Tag: nevin manimala

JMIR Hum Factors. 2025 May 22;12:e62960. doi: 10.2196/62960.

ABSTRACT

BACKGROUND: Problem anger is common after experiencing trauma and is under-recognized relative to other posttraumatic mental health issues. Previous research has shown that digital mental health tools have significant potential to support individuals with problem anger after trauma.

OBJECTIVE: The objective of this study was to describe the co-design and development of a just-in-time adaptive intervention (JITAI) targeting problem anger in individuals who have experienced trauma.

METHODS: We used a participatory design process following the double-diamond framework. Phase 1 involved one-on-one qualitative interviews with trauma-exposed individuals with problem anger (n=10). Using an inductive approach (interpretative phenomenological analysis), we thematically coded interview data to create design principles for this population and generate potential content for the intervention. Phase 2 involved academic and clinical experts in trauma and experts in digital health reviewing the Phase 1 results and an evidence-based cognitive behavioral approach to treating anger. We then created intervention content and prototypes, which we then took to workshops with all participants for feedback, using group discussions and ratings of desirability and feasibility.

RESULTS: From Phase 1, core considerations for a JITAI included look and feel preferences, self-led and personalized support and content, and different support needed for each anger stage. A JITAI was developed with the following components: (1) personalized schedules and content onboarding; (2) psychoeducation about problem anger; (3) crisis support; (4) mood monitoring via anger check-ins; (5) self-led and personalized circuit breakers; (6) cognitive-behavioral based skills; (7) and a digital Coach embedded in the app. Some suggested features, such as social networking and sharing data with loved ones, were not pursued due to feasibility reasons relating to participant safety or technical costs.

CONCLUSIONS: The resulting JITAI, termed “Shift,” is the first digital mental health tool designed with end users to manage anger after trauma.

PMID:40402559 | DOI:10.2196/62960

Interact J Med Res. 2025 May 22;14:e64829. doi: 10.2196/64829.

ABSTRACT

BACKGROUND: The global targets for HIV testing for achieving the Joint United Nations Programme on HIV/AIDS (UNAIDS) 95-95-95 targets are still short. Identifying gaps and opportunities for HIV testing uptake is crucial in fast-tracking the second (initiate people living with HIV on antiretroviral therapy) and third (viral suppression) UNAIDS goals. Machine learning and health technologies can precisely predict high-risk individuals and facilitate more effective and efficient HIV testing methods. Despite this advancement, there exists a research gap regarding the extent to which such technologies are integrated into HIV testing strategies worldwide.

OBJECTIVE: The study aimed to examine the characteristics, citation patterns, and contents of published studies applying machine learning and emerging health technologies in HIV testing from 2000 to 2024.

METHODS: This bibliometric analysis identified relevant studies using machine learning and emerging health technologies in HIV testing from the Web of Science database using synonymous keywords. The Bibliometrix R package was used to analyze the characteristics, citation patterns, and contents of 266 articles. The VOSviewer software was used to conduct network visualization. The analysis focused on the yearly growth rate, citation analysis, keywords, institutions, countries, authorship, and collaboration patterns. Key themes and topics were driven by the authors’ most frequent keywords, which aided the content analysis.

RESULTS: The analysis revealed a scientific annual growth rate of 15.68%, with an international coauthorship of 8.22% and an average citation count of 17.47 per document. The most relevant sources were from high-impact journals such as the Journal of Internet Medicine Research, JMIR mHealth and uHealth, JMIR Research Protocols, mHealth, AIDS Care-Psychological and Socio-Medical Aspects of AI, and BMC Public Health, and PLOS One. The United States of America, China, South Africa, the United Kingdom, and Australia produced the highest number of contributions. Collaboration analysis showed significant networks among universities in high-income countries, including the University of North Carolina, Emory University, the University of Michigan, San Diego State University, the University of Pennsylvania, and the London School of Hygiene and Tropical Medicine. The discrepancy highlights missed opportunities in strategic partnerships between high-income and low-income countries. The results further demonstrate that machine learning and health technologies enhance the effective and efficient implementation of innovative HIV testing methods, including HIV self-testing among priority populations.

CONCLUSIONS: This study identifies trends and hotspots of machine learning and health technology research in relation to HIV testing across various countries, institutions, journals, and authors. The trends are higher in high-income countries with a greater focus on technology applications for HIV self-testing among young people and priority populations. These insights will inform future researchers about the dynamics of research outputs and help them make scholarly decisions to address research gaps in this field.

