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Related Concept Videos

Censoring Survival Data01:09

Censoring Survival Data

Survival analysis is a statistical method used to analyze time-to-event data, often employed in fields such as medicine, engineering, and social sciences. One of the key challenges in survival analysis is dealing with incomplete data, a phenomenon known as "censoring." Censoring occurs when the event of interest (such as death, relapse, or system failure) has not occurred for some individuals by the end of the study period or is otherwise unobservable, and it might have many different reasons...
Statistical Methods for Analyzing Epidemiological Data01:25

Statistical Methods for Analyzing Epidemiological Data

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:
Bias in Epidemiological Studies01:29

Bias in Epidemiological Studies

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:
Actuarial Approach01:20

Actuarial Approach

The actuarial approach, a statistical method originally developed for life insurance risk assessment, is widely used to calculate survival rates in clinical and population studies. This method accounts for participants lost to follow-up or those who die from causes unrelated to the study, ensuring a more accurate representation of survival probabilities.
Consider the example of a high-risk surgical procedure with significant early-stage mortality. A two-year clinical study is conducted,...
Barriers to Effective Communication II01:21

Barriers to Effective Communication II

The barriers to effective communication also include cultural barriers, semantic barriers, gender barriers, and time constraints.
Cultural barriers:
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As a result of their tendency to use...
Diagnostic and Statistical Manual of Mental Disorders (DSM)01:27

Diagnostic and Statistical Manual of Mental Disorders (DSM)

The Diagnostic and Statistical Manual of Mental Disorders (DSM) serves as the primary classification system for mental health disorders, providing standardized diagnostic criteria for clinicians and researchers. First published by the American Psychiatric Association (APA) in 1952, the DSM has undergone several revisions to reflect evolving psychiatric understanding. The fifth edition, DSM-5, released in 2013, introduced key updates that expanded diagnostic categories and modified diagnostic...

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Inverse Probability of Treatment Weighting (Propensity Score) using the Military Health System Data Repository and National Death Index
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[«Bridge Coding» ICD-9, ICD-10 and effects on mortality statistics].

Stefano Brocco1, Piercarlo Vercellino, Carlo Alberto Goldoni

  • 1Servizio epidemiologico regione Veneto.

Epidemiologia E Prevenzione
|September 21, 2010
PubMed
Summary
This summary is machine-generated.

The International Classification of Diseases, 10th Revision (ICD-10) implementation in Italy had minimal impact on major mortality statistics. However, coding variations were noted for less common diseases, highlighting the need for standardized procedures.

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Area of Science:

  • Public Health
  • Medical Informatics
  • Epidemiology

Context:

  • The transition from the International Classification of Diseases, 9th Revision (ICD-9) to ICD-10 is a critical process for national mortality statistics.
  • Accurate cause-of-death reporting is essential for public health surveillance and resource allocation.

Purpose:

  • To evaluate the impact of implementing the International Classification of Diseases, 10th Revision (ICD-10) on Italian mortality statistics.
  • To assess the effectiveness of the "Bridge-Coding" method in standardizing cause-of-death data during the transition.

Summary:

  • A "Bridge-Coding" analysis was conducted on 74,525 death certificates across multiple Italian regions using both ICD-9 and ICD-10.
  • Limited variability was observed for major disease groups (circulatory, neoplasms), indicating a low impact on overall mortality statistics.
  • Higher variability was found for infectious diseases, respiratory diseases, and dementia, suggesting potential challenges in comparability for these categories.

Impact:

  • The "Bridge-Coding" analysis reveals that while major mortality trends remain stable, specific disease categories may show coding-related variability.
  • The study underscores that the transcoding procedure may not fully correct all changes introduced by ICD-10, especially for certain conditions.
  • Ensuring the quality and accuracy of death certificate compilation is crucial, potentially having a greater effect on mortality statistics than coding system variations alone.