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• No-fault compensation schemes for COVID-19 vaccine injury: a mixed bag for claimants and citizens
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• http://orcid.org/0000-0002-5673-8755 Sonia Macleod 1 ,
• http://orcid.org/0000-0002-3430-5705 Francesca Uberti 1 ,
• Enga Kameni 2 , 3
• 1 Centre for Socio-Legal Studies , University of Oxford , Oxford , UK
• 2 Afreximbank , Heliopolis , Egypt
• 3 Faculty of Law , University of the Western Cape , Cape Town , South Africa
• Correspondence to Dr Sonia Macleod, Centre for Socio-Legal Studies, University of Oxford, Manor Road Building, Manor Road, Oxford OX1 3UQ, UK; sonia.macleod{at}csls.ox.ac.uk

The development of vaccines against SARS-CoV-2 (COVID-19) presented a unique set of challenges. There was a global need for safe, effective vaccines against a new virus. In response to the development of vaccines for COVID-19 (some of which used novel technologies), there was a proliferation of no-fault compensation schemes (NFCS) for COVID-19 vaccine injuries. We identified 28 national vaccine injury NFCS operating in December 2019. Just 2 years later, over 130 countries had some NFCS coverage for COVID-19 vaccines. This rapid expansion was primarily driven by the creation of three multinational schemes. The COVID-19 Vaccines Global Access (COVAX) scheme covers vaccines given under the COVAX framework in 92 low and middle-income countries across the globe. The African Vaccines Acquisition Trust (AVAT) scheme covers vaccines administered under the AVAT framework in 36 African and Caribbean countries. The UNICEF scheme covers vaccines administered by UNICEF in 18 Asian countries.

Because of the sudden expansion of no-fault compensation for vaccine injury, especially in developing economies, more research on the foundations, procedures and outcomes of NFCS is needed. In this article, we examine how these NFCS meet the needs of individual claimants and society more widely. To do so, we first review the rationales offered to support the creation of vaccine injury NFCS. We then argue that, in order to achieve their function as compensation mechanisms, NFCS should be accessible and offer substantive and procedural justice to claimants. Finally, we focus on transparency and accountability as necessary requirements to allow scrutiny over existing NFCS and their wider impacts.

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Data are available upon reasonable request.

https://doi.org/10.1136/jme-2024-109900

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Contributors SM and FU made substantial contributions to the conception and design of the work; the acquisition, analysis and interpretation of data for the work; and drafting the work including revising it critically for important intellectual content. EK made a substantial contribution to the design of the work; the acquisition of data for the work; and revising it critically for important intellectual content. SM is the guarantor and accepts full responsibility for the finished work and the conduct of the study, had access to the data, and controlled the decision to publish.

Funding This study was funded by the International Federation of Pharmaceutical Manufacturers and Associations.

Competing interests SM and FU have received research support from the International Federation of Pharmaceutical Manufacturers and Associations (IFPMA).

Provenance and peer review Not commissioned; externally peer reviewed.

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Emergency Medical Service Agency Practices and Cardiac Arrest Survival

• 1 University of Texas Southwestern Medical Center, Dallas
• 2 University of Iowa Carver College of Medicine, Iowa City
• 3 Social Science Research Institute, Duke University, Durham, North Carolina
• 4 Saint Luke’s Mid America Heart Institute, Kansas City, Missouri
• 5 ESO Inc, Austin, Texas
• 6 Department of Emergency Medicine, The Ohio State University, Columbus
• 7 King County Medic One Emergency Medical Services and Harborview Medical Center, University of Washington, Seattle
• 8 Emory University Rollins School of Public Health, Atlanta, Georgia
• 9 Department of Emergency Medicine, Emory University School of Medicine, Atlanta, Georgia

Question   Which emergency medical services (EMS) agency practices are associated with favorable neurological survival for out-of-hospital cardiac arrest (OHCA)?

Findings   This cohort study among 470 EMS agencies in the Cardiac Arrest Registry to Enhance Survival (CARES) for OHCA identified 7 practices related to training, cardiopulmonary resuscitation, and transport that were associated with favorable neurological survival. Adoption of more than half (≥4) of these practices was more common at EMS agencies in the highest quartile of favorable neurological survival vs agencies in the lowest quartile (59.3% vs 35.6%, respectively).

Meaning   Given wide variability in EMS agency rates for OHCA survival, these findings provide initial insights into EMS practices associated with top-performing EMS agencies.

Importance   Survival for out-of-hospital cardiac arrest (OHCA) varies widely across emergency medical service (EMS) agencies in the US. However, little is known about which EMS practices are associated with higher agency-level survival.

Objective   To identify resuscitation practices associated with favorable neurological survival for OHCA at EMS agencies.

Design, Setting, and Participants   This cohort study surveyed EMS agencies participating in the Cardiac Arrest Registry to Enhance Survival (CARES) with 10 or more OHCAs annually during January 2015 to December 2019; data analyses were performed from April to October 2023.

Exposure   Survey of resuscitation practices at EMS agencies.

Main Outcomes and Measures   Risk-standardized rates of favorable neurological survival for OHCA at each EMS agency were estimated using hierarchical logistic regression. Multivariable linear regression then examined the association of EMS practices with rates of risk-standardized favorable neurological survival.

Results   Of 577 eligible EMS agencies, 470 agencies (81.5%) completed the survey. The mean (SD) rate of risk-standardized favorable neurological survival was 8.1% (1.8%). A total of 7 EMS practices across 3 domains (training, cardiopulmonary resuscitation [CPR], and transport) were associated with higher rates of risk-standardized favorable neurological survival. EMS agencies with higher favorable neurological survival rates were more likely to use simulation to assess CPR competency (β = 0.54; P  = .05), perform frequent reassessment (at least once every 6 months) of CPR competency in new staff (β = 0.51; P  = .04), use full multiperson scenario simulation for ongoing skills training (β = 0.48; P  = .01), perform simulation training at least every 6 months (β = 0.63; P  < .001), and conduct training in the use of mechanical CPR devices at least once annually (β = 0.43; P  = .04). EMS agencies with higher risk-standardized favorable neurological survival were also more likely to use CPR feedback devices (β = 0.58; P  = .007) and to transport patients to a designated cardiac arrest or ST-segment elevation myocardial infarction receiving center (β = 0.57; P  = .003). Adoption of more than half (≥4) of the 7 practices was more common at EMS agencies in the highest quartile of favorable neurological survival rates (70 of 118 agencies [59.3%]) vs the lowest quartile (42 of 118 agencies [35.6%]) ( P  < .001).

Conclusions and Relevance   In a national registry for OHCA, 7 practices associated with higher rates of favorable neurological survival were identified at EMS agencies. Given wide variability in neurological survival across EMS agencies, these findings provide initial insights into EMS practices associated with top-performing EMS agencies in OHCA survival. Future studies are needed to validate these findings and identify best practices for EMS agencies.

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Girotra S , Dukes KC , Sperling J, et al. Emergency Medical Service Agency Practices and Cardiac Arrest Survival. JAMA Cardiol. Published online June 05, 2024. doi:10.1001/jamacardio.2024.1189

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What should we call mental ill health? Historical shifts in the popularity of generic terms

Roles Conceptualization, Formal analysis, Funding acquisition, Investigation, Methodology, Project administration, Supervision, Visualization, Writing – original draft

* E-mail: [email protected]

Affiliation Melbourne School of Psychological Sciences University of Melbourne Parkville, Melbourne, Victoria, Australia

Roles Conceptualization, Data curation, Formal analysis, Investigation, Methodology, Software, Writing – review & editing

• Nick Haslam, 

• Published: June 4, 2024
• https://doi.org/10.1371/journal.pmen.0000032
• Reader Comments

Substantial attention has been paid to the language of mental ill health, but the generic terms used to refer to it–“mental illness”, “psychiatric condition”, “mental health problem” and so forth–have largely escaped empirical scrutiny. We examined changes in the prevalence of alternative terms in two large English language text corpora from 1940 to 2019. Twenty-four terms were studied, compounds of four adjectival expressions (“mental”, “mental health”, “psychiatric”, “psychological”) and six nouns (“condition”, “disease”, “disorder”, “disturbance”, “illness”, “problem”). Terms incorporating “condition”, “disease” and “disturbance” became less popular over time, whereas those involving “psychiatric”, “mental health” and “illness” became more popular. Although there were some trends away from terms with medical connotations and towards more normalizing expressions, “mental illness” consolidated its position as the dominant term over the study period.

Citation: Haslam N, Baes N (2024) What should we call mental ill health? Historical shifts in the popularity of generic terms. PLOS Ment Health 1(1): e0000032. https://doi.org/10.1371/journal.pmen.0000032

Editor: Vitalii Klymchuk, University of Luxembourg: Universite du Luxembourg, LUXEMBOURG

Received: January 17, 2024; Accepted: March 7, 2024; Published: June 4, 2024

Copyright: © 2024 Haslam, Baes. This is an open access article distributed under the terms of the Creative Commons Attribution License , which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited.

Data Availability: Data and all scripts are available at OSF: https://osf.io/6egsz/ .

Funding: This work was supported by Australian Research Council Discovery Project DP210103984 to NH. The funders had no role in study design, data collection and analysis, decision to publish, or preparation of the manuscript.

Competing interests: The authors have declared that no competing interests exist.

Introduction

Terminology has long been a vexed issue in the domain of mental ill health. Blatantly stigmatizing colloquial expressions such as “crazy” and “lunatic” have been controversial for many years [ 1 ], and official diagnostic terms such as “schizophrenia” have also been denounced [ 2 ]. Some critics challenge the use of diagnostic terms in general, sometimes out of concern for the ill effects of labelling [ 3 , 4 ] and sometimes driven by a broader critique of medicalization [ 5 ]. Some writers who are comfortable with diagnostic terms criticize disease-first language (e.g., “schizophrenic person”) for reducing people to their illnesses, whereas others criticize person-first language (e.g., “person with schizophrenia”) because some people strongly identify with their diagnosis [ 6 , 7 ]. There is also lively disagreement about appropriate terminology for referring to users of mental health services, such as “patient”, “client”, or “consumer” [ 8 ].

