literature review on risk communication

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The Evolving Field of Risk Communication

Affiliations.

1 Department of Communication, Cornell University, Ithaca, NY, USA.
2 Department of Advertising and Public Relations, Michigan State University, East Lansing, MI, USA.
PMID: 33084114
PMCID: PMC7756860
DOI: 10.1111/risa.13615

The 40th Anniversary of the Society for Risk Analysis presents an apt time to step back and review the field of risk communication. In this review, we first evaluate recent debates over the field's current state and future directions. Our takeaway is that efforts to settle on a single, generic version of what constitutes risk communication will be less productive than an open-minded exploration of the multiple forms that comprise today's vibrant interdisciplinary field. We then review a selection of prominent cognitive, cultural, and social risk communication scholarship appearing in the published literature since 2010. Studies on trust in risk communication messengers continued to figure prominently, while new research directions emerged on the opportunities and critical challenges of enhancing transparency and using social media. Research on message attributes explored how conceptual insights particularly relating to framing, affective and emotional responses, and uncertainty might be operationalized to improve message effectiveness. Studies consistently demonstrated the importance of evaluation and how varying single attributes alone is unlikely to achieve desired results. Research on risk communication audiences advanced on risk perception and multiway engagement with notable interest in personal factors such as gender, race, age, and political orientation. We conclude by arguing that the field's interdisciplinary tradition should be further nurtured to drive the next evolutionary phase of risk communication research.

Keywords: Interdisciplinary; SRA anniversary; literature review; risk communication.

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Open Access

Peer-reviewed

Research Article

A systematic review of risk communication in clinical trials: How does it influence decisions to participate and what are the best methods to improve understanding in a trial context?

Roles Data curation, Formal analysis, Investigation, Project administration, Writing – original draft

Affiliation Health Services Research Unit, University of Aberdeen, Aberdeen, United Kingdom

Roles Conceptualization, Investigation, Methodology, Supervision, Validation, Visualization, Writing – review & editing

* E-mail: [email protected]

Maeve Coyle,
Katie Gillies

Published: November 16, 2020
https://doi.org/10.1371/journal.pone.0242239
Peer Review
Reader Comments

Effective risk communication is challenging. Ensuring potential trial participants’ understand ‘risk’ information presented to them is a key aspect of the informed consent process within clinical trials, yet minimal research has looked specifically at how to communicate probabilities to support decisions about trial participation. This study reports a systematic review of the literature focusing on presentation of probabilistic information or understanding of risk by potential trial participants.

A search strategy for risk communication in clinical trials was designed and informed by systematic reviews of risk communication in treatment and screening contexts and supplemented with trial participation terms. Extracted data included study characteristics and the main interventions/findings of each study. Explanatory studies that investigated the methods for presenting probabilistic information within participant information leaflets for a clinical trial were included, as were interventions that focused on optimising understanding of probabilistic information within the context of a clinical trial.

The search strategy identified a total of 4931 studies. Nineteen papers were selected for full text screening, and seven studies included. All reported results from risk communication studies that aimed to support potential trial participants’ decision making set within hypothetical trials. Five of these were randomised comparisons of risk communication interventions, and two were prospectively designed, non-randomised studies. Study interventions focused on probability presentation, risk framing and risk interpretation with a wide variety of interventions being evaluated and considerable heterogeneity in terms of outcomes assessed. Studies show conflicting findings when it comes to how best to present information, although numerical, particularly frequency formats and some visual aids appear to have promise.

Conclusions

The evidence base surrounding risk communication in clinical trials indicates that there is as yet no clear optimal method for improving participant understanding, or clear consensus on how it affects their willingness to participate. Further research into risk communication within trials is needed to help illuminate the mechanisms underlying risk perception and understanding and provide appropriate ways to present and communicate risk in a trial context so as to further promote informed choices about participation. A key focus for future research should be to investigate the potential for learning in the evidence on risk communication from treatment and screening decisions when applied to decisions about trial participation.

Citation: Coyle M, Gillies K (2020) A systematic review of risk communication in clinical trials: How does it influence decisions to participate and what are the best methods to improve understanding in a trial context? PLoS ONE 15(11): e0242239. https://doi.org/10.1371/journal.pone.0242239

Editor: Dermot Cox, Royal College of Surgeons in Ireland, IRELAND

Received: July 29, 2019; Accepted: October 29, 2020; Published: November 16, 2020

Copyright: © 2020 Coyle, Gillies. This is an open access article distributed under the terms of the Creative Commons Attribution License , which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited.

Data Availability: The data underlying the results presented in the study are available from the published papers. Included studies available here: Reference 22 - DOI: 10.1177/009286150604000302 Reference 23 - DOI: 10.1542/peds.2009-1796 Reference 24 - DOI: 10.1177/1740774515585120 Reference 25 - DOI: 10.1017/S1357530902000558 Reference 26 - DOI: 10.1097/00000539-200302000-00037 Reference 27 - DOI: 10.1186/1472-6947-10-55 Reference 28 - DOI: 10.1177/014107689008300710 .

Funding: This work was supported by personal fellowship award (to KG) from the Medical Research Council’s Strategic Skills Methodology Programme. The Health Services Research Unit is supported by a core grant from the Chief Scientist Office of the Scottish Government Health and Social Care Directorates. The views and opinions expressed therein are those of the authors and do not necessarily reflect those of the Chief Scientist Office, MRC or the Department of Health.

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

Introduction

Clinical trials are now widely accepted as the gold standard of evidence-based medicine for determining treatment effects [ 1 ]. The importance of recruiting adequately informed individuals to participate in clinical trials is paramount. However many studies have demonstrated that participants approached to take part and those consented to participate in trials have a limited understanding of key aspects of the trial [ 2 ]. One of the key areas to consider when presenting information to potential participants is the information on potential risks and benefits. The ethical principles for medical research involving human subjects, enshrined within the Declaration of Helsinki, state that ‘each potential subject must be adequately informed of the anticipated benefits and potential risks of the study’ [ 3 ]. This is echoed by guidelines for good clinical practice, which state that all the information provided to participants should include explanations of the ‘reasonably foreseeable risks or inconveniences’, expected benefits, and where there are no clinical benefits to the participants, they must be made aware of this’ [ 4 ]. However, mechanisms to operationalise the provision of such information are not provided in the guidance.

Risk communication can be defined as communication with individuals that addresses knowledge, perceptions, attitudes and behaviour related to risk, and risk itself can be defined as the probability that a hazard will give rise to harm [ 5 , 6 ]. A correct understanding of risk therefore depends upon an accurate understanding of probabilities, a feat that is determined by several influencing factors, such as individual numeracy levels and cognitive abilities, but not least by the methods used to present probabalistic information [ 7 ]. There is a substantial amount of literature that focuses on risk communication with regard to public health messages, health behaviour, and treatment and screening decisions for patients [ 8 – 11 ]. Speigelhalter et al have shown that probabilities are ‘notoriously difficult to communicate effectively’ to lay audiences in various contexts, including health [ 12 ]. Yet minimal research has looked specifically at how to communicate probabilities within information provided to support decisions about trial participation (or not). In a trial context uncertainties relating to interventions will usually be greater purely by the nature of the trial endeavour—to generate evidence about benefit and harm.

Understanding, or more often mis-understanding, of risk information related to trials has been shown to influence decisions about participation in a range of trials, with those prepared to accept risk more likely to participate [ 13 , 14 ]. Decisions about trial participation are inherently different from decisions about treatment. For example, one of the main influences on clinical trial participation is conditional altruism [ 13 ]. Conditional altruism is the concept that participation in the trial will benefit society but there must be a benefit (which is influenced by perception of risk) for self. Conditional altruism does not exist for decisions about treatment and as such it is important to understand how potential trial participants understand risk in a trial participation context. Additionally, trade-offs between risk and benefit in a trial involve layers of complexity in addition to those for treatment such as: loss of control over which treatment they receive; and potentially greater uncertainties, as often participants have to consider the risks and benefits of a minimum of two competing treatments. Existing studies in the domain of informed consent for clinical trials have repeatedly highlighted significant discrepancies between actual risk and participant interpretation of risk to themselves, or their child, in taking part in a trial [ 15 , 16 ]. Participants frequently underestimate risks, leading them to believe that there would be little to no risk involved in trial participation. This pronounced lack of understanding strongly suggests the need for better communication about trial aims and design, particularly when it comes to the inherent risks, however small, that are almost always present in taking part in a clinical trial [ 15 ]. The intrinsic nature of trials means there is much unknown information and communicating probabilistic information in this context is more challenging as the layers of risk are greater, for example the risk of undertaking a trial as opposed to treatment, the outcome risks, and the risk of randomisation to a drug, procedure or placebo [ 17 ].

Preliminary findings from our group have shown that stakeholders have varied preferences about how probabilistic information relevant to trial participation (e.g. estimates of the likelihood of benefit and/or harm associated with trial interventions) is communicated [ 18 ]. In addition, a pilot study exploring decision support for trial participation decisions highlighted that patients’ preferences for risk information differed in a trial context compared to a treatment context [ 19 ]. Existing research on methods to present probabilistic information to improve patient understanding and decision making about treatment and screening decisions could provide valuable insights for enabling effective risk communication in the context of informed consent for trials [ 20 ]. Yet, surprisingly, the methods shown to be effective to improve understanding of probabilistic information are not routinely employed in participant information leaflets for trial participation [ 17 ].

A small number of studies have evaluated methods for presenting ‘risk’ in patient information leaflets for clinical trials. However, these studies have not been analysed together to allow judgements about optimal methods of presentation. This warrants further investigation both at the level of understanding and on the decision to participate (or not) in the trial. To address this, this study aimed to systematically review the literature focusing on presentation of probabilistic information within the informed consent process for trials. We focused our search on comparative effectiveness studies that tested interventions which varied the presentation of probabilistic information and the effects on potential trial participants’ understanding and/or the decision to participate.

Inclusion criteria

Evaluative studies using qualitative methods that investigated the methods for presenting probabilistic information to potential trial participants during the informed consent process for a trial were considered eligible. Specific study designs could include randomised controlled trials, case series, and prospective cohorts. Interventions that focused on optimising understanding (or another plausible outcome linked to decision making for trial participation) of probabilistic information within the context of a clinical trial were included. We chose to include studies of both real and hypothetical decisions about trial participation.

Exclusion criteria

Papers or articles that present findings on risk communication in a treatment or screening context or consider the decision to participate in research studies that are not RCTs were excluded. Studies investigating participants’ perceptions of receiving risk communication as part of the RCT decision process (which may include studies using methods such as interviews, focus groups and other methods) were not included.

Search methods for identification of studies

A search strategy for risk communication in clinical trials was designed in collaboration with a Senior Information Specialist (skilled in developing and running search strategies to identify relevant scientific literature) and informed by systematic reviews of risk communication in treatment and screening contexts and supplemented with trial participation terms. The search strategy is available on request. Four data bases were searched. Embase was searched from 1980 to 2019. Ovid MEDLINE(R) Epub Ahead of print, In-Process & other Non-Indexed Citations, Ovid MEDLINE(R) Daily and Ovid MEDLINE(R) was searched from 1946 to May 10 th 2019. PsycINFO was searched from 1987 to May week 2 2019. Finally, CINAHL was searched from 1998 to 2019. No restrictions on language were imposed.

Screening and selection of studies

One author (MC) screened all articles identified within the database searches. Duplicate screening was carried out by one other author (KG) on a random sample (10%) of the search output. Papers were assessed at title and abstract level according to the eligibility criteria, and differences of opinion were resolved by discussion between MC and KG. Nineteen full text papers were identified for further investigation, and of these seven studies were deemed eligible for inclusion and progressed to data extraction procedures.

Data collection and analysis

The seven studies were summarised by study characteristics (see details below) and presented in tabular form. Due to the heterogeneous nature of the interventions and/or outcomes reported a meta-analysis was not appropriate. This review is therefore presented in a descriptive narrative form with studies grouped first by design of the embedded study (RCT, non-randomised) and then by content of intervention i.e., probability presentation, risk framing, risk intervention. This structured framework to present narrative findings has been recently proposed by Rowlands et al 2018 [ 21 ].

Data extraction and management

Data were extracted independently by two reviewers (MC & KG). The following summary features of the host trial (i.e. the trial the potential participants were being asked to consider participation in) for each study were summarised in table form: study design; study aim; author details; year and journal of publication; population demographics; sample size; phase of trial; intervention(s). Specific details on the intervention(s) being evaluated (i.e., risk communication tools), embedded study results and associated outcomes were extracted. These included: comparative methods of disseminating probabilistic information to potential trial participants using different communication tools/aids; mode of intervention delivery (i.e., paper, computer, verbal); study outcomes to be extracted; cognitive outcomes (i.e., potential trial participant comprehension of probabilistic information and subsequent risk perception); affective outcomes (i.e., participant preferences and/or satisfaction with communication method, and level of decisional conflict and concern); and behavioural outcomes linked to trial participation (i.e., willingness to participate in clinical trial).

Study selection and summary characteristics

The search strategy identified a total of 4931 studies. Full text papers for 19 potentially eligible studies were sourced, and following full text screening a further 12 studies were excluded from the review ( Fig 1 ). The included seven studies all reported results from studies set within hypothetical randomised controlled trials [ 22 – 28 ]. To provide an example of how this embedded evaluation is operationalised, the studies asked participants to imagine they were being recruited into a clinical trial, provided brief information about the hypothetical trial (such as clinical population, intervention, comparator, outcomes, etc), then provided various formats of risk communication (such as verbal or numerical descriptors) followed by assessment of relevant outcomes.

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https://doi.org/10.1371/journal.pone.0242239.g001

The seven included studies had various designs: five were randomised comparisons of risk communication interventions considering participation in hypothetical RCTs; and two were prospective, non-randomised studies, one being a comparative cohort study (three groups) and the other a single cohort. The included studies spanned a range of clinical settings. Three of the included studies were trials in neurological settings, and the other four were within dermatology, cardiology, oncology and surgery. Only one study was set within a trial considering a non-drug intervention, where the other six were identified as trials testing drug-based interventions. All of the included studies had at least two arms as part of their hypothetical trial design. Six of the seven studies reported trials where an individual was considering consenting for themselves and one study included only parents who were considering participation for their child. Most studies were single centre, however two of the studies did not specify the number of centres involved. The number of participants in the embedded studies ranged from 50 to 4885 with a median of 240. ( Table 1 ).

https://doi.org/10.1371/journal.pone.0242239.t001

The final seven studies were grouped according to the study design (i.e., RCT or prospective cohort) and the topic of the described intervention: ‘probability presentation’ (22, 23), ‘risk framing’ (24, 25, 26, 27), and ‘risk interpretation’ (28). The studies are presented alphabetically based on these similar characteristics under their category headings ( Table 2 ). Further information on each study detailing intervention content, mode, and outcome are presented in Table 3 .

https://doi.org/10.1371/journal.pone.0242239.t002

https://doi.org/10.1371/journal.pone.0242239.t003

RCTs of interventions to explore risk communication in RCTs

Probability presentation interventions..

One study was identified that used a randomised design to investigate different probability presentations in the context of risk communication in clinical trials. Berry & Hochhauser (2006), compared European Union (EU) verbal descriptors only versus verbal descriptors and their associated numerical values (e.g., Common (EU equivalent = 1–10%)) [ 22 ]. Participants were asked to imagine they had been approached to take part in a clinical trial and given a booklet detailing the possible side effects of a new drug (versus nothing) for a skin condition and were asked to complete a questionnaire (N = 96, 48 in each arm). When asked to rate on a scale from 1 to 6 (p = 0.03), those who received only verbal descriptors were significantly less satisfied with the information than those who also had the numerical values. Participants in the verbal descriptors only group also perceived the risk to health to be higher (p<0.0001) and the benefit to be lower (p = 0.03), and were significantly less likely to participate in the trial (p = 0.01). When asked to make probability estimates for experiencing side effects, the verbal only group estimated these approximately three times higher than the combined group. When asked to consider the main reason for participating in the trial, participants in both groups reported long term relief/possible cure and the main reason for not participating was fear of side effects. There were no significant differences between the reasons listed by the two groups.

Risk framing interventions.

Four of the included studies employed randomised designs to explore risk framing in communication within clinical trials. Kim et al (2015) recruited 584 participants to investigate the language framing of benefit statements within a hypothetical trial for amyotrophic lateral sclerosis. An online survey administered one of two statements within a consent form to participants; either ‘there is some but very small chance that you might benefit’ (control group n = 290), or ‘it is not guaranteed you will benefit’ (intervention group n = 294) [ 24 ]. The intervention group had a slightly greater, but not significant, willingness to participate in the trial as scored on a 10 point scale (p = 0.11). However, the average estimate of the likelihood of their condition improving was significantly higher in the intervention group than in the control group (p<0.0001).

