Exposure Covariates Research Articles

The Relationship Of Tobacco Smoking To Depressive Symptoms Among Male Expatriate Workers In Saudi Arabia: A Cross-Sectional Study

Background & Aims: Mental health issues have been associated with tobacco smoking. Several studies conducted in large samples of general population found a significant relationship between tobacco smoking and depressive symptoms. However, previous research conducted in Saudi Arabia reported a high prevalence of depression and smoking among a small-scale sample of male expatriate workers but found no correlation. This study aims at investigating the association between self-reported tobacco smoking (cigarette and water pipe smoking) and depressive symptoms in a large representative sample of expatriate workers. Methods: A cross-sectional survey involving face-to-face interviews of 4575 male expatriate workers in 270 businesses/companies in Riyadh, Saudi Arabia was conducted using a multistage stratified cluster sampling technique to randomly select businesses/companies and participants. Trained research assistants used the Composite International Diagnostic Interview instrument applicable to clinical depression to assess DSM-IV for major depressive disorder and episodes among participants. Smoking was assessed using the WHO standard questions for assessing tobacco exposure and other covariates with a standardized questionnaire. The data were analyzed using logistic regression. Results: Current smoking is associated with significantly increased likelihood of depressive symptoms (Crude Odds Ratio=1.790 - 1.284) 1.516)). The association remained statistically significant after adjusting for age, self-reported health and income (Adjusted Odds Ratio=- 1.341) 1.590 1.886)). Conclusions: This study provides new insights into the relationship between tobacco smoking and depressive symptoms. In expatriates, current smoking is associated with significantly increased risk of depressive symptoms. Future research is needed to clarify directionality and mechanism explaining the relationship. Health professionals and policymakers should be aware of the increased risk of depressive symptoms in expatriate workers who are tobacco smokers.

Longitudinal associations of gross motor development, motor milestone achievement and weight-for-length z score in a racially diverse cohort of US infants

ObjectivesTo investigate longitudinal associations between gross motor development, motor milestone achievement and weight-for-length z scores in a sample of infants. In a secondary aim, we explored potential bidirectional relationships, as...

Bi-level variable selection for case-cohort studies with group variables.

The case-cohort design is an economical approach to estimate the effect of risk factors on the survival outcome when collecting exposure information or covariates on all patients is expensive in a large cohort study. Variables often have group structure such as categorical variables and highly correlated continuous variables. The existing literature for case-cohort data is limited to identifying non-zero variables at individual level only. In this article, we propose a bi-level variable selection method to select non-zero group and within-group variables for case-cohort data when variables have group structure. The proposed method allows the number of variables to diverge as the sample size increases. The asymptotic properties of the estimator including bi-level variable selection consistency and the asymptotic normality are shown. We also conduct simulations to compare our proposed method with some existing method and apply them to the Busselton Health data.

Go Home, Sit Less: The Impact of Home Versus Hospital Rehabilitation Environment on Activity Levels of Stroke Survivors

ObjectiveTo examine whether change in rehabilitation environment (hospital or home) and other factors influence time spent sitting upright and walking after stroke. DesignObservational study. SettingTwo inpatient rehabilitation units and community residences following discharge. ParticipantsParticipants (N=34) with stroke were recruited. Main Outcome MeasureAn activity monitor was worn continuously for 7 days during the final week in the hospital and the first week at home. Other covariates included mood, fatigue, physical function, pain, and cognition. Linear mixed models were performed to examine the associations between the environment (exposure) and physical activity levels (outcome) in the hospital and at home. Interaction terms between the exposure and other covariates were added to the model to determine whether they modified activity with change in environment. ResultsThe mean age of participants was 68±13 years and 53% were male. At home, participants spent 45 fewer minutes sitting (95% CI -84.8, -6.1; P=.02), 45 more minutes upright (95% CI 6.1, 84.8; P=.02), and 12 more minutes walking (95% CI 5, 19; P=.001), and completed 724 additional steps (95% CI 199, 1250; P=.01) each day compared to in the hospital. Depression at discharge predicted greater sitting time and less upright time (P=.03 respectively) at home. ConclusionsEnvironmental change from hospital to home was associated with reduced sitting time and increased the time spent physically active, though depression modified this change. The rehabilitation environment may be a target to reduce sitting and promote physical activity.

The Right Tool for the Job: Choosing Between Covariate-balancing and Generalized Boosted Model Propensity Scores.

