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A systematic literature review of researchers’ and healthcare professionals’ attitudes towards the secondary use and sharing of health administrative and clinical trial data

• Elizabeth Hutchings   ORCID: orcid.org/0000-0002-6030-954X 1 ,
• Max Loomes   ORCID: orcid.org/0000-0003-1042-0968 2 ,
• Phyllis Butow   ORCID: orcid.org/0000-0003-3562-6954 2 , 3 , 4 &
• Frances M. Boyle   ORCID: orcid.org/0000-0003-3798-1570 1 , 5  

Systematic Reviews volume  9 , Article number:  240 ( 2020 ) Cite this article

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A systematic literature review of researchers and healthcare professionals’ attitudes towards the secondary use and sharing of health administrative and clinical trial data was conducted using electronic data searching. Eligible articles included those reporting qualitative or quantitative original research and published in English. No restrictions were placed on publication dates, study design, or disease setting. Two authors were involved in all stages of the review process; conflicts were resolved by consensus. Data was extracted independently using a pre-piloted data extraction template. Quality and bias were assessed using the QualSyst criteria for qualitative studies. Eighteen eligible articles were identified, and articles were categorised into four key themes: barriers, facilitators, access, and ownership; 14 subthemes were identified. While respondents were generally supportive of data sharing, concerns were expressed about access to data, data storage infrastructure, and consent. Perceptions of data ownership and acknowledgement, trust, and policy frameworks influenced sharing practice, as did age, discipline, professional focus, and world region. Young researchers were less willing to share data; they were willing to share in circumstances where they were acknowledged. While there is a general consensus that increased data sharing in health is beneficial to the wider scientific community, substantial barriers remain.

Systematic review registration

PROSPERO CRD42018110559

Peer Review reports

Healthcare systems generate large amounts of data; approximately 80 mB of data are generated per patient per year [ 1 ]. It is projected that this figure will continue to grow with an increasing reliance on technologies and diagnostic capabilities. Healthcare data provides an opportunity for secondary data analysis with the capacity to greatly influence medical research, service planning, and health policy.

There are many forms of data collected in the healthcare setting including administrative and clinical trial data which are the focus of this review. Administrative data collected during patients’ care in the primary, secondary, and tertiary settings can be analysed to identify systemic issues and service gaps, and used to inform improved health resourcing. Clinical trials play an essential role in furthering our understanding of disease, advancing new therapeutics, and developing improved supportive care interventions. However, clinical trials are expensive and can take several years to complete; a frequently quoted figure is that it takes 17 years for 14% of clinical research to benefit the patient [ 2 , 3 ].

Those who argue for increased data sharing in healthcare suggest that it may lead to improved treatment decisions based on all available information [ 4 , 5 ], improved identification of causes and clinical manifestations of disease [ 6 ], and provide increased research transparency [ 7 ]. In rare diseases, secondary data analysis may greatly accelerate the medical community’s understanding of the disease’s pathology and influence treatment.

Internationally, there are signs of movement towards greater transparency, particularly with regard to clinical research data. This change has been driven by governments [ 8 ], peak bodies [ 9 ], and clinician led initiatives [ 5 ]. One initiative led by the International Council of Medical Journal Editors (ICMJE) now requires a data sharing plan for all clinical research submitted for publication in a member scientific journal [ 9 ]. Further, international examples of data sharing can be seen in projects such as The Cancer Genome Atlas (TCGA) [ 10 ] dataset and the Surveillance, Epidemiology, and End Results (SEER) [ 11 ] database which have been used extensively for cancer research.

However, consent, data ownership, privacy, intellectual property rights, and potential for misinterpretation of data [ 12 ] remain areas of concern to individuals who are more circumspect about changing the data sharing norm. To date, there has been no published synthesis of views on data sharing from the perspectives of diverse professional stakeholders. Thus, we conducted a systematic review of the literature on the views of researchers and healthcare professionals regarding the sharing of health data.

This systematic literature review was part of a larger review of articles addressing data sharing, undertaken in accordance with the PRISMA statement for systematic reviews and meta-analysis [ 13 ]. The protocol was prospectively registered on PROSPERO ( www.crd.york.ac.uk /PROSPERO, CRD42018110559).

The following databases were searched: EMBASE/MEDLINE, Cochrane Library, PubMed, CINAHL, Informit Health Collection, PROSPERO Database of Systematic Reviews, PsycINFO, and ProQuest. The final search was conducted on 21 October 2018. No date restrictions were placed on the search; key search terms are listed in Table 1 . Papers were considered eligible if they: were published in English; were published in a peer review journal; reported original research, either qualitative or quantitative with any study design, related to data sharing in any disease setting; and included subjects over 18 years of age. Systematic literature reviews were included in the wider search but were not included in the results. Reference list and hand searching were undertaken to identify additional papers. Papers were considered ineligible if they focused on electronic health records, biobanking, or personal health records or were review articles, opinion pieces/articles/letters, editorials, or theses from masters or doctoral research. Duplicates were removed and title and abstract and full-text screening were undertaken using the Cochrane systematic literature review program Covidence [ 14 ]. Two authors were involved in all stages of the review process; conflicts were resolved by consensus.

Quality and bias were assessed at a study level using the QualSyst system for quantitative and qualitative studies as described by Kmet et al. [ 15 ]. A maximum score of 20 is assigned to articles of high quality and low bias; the final QualSyst score is a proportion of the total, with a possible score ranging from 0.0 to 1.0 [ 15 ].

Data extraction was undertaken using a pre-piloted form in Microsoft Office Excel. Data points included author, country and year of study, study design and methodology, health setting, and key themes and results. Where available, detailed information on research participants was extracted including age, sex, clinical/academic employment setting, publication and grant history, career stage, and world region.

Quantitative data were summarised using descriptive statistics. Synthesis of qualitative findings used a meta-ethnographic approach, in accordance with guidelines from Lockwood et al. [ 16 ].The main themes of each qualitative study were first identified and then combined, if relevant, into categories of commonality. Using a constant comparative approach, higher order themes and subthemes were developed. Quantitative data relevant to each theme were then incorporated. Using a framework analysis approach as described by Gale et al. [ 17 ], the perspectives of different professional groups (researchers, healthcare professionals, data custodians, and ethics committees) towards data sharing were identified. Where differences occurred, they are highlighted in the results. Similarly, where systematic differences according to other characteristics (such as age or years of experience), these are highlighted.

This search identified 4019 articles, of which 241 underwent full-text screening; 73 articles met the inclusion criteria for the larger review. Five systematic literature reviews were excluded as was one article which presented duplicate results; this left a total of 67 articles eligible for review. See Fig. 1 for the PRISMA diagram describing study screening.

PRIMSA flow diagram (attached)

This systematic literature review was originally developed to identify attitudes towards secondary use and sharing of health administrative and clinical trial data in breast cancer. However, as there was a paucity of material identified specifically related to this group, we present the multidisciplinary results of this search, and where possible highlight results specific to breast cancer, and cancer more generally. We believe that the material identified in this search is relevant and reflective of the wider attitudes towards data sharing within the scientific and medical communities and can be used to inform data sharing strategies in breast cancer.

Eighteen [ 18 , 19 , 20 , 21 , 22 , 23 , 24 , 25 , 26 , 27 , 28 , 29 , 30 , 31 , 32 , 33 , 34 , 35 ] of the 67 articles addressed the perspectives of clinical and scientific researchers, data custodians, and ethics committees and were analysed for this paper (Table 2 ). The majority ( n = 16) of articles focused on the views of researchers and health professionals, [ 18 , 19 , 20 , 21 , 22 , 24 , 25 , 26 , 28 , 29 , 30 , 31 , 32 , 33 , 34 , 35 ], only one article focused on data custodians [ 27 ] and ethics committees [ 23 ] respectively. Four articles [ 18 , 19 , 21 , 35 ] included a discussion on the attitudes of both researchers and healthcare professionals and patients; only results relating to researchers/clinicians are included in this analysis (Fig. 1 ).

Study design, location, and disciplines

Several study methodologies were used, including surveys ( n = 11) [ 24 , 25 , 26 , 27 , 29 , 30 , 31 , 32 , 33 , 34 , 35 ], interviews and focus groups ( n = 6) [ 18 , 19 , 20 , 21 , 22 , 23 ], and mixed methods ( n = 1) [ 28 ]. Studies were conducted in a several countries and regions; a breakdown by country and study is available in Table 3 .

In addition to papers focusing on general health and sciences [ 18 , 21 , 22 , 24 , 25 , 26 , 29 , 30 , 31 , 32 , 33 , 34 ], two articles included views from both science and non-science disciplines [ 27 , 28 ]. Multiple sclerosis (MS) [ 19 ], mental health [ 35 ], and human immunodeficiency virus (HIV)/acquired immunodeficiency syndrome (AIDS)/tuberculosis (TB) [ 20 ] were each the subject of one article.

Study quality

Results of the quality assessment are provided in Table 2 . QualSyst [15] scores ranged from 0.7 to 1.0 (possible range 0.0 to 1.0). While none were blinded studies, most provided clear information on respondent selection, data analysis methods, and justifiable study design and methodology.

Four key themes, barriers, facilitators, access, and ownership were identified; 14 subthemes were identified. A graphical representation of article themes is presented in Fig. 2 . Two articles reflect the perspective of research ethics committees [ 23 ] and data custodians [ 27 ]; concerns noted by these groups are similar to those highlighted by researchers and healthcare professionals.

Graphic representation of key themes and subthemes identified (attached)

Barriers and facilitators

Reasons for not sharing.

Eleven articles identified barriers to data sharing [ 20 , 22 , 24 , 25 , 27 , 29 , 30 , 31 , 32 , 33 , 34 ]. Concerns cited by respondents included other researchers taking their results [ 24 , 25 ], having data misinterpreted or misattributed [ 24 , 27 , 31 , 32 ], loss of opportunities to maximise intellectual property [ 24 , 25 , 27 ], and loss of publication opportunities [ 24 , 25 ] or funding [ 25 ]. Results of a qualitative study showed respondents emphasised the competitive value of research data and its capacity to advance an individual’s career [ 20 ] and the potential for competitive disadvantage with data sharing [ 22 ]. Systematic issues related to increased data sharing were noted in several articles where it was suggested the barriers are ‘deeply rooted in the practices and culture of the research process as well as the researchers themselves’ [ 33 ] (p. 1), and that scientific competition and a lack of incentive in academia to share data remain barriers to increased sharing [ 30 ].

Insufficient time, lack of funding, limited storage infrastructure, and lack of procedural standards were also noted as barriers [ 33 ]. Quantitative results demonstrated that the researchers did not have the right to make the data public or that there was no requirement to share by the study sponsor [ 33 ]. Maintaining the balance between investigator and funder interests and the protection of research subjects [ 31 ] were also cited as barriers. Concerns about privacy were noted in four articles [ 25 , 27 , 29 , 30 ]; one study indicated that clinical researchers were significantly more concerned with issues of privacy compared to scientific researchers [ 25 ]. The results of one qualitative study indicated that clinicians were more cautious than patients regarding the inclusion of personal information in a disease specific registry; the authors suggest this may be a result of potential for legal challenges in the setting of a lack of explicit consent and consistent guidelines [ 19 ]. Researchers, particularly clinical staff, indicated that they did not see sharing data in a repository as relevant to their work [ 29 ]

Trust was also identified as a barrier to greater data sharing [ 32 ]. Rathi et al. identified that researchers were likely to withhold data if they mistrusted the intent of the researcher requesting the information [ 32 ]. Ethical, moral, and legal issues were other potential barriers cited [ 19 , 22 ]. In one quantitative study, 74% of respondents ( N = 317) indicated that ensuring appropriate data use was a concern; other concerns included data not being appropriate for the requested purpose [ 32 ]. Concerns about data quality were also cited as a barrier to data reuse; some respondents suggested that there was a perceived negative association of data reuse among health scientists [ 30 ].

Reasons for sharing

Eleven articles [ 19 , 20 , 21 , 22 , 24 , 25 , 29 , 30 , 31 , 32 , 33 ] discussed the reasons identified by researchers and healthcare professionals for sharing health data; broadly the principle of data sharing was seen as a desirable norm [ 25 , 31 ]. Cited benefits included improvements to the delivery of care, communication and receipt of information, impacts on care and quality of life [ 19 ], contributing to the advancement of science [ 20 , 24 , 29 ], validating scientific outputs, reducing duplication of scientific effort and minimising research costs [ 20 ], and promoting open science [ 31 , 32 ]. Professional reasons for sharing data included academic benefit and recognition, networking and collaborative opportunities [ 20 , 24 , 29 , 31 ], and contributing to the visibility of their research [ 24 ]. Several articles noted the potential of shared data for enabling faster access to a wider pool of patients [ 21 ] for research, improved access to population data for longitudinal studies [ 22 ], and increased responsiveness to public health needs [ 20 ]. In one study, a small percentage of respondents indicated that there were no benefits from sharing their data [ 24 ].

Analysis of quantitative survey data indicated that the perceived usefulness of data was most strongly associated with reuse intention [ 30 ]. The lack of access to data generated by other researchers or institutions was seen as a major impediment to progress in science [ 33 ]. In a second study, quantitative data showed no significant differences in reasons for sharing by clinical trialists’ academic productivity, geographic location, trial funding source or size, or the journal in which the results were published [ 32 ]. Attitudes towards sharing in order to receive academic benefits or recognition differed significantly based on the respondent’s geographic location; those from Western Europe were more willing to share compared to respondents in the USA or Canada, and the rest of the world [ 32 ].

Views on sharing

Seven articles [ 19 , 20 , 21 , 29 , 31 , 33 , 34 ] discussed researchers’ and healthcare professionals’ views relating to sharing data, with a broad range of views noted. Two articles, both qualitative, discussed the role of national registries [ 21 ], and data repositories [ 31 ]. Generally, there was clear support for national research registers and an acceptance for their rationale [ 21 ], and some respondents believed that sharing de-identified data through data repositories should be required and that when requested, investigators should share data [ 31 ]. Sharing de-identified data for reasons beyond academic and public health benefit were cited as a concern [ 20 ]. Two quantitative studies noted a proportion of researchers who believed that data should not be made available [ 33 , 34 ]. Researchers also expressed differences in how shared data should be managed; the requirement for data to be ‘gate-kept’ was preferred by some, while others were happy to relinquish control of their data once curated or on release [ 20 ]. Quantitative results indicated that scientists were significantly more likely to rank data reuse as highly relevant to their work than clinicians [ 29 ], but not all scientists shared data equally or had the same views about data sharing or reuse [ 33 ]. Some respondents argued that not all data were equal and therefore should only be shared in certain circumstances. This was in direct contrast to other respondents who suggested that all data should be shared, all of the time [ 20 ].

Differences by age, background, discipline, professional focus, and world region

Differences in attitudes towards shared data were noted by age, professional focus, and world region [ 25 , 27 , 33 , 34 ]. Younger researchers, aged between 20–39 and 40–49 years, were less likely to share their data with others (39% and 38% respectively) compared to other age groups; respondents aged over 50 years of age were more willing (46%) to share [ 33 ]. Interestingly, while less willing to share, younger researchers also believed that the lack of access to data was a major impediment to science and their research [ 33 ]. Where younger researchers were able to place conditions on access to their data, rates of willingness to share were increased [ 33 ].

Respondents from the disciplines of education, medicine/health science, and psychology were more inclined than others to agree that their data should not be available for others to use in the first place [ 34 ]. However, results from one study indicated that researchers from the medical field and social sciences were less likely to share compared to other disciplines [ 33 ]. For example, results of a quantitative study showed that compared to biologists, who reported sharing 85% of their data, medical and social sciences reported sharing their data 65% and 58% percent of the time, respectively [ 33 ].

One of the primary reasons for controlling access to data, identified in a study of data custodians, was due to a desire to avoid data misuse; this was cited as a factor for all surveyed data repositories except those of an interdisciplinary nature [ 27 ]. Limiting access to certain types of research and ensuring attribution were not listed as a concern for sociology, humanities or interdisciplinary data collections [ 27 ]. Issues pertaining to privacy and sensitive data were only cited as concerns for data collections related to humanities, social sciences, and biology, ecology, and chemistry; concerns regarding intellectual property were also noted [ 27 ]. The disciplines of biology, ecology, and chemistry and social sciences had the most policy restrictions on the use of data held in their repositories [ 27 ].

Differences in data sharing practices were also noted by world region. Respondents not from North American and European countries were more willing to place their data on a central repository; however, they were also more likely to place conditions on the reuse of their data [ 33 , 34 ].

Experience of data sharing

The experience of data sharing among researchers was discussed in nine articles [ 20 , 24 , 25 , 26 , 28 , 29 , 30 , 31 , 32 , 33 ]. Data sharing arrangements were highly individual and ranged from ad hoc and informal processes to formal procedures enforced by institutional policies in the form of contractual agreements, with respondents indicating data sharing behaviour ranging from sharing no data to sharing all data [ 20 , 26 , 31 ]. Quantitative data from one study showed that researchers were more inclined to share data prior to publication with people that they knew compared to those they did not; post publication, these figures were similar between groups [ 24 ]. While many researchers were prepared to share data, results of a survey identified a preference of researchers to collect data themselves, followed by their team, or by close colleagues [ 26 ].

Differences in the stated rate of data sharing compared to the actual rate of sharing [ 25 ] were noted. In a large quantitative study ( N = 1329), nearly one third of respondents chose not to answer whether they make their data available to others; of those who responded to the question, 46% reported they do not make their data electronically available to others [ 33 ]. By discipline, differences in the rate of refusal to share were higher in chemistry compared to non-science disciplines such as sociology [ 25 ]. Respondents who were more academically productive (> 25 articles over the past 3 years) reported that they have or would withhold data to protect research subjects less frequently than those who were less academically productive or received industry funding [ 32 ].

Attitudes to sharing de-identified data via data repositories was discussed in two articles [ 29 , 31 ]. A majority of respondents in one study indicated that de-identified data should be shared via a repository and that it should be shared when requested. A lack of experience in uploading data to repositories was noted as a barrier [ 29 ]. When data was shared, most researchers included additional materials to support their data including materials such as metadata or a protocol description [ 29 ].

Two articles [ 28 , 30 ] focused on processes and variables associated with sharing. Factors such as norms, data infrastructure/organisational support, and research communities were identified as important factors in a researcher’s attitude towards data sharing [ 28 , 30 ]. A moderate correlation between data reuse and data sharing suggest that these two variables are not linked. Furthermore, sharing data compared to self-reported data reuse were also only moderately associated (Pearson’s correlation of 0.25 ( p ≤ 0.001)) [ 26 ].

Predictors of data sharing and norms

Two articles [ 26 , 30 ] discussed the role of social norms and an individual’s willingness to share health data. Perceived efficacy and efficiency of data reuse were strong predictors of data sharing [ 26 ] and the development of a ‘positive social norm towards data sharing support(s)[ed] researcher data reuse intention’ [ 30 ] (p. 400).

