Ghana Case Study

Malaria-specific Slides

DATA FOR DECISION MAKING

Class Activity: Is it Monitoring or Is it Evaluation? 1The 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

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

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)

Name of Stakeholder Organiza-tion (and specific individual if required)

Stakeholder Description

Potential Role in Activity and Use of Results

Level of Knowledge of Topic

Level of Commit-ment (positive and negative)

Constraints to Participating in Activity

When to Involve

NMCP–Program Manager

Oversees malaria control policy/ strategy development and coordinates implementation

Primary audience – Access to sites. Service guideline revision

High, extensive

Strongly supports scale-up of malaria control strategies

Busy schedule. Needs at least four weeks lead time

Through-out

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” 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.) 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

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

Development of Policy

Implementation of Policy**

Monitoring of Policy

Re-evaluation of Policy

Updating of Policy

DATA ANALYSIS

Mean

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

n

yy i

Month Cases 2008

Jan 30

Feb 45

Mar 38

April 41

May 37

Jun 40

Jul 70

Aug 270

Sep 280

Oct 200

Nov 100

Dec 29

180,1iy

12n

2.9812

180,1y

Average number of confirmed malaria cases per month

Total number of cases

Number of observations

Mean number of cases

Very sensitive to variation

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

Month Cases2008

Cases2009

Dec 29 24Jan 30 29May 37 32

Mar 38 35Jun 40 39April 41 39Feb 45 42Jul 70 65

Nov 100 80

Oct 200 150

Aug 270 200Sep 280 -

Median number of confirmed malaria cases

Not sensitive to variation

432

4541

median

Median for 2008

Median for 2009

39median

Mode

Value that occurs most frequently

It is the least useful (and least used) of the three measures of central tendency

Month Cases2008

Cases2009

Dec 29 24Jan 30 29May 37 32

Mar 38 35Jun 40 39April 41 39Feb 45 42Jul 70 65

Nov 100 80

Oct 200 150

Aug 270 200Sep 280 -

Mode number of confirmed malaria cases

noneemod

Mode for 2008

Mode for 2009

39median

Annual Parasite Incidence (API)

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

Confirmed malaria cases during 1 yearPopulation under surveillance

API X 1,000

Has the Program Met its Goal?

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.

Question: Are ANC clinics in country X reaching their coverage

targets for IPTp?

Data Source: Routine health information

Practical

Code Variables

1. New ANC clients

2. Group pre-test counseled

3. Individual pre-test counseled

4. Accepted HIV test

5A. HIV test result – Positive

5B. HIV test result – Negative

5C. HIV test result – Indeterminate

6 A. Post-test counseled – Positive

6 B. Post-test counseled – Negative

8A. ARV therapy received – Current NVP

9. IPTp-1

10. IPTp-2

Data Source

General ANC RegistersWhich 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

Code Variables

Facility 1 Facility 2 Facility 3 Facility 4 Facility 5

9. IPTp-1 536 1435 39 969 862

10. IPTp-2 372 542 38 452 780

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 Performance

Code Variables Facility 1 Facility 2 Facility 3 Facility 4 Facility 5

1 New ANC Clients 744 2708 105 1077 908

9. IPTp-1 536 1435 39 969 862

10. IPTp-2 372 542 38 452 780

Number of ANC clients receiving IPTp

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

Indicator Facility 1 Facility 2 Facility 3 Facility 4 Facility 5

% of new ANC clients who receive IPTp-1 in the past year

72% 53% 37% 90% 95%

% of new ANC clients who receive IPTp-2 in the past year

50% 20% 36% 42% 86%

Response: Facility 5

Are facilities reaching coverage targets?

Target-80%

DATA DISSEMINATION AND PRESENTATION

Tables

Year Number of malaria cases (n) Relative frequency (%)

2000 4 216 531 8

2001 3 262 931 6

2002 3 319 339 7

2003 5 338 008 10

2004 7 545 541 15

2005 9 181 224 18

2006 8 926 058 17

2007 9 610 691 19

Total 51 400 323 100.0

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

Source: WHO, World Malaria Report 2009

Bar chart

Bar chart

Source: Quarterly Country Summaries, 2008

Stacked bar chart

Stacked bar chart

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

Histogram

Line graph

0

1

2

3

4

5

6

Year 1 Year 2 Year 3 Year 4

Num

ber o

f clin

icia

ns

Clinic 1

Clinic 2

Clinic 3

*Includes doctors and nurses.

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

Caution: Line graph

0

1

2

3

4

5

6

Clinic 1 Clinic 2 Clinic 3

Num

ber o

f clin

icia

ns

Year 1

Year 2

Year 3

Year 4

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

*Includes doctors and nurses.

Pie chart

5923

10

8

Malaria Cases

1st Qtr

2nd Qtr

3rd Qtr

4th Qtr

Pie chart

59%23%

10%

8%

Percentage of all confirmed malaria cases treated by quarter, Country X, 2008

1st Qtr

2nd Qtr

3rd Qtr

4th Qtr

N=257

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?

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.