To understand the nature of the current measles outbreaks taking place across the United States in 2026, we analyzed historical surveillance data dating back to 1997 using four distinct forecasting models. By withholding data from the first four months of 2026 for out-of-sample validation, we evaluated how well traditional trend tracking and temporal dependence could anticipate this year's trajectory. While models incorporating short-term patterns performed best overall, every single model underestimated the cumulative cases recorded by May 1, 2026. This systematic underprediction reveals that the ongoing resurgence cannot be explained merely as a continuation of past trends or 2025 outbreak effects. Instead, 2026 represents a distinct, more severe outbreak regime, as the projected annual burden was already reached by May. These findings indicate that public health tracking must adapt to this heightened risk environment, and warn that the final 2026 case burden will substantially exceed that of previous years.
Adverse childhood experiences (ACEs), including abuse, neglect, and household dysfunction, are prevalent and represent a major risk factor for psychological distress in adulthood. Although control-related constructs such as self-efficacy, mastery, and perceived control have been widely examined in this context, the role of locus of control (LoC) remains less well understood. The present study investigated the associations between ACEs, LoC, and psychological distress, and examined whether LoC mediates or moderates the relationship between ACE exposure and psychological distress. Data were drawn from a representative survey of the German general population [N = 2,463; age range 16–96 years; female 1,237 female (50.2%)]. ACEs were assessed using the Adverse Childhood Experiences Questionnaire, psychological distress was measured with the Patient Health Questionnaire–4, and LoC was assessed with the Internal–External Locus of Control Short Scale–4. Structural equation modeling with robust diagonally weighted least squares estimation was applied. Higher ACE exposure significantly predicted greater psychological distress and a more external LoC orientation. Mediation analyses indicated that LoC accounted for approximately half of the association between ACE exposure and psychological distress. In contrast, moderation analyses did not reveal a significant interaction effect between ACE exposure and LoC in predicting psychological distress. These findings highlight control beliefs as an important psychological factor, linking early adversity to adult psychological distress and suggest that interventions targeting perceived control may help mitigate long-term mental health consequences of childhood adversity.
ObjectiveTo assess long-term trends, seasonality, forecasts, and ecological associations with tourist visits for reported hepatitis B virus (HBV) and hepatitis C virus (HCV) incidence in Hainan Province from 2005 to 2020.MethodsReported HBV and HCV incidence data from Hainan, national Global Burden of Disease (GBD) estimates for China, and annual tourist visit data were analyzed. Joinpoint regression assessed temporal trends, seasonal-trend decomposition using loess evaluated monthly variation, and seasonal autoregressive integrated moving average models generated forecasts. Spearman rank correlation analysis examined tourism associations, with sensitivity analyses excluding 2020 and using first-differenced and linearly detrended series.ResultsReported HBV incidence declined from 2005 to 2008, increased from 2008 to 2018, and then showed a non-significant decline through 2020; the overall average annual percent change (AAPC) was +3.37%. Reported HCV incidence increased until 2015 and subsequently declined, with an overall AAPC of +14.54%. National GBD estimates showed overall AAPCs of −0.54% for HBV and −1.52% for HCV. No significant differences among calendar months were detected. Under continuation of historical reporting patterns, the annualized projected reported HBV incidence in 2030 was 12.00 per 100,000 population (95% prediction interval: approximately 0.00–121.20), numerically close to the study-defined reference value of 12.20. The corresponding HCV estimate was 22.54 per 100,000 population (approximately 0.00–39.10), approximately 6.4 times the study-defined reference value of 3.55. The wide prediction intervals indicated substantial long-term forecast uncertainty. In 2020, observed mean monthly reported HBV and HCV incidence rates were 27.3% and 9.5% lower than forecasted values, respectively. In raw annual analyses, tourist visits correlated with reported HBV and HCV incidence (r = 0.871 and 0.879; both P < 0.001). After first differencing and linear detrending, the association remained significant only for reported HBV incidence.ConclusionReported HBV and HCV incidence in Hainan showed distinct phased changes without significant monthly differences. The forecasts represent projections under historical reporting patterns and should not be interpreted as evidence of progress toward WHO elimination targets. Tourism associations were exploratory and ecological and do not support causal inference. Continued surveillance and strengthened HCV screening, diagnosis, and linkage to treatment remain important.
