Calibration and XGBoost reweighting to reduce coverage and non-response biases in overlapping panel surveys: Application to the Healthcare and Social Survey

Calibration and XGBoost reweighting to reduce coverage and non-response biases in overlapping panel surveys: Application to the Healthcare and Social Survey | Research Square window.SnipcartSettings = { analytics: { enabled: false } }; (function() { var accessVector = localStorage.getItem('access_vector') || ''; window.dataLayer = window.dataLayer || []; if (accessVector) { window.dataLayer.push({ user: { profile: { profileInfo: { snid: accessVector } } } }); } })(); (function(w,d,s,l,i){w[l]=w[l]||[];w[l].push({'gtm.start':new Date().getTime(),event:'gtm.js'});var f=d.getElementsByTagName(s)[0],j=d.createElement(s),dl=l!='dataLayer'?'&l='+l:'';j.async=true;j.src='https://www.googletagmanager.com/gtm.js?id='+i+dl;f.parentNode.insertBefore(j,f);})(window,document,'script','dataLayer','GTM-K279D39R'); Browse Preprints In Review Journals COVID-19 Preprints AJE Video Bytes Research Tools Research Promotion AJE Professional Editing AJE Rubriq About Preprint Platform In Review Editorial Policies Our Team Advisory Board Help Center Sign In Submit a Preprint Cite Share Download PDF Research Article Calibration and XGBoost reweighting to reduce coverage and non-response biases in overlapping panel surveys: Application to the Healthcare and Social Survey Luis Castro, María del Mar Rueda, Carmen Sánchez-Cantalejo, Ramón Ferri, and 1 more This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-3072394/v1 This work is licensed under a CC BY 4.0 License Status: Published Journal Publication published 15 Feb, 2024 Read the published version in BMC Medical Research Methodology → Version 1 posted 10 You are reading this latest preprint version Abstract Healthcare statistical services worldwide have used probability surveys to provide information on the social, economic and health impact of COVID-19, or its seroprevalence and evolution, or the characteristics of the infected population. The Healthcare and Social Survey (ESSA, Spanish acronym) arises from the need to provide data on the evolution of the COVID-19 impact which can be considered when making decisions, so as to prepare and deliver an effective Public Health response in the different populations concerned. This survey has an overlapping panel survey design with measurements throughout 2020 and 2021, and random samplings stratified by province and degree of urbanization. Each ESSA measurement comprises two samples: a longitudinal sample taken from previous measurements and a new sample taken at each measurement. This design allows longitudinal estimates and more accurate cross-sectional estimates to be obtained thanks to the larger sample size. However, the problem of non-response is particularly aggravated in the case of panel surveys due to population fatigue with repeated surveys. The objective of this research article is to develop a new reweighting method for overlapping panel surveys affected by non-response. Considering the design, timing and objectives of this survey, our reweighting methodological approach produces suitable estimators for both cross-sectional and longitudinal samples. The weights are the result of a two-step process: the original sampling design weights are corrected by modelling non-response with respect to the longitudinal sample obtained in a previous measurement using machine learning techniques, followed by calibration using the auxiliary information available at the population level. The proposed method is applied to the estimation of totals, proportions, ratios, and differences between measurements, and to gender gaps in the variable of self-perceived general health. For addressing future health crises such as COVID-19, it is therefore necessary to reduce potential coverage and non-response biases in surveys by means of utilizing reweighting techniques as proposed in this study. Public health COVID-19 panel surveys sampling machine learning non-response bias Figures Figure 1 Figure 2 Figure 3 Figure 4 Figure 5 Figure 6 Figure 7 Figure 8 Figure 9 Figure 10 Full Text Additional Declarations No competing interests reported. Tables 1-6 is available in the Supplementary Files section. Supplementary Files Table1.xlsx Table2.xlsx Table3.xlsx Table4.xlsx Table5.xlsx Table6.xlsx Cite Share Download PDF Status: Published Journal Publication published 15 Feb, 2024 Read the published version in BMC Medical Research Methodology → Version 1 posted Editorial decision: Revision requested 10 Jan, 2024 Reviews received at journal 15 Dec, 2023 Reviews received at journal 04 Dec, 2023 Reviewers agreed at journal 04 Dec, 2023 Reviewers agreed at journal 21 Nov, 2023 Reviewers invited by journal 21 Nov, 2023 Editor invited by journal 07 Jul, 2023 Editor assigned by journal 07 Jul, 2023 Submission checks completed at journal 07 Jul, 2023 First submitted to journal 16 Jun, 2023 You are reading this latest preprint version Research Square lets you share your work early, gain feedback from the community, and start making changes to your manuscript prior to peer review in a journal. As a division of Research Square Company, we’re committed to making research communication faster, fairer, and more useful. We do this by developing innovative software and high quality services for the global research community. Our growing team is made up of researchers and industry professionals working together to solve the most critical problems facing scientific publishing. Also discoverable on Platform About Our Team In Review Editorial Policies Advisory Board Help Center Resources Author Services Accessibility API Access RSS feed Manage Cookie Preferences © Research Square 2026 | ISSN 2693-5015 (online) Privacy Policy Terms of Service Do Not Sell My Personal Information {"props":{"pageProps":{"initialData":{"identity":"rs-3072394","acceptedTermsAndConditions":true,"allowDirectSubmit":false,"archivedVersions":[],"articleType":"Research Article","associatedPublications":[],"authors":[{"id":216530986,"identity":"54e39c48-982d-420c-802e-2ac6a2514ce9","order_by":0,"name":"Luis Castro","email":"","orcid":"","institution":"University of Granada","correspondingAuthor":false,"prefix":"","firstName":"Luis","middleName":"","lastName":"Castro","suffix":""},{"id":216530987,"identity":"fc398474-137d-4657-9ef6-6651e40093e6","order_by":1,"name":"María del Mar Rueda","email":"","orcid":"","institution":"University of 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5","display":"","copyAsset":false,"role":"figure","size":5713,"visible":true,"origin":"","legend":"\u003cp\u003ePercentages and confidence intervals at 95% level of people with fair or poor self-perceived general health\u003c/p\u003e","description":"","filename":"F5.png","url":"https://assets-eu.researchsquare.com/files/rs-3072394/v1/8977718eb14ad585969b9d19.png"},{"id":39935175,"identity":"f606d536-b168-4a91-a165-2cbfece4d18a","added_by":"auto","created_at":"2023-07-12 18:33:07","extension":"png","order_by":6,"title":"Figure 6","display":"","copyAsset":false,"role":"figure","size":5713,"visible":true,"origin":"","legend":"\u003cp\u003eAbsolute percentage changes and 95% confidence intervals for people with fair or poor self-perceived general 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The Healthcare and Social Survey (ESSA, Spanish acronym) arises from the need to provide data on the evolution of the COVID-19 impact which can be considered when making decisions, so as to prepare and deliver an effective Public Health response in the different populations concerned. This survey has an overlapping panel survey design with measurements throughout 2020 and 2021, and random samplings stratified by province and degree of urbanization.\nEach ESSA measurement comprises two samples: a longitudinal sample taken from previous measurements and a new sample taken at each measurement. This design allows longitudinal estimates and more accurate cross-sectional estimates to be obtained thanks to the larger sample size. However, the problem of non-response is particularly aggravated in the case of panel surveys due to population fatigue with repeated surveys. 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