improving surveillance data quality and use in tanzania

OverviewBackground integrated disease surveillance and response (IDSR)PHRplus/National Institute for Medical Research (NIMR) project in TanzaniaResultsNext stepsLessons learned

Integrated Disease Surveillance and Response (IDSR)WHO strategy for infectious disease surveillance in the African region (1998)

Goal: Improved detection of and response to priority infectious diseases

IDSR functionsIdentify casesReportAnalyze and interpret dataInvestigate and confirm cases/outbreaksRespondProvide feedbackEvaluate and improve system

Overview of USAID-funded IDSR strengthening project in TanzaniaPartners: MOH, National IDSR Task Force, NIMR, PHRplus, CDC, CHANGE Project, WHO/AFROObjective: Improved prevention and control of 13 priority infectious diseases Implementation: 2002-2005Develop, test, refine strategies most effective for improved system performance in 12 districtsFocus on district and facility levels

IDSR priority diseases, Tanzania Epidemic-prone diseasesCholera, bacillary dysentery, plague, measles, yellow fever, cerebrospinal meningitis, rabiesDiseases targeted for eradication/eliminationAcute flaccid paralysis, neonatal tetanusDiseases of public health importanceDiarrhea

Baseline situation: Monthly IDSR report accuracyAccuracy defined as number of cases in submitted report within +/- 5% of number of cases in data audit.

Baseline situation: Routine data analysis

The PHRplus/NIMR IDSR intervention in Tanzania (1)Situation analysisEpidemic preparedness planningDisease outbreak management field manualTraining materials and methodsTraining of trainers; district and facility level IDSR trainings

The PHRplus/NIMR IDSR intervention in Tanzania (2)

Data management, analysis, & interpretation toolsFollow-up visits and district quarterly meetingsMonitoring and evaluationOperational research (costing; analysis and response)

The PHRplus/NIMR IDSR intervention in Tanzania (3)Laboratory job aidsCommunity linkages

Results: IDSR materials and job aidsTested, revised, finalizedEndorsed by IDSR Task ForceReady for scale-up

Results: Improved capacity for sustainable trainingTrained staff at all levels:51 District level trainers96 District Health Management Team members32 Facility level trainers787 Facility health workers in 591 facilitiesStrengthened horizontal and vertical linkages between National level, Zonal Training Centers, Regions, and DistrictsHigh demand for IDSR training in non-project districts

Results: Improved reportingDistrict TrainingFacility Training

Chart1

0.1250.2

0.0750.1

0.050.125

0.1750.225

0.20.225

0.1750.25

0.3250.375

0.250.35

0.2750.35

0.3750.45

0.3750.45

0.350.425

0.2750.4

0.2750.3

0.3250.35

0.3250.375

0.20.25

0.3250.45

0.50.525

0.550.625

0.5250.675

0.4750.525

0.650.65

0.450.575

0.5750.575

0.5250.525

0.70.725

0.5250.55

0.60.625

0.60.6

0.5250.65

0.6750.7

0.7750.775

0.650.65

0.50.65

0.6250.675

0.5750.625

0.7250.725

0.6750.675

0.550.55

0.5750.6

0.60.6

0.650.65

0.450.45

0.70.7

0.650.675

0.70.7

0.6750.675

0.7750.8

0.750.8

0.750.75

0.70.7

0.7250.725

Timeliness

Completeness

Week number

Percent (%)

Timeliness and completeness of weekly IDSR reports, Babati, 2004

Weekly Reporting Data

Timeliness and completeness of weekly facility reports 2004

T

Source(s) of data:log of weekly facility reports or actual weekly reportsTotal expected reports53

Codes:T = received on timeL = received lateW = report not received at district

JanuaryFebruaryMarchQuarter 1 ResultsAprilMayJuneQuarter 2 ResultsJulyAugustSeptemberQuarter 3 ResultsOctoberNovemberDecemberQuarter 4 ResultsTotal reports on time (T)Total reports late (L)Total reports not received (W)Timeliness = 100* T/NCompleteness = 100* N-W) / N

Week12345123412345Reports on time (T)Reports late (L)Reports not received (W)TimelinessCompleteness1234123412345Reports on time (T)Reports late (L)Reports not received (W)TimelinessCompleteness1234123412345Reports on time (T)Reports late (L)Reports not received (W)TimelinessCompleteness1234123451234Reports on time (T)Reports late (L)Reports not received (W)TimelinessCompletenessN=53

Facilities / Week No1234567891011121314151617181920212223242526272829303132333435363738394041424344454647484950515253

1Bashnet DispWWLWWWTWWTTTWW41929%36%WWWLLWTLTTTWT53538%62%TTWWTTWLTTTTW81462%69%WWWWTTTWTLTTT71554%62%2462345%57%

2Bonga DispTWWTTTTTTTTTTT120286%86%TTTWTTWTTTTTT110285%85%TTTTTTTTTTTTT1300100%100%TTTTTTTTTTTTT1300100%100%490492%92%

3Dareda Kati DispWWWWWTTWTTTTTT80657%57%WWWTTTLWTLTWT62546%62%TTTLTTTTTTTTT121092%100%TTTWTTTTTTTTT120192%92%3831272%77%

4Endaberg DispWWWTTWTWTTTTTT90564%64%TTWTTTTTTTTTT120192%92%WWTWTTTTWWTTT80562%62%TTTWTTTTTTTTT120192%92%4101277%77%

5Endagwe DispLWWWWWWWWWWWWL02120%14%WWWWTTLWTTWWT51738%46%TTTWTTTWLTTWW81462%69%WWTTTTTTTTTTT110285%85%2442545%53%

6Endakiso DispWWWWWWWWWWWWWW00140%0%WWWWTTTWTTWWW50838%38%WTTWTTTTTTTTT110285%85%WTTTTTTTTTTTT120192%92%2802553%53%

7Gallapo DispWWWWTWWWTLTWWW311021%29%WWWWTTTTTTTTT90469%69%TTTTTTTTTTTTT1300100%100%TTTTTTTTTTTTT1300100%100%3811472%74%

8Gidas DispWWWWWLLLLWLLLW0770%50%WWWTTTTTTTWWT80562%62%TTTTTTTTTTTTW120192%92%WTWWTTTTTTTTT100377%77%3071657%70%

9Kiongozi DispWWWWWLLLLWLLLW0770%50%WWWTTTTTTTWWT80562%62%TTTTTTTTTWTTT120192%92%WTTTWTTTTTTTT110285%85%3171558%72%

10Kiru DispTTWTTWTTTTTTTT120286%86%TTTTTWTWTTTTT110285%85%TTTTTTTTTTTTT1300100%100%TWTTTTTTTTTTT120192%92%480591%91%

11Madrasat Gallapo DispWWWWWWWWWWWWWT10137%7%WWWWWTTTTTTTT80562%62%TWTTTTTTTTTTT120192%92%TTTTTTTTTTTTT1300100%100%3401964%64%

12Madunga DispWWWWWWWWWWWWWW00140%0%WWWWWTTWWWWTT40931%31%WTWWWTWWWWWWW201115%15%WTWWTWWWTTTWT60746%46%1204123%23%

13Magara DispTWWTWTTWTTTTTT100471%71%WTTTTTTTTLTTT111185%92%TTTTTTTTWTTTT120192%92%WTWWTWWWTTTWT60746%46%3911374%75%

14Magereza Disp (Prison)WWWWWWWWWTWWTW201214%14%WWTWTTTTTWTTT90469%69%TTTTTTTTTTTTT1300100%100%TTTWTWTTTTTTT110285%85%3501866%66%

