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Socioeconomic status and outcomes after in-hospital cardiac arrest

  • Nikola Stankovic
    Affiliations
    Research Center for Emergency Medicine, Aarhus University Hospital, Aarhus, Denmark

    Department of Clinical Medicine, Aarhus University, Aarhus, Denmark
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  • Mathias J. Holmberg
    Affiliations
    Research Center for Emergency Medicine, Aarhus University Hospital, Aarhus, Denmark

    Department of Clinical Medicine, Aarhus University, Aarhus, Denmark

    Department of Anesthesiology and Intensive Care, Randers Regional Hospital, Randers, Denmark
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  • Asger Granfeldt
    Affiliations
    Department of Clinical Medicine, Aarhus University, Aarhus, Denmark

    Department of Anesthesiology and Intensive Care, Aarhus University Hospital, Aarhus, Denmark
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  • Lars W. Andersen
    Correspondence
    Corresponding author at: Research Center for Emergency Medicine, Aarhus University Hospital, Palle Juul Jensens Boulevard 99, Bygning J, Plan 1, 8200 Aarhus N, Denmark.
    Affiliations
    Research Center for Emergency Medicine, Aarhus University Hospital, Aarhus, Denmark

    Department of Clinical Medicine, Aarhus University, Aarhus, Denmark

    Department of Anesthesiology and Intensive Care, Aarhus University Hospital, Aarhus, Denmark

    Prehospital Emergency Medical Services, Central Denmark Region, Denmark
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Open AccessPublished:August 24, 2022DOI:https://doi.org/10.1016/j.resuscitation.2022.08.014

      Abstract

      Aim

      To investigate the association between socioeconomic status and outcomes after in-hospital cardiac arrest in Denmark.

      Methods

      We conducted an observational cohort study based on nationwide registries and prospectively collected data on in-hospital cardiac arrest from 2017 and 2018 in Denmark. Unadjusted and adjusted analyses using regression models were performed to assess the association between socioeconomic status and outcomes after in-hospital cardiac arrest. Outcomes included return of spontaneous circulation (ROSC), survival to 30 days, survival to one year, and the duration of resuscitation among patients without ROSC.

      Results

      A total of 3,223 patients with in-hospital cardiac arrest were included in the study. In the adjusted analyses, high household assets were associated with 1.20 (95 %CI: 0.96, 1.51) times the odds of ROSC, 1.49 (95 %CI: 1.14, 1.96) times the odds of survival to 30 days, 1.40 (95 %CI: 1.04, 1.90) times the odds of survival to one year, and 2.8 (95 %CI: 0.9, 4.7) minutes longer duration of resuscitation among patients without ROSC compared to low household assets. Similar albeit attenuated associations were observed for education. While high household income was associated with better outcomes in the unadjusted analyses, these associations largely disappeared in the adjusted analyses.

      Conclusions

      In this study of patients with in-hospital cardiac arrest, we found that high household assets were associated with a higher odds of survival and a longer duration of resuscitation among patients without ROSC compared to low household assets. However, the effect size may potentially be small. The results varied based on socioeconomic status measure, outcome of interest, and across adjusted analyses.

      Keywords

      Introduction

      Low socioeconomic status (SES) is associated with worse outcomes after various cardiovascular diseases including out-of-hospital cardiac arrest (OHCA).
      • Schultz W.M.
      • Kelli H.M.
      • Lisko J.C.
      • et al.
      Socioeconomic Status and Cardiovascular Outcomes: Challenges and Interventions.
      • van Nieuwenhuizen B.P.
      • Oving I.
      • Kunst A.E.
      • et al.
      Socio-economic differences in incidence, bystander cardiopulmonary resuscitation and survival from out-of-hospital cardiac arrest: A systematic review.
      However, less is known about the association between SES and outcomes after in-hospital cardiac arrest (IHCA).
      A systematic review did not find a clear association between SES and outcomes after IHCA, although the included studies were few and most had a high risk of bias.
      • Stankovic N.
      • Høybye M.
      • Lind P.C.
      • Holmberg M.J.
      • Andersen L.W.
      Socioeconomic status and in-hospital cardiac arrest: A systematic review.
      Recently, a study found that low SES was associated with worse survival to hospital discharge with good neurological outcome and 30-day survival after IHCA compared to high SES.
      • Agerstrom J.
      • Carlsson M.
      • Bremer A.
      • Herlitz J.
      • Israelsson J.
      • Arestedt K.
      Discriminatory cardiac arrest care? Patients with low socioeconomic status receive delayed cardiopulmonary resuscitation and are less likely to survive an in-hospital cardiac arrest.
      However, several aspects remain uncertain. First, no studies have evaluated the association between household assets and outcomes after IHCA. The relative significance of different SES measures varies during the life course.
      • Galobardes B.
      • Shaw M.
      • Lawlor D.A.
      • Lynch J.W.
      • Davey Smith G.
      Indicators of socioeconomic position (part 1).
      • Galobardes B.
      • Shaw M.
      • Lawlor D.A.
      • Lynch J.W.
      • Davey Smith G.
      Indicators of socioeconomic position (part 2).
      • Robert S.
      • House J.S.
      SES differentials in health by age and alternative indicators of SES.
      Since IHCA occurs in a predominantly elderly and retired population,
      • Andersen L.W.
      • Holmberg M.J.
      • Lofgren B.
      • Kirkegaard H.
      • Granfeldt A.
      Adult in-hospital cardiac arrest in Denmark.
      household assets may be a more accurate measure of SES compared to household income.
      • Galobardes B.
      • Shaw M.
      • Lawlor D.A.
      • Lynch J.W.
      • Davey Smith G.
      Indicators of socioeconomic position (part 1).
      • Galobardes B.
      • Shaw M.
      • Lawlor D.A.
      • Lynch J.W.
      • Davey Smith G.
      Indicators of socioeconomic position (part 2).
      • Robert S.
      • House J.S.
      SES differentials in health by age and alternative indicators of SES.
      Second, the outcomes of previous studies have been limited to short-term outcomes only. Thus, it is unknown whether SES is associated with long-term outcomes after IHCA. Third, it is uncertain whether SES is associated with the duration of resuscitation among patients without return of spontaneous circulation (ROSC), which may serve as a proxy for the quality of resuscitation care. Finally, it is unclear how sex may influence the association between SES and outcomes.
      In this study, we aimed to address these knowledge gaps by investigating how different SES measures were associated with short- and long-term outcomes after IHCA.