Policy framework

The establishment of clear policies and procedures to support data sharing was highlighted in two articles [ 22 , 28 ]. The presence of ambiguous data sharing policies was noted as a major limitation, particularly in primary care and the increased adoption of health informatics systems [ 22 ]. Policies that support an efficient exchange system allowing for the maximum amount of data sharing are preferred and may include incentives such as formal recognition and financial reimbursement; a framework for this is proposed in Fecher et al. [ 28 ].

Research funding

The requirement to share data funded by public monies was discussed in one article [ 25 ]. Some cases were reported of researchers refusing to share data funded by tax-payer funds; reasons for refusal included a potential reduction in future funding or publishing opportunities [ 25 ].

Access and ownership

Articles relating to access and ownership were grouped together and seven subthemes were identified.

Access, information systems, and metadata

Ten articles [ 19 , 20 , 21 , 22 , 26 , 27 , 29 , 33 , 34 , 35 ] discussed the themes of access, information systems, and the use of metadata. Ensuring privacy protections in a prospective manner was seen as important for data held in registries [ 19 ]. In the setting of mental health, researchers indicated that patients should have more choices for controlling access to shared registry data [ 35 ]. The use of guardianship committees [ 19 ] or gate-keepers [ 20 ] was seen as important in ensuring the security and access to data held in registries by some respondents; however, many suggested that a researcher should relinquish control of the data collection once curated or released, unless embargoed [ 20 ]. Reasons for maintaining control over registry data included ensuring attribution, restricting commercial research, protecting sensitive (non-personal) information, and limiting certain types of research [ 27 ]. Concerns about security and confidentiality were noted as important and assurances about these needed to be provided; accountability and transparency mechanisms also need to be included [ 21 ]. Many respondents believed that access to the registry data by pharmaceutical companies and marketing agencies was not considered appropriate [ 19 ].

Respondents to a survey from medicine and social sciences were less likely to agree to have all data included on a central repository with no restrictions [ 33 ]; notably, this was also reflected in the results of qualitative research which indicated that health professionals were more cautious than patients about the inclusion of personal data within a disease specific register [ 19 ].

While many researchers stated that they commonly shared data directly with other researchers, most did not have experience with uploading data to repositories [ 29 ]. Results from a survey indicated that younger respondents have more data access restrictions and thought that their data is easier to access significantly more than older respondents [ 34 ]. In the primary care setting, concerns were noted about the potential for practitioners to block patient involvement in a registry by refusing access to a patient’s personal data or by not giving permission for the data to be extracted from their clinical system [ 21 ]. There was also resistance in primary care towards health data amalgamation undertaken for an unspecified purpose [ 22 ]; respondents were not in favour of systems which included unwanted functionality (do not want/need), inadequate attributes (capability and receptivity) of the practice, or undesirable impact on the role of the general practitioner (autonomy, status, control, and workflow) [ 22 ].

Access to ‘comprehensive metadata (is needed) to support the correct interpretation of the data’ [ 26 ] (p. 4) at a later stage. When additional materials were shared, most researchers shared contextualising information or a description of the experimental protocol [ 29 ]. The use of metadata standards was not universal with some respondents using their own [ 33 ].

Several articles highlighted the impact of data curation on researchers’ time [ 20 , 21 , 22 , 29 , 33 ] or finances [ 24 , 28 , 29 , 33 , 34 ]; these were seen as potential barriers to increased registry adoption [ 21 ]. Tasks required for curation included preparing data for dissemination in a usable format and uploading data to repositories. The importance of ensuring that the data is accurately preserved for future reuse was highlighted; it must be presented in a retriable and auditable manner [ 20 ]. The amount of time required to curate data ranged from ‘no additional time’ to ‘greater than ten hours’ [ 29 ]. In one study, no clinical respondent had their data in a sharable format [ 29 ]. In the primary care setting, health information systems which promote sharing were not seen as being beneficial if they required standardisation of processes and/or sharing of clinical notes [ 22 ]. Further, spending time on non-medical issues in a time poor environment [ 22 ] was identified as a barrier. Six articles described the provision of funding or technical support to ensure data storage, maintenance, and the ability to provide access to data when requested. All noted a lack of funding and time as a barrier to increased sharing data [ 20 , 24 , 28 , 29 , 33 , 34 ].

Results of qualitative research indicated a range of views regarding consent mechanisms for future data use [ 18 , 19 , 20 , 23 , 35 ]. Consenting for future research can be complex given that the exact nature of the study will be unknown, and therefore some respondents suggested that a broad statement on future data uses be included [ 19 , 20 ] during the consent process. In contrast, other participants indicated that the current consent processes were too broad and do not reflect patient preferences sufficiently [ 35 ]. The importance of respecting the original consent in all future research was noted [ 20 ]. It was suggested that seeking additional consent for future data use may discourage participation in the original study [ 20 ]. Differences in views regarding the provision of detailed information about sharing individual level data was noted suggesting that the researchers wanted to exert some control over data they had collected [ 20 ]. An opt-out consent process was considered appropriate in some situations [ 18 ] but not all; some respondents suggested that consent to use a patient’s medical records was not required [ 18 ]. There was support by some researchers to provide patients with the option to ‘opt-in’ to different levels of involvement in a registry setting [ 19 ]. Providing patients more granular choices when controlling access to their medical data [ 35 ] was seen as important.

The attitudes of ethics and review boards ( N = 30) towards the use of medical records for research was discussed in one article [ 23 ]. While 38% indicated that no further consent would be required, 47% required participant consent, and 10% said that the requirement for consent would depend on how the potentially identifying variables would be managed [ 23 ]. External researcher access to medical record data was associated with a requirement for consent [ 23 ].

Acknowledgement

The importance of establishing mechanisms which acknowledge the use of shared data were discussed in four articles [ 27 , 29 , 33 , 34 ]. A significant proportion of respondents to a survey believed it was fair to use other researchers’ data if they acknowledged the originator and the funding body in all disseminated work or as a formal citation in published works [ 33 ]. Other mechanisms for acknowledging the data originator included opportunities to collaborate on the project, reciprocal data sharing agreements, allowing the originator to review or comment on results, but not approve derivative works, or the provision of a list of products making use of the data and co-authorship [ 33 , 34 ]. In the setting of controlled data collections, survey results indicated that ensuring attribution was a motivator for controlled access [ 27 ]. Over half of respondents in one survey believed it was fair to disseminate results based either in whole or part without the data provider’s approval [ 33 ]. No significant differences in mechanisms for acknowledgement were noted between clinical and scientific participants; mechanisms included co-authorship, recognition in the acknowledgement section of publications, and citation in the bibliography [ 29 ]. No consentient method for acknowledging shared data reuse was identified [ 29 ].

Data ownership was identified as a potential barrier to increased data sharing in academic research [ 28 ]. In the setting of control of data collections, survey respondents indicated that they wanted to maintain some control over the dataset, which is suggestive of researchers having a perceived ownership of their research data [ 28 ]. Examples of researchers extending ownership over their data include the right to publish first and the control of access to datasets [ 28 ]. Fecher et al. noted that the idea of data ownership by the researcher is not a position always supported legally; ‘the ownership and rights of use, privacy, contractual consent and copyright’ are subsumed [ 28 ] (p. 15). Rather data sharing is restricted by privacy law, which is applied to datasets containing data from individuals. The legal uncertainty about data ownership and the complexity of law can deter data sharing [ 28 ].

Promotion/professional criteria

The role of data sharing and its relation to promotion and professional criteria were discussed in two articles [ 24 , 28 ]. The requirement to share data is rarely a promotion or professional criterion, rather the systems are based on grants and publication history [ 24 , 28 ]. One study noted that while the traditional link between publication history and promotion remains, it is ‘likely that funders will continue to get sub-optimal returns on their investments, and that data will continue to be inefficiently utilised and disseminated’ [ 24 ] (p. 49).

This systematic literature review highlights the ongoing complexity associated with increasing data sharing across the sciences. No additional literature meeting the inclusion criteria were identified in the period between the data search and the submission of this manuscript. Data gaps identified include a paucity of information specifically related to the attitudes of breast cancer researchers and health professionals towards the secondary use and sharing of health administrative and clinical trial data.

While the majority of respondents believed the principles of data sharing were sound, significant barriers remain: issues of consent, privacy, information security, and ownership were key themes throughout the literature. Data ownership and acknowledgement, trust, and policy frameworks influenced sharing practice, as did age, discipline, professional focus, and world region.

Addressing concerns of privacy, trust, and information security in a technologically changing and challenging landscape is complex. Ensuring the balance between privacy and sharing data for the greater good will require the formation of policy and procedures, which promote both these ideals.

Establishing clear consent mechanisms would provide greater clarity for all parties involved in the data sharing debate. Ensuring that appropriate consent for future research, including secondary data analysis and sharing and linking of datasets, is gained at the point of data collection, would continue to promote research transparency and provide healthcare professionals and researchers with knowledge that an individual is aware that their data may be used for other research purposes. The establishment of policy which supports and promotes the secondary use of data and data sharing will assist in the normalisation of this type of health research. With the increased promotion of data sharing and secondary data analysis as an established tool in health research, over time barriers to its use, including perceptions of ownership and concerns regarding privacy and consent, will decrease.

The importance of establishing clear and formal processes associated with acknowledging the use of shared data has been underscored in the results presented. Initiatives such as the Bioresource Research Impact Factor/Framework (BRIF) [ 36 ] and the Citation of BioResources in journal Articles (CoBRA) [ 37 ] have sought to formalise the process. However, increased academic recognition of sharing data for secondary analysis requires further development and the allocation of funding to ensure that collected data is in a usable, searchable, and retrievable format. Further, there needs to be a shift away from the traditional criteria of academic promotion, which includes research outputs, to one which is inclusive of a researcher’s data sharing history and the availability of their research dataset for secondary analysis.

The capacity to identify and use already collected data was identified as a barrier. Moves to make data findable, accessible, interoperable, and reusable (FAIR) have been promoted as a means to encourage greater accessibility to data in a systematic way [ 38 ]. The FAIR principles focus on data characteristics and should be interpreted alongside the collective benefit, authority to control, responsibility, and ethics (CARE) principles established by the Global Indigenous Data Alliance (GIDA) which a people and purpose orientated [ 39 ].

Limitations

The papers included in this study were limited to those indexed on major databases. Some literature on this topic may have been excluded if it was not identified during the grey literature and hand searching phases.

Implications

Results of this systematic literature review indicate that while there is broad agreement for the principles of data sharing in medical research, there remain disagreements about the infrastructure and procedures associated with the data sharing process. Additional work is therefore required on areas such as acknowledgement, curation, and data ownership.

While the literature confirms that there is overall support for data sharing in medical and scientific research, there remain significant barriers to its uptake. These include concerns about privacy, consent, information security, and data ownership.

Availability of data and materials

All data generated or analysed during this study are included in this published article.

Abbreviations

Bioresource Research Impact Factor/Framework

Collective benefit, authority to control, responsibility, and ethics

Citation of BioResources in journal Articles

Findable, accessible, interoperable, and reusable

Global Indigenous Data Alliance

Human immunodeficiency virus/acquired immunodeficiency

International Council of Medical Journal Editors

Multiple sclerosis

Surveillance, Epidemiology, and End Results

Tuberculosis

The Cancer Genome Atlas

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Acknowledgements

The authors would like to thank Ms. Ngaire Pettit-Young, Information First, Sydney, NSW, Australia, for her assistance in developing the search strategy.

This project was supported by the Sydney Vital, Translational Cancer Research, through a Cancer Institute NSW competitive grant. The views expressed herein are those of the authors and are not necessarily those of the Cancer Institute NSW. FB is supported in her academic role by the Friends of the Mater Foundation.

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EH, PB, and FB were responsible for developing the study concept and the development of the protocol. EH and ML were responsible for the data extraction and data analysis. FB and PB supervised this research. All authors participated in interpreting the findings and contributed the intellectual content of the manuscript. All authors have read and approved the manuscript.

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Hutchings, E., Loomes, M., Butow, P. et al. A systematic literature review of researchers’ and healthcare professionals’ attitudes towards the secondary use and sharing of health administrative and clinical trial data. Syst Rev 9 , 240 (2020). https://doi.org/10.1186/s13643-020-01485-5

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Choosing a Review Type

For guidance related to choosing a review type, see:

• "What Type of Review is Right for You?" - Decision Tree (PDF) This decision tree, from Cornell University Library, highlights key difference between narrative, systematic, umbrella, scoping and rapid reviews.
• Reviewing the literature: choosing a review design Noble, H., & Smith, J. (2018). Reviewing the literature: Choosing a review design. Evidence Based Nursing, 21(2), 39–41. https://doi.org/10.1136/eb-2018-102895
• What synthesis methodology should I use? A review and analysis of approaches to research synthesis Schick-Makaroff, K., MacDonald, M., Plummer, M., Burgess, J., & Neander, W. (2016). What synthesis methodology should I use? A review and analysis of approaches to research synthesis. AIMS Public Health, 3 (1), 172-215. doi:10.3934/publichealth.2016.1.172 More information less... ABSTRACT: Our purpose is to present a comprehensive overview and assessment of the main approaches to research synthesis. We use "research synthesis" as a broad overarching term to describe various approaches to combining, integrating, and synthesizing research findings.
• Right Review - Decision Support Tool Not sure of the most suitable review method? Answer a few questions and be guided to suitable knowledge synthesis methods. Updated in 2022 and featured in the Journal of Clinical Epidemiology 10.1016/j.jclinepi.2022.03.004

Types of Evidence Synthesis / Literature Reviews

Literature reviews are comprehensive summaries and syntheses of the previous research on a given topic.  While narrative reviews are common across all academic disciplines, reviews that focus on appraising and synthesizing research evidence are increasingly important in the health and social sciences.  

Most evidence synthesis methods use formal and explicit methods to identify, select and combine results from multiple studies, making evidence synthesis a form of meta-research.  

The review purpose, methods used and the results produced vary among different kinds of literature reviews; some of the common types of literature review are detailed below.

Common Types of Literature Reviews 1

Narrative (literature) review.

• A broad term referring to reviews with a wide scope and non-standardized methodology
• Search strategies, comprehensiveness of literature search, time range covered and method of synthesis will vary and do not follow an established protocol

Integrative Review

• A type of literature review based on a systematic, structured literature search
• Often has a broadly defined purpose or review question
• Seeks to generate or refine and theory or hypothesis and/or develop a holistic understanding of a topic of interest
• Relies on diverse sources of data (e.g. empirical, theoretical or methodological literature; qualitative or quantitative studies)

Systematic Review

• Systematically and transparently collects and categorize existing evidence on a question of scientific, policy or management importance
• Follows a research protocol that is established a priori
• Some sub-types of systematic reviews include: SRs of intervention effectiveness, diagnosis, prognosis, etiology, qualitative evidence, economic evidence, and more.
• Time-intensive and often takes months to a year or more to complete 
• The most commonly referred to type of evidence synthesis; sometimes confused as a blanket term for other types of reviews

Meta-Analysis

• Statistical technique for combining the findings from disparate quantitative studies
• Uses statistical methods to objectively evaluate, synthesize, and summarize results
• Often conducted as part of a systematic review

Scoping Review

• Systematically and transparently collects and categorizes existing evidence on a broad question of scientific, policy or management importance
• Seeks to identify research gaps, identify key concepts and characteristics of the literature and/or examine how research is conducted on a topic of interest
• Useful when the complexity or heterogeneity of the body of literature does not lend itself to a precise systematic review
• Useful if authors do not have a single, precise review question
• May critically evaluate existing evidence, but does not attempt to synthesize the results in the way a systematic review would 
• May take longer than a systematic review

Rapid Review

• Applies a systematic review methodology within a time-constrained setting
• Employs methodological "shortcuts" (e.g., limiting search terms and the scope of the literature search), at the risk of introducing bias
• Useful for addressing issues requiring quick decisions, such as developing policy recommendations

Umbrella Review

• Reviews other systematic reviews on a topic
• Often defines a broader question than is typical of a traditional systematic review
• Most useful when there are competing interventions to consider

1. Adapted from:

Eldermire, E. (2021, November 15). A guide to evidence synthesis: Types of evidence synthesis. Cornell University LibGuides. https://guides.library.cornell.edu/evidence-synthesis/types

Nolfi, D. (2021, October 6). Integrative Review: Systematic vs. Scoping vs. Integrative. Duquesne University LibGuides. https://guides.library.duq.edu/c.php?g=1055475&p=7725920

Delaney, L. (2021, November 24). Systematic reviews: Other review types. UniSA LibGuides. https://guides.library.unisa.edu.au/SystematicReviews/OtherReviewTypes

Further Reading: Exploring Different Types of Literature Reviews

• A typology of reviews: An analysis of 14 review types and associated methodologies Grant, M. J., & Booth, A. (2009). A typology of reviews: An analysis of 14 review types and associated methodologies. Health Information and Libraries Journal, 26 (2), 91-108. doi:10.1111/j.1471-1842.2009.00848.x More information less... ABSTRACT: The expansion of evidence-based practice across sectors has lead to an increasing variety of review types. However, the diversity of terminology used means that the full potential of these review types may be lost amongst a confusion of indistinct and misapplied terms. The objective of this study is to provide descriptive insight into the most common types of reviews, with illustrative examples from health and health information domains.
• Clarifying differences between review designs and methods Gough, D., Thomas, J., & Oliver, S. (2012). Clarifying differences between review designs and methods. Systematic Reviews, 1 , 28. doi:10.1186/2046-4053-1-28 More information less... ABSTRACT: This paper argues that the current proliferation of types of systematic reviews creates challenges for the terminology for describing such reviews....It is therefore proposed that the most useful strategy for the field is to develop terminology for the main dimensions of variation.
• Are we talking the same paradigm? Considering methodological choices in health education systematic review Gordon, M. (2016). Are we talking the same paradigm? Considering methodological choices in health education systematic review. Medical Teacher, 38 (7), 746-750. doi:10.3109/0142159X.2016.1147536 More information less... ABSTRACT: Key items discussed are the positivist synthesis methods meta-analysis and content analysis to address questions in the form of "whether and what" education is effective. These can be juxtaposed with the constructivist aligned thematic analysis and meta-ethnography to address questions in the form of "why." The concept of the realist review is also considered. It is proposed that authors of such work should describe their research alignment and the link between question, alignment and evidence synthesis method selected.
• Meeting the review family: Exploring review types and associated information retrieval requirements Sutton, A., Clowes, M., Preston, L., & Booth, A. (2019). Meeting the review family: Exploring review types and associated information retrieval requirements. Health Information & Libraries Journal, 36(3), 202–222. doi: 10.1111/hir.12276

Integrative Reviews

"The integrative review method is an approach that allows for the inclusion of diverse methodologies (i.e. experimental and non-experimental research)." (Whittemore & Knafl, 2005, p. 547).

