The 'number needed to sample' in primary care research. Comparison of two primary care sampling frames for chronic back pain.

BACKGROUND

Sampling for primary care research must strike a balance between efficiency and external validity. For most conditions, even a large population sample will yield a small number of cases, yet other sampling techniques risk problems with extrapolation of findings.

OBJECTIVE

To compare the efficiency and external validity of two sampling methods for both an intervention study and epidemiological research in primary care--a convenience sample and a general population sample--comparing the response and follow-up rates, the demographic and clinical characteristics of each sample, and calculating the 'number needed to sample' (NNS) for a hypothetical randomized controlled trial.

METHODS

In 1996, we selected two random samples of adults from 29 general practices in Grampian, for an epidemiological study of chronic pain. One sample of 4175 was identified by an electronic questionnaire that listed patients receiving regular analgesic prescriptions--the 'repeat prescription sample'. The other sample of 5036 was identified from all patients on practice lists--the 'general population sample'. Questionnaires, including demographic, pain and general health measures, were sent to all. A similar follow-up questionnaire was sent in 2000 to all those agreeing to participate in further research. We identified a potential group of subjects for a hypothetical trial in primary care based on a recently published trial (those aged 25-64, with severe chronic back pain, willing to participate in further research).

RESULTS

The repeat prescription sample produced better response rates than the general sample overall (86% compared with 82%, P < 0.001), from both genders and from the oldest and youngest age groups. The NNS using convenience sampling was 10 for each member of the final potential trial sample, compared with 55 using general population sampling. There were important differences between the samples in age, marital and employment status, social class and educational level. However, among the potential trial sample, there were no demographic differences. Those from the repeat prescription sample had poorer indices than the general population sample in all pain and health measures.

CONCLUSIONS

The repeat prescription sampling method was approximately five times more efficient than the general population method. However demographic and clinical differences in the repeat prescription sample might hamper extrapolation of findings to the general population, particularly in an epidemiological study, and demonstrate that simple comparison with age and gender of the target population is insufficient.