      Methods

      Study design, setting, and population

      We conducted an observational cohort study based on nationwide registries and prospectively collected data on IHCA in Denmark. Ethical approval for observational register-based studies is not required in Denmark. The Danish welfare model contains a tax-funded healthcare system with universal access along with tax-funded services (e.g., education, unemployment insurance, and disability pensions) that promote health and social equity.
      • Schmidt M.
      • Schmidt S.A.J.
      • Adelborg K.
      • et al.
      The Danish health care system and epidemiological research: from health care contacts to database records.
      Adult patients (≥18 years) with an index IHCA registered in the Danish In-Hospital Cardiac Arrest Registry (DANARREST) from January 1st, 2017 to December 31st, 2018 were included.
      • Andersen L.W.
      • Ostergaard J.N.
      • Antonsen S.
      • et al.
      The Danish in-hospital cardiac arrest registry (DANARREST).
      Patients with an initial pulse-generating rhythm on the first rhythm analysis, patients with missing data on covariates and outcomes, and patients with extreme values on time-based variables were excluded (see Supplemental Materials for details).

      Registries

      Data were obtained from DANARREST, the Danish Civil Registration System, the Income Statistics Register, the Population Education Register, the Employment Classification Module, and the Danish National Patient Registry.
      • Schmidt M.
      • Schmidt S.A.J.
      • Adelborg K.
      • et al.
      The Danish health care system and epidemiological research: from health care contacts to database records.
      Data were linked using the Civil Personal Registration number which is a personal and unique identifier for all Danish citizens.
      • Schmidt M.
      • Schmidt S.A.J.
      • Adelborg K.
      • et al.
      The Danish health care system and epidemiological research: from health care contacts to database records.
      Further information about the Danish registries is available in the Supplemental Materials.

      Socioeconomic status

      SES was defined by several measures, which included household income, household assets, education, and employment.
      • Stankovic N.
      • Holmberg M.J.
      • Granfeldt A.
      • Andersen L.W.
      Socioeconomic status and risk of in-hospital cardiac arrest.
      Data on household income, household assets, education, and employment were retrieved from the Income Statistics Register,
      • Baadsgaard M.
      • Quitzau J.
      Danish registers on personal income and transfer payments.
      the Population’s Education Register
      • Jensen V.M.
      • Rasmussen A.W.
      Danish Education Registers.
      , and the Employment Classification Module,
      • Petersson F.
      • Baadsgaard M.
      • Thygesen L.C.
      Danish registers on personal labour market affiliation.
      respectively. Household income was defined as the average annual equivalized disposable household income in the three years prior to the IHCA. Household assets were defined as the average total assets of the household in the three years prior to the IHCA, which included assets in property, monetary institutions, shares, and bonds. Education was defined as the highest attained educational level according to the International Classification of Education (ISCED) at the time of the IHCA. Employment was based on the year prior to the index IHCA. Further information about the socioeconomic measures is available in the Supplemental Materials.

      Outcomes

      The outcomes of interest included sustained ROSC, survival to 30 days, survival to one year, and duration of resuscitation in patients without ROSC.
      ROSC was defined as a palpable pulse or other signs of circulation sustained for at least 20 min without chest compressions. Duration of resuscitation was defined as the interval in minutes from time of cardiac arrest to termination of resuscitation, which was analyzed in patients without ROSC only. We decided to only analyze patients without ROSC to evaluate clinicians’ efforts to continue resuscitation as a surrogate marker of level of care.

