Tag: pubmed

AIDS Res Ther. 2026 Feb 4. doi: 10.1186/s12981-026-00852-x. Online ahead of print.

ABSTRACT

BACKGROUND: As Uganda scales up integrated chronic care, understanding viral load suppression (VLS) among people living with HIV (PLHIV) with non-communicable diseases (NCDs) is critical for optimizing service delivery. This study thus determined VLS among the PLHIV with NCDs and associated factors.

METHODS: We conducted a cross-sectional analysis using routine program data from 10 districts in Eastern Uganda and 18 health facilities-three general hospitals, seven health centre (HC) IVs, seven HC IIIs and one Special HIV Clinic. We included PLHIV aged 20 + who were active in care during the April-June 2025 quarter and screened for NCDs (hypertension, diabetes mellitus, anxiety/depression and alcohol abuse). VLS was defined as < 1000 copies/ml. Robust Poisson regression estimated crude and adjusted prevalence ratios (aPRs) for VLS using 95% confidence interval (CI) and p < 0.05 for statistical significance. A forest plot visualized effect sizes and confidence intervals. STATA Corp version 15 was used for the analysis.

RESULTS: Among 8,306 PLHIV, 62.4% were female, with a mean (Standard Deviation) age of 46.7 years (13.8). Overall VLS was 94.7% (96.72% for PLHIV with NCDs and 94.53% for those without). NCD comorbidity was 8.45%, predominantly hypertension (7%). Crude analysis showed higher VLS among PLHIV with NCDs (PR = 1.023; p = 0.002), but adjusted estimates attenuated (aPR = 1.015; 95% CI 0.999-1.031; p = 0.065). Older age groups (30-39, 40-49, 50+) had significantly higher VLS than those under 30 ( aPR = 1.063; 95% CI 1.034-1.091; p < 0.001), (aPR = 1.073; 95% CI 1.044-1.102; p < 0.001), (aPR = 1.071; 95% CI 1.043-1.100; p < 0.001) respectively. Males had lower VLS than females (aPR = 0.978, 95% CI [0.968, 0.989] p < 0.001). Clients at Health Centre III had reduced VLS (aPR = 0.979 95%CI [0.961, 0.997]; p = 0.023), while Health Centre IV and hospitals showed no significant difference when compared with those in the special clinic.

CONCLUSION: VLS among PLHIV with NCDs was comparable to those without, suggesting integrated care may mitigate disparities. However, age, sex, and facility level remain key determinants. Targeted interventions are needed for younger clients, men, and health centre IIIs to sustain viral suppression in the era of integrated health services.

PMID:41639705 | DOI:10.1186/s12981-026-00852-x

BMC Oral Health. 2026 Feb 4. doi: 10.1186/s12903-026-07820-x. Online ahead of print.

ABSTRACT

BACKGROUND: Smoking and alcohol consumption are major risk factors for oral potentially malignant disorders (OPMDs), yet the extent to which an OPMD diagnosis prompts behavioral change remains unclear. This study aimed to describe the changes in smoking and alcohol consumption in patients after an OPMD diagnosis and explore the role of demographic and clinical factors in these behavioral changes.

METHODS: A retrospective cohort study was conducted in the Department of Oral and Maxillofacial Surgery at the McGill University Health Centre, including OPMD patients from 2018 to 2023. Eligible patients had a confirmed OPMD diagnosis, no prior history of head and neck cancer, and at least six months of follow-up. Those without smoking or alcohol data were excluded. Descriptive statistics, McNemar’s and Wilcoxon signed-rank tests to assess behavioral changes, and logistic regression to identify factors associated with continued smoking and alcohol use were used.

RESULTS: The sample composed of 82 patients, 30 (36.6%) were female, with a mean age at diagnosis of 64 years. Leukoplakia (82.9%) was the most common OPMD, and the tongue was the most affected site (42.7%). Median follow-up was 29 months. Most patients (76.8%) had a histologic diagnosis of either mild or moderate dysplasia. At the time of diagnosis, about one-third were active smokers or moderate-to-heavy drinkers. Smoking prevalence decreased from 30.5% at diagnosis to 22.5% post-diagnosis, although this reduction did not reach statistical significance (p = 0.06), while moderate-to-heavy alcohol use remained largely unchanged (31.7% vs. 30.5%, p = 1.00). Younger patients were less likely to change smoking habits (OR = 0.96, 95% CI 0.91-1.00; p = 0.042). Patients from lower socioeconomic backgrounds had lower odds of alcohol use post-diagnosis (OR = 0.26, 95% CI 0.08-0.82; p = 0.021).

