Large Number Of Potential Confounders Research Articles

Large-scale societal dynamics are reflected in human mood and brain

The stock market is a bellwether of socio-economic changes that may directly affect individual well-being. Using large-scale UK-biobank data generated over 14 years, we applied specification curve analysis to rigorously identify significant associations between the local stock market index (FTSE100) and 479,791 UK residents’ mood, as well as their alcohol intake and blood pressure adjusting the results for a large number of potential confounders, including age, sex, linear and non-linear effects of time, research site, other stock market indexes. Furthermore, we found similar associations between FTSE100 and volumetric measures of affective brain regions in a subsample (n = 39,755; measurements performed over 5.5 years), which were particularly strong around phase transitions characterized by maximum volatility in the market. The main findings did not depend on applied effect-size estimation criteria (linear methods or mutual information criterion) and were replicated in two independent US-based studies (Parkinson’s Progression Markers Initiative; n = 424; performed over 2.5 years and MyConnectome; n = 1; 81 measurements over 1.5 years). Our results suggest that phase transitions in the society, indexed by stock market, exhibit close relationships with human mood, health and the affective brain from an individual to population level.

Neuroimmunological investigations of cerebrospinal fluid in patients with recent onset depression \u2013 a study protocol

BackgroundA proinflammatory response has been suggested to be involved in the pathophysiology of depression in a subgroup of patients. However, comprehensive largescale studies on neuroimmunological investigations of the cerebrospinal fluid (CSF) are lacking and no largescale longitudinal CSF studies comparing patients with depression to healthy controls currently exist.MethodsA longitudinal case-control study including at least 100 patients with first time depression (ICD-10: F32) within the past year with ongoing symptoms and at least 100 sex and age matched healthy controls with collection of CSF, blood, and fecal samples. All individuals will be evaluated by neurological examination including neurological soft signs, interviewed for psychopathology assessment and have symptomatology evaluated by relevant rating scales. Level of functioning and quality of life will be evaluated by a panel of interview questions and rating scales, and cognitive function assessed by a relevant test battery. In addition, a large number of potential confounders will be registered (BMI, smoking status, current medication etc.). Primary outcomes: CSF white cell count, CSF/serum albumin ratio, CSF total protein levels, IgG index, CSF levels of IL-6 and IL-8, and the prevalence of any CNS-reactive autoantibody in CSF and/or blood. Secondary outcomes: exploratory analyses of a wide range of neuroimmunological markers and specific autoantibodies. Power calculations are computed for all primary outcomes based on previous CSF studies including patients with depression and healthy controls.DiscussionThis study will represent the hitherto largest investigation of CSF in patients with recent onset depression compared to healthy controls. We expect to elucidate neuroimmunological alterations in individuals with depression and characterize an immunological profile paving the way for the development of effective treatments based on biomarkers.Trial registrationThe study is approved by The Regional Committee on Health Research Ethics (Capital Region, j.no: H-16030985) and The Danish Data Protection Agency (j.no: RHP-2016-020, I-Suite no.: 04945).

Examining the association between perceived neighbourhood safety and health services utilization: A cross-sectional study among older adults in the United States of America.

To assess the association between perceived neighbourhood safety and health services use among older adults. The Health and Retirement Study was used to assess the association of perceived neighbourhood safety with inpatient hospital utilization, contact with a physician, home health use, and any health services utilization in the United States of America (n = 10,844). Logistic regression models were used, while controlling for a large number of potential confounders. The odds of any contact with a physician were greater among those who perceived their neighbourhood safety to be excellent (odds ratio (OR): 1.81, 95% confidence interval (CI): 1.20, 2.72) or very good (OR: 1.56, 95% CI: 1.04, 2.32) compared with those who perceived their neighbourhood safety as fair or poor, controlling for all model covariates. The odds of any health services utilization were greater among those who perceived their neighbourhood safety to be excellent (OR: 1.95, 95% CI: 1.26, 3.00) or very good (OR: 1.63, 95% CI: 1.06, 2.50) compared with those who perceived their neighbourhood safety as fair or poor controlling for all other model covariates. The odds of inpatient care were higher among those who perceived their neighbourhood to be excellent compared with those who compared their neighbourhood to be fair/poor (OR: 1.22, 95% CI: 1.01, 1.48). Results were not statistically significant for home health utilization. These analyses show a relationship between perceived neighbourhood safety and contact with a physician and any health services utilization among older adults and a weaker relationship between perceived neighbourhood safety and inpatient services. Future research should continue to examine this relationship between perceived neighbourhood safety and health services utilization among older adults.