Generic terms for mental ill health are one kind of terminology that has largely escaped systematic attention. These expressions serve as umbrella terms that refer to the class of specific conditions. Official psychiatric classifications such as the Diagnostic and Statistical Manual of Mental Disorders (DSM) and the International Classification of Disease (ICD) employ the term “mental disorder,” but laypeople and professionals alike use a wide array of other expressions. These are typically compound, involving an adjectival expression followed by a noun, the former stipulating how the latter applies to the specific domain of mental ill health. Among the most common adjective expressions are “mental”, “psychological”, and “psychiatric”, with some writers now preferring “mental health” despite the occasional clumsiness of three-word terms such as “mental health disorder”. Some of the most common nouns include “condition”, “disease”, “disorder”, “disturbance”, “illness”, and “problem”. All combinations of these adjectives and nouns can be encountered in everyday use.

These adjectival and noun expressions have distinctive connotations. As a generic term, “mental” implies that the common element of the phenomena of interest relates to the mind. Thesauri list “physical” and “somatic” as antonyms of “mental”, arguably suggesting a dualistic contrast between “mental” and “physical” conditions. “Psychological” has a similar connotation (“of, relating to, or occurring in the mind” in the Merriam-Webster dictionary) but with a clearer connection to a specific profession and field of study (“of or relating to psychology” in the same dictionary). “Psychiatric” relates the phenomena of interest to a particular medical specialty. Like “psychiatric”, and unlike “mental” and “psychological,” “mental health”, used adjectivally (e.g., “mental health problem"), invokes a health context but without referring to a specific profession or discipline.

The noun components of generic terms also carry differing meanings, varying in the extent to which they implicate a medical framing of mental ill health. “Disease” and “illness” are arguably the most clearly medical. According to the ‘Small World of Words’ word association norms [ 9 ], the terms are strongly associated with one another and with “sickness”. Conceptually, “disease” refers to an objective organic malfunction, an entity prototypically caused by an external pathological agent such as a bacterium. “Illness,” by contrast, refers to the subjective experience of a state of ill health. “Disorder” and “disturbance” are sometimes used as near synonyms of “disease” and “illness” but imply a functional impairment or aberration rather than a structural pathology. According to the Compact Oxford dictionary [ 10 ], “disorder” is “usually a weaker term than disease, and not implying structural change” (p.449). “Disturbance” is less tied to the health domain than “disorder”, primarily linked to “problem” and “disruption” in Small World of Words. “Condition” is more neutral than most of the other terms, capable of referring to positive and negative states of health. “Problem,” finally, implies a negative state–associated in the word association norms with words such as “solve” and “issue”–without any direct reference to health. In short, the components of generic terms vary widely in their linkage to health and medicine and in their implied normalcy.

Terminology of this sort is one aspect of what Berrios [ 11 ] calls “psychopathological language”, the systematic language that at any point in history is deemed appropriate for referring to the psychopathological domain. Many generic terms have been widely used in recent history. Berrios notes that “insanity” and “madness” were popular terms in the 19 th century. “Mental disease” became a popular expression early in the late 19 th and early 20 th century, featuring in the title of the Journal of Nervous and Mental Disease , the world’s oldest scientific monthly devoted to human behavior. “Mental disorder” became the preferred generic term in organized psychiatry when formal psychiatric classifications were developed, such as DSM’s first edition, published in 1952. The emerging preference for this term may have arisen because it did not presume a biomedical causation and side-stepped debates over the legitimacy of “mental illness” or “mental disease” [ 12 , 13 ]. More recently, terms whose connotations are even less medical have become popular, such as those including “mental health” and “problems.” The emergence of these expressions reflects a desire to destigmatize and normalize mental ill health, akin to the “euphemism treadmill” [ 14 ], whereby new terms replace those that have come to be seen as offensive or pejorative to ameliorate them. As a result of these terminological shifts, many generic terms are now in widespread circulation.

The implications of alternative generic terms are unclear and have attracted little research. Despite having different connotations, several alternative terms (“mental disorder”, “mental illness”, “mental health problem”) do not differ substantially in the range of phenomena to which they refer [ 15 ]. There is also little clear evidence of differential impacts on judgments of people with mental ill health. Szeto, Luong, and Dobson [ 16 ] found that undergraduate participants did not differ in their attitudes towards and desire for social distance from a person labelled as having a “mental disease”, a “mental disorder”, a “mental health problem”, or a “mental illness”. Similarly, Fox et al. [ 17 ] found no effects on a range of stigma measures when the terms “mental illness”, “mental health problem” or “psychological disorder” were used in a large sample of people with a history of mental illness. However, Lawson [ 18 ] found a greater desire for distance from a hypothetical person when they were labelled as having a “mental disorder” rather than a “mental illness” or “mental health condition.” As yet, no studies have examined other dimensions along which alternative terms might have differential implications, such as effects on professionals’ clinical judgments or on laypeople’s beliefs about causes and appropriate treatments. Although there may have been a trend away from directly medical terms towards more normalizing alternatives, there is as yet no evidence to suggest such a trend has had beneficial effects.

The previous empirical work on generic terminology reviewed above has compared four terms at most, and theoretical work has typically addressed the strengths and weakness of single terms (e.g., “mental illness” [ 19 ]). Studies have also restricted their focus to current usage of terms rather than how that usage has evolved over time. The present study therefore investigated historical trends in the popularity of a comprehensive set of generic terms. We examined the frequency of 24 terms relative to all terms and to one another over a 80-year period using two large English language text corpora. The study was primarily descriptive, aiming to characterize shifts in preferred terminology within society at large. However, we expected to find evidence of diminished popularity of more medical terms (e.g., those including “disease”) and rising preference for more normalizing terms (e.g., those including “mental health” and/or “problem”).

Two corpora were used to track the rise and fall of generic terms from 1940 to 2019. These corpora were chosen for their wide historical span, their very large magnitude, and their differing text sources. The first was derived specifically from books published across the Anglophone world, whereas the second includes text from diverse sources in the USA. If historical trends in terminology are robust, they should replicate across these two distinct corpora. The open-access repository contains all preprocessing scripts: https://osf.io/6egsz/

The Google Books corpus contained “books predominantly in the English language published in any country”, incorporating 361 billion words that appear over 40 times across the corpus from the 1500s onwards [ 20 ]. Frequency counts for specific terms were extracted using the `ngramr`package in R Studio [ 21 ], which facilitates direct access to the corpus, and annual total frequencies were downloaded from Google Books Ngram Viewer Exports. This study used the most recently compiled general English version of the corpus (eng_2019), which excludes low optical character recognition quality and serials. The Google Books corpus contains numerals, did not require preprocessing, and contained 1,423,515,352,830 tokens in the 1940–2019 period.

The second corpus is a combination of two closely related corpora: the Corpus of Historical American English (CoHA [ 22 ]) and the Corpus of Contemporary American English (CoCA [ 23 ]). CoHA contains ~400 million words from 1810–2009, drawn from 115,000 texts distributed across everyday publications (fiction, magazines, newspapers, and non-fiction books). CoCA contains 560 million words from 1990–2019 drawn from ~500,000 texts (extracted from spoken language, TV shows, academic journals, fiction, magazines, newspapers, and blogs). A similar merged CoCA/CoHA corpus has been used in previous research [ 24 ].

After merging, the combined corpus spanning 1810–2019 was processed following recommendations from Alatrash et al. [ 25 ] to clean it without compromising the qualitative and distributional properties of the data. This process included first excluding the special token “@”, which appears in 5% of the COHA corpus (introduced for legal reasons), malformed tokens that are possible artifacts of the digitization process or the data processing, and clean-up performed using the web interface (“&c?;”, “q!”, “|p130”, “NUL”), and removing escaped HTML characters (“(STAR)”, “<p>”, “<>”). Other symbols were excluded after manual inspection of the corpus (e.g., “//”, “PHOTO”, “(COLOR)”, “ILLUSTRATION”). Blogs were also excluded (“web” = 89,054 articles; “blog” = 98,788 articles) for not containing associated year data. Forty-one lines were removed for missing text data (3 fiction, 11 news, 25 magazines, 2 spoken text). The cleaned corpus was then lower-cased and punctuation (commas, periods, question marks) was removed. Numerals and function words were retained to mirror the Google Books corpus. The final combined corpus contained 931,569,490 tokens from 370,091 texts from academic articles ( n = 25,418), fiction books ( n = 30,497), magazines ( n = 136,493), newspapers ( n = 113,440), non-fiction books ( n = 2,635), spoken language ( n = 43,210) and TV shows ( n = 18,398). The current study restricted the corpus period from 1940 to 2019 using 716,070,640 tokens from 330,970 articles. Although very large, the combined CoCA/CoHA corpus was therefore 0.05% the size of the Google Books corpus.

Generic terms

We examined 24 generic terms (bigrams and trigrams) by combining four adjectival terms (“mental”, “mental health”, “psychiatric”, “psychological”) with six nouns (“condition”, “disease”, “disorder”, “disturbance”, “illness”, “problem”). The popularity of each term in each time period was examined as its prevalence as a share of all terms in that period.

