new sampling methods for surveying nutritional status megan deitchler june 28, 2007
TRANSCRIPT
New Sampling Methods for
Surveying Nutritional Status
Megan DeitchlerJune 28, 2007
Presentation Outline
Traditional Approach to Assess Nutritional Status
Work to Date on New Sampling Methods
Comparison of New Sampling Methods with Traditional Approach
Data Priorities in Emergencies Statistically representative data on the status of a
situation are usually necessary before resources can be directed to the area
Reliable data are needed rapidly – to allow agencies to make appropriate decisions about interventions and the level and type of aid to provide
Often times recurrent assessments are mandated (quarterly, e.g.) – either by national government or donor agencies
Data on GAM among children 6-59 mo is a key indicator used to measure the severity of a situation
The Traditional Approach30x30 Cluster Survey
# Clusters: 30 # Observations per Cluster: 30 Total Sample Size: 900
How to Collect 30x30 Cluster Data:
1. Obtain a complete sample frame2. Conduct Population Proportionate to Size (PPS) for cluster
selection 3. Establish the random start point within a cluster4. Administer questionnaire based on demographics of HH5. Sample next nearest HH until data are collected for 30 HHs and
30 Children in the Cluster
REPEAT STEPS 3-5 in 29 OTHER CLUSTERS SELECTED BY PPS
The Traditional Approach Challenge Conventional 30x30 cluster
surveys are time and resource intensive – this poses a challenge, particularly in emergency situations, when rapid and appropriate humanitarian response is essential for effective targeting of scarce resources to communities most in need.
New Sampling Methods
Objective To develop a statistically representative assessment method allowing for rapid collection and analysis of data.
Result 1. 33x6 LQAS Design 2. 67x3 LQAS Design
Completed and Forthcoming Work
Collaborators Funded By
1. Development of Designs
FANTA, CRS, OSU USAID/GHAndrew W. Mellon Grant
2. Ethiopia Field Test FANTA, CRS, OSU GH and Ethiopia MissionAndrew W. Mellon Grant
3. Sudan Field Validation
FANTA, SC-US OFDAGHSC-US private funds
4. Simulation Validation and Post-Hoc Correction Formula
FANTA, Harvard GH
5. Field Manual FANTA OFDAGH
New Sampling Methods: 33x6 and 67x3 Design
Comparison of Traditional and New Sampling Methods
30x30 Design, 33x6 Design, 67x3 Design Accuracy Precision
for child- and HH-level indicators
using data from a field validation in West Darfur, Sudan
Vocabulary
Comparison of Results: Child-Level
GAM
33x6
67x330x30
0
5
10
15
20
25
30
Design
Pre
va
len
ce
Comparison of Results: Child-Level
Low MUAC
30x30 33x6 67x30
5
10
15
20
25
30
Design
Pre
vale
nce
Comparison of Results: Child-Level
Stunting
30x30 33x6 67x3
0
10
20
30
40
50
Design
Pre
va
len
ce
Comparison of Results: Child-Level
Underweight
30x3033x6
67x3
0
10
20
30
40
50
Design
Pre
vale
nce
Comparison of Results: HH-Level
Access to Safe Drinking Water
30x3033x6 67x3
0
20
40
60
80
Design
Pre
vale
nce
Comparison of Results: HH-Level
Access to Latrine
30x30 33x6 67x3
010203040
5060708090
Design
Pre
vale
nce
Comparison of Results: HH-Level
Bednet Ownership
30x3033x6
67x3
010203040
5060708090
Design
Pre
vale
nce
Comparison of Results: HH-Level
HH Food Shortage
30x3033x6
67x3
010
203040
506070
8090
Design
Pre
vale
nce
Vocabulary
Intra Cluster Correlation A measure of how similar the responses
are within clusters for an outcome of interest. The intra cluster correlation coefficient is often referred to as rho, and is designated by “p”.
Why Do The LQAS Designs Work?
Inherent to cluster sampling is the possibility for intra cluster correlation.
The greater the intra cluster correlation, the more data (clusters) must be collected to precisely capture the variability that exists across the entire assessment area.
Vocabulary
Design Effect A measure of the extent to which a cluster
design has diminished ability to precisely measure an indicator, as compared to a simple random sample of the same size.
