May measurement month 2018: an analysis of blood pressure screening results from Colombia

High blood pressure (BP) is the leading global preventable cause of death and the most common risk factor for cardiovascular disease (CVD). However, due to its asymptomatic nature, the lack of awareness of this condition causes underdiagnosis and low rates of adherence to pharmacological treatment. Looking for practical approaches to increase awareness worldwide, the International Society of Hypertension (ISH) implemented the 2nd May Measurement Month campaign in 2018 (MMM18). In order to contribute to this initiative, Colombia participated as one of the 89 countries involved in this hypertension screening programme. Blood pressure was measured in subjects from 11 departments in Colombia. Under the leadership of the Fundacio´n Oftalmolo´gica de Santander (FOSCAL), 400 volunteers across the country collected the data following the MMM protocol. Measurements from 35 548 participants with a mean age of 41.9 years were obtained. In total, 9475 (26.7%) of the total population studied had hypertension. Of those with hypertension, 69.9% of these subjects were aware of their condition, 65.0% were on antihypertensive medication, and 43.1% had controlled BP. Of those on medication, 66.3% had controlled BP. Hypertension screening, awareness, treatment, and control should be a priority in public health objectives due to its elevated burden of disease and direct association with increased CVD. The MMM campaign provided a positive impact in the diagnosis of hypertension across Colombia. Although efforts are being made to expand treatment capability and adherence, still more are needed to insure a broader coverage of antihypertensive medication in Colombia

Implementation of the trial emulation approach in medical research: a scoping review

Abstract Background When conducting randomised controlled trials is impractical, an alternative is to carry out an observational study. However, making valid causal inferences from observational data is challenging because of the risk of several statistical biases. In 2016 Hernán and Robins put forward the ‘target trial framework’ as a guide to best design and analyse observational studies whilst preventing the most common biases. This framework consists of (1) clearly defining a causal question about an intervention, (2) specifying the protocol of the hypothetical trial, and (3) explaining how the observational data will be used to emulate it. Methods The aim of this scoping review was to identify and review all explicit attempts of trial emulation studies across all medical fields. Embase, Medline and Web of Science were searched for trial emulation studies published in English from database inception to February 25, 2021. The following information was extracted from studies that were deemed eligible for review: the subject area, the type of observational data that they leveraged, and the statistical methods they used to address the following biases: (A) confounding bias, (B) immortal time bias, and (C) selection bias. Results The search resulted in 617 studies, 38 of which we deemed eligible for review. Of those 38 studies, most focused on cardiology, infectious diseases or oncology and the majority used electronic health records/electronic medical records data and cohort studies data. Different statistical methods were used to address confounding at baseline and selection bias, predominantly conditioning on the confounders (N = 18/49, 37%) and inverse probability of censoring weighting (N = 7/20, 35%) respectively. Different approaches were used to address immortal time bias, assigning individuals to treatment strategies at start of follow-up based on their data available at that specific time (N = 21, 55%), using the sequential trial emulations approach (N = 11, 29%) or the cloning approach (N = 6, 16%). Conclusion Different methods can be leveraged to address (A) confounding bias, (B) immortal time bias, and (C) selection bias. When working with observational data, and if possible, the ‘target trial’ framework should be used as it provides a structured conceptual approach to observational research