Eligibility rates and representativeness of the General Medical Services scheme population in Ireland 2016-2021: A methodological report

Molly Mattsson; Michelle Flood; Emma Wallace; Fiona Boland; Frank Moriarty

doi:10.12688/hrbopenres.13622.2

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Research Article

Revised

Eligibility rates and representativeness of the General Medical Services scheme population in Ireland 2016-2021: A methodological report

[version 2; peer review: 2 approved]

Molly Mattsson ¹, Michelle Flood¹, Emma Wallace², Fiona Boland¹, Frank Moriarty¹

Molly Mattsson ¹, Michelle Flood¹, [...] Emma Wallace², Fiona Boland¹, Frank Moriarty¹

PUBLISHED 18 Oct 2023

Author details Author details

¹ Royal College of Surgeons in Ireland, Dublin, Ireland
² University College Cork, Cork, Ireland

Molly Mattsson
Roles: Formal Analysis, Methodology, Project Administration, Writing – Original Draft Preparation, Writing – Review & Editing

Michelle Flood
Roles: Writing – Review & Editing

Emma Wallace
Roles: Writing – Review & Editing

Fiona Boland
Roles: Writing – Review & Editing

Frank Moriarty
Roles: Conceptualization, Funding Acquisition, Methodology, Visualization, Writing – Original Draft Preparation, Writing – Review & Editing

OPEN PEER REVIEW

REVIEWER STATUS

Abstract

Background

In Ireland, the means tested General Medical Services (GMS) scheme provides access to a range of healthcare services at no or low cost to approximately one third of the population. Individuals eligible for the GMS scheme are often a focus of research, as a population that account for a large proportion of healthcare services use. The aim of this study is to describe the eligibility rates and representativeness of the GMS scheme population over time, with respect to age group, sex, and geographical area in Ireland.

Methods

Population data was obtained from the Central Statistics Office (CSO), using 2016 Census figures and projected population figures for 2017-2021. GMS eligibility figures for 2016-2021 were obtained from the HSE Primary Care Reimbursement Service (PCRS). GMS eligibility rates and relative rates of eligibility were calculated for 2016-2021 by age group and sex. Additionally, 2016 eligibility rates were calculated by geographical area.

Results

The crude eligibility rate decreased from 36.4% in 2016 to 31.2% in 2020, with a slight increase to 31.6% in 2021. In the 75+ years age group, 78.2% of the total population were eligible for the GMS scheme in 2021. The age group with the lowest rate of eligible individuals was the 25–34 age group, with 19.5% eligible in 2021. The eligibility rate was higher among females compared to males throughout the study period. The highest eligibility rate was seen in Donegal, with a crude rate of 52.8%. Dublin had the lowest rate, with a crude rate of 29.3%.

Conclusions

GMS eligibility varies greatly depending on age, sex, and geographical area, and decreased between 2016 and 2021. This study uses the most up-to-date data available to provide age group, sex and area-based figures for GMS eligibility which may inform planning and conduct of research focusing on GMS-eligible individuals.

Keywords

health service executive, eligibility rates, general medical services scheme, local health office

Corresponding author: Molly Mattsson

Competing interests: No competing interests were disclosed.

Grant information: Health Research Board [SDAP-2019-023] as part of the CDRx project through the Secondary Data Analysis Projects scheme.
The funders had no role in study design, data collection and analysis, decision to publish, or preparation of the manuscript.

Copyright: © 2023 Mattsson M et al. This is an open access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.

How to cite: Mattsson M, Flood M, Wallace E et al. Eligibility rates and representativeness of the General Medical Services scheme population in Ireland 2016-2021: A methodological report [version 2; peer review: 2 approved]. HRB Open Res 2023, 5:67 (https://doi.org/10.12688/hrbopenres.13622.2) First published: 20 Oct 2022, 5:67 (https://doi.org/10.12688/hrbopenres.13622.1) Latest published: 18 Oct 2023, 5:67 (https://doi.org/10.12688/hrbopenres.13622.2)

Revised Amendments from Version 1

Based on reviewer comments, this version has been updated with further detail on medications entitlement of private patients (Introduction), the CSO population forecasting approach, our rationale for geographic area groupings, and direct standardisation approaches (Methods), information on changing income thresholds for the GMS scheme, and how this analysis may inform future research (Discussion), updating figure legends and titles, and minor typographical errors.

See the authors' detailed response to the review by Geoffrey M. Curran
See the authors' detailed response to the review by Amelia Smith

Introduction

Ireland currently has a mixed public-private system of healthcare. Entitlement to publicly-funded healthcare services is largely via the General Medical Services (GMS or “medical card”) scheme¹. This scheme provides access to a range of healthcare services at no or low cost to approximately one third of the population². This includes unlimited visits to general practitioners (GP) and other community services, emergency department visits, and hospital inpatient care, free to patients at the point of access. It also includes dispensing of a wide range of prescribed medications which are reimbursed to pharmacies by the Irish health service, where a small prescription charge applies to patients per item dispensed (currently €1.50 or €1 for those aged 70 years and over), with a monthly household cap (€15 or €10 for over 70s).

Entitlement to services under the GMS scheme is mainly based on income, with lower income thresholds for older adults resulting in higher eligibility in this group. The income thresholds have varied over time, based on government budgetary decisions. In a small proportion of cases, eligibility is granted on a discretionary basis if there is evidence of high medical expenses. In 2021, approximately 11% of all medical cards were discretionary³.

Individuals eligible for the GMS scheme are often a focus of research, as a population that account for a large proportion of healthcare services use⁴. In addition, the management of the GMS scheme results in the generation of administrative data, which represent an important resource for health research⁵, and has been linked with other research data to facilitate pharmacoepidemiology studies^6–8. Those without GMS eligibility can avail of the Drugs Payment Scheme, which caps household prescribed medication spend per month, however this yields inconsistent claims data only covering individuals in months where their household prescribed medication spend exceeds this cap. Therefore, for individuals in the privately funded cohort, there is limited information available on healthcare and medication use. An important consideration in any research focussing on GMS scheme eligible individuals as the study population is their representativeness and the potential for selection bias. Therefore, the aim of this study is to describe the eligibility rates and representativeness of the GMS scheme population over time, with respect to age group, sex, and geographical area in Ireland.

Methods

This is a repeated cross-sectional study.

Data sources

Population data for 2016–2021 were obtained from the Central Statistics Office (CSO) via Ireland’s Open Data Portal⁹. The census is a legally mandatory count of the population and is completed by each household every five years. For 2016, census data from April of the same year was used, as this was when the census was last recorded. For 2017–2021, estimated population figures for April each year based on Census 2016 projections were obtained. The CSO produce these by trending forwards Census data, based on the number of males and females in each region by single year of age, each person was aged by one year, births and number of immigrants were added, and deaths and number of emigrants were subtracted). Data included single year of age and sex for the estimated years, while Census 2016 data also included information at county level. The CSO does not provide population estimates by county, age, and sex for non-Census years. Age groups were created to match those reported for the GMS eligibility figures (0–4, 5–11, 12–15, 16–24, 25–34, 35–44, 45–54, 55–64, 65–69, 70–74, and 75+ years).

GMS eligibility figures for 2017–2021 were obtained from the Health Service Executive (HSE) Primary Care Reimbursement Service (PCRS) via the PCRS’s Reporting and Open Data platform, while 2016 figures were requested directly from the PCRS. Aggregate figures for number of GMS-eligible individuals by age group and sex were extracted for April 2016–2021 to correspond with Census population figures. For April 2016, information at HSE Local Health Office (LHO) level (i.e. geographical areas for the administration of healthcare entitlements) was also obtained. As LHOs do not correspond directly to counties (i.e. areas for reporting GMS eligibility may incorporate (parts of) multiple counties which is the basis for population numbers), broader areas combining counties and/or LHOs were created to provide equivalence for analysis purposes (see Table 1).

Table 1. Local health offices (LHOs), Central Statistics Office (CSO) areas, and areas used for analysis.

