Protocol of a Scoping Review on the Use of Transactional Data for Early Diagnosis (TRADED-ScR)

Kurdo Araz; Benjamin M Jacob; James Flanagan; Patrick Redmond

doi:10.12688/hrbopenres.14015.1

Home Browse Protocol of a Scoping Review on the Use of Transactional Data for...

ALL Metrics

-

Views

8

Downloads

Get PDF

Get XML

Export

▬

✚

Study Protocol

Protocol of a Scoping Review on the Use of Transactional Data for Early Diagnosis (TRADED-ScR)

[version 1; peer review: 1 approved with reservations]

Kurdo Araz ^1,2, Benjamin M Jacob², James Flanagan³, Patrick Redmond²

PUBLISHED 10 Jan 2025

Author details Author details

¹ School of Medicine, RCSI University of Medicine and Health Sciences, County Dublin, County Dublin, Ireland
² Department of General Practise, RCSI University of Medicine and Health Sciences, Dublin, County Dublin, Ireland
³ Department of Surgery and Cancer - Faculty of Medicine, Imperial College London, London, England, UK

Kurdo Araz
Roles: Conceptualization, Methodology, Visualization, Writing – Original Draft Preparation, Writing – Review & Editing

Benjamin M Jacob
Roles: Conceptualization, Writing – Original Draft Preparation, Writing – Review & Editing

James Flanagan
Roles: Conceptualization, Writing – Review & Editing

Patrick Redmond
Roles: Conceptualization, Writing – Original Draft Preparation, Writing – Review & Editing

OPEN PEER REVIEW

REVIEWER STATUS

Abstract

Background

The early detection of many diseases is crucial for effective treatment, as it increases the likelihood of successful management and helps prevent or delay complications. However, early detection is often hampered by the asymptomatic nature of initial disease stages and delays in patients seeking care. In cancers such as ovarian, gastrointestinal, and haematological types, delays may result from self-management of non-specific symptoms with over-the-counter medications. Recent studies, such as the Cancer Loyalty Card Study (CLOCS), suggest that transactional data can reveal early self-medicating behaviours indicative of cancer. Despite this potential, a comprehensive understanding of the use of transactional data for early diagnosis across diseases is lacking.

Aim

This scoping review aims to systematically collate and analyse the literature on the use of transactional data for early detection of any disease, assessing its viability as a predictive tool.

Methods

The review will follow the Arksey and O'Malley methodological framework, with enhancements by Levac et al., and will be reported according to PRISMA-P and PRISMA-ScR guidelines. A comprehensive search will be conducted across databases including MEDLINE, Embase, Scopus, Web of Science, and the Cochrane Database of Systematic Reviews. The review will include studies that utilise transactional data to identify early signs of disease, focusing on both peer-reviewed articles and conference abstracts. The data will be thematically charted and synthesised to compare methodologies, disease types, types of transactional data used, key findings, and limitations.

Implications

By mapping the use of transactional data as a non-invasive tool for early detection, this review aims to inform and potentially transform approaches to screening and diagnosis. The findings will provide insights for healthcare professionals, researchers, and policymakers, supporting the development of targeted interventions that leverage transactional data for disease surveillance and early detection.

Keywords

Early diagnosis, Screening, Transactional data, Health informatics, Consumer behaviour, Scoping review

Corresponding author: Kurdo Araz

Competing interests: No competing interests were disclosed.

Grant information: This research was supported by a grant from the HSE National Cancer Control Programme. SDAP-2023-033 Tailoring Lung Cancer Screening for Ireland: Using Existing Datasets to Generate Evidence-Based Recommendations for Health Policy.
The funders had no role in study design, data collection and analysis, decision to publish, or preparation of the manuscript.

Copyright: © 2025 Araz K et al. 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 work is properly cited.

How to cite: Araz K, Jacob BM, Flanagan J and Redmond P. Protocol of a Scoping Review on the Use of Transactional Data for Early Diagnosis (TRADED-ScR) [version 1; peer review: 1 approved with reservations]. HRB Open Res 2025, 8:3 (https://doi.org/10.12688/hrbopenres.14015.1) First published: 10 Jan 2025, 8:3 (https://doi.org/10.12688/hrbopenres.14015.1) Latest published: 10 Jan 2025, 8:3 (https://doi.org/10.12688/hrbopenres.14015.1)

Introduction

Many diseases begin with subtle symptoms, which can lead to delayed diagnoses that occur only after significant, sometimes irreversible, damage has been done. These late diagnoses are linked to lower survival rates and higher morbidity, highlighting the importance of early detection to improve patient outcomes^1–3. Early diagnosis refers to identifying a condition at a stage when interventions can prevent or minimise irreversible damage, thereby improving survival rates, reducing morbidity, and enhancing quality of life^1–3.

However, the early detection of diseases presents substantial challenges. Many diseases in their early stages are asymptomatic or present with non-specific symptoms, which complicates timely diagnosis. Conditions such as cardiovascular diseases, renal diseases, and various forms of cancer exemplify this issue, where delays in seeking care are common due to the subtlety of early symptoms^4–6. These delays are often exacerbated by self-management behaviours, where individuals attempt to alleviate vague symptoms with over-the-counter (OTC) medications. While self-medication can temporarily mask symptoms, it may also obscure the early signs of more serious underlying conditions, such as malignancies^7,8.

