Coronavirus testing in women attending antenatal care

BackgroundUniversal screening has been proposed as a strategy to identify asymptomatic individuals infected with the severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) and mitigate transmission.AimTo investigate the rate of positive tests among pregnant women in Melbourne, Australia.MethodsWe performed a cross-sectional prevalence study at three maternity hospitals (one tertiary referral hospital and two secondary maternities) in Melbourne, Australia. SARS-CoV-2 testing was offered to all pregnant women attending face-to-face antenatal visits and to those attending the hospital with symptoms of possible coronavirus disease, between 6th and 19th of May 2020. Testing was performed by multiplex-tandem polymerase chain reaction (PCR) on combined oropharyngeal and nasopharyngeal swabs. The primary outcome was the proportion of positive SARS-CoV-2 tests.FindingsSARS-CoV-2 testing was performed in 350 women, of whom 19 had symptoms of possible COVID-19. The median maternal age was 32 years (IQR 28–35 years), and the median gestational age at testing was 33 weeks and four days (IQR 28 weeks to 36 weeks and two days). All 350 tests returned negative results (p̂ = 0%, 95% CI 0–1.0%).ConclusionIn a two-week period of low disease prevalence, the rate of asymptomatic coronavirus infection among pregnant women in Australia during the study period was negligible, reflecting low levels of community transmission.     

Supersaturated designs of projectivity P=3 or near P=3

Supersaturated designs offer a potentially useful way to investigate many factors in few experiments i.e. typical screening situations. Their design properties have mainly been evaluated based on their ability to identify and estimate main effects. Projective properties have received little attention. In this paper we show how to construct two-level supersaturated designs for 2(n−2) factors in n runs (n a multiple of four) of projectivity P=3 or near projectivity P=3 from orthogonal non-regular two-level designs. The designs obtained also have favourable properties such as low maximum absolute value of the inner product between a main effect column and a two-factor interaction column and relatively few types of different projections onto subsets consisting of three factor columns.     

Foldover Conference Designs for Screening Experiments

A conference matrix is a square matrix C with zeros on the diagonal and ±1s off the diagonal, such that C ^T C = CC ^T  = (n ? 1)I, where I is the identity matrix. Conference matrices are an important class of combinatorial designs due to their many applications in several fields of science, including statistical-experimental designs, telecommunications, elliptic geometry, and more. In this article, conference matrices and their full foldover design are combined together to obtain an alternative method for screening active factors in complicated problems. This method provides a model-independent estimate of the set of active factors and also gives a linearity test for the underlying model.     

Split-plot designs with mirror image pairs as sub-plots

In this article we investigate two-level split-plot designs where the sub-plots consist of only two mirror image trials. Assuming third and higher order interactions negligible, we show that these designs divide the estimated effects into two orthogonal sub-spaces, separating sub-plot main effects and sub-plot by whole-plot interactions from the rest. Further we show how to construct split-plot designs of projectivity P≥3. We also introduce a new class of split-plot designs with mirror image pairs constructed from non-geometric Plackett-Burman designs. The design properties of such designs are very appealing with effects of major interest free from full aliasing assuming that 3rd and higher order interactions are negligible.     

Do youth in out-of-home care receive recommended mental health and educational services following screening evaluations?

For children in out-of-home care, a significant gap exists between those who need services and those who receive them. Screening all children in out-of-home care is recommended to reduce this gap. This study was designed to determine if recommendations from mental health and educational screening evaluations were related to service implementation for youth in out-of-home care. Screening evaluations were completed with 171 maltreated youth (ages 9 to 11) who had been placed in out-of-home care within the prior year. Written reports summarizing the findings were provided to children's caseworkers. Service utilization was assessed at baseline (T1; before screening reports were completed) and follow-up (T2; 9–12 months later) interviews. For children not already receiving services at T1, logistic regression analyses tested the association between T1 recommendations for services and new service implementation by T2. Mental health (youth-report) and educational (teacher-report) outcomes were analyzed separately. Screening evaluations identified 22% of children with unmet mental health needs and 36% with unmet educational needs at T1. Children who received a recommendation for new services (i.e., all of those with unmet needs) were more likely to receive mental health (OR = 2.50, p = .06) and/or educational (OR = 3.54, p = .04) services by T2 than children who did not receive recommendations for services. While recommendations increased the odds of receiving services, almost half of the children with unmet mental health needs did not receive services, and 84% of children with unmet educational needs did not receive services by T2. Much work remains to ensure youth receive needed services.     

Sure independence screening for analyzing supersaturated designs

ABSTRACT

Supersaturated designs (SSDs) constitute a large class of fractional factorial designs which can be used for screening out the important factors from a large set of potentially active ones. A major advantage of these designs is that they reduce the experimental cost dramatically, but their crucial disadvantage is the confounding involved in the statistical analysis. Identification of active effects in SSDs has been the subject of much recent study. In this article we present a two-stage procedure for analyzing two-level SSDs assuming a main-effect only model, without including any interaction terms. This method combines sure independence screening (SIS) with different penalty functions; such as Smoothly Clipped Absolute Deviation (SCAD), Lasso and MC penalty achieving both the down-selection and the estimation of the significant effects, simultaneously. Insights on using the proposed methodology are provided through various simulation scenarios and several comparisons with existing approaches, such as stepwise in combination with SCAD and Dantzig Selector (DS) are presented as well. Results of the numerical study and real data analysis reveal that the proposed procedure can be considered as an advantageous tool due to its extremely good performance for identifying active factors.     

A review of cancer screening evaluation techniques, with some particular examples in breast cancer screening

Summary.  The instigation of mass screening for breast cancer has, over the last three decades, raised various statistical issues and led to the development of new statistical approaches. Initially, the design of screening trials was the main focus of research but, as the evidence in favour of population-based screening programmes mounts, a variety of other applications have also been identified. These include administrative and quality control tasks, for monitoring routine screening services, as well as epidemiological modelling of incidence and mortality. We review the commonly used methods of cancer screening evaluation, highlight some current issues in breast screening and, using examples from randomized trials and established screening programmes, illustrate the role that statistical science has played in the development of clinical research in this field.     

On multistage group screening designs

In this article we discuss multistage group screening in which group-factors contain differing number of factors. We describe a procedure for grouping the factors in the absence of concrete prior information, so that the relative testing cost is minimal. It Is shown that under quite general conditions, these designs will require fewer runs than the equivalent designs in which the group-factors contain same number of factors.     

Group testing with test error as a function of concentration

A common assumption in group testing applications is that there is no test error, i.e., misclassification of a single item or a group of items cannot occur. Graff and Roeloffs ( 1972 ) have proposed a procedure applicable when there is a known probability of misclassification. We generalize their results to the situation where the probability of misclassification depends on the proportion of defective items in the group.