Nevin Manimala

ATS Sch. 2025 Jun 6. doi: 10.34197/ats-scholar.2024-0134OC. Online ahead of print.

ABSTRACT

Background: Interprofessional teaching (IPT) has the potential to promote teamwork and collaborative patient care, but few studies have explored physician attitudes about the role of nonphysician clinical teachers in graduate medical education. Objective: This study aimed to elucidate critical care attending physician perspectives about the role of nurses, pharmacists, and respiratory therapists in teaching medical residents. Methods: Using a concurrent mixed methods approach, surveys and focus groups were administered to attendings in an urban tertiary academic medical center. Survey data were analyzed with descriptive statistics; focus group data were analyzed using the Framework method of content analysis. Results: Of attendings surveyed, 23/26 (88%) responded. Attendings reported positive attitudes about IPT; highly cited benefits included capitalizing on the unique expertise held by interprofessional providers (21/22, 95%), modeling respectful interprofessional relationships (21/22, 95%), and promoting collaborative patient care (20/22, 91%). Ten attendings participated in focus groups. Qualitative analysis revealed four major themes: overall low rates of IPT that vary by profession, potential role of attending as facilitator of IPT, multiple interpersonal and environmental characteristics that influence IPT, and impacts of IPT on education, patient care, and teamwork. Conclusion: Study results suggest that attending physicians are enthusiastic about the concept of IPT and their potential role in its promotion.

PMID:40479547 | DOI:10.34197/ats-scholar.2024-0134OC

Cancer. 2025 Jun 15;131(12):e35926. doi: 10.1002/cncr.35926.

ABSTRACT

BACKGROUND: The disproportionate burden of obesity among Black women may contribute to disparities in breast cancer survival; yet, associations of body mass index (BMI), a proxy for total adiposity, are inconsistent.

METHODS: To examine racial/ethnic differences in body composition and evaluate associations with breast cancer-specific and all-cause mortality, this study identified 3898 women 18 to <90 years old, diagnosed in 2005-2019 with stage II-III breast cancer at Kaiser Permanente Northern California. The authors measured subcutaneous, visceral, and intermuscular adipose tissue area from computed tomography scans.

RESULTS: Body composition differed by race: compared to other race/ethnicity groups, Black women had higher skeletal muscle and subcutaneous adipose, but lower visceral adipose tissues, whereas Asian/Pacific Islander women had lower intermuscular and subcutaneous adipose tissue. BMI was not significantly associated with mortality in any group. Among Black women, higher subcutaneous adipose tissue was associated with breast cancer-specific mortality (hazard ratio [HR], 1.24; 95% confidence interval [CI], 1.02-1.52) and all adipose tissue measures were associated with increased all-cause mortality (intermuscular HR, 1.26; 95% CI, 1.09-1.46; subcutaneous HR, 1.25; 95% CI, 1.05-1.48; and visceral HR, 1.32; 95% CI, 1.03-1.68, respectively). By contrast, only increased intermuscular adipose tissue was associated with all-cause mortality among White women (HR, 1.08; 95% CI, 1.00-1.16), with null associations for Hispanic and Asian/Pacific Islander women.

CONCLUSIONS: BMI obscures variation in body composition, particularly for Black women, who have more subcutaneous adipose and skeletal muscle but less visceral adipose tissue at higher BMIs. These findings from routine imaging highlight opportunities for tailored lifestyle interventions to improve survivorship and mitigate disparities.

PMID:40479498 | DOI:10.1002/cncr.35926

Int J Surg. 2025 Jun 5. doi: 10.1097/JS9.0000000000002641. Online ahead of print.

ABSTRACT

INTRODUCTION: Due to the high propensity for occult lymph node metastasis (OLNM) in Early-stage oral tongue squamous cell carcinoma (OTSCC), elective neck dissection has become standard practice for many patients with clinically node-negative (cT1-2 N0) disease, which may lead to overtreatment in some patients. Hence, accurate identification and prediction of OLNM are of great significance.

AIM: This study aimed to develop and validate an explainable machine learning (ML) model to predict OLNM in OTSCC.

METHODS: A total of 678 Early-stage OTSCC patients from multiple centers were enrolled and randomly classified into the derivation and external validation cohorts. The variables considered in this study primarily included clinicopathological characteristics associated with the occurrence of OLNM in OTSCC. Feature selection utilized multivariate logistic regression analysis and Lasso regression analysis. Meanwhile, 6 ML algorithms were employed to develop an OLNM diagnostic model, assessed with area under the curve (AUC), calibration curve, decision curve analysis (DCA), sensitivity, specificity, and validation cohorts. Moreover, the Shapley Additive exPlanations (SHAP) method was applied to rank the feature importance and interpret the final model.