15Mamire DispLLLLLWTTWWTWWW35621%57%TTWTTTTTTTTWT110285%85%TTTTTTTWTTTTW110285%85%WTTWWTTTTTTTT100377%77%3551366%75%

16Mayoka DispWWWWWWWWWWWWWW00140%0%WWWWWWWWWWWWW00130%0%WWTTTTTLTWTTT91369%77%TTTTTWTTTTTTT120192%92%2113140%42%

17Minjingu DispWWWWWWWWWWWWWW00140%0%WWWWWWWWWWWWW00130%0%WWWWWWWWWWWWW00130%0%WWTWWWWWWWWWW10128%8%10522%2%

18Mwada DispWWWWWTWTWTTTTW60843%43%TWLWTTTWWWTTT71554%62%WTWTTTWTTWTWT80562%62%TTTWTTTTTTTTT120192%92%3311962%64%

19Mwikantsi DispWWWWTTTTWTWWWW50936%36%TTTTTTTTTTTTT1300100%100%TTTTTTTTTTTTT1300100%100%TWTTTTTTTTTTT120192%92%4301081%81%

20Nkaiti DispWWWWWWWTLLTTLT43729%50%TTWTWWWWWLTTT61646%54%WWWTTTTTTTTTT100377%77%TTTTTTTTTTTTT1300100%100%3341662%70%

21OCM Magugu DispWWWWWWWWWWWWWW00140%0%WWWWWTTWTWWTW40931%31%LTTTTWTTTTTTW101277%85%TTTWWTTTTTTTT110285%85%2512747%49%

22PumaWWWWWWWWWWWWWW00140%0%WWWWWWWWWWWWW00130%0%WWWWWWWWWWWWW00130%0%WWWWWWWWWWWWW00130%0%00530%0%

23Qameyu DispWWWWWWWWWWWWWW00140%0%WWWWWWWWWWTWT201115%15%WWWWWWWWWWWWW00130%0%WWWWWWWWWWWWW00130%0%20514%4%

24RC Mission DispWWWWWWWWWWWWTW10137%7%TTWLTTTTTWWTT91369%77%TWWLTTTLTTTTT92269%85%TWTTTTTTTTTTT120192%92%3131958%64%

25Riroda DispWWWWWTTTTTTWWW60843%43%TLWTTLWTWWTWT62546%62%WTWWTTTTWTWWT70654%54%TTWTTTTTTTTTT120192%92%3122058%62%

26Riroda RCWWWWWWWWWWWTLT211114%21%WWWWWWWWTWWWW10128%8%WWWLWWWWWWTWW11118%15%WWWWTWWWWWWWW10128%8%52469%13%

27Sangaiwe DispWWWWWWWLTTTTLW42829%43%TTWLWWLWTTWWL43631%54%TTTTTTTTTLTTT121092%100%TTTTTTTWTLTTT111185%92%3171558%72%

28Secheda DispWWTTTLWWTTWTWW61743%50%LLLTTLTLTTTTT85062%100%WTWLWTWLLLTWW34623%54%LTTWWLWTLTWWW43631%54%21131940%64%

29Tarangire DispLTLLWWTLWLTTTT65343%79%TTTLWTTTTTTTT111185%92%WTTTLTTLTWTTT92269%85%TTTWTTTTTTTTT120192%92%388772%87%

30Tumaini DispWWWWWWWWWWWWWW00140%0%WWWWWWWWWWWWW00130%0%WWWWWWWWWWWWW00130%0%WWWWWWWWWWWWW00130%0%00530%0%

31Ufana DispWWWWWWWWWWLLWW02120%14%WWWLWLLTTWTTT53538%62%WWTTTTTWWTTTW80562%62%TTTWWTWWTTTTW80562%62%2152740%49%

32Wazazi Babati DispWWWWWWWWWWWWWW00140%0%WWWWWTLTTTTTT71554%62%TTTTTTTTTWTTW110285%85%TTTTTTTTTTTTT1300100%100%3112158%60%

33Wazazi Gallapo DispWWWWWWWWWWWWWW00140%0%WWWWWWWWWWWWW00130%0%WWTWWWWWWTWWW201115%15%TWWTTTTTWWWWW60746%46%804515%15%

34Wazazi MaguguWWWWWWWWWWWWWW00140%0%WWWWWTLTTTTTT71554%62%TWTLTTWWTTTTT91369%77%TTTTTTTTTTTTT1300100%100%2922255%58%

35ZAC Babati DispWWWWWWWWWWWWWW00140%0%WWWWWWWWWWWWW00130%0%TWWWWWWWWWWWW10128%8%WWWWWWWWWWWWW00130%0%10522%2%

36ZAC Ngaranaro DispWWWWWWWWWWWWWW00140%0%WWWWWWWWWWWWW00130%0%WWWWWWWWWWWWW00130%0%WWWWWWWWWWWWW00130%0%00530%0%

37Buma HCWWWWWWWWWWWWWW00140%0%WWWWWWWWWWWWW00130%0%WTWTWTWWTWWTW50838%38%WWWWWWWWTTWWW201115%15%704613%13%

38Magugu HCTWWTTTTTTTTTTT120286%86%TTTWTTTTTLTTT111185%92%TTTTTTTLTTTTT121092%100%TWTTTTTTTTTTT120192%92%472489%92%

39Babati HospitalTTTTTWTTTTTTTT130193%93%TTTTTTTTTTTTT1300100%100%TTTTTTTTTTTTT1300100%100%TTTTTTTTTTTTT1300100%100%520198%98%

40Dareda HospitalWWWWWWTTWTWWWW301121%21%WTWTTWTTWLTTT81462%69%TLWWWTTTTTWTT81462%69%TWWWTWTTTTTTT90469%69%2822353%57%

41

42

43

Total reports expected (N)4040404040404040404040404040560404040404040404040404040405204040404040404040404040404052040404040404040404040404040520TOTALS FOR YEAR2120

Total reports on time (T)53278713101115151411111321313813202221192618232128245212424212731262025232927223202324261828262827313030282934810451045

Total reports late (L)3132132433335137122513620500128110510062200018100001001200058888

Total reports not received (W)3236353131302526262222232428391262530221915131914171719112471815161412914141315111318182161614221213121388101211167987987

Timeliness = 100* T/N13%8%5%18%20%18%33%25%28%38%38%35%28%28%24%33%33%20%33%50%55%53%48%65%45%58%53%70%47%53%60%60%53%68%78%65%50%63%58%73%68%55%62%58%60%65%45%70%65%70%68%78%75%75%70%73%67%49%Yearly Timeliness

Completeness = 100* (N-W) / N20%10%13%23%23%25%38%35%35%45%45%43%40%30%30%0%0%0%0%35%38%25%45%53%63%68%53%65%58%58%53%73%53%0%0%0%0%55%63%60%65%70%78%65%65%68%63%73%68%55%65%0%0%0%0%60%60%65%45%70%68%70%68%80%80%75%70%73%68%0%0%0%0%53%Yearly Completeness

Week No1234567891011121314151617181920212223242526272829303132333435363738394041424344454647484950515253

