Statin Use and Incidence of Parkinson's Disease in Women from the French E3N Cohort Study.

BACKGROUND: Statins represent candidates for drug repurposing in Parkinson's disease (PD). Few studies examined the role of reverse causation, statin subgroups, and dose-response relations based on time-varying exposures. OBJECTIVES: We examined whether statin use is associated with PD incidence while attempting to overcome the limitations described previously, especially reverse causation. METHOD: We used data from the E3N cohort study of French women (follow-up, 2004-2018). Incident PD was ascertained using multiple sources and validated by experts. New statin users were identified through linked drug claims. We set up a nested case-control study to describe trajectories of statin prescriptions and medical consultations before diagnosis. We used time-varying multivariable Cox proportional hazards regression models to examine the statins-PD association. Exposure indexes included ever use, cumulative duration/dose, and mean daily dose and were lagged by 5 years to address reverse causation. RESULTS: The case-control study (693 cases, 13,784 controls) showed differences in case-control trajectories, with changes in the 5 years before diagnosis in cases. Of 73,925 women (aged 54-79 years), 524 developed PD and 11,552 started using statins in lagged analyses. Ever use of any statin was not associated with PD (hazard ratio [HR] = 0.87, 95% confidence interval [CI] = 0.67-1.11). Alternatively, ever use of lipophilic statins was significantly associated with lower PD incidence (HR = 0.70, 95% CI = 0.51-0.98), with a dose-response relation for the mean daily dose (P-linear trend = 0.02). There was no association for hydrophilic statins. CONCLUSION: Use of lipophilic statins at least 5 years earlier was associated with reduced PD incidence in women, with a dose-response relation for the mean daily dose. © 2023 The Authors. Movement Disorders published by Wiley Periodicals LLC on behalf of International Parkinson and Movement Disorder Society.

Comparison of cohort and nested case-control designs for estimating the effect of time-varying drug exposure on the risk of adverse event in the presence of ties.

Cohort and nested case-control (NCC) designs are frequently used in pharmacoepidemiology to assess the associations of drug exposure that can vary over time with the risk of an adverse event. Although it is typically expected that estimates from NCC analyses are similar to those from the full cohort analysis, with moderate loss of precision, only few studies have actually compared their respective performance for estimating the effects of time-varying exposures (TVE). We used simulations to compare the properties of the resulting estimators of these designs for both time-invariant exposure and TVE. We varied exposure prevalence, proportion of subjects experiencing the event, hazard ratio, and control-to-case ratio and considered matching on confounders. Using both designs, we also estimated the real-world associations of time-invariant ever use of menopausal hormone therapy (MHT) at baseline and updated, time-varying MHT use with breast cancer incidence. In all simulated scenarios, the cohort-based estimates had small relative bias and greater precision than the NCC design. NCC estimates displayed bias to the null that decreased with a greater number of controls per case. This bias markedly increased with higher proportion of events. Bias was seen with Breslow's and Efron's approximations for handling tied event times but was greatly reduced with the exact method or when NCC analyses were matched on confounders. When analyzing the MHT-breast cancer association, differences between the two designs were consistent with simulated data. Once ties were taken correctly into account, NCC estimates were very similar to those of the full cohort analysis.

Trends in psychotropic drug consumption among French military personnel during the COVID-19 epidemic.