BackgroundChronic kidney disease (CKD) disproportionately impacts Black individuals. This disparity, coupled with historical and ongoing injustices in medical research, raises significant ethical concerns that may hinder participation in health registries and clinical research, which are critical for advancing patient-centered kidney care. This study sought to identify key ethical challenges from the perspective of Black individuals with CKD to enhance their meaningful and equitable engagement.MethodsWe employed a qualitative approach guided by a pragmatist bioethical framework. We conducted three virtual town hall meetings with a sample of 24 self-identified Black adults with CKD and their caregivers, recruited from the Baltimore Metropolitan Area. Data were analyzed using thematic analysis. To ensure rigor, we employed intercoder reliability checks and member-checking with participants and a Community Advisory Board.ResultsSix primary ethical areas emerged: (1) lack of transparent communication, (2) distrust in the research enterprise, (3) experiences of stigmatization and discrimination, (4) cultural insensitivity, (5) lack of respect and civility, and (6) potential harm from poor-quality staff. Participants proposed and prioritized actionable solutions, including implementing mandatory transparent communication policies, enhancing community-engaged outreach, enforcing anti-discrimination legislation, providing cultural competency training for staff, and establishing rigorous quality control measures with patient input.ConclusionAddressing these multi-faceted ethical challenges through targeted, trust-building strategies is essential to enhance the participation of Black individuals in health registries and clinical studies. This, in turn, can lead to more equitable representation in kidney research and more generalizable findings to reduce CKD disparities.
BackgroundPharmacovigilance is the science and practice aimed at detecting, assessing, understanding, and preventing adverse drug events (ADEs). During the COVID-19 pandemic, a wide range of medications - suchasantivirals, corticosteroids, anticoagulants, immunomodulators, and sedatives - were used during respiratorysupport. Reports of hepatotoxicity, cardiotoxicity, renal impairment, and neurological effects underscored the need for systematic safety monitoring. Since 2012, our research group has been monitoring the clinical use of various prescriptions at the National Institute of Infectious Diseases in Brazil. In this study, we aimed to evaluate factors associated with adverse drug events in hospitalized COVID-19 patients,with emphasis on ICU management practices, polypharmacy and fentanyl exposure.MethodsThis observational study was conducted between March and September 2021 and included patients with a positive molecular test for SARS-CoV-2. Sociodemographic characteristics, including age, sex, educational level, and family income, as well as medical history such as hypertension, diabetes, obesity, dyslipidemia, fever, cough, dyspnea, and anosmia, were collected. Clinical data on hospitalizations, commonly prescribed medications, length of stay in intensive care units, vaccination status, oxygen supplementation, type of respiratory support, deaths, and oxygen saturation were also recorded. All adverse reactions were classified using a validated algorithm and standard terminology, and patients were followed until discharge or death. Data were initially collected on paper forms and subsequently entered into an electronic data capture platform. Associations between patient characteristics, treatments, and adverse reactions were analyzed using Poisson regression, with statistical significance set at p-value < 0.05.ResultsAmong 113 patients, 110 experienced at least one adverse event, totaling 411 events. The mean age was 56.5 years; 54.9% were male, 53.1% had hypertension, 28.3% had diabetes, 22.2% had received at least one vaccine dose, and 17.7% died. Higher rates of adverse events were observed among intubated patients and those who received fentanyl, which is commonly used during the intubation process.ConclusionsIn this study, intubation and fentanyl use were associated with higher rates of ADEs. These findings should be interpreted cautiously given the severity of illness, ICU sedation practices, and the possibility of confounding by indication. Therefore, active pharmacovigilance strategies may contribute to improved medication safety in critically ill populations.
