Causal Association of Social Determinants of Health and Dynamic Impact on Mortality in Patients with Chronic Heart Failure

Causal Association of Social Determinants of Health and Dynamic Impact on Mortality in Patients with Chronic Heart Failure | Research Square window.SnipcartSettings = { analytics: { enabled: false } }; (function() { var accessVector = localStorage.getItem('access_vector') || ''; window.dataLayer = window.dataLayer || []; if (accessVector) { window.dataLayer.push({ user: { profile: { profileInfo: { snid: accessVector } } } }); } })(); (function(w,d,s,l,i){w[l]=w[l]||[];w[l].push({'gtm.start':new Date().getTime(),event:'gtm.js'});var f=d.getElementsByTagName(s)[0],j=d.createElement(s),dl=l!='dataLayer'?'&l='+l:'';j.async=true;j.src='https://www.googletagmanager.com/gtm.js?id='+i+dl;f.parentNode.insertBefore(j,f);})(window,document,'script','dataLayer','GTM-K279D39R'); Browse Preprints In Review Journals COVID-19 Preprints AJE Video Bytes Research Tools Research Promotion AJE Professional Editing AJE Rubriq About Preprint Platform In Review Editorial Policies Our Team Advisory Board Help Center Sign In Submit a Preprint Cite Share Download PDF Research Article Causal Association of Social Determinants of Health and Dynamic Impact on Mortality in Patients with Chronic Heart Failure Yujing Wang, Guisheng Song, Yongfeng Lv, Jingjing Yan, Yajing Wang, and 2 more This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-4496796/v1 This work is licensed under a CC BY 4.0 License Status: Posted Version 1 posted You are reading this latest preprint version Abstract Purpose The causality between social determinants of health (SDoH) and mortality in patients with chronic heart failure (CHF) is uncertain. Herein, we assessed the causality using inverse probability weighting (IPW) of marginal structural models (MSMs) during the course of CHF. Method A multicenter, prospective cohort study of 1377 patients with CHF were enrolled from September 2017. The social domain and two dimensions of Chronic Heart Failure Patient-Reported Outcomes Measure (CHF-PROM) was used to assess the SDoH, social support, and support utilization of patients with CHF. CHF-PROM and mortality information were obtained at 1, 3, and 6 months following patient discharge, and every 6 months thereafter at regular follow-ups. Logistic regression and IPW of MSMs were applied to analyze the SDoH, social support, and support utilization on mortality in patients with CHF. Results Logistic regression showed that at baseline, the SDoH, social support, and support utilization were not associated with mortality in patients with CHF. After adjusting for confounders, MSMs showed that the SDoH and social support were not associated with mortality at baseline. In contrast, low support utilization at baseline and unfavorable SDoH, low social support, and low support utilization during follow-up increased the risk of death in patients with CHF. Conclusion Through follow-up data and MSMs analysis, we found that the long-term out-of-hospital causal effects, but not one-time effects of SDoH, are risk factors for CHF mortality. SDoH should be taken seriously during the entire CHF process to prolong patients’ survival. Trial registration: The cohort number registered in the China Clinical Trial Registry is ChiCTR2100043337. URL: https://www.chictr.org.cn/showproj.html?proj=64980 Registered on February 11, 2021 Social determinants of health Chronic heart failure Inverse probability weighting Marginal structural models Chronic Heart Failure Patient-Reported Outcomes Measure Figures Figure 1 Figure 2 Figure 3 1. Introduction Chronic heart failure (CHF) is a complex clinical syndrome caused by structural and/or functional abnormalities of the heart, with a high prevalence and poor prognosis [ 1 ] . Data from the Global Burden of Disease Study show that heart failure affects more than 64 million people worldwide, with the age-standardized prevalence increasing by 0.6% over three years [ 2 ] . Various pharmacological and mechanical therapies have been developed with improved treatments to address traditional risk factors. However, patients still have a substantial residual risk [ 3 ] . Evaluating the impact of nontraditional risk