Impact of comorbidity on the association between socioeconomic factors and long-term mortality of intensive care patients

doi:10.21203/rs.3.rs-5334737/v1

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Research Article

Impact of comorbidity on the association between socioeconomic factors and long-term mortality of intensive care patients

https://doi.org/10.21203/rs.3.rs-5334737/v1

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Purpose

We aimed to estimate the association between socioeconomic status and long-term mortality after intensive care, with a particular focus on the impact of comorbidity.

Methods

First admissions to the ICU, registered in the national Swedish intensive care register between 2009 to 2012, were linked to information on education and income. We estimated the association between these socioeconomic factors and mortality, using Cox regression with follow-up until 2016. Comorbidity was measured with a multi-dimensional comorbidity measure based on hospital discharge diagnoses.

Results

We identified 101 745 ICU patients ≥30 years old. The group with only elementary school had a higher mortality rate than the group with the highest educational level (adjusted HR, 1.20; 95% CI 1.16-1.23). The association was not notably influenced by adjustment for comorbidity. In a landmark analysis, the association was weaker during the first year after ICU admission (adjusted HR, 1.14; 95% CI 1.09-1.18) than after the first year (adjusted HR, 1.28; 95% CI 1.23-1.35). The associations were stronger in patients with lower comorbidity burden than in patients with more severe comorbidity. The associations were largely consistent when income was used to indicate socioeconomic status.

Conclusion

Low educational level was associated with an increased long-term mortality rate after ICU admission, but the association was not notably related to comorbidity. The association was stronger after the first year of follow-up, suggesting that it may be more related to unmeasured background characteristics such as lifestyle factors that are not reflected in measurable comorbidity rather than the ICU admission.

Socioeconomic status

Intensive care

Critical care

Comorbidity

Mortality

Low educational level was associated with increased long-term mortality after ICU admission, but the association was not notably related to comorbidity. In a landmark analysis, the association was weaker during the first year after ICU admission than after the first year, suggesting that risk factors other than those specifically related to ICU admission may be important.

Low educational level was associated with increased long-term mortality after ICU admission independent of comorbidity.

Socioeconomic status (SES) refers to an individual’s or group's relative position or rank in society based on economic and social factors. From a general healthcare perspective, SES is a relevant characteristic since patients with high socioeconomic status (SES) have better outcomes after advanced care than patients with low SES [1]. This concern also exists in the context of critical illness, where lower SES has been associated with increased mortality [2] and earlier decisions to withdraw life-sustaining therapy [3, 4] after admission to intensive care.

The impact of SES on healthcare outcomes can be studied based on different measurable dimensions of SES, such as educational level, occupational status, and income. A potential complication when understanding the causal impact of SES is that people with low SES may have a more significant comorbidity burden [5–9]. Good comorbidity measures are required to study their relation to SES, but previous studies have used measures that may underestimate the comorbidity burden [4, 10, 11]. We have previously demonstrated that baseline comorbidity measures using increased granularity of the ICU patient’s hospital discharge history can substantially improve long-term mortality prediction after ICU admission, compared to traditional comorbidity indices [12].

This study aimed to estimate the association between SES and long-term mortality after intensive care, with a particular focus on the potential impact of comorbidity.

Study population

We included the first ICU admissions of patients aged 30 years and older registered in the Swedish Intensive Care Registry (SIR) [13] from 2009 to 2012 (eFig S1). The age restriction aimed to exclude younger age groups whose SES may be more dependent on their parents/family and whose own educational/income level does not accurately reflect their SES. The regional Human Ethics Committee approved the study (Approval no 2012/197).

Data sources

In 2012, SIR covered 92% of all ICU admissions in Sweden. SIR contains information on, e.g., the characteristics of patients admitted to an ICU, reasons for admission, and severity scores indicating baseline risk. Using unique patient identity numbers, SIR was linked to the other national healthcare registers [14]. Individual-level information on demographics, level of education, and income were obtained from the Statistics Sweden and the Longitudinal integrated database for health insurance and labour market studies (LISA) [15]. Hospital discharge diagnoses coded according to the Swedish clinical modification of the 10th revision of the International Statistical Classification of Diseases and Related Health Problems (ICD-10-SE) were retrieved from the Patient Register [16]. Reporting to this register is mandatory and has nationwide coverage regarding in-patient care since 1987. The date of death was retrieved from the Cause of Death Register [17].

