Bias Analysis for Uncontrolled Confounding in the Health Sciences

Association Between Gestational Diabetes Mellitus and Hypertension: A Systematic Review and Meta-Analysis of Cohort Studies With a Quantitative Bias Analysis of Uncontrolled Confounding

BACKGROUND

Whether individuals with gestational diabetes mellitus (GDM) had an increased risk of hypertension remains unclear. We conducted a systematic literature review and meta-analysis to examine the association between GDM and hypertension and performed a quantitative bias analysis to quantify the impact of uncontrolled confounding due to antenatal psychological stress.

METHODS

We searched databases (PUBMED, EMBASE, and Web of Science) through 2022/11. Eligible studies were cohort studies that reported the association of GDM with hypertension. We assessed the risk of bias using the Newcastle-Ottawa Scale for cohort studies. We pooled adjusted risk ratios with 95% CIs using a random effects model. We performed the quantitative bias analysis using the bias formula.

RESULTS

We included 15 cohort studies, with a total of 3 959 520 (GDM, 175 378; non-GDM, 3 784 142) individuals. During the follow-up of 2 to 20 years, 106 560 cases of hypertension were reported. We found that GDM was associated with a higher risk of hypertension (pooled risk ratio, 1.78 [95% CI, 1.47, 2.17]). The risk ratio was lower among cohorts assessing incident (1.58 [95% CI, 1.29, 1.95]) than prevalent hypertension (2.60 [95% CI, 2.40, 2.83]). However, other subgroup analyses showed no differences. The quantitative bias analysis revealed that if the uncontrolled confounder of antenatal psychological stress was additionally adjusted, the positive association between GDM and hypertension would attenuate slightly (≤18%) but remains positive.

CONCLUSIONS

Limitations of this study included residual confounding and discrepancies in GDM and hypertension ascertainments. Our findings indicate that GDM is positively associated with hypertension after the index pregnancy.

Raising Epidemiological Awareness: Assessment of Measles/MMR Susceptibility in Highly Vaccinated Clusters within the Hungarian and Croatian Population-A Sero-Surveillance Analysis

Perceptions of the complete eradication of vaccine-preventable diseases such as measles, mumps, and rubella (MMR) may foster complacency and compromise vaccination efforts. Decreased measles vaccination rates during the COVID-19 pandemic have heightened the risk of outbreaks, even in adequately vaccinated populations. To address this, we have aligned with ECDC recommendations, leveraging previous cross-border sero-epidemiological assessments between Pécs, Hungary, and Osijek, Croatia, to identify latent risk groups and uncover potential parallels between our nations. Testing 2680 Hungarian and 1764 Croatian serum samples for anti-MMR IgG via ELISAs revealed anti-measles seropositivity ratios below expectations in Croatian cohorts aged ~20-30 (75.7%), ~30-40 (77.5%) and ~40-50 years (73.3%). Similarly, Hungarian samples also showed suboptimal seropositivity ratios in the ~30-40 (80.9%) and ~40-50 (87.3%) age groups. Considering mumps- and rubella-associated seropositivity trends, in both examined populations, individuals aged ~30-50 years exhibited the highest vulnerability. Additionally, we noted congruent seropositivity trends across both countries, despite distinct immunization and epidemiological contexts. Therefore, we propose expanding research to encompass the intricate dynamics of vaccination, including waning long-term immunity. This understanding could facilitate targeted interventions and bolster public awareness. Our findings underscore persistent challenges in attaining robust immunity against measles despite vaccination endeavors.

Per- and polyfluoroalkyl substances and sleep health in U.S. adults, NHANES 2005-2014

BACKGROUND

Per- and polyfluoroalkyl substances (PFAS) are synthetic chemicals that induce oxidative inflammatory responses and disrupt the endocrine and central nervous systems, all of which can influence sleep.

OBJECTIVE

To investigate the association between PFAS exposure and sleep health measures in U.S. adults.

METHODS

We analyzed serum concentration data of four PFAS [perfluorooctane sulfonic acid (PFOS), perfluorooctanoic acid (PFOA), perfluorohexane sulfonic acid (PFHxS), and perfluorononanoic acid (PFNA)] reported for 8913 adults in NHANES 2005-2014. Sleep outcomes, including trouble sleeping, having a diagnosis of sleep disorder, and recent daily sleep duration classified as insufficient or excessive sleep (<6 or >9 h/day) were examined. Weighted logistic regression was used to estimate the association between the sleep outcomes and each PFAS modeled continuously (log2) or in exposure tertiles. We applied quantile g-computation to estimate the effect of the four PFAS as a mixture on the sleep outcomes. We conducted a quantitative bias analysis to assess the potential influence of self-selection and uncontrolled confounding.

RESULTS

We observed some inverse associations between serum PFAS and trouble sleeping or sleep disorder, which were more consistent for PFOS (e.g., per log2-PFOS (ng/ml) and trouble sleeping OR = 0.93, 95%CI: 0.89, 0.98; sleep disorder OR = 0.89, 95%CI: 0.83, 0.95). Per quartile increase of the PFAS mixture was inversely associated with trouble sleeping and sleep disorder. No consistent associations were found for sleep duration across analyses. Our bias analysis suggests that the finding on sleep disorder could be explained by a moderate level of self-selection and negative confounding effects.

CONCLUSIONS

We found no evidence to suggest exposure to four legacy PFAS worsened self-reported sleep health among U.S. adults. While some inverse associations between specific PFAS and sleep disorder were observed, self-selection and uncontrolled confounding biases may play a role in these findings.

Mendelian Randomization Analysis Reveals Causal Effects of Polyunsaturated Fatty Acids on Subtypes of Diabetic Retinopathy Risk

Polyunsaturated fatty acids (PUFAs) affect several physiological processes, including visual acuity, but their relationship with diabetic retinopathy (DR) remains elusive. The aim of this study was to determine whether PUFAs have a causal effect on DR. PUFAs- (total and omega-3 [FAw3] and omega-6 [FAw6] fatty acids and their ratio) and DR-associated single nucleotide polymorphisms derived from genome-wide association studies; sample sizes were 114,999 for fatty acids and 216,666 for any DR (ADR), background DR (BDR), severe non-proliferative DR (SNPDR), and proliferative DR (PDR). We hypothesized that the intra-body levels of PUFAs have an impact on DR and conducted a two-sample Mendelian randomization (MR) study to assess the causality. Pleiotropy, heterogeneity, and sensitivity analyses were performed to verify result reliability. High levels of PUFAs were found to be associated with reduced risk of both ADR and PDR. Moreover, FAw3 was associated with a decreased risk of PDR, whereas FAw6 demonstrated an association with lowered risks of both BDR and PDR. Our findings provide genetic evidence, for the first time, for a causal relationship between PUFAs and reduced DR risk. Consequently, our comprehensive MR analysis strongly urges further investigation into the precise functions and long-term effects of PUFAs, FAw3, and FAw6 on DR.

