Investigator

Marc Chadeau-Hyam

Professor in Computational Epidemiology and Biostatistics · Imperial College London, School of Public Health

About

Research Interests

MCMarc Chadeau-Hyam
Papers(1)
Association Between P…
Collaborators(5)
Sudha SundarYasemin HirstEric JohnsonHannah R BrewerJames M. Flanagan
Institutions(3)
Imperial College Lond…The University of Bir…University of Lancash…

Papers

Association Between Purchase of Over-the-Counter Medications and Ovarian Cancer Diagnosis in the Cancer Loyalty Card Study (CLOCS): Observational Case-Control Study

Background Over-the-counter (OTC) medications are frequently used to self-care for nonspecific ovarian cancer symptoms prior to diagnosis. Monitoring such purchases may provide an opportunity for earlier diagnosis. Objective The aim of the Cancer Loyalty Card Study (CLOCS) was to investigate purchases of OTC pain and indigestion medications prior to ovarian cancer diagnosis in women with and without ovarian cancer in the United Kingdom using loyalty card data. Methods An observational case-control study was performed comparing purchases of OTC pain and indigestion medications prior to diagnosis in women with (n=153) and without (n=120) ovarian cancer using loyalty card data from two UK-based high street retailers. Monthly purchases of pain and indigestion medications for cases and controls were compared using the Fisher exact test, conditional logistic regression, and receiver operating characteristic (ROC) curve analysis. Results Pain and indigestion medication purchases were increased among cases 8 months before diagnosis, with maximum discrimination between cases and controls 8 months before diagnosis (Fisher exact odds ratio [OR] 2.9, 95% CI 2.1-4.1). An increase in indigestion medication purchases was detected up to 9 months before diagnosis (adjusted conditional logistic regression OR 1.38, 95% CI 1.04-1.83). The ROC analysis for indigestion medication purchases showed a maximum area under the curve (AUC) at 13 months before diagnosis (AUC=0.65, 95% CI 0.57-0.73), which further improved when stratified to late-stage ovarian cancer (AUC=0.68, 95% CI 0.59-0.78). Conclusions There is a difference in purchases of pain and indigestion medications among women with and without ovarian cancer up to 8 months before diagnosis. Facilitating earlier presentation among those who self-care for symptoms using this novel data source could improve ovarian cancer patients’ options for treatment and improve survival. Trial Registration ClinicalTrials.gov NCT03994653; https://clinicaltrials.gov/ct2/show/NCT03994653

Clinical Trials (1)

NCT03994653Imperial College London

Cancer Loyalty Card Study

Approximately 7,400 new cases of ovarian cancer are diagnosed each year in the United Kingdom (UK), and with over 4,000 women dying from the disease each year it is a particularly lethal form of cancer. The symptoms for ovarian cancer are not well known and vague, and most women are diagnosed at a late stage when the cancer has already spread around the abdominal cavity with poor prognosis. Novel methods are needed to improve earlier detection and thereby improve survival from this disease. The Cancer Loyalty Card Study (CLOCS) proposes to use loyalty card data from two participating high street retailers to investigate purchase behaviour as an opportunity for cancer symptom surveillance. The investigators aim to conduct a case-control study of ovarian cancer patients matched with women without ovarian cancer and to explore public preferences for how to communicate potential outcomes of the commercial and health data linkages back to individuals. Eligible participants will be women in the UK who own at least one loyalty card with the participating high street retailers. Of these women, those who have been diagnosed with ovarian cancer are eligible to participate in the study as cases, while women who have not been diagnosed with ovarian cancer are eligible to participate as controls. Upon choosing to participate, all participants will be asked to complete a short questionnaire about well-established ovarian cancer risk factors and common symptoms either in the clinic (cases) or online/from a packet in the mail(controls). This information will be used in risk assessment for ovarian cancer of participants, which will be used at the analysis stage.

235Works
1Papers
5Collaborators
1Trials
Lung NeoplasmsBreast NeoplasmsPrognosisPost-Acute COVID-19 SyndromeNeoplasmsOrthomyxoviridae InfectionsNeoplasm MetastasisCardiovascular Diseases

Positions

2020–

Professor in Computational Epidemiology and Biostatistics

Imperial College London · School of Public Health

2018–

Reader in Computational Epidemiology and Exposome Sciences

Imperial College London · School of Public Health

2015–

Senior Lecturer

Imperial College London · School of Public Health

2011–

Lecturer

Imperial College London · School of Public Health

Education

2005

PhD

University of Paris · Statistics at INSERM

2001

MSc

University of Paris · Statistics at INSERM

2000

DEng

Oniris Nantes - Site de la Géraudière · Statitics