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Uses of the propensity score to obtain estimates of causal effect have been investigated thoroughly under assumptions of linearity and additivity of exposure effect. When the outcome variable is binary relationships such as collapsibility, valid for the linear model, do not always hold. This article examines uses of the propensity score when both exposure and outcome are binary variables and the parameter of interest is the marginal odds ratio. We review stratification and matching by the propensity score when calculating the Mantel-Haenszel estimator and show that it is consistent for neither the marginal nor conditional odds ratio. We also investigate a marginal odds ratio estimator based on doubly robust estimators and summarize its performance relative to other recently proposed estimators under various conditions, including low exposure prevalence and model misspecification. Finally, we apply all estimators to a case study estimating the effect of Medicare plan type on the quality of care received by African-American breast cancer patients.

Numerous studies have reported a protective association between asthma and acute lymphoblastic leukemia (ALL), but the causal structure of this association remains unclear. We present a hybrid simulation to examine the compatibility of this association with uncontrolled confounding by infection or another unmeasured factor. We generated a synthetic cohort using inputs on the interrelations of asthma, ALL, infections, and other suggested risk factors from the literature and the Danish National Birth Cohort. We computed odds ratios (ORs) between asthma and ALL in the synthetic cohort with and without adjustment for infections and other (including unmeasured) confounders. Only if infection was an extremely strong risk factor for asthma (OR of 10) and an extremely strong protective factor against ALL (OR of 0.1) was the asthma-ALL association compatible with the literature (OR of 0.78). Similarly, strong uncontrolled confounding by an unmeasured factor could downwardly bias the asthma-ALL association, but not enough to replicate findings in the literature. This investigation illustrates that the reported protective association between asthma and ALL is unlikely to be entirely due to uncontrolled confounding by infections or an unmeasured confounder alone. Simulation can be used to advance our understanding of risk factors for rare outcomes as demonstrated by this study.

Sleeping pills, more formally defined as hypnotics, are sedatives used to induce and maintain sleep. In a review of publications for the past 30 years, descriptive epidemiologic studies were identified that examined the mortality risk of hypnotics and related sedative-anxiolytics. Of the 34 studies estimating risk ratios, odds ratios, or hazard ratios, excess mortality associated with hypnotics was significant (p < 0.05) in 24 studies including all 14 of the largest, contrasted with no studies at all suggesting that hypnotics ever prolong life. The studies had many limitations: possibly tending to overestimate risk, such as possible confounding by indication with other risk factors; confusing hypnotics with drugs having other indications; possible genetic confounders; and too much heterogeneity of studies for meta-analyses. There were balancing limitations possibly tending towards underestimates of risk such as limited power, excessive follow-up intervals with possible follow-up mixing of participants taking hypnotics with controls, missing dosage data for most studies, and over-adjustment of confounders. Epidemiologic association in itself is not adequate proof of causality, but there is proof that hypnotics cause death in overdoses; there is thorough understanding of how hypnotics euthanize animals and execute humans; and there is proof that hypnotics cause potentially lethal morbidities such as depression, infection, poor driving, suppressed respiration, and possibly cancer. Combining these proofs with consistent evidence of association, the great weight of evidence is that hypnotics cause huge risks of decreasing a patient's duration of survival.

This study investigated whether the relationship between body mass index (BMI) and poor self-rated health differed by sex in low-income countries and middle-income countries. We analyzed data from the World Health Survey (2002-2004) on 160,099 participants from 49 low-income and middle-income countries by using random-intercept multilevel logistic regressions. We found a U-shaped relationship between BMI and poor self-rated health among both sexes in both low-income and middle-income countries, but the relationship differed by sex in strength and direction between low-income countries and middle-income countries. Differential perception of body weight and general health might explain some of the observed sex differences.

Juho Pylväläinen,1–3 Kirsi Talala,4 Teemu Murtola,5–7 Kimmo Taari,8 Jani Raitanen,1,9 Teuvo L Tammela,6 Anssi Auvinen1 1Tampere University, Faculty of Social Sciences (Health Sciences), Tampere, Finland; 2Helsinki University, Faculty of Medicine, Department of Diagnostics and Therapeutics, Helsinki, Finland; 3Helsinki University Central Hospital, Medical Imaging Center, Helsinki, Finland; 4Cancer Society of Finland, Finnish Cancer Registry, Mass Screening Registry, Helsinki, Finland; 5Tampere University, Faculty of Medicine and Health Technology, Prostate Cancer Research Center, Tampere, Finland; 6Tampere University Hospital, Department of Urology, Tampere, Finland; 7Seinäjoki Central Hospital, Department of Surgery, Seinäjoki, Finland; 8University of Helsinki and Helsinki University Hospital, Department of Urology, Helsinki, Finland; 9UKK Institute for Health Promotion Research, Tampere, FinlandCorrespondence: Juho PylväläinenHelsinki University Central Hospital, Medical Imaging Center, Haartmaninkatu 3, PO Box 180, 00029, Helsinki, Uusimaa, FinlandTel +358 503239286Email juho.pylvalainen@hus.fiPurpose: To evaluate the performance of Charlson Comorbidity Index (CCI) calculated using hospitalization and medication reimbursement databases in predicting mortality.Patients and methods: Information on hospitalizations was obtained from the national Care Register for Health Care (HILMO) and on medication reimbursements and entitlements for special reimbursements for medications from the Social Insurance Institution for 77,440 men aged 56–71 years at baseline. The subjects were followed up for mortality via Statistics Finland with 20,562 deaths during a 13-year follow-up.Results: Compared to a CCI score of 0, the age-adjusted hazard ratio for all-cause mortality associated with HILMO-based CCI scores of 1, 2 and 3 or more were 2.39 (95% CI 2.29–2.49), 2.96 (95% CI 2.81–3.13) and 6.42 (95% CI 5.95–6.93) at 13 years. The C-statistic was 0.72 at 1, 0.68 at 5 and 0.66 at 13 years, with only minor improvement over age alone (0.10, 0.06 and 0.04 accordingly). Addition of medication data did not improve predictive abilities and medication-based CCI performed poorly on its own.Conclusion: The hospitalization-based CCI, as well as that based on both databases, predicts relative mortality adequately, but its discriminative ability diminishes over time. Conditions related to hospitalizations affect survival more than medications.Keywords: mortality, comorbidity, follow-up studies, hospitalization/statistics and numerical data, drug prescriptions/statistics and numerical data, confounding factors