PMID:40402556 | DOI:10.2196/64829

JMIR Public Health Surveill. 2025 May 22;11:e68213. doi: 10.2196/68213.

ABSTRACT

BACKGROUND: During the COVID-19 pandemic, several US jurisdictions began to regularly report levels of SARS-CoV-2 in wastewater as a proxy for SARS-CoV-2 incidence. Despite the promise of this approach for improving COVID-19 situational awareness, the degree to which wastewater surveillance data agree with other data has varied, and better evidence is needed to understand the situations in which wastewater surveillance data track closely with traditional surveillance data.

OBJECTIVE: In this study, we quantified the statistical relationship between wastewater data and traditional case-based surveillance data for multiple jurisdictions.

METHODS: We collated data on wastewater SARS-CoV-2 RNA levels and COVID-19 case reports from July 2020 to March 2023 for 107 counties representing a range in terms of geographic location, population size, and urbanicity. For these counties, we used Bayesian hierarchical regression modeling to estimate the statistical relationship between wastewater data and reported cases, allowing for variation in this relationship across counties. We compared different model structural approaches and assessed how the strength of the estimated relationships varied between settings and over time.

RESULTS: Our analyses revealed a strong positive relationship between wastewater data and COVID-19 cases for the majority of locations, with a median correlation coefficient between observed and predicted cases of 0.904 (IQR 0.823-0.943). In total, 23/107 counties (21.5%) had correlation coefficients below 0.8, and 3/107 (2.8%) had values below 0.6. Across locations, the COVID-19 case rate associated with a given level of wastewater SARS-CoV-2 RNA concentration declined over the study period. Counties with greater population size (P<.001) and higher levels of urbanicity (P<.001) had stronger concordance between wastewater data and COVID-19 cases. Measures of model fit, and relationships with urbanicity and population size, were robust to sensitivity analyses in which we varied the time period of analysis and the sample of counties used for model fitting.

CONCLUSIONS: In a sample of 107 US counties, wastewater surveillance had a close relationship with COVID-19 cases reported for the majority of locations, with these relationships found to be stronger in counties with greater population size and urbanicity. In situations where routine COVID-19 surveillance data are less reliable, wastewater surveillance may be used to track local SARS-CoV-2 incidence trends.

PMID:40402554 | DOI:10.2196/68213

JMIR Hum Factors. 2025 May 22;12:e72838. doi: 10.2196/72838.

ABSTRACT

Traditional medical education encounters several challenges. The introduction of advanced facial expression recognition technology offers a new approach to address these issues. The aim of the study is to propose a medical education-assisted teaching and student evaluation method based on facial expression recognition technology. This method consists of 4 key steps. In data collection, multiangle high-definition cameras record students’ facial expressions to ensure data comprehensiveness and accuracy. Facial expression recognition uses computer vision and deep learning algorithms to identify students’ emotional states. The result analysis stage organizes and statistically analyzes the recognized emotional data to provide teachers with students’ learning status feedback. In the teaching feedback stage, teaching strategies are adjusted according to the analysis results. Although this method faces challenges such as technical accuracy, device dependency, and privacy protection, it has the potential to improve teaching effectiveness, optimize personalized learning, and promote teacher-student interaction. The application prospects of this method in medical education are broad, and it is expected to significantly enhance teaching quality and students’ learning experience.

PMID:40402552 | DOI:10.2196/72838

JMIR Mhealth Uhealth. 2025 May 22;13:e56176. doi: 10.2196/56176.

ABSTRACT

BACKGROUND: Exercise therapy is strongly recommended as a treatment for chronic nonspecific low back pain (CNSLBP). However, therapist-guided exercise therapy requires significant medical resources. Ordinary digital telerehabilitation affects efficacy due to a lack of guidance and dynamic support. Artificial intelligence (AI)-assisted interactive health promotion systems may solve these problems.

OBJECTIVE: We aimed to explore whether AI-assisted multimodal exercise telerehabilitation is superior to conventional telerehabilitation in the treatment of people with CNSLBP.

