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Article ; Online: Self-Rated Health in Middle Age and Risk of Hospitalizations and Death: Recurrent Event Analysis of the ARIC Study.

Mu, Scott Z / Hicks, Caitlin W / Daya, Natalie R / Foraker, Randi E / Kucharska-Newton, Anna M / Lutsey, Pamela L / Coresh, Josef / Selvin, Elizabeth

Journal of general internal medicine

2024

Abstract: Background: Self-rated health is a simple measure that may identify individuals who are at a higher risk for hospitalization or death.: Objective: To quantify the association between a single measure of self-rated health and future risk of recurrent ... ...

Abstract	Background: Self-rated health is a simple measure that may identify individuals who are at a higher risk for hospitalization or death. Objective: To quantify the association between a single measure of self-rated health and future risk of recurrent hospitalizations or death. Participants: Atherosclerosis Risk in Communities (ARIC) study, a community-based prospective cohort study of middle-aged men and women with follow-up beginning from 1987 to 1989. Main measures: We quantified the associations between initial self-rated health with risk of recurrent hospitalizations and of death using a recurrent events survival model that allowed for dependency between the rates of hospitalization and hazards of death, adjusted for demographic and clinical factors. Key results: Of the 14,937 ARIC cohort individuals with available self-rated health and covariate information, 34% of individuals reported "excellent" health, 47% "good," 16% "fair," and 3% "poor" at study baseline. After a median follow-up of 27.7 years, 1955 (39%), 3569 (51%), 1626 (67%), and 402 (83%) individuals with "excellent," "good," "fair," and "poor" health, respectively, had died. After adjusting for demographic factors and medical history, a less favorable self-rated health status was associated with increased rates of hospitalization and death. As compared to those reporting "excellent" health, adults with "good," "fair," and "poor" health had 1.22 (1.07 to 1.40), 2.01 (1.63 to 2.47), and 3.13 (2.39 to 4.09) times the rate of hospitalizations, respectively. The hazards of death also increased with worsening categories of self-rated health, with "good," "fair," and "poor" health individuals experiencing 1.30 (1.12 to 1.51), 2.15 (1.71 to 2.69), and 3.40 (2.54 to 4.56) times the hazard of death compared to "excellent," respectively. Conclusions: Even after adjusting for demographic and clinical factors, having a less favorable response on a single measure of self-rated health taken in middle age is a potent marker of future hospitalizations and death.
Language	English
Publishing date	2024-04-10
Publishing country	United States
Document type	Journal Article
ZDB-ID	639008-0
ISSN	1525-1497 ; 0884-8734
ISSN (online)	1525-1497
ISSN	0884-8734
DOI	10.1007/s11606-024-08748-0
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Article ; Online: A systematic review of recurrent firearm injury rates in the United States.

Shayan, Muhammad / Lew, Daphne / Mancini, Michael / Foraker, Randi E / Doering, Michelle / Mueller, Kristen L

Preventive medicine

2023 Volume 168, Page(s) 107443

Abstract: Objectives: To conduct a systematic review of methodologies, data sources, and best practices for identifying, calculating, and reporting recurrent firearm injury rates in the United States.: Methods: In accordance with PRISMA guidelines, we searched ...

Abstract	Objectives: To conduct a systematic review of methodologies, data sources, and best practices for identifying, calculating, and reporting recurrent firearm injury rates in the United States. Methods: In accordance with PRISMA guidelines, we searched seven electronic databases on December 16, 2021, for peer-reviewed articles that calculated recurrent firearm injury in generalizable populations. Two reviewers independently assessed the risk of bias, screened the studies, extracted data, and a third resolved conflicts. Findings: Of the 918 unique articles identified, 14 met our inclusion criteria and reported recurrent firearm injury rates from 1% to 9.5%. We observed heterogeneity in study methodologies, including data sources utilized, identification of subsequent injury, follow-up times, and the types of firearm injuries studied. Data sources ranged from single-site hospital medical records to comprehensive statewide records comprising medical, law enforcement, and social security death index data. Some studies applied machine learning to electronic health records to differentiate subsequent new firearm injuries from the index injury, while others classified all repeat firearm-related hospital admissions after variably defined cut-off times as a new injury. Some studies required a minimum follow-up observation period after the index injury while others did not. Four studies conducted survival analyses, albeit using different methodologies. Conclusions: Variability in both the data sources and methods used to evaluate and report recurrent firearm injury limits individual study generalizability of individual and societal factors that influence recurrent firearm injury. Our systematic review highlights the need for development, dissemination, and implementation of standard practices for calculating and reporting recurrent firearm injury.
MeSH term(s)	Humans ; United States ; Firearms ; Wounds, Gunshot ; Age Distribution ; Population Surveillance/methods ; Electronic Health Records
Language	English
Publishing date	2023-02-03
Publishing country	United States
Document type	Systematic Review ; Journal Article ; Review
ZDB-ID	184600-0
ISSN	1096-0260 ; 0091-7435
ISSN (online)	1096-0260
ISSN	0091-7435
DOI	10.1016/j.ypmed.2023.107443
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Article ; Online: Discovering disease-disease associations using electronic health records in The Guideline Advantage (TGA) dataset.