Recent studies, including the Cancer Loyalty Card Study (CLOCS), have explored the use of transactional data—specifically, the purchase of OTC medications—as a potential tool for early disease detection. These studies suggest that patterns in transactional data can reveal self-medicating behaviours that may be indicative of conditions like cancer, offering a novel and non-invasive method for early diagnosis⁹. Beyond cancer, transactional data has also been used to enhance the detection of infectious disease outbreaks, such as Influenza, by tracking sales of specific OTC medication^10,11.

The relevance of this approach is further supported by research showing that patients with certain cancers, such as ovarian, haematological, and gastrointestinal cancers, often delay seeking professional advice due to non-specific symptoms, which they manage with OTC medications^12–14. For example, a survey of over 1,500 women with ovarian cancer revealed that many attributed early symptoms to non-threatening causes such as irregular menstrual cycles, menopause, or aging, leading to delayed diagnosis¹⁴. Additionally, focus group studies have demonstrated that these women had higher purchases of specific medications prior to diagnosis compared to healthy controls, indicating a potential pattern that could be leveraged for earlier detection¹⁵.

The potential for using transactional data in medical research is further supported by regulatory frameworks like the General Data Protection Regulation (GDPR), which grants patients the right to access their personal data, including transaction records¹⁶. This regulation could facilitate the ethical use of consumer transactional data for medical research, provided that safeguards such as the ability to withdraw consent, data security measures, and transparency in data management are maintained¹⁵.

Given these developments, transactional data, such as purchase histories of OTC medications, has emerged as a promising avenue for the early detection of cancer and other diseases^9–11. The ability to detect early warning signs through consumer purchasing patterns represents a novel, non-invasive diagnostic approach that could significantly improve early diagnosis strategies. However, the existing literature on this topic is fragmented, with studies varying widely in design, disease focus, and outcomes. To date, there has been no comprehensive synthesis that could clarify the overall efficacy of using transactional data for disease detection and its potential applications in public health and clinical practice.

Aims and objectives

The aim of this scoping review is to provide a detailed overview of how transactional data can be used in the early detection of disease. This review intends to map the range, scope, and nature of the existing research, providing critical insights into the feasibility of utilising such data to enhance early diagnosis strategies.

The specific objectives include:

1. to identify all peer-reviewed articles which describe the use transactional data for early detection, cataloguing them according to methodology, disease type and data type;
2. to report, in the case of observational and interventional studies, the population characteristics, the event horizon studied, and how the transactional data and disease diagnosis data were operationalised;
3. to quantify, where the published data allow, the predictive power of the disease signal identified in the transactional data, by calculating the implied positive predictive value;
4. to discuss the logistical, legal and ethical challenges related to the use of transactional data in disease screening and early detection reported by the authors of the primary studies.
5. to conduct this review in two phases, the first phase will focus specifically on the early detection of cancer, while the second phase will broaden the scope to include the early detection of any disease.

Methods

Reporting and registration

We will conduct a scoping review in accordance the Arksey and O'Malley framework enhanced by Levac et al.¹⁷. The protocol will adhere to the PRISMA-P reporting guidelines and will be pre-registered on the Open Science Framework. The results will be reported following the PRISMA-ScR guideline¹⁸. Supplementary files and relevant datasets will be made available via the Open Science Framework¹⁹.

Eligibility criteria

The review will include studies that use transactional data for early detection of diseases. Eligible studies must specifically address the use of transactional data, such as loyalty card purchases or OTC medication sales, with incident disease or disease recurrence as an outcome, regardless of the disease type. Studies from all geographical locations will be considered.

Since our aim is to describe the scope of the literature, all article types will be eligible for inclusion: interventional studies, observational studies, qualitative studies, review articles, letters and editorials will all be included. Both peer-reviewed articles (including doctoral theses) and conference abstracts will be included. We will exclude studies that do not focus on transactional data or are not concerned with diagnosis as an outcome.

Information sources

In Phase 1, the databases to be searched include MEDLINE, Embase, Scopus, Web of Science, and the Cochrane Database of Systematic Reviews. In Phase 2, additional sources such as CINAHL, ProQuest Dissertations and Theses Global (PQDT Global), three clinical trial registries (ClinicalTrials.gov, EU Clinical Trials Register, and International Standard Randomised Controlled Trial Number), and two systematic review registries (PROSPERO and Joanna Briggs Institute) will also be searched.

Search strategy

The search strategy, developed with an information specialist, will combine keywords and subject headings related to the themes of “transactional data” and “early detection”. To ensure thorough coverage, the strategy will also incorporate synonyms and related terms such as "purchase data," "loyalty card data," "retail data," and "consumer behaviour data." The detailed search strategies for both phases are available via the Open Science Framework: https://doi.org/10.17605/OSF.IO/ZRJT5²⁰. Based on pilot searches, it is anticipated the number of papers to be screened in Phase 1 will be less than 20,000, while the number of papers to be screened in Phase 2 is expected to be over 100,000. References of included studies will be reviewed to identify additional relevant works.