CONCLUSION: The findings suggest that smoking cessation is achievable post-diagnosis, whereas reducing alcohol intake may require more targeted interventions. Resistance to behavior change among younger patients may explain rising oral cancer rates in this population.

PMID:41639699 | DOI:10.1186/s12903-026-07820-x

Child Adolesc Psychiatry Ment Health. 2026 Feb 4. doi: 10.1186/s13034-026-01022-7. Online ahead of print.

ABSTRACT

BACKGROUND: Adolescent mental health outcomes are often poorer in rural areas of Australia, and most adolescents do not seek help, highlighting a critical gap in understanding help-seeking behaviours. This study examined mental health help-seeking patterns and associated factors among rural Australian adolescents.

METHODS: Data from Wave 8 of the Longitudinal Study of Australian Children, including 4,837 adolescents aged 14-19 years, were analysed. The prevalence of help-seeking overall and by remoteness, as defined by the Australian Bureau of Statistics were estimated. Cluster-adjusted multiple logistic regression models were used to examine factors associated with help-seeking behaviours.

RESULTS: Help-seeking behaviours were generally lower among adolescents from rural areas compared to their urban counterparts. Seeking face-to-face mental health professional help was significantly less common in outer regional and remote areas (7.72%, 95% CI: 5.39-10.93) compared to urban areas (12.20%, 10.97-13.54). Furthermore, males reported significantly lower professional help-seeking behaviours (2.76%, 1.33-5.63) than females (13.53%, 9.08-19.70) in outer regional and remote areas. Similar sex disparities were observed in non-face-to-face (e.g., internet, phone) help-seeking. The most common predictors of help-seeking behaviours were ongoing anxiety or depression and good parent-child relationships. Other statistically significant predictors included suicidal thoughts and behaviours, single-parent family, community participation, social media exposure and drug use. Two predictors (i.e., financial hardship for formal help-seeking and community engagement for informal help-seeking) varied statistically significantly between rural and urban settings.

CONCLUSION: Strategies to address lower prevalence of mental health help seeking among rural male adolescents in Australia should be sensitive to context-specific barriers and designed to meet their unique needs. Adolescent-focused digital interventions and strengthened family and community engagement are vital to ensuring equitable access to mental health services for adolescents in rural Australia.

PMID:41639672 | DOI:10.1186/s13034-026-01022-7

BMC Psychiatry. 2026 Feb 4. doi: 10.1186/s12888-026-07868-7. Online ahead of print.

ABSTRACT

BACKGROUND: Relatives’ expressed emotions (EE) and caregiving appraisals are associated with the prognosis of individuals with psychotic disorders as well as relatives’ own well-being. The main aim of the present study was to examine whether sociodemographic factors, patients’ clinical characteristics, relatives’ health and quality of life (QoL), and relatives’ perceived support explained a significant amount of the variance in expressed emotion (EE) and caregiving appraisals among relatives of patients with psychotic disorders.

METHODS: Baseline data from The Implementation of guidelines on Family Involvement for persons with Psychotic disorders (IFIP) trial were compiled from 231 patient-relative pairs across 15 Community Mental Health Centres (CMHC). Relatives completed assessments on EE; emotional overinvolvement (EOI) and critical comments (CC) (Family Questionnaire), negative and positive caregiving appraisals (Experience of Caregiving Inventory), health and QoL (Care Related Quality of Life), healthcare professional support (Caregiver Well-being and Support) and sociodemographic factors. Patients reported on their own difficulties with mental health and functioning (Behavior and Symptom Identification scale) while clinicians assessed patients’ functioning (Global Assessment of Functioning) and sociodemographic factors. Pearson and Spearman correlations and hierarchical multiple linear regressions were used for statistical analyses.

RESULTS: Duration since first psychosis diagnosis, patients’ mental health and functioning, and relatives’ health problems explained a substantial proportion of variance in relatives’ EOI and negative caregiving appraisals. Together with household income level, duration since first psychosis diagnosis explained a significant amount of the variance in positive caregiving appraisals. Relatives’ perceived support from healthcare professionals explained a significant amount of the variance in CC and negative caregiving appraisals, even after adjusting for contextual variables.