The relative frequency of generic terms for mental ill health as a proportion of all terms was extracted annually for the Google Books corpus but by decade (i.e., 1940–1949, 1950–1959 etc.) for the combined CoHA/CoCA corpus in view of its smaller size and the relative sparsity of the generic terms. Three sets of historical trends in the popularity of the generic terms were examined in parallel for the two corpora. First, we examined the frequency of the generic terms collectively to assess whether these terms have changed in their overall popularity. Second, we examined the frequency of the alternative adjectival and then noun terms relative to one another, to evaluate which terms have risen and fallen in relative popularity. In these analyses, the frequency of an adjectival expression is summed across all nouns it combines with (e.g., “mental” = “mental condition” + “mental disease” + “mental disorder” + “mental disturbance” + “mental illness” + “mental problem”) and vice versa. Finally, we examined trends in the relative popularity of the 24 compound generic terms to determine which have risen and fallen in dominance.

Figs 1 and 2 presents the combined relative frequency of the 24 generic terms in the Google Books and CoCA/CoHA corpora. Both corpora show strong upward trends, with the generic terms more than twice as prevalent in the most recent time period as at the beginning of the study period. This rise is consistent with the growing cultural salience of mental health and illness, and the rise of psychiatry, clinical psychology, and other mental health professions through the 20 th century and since.

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https://doi.org/10.1371/journal.pmen.0000032.g001

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Figs 3 and 4 displays the relative popularity of the adjectival components of the generic terms, each expressed as a percentage of all such expressions in each time period. The two corpora yield very consistent patterns. “Mental” is clearly the most prominent expression throughout the 80-year study period, reducing its share of all expressions only slightly. “Psychiatric” and “psychological” both emerge as increasingly popular adjectival expressions in the 1960s but then remain relatively stable. “Mental health” emerges in the 2000s but is always less popular than its alternatives.

https://doi.org/10.1371/journal.pmen.0000032.g003

https://doi.org/10.1371/journal.pmen.0000032.g004

Figs 5 and 6 show the corresponding trends for the noun expressions, which are again broadly consistent across corpora. Terms incorporating “disease” fell in popularity over time (especially in Google Books), those incorporating “problem” and “disturbance” were relatively unpopular but stable, and “illness” rapidly becomes the dominant noun term in the 1950s and steadily increased its popularity since then. The trajectories of the moderately popular “disorder” and “condition” terms are less clear, the former rising gradually in Google Books but falling across the first two decades in CoHA/CoCA, and the latter showing a general decline in recent decades.

https://doi.org/10.1371/journal.pmen.0000032.g005

https://doi.org/10.1371/journal.pmen.0000032.g006

Figs 7 and 8 , finally, present the 10 most popular complete terms in each corpus, calculated based on their average relative frequency over the eight decades. The terms are ordered from bottom to top in average relative frequency, with the summed relative frequency of the 14 least popular terms represented by the white “Remainder” band. The two corpora yield highly convergent rankings, sharing nine of the top 10 terms and with their top four (“mental illness”, “mental disorder”, “mental condition”, “mental disease”) in identical order. “Mental illness” steadily rises to be the dominant generic term, “mental disease” steadily falls, “mental disorder” becomes a stable distant second, and two terms with “psychiatric” gain some ground in recent decades. However, most of the terms are of very low prevalence and demonstrate few meaningful historical shifts in popularity. Normalizing or de-medicalizing terms incorporating “problem” appear low in the top 10 once (Google Books) or twice (CoHA/CoCA), but those incorporating “mental health” do not.

https://doi.org/10.1371/journal.pmen.0000032.g007

https://doi.org/10.1371/journal.pmen.0000032.g008

In two large English language text corpora, we found consistent patterns in the popularity of a comprehensive set of generic terms for mental ill health. These patterns can be divided into those that are relatively stable over time and those that represent historical shifts. With regard to stable patterns, “mental” was overwhelmingly the most prevalent adjectival expression within generic terms throughout the 1940–2019 study period, with “psychiatric” and “psychological” far behind and “mental health” a very infrequent alternative. “Illness” was almost equally dominant as a noun expression within generic terms, with “disorder”, “disease”, and “condition” in a second tier and “disturbance” and “problem” rare. The relative unpopularity of “mental disorder” is surprising given the term’s ratification by influential psychiatric classifications. With regard to generic terms rather than their components, “mental illness” is easily the most prevalent in the study period, representing half or more of all uses of the 24 generic terms since the 1960s. Along with “mental disorder”, “mental condition” and “mental disease” it consistently accounts for more than 80% of all uses of generic terms throughout the study period in both corpora.

Patterns of change are also evident. Terms beginning with “psychiatric” and “psychological” made modest gains in popularity over time, as did those ending with “disorder”, whereas “disease” and “condition” tended to decline. Terms commencing with “mental health” grew steeply but from a very low base, and therefore did not feature among the most common generic terms. “Illness” consolidated its high popularity over time and “mental illness” rapidly rose to prominence, increasing its share of usage at least three-fold from 1940 to 2019. “Mental illness” rose most steeply from the 1940s to the 1960s at the apparent expense of “mental disease”, which fell steeply out of fashion during this period.

These patterns present a complex picture of the role of medical framing within generic terminology. Critics of medicalization have opposed terms they see as embodying a medical or disease model and proposed new terms to replace them. Their critique is often motivated by philosophical objections as well as the belief that the replacement terms will reduce stigma. Our findings offer some encouragement to these critics. Terms referring to “disease” have fallen from favor and those that include “psychiatric”, identifying mental ill health with a medical specialty, have not established a strong foothold. Normalizing terms such as “mental health problem”, which has no medical connotation and implies that mental ill health is an everyday dilemma to be solved, have become more prevalent in recent years.

Several findings point in the opposite direction, however. “Illness” may be less wedded to organic pathology than “disease”, but it is medical nonetheless and remains the dominant noun when referring to mental ill health. Its dominance, especially in the expression “mental illness”, has only increased in recent years, despite decades of criticism surrounding its legitimacy [ 13 ]. Less medically saturated terms that incorporate “mental health” or “problem” remain unpopular, at least as indexed by appearance in diverse forms of text, calling into question claims that “mental health” is increasingly used as a euphemism for “mental illness” [ 26 ]. Efforts to overhaul generic terminology have thus far not been effective in bringing about substantial change, and the rising prevalence and historical durability of “mental illness” suggests that altering public preferences for generic terms may be difficult. It could be argued that “mental illness” foregrounds subjective experience in its adjectival and noun components and should therefore be embraced rather than dismissed as medicalizing [ 19 ].

There has been very little systematic research on generic terms for mental ill health, so many possible avenues for future work are open. The corpora examined in the present study primarily represent written language generated by people outside the mental health professions, and it would be informative to assess preferences within and between these professions (e.g., clinical psychology, psychiatry, mental health nursing, social work). It would be equally informative to evaluate how professionals, laypeople and service users construe the differences in connotation between generic terms as well as their preferences among them, just as studies have examined preferences for alternative ways of referring to service users [ 8 ]. One informal exploration found that “some people prefer the phrase ‘mental illness’ as it emphasizes the seriousness of the conditions experienced by people; others prefer ‘mental health problem’ because they see it as less stigmatizing; others prefer mental ‘disorder’ as potentially encompassing both ‘problems’ and ‘illnesses’ while also acknowledging the non-medical dimension” [ 27 ] (p.46)]. A more systematic empirical investigation of understandings and preferences for generic terms is overdue.

Equally important is to establish whether generic terms have differential effects on perceptions of and by people experiencing mental ill health. Although vignette studies find few effects on stigmatizing attitudes [ 16 , 18 ], they are limited in quantity, in realism, and in the range of terms examined. No studies have explored whether generic terms have implications for how people with mental ill health perceive themselves (aside from Fox et al.’s [ 17 ] examination of self-stigma) or for how clinicians view them, including possible effects on the perceived durability, causation, or appropriate treatment implied by different terms. Is a condition described as a “psychiatric disease” likely to be perceived as more serious, organic, and suitable for pharmacological treatment than one described as a “mental health problem”? Examining the possible implications of different terms for how the general public and affected persons perceive and evaluate mental ill health should be a research priority.

Our study has several limitations. First, it only examines terms in English and its findings are unlikely to generalize to other languages. It would be worthwhile exploring shifts in preferred terminology in other linguistic and cultural contexts. Second, the study’s datasets ended in 2019 and there may have been significant changes since that time, during a period of intense attention to mental ill health. Future studies should examine ongoing terminological shifts. Third, the two corpora are drawn entirely (Google Books) or primarily (CoHA/CoCA) from written texts drawn from specific regions and therefore cannot be presumed to correspond to spoken language use or equally to all relevant geographical communities. More colloquial spoken language might employ different terms from written texts, or the same terms with significantly differing frequencies. The corpora are entirely (CoCA/CoHA) or predominantly (Google Books) based on U.S.A. sources, for example, and the extent to which our findings generalize across the Anglosphere is uncertain. Regrettably, addressing these possibilities may be challenging because corpora of comparable size and historical depth that collect spoken language or text from other regions may not exist. Fourth and more generally, while corpus studies enable powerful, large-scale quantitative analyses of language use, they do not allow for more nuanced analyses of connotational meaning or detailed studies of how words are understood or used differently in specific communities or contexts and possibly even replaced. Qualitative studies that illuminate these complexities would be valuable to complement our findings.

Debates over diagnostic labels, person- versus identity-first language, and appropriate ways of referring to people using mental health services reflect a conviction that language use in the field of mental health is profoundly important. The present study points to intriguing shifts in the use of generic terms for mental ill health, but it remains to be seen whether the implications of these terms are equally consequential.

Acknowledgments

The research reported in this manuscript was supported by Australian Research Council Discovery Project DP210103984.

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Researchers provide a review and update on therapy of gastrointestinal tract tumors

by Xia & He Publishing Inc.

