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How are we doing — and how can we do better? These are perhaps the most basic questions a community can ask regarding the health of its residents. Yet communities have not been given the necessary tools to answer these questions with validated, consistent measures, evidence-based policies and practices, and incentives for improvement. In response to this need and with funding from the Robert Wood Johnson Foundation, we initiated a project called Mobilizing Action Toward Community Health (MATCH) at the University of Wisconsin-Madison Population Health Institute (1). We created a logic model (Figure) that guides our work and demonstrates the principal activities of 1) producing county health rankings in all 50 states, 2) examining partnerships and organizational models to increase involvement and accountability for population health improvement, and 3) developing incentive models to encourage and reward communities that implement evidence-based programs and policies that improve population health. Figure The Mobilizing Action Toward Community Health (MATCH) logic model. This model shows how incentives can be used to improve population health and reduce health disparities. We believe that together these efforts will increase awareness of the multiple determinants of health, promote engagement by a more diverse group of stakeholders, and stimulate development of models that promote evidence-based programs and policies — eventually leading to improved health outcomes and reduced health disparities. The most visible product of this effort so far is the county health rankings (2) released in early 2010. Several other components of our project, based in part on a proposed “pay-for-population-health” performance system advanced in 2006 (3), are aimed at understanding how we might best support population health improvement at the community level. To that end, we commissioned 24 essays to critique the assumptions underlying such a system and to suggest approaches for overcoming potential barriers to its implementation. We worked with these authors, MATCH and Robert Wood Johnson Foundation staff, and several guests in a 2-day meeting in late 2009 in Madison to discuss the essays and develop an agenda for future practice and research activities for improving population health. Why Metrics Matter (MP3–3Mb) In this issue of Preventing Chronic Disease, we present the 7 essays on population health metrics (4-10), introduced by 2 commentaries (11,12). These essays describe the types of tools that can be used to measure and monitor the health of populations and are the first of 3 sets of essays to appear in this and the next 2 issues. The next set of essays will describe incentives that can be used to promote programs and policies that improve population health, and the role for population health partnerships in these efforts. The final set will summarize the discussion of the 2009 meeting and outline cross-cutting themes and priorities for research and practice in population health improvement. We hope that the essays will stimulate discussion and mobilize action that improves population health outcomes in the coming decade.
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Conventional frameworks often attribute migrant health disparities to language barriers, legal exclusion, or socioeconomic deprivation. However, it remains unclear whether health inequities persist when these structural barriers are removed. Isolating the impact of historical and intergenerational trauma requires examining a unique minoritized population that shares full citizenship and linguistic heritage with the host society. To quantify the explanatory power of conventional demographic, socioeconomic, and family determinants on health disparities in a fully integrated refugee group and to estimate the magnitude of the unexplained residual disparity attributed to nonsocioeconomic factors. This cross-sectional study analyzed data from the Korea Youth Risk Behavior Survey (2011 to 2024), a nationally representative survey. The study used North Korean refugee-origin youths (NKRY) as a strategic case study of a population sharing ethnicity and citizenship with the host country (South Korean-origin youth, SKY), compared alongside international-origin youths (IOY). Self-reported mental health outcomes and health risk behaviors. Fairlie decomposition analysis was performed to calculate the percentage of the disparity explained by observed conventional determinants vs the unexplained residual component. Among 876 693 participants, SKY (n = 785 462; 392 731 [51.0%] male; median age, 14.97 years), NKRY (n = 547; 317 [58.25%] male; 15.61 years), and IOY (n = 13 800; 6675 [49.45%] male; 14.55 years) were included. NKRY had substantially poorer outcomes compared with SKY. Adjusted odds ratios for suicide planning (aOR, 4.35; 95% CI, 2.97-6.35), suicide attempts (aOR, 4.27; 95% CI, 2.78-6.56), and drug use (aOR, 14.09; 95% CI, 7.48-26.51) remained high. Decomposition analyses revealed that conventional socioeconomic factors explained less than 10% of these disparities. For suicide-related outcomes, observed factors accounted for less than 5% of the gap, leaving more than 95% unexplained. Conversely, disparities in the IOY group were largely explained by these conventional factors. In this cross-sectional study, substantial health inequities persisted even in a minoritized population granted full legal and linguistic integration, with the vast majority of disparities remaining unexplained by conventional socioeconomic metrics. These findings challenge the sufficiency of standard social determinant frameworks and suggest that for displaced populations worldwide, policies must address unmeasured structural drivers such as historical trauma and intergenerational instability.
