Vaccines and the Risk of Hospitalization for Multiple Sclerosis Flare-Ups

Importance

Scientific literature is sparse about the association of vaccination with the onset of multiple sclerosis (MS) flare-ups. Immunization by vaccines of the entire population is crucially important for public health.

Objective

To evaluate the risk of hospitalization for severe MS flare-ups after vaccination in patients with MS.

Design, Setting, Participants

This cohort study included patients diagnosed with MS between January 1, 2007, and December 31, 2017, who were included in the System of National Health Databases, a national health claims database in France. In a nested case-crossover analysis, cases were defined by vaccine exposure prior to the onset of hospitalization due to an MS flare-up, and flare-up rates were compared with those that occurred prior to vaccine exposure in up to 4 control time windows immediately preceding the at-risk time window (ie, the MS flare-up) for each patient. Data were analyzed from January 2022 to December 2022.

Exposure

Receipt of at least 1 vaccination, including the diphtheria, tetanus, poliomyelitis, pertussis, or Haemophilus influenzae (DTPPHi) vaccine, influenza vaccine, and pneumococcal vaccine, during follow-up.

Main Outcomes and Measures

The primary outcome was the risk of hospitalization for an MS flare-up after receipt of a vaccine. Adjusted odds ratios (AORs) and 95% CIs were derived using conditional logistic regression to measure the risk of hospitalization for an MS flare-up associated with vaccination.

Results

A total of 106 523 patients constituted the MS cohort (mean [SD] age, 43.9 [13.8] years; 76 471 females [71.8%]; 33 864 patients [31.8%] had incident MS and 72 659 patients [68.2%] had prevalent MS) and were followed up for a mean (SD) of 8.8 (3.1) years. Of these patients, 35 265 (33.1%) were hospitalized for MS flare-ups during the follow-up period for a total of 54 036 MS-related hospitalizations. The AORs of hospitalization for an MS flare-up and vaccine exposure in the 60 days prior to the flare-up were 1.00 (95% CI, 0.92-1.09) for all vaccines, 0.95 (95% CI, 0.82-1.11) for the DTPPHi, 0.98 (95% CI, 0.88-1.09) for the influenza vaccine, and 1.20 (95% CI, 0.94-1.55) for the pneumococcal vaccine.

Conclusions and Relevance

A nationwide study of the French population found no association between vaccination and the risk of hospitalization due to MS flare-ups. However, considering the number of vaccine subtypes available, further studies are needed to confirm these results.

A statistical algorithm for outbreak detection in multisite settings: an application to sick leave monitoring

Motivation

Public health authorities monitor cases of health-related problems over time using surveillance algorithms that detect unusually high increases in the number of cases, namely aberrations. Statistical aberrations signal outbreaks when further investigation reveals epidemiological significance. The increasing availability and diversity of epidemiological data and the most recent epidemic threats call for more accurate surveillance algorithms that not just detect aberration times but also detect locations. Sick leave data, for instance, can be monitored across companies to identify companies-related aberrations. In this context, we develop an extension to multisite surveillance of a routinely used aberration detection algorithm, the quasi-Poisson regression Farrington Flexible algorithm. The new algorithm consists of a negative-binomial mixed effects regression model with a random effects term for sites and a new reweighting procedure reducing the effect of past aberrations.

Results

A wide range of simulations shows that, compared with Farrington Flexible, the new algorithm produces better false positive rates and similar probabilities of detecting genuine outbreaks, for case counts that exceed historical baselines by 3 SD. As expected, higher surges lead to lower false positive rates and higher probabilities of detecting true outbreaks. The new algorithm provides better detection of true outbreaks, reaching 100%, when cases exceed eight baseline standard deviations. We apply our algorithm to sick leave rates in the context of COVID-19 and find that it detects the pandemic effect. The new algorithm is easily implementable over a range of contrasting data scenarios, providing good overall performance and new perspectives for multisite surveillance.

Availability and implementation

All the analyses are performed in the R statistical software using the package glmmTMB. The code for performing the analyses and for generating the simulations can be found online at the following link: https://github.com/TomDuchemin/mixed_surveillance.

Contact

a.noufaily@warwick.ac.uk.

Sick leave due to COVID-19 during the first pandemic wave in France, 2020

OBJECTIVES

To quantify the burden of COVID-19-related sick leave during the first pandemic wave in France, accounting for sick leaves due to symptomatic COVID-19 ('symptomatic sick leaves') and those due to close contact with COVID-19 cases ('contact sick leaves').

METHODS

We combined data from a national demographic database, an occupational health survey, a social behaviour survey and a dynamic SARS-CoV-2 transmission model. Sick leave incidence from 1 March 2020 to 31 May 2020 was estimated by summing daily probabilities of symptomatic and contact sick leaves, stratified by age and administrative region.