Local Health Office (PCRS)	County and city (CSO)	New area
Cavan/Monaghan	Cavan	Cavan/Monaghan/ Sligo/Leitrim
	Monaghan
Sligo/Leitrim/West Cavan	Sligo
	Leitrim
Donegal	Donegal	Donegal
Galway	Galway City	Galway
	Galway County
Mayo	Mayo	Mayo
Roscommon	Roscommon	Roscommon
Clare	Clare	Clare
Limerick	Limerick	Limerick/Tipperary
North Tipperary/East Limerick	Tipperary
South Tipperary
Kerry	Kerry	Kerry
North Cork	Cork City	Cork
North Lee	Cork County
South Lee
West Cork
Carlow/Kilkenny	Carlow	Carlow/Kilkenny
	Kilkenny
Waterford	Waterford	Waterford
Wexford	Wexford	Wexford
Dublin South East	Dublin City	Dublin
Dún Laoghaire	Dún Laoghaire- Rathdown
Dublin South City	Fingal
Dublin South West	South Dublin
Dublin West
Dublin North
Dublin North Central
Dublin North West
Wicklow	Wicklow	Kildare/Wicklow
Kildare/West Wicklow	Kildare
Laois/Offaly	Laois	Laois/Offaly
	Offaly
Longford/ Westmeath	Longford	Longford/ Westmeath
	Westmeath
Louth	Louth	Louth
Meath	Meath	Meath

Analysis

The characteristics of the GMS population per year were summarised. Crude eligibility rates were calculated for 2016–2021 by age group and sex, and demographic group (children, 0–15 years; adults, 16–64 years; and older adults, 65+ years), along with the relative rate of eligibility (how much more or less likely a member of a particular age or sex group was to be eligible compared to the general population). The crude eligibility rate per year was calculated, and the yearly rates were also directly standardised to the 2016 population based on age group and sex (i.e. eligibility rates in each age-sex strata were weighted according to the age-sex distribution in 2016 to estimate the eligibility rate had the age-sex structure of population remained the same as 2016).

For the area-level analysis, crude eligibility rates were calculated using Census 2016 data and April 2016 eligibility data and directly standardised to national population based on age group and sex. Standardised eligibility rates were plotted on a map by area. Analyses were conducted using Stata version 17¹⁰, and figures were generated in RStudio using the tmap and ggplot2 packages^11,12

Results

Characteristics of the GMS population over time are included in Table 2. The GMS population decreased from 1,735,524 in 2016 to 1,554,759 in 2020, and increased to 1,581,294 in 2021. The 75+ years age group was the largest group throughout the study period, ranging from 13.0% in 2016 to 15.8% in 2021. Conversely, 0–4 years age group was consistently the smallest, ranging from 5.8% in 2016 to 4.4% in 2021. Females made up the majority of the GMS population throughout the study period, increasing from 53.1% in 2016 to 54.1% in 2021.

Table 2. Characteristics of the General Medical Services (GMS) population over time.

	2016	2017	2018	2019	2020	2021
Age group (years) N %
0–4	100,242 5.78%	88,132 5.34%	82,103 5.16%	77,471 4.96%	74,767 4.81%	68,711 4.35%
5–11	187,675 10.81%	174,060 10.56%	166,760 10.47%	162,127 10.39%	160,585 10.33%	160,806 10.17%
12–15	102,509 5.91%	97,485 5.91%	93,662 5.88%	92,568 5.93%	94,723 6.09%	99,054 6.26%
16–24	168,084 9.68%	153,603 9.31%	145,733 9.15%	134,638 8.63%	125,538 8.07%	135,813 8.59%
25–34	178,306 10.27%	151,514 9.19%	132,226 8.30%	120,891 7.74%	116,980 7.52%	118,626 7.50%
35–44	211,173 12.17%	196,223 11.90%	179,404 11.27%	171,786 10.96%	168,337 10.83%	171,482 10.84%
45–54	189,374 10.91%	186,155 11.29%	180,214 11.32%	183,261 11.74%	184,379 11.86%	186,366 11.79%
55–64	168,190 9.69%	168,288 10.21%	168,530 10.58%	171,786 11.00%	174,236 11.21%	177,423 11.22%
65–69	96,420 5.56%	96,125 5.83%	96,556 6.06%	94,450 6.05%	93,391 6.01%	92,771 5.87%
70–74	108,475 6.25%	111,113 6.74%	114,768 7.21%	116,929 7.49%	119,676 7.70%	120,832 7.64%
75+	225,076 12.97%	226,307 13.72%	232,239 14.59%	235,775 15.10%	242,147 15.57%	249,410 15.77%
Sex N %
Female	920,815 53.06%	880,329 53.39%	853,493 53.60%	839,738 53.79%	838,767 53.95%	855,759 54.12%
Male	814,709 46.95%	768,676 46.61%	738,702 46.40%	721,270 46.21%	715,992 46.05%	725,545 45.88%
Total	1,735,524	1,649,005	1,592,195	1,561,008	1,554,759	1,581,294

Table 3 shows the eligibility rates and relative rates by age group and sex. In the 75+ years age group, 85.2% of the total population were eligible for the GMS scheme in 2016, decreasing to 78.2% in 2021. The age group with the lowest rate of eligible individuals was the 25-34 age group, with 27.0% eligible in 2016 and 19.5% in 2021. The eligibility rate was higher among females compared to males throughout the study period, with 38.2% eligible in 2016 and 33.9% in 2021, compared to 34.6% and 29.2% for males. Figure 1 shows a population pyramid, depicting the percentage of males and females across each age group within GMS eligible individuals, compared to the full population for 2016. The 75+ age group was the most overrepresented in the GMS population in 2016. Among males, individuals aged 75 and over made up approximately 5% of the full population and 12% of the GMS population, while for females, individuals aged 75 and over made up approximately 6% of the full population and 15% of the GMS population. Those aged 5–15 years, and to a greater extent those 65+ years, were overrepresented across all years, and females were overrepresented relative to males (see Figure 2 for population pyramids across years).

Table 3. General medical services (GMS) scheme eligibility rate and relative rate over time.

	2016		2017		2018		2019		2020		2021
	Eligibility rate	Relative rate	Eligibility rate	Relative rate	Eligibility rate	Relative rate	Eligibility rate	Relative rate	Eligibility rate	Relative rate	Eligibility rate	Relative rate
Age group (years)
0–4	30.2%	0.83	27.2%	0.789	25.7%	0.784	24.5%	0.775	24.2%	0.773	22.7%	0.72
5–11	38.7%	1.063	35.5%	1.031	33.6%	1.026	32.6%	1.029	32.8%	1.05	33.4%	1.06
12–15	40.6%	1.115	38.3%	1.113	36.3%	1.107	35.3%	1.114	35.2%	1.126	35.7%	1.131
16–24	32.6%	0.896	29.4%	0.855	26.9%	0.822	24.3%	0.767	22.2%	0.71	23.8%	0.755
25–34	27.0%	0.742	23.7%	0.688	21.1%	0.643	19.5%	0.615	19.0%	0.608	19.5%	0.618
35–44	28.3%	0.776	25.9%	0.753	23.4%	0.712	22.0%	0.694	21.6%	0.691	22.0%	0.699
45–54	30.2%	0.83	29.3%	0.85	27.8%	0.849	27.7%	0.873	27.3%	0.875	27.1%	0.858
55–64	33.0%	0.907	32.6%	0.947	32.0%	0.977	31.8%	1.004	31.6%	1.011	31.5%	0.999
65–69	45.6%	1.252	45.5%	1.322	44.8%	1.365	43.1%	1.358	41.6%	1.333	40.5%	1.283
70–74	66.8%	1.834	65.7%	1.908	64.6%	1.971	63.3%	1.994	62.7%	2.008	62.2%	1.971
75+	85.2%	2.339	84.0%	2.441	83.0%	2.531	80.7%	2.544	79.4%	2.531	78.2%	2.479
Demographic group
0–15	36.6%	0.945	33.6%	0.917	31.9%	0.908	30.9%	0.907	30.9%	0.917	31.0%	0.912
16–64	29.9%	0.774	27.9%	0.759	25.9%	0.737	24.8%	0.727	24.1%	0.716	24.6%	0.725
65+	67.4%	1.743	66.7%	1.819	65.9%	1.874	64.2%	1.883	63.2%	1.877	62.4%	1.838
Sex
Female	38.2%	1.049	36.4%	1.057	34.8%	1.062	33.8%	1.066	33.4%	1.069	33.9%	1.072
Male	34.6%	0.949	32.4%	0.942	30.7%	0.937	29.6%	0.933	29.0%	0.93	29.2%	0.927

Figure 1. Percentage of males and females in each age group within the General Medical Services (GMS) scheme (light-coloured, black-bordered bars) overlaid with percentage in the full population (bold-coloured, white-bordered bars) in 2016.

