Surveillance data management and transmission

Integrated Disease Surveillance Programme (IDSP) district surveillance officers (DSO) course

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 • • 1.

2.

3.

Warming up case study Population under surveillance Reporting units Data transmission Closing case study 3

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?

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“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, 1988-91 Month Jan Feb Mar Apr May Jun Jul Aug Slides 1988 414 337 278 334 293 211 326 1009 Positive 2 0 0 0 20 0 0 0 Slides 1989 276 287 263 408 283 324 345 1602 Sep Oct Nov Dec 830 650 438 353 22 0 0 1 1492 862 333 279 Total 5473 45 6754 *1227 Slides still to be examined Positive 5 1 0 0 0 15 1 0 0 0 4 0 1 8 Slides 1990 Positive 273 348 341 252 229 323 550 1440 941 497 289 295 5778 9 0 0 0 14 0 0 0 0 0 0 0 5 Slides 1991 Positive 267 234 259 443 347 372 483 1001 2036 3187 * 8629* 19 104 0 0 0 0 0 0 0 7 130

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 on

Case study

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

Population

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 Urban • Sub-centres (SCs) • Primary health centres (PHCs) and block PHCs • Community health centres (CHCs) • Sub-district/district hospitals • Indian medicine units • Dispensaries • Urban hospitals • Public health labs • ESI/Railways/Defence facilities • Medical colleges 11 • Practitioners • Hospitals • Nursing homes • Hospitals • Medical colleges • Laboratories

Reporting units

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 outbreak

Active 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 15

Reporting units

Routine data are reported weekly • • • • • • Email Electronic Fax Messenger Post Telephone

Data transmission

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

Data transmission

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Quality check before reporting 1.

2.

3.

Filling of forms by health care workers Review by senior staff  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

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Case Lab slip

Immediately

Lab register

+ve slides + sample -ves

Form L Reporting unit Outpatient register

Weekly

Common reporting form P

Weekly Weekly

District public health laboratory Computer (District) Feedback Inpatient slip Inpatient register District surveillance officer

Information flow of the weekly surveillance system Sub-centres Programme officers P.H.C.s

S.S.U.

C.S.U.

C.H.C.s

Dist. hosp.

Med. col.

P.H. lab.

D.S.U.

Other Hospitals: ESI, Municipal Rly., Army etc.

Pvt. practitioners Nursing homes Private hospitals Private labs.

Corporate hospitals

Regular reporting in Integrated Disease Surveillance Programme (IDSP) Day of the week Required activity Monday Tuesday 22 • Primary health centre reports to community health centre • Community health centre reports to district

Data transmission

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.

2.

3.

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

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

Case study

Village Cases of typhoid fever admitted to primary health centre, Galore, Himachal Pradesh, India May-June 1991 Cases by sex, village Male Female Total Lanjiana Daswin Pahal Halti 22 17 1 2 31 1 2 3 53 18 3 5 Ghirmani 5 other villages 4 6 0 12 4 18 Total 52 49

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 31