routine data systems related to case management mac otten surveillance, monitoring, and evaluation...
TRANSCRIPT
Routine data systems related to case management
Mac Otten
Surveillance, Monitoring, and Evaluation
Global Malaria Programme, WHO
Content
• Recommended routine data package for high-burden African countries– Core indicators and data elements– Core analysis– Use of data for decision making
• Impact monitoring
• Gaps in routine data systems
• Proposed remedies
Two types of routine data
• Logistics distribution data (logisticians)– National– Sub-national stores– District distribution to health facilities
• Health facility– Logistics– Disease surveillance
Indicator philosophy
• Simple
• Fit into integrated HMIS
• Full stock data at health facility is too much (stock-outs y/n)
• Operational manual being printed
Routine disease surveillance
• Indicators– Impact
• Confirmed malaria cases
• Test positivity rate• In-patient malaria cases• In-patient malaria
deaths
– Quality• % tested (diagnostic)
• Data elements– Out-patient
• Suspected• Tested• Confirmed
– In-patient• Cases• Deaths
Out-patient data collection form
• Epidemiologic data– Suspected– Tested– Confirmed
• Lab data– Tested– Positive
Routine logistics and reporting indicators
• Logistics– Number treated with ACT– % ANC1 received LLIN– % IPT2
• Stock-outs (yes/no)– ACT, RDT, LLIN
• Completeness of reporting– Health facility, district
Case management
• ACT– Number treated– Stock-out (yes/no)
• RDT– % tested (number tested)– Stock-out (yes/no)
Community data elements
• No. workers expected to report• No. workers reported this month• Suspected malaria cases seen • Suspected cases tested for malaria• Confirmed malaria cases• Cases referred• No. workers with stock-out of ACT• No. workers with stock-out of RDT
Core graphs
Out-patient confirmed malaria cases and % of suspected cases tested
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Out-patient confirmed malaria cases, all ages
% out-patient suspected cases tested
In-patient malaria cases and deaths
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In-patient malaria cases, <5 yo
In-patient non-malaria cases, <5 yo
In-patient malaria deaths, <5 yo
In-patient non-malaria cases, <5 yo
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In-patient malaria cases, <5 yo
In-patient malaria deaths, <5 yo
Confirmed + % tested Inpt cases and deaths
Out-patient malaria test positivity rate
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%
case
s p
osi
tive
<5 yo All ages
Test positivity rate
Percentage coverage of ACT, LLIN, and IPT 2nd dose
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Per
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% cases treated by ACT
% LLIN / ANC1
% IPT 2
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% HF without stock-out of ACT
% HF without stock-out of RDT
% HF without stock-out of LLIN
% treated with ACT
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% HF without stock-out of ACT
% HF without stock-out of RDT
% HF without stock-out of LLIN
% HF with stock-outs
Percentage completeness of reporting by health facilities and by districts
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2007 2008
% c
ompl
eten
ess
of r
epor
ting
Health facility District
Completeness of reporting
Link to decision-making
• Provincial supervision