BACKGROUND: The coronavirus disease (COVID-19) pandemic may have had significant mental health consequences for military personnel, which is a population already exposed to psychological stress. To assess the potential impact of the COVID-19 pandemic, we analyzed the dispensing of three classes of psychotropic drugs (anxiolytics, hypnotics, and antidepressants) among French military personnel. METHODS: A retrospective analysis was conducted using the individualized medico-administrative data of persons insured by the National Military Social Security Fund from the National Health Data System. All active French military personnel aged 18-64 who received outpatient care and to whom drugs were dispensed between January 1, 2019, and April 30, 2021, were included from the French national health database. Rate ratios of dispensed anxiolytics, hypnotics and antidepressants (based on drug reimbursement) were estimated from negative binomial regressions before and after the start of the COVID-19 pandemic. RESULTS: Three hundred eighty-one thousand seven hundred eleven individuals were included. Overall, 45,148 military personnel were reimbursed for anxiolytics, 10,637 for hypnotics, and 4328 for antidepressants. Drugs were dispensed at a higher rate in 2020 and 2021 than in 2019. There was a notable peak at the beginning of the first lockdown followed by a decrease limited to the duration of the first lockdown. During the first lockdown only, there were temporary phenomena including a brief increase in drug dispensing during the first week followed by a decrease during the rest of lockdown, possibly corresponding to a stocking-up effect. For the study period overall, while there was a significant downward trend in psychotropic drug dispensing before the occurrence of COVID-19 (p < 0.001), the pandemic period was associated with an increase in dispensed anxiolytics (rate ratio, 1.03; 95% CI, 1.02-1.04, p < 0.05), hypnotics (rate ratio, 1.13; 95% CI, 1.11-1.16, p < 0.001) and antidepressants (rate ratio, 1.12; 95% CI, 1.10-1.13, p < 0.001) in the military population. CONCLUSIONS: The COVID-19 pandemic has probably had a significant impact on the mental health of French military personnel, as suggested by the trends in dispensed psychotropic drugs. The implementation of mental health prevention measures should be investigated for this population.

Identifying Protective Drugs for Parkinson's Disease in Health-Care Databases Using Machine Learning.

BACKGROUND: Available treatments for Parkinson's disease (PD) are only partially or transiently effective. Identifying existing molecules that may present a therapeutic or preventive benefit for PD (drug repositioning) is thus of utmost interest. OBJECTIVE: We aimed at detecting potentially protective associations between marketed drugs and PD through a large-scale automated screening strategy. METHODS: We implemented a machine learning (ML) algorithm combining subsampling and lasso logistic regression in a case-control study nested in the French national health data system. Our study population comprised 40,760 incident PD patients identified by a validated algorithm during 2016 to 2018 and 176,395 controls of similar age, sex, and region of residence, all followed since 2006. Drug exposure was defined at the chemical subgroup level, then at the substance level of the Anatomical Therapeutic Chemical (ATC) classification considering the frequency of prescriptions over a 2-year period starting 10 years before the index date to limit reverse causation bias. Sensitivity analyses were conducted using a more specific definition of PD status. RESULTS: Six drug subgroups were detected by our algorithm among the 374 screened. Sulfonamide diuretics (ATC-C03CA), in particular furosemide (C03CA01), showed the most robust signal. Other signals included adrenergics in combination with anticholinergics (R03AL) and insulins and analogues (A10AD). CONCLUSIONS: We identified several signals that deserve to be confirmed in large studies with appropriate consideration of the potential for reverse causation. Our results illustrate the value of ML-based signal detection algorithms for identifying drugs inversely associated with PD risk in health-care databases. © 2022 The Authors. Movement Disorders published by Wiley Periodicals LLC on behalf of International Parkinson and Movement Disorder Society.

Analyzing cohort studies with interval-censored data: A new model-based linear rank-type test.

To compare two or more survival distributions with interval-censored data, various nonparametric tests have been proposed. Some are based on the G ρ $$ {G}^{\rho } $$ -family introduced by Harrington and Fleming (1991) that allows flexibility for situations in which the hazard ratio decreases monotonically to unity. However, it is unclear how to choose the appropriate value of the parameter ρ $$ \rho $$ . In this work, we propose a novel linear rank-type test for analyzing interval-censored data that derived from a proportional reversed hazard model. We show its relationship with decreasing hazard ratio. This test statistic provides an alternative to the G ρ $$ {G}^{\rho } $$ -based test statistics by bypassing the choice of the ρ $$ \rho $$ parameter. Simulation results show its good behavior. Two studies on breast cancer and drug users illustrate its practical uses and highlight findings that would have been overlooked if other tests had been used. The test is easy to implement with standard software and can be used for a wide range of situations with interval-censored data to test the equality of survival distributions between two or more independent groups.

