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Body mass index and healthcare costs: using genetic variants from the HUNT study as instrumental variables
BACKGROUND: Past studies have found associations between obesity and healthcare costs, however, these studies have suffered from bias due to omitted variables, reverse causality, and measurement error. METHODS: We used genetic variants related to body mass index (BMI) as instruments for BMI; thereby...
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Formato: | Online Artículo Texto |
Lenguaje: | English |
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BioMed Central
2022
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Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8957125/ https://www.ncbi.nlm.nih.gov/pubmed/35337320 http://dx.doi.org/10.1186/s12913-022-07597-z |
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author | Edwards, Christina Hansen Vie, Gunnhild Åberge Kinge, Jonas Minet |
author_facet | Edwards, Christina Hansen Vie, Gunnhild Åberge Kinge, Jonas Minet |
author_sort | Edwards, Christina Hansen |
collection | PubMed |
description | BACKGROUND: Past studies have found associations between obesity and healthcare costs, however, these studies have suffered from bias due to omitted variables, reverse causality, and measurement error. METHODS: We used genetic variants related to body mass index (BMI) as instruments for BMI; thereby exploiting the natural randomization of genetic variants that occurs at conception. We used data on measured height and weight, genetic information, and sociodemographic factors from the Nord-Trøndelag Health Studies (HUNT), and individual-level registry data on healthcare costs, educational level, registration status, and biological relatives. We studied associations between BMI and general practitioner (GP)-, specialist-, and total healthcare costs in the Norwegian setting using instrumental variable (IV) regressions, and compared our findings with effect estimates from ordinary least squares (OLS) regressions. The sensitivity of our findings to underlying IV-assumptions was explored using two-sample Mendelian randomization methods, non-linear analyses, sex-, healthcare provider-, and age-specific analyses, within-family analyses, and outlier removal. We also conducted power calculations to assess the likelihood of detecting an effect given our sample 60,786 individuals. RESULTS: We found that increased BMI resulted in significantly higher GP costs; however, the IV-based effect estimate was smaller than the OLS-based estimate. We found no evidence of an association between BMI and specialist or total healthcare costs. CONCLUSIONS: Elevated BMI leads to higher GP costs, and more studies are needed to understand the causal mechanisms between BMI and specialist costs. SUPPLEMENTARY INFORMATION: The online version contains supplementary material available at 10.1186/s12913-022-07597-z. |
format | Online Article Text |
id | pubmed-8957125 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2022 |
publisher | BioMed Central |
record_format | MEDLINE/PubMed |
spelling | pubmed-89571252022-03-27 Body mass index and healthcare costs: using genetic variants from the HUNT study as instrumental variables Edwards, Christina Hansen Vie, Gunnhild Åberge Kinge, Jonas Minet BMC Health Serv Res Research BACKGROUND: Past studies have found associations between obesity and healthcare costs, however, these studies have suffered from bias due to omitted variables, reverse causality, and measurement error. METHODS: We used genetic variants related to body mass index (BMI) as instruments for BMI; thereby exploiting the natural randomization of genetic variants that occurs at conception. We used data on measured height and weight, genetic information, and sociodemographic factors from the Nord-Trøndelag Health Studies (HUNT), and individual-level registry data on healthcare costs, educational level, registration status, and biological relatives. We studied associations between BMI and general practitioner (GP)-, specialist-, and total healthcare costs in the Norwegian setting using instrumental variable (IV) regressions, and compared our findings with effect estimates from ordinary least squares (OLS) regressions. The sensitivity of our findings to underlying IV-assumptions was explored using two-sample Mendelian randomization methods, non-linear analyses, sex-, healthcare provider-, and age-specific analyses, within-family analyses, and outlier removal. We also conducted power calculations to assess the likelihood of detecting an effect given our sample 60,786 individuals. RESULTS: We found that increased BMI resulted in significantly higher GP costs; however, the IV-based effect estimate was smaller than the OLS-based estimate. We found no evidence of an association between BMI and specialist or total healthcare costs. CONCLUSIONS: Elevated BMI leads to higher GP costs, and more studies are needed to understand the causal mechanisms between BMI and specialist costs. SUPPLEMENTARY INFORMATION: The online version contains supplementary material available at 10.1186/s12913-022-07597-z. BioMed Central 2022-03-25 /pmc/articles/PMC8957125/ /pubmed/35337320 http://dx.doi.org/10.1186/s12913-022-07597-z Text en © The Author(s) 2022 https://creativecommons.org/licenses/by/4.0/Open AccessThis article is licensed under a Creative Commons Attribution 4.0 International License, which permits use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons licence, and indicate if changes were made. The images or other third party material in this article are included in the article's Creative Commons licence, unless indicated otherwise in a credit line to the material. If material is not included in the article's Creative Commons licence and your intended use is not permitted by statutory regulation or exceeds the permitted use, you will need to obtain permission directly from the copyright holder. To view a copy of this licence, visit http://creativecommons.org/licenses/by/4.0/ (https://creativecommons.org/licenses/by/4.0/) . The Creative Commons Public Domain Dedication waiver (http://creativecommons.org/publicdomain/zero/1.0/ (https://creativecommons.org/publicdomain/zero/1.0/) ) applies to the data made available in this article, unless otherwise stated in a credit line to the data. |
spellingShingle | Research Edwards, Christina Hansen Vie, Gunnhild Åberge Kinge, Jonas Minet Body mass index and healthcare costs: using genetic variants from the HUNT study as instrumental variables |
title | Body mass index and healthcare costs: using genetic variants from the HUNT study as instrumental variables |
title_full | Body mass index and healthcare costs: using genetic variants from the HUNT study as instrumental variables |
title_fullStr | Body mass index and healthcare costs: using genetic variants from the HUNT study as instrumental variables |
title_full_unstemmed | Body mass index and healthcare costs: using genetic variants from the HUNT study as instrumental variables |
title_short | Body mass index and healthcare costs: using genetic variants from the HUNT study as instrumental variables |
title_sort | body mass index and healthcare costs: using genetic variants from the hunt study as instrumental variables |
topic | Research |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8957125/ https://www.ncbi.nlm.nih.gov/pubmed/35337320 http://dx.doi.org/10.1186/s12913-022-07597-z |
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