Analysing predictors for future high-cost patients using German SHI data to identify starting points for prevention

Eur J Public Health. 2016 Aug;26(4):549-55. doi: 10.1093/eurpub/ckv248. Epub 2016 Feb 5.

Abstract

Background: Demographic change influences not only the terms of health care, but also its financing. Hence, prevention is becoming a more important key to facing upcoming challenges. Aim of this study was to identify predictors for future high-cost patients and derive implications for potential starting points for prevention.

Methods: Claims data from a German statutory health insurance agency were used. High-cost patients were defined as the 10% most expensive persons to insure in 2011. The predictors stemmed from the previous year. Logistic regression with stepwise forward selection for 10 sex- and age-specific subgroups was performed. Model fit was assessed by Nagelkerke's R-squared value.

Results: Model fit values indicated well-suited models that yielded better results among younger age-groups. Identified predictors can be summarized as different sets of variables that mostly pertain to diseases. Some are rather broad and include different disorders, like the set of mental/behavioural disorders including depression and schizophrenia; other sets of variables are more homogenous, such as metabolic diseases, with diabetes mellitus (DM) being the dominant member of every subgroup.

Conclusion: Because diabetes was a significant predictor for future high-cost patients in all analysed subgroups, it should be considered as a potential starting point for prevention. The disease is specific enough to allow for the implementation of effective prevention strategies, and it is possible to intervene, even in patients already affected by DM. Furthermore, the monetary savings potential is probably high because the long-term complications of DM are expensive to treat and affect a large part of the population.

Publication types

  • Research Support, Non-U.S. Gov't

MeSH terms

  • Adolescent
  • Adult
  • Aged
  • Aged, 80 and over
  • Cost-Benefit Analysis / economics
  • Cost-Benefit Analysis / methods*
  • Cost-Benefit Analysis / statistics & numerical data*
  • Female
  • Germany
  • Health Care Costs / statistics & numerical data*
  • Humans
  • Male
  • Middle Aged
  • State Medicine / economics*
  • State Medicine / statistics & numerical data*
  • Young Adult