BackgroundBody Mass Index (BMI), a measure based on weight and height, is a key indicator for assessing health status and the risk of chronic diseases such as hypertension, diabetes, and cancer. Both ends of the BMI distribution have been associated with greater healthcare utilization, with prior evidence in general populations suggesting a non-linear (U-shaped) association in which underweight and obese individuals use more healthcare services than those with normal weight. This relationship is particularly important during mass gatherings like the Hajj pilgrimage, where overcrowding, extreme heat, and physical exertion can amplify health risks. This study aimed to investigate how variations in BMI influence clinic visits, hospital stays, and diagnoses among pilgrims during Hajj.MethodsA cross-sectional study was conducted among 4,000 pilgrims randomly selected from the official Hajj registry during the 2024 (1445 H) season. BMI was modelled as a categorical variable (<18.5, 18.5–24.9, 25–29.9, and ≥30 kg/m²), with the normal-weight category as the reference, allowing detection of non-linear (U-shaped) associations. Outcomes were clinic visit (yes/no), hospital admission (yes/no), and primary diagnosis, all collected using a standardized data collection form. Crude and adjusted odds ratios (cOR, aOR) with 95% confidence intervals and exact p-values were estimated using multiple logistic regression, adjusting simultaneously for age, gender, education, WHO region, marital status, employment, and smoking.ResultsUnderweight (BMI <18.5) and obese (BMI ≥30) pilgrims reported higher rates of clinic visits (9% and 10%, respectively) and higher rates of hospital admission than normal-weight and overweight pilgrims, confirming a U-shaped association between BMI and healthcare utilization. The most frequent diagnoses were heat-related illnesses (75% of admissions among underweight, 76% among obese), followed by chronic-disease follow-up, particularly at BMI extremes. In the adjusted analyses, WHO region and employment status remained significantly associated with clinic visits, with lower odds among pilgrims from South-East Asia and higher odds among unemployed individuals with BMI <18.5.ConclusionBoth low and high BMI are linked to greater healthcare utilization during Hajj. Implementing targeted risk assessments, pre-travel health education, and culturally adapted healthcare services for BMI extremes may reduce acute healthcare demand during mass gatherings.
The prevalence and distribution of endemic human coronaviruses (HCoVs) in Ghana remain poorly characterized, particularly in the post-COVID-19 pandemic era. We conducted a cross-sectional molecular and serological survey of 1,000 individuals from five rural and five urban communities in Ghana to assess the prevalence of HCoVs. Reverse transcriptase real-time PCR (RT-qPCR) assays targeted SARS-CoV-2, HCoV-OC43, HCoV-HKU1, HCoV-NL63, and HCoV-229E, while serological assays evaluated IgG antibody responses against SARS-CoV-2, SARS-CoV-1, MERS-CoV, HCoV-OC43, HCoV-HKU1, HCoV-NL63, and HCoV-229E Spike proteins. A broad pan-coronavirus RT-qPCR assay detected HCoV RNA in 6.7% of participants, with SARS-CoV-2 as the most frequent, followed by HCoV-229E, HCoV-HKU1, and HCoV-OC43; HCoV-NL63 was not detected. Serological analyses revealed widespread exposure to SARS-CoV-2, HCoV-OC43, HCoV-229E, and HCoV-HKU1, with a low seroprevalence for HCoV-NL63. Our findings demonstrate ongoing co-circulation of multiple HCoVs in both rural and urban settings in Ghana post Covid-19, suggesting sustained community transmission, increased potential for co-infection, and the need for continued surveillance of endemic and emerging respiratory viruses. The data provides rare insights into the epidemiology of HCoVs in West Africa.
