Explore the vast range of titles available.

Background Sepsis is a life-threatening syndrome characterized by acute loss of organ function due to infection. Sepsis survivors are at risk for long-term comorbidities, have a reduced Quality of Life (QoL), and are prone to increased long-term mortality. The societal impact of sepsis includes its disease burden and indirect economic costs. However, these societal costs of sepsis are not fully understood. This study assessed sepsis’s disease-related and indirect economic costs in the Netherlands. Methods Sepsis prevalence, incidence, sepsis-related mortality, hospitalizations, life expectancy, QoL population norms, QoL reduction after sepsis, and healthcare use post-sepsis were obtained from previous literature and Statistics Netherlands. We used these data to estimate annual Quality-adjusted Life Years (QALYs), productivity loss, and increase in healthcare use post-sepsis. A sensitivity analysis was performed to analyze the burden and indirect economic costs of sepsis under alternative assumptions, resulting in a baseline, low, and high estimated burden. The results are presented as a baseline (low–high burden) estimate. Results The annual disease burden of sepsis is approximately 57,304 (24,398–96,244; low–high burden) QALYs. Of this, mortality accounts for 26,898 (23,166–31,577) QALYs, QoL decrease post-sepsis accounts for 30,406 (1232–64,667) QALYs. The indirect economic burden, attributed to lost productivity and increased healthcare expenditure, is estimated at €416.1 (147.1–610.7) million utilizing the friction cost approach and €3.1 (0.4–5.7) billion using the human capital method. Cumulatively, the combined disease and indirect economic burdens range from €3.8 billion (friction method) to €6.5 billion (human capital method) annually within the Netherlands. Conclusions Sepsis and its complications pose a substantial disease and indirect economic burden to the Netherlands, with an indirect economic burden due to production loss that is potentially larger than the burden due to coronary heart disease or stroke. Our results emphasize the need for future studies to prevent sepsis, saving downstream costs and decreasing the economic burden.

The application of foresight to the field of public health is limited. There is growing need to anticipate uncertain future trends and to plan for them. Foresight provides tools to experts and policymakers to discuss and plan for possible futures. Hence, the aim of this study is to illustrate how the foresight six-step approach can be applied in public health, and to provide recommendations on dealing with challenges, drawn from the Population Health Information Research Infrastructure (PHIRI) foresight exercise. In this tutorial, we describe the six-step approach as part of foresight methodology and give examples of possible challenges. Step 1 comprises the formulation of study objectives. Step 2 focuses on developing a conceptual model and applying the Demographic Economic Sociocultural Technological Ecological and Political-Institutional (DESTEP) framework to identify and prioritize driving forces for the topic of interest. In Step 3, a time horizon and spatial level are defined. Step 4 discusses scenario logics. Steps 5 and 6 discuss different types of scenarios and associated tools for analyses. Possible challenges encountered whilst applying the foresight methodology at each of the steps, were drawn from experiences during PHIRI foresight exercise. Challenges associated with applying the foresight six-step approach included: formulating concise objectives, developing a conceptual model, understanding driving forces and uncertainty and difficulties in building scenarios. Understanding concepts used in the six-step approach and how they relate to each other remained difficult. Support from foresight experts, conducting more foresight exercises, tutorials and guidelines can enhance understanding and support building capacity.

Background Incidence rates and prevalence proportions are commonly used to express the populations health status. Since there are several methods used to calculate these epidemiological measures, good comparison between studies and countries is difficult. This study investigates the impact of different operational definitions of numerators and denominators on incidence rates and prevalence proportions. Methods Data from routine electronic health records of general practices contributing to NIVEL Primary Care Database was used. Incidence rates were calculated using different denominators (person-years at-risk, person-years and midterm population). Three different prevalence proportions were determined: 1 year period prevalence proportions, point-prevalence proportions and contact prevalence proportions. Results One year period prevalence proportions were substantially higher than point-prevalence (58.3 - 206.6%) for long-lasting diseases, and one year period prevalence proportions were higher than contact prevalence proportions (26.2 - 79.7%). For incidence rates, the use of different denominators resulted in small differences between the different calculation methods (-1.3 - 14.8%). Using person-years at-risk or a midterm population resulted in higher rates compared to using person-years. Conclusions All different operational definitions affect incidence rates and prevalence proportions to some extent. Therefore, it is important that the terminology and methodology is well described by sources reporting these epidemiological measures. When comparing incidence rates and prevalence proportions from different sources, it is important to be aware of the operational definitions applied and their impact.

