1. Surveillance data management and transmission Integrated Disease Surveillance Programme (IDSP) district surveillance officers (DSO) course

2. 2 Preliminary questions to the group Were you already involved in a data management and transmission? If yes, what difficulties did you face? What would you like to learn about data management and transmission?

3. 3 Outline of the session Warming up case study 1.Population under surveillance 2.Reporting units 3.Data transmission Closing case study

4. 4 Warming up case study Malaria outbreak, Uttar Pradesh, India, October 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”  Data collected from the malaria form  No compilation of the data Data compiled by the visitor Look at the table and observe Case study

5. 5 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

6. 6 Observations and some interpretations People tend to collect more slides from August to October, each year Collection of slides and positive slides increased in 1991 Why did the local medical officer did not observe anything?  The medical officer did not compile the data  Failure to do so prevented the medical officer to make any comparisons Case study

7. 7 Epilogue Compiled data presented to the medical officer Medical officer agreed that there was a problem of malaria Unless you compile your data, you cannot detect problems Compiling is the number one step (“Count”)  “Dividing” and “Comparing” with time, place and person analysis further transform data in information Compile the data before you pass it on Case study

8. 8 Surveillance: A systematic, ongoing process Data collection Transmission Analysis Feedback Action Population

9. 9 Surveillance in the general population The surveillance system tries to captures events in the whole population All health care facilities report cases Census data may be used to:  Estimate population denominators  Calculate rates Example:  India’s Integrated Disease Surveillance Programme (IDSP) in public health care facilities Population

10. 10 Sentinel surveillance The surveillance system only captures events in selected spots Chosen health care facilities report cases  Sentinel sites No population denominators may be used to calculate rates Example:  Sentinel HIV surveillance  India’s Integrated Disease Surveillance Programme (IDSP) in the private sector Population

11. 11 Reporting units for disease surveillance Public sector (Exhaustive) Private (Sentinel) Rural Sub-centres (SCs) Primary health centres (PHCs) and block PHCs Community health centres (CHCs) Sub-district/district hospitals Indian medicine units Practitioners Hospitals Urban Dispensaries Urban hospitals Public health labs ESI/Railways/Defence facilities Medical colleges Nursing homes Hospitals Medical colleges Laboratories Reporting units

12. 12 Passive surveillance Health care facilities or providers report cases as they present in health care facilities No specific efforts are made to make sure all cases are reported Surveillance is integrated to routine health care delivery Example:  Surveillance of measles in India Active versus passive surveillance

13. 13 Stimulated passive surveillance Health care facilities or providers report cases as they present in health care facilities Special efforts made to maximize reporting  Reminders, visits Surveillance remains integrated to routine health care delivery Example:  Surveillance of acute flaccid paralysis in India  Stimulated surveillance during an outbreak Active versus passive surveillance

14. 14 Active surveillance The system does not wait for:  Case-patients to come to health care facilities  Health care facilities to report cases Health care workers actively reach out to detect cases Surveillance comes in addition to routine health care delivery Example:  Malaria surveillance in India Active versus passive surveillance

15. 15 Active and passive reporting Active reporting  Health workers House visits Passive reporting  All other reporting units Reporting units

16. 16 Routine data are reported weekly Email Electronic Fax Messenger Post Telephone Data transmission

17. 17 Unusual events, outbreaks, clusters are reported immediately Data transmission Telephone Fax E-mail Police wireless Special messenger Follow with written report

18. 18 Quality check before reporting 1.Filling of forms by health care workers 2.Review by senior staff 3.Transmission to the higher level  Copy kept in the facility Data transmission

19. 19 Zero reporting Do not mix up:  Zero  Missing information Zero reporting is mandatory to confirm that the condition was looked for and not found Data transmission

20. Outpatient register Inpatient slip Reporting unit Case Lab slip Inpatient register Lab register Common reporting form P Computer (District) Form L District public health laboratory District surveillance officer Feedback Weekly Immediately +ve slides + sample -ves

21. 21 Information flow of the weekly surveillance system Sub-centres P.H.C.s C.H.C.s Dist. hosp. Programme officers Pvt. practitioners D.S.U. P.H. lab. Med. col. Other Hospitals: ESI, Municipal Rly., Army etc. S.S.U. C.S.U. Nursing homes Private hospitals Private labs. Corporate hospitals

22. 22 Regular reporting in Integrated Disease Surveillance Programme (IDSP) Community health centre reports to district Tuesday Primary health centre reports to community health centre Monday Required activityDay of the week Data transmission

23. 23 Data manager at the district level Receives data from reporting units Enters data into computer Checks data validity Generates reports Submits report to surveillance officer Prepares a report summarizing the analysis Submits report to state surveillance officer and state surveillance unit Data transmission

24. 24 Each level analyzes data at its level Reporting units  COUNT: Compilation, Detection of thresholds District level  DIVIDE: Calculation of rates  COMPARE: Time, place and person analysis State levels  Advanced analyses More complex analyses No need to wait for feedback from the upper level : All levels analyze data Data transmission

25. 25 Each level use the information for action at its level Reporting units  Investigate an outbreak District level  Focus resources on an area with high incidence State levels  Re-design a programme to meet changing needs More complex decisions No need to wait for instructions from the upper level : All levels make decisions Data transmission

26. 26 Example of decisions made on the basis of surveillance data at each level Lower level  Outbreak investigation following a cluster detected at the periphery level Intermediate level  Supplemental immunization campaign following persisting transmission in an area at the intermediate level Higher level  Programme modifications because of changing epidemiology of a disease in the state Data transmission

27. 27 Take home messages 1.Exhaustive surveillance is connected to denominators, sentinel surveillance is not 2.Regular, timely data transmission and nil reporting are vital to an effective surveillance system 3.Analyze the data as you pass it on to make the system alive at all levels

28. 28 Closure case study Typhoid in Galore, Himachal Pradesh Interesting method of data compilation Case study

29. 29 Cases of typhoid fever admitted to primary health centre, Galore, Himachal Pradesh, India May-June 1991 Cases by sex, village VillageMaleFemaleTotal Lanjiana223153 Daswin17118 Pahal123 Halti235 Ghirmani404 5 other villages61218 Total5249101 Case study

30. 30 So where did the typhoid come from? What is special about this compilation?  Distribution by sex Predominance of males in one village, not in another The data tells something:  But to hear it, you need to compile it  The outbreak was caused by drinking water served at a wedding held in Lanjiana (male and female affected)  Only male family members from the bride groom family who was from Daswin came to the wedding (Local custom)  The sex distribution gives you a clue for the cause of the outbreak Case study

31. 31 Additional reading Section 2 and 3 of IDSP operations manual Module 5 of training manual Format and guidelines for reporting of information on disease surveillance (electronic manual) IDSP manual