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Z-Score Burden Metric: A Method for Assessing Burden of Injury and Disease

      Introduction

      Traditional methods of summarizing burden of disease have limitations in terms of identifying communities within a population that are in need of prevention and intervention resources. This paper proposes a new method of burden assessment for use in guiding these decisions.

      Methods

      This new method for assessing burden utilizes the sum of population-weighted age-specific z-scores. This new Z-Score Burden Metric was applied to firearm-related deaths in North Carolina counties using 2010‒2017 North Carolina Violent Death Reporting System data. The Z-Score Burden Metric consists of 4 measures describing various aspects of burden. The Z-Score Burden Metric Overall Burden Measure was compared with 2 traditional measures (unadjusted and age-adjusted death rates) for each county to assess similarities and differences in the relative burden of firearm-related death.

      Results

      Of all 100 North Carolina counties, 73 met inclusion criteria (≥5 actual and expected deaths during the study period in each age strata). Ranking by the Overall Burden Measure produced an ordering of counties different from that of ranking by traditional measures. A total of 8 counties (11.0%) differed in burden rank by at least 10% when comparing the Overall Burden Measure with age-adjusted and unadjusted rates. All the counties with large differences between the measures were substantially burdened by firearm-related death.

      Conclusions

      The use of the Z-Score Burden Metric provides an alternative way of measuring realized community burden of injury while still facilitating comparisons between communities with different age distributions. This method can be used for any injury or disease outcome and may help to prioritize the allocation of resources to communities suffering high burdens of injury and disease.
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      REFERENCES

      1. Hernan MA, Robins JM. Causal Inference: What If. Boca Raton, FL: Chapman & Hall/CRC; 2020. https://www.hsph.harvard.edu/miguel-hernan/causal-inference-book/. Accessed January 6, 2019.

        • Rothman K
        • Greenland S
        • Lash T.
        Modern Epidemiology.
        3rd ed. Lippincott, Williams, & Wilkins, Philadelphia, PA2008
      2. GBD 2016 DALYs and HALE Collaborators. Global, regional, and national disability-adjusted life-years (DALYs) for 333 diseases and injuries and healthy life expectancy (HALE) for 195 countries and territories, 1990–2016: a systematic analysis for the Global Burden of Disease Study 2016 [published correction appears in Lancet. 2017;390(10106):e38].
        Lancet. 2017; 390: 1260-1344https://doi.org/10.1016/S0140-6736(17)32130-X
        • GBD 2016 Risk Factors Collaborators
        Global, regional, and national comparative risk assessment of 84 behavioural, environmental and occupational, and metabolic risks or clusters of risks, 1990–2016: a systematic analysis for the Global Burden of Disease Study 2016 [published correction appears in Lancet. 2017;390(10104):1736] [published correction appears in Lancet. 2017;390(10106):e38].
        Lancet. 2017; 390: 1345-1422https://doi.org/10.1016/S0140-6736(17)32366-8
      3. GBD 2016 Mortality Collaborators. Global, regional, and national under-5 mortality, adult mortality, age-specific mortality, and life expectancy, 1970–2016: a systematic analysis for the Global Burden of Disease Study 2016 [published correction appears in Lancet. 2017;390(10106):e38].
        Lancet. 2017; 390: 1084-1150https://doi.org/10.1016/S0140-6736(17)31833-0
        • GBD 2016 Disease and Injury Incidence and Prevalence Collaborators
        Global, regional, and national incidence, prevalence, and years lived with disability for 328 diseases and injuries for 195 countries, 1990–2016: a systematic analysis for the Global Burden of Disease Study 2016 [published correction appears in Lancet. 2017;390(10106):e38].
        Lancet. 2017; 390: 1211-1259https://doi.org/10.1016/S0140-6736(17)32154-2
      4. GBD 2016 Causes of Death Collaborators. Global, regional, and national age-sex specific mortality for 264 causes of death, 1980–2016: a systematic analysis for the Global Burden of Disease Study 2016 [published correction appears in Lancet. 2017;390(10106):e38].
        Lancet. 2017; 390: 1151-1210https://doi.org/10.1016/S0140-6736(17)32152-9
        • Mokdad AH
        • Ballestros K
        • et al.
        • U.S. Burden of Disease Collaborators
        The state of U.S. health, 1990–2016: burden of diseases, injuries, and risk factors among U.S. states.
        JAMA. 2018; 319: 1444-1472https://doi.org/10.1001/jama.2018.0158
        • Wiebe DJ
        • Nance ML
        • Branas CC.
        Determining objective injury prevention priorities.
        Inj Prev. 2006; 12: 347-350https://doi.org/10.1136/ip.2006.011494
        • Lee CH
        • Cook S
        • Lee JS
        • Han B.
        Comparison of two meta-analysis methods: inverse-variance-weighted average and weighted sum of Z-scores.
        Genomics Inform. 2016; 14: 173-180https://doi.org/10.5808/GI.2016.14.4.173
        • Chung W
        • Park JH
        • Chung HS
        • et al.
        Utility of the Z-score of log-transformed A Body Shape Index (LBSIZ) in the assessment for sarcopenic obesity and cardiovascular disease risk in the United States.
        Sci Rep. 2019; 9: 1-8https://doi.org/10.1038/s41598-019-45717-8
        • Aris IM
        • Rifas-Shiman SL
        • Li LJ
        • et al.
        Association of weight for length vs body mass index during the first 2 years of life with cardiometabolic risk in early adolescence.
        JAMA Netw Open. 2018; 1e182460https://doi.org/10.1001/jamanetworkopen.2018.2460
        • Irving SY
        • Daly B
        • Verger J
        • et al.
        The association of nutrition status expressed as body mass index z-Score With Outcomes in children with severe sepsis: a secondary analysis from the Sepsis Prevalence, Outcomes, and Therapies (SPROUT) study.
        Crit Care Med. 2018; 46: e1029-e1039https://doi.org/10.1097/CCM.0000000000003351
        • Goodman MS.
        Biostatistics for Clinical and Public Health Research.
        Routledge, New York, NY2017https://doi.org/10.4324/9781315155661
        • Dailey NJ
        • Norwood T
        • Moore ZS
        • Fleischauer AT
        Proescholdbell S. Evaluation of the North Carolina violent death reporting system, 2009.
        N C Med J. 2012; 73: 257-262https://doi.org/10.18043/ncm.73.4.257
      5. U.S. Census populations with bridged race categories. National Center for Health Statistics, National Vital Statistics System, Centers for Disease Control and Prevention. https://www.cdc.gov/nchs/nvss/bridged_race.htm. Updated June 20, 2019. Accessed April 14, 2021.

      6. Ingram DD, Parker JD, Schenker N, et al. United States Census 2000 population with bridged race categories. Vital Health Stat 2. 2003;(135):1‒55. https://www.cdc.gov/nchs/data/series/sr_02/sr02_135.pdf. Accessed February 26, 2021.