Guo, Aixia / Khan, Yosef M / Langabeer, James R / Foraker, Randi E

Scientific reports

2021 Volume 11, Issue 1, Page(s) 20969

Abstract: Certain diseases have strong comorbidity and co-occurrence with others. Understanding disease-disease associations can potentially increase awareness among healthcare providers of co-occurring conditions and facilitate earlier diagnosis, prevention and ... ...

Abstract	Certain diseases have strong comorbidity and co-occurrence with others. Understanding disease-disease associations can potentially increase awareness among healthcare providers of co-occurring conditions and facilitate earlier diagnosis, prevention and treatment of patients. In this study, we utilized the valuable and large The Guideline Advantage (TGA) longitudinal electronic health record dataset from 70 outpatient clinics across the United States to investigate potential disease-disease associations. Specifically, the most prevalent 50 disease diagnoses were manually identified from 165,732 unique patients. To investigate the co-occurrence or dependency associations among the 50 diseases, the categorical disease terms were first mapped into numerical vectors based on disease co-occurrence frequency in individual patients using the Word2Vec approach. Then the novel and interesting disease association clusters were identified using correlation and clustering analyses in the numerical space. Moreover, the distribution of time delay (Δt) between pair-wise strongly associated diseases (correlation coefficients ≥ 0.5) were calculated to show the dependency among the diseases. The results can indicate the risk of disease comorbidity and complications, and facilitate disease prevention and optimal treatment decision-making.
MeSH term(s)	Adult ; Aged ; Cluster Analysis ; Comorbidity ; Databases, Factual ; Electronic Health Records ; Female ; Humans ; International Classification of Diseases ; Male ; Middle Aged ; United States
Language	English
Publishing date	2021-10-25
Publishing country	England
Document type	Journal Article
ZDB-ID	2615211-3
ISSN	2045-2322 ; 2045-2322
ISSN (online)	2045-2322
ISSN	2045-2322
DOI	10.1038/s41598-021-00345-z
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Article ; Online: Predicting mortality among patients with liver cirrhosis in electronic health records with machine learning.

Guo, Aixia / Mazumder, Nikhilesh R / Ladner, Daniela P / Foraker, Randi E

PloS one

2021 Volume 16, Issue 8, Page(s) e0256428

Abstract: Objective: Liver cirrhosis is a leading cause of death and effects millions of people in the United States. Early mortality prediction among patients with cirrhosis might give healthcare providers more opportunity to effectively treat the condition. We ... ...