Alterations in executive functions in inmates convicted for violent behavior: a systematic review

Background

The growth of the prison population and the high recidivism rates of inmates represent a major public safety problem.

Objective

This systematic review explored executive functions in inmates convicted of violent behavior compared with inmates convicted of non-violent behavior and healthy controls (HCs).

Methods

Systematic searches were carried out using five databases (PubMed, Scopus, Web of Science, EBSCO, and Embase) until March 6th, 2023. Following Preferred Reporting Items for Systematic Reviews and Meta-analyses (PRISMA) guidelines, two reviewers independently performed the screening, data extraction, and risk of bias assessment of the 8 studies included. The protocol of this study was registered in Prospective Register of Systematic Reviews (PROSPERO), under registration number CRD42021252043.

Results

Consistently, inmates convicted of violent behavior exhibited a greater alteration in inhibition than inmates convicted of non-violent behavior (four out of four studies) and HCs (two out of two studies). In addition, inmates convicted of violent behavior showed greater impairments in cognitive flexibility (two out of three studies) and working memory (two out of three studies) than HCs. Although with limited evidence (only one study), they also showed worse performance in set shifting and planning than HCs.

Conclusion

This study provides evidence of alterations in inhibition in inmates convicted of violent behavior compared to inmates convicted of non-violent behavior and HCs. Even though inmates convicted of violent behavior showed greater impairments in planning and set shifting than HCs, these findings were supported in only one study. In general, more robust evidence is needed to confirm alterations in inmates convicted due to violent behavior. These findings highlight the importance of designing and promoting specific cognitive interventions that contribute to the reintegration of inmates into society.

Systematic review registration

https://www.crd.york.ac.uk/prospero/display_record.php?ID=CRD42021252043, identifier CRD42021252043.

Simultaneous adjustment of uncontrolled confounding, selection bias and misclassification in multiple-bias modelling

BACKGROUND

Adjusting for multiple biases usually involves adjusting for one bias at a time, with careful attention to the order in which these biases are adjusted. A novel, alternative approach to multiple-bias adjustment involves the simultaneous adjustment of all biases via imputation and/or regression weighting. The imputed value or weight corresponds to the probability of the missing data and serves to 'reconstruct' the unbiased data that would be observed based on the provided assumptions of the degree of bias.

METHODS

We motivate and describe the steps necessary to implement this method. We also demonstrate the validity of this method through a simulation study with an exposure-outcome relationship that is biased by uncontrolled confounding, exposure misclassification, and selection bias.

RESULTS

The study revealed that a non-biased effect estimate can be obtained when correct bias parameters are applied. It also found that incorrect specification of every bias parameter by +/-25% still produced an effect estimate with less bias than the observed, biased effect.

CONCLUSIONS

Simultaneous multi-bias analysis is a useful way of investigating and understanding how multiple sources of bias may affect naive effect estimates. This new method can be used to enhance the validity and transparency of real-world evidence obtained from observational, longitudinal studies.

A probabilistic bias analysis on the magnitude of unmeasured confounding: The impact of driving mileage on road traffic crashes

INTRODUCTION

Unmeasured confounding can lead to biased interpretations of empirical findings. This paper aimed to assess the magnitude of suspected unmeasured confounding due to driving mileage and simulate the statistical power required to detect a discrepancy in the effect of polypharmacy on road traffic crashes (RTCs) among older adults.

METHODS

Based on Monte Carlo Simulation (MCS) approach, we estimated 1) the magnitude of confounding of driving mileage on the association of polypharmacy and RTCs and 2) the statistical power of to detect a discrepancy from no adjusted effect. A total of 1000 studies, each of 500000 observations, were simulated.

RESULTS

Under the assumption of a modest adjusted exposure-outcome odds ratio of 1.35, the magnitude of confounding bias by driving mileage was estimated to be 16% higher with a statistical power of 50%. Only an adjusted odds ratio of at least 1.60 would be associated with a statistical power of about 80% CONCLUSION: This applied probabilistic bias analysis showed that not adjusting for driving mileage as a confounder can lead to an overestimation of the effect of polypharmacy on RTCs in older adults. Even considering a large sample, small to moderate adjusted exposure effects were difficult to be detected.

Quantitative bias analysis in practice: review of software for regression with unmeasured confounding

BACKGROUND

Failure to appropriately account for unmeasured confounding may lead to erroneous conclusions. Quantitative bias analysis (QBA) can be used to quantify the potential impact of unmeasured confounding or how much unmeasured confounding would be needed to change a study's conclusions. Currently, QBA methods are not routinely implemented, partly due to a lack of knowledge about accessible software. Also, comparisons of QBA methods have focused on analyses with a binary outcome.

METHODS

We conducted a systematic review of the latest developments in QBA software published between 2011 and 2021. Our inclusion criteria were software that did not require adaption (i.e., code changes) before application, was still available in 2022, and accompanied by documentation. Key properties of each software tool were identified. We provide a detailed description of programs applicable for a linear regression analysis, illustrate their application using two data examples and provide code to assist researchers in future use of these programs.

RESULTS

Our review identified 21 programs with [Formula: see text] created post 2016. All are implementations of a deterministic QBA with [Formula: see text] available in the free software R. There are programs applicable when the analysis of interest is a regression of binary, continuous or survival outcomes, and for matched and mediation analyses. We identified five programs implementing differing QBAs for a continuous outcome: treatSens, causalsens, sensemakr, EValue, and konfound. When applied to one of our illustrative examples, causalsens incorrectly indicated sensitivity to unmeasured confounding whereas the other four programs indicated robustness. sensemakr performs the most detailed QBA and includes a benchmarking feature for multiple unmeasured confounders.

CONCLUSIONS

Software is now available to implement a QBA for a range of different analyses. However, the diversity of methods, even for the same analysis of interest, presents challenges to their widespread uptake. Provision of detailed QBA guidelines would be highly beneficial.

