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Income-related inequity in the use of GP services by children: a comparison of Ireland and Scotland

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Abstract

Equity of access to health care is a key component of national and international health policy, with most countries subscribing to the principle that health care should be allocated on the basis of need, rather than ability to pay or other criteria. The issue of health care entitlements for children is particularly pertinent given the strong causal links that have been demonstrated between eligibility for free care, utilisation and health outcomes. The Irish health care system is unusual in requiring the majority of the population to pay the full out-of-pocket cost of GP care. In contrast, all Scottish residents are entitled to free GP care at the point of use. This difference in public health care entitlements between Ireland and Scotland allows us to examine the impact of differences in financing structures on equity in GP care. In this paper, we use data from two nationally representative surveys of children in Ireland and Scotland to examine the degree of income-related inequity in the utilisation of GP services in both countries. We find that while the distribution of GP care is significantly pro-poor in Ireland, even after adjustment for health need, there is little or no significant inequity in GP utilisation among Scottish children. However, focusing just on children who pay the full price of GP care in Ireland, we find some evidence for a significant pro-rich distribution of GP visits. These results reflect the particular structure of health care entitlements that exist in two systems.

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Notes

  1. Most empirical analyses examine income-related inequity in health care utilisation, but Stirbu et al. [18] and Bago d’Uva et al. [19] examine education-related inequity. A number of studies [7, 16, 17] examine inequities in expenditure, rather than utilisation.

  2. We focus on Scotland (rather than the UK) due to data availability, i.e., data on GP utilisation among children are not collected in the UK-wide Millennium Cohort Study (see “Methods” section for further details).

  3. A number of studies focus on sub-sets of the adult population. Allin et al. [14] focus on those aged 65+ years in the UK, while Cunningham et al. [16] examines health spending in the year prior to death for a sample of British Columbian individuals aged 65+ years.

  4. See www.citizensinformation.ie/en/health/women_s_health/maternity_and_infant_welfare_services/ [last accessed 5 March 2014].

  5. In 2010, an estimated 41 % of the population held PHI only; 6 % held both a full medical/GP visit card and PHI (‘dual cover’); 30 % held a full medical card or GP visit card only; and 23 % of the population had neither a full medical/GP visit card nor PHI [28].

  6. Two other scenarios are possible. First, it is possible that there is differential take-up of the MICS for the different eligibility groups in Table 1. Unfortunately we have no information on take-up of the MICS among different population groups, and so cannot speculate on how our results may be affected. Second, it is also possible that GP visits under the MICS are excluded from our dependent variable (as the MICS specifically excludes visits that relate to the health of the child, rather than the developmental check, and the GUI question specifically asks about visits in relation to the health of the child) (see also Table 2).

  7. In the Irish health care system, full medical and GP visit card holders are typically referred to as ‘public patients’ while those without a full medical card or GP visit card are typically referred to as ‘private patients’.

  8. With the exception of the payments for enhanced services (which are supposed to reflect local health care needs), Scottish GPs operate under the UK-wide GP contract agreed in 2004 [45].

  9. Data for 2002 indicated that PHI cover was much lower in Scotland than in other parts of the UK (8 % in comparison with a rate of 18–20 % in London and South-East England) [47].

  10. Some studies distinguish between the probability of a GP visit, and the conditional number of visits, and often find conflicting results for the two decisions (for instance, van Doorslaer et al. [3] found an insignificant pro-rich distribution for the probability of visiting a GP in Ireland, but a significant pro-poor distribution for the conditional number of visits).

  11. Bago d’Uva et al. [12] discuss this issue in greater detail. In the context of panel data, they argue that the ‘conventional’ HI may overstate the degree of inequity in health care utilisation as the residual variation in utilisation may be picking up some of the variation in unobserved need for health care (see also van Doorslaer et al. [3]).

  12. As is apparent from Table 3, two of our health need variables are binary. In this case, the Wagstaff [49] and Erreygers [50] corrections are applied to the CIs for these variables.

  13. In any case, there is some debate in the literature over whether panel data techniques (which control for unobserved time heterogeneity) are appropriate for analyses of children [51].

