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This volume is a single up-to-date source on the entire global epidemiology of diseases, injuries and risk factors with a comprehensive statement of methods and a complete presentation of results. It includes refined methods to assess data, ensure epidemiological consistency, and summarize the disease burden. Global Burden of Disease and Risk Factors examines the comparative importance of diseases, injuries, and risk factors; it incorporates a range of new data sources to develop consistent estimates of incidence, prevalence, severity and duration, and mortality for 136 major diseases and injuries. Drawing from more than 8,500 data sources that include epidemiological studies, disease registers, and notifications systems, Global Burden of Disease and Risk Factors incorporates information from more than 10,000 datasets relating to population health and mortality, representing one of the largest syntheses of global information on population health to date.

Background The atherogenic index of plasma (AIP) is composed of triglycerides and high-density lipoprotein cholesterol and is a novel marker for assessing the risk of atherogenicity and cardiometabolic health. An association between AIP and greater frequency of major adverse cardiovascular events (MACEs) in patients with type 2 diabetes mellitus and high cardiovascular (CV) disease risk has been reported. However, only few studies have examined the correlation between AIP and CV risk in general populations. We thus aimed to evaluate the relationship between AIP and CV diseases using a large-scale population dataset from the Korean National Health Insurance Service-National Health Screening Cohort (NHIS-HEALS). Methods A total of 514,866 participants were enrolled from the NHIS-HEALS and classified according to the AIP quartiles. We performed univariate and multivariate Cox proportional hazards regression analyses to determine the association between AIP and MACEs, CV events, and CV mortality. Results During follow-up, we documented 12,133, 11,055, and 1942 cases of MACEs, CV events, and CV mortality, respectively. The multivariate-adjusted hazard ratios [HRs; 95% confidence interval (CI)] for MACEs gradually and significantly increased with the AIP quartiles [1.113 (1.054–1.175) in Q2, 1.175 (1.113–1.240) in Q3, and 1.278 (1.209–1.350) in Q4], following an adjustment for the conventional CV risk factors, including age, sex, body mass index, smoking, alcohol drinking, physical activities, household income, fasting glucose, systolic blood pressure, low-density lipoprotein cholesterol, and estimated glomerular filtration rate. In subgroup analyses, the association of AIP with MACEs and CV events was particularly outstanding in patients with diabetes. Conclusions AIP was significantly associated with CV risks after adjusting for the traditional risk factors. Therefore, it may be used as an effective mass screening method to identify patients at a high risk of CV events.

Land-use change has a direct impact on species survival and reproduction, altering their spatio-temporal distributions. It acts as a selective force that favours the abundance and diversity of reservoir hosts and affects host–pathogen dynamics and prevalence. This has led to land-use change being a significant driver of infectious diseases emergence. Here, we predict the presence of rodent taxa and map the zoonotic hazard (potential sources of harm) from rodent-borne diseases in the short and long term (2025 and 2050). The study considers three different land-use scenarios based on the shared socioeconomic pathways narratives (SSPs): sustainable (SSP1-Representative Concentration Pathway (RCP) 2.6), fossil-fuelled development (SSP5-RCP 8.5) and deepening inequality (SSP4-RCP 6.0). We found that cropland expansion into forest and pasture may increase zoonotic hazards in areas with high rodent-species diversity. Nevertheless, a future sustainable scenario may not always reduce hazards. All scenarios presented high heterogeneity in zoonotic hazard, with high-income countries having the lowest hazard range. The SSPs narratives suggest that opening borders and reducing cropland expansion are critical to mitigate current and future zoonotic hazards globally, particularly in middle- and low-income economies. Our study advances previous efforts to anticipate the emergence of zoonotic diseases by integrating past, present and future information to guide surveillance and mitigation of zoonotic hazards at the regional and local scale. This article is part of the theme issue ‘Infectious disease macroecology: parasite diversity and dynamics across the globe’.

