Equity in healthcare can be examined across population groups defined in various ways including economic status, geographic location, social status, demographics, etc. For example, much of the recent empirical literature on equity in healthcare has focused on developing robust measures of socioeconomic-related equity in healthcare utilisation (van Doorslaer and Masseria, 2004; van Doorslaer et al., 2007).
Given the challenges in defining access, it is not surprising that many empirical studies on access focus on one aspect of this multifaceted concept. There is a large body of work that interprets access as a supply concept, concentrating on the geographic availability of services using provider–population ratios. McIntyre et al. (2009) identified three other strands of research on healthcare access wherein access is interpreted as a utilisation concept (e.g. studies of equity in utilisation controlling for needs), a demand concept (focusing on the affordability of services), or as the full cost of using a service, including travel costs, waiting costs, childcare costs, etc.
Many studies acknowledge the limitations of focusing on narrow definitions of access. For example, in their study of primary care, Ryvicker et al. (2012) found that variations in primary care supply cannot fully explain variations in realised access to care, and other access factors such as accessibility of public transport need to be taken into account.
Given the focus of this report on analysing geographic patterns of healthcare supply, it is useful to identify why this can be valuable, notwithstanding the important caveat that supply alone does not capture the full picture of access to a healthcare system. Rice and Smith (2001) outlined three reasons for considering geography when assessing equity in healthcare. First, healthcare systems, including Ireland, are organised on a geographic basis: for example, Health Boards (in the past), Local Health Offices and Regional Integrated Care Organisations (RICOs). Equity across geographic areas is a central concern in the distribution of resources. The focus on the geographic distribution of resources is encompassed in the widely cited ‘inverse-care law’, introduced by Tudor Hart in the 1970s, which highlighted the concern that ‘availability of good medical care tends to vary inversely with the need of the population served’ (Tudor Hart, 1971, p. 412).
Second, Rice and Smith (2001) pointed out that healthcare facilities are concentrated at specific locations, and thus geographic considerations come into play in determining access to healthcare (e.g. distance to healthcare facility). Third, there is evidence that geographic inequalities in health status can exist over and above socioeconomic variations. There may be specific ‘area effects’ (e.g. physical environment, local economic conditions, local social support systems) that directly influence an individual’s health and healthcare needs. In addition to the direct effects of geography on health, there may also be area effects on healthcare production. For example, input prices can vary from one area to another; service provision can be more costly in rural than in urban areas (Rice and Smith, 2001). Thus, geography can play a role in determining the nature of healthcare provided in different areas. Geographic access can also directly affect utilisation whereby individuals may be induced to make more use of services in areas with relatively high provision compared with less well-resourced areas.
Measuring geographic equity in supply
Simple comparisons of supply to population ratios across different areas give a good indication of patterns of geographic inequality, and this is the most common way in which geography is used in assessing equality and/or equity in healthcare access (WHO et al., 2010). For example, supply of doctors is expressed as the number of doctors per population in each area, where ‘area’ is a specific geographic unit of analysis (e.g. county, region). This allows for organisations such as Eurostat, the OECD and the Commonwealth Fund to undertake international comparisons of health and social care supply. Supply ratios are useful for broad comparisons of supply across large areas and are used by policymakers to set minimal standards of supply and to identify underserved areas (Guagliardo, 2004). Researchers in the UK (Gravelle and Sutton, 2001; Hann and Gravelle, 2004; Goddard et al., 2010) have used this approach to examine the geographic distribution of GPs from the mid-1970s to the early 2000s, with some modifications to the supply ratios to take account of geographic variations in healthcare needs. Their methods are based on calculating Gini coefficients (see Section 3.4.1) on the geographic supply of whole-time equivalent GPs per needs-adjusted population for each time period.
As comparisons of raw GP per capita ratios do not allow for differences in needs across areas, Gravelle and Sutton (2001) used alternative indicators to adjust area populations for needs. These include age-related capitation weights,23 age- and sex-specific GP consultation rate adjustments, mortality rate adjustments and morbidity measures. For example, for mortality adjustment, area populations are
weighted by their crude death rate relative to the national crude death rate.24 For morbidity, area age- and sex-specific limiting long-term illness rates have been used to adjust the populations. Results for England and Wales indicate that inequality in the geographic distribution of GPs increased over time, with very few areas changing from having a below-average GP:population ratio to having an above-average ratio from the 1990s to the early 2000s (Hann and Gravelle, 2004). Gini coefficients for the supply of GPs in England and Scotland increased over time (indicating an increase in inequality) between the mid-1970s and 2006 (Goddard et al., 2010). More recent work examining GP supply at more granular geographic levels has shown a small increase in supply, and a reduction in socioeconomic inequities in supply from 2004 to 2014 (Asaria et al., 2016). Similar work from Japan has been used to project physician shortages in the future (Ishikawa et al., 2017). Supply ratios are used in this report to examine patterns of supply of non-acute services across counties in Ireland (see Chapter 3), drawing on the methods used in the UK to adjust for healthcare needs. Supply is measured at the most granular level in which the data are available.
