Journal Article > ResearchFull Text
Confl Health. 30 January 2024; Volume 18 (Issue 1); 13.; DOI:10.1186/s13031-024-00571-y
Baertlein L, Dubad BA, Sahelie B, Damulak IC, Osman M, et al.
Confl Health. 30 January 2024; Volume 18 (Issue 1); 13.; DOI:10.1186/s13031-024-00571-y
BACKGROUND
This study evaluated an early warning, alert and response system for a crisis-affected population in Doolo zone, Somali Region, Ethiopia, in 2019–2021, with a history of epidemics of outbreak-prone diseases. To adequately cover an area populated by a semi-nomadic pastoralist, or livestock herding, population with sparse access to healthcare facilities, the surveillance system included four components: health facility indicator-based surveillance, community indicator- and event-based surveillance, and alerts from other actors in the area. This evaluation described the usefulness, acceptability, completeness, timeliness, positive predictive value, and representativeness of these components.
METHODS
We carried out a mixed-methods study retrospectively analysing data from the surveillance system February 2019–January 2021 along with key informant interviews with system implementers, and focus group discussions with local communities. Transcripts were analyzed using a mixed deductive and inductive approach. Surveillance quality indicators assessed included completeness, timeliness, and positive predictive value, among others.
RESULTS
1010 signals were analysed; these resulted in 168 verified events, 58 alerts, and 29 responses. Most of the alerts (46/58) and responses (22/29) were initiated through the community event-based branch of the surveillance system. In comparison, one alert and one response was initiated via the community indicator-based branch. Positive predictive value of signals received was about 6%. About 80% of signals were verified within 24 h of reports, and 40% were risk assessed within 48 h. System responses included new mobile clinic sites, measles vaccination catch-ups, and water and sanitation-related interventions. Focus group discussions emphasized that responses generated were an expected return by participant communities for their role in data collection and reporting. Participant communities found the system acceptable when it led to the responses they expected. Some event types, such as those around animal health, led to the community’s response expectations not being met.
CONCLUSIONS
Event-based surveillance can produce useful data for localized public health action for pastoralist populations. Improvements could include greater community involvement in the system design and potentially incorporating One Health approaches.
This study evaluated an early warning, alert and response system for a crisis-affected population in Doolo zone, Somali Region, Ethiopia, in 2019–2021, with a history of epidemics of outbreak-prone diseases. To adequately cover an area populated by a semi-nomadic pastoralist, or livestock herding, population with sparse access to healthcare facilities, the surveillance system included four components: health facility indicator-based surveillance, community indicator- and event-based surveillance, and alerts from other actors in the area. This evaluation described the usefulness, acceptability, completeness, timeliness, positive predictive value, and representativeness of these components.
METHODS
We carried out a mixed-methods study retrospectively analysing data from the surveillance system February 2019–January 2021 along with key informant interviews with system implementers, and focus group discussions with local communities. Transcripts were analyzed using a mixed deductive and inductive approach. Surveillance quality indicators assessed included completeness, timeliness, and positive predictive value, among others.
RESULTS
1010 signals were analysed; these resulted in 168 verified events, 58 alerts, and 29 responses. Most of the alerts (46/58) and responses (22/29) were initiated through the community event-based branch of the surveillance system. In comparison, one alert and one response was initiated via the community indicator-based branch. Positive predictive value of signals received was about 6%. About 80% of signals were verified within 24 h of reports, and 40% were risk assessed within 48 h. System responses included new mobile clinic sites, measles vaccination catch-ups, and water and sanitation-related interventions. Focus group discussions emphasized that responses generated were an expected return by participant communities for their role in data collection and reporting. Participant communities found the system acceptable when it led to the responses they expected. Some event types, such as those around animal health, led to the community’s response expectations not being met.
CONCLUSIONS
Event-based surveillance can produce useful data for localized public health action for pastoralist populations. Improvements could include greater community involvement in the system design and potentially incorporating One Health approaches.
