Automating incidence and prevalence analysis in open cohorts.

MOTIVATION: Data is increasingly used for improvement and research in public health, especially administrative data such as that collected in electronic health records. Patients enter and exit these typically open-cohort datasets non-uniformly; this can render simple questions about incidence and prevalence time-consuming and with unnecessary variation between analyses. We therefore developed methods to automate analysis of incidence and prevalence in open cohort datasets, to improve transparency, productivity and reproducibility of analyses. IMPLEMENTATION: We provide both a code-free set of rules for incidence and prevalence that can be applied to any open cohort, and a python Command Line Interface implementation of these rules requiring python 3.9 or later. GENERAL FEATURES: The Command Line Interface is used to calculate incidence and point prevalence time series from open cohort data. The ruleset can be used in developing other implementations or can be rearranged to form other analytical questions such as period prevalence. AVAILABILITY: The command line interface is freely available from https://github.com/THINKINGGroup/analogy_publication .

Using Repeated Measurements to Predict Cardiovascular Risk in Patients With Type 2 Diabetes Mellitus.

The QRISK cardiovascular disease (CVD) risk assessment model is not currently optimized for patients with type 2 diabetes mellitus (T2DM). We aim to identify if the abundantly available repeatedly measured data for patients with T2D improves the predictive capability of QRISK to support the decision-making process regarding CVD prevention in patients with T2DM. We identified patients with T2DM aged 25 to 85, not on statin treatment and without pre-existing CVD from the IQVIA Medical Research Data United Kingdom primary care database and then followed them up until the first diagnosis of CVD, ischemic heart disease, or stroke/transient ischemic attack. We included traditional, nontraditional risk factors and relevant treatments for our analysis. We then undertook a Cox's hazards model accounting for time-dependent covariates to estimate the hazard rates for each risk factor and calculated a 10-year risk score. Models were developed for males and females separately. We tested the performance of our models using validation data and calculated discrimination and calibration statistics. The study included 198,835 (180,143 male with 11,976 outcomes and 90,466 female with 8,258 outcomes) patients. The 10-year predicted survival probabilities for females was 0.87 (0.87 to 0.87), whereas the observed survival estimates from the Kaplan-Meier curve for all female models was 0.87 (0.86 to 0.87). The predicted and observed survival estimates for males were 0.84 (0.84 to 0.84) and 0.84 (0.83 to 0.84) respectively. The Harrell's C-index of all female models and all male models were 0.71 and 0.69 respectively. We found that including time-varying repeated measures, only mildly improved CVD risk prediction for T2DM patients in comparison to the current practice standard. We advocate for further research using time-varying data to identify if the involvement of further covariates may improve the accuracy of currently accepted prediction models.

Prodromal symptoms of rheumatoid arthritis in a primary care database: variation by ethnicity and socioeconomic status.

OBJECTIVES: To assess whether prodromal symptoms of rheumatoid arthritis (RA), as recorded in the Clinical Practice Research Datalink Aurum (CPRD) database of English primary care records, differ by ethnicity and socioeconomic status. METHODS: A cross-sectional study to determine the coding of common symptoms (≥0.1% in the sample) in the 24 months preceding RA diagnosis in CPRD Aurum, recorded between January 1st 2004 to May 1st 2022. Eligible cases were adults with a code for RA diagnosis. For each symptom, a logistic regression was performed with the symptom as dependent variable, and ethnicity and socioeconomic status as independent variables. Results were adjusted for sex, age, BMI, and smoking status. White ethnicity and the highest socioeconomic quintile were comparators. RESULTS: In total, 70115 cases were eligible for inclusion, of which 66.4% female. Twenty-one symptoms were coded in > 0.1% of cases so were included in the analysis. Patients of South Asian ethnicity had higher frequency of codes for several symptoms, with the largest difference by odds ratio being muscle cramps (OR 1.71, 1.44-2.57) and shoulder pain (1.44, 1.25-1.66). Patients of Black ethnicity had higher prevalence of several codes including unintended weight loss (2.02, 1.25-3.28) and ankle pain (1.51, 1.02-2.23). Low socioeconomic status was associated with morning stiffness (1.74, 1.08-2.80) and falls (1.37, 2.03-1.82). CONCLUSION: There are significant differences in coded symptoms between demographic groups, which must be considered in clinical practice in diverse populations and to avoid algorithmic bias in prediction tools derived from routinely collected healthcare data.

Health outcomes in those who have been victims of knife crime: a protocol for a systematic review and meta-analysis.

INTRODUCTION: Knife-enabled crime is a UK public health issue leading to substantial impacts on society, victims and their families, as well as additional strain on the healthcare system. Despite the increase in knife-enabled crime and the overwhelming consequences, there is a lack of comprehensive studies exploring the long-term health outcomes of knife crime victims in the UK. The research gap hinders the development of more targeted secondary preventative interventions, resource allocation and public awareness campaigns. This systematic review aims to identify the long-term health outcomes of knife crime victims, therefore providing valuable knowledge for stakeholders, health practitioners and policymakers for a more effective public health response. METHODS AND ANALYSIS: A comprehensive search strategy was developed, focusing on four key concepts: study design, knife-related offences, outcomes and risk. Databases being searched include MEDLINE, EMBASE, PsycINFO, ProQuest Criminology Collection, Web of Science Core Collection, Google Scholar and OpenGrey. Reference lists and forward citations will be inspected for further suitable literature. The study selection will involve two independent reviewers screening the studies from the search, with disagreements resolved by a third reviewer. All UK quantitative research on long-term health outcomes of knife crime victims will be included in the review. Covidence will be used to efficiently manage data. A data extraction form has been developed which will summarise key aspects of each study that will be included in the review. Methodological Index for Non-Randomised Studies quality assessment checklist will be used to assess the studies and the Newcastle-Ottawa Scale will assess the risk of bias in each study. Findings will be narratively synthesised, and if heterogeneity is sufficient, a meta-analysis will be conducted. ETHICS AND DISSEMINATION: Ethics approval is not required for this study as no original data will be collected. The results will be disseminated through a peer-reviewed publication and conference presentation.