Category: Statistics

Popul Health Metr. 2026 Jan 31. doi: 10.1186/s12963-026-00455-8. Online ahead of print.

ABSTRACT

This study investigates territorial disparities in healthcare outcomes and service provision across Italian regions through a multidimensional analysis based on the BES (Equitable and Sustainable Well-being) framework. Two distinct but complementary sets of indicators are considered: one focusing on health outcomes (life expectancy, healthy life expectancy, and avoidable mortality), and the other on the structural availability and accessibility of healthcare services (residential beds, home care, access difficulties, and unmet needs). Using the DBSCAN (Density-Based Spatial Clustering of Applications with Noise) algorithm, the study identifies spatial clusters of regions with similar profiles. Results reveal persistent North-South divides in both health and service indicators, with southern regions consistently exhibiting lower performance. While the Health dataset shows relatively homogeneous clusters, the Services dataset highlights more marked disparities. The use of DBSCAN proves effective in detecting regional groupings even in a relatively small sample, offering a valuable tool for territorial policy planning and sustainability-oriented healthcare strategies.

PMID:41620739 | DOI:10.1186/s12963-026-00455-8

J Orthop Surg Res. 2026 Jan 31. doi: 10.1186/s13018-026-06670-1. Online ahead of print.

ABSTRACT

OBJECTIVE: Progressive collapsing foot deformity is a complex clinical presentation of combined deformities, which affects articular joint relationships. Herein, the study aimed to characterize joint interactions of the ankle, tibiofibular, talofibular, subtalar, talonavicular, calcaneocuboid, naviculo-cuneiform, and tarso-metatarsal joints.

MATERIALS AND METHODS: We quantified and compared the results between 23 female patients with progressive collapsing foot deformity and 23 female asymptomatic individuals. We used a multi-domain correspondence model from statical shape modeling to compare the alignment and morphology followed by calculating the joint-level measurements including the joint space distance and the congruence index between groups.

RESULTS: From our results we found that almost all bones and joints were affected by the progressive collapsing foot deformity. Our main results for joint space distances were narrowing in the sinus tarsi, gapping at the medial of the calcaneocuboid joint, and narrowing at the 3rd, 4th, and 5th tarso-metatarsal joints. The primary results for the congruence index were shifted to the lateral side in the talonavicular joint and a less congruent middle facet of the subtalar joint in the progressive collapsing foot deformity group. In addition, the joint space distance was mainly influenced by alignment, and the congruence index was influenced by bone morphology.

CONCLUSION: We believe that assessing multi-joint interactions in progressive collapsing foot deformity will lead to a better understanding of the pathophysiology and assist in surgical treatment planning.

PMID:41620735 | DOI:10.1186/s13018-026-06670-1

BMC Pulm Med. 2026 Jan 31. doi: 10.1186/s12890-026-04115-3. Online ahead of print.

ABSTRACT

BACKGROUND: Combined immunodeficiency (CID) involves profound defects in B and T lymphocyte development and function. This study examined clinical and immunological phenotypes of CID patients with and without pulmonary manifestations.

METHODS: This retrospective multicenter study included 53 CID patients diagnosed between 2009 and 2022 with available thoracic computed tomography scans. Patients were categorized based on pulmonary manifestations presence. Demographic, clinical, and laboratory characteristics were compared using conservative statistical thresholds (P < 0.01). All laboratory parameters were interpreted using age-adjusted pediatric reference ranges.

RESULTS: Among 53 patients (56.6% male), 43 had pulmonary abnormalities on HRCT. Common clinical features included skin lesions (43.4%), failure to thrive (34%), and autoimmunity (32.1%). HRCT revealed pneumonia (28.3%), bronchiectasis (18.9%), interstitial lung disease with BOOP-like pattern (3.8%), and other findings. Using age-adjusted pediatric reference ranges, profound immunological defects were confirmed: absolute lymphocyte count below the 5th percentile in 92% (49/53), CD3 + T cells below the 5th percentile in 94% (47/50 tested), CD4 + T cells below the 5th percentile in 96% (51/53), CD19 + B cells below the 5th percentile in 94% (50/53), and hypogammaglobulinaemia (IgG below the 5th percentile) in 98% (52/53). Patients with abnormal HRCT had significantly lower CD4 + T-cell counts (178 vs. 498 cells/µL; P = 0.008) and CD19 + B-cell counts (42 vs. 189 cells/µL; P = 0.009). Bronchoscopy identified Aspergillus fumigatus, Streptococcus pneumoniae, and multidrug-resistantAcinetobacter baumannii. Deceased patients showed significantly lower baseline platelets (183,000 vs. 266,000 cells/µL; P = 0.009), IgG (380 vs. 720 mg/dL; P = 0.007), and IgE (0.8 vs. 12 IU/mL; P = 0.008).