Will SARS or another pandemic influenza reoccur and, if it does, have we learned how to manage pandemics more effectively? In SARS Unmasked risk communication expert Michael Tyshenko offers answers to this and other questions. Cathy Paterson, who worked as a nurse clinician during the Toronto SARS crisis, adds an important view from the frontlines. Their analysis reveals an out-of-control situation with mixed risk communication messages, a lack of leadership, and an overwhelmed health care system that was unable to both cope with the crisis in Toronto and provide adequate support for their most valuable employees at the time - health care workers.

Background Cardiovascular disease (CVD) has become the leading cause of competitive mortality in female breast cancer (BC). Regular aerobic exercise (AE) has been widely accepted as an effective intervention to reduce cardiovascular risk in a variety of different clinical conditions. This study is aimed at evaluating the efficacy and safety of AE on cardiovascular risk factors in female BC and assessing the quality of the synthesized evidence. Methods We searched five English databases (Cochrane Library, PubMed, Embase, Scopus, and Web of Science) from inception to January 2023. Randomized controlled trials (RCTs) and cohort trials studying the effects of AE intervention on cardiovascular disease risk in female breast cancer were included. We used Stata 16 for data synthesis, Risk of Bias 2, and the Newcastle–Ottawa Scale for methodological quality evaluation and assessed the certainty of the synthesized evidence in the Grading of Recommendations, Assessment, Development and Evaluations (GRADE) approach. Results Forty RCTs and 6 cohort trials involving 44,877 BC patients showed AE reduced the incidence of CVD events by 29.4% [risk ratio (RR) = 0.706, 95% confidence interval (CI) (0.659, 0.757), low certainty] and coronary artery disease events by 36% [RR = 0.640, 95% CI (0.561, 0.729), low certainty]. AE improved LVEF, and reduced weight and hip circumference. The subgroup analysis results showed that nonlinear AE increased VO 2 max by 5.354 ml·kg·min −1 [mean difference (MD) = 5.354, 95% CI (2.645, 8.062), very low certainty] and reduced fat mass by 4.256 kg [MD = 4.256, 95% CI (-3.839, -0.094), very low certainty]. While linear AE reduced low-density lipoprotein cholesterol (LDL-C) by 8.534 mg/dL [MD = -8.534, 95% CI (-15.511, -1.557), low certainty]. The sensitivity analysis results showed that each trial did not affect the impact index of the highly heterogeneous outcomes. Conclusions Our study indicates that AE has a positive effect in reducing cardiovascular risk factors. The individualization principle of AE deserves more attention in the future. This will provide new ideas to reduce CVD events and improve the quality of life in female BC patients. However, further research on AE in female BC should take into account long-term and well-designed administration to draw definitive conclusions.

Rapid adaptation in response to novel environments can facilitate species invasions and range expansions. Understanding how invasive disease vectors rapidly evolve to novel conditions—particularly at the edge of its non‐native range—has important implications for mitigating the prevalence and spread of disease. Here, we evaluate the role of local adaptation in overwintering capability of the Asian tiger mosquito, Aedes albopictus. This species invaded the Southern United States in the 1980s and rapidly spread northward into novel climate compared to its native range. Photoperiodically induced egg diapause is a key trait contributing to the establishment and spread of Ae. albopictus in temperate latitudes, and diapause incidence rapidly developed a cline along a latitudinal gradient in the United States shortly after its initial invasion. However, variation in overwintering survival of diapause‐induced eggs along this gradient is not known, but is critical to the fitness‐related role of diapause evolution in the establishment of Ae. albopictus in its northern US range. Using reciprocal transplants, we detected local adaptation in overwinter survival of diapausing Aedes albopictus eggs. In northern range‐edge winters, eggs produced by range‐edge individuals survived better than those produced by range‐core individuals. Diapause eggs from range‐edge and range‐core locations survived equally well in range‐core winters, and no eggs survived a winter beyond the current northern range limit in the United States. Synthesis and applications. These results demonstrate rapid (~3 decades) local adaptation of egg diapause, a key trait facilitating overwinter survival and range expansion for the invasive Asian tiger mosquito. In light of these results, control efforts could shift from targeting satellite populations to a focus on preventing dispersal into locally adapted, range‐edge locations and to aim removal efforts towards areas surrounding locally adapted populations. Adopting new approaches to target rapidly adapting populations will require large‐scale collaboration among control agencies and research institutions, and should begin in the northern US range to better control Aedes albopictus mosquito populations in the face of rapid adaptation.