• Regular meetings– Health facility with community– District with health facility staff– Province with district (quarterly)– Province with national (quarterly)
• Monthly national malaria feedback bulletin
Monthly national feedback bulletin
National impact &logistics
Impact by district Logistics by district
340,493 5.8 mil
410,320 2,435,934
49,202 112,953
10,290 44.0
430,222
390,000 YTD 2008 % Reduction
40,536 17,769 56%
1.5 m 39,299 8,879 77%
1.3 m 4,928 1,332 73%
11.3 m 5,832 964 83%
6.7 m 53%
50%
Out-patient malaria test positivity rate
Out-patient malaria test positivity rate In-patient malaria cases and deaths
In-patient malaria deaths, all ages
In-patient malaria deaths, <5 yo
No. houses sprayed with ≥1 round
No. persons at risk of malaria
No. persons protected with ≥1 round
% protected with ≥1 round
Trends in surveillance/impact indicators
In-patient malaria cases, all ages
In-patient malaria cases, <5 yoNo. houses targeted for ≥1 round
National IRS data, 2008
RDTStock at end of month National-level surveillance data, 2008, Year-To-Date (YTD)
Stock needed for next month Reference period 2007
Commentary:
LLINStock at end of month No. of LLIN district this year (year-to-date)
Estimated coverage with LLIN
No. of persons at risk for malaria
No. of LLIN distributed in past 2 years
National Malaria Programme (Country XXX)
Monthly Surveillance and Logistics ReportBased on data available at end 12.2008
Trend in logistics and reporting completeness indicators
Stock needed for next month
Stock needed for next month
Stock for public sector at national level Estimated national coverage (possession) with LLIN
ACTStock at end of month
Out-patient confirmed malaria cases and % of suspected cases tested Out-patient all-cause and suspected malaria cases
Percentage coverage of ACT, LLIN, and IPT 2nd dose
Percentage of health facilities without stock-outs
of ACT, LLIN, and RDT
Percentage completeness of reporting by health
facilities and by districts
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% c
ase
s p
osi
tive
<5 yo All ages
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Ca
ses
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De
ath
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In-patient malaria cases, <5 yo
In-patient non-malaria cases, <5 yo
In-patient malaria deaths, <5 yo
In-patient non-malaria cases, <5 yo
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1 2 3 4 5 6 7 8 9101112 1 2 3 4 5 6 7 8 9 101112 1 2 3 4 5 6 7 8 9101112 1 2 3 4 5 6 7 8 9 101112
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All-
cau
se c
ase
s
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Out-patient all-cause cases Out-patient suspected malaria cases
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Per
cent
age
% cases treated by ACT
% LLIN / ANC1
% IPT 2
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2007 2008
% h
ea
lth fa
cilit
ies
with
ou
t sto
ck-o
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% HF without stock-out of ACT
% HF without stock-out of RDT
% HF without stock-out of LLIN
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ses
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Pe
rce
nta
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Out-patient confirmed malaria cases, all ages
% suspected cases tested
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2007 2008
% c
ompl
eten
ess
of r
epor
ting
Health facility District