RESULTS: In this study, 192 patients (34.7%) developed OLNM in the derivation cohort, while 38 patients (30.6%) developed OLNM in the external validation cohort. Through feature selection, 9 clinicopathological variables were identified as independent predictive factors for OLNM, and six ML models were developed based on these factors. Among the six evaluated ML models, the Random Forest (RF) model achieved the highest AUC (0.941, 95% CI: 0.907-0.975) for internal validation. External validation further confirmed the RF model’s effectiveness, yielding an AUC of 0.917 (95% CI: 0.868-0.967). The calibration curves also demonstrated a high level of concordance between the anticipated risk and the observed risk of the RF model. Additionally, this study also compared the RF model with the currently accepted traditional statistical methods, including depth of invasion (DOI) and tumor budding (TB), demonstrating superior prediction performance and greater clinical application value. Ultimately, an online computing platform (https://prediction-model-for-olnm.streamlit.app/) for this RF model is freely available to both clinicians and patients.

CONCLUSION: This study innovatively utilized 9 easily obtained clinicopathological features to construct an explainable RF model, providing a practical and reliable tool for predicting OLNM in Early-stage OTSCC. More importantly, it also provided interpretable results, thus overcoming the “impenetrable black box” of conventional ML models.

PMID:40479496 | DOI:10.1097/JS9.0000000000002641

J Med Internet Res. 2025 Jun 6;27:e67201. doi: 10.2196/67201.

ABSTRACT

BACKGROUND: Sepsis is a severe syndrome of organ dysfunction caused by infection; it has high heterogeneity and high in-hospital mortality, representing a grim clinical challenge for precision medicine in critical care.

OBJECTIVE: We aimed to extract reported sepsis biomarkers to provide users with comprehensive biomedical information and integrate retrieval augmented generation (RAG) and prompt engineering to enhance the accuracy, stability, and interpretability of clinical decisions recommended by large language models (LLMs).

METHODS: To address the challenge, we established and updated the first knowledge-enhanced platform, MetaSepsisKnowHub, comprising 427 sepsis biomarkers and 423 studies, aiming to systematically collect and annotate sepsis biomarkers to guide personalized clinical decision-making in the diagnosis and treatment of human sepsis. We curated a tailored LLM framework incorporating RAG and prompt engineering and incorporated 2 performance evaluation scales: the System Usability Scale and the Net Promoter Score.

RESULTS: The overall quantitative ratings of expert-reviewed clinical recommendations based on RAG surpassed baseline responses generated by 4 LLMs and showed a statistically significant improvement in textual questions (GPT-4: mean 75.79, SD 7.11 vs mean 81.59, SD 9.87; P=.02; GPT-4o: mean 70.36, SD 7.63 vs mean 77.98, SD 13.26; P=.02; Qwen2.5-instruct: mean 77.08 SD 3.75 vs mean 85.46, SD 7.27; P<.001; and DeepSeek-R1: mean 77.67, SD 3.66 vs mean 86.42, SD 8.56; P<.001), but no significant statistical differences could be measured in clinical scenarios. The RAG assessment score comparing RAG-based responses and expert-provided benchmark answers illustrated prominent factual correctness, accuracy, and knowledge recall compared to the baseline responses. After use, the average the System Usability Scale score was 82.20 (SD 14.17) and the Net Promoter Score was 72, demonstrating high user satisfaction and loyalty.

CONCLUSIONS: We highlight the pioneering MetaSepsisKnowHub platform, and we show that combining MetaSepsisKnowHub with RAG can minimize limitations on precision and maximize the breadth of LLMs to shorten the bench-to-bedside distance, serving as a knowledge-enhanced paradigm for future application of artificial intelligence in critical care medicine.

PMID:40478618 | DOI:10.2196/67201

Cogn Sci. 2025 Jun;49(6):e70073. doi: 10.1111/cogs.70073.

