DYNAMO-HIA–A Dynamic Modeling Tool for Generic Health Impact Assessments
Publication Date
May 10, 2012
Journal
PLOS ONE
Authors
Stefan K. Lhachimi, Wilma J. Nusselder, Henriette A. Smit, Pieter Van Baal, et al
Volume
7
Issue
5
Pages
e33317
DOI
https://dx.plos.org/10.1371/journal.pone.0033317
Publisher URL
http://journals.plos.org/plosone/article?id=10.1371%2Fjournal.pone.0033317
PubMed
http://www.ncbi.nlm.nih.gov/pubmed/22590491
PubMed Central
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC3349723
Europe PMC
http://europepmc.org/abstract/MED/22590491
Web of Science
000305336400001
Scopus
84860994403
Mendeley
http://www.mendeley.com/research/dynamohiaa-dynamic-modeling-tool-generic-health-impact-assessments
Events
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Mendeley | Further Information

{"title"=>"Dynamo-HIA-a dynamic modeling tool for generic health impact assessments", "type"=>"journal", "authors"=>[{"first_name"=>"Stefan K.", "last_name"=>"Lhachimi", "scopus_author_id"=>"56866086500"}, {"first_name"=>"Wilma J.", "last_name"=>"Nusselder", "scopus_author_id"=>"6603049309"}, {"first_name"=>"Henriette A.", "last_name"=>"Smit", "scopus_author_id"=>"7102782109"}, {"first_name"=>"Pieter", "last_name"=>"van Baal", "scopus_author_id"=>"57191805413"}, {"first_name"=>"Paolo", "last_name"=>"Baili", "scopus_author_id"=>"21738979800"}, {"first_name"=>"Kathleen", "last_name"=>"Bennett", "scopus_author_id"=>"23079596400"}, {"first_name"=>"Esteve", "last_name"=>"Fernández", "scopus_author_id"=>"35232801100"}, {"first_name"=>"Margarete C.", "last_name"=>"Kulik", "scopus_author_id"=>"55039999800"}, {"first_name"=>"Tim", "last_name"=>"Lobstein", "scopus_author_id"=>"56268223800"}, {"first_name"=>"Joceline", "last_name"=>"Pomerleau", "scopus_author_id"=>"7003928005"}, {"first_name"=>"Johan P.", "last_name"=>"Mackenbach", "scopus_author_id"=>"36042840300"}, {"first_name"=>"Hendriek C.", "last_name"=>"Boshuizen", "scopus_author_id"=>"7005542767"}], "year"=>2012, "source"=>"PLoS ONE", "identifiers"=>{"sgr"=>"84860994403", "pmid"=>"22590491", "pui"=>"364802819", "scopus"=>"2-s2.0-84860994403", "doi"=>"10.1371/journal.pone.0033317", "issn"=>"19326203"}, "id"=>"94d5d8db-b717-35f0-9162-8c2c2a1c4da6", "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.\\n\\nMETHODS 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.\\n\\nCONCLUSION: 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.", "link"=>"http://www.mendeley.com/research/dynamohiaa-dynamic-modeling-tool-generic-health-impact-assessments", "reader_count"=>40, "reader_count_by_academic_status"=>{"Professor > Associate Professor"=>1, "Researcher"=>11, "Student > Doctoral Student"=>4, "Student > Ph. D. Student"=>8, "Student > Postgraduate"=>1, "Other"=>4, "Student > Master"=>7, "Lecturer"=>3, "Professor"=>1}, "reader_count_by_user_role"=>{"Professor > Associate Professor"=>1, "Researcher"=>11, "Student > Doctoral Student"=>4, "Student > Ph. D. Student"=>8, "Student > Postgraduate"=>1, "Other"=>4, "Student > Master"=>7, "Lecturer"=>3, "Professor"=>1}, "reader_count_by_subject_area"=>{"Agricultural and Biological Sciences"=>1, "Business, Management and Accounting"=>2, "Chemistry"=>1, "Computer Science"=>1, "Economics, Econometrics and Finance"=>2, "Engineering"=>3, "Environmental Science"=>6, "Biochemistry, Genetics and Molecular Biology"=>1, "Nursing and Health Professions"=>1, "Mathematics"=>1, "Medicine and Dentistry"=>14, "Psychology"=>4, "Social Sciences"=>3}, "reader_count_by_subdiscipline"=>{"Medicine and Dentistry"=>{"Medicine and Dentistry"=>14}, "Social Sciences"=>{"Social Sciences"=>3}, "Psychology"=>{"Psychology"=>4}, "Mathematics"=>{"Mathematics"=>1}, "Environmental Science"=>{"Environmental Science"=>6}, "Engineering"=>{"Engineering"=>3}, "Chemistry"=>{"Chemistry"=>1}, "Economics, Econometrics and Finance"=>{"Economics, Econometrics and Finance"=>2}, "Agricultural and Biological Sciences"=>{"Agricultural and Biological Sciences"=>1}, "Computer Science"=>{"Computer Science"=>1}, "Business, Management and Accounting"=>{"Business, Management and Accounting"=>2}, "Nursing and Health Professions"=>{"Nursing and Health Professions"=>1}, "Biochemistry, Genetics and Molecular Biology"=>{"Biochemistry, Genetics and Molecular Biology"=>1}}, "reader_count_by_country"=>{"Spain"=>1, "India"=>1}, "group_count"=>4}

