Migration Health Evidence Portal for COVID-19
This evidence portal is a repository of research publications and high-yield evidence briefs on COVID-19 and its intersection with migration health. The content of this platform is updated regularly.
The scientific literature and knowledge base on the epidemic rapidly expand daily. Tremendous efforts are being made by the global community of clinicians, researchers, and journal editors to advance scientific evidence to guide policy and decision making at the field level. There is a need to build evidence platforms for sharing and distilling key findings emergent from the growing body of scientific literature, relevant to migration, health, and human mobility. These findings can ultimately assist evidence-informed decision making from a migration lens.
The portal contains:
- An interactive, open-source, searchable (and downloadable) repository of research publications on COVID-19 in relation to migrants, migration, and human mobility based on the quantitative analysis of the thematic trends and impact of relevant publications.
- The full paper of the quantitative analysis of publications on COVID-19 and migration health (i.e., bibliometric analysis), which is regularly updated to include the latest peer-reviewed studies available.
- High-yield evidence briefs that align with the COVID-19 Global Preparedness and Response Plan and with IOM’s Global Strategic Preparedness and Response Plan (SRP).
- Profiling migration health and COVID-19 related analysis, research, and commentaries in partnership with the Migration Health and Development Research Initiative (MHADRI), a global network of migration health research experts/scholars.
Research Publications on COVID-19 and Migration Health
This section reflects the output of the publication mapping exercise (i.e., bibliometric analysis) involving the quantitative assessment of a set of published scientific articles on COVID-19 with reference to migrants, migration, and human mobility. (NOTE: The next round of updates has been put on hold for the time being.) Bibliometric analysis provides an important snapshot of a specific field of interest/domain. The baseline information from bibliometric analysis helps identify research gaps that future studies can investigate. The analysis enables examining the relevant literature in terms of its scope and content, data use, representation of specific research areas, or its development over time. The bibliometric analysis conducted by IOM and MHADRI on international migration and health is one example.
- Overview of search: As of 4 May 2020, the search retrieved 276 publications relevant to migration health and human mobility. The majority of publications were research articles (57%) and mostly covered the following subject areas: medicine (86%), immunology and microbiology (11%), social sciences (7%), and environmental science (7%).
- Research themes: The publications relevant to migration health mapped by research themes included: disease epidemiology and mathematical modelling (41%), public health interventions (38%), and clinical management (19%). Research themes such as: health system capacity, diagnostic and testing strategies, candidate therapeutics and vaccines, and policy analysis remain largely under-represented in the current evidence base (less than 10 per cent of the 276 publications). There were 14 publications that used mathematical modelling to predict spread, and model social distancing, border closures, and impacts on the health care system capacities. Of this number, about 57 per cent covered China (n=8) followed by publications that were global in scope (n=3). There were only three studies (India, Iran, and African countries) that model the importation risk of COVID-19 using the volume of air travel. None hitherto have investigated situations in camps and camp-like settings.
- Migrant groups and mobile populations: Most studies investigated cases of COVID-19 in the context of population movement. A few publications covered specific migrant populations (e.g. international students, asylum seekers, refugees, and internally displaced persons [IDPs]), the majority of which were opinion/ commentary pieces. Evidence with attribution to migrant groups within clinical datasets are seldom reported.
- Refugees: Most of the publications stressed the urgent need for inclusion of refugees in the national and global health response against COVID-19. Further, these publications discussed in length the risks and pre-existing vulnerabilities (i.e. overcrowded and poor living conditions, multiple barriers to health care, and others), and the humanitarian barriers that refugees face due to the mobility restrictions implemented by the governments [1, 2, 3, 4, 5, 6]. There was one publication that discussed a successful phone-based method used by a non-profit group to follow-up war-affected refugee caregivers under COVID-19 lockdown in Tripoli, Lebanon .
- International students: There were two publications that discussed the impact of COVID-19 to the mental health of Chinese students abroad  and foreign students in China  due to the belief that they are seen as potential carriers of the COVID-19 virus and also because of fear and anxiety during the crisis [8, 10].
- Migrant workers: Only three publications (two letters and one research article) retrieved from the search specifically involved migrant workers despite being an important subgroup of migrants greatly affected by the pandemic. The research article discussed the vulnerability among migrant workers  (NB: the term ‘international’ was not used but implied) due to biomass exposure . The two other publications (i.e. letters), which explicitly referred to international migrant workers, discussed the lack or limited response for migrant workers and the emotional impact of COVID-19 among migrants .
