Jove
Visualize
Contact Us
JoVE
x logofacebook logolinkedin logoyoutube logo
ABOUT JoVE
OverviewLeadershipBlogJoVE Help Center
AUTHORS
Publishing ProcessEditorial BoardScope & PoliciesPeer ReviewFAQSubmit
LIBRARIANS
TestimonialsSubscriptionsAccessResourcesLibrary Advisory BoardFAQ
RESEARCH
JoVE JournalMethods CollectionsJoVE Encyclopedia of ExperimentsArchive
EDUCATION
JoVE CoreJoVE BusinessJoVE Science EducationJoVE Lab ManualFaculty Resource CenterFaculty Site
Terms & Conditions of Use
Privacy Policy
Policies

Related Concept Videos

Pareto Chart00:52

Pareto Chart

6.8K
A Pareto chart is a bar graph or a combination of both line and bar graphs. The bar lengths represent the individual values or the frequency, while the lines represent the cumulative total values. In this chart, the longest bars are arranged on the left and the shortest bars on the right, which makes it easier to read and interpret the data. It can also be called a Pareto diagram or Pareto analysis.
The Pareto chart is named after the Italian economist Vilfredo Pareto, who described the Pareto...
6.8K
Types of Biopharmaceutical Studies: Controlled and Non-Controlled Approaches01:23

Types of Biopharmaceutical Studies: Controlled and Non-Controlled Approaches

151
Biopharmaceutical studies constitute a vital field aiming to enhance drug delivery methods and refine therapeutic approaches, drawing upon diverse interdisciplinary knowledge. In research methodologies, the choice between controlled and non-controlled studies significantly influences the study's reliability and accuracy.
Non-controlled studies, commonly employed for initial exploration, lack a control group, rendering them susceptible to biases and external influences. In contrast,...
151
Relative Risk01:12

Relative Risk

261
Relative risk (RR) is a statistical measure commonly used in epidemiology to compare the likelihood of a particular event occurring between two groups. This metric is important for evaluating the relationship between exposure to a specific risk factor and the probability of a particular outcome. It plays a crucial role in medical research, public health studies, and risk assessment. Relative risk quantifies how much more (or less) likely an event is to occur in an exposed group compared to an...
261
Statistical Methods for Analyzing Epidemiological Data01:25

Statistical Methods for Analyzing Epidemiological Data

438
Epidemiological data primarily involves information on specific populations' occurrence, distribution, and determinants of health and diseases. This data is crucial for understanding disease patterns and impacts, aiding public health decision-making and disease prevention strategies. The analysis of epidemiological data employs various statistical methods to interpret health-related data effectively. Here are some commonly used methods:
438
Interval Level of Measurement00:55

Interval Level of Measurement

15.5K
For effective statistical analysis, data are classified into four levels of measurement—nominal, ordinal, interval, and ratio.
Data measured using the interval scale are similar to ordinal level data because they have a definite arrangement. However, in the interval level of measurement, the differences between data values are meaningful even though the data does not have a starting point.
Temperature is measured using the interval scale. It is measurable data, and the difference between...
15.5K
Classification of Illness01:17

Classification of Illness

7.7K
The meaning of illness is individualized to each person who experiences an alteration in health. In contrast, disease is a medical term indicating a pathological change in the structure and function of the body or mind. It is a condition that has specific symptoms and boundaries.
An illness is a response to a disease in which the person's level of functioning is changed compared with a previous level. The general classification of illness includes acute and chronic.
Acute illness is severe...
7.7K

You might also read

Related Articles

Articles linked to this work by shared authors, journal, and citation graph.

Sort by
Same author

Effect of COVID-19 on Acute Ischemic Stroke Hospitalizations and Treatments: Population-Level Experience.

Stroke (Hoboken, N.J.)·2026
Same author

Trends in the Use of Oral Anticoagulants and Associated Expenditures Among Medicaid Programs.

Stroke (Hoboken, N.J.)·2026
Same author

Effects of Metformin on Cancer Survival Among Men Diagnosed with Advanced Prostate Cancer Treated with Androgen-Deprivation Therapy: Emulating a Target Trial.

Cancers·2025
Same author

Trends in glucagon-like peptide 1 receptor agonist prescribing patterns.

The American journal of managed care·2025
Same author

Geographic disparities in access to outpatient stroke rehabilitation in Texas.

PloS one·2025
Same author

Application of Claims-based Frailty Index to a Structured Electronic Health Record Data.

The journals of gerontology. Series A, Biological sciences and medical sciences·2025
Same journal

Barriers and facilitators to implementing discharge preparation services for older adults with multimorbidity: a qualitative study guided by the updated CFIR.

