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. 2026 Jul 14;26:992. doi: 10.1186/s12913-026-15093-x

Regional variance in socioeconomic status among Dutch intensive care units: a nationwide cohort study of 588,568 ICU admissions

Daniëlle J M Koornneef 1,2,✉,#, Jip W T M de Kok 3,4,✉,#, Fabian Termorshuizen 1,2, Ronny M Schnabel 3, Nicolette F de Keizer 1,2, Iwan C C van der Horst 4,5, Bas C T van Bussel 1,3,4,6,#, Sylvia Brinkman 1,2,#
PMCID: PMC13374232  PMID: 42449368

Abstract

Background

Low socioeconomic status (SES) is a known determinant for higher disease incidence and adverse health outcomes. However, the geographical distribution of SES and its impact on intensive care unit (ICU) populations remains unclear. This study aims to examine SES differences across Dutch acute care regions, hospital types, and patient characteristics and to build a national ICU cohort to improve comparisons of intensive care quality and identify potential health inequalities.

Methods

A nationwide cohort study was conducted, linking clinical data of the National Intensive Care Evaluation registry from all Dutch ICU admissions (2014–2022) with SES scores from Statistics Netherlands. The SES score was based on wealth, education, and employment component scores per household. SES distributions were compared across regions, hospital types and volumes, admission types, and number of chronic conditions.

Results

Of 623,838 eligible ICU admissions, 588,568 (94.3%) were successfully linked to SES scores. Differences in SES were observed between regions. The Southwest and Limburg regions showed the lowest mean SES, while Middle and Zwolle regions showed the highest. Lower SES was observed in admissions to non-academic hospitals, smaller volume hospitals, medical admissions, and patients with chronic conditions.

Conclusion

Despite the robust healthcare system, socioeconomic disparities were observed among Dutch ICUs and regions in this novel ICU cohort, highlighting the importance of considering SES when comparing ICUs. Our findings underscore the need for further research into the effects of SES on ICU outcomes within this and other cohorts, including studies in different countries. Additionally, these results suggest that incorporating SES into the ICU performance benchmarking is crucial for fair comparisons across diverse healthcare settings.

Supplementary Information

The online version contains supplementary material available at 10.1186/s12913-026-15093-x.

Keywords: Socioeconomic disparities in health, Critical care, Public health, Regional health, Health inequalities

Introduction

Low socioeconomic status (SES) is a known determinant of adverse health outcomes [1–9]. SES is defined as the relative position in society and typically consists of multiple components, such as wealth, education, and employment [10]. Individuals with lower SES have a higher incidence of various diseases, more interactions with healthcare providers, and more adverse health outcomes in general [3, 5–7, 11]. Health inequalities between socioeconomic groups are present in high- as well as low-income countries and are not driven solely by differences in economic wealth, but also by differences in social skills and level of education [3]. The difficulty in applying a simple definition to SES, compounded by the high administrative burden of collecting it, and the sensitive and confidential nature of SES data, mean that studying and quantifying SES is challenging. Consequently, information on SES is lacking in electronic health records (EHRs). Indeed, although population-wide SES data are registered in the Netherlands, these have not been linked with EHRs.

Research on SES in the intensive care unit (ICU) has shown that lower SES is associated with various patient characteristics such as a higher disease severity, type of admission diagnoses, more chronic conditions, and also higher mortality [12–17]. However, further research on SES across large ICU populations, also in other countries, is required because the distribution of specific socioeconomic groups among regions and hospitals remains unclear. Also, it should be investigated whether socioeconomic disparities contribute to differences in health outcomes in ICU populations. Typically, these differences in terms of the quality of care, type of care, patient demographics, and clinical characteristics are compared through ICU quality registries. Collecting nationwide data on ICU patients allows these quality registries to compare individual ICUs and provides insight into the distribution and quality of care to identify potential are for improvement.

