Metabolic syndrome (MetS) is a cluster of metabolic disorders associated with increased risks of cardiovascular diseases and type 2 diabetes, posing a significant public health challenge globally. Early identification of biomarkers for MetS is crucial for timely intervention. This study aimed to explore the association between 24-hour urine indicators (sodium, potassium, creatinine, and microalbumin) and MetS among Chinese adults, providing evidence for early MetS screening and intervention. A cross-sectional survey was conducted in 2017, involving 1175 Chinese residents aged 18-69 years. Data were collected via questionnaires, physical measurements, and laboratory tests, with 24-hour urine samples analyzed for sodium, potassium, creatinine, and microalbumin excretion. Participants were excluded if they had incomplete data, unqualified urine collection, or a self-reported history of hypertension or diabetes. Of the 1175 participants (median age 42 years; 48.17% male), the prevalence of MetS was 11.4%. The median 24-hour urine excretion levels were 158.42/24 hours mmol (sodium), 34.89 mmol/24 hours (potassium), 9.62 mmol/24 hours (creatinine), and 4.31 mg/24 hours (microalbumin). Specifically, 24-hour urine microalbumin excretion was significantly higher in participants with MetS (6.35 mg/24 h) than in those without (4.12 mg/24 h; P < 0.0001), while no significant differences were observed in sodium, potassium, or creatinine excretion between the two groups. Notably, elevated microalbumin excretion was associated with MetS components: central obesity, elevated blood pressure, elevated fasting blood glucose, and elevated triglycerides (all P < 0.05). When microalbumin excretion was divided into quartiles, adjusted odds ratios (adjusted for age, sex, and other confounders) for MetS and its component (elevated fasting blood glucose) increased with higher quartiles (MetS: 1.00, 1.53, 1.99, 2.75; P for trend = 0.0004; elevated fasting blood glucose: 1.00, 1.31, 1.35, 2.20; P for trend = 0.0005). These findings indicate that elevated 24-hour urine microalbumin excretion is independently associated with MetS in Chinese adults, suggesting its potential as a practical biomarker for early risk assessment and intervention.

Data from wearable devices collected in free-living settings, and labelled with physical activity behaviours compatible with health research, are essential for both validating existing wearable-based measurement approaches and developing novel machine learning approaches. One common way of obtaining these labels relies on laborious human annotation of sequences of images captured by body-worn cameras. The aim of this study was to investigate whether open-source vision-language models could accurately annotate activity intensity classes in wearable camera-based validation studies, thereby reducing the annotation burden. We compared the performance of three vision language models and two discriminative models on two free-living validation studies with 161 and 111 participants, collected in Oxfordshire, United Kingdom and Sichuan, China, respectively, using the Autographer (OMG Life, defunct) wearable camera. We found that the best open-source vision-language model (VLM) and fine-tuned discriminative model (DM) achieved comparable performance when predicting sedentary behaviour from single images on unseen participants in the Oxfordshire study; median F₁-scores: VLM = 0.89 (0.84, 0.92), DM = 0.91 (0.86, 0.95). Performance declined for light [VLM = 0.60 (0.56, 0.67), DM = 0.70 (0.63, 0.79)], and moderate-to-vigorous intensity physical activity [VLM = 0.66 (0.53, 0.85); DM = 0.72 (0.58, 0.84)]. When applied to the external Sichuan study, performance fell across all intensity categories, with median Cohen's κ scores falling from 0.54 (0.49, 0.64) to 0.26 (0.15, 0.37) for the VLM, and from 0.67 (0.60, 0.74) to 0.19 (0.10, 0.30) for the DM. Freely available computer vision models could help annotate sedentary behaviour, typically the most prevalent activity of daily living, from wearable camera images within similar populations to seen data, reducing the annotation burden when using cameras as the source of ground-truth.

Asthma poses a great burden as one of the most common long-term non-communicable diseases. The April 2023 Sudanese war has severely disrupted the healthcare system, greatly affecting asthmatic patients by limiting access to medications and diagnostic services. Therefore, this research aims to investigate asthma control, self-management and healthcare access among asthma patients in Sudan during the ongoing war.