The association between mean arterial blood pressure (MAP) and hematocrit (Hct) as a surrogate for blood viscosity was investigated in a young (average 20.0±2.3 years), healthy population of 174 men and 442 women. Health status was assessed by clinical examination and serological evaluation. Individuals with severe anemia or hemoconcentration, prior traumas or major surgical intervention, smokers, and pregnant or lactating women were excluded from the study. The MAP/Hct association was positive and significant (P=0.04) for women and negative, albeit not significantly so, for men. The MAP/Hct association was also evaluated in subgroups of the same population with a progressive step-by-step exclusion of: individuals with cholesterol >200 mg/dL; triglycerides >200 mg/dL; body mass index >25 kg/m(2); and glucose >100 mg/dL. This consecutively reduced the strength of the positive MAP/Hct association in women, which became negative - although not significantly so - when all anomalously high factors were excluded. The same trend was found in men. Our study indicates that previously reported positive trends in the relationship between the MAP and Hct in the population are not present in a young, healthy population of men or women that excludes individuals with the confounding factors of above normal serological values and BMI.

Rationale: Estimating the impact of ventilator-associated pneumonia (VAP) from routinely collected intensive care unit (ICU) data is methodologically challenging.Objectives: We aim to replicate earlier findings of limited VAP-attributable ICU mortality in an independent cohort. By refining statistical analyses, we gradually tackle different sources of bias.Methods: Records of 2,720 adult patients admitted to Ghent University Hospital ICUs (2013-2017) and receiving mechanical ventilation within 48 hours after admission were extracted from linked Intensive Care Information System and Computer-based Surveillance and Alerting of Nosocomial Infections, Antimicrobial Resistance, and Antibiotic Consumption in the ICU databases. The VAP-attributable fraction of ICU mortality was estimated using a competing risk analysis that is restricted to VAP-free patients (approach 1), accounts for VAP onset by treating it as either a competing (approach 2) or censoring event (approach 3), or additionally adjusts for time-dependent confounding via inverse probability weighting (approach 4).Results: A total of 210 patients (7.7%) acquired VAP. Based on benchmark approach 4, we estimated that (compared with current preventive measures) hypothetical eradication of VAP would lead to a relative ICU mortality reduction of 1.7% (95% confidence interval, -1.3 to 4.6) by Day 10 and of 3.6% (95% confidence interval, 0.7 to 6.5) by Day 60. Approaches 1-3 produced estimates ranging from -0.7% to 2.5% by Day 10 and from 5.2% to 5.5% by Day 60.Conclusions: In line with previous studies using appropriate methodology, we found limited VAP-attributable ICU mortality given current state-of-the-art VAP prevention measures. Our study illustrates that inappropriate accounting of the time dependency of exposure and confounding of its effects may misleadingly suggest protective effects of early-onset VAP and systematically overestimate attributable mortality. [ABSTRACT FROM AUTHOR]

The ornithine decarboxylase-1 (ODC1) polymorphism at position +316 affects binding by transcriptional activators and repressors and modulates the risk of metachronous colorectal adenomas, particularly in association with aspirin use. We investigated the effects of ODC1 after treatment with difluoromethylornithine (eflornithine)/sulindac or placebo. Two hundred twenty-eight colorectal adenoma patients in a randomized phase III trial were genotyped for ODC1. We used Wilcoxon rank sums tests on non-normally distributed continuous variables across two genotype groups, χ(2) or Fisher exact test to assess the association between baseline categorical variables and genotype group, and log binomial regression for the primary (adenoma recurrence) and secondary outcomes (tissue polyamine response, cardiovascular toxicity, gastrointestinal toxicity, and ototoxicity). All statistical tests were two-sided. In binomial regression models with variables age, sex, race, aspirin use, treatment, and ODC1 genotype, treatment was the only statistically significant factor associated with differences in adenoma recurrence or tissue polyamine response. A statistically significant interaction was detected between ODC1 genotype and treatment with respect to adenoma recurrence (placebo group: GG, 50%, AA/GA: 34%; treatment group: GG, 11%, AA/GA, 21%; P(interaction) = .038). Excess ototoxicity was observed among ODC1 AA patients receiving treatment, but the interaction of genotype and treatment on ototoxicity was not statistically significant (P = .45).