To enable fair comparison of ICUs and regions, differences in ICU-specific patient populations should be considered. Therefore, many clinical factors such as age, admission type, acute physiology, chronic conditions, and admission diagnosis are typically used for case-mix correction [18–21]. Although SES is an important determinant of health outcomes, it has not been incorporated into comparisons of Dutch ICU performance. The practical challenge of quantifying SES and integrating it with healthcare data likely contributes to this. Importantly, integrating SES with clinical data might reveal health inequalities among ICUs and regions. Therefore, we hypothesise that SES is distributed unequally among ICUs and regions, and has multiple determinants, such as hospital size and type, and patient characteristics, including the type of admission and chronic conditions. If SES is distributed unequally across ICUs and regions, further research on differences in ICU patient populations and SES-adjusted health outcomes should be undertaken. Therefore, we aimed to build a national ICU cohort containing clinical patient data and SES information to elucidate the distribution of SES among Dutch ICUs and regions and investigated the relationship between SES and hospital and patient characteristics.

Methods

The data

This study built a national ICU cohort integrating patient and hospital data of all Dutch ICUs with national SES data. All 703,773 Dutch ICU admissions of patients aged ≥ 16 years registered in the national intensive care evaluation (NICE) clinical quality registry [19] from 1/1/2014 to 31/12/2022 were transferred to the Statistics Netherlands secure remote access environment to be enriched with SES scores. Data are collected from all Dutch ICU patients admitted to one of the 79 ICUs. The minimally required set of variables are systematically extracted from the EHR and include patient demographics, admission types, discharge status, physiology, laboratory measurements (minimum and maximum values during the first 24 hours of ICU admission), and mortality. In addition, chronic conditions and reasons for admission are currently entered manually, while the length of stay and severity of illness scores are calculated based on the extracted variables [19]. ICU patient transfers between hospitals and separate hospital admissions of the same patient were considered as separate ICU admissions. Admissions that met the APACHE IV exclusion criteria [22] were excluded. For example, for patients who were readmitted to the ICU without leaving the hospital, only the first ICU admission was included in the analysis. Furthermore, data that did not fulfil the benchmark criteria were excluded. Benchmark compliance is assessed during site visits by comparing a random sample of EHR records with submitted data. ICUs that do not meet the NICE benchmark criteria are excluded from research datasets for at least one year, until the data quality is proven to be sufficient. The benchmark exclusion criteria are described in the Supplementary Methods section.

To unify and standardise the data collection, a data dictionary is used to define all registered variables [23]. A more detailed description of data preparation is provided in the Supplementary Methods section. Patient admissions were deterministically linked to their households using specific variables (i.e., hospital of admission, date of birth, sex, postal code, hospital and ICU admission dates, and hospital and ICU discharge dates). Patients who could not be linked to a household or had no SES data were excluded. To gain access to the secure remote access environment of Statistics Netherlands, we followed the Statistics Netherlands procedure, which complies with the General Data Protection Regulation (GDPR) [24].

The SES score

Statistics Netherlands calculated the SES scores at the household level for each Dutch inhabitant from 2014 to 2022. The SES score is based on three component scores; wealth, education level, and employment history. These data were obtained by Statistics Netherlands using multiple sources, including municipal population registers, the Tax and Customs Administration, the Education Executive Agency, the Employee Insurance Agency, business records, and the Ministry of Education, Culture and Science [10]. The SES score ranges between −2.3 and 1.0 and has a non-normal distribution. SES is relative to a mean SES of zero for the complete Dutch population in 2019, with higher scores indicating a more favourable SES. The education component score describes the highest level of education achieved by the main financial provider of a household or a registered partner, using five categories (ranging from the lowest to the highest level of education, defined as; elementary education, lower secondary education, higher secondary education, bachelor’s degree, and master’s degree). The employment component score per household is based on employment status of the main financial provider over the past four years. The employment status is divided into seven categories (ranging from lowest to highest, defined as; not working, employed between 1 month and 1 year, employed between 1 and 2 years, employed between 2 and 3 years, employed between 3 and 4 years, retired, and continuously employed). The wealth component score consists of the household's standardised spendable income and assets (e.g., savings, real estate, and debts) and is adjusted for household size and composition using the Organisation for Economic Co-operation and Development (OECD) equivalence scale. Wealth is divided into deciles, with higher deciles representing more wealth. The SES score is derived through multiple correspondence analysis (MCA) using the three component scores as inputs [10], for the exact MCA coefficients please see Supplementary Table S1.