      Statistics

      Descriptive statistics were used to describe the patient population. Continuous variables are presented as medians with quartiles. Categorical variables are presented as counts with frequencies.

      Categorical socioeconomic status

      To evaluate the association between SES and outcomes after IHCA, we treated household income, household assets, and education as categorical variables. Household income and household assets were divided into three categories (low, medium, high) based on tertiles.
      For binary outcomes, we used logistic regression models to yield odds ratios (ORs) with 95 % confidence intervals. Generalized estimating equations (GEE) were used to account for clustering within hospitals for all models. First, we performed an unadjusted model (Model 1). Second, we adjusted for patient characteristics (Model 2). These included age, sex, number of hospital contacts within the past year, hospital length of stay prior to the index IHCA, and comorbidities. Third, we then adjusted for cardiac arrest characteristics (Model 3). These included initial rhythm, time of day, weekend, location of the cardiac arrest, intubation prior to cardiac arrest, monitored cardiac arrest, and witnessed cardiac arrest. Finally, to assess if each SES measure was independently associated with outcomes after IHCA, we added the remaining SES measures to the model (Model 4). Due to collinearity between household income and household assets (Spearman correlation coefficient = 0.63), these were not included in the same model when each of the two measures were the independent variable.
      For the outcome of duration of resuscitation in patients without ROSC, we used linear regression models to yield mean differences with 95 % confidence intervals. Unadjusted and adjusted models were developed as described previously.
      To evaluate if outcomes after IHCA varied based on sex, subgroup analyses were performed with all analyses stratified by sex. Additionally, we assessed the association between SES and outcomes after IHCA in the subgroup of patients who were not retired.
      Analyses to account for missing data are presented in the Supplemental Materials.

      Continuous socioeconomic status

      For this analysis, household income and household assets were treated as continuous variables. Due to the skewness of the data with severe outliers (e.g., very high income), we ranked the SES measures. Next, we used separate logistic regression models with restricted cubic splines to yield predicted probabilities of outcomes. These models were adjusted for patient characteristics and cardiac arrest characteristics as described previously (i.e., Model 3). GEE were used to account for clustering within hospitals for all models. We used three prespecified knots at the 25 %, 50 %, and 75 % percentiles for each SES measure.