Introduction:Globally, Noncommunicable diseases (NCDs) contribute to 75% of all deaths. The precursors to the development of NCDs include the metabolic syndrome (MetS) and key metabolic risk factors outlined by the World Health Organisation, including overweight/obesity, raised blood pressure, hyperglycemia, and hyperlipidemia. Women in Kirinyaga County in Kenya have the highest prevalence of overweight and obesity at 64.6%, and hypertension at 20%, as per the 2022 Kenyan Demographic Health survey; however, data on MetS, hyperglycemia, and hyperlipidemia in Kirinyaga is scarce. Therefore, this study aimed to determine the relationship between sociodemographic determinants and the prevalence of metabolic risk factors and the metabolic syndrome among women aged 20-49 years in Kirinyaga County, Kenya. Methods:A cross-sectional study design was employed, and multi-stage sampling was used to select 425 women aged 20-49 years in Kirinyaga County, Kenya. Data were collected on sociodemographic determinants and anthropometric and biochemical measurements. Data analysis was conducted using STATA version 17, frequencies, proportions and binary logistic regression. A total of 425 women participated in the study. Results:The prevalence of MetS was 46.4%; 68.87% were obese or overweight; central obesity, measured by waist circumference, was 79.3%, and by waist-hip ratio, 51.06%. Diabetes was at 8.56%, elevated blood pressure at 22.6%, elevated triglycerides at 17.2%, Low HDL-C at 68.6%, and elevated cholesterol at 7.5%. The primary determinants of metabolic health were wealth and age. Increasing age (p > 0.05) elevated the odds of having all risk factors for NCDs and metabolic syndrome, except low HDL cholesterol. Women in the highest wealth quintile had increased odds of obesity (AOR = 1.83, p = 0.036), elevated triglycerides (AOR = 2.22, p = 0.035), and metabolic syndrome (AOR = 1.79, p = 0.040). However, being a student reduced the odds of metabolic syndrome (AOR = 0.24, p = 0.047). Hormonal contraceptive use, on one hand, was associated with reductions in hypertension (AOR = 0.32, p < 0.001), but was a significant risk factor for low HDL cholesterol (AOR = 2.08, p = 0.011). Conclusion:The study reveals a high prevalence of both metabolic syndrome and metabolic risk factors linked to noncommunicable diseases, mainly driven by ageing, multi-parity, and economic affluence. Public health interventions need to focus on wealth-associated lifestyle behaviours.
Background:Mixed-strain Mycobacterium tuberculosis (M. tuberculosis; Mtb) infections and heteroresistance are increasingly recognized as under-detected contributors to drug resistance dynamics. In high-burden rural settings such as the Eastern Cape, these genomic features may reflect both within-host diversity and ongoing transmission. However, their epidemiological and clinical implications remain incompletely understood, particularly in contexts with high HIV co-infection. Methods:We conducted an exploratory whole-genome sequencing (WGS) analysis of 28 drug-resistant M. tuberculosis isolates obtained through routine diagnostic workflows. Mixed-strain candidacy was defined using a composite genomic criterion, including multi-lineage assignments, ≥2 heteroallelic variants [allele frequency (AF): 0.10-0.90], or heteroresistance across multiple drug classes. A Random Forest (RF) model with Leave-One-Out Cross-Validation (LOOCV) was used as a feature-prioritization tool to identify genomic characteristics associated with mixed-strain candidacy. Given the limited sample size, analyses were designed to generate hypotheses rather than support causal inference. Clinical and epidemiological metadata, including HIV status and treatment outcomes, were not available. Results:Lineage 4 (46%) and Lineage 2 (43%) predominated, with 10.7% of isolates classified as probable mixed-strain candidates. Heteroallelic variants were most frequently observed in fabG1/inhA and rpoB, with a median AF of 0.22, consistent with subclonal diversity and possible within-host microevolution. Additional heteroallelic variants were identified in non-resistance-associated genes, suggesting broader genomic heterogeneity. The RF model demonstrated high discriminatory performance (AUC = 1.00), although this likely reflects the small sample size and should be interpreted cautiously. Feature importance analysis identified heteroresistance burden as the most prominent predictor of mixed-strain candidacy. Conclusion:This pilot study provides preliminary genomic evidence of mixed-strain infection and heteroresistance in a high-burden rural setting. While these findings highlight the potential role of subclonal diversity in shaping resistance patterns, their clinical and epidemiological relevance remains uncertain due to the absence of patient-level data and limited sample size. Future studies integrating genomic, clinical, and epidemiological data in larger cohorts and incorporating benchmarking against established tools are required to validate these findings and clarify their implications for tuberculosis control.