Timeliness13%8%5%18%20%18%33%25%28%38%38%35%28%28%33%33%20%33%50%55%53%48%65%45%58%53%70%53%60%60%53%68%78%65%50%63%58%73%68%55%58%60%65%45%70%65%70%68%78%75%75%70%73%

Completeness20%10%13%23%23%25%38%35%35%45%45%43%40%30%35%38%25%45%53%63%68%53%65%58%58%53%73%55%63%60%65%70%78%65%65%68%63%73%68%55%60%60%65%45%70%68%70%68%80%80%75%70%73%

&LDistrict: BABATI&RJanuary - December

&L&"Arial,Italic"Monthly timeliness and completeness

Weekly Reporting Data

00

00

00

00

00

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00

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00

Timeliness

Completeness

Week number

Percent (%)

Timeliness and completeness of weekly IDSR reports, Babati, 2004

Weekly Summary by Facility

Timeliness and completeness of weekly facility reports 2004

TimelinessCompleteness

SnFacilityQuarter 1Quarter 2Quarter 3Quarter 4Annual AverageQuarter 1Quarter 2Quarter 3Quarter 4Annual Average

1Bashnet Disp29%38%62%54%45%36%62%69%62%57%

2Bonga Disp86%85%100%100%92%86%85%100%100%92%

3Dareda Kati Disp57%46%92%92%72%57%62%100%92%77%

4Endaberg Disp64%92%62%92%77%64%92%62%92%77%

5Endagwe Disp0%38%62%85%45%14%46%69%85%53%

6Endakiso Disp0%38%85%92%53%0%38%85%92%53%

7Gallapo Disp21%69%100%100%72%29%69%100%100%74%

8Gidas Disp0%62%92%77%57%50%62%92%77%70%

9Kiongozi Disp0%62%92%85%58%50%62%92%85%72%

10Kiru Disp86%85%100%92%91%86%85%100%92%91%

11Madrasat Gallapo Disp7%62%92%100%64%7%62%92%100%64%

12Madunga Disp0%31%15%46%23%0%31%15%46%23%

13Magara Disp71%85%92%46%74%71%92%92%46%75%

14Magereza Disp (Prison)14%69%100%85%66%14%69%100%85%66%

15Mamire Disp21%85%85%77%66%57%85%85%77%75%

16Mayoka Disp0%0%69%92%40%0%0%77%92%42%

17Minjingu Disp0%0%0%8%2%0%0%0%8%2%

18Mwada Disp43%54%62%92%62%43%62%62%92%64%

19Mwikantsi Disp36%100%100%92%81%36%100%100%92%81%

20Nkaiti Disp29%46%77%100%62%50%54%77%100%70%

21OCM Magugu Disp0%31%77%85%47%0%31%85%85%49%

22Puma0%0%0%0%0%0%0%0%0%0%

23Qameyu Disp0%15%0%0%4%0%15%0%0%4%

24RC Mission Disp7%69%69%92%58%7%77%85%92%64%

25Riroda Disp43%46%54%92%58%43%62%54%92%62%

26Riroda RC14%8%8%8%9%21%8%15%8%13%

27Sangaiwe Disp29%31%92%85%58%43%54%100%92%72%

28Secheda Disp43%62%23%31%40%50%100%54%54%64%

29Tarangire Disp43%85%69%92%72%79%92%85%92%87%

30Tumaini Disp0%0%0%0%0%0%0%0%0%0%

31Ufana Disp0%38%62%62%40%14%62%62%62%49%

32Wazazi Babati Disp0%54%85%100%58%0%62%85%100%60%

33Wazazi Gallapo Disp0%0%15%46%15%0%0%15%46%15%

34Wazazi Magugu0%54%69%100%55%0%62%77%100%58%

35ZAC Babati Disp0%0%8%0%2%0%0%8%0%2%

36ZAC Ngaranaro Disp0%0%0%0%0%0%0%0%0%0%

37Buma HC0%0%38%15%13%0%0%38%15%13%

38Magugu HC86%85%92%92%89%86%92%100%92%92%

39Babati Hospital93%100%100%100%98%93%100%100%100%98%

40Dareda Hospital21%62%62%69%53%21%69%69%69%57%

41

42

43

31



Weekly Summary by Facility

11

#REF!

#REF!

Week

Percentage

Weekly Graph by Week

0.1250.2

0.0750.1

0.050.125

0.1750.225

0.20.225

0.1750.25

0.3250.375

0.250.35

0.2750.35

0.3750.45

0.3750.45

0.350.425

0.2750.4

0.2750.3

0.3250.35

0.3250.375

0.20.25

0.3250.45

0.50.525

0.550.625

0.5250.675

0.4750.525

0.650.65

0.450.575

0.5750.575

0.5250.525

0.70.725

0.5250.55

0.60.625

0.60.6

0.5250.65

0.6750.7

0.7750.775

0.650.65

0.50.65

0.6250.675

0.5750.625

0.7250.725

0.6750.675

0.550.55

0.5750.6

0.60.6

0.650.65

0.450.45

0.70.7

0.650.675

0.70.7

0.6750.675

0.7750.8

0.750.8

0.750.75

0.70.7

0.7250.725

Timeliness

Completeness

Week

Percentage

Facility Reporting by Week, 2004

Monthly Reporting Data

Timeliness and completeness of facility monthly reports 2004

Total number of facilities (N) =40Total number of expected reports12

Source(s) of data:log of monthly facility reports, or actual monthly reports

Codes:T = received on timeL = received lateW = report not received at district

Quarter 1 ResultsQuarter 2 ResultsQuarter 3 ResultsQuarter 4 ResultsAnnual Average

FacilityJanFebMarReports on time (T)Reports late (L)Reports not received (W)TimelinessCompletenessAprMayJunReports on time (T)Reports late (L)Reports not received (W)TimelinessCompletenessJulAugSepReports on time (T)Reports late (L)Reports not received (W)TimelinessCompletenessOctNovDecReports on time (T)Reports late (L)Reports not received (W)TimelinessCompletenessTotal reports on time (T)Total reports late (L)Total reports not received (W)Timeliness = 100* T/N (%)Completeness = 100* (N-W) / N (%)

1Bashnet DispLTT21067%100%LTT21067%100%TLT21067%100%TLL12033%100%75058%100%

2Bonga DispTTT300100%100%TTT300100%100%TTT300100%100%TTT300100%100%1200100%100%

3Dareda Kati DispTTL21067%100%TLT21067%100%TTL21067%100%TTT300100%100%93075%100%

4Endaberg DispLLL0300%100%LTT21067%100%TTL21067%100%TTT300100%100%75058%100%

5Endagwe DispTLL12033%100%LTT21067%100%LLT12033%100%LTL12033%100%57042%100%

6Endakiso DispWWW0030%0%WTT20167%67%TTT300100%100%TTT300100%100%80467%67%

7Gallapo DispTTT300100%100%LTT21067%100%TTT300100%100%TTT300100%100%111092%100%

8Gidas DispTLL12033%100%LLT12033%100%TLT21067%100%TTT300100%100%75058%100%

9Kiongozi DispTTL21067%100%TTT300100%100%LTT21067%100%LTT21067%100%93075%100%

10Kiru DispTTT300100%100%LTT21067%100%TTT300100%100%TTT300100%100%111092%100%

11Madrasat Gallapo DispTTT300100%100%TTL21067%100%TTT300100%100%LTT21067%100%102083%100%