METHODS: This study was a prospective, double-arm, open-label, randomized clinical controlled trial. People with CNSLBP were randomly allocated to either the AI or video group, receiving AI-assisted multimodal exercise therapy or conventional video guidance, respectively, via a WeChat application add-in. The multimodal exercise consisted of deep core muscle, flexibility, Mackenzie, and breathing exercises. The exercises were performed for 30-45 minutes per session, 3 times a week, for 4 weeks. Participants underwent face-to-face assessment at baseline and week 4, and web-based assessment at weeks 2 and 8. The primary outcome was the change in Numerical Rating Scale (NRS) relative to baseline at week 4. Secondary outcomes included changes in the Roland-Morris Disability Questionnaire (RMDQ), Oswestry Disability Index (ODI), Pain Castastrophizing Scale (PCS), Timed Up-and-Go (TUG) test, and thickness of the transverse abdominus (TrA) and multifidus (MF) muscles relative to baseline at week 4. Generalized estimating equation and covariance were used to examine the efficacy of the interventions.

RESULTS: A total of 38 participants (19 participants per group) were recruited. Eighteen participants in the AI group and 16 participants in the video group completed and were included in the final analysis. There was a significant difference in NRS at week 4 between the AI group and video group (most severe NRS: -3.00 vs -1.50; adjusted mean difference -1.08, 95% CI -1.68 to -0.49; P<.001; mean NRS: -2.61 vs -1.62; adjusted mean difference -0.67, 95% CI -1.19 to -0.15; P=.01). The difference in most severe NRS persisted until week 8 (-3.06 vs -1.69; adjusted mean difference -0.95, 95% CI -1.73 to -0.18; P=.02). Compared with the video group at week 4, the AI group showed significant improvement in secondary outcomes, including RMDQ, PCS, and core muscle thickness of left TrA, right TrA, left MF, and right MF.

CONCLUSIONS: We showed that 4 weeks of telerehabilitation based on AI-assisted multimodal exercise has better therapeutic effects compared to conventional exercise telerehabilitation in people with CNSLBP. This study provides guidance for developing effective real-time home-based exercise therapies for people with CNSLBP, which may help reduce economic and human resource costs associated with treatment.

PMID:40402551 | DOI:10.2196/56176

JMIR Public Health Surveill. 2025 May 22;11:e66921. doi: 10.2196/66921.

ABSTRACT

BACKGROUND: Sexual and gender minority (SGM) individuals represent 2%-5% of the US population, yet continue to account for more than two-thirds of new HIV infections annually.

OBJECTIVE: This study seeks to identify multilevel (ie, structural, psychological, and social) and biobehavioral (ie, rectal cytokines or chemokines) determinants of amplified HIV seroconversion risk for SGM individuals, including those who use methamphetamine.

METHODS: The American Transformative HIV Study is an ongoing web-based cohort study of 5364 SGM individuals from all 50 US states and Puerto Rico, enrolled in 2022 and 2023, and will be followed through 2027. We oversampled persons who use methamphetamine (2846/5364, 53.1%). We used established web-based strategies to enroll individuals aged 16-49 years at high risk of HIV acquisition via sexual networking apps. To be eligible, participants had to report meeting objective criteria for HIV pre-exposure prophylaxis (PrEP) care, but not be taking PrEP. Participants complete annual web-based surveys (baseline, 12, 24, and 36 months) and are asked to provide self-collected oral fluid samples for HIV testing and 2 rectal swabs (the Aptima Multitest Swab and the Zymo DNA/RNA Shield swab) following each assessment. Oral fluid samples are analyzed immediately, while rectal swabs are banked for a future nested case-cohort analysis to assess changes in inflammatory markers following a new infection.

RESULTS: Nearly all participants (4542/5364, 84.7%) were cisgender men, 3.7% (201/5364) were transgender women, and 1.1% (61/5364) were transgender men who have sex with men. There were also 560 (10.4%) individuals who self-identified outside of the gender binary-all reported being assigned male sex at birth. In total, 56.5% (3031/5364) were persons of color, and 31.8% (1714/5365) were aged 16 to 29 years. In total, 4054 baseline HIV test kits were returned, including 371 HIV reactive samples-3.3% (69/2210) were HIV-positive among those who did not report methamphetamine use, and 15.5% (302/1944) were HIV-positive among those reporting methamphetamine use. Based on participant’s HIV results as well as self-reporting when their most recent prior HIV-negative test was, we estimated that the incidence rate in this cohort in the 12-month period leading up to study enrollment was 10.06 (95% CI 8.65-11.64) per 100 person-years among those reporting methamphetamine use compared with 2.04 (95% CI 1.49-2.73) among those not reporting methamphetamine use per 100 person-years.