Over the past decade, North American and European health care systems have opted for policies based on the tenets of value-based health care (VBHC), aiming to improve health outcomes, reduce care-related expenditure, and provide care sustainably. As well as promoting healthy competition between health care providers, VBHC aims to drive collaboration between players, including those in primary and secondary care and public and private providers. Long-standing trends toward increased outsourcing of publicly owned facilities to private organizations have generated heated debate, especially in Europe, regarding potential conflicts between ensuring quality of care and implementing cost-reduction strategies to increase economic benefits. In this context, outsourcing to private networks with a strong commitment to VBHC may be a successful strategy for improving health outcomes and lowering costs while overcoming potential drawbacks voiced by opponents of outsourcing. The Regional Health Care System of Madrid (RHSM), Spain, stands out as a best practice model in European health care, providing universal coverage to around 7 million inhabitants (14% of the nation's population) through a robust network of primary care centers and hospitals, of which several are outsourced to private providers. The RHSM is characterized by a centralized database and transparent reporting system to monitor quality metrics across hospitals and other health care structures, as well as a free-choice mandate, which empowers inhabitants to seek care at the center of their choice at zero out-of-pocket cost, thus overcoming social inequalities in access to care. The authors analyzed publicly available data from 25 public hospitals in the RHSM from 2015 to 2023, comparing quality of care, efficiency, and patient experience metrics from four hospitals outsourced to the value-based Quirónsalud Health Care Network and 22 publicly managed hospitals. Their results provide longitudinal evidence that value-based outsourcing is associated with lower standardized inpatient mortality rates, reduced medical and surgical inpatient complications (3.22% vs. 3.75%; P<0.001), lower average lengths of hospital stays (4.93 days vs. 5.83 days; P<0.001; 95% confidence interval [CI], -0.89 to 0.90), and higher patient experience survey scores (93.1 vs. 88.6; P<0.001; 95% CI, 3.75 to 4.93) than the results from other nonoutsourced hospitals. Significantly more patients were transferred to outsourced hospitals than to nonoutsourced hospitals, with more than half of the patients coming from areas with worse socioeconomic status. The authors' findings suggest that, in the context of a regional health care system providing excellent universal coverage to residents at zero out-of-pocket cost, along with free choice of health provider, value-based outsourcing is associated with increased quality of care, efficiency, and patient satisfaction, as well as helping to reduce inequalities in access to care in areas with lower socioeconomic status. Outsourcing to value-based networks may catalyze system transformation toward VBHC, while fostering healthy, patient-centered competition between private providers.
Objective: In the present study, normative data for the Verbal Fluency Test (VFT) were developed from a Greek cohort updating and expanding existing data and examining the relatively less-common metrics of clustering, switching and semantic--phonemic discrepancy. In addition, the association of socioeconomic (SES) and health status (HS) with VFT metrics, which are rarely considered when normative data are calculated, as well as commonly assessed demographic characteristics (sex, age, education) was evaluated. Method: Data from 1657 cognitively healthy Greek adults [(991 women; mean age: 46.09 years (SD = 11.63); 677 (41%) university graduates], drawn from the population-based Epirus Health Study, were included in the study. Scores of interest were the number of words produced, the number of clusters and switches, repetitions, errors, and a semantic-phonemic discrepancy index. Linear regression analyses assessed the association of sociodemographic factors with the VFT scores. Results: Age and education were associated with most VFT scores. Women produced more words (β = -0.071) on the phonemic condition and more phonemic (β = -0.072) and semantic (β = -.091) switches, while men made less phonemic repetitions (β = -0.069), more semantic clusters (β = 0.062) and had a higher semantic-phonemic discrepancy index (β=.079). SES and HS had only limited contribution and did not significantly alter the association between basic demographic variables and VFT scores, when included simultaneously in the model. Conventional and regression-based normative data were suggested according to the observed associations. Conclusions: Updated, demographically stratified norms for direct and derived VFT scores were derived from a large population-based sample of Greek adults.