12Madunga DispLLT12033%100%LTT21067%100%TTT300100%100%LTT21067%100%84067%100%

13Magara DispLTT21067%100%TLT21067%100%TTT300100%100%LTT21067%100%93075%100%

14Magereza Disp (Prison)TTT300100%100%TTT300100%100%TTT300100%100%TTT300100%100%1200100%100%

15Mamire DispTTT300100%100%TTT300100%100%TTL21067%100%LTT21067%100%102083%100%

16Mayoka DispWWW0030%0%WWW0030%0%LLT12033%100%TTT300100%100%42633%50%

17Minjingu DispWWW0030%0%WWW0030%0%WWW0030%0%WWW0030%0%00120%0%

18Mwada DispTTT300100%100%TTL21067%100%TTL21067%100%LTT21067%100%93075%100%

19Mwikantsi DispTTT300100%100%TTT300100%100%LLT12033%100%TTT300100%100%102083%100%

20Nkaiti DispLTT21067%100%LTL12033%100%TTT300100%100%TTT300100%100%93075%100%

21OCM Magugu DispTTT300100%100%TTT300100%100%TTT300100%100%LTT21067%100%111092%100%

22PumaWWW0030%0%WWW0030%0%WWW0030%0%WWW0030%0%00120%0%

23Qameyu DispWWW0030%0%WWW0030%0%WWW0030%0%WWW0030%0%00120%0%

24RC Mission DispLTT21067%100%TLT21067%100%TTT300100%100%TTT300100%100%102083%100%

25Riroda DispLTT21067%100%TLT21067%100%LTT21067%100%TTT300100%100%93075%100%

26Riroda RCWWW0030%0%WWW0030%0%WWW0030%0%WWW0030%0%00120%0%

27Sangaiwe DispTLL12033%100%TTT300100%100%TLT21067%100%TTL21067%100%84067%100%

28Secheda DispLLL0300%100%LTL12033%100%LLL0300%100%LLL0300%100%11108%100%

29Tarangire DispLTT21067%100%TTT300100%100%TTT300100%100%TTL21067%100%102083%100%

30Tumaini DispWWW0030%0%WWW0030%0%WWW0030%0%WWW0030%0%00120%0%

31Ufana DispTTL21067%100%LTT21067%100%LLT12033%100%TLT21067%100%75058%100%

32Wazazi Babati DispTTT300100%100%LTT21067%100%LTT21067%100%TTT300100%100%102083%100%

33Wazazi Gallapo DispLTT21067%100%TTT300100%100%TTT300100%100%TTT300100%100%111092%100%

34Wazazi MaguguLTT21067%100%LTT21067%100%LTT21067%100%TTT300100%100%93075%100%

35ZAC Babati DispWWW0030%0%WWW0030%0%WWW0030%0%WWW0030%0%00120%0%

36ZAC Ngaranaro DispWWW0030%0%WWW0030%0%WWW0030%0%WWW0030%0%00120%0%

37Buma HCWWW0030%0%WWW0030%0%TTT300100%100%TLL12033%100%42633%50%

38Magugu HCTTT300100%100%TTT300100%100%TTT300100%100%TTT300100%100%1200100%100%

39Babati HospitalTTT300100%100%TTT300100%100%TTT300100%100%TTT300100%100%1200100%100%

40Dareda HospitalTTT300100%100%LLT12033%100%TTT300100%100%LTT21067%100%93075%100%

41

42

43

Total reports expected (N)404040120404040120404040120404040120TOTALS FOR YEAR480

Total reports on time (T)19242265172527692425287723292779290290

Total reports late (L)116825136423985221046209090

Total reports not received (W)101010301099287772177721100100

Timeliness = 100* T/N48%60%55%54%43%63%68%58%60%63%70%64%58%73%68%66%60%Yearly Timeliness

Completeness = 100* (N-W) / N75%75%75%75%75%78%78%77%83%83%83%83%83%83%83%83%79%Yearly Completeness

District summary: Performance of Timeliness and Completeness of monthly report 2004

JanFebMarAprMayJunJulAugSepOctNovDec

Timeliness (%)48%60%55%43%63%68%60%63%70%58%73%68%

Completeness (%)75%75%75%75%78%78%83%83%83%83%83%83%



Monthly Summary by Facility

Timeliness and completeness of facility monthly reports 2004

TimelinessCompleteness

SNFacilityQuarter 1Quarter 2Quarter 3Quarter 4Annual AverageQuarter 1Quarter 2Quarter 3Quarter 4Annual Average

1Bashnet Disp67%67%67%33%58%100%100%100%100%100%

2Bonga Disp100%100%100%100%100%100%100%100%100%100%

3Dareda Kati Disp67%67%67%100%75%100%100%100%100%100%

4Endaberg Disp0%67%67%100%58%100%100%100%100%100%

5Endagwe Disp33%67%33%33%42%100%100%100%100%100%

6Endakiso Disp0%67%100%100%67%0%67%100%100%67%

7Gallapo Disp100%67%100%100%92%100%100%100%100%100%

8Gidas Disp33%33%67%100%58%100%100%100%100%100%

9Kiongozi Disp67%100%67%67%75%100%100%100%100%100%

10Kiru Disp100%67%100%100%92%100%100%100%100%100%

11Madrasat Gallapo Disp100%67%100%67%83%100%100%100%100%100%

12Madunga Disp33%67%100%67%67%100%100%100%100%100%

13Magara Disp67%67%100%67%75%100%100%100%100%100%

14Magereza Disp (Prison)100%100%100%100%100%100%100%100%100%100%

15Mamire Disp100%100%67%67%83%100%100%100%100%100%

16Mayoka Disp0%0%33%100%33%0%0%100%100%50%

17Minjingu Disp0%0%0%0%0%0%0%0%0%0%

18Mwada Disp100%67%67%67%75%100%100%100%100%100%

19Mwikantsi Disp100%100%33%100%83%100%100%100%100%100%

20Nkaiti Disp67%33%100%100%75%100%100%100%100%100%

21OCM Magugu Disp100%100%100%67%92%100%100%100%100%100%

22Puma0%0%0%0%0%0%0%0%0%0%

23Qameyu Disp0%0%0%0%0%0%0%0%0%0%

24RC Mission Disp67%67%100%100%83%100%100%100%100%100%

25Riroda Disp67%67%67%100%75%100%100%100%100%100%

26Riroda RC0%0%0%0%0%0%0%0%0%0%

27Sangaiwe Disp33%100%67%67%67%100%100%100%100%100%

28Secheda Disp0%33%0%0%8%100%100%100%100%100%

29Tarangire Disp67%100%100%67%83%100%100%100%100%100%

30Tumaini Disp0%0%0%0%0%0%0%0%0%0%

31Ufana Disp67%67%33%67%58%100%100%100%100%100%

32Wazazi Babati Disp100%67%67%100%83%100%100%100%100%100%

33Wazazi Gallapo Disp67%100%100%100%92%100%100%100%100%100%

34Wazazi Magugu67%67%67%100%75%100%100%100%100%100%

35ZAC Babati Disp0%0%0%0%0%0%0%0%0%0%

36ZAC Ngaranaro Disp0%0%0%0%0%0%0%0%0%0%

37Buma HC0%0%100%33%33%0%0%100%100%50%

38Magugu HC100%100%100%100%100%100%100%100%100%100%

39Babati Hospital100%100%100%100%100%100%100%100%100%100%

40Dareda Hospital100%33%100%67%75%100%100%100%100%100%

41

42

43



Monthly Summary by Facility

11

#REF!