• The integrative review: Updated methodology Whittemore, R., & Knafl, K. (2005). The integrative review: Updated methodology. Journal of Advanced Nursing, 52 (5), 546–553. doi:10.1111/j.1365-2648.2005.03621.x More information less... ABSTRACT: The aim of this paper is to distinguish the integrative review method from other review methods and to propose methodological strategies specific to the integrative review method to enhance the rigour of the process....An integrative review is a specific review method that summarizes past empirical or theoretical literature to provide a more comprehensive understanding of a particular phenomenon or healthcare problem....Well-done integrative reviews present the state of the science, contribute to theory development, and have direct applicability to practice and policy.

• Conducting integrative reviews: A guide for novice nursing researchers Dhollande, S., Taylor, A., Meyer, S., & Scott, M. (2021). Conducting integrative reviews: A guide for novice nursing researchers. Journal of Research in Nursing, 26(5), 427–438. https://doi.org/10.1177/1744987121997907
• Rigour in integrative reviews Whittemore, R. (2007). Rigour in integrative reviews. In C. Webb & B. Roe (Eds.), Reviewing Research Evidence for Nursing Practice (pp. 149–156). John Wiley & Sons, Ltd. https://doi.org/10.1002/9780470692127.ch11

Scoping Reviews

Scoping reviews are evidence syntheses that are conducted systematically, but begin with a broader scope of question than traditional systematic reviews, allowing the research to 'map' the relevant literature on a given topic.

• Scoping studies: Towards a methodological framework Arksey, H., & O'Malley, L. (2005). Scoping studies: Towards a methodological framework. International Journal of Social Research Methodology, 8 (1), 19-32. doi:10.1080/1364557032000119616 More information less... ABSTRACT: We distinguish between different types of scoping studies and indicate where these stand in relation to full systematic reviews. We outline a framework for conducting a scoping study based on our recent experiences of reviewing the literature on services for carers for people with mental health problems.
• Scoping studies: Advancing the methodology Levac, D., Colquhoun, H., & O'Brien, K. K. (2010). Scoping studies: Advancing the methodology. Implementation Science, 5 (1), 69. doi:10.1186/1748-5908-5-69 More information less... ABSTRACT: We build upon our experiences conducting three scoping studies using the Arksey and O'Malley methodology to propose recommendations that clarify and enhance each stage of the framework.
• Methodology for JBI scoping reviews Peters, M. D. J., Godfrey, C. M., McInerney, P., Baldini Soares, C., Khalil, H., & Parker, D. (2015). The Joanna Briggs Institute reviewers’ manual: Methodology for JBI scoping reviews [PDF]. Retrieved from The Joanna Briggs Institute website: http://joannabriggs.org/assets/docs/sumari/Reviewers-Manual_Methodology-for-JBI-Scoping-Reviews_2015_v2.pdf More information less... ABSTRACT: Unlike other reviews that address relatively precise questions, such as a systematic review of the effectiveness of a particular intervention based on a precise set of outcomes, scoping reviews can be used to map the key concepts underpinning a research area as well as to clarify working definitions, and/or the conceptual boundaries of a topic. A scoping review may focus on one of these aims or all of them as a set.

Systematic vs. Scoping Reviews: What's the Difference? 

YouTube Video 4 minutes, 45 seconds

Rapid Reviews

Rapid reviews are systematic reviews that are undertaken under a tighter timeframe than traditional systematic reviews. 

• Evidence summaries: The evolution of a rapid review approach Khangura, S., Konnyu, K., Cushman, R., Grimshaw, J., & Moher, D. (2012). Evidence summaries: The evolution of a rapid review approach. Systematic Reviews, 1 (1), 10. doi:10.1186/2046-4053-1-10 More information less... ABSTRACT: Rapid reviews have emerged as a streamlined approach to synthesizing evidence - typically for informing emergent decisions faced by decision makers in health care settings. Although there is growing use of rapid review "methods," and proliferation of rapid review products, there is a dearth of published literature on rapid review methodology. This paper outlines our experience with rapidly producing, publishing and disseminating evidence summaries in the context of our Knowledge to Action (KTA) research program.
• What is a rapid review? A methodological exploration of rapid reviews in Health Technology Assessments Harker, J., & Kleijnen, J. (2012). What is a rapid review? A methodological exploration of rapid reviews in Health Technology Assessments. International Journal of Evidence‐Based Healthcare, 10 (4), 397-410. doi:10.1111/j.1744-1609.2012.00290.x More information less... ABSTRACT: In recent years, there has been an emergence of "rapid reviews" within Health Technology Assessments; however, there is no known published guidance or agreed methodology within recognised systematic review or Health Technology Assessment guidelines. In order to answer the research question "What is a rapid review and is methodology consistent in rapid reviews of Health Technology Assessments?", a study was undertaken in a sample of rapid review Health Technology Assessments from the Health Technology Assessment database within the Cochrane Library and other specialised Health Technology Assessment databases to investigate similarities and/or differences in rapid review methodology utilised.
• Rapid Review Guidebook Dobbins, M. (2017). Rapid review guidebook. Hamilton, ON: National Collaborating Centre for Methods and Tools.
• NCCMT Summary and Tool for Dobbins' Rapid Review Guidebook National Collaborating Centre for Methods and Tools. (2017). Rapid review guidebook. Hamilton, ON: McMaster University. Retrieved from http://www.nccmt.ca/knowledge-repositories/search/308
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Rulla Alsaedi , Kimberly McKeirnan; Literature Review of Type 2 Diabetes Management and Health Literacy. Diabetes Spectr 1 November 2021; 34 (4): 399–406. https://doi.org/10.2337/ds21-0014

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The purpose of this literature review was to identify educational approaches addressing low health literacy for people with type 2 diabetes. Low health literacy can lead to poor management of diabetes, low engagement with health care providers, increased hospitalization rates, and higher health care costs. These challenges can be even more profound among minority populations and non-English speakers in the United States.

A literature search and standard data extraction were performed using PubMed, Medline, and EMBASE databases. A total of 1,914 articles were identified, of which 1,858 were excluded based on the inclusion criteria, and 46 were excluded because of a lack of relevance to both diabetes management and health literacy. The remaining 10 articles were reviewed in detail.

Patients, including ethnic minorities and non-English speakers, who are engaged in diabetes education and health literacy improvement initiatives and ongoing follow-up showed significant improvement in A1C, medication adherence, medication knowledge, and treatment satisfaction. Clinicians considering implementing new interventions to address diabetes care for patients with low health literacy can use culturally tailored approaches, consider ways to create materials for different learning styles and in different languages, engage community health workers and pharmacists to help with patient education, use patient-centered medication labels, and engage instructors who share cultural and linguistic similarities with patients to provide educational sessions.

This literature review identified a variety of interventions that had a positive impact on provider-patient communication, medication adherence, and glycemic control by promoting diabetes self-management through educational efforts to address low health literacy.

Diabetes is the seventh leading cause of death in the United States, and 30.3 million Americans, or 9.4% of the U.S. population, are living with diabetes ( 1 , 2 ). For successful management of a complicated condition such as diabetes, health literacy may play an important role. Low health literacy is a well-documented barrier to diabetes management and can lead to poor management of medical conditions, low engagement with health care providers (HCPs), increased hospitalizations, and, consequently, higher health care costs ( 3 – 5 ).

The Healthy People 2010 report ( 6 ) defined health literacy as the “degree to which individuals have the capacity to obtain, process, and understand basic health information and services needed to make appropriate health decisions.” Diabetes health literacy also encompasses a wide range of skills, including basic knowledge of the disease state, self-efficacy, glycemic control, and self-care behaviors, which are all important components of diabetes management ( 3 – 5 , 7 ). According to the Institute of Medicine’s Committee on Health Literacy, patients with poor health literacy are twice as likely to have poor glycemic control and were found to be twice as likely to be hospitalized as those with adequate health literacy ( 8 ). Associations between health literacy and health outcomes have been reported in many studies, the first of which was conducted in 1995 in two public hospitals and found that many patients had inadequate health literacy and could not perform the basic reading tasks necessary to understand their treatments and diagnoses ( 9 ).

Evaluation of health literacy is vital to the management and understanding of diabetes. Several tools for assessing health literacy have been evaluated, and the choice of which to use depends on the length of the patient encounter and the desired depth of the assessment. One widely used literacy assessment tool, the Test of Functional Health Literacy in Adults (TOFHLA), consists of 36 comprehension questions and four numeric calculations ( 10 ). Additional tools that assess patients’ reading ability include the Rapid Estimate of Adult Literacy in Medicine (REALM) and the Literacy Assessment for Diabetes. Tests that assess diabetes numeracy skills include the Diabetes Numeracy Test, the Newest Vital Sign (NVS), and the Single-Item Literacy Screener (SILS) ( 11 ).

Rates of both diabetes and low health literacy are higher in populations from low socioeconomic backgrounds ( 5 , 7 , 12 ). People living in disadvantaged communities face many barriers when seeking health care, including inconsistent housing, lack of transportation, financial difficulties, differing cultural beliefs about health care, and mistrust of the medical professions ( 13 , 14 ). People with high rates of medical mistrust tend to be less engaged in their care and to have poor communication with HCPs, which is another factor HCPs need to address when working with their patients with diabetes ( 15 ).

The cost of medical care for people with diabetes was $327 billion in 2017, a 26% increase since 2012 ( 1 , 16 ). Many of these medical expenditures are related to hospitalization and inpatient care, which accounts for 30% of total medical costs for people with diabetes ( 16 ).

People with diabetes also may neglect self-management tasks for various reasons, including low health literacy, lack of diabetes knowledge, and mistrust between patients and HCPs ( 7 , 15 ).

These challenges can be even more pronounced in vulnerable populations because of language barriers and patient-provider mistrust ( 17 – 19 ). Rates of diabetes are higher among racial and ethnic minority groups; 15.1% of American Indians and Alaskan Natives, 12.7% of Non-Hispanic Blacks, 12.1% of Hispanics, and 8% of Asian Americans have diagnosed diabetes, compared with 7.4% of non-Hispanic Whites ( 1 ). Additionally, patient-provider relationship deficits can be attributed to challenges with communication, including HCPs’ lack of attention to speaking slowly and clearly and checking for patients’ understanding when providing education or gathering information from people who speak English as a second language ( 15 ). White et al. ( 15 ) demonstrated that patients with higher provider mistrust felt that their provider’s communication style was less interpersonal and did not feel welcome as part of the decision-making process.

To the authors’ knowledge, there is no current literature review evaluating interventions focused on health literacy and diabetes management. There is a pressing need for such a comprehensive review to provide a framework for future intervention design. The objective of this literature review was to gather and summarize studies of health literacy–based diabetes management interventions and their effects on overall diabetes management. Medication adherence and glycemic control were considered secondary outcomes.

Search Strategy

A literature review was conducted using the PubMed, Medline, and EMBASE databases. Search criteria included articles published between 2015 and 2020 to identify the most recent studies on this topic. The search included the phrases “diabetes” and “health literacy” to specifically focus on health literacy and diabetes management interventions and was limited to original research conducted in humans and published in English within the defined 5-year period. Search results were exported to Microsoft Excel for evaluation.

Study Selection

Initial screening of the articles’ abstracts was conducted using the selection criteria to determine which articles to include or exclude ( Figure 1 ). The initial search results were reviewed for the following inclusion criteria: original research (clinical trials, cohort studies, and cross-sectional studies) conducted in human subjects with type 2 diabetes in the United States, and published in English between 2015 and 2020. Articles were considered to be relevant if diabetes was included as a medical condition in the study and an intervention was made to assess or improve health literacy. Studies involving type 1 diabetes or gestational diabetes and articles that were viewpoints, population surveys, commentaries, case reports, reviews, or reports of interventions conducted outside of the United States were excluded from further review. The criteria requiring articles to be from the past 5 years and from the United States were used because of the unique and quickly evolving nature of the U.S. health care system. Articles published more than 5 years ago or from other health care systems may have contributed information that was not applicable to or no longer relevant for HCPs in the United States. Articles were screened and reviewed independently by both authors. Disagreements were resolved through discussion to create the final list of articles for inclusion.

PRISMA diagram of the article selection process.

Data Extraction

A standard data extraction was performed for each included article to obtain information including author names, year of publication, journal, study design, type of intervention, primary outcome, tools used to assess health literacy or type 2 diabetes knowledge, and effects of intervention on overall diabetes management, glycemic control, and medication adherence.

A total of 1,914 articles were collected from a search of the PubMed, MEDLINE, and EMBASE databases, of which 1,858 were excluded based on the inclusion and exclusion criteria. Of the 56 articles that met criteria for abstract review, 46 were excluded because of a lack of relevance to both diabetes management and health literacy. The remaining 10 studies identified various diabetes management interventions, including diabetes education tools such as electronic medication instructions and text message–based interventions, technology-based education videos, enhanced prescription labels, learner-based education materials, and culturally tailored interventions ( 15 , 20 – 28 ). Figure 1 shows the PRISMA diagram of the article selection process, and Table 1 summarizes the findings of the article reviews ( 15 , 20 – 28 ).

Findings of the Article Reviews (15,20–28)

SAHLSA, Short Assessment of Health Literacy for Spanish Adults.

Medical mistrust and poor communication are challenging variables in diabetes education. White et al. ( 15 ) examined the association between communication quality and medical mistrust in patients with type 2 diabetes. HCPs at five health department clinics received training in effective health communication and use of the PRIDE (Partnership to Improve Diabetes Education) toolkit in both English and Spanish, whereas control sites were only exposed to National Diabetes Education Program materials without training in effective communication. The study evaluated participant communication using several tools, including the Communication Assessment Tool (CAT), Interpersonal Processes of Care (IPC-18), and the Short Test of Functional Health Literacy in Adults (s-TOFHLA). The authors found that higher levels of mistrust were associated with lower CAT and IPC-18 scores.

Patients with type 2 diabetes are also likely to benefit from personalized education delivery tools such as patient-centered labeling (PCL) of prescription drugs, learning style–based education materials, and tailored text messages ( 24 , 25 , 27 ). Wolf et al. ( 27 ) investigated the use of PCL in patients with type 2 diabetes and found that patients with low health literacy who take medication two or more times per day have higher rates of proper medication use when using PCL (85.9 vs. 77.4%, P = 0.03). The objective of the PCL intervention was to make medication instructions and other information on the labels easier to read to improve medication use and adherence rates. The labels incorporated best-practice strategies introduced by the Institute of Medicine for the Universal Medication Schedule. These strategies prioritize medication information, use of larger font sizes, and increased white space. Of note, the benefits of PCL were largely seen with English speakers. Spanish speakers did not have substantial improvement in medication use or adherence, which could be attributed to language barriers ( 27 ).

Nelson et al. ( 25 ) analyzed patients’ engagement with an automated text message approach to supporting diabetes self-care activities in a 12-month randomized controlled trial (RCT) called REACH (Rapid Education/Encouragement and Communications for Health) ( 25 ). Messages were tailored based on patients’ medication adherence, the Information-Motivation-Behavioral Skills model of health behavior change, and self-care behaviors such as diet, exercise, and self-monitoring of blood glucose. Patients in this trial were native English speakers, so further research to evaluate the impact of the text message intervention in patients with limited English language skills is still needed. However, participants in the intervention group reported higher engagement with the text messages over the 12-month period ( 25 ).

Patients who receive educational materials based on their learning style also show significant improvement in their diabetes knowledge and health literacy. Koonce et al. ( 24 ) developed and evaluated educational materials based on patients’ learning style to improve health literacy in both English and Spanish languages. The materials were made available in multiple formats to target four different learning styles, including materials for visual learners, read/write learners, auditory learners, and kinesthetic learners. Spanish-language versions were also available. Researchers were primarily interested in measuring patients’ health literacy and knowledge of diabetes. The intervention group received materials in their preferred learning style and language, whereas the control group received standard of care education materials. The intervention group showed significant improvement in diabetes knowledge and health literacy, as indicated by Diabetes Knowledge Test (DKT) scores. More participants in the intervention group reported looking up information about their condition during week 2 of the intervention and showed an overall improvement in understanding symptoms of nerve damage and types of food used to treat hypoglycemic events. However, the study had limited enrollment of Spanish speakers, making the applicability of the results to Spanish-speaking patients highly variable.

Additionally, findings by Hofer et al. ( 22 ) suggest that patients with high A1C levels may benefit from interventions led by community health workers (CHWs) to bridge gaps in health literacy and equip patients with the tools to make health decisions. In this study, Hispanic and African American patients with low health literacy and diabetes not controlled by oral therapy benefited from education sessions led by CHWs. The CHWs led culturally tailored support groups to compare the effects of educational materials provided in an electronic format (via iDecide) and printed format on medication adherence and self-efficacy. The study found increased adherence with both formats, and women, specifically, had a significant increase in medication adherence and self-efficacy. One of the important aspects of this study was that the CHWs shared cultural and linguistic characteristics with the patients and HCPs, leading to increased trust and satisfaction with the information presented ( 22 ).

Kim et al. ( 23 ) found that Korean-American participants benefited greatly from group education sessions that provided integrated counseling led by a team of nurses and CHW educators. The intervention also had a health literacy component that focused on enhancing skills such as reading food package labels, understanding medical terminology, and accessing health care services. This intervention led to a significant reduction of 1–1.3% in A1C levels in the intervention group. The intervention established the value of collaboration between CHW educators and nurses to improve health information delivery and disease management.

A collaboration between CHW educators and pharmacists was also shown to reinforce diabetes knowledge and improve health literacy. Sharp et al. ( 26 ) conducted a cross-over study in four primary care ambulatory clinics that provided care for low-income patients. The study found that patients with low health literacy had more visits with pharmacists and CHWs than those with high health literacy. The CHWs provided individualized support to reinforce diabetes self-management education and referrals to resources such as food, shelter, and translation services. The translation services in this study were especially important for building trust with non-English speakers and helping patients understand their therapy. Similar to other studies, the CHWs shared cultural and linguistic characteristics with their populations, which helped to overcome communication-related and cultural barriers ( 23 , 26 ).

The use of electronic tools or educational videos yielded inconclusive results with regard to medication adherence. Graumlich et al. ( 20 ) implemented a new medication planning tool called Medtable within an electronic medical record system in several outpatient clinics serving patients with type 2 diabetes. The tool was designed to organize medication review and patient education. Providers can use this tool to search for medication instructions and actionable language that are appropriate for each patient’s health literacy level. The authors found no changes in medication knowledge or adherence, but the intervention group reported higher satisfaction. On the other hand, Yeung et al. ( 28 ) showed that pharmacist-led online education videos accessed using QR codes affixed to the patients’ medication bottles and health literacy flashcards increased patients’ medication adherence in an academic medical hospital.

Goessl et al. ( 21 ) found that patients with low health literacy had significantly higher retention of information when receiving evidence-based diabetes education through a DVD recording than through an in-person group class. This 18-month RCT randomized participants to either the DVD or in-person group education and assessed their information retention through a teach-back strategy. The curriculum consisted of diabetes prevention topics such as physical exercise, food portions, and food choices. Participants in the DVD group had significantly higher retention of information than those in the control (in-person) group. The authors suggested this may have been because participants in the DVD group have multiple opportunities to review the education material.