      Results

      Study population

      We included 4,281 patients with IHCA, of which 3,570 patients met all inclusion criteria (Fig. 1). A total of 347 patients were excluded due to missing data or extreme values on time-based variables. The final study population included 3,223 patients with IHCA.
      Figure thumbnail gr1
      Fig. 1Diagram of the Derivation of the Study Population.
      Of the 3,223 patients with IHCA, the median age was 74 years (quartiles: 65, 81), of which 2,011 (62 %) were men. Patient and cardiac arrest characteristics are shown in Table 1.
      Table 1Patient characteristics.
      Tertiles of Household Assets
      All (n = 3,223)Low (n = 1,074)Medium (n = 1,075)High (n = 1,074)
      Demographics
      Age (years)74 (65, 81)71 (60, 80)75 (67, 83)75 (68, 82)
      Sex (men)2,011 (62)641 (60)629 (59)741 (69)
      Cohabitation
       Unmarried/no registered partnership1,678 (52)782 (73)560 (52)336 (31)
       Married/registered partnership1,541 (48)289 (27)515 (48)737 (69)
       Missing4 (0)3 (0)0 (0)1 (0)
      Immigration
       Non-immigrant3,053 (95)975 (91)1,039 (97)1,039 (97)
       Immigrant or descendant166 (5)96 (9)36 (3)34 (3)
       Missing4 (0)3 (0)0 (0)1 (0)
      Hospital contacts within past year1 (0, 2)1 (0, 2)1 (0, 2)1 (0, 2)
      Hospital length of stay prior to index cardiac arrest (days)2 (0, 7)2 (1, 7)2 (0, 6)2 (0, 7)
      Socioeconomic status
      Household income (DKK)
       Low1,53,014 (138,650, 161,835)1,52,743 (137,258, 161,942)1,53,072 (140,029, 161,390)1,57,057 (146,344, 162,985)
       Medium1,87,172 (176,427, 200,870)1,80,545 (174,372, 193,032)1,87,300 (177,638, 201,879)1,95,424 (185,633, 205,833)
       High2,84,873(245,591, 357,667)2,38,693 (227,966, 262,691)2,60,384 (236,105, 293,363)3,04,420 (257,459, 381,952)
      Household assets (DKK)
       Low20,864 (10,843, 47,432)
       Medium7,31,104 (426,093, 1,050,946)
       High24,96,850 (1,860,598, 3,776,451)
      Education
       Basic1,439 (45)620 (58)505 (47)314 (29)
       Upper secondary1,296 (40)378 (35)443 (41)475 (44)
       Higher488 (15)76 (7)127 (12)285 (27)
      Employment
       Retirement2,333 (72)683 (64)829 (77)821 (76)
       Employment369 (11)62 (6)114 (11)193 (18)
       Unemployment124 (4)94 (9)21 (2)9 (1)
       Other
      Includes students, individuals on sick leave, early retirement, or not otherwise classified.
      397 (12)235 (22)111 (10)51 (5)
      Comorbidities
      Cardiovascular disease
       Ischemic heart disease754 (23)262 (24)248 (23)244 (23)
       Heart failure585 (18)222 (21)193 (18)170 (16)
       Arterial hypertension1,153 (36)409 (38)398 (37)346 (32)
       Cardiac dysrhythmia938 (29)310 (29)310 (29)318 (30)
       Valvular disease347 (11)105 (10)117 (11)125 (12)
       Peripheral vascular disease367 (11)145 (14)108 (10)114 (11)
       Hypercholesterolemia345 (11)127 (12)135 (13)83 (8)
       Venous thromboembolism182 (6)69 (6)50 (5)63 (6)
       Aortic disease112 (3)29 (3)31 (3)52 (5)
       Pulmonary hypertension58 (2)21 (2)16 (1)21 (2)
       Cardiac procedures prior to cardiac arrest
      Includes coronary angiography, percutaneous coronary intervention, and coronary artery bypass graft.
      659 (20)215 (20)209 (19)235 (22)
      Neurological disease
       Cerebrovascular disease399 (12)149 (14)130 (12)120 (11)
       Other neurological disease
      Includes dementia, epilepsy, Parkinson’s disease, hemiplegia, paraplegia, and tetraplegia.
      243 (8)101 (9)73 (7)69 (6)
      Pulmonary disease
       Chronic obstructive pulmonary disease547 (17)206 (19)201 (19)140 (13)
       Asthma105 (3)46 (4)31 (3)28 (3)
      Metabolic disease
       Diabetes mellitus645 (20)276 (26)206 (19)163 (15)
       Overweight and obesity197 (6)105 (10)58 (5)34 (3)
      Gastrointestinal disease
       Liver disease142 (4)73 (7)37 (3)32 (3)
       Peptic ulcer133 (4)57 (5)40 (4)36 (3)
       Pancreatitis61 (2)30 (3)17 (2)14 (1)
      Renal disease462 (14)170 (16)151 (14)141 (13)
      Cancer637 (20)166 (15)213 (20)258 (24)
      Psychiatric disorder443 (14)256 (24)114 (11)73 (7)
      Cardiac arrest characteristics
      Initial recorded cardiac arrest rhythm
      Shockable rhythm
       Ventricular fibrillation412 (13)132 (12)135 (13)145 (14)
       Pulseless ventricular tachycardia181 (6)63 (6)60 (6)58 (5)
      Non-shockable rhythm
       Pulseless electrical activity1,421 (44)458 (43)477 (44)486 (45)
       Asystole1,209 (38)421 (39)403 (37)385 (36)
      Time of day
       7–151,216 (38)397 (37)414 (39)405 (38)
       15–23987 (31)318 (30)346 (32)323 (30)
       23–71,020 (32)359 (33)315 (29)346 (32)
      Weekend
      Defined as the time interval from Saturday at 00:00 to Sunday at 23:59.
      834 (26)271 (25)290 (27)273 (25)
      Location of cardiac arrest
       Hospital ward2,018 (63)683 (64)676 (63)659 (61)
       Emergency department459 (14)138 (13)156 (15)165 (15)
       Intensive care unit347 (11)126 (12)108 (10)113 (11)
       Cardiac catherization laboratory155 (5)52 (5)57 (5)46 (4)
       Operation ward107 (3)28 (3)38 (4)41 (4)
       Other
      Includes outpatient clinic and other.
      137 (4)47 (4)40 (4)50 (5)
      Intubated prior to cardiac arrest309 (10)101 (9)95 (9)113 (11)
      Monitored cardiac arrest1,465 (45)452 (42)500 (47)513 (48)
      Witnessed cardiac arrest2,509 (78)812 (76)848 (79)849 (79)
      DKK: Danish Kroner (1 EUR = 7.44 DKK) Categorical data is presented as counts with frequencies (%). Continuous data is presented as medians with quartiles (quartile 1, quartile 3).
      a Includes students, individuals on sick leave, early retirement, or not otherwise classified.
      b Includes coronary angiography, percutaneous coronary intervention, and coronary artery bypass graft.
      c Includes dementia, epilepsy, Parkinson’s disease, hemiplegia, paraplegia, and tetraplegia.
      d Defined as the time interval from Saturday at 00:00 to Sunday at 23:59.
      e Includes outpatient clinic and other.