  14. Data on prescription medicine consumption are not available in either GUI or GUS.

  15. In the GUI survey, the GP variable includes telephone consultations. While we have no information on the proportion of visits that were classified as a telephone consultation, we recognise the possibility for bias in our results for the GUI cohorts if the different eligibility groups have different proportions of telephone consultations (for which the financial disincentive to visit should be far less).

  16. As discussed in the “Introduction” section, the MICS complicates the picture in relation to the entitlement groups outlined in Table 1 for the GUI infant cohort; it is possible that the higher level of visiting among this group in part reflects the two free developmental checks that are free-of-charge to all infants.

  17. The difference in GP visiting between quintile 1 (lowest) and quintile 5 (highest) is not statistically significant for the GUS birth cohort. Results of these tests are available on request from the authors.

  18. The difference in GP visiting between quintile 1 (lowest) and quintile 5 (highest) is not statistically significant for the GUI child cohort. Once again, the MICS complicates the picture in relation to the entitlement groups outlined in Table 1 for the GUI infant cohort. However, assuming that there is no differential take-up of the MICS among different income groups, the patterns in Fig. 2 should not be affected.

  19. While an indicator of chronic illness incidence is available in both the GUI and GUS surveys, the underlying question differs considerably across the surveys. In the GUI infant cohort, the variable is constructed from responses to the question ‘Has a medical professional ever told you that \(\left\langle {\text{baby}} \right\rangle\) has any of the following conditions? With 16 conditions specified (e.g., asthma, diabetes, epilepsy, etc.). In the GUI child cohort, the variable is constructed from the responses to the question ‘Does the Study Child have any on-going chronic physical or mental health problem, illness or disability?’ In GUS, the question is ‘Does ^childname have any longstanding illness or disability? By longstanding I mean anything that has troubled ^him over a period of time or that is likely to affect ^him over a period of time?’ Due to the differences in the underlying question, and the extent to which the GUI infant cohort indicator is an indicator of health need (rather than utilisation), we exclude the chronic illness indicator from our analyses. However, as detailed in the Appendix, we also check the robustness of the results to the inclusion of this variable (and other health need variables which are not available in comparable form across the four samples).

  20. Summary statistics for all variables in table are presented in Table 7 in Appendix 1.

  21. The CI ranges from −1 to 1, with a value of zero indicating no income inequality/inequity in the underlying variable. A negative value indicates a pro-poor distribution of the variable, while a positive value reflects a pro-rich distribution. Van Doorslaer and Koolman [52] have shown that multiplying the value of the concentration index by 75 gives the percentage of the underlying variable (in this case, GP visits) that would need to be (linearly) redistributed from the poorer half to the richer half of the population to arrive at a distribution with an index value of zero.

  22. Detailed results are presented in Appendix 2.

  23. Due to space constraints, the results of the decomposition analyses for the two-part models are not presented here, but are available on request from the authors.

  24. However, there is some evidence for a significant pro-rich distribution for the probability of a GP visit among GUS 2 year olds.

  25. As noted, the existence of the MICS potentially complicates the picture of entitlements for the analysis of the GUI infant cohort although in the absence of more detailed information on take-up of the MICS, it is impossible to speculate on the implications of the MICS for this analysis.

  26. Bago d’Uva et al. [12] exploit the additional information available in longitudinal data to improve the measurement of income-related inequity in health care utilisation by including the time-invariant part of unobserved heterogeneity in the need standardisation procedure. While they find (using the ECHP), that many of the cross-country comparisons are ‘fairly robust’ to the panel data test, the panel estimates lead to significantly higher estimates of income-related inequity for most countries. This confirms that better estimation and control for need often reveals more pro-rich inequity in health care utilisation (also found by Grasdal and Monstad [15], among others).

  27. For all samples, we tested the inclusion of variables relating to chronic illness incidence, acute illness (GUI infant cohort only), child sleeping problems (GUI infant and both GUS cohorts), breast feeding, mother’s smoking and drinking during pregnancy, and current childcare arrangements.

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Acknowledgments

This research was conducted as part of the research programme ‘The Longitudinal Analysis of Child Health and Development in Ireland’ (2010–2013), funded by the Health Research Board. The authors would like to thank the Central Statistics Office (CSO) for access to the micro-data and an anonymous referee for very helpful comments on an earlier draft of the paper.