To minimise the effect of missing SES data, we used SES scores from the year before ICU admission or otherwise within 2 years before ICU admission. SES scores after ICU admission were not used because SES can be affected by the course of the condition that led to the ICU admission. All analyses were performed using non-public Microdata within the remote access environment of Statistics Netherlands, using R version 4.2.3. Access in the database of Statistics Netherlands was limited to the SES and component scores of the linked admissions. The secondary use of data for scientific research is part of formal agreements with the participating hospitals, which was applicable to the present study. The Medical Research Ethics Review Committee of Amsterdam UMC waived the need for informed consent [reference number: 2023.0259]. We followed the STrengthening the Reporting of OBservational studies in Epidemiology (STROBE) and The Reporting of studies Conducted using Observational Routinely-collected health Data (RECORD) guidelines, and the requirements were met as shown in Supplementary Table S2.

Statistical analysis

The mean SES scores of admitted ICU patients were compared between Dutch regions, hospital volumes, hospital types, patient types, the number of chronic conditions, and time periods. The categorisation of regions was based on the 11 Dutch acute care regions (in Dutch, Regionaal Overleg Acute Zorgketen (ROAZ)). Patient admissions were allocated to the region of the ICU, independent of their place of residence. The SES of the total ICU population was categorised into deciles to compare the distribution of these scores across regions. Differences in SES decile distributions were expressed as absolute differences compared to the total study population in percentage points. To facilitate interpretation, these absolute differences were, where relevant, also expressed as relative differences (relative to the size of a decile, which is approximately 10%). For example, a difference of 2% points in a SES decile corresponds to a relative difference of 20% (i.e., Inline graphic) compared to the total study population. The mean SES score per region was compared across calendar years to assess the potential impact of region-specific changes in SES over time. Hospital volume was categorised into quintiles based on the average annual number of ICU admissions, and referred to as very small, small, medium, large, and very large hospitals. Hospital type describes academic teaching hospitals, non-academic teaching hospitals, and general peripheral hospitals. Patient types were categorised into emergency surgical (surgery could not be postponed for more than 12 hours), planned surgical (surgery could be postponed more than 12 hours), and medical admissions (patients admitted without the involvement of the operation room or recovery unit). Chronic conditions have been precisely described in the NICE data dictionary [23], and include chronic cardiovascular insufficiency, cirrhosis, diabetes mellitus, immunological insufficiency and acquired immunodeficiency syndrome (AIDS) combined, chronic obstructive pulmonary disease (COPD) and chronic respiratory insufficiency combined, chronic renal insufficiency and chronic dialysis combined, and neoplasms and haematological malignancy combined. The number of chronic conditions describes whether a patient had none, one, or more than one chronic condition before hospitalisation. Severity of illness is quantified using the Acute Physiology and Chronic Health Evaluation (APACHE) IV and corresponding mortality risk [22]. No p-values were calculated for this study as they could be potentially misleading and provide little added value to the analyses, particularly given the large sample size [25]. Therefore, the results focus on clinical relevance and effect sizes.

Results

Linkage

Of the 703,773 available ICU admissions from all 79 ICUs in the NICE registry from 2014 to 2022, 64,792 (9.2%) met the APACHE IV exclusion criteria [22], and 15,143 (2.2%) met the NICE benchmark exclusion criteria. Of the remaining 623,838 admissions, 18,082 (2.9%) could not be linked to a Dutch household by Statistics Netherlands, while another 17,188 (2.8%) had no available SES data within 2 years prior to admission. The study population included 588,568 admissions (Fig. 1). Due to benchmark exclusion, nine hospitals were excluded for at least one year; three hospitals were excluded for one year, three for two years, one for three years, one for five years, and one for six years.

Fig. 1.