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

Purpose: Propensity score matching (PSM) is often used to estimate the average treatment effect among the treated (ATT) using observational data. We demonstrate how the use of "double propensity score adjustment" can reduce residual confounding and avoid bias due to incomplete matching compared with traditional PSM methods.Methods: The DC Cohort is an observational clinical HIV cohort in Washington, DC. We compared the mean percent change in non-high-density lipoprotein cholesterol (non-HDL-C) concentration after 3-12 months between participants treated and participants not treated with statin therapy between 2011 and 2018. We conducted traditional PSM procedures (optimal, nearest neighbor, and nearest neighbor caliper matching) and double propensity score adjustment.Results: Among 202 treated and 1252 untreated participants, the ATT was -14.5% (95% CI: -18.4, -10.6) after optimal matching (202 matched pairs; 15/22 covariates balanced), -14.9% (-18.9, -11.0) after nearest neighbor matching (202 matched pairs; 17/22 covariates balanced), and -12.0% (-16.5, -7.5) after nearest neighbor caliper matching (153 matched pairs; 21/22 covariates balanced). After double propensity score adjustment, the ATT was -13.0% (-16.0, -10.1).Conclusions: In PSM analyses, double propensity score adjustment is a readily accessible alternative approach for estimating ATTs when sufficient covariate balance between treatment groups cannot be achieved without excluding treated participants. [ABSTRACT FROM AUTHOR]

Target populations for chemoprevention trials should include those at higher than average risk for the development of prostate cancer as defined by explicit epidemiologic and genetic criteria. Such populations include a "primary prevention" group without histologic or clinical evidence of cancer, and several clinical models of "secondary prevention," including those with clinically evident disease prior to definitive therapy and those at high risk of recurrence after therapy based on histological or biochemical status. Each risk group and clinical model has potential advantages and disadvantages, and the mechanisms that underlie disease development and progression in each group may be unique. These observations give rise to many potential clinical trials of specific agents. These trials should also include collection of data on potentially confounding influences on disease development and progression.

Objective. To evaluate the state of mental health and its relation with associated factors among nursing students. Methods. A cross-sectional study was conducted with 130 students from the Nursing and Midwifery College affiliated to the University of Medical Sciences of Shiraz (Iran). Data was collected through a document that included information on the demographic characteristics, the mean grades of the practical assignments and of the total (practical and theoretical assignments), and the Goldberg Health Questionnaire (GHQ-28) that measures symptoms grouped into four dimensions (somatic symptoms, anxiety and insomnia, social dysfunction, and depression). Results. Most of the participants (65.1%) were women; 5.3% were between 21 and 22 years of age, 84.5% were single, and 33.3% were in the sixth semester; 68.5% of the students had problems with mental health. By dimensions of the GHQ-28, it was found that 7.7% had somatic symptoms, 13.8% symptoms of anxiety and sleep disorders, 52.3% social dysfunction, and 6.2% depression. Males had a higher score of depression than females, and being single was related with higher scores of physical symptoms, anxiety and insomnia, and depression, compared with those who were married. An inverse relationship was found between the GHQ-28 average score and the semester, the grade in practical assignments, and the total grade for physical symptoms and anxiety and insomnia. Conclusion. There is a high proportion of nursing students with suspected mental health disorder. Some demographic and academic factors are related with the mental health of students and must be kept in mind by the institutions training future nurses. How to cite this article: Mohebbi Z, Setoodeh G, Torabizadeh C, Rambod M. State of Mental Health and Associated Factors in Nursing Students from Southeastern Iran. Invest. Educ. Enferm. 2019; 37(3):e04.

Biomarkers are desirable for quantitating human exposure to environmental tobacco smoke (ETS) and for predicting potential health risks for exposed individuals. A number of biomarkers of ETS have been proposed. At present cotinine, measured in blood, saliva, or urine, appears to be the most specific and the most sensitive biomarker. In nonsmokers with significant exposure to ETS, cotinine levels in the body are derived primarily from tobacco smoke, can be measured with extremely high sensitivity, and reflect exposure to a variety of types of cigarettes independent of machine-determined yield. Under conditions of sustained exposure to ETS (i.e., over hours or days), cotinine levels reflect exposure to other components of ETS. Supporting the validity of cotinine as a biomarker, cotinine levels have been positively correlated to the risks of some ETS-related health complications in children who are not cigarette smokers.

The adipocyte hormone, leptin, is secreted in proportion to adipose mass and is implicated in the regulation of energy balance via its central actions on food intake and sympathetic nervous system activity. The placenta was also shown recently to be a possible source of leptin in pregnant women, raising the possibility that the normal relationship between leptin and adiposity may be altered in pre-eclampsia. We therefore sought to assess the extent to which maternal second trimester serum leptin concentrations differed for women who would subsequently develop pre-eclampsia and those who would remain normotensive. This nested case-control study population comprised 38 women with pregnancy-induced hypertension and proteinuria (pre-eclampsia) and 192 normotensive women. Multiple least-squares regression procedures were used to assess the independent relationship between leptin concentrations and risk of pre-eclampsia. Serum leptin concentrations, measured by radioimmunoassay, were highly correlated with maternal pre-pregnancy and second trimester body mass index (r = 0.71 and r = 0.74 respectively; P < 0.001 for both) among normotensive women, and to a lesser extent among women who developed pre-eclampsia (r = 0.29 and r = 0.42; P = 0.09 and 0.02 respectively). Among women with a pre-pregnancy body mass index of < or = 25 kg/m2, pre-eclampsia cases compared with controls had higher mean second trimester leptin concentrations after adjustment for confounding factors. In contrast, pre-eclampsia cases had lower mean leptin concentrations than controls for those women with a pre-pregnancy body mass index above 25 kg/m2. Other factors in addition to the level of adiposity may therefore influence serum leptin concentrations in pre-eclamptic pregnant women. Our results suggest the possibility that leptin, like several other placentally derived substances (e.g. steroid hormones, eicosanoids and cytokines), may be involved in the pathogenesis of pre-eclampsia. Further work is needed to confirm our findings and to assess the metabolic importance and determinants of leptin concentrations in uncomplicated and pre-eclamptic pregnancies.

