The introduction of a data-driven population health management approach in the Netherlands since 2019: The Extramural LUMC Academic Network data infrastructure.

Improving population health and reducing inequalities through better integrated health and social care services is high up on the agenda of policymakers internationally. In recent years, regional cross-domain partnerships have emerged in several countries, which aim to achieve better population health, quality of care and a reduction in the per capita costs. These cross-domain partnerships aim to have a strong data foundation and are committed to continuous learning in which data plays an essential role. This paper describes our approach towards the development of the regional integrative population-based data infrastructure Extramural LUMC (Leiden University Medical Center) Academic Network (ELAN), in which we linked routinely collected medical, social and public health data at the patient level from the greater The Hague and Leiden area. Furthermore, we discuss the methodological issues of routine care data and the lessons learned about privacy, legislation and reciprocities. The initiative presented in this paper is relevant for international researchers and policy-makers because a unique data infrastructure has been set up that contains data across different domains, providing insights into societal issues and scientific questions that are important for data driven population health management approaches.

The Obesity Paradox in Lung Cancer: Associations With Body Size Versus Body Shape.

BACKGROUND: The association between obesity and lung cancer (LC) remains poorly understood. However, other indices of obesity on the basis of body shape instead of body size have not been examined yet. The aim of this study was to evaluate the association between different indices of body size and body shape and the risk of LC. In particular, this study examined the association between A Body Shape Index, a more precise indicator of abdominal fat than traditional anthropometric measures, and the risk of LC. METHODS: In the prospective cohort the Rotterdam Study, we analysed data of 9,689 participants. LC diagnoses were based on medical records and anthropometric measurements were assessed at baseline. Cox-regression analyses with corresponding Hazard Ratios were used to examine the association between the anthropometric measurements and the risk of LC with adjustment for potential confounders. Potential non-linear associations were explored with cubic splines using the Likelihood ratio (LR) test. RESULTS: During follow-up, 319 participants developed LC. Body mass Index (BMI) was inversely associated with the risk of lung cancer (HR 0.94, 95% CI: 0.91-0.97) and persisted after excluding lung cancer cases during the first 10 years of follow-up. There was evidence for a non-linear association between BMI and the risk of lung cancer (0,04, df = 1), which indicated that the inverse association between BMI and lung cancer was mainly present in non-obese participants. Waist circumference (WC) (HR 1.03 95% CI: 1.01-1.05), Waist-to-Hip Ratio (WHR) (HR 1.23 95% CI: 1.09-1.38) and ABSI (A Body Shape Index) (HR 1.17 95% CI: 1.05-1.30) were positively and linearly associated with the risk of lung cancer. CONCLUSIONS: Body shape rather than body size may be an important risk indicator of LC. Future research should focus on the role of visceral fat and the risk of LC as well as the underlying mechanisms.