Analysis and interpretation of data IDSP training module for state and district surveillance officers Module 9.

Analysis and interpretation of data

IDSP training module for state and district surveillance officers

Module 9

Learning objectives

• Identify the role, importance and techniques of data analysis

• Sources and management of data for valid conclusions

• Choose appropriate descriptive and analytical methods

• List outcome measures for feedback• Generate reports with tables and graphs

All levels must analyze surveillance data

• Health workers Increase of cases

• Medical officers in primary health centres Outbreak detection Seasonal trends

• District surveillance officers All of the above Advanced analyses

Selected outcomes of data analysis

• Identification of outbreaks / potential outbreaks• Identification of appropriate and timely control

measures• Prediction of changes in disease trends over time • Identification of problems in health systems• Improvement of the surveillance system

through: Identification of regional differences Identification of differences between the private and

the public sectors

• Identification of high-risk population groups

Sources of data

• Sub-Centre• Primary health centre• Community health centre• District• Private practitioners• Private nursing homes• Identified laboratories • Medical colleges• Police departments• State

Types of data

• Syndromic case data• Presumptive case data• Confirmed case data• Sentinel case data• Regular surveillance data• Urban data• Rural data

Periodicity of data collection

• Weekly• High priority (Acute flaccid paralysis)

As soon as a case is detected

• Data on outbreaks are collected and analyzed separately

Analysis of data at the district surveillance unit

• Computer software provides ready outputs• District surveillance officer prepares a report• Technical committee reviews and needs to

bear in mind: The strength and weakness of data collection

methods? Reliability and validity of data

The separate disease profiles The user-friendliness of graphs The need to calculate rates before comparisons

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

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

Basic surveillance data analysis

1. Count, divide and compare Direct comparisons between number of

cases are not possible in the absence of the calculation of the incidence rate

2. Descriptive epidemiologyA. TimeB. Place C. Person

1. Count, Divide and Compare (CDC)

• Count Count cases that meet the case definition

• Divide Divide cases by the population denominator

• Compare Compare rates across:

• Age groups• Districts• Etc.

2. Time, place and person descriptive analysis

A. Time Graph over time

B. Place Map

C. Person Breakdown by age, sex or personal

characteristics

A. Analysis over time

• Absolute number of cases Does not allow comparisons Analysis by week, month or year

• Incidence Allows comparisons Analysis by week, month or year

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 per week

Reported varicella and typhoid cases, Darjeeling district, West Bengal, India,

2000-4Figure 3: Reported varicella and typhoid cases, Darjeeling

district, WB, India, 2000-2004

1

10

100

1000

10000

100000

2000 2001 2002 2003 2004

Years

Number of cases (Log)

Typhoid

Varicella

Interpretation: The parallel increase between varicella (that should be constant) and typhoid suggests that increasing

rates of typhoid are secondary to improved reporting

Incidence by year

2. Analysis by place

• Number of cases by village or district Does not control for population size Spot map

• Incidence of cases by village or district Controls for population size Incidence map

Mangalore

Nallur

Vridha-chalam

Kattumannar Kail Kumaratchi

Parangipattai

Kamma-puram

Panruti

Cuddalore

Annagraman

Kurinjipadi

Bhuvanagiri

Keerapalayam

Interpretation: Cases were reported from tsunami affected

non-affected areas, thus the cluster was not a consequence of

the tsunami

Reported cases of measles, Cuddalore district, Tamil Nadu, Dec

2004 – Jan 2005

Spot map of absolute number of cases

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

3. Analysis per person

• Distribution of cases by: Age Sex Other characteristics

(e.g., Ethnic group, vaccination status)

• Incidence by: Age Sex Other characteristics

81%

19%

Immunized Unimmunized

Immunization status of probable measles cases, Nai, Uttaranchal,

India, 2004

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

Distribution of cases according to a characteristic

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

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

Report 1: Completeness and timeliness

• A report is said to be 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

Interpretation of timeliness and completeness

Scenario Interpretation

Reporting unit A is timely and complete

•Ideal

Reporting unit B timely but regularly incomplete

•Medical officer of B understands the importance•Sort out problem of non reporting sites

Reporting unit C is late but complete

•Medical officer C don’t understand the importance of timeliness. He needs to be educated

Reporting unit D is late and incomplete

•Major problem. Urgent action required

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 and deaths are

sufficient for a single reporting unit level Incidence rates are required to compare

reporting units

Epidemiological indicators to use in weekly / monthly summary report

• Cases• Deaths• Incidence rate• Case fatality ratio

Report 3: Comparison with previous weeks/ months/ years

• Help detect 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

Acute hepatitis by week of onset in 3 villages, Bhimtal block, Uttaranchal,

India, July 2005

0

10

20

30

40

50

60

70

80

901s

t w

eek

2nd

w

eek

3rd

we

ek

4th

wee

k

1st

wee

k

2nd

w

eek

3rd

we

ek

4th

wee

k

1st

wee

k

2nd

w

eek

3rd

we

ek

4th

wee

k

1st

wee

k

2nd

w

eek

3rd

we

ek

4th

wee

k

1st

wee

k

May June July August September

Week of onset

Num

ber

of c

ases

Interpretation: The second week of July has a clear excess in the number of cases, providing an early warning signal for the

outbreak

Example of weekly analysis

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

Example of monthly and yearly analysis

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

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)

Report 5: Comparison between reporting units

• Compares Incidence rates Case fatality ratios

• Reference period Current month

• Sites concerned Block level and above

Interpretation of the comparison between reporting units

Scenario Interpretation

Incidence rate and case fatality ratio in various reporting units are similar

•May be indicative of good reporting mechanism

Markedly low incidence rate and case fatality ratio in a reporting unit

•Quality of data needs review•Possibility of under-reporting

Markedly high incidence rate and case fatality ratio in a reporting unit

•Quality of data needs review•Possibility of an outbreak•Possibility of a data entry error

Report 6: Comparison between public and private sectors

• Compare trends in incidence 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

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

Frequency of reports and analysisReports Daily Weekly Monthly Yearly

Report 1:Timeliness/completeness

Report 2: Description

Report 3:Trends over time

Report 4:Threshold levels

Report 5:Compare reporting units

Report 6:Compare private / public

Report 7:Compare with laboratory

Review of analysis results by the technical committee

• Meeting on a fixed day of every week• Review of a minimum of:

4 reports weekly 7 reports monthly

• Review by disease wise • Search for missing values• Check the validity• Interpret • Prepare summary reports and share• Take action

Limitations in analysis of surveillance data

• The quality of data may be problematic Poor use of case definition Under-reporting

• There may be a time lag between detection, reporting and analysis

• Under-reporting occurs However, if the level of under-reporting is constant,

trends may still be analyzed and outbreaks may still be detected

• The representativeness may be poor Engage the private sector to diversify reporting sources

Conclusion

• Analysis is a major component of surveillance – links data collection and program implementation

• While it is important to analyze data, its also important that analyzed reports are sent to the appropriate authorities Higher level Lower level

Points to remember

• Surveillance data identifies outbreaks and describe conditions by time, place and person

• Surveillance helps monitor disease control and assess the impact of services

• Data analysis must occur at each level• Analyzed data is presented in tables,

graphs with comparisons with previous data