Gastrointestinal (GI) cancers, encompassing esophageal, gastric, small bowel, and colorectal carcinomas, represent a significant global health burden due to their high incidence and mortality rates. A review by M. Jesús Fernández-Aceñero and team provides an in-depth analysis of the molecular characteristics, prognosis, and current therapeutic strategies for these malignancies, highlighting the latest advancements and challenges in the field. The paper is published in the Journal of Clinical and Translational Pathology .

Esophageal carcinoma is among the ten most prevalent tumors globally, with squamous cell carcinoma (SCC) being the most common subtype. Despite geographical variations, SCC accounts for approximately 85% of esophageal cancer cases. Adenocarcinoma, particularly arising in Barrett's esophagus, is on the rise in Western countries.

Early-stage esophageal cancer is primarily treated with surgery, while advanced SCC relies on cytotoxic therapies. Neoadjuvant treatments are commonly employed to facilitate surgical resection. Despite these interventions, the prognosis remains poor, with less than 15% of patients achieving disease-free status at a five-year follow-up .

Recent genomic studies have provided valuable insights into the genetic alterations driving esophageal SCC. Technologies like whole-genome and whole-exome sequencing have identified potential therapeutic targets , such as the WNT/Notch1 pathway and the CCL2-CCR2 axis. However, targeted therapies, including EGFR inhibitors, have yet to demonstrate clinical efficacy in phase 3 trials, underscoring the need for predictive biomarkers to personalize treatment strategies .

Gastric cancer (GC) ranks as the fifth most common malignancy and the fourth leading cause of cancer-related deaths worldwide. The incidence of GC is particularly high in Eastern Asia and Eastern Europe, with significant differences in screening programs, clinical characteristics, and patient management between Asian and Western regions.

Advances in molecular biology have led to the identification of key genetic alterations in GC, contributing to the development of molecular classifications like those by The Cancer Genome Atlas (TCGA) and the Asian Cancer Research Group (ACRG) .

A promising therapeutic target in GC is the tight junction protein claudin-18 isoform 2 (CLDN 18.2), which is overexpressed in up to 30% of gastric and gastroesophageal carcinomas. Zolbetuximab, an anti-CLDN 18.2 antibody, has shown efficacy in clinical trials, offering a new treatment avenue for HER2-negative gastric and gastroesophageal adenocarcinomas. Despite the potential of fibroblast growth factor receptor (FGFR) inhibitors, their success has been limited, likely due to the diverse genetic alterations affecting FGFR .

Small bowel carcinoma is relatively rare compared to other GI cancers. The molecular landscape of these tumors is less understood, but recent studies have started to uncover the genetic changes involved. Treatment strategies for small bowel carcinoma often mirror those for other GI cancers, including surgical resection and chemotherapy. The identification of specific molecular alterations could lead to more targeted therapies in the future .

Colorectal cancer (CRC) is one of the most common malignancies worldwide. Advances in molecular biology have identified several genetic mutations and pathways involved in CRC pathogenesis, including APC, KRAS, and TP53 mutations. These discoveries have paved the way for targeted therapies and personalized treatment approaches. Immunotherapy, particularly immune checkpoint inhibitors, has shown promise in CRC, especially in microsatellite instability-high (MSI-H) tumors .

The future of gastrointestinal cancer treatment lies in a deeper understanding of the molecular mechanisms underlying these diseases. Research is ongoing to elucidate the molecular pathogenesis of these cancers, which could lead to the development of more effective and personalized therapies. The integration of high-throughput molecular techniques and next-generation sequencing has the potential to identify novel targets and biomarkers, paving the way for personalized medicine in gastrointestinal oncology.

The molecular characterization of GI tumors has significantly advanced our understanding of these malignancies, leading to the development of targeted therapies and personalized treatment strategies. However, challenges remain, including the need for reliable predictive biomarkers and overcoming resistance to targeted treatments. Continued research is essential to improve the prognosis and treatment outcomes for patients with GI cancers.

This comprehensive review underscores the importance of integrating molecular findings into clinical practice to enhance the management of gastrointestinal tumors, ultimately aiming for better patient outcomes and personalized therapeutic approaches .

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Gender, immunological response, and covid-19: an assessment of vaccine strategies in a pandemic region of oaxaca, méxico.

1. Introduction

2. materials and methods, 2.1. study population, inclusion criteria, and vaccines, 2.2. sample collection, 2.3. the enzyme-linked immunosorbent assay (elisa), 2.4. statistical analysis, 4. discussion, 5. conclusions, author contributions, institutional review board statement, informed consent statement, data availability statement, acknowledgments, conflicts of interest.

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Rodríguez-Martínez, L.M.; Chavelas-Reyes, J.L.; Medina-Ramírez, C.F.; Cabrera-Santos, F.J.; Fernández-Santos, N.A.; Aguilar-Durán, J.A.; Pérez-Tapia, S.M.; Rodríguez-González, J.G.; Rodríguez Pérez, M.A. Gender, Immunological Response, and COVID-19: An Assessment of Vaccine Strategies in a Pandemic Region of Oaxaca, México. Microbiol. Res. 2024 , 15 , 1007-1015. https://doi.org/10.3390/microbiolres15020066

Rodríguez-Martínez LM, Chavelas-Reyes JL, Medina-Ramírez CF, Cabrera-Santos FJ, Fernández-Santos NA, Aguilar-Durán JA, Pérez-Tapia SM, Rodríguez-González JG, Rodríguez Pérez MA. Gender, Immunological Response, and COVID-19: An Assessment of Vaccine Strategies in a Pandemic Region of Oaxaca, México. Microbiology Research . 2024; 15(2):1007-1015. https://doi.org/10.3390/microbiolres15020066

Rodríguez-Martínez, Luis M., José L. Chavelas-Reyes, Carlo F. Medina-Ramírez, Francisco J. Cabrera-Santos, Nadia A. Fernández-Santos, Jesús A. Aguilar-Durán, Sonia M. Pérez-Tapia, Josefina G. Rodríguez-González, and Mario A. Rodríguez Pérez. 2024. "Gender, Immunological Response, and COVID-19: An Assessment of Vaccine Strategies in a Pandemic Region of Oaxaca, México" Microbiology Research 15, no. 2: 1007-1015. https://doi.org/10.3390/microbiolres15020066

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This paper is in the following e-collection/theme issue:

Published on 11.6.2024 in Vol 26 (2024)

Integrated Real-World Data Warehouses Across 7 Evolving Asian Health Care Systems: Scoping Review

Authors of this article:

• Wen-Yi Shau 1 * , MD, PhD   ; 
• Handoko Santoso 1 , MD   ; 
• Vincent Jip 2 , BE   ; 
• Sajita Setia 2 * , MD  

1 Regional Medical Affairs, Pfizer Corporation Hong Kong Limited, Hong Kong, Hong Kong, China (Hong Kong)

2 Transform Medical Communications Limited, Wanganui, New Zealand

*these authors contributed equally

Corresponding Author:

Sajita Setia, MD

Transform Medical Communications Limited

184 Glasgow Street

Wanganui, 4500

New Zealand

Phone: 64 276175433

Email: [email protected]

Background: Asia consists of diverse nations with extremely variable health care systems. Integrated real-world data (RWD) research warehouses provide vast interconnected data sets that uphold statistical rigor. Yet, their intricate details remain underexplored, restricting their broader applications.

Objective: Building on our previous research that analyzed integrated RWD warehouses in India, Thailand, and Taiwan, this study extends the research to 7 distinct health care systems: Hong Kong, Indonesia, Malaysia, Pakistan, the Philippines, Singapore, and Vietnam. We aimed to map the evolving landscape of RWD, preferences for methodologies, and database use and archetype the health systems based on existing intrinsic capability for RWD generation.

Methods: A systematic scoping review methodology was used, centering on contemporary English literature on PubMed (search date: May 9, 2023). Rigorous screening as defined by eligibility criteria identified RWD studies from multiple health care facilities in at least 1 of the 7 target Asian nations. Point estimates and their associated errors were determined for the data collected from eligible studies.

Results: Of the 1483 real-world evidence citations identified on May 9, 2023, a total of 369 (24.9%) fulfilled the requirements for data extraction and subsequent analysis. Singapore, Hong Kong, and Malaysia contributed to ≥100 publications, with each country marked by a higher proportion of single-country studies at 51% (80/157), 66.2% (86/130), and 50% (50/100), respectively, and were classified as solo scholars . Indonesia, Pakistan, Vietnam, and the Philippines had fewer publications and a higher proportion of cross-country collaboration studies (CCCSs) at 79% (26/33), 58% (18/31), 74% (20/27), and 86% (19/22), respectively, and were classified as global collaborators . Collaboration with countries outside the 7 target nations appeared in 84.2% to 97.7% of the CCCSs of each nation. Among target nations, Singapore and Malaysia emerged as preferred research partners for other nations. From 2018 to 2023, most nations showed an increasing trend in study numbers, with Vietnam (24.5%) and Pakistan (21.2%) leading the growth; the only exception was the Philippines, which declined by –14.5%. Clinical registry databases were predominant across all CCCSs from every target nation. For single-country studies, Indonesia, Malaysia, and the Philippines favored clinical registries; Singapore had a balanced use of clinical registries and electronic medical or health records, whereas Hong Kong, Pakistan, and Vietnam leaned toward electronic medical or health records. Overall, 89.9% (310/345) of the studies took >2 years from completion to publication.

Conclusions: The observed variations in contemporary RWD publications across the 7 nations in Asia exemplify distinct research landscapes across nations that are partially explained by their diverse economic, clinical, and research settings. Nevertheless, recognizing these variations is pivotal for fostering tailored, synergistic strategies that amplify RWD’s potential in guiding future health care research and policy decisions.