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 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, Czechia, 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.

Abstract Background The use of foresight studies is common in some policy fields, but not in public health. Interest in such studies is growing. This paper gives a general overview of the Dutch Public Health Foresight Study (PHFS) 2018, providing insight into what performing a broad scenario exercise in the field of public health entails and its societal impacts. Methods The aim of the PHFS-2018 was: (a) to show how public health and healthcare in the Netherlands will develop over the next 25 years if we pursue our current course and detect ‘new’ developments; (b) to give options for dealing with the major future societal challenges. Part a was addressed by means of a quantitative business-as-usual scenario exercise complemented with qualitative thematic studies, and part b by elaborating courses of action for three key challenges, based on stakeholder consultation. Typical aspects of the PHFS methods are a multidisciplinary, participatory and conceptual approach and using a broad definition of health. Results The PHFS-2018 is the basis for the upcoming National Health Policy Memorandum and the Trend Scenario is the baseline for the National Prevention Agreement. Unexpectedly, the findings about increasing mental pressure in young people received most attention. There still is room for expanding use of the study to its full potential. Conclusions Long-term thinking could be stimulated by using back casting techniques and stronger involvement of policy-makers in the elaboration of options for action. Lessons learned from developing intersectoral policy at the local level could be applied at the national level.

A growing number of informal caregivers of older adults combine caregiving with a paid job, raising the question of whether they will be able to meet the increasing demand for informal care. The aims of our study are twofold: first, to describe the development of a model providing insight into the factors that play a role in the availability and provision of informal care by working caregivers of older adults, and second, to investigate which societal developments will impact the factors in the model, and thus the future availability of informal care by workers. A mixed-methods approach was applied to integrate evidence from academic and grey literature, with insights from experts through a Group Model Building exercise and a Delphi study. The resulting Working Informal Caregiver (WIC) model presents a range of individual, social and environmental factors that are related to working caregivers’ ability and their willingness to engage in informal care. Experts foresee that future informal care will be impacted most by the increasing participation of women in the workforce, while changing household structures may diminish opportunities to share care tasks within their households or families. The WIC model can be used to gain better insight into the availability of informal care by workers, now and in the future.

Background The Disability Adjusted Life Year (DALY) is a measure to prioritize in the public health field. In the Netherlands, the DALY estimates are calculated since 1997 and are included in the Public Health Status and Foresight studies which is an input for public health priority setting and policy making. Over these 20 years, methodological advancements have been made, including accounting for multimorbidity and performing projections for DALYs into the future. Most important methodological choices and improvements are described and results are presented. Methods The DALY is composed of the two components years of life lost (YLL) due to premature mortality and years lost due to disability (YLD). Both the YLL and the YLD are distinguished by sex, age and health condition, allowing aggregation to the ICD-10 chapters. The YLD is corrected for multimorbidity, assuming independent occurrence of health conditions and a multiplicative method for the calculation of combined disability weights. Future DALYs are calculated based on projections for causes of death, and prevalence and incidence. Results The results for 2015 show that cancer is the ICD-10 chapter with the highest disease burden, followed by cardiovascular diseases and mental disorders. For the individual health conditions, coronary heart disease had the highest disease burden in 2015. In 2040, we see a strong increase in disease burden of dementia and arthrosis. For dementia this is due to a threefold increase in dementia as a cause of death, while for arthrosis this is mainly due to the increase in prevalence. Conclusions To calculate the DALY requires a substantial amount of data, methodological choices, interpretation and presentation of results, and the personnel capacity to carry out all these tasks. However, doing a National Burden of Disease study, and especially doing that for more than 20 years, proved to have an enormous additional value in population health information and thus supports better public health policies.

