TikTok misinformation and user engagement in female pelvic floor conditions.

IMPORTANCE: Social media platforms are increasingly utilized to distribute medical information. Our study emphasizes the need for accuracy in pelvic health education on social media and the involvement of female pelvic floor (FPF) specialists in content creation. AIMS: In this cross-sectional study, we assessed the FPF TikTok videos with the highest engagement for quality of information and misinformation and investigated the relationship between misinformation and user engagement. METHODS: We collected all TikTok videos on the US app with hashtags related to FPF conditions, including 76 on pelvic organ prolapse, 323 on urinary tract infection, 84 on overactive bladder, and 972 on incontinence. The top 20 videos for each FPF condition were selected based on highest engagement, and 74 videos total met inclusion criteria. TikTok videos were scored with the validated DISCERN instrument for quality of consumer health information and a 5-point Likert scale for misinformation. The correlation between misinformation and user engagement was assessed. RESULTS: Our analysis revealed positive correlations among higher average misinformation scores and shares (r = 0.37, p < 0.001), likes (r = 0.23, p = 0.004), and overall engagement (r = 0.25, p = 0.002) in FPF TikTok videos as a group, likely driven by the #UTI category. Most TikTok videos (96%) had poor quality of information (DISCERN score < 3), and 18% of TikTok videos contained misinformation. CONCLUSION: The poor quality and prevalence of misinformation in FPF-related TikTok videos with the highest engagement raise concerns about the propagation of nonevidence-based health information.

Changes in Facility Share of Medicaid-insured Patients With Urologic Cancers Following Implementation of the Patient Protection and Affordable Care Act.

OBJECTIVE: To examine Medicaid-insurance acceptance at facilities treating urologic cancers following implementation of the Affordable Care Act (ACA). METHODS: We conducted a retrospective, longitudinal study with a pre-post design. We accessed 2010-2017 data from the National Cancer Database, calculating the facility-level change in proportion of urologic cancer patients with Medicaid following implementation of the ACA. We used multivariable logistic regression to assess baseline clinical and demographic factors associated with changes in the proportion of patients at a facility insured through Medicaid. RESULTS: We identified 630 facilities, including 287 in Medicaid expansion states and 343 in non-expansion states associated with 436,082 urologic cancer patients. The mean facility-level change in proportion of patients with Medicaid was + 5.8% (95% CI 5.0%-6.5%) in expansion states versus + 0.6% (95% CI 0.2%-0.9%) in non-expansion states. There were 179 facilities that experienced a decrease in the post-ACA period, representing 13.6% of facilities in expansion states and 40.8% in non-expansion states (P <.001). Factors associated with a decrease in proportion of urologic cancer patients insured by Medicaid included non-expansion state status (OR 8.9, 95% CI 5.3-15.6, P <.001), higher baseline proportion of patients with Medicaid (highest quartile vs lowest: OR 4.6, 95% CI 2.3-9.4, P <.001) and high-income zip code (highest vs lowest quartile: OR 3.1, 95% CI 1.5-6.6, P <.001). CONCLUSION: Urologic cancer care for Medicaid-insured Americans remains unevenly distributed across cancer care centers, even in states that expanded coverage. Our findings suggest that this variation may reflect the effort of some facilities to reduce their financial exposure to increased numbers of Medicaid patients in the wake of ACA-supported state expansions.

Association between sociodemographic factors and diagnosis of lethal prostate cancer in early life.