Schwartz & Hasnain (2002) explored the effects of gain and loss framing on risk perception and attitude by randomising 284 participants to one of three groups receiving a consent form about a trial for a new cholesterol lowering drug [ 25 ]. One group were given information where benefits were framed in terms of gains (e.g., ‘Out of 100 people whose lives would likely be cut short by heart disease and begin taking this drug, we expect that 95 will show substantial improvements in their chance of survival and 5 will show no improvement in survival’, n = 98), the ‘loss’ group received benefit information framed as losses (e.g.,. ‘Out of 100 people whose lives would likely be cut short by heart disease and begin taking this drug, we expect that 5 people will go on to die from heart disease, and 95 people will reduce their chance of death’, n = 93), and the third group were given information where both framings were presented (e.g.,. ‘Out of 100 people whose lives would likely be cut short by heart disease and begin taking this drug, we expect that 5 people will show no improvement and will go on to die from heart disease, and 95 people will substantially improve their chance of survival and reduce their chance of death’, n- = 93). The majority of participants (59%) chose to take part in the trial when outcomes were framed as losses, while only 35% of the ‘gain’ group chose to participate. When both framings were presented, 62% of participants chose to participate, making a similar choice to the ‘loss’ group. When it came to perceiving riskiness of participation, the ‘gain’ group were more likely to rate this as riskier than non-participation (66%) compared to the ‘loss’ group (55%). For the ‘both’ group, the results were again similar to the loss condition, with 52% reporting trial participation as riskier than not. Respondents in the gain condition rated participation as significantly riskier (on a 10 point scale) than those in the loss condition (p<0.05), and respondents in the loss condition rated non-participation as significantly riskier than those in the gain condition (p<0.05). There was a significant association between domain (gains vs loss) and relative riskiness of participation vs non-participation (p<0.05).

In the study by Tait et al [ 26 ] 4685 parents were asked to consider their child was being randomised into a trial testing two drugs for post-operative pain, one a standard treatment and the other proven in adults but not in children. The risks and benefits of the two drugs were presented in absolute terms with comparisons presented as incremental changes. Four scenarios that provided different risk/benefit trade-offs were developed and considered: one benefit and 2 risks (a minor and a major), which were varied for Drug B across each scenario but remained static for Drug A. There was one scenario with no trade off, where there was an increase in benefit as well as risk reduction (n = 1171), whereas the other three included a loss of benefit but gains in risk reduction (n = 1184, n = 1196, n = 1134). Overall the study showed that parents who received the ‘no trade off’ (i.e., improvements across benefit and risk) scenario had both improved gist (defined as ‘ability to identify the essential meaning about the observed differences’ and measured using 4 items where ≥3 correct answers were required, p<0.01) and verbatim understanding (defined as understanding or knowledge to ‘correctly report the actual risk and benefit frequencies’ and measured using 7 items where ≥5 correct answers were required, p<0.01). The no trade off scenario also enabled parents to correctly perceive the potential benefits as greater, risks as lower, (p<0.01) and to be more likely to agree to their child participating in the trial (measured using an 11 point scale) compared to the other three groups (p<0.01). Taken together these results suggested the no trade off scenario offering multiple gains resulted in a higher level of scrutiny compared to when only reductions in risk were presented.

Treschan et al (2003) randomised 148 participants to one of three versions of a study protocol to examine how understanding of risk and discomfort associated with a clinical trial influences patients’ decision to participate [ 27 ]. The proposed trial was comparing peri-operative oxygen (30% vs 80%) to reduce the risk of surgical site infections. The control group received a version of the protocol that stated there would be little if any risk or pain involved in participating (n = 47), the ‘pain’ group were told that there would be additional procedures that would cause considerable pain and discomfort (e.g., dressing of wounds, cannulation, blood samples, n = 51)), and for the ‘risk’ group procedures were described as having a high risk of injury (e.g.,. extra oxygen is dangerous, risks of cannulation, risk of blood samples, etc, n = 50). Participants in the control group were more willing to participate in the trial (64%), with significantly fewer consenting in the risky (26%) and painful (35%) groups (p<0.001). There were no significant differences in understanding of the level of risk or pain for the three groups (p = 0.884). Those who correctly understood the risk or pain described in the protocols were twice as likely to consent to participation in the trial (49% vs 24%, p = 0.003).

Prospectively designed, non-randomised studies of interventions to explore risk communication within RCTs

Of the two non-randomised papers that met the inclusion criteria, Cheung et al (2010) is the only study that investigated probability presentation within risk communication for clinical trials [ 23 ]. This study implemented a cognitive experiment (N = 240) and preference survey about risk within a hypothetical trial for pain medication for arthritis. The intervention used a factorial design to study the impact of three formats (frequency (n = 82), percentage (n = 80) and verbal descriptors (n = 78)) and two sequences on willingness to participate and likelihood to change one’s willingness after given additional information. Participants were presented with information in one of the six combinations. Participants were given a card that showed information about side effects of a new medication for pain relief in one of six ways of risk presentation, and then were asked whether they would be willing to take part in the trial. They were then presented with a second card, with the same risk information presented in all three formats being studied. A change in decision would indicate a potential problem in the initial format given to participants. There was no difference in willingness to participate in the trial across all presentations (p = 0.886), and there was also no difference in the likelihood of a participant changing their mind after being given the information in additional formats (p = 0.529). After reading card 2, the proportion of participants in each group showing a willingness to participate increased significantly (p<0.05). With regardto presentation preferences, 43% of participants preferred the frequency format, 32% preferred percentages, and 25% preferred the verbal descriptors.

Risk interpretation interventions.

The remaining non-randomised study (Sutherland et al, 1990) explored risk interpretation within a consent form for a hypothetical drug trial for cancer [ 28 ]. All participants (N = 50) were given a consent form and asked to underline statements that were important to them in terms of making a decision about participating in the trial. They were also asked to indicate if their chosen statements were positive or negative. A questionnaire including preferences for probability descriptors (verbal or numerical) was also administered. Of those who refused to take part in the hypothetical trial, 70% noted only the potential for risk, 10% only for benefit, and the remaining 20% noted both risk and benefit information as important for their decision. Just 33% of those who ‘consented’ identified only risks, 27% noted only benefits, and 30% noted both risk and benefit. The remaining 10% identified neither as important to their decision. One third of participants were unable to identify the correct interpretation of the ‘unlikely’ verbal descriptor, and 54% gave an incorrect interpretation of ‘10% response rate’ meaning. When it came to preferences for benefit descriptors, 16% of patients preferred words, 34% numbers, 48% both and 2% other. For risk communication preference the results were very similar; 16% verbal, 28% numerical, 48% both and 2% other.

The study is one of the first to systematically review the published evidence on methods for communicating risk to potential trial participants during the informed consent process. It has examined and summarised the existing evidence about how risk information is perceived by potential participants and highlights how these factors may influence decisions to participate in a clinical trial context. Only seven studies were identified that have investigated aspects of communicating risk information in a clinical trial setting. Whilst the majority of studies were randomised comparisons, we also identified 2 non-randomised evaluations. Given the heterogeneity of the interventions investigated in the included studies and the variability in outcomes reported, a meta-analysis of these studies was not possible. This work therefore highlights the need for the rigorous development and evaluation of interventions to improve the presentation and communication of risk information for potential trial participants.

One of the studies investigated probabilistic presentation methods and demonstrated that numerical formats appear to be better at communicating risk to potential trial participants, when compared to text [ 22 ]. Participants receiving verbal descriptors alone were less likely to consent to take part in a trial and were less satisfied with the information, perceiving risks of side effects to be much higher than participants receiving both numerical and verbal descriptors. Similar findings can be seen in a review on communicating with patients about evidence (for treatment decisions), which illustrated that patients have a better understanding of risk if probabilistic information is presented numerically rather than verbally [ 29 ]. It is worth considering that studies in a treatment setting have shown that using visual aids such as pictographs or bar charts to present event rates may aid accurate understanding of probabilities, and they can help reduce several biases including framing effects [ 30 ]. There are many variants of visual aids however, and how these are utilised and understood by potential trial participants warrants more investigation using the best practice examples from treatment decision making as a starter.

The second study (Cheung et al, 2010) looking at probabilistic presentation found no difference in willingness to participate between frequency, percentage and verbal conditions; however, it did find a strong preference for numerical presentations over verbal descriptors, particularly for frequency formats [ 23 ]. Research by Price et al (2007) found that frequency statements are generally better understood by participants compared to ratios or percentages [ 31 ]. An important finding from this study highlighted major errors in correctly matching EU descriptors of risk to associated frequencies, findings echoed by the other study which looked at risk interpretation showing a large proportion of participants were unable to correctly interpret verbal descriptors or percentage formats [ 28 ]. A number of studies have demonstrated that many lay persons are unable to understand basic aspects of probabilities that are essential to risk understanding, nor to comprehend the concept of risk in general [ 32 , 33 ]. This poses a challenge to effective risk communication and demonstrates a need for improved methods for better informed consent within the context of clinical trials.

The Sutherland et al (1990) study found that the majority of non-consenters to the trial noted only the potential for risk in the provided information, whereas the information was interpreted very differently by consenters where a minority saw only risks, and many perceived benefits instead [ 28 ]. A qualitative study into patient decisions about taking part in an epilepsy treatment trial noted that participant decision making was most commonly influenced by their perception of harm and benefit [ 34 ]. Those who agreed to take part usually saw the risks involved as acceptable, in this case because of the ‘tried and tested’ nature of treatments. However, the non-consenters viewed participation as ‘an unknown quantity’ and defined the risks of being randomised to an unsuitable drug as being too high or not in their best interest [ 34 ].

When it came to studies looking at risk framing, the results were mixed. The study by Kim et al found no significant difference in willingness to participate in the trial, although participants in the intervention group (no guarantee for benefit statement) were much more likely to believe that their condition would improve [ 24 ]. When benefits were framed as losses participants were more likely to take part in the trial, and when benefits were presented as both losses and gains, participants seemed to respond similarly to the loss group, suggesting that loss framing had more impact on decision making than gain, where perceived risk was higher [ 25 ]. However, many of the statements used in this study were vague and uninformative, putting into question what understanding participants had in relation to these statements in addition to willingness to participate. Conversely, Treschan et al found that when outcomes were framed as gains the majority of participants were less likely to participate [ 27 ]. Earlier research by Tversky & Kahneman (1981) on framing and the psychology of choice demonstrated that framing outcomes in terms of gains does indeed generate risk-averse choices, which could translate to, for example, a decreased willingness to participate in a clinical trial [ 35 ]. A more recent study highlighted the introduction of potential bias in decision making about trial participation when the effects of language framing are not addressed [ 36 ]. This study explored whether presenting health care decisions as ‘opportunity’ rather than ‘choice’ biased individuals’ preferences in the context of trial participation for cancer treatment. They found that a ‘choice’ frame, where all treatment options are explicit, is less likely to bias preferences [ 36 ]. It is therefore of paramount importance that information given to participants include neutral statements, or at a minimum balanced statement about participation or not, so as not to unduly manipulate or ‘nudge’ decisions in ways that are not consistent with the individual’s values and preferences [ 18 ].

Five out of the seven studies included in this review only communicated risk information about the ‘experimental’ treatment [ 22 – 25 , 28 ]. Two studies communicated risk information about both the intervention and its comparator or indeed both active interventions [ 26 , 27 ]. Given that decision making about clinical trials is complex and requires trade-offs between both (or all) options and therefore presenting risk (and benefit) information on these options would be important to support fully informed choices. This should be acknowledged and explored in future studies.

Complex language and details included in participant information leaflets (PILs) and consent forms for trials can be difficult for some people to comprehend properly and may engender more confusion than understanding of trial processes, including risks [ 37 ]. An analysis of PILs used in clinical trials by Gillies et al (2011) found that: explaining trial processes; presenting probabilities; and expressing values, were consistently poor across all PILs when assessed using an informed consent evaluation instrument [ 17 ]. These information leaflets clearly need to be improved to encourage higher quality decision making when it comes to trial participation. It is also clear that potential trial participants continue to have significant deficits in their recall and understanding of trial related information, and that such information is often not presented in a comprehensive way that optimises participant understanding [ 38 , 39 ]. The recent study by Gillies et al (2014) explored whether patient information leaflets (PILs) were able to effectively support decision making about trial participation [ 17 ]. They found that information that demonstrated support for good quality decision making in other contexts was lacking in PILs for UK clinical trials. In particular, the section on ‘presenting probabilities’ was almost always absent, despite its proven importance for supporting good quality decision making [ 17 ].

Whilst not a focus of this review it is important to point out that none of the included studies reported including patients or the public as partners in the research to identify what the content and/or presentation of the information should be for the studies. Also, no input was sought with regard to whether the outcomes being evaluated were appropriate and meaningful for patients faced with decisions about trial participation.

Lessons from effective risk communication in a treatment and/or screening context can provide examples of best practice that could be used for those developing PILs for patients considering clinical trial participation. A systematic review on risk communication published since has shown that visual aids, such as icon arrays and bar graphs, improved both understanding and satisfaction [ 40 ]. Interestingly, this review showed that presenting absolute risk reduction was better at maximising accuracy and less likely to influence decisions. The presentation of information on numbers needed to treat reduced understanding. This review also concluded that due to the quality and heterogeneity of included studies, it is not possible to determine a ‘best’ method for conveying probabilistic information [ 40 ]. However, whilst there might be a paucity of high quality evidence to support an unequivocal ‘best’ method there have been recommendations for guiding principles developed by several groups. The first, developed using an international consensus process involving researchers and patients, provided key considerations for presenting probabilities of outcomes [ 41 ]. These include:

Use event rates to specify the population and time period
Compare outcome probabilities using the same denominator, time period, and scale;
Describe uncertainty around probabilities;
Use visual diagrams;
Use multiple methods to view probabilities (words, numbers, diagrams);
Allow the patient to select a way of viewing the probabilities (words, numbers, diagrams);
Allow patient to view probabilities based on their own situation (e.g. age);
Place probabilities in context of other events;
Use both positive and negative frames (e.g. showing both survival and death rates).

An expert consensus group further developed these IPDAS items to develop a set of guiding principles and key messages which cover eleven components of risk communication and consider what information to present and how it should be presented within tools such as patient decision aids [ 42 ]. The guiding principles range from how best to present the chance an event will occur, to use of interactive web-based platforms for delivery, and narrative methods for communication [ 42 ]. A recent study published ‘good practice statements’ for the development of evidence-based information communicating the effects of healthcare interventions [ 43 ]. Many of these statements would be relevant for developing information related to risk communication to support decision about trial participation. For example: using numerical formats that are easy to understand; present both numbers and words; and report absolute effects [ 43 ]. In summary, whilst there may be a paucity of high quality evidence to underpin decisions about effective risk communication in clinical trial contexts, many of the good practice recommendations developed through empirical research provide sensible frameworks to promote informed choices, enable good quality decision making, and are unlikely to cause significant harm. As such, these guiding principles could also serve as a foundation on which to develop (and test) effective methods of risk communication within the context of clinical trials.

Strengths and limitations

The low number of studies included for review means it is difficult to confidently make far reaching recommendations based on the findings, and the heterogenous nature of the studies mean a meta-analysis was not feasible. The studies in our review included decisions about trial participation that were hypothetical which may limit the extent to which these findings are applicable to a real world setting. Understanding and assessing risk and risk communication is pertinent to the trial phase, as the magnitude of risk is much greater in earlier phases of clinical trials; however, only one of the studies stated the trial phase being investigated. This review is, however, the first to systematically investigate risk communication within a clinical trial context. With ever increasing numbers of trials, the importance of informed consent, and yet no consistent, evidence-based format for presenting probabilistic information in a clinical trial setting, this study supports the argument for effective future research within this area.

The evidence base surrounding risk communication in clinical trials indicates that there is as yet no clear optimal method for improving participant understanding, nor a clear consensus on how understanding affects willingness to participate, indicting a necessity for robust, high quality research in this area. Further research into risk communication during the informed consent process for trials, based on examples of best practice in other settings such as treatment and screening decision making, is needed to help illuminate the mechanisms underlying risk perception and understanding and provide appropriate ways to present and communicate risk in a trial context so as to further promote informed choices about participation.

Supporting information

S1 checklist. prisma checklist..

https://doi.org/10.1371/journal.pone.0242239.s001

S1 Appendix. Search strategy MEDLINE.

https://doi.org/10.1371/journal.pone.0242239.s002

Acknowledgments

The authors would like to acknowledge Cynthia Fraser for help designing and running the search strategies and Paul Manson for updating the search.

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DOI: 10.2900/64747
Corpus ID: 155452482

A literature review on effective risk communication for the prevention and control of communicable diseases in Europe.

J. Infanti , Jane Sixsmith , M. Barry
Published 2013
Medicine, Environmental Science, Sociology

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A literature review on effective risk communication for the prevention and control of communicable diseases in Europe

This review brings together the current body of literature on risk communication (focused on communicable diseases) in a concise reference document which can be used to inform the development of evidence-based risk communication strategies and approaches. The review demonstrates that there is an impressive body of literature on risk communication relevant to the prevention and control of communicable diseases. This literature is complicated, however, by blurred definitions and overlap between risk communication and crisis communication. It is also widely dispersed across academic disciplines, lacking rigorous empirical evidence to demonstrate effectiveness, challenged by the complex and unpredictable ways that individuals perceive risk and the environmental, social, cultural and linguistic factors through which risk communication is viewed.

Executive Summary

Risk communication is seen as one of the essential ways of limiting morbidity and mortality, as well as the damage to national economies and public health infrastructure caused by communicable diseases.

This review highlights the particular challenges faced by a multi-cultural and multi-lingual Europe when designing effective risk communication strategies. National and international collaboration is identified as being vital to deal with these challenges as well as further development of cross-sectoral risk preparedness strategies. Although there are many resources available to risk communicators today, limitations have been exposed once they have been tested. Gaps which the report identified include risk communication messages often failing to reach the intended targets and a lack of resources to meet the new and changing needs of more web-based societies.

