Switching insurer in the Irish voluntary health insurance market: determinants, incentives, and risk equalization

Projected private hospital expenditure in Ireland, 2018-2035: What role for demographics, cost, and Sláintecare?

Despite private hospitals occupying an important role in the delivery of acute hospital care in Ireland, an understanding of future spending pressures on these services is limited. Particularly, a key dimension of Ireland's ambitious roadmap for healthcare reform (Sláintecare) seeks to remove private practice from public hospitals. However, to date, there has been no examination of how this reform could impact private hospital demand and expenditure, and ultimately, the capacity to treat public patients. Using previously unavailable administrative health insurer data and a healthcare macro-simulation projection model, we project real (volume-based) and nominal expenditure on private hospital services over the medium-term (2018-2035). We develop a number of projection scenarios that vary assumptions in relation to population growth and ageing, healthy ageing, and the future cost of care delivery. Additionally, by developing profiles of private activity in public hospitals, we examine how the removal of private practice from public hospitals could impact on demand and expenditure in private hospitals over time. Findings from this analysis have implications for capital investment and workforce planning in private hospitals, and failure to meet future demand could have implications for access to care in public hospitals. Moreover, should private practice be ended in public hospitals, most complex private in-patient and emergency care is likely to remain within the public hospitals with limited capacity benefits for the public system.

Geographic inequalities in non-acute healthcare supply: evidence from Ireland

Background: Recent reforms in Ireland, as outlined in Sláintecare, the report of the cross-party parliamentary committee on health, are focused on shifting from a hospital-centric system to one where non-acute care plays a more central role. However, these reforms were embarked on in the absence of timely and accurate information about the capacity of non-acute care to take on a more central role in the system. To help address this gap, this paper outlines the most comprehensive analysis to date of geographic inequalities in non-acute care supply in Ireland. Methods: Data on the supply of 10 non-acute services including primary care, allied health, and care for older people, were collated. Per capita supply for each service is described for 28 counties in Ireland (Tipperary and Dublin divided into North and South), using 2014 supply and population data. To examine inequity in the geographic distribution of services, raw population in each county was adjusted for a range of needs indicators. Results: The findings show considerable geographic inequalities across counties in the supply of non-acute care. Some counties had low levels of supply of several types of non-acute care. The findings remain largely unchanged after adjusting for need, suggesting that the unequal patterns of supply are also inequitable. Conclusions: In the context of population changes and the influence of non-need factors, the persistence of historical budgeting in Ireland has led to considerable geographic inequities in non-acute supply, with important lessons for Ireland and for other countries. Such inequities come into sharp relief in the context of COVID-19, where non-acute supply plays a crucial role in ensuring that acute services are preserved for treating acutely ill patients.

Does the chronically ill population in the Netherlands switch their health insurer as often as the general population? Empirical evidence from a nationwide survey study

Background

Consumer mobility is an important aspect of a health insurance system based on managed competition. Both the general population and insured with a chronic illness should enjoy an equal opportunity to switch their insurer every year. We studied possible differences in the rates of switching between these two groups in the Netherlands.

Methods

A structured questionnaire was sent to 1500 members of Nivel’s Dutch Health Care Consumer Panel (response rate: 47%) and to 1911 chronically ill members of the National Panel of the Chronically ill and Disabled (response rate: 84%) in February 2016. Associations between switching and background characteristics were estimated using logistic regression analyses with interaction effects.

Results

In general, we did not find significant differences in switching rates between the general population and chronically ill population. However, a combination of the population and background characteristics demonstrated that young insured with a chronic illness switched significantly less often than young insured from the general population (1% versus 17%).

Conclusions

Our results demonstrated that the group of young people with a chronic illness is less inclined to switch insurer. This observation suggests that this group might either face difficulties or barriers which prevents them from switching, or that they experience a high level of satisfaction with their current insurer. Further research should therefore focus on unravelling the mechanisms which explain the differences in switching rates.

Switching benefits and costs in the Irish health insurance market: an analysis of consumer surveys

Relatively little analysis has taken place internationally on the consumer-reported benefits and costs to switching insurer in multi-payer health insurance markets. Ideally, consumers should be willing to switch out of consideration for price and quality and switching should be able to take place without incurring significant switching costs. Costs to switching come in many forms and understanding the nature of these costs is necessary if policy interventions to improve market competition are to be successful. This study utilises data from consumer surveys of the Irish health insurance market collected between 2009 and 2013 (N [Formula: see text] 1703) to examine consumer-reported benefits and costs to switching insurer. Probit regression models are specified to examine the relationship between consumer characteristics and reported switching costs, and switching behaviour, respectively. Overall evidence suggests that switchers in the Irish market mainly did so out of consideration for price. Transaction cost was the most common switching cost identified, reported by just under 1 in 7 non-switchers. Psychological switching costs may also be impacting behaviour. Moreover, high-risk individuals were more likely to experience switching costs and this was reflected in actual switching behaviour. A recent information campaign launched by the market regulator may prove beneficial in reducing perceived transaction costs in the market, however, a more focused campaign aimed at high-risk consumers may be necessary to reduce inequalities. Policy-makers should also consider the impact insurer behaviour may have on decision-making.