Value-based care has long been championed for its potential to align clinical outcomes with financial incentives, but many health systems struggle to implement truly transformative models. Fee-for-service constraints, payer fragmentation, and complex claims processing often limit progress to incremental gains rather than transformation to true value-based arrangements. Herein, the authors present an innovative approach to bundled payments that overcomes many of these hurdles by placing physicians and patients at the center of the redesign process while partnering with self-insured employers. The Vanderbilt University Medical Center MyHealth Bundles program rethinks traditional specialty care episodes through prospective bundled payment arrangements. Rather than layering risk contracts on existing workflows, clinicians are encouraged to envision an ideal care model without the usual fee-for-service rules, prior authorization requirements, or utilization management barriers. This physician-led design then becomes the blueprint for new financial models that take full two-sided risk on common high-cost clinical episodes, such as maternity care, weight loss, and joint replacement. In each instance, the bundle is built around everything a patient needs, from diagnosis to recovery, with the health system assuming accountability for outcomes and utilization throughout that period. One of the most significant innovations is bypassing conventional payer contracts in favor of direct partnerships with employers, many of whom face escalating health care expenditures and seek practical strategies for improving employee health. To address operational complexities inherent in prospective risk-based billing, Vanderbilt University Medical Center collaborated with a specialized third-party administrator to develop claims processing models capable of bundling and adjudicating diverse services under a single invoice. This arrangement helps overcome the structural challenges that often impede new payment models within traditional insurance pathways. Early outcomes suggest meaningful benefits for all stakeholders. Employers benefit from transparent and predictable costs, while patients experience significantly reduced or eliminated out-of-pocket expenses, enhanced care coordination, and more convenient clinical pathways. At the same time, clinicians report greater autonomy and alignment of financial and professional incentives. Preliminary data also point to improved clinical metrics, such as lower cesarean section rates in maternity bundles and decreased need for joint replacement surgeries in orthopedic bundles. By illustrating how prospective bundles, direct contracting, and patient-centered service design can be integrated into a comprehensive value-based care strategy, the MyHealth Bundles program offers a replicable framework for other institutions. Rather than serving as a niche alternative, this model represents a viable pathway for health care systems intent on transcending transactional approaches and moving toward truly transformative value-based care.
The purpose of this study was to estimate the lifetime risk of alcohol-attributable mortality and morbidity in the United States based on a person's average lifetime weekly alcohol consumption to assess the impact of per-occasion alcohol consumption on health. Lifetime risks were estimated using a cause-specific modeling approach that combined exposure data from national health surveys, relative risks, population data from the U.S. Census Bureau, mortality data from the Centers for Disease Control and Prevention, and morbidity data from the Institute for Health Metrics and Evaluation. A narrative review assessed the health impact of per-occasion alcohol consumption on health. At low levels of consumption, no protective net effect of alcohol consumption on health was observed. Elevated mortality and morbidity risks were associated with alcohol consumption starting at relatively low levels. Males consuming >6.5 (95% CI [<1, 13.5]) and females consuming >7.0 (95% CI [<1, 11.5]) drinks per week had life-time alcohol-attributable mortality risks >1:1,000. At >8.5 (95% CI [2.5, 13]) drinks per week for both males and females, these risks increased to >1:100. At 14 drinks per week for males (the upper limit of the former Dietary Guidelines for males), the risk of an alcohol-caused death was 1:25 (4%). Drinking patterns also impacted risk. Above 1 drink per occasion, higher consumption was associated with progressively increased risks of breast cancer, cardiovascular disease, and injury. Alcohol consumption, including at what may be perceived as "moderate" levels, is associated with increased mortality and morbidity risks. These results support tightening alcohol use guidance in the United States, for both males and females, to no more than 1 drink per day.
