Index-linked Assisted HIV Self-Testing for Children 2-14 Years: Results of a Pilot Evaluation in Ethiopia.

BACKGROUND: Case identification remains a challenge to reaching the United Nations 95-95-95 targets for children with HIV. While the World Health Organization approved oral mucosal HIV self-testing (HIVST) for children over 2 years in 2019, there is little information on HIVST for pediatric case identification in Ethiopia. SETTING: Nine health facilities across Ethiopia. METHODS: We implemented a pilot program from November 2021-April 2022 to assess acceptability and feasibility of using HIVST to screen children 2-14 years of adult index clients, (i.e., parents/caregivers living with HIV and on antiretroviral therapy). HIV-positive adults who had children with unknown HIV status were given HIVST kits (OraQuick®) to screen their children at home. Parents/caregivers were asked to report results telephonically and bring children screening positive to the health facility for confirmatory HIV testing. We defined HIVST acceptability as ≥50% of parents/caregivers accepting testing and ≥50% reporting results within seven days of receiving a test kit. Feasibility was defined as ≥60% of children with a reactive HIVST receiving confirmatory testing and <5 serious social harms reported per 1000 kits distributed. RESULTS: Overall, 1496 of 1651 (91%) parents/caregivers accepted HIVST kits to test their children at home and 1204 (71%) reported results within seven days. Of 17 children (1%) with reactive results, 13 (76%) received confirmatory testing; of which 7 (54%) were confirmed to be HIV-positive. One serious social harm was reported. CONCLUSION: Providing adult parents/caregivers with HIVST kits to screen their children at home is an acceptable and feasible strategy to reach untested children and improve pediatric case finding in a low prevalence setting.

Multidimensional determinants of willingness to pay for community-based health insurance in Ethiopia and its implication towards universal health coverage: A narrative synthesis.

Pooling resources to pay for healthcare services and attain universal health coverage is a viable global agenda, especially for underdeveloped health systems. Ethiopia has implemented community-based health insurance (CBHI) since 2011 to improve healthcare funding. However, comprehensive evidence on the demand and determinants of health insurance in Ethiopia is lacking. Therefore, this review aimed at identifying determinants of willingness to pay (WTP) for CBHI in Ethiopia. A narrative review was conducted using search terms from PubMed, Science Direct, Scopus, African Journal Online, and Google Scholar databases. Screening process considered publication year, settings, English language, and study participants. Newcastle Ottawa tool assessed the quality of included studies. A thematic framework was applied. The review protocol was registered in PROSPERO with an ID number CRD42022296840. The review included 10 studies. The synthesis identified 25 determinants of WTP for CBHI in Ethiopia. Socio-demographic and economic, scheme-related, and health-related determinants of WTP for the CBHI were identified. Determinants of household WTP for CBHI in Ethiopia were multi-dimensional. Socio-demographic, socio-economic, scheme-related, and health-related factors are among the common determinants documented. CBHI is thus an alternative and potential source of financing for the healthcare system, primarily for people with low socioeconomic status and a fragile health system. The health system, socioeconomic leaders, and political figures play a significant role in influencing communities towards WTP for CBHI while increasing government spending on health toward UHC.

Automated quality control tool for high-content imaging data by building 2D prediction intervals on reference biosignatures.

Recent advances in automated microscopy and image analysis enables quantitative profiling of cellular phenotypes (Cell Painting). It paves the way for studying the broad effects of chemical perturbations on biological systems at large scale during lead optimization. Comparison of perturbation biosignatures with biosignatures of annotated compounds can inform on both on- and off-target effects. When building databases with phenotypic profiles of thousands of compounds, it is vital to control the quality of Cell Painting assays over time. A tool for this to our knowledge does not yet exist within the imaging community. In this paper, we introduce an automated tool to assess the quality of Cell Painting assays by quantifying the reproducibility of biosignatures of annotated reference compounds. The tool learns the biosignature of those treatments from a historical dataset, and subsequently, it builds a two-dimensional probabilistic quality control (QC) limit. The limit will then be used to detect aberrations in new Cell Painting experiments. The tool is illustrated using simulated data and further demonstrated on Cell Painting data of the A549 cell line. In general, the tool provides a sensitive, detailed and easy-to-interpret mechanism to validate the quality of Cell Painting assays.

Differential detection workflows for multi-sample single-cell RNA-seq data.

In single-cell transcriptomics, differential gene expression (DE) analyses typically focus on testing differences in the average expression of genes between cell types or conditions of interest. Single-cell transcriptomics, however, also has the promise to prioritise genes for which the expression differ in other aspects of the distribution. Here we develop a workflow for assessing differential detection (DD), which tests for differences in the average fraction of samples or cells in which a gene is detected. After benchmarking eight different DD data analysis strategies, we provide a unified workflow for jointly assessing DE and DD. Using simulations and two case studies, we show that DE and DD analysis provide complementary information, both in terms of the individual genes they report and in the functional interpretation of those genes.

Correction to: On the utility of RNA sample pooling to optimize cost and statistical power in RNA sequencing experiments.

An amendment to this paper has been published and can be accessed via the original article.

On the utility of RNA sample pooling to optimize cost and statistical power in RNA sequencing experiments.