factors on mortality in patients with heart failure is important for developing effective management measures to reduce associated residual risk and mortality [ 4 ] . Social determinants of health (SDoH) are pivotal nontraditional risk factors linked to healthcare inequities and impacting disease outcomes significantly [ 5 , 6 ] . Recent research has focused on the role of SDoH in patients with CHF [ 7 ] . The American Heart Association emphasizes that the complexity of heart failure management is compounded by the number of patients who experience the adverse downstream effects of SDoH and recommends using health data other than medical records to evaluate patient SDoH [ 8 ] . Most current studies have applied generic scales of social domains to assess the social status of patients with CHF [ 9 ] . However, these scales are not heart failure-specific and cannot be used to assess patients. Guidelines for managing heart failure recommend patient-reported outcomes to assess SDoH in patients with heart failure [ 10 , 11 ] . The social domain of CHF-Patient-Reported Outcomes Measure (CHF-PROM) developed by our group is more comprehensive than other PROM. The reliability and validity of CHF-PROM have already been verified [ 12 ] . Clarifying the relationship between SDoH and mortality in patients with CHF may guide the development of effective disease management strategies. However, some deficiencies in previous studies may have interfered with the results. First, most previous studies on SDoH were cross-sectional, ignoring the dynamic changes in social factors during progression. Therefore, the causality between SDoH and death cannot be truly reflected [ 13 ] . CHF is a chronic and lingering disease. There are unavoidable fluctuations in the social environment during the course of the disease. One-time data cannot reflect the real status of SDoH. Second, SDoH is uncertain, making randomized controlled trials (RCTs) expensive. Moreover, logistic and Cox regression methods cannot address the time-dependent confounding problem in longitudinal follow-up data and do not fully utilize the information on confounder changes over time. Causal inference can analyze real-world data and obtain effect estimates like RCTs [ 14 ] . The Marginal Structural Model (MSM) proposed by Robins removes the effects of confounders, achieves randomization by employing the inverse probability weighting (IPW) parameter estimation method, and allows accurate assessment of the time-dependent effects of exposure on outcomes [ 15 ] . We conducted a cohort study of patients with CHF in three hospitals in China. We collected information on the dynamic changes of SDoH during the course of the disease to determine the causal effect of SDoH on mortality in patients with CHF. The IPW of MSM was further applied to comprehensively infer the causal relationship between SDoH at baseline, follow-up, and death. The results of this study will provide new intervention targets of clinical treatment and management for CHF from a novel perspective. 2. Methods 2.1. Study population This study was a multicenter prospective cohort study. Patients from three medical centers in the Shanxi Province of the People’s Republic of China were enrolled between September 1, 2017, and August 30, 2022. The cohort number registered in the China Clinical Trial Registry is ChiCTR2100043337. The study was conducted according to the Declaration of Helsinki, and the Ethics Committee of Shanxi Medical University approved the study protocol. All patients provided written informed consent before enrollment. Patients were strictly selected according to the inclusion and exclusion criteria. Inclusion criteria were as follows: (1) age ≥ 18 years, (2) CHF diagnosis following the 2021 ESC Heart Failure Guidelines [ 1 ] , (3) NYHA Cardiac Function Class II-IV, and (4) HF treatment within the past month. The exclusion criteria were as follows: (1) acute cardiovascular events within the past two months, and (2) life expectancy of < 1 year due to other malignant diseases. 