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Publication - Professor Alastair Hay

    Use of primary care data to predict those most vulnerable to cold weather

    a case-crossover analysis

    Citation

    Tammes, P, Sartini, C, Preston, I, Hay, A, Lasserson, D & Morris, R, 2018, ‘Use of primary care data to predict those most vulnerable to cold weather: a case-crossover analysis’. British Journal of General Practice.

    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.

    Full details in the University publications repository