Participant recruitment, baseline characteristics and at-home-measurements of cardiometabolic risk markers: insights from the Supreme Nudge parallel cluster-randomised controlled supermarket trial.

BACKGROUND: Recruiting participants for lifestyle programmes is known to be challenging. Insights into recruitment strategies, enrolment rates and costs are valuable but rarely reported. We provide insight into the costs and results of used recruitment strategies, baseline characteristics and feasibility of at-home cardiometabolic measurements as part of the Supreme Nudge trial investigating healthy lifestyle behaviours. This trial was conducted during the COVID-19 pandemic, requiring a largely remote data collection approach. Potential sociodemographic differences were explored between participants recruited through various strategies and for at-home measurement completion rates. METHODS: Participants were recruited from socially disadvantaged areas around participating study supermarkets (n = 12 supermarkets) across the Netherlands, aged 30-80 years, and regular shoppers of the participating supermarkets. Recruitment strategies, costs and yields were logged, together with completion rates of at-home measurements of cardiometabolic markers. Descriptive statistics are reported on recruitment yield per used method and baseline characteristics. We used linear and logistic multilevel models to assess the potential sociodemographic differences. RESULTS: Of 783 recruited, 602 were eligible to participate, and 421 completed informed consent. Most included participants were recruited via letters/flyers at home (75%), but this strategy was very costly per included participant (89 Euros). Of paid strategies, supermarket flyers were the cheapest (12 Euros) and the least time-invasive (< 1 h). Participants who completed baseline measurements (n = 391) were on average 57.6 (SD 11.0) years, 72% were female and 41% had high educational attainment, and they often completed the at-home measurements successfully (lipid profile 88%, HbA1c 94%, waist circumference 99%). Multilevel models suggested that males tended to be recruited more often via word-of-mouth (ORfemales 0.51 (95%CI 0.22; 1.21)). Those who failed the first attempt at completing the at-home blood measurement were older (ß 3.89 years (95% CI 1.28; 6.49), whilst the non-completers of the HbA1c (ß - 8.92 years (95% CI - 13.62; - 4.28)) and LDL (ß - 3.19 years (95% CI - 6.53; 0.09)) were younger. CONCLUSIONS: Supermarket flyers were the most cost-effective paid strategy, whereas mailings to home addresses recruited the most participants but were very costly. At-home cardiometabolic measurements were feasible and may be useful in geographically widespread groups or when face to face contact is not possible. TRIAL REGISTRATION: Dutch Trial Register ID NL7064, 30 May 2018, https://trialsearch.who.int/Trial2.aspx?TrialID=NTR7302.

Development and validation of an algorithm to estimate the risk of severe complications of COVID-19: a retrospective cohort study in primary care in the Netherlands

ObjectiveTo develop an algorithm (sCOVID) to predict the risk of severe complications of COVID-19 in a community-dwelling population to optimise vaccination scenarios.DesignPopulation-based cohort study.Setting264 Dutch general practices contributing to the NL-COVID database.Participants6074 people aged 0–99 diagnosed with COVID-19.Main outcomesSevere complications (hospitalisation, institutionalisation, death). The algorithm was developed from a training data set comprising 70% of the patients and validated in the remaining 30%. Potential predictor variables included age, sex, chronic comorbidity score (CCS) based on risk factors for COVID-19 complications, obesity, neighbourhood deprivation score (NDS), first or second COVID-19 wave and confirmation test. Six population vaccination scenarios were explored: (1) random (naive), (2) random for persons above 60 years (60plus), (3) oldest patients first in age band of 5 years (oldest first), (4) target population of the annual influenza vaccination programme (influenza), (5) those 25–65 years of age first (worker), and (6) risk based using the prediction algorithm (sCOVID).ResultsSevere complications were reported in 243 (4.8%) people with 59 (20.3%) nursing home admissions, 181 (62.2%) hospitalisations and 51 (17.5%) deaths. The algorithm included age, sex, CCS, NDS, wave and confirmation test (c-statistic=0.91, 95% CI 0.88 to 0.94) in the validation set. Applied to different vaccination scenarios, the proportion of people needed to be vaccinated to reach a 50% reduction of severe complications was 67.5%, 50.0%, 26.1%, 16.0%, 10.0% and 8.4% for the worker, naive, influenza, 60plus, oldest first and sCOVID scenarios, respectively.ConclusionThe sCOVID algorithm performed well to predict the risk of severe complications of COVID-19 in the first and second waves of COVID-19 infections in this Dutch population. The regression estimates can and need to be adjusted for future predictions. The algorithm can be applied to identify persons with highest risks from data in the electronic health records of general practitioners (GPs).