Assessing the effectiveness of health care cost containment measures: evidence from the market for rehabilitation care

Age Structural Transitions and Copayment Policy Effectiveness: Evidence from Taiwan's National Health Insurance System

Background: Population ageing is a worldwide phenomenon that could influence health policy effectiveness. This research explores the impact of age structural transitions on copayment policy responses under Taiwan’s National Health Insurance (NHI) system. Methods: The time-varying parameter vector autoregressive model was applied to create two measures of the copayment policy effectiveness, and multiple linear regression models were used to verify the nonlinear effect of age structural transitions on copayment policy responses. Results: Our results show that copayment policy effectiveness (in terms of the negative response of medical center outpatient visits to upward adjustments in copayment) is positively correlated with the proportions of the population in two older age groups (aged 55–64 and ≥ 65) and children (age < 15), but negatively correlated with the proportion of the population that makes up most of the workforce (aged 15‒54). These tendencies of age distribution, which influence the responses of medical center outpatient visits to copayment policy changes, predict that copayment policy may have a greater influence on medical center outpatient utilization in an ageing society. Conclusions: Policymakers should be concerned about the adverse effects of copayment adjustments on the elderly, such as an increasing financial burden and the effect of pricing some elderly patients out of Taiwan’s NHI system.

Public Healthcare Entitlements and Healthcare Utilisation among the Older Population in Ireland

The use of direct out-of-pocket payments to finance general practitioner (GP) care by the majority of the population in Ireland is unusual in a European context. Currently, approximately 40% of the population have means-tested access to free GP care, while the remainder must pay the full out-of-pocket cost. In this paper, we use data from The Irish Longitudinal Study on Ageing (TILDA) to examine the impact of the current system of public healthcare entitlements on GP utilisation among the older population. Using difference-in-difference propensity score matching methods, we find significant effects of changes in public healthcare entitlements on GP utilisation (i.e. introducing user fees reduces utilisation, while removing them increases utilisation). There is limited evidence of offset effects on other types of healthcare utilisation. The results have direct implications for current Irish health policy, and add to the international literature on the effects of insurance on healthcare utilisation. Copyright © 2016 John Wiley & Sons, Ltd.

Causal inference in multi-state models-sickness absence and work for 1145 participants after work rehabilitation

Background

Multi-state models, as an extension of traditional models in survival analysis, have proved to be a flexible framework for analysing the transitions between various states of sickness absence and work over time. In this paper we study a cohort of work rehabilitation participants and analyse their subsequent sickness absence using Norwegian registry data on sickness benefits. Our aim is to study how detailed individual covariate information from questionnaires explain differences in sickness absence and work, and to use methods from causal inference to assess the effect of interventions to reduce sickness absence. Examples of the latter are to evaluate the use of partial versus full time sick leave and to estimate the effect of a cooperation agreement on a more inclusive working life.

Methods

Covariate adjusted transition intensities are estimated using Cox proportional hazards and Aalen additive hazards models, while the effect of interventions are assessed using methods of inverse probability weighting and G-computation.

Results

Results from covariate adjusted analyses show great differences in sickness absence and work for patients with assumed high risk and low risk covariate characteristics, for example based on age, type of work, income, health score and type of diagnosis. Causal analyses show small effects of partial versus full time sick leave and a positive effect of having a cooperation agreement, with about 5 percent points higher probability of returning to work.

Conclusions

Detailed covariate information is important for explaining transitions between different states of sickness absence and work, also for patient specific cohorts. Methods for causal inference can provide the needed tools for going from covariate specific estimates to population average effects in multi-state models, and identify causal parameters with a straightforward interpretation based on interventions.