Date post: | 25-Dec-2015 |
Category: |
Documents |
Upload: | laura-caldwell |
View: | 223 times |
Download: | 2 times |
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