A cross-sectional study was conducted among Sudanese patients with asthma during the ongoing war using a convenience sampling. An online questionnaire was distributed from September 2024 to June 2025. It covered sociodemographic data, the validated Asthma Control Test (ACT), self-management and healthcare access. Data were analysed using SPSS software, employing descriptive statistics, χ² tests and linear regression, with significance set at p<0.05.

Among the 229 asthmatic patients surveyed, 60.3% had poorly controlled asthma. Since the onset of the conflict, 43.2% had not attended any follow-up visits, while 48.5% required at least one emergency room visit during the war. Despite these challenges, 86.0% reported having quick access to healthcare during active attacks. However, 17.9% were unable to obtain their medications regularly, forcing many to adopt alternative coping strategies: 6.1% sought medical advice through online consultations, 15.7% increased their prescribed doses on their own and 39.3% used whatever medications were available without prescriptions, which was significantly associated with poor asthma control (χ²=29.29, p<0.01). Regression analysis revealed dose reduction was linked to worse control (β=-3.68, p=0.004), while refugee status and better adherence to medications predicted higher ACT scores (β=2.88, p<0.001; β=0.54, p=0.028).

This study highlights a considerable gap in asthma control during the ongoing war, despite the availability of healthcare services in times of urgent need. Many patients adopted coping strategies such as using whatever medications were available without prescriptions or independently increasing their prescribed doses. Enhancing patient education on proper asthma self-management and improving access to healthcare services, including telemedicine consultations, could play a crucial role in achieving better asthma control.

Machine learning (ML), a subset of artificial intelligence, uses large datasets to identify patterns between potential predictors and outcomes. ML involves iterative learning from data and is increasingly used in population and public health. Examples include early warning of infectious disease outbreaks, predicting the future burden of noncommunicable diseases, and assessing public health interventions. However, ML can inadvertently produce biased outputs related to the quality and quantity of data, who is engaged and helping direct the analysis, and how findings are interpreted. Specific guidelines for using ML in population and public health have not yet been created. We assembled a diverse team of experts in computer science, statistical modeling, clinical and population health epidemiology, health economics, ethics, sociology, and public health. Drawing on literature reviews and a modified Delphi process, we identified five key recommendations: (1) prioritize partnerships and interventions to support communities considered structurally disadvantaged; (2) use ML for dynamic situations, such as public health emergencies, while adhering to ethical standards; (3) conduct risk assessments and bias mitigation strategies aligned with identified risks; (4) ensure technical transparency and reproducibility by publicly sharing data sources and methodologies; and (5) foster multidisciplinary dialogue to discuss the potential harms of ML-related bias and raise awareness among the public and public health community. The proposed guidelines provide operational steps for stakeholders, ensuring that ML tools are not only effective but also ethically grounded and feasible in real-world scenarios.

In Latin America and the Caribbean (LAC), many countries face persistent gaps in access to and quality of primary care, which can result in preventable hospitalizations. Ambulatory care sensitive conditions (ACSCs) are conditions for which timely and effective primary care may reduce the risk of hospitalization. We analyze hospital discharges from public sector hospitals in Brazil, Chile, Colombia, Ecuador, El Salvador, Mexico, Paraguay, and Peru between 2015 and 2019 to describe national and subnational variation in hospitalizations due to ACSCs and to assess their contribution to inpatient service utilization.

Using sex-age standardized shares of ACSC discharges and days of stay in public hospitals, we conducted descriptive analyses of patterns across and within countries. We estimated hypothetical reductions in ACSC-related hospitalizations by benchmarking departments or states against those with lower shares of ACSC-related hospitalizations under different scenarios.

On average across countries, ACSCs accounted for 17.4% of discharges and days of stay, with limited variation across countries but significant subnational disparities. Chronic non-communicable diseases (NCDs) were the predominant cause of ACSCs hospitalizations (50.6%), followed by infectious diseases (45.3%). Simulated reductions in subnational variation suggest meaningfully lower ACSCs discharges and days of stay, particularly in Brazil, Ecuador, and El Salvador.

