried out as part of the “Carreras con Impacto” program during the mentorship phase. You can find more information about the program in this entry.

 

Problem Description

Antibiotic resistance is a growing global issue, and in Latin America, surveillance systems and data collection tend to be fragmented and lack transparency. There is a collective shortage of integrated and accessible data, making it difficult to understand the issue on a regional scale and implement effective measures (PAHO, 2021). Antibiotic resistance threatens public health by reducing the effectiveness of treatments, increasing mortality rates, and raising the costs associated with infections.

Moreover, socioeconomic and demographic factors, such as population density, the Human Development Index (HDI), and public health conditions, can have a significant impact on the spread of bacterial resistance. However, these factors are often not effectively integrated into the analysis (PAHO, 2021). The lack of education and clear information about antimicrobial resistance hinders the coordination of efforts to address the issue, increasing the population’s vulnerability to its effects.
 

Project Description

This project addresses the need for more access to spatial information on bacterial resistance in Latin America by integrating data from the World Health Organization's GLASS platform, the Global Health Index (GHI), and the Pan American Health Organization (PAHO). Using this data, correlation analyses were conducted between cases of bacterial infections with antimicrobial resistance and various key indicators, providing a clearer understanding of the current landscape of these infectious diseases. The information is presented in an accessible and visual format through an interactive website. Additionally, the project aims to raise public awareness of antimicrobial resistance as a critical public health issue.

Sources of Information

The information used to develop this project comes from three key sources: the World Health Organization's Global Antimicrobial Resistance Surveillance System (GLASS), the Health Information Platform for the Americas (PLISA), and the Global Health Security Index (GHSI), with a focus on data specifically from the Latin American region.

Motivations

Information on bacterial resistance is often fragmented and difficult to access. Developing a project that facilitates access to relevant data can help raise awareness about the severity of this issue and support decision-making in public health.

Project Objectives

General

Develop an interactive web tool that facilitates the visualization of bacterial infections with antimicrobial resistance in Latin America, correlating them with key indicators to better understand their impact.

Specifics

• Collect and consolidate data on bacterial resistance in Latin America from the WHO’s GLASS platform, the Global Health Index (GHI), and PAHO, ensuring the data is uniformly integrated and easily accessible.
• Apply various spatial analysis algorithms to identify relationships within the collected data.
• Represent data on antibiotic resistance in the region, making the information more accessible to the general public.

Methodology

The bacteria selected for this project are those classified as "priority" according to the WHO’s 2024 list of antibiotic-resistant pathogens.

Relevant information was collected from various international agencies providing open data, such as WHO’s GLASS and PAHO’s PLISA.

The data were obtained in different formats depending on the source and were subsequently processed to create unified databases. Data processing was conducted using Python scripts and the open-source software QGIS. The qgis2web plugin for QGIS, which is based on JavaScript, was configured to make the map interactive.

QGIS was used to apply geographic information layering techniques and cartographic design to display the data visually. Additionally, layer overlapping, statistical variable calculations using GeoDa software, and interpolation were used to search for relevant indicators for spatial analysis.

Finally, the maps were exported in web-compatible formats, preserving interactivity and the styles defined in QGIS. A website was developed using HTML, CSS, and JavaScript, allowing users to explore the bacterial resistance data and describe its key indicators.

Results

The final result of the project is an interactive map that visually presents the relevant data collected, along with descriptions of key indicators and the option to download the dataset in ".csv" format. The map is available for consultation at: https://miguelhg-ta.github.io/CCI/indexcci.html 

In addition to the web-based display of data layers, several findings emerged from the spatial analysis, leading to the following conclusions:

A moderate to strong association exists between the number of reported cases of specific bacterial infections and the Antimicrobial Resistance (AMR) detection and reporting score from the Global Health Security Index (GHSI). This suggests that areas with high infection rates have likely received more resources to strengthen their healthcare systems. However, in countries reporting fewer cases, the low incidence may not necessarily reflect the absence of infections but rather a potential lack of infrastructure for detection. This association was derived from the Moran's Index calculation for all bacteria, which is in correlation with the GHSI AMR detection and reporting score. The same association was also observed with the percentage of cases in which resistance was detected. Notably, there is no similarity in AMR scores between neighbouring countries, suggesting that interventions and policies to control antibiotic resistance should be evaluated nationally.

This pattern highlights the importance of international cooperation and information sharing to effectively tackle antibiotic resistance, as differences between countries can influence regional resistance dynamics.

A direct correlation was found between the number of reported cases, population density, and the risk of infection. Similarly, there is a direct correlation between population density and cases of bacterial resistance. All the bacteria analyzed exhibited similar behavior in this variable. Additionally, this direct correlation was also observed in relation to the Human Development Index (HDI) and low GHSI scores.

The analysis reveals that the distribution of bacterial resistance is strongly correlated with social, economic, and infrastructure factors, highlighting key areas for interventions and improvements in healthcare systems.

Due to a lack of information in several countries and the different methodologies used for data collection, these results may still exhibit significant biases, potentially caused by limitations in infrastructure or access to information. Nevertheless, the findings provide an overall view of the current situation in the analyzed regions, which can be valuable for evidence-based decision-making and the implementation of appropriate policies to improve the public health landscape.

Limitations

The project faced several limitations that impacted its original scope:

• The lack of access to up-to-date information prevented the reflection of the most recent situation.
• A potential lack of transparency, coordination, and institutional cooperation in public health was identified.
• The data comes from various sources, leading to inconsistencies in collection methodologies and available data types.
• The geographic coverage of the data is uneven.
• The conclusions drawn from the correlations are biased by the limited availability of information.

Future Perspectives

Despite the limitations, the project’s scalability allows for the easy integration of new information as it becomes available, ensuring continuous updates. There are also opportunities for improvement in the technical deployment of the website, such as a more intuitive design optimized for a better user experience. As the platform evolves, it could be expanded to include data from other regions, offering a global perspective on antibiotic resistance. Additionally, there is potential to integrate information on antibiotic use in agriculture, animal health, and its presence in the environment, further enhancing the platform's scope.

References

Organización Mundial de la Salud (OMS). (n.d.). Global Antimicrobial Resistance Surveillance System (GLASS). Recuperado de https://www.who.int/initiatives/glass

Organización Panamericana de la Salud (OPS). (n.d.). Plataforma de Información en Salud para las Américas (PLISA). Recuperado de https://www.paho.org/data/index.php/es/plisa 

Nuclear Threat Initiative (NTI), & Johns Hopkins Center for Health Security. (2021). Global Health Security Index 2021: Advancing Collective Action and Accountability Amid Global Crisis. Recuperado de https://www.ghsindex.org/

Organización Panamericana de la Salud (OPS). (2021). Informe sobre la resistencia antimicrobiana en América Latina y el Caribe. Recuperado de https://www.paho.org/ 

World Health Organization. (2023, 21 noviembre). Resistencia a los antimicrobianos. https://www.who.int/es/news-room/fact-sheets/detail/antimicrobial-resistance

World Health Organization. (2024, 17 mayo). La OMS pone al día la lista de bacterias farmacorresistentes más peligrosas para la salud humana. Organización Mundial de la Salud. https://www.who.int/es/news/item/17-05-2024-who-updates-list-of-drug-resistant-bacteria-most-threatening-to-human-health