BMC health services research·2026
Same journal

Coverage, pricing, and reimbursement of telemedicine: a qualitative study among implementers of the Akoma Pa mHealth-based telemedicine intervention in Ghana.

BMC health services research·2026
Same journal

Hospital system resilience in the face of armed conflicts; a cross sectional study from two regions in Lebanon.

BMC health services research·2026
Same journal

Career choice, training satisfaction, and retention intentions among healthcare apprentices: a cross-sectional study in rural Germany.

BMC health services research·2026
Same journal

Assessment of the patient safety culture and the influencing factors among medical staff in a non-public tertiary general hospital in China.

BMC health services research·2026
Same journal

A descriptive analysis of hospital-based palliative care services: a case study from Fort Portal Regional Referral Hospital, Uganda.

BMC health services research·2026
See all related articles

Related Experiment Video

Updated: Aug 1, 2025

Comparing Objective Conjunctival Hyperemia Grading and the Ocular Surface Disease Index Score in Dry Eye Syndrome During COVID-19
06:29

Comparing Objective Conjunctival Hyperemia Grading and the Ocular Surface Disease Index Score in Dry Eye Syndrome During COVID-19

Published on: May 25, 2022

2.4K

COVID-19 severity scale for claims data research.

Trudy Millard Krause1, Raymond Greenberg2, Lopita Ghosh3

  • 1Department of Management, Policy and Community Health, School of Public Health, The University of Texas Health Science Center at Houston, Houston, United States. Trudy.M.Krause@uth.tmc.edu.

BMC Health Services Research
|April 26, 2023
PubMed
Summary
This summary is machine-generated.

A new method categorizes COVID-19 severity using claims data, revealing older individuals and those with more comorbidities face higher risks. This scale aids future research on COVID-19 interventions and outcomes.

Keywords:
Administrative Claims HealthcareCOVID-19Disease SeverityEpidemiological methodsHealthcare costSARS-CoV-2

More Related Videos

Author Spotlight: Self-Assessment Protocol for Predicting Psoriatic Arthritis in Psoriasis Patients
02:28

Author Spotlight: Self-Assessment Protocol for Predicting Psoriatic Arthritis in Psoriasis Patients

Published on: March 1, 2024

457
Inverse Probability of Treatment Weighting Propensity Score using the Military Health System Data Repository and National Death Index
06:55

Inverse Probability of Treatment Weighting Propensity Score using the Military Health System Data Repository and National Death Index

Published on: January 8, 2020

14.5K

Related Experiment Videos

Last Updated: Aug 1, 2025

Comparing Objective Conjunctival Hyperemia Grading and the Ocular Surface Disease Index Score in Dry Eye Syndrome During COVID-19
06:29

Comparing Objective Conjunctival Hyperemia Grading and the Ocular Surface Disease Index Score in Dry Eye Syndrome During COVID-19

Published on: May 25, 2022

2.4K
Author Spotlight: Self-Assessment Protocol for Predicting Psoriatic Arthritis in Psoriasis Patients
02:28

Author Spotlight: Self-Assessment Protocol for Predicting Psoriatic Arthritis in Psoriasis Patients

Published on: March 1, 2024

457
Inverse Probability of Treatment Weighting Propensity Score using the Military Health System Data Repository and National Death Index
06:55

Inverse Probability of Treatment Weighting Propensity Score using the Military Health System Data Repository and National Death Index

Published on: January 8, 2020

14.5K

Area of Science:

  • Health Services Research
  • Epidemiology
  • Data Science

Background:

  • Retrospective analysis of COVID-19 (coronavirus disease 2019) in large-scale claims data is crucial for understanding disease burden.
  • Standardized methods are needed to accurately assess COVID-19 episode severity within these datasets.

Purpose of the Study:

  • To develop and validate a methodology for assigning COVID-19 severity levels using retrospective claims data.
  • To enable robust analysis of interventions, effectiveness, costs, and outcomes related to COVID-19.

Main Methods:

  • Utilized a national dataset of 19,761,754 individuals, including 692,094 with COVID-19 in 2020.
  • Adapted the World Health Organization (WHO) COVID-19 Progression Scale, identifying endpoints like symptoms, respiratory status, treatment, and mortality within claims.
  • Employed Centers for Disease Control and Prevention (CDC) guidance for case identification.

Main Results:

  • Developed a nine-level severity scale, classifying 709,846 individuals (3.6%).
  • Higher severity levels were significantly associated with older age groups (p < 0.001).
  • Increased severity correlated with higher mean and median healthcare costs. Demographic factors and comorbidity count also showed significant associations with severity.

Conclusions:

  • A standardized severity scale for COVID-19 in claims data is feasible and valuable.
  • This methodology facilitates research into the processes, effectiveness, efficiencies, costs, and outcomes of COVID-19 care.