Abstract Background Cardiovascular disease (CVD) risk prediction is important for guiding the intensity of therapy in CVD prevention. Whilst current risk prediction algorithms use traditional statistical approaches, machine learning (ML) presents an alternative method that may improve risk prediction accuracy. This systematic review and meta-analysis aimed to investigate whether ML algorithms demonstrate greater performance compared with traditional risk scores in CVD risk prognostication. Methods and results MEDLINE, EMBASE, CENTRAL, and SCOPUS Web of Science Core collections were searched for studies comparing ML models to traditional risk scores for CVD risk prediction between the years 2000 and 2021. We included studies that assessed both ML and traditional risk scores in adult (≥18 year old) primary prevention populations. We assessed the risk of bias using the Prediction Model Risk of Bias Assessment Tool (PROBAST) tool. Only studies that provided a measure of discrimination [i.e. C-statistics with 95% confidence intervals (CIs)] were included in the meta-analysis. A total of 16 studies were included in the review and meta-analysis (3302 515 individuals). All study designs were retrospective cohort studies. Out of 16 studies, 3 externally validated their models, and 11 reported calibration metrics. A total of 11 studies demonstrated a high risk of bias. The summary C-statistics (95% CI) of the top-performing ML models and traditional risk scores were 0.773 (95% CI: 0.740–0.806) and 0.759 (95% CI: 0.726–0.792), respectively. The difference in C-statistic was 0.0139 (95% CI: 0.0139–0.140), P < 0.0001. Conclusion ML models outperformed traditional risk scores in the discrimination of CVD risk prognostication. Integration of ML algorithms into electronic healthcare systems in primary care could improve identification of patients at high risk of subsequent CVD events and hence increase opportunities for CVD prevention. It is uncertain whether they can be implemented in clinical settings. Future implementation research is needed to examine how ML models may be utilized for primary prevention. This review was registered with PROSPERO (CRD42020220811).

Background According to the Physical Activity Guidelines Advisory Committee Scientific Report, limited evidence is available on sedentary behaviors (screen time) and their joint associations with physical activity (steps) for cardiovascular health in adolescence. The objective of this study was to identify joint associations of screen time and physical activity categories with cardiovascular disease (CVD) risk factors (blood pressure, hemoglobin A1c, cholesterol) in adolescence. Methods This study analyzed data from the Adolescent Brain Cognitive Development (ABCD) Study, comprising a diverse sample of 4,718 U.S. adolescents aged 10–15 years between 2018 and 2021. Steps were measured by a Fitbit wearable device and levels were categorized as low (1,000–6,000), medium (> 6,000–12,000), and high (> 12,000) averaged daily step counts. Self-reported recreational screen time hours per day were classified as low (0–4), medium (> 4–8), and high (> 8) hours per day. CVD risk factors including blood pressure, hemoglobin A1c, and cholesterol (total and HDL) were measured. Results The analytical sample averaged 6.6 h of screen time per day and 9,722 steps per day. In models including both screen time and steps, the high screen time category was associated with a 4.27 higher diastolic blood pressure percentile (95% CI 1.83–6.73) and lower HDL cholesterol (B= -2.85, 95% CI -4.77 to -0.94 mg/dL) compared to the low screen time category. Medium (B = 3.68, 95% CI 1.24–6.11) and low (B = 7.64, 95% CI 4.07–11.20) step categories were associated with higher diastolic blood pressure percentile compared to the high step category. The medium step category was associated with lower HDL cholesterol (B= -1.99, 95% CI -3.80 to -0.19 mg/dL) compared to the high step category. Findings were similar when screen time and step counts were analyzed as continuous variables; higher continuous step count was additionally associated with lower total cholesterol (mg/dL). Conclusions Combinations of low screen time and high steps were generally associated with favorable cardiovascular health markers including lower diastolic blood pressure and higher HDL cholesterol, which can inform future adolescent health guidelines.