Journal Article > ResearchFull Text
ACG Case Rep J. 15 September 2023; Volume 25; e39736.; DOI:10.2196/39736
Orel EB, Ciglenecki I, Thiabaud A, Temerev A, Calmy A, et al.
ACG Case Rep J. 15 September 2023; Volume 25; e39736.; DOI:10.2196/39736
BACKGROUND
Literature reviews (LRs) identify, evaluate, and synthesize relevant papers to a particular research question to advance understanding and support decision-making. However, LRs, especially traditional systematic reviews, are slow, resource-intensive, and become outdated quickly.
OBJECTIVE
LiteRev is an advanced and enhanced version of an existing automation tool designed to assist researchers in conducting LRs through the implementation of cutting-edge technologies such as natural language processing and machine learning techniques. In this paper, we present a comprehensive explanation of LiteRev’s capabilities, its methodology, and an evaluation of its accuracy and efficiency to a manual LR, highlighting the benefits of using LiteRev.
METHODS
Based on the user’s query, LiteRev performs an automated search on a wide range of open-access databases and retrieves relevant metadata on the resulting papers, including abstracts or full texts when available. These abstracts (or full texts) are text processed and represented as a term frequency-inverse document frequency matrix. Using dimensionality reduction (pairwise controlled manifold approximation) and clustering (hierarchical density-based spatial clustering of applications with noise) techniques, the corpus is divided into different topics described by a list of the most important keywords. The user can then select one or several topics of interest, enter additional keywords to refine its search, or provide key papers to the research question. Based on these inputs, LiteRev performs a k-nearest neighbor (k-NN) search and suggests a list of potentially interesting papers. By tagging the relevant ones, the user triggers new k-NN searches until no additional paper is suggested for screening. To assess the performance of LiteRev, we ran it in parallel to a manual LR on the burden and care for acute and early HIV infection in sub-Saharan Africa. We assessed the performance of LiteRev using true and false predictive values, recall, and work saved over sampling.
RESULTS
LiteRev extracted, processed, and transformed text into a term frequency-inverse document frequency matrix of 631 unique papers from PubMed. The topic modeling module identified 16 topics and highlighted 2 topics of interest to the research question. Based on 18 key papers, the k-NNs module suggested 193 papers for screening out of 613 papers in total (31.5% of the whole corpus) and correctly identified 64 relevant papers out of the 87 papers found by the manual abstract screening (recall rate of 73.6%). Compared to the manual full text screening, LiteRev identified 42 relevant papers out of the 48 papers found manually (recall rate of 87.5%). This represents a total work saved over sampling of 56%.
CONCLUSIONS
We presented the features and functionalities of LiteRev, an automation tool that uses natural language processing and machine learning methods to streamline and accelerate LRs and support researchers in getting quick and in-depth overviews on any topic of interest.
Literature reviews (LRs) identify, evaluate, and synthesize relevant papers to a particular research question to advance understanding and support decision-making. However, LRs, especially traditional systematic reviews, are slow, resource-intensive, and become outdated quickly.
OBJECTIVE
LiteRev is an advanced and enhanced version of an existing automation tool designed to assist researchers in conducting LRs through the implementation of cutting-edge technologies such as natural language processing and machine learning techniques. In this paper, we present a comprehensive explanation of LiteRev’s capabilities, its methodology, and an evaluation of its accuracy and efficiency to a manual LR, highlighting the benefits of using LiteRev.
METHODS
Based on the user’s query, LiteRev performs an automated search on a wide range of open-access databases and retrieves relevant metadata on the resulting papers, including abstracts or full texts when available. These abstracts (or full texts) are text processed and represented as a term frequency-inverse document frequency matrix. Using dimensionality reduction (pairwise controlled manifold approximation) and clustering (hierarchical density-based spatial clustering of applications with noise) techniques, the corpus is divided into different topics described by a list of the most important keywords. The user can then select one or several topics of interest, enter additional keywords to refine its search, or provide key papers to the research question. Based on these inputs, LiteRev performs a k-nearest neighbor (k-NN) search and suggests a list of potentially interesting papers. By tagging the relevant ones, the user triggers new k-NN searches until no additional paper is suggested for screening. To assess the performance of LiteRev, we ran it in parallel to a manual LR on the burden and care for acute and early HIV infection in sub-Saharan Africa. We assessed the performance of LiteRev using true and false predictive values, recall, and work saved over sampling.