Scopus | Further Information

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Figshare

  • {"files"=>["https://ndownloader.figshare.com/files/640508"], "description"=>"<p>Each plane is a distinct cohort with varying starting ages for cohorts already existing at the starting year of the simulation and starting age zero for cohorts born during the simulation run. The cohort life tables, consisting of the set of individual risk-factor biographies, follow every already existing birth cohort until the cohort reaches 105 years of age. In addition, every year of the simulation a cohort of newborns is created and – after simulating individual risk-factor biographies for them – is followed through the appropriate disease life tables as well. This allows collecting health data for each cohort according to their risk-factor status (longitudinal) or the health status of the population by age, sex, and risk-factor status by each year of the simulation (cross-sectional).</p>", "links"=>[], "tags"=>["overview", "multistate-life"], "article_id"=>310989, "categories"=>["Medicine", "Biotechnology"], "users"=>["Stefan K. Lhachimi", "Wilma J. Nusselder", "Henriette A. Smit", "Pieter van Baal", "Paolo Baili", "Kathleen Bennett", "Esteve Fernández", "Margarete C. Kulik", "Tim Lobstein", "Joceline Pomerleau", "Johan P. Mackenbach", "Hendriek C. Boshuizen"], "doi"=>"https://dx.doi.org/10.1371/journal.pone.0033317.g004", "stats"=>{"downloads"=>0, "page_views"=>1, "likes"=>0}, "figshare_url"=>"https://figshare.com/articles/_Schematic_overview_of_the_dimension_of_a_multi_cohort_multistate_life_table_/310989", "title"=>"Schematic overview of the dimension of a multi-cohort, multistate-life table.", "pos_in_sequence"=>0, "defined_type"=>1, "published_date"=>"2012-05-10 00:16:29"}
  • {"files"=>["https://ndownloader.figshare.com/files/640897"], "description"=>"<p>\n <i>For data sources see <a href=\"http://www.plosone.org/article/info:doi/10.1371/journal.pone.0033317#pone.0033317.s001\" target=\"_blank\">Table S1</a>, <a href=\"http://www.plosone.org/article/info:doi/10.1371/journal.pone.0033317#pone.0033317.s002\" target=\"_blank\">Table S2</a>, <a href=\"http://www.plosone.org/article/info:doi/10.1371/journal.pone.0033317#pone.0033317.s003\" target=\"_blank\">Table S3</a>, , <a href=\"http://www.plosone.org/article/info:doi/10.1371/journal.pone.0033317#pone.0033317.s005\" target=\"_blank\">Table S5</a>.</i></p>", "links"=>[], "tags"=>["expectancy", "years", "uk"], "article_id"=>311379, "categories"=>["Medicine", "Biotechnology"], "users"=>["Stefan K. Lhachimi", "Wilma J. Nusselder", "Henriette A. Smit", "Pieter van Baal", "Paolo Baili", "Kathleen Bennett", "Esteve Fernández", "Margarete C. Kulik", "Tim Lobstein", "Joceline Pomerleau", "Johan P. Mackenbach", "Hendriek C. Boshuizen"], "doi"=>"https://dx.doi.org/10.1371/journal.pone.0033317.t002", "stats"=>{"downloads"=>3, "page_views"=>3, "likes"=>0}, "figshare_url"=>"https://figshare.com/articles/_Period_based_total_life_expectancy_and_expected_number_of_years_with_a_disease_for_the_UK_example_application_/311379", "title"=>"Period based total life expectancy and expected number of years with a disease for the UK example application.", "pos_in_sequence"=>0, "defined_type"=>3, "published_date"=>"2012-05-10 00:22:59"}