Abstract	Objective: Liver cirrhosis is a leading cause of death and effects millions of people in the United States. Early mortality prediction among patients with cirrhosis might give healthcare providers more opportunity to effectively treat the condition. We hypothesized that laboratory test results and other related diagnoses would be associated with mortality in this population. Our another assumption was that a deep learning model could outperform the current Model for End Stage Liver disease (MELD) score in predicting mortality. Materials and methods: We utilized electronic health record data from 34,575 patients with a diagnosis of cirrhosis from a large medical center to study associations with mortality. Three time-windows of mortality (365 days, 180 days and 90 days) and two cases with different number of variables (all 41 available variables and 4 variables in MELD-NA) were studied. Missing values were imputed using multiple imputation for continuous variables and mode for categorical variables. Deep learning and machine learning algorithms, i.e., deep neural networks (DNN), random forest (RF) and logistic regression (LR) were employed to study the associations between baseline features such as laboratory measurements and diagnoses for each time window by 5-fold cross validation method. Metrics such as area under the receiver operating curve (AUC), overall accuracy, sensitivity, and specificity were used to evaluate models. Results: Performance of models comprising all variables outperformed those with 4 MELD-NA variables for all prediction cases and the DNN model outperformed the LR and RF models. For example, the DNN model achieved an AUC of 0.88, 0.86, and 0.85 for 90, 180, and 365-day mortality respectively as compared to the MELD score, which resulted in corresponding AUCs of 0.81, 0.79, and 0.76 for the same instances. The DNN and LR models had a significantly better f1 score compared to MELD at all time points examined. Conclusion: Other variables such as alkaline phosphatase, alanine aminotransferase, and hemoglobin were also top informative features besides the 4 MELD-Na variables. Machine learning and deep learning models outperformed the current standard of risk prediction among patients with cirrhosis. Advanced informatics techniques showed promise for risk prediction in patients with cirrhosis.
MeSH term(s)	Algorithms ; Cohort Studies ; Electronic Health Records ; Female ; Humans ; Liver Cirrhosis/mortality ; Logistic Models ; Machine Learning ; Male ; Middle Aged ; Models, Theoretical ; Neural Networks, Computer
Language	English
Publishing date	2021-08-31
Publishing country	United States
Document type	Journal Article ; Research Support, N.I.H., Extramural
ZDB-ID	2267670-3
ISSN	1932-6203 ; 1932-6203
ISSN (online)	1932-6203
ISSN	1932-6203
DOI	10.1371/journal.pone.0256428
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Article ; Online: Linking out-of-hospital deaths with a regional hospital-based firearm injury database: a clinical researcher's guide to accessing data from the National Death Index.

Mueller, Kristen / Cooper, Benjamin P / Moran, Vicki / Mancini, Michael / Foraker, Randi E

Injury prevention : journal of the International Society for Child and Adolescent Injury Prevention

2022 Volume 28, Issue 4, Page(s) 374–378

Abstract: IntroductionFirearm injuries are a public health crisis in the US. The National Death Index (NDI) is a well-established, comprehensive database managed by the National Center for Health Statistics at the CDC. In this methodology paper we describe our ... ...

Abstract	IntroductionFirearm injuries are a public health crisis in the US. The National Death Index (NDI) is a well-established, comprehensive database managed by the National Center for Health Statistics at the CDC. In this methodology paper we describe our experience accessing and linking data from the NDI to our regional, hospital-based violent injury database to identify out-of-hospital deaths from firearms. Methods: We outline the key steps of our submission to the NDI. Data were collected from research team meeting notes, team member emails with NDI staff, and information provided from the NDI website and supplementary guides. Few of our collaborators or university partner investigators had accessed or used data from the NDI. We discuss the online NDI Processing Portal data request, data preparation and receipt from the NDI, troubleshooting tips, and a timeline of events. Results: Our query to the NDI returned 12 034 records of 12 219 firearm-injured patient records from 2010 and 2019. The record match rate was 98.5%. Discussion: Linking hospital-based data sets with NDI data can provide valuable information on out-of-hospital deaths. This has the potential to improve the quality of longitudinal morbidity and mortality calculations in hospital-based patient cohorts. We encountered logistic and administrative challenges in completing the online NDI Processing Portal and in preparing and receiving data from the NDI. It is our hope that the lessons learnt presented herein will help facilitate easy and streamlined acquisition of valuable NDI data for other clinical researchers. What this study adds: - A step-by-step guide for clinical researchers of how to apply to access data from the National Death Index (NDI).- Advice and lessons learned on how to efficiently and effectively access data from the NDI.- A well-described methodology to improve the quality of longitudinal morbdity and mortality calculations in hospital-based cohorts of firearm injured patients.What is already known on this subject:- There is a need for robust, longitudinal data sources that reliably track morbidity and mortality among firearm injured patients in the United States.- The NDI is a well-established, comprehensive database that holds death records for all 50 states, which provides valuable mortality data to the public health and medical research community.
MeSH term(s)	Cause of Death ; Firearms ; Hospitals ; Humans ; Population Surveillance ; United States/epidemiology ; Violence ; Wounds, Gunshot
Language	English
Publishing date	2022-02-17
Publishing country	England
Document type	Journal Article ; Research Support, Non-U.S. Gov't
ZDB-ID	1433667-4
ISSN	1475-5785 ; 1353-8047
ISSN (online)	1475-5785
ISSN	1353-8047
DOI	10.1136/injuryprev-2021-044516
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Zs.A 4910: Show issues

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Article ; Online: The impact of neighborhood socioeconomic status on retention in care and viral suppression among people living with HIV.