CONCLUSIONS: A large, US national, and racially diverse web-based cohort of SGM individuals at high risk for HIV has been successfully enrolled and will be followed through 2027. Persons who use methamphetamine have been oversampled and demonstrated an exceptionally greater risk for HIV. Our study will offer insight into the development and implementation of new interventions, which aim to have a meaningful impact on HIV transmission.

PMID:40402549 | DOI:10.2196/66921

JAMA Ophthalmol. 2025 May 22. doi: 10.1001/jamaophthalmol.2025.1262. Online ahead of print.

ABSTRACT

IMPORTANCE: The accuracy of the predicted risk of malignant transformation of a large choroidal nevus or indeterminate melanocytic tumor (IMT) is not known.

OBJECTIVE: To estimate the risk of malignant transformation (predicted risk) in a cohort of patients with IMT of known outcomes (observed status; benign [large nevus] or malignant [small melanoma]).

DESIGN, SETTING, AND PARTICIPANTS: This was a cohort study of patients from a single center. Patients diagnosed with IMTs that were benign (large nevus) or malignant (small melanoma) were included in the analysis. Those lesions classified as large nevus (benign; 0% risk) had documented stability over 24 or more months. IMTs classified as small melanoma (malignant; 100% risk) had quantified growth or confirmatory pathology. Data were analyzed from October to December 2024.

EXPOSURES: Prediction of malignant transformation of a large choroidal nevus or IMT.

MAIN OUTCOMES AND MEASURES: The primary outcome included the predicted 5-year Kaplan-Meier probability of malignant transformation using combinations of risk factors of predictive models, the Collaborative Ocular Melanoma Study (COMS) and Wills Eye Hospital (WEH) model.

RESULTS: A total of 123 patients (median [IQR] age, 63 [56-67] years; 89 male [72%]), 62 with large nevus and 61 with small malignant melanoma, were included in this study. The mean predicted 5-year Kaplan-Meier probability of melanoma for observed melanoma was 0.39 (95% CI, 0.32-0.46) by the COMS model and 0.44 (95% CI, 0.39-0.49) by the WEH model. The difference of -0.05 (95% CI, -0.14 to 0.04) was not statistically significant. However, the mean predicted 5-year Kaplan-Meier probability of melanoma for observed nevus was 0.18 (95% CI, 0.12-0.23) by the COMS model and 0.31 (95% CI, 0.24-0.38) by the WEH model. The difference of -0.13 (95% CI, -0.22 to -0.05) was statistically significant. There was a significant difference in mean 5-year Kaplan-Meier probability of melanoma between observed melanoma and nevus of 0.21 (95% CI, 0.12-0.31) by the COMS model and 0.13 (95% CI, 0.05-0.21) by the WEH model. Optimal cut points of 0.18 and 0.34 for the COMS model and the WEH model, respectively, were identified using the Youden index. The sensitivity was lower for the COMS model than the WEH model (-15.2% difference; 95% CI, -25.6% to -4.8%), and the specificity was higher for the COMS model than the WEH model (11.7% difference; 95% CI, 2.0%-21.4%).

CONCLUSIONS AND RELEVANCE: Findings of this cohort study suggest that predicted risk for malignant transformation estimated by 2 different models based on combinations of risk factors was suboptimal and may lead to overtreatment in approximately 30% of patients. These findings support pursuing other methods for prediction that should be validated before use in clinical practice.

PMID:40402510 | DOI:10.1001/jamaophthalmol.2025.1262

JAMA Netw Open. 2025 May 1;8(5):e2511764. doi: 10.1001/jamanetworkopen.2025.11764.

ABSTRACT

IMPORTANCE: Respiratory viral infections (RVIs) are associated with elevated cardiovascular risk; however, less is known about cardiac complications after hospitalization for respiratory syncytial virus (RSV) vs other vaccine-preventable RVIs (COVID-19 or influenza).

OBJECTIVE: To compare the risk of acute cardiovascular complications in adults hospitalized for RSV vs COVID-19 or influenza.

DESIGN, SETTING, AND PARTICIPANTS: This population-based cross-sectional study, conducted before RSV vaccination rollout in Singapore, assessed all adults hospitalized for RSV or influenza (January 1, 2017, to June 30, 2024) and all adults hospitalized for COVID-19 during Omicron XBB/JN.1 transmission (January 1, 2023, to June 30, 2024).

EXPOSURE: Hospitalization for RSV, influenza (vaccinated or unvaccinated), or COVID-19 (boosted [≥3 vaccine doses] or unboosted [<3 vaccine doses]).

