History of Benzodiazepine Prescriptions and Risk of Dementia

Possible Bias Due to Prevalent Users and Covariate Measurement Timing in a Nested Case-Control Study

Kathryn Richardson; Katharina Mattishent; Yoon K. Loke; Nicholas Steel; Chris Fox; Carlota M. Grossi; Kathleen Bennett; Ian Maidment; Malaz Boustani; Fiona E. Matthews; Phyo K. Myint; Noll L. Campbell; Carol Brayne; Louise Robinson; George M. Savva


Am J Epidemiol. 2019;188(7):1228-1236. 

In This Article

Abstract and Introduction


Previous estimates of whether long-term exposure to benzodiazepines increases dementia risk are conflicting and are compromised by the difficulty of controlling for confounders and by reverse causation. We investigated how estimates for the association between benzodiazepine use and later dementia incidence varied based on study design choices, using a case-control study nested within the United Kingdom's Clinical Practice Research Datalink. A total of 40,770 dementia cases diagnosed between April 2006 and July 2015 were matched on age, sex, available data history, and deprivation to 283,933 control subjects. Benzodiazepines and Z-drug prescriptions were ascertained in a drug-exposure period 4–20 years before dementia diagnosis. Estimates varied with the inclusion of new or prevalent users, with the timing of covariate ascertainment, and with varying time between exposure and outcome. There was no association between any new prescription of benzodiazepines and dementia (adjusted odds ratio (OR) = 1.03, 95% confidence interval (CI): 1.00, 1.07), whereas an inverse association was observed among prevalent users (adjusted OR = 0.91, 95% CI: 0.87, 0.95), although this was likely induced by unintentional adjustment for colliders. By considering the choice of confounders and timing of exposure and covariate measurement, our findings overall are consistent with no causal effect of benzodiazepines or Z-drugs on dementia incidence.


Dementia prevention is a public health priority. More than 152 million people are expected to be living with dementia by 2050. Dementia is recognized as a leading cause of disability, is the fifth most important cause of death, and has a global economic cost of US$1 trillion.[1,2] There is no curative or disease-modifying treatment for dementia, increasing the importance of identifying its risk factors.[3] Authors of several studies have suggested that long-term benzodiazepine use could increase dementia risk.[4] If true, this is an important opportunity to prevent dementia, because 9% of older US adults currently use benzodiazepines, with 31% of these being long-term users.[5,6]

Benzodiazepines, including diazepam (Valium; Hoffmann-La Roche Inc, Little Falls, New Jersey) and alprazolam (Xanax; Pfizer Inc, New York, New York) are the most commonly prescribed sedatives and are typically used for insomnia or anxiety. Despite years of guidance advising against long-term benzodiazepine use, because of adverse effects, addiction, and tolerance,[7] there has been no decline in their use in the past decade in the United States,[8–10] whereas a small decline in the United Kingdom has been accompanied by greater use of benzodiazepine-related drugs, including zopiclone, (e.g., Lunesta; Sunovion Pharmaceuticals Inc. Marlborough, Massachusetts), zolpidem (e.g., Ambien; Sanofi-Aventis U.S. LLC, Bridgewater, New Jersey), and zaleplon (e.g., Sonata; Wyeth Pharmaceuticals Inc., Philadelphia, Pennsylvania), collectively known as Z-drugs.[11]

Benzodiazepines and Z-drugs have dose-related effects on memory and other aspects of cognitive function.[12,13] However, no biological mechanism has been demonstrated to underlie any link to dementia incidence. Although increased risks of dementia with long-term benzodiazepine use[14–16] have been suggested on the basis of studies using insurance records and epidemiologic cohort studies, no association was noted in other recent studies.[17,18] These conflicting results may reflect genuine differences across populations, or different study designs, availability and use of covariate data, or analysis parameters, such as minimum time lag between exposure and outcomes.[19,20]

It is not practical or ethical to randomly assign patients to receive benzodiazepine treatment to estimate harms; therefore, observational studies are central to addressing this important question. Individual, patient-level data sets exist that include detailed histories of benzodiazepine use going back years or decades, details of diagnoses and treatment for cognitive disorders, and records of many possible confounding variables for this relationship. However, several factors complicate any analysis. Benzodiazepine treatment often is initiated before records for a patient begin, precluding the use of the "new-user" design.[21] This is particularly true for those with very long-term use, who may be most at risk.[22] Second, the main indications for benzodiazepines—anxiety and sleep disturbance—are both risk factors for and prodromal symptoms of neurodegenerative disease that may occur many years before dementia diagnosis, necessitating a lag period to avoid protopathic bias.[4] Furthermore, dates associated with diagnoses in electronic health records may reflect the time of the underlying event. Together, these factors make the theoretical identification of confounding from mediating or colliding variables, as is often suggested,[23,24] difficult. This is important because valid causal inference relies on the correct identification of and control for confounders (i.e., variables that are common causes of both the exposure and the outcome), but conditioning on mediators (i.e., variables on the causal pathway from exposure to outcome) or on colliders (i.e., common consequences of the exposure and the outcome) will introduce bias rather than reduce it.[25]

Case-control studies, in which exposures within an exposure period are compared between cases of a disease and matched controls, are often used for estimating the associations between multiple complex exposures and a single outcome. Case-control studies are used particularly when tackling rare adverse events or adverse events that may only become apparent after long-term exposures. However, selection based on outcome rather than exposure status further complicates the ascertainment of confounders. Clearly, it is optimal to measure potential confounders at treatment initiation,[20] but because cases and controls are not matched on exposure, the presence of treatment or time of treatment initiation will vary within a matched set. Hence, it is difficult to know when to optimally ascertain and encode covariates. Measuring covariates recorded only up to the start of an exposure window (possibly years before exposure) risks missing confounders and omitted variable bias, whereas including covariates recorded during or after the exposure window (hence, after the exposure) risks underestimation through unintended adjustment for mediators or colliders.[26]

We conducted a case-control study nested within an electronic health record data set as part of a wider project estimating the associations of drug use on dementia incidence,[27] and we have explored several of these issues. We present estimates for the association between benzodiazepine and Z-drug prescription and dementia incidence, and explore how these depend on 1) the inclusion or exclusion of prevalent users, 2) the timing of covariate ascertainment, and 3) the minimum lag between treatment and dementia incidence. Finally, we explore the role of specific covariates and implications for the conduct and interpretation of future similar studies.