  • {"files"=>["https://ndownloader.figshare.com/files/330628", "https://ndownloader.figshare.com/files/330695", "https://ndownloader.figshare.com/files/330762", "https://ndownloader.figshare.com/files/330861", "https://ndownloader.figshare.com/files/330944"], "description"=>"<div><h3>Background</h3><p>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.</p> <h3>Methods and Results</h3><p>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.</p> <h3>Conclusion</h3><p>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.</p> </div>", "links"=>[], "tags"=>["modeling", "generic", "assessments"], "article_id"=>125289, "categories"=>["Medicine", "Biotechnology"], "users"=>["Stefan K. Lhachimi", "Wilma J. Nusselder", "Henriette A. Smit", "Pieter van Baal", "Paolo Baili", "Kathleen Bennett", "Esteve Fernández", "Margarete C. Kulik", "Tim Lobstein", "Joceline Pomerleau", "Johan P. Mackenbach", "Hendriek C. Boshuizen"], "doi"=>["https://dx.doi.org/10.1371/journal.pone.0033317.s001", "https://dx.doi.org/10.1371/journal.pone.0033317.s002", "https://dx.doi.org/10.1371/journal.pone.0033317.s003", "https://dx.doi.org/10.1371/journal.pone.0033317.s004", "https://dx.doi.org/10.1371/journal.pone.0033317.s005"], "stats"=>{"downloads"=>9, "page_views"=>11, "likes"=>0}, "figshare_url"=>"https://figshare.com/articles/DYNAMO_HIA_A_Dynamic_Modeling_Tool_for_Generic_Health_Impact_Assessments/125289", "title"=>"DYNAMO-HIA–A Dynamic Modeling Tool for Generic Health Impact Assessments", "pos_in_sequence"=>0, "defined_type"=>4, "published_date"=>"2012-05-10 01:28:09"}
  • {"files"=>["https://ndownloader.figshare.com/files/640940"], "description"=>"<p>\n <i>For data sources see <a href=\"http://www.plosone.org/article/info:doi/10.1371/journal.pone.0033317#pone.0033317.s001\" target=\"_blank\">Table S1</a>, <a href=\"http://www.plosone.org/article/info:doi/10.1371/journal.pone.0033317#pone.0033317.s002\" target=\"_blank\">Table S2</a>, <a href=\"http://www.plosone.org/article/info:doi/10.1371/journal.pone.0033317#pone.0033317.s003\" target=\"_blank\">Table S3</a>, <a href=\"http://www.plosone.org/article/info:doi/10.1371/journal.pone.0033317#pone.0033317.s004\" target=\"_blank\">Table S4</a>, <a href=\"http://www.plosone.org/article/info:doi/10.1371/journal.pone.0033317#pone.0033317.s005\" target=\"_blank\">Table S5</a>.</i></p>", "links"=>[], "tags"=>["cases", "prevalence"], "article_id"=>311418, "categories"=>["Medicine", "Biotechnology"], "users"=>["Stefan K. Lhachimi", "Wilma J. Nusselder", "Henriette A. Smit", "Pieter van Baal", "Paolo Baili", "Kathleen Bennett", "Esteve Fernández", "Margarete C. Kulik", "Tim Lobstein", "Joceline Pomerleau", "Johan P. Mackenbach", "Hendriek C. Boshuizen"], "doi"=>"https://dx.doi.org/10.1371/journal.pone.0033317.t001", "stats"=>{"downloads"=>1, "page_views"=>4, "likes"=>0}, "figshare_url"=>"https://figshare.com/articles/_Number_of_disease_cases_and_population_prevalence_in_percent_for_example_applications_/311418", "title"=>"Number of disease cases and population prevalence (in percent) for example applications.", "pos_in_sequence"=>0, "defined_type"=>3, "published_date"=>"2012-05-10 00:23:38"}