High-Dimensional Propensity Score-Adjusted Case-Crossover for Discovering Adverse Drug Reactions from Computerized Administrative Healthcare Databases.

INTRODUCTION: Increasing availability of medico-administrative databases has prompted the development of automated pharmacovigilance signal detection methodologies. Self-controlled approaches have recently been proposed. They account for time-independent confounding factors that may not be recorded. So far, large numbers of drugs have been screened either univariately or with LASSO penalized regressions. OBJECTIVE: We propose and assess a new method that combines the case-crossover self-controlled design with propensity scores (propensity score-adjusted case-crossover) built from high-dimensional data-driven variable selection, to account for co-medications or possibly other measured confounders. METHODS: Comparison with the univariate and LASSO case-crossover was performed from simulations and a real-data study. Multiple regressions (LASSO, propensity score-adjusted case-crossover) accounted for co-medications and no other covariates. For the univariate and propensity score-adjusted case-crossover methods, the detection threshold was based on a false discovery rate procedure, while for LASSO, it relied on the Akaike Information Criterion. For the real-data study, two drug safety experts evaluated the signals generated from the analysis of 4099 patients with acute myocardial infarction from the French national health database. RESULTS: On simulations, our approach ranked the signals similarly to the LASSO and better than the univariate method while controlling the false discovery rate at the prespecified level, contrary to the univariate method. The LASSO provided the best sensitivity at the cost of larger false discovery rate estimates. On the application, our approach showed similar performances to the LASSO and better performances than the univariate method. It highlighted 43 signals out of 609 drug candidates: 22 (51%) were considered as potentially pharmacologically relevant, including seven (16%) regarded as highly relevant. CONCLUSIONS: Our findings show the interest of a propensity score combined with a case-crossover for pharmacovigilance. They also confirm that indication bias remains a challenge when mining medico-administrative databases.

Estimation of mid-and long-term benefits and hypothetical risk of Guillain-Barre syndrome after human papillomavirus vaccination among boys in France: A simulation study.

BACKGROUND: The burden of human papillomavirus (HPV) infection can be substantially reduced through vaccination of girls, and gender-neutral policies are being adopted in many countries to accelerate disease control among women and expand direct benefits to men. Clinical direct benefit of boys HPV vaccination has been established for ano-genital warts and anal cancer. HPV vaccines are considered safe, but an association with Guillain-Barre syndrome has been found in French reimbursement and hospital discharge data. METHODS: We conducted a Monte-Carlo simulation assuming a stable French population of 11- to 14-year-old boys, adult men and men having sex with men. We modelled and quantified the mid-term benefits as the annually prevented ano-genital warts among the 8.72 M men aged 15-35 years and the long-term benefits as the annually prevented anal cancer cases among the 17.4 M men aged 25-65 years. We also estimated the number of Guillain-Barre syndrome cases hypothetically induced by vaccination. RESULTS: With a vaccine coverage of 30%, an annual number of 9310 (95% uncertainty interval [7050-11,200]) first ano-genital warts episodes among the 8.72 M men aged 15-35 years are prevented. According to more or less optimistic hypotheses on the proportion of HPV cancers covered by the vaccine, between 15.1 [11.7-17.7] and 19.2 [15.0-22.6] cases of anal cancer among the 17.4 M men aged 25-65 years would be annually avoided. Among men having sex with men, the corresponding figures were 1907 (1944-2291) for ano-genital warts and between 2.0 [0.23-4.5] and 2.6 [0.29-5.7] for anal cancer. Among 11- to 14-year-old boys, 0.82 (0.15-2.3) Guillain-Barre syndrome cases would be induced annually. INTERPRETATION: A long-term program of HPV vaccination among boys in France would avoid substantially more cancer cases than hypothetically induce Guillain-Barre syndrome cases, in the general and specifically the homosexual population. Additional benefits may arise with the possible vaccine protection against oro-laryngeal and -pharyngeal cancer.

Impact of the SARS-CoV-2 Epidemic on Lung Cancer Surgery in France: A Nationwide Study.

