Nevin Manimala

Neurol Clin Pract. 2026 Jun;16(3):e200618. doi: 10.1212/CPJ.0000000000200618. Epub 2026 May 7.

ABSTRACT

BACKGROUND AND OBJECTIVES: Military sexual trauma (MST) is increasingly recognized in US veterans. MST is associated with psychiatric disease, substance abuse, and pain conditions, including headache. Little is known about the relationship between MST and specific headache disorders.

METHODS: This retrospective cross-sectional study analyzed administrative data from the Women Veterans Cohort Study, a sample of post-9/11 US veterans enrolled for Veterans Health Administration care. A positive MST screen in the electronic medical record defined exposure. We extracted demographic and clinical data from administrative coding for migraine and relevant confounders, comparing between subgroups with χ² tests. Health care utilization variables included designated sites of care and prescribed acute and preventive treatments and were evaluated with multivariable logit, negative binomial (nb), and zero-inflated nb models.

RESULTS: Of 846,435 veterans screened for MST, 4.4% of veterans had a positive screen, whereas 9.5% had migraines. Veterans with migraine and a positive MST screen (21.7%) were more often non-White (45.3% vs 38.6%, p < 0.001), and 33 or less years old (55% vs 53%, p < 0.001) than veterans with migraine and a negative MST screen (9.0%). Adjusting for sex, the odds of migraine were greater for veterans with a positive MST screen (OR 1.62, 95% CI 1.57-1.67). Veterans with migraine and a positive MST screen were no more likely to receive triptan medications than veterans with migraine (46.2% vs 45.7%, p = 0.47) although were more likely to be prescribed opioids (36.1% vs 33.4%, p ≤ 0.001), compared with those with migraine and a negative MST screen. After controlling for sex, comorbidities (including chronic pain conditions), treatments, and other health care use, health care utilization was increased among migraine veterans with a positive MST screen, compared with migraine veterans without a positive MST screen for primary care (IRR 1.06, 95% CI 1.04-1.08, p < 0.001) and emergency department care (IRR 1.14, (95% CI 1.07-1.22), whereas neurology visits were not increased (IRR 0.97, 95% CI 0.92-1.02).

DISCUSSION: Veterans with a positive MST screen constitute a vulnerable population more likely to have migraine, take opioid medications, and use emergency departments for migraine care.

PMID:42114073 | DOI:10.1212/CPJ.0000000000200618

Neurol Clin Pract. 2026 Jun;16(3):e200615. doi: 10.1212/CPJ.0000000000200615. Epub 2026 Apr 28.

ABSTRACT

BACKGROUND AND OBJECTIVES: Refractory migraine (RM) is associated with substantial disability, yet its clinical and health care utilization patterns remain poorly characterized in large, real-world populations. Understanding how preventive treatment progression relates to health care use and patient characteristics may inform earlier identification and care strategies. We sought to evaluate demographic, clinical, and health care utilization patterns in chronic migraine according to the number of preventive medication trials using a large cross-institutional electronic health record (EHR) database.

METHODS: We conducted a retrospective observational study using the Epic Cosmos Cross-institutional EHR Database from January 1, 2016, to December 31, 2024. Adults with chronic migraine (International Classification of Diseases, Tenth Edition code G43.7) were included. Preventive medication trials were categorized into 5 classes: antihypertensives, antidepressants, antiseizure agents, calcitonin gene-related peptide-targeted therapies, and onabotulinumtoxinA. We evaluated demographics, comorbidities, and health care utilization metrics, including inpatient or outpatient dihydroergotamine (DHE) infusions, emergency department (ED) visits for headache, MRI brain orders, and patient EHR portal recency (MyChart). Marginal changes were defined as the percentage point change in outcomes between medication classes. Chi-squared tests and analysis of variance were used with significance set at p < 0.05.