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Open access
Published: 21 June 2024

Factors influencing U.S. women’s interest and preferences for breast cancer risk communication: a cross-sectional study from a large tertiary care breast imaging center

Jessica D. Austin 1 ,
Emily James 2 ,
Rachel L Perez 2 ,
Gina L. Mazza 3 ,
Juliana M. Kling 4 ,
Jessica Fraker 4 ,
Lida Mina 5 ,
Imon Banerjee 6 ,
Richard Sharpe 6 &
Bhavika K. Patel 6

BMC Women's Health volume 24 , Article number: 359 ( 2024 ) Cite this article

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Breast imaging clinics in the United States (U.S.) are increasingly implementing breast cancer risk assessment (BCRA) to align with evolving guideline recommendations but with limited uptake of risk-reduction care. Effectively communicating risk information to women is central to implementation efforts, but remains understudied in the U.S. This study aims to characterize, and identify factors associated with women’s interest in and preferences for breast cancer risk communication.

This is a cross-sectional survey study of U.S. women presenting for a mammogram between January and March of 2021 at a large, tertiary breast imaging clinic. Survey items assessed women’s interest in knowing their risk and preferences for risk communication if considered to be at high risk in hypothetical situations. Multivariable logistic regression modeling assessed factors associated with women’s interest in knowing their personal risk and preferences for details around exact risk estimates.

Among 1119 women, 72.7% were interested in knowing their breast cancer risk. If at high risk, 77% preferred to receive their exact risk estimate and preferred verbal (52.9% phone/47% in-person) vs. written (26.5% online/19.5% letter) communications. Adjusted regression analyses found that those with a primary family history of breast cancer were significantly more interested in knowing their risk (OR 1.5, 95% CI 1.0, 2.1, p = 0.04), while those categorized as “more than one race or other” were significantly less interested in knowing their risk (OR 0.4, 95% CI 0.2, 0.9, p = 0.02). Women 60 + years of age were significantly less likely to prefer exact estimates of their risk (OR 0.6, 95% CI 0.5, 0.98, p < 0.01), while women with greater than a high school education were significantly more likely to prefer exact risk estimates (OR 2.5, 95% CI 1.5, 4.2, p < 0.001).

U.S. women in this study expressed strong interest in knowing their risk and preferred to receive exact risk estimates verbally if found to be at high risk. Sociodemographic and family history influenced women’s interest and preferences for risk communication. Breast imaging centers implementing risk assessment should consider strategies tailored to women’s preferences to increase interest in risk estimates and improve risk communication.

Peer Review reports

Breast cancer remains the most common malignancy in the United States (U.S.) with wide variation in incidence and mortality. This variation is attributable to a myriad of factors such as biological sex, age, reproductive history, hormone use, family history, genetic mutations, breast density, obesity, and alcohol intake, each alone explaining a modest proportion in variation in risk [ 1 , 2 , 3 , 4 ]. Evolving guideline organizations [ 5 , 6 , 7 ], including the National Comprehensive Cancer Network, American College of Radiology, and American Cancer Society, recommend formal breast cancer risk assessment (BCRA) starting between 25 and 30 years of age to guide those at increased risk to appropriate risk-reduction care. Risk-based approaches to breast cancer screening have the potential to decrease harms (i.e., false-positive, overdiagnosis) associated with current age-based approaches and may improve early detection in high-risk women leading to improvements in survival rates and quality of life [ 8 , 9 , 10 ].

Several validated models exist that utilize a combination of women’s patient-reported information and medical records data to accurately quantify a woman’s lifetime, 10-year or 5-year risk of developing breast cancer [ 11 ], and are increasingly used at mammography screening facilities to facilitate guideline-recommended risk-reduction care. Implementation of these models in clinical settings has shown to significantly improve identification of individuals at high-risk for breast cancer [ 12 , 13 , 14 , 15 ]; but have not led to the uptake guideline recommended preventive care including supplemental screening, genetic testing/counseling, or risk reducing medications for those at increased risk [ 13 , 15 , 16 , 17 , 18 , 19 , 20 ] While multifaceted, these findings may be partially explained by ineffective risk communication. Integration of BCRAs into electronic health records lend itself to delivering written communication to clinicians and women via clinical reports or patient portals. Yet, these communications of breast cancer risk results have not led to changes to women’s risk perceptions or uptake of recommended care [ 21 , 22 ] Moreover, implementation of risk-based approaches should be accepted and supported by women [ 23 ]. Limited prior studies suggest that women welcome risk-based screening [ 24 , 25 ], but few include perspectives of women in the U.S.

As efforts to implement BCRAs increase, several questions remain regarding women’s interest in knowing their risk and how best to communicate breast cancer risk estimates in a manner that aligns with their preferences. Effective risk communication is essential for helping women understand their vulnerability to a disease [ 26 , 27 ]. Yet, most women are unaware of or misperceive their breast cancer risk [ 28 , 29 ]. These misperceptions can have harmful consequences, including emotional distress and missed opportunities to utilize guideline recommended preventive care for those at increased risk [ 30 , 31 ] Prior efforts to improve risk communication have largely been tasked to clinicians to address gaps in clinician and organizational barriers to implementation [ 32 , 33 , 34 ]. While important, successful implementation of BCRA requires an understanding of women’s preferences for communication to optimize transfer of risk knowledge and recommendations. This study aims to characterize and identify factors associated interest in and preferences for breast cancer risk communication among a large cohort of U.S. women undergoing mammography screening at a large breast imaging clinic.

This is a cross-sectional, quality improvement, survey study on a convenient sample of 1221 women presenting for screening mammography at the Mayo Clinic in Arizona (MCA) Breast Imaging Clinic between January 2021 and March 2021. The Breast Imaging Clinic provides approximately 14,000 screening mammograms annually, including no-cost screening to underserved populations through community-based partnerships. Informed consent was not required for this study as it was deemed exempt from ethics approval by the Mayo Clinic Institutional Review Board.

Data collection

A paper survey was administered at the time of women’s mammography screening appointment. Adapted from prior assessments [ 35 , 36 ], the 18-item survey (see Appendix) assessed sociodemographic characteristics, known breast cancer risk factors, and included items assessing if the women were ever provided a personal risk estimate, interest in knowing personal risk, and preferences for receiving and communicating risk information.

Interest in knowing breast Cancer risk

Interest in knowing one’s personal risk for breast cancer is the primary outcome of this analysis. Women indicated their level of agreement on a 5-point scale (‘Strongly agree’ to ‘Strongly disagree’) to the following item: “I am interested in knowing my risk for breast cancer”. For this analysis, responses were dichotomized as ‘interested’ (‘strongly agree’/’agree’) and ‘neutral’/’uninterested’ (‘neither agree nor disagree’/’disagree’/ ‘strongly disagree’). Those who did not respond to this item were excluded from the analysis ( n = 10).

Preferred mode of risk communicatio

Women were presented with a hypothetical scenario in which they were at high risk for breast cancer and asked how they prefer to receive this information and in how much detail [ 35 , 36 ]. Women’s preferences for receiving information about their breast cancer risk was assessed using a single item: “If you are found to be at high risk of breast cancer, how would you prefer to receive the result of your estimated breast cancer risk?”. Women had the ability to select multiple options including ‘Face-to-face meeting with the health professional who ordered the mammogram’, ‘Telephone call from the health professional who ordered the mammogram’, ‘Face-to-face meeting with the radiologist who interpreted your mammogram’, ‘Telephone call from the radiologist who interpreted your mammogram’, ‘Face-to-face meeting with a breast risk practitioner’, ‘Telephone call from a breast risk practitioner’, ‘Mailed letter accompanying your annual mammogram result’, ‘Mailed letter separate from your mammogram result’, ‘E-mailed copy separate from your mammogram result’, ‘View the result through Patient Online Services (MyChart)’, and ‘Referral to a high-risk breast center’. For this analysis, responses were categorized as face-to-face with a health care professional, telephone call from a healthcare professional, mailed letter, electronic communication, or referral.

Preferred level of detail for risk communication

Women were also asked how much detail they prefer to receive if they were considered high risk for breast cancer. Response options were ‘less detailed (for example, “your calculated breast cancer risk was high and you may need further testing”)’, ‘moderate detail (for example, “your calculated breast cancer risk was greater than 20% and you may need further testing”)’, ‘very detailed (for example, “your calculated breast cancer risk was 26%, which is considered high risk, and you may need further testing”)’, and ‘I would not like my risk to appear in my radiology report’. Responses were dichotomized as ‘yes’ (‘Very detailed’) or ‘no’ (‘Moderate detail’, ‘Less detail’, ‘No detail’) to wanting their exact risk estimate.

To align with guideline recommendations for initiating breast cancer screening for women at average risk, we excluded women under the age of 40 ( n = 34). We also excluded women with a history or unknown history of breast cancer ( n = 53) or a known genetic mutation for breast cancer ( n = 5) since their interest and preferences for risk communication likely differ from those without a cancer diagnosis. The final sample size for this analysis was 1119 women. Summary statistics were calculated to describe the distribution of key variables. We examined differences in interest in knowing one’s breast cancer risk estimate (interested vs. neutral/not interested) by sociodemographic, breast cancer risk factors, and mammography screening history using Fisher’s exact test. We estimated two multivariable logistic regression models to identify sociodemographic and breast cancer risk factors predictive of one’s interest in (1) knowing their breast cancer risk and (2) knowing their exact breast cancer risk estimate. To provide supplemental information, these multivariable logistic regression models were also estimated while excluding women who reported ever having received an estimate of their breast cancer risk. Results from the multivariable logistic regression models are reported using adjusted odds ratios (OR) and 95% Wald confidence intervals (CI). All analyses were performed using SAS 9.4 with p-values < 0.05 considered statistically significant.

A summary of patient characteristics is provided in Table 1 . Most women were 60 years of age or older (51.2%), self-identified as white (86.8%) and non-Hispanic (92.7%), with greater than a high school education (93.1%). In addition, 81.1% reported no primary family history of breast cancer, 77.2% reported no prior breast biopsy, and 83.5% report receiving a mammogram annually.

Summary of participant characteristics ( N =1119)

Interest in knowing breast cancer risk

Overall, 72.7% of women were interested in knowing their risk for breast cancer though only 13.2% reported ever being provided their personal breast cancer risk. Interest in knowing one’s personal risk differed by mammography frequency, with women receiving mammograms annually reporting higher interest in knowing their risk (Fisher’s exact p = 0.01; Table 2 ). Results from the multivariable logistic regression analysis for our entire sample (see Fig. 1 ) show that women with a primary family history of breast cancer were significantly more interested in knowing their risk compared to women without a primary family history (OR 1.5; 95% CI 1.0, 2.1; p = 0.04), while women categorized as “more than one race or other” were significantly less interested in knowing their risk compared to women identifying as White (OR 0.4; 95% CI 0.2, 0.9; p = 0.02). Mammography frequency was not significantly associated with interest in knowing one’s breast cancer risk when controlling for all other variables in the model. These findings remained in supplementary analyses excluding women who were ever provided their risk estimates. Additionally, women 60 years of age and older were significantly less interested in knowing their breast cancer risk compared to women under the age of 60 (OR 0.7; 95% CI 0.5, 0.9; p = 0.02) after excluding women ever provided their risk estimate.

Differences in interest in knowing breast cancer risk by participant characteristics.

Forest plot of the odds ratio and 95% confidence intervals of factors predicting interest in knowing one’s personal risk for breast cancer for the entire sample ( N =1058). Abbreviations: FHx, Family History; AA, African American; PI, Pacific Islander; AI/AN, American Indian or Alaskan Native; HS, High School

Preferred mechanism for risk communication

Figure 2 describes preferences for risk communication for our entire sample. If considered to be at high risk for breast cancer, 52.9% would prefer to receive the results by telephone with a healthcare professional, followed by 47.1% preferring a face-to-face meeting with a healthcare professional. Some form of verbal communication—whether face-to-face or by telephone—was preferred by 83.4% of women (85.0% when additionally considering that referral to a high-risk breast cancer center may lead to a face-to-face discussion). Of the 402 women who preferred to receive results by mailed letter or electronic communication, 245 (60.9%) also wanted some form of verbal communication (i.e., face-to-face or by telephone; 255 [63.4%] when additionally considering referral to a high-risk breast cancer center). Moreover, 77.2% of women preferred having detailed information about their exact risk estimate.

UpSet plot showing the number of women endorsing each combination of preferences for receivingbreast cancer risk estimates. Notes: 21 patients made no selection regarding their preferences. F2F=face-to-face.

Preferred Level of Detail for Risk Communication

Results from the multivariable logistic regression analyses (see Fig. 3 ) show that women 60 years of age and older were significantly less likely to prefer exact estimates of their risk compared to women under the age of 60 (OR 0, 95% CI 0.5, 0.9; p = 0.003). Women with greater than a high school education were significantly more likely to prefer exact risk estimates, compared to women with a high school degree or less (OR 2.5; 95% CI 1.5, 4, p < 0.001). Based on the full sample, we did not observe significant differences in preferences for detailed risk estimates by race, ethnicity, prior breast biopsy, primary family history of breast cancer, or mammography frequency. Results from the supplementary analysis excluding women who were ever provided their risk estimates show similar patterns by age and education, as well as women with a history of breast biopsy being significantly more likely to prefer detailed risk information compared to those with no history of breast biopsy (OR 1.5; 95% CI 1.0, 2.3; p = 0.05).

Forest plot of the odds ratio and 95% confidence intervals of factors predicting for knowing exact breast cancer for the entire sample ( N =1037). Abbreviations: FHx, Family History; AA, African American; PI, Pacific Islander; AI/AN, American Indian or Alaskan Native; HS, High School

This cross-sectional, quality improvement, survey study of women receiving a screening mammogram adds to the growing empirical evidence supporting women’s interest in and preferences for risk communication. Despite evolving guideline recommendations, only 13.2% of women in our study reported ever being told their personal breast cancer risk though nearly 73% of women were interested in knowing their breast cancer risk. Differences in interest in knowing breast cancer risk were observed by family history and race. We also found that women, if identified as increased risk, would prefer to receive their exact risk estimate verbally (i.e., phone or face-to-face) from a health care professional, though differences in preferences were observed by age and education.

Though multifactorial, women’s interest in knowing their breast cancer risk is key to successful implementation of BCRA programs [ 23 ]. Similar to prior studies [ 37 ], the majority of women in our study expressed a strong interest in knowing their estimated lifetime risk of breast cancer, aligning with the growing emphasis on shared decision-making and women’s autonomy in modern medicine [ 38 , 39 ]. However, we also identified groups who may be less interested in their breast cancer risk including women identifying as “more than one race or other race” in our study. Not much exists in the current literature to explain this phenomenon. One potential explanation could be the low proportion of our sample identifying as “more than once race or other race” ( n = 47/1119, 4.3%). However, we did not see differences among other race groups with similarly lower proportions. Higher levels of perceived risk have been associated with a higher degree of willingness to undergo BCRA in prior studies and is consistent with health behavior theories including the Health Belief Model and Protection Motivation Theory [ 24 , 26 , 40 ]. This could also explain increased interest among women with a primary family history of breast cancer, where their experiences and knowledge might influence their perceived breast cancer risk.

Contrary a prior study [ 41 ], we found that women 60 years of age or older were less likely to prefer exact information about their cancer risk. While breast cancer risk increases with age so do complications from other chronic conditions, which may explain lower preferences for exact breast cancer risk estimates. Additionally, we found that women with a high school degree or less were less likely to prefer exact information about their risk. This finding may be attributed to how the response options were presented, showing numeric risk estimates only. Low numeracy is pervasive, particularly among lower educated populations, and can impair risk communication as it is associated with difficulty in understanding and assessing risk-related information [ 42 , 43 ]. Existing recommendations for risk communication suggest presenting information using a variety of formats including lay language, numerical (e.g., 20%; 1 out of 10) and pictorial information [ 44 ]. Additionally, decision support tools combining these recommendations with experience-based dynamic interfaces, such as games, has shown to significantly improve accuracy of breast cancer risk perceptions among high and low numeracy women [ 45 ]. However, qualitative analyses suggest that accurate risk perceptions alone are insufficient in the adoption of risk-appropriate breast cancer prevention strategies [ 45 , 46 ].

Healthcare systems are encouraged to allow women to view and download their personal health records via electronic health record portals [ 47 ]. While leveraging such systems can support access to and personalization of care, our results support that women prefer verbal rather than written communications about their breast cancer risk [ 35 , 37 ]. Combining written with verbal communication by clinicians has shown to be associated with greater awareness of one’s individual risk and greater adherence to guideline recommended care [ 48 ]. Yet, tailoring and communicating breast cancer risk to each women’s abilities and preferences can present significant challenges for clinicians and organizations, particularly in an era of increasing work volumes. Moreover, a recent study found that tailoring risk presentation formats to women’s preferences does not necessarily translate to improvements in risk comprehension [ 49 ]. Further research is needed to explore the feasibility, workflow challenges, and most effective formats for improving risk comprehension while aligning with women’s preferences [ 50 ].

This study has limitations. The cross-sectional nature of our study limits our ability to determine temporal causality of factors influencing interest and preferences for risk communication. Our study population was also limited to a single academic imaging center serving predominately educated, non-Hispanic, White women, thus limiting our ability to generalize findings to other settings. Despite the relative homogeneity of our sample, our large sample size allowed for detecting differences in interest and risk communication preferences by sociodemographic and clinical factors, critical for hypothesis generation. Specifically, we were able to detect lower levels of interest among women identifying as “more than one race or other race”, emphasizing the need for future studies to understand experiences and preferences of diverse populations. It is also important to note that the level of interest and preferences for knowing exact risk estimates may be overestimations since all women were recruited at the time of their screening mammogram and women demonstrating high levels of routine screening mammography. Future studies should assess acceptability of BCRA, including interest and willingness, among populations who lack access to or have not initiated screening, but may benefit from risk assessment and earlier screening.

