Johan Zetterqvist

Medication for Attention Deficit–Hyperactivity Disorder and Criminality

BACKGROUND Attention deficit-hyperactivity disorder (ADHD) is a common disorder that has been associated with criminal behavior in some studies. Pharmacologic treatment is available for ADHD and may reduce the risk of criminality. METHODS Using Swedish national registers, we gathered information on 25,656 patients with a diagnosis of ADHD, their pharmacologic treatment, and subsequent criminal convictions in Sweden from 2006 through 2009. We used stratified Cox regression analyses to compare the rate of criminality while the patients were receiving ADHD medication, as compared with the rate for the same patients while not receiving medication. RESULTS As compared with nonmedication periods, among patients receiving ADHD medication, there was a significant reduction of 32% in the criminality rate for men (adjusted hazard ratio, 0.68; 95% confidence interval [CI], 0.63 to 0.73) and 41% for women (hazard ratio, 0.59; 95% CI, 0.50 to 0.70). The rate reduction remained between 17% and 46% in sensitivity analyses among men, with factors that included different types of drugs (e.g., stimulant vs. nonstimulant) and outcomes (e.g., type of crime). CONCLUSIONS Among patients with ADHD, rates of criminality were lower during periods when they were receiving ADHD medication. These findings raise the possibility that the use of medication reduces the risk of criminality among patients with ADHD. (Funded by the Swedish Research Council and others.).

Antipsychotics, mood stabilisers, and risk of violent crime.

Summary Background Antipsychotics and mood stabilisers are prescribed widely to patients with psychiatric disorders worldwide. Despite clear evidence for their efficacy in relapse prevention and symptom relief, their effect on some adverse outcomes, including the perpetration of violent crime, is unclear. We aimed to establish the effect of antipsychotics and mood stabilisers on the rate of violent crime committed by patients with psychiatric disorders in Sweden. Methods We used linked Swedish national registers to study 82 647 patients who were prescribed antipsychotics or mood stabilisers, their psychiatric diagnoses, and subsequent criminal convictions in 2006–09. We did within-individual analyses to compare the rate of violent criminality during the time that patients were prescribed these medications versus the rate for the same patients while they were not receiving the drugs to adjust for all confounders that remained constant within each participant during follow-up. The primary outcome was the occurrence of violent crime, according to Swedens national crime register. Findings In 2006–09, 40 937 men in Sweden were prescribed antipsychotics or mood stabilisers, of whom 2657 (6·5%) were convicted of a violent crime during the study period. In the same period, 41 710 women were prescribed these drugs, of whom 604 (1·4 %) had convictions for violent crime. Compared with periods when participants were not on medication, violent crime fell by 45% in patients receiving antipsychotics (hazard ratio [HR] 0·55, 95% CI 0·47–0·64) and by 24% in patients prescribed mood stabilisers (0·76, 0·62–0·93). However, we identified potentially important differences by diagnosis—mood stabilisers were associated with a reduced rate of violent crime only in patients with bipolar disorder. The rate of violence reduction for antipsychotics remained between 22% and 29% in sensitivity analyses that used different outcomes (any crime, drug-related crime, less severe crime, and violent arrest), and was stronger in patients who were prescribed higher drug doses than in those prescribed low doses. Notable reductions in violent crime were also recorded for depot medication (HR adjusted for concomitant oral medications 0·60, 95% CI 0·39–0·92). Interpretation In addition to relapse prevention and psychiatric symptom relief, the benefits of antipsychotics and mood stabilisers might also include reductions in the rates of violent crime. The potential effects of these drugs on violence and crime should be taken into account when treatment options for patients with psychiatric disorders are being considered. Funding The Wellcome Trust, the Swedish Prison and Probation Service, the Swedish Research Council, and the Swedish Research Council for Health, Working Life and Welfare.

Stimulant and non-stimulant attention deficit/hyperactivity disorder drug use : total population study of trends and discontinuation patterns 2006-2009

Zetterqvist J, Asherson P, Halldner L, Långström N, Larsson H. Stimulant and non‐stimulant attention deficit/hyperactivity disorder drug use: total population study of trends and discontinuation patterns 2006–2009.