- Ethnic minorities: As of the last date of search, this mapping exercise retrieved one publication that mentioned ethnic minorities alongside migrants as a high-risk group. However, it should be noted that the methodology of this mapping exercise was not designed to capture ‘ethnic minorities’. Beyond the search results, a relevant systematic review emphasized the importance of gathering robust evidence on the role of ethnicity in COVID-19 . From the said review, several publications in the UK and the US indicated the disproportionate risk of having COVID-19 and suffering from more serious clinical outcomes (e.g. hospitalization, intensive care admission, and deaths) among individuals from Black, Asian, and Minority Ethnic (BAME) groups relative to White patients. Although ethnicity is different from migrant status, the disparities in health outcomes of specific ethnic minority groups may provide a better understanding of the intersection between migrant status and ethnicity.
- Most active countries: Most publications also involved studies conducted in China (34%) and the US (22%) suggesting the limited coverage of relevant research in other countries. The top three active institutions identified were the Ministry of Education China, the University of Hong Kong, and the London School of Hygiene and Tropical Medicine. Africa, South America and the Caribbean, and the Middle East were the least productive countries in terms of the number of research outputs.
- Research collaborations: International research collaboration was strongest between the US and China, followed by the US and the UK, and finally between China and Hong Kong Administrative Region, China. Details on collaboration networks may be useful in developing multi-center research studies when more robust data become available.
- There is limited inclusion of migrant status within data collection practices in routine health information systems, hospital registries, and disease surveillance systems globally. This also extends to research where migrant status remains poorly captured. Data disaggregation by migrant flows and categories on COVID-19 testing, hospitalizations, and deaths by migrant status is a poorly described national data set.
- Efforts to address the challenges of COVID-19 in the context of migration health requires a robust knowledge base generated from the growing body of scientific evidence that carefully considers specific dimensions of COVID-19 and migration health. Although limited to the analysis of relevant publications using the metrics available (i.e. number of publications, co-occurrence of keywords, etc.) the key findings from the analysis can provide a useful starting point that can facilitate ongoing and future research on COVID-19 and migration health in terms of the critical areas that need more attention.
Network map of common keywords
The network map below shows an overview of the common keywords that appear in the title, abstract, and keywords of the relevant publications retrieved on the topic of COVID-19 and migration health. Network maps of keywords reveal key topics in a research area or domain as well as the relationship (co-occurrence) between common keywords. It is a relative indicator of important research areas that are drawing attention in the field. The relationships described are based on the co-occurrence of keywords in publications and do not necessarily represent a structured conceptual framework. Nevertheless, it can provide insight into the extent of the representation of themes on COVID-19 and migration health.
- The large circles in the figure represent the most frequently occurring keywords in the publications retrieved (N=276). The most commonly encountered keywords were: ‘pneumonia’ (n=115), ‘China’ (n=106), ‘pandemic’ (n=97), ‘travel’ (n=93), ‘epidemic’ (n=88), and ‘disease transmission’ (n=66).
- Using a minimum occurrence threshold of 15 (i.e. each keyword appears at least 15 times in the dataset to be included in the map), the visualization of keywords in the retrieved publications included 36 keywords that formed three clusters (red, green, and blue).
- The lines connecting the circles represent the co-occurring keywords. The distance between two keywords approximates how strongly the words are related based on the number of their co-occurrences (i.e., the more publications in which two keywords co-occur, the stronger the relation between them). Thus, the strongly related words appear closer together on the map.
- Each distinct color represents a cluster of keywords that are strongly related to each other. In the figure, ‘pneumonia’, ‘outbreak’, and ‘pathogenicity’ are strongly related to ‘virology’, ‘global health’, ‘United States’, and ‘Wuhan’ (blue cluster). The keyword ‘epidemic’ is strongly related to ‘pandemic’, ‘quarantine’, ‘air travel’, ‘China’, ‘infection control’, and ‘risk assessment’ which are shown to be closer together forming the green cluster. The red cluster is formed by the strong relatedness of the following keywords: ‘travel’, ‘PCR’, ‘fever’, ‘cough’, and ‘tomography’ among others.
- In terms of specific research themes, the map mostly included keywords related to disease epidemiology (‘epidemic’, adult’, ‘male’, ‘female’, ‘elderly’, etc.), public health measures (e.g. ‘quarantine’, ‘social distancing’, ‘risk assessment’, ‘infection prevention and control’) and clinical management (i.e. ‘pneumonia’, ‘incubation time’, etc.). A few keywords were also related to diagnostic procedures – such as ‘PCR’, ‘tomography’, and ‘radiography’.
Note: See the full paper for more detailed findings, including the Methodology and Limitations of this analysis.