Socioeconomic status

Educational level was categorized as low if restricted to compulsory school (9 years), medium if restricted to upper secondary school (additional 2–4 years), and high if it included education beyond that level (university). The educational level was defined from information reported in the calendar year preceding the date of admission to intensive care.

The patient’s annual disposable income was stratified into four arbitrary categories (< 10 000 €, 10 000–29 999 €, 30 000–49 999 €, and ≥50 000 €). It was defined from the income reported in the calendar year preceding the calendar year for admission to intensive care not to be affected by the acute condition causing ICU admission. In a sensitivity analysis, the income variable was defined from the income one year earlier than in the primary analysis.

Comorbidity and other baseline characteristics

We used a previously described and validated method to quantify baseline comorbidity [12]. Hospital discharge diagnoses from in-patient care during the five years preceding the index date for the ICU admission were divided into 36 predefined comorbidity categories. For each comorbidity category, we calculated two variables: (a) one quantifying the total length of hospital stay in days with a primary diagnosis within that category, and (b) the interval from the last hospital admission with a primary diagnosis within that category to the ICU admission date (1–6 months, 6–12 months, 1–3 years, > 3 years). The month preceding the index date was considered a grace period to reduce the inclusion of diagnoses directly related to the current hospitalization and ICU stay and not representing preexisting comorbidity. The described prevalence of comorbidity at baseline was based on both main and secondary hospital discharge diagnoses during the five years preceding the index date.

The simplified acute physiology score (SAPS) version 3 was extracted from SIR [18, 19]

Outcomes

The date of death was retrieved from the Cause of Death Register. Follow-up for all-cause mortality was available until December 31, 2016.

Statistical methods

Follow-up started on the day of ICU admission and ended on the date of death or December 31, 2016, whichever came first. The length of follow-up was calculated from a ‘reverse’ Kaplan-Meier analysis.

First, a Cox model was fitted for the association between all 72 comorbidity variables (two variables for each of the 36 comorbidity categories, quantifying the length of stay and the recency of previous hospital admissions within each category) and survival probability. This model was then applied to the study population, and the estimated linear predictor was used to provide a summary comorbidity score for each individual’s overall baseline comorbidity burden (eFig. S2).

In the regression models including the SAPS3 score, missing SAPS3 values were imputed (5 times) using multiple imputations [20]. The associations between education/income as exposures and survival probability were then estimated in Cox proportional hazard regression models, gradually adjusted for age, sex, SAPS3, and the summary comorbidity score. The continuous variables age, SAPS3 and the summary comorbidity score were modeled as restricted cubic splines with 5 knots. The results were presented as hazard ratios (HR) with 95% confidence intervals (CIs). A secondary analysis was restricted to complete cases.

Subgroup analyses were performed based on age, sex, country of birth, ICU type, and overall burden of comorbidity. In a complementary analysis a landmark was predefined to one year.

The proportional hazards assumption was evaluated by visual inspection of a log-log plot. Since age and some comorbidities are included as components in the SAPS3 score, potential collinearity was evaluated by calculating variance inflation factors (VIF) using linear regression. Data management was done in SAS version 9.4, and all statistical analyses were performed using R version 4.4.0.

Baseline characteristics

During the 4-year study period, 101 745 patients ≥ 30 years old were admitted to the ICU for the first time (eFig. S1). Of these, 91.6% (93 152) had information on the highest attained educational level, and 99.0% (100 778) had information on annual income.

The age distribution differed across the educational level categories, with a higher proportion of older age groups in the compulsory school category (Table 1). Correspondingly, the prevalence of comorbidity was also higher in this educational category. The reasons for admission to the ICU were comparable across educational level categories, but there was a tendency for higher SAPS3 in the compulsory school category.

