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• Supply inducement and mortality rates - bias and confounding
  Grazyna T Adamiak
  Published on: 19 August 2002

• Published on: 19 August 2002

  Supply inducement and mortality rates - bias and confounding

  • Grazyna T Adamiak, PhD Student

  Dear Editor

  The Authors draw conclusion that the use of acute hospital beds does not increase as the population ages, which is a result from a seven year cohort study in Germany. The problem is however that there are supply related factors, which can strongly affect the results. It is known that there are substitutions between different forms of care.[1] The days spent in hospitals and the mortality rates, in part...

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  Dear Editor

  The Authors draw conclusion that the use of acute hospital beds does not increase as the population ages, which is a result from a seven year cohort study in Germany. The problem is however that there are supply related factors, which can strongly affect the results. It is known that there are substitutions between different forms of care.[1] The days spent in hospitals and the mortality rates, in particular among the oldest populations, are affected by the access to elderly care, e.g. providers of palliative care in the terminal stage of life.[2] In Sweden the reform of the care for the elderly meaning transfer of responsibility for the care of the elderly to municipalities led to an increase of death rates in some of the nursing homes providing long term care and after care to the elderly people.[3,4] Also, the supply of beds at geriatric departments might be associated with the number of inhospital deaths as opposed to deaths occurring in other places.

  The stable patterns in inhospital death rates among German inpatients might be to a large degree explained by the growing supply of other forms of care of the elderly, which authors do not appear to take into account in their calculations and the following discussion. The impact of supply of various kinds of inpatient beds is an important confounder in studies that calculate inhospital mortality and beds use as proxies for morbidity and related mortality (or health outcomes) without considering effects of supply and proximity known by all health economists and epidemiologists. Proxy measures or performance indicators such as use of hospital beds are conceptually different from outcome indicators or measures of health care needs.[5-8] Use or supply is often confounded with needs and a serious methodological problem.[9,10]

  The knowledge about the supply inducement is used as a rationale behind many management decisions in health care, so called "chains of care" and by HMO in USA. Patients treated for acute conditions are transferred to other forms of after care when their condition stabilizes. As Ashton et al.[11] point out physicians will discharge patients prematurely if there is a safety net outside hospitals, which can provide prompt aftercare. Some patients are requiring to be discharged and die at home, which is a consequence of free choice, often used by terminally ill cancer patients. Thus, the conclusion about health care needs of acute care among the German as well other populations based on consumption of hospital days as proxy for health care needs is strongly biased. It is rather a matter of provision of inpatients beds and integration between various providers.[4] Inpatient beds might in fact be located outside hospitals or in patient homes, and those deaths are not registered in hospital discharge data bases, which often serve as the primary source of information in many studies. It must be more appropriate to calculate total death rates to make inferences about changing health care needs among populations. A health care system perspective[12] and account into total population instead solely into use of acute care hospitals and inhospital deaths would be more fruitful in the context of projections of health care needs.

  Grazyna Adamiak

  References

  (1) Culyer AJ. The morality of efficiency in health care – some uncomfortable implications. Economics of health care systems. Health Economics 1992;1:7-18.

  (2) Fisher J, Wennberg JE, Stukel TA, Sharp SM. Hospital readmission rates for cohorts of medical beneficiaries in Boston and New Haven. New England Journal of Medicine 1994;331:989-95.

  (3) The Federation of County Councils. Place of death project (Plats för död-projektet) http://www.lf.se

  (4) Andersson G, Karlberg I. Integrated care for the elderly. The background and effects of the reform of Swedish care of the elderly. International Journal of Integrated Care (IJIC) 2000. http://www.ijic.org/

  (5) Morrill RL, Earickson R. Hospital Variation and Patient Travel Distances. Inquiry 1968;5:26-34.

  (6) Wennberg JE, Barnes BA, Zubkoff M. Professional uncertainty and the problem of supplier-induced demand. Social Science and Medicine 1982;16:811-834.

  (7) Hornbrook MC. Practice Mode and Payment Method. Effects on Use, Costs, Quality and Access. Medical Care 1985;23:484-511.

  (8) Giuffrida A, Gravelle H, Roland M. General Practice. Measuring quality of care with routine data: avoiding confusion between performance indicators and health outcomes. British Medical Journal 1999;319:94-98.

  (9) Carr-Hill RA, Jamison JQ, O'Reilly D, Stevenson MR, Reid J, Merriman B. Risk adjustment for hospital use using social security data: cross sectional small area analysis. British Medical Journal 2002;324:390.

  (10) Gibson A, Asthana S, Brigham P, Moon G, Dicker J. Geographies of need and the new NHS: methodological issues in the definition and measurement of the health needs of local populations. Health and Place 2002;8:47-60.

  (11) Ashton CM, Wray NP. A conceptual framework for the study of early readmission as an indicator of quality of care. Social Science and Medicine 1996;43(11):1533-1541.

  (12) Nutting PA, Shorr GI, Burkhalter BR. Assessing the Performance of Medical Care Systems: A Method and its Application. Medical Care 1981;19(3):281-296.

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  Conflict of Interest:
  None declared.

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