      Outcomes

      Among 3,223 patients with IHCA, 1,552 (48 %) patients had ROSC, 729 (23 %) patients survived to 30 days, and 568 (18 %) patients survived to one year (Table 2). The median duration of resuscitation among patients without ROSC was 20 (quartiles: 13, 29) minutes (Table 2). Outcomes across SES are presented in Table 2.
      Table 2Outcomes.
      AllHousehold incomeHousehold assetsEducation
      (n = 3,223)Low (n = 1,074)Medium (n = 1,075)High (n = 1,074)Low (n = 1,074)Medium (n = 1,075)High (n = 1,074)Basic (n = 1,439)
      Return of spontaneous circulation1,552 (48)484 (45)531 (49)537 (50)500 (47)523 (49)529 (49)663 (46)
      Survival to 30 days729 (23)212 (20)237 (22)280 (26)220 (20)237 (22)272 (25)280 (19)
      Survival to one year568 (18)168 (16)171 (16)229 (21)175 (16)180 (17)213 (20)214 (15)
      Missing
      One patient emigrated within one year after the index in-hospital cardiac arrest.
      1 (0)0 (0)0 (0)1 (0)0 (0)0 (0)1 (0)0 (0)
      Duration of resuscitation among patients without return of spontaneous circulation
      A total of 1,671 patients did not achieve return of spontaneous circulation.
      20 (13, 29)19 (12, 28)18 (12, 27)21 (14, 30)20 (12, 28)19 (12, 28)20 (13, 30)20 (13, 29)
      Categorical data is presented as counts with frequencies (%).
      Continuous data is presented as medians with quartiles (quartile 1, quartile 3).
      a One patient emigrated within one year after the index in-hospital cardiac arrest.
      b A total of 1,671 patients did not achieve return of spontaneous circulation.

      Household income

      In the unadjusted analyses, patients with high household income had 1.22 (95 %CI: 1.04, 1.43) times the odds of ROSC, 1.43 (95 %CI: 1.17, 1.75) times the odds of survival to 30 days, and 1.46 (95 %CI: 1.19, 1.80) times the odds of survival to one year compared to patients with low household income (Fig. 2). However, there was no association after adjustment for patient and cardiac arrest characteristics (Fig. 2).
      Figure thumbnail gr2
      Fig. 2Association between socioeconomic status and outcomes after in-hospital cardiac arrest Analyses are presented as odds ratios (ORs) with 95 % confidence intervals. Thus, an OR > 1 indicates an association with better outcomes compared to low SES/basic education. Conversely, an OR < 1 indicates an association with worse outcomes compared to low SES/basic education. Model 1: Unadjusted. Model 2: Adjusted for patient characteristics. Model 3: Adjusted for patient characteristics and cardiac arrest characteristics. Model 4: Adjusted for patient characteristics, cardiac arrest characteristics, and the remaining SES measures.
      Among patients without ROSC, high household income was associated with 2.2 (95 %CI: 0.0, 4.4) minutes longer duration of resuscitation compared to low household income (Fig. 3). There was no clear association between household income and duration of resuscitation after adjusting for patient and cardiac arrest characteristics (Fig. 3).
      Figure thumbnail gr3
      Fig. 3Association between socioeconomic status and duration of resuscitation among patients without return of spontaneous circulation. Analyses are presented as mean differences in minutes with 95 % confidence intervals. Thus, a mean difference > 0 indicates an association with a longer duration of resuscitation compared to low SES/basic education. Conversely, a mean difference < 0 indicates an association with a shorter duration of resuscitation compared to low SES/basic education. Model 1: Unadjusted. Model 2: Adjusted for patient characteristics. Model 3: Adjusted for patient characteristics and cardiac arrest characteristics. Model 4: Adjusted for patient characteristics, cardiac arrest characteristics, and the remaining SES measures.
      In the analyses of continuous household income and outcomes, the estimated outcomes increased marginally across the range of household income (Fig. 4).
      Figure thumbnail gr4
      Fig. 4Association between household income and assets and outcomes after in-hospital cardiac arrest. Analyses are presented as predicted probabilities of outcomes with 95% confidence intervals. All models are adjusted for patient characteristics and cardiac arrest characteristics (Model 3). Restricted cubic splines and GEE were used. Three prespecified knots were used at the 25%, 50%, and 75%-percentiles for each SES measure.

      Household assets

      In the unadjusted analyses, patients with high household assets had 1.11 (95 %CI: 0.92, 1.35) times the odds of ROSC, 1.32 (95 %CI: 1.03, 1.68) times the odds of survival to 30 days, and 1.27 (95 %CI: 0.96, 1.68) times the odds of survival to one year compared to patients with low household assets (Fig. 2). These associations remained relatively constant across analyses adjusted for patient and cardiac arrest characteristics (Fig. 2).
      Among patients without ROSC, high household assets were associated with 2.0 (95 %CI: −0.2, 4.2) minutes longer duration of resuscitation compared to low household assets. This association became stronger in the adjusted analyses (Fig. 3).
      In the analyses of continuous household assets and outcomes, the estimated outcomes increased across the range of household assets (Fig. 4).

