Estimating the potential impact of behavioral public health interventions nationally while maintaining agreement with global patterns on relative risks
This paper introduces a novel method to evaluate the local impact of behavioral scenarios on disease prevalence and burden with representative individual level data while ensuring that the model is in agreement with the qualitative patterns of global relative risk (RR) estimates. The method is used...
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| description | This paper introduces a novel method to evaluate the local impact of behavioral scenarios on disease prevalence and burden with representative individual level data while ensuring that the model is in agreement with the qualitative patterns of global relative risk (RR) estimates. The method is used to estimate the impact of behavioral scenarios on the burden of disease due to ischemic heart disease (IHD) and diabetes in the Turkish adult population.
Disease specific Hierarchical Bayes (HB) models estimate the individual disease probability as a function of behaviors, demographics, socio-economics and other controls, where constraints are specified based on the global RR estimates. The simulator combines the counterfactual disease probability estimates with disability adjusted life year (DALY)-per-prevalent-case estimates and rolls up to the targeted population level, thus reflecting the local joint distribution of exposures. The Global Burden of Disease (GBD) 2016 study meta-analysis results guide the analysis of the Turkish National Health Surveys (2008 to 2016) that contain more than 90 thousand observations.
The proposed Qualitative Informative HB models do not sacrifice predictive accuracy versus benchmarks (logistic regression and HB models with non-informative and numerical informative priors) while agreeing with the global patterns. In the Turkish adult population, Increasing Physical Activity reduces the DALYs substantially for both IHD by 8.6% (6.4% 11.2%), and Diabetes by 8.1% (5.8% 10.6%), (90% uncertainty intervals). Eliminating Smoking and Second-hand Smoke predominantly decreases the IHD burden 13.1% (10.4% 15.8%) versus Diabetes 2.8% (1.1% 4.6%). Increasing Fruit and Vegetable Consumption, on the other hand, reduces IHD DALYs by 4.1% (2.8% 5.4%) while not improving the Diabetes burden 0.1% (0% 0.1%).
While the national RR estimates are in qualitative agreement with the global patterns, the scenario impact estimates are markedly different than the attributable risk estimates from the GBD analysis and allow evaluation of practical scenarios with multiple behaviors. |
| doi_str_mv | 10.1371/journal.pone.0232951 |
| format | Article |
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Disease specific Hierarchical Bayes (HB) models estimate the individual disease probability as a function of behaviors, demographics, socio-economics and other controls, where constraints are specified based on the global RR estimates. The simulator combines the counterfactual disease probability estimates with disability adjusted life year (DALY)-per-prevalent-case estimates and rolls up to the targeted population level, thus reflecting the local joint distribution of exposures. The Global Burden of Disease (GBD) 2016 study meta-analysis results guide the analysis of the Turkish National Health Surveys (2008 to 2016) that contain more than 90 thousand observations.
The proposed Qualitative Informative HB models do not sacrifice predictive accuracy versus benchmarks (logistic regression and HB models with non-informative and numerical informative priors) while agreeing with the global patterns. In the Turkish adult population, Increasing Physical Activity reduces the DALYs substantially for both IHD by 8.6% (6.4% 11.2%), and Diabetes by 8.1% (5.8% 10.6%), (90% uncertainty intervals). Eliminating Smoking and Second-hand Smoke predominantly decreases the IHD burden 13.1% (10.4% 15.8%) versus Diabetes 2.8% (1.1% 4.6%). Increasing Fruit and Vegetable Consumption, on the other hand, reduces IHD DALYs by 4.1% (2.8% 5.4%) while not improving the Diabetes burden 0.1% (0% 0.1%).
