How to mitigate selection bias in COVID-19 surveys: evidence from five national cohorts.

Non-response to surveys is a common problem; even more so during the COVID-19 pandemic with social distancing measures challenging data collection. As respondents often differ from non-respondents, this can introduce bias. The goal of the current study was to see if we can reduce bias and restore sample representativeness in a series of COVID-19 surveys embedded within five UK cohort studies by using the rich data available from previous waves of data collection.

Impact of the COVID-19 pandemic on health-care use among patients with cancer in England, UK: a comprehensive phase-by-phase time-series analysis across attendance types for 38 cancers.

Background: The COVID-19 pandemic resulted in the widespread disruption of cancer health provision services across the entirety of the cancer care pathway in the UK, from screening to treatment. The potential long-term health implications, including increased mortality for individuals who missed diagnoses or appointments, are concerning. However, the precise impact of lockdown policies on national cancer health service provision across diagnostic groups is understudied.

Prevalence of chronic pain or analgesic use in children and young people and its long-term impact on substance misuse, mental illness, and prescription opioid use: a retrospective longitudinal cohort study.

Background: Epidemiological studies suggest chronic and recurrent pain affects around a quarter of children, while 8% report intense and frequent pain. The long-term implications of chronic pain in childhood are uncertain. Using electronic health records (EHRs) we used both disease codes and medicines prescription records to investigate the scale of chronic pain and long-term analgesic use in children and young people (CYP), and if chronic pain and/or use of analgesic medicines at an early age is associated with substance misuse, use of prescription opioids, and poor mental health in adulthood.

On the estimation of the effect of weight change on a health outcome using observational data, by utilising the target trial emulation framework.

Background/objectives: When studying the effect of weight change between two time points on a health outcome using observational data, two main problems arise initially (i) 'when is time zero?' and (ii) 'which confounders should we account for?' From the baseline date or the 1st follow-up (when the weight change can be measured)? Different methods have been previously used in the literature that carry different sources of bias and hence produce different results.

Methods: We utilised the target trial emulation framework and considered weight change as a hypothetical intervention. First, we used a simplified example from a hypothetical randomised trial where no modelling is required.