CONCLUSION: Psychotic disorders entail significant burdens and shape the family climate for both patients and their relatives. Negative caregiving appraisals and EOI may reflect normal reactions to the responsibilities and challenges relatives face. EOI and CC, however, may be associated with distinct factors, necessitating tailored psychoeducational and support interventions. The findings suggest that relatives’ perceived support from healthcare professionals could have a substantial positive impact on CC and negative caregiving appraisals, which is important for clinicians to recognise and address.

PMID:41639671 | DOI:10.1186/s12888-026-07868-7

BMC Public Health. 2026 Feb 4. doi: 10.1186/s12889-026-26520-3. Online ahead of print.

NO ABSTRACT

PMID:41639656 | DOI:10.1186/s12889-026-26520-3

BMC Psychiatry. 2026 Feb 4. doi: 10.1186/s12888-026-07867-8. Online ahead of print.

NO ABSTRACT

PMID:41639644 | DOI:10.1186/s12888-026-07867-8

BMC Public Health. 2026 Feb 5. doi: 10.1186/s12889-026-26453-x. Online ahead of print.

ABSTRACT

BACKGROUND: Despite the crucial distinction between insomnia symptoms and a diagnosed disorder, population-level studies based on contemporary criteria and clinical interviews are scarce. This study therefore examined the insomnia spectrum by assessing the prevalence, identifying subtypes, and exploring associations with sociodemographic factors and comorbid mental disorders for both conditions.

METHODS: This large-scale, community-based cross-sectional study was conducted in Beijing from October to December 2021. A sample of 10,778 adults was recruited via multistage stratified random sampling. Trained psychiatrists conducted standardized diagnostic interviews based on the Diagnostic and Statistical Manual of Mental Disorders, Fifth Edition (DSM-5) to collect data on insomnia disorder and mental disorders. Descriptive analysis and weighted Rao-Scott chi-square tests, were performed using the Statistical Analysis System (SAS, version 9.4).

RESULTS: The weighted prevalence rates were 17.7% for subjective sleep problems, 10.7% for clinically assessed insomnia symptoms, and 3.0% for DSM-5-diagnosed insomnia disorder. Among insomnia subtypes, initial insomnia was most prevalent, both as a symptom (8.1%) and a disorder (2.6%). Furthermore, insomnia disorder prevalence varied by sociodemographics, being higher in females, older adults (≥ 60 years), and those with lower education. Among individuals with insomnia disorder, 31.3% had comorbid other mental disorders, particularly alcohol-related disorders (13.4%). Conversely, insomnia disorder was observed in 14.4% of individuals with other mental disorders, with the highest prevalence in depressive (21.4%) and anxiety disorders (19.2%).

CONCLUSIONS: The marked disparity between prevalent insomnia symptoms and formal diagnoses, compounded by significant psychiatric comorbidity, mandates a public health shift toward population-level screening and early intervention. This imperative includes adopting standardized diagnostics and implementing integrated, transdiagnostic treatment models as a pivotal preventive strategy.

PMID:41639643 | DOI:10.1186/s12889-026-26453-x

BMC Geriatr. 2026 Feb 4. doi: 10.1186/s12877-026-07011-x. Online ahead of print.

ABSTRACT

INTRODUCTION: Older adults are more likely than younger people to have multiple chronic health conditions and increased health and/or social needs. As older people generally prefer living at home in the community as they age and residential care can be expensive, there is a need for effective alternatives to residential care in the community. The objective of this review was to synthesize evidence about programs aimed at enabling older people with ongoing health and social care needs to remain in the community.

METHODS: This review followed the JBI methodology for systematic reviews of effectiveness. Included studies reported on complex, multifactorial interventions that were based in the community and included more than one type of service. Six databases and gray literature were searched for published and unpublished research. Titles and abstracts, and full-text selections were screened by two or more reviewers and assessed for methodological quality using JBI critical appraisal tools. Results related to quality of life and healthcare outcomes were extracted.

