Inquiry-The Journal of Health Care Organization Provision and Financing最新文献

This study examined gender differences in sleep outcomes among spousal caregivers of disabled partners in China and the relationship between the breadth of a care recipient's functional disability and a caregiver's sleep. This study used the 2020 data from the fifth wave of the China Health and Retirement Longitudinal Study (CHARLS), including 13 243 participants, of whom 1600 were caregivers for disabled spouses. The degree of disability was assessed using the Basic/Instrumental Activities of Daily Living (BADL/IADL) scale. Sleep quality and duration were measured by self-report. Statistical analyses included chi-square tests and binary logistic regression, adjusting for potential confounders such as sociodemographic characteristics, health status, and lifestyle factors. Women spousal caregivers had shorter sleep duration (OR = 1.309, 95% CI: 1.098-1.561) and poorer sleep quality (OR = 1.200, 95% CI: 1.011-1.424) versus non-caregivers, whereas no significant differences were observed among men caregivers. Analysis of the parametric relationship revealed that when the number of types of disabilities in care recipients reached 5 to 8, caregivers' sleep duration was significantly reduced (OR = 1.238, 95% CI: 1.007-1.522); when the number reached 9 to 12, both sleep duration (OR = 1.616, 95% CI: 1.096-2.382) and sleep quality (OR = 1.774, 95% CI: 1.220-2.579) of caregivers deteriorated significantly. Caregiving for a disabled spouse impairs sleep among women. A clear graded relationship exists between care recipients' functional disability types and caregivers' sleep problems. Support policies should consider gender differences and target women undertaking high-intensity care.

Using AI-powered mobile applications for mental health screening can help reduce maternal mental health disparities among Black mothers who are pregnant or parenting in the United States. A maternal health education question and answer mobile application chatbot has the potential to intervene in the maternal depression cascade, specifically screening. Extant research demonstrates the usability of mobile applications addressing mental health. However, limited scholarship explores the intersection between AI-powered mobile application chatbots and maternal mental health. This study uses a multimethod analysis to evaluate the usability of an AI-powered mobile application to address maternal mental health among Black women. Data sources, including mobile application engagement, mental health disorder scales, and secondary qualitative analysis from focus group discussions (n = 5), will be assessed through a multimethod approach. The study team previously collected data across the United States for this clinical intervention in 2022. Findings indicate that the mobile application demonstrated promise in the application's usability to screen for maternal health depression indicators. This was achieved using the mobile application's intent classification functionality that classified users' questions that contained targeted search terms (e.g., postpartum depression) or specific inquiries about mental health and appropriate follow-up from the study team to provide mental health resources. Critical interconnected themes were assessed and reflected high confidence, acceptance, and usability of the mobile application in addressing maternal mental health inquiries. Findings contribute to evidence about the usability of AI-powered mobile applications informed by Black mothers in appropriate screening for maternal depression indicators and inquiries. This study provides insight into closing the gap in maternal health disparities in depression outcomes for Black mothers.Trial Registration: ClinicalTrials.gov NCT06053515; https://clinicaltrials.gov/study/NCT06053515.

This study aims to conduct a systematic quality assessment of frailty assessment tools for elderly Chinese speakers in order to screen and recommend high-quality assessment tools and provide theoretical support for healthcare professionals. We searched multiple databases, including PubMed, Embase, Web of Science, CINAHL, China National Knowledge Infrastructure (CNKI), China Science and Technology Journal(VIP), and WanFang Data, for contents related to the psychometric properties, scale localization, and cross-cultural studies of Chinese frailty assessment instruments for the elderly. The search period was from the establishment of the databases to April 1, 2025. Two researchers independently screened the literature and extracted data. The risk of bias for the instruments was assessed using the bias risk assessment form of Consensus-based Standards for the Selection of Health Measurement Instruments (COSMIN). The measurement properties were evaluated using the assessment form of COSMIN, and the modified Grading of Recommendations, Assessment, Development, and Evaluation (GRADE) system were used to determine the recommendation level. A total of 35 studies involving 25 Chinese frailty assessment scales were included, none of which reported cross-cultural validity or responsiveness. Based on evidence of low quality or above, the C-RGA and GFI-C frailty assessment scales for hospitalized elderly patients were deemed to possess "adequate" content validity and internal consistency. These 3 assessment tools were recommended as Grade A, with 12 others classified as Grade B, 7 as Grade C, and 3 as Grade D. There are various Chinese frailty assessment instruments for the elderly, but their quality varies greatly. The Frailty Assessment Scale for Elderly Hospitalized Patients, C-RGA, and GFI-C are suitable for the assessment needs of most elderly populations are provisionally recommended for use in clinical and institutional settings pending further validation. Therefore, none of the tools can yet be considered fully validated for broad clinical or research use without further rigorous psychometric evaluation.Trial registration: PROSPERO registration number withheld for anonymous review.