Table 1

Baseline characteristics of 93 152 ICU patients with individual-level information on educational level, categorized based on the maximum level of education completed.
	Educational level
	Compulsory school	Upper secondary	Higher
	(N = 35 778)	(N = 39 613)	(N = 17 751)
Age (years), % (n)
30–49	10% (3596)	22% (8870)	25% (4359)
50–69	37% (13 122)	47% (18 578)	47% (8357)
70–89	51% (18 221)	30% (11 809)	28% (4907)
90+	2% (849)	1% (356)	1% (128)
Sex, % (n)
Female	41% (14 612)	40% (15 776)	43% (7615)
Admission diagnoses, % (n)
Respiratory, other	24% (8606)	23% (9143)	22% (3934)
Neurological	21% (7410)	22% (8883)	22% (3847)
Circulatory (excl. septic shock)	21% (7351)	17% (6907)	17% (2990)
Gastrointestinal	14% (4981)	12% (4673)	11% (2006)
Metabolic	13% (4613)	12% (4690)	10% (1768)
Renal	11% (3860)	8% (3348)	7% (1283)
Trauma	6% (1972)	7% (2724)	7% (1166)
Septic shock	5% (1768)	4% (1726)	4% (713)
Respiratory, acute-on-chronic	5% (1953)	4% (1601)	3% (498)
Haematological	4% (1302)	3% (1374)	4% (691)
Missing	23% (8332)	25% (9773)	29% (5139)
SAPS3
Median (interquartile range)	57 (47–67)	51 (41–63)	50 (40–62)
Missing, % (n)	23% (8332)	25% (9773)	29% (5139)
Comorbidities^a, % (n)
Hypertension	29% (10 502)	22% (8838)	19% (3352)
Infectious disease	21% (7678)	17% (6635)	15% (2624)
Ischemic heart disease	17% (6042)	12% (4749)	10% (1798)
Cardiac arrhythmias	15% (5356)	11% (4270)	10% (1857)
Diabetes mellitus	15% (5238)	11% (4541)	8% (1502)
Bone/muscle disease	13% (4803)	11% (4435)	9% (1644)
Neurological disease	13% (4586)	11% (4195)	10% (1764)
Injury	13% (4510)	11% (4324)	9% (1591)
Heart failure	12% (4331)	8% (3207)	7% (1158)
Chronic pulmonary disease	12% (4120)	9% (3466)	6% (986)
Tumour, non-metastatic	9% (3100)	8% (3208)	9% (1626)
Cerebrovascular disease	8% (2887)	6% (2380)	5% (894)
Alcohol abuse	6% (2106)	7% (2863)	5% (800)
Other anaemias	7% (2670)	6% (2226)	5% (870)
Peripheral vascular disease	7% (2564)	6% (2253)	5% (836)
Valvular disease	6% (2010)	5% (1918)	6% (1002)
Renal disease	6% (2181)	5% (1963)	4% (764)
Depression	4% (1483)	5% (1968)	5% (821)
Other endocrine disease	5% (1754)	4% (1618)	4% (670)
Drug abuse	4% (1373)	4% (1566)	2% (350)
Fluid balance disorder	4% (1431)	3% (1275)	3% (522)
Hepatic disease	3% (1203)	3% (1379)	2% (420)
Rheumatic/autoimmune disease	4% (1351)	3% (1146)	2% (424)
Poisoning	2% (770)	3% (1050)	2% (360)
Obesity	2% (669)	2% (919)	1% (229)
Tumour, metastatic	2% (570)	2% (750)	3% (447)
Pulmonary circulation disorders	2% (727)	2% (630)	1% (253)
Haematological disease	2% (545)	2% (648)	2% (337)
Haematological malignancy	1% (512)	1% (585)	2% (333)
Psychoses	2% (577)	2% (633)	1% (212)
Blood loss anaemia	1% (380)	1% (352)	1% (151)
Transplantation-related disorder	1% (182)	1% (274)	1% (138)
Malnutrition	1% (203)	1% (201)	0% (76)
Coagulopathy	1% (185)	0% (174)	0% (83)
Immunodeficiency	0% (72)	0% (89)	0% (38)
^a A patient may have multiple comorbidity categories

There were also notable differences in age and sex distribution across income categories (eTable S1). Furthermore, the baseline prevalence of comorbidity varied across income level categories. The reason for admission to the ICU was broadly comparable across income categories.

