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

Methods Of Healthcare Delivery System01:26

Methods Of Healthcare Delivery System

3.5K
At the different levels of the healthcare system, we see varying methods of healthcare used. These methods include managed care systems, case management, and primary healthcare.
Managed Care System:
The managed care system is designed to control the cost while maintaining the quality of care. The patient's care from admission to discharge is planned by the primary care provider or the case manager, also known as the gatekeeper. In a managed care system, the number of care providers is...
3.5K
Bias in Epidemiological Studies01:29

Bias in Epidemiological Studies

687
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:  
687
Statistical Methods for Analyzing Epidemiological Data01:25

Statistical Methods for Analyzing Epidemiological Data

537
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:
537
Data Reporting and Recording01:24

Data Reporting and Recording

4.9K
Reporting and recording are crucial in data documentation. The timely, thorough, and accurate documentation of facts is essential when recording patient data. Failure to record findings during an assessment or interpretation of a problem will result in loss of information and make the patient document unreliable. The reader is left with general impressions if the information is not specific. A recording is documenting data of the individual's health information in a traceable, secure, and...
4.9K
Data Validation01:03

Data Validation

5.3K
Data validation is an essential part of a comprehensive assessment. Validation is confirming or verifying and opening the door to gathering more assessment data as it clarifies vague or unclear data. The process of checking and verifying the collected information is called data validation. The primary purpose of data validation is to ensure data is as free from error, bias, and misinterpretation as possible.
Nursing assessment guides are generally based on holistic models rather than medical...
5.3K
Quality Control01:05

Quality Control

290
Quality control is one of the three cyclical quality assurance activities that help keep a system under statistical control. Typical quality control activities include creating quality control charts, conducting proficiency testing, and documenting and archiving results.
Quality control helps track data, visualize trends, and identify variations, making it easier to detect deviations that may affect the accuracy of an analysis. One way to do this is by generating a quality control chart, which...
290

You might also read

Related Articles

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

Sort by
Same author

Prevalence and correlates of 24-hour movement behaviors among Thai preschoolers.

Journal of exercise science and fitness·2026
Same author

Timing and causes of neonatal deaths in India: a systematic review and meta-analysis.

JBI evidence synthesis·2026
Same author

Decomposing Wealth-Based Inequalities in Neonatal Mortality in India: Evidence from National Family Health Survey (2019-2021).

International journal of environmental research and public health·2026
Same author

Leave policy after stillbirth in LMICs: how much are we thinking about bereaved mothers? A scoping review.

BMJ open·2026
Same author

Breaking Barriers: Strategies for Inclusive Transgender Healthcare in Indian Hospitals-A Commentary.

The International journal of health planning and management·2026
Same author

National, state and district-level estimates of stillbirth in India at 20 weeks' gestation or longer using national family health survey data (2005-21).

The Lancet regional health. Southeast Asia·2026

Related Experiment Video

Updated: Sep 13, 2025

A Novel Method for Involving Women of Color at High Risk for Preterm Birth in Research Priority Setting
14:43

A Novel Method for Involving Women of Color at High Risk for Preterm Birth in Research Priority Setting

Published on: January 12, 2018

12.0K

'Silent Losses-Silent Data': Reviewing Stillbirth Data Quality in Low- and Middle-Income Countries Using Data Quality

Anuj Kumar Pandey1,2, Sutapa Bandyopadhyay Neogi1, Diksha Gautam1,3

  • 1Department of Health Systems and Implementation Research, International Institute of Health Management Research- New Delhi, Delhi, India.

The International Journal of Health Planning and Management
|July 28, 2025
PubMed
Summary

Improving stillbirth data quality is essential for effective policymaking. Addressing data gaps and enhancing reporting in low- and middle-income countries (LMICs) can help prevent future tragedies.

Keywords:
LMICsSDGdata qualitystillbirth

More Related Videos

Author Spotlight: Developing a Point-of-Care Hemoglobin Estimation Method for Anemia Management
05:35

Author Spotlight: Developing a Point-of-Care Hemoglobin Estimation Method for Anemia Management

Published on: January 19, 2024

929
Transcutaneous Microcirculatory Imaging in Preterm Neonates
06:27

Transcutaneous Microcirculatory Imaging in Preterm Neonates

Published on: December 31, 2015

8.3K

Related Experiment Videos

Last Updated: Sep 13, 2025

A Novel Method for Involving Women of Color at High Risk for Preterm Birth in Research Priority Setting
14:43

A Novel Method for Involving Women of Color at High Risk for Preterm Birth in Research Priority Setting

Published on: January 12, 2018

12.0K
Author Spotlight: Developing a Point-of-Care Hemoglobin Estimation Method for Anemia Management
05:35

Author Spotlight: Developing a Point-of-Care Hemoglobin Estimation Method for Anemia Management

Published on: January 19, 2024

929
Transcutaneous Microcirculatory Imaging in Preterm Neonates
06:27

Transcutaneous Microcirculatory Imaging in Preterm Neonates

Published on: December 31, 2015

8.3K

Area of Science:

  • Public Health
  • Healthcare Policy
  • Data Science

Background:

  • Accurate stillbirth data is critical for policy decisions, impacting healthcare management and research.
  • Stillbirth represents a profound loss, necessitating a shift from viewing it solely as a birth outcome to acknowledging it as a life lost.
  • Current data collection in low- and middle-income countries (LMICs) often overlooks social and emotional dimensions, hindering effective policymaking.

Purpose of the Study:

  • To compile information on key issues in stillbirth reporting and recording in LMICs, focusing on data quality.
  • To propose strategies for improving stillbirth data quality, including definition harmonization and enhanced data capture.
  • To advocate for increased funding for stillbirth research and improved data systems.

Main Methods:

  • Literature review and synthesis of current practices in stillbirth data collection and reporting in LMICs.
  • Analysis of data quality challenges, including definition inconsistencies and system fragmentation.
  • Development of actionable strategies for data quality improvement.

Main Results:

  • Identified significant challenges in stillbirth data quality across LMICs, affecting policy and intervention effectiveness.
  • Highlighted the need for harmonized stillbirth definitions to ensure consistent data interpretation.
  • Proposed practical solutions for enhancing data capture and linkage between different health information systems.

Conclusions:

  • Enhancing stillbirth data quality is paramount for developing targeted policies and interventions to reduce stillbirth rates.
  • A multi-faceted approach, including improved definitions, data linkage, and increased research funding, is necessary.
  • Recognizing stillbirth as a loss of life, supported by robust data, is crucial for both policy and compassionate care.