NUTRITION DAE PROJECT June 1, 2017 Produced by: Lola Arakaki, Dylan Green, Andrew Kwist, Jennifer Slyker |
Meeting Agenda ¨ Background ¨ Objective 1: Surveys ¤ Methodology and Results ¨ Objective 2: Health Management Information Systems ¤ Methodology and Results ¨ Objective 3: Databases ¤ Methodology and Results ¨ Discussion ¨ Conclusion 6/1/2017 | 2
Background Motivation for measuring nutrition ¨ The Bill and Melinda Gates Foundation (BMGF) Nutrition team, Data, Analysis, and Evidence (DAE) initiative seeks to strengthen country data and information systems for nutrition ¨ The 2013 Lancet Maternal and Child Nutrition Series highlights evidence supporting nutrition-specific interventions 1 6/1/2017 | 3
Background Objectives for START Team 1. Conduct review and comparison of nutrition indicators and measurement methods in the current versions of the: Demographic and Health Survey (DHS), ¤ Multiple Indicator Cluster Survey (MICS), and ¤ Standardized Monitoring and Assessment of Relief and Transitions (SMART) ¤ 2. Review published and gray literature documenting inclusion of nutrition indicators in routine health management information systems, with particular focus on the District Health Information System (DHIS2) platform and summarize results 3. Review global databases of nutrition data and provide summary of use 6/1/2017 | 4
Objective 1: Surveys To review surveys and compare nutrition indicators 6/1/2017 | 5
Objective 1: Surveys Methodology • Reviewed • Demographic and Health Survey (DHS) 2,3 • Multiple Indicator Cluster Survey (MICS) 4-6 Survey • Standardized Monitoring and Assessment of Relief and Transitions (SMART) 7,8 • Living Standard Measurement Study (LSMS) review • Based on 5 broad categories, extracted nutrition-specific indicators from DHS and MICS and compare Indicator • Compared results from DHS and MICS reports from Nigeria as a case study 9,10 review • Summarized similarities and differences Analysis 6/1/2017 | 6
Objective 1: Surveys Surveys Survey DHS MICS SMART LSMS Type Nationally- Household survey for Iterative survey Nationally- representative national and sub- methodology used in representative and household surveys national populations emergency and sub-national household developing settings surveys Time frame Typically conducted Rounds administered Variable Variable every 5 years (with 3-5 years interim surveys) # of 90 107 143 (using downloads 38 countries as of 2015) For more information, see Summary Workbook 6/1/2017 | 7
Objective 1: Surveys More on SMART ¨ SMART is a methodology that improves upon survey methods by balancing simplicity for rapid assessment in acute emergencies and technical soundness ¨ Typically collects ¤ Nutritional status of children under-five years n Anthropometric measures – SAM, GAM, MUAC ¤ Mortality rate of population ¤ Food security (optional) ¨ Also offers software to help collect data 6/1/2017 | 8
Objective 1: Surveys More on SMART ¨ In response to SMART, the Centre for Research on the Epidemiology of Disasters (CRED) at the Catholic University of Louvain set up the Complex Emergency Database (CEDAT) as a central repository of survey results ¤ 3432 surveys in the database ¤ Not available for public use ¤ Unclear if still active ¨ Some application of SMART to conduct country-wide surveys 6/1/2017 | 9
Objective 1: Surveys Country-specific surveys ¨ Bangladesh ¤ Utilization of Essential Service Delivery (UESD) n Interim survey to monitor program performance between DHS years n Survey scheme similar to DHS n Uncertain if UESD will be conducted in future years ¤ Food Security and Nutrition Surveillance (FSNS) n Annual surveillance ¨ India ¤ National Family Health Survey (NFHS) n On DHS website, managed by Government of India, TA by USAID and BMGF ¨ Nigeria ¤ National Nutrition and Health Survey (NNHS) n Nationally-representative survey using SMART methodology 6/1/2017 | 10
Objective 1: Surveys Yearly co-occurrence of surveys in BMGF focus countries since 2010 6/1/2017 | 11
Objective 1: Surveys Geographic co-occurrence of DHS and MICS since 2010 India Burkina Faso Bangladesh Ethiopia Nigeria # of Surveys Countries DHS only 55 MICS only 46 Both DHS and MICS 24 6/1/2017 | 12
Objective 1: Surveys Summary of nutrition-related indicators by domain and survey Total Nutrition Individual nutrition Individual nutrition Overlapping Indicators Collected indicators collected indicators collected indicators between between DHS/MICS in DHS in MICS surveys Diet Quality & 18 18 16 16 Feeding Practices Anthropometry 9 9 4 4 Micronutrients 13 13 2 2 Care-Seeking 18 17 15 15 WASH 4 3 4 3 Total 62 60 41 41 6/1/2017 | 13
