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Long-term cost-effectiveness of interventions for obesity: A mendelian randomisation study
The prevalence of obesity has increased in the United Kingdom, and reliably measuring the impact on quality of life and the total healthcare cost from obesity is key to informing the cost-effectiveness of interventions that target obesity, and determining healthcare funding. Current methods for esti...
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Published in: | PLoS medicine 2021-08, Vol.18 (8), p.e1003725-e1003725 |
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description | The prevalence of obesity has increased in the United Kingdom, and reliably measuring the impact on quality of life and the total healthcare cost from obesity is key to informing the cost-effectiveness of interventions that target obesity, and determining healthcare funding. Current methods for estimating cost-effectiveness of interventions for obesity may be subject to confounding and reverse causation. The aim of this study is to apply a new approach using mendelian randomisation for estimating the cost-effectiveness of interventions that target body mass index (BMI), which may be less affected by confounding and reverse causation than previous approaches.
We estimated health-related quality-adjusted life years (QALYs) and both primary and secondary healthcare costs for 310,913 men and women of white British ancestry aged between 39 and 72 years in UK Biobank between recruitment (2006 to 2010) and 31 March 2017. We then estimated the causal effect of differences in BMI on QALYs and total healthcare costs using mendelian randomisation. For this, we used instrumental variable regression with a polygenic risk score (PRS) for BMI, derived using a genome-wide association study (GWAS) of BMI, with age, sex, recruitment centre, and 40 genetic principal components as covariables to estimate the effect of a unit increase in BMI on QALYs and total healthcare costs. Finally, we used simulations to estimate the likely effect on BMI of policy relevant interventions for BMI, then used the mendelian randomisation estimates to estimate the cost-effectiveness of these interventions. A unit increase in BMI decreased QALYs by 0.65% of a QALY (95% confidence interval [CI]: 0.49% to 0.81%) per year and increased annual total healthcare costs by £42.23 (95% CI: £32.95 to £51.51) per person. When considering only health conditions usually considered in previous cost-effectiveness modelling studies (cancer, cardiovascular disease, cerebrovascular disease, and type 2 diabetes), we estimated that a unit increase in BMI decreased QALYs by only 0.16% of a QALY (95% CI: 0.10% to 0.22%) per year. We estimated that both laparoscopic bariatric surgery among individuals with BMI greater than 35 kg/m2, and restricting volume promotions for high fat, salt, and sugar products, would increase QALYs and decrease total healthcare costs, with net monetary benefits (at £20,000 per QALY) of £13,936 (95% CI: £8,112 to £20,658) per person over 20 years, and £546 million (95% CI: £435 million to £6 |
doi_str_mv | 10.1371/journal.pmed.1003725 |
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We estimated health-related quality-adjusted life years (QALYs) and both primary and secondary healthcare costs for 310,913 men and women of white British ancestry aged between 39 and 72 years in UK Biobank between recruitment (2006 to 2010) and 31 March 2017. We then estimated the causal effect of differences in BMI on QALYs and total healthcare costs using mendelian randomisation. For this, we used instrumental variable regression with a polygenic risk score (PRS) for BMI, derived using a genome-wide association study (GWAS) of BMI, with age, sex, recruitment centre, and 40 genetic principal components as covariables to estimate the effect of a unit increase in BMI on QALYs and total healthcare costs. Finally, we used simulations to estimate the likely effect on BMI of policy relevant interventions for BMI, then used the mendelian randomisation estimates to estimate the cost-effectiveness of these interventions. A unit increase in BMI decreased QALYs by 0.65% of a QALY (95% confidence interval [CI]: 0.49% to 0.81%) per year and increased annual total healthcare costs by £42.23 (95% CI: £32.95 to £51.51) per person. When considering only health conditions usually considered in previous cost-effectiveness modelling studies (cancer, cardiovascular disease, cerebrovascular disease, and type 2 diabetes), we estimated that a unit increase in BMI decreased QALYs by only 0.16% of a QALY (95% CI: 0.10% to 0.22%) per year. We estimated that both laparoscopic bariatric surgery among individuals with BMI greater than 35 kg/m2, and restricting volume promotions for high fat, salt, and sugar products, would increase QALYs and decrease total healthcare costs, with net monetary benefits (at £20,000 per QALY) of £13,936 (95% CI: £8,112 to £20,658) per person over 20 years, and £546 million (95% CI: £435 million to £671 million) in total per year, respectively. The main limitations of this approach are that mendelian randomisation relies on assumptions that cannot be proven, including the absence of directional pleiotropy, and that genotypes are independent of confounders.