Amid global population aging, evidence-based geriatric assessment tools are essential for clinical decision-making and risk stratification. Despite growing interest, few studies have comprehensively compared the discriminative ability of existing tools, particularly as new tools have recently become available. In this study, we aimed to perform such a comparison across a wide range of patient-relevant health outcomes. This population-based prospective cohort study used data from the Swedish National study on Aging and Care in Kungsholmen (SNAC-K). We included 3,108 adults aged ≥ 60 years at baseline (2001-2004), who were followed up for up to six years. Seven tools (Health Assessment Tool [HAT], SNAC-K Frailty Index (FI) [SNACK-FI], Primary Care FI [PC-FI], Intrinsic Capacity [IC], Geriatric 8 [G8], Charlson Comorbidity Index [CCI], and Cumulative Illness Rating Scale [CIRS]) were evaluated in terms of their discriminative ability for formal care use, institutionalization, dementia, disability, injurious falls, self-rated health, quality of life, unplanned hospitalization, and mortality, using Harrell's C-index estimated from unadjusted cause-specific Cox models. HAT, IC, and SNACK-FI consistently ranked among the top three performers across all outcomes. The highest C-indices were observed for institutionalization (HAT 0.93 [0.91, 0.95], IC 0.93 [0.90, 0.94], SNACK-FI 0.92 [0.89, 0.94]); 1-year mortality (HAT 0.88 [0.85, 0.91], SNACK-FI 0.87 [0.84, 0.91], IC 0.86 [0.82, 0.89]); dementia (HAT 0.87 [0.85, 0.89], IC 0.88 [0.86, 0.90], SNACK-FI 0.86 [0.83, 0.88]); and formal care use (HAT 0.83 [0.81, 0.86], IC 0.85 [0.83, 0.88], SNACK-FI 0.80 [0.77, 0.83]). Tools incorporating physical function metrics (e.g., gait speed) showed higher discriminative ability than those that omitted them. IC and SNACK-FI showed marginal and clinically negligible occasional gains over HAT ( ≤ 0.02 differences in C-index), despite greater complexity and a larger number of indicators. Guideline-endorsed tools (e.g., G8, CCI, CIRS) showed comparatively lower discrimination across outcomes. Contemporary geriatric assessment tools show promise. Tools incorporating physical function metrics demonstrated superior discriminative ability, suggesting these measures may be integral to geriatric prognosis and risk stratification. Given the underperformance of several established tools, reappraisal of current guideline recommendations may be warranted.
The Fracture Risk Assessment Tool (FRAX) excludes objective skeletal muscle health and genetic variables. We evaluated the prognostic associations of handgrip-defined probable/possible sarcopenia and genome-wide polygenic scores (GPS) with 10-year fracture risk, and their incremental predictive value beyond FRAX across racial/ethnic groups and GPS strata. We analyzed 2,051 postmenopausal women from the Women's Health Initiative. Race-specific analyses focused on Black, Hispanic, and White participants (n=2,009), excluding American Indian/Alaska Native and Asian/Pacific Islander individuals due to sparse fracture events. Sarcopenia status was operationalized by low handgrip strength alone via EWGSOP2 (<16.0 kg) and AWGS 2025 (<18.0-20.0 kg) criteria. Fine-Gray models estimated subdistribution hazard ratios (sHR), treating death as a competing risk. Predictive performance at 10 years was assessed using time-dependent AUC, Brier scores, and decision curve analysis (DCA). Handgrip-defined probable or possible sarcopenia prevalence was 4.4% (EWGSOP2) and 6.4% (AWGS 2025). Black women demonstrated lower risk for major osteoporotic fractures (MOF) (adjusted sHR=0.19, 95% CI: 0.08-0.48) and hip fractures (adjusted sHR=0.07, 95% CI: 0.01-0.52) compared to White women. Neither sarcopenia status nor high GPS showed statistically significant independent associations with fractures after FRAX adjustment. Adding sarcopenia status to baseline FRAX (AUC: 0.71 for MOF; 0.69 for hip) yielded near-identical AUCs, Brier scores, and within-sample net benefit. Handgrip-defined probable/possible sarcopenia and current GPS do not provide independent or incremental predictive value beyond the clinical FRAX framework within this genomic sub-sample of older women. The Fracture Risk Assessment Tool (FRAX) currently excludes skeletal muscle and genomic metrics. In 2,051 postmenopausal women, incorporating handgrip-defined sarcopenia and polygenic scores provided no incremental predictive benefit beyond standard FRAX. Within this study population, separate integration of these markers may not enhance routine fracture risk stratification.
Adverse childhood experiences (ACEs) and neurodevelopmental disorders (NDDs) are frequent yet the relationship between cumulative ACEs and multiple NDDs remains poorly understood. Using data from 120,629 children aged 3-17 years in the 2021-2023 US National Survey of Children's Health, we examined associations between ACEs and multiple NDDs, defined as the presence of ≥ 2 NDDs. Logistic regression, adjusted for sociodemographic and health covariates, estimated associations for ACEs treated as a continuous variable and as categorical groups (0, 1, 2, and ≥ 3 ACEs). We also assessed associations for common NDD combinations and individual ACE. The prevalence of multiple NDDs increased with the number of ACEs: 4.5% (95% confidence interval [CI]: 4.2-4.8) for 0 ACE, 8.4% (95% CI: 7.7-9.1) for 1 ACE, 11.9% (95% CI: 10.7-13.2) for 2 ACEs, and 17.8% (95% CI: 16.5-19.2) for ≥ 3 ACEs. Each additional ACE was associated with 13% higher odds of multiple NDDs (adjusted odds ratio [aOR] = 1.13, 95% CI: 1.09-1.17). Compared with no ACE, the odds of multiple NDDs were higher for 1 ACE (aOR = 1.48, 95% CI: 1.30-1.68), 2 ACEs (aOR = 1.78, 95% CI: 1.52-2.09), and ≥ 3 ACEs (aOR = 1.91, 95% CI: 1.62-2.26). Associations were consistent across three NDD combinations. Individually, health- or disability-related mistreatment, household mental illness, and economic hardship showed significant associations. Cumulative ACEs are associated with a greater likelihood of multiple NDDs, suggesting future research, health policy, system and service planning require coordinated responses that account for the diverse adversities experienced by many families and children.
