Category: Nevin Manimala

Chaos. 2025 May 1;35(5):053156. doi: 10.1063/5.0263273.

ABSTRACT

Understanding coupling correlations in multivariate time series is crucial for analyzing the complex dynamics of real world systems, where interactions often vary across different time scales. In this paper, we propose two novel methods: temporal weighted coupling correlation detrended analysis and its multiscale extension and multiscale temporal weighted coupling correlation detrended analysis (MTWCCDA). These two methods improve the trend removal process by incorporating temporal weighting, leading to a more robust and accurate characterization of coupling correlation behavior. We evaluate the performance of the proposed methods using synthetic data from several perspectives: accuracy of scaling index estimation, robustness to trends, sensitivity to noise and various components, and the necessity of MTWCCDA for capturing coupling behavior across scales. Additionally, MTWCCDA is applied to study the coupling behaviors of six air pollutants in Hunan Province, China, at different time scales. A Q-statistic quantifies each pollutant’s contribution to the system’s multifractal coupling correlation across scales, while scaling indices are used for clustering 14 cities, revealing regional variations in coupling behavior. The results provide valuable insights into pollutant interdependencies, aiding in the development of targeted air quality management strategies. The proposed methods offer a robust framework for investigating dynamic coupling behaviors in complex, multiscale, and non-stationary multivariate time series.

PMID:40408565 | DOI:10.1063/5.0263273

PLoS One. 2025 May 23;20(5):e0323710. doi: 10.1371/journal.pone.0323710. eCollection 2025.

ABSTRACT

Pain negatively impacts animal welfare and it is still neglected in ruminants. This original study aimed to develop and validate the Bovine Pain Scale (BPS) for acute pain assessment in hospitalized cattle undergoing surgery. This was a blinded, randomized, prospective clinical study. Thirty-six animals were included in the study. The Pain Group (n = 25) included patients admitted to a veterinary teaching hospital requiring any type of soft tissue or orthopedic surgery. Videos were recorded before, 2-6 hours after surgery, 1 hour after the administration of analgesia and 24 hours after surgery. The Control Group (n = 11) included healthy animals that were video recorded twice within a 24-48h interval. The BPS was developed using content validity. A total of 118 videos of 6 minutes were randomized and analyzed by four raters who were unaware of groups, time-points and procedures in two phases with a five-week interval. Statistical analysis was performed using R software. Intra and inter-rater reliability (intra-class correlation coefficient) was very good (0.83-0.94) and ranged from good to very good, respectively (0.65-0.81). The correlation between the BPS and the Visual Analog Scale (VAS) was strong (rho = 0.77, p < 0.0001) confirming criterion validity. Item-total correlation was acceptable for 3 of 9 items (0.33-0.43) and internal consistency was below the acceptable value (0.6). The scale was responsive to pain but not the administration of analgesia. It was specific for five items, but no items showed sensitivity. The area under the curve of 0.90 demonstrated high discriminatory capacity. According to the receiver operating characteristic curve, the cut-off point for rescue analgesia was ≥ 5 of 18. The BPS is reliable and reproducible, showed content and criterion validity, and may be used in veterinary hospitals for assessing post-operative pain in cattle to guide decision-making towards rescue analgesia. Future studies should refine the instrument to guarantee construct validity and sensitivity.

PMID:40408538 | DOI:10.1371/journal.pone.0323710

PLoS One. 2025 May 23;20(5):e0324999. doi: 10.1371/journal.pone.0324999. eCollection 2025.

ABSTRACT

This study aimed to examine prescribing trends for Parkinson’s disease (PD) medications in the United Kingdom from 2019 to 2024, focusing on the impact of guidelines from the American Academy of Neurology (AAN) and the National Institute for Health and Care Excellence (NICE) on the use of levodopa and dopamine agonists (DAs). A repeated cross-sectional design was employed, using publicly available data to assess prescribing patterns across the four UK countries. An interrupted time series analysis with linear regression was performed to identify trends, comparing regions with England as the reference point. Levodopa remained the most prescribed PD medication across all UK regions, as revealed by the analysis. In England, levodopa prescriptions increased significantly after the introduction of AAN guidelines, while other regions displayed more stable trends. Northern Ireland exhibited a distinct pattern, with DAs prescribed more frequently than levodopa. The findings also indicated that Scotland and Wales were less responsive to AAN guidance. This study highlights the influence of clinical guidelines on PD prescribing practices in the UK, with regional variations suggesting possible demographic or healthcare system factors. Further research is required to understand these disparities and their implications for PD management.

