Epidemiologic Reviews最新文献

The health benefits of physical activity (PA) have been widely recognized, yet traditional measures of PA, including questionnaires and category-based assessments of volume and intensity, provide only broad estimates of daily activities. Accelerometers have advanced epidemiologic research on PA by providing objective and continuous measurement of PA in free-living conditions. Wrist-worn accelerometers have become especially popular because of low participant burden. However, the validity and reliability of wrist-worn devices for adults have yet to be summarized. Moreover, accelerometer data provide rich information on how PA is accumulated throughout the day, but only a small portion of these rich data have been used by researchers. Last, new methodological developments are emerging that aim to overcome some of the limitations of accelerometers. In this review, we provide an overview of accelerometry research, with a special focus on wrist-worn accelerometers. We describe briefly how accelerometers work; summarize the validity and reliability of wrist-worn accelerometers; discuss the benefits of accelerometers, including measuring light-intensity PA; and discuss pattern metrics of daily PA recently introduced in the literature. A summary of large-scale cohort studies and randomized trials that implemented wrist-worn accelerometry is provided. We conclude the review by discussing new developments and directions of research using accelerometers, with a focus on wrist-worn accelerometers.

Simulation models are increasingly being used to inform epidemiologic studies and health policy, yet there is great variation in their transparency and reproducibility. In this review, we provide an overview of applications of simulation models in health policy and epidemiology, analyze the use of best reporting practices, and assess the reproducibility of the models using predefined, categorical criteria. We identified and analyzed 1,613 applicable articles and found exponential growth in the number of studies over the past half century, with the highest growth in dynamic modeling approaches. The largest subset of studies focused on disease policy models (70%), within which pathological conditions, viral diseases, neoplasms, and cardiovascular diseases account for one-third of the articles. Model details were not reported in almost half of the studies. We also provide in-depth analysis of modeling best practices, reporting quality and reproducibility of models for a subset of 100 articles (50 highly cited and 50 randomly selected from the remaining articles). Only 7 of 26 in-depth evaluation criteria were satisfied by more than 80% of samples. We identify areas for increased application of simulation modeling and opportunities to enhance the rigor and documentation in the conduct and reporting of simulation modeling in epidemiology and health policy.

In many perinatal pharmacoepidemiologic studies, exposure to a medication is classified as "ever exposed" versus "never exposed" within each trimester or even over the entire pregnancy. This approach is often far from real-world exposure patterns, may lead to exposure misclassification, and does not to incorporate important aspects such as dosage, timing of exposure, and treatment duration. Alternative exposure modeling methods can better summarize complex, individual-level medication use trajectories or time-varying exposures from information on medication dosage, gestational timing of use, and frequency of use. We provide an overview of commonly used methods for more refined definitions of real-world exposure to medication use during pregnancy, focusing on the major strengths and limitations of the techniques, including the potential for method-specific biases. Unsupervised clustering methods, including k-means clustering, group-based trajectory models, and hierarchical cluster analysis, are of interest because they enable visual examination of medication use trajectories over time in pregnancy and complex individual-level exposures, as well as providing insight into comedication and drug-switching patterns. Analytical techniques for time-varying exposure methods, such as extended Cox models and Robins' generalized methods, are useful tools when medication exposure is not static during pregnancy. We propose that where appropriate, combining unsupervised clustering techniques with causal modeling approaches may be a powerful approach to understanding medication safety in pregnancy, and this framework can also be applied in other areas of epidemiology.

