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

Analysis of Population Pharmacokinetic Data01:12

Analysis of Population Pharmacokinetic Data

382
Analysis of population pharmacokinetic data involves studying the behavior of drugs within diverse populations to understand their pharmacokinetic parameters. Traditional pharmacokinetic methods typically involve collecting samples from a few individuals and estimating these parameters. While these methods are commonly used, they have limitations in capturing the variability in drug response among individuals or heterogeneous populations. Population pharmacokinetics is employed to address these...
382
Statistical Methods for Analyzing Epidemiological Data01:25

Statistical Methods for Analyzing Epidemiological Data

532
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:
532
Model-Independent Approaches for Pharmacokinetic Data: Noncompartmental Analysis00:59

Model-Independent Approaches for Pharmacokinetic Data: Noncompartmental Analysis

124
Noncompartmental analyses offer an alternative method for describing drug pharmacokinetics without relying on a specific compartmental model. In this approach, the drug's pharmacokinetics are assumed to be linear, with the terminal phase log-linear. This assumption allows for simplified analysis and interpretation of the drug's behavior in the body.
One important characteristic of noncompartmental analyses is that drug exposure increases proportionally with increasing doses. This...
124
Bias in Epidemiological Studies01:29

Bias in Epidemiological Studies

675
Biases can arise at various stages of research, from study design and data collection to analysis and interpretation. Recognizing and addressing these biases is essential to ensure the validity and reliability of epidemiological findings.Broadly speaking, biases in epidemiology fall into three main categories: selection bias, information bias, and confounding. A more detailed description of possible biases is:  
675
Immunodeficiency Diseases01:25

Immunodeficiency Diseases

1.2K
Immunodeficiency disorders are conditions in which the immune system's ability to fight infectious disease and cancer is compromised or entirely absent. The immune system comprises a complex network of cells, tissues, and organs that work together to protect the body from potentially harmful invaders. When this system is deficient or not functioning properly, it leaves the body susceptible to infections, diseases, or other complications.
There are three main causes of immunodeficiency...
1.2K
Analysis Methods of Pharmacokinetic Data: Model and Model-Independent Approaches01:14

Analysis Methods of Pharmacokinetic Data: Model and Model-Independent Approaches

224
Drug disposition in the body is a complex process and can be studied using two major approaches: the model and the model-independent approaches.
The model approach uses mathematical models to describe changes in drug concentration over time. Pharmacokinetic models help characterize drug behavior in patients, predict drug concentration in the body fluids, calculate optimum dosage regimens, and evaluate the risk of toxicity. However, ensuring that the model fits the experimental data accurately...
224

You might also read

Related Articles

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

Sort by
Same author

Evaluating Late-Season Reminder-Recall for Influenza Vaccine Uptake Among Older Adults in Louisiana.

Journal of public health management and practice : JPHMP·2026
Same journal

Real-World Mapping of Multiple Primary Carcinoma Combinations and Survival Outcomes in Shanghai, China: Retrospective Registry-Based Study.

JMIR public health and surveillance·2026
Same journal

Adapting the Tobacco Pack Surveillance System Protocol to Assess Electronic Cigarette Packaging: Protocol for a Content Analysis.

JMIR public health and surveillance·2026
Same journal

Comparison of Measured 24-Hour Urinary Salt Excretion With Spot Urine and 24-Hour Dietary Recall Estimates Among Adolescents and Parents: Cross-Sectional Study.

JMIR public health and surveillance·2026
Same journal

Predicting Tuberculosis Outcomes Using Routine Surveillance Data in Chiang Mai, Thailand: Retrospective Cohort Study.

JMIR public health and surveillance·2026
Same journal

Multimodal Data Approaches for Examining the 2024-2025 Highly Pathogenic Avian Influenza Outbreak in the United States: Descriptive Study.

JMIR public health and surveillance·2026
Same journal

Encouraging Adults at Risk for Type 2 Diabetes to Enroll in Diabetes Prevention Programs Through a Media Campaign in Hawai'i: Cross-Sectional Study.