Varenicline and risk of psychiatric conditions, suicidal behaviour, criminal offending, and transport accidents and offences: population based cohort study

Objective To examine associations between varenicline and the incidence of a range of adverse outcomes. Design Population based cohort study using within person analyses to control for confounding by indication. Setting Whole population of Sweden. Participants 7 917 436 people aged 15 and over, of whom 69 757 were treated with varenicline between 2006 and 2009. Main outcome measures Incidence of new psychiatric conditions, suicidal behaviour, suspected and convicted criminal offending, transport accidents, and suspected and convicted traffic offences. Results In the whole population, 337 393 new psychiatric conditions were diagnosed during follow-up. In addition, 507 823 suspected and 338 608 convicted crimes, 40 595 suicidal events, 124 445 transport accidents, and 99 895 suspected and 57 068 convicted traffic crimes were recorded. Within person analyses showed that varenicline was not associated with significant hazards of suicidal behaviour, criminal offending, transport accidents, traffic offences, or psychoses. However, varenicline was associated with a small increase in the risk of anxiety conditions (hazard ratio 1.23, 95% confidence interval 1.01 to 1.51) and mood conditions (1.31, 1.06 to 1.63), which was only seen in people with pre-existing psychiatric disorders. Conclusions Concerns that varenicline is associated with an increased risk of many adverse outcomes, including suicidality and accidents, are not supported in this observational study. The small increase in risk of two psychiatric conditions in people with pre-existing psychiatric disorders needs to be confirmed using other research designs.

Selective serotonin reuptake inhibitors and violent crime: a cohort study

Background Although selective serotonin reuptake inhibitors (SSRIs) are widely prescribed, associations with violence are uncertain. Methods and Findings From Swedish national registers we extracted information on 856,493 individuals who were prescribed SSRIs, and subsequent violent crimes during 2006 through 2009. We used stratified Cox regression analyses to compare the rate of violent crime while individuals were prescribed these medications with the rate in the same individuals while not receiving medication. Adjustments were made for other psychotropic medications. Information on all medications was extracted from the Swedish Prescribed Drug Register, with complete national data on all dispensed medications. Information on violent crime convictions was extracted from the Swedish national crime register. Using within-individual models, there was an overall association between SSRIs and violent crime convictions (hazard ratio [HR] = 1.19, 95% CI 1.08–1.32, p < 0.001, absolute risk = 1.0%). With age stratification, there was a significant association between SSRIs and violent crime convictions for individuals aged 15 to 24 y (HR = 1.43, 95% CI 1.19–1.73, p < 0.001, absolute risk = 3.0%). However, there were no significant associations in those aged 25–34 y (HR = 1.20, 95% CI 0.95–1.52, p = 0.125, absolute risk = 1.6%), in those aged 35–44 y (HR = 1.06, 95% CI 0.83–1.35, p = 0.666, absolute risk = 1.2%), or in those aged 45 y or older (HR = 1.07, 95% CI 0.84–1.35, p = 0.594, absolute risk = 0.3%). Associations in those aged 15 to 24 y were also found for violent crime arrests with preliminary investigations (HR = 1.28, 95% CI 1.16–1.41, p < 0.001), non-violent crime convictions (HR = 1.22, 95% CI 1.10–1.34, p < 0.001), non-violent crime arrests (HR = 1.13, 95% CI 1.07–1.20, p < 0.001), non-fatal injuries from accidents (HR = 1.29, 95% CI 1.22–1.36, p < 0.001), and emergency inpatient or outpatient treatment for alcohol intoxication or misuse (HR = 1.98, 95% CI 1.76–2.21, p < 0.001). With age and sex stratification, there was a significant association between SSRIs and violent crime convictions for males aged 15 to 24 y (HR = 1.40, 95% CI 1.13–1.73, p = 0.002) and females aged 15 to 24 y (HR = 1.75, 95% CI 1.08–2.84, p = 0.023). However, there were no significant associations in those aged 25 y or older. One important limitation is that we were unable to fully account for time-varying factors. Conclusions The association between SSRIs and violent crime convictions and violent crime arrests varied by age group. The increased risk we found in young people needs validation in other studies.