PMID:40408537 | DOI:10.1371/journal.pone.0324999

PLoS One. 2025 May 23;20(5):e0320321. doi: 10.1371/journal.pone.0320321. eCollection 2025.

ABSTRACT

This retrospective observational study aims to evaluate the incidence, disease burden, and geographic distribution of bipolar disorder based on hospital records in Ecuador over an eleven-year span. Hospital discharge data, publicly available from 2011 to 2021, were analyzed to assess incidence, DALYs, and the spatial distribution of hospitalized cases during this period. Between 2010 and 2021, a total of 6,821 hospitalized cases of bipolar disorder were documented in Ecuador, comprising 2,423 males and 4,398 females. The incidence rate peaked in 2019, with the lowest rate reported in 2020. There was no linear association between time and incidence rates or number of cases, but a significant increase was observed from 2017 to 2019 (p < 0.0001). The incidence rate was significantly higher in females compared to males (p < 0.0001). The average annual incidence was 3.47 cases per 100,000 person-years. The mean age at diagnosis was 40.76 years, with females being diagnosed at a younger age than males (p = 0.01548). Bipolar disorder-related deaths totaled 27 (12 males, 15 females). The burden of disease, expressed in DALYs, ranged from 66.769 to 126.98 per 100,000 population, with the hospitals from the private sector contributing most to the average DALYs. YLDs represented over 99% of the total burden. This study highlights the significant gender differences and temporal trends in bipolar disorder incidence in Ecuador, emphasizing the need for targeted public health strategies.

PMID:40408536 | DOI:10.1371/journal.pone.0320321

Biostatistics. 2024 Dec 31;26(1):kxaf013. doi: 10.1093/biostatistics/kxaf013.

ABSTRACT

Object-oriented data analysis is a fascinating and evolving field in modern statistical science, with the potential to make significant contributions to biomedical applications. This statistical framework facilitates the development of new methods to analyze complex data objects that capture more information than traditional clinical biomarkers. This paper applies the object-oriented framework to analyze physical activity levels, measured by accelerometers, as response objects in a regression model. Unlike traditional summary metrics, we utilize a recently proposed representation of physical activity data as a distributional object, providing a more nuanced and complete profile of individual energy expenditure across all ranges of monitoring intensity. A novel hybrid Fréchet regression model is proposed and applied to US population accelerometer data from National Health and Nutrition Examination Survey (NHANES) 2011 to 2014. The semi-parametric nature of the model allows for the inclusion of nonlinear effects for critical variables, such as age, which are biologically known to have subtle impacts on physical activity. Simultaneously, the inclusion of linear effects preserves interpretability for other variables, particularly categorical covariates such as ethnicity and sex. The results obtained are valuable from a public health perspective and could lead to new strategies for optimizing physical activity interventions in specific American subpopulations.

PMID:40408136 | DOI:10.1093/biostatistics/kxaf013

JAMA Netw Open. 2025 May 1;8(5):e2511922. doi: 10.1001/jamanetworkopen.2025.11922.

ABSTRACT

IMPORTANCE: Large language models (LLMs) and text-embedding models have shown potential in assessing mental health risks based on narrative data from psychiatric patients.

OBJECTIVE: To assess whether LLMs and text-embedding models can identify depression and suicide risk based on sentence completion test (SCT) narratives of psychiatric patients.

DESIGN, SETTING, AND PARTICIPANTS: This cross-sectional study, conducted at Seoul Metropolitan Government-Seoul National University Boramae Medical Center, analyzed SCT data collected from April 1, 2016, to September 30, 2021. Participants included psychiatric patients aged 18 to 39 years who completed SCT and self-assessments for depression (Beck Depression Inventory-II or Zung Self-Rating Depression Scale) and/or suicide (Beck Scale for Suicidal Ideation). Patients confirmed to have an IQ below 70 were excluded, leaving 1064 eligible SCT datasets (52 627 completed responses). Data processing with LLMs (GPT-4o, May 13, 2024, version; OpenAI [hereafter, LLM1]; gemini-1.0-pro, February 2024 version; Google DeepMind [hereafter, LLM2]; and GPT-3.5-turbo-16k, January 25, 2024, version; OpenAI) and text-embedding models (text-embedding-3-large, OpenAI [hereafter, text-embedding 1]; text-embedding3-small; OpenAI; and text-embedding-ada-002; OpenAI) was performed between July 4 and September 30, 2024.

