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Getting the data to speak
Analysis of surveillance data
2
Warming up case study
• Malaria outbreak, Uttar Pradesh, India, 1991• Visit of a primary health centre:
– Do you have a problem in your centre?• “No, thank you!, We have sent our people to help the
neighbouring facilities where they do have malaria”– No compilation of the data– Data collected from the malaria form
• Data compiled by the visitor• Look at the table and observe
3
Malaria in primary health centre, Jalalabad, Uttar Pradesh, India, 1988-
911988 1989 1990 1991
Month Slides Positive
Slides Positive
Slides Positive
Slides Positive
Jan 414 0 276 1 273 0 267 0
Feb 337 0 287 0 348 0 234 0
Mar 278 0 263 0 341 0 259 0
Apr 334 2 408 0 252 0 443 0
May 293 0 283 4 229 0 347 0
Jun 211 0 324 0 323 0 372 0
Jul 326 0 345 1 550 0 483 0
Aug 1009 20 1602 8 1440 5 1001 7
Sep 830 22 1492 1 941 9 2036 19
Oct 650 0 862 0 497 0 3187 * 104
Nov 438 0 333 0 289 0
Dec 353 1 279 0 295 0
Total 5473 45 6754 15 5778 14 8629* 130
*1227 Slides still to be examined
Observations and some interpretations
• People collect more slides from August to October
• Collection of slides and % positive increased in 1991
• Why was the outbreak missed?– Failure to compile data
prevented any comparisons
Key issues
• Rationale
• Counting
• Dividing
• Comparing
• Ongoing analysis
Why analyse surveillance data?
• Turn data into information
• Use information for action
• Close the surveillance loop
• Assess data quality
Count: Adding up cases is like sorting beads
• Unsorted beads (Individual data)– Sort in drawers
– Count beads in each drawer
• Beads pre-sorted in bags (aggregated data)– Place smaller bags in the drawers
– Add the content of the smaller bags in each drawer
Individual and aggregated data
ID Place Count
1 Block A 12
2 Block B 1
3 Block C 35
4 Block D 24
ID Place Age Sex Onset
1 Block A 3 1 1 Jan 06
2 Block A 1 2 1 Jan 06
3 Block C 35 2 3 Jan 06
4 Block D 67 1 4 Jan 06
… … … … …
Individual line listing
Aggregated file
Counting individual and aggregated cases
• Aggregate database of individual cases– One line per record / case
• “count” variable in the aggregated database
• Sum up aggregated database– Records with characteristics and a “count” variable
• Files merged to sum up the count variable
Aggregating cases by time
• Choose a time unit– Year, month, week
• Define a variable reflecting the time unit– Extract year, month, week
• 12/11/2004 becomes 2004
• 14/07/2005 becomes 2005_07
• 2/01/2006 becomes 2006_Week1
• Aggregate cases using the time variable
Aggregating cases by place or by person
• Select / define a group with denominators – Person
• Age group
• Gender
– Location
• Use variable name identical to denominator file
• Aggregate by the chosen group
Example: Aggregating a line listing by place
ID Place Count
1 Block A 12
2 Block B 1
3 Block C 35
4 Block D 24
ID Place Age Sex Onset
1 Block A 3 1 1 Jan 06
2 Block A 1 2 1 Jan 06
3 Block C 35 2 3 Jan 06
4 Block D 67 1 4 Jan 06
… … … … …
Individual line listing
Aggregated file
Follow the same procedure for “time” and “person” variables
Population denominators
• Available on the Internet• Heterogeneous
– Standardize uses across institutions
• Out-dated– Project with standardized procedures and factors
• Structured differently from numerator file – Identify variables for which denominators are
available
Merging numerator and denominator files to calculate incidences
ID Place Count
1 Block A 12
2 Block B 1
3 Block C 35
4 Block D 24
Place Population
Block A 3,567
Block B 1,231
Block C 2,745
Block D 724
ID Place Count Population Incidence
1 Block A 12 3,567 xxx
2 Block B 1 1,231 xxx
3 Block C 35 2,745 xxx
4 Block D 24 724 xxx
Aggregated numerator file
Denominator file
Files merged side to side
Incidence may now
be calculated
Compare
• Time– Incidence over time
• Place – Incidence between different geographical areas
• Person– Incidence between different population groups
0
5
10
15
20
25
30
35
40
45
4 6 8 10 12 14 16 18 20 22 24 26 28 30 2 4 6 8 10April Date of Onset May
No.
