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Analysis and interpretation of surveillance data

Analysis and interpretation of surveillance data

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Analysis and interpretation of surveillance data

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  1. 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? 2

  3. Outline of this session • The concept of data analysis • CDC for TPP • Reports • Interpretation of the information 3

  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 Action Data Information Interpretation Analysis 4 Why analyze? 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 Distribution of cases by sex Table Data Analysis Information Graph 5 Why analyze?

  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. 6 CDC for TPP

  7. Exercise • How would you find out if diphtheria is more common among people who are below the poverty line? 7 CDC for TPP

  8. 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 8 CDC for TPP

  9. 2. Time, place and person descriptive analysis • Time • Incidence over time (Graph) • Place • Map of incidence by area • Person • Breakdown by age, sex or personal characteristics • Table of incidence by age and sex 9 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 10 CDC for TPP

  11. Absolute number of cases for analysis over a short time period Acute hepatitis (E) by week, Hyderabad, AP, India, March-June 2005 120 100 80 Number of cases 60 40 20 0 1 8 15 22 29 4 12 19 26 3 10 17 24 31 7 14 21 28 March April May June First day of week of onset Interpretation: The source of infection is persisting and continues to cause cases 11 CDC for TPP

  12. Incidence rates for analysis over a longer time period Malaria in Kurseong block, Darjeeling District, West Bengal, India, 2000-2004 45 40 Incidence of malaria 35 Incidence of Pf malaria 30 25 Incidence of malaria per 10,000 20 15 10 5 0 May July July July July July May May May May April April April April April June June June June June February March March March March March August August August August August January October January October January October January October January October February February February February November December November December November December November December November December September September September September September 2000 2001 2002 2003 2004 Months Interpretation: There is a seasonality in the end of the year and a trend towards increasing incidence year after year 12 Reports

  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 13 CDC for TPP

  14. Incidence by area Incidence of acute hepatitis (E) by block, Hyderabad, AP, India, March-June 2005 Attack rate per100,000 population 0 1-19 20-49 50-99 100+ Open drain Interpretation: Blocks with hepatitis are those supplied by pipelines crossing open sewage drains Pipeline crossing open sewage drain 14

  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 15 CDC for TPP

  16. Incidence according to a characteristic 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 A g e g r o up 0 t o4 6 1 1 3 5 . 3 % ( In y e ar s ) 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 % > 6 5 3 8 7 3 . 4 % S ex M a l e 1 7 4 8 1 3 . 5 % 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 16 CDC for TPP

  17. Distribution of cases according to a characteristic Immunization status of measles cases, Nai, Uttaranchal, India, 2004 19% 81% Immunized Unimmunized Interpretation: The outbreak is probably caused by a failure to vaccinate CDC for TPP

  18. 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 18 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 19 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 20 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 21 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) 22 Reports

  23. Report 5: Comparison between reporting units • Compares • Incidence rates • Case fatality ratios • Reference period • Current month • Sites concerned • Block level and above 23 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 24 Reports

  25. Report 7: Comparison of reports between the public health system and the laboratory 25 Reports

  26. 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 oranges • Use 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 26 Interpretation

  27. 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 What computers cannot do 27 Interpretation

  28. Concerns commonly expressed Statistics are difficult Multivariate analysis is complex Presentation of data is challenging Expressed concerns versus reality Mistake commonly observed • Data are not looked at 28 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 29 Interpretation

  30. Take home messages • Link data collection and program implementation • Data > Information > Action • Count, divide and compare for time, place and person description • Share information through reports • Interpret with the technical committee to decide action on the basis of the information 30