Journal Article > ReviewFull Text
East Afr Med J. 2016 October 1; Volume 93 (Issue 10); S55-S57.
Gituma KS, Hussein S, Mwitari J, Kizito W, Edwards JK, et al.
East Afr Med J. 2016 October 1; Volume 93 (Issue 10); S55-S57.
Journal Article > LetterFull Text
Lancet Diabetes Endocrinol. 2022 February 7; 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. 2022 February 7; Volume S2213-8587 (Issue 22); 00036-5.; DOI:10.1016/S2213-8587(22)00036-5
Journal Article > ReviewFull Text
Clin Microbiol Infect. 2021 October 1; 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. 2021 October 1; 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.
Journal Article > Meta-AnalysisFull Text
Int Health. 2023 January 11; 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. 2023 January 11; 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.
Journal Article > ResearchFull Text
Confl Health. 2024 January 30; 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. 2024 January 30; 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
BMC Public Health. 2021 December 13; Volume 21 (Issue 1); 2269.; DOI:10.1186/s12889-021-12206-5
Perrocheau A, Brindle H, Roberts CH, Murthy S, Shetty S, et al.
BMC Public Health. 2021 December 13; Volume 21 (Issue 1); 2269.; DOI:10.1186/s12889-021-12206-5
BACKGROUND
Timely but accurate data collection is needed during health emergencies to inform public health responses. Often, an abundance of data is collected but not used. When outbreaks and other health events occur in remote and complex settings, operatives on the ground are often required to cover multiple tasks whilst working with limited resources. Tools that facilitate the collection of essential data during the early investigations of a potential public health event can support effective public health decision-making. We proposed to define the minimum set of quantitative information to collect whilst using electronic device or not. Here we present the process used to select the minimum information required to describe an outbreak of any cause during its initial stages and occurring in remote settings.
METHODS
A working group of epidemiologists took part in two rounds of a Delphi process to categorise the variables to be included in an initial outbreak investigation form. This took place between January-June 2019 using an online survey.
RESULTS
At a threshold of 75 %, consensus was reached for nineteen (23.2%) variables which were all classified as 'essential'. This increased to twenty-six (31.7%) variables when the threshold was reduced to 60% with all but one variable classified as 'essential'. Twenty-five of these variables were included in the 'Time zero initial case investigation' '(T0)' form which was shared with the members of the Rapid Response Team Knowledge Network for field testing and feedback. The form has been readily available online by WHO since September 2019.
CONCLUSION
This is the first known Delphi process used to determine the minimum variables needed for an outbreak investigation. The subsequent development of the T0 form should help to improve the efficiency and standardisation of data collection during emergencies and ultimately the quality of the data collected during field investigation.
Timely but accurate data collection is needed during health emergencies to inform public health responses. Often, an abundance of data is collected but not used. When outbreaks and other health events occur in remote and complex settings, operatives on the ground are often required to cover multiple tasks whilst working with limited resources. Tools that facilitate the collection of essential data during the early investigations of a potential public health event can support effective public health decision-making. We proposed to define the minimum set of quantitative information to collect whilst using electronic device or not. Here we present the process used to select the minimum information required to describe an outbreak of any cause during its initial stages and occurring in remote settings.
METHODS
A working group of epidemiologists took part in two rounds of a Delphi process to categorise the variables to be included in an initial outbreak investigation form. This took place between January-June 2019 using an online survey.
RESULTS
At a threshold of 75 %, consensus was reached for nineteen (23.2%) variables which were all classified as 'essential'. This increased to twenty-six (31.7%) variables when the threshold was reduced to 60% with all but one variable classified as 'essential'. Twenty-five of these variables were included in the 'Time zero initial case investigation' '(T0)' form which was shared with the members of the Rapid Response Team Knowledge Network for field testing and feedback. The form has been readily available online by WHO since September 2019.
CONCLUSION
This is the first known Delphi process used to determine the minimum variables needed for an outbreak investigation. The subsequent development of the T0 form should help to improve the efficiency and standardisation of data collection during emergencies and ultimately the quality of the data collected during field investigation.
Conference Material > Poster
Rau C, Lüdecke D, Dumolard LB, Grevendonk J, Wiernik BM, et al.
MSF Paediatric Days 2022. 2022 November 30; DOI:10.57740/m9jz-9n26
Journal Article > ResearchFull Text
Glob Health Action. 2022 October 6; Volume 15 (Issue 1); DOI:10.1080/16549716.2022.2128281
Cubides JC, Jorgensen N, Peiter PC
Glob Health Action. 2022 October 6; 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 > ResearchFull Text
ACG Case Rep J. 2023 September 15; Volume 25; e39736.; DOI:10.2196/39736
Orel EB, Ciglenecki I, Thiabaud A, Temerev A, Calmy A, et al.