Shift Work Domains and their Interactions with Empty Calorie Food/Beverage Consumption: A 14-day Intensive Longitudinal Study

BACKGROUND/OBJECTIVES

Shift work has been linked to unhealthy eating behaviors such as imbalanced diet, or increased empty calorie food/beverage consumption. However, most research has focused on the impact of shift timing. The concept of shift work is complex, and it contains several domains such as shift timing, intensity, and speed. Previous studies have suggested that greater shift intensity and quicker shift speed may contribute to adverse health effects. However, evidence regarding associations between other domains of shift work and empty calorie food/beverage consumption has been relatively lacking. Therefore, the purpose of this study was to evaluate how other shift work domains related to empty calorie food/beverage consumption and whether different shift work domains interacted to influence the intake of foods or beverages.

DESIGN

A 14-day intensive longitudinal study employing ecological momentary assessment.

SETTINGS AND PARTICIPANTS

Eighty registered nurses working in 24 accredited Taiwanese hospitals (i.e., 9 medical centers, 12 regional hospitals, and 3 district hospitals) were recruited.

METHODS

During the study period, a convenience sample of 77 participants completed 2444 momentary surveys about empty calorie food/beverage consumption on a smartphone. Three shift work domains (shift timing, intensity, and speed) were evaluated based on registry-based work schedules. To study how these shift work domains influenced empty calorie food/beverage consumption, we employed three-level mixed-effects regression models for data analyses.

RESULTS

Findings suggested that greater night shift intensity increased the likelihood of sugar-sweetened beverage intake (odds ratio = 1.64, 95% confidence interval [1.01, 2.68]). The impacts of work shift intensity and shift timing on sugar-sweetened beverage consumption varied by shift speed. Among participants assigned a schedule with either medium or rapid shift speed, higher work shift intensity was associated with a higher probability of sugar-sweetened beverage consumption. Compared to day shifts, those who were assigned a quicker shift speed on evening shifts were more likely to consume sugar-sweetened beverages. However, associations between night shift intensity and sugar-sweetened beverage intake did not change by shift speed. Furthermore, shift intensity and shift timing did not interact to affect empty calorie food/beverage consumption.

CONCLUSIONS

This study demonstrated assignments of shift schedules (i.e., high night shift intensity, more changes in shift timings) might influence workers' consumption of empty calorie foods/beverages. Therefore, identifying and mitigating hazardous shift schedules may help to improve shift workers' eating behaviors and benefit their overall health.

Cohort Study and Bias Analysis of the Obesity Paradox Across Stages of Chronic Kidney Disease

OBJECTIVE

In advanced chronic kidney disease (CKD), patients with obesity often have better outcomes than patients without obesity, often called the 'obesity paradox'. Yet, in CKD, the prevalence of inflammation increases as CKD progresses. Although a potential confounder, inflammation may be left unaccounted in obesity-mortality studies. We examined the associations of body mass index (BMI) with all-cause and cause-specific mortality across CKD stages, with consideration for uncontrolled confounding due to unmeasured inflammation.

METHODS

We investigated 2,703,512 patients with BMI data between 2004 and 2006. We used Cox models to examine the associations of BMI with all-cause, cardiovascular, and cancer mortality, (ref: BMI 25-<30 kg/m2), adjusted for clinical characteristics and stratified by CKD stages. To address uncontrolled confounding, we performed bias analysis using a weighted probabilistic model of inflammation given the observed data applied to weighted Cox models.

RESULTS

The cohort included 5% females and 14% African Americans. In adjusted analyses, the associations of the BMI with all-cause and cardiovascular mortality showed a reverse J-shape, where a higher BMI (>40 kg/m2) was associated with a higher risk. Conversely, a lower mortality risk was observed with a BMI 30-<35 kg/m2 across all CKD stages and for BMI >40 kg/m2 in CKD stage 4/5. Cancer mortality analyses showed an inverse relationship. Bias analysis for uncontrolled confounding suggested that independent of inflammation, the obesity paradox was present.

CONCLUSION

We observed the presence of the obesity paradox in this study. This association was consistent in advanced CKD and in our bias analysis, suggesting that inflammation may not fully explain the observed BMI-mortality associations including in patients with CKD.

A Review of Causal Inference for External Comparator Arm Studies

Randomized controlled trials (RCTs) are the gold standard design to establish the efficacy of new drugs and to support regulatory decision making. However, a marked increase in the submission of single-arm trials (SATs) has been observed in recent years, especially in the field of oncology due to the trend towards precision medicine contributing to the rise of new therapeutic interventions for rare diseases. SATs lack results for control patients, and information from external sources can be compiled to provide context for better interpretability of study results. External comparator arm (ECA) studies are defined as a clinical trial (most commonly a SAT) and an ECA of a comparable cohort of patients-commonly derived from real-world settings including registries, natural history studies, or medical records of routine care. This publication aims to provide a methodological overview, to sketch emergent best practice recommendations and to identify future methodological research topics. Specifically, existing scientific and regulatory guidance for ECA studies is reviewed and appropriate causal inference methods are discussed. Further topics include sample size considerations, use of estimands, handling of different data sources regarding differential baseline covariate definitions, differential endpoint measurements and timings. In addition, unique features of ECA studies are highlighted, specifically the opportunity to address bias caused by unmeasured ECA covariates, which are available in the SAT.

Role of Real-World Data in Assessing Cardiac Toxicity After Lung Cancer Radiotherapy

Radiation-induced heart disease (RIHD) is a recent concern in patients with lung cancer after being treated with radiotherapy. Most of information we have in the field of cardiac toxicity comes from studies utilizing real-world data (RWD) as randomized controlled trials (RCTs) are generally not practical in this field. This article is a narrative review of the literature using RWD to study RIHD in patients with lung cancer following radiotherapy, summarizing heart dosimetric factors associated with outcome, strength, and limitations of the RWD studies, and how RWD can be used to assess a change to cardiac dose constraints.

Causal Effect of Chronic Pain on Mortality Through Opioid Prescriptions: Application of the Front-Door Formula

BACKGROUND

Chronic pain is the leading cause of disability worldwide and is strongly associated with the epidemic of opioid overdosing events. However, the causal links between chronic pain, opioid prescriptions, and mortality remain unclear.

METHODS

This study included 13,884 US adults aged ≥20 years who provided data on chronic pain in the National Health and Nutrition Examination Survey 1999-2004 with linkage to mortality databases through 2015. We employed the generalized form of the front-door formula within the structural causal model framework to investigate the causal effect of chronic pain on all-cause mortality mediated by opioid prescriptions.