Risk assessment at the time of mammography screening has the potential to reach a wide audience, but ineffective risk communication may continue to undermine effective implementation and uptake of guideline recommended care. Our study adds to the growing empirical literature demonstrating that women are interested in and prefer to receive detailed breast cancer risk estimates verbally, though these preferences may differ by sociodemographic characteristics [ 35 , 37 ]. These findings suggest that clinicians and organizations implementing risk assessment as part of routine mammography screening carefully consider methods for incorporating women’s communication preferences as part of an integrated, standardized workflow [ 15 ]. While combining written with verbal communication by clinicians was shown to be associated with greater awareness of one’s individual risk and greater adherence to supplemental MRI screening [ 48 ], presenting and discussing risk alone may not improve uptake of guideline recommended care [ 50 , 51 ]. Providing risk information in conjunction with education on how to reduce risk has shown promising results [ 52 ], but education does not address other barriers that hinder uptake of recommended care including psychological distress, cost, transportation, and time [ 34 , 53 ]. To this end, more research is needed to identify effective approaches to risk communication that also improve uptake on guideline recommended care. Additionally, our findings emphasize the need for more research to understand interest and preferences for risk communication among racially/ethnically diverse populations under the age of 40 who are not eligible for routine mammography screening but should undergo risk assessment in primary care settings.

Data Availability

The datasets used and/or analyzed during the current study are available from the corresponding author on reasonable request.

Abbreviations

Mayo Clinic Arizona

Confidence Interval

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Acknowledgements

We would like to thank Jhenitza P. Raygoza Tapia for the preparation of this manuscript for submission to BMC Women’s Health.

This study is funded by the Mayo Clinic Transform the Practice Award.

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J.D.A. and B.K.P. conceived the study. J.D.A., G.M., B.K.P. contributed to data collection and analysis. J.D.A., E.J., and R.L.P. drafted the manuscript. G.L.M., J.M.K., J.F., L.M., I.B., R.S., and B.K.P. edited and reviewed various versions of the manuscript. All authors read and approved the final manuscript and are accountable for all aspects of the work. Funding for this study was received by B.K.P.

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Austin, J.D., James, E., Perez, R.L. et al. Factors influencing U.S. women’s interest and preferences for breast cancer risk communication: a cross-sectional study from a large tertiary care breast imaging center. BMC Women's Health 24 , 359 (2024). https://doi.org/10.1186/s12905-024-03197-7

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DOI : https://doi.org/10.1186/s12905-024-03197-7

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Strategic approaches in network communication and information security risk assessment.

1. Introduction

2. information security management, 3. risk assessment, 3.1. iso/iec 27005 risk management standard, iso/iec 27005 information security risk assessment, 3.2. nist (sp-800-30), 3.3. operationally critical threat, asset, and vulnerability evaluation (octave), 3.4. information risk analysis methodology (iram), 3.5. expression of needs and identification of security objectives (ebios), 3.6. cramm method, 3.7. statistical design of expression approach, 3.8. multi-criteria evaluation methods, 3.9. cyber investment analysis methodology (ciam), 3.10. enhanced grey risk assessment model supporting cloud service provider, 3.11. a situation awareness model for information security risk management, 3.12. a hybrid model for information security assessment, 3.13. bwm-swara approach, 3.14. an integrated model to enhance security, 3.15. ai-powered cyber insurance risk assessment, 4. challenges in control assessment methods, 5. future directions, 6. conclusions, author contributions, conflicts of interest.

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Click here to enlarge figure

SDLC Phases	Phase Characteristics	RMA Support
Phase 1: Initiation	An IT system is deemed necessary, and the goal and scope of the IT system are defined	The identified dangers aid in the formulation of the system requirements.
Phase 2: Development or Acquisition	Design, procurement, development of programming, or other construction of the information technology system.	The hazards found during this phase can be utilized to help with the IT system’s security analysis.
Phase 3: Implementation	Configuration, enablement, testing, and verification of system security features are required.	System implementation is evaluated against its specifications and within a modeled operating environment using the risk management method.
Phase 4: Operation Maintenance	The system goes about its business. Adding hardware and software to the system is a common practice for system modifications.	Periodic system reauthorization (or reaccreditation) or substantial modifications to an IT system necessitate risk management actions.
Phase 5: Disposal	This step may include the disposal of data, hardware, and software.	System components that will be discarded or replaced will have risk management actions carried out to guarantee that old hardware and software are disposed of correctly.

Ref.	Model	Results
[ ]	ISO/IEC 27005	This standard offers a comprehensive structure for effectively handling and mitigating risks related to information security. The main focus is on the identification, assessment, and mitigation of risks to establish efficient information security protocols inside businesses. Despite its widespread acceptance, this framework presents a multitude of intricate regulations that might be difficult to implement without the assistance of specific examples.
[ ]	NIST SP800-30	Information technology risk management is rigorous with NIST principles. Risk assessment, mitigation, and monitoring demonstrate the necessity for an IT-specific risk management plan. Its various, intricate processes need competence to perform.
[ ]	OCTAVE	The OCTAVE risk management method addresses risks inside organizations by conducting an Operationally Critical Threat, Asset, and Vulnerability Evaluation. The methodology encompasses the process of identifying assets, profiling potential threats, and developing plans for protection. Qualitative approaches may not provide explicit organizational rules.
[ ]	IRAM	The Information Risk Assessment Methodology (IRAM) is a risk assessment technique proposed by the Information Security Forum. It connects security measures with the organization’s goals. Its generalizability may be limited due to its heavy reliance on expert opinion and organizational context.
[ ]	EBIOS	EBIOS offers a thorough method for detecting and evaluating security requirements. It is extensively used in French enterprises and the government, providing a methodical approach to controlling security threats. However, it often prioritizes broad assessments without a thorough and specific methodology.
[ , ]	CRAMM	The CRAMM technique provides a comprehensive strategy for effectively managing information security risks via the use of statistical and analytical tools. The document offers a well-organized strategy for establishing a strong security system by doing thorough analysis and using effective risk management techniques.
[ ]	SDEA	This methodology uses statistical experimental design to address risk management, offering a quantitative structure for assessing and reducing hazards. The risk management strategy integrates statistical and qualitative methodologies to provide a comprehensive analysis.
[ ]	McEM	This approach assesses information security measures by using several criteria to strike a balance between different elements and attain optimum security management. It provides a systematic method for making decisions when implementing security controls.
[ ]	CIAM	CIAM is a systematic approach that uses data to choose and rank security measures. The main objective is to assess the efficiency of controls and prioritize high-priority threats to improve overall security measures.

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Alsafwani, N.; Fazea, Y.; Alnajjar, F. Strategic Approaches in Network Communication and Information Security Risk Assessment. Information 2024 , 15 , 353. https://doi.org/10.3390/info15060353

Alsafwani N, Fazea Y, Alnajjar F. Strategic Approaches in Network Communication and Information Security Risk Assessment. Information . 2024; 15(6):353. https://doi.org/10.3390/info15060353

Alsafwani, Nadher, Yousef Fazea, and Fuad Alnajjar. 2024. "Strategic Approaches in Network Communication and Information Security Risk Assessment" Information 15, no. 6: 353. https://doi.org/10.3390/info15060353

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A literature review on effective risk communication for the prevention and control of communicable diseases in Europe.

Executive Summary This review examines the current body of literature on risk communication related to communicable diseases, focusing on: (i) definitions and theories of risk communication; (ii) methodologies, tools and guidelines for risk communication research, policy and implementation; and (iii) implications, insights and key lessons learned from the application of risk communication principles in real-world settings. Effective risk communication is essential to limiting morbidity and mortality caused by communicable diseases, in addition to minimising the damage that communicable diseases can cause to national economies and public health infrastructure. The aim of the review was thus to uncover the general principles of effective risk communication that can assist with the prevention and control of communicable diseases in the European context, as well as specific examples of good practice that can be built on in future risk communication policies, guidance, research and implementation scenarios. The review brings together the current body of literature on risk communication on communicable diseases in a concise reference document that can be used to inform the development of evidence- based risk communication strategies and approaches. To conduct the review, various databases were searched to locate published academic literature on the topic of risk communication for communicable diseases. In addition, both general and targeted internet searches were undertaken to locate relevant unpublished literature on the topic, such as conference presentations, reports and other technical documents. The literature search prioritised documents produced over the past ten years and relevant to the European context. Following the collection of relevant literature, key themes were identified and analysed and findings synthesised for the production of this report. The review revealed that the multi-cultural and multi-lingual environment of Europe presents a unique set of challenges to effective risk communication on communicable diseases to which the most promising solutions include: (i) collaboration of an international coordinating body with both national and local non-governmental organisations; and (ii) focusing on the development of cross-sectoral and cross-national risk preparedness, surveillance, response and monitoring strategies in the region. In addition, both quantitative and qualitative studies on risk communication have brought valuable insights to light on the topic, but there is an important need for evaluation research to better understand the effectiveness of risk communication as it unfolds during real-life events. In terms of risk communication practice, there are various resources available to risk communicators today (for example, toolkits, training modules, guidance frameworks), but when tested, challenges, gaps and limitations have been exposed. The review indicates that risk communication resources need to be continuously updated to meet new and developing needs (for example, strategies for effective web-based and social networking communication are notably absent yet highly relevant in today’s world); and the scope of the resources must be broadened beyond the current focus on risk communication in the context of emergency and outbreak situations. Other principal conclusions of the review are that risk communication messages often fail to reach the intended communities, including those people most at risk of the disease. Similarly, despite the availability of planning tools and pre-crisis event and readiness efforts, many countries in the European region still need to concentrate on advanced risk communication planning efforts at all levels of public health, such as needs assessments and public engagement plans.

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The good, the bad, and the ugly: how counterfeiting is addressed in operations and supply chain management literature

Published: 28 June 2024

Cite this article

Raul Beal Partyka ORCID: orcid.org/0000-0001-7941-2152 1 ,
Rafael Teixeira ORCID: orcid.org/0000-0002-7643-6084 2 ,
Roger Augusto Luna ORCID: orcid.org/0000-0003-2827-4719 1 &
Ely Laureano Paiva ORCID: orcid.org/0000-0003-1203-0584 1

This article aims to identify counterfeiting state-of-the-art and expand the Operations and Supply chain Management (OSCM) field from the identified gaps and bottlenecks to understand the real-life phenomenon and critically evaluate the existing body of knowledge. This is a systematic literature review from 63 relevant articles identified from Scopus and Web of Science. This is a reflection exercise to identify gaps and bottlenecks to subsidy research opportunities. Clearly, the strategies for combating counterfeiting could be more reactive or proactive, for example, reactive in the purchasing/co-opting offenders and proactive by blockchain adoption and marketing communication with tips to identify fake products. Therefore, the results also identified some central aspects related to the evolution of counterfeiting studies in the OSCM field and relevant gaps. We provide theoretical evidence that an interesting and broad field exists to expand from the identified gaps and bottlenecks. We also present up-to-date, state-of-the-art literature on all the aspects and facets of counterfeiting.

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Risk communication and community engagement during COVID-19

Shabana khan.

a Indian Research Academy, India

Jyoti Mishra

b University of Leeds, United Kingdom

c North South University, Bangladesh

Chioma Daisy Onyige

d University of Port Harcourt, Nigeria

Kuanhui Elaine Lin

e National Taiwan Normal University, Taiwan

Renard Siew

f UNITAR University, Malaysia

Boon Han Lim

g Universiti Tunku Abdul Rahman, Malaysia

In today's information age, both excess and lack of information can cause a disaster. COVID-19 pandemic not only highlighted the significance of risk communication but also pointed out several unintended and distressing consequences due to information gaps and miscommunications. Despite facing a common threat, the local communities suffered differential impacts during the pandemic. This paper classifies the nature of risk communications experienced across different countries into three categories, namely: inadequate, ideal, and infodemic risk communication that influenced the local perceptions and responses. It further argues that inadequately planned risk communications tend to create new risks and compromise the efforts towards managing a disaster. As global risks are responded locally, there is a need for more inclusive and engaging risk communication that involves communities as responsible stakeholders who understand, plan, and respond to risks to increase their propensity for resilience during disasters and crisis situations.

1. Introduction

COVID-19, the novel coronavirus disease caused by severe acute respiratory syndrome coronavirus 2 (SARS-CoV2), not only challenged how people think about a disaster but also exposed unaddressed intertwined risks in the globalized world [ 1 ]. As of October 20, 2021, the unprecedented disaster has infected nearly 242, 348, 657 people, with 4,927,723 casualties across 224 countries and territories [ 2 ]. The pandemic led to an outcry for accurate risk communication and an unparalleled flow of information and messages communicating risks worldwide, thereby generated diverse risk perceptions and responses to the disaster [ 3 ]. This paper evaluates risk communication and community engagement in both theory and practice aspects by drawing upon the examples and experiences of COVID-19 in 2020 and 2021.

The paper discusses various risk communications and responses in an international context considering the realistic constraints and opportunities. Risk communication here is used as an overarching concept that includes various communications pertaining to disaster risks including but not limited to risk assessment, warnings, forecasts, risk awareness, and crisis communication [ 4 ]. It provides a comprehensive desktop review of various sources and literature, including websites and online newspapers covering risks and responses to COVID-19. The location of authors in different parts of the world helped to verify and provide the local perspectives from countries in Asia, Africa, and Europe. As the paper is written under an ongoing disaster situation, the assessment represents the situation till October 2021. It provides a brief background of risk communication in public health emergencies and disasters in theory followed by specific characteristics of risk communication during COVID-19. Further, it explores common gaps pertaining to the involvement of communities in risk communication, and evaluates findings in the discussions section. It concludes with recommendations and future research.

2. Communities in public health emergencies and disaster risk communications

The term communication finds its root in the Latin word 'communis' that also means common or public, which evolved to become ‘communicare’, i.e. to share, and finally to ‘communication’ in the early 15th century [ 147 ]. It indicates that both community and sharing rest at the heart of communication. When it comes to risk communication, the meaning of the term has changed in both understanding and implementation.

Risk communication is an interactive process of exchanging information about and beyond risk among individuals, groups, and institutions [ 5 ]. It is not only limited to providing information about risks in the form of messages or opinions expressing concerns and reactions, but it also includes actionable information for how to prepare, protect, respond, and recover from the risk [ 6 ]. It is critical because it allows people to make informed decisions and influences public perception and response to varied risks [ 7 , 8 ]. Risk communication notably plays a central role in the risk management cycle, where it is necessary to identify a hazard, conduct risk analysis, and develop, implement, and evaluate policies [ 9 ]. Besides, if not managed well, it also carries the potential to induce severe consequences due to misunderstanding of associated uncertainty by different stakeholders [ 10 , 11 ].

Risk communication is also considered a two-way process wherein information is exchanged back and forth to close the gap between expert and lay assessment [ 8 , 12 ]. However, studies have recurrently pointed out the intermittent application and inefficiencies of top-down communication applied for disaster preparedness and responses [ 13 , 14 ]. In theory, various models of risk communication have been proposed, e.g., mental model [ 15 ], value model [ 16 ], evaluation framework [ 17 ], IDEA model [ 148 ], along with various other models at the individual-psychological and organizational level for mitigation, preparedness, response and recovery [ 18 ]. Despite the availability of various models, their applications are found limited during an emergency or disaster response [ 19 ]. Most discussions around risk communications in disasters tend to focus on using communication tools to manage information flow, build awareness, generate a warning and govern response by following a top-down approach [ [20] , [21] , [22] , [23] ]. It has also shaped the use of information and communication technologies (ICTs) to design and enhance the effectiveness of risk communication [ [24] , [25] , [26] , [27] ].

Recent studies, however, point towards the role of human subjectivities, such as trust, past experiences, heuristics, socio-cultural context, scale or complexities for their influences on risk communications [ [28] , [29] , [30] , [31] ]. Meredith et al. [ 6 ] noted a critical gap in communicating risks with vulnerable populations such as children, senior citizens, and pregnant women in emergencies. Eisenman et al. [ 32 ] emphasized that the involvement of the vulnerable communities in devising and pretesting risk communication can help to create effective crisis messages that are consistent, timely, actionable, and empathic to communities in the complex situation, and are more likely to get a positive response from the public. However, expanding risk communication to cover the wide spectrum of the population would require an understanding that there is no “public” in the crisis, but all are stakeholders [ 33 ].

Public involvement, on the other hand, is frequently limited in disasters due to the traditional push strategy applied in the process that is found to be insufficient in generating an ideal response and, in turn, cause reduced trust in the organizations communicating risks [ 34 , 35 ]. The problem has been further aggravated with the emergence of ICTs that play a pivotal role in disseminating and spreading information about potential risks [ 36 ]. Gamhewage [ 37 ] notes three major shifts in the risk communication of the 21st century:

1. Decreasing trust in experts and authorities surrounded by the issues of real and perceived trust;
2. Change in the mode of seeking public advice from direct contact or public warning to online sources and social networks;
3. 24-hour journalism favours citizens' participation that beats limited experts and resources that impact opinions compared to evidence-based risk communication.

The increasing role of ICTs might achieve efficiency in information flow. However, new challenges accompanying innovation have arisen to even complicate the communication processes in emergencies. These emerging risks include amplifying the voice of elites and their social power, content filtering, and manipulating information [ 38 ]. The role of the community and the form of their engagement in risk communication have turned out to be significant in the era of “Fake News” and the description of a “post-truth” society [ 39 ]. The widespread existence of misinformation and myths around science has led to a need for efforts towards retracting, correcting, and debiasing by the public campaigns as well as social media [ 40 ; 43 ]. However, the cause of misinformation is not just limited to the people expressing their thoughts on social media but also includes the communication of uncertainty by experts or scientific organizations [ [41] , [42] , [43] ]. While an increasing number of stakeholders modify the risk perception and communication, the exclusion of the public in the formal risk communication tends to manifest as a gap reflected in the misinformation or infodemic [ 44 ]. Lipshitz et al. [ 45 ] argued that there is a need to understand an individual's dynamic reality of the world as influenced by communication. This need is rather an essential gap when the formal participation of the public in risk communication is limited along with the understanding of processes influencing the risks at the local level.