#REF!

Months

Percentage

Monthly graph by month

0.4750.75

0.60.75

0.550.75

0.4250.75

0.6250.775

0.6750.775

0.60.825

0.6250.825

0.70.825

0.5750.825

0.7250.825

0.6750.825

Timeliness

Completeness

Month

Percentage

Facility Reporting by Month, 2004

case-investigation forms

Reporting Using Case Investigation Forms 2004

Source(s) of data:Forms: case investigation forms Cases: Weekly facility reports

JanuaryFebruaryMarchAprilMayJuneJulyAugustSeptemberOctoberNovemberDecemberTotal for Quarter 1Total for Quarter 2Total for Quarter 3Total for Quarter 4Total for Year

Priority Disease# Cases# Forms# Cases# Forms# Cases# Forms# Cases# Forms# Cases# Forms# Cases# Forms# Cases# Forms# Cases# Forms# Cases# Forms# Cases# Forms# Cases# Forms# Cases# Forms# Cases# Forms%# Cases# Forms%# Cases# Forms%# Cases# Forms%# Cases# Forms%

1AFP0000000000

2NNT0000000000

3Measles0000000000

4Meningitis0000000000

5Cholera0000000000

6Plague0000000000

7Yellow Fever0000000000

8VHF0000000000

Monthly /Quarter Total0000000000000000000000000000000000

Indicator value000000000000



Results: Improved reporting

Chart2

2.759.1

35.660

35.870

35.944.6

41.466.4

37.862.8

Pre-training

Post-training

District

Percent (%)

Average completeness of weekly IDSR reports, 2004

Summary all districts

Timeliness - Monthly Reports

Q 1Q 2Q 3Q 4Improved?% changedifference

Babati54%58%64%66%Yes22%12%

Dodoma R.53%67%85%86%Yes62%33%

Igunga36%33%40%48%Yes33%12%

Masasi10%14%50%89%Yes790%79%

Mbulu27%63%74%67%Yes148%40%

Mpwapwa43%61%83%54%Yes26%11%

Muleba14%28%41%40%Yes186%26%

Mwanza CityNo0%

Nkasi29%48%64%71%Yes145%42%

Sumbawanga R.30%29%54%51%Yes70%21%

Tabora U.93%90%90%86%No-8%-7%

Tunduru59%75%82%79%Yes34%20%

Districts with improved performance91%

Timeliness - Weekly Reports


Babati24%47%62%67%Yes179%43%

Dodoma R.0%43%94%93%Yes0%93%

Igunga6%2%7%3%No-50%-3%

Masasi0%1%30%84%Yes0%84%

Mbulu5%49%55%55%Yes1000%50%

Mpwapwa3%41%70%53%Yes1667%50%

Muleba35%40%39%37%Yes6%2%

Mwanza CityNo0%

Nkasi7%18%31%52%Yes643%45%


Tabora U.85%85%95%91%Yes7%6%

Tunduru2%1%29%73%Yes3550%71%


Completeness - Monthly Reports


Babati75%77%83%83%Yes11%8%

Dodoma R.94%95%95%91%No-3%-3%

Igunga71%74%69%59%No-17%-12%

Masasi29%28%58%95%Yes228%66%

Mbulu32%65%76%74%Yes131%42%

Mpwapwa70%69%83%55%No-21%-15%

Muleba55%63%69%65%Yes18%10%

Mwanza CityNo0%

Nkasi45%62%67%75%Yes67%30%


Tabora U.97%97%97%97%No0%0%

Tunduru100%100%100%100%No0%0%


Completeness - Weekly Reports


Babati30%53%65%68%Yes127%38%

Dodoma R.0%97%100%100%Yes0%100%

Igunga30%25%46%36%Yes20%6%

Masasi0%5%33%91%Yes0%91%

Mbulu10%61%69%71%Yes610%61%

Mpwapwa21%54%72%54%Yes157%33%

Muleba63%73%65%66%Yes5%3%

Mwanza CityNo0%

Nkasi27%44%51%69%Yes156%42%


Tabora U.97%97%97%97%No0%0%

Tunduru3%3%36%82%Yes2633%79%


Summary, 2nd 4 districts

Second 4 districts trained in IDSR

Masasi, Tunduru, Sumbawanga, Nkasi

District IDSR training: Quarter 3, 2004

Facility IDSR training: Quarter 3, 2004

Tunduru data under investigation


Pre-trainingPost-trainingp-valueP-value < 0.05 indicates

Nkasi38.767.6


Chart3

12.170.8

29.352.8

38.767.6

45.170.4

51.768.4

55.965.1

59.985.6

67.180.4

Pre-training

Post-training

District

Percent (%)

Average timeliness of monthly IDSR reports, 2004




Babati54%58%64%66%Yes22%12%

Dodoma R.53%67%85%86%Yes62%33%

Igunga36%33%40%48%Yes33%12%

Masasi10%14%50%89%Yes790%79%

Mbulu27%63%74%67%Yes148%40%

Mpwapwa43%61%83%54%Yes26%11%

Muleba14%28%41%40%Yes186%26%

Mwanza CityNo0%

Nkasi29%48%64%71%Yes145%42%


Tabora U.93%90%90%86%No-8%-7%

Tunduru59%75%82%79%Yes34%20%




Babati24%47%62%67%Yes179%43%

Dodoma R.0%43%94%93%Yes0%93%

Igunga6%2%7%3%No-50%-3%

Masasi0%1%30%84%Yes0%84%

Mbulu5%49%55%55%Yes1000%50%

Mpwapwa3%41%70%53%Yes1667%50%

Muleba35%40%39%37%Yes6%2%

Mwanza CityNo0%

Nkasi7%18%31%52%Yes643%45%


Tabora U.85%85%95%91%Yes7%6%

Tunduru2%1%29%73%Yes3550%71%




Babati75%77%83%83%Yes11%8%

Dodoma R.94%95%95%91%No-3%-3%

Igunga71%74%69%59%No-17%-12%

Masasi29%28%58%95%Yes228%66%

Mbulu32%65%76%74%Yes131%42%

Mpwapwa70%69%83%55%No-21%-15%

Muleba55%63%69%65%Yes18%10%

Mwanza CityNo0%

Nkasi45%62%67%75%Yes67%30%


Tabora U.97%97%97%97%No0%0%

Tunduru100%100%100%100%No0%0%




Babati30%53%65%68%Yes127%38%

Dodoma R.0%97%100%100%Yes0%100%

Igunga30%25%46%36%Yes20%6%

Masasi0%5%33%91%Yes0%91%

Mbulu10%61%69%71%Yes610%61%

Mpwapwa21%54%72%54%Yes157%33%

Muleba63%73%65%66%Yes5%3%

Mwanza CityNo0%

Nkasi27%44%51%69%Yes156%42%


Tabora U.97%97%97%97%No0%0%

Tunduru3%3%36%82%Yes2633%79%










Nkasi38.767.6

Next stepsMaterials & methods in place, but funds lackingZonal Training Centers expanding training using methods and materials Global Fund proposalScale up IDSR plus other health system strengthening needs

Lessons learnedUnderstand the system and define standardsAdapt materials to local contextFocus on facility and district levelsTraining necessary, but not sufficientIntegration challenging

