{"id":427360,"date":"2025-06-20T09:38:12","date_gmt":"2025-06-20T07:38:12","guid":{"rendered":"https:\/\/www.wifo.ac.at\/publication\/427360\/"},"modified":"2025-06-20T09:57:13","modified_gmt":"2025-06-20T07:57:13","slug":"predicting-the-uptake-of-long-term-care-benefits-in-austria","status":"publish","type":"publication","link":"https:\/\/www.wifo.ac.at\/en\/publication\/427360\/","title":{"rendered":"Predicting the Uptake of Long-Term Care Benefits in Austria"},"content":{"rendered":"","protected":false},"featured_media":0,"template":"","class_list":["post-427360","publication","type-publication","status-publish","hentry"],"acf":{"subtitle":"","text":"We use administrative microdata and statistical learning methods to analyse how personal characteristics and the consumption of healthcare services help predict the first-time receipt of \"long-term care allowance\" (LTCA), a needs-tested cash-for-care benefit in Austria. Our findings suggest that short-term information from the health-care sector, particularly in the quarter prior to LTCA enrolment, provides substantial explanatory power. Apart from old age, the most influential predictors include the frequency of doctor visits and hospital stays as well as diagnoses such as dementia, cerebral infarction, and hypertension. Our findings emphasise the importance of data-driven approaches in anticipating the uptake of long-term care benefits and informing policy, especially against the background of the demographic transition.","onlinedate":"2025-06-20 07:38:13","lang":"English","publication_series":"","publication_date":"20250620","publication_date_year":"","publication_date_full":true,"publication_num_pages":"32","keywords":"[]","jelcodes":"[]","related_publications":"[]","pdf":427364,"zip":null,"link":"","monthly_report":true,"monthly_report_main":false,"monthly_report_volume":"","monthly_report_pages":"","monthly_report_pages_sort":"0","issue":"707","journal":"WIFO Working Papers","publisher":"","invisible":false,"external":false,"embargo_date":null,"types":[44410],"research_groups":[3510,3481],"collaboration":"","persons":[4401,3755],"persons_data":"[{\"personId\":4401,\"personName\":\"Ulrike Famira-M\u00fchlberger\",\"organisationNames\":[]},{\"personId\":3755,\"personName\":\"Klaus Nowotny\",\"organisationNames\":[]}]","clients":[],"subclients":[],"partners":[],"topics":[],"host_publication_title":"","host_publication_subtitle":"","place_of_publication":"","host_publication_editors":"[]","type_description":"","output_media":"","chapter":"","article_number":"","citations":"{\"apa\":\"<div class=\\\"rendering rendering_researchoutput  rendering_researchoutput_apa rendering_contributiontoperiodical rendering_apa rendering_contributiontoperiodical_apa\\\"><span>Famira-M\u00fchlberger, U.<\\\/span><span>, &amp; Nowotny, K.<\\\/span> (2025). <span>Predicting the Uptake of Long-Term Care Benefits in Austria<\\\/span>. <span><em>WIFO Working Papers<\\\/em><\\\/span>, (707).<\\\/div>\",\"vancouver\":\"<div class=\\\"rendering rendering_researchoutput  rendering_researchoutput_vancouver rendering_contributiontoperiodical rendering_vancouver rendering_contributiontoperiodical_vancouver\\\"><span>Famira-M\u00fchlberger U<\\\/span><span>, Nowotny K<\\\/span>. <span class=\\\"title\\\"><span>Predicting the Uptake of Long-Term Care Benefits in Austria<\\\/span><\\\/span>. <span>WIFO Working Papers<\\\/span>. 2025 Jun 20;(707).<\\\/div>\",\"bibtex\":\"<div class=\\\"rendering rendering_researchoutput  rendering_researchoutput_bibtex rendering_contributiontoperiodical rendering_bibtex rendering_contributiontoperiodical_bibtex\\\"><div>@misc{26c9935e2c8f47c984acf14229fb3d12,<\\\/div><div>  title    = \\\"Predicting the Uptake of Long-Term Care Benefits in Austria\\\",<\\\/div><div>  abstract = \\\"We use administrative microdata and statistical learning methods to analyse how personal characteristics and the consumption of healthcare services help predict the first-time receipt of {\\\"}long-term care allowance{\\\"} (LTCA), a needs-tested cash-for-care benefit in Austria. Our findings suggest that short-term information from the health-care sector, particularly in the quarter prior to LTCA enrolment, provides substantial explanatory power. Apart from old age, the most influential predictors include the frequency of doctor visits and hospital stays as well as diagnoses such as dementia, cerebral infarction, and hypertension. Our findings emphasise the importance of data-driven approaches in anticipating the uptake of long-term care benefits and informing policy, especially against the background of the demographic transition.