ACG Case Rep J. 2023 September 15; 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
Confl Health. 2012 November 27; Volume 6 (Issue 1); 11.; DOI:10.1186/1752-1505-6-11
Bowden S, Braker K, Checchi F, Wong S
Confl Health. 2012 November 27; Volume 6 (Issue 1); 11.; DOI:10.1186/1752-1505-6-11
BACKGROUND
Prospective surveillance is a recognised approach for measuring death rates in humanitarian emergencies. However, there is limited evidence on how such surveillance should optimally be implemented and on how data are actually used by agencies. This case study investigates the implementation and utilisation of mortality surveillance data by Médecins Sans Frontières (MSF) in eastern Chad. We aimed to describe and analyse the community-based mortality surveillance system, trends in mortality data and the utilisation of these data to guide MSF's operational response.
METHODS
The case study included 5 MSF sites including 2 refugee camps and 3 camps for internally displaced persons (IDPs). Data were obtained through key informant interviews and systematic review of MSF operational reports from 2004-2008.
RESULTS
Mortality data were collected using community health workers (CHWs). Mortality generally decreased progressively. In Farchana and Breidjing refugee camps, crude death rates (CDR) decreased from 0.9 deaths per 10,000 person-days in 2004 to 0.2 in 2008 and from 0.7 to 0.1, respectively. In Gassire, Ade and Kerfi IDP camps, CDR decreased from 0.4 to 0.04, 0.3 to 0.04 and 1.0 to 0.3. Death rates among children under 5 years (U5DR) followed similar trends. CDR and U5DR crossed emergency thresholds in one site, Kerfi, where CDR rapidly rose to 2.1 and U5DR to 7.9 in July 2008 before rapidly decreasing to below emergency levels by September 2008.
DISCUSSION
Mortality data were used regularly to monitor population health status and on two occasions as a tool for advocacy. Lessons learned included the need for improved population estimates and standardized reporting procedures for improved data quality and dissemination; the importance of a simple and flexible model for data collection; and greater investment in supervising CHWs.
CONCLUSIONS
This model of community based mortality surveillance can be adapted and used by humanitarian agencies working in complex settings. Humanitarian organisations should however endeavour to disseminate routinely collected mortality data and improve utilisation of data for operational planning and evaluation. Accurate population estimation continues to be a challenge, limiting the accuracy of mortality estimates.
Prospective surveillance is a recognised approach for measuring death rates in humanitarian emergencies. However, there is limited evidence on how such surveillance should optimally be implemented and on how data are actually used by agencies. This case study investigates the implementation and utilisation of mortality surveillance data by Médecins Sans Frontières (MSF) in eastern Chad. We aimed to describe and analyse the community-based mortality surveillance system, trends in mortality data and the utilisation of these data to guide MSF's operational response.
METHODS
The case study included 5 MSF sites including 2 refugee camps and 3 camps for internally displaced persons (IDPs). Data were obtained through key informant interviews and systematic review of MSF operational reports from 2004-2008.
RESULTS
Mortality data were collected using community health workers (CHWs). Mortality generally decreased progressively. In Farchana and Breidjing refugee camps, crude death rates (CDR) decreased from 0.9 deaths per 10,000 person-days in 2004 to 0.2 in 2008 and from 0.7 to 0.1, respectively. In Gassire, Ade and Kerfi IDP camps, CDR decreased from 0.4 to 0.04, 0.3 to 0.04 and 1.0 to 0.3. Death rates among children under 5 years (U5DR) followed similar trends. CDR and U5DR crossed emergency thresholds in one site, Kerfi, where CDR rapidly rose to 2.1 and U5DR to 7.9 in July 2008 before rapidly decreasing to below emergency levels by September 2008.
DISCUSSION
Mortality data were used regularly to monitor population health status and on two occasions as a tool for advocacy. Lessons learned included the need for improved population estimates and standardized reporting procedures for improved data quality and dissemination; the importance of a simple and flexible model for data collection; and greater investment in supervising CHWs.
CONCLUSIONS
This model of community based mortality surveillance can be adapted and used by humanitarian agencies working in complex settings. Humanitarian organisations should however endeavour to disseminate routinely collected mortality data and improve utilisation of data for operational planning and evaluation. Accurate population estimation continues to be a challenge, limiting the accuracy of mortality estimates.