RESULTS

We identified a total of 718 participants at 3 years of follow-up and 1260 participants at 5 years as having died from all causes. Opioid prescriptions increased the risk of all-cause mortality with an estimated odds ratio (OR) (95% confidence interval) = 1.5 (1.1, 1.9) at 3 years and 1.3 (1.1, 1.6) at 5 years. The front-door formula revealed that chronic pain increased the risk of all-cause mortality through opioid prescriptions; OR = 1.06 (1.01, 1.11) at 3 years and 1.03 (1.01, 1.06) at 5 years. Our bias analysis showed that our findings based on the front-door formula were likely robust to plausible sources of bias from uncontrolled exposure-mediator or mediator-outcome confounding.

CONCLUSIONS

Chronic pain increased the risk of all-cause mortality through opioid prescriptions. Our findings highlight the importance of careful guideline-based chronic pain management to prevent death from possibly inappropriate opioid prescriptions driven by chronic pain.

Surgical data science - from concepts toward clinical translation

Recent developments in data science in general and machine learning in particular have transformed the way experts envision the future of surgery. Surgical Data Science (SDS) is a new research field that aims to improve the quality of interventional healthcare through the capture, organization, analysis and modeling of data. While an increasing number of data-driven approaches and clinical applications have been studied in the fields of radiological and clinical data science, translational success stories are still lacking in surgery. In this publication, we shed light on the underlying reasons and provide a roadmap for future advances in the field. Based on an international workshop involving leading researchers in the field of SDS, we review current practice, key achievements and initiatives as well as available standards and tools for a number of topics relevant to the field, namely (1) infrastructure for data acquisition, storage and access in the presence of regulatory constraints, (2) data annotation and sharing and (3) data analytics. We further complement this technical perspective with (4) a review of currently available SDS products and the translational progress from academia and (5) a roadmap for faster clinical translation and exploitation of the full potential of SDS, based on an international multi-round Delphi process.

The Measurement Error Elephant in the Room: Challenges and Solutions to Measurement Error in Epidemiology

Measurement error, although ubiquitous, is uncommonly acknowledged and rarely assessed or corrected in epidemiologic studies. This review offers a straightforward guide to common problems caused by measurement error in research studies and a review of several accessible bias-correction methods for epidemiologists and data analysts. Although most correction methods require criterion validation including a gold standard, there are also ways to evaluate the impact of measurement error and potentially correct for it without such data. Technical difficulty ranges from simple algebra to more complex algorithms that require expertise, fine tuning, and computational power. However, at all skill levels, software packages and methods are available and can be used to understand the threat to inferences that arises from imperfect measurements.

Monte Carlo Simulation Approaches for Quantitative Bias Analysis: A Tutorial

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 dataset. In this article, we review two strategies for quantitative bias analysis: 1) traditional probabilistic quantitative bias analysis and 2) quantitative bias analysis with generated data. An important difference between the two strategies relates to the type of data (real vs. generated data) used in the analysis. As we describe, 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.

Uncovering Sociological Effect Heterogeneity Using Tree-Based Machine Learning

Individuals do not respond uniformly to treatments, such as events or interventions. Sociologists routinely partition samples into subgroups to explore how the effects of treatments vary by selected covariates, such as race and gender, on the basis of theoretical priors. Data-driven discoveries are also routine, yet the analyses by which sociologists typically go about them are often problematic and seldom move us beyond our biases to explore new meaningful subgroups. Emerging machine learning methods based on decision trees allow researchers to explore sources of variation that they may not have previously considered or envisaged. In this article, the authors use tree-based machine learning, that is, causal trees, to recursively partition the sample to uncover sources of effect heterogeneity. Assessing a central topic in social inequality, college effects on wages, the authors compare what is learned from covariate and propensity score-based partitioning approaches with recursive partitioning based on causal trees. Decision trees, although superseded by forests for estimation, can be used to uncover subpopulations responsive to treatments. Using observational data, the authors expand on the existing causal tree literature by applying leaf-specific effect estimation strategies to adjust for observed confounding, including inverse propensity weighting, nearest neighbor matching, and doubly robust causal forests. We also assess localized balance metrics and sensitivity analyses to address the possibility of differential imbalance and unobserved confounding. The authors encourage researchers to follow similar data exploration practices in their work on variation in sociological effects and offer a straightforward framework by which to do so.

Association Between Neutrophil-Lymphocyte Ratio and Gestational Diabetes-A Systematic Review and Meta-Analysis

A growing body of evidence shows that the neutrophil-lymphocyte ratio (NLR) is a surrogate index of systemic inflammation in several chronic diseases. Conflicting associations between NLR and gestational diabetes mellitus (GDM) have been reported in individual studies. This meta-analysis sought to investigate the association between NLR and GDM. The PubMed, EMBASE, and Google Scholar databases were searched to identify relevant articles. The pooled standardized mean difference with 95% CI was calculated using a random-effects model. Subgroup and meta-regression analysis were carried out to control for the effects of GDM diagnostic criteria, ethnicity, body mass index (BMI), and age. Eleven eligible articles were included, containing 1271 participants with GDM and 1504 controls. Pooled outcomes indicated a higher NLR in GDM pregnancies than in normoglycemic controls (SMD = 0.584; 95% CI, 0.339-0.830; P < .001), although extensive heterogeneity between studies was noted. Subgroup analysis revealed that the higher pooled estimate in GDM was not affected by diagnostic criteria, ethnicity, or BMI, although matching for BMI reduced heterogeneity between studies. This meta-analysis supports the higher NLR in GDM described by some individual studies.

Autoregulated and individualised resistance training versus predetermined and standardised resistance training in tendinopathy: A systematic review protocol

REVIEW OBJECTIVE

To synthesise the best available evidence on the effectiveness of interventions that have used autoregulated and individualised resistance training versus predetermined and standardised resistance training in treating any tendinopathy.

INTRODUCTION

Recent evidence suggests that individualised exercise may be more effective than standardised exercise for musculoskeletal disorders such as tendinopathy. However, no systematic reviews have been conducted on the topic and optimal treatment protocols and clinical recommendations are lacking.

INCLUSION CRITERIA

Randomised controlled trials assessing the effectiveness of autoregulated and individualised resistance training versus predetermined and standardised resistance training for tendinopathy in adults will be included.

METHODS

The authors will search for a wide range of sources to find both published and unpublished studies via EBSCOhost, including, but not limited to, MEDLINE, SPORTDiscus, CINAHL, Cochrane Central Register of Controlled Trials and Allied and Complementary Medicine Database. Studies published in a language other than English will only be considered if a translation is available. The Joanna Briggs Institute systematic review methodology will be followed when conducting the review. Data synthesis will be conducted using meta-analysis or narrative synthesis, where appropriate.