International Registered Report Identifier (IRRID): RR2-10.2196/43741

Introduction

Asia is a vast and diverse continent that also represents varied health care systems and socioeconomic challenges. Multiple evidence-driven approaches tailored to each nation’s unique health care and research context are required to draw essential data to support the ambitious goals for universal health coverage in each country [ 1 , 2 ]. The strength and necessity of real-world data (RWD) and their concrete data analytical interference in terms of real-world evidence (RWE) are integral to this evidence generation. RWE has the potential to inform health technology assessments (HTAs), guide evidence-driven policies, and streamline service delivery [ 3 ]. However, as crucial as RWE is, the Asian health care landscape lacks a cohesive framework to harness its full potential despite its promise in pharmacoeconomics, pharmacovigilance, and pharmacoepidemiology [ 3 , 4 ].

The utility of RWD and RWE becomes even more apparent with large integrated research databases within health systems. The integrated warehouses offer vast connected data sets that sustain the statistical rigor and can assist in providing insights with minimal bias and confounding [ 5 ]. However, these data reservoirs have not been vastly studied across health care systems, which limits their broader utility in health care research; RWE data generation; and, consequently, universal health coverage [ 6 ].

Recognizing this potential, our previous research explored integrated RWD warehouses within 3 diverse health care pilots for Taiwan, India, and Thailand [ 7 ]. Our systematic research identified strong differences in the types of RWD and their warehouses in the 3 countries. Still, the results only partly reflected their divergent economic, social, and clinical settings. Hence, we continued to conduct similar research in many other diverse Asian health care systems in line with our published protocol [ 8 ].

The literature on RWD practices and awareness of corresponding warehouses in certain Asian countries such as China, Japan, and South Korea is significant [ 5 , 9 - 17 ], partly because these countries also have recommendations on the utility of RWE by external regulators [ 3 ]. This study sought to understand the evolving landscape of RWD use and its implications across Hong Kong, Indonesia, Malaysia, Pakistan, the Philippines, Singapore, and Vietnam, where RWD practices are emerging or undergoing significant development [ 4 ]. Our selection of countries for this scoping review was strategically based on selecting a contrasting spectrum of HTA maturity across countries with evolving HTA systems, ranging from relatively mature systems in Singapore, Thailand, and Malaysia to emerging frameworks in Indonesia, the Philippines, and Vietnam and systems in the nascent stages in Pakistan [ 18 , 19 ]. Each nation, with its individualistic health care challenges and unique research capabilities, underscores the need for understanding recent patterns in RWD research and use of clinical research warehouses, especially in light of the marked underrepresentation of specific Asian demographics in traditional randomized clinical trials [ 20 ]. By systematically analyzing both single-country studies (SCSs) and cross-country collaboration studies (CCCSs), this research aimed to delineate the current state of RWE generation and collaborative research initiatives for RWE from integrated databases across different nations in Asia. Our objectives also included obtaining a comprehensive understanding of the preference for RWD methodologies by contrasting the emphasis on comparative effectiveness research (CER) with descriptive studies and discerning the preferred and popular real-world research databases.

The cyclical interplay between a nation’s economic strength, health care infrastructure, and research capacity perpetuates disparities in RWD generation. We hypothesized that Asian countries with less extensively documented RWD research trends could be effectively clustered based on systematic patterns in RWD generation. This streamlined our objective to evaluate trends in RWD generation and shed light on targeted capacity-building strategies essential for informed health care policy making. Through this rigorous extended scoping research, we aimed to present insights that resonate with clinical stakeholders, medical researchers, and health policy makers, thereby guiding the formulation of strategies attuned to each nation’s health care challenges and research diversities and complexities.

Research Approach

Our research approach was methodically aligned with the guidelines set forth by the PRISMA-ScR (Preferred Reporting Items for Systematic Reviews and Meta-Analyses extension for Scoping Reviews) [ 21 ]. Our published protocol specified a preliminary focus on 3 countries—India, Thailand, and Taiwan—as a representative pilot to explore the diversity of health care systems and RWD use in Asia [ 8 ]. The outcomes from our initial study covering Taiwan, India, and Thailand have been previously published [ 7 ]. Relevant insights from the latter publication were incorporated into the archetyping of the nations wherever applicable. In this study, we expanded our protocol to 7 other countries. However, we maintained consistency with the original protocol’s methodological framework to ensure comparability across all countries studied. This expansion was aligned with our initial intent to potentially include more countries following the first study across 3 countries.

The search strategy is described in Table S1 in Multimedia Appendix 1 . We filtered our search to include only English-language publications from the last 5 years, aiming to highlight current and internationally relevant RWE or RWD. As the conversion of RWD to RWE emphasizes the stringent analytical processes necessary to yield valuable and credible findings, we intentionally chose to rely on PubMed as an exclusive source of relevant citations for screening. Our goal was to assess studies yielding robust RWD featured in esteemed, indexed, and peer-reviewed journals while reducing potential duplicates. By focusing solely on PubMed, we tried to identify research representing this standard and offering evidence of the utmost scientific integrity. This strategy aligns with the specifications outlined in our protocol [ 8 ].

Screening Eligible Studies for Data Analysis

All retrieved study abstracts were directly imported into the Covidence software (Veritas Health Innovation) for subsequent screening and data extraction. Studies were initially screened against predefined eligibility criteria to capture research from integrated RWD. The criteria encompassed 4 domains described in the original protocol: database type and requirement for research across >1 hospital or clinic, publication nature, RWD study type, and publication scope [ 8 ]. The scope of publication was adapted in this study to include citations on databases involving 1 of the target nations (Hong Kong, Indonesia, Malaysia, Pakistan, Philippines, Singapore, or Vietnam). The inclusion criteria also considered studies featuring nontarget countries as long as 1 of the 7 target nations was involved. Table S2 in Multimedia Appendix 1 provides a snapshot of the eligibility criteria used in this study.

Duplicate removal and a 2-step eligibility screening process were conducted in Covidence. The initial step (phase 1) assessed titles and abstracts, with relevant studies advancing to full-text evaluation in the second phase. Given the study volume, the screening for both phases was divided between 2 reviewers. An independent reviewer examined a random sample of 20% of the studies to maintain accuracy. Any ambiguities or discrepancies were collaboratively resolved, and another reviewer was consulted if needed. The final step involved data extraction and data analysis for eligible studies.

Data Extraction and Analysis

We used Covidence for data extraction through a custom template that covered the following:

• Basic study details: Covidence identifier (ID) based on the first author’s last name and publication year and title.
• Presence of cross-country collaboration in the research (CCCS or SCS).
• Nature of publication (clinical study or protocol).
• Study categorization: CER versus descriptive study (non-CER), with CER definitions adapted from Medical Subject Headings. We expanded the criteria for CER to standardize its meaning in the context of this research as the “studies comparing interventions and strategies (including the comparison between active and nonactive interventions and strategies) to prevent, diagnose, treat, and monitor health conditions using validated methods for confounders elimination, e.g., matching, and statistical adjustments like stratification, weighting, regression, instrumental variable analysis etc” [ 7 ].
• Research source database classification involving medical records, health insurance claims, clinical registries, pharmacy claims, or composite databases.
• Disease specifics: name and area of the target disease under study (defined by primary diagnosis and pathophysiology or by prime medical specialty in charge if they intersected). The disease categories encompassed cardiology and metabolic disorders (CVM), oncology, inflammatory and autoimmune disorders, infectious diseases and vaccines (IDV), and others. These categories represent major research fields in clinical medicine with significant disease burdens, selected to provide pertinent insights into RWD and RWE applications within these critical domains.
• Outcome types: clinical (treatment effect or safety), cost, or patient-reported outcomes (PROs), with PROs capturing direct patient responses.
• Demographics (adults, children, or both), number of centers, study participants, and length of study and duration between last data collected and year of publication. The length or duration represented the span from the study’s commencement to completion as specified by the authors.
• The unique names for the databases used. When provided, the name of the specific database used in each study was collected and organized by target nation, database type, and disease area.

A total of 2 reviewers collaboratively managed data extraction, and all extractions underwent quality checks by another reviewer to ensure the accuracy and reliability of the extracted data. However, this process was not conducted independently or blinded to the other reviewer’s decisions. Disagreements between the 2 reviewers were settled through discussions, and an additional reviewer was involved whenever there was a need for a consensus.

The final search was conducted on May 9, 2023, covering the preceding 5 years; to account for partial yearly data in 2018 and 2023, we calculated the equivalent of annual publication count using 365 multiplied by the average daily number of publications. We used linear regression, using the year as a continuous predictor variable, to understand the annual trend in nation study counts. This provided insights into the average annual trend in study numbers throughout the search period. To further even out year-to-year variations, a 2-year simple moving average (SMA) was applied to enhance the clarity of the data trends. This SMA approach was consistent with our previous research methodologies [ 7 ]. Given the study’s descriptive nature, there was no a priori statistical hypothesis. Statistical analyses were conducted to calculate point estimates and their associated errors. Categorical data were presented as frequencies and percentages, whereas continuous data were presented as means and SDs. We used Microsoft Excel (Microsoft Corp) for all data analyses. Adobe Illustrator was used for crafting high-definition figures for the main manuscript.

Eligible Studies

The search was conducted on May 9, 2023, and yielded 1483 studies with 1 duplicate. Of these 1483 studies, 553 (37.3%) were included in phase-2 screening, and 369 (24.9%) studies were eligible for data extraction ( Figure 1 ).

The vast majority of the publications (361/369, 97.8%) were original research, whereas the remaining 2.2% (8/369) were study protocols. The country-wise distribution of SCSs and CCCSs is illustrated in Multimedia Appendix 2 .