The PHRplus Project is funded by U.S. Agency for International Development and implemented by: Abt Associates Inc. and partners, Development Associates, Inc.; Emory University Rollins School of Public Health; Philoxenia International Travel, Inc. Program for Appropriate Technology in Health; SAG Corp.; Social Sectors Development Strategies, Inc.; Training Resources Group; Tulane University School of Public Health and Tropical Medicine; University Research Co., LLC. URL: http://www.phrplus.org

Reports related to this presentation are available at www.phrplus.orgThank You

Dodoma RuralBabatiProject districts


Chart1

0.30.530.650.68

00.9711

0.10.610.690.71

0.210.540.720.54

Quarter 1

Quarter 2

Quarter 3

Quarter 4

District

Percent (%)

Completeness of weekly IDSR reports, 2004District training in Q1 and facility training in Q2




Babati54%58%64%66%Yes22%12%

Dodoma R.53%67%85%86%Yes62%33%

Igunga36%33%40%48%Yes33%12%

Masasi10%14%50%89%Yes790%79%

Mbulu27%63%74%67%Yes148%40%

Mpwapwa43%61%83%54%Yes26%11%

Muleba14%28%41%40%Yes186%26%

Mwanza CityNo0%

Nkasi29%48%64%71%Yes145%42%


Tabora U.93%90%90%86%No-8%-7%

Tunduru59%75%82%79%Yes34%20%




Babati24%47%62%67%Yes179%43%

Dodoma R.0%43%94%93%Yes0%93%

Igunga6%2%7%3%No-50%-3%

Masasi0%1%30%84%Yes0%84%

Mbulu5%49%55%55%Yes1000%50%

Mpwapwa3%41%70%53%Yes1667%50%

Muleba35%40%39%37%Yes6%2%

Mwanza CityNo0%

Nkasi7%18%31%52%Yes643%45%


Tabora U.85%85%95%91%Yes7%6%

Tunduru2%1%29%73%Yes3550%71%




Babati75%77%83%83%Yes11%8%

Dodoma R.94%95%95%91%No-3%-3%

Igunga71%74%69%59%No-17%-12%

Masasi29%28%58%95%Yes228%66%

Mbulu32%65%76%74%Yes131%42%

Mpwapwa70%69%83%55%No-21%-15%

Muleba55%63%69%65%Yes18%10%

Mwanza CityNo0%

Nkasi45%62%67%75%Yes67%30%


Tabora U.97%97%97%97%No0%0%

Tunduru100%100%100%100%No0%0%




Babati30%53%65%68%Yes127%38%

Dodoma R.0%97%100%100%Yes0%100%

Igunga30%25%46%36%Yes20%6%

Masasi0%5%33%91%Yes0%91%

Mbulu10%61%69%71%Yes610%61%

Mpwapwa21%54%72%54%Yes157%33%

Muleba63%73%65%66%Yes5%3%

Mwanza CityNo0%

Nkasi27%44%51%69%Yes156%42%


Tabora U.97%97%97%97%No0%0%

Tunduru3%3%36%82%Yes2633%79%










Nkasi38.767.6


Chart5

0.270.440.510.69

0.350.370.490.4

00.050.330.91

0.030.030.360.82

Quarter 1

Quarter 2

Quarter 3

Quarter 4

District

Percent (%)

Completeness of weekly IDSR reports, 2004IDSR district & facility training in quarter 3




Babati54%58%64%66%Yes22%12%

Dodoma R.53%67%85%86%Yes62%33%

Igunga36%33%40%48%Yes33%12%

Masasi10%14%50%89%Yes790%79%

Mbulu27%63%74%67%Yes148%40%

Mpwapwa43%61%83%54%Yes26%11%

Muleba14%28%41%40%Yes186%26%

Mwanza CityNo0%

Nkasi29%48%64%71%Yes145%42%


Tabora U.93%90%90%86%No-8%-7%

Tunduru59%75%82%79%Yes34%20%




Babati24%47%62%67%Yes179%43%

Dodoma R.0%43%94%93%Yes0%93%

Igunga6%2%7%3%No-50%-3%

Masasi0%1%30%84%Yes0%84%

Mbulu5%49%55%55%Yes1000%50%

Mpwapwa3%41%70%53%Yes1667%50%

Muleba35%40%39%37%Yes6%2%

Mwanza CityNo0%

Nkasi7%18%31%52%Yes643%45%


Tabora U.85%85%95%91%Yes7%6%

Tunduru2%1%29%73%Yes3550%71%




Babati75%77%83%83%Yes11%8%

Dodoma R.94%95%95%91%No-3%-3%

Igunga71%74%69%59%No-17%-12%

Masasi29%28%58%95%Yes228%66%

Mbulu32%65%76%74%Yes131%42%

Mpwapwa70%69%83%55%No-21%-15%

Muleba55%63%69%65%Yes18%10%

Mwanza CityNo0%

Nkasi45%62%67%75%Yes67%30%


Tabora U.97%97%97%97%No0%0%

Tunduru100%100%100%100%No0%0%




Babati30%53%65%68%Yes127%38%

Dodoma R.0%97%100%100%Yes0%100%

Igunga30%25%46%36%Yes20%6%

Masasi0%5%33%91%Yes0%91%

Mbulu10%61%69%71%Yes610%61%

Mpwapwa21%54%72%54%Yes157%33%

Muleba63%73%65%66%Yes5%3%

Mwanza CityNo0%

Nkasi27%44%51%69%Yes156%42%


Tabora U.97%97%97%97%No0%0%

Tunduru3%3%36%82%Yes2633%79%










Nkasi38.767.6


Chart2

0.240.470.620.67

00.430.940.93

0.050.490.550.55

0.030.410.70.53

Quarter 1

Quarter 2

Quarter 3

Quarter 4

District

Percent (%)

Timeliness of weekly IDSR reports, 2004District training in Q1 and facility training in Q2




Babati54%58%64%66%Yes22%12%

Dodoma R.53%67%85%86%Yes62%33%

Igunga36%33%40%48%Yes33%12%

Masasi10%14%50%89%Yes790%79%

Mbulu27%63%74%67%Yes148%40%

Mpwapwa43%61%83%54%Yes26%11%

Muleba14%28%41%40%Yes186%26%

Mwanza CityNo0%

Nkasi29%48%64%71%Yes145%42%


Tabora U.93%90%90%86%No-8%-7%

Tunduru59%75%82%79%Yes34%20%




Babati24%47%62%67%Yes179%43%

Dodoma R.0%43%94%93%Yes0%93%

Igunga6%2%7%3%No-50%-3%

Masasi0%1%30%84%Yes0%84%

Mbulu5%49%55%55%Yes1000%50%

Mpwapwa3%41%70%53%Yes1667%50%

Muleba35%40%39%37%Yes6%2%

Mwanza CityNo0%

Nkasi7%18%31%52%Yes643%45%


Tabora U.85%85%95%91%Yes7%6%

Tunduru2%1%29%73%Yes3550%71%




Babati75%77%83%83%Yes11%8%

Dodoma R.94%95%95%91%No-3%-3%

Igunga71%74%69%59%No-17%-12%

Masasi29%28%58%95%Yes228%66%

Mbulu32%65%76%74%Yes131%42%

Mpwapwa70%69%83%55%No-21%-15%

Muleba55%63%69%65%Yes18%10%

Mwanza CityNo0%

Nkasi45%62%67%75%Yes67%30%


Tabora U.97%97%97%97%No0%0%

Tunduru100%100%100%100%No0%0%




Babati30%53%65%68%Yes127%38%

Dodoma R.0%97%100%100%Yes0%100%

Igunga30%25%46%36%Yes20%6%

Masasi0%5%33%91%Yes0%91%

Mbulu10%61%69%71%Yes610%61%

Mpwapwa21%54%72%54%Yes157%33%

Muleba63%73%65%66%Yes5%3%

Mwanza CityNo0%

Nkasi27%44%51%69%Yes156%42%


Tabora U.97%97%97%97%No0%0%

Tunduru3%3%36%82%Yes2633%79%










Nkasi38.767.6


Chart4

0.070.180.310.52

0.020.060.20.2

00.010.30.84

0.020.010.290.73

Quarter 1

Quarter 2

Quarter 3

Quarter 4

District

Percent (%)