The C-reactive protein-triglyceride glucose index (CTI) has emerged as a promising composite biomarker for cardiovascular disease (CVD) risk. However, whether incorporating obesity-related metrics such as waist circumference (WC), body mass index (BMI), or waist-to-height ratio (WHtR) into CTI to form modified indices such as CTI-WC, CTI-BMI, and CTI-WHtR improves predictive performance remains uncertain. The performance of these modified indices requires validation in large-scale prospective cohorts stratified by glycemic status. This study used data from the China Health and Retirement Longitudinal Study (CHARLS) from 2011 to 2020, involving 7,579 participants aged ≥ 45 years. Multivariate Cox regression and restricted cubic splines (RCSs) analyses were used to assess the associations of the CTI and its modified indices with CVD risk. To compare the predictive performance, time-dependent Harrell's C-indices, integrated discrimination improvement and net reclassification index were utilized. Weighted quantile sum (WQS) regression was used to evaluate component contributions. During a mean follow-up of 8.28 years, 1,871 (24.69%) participants experienced their first CVD event. The CTI and its modified indices were effective in predicting CVD incidence in the general population. RCS analysis revealed positive linear dose-response relationships between these indices and CVD risk in the general population, which persisted in both normal glucose regulation (NGR) and prediabetes mellitus (Pre-DM) patients. WQS regression analysis revealed that, in the general population, TG contributed the most to CVD risk in the CTI, while WC, BMI, and WHtR had greater weights in their modified indices. In the general population, all modified CTI indices demonstrated superior predictive ability than did the original CTI (C-index: CTI-WC 0.619, CTI-WHtR 0.616, CTI-BMI 0.614, and CTI 0.612). In the population with NGR, Pre-DM, and DM, the predictive capability of CTI-WC is superior to that of the original CTI, with its C-index being greater across all these populations. The CTI and its modified indices are effective in predicting CVD risk in the general population. The modified CTI indices, especially the CTI-WC, show superior predictive ability across different glycemic statuses. These findings suggest that incorporating obesity-related metrics into the CTI may enhance its utility for CVD risk prediction.
Diabetic retinopathy (DR) is a major microvascular complication of diabetes and is the leading cause of blindness and visual impairment among working-age populations worldwide. The loss of vision caused by diabetes significantly reduces the quality of life and has a major impact on the overall burden of visual impairment. This study assessed the global burden of blindness and visual impairment caused by diabetes among the working population from 1990 to 2021, and predicted the future trends in 2050. Based on data from the Global Burden of Disease Study 2021, we analyzed trends from 1990 to 2021 in the burden of diabetes-related blindness and visual impairment. Key metrics included case numbers and rates of prevalence and years lived with disability (YLDs). Decomposition analysis identified drivers of burden changes, and health inequality analysis assessed disparities by socioeconomic status. We used ARIMA and exponential smoothing models to forecast prevalence and YLDs from 2022 to 2050. From 1990 to 2021, the global burden of blindness and visual impairment caused by diabetes among the working population significantly increased.From 1990 to 2021, the global burden of blindness and visual impairment caused by diabetes among the working population significantly increased. Comparative analysis shows that the number of cases of type 2 diabetes and the number of YLDs are much higher than those of type 1 diabetes. Moreover, the burden on women has always been higher than that on men. Among the working population, blindness and visual impairments caused by diabetes pose a serious and increasingly severe public health threat worldwide, with the impact on women being particularly significant.Among the working population, blindness and visual impairments caused by diabetes pose a serious and increasingly severe public health threat worldwide, with the impact on women being particularly significant. Therefore, urgent action is needed to raise awareness of diabetic retinopathy among both clinicians and the general public, improve the level of early detection and treatment, reduce preventable vision loss, and alleviate the impact on family life and social economic burden caused by this decline in quality of life. Therefore, urgent action is needed to raise awareness of diabetic retinopathy among both clinicians and the general public, improve the level of early detection and treatment, reduce preventable vision loss, and alleviate the impact on family life and social economic burden resulting from this decline in quality of life.