Geographic Distribution and Collaboration Relationship

Among the 369 studies that qualified for data extraction, Singapore, Hong Kong, and Malaysia each contributed to ≥100 publications, with respective counts of 157 (42.5%), 130 (35.2%), and 100 (27.1%). The other 4 nations—Indonesia, Pakistan, Vietnam, and the Philippines—were involved in fewer publications, with 8.9% (33/369), 8.4% (31/369), 7.3% (27/369), and 6% (22/369), respectively, and each had >50% of their studies classified as CCCSs (Table S3 in Multimedia Appendix 1 ). Given their lower overall study numbers and the predominance of CCCSs, these 4 nations were categorized as global collaborators in certain subsequent analyses ( Figure 2 [ 7 ]).

Countries beyond the 7 target nations of this study that were involved in collaborations were labeled as nontarget countries. The cross-country collaboration network across the 7 target nations and nontarget countries is described in Table S4 in Multimedia Appendix 1 . The average number of collaborative countries (ANC) indicates the cross-country interconnection for research of a given nation. The ANC varied from 2.2 for Singapore to 4.1 for the Philippines. Despite having the lowest ANC, Singapore was involved in 30.3% (77/254) of the studies, making it the highest contributor to CCCSs. On the other hand, Malaysia, with a higher ANC of 3.1, participated in 50% (50/100) of CCCSs, making it the most engaged collaborator within the solo scholars cluster. Malaysia participated in 56% (10/18) of CCCSs from Pakistan, 75% (15/20) of CCCSs from Vietnam, 81% (21/26) of CCCSs from Indonesia, and 84% (16/19) of CCCSs from the Philippines.

Nontarget countries were common collaborators in CCCSs across all 7 target countries, with their involvement ranging from 84% (16/19) to 98% (43/44).

Figures 3 and 4 depict the yearly average counts and growth rates of SCSs and CCCSs across the 7 target nations from the solo scholar and global collaborator clusters, respectively. Between 2018 and 2023, every target nation except for the Philippines, which experienced a decline of –14.5%, exhibited an upward trend in the average number of studies published. Vietnam led with the steepest growth rate at 24.5%, trailed by Pakistan at 21.2%. Among solo scholars, there were growing trends for all 3 nations, with a growth rate of 6.2% for Hong Kong, 8.7% for Singapore, and 16.3% for Malaysia. Due to the small average number of studies by year in some of the individual global collaborator nations, growth trends are also presented collectively for global collaborators as a cluster in Figure 4 . Duplicate studies within clusters were adjusted; thus, the sum of CCCSs from all 4 nations might be larger than the number of CCCSs of the cluster as whole. The growth rate was 21.2% in the global collaborators cluster after adjusting for duplicates.

Overall Attributes

Tables 1 and 2 show the primary attributes of the eligible studies from each target nation by SCS and CCCS. The variables presented were study type, disease domain, data source, outcomes, participant demographics, length of study duration, time gap from last data collected to publication, sample size, and number of research centers.

a Study numbers for database types and study outcomes may appear as duplicates; hence, the total percentage may not add up to 100. CCCS numbers may appear as duplicates for studies conducted in multiple target countries. The percentages may add up to less or more than 100 because of rounding.

b CER: comparative effectiveness research.

c CVM: cardiology and metabolic disorders.

d IDV: infectious diseases and vaccines.

e IAD: inflammatory and autoimmune disorders.

f EMR: electronic medical record.

g EHR: electronic health record.

h PRO: patient-reported outcome.

i Not applicable.

Study Type: CER or Non-CER (Descriptive)

Of the 369 studies, 221 (59.9%) were CER studies, with the remaining 148 (40.1%) being non-CER or descriptive. The relative representation of CER versus non-CER for SCSs and CCCSs is illustrated in Multimedia Appendix 2 . Singapore, Hong Kong, Malaysia, and Vietnam had a higher number of CER studies in both their SCSs and CCCSs. Vietnam’s SCSs had the predominant CER representation at 71% (5/7), followed by Singapore at 68% (54/80) and Hong Kong at 66% (57/86). Among CCCSs, Hong Kong led with 70% (31/44) CER studies, followed by Pakistan with 67% (12/18) and Singapore with 57% (44/77). There were more descriptive non-CER studies in SCSs from Pakistan, the Philippines, and Indonesia, resulting in the CER study percentages being 8% (1/13), 33% (1/3), and 43% (3/7), respectively.

Figure 5 shows the yearly trends of CER percentages from 2018 to 2023 broken down by SCS and CCCS. The consistency in trends was more noticeable in SCSs across the 7 target nations compared to CCCSs. An upward trend in CER study percentage was observed in SCSs from Hong Kong and the global collaborators. Conversely, Malaysia’s SCSs experienced a steady decrease in CER contribution over the same period.

The 2-year SMA trends for CER and descriptive studies are illustrated in Multimedia Appendix 2 for the biennial average from 2018 to 2023. Hong Kong consistently increased its CER contributions in both SCSs and CCCSs, increasing from 47% (9/19) between 2018 and 2019 to 73% (24/33) between 2022 and 2023 for SCSs and similarly from 61% (11/18) between 2018 and 2019 to 73% (8/11) between 2022 and 2023 for CCCSs. Other notable rises in CER contributions in CCCSs were observed in Malaysia (from 4/12, 33% between 2018 and 2019 to 12/16, 75% between 2022 to 2023), Indonesia (from 3/9, 33% between 2018 to 2019 to 7/10, 70% between 2022 and 2023), Pakistan (from 1/4, 25% between 2018 and 2019 to 8/9, 89% between 2022 and 2023), and Vietnam (from 4/9, 44% between 2019 and 2020 to 6/7, 86% between 2022 and 2023). Conversely, Malaysia’s SCSs saw a consistent decline in CER contribution over the 5 years, dropping from 67% (8/12) between 2018 and 2019 to 53% (10/19) between 2022 and 2023. Furthermore, all of Pakistan’s SCSs (12/12, 100%) were non-CER between 2018 and 2022.

Database Type

Of the 369 studies, 341 (92.4%) used a single database. Exclusive use of clinical registry databases was most common at 50.9% (188/369), followed by electronic medical records (EMRs) or electronic health records (EHRs) at 39.3% (145/369), health insurance and administrative claims at 1.4% (5/369), and pharmacy claims at 0.8% (3/369). The use of multiple databases was found in 7.6% (28/369) of the studies, primarily combining clinical registries and EMRs or EHRs ( Multimedia Appendix 2 ). Use of EMR or EHR databases was more common for SCSs (120/246, 48.8%; Multimedia Appendix 2 ). On the other hand, the predominant exclusive database warehouse for CCCSs was clinical registries, used in 73.2% (9/123) of the studies. EMRs’ or EHRs’ contribution to CCCSs was lower, representing only 20.3% (25/123) of CCCSs, which is considerably lower than their share in SCSs ( Tables 1 and 2 and Multimedia Appendix 2 ).

The use of the clinical registry database type consistently dominated across all CCCSs from all target nations, whether used on its own or in combination with other databases. For SCSs, (1) there were more clinical registries over EMRs or EHRs used in Indonesia, Malaysia, and the Philippines—the figures were 57% (4/7) versus 29% (2/7), 78% (39/50) versus 22% (11/50), and 67% (2/3) versus 33% (1/3), respectively; (2) Singapore’s use was almost even, with 58% (46/80) of the studies using clinical registries and 54% (43/80) using EMRs or EHRs; and (3) conversely, Hong Kong, Pakistan, and Vietnam used more EMRs or EHRs than clinical registries—80% (69/86) versus 26% (22/86), 69% (9/13) versus 23% (3/13), and 57% (4/7) versus 14% (1/7), respectively.

Figure 6 reveals the evolution of EMR or EHR contributions, both exclusively and in combination with other databases, in the previous 5 years. In SCSs, Malaysia and the global collaborators experienced a consistent decline in EMR or EHR use, whereas Hong Kong exhibited an increase. Malaysia’s EMR or EHR use in SCSs remained consistently at <50% during this period. In contrast to SCSs, where EMR or EHR use was predominant, EMR or EHR use in CCCSs from the target countries was always at <50% from 2018 to 2023, and no consistent time trend pattern was observed.

The 2-year SMA over the previous 5 years indicated that the exclusive use of EMRs or EHRs in SCSs from the 7 target nations increased from 46% (26/56) to 60% (49/82). In contrast, the reliance on clinical registry databases dipped from 50% (28/56) to 38% (31/82); Multimedia Appendix 2 ). For CCCSs, the distribution between clinical registries and EMRs or EHRs remained relatively steady, with clinical registries being the most common ( Multimedia Appendix 2 ).

Disease Area

The leading medical research area was CVM, accounting for 36.9% (136/369) of the studies, trailed by oncology and IDV, each with 14.9% (55/369). Inflammatory and autoimmune disorders was the least prevalent area, representing 6.2% (23/369) of the studies. The remaining 27.1% (100/369) of the studies pertained to various other diseases. The proportion of CVM studies grew from 28% (11/39) in 2018 to 39% (14/36) in 2023, peaking in 2020 with 49% (36/73). Conversely, the share contributed by IDV medical area increased from 8% (6/73) in 2020 to 25% (21/84) in 2022 and 17% (6/36) in 2023, surpassing oncology as the second most common disease and therapeutics research area in recent years ( Multimedia Appendix 2 ).

Study Outcomes

Most of the studies (348/369, 94.3%) presented clinical outcomes whether in terms of treatment effect or safety. There were 5.7% (21/369) of the studies that discussed cost outcomes, PROs, or a combination of these with clinical results. In the SCS category, every study from Pakistan focused on clinical outcomes, whereas cost outcomes were observed in SCSs from all countries except Malaysia and Pakistan. One study each from Hong Kong (1/86, 1%) and Malaysia (1/50, 2%) included PRO outcomes. In the CCCS category, none of the selected nations published studies focusing on cost outcomes ( Tables 1 and 2 ).