The high share of ACSC-related hospitalizations indicated substantial use of hospital resources for conditions that could be managed with timely and effective primary care. Addressing subnational disparities and strengthening PHC can improve health outcomes, reduce avoidable hospitalizations, and save health system resources. The predominance of NCDs among ACSC hospitalizations highlights the need to adapt PHC strategies to address the high burden of chronic conditions across the region. Routine monitoring of ACSCs may help identify areas with potential PHC gaps and support targeted improvements in care delivery in LAC.

This study received financial support from the Inter-American Development Bank.

Good health is viewed as essential to enable citizens to live fulfilling lives, shape communities, and drive economic growth. However, health is socially patterned. Low socioeconomic status is associated with an increased risk of non-communicable diseases, where poor dietary patterns and diet-related obesity are likely contributors. Food purchasing can be influenced by many factors, including cost and income. Most food purchased to be consumed at home is acquired from supermarkets, and any increase in food prices disproportionately impacts low-income households, contributing to food insecurity. This study explored the factors that helped and hindered people living with obesity and food insecurity in purchasing healthy, environmentally sustainable food from supermarkets. Semi-structured interviews (n = 25) and focus groups (n = 7) were conducted between June and December 2023 with adults living in Scotland and England who self-identified as living with obesity and food insecurity. Using thematic analysis, six main themes were identified: (1) Supermarket deals: perceptions surrounding the good, the bad, and the ugly side of supermarket offers and promotions; (2) Skepticism about supermarkets and the wider food system: questioning supermarket pricing motives but recognizing the role of the wider food system in food pricing; (3) Other peoples' role in enhancing or undermining healthy diet intentions: the impact of others in shaping food purchases; (4) Financial restrictions facing non-UK nationals: additional challenges faced by those with no recourse to public funds; (5) The overwhelming in-store supermarket experience: sensory overload and attempts to prevent unintended, impulse purchases; (6) Unconscious, environmentally sustainable shopping practices: cost saving practices that lead to environmentally sustainable purchasing patterns and behaviors as a unintentionally created outcome of budget maximizing strategies. However, such strategies, that is, limiting food waste and purchasing less meat, although beneficial for environmental sustainability, do not necessarily indicate that a healthier diet is being purchased or consumed. While views on some factors believed to help or hinder healthy, environmentally sustainable food purchases varied, there was general agreement amongst participants on the need for upstream changes, including having access to adequate benefits and wages.

This study aims to analyse the crowding-out and crowding-in effects of non-communicable diseases (NCDs) related out-of-pocket (OOP) health expenditures on household consumption in Bangladesh.

DESIGN: This study used data from the nationally representative Bangladesh household income and expenditure survey (HIES) 2022.

Eight divisions of Bangladesh.

14 395 households.

We examined how household consumption patterns across 20 expenditure categories were impacted by OOP health spending for NCD management, with a focus on income-level disparities.

In Bangladesh, OOP health expenditures for NCDs crowded out household expenditures on essential items like food and rent. Across all households, OOP health expenditures for NCDs by 10 US$ crowded out -3.8 US$ of expenditure on food (95% CI -5.1 to -2.5), and more specifically on protein-rich foods (-2.0 US$, 95% CI -2.8 to -1.2), spices (-0.2 US$, 95% CI -0.3 to -0.1), and restaurant and café meals (-0.9 US$, 95% CI -1.4 to -0.5). Crowding-out was also seen for tobacco, rent, durable goods and miscellaneous. In lower-income households, expenditures on food (-4.1 US$, 95% CI -7.2 to -1.1), restaurant and café meals (-2.0 US$, 95% CI -3.1 to -0.8), spices (-0.4 US$, 95% CI -0.7 to -0.09), and rent (-3.1 US$, 95% CI -4.5 to -1.6) were significantly crowded out.

This research demonstrates that NCD-related spending in Bangladesh reduces budgets for both food and non-food expenditures, with a stronger crowding-out effect on food items and rent, particularly in lower-income households. Effective financial and social protection mechanisms against NCDs are warranted to safeguard the consumption of the NCD-affected households in Bangladesh.

Implementing healthy behaviors, particularly a healthy plant-based diet, can play a key role in preventing non-communicable diseases (NCDs). However, behavior change initiation and maintenance can be challenging. The objective was to test, if the Healthy Lifestyle Community Program (cohort 2; HLCP-2) was effective in changing psychological constructs regarding eating behavior.