Colorectal cancer (CRC) remains a leading cause of cancer-related morbidity and mortality worldwide yet is largely preventable through effective screening and surveillance. While most CRC cases are sporadic, a substantial proportion occur in individuals at increased risk due to hereditary cancer syndromes or family history who require tailored screening strategies different from population-based approaches with respect to age of initiation, surveillance intervals, and modality. This review summarizes current evidence on CRC risk across higher risk groups, including Lynch syndrome, polyposis syndromes, carriers of moderate-penetrance genes, and individuals with a family history of CRC. Efficacy of colonoscopic surveillance and the potential roles of emerging biomarker tests and artificial intelligence-assisted technologies for detection of colorectal neoplasia are discussed. Current CRC surveillance guidelines, quality metrics and adherence in higher risk groups are reviewed. As research in genomics, biomarkers, microbiome, and artificial intelligence evolves, personalized risk-based screening strategies hold promise for optimizing CRC prevention. High-quality, population-specific data will be essential to refine surveillance intensity, improve adherence, and reduce CRC burden in higher risk populations.
Tackle football is the most participated youth sport in the U.S. with leagues beginning as early as age 5. Exposure to cumulative repetitive head impacts (RHI) over years of play is increasingly viewed as a major contributor to chronic traumatic encephalopathy (CTE), a progressive neurodegenerative disease documented in contact sport athletes. Amid growing awareness of CTE, parents may turn to online information to guide decisions about youth tackle football participation. This cross‑sectional study examined the readability of online CTE information. Using the search term, 'CTE,' 68 URLs providing non‑technical information were identified after applying exclusion criteria. Online software was used to generate metrics from six widely-used readability formulas. Grade-level readability scores were categorized as ≤ Grade 8, 9-12, and ≥ 13 and summarized using descriptive statistics; distributions were compared by URL designation using chi-square tests (P < 0.05). Web page publication/revision date and presence of references were recorded. Median readability scores ranged from high school to early college with few pages meeting the recommended ≤ Grade 8 reading level for the general population. Levels were similarly high across non-commercial (.org,.gov,.edu) and commercial (.com) domains. Nearly 40% lacked clear publication or revision dates; fewer than half (47.1%) included references. Commonly accessed online CTE resources exceed recommended reading levels. This digital barrier impairs parents' functional health literacy and capacity for informed decision-making. As research on CTE and tackle football participation evolves, there is a need for plain‑language, clearly-sourced, updated online resources tailored to this decisional context.
To reconstruct the identity of an individual, sex assessment is an essential step in the estimation of the biological profile of a skeleton. For that purpose, forensic anthropologists apply both morphological and metric methods based in skeletal elements. In 2000, Wasterlain used a sample from the Identified Skeletal Collection of the University of Coimbra (19th -20th centuries) to develop a method to estimate sex on the basis of the limb bones metrics. In the present study, a sample of 202 individuals (92 males and 110 females) from the 21st Century Identified Skeletal Collection (University of Coimbra) was used to revise the Wasterlain method to better suit the contemporary Portuguese population. In all, 22 measurements were performed in the femur, tibia, calcaneus, and talus. Although the Wasterlain method has shown satisfactory results, new cut-off points and discriminant functions were developed to promote accurate sex classifications in the contemporary Portuguese population. Regarding the cut-off points, the femoral vertical head diameter, the bilateral width of the tibia, the length of the calcaneus, and the maximum length of the talus presented the best results for sex estimation (89.8%, 88.0%, 85.6%, 85.9%, respectively). Discriminant functions presented correct classification percentages varying between 78.7% and 91.5% for femur, 83.7% and 93.0% for tibia, 84.9% and 86.6% for calcaneus, and 86.6% and 88.5% for talus. In sum, the new cut-off points and discriminant functions presented an overall good performance, constituting reliable methods to be applied in forensic cases involving individuals of Portuguese origin.