Many studies have focused on what Guagliardo (2004) refers to as spatial accessibility. These involve methods, such as gravity models, that move beyond provider-to-population ratios to combine the dimensions of availability (level of supply) and accessibility (distance/time between patient location and healthcare supply), within the broader concept of access. Advances made in geocoding have facilitated this type of research in many countries, including Ireland (Mohan et al., 2019).
Siegel et al. (2016) used an ‘improved gravity model’ to examine spatial accessibility of a range of ambulatory and inpatient services in Germany. For each type of healthcare service, the model measured the provider to population ratios in a given district, but also incorporated supply and demand potential in all other districts (allowing for the fact that individuals living in one area can travel to other areas to receive services), while taking account of travel times between regions. A distance decay function was used to ensure that the more distant providers and potential users contribute less to the measure of spatial accessibility than those who are closer.
Other studies report links between spatial accessibility and how the healthcare system is used (Guagliardo, 2004). For example, Gulliford (2009) found that lower primary care availability is associated with higher rates of hospital admissions for ambulatory care sensitive conditions. Some studies have also demonstrated an association between access (e.g. supply of services) and health outcomes. Shi et al. (2002) observed a positive association between physician supply levels and self-
rated health. Okumura et al. (2013) used survey data to examine changes in health status and access to care over time in adolescents with special healthcare needs. Access is measured using supply variables (e.g. whether the adolescents had a usual source of care, a personal doctor or nurse) and insurance status. Results showed that significant deterioration in access factors over time was associated with decline in health status among adolescents with special healthcare needs.
Measuring equity in access in the Irish context
One common concern in studies of equity in the Irish healthcare system is the extent to which the eligibility structures influence the patterns of healthcare use. A number of studies have shown that individuals with a Medical Card or GP Visit Card have higher rates of use of non-acute and acute care services, ceteris paribus (Nolan and Layte, 2017; Hudson and Nolan, 2015; O’Callaghan et al.,2018; Walsh et al., 2012, 2019a). As outlined in Chapter 1, there are complex eligibility structures in the Irish system, with different payment mechanisms for different parts of the system and for different groups of the population. There has been widespread discussion of two-tier access to acute care in the system, with privately financed patients getting preferential access to public hospital care, and long waiting lists for public patients (Wren and Connolly, 2016). In primary care, for those without a Medical Card there are high user charges and although some community services may in principle be available free at the point of use, in areas where there is limited supply, Medical Card holders are granted priority (Citizens Information, 2019).
Studies of equity in access in the Irish context include studies on socioeconomic equity in utilisation (interpreting access as utilisation) (Layte and Nolan, 2004; Layte 2007) and assessments of geographic profiles and inequalities in healthcare supply, in particular of GPs (e.g. Layte et al., 2009; Teljeur et al., 2010, 2014). There has also been analysis of spatial accessibility in the supply of GPs in Ireland (Mohan et al., 2019; Teljeur et al., 2010; Morrissey et al., 2008) but to date extending this type of analysis to other non-acute healthcare services in Ireland is hindered by the absence of adequately geocoded data. Teljeur et al. (2010) used a gravity model to examine the geographic distribution of GPs in Ireland taking account of the distribution of deprivation. The authors found a relatively equitable distribution of GP practices, but less equity in the distribution of the GP workforce. Rural areas with the highest deprivation scores had the longest average travel times to GPs, but in general the study found only modest variation in travel times across levels of the deprivation proxy. Estimated GP workloads increased with the deprivation proxy in cities, villages and rural areas, with a more complicated pattern in towns. Mohan et al. (2019) estimated individual-level regression models of GP utilisation using data from TILDA and three accessibility indicators based on the physical proximity of each household to nearby GPs. They found no evidence of a general
effect on utilisation from accessibility to GP services. However, for respondents able to exercise significant choice about which GP to use (those without a Medical Card), the study found that the number of nearby GPs was positively associated with utilisation.