MAIN OUTCOMES AND MEASURES: Outcomes included the performance of LLMs and text-embedding models in detecting depression and suicide, as measured by the area under the receiver operating characteristic curve (AUROC), balanced accuracy, and macro F1-score. Performance was evaluated across concatenated narratives of SCT, including self-concept, family, gender perception, and interpersonal relations narratives.

RESULTS: Based on SCT narratives from 1064 patients (mean [SD] age, 25.4 [5.5] years; 673 men [63.3%]), LLM1 showed strong performance in zero-shot learning, with an AUROC of 0.720 (95% CI, 0.689-0.752) for depression and 0.731 (95% CI, 0.704-0.762) for suicide risk using self-concept narratives. Few-shot learning for depression further improved the performance of LLM1 (AUROC, 0.754 [95% CI, 0.721-0.784]) and LLM2 (AUROC, 0.736 [95% CI, 0.704-0.770]). The text-embedding 1 model paired with extreme gradient boosting outperformed other models, achieving an AUROC of 0.841 (95% CI, 0.783-0.897) for depression and 0.724 (95% CI, 0.650-0.795) for suicide risk. Overall, self-concept narratives showed the most accurate detections across all models.

CONCLUSIONS AND RELEVANCE: This cross-sectional study of SCT narratives from psychiatric patients suggests that LLMs and text-embedding models may effectively detect depression and suicide risk, particularly using self-concept narratives. However, while these models demonstrated potential for detecting mental health risks, further improvements in performance and safety are essential before clinical application.

PMID:40408109 | DOI:10.1001/jamanetworkopen.2025.11922

JAMA Netw Open. 2025 May 1;8(5):e2512064. doi: 10.1001/jamanetworkopen.2025.12064.

ABSTRACT

IMPORTANCE: Depression detection in primary care remains limited in low- and middle-income countries despite increasing use of the World Health Organization Mental Health Gap Action Programme-Intervention Guide (mhGAP-IG).

OBJECTIVE: To test an app version of the mhGAP-IG (e-mhGAP-IG) in Nepal and Nigeria to improve depression detection.

DESIGN, SETTING, AND PARTICIPANTS: In this feasibility cluster randomized clinical trial conducted from February 14, 2021, to March 25, 2022, primary care facilities (unit of clustering) in Nepal and Nigeria were randomized to the standard mhGAP-IG training arm (control) or to training using the e-mhGAP-IG app (intervention). Primary care workers (PCWs) received training based on the arm assignment of their health care facility. Statistical analysis was conducted from July 20, 2022, through September 27, 2024.

INTERVENTION: Training using standard mhGAP-IG vs training using the e-mhGAP-IG.

MAIN OUTCOMES AND MEASURES: Analysis was performed on an intention-to-treat basis. The main outcome was accuracy of depression detection rates by PCWs, evaluated prior to mhGAP training and 5 to 8 months after training, measured as the percentage of patients who received a depression diagnosis by their PCWs compared with the number of patients who scored 10 or more on the locally validated 9-item Patient Health Questionnaire. Costs per patient detected were calculated.

RESULTS: In Nepal, 25 facilities (67 PCWs; mean [SD] age, 35.3 [9.2] years; 52 men [78%]) were randomized: 13 facilities to standard mhGAP-IG training (36 PCWs) and 12 facilities to e-mhGAP-IG (31 PCWs). In Nigeria, 10 facilities (47 PCWs; mean [SD] age, 46.9 [7.5] years; 44 women [94%]) were randomized: 5 facilities to standard mhGAP-IG (25 PCWs) and 5 facilities to e-mhGAP-IG (22 PCWs). In Nepal, depression detection by PCWs in the standard mhGAP-IG arm increased from 0 of 43 patients before training to 15 of 92 patients after training (adjusted mean change [AMC], 16% [95% CI, 5%-28%]), and depression detection in the e-mhGAP-IG arm increased from 0 of 49 before training to 22 of 91 after training (AMC, 24% [95% CI, 12%-36%]). In Nigeria, depression detection in the standard mhGAP-IG arm increased from 5 of 36 patients before training to 25 of 75 patients after training (AMC, 19% [95% CI, 2%-37%]), and depression detection in the e-mhGAP-IG arm increased from 6 of 35 patients before training to 67 of 76 patients after training (AMC, 71% [95% CI, 57%-85%]). In facilities in the e-mhGAP-IG arm, the app was used for 59 of 616 assessments (10% of patients) in Nepal and 883 of 1077 assessments (82% of patients) in Nigeria. Cost per patient with depression detected using the e-mhGAP-IG was Nepali Rupiya (NPR) 1980 (US $14.79) in Nepal and naira (₦) 1462 (US $0.91) in Nigeria.