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Nonexperimental studies of the effectiveness of seasonal influenza vaccine in older adults have found 40%–60% reductions in all-cause mortality associated with vaccination, potentially due to confounding by frailty. We restricted our cohort to initiators of medications in preventive drug classes (statins, antiglaucoma drugs, and β blockers) as an approach to reducing confounding by frailty by excluding frail older adults who would not initiate use of these drugs. Using a random 20% sample of US Medicare beneficiaries, we framed our study as a series of nonrandomized "trials" comparing vaccinated beneficiaries with unvaccinated beneficiaries who had an outpatient health-care visit during the 5 influenza seasons occurring in 2010–2015. We pooled data across trials and used standardized-mortality-ratio–weighted Cox proportional hazards models to estimate the association between influenza vaccination and all-cause mortality before influenza season, expecting a null association. Weighted hazard ratios among preventive drug initiators were generally closer to the null than those in the nonrestricted cohort. Restriction of the study population to statin initiators with an uncensored approach resulted in a weighted hazard ratio of 1.00 (95% confidence interval: 0.84, 1.19), and several other hazard ratios were above 0.95. Restricting the cohort to initiators of medications in preventive drug classes can reduce confounding by frailty in this setting, but further work is required to determine the most appropriate criteria to use. [ABSTRACT FROM AUTHOR]

Baseline neuropsychological function was assessed in 2,123 Framingham Heart Study participants and was related to mortality over an 8- to 10-year follow-up period. During that time, 573 persons died. Using Cox proportional hazards models, the authors showed poor cognitive function to be consistently associated with an increased risk of death. This association persisted after adjustment for the confounding effects of age, education, and illness. Subjects scoring below the 26th percentile of performance were at increased risk of mortality compared with high scorers (the relative risk was 1.3 for the 11th percentile-25th percentile and 1.7 for the 1st percentile-10th percentile).

Confounding may account for the apparently improved infant outcomes after prenatal exposure to buprenorphine versus methadone. We used Massachusetts Medicaid Analytic eXtract (MAX) data to identify a cohort of opioid-dependent mother-infant pairs (2006-2011), supplemented with confounder data from an external Boston, Massachusetts, cohort (2015-2016). Associations between prenatal buprenorphine exposure versus methadone exposure and infant outcomes in the MAX cohort were adjusted for measured MAX confounders and were additionally adjusted for unmeasured confounders with bias analysis using external cohort data. A total of 477 women in MAX were treated with methadone and 543 with buprenorphine. More buprenorphine users than methadone users were white and used psychotropic medications. After adjustment forMAX confounders, risk ratios among infants exposed to buprenorphine versus those exposed tomethadone were 0.45 (95% confidence interval (CI): 0.34, 0.61) for preterm birth (birth at <37 weeks) and 0.75 (95%CI: 0.51, 1.11) for low birth weight for gestational age. The mean difference in infant hospitalization was -7.35 days (95% CI: -9.16, -5.55). After further adjustment with bias analysis, the risk ratios were 0.53 (95%CI: 0.39, 0.71) for preterm birth and 1.14 (95%CI: 0.77, 1.69) for low birth weight for gestational age, and the mean difference in infant hospitalization was -3.66 days (95% CI:-5.46,-1.87). External confounder data can be used to adjust for unmeasured confounding in studies of prenatal outcomes among women on opioid agonist therapy based on administrative databases. [ABSTRACT FROM AUTHOR]

In longitudinal studies, if the time-dependent covariates are affected by the past treatment, time-dependent confounding may be present. For a time-to-event response, marginal structural Cox models are frequently used to deal with such confounding. To avoid some of the problems of fitting marginal structural Cox model, the sequential Cox approach has been suggested as an alternative. Although the estimation mechanisms are different, both approaches claim to estimate the causal effect of treatment by appropriately adjusting for time-dependent confounding. We carry out simulation studies to assess the suitability of the sequential Cox approach for analyzing time-to-event data in the presence of a time-dependent covariate that may or may not be a time-dependent confounder. Results from these simulations revealed that the sequential Cox approach is not as effective as marginal structural Cox model in addressing the time-dependent confounding. The sequential Cox approach was also found to be inadequate in the presence of a time-dependent covariate. We propose a modified version of the sequential Cox approach that correctly estimates the treatment effect in both of the above scenarios. All approaches are applied to investigate the impact of beta-interferon treatment in delaying disability progression in the British Columbia Multiple Sclerosis cohort (1995-2008). [ABSTRACT FROM AUTHOR]

Confounding is an important source of bias, but it is often misunderstood. We consider how confounding occurs and how to address confounding using examples. Study results are confounded when the effect of the exposure on the outcome, mixes with the effects of other risk and protective factors for the outcome. This problem arises when these factors are present to different degrees among the exposed and unexposed study participants, but not all differences between the groups result in confounding. Thinking about an ideal study where all of the population of interest is exposed in one universe and is unexposed in a parallel universe helps to distinguish confounders from other differences. In an actual study, an observed unexposed population is chosen to stand in for the unobserved parallel universe. Differences between this substitute population and the parallel universe result in confounding. Confounding by identified factors can be addressed analytically and through study design, but only randomization has the potential to address confounding by unmeasured factors. Nevertheless, a given randomized study may still be confounded. Confounded study results can lead to incorrect conclusions about the effect of the exposure of interest on the outcome. [ABSTRACT FROM AUTHOR]

Confounding biases study results when the effect of the exposure on the outcome mixes with the effects of other risk and protective factors for the outcome that are present differentially by exposure status. However, not all differences between the exposed and unexposed group cause confounding. Thus, sources of confounding must be identified before they can be addressed. Confounding is absent in an ideal study where all of the population of interest is exposed in one universe and is unexposed in a parallel universe. In an actual study, an observed unexposed population represents the unobserved parallel universe. Thinking about differences between this substitute population and the unexposed parallel universe helps identify sources of confounding. These differences can then be represented in a diagram that shows how risk and protective factors for the outcome are related to the exposure. Sources of confounding identified in the diagram should be addressed analytically and through study design. However, treating all factors that differ by exposure status as confounders without considering the structure of their relation to the exposure can introduce bias. For example, conditions affected by the exposure are not confounders. There are also special types of confounding, such as time-varying confounding and unfixable confounding. It is important to evaluate carefully whether factors of interest contribute to confounding because bias can be introduced both by ignoring potential confounders and by adjusting for factors that are not confounders. The resulting bias can result in misleading conclusions about the effect of the exposure of interest on the outcome. [ABSTRACT FROM AUTHOR]