Selection of sources of evidence

Two reviewers will independently screen titles and abstracts for eligibility using Rayyan, a software that designed to facilitate efficient screening of abstracts and titles²¹. Reviewers will follow the screening algorithm (Table 1) to determine included studies and excluded studies. Full-text screening will follow for potentially relevant studies. Discrepancies will be resolved through discussion or consultation with a third reviewer. A PRISMA flow diagram will document the selection process. The screening algorithm, which will be piloted using a limited search of the MEDLINE database, is outlined on Open Science Framework: https://doi.org/10.17605/OSF.IO/ZRJT5²⁰.

Table 1. Screening algorithm.

Step	Concept	Question	Exclusion label
1	Transaction	Does this paper deal with transaction data?	“NotTransactional”
2	Diagnosis	Does this paper discuss the association between patterns in transaction data and a subsequent disease diagnosis/incidence/risk?	“NotDiagnosis”
3	(INCLUDE)	If negative to all the above, then include	n/a

Given the high volume of articles anticipated in Phase 2, title and abstract screening may be assisted by an artificial intelligence tool based on a large language model. The validity of this approach will be assessed by comparing the results of Phase 1 screening with the authors' results. The detailed approach will be outlined in a subsequent protocol akin to a previous study implementing a similar approach²².

Data collection and data management

A standardised form will be used to extract data from selected studies based on Table 2. Extracted data will include study characteristics (e.g. author, year, disease type), transactional data specifics (data sources, items tracked), methodology (e.g. data collection, analysis techniques), and key findings (e.g. disease symptoms identified, effectiveness of detection, and limitations).

Table 2. Data charting pro-forma.

Step	Concept	Question	Inclusion label example
1	Publication details	(Automatically extracted by Rayyan software)	Title, Authors, Publication Year, Data Source (e.g., Journal, Registry)
2	Publication type	What type of record/publication is this?	P-Journal, P-Conference, P-Registration,
3	Methods	What type of study is this?	M-Review, M-Trial, M-CCC, M-Cohort, M-Other
4	Location	Where did the study population live?	[Coded as “Note”] L-Korea, L-UK, L-NA
5	Disease type	What disease was being studied?	[Coded as “Note”] C-Pancreas, C-CRC, C- Ovarian, C-Lung
6	Disease data	What data was used to determine disease diagnosis?	[Coded as “Note”] D-CPRD, D-THIN
7	Transactional data	What type of transactional data was used?	[Coded as “Note”] T-Statement, T-Loyalty

Data items

This review aims to identify and catalogue peer-reviewed articles that utilise transactional data for early disease detection, categorising them based on methodology, disease type, and data type. For observational and interventional studies, it will report population characteristics, the event horizon studied, and operationalisation of transactional and disease diagnosis data. Additionally, it will quantify the predictive power of the studied transactional data signal, when possible, by calculating the implied positive predictive value.

Critical appraisal

While scoping reviews do not typically involve a quality assessment, we will use the Robins-E and Rob-2/Robins-I tools to critically appraise the observational and interventional studies identified, respectively^23,24. Meta-biases and confidence in cumulative evidence will not be reported since we are conducting a scoping review that does not focus on a single primary outcome.

Data synthesis

The data synthesis will proceed through four steps. First, a bibliometric analysis will map the research landscape by categorising identified articles by methodology, disease type, and data type, providing a high-level overview of the field. Second, study details will be analysed, including population characteristics, study durations, and the operational definitions and uses of transactional and disease diagnosis data, using simple descriptive statistics and narrative approaches as appropriate. Third, where data is sufficient, the review will calculate the implied positive predictive value for the disease signal identified in transactional data. Finally, the review will discuss the logistical, legal, and ethical challenges highlighted in the studies, addressing the complexities of using transactional data in disease detection.

Discussion

Summary

This scoping review protocol presents a systematic approach to examining the use of transactional data in the early detection of diseases. It highlights key areas of interest, including the types of diseases studied, the nature of the data utilised, the methodologies employed, and the challenges encountered in this emerging field. The significance of this review lies in its potential to clarify the utility of transactional data—such as loyalty card purchases and OTC medication sales—as a tool for early diagnosis across a range of conditions.

Strengths and limitations

A major strength of this protocol is its comprehensive and methodical approach to literature search and data extraction, ensuring that the review will thoroughly explore the landscape of research in this area. The inclusion of various study types and the systematic categorisation of findings by disease type, data type, and methodology will provide a broad and detailed overview of the current state of the field.

However, several limitations must be acknowledged. First, there is the issue of publication bias, where studies with negative or inconclusive results may not have been published, potentially skewing the literature. Additionally, language bias could limit the scope of this review, as studies published in languages other than English may be excluded. Another challenge arises from the need to synthesise a large and diverse body of literature into a coherent summary, which may overlook some nuances of individual studies.

A significant limitation specific to this area of research is the restricted access to raw transactional data. For example, in studies like the Cancer Loyalty Card Study (CLOCS), the raw data cannot be shared with other researchers due to privacy and legal constraints. This limitation directly impacts our ability to fully answer the research question, particularly concerning the detailed analysis of transactional patterns associated with early disease detection. While we can report on the scope of the literature in terms of publication details, publication type, methods, location, cancer type, and general descriptions of the datasets used, the lack of access to raw data means that our ability to assess the robustness of the findings reported in primary studies is inherently constrained. However, most primary studies are expected to provide sufficient descriptions of the datasets and methodologies used, allowing us to still draw meaningful insights into the use of transactional data for early diagnosis.

