Evaluation & the Health Professions

This special issue of Evaluation and the Health Professions is dedicated to methods for causal mediation analysis in Single Case Experimental Designs (SCEDs). Mediation analysis is used to identify intermediate variables that transmit the effect of the independent variable on the outcome. Until recently, mediation analysis was mostly confined to between-subjects designs and panel studies with few exceptions. Consequently, most of the developments in causal mediation analysis have also been restricted to such designs. In applied health research, SCEDs have been used to evaluate total effects of treatments on outcomes of interest. Providing researchers with the methods for evaluating causal indirect effects for individual participants can lead to important improvements in diagnosis, treatment, and prevention. This special issue includes articles that describe advanced quantitative methods for testing mediators in SCEDs, propose and test approaches that allow for relaxing statistical assumptions that may not hold in real data, and illustrate mediation analysis for a single participant in real and simulated SCEDs data.

In this article, we present single-case causal mediation analysis as the application of causal mediation analysis to data collected within a single-case experiment. This method combines the focus on the individual with the focus on mechanisms of change, rendering it a promising approach for both mediation and single-case researchers. For this purpose, we propose a new method based on time-discrete state-space modeling to estimate the direct and indirect treatment effects. We demonstrate how to estimate the model for a single-case experiment on stress and craving in a routine alcohol consumer before and after an imposed period of abstinence. Furthermore, we present a simulation study that examines the estimation and testing of the standardized indirect effect. All parameters used to generate the data were recovered with acceptable precision. We use maximum likelihood and permutation procedures to calculate p-values and standard errors of the parameters estimates. The new method is promising for testing mediated effects in single-case experimental designs. We further discuss limitations of the new method with respect to causal inference, as well as more technical concerns, such as the choice of the time lags between the measurements.

Mediation analysis is widely adopted by researchers to disentangle the causal pathways by which an intervention affects an outcome. This paper describes a model to estimate the direct and indirect effect from a single subject AB-design with repeated assessments of both the mediator and the outcome. We discuss the plausibility of the modeling assumptions and contrast different approaches to deal with the autocorrelation in the time series data. While there are only small differences between those approaches when the number of timepoints is small (T = 15), the Generalized Least Squares approach performs best in medium (T = 30) to large (T = 90) time series. We apply the proposed methodology to data from a single case AB-design that was conducted with a mother of an excessively crying baby. Daily crying and baby sleep during the night were assessed with online diaries during a baseline phase and intervention phase. Between both phases, the pediatrician instructed the mother how to apply a responsive soothing intervention, the happiest baby (THB) method. We find that the direct effect of THB on sleeping is positive. THB also reduces crying but decreased crying during the day is associated with decreased sleeping during the night and hence a negative indirect effect of THB on sleeping via crying is found.

Single-Case Experimental Designs (SCEDs) are increasingly recognized as a valuable alternative to group designs. Mediation analysis is useful in SCEDs contexts because it informs researchers about the underlying mechanism through which an intervention influences the outcome. However, methods for conducting mediation analysis in SCEDs have only recently been proposed. Furthermore, repeated measures of a target behavior present the challenges of autocorrelation and missing data. This paper aims to extend methods for estimating indirect effects in piecewise regression analysis in SCEDs by (1) evaluating three methods for modeling autocorrelation, namely, Newey-West (NW) estimation, feasible generalized least squares (FGLS) estimation, and explicit modeling of an autoregressive structure of order one (AR(1)) in the error terms and (2) evaluating multiple imputation in the presence of data that are missing completely at random. FGLS and AR(1) outperformed NW and OLS estimation in terms of efficiency, Type I error rates, and coverage, while OLS was superior to the methods in terms of power for larger samples. The performance of all methods is consistent across 0% and 20% missing data conditions. 50% missing data led to unsatisfactory power and biased estimates. In light of these findings, we provide recommendations for applied researchers.

In response to the importance of individual-level effects, the purpose of this paper is to describe the new randomization permutation (RP) test for a mediation mechanism for a single subject. We extend seminal work on permutation tests for individual-level data by proposing a test for mediation for one person. The method requires random assignment to the levels of the treatment variable at each measurement occasion, and repeated measures of the mediator and outcome from one subject. If several assumptions are met, the process by which a treatment changes an outcome can be statistically evaluated for a single subject, using the permutation mediation test method and the permutation confidence interval method for residuals. A simulation study evaluated the statistical properties of the new method suggesting that at least eight repeated measures are needed to control Type I error rates and larger sample sizes are needed for power approaching .8 even for large effects. The RP mediation test is a promising method for elucidating intraindividual processes of change that may inform personalized medicine and tailoring of process-based treatments for one subject.