Abstract: The basic assumptions of the Cox proportional hazards regression model are rarely questioned. This study addresses whether hazard ratio, i.e., relative risk (RR), estimates using the Cox model are biased when these assumptions are violated. We investigated also the dependence of RR estimates on temporal exposure characteristics, and how inadequate control for a strong, time‐dependent confounder affects RRs for a modest, correlated risk factor. In a realistic cohort of 500,000 adults constructed using the National Cancer Institute Smoking History Generator, we used the Cox model with increasing control of smoking to examine the impact on RRs for smoking and a correlated covariate X. The smoking‐associated RR was strongly modified by age. Pack‐years of smoking did not sufficiently control for its effects; simultaneous control for effect modification by age and time‐dependent cumulative exposure, exposure duration, and time since cessation improved model fit. Even then, residual confounding was evident in RR estimates for covariate X, for which spurious RRs ranged from 0.980 to 1.017 per unit increase. Use of the Cox model to control for a time‐dependent strong risk factor yields unreliable RR estimates unless detailed, time‐varying information is incorporated in analyses. Notwithstanding, residual confounding may bias estimated RRs for a modest risk factor. [ABSTRACT FROM AUTHOR]

Propensity score methods are a popular tool with which to control for confounding in observational data, but their bias-reduction properties--as well as internal validity, generally--are threatened by covariate measurement error. There are few easy-to-implement methods of correcting for such bias. In this paper, we describe and demonstrate how existing sensitivity analyses for unobserved confounding--propensity score calibration, VanderWeele and Arah's bias formulas, and Rosenbaum's sensitivity analysis--can be adapted to address this problem. In a simulation study, we examine the extent to which these sensitivity analyses can correct for several measurement error structures: classical, systematic differential, and heteroscedastic covariate measurement error. We then apply these approaches to address covariate measurement error in estimating the association between depression and weight gain in a cohort of adults in Baltimore, Maryland. We recommend the use of VanderWeele and Arah's bias formulas and propensity score calibration (assuming it is adapted appropriately for the measurement error structure), as both approaches perform well for a variety of propensity score estimators and measurement error structures. [ABSTRACT FROM AUTHOR]

We present a method for improving estimation in linear regression models in samples ofmoderate size, using shrinkage techniques. Our work connects the theory of causal inference, which describes how variable adjustment should be performed with large samples, with shrinkage estimators such as ridge regression and the least absolute shrinkage and selection operator (LASSO), which can perform better in sample sizes seen in epidemiologic practice. Shrinkagemethods reduce mean squared error by trading off some amount of bias for a reduction in variance. However, when inference is the goal, there are no standard methods for choosing the penalty "tuning" parameters that govern these tradeoffs. We propose selecting the penalty parameters for these shrinkage estimators byminimizing bias and variance in future similar data sets drawn from the posterior predictive distribution. Our method provides both the point estimate of interest and corresponding standard error estimates. Through simulations, we demonstrate that it can achieve better mean squared error than using cross-validation for penalty parameter selection. We apply our method to a crosssectional analysis of the association between smoking and carotid intima-media thickness in the Multi-Ethnic Study of Atherosclerosis (multiple US locations, 2000-2002) and compare it with similar analyses of these data. [ABSTRACT FROM AUTHOR]

Aims Previous studies have reported diverging results on the association between benzodiazepine use and cancer risk. Methods We investigated this association in a matched case-control study including incident cancer cases during 2002-2009 in the Danish Cancer Registry ( n = 94 923) and age- and sex-matched (1:8) population controls ( n = 759 334). Long-term benzodiazepine use was defined as ≥500 defined daily doses 1-5 years prior to the index date. We implemented propensity score (PS) calibration using external information on confounders available from a survey of the Danish population. Two PSs were used: The error-prone PS using register-based confounders and the calibrated PS based on both register- and survey-based confounders, retrieved from the Health Interview Survey. Results Register-based data showed that cancer cases had more diagnoses, higher comorbidity score and more co-medication then population controls. Survey-based data showed lower self-rated health, more self-reported diseases, and more smokers as well as subjects with sedentary lifestyle among benzodiazepine users. By PS calibration, the odds ratio for cancer overall associated with benzodiazepine use decreased from 1.16 to 1.09 (95% confidence interval 1.00-1.19) and for smoking-related cancers from 1.20 to 1.10 (95% confidence interval 1.00-1.21). Conclusion We conclude that the increased risk observed in the solely register-based study could partly be attributed to unmeasured confounding. [ABSTRACT FROM AUTHOR]

Background Potential confounding or effect modification by employment status is frequently overlooked in pregnancy outcome studies. Methods To characterize how employed and non-employed women differ, we compared demographics, behaviors, and reproductive histories by maternal employment status for 8,343 mothers of control (non-malformed) infants in the National Birth Defects Prevention Study (1997-2007) and developed a multivariable model for employment status anytime during pregnancy and the 3 months before conception. Results Sixteen factors were independently associated with employment before or during pregnancy, including: maternal age, pre-pregnancy body mass index, pregnancy intention, periconceptional/first trimester smoking and alcohol consumption, and household income. Conclusions Employment status was significantly associated with many common risk factors for adverse pregnancy outcomes. Pregnancy outcome studies should consider adjustment or stratification by employment status. In studies of occupational exposures, these differences may cause uncontrollable confounding if non-employed women are treated as unexposed instead of excluded from analysis. Am. J. Ind. Med. 60:329-341, 2017. © 2017 Wiley Periodicals, Inc. [ABSTRACT FROM AUTHOR]