Implications

The findings of this review will be instrumental in shaping future research on the use of transactional data in disease detection. By identifying gaps in the existing literature and highlighting under-researched areas, this review will guide researchers towards optimal study designs and methodologies. Moreover, it will provide healthcare professionals with the latest insights into innovative tools for early detection, potentially leading to advancements in screening and diagnosis practices that could improve patient outcomes.

In addition, this review aims to contribute to the ongoing dialogue on the ethical and privacy concerns associated with the use of transactional data in medical research. By addressing these concerns, we hope to influence the development of guidelines that balance the potential health benefits of this research with the need to protect patient privacy and data security.

Conclusion

In conclusion, the proposed scoping review will provide valuable contributions to our understanding of the potential role of transactional data in early diagnosis. By mapping the research landscape, evaluating methodologies, synthesising key findings, and identifying gaps, this review will inform both current practices and future directions in disease detection research. Despite the limitations posed by the inaccessibility of raw data, this review will offer a comprehensive overview of how transactional data is currently being used and its potential to enhance early detection strategies, ultimately aiming to improve patient outcomes.

Reporting guidelines

PRISMA-P (certain elements modified and added to reflect the specific PRISMA-ScR guideline, as outlined in PRISMA-P checklist) checklist is available via the Open Science Framework.

https://doi.org/10.17605/OSF.IO/ZRJT5 under CC-By Attribution 4.0 International license²⁰.

Ethics and consent

Ethical approval and consent were not required.

Data availability

No data associated with this article since it is a research protocol and does not include associated datasets.

Datasets generated during research will be uploaded permanently on the Open Science Framework: Protocol of a Scoping Review on the Use of Transactional Data for Early Diagnosis (TRADED-ScR): https://doi.org/10.17605/OSF.IO/ZRJT5 under CC-By Attribution 4.0 International license²⁰.

Extended data

The full search strategy and PRISMA-P checklist are available via the Open Science Framework: Protocol of a Scoping Review on the Use of Transactional Data for Early Diagnosis (TRADED-ScR): https://doi.org/10.17605/OSF.IO/ZRJT5 under CC-By Attribution 4.0 International license²⁰.