Few studies have investigated the link between SARS-CoV-2 and health restrictions and its effects on the health of lung cancer (LC) patients. The aim of this study was to assess the impact of the SARS-CoV-2 epidemic on surgical activity volume, postoperative complications and in-hospital mortality (IHM) for LC resections in France. All data for adult patients who underwent pulmonary resection for LC in France in 2020, collected from the national administrative database, were compared to 2018-2019. The effect of SARS-CoV-2 on the risk of IHM and severe complications within 30 days among LC surgery patients was examined using a logistic regression analysis adjusted for age, sex, comorbidities and type of resection. There was a slight decrease in the volume of LC resections in 2020 (n = 11,634), as compared to 2018 (n = 12,153) and 2019 (n = 12,227), with a noticeable decrease in April 2020 (the peak of the first wave of epidemic in France). We found that SARS-CoV-2 (0.43% of 2020 resections) was associated with IHM and severe complications, with, respectively, a sevenfold (aOR = 7.17 (3.30-15.55)) and almost a fivefold (aOR = 4.76 (2.31-9.80)) increase in risk. Our study suggests that LC surgery is feasible even during a pandemic, provided that general guidance protocols edited by the surgical societies are respected.

New adaptive lasso approaches for variable selection in automated pharmacovigilance signal detection.

BACKGROUND: Adverse effects of drugs are often identified after market introduction. Post-marketing pharmacovigilance aims to detect them as early as possible and relies on spontaneous reporting systems collecting suspicious cases. Signal detection tools have been developed to mine these large databases and counts of reports are analysed with disproportionality methods. To address disproportionality method biases, recent methods apply to individual observations taking into account all exposures for the same patient. In particular, the logistic lasso provides an efficient variable selection framework, yet the choice of the regularization parameter is a challenging issue and the lasso variable selection may give inconsistent results. METHODS: We propose a new signal detection methodology based on the adaptive lasso. We derived two new adaptive weights from (i) a lasso regression using the Bayesian Information Criterion (BIC), and (ii) the class-imbalanced subsampling lasso (CISL), an extension of stability selection. The BIC is used in the adaptive lasso stage for variable selection. We performed an extensive simulation study and an application to real data, where we compared our methods to the existing adaptive lasso, and recent detection approaches based on lasso regression or propensity scores in high dimension. For both studies, we evaluate the methods in terms of false discoveries and sensitivity. RESULTS: In the simulations and the application, both proposed adaptive weights show equivalent or better performances than the other competitors, with an advantage for the CISL-based adaptive weights. CISL and lasso regression using BIC are solid alternatives. CONCLUSION: Our proposed adaptive lasso is an appealing methodology for signal detection in pharmacovigilance. Although we cannot rely on test theory, our approaches show a low and stable False Discovery Rate in all simulation settings. All methods evaluated in this work are implemented in the adapt4pv R package.

Impact of COVID-19 and lockdowns on pulmonary embolism in hospitalized patients in France: a nationwide study.

BACKGROUND: This study assessed the impact of the COVID-19 epidemic on overall hospitalizations for pulmonary embolism (PE) in France in comparison with previous years, and by COVID-19 and non-COVID-19 status. METHODS: Hospitalization data (2017-2020) were extracted from the French National Discharge database (all public and private hospitals). We included all patients older than 18 years hospitalized during the 3 years and extracted PE status and COVID-19 status (from March 2020). Age, sex and risk factors for PE (such as obesity, cancer) were identified. We also extracted transfer to an intensive care unit (ICU) and hospital death. The number of PE and the frequency of death in patients in 2019 and 2020 were described by month and by COVID-19 status. Logistic regressions were performed to identify the role of COVID-19 among other risk factors for PE in hospitalized patients. RESULTS: The overall number of patients hospitalized with PE increased by about 16% in 2020 compared with 2019, and mortality also increased to 10.3% (+ 1.2%). These increases were mostly linked to COVID-19 waves, which were associated with PE hospitalization in COVID-19 patients (PE frequency was 3.7%; 2.8% in non-ICU and 8.8% in ICU). The final PE odds ratio for COVID-19 hospitalized patients was 4 compared with other hospitalized patients in 2020. The analyses of PE in non-COVID-19 patients showed a 2.7% increase in 2020 compared with the previous three years. CONCLUSION: In 2020, the overall number of patients hospitalized with PE in France increased compared to the previous three years despite a considerable decrease in scheduled hospitalizations. Nevertheless, proactive public policy focused on the prevention of PE in all patients should be encouraged.

