Novel application of one-step pooled molecular testing and maximum likelihood approaches to estimate the prevalence of malaria parasitaemia among rapid diagnostic test negative samples in western Kenya

Table 2 Validation study of prevalence estimation approaches and accounting for variability in sensitivity and specificity of one-step pooled testing

qPCR testing strategy	Prevalence estimation method	Samples or pools tested, N	Positive, n	Pool size	Prevalence, % (95% CI)
Individual	Binomial (validation subset)^a	1000	141	1	14.1 (12.1–16.4)
Individual	Binomial (main subset)^b	3670	496	1	13.5 (12.4–14.7)
One-step pooled	MLE, se = sp = 1	734	273	5	8.9 (7.9–9.9)
One-step pooled	MLE, se = 0.796^a, sp = 0.980^a	734	273	5	11.4 (9.9–12.9)
One-step pooled	MLE, se = 0.796^a, sp = 0.980^a, accounting for σ²_SE and σ²_SP	734	273	5	11.4 (9.2–13.6)
One-step pooled	Inverse-weighted prevalence estimator accounting for σ²_SE and σ²_SP	734	273	5	12.8 (11.2–14.3)

CI confidence interval, MLE maximum likelihood estimation, qPCR quantitative polymerase chain reaction, Se sensitivity, Sp specificity, σ²_SE variance in sensitivity, σ²_SP variance in specificity
^aEstimated from validation subset (n = 200 pools, n = 1000 samples); se = 0.796 (95% CI: 0.716–0.876), sp = 0.980 (95%CI: 0.953–1.000)
^bTypically unknown in a study, but it is included here to illustrate the gold standard estimate

ISSN: 1475-2875