Study Population

Of the 369 studies obtained, 273 (74%) investigated adults, 24 (6.5%) focused on the pediatric age group, and 72 (19.5%) encompassed both adult and pediatric participants. Notably, in the SCS category ( Table 2 ), Pakistan (9/13, 69%) and the Philippines (2/3, 67%) reported higher proportions of mixed populations than of solely adult participants (Pakistan: 3/13, 23%; the Philippines: 1/3, 33%).

Pediatric representation in the CCCSs was 8.1% (10/123), slightly higher than in SCSs (14/246, 5.7%). Within CCCSs ( Table 2 ), Vietnam led in pediatric-focused research with 15% (3/20) of the studies, followed by Indonesia (3/26, 12%) and Pakistan (2/18, 11%).

Study Duration

Information about study duration was reported for 94.6% (349/369) of the studies. The average duration was 7.4 (SD 6.3) years, ranging from 0.01 to 35.8 years. In total, 3.5% (13/369) of the studies had a duration of >20 years. The mean for SCSs was higher at 7.5 (SD 6.0) years compared to that for CCCSs at 7.1 (SD 6.8) years ( Multimedia Appendix 2 ).

Among SCSs, the Philippines ( Table 2 ) topped the list with the longest average study duration of 12.0 (SD 8.2) years. As shown in Table 1 , Hong Kong followed closely with an average of 9.8 (SD 7.3) years. Conversely, Pakistan and Indonesia registered the shortest mean study durations with 2.1 (SD 1.9) years and 3.1 (SD 3.3) years, respectively. Over a 5-year span, based on a 2-year rolling average, the study duration in Indonesia showed an uptick, increasing from 0.6 years between 2018 and 2019 to 1.9 years between 2022 and 2023. Other target countries did not exhibit any consistent study duration trend patterns ( Multimedia Appendix 2 ).

For CCCSs, Pakistan ( Table 2 ) led with the longest mean study duration of 9.1 (SD 11.2) years, closely followed by the Philippines with 8.4 (SD 9.8) years. Malaysia ( Table 1 ) recorded the shortest average study duration at 5.5 (SD 7.4) years. The study duration in Indonesia’s CCCSs averaged 7.1 (SD 9.6) years, which was notably longer than that of its SCSs. Observing trends, there was a decline in the mean study duration of CCCSs in Malaysia, Pakistan, the Philippines, and Vietnam. Conversely, the average study duration in Singapore’s CCCSs steadily rose, increasing from 5.6 years between 2018 and 2019 to 9.1 years between 2022 and 2023 ( Multimedia Appendix 2 ).

Lag Between the Research Period and Publication

Of the eligible studies, most (205/369, 55.6%) were published between 2 and 5 years after the time of latest available data studied, 28.5% (105/369) were published after >6 years, and 9.5% (35/369) were published within 2 years. The remaining 6.5% (24/369) of the studies had unspecified year or years of research completion ( Multimedia Appendix 2 ).

Eligible studies from all the target nations showed a similar trend, with most (205/345, 59.4%) being published within 2 to 5 years after the research period. However, in both Singapore and Hong Kong, the time taken from research completion to publication was notably longer for both SCSs and CCCSs, averaging 5.8 (SD 3.0) years and 5.1 (SD 2.9) years for SCSs and 4.8 (SD 2.5) years and 5.0 (SD 2.6) years for CCCSs, respectively ( Table 1 ).

It is worth noting that 15.9% (17/107) of CCCSs were published within 2 years of the research period but only 7.6% (18/238) of SCSs were published within this time frame. We observed upward trends in the time to publication within 2 years from 2018 to 2023. For SCSs, it was from 3% (2/63) to 15% (12/83), and for CCCSs, it was from 16% (6/38) to 20% (5/25; Multimedia Appendix 2 ).

The publication time lag also varied according to the RWD source ( Multimedia Appendix 2 ). Among studies that relied on a single database, the highest percentage of those published within 2 years after the research consistently used the EMR or EHR database type. This trend held true for both SCSs and CCCSs, with EMRs or EHRs dominating the quick turnaround for publications. Specifically, among SCSs, 12.8% (15/117) of studies using the EMR or EHR database type were published within this 2-year time frame, whereas only 2% (2/94) of those using clinical registry databases had the same publishing speed. CCCSs had a consistent pattern—26% (6/23) of the studies that used EMRs or EHRs were published within 2 years, in contrast to the 14% (11/78) of the studies that used clinical registry databases. Notably, no studies published within the 2-year window used the health insurance and medical claims database type or the pharmacy claims database type.

Study Size: Sample Size and Number of Centers

The sample size was specified in 98.1% (362/369) of the studies and varied considerably, ranging from as few as 16 to >154,500,000. The average sample size was 672,352 (SD 8,364,280). The average in CCCSs was much higher at 1,824,035 (SD 14,530,091) compared to 106,029 (SD 397,050) in SCSs ( Multimedia Appendix 2 ). The 2-year SMA of sample size in SCSs indicated an increasing trend from 51,706 to 178,675 from 2018 to 2023. In contrast, a sharp decline was observed in CCCS sample sizes from 5,543,271 to 548,107 during the same period ( Multimedia Appendix 2 ). Hong Kong had the highest average sample size in SCSs (205,006, SD 607,767) as well as in CCCSs (4,187,122, SD 23,979,119; Tables 1 and 2 ).

The number of participating centers was only specified in 44.4% (164/369) of the studies. The mean number of centers was 44.3 (SD 120.3), ranging from 2 to 1119. As noted in Tables 1 and 2 , Hong Kong reported the highest average number of study centers at 42.7 (SD 60.2) for SCSs, followed by Pakistan at 19.9 (SD 13.4) and Malaysia at 19.5 (SD 15.4). For CCCSs, Vietnam had the highest mean number of study centers at 181.9 (SD 236.9), which was higher than twice the overall CCCS mean of 78.4 (SD 178.6).

The 2-year SMA for the number of study centers in SCSs initially rose from 81.8 between 2018 and 2019, reaching a peak of 131.9 between 2019 and 2020 before declining to 99.1; 37.1; and, finally, 30.8 in the subsequent years. In CCCSs, there was a downtrend starting from 16.1 between 2018 and 2019 to 10.7 centers at its lowest point between 2020 and 2021 before bouncing back to 33.9 centers between 2022 and 2023 ( Multimedia Appendix 2 ).

Table S5 in Multimedia Appendix 1 provides and overview of the number of centers involved in RWD studies across various database types in the target countries. The Philippines reported the highest average number of centers (197.0) in studies using EMR or EHR databases, whereas Vietnam had the highest average (65.8) in studies associated with clinical registry databases. Information on the number of centers was not reported in many studies, particularly those using health insurance and claims databases.

Database Names

Multimedia Appendix 3 provides the specific name or names of the databases used in each study organized by target country, database type, and disease area.

Principal Findings

This scoping review was based on our earlier research completed for Taiwan, India, and Thailand in Asia [ 7 ]. We have now expanded insights on integrated real-world study databases across 7 additional diverse Asian health care systems in Hong Kong, Indonesia, Malaysia, Pakistan, the Philippines, Singapore, and Vietnam. This has enabled us to provide a comprehensive perspective on the current landscape of RWE generation in these nations, thus aiding stakeholders in formulating informed research and policy decisions [ 22 , 23 ].

The archetyping of the target nations into 2 country clusters (ie, solo scholars and global collaborators) allowed us to uncover distinct patterns reflective of differing resources, priorities, and strategic objectives. Solo scholars tend to conduct independent studies, which exemplifies that these nations are equipped with robust research infrastructures. This autonomy allows for a deep dive into national health issues tailored to specific local contexts and country priorities [ 24 ]. On the other hand, global collaborators frequently engaged in international partnerships, a strategy that would be likely born out of necessity due to limited research funding and resources within the nation and, hence, a greater reliance on collaborative networks [ 25 ]. We found that >90% of CCCSs from global collaborators involving nontarget countries (76/83, 92%) also partnered with some of the 7 target nations. This collaborative pattern possibly indicated a stronger network of research collaboration within the neighboring regions of Asia. In contrast, among solo scholars such as Singapore and Hong Kong, despite the high number of CCCSs involving nontarget countries (>94%; 73/77, 95% in Singapore and 43/44, 98% in Hong Kong), there were approximately 40% of studies (33/77, 43%; 17/44, 39%) not partnered with other target nations. However, the emphasis on global or regional priorities could overshadow the unique health challenges and priorities of these nations. This imbalance can lead to a scarcity of data and insights directly applicable to domestic health care [ 26 ].

Among solo scholars, Hong Kong, Malaysia, and Singapore emerged as significant contributors to domestic RWD publications, showcasing their robust research infrastructure and commitment to harnessing RWD. In contrast, global collaborators, and especially the Philippines, had fewer studies, which could be attributed to various reasons, from funding sources to bureaucracies in research grant administration [ 27 , 28 ]. Singapore emerged as the predominant contributor to the CCCS pool, but on average, Malaysia collaborated with more countries. It is also particularly noteworthy that, from 2018 to 2023, most of our target nations (except the Philippines) manifested an increasing trend in the average number of studies published annually, with Vietnam leading in growth. Vietnam’s health care system has been consistently advancing, and the nation is enhancing its research capabilities; this progress has been widely acknowledged in the literature [ 23 , 29 ].