CONCLUSIONS AND RELEVANCE: This feasibility cluster randomized clinical trial demonstrated that the use, cost, and potential clinical benefit of the e-mhGAP-IG varied by setting, highlighting the importance of multisite feasibility studies when evaluating digital innovations intended for health care systems worldwide.

TRIAL REGISTRATION: ClinicalTrials.gov Identifier: NCT04522453.

PMID:40408108 | DOI:10.1001/jamanetworkopen.2025.12064

JAMA Netw Open. 2025 May 1;8(5):e2512081. doi: 10.1001/jamanetworkopen.2025.12081.

ABSTRACT

IMPORTANCE: Suicide risk may be elevated among individuals who separate from military service with a character of discharge or service that was not honorable (ie, dishonorable, bad conduct, other than honorable, general, or uncharacterized), but a comprehensive evaluation of this possibility has not been previously published.

OBJECTIVE: To examine suicide mortality by character of discharge compared with individuals who received an honorable discharge and the full veteran population recently separated from service.

DESIGN, SETTING, AND PARTICIPANTS: This retrospective cohort study included all individuals who separated from the active component of the US military between January 1, 2002, and December 31, 2021. Data were obtained from the US Department of Veterans Affairs/Department of Defense Mortality Data Repository. Suicide rates for each character of discharge were compared with those among individuals receiving an honorable discharge and the full veteran population recently separated from service. Analyses were conducted between December 21, 2023, and June 1, 2024.

EXPOSURE: Character of discharge assigned at separation from the active component of military service.

MAIN OUTCOMES AND MEASURES: Crude suicide rates up to 5 years after separation were examined. Age-standardized rate ratios were used for comparisons within the cohort between strata. Age-standardized mortality ratios (SMRs) were used to compare suicide mortality by character of discharge with the full separation cohort and within specific military service branches.

RESULTS: Among 3 627 653 individuals (mean [SD] age at separation, 28.4 [8.6] years; 83% men), 5599 deaths by suicide occurred during the study period. All character of discharge groups had significantly higher suicide rates than the honorable group, with standardized rate ratios ranging from 1.91 (95% CI, 1.69-2.17) for uncharacterized to 2.77 (95% CI, 2.52-3.05) for general. Veterans who received an honorable discharge were less likely to die by suicide than the overall cohort population (SMR, 0.82 [95% CI, 0.79-0.85]), while veterans with all other characters of discharge were more likely to die by suicide, with SMRs ranging from 1.23 (95% CI, 1.15-1.31) for uncharacterized to 1.84 (95% CI, 1.72-1.97) for general. Common demographic risk factors for suicide (eg, age, race) were replicated within character groups in most cases.

CONCLUSIONS AND RELEVANCE: In this cohort study, individuals who did not receive an honorable character of discharge were at significantly higher risk of suicide compared with veterans who received an honorable discharge and the veteran population recently separated from service. These findings suggest that character of discharge may be a helpful risk factor to consider for ongoing suicide prevention efforts.

PMID:40408107 | DOI:10.1001/jamanetworkopen.2025.12081

JAMA Netw Open. 2025 May 1;8(5):e2512189. doi: 10.1001/jamanetworkopen.2025.12189.

ABSTRACT

IMPORTANCE: A recent increase in state policies targeting gender minority (GM; transgender and gender-diverse) people may affect the mental health of sexual and gender minority (SGM; nonheterosexual and/or GM) people and GM people specifically.

OBJECTIVE: To estimate changes in mental health symptoms associated with enactment of anti-GM state policies among SGM people and GM people specifically.

DESIGN, SETTING, AND PARTICIPANTS: This repeated cross-sectional study used a staggered difference-in-differences analysis to examine the associations between anti-GM policies in the US and mean changes in mental health symptoms among 8733 SGM adults who completed annual questionnaires between April 1, 2020, and June 1, 2023, for The Population Research in Identity and Disparities for Equality (PRIDE) Study, a national, prospective, continuously enrolling online cohort study of SGM adults.

EXPOSURES: Living in a state with 1 or more of the following enacted policies: (1) bathroom restrictions for GM people, (2) sports bans for GM young people participating in school sports, and (3) bans on gender-affirming care for young people.

MAIN OUTCOMES AND MEASURES: Mean levels of anxiety (measured using the 7-item Generalized Anxiety Disorder scale [GAD-7]; total score range, 0-21), depression (measured using the 9-item Patient Health Questionnaire scale [PHQ-9]; total score range, 0-27), and posttraumatic stress disorder (PTSD) symptoms (measured using the 6-item PTSD Checklist scale [PCL-6]; total score range, 6-30). For all 3 scales, higher scores indicate more severe symptoms.