Figure 2. Percentage of males and females in each age group within the General Medical Services (GMS) scheme (light-coloured, black-bordered bars) overlaid with percentage in the full population (bold-coloured, white-bordered bars) for 2016-2021.

Crude and adjusted eligibility rates over time are included in Table 4. The crude rate decreased from 36.4% in 2016 to 31.2% in 2020, with a slight increase to 31.6% in 2021. After directly standardising the rate to the 2016 population based on age group and sex, a similar pattern was observed with 30.8% (95% confidence interval 30.76% to 30.84%) adjusted eligibility rate in 2021.

Table 4. General Medical Services (GMS) scheme crude eligibility rate over time, and rate directly standardised to 2017 population based on age group and sex.

	Total estimated population	Total eligible	Crude rate	Standardised rate (95% CI)
2016	4,761,865	1,735,524	36.4%	36.4%
2017	4,792,490	1,649,005	34.4%	34.29% (34.25-34.33)
2018	4,857,015	1,592,195	32.8%	32.50% (32.46-32.54)
2019	4,921,496	1,561,008	31.7%	31.28% (31.25-31.32)
2020	4,977,443	1,554,759	31.2%	30.66% (30.62-30.70)
2021	5,011,460	1,581,294	31.6%	30.80% (30.76-30.84)

Table 5 shows the crude eligibility rate by area for 2016, and rates directly standardised to the national population based on age group and sex. Population pyramids by area are shown in Figure 3. The highest rate was seen in Donegal, with a crude rate of 52.8% and an adjusted rate of 52.0% (95% CI 51.7% to 52.2%). Dublin had the lowest rate, with a crude rate of 29.3% and an adjusted rate of 30.1% (95% CI 30.0% to 30.2%). Figure 4 shows a map of the adjusted eligibility rates across areas.

Table 5. General Medical Services (GMS) scheme crude eligibility rate by area for 2016, and rate directly standardised to national population based on age group and sex.

	Total estimated population	Total eligible	Crude rate	Standardised rate (95% CI)
Carlow/Kilkenny	156,164	57,933	37.1%	37.0% (36.8-37.2)
Cavan/Monaghan/Sligo/Leitrim	235,141	96,135	40.9%	40.3% (40.1-40.5)
Clare	118,817	45,233	38.1%	37.7% (37.4-37.9)
Cork	542,868	189,302	34.9%	34.8% (34.6-34.9)
Donegal	159,192	84,050	52.8%	52.0% (51.7-52.2)
Dublin	1,347,359	394,717	29.3%	30.1% (30.0-30.2)
Galway	258,058	95,684	37.1%	37.0% (36.8-37.2)
Kerry	147,707	58,482	39.6%	38.6% (38.4-38.9)
Kildare/Wicklow	364,929	115,644	31.7%	32.7% (32.5-32.8)
Laois/Offaly	162,658	67,396	41.4%	41.7% (41.5-42.0)
Limerick/Tipperary	354,452	145,559	41.1%	40.6% (40.4-40.7)
Longford/Westmeath	129,643	55,735	43.0%	43.0% (42.7-43.2)
Louth	128,884	57,679	44.8%	45.0% (44.7-45.2)
Mayo	130,507	60,788	46.6%	45.1% (44.8-45.3)
Meath	195,044	61,853	31.7%	32.9% (32.7-33.1)
Roscommon	64,544	27,731	43.0%	41.7% (41.3-42.0)
Waterford	116,176	54,180	46.6%	46.0% (45.8-46.3)
Wexford	149,722	67,423	45.0%	44.5% (44.3-44.8)

Figure 3. Percentage of males and females in each age group within the GMS scheme (light-coloured, black-bordered bars) overlaid with percentage in the full population (bold-coloured, white-bordered bars) for 2016 across geographic areas.

Figure 4. Eligibility rate for the General Medical Services (GMS) scheme by geographical area for 2016, standardised to the national population by age group and sex.

Discussion

The decreasing trend in eligibility rate from 2017 is consistent with previous analysis, which identified a rise from approximately 30% to over 40% from 2008 to 2013 (during the economic crisis affecting Ireland), followed by a decrease in 2014 and 2015¹³. The income threshold for GMS eligibility remained the same from 2013 to November 2020, suggesting the declining eligibility rate may have been due to improved financial circumstances of individuals. The maximum income threshold was increased in November 2020, which may explain the slight increase in eligibility rate from 2020 to 2021, and this could also be partially attributable to the adverse financial impact of the coronavirus disease 2019 (COVID-19) pandemic¹⁴.

As expected given that eligibility is often based on income, the areas we identified as having the highest rates are somewhat similar to those with higher levels of deprivation¹⁵. However, because of the grouping of areas in our analysis to allow for comparable population and eligibility figures, many of the highest deprivation areas were grouped with others, precluding direct comparison.

Considering representativeness of health data drawn from routine sources (as non-random samples) is important for assessing the external validity of research. A recently published analysis of the OpenSAFELY data, derived from GP records in England and used extensively since the onset of the COVID-19 pandemic to generate evidence, suggests some geographic variation but otherwise good population representativeness¹⁶. Even for prospectively collected research data, such as the UK BioBank, consideration of representativeness and the potential for healthy user bias is important¹⁷. As well as undermining generalisability, selection bias may also impact internal validity, where sampling into the study is affected by both the exposure and outcome of interest, and thus the exposure-outcome association may be biased¹⁸. As the GMS scheme is means tested, older people and individuals from lower socioeconomic backgrounds are overrepresented, which may have implications for research using this data and the conclusions drawn from it. The analysis presented may inform future research, for example for sample size estimates if including only GMS-eligible patients or extrapolating findings from analysis of GMS-eligible patients to the wider population. The overrepresentation of older adults has important policy and fiscal implications, particularly as Ireland is projected to see significant growth in the 65 years and over age group in the coming years. Financing to support medicines expenditure in this growing older cohort is an important consideration for health care planning.

Limitations

For the year-by-year analysis, the population figures for 2017–2021 are estimated based on projections from the census, which may not fully capture true population changes. The next census was conducted in April 2022 (delayed from April 2021 due to the COVID-19 pandemic). The publication of data from this (expected to be released in stages between 2023 and 2024¹⁹) will provide an opportunity to evaluate contemporary eligibility rate using actual population figures, and to evaluate how eligibility patterns have changed by area over time.

Our area-level analysis required grouping of several counties and included only 18 areas, compared to 32 LHOs and 31 local authority areas. All of Dublin’s local authority areas were grouped into one area, resulting in an area with a very high population and no possibility of within area comparisons. As mentioned above, this meant that a closer examination of deprived and affluent areas was not possible. Releasing data at a more granular geographic area, or providing information to map townland-level Census figures to LHO areas for example, would allow for a more detailed analysis. In general alignment and harmonisation of how data is presented by various statutory bodies in health and elsewhere would enhance the secondary use of such data for research and other purposes.

Conclusion

This study uses the most up-to-date data available to provide age group, sex and area-based figures for GMS eligibility which may inform planning and conduct of research focusing on GMS-eligible individuals. We also provide the statistical code to import open data (where available) and conduct analysis, along with extracted data from the PCRS portal. Provision of open, interoperable PCRS eligibility data per month via Ireland’s Open Data Portal would enhance the usability of this data for research and wider purposes.

Data availability

Underlying data

Zenodo: Data and code related to “Eligibility rates and representativeness of the General Medical Services scheme population in Ireland 2017–2021: A methodological report.” https://doi.org/10.5281/zenodo.7097254⁹.

This project contains the following underlying data:

- age_sex_lho_eligibility_2016.xlsx (GMS eligibility figures by age group, sex, and LHO for 2016)
- age_sex_lho_eligibility_2017-2021.xlsx (GMS eligibility figures by age group, sex, and LHO for 2017–2021)

Additionally, data from the CSO datasets E3003 (Population 2011 to 2016) and PEA11 (Population estimates from 1926) were downloaded from the CSO website as described in the Stata do-file included in the repository.

Data are available under the terms of the Creative Commons Attribution 4.0 International license (CC-BY 4.0).