Diving deeper into the nature of these studies, there was an evident leaning toward CER over descriptive studies in several nations, such as Vietnam, Singapore, and Hong Kong. The proportion of CER to non-CER studies offers insights into the nature of the questions that researchers in different regions were keen to address. Regions with a higher proportion of CER studies, such as Vietnam’s SCSs, suggest an active interest in comparing the outcomes of different interventions and preventive and prognostic strategies, which can be crucial for policy decisions, including effective containment of COVID-19. Vietnam, along with Singapore and Hong Kong, has been extensively praised for effectively controlling the spread of COVID-19, especially during the early stages of the pandemic [ 30 , 31 ]. While we cannot assert a direct link with certainty, the possibility of a connection through RWD from CER also playing a role in fostering better-informed public health decisions cannot be denied [ 32 ]. Moreover, Singapore and Hong Kong’s well-established reputation as quality research hubs in Asia could further underscore the potential impact of robust research frameworks [ 33 , 34 ].

The predilection for certain database types, be it clinical registries or EMRs or EHRs, was due to a combination of availability and convenience and, hence, the ease of use of these databases in different regions. The 5-year trend showcased the evolving dynamics in RWD research. Nations leaning toward EMR or EHR databases, such as Hong Kong, might be signaling greater digitization of their health records or the perceived value in this data type [ 35 , 36 ], whereas nations such as Indonesia, Malaysia, and the Philippines primarily leveraged clinical registries. Apart from Hong Kong, Pakistan and Vietnam also displayed a marked inclination toward EMR or EHR use.

The variance in research duration offers a window into research efficiency and the possible administrative or infrastructural bottlenecks. Longer durations in nations such as the Philippines could indicate complex, long-term studies or challenges in study execution and continuity [ 27 ]. In addition, the expectation that RWD accelerates evidence generation is not reflected in our findings, where there was an average lag of approximately 2 to 5 years from research completion to publication. Notably, around 90% of the studies (310/345, 89.9%) extended beyond 2 years to reach publication, suggesting room for enhancing the efficiency of evidence generation, potentially through targeted support mechanisms. Interestingly, a comparatively higher proportion of CCCSs in contrast to SCSs (17/107, 15.9% vs 18/238, 7.6%) were published within 2 years, hinting at the efficiency benefits that international partnerships might offer in expediting research outputs. Larger study sample sizes and a greater number of centers, as observed in Hong Kong, reflect the ability to conduct expansive studies from territory-wide linked databases, indicating a propensity for large-scale, nationally representative research [ 37 ]. Similarly, the number of centers involved could also indicate the collaborative spirit within the research community or the need to pool resources.

The strategic application of RWE in health care research and policy formation is clearly evident on a global scale. Although there is an increasing reliance on HTA as a pivotal tool for informed health care decision-making, the nations categorized largely under the global collaborators cluster face a tremendous challenge in health care infrastructure and economic constraints to lead independent RWE that could shape local health care policy and reimbursement decision-making [ 38 , 39 ]. Hence, open data initiatives and international collaborations such as the Observational Health Data Sciences and Informatics and the HTAsiaLink, respectively, are crucial in this regard [ 40 , 41 ]. There is also the potential for other international networks (eg, the International Network of Agencies for Health Technology Assessment) in facilitating the alignment of health care policies with benchmark evidence-based practices [ 42 ].

Limitations

In our efforts to comprehensively assess the landscape of research in the field, we encountered challenges in data extraction, mainly due to inconsistencies in how various studies reported certain characteristics such as the number of centers or study periods. For instance, while some studies provided clear details on their duration, others only specified the enrollment phase, leaving us to speculate on the follow-up or observation period. In addition, the screening and extraction process involved multiple reviewers working under tight schedules. We acknowledge that this approach diverges from the ideal practice of having at least 2 independent investigators screen each title, abstract, and full text and subsequently extract data blinded to each other’s decisions. While we implemented quality checks, including spot assessments and team discussions, the constraints may have inevitably introduced occasional inaccuracies.

Moreover, in some nations, the limited number of studies could make percentage-based analyses less reflective of the true study landscape. Still, these analyses offer a preliminary understanding of research trends in those regions. We must also acknowledge that our analysis, with a search conducted in May 2023, assumes a linear distribution for studies in 2019 and 2023, which might slightly deviate from the actual figures due to approximations.

We might have inadvertently overlooked some relevant studies, especially if abstracts failed to mention key terms such as RWD or RWE. Our decision to focus on English-language publications and rely solely on PubMed for citations, while strategic, may have missed a handful of non–English-language studies or those in other databases. Nonetheless, the vast number of studies that we analyzed offers valuable insights into using RWD to produce RWE in our target countries. In addition, we did not report study design and funding sources for the included studies. While this information may have been valuable, we faced challenges in the extraction of study design and funding information due to lack of clear and consistent reporting across publications. This necessitated the exclusion of these variables to prevent any subjective interpretations.

Despite these challenges, our findings underscore a need in the research community—a call for clearer, more standardized reporting on the databases used, study design, analysis methods, and important time points. A particular area that warrants attention is the clarity in detailing study duration (which should encompass the recruitment and observation periods), study design, and funding sources. While we recognize this study’s limitations, we believe that it also paves the way for refining research methodologies in the future.

Conclusions: The Path Forward

Our comprehensive assessment of studies across the selected nations reveals intricate patterns that explain the diverse research landscape for RWD generation. Each nation’s unique landscape for contemporary integrated RWD warehouses tells a narrative that is partially attributed to their economic, clinical, and research settings. Delving deeper into these patterns aids in formulating robust insights for future endeavors in health care research and policy making, including prioritization of competency building based on a nation’s unique infrastructure, skill sets, and research strengths and weaknesses [ 4 ]. As the health care landscape evolves, there is an undeniable value in understanding and leveraging RWD [ 43 ]. Recognizing the diverse approaches and challenges across countries can lead to more collaborative and informed strategies [ 4 ]. The goal should be to address the present gaps and pave the way for future synergistic, impactful, and patient-centric research [ 3 ].

In conclusion, the observed variations across nations reiterate the essence of context in health care research. Every nation’s unique story, as told by their data, accentuates the need for a tailored approach in using RWD—ensuring that they truly serve the multifaceted needs of health care research and decision-making.

Acknowledgments

This study was funded by Pfizer, and the research was conducted by Transform Medical Communications Limited (Transform Medcomms), New Zealand. The authors would like to thank Dr Veena Shetye and Dr Daniel Furtner for their assistance in screening and data extraction provided on behalf of Transform Medcomms. The views and opinions expressed herein are solely those of the authors and do not reflect the views or positions of their employers.

Data Availability

Data generated or analyzed during this study are included in Multimedia Appendices 1 - 3 .

Authors' Contributions

All the authors were involved in the study conception, study design, and interpretation of the facts and data. WYS and VJ were involved in data analysis. SS led the manuscript writing, and all authors were engaged in revising it for scientific content and final approval before submission for publication.

Conflicts of Interest

WYS and HS declare that, while they are employees of Pfizer, there is no conflict of interest in relation to the work presented in this paper.

Supplementary tables.

Supplementary figures.

Identified databases.

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Abbreviations

Edited by G Eysenbach, T de Azevedo Cardoso; submitted 23.01.24; peer-reviewed by A R, O Steichen, S Shao; comments to author 20.02.24; revised version received 11.03.24; accepted 07.05.24; published 11.06.24.

©Wen-Yi Shau, Handoko Santoso, Vincent Jip, Sajita Setia. Originally published in the Journal of Medical Internet Research (https://www.jmir.org), 11.06.2024.

This is an open-access article distributed under the terms of the Creative Commons Attribution License (https://creativecommons.org/licenses/by/4.0/), which permits unrestricted use, distribution, and reproduction in any medium, provided the original work, first published in the Journal of Medical Internet Research (ISSN 1438-8871), is properly cited. The complete bibliographic information, a link to the original publication on https://www.jmir.org/, as well as this copyright and license information must be included.

Journal of Medical Internet Research

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• Health Informatics

JMIR Publications Inc.

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14388871, 14394456

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[email protected]

The set of journals have been ranked according to their SJR and divided into four equal groups, four quartiles. Q1 (green) comprises the quarter of the journals with the highest values, Q2 (yellow) the second highest values, Q3 (orange) the third highest values and Q4 (red) the lowest values.

The SJR is a size-independent prestige indicator that ranks journals by their 'average prestige per article'. It is based on the idea that 'all citations are not created equal'. SJR is a measure of scientific influence of journals that accounts for both the number of citations received by a journal and the importance or prestige of the journals where such citations come from It measures the scientific influence of the average article in a journal, it expresses how central to the global scientific discussion an average article of the journal is.

Evolution of the number of published documents. All types of documents are considered, including citable and non citable documents.

This indicator counts the number of citations received by documents from a journal and divides them by the total number of documents published in that journal. The chart shows the evolution of the average number of times documents published in a journal in the past two, three and four years have been cited in the current year. The two years line is equivalent to journal impact factor ™ (Thomson Reuters) metric.

Evolution of the total number of citations and journal's self-citations received by a journal's published documents during the three previous years. Journal Self-citation is defined as the number of citation from a journal citing article to articles published by the same journal.

Evolution of the number of total citation per document and external citation per document (i.e. journal self-citations removed) received by a journal's published documents during the three previous years. External citations are calculated by subtracting the number of self-citations from the total number of citations received by the journal’s documents.

International Collaboration accounts for the articles that have been produced by researchers from several countries. The chart shows the ratio of a journal's documents signed by researchers from more than one country; that is including more than one country address.

Not every article in a journal is considered primary research and therefore "citable", this chart shows the ratio of a journal's articles including substantial research (research articles, conference papers and reviews) in three year windows vs. those documents other than research articles, reviews and conference papers.

Ratio of a journal's items, grouped in three years windows, that have been cited at least once vs. those not cited during the following year.

Evolution of the percentage of female authors.

Evolution of the number of documents cited by public policy documents according to Overton database.

Evoution of the number of documents related to Sustainable Development Goals defined by United Nations. Available from 2018 onwards.

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