Estimating the causal effect of an exposure (vs. some control) on an outcome using observational data often requires addressing the fact that exposed and control groups differ on pre-exposure characteristics that may be related to the outcome (confounders). Propensity score methods have long been used as a tool for adjusting for observed confounders in order to produce more valid causal effect estimates under the strong ignorability assumption. In this article, we compare two promising propensity score estimation methods (for time-invariant binary exposures) when assessing the average treatment effect on the treated: the generalized boosted models and covariate-balancing propensity scores, with the main objective to provide analysts with some rules-of-thumb when choosing between these two methods. We compare the methods across different dimensions including the presence of extraneous variables, the complexity of the relationship between exposure or outcome and covariates, and the residual variance in outcome and exposure. We found that when noncomplex relationships exist between outcome or exposure and covariates, the covariate-balancing method outperformed the boosted method, but under complex relationships, the boosted method performed better. We lay out criteria for when one method should be expected to outperform the other with no blanket statement on whether one method is always better than the other.

Maternal depressive symptoms linked to reduced fecal Immunoglobulin A concentrations in infants

Secretory Immunoglobulin A (sIgA) plays a critical role to infant gut mucosal immunity. Delayed IgA production is associated with greater risk of allergic disease. Murine models of stressful events during pregnancy and infancy show alterations in gut immunity and microbial composition in offspring, but little is known about the stress-microbiome-immunity pathways in humans. We investigated differences in infant fecal sIgA concentrations according to the presence of maternal depressive symptoms during and after pregnancy. A subsample of 403 term infants from the Canadian Healthy Infant Longitudinal Development (CHILD) cohort were studied. Their mothers completed the Center of Epidemiologic Studies Depression Scale when enrolled prenatally and again postpartum. Quantified by Immundiagnostik sIgA ELISA kit, sIgA from infant stool was compared across maternal depressive symptom categories using Mann-Whitney U-tests and logistic regression models that controlled for various covariates. Twelve percent of women reported clinically significant depressive symptoms only prenatally, 8.7% had only postpartum symptoms and 9.2% had symptoms both pre and postnatally. Infants born to mothers with pre and postnatal symptoms had significantly lower median sIgA concentrations than those in the reference group (4.4 mg/g feces vs. 6.3 mg/g feces; p = 0.033). The odds for sIgA concentrations in the lowest quartile was threefold higher (95% CI: 1.25–7.55) when mothers had pre and postnatal symptoms, after controlling for breastfeeding status, infant age, antibiotics exposure and other covariates. Postnatal symptoms were not associated with fecal sIgA, independently of breastfeeding status. Infants born to mothers with depressive symptoms appear to have lower fecal sIgA concentrations, predisposing them to higher risk for allergic disease.

Using Structural Equation Modeling to Assess the Links between Tobacco Smoke Exposure, Volatile Organic Compounds, and Respiratory Function for Adolescents Aged 6 to 18 in the United States.

Asthma is an inflammatory airway disease that affects 22 million Americans in the United States. Research has found associations between impaired respiratory function, including asthma and increased symptoms among asthmatics, and common indoor air pollutants, including tobacco smoke exposure and volatile organic compounds (VOCs). However, findings linking VOC exposure and asthma are inconsistent and studies are of mixed quality due to design limitations, challenges measuring VOC exposure, small sample sizes, and suboptimal statistical methodologies. Because of the correlation between tobacco smoke exposure and VOCs, and associations between both tobacco smoke and VOCs with respiratory function, it is crucial that statistical methodology employed to assess links between respiratory function and individual air pollutants control for these complex relationships. This research uses Structural Equation Modeling (SEM) to assess the relationships between respiratory function, tobacco smoke exposure, and VOC exposure among a nationally-representative sample of adolescents. SEM allows for multiple outcome variables, the inclusion of both observed and latent variables, and controls the effects of confounding and correlated variables, which is critically important and is lacking in earlier studies when estimating the effects of correlated air pollutants on respiratory function. We find evidence of associations between respiratory function and some types of VOCs, even when controlling for the effects of tobacco smoke exposure and additional covariates. Furthermore, we find that poverty has an indirect effect on respiratory function through its relationships with tobacco smoke exposure and some types of VOCs. This analysis demonstrates how SEM is a robust analytic tool for assessing associations between respiratory function and multiple exposures to pollutants.

