Associations between socioeconomic deprivation and pharmaceutical prescribing in primary care in England.

BACKGROUND: Socioeconomic deprivation is associated with health inequality. Previous studies have described associations between primary care prescribing rates and deprivation for individual drugs or drug classes. We explore the correlation between socioeconomic deprivation and the rate of prescribing of individual pharmaceutical drugs, and drug classes, in primary care in England, to identify prescribing inequalities that would require further investigation. METHODS: In this cross-sectional study, national primary care prescribing data, by primary care practice, were retrieved for the calendar year 2019 in England. Socioeconomic deprivation was quantified using the Index of Multiple Deprivation (IMD) score. Correlations were calculated using Spearman's rank correlation coefficient (ρ), adjusting for practice list size and demographics, with a Bonferroni-corrected p value threshold of 5×10-5. RESULTS: We included 1.05 billion prescription items dispensed from 6896 England practices. 142/206 (69%) drug classes and 505/774 (65%) drugs were significantly correlated with IMD score (p<5×10-5). Of the 774 included drugs, 31 (4%) were moderately positively associated with IMD score (ρ>0.4). Only one was moderately negatively correlated with IMD score (ρ<-0.4), suggesting higher prescribing rates in more affluent areas. The drug classes most strongly associated with IMD score included opioid and non-opioid analgesics, antipsychotics and reflux medications. Drug classes most strongly associated with affluence included epinephrine, combined oral contraceptives and hormone replacement therapy. CONCLUSION: We identify novel associations of prescribing with deprivation. Further work is required to identify the underlying reasons for these associations so that appropriate interventions can be formulated to address drivers of inequality.

Contrasting factors associated with COVID-19-related ICU admission and death outcomes in hospitalised patients by means of Shapley values.

Identification of those at greatest risk of death due to the substantial threat of COVID-19 can benefit from novel approaches to epidemiology that leverage large datasets and complex machine-learning models, provide data-driven intelligence, and guide decisions such as intensive-care unit admission (ICUA). The objective of this study is two-fold, one substantive and one methodological: substantively to evaluate the association of demographic and health records with two related, yet different, outcomes of severe COVID-19 (viz., death and ICUA); methodologically to compare interpretations based on logistic regression and on gradient-boosted decision tree (GBDT) predictions interpreted by means of the Shapley impacts of covariates. Very different association of some factors, e.g., obesity and chronic respiratory diseases, with death and ICUA may guide review of practice. Shapley explanation of GBDTs identified varying effects of some factors among patients, thus emphasising the importance of individual patient assessment. The results of this study are also relevant for the evaluation of complex automated clinical decision systems, which should optimise prediction scores whilst remaining interpretable to clinicians and mitigating potential biases.

The use of magnetic resonance imaging (MRI) of the knee in current clinical practice: A retrospective evaluation of the MRI reports within a large NHS trust.

BACKGROUND: Magnetic resonance imaging (MRI) is one of the most widely used investigations for knee pain as it provides detailed assessment of the bone and soft tissues. The aim of this study is to report the frequency of each diagnosis identified on MRI scans of the knee and explore the relationship between MRI results and onward treatment. METHODS: Consecutive MRI reports from a large NHS trust performed in 2017 were included in this study. The hospital electronic system was consulted to identify whether a patient underwent x-ray prior to the MRI, attended an outpatient appointment or underwent surgery. RESULTS: 4466 MRI knees were performed in 2017 with 71.2% requested in primary care and 28.1% requested in secondary care. The most common diagnosis was signs of arthritis (55.2%), followed by meniscal tears (42.8%) and ACL tears (8.3%). 49.4% of patients who had an MRI attended outpatients and 15.6% underwent surgery. The rate of knee surgery was significantly higher for patients who had their scans requested in secondary care (32.9% vs 8.9%, p < 0.001). CONCLUSION: The rate of surgical intervention following MRI is low and given these results it seems unlikely that the scan changes practice in most cases. The rate of surgery and outpatient follow up was significantly higher in scans requested by secondary care. We urge clinicians avoid wasteful use of MRI and recommend the use of plain radiography prior to MRI where arthritis may be present.