Timeliness of weekly IDSR reports, 2004IDSR district & facility training in quarter 3




Babati54%58%64%66%Yes22%12%

Dodoma R.53%67%85%86%Yes62%33%

Igunga36%33%40%48%Yes33%12%

Masasi10%14%50%89%Yes790%79%

Mbulu27%63%74%67%Yes148%40%

Mpwapwa43%61%83%54%Yes26%11%

Muleba14%28%41%40%Yes186%26%

Mwanza CityNo0%

Nkasi29%48%64%71%Yes145%42%


Tabora U.93%90%90%86%No-8%-7%

Tunduru59%75%82%79%Yes34%20%




Babati24%47%62%67%Yes179%43%

Dodoma R.0%43%94%93%Yes0%93%

Igunga6%2%7%3%No-50%-3%

Masasi0%1%30%84%Yes0%84%

Mbulu5%49%55%55%Yes1000%50%

Mpwapwa3%41%70%53%Yes1667%50%

Muleba35%40%39%37%Yes6%2%

Mwanza CityNo0%

Nkasi7%18%31%52%Yes643%45%


Tabora U.85%85%95%91%Yes7%6%

Tunduru2%1%29%73%Yes3550%71%




Babati75%77%83%83%Yes11%8%

Dodoma R.94%95%95%91%No-3%-3%

Igunga71%74%69%59%No-17%-12%

Masasi29%28%58%95%Yes228%66%

Mbulu32%65%76%74%Yes131%42%

Mpwapwa70%69%83%55%No-21%-15%

Muleba55%63%69%65%Yes18%10%

Mwanza CityNo0%

Nkasi45%62%67%75%Yes67%30%


Tabora U.97%97%97%97%No0%0%

Tunduru100%100%100%100%No0%0%




Babati30%53%65%68%Yes127%38%

Dodoma R.0%97%100%100%Yes0%100%

Igunga30%25%46%36%Yes20%6%

Masasi0%5%33%91%Yes0%91%

Mbulu10%61%69%71%Yes610%61%

Mpwapwa21%54%72%54%Yes157%33%

Muleba63%73%65%66%Yes5%3%

Mwanza CityNo0%

Nkasi27%44%51%69%Yes156%42%


Tabora U.97%97%97%97%No0%0%

Tunduru3%3%36%82%Yes2633%79%










Nkasi38.767.6

Good morning. Today I will present a summary of a 3-year project that has been undertaken to improve infectious disease surveillance data quality and use in Tanzania.I will begin with some brief background on integrated disease surveillance and response (IDSR). I will then summarize the PHRplus/NIMR project activities and results in Tanzania, and discuss the next steps and how the lessons learned from the PHRplus experience can benefit other surveillance initiatives in resource-poor environments.Experience has shown that disease control and prevention objectives are successfully met when resources are dedicated to improving the ability of health officials to detect the targeted diseases, obtain laboratory confirmation of outbreaks, and use action thresholds at the district level. Based on this experience, in 1998 the World Health Organization adopted the integrated disease surveillance and response (IDSR) strategy linking community, health facility, district and national levels in the African region.

The goal of IDSR is to improve the ability of districts to detect and respond to diseases and conditions that cause high levels of death, illness and disability.

This slide summarizes the core functions of IDSR. Different levels of the health system play different roles in each of these functions.First, using standard case definitions, health workers must identify cases of priority infectious diseases.These cases should then be reported regularly to the next level of the health system. Surveillance data collected should be analyzed regularly for trends and should be compared with data from earlier time periods. Analyzed data should be interpreted to determine possible causes and to take appropriate actions.Suspected cases and outbreaks should be investigated and confirmed immediately, using laboratory confirmation whenever possible. Resources and personnel must be mobilized to implement the appropriate outbreak or public health response.It is important to provide feedback to the levels that reported the suspected cases or outbreaks to ensure their continued cooperation and to inform them of the investigation and response results.Finally, the effectiveness of the surveillance system must be routinely evaluated so that problems can be corrected and improvements made.

Tanzania was one of the first countries in the African region to adopt the IDSR strategy in 1998. However, implementation of the strategy needed additional support to ensure its effectiveness.

To address this need, USAID initiated a 3-year project to strengthen surveillance and response capacity at the district and facility levels. The project was developed in close collaboration with the Tanzania Ministry of Health, the National IDSR Task Force, and the National Institute for Medical Research in Tanzania, which is the primary implementing organization. Technical assistance was provided by PHRplus, CDC, and the CHANGE Project. The project built on the WHO/AFRO IDSR strategy and materials, adapting them to the Tanzania context.

The objective of this project was to improve the prevention and control of 13 priority infectious diseases. Project implementation began in 2002 and will be completed in September 2005.

The project was implemented in 12 districts in 8 regions throughout the country. In these districts, PHRplus and NIMR collaborated with partners to develop, test, and refine strategies to improve IDSR performance, with a focus on the district and facility levels. The intention of the project was then to roll out the successful strategies to the rest of the country.

IDSR in Tanzania covers thirteen priority infectious diseases which are divided into three categories: epidemic-prone diseases, diseases targeted for eradication or elimination, and diseases of public health importance.

It is important to note that HIV/AIDS and tuberculosis are not currently included in the thirteen IDSR diseases. These diseases both had vertical disease control and surveillance systems in place when the IDSR strategy was adopted in Tanzania. However, when the national IDSR guidelines are reviewed during the next year, inclusion of these diseases into the IDSR framework will be discussed.Before the PHRPlus/NIMR interventions in project districts, accuracy of IDSR data reported from facilities to districts and from districts to regions was poor. This table shows the proportion of monthly IDSR reports for which the number of cases of three priority diseases in the submitted report was within 5% of the number of cases found using a record review (or data audit). Data accuracy was thus one aspect of IDSR requiring strengthening at each level.Baseline data also revealed the need to strengthen routine surveillance data analysis at the facility and district levels.

This table shows the proportion of facilities and districts conducting regular analyses expected to be done for priority diseases. Malaria is the leading cause of death in Tanzania and a leading cause of morbidity. However, just 28% of facilities and 16% of project districts were regularly analyzing malaria trends, and almost none were looking at trends in the highly vulnerable under 5 age group.

In addition, feedback from the national level to regions, regions to districts, and districts to facilities was quite low, with just 33% of districts and 7% of facilities reporting receipt of aggregated or compared IDSR data from the next level.The information in the previous slides was collected during an initial situation analysis in 2 districts and a full baseline assessment in all 12 districts. These identified areas needing strengthening and informed the development of the project strategy, along with the definition of roles and responsibilities for persons at the facility and district levels.