RESULTS

There were an estimated 1.70M COVID-19-related sick leaves among France's 40M working-age adults during the first pandemic wave, including 0.42M due to COVID-19 symptoms and 1.28M due to COVID-19 contacts. There was great geographical variation, with peak daily sick leave incidence ranging from 230 in Corse (Corsica) to 33 000 in Île-de-France (the greater Paris region), and greatest overall burden in regions of north-eastern France. Regional sick leave burden was generally proportional to local COVID-19 prevalence, but age-adjusted employment rates and contact behaviours also contributed. For instance, 37% of symptomatic infections occurred in Île-de-France, but 45% of sick leaves. Middle-aged workers bore disproportionately high sick leave burden, owing predominantly to greater incidence of contact sick leaves.

CONCLUSIONS

France was heavily impacted by sick leave during the first pandemic wave, with COVID-19 contacts accounting for approximately three-quarters of COVID-19-related sick leaves. In the absence of representative sick leave registry data, local demography, employment patterns, epidemiological trends and contact behaviours can be synthesised to quantify sick leave burden and, in turn, predict economic consequences of infectious disease epidemics.

Distinct Microbiotas Are Associated with Different Production Lines in the Cutting Room of a Swine Slaughterhouse

The microorganisms found on fresh, raw meat cuts at a slaughterhouse can influence the meat's safety and spoilage patterns along further stages of processing. However, little is known about the general microbial ecology of the production environment of slaughterhouses. We used 16s rRNA sequencing and diversity analysis to characterize the microbiota heterogeneity on conveyor belt surfaces in the cutting room of a swine slaughterhouse from different production lines (each associated with a particular piece/cut of meat). Variation of the microbiota over a period of time (six visits) was also evaluated. Significant differences of alpha and beta diversity were found between the different visits and between the different production lines. Bacterial genera indicative of each visit and production line were also identified. We then created random forest models that, based on the microbiota of each sample, allowed us to predict with 94% accuracy to which visit a sample belonged and to predict with 88% accuracy from which production line it was taken. Our results suggest a possible influence of meat cut on processing surface microbiotas, which could lead to better prevention, surveillance, and control of microbial contamination of meat during processing.

Monitoring sick leave data for early detection of influenza outbreaks

Background

Workplace absenteeism increases significantly during influenza epidemics. Sick leave records may facilitate more timely detection of influenza outbreaks, as trends in increased sick leave may precede alerts issued by sentinel surveillance systems by days or weeks. Sick leave data have not been comprehensively evaluated in comparison to traditional surveillance methods. The aim of this paper is to study the performance and the feasibility of using a detection system based on sick leave data to detect influenza outbreaks.

Methods

Sick leave records were extracted from private French health insurance data, covering on average 209,932 companies per year across a wide range of sizes and sectors. We used linear regression to estimate the weekly number of new sick leave spells between 2016 and 2017 in 12 French regions, adjusting for trend, seasonality and worker leaves on historical data from 2010 to 2015. Outbreaks were detected using a 95%-prediction interval. This method was compared to results from the French Sentinelles network, a gold-standard primary care surveillance system currently in place.

Results

Using sick leave data, we detected 92% of reported influenza outbreaks between 2016 and 2017, on average 5.88 weeks prior to outbreak peaks. Compared to the existing Sentinelles model, our method had high sensitivity (89%) and positive predictive value (86%), and detected outbreaks on average 2.5 weeks earlier.

Conclusion

Sick leave surveillance could be a sensitive, specific and timely tool for detection of influenza outbreaks.

Supplementary Information

The online version contains supplementary material available at 10.1186/s12879-020-05754-5.

Modeling sickness absence data: A scoping review

The identification of sick leave determinants could positively influence decision making to improve worker quality of life and to reduce consequently costs for society. Sick leave is a research topic of interest in economics, psychology, health and social behaviour. The question of choosing an appropriate statistical tool to analyse sick leave data can be challenging. In fact, sick leave data have a complex structure, characterized by two dimensions: frequency and duration, and involve numerous features related to individual and environmental factors. We conducted a scoping review to characterize statistical approaches to analyse individual sick leave data in order to synthesise key insights from the extensive literature, as well as to identify gaps in research. We followed the PRISMA methodology for scoping reviews and searched Medline, World of Science, Science Direct, Psycinfo and EconLit for publications using statistical modeling for explaining or predicting sick leave at the individual level. We selected 469 articles from the 5983 retrieved, dated from 1981 to 2019. In total, three types of model were identified: univariate outcome modeling using for the most part count models (438 articles), bivariate outcome modeling (14 articles), such as multistate models and structural equation modeling (22 articles). The review shows that there was a lack of evaluation of the models as predictive accuracy was only evaluated in 18 articles and the explanatory accuracy in 43 articles. Further research based on joint models could bring more insights on sick leave spells, considering both their frequency and duration.