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Analysis and interpretation of surveillance data Integrated Disease Surveillance Programme (IDSP)...

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Analysis and interpretation of surveillance data Integrated Disease Surveillance Programme (IDSP) district surveillance officers (DSO) course
Transcript

Analysis and interpretation of surveillance data

Integrated Disease Surveillance Programme (IDSP) district surveillance

officers (DSO) course

2

Preliminary questions to the group

• Have you been involved in surveillance data analysis?

• What difficulties have you encountered in analyzing surveillance data?

• What would you like to learn about surveillance data analysis?

3

Outline of this session

1. The concept of data analysis2. CDC for TPP3. Reports4. Interpretation of the information

4

What is data analysis?

• Data reduction Reduces the quantity of numbers to examine

Because the human mind cannot handle too many bits of information at the same time

• Transforms raw data into information A list of cases becomes a monthly rate

Why analyze?

Data Information Action

Analysis Interpretation

Today we will focus on analysis

5

REC SEX--- ---- 1 M 2 M 3 M 4 F 5 M 6 F 7 F 8 M 9 M 10 M 11 F 12 M 13 M 14 M 15 F 16 F 17 F 18 M 19 M 20 M 21 F 22 M 23 M 24 F 25 M 26 M 27 M 28 F 29 M 30 M

Sex Frequency Proportion

Female 10 33.3%

Male 20 66.7%

Total 30 100.0%

FemaleMale

Data

Information

Distribution of cases by sex

Table

Graph

Why analyze?

Analysis

6

1. Count, Divide and Compare (CDC): An epidemiologist

calculates rates and compare them

• Direct comparisons of absolute numbers of cases are not possible in the absence of rates

• CDC Count

• Count (compile) cases that meet the case definition

Divide• Divide cases by the corresponding population denominator

Compare• Compare rates across age groups, districts etc.

CDC for TPP

7 CDC for TPP

Exercise

• How would you find out if diphtheria is more common among people who are below the poverty line?

8 CDC for TPP

Is diphtheria more common among poorer people?

• Count Count cases of diphtheria among families with and without a Below Poverty Line (BPL) card

• Divide Divide the cases of diphtheria among BPL people by the estimated BPL population size (e.g., census) to get the rate

Divide the cases of diphtheria among non BPL people by the estimated non BPL population size (e.g., census) to get the rate

• Compare Compare the rates of diphtheria among BPL and non BPL people

9

2. Time, place and person descriptive analysis

A. Time Incidence over time (Graph)

B. Place Map of incidence by area

C. Person Breakdown by age, sex or personal

characteristics Table of incidence by age and sex

CDC for TPP

10

A. Present the results of the analysis over time using a

GRAPH• Absolute number of cases

Avoid analysis over longer time period as the population size increases

• Incidence rates Allows analysis over longer time period

Analysis by week, month or year

CDC for TPP

11

Acute hepatitis (E) by week, Hyderabad, AP, India, March-

June 2005

0

20

40

60

80

100

120

1 8 15 22 29 4 12 19 26 3 10 17 24 31 7 14 21 28

Num

ber

of

case

s

March April May June First day of week of onset

Interpretation: The source of infection is persisting and continues to cause cases

Absolute number of cases for analysis over a short time period

CDC for TPP

12

Malaria in Kurseong block, Darjeeling District, West Bengal, India, 2000-2004

0

5

10

15

20

25

30

35

40

45

Janu

ary

Feb

ruar

y

Mar

chA

pril

May

June

July

Aug

ust

Sep

tem

ber

Oct

ober

Nov

embe

r

Dec

embe

rJa

nuar

yF

ebru

ary

Mar

chA

pril

May

June

July

Aug

ust

Sep

tem

ber

Oct

ober

Nov

embe

r

Dec

embe

r

Janu

ary

Feb

ruar

yM

arch

Apr

ilM

ayJu

neJu

lyA

ugus

t

Sep

tem

ber

Oct

ober

Nov

embe

r

Dec

embe

rJa

nuar

y

Feb

ruar

yM

arch

Apr

ilM

ay

June

July

Aug

ust

Sep

tem

ber

Oct

ober

Nov

embe

rD

ecem

ber

Janu

ary

Feb

ruar

yM

arch

Apr

il

May

June

July

Aug

ust

Sep

tem

ber

Oct

ober

Nov

embe

rD

ecem

ber

2000 2001 2002 2003 2004

Months

Inci

denc

e of

mal

aria

per

10,

000 Incidence of malaria

Incidence of Pf malaria

Interpretation: There is a seasonality in the end of the year and a trend towards increasing incidence year after

yearReports

Incidence rates for analysis over a longer time period

13

2. Present the results of the analysis by place using a MAP

• Number of cases Spot map Does not control for population size Concentration of dots may represent high population density only

May be misleading in areas with heterogeneous population density (e.g., urban areas)