Chronic respiratory diseases are predictors of severe outcome in COVID-19 hospitalised patients: a nationwide study.

BACKGROUND: Influenza epidemics were initially considered to be a suitable model for the COVID-19 epidemic, but there is a lack of data concerning patients with chronic respiratory diseases (CRDs), who were supposed to be at risk of severe forms of COVID-19. METHODS: This nationwide retrospective cohort study describes patients with prior lung disease hospitalised for COVID-19 (March-April 2020) or influenza (2018-2019 influenza outbreak). We compared the resulting pulmonary complications, need for intensive care and in-hospital mortality depending on respiratory history and virus. RESULTS: In the 89 530 COVID-19 cases, 16.03% had at least one CRD, which was significantly less frequently than in the 45 819 seasonal influenza patients. Patients suffering from chronic respiratory failure, chronic obstructive pulmonary disease, asthma, cystic fibrosis and pulmonary hypertension were under-represented, contrary to those with lung cancer, sleep apnoea, emphysema and interstitial lung diseases. COVID-19 patients with CRDs developed significantly more ventilator-associated pneumonia and pulmonary embolism than influenza patients. They needed intensive care significantly more often and had a higher mortality rate (except for asthma) when compared with patients with COVID-19 but without CRDs or patients with influenza. CONCLUSIONS: Patients with prior respiratory diseases were globally less likely to be hospitalised for COVID-19 than for influenza, but were at higher risk of developing severe COVID-19 and had a higher mortality rate compared with influenza patients and patients without a history of respiratory illness.

Comparison of Cancer Patients to Non-Cancer Patients among COVID-19 Inpatients at a National Level.

(1) Background: Several smaller studies have shown that COVID-19 patients with cancer are at a significantly higher risk of death. Our objective was to compare patients hospitalized for COVID-19 with cancer to those without cancer using national data and to study the effect of cancer on the risk of hospital death and intensive care unit (ICU) admission. (2) Methods: All patients hospitalized in France for COVID-19 in March-April 2020 were included from the French national administrative database, which contains discharge summaries for all hospital admissions in France. Cancer patients were identified within this population. The effect of cancer was estimated with logistic regression, adjusting for age, sex and comorbidities. (3) Results: Among the 89,530 COVID-19 patients, we identified 6201 cancer patients (6.9%). These patients were older and were more likely to be men and to have complications (acute respiratory and kidney failure, venous thrombosis, atrial fibrillation) than those without cancer. In patients with hematological cancer, admission to ICU was significantly more frequent (24.8%) than patients without cancer (16.4%) (p < 0.01). Solid cancer patients without metastasis had a significantly higher mortality risk than patients without cancer (aOR = 1.4 [1.3-1.5]), and the difference was even more marked for metastatic solid cancer patients (aOR = 3.6 [3.2-4.0]). Compared to patients with colorectal cancer, patients with lung cancer, digestive cancer (excluding colorectal cancer) and hematological cancer had a higher mortality risk (aOR = 2.0 [1.6-2.6], 1.6 [1.3-2.1] and 1.4 [1.1-1.8], respectively). (4) Conclusions: This study shows that, in France, patients with COVID-19 and cancer have a two-fold risk of death when compared to COVID-19 patients without cancer. We suggest the need to reorganize facilities to prevent the contamination of patients being treated for cancer, similar to what is already being done in some countries.

Hospitalizations for Stroke in France During the COVID-19 Pandemic Before, During, and After the National Lockdown.

