This document discusses analyzing and interpreting surveillance data. It outlines key steps in the process including counting cases, dividing by population to calculate rates, and comparing rates over time, place, and person. Common reports generated from surveillance data are described such as timeliness, descriptive analyses, trend analyses, and comparisons between reporting units. Interpretation of results involves looking for missing or invalid data, considering disease profiles and rates, and taking action based on the information. Technical committees regularly review analyses to guide public health responses. The goal is to transform raw surveillance data into useful information that can inform program implementation and action.

1. Analysis and interpretation of
surveillance data
Integrated Disease Surveillance Programme (IDSP)

2. Preliminary questions to the group
• Have you been involved in surveillance data
analysis?
• What difficulties have you encountered in
analyzing surveillance data?
• What would you like to learn about
surveillance data analysis?
2

3. Outline of this session
1. The concept of data analysis
2. CDC for TPP
3. Reports
4. Interpretation of the information
3

4. What is data analysis?
• Data reduction
 Reduces the quantity of numbers to examine
 Because the human mind cannot handle too many
bits of information at the same time
• Transforms raw data into information
 A list of cases becomes a monthly rate
Data Information Action
Analysis Interpretation
4
Today we will focus on analysis
Why analyze?

5. REC SEX Distribution of cases by sex
--- ----
1 M
2 M Table
3 M
4 F
5 M
Data
6 F Sex Frequency Proportion
7 F
8 M Female 10 33.3%
9 M
10 M Analysis Male 20 66.7%
11 F
12 M
13 M Total 30 100.0%
14 M
15 F Information Graph
16 F
17 F
18 M
19 M Female
20 M Male
21 F
22 M
23 M
24 F
25 M
26 M
27 M
28 F
29 M
30 M 5 Why analyze?

6. 1. Count, Divide and Compare (CDC): An
epidemiologist calculates rates and
compare them
• Direct comparisons of absolute numbers of
cases are not possible in the absence of rates
• CDC
 Count
• Count (compile) cases that meet the case definition
 Divide
• Divide cases by the corresponding population
denominator
 Compare
• Compare rates across age groups, districts etc.
6 CDC for TPP

7. Exercise
• How would you find out if diphtheria is more
common among people who are below the poverty
line?
7 CDC for TPP

8. Is diphtheria more common among
poorer people?
• Count
 Count cases of diphtheria among families with and without
a Below Poverty Line (BPL) card
• Divide
 Divide the cases of diphtheria among BPL people by the
estimated BPL population size (e.g., census) to get the rate
 Divide the cases of diphtheria among non BPL people by
the estimated non BPL population size (e.g., census) to get
the rate
• Compare
 Compare the rates of diphtheria among BPL and non BPL
people
8 CDC for TPP

9. 2. Time, place and person
descriptive analysis
A. Time
 Incidence over time (Graph)
A. Place
 Map of incidence by area
A. Person
 Breakdown by age, sex or personal
characteristics
 Table of incidence by age and sex
9 CDC for TPP

10. A. Present the results of the analysis
over time using a GRAPH
• Absolute number of cases
 Avoid analysis over longer time period as the
population size increases
• Incidence rates
 Allows analysis over longer time period
 Analysis by week, month or year
10 CDC for TPP

11. Absolute number of cases for analysis over a short time period
Acute hepatitis (E) by week, Hyderabad,
120
AP, India, March-June 2005
100
Number of cases
80
60
40
20
0
1 8 15 22 29 4 12 19 26 3 10 17 24 31 7 14 21 28
March April May June
First day of week of onset
Interpretation: The source of infection is persisting and continues to cause cases
11 CDC for TPP

12. December
November
Reports
October
Interpretation: There is a seasonality in the end of the year and a trend towards
September
August
July
2004
District, West Bengal, India, 2000-2004
June
May
Malaria in Kurseong block, Darjeeling
April
March
February
January
December
November
October
September
August
July
2003
June
May
increasing incidence year after year
Incidence rates for analysis over a longer time period
April
March
February
January
December
November
October
September
August
July
2002
June
Months
May
April
March
February
January
December
November
12
October
September
August
Incidence of Pf malaria
July
2001
Incidence of malaria
June
May
April
March
February
January
December
November
October
September
August
July
2000
June
May
April
March
February
January
5
0
45
40
35
30
25
20
15
10
Incidence of malaria per 10,000

13. 2. Present the results of the analysis by
place using a MAP
• Number of cases
 Spot map
 Does not control for population size
 Concentration of dots may represent high
population density only
 May be misleading in areas with heterogeneous
population density (e.g., urban areas)
• Incidence rates
 Incidence rate map
 Controls for population size
13 CDC for TPP

14. Incidence by area
Incidence of acute hepatitis (E) by block,
Hyderabad, AP, India, March-June 2005
Attack rate per
100,000
population
0
1-19
20-49
50-99
100+
Open drain
Interpretation: Blocks with hepatitis
are those supplied by pipelines
Pipeline crossing
open sewage drain 14 crossing open sewage drains

