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Analysis of Surveillance Data. Philippe Dubois From Denis Coulombier, Julia Fitzner, Augusto Pinto & Marta Valenciano, WHO-HQ/LYON. Analysis of Surveillance Data. Data characteristics Data validation Descriptive analysis Hypothesis testing. Data Characteristics.
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Analysis of Surveillance Data Philippe Dubois From Denis Coulombier, Julia Fitzner, Augusto Pinto & Marta Valenciano, WHO-HQ/LYON Phom Penh, Cambodia
Analysis of Surveillance Data • Data characteristics • Data validation • Descriptive analysis • Hypothesis testing
Data Characteristics • Various sources of notification • Various levels of qualification • Continuous data collection subject to change
Surveillance Data Validation • Frequency distributions • missing values • expected distribution • digit attraction • Cross-Tabulations • age, sex, logical errors • by source: collect bias ?
Notifications of All Notifiable Diseasesby Date of Onset, USA, 1989
Birth weight Distribution, in PoundsFermattes Hospital, Haiti, February 1994
Descriptive Approach • Time • Place • Persons • Generating hypotheses
Analyzing Time Characteristics • Graphical analysis • The 3 data components • secular trend • seasonal variations • accidental variations
Notifications of Foodborne Outbreaks in France, 1996-1998 1996 1997 1998
Components of Surveillance Data • Signal • secular trend • seasonal variations • accidental variations
Descriptive analysis of Components • Moving averages • empirical method • for reducing variability • same area under the curve • Logarithmic scale • dynamic analysis of changes • difficult to interpret
Calculation of moving averages 822 Jan 654 Feb Apr 546 3622/5=724,4 728 3690/5=738.0 Mar 3836/5=767.2 May 872 Jun 890 1993 Jul 692 465 Aug Sep 869 5 month window Oct 726 Nov 834 Dec 945
Notification of giardiasis in Delaware, 03/1991-03/1995 Crude Weekly Data
Notification of giardiasis in Delaware, 03/1991-03/1995 12 Week Moving Average
Notification of giardiasis in Delaware, 03/1991-03/1995 52 Week Moving Average
Notification of giardiasis in Delaware, 03/1991-03/1995 Aggregated data
Size of the Moving Average Window 1. Showing cyclical variations by removing accidental variations • empirical approach: the visual impression • inversely proportional to the number of cases • increases as the variance increases
Effect of the Moving Average Window Size Weekly Notifications of Salmonellosis, Georgia, 1993-1994 3 weeks 5 weeks 7 weeks 10 weeks
Size of the Moving Average Window 2. Showing secular trend by removing cyclical variations • Cycle span • 52 for weekly data • 12 for monthly data • 4 for quarterly data
Week 10 of 1994 and 208 Previous Weeks Cases of Gonorrhea in Michigan
Cases of Gonorrhea in Michigan Week 10 of 1994 and 208 Previous Weeks
Descriptive Analysis of the 3 Components • Moving average • empirical method • variability reduction • same area under the curve • Logarithmic scale • dynamic analysis of changes • difficult to interpret
MALARIA- By year, United States, 1930-1992 MALARIA- By Year, United States, 1930-1992
GONORRHEA - By race and ethnicity, United States, 1981-1993 Arithmetic scale
GONORRHEA - By race and ethnicity, United States, 1981-1993 GONORRHEA - By race and ethnicity, United States, 1981-1993 Logarithmic scale Source: Summary of Notifiable Diseases, United States 1993
Typhoid Notifications in France Typhoid Notifications in France
Interpreting the results • Role of chance • Role of bias • True disease pattern
Conclusions • Analysis to draw attention • Validation by investigation