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

Peter Tammes, Claudio Sartini, Ian Preston, Alastair Hay, Daniel Lasserson, Richard Morris

Research output: Contribution to journalArticle (Academic Journal)peer-review

4 Citations (Scopus)
217 Downloads (Pure)

Abstract

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<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.

Original languageEnglish
Number of pages10
JournalBritish Journal of General Practice
Early online date29 Jan 2018
DOIs
Publication statusE-pub ahead of print - 29 Jan 2018

Keywords

  • case-crossover design
  • cold weather
  • England
  • longitudinal data
  • mortality
  • primary care

Fingerprint Dive into the research topics of 'Use of primary care data to predict those most vulnerable to cold weather: a case-crossover analysis'. Together they form a unique fingerprint.

Cite this