Electronic health records (EHRs) are an increasingly utilized resource for clinical research. While their size allows for many analytical opportunities, as with most observational data there is also the potential for bias. One of the key sources of bias in EHRs is what we term informed presence--the notion that inclusion in an EHR is not random but rather indicates that the subject is ill, making people in EHRs systematically different from those not in EHRs. In this article, we use simulated and empirical data to illustrate the conditions under which such bias can arise and how conditioning on the number of health-care encounters can be one way to remove this bias. In doing so, we also show when such an approach can impart M bias, or bias from conditioning on a collider. Finally, we explore the conditions under which number of medical encounters can serve as a proxy for general health. We apply these methods to an EHR data set from a university medical center covering the years 2007-2013. [ABSTRACT FROM AUTHOR]

In time-to-event analyses of observational studies of drug effectiveness, incorrect handling of the period between cohort entry and first treatment exposure during follow-up may result in immortal time bias. This bias can be eliminated by acknowledging a change in treatment exposure status with time-dependent analyses, such as fitting a time-dependent Cox model. The prescription time-distribution matching (PTDM) method has been proposed as a simpler approach for controlling immortal time bias. Using simulation studies and theoretical quantification of bias, we compared the performance of the PTDM approach with that of the time-dependent Cox model in the presence of immortal time. Both assessments revealed that the PTDM approach did not adequately address immortal time bias. Based on our simulation results, another recently proposed observational data analysis technique, the sequential Cox approach, was found to be more useful than the PTDM approach (Cox: bias = -0.002, mean squared error = 0.025; PTDM: bias = -1.411, mean squared error = 2.011). We applied these approaches to investigate the association of β-interferon treatment with delaying disability progression in a multiple sclerosis cohort in British Columbia, Canada (Long-Term Benefits and Adverse Effects of Beta-Interferon for Multiple Sclerosis (BeAMS) Study, 1995-2008). [ABSTRACT FROM AUTHOR]

Background: In addition to acute hospital mortality, sepsis is associated with higher risk of death following hospital discharge. We assessed the strength of epidemiological evidence supporting a causal link between sepsis and mortality after hospital discharge by systematically evaluating the available literature for strength of association, bias, and techniques to address confounding.Methods: We searched Medline and Embase using the following 'mp' terms, MESH headings and combinations thereof - sepsis, septic shock, septicemia, outcome. Studies published since 1992 where one-year post-acute mortality in adult survivors of acute sepsis could be calculated were included. Two authors independently selected studies and extracted data using predefined criteria and data extraction forms to assess risk of bias, confounding, and causality. The difference in proportion between cumulative one-year mortality and acute mortality was defined as post-acute mortality. Meta-analysis was done by sepsis definition categories with post-acute mortality as the primary outcome.Results: The literature search identified 11,156 records, of which 59 studies met our inclusion criteria and 43 studies reported post-acute mortality. In patients who survived an index sepsis admission, the post-acute mortality was 16.1% (95% CI 14.1, 18.1%) with significant heterogeneity (p < 0.001), on random effects meta-analysis. In studies reporting non-sepsis control arm comparisons, sepsis was not consistently associated with a higher hazard ratio for post-acute mortality. The additional hazard associated with sepsis was greatest when compared to the general population. Older age, male sex, and presence of comorbidities were commonly reported independent predictors of post-acute mortality in sepsis survivors, challenging the causality relationship. Sensitivity analyses for post-acute mortality were consistent with primary analysis.Conclusions: Epidemiologic criteria for a causal relationship between sepsis and post-acute mortality were not consistently observed. Additional epidemiologic studies with recent patient level data that address the pre-illness trajectory, confounding, and varying control groups are needed to estimate sepsis-attributable additional risk and modifiable risk factors to design interventional trials. [ABSTRACT FROM AUTHOR]

OBJECTIVE: To investigate whether a short interpregnancy interval is a risk factor for preterm birth in Emirati women, where there is a wide range of interpregnancy intervals and uniformity in potentially confounding factors. METHODS: A case-control design based on medical records was used. A case was defined as a healthy multiparous Emirati woman delivering a healthy singleton spontaneously before 37 weeks of gestation between 1997 and 2000, and a control was defined as the next eligible similar woman delivering after 37 weeks of gestation. Women were excluded if there was no information available about their most recent previous pregnancy or if it had resulted in a multiple or preterm birth. Data collected from charts and delivery room records were analysed using the STATA statistical package. All variables found to be valid, stable and significant by univariate analysis were included in multivariate logistic regression analysis. FINDINGS: There were 128 cases who met the eligibility criteria; 128 controls were selected. Short interpregnancy intervals were significantly associated with case status (P

