Related Experiment Video
Updated: Jul 4, 2026
07:40
Validation of a Psychosocial Intervention on Body Image in Older People: An Experimental Design
Published on: May 31, 2021
[Constructing a deprivation index based on census data in large Spanish cities(the MEDEA project)]
M Felícitas Domínguez-Berjón1, Carme Borrell, Gemma Cano-Serral
1Servicio de Informes de Salud y Estudios, Instituto de Salud Pública, Dirección General de Salud Pública y Alimentación, Consejería de Sanidad, Comunidad de Madrid, Madrid, España. felicitas.dominguez@salud.madrid.org
Gaceta Sanitaria
|June 27, 2008
Summary
A new deprivation index, based on work and education indicators, effectively identifies areas with unfavorable socioeconomic conditions and higher mortality rates in Spanish cities.
Area of Science:
- Socioeconomic Determinants of Health
- Public Health
- Urban Health Research
Context:
- Urban areas face challenges in identifying localized socioeconomic disparities.
- Understanding the link between socioeconomic status and mortality is crucial for public health interventions.
Purpose:
- To develop a deprivation index by census tract for major Spanish cities.
- To identify areas with the least favorable socioeconomic conditions.
- To analyze the association between this index and overall mortality.
Summary:
- A deprivation index was constructed using 2001 Census data from Barcelona, Bilbao, Madrid, Seville, and Valencia.
- Key indicators included unemployment, manual/eventual workers, and education levels.
- This index, weighted by principal component factor loadings, explained over 75% of variability and showed stronger correlations with mortality than individual indicators.
Impact:
- The proposed deprivation index serves as a valuable tool for health planning in identifying areas with unfavorable socioeconomic characteristics.
- It aids in detecting small areas within large cities that are associated with higher mortality.
- This index can contribute to the study of social inequalities in health within Spain.
Related Concept Videos
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:
00:57Deindividuation
Deindividuation is a form of social influence on an individual’s behavior such that the individual engages in unusual or non-normal behavior while in a group setting. Why? Because in these group settings, the individual no longer sees themselves as an individual anymore, disinhibiting their behavior and personal restraint.
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:
01:22Estimating Population Mean with Unknown Standard Deviation
In practice, we rarely know the population standard deviation. In the past, when the sample size was large, this did not present a problem to statisticians. They used the sample standard deviation s as an estimate for σ and proceeded as before to calculate a confidence interval with close enough results. However, statisticians ran into problems when the sample size was small. A small sample size caused inaccuracies in the confidence interval.
William S. Gosset (1876–1937) of the Guinness...
William S. Gosset (1876–1937) of the Guinness...
01:50Cross-Sectional Research
In cross-sectional research, a researcher compares multiple segments of the population at the same time. If they were interested in people's dietary habits, the researcher might directly compare different groups of people by age. Instead of following a group of people for 20 years to see how their dietary habits changed from decade to decade, the researcher would study a group of 20-year-old individuals and compare them to a group of 30-year-old individuals and a group of 40-year-old...
01:16Estimating Population Mean with Known Standard Deviation
To construct a confidence interval for a single unknown population mean μ, where the population standard deviation is known, we need sample mean as an estimate for μ and we need the margin of error. Here, the margin of error (EBM) is called the error bound for a population mean (abbreviated EBM). The sample mean is the point estimate of the unknown population mean μ.
The confidence interval estimate will have the form as follows:
(point estimate - error bound, point estimate + error bound)
The...
The confidence interval estimate will have the form as follows:
(point estimate - error bound, point estimate + error bound)
The...