Relationship between Enviromental Factors and Infant Mortality in Madrid 1986 – 1997 Julio Díaz Jiménez (1), César López Santiago (1), Cristina Linares.

Slides:



Advertisements
Similar presentations
Significance Testing.  A statistical method that uses sample data to evaluate a hypothesis about a population  1. State a hypothesis  2. Use the hypothesis.
Advertisements

Chapter 12 Inference for Linear Regression
1 9 th International Conference Zaragoza-Pau on Applied Mathematics and Statistics On heat wave definition Abaurrea J., Cebrián A.C., Asín J., Centelles.
Measuring the Health Impacts of Air Pollution in Toronto and Hamilton Murray M. Finkelstein PhD MD Associate Professor, Department of Family Medicine and.
Inference for Regression
CH 27. * Data were collected on 208 boys and 206 girls. Parents reported the month of the baby’s birth and age (in weeks) at which their child first crawled.
Cold and Health James Goodwin Head of Research. Hippocrates 400BC Whoever wishes to investigate medicine properly, should proceed thus: in the first place.
Snow Trends in Northern Spain. Analysis and Simulation with Statistical Downscaling Methods Thanks to: Daniel San Martín, Sixto.
1 CLUSTER ANALYSIS OF EUROPEAN DAILY TEMPERATURE SERIES: AN EXTREME VALUE APPROACH Andrés M. Alonso Departamento de Estadística Universidad Carlos II de.
Fighting the Great Challenges in Large-scale Environmental Modelling I. Dimov n Great challenges in environmental modelling n Impact of climatic changes.
High Summer Air Temperatures and Public Health : Tver Case Study Boris Revich 1, Dmitry Shaposhnikov* 2 1 Center for Demography and Human Ecology RAS,
Markus Amann The RAINS model: Modelling of health impacts of PM and ozone.
Chapter 13 Introduction to Linear Regression and Correlation Analysis
Fall 2006 – Fundamentals of Business Statistics 1 Chapter 13 Introduction to Linear Regression and Correlation Analysis.
Linear Regression and Correlation Analysis
Lecture 24: Thurs., April 8th
Model Choice in Time Series Studies of Air Pollution and Health Roger D. Peng, PhD Department of Biostatistics Johns Hopkins Blomberg School of Public.
Chapter 13 Introduction to Linear Regression and Correlation Analysis
PY 427 Statistics 1Fall 2006 Kin Ching Kong, Ph.D Lecture 6 Chicago School of Professional Psychology.
C82MCP Diploma Statistics School of Psychology University of Nottingham 1 Linear Regression and Linear Prediction Predicting the score on one variable.
Correlation and Regression Analysis
Time series study on air pollution and mortality in Indian cities R Uma, Kaplana Balakrishnan, Rajesh kumar.
Correlation & Regression
Regression and Correlation Methods Judy Zhong Ph.D.
Stratification and Adjustment
Cohort Study.
Introduction to Linear Regression and Correlation Analysis
Copyright © 2013, 2009, and 2007, Pearson Education, Inc. Chapter 12 Analyzing the Association Between Quantitative Variables: Regression Analysis Section.
Inference for regression - Simple linear regression
1 HEALTH IMPACTS OF AIR QUALITY ON THE BISHOP PAIUTE RESERVATION FOCUS ON PARTICULATE MATTER TONI RICHARDS, Ph.D., AIR QUALITY SPECIALIST ENVIRONMENTAL.
Chapter 14 – Correlation and Simple Regression Math 22 Introductory Statistics.
Anthony Greene1 Correlation The Association Between Variables.
© The McGraw-Hill Companies, Inc., 2000 Business and Finance College Principles of Statistics Lecture 10 aaed EL Rabai week
BPS - 3rd Ed. Chapter 211 Inference for Regression.
AP Statistics Section 15 A. The Regression Model When a scatterplot shows a linear relationship between a quantitative explanatory variable x and a quantitative.
Inference for Linear Regression Conditions for Regression Inference: Suppose we have n observations on an explanatory variable x and a response variable.
Results of the 3 pilot studies conducted near waste incinerators in Dorog, Forlí and Warsaw.
Introduction to Linear Regression
+ Chapter 12: More About Regression Section 12.1 Inference for Linear Regression.
HOW HOT IS HOT? Paul Wilkinson Public & Environmental Health Research Unit London School of Hygiene & Tropical Medicine Keppel Street London WC1E 7HT (UK)
11/4/2015Slide 1 SOLVING THE PROBLEM Simple linear regression is an appropriate model of the relationship between two quantitative variables provided the.
P. Otorepec, M. Gregorič IVZ RS Use of rutinely collected air pollution and health data on local level for simple evaluation of health impact.
1 HEALTH IMPACTS OF AIR QUALITY ON THE BISHOP PAIUTE RESERVATION FOCUS ON PARTICULATE MATTER TONI RICHARDS, Ph.D., AIR QUALITY SPECIALIST ENVIRONMENTAL.
ENVIRONMENTAL FACTOR EFFECTS OF SIBERIAN NORTHERN TERRITORIES OF POPULATION HEALTH Efimova N.V., Nikiforova V.A., Pertseva T.G. Russia.
Chapter 12 More About Regression Let’s look at the Warm-Up first to remind ourselves what we did with regression! Remember FODS!
The impact on mortality of heat waves in Budapest, Hungary R Sari Kovats, Shakoor Hajat, London School of Hygiene and Tropical Medicine, London, United.
Estimation of the risk factors in chronic respiratory diseases of the children Cebanu Sergiu State Medical and Pharmaceutical University “Nicolae Testemitanu”
Scatter Diagrams scatter plot scatter diagram A scatter plot is a graph that may be used to represent the relationship between two variables. Also referred.
26134 Business Statistics Week 4 Tutorial Simple Linear Regression Key concepts in this tutorial are listed below 1. Detecting.
AMGI/EURASAP workshop, Zagreb 25 May 2007 Nenad Kezele, Ruđer Bošković Institute, Bijenička 54, Zagreb, Croatia Effect of O 3 and PM10 on mortality increase.
AP Statistics Section 15 A. The Regression Model When a scatterplot shows a linear relationship between a quantitative explanatory variable x and a quantitative.
Heat waves in Budapest A Páldy *, J Bobvos **, A Vámos ** * - “Fodor József” National Center for Public Health, National Institute of Environmental.
1 “Experiences of economic valuation applied to air quality and pollultion management : Examples of experiences, political implications and application.
Time-series studies for the relationship between air pollution and the population health in Beijing Xiao-chuan Pan Dept. of Occupational and Environmental.
Methodological Considerations in Assessing Effects of Air Pollution on Human Health Rebecca Klemm, Ph.D. Klemm Analysis Group, Inc. American Public Health.
Chapter 12 Inference for Linear Regression. Reminder of Linear Regression First thing you should do is examine your data… First thing you should do is.
BPS - 5th Ed. Chapter 231 Inference for Regression.
26134 Business Statistics Week 4 Tutorial Simple Linear Regression Key concepts in this tutorial are listed below 1. Detecting.
Inter-American Development Bank Regional Policy Dialogue
Lecture 10 Regression Analysis
EUROEPI2010 -EPIDEMIOLOGY AND PUBLIC HELTH IN AN EVOLVING EUROPE
Dr.MUSTAQUE AHMED MBBS,MD(COMMUNITY MEDICINE), FELLOWSHIP IN HIV/AIDS
Inference for Regression
Which method is most appropriate for assessing exposure?
Estimating changes in mortality due to climate change
Fernando Pereira, PhD, Institute Polytechnic of Bragança, Portugal
Inference for Regression
Introduction to Hypothesis Testing
Descriptive Statistics Univariate Data
Presentation transcript:

Relationship between Enviromental Factors and Infant Mortality in Madrid 1986 – 1997 Julio Díaz Jiménez (1), César López Santiago (1), Cristina Linares Gil (1) Ricardo García Herrera (2) (1) Centro Universitario de Salud Pública. Madrid. (2) Facultad de Ciencias Físicas. Universidad Complutense de Madrid.

OBJECTIVE To analyse the effects of extreme temperatures and main air pollutants on daily mortality of children up to 10 years of age. Madrid (Spain) since 1986 to 1997.

MATERIALS & METHODS DEPENDENT VARIABLE: – Daily mortality data from 1/01/1986 to 31/12/1997: children residents in Madrid under ten years of age. –All mortality causes were considered, except accidents ICD-9 (1-799). –Age groups have considered: from 0 to 9 years old less than 1 year old from 1 to 5 years old from 5 to 9 years old.

MATERIALS & METHODS INDEPENDENT VARIABLES: –Daily temperature: average, maximum and minimum –Relative Humidity –Air pollution: daily average concentrations of SO 2, NOx, TSP, NO 2, O 3 ). CONTROL VARIABLES: –Influenza epidemics. –Day of the week

MATERIALS & METHODS Poisson regression was used to model the association between infant mortality in Madrid and the environmental risk factor considered. The independent variables impact on mortality was assessed through the atributable risk (AR), with the assumption that the whole population could be exposed to its effect. Attributable risk can be easily computed as follows: AR = (RR-1)/RR (23), where RR is the relative risk obtained by Poisson models. The analysis was carried out using statistic pack S- Plus 2000.

Descriptive statistics for children mortality, air pollution and meteorological variables series

RESULTS Lags in which are established significant associations between the children sample (0-9 years old) mortality and the independent variables (outcome of the pre-whitening series residuals CCF analysis).

RESULTS Poisson Regression Models for Children (0-9 years old) mortality and air pollutants.

RESULTS Scatter-plot of TSP concentration and mortality in the group of 0-9 years old. Daily mortality

RESULTS Scatter-plot of Tmax and mortality in the group of 0-9 years old.

V-SHAPED RELATIONS Scatter-plot of TSP concentration and mortality for the whole population in Madrid (same period) Maximum daily temperature(ºC) All causes mortality

RESULTS Statistically significant variables. Poisson Regression for all the variables considered and mortality in the group of 0-9 years old. * RR for an increase of 25 micg/m 3 ** RR for each degree of Tmax under 30ºC. *** RR for each 1% that realative humidity increases.

RESULTS Scatter Plot Diagram for Tmax lagged 7 days with mortality in winter.

RESULTS Extremely cold days influence on mortality of the different children age groups considered. * for each degree in which daily Tmax is under 6ºC.

MAIN CONCLUSIONS  1. TSP presents an association with mortality in the very short term, while SO 2 and NO x present the association lagged one day.  2. There is no association found between mortality and troposphere ozone.  3. The maximum daily temperature shows a significant relationship with child mortality, but not the minimum daily temperature.