Analysis of Surveillance Data Philippe Dubois From Denis Coulombier, Julia Fitzner, Augusto Pinto & Marta Valenciano, WHO-HQ/LYON April 8 – 12, 2013 Phom Penh, Cambodia

 Data characteristics  Data validation  Descriptive analysis  Hypothesis testing Analysis of Surveillance Data

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 Diseases by Date of Onset, USA, 1989

Birth weight Distribution, in Pounds Fermattes 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,

Components of Surveillance Data  Signal secular trend seasonal variations accidental variations

Decomposition of Surveillance Data Signal

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 1993 Jan Feb Apr Jun Aug Jul Sep Oct Dec Mar May Nov /5=724,4 3690/5= /5= month window 890

Notification of giardiasis in Delaware, 03/ /1995 Crude Weekly Data

12 Week Moving Average Notification of giardiasis in Delaware, 03/ /1995

52 Week Moving Average

Notification of giardiasis in Delaware, 03/ /1995 Aggregated data

Size of the Moving Average Window  empirical approach: the visual impression  inversely proportional to the number of cases  increases as the variance increases 1. Showing cyclical variations by removing accidental variations

Effect of the Moving Average Window Size Weekly Notifications of Salmonellosis, Georgia, weeks 7 weeks 5 weeks 10 weeks

Size of the Moving Average Window  Cycle span 52 for weekly data 12 for monthly data 4 for quarterly data 2. Showing secular trend by removing cyclical variations

Cases of Gonorrhea in Michigan Week 10 of 1994 and 208 Previous Weeks

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

Annual Rate of Tuberculosis, United States,

MALARIA- By year, United States,

MALARIA- By Year, United States, MALARIA- By year, United States,

GONORRHEA - By race and ethnicity, United States, Arithmetic scale

GONORRHEA - By race and ethnicity, United States, Logarithmic scale Source: Summary of Notifiable Diseases, United States 1993 GONORRHEA - By race and ethnicity, United States,

Typhoid Notifications in France

Interpreting the results  Role of chance  Role of bias  True disease pattern

Conclusions  Analysis to draw attention  Validation by investigation

Questions? Comments? Discussions?