BACKGROUND AND PURPOSE: In France, the entire population was put under a total lockdown from March 17 to May 11, 2020 during the peak of the coronavirus disease 2019 (COVID-19) pandemic. Whether the lockdown had consequences on the management of medical emergencies such as stroke and transient ischemic attack (TIA) has yet to be fully evaluated. This article describes hospitalization rates for acute stroke in 2 French regions that experienced contrasting rates of COVID-19 infection, before, during, and after the nationwide lockdown (January to June 2020). METHODS: All patients admitted for acute stroke/TIA into all public and private hospitals of the 2 study regions were included. Data were retrieved from the National Hospitalization Database (PMSI). In the most affected region (Grand-Est), the hospitalization rates observed in April 2020 were compared with the rates in the same period in the least affected region (Occitanie) and in the 3 prior years (2017-2019). RESULTS: There was a significant decline in hospitalization rates for stroke/TIA within the region most affected by COVID-19 during the month of April 2020 compared with previous years, while no significant change was seen in the least affected region. After lockdown, we observed a fast rebound in the rate of hospitalization for stroke/TIA in the most affected region, contrasting with a slower rebound in the least affected region. In both regions, patients with COVID-19 stroke more frequently had ischemic stroke, a nonsignificant greater prevalence of diabetes, they were less frequently admitted to stroke units, and mortality was higher than in patients without COVID-19. CONCLUSIONS: Our results demonstrates a significant drop in stroke/TIA hospitalizations and a fast recovery after the end of the French lockdown in the most affected region, while the least affected region saw a nonsignificant drop in stroke/TIA hospitalizations and a slow recovery. These results and recommendations could be used by the health authorities to prepare for future challenges.

Comparison of the characteristics, morbidity, and mortality of COVID-19 and seasonal influenza: a nationwide, population-based retrospective cohort study.

BACKGROUND: To date, influenza epidemics have been considered suitable for use as a model for the COVID-19 epidemic, given that they are respiratory diseases with similar modes of transmission. However, data directly comparing the two diseases are scarce. METHODS: We did a nationwide retrospective cohort study using the French national administrative database (PMSI), which includes discharge summaries for all hospital admissions in France. All patients hospitalised for COVID-19 from March 1 to April 30, 2020, and all patients hospitalised for influenza between Dec 1, 2018, and Feb 28, 2019, were included. The diagnosis of COVID-19 (International Classification of Diseases [10th edition] codes U07.10, U07.11, U07.12, U07.14, or U07.15) or influenza (J09, J10, or J11) comprised primary, related, or associated diagnosis. Comparisons of risk factors, clinical characteristics, and outcomes between patients hospitalised for COVID-19 and influenza were done, with data also stratified by age group. FINDINGS: 89 530 patients with COVID-19 and 45 819 patients with influenza were hospitalised in France during the respective study periods. The median age of patients was 68 years (IQR 52-82) for COVID-19 and 71 years (34-84) for influenza. Patients with COVID-19 were more frequently obese or overweight, and more frequently had diabetes, hypertension, and dyslipidaemia than patients with influenza, whereas those with influenza more frequently had heart failure, chronic respiratory disease, cirrhosis, and deficiency anaemia. Patients admitted to hospital with COVID-19 more frequently developed acute respiratory failure, pulmonary embolism, septic shock, or haemorrhagic stroke than patients with influenza, but less frequently developed myocardial infarction or atrial fibrillation. In-hospital mortality was higher in patients with COVID-19 than in patients with influenza (15 104 [16·9%] of 89 530 vs 2640 [5·8%] of 45 819), with a relative risk of death of 2·9 (95% CI 2·8-3·0) and an age-standardised mortality ratio of 2·82. Of the patients hospitalised, the proportion of paediatric patients (<18 years) was smaller for COVID-19 than for influenza (1227 [1·4%] vs 8942 [19·5%]), but a larger proportion of patients younger than 5 years needed intensive care support for COVID-19 than for influenza (14 [2·3%] of 613 vs 65 [0·9%] of 6973). In adolescents (11-17 years), the in-hospital mortality was ten-times higher for COVID-19 than for influenza (five [1·1% of 458 vs one [0·1%] of 804), and patients with COVID-19 were more frequently obese or overweight. INTERPRETATION: The presentation of patients with COVID-19 and seasonal influenza requiring hospitalisation differs considerably. Severe acute respiratory syndrome coronavirus 2 is likely to have a higher potential for respiratory pathogenicity, leading to more respiratory complications and to higher mortality. In children, although the rate of hospitalisation for COVID-19 appears to be lower than for influenza, in-hospital mortality is higher; however, low patient numbers limit this finding. These findings highlight the importance of appropriate preventive measures for COVID-19, as well as the need for a specific vaccine and treatment. FUNDING: French National Research Agency.

