Modelling study of the ability to diagnose acute rheumatic fever at different levels of the Ugandan healthcare system

Abstract

Objective: To determine the ability to accurately diagnose acute rheumatic fever (ARF) given the resources available at three levels of the Ugandan healthcare system.

Methods: Using data obtained from a large epidemiological database on ARF conducted in three districts of Uganda, we selected variables that might positively or negatively predict rheumatic fever based on diagnostic capacity at three levels/tiers of the Ugandan healthcare system. Variables were put into three statistical models that were built sequentially. Multiple logistic regression was used to estimate ORs and 95% CI of predictors of ARF. Performance of the models was determined using Akaike information criterion, adjusted R2, concordance C statistic, Brier score and adequacy index.

Results: A model with clinical predictor variables available at a lower-level health centre (tier 1) predicted ARF with an optimism corrected area under the curve (AUC) (c-statistic) of 0.69. Adding tests available at the district level (tier 2, ECG, complete blood count and malaria testing) increased the AUC to 0.76. A model that additionally included diagnostic tests available at the national referral hospital (tier 3, echocardiography, anti-streptolysin O titres, erythrocyte sedimentation rate/C-reactive protein) had the best performance with an AUC of 0.91.

Conclusions: Reducing the burden of rheumatic heart disease in low and middle-income countries requires overcoming challenges of ARF diagnosis. Ensuring that possible cases can be evaluated using electrocardiography and relatively simple blood tests will improve diagnostic accuracy somewhat, but access to echocardiography and tests to confirm recent streptococcal infection will have the greatest impact.

Keywords: health policy; public health; quality in health care.

Figure 1

Observed receiver operating curves. AUC, area under the curve.