Background Social determinants of health (SDoH) are known to have a large impact on health outcomes, but their effects are difficult to make visible. They are part of complex systems of variables largely indirect effects on multiple levels, constituting so-called wicked problems. This study describes a participatory approach using group model building (GMB) with stakeholders, in order to develop a qualitative causal model of the health effects of SDoH, taking poverty and debt in the Dutch city of Utrecht as a case study. Methods With GMB we utilised the perspective of stakeholders who are directly involved in policy and practice regarding poverty, debt, and/or health. This was done using system dynamic modelling, in three interactive sessions lasting three hours each. In these sessions, they constructed a model, resulting in a system of variables with causal relationships and feedback loops. Subsequently, the results of these GMB sessions were compared to scientific literature and reviewed by a panel of researchers with extensive experience in relevant scientific fields. Results The resulting model contains 71 causal relationships between 39 variables, 29 of which are present in feedback loops. The variables of participation in society, stress, shame, social contacts and use of services/provisions appear to hold prominent roles in the model’s mechanisms. Most of the relationships in the model are supported by scientific literature. The researchers reviewing the model in the scientific meeting agreed that the vast majority of relationships would concur with scientific knowledge, but that the model constructed by the stakeholders consists mostly of individual-level factors, while important conditions usually relate to systemic variables. Conclusions Building a model with GMB helps grasp the complex situation of a wicked problem, for which it is unlikely that its interrelationships result in a fully intuitive understanding with linear mechanisms. Using this approach, effects of SDoH can be made visible and the body of evidence expanded. Importantly, it elicits stakeholders’ perspectives on a complex reality and offers a non-arbitrary way of formulating the model structure. This qualitative model is also well suited to serve as conceptual input for a quantitative model, which can be used to test and estimate the relationships.
Background: Evidence has emerged showing that elderly people and those with pre-existing chronic health conditions may be at higher risk of developing severe health consequences from COVID-19. In Europe, this is of particular relevance with ageing populations living with non-communicable diseases, multi-morbidity and frailty. Published estimates of Years Lived with Disability (YLD) from the Global Burden of Disease (GBD) study help to characterise the extent of these effects. Our aim was to identify the countries across Europe that have populations at highest risk from COVID-19 by using estimates of population age structure and YLD for health conditions linked to severe illness from COVID-19.Methods: Population and YLD estimates from GBD 2017 were extracted for 45 countries in Europe. YLD was restricted to a list of specific health conditions associated with being at risk of developing severe consequences from COVID-19 based on guidance from the United Kingdom Government. This guidance also identified individuals aged 70 years and above as being at higher risk of developing severe health consequences. Study outcomes were defined as: (i) proportion of population aged 70 years and above; and (ii) rate of YLD for COVID-19 for vulnerable health conditions across all ages. Bivariate groupings were established for each outcome and combined to establish overall population-level vulnerability. Results:Countries with the highest proportions of elderly residents were Italy, Greece, Germany, Portugal and Finland. When assessments of population-level YLD rates for COVID-19 vulnerable health conditions were made the highest rates were observed for Bulgaria, Czech Republic, Croatia, Hungary and Bosnia and Herzegovina. A bivariate analysis indicated that the countries at high-risk across both measures of vulnerability were: Bulgaria; Portugal; Latvia; Lithuania; Greece; Germany; Estonia; and Sweden.Conclusion: Routine estimates of population structures and non-fatal burden of disease measures can be usefully combined to create composite indicators of vulnerability for rapid assessments, in this case to severe health consequences from COVID-19. Countries with available results for sub-national regions within their country, or national burden of disease studies that also use sub-national levels for burden quantifications, should consider using non-fatal burden of disease estimates to estimate geographical vulnerability to COVID-19.
Background Burden of disease analyses quantify population health and provide comprehensive overviews of the health status of countries or specific population groups. The comparative risk assessment (CRA) methodology is commonly used to estimate the share of the burden attributable to risk factors. The aim of this paper is to identify and address some selected important challenges associated with CRA, illustrated by examples, and to discuss ways to handle them. Further, the main challenges are addressed and finally, similarities and differences between CRA and health impact assessments (HIA) are discussed, as these concepts are sometimes referred to synonymously but have distinctly different applications. Results CRAs are very data demanding. One key element is the exposure-response relationship described e.g. by a mathematical function. Combining estimates to arrive at coherent functions is challenging due to the large variability in risk exposure definitions and data quality. Also, the uncertainty attached to this data is difficult to account for. Another key issue along the CRA-steps is to define a theoretical minimal risk exposure level for each risk factor. In some cases, this level is evident and self-explanatory (e.g., zero smoking), but often more difficult to define and justify (e.g., ideal consumption of whole grains). CRA combine all relevant information and allow to estimate population attributable fractions (PAFs) quantifying the proportion of disease burden attributable to exposure. Among many available formulae for PAFs, it is important to use the one that allows consistency between definitions, units of the exposure data, and the exposure response functions. When combined effects of different risk factors are of interest, the non-additive nature of PAFs and possible mediation effects need to be reflected. Further, as attributable burden is typically calculated based on current exposure and current health outcomes, the time dimensions of risk and outcomes may become inconsistent. Finally, the evidence of the association between exposure and outcome can be heterogeneous which needs to be considered when interpreting CRA results. Conclusions The methodological challenges make transparent reporting of input and process data in CRA a necessary prerequisite. The evidence for causality between included risk-outcome pairs has to be well established to inform public health practice.
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-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, and 6. Choose the appropriate methodological approaches. 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.
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