© professor rifat atun. imperial college london, 2008 positive synergies between global health...

© Professor Rifat Atun. Imperial College London, 2008

Positive Synergies between Global Health Initiatives

Professor Rifat AtunProfessor of International Health Management, Imperial College London &Director Strategy, Policy and Performance Cluster, The Global Fund to Fight AIDS, TB & Malaria


Key Research Questions

• What are the extent and nature of integration of GHIs (programmes they finance) and health systems to achieve synergies in varied contexts?

• Which factors influence the extent and nature of integration?

• How the varied health system designs and delivery structures influence outcomes?

© Professor Rifat Atun. Imperial College London, 2008Atun, Ohiri, Adeyi, 2008

Key variables affecting the nature and extent of integration

1. The Problem being addressed

2. The Intervention

3. The Adoption System

4. The Health System characteristics

5. The Broad Context


Integrate or not to integrate: framework for analysis

InterventionAdoption

System

Broad Context

Broad Context

Health System Characteristics

Problem


The Problem

• Necessity and Urgency • Burden– Economic and social

• Perceived and real• Social Narrative

• Transmission dynamics


The Intervention

• Complexity

• Scalability

• Simpler to more complex*

• Replicability

* See next slide


Intervention: simple versus complex

Single episode

Multiple episodes

Few elements

Multipleelements

Less complex

More complex

Atun and Kyratsis 2007



Few stakeholders

Multiple stakeholders

Few levels

Multiplelevels

Less complex

More complex




User engagement lower

User engagement higher

Technologydominates

Less complex

More complex


Behaviourdominates


The Adoption System

• Receptivity • Individual & organisational

• Political economy• Incentives

– agency/provider/user incentive alignment

• Legitimacy– Cognitive

– Technical

– Normative

– Economic


Health System Characteristics

• Feasibility • Governance

• Structure and organization

• Financing

• Provider payment methods

• Resource availability

• Service delivery

• M&E system


The Context

• Sustainability

• Attributability

• Fiscal space– Overall and health sector

specific

• Frailty

• Reporting needs


The Context

• Opportunity

• Desirability

• Critical events– Visibility

• Synergy

• Technology / innovation

• Political economy

• Socio-cultural factors


Analysing the extent and nature of integration


Integration into Critical Health System Functions

1. Governance– Reporting– Accountability

2. Financing– Pooling– Provider payment

3. Planning – Needs assessment– Priority setting – Resource allocation


Integration into Critical Health System Functions

4. Service Delivery– Structural

• Human resources, • Shared infrastructure

– Operational integration• Supply chain• Guidelines• Procurement

5. Monitoring and Evaluation– Data collection and analysis

6. Demand Generation– Financial incentives – e.g. CCT, insurance– Population interventions – e.g. education and promotion


Some Initial Results


Intervention Complexity

Few Many

Single

Multiple

Dengue - Cuba Malaria - Colombia

Schistosomiasis - Brazil, Burundi, Cameroon, China, Saudi Arabia, Uganda

Leprosy - India, Sri Lanka

Onchocerciasis - Uganda

Nutrition - Peru, etc.

IMCIICDS

Dular - India

FP/MCH - Matlab, Bangladesh

FP/MCH - Pakistan - LHWPFP/MCH - Nepal (Tuladhar)

HIV/AIDS - Haiti

STD - Mbofana

FP; STD - Lafort

Mental health - WhettenSubstance abuse - Friedmann

Intervention

elements

Intervention frequency/number of episodes


Extent of integration & success as documented in studies

Fully integrated

Partially integrated

Not integrated

? Unknown

Most to all outcomes

Mixed outcomes

Few to no outcomes

? Unknown



Success

Cuba (ToledoRomani2007)

Colombia (Rojas2001)Malaria

Dengue

Service deliv

ery

Finance

Governance

Demand generatio

n

Monitorin

g & E

valuation

?

