Lower Urinary Tract Symptoms in US Women: Contemporary Prevalence Estimates from the RISE FOR HEALTH Study.

PURPOSE: We aimed to estimate the prevalence of a wide range of lower urinary tract symptoms (LUTS) in US women, and explore associations with bother and discussion with health care providers, friends, and family. MATERIALS AND METHODS: We analyzed baseline data collected from May 2022 to December 2023 in the RISE FOR HEALTH study-a large, regionally representative cohort study of adult female community members. LUTS and related bother were measured by the 10-item Symptoms of Lower Urinary Tract Dysfunction Research Network Symptom Index, and discussion was assessed by a study-specific item. RESULTS: Of the 3000 eligible participants, 73% (95% CI 71%-74%) reported any storage symptoms, 52% (95% CI 50%-53%) any voiding or emptying symptoms, and 11% (95% CI 10%-13%) any pain with bladder filling, for an overall LUTS prevalence of 79% (95% CI 78%-81%). This prevalence estimate included 43% (95% CI 41%-45%) of participants with mild to moderate symptoms and 37% (95% CI 35%-38%) with moderate to severe symptoms. Over one-third of participants reported LUTS-related bother (38%, 95% CI 36%-39%) and discussion (38%, 95% CI 36%-40%), whereas only 7.1% (95% CI 6.2%-8.1%) reported treatment. Urgency and incontinence (including urgency and stress incontinence) were associated with the greatest likelihood of bother and/or discussion (adjusted prevalence ratios = 1.3-2.3), even at mild to moderate levels. They were also the most commonly treated LUTS. CONCLUSIONS: LUTS, particularly storage LUTS such as urgency and incontinence, were common and bothersome in the RISE study population, yet often untreated. Given this large burden, both prevention and treatment-related interventions are warranted to reduce the high prevalence and bother of LUTS.

Associations of Financial Strain and Unmet Social Needs with Women's Bladder Health.

OBJECTIVE: Financial strain and unmet social needs are associated with greater risk for lower urinary tract symptoms. Little research has examined financial strain and unmet social needs in relation to the more holistic concept of bladder health. This study utilizes baseline data from RISE FOR HEALTH: A U.S. Study of Bladder Health to examine whether financial strain, unmet social needs, and meeting specific federal poverty level threshold levels are associated with lower urinary tract symptoms and poorer perceived bladder health, well-being, and function. STUDY DESIGN: Participants were 18 years or older, born female or currently identified as a woman, and from the civilian, noninstitutionalized population residing in 50 counties in the United States that included or surrounded nine recruitment centers. Data were collected through mailed or internet-based surveys. To address research questions, the 10-item Lower Urinary Tract Dysfunction Research Network Symptom Index and selected Prevention of Lower Urinary Tract Symptoms Research Consortium bladder health scores were separately regressed on each financial strain, unmet social need, and federal poverty level variable, using linear regression adjusting for covariates (age, race/ethnicity, education, and vaginal parity) and robust variance estimation for confidence intervals. Participants with no missing data for a given analysis were included (range of n=2,564 to 3,170). In separate sensitivity analyses, body mass index, hypertension, and diabetes were added as covariates and missing data were imputed. RESULTS: The mean age of participants was 51.5 years (standard deviation=18.4). Not having enough money to make ends meet, housing insecurity, food insecurity, unreliable transportation, and percent federal poverty levels of 300% or less were consistently associated with more reported lower urinary tract symptoms and poorer perceived bladder health. For example, compared to food secure participants, women who worried that their food would run out at the end of the month had a Lower Urinary Tract Dysfunction Research Network - Symptom Index score that was 3.4 points higher (95% CI: 2.5, 4.3), on average. They also had lower mean scores across different bladder health measures, each assessed using a 100-point scale: global bladder health (-8.2, 95% CI: -10.8,-5.7), frequency (-10.2, 95% CI: -13.8,-6.7), sensation (-11.6, 95% CI: -15.1,-8.2), continence (-13.3, 95% CI: -16.7,-9.9), and emotional impact of bladder health status (-13.2, 95% CI: -16.5,-9.9). Across analyses, associations largely remained significant after additional adjustment for body mass index, hypertension, and diabetes. The pattern of results when imputing missing data was similar to that observed with complete case analysis; all significant associations remained significant with imputation. CONCLUSION: Financial strain and unmet social needs are associated with worse LUTS and poorer bladder health. Longitudinal research is needed to examine whether financial strain and unmet social needs influence the development, maintenance, and worsening of lower urinary tract symptoms; different mechanisms by which financial strain and unmet social needs may impact symptoms; and the degree to which symptoms contribute to financial strain. If supported by etiologic research, prevention research can be implemented to determine whether the amelioration of financial strain and social needs, including enhanced access to preventative care, may promote bladder health across the life course.

