Dataset Information

DYNAMO-HIA--a Dynamic Modeling tool for generic Health Impact Assessments.

ABSTRACT:

Background

Currently, no standard tool is publicly available that allows researchers or policy-makers to quantify the impact of policies using epidemiological evidence within the causal framework of Health Impact Assessment (HIA). A standard tool should comply with three technical criteria (real-life population, dynamic projection, explicit risk-factor states) and three usability criteria (modest data requirements, rich model output, generally accessible) to be useful in the applied setting of HIA. With DYNAMO-HIA (Dynamic Modeling for Health Impact Assessment), we introduce such a generic software tool specifically designed to facilitate quantification in the assessment of the health impacts of policies.

Methods and results

DYNAMO-HIA quantifies the impact of user-specified risk-factor changes on multiple diseases and in turn on overall population health, comparing one reference scenario with one or more intervention scenarios. The Markov-based modeling approach allows for explicit risk-factor states and simulation of a real-life population. A built-in parameter estimation module ensures that only standard population-level epidemiological evidence is required, i.e. data on incidence, prevalence, relative risks, and mortality. DYNAMO-HIA provides a rich output of summary measures--e.g. life expectancy and disease-free life expectancy--and detailed data--e.g. prevalences and mortality/survival rates--by age, sex, and risk-factor status over time. DYNAMO-HIA is controlled via a graphical user interface and is publicly available from the internet, ensuring general accessibility. We illustrate the use of DYNAMO-HIA with two example applications: a policy causing an overall increase in alcohol consumption and quantifying the disease-burden of smoking.

Conclusion

By combining modest data needs with general accessibility and user friendliness within the causal framework of HIA, DYNAMO-HIA is a potential standard tool for health impact assessment based on epidemiologic evidence.

SUBMITTER: Lhachimi SK

PROVIDER: S-EPMC3349723 | biostudies-literature | 2012

REPOSITORIES: biostudies-literature

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Publications

DYNAMO-HIA--a Dynamic Modeling tool for generic Health Impact Assessments.

Lhachimi Stefan K SK Nusselder Wilma J WJ Smit Henriette A HA van Baal Pieter P Baili Paolo P Bennett Kathleen K Fernández Esteve E Kulik Margarete C MC Lobstein Tim T Pomerleau Joceline J Mackenbach Johan P JP Boshuizen Hendriek C HC

PloS one 20120510 5

<h4>Background</h4>Currently, no standard tool is publicly available that allows researchers or policy-makers to quantify the impact of policies using epidemiological evidence within the causal framework of Health Impact Assessment (HIA). A standard tool should comply with three technical criteria (real-life population, dynamic projection, explicit risk-factor states) and three usability criteria (modest data requirements, rich model output, generally accessible) to be useful in the applied sett ...[more]

PMID: 22590491

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Project description:BackgroundThere are several types of tobacco control interventions/policies which can change future smoking exposure. The most basic intervention types are 1) smoking cessation interventions 2) preventing smoking initiation and 3) implementation of a nationwide policy affecting quitters and starters simultaneously. The possibility for dynamic quantification of such different interventions is key for comparing the timing and size of their effects.Methods and resultsWe developed a software tool, DYNAMO-HIA, which allows for a quantitative comparison of the health impact of different policy scenarios. We illustrate the outcomes of the tool for the three typical types of tobacco control interventions if these were applied in the Netherlands. The tool was used to model the effects of different types of smoking interventions on future smoking prevalence and on health outcomes, comparing these three scenarios with the business-as-usual scenario. The necessary data input was obtained from the DYNAMO-HIA database which was assembled as part of this project. All smoking interventions will be effective in the long run. The population-wide strategy will be most effective in both the short and long term. The smoking cessation scenario will be second-most effective in the short run, though in the long run the smoking initiation scenario will become almost as effective. Interventions aimed at preventing the initiation of smoking need a long time horizon to become manifest in terms of health effects. The outcomes strongly depend on the groups targeted by the intervention.ConclusionWe calculated how much more effective the population-wide strategy is, in both the short and long term, compared to quit smoking interventions and measures aimed at preventing the initiation of smoking. By allowing a great variety of user-specified choices, the DYNAMO-HIA tool is a powerful instrument by which the consequences of different tobacco control policies and interventions can be assessed.