The following section collates the observations, challenges, and interpretations of COVID-19 risk communications and their impacts on the ground.

3. Risk communications and impact on communities during COVID-19

Cheng et al. [ 149 ] not only indicated the risk but also emphasized the need to prepare for the re-emergence of SARS and novel coronaviruses. Numerous government authorities also prepared documents for such events, for example, National Strategy for Pandemic Influenza in the USA or National Disaster Management Guidelines for Biological Disasters in India [ 46 , 47 ]. However, the local responses to COVID-19 exposed inadequacies of preparedness measures to address the pandemic.

The disease appeared in China in December 2019, but no death was officially acknowledged by the World Health Organisation (WHO). The first death was reported on the January 9, 2020 [ 48 ]. The country sent an official confirmation to WHO regarding novel coronavirus and human-to-human transmission on the 20th of January 2020 [ 49 ]. WHO declared the outbreak a ‘Public Health Emergency’ of international concern on the January 30, 2020, and on the February 11, 2020, it is named as COVID-19 [ 50 ]. Three weeks after informing the WHO about the health emergency in Wuhan City, the government of China put 11 million people in the city on lockdown, followed by the lockdown of other cities in the province [ 51 ]. Even though it was a clear warning sign combined with the declaration of a pandemic by WHO, the environment of confusion around timely responses prevailed across the world. While the number of infections and deaths continuously increased, the process of countries adopting measures such as localized or national recommendations or lockdown was found to be ad-hoc and irregular [ 52 ].

The data shows that countries that took early measures benefited more in controlling disease transmission. For example, following the initial speculation of unknown pneumonia cases in Wuhan, on the December 31, 2019, Taiwan started implementing onboard checking of all passengers on direct flights from Wuhan for fever and pneumonia symptoms [ 150 ]. By January 20, 2020, Taiwan Central Epidemic Command Center (CECC) had implemented at least 124 action items, including border control, case identification, quarantine of suspicious cases, proactive case finding, education of the public while fighting misinformation, formulation of policies toward schools and child care services [ 150 ]. On the other hand, in many other countries like Indonesia, under-reporting of the number of total infected cases was observed during this time [ 53 ]. Internationally, much of the risk communication remained focused on controlling the disease transmission, with guidelines for testing general people for symptoms. In the early stage, the WHO guidelines were found to be generic and non-specific, focusing on the management of symptoms and advice to be cautious with patients with co-morbidities or pregnancy [ 54 ]. In the absence of any vaccination, both doctors and the public remained confused about how to save lives. In such a situation, rumors and misinformation spread widely through different communication channels.

On March 7, 2021, after a global milestone of 100,000 confirmed cases globally, WHO released the statement that called every country, leader, and community “to stop, contain, control, delay and reduce the impact of this virus at every opportunity” [ 151 ]. On March 11, 2021, WHO released a call for urgent actions by every country that led to a significant surge in the number of countries choosing to adopt localized or nationalized lockdowns [ 8 ], 52 ]. The WHO also released the guidelines for strategic preparedness and responses to manage COVID-19. It asked countries to do a risk assessment and prepare for a surge in testing and clinical care, to reduce transmission and impacts [ 152 ]. It was followed by the launch of the appeal for a Global Humanitarian Response Plan. WHO asked countries to support it financially and politically to contain the spread of the virus [ 153 ].

While ICTs played a critical role in communicating the risk of COVID-19, it also became a source of rampant and recurrent risk of misinformation and miscommunication. The flooding of information and messages occurred through various online platforms and news channels along with social media and personal messaging on smartphones. Several Facebook and WhatsApp messages and videos sharing information and conspiracy theories reached thousands of people unchecked, and a rapid circulation of misleading information became a cause of concern [ 55 ]; [ 159 ]. On March 31, 2020, WHO issued a ‘Medical Product Alert’ warning consumers, health care professionals, and authorities for faulty products claiming a cure for COVID-19 (WHO, January 29, 2021). The extraordinary quantity and impact of the unofficial communications were later described as an ‘infodemic’ by the Director-General of WHO, when he expressed his concerns by saying, “We're not just fighting an epidemic; we're fighting an infodemic” [ 56 ]. It also led to the emergence of the first conference of infodemiology by the WHO on June 29, 2020 [ 8 ].

The infodemic was, however, not just an information management issue but can also be seen as the failures of risk communication in addressing various factors of vulnerability at the local and global levels. In the study conducted by Wong et al. [ 57 ], it was identified that adults over 60 showed anxiety symptoms due to contradictory information found on social media. Table 1 highlights some of the unintended consequences related to unplanned or inadequate risk communications either directly or indirectly. While it was challenging to cover all impacts worldwide, some of the typical impacts noted across different countries are highlighted in Table 1 . As Gottlieb and Dyer (2020) highlighted, there is a need to assign third parties to verify social media outlets.

Risk communication in different phases of COVID-19 and impact on local communities.

Phase	Risk Communication	Local community impacts (countries)**
	Top-down Disease transmission control Lockdown, social-distancing, quarantine centers, hospitalization High High	Drinking industrial alcohol to cure coronavirus led to 480 deaths and 1000 sick; chloroquine poisoning. A sudden lockdown led to unemployment that generated reverse migration of migrant laborers. Both educated and uneducated populations chose to run away, causing the further spread of the virus. : Hopelessness, despair, and increase in psychological impacts resulted in over 300 deaths in India. Doctors, nurses, and health care workers; flight crew engaged in the rescue of people; sanitary workers; people going out to feed stray dogs. Cases of racism noted against Chinese, Asian, and African communities. : Medical supplies, food, and other necessities, even toilet paper and flour. Elderly, poor income, less educated, marginal workers. Increased family tensions, fights, divorces, and separation. There were reported cases of rapes of minors during the lockdowns. Tablighi Jamaat in Malaysia spread the disease across the region. People had to pay fines, face lathicharge, legal actions for violating the rules of social distancing
	Top-down with engagement with associations. Quarantine and treatment Medication, hospitalization, oxygen availability, work from home. Medium High	Deaths after vaccination, reduced level of trust in vaccination and government, hesitancy due to cultural and religious reasons. Lack of hospital beds, critical drugs and oxygen in rural areas, and a severe dip in income of informal migrants, the elderly, single mothers, pregnant women, people with disabilities, children, Dalit, and tribal population. : Medical supplies and oxygen cylinders A gap in real and reported cases No space and woods in crematoriums lead the dead bodies floating in the River Ganges. : Mass gatherings in election rallies and Kumbh Mela (9 million people) without adequate protocols increased cases.
	Top-down with associations Vaccination Vaccination Low High	Vaccine hesitancy, particularly in areas of trust deficits or to overcome rumors Rural-urban divide in vaccination Promotion of vaccination along with easy services. Parents scared to send children to schools The third wave will impact children Cases of poisoning from self-medication Travelling without masks, putting white flag

Source: [ [58] , [59] , [60] , [61] , [62] , [63] , [64] , [65] , [66] , [67] , [68] , [69] , [70] , [71] , [72] , [73] , [74] , [75] , [76] , [77] , [78] , [79] , [80] , [81] , [82] , [83] , [84] , [85] , [86] , [87] , [88] , [89] , [90] , [91] , [92] , [93] , [94] , [95] , [96] , [97] , [98] , [99] , [100] , [101] , [102] , [103] , [155] ], [ [104] , [105] , [106] , [107] ].

Interestingly, many responses, such as hoarding, harassment, or discrimination, are noted in both developed and developing nations, not just in the beginning but even after a year in the second wave. Besides, occasional incidents were also reported, such as the claim suggesting 5G networks accelerate the spread of COVID-19, which led to the attacks on several 5G phone masts in the UK. In India, pets were killed, and pet lovers were attacked due to WhatsApp messages spreading misinformation that ‘animals spread coronavirus’ [ 72 ]. Many African countries, such as Nigeria, a multi-ethnic, multi-cultural and multi-religious country, reported vaccine hesitancy, especially within religious belief systems that see causation as coincidences rather than finding answers to phenomena that seem coincidental [ 108 ]. The impacts were certainly beyond these reported events and would require further studies to get the complete scenario. The rapid flow of misinformation led WHO and various countries to take several actions to control rumors and myths. In countries affected by infodemics, such as India and Bangladesh, the health authorities also issued notices for limiting the COVID communications on social platforms to be followed by legal actions [ 95 , 109 ]. Despite several efforts to bust the myths, the gap in actual and perceived risk continued, affecting response at different levels.

4. The gap: unclear community engagement in risk communication

The WHO, in its six-point action plan, addresses the public in its very first point and notes that “the public must be effectively prepared for the critical measures that are needed to help suppress the spread and protect vulnerable groups, like the elderly and those with underlying health conditions” [ 153 ]. The reality portrayed a different scenario. In many countries, top-down communication and information flow can be linked with the gaps in response that occurred at the local level. Various extreme measures suggested and implemented across different countries caught the people unprepared to either understand or adhere to the extreme conditions imposed. The following paragraphs discuss some of these measures:

Lockdown: The administrative risk communication suggesting lockdown was purely focused on the need to control COVID-19 transmission without adequate explanations for its possible impacts or responses to other related uncertainties. On the other hand, the local population feared the uncertainties relating to food, employment, and their overall future beyond the crisis [ 110 ]. In India, thousands of laborers were stranded without work, money, or any other option except to return to their hometown due to the total lockdown in India declared on March 24, 2020. The concerns of people ranged from affording expenses of childrens' education fees to feeding their families in both short and long-term. As quoted by Prakash, an auto-rickshaw driver in Kerala (the first state in India to report COVID-19), the “virus doesn't worry me much as the uncertainty that awaits on the other side of the crisis” [ 111 ]. The decision of lockdown caused a mass exodus of laborers, industrial workers, and unorganized sector employees from the megacities like Delhi and Mumbai to rural areas across the country, which indicated both unpreparedness and inadequacy of risk communication of measures taken or promised to the affected population [ 112 ]. Along with a fear of hunger and loss of livelihood, many people also carried the risk of spreading COVID-19 to distant rural areas. A similar trend of domestic migration is also noted in Bangladesh, Malaysia, and New Zealand. However, in contrast to New Zealand, in developing countries like India or Bangladesh, the people are given very limited time to travel or make arrangements to follow the guidelines of lockdown or social distancing [ [84] , [112] , [113] ]). Many countries, including India and Taiwan, adopted partial lockdown during the second wave attributed to widespread social and economic impacts. Even in remote indigenous communities, small business and tourism were ceased in Taiwan. By realizing how insufficient health and medicine resources are in the marginal area, local communities adopted strict attitudes to prevent people from entering the community, including their younger generations studying and working in the cities. Public participation in implementing governmental instructions in Taiwan is noted to be very high. A similar situation is reported in African indigenous communities, which indicates varying levels of trust and cooperation between the local communities and government in different countries that are not often addressed in global risk communications.

Quarantine: Quarantine measures are vital in controlling the spread of COVID-19, as they reduce social interaction, maintain physical distancing to prevent spread, and also help to facilitate the contact tracing processes needed to limit outbreak cluster growth [ 114 ]. However, compliance with quarantine orders requires high levels of trust and confidence in officials, as well as adequate risk communication to develop an understanding of the risk posed by breaking quarantine [ 115 ]. Unfortunately, inadequately planned communication also led to the loss of trust in the planned response by the government. As the governments placed quarantine measures for public safety, even the educated crowd and professionals chose to run away, as seen in India and Nigeria. On March 11, 2020, three out of the 17 pilots and flight attendants who flew 14 Chinese medical doctors and medical supplies from China to Nigeria, boycotted the Lagos quarantine center provided by the Lagos State Government. The pilots left the quarantine center and went to their homes, despite knowing that it could put their family members and people with whom they may come in contact in grave danger [ 65 ]. Similarly, many migrating laborers in India in the state of Uttar Pradesh and Bihar broke the quarantine to go to a home in the absence of any police or security personnel to stop them [ 66 ]. While inadequate arrangements can be argued as a cause, such incidences also indicate a gap in risk communication about the safety of the affected individuals and their families while they stay in quarantine.

Social-distancing: The term social distancing refers to the practices of maintaining a greater physical distance from people, usually 6 feet or more, to avoid the spread of the disease . However, it comes with other challenges such as psychological fallouts or mental health problems or a decline in care for those who need it the most, e.g. elderly [ 116 ]. Besides, the solution is not applicable in many high-risk areas, e.g. the marginal communities residing in densely populated regions such as slum areas of Bangladesh [ 156 ]. These communities living in close proximity cannot maintain social distance despite official orders, which gives little detail of what to do or practice in such situations. The inadequate communication and understanding of people about social distancing were also reflected in the immediate rush and breach of social distancing to buy liquor in India as the government eased some of the lockdown conditions [ 117 ]. It clearly indicates that the communication didn't address the risk involved or how to manage it when services resume. Besides, the usage of the term also attracted reactions for its literal meaning, which is also acknowledged by the Risk Communication & Community Engagement Technical Officer WHO Regional Office for South-East Asia, who suggested that social distancing should be understood as physical distance and social connection [ 118 ] . However, real-time communication and practices didn't reflect this understanding on the ground level. Lack of community participation is witnessed in the fact that even the community leaders became sources of misinformation or breach of social distancing in many cases. In South Korea, a religious leader said God would protect people who attended the gathering and not be infected by COVID-19 [ 119 ]. However, this cluster of people was infected by the virus and became a key source of COVID-19 spreading in South Korea. Similar cases occurred in Malaysia, where two major clusters of infection originated from religious gatherings that ignored the Ministry of Health's advice. The first gathering was a three-day Islamic Tablighi gathering at the Sri Petaling mosque, Kuala Lumpur, held from February 27 to March 1, attended by nearly 16,000 people [ 120 ]. By April 11th, 2020, 40.2% of the cases in Malaysia were related to this Tablighi gathering . The gathering had even become the source of virus spread [ 121 ] internationally in Brunei, India, and Indonesia. In India, legal cases were registered against Tabligh participants for spreading the disease [ 83 ]. In Taiwan, on the other hand, ICTs is used to alert people for social distancing rules.

Vaccination: Throughout the COVID-19 exposure, vaccination remained an important point of discussion. The communication of pandemic initially projected herd immunity, which was soon replaced by the urgent need to vaccinate the entire world [ 122 , 123 ]. The formal process of vaccination could only start by the end of 2020. Although it was not made mandatory, it created misunderstandings and apprehensions among people and government agencies for those without vaccination. The reason for vaccine hesitancy, on the one hand, was apparent due to untimely deaths without COVID-19 or other diseases after vaccination [ 124 ]. On the other hand, governments kept on vaccinating people with and without being fully approved by the WHO [ 125 ]. It also led to a drive for building trust in COVID-19 vaccination. Many governments used multiple channels, social media, and ICTs to alter public perception and implement vaccination to the entire population. A survey revealed that more than 80% of the Taiwanese people approved the government's efficacy for handling the crisis (Wang et al., 2020). And for vaccination, although there were rumors about severe side effects and high mortality rate after vaccination, the CECC, together with third parties of experts in medical science, have voiced to correct the rumors and justified the effectiveness of the vaccines and the importance of getting herd immunity for the society. As of October 2021, the coverage of first-dose in Taiwan has reached 70%. India also completed and celebrated the mark of 1 billion vaccination by the end of October 2021[ 69 ]. This left the gap in addressing the concerns of people who lost lives either due to COVID19 extreme measures or vaccination.

The aforementioned examples highlight that while the public was the main target of the risk communication, they were excluded from the formal risk communication process. The communications also lacked clarity of their role as a key stakeholder in risk communication beyond the expectations that they would follow the orders, which can be seen as a direct cause behind the info-demic and subsequent unintended impacts.

5. Discussion

Public participation or involvement in the understanding, communication, and management of global risks is not just a recommendation for good governance but also crucial for its success [ 126 ]. Renn [ 127 ] identified four different types of risk communication, i.e. documentation, information, dialogue and involvement. Although all four types of communications were observed for COVID-19, the first two dominated the process across most countries. The dialogue and involvement of the public were not just insufficient at times but also discouraged in several instances. The scenario, however, varied across different countries depending on varied risk perceptions rooted in the complexity of the situation, past experiences, or socio-cultural context. The review of COVID-19 information flow highlighted some trends in the risk communication leading to differential impacts on the ground. The nature of risk communication as experienced in different countries can be classified into three broad categories (see Fig. 1 ):

Diagrammatic illustration of levels of risk communication and outcome.

Info-demic: Info-demic represents a situation of excessive risk communications, wherein multiple stakeholders share their perceptions, fears, knowledge, or thoughts about risk or response without much consideration to its overall impact. A mix of risk information from official and multiple unofficial sources creates confusion, fear, stress, and loss of trust. The impact of such communications is rather severe, such as suicides, harassment, xenophobia, or hoardings of essential goods, as noted in countries like India, Bangladesh, Nigeria or the USA (see Table 1 ). It is important to note that many of these incidents occurred in urban areas which became the epicentre of pandemic with excessive concentration of people, pre-existing inequalities and hightened socio-economic impacts of lockdown [ 128 ]. While the ‘public’ is seen as the dominant source of misinformation or info-demic, in many of these cases, the exclusion of the community as a responsible stakeholder in timely risk communication can also be an important cause. The excessive fear in the situation of very high uncertainty can be seen as a reason behind such outbursts of miscommunication. Further, the governments' interpretation and usage of the terms and war approach also added to this fear (see [ [129] , [130] ]). In this situation, the administration not only had to deal with the real cases of COVID-19 but also with several other issues that emerged from the miscommunication, including violence, large-scale unemployment or loss of trust in the government.