Mendelian randomisation can be used to estimate the impact of interventions on quality of life and healthcare costs. We observed that the effect of increasing BMI on health-related quality of life is much larger when accounting for 240 chronic health conditions, compared with only a limited selection. This means that previous cost-effectiveness studies have likely underestimated the effect of BMI on quality of life and, therefore, the potential cost-effectiveness of interventions to reduce BMI.</description><identifier>ISSN: 1549-1676</identifier><identifier>ISSN: 1549-1277</identifier><identifier>EISSN: 1549-1676</identifier><identifier>DOI: 10.1371/journal.pmed.1003725</identifier><identifier>PMID: 34449774</identifier><language>eng</language><publisher>United States: Public Library of Science</publisher><subject>Adult ; Aged ; Bias ; Biobanks ; Biology and Life Sciences ; Body Mass Index ; Cardiovascular disease ; Cardiovascular diseases ; Care and treatment ; Cerebrovascular diseases ; Cost analysis ; Cost benefit analysis ; Diabetes mellitus (non-insulin dependent) ; Epidemiologic methods ; Estimates ; Female ; Gastrointestinal surgery ; Genetic aspects ; Genome-wide association studies ; Genomes ; Genotypes ; Health care ; Health Care Costs - statistics & numerical data ; Health risk assessment ; Humans ; Intervention ; Laparoscopy ; Male ; Medicine and Health Sciences ; Mendelian Randomization Analysis ; Methods ; Middle Aged ; Obesity ; Obesity - economics ; Obesity - prevention & control ; People and places ; Pleiotropy ; Primary care ; Primary Health Care - economics ; Quality of life ; Quality-Adjusted Life Years ; Secondary Care - economics ; Simulation ; Social Sciences ; Surgery</subject><ispartof>PLoS medicine, 2021-08, Vol.18 (8), p.e1003725-e1003725</ispartof><rights>COPYRIGHT 2021 Public Library of Science</rights><rights>2021 Harrison 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>2021 Harrison et al 2021 Harrison et al</rights><lds50>peer_reviewed</lds50><oa>free_for_read</oa><woscitedreferencessubscribed>false</woscitedreferencessubscribed><citedby>FETCH-LOGICAL-c694t-dd75a4488c91e7322f1b52a0f507a754a6a0710dad2f818a4f7523daebb1a1563</citedby><cites>FETCH-LOGICAL-c694t-dd75a4488c91e7322f1b52a0f507a754a6a0710dad2f818a4f7523daebb1a1563</cites><orcidid>0000-0003-3357-2796 ; 0000-0001-5285-409X ; 0000-0002-2460-0508 ; 0000-0002-4265-2854 ; 0000-0002-7966-0700</orcidid></display><links><openurl>$$Topenurl_article</openurl><openurlfulltext>$$Topenurlfull_article</openurlfulltext><thumbnail>$$Tsyndetics_thumb_exl</thumbnail><linktopdf>$$Uhttps://www.proquest.com/docview/2573454174/fulltextPDF?pq-origsite=primo$$EPDF$$P50$$Gproquest$$Hfree_for_read</linktopdf><linktohtml>$$Uhttps://www.proquest.com/docview/2573454174?pq-origsite=primo$$EHTML$$P50$$Gproquest$$Hfree_for_read</linktohtml><link.rule.ids>230,314,727,780,784,885,25751,27922,27923,37010,37011,44588,53789,53791,74896</link.rule.ids><backlink>$$Uhttps://www.ncbi.nlm.nih.gov/pubmed/34449774$$D View this record in MEDLINE/PubMed$$Hfree_for_read</backlink></links><search><contributor>Veerman, J. Lennert</contributor><creatorcontrib>Harrison, Sean</creatorcontrib><creatorcontrib>Dixon, Padraig</creatorcontrib><creatorcontrib>Jones, Hayley E</creatorcontrib><creatorcontrib>Davies, Alisha R</creatorcontrib><creatorcontrib>Howe, Laura D</creatorcontrib><creatorcontrib>Davies, Neil M</creatorcontrib><title>Long-term cost-effectiveness of interventions for obesity: A mendelian randomisation study</title><title>PLoS medicine</title><addtitle>PLoS Med</addtitle><description>The prevalence of obesity has increased in the United Kingdom, and reliably measuring the impact on quality of life and the total healthcare cost from obesity is key to informing the cost-effectiveness of interventions that target obesity, and determining healthcare funding. Current methods for estimating cost-effectiveness of interventions for obesity may be subject to confounding and reverse causation. The aim of this study is to apply a new approach using mendelian randomisation for estimating the cost-effectiveness of interventions that target body mass index (BMI), which may be less affected by confounding and reverse causation than previous approaches.
