The use of infodemiology in the development of targeted public health actions on endometriosis in France: a mixed & patient-centric methodology (Preprint)

B. Derycke, Emma Le Priol, Pierre Foulquié, Tom Marty, Anaïs Gedik, Stéphane Schück, Nathalie Texier, Adel Mebarki, Paul Loussikian
2023 · doi:10.2196/preprints.49048 · W4376876095
preprint OA: gold CC0
🔓 Open OA copy Full text JSON View on OpenAlex View at publisher
⚙ AI-generated summary by claude@2026-06, 2026-06-06 ⓘ

This study used infodemiology and patient interviews to identify major difficulties faced by endometriosis patients in France, including care pathway issues, pathology-related problems, and treatment challenges.

One-sentence paraphrase of the abstract; not a substitute for reading it. No clinical advice. How this works

⚙ AI-generated deep summary by claude@2026-06, 2026-06-06 · read from full text ⓘ

This unreviewed preprint studied whether infodemiology (analyzing internet health-related content) can reliably inform targeted public health actions for endometriosis in France, combining social media mining with qualitative validation. Geolocated social media posts from January 2020 to 2022 were collected and analyzed using natural language processing to identify endometriosis-related posts expressing difficulties and unmet needs, followed by semi-structured interviews in July 2022 with 9 women with endometriosis and healthcare professionals; a thematic analysis was used to generate general statistics. From 2,396 written posts by 1,742 patients, 96.7% were identified as containing at least one difficulty, with unmet needs/difficulties most often related to issues across the care pathway and pathology-related aspects, and interview-derived “top difficulties” including treatment-related issues and environmental impact. The authors acknowledge that it is a preprint with no peer-reviewed confirmation. This paper is centrally about endometriosis — it develops a mixed patient-centric infodemiology methodology to characterize endometriosis-related difficulties and unmet needs in France.

Read from the paper's body, not the abstract. Not a substitute for reading the paper. No clinical advice. How this works

Abstract

BACKGROUND Endometriosis is a chronic gynaecological disease of women of reproductive age linked to menstruation and the hormones that trigger it. The symptoms of endometriosis have a major impact on women’s quality of life, either on a psychological level or on a societal one. Today, as many other female-specific topics, endometriosis is under-researched and under-diagnosed and many causes remain unknown. Although literature suggests that endometriosis patients are prone to use social network as a self-management tool and discuss various topics related to their disease, very few papers applied infodemiological (epidemiology based on internet health-related content) methods to endometriosis. OBJECTIVE The main objective of this study is to understand whether infodemiology could be useful for public health decision makers in the management of patients with endometriosis and to prove it to be a reliable study methodology. METHODS Firstly, social media post were collected between January 2020 and 2022 from France geolocalized sites using keyword for endometriosis. Automatic natural language processing methods were used to identify relevant patients with endometriosis posts expressing difficulties and unmet needs. Then, semi-structured interviews were conducted during July 2022 with 9 women with endometriosis and healthcare professionals. Discussions were recorded and transcribed verbatims were analysed through a thematic analysis allowing to generate general statistics on the most discussed topics. RESULTS A total of 2,396 written by 1,742 patients with endometriosis were included. 2,317 (96.70%) were identified as containing at least one difficulty. The top 3 unmet needs and difficulties were difficulties present throughout the care pathway (71.1%; n = 1701), pathology-related (57.2%; n = 1,356) and treatments and medical procedures (20.6 %; n = 492). Semi-structured interviews show that the top 3 main difficulties for patients with endometriosis were difficulties related to treatments (30% of the detected medical concepts), impact of the disease on the patient's environment (29% of the concepts) and transversal difficulties (16% of the concepts). CONCLUSIONS The bi-patient-centric approach with limited manual work allows to identify difficulties encountered by patients with endometriosis in France. The mixed-methodology of this study appears ideal to demonstrate their complementarity and to justify the use of AI algorithms for public health decision-making. Furthermore, endometriosis is a disease affecting a wide range of women, each with their own specific difficulties. They now expect societal and medical recognition of their illness and care pathways adapted to their individual symptoms.
Full text 5,508 characters · extracted from oa-html · 5 sections · click to expand