Does Collective Efficacy Matter at the Micro Geographic Level?: Findings from a Study Of Street Segments.

Many scholars argue that collective efficacy is not relevant to understanding crime at the microgeographic level. We examine variation in collective efficacy across streets with different levels of crime in Baltimore City, MD, and, then, employ multilevel modelling to assess this relationship. We find that people who live in crime hot spots have much lower levels of collective efficacy than people who live in non-hot spot streets and that this relationship persists when controlling for a large number of potential confounders both at the street and community levels. These findings suggest the importance of collective efficacy both in understanding and controlling crime at microgeographic units.

Treatment Effect Estimation via Differentiated Confounder Balancing and Regression

Treatment effect plays an important role on decision making in many fields, such as social marketing, healthcare, and public policy. The key challenge on estimating treatment effect in the wild observational studies is to handle confounding bias induced by imbalance of the confounder distributions between treated and control units. Traditional methods remove confounding bias by re-weighting units with supposedly accurate propensity score estimation under the unconfoundedness assumption. Controlling high-dimensional variables may make the unconfoundedness assumption more plausible, but poses new challenge on accurate propensity score estimation. One strand of recent literature seeks to directly optimize weights to balance confounder distributions, bypassing propensity score estimation. But existing balancing methods fail to do selection and differentiation among the pool of a large number of potential confounders, leading to possible underperformance in many high-dimensional settings. In this article, we propose a data-driven Differentiated Confounder Balancing (DCB) algorithm to jointly select confounders, differentiate weights of confounders and balance confounder distributions for treatment effect estimation in the wild high-dimensional settings. Besides, under some settings with heavy confounding bias, in order to further reduce the bias and variance of estimated treatment effect, we propose a Regression Adjusted Differentiated Confounder Balancing (RA-DCB) algorithm based on our DCB algorithm by incorporating outcome regression adjustment. The synergistic learning algorithms we proposed are more capable of reducing the confounding bias in many observational studies. To validate the effectiveness of our DCB and RA-DCB algorithms, we conduct extensive experiments on both synthetic and real-world datasets. The experimental results clearly demonstrate that our algorithms outperform the state-of-the-art methods. By incorporating regression adjustment, our RA-DCB algorithm achieves more precise estimation on treatment effect than DCB algorithm, especially under the settings with heavy confounding bias. Moreover, we show that the top features ranked by our algorithm generate accurate prediction of online advertising effect.

Safety surveillance and the estimation of risk in select populations: Flexible methods to control for confounding while targeting marginal comparisons via standardization.

We consider the critical problem of pharmacosurveillance for adverse events once a drug or medical product is incorporated into routine clinical care. When making inference on comparative safety using large-scale electronic health records, we often encounter an extremely rare binary adverse outcome with a large number of potential confounders. In this context, it is challenging to offer flexible methods to adjust for high-dimensional confounders, whereas use of the propensity score (PS) can help address this challenge by providing both confounding control and dimension reduction. Among PS methods, regression adjustment using the PS as a covariate in an outcome model has been incompletely studied and potentially misused. Previous studies have suggested that simple linear adjustment may not provide sufficient control of confounding. Moreover, no formal representation of the statistical procedure and associated inference has been detailed. In this paper, we characterize a three-step procedure, which performs flexible regression adjustment of the estimated PS followed by standardization to estimate the causal effect in a select population. We also propose a simple variance estimation method for performing inference. Through a realistic simulation mimicking data from the Food and Drugs Administration's Sentinel Initiative comparing the effect of angiotensin-converting enzyme inhibitors and beta blockers on incidence of angioedema, we show that flexible regression on the PS resulted in less bias without loss of efficiency, and can outperform other methods when the PS model is correctly specified. In addition, the direct variance estimation method is a computationally fast and reliable approach for inference.

Spatial competition and quality: Evidence from the English family doctor market

We examine whether family doctor firms in England respond to local competition by increasing their quality. We measure quality in terms of clinical performance and patient-reported satisfaction to capture its multi-dimensional nature. We use a panel covering 8 years for over 8000 English general practices. We measure competition as the number of rival doctors within a small distance and control for a large number of potential confounders. We find that increases in local competition are associated with increases in patient satisfaction and to a lesser extent in clinical quality. However, the magnitude of the effect is small.

