Trends in work-related mental disorders by sector in France.

BACKGROUND: Notifications of work-related mental disorders (WRMDs) are increasing while little data are available on trends by industrial sector. AIMS: To assess WRMD notifications and trends from 2001 to 2011 by industrial sector using data collected by the French National Network for Occupational Disease Vigilance and Prevention (RNV3P) network. METHODS: We calculated standardized notification ratios (SNRs) per sector. To analyse trends over time, we used odds ratios, analysed by logistic regression, including time as a discrete variable or as a continuous variable. RESULTS: We found higher than expected WRMD notifications in manufacturing; paper and book (SNR = 2.16; 95% CI [1.88-2.48]), chemical and pharmaceutical (SNR = 1.79; 95% CI [1.58-2.03]), textile and clothing (SNR = 1.27; 95% CI [1.04-1.54]). In the trade sectors, retail (SNR = 1.60; 95% CI [1.52-1.68]), motor vehicle and cycle trade and repair (SNR = 1.19; 95% CI [1.05-1.34]). In the services sector personal (SNR = 2.09; 95% CI [1.83-2.38]), information technology (SNR = 1.54; 95% CI [1.36-1.74]), financial and insurance (SNR = 1.42; 95% CI [1.31-1.53]), post and telecommunication (SNR = 1.44; 95% CI [1.30-1.60]), human health and social work (SNR = 1.29; 95% CI [1.24-1.34]). We found an increase in notifications in agriculture, post-telecommunication services and the human health/social work sectors and a decrease in chemical/pharmaceutical, metal, and paper and book manufacturing sectors (P < 0.05). CONCLUSIONS: These results reveal trends in notifications for WRMDs by industrial sector. This highlights the importance of monitoring trends in WRMDs in specific worker subsets, using specialized networks such as the RNV3P.

Progress Towards Using Linked Population-Based Data For Geohealth Research: Comparisons Of Aotearoa New Zealand And The United Kingdom.

Globally, geospatial concepts are becoming increasingly important in epidemiological and public health research. Individual level linked population-based data afford researchers with opportunities to undertake complex analyses unrivalled by other sources. However, there are significant challenges associated with using such data for impactful geohealth research. Issues range from extracting, linking and anonymising data, to the translation of findings into policy whilst working to often conflicting agendas of government and academia. Innovative organisational partnerships are therefore central to effective data use. To extend and develop existing collaborations between the institutions, in June 2019, authors from the Leeds Institute for Data Analytics and the Alan Turing Institute, London, visited the Geohealth Laboratory based at the University of Canterbury, New Zealand. This paper provides an overview of insight shared during a two-day workshop considering aspects of linked population-based data for impactful geohealth research. Specifically, we discuss both the collaborative partnership between New Zealand's Ministry of Health (MoH) and the University of Canterbury's GeoHealth Lab and novel infrastructure, and commercial partnerships enabled through the Leeds Institute for Data Analytics and the Alan Turing Institute in the UK. We consider the New Zealand Integrated Data Infrastructure as a case study approach to population-based linked health data and compare similar approaches taken by the UK towards integrated data infrastructures, including the ESRC Big Data Network centres, the UK Biobank, and longitudinal cohorts. We reflect on and compare the geohealth landscapes in New Zealand and the UK to set out recommendations and considerations for this rapidly evolving discipline.