Effect of hand hygiene on infectious diseases in the office workplace: A systematic review

Background: Extensive data suggests that hand hygiene is a critical intervention for reducing infectious disease transmission in the clinical setting. However, it is unclear whether hand hygiene is effective at cutting down on infectious illnesses in non-clinical workplaces. The aim of this review is to assess the current literature concerning the effects of hand-washing interventions on infectious disease prevention among employees in nonclinical, office-based workplaces. Methods: In compiling this review, PubMed, Scopus, and Business Source Premier were examined for studies published from 1960 through 2016. Results: Eleven studies (eight experimental, two observational, one a simulation) were identified as eligible for inclusion. Hand-hygiene interventions at various levels of rigor were shown to reduce self-reported illness symptoms. Conclusions: Hand hygiene is thought to be more effective against gastrointestinal illness than it is against respiratory illness, but no clear consensus has been reached on this point. Minimal hand-hygiene interventions seem to be effective at reducing the incidence of employee illness. Along with reducing infections among employees, hand-hygiene programs in the workplace may provide additional benefits to employers by reducing the number of employee health insurance claims and improving employee morale. Future research should use objective measures of hand hygiene and illness, and explore economic impacts on employers more fully

TWISTER PLOTS FOR TIME-TO-EVENT STUDIES

Results of randomized trials and observational studies can be difficult to communicate. Results are often presented as risk or survival functions stratified by the treatment or exposure. However, a contrast between the stratified risk functions is often of primary interest. Here we propose a “twister” plot to visualize contrasts in risk over the duration of a study. The twister plot is a −90-degree rotation of a typical contrast measure plot (e.g., Figure 3 in Cole et al.), whereby the contrast measure is instead on the abscissa (x-axis) and time on the ordinate (y-axis). Pointwise confidence intervals are similarly added as a shaded region that typically widens as follow-up duration increases, giving twister plots their characteristic shape that resembles their namesake. To ease application, we provide SAS, R, and Python code on GitHub

Identifying and estimating effects of sustained interventions under parallel trends assumptions

Many research questions in public health and medicine concern sustained interventions in populations defined by substantive priorities. Existing methods to answer such questions typically require a measured covariate set sufficient to control confounding, which can be questionable in observational studies. Differences-in-differences rely instead on the parallel trends assumption, allowing for some types of time-invariant unmeasured confounding. However, most existing difference-in-differences implementations are limited to point treatments in restricted subpopulations. We derive identification results for population effects of sustained treatments under parallel trends assumptions. In particular, in settings where all individuals begin follow-up with exposure status consistent with the treatment plan of interest but may deviate at later times, a version of Robins' g-formula identifies the intervention-specific mean under stable unit treatment value assumption, positivity, and parallel trends. We develop consistent asymptotically normal estimators based on inverse-probability weighting, outcome regression, and a double robust estimator based on targeted maximum likelihood. Simulation studies confirm theoretical results and support the use of the proposed estimators at realistic sample sizes. As an example, the methods are used to estimate the effect of a hypothetical federal stay-at-home order on all-cause mortality during the COVID-19 pandemic in spring 2020 in the United States

On the Use of Covariate Supersets for Identification Conditions

The union of distinct covariate sets, or the superset, is often used in proofs for the identification or the statistical consistency of an estimator when multiple sources of bias are present. However, the use of a superset can obscure important nuances. Here, we provide two illustrative examples: one in the context of missing data on outcomes, and one in which the average causal effect is transported to another target population. As these examples demonstrate, the use of supersets may indicate a parameter is not identifiable when the parameter is indeed identified. Furthermore, a series of exchangeability conditions may lead to successively weaker conditions. Future work on approaches to address multiple biases can avoid these pitfalls by considering the more general case of nonoverlapping covariate sets

Optimizing SARS-CoV-2 Pooled Testing Strategies Through Differentiated Pooling for Distinct Groups

Pooled testing has been successfully used to expand SARS-CoV-2 testing, especially in settings requiring high volumes of screening of lower-risk individuals, but efficiency of pooling declines as prevalence rises. We propose a differentiated pooling strategy that independently optimizes pool sizes for distinct groups with different probabilities of infection to further improve the efficiency of pooled testing. We compared the efficiency (results obtained per test kit used) of the differentiated strategy with a traditional pooling strategy in which all samples are processed using uniform pool sizes under a range of scenarios. For most scenarios, differentiated pooling is more efficient than traditional pooling. In scenarios examined here, an improvement in efficiency of up to 3.94 results per test kit could be obtained through differentiated versus traditional pooling, with more likely scenarios resulting in 0.12 to 0.61 additional results per kit. Under circumstances similar to those observed in a university setting, implementation of our strategy could result in an improvement in efficiency between 0.03 to 3.21 results per test kit. Our results can help identify settings, such as universities and workplaces, where differentiated pooling can conserve critical testing resources

When Does Differential Outcome Misclassification Matter for Estimating Prevalence?