Faculty Opinions recommended

References

1. Yuan X, Chen S, Sun C, et al.: A novel early diagnostic framework for chronic diseases with class imbalance. Sci Rep. 2022; 12(1): 8614. PubMed Abstract | Publisher Full Text | Free Full Text
2. Whitaker K: Earlier diagnosis: the importance of cancer symptoms. Lancet Oncol. 2020; 21(1): 6–8. PubMed Abstract | Publisher Full Text
3. Varghese SE, El Otol RHM, Al Olama FS, et al.: The importance of early detection of genetic diseases. Dubai Med J. 2021; 4(2): 133–41. Publisher Full Text
4. Cohn JN, Hoke L, Whitwam W, et al.: Screening for early detection of cardiovascular disease in asymptomatic individuals. Am Heart J. 2003; 146(4): 679–85. PubMed Abstract | Publisher Full Text
5. McClellan WM, Knight DF, Karp H, et al.: Early detection and treatment of renal disease in hospitalized diabetic and hypertensive patients: important differences between practise and published guidelines. Am J Kidney Dis. 1997; 29(3): 368–75. PubMed Abstract | Publisher Full Text
6. Whitaker KL, Winstanley K, Macleod U, et al.: Low cancer suspicion following experience of a cancer ‘warning sign’. Eur J Cancer. 2015; 51(16): 2473–9. PubMed Abstract | Publisher Full Text | Free Full Text
7. Davies A, Green MA, Singleton AD: Using machine learning to investigate self-medication purchasing in England via high street retailer loyalty card data. Rao P, editor. PLoS One. 2018; 13(11): e0207523. PubMed Abstract | Publisher Full Text | Free Full Text
8. Brewer HR, Chadeau-Hyam M, Johnson E, et al.: Cancer Loyalty Card Study (CLOCS): feasibility outcomes for an observational case-control study focusing on the patient interval in ovarian cancer. BMJ Open. 2023; 13(6): e066022. PubMed Abstract | Publisher Full Text | Free Full Text
9. Brewer HR, Hirst Y, Chadeau-Hyam M, et al.: Association between purchase of over-the-counter medications and ovarian cancer diagnosis in the Cancer Loyalty Card Study (CLOCS): Observational Case-Control Study. JMIR Public Health Surveill. 2023; 9: e41762. PubMed Abstract | Publisher Full Text | Free Full Text
10. Das D, Metzger K, Heffernan R, et al.: Monitoring over-the-counter medication sales for early detection of disease outbreaks—New York City. MMWR Suppl. 2005; 54: 41–6. PubMed Abstract
11. Qin Q, Cheng L, Tan L, et al.: Over-the-counter medication sales surveillance for early detection of respiratory epidemics in rural China. Online J Public Health Inform. 2014; 6(1): e61404. Publisher Full Text | Free Full Text
12. Dapkevičiūtė A, Šapoka V, Martynova E, et al.: Time from symptom onset to diagnosis and treatment among haematological malignancies: influencing factors and associated negative outcomes. Medicina (Kaunas). 2019; 55(6): 238. PubMed Abstract | Publisher Full Text | Free Full Text
13. Dulal S, Paudel BD, Wood LA, et al.: Reliance on self-medication increase delays in diagnosis and management of GI cancers: results from Nepal. JCO Glob Oncol. 2020; 6: 1258–63. PubMed Abstract | Publisher Full Text | Free Full Text
14. Goff BA, Mandel L, Muntz HG, et al.: Ovarian carcinoma diagnosis: results of a national ovarian cancer survey. Cancer. 2000; 89(10): 2068–75. PubMed Abstract | Publisher Full Text
15. Flanagan JM, Skrobanski H, Shi X, et al.: Self-care behaviors of ovarian cancer patients before their diagnosis: proof-of-concept study. JMIR Cancer. 2019; 5(1): e10447. PubMed Abstract | Publisher Full Text | Free Full Text
16. Dolan EH, Goulding J, Tata LJ, et al.: Using shopping data to improve the diagnosis of ovarian cancer: computational analysis of a web-based survey. JMIR Cancer. 2023; 9: e37141. PubMed Abstract | Publisher Full Text | Free Full Text
17. Levac D, Colquhoun H, O’Brien KK: Scoping studies: advancing the methodology. Implement Sci. 2010; 5(1): 69. PubMed Abstract | Publisher Full Text | Free Full Text
18. Page MJ, McKenzie JE, Bossuyt PM, et al.: The PRISMA 2020 statement: an updated guideline for reporting systematic reviews. BMJ. 2021; 372: n71. PubMed Abstract | Publisher Full Text | Free Full Text
19. Foster ED, Deardorff A: Open Science Framework (OSF). JMLA. 2017; 105(2). Publisher Full Text
20. Jacob BM, Araz K: Protocol of a Scoping Review on the Use of Transactional Data for Early Diagnosis (TRADED-ScR). OSF Registries; 2024; [cited 2024 Oct 21]. Reference Source
21. Ouzzani M, Hammady H, Fedorowicz Z, et al.: Rayyan—a web and mobile app for systematic reviews. Syst Rev. 2016; 5(1): 210. PubMed Abstract | Publisher Full Text | Free Full Text
22. Chai KEK, Lines RLJ, Gucciardi DF, et al.: Research Screener: a machine learning tool to semi-automate abstract screening for systematic reviews. Syst Rev. 2021; 10(1): 93. PubMed Abstract | Publisher Full Text | Free Full Text
23. Bero L, Chartres N, Diong J, et al.: The Risk of Bias in Observational Studies of Exposures (ROBINS-E) tool: concerns arising from application to observational studies of exposures. Syst Rev. 2018; 7(1): 242. PubMed Abstract | Publisher Full Text | Free Full Text
24. Sterne JA, Hernán MA, Reeves BC, et al.: ROBINS-I: a tool for assessing risk of bias in non-randomised studies of interventions. BMJ. 2016; 355: i4919. PubMed Abstract | Publisher Full Text | Free Full Text

Comments on this article Comments (0)

Version 1

VERSION 1 PUBLISHED 10 Jan 2025

Author details Author details

¹ School of Medicine, RCSI University of Medicine and Health Sciences, County Dublin, County Dublin, Ireland
² Department of General Practise, RCSI University of Medicine and Health Sciences, Dublin, County Dublin, Ireland
³ Department of Surgery and Cancer - Faculty of Medicine, Imperial College London, London, England, UK

Kurdo Araz
Roles: Conceptualization, Methodology, Visualization, Writing – Original Draft Preparation, Writing – Review & Editing

Benjamin M Jacob
Roles: Conceptualization, Writing – Original Draft Preparation, Writing – Review & Editing

James Flanagan
Roles: Conceptualization, Writing – Review & Editing

Patrick Redmond
Roles: Conceptualization, Writing – Original Draft Preparation, Writing – Review & Editing

Competing interests

No competing interests were disclosed.

Grant information

This research was supported by a grant from the HSE National Cancer Control Programme. SDAP-2023-033 Tailoring Lung Cancer Screening for Ireland: Using Existing Datasets to Generate Evidence-Based Recommendations for Health Policy.
The funders had no role in study design, data collection and analysis, decision to publish, or preparation of the manuscript.

Article Versions (1)

version 1

Published: 10 Jan 2025, 8:3

https://doi.org/10.12688/hrbopenres.14015.1

Copyright

© 2025 Araz K et al. 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 work is properly cited.

Download

Export To

metrics

VIEWS

352

downloads

8

Citations

0

SEE MORE DETAILS

CITE

how to cite this article

Araz K, Jacob BM, Flanagan J and Redmond P. Protocol of a Scoping Review on the Use of Transactional Data for Early Diagnosis (TRADED-ScR) [version 1; peer review: 1 approved with reservations]. HRB Open Res 2025, 8:3 (https://doi.org/10.12688/hrbopenres.14015.1)

NOTE: If applicable, it is important to ensure the information in square brackets after the title is included in all citations of this article.

track

receive updates on this article

Track an article to receive email alerts on any updates to this article.