RESULTS: Among all 8733 SGM participants in the sample (median age, 32.5 years [IQR, 26.0-45.0 years]; 2024 cisgender men [23.2%], 2355 cisgender women [27.0%], 2198 gender-diverse adults assigned female at birth [25.2%], 321 gender-diverse adults assigned male at birth [3.7%], 1294 transgender men [14.8%], and 541 transgender women [6.2%]), anti-GM policy enactment was associated with significant increases in anxiety (GAD-7 score, 0.8 points [95% CI, 0.2-1.4 points]) and PTSD (PCL-6 score, 0.8 points [95% CI, 0.1-1.4 points]) symptoms in states that enacted anti-GM policies compared with states that did not but was not associated with significant increases in depression symptoms (PHQ-9 score, 0.6 points [95% CI, -0.1 to 1.4 points]). In the GM subsample (n = 4354), nonsignificant changes in anxiety (GAD-7 score, 0.6 points [95% CI, -0.2 to 1.4 points]), depression (PHQ-9 score, 0.1 points [95% CI, -0.9 to 1.1 points]), and PTSD (PCL-6 score, 0.7 points [95% CI, -0.2 to 1.6 points]) symptoms were observed after policy enactment in states that enacted anti-GM policies compared with states that did not. Gender minority adults had high mental health symptoms across the study period.

CONCLUSIONS AND RELEVANCE: In this study of 8733 SGM adults using difference-in-differences analysis, anti-GM policies were associated with worse mental health symptoms among SGM adults but no changes in mental health symptoms among GM adults. As these policies proliferate, it is important to consider how they may affect mental health.

PMID:40408106 | DOI:10.1001/jamanetworkopen.2025.12189

JAMA Netw Open. 2025 May 1;8(5):e2512198. doi: 10.1001/jamanetworkopen.2025.12198.

ABSTRACT

IMPORTANCE: Life expectancy is a composite health measure reflecting acute and life-course exposures. Identifying conditions behind disparities in life expectancy can guide policy, planning, and financing to battle the most urgent health problems.

OBJECTIVE: To examine the contribution of 33 causes of death to life expectancy disparities, highlighting 2 sets of priority conditions-8 infectious and maternal and child health conditions (I-8) and 7 noncommunicable diseases and injuries (NCD-7).

DESIGN, SETTING, AND PARTICIPANTS: This cross-sectional study examined life expectancy disparities in 7 global regions and 165 countries from 2000 to 2021. Western Europe and Canada (hereafter referred to as the North Atlantic) in 2019 were used as a benchmark for life expectancy achievable with advanced health care and living standards. Life expectancy gaps in locations with life expectancy lower than the benchmark were decomposed by cause of death using the Pollard decomposition on the Global Health Estimates from the World Health Organization. Data were analyzed from February to March 2025.

EXPOSURE: Geographic location (countries and regions).

MAIN OUTCOME AND MEASURE: Life expectancy at birth.

RESULTS: In the median country in 2019, the I-8 and NCD-7 together accounted for 80% (IQR, 71%-88%) of the life expectancy gap compared with the North Atlantic. Outside sub-Saharan Africa, the NCD-7 accounted for the largest share of the gap; for example, more than the total life expectancy gap in China, or 5.5 (95% uncertainty bounds [UB], 5.0-6.0) years of a 4.3-year life expectancy gap; and 6.4 (95% UB, 5.9-6.8) years of a 11.5-year gap in India. However, reduced mortality from the I-8 contributed to enormous improvements in sub-Saharan Africa, accounting for 21.4 (95% UB, 20.6-22.2) years of a 31-year gap in 2000 and 11.4 (95% UB, 10.9-11.8) years of a 22-year gap in 2019. India transitioned from having most of the gap accounted for by the I-8 in 2000, or 11.9 (95% UB, 11.0-13.0) years of a 19.6-year life expectancy gap, to having a larger share accounted for by the NCD-7 in 2019.

CONCLUSIONS AND RELEVANCE: This cross-sectional study suggests that a limited number of causes account for most life expectancy disparities. Together with current information on risk factors, interventions, and morbidity not yet reflected in life expectancy, the varying contributions of these causes to gaps in life expectancy can help focus health policy and guide interventions to reduce risk factors and treat conditions.

PMID:40408105 | DOI:10.1001/jamanetworkopen.2025.12198