Single-case designs (SCDs) are used to evaluate the effects of interventions on individual participants. By repeatedly measuring participants under different conditions, SCD studies focus on individual effects rather than on group summaries. The main limitation of SCDs remains its generalisability to wider populations, reducing the relevance of their findings for practice and policy making. With this limitation in mind, methodological developments for synthesising SCD data from different studies that investigate the same research question have intensified in the past decades (e.g. multilevel modelling). However, these techniques are restricted to comparing two interventions at a time and can only incorporate evidence from studies that directly compare the two treatments of interest. These limitations could be addressed by using network meta-analysis that incorporates both direct and indirect evidence to simultaneously compare multiple interventions. Despite its potential, network meta-analytical techniques have yet to be applied to SCD data. Thus, in this paper, we argue that network meta-analysis can be a valuable tool to synthesise SCD data. We demonstrate the use of network meta-analysis in SCD data using a real dataset, and we conclude by reflecting on the challenges that SCD researchers might face when applying network meta-analysis methods to their data.

Healthcare workers worldwide have been exposed to extraordinary stress during COVID-19 pandemic. This study aimed to investigate health-related quality of life (HRQoL) level and its health and occupational associated factors among Jordanian physicians during COVID-19 pandemic. A cross-sectional design using an online survey was adopted targeting physicians at different Jordanian hospitals. The study survey included demographics, HRQoL measured by 12-item Short Form health survey (SF-12) mental and physical components, physicians’ evaluation of work conditions during COVID-19, Neck Disability Index (NDI), Depression Anxiety Stress Scale (DASS 21), and International Physical Activity Questionnaire (IPAQ). Descriptive analyses were conducted to summarize primary data. Factors associated with HRQoL were determined using a multiple variable regression analysis. In total, 326 physicians successfully completed the survey, 44.2% were males with mean age of 32.08 (±6.93). SF-12 mental component mean was 52.13 (±20.84) and physical component mean was 69.24 (±18.1). Physicians HRQoL level was significantly associated with levels of stress (β = −0.23, 95% CI −1.05 to −0.27), depression (β = −0.22, 95% CI −1.09 to −0.28), neck disability (β = −0.30, 95% CI −1.08 to −0.57), health self-evaluation (β = 0.14, 95% CI 1.66–7.87), sleep self-evaluation (β = 0.09, 95% CI 0.16–3.58), and physical activity level (β = 0.09, 95% CI 0.00–0.001). Jordanian physicians’ level of HRQoL was relatively low during COVID-19. Healthcare facilities administrators should take into consideration factors associated with physicians’ HRQoL level when planning for future healthcare emergencies.

This study aimed to determine the depression, anxiety and stress levels that have negatively impacted nurses’ mental health during the COVID-19 pandemic. A sample group of 826 nurses working in Turkey were asked to fill in an online questionnaire in order to evaluate their psychological responses and the related factors that have adversely affected their mental health during the COVID-19 pandemic. In total, 696 nurses (84.3%) showed symptoms of depression, 644 (78%) reported anxiety and 543 (65.74%) reported stress. This study also highlighted that the most concerning factor for the nurses was the risk of transmitting the COVID-19 infection to their household members (89.2%). The most important problems faced by the nurses during COVID-19 included equipment shortages (50.7%), administrative problems (38.5%) and issues such as accommodation and nutrition (27.4%). These were found to have a statistically significant correlation with the nurses’ levels of depression, anxiety and stress. The fear of losing a household member, the inability to their household’s social needs and the fear of death were among the factors that concerned nurses during the COVID-19 pandemic, significantly affecting their levels of depression, anxiety and stress. Taking the necessary measures to deal with the aforementioned problems and fears is important to protect the health, productivity and efficiency of nurses during the pandemic period.

The aim of the study is to investigate the effects of intense anxiety and hopelessness experienced by healthcare workers during the pandemic on their quality of life. This cross-sectional, online questionnaire-based study was conducted between August 31, 2020 and October 31, 2020, with 729 healthcare workers in Turkey. The study showed that hopelessness, the weekly working time, fatigue, and the workload of healthcare workers negatively affected their quality of life, those who found the pandemic measures inadequate had a lower quality of life and higher hopelessness levels, and those who needed knowledge on various issues to improve their skills had lower quality of life and higher levels of anxiety and hopelessness. Increasing the measures to make healthcare workers feel competent and ready during the COVID-19 pandemic and meet their information needs to improve their skills will reduce their anxiety and hopelessness and improve their quality of life. Identifying the factors affecting anxiety, hopelessness, and quality of life will help achieve sustainable success in the delivery of health services and promote employee health and safety.