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 analysis 2.CDC for TPP 3.Reports 4.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? DataInformation Action Analysis Interpretation Today we will focus on analysis

5 REC SEX 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 SexFrequencyProportion Female1033.3% Male2066.7% Total % 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 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 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 for analysis over a short time period CDC for TPP

12 Malaria in Kurseong block, Darjeeling District, West Bengal, India, 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 Months Incidence of malaria 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 year Reports 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

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

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, 2003 NumberofcasesPopulationIncide e 0 to % 5 to % 15to % % % % % Age group (Inyears) > % Male %Sex Female % TotalTotal % Interpretation: Older adults and women are at increased risk of cholera Incidence according to a characteristic CDC for TPP

81% 19% ImmunizedUnimmunized 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/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 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 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 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 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

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

30 Take home messages 1.Link data collection and program implementation Data > Information > Action 2.Count, divide and compare for time, place and person description 3.Share information through reports 4.Interpret with the technical committee to decide action on the basis of the information