PurposeVisual impairment is associated with falls in older adults, but whether changes in vision alter this risk is unclear.MethodsWe performed a retrospective cohort study using data from the National Health and Aging Trends Study (NHATS), a nationally representative survey of Medicare beneficiaries aged ≥65 years. Distance acuity, near acuity, and contrast sensitivity were measured in 2022 and 2023. Change from 2022–2023 was calculated and categorized as stable (to ±0.1 logMAR), improving, or worsening. Using multivariable survey-weighted logistic regression, we compared the following fall-related outcomes in 2024: any fall in the last month, any fall in the last year, or multiple falls in the last year.ResultsAmong 6,327 eligible participants, distance acuity trends from 2022–2023 were stable in 50.3%, improved in 19.5%, and worsened in 30.2%. Similar patterns were observed for near acuity and contrast sensitivity. Worsening distance acuity was associated with higher odds of a fall in the past year (aOR 1.44, 95% CI 1.03–2.02), and worsening near acuity was associated with higher odds of a fall in the last month (aOR 1.44, 95% CI 1.3–2.02). Improvements in distance acuity, near acuity, and contrast sensitivity from 2022–23 were not associated with lower odds of falls in 2024.ConclusionsWorsening vision is associated with an increased risk of falls, but improving vision does not appear to reduce this risk. These findings suggest that while efforts to improve vision may not prevent falls, preventing a decline in visual function may help to reduce fall risk.
Coxiella burnetii is a zoonotic bacterial agent responsible for Q fever in both humans and animals. Ruminants are the most common livestock species associated with Q fever infections in humans. The disease presentation in humans range from asymptomatic, non-specific symptoms to fatal illness. In Kenya, no healthcare indicators seek to clinically diagnose and report Q fever because there are no readily available diagnostic technologies. We conducted a prospective observational study with paired serological sampling leveraging on the existing equipment in the Kajiado County referral laboratory to demonstrate antibodies to Coxiella burnetii in sera of febrile patients presenting with Brucella-like symptoms using the indirect immunofluorescent assay (IFA) and a fluorescent microscope provided for diagnosis of tuberculosis. A total of 100 paired blood samples were obtained from consenting and assenting study subjects. A pilot-tested questionnaire was used to collect patient's socio-demographic information, knowledge of Q fever disease, and community practices that put them at risk of exposure. Coxiella burnetii phase I (IgG) and phase II (IgM) antibodies were characterized, while Brucella spp. IgG antibodies were demonstrated using an indirect enzyme-linked immunosorbent assay (iELISA) and febrile Brucella agglutination test (FBAT). The overall seroprevalence of C. burnetii IgG and IgM antibodies was 49% and 27%, respectively, compared to only 13% reactivity with Brucella ELISA. Q fever prevalence substantially exceeded that of brucellosis in patients presenting with brucellosis-like symptoms in this pastoral community, suggesting a substantial burden of undiagnosed Q fever.
Background:It remains unknown whether a link exists between military employment and alterations in cognitive function later in life. Aim:To examine the association between military employment and cognitive impairment in adults. Methods:Chinese Longitudinal Healthy Longevity Survey data from 2018 were retrospectively analyzed. The participants included Chinese military veterans and non-veterans aged ≥65 years. Veteran status was defined according to the self-reported prior service as uniformed military personnel in the armed forces. Cognitive function was assessed using the Chinese version of the Mini-Mental State Examination. Nearest neighbor caliper matching without replacement was conducted to generate matched pairs. Binary logistic regression models were used to examine the association between military employment and cognitive impairment in the matched cohort, with adjustments for age, sex, education level, marital status, current smoking status, and chronic disease history. To assess the robustness of the findings, sensitivity analyses were conducted by defining cognitive impairment according to higher education-specific cut-off values. Results:Data from 102 matched pairs were analyzed. The logistic regression analyses demonstrated that military employment was not significantly associated with the risk of cognitive impairment (odds ratio (OR) [95% confidence interval (CI)]: 0.61 [0.24-1.55). The sensitivity analyses yielded similar results. Conclusions:There was no evidence to suggest military employment affected the risk of cognitive impairment later in life in this Chinese cohort. Nevertheless, owing to the high prevalence of impaired cognition among Chinese veterans, it remains an important health condition requiring further study.
