Presentation by Neeraj Trivedi on "Training of district officials in Bihar" at Developing a nutrition training roadmap to support India’s nutrition progress (17-18 Dec 2019)

1. Reflections from the Patna Winter
School (29th-30th Nov, 2019)
Neeraj Trivedi and Sameeksha Jain,
Evidence for Policy Design (EPoD) India at IFMR
17 December 2019,
Nutrition Training Roadmap Workshop, Bhubaneswar

2. Background – Capacity Building Needs Assessment
• Objective: Commissioned by the IGC for building capacities of state and district officials
in Bihar to use data for decision-making (July 2018)
• Needs Assessment with state and district officials from various organizations - 6 govt.
departments, 1 non-profit, 1 academic institute (July – Dec 2018 )
• Key Insights
• Input and process-based data, primarily for tracking targets
• Misaligned incentives for collecting quality data; Multiple parallel formats
• Sub-optimal use of data for problem identification and diagnosis
• Data meant to be sent up to the higher officials for reporting! Lack of feedback loops
• Demand for data validation and verification skills

3. Winter SchoolTraining Program
• SecondWinter School conducted on 29th and 30th Nov, 2019
• Participants:
• 23 State-level
• 11 District-level
• 4 Block-level
• Organizations:
• ICDS (12)
• JEEViKA (8)
• Women
Development
Corporation (4)
• UNICEF (4)
• Centre for Catalyzing Change (3)
• Directorate of Economics and
Statistics (5)
• Pratham Bihar (1)
• Centre for Health Policy (1)

4. Content – Day 1
• Motivational talk
by Shri Balamurugan,
CEO-JEEViKA
• Introduction :
insightful icebreaker –
“the birthday
paradox”)
• Using Data
Systems : blended-
learning module

5. Key Principles in Using Data Systems module
1. Begin with the goal in mind Know (and apply) your theory of change
Plan for efficiencies
Apply the principal-agent framework to data collection
Align incentives
Focus on data quality
Consult with users
Understand the optimal level of aggregation
Build in feedback loops
Link across datasets
Ensure international comparability
Take an iterative approach
Invest in staff and system capacity
2. Understand who is collecting the
data
3. Understand who is using the
data
4. Don’t let data live alone
5. Make your data system a living
system

6. Content – Day 1
• Participants divided into groups and each group given a sectoral goal. Groups
asked to identify
1. Outcomes, process, inputs;
2. Indicators for each step;
3. Assumptions at each step;
4. Who will collect data, what can be potential biases and how can you
minimize
Group work: Building aTheory of Change and aligning
stakeholder incentives

7. Content – Day 2 (Theory of Change & Incentive
alignment)
• Group presentations with Instructor
feedback
• In-class presentation on how to identify
implementation gaps when you have
indicators along aTheory of Change
• Participants went through IDInsight’s
POSHANAbhiyaan Organizing
frameworks

8. Content – Day 2 (Problem Identification &
Diagnosis)
• In-class presentation on using data to
understand problems; Looking at
outcomes for different population
segments (by age, gender, geography etc)
• In-class presentation on using descriptive
data for differential diagnosis
• If this was the underlying cause, what
should I see in the data
• Think like a doctor; the data shows
symptoms to the underlying disease

9. Content – Day 2 (Problem Identification in nutrition
using IFPRI’s District Nutrition Profiles & Data Notes)
• Criticality of the 1000-day period
• Lancet Framework
• Immediate & Underlying determinants of
malnutrition
• Nutrition-specific & nutrition-sensitive
interventions
• Missed opportunities
• What “convergence” should really mean?
• Individual exercise: Participants given DNP &
State Data Note. Asked to do a diagnosis of their own
districts
• 5-7 individuals asked to present

10. Content – Day 2 (Working with administrative
data)
• Feedback loop. Making data meaningful for those who collect it.
• Video of AAA integration work of Antara Foundation
• How visualization can help identify problems and observe interesting
patterns and correlations
• Examples using HMIS, SNCU, MPR data
• Significance of disaggregated data.
• Data quality checks (several examples from ICDS MPR, HMIS and Jeevika data)
• Data type and range checks
• Logical consistency checks
• Patterns andTrends Analysis

11. Logical consistency
• Newborns breast fed within first one hour are less than or equal to total live
births (from HMIS)
• Newborns weighted at birth are less than or equal to live births (from HMIS)
• Number of members trained on HNS should be less than or equal to number of
SHGs trained (from JEEViKA)
• At any point in time,Total number of CMs trained should be less than or equal to
Total number of CMs mobilized (from JEEViKA)

12. Logical Consistency
Type of toilet needs to be filled even when AWW enters there is no toilet in AWC

13. Patterns and trends analysis
 See patterns in data
THR has to be given 2 times a month
for 15 days each.
Here, this is a case of AWW not
being sure of what is asked in the
format.
Some understand it as “THR given
for how many days”
“Others interpret it asTHR
distributed on how many days”

14. Participant reflections…
• “I would recommend authorities to make data collection simple, little informal
and digital so that it is more quicker, accurate and easy to analysis. For
example, using text messages/google sheet for collection of data is not yet
being used at organisation”
• “My agent should get the right incentives, for this I will regularly counsel my
agents and ask them to give actual data and not to manipulate it, and if data
shows poor performance then I will not disincentivize them for this. By regular
support and counselling agents can be incentivised well.”

15. Learnings and next steps
Learnings:
• Need to increase training duration; Never rush group work and group presentations!
• Reconsider blended-learning modules at district/sub-district levels
• Value in collaborations with like-minded organizations
• Value in participants from different departments and combining state, district and block
BUT better targeting needed
Next steps:
• Significant demand from ICDS and JEEViKA for department-focused trainings
• Identify follow up approaches –Whatsapp group? Reminders?
• Follow up survey to assess practice change