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

2. 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

3. 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

4. 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

5. Sources of data Sub-Centre Primary health centre Community health centre District Private practitioners Private nursing homes Identified laboratories Medical colleges Police departments State

6. Types of data Syndromic case data Presumptive case data Confirmed case data Sentinel case data Regular surveillance data Urban data Rural data

7. 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

8. 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

9. 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

10. 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

11. 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 epidemiology A.Time B.Place C.Person

12. 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.

13. 2. Time, place and person descriptive analysis A.Time  Graph over time B.Place  Map C.Person  Breakdown by age, sex or personal characteristics

14. 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

15. Acute hepatitis (E) by week, Hyderabad, AP, India, March-June 2005 0 20 40 60 80 100 120 1815222941219263101724317142128 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

16. 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

17. 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

18. 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

19. 20-49 50-99 100+ 1-19 0 Attack rate per 100,000 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

20. 3. Analysis per person Distribution of cases by:  Age  Sex  Other characteristics (e.g., Ethnic group, vaccination status) Incidence by:  Age  Sex  Other characteristics

21. 81% 19% ImmunizedUnimmunized 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

22. Probable cases of cholera by age and sex, Parbatia, Orissa, India, 2003 NumberofcasesPopulationIncide e 0 to461135.3% 5 to1441902.1% 15to2451283.9% 25 3451443.5% 35 4461294.7% 45 544884.5% 55 6486711.9% Age group (Inyears) > 653873.4% Male174813.5%Sex Female244655.2% TotalTotal419464.3% Interpretation: Older adults and women are at increased risk of cholera Incidence according to a characteristic

23. Seven reports to be generated 1.Timeliness/completeness 2.Description by time, place and person 3.Trends over time 4.Threshold levels 5.Compare reporting units 6.Compare private / public 7.Compare providers with laboratory

24. 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

25. Interpretation of timeliness and completeness ScenarioInterpretation 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

26. 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

27. Epidemiological indicators to use in weekly / monthly summary report Cases Deaths Incidence rate Case fatality ratio

28. 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

29. 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 week4th week 1st week 2nd week 3rd week4th week 1st week MayJuneJulyAugustSeptember 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

30. 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 NovemberDecember January February March April May June July August September October NovemberDecember January February March April May June July August September October NovemberDecember January February March April May June July August September October NovemberDecember January February March April May June July August September October NovemberDecember 20002001200220032004 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

31. 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)

32. Report 5: Comparison between reporting units Compares  Incidence rates  Case fatality ratios Reference period  Current month Sites concerned  Block level and above

33. Interpretation of the comparison between reporting units ScenarioInterpretation 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

34. 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

35. Report 7: Comparison of reports between the public health system and the laboratory Elements to compare Public health systemLaboratories 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

36. Frequency of reports and analysis ReportsDailyWeeklyMonthlyYearly 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 

37. 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

38. 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

39. 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

40. 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