Epidemiological analysis of ozone and nitrogen impacts on vegetation – Critical evaluation and recommendations

For human health studies, epidemiology has been established as important tool to examine factors that affect the frequency and distribution of disease, injury, and other health-related events in a defined population, serving the purpose of establishing prevention and control programs. On the other hand, gradient studies have a long tradition in the research of air pollution effects on plants. While there is no principal difference between gradient and epidemiological studies, the former address more one-dimensional transects while the latter focus more on populations and include more experience in making quantitative predictions, in dealing with confounding factors and in taking into account the complex interplay of different factors acting at different levels. Epidemiological analyses may disentangle and quantify the contributions of different predictor variables to an overall effect, e.g. plant growth, and may generate hypotheses deserving further study in experiments. Therefore, their use in ecosystem research is encouraged. This article provides a number of recommendations on: (1) spatial and temporal aspects in preparing predictor maps of nitrogen deposition, ozone exposure and meteorological covariates; (2) extent of a dataset required for an analysis; (3) choice of the appropriate regression model and conditions to be satisfied by the data; (4) selection of the relevant explanatory variables; (5) treatment of interactions and confounding factors; and (6) assessment of model validity.

The utility of historical electoral roll records and their effect on the association of regularity of GP contact and potentially preventable diabetic hospitalisations

ABSTRACTObjectivesCorrectly ascertaining person-time at risk is paramount to longitudinal studies of health services research and relies on the ability to track the status of individuals throughout the study. Administrative health data contain limited information on where study subjects live in the time between episodes of health service use. Accurate ascertainment of person-time at risk is important particularly when it varies differentially across exposure groups or covariates. Historical electoral roll data allows better specification of person-time and also provides longitudinal information on geographic location facilitating the inclusion of accessibility and socio-economic status longitudinally. This study evaluated the utility of Australian historical electoral roll data to capture place of residence throughout the time line of a whole of population longitudinal cohort study over 20 years to better ascertain person-time at risk and changes in socio-economic status (SES).ApproachThe association between regularity of GP contact and potentially preventable hospitalisations (PPHs) in WA was modelled using person-level linked data where the time at risk and socio-economic status for both the exposure (regularity of GP contact) and outcome (PPHs) was assumed to be constant throughout the follow up until death. The analysis was then repeated incorporating historical (longitudinal) electoral roll data. These data partitioned follow up time and socio-economic status according to location of residence within the State to better characterise access and SES and included removal (out-of-State/country migration) and re-enrolment records.ResultsSubstantial differences were found in the number of people at risk (46,625 (13%) of people were never at risk) and person-time at risk (reduction of 473,708 (22%) person-years at risk) when cross-sectional electoral roll and health administrative data were augmented using historical electoral roll data. Substantial changes in residential postcode (up to 25 changes) were observed and these impacted on the accessibility and SES classification across the duration of the study. These changes significantly impacted the magnitude of the relationship between GP contacts on PPHs determined by models.ConclusionsCurrently cross-sectional electoral roll data are available to researchers in WA solely for the purposes of identifying and characterising a cohort at baseline. However these data are longitudinal and contain important information that improve analyses. Information on their utility is important so as to leverage their availability from the Australian Electoral Commission.

Fruit Juice and Change in BMI: A Meta-analysis.

Whether 100% fruit juice consumption causes weight gain in children remains controversial. To determine the association between 100% fruit juice consumption and change in BMI or BMI z score in children. PubMed, Embase, CINAHL, and Cochrane databases. Longitudinal studies examining the association of 100% fruit juice and change in BMI measures were included. Two independent reviewers extracted data using a predesigned data collection form. Of the 4657 articles screened, 8 prospective cohort studies (n = 34 470 individual children) met the inclusion criteria. Controlling for total energy intake, 1 daily 6- to 8-oz serving increment of 100% fruit juice was associated with a 0.003 (95% CI: 0.001 to 0.004) unit increase in BMI z score over 1 year in children of all ages (0% increase in BMI percentile). In children ages 1 to 6 years, 1 serving increment was associated with a 0.087 (95% confidence interval: 0.008 to 0.167) unit increase in BMI z score (4% increase in BMI percentile). 100% fruit juice consumption was not associated with BMI z score increase in children ages 7 to 18 years. All observational studies; studies differed in exposure assessment and covariate adjustment. Consumption of 100% fruit juice is associated with a small amount of weight gain in children ages 1 to 6 years that is not clinically significant, and is not associated with weight gain in children ages 7 to 18 years. More studies are needed in children ages 1 to 6 years.

