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

Background: NICE recommends GPs use routinely available data to identify patients most at risk of death and ill-health from living in cold homes.
Aim: We investigated whether socio-demographic, medical and house quality characteristics could predict cold-related mortality.
Design and Setting: A case-crossover analysis was conducted on 34,777 patients aged 65+ from the Clinical Practice Research Datalink who died between April 2012 and March 2014. From Meteorological Office data, we calculated average temperature of date of death and 3 days previously. We also calculated the average 3-day temperature for the 28th day before/after date of death, and compared those temperatures with 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 socio-demographic, medical and house quality characteristics, expressed as relative odds ratios (RORs).
Results and Conclusion: Lower 3-day temperature was associated with higher risk of death (OR 1.011 per 1o 21 C fall; 95%CI 1.007-1.015; p<0.001). No modifying effects were observed for socio-demographic, medical and house quality characteristics. Analysis of winter deaths for causes typically associated with excess winter mortality (N=7,710) showed some evidence of a weaker effect of lower 3-day temperature for women (ROR 0.980 per 1°C, 95%CI 0.959-1.002, p=0.082), and a stronger effect for patients living in northern England (ROR 1.040 per 1°C, 95%CI 1.013-1.066, p=0.002). It is unlikely GPs can identify older patients at highest risk of cold-related death using routinely available data, and NICE may need to refine its guidance.