Epidemiologic Reviews最新文献

Gastric cancer is the fifth most common cancer and a leading cause of death worldwide. Despite a substantial body of research exploring individual food groups or dietary patterns in isolation, few studies have assessed the overall strength of the association between multiple dietary factors and gastric cancer risk. The aim for this meta-analysis was to identify the associations between dietary factors and gastric cancer risk in the Asian population. The PubMed, Web of Science, Embase, and Scopus databases were systematically searched up to December 31, 2022. Hazard ratios (HRs) with their 95% CIs were used to calculate pooled risk estimates, and Cochran's Q and I2 statistics were used to assess heterogeneity. Funnel plot and Egger's tests were used to estimate publication bias. Through stepwise screening, 30 cohort studies were deemed eligible for inclusion in this review. Results indicated a high total fruit consumption may reduce gastric cancer risk by 11% (HR = 0.89; 95% CI, 0.83-0.96), whereas high salt consumption potentially increases the risk by 97% (HR = 1.97; 95% CI, 1.34-2.90) in the Asian population. However, no associations of the other 21 food groups and 2 dietary patterns (healthy and unhealthy dietary patterns) with gastric cancer risk were found. Overall, this review demonstrates that consumption of high amounts of fruit and low amounts of salt may effectively prevent gastric cancer incidence in the Asian population. More cohort studies based on the Asian population are required to confirm the association of fish, meat, coffee consumption, and dietary pattern with gastric cancer risk.

For interventions aimed at redressing health disparities in breast cancer to be effective, a clear understanding of the nature and causes of these disparities is required. Our questions were: what is the current evidence for ethnoracial disparities in time-to-treatment initiation and survival in breast cancer, and how are the causal mechanisms of these disparities conceptualized in the literature? A comprehensive systematic search of studies on cohorts of female patients with breast cancer diagnosed with stage I-III was performed. Directed acyclic graphs were used to describe implicit causal relationships between racial/ethnic group membership and time-to-treatment initiation and survival outcomes. This review revealed strong evidence for ethnoracial disparities in both time to treatment and survival among patients with breast cancer. Unmeasured factors identified by the authors highlighted gaps in data sources and opportunities for causal reasoning. Although the existing literature describes ethnoracial disparities, there is very limited discussion of causal mechanisms and no discussion of system-level rather than individual-level effects. Addressing established ethnoracial disparities in breast cancer requires new research that explicitly considers the causal mechanisms of potential interventions, incorporating unmeasured factors contributing to these disparities. Trial registration: PROSPERO identifier: CRD42023391901.

Bloodstream infection (BSI) can be a serious complication among injured patients. Understanding the burden of injury-related BSI is important for early detection and implementing appropriate treatments to improve patient outcomes. Incidence rates and risk factors are important measures that help provide insights into the burden of injury-related BSIs and early diagnosis of patients. In this review, the aim was to comprehensively summarize incidence rates and risk factors for injury-related BSIs from scientific literature. Four electronic databases (PubMed, CINAHL [via EBSCOhost], Embase, and Web of Science) were searched. There were no limitations on the language. Studies reporting the incidence rates or risk factors associated with incidence or adverse outcomes from injury-related BSIs were included. Database searches returned 9830 articles, of which 48 were included. Incidence rates of injury-related BSIs ranged from 0.71 to 27.4 episodes per 1000 patient-days. A total of 237 potential factors associated with the development and/or outcomes of injury-related BSIs were identified and classified into 8 broad categories: demographics, prognostic scores, burn extent, clinical and patient health factors, biomarkers, resource utilization and treatments, pathogens and injuries, and mechanisms. Older age, male sex, higher injury severity score, longer length of stay, greater total body surface burn area, and inhalation injuries were the most frequently reported risk factors. This review identified a large variation in reported incidence rates but no population-based studies. Many factors have been associated with injury-related BSIs; however, the direction of association and effect sizes vary across the studies, which can be attributed to the differences in study design.

COVID-19 has transitioned from a pandemic to an endemic state, but the emergence of novel variants continues to pose significant public health challenges. In this study, the application of spatial and spatiotemporal machine learning (ML) models in understanding the dynamics of COVID-19 was systematically reviewed, as were contextual local-level comprehensive socio-environmental drivers. A systematic search was conducted across the Scopus, Web of Science, PubMed, Emcare (via Ovid), and the World Health Organization COVID-19 databases, and gray literature, adhering to Preferred Reporting Items for Systematic Reviews and Meta-Analyses guidelines. Data extraction was conducted according to the Critical Appraisal and Data Extraction for Systematic Reviews of Prediction Modeling Studies checklist, and study quality was assessed using a validated scoring system. A total of 42 studies met the inclusion criteria. The review Findings indicate that global-scale spatial and spatiotemporal ML models dominate the field. Long-standing standalone factors in the demographic, environmental, and socioeconomic domains are frequently used as local-level drivers. However, the integration of composite indicators, aggregating multiple standalone factors into a single score, is notably lacking. Such composite indicators have the potential to reduce model complexity, improve interpretability, and enhance performance by capturing multidimensional aspects of vulnerability or risk in a more simplified form. This review highlights critical gaps in the current use of spatial and spatiotemporal ML models to understand the spatial epidemiology of COVID-19. Addressing these gaps could significantly enhance the understanding of COVID-19 dynamics and inform the development of effective public health strategies to mitigate future threats.