Marginal structural models in clinical research: when and how to use them?

Marginal structural models are a multi-step estimation procedure designed to control for the effect of confounding variables that change over time, and are affected by previous treatment. When a time-varying confounder is affected by prior treatment standard methods for confounding control are inappropriate, because over time the covariate plays both the role of confounder and mediator of the effect of treatment on outcome. Marginal structural models first calculate a weight to assign to each observation. These weights reflect the extent to which observations with certain characteristics (covariate values) are under-represented or over-represented in the sample with the respect to a target population in which these characteristics are balanced across treatment groups. Then, marginal structural models estimate the outcome of interest taking into account these weights. Marginal structural models are a powerful method for confounding control in longitudinal study designs that collect time-varying information on exposure, outcome and other covariates.

Additive rate model with auxiliary covariates

In this paper, we consider an inference method for recurrent event data in which the primary exposure covariate is assessed only in a validation set, while as an auxiliary covariate for the main exposure is available for the full cohort. Additive rate model is considered. The existing estimating equations in the absence of primary exposure are corrected by taking use of the validation data and auxiliary information, which yield consistent and asymptotically normal estimators of the regression parameters. The estimated baseline mean process is shown to converge weakly to a zero-mean Gaussian process. Extensive simulations are conducted to evaluate finite sample performance.

Abstract 12519: Identification of Novel Atrial Fibrillation Risk Factors in The Women’s Health Initiative Database Utilizing the Random Generalized Linear Model

Introduction: Atrial fibrillation (AF) is the most common cardiac arrhythmia and confers significant risk of morbidity and mortality. The etiology of AF remains incompletely understood. The Women’s Health Initiative (WHI) provides an opportunity to evaluate an extensive and diverse set of covariates in a large population to identify novel risk factors. Hypothesis: We hypothesized that we would validate existing predictors of AF and discover novel markers associated with AF using a random generalized linear model (RGLM). Methods: A training subset of women enrolled into the WHI without pre-existing AF were included. Women were followed for AF using self-report and medical records coding. 104 demographic, medical history and environmental exposure covariates were selected for analysis in a RGLM. RGLM is a bootstrapped aggregated generalized linear model (GLM) that combines the predictive accuracy of random forests, a machine-learning algorithm, with the interpretability of forward selected GLM’s. Variables that are most commonly found in the best-performing regression models with randomly generated covariates are determined the most influential. Results: Of the 60,000 women included in this training set, 5,047 developed AF over an average of 16.3 years. The women were on average 61.9 years old at enrollment, 1.7% were African American, 0.9% were Hispanic, 35.2% had hypertension and 4.2% had diabetes. The RGLM generated 100 sub-cohorts with 75 covariates through random sampling. The top 50 covariates, based on correlation with incident AF, were evaluated by forward selection to generate GLM’s. The variables identified by the RGLM as being the most influential predictors were history of leukemia, prior pregnancy, systolic blood pressure, waist circumference, history of DVT, depression, and vegetable consumption. Conclusions: Using RGLM, we validated several established risk factors for AF. We also, for the first time in a large population cohort, identified history of leukemia as an influential risk marker of AF. Additional study will be needed to determine if leukemia-specific chemotherapy mediates this association. Finally, these novel markers will need to be validated in additional cohorts.

The effect of weight, body mass index, age, sex , and race on plasma concentrations of subcutaneous sumatriptan: a pooled analysis

