{"title":"重新定义疾病负担：每例daly作为每次诊断严重程度指标的验证。","authors":"Omar Freihat","doi":"10.1186/s12963-026-00469-2","DOIUrl":null,"url":null,"abstract":"<p><strong>Background: </strong>Population-weighted metrics (incidence, mortality, disability-adjusted life years (DALYs), mortality to incidence ratio (MIR) can obscure per-case severity for less prevalent but high-impact conditions. This paper introduces DALY per case, total DALYs divided by incident cases, as a standardized estimate of healthy life-years lost per new diagnosis, integrating years of life lost (YLL) and years lived with disability (YLD). Validated using cancers and applied across diverse diseases, the metric enables prevalence-independent severity comparisons.</p><p><strong>Methods: </strong>Using GBD 2021, we computed DALY per case across diseases (all ages, both sexes), validated on 34 cancers, and tested generalizability in five non-cancer conditions (type 2-diabetes, tuberculosis, HIV/AIDS, ischemic heart disease, Alzheimer's). We compared rankings with incidence, mortality, and total DALYs. A 2-Dimensional framework plotted total DALYs (population burden) vs. DALY-per-case (individual severity) with median-based quadrant thresholds. Uncertainty intervals (UIs) were propagated per GBD conventions; stability was assessed via relative UI width, band-crossing, and sensitivity analyses. Construct/convergent validity used correlations with 5-year survival Surveillance, Epidemiology, and End Results Program (SEER) and MIR; full and reduced regressions tested independence.</p><p><strong>Results: </strong>High-severity cancers included malignant bone tumours (27.6 DALYs/case), neuroblastoma (26.3), and brain/CNS (24.9), contrasting with population-dominant burdens such as lung (46.5 million DALYs; 20.4/case) and colorectal (24.4 million; 11.1/case). Relative uncertainty spanned 27% (breast) to 96% (Hodgkin lymphoma); rankings were largely preserved despite wide UIs in select sites. DALY-per-case correlated inversely with 5-year survival (r=-0.72, p < 0.001) and positively with MIR (r = 0.75, p < 0.001). In regression, MIR showed the strongest effect (β = 0.52, p = 0.06); survival lost significance when MIR was included, indicating shared but non-redundant variance.</p><p><strong>Conclusions: </strong>DALY-per-case provides a disease-agnostic toolkit, including a 2Dimensional burden-severity framework and validation against existing indicators, to quantify per-diagnosis severity and inform policy across communicable and non-communicable diseases.</p>","PeriodicalId":51476,"journal":{"name":"Population Health Metrics","volume":" ","pages":""},"PeriodicalIF":2.5000,"publicationDate":"2026-03-08","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC13081330/pdf/","citationCount":"0","resultStr":"{\"title\":\"Reframing disease burden: validation of DALY-per-case as a per-diagnosis severity metric.\",\"authors\":\"Omar Freihat\",\"doi\":\"10.1186/s12963-026-00469-2\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"<p><strong>Background: </strong>Population-weighted metrics (incidence, mortality, disability-adjusted life years (DALYs), mortality to incidence ratio (MIR) can obscure per-case severity for less prevalent but high-impact conditions. This paper introduces DALY per case, total DALYs divided by incident cases, as a standardized estimate of healthy life-years lost per new diagnosis, integrating years of life lost (YLL) and years lived with disability (YLD). Validated using cancers and applied across diverse diseases, the metric enables prevalence-independent severity comparisons.</p><p><strong>Methods: </strong>Using GBD 2021, we computed DALY per case across diseases (all ages, both sexes), validated on 34 cancers, and tested generalizability in five non-cancer conditions (type 2-diabetes, tuberculosis, HIV/AIDS, ischemic heart disease, Alzheimer's). We compared rankings with incidence, mortality, and total DALYs. A 2-Dimensional framework plotted total DALYs (population burden) vs. DALY-per-case (individual severity) with median-based quadrant thresholds. Uncertainty intervals (UIs) were propagated per GBD conventions; stability was assessed via relative UI width, band-crossing, and sensitivity analyses. Construct/convergent validity used correlations with 5-year survival Surveillance, Epidemiology, and End Results Program (SEER) and MIR; full and reduced regressions tested independence.</p><p><strong>Results: </strong>High-severity cancers included malignant bone tumours (27.6 DALYs/case), neuroblastoma (26.3), and brain/CNS (24.9), contrasting with population-dominant burdens such as lung (46.5 million DALYs; 20.4/case) and colorectal (24.4 million; 11.1/case). Relative uncertainty spanned 27% (breast) to 96% (Hodgkin lymphoma); rankings were largely preserved despite wide UIs in select sites. DALY-per-case correlated inversely with 5-year survival (r=-0.72, p < 0.001) and positively with MIR (r = 0.75, p < 0.001). In regression, MIR showed the strongest effect (β = 0.52, p = 0.06); survival lost significance when MIR was included, indicating shared but non-redundant variance.