Initiation of antidepressant medication and risk of incident stroke: using the Adult Changes in Thought cohort to address time-varying confounding.
Ontology highlight
ABSTRACT: Purpose
Depression strongly predicts stroke incidence, suggesting that treating depression may reduce stroke risk. Antidepressant medications, however, may increase stroke risk via direct pathways. Previous evidence on antidepressant medication and stroke incidence is mixed. We evaluated associations between antidepressant use and incident stroke.Methods
For 2302 Adult Changes in Thought cohort participants with no stroke at study entry, we characterized antidepressant use from pharmacy records, biennial depressive symptoms with a 10-item Centers for Epidemiologic Study-Depression scale, and incident strokes from ICD codes. We used discrete-time survival models with inverse probability weighting to compare stroke risk associated with filling antidepressant prescriptions and by medication category: tricyclic antidepressants (TCAs), selective serotonin reuptake inhibitors, or other.Results
Over an average 8.4-year follow-up, 441 incident strokes occurred. Filling antidepressant medications 3+ times versus 0-2 times predicted 35% increased odds of stroke (OR = 1.35; 95% CI: 0.98, 1.66). Use of TCAs was associated with stroke onset (OR per 10 fills = 1.28; CI: 1.04, 1.57), but use of selective serotonin reuptake inhibitors (OR = 0.98; CI: 0.80, 1.20) or other antidepressants (OR = 0.99; CI: 0.67, 1.45) was not.Conclusions
Although patients who received antidepressant medication were at higher risk of stroke, this association appeared specific to TCA prescriptions.
SUBMITTER: Glymour MM
PROVIDER: S-EPMC8074509 | biostudies-literature |
REPOSITORIES: biostudies-literature
Similar Datasets
Project description:We validated an algorithm designed to identify new or prevalent users of antidepressant medications via population-based drug prescription records.We obtained population-based drug prescription records for the entire Olmsted County, Minnesota, population from 2011 to 2012 (N=149,629) using the existing electronic medical records linkage infrastructure of the Rochester Epidemiology Project (REP). We selected electronically a random sample of 200 new antidepressant users stratified by age and sex. The algorithm required the exclusion of antidepressant use in the 6 months preceding the date of the first qualifying antidepressant prescription (index date). Medical records were manually reviewed and adjudicated to calculate the positive predictive value (PPV). We also manually reviewed the records of a random sample of 200 antihistamine users who did not meet the case definition of new antidepressant user to estimate the negative predictive value (NPV).161 of the 198 subjects electronically identified as new antidepressant users were confirmed by manual record review (PPV 81.3%). Restricting the definition of new users to subjects who were prescribed typical starting doses of each agent for treating major depression in non-geriatric adults resulted in an increase in the PPV (90.9%). Extending the time windows with no antidepressant use preceding the index date resulted in only modest increases in PPV. The manual abstraction of medical records of 200 antihistamine users yielded an NPV of 98.5%.Our study confirms that REP prescription records can be used to identify prevalent and incident users of antidepressants in the Olmsted County, Minnesota, population.
| S-EPMC4147111 | biostudies-literature
Project description:ObjectivesTo evaluate associations between high-density lipoprotein cholesterol (HDL) and non-HDL-C levels at specific ages and subsequent Alzheimer's disease (AD) risk.DesignProspective population-based cohort study.SettingAdult Changes in Thought (ACT) Study.ParticipantsIndividuals aged 65 and older with no dementia at ACT Study entry. We identified separate, partially overlapping subcohorts of ACT participants who were eligible for each age band-specific analysis (50-59, n = 1,088; 60-69, n = 2,852; 70-79, n = 2,344; 80-89, n = 537).MeasurementsExposure consisted of clinical measures of total cholesterol (TC) and HDL-C from laboratory data during a given age band. Outcomes of incident AD were assessed post-age band using standard research diagnostic criteria. Statistical analyses used adjusted Cox proportional hazards regression models for each exposure and outcome pair within an age band. Cholesterol exposures were modeled using cubic splines.ResultsFor non-HDL-C, we found a statistically significant association with AD risk in the 60 to 69 (omnibus p = .005) and 70 to 79 (omnibus p = .04) age bands, suggesting a potential U-shaped relationship (greater risk at low and high levels). For example, in people aged 60 to 69, those with an average non-HDL-C level of 120 mg/DL had a 29% greater AD hazard (hazard ratio (HR)=1.29, 95% confidence interval (CI)=1.04-1.61) than those with an average non-HDL-C level of 160 mg/dL, whereas those with an average non-HDL-C level of 210 mg/dL had a 16% greater hazard (HR=1.16, 95% CI=1.01-1.33). We did not find a statistically significant association between HDL-C and AD risk.ConclusionPeople with low (120 mg/dL) and high (210 mg/dL) non-HDL-C levels during their 60s and 70s had modestly higher risk of AD than those with intermediate (160 mg/dL) levels. The extreme age bands (50s and 80s) had small sample sizes. J Am Geriatr Soc 66:2344-2352, 2018.
