Redistribution of garbage codes to underlying causes of death: a systematic analysis on Italian data based on the...

to describe the garbage codes (GCs) identified in the Global Burden of Disease Study (GBD) 2023 and their redistribution to underlying causes of death in Italy in 2021. Specifically, the study aims to: 1. compare temporal trends in the proportion of GCs in Italy with those of other Western European countries with similar population sizes; 2. identify the most frequent GC packages, analyze their geographic distribution, and determine the causes to which they are reassigned; 3. examine the relationship between the proportion of GCs and contextual factors related to death certification, including the type of certifier, place of death, and requests for autopsy.

descriptive epidemiological study based on GBD 2023 estimates.

the analysis focuses on the Italian population in 2021, stratified by 21 subnational units, including 19 regions and 2 autonomous provinces.

total number of deaths; number of GCs, defined as deaths attributed to causes that do not represent underlying causes of death; percentage of GCs, calculated as the number of GCs divided by total deaths and multiplied by 100.

the proportion of GCs in Italy gradually decreased over time, from 34.6% in 1990 to 28.8% in 2021. In 2021, the three most frequent GC packages at the national level were 'unspecified type of stroke' (4.28% of total deaths), 'unspecified type of diabetes' (2.44%), and 'heart failure, right or left' (2.38%). In the same year, the proportion of GCs was positively correlated with the share of deaths occurring at home (r 0.71; p <0.001), with missing data on the type of certifying physician (r 0.54, p=0.020), on place of death (r 0.77, p <0.001), and on autopsy requests (r 0.76, p <0.001).

misreporting of causes of death arises from multiple mechanisms, reflecting errors of different nature and severity, with important implications for public health policies and health information systems. While redistribution methods are essential to produce comparable and policy-relevant estimates, improving data quality at the source remains a critical priority.