RESULTS
LiteRev extracted, processed, and transformed text into a term frequency-inverse document frequency matrix of 631 unique papers from PubMed. The topic modeling module identified 16 topics and highlighted 2 topics of interest to the research question. Based on 18 key papers, the k-NNs module suggested 193 papers for screening out of 613 papers in total (31.5% of the whole corpus) and correctly identified 64 relevant papers out of the 87 papers found by the manual abstract screening (recall rate of 73.6%). Compared to the manual full text screening, LiteRev identified 42 relevant papers out of the 48 papers found manually (recall rate of 87.5%). This represents a total work saved over sampling of 56%.
CONCLUSIONS
We presented the features and functionalities of LiteRev, an automation tool that uses natural language processing and machine learning methods to streamline and accelerate LRs and support researchers in getting quick and in-depth overviews on any topic of interest.
Journal Article > ResearchFull Text
Front Public Health. 31 August 2023; Volume 11; DOI:10.3389/fpubh.2023.1185330
Malaeb R, Haider A, Abdulateef MM, Hameed M, Daniel U, et al.
Front Public Health. 31 August 2023; Volume 11; DOI:10.3389/fpubh.2023.1185330
BACKGROUND
The Coronavirus Disease 2019 (COVID-19) pandemic has highlighted the challenges of the healthcare system in Iraq, which has limited intensive care unit beds, medical personnel, and equipment, contributing to high infection rates and mortality. The main purpose of the study was to describe the clinical characteristics, the length of Intensive Care Unit (ICU) stay, and the mortality outcomes of COVID-19 patients admitted to the ICU during the first wave and two subsequent surges, spanning from September 2020 to October 2021, in addition to identify potential risk factors for ICU mortality.
METHODS
This retrospective cohort study analyzed data from COVID-19 patients admitted to the COVID-19 ICU at Al-Kindi Ministry of Health hospital in Baghdad, Iraq, between September 2020 and October 2021.
RESULTS
The study included 936 COVID-19 patients admitted to the ICU at Al-Kindi Hospital. Results showed a high mortality rate throughout all waves, with 60% of deaths due to respiratory failure. Older age, male gender, pre-existing medical conditions, ICU procedures, and complications were associated with increased odds of ICU mortality. The study also found a decrease in the number of complications and ICU procedures between the first and subsequent waves. There was no significant difference in the length of hospital stay between patients admitted during different waves.
CONCLUSION
Despite improvements in critical care practices, the mortality rate did not significantly decrease during the second and third waves of the pandemic. The study highlights the challenges of high mortality rates among critical COVID-19 patients in low-resource settings and the importance of effective data collection to monitor clinical presentations and identify opportunities for improvement in ICU care.
The Coronavirus Disease 2019 (COVID-19) pandemic has highlighted the challenges of the healthcare system in Iraq, which has limited intensive care unit beds, medical personnel, and equipment, contributing to high infection rates and mortality. The main purpose of the study was to describe the clinical characteristics, the length of Intensive Care Unit (ICU) stay, and the mortality outcomes of COVID-19 patients admitted to the ICU during the first wave and two subsequent surges, spanning from September 2020 to October 2021, in addition to identify potential risk factors for ICU mortality.
METHODS
This retrospective cohort study analyzed data from COVID-19 patients admitted to the COVID-19 ICU at Al-Kindi Ministry of Health hospital in Baghdad, Iraq, between September 2020 and October 2021.