Open Peer Review

Version 1

VERSION 1

PUBLISHED 10 Jan 2025

Views

9

Reviewer Report 30 Jun 2025

Paul A. Townsend, University of Stirling, Stirling, UK

Approved with Reservations

https://doi.org/10.21956/hrbopenres.15383.r47866

This protocol describes a scoping review looking at how transactional data (like loyalty card purchases and over-the-counter medicine sales) might help detect diseases early.

The idea is that people's shopping patterns could reveal when they are self-medicating, ... Continue reading

This protocol describes a scoping review looking at how transactional data (like loyalty card purchases and over-the-counter medicine sales) might help detect diseases early.

The idea is that people's shopping patterns could reveal when they are self-medicating, which might indicate underlying health problems, particularly cancer.

Strengths

Clear reasoning: The authors explain convincingly why this approach could be useful - many people delay seeking medical help and try to manage symptoms themselves with shop-bought medicines.

Sensible methodology: A scoping review is the right approach for this emerging field where research is likely to be patchy and varied. Using the established Arksey and O'Malley framework shows good practice.

Thorough search plan: They have planned to search multiple databases comprehensively, with a sensible two-phase approach (cancer first, then other diseases).

Proper guidelines: The protocol follows recognised reporting standards (PRISMA-P and PRISMA-ScR), which is reassuring.

Main Concerns

1. Missing methodological details
Search strategy: Whilst they say full search terms are available online, they should include at least one example in the paper itself so readers can assess the approach.

AI screening: They mention possibly using AI to help screen papers in Phase 2, but do not explain which tool, how it would be validated, or what safeguards would be in place.

Screening criteria: Table 1 is rather basic. They need clearer definitions of what counts as "transactional data" and how they will handle borderline cases.

2. Data handling issues
Extraction form: Table 2 uses confusing codes ("P-Journal," "M-Review") without explanation. They need clearer categories and better definitions of what constitutes "early detection."

Analysis approach: They mention calculating predictive values but do not explain how they'll do this when studies might not provide complete data, or how they will handle different study designs.

3. Quality assessment gaps
The authors plan to use standard risk-of-bias tools but do not explain how these will work for transactional data studies, which are quite different from typical medical research.

4. Ethics and practicalities
There' i limited discussion of how they will handle studies with questionable ethics, privacy issues, or potential commercial conflicts of interest - important given the commercial nature of much transactional data.

Minor points

The term "event horizon" is confusing in this context
No timeline provided for completing the work
Some terminology could be clearer throughout

Essential changes needed:

Include search strategy examples in the main paper
Expand screening criteria with clearer definitions
Clarify AI screening plans with proper validation details
Improve data extraction forms with better coding systems
Explain analysis methods more clearly, especially for calculating predictive values

Would be helpful:

Add a realistic timeline
Strengthen the quality assessment section
Address ethical considerations more thoroughly

This is interesting and potentially important research tackling a novel area. The overall approach is sound, but several key methodological details need adding/clarifying to make the review reproducible and reliable. The authors deserve credit for taking on this innovative topic, but the protocol needs tightening up to meet proper systematic review standards.
Once these issues are addressed, this could provide a valuable foundation for understanding how shopping data might help with early disease detection.

Is the rationale for, and objectives of, the study clearly described?

Yes
Is the study design appropriate for the research question?

Yes
Are sufficient details of the methods provided to allow replication by others?

Partly
Are the datasets clearly presented in a useable and accessible format?

Not applicable

Competing Interests: No competing interests were disclosed.

Reviewer Expertise: cancer early detection, machine learning, multiple omics and mass spec

I confirm that I have read this submission and believe that I have an appropriate level of expertise to confirm that it is of an acceptable scientific standard, however I have significant reservations, as outlined above.

CITE

Report a concern

Respond or Comment

Comments on this article Comments (0)

Version 1

VERSION 1 PUBLISHED 10 Jan 2025

Open Peer Review

Reviewer Status

Reviewer Reports

	Invited Reviewers
	1
Version 1 10 Jan 25	read

Paul A. Townsend, University of Stirling, Stirling, UK

Comments on this article

All Comments(0)

Add a comment

Sign up for content alerts

Back to all reports

Reviewer Report

9 Views

30 Jun 2025 | for Version 1

Paul A. Townsend, University of Stirling, Stirling, UK

9 Views Cite this report Responses(0)

Approved With Reservations

This protocol describes a scoping review looking at how transactional data (like loyalty card purchases and over-the-counter medicine sales) might help detect diseases early.

The idea is that people's shopping patterns could reveal when they are self-medicating, which might indicate underlying health problems, particularly cancer.

Strengths

Clear reasoning: The authors explain convincingly why this approach could be useful - many people delay seeking medical help and try to manage symptoms themselves with shop-bought medicines.

Sensible methodology: A scoping review is the right approach for this emerging field where research is likely to be patchy and varied. Using the established Arksey and O'Malley framework shows good practice.

Thorough search plan: They have planned to search multiple databases comprehensively, with a sensible two-phase approach (cancer first, then other diseases).