Benign prostatic hyperplasia (BPH) is highly prevalent among older men, yet its population-level assessment often depends on physician diagnosis, which may vary by healthcare access and reporting behavior. In China, substantial urban-rural differences in healthcare utilization raise questions about whether reported differences in BPH reflect true variation or diagnostic patterns. This study therefore examined urban-rural differences in self-reported physician-diagnosed BPH and associated factors using nationally representative data. We conducted a cross-sectional analysis of 8455 men aged 45 years and older using data from Wave 4 (2018) of the China Health and Retirement Longitudinal Study (CHARLS). BPH status was defined based on self-reported physician diagnosis. Urban-rural differences in reported BPH were compared, and multivariable logistic regression models were used to examine factors associated with reported BPH diagnosis. Of 8455 participants, the overall proportion reported a physician diagnosis of BPH was 11.9%, with subgroup-specific proportions of 18.4% in urban men and 10.1% in rural men. After adjustment for sociodemographic, lifestyle, and health-related variables, rural residence was associated with lower odds of reporting BPH (aOR 0.61, 95% CI 0.51-0.72). Several associations differed by residence, including a positive association between moderate physical activity and reported BPH in urban men, and inverse associations for longer sleep duration and regular alcohol consumption in rural men (P < .05). Urban men were more likely than rural men to report a physician diagnosis of BPH. Multiple sociodemographic and lifestyle factors were statistically associated with reported BPH, with some variation by residence. Given the cross-sectional design and self-reported measures, these findings reflect patterns of reporting and diagnosis rather than confirmed differences in underlying disease prevalence. Longitudinal studies using validated clinical assessments are needed to further clarify these relationships.

ML-based mental disorder prediction plays an increasingly important role in early screening, clinical decision support, and personalized mental healthcare. However, reliable multi-class classification remains challenging due to high dimensionality, class imbalance, and subtle psychological features. This study describes an interpretable, RAM-based WiSARD classifier for the multi-disorder mental health prediction problem and compares its performance to established models. A retrospective experimental study was carried out in the year 2024 using publicly available mental-health diagnostic data from the Kaggle Mental Disorders Dataset. The dataset consisted of 637 records and 29 symptom-based features representing disorders such as Major Depressive Disorder, Anxiety, PTSD, OCD, ADHD, Bipolar Disorder, and others. Records with missing values, incomplete diagnostic labels, or duplicated entries were excluded. Thus, 637 complete cases were selected for analysis. No clinical identifiers were involved, and hence, ethical clearance was not required. In the present study, WiSARD was tested using a 10-fold stratified cross-validation design against Multilayer Perceptron, Naïve Bayes, DTNB, IB1, and A1DE. The performance was computed using precision, recall, F-measure, accuracy, MCC, MAE, and KS. The study was geographically conducted in Pakistan as part of computational healthcare research. WiSARD classifier achieved the best overall performance with an overall accuracy of 98.27%, F-measure of 0.983, MCC of 0.982, and KS of 0.981, outperforming all baseline models under the same evaluation conditions. Analysis of ROC-AUC, TPR, TNR, and error distributions further showed that WiSARD was more tolerant of misclassifications associated with minority disorder classes, thereby addressing the imbalance present in the dataset. The ablation study verified its contribution to improved reliability and interpretability through RAM-based pattern recognition. The results have shown that WiSARD is a promising, interpretable model for multi-class mental disorder prediction in data-imbalanced settings. At the same time, results are limited to a single non-clinical Kaggle dataset with self-reported observations and without formal psychiatric validation. For this reason, the findings should be interpreted as indicative rather than definitive.

Long COVID increases healthcare utilization, yet differences in healthcare spending patterns between individuals with and without long COVID remain poorly characterized, especially at the national level. To evaluate differences in healthcare expenditures among U.S. adults with and without long COVID using nationally representative data. This cross-sectional study analyzed data from the 2022 Medical Expenditure Panel Survey (MEPS), including 16 762 unweighted adults (weighted population: 239 915 159). Healthcare spending outcomes included total expenditures and specific categories including office-based care, outpatient services, emergency room visits, hospital admissions, home healthcare, and prescription medications. A survey-weighted generalized linear model (GLM) with a log link and gamma distribution was used to estimate adjusted differences in expenditures between groups. Individuals with long COVID had significantly higher total healthcare expenditures (mean $11 567; SD $25 334) compared to those without long COVID ($7448; SD $21 734, P < .01). After adjusting for demographic characteristics, insurance status, chronic conditions, and other potential confounders, individuals with long COVID incurred 40% higher total expenditures (β = 1.40, P = .01). Expenditures were significantly elevated for office-based visits (35% higher; β = 1.35, P = .02) and outpatient services (118% higher; β = 2.18, P < .01). No significant differences were found in emergency room, hospital admissions, or dental care expenditures. Long COVID imposes a substantial financial burden on individuals and healthcare systems, primarily through increased outpatient and office-based service utilization. Understanding these spending patterns can help inform policy decisions, optimize healthcare resource allocation, and guide targeted interventions to manage long COVID more effectively.

