5 results on '"Rossen, Lauren"'
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2. Mortality Surveillance for the COVID-19 Pandemic: Review of the Centers for Disease Control and Prevention's Multiple System Strategy.
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Khan, Diba, Park, Meeyoung, Grillo, Peter, Rossen, Lauren, Lyons, B. Casey, David, Sarah, Ritchey, Matthew D., Ahmad, Farida B., McNaghten, A. D., Gundlapalli, Adi V., and Suthar, Amitabh B.
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PUBLIC health surveillance ,DASHBOARDS (Management information systems) ,MORTALITY ,PUBLIC health laws ,SEVERITY of illness index ,PUBLIC health records ,DEATH certificates ,CONTENT mining ,PUBLIC health ,SOCIODEMOGRAPHIC factors ,HEALTH equity ,COVID-19 pandemic ,COVID-19 - Abstract
Mortality surveillance systems can have limitations, including reporting delays, incomplete reporting, missing data, and insufficient detail on important risk or sociodemographic factors that can impact the accuracy of estimates of current trends, disease severity, and related disparities across subpopulations. The Centers for Disease Control and Prevention used multiple data systems during the COVID-19 emergency response—line-level case‒death surveillance, aggregate death surveillance, and the National Vital Statistics System—to collectively provide more comprehensive and timely information on COVID-19‒associated mortality necessary for informed decisions. This article will review in detail the line-level, aggregate, and National Vital Statistics System surveillance systems and the purpose and use of each. This retrospective review of the hybrid surveillance systems strategy may serve as an example for adaptive informational approaches needed over the course of future public health emergencies. (Am J Public Health. 2024;114(10):1071–1080. https://doi.org/10.2105/AJPH.2024.307743) [ABSTRACT FROM AUTHOR]
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- 2024
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3. Technical Guidance for Using the Modified Kalman Filter in Small-domain Estimation at the National Center for Health Statistics: Data Evaluation and Methods Research.
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Talih, Makram, Rossen, Lauren M., Patel, Priyam, Earp, Morgan, and Parker, Jennifer D.
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- 2024
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4. Excess deaths associated with COVID‐19 by rurality and demographic factors in the United States.
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Ahrens, Katherine A., Rossen, Lauren M., Milkowski, Carly, Gelsinger, Catherine, and Ziller, Erika
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POISSON distribution ,RESEARCH funding ,SEX distribution ,RESIDENTIAL patterns ,AGE distribution ,DESCRIPTIVE statistics ,RACE ,RURAL conditions ,METROPOLITAN areas ,SOCIODEMOGRAPHIC factors ,HEALTH equity ,CONFIDENCE intervals ,COVID-19 ,COVID-19 pandemic ,REGRESSION analysis - Abstract
Purpose: To estimate percent excess deaths during the COVID‐19 pandemic by rural‐urban residence in the United States and to describe rural‐urban disparities by age, sex, and race/ethnicity. Methods: Using US mortality data, we used overdispersed Poisson regression models to estimate monthly expected death counts by rurality of residence, age group, sex, and race/ethnicity, and compared expected death counts with observed deaths. We then summarized excess deaths over 6 6‐month time periods. Findings: There were 16.9% (95% confidence interval [CI]: 16.8, 17.0) more deaths than expected between March 2020 and February 2023. The percent excess varied by rurality (large central metro: 18.2% [18.1, 18.4], large fringe metro: 15.6% [15.5, 15.8], medium metro: 18.1% [18.0, 18.3], small metro: 15.5% [15.3, 15.7], micropolitan rural: 16.3% [16.1, 16.5], and noncore rural: 15.8% [15.6, 16.1]). The percent excess deaths were 20.2% (20.1, 20.3) for males and 13.6% (13.5, 13.7) for females, and highest for Hispanic persons (49% [49.0, 49.6]), followed by non‐Hispanic Black persons (28% [27.5, 27.9]) and non‐Hispanic White persons (12% [11.6, 11.8]). The 6‐month time periods with the highest percent excess deaths for large central metro areas were March 2020‐August 2020 and September 2020‐February 2021; for all other areas, these time periods were September 2020‐February 2021 and September 2021‐February 2022. Conclusion: Percent excess deaths varied by rurality, age group, sex, race/ethnicity, and time period. Monitoring excess deaths by rurality may be useful in assessing the impact of the pandemic over time, as rural‐urban patterns appear to differ. [ABSTRACT FROM AUTHOR]
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- 2024
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5. Preventable Premature Deaths from the Five Leading Causes of Death in Nonmetropolitan and Metropolitan Counties, United States, 2010-2022.
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García, Macarena C., Rossen, Lauren M., Matthews, Kevin, Guy, Gery, Trivers, Katrina F., Thomas, Cheryll C., Schieb, Linda, and Iademarco, Michael F.