Background Lipoprotein(a) [Lp(a)] plasma level is a well‐known risk factor for coronary artery disease (CAD). Existing data regarding the influence of sex on the Lp(a)‐CAD relationship are inconsistent. Objective To investigate the relationship between Lp(a) and CAD in men and women and to elucidate any sex‐specific differences that may exist. Methods Data of patients with Lp(a) measurements who were admitted to a tertiary university hospital, Koc University Hospital, were analyzed. The relationship between Lp(a) levels and CAD was explored in all patients and in subgroups created by sex. Two commonly accepted Lp(a) thresholds ≥ 30 and ≥ 50 mg/dL were analyzed. Results A total of 1858 patients (mean age 54 ± 17 years; 53.33% females) were included in the analysis. Lp(a) was an independent predictor of CAD according to the multivariate regression model for the entire cohort. In all cohort, both cut‐off values (≥ 30 and ≥ 50 mg/dL) were detected as independent predictors of CAD (p < 0.001). In sex‐specific analysis, an Lp(a) ≥ 30 mg/dL was an independent predictor of CAD only in women (p < 0.001), but Lp(a) ≥ 50 mg/dL was a CAD predictor both in men and women (men, p = 0.004; women, p = 0.047). Conclusion The findings of this study may suggest that different thresholds of Lp(a) level can be employed for risk stratification in women compared to men.

Background Community level screening, referral and care intervention for the rising burden of cardiovascular diseases (CVD) and its risk factors in sub-Saharan Africa has been advocated. However, very low completed referral rates among those identified at risk has been reported. The goal of the current study was to use a citizen science-based approach to identify and refer those at high CVD risk to local health clinics, assess the referral rates in each community, and explore reasons for non-attendance by urban or rural location. Methods Twelve trained health extension workers (HEWs) screened 772 community members aged ≥ 35 year from 10 randomly selected health clinic catchment areas in an urban and a rural community in Adama district and Addis Ababa, Ethiopia with a mobile app-based on the Framingham 10-year CVD risk algorithm. HEWs also provided simple educational tools to support communicating CVD risk and counselling, and referral of at-risk persons for further care. Participants were followed up for four weeks after referral. Results The proportions of participants with high (> 20%), moderate (10–20%), and pooled moderate and high risk (> 10%) were 6.0%, 12.6%, and 18.5% respectively. The most common risk factors identified included hypertension, diabetes mellitus (DM), and tobacco use. Of the 143 at risk participants identified, 124 were interviewed at 4-weeks follow-up (86.7%), and 80/124 (64.5%) voluntarily attended a local clinic for further assessment and management; rural at-risk participants ( n  = 42) had higher rates of follow-up (72.4%) compared with urban (57.6%) dwellers ( p  = 0.08). Those without prior hypertension or diabetes had lower rates of follow-up (57.1%) compared to those with at least one of these risk factors (74.1%, p  = 0.05). The most common reasons for not attending clinic were inconvenience (63.4%), feeling fine (24.4%), and financial challenge (12.2%). Conclusions We observed high rates of voluntary clinic attendance among community members screened with moderate to high CVD risk scores in both rural and urban communities of Ethiopia. These findings imply that this novel approach may be useful for scaling up CVD risk screening in regions of Ethiopia.