ABSTRACT

Previous research suggests that statistical learning enhances memory for self-related information at the individual level and that individuals exhibit better memory for partner-related items than they do for irrelevant items in joint contexts (i.e., the joint memory effect, JME). However, whether statistical learning improves memory for partner-related information in joint contexts remains unclear. This study investigated memory performance for partner-related words when higher level statistical regularities were embedded in word streams during a joint memory task. Participants performed a word categorization task, followed by a surprise free recall task across four experiments. Experiment 1 replicated the JME, revealing improved memory for partner-related items than for irrelevant items when using Chinese words with increased repetition. Experiment 2 embedded semantic regularities within partners’ word streams; Experiment 3a employed regularities based on non-adjacent fixed temporal positions; and Experiment 3b employed regularities based on adjacent fixed temporal positions. Results showed that the JME was enhanced only when semantic regularities were present (Experiment 2) and not with temporal positional rules (Experiments 3a and 3b). These findings suggest a hierarchical structure of co-representation and show that co-represented statistical regularities facilitate the processing of partner-related words, but only when the regularities align with partners’ intentions. This study advances our understanding of co-representation in joint action by highlighting its hierarchical nature, and the top-down interaction between structural levels.

PMID:40478612 | DOI:10.1111/cogs.70073

Womens Health (Lond). 2025 Jan-Dec;21:17455057251343953. doi: 10.1177/17455057251343953. Epub 2025 Jun 6.

ABSTRACT

BACKGROUND: Postpartum depression is a significant public health challenge. Understanding the predictors of postpartum depression can inform targeted interventions and support systems for new mothers.

OBJECTIVES: To identify and quantify sociodemographic and obstetric predictors of postpartum depression among mothers in Kumasi, Ghana.

DESIGN: A cross-sectional multicenter prospective study.

METHODS: A total of 502 postpartum mothers from five hospitals were included. Bayesian logistic regression analysis was used to assess model uncertainty and complex interactions between sociodemographic, economic, and obstetric factors on postpartum depression.

RESULTS: The pooled prevalence of postpartum depression was 25% (range 13% to 31%). Education attainment [coefficient = -0.43, 95% credible interval: -0.57 to -0.29, (adjusted odds ratio (aOR) = 0.65] and economic support from multiple earning members (coefficient = -0.28, 95% credible interval: -0.33 to -0.22, aOR = 0.75) substantially reduced the likelihood of postpartum depression. Being a single mother (coefficient = 0.34, 95% credible interval: 0.24 to 0.44, aOR = 1.40) increased the risk of postpartum depression. Planned pregnancies (coefficient = -0.25, 95% credible interval: -0.28 to -0.21, aOR = 0.78), doing physical exercise (coefficient = -0.26, 95% credible interval: -0.30 to -0.23, aOR = 0.77), and exclusive breastfeeding (coefficient = -0.23, 95% credible interval: -0.28 to -0.19, aOR = 0.79) were protective factors for postpartum depression. On the other hand, cesarean sections (coefficient = 0.34, 95% credible interval: 0.24 to 0.43, aOR = 1.40) and spontaneous vaginal deliveries (coefficient = 0.56, 95% credible interval: 0.47 to 0.65, aOR = 1.75) increased the risk of postpartum depression.

CONCLUSION: Our findings emphasize the importance of identifying modifiable predictors of postpartum depression, including sociodemographic, economic, and obstetrical factors, in Kumasi, Ghana. Interventions addressing these factors, such as financial support programs, maternal education, and physician training, may significantly reduce the regional burden. Policies tailored to low-resource contexts and exhibiting local cultural sensitivity are needed for enhancing maternal-child health outcomes in Ghana and comparable regions.

PMID:40478594 | DOI:10.1177/17455057251343953

JAMA Netw Open. 2025 Jun 2;8(6):e2512397. doi: 10.1001/jamanetworkopen.2025.12397.

ABSTRACT

IMPORTANCE: Patients from socially disadvantaged backgrounds experience disproportionately worse health care outcomes. Nurses provide most care to patients in hospitals and are informants of health care quality and equity.

OBJECTIVE: To understand what hospital nurses say helps or hinders their ability to provide quality care to socially disadvantaged populations.

DESIGN, SETTING, AND PARTICIPANTS: This qualitative study involved a directed content analysis of open-text responses from the RN4CAST-NY/IL survey, which was conducted between April and June 2021. Participants were registered nurses licensed to work in 58 New York and Illinois hospitals identified as high-performing (25 hospitals) and low-performing (33 hospitals) for COVID-19 mortality outcomes in 2021 from a larger quantitative study. The Social Ecological Model informed the study codebook; inductive and deductive coding of the data and content analysis were conducted from March to October 2024.