OBJECTIVE: To collect accurate data on disease- and microbial-specific causes and avoidable factors in child deaths in a developing country. METHODS: A systematic prospective audit of deaths of children seen at Goroka Hospital in the highlands of Papua New Guinea was carried out. Over a 24-month period, we studied 353 consecutive deaths of children: 126 neonates, 186 children aged 1-59 months, and 41 children aged 5-12 years. FINDINGS: The most frequent age-specific clinical diagnoses were as follows: for neonates - very low birth weight, septicaemia, birth asphyxia and congenital syphilis; for children aged 1-59 months - pneumonia, septicaemia, marasmus and meningitis; and for children aged 5-12 years - malignancies and septicaemia. At least one microbial cause of death was identified for 179 (50.7%) children and two or more were identified for 37 (10.5%). Nine microbial pathogens accounted for 41% of all childhood deaths and 76% of all deaths that had any infective component. Potentially avoidable factors were identified for 177 (50%) of deaths. The most frequently occurring factors were as follows: no antenatal care in high-risk pregnancies (8.8% of all deaths), very delayed presentation (7.9%), vaccine-preventable diseases (7.9%), informal adoption or child abandonment leading to severe malnutrition (5.7%), and lack of screening for maternal syphilis (5.4%). Sepsis due to enteric Gram-negative bacilli occurred in 87 (24.6%). The strongest associations with death from Gram- negative sepsis were adoption/abandonment leading to severe malnutrition, village births, and prolonged hospital stay. CONCLUSIONS: Reductions in child mortality will depend on addressing the commonest causes of death, which include disease states, microbial pathogens, adverse social circumstances and health service failures. Systematic mortality audits in selected regions where child mortality is high may be useful for setting priorities, estimating the potential benefit of specific and non-specific interventions, and providing continuous feedback on the quality of care provided and the outcome of health reforms.

OBJECTIVE: To evaluate the impact of the National Control of Diarrhoeal Disease Programme (NCDDP) in the Philippines over the period 1980-93, describing levels and trends in programme activities, and relating them to severe diarrhoea morbidity and mortality among under-5-year-olds. METHODS: Routinely collected data on morbidity and mortality trends were obtained from health statistics reports of the Health Intelligence Service and the NCDDP. Socioeconomic indicators, including annual average family income and expenditures, gross national product, and unemployment rates, were derived from the Philippine population census data collected by the National Statistics Office. FINDINGS: In relation to baseline levels, diarrhoea mortality among infants and young children fell by about 5% annually over the 18-year period under review. The decline was faster than those related to acute respiratory infections (ARIs) among children of similar age and to perinatal causes. Diarrhoea hospital admission rates registered an annual decline of 2.4% relative to the baseline level. CONCLUSION:These findings suggest that the programme had a substantial impact; the period under review also witnessed some degree of improvement in other factors with positive influences on health, such as exclusive breastfeeding, nutrition and environmental sanitation. The quality, particularly completeness and reliability, of the existing data did not allow further analysis, thus, making it difficult to conclude beyond doubt that the observed trends indicate that they were solely due to NCDDP.

Background: Failure to account for the etiological diversity that typically occurs in psychiatric cohorts may increase the potential for confounding, as a proportion of genetic variance will be specific to exposures that have variable distribution in cases. This study investigated whether minimizing the potential for such confounding strengthened the evidence for a genetic candidate currently unsupported at the genome-wide level.Methods: 291 first-episode psychosis cases from South London UK, and 218 unaffected controls were evaluated for a functional polymorphism at the rs1360780 locus in FKBP5. The relationship between FKBP5 and psychosis was modelled using logistic regression. Cannabis use (Cannabis Experiences Questionnaire) and parental separation (Childhood Experience of Care and Abuse Questionnaire) were modelled as confounders in the analysis.Results: Association at rs1360780 was not detected until the effects of the two environmental factors had been adjusted for in the model (OR=2.81, 95% CI 1.23-6.43, p=0.02). A statistical interaction between rs1360780 and parental separation was confirmed by stratified tests (OR=2.8, p=0.02 vs. OR=0.89, p=0.80). The genetic main effect was directionally-consistent with findings in other (stress-related) clinical phenotypes. Moreover, the variation in effect magnitude was explained by the level of power associated with different cannabis constructs used in the model (r=0.95).Conclusions: Our results suggest that the extent to which genetic variants in FKBP5 can influence susceptibility to psychosis may depend on the other etiological factors involved. This finding requires further validation in other large independent cohorts. Potentially this work could have translational implications, as the ability to discriminate between genetic etiologies, based on a case-by-case understanding of exposure history would confer an important clinical advantage that would benefit the delivery of personalizable treatment strategies

Nonexperimental studies of preventive interventions are often biased because of the healthy-user effect and, in frail populations, because of confounding by functional status. Bias is evident when estimating influenza vaccine effectiveness, even after adjustment for claims-based indicators of illness. We explored bias reduction methods while estimating vaccine effectiveness in a cohort of adult hemodialysis patients. Using the United States Renal Data System and linked data from a commercial dialysis provider, we estimated vaccine effectiveness using a Cox proportional hazards marginal structural model of all-cause mortality before and during 3 influenza seasons in 2005/2006 through 2007/2008. To improve confounding control, we added frailty indicators to the model, measured time-varying confounders at different time intervals, and restricted the sample in multiple ways. Crude and baseline-adjusted marginal structural models remained strongly biased. Restricting to a healthier population removed some unmeasured confounding; however, this reduced the sample size, resulting in wide confidence intervals. We estimated an influenza vaccine effectiveness of 9% (hazard ratio = 0.91, 95% confidence interval: 0.72, 1.15) when bias was minimized through cohort restriction. In this study, the healthy-user bias could not be controlled through statistical adjustment; however, sample restriction reduced much of the bias. [ABSTRACT FROM AUTHOR]

Longitudinal observational data are required to assess the association between exposure to β-interferon medications and disease progression among relapsing-remitting multiple sclerosis (MS) patients in the “real-world” clinical practice setting. Marginal structural Cox models (MSCMs) can provide distinct advantages over traditional approaches by allowing adjustment for time-varying confounders such as MS relapses, as well as baseline characteristics, through the use of inverse probability weighting. We assessed the suitability of MSCMs to analyze data from a large cohort of 1,697 relapsing-remitting MS patients in British Columbia, Canada (1995–2008). In the context of this observational study, which spanned more than a decade and involved patients with a chronic yet fluctuating disease, the recently proposed “normalized stabilized” weights were found to be the most appropriate choice of weights. Using this model, no association between β-interferon exposure and the hazard of disability progression was found (hazard ratio = 1.36, 95% confidence interval: 0.95, 1.94). For sensitivity analyses, truncated normalized unstabilized weights were used in additional MSCMs and to construct inverse probability weight-adjusted survival curves; the findings did not change. Additionally, qualitatively similar conclusions from approximation approaches to the weighted Cox model (i.e., MSCM) extend confidence in the findings. [ABSTRACT FROM PUBLISHER]