Acknowledgements

We would like to thank the HSE PCRS for providing data on the number of eligible individuals for April 2016, which were not openly available via their Reporting and Open Data platform (https://www.sspcrs.ie/portal/annual-reporting/).

Faculty Opinions recommended

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9. Mattsson M, Flood M, Wallace E, et al.: Data and code related to "Eligibility rates and representativeness of the General Medical Services scheme population in Ireland 2017-2021: A methodological report". Zenodo. [Dataset]. 2022. http://www.doi.org/10.5281/zenodo.7097254
10. Stata Statistical Software: Release 17. StataCorp LLC; 2022. Reference Source
11. Tennekes M: tmap: Thematic Maps in R. Journal of Statistical Software. 2018; 84(6): 1–39. Publisher Full Text
12. ggplot2: Elegant Graphics for Data Analysis. Springer-Verlag; 2016. Publisher Full Text
13. Burke SA, Normand C, Barry S, et al.: From universal health insurance to universal healthcare? The shifting health policy landscape in Ireland since the economic crisis. Health Policy. 2016; 120(3): 235–240. PubMed Abstract | Publisher Full Text
14. McGlacken-Byrne D, Parker S, Burke S: Tracking aspects of healthcare activity during the first nine months of COVID-19 in Ireland: a secondary analysis of publicly available data [version 1; peer review: 1 not approved]. HRB Open Res. 2021; 4(98): 98. Publisher Full Text
15. Teljeur C, Darker C, Barry J, et al.: The Trinity National Deprivation Index for Health and Health Services Research 2016. 2019. Reference Source
16. Andrews CD, Schultze A, Curtis HJ, et al.: OpenSAFELY: Representativeness of Electronic Health Record platform OpenSAFELY-TPP data compared to the population of England. medRxiv. 2022; 2022.06.23.22276802. Publisher Full Text
17. Fry A, Littlejohns TJ, Sudlow C, et al.: Comparison of Sociodemographic and Health-Related Characteristics of UK Biobank Participants With Those of the General Population. Am J Epidemiol. 2017; 186(9): 1026–1034. PubMed Abstract | Publisher Full Text | Free Full Text
18. Griffith GJ, Morris TT, Tudball MJ, et al.: Collider bias undermines our understanding of COVID-19 disease risk and severity. Nat Commun. 2020; 11(1): 5749. PubMed Abstract | Publisher Full Text | Free Full Text
19. Census 2022 - Dissemination strategy. Stationery Office, 2022. Reference Source

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Version 2

VERSION 2 PUBLISHED 20 Oct 2022

Author details Author details

¹ Royal College of Surgeons in Ireland, Dublin, Ireland
² University College Cork, Cork, Ireland

Molly Mattsson
Roles: Formal Analysis, Methodology, Project Administration, Writing – Original Draft Preparation, Writing – Review & Editing

Michelle Flood
Roles: Writing – Review & Editing

Emma Wallace
Roles: Writing – Review & Editing

Fiona Boland
Roles: Writing – Review & Editing

Frank Moriarty
Roles: Conceptualization, Funding Acquisition, Methodology, Visualization, Writing – Original Draft Preparation, Writing – Review & Editing

Competing interests

No competing interests were disclosed.

Grant information

Health Research Board [SDAP-2019-023] as part of the CDRx project through the Secondary Data Analysis Projects scheme.
The funders had no role in study design, data collection and analysis, decision to publish, or preparation of the manuscript.

Article Versions (2)

version 2

Revised

Published: 18 Oct 2023, 5:67

https://doi.org/10.12688/hrbopenres.13622.2

version 1

Published: 20 Oct 2022, 5:67

https://doi.org/10.12688/hrbopenres.13622.1

© 2023 Mattsson M et al. This is an open access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.

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Mattsson M, Flood M, Wallace E et al. Eligibility rates and representativeness of the General Medical Services scheme population in Ireland 2016-2021: A methodological report [version 2; peer review: 2 approved]. HRB Open Res 2023, 5:67 (https://doi.org/10.12688/hrbopenres.13622.2)

NOTE: If applicable, it is important to ensure the information in square brackets after the title is included in all citations of this article.

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Reviewer Report 13 Sep 2023

Geoffrey M. Curran, Department of Pharmacy Practice, University of Arkansas for Medical Sciences, Little Rock, AR, USA

Approved

https://doi.org/10.21956/hrbopenres.14894.r35745

I found the article to be a relatively straightforward presentation of potential trends in eligibility for the Irish GMS scheme. As an outsider to the Irish health system, it helped me to better understand the GMS scheme and to appreciate geographic differences in Ireland in terms of economic outlook and resultant eligibility. I appreciated the transparency of the authors and their support of others gaining access to the same data for their own analysis.

There were several areas where I would have liked more description and/or discussion. I would have appreciated more information on the basis of (or assumptions applied for) the projections for the "out years." (I was not sure what the authors meant by "rated were directly standardized" to the 2016 population). I also could have used some more information on the possible economic or other social forces that could have been driving the projected changes in eligibility and geographic differences. Also, I am not sure that the readers were given a reason why they were required to group several counties. I also could have used a brief discussion of how specifically these data could be used in the "planning and conduct of research focusing on GMS-eligible individuals."

In general, I found the tables followable and useful. I struggled some with the figures. It took me a while see and understand the white/black bar distinction.

I agree with the feedback provided by the first reviewer. I appreciated the opportunity to review the manuscript. I had not seen previously a system whereby a review is sought after publication.

Is the work clearly and accurately presented and does it cite the current literature?

Yes
Is the study design appropriate and is the work technically sound?

Yes
Are sufficient details of methods and analysis provided to allow replication by others?

Yes
If applicable, is the statistical analysis and its interpretation appropriate?

I cannot comment. A qualified statistician is required.
Are all the source data underlying the results available to ensure full reproducibility?

Yes
Are the conclusions drawn adequately supported by the results?

Yes

Competing Interests: No competing interests were disclosed.

Reviewer Expertise: Health services research, implementation science

I confirm that I have read this submission and believe that I have an appropriate level of expertise to confirm that it is of an acceptable scientific standard.

CITE

Report a concern

Author Response 29 Nov 2023

Frank Moriarty, Royal College of Surgeons in Ireland, Dublin, Ireland

29 Nov 2023

Author Response

Thanks very much for reviewing this manuscript and for the helpful comments. We have amended the manuscript based on them and have submitted an updated version incorporating these changes.

... Continue reading Thanks very much for reviewing this manuscript and for the helpful comments. We have amended the manuscript based on them and have submitted an updated version incorporating these changes.

Comments
There were several areas where I would have liked more description and/or discussion. I would have appreciated more information on the basis of (or assumptions applied for) the projections for the "out years." (I was not sure what the authors meant by "rated were directly standardized" to the 2016 population).

Response: We have elaborated on the CSO’s methods for estimating population projects: “The CSO produce these by trending forwards Census data, based on the number of males and females in each region by single year of age, each person was aged by one year, births and number of immigrants were added, and deaths and number of emigrants were subtracted).” We have also provided more detail about the direct standardisation in the Analysis section: “The crude eligibility rate per year was calculated, and the yearly rates were also directly standardised to the 2016 population based on age group and sex (i.e. eligibility rates in each age-sex strata were weighted according to the age-sex distribution in 2016 to estimate the eligibility rate had the age-sex structure of population remained the same as 2016).”

I also could have used some more information on the possible economic or other social forces that could have been driving the projected changes in eligibility and geographic differences.

Response: We have now added detail on the likely explanation for eligibility changes over time (in addition to the existing discussion of geographical deprivation and eligibility): “The income threshold for GMS eligibility remained the same from 2013 to November 2020, suggesting the declining eligibility rate may have been due to improved financial circumstances of individuals. The maximum income threshold was increased in November 2020, which may explain the slight increase in eligibility rate from 2020 to 2021, and this could also be partially attributable to the adverse financial impact of the COVID-19 pandemic¹³.”

Also, I am not sure that the readers were given a reason why they were required to group several counties.

Response: We have now expanded the explanation of this in the Methods: For April 2016, information at HSE Local Health Office (LHO) level (i.e. geographical areas for the administration of healthcare entitlements) was also obtained. As LHOs do not correspond directly to counties (i.e. areas for reporting GMS eligibility may incorporate (parts of) multiple counties which is the basis for population numbers), broader areas combining counties and/or LHOs were created to provide equivalence for analysis purposes (see Table 1).