RESULTS
The study included 936 COVID-19 patients admitted to the ICU at Al-Kindi Hospital. Results showed a high mortality rate throughout all waves, with 60% of deaths due to respiratory failure. Older age, male gender, pre-existing medical conditions, ICU procedures, and complications were associated with increased odds of ICU mortality. The study also found a decrease in the number of complications and ICU procedures between the first and subsequent waves. There was no significant difference in the length of hospital stay between patients admitted during different waves.
CONCLUSION
Despite improvements in critical care practices, the mortality rate did not significantly decrease during the second and third waves of the pandemic. The study highlights the challenges of high mortality rates among critical COVID-19 patients in low-resource settings and the importance of effective data collection to monitor clinical presentations and identify opportunities for improvement in ICU care.
Journal Article > ResearchFull Text
Confl Health. 30 August 2023; Volume 17 (Issue 1); 41.; DOI:10.1186/s13031-023-00536-7
OKeeffe J, Takahashi E, Otshudiema JO, Malembi E, Ndaliko C, et al.
Confl Health. 30 August 2023; Volume 17 (Issue 1); 41.; DOI:10.1186/s13031-023-00536-7
English
Français
INTRODUCTION
There has been little documentation of the large networks of community health workers that contributed to Ebola Virus Disease (EVD) surveillance during the 2018–2020 Democratic Republic of Congo (DRC) epidemic in the form of community-based surveillance (CBS). These networks, comprised entirely of local community members, were a critical and mostly unrecognized factor in ending the epidemic. Challenges with collection, compilation, and analysis of CBS data have made their contribution difficult to quantify. From November 2019 to March 2020, the DRC Ministry of Health (MoH), the World Health Organization (WHO), and Médecins Sans Frontières (MSF) worked with communities to strengthen existing EVD CBS in two key health areas in Ituri Province, DRC. We describe CBS strengthening activities, detail collaboration with communities and present results of these efforts. We also provide lessons learned to inform future outbreak responses.
METHODS
As the foundation of CBS, community health workers (CHW) completed training to identify and report patients who met the EVD alert definitions. Alerts were investigated and if validated, the patient was sent for isolation and EVD testing. Community members provided early and ongoing input to the CBS system. We established a predefined ratio of community- elected CHW, allocated by population, to assure equal and adequate coverage across areas. Strong performing CHW or local leaders managed the CHWs, providing a robust supervision structure. We made additional efforts to integrate rural villages, revised tools to lighten the reporting burden and focused analysis on key indicators. Phased roll-out of activities ensured time for community discussion and approval. An integrated treatment center (ITC) combined EVD testing and isolation with free primary health care (PHC), referral services, and an ambulance network.
RESULTS
A total of 247 CHW and supervisors completed training. CBS had a retention rate of 94.3% (n?=?233) with an average daily reporting rate of 97.4% (range 75.0-100.0%). Local chiefs and community leaders participated in activities from the early stages. Community feedback, including recommendations to add additional CHW, run separate meetings in rural villages, and strengthen PHC services, improved system coverage and performance. Of 6,711 community referrals made, 98.1% (n?=?6,583) were classified as alerts. Of the alerts, 97.4% (n?=?6,410) were investigated and 3.0% (n?=?190) were validated. Of the community referrals, 73.1% (n?=?4,905) arrived for care at the ITC. The contribution of CBS to total alerts in the surveillance system increased from an average of 47.3% in the four weeks prior to system strengthening to 69.0% after. In one of the two health areas, insufficient reporting in rural villages suggested inadequate coverage, with 8.3% of the total population contributing 6.1% of alerts.
DISCUSSION
CBS demonstrated the capacity of community networks to improve early disease detection and expand access to healthcare. Early and consistent community involvement proved vital to CBS, as measured by system performance, local acceptance of EVD activities, and health service provision. The CBS system had high reporting rates, number of alerts signaled, proportion of alerts investigated, and proportion of community referrals that arrived for care. The change in contribution of CBS to total alerts may have been due in part to system strengthening, but also to the expansion in the EVD suspect case definition. Provision of PHC, referral services, and an ambulance network linked EVD response activities to the existing health system and facilitated CBS performance. More importantly, these activities provided a continuum of care that addressed community prioritized health needs. The involvement of local health promotion teams was vital to the CBS and other EVD and PHC activities. Lessons learned include the importance of early and consistent community involvement in surveillance activities and the recommendation to assure local representation in leadership positions.