Early pregnancy bisphenol and phthalate metabolite levels, maternal hemodynamics and gestational hypertensive disorders.

Are early-pregnancy urinary bisphenol and phthalate metabolite concentrations associated with placental function markers, blood pressure (BP) trajectories during pregnancy and risk of gestational hypertensive disorders? Early-pregnancy bisphenols and phthalate metabolites were not consistently associated with maternal BP changes or gestational hypertensive disorders, but subclinical, statistically significant associations with placental angiogenic markers and placental hemodynamics were identified. In vitro studies suggest that bisphenols and phthalate metabolites may disrupt early placental development and affect the risk of gestational hypertensive disorders. Previous studies investigating effects of bisphenols and phthalate metabolites on gestational hypertensive disorders reported inconsistent results and did not examine placental function or BP throughout pregnancy. In a population-based prospective cohort study, bisphenol and phthalate metabolite concentrations were measured in a spot urine sample in early pregnancy among 1396 women whose children participated in postnatal follow-up measurements. After exclusion of women without any BP measurement or with pre-existing hypertension, 1233 women were included in the analysis. Urinary bisphenol and phthalate metabolite concentrations were measured in early-pregnancy [median gestational age 13.1 weeks, inter-quartile range 12.1-14.5]. Molar sums of total bisphenols and of low molecular weight phthalate, high molecular weight (HMW) phthalate, di-2-ethylhexylphthalate, and di-n-octylphthalate metabolites were calculated. Placental angiogenic markers (placental growth factor (PlGF), soluble fms-like tyrosine kinase (sFlt)-1), placental hemodynamic function measures (umbilical artery pulsatility index (PI), uterine artery resistance index (RI), notching and placental weight), and maternal BP were measured in different trimesters. Information on gestational hypertensive disorders was obtained from medical records. Each log unit increase in HMW phthalate metabolites was associated with a 141.72 (95% CI: 29.13, 373.21) higher early pregnancy sFlt-1/PlGF ratio (range in total sample 9-900). This association was driven by mono-[(2-carboxymethyl)hexyl]phthalate. In the repeated measurements regression models, each log unit increase in bisphenol A was associated with a 0.15 SD (95% CI: 0.03, 0.26) higher intercept and -0.01 SD (95% CI: -0.01, -0.00) decreasing slope of the umbilical artery PI Z-score and a -1.28 SD (95% CI: -2.24, -0.33) lower intercept and 0.06 SD (95% CI: 0.02, 0.11) increasing slope of the uterine artery RI Z-score. These associations remained significant after Bonferroni correction. Early-pregnancy bisphenols or phthalate metabolites showed no consistent associations with any other outcome. Information on a large number of potential confounders was available but was partly self-reported. Bisphenols and phthalate metabolites, which typically have a half-life of 24-48 h, were measured via single spot urine samples in early-pregnancy. In addition, at the current sample size, the study was powered to detect an odds ratio of 1.57 for gestational hypertension and 1.78 for pre-eclampsia, but was underpowered to perform multivariable analyses for these outcomes. Further studies combining data from different cohorts may be necessary to increase power. These limitations are possible sources of non-differential misclassification leading to bias toward the null. Bisphenols and phthalate metabolites were not associated with longitudinal changes in BP in pregnancy in our low-risk population. The observed subclinical associations of phthalates with the sFlt-1/PlGF ratio and of bisphenol A with placental hemodynamics may contribute to adverse pregnancy outcomes. Our results are therefore more supportive of an association of early pregnancy bisphenols and phthalate metabolites with risk for pre-eclampsia than with gestational hypertension. This analysis was supported by Grant (ES022972) from the National Institutes of Health, USA. The content is solely the responsibility of the authors and does not represent the official views of the National Institutes of Health. The authors report no conflicts of interest.

