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Getting the data to speak Analysis of surveillance data.

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Presentation on theme: "Getting the data to speak Analysis of surveillance data."— Presentation transcript:

1 Getting the data to speak Analysis of surveillance data

2 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 3 Malaria in primary health centre, Jalalabad, Uttar Pradesh, India, 1988-91 1988198919901991 Month SlidesPositiveSlidesPositiveSlidesPositiveSlidesPositive Jan4140276127302670 Feb3370287034802340 Mar2780263034102590 Apr3342408025204430 May2930283422903470 Jun2110324032303720 Jul3260345155004830 Aug100920160281440510017 Sep83022149219419203619 Oct6500862049703187 *104 Nov438033302890 Dec353127902950 Total5473456754155778148629*130 *1227 Slides still to be examined

4 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

5 Key issues Rationale Counting Dividing Comparing Ongoing analysis

6 Why analyse surveillance data? Turn data into information Use information for action Close the surveillance loop Assess data quality

7 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

8 Individual and aggregated data IDPlaceCount 1Block A12 2Block B1 3Block C35 4Block D24 IDPlaceAgeSexOnset 1Block A311 Jan 06 2Block A121 Jan 06 3Block C3523 Jan 06 4Block D6714 Jan 06 …………… Individual line listing Aggregated file

9 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

10 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

11 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

12 Example: Aggregating a line listing by place IDPlaceCount 1Block A12 2Block B1 3Block C35 4Block D24 IDPlaceAgeSexOnset 1Block A311 Jan 06 2Block A121 Jan 06 3Block C3523 Jan 06 4Block D6714 Jan 06 …………… Individual line listing Aggregated file Follow the same procedure for time and person variables

13 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

14 Merging numerator and denominator files to calculate incidences IDPlaceCount 1Block A12 2Block B1 3Block C35 4Block D24 PlacePopulation Block A3,567 Block B1,231 Block C2,745 Block D724 IDPlaceCountPopulationIncidence 1Block A123,567xxx 2Block B11,231xxx 3Block C352,745xxx 4Block D24724xxx Aggregated numerator fileDenominator file Files merged side to side Incidence may now be calculated

15 Compare Time –Incidence over time Place –Incidence between different geographical areas Person –Incidence between different population groups

16 0 5 10 15 20 25 30 35 40 45 468 1012141618202224262830 2468 10 April 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

17 Distribution of diarrhoea cases by households, Garulia, West Bengal, India, 2006 Index case Household with 1 case Household with 2-3 cases Household with 4-5 cases Household with 6+ cases Water pipeline Road Overhead tank Leakage point PLACE= Map

18 Attack rate of diarrhoea by age and sex, Garulia, West Bengal, India, 2006 CharacteristicsNumber of cases Population, 2006 Attack rate per 1000 Age0 - 4 518,0306.4 5 -14 6820,0663.4 15 - 24 3915,4932.5 25 - 34 4214,1073.0 35 - 44 4211,1913.8 45 + 5615,6373.6 GenderMale 15843,7163.6 Female 14040,8093.4 Total298 84,5253.5 PERSON = Table

19 Analysis at each level Lower level –Count –Detect clusters –Threshold analysis Intermediate analysis –Rates –Time, place and person Higher level –Advanced analyses

20 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

21 Public health emergency in Kano state, Nigeria, 1996 An example

22 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

23 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

24 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 95/4995/5095/5195/5296/0196/0296/0396/0496/0596/0696/0796/0896/0996/10 Weeks Incidence per 100,000 Nassarawa Dawakin-Tofa Bagwai

25 Weekly incidence of meningitis by Local Government Areas (LGAs), Kano state, Nigeria, 1995- 1996

26 Attack rate of meningitis by age, Kano state, Nigeria, 1995- 1996

27 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

28 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

29 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

30 Cholera cases by week of onset, Kano state, Nigeria, 1995- 1996 0 200 400 600 800 1000 1200 1400 95/4995/5195/5396/0296/0496/0696/0896/1096/1296/1496/1696/1896/20 Surveillance week Number of cases

31 Weekly rates of cholera by Local Government Areas (LGAs), Kano state, Nigeria, 1995- 1996

32 Attack rate of cholera by age group, Kano state, Nigeria 1995-1996

33 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

34 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

35 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 Dont second guess data quality –Analyse the data to understand them

36 A Bhutanese village close to India Number of blood slides collected for malaria monitored over time Data analysed for 2001-4

37 Number of slides tested for malaria in Phuentsholing, Bhutan, 2001-2004 0 100 200 300 400 500 600 13579 111315171921232527293133353739414345474951 Weeks Number of slides tested for malaria 2004 2003 2002 2001 The 2004 Dengue outbreak was detected A 2003 falciparum outbreak was missed


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