\\\",<\\\/div><div>  author   = \\\"Ulrike Famira-M{\\\\\\\"u}hlberger and Klaus Nowotny\\\",<\\\/div><div>  year     = \\\"2025\\\",<\\\/div><div>  month    = jun,<\\\/div><div>  day      = \\\"20\\\",<\\\/div><div>  language = \\\"English\\\",<\\\/div><div>  journal  = \\\"WIFO Working Papers\\\",<\\\/div><p>}<\\\/p><\\\/div>\",\"ris\":\"<div class=\\\"rendering rendering_researchoutput  rendering_researchoutput_ris rendering_contributiontoperiodical rendering_ris rendering_contributiontoperiodical_ris\\\"><p>TY  - GEN<\\\/p><p>T1  - Predicting the Uptake of Long-Term Care Benefits in Austria<\\\/p><p>AU  - Famira-M\u00fchlberger, Ulrike<\\\/p><p>AU  - Nowotny, Klaus<\\\/p><p>PY  - 2025\\\/6\\\/20<\\\/p><p>Y1  - 2025\\\/6\\\/20<\\\/p><p>N2  - We use administrative microdata and statistical learning methods to analyse how personal characteristics and the consumption of healthcare services help predict the first-time receipt of \\\"long-term care allowance\\\" (LTCA), a needs-tested cash-for-care benefit in Austria. Our findings suggest that short-term information from the health-care sector, particularly in the quarter prior to LTCA enrolment, provides substantial explanatory power. Apart from old age, the most influential predictors include the frequency of doctor visits and hospital stays as well as diagnoses such as dementia, cerebral infarction, and hypertension. Our findings emphasise the importance of data-driven approaches in anticipating the uptake of long-term care benefits and informing policy, especially against the background of the demographic transition.<\\\/p><p>AB  - We use administrative microdata and statistical learning methods to analyse how personal characteristics and the consumption of healthcare services help predict the first-time receipt of \\\"long-term care allowance\\\" (LTCA), a needs-tested cash-for-care benefit in Austria. Our findings suggest that short-term information from the health-care sector, particularly in the quarter prior to LTCA enrolment, provides substantial explanatory power. Apart from old age, the most influential predictors include the frequency of doctor visits and hospital stays as well as diagnoses such as dementia, cerebral infarction, and hypertension. Our findings emphasise the importance of data-driven approaches in anticipating the uptake of long-term care benefits and informing policy, especially against the background of the demographic transition.<\\\/p><p>M3  - WIFO series<\\\/p><p>JO  - WIFO Working Papers<\\\/p><p>JF  - WIFO Working Papers<\\\/p><p>ER  - <\\\/p><\\\/div>\"}","scientific_assistance":"[\"Grandner Lydia\",\"Zerobin Manuel\"]","scientific_review":"[]","version":"","release_date":null,"expiration_date":null,"surveyor":"","research_assistance":"","edv":"","additional_info_de":"","additional_info_en":""},"yoast_head":"<!-- This site is optimized with the Yoast SEO Premium plugin v28.0 (Yoast SEO v28.0) - https:\/\/yoast.com\/product\/yoast-seo-premium-wordpress\/ -->\n<title>Predicting the Uptake of Long-Term Care Benefits in Austria - WIFO<\/title>\n<meta name=\"robots\" content=\"index, follow, max-snippet:-1, max-image-preview:large, max-video-preview:-1\" \/>\n<link rel=\"canonical\" href=\"https:\/\/www.wifo.ac.at\/en\/publication\/427360\/\" \/>\n<meta property=\"og:locale\" content=\"en_US\" \/>\n<meta property=\"og:type\" content=\"article\" \/>\n<meta property=\"og:title\" content=\"Predicting the Uptake of Long-Term Care Benefits in Austria\" \/>\n<meta property=\"og:url\" content=\"https:\/\/www.wifo.ac.at\/en\/publication\/427360\/\" \/>\n<meta property=\"og:site_name\" content=\"WIFO\" \/>\n<meta property=\"article:publisher\" content=\"https:\/\/www.facebook.com\/WIFOat\/\" \/>\n<meta property=\"article:modified_time\" content=\"2025-06-20T07:57:13+00:00\" \/>\n<meta property=\"og:image\" content=\"https:\/\/www.wifo.ac.at\/wp-content\/uploads\/2024\/05\/WIFO-Gebaeude-FotoAlexanderMueller-www.alexandermueller.at-IMG_6326-Bearbeitet-1.jpg\" \/>\n\t<meta property=\"og:image:width\" content=\"1920\" \/>\n\t<meta property=\"og:image:height\" content=\"1280\" \/>\n\t<meta property=\"og:image:type\" content=\"image\/jpeg\" \/>\n<meta name=\"twitter:card\" content=\"summary_large_image\" \/>\n<meta name=\"twitter:site\" content=\"@WIFOat\" \/>\n<script type=\"application\/ld+json\" class=\"yoast-schema-graph\">{\"@context\":\"https:\\\/\\\/schema.org\",\"@graph\":[{\"@type\":\"WebPage\",\"@id\":\"https:\\\/\\\/www.wifo.ac.at\\\/en\\\/publication\\\/427360\\\/\",\"url\":\"https:\\\/\\\/www.wifo.ac.at\\\/en\\\/publication\\\/427360\\\/\",\"name\":\"Predicting the Uptake of Long-Term Care Benefits in Austria - 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