Major depressive disorder (MDD) is a brain-related psychiatric disorder. Accurate diagnosis of MDD has long remained a challenge in clinical practice due to the high heterogeneity of its clinical manifestations. Graph neural network-based brain network analysis has shown great potential for MDD diagnosis. However, existing methods for constructing brain networks often overlook the continuous hierarchical organization of brain function or fail to effectively incorporate non-imaging information as a crucial complement for disease modeling. To address these, we propose a gradient-guided spatio-temporal graph convolutional network (GST-GCN) for population-level MDD identification. Specifically, GST-GCN first performs multi-scale modeling of the long-range dependencies and short-term dynamic fluctuations of blood oxygen level dependent signals along the temporal dimension, thereby capturing key temporal patterns associated with MDD. It then innovatively replaces conventional graph pooling with a gradient-guided hierarchical pooling to construct a spatial topology consistent with brain's intrinsic organizational principles. Within this well-structured spatial framework, the model is able to learn brain embeddings that more faithfully reflect individual functional organization characteristics. Furthermore, to incorporate non-imaging information, we integrate embeddings learned from individual brain graphs with non-imaging features to build a subject-level population graph. This explicit modeling of inter-subject relationships effectively enhances classification performance. Experimental results on the publicly available REST-meta-MDD dataset demonstrate that GST-GCN achieves a classification accuracy of 91.17%, with all performance metrics outperforming state-of-the-art methods. Moreover, the framework reveals alterations in functional gradients and identifies crucial biomarker regions for MDD classification, thereby exhibiting significant neurobiological interpretability.
While inflammation is recognized as an independent risk factor for atherosclerotic cardiovascular disease (ASCVD), with high-sensitivity C-reactive protein (hs-CRP) serving as a prognostic indicator, the degree to which elevated hs-CRP combined with different atherogenic lipid measures increases long-term ASCVD risk remains unclear in primary prevention cohorts of Mediterranean origin, who exhibit a comparatively favorable inflammatory profile due to lifestyle and dietary habits. Thus, this study aimed to examine the association between combined hs-CRP and various atherogenic lipid measures and long-term 3-point MACE risk in apparently healthy adults from the general population. A cohort of 3,042 ASCVD-free adults residing in greater Athens, Greece, was recruited in 2002. A 20-year follow-up was conducted in 2022, comprising n = 2,169 participants, of which n = 1,988 had complete data for cardiovascular disease incidence. Participants were grouped based on discordance between biomarkers, defined by established guideline-recommended thresholds. Hs-CRP was an independent predictor of incident 3-point MACE after adjusting for low-density lipoprotein cholesterol (LDL-C) or non-high-density lipoprotein cholesterol (non-HDL-C), but not apolipoprotein B (apoB) or lipoprotein(a) [Lp(a)]. High levels of apoB (≥ 100 mg/dL) were associated with an increased 3-point MACE risk regardless of hs-CRP levels. However, elevated LDL-C (≥ 100 mg/dL) and non-HDL-C (≥ 131 mg/dL) showed significant associations only in the presence of hs-CRP ≥2 mg/L. Hs-CRP provides independent, incremental predictive value, even beyond advanced lipid metrics, and is a potent residual risk factor in contexts traditionally considered low-risk.
Lung and bronchus cancer remains the leading cause of cancer related mortality in the United States, yet the influence of climatic factors on incidence rates remains understudied. This research investigates the association between temperature, severe drought duration, and cancer incidence rates among Non-Hispanic (NH) Whites in Texas. A cross-sectional population-based analysis using Texas Cancer Registry county-data (n = 98) for age-adjusted incidence rates of lung and bronchus cancer for NH Whites from 2013 to 2022. Annual ambient temperature data from NOAA and drought severity metrics from the US Drought Monitor were associated with cancer incidence rates. After controlling for key covariates, adjusted associations were conducted using Poisson regression and were stratified by various phases of drought periods. Adjusted associations were reported as incidence rate ratio (IRR). Findings indicate a significant interaction (P < 0.05) between annual temperatures and the number of weeks of severe drought. Hence drought duration is found to be an associational effect modifier between cancer incidence and ambient temperature among all ages for NH Whites in Texas. Upon adjusted for covariates, each 1 °F in annual temperatures increased the risk for lung and bronchus cancer incidence for droughts duration of ≤ 7 weeks [IRR 1.012 (1.001-1.023), P = 0.04] and ≥ 21 weeks [IRR 1.009 (1.001-1.017), P = 0.039] among NH Whites in Texas. This study provides unique racial specific evidence linking climatic variables to lung and bronchus cancer. These findings contribute to a growing body of evidence suggesting that climate variability is a critical determinant of respiratory cancer outcomes.