Management of type 2 diabetes remains a challenge for HCPs and patients, in part because of the challenges discussed in this review, including communication barriers between patients and HCPs and knowledge deficits about medications and disease states ( 29 ). HCPs can have a positive impact on the health outcomes of their patients with diabetes by improving patients’ disease state and medication knowledge.

One of the common themes identified in this literature review was the prevalence of culturally tailored diabetes education interventions. This is an important strategy that could improve diabetes outcomes and provide an alternative approach to diabetes self-management education when working with patients from culturally diverse backgrounds. HCPs might benefit from using culturally tailored educational approaches to improve communication with patients and overcome the medical mistrust many patients feel. Although such mistrust was not directly correlated with diabetes management, it was noted that patients who feel mistrustful tend to have poor communication with HCPs ( 20 ). Additionally, Latino/Hispanic patients who have language barriers tend to have poor glycemic control ( 19 ). Having CHWs work with HCPs might mitigate some patient-provider communication barriers. As noted earlier, CHWs who share cultural and linguistic characteristics with their patient populations have ongoing interactions and more frequent one-on-one encounters ( 12 ).

Medication adherence and glycemic control are important components of diabetes self-management, and we noted that the integration of CHWs into the diabetes health care team and the use of simplified medication label interventions were both successful in improving medication adherence ( 23 , 24 ). The use of culturally tailored education sessions and the integration of pharmacists and CHWs into the management of diabetes appear to be successful in reducing A1C levels ( 12 , 26 ). Electronic education tools and educational videos alone did not have an impact on medication knowledge or information retention in patients with low health literacy, but a combination of education tools and individualized sessions has the potential to improve diabetes medication knowledge and overall self-management ( 20 , 22 , 30 ).

There were several limitations to our literature review. We restricted our search criteria to articles published in English and studies conducted within the United States to ensure that the results would be relevant to U.S. HCPs. However, these limitations may have excluded important work on this topic. Additional research expanding this search beyond the United States and including articles published in other languages may demonstrate different outcomes. Additionally, this literature review did not focus on A1C as the primary outcome, although A1C is an important indicator of diabetes self-management. A1C was chosen as the method of evaluating the impact of health literacy interventions in patients with diabetes, but other considerations such as medication adherence, impact on comorbid conditions, and quality of life are also important factors.

The results of this work show that implementing health literacy interventions to help patients manage type 2 diabetes can have beneficial results. However, such interventions can have significant time and monetary costs. The potential financial and time costs of diabetes education interventions were not evaluated in this review and should be taken into account when designing interventions. The American Diabetes Association estimated the cost of medical care for people with diabetes to be $327 billion in 2017, with the majority of the expenditure related to hospitalizations and nursing home facilities ( 16 ). Another substantial cost of diabetes that can be difficult to measure is treatment for comorbid conditions and complications such as cardiovascular and renal diseases.

Interventions designed to address low health literacy and provide education about type 2 diabetes could be a valuable asset in preventing complications and reducing medical expenditures. Results of this work show that clinicians who are considering implementing new interventions may benefit from the following strategies: using culturally tailored approaches, creating materials for different learning styles and in patients’ languages, engaging CHWs and pharmacists to help with patient education, using PCLs for medications, and engaging education session instructors who share patients’ cultural and linguistic characteristics.

Diabetes self-management is crucial to improving health outcomes and reducing medical costs. This literature review identified interventions that had a positive impact on provider-patient communication, medication adherence, and glycemic control by promoting diabetes self-management through educational efforts to address low health literacy. Clinicians seeking to implement diabetes care and education interventions for patients with low health literacy may want to consider drawing on the strategies described in this article. Providing culturally sensitive education that is tailored to patients’ individual learning styles, spoken language, and individual needs can improve patient outcomes and build patients’ trust.

Duality of Interest

No potential conflicts of interest relevant to this article were reported.

Author Contributions

Both authors conceptualized the literature review, developed the methodology, analyzed the data, and wrote, reviewed, and edited the manuscript. R.A. collected the data. K.M. supervised the review. K.M. is the guarantor of this work and, as such, has full access to all the data in the study and takes responsibility for the integrity of the data and the accuracy of the data analysis.

Prior Presentation

Portions of this research were presented at the Washington State University College of Pharmacy and Pharmaceutical Sciences Honors Research Day in April 2019.

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A literature review provides an overview of what's been written about a specific topic. There are many different types of literature reviews. They vary in terms of comprehensiveness, types of study included, and purpose. 

The other pages in this guide will cover some basic steps to consider when conducting a traditional health sciences literature review. See below for a quick look at some of the more popular types of literature reviews.

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Grant MJ, Booth A. A typology of reviews: an analysis of 14 review types and associated methodologies. Health Info Libr J. 2009 Jun;26(2):91-108. doi: 10.1111/j.1471-1842.2009.00848.x. Review. PubMed PMID: 19490148.

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

Published on 19.8.2024 in Vol 26 (2024)

This is a member publication of Open University

Prevalence of Health Misinformation on Social Media—Challenges and Mitigation Before, During, and Beyond the COVID-19 Pandemic: Scoping Literature Review

Authors of this article:

• Dhouha Kbaier 1 , PhD   ; 
• Annemarie Kane 2 , PhD   ; 
• Mark McJury 3   ; 
• Ian Kenny 1 , PhD  

1 School of Computing and Communications, The Open University, Milton Keynes, United Kingdom

2 Faculty of Arts and Social Sciences, The Open University, Milton Keynes, United Kingdom

3 School of Physical Sciences, The Open University, Milton Keynes, United Kingdom

Corresponding Author:

Dhouha Kbaier, PhD

School of Computing and Communications

The Open University

Walton Hall

Milton Keynes, MK7 6AA

United Kingdom

Email: [email protected]

Background: This scoping review accompanies our research study “The Experience of Health Professionals With Misinformation and Its Impact on Their Job Practice: Qualitative Interview Study.” It surveys online health misinformation and is intended to provide an understanding of the communication context in which health professionals must operate.

Objective: Our objective was to illustrate the impact of social media in introducing additional sources of misinformation that impact health practitioners’ ability to communicate effectively with their patients. In addition, we considered how the level of knowledge of practitioners mitigated the effect of misinformation and additional stress factors associated with dealing with outbreaks, such as the COVID-19 pandemic, that affect communication with patients.

Methods: This study used a 5-step scoping review methodology following Arksey and O’Malley’s methodology to map relevant literature published in English between January 2012 and March 2024, focusing on health misinformation on social media platforms. We defined health misinformation as a false or misleading health-related claim that is not based on valid evidence or scientific knowledge. Electronic searches were performed on PubMed, Scopus, Web of Science, and Google Scholar. We included studies on the extent and impact of health misinformation in social media, mitigation strategies, and health practitioners’ experiences of confronting health misinformation. Our independent reviewers identified relevant articles for data extraction.

Results: Our review synthesized findings from 70 sources on online health misinformation. It revealed a consensus regarding the significant problem of health misinformation disseminated on social network platforms. While users seek trustworthy sources of health information, they often lack adequate health and digital literacies, which is exacerbated by social and economic inequalities. Cultural contexts influence the reception of such misinformation, and health practitioners may be vulnerable, too. The effectiveness of online mitigation strategies like user correction and automatic detection are complicated by malicious actors and politicization. The role of health practitioners in this context is a challenging one. Although they are still best placed to combat health misinformation, this review identified stressors that create barriers to their abilities to do this well. Investment in health information management at local and global levels could enhance their capacity for effective communication with patients.

Conclusions: This scoping review underscores the significance of addressing online health misinformation, particularly in the postpandemic era. It highlights the necessity for a collaborative global interdisciplinary effort to ensure equitable access to accurate health information, thereby empowering health practitioners to effectively combat the impact of online health misinformation. Academic research will need to be disseminated into the public domain in a way that is accessible to the public. Without equipping populations with health and digital literacies, the prevalence of online health misinformation will continue to pose a threat to global public health efforts.

Introduction

The global adoption of the internet has made health information more accessible, and the development of digital technology has enabled its rapid dissemination. However, the internet has also made possible the dissemination of false and misleading health misinformation and disinformation, with negative consequences, including the potential to exacerbate health inequalities. Health practitioners have found themselves at the forefront of communicating with patients who have taken on board health misinformation in the context of its proliferation on the web. This paper (associated with the study by Ismail et al [ 1 ]) surveyed the current literature concerning online health misinformation to establish the extent and scope of the problem, with special reference to the needs of health practitioners in their efforts to mitigate its impact. Several studies have established useful definitions of the terms misinformation and disinformation and distinctions between them. Misinformation has been defined as information that is not supported by evidence and contradicts the best-supported evidence available [ 2 , 3 ]. Wang et al [ 4 ] made a further distinction between online misinformation and disinformation, in particular on social media platforms. For Wang et al [ 4 ], misinformation is information that is not known to be false and is shared without malice. By contrast, disinformation involves the knowing and sharing of false information with the purpose of causing harm. This paper follows the distinctions of Wang et al [ 4 ] to use the terms misinformation and disinformation as appropriate.

It is important to acknowledge at the outset that digital technology in health and social contexts presents both risks and opportunities for equity among different information audiences [ 5 ]. However, there has recently been a change in the reception and assessment of the role of the internet, social media in particular, among researchers, even predating the COVID-19 pandemic. In the early days of social media, researchers largely identified prosocial and altruistic uses of social media platforms such as Facebook and Twitter by the public. However, considerable disquiet about the impact of social media and its potential for the spread of “fake news” and the amplification of conspiracy theories has displaced the more positive evaluation that was apparent when social media was in its infancy [ 6 ]. In the majority of the current research, there is a view that digital technology, particularly social media, has amplified the problem of health misinformation. The risk most frequently identified, either explicitly or implicitly, is the susceptibility of ordinary users, who may be lacking sophisticated levels of health and digital literacies, to health misinformation. Further risks noted in the literature include disinformation disseminated by organized trolling networks and bots that can be hard to distinguish from human users. The recognition of these risks underpins an emerging policy discourse about the threat of health misinformation, particularly the role of social media in its spread, in which health information and misinformation has become a politicized issue. From one policy perspective, there is an assumption that social media users are vulnerable, even passive, recipients of health misinformation rather than reflective interpreters of the available information. The corollary of this is that correcting misinformation with authoritative knowledge will solve the problem. However, a survey of the literature suggested that neither assumption fully expresses the complexity of how health misinformation is disseminated, received, and used via the internet. This may be because although there is a growing body of evidence on the extent of online health misinformation, there is much less research about what users do with health misinformation, why users consume health misinformation, and why (and which) people believe health misinformation [ 7 - 9 ].

In this scoping review, we reviewed the current state of knowledge regarding the prevalence of online misinformation before and during the COVID-19 pandemic and the impact that has on users’ understanding of health information. We considered this context with special reference to patients’ understanding, health practitioners’ practice in response to that, and policy makers’ concerns. The pressures and distractions that health professionals face in attempting to mitigate the impacts of online health misinformation are discussed in relation to patients’ health and digital literacies and the politicization of health information and misinformation.

Information Sources

We conducted a comprehensive literature search to identify relevant studies that explored health misinformation on social media platforms. The search was conducted across multiple electronic databases, including PubMed, Scopus, Web of Science, and Google Scholar.

The search terms included a combination of relevant keywords and phrases, including “health misinformation,” “social media,” “online health communities,” and “COVID-19 pandemic.” The search was not limited by publication date. Detailed search strategies are provided in Multimedia Appendix 1 .

Study Selection

Our study selection process followed a scoping approach, where we aimed to identify and include studies that provided insights into the prevalence and challenges of health misinformation on social media platforms. Initially, 2 researchers independently screened titles and abstracts of the identified articles to determine their relevance. Articles that did not meet the inclusion criteria were excluded at this stage.

Inclusion Criteria

Articles were included if they discussed health misinformation on social media, addressed the challenges posed by health misinformation, or were relevant to the period before, during, and after the COVID-19 pandemic.

Any disagreements between the 2 researchers were resolved through discussion and consensus. Full-text articles were then retrieved for the remaining studies, and a further assessment of eligibility was conducted based on the same inclusion criteria.

Data Extraction

We gathered information on (1) study objectives, (2) research methods, (3) findings, and (4) key themes related to health misinformation. This process was performed independently by 2 researchers, and any discrepancies were resolved through discussion.

Data Synthesis and Analysis

We adopted a scoping review content analysis approach to analyze the data extracted from the selected articles. The analysis process involved identifying key themes and patterns related to health misinformation on social media. The content analysis allowed us to gain a deeper understanding of the challenges posed by health misinformation and the strategies for its mitigation, both before and during the COVID-19 pandemic.

Results of Search

In our article selection process ( Figure 1 ), we initiated our search by identifying a total of 4563 articles from various databases. Following the removal of duplicates, 1295 articles were excluded, leaving us with 3268 unique articles. Subsequently, these articles underwent an initial screening, which involved evaluating their abstracts and titles, resulting in the exclusion of 2635 articles that did not align with our inclusion criteria. Further scrutiny was applied during full-text screening, which was conducted on 633 articles. Among these, 563 articles were found ineligible due to reasons such as not meeting the inclusion criteria (n=378 articles), being classified as literature reviews, editorials, or letters (n=174 articles), or the full texts being inaccessible (n=11 articles). A total of 70 articles were ultimately included in this scoping review.

Characteristics of Included Documents (n=70)

The majority (65/70, 93%) of documents were published in the last 10 years and originated predominantly in North America (42/70, 60%), Europe (19/70, 27%), and Asia (11/70, 16%). The funding sources were mainly public (61/70, 87%). The documents were classified as original research papers (38/70, 54%), subjective “opinion” articles (editorials, viewpoints, commentaries, and letters to the journal; 11/70, 16%), and knowledge syntheses or reviews (9/70, 13%) which included systematic reviews (n=6), descriptive reviews (n=2), and 1 integrated theoretic review.

Extent and Impact of Health Misinformation Disseminated Across a Range of Outlets

This section will review the literature concerning the extent and impact of the problem of health misinformation, including the spread of antivaccination discourse. In a study by Wood et al [ 10 ] among health practitioners in North Carolina, 94.2% of the respondents reported encounters with patient health misinformation within the previous year. While the sources of this misinformation were not broken down and identified by Wood et al [ 10 ], several other studies linked patient health misinformation to the prevalence of health misinformation on social media sites, identifying the latter as a significant problem [ 4 , 11 - 15 ]. There is a growing consensus among researchers, health professionals, and policy makers about the need to confront, challenge, and even prevent the online dissemination of health misinformation [ 16 ]. Since the emergence of online social networks, users have increasingly sought and shared health information on social media sites. It is estimated that around 70% of adult internet users search health matters on the web. With the emergence of social media platforms, there has been a rise in “peer-to-peer health care,” through which individuals seek and share health information, forming online health communities with others who have similar health concerns [ 3 ]. In addition, health organizations and health professionals are increasingly using social media to disseminate and promote health information and advice. The opportunities for sharing and promoting good health information via the internet are evident, and it is important to acknowledge that in online health communities, users share experiences and receive and give different kinds of support, including emotional support, to cope with specific health conditions. However, the medium has also enabled the dissemination of health misinformation, and the prosocial aspects of sharing are also likely to involve the sharing of misinformation, putting the health of users at risk [ 3 ].

Misinformation Spreads on Social Media

There is a high degree of consensus among researchers that mainly because of the increasing popularity of social media, the internet has become a space for the dissemination and amplification of “fake news,” misleading information, and rumor, including health misinformation and antivaccine conspiracy theories [ 17 ]. The COVID-19 pandemic has heightened these concerns, resulting in a proliferation of recent studies and rapid reviews focusing on the online spread of misinformation. Lee et al [ 18 ] proposed that the proliferation of health misinformation during the COVID-19 pandemic became a major public health issue. At the earliest signs of the emerging COVID-19 pandemic, the director-general of the World Health Organization, Tedros Adhanom Ghebreyesus, speaking at the February 2020 Munich Security Conference, expressed concern about the risk of an infodemic of health misinformation disseminated via social media, identifying “vaccine hesitancy” as 1 of the top 10 global health threats [ 19 ]. Bapaye and Bapaye [ 20 ] agreed that the risks of misinformation on social networking sites constitute a global issue, referring specifically to the COVID-19 infodemic.

However, this is not in itself a new problem; longstanding concerns about “fake news” and misinformation in traditional media have been evident since the early decades of the 20th century [ 21 ], and the prevalence of misinformation on internet platforms certainly predates the COVID-19 pandemic. Therefore, because the COVID-19 pandemic has only intensified the concern regarding health misinformation, it might be more appropriate to see the pandemic as symptomatic of, and crystallizing, the challenges of countering health misinformation in the digital age, as the development of digital technology and the internet have brought about profound changes in the capacity of both misinformation and disinformation to spread globally and amplify rapidly [ 4 ].

Suarez-Lledo and Alvarez-Galvez [ 16 ] undertook a review of 69 studies of health misinformation on social media to identify the main health misinformation topics and their frequency on different social media platforms. The studies surveyed used a variety of research methods, including social network analysis (28%), evaluation of content (26%), evaluation of quality (24%), content/text analysis (16%), and sentiment analysis (6%). Suarez-Lledo and Alvarez-Galvez [ 16 ] concluded that the incidence of health misinformation was highest on Twitter, in particular, regarding the use of tobacco and other drugs, with some studies citing 87% of such posts containing misinformation. However, health misinformation about vaccines was also prevalent, with around 43% of posts containing misinformation, with the human papillomavirus vaccine being the most affected. This review by Suarez-Lledo and Alvarez-Galvez [ 16 ] confirmed many of the findings from earlier surveys. For example, in their survey of 57 articles, Wang et al [ 4 ] found that the most frequently discussed topics were regarding vaccination and infectious diseases, including Ebola and the Zika virus. Other topics such as nutrition, cancer, water fluoridation, and smoking were also prevalent. The studies they surveyed had tended to find that a high degree of misinformation on these topics was being shared and liked on social media.

Lee et al [ 18 ] conducted a cross-sectional online survey in South Korea to examine the prevalence of COVID-19 misinformation and the impact of exposure to COVID-19 misinformation on beliefs and behaviors. They found that exposure to COVID-19 misinformation was associated with misinformation belief, which then resulted in fewer preventive behaviors. Therefore, they highlighted the potential of misinformation to undermine global efforts in disease control and argued that public health strategies are needed to combat the proliferation of misinformation. Bapaye and Bapaye [ 20 ] conducted a cross-sectional online questionnaire survey of 1137 WhatsApp users in India. They noted that most research on the prevalence of misinformation in social media has focused on Twitter and Facebook and on the Global North. Measured by age, researchers found that users aged >65 years were the most vulnerable to accepting the veracity of messages containing health misinformation (K=0.38, 95% CI 0.341-0.419) Respondents aged 19 to 25 years displayed much lower vulnerability (K=0.31, 95% CI 0.301-0.319) than those aged >25 years ( P <.05). Measured by occupational category, users employed in nonprofessional occupations had the highest vulnerability (K=0.38, 95% CI 0.356-0.404); this was significantly higher than those of professionals and students ( P <.05). Notably, the vulnerability of health professionals was not significantly different from those of other occupation groups ( P >.05).