2.2. Data collection The baseline was defined as the first hospitalization for patients diagnosed with CHF during the study period. General information and CHF-PROM were assessed at baseline during hospitalization. CHF-PROM and outcome were collected by face-to-face counseling or telephone at months 1, 3, and 6 after discharge and every 6 months after that. Professionally trained individuals entered all data into the system to ensure quality. 2.2.1. General information General information included patient demographics, clinical information, comorbidities, and treatments. Demographics included age, sex, body mass index (BMI), marital status, education, occupation, household income, and health insurance type. Clinical information included heart rate, blood pressure, and medical history. Patient comorbidities included atrial fibrillation, coronary artery disease, hypertension, valvular disease, hyperlipidemia, diabetes mellitus, central nervous system disorders, chronic obstructive pulmonary disease, chronic renal insufficiency, and cancer. Treatments included medication and revascularization. Medications included antiplatelets, statins, nitrates, beta receptor inhibitors, angiotensin-converting enzyme inhibitors (ACEIs)/angiotensin II receptor blockers (ARBs), aldosterone receptor antagonists, diuretics, and digoxin. Revascularization included percutaneous coronary intervention (PCI) and coronary artery bypass grafting (CABG). Age, BMI, heart rate, and blood pressure were continuous variables, and all other variables were transformed into categorical variables, as shown in Table 1 . Table 1 Baseline characteristics and clinical profiles of patients with CHF Characteristics number (%)/median (IQR) Unfavorable SDoH (n = 630) Favorable SDoH (n = 747) χ 2 /Z P Age , median (IQR), years 67.47(59.02, 77.00) 66.61(56.59, 76.07) 1.286 0.20 Sex , n(%) - - - - Female 279(44.29%) 327(43.78%) 0.036 0.85 BMI , median (IQR), kg/m 2 23.94(21.45,27.06) 23.88(20.98,26.91) 1.515 0.13 Heart rate , median (IQR), bpm 75(67,86.25) 75(66,87) 0.103 0.92 Blood pressure , median (IQR), mmHg - - - - Systolic blood pressure 124(111,139) 125(111,140) 0.083 0.93 Diastolic blood pressure 75(67,84) 75(67,83) -1.023 0.31 Marriage , n(%) - - - - Single 10(1.59%) 15(2.01%) 1.587 0.66 Married 539(85.56%) 636(85.14%) - - Divorced 6(0.95%) 12(1.61%) - - Widowed 75(11.90%) 84(11.24%) - - Education , n(%) - - - - Illiterate 42(6.67%) 67(8.97%) 2.532 0.28 Low-level 184(29.21%) 209(27.98%) - - High-level 404(64.13%) 471(63.05%) - - Occupation , n(%) - - - - Manual labor 347(55.08%) 428(57.30%) 0.682 0.41 Non-manual labor 283(44.92%) 319(42.70%) - - Household income , n(%) - - - - Low-level 299(47.46%) 395(52.88%) 5.050 0.08 Moderate-level 324(51.43%) 348(46.59%) - - High-level 7(1.11%) 4(0.54%) - - Medical insurance , n(%) - - - - Urban 383(60.79%) 463(61.98%) 3.942 0.14 Rural 243(38.57%) 271(36.28%) - - Own expense 4(0.63%) 13(1.74%) - - Family history , n(%) 156(24.76%) 193(25.84%) 0.209 0.65 Past history , n(%) 418(66.35%) 454(60.78%) 4.570 0.03 Smoking , n(%) - - - - Never 305(48.41%) 374(50.07%) 1.645 0.44 Former 193(30.63%) 237(31.73%) - - Current 132(20.95%) 136(18.21%) - - Drinking , n(%) - - - - Never 421(66.83%) 514(68.81%) 0.631 0.73 Former 116(18.41%) 128(17.14%) - - Current 93(14.76%) 105(14.06%) - - Complications , n(%) - - - - Atrial fibrillation 228(36.19%) 300(40.16%) 2.279 0.13 Coronary heart disease 422(66.98%) 465(62.25%) 3.343 0.07 Hypertension 454(72.06%) 483(64.66%) 8.619 0.003 Valvular heart disease 325(51.59%) 402(53.82%) 0.681 0.41 Hyperlipidemia 425(67.46%) 482(64.52%) 1.310 0.25 Diabetes 218(34.60%) 251(33.60%) 0.153 0.70 Encephalopathy 104(16.51%) 126(16.87%) 0.032 0.86 COPD 142(22.54%) 139(18.61%) 3.253 0.07 Chronic renal failure 237(37.62%) 301(40.29%) 1.028 0.31 Cancers 7(1.11%) 14(1.87%) 1.325 0.25 Drugs , n(%) - - - - Antiplatelet drugs 450(71.43%) 487(65.19%) 6.109 0.01 Statins 467(74.13%) 517(69.21%) 4.051 0.04 Nitrates 263(41.75%) 287(38.42%) 1.576 0.21 β-receptor blockers 470(74.60%) 571(76.44%) 0.624 0.43 ACEIs/ARBs 251(39.84%) 247(33.07%) 6.796 0.009 Aldosterone receptor antagonist 382(60.63%) 479(64.12%) 1.775 0.18 Diuretics 429(68.10%) 523(70.01%) 0.589 0.44 Digoxin 116(18.41%) 145(19.41%) 0.222 0.64 PCI/CABG , n(%) 157(24.92%) 171(22.89%) 0.775 0.38 Abbreviations: ACEIs, angiotensin-converting enzyme inhibitors; ARBs, angiotensin receptor blockers; BMI, body mass