      Education

      In the unadjusted analyses, patients with higher education had 1.17 (95 %CI: 0.95, 1.44) times the odds of ROSC, 1.43 (95 %CI: 1.14, 1.78) times the odds of survival to 30 days, and 1.46 (95 %CI: 1.15, 1.85) times the odds of survival to one year compared to patients with basic education only (Fig. 2). These associations remained relatively constant across analyses adjusted for patient and cardiac arrest characteristics (Fig. 2). Upper secondary education was significantly associated with higher survival to 30 days and survival to one year compared to basic education across the adjusted analyses (Fig. 2).
      Among patients without ROSC, patients with higher education had 1.6 (95 %CI: −0.6, 3.9) minutes longer duration of resuscitation compared to patients with basic education only (Fig. 3). This association remained relatively constant across analyses adjusted for patient and cardiac arrest characteristics (Fig. 3). Conversely, upper secondary education was associated a with shorter duration of resuscitation compared to basic education in the adjusted analyses (Fig. 3).

      Subgroup and sensitivity analyses

      Analyses stratified by sex are presented in eFigure 1–3. The association between SES and outcomes did not vary substantially between men and women. However, while the association between household assets and survival was stronger in women, the association between household assets and a longer duration of resuscitation was stronger in men. Analyses of patients who were not retired are presented in eFigure 4–5. The findings among patients who were not retired were generally similar to the findings of the overall population, although the association between household assets and survival to one year was attenuated.
      Analyses to account for missing data are presented in eFigure 6–7. The results from these analyses did not differ substantially from the results of the primary analyses.