RESULTS: A total of 1,572,698 patients were identified by our search criteria; 21.2% were prescribed no preventive medications and 2.5% were prescribed all 5 classes, meeting the study’s definition of RM. Health care utilization increased significantly with each additional medication class. The greatest marginal increases occurred between zero to 1 classes for MyChart access (43-day decrease), 1 to 2 classes for ED visits (+9.2%), and 4 to 5 classes for DHE administration (+6.1%) and MRI brain orders (+4.9%). Patients prescribed more preventive classes were older, a higher percentage female sex, White race, with public insurance, residence in the Northeast United States, and live in less socially vulnerable areas. Comorbidity burden increased progressively, with 94.6% of refractory patients having at least 1 comorbidity, most commonly anxiety (78.1%), depression (71.5%), hypertension (56.0%), and asthma (36.3%). All differences were statistically significant (p < 0.001).

DISCUSSION: Higher health care utilization, greater comorbidity burden, and distinct geographic patterns are observed with increasing numbers of preventive medication trials in chronic migraine. These findings highlight the complexity of RM and underscore the need for earlier identification and more equitable access to comprehensive migraine care.

PMID:42114071 | DOI:10.1212/CPJ.0000000000200615

JMIR Hum Factors. 2026 May 11;13:e67944. doi: 10.2196/67944.

ABSTRACT

BACKGROUND: There are around 20,000 mental health apps available in app stores. The Organisation for the Review of Care and Health Apps (ORCHA), a United Kingdom digital health compliance company, has assessed a number of digital mental health apps with regard to their quality, professional and clinical assurance, data privacy, and user experience. This study analyzes the data that were collected by ORCHA when they assessed mental health apps.

OBJECTIVE: This study aimed to examine the characteristics of mental health apps regarding their quality, target users, features, underpinning evidence, and data privacy.

METHODS: A dataset comprising ORCHA Baseline Review assessments of over 2000 digital health apps, including 436 mental health apps, was used. This study uses exploratory data analysis to gain insight into the quality and characteristics of mental health apps. Methods such as descriptive and inferential statistics, k-modes clustering, and association rule mining were used to explore the quality of mental health apps as well as reveal insights into the different cost types, target users, app features, data types, and evidence of app content.

RESULTS: Information provision, data capture, and data sharing were the most common features within the 436 mental health apps. The examined apps primarily targeted the following groups: adults (n=229, 52.5%), everyone (n=184, 42.2%), and teens (n=135, 31%). The cost of apps has not been linked to the quality of mental health apps, although paid apps or apps with in-app purchases may include additional services. Indicated user acceptance or benefit is the most common type of evidence provided by these mental health apps. A total of 241 (55.3%) apps included a qualified professional in app development, and 251 (57.6%) apps provided evidence within the app that the developer validated any guidance with relevant reliable information sources or references. Usage data and email were the most commonly collected data types. Association rule mining showed that email, IP address, name, and usage data are often co-collected by the same apps. K-modes cluster analysis showed that mental health apps can be categorized into 2 clusters, where one cluster of apps (n=182, 41.7%) collected more data than apps in the other cluster.

CONCLUSIONS: Mental health apps are commonly targeted for everyone to use, but many apps are targeted toward teens or adults. Our study suggests that many publicly available mental health apps did not take the precautions (such as the involvement of appropriate health professionals, literature references, or conducting tests) to ensure that their content is valid and research based. Greater effort on behalf of mental health app developers is needed to ensure that the public is provided with high-quality apps. Moreover, our study indicates that the mental health apps that collect more data tend to score better on the ORCHA Baseline Review assessment.

PMID:42114062 | DOI:10.2196/67944

J Med Internet Res. 2026 May 11;28:e88374. doi: 10.2196/88374.

ABSTRACT

BACKGROUND: Frailty and sarcopenia represent substantial global health challenges, frequently diminishing patients’ quality of life through impaired muscle function and physical performance. Digital health interventions (DHIs) have shown promise in mitigating these conditions among older adults. However, outcomes of such interventions in this demographic are inconsistent, and a thorough synthesis of existing evidence is lacking.