EXPOSURE: Direct care hospital nurses who participated in the RN4CAST-NY/IL survey.

MAIN OUTCOMES AND MEASURES: Open-text responses were from nurses who answered the survey question, “What helps (or hinders) your ability to provide quality care to vulnerable populations? (e.g. low SES, housing insecurity/homeless, racial/ethnic minorities, immigrant, limited English proficiency)?”

RESULTS: A total of 1084 nurses (mean [SD] age, 47.1 [18.2] years) responded to the survey. Most respondents identified as female (986 respondents [91.0%]) and were staff or direct care nurses (765 respondents [70.6%]) with at least a bachelor’s degree (968 respondents [89.6%]). With regard to race and ethnicity, 127 respondents (11.8%) were Asian, 156 (14.5%) were Black or African American, 89 (8.3%) were Hispanic, 693 (64.2%) were White, and 97 (8.9%) were other races. They had a mean (SD) of 18.9 (14.0) years of experience. Six themes described what helped or hindered quality care: (1) profits over patients, (2) care continuity and hospital-community partnerships, (3) insufficient staffing and time constraints, (4) technology to address language barriers, (5) patients’ determinants of health, and (6) individual nurses’ beliefs and backgrounds. Nurses proposed improving health care workforce diversity, strengthening community resources for patients, and advancing tailored cultural competency education as solutions to improve care for socially disadvantaged patients.

CONCLUSIONS AND RELEVANCE: In this qualitative study and directed content analysis of 1084 open-text responses, nurses identified systemic, institutional, community, and individual clinician-level approaches to improve care for socially disadvantaged populations for equitable care delivery. Nurses’ direct care experiences can inform hospital strategies to improve care for this population.

PMID:40478576 | DOI:10.1001/jamanetworkopen.2025.12397

JAMA Netw Open. 2025 Jun 2;8(6):e2512681. doi: 10.1001/jamanetworkopen.2025.12681.

ABSTRACT

IMPORTANCE: For breast cancer risk prediction to be clinically useful, it must be accurate and applicable to diverse groups of women across multiple settings.

OBJECTIVE: To examine whether a dynamic risk prediction model incorporating prior mammograms, previously validated in Black and White women, could predict future risk of breast cancer across a racially and ethnically diverse population in a population-based screening program.

DESIGN, SETTING, AND PARTICIPANTS: This prognostic study included women aged 40 to 74 years with 1 or more screening mammograms drawn from the British Columbia Breast Screening Program from January 1, 2013, to December 31, 2019, with follow-up via linkage to the British Columbia Cancer Registry through June 2023. This provincial, organized screening program offers screening mammography with full field digital mammography (FFDM) every 2 years. Data were analyzed from May to August 2024.

EXPOSURE: FFDM-based, artificial intelligence-generated mammogram risk score (MRS), including up to 4 years of prior mammograms.

MAIN OUTCOMES AND MEASURES: The primary outcomes were 5-year risk of breast cancer (measured with the area under the receiver operating characteristic curve [AUROC]) and absolute risk of breast cancer calibrated to the US Surveillance, Epidemiology, and End Results incidence rates.

RESULTS: Among 206 929 women (mean [SD] age, 56.1 [9.7] years; of 118 093 with data on race, there were 34 266 East Asian; 1946 Indigenous; 6116 South Asian; and 66 742 White women), there were 4168 pathology-confirmed incident breast cancers diagnosed through June 2023. Mean (SD) follow-up time was 5.3 (3.0) years. Using up to 4 years of prior mammogram images in addition to the most current mammogram, a 5-year AUROC of 0.78 (95% CI, 0.77-0.80) was obtained based on analysis of images alone. Performance was consistent across subgroups defined by race and ethnicity in East Asian (AUROC, 0.77; 95% CI, 0.75-0.79), Indigenous (AUROC, 0.77; 95% CI 0.71-0.83), and South Asian (AUROC, 0.75; 95% CI 0.71-0.79) women. Stratification by age gave a 5-year AUROC of 0.76 (95% CI, 0.74-0.78) for women aged 50 years or younger and 0.80 (95% CI, 0.78-0.82) for women older than 50 years. There were 18 839 participants (9.0%) with a 5-year risk greater than 3%, and the positive predictive value was 4.9% with an incidence of 11.8 per 1000 person-years.

CONCLUSIONS AND RELEVANCE: A dynamic MRS generated from both current and prior mammograms showed robust performance across diverse racial and ethnic populations in a province-wide screening program starting from age 40 years, reflecting improved accuracy for racially and ethnically diverse populations.

