SUCCESSFUL LEADERSHIP FOR

MATERNAL, NEWBORN & CHILD HEALTH

A POLICY ANALYSIS OF FACTORS ASSOCIATED WITH

COUNTRIES’ PROGRESS TOWARDS MDGS 4 & 5

Work in Progress –

Discussion of Methods and Preliminary Findings

Sadia Chowdhury, World Bank (schowdhury3@worldbank.org)

Shyama Kuruvilla, PMNCH (kuruvillas@who.int)

Henrik Axelson, PMNCH (axelsonh@who.int)

Daniele Caramani, Univ. of St. Gallen (daniele.caramani@unisg.ch)

From Pledges to Action Pre-Forum Technical session, New Delhi, 12 November 2010

PART I

BACKGROUND AND

ANALYTICAL FRAMEWORK

Background for

policy analysis

•Decade of progress -

declining maternal and

child mortality rates

•Global policy agenda -

G8, African Union, UN

MDG Summit

•Variable progress -

country differences, but

not clear why

Defining leadership

Leadership is the

ability to influence,

motivate, and enable

individuals and

organizations toward

achieving agreed

goals and

commitments.

Analytical framework

PART II

PRELIMINARY FINDINGS OF

COUNTRY CASE STUDIES

Case study: Nepal

• Reproductive health

rights in Constitution

• Long-term health plans

and safe motherhood

policies

• Innovations for targeted

groups

• Remote area strategy for

SM

• Community based

newborn care packages and

insurance

• Contraception

• Challenges:

• Last mile difficult to

achieve

• Scaling up

Case study: Bolivia

• Address barriers to

access:

• Financial: Maternal

and infant insurance

program

• Geographical:

Extensa progam

• Challenges:

• Initial progress, but

plateau

• Inequity

• Neonatal

PART III

DATABASE AND INDICATOR

DEFINITIONS + PRELIMINARY

FINDINGS OF BIVARIATE

REGRESSION ANALYSIS

Domains and sub-categories

• Dependent variables

Progress on MDGs 4 and 5 (e.g. average annual rate of

mortality reduction, 1990-2008)

• Independent variables (critical for progress on MDGs)

Governance

Leadership

Entitlements (policies/laws + financing)

• Mediating variables (health-related mechanisms through

which leadership inputs are channelled towards MDGs)

Human resources and infrastructure

Interventions delivered in the health system

Domains and sub-categories cont.

• Mediating variables cont.

Interventions delivered in the community

Intersectoral interventions (watsan + nutrition)

Equity

• Moderating variables (contextual factors)

Socioeconomic development

Environment

Sociocultural context (e.g. religious, ethnic and linguistic

diversity)

Gender

Education

Demography

Criteria for indicator selection

• Variables hypothesized to influence MNCH outcomes

Informed by literature review

• Consensus on indicator

As evidenced by systematic and standardized use of

indicator in monitoring and evaluation, e.g. indicators

in DHS and MICS, Countdown to 2015

• Regularly collected and publicly available

• Use of indices if possible

E.g. World Governance Index, Global Innovation Index,

Gender-related Development Index

Set-up of database

• Set up Excel-file with indicators and info on data source,

# of data points, etc

• Iterative process to reduce number of indicators

Eliminate if adequately captured by indices and/or other

indicators

Current number: 79

• Data collection, entry and review

Some indictors dropped because too many missing

values

• Preparation for Boolean and regression analysis

Determination of cut-off points

Coding: transfer values to dichotomous + ordinal

Bivariate regression

• Explore associations between dependent (progress and

MDGs 4 and 5) and independent/mediating variables

To identify variables that could be explored more fully

through:

Boolean analysis and further regression analysis

Country case studies

Bivariate regression cont.Variables with association with independent variables

MDG 4 MDG 5

Governance (several indicators) √

Leadership (some of the indicators) √ √

Abortion policy √

Total health spending per capita √ √

ODA per child under five √

Number of doctors and nurses √ √

Malaria treatment √

Child vaccination √

Water and sanitation √

Human Development Index √ √

GDP per capita √

Gender Development Index √ √

PART IV

BOOLEAN ANALYSIS:

RESEARCH DESIGN AND

PRELIMINARY RESULTS

The Nature of Boolean Analysis

Advantages:

1. Comparative approach: analytical ("why" question:

relationship between variables)

2. Few cases and many variables

3. Qualitative data: dichotomous dependent variable

(presence/absence of phenomenon), non-quantifiable

properties

A different logic than statistical analysis:

1. Multiple causation

2. Combinatorial logic: configurations of factors

3. Analysis of necessary and sufficient conditions: crisp-set

and fuzzy-set.

What We Want to Explain

Why are some countries on track and why other are not?

Operational definition depending on MDG4 and 5:

• 20 on-track countries for either/or MDG4 and MDG5. The

countries are all those listed above.

• 6 on-track countries for both MDG4 and MDG5. The

countries are the following: Bolivia, China, Egypt, Eritrea,

Romania, Vietnam.

• 50 countries which are on track for neither MGDs.

Distribution of 70 Country Cases

On track vs. not on track:

On track, progressing regressing:

Independent Variables with

Explanatory Potential (MDG4)

Stronger results for MDG4 than MDG5.

Being on track:

1. Necessary conditions

Leadership culture (index)(100%), proportion of vaccination above 75%

for measles and for 3dose, proportion of population with access to good

quality water and sanitation above 75%, proportion of women who

attended at least once skilled personnel above 50%.

2. Sufficient conditions

gender development indices above .70 and protective leadership culture

above 4 (on the 1 to 7 scale)(100%) .

Summing up: what would "guarantee" outcome (be sufficient)?

[Bold meaning necessary but not sufficient.]

Independent Variables with

Explanatory Potential (MDG5)

As for MDG4: hygiene factors inclusive and protective leadership and

high gender development index: necessary conditions for

a country being on track BUT other factors not helpful

(keep in mind that all countries which are on track for MDG5

are also on track for MDG4 with 1 exception).

MDG5: more prominent role for :

• proportion of women who attended at least once skilled

personnel above 50%;

• attendance of birth by at least 50% of women;

• gender development index above .70.

Model has high "coverage" (0.85) meaning these are necessary

factors BUT low "consistency" (0.50) indicating that it is not

sufficient to lead a country toward being on track for MDG5.

Points for Discussion and Open Questions

1. Triangulation and further analysis: (1) logistic regression (2) case

studies (e.g., no information in dataset on "international linkage") (3)

fuzzy-set analysis.

2. Policy advice: "taking action" factors.

3. The nature of the dependent variable:

- On track vs. not on track is one possibility;

-The other possibility being to try to explain absolute rates of

mortalities rather than progression over time.

4. Definition of leadership; relationship between leadership and

policies, expenditure.

5. Plausibility of operationalisation (examples):

- Gender development index and HDI at .70;

- Immunisations, vaccinations, etc. at 50%;

- Leadership culture indices at 4 (on 1 to 7 scale).

Thank you