| S-EPMC6289681 | biostudies-literature
Project description:Scientists aspire to measure cause and effect. Unfortunately confounding variables, ones that are associated with both the probable cause and the outcome, can lead to an association that is true but potentially misleading. For example, altered body weight is often observed in a gene knockout; however, many other variables, such as lean mass, will also change as the body weight changes. This leaves the researcher asking whether the change in that variable is expected for that change in weight. Ratio correction, which is often referred to as normalization, is a method used commonly to remove the effect of a confounding variable. Although ratio correction is used widely in biological research, it is not the method recommended in the statistical literature to address confounding factors; instead regression methods such as the analysis of covariance (ANCOVA) are proposed. This method examines the difference in means after adjusting for the confounding relationship. Using real data, this manuscript demonstrates how the ratio correction approach is flawed and can result in erroneous calls of significance leading to inappropriate biological conclusions. This arises as some of the underlying assumptions are not met. The manuscript goes on to demonstrate that researchers should use ANCOVA, and discusses how graphical tools can be used readily to judge the robustness of this method. This study is therefore a clear example of why assumption testing is an important component of a study and thus why it is included in the Animal Research: Reporting of In Vivo Experiment (ARRIVE) guidelines.
| S-EPMC4152922 | biostudies-other
Project description:BackgroundYouth e-cigarette use is associated with the initiation of combustible cigarette smoking, but prior studies have rarely accounted for time-varying measures of e-cigarette exposure or time-dependent confounding of e-cigarette use and smoking initiation.MethodsUsing five waves of the Population Assessment of Tobacco and Health (2013-2019), we estimated marginal structural models with inverse probability of treatment and censoring weights to examine the association between time-varying e-cigarette initiation and subsequent cigarette smoking initiation among e-cigarette- and cigarette-naïve youth (12-17 years) at baseline. Time-dependent confounders used as predictors in inverse probability weights included tobacco-related attitudes or beliefs, mental health symptoms, substance use, and tobacco-marketing exposure.ResultsAmong 9,584 youth at baseline, those who initiated e-cigarettes were 2.4 times as likely to subsequently initiate cigarette smoking as youth who did not initiate e-cigarettes (risk ratio = 2.4, 95% confidence interval [CI] = 2.1, 2.7), after accounting for time-dependent confounding and selection bias. Among youth who initiated e-cigarettes, more frequent vaping was associated with greater risk of smoking initiation (risk ratio ≥3 days/month = 1.8, 95% CI = 1.4, 2.2; 1-2 days/month = 1.2; 95% CI = 0.93, 1.6 vs. 0 days/month). Weighted marginal structural model estimates were moderately attenuated compared with unweighted estimates adjusted for baseline-only confounders. At the US population level, we estimated over half a million youth initiated cigarette smoking because of prior e-cigarette use over follow-up.ConclusionsThe association between youth vaping and combustible cigarette smoking persisted after accounting for time-dependent confounding. We estimate that e-cigarette use accounts for a considerable share of cigarette initiation among US youth. See video abstract at, http://links.lww.com/EDE/B937.