CONCLUSION: Pulmonary manifestations affect 81.1% of Iranian CID patients. Low baseline platelets, IgG, and IgE constitute a robust prognostic triad for mortality (P = 0.009, P = 0.007, P = 0.008 respectively). Application of age-adjusted reference ranges revealed profound immunological defects. Systematic HRCT surveillance using low-dose protocols and distinguishing infectious sequelae from immune-mediated lung disease guides targeted management in resource-limited settings.

PMID:41620725 | DOI:10.1186/s12890-026-04115-3

BMC Med. 2026 Jan 31. doi: 10.1186/s12916-026-04621-5. Online ahead of print.

ABSTRACT

BACKGROUND: Existing aging clocks, designed to quantify biological aging, primarily capture systemic changes and may overlook alterations crucial for cardiometabolic diseases (CMDs).

METHODS: In this study, we developed the CardioMetAge model, an aging clock tailored to predict CMD-related outcomes. Trained in the NHANES-III, the model was applied to the continuous NHANES and UK Biobank. Its associations with cardiometabolic mortality, disease incidence, and transitions between disease states were examined, and its performance in predicting 10-year CMD incidence was also evaluated. We further investigated associations of proteomic pathways, lifestyle factors, and socioeconomic status with CardioMetAge, as well as the impact of caloric restriction intervention on its change.

RESULTS: The final CardioMetAge was constructed as a linear combination of chronological age and 12 common clinical biomarkers. Its age deviation (CardioMetAgeDev) showed stronger associations with CMD mortality (HR per SD [95% CI]: 1.87 [1.83, 1.91]), CMD incidence (1.35 [1.33, 1.37]), and disease progression, including transitions from no CMD to first CMD (1.34 [1.32, 1.35]) and from first CMD to cardiometabolic multimorbidity (1.25 [1.21, 1.30]), compared with deviations of PhenoAge and other traditional biological age models. CardioMetAge also consistently outperformed these models in predicting 10-year CMD incidence. Our findings also highlighted the biological determinants of cardiometabolic aging, with proteomic analyses linking CardioMetAgeDev to inflammatory activation and metabolic disorders. Analysis of modifiable factors revealed that lifestyle and socioeconomic status were associated with CMD risks, partly via CardioMetAgeDev (mediation proportions: 34.5% and 10.7%, respectively). Additionally, two-year caloric restriction slowed the progression of CardioMetAge by 1.23 years (95% CI: [0.61, 1.84]) relative to the ad libitum control.

CONCLUSIONS: CardioMetAge outperformed existing aging clocks in ease of use and in predicting CMD-related outcomes. It provides valuable insights into the mechanisms of cardiometabolic aging and holds potential for clinical monitoring and evaluating the effectiveness of interventions.

PMID:41620721 | DOI:10.1186/s12916-026-04621-5

Child Adolesc Psychiatry Ment Health. 2026 Jan 31. doi: 10.1186/s13034-025-01021-0. Online ahead of print.

NO ABSTRACT

PMID:41620718 | DOI:10.1186/s13034-025-01021-0

BMC Health Serv Res. 2026 Jan 31. doi: 10.1186/s12913-025-13400-6. Online ahead of print.

ABSTRACT

BACKGROUND: Availability of mental health services is often limited, with many patients suffering from depression or anxiety either unidentified or unable to access care. Community pharmacists may be well-positioned to serve as behavioral healthcare extenders by offering timely screening, referrals, education, and medication management. However, evidence supporting feasibility and effectiveness of behavioral health (BH) care interventions in community pharmacy settings remains anecdotal. The purpose of this article is to summarize the findings from a feasibility evaluation of a BH intervention in 7 U.S. community pharmacies.

METHODS: The BH intervention, delivered over 6-11 months, consisted of a screening and referral program and a 6-session education and medication management program. Participating pharmacies benefited from a multi-faceted implementation strategy (e.g., coaching, toolkits). An effectiveness-implementation hybrid Type II design was used to assess effectiveness of the intervention, while evaluating its implementation. Implementation outcomes involved program adoption rates, levels of program acceptability, appropriateness, and feasibility, intent to sustain, and fidelity rates. Intervention outcomes included: patient referral rates, perceived benefits, and changes in patient knowledge, clinical symptoms, and medication adherence. The data were collected using multiple methods (e.g., surveys, interviews, administrative data) and analyzed accordingly.

RESULTS: Results indicated a 100% adoption rate by the pharmacies with intent to continue past the project period; significant increases in program acceptability, appropriateness, and feasibility; and high levels of program fidelity. All 206 patients were appropriately referred as needed, with patients enrolled in the 6-session program reporting statistically significant changes in knowledge, clinical symptoms, and a nonadherence to medications.