Ideal risk communication: Ideal risk communication represents a situation where varied risk communications from different stakeholders are aligned to resolve the issues associated with a hazard. Various governments tried to achieve this with or without sufficient public participation. While control measures can help manage risk for a short time span, it becomes problematic when hazard exposure is prolonged. Some of the countries, however, not only acted proactively but also encouraged public participation as a shared responsibility for risk communication and management. The Ubuntu philosophy of Africa is rather noted as a framework for dealing with COVID-19 in social psychology that is based on community consensus and participation [ 131 ]. It is noted that past experiences of dealing with Ebola and community participation not only helped west Africa and Democratic Republic of Congo in managing COVID-19 response but also found essential for avoiding misinformation during crisis [ 132 ]. Several efforts to enhance community participation are also observed in other countries. Mongolia's preliminary stakeholder engagement plan emphasized inclusive and culturally sensitive risk communication for various affected, interested and vulnerable groups with a clear operational procedure for grievances redress mechanism [ 2 ]. Sweden, on the other hand, is seen as an outlier in Europe when it adopted a different approach to deal with COVID-19 by allowing essential services to be open. However, the plan is not just backed by the local people, but the impact of the shared responsibility was witnessed in voluntary social distancing, reduced mobility and precautionary public behavior [ 157 ]. While the number of cases affected by COVID-19 is found to be high in the country, the response seems to have reduced side-effects of COVID-19 in terms of its impact on the economy or mental health [ [133] , [134] ]. New Zealand is another good example where local people are not only given time to prepare for a lockdown but physical movements were also allowed locally for better health and well-being [ 113 ], [ 135 ]. While which country's approach can be considered is ideal, it can be argued, however, that enhanced degree of community engagement as responsible stakeholder is an essential element for ideal risk communication.

Inadequate risk communications: This reflects a situation of inadequate communication giving little or no clarity about the hazard, impact, or measures to manage the risk. The situation leads to a high dependence on rumors that cause anxiety, fear, confusion or loss of trust among people. While the reason for inadequate risk communication could vary, such as high uncertainty or insufficient information, the gap in risk communication results in high exposure and loss of lives, as seen in Iran and Italy in the first wave [ 136 ]. While local vulnerabilities and situations can be seen as the cause behind the high mortality rate, the existence of gap in risk communication that can ensure trust in people about their safety cannot be denied.

As the public is frequently the ultimate target of risk communication, their understanding, concerns, role, and participation become the critical aspects of risk communication. Contrary to this, the information shared to them depends on the availability of information about the hazard, associated uncertainties and the previous knowledge of best practices to manage the situation that follows a certain direction of formal information flow, as noted in COVID-19 ( Table 2 ). The table highlights the change in the nature of information in terms of its quantity, emotions, uncertainty and understanding as it moves from the scientists assessing risks to various bodies understanding and communicating risks for its management. By the time information reaches the public, it is diversified and tends to be more confusing, high in emotion, and generate varied responses from different communities.

Tentative flow of risk communication across various stakeholders during COVID-19 to date.

A sudden emergence of information having the potential to disrupt normal life not only creates fear but also generates varied reactions from the public. In such a case, understanding the risk and even vulnerabilities only fulfils the partial purpose of risk communication. For example, the warning of health authorities regarding the vulnerability of the elderly population led to their further isolation and ageism [ 81 , 137 ]. Similarly, the increasing cases of racism during the pandemic led to the risk communication articles focusing on how to communicate without fueling anti-Chinese sentiments [ 138 ]. Although modification of the risk communication can help in managing a specific situation, it is difficult to address every problem for every section of the society. It is noted that despite communicating the risks and various efforts to bust the myths at different levels, the gaps tend to continue in terms of addressing all issues or reaching out to every community, particularly those which did not have access to the internet [ 152 ]; [ 97 ]. Studies argue that it is a mistake to consider ‘public’ as one stakeholder as it represents strata of varied socio-economic, cultural and political communities [ 27 ].

At the same time, there are examples of countries, which effectively engaged communities in risk communication with top-down apporach. For example, in Taiwan everyday press conference by the CECC authority, the deployment of ICTs during covid-19 has been further invented for information exposure and dissimilation of infection, resource allocation (such as where to get masks and tests nearby your neighborhood), social distancing and vaccination nationalwide. Here, apart from a top-down approach, a bottom-up mechanism was established through several smart phone apps to facilitate citizen participatory communication. Singapore is also noted globally for its high preparedness and successful risk communication, promoting a strong community engagement and less emphasis on extreme measures such as lockdown despite following a top-down approach [ 139 ].

The increasingly homogenizing response across the countries has an underlying assumption that the information shared about risk is likely to be received, understood, and responded in a similar manner with some modifications. Subsequently, the efforts are focused on either improvising the risk communication on the basis of overall feedback from the ground or busting individual myths (e.g. [ 27 ]. However, the compromised role of the public as a key stakeholder in risk communication not only creates a wider gap in the way the information is received but also how it is responded to, as seen in the cases of info-demic or inadequate risk communications. Besides, though the rapid transmission tends to bring valuable data such as concentrated impacts and measures, e.g. demography informed COVID-19 policy [ 140 ], it takes time for the governments to mobilize and make policy decisions at the national level. Adapting the messages and responses can be managed with ease and rigor at the local level by using a participatory approach.

At any given time, the local population tends to deal with various circumstances situated in a dynamic reality, wherein they are not only pressed by their day-to-day concerns but also exposed to multiple hazards [ 141 ]. Frequently, a specific risk communication doesn't address most of the other concerns people may be dealing with. For example, earthquakes in Croatia or Delhi that remind us of the vulnerability to other natural hazards that continue to exist while all the response mechanisms were focused on the pandemic [ 142 ]. In Delhi, the COVID-19 led to a complete closure of all public spaces, including park gates during lockdown, which left people confused without any option to move out of their houses or apartments, putting them at a higher risk of earthquakes. Such events, however, give little time or scope for improvisation of the risk communication, and the role of communities in managing local risks becomes all the more important. Studies note that a participative approach for risk communication can effectively trigger adaptive behaviors [ 143 ]. COVID-19 also created a situation of increased mental and emotional stress, which further suggests the significance of community for not just informing risks and responsible behavior for safety but also providing physical, psychological and emotional support during a disaster situation. The role of community is also essential in establishing trust in information and support provided by the government or international organizations like WHO attributed to varied impacts and socio-cultural responses [ 158 ].

Effective public participation is not only essential to deal with the info-demic but also for the effective use of the local indigenous knowledge and wisdom to deal with disasters. The value of public participation and indigenous knowledge has been recurrently emphasized in the disaster literature in the form of community-based disaster risk reduction [ 144 , 145 ]. However, it has yet to be explored in terms of risk communication. Although the use of ICTs has tremendously increased in informing the public about best practices and busting myths and rumors, its use for effective community engagement is still in its nascent stage and requires further research. It is essential that risk communication is interactive and inclusive as risk emerges in a complex socio-economic and political context and accordingly perceived and responded to [ 146 ]. To achieve this effect, it would require comprehensive planning and evaluation of risk communications that addresses the local context, constraints, and the local knowledge to facilitate effective and responsible community participation.

6. Conclusion

In this rapidly changing world, it is crucial that disaster risk communications address the increased public exposure and participation in the globalization of knowledge, economy, and information flow. While global guidelines are useful, they lack structures, specific guidelines, and to an extent, scope to encompass all possible diversities. Although countries choose their response and methods of risk communication, a gap is noted in the top-down risk communication that may overlook various local risks. Communities, on the other hand, not only face multiple risks but also receive information and risk messages from multiple sources that could induce confusion or dilemmas in their decision-making. Ensuring effective response of communities thus requires a shift from a top-down approach focusing on what should or should not be done to an exploratory and interactive risk communication that recognizes public emotions, builds trust, understands heuristics, and the socio-cultural context of power relations and cultural practices that affect the local response. For this, it is essential that risk communication for emergencies or disasters is inclusive and engages with communities at the local level, where new risks are formed and responded to.

Although several models have been developed for engaging communities and using a participatory approach for risk communication, there is limited research on their applications in different socio-cultural contexts. There is a need to call for further research on various structures and guidelines to engage communities as responsible stakeholders in the process of risk communication where they are not only informed about the risks but also empowered to make informed decisions that incorporate and respect local socio-economic and cultural diversity, varied risks, and the local governance system.

Declaration of competing interest

The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper.

Acknowledgement

The authors are grateful to Dr Emma Hudson Doyle, World Social Science Fellow and Senior Lecturer at the Massey University, for her timely and detailed comments that helped to improve the paper. The authors are also grateful to Global Young Academy and International Science Council for providing the platforms for researchers to connect and encourage them to work together, which made this paper possible. The authors also thank the reviewers for their valuable comments and suggestions.

Open access
Published: 22 June 2024

Hepatitis B virus infection as a risk factor for chronic kidney disease: a systematic review and meta-analysis

Danjing Chen 1 na1 ,
Rong Yu 1 na1 ,
Shuo Yin 1 na1 ,
Wenxin Qiu 1 ,
Jiangwang Fang 1 &
Xian-e Peng 1 , 2

BMC Infectious Diseases volume 24 , Article number: 620 ( 2024 ) Cite this article

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Currently, several studies have observed that chronic hepatitis B virus infection is associated with the pathogenesis of kidney disease. However, the extent of the correlation between hepatitis B virus infection and the chronic kidney disease risk remains controversial.

In the present study, we searched all eligible literature in seven databases in English and Chinese. The random effects model was used to conduct a meta-analysis. Quality of included studies was assessed using the Newcastle-Ottawa Quality Scale.

In this analysis, a total of 31 studies reporting the association between hepatitis B virus infection and chronic kidney disease risk were included. The results showed a significant positive association between hepatitis B virus infection and the risk of chronic kidney disease (pooled OR , 1.20; 95% CI , 1.12–1.29), which means that hepatitis B virus increases the risk of developing chronic kidney disease.

This study found that hepatitis B virus infection was associated with a significantly increased risk of chronic kidney disease. However, the current study still cannot directly determine this causal relationship. Thus, more comprehensive prospective longitudinal studies are needed in the future to provide further exploration and explanation of the association between hepatitis B virus and the risk of developing chronic kidney disease.

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Introduction

Chronic kidney disease (CKD) is the primary non-infectious disease associated with high morbidity and mortality and is commonly defined as persistent urinary abnormalities, structural abnormalities, or impaired renal excretory function [ 1 , 2 ]. When diagnosed with CKD, kidney function gradually declines and progresses to end-stage renal disease (ESRD) with irreversible damage [ 3 ]. It is estimated that patients with CKD account for more than 10% of the world’s population, and the prevalence increases with age [ 4 , 5 ]. In addition, the researchers found that both morbidity and mortality from CKD have risen dramatically over the past 30 years, and that this upward trend will continue through 2029 [ 6 ]. Therefore, CKD is considered a growing global public health problem.

Currently, about 296 million people worldwide are infected with hepatitis B virus (HBV), which is the main cause of cirrhosis and liver cancer [ 7 ]. Besides the effects on the liver, several studies have found that chronic HBV infection is associated with the pathogenesis of kidney diseases such as polyarteritis nodosa (PAN) catheterization and glomerulonephritis (GN) [ 8 ]. Recently, an increasing number of studies have been conducted on the relationship between HBV infection and CKD. However, the extent of the association between the two remains controversial. A large U.S. cohort study found that HBV infection was associated with an increased risk of developing CKD and ESRD [ 9 ]. However, a cross sectional study based on a Chinese population did not find any direct relationship between HBV infection and the risk of developing CKD [ 10 ]. Recently, a meta-analysis showed that HBV infection is related to an increased risk of CKD in the general adult population [ 11 ]. The recently publication on the relationship between HBV and CKD provides an opportunity to assess again the association between HBV and CKD, which may provide additional scientific evidence [ 12 , 13 , 14 ]. Therefore, in this study, we assessed the association between HBV and the risk of CKD prevalence in the general adult population through a meta-analysis of observational studies.

Materials and methods

Literature search strategy.

All relevant studies up to March 20, 2023 were searched all eligible literature in seven databases in English and Chinese, including Chinese National Knowledge Infrastructure (CNKI), China Science, Wanfang and Technology Journal (VIP), PubMed, Web of Science, Embase databases and Cochrane Library. The search terms included “hepatitis B virus infection”, “chronic hepatitis B”, “HBV”, “chronic kidney disease “, “CKD”, and “chronic renal insufficiency”. The search formulas have been adjusted to the requirements of each database separately. Besides the above search methods, manual searches were performed for references to reviews and original articles. Supplementary Material 1 shows in detail the specific search formulas used for each database.

Study selection

There were no language limitations for studies included in the analysis, but review articles, abstracts, reviews, letters, and articles without complete text or valid data were excluded. When more than one study reported similar data, the most recent study was included in this analysis. In addition, for inclusion, the following requirements were met: (a) the type of study design was a cohort study, case-control study, or cross-sectional study; (b) HBV infection is defined as detection of HBsAg in serum and/or HBV DNA by PCR [ 15 ]; (c) the study outcome was the incidence or prevalence of CKD (glomerular filtration rate (GFR) < 60 mL/min/1.73 m 2 or albuminuria ≥ 30 mg/24 hours) or ESRD or composite renal outcome due to CKD [ 1 ]; (d) an adjusted risk estimates or sufficient data to calculate the above metrics.

Data extraction

Information was independently extracted from the retrieved literature by two authors according to the inclusion exclusion criteria. When disagreements arose, they were analyzed and resolved by a third researcher. Information extracted from the literature included mainly (a) the sample size of the study, (b) details of the study design, (c) patient characteristics, (d) outcome indicators as defined above.

Quality assessment

The quality of the included 20 case-control studies and cohort studies was assessed using the Newcastle-Ottawa Quality Scale (NOS) [ 16 ]. The NOS scoring criteria included three main components: selection of study subjects, comparability between groups, and outcome/exposure assessment. Points were assigned when the information contained in the articles matched the scale description. Of these, those scoring below 4 were classified as low-quality studies, those scoring 5–6 as moderate-quality studies, and those scoring above 7 as high-quality studies. In addition, the quality of the 11 included cross-sectional studies was assessed according to the adapted NOS [ 17 ]. Studies with scores of 6–10, 4–5, or 0–3 were rated as high quality, moderate quality, and low quality, respectively. Only articles rated as moderate and high quality were included in the meta-analysis.

Statistical analysis

A meta-analysis of the included literature was performed using Stata 17.0 software. Odds risks ( OR ) or hazard ratios ( HR ) and their 95% confidence intervals ( CI ) were used to estimate effect sizes. Meanwhile, the I 2 statistic and Q test were used to assess possible heterogeneity between different study results. Included studies were considered to have large heterogeneity when I 2 ⩾ 50% or P < 0.05. When study heterogeneity existed, a random effects model was used to calculate pooled effect sizes. Conversely, a fixed-effects model was used. Besides, when there was significant heterogeneity across studies, meta-regression and subgroup analysis were used to explore the sources of heterogeneity. Also, sensitivity analysis was performed using the one-by-one exclusion method. Begg’s test, Egger’s test, and funnel plot were used to assess the potential publication bias of the included literature. All P -values were obtained in a two-sided test.

Study selection and study characteristics

A total of 12,801 studies were collected by a search of seven Chinese and English databases and a manual search of references. The retrieved articles were managed using EndNote software. The literature was selected based on inclusion and exclusion criteria, and a total of 31 studies were eligible [ 10 , 12 , 13 , 14 , 18 , 19 , 20 , 21 , 22 , 23 , 24 , 25 , 26 , 27 , 28 , 29 , 30 , 31 , 32 , 33 , 34 , 35 , 36 , 37 , 38 , 39 , 40 , 41 , 42 , 43 , 44 ], which included three manually searched articles. Among them, studies by Hwang JC et al. [ 25 ] and Tartof SY et al. [ 37 ] were included for the first time in 2019 [ 45 ], whereas Chen YC et al. [ 19 ] were included for the first time in 2020 [ 11 ] in the systematic review and meta-analysis. Finally, 11 of the included articles were cross-sectional studies, 16 were cohort studies, and the remaining 4 were case-control studies. Figure 1 shows the specific process of literature screening. The general characteristics of the final included studies are shown in Table 1 .

Flowchart of the selection of studies for inclusion in the meta-analysis

According to the NOS quality assessment of the included literature, a total of 13 case-control or cohort studies and 11 cross-sectional studies were considered to be of high quality, and the other 7 included cohort studies were of moderate quality. The proportion of high-quality studies is 77.4% (24/31). Details used to rate the quality of the studies are shown in Supplementary Tables 1 and 2 .

Meta-analysis

A random-effects model was used to perform a meta-analysis of the 31 included studies reporting the association between HBV and CKD risk. As the result is shown in Fig. 2 , there was a significant positive association between HBV infection and the risk of CKD (pooled OR , 1.20; 95% CI , 1.12–1.29), which means that HBV infection increases the risk of developing CKD. Furthermore, a large statistical heterogeneity was found in this meta-analysis ( I 2 = 85.7%, P < 0.001).