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% c
ase
s p
osi
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<5 yo All ages
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De
ath
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In-patient malaria cases, <5 yo
In-patient malaria deaths, <5 yo
Region District Pop (x1000)Ref.
periodCurrent
year % declineRef.
periodCurrent
year % decline <5 yo All ages Con- firmedTotal malaria
casesRate / 1000 All ages
Centrale Blitta 129 155 93 40 48 - - 59 54 5,838 17,025 13 63
CHR Sokodé - 0 136 - 0 32 - 58 52 322 605 - 102
Sotouboua 162 468 528 -13 18 40 -122 80 53 8,506 37,340 23 43
Tchamba 96 107 23 79 6 5 17 69 51 5,604 30,570 32 36
Tchaoudjo 198 667 589 12 117 85 27 64 54 18,030 55,246 28 60
sub total 585 - 1,369 - - 162 - 329 53 38,300 140,786 36 34
Kara Assoli 58 3 6 -100 1 0 100 68 59 4,271 16,097 28 45
Bassar 119 69 87 -26 10 6 40 71 46 5,261 23,382 20 49
Binah 75 98 118 -20 6 11 -83 60 5,642 15,980 21 58
CHU Kara - 0 83 - 0 23 - - - - 108 - -
Dankpen 85 51 57 -12 10 4 60 74 68 2,366 6,127 7 56
Doufelgou 95 54 67 -24 9 5 44 74 64 8,750 20,494 22 67
Kéran 81 145 136 6 10 9 10 67 59 5,969 14,755 18 68
Kozah 240 289 555 -92 143 144 -1 67 57 22,969 56,505 24 71
sub total 753 - 1,109 - - 202 - 70 58 55,228 153,448 33 38
Lomé CHR-LC - 60 70 -17 0 1 - 76 28 230 962 - 87
DDS1 43 0 36 - 0 1 - - 33 3,153 11,545 27 82
DDS2 309 0 - - 0 - - 74 43 2,693 18,061 6 35
DDS3 251 356 626 -76 33 64 -94 74 24 3,258 17,637 7 77
DDS4 79 0 0 - 0 0 - 67 38 3,007 12,465 16 64
DDS5 279 987 1,081 -10 78 90 -15 67 41 7,079 22,013 8 78
sub total 962 - 1,813 - - 156 - 19,420 82,683 14 40
Maritime Avé 100 24 20 17 2 4 -100 31 58 5,304 15,095 15 60
CHR Tsévié - 145 189 -30 10 13 -30 37 44 2,057 2,743 - 172
Golfe 353 0 - - 0 - - 68 45 12,219 57,026 16 48
Lacs 265 605 885 -46 51 91 -78 27 39 12,462 47,913 18 67
Vo 256 11 12 -9 0 0 - 42 58 8,628 34,514 13 43
Yoto 174 345 399 -16 2 30 -1400 46 39 4,288 13,303 8 84
Zio 286 0 - - 0 - - 251 51 5,998 19,124 7 61
sub total 1,435 - 1,505 - - 138 - 77 46 50,956 189,718 21 37
Plateaux Agou 106 14 101 -621 1 10 -900 50 52 5,292 14,752 14 69
Amou 104 132 142 -8 7 5 29 44 60 5,862 13,291 13 73
CHR-Atakpamé - 37 39 -5 2 0 100 50 31 129 235 - 174
Danyi 51 6 1 83 0 0 - 75 47 1,446 6,272 12 49
Est mono 93 284 261 8 43 41 5 48 61 8,955 20,086 22 73
Haho 227 574 544 5 28 30 -7 92 77 13,184 27,987 12 61
Kloto 222 403 286 29 56 49 13 436 60 23,146 38,856 18 99
Moyen mono 86 5 0 100 0 1 - 72 69 2,829 6,035 7 68
Ogou 303 690 1,096 -59 57 68 -19 72 69 16,853 37,006 12 66
Wawa 198 103 73 29 15 9 40 47 56 6,670 15,205 8 78
sub total 1,390 - 2,543 - - 213 - 64 63 84,366 179,725 23 43
Savanes CHR Dapaong - 1 - - 0 - - 75 - - - - -
Kpendjal 128 2 2 0 1 0 100 91 53 3,749 17,024 13 42
Oti 146 216 252 -17 15 25 -67 75 57 6,373 20,398 14 55
Tandjoare 99 0 8 - 0 0 - 84 55 4,574 17,938 18 47
Tone 293 258 278 -8 57 68 -19 81 40 11,195 57,366 20 48
sub total 666 - 540 - - 93 - 47 48 25,891 112,726 25 33
Total 5,790 - 8,879 - - 964 - 63 53 274,161 859,086 24 38
Surveillance data by district, 2008
Year-To-Date since the beginning of the year, compared to the same period during the reference year(s) (2007.1 - 2007.12)
<5 years old <5 years old
deathscases
In-patientIn-patient Out-patients
% cases positive / tested
% tested / suspected
Incidence, all ages
Region District
No. HF-month reports