PMID:40478575 | DOI:10.1001/jamanetworkopen.2025.12681

JAMA Netw Open. 2025 Jun 2;8(6):e2514212. doi: 10.1001/jamanetworkopen.2025.14212.

ABSTRACT

IMPORTANCE: Work-related hazards and stress have been shown to be associated with mental health, with suicide rates among adult workers increasing since 2000.

OBJECTIVE: To determine if self-reported lifetime diagnosed depression, frequent mental distress (FMD), extreme distress prevalences, and mean mentally unhealthy days (MUD) varied among current workers by industry or occupation.

DESIGN, SETTING, AND PARTICIPANTS: This cross-sectional study used Behavioral Risk Factor Surveillance System (BRFSS) data from 37 states reporting workers’ industry and occupation in 1 or more years between 2015 and 2019. The target population was currently employed civilian adults aged 18 years or older. Analyses were conducted in 2022 and 2023.

EXPOSURES: Workers’ current industry and occupation were the primary exposures of interest. Self-reported sociodemographic covariates included sex, age, race and ethnicity, education, coupled status, and health care coverage.

MAIN OUTCOMES AND MEASURES: Self-reported lifetime diagnosed depression, FMD, extreme distress, and MUD.

RESULTS: Of a total 536 279 workers assessed (unweighted sample, 535 997 workers; 263 007 female [49.1%]; 48 279 Hispanic [9.0%], 40 188 non-Hispanic Black [7.5%], 400 604 non-Hispanic White [74.7%]), 469 129 reported their industry or occupation. Lifetime diagnosed depression was reported by 80 319 of 534 342 workers (14.2% [95% CI, 13.9%-14.4%]). Mean MUD was 9.5 days (95% CI, 9.4-9.7 days) among 530 309 workers, and in all sociodemographic groups the mean MUD was 3 to 5 times higher among workers who reported lifetime diagnosed depression. Higher prevalences than all workers for lifetime diagnosed depression, FMD, and extreme distress were reported by workers who were female (lifetime diagnosed depression, 19.5% [95% 19.1%-19.9%]; FMD, 11.6% [95% CI, 11.3%-11.9%]; extreme distress, 4.8% [95% CI, 4.6%-5.1%]), ages 18 to 34 years (lifetime diagnosed depression, 16.9% [95% CI, 16.4%-17.3%]; FMD, 13.6% [95% CI, 13.1%-14.0%]; extreme distress, 5.5% [95% CI, 5.2%-5.8%]), and no longer or never in a couple (lifetime diagnosed depression, 18.0% [95% CI, 17.6%-18.4%]; FMD, 13.3% [95% CI, 12.9%-13.7%]; extreme distress, 5.7% [95% CI, 5.4%-6.0%]). By industry, retail trade (lifetime diagnosed depression: APR, 1.15 [95% CI, 1.05-1.25]; FMD: APR, 1.23 [95% CI, 1.10-1.39]) and accommodation and food services (lifetime diagnosed depression: APR, 1.13 [95% CI, 1.03-1.25]; FMD: APR, 6.8 [95% CI, 6.0-7.7]) had higher adjusted prevalences of lifetime diagnosed depression and FMD. By occupation, arts, design, entertainment, sports, and media (1.32 [95% CI, 1.09-1.60]); health care support (1.19 [95% CI, 1.03-1.38]); food preparation and serving (1.20 [95% CI, 1.05-1.36]); and sales and related occupations (1.13 [95% CI, 1.01-1.27]) had higher adjusted prevalences of FMD than the comparison group. Health care support (6.6% [95% CI, 5.5%-7.8%]), food preparation and service (6.9% [95% CI, 5.9%-7.8%]), building and grounds cleaning and maintenance (5.2% [95% CI, 4.4%-6.0%]), personal care and service (5.8% [95% CI, 4.9%-6.8%]), and sales and related occupations (4.8% [95% CI, 4.3%-5.3%]) had higher unadjusted extreme distress than all workers.

CONCLUSIONS AND RELEVANCE: In this cross-sectional study, poor mental health among workers varied significantly by sociodemographic categories; significant differences among industry and occupation groups remained after adjustment. More research is needed on the effects of work-related factors on mental health, which may inform tailored treatment and prevention strategies.