Forest plot of association meta-analysis of HBV and CKD risk

Meta-regression analysis was performed on five factors including type of study, region, reference year, study outcome, and sample size of the included articles to explore sources of heterogeneity. The result is shown in Table 2 , and no heterogeneity was generated by including these five variables in the regression model simultaneously. In addition, subgroup analyses were conducted on the five factors mentioned above, and the results, as shown in Supplementary Figs. 1 – 5 , did not reveal a source of heterogeneity.

Sensitivity analysis and publication bias

As shown in Fig. 3 , a sensitivity analysis of the included studies was performed using a case-by-case exclusion method to evaluate the impact of individual studies on the newly generated pooled OR . As the results showed, the results of the meta-analysis were comparatively stable after excluding any of the studies, and ranged from 1.17 (95% CI , 1.09–1.26) to 1.22 (95% CI , 1.13–1.31). The P values of the regression tests of Egger and Begg used to test for publication bias were 0.862 and 0.139, which were consistent with the results suggested by the funnel plot (Fig. 4 ), and there was no publication bias in this study.

Sensitivity analysis of the association between HBV and CKD risk

Funnel plot of the association between HBV and CKD risk

Over the past few decades, a strong link between HBV and kidney disease has been known to exist [ 46 , 47 ]. However, controversy remains regarding the relationship between HBV infection and CKD risk. This study summarized and pooled the relevant existing studies to perform a meta-analysis of the risk of CKD in the adult general population infected with HBV. The results showed that people infected with HBV had a higher risk of developing CKD compared to those who were not infected with HBV (pooled OR , 1.20; 95% CI , 1.12–1.29). Also, no literature was observed in the sensitivity analysis that had a significant impact on the study results, and no publication bias was observed.

Increasingly, studies have examined the relationship between HBV infection and the risk of CKD prevalence. Several previous meta-analyses have not observed a significant correlation between HBV infection and risk of CKD prevalence, with pooled effect estimates and their 95% CIs were 1.05 (0.56, 1.98) and 2.22 (0.95; 3.50), respectively [ 17 , 48 ]. A recently published meta-analysis by Fabrizi F et al. found that HBV infection increased the risk of CKD ( OR , 1.19; 95% CI 1.11–1.27) [ 11 ]. A recently published case-control study based on a Chinese population found that HBV infection promoted an increased risk of CKD ( OR , 2.099; 95% CI 1.128–3.907) [ 14 ]. In this study, we found that HBV infection was associated with an increased risk of developing chronic kidney disease, which is consistent with the results of a recently published meta-analysis.

Unfortunately, our analysis found substantial heterogeneity in prior published studies ( I 2 = 85.7%, P < 0.001). In order to explore sources of heterogeneity, heterogeneity was assessed using meta-regression and subgroup analyses. However, study type, region, reference year, study outcome, and sample size were not sources of heterogeneity. Although studies providing adjusted outcome estimates ( HR / OR ) were included in our study, there may still be residual confounding factors. Therefore, sources of article heterogeneity could not be easily excluded. Meanwhile, because complete covariate information was not given across studies, we were unable to conduct a more comprehensive exploration of the sources of heterogeneity. For example, the specific inclusion and exclusion criteria for studies included in the literature may vary, which may account for the high degree of heterogeneity.

The mechanisms underlying the association between HBV and CKD development have not been fully elucidated. Nonetheless, the relationship between chronic HBV infection and kidney disease was reported in an article more than fifty years ago [ 49 ]. It has been suggested that the deposition of immune complexes in the kidney plays a key role in the pathogenesis of HBV-related nephropathy [ 50 ]. It is likely due to low molecular weight HBeAg (3 × 10 5 Da) crossing the glomerular basement membrane to form subepithelial immune deposits, which leads to glomerular and interstitial tubular damage and contributes to the decline in renal function [ 51 , 52 ]. Secondly, Deng et al. showed that excessive apoptosis of renal proximal tubular cells may also be associated with renal injury in patients with chronic HBV infection [ 53 ]. In addition, six nucleotide analogues (NAs) have been approved for the treatment of chronic HBV. Nevertheless, all NAs are excreted via the renal route and suffer from some degree of nephrotoxicity [ 54 ]. Therefore, dosing adjustments should be made according to the overall clinical status of chronic HBV infection to avoid causing renal impairment [ 55 ].

Our study has several advantages. Firstly, this study synthesizes several recently published large studies on the relationship between HBV infection and the risk of CKD, and provides more reliable evidence. Secondly, the study area included Asia, Europe, and the Americas, which can better represent the international research landscape. Generally, the results of our meta-analysis are similar to related articles recently published by other scholars.

Nevertheless, there are some limitations to this study. Firstly, the included studies contained a large proportion of case-control studies and cohort studies, which may be subject to selection bias and recall bias. Secondly, the inclusion of a large proportion of cross-sectional studies in this study made it difficult to establish a causal association between HBV infection and risk of CKD. Thirdly, our subgroup analysis could not explain the source of heterogeneity. In addition, although this study developed strict inclusion and exclusion criteria and used the NOS scale to assess the quality of the included articles during the screening process, there was still a degree of subjectivity in the assessment of the literature.

Conclusions

In conclusion, this study found that HBV infection was associated with a significant increase in the risk of CKD. However, the current study still cannot directly determine this cause-and-effect relationship. Thus, more comprehensive prospective longitudinal studies are needed in the future to provide further exploration and explanation of the association between hepatitis B virus and the risk of developing chronic kidney disease.

Data availability

Data sharing is not applicable to this paper as no datasets were generated or analyzed for this study.

Abbreviations

95% confidence intervals

Chronic kidney disease

Chinese National Knowledge Infrastructure

End-stage renal disease

Glomerular filtration rate

Glomerulonephritis

Hepatitis B virus

Hazard ratios

Nucleotide analogues

Newcastle-Ottawa Quality Scale

Polyarteritis nodosa

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Acknowledgements

The authors would like to express their gratitude to all participants for their cooperation.

This work was supported by the Natural Science Foundation of Fujian Province (No. 2020J01607) and Natural Science Foundation of Fujian Province (No. 2023J01628).

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Danjing Chen, Rong Yu and Shuo Yin contributed equally to this work.

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Department of Epidemiology and Health Statistics, Fujian Provincial Key Laboratory of Environment Factors and Cancer, School of Public Health, Fujian Medical University, Fuzhou, 350122, People’s Republic of China

Danjing Chen, Rong Yu, Shuo Yin, Wenxin Qiu, Jiangwang Fang & Xian-e Peng

Department of Epidemiology and Health Statistics, Key Laboratory of Gastrointestinal Cancer, School of Basic Medical Sciences, Fujian Medical University, Ministry of Education, Fujian Medical University, Xuefu North Road 1st, Shangjie Town, Minhou Country, Fuzhou, Fujian, 350108, China

Xian-e Peng

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Study concept and design: PXE; Collection and assembly of data: CDJ, YR, YS and QWX; Data analysis and interpretation: CDJ, YR, YS and FJW; Manuscript writing and review: CDJ, YR, YS and PXE. All authors read and approved the final manuscript.

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Chen, D., Yu, R., Yin, S. et al. Hepatitis B virus infection as a risk factor for chronic kidney disease: a systematic review and meta-analysis. BMC Infect Dis 24 , 620 (2024). https://doi.org/10.1186/s12879-024-09546-z

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Development of a quantitative index system for evaluating the quality of electronic medical records in disease risk intelligent prediction

Jiayin Zhou 1 na1 ,
Jie Hao 1 na1 ,
Mingkun Tang 1 ,
Haixia Sun 1 ,
Jiayang Wang 2 ,
Jiao Li 1 &
Qing Qian 1

BMC Medical Informatics and Decision Making volume 24 , Article number: 178 ( 2024 ) Cite this article

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Metrics details

This study aimed to develop and validate a quantitative index system for evaluating the data quality of Electronic Medical Records (EMR) in disease risk prediction using Machine Learning (ML).

Materials and methods

The index system was developed in four steps: (1) a preliminary index system was outlined based on literature review; (2) we utilized the Delphi method to structure the indicators at all levels; (3) the weights of these indicators were determined using the Analytic Hierarchy Process (AHP) method; and (4) the developed index system was empirically validated using real-world EMR data in a ML-based disease risk prediction task.

The synthesis of review findings and the expert consultations led to the formulation of a three-level index system with four first-level, 11 second-level, and 33 third-level indicators. The weights of these indicators were obtained through the AHP method. Results from the empirical analysis illustrated a positive relationship between the scores assigned by the proposed index system and the predictive performances of the datasets.

The proposed index system for evaluating EMR data quality is grounded in extensive literature analysis and expert consultation. Moreover, the system’s high reliability and suitability has been affirmed through empirical validation.

The novel index system offers a robust framework for assessing the quality and suitability of EMR data in ML-based disease risk predictions. It can serve as a guide in building EMR databases, improving EMR data quality control, and generating reliable real-world evidence.

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Introduction

The onset of the digital health era has led to a paradigm shift in health management, transitioning from a focus on reactive treatment to proactive prevention [ 1 ]. Disease risk intelligent prediction has become a vital strategy in proactive health management, aiming to identify potential risk factors and prevent the progression of diseases. By harnessing the capabilities of Artificial Intelligence (AI) technologies and Machine Learning (ML) approaches, healthcare professionals can gain valuable insights into diseases, enabling the development of more effective preventive treatment plans [ 2 , 3 ].

Johnson [ 4 ] applied four different ML-based models to predict subsequent deaths or cardiovascular events in a cohort of 6,892 patients. The study found that the ML-based model had superior discrimination ability compared to traditional coronary Computed Tomography (CT) scores in identifying patients at risk of adverse cardiovascular events. Electronic medical records (EMR) data, as a valuable real-world data source, plays a critical role in disease risk prediction using ML techniques [ 4 ]. An EMR refers to a digital version of a patient’s medical record, encompassing medical history, medications, test results, and other relevant information [ 5 , 6 ]. Healthcare providers commonly utilize EMRs to document and track patient information, enabling comprehensive decision-making regarding patient care. Furthermore, clinical researchers can leverage de-identified EMR data to study disease patterns, develop novel treatments, and advance medical knowledge. The integration of ML with EMRs has recently shown significant improvements in predicting patient outcomes, such as identifying individuals with suspected coronary artery disease [ 7 ] or forecasting the likelihood of open-heart surgery [ 8 ]. These advancements highlight the potential of ML in enhancing the efficiency of clinical decision-making [ 9 ].

Nevertheless, several studies have raised concerns about the quality of EMRs in clinical research, emphasizing issues such as lack of data standardization, incomplete or missing clinical data, and discrepancies in data types and element representations [ 10 , 11 ]. Ensuring the quality of EMR data is crucial, as it forms the bedrock for effective utilization of EMRs. High-quality EMR data not only supplies robust evidence, but also accelerates the clinical research process, shortens its timeline, and reduces associated risks. Therefore, controlling and evaluating EMR data quality are pivotal in upholding the overall quality and integrity of clinical research.

Despite numerous studies investigating the assessment of EMR data quality in clinical research, it is noteworthy that the body of literature evaluating EMR data quality is growing [ 12 , 13 ]. However, publicly published clinical studies employing ML techniques and utilizing EMR data frequently overlook data quality or implement methods lacking expert knowledge or evidential support. While methods for data quality evaluation have been described in the informatics literature, researchers without specialized knowledge in this field may find difficulty choosing the appropriate evaluation method in line with the available data and research problems [ 14 ]. Furthermore, the existing quality assessment framework primarily relies on qualitative approaches, making objective measurement of quality and suability challenging.

In this paper, we aim to develop and validate a quantitative index system for evaluating the quality of EMR in disease risk prediction using ML. The proposed index system is intended to provide guidance for utilizing EMR data in research, enhance the quality of EMR data within a Hospital Information System (HIS), and facilitate the implementation of clinical decision-making research based on EMR data. By applying the proposed index system, researchers and healthcare professionals can make knowledgeable decisions regarding the use of EMR data for ML-based disease prediction research, ultimately improving patient care and advancing medical knowledge.

In this paper, we present the development of a quantitative index system, depicted in Fig. 1 , designed to ensure the quality control of EMR data in disease prediction models. The development process incorporated the use of the Delphi method and the analytic hierarchy process (AHP). In addition, an empirical study was undertaken to validate the effectiveness of the developed index system using real-world EMR data in disease risk intelligent prediction.

Workflow of the study

Sketching the preliminary index system

Preliminary indicator identification, definition, and organization.

The initial set of indicators was determined through a comprehensive literature review of studies published before September 27, 2021, obtained from the PubMed database. The search query used was “(machine learning) AND (electronic medical records) AND (disease prediction)”, which resulted in 549 papers. The inclusion criteria required that the research data be related to EMR or HIS and that disease risk was predicted using ML techniques. Review articles and papers deemed to have low relevance were excluded, leading to the removal of 225 papers based on the fulfillment of the exclusion criteria after reading abstracts.

Further screening was conducted by reading the full papers to eliminate studies that did not involve EMR or HIS data or utilized disease prediction methods other than machine learning. Additionally, 18 relevant papers were included by examining the reference lists of the selected studies. Ultimately, a total of 229 papers were retained for the development of the preliminary index system. The detailed process of paper screening is illustrated in Fig. 2 .

Flowchart of paper screening

Upon analyzing the review results, we formulated an initial multi-level index system consisting of four first-level, 11 second-level, and 33 third-level indicators. The first-level indicators represent broad dimensions of data quality, while the second-level indicators correspond to the general dimensions specifically for EMR data quality. The third-level indicators capture specific dimensions relevant to EMR-based disease prediction models.

Calculation methods

We utilized the AHP method to determine the weights of the first- and second-level indicators in the three-level index system. The weights of the third-level indicators were calculated using percentages or binary values according to their definitions. The calculation formulas of these third-level indicators will be assessed in the forthcoming Delphi consultation.

Developing a three-level index system using the Delphi method

Questionnaire compilation and expert consultation.

We conducted a Delphi consultation to gather feedback from experts based on the preliminary index system. The consultation questionnaire, provided in Additional file 1 , consists of four parts: experts' basic information (see Table S1), familiarity and judgment basis with AI-based disease prediction (see Table S2-S3), evaluation tables for the preliminary index system (see Table S4-S6), and an evaluation table for the calculation formulas of the third-level indicators (see Table S7). The importance of the preliminary indicators was measured using a 5-point Likert scale, ranging from “very unimportant” to “very important”. To ensure the extensibility of the preliminary index system, three additional options were included: delete, modify, and new indicator(s). For the calculation formulas part, experts were asked to provide a yes or no response, and if the answer was no, a suggestion for modification was requested.

A total of twenty experts specializing in healthcare/EMR data governance and medical AI were selected for the Delphi consultation. The inclusion criteria for the selection were as follows: (1) holding a Ph.D. degree or being a senior technical associate; (2) possessing more than two years of research experience in related fields; (3) being familiar with the construction and evaluation of EMR data; and (4) being able to give feedback in a timely manner. We conducted a single-round consultation since the nature of our consulting panel was relatively small and homogeneous [ 15 ].

Key coefficients and statistical analyses

To achieve relatively consistent and reliable feedback from the questionnaire, we calculated four metrics: the experts' positive coefficients, expert authority coefficients (Cr), coefficient of variation (CV), and Kendall's coefficient of concordance. The experts' positive coefficients were determined based on the response rate to the questionnaire. A response rate of 70% or higher is considered satisfactory [ 16 ]. The Cr was calculated as the average of the familiarity coefficient (Cs) and the judgment coefficient (Ca), reflecting the reliability of the expert consultation. A Cr value of 0.7 or above is considered acceptable. The CV measures the consistency of indicators on the same level. A CV value less than 0.25 is expected, indicating a high level of consistency [ 17 ]. Kendall's coefficient of concordance evaluates the overall consistency of all indicators in the system. It ranges from zero to one, with a value greater than 0.2 considered acceptable [ 18 ]. All statistical analyses were performed using Microsoft Excel/IBM SPSS 25.0.

Using the AHP method for weight assignment

We applied the AHP method to determine the weights of indicators at each level, which is a well-known technique in multiple criteria decision-making [ 19 ]. AHP enables the quantification of criteria and opinions that are difficult to measure numerically, and its outcomes are free from subjective influence due to its use of pairwise comparisons and eigenvalues [ 20 ].

In this study, our AHP method was conducted in three steps. First, we obtained the importance ratings of experts for each indicator. Then, we averaged these ratings for each indicator and performed pairwise comparisons among indicators at the same level that belong to the same upper level. This step allowed us to construct multiple judgment matrices based on their ratios.

Second, we calculated the eigenvectors of each indicator by normalizing the judgement matrix. A larger eigenvector for an indicator represents a higher relative importance. The relative weights of indicators at the same level were determined by standardizing the eigenvectors. For the first-level indicators, their relative weights were equal to their absolute weights. For the second- and third-level indicators, their absolute weights were calculated by multiplying their relative weights with the absolute weight of the upper level.

Third, we performed a consistency test using the consistency ratio (CR) to evaluate the consistency of the judgment matrices. A CR below 0.1 indicated that the judgment matrices were consistent and that the obtained weights were considered valid [ 21 ]. The steps of the AHP method are illustrated in Fig. 3 .

Flowchart of the AHP method

Evaluating the index system through prediction tasks

To further validate the suitability of the proposed index system, an empirical study was conducted using real-world EMR data for disease risk prediction.

Dataset construction

To ensure a fair assessment, we opted to generate multiple datasets from a single EMR data resource. The chosen data resource needed to be large-scale, open-access, and regularly updated. Once the data resource was identified, we constructed several datasets with varying sample types but maintaining the same set of attributes.