Older patients are heavy users of emergency department (ED) resources and are at high risk for short-term ED visits, often leading to adverse outcomes. We aim to elucidate the characteristics of older patients who undergo 72-h ED returns, and develop a prediction model for unfavorable outcomes to facilitate clinical practices. This retrospective observational study enrolled older patients who shortly returned to the ED of a tertiary hospital within 72 h between 2019 and 2020. The study population was divided into development and validation datasets. The primary outcome was high-risk ED returns, defined as intensive care unit admission or in-hospital mortality after ED returns. Multivariable logistic regression was performed to identify predictors of high-risk returns, and a prediction model was built accordingly. A total of 1118 encounters were enrolled in our development dataset, with a mean age of 79.4 ± 9.5 years. Through multivariable analysis, independent predictors of high-risk ED returns were identified. A simple prediction model (ReC-FLASH) was developed, demonstrating a C-statistic of 0.862 (95% CI: 0.822-0.903, P < .001), incorporating "Return" triage ≤ 2, "Cancer," "Functional" bed-ridden status, "Liver" disease, complaint of "Air" hunger, "Stroke," and "Hypertension." This is the first study to propose a risk prediction model for older patients who undergo short-term ED returns. The ReC-FLASH model is straightforward and practical, facilitating early identification and management of high-risk patients, thereby improving outcomes for this vulnerable population and potentially rescuing more lives.

Informal caregivers provide essential support to cancer patients from diagnosis through treatment, palliative care, and end-of-life care. While their practical contributions are widely acknowledged, the emotional and psychological experiences of caregivers remain underexplored, particularly in home-based palliative care settings. Understanding these experiences is crucial to improving support and interventions for caregivers. This study explored the emotional and subjective experiences of informal caregivers providing home-based palliative care for family members with cancer, using retrospective data collected after the patient's death. A qualitative exploratory study was conducted using semi-structured interviews with 12 informal caregivers recruited from Primorsko-Goranska County, Croatia. Interviews were conducted face-to-face, audio-recorded, transcribed verbatim, and analyzed using thematic analysis to capture patterns and insights in caregivers' experiences. Analysis revealed one overarching theme, "Still Invisible but Essential," and 2 major themes: "Confronting the reality of an incurable cancer diagnosis" and "Facing the paradox - choosing between length and quality of life," encompassing 5 sub-themes. Caregivers described both rewarding and stressful aspects of care, including emotional challenges, communication difficulties, decision-making burdens, and unmet needs for professional support, alongside practical caregiving responsibilities. Informal caregivers are indispensable to home-based palliative care, yet their emotional and subjective experiences are often overlooked. Findings highlight the need for tailored interventions and support services addressing both practical and emotional aspects of caregiving, informing policy, clinical practice, and caregiver support in Croatia and similar contexts.

Telehealth has expanded access to psychotherapy for veterans, yet little is known about how art therapy is experienced in remote formats. In this paper, we describe a thematic analysis that involved male and female veterans with PTSD and TBI (aside from one participant without a TBI) in individual telehealth art therapy. This paper explores the similarities and differences in male and female veterans' engagement in art therapy. Nine veterans (6 male, 3 female) diagnosed with PTSD (with most also having TBI) participated in eight sessions of individual telehealth art therapy. Clinical notes, session summaries, and artwork were analyzed using thematic analysis to identify key patterns in veterans' therapeutic experiences. Three primary themes emerged from the data: a) Striving to manage self and emotions; b) Diverse relationships with artmaking and media; and c) Seeking to improve interpersonal connection. Telehealth art therapy supported emotional expression, trauma processing, and relational insight in veterans with PTSD and TBI. The findings underscore the value of flexible, personalized approaches in remote care and demonstrate how art therapy can be meaningfully adapted for telehealth delivery and meet the needs of diverse participants. Future research should explore hybrid models, individualized interventions, and the influence of identity and environment on therapeutic engagement and outcomes, as well as the possibly unique experiences of male and female veterans.