I also could have used a brief discussion of how specifically these data could be used in the "planning and conduct of research focusing on GMS-eligible individuals."

Response: We added some detail on potential research applications for the findings of this study: “The analysis presented may inform future research, for example for sample size estimates if including only GMS-eligible patients or extrapolating findings from analysis of GMS-eligible patients to the wider population.”

In general, I found the tables followable and useful. I struggled some with the figures. It took me a while see and understand the white/black bar distinction.

Response: As suggested by reviewer 1, we have removed the GMS/full population part of the legend, leaving the legend description which is hopefully clearer.
Thanks very much for reviewing this manuscript and for the helpful comments. We have amended the manuscript based on them and have submitted an updated version incorporating these changes.

Comments
There were several areas where I would have liked more description and/or discussion. I would have appreciated more information on the basis of (or assumptions applied for) the projections for the "out years." (I was not sure what the authors meant by "rated were directly standardized" to the 2016 population).

Response: We have elaborated on the CSO’s methods for estimating population projects: “The CSO produce these by trending forwards Census data, based on the number of males and females in each region by single year of age, each person was aged by one year, births and number of immigrants were added, and deaths and number of emigrants were subtracted).” We have also provided more detail about the direct standardisation in the Analysis section: “The crude eligibility rate per year was calculated, and the yearly rates were also directly standardised to the 2016 population based on age group and sex (i.e. eligibility rates in each age-sex strata were weighted according to the age-sex distribution in 2016 to estimate the eligibility rate had the age-sex structure of population remained the same as 2016).”

I also could have used some more information on the possible economic or other social forces that could have been driving the projected changes in eligibility and geographic differences.

Response: We have now added detail on the likely explanation for eligibility changes over time (in addition to the existing discussion of geographical deprivation and eligibility): “The income threshold for GMS eligibility remained the same from 2013 to November 2020, suggesting the declining eligibility rate may have been due to improved financial circumstances of individuals. The maximum income threshold was increased in November 2020, which may explain the slight increase in eligibility rate from 2020 to 2021, and this could also be partially attributable to the adverse financial impact of the COVID-19 pandemic¹³.”

Also, I am not sure that the readers were given a reason why they were required to group several counties.

Response: We have now expanded the explanation of this in the Methods: For April 2016, information at HSE Local Health Office (LHO) level (i.e. geographical areas for the administration of healthcare entitlements) was also obtained. As LHOs do not correspond directly to counties (i.e. areas for reporting GMS eligibility may incorporate (parts of) multiple counties which is the basis for population numbers), broader areas combining counties and/or LHOs were created to provide equivalence for analysis purposes (see Table 1).

I also could have used a brief discussion of how specifically these data could be used in the "planning and conduct of research focusing on GMS-eligible individuals."

Response: We added some detail on potential research applications for the findings of this study: “The analysis presented may inform future research, for example for sample size estimates if including only GMS-eligible patients or extrapolating findings from analysis of GMS-eligible patients to the wider population.”

In general, I found the tables followable and useful. I struggled some with the figures. It took me a while see and understand the white/black bar distinction.

Response: As suggested by reviewer 1, we have removed the GMS/full population part of the legend, leaving the legend description which is hopefully clearer.
Competing Interests: None. Close
Report a concern
Respond or Comment

COMMENTS ON THIS REPORT

Author Response 29 Nov 2023

Frank Moriarty, Royal College of Surgeons in Ireland, Dublin, Ireland

29 Nov 2023

Author Response

Thanks very much for reviewing this manuscript and for the helpful comments. We have amended the manuscript based on them and have submitted an updated version incorporating these changes.

... Continue reading Thanks very much for reviewing this manuscript and for the helpful comments. We have amended the manuscript based on them and have submitted an updated version incorporating these changes.

Comments
There were several areas where I would have liked more description and/or discussion. I would have appreciated more information on the basis of (or assumptions applied for) the projections for the "out years." (I was not sure what the authors meant by "rated were directly standardized" to the 2016 population).

Response: We have elaborated on the CSO’s methods for estimating population projects: “The CSO produce these by trending forwards Census data, based on the number of males and females in each region by single year of age, each person was aged by one year, births and number of immigrants were added, and deaths and number of emigrants were subtracted).” We have also provided more detail about the direct standardisation in the Analysis section: “The crude eligibility rate per year was calculated, and the yearly rates were also directly standardised to the 2016 population based on age group and sex (i.e. eligibility rates in each age-sex strata were weighted according to the age-sex distribution in 2016 to estimate the eligibility rate had the age-sex structure of population remained the same as 2016).”

I also could have used some more information on the possible economic or other social forces that could have been driving the projected changes in eligibility and geographic differences.

Response: We have now added detail on the likely explanation for eligibility changes over time (in addition to the existing discussion of geographical deprivation and eligibility): “The income threshold for GMS eligibility remained the same from 2013 to November 2020, suggesting the declining eligibility rate may have been due to improved financial circumstances of individuals. The maximum income threshold was increased in November 2020, which may explain the slight increase in eligibility rate from 2020 to 2021, and this could also be partially attributable to the adverse financial impact of the COVID-19 pandemic¹³.”

Also, I am not sure that the readers were given a reason why they were required to group several counties.

Response: We have now expanded the explanation of this in the Methods: For April 2016, information at HSE Local Health Office (LHO) level (i.e. geographical areas for the administration of healthcare entitlements) was also obtained. As LHOs do not correspond directly to counties (i.e. areas for reporting GMS eligibility may incorporate (parts of) multiple counties which is the basis for population numbers), broader areas combining counties and/or LHOs were created to provide equivalence for analysis purposes (see Table 1).

I also could have used a brief discussion of how specifically these data could be used in the "planning and conduct of research focusing on GMS-eligible individuals."

Response: We added some detail on potential research applications for the findings of this study: “The analysis presented may inform future research, for example for sample size estimates if including only GMS-eligible patients or extrapolating findings from analysis of GMS-eligible patients to the wider population.”

In general, I found the tables followable and useful. I struggled some with the figures. It took me a while see and understand the white/black bar distinction.

Response: As suggested by reviewer 1, we have removed the GMS/full population part of the legend, leaving the legend description which is hopefully clearer.
Thanks very much for reviewing this manuscript and for the helpful comments. We have amended the manuscript based on them and have submitted an updated version incorporating these changes.

Comments
There were several areas where I would have liked more description and/or discussion. I would have appreciated more information on the basis of (or assumptions applied for) the projections for the "out years." (I was not sure what the authors meant by "rated were directly standardized" to the 2016 population).

Response: We have elaborated on the CSO’s methods for estimating population projects: “The CSO produce these by trending forwards Census data, based on the number of males and females in each region by single year of age, each person was aged by one year, births and number of immigrants were added, and deaths and number of emigrants were subtracted).” We have also provided more detail about the direct standardisation in the Analysis section: “The crude eligibility rate per year was calculated, and the yearly rates were also directly standardised to the 2016 population based on age group and sex (i.e. eligibility rates in each age-sex strata were weighted according to the age-sex distribution in 2016 to estimate the eligibility rate had the age-sex structure of population remained the same as 2016).”

I also could have used some more information on the possible economic or other social forces that could have been driving the projected changes in eligibility and geographic differences.

Response: We have now added detail on the likely explanation for eligibility changes over time (in addition to the existing discussion of geographical deprivation and eligibility): “The income threshold for GMS eligibility remained the same from 2013 to November 2020, suggesting the declining eligibility rate may have been due to improved financial circumstances of individuals. The maximum income threshold was increased in November 2020, which may explain the slight increase in eligibility rate from 2020 to 2021, and this could also be partially attributable to the adverse financial impact of the COVID-19 pandemic¹³.”

Also, I am not sure that the readers were given a reason why they were required to group several counties.

Response: We have now expanded the explanation of this in the Methods: For April 2016, information at HSE Local Health Office (LHO) level (i.e. geographical areas for the administration of healthcare entitlements) was also obtained. As LHOs do not correspond directly to counties (i.e. areas for reporting GMS eligibility may incorporate (parts of) multiple counties which is the basis for population numbers), broader areas combining counties and/or LHOs were created to provide equivalence for analysis purposes (see Table 1).

I also could have used a brief discussion of how specifically these data could be used in the "planning and conduct of research focusing on GMS-eligible individuals."