The authors concluded that in a developing country, WhatsApp users aged >65 years and those involved in nonprofessional occupations are the most vulnerable to false information disseminated via WhatsApp. Crucially, they noted that health care workers, who might be expected by laypersons to have expert knowledge, were as likely to be vulnerable to health misinformation as other occupation groups.

Antivaxxer Spread Before, During, and Beyond the COVID-19 Pandemic

Much of the current unease from researchers, understandably, centers on health misinformation about vaccines in the wake of the COVID-19 pandemic. In particular, there is concern about the growth and spread of so-called antivaxxer misinformation and beliefs. In 2019, the United States had its biggest measles outbreak in 30 years, with most cases involving people who had not been vaccinated. Hotez [ 22 ] claimed that much of the reason for the growth of antivaccine beliefs is because of a campaign of misinformation. He argued that social media sites are meeting places for the sharing of antivaccine views. To evade social media platforms’ automated moderation tools, which tend to focus on words, several antivaxxer groups, including one with around 250,000 members, began using visual codes, such as the carrot emoji, to hide antivaxxer content.

However, some of the misinformation has gained credibility because it has come from sources that laypersons would expect to be trustworthy. For example, in 1998, the British medical journal The Lancet published a paper by Dr Andrew Wakefield claiming a link between the measles, mumps, rubella vaccine and the onset of autism spectrum disorder. Wakefield’s paper was later rebutted, and an overwhelming body of evidence now refutes its conclusions [ 23 ]. However, despite long being discredited, Wakefield’s claims have remained a part of the antivaccine discourse. The persistence of the antivaccination narrative demonstrates the power of such discourses even in the face of evidence to challenge them.

Although strong antivaccine beliefs, and the more ambivalent attitude of vaccine hesitancy, have been around as long as there have been vaccines, until recent decades, they were on the margins. However, evidence supports the claim that they have been gaining momentum in the United States and Europe.

A survey by Skafle et al [ 24 ] aimed to synthesize the results from 19 studies in which the effect of social media misinformation on vaccine hesitancy was measured or discussed. The authors noted that the “vast majority” of studies were from industrialized Western countries. Only 1 study contained misinformation about autism as a side effect of COVID-19 vaccines. Nevertheless, the studies implied that information spread on social media had a negative effect on vaccine hesitancy and uptake. The conclusions from Skafle et al [ 24 ] were supported by data from online polling agencies. For example, a US YouGov poll from May 2020 found that only 55% of respondents would definitely take a COVID-19 vaccine if one were to become available, whereas 19% of respondents said that they would refuse and 26% were still undecided [ 25 ].

While much of the research about online vaccine discourse comes from the United States, there is also evidence that vaccine hesitancy has risen elsewhere. For example, in an Ipsos-MORI survey taken in December 2020, only 40% of respondents in France said they would take a COVID-19 vaccine, a figure symptomatic of a steep and swift decline in vaccine confidence in France [ 26 ]. However, interestingly, the same Ipsos-MORI poll indicated a rise in vaccine confidence among respondents in the United States since the earlier YouGov poll, cited earlier, by approximately 10% to 65%, and respondents in the United Kingdom expressed a still higher willingness to take a COVID-19 vaccine at approximately 77%. It is notable that in the United States and United Kingdom, the Ipsos-MORI results came after a period of intermittent lockdowns. The contrast with the results from France is, nevertheless, striking.

Understanding the Challenges Surrounding Health Misinformation

Here, we consider the challenges created by health misinformation on the web: (1) the role played by malicious actors on social media in spreading vaccine disinformation and misinformation and (2) how contextual and cultural issues have different effects on patients’ understanding of what is considered genuine, valid, and authentic health information.

Spread of Health Misinformation on Social Media by Malicious Actors

One strand of research presents the issue of health misinformation as a contest between trolls and bots on the one hand and the voices of trustworthy public health agencies on the other [ 6 ]. This view was supported by Hotez [ 22 ] and Broniatowski et al [ 11 ]. The latter investigated the role of bots and trolls as malicious actors mobilizing vaccination discourse on the web. Their study focused specifically on vaccine-related health messaging on Twitter. Comparing the rates of vaccine-related messages, they found that sophisticated bots and Russian trolls tweeted at higher rates than “average users.” However, the respective content from bots and trolls differed. Whereas bots communicated antivaccine messages, Russian troll accounts provided a seemingly balanced discussion of both provaccination and antivaccination arguments, implying an equivalence between them. The authors argued that amplifying and normalizing a debate is done with the purpose of sowing discord and may lead to undermining public confidence in scientific consensus about the effectiveness of vaccines. Wang et al [ 4 ] acknowledged that it is a challenge to readily distinguish between misinformation and disinformation on the web. They noted that disinformation, such as antivaccine propaganda, can unknowingly be spread by users with genuine concerns [ 4 ], as individuals increasingly seek health and healthy lifestyle information via the internet.

Contextual Factors Influencing the Reception of and Responses to Misinformation: Politicization of the Problem of Health Misinformation

The identification of online trolls, bots, and orchestrated networks as major contributors to the spread of health disinformation and misinformation is now part of mainstream political discourse in the United States. On July 16, 2021, a quarrel broke out between the president of the United States, Joe Biden, and Facebook over the spread of health misinformation on the company’s social media platforms. Speaking to journalists, Biden blamed social media companies for a rise in the number of deaths from COVID-19 among the unvaccinated in the United States. Referring explicitly to Facebook, the president claimed that by allowing the proliferation of health misinformation on its platforms, the company was “killing people” [ 27 ]. Discursive interventions from politicians are never neutral; nevertheless, Biden’s claim about the impact of health misinformation on social media is backed up by many of the studies surveyed for this paper. Facebook immediately rebutted Biden’s accusation by citing their rules, introduced in February 2021, which banned posts that make identifiably false claims about vaccines. Furthermore, Facebook challenged Biden’s claim by asserting that not only has Facebook provided more authoritative information about COVID-19 and vaccines than any other internet site, reaching 2 billion people with such posts, but also that the platform’s vaccine finder tool had been used by more than 3 million Americans.

These figures suggest that although antivaxxer groups find ways to evade detection, their reach may be countered by that of information grounded in current science. A spokesperson for the company said that, far from killing people, “The facts show that Facebook is helping save lives. Period” [ 27 ]. The argument between Biden and Facebook may indeed signal more lay awareness of the problem and echo the concerns of the recent academic research about the dissemination of health misinformation by organized bot and troll networks. Framed as it is, in terms of apportioning the blame for the spread of health misinformation, Biden’s intervention mirrors much of the academic discourse in the United States on the subject. However, it is also symptomatic of the politicization of health misinformation, arguably accelerated by the COVID-19 pandemic, which may thwart evidence-based decision-making. This point was emphasized strongly by Kyabaggu et al [ 5 ]. They framed the problem of pervasive misinformation and disinformation in terms of prime movers and beneficiaries who use it to advance sociopolitical agendas and entrench asymmetrical power, especially in times of uncertainty and threat, such as the COVID-19 pandemic.

Kyabaggu et al [ 5 ] identified government failures to adopt evidence-informed decision-making. They noted that such failures have costs that not only are economic but, crucially, result in poorer health outcomes. They cited as an example the United Kingdom government’s initial prevaccine herd immunity strategy. The intention of this strategy was to allow SARS-CoV-2 to indiscriminately spread to a critical mass to build up population immunity. The authors noted that this was “a particularly concerning example of evidence framing by a government.” Kyabaggu et al [ 5 ] argued that public acceptance of health risk messages and adoption of health-protecting behaviors is highly contingent on the degree to which governments engage in evidence-informed decision-making and communicate this basis effectively. The authors cited several instances of government actors failing to recognize misinformation, disseminating inconsistent or inaccurate information, and not using evidence- and information-based decision-making processes. In recent years, the public policy discourse in the United Kingdom has been veering away from evidence- and information-based decision-making, as politicians have denounced “experts” and their “influence” on policy [ 28 , 29 ].

Finally, Gruzd et al [ 30 ] reported on the impact of coordinated link-sharing behavior to spread and amplify conspiracy-related misinformation. They found a coalition of Facebook accounts that engaged in coordinated link sharing behavior to promote COVID-19 related misinformation. This coalition included US-based pro-Trump, QAnon, and antivaccination accounts.

Contextual Factors Influencing the Reception of and Responses to Misinformation: Health Literacies and Inequality

While the approach of Broniatowski et al [ 11 ], for example, provided a persuasive account of ways in which online health misinformation can be disseminated, there are limitations to this approach, as it did not provide an account of how users respond to the misinformation they encounter. The responses of ordinary users were assumed rather than investigated. Research by Vosoughi et al [ 31 ] provided a caveat to the claim that it is bots that accelerate the spread of misinformation. Their work supported that of Broniatowski et al [ 11 ] in suggesting that bots spread accurate and false information at the same rate. However, Vosoughi et al [ 31 ] also explained that misinformation spreads more rapidly than accurate information because humans, rather than bots, are more likely to spread misinformation [ 31 ]. This claim was further supported by Wang [ 32 ], who suggested that in democracies, where ideas compete for attention in a marketplace, accurate scientific information, which, for the layperson, may be boring or difficult to understand, is easily crowded out by information that is more easily grasped or sensational. Mokhtari and Mirzaei [ 12 ] located this problem specifically in the context of the COVID-19 pandemic. They considered that high mortality from COVID-19, its complexity, and its unknown features resulted in fear, anxiety, and mental pressure among people worldwide. To allay anxiety, people needed health information literacy, defined by the American Library Association as a set of abilities individuals require to recognize when information is needed and to locate, evaluate, and use it effectively [ 33 ]. In addition, Wang [ 32 ] noted that individuals are differentially vulnerable to health misinformation depending on their level of health literacy and that models need to account for this. Mokhtari and Mirzaei [ 12 ] argued that not only information and health literacies but also media literacy are needed. However, studies in the field of health literacy suggest that significant inequalities in health and digital literacies exist.

Researchers have argued that “vastly undervalued and unrecognized” health literacy ought to be considered the best “social vaccine” for preventing COVID-19 in populations [ 5 ]. However, inequalities in health literacy persist. Kyabaggu et al [ 5 ] defined health literacy as encompassing cognitive and social skills that determine individuals’ motivation and ability to access, understand, and use information, including quantitative health risk information, in ways that promote and maintain good health across the life course. They asserted that health literacy is an essential self-management skill and community resource for health, noting that health literacy is positively associated with patients’ involvement in clinical decision-making, willingness to express health concerns, and compliance with clinical guidance. However, despite research demonstrating the importance of health literacy, evidence, even from high-income countries, suggested relatively low levels of health literacy.

Kyabaggu et al [ 5 ] drew a link between health literacy and digital literacy. They suggested that the latter can be understood as health literacy in digital information and technology spaces. They argued that inequalities in health outcomes are exacerbated by a widening digital divide. While digital technology in health and social contexts presents both new risks and opportunities for equity in different information audiences, the ways in which power and privilege operated in the COVID-19 misinformation discourse have not been sufficiently examined. Although socially and economically disadvantaged groups were at a greater risk of exposure to COVID-19, their voices and experiences were often marginalized. In addition, inequalities in access to accurate information are not only related to issues of digital access and literacy but are also situational. For example, disadvantaged individuals may have fewer social connections, and low pay may necessitate longer working hours, militating against individuals having the resources of time and energy to seek out accurate health information and enhance their level of health literacy.

The experiences of specific groups may also go unreported. Quraishi [ 34 ] addressed the impact of misinformation on South Asian students—a fast-growing group in the United States, but one that often receives little media attention. Quraishi [ 34 ] concluded that there is a relationship between the COVID-19 pandemic and students’ academic performance and mental health, as well as an increase in the spread of misinformation regarding COVID-19 public safety guidelines.

Older adults can be a vulnerable group in relation to their comparatively poor digital literacy. Zhou et al [ 35 ] reported on the accuracy of older adults in judging health information credibility. They found that on average, participants only successfully judged 41.38% of health articles. Attractive headlines increased participant credibility judgments on the content, and of the articles shared with others, 62.5% contained falsehoods.

Contextual Factors Influencing the Reception of and Responses to Misinformation: Cultures and Values

Larson and Broniatowski [ 19 ] argued that developing the kinds of literacy advocated by Mokhtari and Mirzaei [ 12 ] and Tully et al [ 2 ] will not address the deep-seated problems they identified. The work by Kyabaggu et al [ 5 ] supported this, and noted that the infodemic crisis is not merely a health and digital literacy issue. Some demographics may be more vulnerable to persuasive communication from broader sociocultural forces. Kyabaggu et al [ 5 ] argued that in considering the social determinants of health, attention must be paid not only to digital and health literacies but also to the ways in which these literacies coexist and interact with other influences. Larson and Broniatowski [ 19 ] suggested that one of the strongest determinants of vaccine confidence or vaccine hesitancy is the level of trust or distrust in the institutions that produce vaccines. A higher level of trust encourages the willingness to accept a high level of risk for a greater benefit. A lower level of trust militates against the acceptance of even a low level of perceived risk. For Larson and Broniatowski [ 19 ], it is not simply the presence of misinformation on social media networks but the social and cultural context of users’ reception of that information that influences responses. Health information operates in a complex and contentious social world. Individuals and communities respond to new information in terms of already developed political, cultural, and social values that influence whether they trust or distrust authority. Populations may be characterized by trust or mistrust of scientific institutions and government. Trust has been eroded through the exposure of fraud, research scandals, and misconduct by major multinational pharmaceutical companies, for example. Communities may be predisposed to distrust the government and its agents depending on their own status or identity. According to Goldenberg [ 36 ], these contexts can make misinformation and health conspiracy theories compelling.

Strategies to Correct Online Misinformation

We address the additional pressures on health professionals in communicating accurate information to mitigate the effects of misinformation, particularly with regard to the additional requirements imposed as a result of the precautions being taken during the pandemic. One area of disagreement in the literature concerns the usefulness of user correction response.

Research Into User Correction Strategies

There is some disagreement as to whether engagement with misinformation by users spreads and reinforces it or even whether extended debates over health misinformation cause users to doubt the possibility of knowable facts. For example, Broniatowski et al [ 11 ] argued that when ordinary users directly confront vaccine-skeptic messages from bots, it only serves to legitimize the “debate.” By contrast, Tully et al [ 2 ] argued that social media users have a role to play in either spreading or stopping the spread of misinformation across platforms. Their research aimed to uncover what factors influenced users’ responses. Tully et al [ 2 ] acknowledged that a range of factors can influence the spread or prevention of misinformation, including the behavior of malicious actors such as bots and trolls; the platform’s terms of service; and content moderation policies. As already noted, while most users are not creators of misinformation, they may spread and amplify it by liking, sharing, or replying. In opposition to the work of Broniatowski et al [ 11 ], Tully et al [ 2 ] argued that the content of engagement is particularly important, as their research suggested that multiple corrections by social media users may be required to reduce misperceptions. However, they claimed that most people simply ignore misinformation when they see it on social media.

Tully et al [ 2 ] noted the promise in mobilizing users to engage in such correction, given the vast numbers of users on these sites, in comparison with professional fact-checkers and health authorities.

They considered whether the tone of a correction would influence perceptions of the credibility of the message. However, despite some mixed evidence, they concluded that overall, the tone was not a significant factor and that neutral, affirmative, and uncivil corrections were all effective at reducing misperceptions. They found that participants were generally unlikely to reply to the misinformation tweet. However, their content analysis of hypothetical replies suggested that when users did reply, they mainly provided correct information, particularly after seeing other corrections. Tully et al [ 2 ] concluded that user corrections offer “untapped potential” in responding to misinformation on social media, but further work is needed to consider how users can be mobilized to provide corrections, given their overall unwillingness to reply. However, a limitation of the experimental approach of Tully et al [ 2 ], acknowledged by the researchers, is that in asking individuals what they would hypothetically do, this may not reflect what they actually do in a real social media setting, especially in relation to an issue they care more strongly about. Although the experiment gauged attitudes, it did not delve into how strongly these attitudes were held. It is also not clear to what degree corrections were effective at reducing misperceptions and how reductions were measured.

By contrast, the results of experimental studies by Ittefaq [ 37 ] and Mourali and Drake [ 38 ] suggested that correcting misinformation is by no means a straightforward proposition. They noted the previous research on rebuttal, which suggested that properly designed corrections can mitigate the effects of misinformation. However, such studies have tended to compare responses to misinformation followed by correction with responses of a control group that receives no correction or receives an alternative correction. Mourali and Drake [ 38 ] argued that this static approach misses the dynamic nature of social media debate. They noted that the correction of misinformation is generally followed up with a rebuke by the original poster, inciting further correction and prolonged back-and-forth debate. Mourali and Drake [ 38 ] cited previous studies showing that exposure to conflicting information about health topics, including mammography, nutrition, and the human papillomavirus vaccine, may increase confusion and negative attitudes toward that particular health topic. The researchers found that initial exposure to misinformation had a negative impact on attitudes and intentions toward masking, consistent with previous studies that concluded that exposure to misinformation negatively impacts attitudes and intentions toward behaviors favored by science. Also consistent with previous research, they found that the first correction of the false claim improved attitudes and intentions toward masking. The authors suggested that this effect is partially explained by a decrease in the perceived strength of the argument underlying the false claim. However, this initial improvement diminished on further exposure to false claims and refutation attempts. This finding confirmed their hypothesis that extended exposure to false claims and refutation attempts appears to weaken belief in the possibility of objective knowledge, leading to less positive reactions toward masking as a science-based behavior. They concluded that the level of exposure to contradictory information needs to reach a certain threshold before it affects perceived truth objectivity. However, although people are more likely to share misinformation when its content is consistent with their existing beliefs or when its message is simple, direct, or sensational, correcting misinformation does reduce its likelihood of being shared on social media, an effect that persists even after multiple exposures.

Mourali and Drake [ 38 ] noted that each social media platform exhibits particular interaction norms, which may impact how users interpret the conversation. As their study was limited to a single platform, Reddit, and the debate was restricted to 4 exchanges between only 2 protagonists, the researchers acknowledged that these aspects limit the generalizability of the results. They suggested that future research could attempt to replicate their findings on different social media platforms, and to include more than 2 protagonists and more than 4 exchanges. They noted further that although extended debates are common on social media, it is not known how frequently they occur, echoing the comments by Suarez-Lledo and Alvarez-Galvez [ 16 ] that the extent of misinformation is not clear.

