The first COVID-19 case in a healthcare worker in Kenya was reported on March 30, 2020, in Nairobi, leading to a 41-day closure of the health facility where he had worked. We assessed infection prevention and control (IPC) activities and implemented recommendations to re-open and operate the facility. We conducted a risk assessment of the facility in April 2020 using a modified World Health Organization, six-element IPC facility risk assessment tool. IPC recommendations were made, and a follow-up assessment of their implementation was conducted in July 2020. Breaches in IPC measures included poor ventilation in most service delivery areas; lack of physical distancing between patients; inadequate COVID-19 information, education, and communication materials; lack of standard operating procedures on cleaning and disinfecting high-touch areas; insufficient IPC training; inadequate hand hygiene facilities; insufficient personal protective equipment supplies; and an inactive IPC committee. Strengthening IPC measures is critical to prevent healthcare facility closures.

BACKGROUND: Measures of diagnostic test accuracy provide evidence of how well a test correctly identifies or rules-out disease. Commonly used diagnostic accuracy measures (DAMs) include sensitivity and specificity, predictive values, likelihood ratios, area under the receiver operator characteristic curve (AUROC), area under precision-recall curves (AUPRC), diagnostic effectiveness (accuracy), disease prevalence, and diagnostic odds ratio (DOR) etc. Most available analysis tools perform accuracy testing for a single diagnostic test using summarized data. We developed a SAS macro for evaluating multiple diagnostic tests using individual-level data that creates a 2 × 2 summary table, AUROC and AUPRC as part of output. METHODS: The SAS macro presented here is automated to reduce analysis time and transcription errors. It is simple to use as the user only needs to specify the input dataset, "standard" and "test" variables and threshold values. It creates a publication-quality output in Microsoft Word and Excel showing more than 15 different accuracy measures together with overlaid AUROC and AUPRC graphics to help the researcher in making decisions to adopt or reject diagnostic tests. Further, it provides for additional variance estimation methods other than the normal distribution approximation. RESULTS: We tested the macro for quality control purposes by reproducing results from published work on evaluation of multiple types of dried blood spots (DBS) as an alternative for human immunodeficiency virus (HIV) viral load (VL) monitoring in resource-limited settings compared to plasma, the gold-standard. Plasma viral load reagents are costly, and blood must be prepared in a reference laboratory setting by a qualified technician. On the other hand, DBS are easy to prepare without these restrictions. This study evaluated the suitability of DBS from venous, microcapillary and direct spotting DBS, hence multiple diagnostic tests which were compared to plasma specimen. We also used the macro to reproduce results of published work on evaluating performance of multiple classification machine learning algorithms for predicting coronary artery disease. CONCLUSION: The SAS macro presented here is a powerful analytic tool for analyzing data from multiple diagnostic tests. The SAS programmer can modify the source code to include other diagnostic measures and variance estimation methods. By automating analysis, the macro adds value by saving analysis time, reducing transcription errors, and producing publication-quality outputs.

BACKGROUND: Estimating cause-related mortality among the dead is not common, yet for clinical and public health purposes, a lot can be learnt from the dead. HIV/AIDS accounted for the third most frequent cause of deaths in Kenya; 39.7 deaths per 100,000 population in 2019. OraQuick Rapid HIV-1/2 has previously been validated on oral fluid and implemented as a screening assay for HIV self-testing in Kenya among living subjects. We assessed the feasibility and diagnostic accuracy of OraQuick Rapid HIV-1/2 for HIV screening among decedents. METHODS: Trained morticians collected oral fluid from 132 preembalmed and postembalmed decedents aged >18 months at Jaramogi Oginga Odinga Teaching and Referral Hospital mortuary in western Kenya and tested for HIV using OraQuick Rapid HIV-1/2. Test results were compared with those obtained using the national HIV Testing Services algorithm on matched preembalming whole blood specimens as a gold standard (Determine HIV and First Response HIV 1-2-O). We calculated positive predictive values, negative predictive values, area under the curve, and sensitivity and specificity of OraQuick Rapid HIV-1/2 compared with the national HTS algorithm. RESULTS: OraQuick Rapid HIV-1/2 had similar sensitivity of 92.6% [95% confidence interval (CI): 75.7 to 99.1] on preembalmed and postembalmed samples compared with the gold standard. Specificity was 97.1% (95% CI: 91.9 to 99.4) and 95.2% (95% CI: 89.2 to 98.4) preembalming and postembalming, respectively. Preembalming and postembalming positive predictive value was 89.3% (95% CI: 71.8 to 97.7) and 83.3% (95% CI: 65.3 to 94.4), respectively. The area under the curve preembalming and postembalming was 94.9% (95% CI: 89.6 to 100) and 93.9% (95% CI: 88.5 to 99.4), respectively. CONCLUSIONS: The study showed a relatively high-performance sensitivity and specificity of OraQuick Rapid HIV-1/2 test among decedents, similar to those observed among living subjects. OraQuick Rapid HIV-1/2 presents a convenient and less invasive screening test for surveillance of HIV among decedents within a mortuary setting.