Living With and Managing Uncomplicated Urinary Tract Infection: Mixed Methods Analysis of Patient Insights From Social Media
Bibliographic record
Abstract
BACKGROUND: Uncomplicated urinary tract infections (uUTIs) affect more than half of women in their lifetime and can impact on quality of life. We analyzed social media posts discussing uUTIs to gather insights into the patient experience, including aspects of their disease management journey and associated opinions and concerns. OBJECTIVE: This study aims to gather patient experience insights by analyzing social media posts that discussed uUTI. METHODS: A search string ("urinary tract infection" [UTI] or "bladder infection" or "cystitis" or "UTI" not "interstitial cystitis") was used to identify posts from public blogs and patient forums (June 2021 to June 2023). Posts were excluded if they were not written in English or discussed complicated UTI (posts that mentioned "pregnancy" or "pregnant" or "trimester" or "catheter" or "interstitial"). Posts were limited to publicly available sources and anonymized. The primary objective was to gather patient perspectives on key elements of the uUTI experience, including health care professional (HCP) interactions, diagnosis, treatment, and recurrence. RESULTS: In total, more than 42,000 unique posts were identified (mostly from reddit.com; 29,506/42,265, 70%) and >3600 posts were analyzed. Posts were most commonly from users in the United States (6707/11,180, 60%), the United Kingdom (2261/11,180, 20%), Canada (509/11,180, 5%), Germany (356/11,180, 3%), or India (320/11,180, 3%). Six main themes were identified: symptom awareness and information seeking, HCP interactions, diagnosis and management challenges, management with antibiotics, self-management, and challenges with recurrent UTI. Most posts highlighted the importance of seeking professional medical advice, while some patients raised concerns regarding their HCP interactions and lack of shared decision-making. Patients searched for advice and guidance on the web prior to consulting an HCP, described their symptoms, and discussed lifestyle adjustments. Most patients tried self-management and shared their experiences with nonprescribed treatment options. There was general agreement among posts that antibiotics are necessary to cure UTIs and prevent associated complications. CONCLUSIONS: Social media posts provide valuable insight into the experiences and opinions of patients with uUTIs in Canada, Germany, India, the United Kingdom, and the United States. The insights from this study provide a more complete picture of patient behaviors and highlight the potential for HCP and patient education, as well as better communication through shared decision-making to improve care.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.014 | 0.052 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.004 | 0.003 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 0.001 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".