Response: We added some detail on potential research applications for the findings of this study: “The analysis presented may inform future research, for example for sample size estimates if including only GMS-eligible patients or extrapolating findings from analysis of GMS-eligible patients to the wider population.”

In general, I found the tables followable and useful. I struggled some with the figures. It took me a while see and understand the white/black bar distinction.

Response: As suggested by reviewer 1, we have removed the GMS/full population part of the legend, leaving the legend description which is hopefully clearer.
Competing Interests: None. Close
Report a concern

Views

Reviewer Report 22 Nov 2022

Amelia Smith, Department of Pharmacology and Therapeutics, Trinity College Dublin, Trinity Centre for Health Sciences, St. James’ Hospital, Dublin, Ireland; Medicines Management Programme, Health Service Executive, Trinity Centre for Health Sciences, St. James’ Hospital, Dublin, Ireland

Approved

https://doi.org/10.21956/hrbopenres.14894.r33160

Well done to the authors on a well-written and informative study.

Introduction:

Final paragraph; include sentence to explain that there’s very limited information on healthcare use, dispensed claims, etc. in the privately funded

Well done to the authors on a well-written and informative study.

Introduction:

Final paragraph; include sentence to explain that there’s very limited information on healthcare use, dispensed claims, etc. in the privately funded cohort.
The authors could include a brief description of the Drugs Payment Scheme (DPS) as all persons not eligible for GMS are eligible for DPS. This could sit in the discussion, either.

Results:

‘The GMS population decreased from 1,735,534 in 2016 to 1,554,759 in 2020, and increased to 1,581,294 in 2021.’ Double check figures in Table 2 – states the 2016 as 1,735,524.
Check total figures for 2019 and 2020 in Table 2.
For the population pyramids, the grey legend for cohort (GMS or Population) isn’t intuitive. I think this could be removed/changed, as the Figure description is more informative.
Page 4, 2^nd column, 3^rd paragraph, final sentence: ‘(95% confidence interval 30.88% to 30.96%)’ these CI’s are different to that in Table 4.

Discussion:

Authors could include a brief discussion on the practical implications of the difference in eligibility rates across age groups – the aging population and fiscal implications going forward, etc. However, this is just a suggestion – not a requirement.

Is the work clearly and accurately presented and does it cite the current literature?

Yes
Is the study design appropriate and is the work technically sound?

Yes
Are sufficient details of methods and analysis provided to allow replication by others?

Yes
If applicable, is the statistical analysis and its interpretation appropriate?

Yes
Are all the source data underlying the results available to ensure full reproducibility?

Yes
Are the conclusions drawn adequately supported by the results?

Yes

Competing Interests: No competing interests were disclosed.

Reviewer Expertise: Drug utilisation studies using PCRS pharmacy claims data. Health technology management.

I confirm that I have read this submission and believe that I have an appropriate level of expertise to confirm that it is of an acceptable scientific standard.

CITE

Report a concern

Author Response 29 Nov 2023

Frank Moriarty, Royal College of Surgeons in Ireland, Dublin, Ireland

29 Nov 2023

Author Response

Thanks very much for reviewing this manuscript and for the helpful comments. We have amended the manuscript based on them and have submitted an updated version incorporating these changes.

... Continue reading Thanks very much for reviewing this manuscript and for the helpful comments. We have amended the manuscript based on them and have submitted an updated version incorporating these changes.

Final paragraph; include sentence to explain that there’s very limited information on healthcare use, dispensed claims, etc. in the privately funded cohort.
The authors could include a brief description of the Drugs Payment Scheme (DPS) as all persons not eligible for GMS are eligible for DPS. This could sit in the discussion, either.

Response: Thank you for these comments, we have addressed them together in the introduction as follows: “Those without GMS eligibility can avail of the Drugs Payment Scheme, which caps household prescribed medication spend per month, however this yields inconsistent claims data only covering individuals in months where their household prescribed medication spend exceeds this cap. Therefore, for individuals in the privately funded cohort, there is limited information available on healthcare and medication use.”

Results:
‘The GMS population decreased from 1,735,534 in 2016 to 1,554,759 in 2020, and increased to 1,581,294 in 2021.’ Double check figures in Table 2 – states the 2016 as 1,735,524.

Response: We have now amended the figure in the text, to match the figure in Table 2 which is accurate.

Check total figures for 2019 and 2020 in Table 2.

Response: These figures have been checked and amended.

For the population pyramids, the grey legend for cohort (GMS or Population) isn’t intuitive. I think this could be removed/changed, as the Figure description is more informative.

Response: We have now removed the legend for cohort in Figure 1 and supplementary figures 1 and 2 and edited the legends slightly to further explain what each set of bars relates to: “Percentage of males and females in each age group within the GMS scheme (light-coloured, black-bordered bars) overlaid with percentage in the full population (bold-coloured, white-bordered bars) in 2016
Page 4, 2nd column, 3rd paragraph, final sentence: ‘(95% confidence interval 30.88% to 30.96%)’ these CI’s are different to that in Table 4.

Response: We have now updated the numbers in the text to match the table.

Discussion:
Authors could include a brief discussion on the practical implications of the difference in eligibility rates across age groups – the aging population and fiscal implications going forward, etc. However, this is just a suggestion – not a requirement.

Response: This is an important point and we have now added the following to the Discussion as suggested: “The overrepresentation of older adults has important fiscal implications, particularly as Ireland is projected to see significant growth in the 65 years and over age group in the coming years. Financing to support medicines expenditure in this growing older cohort is an important consideration for health care planning.”
Thanks very much for reviewing this manuscript and for the helpful comments. We have amended the manuscript based on them and have submitted an updated version incorporating these changes.

Final paragraph; include sentence to explain that there’s very limited information on healthcare use, dispensed claims, etc. in the privately funded cohort.
The authors could include a brief description of the Drugs Payment Scheme (DPS) as all persons not eligible for GMS are eligible for DPS. This could sit in the discussion, either.

Response: Thank you for these comments, we have addressed them together in the introduction as follows: “Those without GMS eligibility can avail of the Drugs Payment Scheme, which caps household prescribed medication spend per month, however this yields inconsistent claims data only covering individuals in months where their household prescribed medication spend exceeds this cap. Therefore, for individuals in the privately funded cohort, there is limited information available on healthcare and medication use.”

Results:
‘The GMS population decreased from 1,735,534 in 2016 to 1,554,759 in 2020, and increased to 1,581,294 in 2021.’ Double check figures in Table 2 – states the 2016 as 1,735,524.

Response: We have now amended the figure in the text, to match the figure in Table 2 which is accurate.

Check total figures for 2019 and 2020 in Table 2.

Response: These figures have been checked and amended.

For the population pyramids, the grey legend for cohort (GMS or Population) isn’t intuitive. I think this could be removed/changed, as the Figure description is more informative.

Response: We have now removed the legend for cohort in Figure 1 and supplementary figures 1 and 2 and edited the legends slightly to further explain what each set of bars relates to: “Percentage of males and females in each age group within the GMS scheme (light-coloured, black-bordered bars) overlaid with percentage in the full population (bold-coloured, white-bordered bars) in 2016
Page 4, 2nd column, 3rd paragraph, final sentence: ‘(95% confidence interval 30.88% to 30.96%)’ these CI’s are different to that in Table 4.

Response: We have now updated the numbers in the text to match the table.

Discussion:
Authors could include a brief discussion on the practical implications of the difference in eligibility rates across age groups – the aging population and fiscal implications going forward, etc. However, this is just a suggestion – not a requirement.

Response: This is an important point and we have now added the following to the Discussion as suggested: “The overrepresentation of older adults has important fiscal implications, particularly as Ireland is projected to see significant growth in the 65 years and over age group in the coming years. Financing to support medicines expenditure in this growing older cohort is an important consideration for health care planning.”
Competing Interests: None. Close
Report a concern
Respond or Comment

COMMENTS ON THIS REPORT

Author Response 29 Nov 2023

Frank Moriarty, Royal College of Surgeons in Ireland, Dublin, Ireland

29 Nov 2023

Author Response

Thanks very much for reviewing this manuscript and for the helpful comments. We have amended the manuscript based on them and have submitted an updated version incorporating these changes.