In contrast to the fairly sanguine view of Tully et al [ 2 ] about the potential of users to spread corrective information, Mourali and Drake [ 38 ] problematized the position, pointing to the potential for more complex and uncertain outcomes, whereas Larson and Broniatowski [ 19 ] argued that although the importance of correcting misinformation, item by item, should not be diminished, only if underlying issues driving misinformation are addressed can, for example, long-term vaccine confidence in populations be sustained. They argue that simply responding to misinformation with factual corrections is not likely to reverse the dissent that has been evident among antivaxxers or to necessarily persuade the more ambivalent vaccine-hesitant individuals. They identified deeper social and cultural issues at play, which have been discussed in this paper in the previous sections.

Research Into Effective Models to Accomplish the Automatic Detection of Health Misinformation in Online Health Communities

Here, we consider examples of research into the automatic detection of health misinformation in online health communities. Zhao et al [ 3 ] began from the premise that there is a vast amount of health misinformation, creating a challenge for health communities in identifying misinformation. Rather than relying on users’ ability to correct misinformation, they proposed that there is a need for an effective model to achieve automatic detection of health misinformation in online health communities. This view was also put forward by Weinzierl and Harabagiu [ 39 ]. Focusing specifically on COVID-19 vaccine misinformation, they argued that automatic detection of misinformation on social media is an essential first step in delivering interventions designed to address vaccine hesitancy.

Zhao et al [ 3 ] identified much of the existing analysis as concentrating on the linguistic features of communications only. They wanted to examine the underresearched area of whether integrating user behavioral features with linguistic features, sentiment features, and topic features could effectively distinguish misinformation from accurate information in online health communities. Their study combined the aforementioned features to build a detection model targeting misinformation in online health communities’ contexts. The behavioral features targeted were discussion initiation, interaction engagement, influential scope, relational mediation, and informational independence. Descriptions of these behavioral features are reproduced in Table 1 .

The authors tested their detection model on a data set collected from a real online health community, selecting as their data source Zibizheng Ba, an autism forum on the Baidu Tieba online health community site hosted by the Chinese web service Baidu. Baidu Tieba claims to be one of the largest interest-based discussion platforms in China. Users can generate topic-based discussion forums on the platform, share information, and make friends with other users. Posts on Baidu Tieba are indexed by Baidu, China’s most popular search engine, so users can readily find misinformation when searching for health-related information through the search engine. The authors developed a python-based web crawler to collect data from the forum. To train the health misinformation detection model, 5000 records were sampled from the whole data set by stratification according to 3 types of records (ie, thread, reply, and reply to reply) using stratified sampling methods. Therefore, the constituent types of the records (ie, thread, reply, and reply to reply) in the sample data set were consistent with the composition of the whole data set.

The researchers applied the elaboration likelihood model (ELM). The model, originally developed by Petty and Cacioppo [ 40 ] to explain attitude change, has been used extensively in advertising to try to influence consumers.

Overall, 4 types of misinformation were identified through their coding analysis, and the model correctly detected about 85% of the health misinformation. Their results also indicated that behavioral features were more informative than linguistic features in detecting misinformation. The authors concluded that their results not only demonstrated the efficacy of behavioral features in health misinformation detection but also offered both methodological and theoretical contributions to misinformation detection by integrating the features of messages as well as the features of message creators. Others have also highlighted the problems posed by misleading visual information [ 41 ].

It is worth noting that during the pandemic, the UK National Health Service (NHS) began using Twitter to promote provaccine messaging, which closely follows a combination of the features suggested by Zhao et al [ 3 ]. When users searched for the term “vaccine” or related terms, the top post was a message prominently displaying the NHS logo, identifying it as reputable and trustworthy. The tweets contained links to NHS websites providing information about vaccines and COVID-19. The posts differed in linguistic content and visual design. For example, one featured only written text on a white background and stated in bold, “Know the facts.” Another featured a large image of a happy minority ethnic family, washing dishes together, with the message that the COVID-19 vaccine decreases household transmission by up to half. The contrasting designs suggest that the message was targeted specifically to users’ timelines. It was also apparent that elements of ELM were being applied, combining the features identified by Zhao et al [ 3 ] in different ways.

Weinzierl and Harabagiu [ 39 ] adopted a different method than Zhao et al [ 3 ], reversing the more commonly used classification approach. The authors of each study claimed strong results in identifying health misinformation on social media platforms. However, Nabożny et al [ 42 ] argued that the current automatic systems for assessing the credibility of health information are not sufficiently precise to be used without supervision by human medical expert annotators.

Barve and Saini [ 43 ] have reported on their use of automated fact-checking using a coded content similarity measure (CSM). In this approach, the CSM showed improved accuracy (91.06%) compared to the accuracy of the Jaccard similarity measure (74.26%). Further, the algorithmic approach outperformed the feature-based method.

Neither Zhao et al [ 3 ] nor Weinzierl and Harabagiu [ 39 ] recorded what happens when misinformation is detected. Research from Broniatowksi et al [ 44 ] suggested that once detected, steps taken by social media platforms such as content removal or deplatforming may not be effective in stemming the spread of misinformation and may even be counterproductive. Social media platforms use a combination of “hard” and “soft” content remedies to reduce the spread of health misinformation. Soft remedies include warning labels attached to content and downranking of some content in web searches, whereas hard remedies include content removal and deplatforming of accounts. Hard remedies are controversial and have given rise to accusations of censorship. For the authors, short-term evidence for the effectiveness of hard remedies is in any case mixed, and long-term evidence is yet to be examined. Their study focused on Facebook and found that while hard remedies did reduce the number of antivaccine posts, they also produced unintended consequences. Provaccine content was removed, and engagement with the remaining antivaccine content repeatedly recovered to prepolicy levels. Worryingly, this content became more misinformative, more politically polarized, and more likely to be seen in users’ news feeds. The authors explain these results as a product of Facebook’s architecture, which is designed to promote community formation. Members of communities dedicated to vaccine refusal seek out misinformation. To meet this demand, and to circumvent content moderation efforts, antivaccine content producers post links to external sources of misinformative content, such as Bitchute, Rumble, Gab, and Telegram, in lieu of more mainstream platforms that had implemented similar content removal policies (eg, YouTube and Twitter). Broniatowski et al [ 44 ] argued that Facebook’s policy reduced the number of posts in antivaccine venues but was not successful in inducing a sustained reduction in engagement with antivaccine content, including misinformation. The authors noted that alternative platforms often host politically extreme right-wing content. Therefore, they argued that Facebook’s content removal policies may have the unintended consequence of radicalizing their audiences, and their findings suggested the need to address how social media platform architecture enables community formation and mobilization around misinformative topics when managing the spread of online content.

These studies advocate for the automatic detection of health misinformation. However, work that calls into question the ability of automatic detection to operate without human intervention has also been discussed. In addition, there are questions raised in the literature about what should be done when misinformation is detected and concerns about whether content removal or deplatforming of accounts are the most effective ways to reduce the spread of health misinformation or may even be counterproductive.

The Roles of Health Practitioners

The discussion so far has highlighted the complex and multifaceted dimensions of the context of online health misinformation in which health practitioners must operate. As noted in our introduction, a study of health practitioners in North Carolina found that nearly 95% had encountered patient health misinformation within the previous year [ 10 ]. There is very little research on the amount or effectiveness of training received by health professionals to prepare them for engaging with patients about health misinformation. Wood et al [ 10 ] found that most respondents had not received relevant training despite overwhelmingly reporting encountering health misinformation.

Nevertheless, within the literature, there is no shortage of advice from researchers and health professionals addressed to health practitioners on how to approach and correct health misinformation. This advice stems from both original research studies and reviews of best practices featured in peer-reviewed medical and health journals. Such advice centers on the need for health practitioners to understand misinformation and how to address it. Health practitioners are advised of the need to be aware of health myths and urged to dismantle them in providing accurate health guidance [ 45 , 46 ]. Practitioners are further advised that misinformation and pseudoscience are appealing to those seeking certainty because they present information in absolutes, whereas medical science is often ambiguous and contingent. Health practitioners are also encouraged to learn how to message more clearly and to mimic the strategies of misinformation [ 45 ]. One study recommends that “practitioners familiarize themselves with the tools of scientific enquiry and consider the pros and cons of various conspiracy evaluation guidelines” [ 47 ]. Thompson [ 48 ] reports on the activity of health professional influencers and pedagogues in combating misinformation. However, the effectiveness of such social media influencers who are also health professionals remains unclear. At the same time, there is some acknowledgment in this body of literature that misinformation cannot simply be offset with facts, confirming the challenges, discussed earlier, of simply engaging in online refutation. Addressing misinformation also depends on meeting patients’ emotional needs [ 45 , 49 ].

In this context, the one-to-one patient-provider relationship in the practice setting is perceived as paramount [ 45 ]. As suggested by much of the research, source credibility, or trust, is understood to be the strongest driver of effective correction strategies [ 50 ]. It is argued that health care practitioners have the unique opportunity to guide patients toward high-quality, evidence-based medical information [ 10 ]. However, it is also noted that practitioners will need patience in their efforts to persuade patients to abandon strongly held self-beliefs, however harmful. Doing so may mean patients relinquishing membership of online communities that have become integral in their lives and even their identities. As noted earlier, belief in misinformation is often persistent in the face of evidence. Success is more likely when individuals are encouraged to reexamine their information sources, alongside new information providing additional context, rather than simply characterizing the individual’s beliefs as wrong [ 51 ]. Kyabaggu et al [ 5 ] commented that good health communication needs to be tailored to the underlying cause of the misinformation problem, and efforts should be made to take on board inequalities within populations to create accurate, low-barrier, targeted health risk messaging. Skafle et al [ 24 ] contended that to challenge misconceptions, false claims need to be openly addressed and discussed with both cultural and religious awareness in mind. Guidance for practitioners noted that while responding to patient questions about alternative or unproven therapies may become laborious, a strong bond of trust between health practitioner and patient gives a patient a feeling of being supported and increases their adherence to treatment [ 52 ]. Rather than waiting for patients to raise misinformation issues, health care practitioners are advised to anticipate and proactively address potential misinformation and myths with patients. For example, the mortality rate for pediatric cancer has risen during the COVID-19 pandemic because of delayed access to medical care, but misinformation related to COVID-19 may also be a contributing factor [ 53 ]. The literature highlights the challenge of navigating the information and misinformation and the need for health practitioners to communicate with their patients more effectively. However, such efforts are not always successful. Some of the factors that may prevent effective communication of good health information have already been raised in this paper. They are revisited and discussed in the next section, along with other stressors for health practitioners.

Stressors for Health Practitioners

Challenges for health practitioners include time pressures and the additional burdens placed on them during the COVID-19 pandemic. These additional pressures add to the issues health practitioners face in trying to mitigate the impact of misinformation. The following is a brief overview of these issues.

On the one hand, administrative burdens placed on practitioners frequently deny them time for dialogue with their patients [ 52 ]. On the other, in different contexts, practitioners may be coping with a lack of proper facilities; poor infrastructure for patient care; insufficient or ineffective personal protective equipment; lack of awareness among the general population; poor compliance with preventive methods; and the fear of being infected with the virus, as they too are exposed to misinformation. During the COVID-19 pandemic, health practitioners were considered more vulnerable than other workers to developing psychological problems and other stress-related disorders, as they treated patients confirmed with COVID-19 while also dealing with misinformation [ 54 ].

As noted above, practitioners are recommended to invest in developing high levels of patient trust and to proactively correct health misinformation. However, recommendations presuppose that health practitioners necessarily have the resources to do these things well. Some of the materials produced to educate patients are not always reliable or evidence based, resulting ultimately in a loss of trust on the part of patients [ 52 ]. In addition, as noted previously, health practitioners themselves are not necessarily immune from accepting health misinformation as credible. Evidence about the level of knowledge and understanding of COVID-19 among practitioners reveals its unevenness. A study of dentists and oral health practitioners’ knowledge about COVID-19 suggested that their knowledge was at a relatively high level [ 55 ]. By contrast, a study of 310 eye care professionals in Nepal revealed some knowledge but also some acceptance of misinformation. Symptoms of COVID-19 were known to 94% of participants, but only 49% of participants were aware of how the disease is transmitted. More significantly, 41% of participants believed that the consumption of hot drinks helps to destroy the virus, in contradiction to World Health Organization information. The mean overall “knowledge” performance score, as measured by the benchmarks set by the researchers, was 69.65% [ 56 ].

A qualitative study to investigate primary health care practitioners’ perceptions and understanding of the COVID-19 pandemic was conducted in KwaZulu-Natal, South Africa. The study collected data from 15 participants at 2 different clinics situated in rural KwaZulu-Natal. Participants comprised nurses, physiotherapists, pharmacists, community caregivers, social workers, and clinical associates. Data were collected through individual, in-depth face-to-face interviews using a semistructured interview guide. The participants reported prepandemic and pandemic experiences of fear or denial. There was a perception of poor preparation for the COVID-19 outbreak. The findings also revealed participants’ misperceptions regarding the nature of the COVID-19 pandemic. Researchers concluded that respondents’ misunderstandings regarding the pandemic were primarily a result of misinformation found on social media [ 57 ].

The discussion in this section so far has highlighted the significant potential of health practitioners in mitigating the impact of online health misinformation. However, it has also underlined factors that may militate against health practitioners’ ability to do so effectively. Not least of these is the issue of health practitioners’ own knowledge, which coexists with other stressors for health practitioners in combating misinformation. The discussion will now consider health information management (HIM) as a tool for supporting health practitioners’ knowledge base as one element in a multifaceted strategy for combating misinformation on the web.

HIM as a Mitigation Strategy

We have seen there is a need for health practitioners to be supported with evidence-based knowledge that they can share with patients. Kyabaggu et al [ 5 ] argued that the COVID-19 pandemic has demonstrated that in an infectious health crisis, the gathering of accurate and reliable data to assist with the public health response is essential. They highlighted the importance of HIM professionals in supporting contact tracing and syndromic surveillance, as well as in mapping and forecasting health data. They noted that the generation of health information supports the continuum of care and the setting of targets and indicators and aids the planning, monitoring, and evaluation of health programs locally and globally. The health information produced also underpins the development of equitable, efficient, and accessible health care systems, contributing to improving public health initiatives and outcomes. Kyabaggu et al [ 5 ] emphasized the importance of an area of HIM, currently in its early stages, that deals with gathering and identifying evidence about the structural inequalities that underlie the disparities in vulnerability to health misinformation discussed in this paper. The collection of rich, high-quality information, including patient-reported experience, outcome measures, and culturally appropriate identity data, can enable health practitioners and public health advisers serving the most disadvantaged and underrepresented communities to use more tools of advocacy for patients.

The authors noted that advances in technology, including artificial intelligence, have the potential to relieve some of the pressures and constraints on health practitioners working on the front line during crises such as the COVID-19 pandemic, allowing more time for one-to-one engagement with patients. Kyabaggu et al [ 5 ] advocated for the content expertise of health information managers to serve health practitioners by delivering patient-facing information triaging services; constructing user-friendly knowledge representations, such as data visualizations; and developing information interpretation tools, such as decision aids, plain language summaries, and supplementary explanatory information and metadata. Kyabaggu et al [ 5 ] identified the interdisciplinary underpinnings of HIM as essential in contributing to the educational, informational, and decision-making support for addressing current and future infodemic management crises.

Summary of Results

Within the literature, there is a consensus that there exists a significant problem of online health misinformation disseminated via the internet on social network platforms, often by online health communities. It is apparent that while users seek trustworthy sources of health information, they are unequally equipped to assess its credibility. This is partly because some groups lack sufficient levels of health and digital literacies, which may be exacerbated by concomitant social and economic inequalities. Reception of, and response to, online health misinformation is also shaped by users’ cultural contexts, values, and experiences, which may hinder trust in scientific institutions and governments. Evidence suggests that some demographics are more vulnerable to accepting health misinformation as credible and that health practitioners are unevenly prepared in the context of new global health crises, such as the COVID-19 pandemic. Furthermore, the evidence of disparities in positive and negative attitudes toward vaccination highlights a need to pay specific attention to regional and national settings, even in the current global context. Preexisting levels of local trust in vaccine providers may be a significant factor to consider. While the validity and reliability of YouGov polls are limited, nevertheless, the data from an admittedly narrow range of sources suggests that vaccine confidence may have become more fluctuating and potentially vulnerable to destabilization in the digital era.

While online mitigation strategies such as user correction and automatic detection may have their uses, their effectiveness is contested, and some studies suggest they may even be counterproductive. Our analysis of the available literature indicates that the effectiveness of these strategies varies and needs further evaluation [ 42 , 58 ]. The issue of online health misinformation is further complicated by the operation of malicious actors and politicization of the issue, particularly during the COVID-19 pandemic, militating against the equitable and trusted dissemination of evidence-based knowledge. The role of health practitioners in this context is a challenging one. Research suggests that on the one hand, they are still best placed, at the front line of care, to combat health misinformation with science-based knowledge and advice. On the other hand, the stressors identified in this review create barriers to their abilities to do this well. Constraints of time and lack of supporting infrastructure add to the knowledge deficit noted earlier. Our review underlines the complexity of the environment in which health practitioners operate and calls for greater support and resources to enable effective mitigation of health misinformation [ 59 ]. Investment in HIM at local and global levels could address all 3 deficits, creating the potential for health practitioners to enhance their capacity to build trust via knowledgeable one-to-one communication with patients.

Limitations

The limitations of this study are the following: First, the constraints of time and space have necessarily limited the scale and scope of the survey. Second, the study of online health misinformation is a growing field, and inevitably, the nature of the issue means that new evidence is emerging at a rapid rate. In particular, new knowledge and further reflection in the wake of the COVID-19 pandemic will continue to shed new light on the subject. Our study acknowledges these limitations and emphasizes the dynamic nature of the field.

Conclusions

Our survey of the literature on online health misinformation has revealed a complex and multifaceted context in which health practitioners must operate. As the world renormalizes following the pandemic, a collaborative global interdisciplinary effort to provide equitable access to timely, accurate, and complete health information will be needed to support health practitioners in combating the impact of online health misinformation. Academic research will need to be disseminated into the public domain in a way that is accessible to the public to counter misinformation and educate populations concerning how science is carried out. Our conclusions drawn from this review stress the urgency of effective strategies and collaborative efforts to mitigate the prevalence and impact of health misinformation on a global scale. Without strategies for equipping populations with the health and digital literacies required to interpret and use information appropriately, the prevalence of online health misinformation will continue to pose a threat to global public health efforts, disproportionately affecting vulnerable and resource-limited populations. Although social media platforms have a responsibility to correct misinformation, governments will need to engage in evidence-informed decision-making and invest in HIM to support frontline health practitioners in their work, enhance population health literacy, and strengthen evidence-informed decision-making at all levels.