Burnout in health care workers is caused by a complex interplay of psychosocial, organizational, and broader environmental factors. Understanding these causes can help us develop coordinated, comprehensive, and effective solutions. Employers must assess the cost of burnout to their institution, acknowledge tradeoffs, communicate with their employees about burnout, and think comprehensively as they develop policy, programs, and environmental supports to mitigate burnout.

Although the economic impact of guaranteed income (GI) (recurring, unconditional, and unrestricted cash transfers intended to supplement the income of participants) is well studied, much less is known about how GI may affect health, especially in the context of high-income countries like the United States and Canada. We searched 5 electronic databases for terms related to "guaranteed income" and "cash transfer" through April 23, 2022. Among 5340 records originally identified, 25 met our inclusion criteria and represented 16 unique GI initiatives. Most included studies used a quantitative approach (n = 22; 88%), were published between 2000 and 2022 (n = 21; 84%), and were conducted in the United States (n = 15; 60%). Health outcomes included maternal and child health (eg, preterm births, breastfeeding initiation), healthcare utilization (eg, hospital admissions), mental health (eg, depression), physical health (eg, body mass index), and behavioral health (eg, substance use). Maternal, infant, and child health were the most highly represented health outcomes. Guaranteed-income initiatives generally had significant positive impacts on health outcomes, especially among the most vulnerable recipients. Data were absent on neighborhood-level health outcomes, chronic and infectious diseases, potential unintended consequences, and long-term impacts of GI on health. Studies on the impact of GI on health suggest GI has the potential to positively affect many, but not all, health outcomes. Rigorous assessment of health outcomes is still needed, and additional health outcomes should be considered in the design and evaluation of GI initiatives.

Comparisons between randomized trial analyses and observational analyses that attempt to address similar research questions have generated many controversies in epidemiology and the social sciences. There has been little consensus on when such comparisons are reasonable, what their implications are for the validity of observational analyses, or whether trial and observational analyses can be integrated to address effectiveness questions. Here, we consider methods for using observational analyses to complement trial analyses when assessing treatment effectiveness. First, we review the framework for designing observational analyses that emulate target trials and present an evidence map of its recent applications. We then review approaches for estimating the average treatment effect in the target population underlying the emulation, using observational analyses of the emulation data alone and using transportability analyses to extend inferences from a trial to the target population. We explain how comparing treatment effect estimates from the emulation against those from the trial can provide evidence on whether observational analyses can be trusted to deliver valid estimates of effectiveness-a process we refer to as benchmarking-and, in some cases, allow the joint analysis of the trial and observational data. We illustrate different approaches using a simplified example of a pragmatic trial and its emulation in registry data. We conclude that synthesizing trial and observational data-in transportability, benchmarking, or joint analyses-can leverage their complementary strengths to enhance learning about comparative effectiveness, through a process combining quantitative methods and epidemiologic judgments.

For lethal means safety counseling (LMSC) interventions to reduce population-level suicide rates, interventions must be deployed across many settings and populations. We conducted a systematic search in 6 databases to review the current state of LMSC interventions across study designs, settings, intervention providers, populations, and injury prevention levels (eg, universal). Eligibility criteria were as follows: any individual or group receiving an LMSC intervention involving a human-to-human component aiming to influence adult behaviors related to lethal suicide methods, and outcome assessment of storage behaviors and/or suicidal self-directed violence (SDV). Risk of bias was assessed using the Effective Public Health Practice Project quality assessment tool. A descriptive synthesis approach was used for analysis. Twenty-two studies were included that reported medication- and/or firearm-storage behaviors and/or SDV after LMSC. Of the 19 studies assessing behavioral change, 14 reported a significant improvement in safe storage behaviors, and all studies measuring acceptability reported that participants found the interventions favorable. The quality of evidence was limited. No studies were rated low risk of bias, and 77% were rated high risk of bias. There was substantial heterogeneity in the settings, populations, injury prevention levels, delivery methods, and intervention elements. Many included studies focused on caregivers of pediatric populations, and few studies assessed SDV outcomes. Higher-quality trials conducted across a variety of settings, particularly those focusing on adults at risk of suicide, are needed. This review was preregistered with the International Prospective Register of Systematic Reviews (no. CRD42021230668).