MAIN OUTCOMES AND MEASURES: Cardiovascular events during RSV, influenza, or COVID-19 hospitalization, defined as any cardiac, cerebrovascular, or thrombotic event, occurring from admission until discharge or death. Odds of any cardiovascular event (RSV vs COVID-19 or RSV vs influenza) and severe RVI (intensive care unit admission) with or without an acute cardiovascular event were estimated using multivariate logistic regression, adjusted for sociodemographic and clinical characteristics.

RESULTS: A total of 32 960 RVI hospitalizations (mean [SD] patient age, 66.58 [18.99] years; 17 056 [51.7%] female) were included (2148 for RSV, 14 389 for influenza, and 16 423 for COVID-19). Of the 2148 patients hospitalized for RSV, 234 (10.9%) had an acute cardiovascular event. Higher odds of any acute cardiovascular event (adjusted odds ratio [AOR], 1.31; 95% CI, 1.12-1.54) as well as other individual cardiac events were observed in RSV hospitalizations vs boosted COVID-19 (dysrhythmia: AOR, 1.52; 95% CI, 1.19-1.94; heart failure: AOR, 1.75; 95% CI, 1.30-2.35). Similarly, higher odds of any acute cardiovascular event (AOR, 1.58; 95% CI, 1.24-2.01) as well as dysrhythmias or heart failure were observed in patients hospitalized for RSV vs unboosted COVID-19. Odds of a cardiovascular event were not significantly different in RSV vs influenza, except among contemporaneous hospitalizations after the pandemic (2023-2024), where odds of heart failure (AOR, 2.09; 95% CI, 1.21-3.59) were significantly higher in RSV hospitalizations vs vaccine-breakthrough influenza hospitalizations. Occurrence of a cardiovascular event was associated with greater odds of severe RSV requiring intensive care unit admission (AOR, 2.36; 95% CI, 1.21-4.62).

CONCLUSIONS AND RELEVANCE: In this cross-sectional study, 1 in 10 patients hospitalized for RSV had a concurrent acute cardiovascular event. Odds of cardiac events were significantly higher in RSV vs COVID-19 hospitalizations in both vaccine-boosted and unboosted individuals. In contemporaneous hospitalizations for RSV or influenza after the pandemic (2023-2024), odds of heart failure were significantly higher in RSV hospitalizations vs vaccine-breakthrough influenza hospitalizations. These findings suggest that patients with preexisting cardiovascular risk should consider vaccination against RVIs.

PMID:40402498 | DOI:10.1001/jamanetworkopen.2025.11764

JAMA Netw Open. 2025 May 1;8(5):e2512032. doi: 10.1001/jamanetworkopen.2025.12032.

ABSTRACT

IMPORTANCE: An estimated half of all long-term care facility (LTCF) residents are colonized with antimicrobial-resistant organisms, and early identification of these patients on admission to acute care hospitals is a core strategy for preventing intrahospital spread. However, because LTCF exposure is not reliably captured in structured electronic health record data, LTCF-exposed patients routinely go undetected. Large language models (LLMs) offer a promising, but untested, opportunity for extracting this information from patient admission histories.

OBJECTIVE: To evaluate the performance of an LLM against human review for identifying recent LTCF exposure from identifiable patient admission histories.

DESIGN, SETTING, AND PARTICIPANTS: This cross-sectional, multicenter study used the history and physical (H&P) notes from unique, randomly sampled adult admissions occurring between January 1, 2016, and December 31, 2021, at 13 hospitals in the University of Maryland Medical System (UMMS) and the John Hopkins (Hopkins) health care system to compare the performance of an LLM (GPT-4-Turbo) using zero-shot learning and prompting against humans in identifying patients with recent LTCF exposure. LLM analyses were conducted from August to September 2024.

EXPOSURE: Recent (≤12 months) LTCF exposure documented in the H&P note, as adjudicated by (1) humans and (2) an LLM.

MAIN OUTCOMES AND MEASURES: LLM sensitivity and specificity with Clopper-Pearson 95% CIs. Secondary outcomes were note review time and cost. The LLM was also prompted to provide a rationale and supporting note-text for each classification.