For each dataset, we computed the scores of 33 third-level indicators using the established calculation formulas. The weights of the proposed index system were applied to obtain weighted scores for all indicators within each dataset. The overall score of a dataset was subsequently computed by summing the scores of the first-level indicators.

Predictive modeling

In the context of disease risk prediction, we considered three widely used ML models: logistic regression (LR), support vector machine (SVM), and random forest (RF). LR is a traditional classification algorithm used to estimate the probability of an event occurring [ 22 ]. SVM, a nonlinear classifier, employs a kernel function to transform input data into a higher-dimensional space, making it effective in handling complex relationships and nonlinear patterns [ 23 ]. RF is an ensemble method that combines the predictions from multiple decision trees. It has shown great success in disease risk prediction tasks by reducing overfitting and improving predictive accuracy [ 24 ]. For our analysis, we used the scikit-learn python library [ 25 ] to implement LR, SVM, and RF.

Reliability analysis

In our study, we conducted reliability analysis to examine the relationship between the scores obtained from our constructed datasets and the performance of predictive models. we applied Pearson correlation for assessing linear relationships [ 26 ] and Spearman correlation for nonlinearity [ 27 ]. The Pearson correlation coefficient was calculated using the formula:

Here, \({x}_{i}\) and \({y}_{i}\) represent individual data points from the two respective datasets, while \(\overline{x }\) and \(\overline{{\text{y}} }\) denote the mean values of these datasets. A Pearson correlation coefficient near 1 or -1 indicates a strong linear relationship between dataset scores and model performance, whereas a value close to 0 suggests a very weak linear relationship.

Similarly, the Spearman correlation coefficient was calculated using the formula:

Here, \({d}_{i}\) represents the difference in rank between the two datasets for the i -th observation, and n denotes the total number of observations. A Spearman correlation coefficient near 1 or -1 indicates a strong nonlinear relationship, while a value close to 0 suggests a very weak relationship.

In both analyses, statistical significance was established with a p-value less than 0.05. This finding indicates a significant correlation between the scores of our constructed datasets and the performance of predictive models. Thus, this statistically significant outcome supports the reliability of our proposed index system in evaluating the data quality of EMR for intelligent disease risk prediction.

The characteristics of the experts

In the Delphi consultation, a total of twenty experts were invited, of which 17 actively participated, yielding a response rate of 85.0%. Out of the 17 experts, 16 provided feedback that met the credibility criteria for a Delphi study, resulting in an effective response rate of 94.1%. These response rates reflect a high degree of expert engagement.

Most of the participating experts were male, held Ph.D. degrees, and specialized in medical informatics or medical AI. Over half of the experts were aged between 40 and 50 years, and 62.5% had between 10 and 20 years of work experience. Moreover, 68.7% of the experts occupied senior associate positions or higher. For detailed information, see Table S8 in Additional file 3 .

The key coefficients of the Delphi method

The degree of expert authority (Cr) is defined by two factors: the expert's familiarity with the consultation content (Cs) and the basis of expert judgment (Ca). Of the 16 participating experts, 7 were found to be very familiar with the content, while 9 were relatively familiar. This indicates an overall sound understanding of the field among the experts. Only two experts exhibited a low judgment basis, suggesting that the majority of the experts were well-equipped to offer informed judgment. Details of expert familiarity and judgment basis can be found in Table S9 in Additional file 3 .

Cr was calculated to be 0.89, with Cs and Ca values of 0.88 and 0.90, respectively. These values indicate a high level of expert authority and reliability in the consultation results. The CV values for the first-level indicators were less than 0.16, for the second-level indicators were less than 0.20, and for the third-level indicators were no more than 0.25. These low CV values indicate a high level of consistency among experts' scores for the preliminary indicators at each level. Kendall's coefficients of concordance for all three levels were greater than 0.30, indicating a substantial level of agreement among the experts. Additionally, the p-values for the preliminary second- and third-level indicators were very small, further confirming the consistency of experts' scores for each preliminary indicator. Overall, the results demonstrate a high level of consistency and reliability in the experts' assessments for each preliminary indicator.

The final weighted three-level index system

Experts' comments focused on changes to the definition of indicators. After further discussions with experts, all preliminary indicators were included in the final weighted three-level index system, as shown in Table 1 . No new indicators were added to the system. The index system comprises four first-level indicators, 11 second-level indicators, and 33 third-level indicators, with the weights determined using the AHP method and percentages.

The first-level indicators represent a series of data quality characteristics that determine the suitability of EMR data for disease risk intelligent prediction research. The second-level indicators provide a concrete representation or evaluation of the first-level indicators, making it easier for users to understand their extension or evaluation. The third-level indicators further specify the second-level indicators, providing clear quality requirements for different levels of granularity in the EMR dataset, such as data records, data elements, and data element values. This facilitates users in understanding the evaluation needs and contents more clearly. For detailed information on the indicators, please see Additional file 2 .

Data preprocessing

In this empirical study, the MIMIC-III clinical database was chosen as the representative real-world EMR data resource. MIMIC-III Footnote 1 is an extensive and freely accessible database that contains comprehensive health-related data from more than 46,000 patients admitted to intensive care unit (ICU) at the Beth Israel Deaconess Medical Center between 2001 and 2012 [ 28 ]. For this study, we utilized MIMIC-III v1.4, which is the latest version released in 2016 [ 29 ] and ensures effective control over EMR data.

Sepsis is a leading cause of mortality among ICU patients, highlighting the importance of accurate sepsis risk prediction for precise treatments in the ICU [ 30 ]. Hence, we selected sepsis as the disease prediction task using the MIMIC-III database. Potential predictors were extracted from the records of vital signs, routine blood examinations [ 31 ], liver function tests [ 32 ] and demographic information. The outcome variable for the prediction task is the occurrence of sepsis. Furthermore, we obtained five different populations of ICU patients with a high risk of sepsis from the MIMIC-III database. The number of patients in each population, categorized as elderly (> 80 years old), long-stay (> 30 days of length of stay, LLOS), ischemic stroke, acute renal failure (ARF), and cirrhosis (CIR), is presented in Table S10 in Additional file 3 .

Scoring datasets

According to the proposed index system, we evaluated the five datasets and assigned scores to each indicator based on their respective weights in the system. The detailed list of scores of divergent indicators for each dataset can be found in Table S11 in Additional file 3 . In Table 2 , we present the scores of first-level indicators. It is important to note that the scores for the operability indicator were consistent across all five datasets, with a value of 0.251. This is because these datasets were obtained from a single resource.

When considering the overall scores, the LLOS dataset achieved the highest score of 0.966, indicating a higher level of quality, while the ARF dataset obtained the lowest score of 0.907. These scores provide an assessment of the datasets' suitability and quality for disease risk prediction using the proposed index system.

Experimental results

Additional data processing was conducted to prepare the datasets for training ML models. To address the missing values, median imputation was applied to predictors with a small proportion of missing values in each dataset. To mitigate potential bias arising from imbalanced datasets, we applied undersampling on the majority class to achieve a balanced ratio of 1:1. Each dataset was then randomly split into 80% for training and 20% for testing. To ensure fairness in model comparison, the predictors were normalized, and a tenfold cross-validation was performed during the training process.

Regarding model hyperparameters, the LR model applied the 'liblinear' solver method. The SVM model utilized a Radial Basis Function kernel, with a regularization parameter (C) set to 1.0, and the gamma value was set to 'scale'. For the RF model, it was constructed with 10 trees (n_estimators = 10), a maximum tree depth of 7 (max_depth = 7), and optimal feature selection (max_features = ‘auto’).

The evaluation of model performance was based on accuracy (ACC), precision, and area under the curve (AUC). Accuracy represents the proportion of correct predictions made by a model among all predictions. Precision measures the proportion of true positive predictions among all positive predictions made by a model. AUC, also known as the area under the receiver operating characteristic curve, is a metric used to evaluate the performance of binary classification models [ 33 ].

Table 3 displays the model performance on the five datasets. Among the three models, LLOS achieved the highest performance across all three evaluation metrics. On the other hand, ARF had the lowest performance in most cases, except for precision in the LR model.

Association analysis

The relationships between the scores of datasets and the performance metrics of the models were analyzed as follows. First, a normality test was conducted on each pair of scores. If the scores passed the normality test, a Pearson correlation analysis was performed. Otherwise, a Spearman correlation analysis was conducted. Table 4 shows that all correlations, except for LR-Precision, were strongly positive and statistically significant. The SVM-Precision correlation showed the strongest effect among them.

We have developed a quantitative evaluation index system to assess the suitability of EMRs in disease risk intelligent prediction research. The proposed index system was validated through an empirical study using MIMIC-III datasets for predicting sepsis. Three popular ML models were performed, and the predictive results demonstrated that datasets with higher scores achieved better performance across three ML models. Our result is consistent with a previous study that showed the impact of data quality on prediction performance [ 34 ]. Additionally, the association analyses revealed a strong positive relationship between the scores of datasets and the combination of the ML model and evaluation metric. These findings confirm that the proposed index system was effective in evaluating the quality of EMR data in disease risk prediction using ML techniques.

Compared to the general framework for evaluating EMR data quality, our proposed index system was constructed by incorporating both the quality characteristics of EMR data and the specific research activities in ML-based disease risk prediction. It differs from the frameworks developed by Johnson [ 35 ] and Lv [ 36 ], which focused on summarizing literature on general medical data rather than specifically on EMR data. Although Weiskopf [ 37 ] specified EMR data as a required condition for a literature search, they did not explicitly address the situation of using EMR data in their development. The proposed index system considers not only the practical foundation of EMR data but also the data processing operations and operational objectives of EMRs at different stages of prediction model construction. This approach makes the evaluation index system more focused on its research purpose and enhances its explanatory power.

Another significant contribution of the proposed index system is the quantitative evaluation of EMR data quality in disease risk prediction. This provides researchers with guidance or standards for quantifying the EMR datasets for specific research purposes. Most current EMR data quality evaluation systems for clinical research rely on qualitative indicators [ 38 ]. Qualitative indicators are often based on typical cases, statements, and supporting materials, which may lack objectivity. While the study of Weiskopf [ 37 ] incorporated quantitative evaluation, it still relied on subjective scoring of each dimension by experts to calculate the mean value. The evaluation model proposed by Zan [ 39 ] utilized objective measurement indicators, but it primarily focused on binary classification and only included first-level indicators.

The proposed three-level index system was developed using a combination of qualitative and quantitative approaches. The naming and definition of all three levels of indicators were constructed through an extensive literature review and expert consultation. The first-level indicators correspond to the core qualitative aspects of evaluating EMR data quality in ML-based disease risk prediction. The second-level indicators serve as a refinement of the first-level qualitative indicators. The third-level indicators are quantitative in nature and can be obtained through objective quantitative calculations, such as assessing the coverage bias of the outcome variables in the integrity of the third-level indicators. Through the AHP method, the weights of the first- and second-level indicators can be obtained by the weights of third-level indicators in a hierarchical way.

Our study has several limitations. First, the calculation of the weights for the third-level indicators was based on simple percentages. This calculation method may neglect variations in the importance of different indicators. Second, the empirical study was conducted using the MIMIC-3 v1.4 database. Although the MIMIC-3 dataset is a widely used resource in research, the use of a single data resource may restrict the generalizability of our findings. Certain indicators for comparing data resources may be hard to validate without diverse EMR data resources. Hence, future validation studies using another data resource should be conducted to ensure the robustness of the proposed index system.

In this paper, we developed a quantitative three-level index system, which included four first-level, 11 second-level, and 33 third-level indicators, to evaluate the EMR data quality in ML-based disease risk prediction. The reliability of the proposed index system has been verified through an empirical study with real-world data.

The proposed index system can benefit both EMR users for research and data managers. For EMR users for research, the proposed index system could provide them with a measurement for the suitability of EMR data in ML-based disease risk predictions. For EMR data managers, it could guide the direction of EMR database construction and improve the EMR data quality control. Eventually, we hope that the proposed index system can promote the generation of real-world evidence from reliable real-world EMR data.

Availability of data and materials

All data generated or analyzed during this study are included in this published article and its additional files.

https://physionet.org/content/mimiciii/1.4/

Abbreviations

Artificial intelligence

Analytic hierarchy process

Acute renal failure

Area under curve

Judgment coefficient

Consistency ratio

Expert authority coefficients

Familiarity coefficient

Computed tomography

Coefficient of variation

Electronic medical record

Hospital information system

Intensive care units

> 30 Days of length of stay

Logistic regression

Machine learning

Support vector machine

Random forest

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Acknowledgements

The authors would like to thank all those who participated in the expert consultation.

This work was supported by the Chinese Academy of Medical Sciences Initiative for Innovative Medicine (Grant No. 2021-I2M-1–057 and Grant No. 2021-I2M-1–056), National Key Research and Development Program of China (Grant No. 2022YFC3601001), and National Social Science Fund of China (Grant No. 21BTQ069).

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Jiayin Zhou and Jie Hao contributed equally to this work.

Authors and Affiliations

Institute of Medical Information, Chinese Academy of Medical Sciences & Peking Union Medical College, Beijing, People’s Republic of China

Jiayin Zhou, Jie Hao, Mingkun Tang, Haixia Sun, Jiao Li & Qing Qian

Chinese Academy of Medical Sciences & Peking Union Medical College, Beijing, People’s Republic of China

Jiayang Wang

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Contributions

All authors contributed to this study. QQ led and designed the study. HS designed the study and structured the manuscript. JZ drafted and revised the manuscript. JH drafted, revised the manuscript, and provided assistance with experiment interpretation. JW and MT conducted the empirical study. JL provided critical revision. All authors read and approved the final manuscript.

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Correspondence to Qing Qian .

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Ethics approval was not deemed necessary for this study by the Ethics Committee at the Institute of Medical Information & Library, Chinese Academy of Medical Sciences, in accordance with national guidelines and local legislation. Written informed consent was obtained from all participants. All methods were performed in accordance with the relevant guidelines and regulations. This study was conducted in compliance with the Declaration of the Helsinki.

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Zhou, J., Hao, J., Tang, M. et al. Development of a quantitative index system for evaluating the quality of electronic medical records in disease risk intelligent prediction. BMC Med Inform Decis Mak 24 , 178 (2024). https://doi.org/10.1186/s12911-024-02533-z

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DOI : https://doi.org/10.1186/s12911-024-02533-z

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http://orcid.org/0000-0003-3918-5869 Mark Davison 1 ,
Maximos McCune 2 ,
Nishanth Thiyagarajah 2 ,
http://orcid.org/0000-0002-2045-972X Ahmed Kashkoush 1 ,
http://orcid.org/0009-0001-1362-1944 Rebecca Achey 1 ,
Michael Shost 3 ,
http://orcid.org/0000-0002-3646-3635 Gabor Toth 2 ,
Mark Bain 1 , 2 ,
Nina Moore 1 , 2 , 4
1 Department of Neurosurgery , Cleveland Clinic Foundation , Cleveland , Ohio , USA
2 Cerebrovascular Center , CCF , Cleveland Heights , Ohio , USA
3 Case Western Reserve University School of Medicine , Cleveland , OH , USA
4 Department of Biomedical Engineering , Lerner Research Institute , Cleveland , OH , USA
Correspondence to Dr Mark Davison, Neurological Surgery, Cleveland Clinic Foundation, Cleveland, Ohio, USA; davisom{at}ccf.org

Background Arteriovenous malformation (AVM)-associated aneurysms represent a high-risk feature predisposing them to rupture. Infratentorial AVMs have been shown to have a greater incidence of associated aneurysms, however the existing data is outdated and biased. The aim of our research was to compare the incidence of supratentorial vs infratentorial AVM-associated aneurysms.

Methods Patients were identified from our institutional AVM registry, which includes all patients with an intracranial AVM diagnosis since 2000, regardless of treatment. Records were reviewed for clinical details, AVM characteristics, nidus location (supratentorial or infratentorial), and presence of associated aneurysms. Statistical comparisons were made using Fisher’s exact or Wilcoxon rank sum tests as appropriate. Multivariable logistic regression analysis determined independent predictors of AVM-associated aneurysms. As a secondary analysis, a systematic literature review was performed, where studies documenting the incidence of AVM-associated aneurysms stratified by location were of interest.

Results From 2000–2024, 706 patients with 720 AVMs were identified, of which 152 (21.1%) were infratentorial. Intracranial hemorrhage was the most common AVM presentation (42.1%). The incidence of associated aneurysms was greater in infratentorial AVMs compared with supratentorial cases (45.4% vs 20.1%; P<0.0001). Multivariable logistic regression demonstrated that infratentorial nidus location was the singular predictor of an associated aneurysm, odds ratio: 2.9 (P<0.0001). Systematic literature review identified eight studies satisfying inclusion criteria. Aggregate analysis indicated infratentorial AVMs were more likely to harbor an associated aneurysm (OR 1.7) and present as ruptured (OR 3.9), P<0.0001.

Conclusions In this modern consecutive patient series, infratentorial nidus location was a significant predictor of an associated aneurysm and hemorrhagic presentation.

Arteriovenous Malformation
Posterior fossa
Angiography

https://doi.org/10.1136/jnis-2024-022003

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Contributors All authors contributed to the study conception and design. Material preparation, data collection and analysis were performed by MD, MM, and NT. Manuscript preparation was performed by MD and all authors participated in manuscript revisions. All authors reviewed and approved the final manuscript.

Funding The authors have not declared a specific grant for this research from any funding agency in the public, commercial or not-for-profit sectors.

Competing interests None declared.

Provenance and peer review Not commissioned; externally peer reviewed.

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