We estimated health-related quality-adjusted life years (QALYs) and both primary and secondary healthcare costs for 310,913 men and women of white British ancestry aged between 39 and 72 years in UK Biobank between recruitment (2006 to 2010) and 31 March 2017. We then estimated the causal effect of differences in BMI on QALYs and total healthcare costs using mendelian randomisation. For this, we used instrumental variable regression with a polygenic risk score (PRS) for BMI, derived using a genome-wide association study (GWAS) of BMI, with age, sex, recruitment centre, and 40 genetic principal components as covariables to estimate the effect of a unit increase in BMI on QALYs and total healthcare costs. Finally, we used simulations to estimate the likely effect on BMI of policy relevant interventions for BMI, then used the mendelian randomisation estimates to estimate the cost-effectiveness of these interventions. A unit increase in BMI decreased QALYs by 0.65% of a QALY (95% confidence interval [CI]: 0.49% to 0.81%) per year and increased annual total healthcare costs by £42.23 (95% CI: £32.95 to £51.51) per person. When considering only health conditions usually considered in previous cost-effectiveness modelling studies (cancer, cardiovascular disease, cerebrovascular disease, and type 2 diabetes), we estimated that a unit increase in BMI decreased QALYs by only 0.16% of a QALY (95% CI: 0.10% to 0.22%) per year. We estimated that both laparoscopic bariatric surgery among individuals with BMI greater than 35 kg/m2, and restricting volume promotions for high fat, salt, and sugar products, would increase QALYs and decrease total healthcare costs, with net monetary benefits (at £20,000 per QALY) of £13,936 (95% CI: £8,112 to £20,658) per person over 20 years, and £546 million (95% CI: £435 million to £671 million) in total per year, respectively. The main limitations of this approach are that mendelian randomisation relies on assumptions that cannot be proven, including the absence of directional pleiotropy, and that genotypes are independent of confounders.
Mendelian randomisation can be used to estimate the impact of interventions on quality of life and healthcare costs. We observed that the effect of increasing BMI on health-related quality of life is much larger when accounting for 240 chronic health conditions, compared with only a limited selection. This means that previous cost-effectiveness studies have likely underestimated the effect of BMI on quality of life and, therefore, the potential cost-effectiveness of interventions to reduce BMI.</description><subject>Adult</subject><subject>Aged</subject><subject>Bias</subject><subject>Biobanks</subject><subject>Biology and Life Sciences</subject><subject>Body Mass Index</subject><subject>Cardiovascular disease</subject><subject>Cardiovascular diseases</subject><subject>Care and treatment</subject><subject>Cerebrovascular diseases</subject><subject>Cost analysis</subject><subject>Cost benefit analysis</subject><subject>Diabetes mellitus (non-insulin dependent)</subject><subject>Epidemiologic methods</subject><subject>Estimates</subject><subject>Female</subject><subject>Gastrointestinal surgery</subject><subject>Genetic aspects</subject><subject>Genome-wide association studies</subject><subject>Genomes</subject><subject>Genotypes</subject><subject>Health care</subject><subject>Health Care Costs - statistics & numerical data</subject><subject>Health risk assessment</subject><subject>Humans</subject><subject>Intervention</subject><subject>Laparoscopy</subject><subject>Male</subject><subject>Medicine and Health Sciences</subject><subject>Mendelian Randomization Analysis</subject><subject>Methods</subject><subject>Middle Aged</subject><subject>Obesity</subject><subject>Obesity - economics</subject><subject>Obesity - prevention & control</subject><subject>People and places</subject><subject>Pleiotropy</subject><subject>Primary care</subject><subject>Primary Health Care - economics</subject><subject>Quality of life</subject><subject>Quality-Adjusted Life Years</subject><subject>Secondary