While the national RR estimates are in qualitative agreement with the global patterns, the scenario impact estimates are markedly different than the attributable risk estimates from the GBD analysis and allow evaluation of practical scenarios with multiple behaviors.</description><identifier>ISSN: 1932-6203</identifier><identifier>EISSN: 1932-6203</identifier><identifier>DOI: 10.1371/journal.pone.0232951</identifier><identifier>PMID: 32401782</identifier><language>eng</language><publisher>United States: Public Library of Science</publisher><subject>Age ; Bayesian analysis ; Behavior ; Benchmarking ; Benchmarks ; Biology and Life Sciences ; Cardiovascular diseases ; Computer simulation ; Coronary artery disease ; Demographic aspects ; Demographics ; Demography ; Diabetes ; Diabetes mellitus ; Disabilities ; Disease ; Diseases ; Estimates ; Exercise ; Gender ; Health behavior ; Health care ; Health risks ; Health surveys ; Heart diseases ; Ischemia ; Medical research ; Medicine and Health Sciences ; Meta-analysis ; Methods ; Model accuracy ; Myocardial ischemia ; Novels ; Passive smoking ; Physical activity ; Physical fitness ; Physical Sciences ; Public health ; Public health movements ; Qualitative analysis ; Regression analysis ; Research and Analysis Methods ; Risk assessment ; Risk factors ; Smoke ; Smoking ; Social Sciences ; Socioeconomic factors ; Statistical analysis</subject><ispartof>PloS one, 2020-05, Vol.15 (5), p.e0232951-e0232951</ispartof><rights>COPYRIGHT 2020 Public Library of Science</rights><rights>2020 Ali et al. This is an open access article distributed under the terms of the Creative Commons Attribution License: http://creativecommons.org/licenses/by/4.0/ (the “License”), which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited. Notwithstanding the ProQuest Terms and Conditions, you may use this content in accordance with the terms of the License.</rights><rights>2020 Ali et al 2020 Ali et al</rights><lds50>peer_reviewed</lds50><oa>free_for_read</oa><woscitedreferencessubscribed>false</woscitedreferencessubscribed><citedby>FETCH-LOGICAL-c692t-c8cc01332cd626fdb33baa2cba32d63fcefa89df7301a04da8d050166def13e43</citedby><cites>FETCH-LOGICAL-c692t-c8cc01332cd626fdb33baa2cba32d63fcefa89df7301a04da8d050166def13e43</cites><orcidid>0000-0002-9409-4532</orcidid></display><links><openurl>$$Topenurl_article</openurl><openurlfulltext>$$Topenurlfull_article</openurlfulltext><thumbnail>$$Tsyndetics_thumb_exl</thumbnail><linktopdf>$$Uhttps://www.ncbi.nlm.nih.gov/pmc/articles/PMC7219750/pdf/$$EPDF$$P50$$Gpubmedcentral$$Hfree_for_read</linktopdf><linktohtml>$$Uhttps://www.ncbi.nlm.nih.gov/pmc/articles/PMC7219750/$$EHTML$$P50$$Gpubmedcentral$$Hfree_for_read</linktohtml><link.rule.ids>230,314,724,777,781,861,882,2096,2915,23847,27905,27906,53772,53774,79349,79350</link.rule.ids><backlink>$$Uhttps://www.ncbi.nlm.nih.gov/pubmed/32401782$$D View this record in MEDLINE/PubMed$$Hfree_for_read</backlink></links><search><contributor>Devleesschauwer, Brecht</contributor><creatorcontrib>Ali, Ozden Gur</creatorcontrib><creatorcontrib>Ghanem, Angi Nazih</creatorcontrib><creatorcontrib>Ustun, Bedirhan</creatorcontrib><title>Estimating the potential impact of behavioral public health interventions nationally while maintaining agreement with global patterns on relative risks</title><title>PloS one</title><addtitle>PLoS One</addtitle><description>This paper introduces a novel method to evaluate the local impact of behavioral scenarios on disease prevalence and burden with representative individual level data while ensuring that the model is in agreement with the qualitative patterns of global relative risk (RR) estimates. The method is used to estimate the impact of behavioral scenarios on the burden of disease due to ischemic heart disease (IHD) and diabetes in the Turkish adult population.
Disease specific Hierarchical Bayes (HB) models estimate the individual disease probability as a function of behaviors, demographics, socio-economics and other controls, where constraints are specified based on the global RR estimates. The simulator combines the counterfactual disease probability estimates with disability adjusted life year (DALY)-per-prevalent-case estimates and rolls up to the targeted population level, thus reflecting the local joint distribution of exposures. The Global Burden of Disease (GBD) 2016 study meta-analysis results guide the analysis of the Turkish National Health Surveys (2008 to 2016) that contain more than 90 thousand observations.
The proposed Qualitative Informative HB models do not sacrifice predictive accuracy versus benchmarks (logistic regression and HB models with non-informative and numerical informative priors) while agreeing with the global patterns. In the Turkish adult population, Increasing Physical Activity reduces the DALYs substantially for both IHD by 8.6% (6.4% 11.2%), and Diabetes by 8.1% (5.8% 10.6%), (90% uncertainty intervals). Eliminating Smoking and Second-hand Smoke predominantly decreases the IHD burden 13.1% (10.4% 15.8%) versus Diabetes 2.8% (1.1% 4.6%). Increasing Fruit and Vegetable Consumption, on the other hand, reduces IHD DALYs by 4.1% (2.8% 5.4%) while not improving the Diabetes burden 0.1% (0% 0.1%).