Fig. 1

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Flowchart of the inclusion criteria of the study population

Compared to all NICE admissions, the study population was less severely ill (APACHE IV mortality risk: 16.3% vs. 17.2%), had a lower hospital mortality rate (12.1% vs. 13.2%), and consisted of more planned admissions (34.1% vs. 31.1%) (Table 1). Differences in SES scores were observed in APACHE IV and benchmark exclusion steps; however, the SES score remained similar between all NICE admissions and the study population (Supplementary Table S3).

Table 1.

Descriptive statistics of all National Intensive Care Evaluation (NICE) admissions and study population used in this study

All NICE admissions
(N = 703,773)		 Study population
(N = 588,568)		 Mean difference
SES, mean (SD) −0.19 (0.77) −0.19 (0.77) 0.00
Wealth component score −0.03 (0.36) −0.03 (0.36) 0.00
Education component score −0.08 (0.31) −0.08 (0.31) 0.00
Employment component score −0.08 (0.32) −0.08 (0.32) 0.00
Age, mean (SD) 63.13 (15.66) 63.51 (15.32) 0.38
Sex (male), N (%) 428,619 (60.90%) 358,847 (60.97%) 0.07
Missing, N 41 0
BMI, mean (SD) 26.85 (5.54) 26.91 (5.50) −0.05
Planned admission, N (%) 219,120 (31.14%) 200,427 (34.05%) 2.92
Mechanical ventilation (within first 24 h), N (%) 339,276 (48.21%) 289,329 (49.16%) 0.95
ICU LOS, mean (SD) 3.36 (7.35) 3.29 (6.99) −0.07
Hospital LOS, mean (SD) 13.25 (17.00) 12.13 (14.50) −1.13
APS score, mean (SD) 44.95 (26.32) 44.65 (25.75) −0.31
APACHE IV mortality risk, mean (SD) 17.21% (22.78) 16.33% (22.25) −0.89
Hospital mortality, N (%) 93,038 (13.22%) 71,479 (12.14%) −1.08
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The mean difference column indicates the mean difference between the study population and the set of all NICE admissions in percentage points. SES Socioeconomic status, BMI Body mass index, ICU Intensive care unit, LOS Length of Stay, APS Acute Physiology Score, APACHE Acute Physiology And Chronic Health Evaluation

Regional differences

Differences in mean SES between the 11 Dutch acute care regions are shown in Fig. 2. Regions Southwest, Limburg, and SpoedZorgNet Academisch Medisch Centrum (AMC) had the lowest mean SES scores and Middle, Zwolle, and Brabant regions had the highest. The distribution of SES deciles among the 11 Dutch acute care regions is illustrated in Fig. 3. The deciles did not contain exactly 10% of the ICU population because of the discrete nature of the SES score. Deciles 1 to 5 were below the average of the general Dutch population (negative SES), deciles 7 to 10 were above this average (positive SES), and decile 6 contained both negative and positive values. Region East was most similar to the total study population. The lowest SES regions Southwest and Limburg had different underlying SES distributions. Region Southwest had 2.1% points more admissions in the lowest SES decile, corresponding to a relative increase of 21%, while Limburg had approximately 4.4% points fewer admissions in the 2 highest SES deciles combined, a relative decrease of 22%. In contrast, the Middle region had 5.3% points more admissions in the two highest SES deciles combined (a relative increase of 27%) and 5.9% points fewer admissions in the 4 lowest deciles combined (a relative decrease of 14%). Region Zwolle had 3.6% points fewer admissions in the lowest 2 deciles combined, corresponding to a relative decrease of 18%. The distribution of the SES component scores per Dutch region are given in the Supplementary figures S1 – S3. Region Southwest scored lowest on the wealth component score, while Limburg scored lowest on the education and employment component scores. The middle region was highest on all component scores. All regions experienced fluctuations in SES over time, however, these fluctuations were relatively consistent among regions (Supplementary Fig. S4).

Fig. 2.

Fig. 2

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Choropleth map indicating the mean socioeconomic status (SES) score for each of the 11 Dutch acute care regions. AMC SpoedZorgNet Academisch Medisch Centrum

Fig. 3.