PMID:40478574 | DOI:10.1001/jamanetworkopen.2025.14212

JAMA Netw Open. 2025 Jun 2;8(6):e2514248. doi: 10.1001/jamanetworkopen.2025.14248.

ABSTRACT

IMPORTANCE: Interactions between race and county-level factors associated with mortality, such as employment, education, income, and population density, are understudied among Asian American and Pacific Islander populations.

OBJECTIVE: To compare all-cause, cancer, and heart disease mortality rates between Pacific Islander and Asian American adults across county-level factors.

DESIGN, SETTING, AND PARTICIPANTS: This cross-sectional study examined National Center for Health Statistics death certificate data on non-Hispanic Asian American and Pacific Islander adults (aged 20-84 years) between January 1, 2018, and December 31, 2020. County-level socioeconomic data were obtained from the American Community Survey, and population density was determined using Rural-Urban Continuum Codes. Analyses were conducted between August 1, 2023, and September 4, 2024.

EXPOSURES: County-level unemployment, educational attainment, median household income, and population density.

MAIN OUTCOMES AND MEASURES: Age-standardized all-cause, cancer, and heart disease mortality rates and mortality rate ratios (MRRs), comparing Pacific Islander with Asian American individuals by sex and age. Interactions between race and county-level factors associated with MRRs were evaluated using P value for trend across county-level factors.

RESULTS: During 2018 to 2020, 43 221 696 Asian American and 1 281 221 Pacific Islander adults resided in the US. A total of 148 939 Asian American individuals (16.7% aged 20-54 years, 17.2% aged 55-64 years, and 66.1% aged ≥65 years; 57.5% male) and 9628 Pacific Islander individuals (29.9% aged 20-54 years, 23.0% aged 55-64 years, and 47.1% aged ≥65 years; 57.2% male) died of any cause. Across all county-level factors, Pacific Islander adults had elevated all-cause, cancer, and heart disease mortality rates compared with Asian American adults (female: MRR range from 1.82 [95% CI, 1.67-1.98] for population <250 000 to 2.93 [95% CI, 2.73-3.14] for lowest unemployment tertile; male: MRR range from 1.64 [95% CI, 1.50-1.78] for lowest income tertile to 2.47 [95% CI, 2.31-2.63] for lowest unemployment tertile). Across all county-level factors, the largest relative all-cause mortality differences between Pacific Islander and Asian American adults occurred in counties with the lowest unemployment (female: MRR, 2.93 [95% CI, 2.73-3.14]; male: MRR, 2.47 [95% CI, 2.31-2.63]), highest educational attainment (female: MRR, 2.71 [95% CI, 2.53-2.90]; male: MRR, 2.39 [95% CI, 2.25-2.54]), highest median household income (female: MRR, 2.67 [95% CI, 2.56-2.79]; male: MRR, 2.25 [95% CI, 2.17-2.33]), and highest population density (female: MRR, 2.79 [95% CI, 2.67-2.92]; male: MRR, 2.37 [95% CI, 2.28-2.47]). No trends in relative cancer mortality differences between Pacific Islander and Asian American adults across county-level factors were observed overall except for greater population density among women (<250 000 population: MRR, 1.49 [95% CI, 1.25-1.76; >1 000 000 population, 2.13 [95% CI, 1.95-2.32]). The largest heart disease MRRs for Pacific Islander compared with Asian American individuals occurred among those younger than 65 years, with the greatest relative mortality among those aged 20 to 54 years in counties with the lowest unemployment (female: MRR, 14.21 [95% CI, 9.89-20.04]; male: MRR, 5.75 [95% CI, 4.58-7.15]) and highest educational attainment (female: MRR, 13.69 [95% CI, 9.68-18.94]; male: MRR, 6.17 [95% CI, 5.00-7.54]), median household income (female: MRR, 11.97 [95% CI, 9.55-14.91]; male: MRR, 5.16 [95% CI, 4.49-5.91]), and population density (female: MRR, 11.77 [95% CI, 9.39-14.62]; male: MRR, 5.48 [95% CI, 4.76-6.29]).

CONCLUSIONS AND RELEVANCE: In this cross-sectional study, all-cause mortality disparities between Asian American and Pacific Islander populations worsened in counties with higher socioeconomic status and greater population density. Historical aggregation of Pacific Islander with Asian American individuals may have misled health improvement efforts, especially for Pacific Islander adults who lived in high socioeconomic and more populated areas.

PMID:40478573 | DOI:10.1001/jamanetworkopen.2025.14248