Association between educational level and mortality rate

ICU patients with compulsory school as the highest attained educational level had a higher mortality rate than those with the highest educational level (crude HR, 1.82; 95% CI 1.77–1.87) (Table 2). After adjustment for age, sex, and SAPS3, an increased mortality rate remained (adjusted HR, 1.21; 95% CI 1.18–1.25). Additional adjustment for comorbidity did not notably change the estimated association (adjusted HR, 1.20; 95% CI 1.16–1.23). The intermediate educational level had an association with intermediate strength. A complete case analysis showed largely comparable results (Table 2).

Table 2

Association between highest attained educational level and mortality rate, estimated in Cox regression models incrementally adjusted for age, sex, SAPS3, and comorbidity. Results are presented both after multiple imputation of missing SAPS3 values and for a complete case analysis.
Handling of missing SAPS3	Highest educational level	Number of patients	Number of deaths	HR (95% CI)
				Unadjusted	Adjusted for age and sex	Adjusted for age, sex, and SAPS3	Adjusted for age, sex, SAPS3, and comorbidity
Multiple imputation	Compulsory school	35 788	18 842	1.82 (1.77–1.87)	1.29 (1.25–1.33)	1.21 (1.18–1.25)	1.20 (1.16–1.23)
	Upper secondary	39 613	15 488	1.21 (1.18–1.25)	1.17 (1.13–1.2)	1.13 (1.1–1.17)	1.12 (1.09–1.16)
	Higher	17 751	5927	Reference
Complete case	Compulsory school	27 456	15 664	1.77 (1.72–1.83)	1.22 (1.18–1.26)	1.19 (1.15–1.23)	1.18 (1.14–1.22)
	Upper secondary	29 840	12 652	1.17 (1.13–1.21)	1.12 (1.08–1.16)	1.11 (1.07–1.15)	1.11 (1.07–1.14)
	Higher	12 612	4715	Reference

In a landmark analysis, the association between compulsory school educational level and mortality rate was found to be weaker during the first year after ICU admission (adjusted HR, 1.14; 95% CI 1.09–1.18) than among those patients that had survived the first year after ICU admission (adjusted HR, 1.28; 95% CI 1.23–1.35) (Table 3).

Table 3

Landmark analysis of the association between educational level and death adjusted for age, sex, SAPS3 and comorbidity within the first year and restricted to patients surviving the first year after ICU admission.
Period	Highest educational level	Adjusted^a HR (95% CI)
First year after ICU admission	Compulsory school	1.14 (1.09–1.18)
	Upper secondary	1.08 (1.04–1.13)
	Higher	Reference
After 1st year	Compulsory school	1.28 (1.23–1.35)
	Upper secondary	1.18 (1.12–1.24)
	Higher	Reference
^a Cox regression with multiple imputation of SAPS3 and adjustment for age, sex, SAPS3 and comorbidity.

The strength of the association between low educational level and mortality rate during the first 28 days (adjusted HR, 1.14; 95% CI 1.09–1.18) was identical to that with the 1-year mortality rate.

In subgroup analyses, the associations between educational level and mortality rate were largely consistent across subgroups defined by age, sex, and country of birth (Fig. 1). The subgroup analysis for type of ICU showed stronger associations for cardiothoracic ICUs but with poor precision. The associations were also stronger in patients with lower comorbidity burden compared to patients with more severe comorbidity.

Association between income level and mortality rate

The crude associations between income level and mortality rate were notably changed by adjustment for covariates (Table 4). The lowest income level was, after adjustment for age, sex, and SAPS3, associated with higher mortality rates (adjusted HR, 1.40; 95% CI 1.30–1.51). Additional adjustment for comorbidity did not notably change the estimated association (adjusted HR, 1.38; 95% CI 1.28–1.49). The second lowest income level had a somewhat attenuated strength of the association, while the second highest income level had a largely similar mortality rate as the highest income level. A complete case analysis showed slightly weaker associations but a comparable pattern (Table 4).