Advanced Therapy in Traumatic Brain Injury Inpatient Rehabilitation: Effects on Outcomes During the First Year After Discharge

ObjectiveTo use causal inference methods to determine if receipt of a greater proportion of inpatient rehabilitation treatment focused on higher level functions, for example, executive functions, ambulating over uneven surfaces (advanced therapy [AdvTx]), results in better rehabilitation outcomes. DesignA cohort study using propensity score methods applied to the traumatic brain injury practice-based evidence (TBI-PBE) database, a database consisting of multisite, prospective, longitudinal observational data. SettingAcute inpatient rehabilitation facilities. ParticipantsPatients enrolled in the TBI-PBE study (N=1843), aged 14 years or older, who sustained a severe, moderate, or complicated mild TBI, receiving their first inpatient rehabilitation facility admission to 1 of 9 sites in the United States, and consented to follow-up 3 and 9 months postdischarge from inpatient rehabilitation. InterventionsNot applicable. Main Outcome MeasuresParticipation Assessment with Recombined Tools-Objective-17, FIM motor and cognitive scores, Satisfaction with Life Scale, and Patient Health Questionnaire-9. ResultsControlling for measured potential confounders, increasing the percentage of AdvTx during inpatient TBI rehabilitation was found to be associated with better community participation, functional independence, life satisfaction, and decreased likelihood of depression during the year after discharge from inpatient rehabilitation. Participants who began rehabilitation with greater disability experienced larger gains on some outcomes than those who began rehabilitation with more intact abilities. ConclusionsIncreasing the proportion of treatment targeting higher level functions appears to have no detrimental and a small, beneficial effect on outcome. Caution should be exercised when inferring causality given that a large number of potential confounders could not be completely controlled with propensity score methods. Further, the extent to which unmeasured confounders influenced the findings is not known and could be of particular concern due to the potential for the patient’s recovery trajectory to influence therapists’ decisions to provide a greater amount of AdvTx.

A simple, doubly robust, efficient estimator for survival functions using pseudo observations

Survival functions are often estimated by nonparametric estimators such as the Kaplan-Meier estimator. For valid estimation, proper adjustment for confounding factors is needed when treatment assignment may depend on confounding factors. Inverse probability weighting is a commonly used approach, especially when there is a large number of potential confounders to adjust for. Direct adjustment may also be used if the relationship between the time-to-event and all confounders can be modeled. However, either approach requires a correctly specified model for the relationship between confounders and treatment allocation or between confounders and the time-to-event. We propose a pseudo-observation-based doubly robust estimator, which is valid when either the treatment allocation model or the time-to-event model is correctly specified and is generally more efficient than the inverse probability weighting approach. The approach can be easily implemented using standard software. A simulation study was conducted to evaluate this approach under a number of scenarios, and the results are presented and discussed. The results confirm robustness and efficiency of the proposed approach. A real data example is also provided for illustration.

Increased concentrations of soluble ST2 independently predict cardiac and total mortality but not non-fatal cardiovascular events in stable coronary heart disease patients

Aim: Soluble (s) ST2, a member of the interleukin-1 family, has been suggested to play a role in cardiac remodelling and inflammatory signalling. We assessed the long-term predictive value of sST2 in stable coronary heart disease (CHD) patients, simultaneously controlling for a large number of potential confounders.

Association Between Serotonergic Antidepressant Use During Pregnancy and Autism Spectrum Disorder in Children

Previous observations of a higher risk of child autism spectrum disorder with serotonergic antidepressant exposure during pregnancy may have been confounded. To evaluate the association between serotonergic antidepressant exposure during pregnancy and child autism spectrum disorder. Retrospective cohort study. Health administrative data sets were used to study children born to mothers who were receiving public prescription drug coverage during pregnancy in Ontario, Canada, from 2002-2010, reflecting 4.2% of births. Children were followed up until March 31, 2014. Serotonergic antidepressant exposure was defined as 2 or more consecutive maternal prescriptions for a selective serotonin or serotonin-norepinephrine reuptake inhibitor between conception and delivery. Child autism spectrum disorder identified after the age of 2 years. Exposure group differences were addressed by inverse probability of treatment weighting based on derived high-dimensional propensity scores (computerized algorithm used to select a large number of potential confounders) and by comparing exposed children with unexposed siblings. There were 35 906 singleton births at a mean gestational age of 38.7 weeks (50.4% were male, mean maternal age was 26.7 years, and mean duration of follow-up was 4.95 years). In the 2837 pregnancies (7.9%) exposed to antidepressants, 2.0% (95% CI, 1.6%-2.6%) of children were diagnosed with autism spectrum disorder. The incidence of autism spectrum disorder was 4.51 per 1000 person-years among children exposed to antidepressants vs 2.03 per 1000 person-years among unexposed children (between-group difference, 2.48 [95% CI, 2.33-2.62] per 1000 person-years; hazard ratio [HR], 2.16 [95% CI, 1.64-2.86]; adjusted HR, 1.59 [95% CI, 1.17-2.17]). After inverse probability of treatment weighting based on the high-dimensional propensity score, the association was not significant (HR, 1.61 [95% CI, 0.997-2.59]). The association was also not significant when exposed children were compared with unexposed siblings (incidence of autism spectrum disorder was 3.40 per 1000 person-years vs 2.05 per 1000 person-years, respectively; adjusted HR, 1.60 [95% CI, 0.69-3.74]). In children born to mothers receiving public drug coverage in Ontario, Canada, in utero serotonergic antidepressant exposure compared with no exposure was not associated with autism spectrum disorder in the child. Although a causal relationship cannot be ruled out, the previously observed association may be explained by other factors.