• Incidence rates Incidence rate map Controls for population size

CDC for TPP

14

20-49

50-99

100+

1-19

0

Attack rate per100,000 population

Pipeline crossing open sewage drain

Open drain

Incidence of acute hepatitis (E) by block, Hyderabad, AP, India,

March-June 2005

Interpretation: Blocks with hepatitis are those supplied by pipelines

crossing open sewage drains

Incidence by area

15

3. Present the results of the analysis per person using an

incidence TABLE• Distribution of cases by:

Age Sex Other characteristics(e.g., ethnic group, vaccination status)

• Incidence rate by: Age Sex Other characteristics

CDC for TPP

16

Probable cases of cholera by age and sex, Parbatia, Orissa,

India, 2003Number of cases Population Incidence

0 to4 6 113 5.3%5 to14 4 190 2.1%15 to24 5 128 3.9%25 to34 5 144 3.5%35 to44 6 129 4.7%45 to54 4 88 4.5%55 to64 8 67 11.9%

Age group(In years)

> 65 3 87 3.4%Male 17 481 3.5%SexFemale 24 465 5.2%

Total Total 41 946 4.3%

Interpretation: Older adults and women are at increased risk of cholera

Incidence according to a characteristic

CDC for TPP

81%

19%

Immunized Unimmunized

Immunization status of measles cases, Nai, Uttaranchal,

India, 2004

Interpretation: The outbreak is probably caused by a failure to vaccinate

Distribution of cases according to a characteristic

CDC for TPP

18

Seven reports to be generated

1.Timeliness/completeness2.Description by time, place and

person3.Trends over time4.Threshold levels5.Compare reporting units6.Compare private / public7.Compare providers with laboratory

Reports

19

Report 1: Completeness and timeliness

• A report is considered on time if it reaches the designated level within the prescribed time period Reflects alertness

• A report is said to be complete if all the reporting units within its catchment area submitted the reports on time Reflects reliability

Reports

20

Report 2: Weekly/ monthly summary report

• Based upon compiled data of all the reporting units

• Presented as tables, graphs and maps

• Takes into account the count, divide and compare principle: Absolute numbers of cases, deaths and case fatality ratio are sufficient for a single reporting unit level

Incidence rates are required to compare reporting units

Reports

21

Report 3: Comparison with previous weeks/ months/ years

• Help examine trend of diseases over time

• Weekly analysis compare the current week with data from the last three weeks Alerts authorities for immediate action

• Monthly and yearly analysis examine: Long term trends Cyclic pattern Seasonal patterns

Reports

22

Report 4: Crossing threshold values

• Comparison of rates with thresholds• Thresholds that may be used:

Pre-existing national/international thresholds

Thresholds based on local historic data • Monthly average in the last three years (excluding epidemic periods)

Increasing trends over a short duration of time (e.g., Weeks)

Reports

23

Report 5: Comparison between reporting units

• Compares Incidence rates Case fatality ratios

• Reference period Current month

• Sites concerned Block level and above

Reports

24

Report 6: Comparison between public and private sectors

• Compare trends in number of new cases/deaths Incidences are not available for private provider since no population denominators are available

• Good correlation may imply: The quality of information is good Events in the community are well represented

• Poor correlation may suggest: One of the data source is less reliable

Reports

25

Report 7: Comparison of reports between the public

health system and the laboratory

Elements to compare

Public health system

Laboratories

Validation of reporting

•Number of cases seen by providers

•Number of laboratory diagnoses

Water borne disease

•Cases of diarrheal diseases

•Water quality

Vector borne disease

•Cases of vector borne diseases

•Entomological data

Reports

26 Interpretation

Making sense of different sources of information (“S”

and “P” forms) It is not possible to mix data from different case definitions One cannot add cases coming from “S” and “P” forms (syndromic and presumptive diagnoses)

It is not possible to add apples and orangesUse the different sources of information to cross validate (or “triangulate”) If there is an increase in the cases of dengue in the “P” forms, check if there is a surge in the number of fever cases in the “S” forms

27

What computers cannot do

Skills• Contact reporting units for missing information

• Interpret laboratory tests

• Make judgment about: Epidemiologic linkage Duplicate records Data entry errors

• Declare a state of outbreak

Attitudes• Looking• Thinking • Discussing• Taking action

Interpretation

28

Expressed concerns versus reality

Concerns commonly expressed

• Statistics are difficult

• Multivariate analysis is complex

• Presentation of data is challenging

Mistake commonly observed

• Data are not looked at

Interpretation

29

Review of analysis results by the technical committee

• Meeting on a fixed day of the week• Search for missing values• Validity check• Interpretation of the analysis bearing in mind The strength and weakness of data The disease profiles The need to calculate rates before comparisons Meeting on a fixed day of every week

• Summary reports for dissemination• Action

Interpretation

30

Take home messages

1. Link data collection and program implementation• Data > Information > Action

2. Count, divide and compare for time, place and person description

3. Share information through reports4. Interpret with the technical

committee to decide action on the basis of the information


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