Table 4

Association between annual income and mortality rate, estimated in Cox regression models incrementally adjusted for age, sex, SAPS3, and comorbidity. Results are presented both after multiple imputation of missing SAPS3 values and for a complete case analysis.
Handling of missing SAPS3	Annual income (Euro)	Number of patients	Number of deaths	HR (95% CI)
				Unadjusted	Adjusted for age and sex	Adjusted for age, sex, and SAPS3	Adjusted for age, sex, SAPS3, and comorbidity
Multiple imputation	0-9999	7874	3716	1.62 (1.51–1.73)	1.49 (1.39–1.6)	1.40 (1.30–1.51)	1.38 (1.28–1.49)
	10 000–29 999	76453	37950	1.73 (1.63–1.84)	1.4 (1.32–1.49)	1.34 (1.25–1.43)	1.30 (1.21–1.39)
	30 000–49 999	13108	3481	0.80 (0.74–0.85)	0.94 (0.88–1.01)	0.96 (0.89–1.04)	0.97 (0.9–1.05)
	≥ 50 000	3343	1073	Reference
Complete case	0-9999	5975	2921	1.37 (1.27–1.48)	1.32 (1.22–1.43)	1.26 (1.17–1.36)	1.24 (1.15–1.35)
	10 000–29 999	57221	30302	1.53 (1.43–1.64)	1.26 (1.18–1.35)	1.21 (1.13–1.3)	1.18 (1.1–1.27)
	30 000–49 999	9010	2676	0.74 (0.68–0.8)	0.91 (0.84–0.98)	0.92 (0.85-1.00)	0.92 (0.86-1.00)
	≥ 50 000	2169	823	Reference

In a sensitivity analysis, defining income from a calendar year earlier, the results were essentially identical (data not shown).

Check of assumptions

For the main analysis, the log-log plot did not suggest any concern with the proportional hazards assumption (eFig. S3). Calculated VIFs did not indicate any relevant degree of collinearity between the independent variables in the analysis (eFig. S4).

Summary of findings

ICU patients with the lowest educational level were older and, therefore, expectedly had a higher prevalence of comorbidity. They also tended to be admitted with higher SAPS3 severity scores. Low educational level was associated with an increased long-term overall mortality rate after adjustment for age, sex, and SAPS3. Still, the strength of this association was only to a negligible extent influenced by adjustment for comorbidity. The association also tended to be stronger in a subpopulation with a lower comorbidity burden, compared to the subgroup with a higher comorbidity burden.

It is not self-evident how to consider comorbidity to SES in a causal framework. As indicated in the directed acyclic graph (eFig. S5), some comorbidity may influence SES, e.g., if the ability to pursue higher education is limited and the ability to work full time is restricted. In this situation, comorbidity is a true confounder and should be included in the adjustment. Low SES could also lead to lifestyle choices that generate increased comorbidity and consequently lie in the causal pathway [21–23]. In this situation, adjustment for comorbidity in the regression analysis is inappropriate. Our results, however, do not support a significant overall contribution of comorbidity to the association between SES and mortality.

In a landmark analysis, the association between low educational level and the mortality rate was weaker during the first year after ICU admission compared to the period after the first year of follow-up, possibly suggesting that the underlying causal pathways may be more dependent on risk factors and other than those specifically related to the ICU admission.

In the current study, we used educational level as the primary measure of SES, but also verified consistency with income as exposure. A comparable association of similar magnitude was seen between income and mortality, providing some support for consistency across different measures of SES. However, no apparent adverse influence was detectable in a subgroup analysis based on country of birth. Similar findings have been made in other settings [24].

Which indicators best describe SES likely depends on the target population for the study [25]. Since we used education and income as indicators of SES, we restricted the study population to patients 30 years of age or older. In younger age groups, SES may be more dependent on parents/family SES, and educational/income level is not a mature and reliable representation of their SES. Our study also focused on comorbidity in relation to SES and the prevalence of comorbidity was too low in the youngest age groups to be able to draw meaningful conclusions. The relevance of SES measures may also be a concern in older and retired patients, where a low income may not correlate well with actual SES. This may contribute to the slightly lower strength of the association between SES and mortality seen in the subgroup analysis of patients older than 65 years.

