BMJ Medicine
@bmjmedicine.bsky.social
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An #openaccess journal from #TheBMJ for specialist research that promotes multidisciplinary collaboration to improve the health of patients
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bmjmedicine.bsky.social
This study by Jennifer Hirst and colleagues highlights a critical need for further exploration into the inequalities in HRT prescribing across England.

https://bit.ly/3KpaeKg

#HealthDisparities #SDOH
bmjmedicine.bsky.social
Live webinar: Hear from experts, including two authors of a new BMJ Oncology paper that assesses the contribution of germline and somatic gene variants to prostate cancer.
📅 1 Oct, 12pm BST | Register here: https://bit.ly/4n0TTsZ
#ProstateCancer #BMJMedicine
bmjmedicine.bsky.social
Research by Jennifer Hirst and colleagues reveal disparities in HRT prescribing across England. Differences were identified based on ethnic group, socioeconomic status, and geographical location. This highlights critical areas for improving equitable access to care.

https://bit.ly/3KpaeKg
Inequalities in hormone replacement therapy prescribing in UK primary care: population based cohort study
Objective To quantify prescribing of hormone replacement therapy (HRT) in women aged 40-60 years by type of HRT and length of use, and to determine sociodemographic factors associated with receiving a...
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bmjmedicine.bsky.social
Join us as we uncover critical insights into health equity as Victor Sartorius and colleagues investigate inequalities in neonatal mortality, finding a strong link between a mother's residential socioeconomic level and neonatal mortality in France.

Read the full study now: https://bit.ly/46t4UNk
Evaluation of area based socioeconomic inequalities and neonatal mortality rates in France: national population based study
Objective To investigate the magnitude and evolution of inequalities in neonatal mortality rates by using area based socioeconomic indices in France.Design National population based study.Setting For ...
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bmjmedicine.bsky.social
How do I conduct a high quality case study?

Find a step-by-step guide below! 🧵
Promotional graphic from BMJ Medicine titled "NEW: Research Methods and Reporting". Includes steps 1 and 2 on conducting a social science case study, with steps such as reviewing literature and defining research purpose. Features an icon of a person climbing a ladder inside a yellow circle, and a "READ NOW" button at the bottom.
bmjmedicine.bsky.social
In summary, generally confounders, but not mediators or colliders, should be adjusted in causal inference. Identifying confounders needs to be based on subject knowledge and is not always clear. Read the full open-access article in BMJ Medicine bmjmedicine.bmj.com/content/4/1/...
Regression adjustment for causal inference
Causal inference identifies and quantifies the effect of an exposure (the hypothetical cause) to an outcome. While randomised controlled trial design is the preferred method for establishing and measu...
bmjmedicine.bmj.com
bmjmedicine.bsky.social
Sensitivity analysis can help to show whether results are robust to causal assumptions. In this example, researchers may pick model 1 as their primary analysis and supplement with results from models 2 and 3, acknowledging that causal assumptions 2 and 3 are also possible.
bmjmedicine.bsky.social
Confounders, mediators and colliders can be visualised in a direct acyclic graph (DAG). This example shows a model for the causal relationship between physical activity and HbA1c.
bmjmedicine.bsky.social
Colliders are the common effect of the exposure and the outcome. Adjusting for a collider will bias estimates of the exposure-outcome association away from the causal effect of the exposure to the outcome.
bmjmedicine.bsky.social
Mediators are intermediate variables between the exposure and outcome, and should not be adjusted if the analysis aims to uncover the total effect of the exposure.
bmjmedicine.bsky.social
Is it a confounder, a mediator or a collider? A confounder is a variable that causes the exposure and outcome. Adjusting for a confounder will eliminate any bias it causes in the adjusted exposure-outcome association, but only if the confounder is correctly measured and modelled.
bmjmedicine.bsky.social
Many observational studies use regression adjustment to deal with confounding bias. A methods primer in BMJ Medicine by Frederick Ho explains the basic principles, including why researchers must choose their confounders carefully 🧵
bmjmedicine.bsky.social
Calling all researchers and educators📣 This new RMR by @trishgreenhalgh.bsky.social on how to conduct effective case studies, offers guidance for using them for learning & decision-making.

Essential reading for robust research!

https://bit.ly/47PL01l
Promotional graphic for BMJ Medicine featuring an illustration of a person with a magnifying glass, analysing icons representing research methods and reporting, including charts and gears. The image includes text that reads 'NEW: Research Methods and Reporting,' with a call to action reading 'READ NOW.' There's also a reference to case studies and a guide for researchers
bmjmedicine.bsky.social
Hear from experts, including the author of a new BMJ Mental Health paper, on the risk of first onset psychosis or mania requiring hospitalisation in adults diagnosed with ADHD after starting ADHD medication.
📅 24 Sep, 12pm BST | Register here: https://bit.ly/4myGUye
bmjmedicine.bsky.social
From public health crises to evaluating interventions, case studies offer invaluable insights into real-world phenomena. But how do we ensure quality & consistency?

This new paper by @trishgreenhalgh.bsky.social explores issues in case study research.

https://bit.ly/47PL01l
bmjmedicine.bsky.social
However, one approach was found to be effective and supported by moderate certainty evidence: combining provider education with audit and feedback (Odds ratio for reducing use of targeted low value care = 0.73, 95% CI 0.63 to 0.84).
bmjmedicine.bsky.social
Overall, evidence certainty for most de-implementation approaches was low, including for audit and feedback of clinical practice, and educating patients or providers.
Effectiveness of de-implementation strategies compared with no intervention in reducing the use of low value care. CI=confidence interval