Comparison of quantitative real-time PCR and direct immunofluorescence for the detection of <i>Pneumocystis jirovecii</i>

<div><p>Background</p><p><i>Pneumocystis</i> pneumonia (PCP) is a serious risk for HIV-positive patients. Asymptomatic infection or colonisation with <i>P</i>. <i>jirovecii</i> has been shown to occur frequently. PCR assays frequently identify such cases, due to their high sensitivity. Quantitative real-time PCR (qPCR) gene copy number cut-off values have been suggested to differentiate colonisation and infection; these need to be standardised for routine use. We compared the results of qPCR with an immunofluorescence assay (IFA) to determine a specific cut-off value.</p><p>Methods</p><p>From March 2005 through June 2009, induced sputum specimens were collected from adult patients who were clinically suspected of having PCP, at the Chris Hani Baragwanath Hospital in Gauteng, South Africa. Laboratory diagnosis of PCP was done by a conventional direct IFA and a qPCR assay. A receiver operating characteristic (ROC) analysis was performed to determine a suitable copy number cut-off value.</p><p>Results</p><p><i>P</i>. <i>jirovecii</i> was identified in 51% (156/305) and 67% (204/305) of specimens using IFA and qPCR, respectively. The cut-off value for the qPCR that best predicted the IFA results was 78 copies/5 μl (area under ROC curve 0.92). The sensitivity and specificity of qPCR using this cut-off was 94.6% and 89.1%, respectively, compared with the IFA.</p><p>Discussion</p><p>The results of the ROC curve analysis indicate an excellent predictive value of the qPCR using the proposed cut-off. However, the IFA test is an imperfect gold standard and so this cut-off should not be used in isolation; clinical data should also contribute to the interpretation of the qPCR result.</p></div

Micrograph showing two fluorescing green clusters of <i>P</i>. <i>jirovecii</i> cysts on a red-stained background, 400x magnification.

<p>Micrograph showing two fluorescing green clusters of <i>P</i>. <i>jirovecii</i> cysts on a red-stained background, 400x magnification.</p

Distribution of the initial effective reproduction number (<i>R_t</i>) across 100 simulations for the pandemic influenza A(H1N1)pdm09 epidemic in South Africa, assuming known serial interval (SI) estimates derived from (A) confirmed secondary cases only (SI: 2.3 days) and (B) confirmed plus suspected secondary cases (SI: 2.7 days) in the transmission chain (method 2).

<p>Distribution of the initial effective reproduction number (<i>R_t</i>) across 100 simulations for the pandemic influenza A(H1N1)pdm09 epidemic in South Africa, assuming known serial interval (SI) estimates derived from (A) confirmed secondary cases only (SI: 2.3 days) and (B) confirmed plus suspected secondary cases (SI: 2.7 days) in the transmission chain (method 2).</p

Distribution of serial interval and initial effective reproductive number (<i>R_t</i>) across 100 simulations for the influenza A(H1N1)pdm09 epidemic in South Africa using the likelihood-based simultaneous estimation method (method 1).

<p>Distribution of serial interval and initial effective reproductive number (<i>R_t</i>) across 100 simulations for the influenza A(H1N1)pdm09 epidemic in South Africa using the likelihood-based simultaneous estimation method (method 1).</p

Temporal variation in the mean effective reproductive number () of influenza A(H1N1)pdm09 in South Africa, June 15 to October 4, 2009 (method 3).

<p>Temporal variation in the mean effective reproductive number () of influenza A(H1N1)pdm09 in South Africa, June 15 to October 4, 2009 (method 3).</p

Observed lag-time between date of symptom onset and date of specimen collection, incidence rate ratio (IRR) and significance value of the covariates significant in the Poisson regression model.

a<p>Pooled p-value for province covariate.</p

Epidemic curve of laboratory-confirmed influenza A(H1N1)pdm09 cases, South Africa, June 12 to September 30, 2009.

<p>Bars show original recorded data applying date of symptom onset where available (n = 758) and substitute by date of specimen collection where onset was unavailable (total n = 12,526). The line shows imputed data where date of symptom onset for missing case-based data was obtained by multiple imputations adjusted by provincial location of specimen collection and the occurrence of a case on a weekend day (n = 12,491).</p

Additional results and analyses.

ObjectivesThe aim of this study was to quantify transmission trends in South Africa during the first four waves of the COVID-19 pandemic using estimates of the time-varying reproduction number (R) and to compare the robustness of R estimates based on three different data sources, and using data from public and private sector service providers.MethodsR was estimated from March 2020 through April 2022, nationally and by province, based on time series of rt-PCR-confirmed cases, hospitalisations, and hospital-associated deaths, using a method that models daily incidence as a weighted sum of past incidence, as implemented in the R package EpiEstim. R was also estimated separately using public and private sector data.ResultsNationally, the maximum case-based R following the introduction of lockdown measures was 1.55 (CI: 1.43–1.66), 1.56 (CI: 1.47–1.64), 1.46 (CI: 1.38–1.53) and 3.33 (CI: 2.84–3.97) during the first (Wuhan-Hu), second (Beta), third (Delta), and fourth (Omicron) waves, respectively. Estimates based on the three data sources (cases, hospitalisations, deaths) were generally similar during the first three waves, but higher during the fourth wave for case-based estimates. Public and private sector R estimates were generally similar except during the initial lockdowns and in case-based estimates during the fourth wave.ConclusionAgreement between R estimates using different data sources during the first three waves suggests that data from any of these sources could be used in the early stages of a future pandemic. The high R estimates for Omicron relative to earlier waves are interesting given a high level of exposure pre-Omicron. The agreement between public and private sector R estimates highlights that clients of the public and private sectors did not experience two separate epidemics, except perhaps to a limited extent during the strictest lockdowns in the first wave.</div

R estimates by sector, based on rt-PCR-confirmed COVID-19 cases, hospitalisations, and deaths.

R estimates by sector, based on rt-PCR-confirmed COVID-19 cases (upper panel), hospitalisations (middle panel), and deaths (lower panel), South Africa. R estimates were generated using 7-day sliding windows. Results reflect median values (between imputations) of median R estimates and associated 2.5% and 97.5% credible intervals. L = Level. Red-shaded areas indicate the period during which civil unrest caused severe disruptions to surveillance in KwaZulu-Natal and Gauteng provinces; grey-shaded areas indicate gradually diminishing effects on R estimates.</p

Province-level R estimates for each data endpoint from early March 2020 through 25 October 2022.

R estimated on 7-day sliding windows. Results reflect median values (between imputations) of median R estimates and associated 2.5% and 97.5% credible intervals. L = Level. Red-shaded areas indicate the period during which civil unrest caused severe disruptions to surveillance in KwaZulu-Natal and Gauteng provinces; grey-shaded areas indicate gradually diminishing effects on R estimates.</p