OBJECTIVE: This study aimed to evaluate the effectiveness of DHIs in older adults with frailty and sarcopenia.

METHODS: A comprehensive search of PubMed, Web of Science, MEDLINE, Embase, and Cochrane Library was conducted from their inception until January 2026 to identify randomized controlled trials. Meta-analyses were performed using R software (R Foundation for Statistical Computing). Study quality was evaluated using the revised Cochrane Risk of Bias Tool 2.0 (Cochrane Collaboration), and evidence certainty was assessed using GRADE (Grading of Recommendations, Assessment, Development, and Evaluation).

RESULTS: From 3506 records, 16 studies were included. DHIs significantly improved total skeletal muscle mass (weighted mean difference [WMD] 1.01, 95% CI 0.08-1.94, 95% prediction interval [PI] -0.95 to 2.96), gait speed (WMD 0.09, 95% CI 0.03-0.15, 95% PI -0.1 to 0.26), Timed Up and Go Test (TUGT: WMD -0.52, 95% CI -1.02 to -0.03, 95% PI -1.93 to 0.85), 30-second Chair Stand Test (30CST: WMD 2.19, 95% CI 0.89-3.48, 95% PI -1.59 to 5.66), balance (standardized mean difference [SMD] 0.61, 95% CI 0-1.21, 95% PI -0.94 to 2.13), and quality of life (SMD 0.16, 95% CI 0.05-0.27, 95% PI 0.04-0.28). No significant improvements were observed in Appendicular Skeletal Muscle Mass Index (ASMI), grip strength, 6-minute walk test (6MWT), 2-minute walk test (2MWT), Short Physical Performance Battery (SPPB), or BMI. Although the pooled effect was favorable, the wide 95% PI suggests substantial between-study heterogeneity. Subgroup analysis stratified by intervention duration revealed significant intersubgroup differences in ASMI (χ²₁=9.93; P=.0016), indicating interventions lasting ≥12 weeks were more effective for improving ASMI (WMD 0.28, 95% CI 0.06-0.50, 95% PI -0.30 to 0.83). Subgroup analysis stratified by intervention type showed significant intersubgroup differences in balance (χ²₃=9.89; P=.0195), with exergame-based interventions showing significant effects (SMD 0.83, 95% CI 0.26-1.40).

CONCLUSIONS: This systematic review is the first to quantify the disease-specific efficacy of DHIs in improving muscle function, physical performance, and quality of life among older adults with frailty and sarcopenia, demonstrating their unique value as a scalable complementary approach. By overcoming geographical and resource constraints, DHIs support underserved populations. However, low evidence quality and heterogeneity warrant cautious interpretation. The 95% PIs indicate that actual effects may vary with population characteristics and implementation contexts. Nonetheless, DHIs represent a promising and cost-effective strategy for service expansion. Future high-quality studies are needed to better understand their effectiveness and implementation across settings.

PMID:42114061 | DOI:10.2196/88374

JMIR Mhealth Uhealth. 2026 May 11;14:e81397. doi: 10.2196/81397.

ABSTRACT

BACKGROUND: Continuous follow-up for patients with major depressive disorder (MDD) is essential for treatment decisions and a better prognosis. There remains limited evidence regarding the critical issue of depression variation trajectory prediction using mobile health (mHealth) measures. Moreover, the temporal dynamics of mHealth measures have not been fully modeled in previous studies, and the poor patient adherence to mHealth records poses great challenges to the dynamic feature modeling.

OBJECTIVE: This study aimed to examine the contribution of mHealth measures in predicting depression variation trajectory for patients with MDD, with full consideration of the temporal dynamics of mHealth measures.

