Impact of unmeasured covariates on bias and statistical power in health administrative databases: a simulation study

International audienceFrench health insurance databases (SNIIRAM) cover the entire French population. These databases include demographic (age, gender, city of residence), and out-hospital reimbursement (drug dispensing and long-term diseases). The use of these administrative databases for epidemiological research has the strengths of being readily available and relatively inexpensive. Also, the large number of patients, without loss of follow-up, allows for sufficient powering of studies. Furthermore, the information is large, comprehensive and detailed, without any exclusion.An example of use is given by the CESIR (Combination of Studies on Health and Road Safety) project. To assess the impact of medicines use on the risk of injury road traffic crashes, data from the health care insurance database were matched with data from the National police database of injurious road traffic crashes. More than seventy thousand drivers involved in an injurious crash in France, between July 2005 and May 2008, were included in the study. Their reimbursement data for drugs dispensed within six months of the road traffic crash were retrieved.Administrative databases are not without limitations. Concerning medical drugs, no information about the use of over-the-counter drugs, prescription drug misuse or medication adherence is available. Diagnoses of chronic diseases could be inaccurate since data are collected and coded in an “administrative way”. There is also a lack of information on potential confounders.In this work we explored different observational study designs that can address the research question of the CESIR project (case–control, matched case–control, case–crossover, case series. . . ). For each study design, we aimed to evaluate the impact of unmeasured confounders onbias and statistical power through data simulation. The simulation study was set up to mimic the real CESIR data in several respects. To generate event times conditional on time-dependent covariates, we adapted the permutational algorithm implemented in the publicly available R packagePermAlgo. We focused on two medicinal drugs: Benzodiazepines (whose effects on crash risk are well established in the literature) and Antihistamines for systemic use (whose effects are controversial). Our results allowed us to develop several recommendations to guide future analyses of the second phase of the CESIR project (comprising the period June 2008 to December 2011)

Etude de l’association entre consommation médicamenteuse et risque d’accident de la route : exploration par simulation de schémas d’études épidémiologiques applicables à partir des données médico-administratives

National audienceLes bases de données médico-administratives sont de plus en plus utilisées en pharmacoépidémiologie et présentent de nombreux avantages : large échantillon, faibles coûts… (Données de santé : données sensibles, Statistique et société, Vol. 2, N° 2 mai 2014). Le projet CESIR, dont lesdonnées sont issues d’un appariement entre les données de la CNAM-TS et des accidents corporels de la circulation, en est un exemple. Il est constitué d’un échantillon de 72685 conducteurs impliqués dans un accident entre juillet 2005 et mai 2008. Le choix du schéma d’étude optimal permettant d’éviter l’introduction de biais tout en conservant une bonne puissance peut s’avérer complexe du fait des particularités de ces données (absence de facteurs de confusion, pas de sujets n’ayant pas connu l’événement –témoins–…). Nous avons exploré, via des simulations, différents schemas d’étude (cas-témoin, cas-croisé...) permettant d’étudier le lien entre la consommation de deux classes de médicaments : les benzodiazépines (dont les effets sur la conduite ont été établis) et les antihistaminiques (dont les effets sont plus discutés) et le risque de survenue d’un accident de lacirculation. Les données ont été simulées en utilisant une adaptation de l’algorithme de permutation proposé par Abrahamowicz et MacKenzie (JASA, 1996). La distribution des variables, dont certaines dépendent du temps, ainsi que leur effet ont été fixés en se basant sur les données de CESIR. Lesrésultats ont permis de répondre à des questions épidémiologiques soulevées lors des analyses préalablement réalisées à partir de CESIR et d’émettre des recommandations en vue de l’analyse de la deuxième vague de données CESIR

Etude de l’association entre consommation médicamenteuse et risque d’accident de la route : exploration par simulation de schémas d’études épidémiologiques applicables à partir des données médico-administratives

National audienceLes bases de données médico-administratives sont de plus en plus utilisées en pharmacoépidémiologie et présentent de nombreux avantages : large échantillon, faibles coûts… (Données de santé : données sensibles, Statistique et société, Vol. 2, N° 2 mai 2014). Le projet CESIR, dont lesdonnées sont issues d’un appariement entre les données de la CNAM-TS et des accidents corporels de la circulation, en est un exemple. Il est constitué d’un échantillon de 72685 conducteurs impliqués dans un accident entre juillet 2005 et mai 2008. Le choix du schéma d’étude optimal permettant d’éviter l’introduction de biais tout en conservant une bonne puissance peut s’avérer complexe du fait des particularités de ces données (absence de facteurs de confusion, pas de sujets n’ayant pas connu l’événement –témoins–…). Nous avons exploré, via des simulations, différents schemas d’étude (cas-témoin, cas-croisé...) permettant d’étudier le lien entre la consommation de deux classes de médicaments : les benzodiazépines (dont les effets sur la conduite ont été établis) et les antihistaminiques (dont les effets sont plus discutés) et le risque de survenue d’un accident de lacirculation. Les données ont été simulées en utilisant une adaptation de l’algorithme de permutation proposé par Abrahamowicz et MacKenzie (JASA, 1996). La distribution des variables, dont certaines dépendent du temps, ainsi que leur effet ont été fixés en se basant sur les données de CESIR. Lesrésultats ont permis de répondre à des questions épidémiologiques soulevées lors des analyses préalablement réalisées à partir de CESIR et d’émettre des recommandations en vue de l’analyse de la deuxième vague de données CESIR

The distracted mind on the wheel: Overall propensity to mind wandering is associated with road crash responsibility.