In any research study, there is an underlying process that should begin with a clear articulation of the study's goal. The study's goal drives this process; it determines many study features, including the estimand of interest, the analytic approaches that can be used to estimate it, and which coefficients, if any, should be interpreted. Misalignment can occur in this process when analytic approaches and/or interpretations do not match the study's goal; misalignment is potentially more likely to arise when study goals are ambiguously framed. In this study, misalignment in the observational epidemiologic literature was documented and how the framing of study goals contributes to misalignment was explored. The following 2 misalignments were examined: use of an inappropriate variable selection approach for the goal (a "goal-methods" misalignment) and interpretation of coefficients of variables for which causal considerations were not made (e.g., Table 2 Fallacy, a "goal-interpretation" misalignment). A random sample of 100 articles published 2014-2018 in the top 5 general epidemiology journals were reviewed. Most reviewed studies were causal, with either explicitly stated (n = 13; 13%) or associational-framed (n = 71; 69%) aims. Full alignment of goal-methods-interpretations was infrequent (n = 9; 9%), although clearly causal studies (n = 5 of 13; 38%) were more often fully aligned than were seemingly causal ones (n = 3 of 71; 4%). Goal-methods misalignments were common (n = 34 of 103; 33%), but most frequently, methods were insufficiently reported to draw conclusions (n = 47; 46%). Goal-interpretations misalignments occurred in 31% (n = 32) of the studies and occurred less often when the methods were aligned (n = 2; 2%) compared with when the methods were misaligned (n = 13; 13%).

The increased focus on the public health burden of antimicrobial resistance (AMR) raises conceptual challenges, such as determining how much harm multidrug-resistant organisms do compared to what, or how to establish the burden. Here, we present a counterfactual framework and provide guidance to harmonize methodologies and optimize study quality. In AMR-burden studies, 2 counterfactual approaches have been applied: the harm of drug-resistant infections relative to the harm of the same drug-susceptible infections (the susceptible-infection counterfactual); and the total harm of drug-resistant infections relative to a situation where such infections were prevented (the no-infection counterfactual). We propose to use an intervention-based causal approach to determine the most appropriate counterfactual. We show that intervention scenarios, species of interest, and types of infections influence the choice of counterfactual. We recommend using purpose-designed cohort studies to apply this counterfactual framework, whereby the selection of cohorts (patients with drug-resistant, drug-susceptible infections, and those with no infection) should be based on matching on time to infection through exposure density sampling to avoid biased estimates. Application of survival methods is preferred, considering competing events. We conclude by advocating estimation of the burden of AMR by using the no-infection and susceptible-infection counterfactuals. The resulting numbers will provide policy-relevant information about the upper and lower bound of future interventions designed to control AMR. The counterfactuals should be applied in cohort studies, whereby selection of the unexposed cohorts should be based on exposure density sampling, applying methods avoiding time-dependent bias and confounding.

Quantitative bias analysis can be used to empirically assess how far study estimates are from the truth (i.e., an estimate that is free of bias). These methods can be used to explore the potential impact of confounding bias, selection bias (collider stratification bias), and information bias. Quantitative bias analysis includes methods that can be used to check the robustness of study findings to multiple types of bias and methods that use simulation studies to generate data and understand the hypothetical impact of specific types of bias in a simulated data set. In this article, we review 2 strategies for quantitative bias analysis: 1) traditional probabilistic quantitative bias analysis and 2) quantitative bias analysis with generated data. An important difference between the 2 strategies relates to the type of data (real vs. generated data) used in the analysis. Monte Carlo simulations are used in both approaches, but the simulation process is used for different purposes in each. For both approaches, we outline and describe the steps required to carry out the quantitative bias analysis and also present a bias-analysis tutorial demonstrating how both approaches can be applied in the context of an analysis for selection bias. Our goal is to highlight the utility of quantitative bias analysis for practicing epidemiologists and increase the use of these methods in the epidemiologic literature.