Care - economics</subject><subject>Simulation</subject><subject>Social Sciences</subject><subject>Surgery</subject><issn>1549-1676</issn><issn>1549-1277</issn><issn>1549-1676</issn><fulltext>true</fulltext><rsrctype>article</rsrctype><creationdate>2021</creationdate><recordtype>article</recordtype><sourceid>PIMPY</sourceid><sourceid>DOA</sourceid><recordid>eNqVk02P0zAQhiMEYpeFf4AgEhKCQ4qd2HHCAala8VGpYiW-DlysSWynrlK72M6K_nvcbXbVoB5AOSS2n3k9804mSZ5iNMMFw2_WdnAG-tl2I8UMI1SwnN5LzjEldYZLVt4_-j5LHnm_RiivUY0eJmcFIaRmjJwnP5fWdFmQbpO21odMKiXboK-lkd6nVqXaxMO4DNoanyrrUttIr8PubTpPN9II2WswqQMj7EZ72HOpD4PYPU4eKOi9fDK-L5LvH95_u_yULa8-Li7ny6wtaxIyIRgFQqqqrbFkRZ4r3NAckKKIAaMESkAMIwEiVxWugChG80KAbBoMmJbFRfL8oLvtreejLZ7nlBWEEsxIJBYHQlhY863TG3A7bkHzmw3rOg4u6LaXnKKSoFrmhBFMcF02MYemagsskCK1VFHr3Xjb0ETn2-iMg34iOj0xesU7e80rEjtU0SjwahRw9tcgfeDRtlb2PRhph33eZYmKumIsoi_-Qk9XN1IdxAK0UTbe2-5F-byMZVQlJXmkshNUF_sck7RGKh23J_zsBB8fITe6PRnwehIQmSB_hw4G7_ni65f_YD__O3v1Y8q-PGJXEvqw8rYfbn7eKUgOYOus906quwZixPfjdes0348XH8crhj07bv5d0O08FX8AGdkePQ</recordid><startdate>20210827</startdate><enddate>20210827</enddate><creator>Harrison, Sean</creator><creator>Dixon, Padraig</creator><creator>Jones, Hayley E</creator><creator>Davies, Alisha R</creator><creator>Howe, Laura D</creator><creator>Davies, Neil M</creator><general>Public Library of Science</general><general>Public Library of Science (PLoS)</general><scope>CGR</scope><scope>CUY</scope><scope>CVF</scope><scope>ECM</scope><scope>EIF</scope><scope>NPM</scope><scope>AAYXX</scope><scope>CITATION</scope><scope>IOV</scope><scope>ISN</scope><scope>ISR</scope><scope>3V.</scope><scope>7TK</scope><scope>7X7</scope><scope>7XB</scope><scope>88E</scope><scope>8FI</scope><scope>8FJ</scope><scope>8FK</scope><scope>ABUWG</scope><scope>AEUYN</scope><scope>AFKRA</scope><scope>AZQEC</scope><scope>BENPR</scope><scope>CCPQU</scope><scope>DWQXO</scope><scope>FYUFA</scope><scope>GHDGH</scope><scope>K9.</scope><scope>M0S</scope><scope>M1P</scope><scope>PIMPY</scope><scope>PQEST</scope><scope>PQQKQ</scope><scope>PQUKI</scope><scope>PRINS</scope><scope>7X8</scope><scope>5PM</scope><scope>DOA</scope><scope>CZK</scope><orcidid>https://orcid.org/0000-0003-3357-2796</orcidid><orcidid>https://orcid.org/0000-0001-5285-409X</orcidid><orcidid>https://orcid.org/0000-0002-2460-0508</orcidid><orcidid>https://orcid.org/0000-0002-4265-2854</orcidid><orcidid>https://orcid.org/0000-0002-7966-0700</orcidid></search><sort><creationdate>20210827</creationdate><title>Long-term cost-effectiveness of interventions for obesity: A mendelian randomisation study</title><author>Harrison, Sean ; Dixon, Padraig ; Jones, Hayley E ; Davies, Alisha R ; Howe, Laura D ; Davies, Neil M</author></sort><facets><frbrtype>5</frbrtype><frbrgroupid>cdi_FETCH-LOGICAL-c694t-dd75a4488c91e7322f1b52a0f507a754a6a0710dad2f818a4f7523daebb1a1563</frbrgroupid><rsrctype>articles</rsrctype><prefilter>articles</prefilter><language>eng</language><creationdate>2021</creationdate><topic>Adult</topic><topic>Aged</topic><topic>Bias</topic><topic>Biobanks</topic><topic>Biology and Life Sciences</topic><topic>Body Mass Index</topic><topic>Cardiovascular disease</topic><topic>Cardiovascular diseases</topic><topic>Care and treatment</topic><topic>Cerebrovascular diseases</topic><topic>Cost analysis</topic><topic>Cost benefit analysis</topic><topic>Diabetes mellitus (non-insulin dependent)</topic><topic>Epidemiologic methods</topic><topic>Estimates</topic><topic>Female</topic><topic>Gastrointestinal surgery</topic><topic>Genetic aspects</topic><topic>Genome-wide association studies</topic><topic>Genomes</topic><topic>Genotypes</topic><topic>Health care</topic><topic>Health Care Costs - statistics & numerical data</topic><topic>Health risk assessment</topic><topic>Humans</topic><topic>Intervention</topic><topic>Laparoscopy</topic><topic>Male</topic><topic>Medicine and Health Sciences</topic><topic>Mendelian Randomization Analysis</topic><topic>Methods</topic><topic>Middle Aged</topic><topic>Obesity</topic><topic>Obesity - economics</topic><topic>Obesity - prevention & control</topic><topic>People