METHODS: A total of 229 patients with MDD from a multiple-center, prospective cohort were included. A 12-week follow-up was conducted involving the collection of the Hamilton Depression Rating Scale (HAMD-17), along with patient-reported outcomes (Immediate Mood Scaler and Altman Self-Rating Mania Scale) via mobile devices and sleep duration through wearable wristbands. We used functional data analysis to extract dynamic features from the sparse mHealth records, rather than aggregating the data to a single scalar summary measure through collapsing over time. Subsequently, 3 machine learning models were applied to predict the depression variation trajectory classes based on the baseline characteristics and these extracted dynamic features.

RESULTS: Based on the variation of HAMD-17 scores within 12 weeks, the participants were labeled into 4 classes through the k-means algorithm. The classes included stable decline (n=93), fluctuate decline (n=44), fast decline (n=60), and delayed and fluctuate (n=32), in light of the shape of depression trajectories. With both baseline features and dynamic features of the mHealth measures, accuracy rates for the overall data were 54.35%, 60.87%, and 56.52%, for the stable decline patients were 78.95%, 84.21%, and 73.68%, for the nonstable decline patients were 59.26%, 62.96%, and 70.37% based on the 3 machine learning models, respectively. The results were significantly superior to the prediction obtained without mHealth measures (with an overall accuracy below 50%) and only showed a marginal reduction in accuracy relative to the ideal prediction with assessment obtained from clinical visits. Moreover, in the construction of the most accurate prediction model, dynamic features of the Immediate Mood Scaler, the Altman Self-Rating Mania Scale, and sleep duration emerged as the most influential predictors, ranking first, third, and fourth, respectively, in terms of their relative importance.

CONCLUSIONS: Longitudinal mHealth measures show potential in depression variation trajectory monitoring for patients with MDD even under poor patient adherence. Our work provides practical help in alleviating the follow-up burden for patients with MDD and validates the effectiveness of mHealth measures in clinical applications.

PMID:42114060 | DOI:10.2196/81397

J Neonatal Perinatal Med. 2026 May 11:19345798261450458. doi: 10.1177/19345798261450458. Online ahead of print.

ABSTRACT

BackgroundMechanical ventilation is an essential component of the management of respiratory failure in newborn infants in the neonatal intensive care unit (NICU). The effects of invasive and noninvasive ventilation strategies on the Cerebral Blood Flow Velocity (CBFV) have not been fully studied. The objective was to assess the influence of the mode of respiratory support, invasive or noninvasive, on CBFV in newborn infants admitted to the NICU.MethodsThis is a prospective observational study of 90 neonates. Participants were allocated into three groups according to the need for respiratory support: invasive ventilation, noninvasive ventilation, and a control group. Doppler ultrasonography of the middle cerebral artery was performed at the first hour and on the third day of respiratory support.ResultsThere were no statistically significant differences between the Doppler indices at the first hour of starting respiratory support between the studied groups. However, the Vmax of the middle cerebral artery was decreased significantly in the invasive mechanical ventilation group on the 3rd day of ventilation compared to the noninvasive group. Preterm infants exhibited a significant decrease in the mean values of Vmax, compared to full-term infants in the invasive group. Reduction of CBFV was reported in relation to seizures and sepsis. The cutoff value of CBFV for mortality has 100% sensitivity and 94.74% specificity.ConclusionsIn this observational study, Lower Vmax was observed among infants receiving invasive ventilation, a group that was also more premature and clinically unstable. Noninvasive ventilation was associated with stable cerebral hemodynamics.

PMID:42114052 | DOI:10.1177/19345798261450458

JAMA Neurol. 2026 May 11. doi: 10.1001/jamaneurol.2026.1206. Online ahead of print.

ABSTRACT

IMPORTANCE: Reliable global estimates of the incidence and prevalence of dementia with Lewy bodies (DLB) are lacking, limiting understanding of its epidemiology and burden.

OBJECTIVE: To estimate pooled incidence and prevalence of DLB from population-based studies worldwide, overall and by age and sex.

