Assessing the effect of COVida orphans and vulnerable children support services on viral load coverage and suppression among children and adolescents living with HIV in four provinces in Mozambique.

Orphans and vulnerable children (OVC) programs focusing on improving HIV outcomes for children and adolescents living with HIV (C&ALHIV) may improve viral load (VL) testing coverage, a critical step toward achieving VL suppression. In Mozambique, we conducted a retrospective medical record review comparing VL testing coverage and suppression between C&ALHIV receiving OVC support and two cohorts of non-participants constructed using propensity score matching. We collected data for 25,783 C&ALHIV in Inhambane, Maputo City, Nampula, and Tete between October 2020-September 2021. Unadjusted rates of VL testing were 62.9% among OVC participants compared with 39.2% and 50.4% of non-participants in OVC support and non-OVC support districts, respectively. In multivariate models, OVC participants were 18 and 10 percentage points more likely to have received a VL test than non-participants in OVC districts (p < 0.01) and non-OVC districts (p < 0.01), respectively. OVC participants under 5 years old were significantly more likely to have received a VL test than their same-age counterparts in both comparison groups. Overall, the OVC program did not demonstrate significant effects on VL suppression. This approach could be replicated in other contexts to improve testing coverage. It is crucial that clinical partners and governments continue to share data to enable timely monitoring through OVC programming.

Correlation of population mortality of COVID-19 and testing coverage: a comparison among 36 OECD countries.

Although testing is widely regarded as critical to fighting the COVID-19 pandemic, what measure and level of testing best reflects successful infection control remains unresolved. Our aim was to compare the sensitivity of two testing metrics - population testing number and testing coverage - to population mortality outcomes and identify a benchmark for testing adequacy. We aggregated publicly available data through 12 April on testing and outcomes related to COVID-19 across 36 OECD (Organization for Economic Development) countries and Taiwan. Spearman correlation coefficients were calculated between the aforementioned metrics and following outcome measures: deaths per 1 million people, case fatality rate and case proportion of critical illness. Fractional polynomials were used to generate scatter plots to model the relationship between the testing metrics and outcomes. We found that testing coverage, but not population testing number, was highly correlated with population mortality (rs = -0.79, P = 5.975 × 10-9vs. rs = -0.3, P = 0.05) and case fatality rate (rs = -0.67, P = 9.067 × 10-6vs. rs = -0.21, P = 0.20). A testing coverage threshold of 15-45 signified adequate testing: below 15, testing coverage was associated with exponentially increasing population mortality; above 45, increased testing did not yield significant incremental mortality benefit. Taken together, testing coverage was better than population testing number in explaining country performance and can serve as an early and sensitive indicator of testing adequacy and disease burden.

Strategic HIV Case Findings among Infants at Different Entry Points of Health Facilities in Cameroon: Optimizing the Elimination of Mother-To-Child Transmission in Low- and- Middle-Income Countries.

BACKGROUND: HIV case finding is an essential component for ending AIDS, but there is limited evidence on the effectiveness of such a strategy in the pediatric population. We sought to determine HIV positivity rates among children according to entry points in Cameroon. METHODS: A facility-based survey was conducted from January 2015 to December 2019 among mother-child couples at various entry points of health facilities in six regions of Cameroon. A questionnaire was administered to parents/guardians. Children were tested by polymerase chain reaction (PCR). Positivity rates were compared between entry points. Associations were quantified using the unadjusted positivity ratio (PR) for univariate analyses and the adjusted positivity ratio (aPR) for multiple Poisson regression analyses with 95% confidence intervals (CIs). p-values < 0.05 were considered significant. RESULTS: Overall, 24,097 children were enrolled. Among them, 75.91% were tested through the HIV prevention of mother-to-child transmission (PMTCT) program, followed by outpatient (13.27%) and immunization (6.27%) services. In total, PMTCT, immunization, and outpatient services accounted for 95.39% of children. The overall positivity was 5.71%, with significant differences (p < 0.001) between entry points. Univariate analysis showed that inpatient service (PR = 1.45; 95% CI: [1.08, 1.94]; p = 0.014), infant welfare (PR = 0.43; 95% CI: [0.28, 0.66]; p < 0.001), immunization (PR = 0.56; 95% CI: [0.45, 0.70]; p < 0.001), and PMTCT (PR = 0.41; 95% CI: [0.37, 0.46]; p < 0.001) were associated with HIV transmission. After adjusting for other covariates, only PMTCT was associated with transmission (aPR = 0.66; 95% CI: [0.51, 0.86]; p = 0.002). CONCLUSIONS: While PMTCT accounts for most tested children, high HIV positivity rates were found among children presenting at inpatient, nutrition, and outpatient services and HIV care units. Thus, systematic HIV testing should be proposed for all sick children presenting at the hospital who have escaped the PMTCT cascade.