| S-EPMC9156560 | biostudies-literature
Project description:PurposeEmerging evidence suggests metformin compared with sulfonylurea is associated with an 8% to 10% lower risk for dementia. Guidelines recommend metformin as initial diabetes treatment, but there is still the question of treatment timing. Thus, the risk of dementia associated with initiating metformin compared with not initiating or delaying treatment was examined.MethodsA retrospective cohort study (1996 to 2015) was conducted with electronic health records from Veteran Health Affairs (VHA; n = 112 845) and Kaiser Permanente Washington (KPW; n = 14 333) healthcare systems. Patients were aged ≥50 years, had a hemoglobin A1c (HbA1c) between 6.5 and <9.5 mg/dL, and did not have dementia or fills for antidiabetic medications before cohort entry. Initiators started metformin monotherapy and noninitiators used no antidiabetic medications in the 6 months after the first qualifying HbA1c. The primary outcome was incident dementia. Propensity scores and inverse probability of treatment weighting (IPTW) controlled for confounding in Cox proportional hazards models.ResultsDuring a median follow-up of 6.2 years in VHA and 6.8 years in KPW, there were 7547 new dementia cases in VHA and 1090 in KPW. After IPTW, there was no association between initiation of metformin (vs no initial treatment) and incident dementia in VHA (HR = 1.04; 95% confidence interval [CI]: 0.95-1.13) or KPW (HR = 0.81; 95% CI: 0.51-1.28). Results did not differ by age, baseline HbA1c, or race.ConclusionsResults do not support initiating metformin earlier to prevent cognitive decline and, thus, may dampen enthusiasm for metformin as a potential antidementia drug. Randomized clinical trials could help clarify the relationship between metformin and cognitive decline.
| S-EPMC8457517 | biostudies-literature
Project description:The effects of joint exposures (or exposure regimes) include those of adhering to assigned treatment versus placebo in a randomized controlled trial, duration of exposure in a cohort study, interactions between exposures, and direct effects of exposure, among others. Unlike the setting of a single point exposure (e.g., propensity score matching), there are few tools to describe confounding for joint exposures or how well a method resolves it. Investigators need tools that describe confounding in ways that are conceptually grounded and intuitive for those who read, review, and use applied research to guide policy. We revisit the implications of exchangeability conditions that hold in sequentially randomized trials, and the bias structure that motivates the use of g-methods, such as marginal structural models. From these, we develop covariate balance diagnostics for joint exposures that can (1) describe time-varying confounding, (2) assess whether covariates are predicted by prior exposures given their past, the indication for g-methods, and (3) describe residual confounding after inverse probability weighting. For each diagnostic, we present time-specific metrics that encompass a wide class of joint exposures, including regimes of multivariate time-varying exposures in censored data, with multivariate point exposures as a special case. We outline how to estimate these directly or with regression and how to average them over person-time. Using a simulated example, we show how these metrics can be presented graphically. This conceptually grounded framework can potentially aid the transparent design, analysis, and reporting of studies that examine joint exposures. We provide easy-to-use tools to implement it.
| S-EPMC5308856 | biostudies-literature
Project description:BackgroundAnticholinergic medications may increase risk of dementia and stroke, but prospective studies in healthy older people are lacking.ObjectiveCompare risk of incident dementia and stroke by anticholinergic burden among initially healthy older people.DesignProspective cohort study.SettingPrimary care (Australia and USA).Participants19,114 community-dwelling participants recruited for the ASPREE trial, aged 70+ years (65+ if US minorities) without major cardiovascular disease, dementia diagnosis, or Modified Mini-Mental State Examination score below 78/100.MeasurementsBaseline anticholinergic exposure was calculated using the Anticholinergic Cognitive Burden (ACB) score. Dementia was adjudicated using Diagnostic and Statistical Manual of Mental Disorders volume IV criteria, and stroke using the World Health Organization definition.ResultsAt baseline, 15,000 participants (79%) had an ACB score of zero, 2930 (15%) a score of 1-2, and 1184 (6%) a score of ≥ 3 (indicating higher burden). After a median follow-up of 4.7 years and adjusting for baseline covariates, a baseline ACB score of ≥ 3 was associated with increased risk of ischemic stroke (adjusted HR 1.58, 95% CI 1.06, 2.35), or dementia (adjusted HR 1.36, 95% CI 1.01, 1.82), especially of mixed etiology (adjusted HR 1.53, 95% CI 1.06, 2.21). Results were similar for those exposed to moderate/highly anticholinergic medications.LimitationsResidual confounding and reverse causality are possible. Assessment of dose or duration was not possible.ConclusionsHigh anticholinergic burden in initially healthy older people was associated with increased risk of incident dementia and ischemic stroke. A vascular effect may underlie this association. These findings highlight the importance of minimizing anticholinergic exposure in healthy older people.