There has been little documentation of the large networks of community health workers that contributed to Ebola Virus Disease (EVD) surveillance during the 2018–2020 Democratic Republic of Congo (DRC) epidemic in the form of community-based surveillance (CBS). These networks, comprised entirely of local community members, were a critical and mostly unrecognized factor in ending the epidemic. Challenges with collection, compilation, and analysis of CBS data have made their contribution difficult to quantify. From November 2019 to March 2020, the DRC Ministry of Health (MoH), the World Health Organization (WHO), and Médecins Sans Frontières (MSF) worked with communities to strengthen existing EVD CBS in two key health areas in Ituri Province, DRC. We describe CBS strengthening activities, detail collaboration with communities and present results of these efforts. We also provide lessons learned to inform future outbreak responses.
METHODS
As the foundation of CBS, community health workers (CHW) completed training to identify and report patients who met the EVD alert definitions. Alerts were investigated and if validated, the patient was sent for isolation and EVD testing. Community members provided early and ongoing input to the CBS system. We established a predefined ratio of community- elected CHW, allocated by population, to assure equal and adequate coverage across areas. Strong performing CHW or local leaders managed the CHWs, providing a robust supervision structure. We made additional efforts to integrate rural villages, revised tools to lighten the reporting burden and focused analysis on key indicators. Phased roll-out of activities ensured time for community discussion and approval. An integrated treatment center (ITC) combined EVD testing and isolation with free primary health care (PHC), referral services, and an ambulance network.
RESULTS
A total of 247 CHW and supervisors completed training. CBS had a retention rate of 94.3% (n?=?233) with an average daily reporting rate of 97.4% (range 75.0-100.0%). Local chiefs and community leaders participated in activities from the early stages. Community feedback, including recommendations to add additional CHW, run separate meetings in rural villages, and strengthen PHC services, improved system coverage and performance. Of 6,711 community referrals made, 98.1% (n?=?6,583) were classified as alerts. Of the alerts, 97.4% (n?=?6,410) were investigated and 3.0% (n?=?190) were validated. Of the community referrals, 73.1% (n?=?4,905) arrived for care at the ITC. The contribution of CBS to total alerts in the surveillance system increased from an average of 47.3% in the four weeks prior to system strengthening to 69.0% after. In one of the two health areas, insufficient reporting in rural villages suggested inadequate coverage, with 8.3% of the total population contributing 6.1% of alerts.
DISCUSSION
CBS demonstrated the capacity of community networks to improve early disease detection and expand access to healthcare. Early and consistent community involvement proved vital to CBS, as measured by system performance, local acceptance of EVD activities, and health service provision. The CBS system had high reporting rates, number of alerts signaled, proportion of alerts investigated, and proportion of community referrals that arrived for care. The change in contribution of CBS to total alerts may have been due in part to system strengthening, but also to the expansion in the EVD suspect case definition. Provision of PHC, referral services, and an ambulance network linked EVD response activities to the existing health system and facilitated CBS performance. More importantly, these activities provided a continuum of care that addressed community prioritized health needs. The involvement of local health promotion teams was vital to the CBS and other EVD and PHC activities. Lessons learned include the importance of early and consistent community involvement in surveillance activities and the recommendation to assure local representation in leadership positions.
Journal Article > ResearchFull Text
Int Health. 11 January 2023; Volume 15 (Issue 5); 537-546.; DOI:10.1093/inthealth/ihac088
Perrocheau A, Jephcott F, Asgari-Jirhanden N, Greig J, Peyraud N, et al.
Int Health. 11 January 2023; Volume 15 (Issue 5); 537-546.; DOI:10.1093/inthealth/ihac088
BACKGROUND
Outbreaks of unknown aetiology in complex settings pose challenges and there is little information about investigation methods. We reviewed investigations into such outbreaks to identify methods favouring or impeding identification of the cause.