Largest study to date shows overall use of antipsychotics in pregnancy does not appear to significantly increase the risk of congenital malformations

ABSTRACT FROM: Huybrechts KF, Hernandez-Diaz S, Patorno E, et al . Antipsychotic use in pregnancy and the risk for congenital malformations. JAMA Psychiatry 2016;73:938–46. Atypical and typical antipsychotics (APs) are...

Increased Plasma Concentrations of Soluble ST2 Independently Predict Mortality but not Cardiovascular Events in Stable Coronary Heart Disease Patients: 13-Year Follow-up of the KAROLA Study.

sST2 (soluble suppression of tumorigenicity 2), a member of the interleukin-1 family, has been suggested to play a role in cardiac remodeling and inflammatory signaling. We assessed the association between sST2 in patients with stable coronary heart disease (CHD) with multiple cardiovascular outcomes and total mortality, simultaneously controlling for a large number of potential confounders. Plasma concentrations of sST2 (ELISA, Critical Diagnostics) were measured at baseline in a cohort of 1081 patients. The Cox-proportional hazards model was used to determine the prognostic value of sST2 on a combined cardiovascular disease (CVD) endpoint, on cardiovascular death, and on total mortality after adjustment for covariates. The median sST2 level was 28.9ng/mL (IQR 23.8, 35.1) (mean age at baseline 58.9years, 84.6% male). sST2 concentration was positively correlated with inflammatory markers and emerging risk factors, e.g., cystatin C, N-terminal probrainnatriuretic peptide (NT-proBNP), high-sensitivity (hs)-Troponin T and I, mid-regional pro-atrial natriuretic peptide (MR-proANP), and growth differentiation factor 15 (GDF-15). Results after short- and long-term (4.5 and 12.3years, respectively) follow-up (FU) displayed no statistically significant association with the combined endpoint of non-fatal and fatal CVD events when the top quartile (Q4) of sST2 concentration was compared to the bottom quartile (Q1). A relationship during long-term FU was seen with CVD mortality even after multivariable adjustments including clinical risk variables (HR 1.65; 95% CI 1.02-2.86), but not in a fully adjusted model whereas, in contrast, it was still highly significant after short-term FU (HR (5.97 (95%CI 1.32-27.06)). In addition, the sST2 concentration was still strongly associated with total mortality in the fully adjusted model including clinical variables and cystatin C based estimated glomerular filtration rate, NT-proBNP, hsCRP and hs-TnI comparing Q4 vs Q1 during long-term FU (HR of 1.48 (95% CI 1.03-2.13)) and short-term FU (HR 3.06 (95% CI 1.29-7.24)). Elevated levels of sST2 concentration in stable CHD patients may independently predict short- and long-term risk for fatal CVD events and total mortality but not non-fatal CVD events.

Causal effect estimation in sequencing studies: a Bayesian method to account for confounder adjustment uncertainty.

Estimating the causal effect of a single nucleotide variant (SNV) on clinical phenotypes is of interest in many genetic studies. The effect estimation may be confounded by other SNVs as a result of linkage disequilibrium as well as demographic and clinical characteristics. Because a large number of these other variables, which we call potential confounders, are collected, it is challenging to select and adjust for the variables that truly confound the causal effect. The Bayesian adjustment for confounding (BAC) method has been proposed as a general method to estimate the average causal effect in the presence of a large number of potential confounders under the assumption of no unmeasured confounders. In this paper, we explore the application of BAC in genetic studies using Genetic Analysis Workshop 19 exome sequencing data. Our results show that BAC can efficiently estimate the causal effect of genetic variants with adjustment for confounding. Consequently, BAC may serve as a useful tool for genome-wide association studies data analysis to effectively assess the causal effect of genetic variants and the impact of potential interventions.