The role of distractions on attentional lapses that place road users in higher risk of crash remains poorly understood. We aimed to assess the respective impact of (i) mind wandering trait (propensity to mind wander in the everyday life as measured with a set of 4 questions on the proportion of time spent mind wandering in 4 different situations) and (ii) mind wandering state (disturbing thoughts just before the crash) on road crash risk using a comparison between responsible and non-responsible drivers. 954 drivers injured in a road crash were interviewed at the adult emergency department of the Bordeaux university hospital in France (2013-2015). Responsibility for the crash, mind wandering (trait/state), external distraction, alcohol use, psychotropic drug use, and sleep deprivation were evaluated. Based on questionnaire reports, 39% of respondents were classified with a mind wandering trait and 13% reported a disturbing thought just before the crash. While strongly correlated, mind wandering state and trait were independently associated with responsibility for a traffic crash (State: OR = 2.51, 95% CI: 1.64-3.83 and Trait: OR = 1.62, 95% CI: 1.22-2.16 respectively). Self-report of distracting thoughts therefore did not capture the entire risk associated with the propensity of the mind to wander, either because of under-reported thoughts and/or other deleterious mechanisms to be further explored

Sensitivity analyses of responsibility risk factors in road accidents.

<p>Sensitivity analyses of responsibility risk factors in road accidents.</p

Characteristics of the study population—Comparison between drivers responsible and not responsible for the road crash (n = 954).

<p>Characteristics of the study population—Comparison between drivers responsible and not responsible for the road crash (n = 954).</p

Prescription medicine use by pedestrians and the risk of injurious road traffic crashes: A case-crossover study

<div><p>Background</p><p>While some medicinal drugs have been found to affect driving ability, no study has investigated whether a relationship exists between these medicines and crashes involving pedestrians. The aim of this study was to explore the association between the use of medicinal drugs and the risk of being involved in a road traffic crash as a pedestrian.</p><p>Methods and findings</p><p>Data from 3 French nationwide databases were matched. We used the case-crossover design to control for time-invariant factors by using each case as its own control. To perform multivariable analysis and limit false-positive results, we implemented a bootstrap version of Lasso. To avoid the effect of unmeasured time-varying factors, we varied the length of the washout period from 30 to 119 days before the crash. The matching procedure led to the inclusion of 16,458 pedestrians involved in an injurious road traffic crash from 1 July 2005 to 31 December 2011. We found 48 medicine classes with a positive association with the risk of crash, with median odds ratios ranging from 1.12 to 2.98. Among these, benzodiazepines and benzodiazepine-related drugs, antihistamines, and anti-inflammatory and antirheumatic drugs were among the 10 medicines most consumed by the 16,458 pedestrians. Study limitations included slight overrepresentation of pedestrians injured in more severe crashes, lack of information about self-medication and the use of over-the-counter drugs, and lack of data on amount of walking.</p><p>Conclusions</p><p>Therapeutic classes already identified as impacting the ability to drive, such as benzodiazepines and antihistamines, are also associated with an increased risk of pedestrians being involved in a road traffic crash. This study on pedestrians highlights the necessity of improving awareness of the effect of these medicines on this category of road user.</p></div

The case-crossover design of the study, with multiple drug exposures and a varying washout period.

<p>In the case-crossover design, only individuals with unequal exposures for the control period and the case period contribute to the analysis. For instance, with a control day defined at 120 days before the crash day, the individual shown in this figure has unequal exposures for the second drug (exposed during case day and unexposed during control day), but concordant exposures for the first drug (exposed both days) and the third drug (unexposed both days). In a case-crossover analysis, only the exposure to the second drug is used.</p

The 10 most consumed medicines among those listed in Table 2 as associated with road traffic crash involvement.

<p>The 10 most consumed medicines among those listed in <a href="http://www.plosmedicine.org/article/info:doi/10.1371/journal.pmed.1002347#pmed.1002347.t002" target="_blank">Table 2</a> as associated with road traffic crash involvement.</p

Results of the 90 case-crossover designs obtained when varying the washout period from 30 days to 119 days.

<p>A blank cell means that the medicine class was not retained in the final model for this control period, and a colored square means that the medicine class was selected by the model. Both the size and color intensity of the squares depend on the absolute value of the bias-corrected estimated coefficients. When varying the washout period, the frequency thresholds estimated using the Akaike information criterion varied from a minimum of 50% (washout = 40) to a maximum of 74% (washout = 104). A frequency threshold of 74% means that medicines selected in at least 74% of the 1,000 bootstraps were considered as associated risk factors for pedestrian road crash. The different colored forms on the far left indicate groups of medicines according to the location of the control periods (with respect to the crash day) for which there was an association of the medicine with increased risk of being involved in a road crash as a pedestrian: blue stars indicate increased risk in control periods close to the crash; yellow squares indicate increased risk in control periods far from the crash; green circles indicate increased risk in control periods both close to and far from the crash; black squares indicate increased risk in discontinuous control periods.</p