and places</topic><topic>Pleiotropy</topic><topic>Primary care</topic><topic>Primary Health Care - economics</topic><topic>Quality of life</topic><topic>Quality-Adjusted Life Years</topic><topic>Secondary Care - economics</topic><topic>Simulation</topic><topic>Social Sciences</topic><topic>Surgery</topic><toplevel>peer_reviewed</toplevel><toplevel>online_resources</toplevel><creatorcontrib>Harrison, Sean</creatorcontrib><creatorcontrib>Dixon, Padraig</creatorcontrib><creatorcontrib>Jones, Hayley E</creatorcontrib><creatorcontrib>Davies, Alisha R</creatorcontrib><creatorcontrib>Howe, Laura D</creatorcontrib><creatorcontrib>Davies, Neil M</creatorcontrib><collection>Medline</collection><collection>MEDLINE</collection><collection>MEDLINE (Ovid)</collection><collection>MEDLINE</collection><collection>MEDLINE</collection><collection>PubMed</collection><collection>CrossRef</collection><collection>Opposing Viewpoints Resource Center</collection><collection>Gale In Context: Canada</collection><collection>Science in Context</collection><collection>ProQuest Central (Corporate)</collection><collection>Neurosciences Abstracts</collection><collection>Health & Medical Collection</collection><collection>ProQuest Central (purchase pre-March 2016)</collection><collection>Medical Database (Alumni Edition)</collection><collection>Hospital Premium Collection</collection><collection>Hospital Premium Collection (Alumni Edition)</collection><collection>ProQuest Central (Alumni) (purchase pre-March 2016)</collection><collection>ProQuest Central (Alumni)</collection><collection>ProQuest One Sustainability</collection><collection>ProQuest Central</collection><collection>ProQuest Central Essentials</collection><collection>ProQuest Databases</collection><collection>ProQuest One Community College</collection><collection>ProQuest Central</collection><collection>Health Research Premium Collection</collection><collection>Health Research Premium Collection (Alumni)</collection><collection>ProQuest Health & Medical Complete (Alumni)</collection><collection>Health & Medical Collection (Alumni Edition)</collection><collection>Medical Database</collection><collection>Publicly Available Content Database</collection><collection>ProQuest One Academic Eastern Edition (DO NOT USE)</collection><collection>ProQuest One Academic</collection><collection>ProQuest One Academic UKI Edition</collection><collection>ProQuest Central China</collection><collection>MEDLINE - Academic</collection><collection>PubMed Central (Full Participant titles)</collection><collection>DOAJ Directory of Open Access Journals</collection><collection>PLoS Medicine</collection><jtitle>PLoS medicine</jtitle></facets><delivery><delcategory>Remote Search Resource</delcategory><fulltext>fulltext</fulltext></delivery><addata><au>Harrison, Sean</au><au>Dixon, Padraig</au><au>Jones, Hayley E</au><au>Davies, Alisha R</au><au>Howe, Laura D</au><au>Davies, Neil M</au><au>Veerman, J. Lennert</au><format>journal</format><genre>article</genre><ristype>JOUR</ristype><atitle>Long-term cost-effectiveness of interventions for obesity: A mendelian randomisation study</atitle><jtitle>PLoS medicine</jtitle><addtitle>PLoS Med</addtitle><date>2021-08-27</date><risdate>2021</risdate><volume>18</volume><issue>8</issue><spage>e1003725</spage><epage>e1003725</epage><pages>e1003725-e1003725</pages><issn>1549-1676</issn><issn>1549-1277</issn><eissn>1549-1676</eissn><abstract>The prevalence of obesity has increased in the United Kingdom, and reliably measuring the impact on quality of life and the total healthcare cost from obesity is key to informing the cost-effectiveness of interventions that target obesity, and determining healthcare funding. Current methods for estimating cost-effectiveness of interventions for obesity may be subject to confounding and reverse causation. The aim of this study is to apply a new approach using mendelian randomisation for estimating the cost-effectiveness of interventions that target body mass index (BMI), which may be less affected by confounding and reverse causation than previous approaches.