A Healthy Dietary Pattern at Midlife, Combined with a Regulated Energy Intake, Is Related to Increased Odds for Healthy Aging

Background: Few studies have investigated the long-term impact of overall dietary patterns (DPs) on healthy aging (HA), and current findings are inconsistent.Objective: Our study’s objective was to investigate the association between empirically derived DPs in midlife and HA after 13 y of follow-up.Methods: Baseline dietary data from repeated 24-h dietary records (on average, 10 records per participant) of a subsample of the SU.VI.MAX (SUpplémentation en Vitamines et Minéraux AntioXydants) study allowed extraction of 2 DPs with the use of principal components analysis on 37 food groups. HA was assessed in 2007–2009 among 2796 participants of the SU.VI.MAX study aged 45–60 y at baseline (1994–1995), who were initially free of diabetes, cardiovascular disease, and cancer. HA was defined as not developing any major chronic disease, good physical and cognitive functioning, no limitations in instrumental activities of daily living, no depressive symptoms, no health-related limitations in social life, good overall self-perceived health, and no function-limiting pain. The association between DPs (in tertiles) and HA was evaluated by using multivariable logistic regression, and a potential interaction with energy intake was investigated.Results: A “Western” and a “healthy” DP were identified. After adjustment for a large number of potential confounders, there was no significant association between the Western DP and HA. Moreover, the healthy pattern was not associated with HA among subjects with high (i.e., greater than or equal to the median) energy intake. Among subjects with low (i.e., less than the median) energy intake, on the other hand, higher scores on the healthy DP were related to higher odds of HA (OR for tertile 3 vs. tertile 1: 1.49; 95% CI: 1.11, 2.00; P-trend = 0.01).Conclusion: Adherence to a healthy diet in midlife that provides micronutrients, fiber, and antioxidants while regulating energy intake may help to promote HA.

Accounting for uncertainty in confounder and effect modifier selection when estimating average causal effects in generalized linear models.

Confounder selection and adjustment are essential elements of assessing the causal effect of an exposure or treatment in observational studies. Building upon work by Wang et al. (2012, Biometrics 68, 661-671) and Lefebvre et al. (2014, Statistics in Medicine 33, 2797-2813), we propose and evaluate a Bayesian method to estimate average causal effects in studies with a large number of potential confounders, relatively few observations, likely interactions between confounders and the exposure of interest, and uncertainty on which confounders and interaction terms should be included. Our method is applicable across all exposures and outcomes that can be handled through generalized linear models. In this general setting, estimation of the average causal effect is different from estimation of the exposure coefficient in the outcome model due to noncollapsibility. We implement a Bayesian bootstrap procedure to integrate over the distribution of potential confounders and to estimate the causal effect. Our method permits estimation of both the overall population causal effect and effects in specified subpopulations, providing clear characterization of heterogeneous exposure effects that may vary considerably across different covariate profiles. Simulation studies demonstrate that the proposed method performs well in small sample size situations with 100-150 observations and 50 covariates. The method is applied to data on 15,060 US Medicare beneficiaries diagnosed with a malignant brain tumor between 2000 and 2009 to evaluate whether surgery reduces hospital readmissions within 30 days of diagnosis.

Silent cerebral events/lesions related to atrial fibrillation ablation: a clinical review.

