CDC Wonder

Clink, Kellian

doi:10.1108/rr-08-2016-0213

Cited by 25 publications

(6 citation statements)

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“…With overdose deaths escalating 1,789% from 1999 to 2017 (Clink, 2017), the potent psychostimulant methamphetamine (METH) potentiates multiple mechanisms that lead to abuse potential and health harm. METH pushes biological systems into hyperactivation, creating a spiral of dysfunction afflicting not only the central nervous system (CNS) (Kish, 2008) but also multiple cell and tissue types in the periphery (Martin et al, 1971;Kaye et al, 2007).…”

Section: Introductionmentioning

confidence: 99%

Methamphetamine Dysregulation of the Central Nervous System and Peripheral Immunity

Miller

Gopinath

et al. 2021

The Journal of Pharmacology and Experimental Therapeutics

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Methamphetamine (METH) is a potent psychostimulant that increases extracellular monoamines such as dopamine and norepinephrine and affects multiple tissue and cell types in the CNS and peripheral immune cells. The reinforcing properties of METH underlie its significant abuse potential and dysregulation of peripheral immunity and central nervous system functions. Together, the constellation of METH's effects on cellular targets and regulatory processes has led to immune suppression and neurodegeneration in METH addicts and animal models of METH exposure. Here we extensively review many of the cell types and mechanisms of METH-induced dysregulation of the central nervous and peripheral immune systems. Significance StatementEmerging research has begun to show that methamphetamine regulates dopaminergic neuronal activity. In addition, METH affects non-neuronal brain cells, such as microglia and astrocytes, and immunological cells of the periphery. Concurrent disruption of bidirectional communication between dopaminergic neurons and glia in the CNS and peripheral immune cell dysregulation gives rise to a constellation of dysfunctional neuronal, cell, and tissue types. Therefore, understanding the pathophysiology of METH requires consideration of the multiple targets at the interface between basic and clinical neuroscience.

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Section: Introductionmentioning

confidence: 99%

Methamphetamine Dysregulation of the Central Nervous System and Peripheral Immunity

Miller

Gopinath

et al. 2021

The Journal of Pharmacology and Experimental Therapeutics

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“…We collected race and ethnicity data to identify potential racial and ethnic disparities in head and neck cancer mortality rates and outcomes. CDC WONDER is a public online database allowing for comprehensive health-trend analysis using ICD-10 codes . Annual percentage changes (APCs) in AAMRs were estimated using linear regression models with log-scale AAMR, incorporating population size as weights.…”

Section: Methodsmentioning

confidence: 99%

“…CDC WONDER is a public online database allowing for comprehensive health-trend analysis using ICD-10 codes. 3 Annual percentage changes (APCs) in AAMRs were estimated using linear regression models with log-scale AAMR, incorporating population size as weights. Differential changes over time by geographic area were assessed with interaction tests.…”

mentioning

confidence: 99%

Geographic Disparities in Head and Neck Cancer Mortality and Place of Death

Balanchivadze,

Blackford,

Sedhom

et al. 2024

JAMA Otolaryngol Head Neck Surg

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“…Freely available national databases were utilized to retrieve the variables of interest for our exploratory analysis. Crude mortality data was obtained from CDC WONDER [29], and the rural-urban county designation and years were obtained from the US Department of Agriculture [30]. The CDC WONDER website's graphical user interface has several selection choices.…”

Section: Data Files and Mergingmentioning

confidence: 99%

“…After our data processing and merging steps, we were able to retain more than 99% of all US counties in our final analysis. The data accessible from CDC WONDER [29], one of our data sources, is considered the gold standard for these reasons: (i) standard data-collection methods, (ii) rigorous peer-reviewed methods, (iii) ease of geographic search and access to downloadable data for FIPS, (iv) timeliness for data archiving, and (v) adhering to ethical guidelines for research protocols [51][52][53]. Similarly, our second data source from the US Census [30,54] demonstrates similar characteristics with regard to its (i) transparency and reproducibility, (ii) quality control and checks, (iii) high geographic area granularity with access to FIPS codes, and (iv) availability of data via an application programming interface [55][56][57].…”

Section: Strengths Of Our Studymentioning

confidence: 99%

Mapping the Heartbeat of America with ChatGPT-4: Unpacking the Interplay of Social Vulnerability, Digital Literacy, and Cardiovascular Mortality in County Residency Choices

Ali,

Gandhi,

Sulaiman

et al. 2023

JPM

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Cardiovascular disease remains a leading cause of morbidity and mortality in the United States (US). Although high-quality data are accessible in the US for cardiovascular research, digital literacy (DL) has not been explored as a potential factor influencing cardiovascular mortality, although the Social Vulnerability Index (SVI) has been used previously as a variable in predictive modeling. Utilizing a large language model, ChatGPT4, we investigated the variability in CVD-specific mortality that could be explained by DL and SVI using regression modeling. We fitted two models to calculate the crude and adjusted CVD mortality rates. Mortality data using ICD-10 codes were retrieved from CDC WONDER, and the geographic level data was retrieved from the US Department of Agriculture. Both datasets were merged using the Federal Information Processing Standards code. The initial exploration involved data from 1999 through 2020 (n = 65,791; 99.98% complete for all US Counties) for crude cardiovascular mortality (CCM). Age-adjusted cardiovascular mortality (ACM) had data for 2020 (n = 3118 rows; 99% complete for all US Counties), with the inclusion of SVI and DL in the model (a composite of literacy and internet access). By leveraging on the advanced capabilities of ChatGPT4 and linear regression, we successfully highlighted the importance of incorporating the SVI and DL in predicting adjusted cardiovascular mortality. Our findings imply that just incorporating internet availability in the regression model may not be sufficient without incorporating significant variables, such as DL and SVI, to predict ACM. Further, our approach could enable future researchers to consider DL and SVI as key variables to study other health outcomes of public-health importance, which could inform future clinical practices and policies.

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CDC Wonder

Cited by 25 publications

References 0 publications

Methamphetamine Dysregulation of the Central Nervous System and Peripheral Immunity

Methamphetamine Dysregulation of the Central Nervous System and Peripheral Immunity

Geographic Disparities in Head and Neck Cancer Mortality and Place of Death

Mapping the Heartbeat of America with ChatGPT-4: Unpacking the Interplay of Social Vulnerability, Digital Literacy, and Cardiovascular Mortality in County Residency Choices

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