A 24-month non-randomized controlled intervention study with a community-based approach in rural Germany was conducted. The intervention group (IG) received a 10-week intensive lifestyle intervention aiming to improve NCD risk factors, followed by a 22-month alumni phase. The control group (CG) received no intervention. Participants completed questionnaires at six measurement time points to assess psychosocial constructs of behavior change derived from the Health Action Process Approach, including action/coping planning, action self-efficacy (SE), maintenance SE, recovery SE, and eating behavior. An exploratory analysis with inter- and intra-group comparisons regarding different scores of HAPA constructs was conducted. Covariate-adjusted comparisons were performed using multiple linear regression models. Additionally, bivariate correlations between these constructs and the healthy plant-based diet index (hPDI) were examined.

A total of 186 participants (IG: n = 111; CG: n = 75) were analyzed. In the IG, all HAPA scores increased significantly at all measurement time points compared to baseline, with the highest impact after the intensive phase (p < 0.001). Between-group comparisons for action/coping planning and action SE were significant at all measurement time points, while results for maintenance and recovery SE were inconsistent in the study course. Adjusting for covariates did not substantially alter the results. After 10 weeks, only recovery SE correlated significantly with hPDI (ρ = 0.289, p = 0.004).

Participation in the HLCP-2 intervention resulted in improvements in planning health-promoting behaviors, action, maintenance, and recovery SE. Further research is required to determine whether an increase in action planning and SE leads to changes in dietary behavior.

German Clinical Trials Register DRKS (https://drks.de/search/de; reference: DRKS00018775; retrospectively registered).

Childhood obesity has emerged as a global health challenge, with significant long-term health consequences, including an increased risk of non-communicable diseases. The "first 1,000 days" period of life is a critical window for shaping long-term health outcomes. This narrative review aims to explore the role of environmental exposures, categorized within the exposome framework, in developing childhood obesity. The exposome encompasses three domains: general external exposures (e.g., air pollution, urbanization), specific external exposures [e.g., nutrition, physical activity, socioeconomic status (SES)], and internal exposures (e.g., metabolic responses, oxidative stress). Evidence identifies risk factors such as maternal smoking during pregnancy, early-life exposure to endocrine-disrupting chemicals, and air pollution, which contribute to obesogenic processes. In contrast, protective factors include access to green and blue spaces, exclusive breastfeeding, adequate complementary feeding, regular physical activity, limited screen time, and sufficient sleep, which support healthy growth trajectories. Findings regarding SES, perfluoroalkyl and polyfluoroalkyl substances exposure, and human breast milk macronutrient composition remain heterogeneous and context-dependent. The findings highlight the need to integrate public health strategies addressing modifiable environmental and lifestyle factors. Identifying a "healthy exposome" that protects against obesity risk can steer the development of personalized prevention strategies, ultimately reducing the burden of obesity and associated diseases.

This study audited the Medical Certification of Cause of Death (MCCD) forms issued at Korle-Bu Teaching Hospital, assessing their accuracy, completeness, and consistency while analysing trends in mortality causes.

Retrospective review of completed MCCD forms.

Korle-Bu Teaching Hospital, Department of Medicine.

All duplicate MCCD forms issued between January 1^st, 2019, and December 31^st, 2023, were included.

No intervention.

Frequency of major and minor errors in completing the MCCD forms and leading causes of death in the department.

Of 4,544 MCCD forms audited, 4,460 (98.17%) contained errors. Major errors were observed in 4,028 (88.64%) forms; the commonest being incorrect reporting of the underlying cause of death (3,483; 76.65%). Minor errors were nearly universal (99.82%), with omission of the full address of the deceased (4,530; 99.69%) being most frequent. Over half of the recorded deaths each year were attributable to non-communicable diseases (NCDs), with cardiovascular-related conditions being the leading cause of death.

There was a high prevalence of errors in MCCD forms, highlighting the need for regular training of healthcare professionals to improve accuracy in death certification. Additionally, the high burden of NCD-related deaths emphasises the need to address modifiable risk factors, strengthen health systems, and foster multisectoral collaboration to mitigate the growing NCD epidemic.

None declared.