Brain magnetic resonance imaging (MRI) has identified a high incidence of cerebral ischemia in asymptomatic patients after atrial fibrillation (AF) ablation (silent). Detection of cerebral ischemic events on MRI is based on acute hyperintense lesions on diffusion-weighted imaging. In the literature, the incidence is related to specifications of MRI and depends on the definition applied. In comparative studies, silent cerebral events (SCE, diffusion-weighted MRI [DWI] positive only) appear to be approximately 3 times more common compared to using a definition of silent cerebral lesions (SCL; without fluid attenuated inverse recovery sequence [FLAIR] positivity). Whereas the FLAIR sequence may turn positive within days after the ischemic event, SCE definition is highly sensitive for early phases of ischemic brain damage. SCE/SCL appear to represent cerebral ischemic infarcts and determine the "embolic fingerprint" of a specific ablation technology and strategy used. The optimum time point for detecting SCE is early after AF ablation (24-72 hours), whereas detection of SCL can only be performed within the first 2-7 days (due to delay of FLAIR positivity). Different technology-, procedure-, and patient-related parameters have been identified to play a role in the multifactorial genesis of SCE/SCL. In recent years, evidence has been gathered that there may be differences of SCE/SCL rates depending upon the ablation technology used, but small patient numbers and a large number of potential confounders hamper all studies. As major findings of recent studies, mode of periprocedural and intraprocedural anticoagulation has been identified as a major predictor for incidences of SCE/SCL. Whereas procedural characteristics related to higher SCE/SCL-rates may be modified, unchangeable patient-related factors should be taken into account for future individualized risk assessment. Novel ablation devices introduced into the market should be tested for their potential embolic fingerprint and refinements of ablation procedures to reduce their embolic potential should be prompted. The knowledge of "best practice" in terms of low SCE/SCL rates has prompted changes in work-flow, which have been implemented into ablation procedures using novel ablation devices. So far, no study has linked SCE/SCL to neuropsychological decline and the low number of AF-ablation-associated events needs to be weighted against the multitude of preexisting asymptomatic MRI-detected brain lesions related to the course of AF itself. Future studies are needed to evaluate if more white matter hyperintensities due to AF may be prevented by AF ablation (producing only a small number of SCE/SCL).

Problem drinking as a risk factor for tuberculosis: a propensity score matched analysis of a national survey

BackgroundEpidemiological and other evidence strongly supports the hypothesis that problem drinking is causally related to the incidence of active tuberculosis and the worsening of the disease course. The presence of a large number of potential confounders, however, complicates the assessment of the actual size of this causal effect, leaving room for a substantial amount of bias. This study aims to contribute to the understanding of the role of confounding in the observed association between problem drinking and tuberculosis, assessing the effect of the adjustment for a relatively large number of potential confounders on the estimated prevalence odds ratio of tuberculosis among problem drinkers vs. moderate drinkers/abstainers in a cross-sectional, nationally representative sample of the South African adult population.MethodsA propensity score approach was used to match each problem drinker in the sample with a subset of moderate drinkers/abstainers with similar characteristics in respect to a set of potential confounders. The prevalence odds ratio of tuberculosis between the matched groups was then calculated using conditional logistic regression. Sensitivity analyses were conducted to assess the robustness of the results in respect to misspecification of the model.ResultsThe prevalence odds ratio of tuberculosis between problem drinkers and moderate drinkers/abstainers was 1.97 (95% CI: 1.40 to 2.77), and the result was robust with respect to the matching procedure as well as to incorrect adjustment for potential mediators and to the possible presence of unmeasured confounders. Sub-population analysis did not provide noteworthy evidence for the presence of interaction between problem drinking and the observed confounders.ConclusionIn a cross-sectional national survey of the adult population of a middle income country with high tuberculosis burden, problem drinking was associated with a two fold increase in the odds of past TB diagnosis after controlling for a large number of socio-economic and biological confounders. Within the limitations of a cross-sectional study design with self-reported tuberculosis status, these results adds to previous evidence of a causal link between problem drinking and tuberculosis, and suggest that the observed higher prevalence of tuberculosis among problem drinkers commonly found in population studies cannot be attributed to the confounding effect of the uneven distribution of other risk factors.

On a closed-form doubly robust estimator of the adjusted odds ratio for a binary exposure.

Epidemiologic studies often aim to estimate the odds ratio for the association between a binary exposure and a binary disease outcome. Because confounding bias is of serious concern in observational studies, investigators typically estimate the adjusted odds ratio in a multivariate logistic regression which conditions on a large number of potential confounders. It is well known that modeling error in specification of the confounders can lead to substantial bias in the adjusted odds ratio for exposure. As a remedy, Tchetgen Tchetgen et al. (Biometrika. 2010;97(1):171-180) recently developed so-called doubly robust estimators of an adjusted odds ratio by carefully combining standard logistic regression with reverse regression analysis, in which exposure is the dependent variable and both the outcome and the confounders are the independent variables. Double robustness implies that only one of the 2 modeling strategies needs to be correct in order to make valid inferences about the odds ratio parameter. In this paper, I aim to introduce this recent methodology into the epidemiologic literature by presenting a simple closed-form doubly robust estimator of the adjusted odds ratio for a binary exposure. A SAS macro (SAS Institute Inc., Cary, North Carolina) is given in an online appendix to facilitate use of the approach in routine epidemiologic practice, and a simulated data example is also provided for the purpose of illustration.