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Ghana Case Study. Malaria-specific Slides. Data for Decision Making. Class Activity: Is it Monitoring or Is it Evaluation? 1. The Director of Health wants to know if interventions being implemented in Region A are increasing ITN use in pregnant women and children under five in that region

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ghana case study

Ghana Case Study

Malaria-specific Slides

class activity is it monitoring or is it evaluation 1
Class Activity: Is it Monitoring or Is it Evaluation? 1
  • The Director of Health wants to know if interventions being implemented in Region A are increasing ITN use in pregnant women and children under five in that region
  • Minister of Health requires information on quantities of RDTs used in health facilities in 2009
  • A country director is interested in finding out if the population knows about and is using the voucher scheme for ITNs
class activity is it monitoring or is it evaluation 2
Class Activity: Is it Monitoring or Is it Evaluation? 2
  • The Director of MCH wants info on # of pregnant women receiving two or three doses of SP (IPT1 and IPT2)
  • Current under-five mortality rate needs to be determined

NOTE: DATA ARE KEY TO MONITORING AND EVALUATION

component 1a decision maker
Component 1a: Decision Maker
  • Decision maker is a person responsible for acting at any level:
    • Lower levels: Community Leader
    • Middle level: DDHS
    • Top level: Program Manager, D-G
    • Global level: RBM Executive Director/ WHO DIR-GEN
stakeholder analysis tool on pen drive useful for m e plan to identify stakeholders and their needs
Stakeholder Analysis (Tool on pen drive, useful for M&E plan to identify stakeholders and their needs)
examples of decisions could be deductive inductive or logical
Examples of Decisions (could be Deductive, Inductive, or Logical)
  • Policymaking: e.g., ITN Policy
  • Strategic Planning: RDTs/ IRS Targeted
  • Program Management: e.g., Zoning with staff to enhance monitoring
  • Resource Allocation: e.g., GBF Budget
  • Drugs and Commodities, Human Resources, Infrastructure and Equipment
making data speak
“Making Data Speak”
  • Results:
    • Stakeholders took informed decision to change from chloroquine to ACTs
    • Implementation Framework drawn with timelines, persons responsible, resources needed
    • Task Teams formed to address various aspects
    • Development of Anti-Malaria Drug Policy/Procurement of ACTs
    • Incorporation into Country Drug Policy and Standard Treatment guidelines
making data speak cont
“Making Data Speak” (cont.)
  • Results:
    • Updating of training manuals, guidelines
    • Communication and behavior change communication
    • Launching/adoption of new policy
    • Training/capacity strengthening
    • Monitoring: drug quality, pharmacovigilance, use, prescriber habits
slide10

Anti-malaria drug policy change is an on-going process

Development of Policy

Updating of Policy

Implementation of Policy**

Re-evaluation of Policy

Monitoring of Policy

slide12
Mean

Average number of confirmed malaria cases per month

Sum of the values, divided by the number of cases – also called average

Total number of cases

Number of observations

Mean number of cases

Very sensitive to variation

median
Median
  • Represents the middle of the ordered sample data
  • For odd sample size, the median is the middle value
  • For even, the median is the midpoint/mean of the two middle values

Median number of confirmed malaria cases

Median for 2008

Median for 2009

Not sensitive to variation

slide14
Mode
  • Value that occurs most frequently
  • It is the least useful (and least used) of the three measures of central tendency

Mode number of confirmed malaria cases

Mode for 2008

Mode for 2009

annual parasite incidence api
Annual Parasite Incidence (API)

Number of microscopically confirmed malaria cases detected during one year per unit population

Confirmed malaria cases during 1 year

API

X

1,000

Population under surveillance

interpreting data
Interpreting Data
  • Does the indicator meet the target?
  • What is the programmatic relevance of the finding?
  • What are the potential reasons for the finding?
  • What other data should be reviewed to understand the finding (triangulation)?
  • How does it compare (trends, group differences)?
  • Conduct further analysis.
practical
Practical
  • Question:
    • Are ANC clinics in country X reaching their coverage targets for IPTp?
  • Data Source:
    • Routine health information
slide19

Data Source

General ANC Registers

  • Which of these variables are relevant for answering your question?
  • Have you defined the use of each relevant variable?
  • Answers:
    • 1) New ANC clients, IPTp-1
    • 2) New ANC clients = Denominator,
    • IPTp-1 and IPTp-2 = Numerator
iptp coverage facility performance
IPTp Coverage – Facility Performance

Number of ANC clients receiving IPTp

  • Question:
    • Among the five facilities, which one performed better?
  • Answer:
    • Cannot tell because we don’t know the denominators
iptp coverage facility performance1
IPTp Coverage – Facility Performance

Number of ANC clients receiving IPTp

Question: Now that you have the denominators, which facility performed better?

Response: Facility 5

tables
Tables

Percentage contribution of reported malaria cases, by year (2000–2007), Kenya

Source: WHO, World Malaria Report 2009

bar chart1
Bar chart

Source: Quarterly Country Summaries, 2008

stacked bar chart1
Stacked bar chart

% Children <5 with Fever who Took Specific Anti-Malarial, 2007–2008

line graph
Line graph

Number of Clinicians* Working in Each Clinic During Years 1-4, Country Y

*Includes doctors and nurses.

caution line graph
Caution: Line graph

Number of Clinicians* Working in Each Clinic During Years 1-4, Country Y

*Includes doctors and nurses.

how should you present
How should you present…
  • Prevalence of malaria in Ghana over a 30-year period?
  • Data comparing prevalence of malaria in 10 different countries?
  • Data on reasons why individuals are not using ITNs (out of all individuals surveyed who own an ITN and are not using it)?
  • Distribution of patients tested for malaria by parasite density?
slide35
MEASURE Evaluation is a MEASURE project funded by the

U.S. Agency for International Development and implemented by

the Carolina Population Center at the University of North Carolina

at Chapel Hill in partnership with Futures Group International,

ICF Macro, John Snow, Inc., Management Sciences for Health,

and Tulane University. Views expressed in this presentation do not

necessarily reflect the views of USAID or the U.S. Government.

MEASURE Evaluation is the USAID Global Health Bureau's

primary vehicle for supporting improvements in monitoring and

evaluation in population, health and nutrition worldwide.