BACKGROUND: In gene expression studies, RNA sample pooling is sometimes considered because of budget constraints or lack of sufficient input material. Using microarray technology, RNA sample pooling strategies have been reported to optimize both the cost of data generation as well as the statistical power for differential gene expression (DGE) analysis. For RNA sequencing, with its different quantitative output in terms of counts and tunable dynamic range, the adequacy and empirical validation of RNA sample pooling strategies have not yet been evaluated. In this study, we comprehensively assessed the utility of pooling strategies in RNA-seq experiments using empirical and simulated RNA-seq datasets. RESULT: The data generating model in pooled experiments is defined mathematically to evaluate the mean and variability of gene expression estimates. The model is further used to examine the trade-off between the statistical power of testing for DGE and the data generating costs. Empirical assessment of pooling strategies is done through analysis of RNA-seq datasets under various pooling and non-pooling experimental settings. Simulation study is also used to rank experimental scenarios with respect to the rate of false and true discoveries in DGE analysis. The results demonstrate that pooling strategies in RNA-seq studies can be both cost-effective and powerful when the number of pools, pool size and sequencing depth are optimally defined. CONCLUSION: For high within-group gene expression variability, small RNA sample pools are effective to reduce the variability and compensate for the loss of the number of replicates. Unlike the typical cost-saving strategies, such as reducing sequencing depth or number of RNA samples (replicates), an adequate pooling strategy is effective in maintaining the power of testing DGE for genes with low to medium abundance levels, along with a substantial reduction of the total cost of the experiment. In general, pooling RNA samples or pooling RNA samples in conjunction with moderate reduction of the sequencing depth can be good options to optimize the cost and maintain the power.

SPsimSeq: semi-parametric simulation of bulk and single-cell RNA-sequencing data.

SUMMARY: SPsimSeq is a semi-parametric simulation method to generate bulk and single-cell RNA-sequencing data. It is designed to simulate gene expression data with maximal retention of the characteristics of real data. It is reasonably flexible to accommodate a wide range of experimental scenarios, including different sample sizes, biological signals (differential expression) and confounding batch effects. AVAILABILITY AND IMPLEMENTATION: The R package and associated documentation is available from https://github.com/CenterForStatistics-UGent/SPsimSeq. SUPPLEMENTARY INFORMATION: Supplementary data are available at Bioinformatics online.

Differential gene expression analysis tools exhibit substandard performance for long non-coding RNA-sequencing data.

BACKGROUND: Long non-coding RNAs (lncRNAs) are typically expressed at low levels and are inherently highly variable. This is a fundamental challenge for differential expression (DE) analysis. In this study, the performance of 25 pipelines for testing DE in RNA-seq data is comprehensively evaluated, with a particular focus on lncRNAs and low-abundance mRNAs. Fifteen performance metrics are used to evaluate DE tools and normalization methods using simulations and analyses of six diverse RNA-seq datasets. RESULTS: Gene expression data are simulated using non-parametric procedures in such a way that realistic levels of expression and variability are preserved in the simulated data. Throughout the assessment, results for mRNA and lncRNA were tracked separately. All the pipelines exhibit inferior performance for lncRNAs compared to mRNAs across all simulated scenarios and benchmark RNA-seq datasets. The substandard performance of DE tools for lncRNAs applies also to low-abundance mRNAs. No single tool uniformly outperformed the others. Variability, number of samples, and fraction of DE genes markedly influenced DE tool performance. CONCLUSIONS: Overall, linear modeling with empirical Bayes moderation (limma) and a non-parametric approach (SAMSeq) showed good control of the false discovery rate and reasonable sensitivity. Of note, for achieving a sensitivity of at least 50%, more than 80 samples are required when studying expression levels in realistic settings such as in clinical cancer research. About half of the methods showed a substantial excess of false discoveries, making these methods unreliable for DE analysis and jeopardizing reproducible science. The detailed results of our study can be consulted through a user-friendly web application, giving guidance on selection of the optimal DE tool ( http://statapps.ugent.be/tools/AppDGE/ ).

Blood pressure control status and associated factors among adult hypertensive patients on outpatient follow-up at University of Gondar Referral Hospital, northwest Ethiopia: a retrospective follow-up study.

BACKGROUND: Large segments of the hypertensive population in the world are either untreated or inadequately treated. The incidence of heart failure and mortality from cardiovascular complications of hypertension is high among patients with uncontrolled blood pressure (BP). But BP control status of hypertensive patients has not been investigated in the study area. The study aimed to assess BP control status and determinant factors among adult hypertensive patients on antihypertensive medication attending outpatient follow-up at University of Gondar Referral Hospital, northwest Ethiopia. METHODS: An institution-based retrospective follow-up study was conducted from September 2015 to April 2016. Data were collected using a structured and pretested questionnaire adopted from the World Health Organization STEPwise approach. BP records of 6 months were used, and patients were classified as having controlled BP if their BP readings were <140/90 mmHg for all adults ≥18 years of age and <150/90 mmHg for adults aged ≥60 years. A generalized estimating equation was fitted, and the odds ratio with a 95% confidence level was used to determine the effect of covariates on BP control status. RESULTS: Among 395 participants, 50.4% (95% CI: 45-55) of them controlled their BP in the last 6 months of the survey. Physical activity (adjusted odds ratio [AOR]=1.95, 95% CI: 1.41-2.68), duration on antihypertensive drugs of 2-4 years (AOR=1.70, 95% CI: 1.13-2.56) and 5 years or more (AOR=1.96, 95% CI: 1.32-2.92), and high adherence (AOR=2.18, 95% CI: 1.14-4.15) to antihypertensive drugs were positively associated with BP control, while salt intake (AOR=0.67, 95% CI: 0.49-0.93), overweight (AOR=0.50, 95% CI: 0.36-0.68), and obesity (AOR=0.56, 95% CI: 0.36-0.87) were inversely associated with BP control. CONCLUSION: In this study, only half of the hypertensive patients controlled their BP. Thus, health care providers need to be made aware about the importance of counseling hypertensive patients on drug adherence, moderate physical activity, and salt restriction to improve BP control.