... Continue reading Thanks very much for reviewing this manuscript and for the helpful comments. We have amended the manuscript based on them and have submitted an updated version incorporating these changes.

Final paragraph; include sentence to explain that there’s very limited information on healthcare use, dispensed claims, etc. in the privately funded cohort.
The authors could include a brief description of the Drugs Payment Scheme (DPS) as all persons not eligible for GMS are eligible for DPS. This could sit in the discussion, either.

Response: Thank you for these comments, we have addressed them together in the introduction as follows: “Those without GMS eligibility can avail of the Drugs Payment Scheme, which caps household prescribed medication spend per month, however this yields inconsistent claims data only covering individuals in months where their household prescribed medication spend exceeds this cap. Therefore, for individuals in the privately funded cohort, there is limited information available on healthcare and medication use.”

Results:
‘The GMS population decreased from 1,735,534 in 2016 to 1,554,759 in 2020, and increased to 1,581,294 in 2021.’ Double check figures in Table 2 – states the 2016 as 1,735,524.

Response: We have now amended the figure in the text, to match the figure in Table 2 which is accurate.

Check total figures for 2019 and 2020 in Table 2.

Response: These figures have been checked and amended.

For the population pyramids, the grey legend for cohort (GMS or Population) isn’t intuitive. I think this could be removed/changed, as the Figure description is more informative.

Response: We have now removed the legend for cohort in Figure 1 and supplementary figures 1 and 2 and edited the legends slightly to further explain what each set of bars relates to: “Percentage of males and females in each age group within the GMS scheme (light-coloured, black-bordered bars) overlaid with percentage in the full population (bold-coloured, white-bordered bars) in 2016
Page 4, 2nd column, 3rd paragraph, final sentence: ‘(95% confidence interval 30.88% to 30.96%)’ these CI’s are different to that in Table 4.

Response: We have now updated the numbers in the text to match the table.

Discussion:
Authors could include a brief discussion on the practical implications of the difference in eligibility rates across age groups – the aging population and fiscal implications going forward, etc. However, this is just a suggestion – not a requirement.

Response: This is an important point and we have now added the following to the Discussion as suggested: “The overrepresentation of older adults has important fiscal implications, particularly as Ireland is projected to see significant growth in the 65 years and over age group in the coming years. Financing to support medicines expenditure in this growing older cohort is an important consideration for health care planning.”
Thanks very much for reviewing this manuscript and for the helpful comments. We have amended the manuscript based on them and have submitted an updated version incorporating these changes.

Final paragraph; include sentence to explain that there’s very limited information on healthcare use, dispensed claims, etc. in the privately funded cohort.
The authors could include a brief description of the Drugs Payment Scheme (DPS) as all persons not eligible for GMS are eligible for DPS. This could sit in the discussion, either.

Response: Thank you for these comments, we have addressed them together in the introduction as follows: “Those without GMS eligibility can avail of the Drugs Payment Scheme, which caps household prescribed medication spend per month, however this yields inconsistent claims data only covering individuals in months where their household prescribed medication spend exceeds this cap. Therefore, for individuals in the privately funded cohort, there is limited information available on healthcare and medication use.”

Results:
‘The GMS population decreased from 1,735,534 in 2016 to 1,554,759 in 2020, and increased to 1,581,294 in 2021.’ Double check figures in Table 2 – states the 2016 as 1,735,524.

Response: We have now amended the figure in the text, to match the figure in Table 2 which is accurate.

Check total figures for 2019 and 2020 in Table 2.

Response: These figures have been checked and amended.

For the population pyramids, the grey legend for cohort (GMS or Population) isn’t intuitive. I think this could be removed/changed, as the Figure description is more informative.

Response: We have now removed the legend for cohort in Figure 1 and supplementary figures 1 and 2 and edited the legends slightly to further explain what each set of bars relates to: “Percentage of males and females in each age group within the GMS scheme (light-coloured, black-bordered bars) overlaid with percentage in the full population (bold-coloured, white-bordered bars) in 2016
Page 4, 2nd column, 3rd paragraph, final sentence: ‘(95% confidence interval 30.88% to 30.96%)’ these CI’s are different to that in Table 4.

Response: We have now updated the numbers in the text to match the table.

Discussion:
Authors could include a brief discussion on the practical implications of the difference in eligibility rates across age groups – the aging population and fiscal implications going forward, etc. However, this is just a suggestion – not a requirement.

Response: This is an important point and we have now added the following to the Discussion as suggested: “The overrepresentation of older adults has important fiscal implications, particularly as Ireland is projected to see significant growth in the 65 years and over age group in the coming years. Financing to support medicines expenditure in this growing older cohort is an important consideration for health care planning.”
Competing Interests: None. Close
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Version 2

VERSION 2 PUBLISHED 20 Oct 2022

Open Peer Review

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Reviewer Reports

	Invited Reviewers
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Version 2 (revision) 18 Oct 23
Version 1 20 Oct 22	read	read

Amelia Smith, St. James’ Hospital, Dublin, Ireland; St. James’ Hospital, Dublin, Ireland
Geoffrey M. Curran, University of Arkansas for Medical Sciences, Little Rock, USA

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Reviewer Report

39 Views

13 Sep 2023 | for Version 1

Geoffrey M. Curran, Department of Pharmacy Practice, University of Arkansas for Medical Sciences, Little Rock, AR, USA

39 Views Cite this report Responses(1)

Approved

Is the work clearly and accurately presented and does it cite the current literature?

Yes
Is the study design appropriate and is the work technically sound?

Yes
Are sufficient details of methods and analysis provided to allow replication by others?

Yes
If applicable, is the statistical analysis and its interpretation appropriate?

I cannot comment. A qualified statistician is required.
Are all the source data underlying the results available to ensure full reproducibility?

Yes
Are the conclusions drawn adequately supported by the results?

Yes

Competing Interests

No competing interests were disclosed.

Reviewer Expertise

Health services research, implementation science

I confirm that I have read this submission and believe that I have an appropriate level of expertise to confirm that it is of an acceptable scientific standard.

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Responses (1)

Author Response

29 Nov 2023

Frank Moriarty, Royal College of Surgeons in Ireland, Dublin, Ireland

Thanks very much for reviewing this manuscript and for the helpful comments. We have amended the manuscript based on them and have submitted an updated version incorporating these changes.

Comments
There were several areas where I would have liked more description and/or discussion. I would have appreciated more information on the basis of (or assumptions applied for) the projections for the "out years." (I was not sure what the authors meant by "rated were directly standardized" to the 2016 population).

Response: We have elaborated on the CSO’s methods for estimating population projects: “The CSO produce these by trending forwards Census data, based on the number of males and females in each region by single year of age, each person was aged by one year, births and number of immigrants were added, and deaths and number of emigrants were subtracted).” We have also provided more detail about the direct standardisation in the Analysis section: “The crude eligibility rate per year was calculated, and the yearly rates were also directly standardised to the 2016 population based on age group and sex (i.e. eligibility rates in each age-sex strata were weighted according to the age-sex distribution in 2016 to estimate the eligibility rate had the age-sex structure of population remained the same as 2016).”

I also could have used some more information on the possible economic or other social forces that could have been driving the projected changes in eligibility and geographic differences.

Response: We have now added detail on the likely explanation for eligibility changes over time (in addition to the existing discussion of geographical deprivation and eligibility): “The income threshold for GMS eligibility remained the same from 2013 to November 2020, suggesting the declining eligibility rate may have been due to improved financial circumstances of individuals. The maximum income threshold was increased in November 2020, which may explain the slight increase in eligibility rate from 2020 to 2021, and this could also be partially attributable to the adverse financial impact of the COVID-19 pandemic¹³.”

Also, I am not sure that the readers were given a reason why they were required to group several counties.

Response: We have now expanded the explanation of this in the Methods: For April 2016, information at HSE Local Health Office (LHO) level (i.e. geographical areas for the administration of healthcare entitlements) was also obtained. As LHOs do not correspond directly to counties (i.e. areas for reporting GMS eligibility may incorporate (parts of) multiple counties which is the basis for population numbers), broader areas combining counties and/or LHOs were created to provide equivalence for analysis purposes (see Table 1).

I also could have used a brief discussion of how specifically these data could be used in the "planning and conduct of research focusing on GMS-eligible individuals."