Needle and syringe programs (NSPs) are among the most effective interventions for controlling the transmission of infection among people who inject drugs in prisons. We evaluated the availability, accessibility, and coverage of NSPs in prisons in European Union (EU) countries. In line with the Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) criteria, we systematically searched 4 databases of peer-reviewed publications (MEDLINE (PubMed), ISI Web of Science, EBSCO, and ScienceDirect) and 53 databases containing gray literature to collect data published from January 2008 to August 2018. A total of 23,969 documents (17,297 papers and 6,672 gray documents) were identified, of which 26 were included in the study. In 2018, imprisonment rates in 28 EU countries ranged between 51 per 100,000 population in Finland and 235 per 100,000 population in Lithuania. Only 4 countries were found to have NSPs in prisons: Germany (in 1 prison), Luxembourg (no coverage data were found), Romania (available in more than 50% of prisons), and Spain (in all prisons). Portugal stopped an NSP after a 6-month pilot phase. Despite the protective impact of prison-based NSPs on infection transmission, only 4 EU countries distribute sterile syringes among people who inject drugs in prisons, and coverage of the programs within these countries is very low. Since most prisoners will eventually return to the community, lack of NSPs in EU prisons not only is a threat to the health of prisoners but also endangers public health.

The effectiveness of opiate treatment programs (OTPs) can be significantly influenced by co-occurring substance use, yet there are no standardized guidelines for assessing the influence of co-occurring substance use on treatment outcomes. In this review, we aim to provide an overview on the status of the assessment of co-occurring substance use during participation in OTPs in the United States. We searched 4 databases-MEDLINE/PubMed, EMBASE, PsychINFO, and the Cumulative Index to Nursing and Allied Health Literature (CINAHL)-from database inception to November 2018 to select relevant publications on OTPs that assessed participants' co-occurring substance use. We used a standardized protocol to extract study, intervention, and co-occurring substance use characteristics. Methodological quality was assessed using the Quality in Prognosis Studies tool. Of the 3,219 titles screened, 614 abstracts and 191 full-text original publications were assessed, leaving 85 eligible articles. Co-occurring substance use was most often assessed during opioid treatments using combined (pharmacological and behavioral) (n = 57 studies) and pharmacological (n = 25 studies) interventions. Cocaine, alcohol, marijuana, and benzodiazepines were frequently measured, while amphetamines and tobacco were rarely assessed. Great variation existed between studies in the timing and measurement of co-occurring substance use, as well as definitions for substances and polysubstance/polydrug use. Inconsistencies in the investigation of co-occurring substance use make comparison of results across studies challenging. Standardized measures and consensus on research on co-occurring substance use is needed to produce the evidence required to develop personalized treatment programs for persons using multiple substances and to inform best-practice guidelines for addressing polydrug use during participation in OTPs.

Drug-law enforcement constitutes a structural determinant of health among people who inject drugs (PWID). Street encounters between police and PWID (e.g., syringe confiscation, physical assault) have been associated with health harms, but these relationships have not been systematically assessed. We conducted a systematic literature review to evaluate the contribution of policing to risk of human immunodeficiency virus (HIV) infection among PWID. We screened MEDLINE, sociological databases, and gray literature for studies published from 1981 to November 2018 that included estimates of HIV infection/risk behaviors and street policing encounters. We extracted and summarized quantitative findings from all eligible studies. We screened 8,201 abstracts, reviewed 175 full-text articles, and included 27 eligible analyses from 9 countries (Canada, China, India, Malaysia, Mexico, Russia, Thailand, Ukraine, and the United States). Heterogeneity in variable and endpoint selection precluded meta-analyses. In 5 (19%) studies, HIV infection among PWID was significantly associated with syringe confiscation, reluctance to buy/carry syringes for fear of police, rushed injection due to a police presence, fear of arrest, being arrested for planted drugs, and physical abuse. Twenty-one (78%) studies identified policing practices to be associated with HIV risk behaviors related to injection drug use (e.g., syringe-sharing, using a "shooting gallery"). In 9 (33%) studies, policing was associated with PWID avoidance of harm reduction services, including syringe exchange, methadone maintenance, and safe consumption facilities. Evidence suggests that policing shapes HIV risk among PWID, but lower-income settings are underrepresented. Curbing injection-related HIV risk necessitates additional structural interventions. Methodological harmonization could facilitate knowledge generation on the role of police as a determinant of population health.