Implementing guidelines in primary care: can population impact measures help?
Evidence for Population Health Unit, School of Epidemiology and Health Sciences, The Medical School, The University of Manchester, UK
BMC Public Health 2003, 3:7 doi:10.1186/1471-2458-3-7Published: 23 January 2003
Primary care organisations are faced with implementing a large number of guideline recommendations. We present methods by which the number of eligible patients requiring treatment, and the relative benefits to the whole population served by a general practice or Primary Care Trust, can be calculated to help prioritise between different guideline recommendations.
We have developed measures of population impact, "Number to be Treated in your Population (NTP)" and "Number of Events Prevented in your Population (NEPP)". Using literature-based estimates, we have applied these measures to guidelines for pharmacological methods of secondary prevention of myocardial infarction (MI) for a hypothetical general practice population of 10,000.
Implementation of the NICE guidelines for the secondary prevention of MI will require 176 patients to be treated with aspirin, 147 patients with beta-blockers and with ACE-Inhibitors and 157 patients with statins (NTP). The benefit expressed as NEPP will range from 1.91 to 2.96 deaths prevented per year for aspirin and statins respectively. The drug cost per year varies from €1940 for aspirin to €60,525 for statins. Assuming incremental changes only (for those not already on treatment), aspirin post MI will be added for 37 patients and produce 0.40 of a death prevented per year at a drug cost of €410 and statins will be added for 120 patients and prevent 2.26 deaths per year at a drug cost of €46,150. An appropriate policy might be to reserve the use of statins until eligible patients have been established on aspirin, ACE-Inhibitors and beta blockers.
The use of population impact measures could help the Primary Care Organisation to prioritise resource allocation, although the results will vary according to local conditions which should be taken into account before the measures are used in practice.