While the national RR estimates are in qualitative agreement with the global patterns, the scenario impact estimates are markedly different than the attributable risk estimates from the GBD analysis and allow evaluation of practical scenarios with multiple behaviors.</description><subject>Age</subject><subject>Bayesian analysis</subject><subject>Behavior</subject><subject>Benchmarking</subject><subject>Benchmarks</subject><subject>Biology and Life Sciences</subject><subject>Cardiovascular diseases</subject><subject>Computer simulation</subject><subject>Coronary artery disease</subject><subject>Demographic aspects</subject><subject>Demographics</subject><subject>Demography</subject><subject>Diabetes</subject><subject>Diabetes mellitus</subject><subject>Disabilities</subject><subject>Disease</subject><subject>Diseases</subject><subject>Estimates</subject><subject>Exercise</subject><subject>Gender</subject><subject>Health behavior</subject><subject>Health care</subject><subject>Health 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Nazih</au><au>Ustun, Bedirhan</au><au>Devleesschauwer, Brecht</au><format>journal</format><genre>article</genre><ristype>JOUR</ristype><atitle>Estimating the potential impact of behavioral public health interventions nationally while maintaining agreement with global patterns on relative risks</atitle><jtitle>PloS one</jtitle><addtitle>PLoS One</addtitle><date>2020-05-13</date><risdate>2020</risdate><volume>15</volume><issue>5</issue><spage>e0232951</spage><epage>e0232951</epage><pages>e0232951-e0232951</pages><issn>1932-6203</issn><eissn>1932-6203</eissn><abstract>This paper introduces a novel method to evaluate the local impact of behavioral scenarios on disease prevalence and burden with representative individual level data while ensuring that the model is in agreement with the qualitative patterns of global relative risk (RR) estimates. The method is used to estimate the impact of behavioral scenarios on the burden of disease due to ischemic heart disease (IHD) and diabetes in the Turkish adult population.
Disease specific Hierarchical Bayes (HB) models estimate the individual disease probability as a function of behaviors, demographics, socio-economics and other controls, where constraints are specified based on the global RR estimates. The simulator combines the counterfactual disease probability estimates with disability adjusted life year (DALY)-per-prevalent-case estimates and rolls up to the targeted population level, thus reflecting the local joint distribution of exposures. The Global Burden of Disease (GBD) 2016 study meta-analysis results guide the analysis of the Turkish National Health Surveys (2008 to 2016) that contain more than 90 thousand observations.
The proposed Qualitative Informative HB models do not sacrifice predictive accuracy versus benchmarks (logistic regression and HB models with non-informative and numerical informative priors) while agreeing with the global patterns. In the Turkish adult population, Increasing Physical Activity reduces the DALYs substantially for both IHD by 8.6% (6.4% 11.2%), and Diabetes by 8.1% (5.8% 10.6%), (90% uncertainty intervals). Eliminating Smoking and Second-hand Smoke predominantly decreases the IHD burden 13.1% (10.4% 15.8%) versus Diabetes 2.8% (1.1% 4.6%). Increasing Fruit and Vegetable Consumption, on the other hand, reduces IHD DALYs by 4.1% (2.8% 5.4%) while not improving the Diabetes burden 0.1% (0% 0.1%).
While the national RR estimates are in qualitative agreement with the global patterns, the scenario impact estimates are markedly different than the attributable risk estimates from the GBD analysis and allow evaluation of practical scenarios with multiple behaviors.</abstract><cop>United States</cop><pub>Public Library of Science</pub><pmid>32401782</pmid><doi>10.1371/journal.pone.0232951</doi><tpages>e0232951</tpages><orcidid>https://orcid.org/0000-0002-9409-4532</orcidid><oa>free_for_read</oa></addata></record> |
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| subjects | Age Bayesian analysis Behavior Benchmarking Benchmarks Biology and Life Sciences Cardiovascular diseases Computer simulation Coronary artery disease Demographic aspects Demographics Demography Diabetes Diabetes mellitus Disabilities Disease Diseases Estimates Exercise Gender Health behavior Health care Health risks Health surveys Heart diseases Ischemia Medical research Medicine and Health Sciences Meta-analysis Methods Model accuracy Myocardial ischemia Novels Passive smoking Physical activity Physical fitness Physical Sciences Public health Public health movements Qualitative analysis Regression analysis Research and Analysis Methods Risk assessment Risk factors Smoke Smoking Social Sciences Socioeconomic factors Statistical analysis |
| title | Estimating the potential impact of behavioral public health interventions nationally while maintaining agreement with global patterns on relative risks |
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