Response: We added some detail on potential research applications for the findings of this study: “The analysis presented may inform future research, for example for sample size estimates if including only GMS-eligible patients or extrapolating findings from analysis of GMS-eligible patients to the wider population.”

In general, I found the tables followable and useful. I struggled some with the figures. It took me a while see and understand the white/black bar distinction.

Response: As suggested by reviewer 1, we have removed the GMS/full population part of the legend, leaving the legend description which is hopefully clearer.

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Competing Interests

None.

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Reviewer Report

51 Views

22 Nov 2022 | for Version 1

51 Views Cite this report Responses(1)

Approved

Well done to the authors on a well-written and informative study.

Introduction:

Final paragraph; include sentence to explain that there’s very limited information on healthcare use, dispensed claims, etc. in the privately funded cohort.
The authors could include a brief description of the Drugs Payment Scheme (DPS) as all persons not eligible for GMS are eligible for DPS. This could sit in the discussion, either.

Results:

‘The GMS population decreased from 1,735,534 in 2016 to 1,554,759 in 2020, and increased to 1,581,294 in 2021.’ Double check figures in Table 2 – states the 2016 as 1,735,524.
Check total figures for 2019 and 2020 in Table 2.
For the population pyramids, the grey legend for cohort (GMS or Population) isn’t intuitive. I think this could be removed/changed, as the Figure description is more informative.
Page 4, 2^nd column, 3^rd paragraph, final sentence: ‘(95% confidence interval 30.88% to 30.96%)’ these CI’s are different to that in Table 4.

Discussion:

Authors could include a brief discussion on the practical implications of the difference in eligibility rates across age groups – the aging population and fiscal implications going forward, etc. However, this is just a suggestion – not a requirement.

Is the work clearly and accurately presented and does it cite the current literature?

Yes
Is the study design appropriate and is the work technically sound?

Yes
Are sufficient details of methods and analysis provided to allow replication by others?

Yes
If applicable, is the statistical analysis and its interpretation appropriate?

Yes
Are all the source data underlying the results available to ensure full reproducibility?

Yes
Are the conclusions drawn adequately supported by the results?

Yes

Competing Interests

No competing interests were disclosed.

Reviewer Expertise

Drug utilisation studies using PCRS pharmacy claims data. Health technology management.

I confirm that I have read this submission and believe that I have an appropriate level of expertise to confirm that it is of an acceptable scientific standard.

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Responses (1)

Author Response

29 Nov 2023

Frank Moriarty, Royal College of Surgeons in Ireland, Dublin, Ireland

Thanks very much for reviewing this manuscript and for the helpful comments. We have amended the manuscript based on them and have submitted an updated version incorporating these changes.

Final paragraph; include sentence to explain that there’s very limited information on healthcare use, dispensed claims, etc. in the privately funded cohort.
The authors could include a brief description of the Drugs Payment Scheme (DPS) as all persons not eligible for GMS are eligible for DPS. This could sit in the discussion, either.

Response: Thank you for these comments, we have addressed them together in the introduction as follows: “Those without GMS eligibility can avail of the Drugs Payment Scheme, which caps household prescribed medication spend per month, however this yields inconsistent claims data only covering individuals in months where their household prescribed medication spend exceeds this cap. Therefore, for individuals in the privately funded cohort, there is limited information available on healthcare and medication use.”

Results:
‘The GMS population decreased from 1,735,534 in 2016 to 1,554,759 in 2020, and increased to 1,581,294 in 2021.’ Double check figures in Table 2 – states the 2016 as 1,735,524.

Response: We have now amended the figure in the text, to match the figure in Table 2 which is accurate.

Check total figures for 2019 and 2020 in Table 2.

Response: These figures have been checked and amended.

For the population pyramids, the grey legend for cohort (GMS or Population) isn’t intuitive. I think this could be removed/changed, as the Figure description is more informative.

Response: We have now removed the legend for cohort in Figure 1 and supplementary figures 1 and 2 and edited the legends slightly to further explain what each set of bars relates to: “Percentage of males and females in each age group within the GMS scheme (light-coloured, black-bordered bars) overlaid with percentage in the full population (bold-coloured, white-bordered bars) in 2016
Page 4, 2nd column, 3rd paragraph, final sentence: ‘(95% confidence interval 30.88% to 30.96%)’ these CI’s are different to that in Table 4.

Response: We have now updated the numbers in the text to match the table.

Discussion:
Authors could include a brief discussion on the practical implications of the difference in eligibility rates across age groups – the aging population and fiscal implications going forward, etc. However, this is just a suggestion – not a requirement.

Response: This is an important point and we have now added the following to the Discussion as suggested: “The overrepresentation of older adults has important fiscal implications, particularly as Ireland is projected to see significant growth in the 65 years and over age group in the coming years. Financing to support medicines expenditure in this growing older cohort is an important consideration for health care planning.”

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Competing Interests

None.

Alongside their report, reviewers assign a status to the article:

Approved - the paper is scientifically sound in its current form and only minor, if any, improvements are suggested

Approved with reservations - A number of small changes, sometimes more significant revisions are required to address specific details and improve the papers academic merit.

Not approved - fundamental flaws in the paper seriously undermine the findings and conclusions

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Eligibility rates and representativeness of the General Medical Services scheme population in Ireland 2016-2021: A methodological report

Abstract

Background

Methods

Results

Conclusions

Keywords

Revised Amendments from Version 1

Introduction

Methods

Data sources

Table 1. Local health offices (LHOs), Central Statistics Office (CSO) areas, and areas used for analysis.

Analysis

Results

Table 2. Characteristics of the General Medical Services (GMS) population over time.

Table 3. General medical services (GMS) scheme eligibility rate and relative rate over time.

Figure 1. Percentage of males and females in each age group within the General Medical Services (GMS) scheme (light-coloured, black-bordered bars) overlaid with percentage in the full population (bold-coloured, white-bordered bars) in 2016.

Figure 2. Percentage of males and females in each age group within the General Medical Services (GMS) scheme (light-coloured, black-bordered bars) overlaid with percentage in the full population (bold-coloured, white-bordered bars) for 2016-2021.

Table 4. General Medical Services (GMS) scheme crude eligibility rate over time, and rate directly standardised to 2017 population based on age group and sex.

Table 5. General Medical Services (GMS) scheme crude eligibility rate by area for 2016, and rate directly standardised to national population based on age group and sex.

Figure 3. Percentage of males and females in each age group within the GMS scheme (light-coloured, black-bordered bars) overlaid with percentage in the full population (bold-coloured, white-bordered bars) for 2016 across geographic areas.

Figure 4. Eligibility rate for the General Medical Services (GMS) scheme by geographical area for 2016, standardised to the national population by age group and sex.

Discussion

Limitations

Conclusion

Data availability

Underlying data

Acknowledgements

References

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Eligibility rates and representativeness of the General Medical Services scheme population in Ireland 2016-2021: A methodological report

Abstract

Background

Methods

Results

Conclusions

Keywords

Revised Amendments from Version 1

Introduction

Methods

Data sources

Table 1. Local health offices (LHOs), Central Statistics Office (CSO) areas, and areas used for analysis.

Analysis

Results

Table 2. Characteristics of the General Medical Services (GMS) population over time.

Table 3. General medical services (GMS) scheme eligibility rate and relative rate over time.

Figure 1. Percentage of males and females in each age group within the General Medical Services (GMS) scheme (light-coloured, black-bordered bars) overlaid with percentage in the full population (bold-coloured, white-bordered bars) in 2016.

Figure 2. Percentage of males and females in each age group within the General Medical Services (GMS) scheme (light-coloured, black-bordered bars) overlaid with percentage in the full population (bold-coloured, white-bordered bars) for 2016-2021.

Table 4. General Medical Services (GMS) scheme crude eligibility rate over time, and rate directly standardised to 2017 population based on age group and sex.

Table 5. General Medical Services (GMS) scheme crude eligibility rate by area for 2016, and rate directly standardised to national population based on age group and sex.

Figure 3. Percentage of males and females in each age group within the GMS scheme (light-coloured, black-bordered bars) overlaid with percentage in the full population (bold-coloured, white-bordered bars) for 2016 across geographic areas.

Figure 4. Eligibility rate for the General Medical Services (GMS) scheme by geographical area for 2016, standardised to the national population by age group and sex.

Discussion

Limitations

Conclusion

Data availability

Underlying data

Acknowledgements

References

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