The SMART & SQUAC Surveys

ARDHO, in cooperation with Action Against Hunger (AAH/ACF), conducted the SMART & SQUAC Surveys project in Helmand Province completed on Nov 30, 2019. SQUEAC is a coverage assessment method developed by Valid International, FHI 360/FANTA, UNICEF, Concern Worldwide, World Vision International, Action Against Hunger, Tufts University, and Brixton Health. After discussions with implementing partners in the NGO, U.N., and government sectors, the following attributes were considered important:

• • The method must be both quick and cheap to allow frequent and ongoing evaluation of program coverage and identification of barriers to service access and uptake.
• • The method must provide a similar richness of information as that provided by the CSAS method, including:
• • Evaluation of the spatial pattern of coverage
• • Identification of barriers to service access and uptake
• • Estimation of overall program coverage was considered to be desirable but not essential.
• • The method should encourage the routine collection, analysis, and use of program planning and evaluation data.
• • Individual components of the method should provide information capable of informing program activities and reforms.
• • The method should not require the use of computers.

The SQUEAC method ARDHO conducted:

• • Is semi-quantitative, using a mixture of quantitative (numerical) data collected from routine program monitoring activities, small studies, small surveys, and small-area surveys, as well as qualitative data collected using informal group discussions and interviews with a variety of informants.
• • Makes use of routine program monitoring data (e.g., charts of trends in admission, exit, recovery, in-program deaths, and defaulting) and data that are already collected on beneficiary record cards (e.g., admission MUAC and the home villages of program beneficiaries).
• • Makes use of data such as agriculture, labor, disease, and food-consumption calendars as well as market price monitoring data that might already be available from such sources as nutritional anthropometry surveys, agricultural assessments, livelihood surveys, and food security assessments. When these data are not readily available, they may be collected using informal group discussions and interviews with a variety of informants.
• • Makes use of data that may already be collected routinely by programs or may be collected with little additional work. These additional data have been selected to provide benefits to programs outside the narrow requirement of evaluating access and coverage.
• • Uses small studies, small surveys, and small-area surveys to confirm or deny hypotheses about program coverage that arise from the analysis of program and qualitative data.
• • Uses Bayesian techniques to estimate overall program coverage with a small-sample survey.

The SQUEAC method achieves rapidity and low cost by collecting and analyzing diverse data intelligently, rather than by using the mechanistic and more focused data collection and analysis techniques employed by the CSAS method. The SQUEAC method uses a two-stage screening test model:
Stage 1: identifies areas of low and high coverage as well as reasons for coverage failure using routine program data, already available data, quantitative data that may be collected with little additional work, and qualitative data.
Stage 2: confirms the location of areas of high and low coverage and the reasons for coverage failure identified in Stage 1 using small studies, small surveys, small-area surveys. If appropriate and required, an additional stage may be performed:
Stage 3: provides an estimate of overall program coverage using Bayesian techniques.
SQUEAC consists of a set of tools each of which is designed to identify and investigate coverage and factors influencing coverage. The tools presented here have been developed and tested in use-studies and by SQUEAC practitioners that have undertaken more than 50 SQUEAC investigations of CMAM programs in many countries in Asia and Africa. It is expected that new tools will be added and existing tools refined as practitioners gain more experience with the SQUEAC method. A SQUEAC investigation will typically use some (but not all) of the tools described here.