</p><p><strong>Conclusions: </strong>DALY-per-case provides a disease-agnostic toolkit, including a 2Dimensional burden-severity framework and validation against existing indicators, to quantify per-diagnosis severity and inform policy across communicable and non-communicable diseases.</p>\",\"PeriodicalId\":51476,\"journal\":{\"name\":\"Population Health Metrics\",\"volume\":\" \",\"pages\":\"\"},\"PeriodicalIF\":2.5000,\"publicationDate\":\"2026-03-08\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC13081330/pdf/\",\"citationCount\":\"0\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"Population Health Metrics\",\"FirstCategoryId\":\"3\",\"ListUrlMain\":\"https://doi.org/10.1186/s12963-026-00469-2\",\"RegionNum\":2,\"RegionCategory\":\"医学\",\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"Q2\",\"JCRName\":\"PUBLIC, ENVIRONMENTAL & OCCUPATIONAL HEALTH\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"Population Health Metrics","FirstCategoryId":"3","ListUrlMain":"https://doi.org/10.1186/s12963-026-00469-2","RegionNum":2,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"Q2","JCRName":"PUBLIC, ENVIRONMENTAL & OCCUPATIONAL HEALTH","Score":null,"Total":0}
Reframing disease burden: validation of DALY-per-case as a per-diagnosis severity metric.
Background: Population-weighted metrics (incidence, mortality, disability-adjusted life years (DALYs), mortality to incidence ratio (MIR) can obscure per-case severity for less prevalent but high-impact conditions. This paper introduces DALY per case, total DALYs divided by incident cases, as a standardized estimate of healthy life-years lost per new diagnosis, integrating years of life lost (YLL) and years lived with disability (YLD). Validated using cancers and applied across diverse diseases, the metric enables prevalence-independent severity comparisons.
Methods: Using GBD 2021, we computed DALY per case across diseases (all ages, both sexes), validated on 34 cancers, and tested generalizability in five non-cancer conditions (type 2-diabetes, tuberculosis, HIV/AIDS, ischemic heart disease, Alzheimer's). We compared rankings with incidence, mortality, and total DALYs. A 2-Dimensional framework plotted total DALYs (population burden) vs. DALY-per-case (individual severity) with median-based quadrant thresholds. Uncertainty intervals (UIs) were propagated per GBD conventions; stability was assessed via relative UI width, band-crossing, and sensitivity analyses. Construct/convergent validity used correlations with 5-year survival Surveillance, Epidemiology, and End Results Program (SEER) and MIR; full and reduced regressions tested independence.
Results: High-severity cancers included malignant bone tumours (27.6 DALYs/case), neuroblastoma (26.3), and brain/CNS (24.9), contrasting with population-dominant burdens such as lung (46.5 million DALYs; 20.4/case) and colorectal (24.4 million; 11.1/case). Relative uncertainty spanned 27% (breast) to 96% (Hodgkin lymphoma); rankings were largely preserved despite wide UIs in select sites. DALY-per-case correlated inversely with 5-year survival (r=-0.72, p < 0.001) and positively with MIR (r = 0.75, p < 0.001). In regression, MIR showed the strongest effect (β = 0.52, p = 0.06); survival lost significance when MIR was included, indicating shared but non-redundant variance.
Conclusions: DALY-per-case provides a disease-agnostic toolkit, including a 2Dimensional burden-severity framework and validation against existing indicators, to quantify per-diagnosis severity and inform policy across communicable and non-communicable diseases.
期刊介绍：
Population Health Metrics aims to advance the science of population health assessment, and welcomes papers relating to concepts, methods, ethics, applications, and summary measures of population health. The journal provides a unique platform for population health researchers to share their findings with the global community. We seek research that addresses the communication of population health measures and policy implications to stakeholders; this includes papers related to burden estimation and risk assessment, and research addressing population health across the full range of development. Population Health Metrics covers a broad range of topics encompassing health state measurement and valuation, summary measures of population health, descriptive epidemiology at the population level, burden of disease and injury analysis, disease and risk factor modeling for populations, and comparative assessment of risks to health at the population level. The journal is also interested in how to use and communicate indicators of population health to reduce disease burden, and the approaches for translating from indicators of population health to health-advancing actions. As a cross-cutting topic of importance, we are particularly interested in inequalities in population health and their measurement.
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