index; CABG, coronary artery bypass grafting; CHF, chronic heart failure; COPD, chronic obstructive pulmonary disease; IQR, interquartile range; PCI, percutaneous coronary intervention Table 2 Impact of patient SDoH, social support, and support utilization scores at baseline upon death Exposure Model β SE Wald χ 2 P HR (95% CI ) SDoH - - - - - - Logistic regression - - - - - Statins 0.268 0.170 2.472 0.12 1.307(0.936,1.826) ACEIs/ARBs 0.600 0.184 10.626 0.001 1.821(1.270,2.612) SDoH 0.135 0.160 0.706 0.40 1.144(0.836,1.567) Constant -2.436 0.183 177.091 < 0.001 0.088 MSM - - - - - SDoH -0.134 0.124 1.421 0.23 0.875(0.702,1.090) Constant -1.809 0.775 545.117 < 0.001 0.164(0.141,0.191) Social support - - - - - - Logistic regression - - - - - Social support -0.015 0.161 0.009 0.93 0.985(0.719,1.350) Constant -1.867 0.122 232.405 < 0.001 0.155 MSM - - - - - Social support -0.015 0.161 0.009 0.93 0.985(0.719,1.350) Constant -1.867 0.122 232.405 < 0.001 0.155 Support utilization - - - - - - Logistic regression - - - - - Age 0.042 0.007 35.018 < 0.001 1.043(1.029,1.058) Atrial fibrillation -0.095 0.171 0.309 0.58 0.909(0.650,1.272) Statins 0.464 0.184 6.361 0.01 1.590(1.109,2.280) ACEIs/ARBs 0.368 0.190 3.775 0.05 1.445(0.997,2.096) Support utilization -0.318 0.183 3.027 0.08 0.728(0.509,1.041) Constant -4.998 0.546 83.851 < 0.001 0.007 MSM - - - - - Support utilization 0.367 0.1742 4.442 0.04 1.444(1.026,2.031) Constant -2.141 0.1485 207.804 < 0.001 0.118(0.088,0.157) Abbreviations: ACEIs, angiotensin-converting enzyme inhibitors; ARBs, angiotensin receptor blockers; CI, confidence interval; HR, hazard ratio; MSM, marginal structural model; SDoH, Social Determinants of Health; SE, standard error 2.2.2. SDoH CHF-PROM is divided into four domains, 12 dimensions, and 57 entries [ 12 ] . Specific scale information is detailed in Table S1 . In this study, the social domain of CHF-PROM was used to assess the SDoH status of patients. The social domain was divided into social support and support utilization. The items for social support included SOY1 (My family members care about my illness), SOY2 (My relatives, neighbors, and friends have asked about my illness), SOY3 (My colleagues care about my illness), SOY4 (I have received financial support from my relatives and friends), and SOY5 (I had received comfort and care from my family, relatives, and friends when I was in trouble). The items for support utilization included SOY6 (I am deeply involved in controlling the risk factors of heart failure), SOY7 (I talk to others voluntarily when I am in trouble), and SOY8 (I ask for help from others when I am in trouble) [ 12 ] . Each item was measured on a five-point Likert scale ranging from 0 to 4 to reflect the frequency of occurrence of each issue during the past two weeks (0 = never, 1 = occasionally, 2 = approximately half of the time, 3 = often, and 4 = almost every day). Items were set as positive or negative to facilitate patient understanding. Positively scored items were recorded as the original score plus 1, whereas negatively scored items were recorded as five minus the original score. The final scores of the SDoH, social support, and social utilization of the CHF-PROM were calculated by adding the scores of the corresponding items. A higher score represented a better SDoH status. This study defined exposure as an unfavorable SDoH status in patients with CHF. The overall social domain score of the scale was used to reflect the SDoH status of patients. The overall scores for the SDoH, social support dimension, and support utilization dimension were 40, 25, and 15, respectively. The median scores were used as nodes [ 16 ] . According to the nodes, patients were divided into unfavorable SDoH (< 27) and favorable SDoH (≥ 27) groups; low social support (< 17) and high social support (≥ 17) groups; and low support utilization (< 11) and high support utilization (≥ 11) groups. The lowest score was defined as the exposure group. Cronbach’s alpha coefficient assessed the quality of CHF-PROM data, yielding coefficients of 0.868, 0.726, and 0.809 for the SDoH, social support, and support utilization dimensions, respectively. 