DATA SOURCES AND STUDY SELECTION: PubMed, Embase, and Scopus were systematically searched from inception to October 22, 2024, for population-based studies reporting DLB incidence and/or prevalence based on validated diagnostic criteria.

DATA EXTRACTION AND SYNTHESIS: Three reviewers independently screened studies, extracted data, and assessed risk of bias according to PRISMA guidelines. Incidence and prevalence estimates were pooled using random-effects meta-analysis. Subgroup and sensitivity analyses explored variation by age, sex, and study design.

MAIN OUTCOMES AND MEASURES: Incident and prevalent DLB cases defined by consensus, Diagnostic and Statistical Manual of Mental Disorders or International Classification of Diseases diagnostic criteria, with denominators based on census or author-defined population at risk.

RESULTS: From 2520 records screened, 16 population-based studies were included and 12 contributed to meta-analyses. In individuals 65 years or older, pooled incidence was 46.85 per 100 000 person-years (95% CI, 23.78-92.30) and pooled prevalence 352.26 per 100 000 population (95% CI, 112.25-1099.79). In individuals younger than 65 years, pooled incidence was 0.34 per 100 000 person-years (95% CI, 0.14-0.83) and prevalence 2.52 per 100 000 population (95% CI, 1.43-4.44). Incidence was higher in males (5.45; 95% CI, 4.13-7.19) than females (4.32; 95% CI, 2.48-7.52). Across all ages, pooled crude incidence was 4.79 (95% CI, 3.90-5.88). Only 1 study reported all-age prevalence (19.13; 95% CI, 15.38-23.51). Between-study heterogeneity was high (I2 ≥ 85%).

CONCLUSIONS AND RELEVANCE: In this systematic review and meta-analysis of population-based studies, clinically diagnosed DLB was uncommon, likely reflecting underdiagnosis and diagnostic insensitivity. Reported incidence and prevalence rose steeply with age, were higher in men, and varied widely across settings. These findings provide a robust reference for future epidemiologic research and public health planning, underscoring the need for standardized diagnostic approaches and inclusion of underrepresented populations to refine global burden estimates.

PMID:42113545 | DOI:10.1001/jamaneurol.2026.1206

Vet Med Sci. 2026 May;12(3):e70991. doi: 10.1002/vms3.70991.

ABSTRACT

Eimeria spp. are major protozoan parasites of cattle, causing coccidiosis with substantial economic and animal health impacts worldwide. This study systematically reviewed and meta-analysed the global prevalence, species distribution and associated risk factors of Eimeria spp. in cattle. Various international databases were searched from inception to 16 April 2025. Eligible studies reported extractable prevalence data for naturally infected cattle. Pooled prevalence was estimated using a random-effects model, with heterogeneity assessed by I² statistic. Subgroup analyses were conducted by publication year, continent, country and sample size. Age- and sex-specific data were analysed descriptively due to missing denominators. Genetic diversity and seasonal patterns were summarized descriptively. Meta-regression evaluated sample size, annual precipitation, publication year and national cattle population. Sensitivity analysis and funnel plot (Egger’s test) assessed robustness and publication bias. A total of 203 studies including 133,740 cattle from 55 countries were analysed. The global pooled prevalence of Eimeria spp. in cattle was 33.6% (95% confidence interval [CI]: 29.6%-37.8%), with substantial heterogeneity (I² = 99.4%). Prevalence ranged from 27.1% (2012-2018) to 40.8% (≤2011), 29.5% in Asia to 67.4% in Central America and 1% (Macedonia) to 94.2% (Costa Rica), though some national estimates were based on single studies. Calves <1 year accounted for the highest proportion of positives (56.3%, 95% CI: 46.2-65.8), and females showed higher infection rates (66.7%, 95% CI: 61.7-71.4). Infections peaked during warm and humid periods. Sixteen Eimeria species were identified in cattle; E. bovis and E. zuernii predominated, followed by E. auburnensis, E. ellipsoidalis, E. cylindrica and E. alabamensis. Sensitivity analyses confirmed estimate stability. Meta-regression identified sample size as the only significant predictor, explaining 4% of heterogeneity. Publication bias was detected (p < 0.05). Eimeria infection imposes a substantial global burden in cattle, particularly among calves and females. Although sample size influenced reported prevalence, marked heterogeneity persists. Standardized reporting and geographically balanced studies are needed to better inform global coccidiosis control strategies.