Planning



Success

Service d

elivery

Finance

Governance

Demand g

eneratio

n

Monitorin

g & E

valuatio

n

Planning

Schistosomiasis control

Burundi (Engels1993,1995)

Cameroon (Bausch1995,Cline1996)

China (Sleigh1998)

Saudi Arabia (Ageel 1997)

Brazil (Filho1992)

?

?

?

?

?

?

?

?

?

?

?

Uganda (Kabatereine 2006)



Success

Service d

elivery

Finance

Steward

ship/G

overnance

Demand g

eneratio

n

Monitorin

g & E

valuatio

n

Planning

India (Rao 2002, Thakar 2003)

Leprosy

Sri-Lanka (Kasturiaratchi 2002)

??



Success

Service d

elivery

Finance

Governance

Demand g

eneratio

n

Monitorin

g & E

valuatio

n

Planning

Peru

Nutrition

Bangladesh (Hossain2005) ??

Various (Deitchler2004) ?



Success

Service d

elivery

Finance

Governance

Demand g

eneratio

n

Monitorin

g & E

valuatio

n

Planning

IMCI*

ICDS - India (Agarwal2000, Kapil1999)

Child health & development

Dular - India (Dubowitz2007)

?

?

?



Success

Service d

elivery

Finance

Governance

Demand g

eneratio

n

Monitorin

g & E

valuatio

n

Planning

Bangladesh – FPHSP (Philips1984, de Graff 1986)

Pakistan – LHWP (Douthwaite 2005)

Family Planning services

Nepal (Tuladhar 1982)

?

?

?

?

?

?

?

?

?



Success

Service d

elivery

Finance

Steward

ship/G

overnance

Demand g

eneratio

n

Monitorin

g & E

valuatio

n

Planning

Haiti (Peck 2003)

HIV/AIDS & STD services

? ??


Conclusions

1. Extent and nature of integration varies

2. Context matters: complex adaptive systems at play

3. Reductionist approaches counterproductive: aim to ‘unpack’ what is meant by integration


Empirical Research


Case Study Approach

• Exploratory

• Descriptive

• Explanatory


Design

Logic of design key

Single Multiple

Holistic

Embedded

Russia TB

Estonia PHC

Russia HIV

Africa HIV

Euro PHC

Tech adoption

Baltic PPP


Embedded units

• HIV

• TB

• Malaria

• NTDs

• Regions


Analytic vs. Statistical Generalisation

• Cases not sampling units but each akin to an individual ‘experiment’

• Analytic generalisation using theory developed a prioriReplication logic– n number of case studies support the same theory– n number of case studies do not support a rival theory

• Statistical generalisationSampling logic


Careful case selection

• Literal replication– Each predict similar results (n=4)

• Theoretical replication– Predict contrasting results --- but for predictable

reasons (n=4)


Theoretical framework and propositions key

• State the conditions under which particular phenomena are likely to be found– Allows literal replication

• State the conditions when particular phenomena are not likely to be found– Allows theoretical replication


Closed vs. flexible design

• Closed but with inductive analysis– Retain replication logic– Build theory as an output– Test ‘additional’ new/alternative propositions

• Flexible and inductive– Risk of drift


One or two tail design

• Good outcome

• Good and poor outcome


Data

• Mixed methods

• Multiple sources

• Inductive

• Iterative

• Triangulation


Process

Agree theory

Generate

propositions

Analytical tools

approach & d/base

Pilot cases

Case studies Case studies

Refine tools

Literal replication Theoretical replication

Explanatory theory & Evidence

Propositions Rival propositions

Construct validity Reliability

Construct validity

Internal validity

Internal validity External validity


Cases

Africa

• Tanzania

• Ghana

SE Asia

• Thailand

• Viet Nam

Embedded units of analysis

• NTDs + malaria + TB + HIV

• NTDs + malaria + TB + HIV

• Malaria, TB, HIV?

• Malaria, TB, HIV?

© professor rifat atun. imperial college london, 2008 positive synergies between global health...

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