Recommendations to plan a national burden of disease study.

BACKGROUND: The InfAct (Information for Action) project is a European Commission Joint Action on Health Information which has promoted the potential role of burden of disease (BoD) approaches to improve the current European Union-Health Information System (EU-HIS). It has done so by raising awareness of the concept, the methods used to calculate estimates and their potential implications and uses in policymaking. The BoD approach is a systematic and scientific effort to quantify and compare the magnitude of health loss due to different diseases, injuries, and risk factors with estimates produced by demographic characteristics and geographies for specific points in time. Not all countries have the resources to undertake such work, and may therefore start with a more restricted objective, e.g., a limited number of diseases, or the use of simple measures of population health such as disease prevalence or life expectancy. The main objective to develop these recommendations was to facilitate those countries planning to start a national burden of disease study. RESULTS: These recommendations could be considered as minimum requirements for those countries planning to start a BoD study and includes following elements: (1) Define the objectives of a burden of disease study within the context of your country, (2) Identify, communicate and secure the benefits of performing national burden of disease studies, (3) Secure access to the minimum required data sources, (4) Ensure the minimum required capacity and capability is available to carry out burden of disease study, (5) Establish a clear governance structure for the burden of disease study and stakeholder engagement/involvement, (6) Choose the appropriate methodological approaches and (7) Knowledge translation. These were guided by the results from our survey performed to identify the needs of European countries for BoD studies, a narrative overview from four European countries (Belgium, Germany, The Netherlands and Scotland) and the summary of a comparative study of country health profiles with national health statistics. CONCLUSIONS: These recommendations as minimum requirements would facilitate efforts by those European countries who intend to perform national BoD studies.

Socioeconomic inequality in recent adverse all-cause mortality trends in Scotland.

BACKGROUND: Gains in life expectancies have stalled in Scotland, as in several other countries, since around 2012. The relationship between stalling mortality improvements and socioeconomic inequalities in health is unclear. METHODS: We calculate the difference, as percentage change, in all-cause, all-age, age-standardised mortality rates (ASMR) between 2006 and 2011 (period 1) and between 2012 and 2017 (period 2), for Scotland overall, by sex, and by Scottish Index of Multiple Deprivation (SIMD) quintile. Linear regression is used to summarise the relationship between SIMD quintile and mortality rate change in each period. RESULTS: Between 2006 and 2011, the overall ASMR fell by 10.6% (138/100 000), by 10.1% in women, and 11.8% in men, but between 2012 and 2017 the overall ASMR fell by only 2.6% (30/100 000), by 3.5% in women, and by 2.0% in men. Within the most deprived quintile, the overall ASMR fell by 8.6% (143/100 000) from 2006 to 2011 (7.2% in women; 9.8% in men), but rose by 1.5% (21/100 000) from 2012 to 2017 (0.7% in women; 2.1% in men).The socioeconomic gradient in ASMR improvement more than quadrupled, from 0.4% per quintile in period 1, to 1.7% per quintile in period 2. CONCLUSION: From 2012 to 2017, socioeconomic gradients in mortality improvement in Scotland were markedly steeper than over the preceding 6 years. As a result, there has not only been a slowdown in overall reductions in mortality, but a widening of socioeconomic mortality inequalities.

A Position Statement on Population Data Science: The Science of Data about People.

Information is increasingly digital, creating opportunities to respond to pressing issues about human populations using linked datasets that are large, complex, and diverse. The potential social and individual benefits that can come from data-intensive science are large, but raise challenges of balancing individual privacy and the public good, building appropriate socio-technical systems to support data-intensive science, and determining whether defining a new field of inquiry might help move those collective interests and activities forward. A combination of expert engagement, literature review, and iterative conversations led to our conclusion that defining the field of Population Data Science (challenge 3) will help address the other two challenges as well. We define Population Data Science succinctly as the science of data about people and note that it is related to but distinct from the fields of data science and informatics. A broader definition names four characteristics of: data use for positive impact on citizens and society; bringing together and analyzing data from multiple sources; finding population-level insights; and developing safe, privacy-sensitive and ethical infrastructure to support research. One implication of these characteristics is that few people possess all of the requisite knowledge and skills of Population Data Science, so this is by nature a multi-disciplinary field. Other implications include the need to advance various aspects of science, such as data linkage technology, various forms of analytics, and methods of public engagement. These implications are the beginnings of a research agenda for Population Data Science, which if approached as a collective field, can catalyze significant advances in our understanding of trends in society, health, and human behavior.