Proper guidelines: The protocol follows recognised reporting standards (PRISMA-P and PRISMA-ScR), which is reassuring.

Main Concerns

1. Missing methodological details
Search strategy: Whilst they say full search terms are available online, they should include at least one example in the paper itself so readers can assess the approach.

AI screening: They mention possibly using AI to help screen papers in Phase 2, but do not explain which tool, how it would be validated, or what safeguards would be in place.

Screening criteria: Table 1 is rather basic. They need clearer definitions of what counts as "transactional data" and how they will handle borderline cases.

2. Data handling issues
Extraction form: Table 2 uses confusing codes ("P-Journal," "M-Review") without explanation. They need clearer categories and better definitions of what constitutes "early detection."

Analysis approach: They mention calculating predictive values but do not explain how they'll do this when studies might not provide complete data, or how they will handle different study designs.

3. Quality assessment gaps
The authors plan to use standard risk-of-bias tools but do not explain how these will work for transactional data studies, which are quite different from typical medical research.

4. Ethics and practicalities
There' i limited discussion of how they will handle studies with questionable ethics, privacy issues, or potential commercial conflicts of interest - important given the commercial nature of much transactional data.

Minor points

The term "event horizon" is confusing in this context
No timeline provided for completing the work
Some terminology could be clearer throughout

Essential changes needed:

Include search strategy examples in the main paper
Expand screening criteria with clearer definitions
Clarify AI screening plans with proper validation details
Improve data extraction forms with better coding systems
Explain analysis methods more clearly, especially for calculating predictive values

Would be helpful:

Add a realistic timeline
Strengthen the quality assessment section
Address ethical considerations more thoroughly

This is interesting and potentially important research tackling a novel area. The overall approach is sound, but several key methodological details need adding/clarifying to make the review reproducible and reliable. The authors deserve credit for taking on this innovative topic, but the protocol needs tightening up to meet proper systematic review standards.
Once these issues are addressed, this could provide a valuable foundation for understanding how shopping data might help with early disease detection.

Is the rationale for, and objectives of, the study clearly described?

Yes
Is the study design appropriate for the research question?

Yes
Are sufficient details of the methods provided to allow replication by others?

Partly
Are the datasets clearly presented in a useable and accessible format?

Not applicable

Competing Interests

No competing interests were disclosed.

Reviewer Expertise

cancer early detection, machine learning, multiple omics and mass spec

I confirm that I have read this submission and believe that I have an appropriate level of expertise to confirm that it is of an acceptable scientific standard, however I have significant reservations, as outlined above.

Respond to this report

Responses (0)

[1] 1. Yuan X, Chen S, Sun C, et al.: A novel early diagnostic framework for chronic diseases with class imbalance. Sci Rep. 2022; 12(1): 8614. PubMed Abstract | Publisher Full Text | Free Full Text

[2] 2. Whitaker K: Earlier diagnosis: the importance of cancer symptoms. Lancet Oncol. 2020; 21(1): 6–8. PubMed Abstract | Publisher Full Text

[3] 3. Varghese SE, El Otol RHM, Al Olama FS, et al.: The importance of early detection of genetic diseases. Dubai Med J. 2021; 4(2): 133–41. Publisher Full Text

[4] 4. Cohn JN, Hoke L, Whitwam W, et al.: Screening for early detection of cardiovascular disease in asymptomatic individuals. Am Heart J. 2003; 146(4): 679–85. PubMed Abstract | Publisher Full Text

[5] 5. McClellan WM, Knight DF, Karp H, et al.: Early detection and treatment of renal disease in hospitalized diabetic and hypertensive patients: important differences between practise and published guidelines. Am J Kidney Dis. 1997; 29(3): 368–75. PubMed Abstract | Publisher Full Text

[6] 6. Whitaker KL, Winstanley K, Macleod U, et al.: Low cancer suspicion following experience of a cancer ‘warning sign’. Eur J Cancer. 2015; 51(16): 2473–9. PubMed Abstract | Publisher Full Text | Free Full Text

[7] 7. Davies A, Green MA, Singleton AD: Using machine learning to investigate self-medication purchasing in England via high street retailer loyalty card data. Rao P, editor. PLoS One. 2018; 13(11): e0207523. PubMed Abstract | Publisher Full Text | Free Full Text

[8] 8. Brewer HR, Chadeau-Hyam M, Johnson E, et al.: Cancer Loyalty Card Study (CLOCS): feasibility outcomes for an observational case-control study focusing on the patient interval in ovarian cancer. BMJ Open. 2023; 13(6): e066022. PubMed Abstract | Publisher Full Text | Free Full Text

[9] 9. Brewer HR, Hirst Y, Chadeau-Hyam M, et al.: Association between purchase of over-the-counter medications and ovarian cancer diagnosis in the Cancer Loyalty Card Study (CLOCS): Observational Case-Control Study. JMIR Public Health Surveill. 2023; 9: e41762. PubMed Abstract | Publisher Full Text | Free Full Text

[10] 10. Das D, Metzger K, Heffernan R, et al.: Monitoring over-the-counter medication sales for early detection of disease outbreaks—New York City. MMWR Suppl. 2005; 54: 41–6. PubMed Abstract

