Post on 15-Jan-2016
description
Surveillance data management and transmission
Integrated Disease Surveillance
Programme (IDSP) district surveillance officers (DSO) course
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
Outline of the session
• Warming up case study1. Population under surveillance2. Reporting units3. Data transmission• Closing case study
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
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
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
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 onCase study
8
Surveillance: A systematic, ongoing process
• Data collection• Transmission• Analysis• Feedback• Action
Population
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
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
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
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
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 outbreakActive versus passive surveillance
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
Active and passive reporting
• Active reporting Health workers
• House visits
• Passive reporting All other reporting units
Reporting units
16
Routine data are reported weekly
• Email• Electronic• Fax• Messenger • Post• Telephone
Data transmission
17
Unusual events, outbreaks, clusters are reported
immediately
Data transmission
• Telephone• Fax• E-mail• Police wireless• Special messenger• Follow with written report
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
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
Outpatient register Inpatient
slip
Reporting unit
Case
Lab slip
Inpatient register
Lab registerCommon
reporting form P
Computer(District)
Form L
District public health
laboratory
District surveillance
officer
Feedback
Weekly
Weekly
Weekly
Immediately
+ve slides + sample -ves
21
Information flow of the weekly
surveillance systemSub-centres
P.H.C.s
C.H.C.s
Dist. hosp.
Programmeofficers
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
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
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
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
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
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
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
Closure case study
• Typhoid in Galore, Himachal Pradesh
• Interesting method of data compilation
Case study
29
Cases of typhoid fever admitted to primary health centre, Galore,
Himachal Pradesh, India May-June 1991 Cases by sex, village
Village Male Female Total
Lanjiana 22 31 53
Daswin 17 1 18
Pahal 1 2 3
Halti 2 3 5
Ghirmani 4 0 4
5 other villages 6 12 18
Total 52 49 101
Case study
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
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