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 ([email protected])
Shyama Kuruvilla, PMNCH ([email protected])
Henrik Axelson, PMNCH ([email protected])
Daniele Caramani, Univ. of St. Gallen ([email protected])
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