Related Experiment Video
Updated: May 27, 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
Estimating the completeness of death registration.
Population Studies
|November 15, 2011
Summary
Estimating death registration completeness using age distribution is possible, even with incomplete data. Two methods, one from stable population theory and another using census data, were successfully applied to Thailand, demonstrating their utility in mortality analysis.
Area of Science:
- Demography
- Mortality Statistics
- Population Studies
Background:
- Incomplete death registration statistics can still offer valuable mortality insights.
- The age structure of deaths is a key indicator for assessing registration completeness, assuming age-independent completeness.
Purpose of the Study:
- To describe and evaluate two methods for estimating death registration completeness using age distribution.
- To assess the utility of these methods in demographic analysis.
Main Methods:
- Method 1: Stable population theory to estimate completeness and rate of natural increase.
- Method 2: Utilizes two census age distributions and intercensal deaths to estimate enumeration and registration completeness for closed populations.
Main Results:
- Both methods were applied to Thailand's 1960-70 data.
- The methods proved satisfactory in estimating death registration completeness and related demographic parameters.
Conclusions:
- Age distribution of deaths is a viable tool for estimating registration completeness.
- The described methods offer robust approaches for demographic analysis, even with imperfect data.
Related Concept Videos
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,...
Consider the example of a high-risk surgical procedure with significant early-stage mortality. A two-year clinical study is conducted,...
Life Tables
A life table is a statistical tool that summarizes the mortality and survival patterns of a population, providing detailed insights into the likelihood of survival or death across different age intervals within a cohort. By organizing data on survival probabilities and mortality rates, life tables offer a clear snapshot of population dynamics over time. They are extensively used in demography, public health, actuarial science, and ecology to analyze life expectancy, design health interventions,...
Applications of Life Tables
Life tables are versatile across various fields, providing a quantitative basis for analyzing mortality and survival rates. Whether used by demographers, actuaries, epidemiologists, or sociologists, life tables offer valuable insights into the dynamics of life and death, facilitating informed decisions in public health, insurance, conservation, and beyond. Their broad applicability highlights the interconnectedness of demographic data with practical outcomes in everyday life and strategic...
Kaplan-Meier Approach
The Kaplan-Meier estimator is a non-parametric method used to estimate the survival function from time-to-event data. In medical research, it is frequently employed to measure the proportion of patients surviving for a certain period after treatment. This estimator is fundamental in analyzing time-to-event data, making it indispensable in clinical trials, epidemiological studies, and reliability engineering. By estimating survival probabilities, researchers can evaluate treatment effectiveness,...
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...
Assumptions of Survival Analysis
Survival models analyze the time until one or more events occur, such as death in biological organisms or failure in mechanical systems. These models are widely used across fields like medicine, biology, engineering, and public health to study time-to-event phenomena. To ensure accurate results, survival analysis relies on key assumptions and careful study design.