Thyroid cancer is the most common and rapidly increasing malignancy among adolescents and young adults (AYAs, aged 15-39 years). While this trend is well-documented, a critical gap exists in understanding how the disease burden varies with socioeconomic development. This study analyzes global disparities in the AYAs thyroid cancer burden across Socio-demographic Index (SDI) levels, identifying ecological patterns that suggest the dual challenges of overdiagnosis and inadequate care. We explored the global disparity in disease burden of thyroid cancer among AYAs utilizing the Global Burden of Disease Study 2021. Age standardized incidence rate (ASIR), age standardized prevalence rate (ASPR), age standardized mortality rate (ASMR), and age standardized disability-adjusted life years (DALYs) rate (ASDR) were extracted for analysis. Temporal trends were assessed using Average Annual Percentage Change (AAPC), estimated through joinpoint regression analysis. To further explore the relationship between development level and disease burden, restricted cubic splines were employed to model the non-linear relationship between SDI and AAPC. From 1990-2021, the global ASIR and ASPR for thyroid cancer in AYAs increased significantly (P < 0.001), with ASIR rising from 0.93 (95% UI: 0.83-1.06) to 1.59 (95% UI: 1.34-1.92) per 100,000 population. In contrast, the ASMR and ASDR remained consistently low and stable. In 2021, high SDI regions recorded the highest ASIR and ASPR, whereas low SDI regions showed the highest ASMR and ASDR. Notably, an inverse U-shaped curve was observed when exploring the correlation between SDI and the AAPC, with AAPC peaking at an SDI of approximately 0.55. Socioeconomic development acts as a double-edged sword in the AYA thyroid cancer epidemic. High-SDI regions exhibit a burden pattern indicative of potential overdiagnosis, while low-SDI regions experiencing higher mortality likely reflect gaps in timely diagnosis and care capacity. The peak burden increase at a low-middle SDI of 0.55 signals a critical transition point. These findings support differentiated strategies that emphasize diagnostic appropriateness, risk stratification, and avoidance of unnecessary detection in high-SDI regions while strengthening essential treatment capacities in resource-limited settings.
Variation in life expectancy has become a critical dimension in understanding inequality in human populations, complementing average longevity by capturing how unequally years of life are distributed across individuals. Reducing such variation, especially by preventing premature mortality, has emerged as a key priority in global health and demographic research. For low- and middle-income countries, monitoring lifespan variation alongside life expectancy enables the detection of persistent mortality inequalities and uneven survival gains that average indicators alone may fail to reveal. Given its ongoing demographic and epidemiological changes, Bangladesh offers a relevant setting for studying these patterns. This paper examines lifespan variation in Bangladesh during its mortality transition. We used two different sources of mortality data from 1974 to 2019: the Matlab Health and Demographic Surveillance System (HDSS), representing a long-standing rural demographic site, and the World Population Prospects 2024 (WPP), representing national-level trends. Comparing HDSS and WPP estimates allows us to assess how data resolution and national-level modeling affect the measurement of lifespan variation, which is critical for population health surveillance. We used multiple indicators of lifespan variation, including lifespan disparity, life table entropy, the Gini coefficient, standard deviation and interquartile range (IQR) of age-at-death distribution, to examine trends over time. We calculated 95% bootstrap confidence intervals to account for sampling variability and smoothing effects. The study finds a consistent long-term decline in lifespan variation for both sexes across both datasets. Lifespan disparity declined from 19.6 to 13.8 years for Matlab males and from 20.9 to 12.4 years for Matlab females; in WPP, it dropped from 24.4 to 13.1 years for males and from 24.5 to 12.1 years for females. Similar downward trends are observed for life table entropy and Gini coefficient, with identifiable peaks during mortality crises, such as the 1974-75 famine and the 1991 cyclone. Since the early 2000s, the highest demographic variation has been observed among Matlab males, while WPP data show higher values for statistical measures in recent years- partly due to smoothing and national-level heterogeneity. Sex-specific differences and regional disparities persist, while bootstrapped confidence intervals indicate that these trends are robust despite smoothing and national-level aggregation in WPP data. This study provides empirical evidence on lifespan variation trends during Bangladesh's mortality transition and illustrates the utility of high-resolution mortality data for monitoring inequality in LMIC populations.