English (United Kingdom)

https://curated-unify.zendy.io/wp-json/zendy-region/v1/featured_content/oa?rat=en

https://curated-unify.zendy.io/wp-json/zendy-region/v1/highlighted_journal/

Global: Zendy Plus annual

Presents the access of premium content as premium feature

Premium Content

Presents the keyphrase highlighting as premium feature

Keyphrase Highlighting

Presents the summarisation as premium feature

Summarisation

Insights

Presents the pdf analysis as premium feature

PDF Analysis

Presents the zaia usage as premium feature

ZAIA

Global: Zendy Plus monthly

Global: Zendy Tools annual

Global: Zendy Tools monthly

Global: Zendy Free Plan

Background  The National Institute for Health and Care Excellence (NICE) recommends that GPs use routinely available data to identify patients most at risk of death and ill health from living in cold homes.    Aim  To investigate whether sociodemographic characteristics, clinical factors, and house energy efficiency characteristics could predict cold-related mortality.    Design and setting  A case-crossover analysis was conducted on 34 777 patients aged ≥65 years from the Clinical Practice Research Datalink who died between April 2012 and March 2014. The average temperature of date of death and 3 days previously were calculated from Met Office data. The average 3-day temperature for the 28th day before/after date of death were calculated, and comparisons were made between these temperatures and those experienced around the date of death.    Method  Conditional logistic regression was applied to estimate the odds ratio (OR) of death associated with temperature and interactions between temperature and sociodemographic characteristics, clinical factors, and house energy efficiency characteristics, expressed as relative odds ratios (RORs).    Results  Lower 3-day temperature was associated with higher risk of death (OR 1.011 per 1°C fall; 95% CI = 1.007 to 1.015;  P &lt;0.001). No modifying effects were observed for sociodemographic characteristics, clinical factors, and house energy efficiency characteristics. Analysis of winter deaths for causes typically associated with excess winter mortality ( N  = 7710) showed some evidence of a weaker effect of lower 3-day temperature for females (ROR 0.980 per 1°C, 95% CI = 0.959 to 1.002,  P  = 0.082), and a stronger effect for patients living in northern England (ROR 1.040 per 1°C, 95% CI = 1.013 to 1.066,  P  = 0.002).    Conclusion  It is unlikely that GPs can identify older patients at highest risk of cold-related death using routinely available data, and NICE may need to refine its guidance.

Use of primary care data to predict those most vulnerable to cold weather: a case-crossover analysis