Related Experiment Video
Updated: Jun 14, 2026
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
The reliability of cause-of-death coding in The Netherlands
Peter Harteloh1, Kim de Bruin, Jan Kardaun
1Statistics Netherlands, POB 24 500, 2490 HA, The Hague, The Netherlands. ppm.harteloh@cbs.nl
European Journal of Epidemiology
|March 24, 2010
Summary
Cause-of-death statistics reliability in the Netherlands was assessed. While reliable for major causes like cancer, chronic diseases showed low reliability, necessitating caution in interpretation and improved coding guidelines.
Area of Science:
- Public Health
- Epidemiology
- Medical Statistics
Background:
- Cause-of-death statistics are crucial for epidemiological research and policy.
- Understanding the reliability of these statistics is vital for accurate trend interpretation and population comparisons.
- Coding variations can obscure true differences in health determinants.
Purpose of the Study:
- To evaluate the reliability of cause-of-death statistics in The Netherlands.
- To assess both intercoder and intracoder agreement in assigning underlying causes of death.
Main Methods:
- A double coding study was conducted on 10,833 death certificates from May 2005, re-coded in 2007.
- Four coders manually assigned codes to each death certificate.
- Reliability was measured using intercoder agreement and intracoder consistency.
Main Results:
- Overall intercoder agreement for the underlying cause of death was 78%.
- Mean intracoder agreement was 89%, with 2.2% of cases showing code changes upon re-coding.
- Reliability was high (>90%) for major causes (e.g., cancers, myocardial infarction) but low (<70%) for chronic diseases (e.g., diabetes, renal insufficiency).
- Agreement varied with ICD-10 code specificity, age, number of coders, and reported diseases.
Conclusions:
- The reliability of cause-of-death statistics in The Netherlands is variable, being high for some causes and low for others.
- Statistical offices should develop and regularly evaluate specific coding rules for low-reliability diseases.
- Users must exercise caution when interpreting cause-of-death data, especially for chronic conditions.
Related Concept Videos
01:20Actuarial 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,...
Consider the example of a high-risk surgical procedure with significant early-stage mortality. A two-year clinical study is conducted,...
01:10Documentation of Nursing Diagnosis
The nurse documents nursing diagnoses and enters them into the patient record. The identified patient's nursing diagnosis is either written out with a plan of care or entered into the electronic health record.
In some settings, data-driven computerized decision support systems are in place, allowing for more accurate nursing diagnoses. The database within one of these systems includes diagnostic labels defining characteristics, activities, and indicators for nursing. A nurse enters assessment...
In some settings, data-driven computerized decision support systems are in place, allowing for more accurate nursing diagnoses. The database within one of these systems includes diagnostic labels defining characteristics, activities, and indicators for nursing. A nurse enters assessment...
01:29Bias 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:
01:25Statistical 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:
01:15Systematic Error: Methodological and Sampling Errors
In the case of systematic errors, the sources can be identified, and the errors can be subsequently minimized by addressing these sources. According to the source, systematic errors can be divided into sampling, instrumental, methodological, and personal errors.
Sampling errors originate from improper sampling methods or the wrong sample population. These errors can be minimized by refining the sampling strategy. Defective instruments or faulty calibrations are the sources of instrumental...
Sampling errors originate from improper sampling methods or the wrong sample population. These errors can be minimized by refining the sampling strategy. Defective instruments or faulty calibrations are the sources of instrumental...
01:19The Mantel-Cox Log-Rank Test
The Mantel-Cox log-rank test is a widely used statistical method for comparing the survival distributions of two groups. It tests whether a statistically significant difference exists in survival times between the groups without assuming a specific distribution for the survival data, making it a non-parametric test. This flexibility makes the log-rank test particularly valuable in medical research and other fields where the timing of an event, such as death or disease recurrence, is of interest.