Abstract

Background: Endometriosis is a chronic gynaecological disease of women of reproductive age linked to menstruation and the hormones that trigger it. The symptoms of endometriosis have a major impact on women’s quality of life, either on a psychological level or on a societal one. Today, as many other female-specific topics, endometriosis is under-researched and under-diagnosed and many causes remain unknown. Although literature suggests that endometriosis patients are prone to use social network as a self-management tool and discuss various topics related to their disease, very few papers applied infodemiological (epidemiology based on internet health-related content) methods to endometriosis.

Objective

The main objective of this study is to understand whether infodemiology could be useful for public health decision makers in the management of patients with endometriosis and to prove it to be a reliable study methodology.

Methods

Firstly, social media post were collected between January 2020 and 2022 from France geolocalized sites using keyword for endometriosis. Automatic natural language processing methods were used to identify relevant patients with endometriosis posts expressing difficulties and unmet needs. Then, semi-structured interviews were conducted during July 2022 with 9 women with endometriosis and healthcare professionals. Discussions were recorded and transcribed verbatims were analysed through a thematic analysis allowing to generate general statistics on the most discussed topics.

Results

A total of 2,396 written by 1,742 patients with endometriosis were included. 2,317 (96.70%) were identified as containing at least one difficulty. The top 3 unmet needs and difficulties were difficulties present throughout the care pathway (71.1%; n = 1701), pathology-related (57.2%; n = 1,356) and treatments and medical procedures (20.6 %; n = 492). Semi-structured interviews show that the top 3 main difficulties for patients with endometriosis were difficulties related to treatments (30% of the detected medical concepts), impact of the disease on the patient's environment (29% of the concepts) and transversal difficulties (16% of the concepts).

Conclusions

The bi-patient-centric approach with limited manual work allows to identify difficulties encountered by patients with endometriosis in France. The mixed-methodology of this study appears ideal to demonstrate their complementarity and to justify the use of AI algorithms for public health decision-making. Furthermore, endometriosis is a disease affecting a wide range of women, each with their own specific difficulties. They now expect societal and medical recognition of their illness and care pathways adapted to their individual symptoms. Citation Request queued. Please wait while the file is being generated. It may take some time. Copyright © The authors. All rights reserved. This is a privileged document currently under peer-review/community review (or an accepted/rejected manuscript). Authors have provided JMIR Publications with an exclusive license to publish this preprint on it's website for review and ahead-of-print citation purposes only. While the final peer-reviewed paper may be licensed under a cc-by license on publication, at this stage authors and publisher expressively prohibit redistribution of this draft paper other than for review purposes.

Text is read by the "Ask this paper" AI Q&A widget below. Extraction quality varies by source — PMC NXML preserves structure cleanly, OA-HTML may include some navigation residue, and OA-PDF can have broken hyphenation. The publisher copy (via DOI) is the canonical version.

My notes (saved in your browser only)

⚙ Ask this paper AI returns verbatim quotes from the full text · source: oa-html ⓘ

Answers must be backed by verbatim quotes from this paper's full text. Hallucinated quotes are dropped automatically; if no verbatim passage answers the question, we say so. How this works

Condition tags

endometriosis

Citation neighborhood

Papers in the corpus that this work cites (lower rings, blue) and that cite this one (upper rings, green). Dot size scales with the paper's in-corpus citation count — bigger dot = more influential within the endo/adeno field. Click a dot to open that paper. [ expand to 2 hops ] — adds papers reached through this work's immediate citers/citees. Heavier; up to 60 extra dots.

References (32)

Source provenance

openalex
last seen: 2026-06-10T17:14:06.276822+00:00
License: CC0 · commercial use OK