Background Various Burden of Disease (BoD) studies do not account for multimorbidity in their BoD estimates. Ignoring multimorbidity can lead to inaccuracies in BoD estimations, particularly in ageing populations that include large proportions of persons with two or more health conditions. The objective of this study is to improve BoD estimates for the Netherlands by accounting for multimorbidity. For this purpose, we analyzed different methods for 1) estimating the prevalence of multimorbidity and 2) deriving Disability Weights (DWs) for multimorbidity by using existing data on single health conditions. Methods We included 25 health conditions from the Dutch Burden of Disease study that have a high rate of prevalence and that make a large contribution to the total number of Years Lived with a Disability (YLD). First, we analyzed four methods for estimating the prevalence of multimorbid conditions (i.e. independent, independent age- and sex-specific, dependent, and dependent sex- and age-specific). Secondly, we analyzed three methods for calculating the Combined Disability Weights (CDWs) associated with multimorbid conditions (i.e. additive, multiplicative and maximum limit). A combination of these two approaches was used to recalculate the number of YLDs, which is a component of the Disability-Adjusted Life Years (DALY). Results This study shows that the YLD estimates for 25 health conditions calculated using the multiplicative method for Combined Disability Weights are 5 % lower, and 14 % lower when using the maximum limit method, than when calculated using the additive method. Adjusting for sex- and age-specific dependent co-occurrence of health conditions reduces the number of YLDs by 10 % for the multiplicative method and by 26 % for the maximum limit method. The adjustment is higher for health conditions with a higher prevalence in old age, like heart failure (up to 43 %) and coronary heart diseases (up to 33 %). Health conditions with a high prevalence in middle age, such as anxiety disorders, have a moderate adjustment (up to 13 %). Conclusions We conclude that BoD calculations that do not account for multimorbidity can result in an overestimation of the actual BoD. This may affect public health policy strategies that focus on single health conditions if the underlying cost-effectiveness analysis overestimates the intended effects. The methodology used in this study could be further refined to provide greater insight into co-occurrence and the possible consequences of multimorbid conditions in terms of disability for particular combinations of health conditions.

Background This study takes on the challenge of quantifying a complex causal loop diagram describing how poverty and health affect each other, and does so using longitudinal data from The Netherlands. Furthermore, this paper elaborates on its methodological approach in order to facilitate replication and methodological advancement. Methods After adapting a causal loop diagram that was built by stakeholders, a longitudinal structural equation modelling approach was used. A cross-lagged panel model with nine endogenous variables, of which two latent variables, and three time-invariant exogenous variables was constructed. With this model, directional effects are estimated in a Granger-causal manner, using data from 2015 to 2019. Both the direct effects (with a one-year lag) and total effects over multiple (up to eight) years were calculated. Five sensitivity analyses were conducted. Two of these focus on lower-income and lower-wealth individuals. The other three each added one exogenous variable: work status, level of education, and home ownership. Results The effects of income and financial wealth on health are present, but are relatively weak for the overall population. Sensitivity analyses show that these effects are stronger for those with lower incomes or wealth. Physical capability does seem to have strong positive effects on both income and financial wealth. There are a number of other results as well, as the estimated models are extensive. Many of the estimated effects only become substantial after several years. Conclusions Income and financial wealth appear to have limited effects on the health of the overall population of The Netherlands. However, there are indications that these effects may be stronger for individuals who are closer to the poverty threshold. Since the estimated effects of physical capability on income and financial wealth are more substantial, a broad recommendation would be that including physical capability in efforts that are aimed at improving income and financial wealth could be useful and effective. The methodological approach described in this paper could also be applied to other research settings or topics.