RESULTS: Fifty-five full text articles, reporting on 51 unique complex interventions, were included in the review. Studies were predominantly randomized controlled trials (n=24) and quasi-experimental studies (n=23), with five cohort and three case series studies included. The overall quality of the included studies was moderate. Key characteristics of the interventions included case management, care planning, a comprehensive assessment, and in-home visits. Comparative meta-analyses were completed for five of the outcomes (hospital admission, emergency department visits, long-term care use, primary care use and quality of life). The results showed effects in the direction of interventions for the number of hospital admissions and LTC use, however, none of the meta-analyses were statistically significant.

CONCLUSIONS: There is little agreement about the effectiveness of complex interventions on quality of life and health system outcomes. Jurisdictional differences may make the integration of literature reporting on such interventions particularly difficult. There is an ongoing need to understand what helps older people with complex needs live well in the community and what level of health system engagement is optimal.

SYSTEMATIC REVIEW REGISTRATION: PROSPERO reference number CRD42022324061.

PMID:41639636 | DOI:10.1186/s12877-026-07011-x

BMC Med Res Methodol. 2026 Feb 4. doi: 10.1186/s12874-025-02762-4. Online ahead of print.

ABSTRACT

BACKGROUND: Analyses of craniofacial morphology are essential for various medical and research applications, including the study of midfacial development, dysmorphologies, and planning surgical interventions. Incomplete CT scans often due to patient movement, imaging artifacts, or obscured landmarks which can result in missing data. If not properly addressed, such missingness may bias conclusions and weaken statistical power.

OBJECTIVE: This paper evaluates imputation techniques to identify the most suitable method for handling missing completely at random values in small, high-dimensional, and highly correlated craniofacial morphometric datasets.

METHODS: 42 craniofacial variables were measured from 32 observations. The missing data structure was set to be at random with 268 (20%) missing values. Five common imputation techniques namely Mean/Median imputation, k-Nearest Neighbors (kNN), Multiple Imputation by Chained Equations (MICE), Random Forest (RF), and Decision Tree, were considered. The performance of the imputation technique was quantified using Root Mean Squared Error (RMSE), Mean Absolute Error (MAE), and Variance Preservation.

RESULTS: RF Imputation demonstrated the best overall performance, with the lowest RMSE (1.3987) and MAE (0.4902), indicating a high level of accuracy in imputing missing values. It also maintained a relatively close to 1 variance preservation (0.8961), suggesting its effectiveness in retaining the original variability in the dataset. MICE present lower accuracy with high RMSE (3.0869) and MAE (1.1246) however appear to have the closest variance preservation to 1 (1.0580).

CONCLUSION: The findings emphasize the importance of choosing suitable imputation techniques for small, high-dimensional, and correlated datasets such as those in craniofacial morphometry. RF emerged as the most effective method, offering a strong balance between accuracy and variance preservation.

PMID:41639629 | DOI:10.1186/s12874-025-02762-4

Nature. 2026 Feb;650(8100):110-115. doi: 10.1038/s41586-025-09951-7. Epub 2026 Feb 4.

ABSTRACT

Fast and reliable validation of new designs in complex physical systems such as batteries is critical to accelerating technological innovation. However, battery development remains bottlenecked by the high time and energy costs required to evaluate the lifetime of new designs^1,2. Notably, existing lifetime forecasting approaches require datasets containing battery lifetime labels for target designs to improve accuracy and cannot make reliable predictions before prototyping, thus limiting rapid feedback^3,4. Here we introduce Discovery Learning, a scientific machine learning approach that integrates active learning⁵, physics-guided learning⁶ and zero-shot learning⁷ into a human-like reasoning loop, drawing inspiration from educational psychology. Discovery Learning can learn from historical battery designs and reduce the need for prototyping, thereby predicting the lifetime of new designs from minimal experiments. To test Discovery Learning, we present industrial-grade battery data comprising 123 large-format lithium-ion pouch cells, including diverse material-design combinations and cycling protocols. Trained on public datasets of cell designs different from ours, Discovery Learning achieves 7.2% test error in predicting cycle life using physical features from the first 50 cycles of 51% of cell prototypes. Under conservative assumptions, this results in savings of 98% in time and 95% in energy compared with conventional practices. Discovery Learning represents a key advance in accurate and efficient battery lifetime prediction and, more broadly, helps realize the promise of machine learning to accelerate scientific discovery⁸.

PMID:41639573 | DOI:10.1038/s41586-025-09951-7