BackgroundInfluenza remains an important cause of morbidity among populations with underlying medical conditions associated with increased risk of severe disease. This study aimed to estimate the size of populations eligible for influenza vaccination according to the Mexican Universal Vaccination Program and to evaluate vaccination coverage gaps among populations at risk in Mexico between 2010 and 2021.MethodsA retrospective analytical study was conducted using epidemiological and administrative healthcare databases from Mexican public healthcare institutions. Population estimates were constructed using prevalence-based epidemiological projections, healthcare system records, and attended patient data from populations at risk. Descriptive analyses and time-series modeling were performed to evaluate vaccination coverage patterns and healthcare demand over time.ResultsAcross all analyzed populations at risk, the number of individuals receiving healthcare services was consistently lower than the number of notified cases and substantially lower than prevalence-based epidemiological estimates. Vaccination coverage varied considerably across populations at risk and remained incomplete throughout the study period. In 2021, although approximately 12.5 million influenza vaccine doses were administered among populations at risk, a substantial proportion of potentially eligible individuals remained unvaccinated. Forecasting analyses suggested a progressive increase in healthcare demand among populations at risk over time.ConclusionsImportant gaps persist between the estimated population at risk, diagnosed individuals, healthcare utilization, and influenza vaccination coverage in Mexico. The analytical framework proposed in this study integrates epidemiological prevalence estimates, healthcare utilization patterns, and vaccination data to identify unmet vaccination needs and may support improved public health planning, prioritization strategies, and strengthening of influenza vaccination programs in Mexico and similar settings.
IntroductionThe Cox proportional hazards (PH) model is widely used in time-to-event research, but its validity depends on the PH assumption, which can be violated in child mortality studies where hazards vary with age. Piecewise exponential models (PEMs) relax this assumption by partitioning follow-up time into intervals. However, standard formulations impose constant hazards within each interval and typically ignore spatial heterogeneity, limiting their usefulness for public health analyses in which geographical variation in risk is important. Existing extensions address non-proportional hazards or spatial dependence separately, but rarely combine a smooth baseline hazard, interval-level temporal heterogeneity, and structured spatial frailty within a single, computationally tractable Bayesian framework. This study proposes and applies such a framework to under-five mortality (U5M) in Nigeria.MethodsWe formulated a Bayesian spline-augmented PEM incorporating a cubic-spline-smoothed baseline hazard, interval-specific Gaussian random effects, and a spatial frailty term with an intrinsic conditional autoregressive (ICAR) prior, within the latent Gaussian modeling framework of integrated nested Laplace approximation (INLA), with survival reformulated as a Poisson likelihood. The model was applied to U5M data for 103,439 children from the 2024 Nigeria Demographic and Health Survey (NDHS). Five nested model specifications (basic PEM, spline-augmented, spline plus interval effects, spline plus spatial effects, and the full spline-interval-spatial model) were compared across three sample sizes using DIC, WAIC, conditional predictive ordinates, posterior predictive checks, and calibration metrics.ResultsThe global test of the PH assumption was significant (χ2 = 898.66, p < 2 x 10-16), with all individual covariates significant at p < 0.05. Across all three sample sizes, the full spline-interval-spatial model achieved the lowest DIC and WAIC, the lowest CPO, and the highest posterior predictive correlation and calibration R2 among the five candidates, with the largest single reduction in DIC (approximately 17,884 units at n = 104,557) attributable to the addition of interval-specific random effects. In the fitted model, twin birth (HR = 2.82; 95% CrI: 2.62–3.03), breastfeeding status (HR = 0.45; 95% CrI: 0.42–0.49), and term delivery (HR = 0.47; 95% CrI: 0.41–0.53) had the largest effects on U5M, with additional protective effects for longer birth intervals and higher maternal education, and elevated risk in the North West (HR = 1.39) and North East (HR = 1.43) relative to the North Central region. Posterior spatial frailty estimates showed positive residual clustering concentrated in the North West and North East zones.DiscussionThe results indicate that jointly modeling temporal and spatial heterogeneity yields the best-fitting and best-calibrated specification, although spatial frailty adds comparatively little once interval-level temporal effects are included, suggesting that residual heterogeneity in this setting is predominantly temporal rather than spatial. The Poisson-INLA formulation provides a computationally efficient alternative to MCMC-based spatial survival models, making it well suited to large-scale demographic surveys. From a public health perspective, the identified biological, maternal, and socioeconomic determinants point to the need for integrated interventions, while the persistence of unexplained spatial clustering after covariate adjustment indicates structural or contextual vulnerabilities in the northern zones in Nigeria.