Several issues for further investigation arise from the findings of this review. These include the following:

• The long-term impact of COVID-19 vaccine hesitancy
• Whether the COVID-19 pandemic has intensified or diminished information literacy, and the related question of whether the pandemic will incentivize health information literacy
• The effects of social and cultural differences on the long-term traction of future health misinformation
• Whether social and economic inequalities will become less or more pronounced in the face of a global pandemic
• The comparative effectiveness of strategies to enhance populations’ media and digital literacies to facilitate the mitigation of health misinformation and its effects
• The influence of state actors on the propagation of health misinformation on the web
• The extent to which academic research has been disseminated into the public domain in a way that is accessible to the public, and the effectiveness of strategies to do so to counter misinformation and educate populations concerning how science is carried out

Acknowledgments

This research was funded by the School of Computing and Communications at the Open University. It allowed researchers across several faculties to collaborate and build a research team that focused on the experience of health practitioners with misinformation and its impact on their job practice. The authors would also like to thank Tracie Farrell and Nashwa Ismail for their invaluable suggestions and recommendations, as well as their assistance in the article screening process.

Data Availability

The data analyzed in this study are derived from published articles available on Google Scholar. All articles included in the review are cited in the reference list. No additional data or code were collected or generated as part of this study.

Authors' Contributions

The study was conceptualized by DK; funding acquisition was managed by DK; data were curated by DK, AK, MM, and IK; formal analysis was conducted by DK and MM; the investigation was carried out by AK and MM; the methodology was designed by DK and MM; project administration was overseen by DK; resources were provided by DK; supervision was carried out by DK; validation was conducted by DK, AK, MM, and IK; visualization was handled by DK and MM; writing (original draft preparation) was done by DK; and writing (review and editing) was carried out by DK, AK, and MM. All authors reviewed and approved the final version.

Conflicts of Interest

None declared.

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Abbreviations

Edited by G Eysenbach, T Leung; submitted 15.04.22; peer-reviewed by G Nneji, S-F Tsao; comments to author 07.06.22; revised version received 29.09.22; accepted 12.07.24; published 19.08.24.

©Dhouha Kbaier, Annemarie Kane, Mark McJury, Ian Kenny. Originally published in the Journal of Medical Internet Research (https://www.jmir.org), 19.08.2024.

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.

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CARDIOVASCULAR DISEASE AND THE ROLE OF ARTIFICIAL INTELLIGENCE: LITERATURE REVIEW

Main article content.

Cardiovascular Disease, Artificial Intelligence, Machine Learning, Risk Assessment, Predictive Analytics

Background: Cardiovascular diseases (CVDs) remain a leading global health issue, with increasing prevalence and economic impact. This study systematically reviews the current literature on CVD prevalence, prevention, management strategies, and the integration of artificial intelligence (AI) in cardiovascular medicine.

Methods: A comprehensive literature search was conducted using databases such as PubMed and Google Scholar. Studies were screened for relevance, and data were synthesized to evaluate trends, interventions, and AI applications in cardiovascular care.

Results: The review found a rising prevalence of CVDs and substantial economic burden. Behavioral interventions, including weight loss and dietary counseling, are effective in reducing cardiovascular risk. AI has shown potential in enhancing diagnostic accuracy and personalizing treatment, though challenges in validation and implementation remain.

Conclusion: Addressing the growing burden of CVDs requires effective prevention strategies and the integration of validated AI tools into clinical practice. Future research should focus on optimizing these approaches and assessing their cost-effectiveness to improve cardiovascular health outcomes and reduce healthcare costs.

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Article details.

This work is licensed under a Creative Commons Attribution-NonCommercial 4.0 International License .

All articles published in JPTCP  are licensed under Copyright Creative Commons Attribution-NonCommercial 4.0 International License.

Lakachew Bekele

ECFMG Certified Medical Doctor, Department of Medicine, Holy Cross Hospital, Silver Spring, Maryland, USA.

Dr. Asfandiyar Khan

Postgraduate Resident, Department of Cardiology, Lady Reading Hospital Peshawar, Pakistan.

Affan Tasleem

4th Year MBBS Student, Nishtar Medical University Multan, Pakistan.

Dr. Nabeel Baig

Medical Office Internal Medicine Changsha Medical University, China.

Shaheed Benazir Bhutto Women University, Pakistan.

Sundas Kanwal

SHO Department of Paediatrics, Tipperary University Hospital Ireland.

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Post-Consumer Textile Waste Management Practices and Challenges in India: A Systematic Literature Review

1 Symbiosis Centre for Management Studies (SCMS), Noida, INDIA

2 Symbiosis International (Deemed to be University), Pune, INDIA

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INTRODUCTION

Literature review, findings and discussion, abbreviations.

This review systematically maps the scholarly articles on waste management practices in the textile sector in light of consumer-disposed textiles and the adoption of the circular economy. It emphasizes the significance of scholarly research to the existing knowledge on waste management practices in the context of India’s textile sector.

Materials and Methods

A thorough Systematic Literature Review (SLR) was employed, which included an analysis of the content of 58 papers published in academic journals in the last 6 years.

The integration of textile waste management methods/practices in this review depicts the varied and challenging domain of waste management in the textile industry. Conversely, there are very limited studies conducted in developing economies, where most textile manufacturing occurs.

Research Limitations

The study includes literature from selected databases published between 2015 and 2021. More comprehensive research coverage and continuous evaluation of the health and status of the textile industry are required for valuable insights to effectively adopt circular economy practices from the perspective of developing countries like India.

Environmental Concerns and the Clothing Industry

Clothing and fashion ingestion have grown in the twenty-first century. Profoundly due to various effects on global population expansion, overall upgrade in standard of living, and the upsurge of fast fashion. [ 1 – 3 ] With technological improvements, globalisation, and changing customer demands, the garment business has seen incredible expansion and transformation over the last few decades. [ 4 ] However, because of the industry’s substantial resource use and the manufacturing of vast volumes of textile waste, its rise has also raised environmental issues. [ 5 ] The clothing industry’s environmental effect extends beyond the manufacturing phase, including the full textile lifecycle, from beginning with raw material extraction to final disposal. [ 6 ] This has increased public awareness of the importance of sustainable methods in the textile and garment industries.

Textile Waste and the Linear Economy Model[7]

In light of how we create and consume products, professionals have significantly increased the demand for global ecological sustainability. [ 8 ] For a considerable time, buyers and suppliers have followed the Linear Economy’s (LE) take-make-use-waste model, wherein massive volumes of finite resources are retrieved to create goods that are employed several times by buyers before being discarded in dump sites, generating wastages. [ 9 ] Consequently, textile waste accumulates in landfills, decaying slowly and emitting dangerous toxins into the environment. [ 10 ] This linear model is founded on a manufacturing mindset that believes resources are limitless. Industries have constantly retrieved resources from the world by embracing this outlook and economy without any strategies for reusing or rejuvenating the natural sources from where the materials have been derived. [ 11 ] Approximately 80% of raw materials are discarded after a single use, experiencing severe adverse effects on the environment, economy, and society, [ 12 ] depletion of natural resources, vegetation cover loss, shifts in global temperature, and economic fallout from global weather catastrophe are all threats to human wellness and well-being.

Global Circular Economy Measures

In response to the environmental problems created by the linear economy, [ 13 ] theorists have proposed new business models that could comply with textile waste management concerning these resources to one’s complete capability; such practices are incorporated into circular production models. [ 14 ] In brief, the concept of a circular economy has gained popularity as an environmentally sound alternative. Circular practices increase the volume of resource utilisation while limiting environmental effects throughout the manufacturing and consumption phases. [ 15 ] As per the circular economy, the primary focus on textile management of waste should be on maximising garment usage, reducing consumption, and emphasising recycling resources. The textile and garment sector may dramatically minimise its environmental impact and enhance resource efficiency by adopting a circular strategy [ 16 , 17 ] Since textile waste accounts for a comparatively tiny proportion of total waste, its effect on our well-being and our surroundings is significant and growing owing to the existing manufacturing framework. As a result, because the clothing industry functions in a linear fashion, struggling with environmental, social, and financial constraints, the Circular Economy (CE) can serve as a recuperative and reformative framework, attempting to keep goods, constituents, and materials at one’s maximum degree of utility and value.

The Challenges of the Indian Textile Industry

India is a significant player in the global textile business and is one of the top manufacturers and exporters of textiles and clothing. [ 18 ] While the sector contributes considerably to the country’s economic growth and jobs, it has various issues in handling post-consumer textile waste. [ 19 ] The particular challenges of the Indian textile industry, such as the wide range of textiles produced, the prevalence of unorganised sectors, and insufficient waste treatment facilities, add to the difficulty of solving textile waste concerns. [ 20 ]

According to a report by Quantis, the textile sector ranks second after the oil industry in terms of pollution, accounting for around 1.2 billion tonnes of greenhouse gas (GHG) emissions each year. Every year, tonnes of clothing are produced, used, and discarded. Moreover, the textile and garment industry is also a significant source of plastic microfibres in the seas and is expected to consume up to 25% of the world’s carbon budget by 2050. Hence, transitioning from a linear to a circular economy is the need of the hour. In addition, textile waste management is a crucial component of the circular economy for textiles.

PCTW Management in India[21]

India positions 6 th on the planet in material and attire trades. The material and attire area in the nation is the second area that does the most work after farming, providing direct employment to 45 million people and 100 million people from related areas. India’s domestic apparel and fashion industry contributes 5% to the nation’s GDP, 7% of modern results in esteem terms, and 12% of the nation’s product profit.

Nowadays, [ 22 ] the brief life spans of clothing items led to fast fashion cycles and a higher purchasing majority of customers of India live in metropolitan areas that create substantial quantities of (PCTW) in the form of worn clothes or possibly even Second-Hand Clothing (SHC). [ 23 ] India is likewise perhaps the biggest beneficiary of worldwide post-customer textile waste, with a significant volume of more than €100m, of disposing of material imported and manually sorted in different hubs. Because the bulk of this post-consumer textile waste is generated domestically, its management is challenging; industrial waste is easier to recycle than residential waste.

The literature highlights the pressing need for sustainable solutions in the Indian textile and apparel industry. [ 24 , 25 ] Initiatives and practices geared towards sustainability, circular economy, and reverse logistics are emerging as potential strategies to address the challenges of textile waste and fast fashion, as discussed in the following research studies. [ 26 – 31 ] Additionally, the economic viability and challenges of the second-hand clothing market are explored as a promising avenue for reducing textile waste and fostering sustainability. [ 31 ] These studies collectively underline the importance of sustainable practices and waste management in the Indian textile and apparel industry, which is essential for the sector’s long-term growth and environmental responsibility.

Recycling Processes in Textile

Mechanical recycling process[32].

Mechanical recycling is a simple, easy and cost-effective method. [ 33 ] This recycling technique is preferred for many clothes. [ 34 ] Mechanical recycling is commonly used to recycle discarded consumer textile waste. [ 35 ] Mechanical recycling breaks textiles into fibres through cutting, shredding, carding, or other processes. Machines progressively break down the textile and contribute to making it fibrous, and the resulting fibers are used again/utilised in the manufacturing of yarns. Subsequently, waste materials are sorted in the mechanical recycling process. [ 36 ] Metals and labels are removed as foreign constituents. The textile is divided into fibers after trimming into smaller bits with rotary blades.

Thermal Recycling Process[37,38]

Synthetic materials are disintegrated and realigned in thermal recycling. Thermal recycling is the preferred approach for recycling synthetic materials. [ 39 ] Melt extrusion transforms chips and pellets procured mechanically from synthetic waste materials into fibers.

Chemical Recycling Process[40]

Chemical recycling is the process of recycling, which includes the depolymerisation of polymers which is the method of diluting polymers. [ 41 , 42 ] Chemical and biological processes are used to transform or disintegrate polymers into their monomeric building blocks. The two forms of chemical recycling are-Monomer and Polymer. During polymer recycling, the polymer chain is often diminished. As an outcome, the recycled fibre’s quality deteriorates. Original quality fibres are extracted through monomer recycling. [ 43 ] While monomer recycling is exclusively utilised for synthetic fabrics, chemical recycling can be used for various textiles.

Downcycling[36-39]

Downcycling tends to happen whenever the quality and monetary value of recyclable material is relatively lower than the original item. Examples of downcycling are insulation materials, upholstery textiles, etc.

Upcycling[39]

Occurs when the recycled material’s quality is comparable to or better than that of the actual product. [ 44 ] Upcycling is the method of utilizing materials to produce more valuable items. This ecologically safe method is an essential first phase towards a zero-waste approach. [ 40 ] Under the circular economy context, manufacturing raw materials like cotton yarns or fibers is an example of upcycling.

Open-loop Channel of Recycling[36]

The practice of recycling an item’s raw materials in a different manufacturing area is called an open-loop or linear recycling channel. Secondary items acquired through linear recycling are typically disrupted at the end of their useful life.

Closed-loop Channel of Recycling[40]

Closed-loop Channel of Recycling refers to the reusability of recyclable textile waste materials in the textile industry. [ 36 ] This recycling method is demonstrated by incorporating mechanically recycled pre-consumer or PCTW in textile manufacturing.

Circular Economy and India[45]

The “circular economy” phenomenon as a waste-reduction approach has gained widespread acceptance in the commercial world. Organizations have attempted to attain “zero waste” by discovering new applications for discarded materials and closing the supply chain loop. This entails shifting the flow of materials from LE (take-create-waste) to CE (take-create-waste-new product). Moreover, [ 46 ] a circular economy model has long been recognized for its importance, validity, and scope. But nevertheless, owing to the dominance of the linear model in academic and research contexts, it has persisted as an underresearched concept. Figure 1 depicts a layout view of the connection among resources, byproducts, and processes that are involved used in the textile sector. [ 47 ] Several typical operating steps are required to create clothing from renewable or fossil resources. [ 15 ]

Figure 1: Linear and the circular process of recycling. [ 46 ]

India is at a crossroads in its economic growth journey. Incorporating circularity into India’s economic development is imperative to counteract the adverse effects of rapid urbanisation, industrialisation, population growth, and climate change. According to the Ellen MacArthur Foundation, adopting the circular economy in India will result in an annual benefit of Rs 40 lakh crore ($624 billion) in 2050 and a 44% reduction in GHG emissions. [ 48 ] Following a recent Accenture research, India can unleash nearly $500 billion in economic value by 2030 by adopting Circular Economy business models, as shown in Figure 2 .

In nations like India, waste management is mostly an unorganized, diverse sector. [ 46 ] Numerous legislations enacted by the Ministry of Forests, Ministry of Environment and Ministry of Climate Change in collaboration with state board of pollution control, state governments, and municipal corporations regulate the said sector.

Origin of the problem

From the literature study, it is evident that there is a dearth of studies in the field of post-consumer textile waste management in India broadly under the circular economy context.

The new circular economy’s goals entail redesigning and reshaping the linear economy towards many more sustainable alternatives. [ 49 ] This could possibly be concentrated on four key aspects of the textile cycle as follows; (1) resources, (2) manufacturing, (3) consumption, and (4) post-consumption. [ 50 ] The environmentally-unfriendly characteristics of present post-consumer textile waste treatment procedures compel the discovery of various alternative strategies to handle the textile waste. [ 51 ] Discarded waste textiles are widely regarded as a new source and a revenue prospect. Whereas, [ 50 ] in the past decades, the recycling/reusability of PCTW goods has developed as a viable approach to solving this problem. [ 52 ] Well-thought-out sustainable fashion projects and their implementation have the potential to considerably aid in involving the general public in initiatives aimed at addressing climate change as well as environmental inequities. According to Bernardes et al ., [ 53 ] consumers assert the absence of information about corporations distributing sustainable products and express a desire to gain more knowledge. Promotional strategies, events, and advertising schemes must concentrate on appropriate communications emphasizing sustainable items’ advantages and describing why they are significantly more costly than non-sustainable items owing to greater quality and restricted availability. Most of the research concentrated on nations like the United States, China, and Europe. As a result, [ 54 ] further research on circular fashion should be conducted in growing nations such as India, which are additionally significant contributors to global pollution and might have constraints, motivations, and efforts that vary from those previously investigated. [ 55 ] Textile waste management study in India is mostly unexplored. In the Indian context, secondary research on textile waste management is scarce. Recent studies have shed light on various aspects of circular economy implementation, sustainable practices, and post-consumer textile waste management in the Indian context, emphasising the need for more extensive research and the development of tailored strategies to address this critical issue. [ 56 – 64 ]

Figure 2: Potential value creation from circular business models by 2030. [ 47 ]

Research Objectives

As determined by systematic literature analysis, specific study objectives are as follows:

Objective: To identify the PCTW management practices in India.

Sub-objective:

(i)To conduct a systematic literature review on PCTW management practices in India. (ii)To identify the waste management practices used in the textile industry. (iii)To identify the critical challenges through the literature

Research Methodology

A systematic strategy for literature analysis was used to extract data for this study. [ 65 ] An SLR is a research method for collecting, synthesising, and reviewing the recorded study results surrounding a certain subject or issue, which helps reduce bias related to non-systematic reviews. [ 53 ] Despite being initiated and used first in medical and healthcare research, SLR is progressively booming in the social sciences, notably in management and business disciplines.

This analysis technique was chosen since there is a lack of clarity and consensus in the PCTW management domain. For this study, current literature and empirical findings were reconstructed as transparent and reproducible, identifying areas where knowledge is still sparse and suggesting future research implications to industry practitioners by carefully examining the literature.

Firstly , the scope of the SLR was outlined. It determines searched keywords and a range of assessment criteria depending on the proposed research questions.

Secondly , after the research plan is defined, a comprehensive literature search is conducted from 2015 to 2021 by employing both inclusion and exclusion criteria while gathering data supported by a broad corpus of available scholarly material on the topic. Academic article publications give valuable information at the moment on a wide range of issues, which is sufficient for achieving research objectives. The Scopus and Web of Science (WoS) databases were included in the inclusion criteria since they are widely regarded as the most extensive research datasets available. In both the databases, specific keywords-”textile”, “waste management”, and “post-consumer waste management” for search strings in the title, abstract, and keywords of the published articles were used. Table 1 shows the range of combinations searched and replicated in the two databases, ranging from more complex strings to simpler combinations.

Thirdly , screening and data extraction is conducted to identify the potentially relevant articles. During the third phase of the inclusion search process, 70 papers were found in WOS, and 455 papers were found in Scopus. After removing 40 duplicate papers and 427 irrelevant papers, 58 articles remained.

A total of 427 articles were excluded during the screening and data extraction process as they were deemed irrelevant to the study’s research questions despite initially meeting the inclusion criteria. The specifics of why these articles were considered irrelevant may include factors such as not addressing the topic adequately, not containing substantial data or analysis related to the study, or not aligning with the objectives of the research. These exclusion criteria were applied systematically to ensure that only the most relevant, recent, and English-language articles and papers meeting the specified keyword criteria were included in the final analysis. Figure 3 depicts the data selection and extraction steps.

Outline of descriptive content analysis

The literature review indicates that various academic investigations concentrated on waste management strategies in textiles, with an increasing emphasis on transitioning from a linear to a circular economy. While demonstrating the necessity for an expanding amount of literature on this subject with practical implications.