| S-EPMC8175463 | biostudies-literature
Project description:Antidepressants exhibit similar efficacy, but varying tolerability, in randomized controlled trials. Predicting tolerability in real-world clinical populations may facilitate personalization of treatment and maximize adherence. This retrospective longitudinal cohort study aimed to determine the extent to which incorporating patient history from electronic health records improved prediction of unplanned treatment discontinuation at index antidepressant prescription. Clinical data were analyzed from individuals from health networks affiliated with two large academic medical centers between March 1, 2008 and December 31, 2014. In total, the study cohorts included 51,683 patients with at least one International Classification of Diseases diagnostic code for major depressive disorder or depressive disorder not otherwise specified who initiated antidepressant treatment. Among 70,121 total medication changes, 16,665 (23.77%) of them were followed by failure to return; maximum risk was observed with paroxetine (27.71% discontinuation), and minimum with venlafaxine (20.78% discontinuation); Mantel-Haenzel ?2 (8?df)?=?126.44, p?=?1.54e-23 <1e-6. Models incorporating diagnostic and procedure codes and medication prescriptions improved per-medication Areas Under the Curve (AUCs) to a mean of 0.69 [0.64-0.73] (ranging from 0.62 for paroxetine to 0.80 for escitalopram), with similar performance in the second, replication health system. Machine learning applied to coded electronic health records facilitates identification of individuals at high-risk for treatment dropout following change in antidepressant medication. Such methods may assist primary care physicians and psychiatrists in the clinic to personalize antidepressant treatment on the basis not solely of efficacy, but of tolerability.
| S-EPMC7026064 | biostudies-literature
Project description:OBJECTIVE::Pregnant women are often concerned about the impact of medication use on their pregnancy, such as congenital abnormalities. This study examined the rate of adherence to and persistence with antidepressant medications during pregnancy based on the class of antidepressants prescribed. METHODS::Women who gave birth between 2012 and 2015 in Alberta, Canada; had ≥1 diagnosis of depression within 1 year of preconception in outpatient physician claims, emergency department, or hospitalization administrative data; and were adherent (medication possession ratio ≥80%) to ≥2 consecutive antidepressant prescriptions during the preconception year ( n = 1865) were included in this retrospective cohort study. The rates of adherence and persistence (prescription refill gap ≤30 days) were calculated by antidepressant class and were compared using chi-square tests. RESULTS::During pregnancy, 834 (44.7%; 95% CI, 42.4% to 47.0%) women discontinued antidepressants. Among those continuing antidepressants, the overall rate of adherence was 62.6% (95% CI, 59.4% to 65.7%). The rate differed significantly by medication class ( P < 0.0001), with a rate of 75.1% (95% CI, 68.3% to 80.9%) for serotonin-norepinephrine inhibitors, 60.9% (95% CI, 57.2% to 64.5%) for selective serotonin reuptake inhibitors, 42.8% (95% CI, 19.9% to 69.3%) for nonselective monoamine reuptake inhibitors, and 37.5% (95% CI, 22.5% to 55.4%) for atypical antidepressants. Only, 40.7% (95% CI, 37.5 to 44.1) of women were persistent with antidepressants for the full pregnancy period-the rate differed significantly by medication class ( P < 0.0001). CONCLUSIONS::Adherence to and persistence with antidepressants is low during pregnancy and varies by medication class. Low adherence and persistence can interfere with a therapeutic effect of antidepressants, which may contribute to the worsening of depression symptoms.
| S-EPMC6405814 | biostudies-other
Project description:Psychology researchers are often interested in mechanisms underlying how randomized interventions affect outcomes such as substance use and mental health. Mediation analysis is a common statistical method for investigating psychological mechanisms that has benefited from exciting new methodological improvements over the last 2 decades. One of the most important new developments is methodology for estimating causal mediated effects using the potential outcomes framework for causal inference. Potential outcomes-based methods developed in epidemiology and statistics have important implications for understanding psychological mechanisms. We aim to provide a concise introduction to and illustration of these new methods and emphasize the importance of confounder adjustment. First, we review the traditional regression approach for estimating mediated effects. Second, we describe the potential outcomes framework. Third, we define what a confounder is and how the presence of a confounder can provide misleading evidence regarding mechanisms of interventions. Fourth, we describe experimental designs that can help rule out confounder bias. Fifth, we describe new statistical approaches to adjust for measured confounders of the mediator-outcome relation and sensitivity analyses to probe effects of unmeasured confounders on the mediated effect. All approaches are illustrated with application to a real counseling intervention dataset. Counseling psychologists interested in understanding the causal mechanisms of their interventions can benefit from incorporating the most up-to-date techniques into their mediation analyses. (PsycINFO Database Record
| S-EPMC5726285 | biostudies-literature