2 Sampling frames

We now know that in order to sample from a population, we need to first identify the target population and the source (study) population. We then select the study sample. But how do we identify sampling units that form part of the source population? We need a sampling frame.

A sampling frame might be a list of all of the poultry farms in the province(s) selected as the source population, for example. In human health, a sampling frame might consist of a list of all hospitals in a region, along with a list of all the patients being treated at these hospitals (either as inpatients or outpatients, depending on the topic of interest) in a defined time period. Note that because sampling frames include identifying information such as farmers’ names and farm addresses, extra attention must be paid to ensuring that this information is stored and accessed securely by authorised members of the research team or surveillance programme (see the Legal and ethical considerations in AMR data module).

Because sampling frames constitute a data-collection process, they are subject to the same risks of error and bias as all other AMR-related data collection and analysis (see the Fundamentals of data for AMR module for more information on error and bias). Most of the time, available sampling frames do not perfectly correspond to the actual source population: for example, a sampling frame for AMR surveillance in poultry might consist of a list of registered poultry slaughterhouses. However, not all slaughterhouses might be registered – the list might be out of date, or only record slaughterhouses of a minimum processing capacity. Further, not all poultry are processed through slaughterhouses: some are slaughtered at the point of sale in live animal markets, or in backyards for home consumption. In general, a sampling frame should include facilities (such as slaughterhouses or farms) that account for at least 80% of the target population.

In reality, sampling frames are not always available or complete, and therefore might not include all units in the source population, and so by extension are not representative of the target population. The lack of a reliable sampling frame is particularly common in livestock and aquaculture studies and surveillance programmes, compared to human clinical studies and public health surveillance. Examples include when there is no list of farms in a region, or when a list of individual animals within a flock, herd or pen does not exist: fish would need to be selected from a pen, but the fish farm does not keep a list of individual fish within a pen.

Three potential solutions to achieving representative sampling when no adequate sampling frame exists are summarised below:

• Random geographic coordinates sampling: An online tool generates a series of randomly selected map coordinates that fall within certain geographic boundaries. Research teams then need to travel to each specific point (or as close as possible). Once the point is reached, sampling occurs as close to that point as possible. This might be used for sampling wild or feral animals, for example.
• Systematic random sampling: The nth unit is selected from a series of units as each unit presents; for example, selecting every tenth fish in a pen that swims through a race. This is best performed when the total number of sampling units and the required sample size is known. The first unit to be selected should be decided by randomly generating a number. This method is also included as a sampling method in the next section.
• Stakeholder consultation: In some circumstances, it may be possible to develop a sampling frame through stakeholder consultation. For example, if there is no list of farms in a particular region, it may be possible to arrange a meeting with local leaders or community members and ask them to list the farms they are aware of in the region. This approach is not perfect, but might be preferable to having no sampling frame at all.