Objective/backgroundFactors such as body size (weight and body mass index [BMI]), age, sex, and race might influence the clinical response to sumatriptan. We evaluated the impact of these covariates on the plasma concentration (Cp) profile of sumatriptan administered subcutaneously.MethodsWe conducted three pharmacokinetic studies of subcutaneous sumatriptan in 98 healthy adults. Sumatriptan was administered subcutaneously (236 administrations) as either DFN-11 3 mg, a novel 0.5 mL autoinjector being developed by Dr. Reddy’s Laboratories; Imitrex® (Sumatriptan) injection 3 mg or 6 mg (6 mg/0.5 mL); or Imitrex STATdose 4 mg or 6 mg (0.5 mL). Blood was sampled for 12 hours to determine sumatriptan Cp. Maximum Cp (Cmax), area under the curve during the first 2 hours (AUC0–2), and total area under the curve (AUC0–∞) were determined using noncompartmental methods. Post hoc analyses were conducted to determine the relationship between these exposure metrics and each of body weight, BMI, age, sex, and race (categorized as white, black, or others).ResultsBoth weight and BMI correlated negatively with each exposure metric for each treatment group. Across all treatment groups, AUC0–2 for subjects with BMI less than or equal to median value was 1.03–1.12 times the value for subjects with BMI more than median value. For subjects with BMI less than or equal to median value receiving DFN-11, median AUC0–2 was slightly less than that for subjects with BMI more than median value receiving Imitrex 4 mg and larger than that for subjects with BMI more than median value receiving Imitrex 3 mg. Results were similar for the other exposure metrics and for weight. Exposure was higher in women than in men, which can be attributed in part to differences in weight. There was no relationship between exposure and age. For DFN-11, AUC0–2 and AUC0–∞ were lower in nonwhites compared with whites; the ratio of median values was 0.84 and 0.89, respectively. A similar, nonstatistically significant, trend was observed in the other products (ratio of median values ranging from 0.84 to 0.89).ConclusionWeight and BMI appear to be important covariates for sumatriptan exposure: subjects with lower values for either metric of body size have higher systemic exposure compared with subjects with higher values. Additional studies are required to determine if doses of subcutaneous sumatriptan may be adjusted based on BMI for comparable efficacy and a potentially improved tolerability profile.

Estimating relative risk of a log-transformed exposure measured in pools.

Pooling biospecimens prior to performing laboratory assays is a useful tool to reduce costs, achieve minimum volume requirements and mitigate assay measurement error. When estimating the risk of a continuous, pooled exposure on a binary outcome, specialized statistical techniques are required. Current methods include a regression calibration approach, where the expectation of the individual-level exposure is calculated by adjusting the observed pooled measurement with additional covariate data. While this method employs a linear regression calibration model, we propose an alternative model that can accommodate log-linear relationships between the exposure and predictive covariates. The proposed model permits direct estimation of the relative risk associated with a log-transformation of an exposure measured in pools. Published 2016. This article is a U.S. Government work and is in the public domain in the USA.

Assessing natural direct and indirect effects for a continuous exposure and a dichotomous outcome.

Recent advances in the literature on mediation have extended from traditional linear structural equation modeling approach to causal mediation analysis using potential outcomes framework. Pearl proposed a mediation formula to calculate expected potential outcomes used in the natural direct and indirect effects definition under the key sequential ignorability assumptions. Current methods mainly focused on binary exposure variables, and in this article, this approach is further extended to settings in which continuous exposures may be of interest. Focusing on a dichotomous outcome, we give precise definitions of the natural direct and indirect effects on both the risk difference and odds ratio scales utilizing the empirical joint distribution of the exposure and baseline covariates from the whole sample analysis population. A mediation-formula based approach is proposed to estimate the corresponding causal quantities. Simulation study is conducted to assess the statistical properties of the proposed method and we illustrate our approach by applying it to the Jackson Heart Study to estimate the mediation effects of diabetes on the relation between obesity and chronic kidney disease. Sensitivity analysis is performed to assess the impact of violation of no unmeasured mediator-outcome confounder assumption.

Constructing Causal Diagrams for Common Perinatal Outcomes: Benefits, Limitations and Motivating Examples with Maternal Antidepressant Use in Pregnancy.

Covariate selection to reduce bias in observational data analysis has primarily relied upon statistical criteria to guide researchers. This approach may lead researchers to condition on variables that ultimately increase bias in the effect estimates. The use of directed acyclic graphs (DAGs) aids researchers in constructing thoughtful models based on hypothesised biologic mechanisms to produce the least biased effect estimates possible. After providing an overview of different relations in DAGs and the prevailing mechanisms by which conditioning on variables increases or reduces bias in a model, we illustrate examples of DAGs for maternal antidepressants in pregnancy and four separate perinatal outcomes. By comparing and contrasting the diagrams for maternal antidepressant use in pregnancy and spontaneous abortion, major malformations, preterm birth, and postnatal growth, we illustrate the different conditioning sets required for each model. Moreover, we illustrate why it is not appropriate to condition on the same set of covariates for the same exposure and different perinatal outcomes. We further discuss potential selection biases, overadjustment of mediators on the causal path, and sufficient sets of conditioning variables. In our efforts to construct parsimonious models that minimise confounding and selection biases, we must rely upon our scientific knowledge of the causal mechanism. By structuring data collection and analysis around hypothesised DAGs, we ultimately aim to validly estimate the causal effect of interest.