      Discussion

      We found that high household assets were associated with a higher odds of survival and a longer duration of resuscitation among patients without ROSC compared to low household assets. However, the effect size may potentially be small. The results varied based on SES measure, outcome of interest, and across analyses adjusted for patient and cardiac arrest characteristics.
      A systematic review previously found that the literature on the association between SES and outcomes after IHCA was sparse, had limitations, and the results were inconclusive.
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      • Høybye M.
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      • Andersen L.W.
      Socioeconomic status and in-hospital cardiac arrest: A systematic review.
      In contrast to our study, the included studies addressed income at the area-level. Compared to individual-level measures, area-level measures may be prone to misclassification and ecological fallacy, which could underestimate the associations.
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      This aspect was partly addressed by a recent study that found high individual-level income to be associated with better outcomes after IHCA.
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      • Bremer A.
      • Herlitz J.
      • Israelsson J.
      • Arestedt K.
      Discriminatory cardiac arrest care? Patients with low socioeconomic status receive delayed cardiopulmonary resuscitation and are less likely to survive an in-hospital cardiac arrest.
      It is unclear whether personal or household income was used in this study. Equivalized disposable household income adjusts for the size of the household and its associated costs of living, which may make it a more useful SES measure compared to personal income.
      • Galobardes B.
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      • Davey Smith G.
      Indicators of socioeconomic position (part 1).
      In our study, high household income was associated with a higher odds of ROSC and survival, and a longer duration of resuscitation in the unadjusted analyses. However, after adjusting for patient and cardiac arrest characteristics, these associations largely disappeared. Our results for survival to one year contrast the findings observed in other patient populations (e.g., patients with OHCA,
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      • Drory Y.
      • I.
      Israel Study Group on First Acute Myocardial. Neighborhood socioeconomic context and long-term survival after myocardial infarction.
      and stroke
      • Vivanco-Hidalgo R.M.
      • Ribera A.
      • Abilleira S.
      Association of Socioeconomic Status With Ischemic Stroke Survival.
      in which high income has been associated with a higher survival to one year in adjusted analyses. However, the use of household income may have its limitations in an elderly and retired population. The patients with IHCA in the present study had a median age of 74 years, of which 72 % were retired. Since the relative significance of household assets may increase with age compared to household income,
      • Galobardes B.
      • Shaw M.
      • Lawlor D.A.
      • Lynch J.W.
      • Davey Smith G.
      Indicators of socioeconomic position (part 1).
      • Galobardes B.
      • Shaw M.
      • Lawlor D.A.
      • Lynch J.W.
      • Davey Smith G.
      Indicators of socioeconomic position (part 2).
      • Robert S.
      • House J.S.
      SES differentials in health by age and alternative indicators of SES.
      household assets may be a more accurate estimator of SES in patients with IHCA. Consequently, the use of income as the measure of SES may potentially underestimate the true association, which could potentially explain our findings. Household assets were consistently associated with a higher odds of survival compared to low household assets in both unadjusted and adjusted analyses. This included survival to one year. No studies have previously evaluated the association between SES and survival to one year in patients with IHCA. While socioeconomic inequalities in short-term outcomes after IHCA may relate to hospital-level treatment (e.g., post-cardiac arrest procedures
      • Moller S.
      • Wissenberg M.
      • Kragholm K.
      • et al.
      Socioeconomic differences in coronary procedures and survival after out-of-hospital cardiac arrest: A nationwide Danish study.
      and the duration of resuscitation), socioeconomic inequalities in long-term outcomes may relate to additional factors that are less related to the initial hospital treatment. These may potentially include differences in adequate healthcare after hospital discharge
      • Burge F.I.
      • Lawson B.
      • Johnston G.
      Home visits by family physicians during the end-of-life: Does patient income or residence play a role?.
      • Davies J.M.
      • Sleeman K.E.
      • Leniz J.
      • et al.
      Socioeconomic position and use of healthcare in the last year of life: A systematic review and meta-analysis.
      along with the provision of advance directives,
      • Barwise A.
      • Juhn Y.J.
      • Wi C.I.
      • et al.
      An Individual Housing-Based Socioeconomic Status Measure Predicts Advance Care Planning and Nursing Home Utilization.
      social support,
      • Lewis J.M.
      • DiGiacomo M.
      • Currow D.C.
      • Davidson P.M.
      Social capital in a lower socioeconomic palliative care population: a qualitative investigation of individual, community and civic networks and relations.
      • Pantell M.
      • Rehkopf D.
      • Jutte D.
      • Syme S.L.
      • Balmes J.
      • Adler N.
      Social isolation: a predictor of mortality comparable to traditional clinical risk factors.
      and nursing home utilization.
      • Moller S.
      • Wissenberg M.
      • Sondergaard K.
      • et al.
      Long-term outcomes after out-of-hospital cardiac arrest in relation to socioeconomic status.
      • Barwise A.
      • Juhn Y.J.
      • Wi C.I.
      • et al.
      An Individual Housing-Based Socioeconomic Status Measure Predicts Advance Care Planning and Nursing Home Utilization.
      In Denmark, studies have indicated that socioeconomic differences in utilizing and obtaining care may exist,
      • Edwards N.M.
      • Varnum C.
      • Overgaard S.
      • Pedersen A.B.
      The impact of socioeconomic status on the utilization of total hip arthroplasty during 1995–2017: 104,055 THA cases and 520,275 population controls from national databases in Denmark.
      • Sovso M.B.
      • Bech B.H.
      • Christensen H.C.
      • Huibers L.
      • Christensen E.F.
      • Christensen M.B.
      Sociodemographic Characteristics Associated with Contacts to Emergency Medical Services and Out-of-Hours Primary Care: An Observational Study of 2.3 Million Citizens.
      • Frydenlund J.
      • Mackenhauer J.
      • Christensen E.F.
      • et al.
      Socioeconomic Disparities in Prehospital Emergency Care in a Danish Tax-Financed Healthcare System: Nationwide Cohort Study.
      • Hyldgard V.B.
      • Johnsen S.P.
      • Stovring H.
      • Sogaard R.
      Socioeconomic Status And Acute Stroke Care: Has The Inequality Gap Been Closed?.
      • Sortso C.
      • Lauridsen J.
      • Emneus M.
      • Green A.
      • Jensen P.B.
      Socioeconomic inequality of diabetes patients' health care utilization in Denmark.
      which might partly explain our results for survival to one year after IHCA. Moreover, we observed a higher proportion of patients with psychiatric disorders among patients with low SES. Since psychiatric disorders may impact health-seeking behavior and access to care,
      • Knaak S.
      • Mantler E.
      • Szeto A.
      Mental illness-related stigma in healthcare: Barriers to access and care and evidence-based solutions.
      this may further explain our findings.
      We evaluated the association between SES and ROSC and survival as an indirect measure of the quality of treatment in patients with IHCA. Accordingly, underlying outcome differences in the adjusted analyses may be perceived as an indirect measure of treatment disparities observed during and after resuscitation. First, high household assets were associated with a higher odds of survival compared to low household assets in the unadjusted analyses. Similar associations were observed for household income and education. As such, these findings may potentially be explained by differences in underlying patient and cardiac arrest characteristics. Patients with low SES had a higher proportion of specific non-favorable factors (i.e., renal disease, non-witnessed cardiac arrest, and non-monitored cardiac arrest).
      • Fernando S.M.
      • Tran A.
      • Cheng W.
      • et al.
      Pre-arrest and intra-arrest prognostic factors associated with survival after in-hospital cardiac arrest: systematic review and meta-analysis.
      Although patients with high SES also had a higher proportion of several other non-favorable factors (i.e., higher age, male sex, and cancer),
      • Fernando S.M.
      • Tran A.
      • Cheng W.
      • et al.
      Pre-arrest and intra-arrest prognostic factors associated with survival after in-hospital cardiac arrest: systematic review and meta-analysis.
      these did not translate into less favorable cardiac arrest characteristics. After adjusting for both patient and cardiac arrest characteristics, we found that high household assets were consistently associated with a higher odds of survival. Similar findings were observed when treating household assets as a continuous measure. Thus, this may suggest that two patients who are similar in patient and cardiac arrest characteristics may experience different outcomes as a result of their SES measured by household assets. Speculatively, this may indicate that patients with high SES experience a better treatment prior to IHCA (i.e., higher proportion of witnessed and monitored cardiac arrest) and after IHCA (i.e., higher odds of survival). While similar albeit attenuated associations were observed for the association between education and outcomes, the association between household income and outcomes largely disappeared in the adjusted analyses.
      Only one study has previously investigated the association between SES and the duration of resuscitation in patients with IHCA, and no association was found.
      • Agerstrom J.
      • Carlsson M.
      • Bremer A.
      • Herlitz J.
      • Israelsson J.
      • Arestedt K.
      Discriminatory cardiac arrest care? Patients with low socioeconomic status receive delayed cardiopulmonary resuscitation and are less likely to survive an in-hospital cardiac arrest.
      However, the study evaluated both patients with and without ROSC. This may potentially have underestimated the socioeconomic disparities since high SES was associated with ROSC and the timing of ROSC was not considered. By examining patients without ROSC only, we evaluated potential socioeconomic treatment inequalities in a patient population that may benefit from a longer duration of resuscitation.
      • Goldberger Z.D.
      • Chan P.S.
      • Berg R.A.
      • et al.
      Duration of resuscitation efforts and survival after in-hospital cardiac arrest: an observational study.
      We found that high household assets were consistently associated with a longer duration of resuscitation compared to low household assets. However, the effect size was small and it is unlikely to fully explain the survival benefit in patients with high SES. It is unclear why high household assets may be associated with a longer duration of resuscitation. However, previous studies have found that negative biases towards low SES among healthcare providers may exist.
      • Zestcott C.A.
      • Blair I.V.
      • Stone J.
      Examining the Presence, Consequences, and Reduction of Implicit Bias in Health Care: A Narrative Review.
      • van Ryn M.
      • Burke J.
      The effect of patient race and socio-economic status on physicians' perceptions of patients.
      It is unknown whether such negative biases exist in the field of resuscitation, and whether these lead to socioeconomic disparities in the termination of resuscitation. Another explanation may relate to socioeconomic differences in the severity of underlying comorbidities, which may potentially influence clinicians’ decision to terminate resuscitation. While similar, albeit weaker, associations were observed for household income and education, the accompanying confidence intervals were wide.
      This study provides an insight into socioeconomic inequalities in outcomes after IHCA in Denmark. While socioeconomic inequalities in outcomes have been observed in multiple cardiovascular diseases,
      • Schultz W.M.
      • Kelli H.M.
      • Lisko J.C.
      • et al.
      Socioeconomic Status and Cardiovascular Outcomes: Challenges and Interventions.
      • Marshall I.J.
      • Wang Y.
      • Crichton S.
      • McKevitt C.
      • Rudd A.G.
      • Wolfe C.D.
      The effects of socioeconomic status on stroke risk and outcomes.
      the association between SES and outcomes after IHCA appears less uniform. Our results varied pertinent to the SES measure, outcome of interest, and across analyses adjusted for patient and cardiac arrest characteristics. Given that a recent study found that substantial socioeconomic inequalities exist in the risk of IHCA,
      • Stankovic N.
      • Holmberg M.J.
      • Granfeldt A.
      • Andersen L.W.
      Socioeconomic status and risk of in-hospital cardiac arrest.
      the present study underlines the importance of mitigating socioeconomic inequalities in both the risk of and the outcomes after IHCA. In turn, this may potentially reduce the absolute and relative numbers of patients from disadvantaged populations that suffer from the detrimental consequences of IHCA.