Black Lives Matter: A Decomposition of Racial Inequalities in Oral Cancer Screening

Simple Summary

Black Lives Matter has highlighted the increased social discrepancies that exist not only in the context of social justice but also in public health. Oral cancer screening is not exempt from disparity, with Black Americans less likely to seek screening leading to higher incidence and worse outcomes of oropharyngeal cancers. We investigate interaction analysis and Blinder–Oaxaca decomposition as tools to guide policy to address this disparity. Using National Health and Nutrition Examination Survey (NHANES) data from 2011–2018 we find that being both in poverty and Black results in sub-additive interaction, which is further deconstructed into differences in higher education levels and poverty status.

Abstract

(1) Background: The Black Lives Matter movement has highlighted the discrepancies in public health in regard to race. This study aims to investigate tools that can be used to analyze and investigate this discrepancy, which can be applied to policymaking. (2) Methods: National Health and Nutrition Examination Survey (NHANES) data from 2011–2018 was combined (N = 22,617) to investigate discrepancies of oral cancer screening in Black Americans. We give examples of counterfactual techniques that can be used to guide policy. Inverse probability treatment weighting (IPTW) was used to remove all measured confounding in an interaction analysis to assess the combined effect of socioeconomic status and race. Blinder–Oaxaca decomposition was then used to investigate the intervenable factors associated with differences in race. (3) Results: Sub-additive interaction was found on additive and multiplicative scales when all measured confounding was removed via IPTW (relative excess risk due to interaction (RERI)(OR) = −0.55 (−0.67–−0.42)). Decomposition analysis found that 32% of the discrepancy could be explained by characteristics of higher education and poverty status. (4) Conclusions: Black Americans in poverty are less likely to seek oral cancer screening than the additive likelihood would suggest. Blinder–Oaxaca decomposition is a strong tool to use for guiding policy as it quantifies clear breakdowns of what intervenable factors there are that would improve the discrepancy the most.

Cortisol Co-Secretion and Clinical Usefulness of ACTH Stimulation Test in Primary Aldosteronism: A Systematic Review and Biases in Epidemiological Studies

The hypothalamus-pituitary-adrenal (HPA) axis plays an important role in primary aldosteronism. Aldosterone biosynthesis is regulated not only by angiotensin II in the renin-angiotensin-aldosterone system, but also by adrenocorticotropic hormone (ACTH), one of the key components of the HPA axis. Although previous studies have reported cortisol cosecretion in primary aldosteronism, particularly aldosterone-producing adenoma (APA), the clinical relevance of such aldosterone and cortisol cosecretion from APA and hypertension or other metabolic disorders has not been fully established. Several somatic mutations including KCNJ5 and CACNA1D are known to induce autonomous production of aldosterone in APA, and the aldosterone responsiveness to ACTH may vary according to each mutation. The ACTH stimulation test has been reported to be a useful tool to distinguish the subtypes of primary aldosteronism (e.g., unilateral vs bilateral) in some studies, but it has not been commonly applied in clinical practice due to limited evidence. Given the recent advancement of imaging, omics research, and computational approach, it is important to summarize the most updated evidence to disentangle the potential impact of cortisol excess in primary aldosteronism and whether the ACTH stimulation test needs to be considered during the diagnostic process of primary aldosteronism. In this article, we conducted a systematic review of epidemiological studies about (i) cortisol cosecretion in primary aldosteronism and (ii) the ACTH stimulation test for the diagnosis of primary aldosteronism (including subtype diagnosis). Then, we discussed potential biases (e.g., confounding bias, overadjustment, information bias, selection bias, and sampling bias) in the previous studies and introduced some advanced epidemiological/statistical methods to minimize these limitations. A better understanding of biases and epidemiological perspective on this topic would allow us to produce further robust evidence and balanced discussion about the causal mechanisms involving the HPA axis and clinical usefulness of the ACTH stimulation test among patients with primary aldosteronism.

Depression and cardiovascular disease events among patients with type 2 diabetes: A systematic review and meta-analysis with bias analysis

AIMS

To provide updated systematic and quantitative summary of the association between depression and the risk of CVD events among individuals with type 2 diabetes. We also aimed to examine the sensitivity of the association to uncontrolled confounding.

METHODS

Data sources included Medline, Embase, and PsycInfo through September 2019. Two independent reviewers selected cohort studies that evaluated the association between depression and fatal or non-fatal CVD events among individuals with type 2 diabetes. Bias analysis was performed using the bias formula approach.

RESULTS

Of 2527 citations screened, 17 eligible studies with a total of 1,033,131 participants were identified. Based on random-effects meta-analysis, depression was associated with higher risks of non-fatal CVD events (relative risk 1.35, 95% confidence interval [CI] 1.20 to 1.53) and fatal CVD event (relative risk 1.47, 95% CI 1.21 to 1.77). Bias analysis indicated that unmeasured confounders alone may not explain the observed association between depression and CVD events among individuals with type 2 diabetes.

CONCLUSIONS

Depression was associated with a higher risk of non-fatal and fatal CVD events among individuals with type 2 diabetes. Our findings provide updated and robust evidence about the association between depression and CVD events among individuals with type 2 diabetes.

Effectiveness of combined shockwave therapy and plantar fascia stretching interventions in treating plantar heel pain: a systematic review and meta-analysis protocol

OBJECTIVE

The objective of this review is to synthesize the best available evidence on the effectiveness of interventions that have used a combination of extracorporeal shockwave therapy and plantar fascia-specific stretching to treat plantar heel pain compared to any other non-surgical intervention.

INTRODUCTION

Recent evidence suggests combining shockwave therapy and plantar fascia stretching may be more effective than other treatments for plantar heel pain. However, no systematic reviews have been conducted on the topic and optimal treatment protocols and clinical recommendations are lacking.

INCLUSION CRITERIA

Randomized controlled trials assessing the effectiveness of combined shockwave therapy and plantar stretching for plantar heel pain in adults will be included.

METHODS

The authors will search a wide range of sources to identify both published and unpublished studies via EBSCOhost, including, but not limited to MEDLINE, SPORTDiscus, CINAHL, Cochrane Central Register of Controlled Trials (CENTRAL), and Allied and Complementary Medicine Database (AMED). Studies published in a language other than English will only be considered if a translation is available. The JBI systematic review methodology will be followed when conducting the review. Data synthesis will be conducted using meta-analysis or narrative synthesis, where appropriate.

