The importance of context in malaria diagnosis and treatment decisions - a quantitative analysis of observed clinical encounters in Tanzania

OBJECTIVE To gain a better understanding of the decision-making context in the diagnosis of malaria in order to inform behaviour change strategies, using quantitative methods. METHODS We observed hospital outpatient and inpatient consultations in northeast Tanzania where malaria testing was routinely available, recording potential influences on testing and prescribing decisions. We analysed the effects of variables at patient, clinical context and clinician levels on three key decisions in malaria diagnosis and treatment: decision to test for malaria, presumptive treatment and treatment of test-negative patients with antimalarials. RESULTS Observation of 2082 consultations took place during 120 clinics (different shifts on different days and in different departments) with 34 clinicians. Malaria tests were requested for 16.9% of patients. This decision was driven primarily by clinical symptoms. Of patients not tested for malaria, 36.0% were prescribed antimalarials, this decision being associated with both clinical and non-clinical factors. In outpatients fever was a strong predictor of presumptive treatment [adjusted odds ratio (AOR): 45.9, 95% CI: 30-73], in inpatients this was less so (AOR: 2.7, 95% CI: 0.98-7.7). Outpatient clinicians who were working alone or who had attended < 2 in-service training sessions in the past year were more likely to prescribe antimalarials presumptively. The decision to prescribe antimalarials without also prescribing antibiotic treatment to 22.8% patients who tested negative for malaria was not driven by clinical symptoms but was associated with age over 5 years, lower patient load and male sex of clinician. CONCLUSIONS Non-clinical factors are important in the overdiagnosis of malaria. Strategies to target antimalarials and antibiotics better need to use methods that address the context of clinical decision making in addition to the dissemination of conventional clinical algorithms.