15. 3. Present the results of the analysis per
person using an incidence TABLE
• Distribution of cases by:
 Age
 Sex
 Other characteristics
(e.g., ethnic group, vaccination status)
• Incidence rate by:
 Age
 Sex
 Other characteristics
15 CDC for TPP

16. Incidence according to a characteristic
Probable cases of cholera by age and
sex, Parbatia, Orissa, India, 2003
Number of cases Population Incidence
Age group 0 to4 6 113 5.3%
(In years) 5 to14 4 190 2.1%
15 to24 5 128 3.9%
25 to34 5 144 3.5%
35 to44 6 129 4.7%
45 to54 4 88 4.5%
55 to64 8 67 11.9%
> 65 3 87 3.4%
Sex Male 17 481 3.5%
Female 24 465 5.2%
Total Total 41 946 4.3%
Interpretation: Older adults and women are
at increased risk of cholera
16 CDC for TPP

17. Distribution of cases according to a characteristic
Immunization status of measles cases,
Nai, Uttaranchal, India, 2004
19%
81%
Immunized Unimmunized
Interpretation: The outbreak is probably caused
by a failure to vaccinate
CDC for TPP

18. Seven reports to be generated
1. Timeliness/completeness
2. Description by time, place and person
3. Trends over time
4. Threshold levels
5. Compare reporting units
6. Compare private / public
7. Compare providers with laboratory
18 Reports

19. Report 1: Completeness and timeliness
• A report is considered on time if it reaches
the designated level within the prescribed
time period
 Reflects alertness
• A report is said to be complete if all the
reporting units within its catchment area
submitted the reports on time
 Reflects reliability
19 Reports

20. Report 2: Weekly/ monthly
summary report
• Based upon compiled data of all the
reporting units
• Presented as tables, graphs and maps
• Takes into account the count, divide and
compare principle:
 Absolute numbers of cases, deaths and case
fatality ratio are sufficient for a single reporting
unit level
 Incidence rates are required to compare
reporting units
20 Reports

21. Report 3: Comparison with previous
weeks/ months/ years
• Help examine trend of diseases over time
• Weekly analysis compare the current week
with data from the last three weeks
 Alerts authorities for immediate action
• Monthly and yearly analysis examine:
 Long term trends
 Cyclic pattern
 Seasonal patterns
21 Reports

22. Report 4: Crossing threshold values
• Comparison of rates with thresholds
• Thresholds that may be used:
 Pre-existing national/international thresholds
 Thresholds based on local historic data
• Monthly average in the last three years
(excluding epidemic periods)
 Increasing trends over a short duration of time
(e.g., Weeks)
22 Reports

23. Report 5: Comparison between
reporting units
• Compares
 Incidence rates
 Case fatality ratios
• Reference period
 Current month
• Sites concerned
 Block level and above
23 Reports

24. Report 6: Comparison between public
and private sectors
• Compare trends in number of new
cases/deaths
 Incidences are not available for private provider
since no population denominators are available
• Good correlation may imply:
 The quality of information is good
 Events in the community are well represented
• Poor correlation may suggest:
 One of the data source is less reliable
24 Reports

25. Report 7: Comparison of reports between
the public health system and the
laboratory
Elements to compare
Public health system Laboratories
Validation of •Number of cases •Number of laboratory
reporting seen by providers diagnoses
Water borne •Cases of diarrheal •Water quality
disease diseases
Vector borne •Cases of vector •Entomological data
disease borne diseases
25 Reports

26. Making sense of different sources of
information (“S” and “P” forms)
 It is not possible to mix data from different
case definitions
 One cannot add cases coming from “S” and “P”
forms (syndromic and presumptive diagnoses)
 It is not possible to add apples and oranges
 Use the different sources of information to
cross validate (or “triangulate”)
 If there is an increase in the cases of dengue in
the “P” forms, check if there is a surge in the
number of fever cases in the “S” forms
26 Interpretation

27. What computers cannot do
Skills Attitudes
• Contact reporting units • Looking
for missing information
• Thinking
• Interpret laboratory
tests • Discussing
• Make judgment about: • Taking action
 Epidemiologic linkage
 Duplicate records
 Data entry errors
• Declare a state of
outbreak
27 Interpretation

28. Expressed concerns versus reality
Concerns Mistake commonly
commonly expressed observed
• Statistics are difficult • Data are not looked at
• Multivariate analysis is
complex
• Presentation of data is
challenging
28 Interpretation

29. Review of analysis results by the
technical committee
• Meeting on a fixed day of the week
• Search for missing values
• Validity check
• Interpretation of the analysis bearing in mind
 The strength and weakness of data
 The disease profiles
 The need to calculate rates before comparisons Meeting on
a fixed day of every week
• Summary reports for dissemination
• Action
29 Interpretation

30. Take home messages
1. Link data collection and program
implementation
• Data > Information > Action
1. Count, divide and compare for time, place
and person description
2. Share information through reports
3. Interpret with the technical committee to
decide action on the basis of the
information
30

31. Thank you.
31