  • {"files"=>["https://ndownloader.figshare.com/files/640227"], "description"=>"<p>The disease life tables contain disease clusters. Each disease cluster consists of one or more diseases. Within disease clusters, intermediate diseases – that is, a disease that increase the risk of getting another disease – can be specified (e.g. having diabetes increases the risk of getting IHD). All diseases are chronic diseases, i.e. excess mortality depends on age and sex and not on time since onset of disease. However, acutely fatal and/or cured fraction can be specified for diseases. The disease life table assumes independence between disease clusters. The user can freely specify the relative risks from risk-factor to disease, from risk-factor to death, and from intermediate disease to other diseases.</p>", "links"=>[], "tags"=>["public health and epidemiology", "Non-clinical medicine"], "article_id"=>310709, "categories"=>["Medicine", "Biotechnology"], "users"=>["Stefan K. Lhachimi", "Wilma J. Nusselder", "Henriette A. Smit", "Pieter van Baal", "Paolo Baili", "Kathleen Bennett", "Esteve Fernández", "Margarete C. Kulik", "Tim Lobstein", "Joceline Pomerleau", "Johan P. Mackenbach", "Hendriek C. Boshuizen"], "doi"=>"https://dx.doi.org/10.1371/journal.pone.0033317.g002", "stats"=>{"downloads"=>0, "page_views"=>12, "likes"=>0}, "figshare_url"=>"https://figshare.com/articles/_Stylized_structure_of_disease_life_table_/310709", "title"=>"Stylized structure of disease life table.", "pos_in_sequence"=>0, "defined_type"=>1, "published_date"=>"2012-05-10 00:11:49"}
  • {"files"=>["https://ndownloader.figshare.com/files/640812"], "description"=>"<p>Overview of required input data (age- and sex-specific).</p>", "links"=>[], "tags"=>["public health and epidemiology", "Non-clinical medicine"], "article_id"=>311293, "categories"=>["Medicine", "Biotechnology"], "users"=>["Stefan K. Lhachimi", "Wilma J. Nusselder", "Henriette A. Smit", "Pieter van Baal", "Paolo Baili", "Kathleen Bennett", "Esteve Fernández", "Margarete C. Kulik", "Tim Lobstein", "Joceline Pomerleau", "Johan P. Mackenbach", "Hendriek C. Boshuizen"], "doi"=>"https://dx.doi.org/10.1371/journal.pone.0033317.g006", "stats"=>{"downloads"=>1, "page_views"=>11, "likes"=>0}, "figshare_url"=>"https://figshare.com/articles/_Overview_of_required_input_data_age_and_sex_specific_/311293", "title"=>"Overview of required input data (age- and sex-specific).", "pos_in_sequence"=>0, "defined_type"=>1, "published_date"=>"2012-05-10 00:21:33"}
  • {"files"=>["https://ndownloader.figshare.com/files/640327"], "description"=>"<p>For every risk-factor biography, a disease life table is constructed. Diseases incidence, i.e. transition from a healthy to a diseased status, equals the baseline incidence – that is, the incidence when in a risk-factor class with a relative risk of one for the specific age- and sex-category – times the relative risk due to the given risk-factor and diseases status (in the case of an intermediate disease). The transition from healthy to dead equals the baseline other-cause mortality of the healthy, i.e. age- and sex-specific total mortality rate minus the excess mortality rate of the diseases included in the disease life table, multiplied by the relative risk due to the given risk-factor status on other-cause mortality. The transition from diseased to dead equals the sum of the excess mortality of the disease (given each age and sex) and the baseline other-cause mortality of the healthy, multiplied by the relative risk in the given risk-factor status. Remission is not explicitly modeled, but for diseases with cured fraction the excess mortality is zero in a “cured”, i.e. user-specified, fraction. Partly acutely fatal diseases, i.e. diseases with very high mortality immediately after contracting the disease while for those who survive this critical period the excess mortality only depends on age and sex, are modeled by specifying the fraction of the incidence cases that die immediately.</p>", "links"=>[], "tags"=>["cohort", "tables"], "article_id"=>310812, "categories"=>["Medicine", "Biotechnology"], "users"=>["Stefan K. Lhachimi", "Wilma J. Nusselder", "Henriette A. Smit", "Pieter van Baal", "Paolo Baili", "Kathleen Bennett", "Esteve Fernández", "Margarete C. Kulik", "Tim Lobstein", "Joceline Pomerleau", "Johan P. Mackenbach", "Hendriek C. Boshuizen"], "doi"=>"https://dx.doi.org/10.1371/journal.pone.0033317.g003", "stats"=>{"downloads"=>1, "page_views"=>3, "likes"=>0}, "figshare_url"=>"https://figshare.com/articles/_Stylized_cohort_life_tables_with_only_one_disease_three_different_biographies_and_five_time_steps_/310812", "title"=>"Stylized cohort life tables (with only one disease, three different biographies, and five time steps).", "pos_in_sequence"=>0, "defined_type"=>1, "published_date"=>"2012-05-10 00:13:32"}