[11] 11. Qin Q, Cheng L, Tan L, et al.: Over-the-counter medication sales surveillance for early detection of respiratory epidemics in rural China. Online J Public Health Inform. 2014; 6(1): e61404. Publisher Full Text | Free Full Text

[12] 12. Dapkevičiūtė A, Šapoka V, Martynova E, et al.: Time from symptom onset to diagnosis and treatment among haematological malignancies: influencing factors and associated negative outcomes. Medicina (Kaunas). 2019; 55(6): 238. PubMed Abstract | Publisher Full Text | Free Full Text

[13] 13. Dulal S, Paudel BD, Wood LA, et al.: Reliance on self-medication increase delays in diagnosis and management of GI cancers: results from Nepal. JCO Glob Oncol. 2020; 6: 1258–63. PubMed Abstract | Publisher Full Text | Free Full Text

[14] 14. Goff BA, Mandel L, Muntz HG, et al.: Ovarian carcinoma diagnosis: results of a national ovarian cancer survey. Cancer. 2000; 89(10): 2068–75. PubMed Abstract | Publisher Full Text

[15] 15. Flanagan JM, Skrobanski H, Shi X, et al.: Self-care behaviors of ovarian cancer patients before their diagnosis: proof-of-concept study. JMIR Cancer. 2019; 5(1): e10447. PubMed Abstract | Publisher Full Text | Free Full Text

[16] 16. Dolan EH, Goulding J, Tata LJ, et al.: Using shopping data to improve the diagnosis of ovarian cancer: computational analysis of a web-based survey. JMIR Cancer. 2023; 9: e37141. PubMed Abstract | Publisher Full Text | Free Full Text

[17] 17. Levac D, Colquhoun H, O’Brien KK: Scoping studies: advancing the methodology. Implement Sci. 2010; 5(1): 69. PubMed Abstract | Publisher Full Text | Free Full Text

[18] 18. Page MJ, McKenzie JE, Bossuyt PM, et al.: The PRISMA 2020 statement: an updated guideline for reporting systematic reviews. BMJ. 2021; 372: n71. PubMed Abstract | Publisher Full Text | Free Full Text

[19] 19. Foster ED, Deardorff A: Open Science Framework (OSF). JMLA. 2017; 105(2). Publisher Full Text

[20] 20. Jacob BM, Araz K: Protocol of a Scoping Review on the Use of Transactional Data for Early Diagnosis (TRADED-ScR). OSF Registries; 2024; [cited 2024 Oct 21]. Reference Source

[21] 21. Ouzzani M, Hammady H, Fedorowicz Z, et al.: Rayyan—a web and mobile app for systematic reviews. Syst Rev. 2016; 5(1): 210. PubMed Abstract | Publisher Full Text | Free Full Text

[22] 22. Chai KEK, Lines RLJ, Gucciardi DF, et al.: Research Screener: a machine learning tool to semi-automate abstract screening for systematic reviews. Syst Rev. 2021; 10(1): 93. PubMed Abstract | Publisher Full Text | Free Full Text

[23] 23. Bero L, Chartres N, Diong J, et al.: The Risk of Bias in Observational Studies of Exposures (ROBINS-E) tool: concerns arising from application to observational studies of exposures. Syst Rev. 2018; 7(1): 242. PubMed Abstract | Publisher Full Text | Free Full Text

[24] 24. Sterne JA, Hernán MA, Reeves BC, et al.: ROBINS-I: a tool for assessing risk of bias in non-randomised studies of interventions. BMJ. 2016; 355: i4919. PubMed Abstract | Publisher Full Text | Free Full Text

Protocol of a Scoping Review on the Use of Transactional Data for Early Diagnosis (TRADED-ScR)

Abstract

Background

Aim

Methods

Implications

Keywords

Introduction

Aims and objectives

Methods

Reporting and registration

Eligibility criteria

Information sources

Search strategy

Selection of sources of evidence

Table 1. Screening algorithm.

Data collection and data management

Table 2. Data charting pro-forma.

Data items

Critical appraisal

Data synthesis

Discussion

Summary

Strengths and limitations

Implications

Conclusion

Reporting guidelines

Ethics and consent

Data availability

Extended data

References

Comments on this article Comments (0)

Open Peer Review

Comments on this article Comments (0)

Open Peer Review

Reviewer Status

Reviewer Reports

Comments on this article

Are you a HRB-funded researcher?

Thank you!

Protocol of a Scoping Review on the Use of Transactional Data for Early Diagnosis (TRADED-ScR)

Abstract

Background

Aim

Methods

Implications

Keywords

Introduction

Aims and objectives

Methods

Reporting and registration

Eligibility criteria

Information sources

Search strategy

Selection of sources of evidence

Table 1. Screening algorithm.

Data collection and data management

Table 2. Data charting pro-forma.

Data items

Critical appraisal

Data synthesis

Discussion

Summary

Strengths and limitations

Implications

Conclusion

Reporting guidelines

Ethics and consent

Data availability

Extended data

References

Comments on this article Comments (0)

Open Peer Review

Comments on this article Comments (0)

Open Peer Review

Reviewer Status

Reviewer Reports

Comments on this article

Competing Interests Policy

Stay Updated

Are you a HRB-funded researcher?

Thank you!