expected
No. HF reports
received % ACT RDT LLIN No. ACT % ACTNo. LLIN at
ANC% LLIN /
ANC1No. IPT 2nd
dose% IPT2 /
ANC1
Centrale Blitta - - - - - 27 4615 29 1,989 61 1,666 51
CHR Sokodé - - - - - 0 0 0 40 32 51 41
Sotouboua - - - - - 46 17186 48 3,305 76 3,252 75
Tchamba - - - - - 28 8548 29 1,816 45 2,158 53
Tchaoudjo - - - - - 39 21563 41 4,057 70 3,153 55
sub total 0 0 - 0 0 37 51,912 39 11207 64 10,280 58
Kara Assoli - - - - - 36 5723 37 990 73 1,105 82
Bassar - - - - - 40 9464 43 1,305 35 2,562 68
Binah - - - - - 34 5385 35 1,459 60 1,266 52
CHU Kara - - - - - - 0 - 20 100 55 275
Dankpen - - - - - 32 1961 34 1,412 62 1,745 77
Doufelgou - - - - - 67 13821 72 1,099 50 1,629 74
Kéran - - - - - 15 2230 17 765 31 1,566 63
Kozah - - - - - 37 21174 40 3,810 67 4,292 75
sub total 0 0 - 0 0 39 59,758 42 10860 54 14,220 70
Lomé CHR-LC - - - - - 82 791 85 0 0 113 58
DDS1 - - - - - 51 5845 54 691 21 1,813 55
DDS2 - - - - - 34 6063 35 3,662 79 2,489 54
DDS3 - - - - - 17 2943 17 2,426 64 2,479 65
DDS4 - - - - - 34 4276 36 753 70 471 43
DDS5 - - - - - 48 10509 58 1,930 61 2,053 65
sub total 0 0 - 0 0 37 30,427 40 9462 59 9,418 58
Maritime Avé - - - - - 41 6180 44 1,158 48 1,243 52
CHR Tsévié - - - - - 53 1464 59 231 86 94 35
Golfe - - - - - 26 14954 28 3,667 42 6,236 71
Lacs - - - - - 33 15751 34 3,086 51 3,642 60
Vo - - - - - 22 7639 23 2,058 56 1,943 53
Yoto - - - - - 44 5910 47 1,529 52 1,908 65
Zio - - - - - 50 9594 55 2,696 52 3,233 63
sub total 0 0 - 0 0 32 61,492 34 14425 49 18,299 63
Plateaux Agou - - - - - 33 4822 35 930 40 1,452 62
Amou - - - - - 34 4567 38 911 29 1,648 53
CHR-Atakpamé - - - - - 0 0 0 0 - - -
Danyi - - - - - 27 1691 28 673 58 549 47
Est mono - - - - - 55 11013 59 1,524 39 2,034 52
Haho - - - - - 30 8454 32 2,611 43 3,644 60
Kloto - - - - - 38 14785 40 1,708 25 4,267 62
Moyen mono - - - - - 63 3783 68 877 57 1,034 67
Ogou - - - - - 33 12262 36 2,902 38 4,549 59
Wawa - - - - - 50 7660 52 1,361 38 2,363 65
sub total 0 0 - 0 0 38 69,037 41 13497 37 21,540 59
Savanes CHR Dapaong - - - - - - 0 - 0 - - -
Kpendjal - - - - - 32 5375 33 1,550 56 1,499 54
Oti - - - - - 55 11256 61 2,025 46 1,744 40
Tandjoare - - - - - 59 10673 64 1,955 80 2,105 86
Tone - - - - - 51 29,113 53 3469 38 6,182 68
sub total 0 0 - 0 0 50 56,417 53 8999 48 11,530 62
Total - - - - - 38 329,043 41 68450 50 85,287 62
IPT 2nd dose
Logistics and completeness of reporting data - latest month
Completeness of health facility reporting
Treatment with ACT
% health facilities without stock-outs
LLIN distributed at antenatal clinics
(ANC)
Case management and disease surveillance
• Resistance– Trape: West and central Africa– Greenburg: Kinshasa– Kilifi: Lancet 2008– Gambia: Lancet 2008
• Impact– Zanzibar– Macha, Zambia
• Case-based– Fake drugs
Impact of ACT use for 24 months in 13 public health facilities, North A district, Zanzibar, 2002-2005
Measure of impact Measure- ment
method
Before ACT intervention,
2002
After ACT intervention,
2005
% decline
ACTs only, public sector
<5y in-patient malaria cases Routine 1261 296 77
<5y in-patient malaria deaths Routine 40 10 75
<5y out-patient malaria cases Routine 20634 4817 77
<5y % asexual parasite + Survey 9.0 5.3 41
<5y all-cause mortality Vital event
registration
133 64 52
Source: Bhattarai et al. Impact of artemisinin-combination therapy and insecticide treated nets on malaria burden in Zanzibar. PLOS November 2007.
