Gonzalez Gabriela, Vaculik Kristina, Khalil Carine, Zektser Yuliya, Arnold Corey W, Almario Christopher V, Spiegel Brennan M R, Anger Jennifer T
Department of Urology, University of California, Davis School of Medicine, Sacramento, California.
Cedars-Sinai Center for Outcomes Research and Education (CS-CORE), Los Angeles, California.
J Urol. 2023 Jan;209(1):208-215. doi: 10.1097/JU.0000000000002955. Epub 2022 Sep 8.
Interstitial cystitis/bladder pain syndrome is a debilitating chronic condition that disproportionately affects women at a ratio of 5:1. We sought to capture women's experiences with interstitial cystitis/bladder pain syndrome by conducting a large-scale digital ethnographic analysis of anonymous posts on Internet forums.
Online posts were identified using condition-specific keywords and data mining extraction services. Once posts were identified, a random sample of 200 online posts was coded and analyzed by hand using qualitative methods. A Latent Dirichlet Allocation probabilistic topic model was applied to the complete dataset to substantiate the qualitative analysis and allow for further thematic discovery.
A total of 6,842 posts written by 3,902 unique users from 224 websites were identified. There was a significant overlap between the hand coding and Latent Dirichlet Allocation themes. Our analysis yielded the following themes: online community engagement, triggers and disease etiologies, medical comorbidities, quality of life impact, patient experience with medical care, and alternative therapies and self-management strategies. Additionally, our population appeared to have a high burden of nonurological associated syndromes. We identified barriers to patient-centered care and found that online peer support was important for women.
Our digital ethnographic analysis is a novel application of qualitative methods using online sources. Social media analytics appears to capture a broader patient population than that typically included in clinic-based qualitative studies, such as patient interviews and focus groups. Understanding patient behaviors and concerns are important to guide strategies for improving care and the overall experience with this difficult-to-treat condition.
间质性膀胱炎/膀胱疼痛综合征是一种使人衰弱的慢性疾病，女性受其影响的比例高达5：1。我们试图通过对互联网论坛上的匿名帖子进行大规模数字人种学分析，来了解女性患间质性膀胱炎/膀胱疼痛综合征的经历。
使用特定疾病关键词和数据挖掘提取服务来识别在线帖子。一旦识别出帖子，就对200个在线帖子的随机样本进行手工编码，并采用定性方法进行分析。将潜在狄利克雷分配概率主题模型应用于完整数据集，以证实定性分析并实现进一步的主题发现。
共识别出224个网站上3902名独特用户撰写的6842篇帖子。手工编码主题与潜在狄利克雷分配主题之间存在显著重叠。我们的分析得出了以下主题：在线社区参与、触发因素和疾病病因、合并症、对生活质量的影响、患者的就医体验以及替代疗法和自我管理策略。此外，我们的研究人群似乎患有非泌尿系统相关综合征的负担较高。我们确定了以患者为中心的护理障碍，并发现在线同伴支持对女性很重要。
我们的数字人种学分析是定性方法在在线资源中的一种新应用。社交媒体分析似乎涵盖了比基于诊所的定性研究（如患者访谈和焦点小组）中通常纳入的患者群体更广泛的人群。了解患者行为和担忧对于指导改善护理的策略以及应对这种难以治疗的疾病的总体体验非常重要。