Accounting for indirect protection in the benefit-risk ratio estimation of rotavirus vaccination in children under the age of 5 years, France, 2018.

Background Rotavirus is a major cause of severe gastroenteritis in children worldwide. The disease burden has been substantially reduced in countries where rotavirus vaccines are used. Given the risk of vaccine-induced intussusception, the benefit­risk balance of rotavirus vaccination has been assessed in several countries, however mostly without considering indirect protection effects. Aim We performed a benefit­risk analysis of rotavirus vaccination accounting for indirect protection in France among the 2018 population of children under the age of 5 years. Methods To incorporate indirect protection effects in the benefit formula, we adopted a pseudo-vaccine approach involving mathematical approximation and used a simulation design to provide uncertainty intervals. We derived background incidence distributions from quasi-exhaustive health claim data. We examined different coverage levels and assumptions regarding the waning effects and intussusception case fatality rate. Results With the current vaccination coverage of < 10%, the indirect effectiveness was estimated at 6.4% (+/− 0.4). For each hospitalisation for intussusception, 277.0 (95% uncertainty interval: (165.0­462.1)) hospitalisations for rotavirus gastroenteritis were prevented. Should 90% of infants be vaccinated, indirect effectiveness would reach 57.9% (+/− 3.7) and the benefit­risk ratio would be 192.4 (95% uncertainty interval: 116.4­321.3). At a coverage level of 50%, indirect protection accounted for 27% of the prevented rotavirus gastroenteritis cases. The balance remained in favour of the vaccine even in a scenario with a high assumption for intussusception case fatality. Conclusions These findings contribute to a better assessment of the rotavirus vaccine benefit­risk balance.

Evaluation of the Quality of French Hospital Data for Perinatal Algorithms.

The aim of our validation study was to assess the quality of hospital data for perinatal algorithms on a national level. In each hospital, we selected 150 discharge abstracts of delivery (after 22 weeks of gestation), in 2014, and their corresponding medical records. Overall, 23 hospitals were included and 3,246 discharge abstracts were studied. This first national validation study of several case-funding algorithms using various perinatal variables suggests that the French national hospital discharge abstracts database is an appropriate data source for epidemiological studies.

Validation study: evaluation of the metrological quality of French hospital data for perinatal algorithms.

OBJECTIVE: The aim of our validation study was to assess the metrological quality of hospital data for perinatal algorithms on a national level. DESIGN: Validation study. SETTING: This was a multicentre study of the French medicoadministrative database on perinatal indicators. PARTICIPANTS: In each hospital, we selected 150 discharge abstracts for delivery (after 22 weeks of gestation), in 2014, and their corresponding medical records. Overall, 22 hospitals were included. INTERVENTIONS: A single investigator performed blind data collection from medical records in order to compare data from discharge abstracts with data from medical records. Finally, 3246 discharge abstracts were studied. PRIMARY AND SECONDARY OUTCOME MEASURES: Seventy items, including maternal and delivery characteristics and maternal morbidity, were collected for each delivery stay. RESULTS: The concordance rate of maternal age at delivery was 94.8% (95% CI 93.8 to 95.4). Combining the two forms of pre-existing diabetes, the algorithm presented a PPV of 65.9% and a sensitivity of 75.7%. The concordance rate of gestational age at delivery was 91.8% (90.9 to 92.7). Regarding gestational diabetes, the PPV was 80.8% (79.4 to 82.2) and the sensitivity was 79.5% (78.1 to 80.9). Regardless of the algorithm explored, the PPV for vaginal delivery was over 99%. For the diagnosis codes corresponding to immediate postpartum haemorrhage, the PPV was 77.7% (76.3 to 79.1) and the sensitivity was 75.5% (74.0 to 77.0). The algorithm for stillbirth presented a PPV of 89.4% (88.3 to 90.5) and a sensitivity of 95.4% (94.7 to 96.1). CONCLUSIONS: This first national validation study of many perinatal algorithms suggests that the French national hospital database is an appropriate data source for epidemiological studies, except for some indicators which presented low PPV and/or sensitivity.