CONCLUSIONS: These findings lend support to the role that community pharmacists can play in bridging the mental health care gap and improving population health.

PMID:41620717 | DOI:10.1186/s12913-025-13400-6

BMC Public Health. 2026 Jan 31. doi: 10.1186/s12889-026-26440-2. Online ahead of print.

NO ABSTRACT

PMID:41620698 | DOI:10.1186/s12889-026-26440-2

BMC Public Health. 2026 Jan 31. doi: 10.1186/s12889-026-26336-1. Online ahead of print.

NO ABSTRACT

PMID:41620692 | DOI:10.1186/s12889-026-26336-1

BMC Med Educ. 2026 Jan 31. doi: 10.1186/s12909-026-08707-9. Online ahead of print.

ABSTRACT

This study investigates health sciences students’ attitudes toward artificial intelligence (AI) and the implications for ethical awareness, clinical decision-making, and public health. A cross-sectional survey was conducted between April 27 and May 15 2025, with 668 students from five departments at Gümüşhane University, employing the validated Artificial Intelligence Attitude Scale, which measures benefits, risks, and use, alongside 12 binary-response items assessing ethical, clinical, and public health judgments. Descriptive statistics, t-tests, ANOVA, and logistic regression analyses were applied. Findings indicate that students perceive AI as highly beneficial (M = 4.05) but also associate it with notable risks (M = 2.52; where lower scores indicate a higher level of perceived risk due to reverse coding). Logistic regression analyses revealed that risk perception (reverse-coded; higher scores indicating lower perceived risk) was the most consistent predictor across all dimensions. Specifically, students with lower perceived risk were significantly more likely to reject concerns regarding patient privacy (OR = 2.55, 95% CI [2.03-3.21], p < 0.001), dismiss the idea that relying on AI instead of human expertise is problematic (OR = 1.57, 95% CI [1.25-1.96], p < 0.001), and reject the notion that AI systems may harm public health (OR = 2.52, 95% CI [1.98-3.20], p < 0.001). While participants endorsed AI’s potential in enhancing patient safety, chronic disease management, and preventive care, they expressed significant concerns about privacy, legal responsibility, and a potential weakening of patient-clinician communication. Gender, academic discipline, and prior AI use further differentiated attitudes. The results highlight a dual perception of AI as both an opportunity and a threat, emphasizing that successful integration in healthcare requires not only technical competence but also ethical, legal, and communicative safeguards.

PMID:41620691 | DOI:10.1186/s12909-026-08707-9

BMC Pediatr. 2026 Jan 31. doi: 10.1186/s12887-026-06526-x. Online ahead of print.

ABSTRACT

BACKGROUND: Child malnutrition is a critical public health issue in Bangladesh, significantly affecting child development and health. This study analyzes the prevalence and determinants of malnutrition among children under five years old using data from the Bangladesh Demographic and Health Survey (BDHS) 2017-18.

METHODS: The study employed Generalized Linear Mixed Models (GLMM) and Generalized Estimating Equations (GEE) to examine socio-economic, demographic, and health-related factors associated with stunting, wasting, and underweight conditions among children.

RESULTS: The study found that 31% of children were stunted, 22% were underweight, and 8% were wasted in Bangladesh in 2017-18, with overlaps likely among these forms of malnutrition. Higher parental education levels and wealthier household status were significantly (p < 0.05) associated with a lower prevalence of malnutrition. Children from Sylhet had 1.3 times higher odds of being stunted (AOR = 1.3, 95% C.I. = 1.05-1.65) and 1.46 times higher odds of being underweight (AOR = 1.46, 95% C.I. = 1.14-1.88) compared to children from Barisal. Mothers with normal BMI were significantly less likely to have stunted (AOR = 0.67, 95% C.I. = 0.56-0.79), wasted (AOR = 0.49, 95% C.I. = 0.37-0.66), and underweight (AOR = 0.63, 95% C.I. = 0.53-0.75) children compared to mothers with underweight BMI. Both GLMM and GEE models identified the same associated factors for stunting, wasting, and underweight, with close estimates. However, GLMM was found to have better predictive power for all three models, as indicated by higher area under the curve (AUC) values.

CONCLUSION: The findings emphasize the association of poor parental education, economic conditions, and maternal health with child malnutrition in Bangladesh. The GLMM demonstrated better predictive power based on AUC values across all three outcomes, making it a more reliable choice for this type of analysis. Policymakers should prioritize enhancing maternal education, household economic status, and access to healthcare services.

PMID:41620673 | DOI:10.1186/s12887-026-06526-x