2.2.3. Outcomes The outcome was defined as all-cause death during the follow-up period, including both cardiac (due to cardiovascular diseases such as heart failure, acute myocardial infarction, and arrhythmias) and non-cardiac death unrelated to organic and functional cardiac disease. The death information was obtained from regular telephone follow-ups and inquiries to the death information system for reports on the cause of death registrations in Shanxi Province based on the patient’s identification number. 2.3. Missing values processing Marginal structural models require sufficient repeated-measures data points to estimate model parameters for reliable results, and deleting patients with < 3 follow-up visits avoid model overfitting, which produces unstable results [ 17 ] . Patients with 30% missing PROM data, or those who refused follow-up were excluded. Missing data included both non-time-varying and time-varying variables. For missing values in time-varying PROM variables, the average value of the two points in time before and after deletion was used to fill in the values [ 18 ] . Missing values of non-time-varying variables were implemented using the MissForest package in R 4.3.2 software [ 19 ] . 2.4. Descriptive analysis Data were described and analyzed using SPSS 26 software. Means and standard deviations (SDs) were used to describe normally distributed data. Medians and interquartile ranges were used to describe data that were not normally distributed. Qualitative data are presented as numbers (percentages). Depending on the type of data distribution, t -tests, chi-square tests, or Mann-Whitney U tests were used to compare the differences between variables in the death and survival groups. All statistical analyses were performed using two-tailed tests. 2.5. Screening confounding factors In this study, we performed a correlation analysis to screen for general information on variables associated with both exposure and outcome. We defined clusters of confounders screened for associations with unfavorable SDoH, low social support, and low support utilization with mortality as Clusters 1, 2, and 3, respectively. Cluster (a) represented baseline confounders, and cluster (b) represented confounders during long-term follow-up. The screened confounders were used to construct the MSM. 2.6 Marginal structural model This model has been widely used for causal inference in real-world research, especially for analyzing repeated measurements in longitudinal studies [ 20 ] . The core step of MSM is to control for confounders by assigning each individual a certain weight using IPW to obtain a virtual population. Analyzing this virtual population provides an unbiased estimation of treatment effects. This analysis ensures that the relationship between treatment and outcomes remains consistent with the original population rather than affected by confounders [ 15 , 21 ] . The probability of treatment levels for individuals must be estimated before using IPW. This study used classical logistic regression analysis to estimate treatment probability. Predicted probabilities of patients receiving treatment were calculated using each confounding factor as an independent variable and the exposure factor as the outcome [ 22 ] . Based on the probability estimation, the IPWs of patients were further calculated to balance confounding factors and construct a comparable virtual population. The formula is: A(k) indicates whether an individual receives exposure at time point k (unfavorable SDoH = 1, low social support = 1, low support utilization = 1). X represents baseline confounders. L represents time-dependent confounders. Hence, the numerator and denominator represent the probability that an individual receives exposure at time point k under the influence of baseline and time-dependent confounders, respectively. The IPW can be adjusted for treatment-level groups (e.g., unfavorable SDoH and high SDoH groups) to have the same distribution in population subgroups characterized by different confounders. $$SW={\prod }_{k=0}^{t}\frac{\left.f\left[A\left(k\right)\right]\right|\stackrel{-}{A}\left(k-1\right),X]}{\left.f[A（k）\right|\stackrel{-}{A}\left(k-1\right),\stackrel{-}{L}（k）]}$$ Percentile truncation was applied to prevent extreme weights from occurring during the calculations. The IPW obtained above was truncated (replacing the extreme values on both sides of the 2.5 and 97.5 percentiles with the value of that percentile point) to obtain stabilized weights (SWs) for each individual [ 23 ] . 