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 Confirmed + % testedInpt cases and deathsTest positivity rate % treated with ACT% HF with stock-outsCompleteness 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 districtLogistics by district

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, Measure of impactMeasure- ment method Before ACT intervention, 2002 After ACT intervention, 2005 % decline ACTs only, public sector <5y in-patient malaria cases Routine <5y in-patient malaria deaths Routine <5y out-patient malaria cases Routine <5y % asexual parasite + Survey <5y all-cause mortality Vital event registration 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 Zambia

Percentage of <5yo fever cases that went to public facility for treatment Median 40% Source: R. Cibulskis, WHO, 2008

Source: MOH/WHO Rapid Impact Assessement, 2009

Routine data systems are not difficult to establish Monthly confirmed cases from all countries in Africa as of 31 May

Remedies National-level stock-outs –Monitor each month Logistics distribution data  More TA –Logisticians –Data systems –Analysis Health facility  More 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