SYSTEMATIC REVIEW REGISTRATION NUMBER

PROSPERO CRD42020171538.

Inference without randomization or ignorability: A stability-controlled quasi-experiment on the prevention of tuberculosis

The stability-controlled quasi-experiment (SCQE) is an approach to study the effects of nonrandomized, newly adopted treatments. While covariate adjustment techniques rely on a "no unobserved confounding" assumption, SCQE imposes an assumption on the change in the average nontreatment outcome between successive cohorts (the "baseline trend"). We provide inferential tools for SCQE and its first application, examining whether isoniazid preventive therapy (IPT) reduced tuberculosis (TB) incidence among 26 715 HIV patients in Tanzania. After IPT became available, 16% of untreated patients developed TB within a year, compared with only 0.5% of patients under treatment. Thus, a simple difference in means suggests a 15.5 percentage point (pp) lower risk (p ≪ .001). Adjusting for covariates using numerous techniques leaves this effectively unchanged. Yet, due to confounding biases, such estimates can be misleading regardless of their statistical strength. By contrast, SCQE reveals valid causal effect estimates for any chosen assumption on the baseline trend. For example, assuming a baseline trend near 0 (no change in TB incidence over time, absent this treatment) implies a small and insignificant effect. To argue IPT was beneficial requires arguing that the nontreatment incidence would have risen by at least 0.7 pp per year, which is plausible but far from certain. SCQE may produce narrow estimates when the plausible range of baseline trends can be sufficiently constrained, while in every case it tells us what baseline trends must be believed in order to sustain a given conclusion, protecting against inferences that rely upon infeasible assumptions.

The role of dwelling type when estimating the effect of magnetic fields on childhood leukemia in the California Power Line Study (CAPS)

PURPOSE

The type of dwelling where a child lives is an important factor when considering residential exposure to environmental agents. In this paper, we explore its role when estimating the potential effects of magnetic fields (MF) on leukemia using data from the California Power Line Study (CAPS). In this context, dwelling type could be a risk factor, a proxy for other risk factors, a cause of MF exposure, a confounder, an effect-measure modifier, or some combination.

METHODS

We obtained information on type of dwelling at birth on over 2,000 subjects. Using multivariable-adjusted logistic regression, we assessed whether dwelling type was a risk factor for childhood leukemia, which covariates and MF exposures were associated with dwelling type, and whether dwelling type was a potential confounder or an effect-measure modifier in the MF-leukemia relationship under the assumption of no-uncontrolled confounding.

RESULTS

A majority of children lived in single-family homes or duplexes (70%). Dwelling type was associated with race/ethnicity and socioeconomic status but not with childhood leukemia risk, after other adjustments, and did not alter the MF-leukemia relationship upon adjustment as a potential confounder. Stratification revealed potential effect-measure modification by dwelling type on the multiplicative scale.

CONCLUSION

Dwelling type does not appear to play a significant role in the MF-leukemia relationship in the CAPS dataset as a leukemia risk factor or confounder. Future research should explore the role of dwelling as an effect-measure modifier of the MF-leukemia association.

Invited Commentary: Making Causal Inference More Social and (Social) Epidemiology More Causal

A society's social structure and the interactions of its members determine when key drivers of health occur, for how long they last, and how they operate. Yet, it has been unclear whether causal inference methods can help us find meaningful interventions on these fundamental social drivers of health. Galea and Hernán propose we place hypothetical interventions on a spectrum and estimate their effects by emulating trials, either through individual-level data analysis or systems science modeling (Am J Epidemiol. 2020;189(3):167-170). In this commentary, by way of example in health disparities research, we probe this "closer engagement of social epidemiology with formal causal inference approaches." The formidable, but not insurmountable, tensions call for causal reasoning and effect estimation in social epidemiology that should always be enveloped by a thorough understanding of how systems and the social exposome shape risk factor and health distributions. We argue that one way toward progress is a true partnership of social epidemiology and causal inference with bilateral feedback aimed at integrating social epidemiologic theory, causal identification and modeling methods, systems thinking, and improved study design and data. To produce consequential work, we must make social epidemiology more causal and causal inference more social.

Heterogeneous treatment effects on Children's cognitive/non-cognitive skills: A reevaluation of an influential early childhood intervention

The 1962-67 High/Scope Perry Preschool Program, a well-known experimental early childhood intervention study that provided quality preschool education to disadvantaged children, has been shown to have had positive impacts on early child development and on a variety of adulthood outcomes. However, most previous analyses have only examined average treatment effects across all program participants without exploring possible effect heterogeneity by children's background characteristics. We investigated this question by first using the 1964-65 Current Population Survey data in combination with the Perry data to construct a scale of child socioeconomic status based on the estimated propensity for inclusion in the Perry program, then analyzing effect heterogeneity within the Perry sample by strata of our socioeconomic scale. We found that the treatment effects of enrollment in the Perry preschool on cognitive and non-cognitive skills were much larger and more persistent among the most disadvantaged children than among others in the Perry program. Furthermore, among the most disadvantaged children, the treatment (i.e., preschool enrollment) affects later outcomes through a reinforcement mechanism of skill development (i.e., early cognitive gain leads to a non-cognitive gain, which in turn leads to later cognitive gain) and a sequential improvement of cognitive skills over time. These findings have important implications for the evaluation of policy interventions in early child development using experimental data.

Maternal diabetes during pregnancy and early onset of cardiovascular disease in offspring: population based cohort study with 40 years of follow-up

OBJECTIVE

To evaluate the associations between maternal diabetes diagnosed before or during pregnancy and early onset cardiovascular disease (CVD) in offspring during their first four decades of life.

DESIGN

Population based cohort study.

SETTING

Danish national health registries.

PARTICIPANTS

All 2 432 000 liveborn children without congenital heart disease in Denmark during 1977-2016. Follow-up began at birth and continued until first time diagnosis of CVD, death, emigration, or 31 December 2016, whichever came first.

EXPOSURES FOR OBSERVATIONAL STUDIES

Pregestational diabetes, including type 1 diabetes (n=22 055) and type 2 diabetes (n=6537), and gestational diabetes (n=26 272).

MAIN OUTCOME MEASURES

The primary outcome was early onset CVD (excluding congenital heart diseases) defined by hospital diagnosis. Associations between maternal diabetes and risks of early onset CVD in offspring were studied. Cox regression was used to assess whether a maternal history of CVD or maternal diabetic complications affected these associations. Adjustments were made for calendar year, sex, singleton status, maternal factors (parity, age, smoking, education, cohabitation, residence at childbirth, history of CVD before childbirth), and paternal history of CVD before childbirth. The cumulative incidence was averaged across all individuals, and factors were adjusted while treating deaths from causes other than CVD as competing events.