RESULTS: The study included 359 601 eligible adult admissions, of which 2087 randomly sampled H&P notes were manually reviewed at UMMS (1020 individuals; median [IQR] age, 58 [41-71] years; 493 [48%] male) and Hopkins (1067 individuals; median [IQR] age, 58 [48-67] years; 561 [53%] male) for LTCF residence. Compared with human review, the LLM achieved a sensitivity of 97% (95% CI, 91%-100%) and a specificity of 98% (95% CI, 97%-99%) at UMMS, and 96% (95% CI, 86%-100%) and 93% (95% CI, 92%-95%) sensitivity and specificity, respectively, at Hopkins; specificity at Hopkins improved with prompt revision (96% [95% CI, 95%-97%]). Of 117 manually reviewed LLM rationales, all were factually correct and quoted note-text accurately, and some demonstrated inferential logic and external knowledge. The LLM identified 37 (1.8%) human errors. Human review time had a mean of 2.5 minutes and cost $0.63 to $0.83 per note vs a mean of 4 to 6 seconds and $0.03 per note for LLM review.

CONCLUSIONS AND RELEVANCE: In this 13-hospital study of 2087 adult admissions, an LLM accurately identified LTCF residence from H&P notes and was more than 25 times faster and 20 times less expensive than human review.

PMID:40402496 | DOI:10.1001/jamanetworkopen.2025.12032

JAMA Netw Open. 2025 May 1;8(5):e2512075. doi: 10.1001/jamanetworkopen.2025.12075.

ABSTRACT

IMPORTANCE: Women experiencing more severe menopausal symptoms exhibit poorer quality of life, and those with early menopause have a higher risk of developing chronic diseases. However, the extent to which neighborhood disadvantage contributes to menopause onset and symptom severity remains understudied.

OBJECTIVE: To examine the association of Social Vulnerability Index (SVI) with age of natural menopause onset and menopausal symptom severity.

DESIGN, SETTING, AND PARTICIPANTS: This cohort study used data from a prospective cohort of women participating in Project Viva who were initially enrolled in eastern Massachusetts and followed up from pregnancy to midlife between April 1999 and August 2021. Participant inclusion required geocoded residential addresses at enrollment (1999-2002), 8-year follow-up (2006-2010), and 13-year follow-up (2012-2016); age at natural menopause; and menopausal symptoms. Data were analyzed between March 1 and June 30, 2024.

EXPOSURES: SVI grouped into 5 categories: very low (<20th percentile), low (20th to <40th percentile), moderate (40th to <60th percentile), high (60th to <80th percentile), or very high (≥80th percentile) vulnerability.

MAIN OUTCOMES AND MEASURES: Age at natural menopause and self-reported menopausal symptoms based on the presence and severity of 11 symptoms over the past year. These symptoms were assessed using the Menopause Rating Scale (total score range: 0-44, with higher scores indicating greater severity).

RESULTS: Of the 691 women included in the study (mean [SD] enrollment age, 33.7 [3.8] years; 41 with Asian [6.0%], 79 with Black [11.5%], 39 with Hispanic [5.7%], 507 with White [73.6%], and 23 with other [3.3%] race and ethnicity), 87 (12.6%) resided in neighborhoods with very high SVI at enrollment, 38 of 635 (6.0%) at 8-year follow-up, and 41 of 660 (6.2%) at 13-year follow-up. The Kaplan-Meier estimate for median age of natural menopause was earlier in women residing in neighborhoods with very high vs very low SVI at enrollment (52.0 [95% CI, 51.0-53.0] years vs 53.0 [95% CI, 53.0-54.0] years), 8-year follow-up (51.0 [95% CI, 50.0-53.0] years vs 53.0 [95% CI, 53.0-54.0] years), and 13-year follow-up (51.0 [95% CI, 50.0-53.0] years vs 53.0 [95% CI, 53.0-54.0] years). After adjusting for covariates, residence in neighborhoods with very high (but not low, moderate, or high) vs very low SVI at enrollment (adjusted hazard ratio [AHR], 1.36; 95% CI, 0.90-2.06), 8-year follow-up (AHR, 2.23 (95% CI, 1.29-3.85), and 13-year follow-up (AHR, 2.18 (95% CI, 1.30-3.66) was associated with higher risk of earlier natural menopause. SVI was not associated with menopausal symptoms.

CONCLUSIONS AND RELEVANCE: In this cohort study, women who resided in neighborhoods with very high vulnerability within 10 years of the perimenopause period exhibited higher risk of earlier natural menopause. Future research is warranted to explore whether initiatives to improve neighborhood conditions could mitigate the association of neighborhood disadvantage with earlier menopause onset.

PMID:40402495 | DOI:10.1001/jamanetworkopen.2025.12075