Identifying Drugs Inducing Prematurity by Mining Claims Data with High-Dimensional Confounder Score Strategies.

BACKGROUND: Pregnant women are largely exposed to medications. However, knowledge is lacking about their effects on pregnancy and the fetus. OBJECTIVE: This study sought to evaluate the potential of high-dimensional propensity scores and high-dimensional disease risk scores for automated signal detection in pregnant women from medico-administrative databases in the context of drug-induced prematurity. METHODS: We used healthcare claims and hospitalization discharges of a 1/97th representative sample of the French population. We tested the association between prematurity and drug exposure during the trimester before delivery, for all drugs prescribed to at least five pregnancies. We compared different strategies (1) for building the two scores, including two machine-learning methods and (2) to account for these scores in the final logistic regression models: adjustment, weighting, and matching. We also proposed a new signal detection criterion derived from these scores: the p value relative decrease. Evaluation was performed by assessing the relevance of the signals using a literature review and clinical expertise. RESULTS: Screening 400 drugs from a cohort of 57,407 pregnancies, we observed that choosing between the two machine-learning methods had little impact on the generated signals. Score adjustment performed better than weighting and matching. Using the p value relative decrease efficiently filtered out spurious signals while maintaining a number of relevant signals similar to score adjustment. Most of the relevant signals belonged to the psychotropic class with benzodiazepines, antidepressants, and antipsychotics. CONCLUSIONS: Mining complex healthcare databases with statistical methods from the high-dimensional inference field may improve signal detection in pregnant women.

Seasonal influenza vaccine and Guillain-Barré syndrome: A self-controlled case series study.

OBJECTIVE: To evaluate the risk of Guillain-Barré syndrome (GBS) following seasonal influenza vaccination based on French nationwide data. METHODS: All cases of GBS occurring in metropolitan France between September 1 and March 31 from 2010 to 2014 were identified from the French national health data system. Data were analyzed according to the self-controlled case series method. The risk period started 1 day after the patient received vaccine (D1) until 42 days after vaccination (D42). The incidence of GBS during this risk period was compared to that of the control period (D43-March 31). The incidence rate ratio (IRR) was estimated after adjusting for seasonality and presence or not of acute infections. RESULTS: Between September and March, of the 2010/2011 to 2013/2014 influenza vaccination seasons, 3,523 cases of GBS occurred in metropolitan France and were included in the study. Among them, 15% (527 patients) had received influenza vaccination. A total of 140 patients developed GBS during the 42 days following influenza vaccination. The crude risk of developing GBS was not significantly increased during the 42 days following influenza vaccination (IRR, 1.02; 95% confidence interval [CI], 0.83-1.25; p = 0.85). This result remained nonsignificant after adjustment for calendar months and the incidence of acute gastrointestinal and respiratory tract infections (IRR, 1.10; 95% CI, 0.89-1.37; p = 0.38). In contrast, the risk of GBS was fourfold higher after acute respiratory tract infection (IRR, 3.89; 95% CI, 3.52-4.30; p < 0.0001) or gastrointestinal infection (IRR, 3.64; 95% CI, 3.01-4.40; p < 0.0001). CONCLUSIONS: No association between seasonal influenza vaccination and GBS was shown during the 42 days following vaccination.