METHODS
We used two approaches: reviewing scientific literature and soliciting key informants. Case studies were developed through interviews with people involved and triangulated with documents available from the time of the investigation.
RESULTS
Ten outbreaks in African or Asian countries within the period 2007–2017 were selected. The cause was identified in seven, of which two had an unclear mode of transmission, and in three, neither origin nor transmission mode was identified. Four events were caused by infectious agents and three by chemical poisoning. Despite differences in the outbreaks, similar obstacles were noted: incomplete or delayed description of patients, comorbidities confounding clinical pictures and case definitions wrongly attributed. Repeated rounds of data collection and laboratory investigations were common and there was limited capacity to ship samples.
DISCUSSION
It was not possible to define activities that led to prompt identification of the cause in the case studies selected. Based on the observations, we conclude that basing case definitions on precise medical observations, implementing initial comprehensive data collection, including environmental, social and behavioural information; and involving local informants could save precious time and hasten implementation of control measures.
Outbreaks of unknown aetiology in complex settings pose challenges and there is little information about investigation methods. We reviewed investigations into such outbreaks to identify methods favouring or impeding identification of the cause.
METHODS
We used two approaches: reviewing scientific literature and soliciting key informants. Case studies were developed through interviews with people involved and triangulated with documents available from the time of the investigation.
RESULTS
Ten outbreaks in African or Asian countries within the period 2007–2017 were selected. The cause was identified in seven, of which two had an unclear mode of transmission, and in three, neither origin nor transmission mode was identified. Four events were caused by infectious agents and three by chemical poisoning. Despite differences in the outbreaks, similar obstacles were noted: incomplete or delayed description of patients, comorbidities confounding clinical pictures and case definitions wrongly attributed. Repeated rounds of data collection and laboratory investigations were common and there was limited capacity to ship samples.
DISCUSSION
It was not possible to define activities that led to prompt identification of the cause in the case studies selected. Based on the observations, we conclude that basing case definitions on precise medical observations, implementing initial comprehensive data collection, including environmental, social and behavioural information; and involving local informants could save precious time and hasten implementation of control measures.
Conference Material > Poster
Rau C, Lüdecke D, Dumolard LB, Grevendonk J, Wiernik BM, et al.
MSF Paediatric Days 2022. 30 November 2022; DOI:10.57740/m9jz-9n26
Journal Article > ResearchFull Text
Glob Health Action. 6 October 2022; Volume 15 (Issue 1); DOI:10.1080/16549716.2022.2128281
Cubides JC, Jorgensen N, Peiter PC
Glob Health Action. 6 October 2022; Volume 15 (Issue 1); DOI:10.1080/16549716.2022.2128281
In the medical humanitarian context, the challenging task of collecting health information from people on the move constitutes a key element to identifying critical health care needs and gaps. Médecins Sans Frontières (MSF), during its long history of working with migrants, refugees and mobile populations in different contexts, has acknowledged how crucial it is to generate detailed context-related data on migrant and refugee populations in order to adapt the response interventions to their needs and circumstances. In 2019, the Brazilian Medical Unit/MSF developed the Migration History Tool (MHT), an application based on the life history method which was created in close dialogue with field teams in order to respond to information needs emerging from medical operations in mobile populations. The tool was piloted in two different contexts: firstly, among mobile populations transiting and living in Beitbridge and Musina, at the Zimbabwe-South Africa border; and, secondly, among Venezuelan migrants and refugees in Colombia. This article describes the implementation of this innovative method for collecting quantitative retrospective data on mobility and health in the context of two humanitarian interventions. The results have proven the flexibility of the methodology, which generated detailed information on mobility trajectories and on the temporalities of migration in two different contexts. It also revealed how health outcomes are not only associated with the spatial dimensions of movement, but also with the temporalities of mobility trajectories.
Journal Article > ReviewFull Text
East Afr Med J. 1 October 2016; Volume 93 (Issue 10); S55-S57.