Fig. 3

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Distribution of socioeconomic status (SES) deciles per Dutch acute care region, with deciles determined on the total study population. The lollipop charts indicate the percentage point difference of occurrence of each decile compared to the total study population, where orange dots indicate an increase and blue dots indicate a decrease compared to the total study population (a-k). The bar chart illustrates the distribution of the SES deciles in the total study population. Each bar indicates what percentage of the population belongs to the corresponding decile (l). All plots share the same x-axis, with each tick representing a SES decile, ranging from lowest to highest SES. The value range of each decile is indicated by the numbered bottom legend. The acute care regions (a-k) are sorted from lowest to highest mean SES. SES socioeconomic status, AMC SpoedZorgNet Academisch Medisch Centrum

Characteristics of the study population

The study population had a mean SES of −0.19 and wealth, education, and employment scores of −0.03, −0.08, and −0.08, respectively. Most of the study population consisted of patients who were retired (54%), had higher secondary education (41%), and patients from wealth deciles 2 and 3 (12.3% and 12.5%, respectively). The distributions per component score are shown in supplementary figures S1–S3. The Southwest region had the lowest mean SES score of −0.26, the lowest wealth component score of −0.07, and had 9.5% points fewer planned admissions compared to the total study population. The Limburg region had the lowest education and employment component scores of −0.11 and −0.10 respectively. ICU patients in the Limburg region were on average 1.2 years older and had 7.6% points fewer planned admissions. The Middle region had the highest SES wealth, education, and employment scores at 0.03, −0.05, and −0.06, respectively. The Middle region had 13.2% points more planned admissions and 22.6% points more admissions were treated in the largest hospital volume group. Region Zwolle had the second−highest mean SES wealth and employment scores of −0.13 and −0.06, respectively, and 5.1% points more admissions without chronic conditions (Fig. 4). A more detailed comparison is provided in Supplementary table S4.

Fig. 4.

Fig. 4

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Heatmap of hospital and patient characteristics across 11 Dutch acute care regions. The linear colour scale, as depicted in the bottom legend, illustrates the differences between the regions for each characteristic (per row), ranging from dark blue (the lowest value of a characteristic) to light blue (the highest value of a characteristic). The acute care regions were sorted from the lowest to the highest mean SES. The hospital size quintile values in terms of average number of ICU admissions per year are as follows: very small (≤344); small (345–607); medium (608–840); large (841–1,762); and very large (>1,762). AMC SpoedZorgNet Academisch Medisch Centrum, SES socioeconomic status

Differences in patient and hospital characteristics

The differences in the SES score distribution across hospital types, volume groups, admission types, and number of chronic conditions are shown in Fig. 5. Teaching (academic) hospitals showed more admissions with higher SES, and fewer with lower SES, opposing results were found for general peripheral hospitals. Teaching (non-academic) hospitals had a comparable distribution of SES deciles to the total study population. Very large hospitals had more admissions with high SES. Planned surgical admissions had the highest SES, whereas medical admissions comprised patients with lower SES. Admissions without chronic conditions had higher SES than those with one or more chronic conditions. In the general peripheral hospitals, 24.4% of admission were planned surgeries, this was 41.7% and 46.5% for non-academic and academic teaching hospitals respectively. These hospital and patient characteristics are comprehensively described in Supplementary tables S5 – S8. The hospital types and volumes illustrated different SES distributions and were unevenly distributed across regions (Fig. 4). Supplementary figures S5 – S6 show that differences in mean SES were also observed between individual ICUs and demonstrate that very large and academic teaching hospitals typically treat patients with higher SES.

Fig. 5.