      Limitations

      This study needs to be interpreted relative to the observational cohort design and sample size. First, residual and unmeasured confounding may be present. Second, the magnitude of the socioeconomic disparities needs to be interpreted pertinent to the range of the confidence intervals for the point estimates. Third, the distribution of do-not-resuscitate orders across SES may have influenced the underlying patient population who received cardiopulmonary resuscitation.
      • de Decker L.
      • Annweiler C.
      • Launay C.
      • Fantino B.
      • Beauchet O.
      Do not resuscitate orders and aging: impact of multimorbidity on the decision-making process.
      • Hanson L.C.
      • Rodgman E.
      The use of living wills at the end of life.
      It is unclear in which direction this may have affected the results. Finally, the findings should be interpreted in the context of the universal healthcare system and welfare model in Denmark. Thus, the findings of this study may not generalize to other health systems and welfare models.

      Conclusions

      In this study of patients with IHCA, we found that high household assets were associated with a higher odds of survival and a longer duration of resuscitation among patients without ROSC compared to low household assets. However, the effect size may potentially be small. The results varied based on SES measure, outcome of interest, and across analyses adjusted for patient and cardiac arrest characteristics.

      CRediT authorship contribution statement

      Nikola Stankovic: Methodology, Software, Formal analysis, Investigation, Data curation, Writing – original draft, Writing – review & editing, Visualization, Project administration. Mathias J. Holmberg: Methodology, Software, Validation, Data curation, Writing – review & editing, Supervision, Project administration. Asger Granfeldt: Conceptualization, Methodology, Writing – review & editing, Supervision, Project administration. Lars W. Andersen: Conceptualization, Methodology, Validation, Resources, Writing – review & editing, Supervision, Project administration, Funding acquisition.

      Declaration of Competing Interest

      The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper.

      Acknowledgements

      This study was supported by the Karen Elise Jensen’s Foundation and Helsefonden.

      Appendix A. Supplementary data

      The following are the Supplementary data to this article:

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