We estimated health-related quality-adjusted life years (QALYs) and both primary and secondary healthcare costs for 310,913 men and women of white British ancestry aged between 39 and 72 years in UK Biobank between recruitment (2006 to 2010) and 31 March 2017. We then estimated the causal effect of differences in BMI on QALYs and total healthcare costs using mendelian randomisation. For this, we used instrumental variable regression with a polygenic risk score (PRS) for BMI, derived using a genome-wide association study (GWAS) of BMI, with age, sex, recruitment centre, and 40 genetic principal components as covariables to estimate the effect of a unit increase in BMI on QALYs and total healthcare costs. Finally, we used simulations to estimate the likely effect on BMI of policy relevant interventions for BMI, then used the mendelian randomisation estimates to estimate the cost-effectiveness of these interventions. A unit increase in BMI decreased QALYs by 0.65% of a QALY (95% confidence interval [CI]: 0.49% to 0.81%) per year and increased annual total healthcare costs by £42.23 (95% CI: £32.95 to £51.51) per person. When considering only health conditions usually considered in previous cost-effectiveness modelling studies (cancer, cardiovascular disease, cerebrovascular disease, and type 2 diabetes), we estimated that a unit increase in BMI decreased QALYs by only 0.16% of a QALY (95% CI: 0.10% to 0.22%) per year. We estimated that both laparoscopic bariatric surgery among individuals with BMI greater than 35 kg/m2, and restricting volume promotions for high fat, salt, and sugar products, would increase QALYs and decrease total healthcare costs, with net monetary benefits (at £20,000 per QALY) of £13,936 (95% CI: £8,112 to £20,658) per person over 20 years, and £546 million (95% CI: £435 million to £671 million) in total per year, respectively. The main limitations of this approach are that mendelian randomisation relies on assumptions that cannot be proven, including the absence of directional pleiotropy, and that genotypes are independent of confounders.
Mendelian randomisation can be used to estimate the impact of interventions on quality of life and healthcare costs. We observed that the effect of increasing BMI on health-related quality of life is much larger when accounting for 240 chronic health conditions, compared with only a limited selection. This means that previous cost-effectiveness studies have likely underestimated the effect of BMI on quality of life and, therefore, the potential cost-effectiveness of interventions to reduce BMI.</abstract><cop>United States</cop><pub>Public Library of Science</pub><pmid>34449774</pmid><doi>10.1371/journal.pmed.1003725</doi><orcidid>https://orcid.org/0000-0003-3357-2796</orcidid><orcidid>https://orcid.org/0000-0001-5285-409X</orcidid><orcidid>https://orcid.org/0000-0002-2460-0508</orcidid><orcidid>https://orcid.org/0000-0002-4265-2854</orcidid><orcidid>https://orcid.org/0000-0002-7966-0700</orcidid><oa>free_for_read</oa></addata></record> |
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recordid | cdi_plos_journals_2573454174 |
source | Open Access: PubMed Central; Publicly Available Content Database |
subjects | Adult Aged Bias Biobanks Biology and Life Sciences Body Mass Index Cardiovascular disease Cardiovascular diseases Care and treatment Cerebrovascular diseases Cost analysis Cost benefit analysis Diabetes mellitus (non-insulin dependent) Epidemiologic methods Estimates Female Gastrointestinal surgery Genetic aspects Genome-wide association studies Genomes Genotypes Health care Health Care Costs - statistics & numerical data Health risk assessment Humans Intervention Laparoscopy Male Medicine and Health Sciences Mendelian Randomization Analysis Methods Middle Aged Obesity Obesity - economics Obesity - prevention & control People and places Pleiotropy Primary care Primary Health Care - economics Quality of life Quality-Adjusted Life Years Secondary Care - economics Simulation Social Sciences Surgery |
title | Long-term cost-effectiveness of interventions for obesity: A mendelian randomisation study |
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