Fig. 5

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Line chart of the occurrence rate of socioeconomic status (SES) deciles for different hospital types (a), volume groups (b), admission types (c), and number of chronic conditions (d). The dots represent the occurrence rate of SES score deciles for the total study population. A higher occurrence rate indicates more observations in the according SES decile. The lines are linear interpolations between points for an intuitive visual comparison of SES distribution. The SES deciles were based on the total study population. The value range of each SES decile is indicated by the numbered bottom legend. The hospital size quintile values in terms of average number of ICU admissions per year are as follows: very small (≤344); small (345–607); medium (608–840); large (841–1,762); and very large (>1,762)

Discussion

In this comprehensive nationwide cohort study, we successfully integrated clinical data from 588,568 Dutch ICU admissions from the NICE registry with household-level SES information from Statistics Netherlands, providing novel insights into socioeconomic disparities across Dutch ICUs and acute care regions. Our findings reveal evident variations in SES distributions, not only between geographical regions and individual ICUs, but also across different hospital types, hospital volumes, admission types, and number of chronic conditions. By demonstrating the feasibility of linking large-scale clinical datasets with national socioeconomic data, this study establishes a robust foundation for future research into the complex interplay between SES and critical care outcomes, in both the Netherlands and for other countries.

The mean SES of the study population was −0.19, indicating that the Dutch ICU population had a lower mean SES compared to the general Dutch population in 2019. These findings comply with previous research, where total healthcare costs and healthcare utilisation were higher in low-education and low-income groups [26–29]. This might be attributed to a higher prevalence of chronic diseases and other risk factors such as unhealthy lifestyle [26, 30–32]. Previous studies have shown that SES varies across hospitals and regions [33–35]. Our study builds on this evidence by using nationwide data to examine SES at the household level, rather than relying on regional proxies, and by disentangling SES into the component domains of wealth, employment, and education across Dutch acute care regions. Differences were observed in SES and component scores across Dutch acute care regions, despite the Netherlands’ prosperity and well-organised healthcare system. Although the Southwest and Limburg regions had similarly low mean SES scores, differences were observed in component scores and the distribution of SES. The Southwest region had the lowest wealth component score, while Limburg had the lowest education and employment component scores. These differences were also reflected in the SES distribution as the Limburg region had fewer ICU patients with very high SES, while the Southwest region had more patients with very low SES. These regional differences in SES are substantial. Therefore, policymakers and healthcare providers should consider these findings for preventive measures, healthcare delivery, and ICU benchmarking. For example, regions with a higher proportion of patients with low education levels might benefit from simplified discharge communication, enhanced coordination with primary care, or an educational campaign on health literacy [36, 37]. Alternatively, areas with higher unemployment rates might benefit from additional social support.

Differences in SES were also observed between hospital types, volume groups, admission types, and number of chronic conditions. Academic hospitals treat a greater proportion of higher SES patients compared to non-academic hospitals. This pattern likely results from an interplay of multiple factors. First, hospital choice is influenced by geographical proximity [38, 39], which typically results in admission to the nearest hospital, meaning that the SES in the surrounding area directly impacts the SES of a specific hospital. Therefore, academic hospitals, often large hospitals located in densely populated cities, will reflect the SES of the surrounding area. Alternatively, patients who need less acute care (e.g., planned surgery) generally had higher SES (Fig. 5c), an observation supported by other evidence [13, 28, 40]. Patients with higher SES also possess greater resources, be it educational, intellectual, or financial, enabling them to seek treatment in academic hospitals. However, Supplementary Table S6 shows that academic teaching hospitals only treat 4.8% points more planned surgeries compared to non-academic teaching hospitals, which suggests that this cannot be the only explanation. Further exploration of the role of geographical distance could offer additional insight. However, this would require adding geospatial data in the current cohort. Furthermore, patients with low SES more frequently presented with one or more chronic conditions (Fig. 5d), which aligns with previous evidence [13, 31]. These findings suggest that patients with lower SES may face a double burden: higher prevalence of medical conditions (Fig. 5c) and potentially greater healthcare access barriers. The trend of care concentration into fewer, larger centres further complicates these burdens by extending the patient-hospital distance and potentially altering referral practices. Taken together, these results highlight the multifaceted nature of SES in the ICU and how many different aspects are involved.

The linkage of ICU admissions from the NICE registry with Statistics Netherlands data was successful. Only some minor differences were observed between the in- and excluded admissions. Each exclusion step was evaluated for selection bias based on several patient and hospital characteristics (Supplementary Table S3). For example, admissions that could not be linked by Statistics Netherlands or lacked SES data were younger than other NICE admissions (Supplementary Table S3). This could possibly be caused by unregistered expatriate workers and tourists. Nevertheless, only 5.7% of eligible admissions could not be used, thereby, minimising potential selection bias. These findings suggest that the study population was representative of the Dutch ICU population [18].

