Surveillance data management and transmission

Integrated Disease Surveillance

Programme (IDSP) district surveillance officers (DSO) course

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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?

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Outline of the session

• Warming up case study1. Population under surveillance2. Reporting units3. Data transmission• Closing case study

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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

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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

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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

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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

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Surveillance: A systematic, ongoing process

• Data collection• Transmission• Analysis• Feedback• Action

Population

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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

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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

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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

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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

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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

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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

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Active and passive reporting

• Active reporting Health workers

• House visits

• Passive reporting All other reporting units

Reporting units

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Routine data are reported weekly

• Email• Electronic• Fax• Messenger • Post• Telephone

Data transmission

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Unusual events, outbreaks, clusters are reported

immediately

Data transmission

• Telephone• Fax• E-mail• Police wireless• Special messenger• Follow with written report

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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

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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

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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

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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

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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

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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

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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

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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

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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

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Closure case study

• Typhoid in Galore, Himachal Pradesh

• Interesting method of data compilation

Case study

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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

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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

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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