  • {"files"=>["https://ndownloader.figshare.com/files/640140"], "description"=>"<p>DYNAMO-HIA simulates individuals and projects their risk-factor biographies. The risk-factor status is being updated in one-year increments, given age- and sex-specific transition probabilities. The age- and sex-specific risk-factor status determines the relative risk of a person to contract a disease or to die. DYNAMO-HIA allows one risk-factor per scenario. This risk-factor can be either categorical (up to ten categories), duration dependent (up to ten categories, of which one is duration dependent, i.e. the risk on disease in this category depends on how long a person is in the category), or a continuous distribution (normal or log-normal, specified by entering mean, standard deviation, and, in the case of the log-normal, skewness).</p>", "links"=>[], "tags"=>["risk-factor", "biographies"], "article_id"=>310620, "categories"=>["Medicine", "Biotechnology"], "users"=>["Stefan K. Lhachimi", "Wilma J. Nusselder", "Henriette A. Smit", "Pieter van Baal", "Paolo Baili", "Kathleen Bennett", "Esteve Fernández", "Margarete C. Kulik", "Tim Lobstein", "Joceline Pomerleau", "Johan P. Mackenbach", "Hendriek C. Boshuizen"], "doi"=>"https://dx.doi.org/10.1371/journal.pone.0033317.g001", "stats"=>{"downloads"=>0, "page_views"=>2, "likes"=>0}, "figshare_url"=>"https://figshare.com/articles/_Example_of_risk_factor_biographies_for_a_risk_factor_with_three_categories_/310620", "title"=>"Example of risk-factor biographies for a risk-factor with three categories.", "pos_in_sequence"=>0, "defined_type"=>1, "published_date"=>"2012-05-10 00:10:20"}
  • {"files"=>["https://ndownloader.figshare.com/files/640675"], "description"=>"<p>Swedish prevalence of alcohol consumption intervention scenario compared with reference scenario (Alcohol consumption is measured by five categories of daily intake of grams of pure alcohol: 0–<0.25 g/d, 0.25–<20 g/d, 20–<40 g/d, 40–<60 g/d, ≥60 g/d).</p>", "links"=>[], "tags"=>["prevalence", "compared", "categories", "intake", "grams"], "article_id"=>311155, "categories"=>["Medicine", "Biotechnology"], "users"=>["Stefan K. Lhachimi", "Wilma J. Nusselder", "Henriette A. Smit", "Pieter van Baal", "Paolo Baili", "Kathleen Bennett", "Esteve Fernández", "Margarete C. Kulik", "Tim Lobstein", "Joceline Pomerleau", "Johan P. Mackenbach", "Hendriek C. Boshuizen"], "doi"=>"https://dx.doi.org/10.1371/journal.pone.0033317.g005", "stats"=>{"downloads"=>1, "page_views"=>12, "likes"=>0}, "figshare_url"=>"https://figshare.com/articles/_Swedish_prevalence_of_alcohol_consumption_intervention_scenario_compared_with_reference_scenario_Alcohol_consumption_is_measured_by_five_categories_of_daily_intake_of_grams_of_pure_alcohol_0_8211_lt_0_25_g_d_0_25_8211_lt_20_g_d_20_8211_lt_40_g_d_40_8211_/311155", "title"=>"Swedish prevalence of alcohol consumption intervention scenario compared with reference scenario (Alcohol consumption is measured by five categories of daily intake of grams of pure alcohol: 0–<0.25 g/d, 0.25–<20 g/d, 20–<40 g/d, 40–<60 g/d, ≥60 g/d).", "pos_in_sequence"=>0, "defined_type"=>1, "published_date"=>"2012-05-10 00:19:15"}

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Relative Metric

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