Systematic Dimension and Methodical Aspect

Five systematic dimensions were identified in the post-initial reviewing of papers and examining components using inductive approaches to evaluate the topic and research directions. Methodical aspects were utilized to place material orders within each dimension, as depicted in Table 2 . One paper may fall into multiple categories.

Systematic Dimension: Types of Paper

Methodical Aspect: Qualitative and quantitative papers (i.e., reviews, conceptual, case studies, surveys, exploratory, modelling).

In the examination of the academic landscape on Post-Consumer Textile Waste (PCTW) management within the circular economy context, it is crucial to categorize and evaluate the types of papers that contribute to our understanding of this multifaceted subject. The classification includes a wide spectrum of research methodologies, such as qualitative and quantitative approaches, encompassing literature reviews, conceptual papers, case studies, surveys, exploratory studies, and modelling. By encompassing such diverse types of papers, we are better equipped to comprehend the intricate dimensions of PCTW management, from theoretical conceptualizations to real-world applications.

Systematic Dimension: Types of Research

Methodical Aspect: Conceptual framework, CE perspective, PCTW management practices in the industry, zero waste fashion, innovation in the processes, design thinking, channels for textile reuse.

Figure 3: Systematic Literature Review Summary.

The exploration of post-consumer textile waste management reveals various dimensions within the circular economy context, encompassing the development of conceptual frameworks, Circular Economy (CE) perspectives, PCTW management practices within the industry, zero waste fashion initiatives, innovations in waste reduction processes, the application of design thinking, and the establishment of channels for textile reuse. This comprehensive spectrum of research directions allows for a holistic view of how PCTW management is evolving, from theoretical underpinnings to practical strategies, and highlights the interplay of sustainability, innovation, and design thinking in reshaping the textile industry.

Figure 4: No. of publications on textile waste management in India from 2015–2021.

Systematic Dimension: Waste Management Practices

Methodical Aspect: Mechanical recycling, chemical recycling, incineration, decomposition, special techniques, and mixed techniques.

One of the core components of effective post-consumer textile waste management is the methodology employed in waste treatment and recycling. This dimension encompasses a range of practices, including mechanical recycling, chemical recycling, incineration, decomposition, and the utilization of special and mixed techniques. Each approach has unique implications for environmental sustainability, resource conservation, and economic viability, making it essential to scrutinize the merits and limitations of these techniques to devise the most effective strategies for PCTW management.

Systematic Dimension: CE Principles (R’s)

Methodical Aspect: Recover, refurbish, recycle, repair, repurpose, reuse, remanufacture, reduce, rethink, refuse.

The principles of the circular economy, often encapsulated by the “R’s,” offer a structured framework for sustainable waste management. In the context of post-consumer textile waste, these principles comprise a roadmap for action. They include strategies such as recovering valuable materials, refurbishing products, recycling textiles, repairing items, repurposing materials, reusing products, remanufacturing goods, reducing waste generation, rethinking consumption patterns, and refusing disposable and non-recyclable items. Each of these principles represents a facet of sustainability, and their integration into textile waste management practices is essential for achieving circularity in the textile industry.

Systematic Dimension: Geographical Focus

Methodical Aspect: Developing nations and regions where the frequency of purchase is high.

Geographical focus is a significant aspect of post-consumer textile waste management research. The research explores the varying dynamics of textile waste generation and management in developing nations and regions characterized by high consumption frequency. By concentrating on these specific areas, the research aims to address the distinctive challenges and opportunities faced by regions with burgeoning consumer markets and emerging economies. Understanding the geographical nuances is vital in formulating targeted strategies that align with each region’s cultural, economic, and environmental characteristics.

Figure 5: Number of documents in top 10 key journals-Scopus.

Figure 6: Prevalence of keyword similarity in selected journal articles.

Figure 7: Number of publications per country.

Paper distribution and Evolution

Figure 4 depicts the complete study of chosen publications and paper distributions from 2015 to 2021. Textile waste management research in India elevated from 2015 to 2018 and expedited in 2019, indicating increased academic awareness and interest in the subject. Because many research questions remain unanswered, a further upward research trend is in anticipation. Figure 5 and Table 3 depict the allocation of publications showcasing important journals’ contributions to the topic.

During this step, keywords were extracted from the selected research papers. As shown in Figure 6 , keyword groups were established to demonstrate how additional topics and synonyms are connected to textile waste management methods in India.

Geographical Focus

As illustrated in Figure 7 , the number of papers published for each nation is governed by the affiliation country of the first or corresponding author. This clearly depicts the increasing number of papers published on the topic in India, followed by South Korea and China.

Figure 8: Research paper distribution.

Types of Research Papers

Figure 8 depicts the distribution of types of research papers, with quantitative papers being the most popular method, followed by qualitative papers. Regarding the literature study for this paper, 23 papers were based on qualitative studies, while 35 were based on quantitative studies. This suggests that even after more and more empirical tests of the measures suggested in the quantitative literature, there is still a lag in the qualitative research that hinders the scalability of the post-consumer textile waste management research. For keyword occurrence set to 2 Fractional counting which is based on theoretical considerations and on the empirical analyses, we conclude that for many purposes, the fractional counting approach is preferable over the full counting one. [ 66 ]

Waste Management Practices (because is a more relevent part of the research)

The major categories of PCTW management methods identified in the literature are:

Mechanical recycling[67]

This recycling method includes manually tearing fabrics and ripping them into several fibers. After that, the fibers are usually mixed with other fiber types to reinforce them before being spun and woven into a new fabric. [ 32 ] Through melt deformation, this recycling process involves the extraction of fibers from waste materials, which may be spun into yarns. As a result, to achieve high fiber tenacity and fineness, this process must be employed for virgin fibers.

Chemical recycling[67]

This recycling method includes the application of chemical solvents to dilute textile waste fibers so that they may be removed, reclaimed, and converted into a new fabric. [ 32 ] The dissolving method is also employed in the chemical recycling of cellulose fabrics like cotton or viscose. Simultaneously time, organic solvents use ionic liquids to disintegrate cellulosic polymers.

Physical methods[68]

Physical processes, such as disintegrating or diluting, are used to develop fibers or polymers appropriate for reprocessing. The formation of the fibers is altered during physical recycling, but the polymer molecules that compose the fibers stay unchanged. Melt spinning or solution spinning can be employed to create a new filament after disintegrating or diluting.

Key Challenges

The critical challenges of textile waste management in India, as identified through the current literature, are the lack of an organized collection, segregation, and aggregation system, importing pre-consumer waste, the gap with recyclers and solution providers, waste flow traceability, reclaiming the lost value of discarded clothes and supply chain design. [ 69 , 70 ]

Waste management practices in the textile sector emerge as a challenging and competitive area of the study. This study systematically examines the scholarly literature on PCTW practices in the textile sector in India from a CE perspective to the best of the author’s knowledge. This research identified essential waste management practices that will influence the evolution of the textile sector’s post-consumer textile waste management, recycling strategies, and transitioning to circular approaches. [ 71 – 74 ] This could help researchers further explore, comprehend, and address a broader spectrum of PCTW practices and supplement prior work on textile waste management problems. [ 75 – 78 ] It exemplifies the range of textile sector studies and summarises the study on green practices. As a result, this research concluded with a coherent depiction of waste management practices and challenges related to various aspects of India’s post-consumer textile waste management domain. Whereas, under the Indian context, this study provided a more comprehensive view of the existing waste management methods in the industry.

Cite this article:

Das B, Dwivedi S. Post-Consumer Textile Waste Management Practices and Challenges in India: A Systematic Literature Review. J Scientometric Res. 2024;13(2):419-29.

Implications

A few implications by integrating a diverse array of literature review, the findings could perhaps aid practitioners in building firm sustainability capabilities with clearer steps. The systematic literature analysis will assist managers in determining priorities and essential features of sustainable practices related to strategic needs, avoiding dispersing resources and investment in various directions, hence decreasing operational waste, risks, and failure costs. Subsequently, the study presents practitioners with recommendations for structuring the sustainability journey and adequately resetting natural resources to embark on opportunities and challenges, set priorities, anticipate possible vulnerable areas, and comprehend how subtleties can be avoided.

Limitations

Only PCTW management practices have been considered for this study. This review has not included aspects of waste management such as behavior change and community engagement, waste collection schemes, and waste transportation. This review is limited to consumer textile waste disposal and textile recycling methods. The findings are based on academic papers that were published between 2015 and 2021; further comprehensive coverage may give more valuable insights. The expansion of sustainable environmental practices is actively developing in the various textile industry setting, and progressing assessment of the data is necessary to allow industry specialists, regulators, and stakeholders to comprehend the sector’s health and position.

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Literature Review Overview

What is a Literature Review? Why Are They Important?

A literature review is important because it presents the "state of the science" or accumulated knowledge on a specific topic. It summarizes, analyzes, and compares the available research, reporting study strengths and weaknesses, results, gaps in the research, conclusions, and authors’ interpretations.

Tips and techniques for conducting a literature review are described more fully in the subsequent boxes:

• Literature review steps
• Strategies for organizing the information for your review
• Literature reviews sections
• In-depth resources to assist in writing a literature review
• Templates to start your review
• Literature review examples

Literature Review Steps

Graphic used with permission: Torres, E. Librarian, Hawai'i Pacific University

1. Choose a topic and define your research question

• Try to choose a topic of interest. You will be working with this subject for several weeks to months.
• Ideas for topics can be found by scanning medical news sources (e.g MedPage Today), journals / magazines, work experiences, interesting patient cases, or family or personal health issues.
• Do a bit of background reading on topic ideas to familiarize yourself with terminology and issues. Note the words and terms that are used.
• Develop a focused research question using PICO(T) or other framework (FINER, SPICE, etc - there are many options) to help guide you.
• Run a few sample database searches to make sure your research question is not too broad or too narrow.
• If possible, discuss your topic with your professor. 

2. Determine the scope of your review

The scope of your review will be determined by your professor during your program. Check your assignment requirements for parameters for the Literature Review.

• How many studies will you need to include?
• How many years should it cover? (usually 5-7 depending on the professor)
• For the nurses, are you required to limit to nursing literature?

3. Develop a search plan

• Determine which databases to search. This will depend on your topic. If you are not sure, check your program specific library website (Physician Asst / Nursing / Health Services Admin) for recommendations.
• Create an initial search string using the main concepts from your research (PICO, etc) question. Include synonyms and related words connected by Boolean operators
• Contact your librarian for assistance, if needed.

4. Conduct searches and find relevant literature

• Keep notes as you search - tracking keywords and search strings used in each database in order to avoid wasting time duplicating a search that has already been tried
• Read abstracts and write down new terms to search as you find them
• Check MeSH or other subject headings listed in relevant articles for additional search terms
• Scan author provided keywords if available
• Check the references of relevant articles looking for other useful articles (ancestry searching)
• Check articles that have cited your relevant article for more useful articles (descendancy searching). Both PubMed and CINAHL offer Cited By links
• Revise the search to broaden or narrow your topic focus as you peruse the available literature
• Conducting a literature search is a repetitive process. Searches can be revised and re-run multiple times during the process.
• Track the citations for your relevant articles in a software citation manager such as RefWorks, Zotero, or Mendeley

5. Review the literature

• Read the full articles. Do not rely solely on the abstracts. Authors frequently cannot include all results within the confines of an abstract. Exclude articles that do not address your research question.
• While reading, note research findings relevant to your project and summarize. Are the findings conflicting? There are matrices available than can help with organization. See the Organizing Information box below.
• Critique / evaluate the quality of the articles, and record your findings in your matrix or summary table. Tools are available to prompt you what to look for. (See Resources for Appraising a Research Study box on the HSA, Nursing , and PA guides )
• You may need to revise your search and re-run it based on your findings.

6. Organize and synthesize

• Compile the findings and analysis from each resource into a single narrative.
• Using an outline can be helpful. Start broad, addressing the overall findings and then narrow, discussing each resource and how it relates to your question and to the other resources.
• Cite as you write to keep sources organized.
• Write in structured paragraphs using topic sentences and transition words to draw connections, comparisons, and contrasts.
• Don't present one study after another, but rather relate one study's findings to another. Speak to how the studies are connected and how they relate to your work.

Organizing Information

Options to assist in organizing sources and information :

1. Synthesis Matrix

• helps provide overview of the literature
• information from individual sources is entered into a grid to enable writers to discern patterns and themes
• article summary, analysis, or results
• thoughts, reflections, or issues
• each reference gets its own row
• mind maps, concept maps, flowcharts
• at top of page record PICO or research question
• record major concepts / themes from literature
• list concepts that branch out from major concepts underneath - keep going downward hierarchically, until most specific ideas are recorded
• enclose concepts in circles and connect the concept with lines - add brief explanation as needed

3. Summary Table

• information is recorded in a grid to help with recall and sorting information when writing
• allows comparing and contrasting individual studies easily
• purpose of study
• methodology (study population, data collection tool)

Efron, S. E., & Ravid, R. (2019). Writing the literature review : A practical guide . Guilford Press.

Literature Review Sections

• Lit reviews can be part of a larger paper / research study or they can be the focus of the paper
• Lit reviews focus on research studies to provide evidence
• New topics may not have much that has been published

* The sections included may depend on the purpose of the literature review (standalone paper or section within a research paper)

Standalone Literature Review (aka Narrative Review):

• presents your topic or PICO question
• includes the why of the literature review and your goals for the review.
• provides background for your the topic and previews the key points
• Narrative Reviews: tmay not have an explanation of methods.
• include where the search was conducted (which databases) what subject terms or keywords were used, and any limits or filters that were applied and why - this will help others re-create the search
• describe how studies were analyzed for inclusion or exclusion
• review the purpose and answer the research question
• thematically - using recurring themes in the literature
• chronologically - present the development of the topic over time
• methodological - compare and contrast findings based on various methodologies used to research the topic (e.g. qualitative vs quantitative, etc.)
• theoretical - organized content based on various theories
• provide an overview of the main points of each source then synthesize the findings into a coherent summary of the whole
• present common themes among the studies
• compare and contrast the various study results
• interpret the results and address the implications of the findings
• do the results support the original hypothesis or conflict with it
• provide your own analysis and interpretation (eg. discuss the significance of findings; evaluate the strengths and weaknesses of the studies, noting any problems)
• discuss common and unusual patterns and offer explanations
•  stay away from opinions, personal biases and unsupported recommendations
• summarize the key findings and relate them back to your PICO/research question
• note gaps in the research and suggest areas for further research
• this section should not contain "new" information that had not been previously discussed in one of the sections above
• provide a list of all the studies and other sources used in proper APA 7

Literature Review as Part of a Research Study Manuscript:

• Compares the study with other research and includes how a study fills a gap in the research.
• Focus on the body of the review which includes the synthesized Findings and Discussion

Literature Reviews vs Systematic Reviews

Systematic Reviews are NOT the same as a Literature Review:

Literature Reviews:

• Literature reviews may or may not follow strict systematic methods to find, select, and analyze articles, but rather they selectively and broadly review the literature on a topic
• Research included in a Literature Review can be "cherry-picked" and therefore, can be very subjective

Systematic Reviews:

• Systemic reviews are designed to provide a comprehensive summary of the evidence for a focused research question
• rigorous and strictly structured, using standardized reporting guidelines (e.g. PRISMA, see link below)
• uses exhaustive, systematic searches of all relevant databases
• best practice dictates search strategies are peer reviewed
• uses predetermined study inclusion and exclusion criteria in order to minimize bias
• aims to capture and synthesize all literature (including unpublished research - grey literature) that meet the predefined criteria on a focused topic resulting in high quality evidence

Literature Review Examples

• Breastfeeding initiation and support: A literature review of what women value and the impact of early discharge (2017). Women and Birth : Journal of the Australian College of Midwives
• Community-based participatory research to promote healthy diet and nutrition and prevent and control obesity among African-Americans: A literature review (2017). Journal of Racial and Ethnic Health Disparities

• Vitamin D deficiency in individuals with a spinal cord injury: A literature review (2017). Spinal Cord

Resources for Writing a Literature Review

These sources have been used in developing this guide.

Resources Used on This Page

Aveyard, H. (2010). Doing a literature review in health and social care : A practical guide . McGraw-Hill Education.

Purdue Online Writing Lab. (n.d.). Writing a literature review . Purdue University. https://owl.purdue.edu/owl/research_and_citation/conducting_research/writing_a_literature_review.html

Torres, E. (2021, October 21). Nursing - graduate studies research guide: Literature review. Hawai'i Pacific University Libraries. Retrieved January 27, 2022, from https://hpu.libguides.com/c.php?g=543891&p=3727230

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The pathophysiological mechanism and clinical treatment of polycystic ovary syndrome: a molecular and cellular review of the literature.

1. Introduction

2. research method and literature review, 3. diagnosis of pcos, 3.1. the nih criteria, 3.2. the rotterdam criteria, 3.3. the ae-pcos criteria, 4. pathogenesis and pathophysiology, 4.1. genetic predisposition, 4.1.1. the genetics of pcos, 4.1.2. the candidate genes for pcos, 4.2. hyperandrogenism, 4.2.1. steroidogenesis, 4.2.2. genes involved in hyperandrogenism, brief summary of cyp genes, 4.3. hyperinsulinemia and insulin resistance, 4.3.1. insulin signaling pathways, 4.3.2. insulin function and glucose utilization in different sites of human body, 4.3.3. genes involved in hyperinsulinemia and insulin resistance (ir), 4.4. hyperandrogenism (ha), insulin resistance (ir), and pcos, 5. management of pcos, 5.1. lifestyle and diet modification, 5.2. medication of pcos, 5.2.1. improvement in hyperandrogenic features, 5.2.2. management of metabolic disorders, thiazolidinediones (tzds), other drugs for metabolic control, 5.2.3. fertility concerns: ovulation and contraception, contraception, 5.3. surgical intervention, 6. conclusions and future perspectives, author contributions, conflicts of interest.

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Chang, K.-J.; Chen, J.-H.; Chen, K.-H. The Pathophysiological Mechanism and Clinical Treatment of Polycystic Ovary Syndrome: A Molecular and Cellular Review of the Literature. Int. J. Mol. Sci. 2024 , 25 , 9037. https://doi.org/10.3390/ijms25169037

Chang K-J, Chen J-H, Chen K-H. The Pathophysiological Mechanism and Clinical Treatment of Polycystic Ovary Syndrome: A Molecular and Cellular Review of the Literature. International Journal of Molecular Sciences . 2024; 25(16):9037. https://doi.org/10.3390/ijms25169037

Chang, Kai-Jung, Jie-Hong Chen, and Kuo-Hu Chen. 2024. "The Pathophysiological Mechanism and Clinical Treatment of Polycystic Ovary Syndrome: A Molecular and Cellular Review of the Literature" International Journal of Molecular Sciences 25, no. 16: 9037. https://doi.org/10.3390/ijms25169037

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