López, Julia D / Qiao, Qisha / Presti, Rachel M / Hammer, Rachel A / Foraker, Randi E

AIDS care

2022 Volume 34, Issue 11, Page(s) 1383–1389

Abstract: Our study combined publicly available neighborhood socioeconomic status (nSES) data from the U.S. Census and clinical data to investigate the relationships between nSES, retention in care (RIC) and viral suppression (VS). Data from 2275 patients were ... ...

Abstract	Our study combined publicly available neighborhood socioeconomic status (nSES) data from the U.S. Census and clinical data to investigate the relationships between nSES, retention in care (RIC) and viral suppression (VS). Data from 2275 patients were extracted from 2009 to 2015 from a midwestern infectious diseases clinic. RIC was defined as patients who kept ≥ 3 visits and VS as an average viral load <200 copies/mL during their index year of study. Logistic regression models provided estimates for neighborhood-level and patient-level variables. In multivariable models, patients living in zip codes with low disability rates (1.50, 1.30-1.70), who wereolder (1.02, 1.01-1.03), and receiving antiretroviral therapy (ART; 3.81, 3.56-4.05) were more likely to have RIC, while those who were unemployed (0.72, 0.45-0.98) and self-reported as BIPOC (0.79, 0.64-0.97) were less likely to have RIC. None of the nSES variables were significantly associated with VS in multivariable models, yet older age (1.05, 1.04-1.05) and self-reported as BIPOC (1.68, 1.36-2.09) were modestly associated with VS, and receiving ART (6.14, 5.86-6.42) was a strong predictor of VS. In multivariable models, nSES variables were independently predictive more than of patient-level variables, for RIC but not VS.
MeSH term(s)	Humans ; HIV Infections ; Retention in Care ; Social Class ; Viral Load
Language	English
Publishing date	2022-02-14
Publishing country	England
Document type	Journal Article
ZDB-ID	1012651-x
ISSN	1360-0451 ; 0954-0121
ISSN (online)	1360-0451
ISSN	0954-0121
DOI	10.1080/09540121.2022.2040724
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Zs.A 2799: Show issues

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Article ; Online: An exploration of factors impacting implementation of a multisystem hospital-based violence intervention program.

Mueller, Kristen L / Moran, Vicki / Anwuri, Victoria / Foraker, Randi E / Mancini, Michael A

Health & social care in the community

2022 Volume 30, Issue 6, Page(s) e6577–e6585

Abstract: Community violence, particularly gun violence, is a leading cause of morbidity and mortality in young people in the United States. Because persons experiencing violence-related injuries are likely to receive medical care through emergency departments, ... ...

Abstract	Community violence, particularly gun violence, is a leading cause of morbidity and mortality in young people in the United States. Because persons experiencing violence-related injuries are likely to receive medical care through emergency departments, hospitals are increasingly seen as primary locations for violence intervention services. Currently, there is little research on how best to implement hospital-based violence intervention programs (HVIPs) across large hospital systems. This study explored the factors influencing the implementation of a multi-site HVIP using qualitative interviews with a purposive sample of 20 multidisciplinary stakeholders. Thematic analysis was used to generate several themes that included: (1) reframing gun violence as a public health issue; (2) developing networks of community-hospital-university partners; (3) demonstrating effectiveness and community benefit; and (4) establishing patient engagement pathways. Effective implementation and sustainment of HVIPs requires robust and sustained multidisciplinary partnerships within and across hospital systems and the establishment of HVIPs as a standard of care.
MeSH term(s)	Humans ; United States ; Adolescent ; Violence/prevention & control ; Emergency Service, Hospital ; Hospitals, University
Language	English
Publishing date	2022-11-14
Publishing country	England
Document type	Journal Article ; Research Support, Non-U.S. Gov't
ZDB-ID	1155902-0
ISSN	1365-2524 ; 0966-0410
ISSN (online)	1365-2524
ISSN	0966-0410
DOI	10.1111/hsc.14107
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Zs.A 3728: Show issues

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Article ; Online: Segregated Patterns of Hospital Care Delivery and Health Outcomes.

Lin, Sunny C / Hammond, Gmerice / Esposito, Michael / Majewski, Cassandra / Foraker, Randi E / Joynt Maddox, Karen E

JAMA health forum

2023 Volume 4, Issue 11, Page(s) e234172

Abstract: Importance: Residential segregation has been shown to be a root cause of racial inequities in health outcomes, yet little is known about current patterns of racial segregation in where patients receive hospital care or whether hospital segregation is ... ...