JMIR public health and surveillance·2026
See all related articles

Related Experiment Video

Updated: Sep 10, 2025

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

14.6K

Comparing Multiple Imputation Methods to Address Missing Patient Demographics in Immunization Information Systems:

Sara Brown1, Ousswa Kudia1, Kaye Kleine1

  • 1Scientific Services - Analytics, Scientific Technologies Corporation (United States), 411 S 1st St, Phoenix, AZ, 85004, United States, 1 480-745-8500.

JMIR Public Health and Surveillance
|August 26, 2025
PubMed
Summary
This summary is machine-generated.

Multiple imputation methods like MICE and miceforest effectively manage missing race/ethnicity data in immunization surveillance, preserving demographics and improving accuracy for public health interventions. Miceforest offers better computational efficiency for large datasets.

Keywords:
data scienceimmunization information systemimputation methodsmachine learningmissing datamultiple imputationstatistical modeling

More Related Videos

Author Spotlight: Unlocking Insights into the Immune Cell Landscape of Tumors
06:32

Author Spotlight: Unlocking Insights into the Immune Cell Landscape of Tumors

Published on: August 18, 2023

2.3K
A Semi-automated Approach to Preparing Antibody Cocktails for Immunophenotypic Analysis of Human Peripheral Blood
08:17

A Semi-automated Approach to Preparing Antibody Cocktails for Immunophenotypic Analysis of Human Peripheral Blood

Published on: February 8, 2016

10.9K

Related Experiment Videos

Last Updated: Sep 10, 2025

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

14.6K
Author Spotlight: Unlocking Insights into the Immune Cell Landscape of Tumors
06:32

Author Spotlight: Unlocking Insights into the Immune Cell Landscape of Tumors

Published on: August 18, 2023

2.3K
A Semi-automated Approach to Preparing Antibody Cocktails for Immunophenotypic Analysis of Human Peripheral Blood
08:17

A Semi-automated Approach to Preparing Antibody Cocktails for Immunophenotypic Analysis of Human Peripheral Blood

Published on: February 8, 2016

10.9K

Area of Science:

  • Public Health Surveillance
  • Biostatistics
  • Health Informatics

Background:

  • Immunization Information Systems (IIS) and surveillance data are crucial for public health but often suffer from missing data, potentially biasing vaccine coverage assessments and hindering efforts to address health disparities.
  • Accurate assessment of vaccine coverage is essential for effective public health programming and interventions, especially when aiming to reduce inequities.

Purpose of the Study:

  • To evaluate the performance of three multiple imputation methods—MICE, Iterative-Imputer, and miceforest—in handling missing race and ethnicity data within large-scale public health surveillance datasets.
  • To compare these imputation methods based on their ability to preserve demographic distributions, computational efficiency, and their impact on assessing the association between race/ethnicity and flu vaccination status.

Main Methods:

  • A retrospective cohort study analyzed 2021-2022 flu vaccination and demographic data from the West Virginia Immunization Information System (N=2,302,036), with significant missingness in race (15%) and ethnicity (34%).
  • Three multiple imputation techniques (MICE, Iterative-Imputer, miceforest) were applied to generate 15 imputed datasets each.
  • Performance was assessed by comparing demographic distribution preservation, computational efficiency, and spatial clustering patterns using G-statistics and likelihood ratio statistics.

Main Results:

  • All imputation methods showed significant spatial clustering for race imputation. MICE and miceforest demonstrated superior preservation of demographic proportional distributions compared to Iterative-Imputer.
  • Computational efficiency varied significantly: MICE took 14 hours, Iterative-Imputer 2 minutes, and miceforest 10 minutes for 15 imputations.
  • Post-imputation analysis revealed reductions in stratified flu vaccination coverage rates (0.87%-18%) and an overall decrease from 26% to 19%, highlighting the impact of missing data on estimates.

Conclusions:

  • MICE and miceforest provide reliable methods for imputing missing demographic data, mitigating bias more effectively than Iterative-Imputer. Miceforest offers enhanced computational efficiency, especially for large datasets, through cloud-based processing.
  • The choice of imputation method significantly impacts research findings, underscoring the need for careful selection.
  • The substantial decrease in estimated vaccination coverage emphasizes how missing data can obscure true disparities. Regular application of imputation methods is recommended to improve health equity evaluations and guide targeted public health interventions.