The Impact of Hazardous Alcohol Use on Behavioral Healthcare Utilization Among National Guard Service Members

ABSTRACTBackground: Prior research suggests that both posttraumatic stress disorder (PTSD) and alcohol abuse affect behavioral healthcare utilization among combat-exposed military populations. However, their interactive effect is not well documented, especially after experiencing psychological trauma. Objective: This study examined the role of hazardous alcohol use (i.e. repeated patterns of drinking which lead to harmful consequences) on behavioral healthcare utilization among service members stratified by past-year combat exposure. Method: This study utilized a sample of National Guard service members who participated in an in-depth survey 2–4 months after returning from Operation Enduring Freedom, Operation Iraqi Freedom, or Operation New Dawn deployments (2011–2013) (n = 467). We examine the marginal effect (the change in the probability) of hazardous alcohol use on utilization while controlling for trauma exposure, PTSD and other potential covariates. Results: In the unadjusted logistic model, hazardous alcohol use reduced the probability of behavioral healthcare utilization by 77% among service members who had been exposed to combat within the past year. In the adjusted model, which controlled for socio-demographics (age, gender, and race), health status (PTSD symptoms, depression and physical health), and measures of stigma (perception of services as embarrassing or harmful to one's career or social networks), hazardous alcohol use further reduced the utilization probability by 302%. Conclusion/Importance: Although these findings require replication, they appear to demonstrate that when combat-exposed service members engaged in hazardous alcohol use at postdeployment, they were much less likely to utilize behavioral healthcare to manage their posttraumatic stress symptoms during this period.

Association Between Schistosoma haematobium Exposure and Human Immunodeficiency Virus Infection Among Females in Mozambique.

Recent evidence suggests an association between human immunodeficiency virus (HIV) and female genital schistosomiasis (FGS) in sub-Saharan Africa, especially in Mozambique, South Africa, Tanzania, and Zimbabwe. Women with FGS have increased numbers of HIV target cells and cell receptors in genital and blood compartments, potentially increasing the risk of HIV transmission per sexual exposure, and the association may explain the high female:male ratio of HIV prevalence unique to sub-Saharan Africa. We investigate this association in Mozambique by linking two georeferenced, high-quality secondary data sources on HIV prevalence and Schistosoma haematobium: the AIDS Indicator Survey, and the Global Neglected Tropical Diseases (GNTD) open-source database, respectively. We construct a schistosomiasis exposure covariate indicating women reporting "unimproved" daily drinking water sources and living no more than 2-5 km from high-endemic global positioning system (GPS) coordinates in the GNTD. In logistic regression analyses predicting HIV-positive status, we show that exposure increases the odds of HIV-positive status by three times, controlling for demographic and sexual risk factors.

Atrial Fibrillation is Associated With Morphine Treatment in Female Breast Cancer Patients: A Retrospective Population-Based Time-Dependent Cohort Study.

We investigated the relationship between morphine treatment and the risk of atrial fibrillation (AF) in female patients with breast cancer.We identified a malignancy cohort of 73,917 female breast cancer patients without an AF history before the date of breast cancer diagnosis between 2000 and 2010 by using the Longitudinal Health Insurance Database for Catastrophic Illness Patients in Taiwan. This malignancy cohort was divided into morphine and comparison cohorts comprising 18,671 and 55,246 patients, respectively, and the incidences of newly diagnosed AF were calculated. We used the Cox proportional hazard model with time-dependent exposure covariates to estimate the risk of AF. The effect of morphine was assessed through multivariable Cox proportional hazard regression controlling for age, the Charlson comorbidity index (CCI) score, and the use of bisphosphonates and paclitaxel.Compared with nonmorphine users, patients who received morphine exhibited a 4.37-fold (95% CI = 3.56–5.36) increase in the risk of developing AF. The risk of AF increased as the CCI score increased, but decreased in patients with tamoxifen treatment. This risk is especially significant in current morphine users of all ages and with low CCI score. AF risk increased as the duration of morphine use lengthened (P for trend <0.0001).The incidence of AF in female breast cancer patients in Taiwan is associated with morphine, but prevented by tamoxifen treatment.