Preinfluenza periods have been used to test for uncontrolled confounding in studies of influenza vaccine effectiveness, but some authors have claimed that confounding differs in preinfluenza and influenza periods. We tested this claim by comparing estimates of the vaccine-mortality association during the 2009/2010 influenza year, when there was essentially no circulation of seasonal influenza in the United States, and 2007/2008, a typical influenza year. We pooled data on seniors (adults aged ≥65 years) from 7 US managed care organizations that participated in the Vaccine Safety Datalink Project. We defined influenza vaccination, all-cause mortality, and potential confounders from administrative databases. We quantified the vaccine-mortality association using Cox regression. During 2007/2008, the adjusted hazard ratio was 0.44 prior to influenza season, 0.62 during influenza season, and 0.71 after influenza season. A similar pattern was observed during 2009/2010, when any effect of seasonal influenza vaccine observed during all time periods must have resulted from confounding: 0.65 during the autumn, 0.80 during the winter, and 0.84 during the summer. In a year with minimal seasonal influenza, we found no evidence that confounding in autumn preinfluenza periods is qualitatively different from confounding in winter. This supports the use of preinfluenza periods as control time periods in studies of influenza vaccine effectiveness. [ABSTRACT FROM PUBLISHER]

Objectives: Estimates of treatment effectiveness in epidemiologic studies using large observational health care databases may be biased owing to inaccurate or incomplete information on important confounders. Study methods that collect and incorporate more comprehensive confounder data on a validation cohort may reduce confounding bias. Study Design and Setting: We applied two such methods, namely imputation and reweighting, to Group Health administrative data (full sample) supplemented by more detailed confounder data from the Adult Changes in Thought study (validation sample). We used in- fluenza vaccination effectiveness (with an unexposed comparator group) as an example and evaluated each method's ability to reduce bias using the control time period before influenza circulation. Results: Both methods reduced, but did not completely eliminate, the bias compared with traditional effectiveness estimates that do not use the validation sample confounders. Conclusion: Although these results support the use of validation sampling methods to improve the accuracy of comparative effectiveness findings from health care database studies, they also illustrate that the success of such methods depends on many factors, including the ability to measure important confounders in a representative and large enough validation sample, the comparability of the full sample and validation sample, and the accuracy with which the data can be imputed or reweighted using the additional validation sample information. [ABSTRACT FROM AUTHOR]

Background: Hepatitis B virus (HBV) is one of the most common chronic viral infections in the world. Iran has a low to intermediate HBV prevalence and approximately 1.5 million people are living with HBV infection. The impact of HBV in Iran is unknown and given the very low levels of alcohol consumption, this region provides an opportunity to examine the impact of isolated chronic HBV infection. Objectives: To examine and evaluate outcome and prognosis of HBV in Iran. Patients and Methods: A longitudinal cohort study dating from 2003-2010 was performed. The patients were assessed six months after their first visit and then during periodic visits for the subsequent seven years. The patients' medical history, route of diagnosis of infection, family history, and liver diseases status including: carrier state of HBV, chronic HBV, cirrhosis, and HCC were recorded. Descriptive and analytic statistics were performed, using SPSS software version 18. Results: 275 HBsAg positive patients, who had completed a 7 year follow up period, were selected. The annual incidence rate for chronic hepatitis B in inactive carrier states and cirrhosis were 0.46% and 0.2% respectively. Over seven years, the rate of inactive carriers decreased by eight percent (They turned into chronic HBV or became HBSAg negative). No significant association was found between HBSAg seroclearance, HBeAg seroconversion and the outcome in the end of each year of follow up. Different treatment regimens did not have any statistically significant difference regarding HBeAg seroconversion. There was no significant association between the outcome and different habitual characteristics, especially smoking, as well as family history on HBsAg, HBsAb, HBeAg, and Anti-HBeAg. Values of platelets and ALT showed a significant change during the follow ups. Annual incidence rate of HCC in the present study was in the range of other studies. Conclusions: These data confirm and extend data from other populations showing a low incidence of significant change in chronic HBV infection in short term with good responses to currently available therapeutics. [ABSTRACT FROM AUTHOR]

ABSTRACT Purpose To systematically examine trends and applications of the disease risk score (DRS) as a confounder summary method. Methods We completed a systematic search of MEDLINE and Web of Science® to identify all English language articles that applied DRS methods. We tabulated the number of publications by year and type (empirical application, methodological contribution, or review paper) and summarized methods used in empirical applications overall and by publication year (<2000, ≥2000). Results Of 714 unique articles identified, 97 examined DRS methods and 86 were empirical applications. We observed a bimodal distribution in the number of publications over time, with a peak 1979-1980, and resurgence since 2000. The majority of applications with methodological detail derived DRS using logistic regression (47%), used DRS as a categorical variable in regression (93%), and applied DRS in a non-experimental cohort (47%) or case-control (42%) study. Few studies examined effect modification by outcome risk (23%). Conclusion Use of DRS methods has increased yet remains low. Comparative effectiveness research may benefit from more DRS applications, particularly to examine effect modification by outcome risk. Standardized terminology may facilitate identification, application, and comprehension of DRS methods. More research is needed to support the application of DRS methods, particularly in case-control studies. Copyright © 2012 John Wiley & Sons, Ltd. [ABSTRACT FROM AUTHOR]