Quality of causes of death data in Minnesota from 2011 to 2021 – A descriptive analysis of the usage and correlates of garbage codes

Objectives: This study aimed to evaluate the cause-of-death data in Minnesota (2011-2021) to understand the usage of "garbage codes" on death certificates. Study design: We conducted a logistic regression analysis using death data from the Minnesota Vital Statistics System that compiles statistical data on all births, deaths, infant deaths, and fetal deaths in Minnesota. Methods: Death certificate data from the Minnesota Department of Health were analyzed, and garbage codes were classified using ANACONDA criteria. Logistic regression assessed associations with socioeconomic variables, considering demographic factors, county characteristics, and fixed effects. Results: Garbage codes constituted 3-4% of deaths, with variations by location, demographics, and office affiliation. Logistic regression revealed significant odds variations, notably related to age, rural residence, education, marital status, and place of death. Conclusions: The study unveiled variations in cause-of-death data reliability in Minnesota, emphasizing the prevalence of garbage codes. Enhancing cause-of-death data accuracy is pivotal for informed public health decisions and accurate death statistics to guide targeted public health interventions and mitigate health disparities.