In-patient predictive value is good,Evidence from national data
• Matches with out-patient lab-confirmed malaria cases
• ~90% decline in Zanzibar and Sao Tome and Principe
• Pronounced seasonality
• In-patient malaria trends nearly identical to very severe anemia trends
Limited impact/wasted resources in many countries
• Severe cases and deaths should be rapidly reducing
• Reasons– Stock-outs at national level
• Global supply chain issues
– Stock-outs at health facility• Weak routine data
• Weak supervision
• Inadequate analysis for action
Zanzibar
0
2000
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14000
16000
2000 2001 2002 2003 2004 2005 2006 2007
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tpat
ien
t co
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rmed
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atie
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s
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2001 2002 2003 2004 2005 2006 2007 2008
In-p
atie
nt
case
s
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1000000
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4500000
5000000
Ou
t-p
atie
nt
case
s
In-patient malaria cases
In-patient non-malaria, non-anemia cases
Out-patient non-malaria, non-anemia cases
Zambia
Percentage of <5yo fever cases that went to
public facility for treatment
Country Source
% fever cases went to public
facility Country Source
% fever cases went to public facility
Sao Tome MICS 2000 72% Mali DHS 2006 37%Gambia MICS 2006 67% Cameroon DHS 2004 35%Guinea-Bissau MICS 2000 60% Burkina Faso DHS 2003 32%Mozambique DHS 2003 55% DR Congo MICS 2001 32%Tanzania DHS 2004 55% Kenya DHS 2003 30%Namibia DHS 2000 54% Guinea DHS 2005 29%Zambia DHS 2001 54% Togo DHS 1998 29%Congo DHS 2005 51% Niger DHS 2006 29%Burundi MICS 2000 47% Uganda DHS 2006 28%Equatorial Guinea MICS 2000 47% Madagascar DHS 2003 27%Sierra Leone MICS 2005 42% Côte d'Ivoire MICS 2006 27%Gabon DHS 2000 42% Malawi DHS 2004 26%Senegal DHS 2005 42% Rwanda DHS 2005 26%Benin DHS 2006 41% Nigeria DHS 2003 25%Ghana DHS 2003 41% Chad DHS 2004 12%
Median 40%Source: R. Cibulskis, WHO, 2008
Percentage of health facility-months with stock out of any prepack type in 24 facilities, 2006-2008, Uganda.
0
10
20
30
40
50
60
1 2 3 4 5 6 7 8 9 10 11 12 1 2 3 4 5 6 7 8 9 10 11 12 1 2 3 4 5 6 7 8 9 10 11 12 1 2
2006 2007 2008 2009
Source: MOH/WHO Rapid Impact Assessement, 2009
Routine data systems are not difficult to establishMonthly confirmed cases from all countries in Africa as of 31 May
Remedies
• National-level stock-outs– Monitor each month
• Logistics distribution dataMore TA– Logisticians– Data systems– Analysis
• Health facilityMore routine M&E TA– Data systems– Analysis– Supervision: data and case management– Performance assessments at regular meetings– Monthly bulletin with data by district
Summary
• Routine data important to minimize stock-outs at health facility level and avoid wasted resources
• Routine surveillance can monitor impact and contribute to monitoring drug resistance and fake drugs
• Routine data systems are not difficult to establish– Operational manual ready– Funds available at country level (GF M&E)– Major gap: technical assistance and electronic tools
End