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STROKE-related mortality , *HEART disease related mortality , *WOUNDS & injuries , *HEALTH services accessibility , *RISK assessment , *SOCIAL determinants of health , *CAUSES of death , *POPULATION geography , *DESCRIPTIVE statistics , *RESPIRATORY diseases , *CHRONIC diseases , *RURAL conditions , *METROPOLITAN areas , *TUMORS - Abstract
Problem/Condition: A 2019 report quantified the higher percentage of potentially excess (preventable) deaths in U.S. nonmetropolitan areas compared with metropolitan areas during 2010-2017. In that report, CDC compared national, regional, and state estimates of preventable premature deaths from the five leading causes of death in nonmetropolitan and metropolitan counties during 2010-2017. This report provides estimates of preventable premature deaths for additional years (2010-2022). Period Covered: 2010-2022. Description of System: Mortality data for U.S. residents from the National Vital Statistics System were used to calculate preventable premature deaths from the five leading causes of death among persons aged <80 years. CDC's National Center for Health Statistics urban-rural classification scheme for counties was used to categorize the deaths according to the urban-rural county classification level of the decedent's county of residence (1: large central metropolitan [most urban], 2: large fringe metropolitan, 3: medium metropolitan, 4: small metropolitan, 5: micropolitan, and 6: noncore [most rural]). Preventable premature deaths were defined as deaths among persons aged <80 years that exceeded the number expected if the death rates for each cause in all states were equivalent to those in the benchmark states (i.e., the three states with the lowest rates). Preventable premature deaths were calculated separately for the six urban-rural county categories nationally, the 10 U.S. Department of Health and Human Services public health regions, and the 50 states and the District of Columbia. Results: During 2010-2022, the percentage of preventable premature deaths among persons aged <80 years in the United States increased for unintentional injury (e.g., unintentional poisoning including drug overdose, unintentional motor vehicle traffic crash, unintentional drowning, and unintentional fall) and stroke, decreased for cancer and chronic lower respiratory disease (CLRD), and remained stable for heart disease. The percentages of preventable premature deaths from the five leading causes of death were higher in rural counties in all years during 2010-2022. When assessed by the six urban-rural county classifications, percentages of preventable premature deaths in the most rural counties (noncore) were consistently higher than in the most urban counties (large central metropolitan and fringe metropolitan) for the five leading causes of death during the study period. During 2010-2022, preventable premature deaths from heart disease increased most in noncore (+9.5%) and micropolitan counties (+9.1%) and decreased most in large central metropolitan counties (-10.2%). Preventable premature deaths from cancer decreased in all county categories, with the largest decreases in large central metropolitan and large fringe metropolitan counties (-100.0%; benchmark achieved in both county categories in 2019). In all county categories, preventable premature deaths from unintentional injury increased, with the largest increases occurring in large central metropolitan (+147.5%) and large fringe metropolitan (+97.5%) counties. Preventable premature deaths from CLRD decreased most in large central metropolitan counties where the benchmark was achieved in 2019 and increased slightly in noncore counties (+0.8%). In all county categories, preventable premature deaths from stroke decreased from 2010 to 2013, remained constant from 2013 to 2019, and then increased in 2020 at the start of the COVID-19 pandemic. Percentages of preventable premature deaths varied across states by urban-rural county classification during 2010-2022. Interpretation: During 2010-2022, nonmetropolitan counties had higher percentages of preventable premature deaths from the five leading causes of death than did metropolitan counties nationwide, across public health regions, and in most states. The gap between the most rural and most urban counties for preventable premature deaths increased during 2010-2022 for four causes of death (cancer, heart disease, CLRD, and stroke) and decreased for unintentional injury. Urban and suburban counties (large central metropolitan, large fringe metropolitan, medium metropolitan, and small metropolitan) experienced increases in preventable premature deaths from unintentional injury during 2010-2022, leading to a narrower gap between the already high (approximately 69% in 2022) percentage of preventable premature deaths in noncore and micropolitan counties. Sharp increases in preventable premature deaths from unintentional injury, heart disease, and stroke were observed in 2020, whereas preventable premature deaths from CLRD and cancer continued to decline. CLRD deaths decreased during 2017-2020 but increased in 2022. An increase in the percentage of preventable premature deaths for multiple leading causes of death was observed in 2020 and was likely associated with COVID-19-related conditions that contributed to increased mortality from heart disease and stroke. Public Health Action: Routine tracking of preventable premature deaths based on urban-rural county classification might enable public health departments to identify and monitor geographic disparities in health outcomes. These disparities might be related to different levels of access to health care, social determinants of health, and other risk factors. Identifying areas with a high prevalence of potentially preventable mortality might be informative for interventions. [ABSTRACT FROM AUTHOR]
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- 2024
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