Multi-release software model based on testing coverage incorporating random effect (SDE).

In the past, various Software Reliability Growth Models (SRGMs) have been proposed using different parameters to improve software worthiness. Testing Coverage is one such parameter that has been studied in numerous models of software in the past and it has proved its influence on the reliability models. To sustain themselves in the market, software firms keep upgrading their software with new features or enhancements by rectifying previously reported faults. Also, there is an impact of the random effect on testing coverage during both the testing and operational phase. In this paper, we have proposed a Software reliability growth model based on testing coverage with random effect along with imperfect debugging. Later, the multi-release problem is presented for the proposed model. The proposed model is validated on the dataset from Tandem Computers. The results for each release of the models have been discussed based on the different performance criteria. The numerical results illustrate that models fit the failure data significantly.•The random effect in the testing coverage rate is handled using Stochastic Differential Equations (SDE).•Three testing coverage functions used are Exponential, Weibull, and S-shaped.•Four Releases of the software model has been presented.

COVID-19 Incidence Proportion as a Function of Regional Testing Strategy, Vaccination Coverage, and Vaccine Type.

Introduction: The COVID-19 pandemic has become a serious challenge for humanity almost everywhere globally. Despite active vaccination around the world, the incidence proportion in different countries varies significantly as of May 2022. The reason may be a combination of demographic, immunological, and epidemiological factors. The purpose of this study was to analyze possible relationships between COVID-19 incidence proportion in the population and the types of SARS-CoV-2 vaccines used in different countries globally, taking into account demographic and epidemiological factors. Materials and methods: An initial database was created of demographic and immunoepidemiological information about the COVID-19 situation in 104 countries collected from published official sources and repository data. The baseline included, for each country, population size and density; SARS-CoV-2 testing coverage; vaccination coverage; incidence proportion; and a list of vaccines that were used, including their relative share among all vaccinations. Subsequently, the initial data set was stratified by population and vaccination coverage. The final data set was subjected to statistical processing both in general and taking into account population testing coverage. Results: After formation of the final data set (including 53 countries), it turned out that reported COVID-19 case numbers correlated most strongly with testing coverage and the proportions of vaccine types used, specifically, mRNA (V1); vector (V2); peptide/protein (V3); and whole-virion/inactivated (V4). Due to the fact that an inverse correlation was found between 'reported COVID-19 case numbers' with V2, V3, and V4, these three vaccine types were also combined into one analytic group, 'non-mRNA group' vaccines (Vnmg). When the relationship between vaccine type and incidence proportion was examined, minimum incidence proportion was noted at V1:Vnmg ratios (%:%) from 0:100 to 30:70. Maximum incidence proportion was seen with V1:Vnmg from 80:20 to 100:0. On the other hand, we have shown that the number of reported COVID-19 cases in different countries largely depends on testing coverage. To offset this factor, countries with low and extremely high levels of testing were excluded from the data set; it was then confirmed that the largest number of reported COVID-19 cases occurred in countries with a dominance of V1 vaccines. The fewest reported cases were seen in countries with a dominance of Vnmg vaccines. Conclusion: In this paper, we have shown for the first time that the level of reported COVID-19 incidence proportion depends not only on SARS-CoV-2 testing and vaccination coverage, which is quite logical, but probably also on the vaccine types used. With the same vaccination level and testing coverage, those countries that predominantly use vector and whole-virion vaccines feature incidence proportion that is significantly lower than countries that predominantly use mRNA vaccines.

Estimating actual COVID-19 case numbers using cumulative death count-A method of measuring effectiveness of lockdown of non-essential activities: a South African case study.