RESULTS

During up to 40 years of follow-up, 1153 offspring of mothers with diabetes and 91 311 offspring of mothers who did not have diabetes were diagnosed with CVD. Offspring of mothers with diabetes had a 29% increased overall rate of early onset CVD (hazard ratio 1.29 (95% confidence interval 1.21 to 1.37); cumulative incidence among offspring unexposed to maternal diabetes at 40 years of age 13.07% (12.92% to 13.21%), difference in cumulative incidence between exposed and unexposed offspring 4.72% (2.37% to 7.06%)). The sibship design yielded results similar to those of the unpaired design based on the whole cohort. Both pregestational diabetes (1.34 (1.25 to 1.43)) and gestational diabetes (1.19 (1.07 to 1.32)) were associated with increased rates of CVD in offspring. We also observed varied increased rates of specific early onset CVDs, particularly heart failure (1.45 (0.89 to 2.35)), hypertensive disease (1.78 (1.50 to 2.11)), deep vein thrombosis (1.82 (1.38 to 2.41)), and pulmonary embolism (1.91 (1.31 to 2.80)). Increased rates of CVD were seen in different age groups from childhood to early adulthood until age 40 years. The increased rates were more pronounced among offspring of mothers with diabetic complications (1.60 (1.25 to 2.05)). A higher incidence of early onset CVD in offspring of mothers with diabetes and comorbid CVD (1.73 (1.36 to 2.20)) was associated with the added influence of comorbid CVD but not due to the interaction between diabetes and CVD on the multiplicative scale (P value for interaction 0.94).

CONCLUSIONS

Children of mothers with diabetes, especially those mothers with a history of CVD or diabetic complications, have increased rates of early onset CVD from childhood to early adulthood. If maternal diabetes does have a causal association with increased CVD rate in offspring, the prevention, screening, and treatment of diabetes in women of childbearing age could help to reduce the risk of CVD in the next generation.

The sensitivity of reported effects of EMF on childhood leukemia to uncontrolled confounding by residential mobility: a hybrid simulation study and an empirical analysis using CAPS data

PURPOSE

Residential mobility is considered as a potential source of confounding in studies assessing environmental exposures, including in studies of electromagnetic field (EMF) exposures and childhood leukemia.

METHODS

We present a hybrid simulation study where we simulate a synthetic dataset based on an existing study and use it to assess the sensitivity of EMF-leukemia associations to different scenarios of uncontrolled confounding by mobility under two major hypotheses of the infectious etiology of childhood leukemia. We then used the findings to conduct sensitivity analysis and empirically offset the potential bias due to unmeasured mobility in the California Power Line Study dataset.

RESULTS

As expected, the stronger the assumed relationship between mobility and exposure and outcome, the greater the potential bias. However, no scenario created a bias strong enough to completely explain away previously observed associations.

CONCLUSIONS

We conclude that uncontrolled confounding by residential mobility had some impact on the estimated effect of EMF exposures on childhood leukemia, but that it was unlikely to be the primary explanation behind previously observed largely consistent, but unexplained associations.

Learning climate positively influences residents' work-related well-being

An optimal learning climate is crucial for the quality of residency training and may also improve residents' well-being and empathy. We investigated the associations of learning climate with residents' work-related well-being. A multicenter questionnaire study was performed among 271 surgery and gynaecology residents in 21 training programs from September 2012 to February 2013. Residents were asked to complete work-related well-being measurements: work engagement (Utrecht Work Engagement Scale), job and specialty satisfaction (measures from Physician Worklife Study), and physician empathy (Jefferson Scale of Physician Empathy). The Dutch Residency Educational Climate Test was used to evaluate learning climate. Multivariate adjusted linear regression analyses were used to estimate associations of learning climate with work-related well-being measures. Well-being measures were completed by 144 residents (53.1%). Learning climate was evaluated by 193 residents, yielding 9.2 evaluations per training program on average. Overall learning climate score was positively associated with work engagement [regression coefficient b = 0.58; 95% confidence interval (CI) 0.18-0.98; p = 0.004] and job satisfaction (b = 0.80; 95% CI 0.48-1.13; p < 0.001). No associations were found between learning climate and empathy and specialty satisfaction. Residents' work engagement and job satisfaction are positively related to the learning climate and may be further enhanced by improved learning climates of training programs.

Defining and Adopting Clinical Performance Measures in Graduate Medical Education: Where Are We Now and Where Are We Going?

Assessment and evaluation of trainees' clinical performance measures is needed to ensure safe, high-quality patient care. These measures also aid in the development of reflective, high-performing clinicians and hold graduate medical education (GME) accountable to the public. Although clinical performance measures hold great potential, challenges of defining, extracting, and measuring clinical performance in this way hinder their use for educational and quality improvement purposes. This article provides a way forward by identifying and articulating how clinical performance measures can be used to enhance GME by linking educational objectives with relevant clinical outcomes. The authors explore four key challenges: defining as well as measuring clinical performance measures, using electronic health record and clinical registry data to capture clinical performance, and bridging silos of medical education and health care quality improvement. The authors also propose solutions to showcase the value of clinical performance measures and conclude with a research and implementation agenda. Developing a common taxonomy of uniform specialty-specific clinical performance measures, linking these measures to large-scale GME databases, and applying both quantitative and qualitative methods to create a rich understanding of how GME affects quality of care and patient outcomes is important, the authors argue. The focus of this article is primarily GME, yet similar challenges and solutions will be applicable to other areas of medical and health professions education as well.

Why Does Parental Divorce Lower Children's Educational Attainment? A Causal Mediation Analysis

Mechanisms explaining the negative effects of parental divorce on children's attainment have long been conjectured and assessed. Yet few studies of parental divorce have carefully attended to the assumptions and methods necessary to estimate causal mediation effects. Applying a causal framework to linked U.S. panel data, we assess the degree to which parental divorce limits children's education among whites and nonwhites and whether observed lower levels of educational attainment are explained by postdivorce family conditions and children's skills. Our analyses yield three key findings. First, the negative effect of divorce on educational attainment, particularly college, is substantial for white children; by contrast, divorce does not lower the educational attainment of nonwhite children. Second, declines in family income explain as much as one- to two-thirds of the negative effect of parental divorce on white children's education. Family instability also helps explain the effect, particularly when divorce occurs in early childhood. Children's psychosocial skills explain about one-fifth of the effect, whereas children's cognitive skills play a minimal role. Third, among nonwhites, the minimal total effect on education is explained by the offsetting influence of postdivorce declines in family income and stability alongside increases in children's psychosocial and cognitive skills.