Project description:PurposeThe aim of this study was to compare associations between generic versus disease-specific functional health status assessments and patient and clinical characteristics for patients with severe congenital heart disease.MethodsThis was a cross-sectional observational study involving 325 single ventricle patients, aged 10-18 years, after Fontan procedure. Enrolled patients underwent a medical history review, laboratory testing, and assessment of the functional health status by completion of the generic Child Report Child Health Questionnaire and the disease-specific Congenital Heart Adolescent and Teenage questionnaire. Correlated conceptually equivalent domains from both questionnaires were identified and their associations with patient and clinical variables were compared.ResultsFrom the generic assessment, patients perceived marginally lower physical functioning (p = 0.05) but greater freedom from bodily pain compared with a normal population (p < 0.001). The equivalent physical functioning/limitations domain of the generic instrument, compared with the disease-specific instrument, had similar associations (higher multi-variable model R²) with medical history variables (R² = 0.14 versus R² = 0.12, respectively) and stronger associations with exercise testing variables (R² = 0.22 versus R² = 0.06). Similarly, the corresponding freedom from bodily pain/symptoms domains from both questionnaires showed a greater association for the generic instrument with medical history variables (R² = 0.15 versus R² = 0.09, respectively) and non-cardiac conditions (R² = 0.13 versus R² = 0.06). The associations of each questionnaire with echocardiographic results, cardiac magnetic resonance imaging results, and serum brain natriuretic peptide levels were uniformly weak (R² range <0.01 to 0.04).ConclusionsAssessment of the physical functional health status using generic and disease-specific instruments yields few differences with regard to associations between conceptually similar domains and patient and clinical characteristics for adolescents after Fontan procedure.

Project description:EFSA Strategy 2027 outlines the need for fit-for-purpose protocols for EFSA generic scientific assessments to aid in delivering trustworthy scientific advice. This EFSA Scientific Committee guidance document helps address this need by providing a harmonised and flexible framework for developing protocols for EFSA generic assessments. The guidance replaces the 'Draft framework for protocol development for EFSA's scientific assessments' published in 2020. The two main steps in protocol development are described. The first is problem formulation, which illustrates the objectives of the assessment. Here a new approach to translating the mandated Terms of Reference into scientifically answerable assessment questions and sub-questions is proposed: the 'APRIO' paradigm (Agent, Pathway, Receptor, Intervention and Output). Owing to its cross-cutting nature, this paradigm is considered adaptable and broadly applicable within and across the various EFSA domains and, if applied using the definitions given in this guidance, is expected to help harmonise the problem formulation process and outputs and foster consistency in protocol development. APRIO may also overcome the difficulty of implementing some existing frameworks across the multiple EFSA disciplines, e.g. the PICO/PECO approach (Population, Intervention/Exposure, Comparator, Outcome). Therefore, although not mandatory, APRIO is recommended. The second step in protocol development is the specification of the evidence needs and the methods that will be applied for answering the assessment questions and sub-questions, including uncertainty analysis. Five possible approaches to answering individual (sub-)questions are outlined: using evidence from scientific literature and study reports; using data from databases other than bibliographic; using expert judgement informally collected or elicited via semi-formal or formal expert knowledge elicitation processes; using mathematical/statistical models; and - not covered in this guidance - generating empirical evidence ex novo. The guidance is complemented by a standalone 'template' for EFSA protocols that guides the users step by step through the process of planning an EFSA scientific assessment.

Dataset Information

DYNAMO-HIA--a Dynamic Modeling tool for generic Health Impact Assessments.

Background

Methods and results

Conclusion

Publications

DYNAMO-HIA--a Dynamic Modeling tool for generic Health Impact Assessments.

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