Gituma KS, Hussein S, Mwitari J, Kizito W, Edwards JK, et al.
East Afr Med J. 1 October 2016; Volume 93 (Issue 10); S55-S57.
Journal Article > LetterFull Text
Lancet Diabetes Endocrinol. 7 February 2022; Volume S2213-8587 (Issue 22); 00036-5.; DOI:10.1016/S2213-8587(22)00036-5
Kehlenbrink S, Mahboob O, Al-Zubi S, Boulle P, Aebischer-Perone S, et al.
Lancet Diabetes Endocrinol. 7 February 2022; Volume S2213-8587 (Issue 22); 00036-5.; DOI:10.1016/S2213-8587(22)00036-5
Journal Article > ReviewFull Text
Clin Microbiol Infect. 1 October 2021; Volume 27 (Issue 10); 1414-1421.; DOI:10.1016/j.cmi.2021.04.015
Ronat JB, Natale A, Kesteman T, Andremont A, Elamin W, et al.
Clin Microbiol Infect. 1 October 2021; Volume 27 (Issue 10); 1414-1421.; DOI:10.1016/j.cmi.2021.04.015
BACKGROUND
In low- and middle-income countries (LMICs), data related to antimicrobial resistance (AMR) are often inconsistently collected. Humanitarian, private and non-governmental medical organizations (NGOs), working with or in parallel to public medical systems, are sometimes present in these contexts. Yet, what is the role of NGOs in the fight against AMR, and how can they contribute to AMR data collection in contexts where reporting is scarce? How can context-adapted, high-quality clinical bacteriology be implemented in remote, challenging and underserved areas of the world?
OBJECTIVES
The aim was to provide an overview of AMR data collection challenges in LMICs and describe one initiative, the Mini-Lab project developed by Médecins Sans Frontières (MSF), that attempts to partially address them.
SOURCES
We conducted a literature review using PubMed and Google scholar databases to identify peer-reviewed research and grey literature from publicly available reports and websites.
CONTENT
We address the necessity of and difficulties related to obtaining AMR data in LMICs, as well as the role that actors outside of public medical systems can play in the collection of this information. We then describe how the Mini-Lab can provide simplified bacteriological diagnosis and AMR surveillance in challenging settings.
IMPLICATIONS
NGOs are responsible for a large amount of healthcare provision in some very low-resourced contexts. As a result, they also have a role in AMR control, including bacteriological diagnosis and the collection of AMR-related data. Actors outside the public medical system can actively contribute to implementing and adapting clinical bacteriology in LMICs and can help improve AMR surveillance and data collection.
In low- and middle-income countries (LMICs), data related to antimicrobial resistance (AMR) are often inconsistently collected. Humanitarian, private and non-governmental medical organizations (NGOs), working with or in parallel to public medical systems, are sometimes present in these contexts. Yet, what is the role of NGOs in the fight against AMR, and how can they contribute to AMR data collection in contexts where reporting is scarce? How can context-adapted, high-quality clinical bacteriology be implemented in remote, challenging and underserved areas of the world?
OBJECTIVES
The aim was to provide an overview of AMR data collection challenges in LMICs and describe one initiative, the Mini-Lab project developed by Médecins Sans Frontières (MSF), that attempts to partially address them.
SOURCES
We conducted a literature review using PubMed and Google scholar databases to identify peer-reviewed research and grey literature from publicly available reports and websites.
CONTENT
We address the necessity of and difficulties related to obtaining AMR data in LMICs, as well as the role that actors outside of public medical systems can play in the collection of this information. We then describe how the Mini-Lab can provide simplified bacteriological diagnosis and AMR surveillance in challenging settings.
IMPLICATIONS
NGOs are responsible for a large amount of healthcare provision in some very low-resourced contexts. As a result, they also have a role in AMR control, including bacteriological diagnosis and the collection of AMR-related data. Actors outside the public medical system can actively contribute to implementing and adapting clinical bacteriology in LMICs and can help improve AMR surveillance and data collection.