Measurement Error and Misclassification in Electronic Medical Records: Methods to Mitigate Bias

PURPOSE OF REVIEW

We sought to: 1) examine common sources of measurement error in research using data from electronic medical records (EMR), 2) discuss methods to assess the extent and type of measurement error, and 3) describe recent developments in methods to address this source of bias.

RECENT FINDINGS

We identified eight sources of measurement error frequently encountered in EMR studies, the most prominent being that EMR data usually reflect only the health services and medications delivered within the specific health facility/system contributing to the EMR data. Methods for assessing measurement error in EMR data usually require gold standard or validation data, which may be possible using data linkage. Recent methodological developments to address the impact of measurement error in EMR analyses were particularly rich in the multiple imputation literature.

SUMMARY

Presently, sources of measurement error impacting EMR studies are still being elucidated, as are methods for assessing and addressing them. Given the magnitude of measurement error that has been reported, investigators are urged to carefully evaluate and rigorously address this potential source of bias in studies based in EMR data.

The Association Between Learning Climate and Adverse Obstetrical Outcomes in 16 Nontertiary Obstetrics-Gynecology Departments in the Netherlands

PURPOSE

To investigate the association between learning climate and adverse perinatal and maternal outcomes in obstetrics-gynecology departments.

METHOD

The authors analyzed 23,629 births and 103 learning climate evaluations from 16 nontertiary obstetrics-gynecology departments in the Netherlands in 2013. Multilevel logistic regressions were used to calculate the odds of adverse perinatal and maternal outcomes, by learning climate score tertile, adjusting for maternal and department characteristics. Adverse perinatal outcomes included fetal or early neonatal mortality, five-minute Apgar score < 7, or neonatal intensive care unit admission for ≥ 24 hours. Adverse maternal outcomes included postpartum hemorrhage and/or transfusion, death, uterine rupture, or third- or fourth-degree perineal laceration. Bias analyses were conducted to quantify the sensitivity of the results to uncontrolled confounding and selection bias.

RESULTS

Learning climate scores were significantly associated with increased odds of adverse perinatal outcomes (aOR 2.06, 95% CI 1.14-3.72). Compared with the lowest tertile, departments in the middle tertile had 46% greater odds of adverse perinatal outcomes (aOR 1.46, 95% CI 1.09-1.94); departments in the highest tertile had 69% greater odds (aOR 1.69, 95% CI 1.24-2.30). Learning climate was not associated with adverse maternal outcomes (middle vs. lowest tertile: OR 1.04, 95% CI 0.93-1.16; highest vs. lowest tertile: OR 0.98, 95% CI 0.88-1.10).

CONCLUSIONS

Learning climate was associated with significantly increased odds of adverse perinatal, but not maternal, outcomes. Research in similar clinical contexts is needed to replicate these findings and explore potential mechanisms behind these associations.

Propensity Scores in Pharmacoepidemiology: Beyond the Horizon

Purpose of review

Propensity score methods have become commonplace in pharmacoepidemiology over the past decade. Their adoption has confronted formidable obstacles that arise from pharmacoepidemiology's reliance on large healthcare databases of considerable heterogeneity and complexity. These include identifying clinically meaningful samples, defining treatment comparisons, and measuring covariates in ways that respect sound epidemiologic study design. Additional complexities involve correctly modeling treatment decisions in the face of variation in healthcare practice, and dealing with missing information and unmeasured confounding. In this review, we examine the application of propensity score methods in pharmacoepidemiology with particular attention to these and other issues, with an eye towards standards of practice, recent methodological advances, and opportunities for future progress.

Recent findings

Propensity score methods have matured in ways that can advance comparative effectiveness and safety research in pharmacoepidemiology. These include natural extensions for categorical treatments, matching algorithms that can optimize sample size given design constraints, weighting estimators that asymptotically target matched and overlap samples, and the incorporation of machine learning to aid in covariate selection and model building.

Summary

These recent and encouraging advances should be further evaluated through simulation and empirical studies, but nonetheless represent a bright path ahead for the observational study of treatment benefits and harms.

Physical activity and 22-year all-cause and coronary heart disease mortality

BACKGROUND

This study explores the effects of occupational (OPA) and leisure time physical activity (LTPA) on mortality relative to cardiorespiratory fitness and pre-existing coronary heart disease (CHD).

METHODS

Associations between OPA, measured as energy expenditure (kcal/day) and relative aerobic workload (%VO₂ max), LTPA, and 22-year mortality among 1891 Finnish men were assessed by Cox regression models stratified by CHD and adjusted for 19 confounders.

RESULTS

In fully adjusted models, each 10% of relative aerobic workload increased all-cause mortality by 13% and CHD mortality 28% (P < 0.01). Compared to healthy subjects, men with CHD experienced lower mortality risks due to OPA and higher risks due to LTPA. While LTPA had no effect among healthy men, in men with CHD each weekly hour of conditioning LTPA increased all-cause mortality risks by 10% and CHD mortality by14%.

CONCLUSION

OPA was positively associated with both all-cause and CHD mortality. LTPA was not protective. Among men with CHD, LTPA increased mortality risks.

Mediators of socioeconomic differences in adiposity among youth: a systematic review

BACKGROUND

Youth with a low socioeconomic position in developed countries are at a disproportionately higher risk of being overweight or obese than their counterparts. Tackling these inequalities requires that the mechanisms behind them are well understood.

OBJECTIVES

The aim of this review was to summarize existing evidence regarding the factors that mediate or contribute to the explanation of the relationship between socioeconomic position and adiposity among youth.

METHODS AND RESULTS

A systematic literature search, conducted using the databases Medline, Embase, Web of Science and PsycINFO, yielded 28 eligible studies. These studies were of low to moderate methodological quality. The most consistent mediators of the association between socioeconomic position and adiposity identified in this review were as follows: consumption of sugar-sweetened beverages, television viewing, computer use, parental body mass index, breastfeeding duration, breakfast consumption, maternal smoking during pregnancy and infant feeding practices. The mediating role of physical activity as well as fruit and vegetable consumption was found to be indeterminate. Other potential mediators were explored in too few studies to make conclusions about their mediating role.

CONCLUSION

The review found several modifiable factors that could be targeted as feasible in interventions aimed at reducing socioeconomic differences in overweight and obesity among youth.