ArticleThe European journal of health economics : HEPAC : health economics in prevention and care2020
Incorporating self-reported health measures in risk equalization through constrained regression.
Article in The European journal of health economics : HEPAC : health economics in prevention and care, 2020. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 10 papers.
What it found
Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.
The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
The trial behind it
Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.
Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.
Who cites it
10 citing papers in PubMed.
- Exploration of using constrained regression in Germany's morbidity-based risk adjustment.The European journal of health economics : HEPAC : health economics in prevention and care · 2026Article
- A critical review of the use of RThe European journal of health economics : HEPAC : health economics in prevention and care · 2025Review
- A Framework for the Design of Risk-Adjustment Models in Health care Provider Payment Systems.Medical care research and review : MCRR · 2025Review
- A framework for ex-ante evaluation of the potential effects of risk equalization and risk sharing in health insurance markets with regulated competition.Health economics review · 2024Review
- High-risk pooling for mitigating risk selection incentives in health insurance markets with sophisticated risk equalization: an application based on health survey information.BMC health services research · 2024Article
- Association analysis of self-reported outcomes with a validated subset.Statistics in medicine · 2024Article
- Improving diagnosis-based cost groups in the Dutch risk equalization model: the effects of a new clustering method and allowing for multimorbidity.International journal of health economics and management · 2023Article
- Selection Incentives in the Dutch Basic Health Insurance: To What Extent Does End-of-Life Spending Contribute to Predictable Profits and Losses for Selective Groups?Medical care research and review : MCRR · 2022Article
- How to deal with persistently low/high spenders in health plan payment systems?Health economics · 2022Article
- The pattern of health insurance economic resilience in the Covid 19 pandemic shock.BMC research notes · 2021Article
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
3 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Most health insurance markets with premium-rate restrictions include a risk equalization system to compensate insurers for predictable variation in spending. Recent research has shown, however, that even the most sophisticated risk equalization systems tend to undercompensate (overcompensate) groups of people with poor (good) self-reported health, confronting insurers with incentives for risk selection. Self-reported health measures are generally considered infeasible for use as an explicit 'risk adjuster' in risk equalization models. This study examines an alternative way to exploit this information, namely through 'constrained regression' (CR). To do so, we use administrative data (N = 17 m) and health survey information (N = 380 k) from the Netherlands. We estimate five CR models and compare these models with the actual Dutch risk equalization model of 2016 which was estimated by ordinary least squares (OLS). In the CR models, the estimated coefficients are restricted, such that the under-/overcompensation for groups based on self-reported general health is reduced by 20, 40, 60, 80, or 100%. Our results show that CR can improve outcomes for groups that are not explicitly flagged by risk adjuster variables, but worsens outcomes for groups that are explicitly flagged by risk adjuster variables. Using a new standardized metric that summarizes under-/overcompensation for both types of groups, we find that the lighter constraints can lead to better outcomes than OLS.
Indexed as
Identifiers
What OpenQuestion holds
Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.