Abstract	Importance: Residential segregation has been shown to be a root cause of racial inequities in health outcomes, yet little is known about current patterns of racial segregation in where patients receive hospital care or whether hospital segregation is associated with health outcomes. Filling this knowledge gap is critical to implementing policies that improve racial equity in health care. Objective: To characterize contemporary patterns of racial segregation in hospital care delivery, identify market-level correlates, and determine the association between hospital segregation and health outcomes. Design, setting, and participants: This cross-sectional study of US hospital referral regions (HRRs) used 2018 Medicare claims, American Community Survey, and Agency for Healthcare Research and Quality Social Determinants of Health data. Hospitalization patterns for all non-Hispanic Black or non-Hispanic White Medicare fee-for-service beneficiaries with at least 1 inpatient hospitalization in an eligible hospital were evaluated for hospital segregation and associated health outcomes at the HRR level. The data analysis was performed between August 10, 2022, and September 6, 2023. Exposures: Dissimilarity index and isolation index for HRRs. Main outcomes and measures: Health outcomes were measured using Prevention Quality Indicator (PQI) acute and chronic composites per 100 000 Medicare beneficiaries, and total deaths related to heart disease and stroke per 100 000 residents were calculated for individuals aged 74 years or younger. Correlation coefficients were used to compare residential and hospital dissimilarity and residential and hospital isolation. Linear regression was used to examine the association between hospital segregation and health outcomes. Results: This study included 280 HRRs containing data for 4386 short-term acute care and critical access hospitals. Black and White patients tended to receive care at different hospitals, with a mean (SD) dissimilarity index of 23 (11) and mean (SD) isolation index of 13 (13), indicating substantial variation in segregation across HRRs. Hospital segregation was correlated with residential segregation (correlation coefficients, 0.58 and 0.90 for dissimilarity and isolation, respectively). For Black patients, a 1-SD increase in the hospital isolation index was associated with 204 (95% CI, 154-254) more acute PQI hospitalizations per 100 000 Medicare beneficiaries (28% increase from the median), 684 (95% CI, 488-880) more chronic PQI hospitalizations per 100 000 Medicare beneficiaries (15% increase), and 6 (95% CI, 2-9) additional deaths per 100 000 residents (6% increase) compared with 68 (95% CI, 24-113; 6% increase), 202 (95% CI, 131-274; 8% increase), and 2 (95% CI, 0 to 4; 3% increase), respectively, for White patients. Conclusions and relevance: This cross-sectional study found that higher segregation of hospital care was associated with poorer health outcomes for both Black and White Medicare beneficiaries, with significantly greater negative health outcomes for Black populations, supporting racial segregation as a root cause of health disparities. Policymakers and clinical leaders could address this important public health issue through payment reform efforts and expansion of health insurance coverage, in addition to supporting upstream efforts to reduce racial segregation in hospital care and residential settings.
MeSH term(s)	United States/epidemiology ; Humans ; Aged ; Cross-Sectional Studies ; Medicare ; Hospitals ; Social Segregation ; Delivery of Health Care ; Outcome Assessment, Health Care
Language	English
Publishing date	2023-11-03
Publishing country	United States
Document type	Journal Article
ISSN	2689-0186
ISSN (online)	2689-0186
DOI	10.1001/jamahealthforum.2023.4172
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Article ; Online: Implementing a hospital-based violence intervention program for assault-injured youth: implications for social work practice.

Mancini, Michael A / Mueller, Kristen L / Moran, Vicki / Anwuri, Victoria / Foraker, Randi E / Chapman-Kramer, Kateri

Social work in health care

2023 Volume 62, Issue 8-9, Page(s) 280–301

Abstract: Youth in the U.S. experience a high rate of assault-related injuries resulting in physical, psychological and social sequelae that require a wide range of services after discharge from the hospital. Hospital-based violence intervention programs (HVIP's) ... ...