Objective 1: Surveys Summary of differences in indicators collected by both surveys Indicator Category (total n) Population of interest WASH (n=3) Question syntax Care-Seeking (n=15) Numerator/denominator Micronutrients (n=2) Anthropometry (n=4) Diet Quality & Feeding (n=16) 0 5 10 15 Count of Differences For more information, see Summary Workbook 6/1/2017 | 14
Objective 1: Surveys Comparison of MICS 2011 and DHS 2013 results for Nigeria MICS DHS 100.0% 80.0% 70.1% 60.0% 44.6% 40.0% 30.1% 17.4% 16.4% 15.1% 20.0% 0.0% Milk Feeding Frequency Care Seeking for Fever Exclusive Breastfeeding Difference Difference Similarity Expected Surprising expected 6/1/2017 | 15
Objective 1: Surveys Gaps in nutrition indicators measured Diet Quality & Anthropometry Micronutrients Care Seeking WASH Feeding Practices • 8 Dashboard Indicators • 1 other indicator • 1 Dashboard Indicator • None • 3 other indicators • 6 other indicators • 4 other indicators Nutrition education during Middle Upper Arm Calcium, Vitamin K, Zinc None Hand washing at pregnancy, household food Circumference supplementation for growth critical periods, security, food in children, zinc environmental enteric supplementation, unhealthy supplementation during dysfunction, snack food/beverage pregnancy, folic acid Community Led Total consumption, formula milk Sanitation consumption, women’s dietary diversity, breastfeeding counseling and support … • ‘Dashboard Indicators’ identified from BMGF’s Nutrition Dashboard • ‘Other indicators’ identified through topic experts 6/1/2017 | 16
Objective 1: Surveys Summary ¨ Attempts to harmonize DHS and MICS data, but still marked differences in measurement ¨ Most differences exist in Diet Quality and Feeding Practices, none exist for Micronutrients and anthropometry ¤ Discrepancy between categories may be due to ease of measurement ¨ Room for improvement in scope of indicators captured in surveys 6/1/2017 | 17
Objective 2: Health Management Information Systems (HMIS) To review the collection of nutrition data via health management information systems, especially DHIS2 6/1/2017 | 18
Objective 2: HMIS Methodology • Conducted literature search in published and gray literature 11-14 • Reviewed DHIS2 materials Literature review • Interviewed: • M&E team at Kenya MoH Informant • Managing Director of Systems Innovations for Global Health Technology at I-TECH Interviews • Summarized findings and built case studies for Kenya and Bangladesh Case studies 6/1/2017 | 19
Objective 2: HMIS DHIS2 is a tool for health data management ¨ DHIS2 is an open source, web-based platform for the management and visualization of data ¨ Accessible on mobile devices, has off-line capabilities, and can allow for customizable graphs and maps ¨ Typically stores aggregate facility and/or community data, not individual case-based data (e.g., data stored in an EMR) ¨ Used in 47 countries, national roll-out in 17 countries* *India roll-out includes Bihar, Orissa, Maharashtra, Kerala, Punjab, Haryana, H Pradesh 6/1/2017 | 20
Objective 2: HMIS Kenya Case Study: Using DHIS2 nationwide ¨ Kenya uses DHIS2 nationally for entire health sector – including nutrition ¤ 11 core indicators, 50-60 data elements with all disaggregates Underweight Vitamin A Supplementation Iron/Folate Fortification Stunting Micronutrient Powder Treatment of SAM Treatment of MAM Early Breastfeeding Exclusive Breastfeeding Growth Monitoring Deworming for Children 6/1/2017 | 21
Objective 2: HMIS Kenya Case Study: DHIS2 nutrition has specific scope ¨ Data aggregated at facility level monthly from paper records ¤ Community-level data only included if resulting from push from health facility ¨ System dedicated to routine surveillance ¤ Leverages existing processes and structures for nutrition surveillance ¤ Other systems used to house survey data such as DHS or MICS ¨ More frequent collection of key data could be used by policy makers ¤ Understand time trends in progress ¤ Potential identification of emergency or deteriorating nutrition situations 6/1/2017 | 22
Objective 2: HMIS Kenya Case Study: DHIS2 has limitations ¨ Primary limitation - human resources and capacity ¨ Continuous QI/QA ¤ Technical working group and advisory committee organize routine meetings ¤ Standards set for data quality and completeness ¨ Successful implementation linked to existing mechanisms and framework for nutrition surveillance 6/1/2017 | 23
Recommend
More recommend
