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Age-specific and year of birth changes in hospital admissions during a period of unexplained higher deaths in England

Published:October 04, 2017DOI:https://doi.org/10.1016/j.ejim.2017.09.039

      Highlights

      • Since 2011 the UK has experienced a period of unexplained higher medical admissions and deaths.
      • Population-adjusted admissions by age band show inexplicable age-specific changes.
      • Statistically significant changes in deaths are associated with the patients' year of birth.
      • Possible explanations for these age and year of birth specific changes are discussed.

      Abstract

      Background

      Policy makers have assumed that increase in medical demands and costs are attributable to the increasing age of the population and the inability of health and social care to limit demand.

      Methods

      Analysis of data obtained from NHS and Office of National Statistics.

      Results

      Population-adjusted age-specific patterns in medical admissions and deaths have increased over the period 2012/13 to 2015/16 in the NHS in England. The growth is both age and year of birth specific, and the youngest appear to be worst affected. Overall there has been a growth of 30,870 admissions (15% increase) in 25–29 year olds compared to 119,280 extra admissions (7% increase) for 70–74 year olds. Admissions of younger medical patients have also increased more so than for pneumonia, and the increase in all-cause mortality appears to be influenced by the patients' year of birth.

      Conclusion

      In England, medical admissions and deaths (all-cause mortality) have recently displayed very high unexplained growth. The fact that these are associated with patient year of birth suggests that the cause(s) may be related to infectious or other environmental factors encountered earlier in life.

      Keywords

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      References

      1. Jones R. Government data on death rates don't tell the full story. Br J Healthc Manag 23(8): 396–397 (in press).

        • Fransham M.
        • Dorling D.
        Have mortality improvements stalled in England?.
        BMJ. 2017; 357: j1946
        • Melzer D.
        • Tavakoly B.
        • Winder R.
        • Masou J.
        • Henley J.
        • Ble A.
        • et al.
        Much more medicine for the oldest old: trends in UK electronic clinical records.
        Age Ageing. 2015; 44: 46-53
        • Ahmadi-Abhari S.
        • Guzman-Castillo M.
        • Bandosz P.
        • Shipley M.
        • Muniz-Terrera G.
        • et al.
        Temporal trend in dementia incidence since 2002 and projections for prevalence in England and Wales to 2040: modelling study.
        BMJ. 2017; 358: j2856
        • Walpole R.
        Introduction to statistics.
        2nd edition. Macmillan Publishing Co., Inc., New York1974
        • Office for National Statistics. Births in England and Wales
        • Jones R.
        Recurring outbreaks of an infection apparently targeting immune function, and consequent unprecedented growth in medical admission and costs in the United Kingdom: a review.
        Brit J Med Medical Res. 2015; 6: 735-770https://doi.org/10.9734/BJMMR/2015/14845
        • Willets R.
        The cohort effect: insights and explanations.
        Institute of Actuaries, 2004 (file:///C:/Users/Owner/Downloads/sm20040426cohort.pdf)
        • Keyes K.
        • Utz R.
        • Robinson W.
        • Guohua L.
        What is a cohort effect? Comparison of three statistical methods for modeling cohort effects in obesity prevalence in the United States, 1971–2006.
        Soc Sci Med. 2010; 70: 1100-1108
        • Jones E.
        • Palmer I.
        • Wessely S.
        Enduring beliefs about effects of gassing in war: qualitative study.
        BMJ. 2007; 335: 1313-1315https://doi.org/10.1136/bmj.39420.533461.25
        • Oksa P.
        • Klockars M.
        • Karjalainen A.
        • Huuskonen M.S.
        • Vattulainen K.
        • Pukkala E.
        • et al.
        Progression of asbestosis predicts lung cancer.
        Chest. 1998 Jun; 113: 1517-1521
        • McNally T.
        • Yorkston K.
        • Jensen M.
        • Truitt A.
        • Schomer K.
        • et al.
        A review of secondary health conditions in post-polio syndrome: prevalence and effects of aging.
        Am J Phys Med Rehabil. 2015 Feb; 94: 139-145https://doi.org/10.1097/PHM.0000000000000166
        • Keith J.
        The prognosis in rheumatic heart disease.
        Can Med Assoc J. 1941; 45: 119-127
        • Goetzler R.
        • Stokes J.
        • Anderson K.
        Prognosis of subjects in the Framingham Study with rheumatic heart disease.
        J Am Geriatr Soc. 1985; 33: 693-697
        • Gerstoft S.
        • Balslov J.
        • Brahm M.
        • Brun C.
        • Jorgensen F.
        • Jorgensen H.
        • et al.
        Prognosis in glomerulonephritis II. Regression analyses of prognostic factors affecting the course of renal function and the mortality in 395 patients.
        Acta Med Scand. 1986; 219: 179-187
        • Hiam L.
        • Dorling D.
        • Harrison D.
        • McKee M.
        Why has mortality in England and Wales been increasing? An iterative demographic analysis.
        J R Soc Med. 2017; 110: 153-162