Background: When accounting for misclassification, investigators make assumptions about whether misclassification is "differential" or "nondifferential." Most guidance on differential misclassification considers settings where outcome misclassification varies across levels of exposure, or vice versa. Here, we examine when covariate-differential misclassification must be considered when estimating overall outcome prevalence. Methods: We generated datasets with outcome misclassification under five data generating mechanisms. In each, we estimated prevalence using estimators that (a) ignored misclassification, (b) assumed misclassification was nondifferential, and (c) allowed misclassification to vary across levels of a covariate. We compared bias and precision in estimated prevalence in the study sample and an external target population using different sources of validation data to account for misclassification. We illustrated use of each approach to estimate HIV prevalence using self-reported HIV status among people in East Africa cross-border areas. Results: The estimator that allowed misclassification to vary across levels of the covariate produced results with little bias for both populations in all scenarios but had higher variability when the validation study contained sparse strata. Estimators that assumed nondifferential misclassification produced results with little bias when the covariate distribution in the validation data matched the covariate distribution in the target population; otherwise estimates assuming nondifferential misclassification were biased. Conclusions: If validation data are a simple random sample from the target population, assuming nondifferential outcome misclassification will yield prevalence estimates with little bias regardless of whether misclassification varies across covariates. Otherwise, obtaining valid prevalence estimates requires incorporating covariates into the estimators used to account for misclassification

Missing Outcome Data in Epidemiologic Studies

Missing data are pandemic and a central problem for epidemiology. Missing data reduce precision and can cause notable bias. There remain too few simple published examples detailing types of missing data and illustrating their possible impact on results. Here we take an example randomized trial that was not subject to missing data and induce missing data to illustrate 4 scenarios in which outcomes are 1) missing completely at random, 2) missing at random with positivity, 3) missing at random without positivity, and 4) missing not at random. We demonstrate that accounting for missing data is generally a better strategy than ignoring missing data, which unfortunately remains a standard approach in epidemiology

Illustration of 2 Fusion Designs and Estimators

"Fusion" study designs combine data from different sources to answer questions that could not be answered (as well) by subsets of the data. Studies that augment main study data with validation data, as in measurement-error correction studies or generalizability studies, are examples of fusion designs. Fusion estimators, here solutions to stacked estimating functions, produce consistent answers to identified research questions using data from fusion designs. In this paper, we describe a pair of examples of fusion designs and estimators, one where we generalize a proportion to a target population and one where we correct measurement error in a proportion. For each case, we present an example motivated by human immunodeficiency virus research and summarize results from simulation studies. Simulations demonstrate that the fusion estimators provide approximately unbiased results with appropriate 95% confidence interval coverage. Fusion estimators can be used to appropriately combine data in answering important questions that benefit from multiple sources of information

Longitudinal effects of perinatal social support on maternal depression: A marginal structural modelling approach

Background Depression in the perinatal period, during pregnancy or within 1 year of childbirth, imposes a high burden on women with rippling effects through her and her child's life course. Social support may be an important protective factor, but the complex bidirectional relationship with depression, alongside a paucity of longitudinal explorations, leaves much unknown about critical windows of social support exposure across the perinatal period and causal impacts on future depressive episodes. Methods This study leverages marginal structural models to evaluate associations between longitudinal patterns of perinatal social support and subsequent maternal depression at 6 and 12 months postpartum. In a cohort of women in rural Pakistan (n=780), recruited in the third trimester of pregnancy and followed up at 3, 6 and 12 months postpartum, we assessed social support using two well-validated measures: the Multidimensional Scale of Perceived Social Support (MSPSS) and the Maternal Social Support Index (MSSI). Major depressive disorder was assessed with the Structured Clinical Interview for Diagnostic and Statistical Manual of Mental Disorders (DSM IV). Results High and sustained scores on the MSPSS through the perinatal period were associated with a decreased risk of depression at 12 months postpartum (0.35, 95% CI: 0.19 to 0.63). Evidence suggests the recency of support also matters, but estimates are imprecise. We did not find evidence of a protective effect for support based on the MSSI. Conclusions This study highlights the protective effect of sustained social support, particularly emotional support, on perinatal depression. Interventions targeting, leveraging and maintaining this type of support may be particularly important for reducing postpartum depression

Transmission of viral pathogens in a social network of university students: the eX-FLU study

Previous research on respiratory infection transmission among university students has primarily focused on influenza. In this study, we explore potential transmission events for multiple respiratory pathogens in a social contact network of university students. University students residing in on-campus housing (n = 590) were followed for the development of influenza-like illness for 10-weeks during the 2012–13 influenza season. A contact network was built using weekly self-reported contacts, class schedules, and housing information. We considered a transmission event to have occurred if students were positive for the same pathogen and had a network connection within a 14-day period. Transmitters were individuals who had onset date prior to their infected social contact. Throat and nasal samples were analysed for multiple viruses by RT-PCR. Five viruses were involved in 18 transmission events (influenza A, parainfluenza virus 3, rhinovirus, coronavirus NL63, respiratory syncytial virus). Transmitters had higher numbers of co-infections (67%). Identified transmission events had contacts reported in small classes (33%), dormitory common areas (22%) and dormitory rooms (17%). These results suggest that targeting person-to-person interactions, through measures such as isolation and quarantine, could reduce transmission of respiratory infections on campus