ArticleAnnals of medicine and surgery (2012)2026
Mortality attributed to complications of surgical operations in the United States: a retrospective cohort study of death certificate data, 1999-2024.
Article in Annals of medicine and surgery (2012), 2026. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.
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The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
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13 authors.
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Abstract
Background: Complications of surgical operations remain an important contributor to preventable mortality, despite advances in perioperative care, anesthesia, and surgical safety. However, long-term national mortality trends and demographic disparities associated with surgical complications remain poorly characterized at the population level. Methods: We conducted a retrospective analysis of U.S. mortality data from the CDC WONDER Multiple Cause of Death database between 1999 and 2024. Deaths attributed to complications of surgical operations were identified using ICD-10 Y83 codes. Age-adjusted mortality rates (AAMRs) per 100 000 population were calculated using the 2000 U.S. standard population. Temporal trends were evaluated using Joinpoint regression analysis to estimate annual percentage changes (APCs) with 95% confidence intervals (CIs). Mortality patterns were stratified by sex, race/ethnicity, census region, urbanization status, and state. Results: A total of 470 098 deaths were attributed to complications of surgical operations during the study period. The overall AAMR was 5.3 (95% CI: 5.2-5.4). Mortality declined significantly from 8.1 in 1999 to 3.4 in 2015 (APC: -5.56, Conclusion: Mortality associated with complications of surgical operations in the United States has demonstrated substantial temporal variation and persistent demographic and geographic disparities between 1999 and 2024. Further studies using patient-level clinical datasets are needed to clarify the underlying contributors to these patterns and identify potential targets for intervention.
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