Cardiovascular disease (CVD) remains the leading cause of premature global mortality and one of the largest contributors to disability-adjusted life years lost. Over the past decade, the field has been transformed by the convergence of population biobanks, deep learning applied to imaging and electrocardiography, polygenic risk scores, wearable biosensors, and methodological advances in causal inference and target trial emulation. These innovations are reshaping precision public health for the general population. Yet the gains have not been equitably distributed. People with disability (PwD), comprising approximately 16 per cent of the global population and recognised by the United States National Institute on Minority Health and Health Disparities as a population experiencing health disparities are systematically under-represented in clinical trials, biobanks, electronic health records and the artificial-intelligence (AI) models trained upon them. Their cardiovascular health is therefore both worse and less precisely characterised than that of the general population. This article maps the key methodological vectors of change in CVD epidemiology, explains why each has so far failed to reach PwD, and presents a layered, defendable framework for disability-inclusive big-data and AI-enabled CVD research. It argues that disability inclusion is not a peripheral equity concern but a stress-test for the validity, generalisability and ethical legitimacy of the entire precision-cardiovascular enterprise.
IntroductionMalawi has made substantial progress in HIV prevention and treatment, yet HIV prevalence remains unevenly distributed across the country. Sub-national estimates are needed to guide targeted interventions.MethodsWe analyzed individual-level HIV biomarker data from the 2016 Malawi Demographic and Health Survey. A spatial modeling approach was applied to capture broad geographic patterns alongside sociodemographic determinants. High-resolution maps of predicted HIV prevalence were generated to visualize fine-scale differences across districts.ResultsThe analysis revealed persistent geographic disparities, with the highest prevalence concentrated in southern Malawi and more varied patterns in central and northern regions. Although geography contributed to explaining HIV variation, sociodemographic factors—including age, education, sex, and household characteristics—were the primary drivers in most districts. Geography emerged as the leading contributor in only 18% of areas.Discussion and ConclusionThese findings provide policy-relevant, sub-national evidence to support more precise targeting of HIV prevention, testing, and treatment efforts. They underscore the importance of tailoring interventions to both geographic and sociodemographic contexts to accelerate progress toward epidemic control.
Introduction:Air pollution (AP) contributes to over 4.2 million premature deaths annually, with approximately 20% linked to cardiovascular disease (CVD)-related deaths. Despite robust scientific evidence, information-seeking behavior remains poorly characterized. This study applies an infodemiological approach to assess global information-seeking behavior related to AP and its cardiovascular implications. Methods:A retrospective analysis was conducted using Google Trends data from June 2020 to June 2025. Five AP-related search terms (e.g., "air pollution," "PM₂.₅," "air quality index," "environmental pollution," and "mask") and five CVD-related search terms (e.g., "cardiovascular disease", "heart disease," "heart attack," "chest pain," and "high blood pressure") were analyzed globally. Pearson correlation coefficients, partial correlation analysis controlling for pandemic-related confounding, and time-series approaches including ARIMAX modeling and Granger causality testing were applied to assess temporal and correlational patterns in information-seeking behavior. Results:Global information-seeking behavior showed marked variability across AP and CVD search terms. AP showed moderate positive correlations with "CVD", "heart disease", and "high blood pressure" (r = 0.275;0.295;0.386, p < 0.01). "Environmental pollution" showed a moderate correlation with "heart disease" (r = 0.417, p < 0.01) and "blood pressure" (r = 0.315, p < 0.01). Technical AP indicators showed weak or negative zero-order associations with most CVD terms; however, sensitivity analysis controlling for pandemic-related confounding revealed complete directional reversal for PM₂.₅, with partial correlations becoming significantly positive, indicating that original negative associations were pandemic-driven artefacts. Search volumes were highest for Chest Pain and High Blood Pressure, with low global engagement for PM₂.₅ and AQI. Twenty-seven countries, including Pakistan, India, and Australia, demonstrated concurrent AP and CVD information-seeking behavior. Conclusion:This analysis indicates moderate information-seeking behavior in the association between AP and CVD, with geographic variation. Findings highlight the need for targeted public health communication strategies that translate AP risk into accessible cardiovascular health messages.