PMID:42113544 | DOI:10.1002/vms3.70991

JAMA Netw Open. 2026 May 1;9(5):e2611774. doi: 10.1001/jamanetworkopen.2026.11774.

ABSTRACT

IMPORTANCE: Robotic-assisted surgery is increasingly used in acute care surgery, but the impact of individual surgeon robotic case volume on outcomes for both elective and emergency general surgery procedures remains uncertain.

OBJECTIVE: To evaluate the association between annual surgeon robotic case volume and patient outcomes following robotic-assisted elective and emergency general surgery.

DESIGN, SETTING, AND PARTICIPANTS: This retrospective cohort study used data from the Premier Healthcare Database (PHD), a large US all-payer hospital database, from January 2021 to December 2023. The PHD aggregates data from nonprofit, community, and teaching hospitals across rural and urban areas, representing 25% of all US inpatient admissions. Adult patients (aged ≥18 years) undergoing robotic-assisted cholecystectomy, colectomy, appendectomy, small bowel resection, or ventral hernia repair were included.

EXPOSURE: Annual surgeon-level robotic case volume, categorized as low (≤25), intermediate (26-75), high (76-150), or very high (≥151).

MAIN OUTCOMES AND MEASURES: The primary outcome was conversion to open surgery; secondary outcomes included postoperative complications, intensive care unit (ICU) admission, 30-day readmission, operative time, hospital length of stay, total hospital cost, and in-hospital mortality. Multivariable logistic and linear regression models, respectively, were used to estimate adjusted odds ratios (AORs) and mean ratios with 95% CIs. Models adjusted for patient demographics, hospital characteristics, and surgeon specialty.

RESULTS: Among 185 924 patients undergoing robotic procedures (137 879 elective and 48 045 emergency), most (58.2%) were female (57.1% of elective and 61.5% of emergency cases). Mean (SD) patient age was 54.9 (16.6) years overall (55.6 [15.8] years for elective and 53.0 [18.5] years for emergency procedures). In elective procedures, increasing annual surgeon volume was associated with stepwise improvements across most outcomes; compared with low volume surgeons, very high volume surgeons had lower odds of conversion to open surgery (AOR, 0.45; 95% CI, 0.36-0.56), complications (AOR, 0.87; 95% CI, 0.79-0.96), readmission (AOR, 0.79; 95% CI, 0.68-0.91), and ICU admission (AOR, 0.61; 95% CI, 0.46-0.82). Operative time (mean ratio, 0.77; 95% CI, 0.75-0.79), hospital length of stay (mean ratio, 0.89; 95% CI, 0.88-0.91), and costs (mean ratio, 0.83; 95% CI, 0.82-0.84) were also significantly lower. In emergency procedures, very high vs low surgeon volume was associated with lower odds of conversion to open surgery (AOR, 0.73; 95% CI, 0.54-1.00) and modest reductions in operative time (mean ratio, 0.88; 95% CI, 0.85-0.91) and cost (mean ratio, 0.92; 95% CI, 0.89-0.94). No association was observed between surgeon volume and in-hospital mortality in either cohort.

CONCLUSIONS AND RELEVANCE: In this cohort study, greater annual surgeon robotic case volume was associated with better patient outcomes in elective general surgery and, to a lesser degree, in emergency procedures. These findings highlight the importance of surgeon-specific experience in robotic surgery and may inform training, credentialing, and strategies for safe expansion of robotic capabilities in acute care surgery.