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  1. Economic and Social Research Institute, Whitaker Square, Sir John Rogerson’s Quay, Dublin 2, Ireland

    Richard Layte & Anne Nolan

  2. Trinity College, Dublin 2, Ireland

    Richard Layte & Anne Nolan

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Correspondence to Anne Nolan.

Appendices

Appendix 1

See Table 7.

Table 7 Dependent and independent variable summary statisticsa
Full size table

Appendix 2

To ensure that our results were robust to the particular data and methods used for analysis, we undertook a number of robustness checks. First, as noted, for both surveys, the majority of the missing observations arose due to missing information on household income, with the problem more serious for GUS (approximately 7 % of GUI observations are missing information on income, while the corresponding figure for GUS is approximately 18 %). To ensure that our results were robust to the exclusion of these cases, we also ran the analysis using imputed income values. Household income was imputed using the ‘uvis’ imputation command in STATA 12.1 [58], and we used indicators of maternal age, employment status, education, ethnicity and lone parent status to predict income values for missing cases. For all analyses presented in Tables 3, 4, 5 and 6, and Figs. 3 and 4, the results are robust to the inclusion of additional observations with imputed income values.

Second, the crucial assumption of this approach to the measurement of income-related inequity in health care utilisation is that the average relationship between utilisation and need is the implied norm for assessing equity in the health care system [3]. A number of recent applications have questioned the validity of this approach [13, 48, 59], particularly in developing countries where resource constraints may mean that the average relationship between need and utilisation is not an accurate reflection of the true relationship. In the Irish context, it is possible that the sharp dichotomy between those with access to free GP care and those without may imply that the observed average relationship between need and GP utilisation in the Irish system is not appropriate, i.e., for the large proportion of the population who must pay user fees for GP care, the amount of care received may not be an accurate reflection of need. Most analyses chose the group for which financial and other (e.g., geographic) barriers in accessing health care are least likely. To test whether utilisation responds differently to need, we followed the approach of van de Poel et al. [13]. We regressed utilisation on health need in each of the five health care entitlement groups in the GUI samples test for the equality of the health need coefficients across the various health care entitlement groups. All tests were not rejected, and so we assumed that the average relationship between utilisation and need was appropriate.

Third, the need-standardised CIs derived from non-linear models (such as the negative binomial, probit and truncated negative binomial which were used in our analysis) were contingent on the values used for the non-need variables, and therefore contained approximation errors. We therefore ran the analysis using median rather than mean values for the non-need variables [15], and found no differences in the estimated CIs and HIs.

Fourth, in the case of a non-linear model, the decomposition is an approximation only (and therefore the CI of the error term includes both an estimation error and an approximation error). In common with others in the literature (e.g., van Doorslaer et al. [3]), we based the decomposition results presented in Figs. 3 and 4 on those from the linear model. However, we also ran the decomposition using the results from the non-linear models, and while the residual component is higher (as expected), the broad patterns observed in Figs. 3 and 4 holds across the various components, both within and across cohorts.

Finally, a particular feature of these types of analyses is that the more extensive the indicators of health need used in the need standardisation process, the smaller the extent of pro-poor inequity [10]. This is because poor health tends to be concentrated among those on lower incomes. As noted, to ensure comparability between and within the GUI and GUS samples used in this analysis, we focused on a comparable group of health need indicators (i.e., birth weight, gestation, parental assessed health and accidents). However, we also tested a wider set of health need indicators (all of which are not available in all four samples),Footnote 27 and while the extent of pro-poor inequity in the GUI samples falls as expected, the pro-poor distribution of GP visits remains significant. The results for the GUS samples remain unchanged.

More detailed results of all these robustness checks are available on request from the authors.

Appendix 3

See Tables 8 and 9.

Table 8 Decomposition Resultsa
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Table 9 Decomposition results (GUI private samples)a
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Layte, R., Nolan, A. Income-related inequity in the use of GP services by children: a comparison of Ireland and Scotland. Eur J Health Econ 16, 489–506 (2015). https://doi.org/10.1007/s10198-014-0587-3

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