Abstract	Youth in the U.S. experience a high rate of assault-related injuries resulting in physical, psychological and social sequelae that require a wide range of services after discharge from the hospital. Hospital-based violence intervention programs (HVIP's) have been developed to engage youth in services designed to reduce the incidence of violent injury in young people. HVIP's combine the efforts of medical staff with community-based partners to provide trauma-informed care to violently-injured people and have been found to be a cost-effective means to reduce re-injury rates and improve social and behavioral health outcomes. Few studies have explored the organizational and community level factors that impact implementation of these important and complex interventions. The objective of this study was to develop an in-depth understanding of the factors that impact HVIP implementation from the perspectives of 41 stakeholders through qualitative interviews. Thematic analysis generated three themes that included the importance of integrated, collaborative care, the need for providers who can perform multiple service roles and deploy a range of skills, and the importance of engaging clients through extended contact. In this article we explore these themes and their implications for healthcare social work.
MeSH term(s)	Humans ; Adolescent ; Violence/prevention & control ; Hospitals ; Risk Factors
Language	English
Publishing date	2023-07-18
Publishing country	United States
Document type	Journal Article
ZDB-ID	197616-3
ISSN	1541-034X ; 0098-1389
ISSN (online)	1541-034X
ISSN	0098-1389
DOI	10.1080/00981389.2023.2238025
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Article ; Online: Cardiovascular health assessment in routine cancer follow-up in community settings: survivor risk awareness and perspectives.

Weaver, Kathryn E / Dressler, Emily V / Smith, Sydney / Nightingale, Chandylen L / Klepin, Heidi D / Lee, Simon Craddock / Wells, Brian J / Hundley, W Gregory / DeMari, Joseph A / Price, Sarah N / Foraker, Randi E

BMC cancer

2024 Volume 24, Issue 1, Page(s) 158

Abstract: Background: Guidelines recommend cardiovascular risk assessment and counseling for cancer survivors. For effective implementation, it is critical to understand survivor cardiovascular health (CVH) profiles and perspectives in community settings. We ... ...

Abstract	Background: Guidelines recommend cardiovascular risk assessment and counseling for cancer survivors. For effective implementation, it is critical to understand survivor cardiovascular health (CVH) profiles and perspectives in community settings. We aimed to (1) Assess survivor CVH profiles, (2) compare self-reported and EHR-based categorization of CVH factors, and (3) describe perceptions regarding addressing CVH during oncology encounters. Methods: This cross-sectional analysis utilized data from an ongoing NCI Community Oncology Research Program trial of an EHR heart health tool for cancer survivors (WF-1804CD). Survivors presenting for routine care after potentially curative treatment recruited from 8 oncology practices completed a pre-visit survey, including American Heart Association Simple 7 CVH factors (classified as ideal, intermediate, or poor). Medical record abstraction ascertained CVD risk factors and cancer characteristics. Likert-type questions assessed desired discussion during oncology care. Results: Of 502 enrolled survivors (95.6% female; mean time since diagnosis = 4.2 years), most had breast cancer (79.7%). Many survivors had common cardiovascular comorbidities, including high cholesterol (48.3%), hypertension or high BP (47.8%) obesity (33.1%), and diabetes (20.5%); 30.5% of survivors received high cardiotoxicity potential cancer treatment. Less than half had ideal/non-missing levels for physical activity (48.0%), BMI (18.9%), cholesterol (17.9%), blood pressure (14.1%), healthy diet (11.0%), and glucose/ HbA1c (6.0%). While > 50% of survivors had concordant EHR-self-report categorization for smoking, BMI, and blood pressure; cholesterol, glucose, and A1C were unknown by survivors and/or missing in the EHR for most. Most survivors agreed oncology providers should talk about heart health (78.9%). Conclusions: Tools to promote CVH discussion can fill gaps in CVH knowledge and are likely to be well-received by survivors in community settings. Trial registration: NCT03935282, Registered 10/01/2020.
MeSH term(s)	Female ; Humans ; Male ; Blood Pressure ; Breast Neoplasms ; Cardiovascular Diseases/epidemiology ; Cardiovascular Diseases/etiology ; Cholesterol ; Cross-Sectional Studies ; Follow-Up Studies ; Glucose ; Health Status ; Risk Assessment ; Risk Factors ; Survivors ; United States ; Clinical Trials as Topic
Chemical Substances	Cholesterol (97C5T2UQ7J) ; Glucose (IY9XDZ35W2)
Language	English
Publishing date	2024-01-31
Publishing country	England
Document type	Journal Article
ZDB-ID	2041352-X
ISSN	1471-2407 ; 1471-2407
ISSN (online)	1471-2407
ISSN	1471-2407
DOI	10.1186/s12885-024-11912-8
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