Optimizing the measurement of sample entropy in resting-state fMRI data.

Introduction: The complexity of brain signals may hold clues to understand brain-based disorders. Sample entropy, an index that captures the predictability of a signal, is a promising tool to measure signal complexity. However, measurement of sample entropy from fMRI signals has its challenges, and numerous questions regarding preprocessing and parameter selection require research to advance the potential impact of this method. For one example, entropy may be highly sensitive to the effects of motion, yet standard approaches to addressing motion (e.g., scrubbing) may be unsuitable for entropy measurement. For another, the parameters used to calculate entropy need to be defined by the properties of data being analyzed, an issue that has frequently been ignored in fMRI research. The current work sought to rigorously address these issues and to create methods that could be used to advance this field. Methods: We developed and tested a novel windowing approach to select and concatenate (ignoring connecting volumes) low-motion windows in fMRI data to reduce the impact of motion on sample entropy estimates. We created utilities (implementing autoregressive models and a grid search function) to facilitate selection of the matching length m parameter and the error tolerance r parameter. We developed an approach to apply these methods at every grayordinate of the brain, creating a whole-brain dense entropy map. These methods and tools have been integrated into a publicly available R package ("powseR"). We demonstrate these methods using data from the ABCD study. After applying the windowing procedure to allow sample entropy calculation on the lowest-motion windows from runs 1 and 2 (combined) and those from runs 3 and 4 (combined), we identified the optimal m and r parameters for these data. To confirm the impact of the windowing procedure, we compared entropy values and their relationship with motion when entropy was calculated using the full set of data vs. those calculated using the windowing procedure. We then assessed reproducibility of sample entropy calculations using the windowed procedure by calculating the intraclass correlation between the earlier and later entropy measurements at every grayordinate. Results: When applying these optimized methods to the ABCD data (from the subset of individuals who had enough windows of continuous "usable" volumes), we found that the novel windowing procedure successfully mitigated the large inverse correlation between entropy values and head motion seen when using a standard approach. Furthermore, using the windowed approach, entropy values calculated early in the scan (runs 1 and 2) are largely reproducible when measured later in the scan (runs 3 and 4), although there is some regional variability in reproducibility. Discussion: We developed an optimized approach to measuring sample entropy that addresses concerns about motion and that can be applied across datasets through user-identified adaptations that allow the method to be tailored to the dataset at hand. We offer preliminary results regarding reproducibility. We also include recommendations for fMRI data acquisition to optimize sample entropy measurement and considerations for the field.

Psychiatric Diagnoses and Treatment in Nine- to Ten-Year-Old Participants in the ABCD Study.

Objective: Psychiatric disorders commonly emerge prior to adulthood. Identification and intervention may vary significantly across populations. We leveraged a large population-based study to estimate the prevalence of psychiatric disorders and treatments, and evaluate predictors of treatment, in children ages 9-10 in the United States. Method: We analyzed cross-sectional data from the Adolescent Brain Cognitive Developmental (ABCD) Study. The Computerized Kiddie Schedule for Affective Disorders and Schizophrenia (KSADS-COMP) was used to estimate clinical diagnoses, and the Child Behavior Checklist (CBCL) was used to assess internalizing and externalizing psychopathology. Parents reported on prescription medications and other mental health interventions. Prevalence rates of KSADS diagnoses and treatments were calculated. Logistic regression analyses estimated associations between clinical and sociodemographic predictors (sex at birth, race, ethnicity, income, education, urbanicity) and treatments. Results: The most common KSADS diagnoses were anxiety disorders, followed by attention deficit/hyperactivity disorder (ADHD) and oppositional defiant disorder. ADHD and depression diagnoses predicted stimulant and antidepressant medication use, respectively. Bipolar and ADHD diagnoses also predicted antidepressant medications, outpatient treatment and psychotherapy. The odds of reporting specific treatments varied by sex, ethnic and racial identities, urbanicity, and income. Conclusion: Expected rates of KSADS-based psychiatric symptoms are present in the ABCD sample at ages 9-10, with treatment patterns broadly mapping onto psychopathology in expected ways. However, we observed important variations in reported treatment utilization across sociodemographic groups, likely reflecting societal and cultural influences. Findings are considered in the context of potential mental health disparities in U.S. children.