Carryover Effects in Sibling Comparison Designs

A convenient way of dealing with confounding is the sibling comparison design, where the outcome in exposed individuals is compared with the outcome in their unexposed siblings. The standard analysis of sibling comparison designs assumes that the exposure and outcome of an individual do not affect the exposure and outcome of his/her siblings, sometimes referred to as an absence of sibling carryover or contagion effects. Unfortunately, there are many situations where carryover effects are likely to be present. In this article, we explore the consequences of carryover effects for sibling comparison designs. We show, using causal diagrams, when and why carryover effects lead to bias, and we investigate the sign and magnitude of this bias under various scenarios.

Doubly robust methods for handling confounding by cluster

In clustered designs such as family studies, the exposure-outcome association is usually confounded by both cluster-constant and cluster-varying confounders. The influence of cluster-constant confounders can be eliminated by studying the exposure-outcome association within (conditional on) clusters, but additional regression modeling is usually required to control for observed cluster-varying confounders. A problem is that the working regression model may be misspecified, in which case the estimated within-cluster association may be biased. To reduce sensitivity to model misspecification we propose to augment the standard working model for the outcome with an auxiliary working model for the exposure. We derive a doubly robust conditional generalized estimating equation (DRCGEE) estimator for the within-cluster association. This estimator combines the two models in such a way that it is consistent if either model is correct, not necessarily both. Thus, the DRCGEE estimator gives the researcher two chances instead of only one to make valid inference on the within-cluster association. We have implemented the estimator in an R package and we use it to examine the association between smoking during pregnancy and cognitive abilities in offspring, in a sample of siblings.

Model-based estimation of the attributable fraction for cross-sectional, case–control and cohort studies using the R package AF

The attributable fraction (or attributable risk) is a widely used measure that quantifies the public health impact of an exposure on an outcome. Even though the theory for AF estimation is well developed, there has been a lack of up-to-date software implementations. The aim of this article is to present a new R package for AF estimation with binary exposures. The package AF allows for confounder-adjusted estimation of the AF for the three major study designs: cross-sectional, (possibly matched) case–control and cohort. The article is divided into theoretical sections and applied sections. In the theoretical sections we describe how the confounder-adjusted AF is estimated for each specific study design. These sections serve as a brief but self-consistent tutorial in AF estimation. In the applied sections we use real data examples to illustrate how the AF package is used. All datasets in these examples are publicly available and included in the AF package, so readers can easily replicate all analyses.

Doubly Robust Estimation with the R Package drgee

Abstract A common goal of epidemiologic research is to study the association between a certain exposure and a certain outcome, while controlling for important covariates. This is often done by fitting a restricted mean model for the outcome, as in generalized linear models (GLMs) and in generalized estimating equations (GEEs). If the covariates are high-dimensional, then it may be difficult to well specify the model. This is an important concern, since model misspecification may lead to biased estimates. Doubly robust estimation is an estimation technique that offers some protection against model misspecification. It utilizes two models, one for the outcome and one for the exposure, and produces unbiased estimates of the exposure-outcome association if either model is correct, not necessarily both. Despite its obvious appeal, doubly robust estimation is not used on a regular basis in applied epidemiologic research. One reason for this could be the lack of up-to-date software. In this paper we describe a new R package, drgee, which carries out doubly robust estimation in restricted mean models. The package is constructed to be user-friendly and fast, to facilitate routine use of doubly robust estimation. The paper is structured into theory sections and example sections. The former are intended to serve as a brief but self-consistent tutorial in doubly robust estimation. The latter illustrate the use of the drgee package through practical examples. We have used publically available data throughout the paper, so that the reader can easily replicate all examples.

Confounders, Mediators, or Colliders: What Types of Shared Covariates Does a Sibling Comparison Design Control For?

The sibling comparison design is an important epidemiologic tool to control for unmeasured confounding, in studies of the causal effect of an exposure on an outcome. It is routinely argued that within-family associations are automatically controlled for all measured and unmeasured covariates that are shared (constant) within sets of siblings, such as early childhood environment and parental genetic makeup. However, an important lesson from modern causal inference theory is that not all types of covariate control are desirable. In particular, it has been argued that collider control always leads to bias, and that mediator control may or may not lead to bias, depending on the research question. In this article, we use directed acyclic graphs (DAGs) to distinguish between shared confounders, shared mediators and shared colliders, and we examine which of these shared covariates the sibling comparison design really controls for.