Analysis of reasons for loss to follow up in a prospective study in Chandigarh, India and impact from telecom changes

Objective Mobile phones are used in research studies, to enroll and follow-up participants, collect data, and implement mHealth initiatives. We conducted a longitudinal study in a birth cohort, where infants were required to make four scheduled visits by 12 months of age. Families of those failing to attend scheduled follow-up visits, were contacted telephonically to ascertain the reasons, which were categorized as: not interested to continue participating, migrated, phone disconnected due to telecom change, or other reason. Results A total of 413 mother-infant dyads were enrolled. The overall attrition was 56%, with majority occurring at the first follow-up visit. This temporally coincided with a telecom service provider announcing strong incentives to switch providers. Attrition monotonically decreased at subsequent visits. The reasons were: moved away (13%), no longer interested (8%), phone disconnected (7%), and multiple other reasons (28%), the majority of whom had unreachable phones. Those who remained in the study and those lost to follow-up were similar on most demographic variables. Among common reasons for attrition in cohort studies, we experienced a new dimension introduced by telecom changes. These findings underscore the need to consider unexpected reasons for attrition in longitudinal studies, and design more robust methods to follow-up participants.

Quantitative serology for SARS-CoV-2 using self-collected saliva and finger-stick blood

Convenient and widespread serology testing may alter the trajectory of the COVID-19 pandemic. This study seeks to leverage high-throughput, multiplexed serologic assays, which have been adopted as benchmarks for vaccine efficacy, to support large-scale surveys of SARS-CoV-2 immunity using finger-stick blood and/or saliva. Specifically, we optimized MSD’s serology assays, which were analytically validated for serum, to test self-collected finger-stick blood and saliva samples. We show that these assays can be used with FDA-registered specimen collection devices to obtain quantitative measurements for self-collected samples. Antibody levels were measured using an electrochemiluminescent (ECL) multiplex immunoassay, which has been used to measure humoral responses to several COVID-19 vaccines, including those funded by the U.S. Government’s Operation Warp Speed. First, we show that salivary antibodies are stable without refrigeration or preservatives for at least five days. Using matched samples, we show that testing of saliva and finger-stick blood equivalently identified individuals with humoral responses to CoV-2 antigens. Moreover, we piloted a simple saliva collection kit that can be used to safely send samples through the mail. This work demonstrates that robust methods for self-collection of finger-stick blood and saliva, in combination with quantitative, automated immunoassays, provide the technical capabilities needed to support large-scale serology testing.

Robust Methods for Soft Clustering of Multidimensional Time Series

Three robust algorithms for clustering multidimensional time series from the perspective of underlying processes are proposed. The methods are robust extensions of a fuzzy C-means model based on estimates of the quantile cross-spectral density. Robustness to the presence of anomalous elements is achieved by using the so-called metric, noise and trimmed approaches. Analyses from a wide simulation study indicate that the algorithms are substantially effective in coping with the presence of outlying series, clearly outperforming alternative procedures. The usefulness of the suggested methods is also highlighted by means of a specific application.

10 easy steps to conducting transparent, reproducible meta-analyses for infant researchers

Meta-analyses provide researchers with an overview of the body of evidence in a topic, with quantified estimates of effect sizes and the role of moderators, and weighting studies according to their precision. We provide a guide for conducting a transparent and reproducible meta-analysis in the field of developmental psychology within the framework of the MetaLab platform, in 10 steps: 1) Choose a topic for your meta-analysis, 2) Formulate your research question and specify inclusion criteria, 3) Preregister and carefully document all stages of your meta-analysis, 4) Conduct the literature search, 5) Collect and screen records, 6) Extract data from eligible studies, 7) Read the data into analysis software and compute effect sizes, 8) Create meta-analytic models to assess the strength of the effect and investigate possible moderators, 9) Visualize your data, 10) Write up and promote your meta-analysis. Meta-analyses can inform future studies, through power calculations, by identifying robust methods and exposing research gaps. By adding a new meta-analysis to MetaLab, datasets across multiple topics of developmental psychology can be synthesized, and the dataset can be maintained as a living, community-augmented meta-analysis to which researchers add new data, allowing for a cumulative approach to evidence synthesis.

Robust official business statistics methodology during COVID-19-related and other economic downturns

Official statistics has not properly researched and understood how its methods and models behave at times of downturns (and potentially in the corresponding situation of similarly paced (unpredictable and fast) growths). There is generally a wish to make methods robust to unusual changes, but these are often tackled situation by situation. Production of official statistics during COVID-19 has necessitated some radical changes in both data collection and statistical methods; these have been introduced with admirable speed and dedication, but this process would have been made easier with a body of research already in place to draw from. We discuss the issues with the robustness of statistical methods during downturns, and highlight the opportunity to gather data which can be analysed to give evidence for the most robust methods to use as protection against poor measurement during future downturns.

861Mendelian randomization on habitual coffee intake and plasma lipid profile: evidence from UK Biobank

Background Long-term heavy coffee consumption may adversely affect individuals’ cardiovascular disease (CVD) risk. As hyperlipidemia is a well-established contributor to CVD, we investigated the association between habitual coffee intake and plasma lipid profile. Methods We used data from up to 362,571 UK Biobank participants to examine associations between coffee intake and plasma lipid profiles, including LDL-C, HDL-C, total-C, triglycerides, ApoA1 and ApoB. Inverse variance weighted mendelian randomization (MR) was used to interrogate the causal nature of coffee-lipid associations, complemented by pleiotropy-robust methods, including MR-median, MR-Mode, MR-PRESSO and MR-Egger. Results We observed positive dose-dependent associations between self-reported coffee intake and plasma concentration of LDL-C, ApoB and total-C, with the highest lipid levels seen among participants drinking >6 cups/day (Plinear trend≤1.97E-57 for all). Genetic instrument for coffee intake was robustly associated with self-reported intake in the UK Biobank (F-statistic = 416). One cup increase in genetically instrumented intake was associated with 0.07 mmol/L (95%CI 0.03 to 0.12), 0.02 g/L (95%CI 0.01 to 0.03), and 0.09 mmol/L (95%CI 0.04 to 0.14) increase in LDL-C, ApoB, and total-C, respectively. Pleiotropy-robust methods provided largely consistent results albeit with greater imprecision when using MR-Egger. Conclusions Our phenotypic and genetic analysis consistently suggests that long-term heavy coffee consumption can lead to unfavourable lipid profile, which could potentially increase individuals’ risk for CVD. Individuals with elevated cholesterol may need to reduce their daily coffee intake. Key messages Our study provides evidence that long-term heavy coffee consumption can lead to unfavourable lipid profile.

Implementing A Human Factors Approach To Rca2: Analysis Of Resultant Safety Recommendations

Root Cause Analysis and Action (RCA2) guidelines offer fundamental improvements to traditional RCA. Yet, these guidelines lack robust methods to support a human factors analysis of patient harm events and the development of systems-level interventions. We previously described how human factors tools can be integrated into RCA2 to create a robust process called HFACS-RCA2. Prior analyses of qualitative data associated with an 18-month implementation project at a large academic health center indicated that HFACS-RCA2 fosters a more comprehensive human factors analysis of serious patient harm events and the identification of broader system interventions. The present study builds on this prior research by presenting the analysis of actual recommendations extracted from RCA reports. Results corroborate qualitative stakeholder findings that HFACS-RCA2 produced recommendations that were stronger and included more substantive changes compared to former RCA methods.