DE=1 + p (m-1), where m=# obs per
cluster
Comparison of Design Effects
Indicator 30x30 33x6 67x3
GAM 1.422 0.804 0.831
Low MUAC 1.284 1.351 0.953
Stunting 1.364 1.151 0.955
Underweight 1.285 1.006 1.058
Comparison of Design Effects
Indicator 30x30 33x6 67x3
Safe Water 27.651 5.791 2.924
LatrineAccess
11.402 3.271 1.955
Bed-net Ownership
12.179 3.421 2.202
HH Food Shortage
15.075 3.609 2.168
Vocabulary
Comparison of CI Widths (in ppt)
Indicator 30x30 33x6 67x3
GAM +/- 2.0 +/- 2.6 +/- 3.4
Low MUAC +/- 1.4 +/- 3.1 +/- 2.3
Stunting +/- 3.4 +/- 6.5 +/- 5.9
Underweight +/- 3.3 +/- 5.9 +/- 6.4
Comparison of CI Widths (in ppt)
Indicator 30x30 33x6 67x3
Safe Water +/- 17.0 +/- 16.8 +/- 11.8
LatrineAccess
+/- 11.0 +/- 12.6 +/- 9.7
Bed-net Ownership
+/- 10.9 +/- 11.7 +/- 9.9
HH Food Shortage
+/- 12.3 +/- 12.4 +/- 9.9
Underlying Principles of LQAS LQAS analysis methods can be used to assess
binary outcomes
Allows for hypothesis testing of an indicator against threshold prevalence levels
Statistical principles of the method are based on cumulative probabilities of binomials
Decision rules are used to judge whether the threshold prevalence of an indicator has been reached
Requires observations be independently and randomly selected (SRS)
Why Call the Methods LQAS Designs?
The 33x6 and 67x3 designs allow for LQAS hypothesis tests of GAM prevalence because the designs approximate a SRS for assessment of GAM
10% and 15% prevalence levels (i.e. upper thresholds) of primary interest, 20% prevalence of secondary interest
Useful if 95% CI overlaps with a threshold level necessary for decision making about response
Comparison of Design Effects
Indicator 30x30 33x6 67x3
GAM 1.422 0.804 0.831
Low MUAC 1.284 1.351 0.953
Stunting 1.364 1.151 0.955
Underweight 1.285 1.006 1.058
Time and Cost Comparison 30x30 33x6 67x3
Time 30.00 team-days
8.25 team-days
11.17 team-days
Cost 4606 US $ 1232 US $ 1630 US $
Summary of Main Points 33x6 and 67x3 designs provide accurate results for child-
and HH-level indicators
33x6 and 67x3 designs provide reasonably precise results for child-level indicators, with a CI only slightly wider than that of a 30x30 design for most indicators.
Note: the 67x3 design provides more narrow CIs than the 30x30 design for child-level indicators with high intra-cluster correlation (eg. VAC suppl., diarrhea, malaria).
33x6 and 67x3 designs provide reasonably precise results for HH-level indicators (notable exception is mortality).
Note: the 67x3 design provides more narrow CIs than the 30x30 design for all HH-level indicators tested (exception is mortality).
Summary of Main Points
33x6 and 67x3 designs allow for LQAS hypothesis testing of GAM threshold prevalence levels (using cumulative binomial probabilities)
33x6 and 67x3 designs offer substantial benefit over 30x30 cluster design in terms of sample size, time, and cost required for data collection
33x6 and 67x3 design are statistically appropriate alternatives for purpose of obtaining rapid, reliable assessment data in food insecure areas
Financial support for this study was provided to the Academy for Educational Development (AED), Food and Nutrition Technical Assistance (FANTA) Project by the Office of Foreign Disaster Assistance (OFDA) of the Bureau for Democracy, Conflict and Humanitarian Assistance, and the Office of Health, Infectious Diseases and Nutrition of the Bureau for Global Health at the US Agency for International Development (USAID), under terms of Cooperative Agreement No HRN-A-00-98-00046-00. The opinions expressed herein are those of the author and do not necessarily reflect the views of USAID.
Acknowledgments