This study also had some limitations. As this study compared data over 9 years, we evaluated the mean SES per region per year (Supplementary Fig. S4). Although SES fluctuated over the years, the relative differences between regions remained stable over the years, suggesting little impact on the aggregate results. Additionally, we specifically compared Dutch acute care regions, which are commonly used to coordinate and concentrate healthcare. This resulted in meaningful and interpretable results for regions with comparable numbers of admissions. However, as these regions are relatively large, our results might be diluted as more specific regions might exhibit even greater differences in SES. Although this study showed geographical differences in SES, these were country-specific. Therefore, these results are likely not generalisable to countries with a different SES distribution and alternative quantification and documentation of SES [3, 11]. Furthermore, ethnicity, language barriers, cultural preferences, and genetic risk factors might be involved in these observed regional differences in SES. However, these data were not available for the present study. We also recognise that the Netherlands is relatively wealthy and has an ‘access to healthcare for all’ policy [26, 41, 42]. Nevertheless, socioeconomic disparities were observed between regions and ICUs, which in this study were identified as health inequalities (i.e., geographical discrepancies in healthcare access by socioeconomic groups) and not inequalities of service, as ICU care is accessible to all Dutch inhabitants through mandatory health insurance. Assessing health inequities would require comparison of healthcare outcomes, which is out of scope for the present study.

The regional differences in SES identified in this study were substantial. In fact, the Southwest region had 2.1% points more admissions in the lowest SES decile, which, in a group containing roughly 10% of the total population, corresponds to a relative increase of approximately 21%. Similarly, the Limburg region had 4.4% points fewer admissions in the two highest SES deciles, corresponding to a decrease of approximately 22% over these two deciles. As these differences relate to the overall population mean, interregional differences are even more apparent. For example, region Middle had 5.3% points more admissions in the highest two deciles, meaning that compared to region Middle, Limburg had approximately 49% fewer admissions in the two highest SES deciles. Other countries with different healthcare policies, higher costs for patients, more pronounced wealth disparities, or lower overall wealth and living standards might have even larger differences in the distribution of SES across regions and ICUs, potentially resulting in more severe health inequalities. This underscores the importance of investigating SES in ICU populations across different countries. However, such international research is complicated by cross-country differences in how SES is defined and measured, variations in healthcare systems, and data availability. Our comprehensive, nationwide approach may serve as a valuable foundation for similar studies in other countries, thereby facilitating more robust international comparisons and contributing to a deeper understanding of health inequalities in critical care. Furthermore, individual components of SES, such as wealth, might be more suitable to compare internationally [9]. Although investigating the association between SES and ICU outcomes was beyond the scope of the present study, such analyses warrant further investigation. If SES is found to be independently associated with outcomes, this would emphasise the need for interventions and improve post-ICU support for socioeconomically disadvantaged groups. However, caution is required when considering SES adjustment in performance metrics. Some have argued that adjusting outcome measures for SES may unintentionally allow poorer outcomes for disadvantaged populations to be considered acceptable, potentially reinforcing existing health disparities [8]. Different approaches may be appropriate depending on the objective of the study, such as identifying disparities, benchmarking ICU performance, or improving the quality of healthcare systems. This study focused on SES-related disparities in intensive care across regions, highlighting the importance of linking socioeconomic information to critical care data.

Conclusion

This study’s successful integration of a national ICU data registry with household-level SES data revealed socioeconomic disparities among Dutch ICUs and regions. Notably, patients with higher SES were more often admitted to large academic teaching hospitals, had fewer chronic conditions, and more frequently underwent planned surgeries. Substantial regional differences in mean SES as well as variations in SES distribution were observed. These findings suggest that adjustment for SES could improve comparison of ICUs and regions to ensure fair and accurate healthcare benchmarking, provide valuable insights about equitable healthcare decision-making, and identify potential areas for improvement. Hence, further research is required into how SES disparities might affect comparisons between ICUs and regions. Despite the prosperity and ‘access to healthcare for all’ policy in the Netherlands, socioeconomic disparities were identified. Therefore, we invite other countries, with differing levels of prosperity and healthcare policies to also investigate SES in the ICU. This cohort lays a foundation for more comprehensive assessments of intensive care quality and outcomes, potentially informing more equitable healthcare policies in the future.