OBJECTIVE: A subset of patients are diagnosed with lethal prostate cancer (CaP) early in life before prostate-specific antigen (PSA) screening is typically initiated. To identify opportunities for improved detection, we evaluated patient sociodemographic factors associated with advanced vs. localized (CaP) diagnosis across the age spectrum. METHODS: We conducted a retrospective cohort study using the National Cancer Database, identifying patients diagnosed with CaP from 2004 to 2020. We compared characteristics of patients diagnosed at the advanced (cN1 or M1) versus localized (cT1-4N0M0) stage. Using multivariable logistic regression, we evaluated the associations among patient clinical and sociodemographic factors and advanced diagnosis, stratifying patients by age as ≤55 (before screening is recommended for most patients), 56 to 65, 66 to 75, and ≥76 years. RESULTS: We identified 977,722 patients who met the inclusion criteria. The mean age at diagnosis was 65.3 years and 50,663 (5.1%) had advanced disease. Overall, uninsured (OR = 3.20, 95% CI 3.03-3.78) and Medicaid-insured (OR 2.58, 95% CI 2.48-2.69) vs. privately insured status was associated with higher odds of diagnosis with advanced disease and this effect was more pronounced for younger patients. Among patients ≤55 years, uninsured (OR 4.14, 95% CI 3.69-4.65) and Medicaid-insured (OR 3.39, 95% CI 3.10-3.72) vs. privately insured patients were associated with higher odds of advanced cancer at diagnosis. Similarly, residence in the lowest vs. highest income quartile was associated with increased odds of advanced CaP in patients ≤55 years (OR 1.15, 95% CI 1.02-1.30). Black vs. White race was associated with increased odds of advanced CaP at diagnosis later in life (OR 1.17, 95% CI 1.09-1.25); however, race was not significantly associated with advanced stage CaP in those ≤55 years (P = 0.635). CONCLUSIONS: Sociodemographic disparities in diagnosis at advanced stages of CaP were more pronounced in younger patients, particularly with respect to insurance status. These findings may support greater attention to differential use of early CaP screening based on patient health insurance.

High and low frequency anterior nucleus of thalamus deep brain stimulation: Impact on memory and mood in five patients with treatment resistant temporal lobe epilepsy.

High frequency anterior nucleus of the thalamus deep brain stimulation (ANT DBS) is an established therapy for treatment resistant focal epilepsies. Although high frequency-ANT DBS is well tolerated, patients are rarely seizure free and the efficacy of other DBS parameters and their impact on comorbidities of epilepsy such as depression and memory dysfunction remain unclear. The purpose of this study was to assess the impact of low vs high frequency ANT DBS on verbal memory and self-reported anxiety and depression symptoms. Five patients with treatment resistant temporal lobe epilepsy were implanted with an investigational brain stimulation and sensing device capable of ANT DBS and ambulatory intracranial electroencephalographic (iEEG) monitoring, enabling long-term detection of electrographic seizures. While patients received therapeutic high frequency (100 and 145 Hz continuous and cycling) and low frequency (2 and 7 Hz continuous) stimulation, they completed weekly free recall verbal memory tasks and thrice weekly self-reports of anxiety and depression symptom severity. Mixed effects models were then used to evaluate associations between memory scores, anxiety and depression self-reports, seizure counts, and stimulation frequency. Memory score was significantly associated with stimulation frequency, with higher free recall verbal memory scores during low frequency ANT DBS. Self-reported anxiety and depression symptom severity was not significantly associated with stimulation frequency. These findings suggest the choice of ANT DBS stimulation parameter may impact patients' cognitive function, independently of its impact on seizure rates.

A platform for brain network sensing and stimulation with quantitative behavioral tracking: Application to limbic circuit epilepsy.

Temporal lobe epilepsy is a common neurological disease characterized by recurrent seizures. These seizures often originate from limbic networks and people also experience chronic comorbidities related to memory, mood, and sleep (MMS). Deep brain stimulation targeting the anterior nucleus of the thalamus (ANT-DBS) is a proven therapy, but the optimal stimulation parameters remain unclear. We developed a neurotechnology platform for tracking seizures and MMS to enable data streaming between an investigational brain sensing-stimulation implant, mobile devices, and a cloud environment. Artificial Intelligence algorithms provided accurate catalogs of seizures, interictal epileptiform spikes, and wake-sleep brain states. Remotely administered memory and mood assessments were used to densely sample cognitive and behavioral response during ANT-DBS. We evaluated the efficacy of low-frequency versus high-frequency ANT-DBS. They both reduced seizures, but low-frequency ANT-DBS showed greater reductions and better sleep and memory. These results highlight the potential of synchronized brain sensing and behavioral tracking for optimizing neuromodulation therapy.