{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,8,19]],"date-time":"2026-08-19T19:39:47Z","timestamp":1787168387838,"version":"build-2736575974"},"reference-count":20,"publisher":"Oxford University Press (OUP)","issue":"3","license":[{"start":{"date-parts":[[2022,1,21]],"date-time":"2022-01-21T00:00:00Z","timestamp":1642723200000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"DOI":"10.13039\/100000865","name":"Bill & Melinda Gates Foundation","doi-asserted-by":"publisher","id":[{"id":"10.13039\/100000865","id-type":"DOI","asserted-by":"publisher"}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":[],"published-print":{"date-parts":[[2022,6,1]]},"abstract":"<jats:title>Abstract<\/jats:title>\n                  <jats:sec>\n                    <jats:title>Background<\/jats:title>\n                    <jats:p>The proportion of reported causes of death (CoDs) that are not underlying causes can be relevant even in high-income countries and seriously affect health planning. The Global Burden of Disease (GBD) study identifies these \u2018garbage codes\u2019 (GCs) and redistributes them to underlying causes using evidence-based algorithms. Planners relying on vital registration data will find discrepancies with GBD estimates. We analyse these discrepancies, through the analysis of GCs and their redistribution.<\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Methods<\/jats:title>\n                    <jats:p>We explored the case of Italy, at national and regional level, and compared it to nine other Western European countries with similar population sizes. We analysed differences between official data and GBD 2019 estimates, for the period 1990\u20132017 for which we had vital registration data for most select countries.<\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Results<\/jats:title>\n                    <jats:p>In Italy, in 2017, 33\u2009000 deaths were attributed to unspecified type of stroke and 15\u2009000 to unspecified type of diabetes, these making a fourth of the overall garbage. Significant heterogeneity exists on the overall proportion of GCs, type (unspecified or impossible underlying causes), and size of specific GCs among regions in Italy, and among the select countries. We found no pattern between level of garbage and relevance of specific GCs. Even locations performing below average show interesting lower levels for certain GCs if compared to better performing countries.<\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Conclusions<\/jats:title>\n                    <jats:p>This systematic analysis suggests the heterogeneity in GC levels and causes, paired with a more detailed analysis of local practices, strengths and weaknesses, could be a positive element in a strategy for the reduction of GCs in Italy.<\/jats:p>\n                  <\/jats:sec>","DOI":"10.1093\/eurpub\/ckab194","type":"journal-article","created":{"date-parts":[[2021,10,26]],"date-time":"2021-10-26T07:08:52Z","timestamp":1635232132000},"page":"456-462","source":"Crossref","is-referenced-by-count":32,"title":["Redistribution of garbage codes to underlying causes of death: a systematic analysis on Italy and a comparison with most populous Western European countries based on the Global Burden of Disease 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