BackgroundObesity remains a major public health concern in the United States, with evidence suggesting that its prevalence varies significantly across socioeconomic groups. This study investigates how annual changes in obesity rates differ by income quantiles across U.S. states from 2012 to 2020.MethodsAn empirical model was developed incorporating income-quantile dummy variables and a linear time trend, allowing for differences in average annual changes in obesity prevalence across income groups. Annual change is defined as the year-to-year percentage change in age-adjusted obesity prevalence relative to the preceding year. The analysis draws on state-level data to evaluate baseline differences and subsequent yearly changes in obesity prevalence.ResultsObesity prevalence differs substantially across income groups in the baseline year, with lower-income groups exhibiting higher prevalence levels. All income groups experienced positive annual increases in obesity prevalence over the study period. Differences in annual changes among lower- and middle-income categories are relatively small in magnitude, while the highest income group (annual income above $75,000) exhibits a larger average annual increase relative to other groups. The estimated time trend is not statistically significant, indicating stable differences in annual changes across income groups over time.ConclusionThe findings highlight persistent socioeconomic differences in obesity prevalence across U.S. states. While lower-income groups bear a higher overall obesity burden, rising obesity prevalence is observed across the entire income distribution. These patterns suggest that public health responses may benefit from accounting for income-related differences in obesity prevalence, particularly through voluntary approaches such as information campaigns, expanded access to healthy foods and opportunities for physical activity, and initiatives related to the built environment.
BackgroundThe HPAI H5N1 panzootic represents a critical threat to human health in Africa, where traditional poultry systems and dense human-animal interfaces facilitate frequent zoonotic spillover. While sporadic human cases raise pandemic concerns, continent-wide integration of spatial dynamics, transmissibility indicators, and surveillance performance has been lacking. This study quantifies avian influenza transmission over two decades across Africa, identifies geographical hotspots, and evaluates the responsiveness of current surveillance systems.MethodsWe analysed 8,037 avian influenza outbreak events and 369 laboratory-confirmed human cases, predominantly caused by HPAI H5N1 (2004–2025), using harmonised data from FAO (EMPRES-i+), WHO, and WOAH. A Bayesian Besag-York-Mollié (BYM) spatiotemporal model estimated residual transmission risks and Incidence Rate Ratios (IRR) by subtype. The basic reproduction number (R₀) was derived via an exponential growth model applied to human outbreak phases across infectious durations of 7–30 days. Surveillance responsiveness was assessed by quantifying notification delays between clinical observation and official reporting.ResultsRisk of infection in animals: HPAI H5N1 was the dominant strain, representing 87.8% of animal cases, with Egypt acting as the primary epidemiological epicentre (66% of total records). The spatiotemporal model revealed that H5N1 is associated with a significantly higher risk of animal infection (IRR = 8.37; 95% CI: 6.65–10.53). Although 71% of outbreaks were reported within 5 days of detection, significant delays (≥15 days) occurred in 12% of cases, with notable regional disparities. Risk of infection in human: H5N1 was associated with a 67-fold increase in the incidence of human cases compared to other subtypes (IRR = 66.78; 95% CI: 25.29–176.37). Sensitivity analyses yielded R0 estimates ranging from 1.05 (95% CI: 0.91–1.31) to 1.23 (95% CI: 0.60–2.33), indicating localised epidemic potential.ConclusionOur findings highlight a persistent and geographically heterogeneous H5N1 reservoir in Africa with high zoonotic affinity. Although sustained human-to-human transmission remains limited, the identification of dual poultry-human hotspots and localised R0 peaks underscores the urgent need for geographically targeted One Health interventions. Strengthening real-time reporting systems and improving biosecurity in high-risk poultry value chains are critical to mitigating future pandemic threats on the continent.