Electronic supplementary material

Below is the link to the electronic supplementary material.

Supplementary Material 1 (35.9MB, pdf)

Acknowledgements

We would like to thank all ICUs for their efforts to provide high-quality data to the National Intensive Care Evaluation (NICE) registry and made it available for this research. We would also like to thank in particular the following individuals for their assistance in the linkage process: Annemieke Redeman (Statistics Netherlands), Ferishta Bakhshi-Raiez (National Intensive Care Evaluation Research & Support), and Eric van der Zwan (National Intensive Care Evaluation Research & Support)).

Abbreviations

AIDS

Acquired immunodeficiency syndrome

AMC

SpoedZorgNet Academisch Medisch Centrum

APACHE

Acute Physiology and Chronic Health Evaluation

APS

Acute Physiology Score

BMI

Body mass index

COPD

Chronic obstructive pulmonary disease

EHRs

Electronic health records

GDPR

General Data Protection Regulation

ICU

Intensive care unit

LOS

Length of stay

MCA

Multiple correspondence analysis

NICE

National Intensive Care Evaluation

ROAZ

Regionaal Overleg Acute Zorgketen

SES

Socioeconomic status

Author contributions

All authors contributed substantially to this study. DK and JdK contributed equally to this study as shared first authors. SB and BvB contributed equally to this study as shared last authors. Conceptualisation: DK, JdK, FT, RS, NdK, IvdH, BvB and SB. Data curation: DK, JdK, and SB. Formal analysis: DK and JdK. Funding acquisition: NdK, IvdH, and BvB. Investigation: DK, JdK, BvB, and SB. Methodology: DK, JdK, FT, BvB, and SB. Project administration: BvB and SB. Software: DK and JdK. Resources: NdK and IvdH Supervision: FT, NdK, IvdH, BvB, and SB. Visualisation: DK, JdK, NdK, IvdH, and SB. Writing – original draft: DK, JdK, BvB, and SB. Writing – review & editing: DK, JdK, FT, RS, NdK, IvdH, BvB and SB. All authors have read and approved the final manuscript.

Funding

This project is funded by the board of directors of the Maastricht University Medical Centre+ and the National Intensive Care Evaluation.

Data availability

The data used during the current study are not publicly available because they contain sensitive patient information and non-public microdata from Statistics Netherlands. The underlying code and documentation for this research are openly available via a GitHub repository (https://github.com/DAM-IC/Regional-variance-in-socioeconomic-status-among-Dutch-intensive-care-units).

Declarations

Ethics approval and consent to participate

This is an observational study. The Medical Ethics Review Committee of Amsterdam UMC has confirmed that no ethical approval is required and waived the need for informed consent [reference number: 2023.0259].

Consent for publication

Not applicable.

Competing interests

The authors declare no competing interests.

Footnotes

Publisher’s Note

Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.

Daniëlle J.M. Koornneef and Jip W.T.M. de Kok shared first author and corresponding author.

Bas C.T. van Bussel and Sylvia Brinkman shared last author.

Contributor Information

Daniëlle J. M. Koornneef, Email: d.j.m.koornneef@amsterdamumc.nl

Jip W. T. M. de Kok, Email: jip.de.kok@mumc.nl

References

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Associated Data

This section collects any data citations, data availability statements, or supplementary materials included in this article.

Supplementary Materials

Supplementary Material 1 (35.9MB, pdf)

Data Availability Statement

The data used during the current study are not publicly available because they contain sensitive patient information and non-public microdata from Statistics Netherlands. The underlying code and documentation for this research are openly available via a GitHub repository (https://github.com/DAM-IC/Regional-variance-in-socioeconomic-status-among-Dutch-intensive-care-units).

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