INTRODUCTION: Estimating the number of SARS-CoV-2 infected individuals at any specific time point is always a challenge due to asymptomatic cases, the incubation period and testing delays. Here we use an empirical analysis of cumulative death count, transmission-to-death time lag, and infection fatality rate (IFR) to evaluate and estimate the actual cases at a specific time point as a strategy of tracking the spread of COVID-19. METHODS: This method mainly uses death count, as COVID-19 related deaths are arguably more reliably reported than infection case numbers. Using an IFR estimate of 0.66%, we back-calculate the number of cases that would result in the cumulative number of deaths at a given time point in South Africa between 27 February and 14 April. We added the mean incubation period (6.4 days) and the onset-to-death time lag (17.8 days) to identify the estimated time lag between transmission and death (25 days, rounded up). We use the statistical programming language R to analyze the data and produce plots. RESULTS: We estimate 28,182 cases as of 14 April, compared with 3,465 reported cases. Weekly growth rate of actual cases dropped immediately after lockdown implementation and has remained steady, measuring at 51.2% as of 14 April. The timing of drop in growth rate suggests that South Africa's infection prevention strategy may have been effective at reducing viral transmission. CONCLUSION: Estimating the actual number of cases at a specific time point can support evidence-based policies to reduce and prevent the spread of COVID-19. Non-reported, asymptomatic, hard to reach and, mild cases are possible sources of outbreaks that could emerge after lockdown. Therefore, close monitoring, optimized screening strategy and prompt response to COVID-19 could help in stopping the spread of the virus.

The HOSENG trial - Effect of the provision of oral self-testing for absent and refusing individuals during a door-to-door HIV-testing campaign on testing coverage: protocol of a cluster-randomized clinical trial in rural Lesotho.

BACKGROUND: HIV-testing coverage remains below the targeted 90% despite efforts and resources invested. Home-based HIV-testing is a key approach endorsed by the World Health Organization (WHO), especially to reach individuals who might not seek testing otherwise. Although acceptance of testing during such campaigns is high, coverage remains low due to absent household members. This cluster-randomized trial aims to assess increase in testing coverage using oral HIV self-testing (HIVST) among individuals who are absent or decline testing during home-based HIV-testing. METHODS: The HOSENG (HOme-based SElf-testiNG) trial is a cluster-randomized, parallel-group, superiority trial in two districts of Lesotho, Southern Africa. Clusters are stratified by district, village size, and village access to the nearest health facility. Cluster eligibility criteria include: village is in catchment area of one of the study facilities, village authority provides consent, and village has a registered, capable, and consenting village health worker (VHW). In intervention clusters, HIV self-tests are provided for eligible household members who are absent or decline HIV-testing in the presence of the campaign team. In control clusters, standard of care for absent and refusing individuals applies, i.e., referral to a health facility. The primary outcome is HIV-testing coverage among individuals aged 12 years or older within 120 days after enrollment. Secondary objectives include HIV-testing coverage among other age groups, and uptake of the different testing modalities. Statistical analyses will be conducted and reported in line with CONSORT guidelines. The HOSENG trial is linked to the VIBRA (Village-Based Refill of ART) trial. Together, they constitute the GET ON (GETting tOwards Ninety) research project. DISCUSSION: The HOSENG trial tests whether oral HIVST may be an add-on during door-to-door testing campaigns towards achieving optimal testing coverage. The provision of oral self-test kits, followed up by VHWs, requires little additional human resources, finances and logistics. If cost-effective, this approach should inform home-based HIV-testing policies not only in Lesotho, but in similar high-prevalence settings. TRIAL REGISTRATION: ClinicalTrials.gov, (ID: NCT03598686 ). Registered on 25 July 2018. More information is available at www.getonproject.wordpress.com .

Assessing the effect of COVida orphans and vulnerable children support services on viral load coverage and suppression among children and adolescents living with HIV in four provinces in Mozambique

Orphans and vulnerable children (OVC) programs focusing on improving HIV outcomes for children and adolescents living with HIV (C&ALHIV) may improve viral load (VL) testing coverage, a critical step toward achieving VL suppression. In Mozambique, we conducted a retrospective medical record review comparing VL testing coverage and suppression between C&ALHIV receiving OVC support and two cohorts of non-participants constructed using propensity score matching. We collected data for 25,783 C&ALHIV in Inhambane, Maputo City, Nampula, and Tete between October 2020-September 2021. Unadjusted rates of VL testing were 62.9% among OVC participants compared with 39.2% and 50.4% of non-participants in OVC support and non-OVC support districts, respectively. In multivariate models, OVC participants were 18 and 10 percentage points more likely to have received a VL test than non-participants in OVC districts (p < 0.01) and non-OVC districts (p < 0.01), respectively. OVC participants under 5 years old were significantly more likely to have received a VL test than their same-age counterparts in both comparison groups. Overall, the OVC program did not demonstrate significant effects on VL suppression. This approach could be replicated in other contexts to improve testing coverage. It is crucial that clinical partners and governments continue to share data to enable timely monitoring through OVC programming.