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Apart from the measures of central tendency, distribution, outliers, and the changing characteristics of data with time, an important characteristic of any data set is its variation or spread. In some data sets, the data values are concentrated closely near the mean; in others, the data values are more widely spread out from the mean.
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Exploring variation in low-value care: a multilevel modelling study.

Tim Badgery-Parker1, Yingyu Feng2, Sallie-Anne Pearson3

  • 1Faculty of Medicine and Health, School of Public Health, Menzies Centre for Health Policy, Charles Perkins Centre, The University of Sydney, Level 2, Charles Perkins Centre D17, Sydney, NSW, 2006, Australia. tim.badgeryparker@sydney.edu.au.

BMC Health Services Research
|June 1, 2019
PubMed
Summary

Hospital-level factors significantly influence low-value care, more than local health districts or patient residence areas. Targeting hospital-level interventions is key to reducing unnecessary medical procedures.

Keywords:
Choosing wiselyDisinvestmentLow-value careMultilevel logistic regression

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Area of Science:

  • Health Services Research
  • Health Economics
  • Clinical Quality Improvement

Background:

  • Low-value hospital care, defined as procedures not providing net benefit, may be influenced by unmeasured factors at the hospital, Local Health District (LHD), or patient residence areas.
  • Multilevel modeling is a statistical approach to simultaneously examine the effects of different hierarchical levels (e.g., patient, hospital, region) on health outcomes.
  • Identifying the level with the greatest contextual influence is crucial for directing efforts to reduce low-value care.

Purpose of the Study:

  • To determine the relative importance of hospital, Local Health District (LHD), and Statistical Local Area (SLA) of residence in influencing patient receipt of low-value hospital care.
  • To assess the contribution of different geographical and administrative levels to the variation in low-value care using multilevel modeling.

Main Methods:

  • Multilevel logistic regression was employed to analyze nine low-value hospital procedures.
  • A series of models were fitted, starting with episode-level variables and progressively adding hospital, LHD, and SLA levels.
  • The change in the c statistic, variance partition coefficients (VPCs), and median odds ratios were used to quantify the contribution of each level.

Main Results:

  • Hospital level was found to be more influential than LHD or SLA in explaining variations in low-value care.
  • The hospital level accounted for the largest proportion of unexplained variance, reaching up to 36.8% for knee arthroscopy.
  • While SLAs showed a small increase in explanatory power compared to the baseline, they did not add significant value beyond the hospital level.

Conclusions:

  • Findings suggest that interventions aimed at reducing low-value care should primarily target the hospital level due to its significant contextual effects.
  • The substantial variation observed at the hospital level indicates the greatest potential for improvement and reduction of unnecessary medical procedures.
  • Further investigation and targeted initiatives at the hospital level are recommended to enhance the value of healthcare delivery.