SLEAC Survey Sample Design

Mark Myatt

The SLEAC sample design uses a two-stage cluster sample survey.

First stage sampling method

This is the sampling method that is used to select the villages to be sampled. A centric systematic area sampling (CSAS)¹ method or quadrat method could be used to select villages to be sampled for SLEAC surveys. The villages to be sampled may be selected by their proximity to the centre of each quadrat. The number/size of quadrats should be selected so as to spread the sample of villages over the entire program area. Many small quadrats are better than few large quadrats. For example, the sample illustrated im Figure 1B (nineteen quadrats) spreads the sample more evenly and over more of the program catchment area than the sample illustrated in Figure 1A (eight quadrats). You should use as many quadrats as is feasible with the time and resources available for the survey.

Figure 1: A. Sampling plan with 8 quadrats B. Sampling plan with 18 quadrats

The CSAS/quadrat sampling method is appropriate for estimating coverage over a wide area such as a health district. Another useful approach is to stratify by clinic catchment area with a fixed number of villages selected systematically from a complete list of villages within each clinic catchment area (Figure 2) or within the boundaries of (e.g.) administrative areas for which lists of village or localities are available.

Figure 2: Selection of villages to be sampled using spatially stratified sampling

This first stage sampling method should be a spatial sampling method that yields a reasonably even spatial sample from the survey area. Cluster sampling using population proportional sampling (PPS), such as that used for SMART surveys, is not appropriate. The stratified approach outlined above and illustrated in Figure 2 also provides a reasonably even spatial sample using village lists and does not require the use of maps. It is important to note that sampling should not stop when the survey has reached its required sample size. Sampling only stops after you have sampled all of the the selected villages

The list-based method for stage 1 sampling is implemented in the sleacr package through the get_sampling_list() function. Using the same list of villages/communities described above and given a target number of villages/communities to sample, a sampling list can be created. Using the village_list dataset and for a target of 40 sampling clusters, a sampling list can be drawn as follows:

get_sampling_list(village_list, 40)

which produces the following sampling list:

	id	chiefdom	section	village
13	13	Badjia	Kpallay	Kugbahun
38	38	Bagbe	Jongo	Bandajuma
63	63	Bagbe	Nyallay	Fuinda
88	88	Bagbo	Gorapon	Kassay
113	113	Bagbo	Kpangbalia	Kpangbalia
138	138	Bagbo	Tissawa	Monjemei
163	163	Baoma	Bambawo	Feiba
188	188	Baoma	Mawojeh	Masao
213	213	Baoma	Upper Pataloo	Komende
238	238	Bumpe Ngao	Bumpe	Nguabu
263	263	Bumpe Ngao	Bumpe	Sembehun
288	288	Bumpe Ngao	Sewama	Juhun
313	313	Bumpe Ngao	Sahn	Sembehun
338	338	Bumpe Ngao	Taninahun	Nyandehun
363	363	Bumpe Ngao	Taninahun	Waterloo
388	388	Bumpe Ngao	Taninahun	Kangama
413	413	Bumpe Ngao	Yengema	Yengema
438	438	Gbo	Maryu	Kama
463	463	Jaiama Bongor	Lower Kama	Bangema
488	488	Jaiama Bongor	Tongowa	Lalewahun
513	513	Jaiama Bongor	Upper Kama	Bowohun
538	538	Kakua	Kpandobu	Manguama
563	563	Kakua	Nguabu	Gandorhun
588	588	Kakua	Samamie	Gbanja Town
613	613	Komboya	Kemoh	Manyama
638	638	Komboya	Mangaru	Kpamajama
663	663	Lugbu	Yalenga	Kpetema
688	688	Niawa Lenga	Kaduawo	Huawuma
713	713	Niawa Lenga	Yalenga	Kpah
738	738	Selenga	Mambawa	Gbangaima
763	763	Selenga	Old Town	Korwama
788	788	Tikonko	Seiwa	Kapima
813	813	Tikonko	Njagbla II	Failor
838	838	Tikonko	Seiwa	Gbanahun
863	863	Valunia	Deilenga	Konima
888	888	Valunia	Kendebu	Kpetema
913	913	Valunia	Lunia	Levuma
938	938	Valunia	Lunia	Njala
963	963	Valunia	Seilenga	Foya
988	988	Wonde	Central Kargoi	YawaJu

Second stage within-community sampling method

This will usually be an active and adaptive case-finding method or a house-to-house census sampling method. These method find all, or nearly all, current and recovering SAM cases in a sampled village. Sampling should be exhaustive. This means that you only stop sampling when you are sure that you have found all cases in the community. Sampling should not stop when you have met a quota or the wider survey has reached its required sample size.

This is a two-stage sample because a sample of villages in the survey area is taken first (stage one) and then a “census” sample of current and recovering SAM cases is taken from each and every of the selected villages (stage two).

SLEAC survey sample size

SLEAC uses a target sample size (n) which, together with prevalence and population estimates, is used to decide the number of villages (\(n_{villages}\)) that should be sampled in order to achieve the target sample size.

A target sample size (n) of forty (40) cases from each service delivery unit in which coverage is to be classified is usually large enough for most SLEAC applications.

In some settings it may be difficult or even impossible to find forty (40) cases. This will be the case if service delivery units are small and/or the prevalence of SAM is low. In these situations it is possible to use a smaller target sample size without increasing error. Table 1 below shows target sample sizes that may be used when the total number of cases in a service delivery unit is likely to be small.

Table 1: Target sample sizes for 50% and 70% coverage standards for use when surveying small service delivery units and/or the prevalence of SAM is low

Total number of cases in the service delivery unit 2	50% standard	70% standard or 30%/70% class thresholds
500	37	33
250	35	32
125	31	29
100	29	26
80	27	26
60	25	25
50	23	22
40	21	19
30	17	18
20	15	15

If, for example, the total number of cases in a service delivery unit is estimated to be about sixty (60) cases then a target sample size of twenty-five (25) cases may be used.

This sample size estimation can be performed using the sleacr package using the get_sample_n() function.

get_sample_n(N = 60, dLower = 0.5, dUpper = 0.7)

which gives the following:

#> $n
#> [1] 24
#> 
#> $d
#> [1] 14
#> 
#> $alpha
#> [1] 0.09353083
#> 
#> $beta
#> [1] 0.09359355

The target sample size (n) together with estimates of the prevalence of severe acute undernutrition in the survey area and population data is used to calculate the number of villages (\(n_{villages}\)) that will need to be sampled in order to achieve the target sample size:

\[n_{villages} ~ \times ~ \left \lceil \frac{n}{\text{average village population}_{\text{all ages}} ~ \times ~ \frac{\text{Percentage of population}_{\text{all ages}}}{100} ~ \times ~ \frac{SAM prevalence}{100}} \right \rceil\]

This calculation can be performed using the get_n_clusters() function in sleacr. If the target sample size is 40 and the average population size per village/cluster is 600:

get_n_clusters(n = 40, n_cluster = 600, u5 = 0.17, p = 0.02)
#> [1] 20