PMID:42113516 | DOI:10.1001/jamanetworkopen.2026.11774

JAMA Netw Open. 2026 May 1;9(5):e2611835. doi: 10.1001/jamanetworkopen.2026.11835.

ABSTRACT

IMPORTANCE: Telemedicine is now widely used, stimulated by pandemic-era expansion rules and payment parity to in-person visits. Lawmakers continue to consider how to revise existing policies because of uncertainty about the potential for telemedicine to increase utilization and spending.

OBJECTIVE: To quantify the association between telemedicine adoption and visits and spending.

DESIGN, SETTING, AND PARTICIPANTS: This cohort study used multipayer medical claims data from MedInsight’s research database for a national sample of adults continuously enrolled in Medicare fee-for-service, Medicare Advantage, dual-eligible, Medicaid, or commercial insurance, from January 1, 2019, to October 31, 2023. Data were analyzed from April 30, 2024, to February 10, 2026.

EXPOSURE: Regional telemedicine adoption (measured at the hospital referral region [HRR] level).

MAIN OUTCOMES AND MEASURES: The primary outcomes were (1) total combined telemedicine and in-person ambulatory visits (primary care, specialist, and preventive screening visits), and (2) total combined per-member-per-month spending on professional, inpatient, facility outpatient, prescription drug, and ancillary payments. By use of difference-in-differences analyses on visit-level and spending-level changes before (January 1, 2019, to December 31, 2019) vs after (January 1, 2021, to October 31, 2023) telemedicine expansion in high-telemedicine vs low-telemedicine adoption quintiles (measured at the HRR level), age-adjusted, sex-adjusted, and diagnosis-adjusted Poisson regressions were estimated, accounting for repeated measurements over time. Analysis was also stratified by urbanicity, payer, and Centers for Disease Control and Prevention Social Vulnerability Index quintiles to explore heterogeneous associations.

RESULTS: The sample included 3.04 million US individuals (mean [SD] age, 54.2 [17.2] years; 55.7% female) who utilized 120 million visits and incurred $178.4 billion in spending during 2019 to 2023. In 2019, the mean (SD) visit rate was 0.66 (0.035), and the mean (SD) spending rate was $774.59 ($36.78) per-member-per-month. Overall, point estimates suggested high-adopting areas had 2.4% (95% CI, -8.1% to 3.6%) fewer visits and 0.5% (95% CI, -13.1% to 13.9%) lower spending; however, 95% CIs crossed the null. Similarly, point estimates varied across subgroups but none achieved statistical significance: there were 4.4% (95% CI, -11.2% to 3.0%) fewer visits and 2.3% (95% CI, -18.9% to 17.8%) lower spending among urban populations, 2.5% (95% CI, -12.9% to 8.0%) lower spending for Medicaid-insured individuals, 5.3% (95% CI, -47.1% to 66.2%) lower spending for dual-eligible individuals, 3.0% (95% CI, -9.2% to 3.5%) lower spending for Medicare Advantage-insured individuals, and 1.5% (95% CI, -19.1% to 19.8%) lower spending among the most socially vulnerable populations. Conversely, point estimates suggested 3.4% (95% CI, -4.9% to 12.5%) greater visits and 3.8% (95% CI, -12.2% to 21.4%) higher spending in rural areas, 1.1% (95% CI, -12.8% to 17.3%) higher spending for commercially insured individuals, 1.0% (95% CI, -7.1% to 11.5%) higher spending for Medicare fee-for-service-insured individuals, and 4.5% (95% CI, -12.7% to 23.1%) higher spending among the least socially vulnerable groups. All 95% CIs crossed the null.

CONCLUSIONS AND RELEVANCE: Nationwide telemedicine adoption was not significantly associated with changes in visits or spending, either overall or when stratified by urbanicity, payer type, or area-level social vulnerability, thus easing concerns about large utilization and spending increases from telemedicine expansion.

PMID:42113515 | DOI:10.1001/jamanetworkopen.2026.11835