Some strengths of our study are the large population-based study population with almost complete coverage of all Swedish ICUs, comprehensive data collection, and long-term follow-up. Sweden's predominantly tax-funded national health care system that provides care to all citizens at a reasonable cost is an advantage. The potential impact of the cost of healthcare as a barrier for patients with lower SES to seek timely medical attention and afford appropriate treatment should be limited. Therefore, differences seen in relation to SES should mainly be related to other mechanisms. We could also more reliably evaluate the potential impact comorbidity has by using optimized measures of this baseline patient characteristic [12, 26, 27].

A potential limitation is the inability to account for potential selection mechanisms for admission to the ICU. The process leading up to ICU admission could be related to SES and also influence long-term mortality risk. Differences between countries regarding healthcare funding, healthcare system characteristics, and coding practices are also expected to limit generalizability and hamper international comparisons [28, 29]. The fact that follow-up is only available until December 2016 can be considered a limitation. It should, however, be acknowledged that the inclusion of data from 2019 inevitably would have compromised the interpretation of results because of the COVID-19 pandemic. When we use SIR to compare ICU case-mix and mortality in 2008 with 2023, they are very similar. We pre-specified the landmark to one year to isolate long-term effects from the acute phase. This selection may have influenced the results but has a clinical rationale [30].

While we could use optimized comorbidity measures in our study, the lack of information on lifestyle factors such as previous physical activity and fitness, dietary habits, smoking, alcohol consumption, and obesity is an explicit limitation of our analyses (eFig S5). The impact of SES on health outcomes may at least partly be mediated by such lifestyle factors. Lifestyle factors are associated with mortality in different populations and may be essential determinants for long-term health outcomes [31].

The health care system's accessibility at a reasonable cost reduces the potential impact of SES, but there may still be an association between low SES and geographical proximity to high-level ICU care. Our study's inability to account for such factors is another limitation, and they represent another possible mechanism for the association between SES and mortality seen in our study.

Low educational level was associated with an increased long-term mortality rate after ICU admission, but the association was not notably related to comorbidity. In a landmark analysis, the association was weaker during the first year after ICU admission than after the first year, suggesting that risk factors other than those specifically related to ICU admission may be important.

Disclaimer: Rolf Gedeborg is also employed by the Medical Products Agency (MPA) in Sweden. The MPA is a Swedish Government Agency. The views expressed in this article may not represent the views of the MPA.

Consent for publication: Not applicable

Availability of data and materials: The data that support the findings of this study are available from the Swedish Intensive Care Register, National Patient Register, the Statistics Sweden and the Longitudinal integrated database for health insurance and labour market studies (LISA) and the Cause of Death Register but restrictions apply to the availability of these data, which were used under license for the current study, and so are not publicly available. Data are however available from the authors upon reasonable request and with permission of the registers.

Authors' contributions: AAD and RG conceptualised and designed the study. AAD and RG performed data collection and data management. RG, BS, and AAD performed analyses. AAD wrote the first draft of the manuscript, and all authors contributed to the critical revision of the manuscript. All authors read and approved the final manuscript.

Acknowledgements: Not applicable

Funding: The study was not supported by any external funding.

Approval by the Swedish Ethical Review Authority: Dnr 2012/197

ClinicalTrials.gov ID: NCT06554808, date 20/08/2024

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No competing interests reported.

SupplementaryImpactofcomorbidityontheassociationbetweensocioeconomicfactorsandlongtermmortalityofintensivecarepatientsCritCare.docx

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Impact of comorbidity on the association between socioeconomic factors and long-term mortality of intensive care patients

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Abstract

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Introduction

Methods

Study population

Data sources

Socioeconomic status

Comorbidity and other baseline characteristics

Outcomes

Statistical methods

Results

Baseline characteristics

Association between educational level and mortality rate

Association between income level and mortality rate

Check of assumptions

Discussion

Summary of findings

Conclusion

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