Initial activities included working with districts to develop epidemic preparedness plans, and consolidating information on management of disease outbreaks into a field reference manual.

A focus of the project was on training health system personnel in their IDSR responsibilities. WHO/AFRO had developed generic IDSR training materials for the district level, but substantial efforts were required to adapt these materials to the Tanzanian context. No training materials existed for the facility level, but given the facilities important role in identifying and reporting cases, this was a critical gap. A task-oriented, interactive approach to IDSR training, using adult learning methods, was used to design training materials for both the facility and district levels.

In each of the 12 districts, a cascade approach was taken for IDSR training. First, trainers from the zonal, regional, and national levels were trained to be trainers, using training of trainer manuals that the project developed. They then trained groups of 4 district health management teams. From each district team, 4 people were trained to be trainers and they in turn conducted training for representatives from all health facilities in the district.Immediately following training, a significant increase was observed in weekly and monthly IDSR reports submitted by facilities to districts. The project then provided districts with the tools needed to manage, analyze, interpret, and use these data. An Excel-based program was installed to assist with data management and to produce standardized analyses of the surveillance data. A data interpretation job aid was distributed to assist the IDSR focal person to interpret the analyzed data.

Districts received additional post-training support via follow-up visits and district quarterly meetings focused on reviewing IDSR performance and problem-solving to address challenges they faced related to surveillance.

Monitoring and evaluation of system and performance indicators pre- and post-intervention has been key to identifying both strengths and challenges, and providing feedback, in all districts. The project developed and field tested several indicators based on WHO/AFRO guidelines.

Two operational research studies were also carried out to assess the costs of IDSR and changes in district and facility analysis and response capacity. Final data collection for these studies is being completed and comparisons will be made with baseline data.

CDC provided technical assistance to develop laboratory job aids for use by districts and facilities to collect specimens and by laboratories to test specimens. These job aids detail materials and steps needed for specimen collection, handling, transport, and testing for confirmation of the 13 priority diseases.

Finally, the CHANGE Project provided technical assistance to increase linkages between the health sector and other sectors and to foster dialogue between the formal health sector and traditional healers. They developed and distributed an information package for district local officials, such as the district executive directors and heads of the district education, sanitation, and other departments. These packages provided key facts about each of the 13 diseases and offered suggestions for how the non-health sector could become involved in disease surveillance and response. The project also oriented traditional healers in one district to improve reporting and referral of cases of priority infectious diseases.

Data collection is currently being completed to quantify project results. However, I will now summarize several preliminary accomplishments.

The IDSR training materials and supporting job aids were tested, using a phased approach, in the project districts and revised after each phase before being finalized. They were presented to the National IDSR Task Force in November 2004 and were endorsed for use nationwide. These materials are ready for production and dissemination.The project is leaving a large number of persons trained at different levels of the health system who can be drawn upon for training in other districts. 51 regional, zonal, and national staff were trained to conduct district-level IDSR training. They in turn trained 96 members of the district health management teams. 32 district team members were trained to conduct facility-level IDSR training, and they trained 787 facility health workers in 591 facilities.

The training and follow-up approaches strengthened linkages among districts and sectors and between different levels of the health system. This is expected to contribute to improved collaboration and capacity for surveillance and response.

Zonal and regional staff involved in the project have expressed their desire to use the projects training materials and methods to expand IDSR training to other non-project districts in their areas.One of the most immediate results seen in project districts after IDSR training was a rapid improvement in timeliness and completeness of IDSR weekly and monthly reports sent from facilities to each district. Timeliness is defined as the proportion of facility reports received by the district according to established deadlines, and completeness is defined as the proportion of facilities submitting IDSR reports to the district, regardless of timeliness.

This graph shows timeliness and completeness of weekly IDSR reports from Babati district in 2004. Before training, both were in the 10-30% range. After district and facility IDSR training, timeliness and completeness rose to between 50 and 70%. This means that the district has significantly more complete and timely data to identify possible outbreaks and take actions early to contain them and reduce morbidity and mortality.

Similar patterns were seen in other project districts. This graph shows the average proportion of facilities sending weekly IDSR reports to six districts with complete pre- and post-training data. Completeness improved in each district following training. Districts also showed increased timeliness of IDSR reports submitted by facilities. This graph shows, for 8 districts with complete data, that the proportion of facilities submitting monthly IDSR reports on time rose after training. Similar patterns were observed for weekly IDSR reports.As the project nears completion, it has left the structure in place to expand IDSR strengthening efforts throughout the country. The materials and methods are endorsed and ready for scaling up, but funds are lacking.

Materials are being distributed to zonal training centers so that they can expand the IDSR training to other districts, as resources permit.

A proposal has been written and is being considered for submission to the Global Fund for AIDS, TB and Malaria to both scale up IDSR training and to provide general health system strengthening support to build Tanzanian capacity to meet the surveillance and data needs of all disease prevention and control programs. The country may seek additional support from other sources as well.

I would like to finish with a brief discussion of a few of the main lessons learned during project implementation.First, before embarking on a health information systems or surveillance system strengthening project, it is critical to begin by analyzing the system in detail to ensure that standards exist and are consistent. This is particularly important when taking a task-oriented training approach that clarifies roles and responsibilities of specific individuals at different levels in the health system.On a related note, substantial time and effort is required to adapt generic reference or training materials to the needs of a specific country. Our experience reinforced the need to focus surveillance strengthening efforts at the facility and district levels. Too often, surveillance and information systems strengthening starts from the national level and works its way down. However, the actual data flowing upwards in the system emanate from the health facilities, and thus it is critical to work with health workers to improve the quality of data as well as their reporting to districts. Districts need assistance to be able to use these data for outbreak detection and to analyze them appropriately for their own use, rather than simply sending data up to the national level without critical review.Our experience also reinforced that training is an important first step for improving data quality and use, but that follow-up support is equally important to maintain these improvements.Finally, the issue of integration of surveillance data from multiple vertical programs remains a big challenge and will require substantial efforts from a wide variety of stakeholders to be successful. Each vertical program has its own demands for data, yet there is very limited capacity at the facility and district levels to meet these often competing demands. Partners who work to improve surveillance and information systems must be mindful of how to best integrate existing efforts to minimize the burden on health staff stretched too thin at the district level.

This map shows the location of the twelve districts in which the project was implemented. Districts were chosen to ensure a mixture of rural and urban locations and to provide the opportunity to work to improve system performance in a variety of settings throughout the country.This graph shows completeness of weekly IDSR reports for the first four districts trained in quarters 1 and 2 of 2004. With the exception of Dodoma Rural district, which had unusually good performance throughout the year, the districts showed substantial improvements after training in the proportion of facilities sending weekly reports to the districts.The second group of four districts that were trained in the third quarter showed similar improvements in completeness of facility weekly reports to the districts. Two districts, Masasi and Tunduru, averaged 5% completeness before training and over 80% completeness after training. In addition to getting IDSR data from all facilities in the district, it is important that the data be received in a timely manner to ensure that unusual events, such as outbreaks, will be detected and responded to quickly.

This graph shows the percentage of weekly IDSR reports received on time by each of the first four districts trained. The general trend showed improvements in report timeliness after IDSR training, though there is still room for improvement.Timeliness of weekly reports also improved after training in the second set of four districts trained.

improving surveillance data quality and use in tanzania

Documents

idsr materials

funded idsr

facility level idsr

idsr task forceready

national idsr task force

log of weekly facility

data management

national level