IDSP 9 www.medicos11.com : IDSP 9 www.medicos11.com
Analysis and interpretation of data : Analysis and interpretation of data IDSP training module for state and district surveillance officers
Module 9
Learning objectives : 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 : 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 : 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 : 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 : 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 : 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 : 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 : 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 : 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 : Basic surveillance data analysis Count, divide and compare
Direct comparisons between number of cases are not possible in the absence of the calculation of the incidence rate
Descriptive epidemiology
Time
Place
Person
1. Count, Divide and Compare (CDC) : 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 : 2. Time, place and person descriptive analysis Time
Graph over time
Place
Map
Person
Breakdown by age, sex or personal characteristics
A. Analysis over time : 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 : 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 Number of cases 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, 2000-4 : Reported varicella and typhoid cases, Darjeeling district, West Bengal, India, 2000-4 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 : 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
Reported cases of measles, Cuddalore district, Tamil Nadu, Dec 2004 – Jan 2005 : 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
Incidence of acute hepatitis (E) by block, Hyderabad, AP, India, March-June 2005 : 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 : 3. Analysis per person Distribution of cases by:
Age
Sex
Other characteristics(e.g., Ethnic group, vaccination status)
Incidence by:
Age
Sex
Other characteristics
Slide 22 : 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, 2003 : Probable cases of cholera by age and sex, Parbatia, Orissa, India, 2003 Nu m b e r of c a s es Po pu l a t i on I nc i d e nc e 0 t o4 6 1 1 3 5 . 3 % 5 t o1 4 4 1 9 0 2 . 1 % 1 5 to 2 4 5 1 2 8 3 . 9 % 2 5 to 3 4 5 1 4 4 3 . 5 % 3 5 to 4 4 6 1 2 9 4 . 7 % 4 5 to 5 4 4 8 8 4 . 5 % 5 5 to 6 4 8 6 7 1 1 . 9 % A g e g r o up ( In y e ar s ) > 6 5 3 8 7 3 . 4 % M a l e 1 7 4 8 1 3 . 5 % S ex F e m a l e 2 4 4 6 5 5 . 2 % Tot al T ot a l 4 1 9 4 6 4 . 3 % Interpretation: Older adults and women are at increased risk of cholera Incidence according to a characteristic
Seven reports to be generated : Seven reports to be generated Timeliness/completeness
Description by time, place and person
Trends over time
Threshold levels
Compare reporting units
Compare private / public
Compare providers with laboratory
Report 1: Completeness and timeliness : 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 : Interpretation of timeliness and completeness
Report 2: Weekly/ monthly summary report : 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 : Epidemiological indicators to use in weekly / monthly summary report Cases
Deaths
Incidence rate
Case fatality ratio
Report 3: Comparison with previous weeks/ months/ years : 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 : Acute hepatitis by week of onset in 3 villages, Bhimtal block, Uttaranchal, India, July 2005 0 10 20 30 40 50 60 70 80 90 1st week 2nd week 3rd week 4th week 1st week 2nd week 3rd week 4th week 1st week 2nd week 3rd week 4th week 1st week 2nd week 3rd week 4th week 1st week May June July August September Week of onset Number of cases 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 : Malaria in Kurseong block, Darjeeling District, West Bengal, India, 2000-2004 0 5 10 15 20 25 30 35 40 45 January February March April May June July August September October November December January February March April May June July August September October November December January February March April May June July August September October November December January February March April May June July August September October November December January February March April May June July August September October November December 2000 2001 2002 2003 2004 Months Incidence of malaria 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 : 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 : 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 : Interpretation of the comparison between reporting units
Report 6: Comparison between public and private sectors : 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 : Report 7: Comparison of reports between the public health system and the laboratory
Frequency of reports and analysis : Frequency of reports and analysis
Review of analysis results by the technical committee : 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 : 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 : 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 : 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