of
cases
4 per 1000 attack rate; No deaths
Cases of diarrhoea by date of onset, Garulia, West Bengal, 2006 (n=298)
TIME = Graph
Distribution of diarrhoea cases by households, Garulia, West Bengal, India, 2006
Index caseHousehold with 1 caseHousehold with 2-3 cases Household with 4-5 casesHousehold with 6+ cases
Water pipelineRoadOverhead tankLeakage point
PLACE= Map
Attack rate of diarrhoea by age and sex, Garulia, West Bengal, India, 2006
Characteristics Number of cases
Population,
2006
Attack rate per 1000
Age 0 - 4 51 8,030 6.4
5 -14 68 20,066 3.4
15 - 24 39 15,493 2.5
25 - 34 42 14,107 3.0
35 - 44 42 11,191 3.8
45 + 56 15,637 3.6
Gender Male 158 43,716 3.6
Female 140 40,809 3.4
Total 298 84,525 3.5
PERSON = Table
Analysis at each level
• Lower level– Count– Detect clusters– Threshold analysis
• Intermediate analysis– Rates – Time, place and person
• Higher level– Advanced analyses
Automated analysis
• Rationale– Facilitate operations– Reduce the risk of mistakes
• Practical methods– Prepare data entry forms– Allow regular updates
• Numerator• Denominator
– Programme analysis– Programme reporting
Public health emergency in Kano state, Nigeria, 1996
An example
A large outbreak of meningitis in Kano state, Nigeria, 1996
• Health care system overwhelmed
• Intervention of Non Governmental Organizations– Médecins Sans Frontières
• Operational question:– Where can we use vaccine?
• Routine surveillance considered “unreliable”
Stimulated passive surveillance methods for meningitis, Kano state,
Nigeria, 1996 • Data collection
– Line listings sent to all local government areas
• Data transmission– Line listings sent to the
state level
• Data analysis– Data entry– Automated time, place
and person analysis
• Feedback– Short report with maps,
curves and tables
Weekly incidence of meningitis in three Local Government Areas (LGAs), Kano state, Nigeria, 1995- 1996
0.0
20.0
40.0
60.0
80.0
100.0
120.0
95/49 95/50 95/51 95/52 96/01 96/02 96/03 96/04 96/05 96/06 96/07 96/08 96/09 96/10
Weeks
Incid
en
ce p
er
10
0,0
00
Nassarawa
Dawakin-Tofa
Bagwai
Weekly incidence of meningitis by Local Government Areas (LGAs),
Kano state, Nigeria, 1995- 1996
Attack rate of meningitis by age, Kano state, Nigeria, 1995- 1996
Conclusions and recommendations, meningitis outbreak, Kano state,
Nigeria, 1996 • Conclusion
– The meningitis epidemic remains heterogeneous– While some areas passed a peak – Many are still at an early stage
• Recommendation– Vaccinate in LGAs still early in the course of the
outbreak
A cholera outbreak during the meningitis epidemic, Kano state,
Nigeria, 1996
• Concurrent large cholera outbreak
• Use descriptive epidemiology to raise hypotheses?
• Use activated system to describe the outbreak
Stimulated passive surveillance methods for cholera, Kano state,
Nigeria, 1996 • Previous line listings used for cholera• Line listings sent to the state level• Additional “disease” variable created
– 1= Meningitis– 2= Cholera
• Short report with maps, curves and tables
Cholera cases by week of onset, Kano state, Nigeria, 1995- 1996
0
200
400
600
800
1000
1200
1400
95/4
995
/51
95/5
396
/02
96/0
496
/06
96/0
896
/10
96/1
296
/14
96/1
696
/18
96/2
0
Surveillance week
Nu
mb
er o
f ca
ses
Weekly rates of cholera by Local Government Areas (LGAs),
Kano state, Nigeria, 1995- 1996
Attack rate of cholera by age group, Kano state, Nigeria 1995-1996
Conclusions and recommendations, cholera outbreak, Kano state, Nigeria,
1996
• Conclusion– The epidemiological characteristics
suggest person to person transmission
– Subsequent case control study pointed to street-vended water and poor hygiene
• Recommendations– Promote hand washing
– Chlorinate water at the point of use
Lesson learnt in Kano, Nigeria, 1996
• Two outbreaks with different epidemiological features
• Two different decisions• Reliable or not, the data was used to make
decisions • Analyzing data made the surveillance system
useful
Key messages
• Analyze your data to make the system more useful
• Aggregate cases with the denominator in mind
• Merge numerator and denominator to calculate rates
• Compare for the time, place and person analysis
• Make data analysis a routine affair
• Don’t second guess data quality– Analyse the data to understand them
A Bhutanese village close to India
• Number of blood slides collected for malaria monitored over time
• Data analysed for 2001-4
Number of slides tested for malaria in Phuentsholing, Bhutan, 2001-2004
0
100
200
300
400
500
600
1 3 5 7 9 11 13 15 17 19 21 23 25 27 29 31 33 35 37 39 41 43 45 47 49 51
Weeks
Nu
mb
er
of
slid
es t
este
d f
or
mala
ria
2004
2003
2002
2001
The 2004 Dengue outbreak was detected
A 2003 falciparum outbreak was missed