2.7. Estimate effect We further applied a generalized estimating equation model to estimate the effect values and 95% confidence intervals (95% CI) between conditions such as SDoH and outcome events of patients. Unfavorable SDoH, low social support, and low support utilization were used as independent variables, respectively, patient death as the outcome, and truncated SW was included in the model. The effect values obtained from MSM represent the true causal effects after controlling time-dependent confounders. 2.8. Subgroup analysis In this study, patients with CHF were divided into subgroups according to age and sex, with long-term follow-up. In the subgroups, the relationships between unfavorable SDoH, low social support, low support utilization, and death were estimated using the IPW of MSM. 3. Results 3.1. Descriptive analysis A total of 1497 patients participated at baseline. During this period, 120 patients (8.02%) were excluded from follow-up owing to refusal of follow-up visits (n = 48) and died prematurely (n = 24). Before the end of the study, < 3 follow-up visits (n = 32), patients felt trial was too demanding and long (n = 10) and inability to contact patients by phone (n = 6). Finally, data from 1377 (91.98%) patients were analyzed. The baseline patient characteristics are shown in Table 1 . A total of 183 patients (13.29%) died during the follow-up period. Deceased individuals were older, had a lower BMI, were more likely to have a lower educational level, and had a higher likelihood of being unmarried or widowed. They had a higher prevalence of comorbidities, such as atrial fibrillation, valvular heart disease, chronic renal failure, and cancer. Patients who died were less likely to receive statins, β-receptor blockers, ACEIs/ARBs, or revascularization; however, they were more likely to be treated with diuretics. 3.2. Dynamic changes in SDoH scores The dynamic changes in SDoH reflected by the social domain scores of the CHF-PROM and the proportion of patients are shown in Fig. 2. Fig. 2A shows that the scores for SDoH, social support, and support utilization initially increased, continued to increase gradually, peaked at the 42nd-month post-discharge, and then sharply declined. This trend in Fig. 2B synchronized with the change in SDoH scores. 3.3. Screening of confounding factors The results of screening for confounding factors are shown in supplementary figure. The results of the univariate analysis at baseline showed that the confounders in Cluster 1(a) were statins and ACEIs/ARBs. Those in Cluster 2(a) were empty, and those in Cluster 3(a) were age, atrial fibrillation, statins, and ACEIs/ARBs. The results of the univariate analysis during follow-up showed that the confounders in Cluster 1(b) were BMI, education, valvular heart disease, ACEIs/ARBs, statins, diuretics, and revascularization. Cluster 2(b) confounders were age, BMI, valvular heart disease, cancers, ACEIs/ARBs, statins, and revascularization. Cluster 3(b) confounders were BMI, marital status, education, chronic renal failure, ACEIs/ARBs, statins, diuretics, and revascularization. 3.4. Causality estimation The results of the impact of patient SDoH, social support, and support utilization on death at baseline are shown in Table 2. The logistic regression model and IPW of MSM showed that SDoH and social support were not associated with mortality. The logistic regression model showed that support utilization was not associated with death. MSM showed that low support utilization increased the risk of death ( HR = 1.444, P = 0.035). The results of SDoH, social support, and support utilization during follow-up are presented in Fig. 3. Logistic regression modeling showed that patients with a favorable SDoH had an increased mortality risk. Revascularization had the greatest effect on patient mortality. MSM showed that patients with unfavorable SDoH had an increased risk of death. Logistic regression modeling results showed that patients with higher social support had a higher risk of death. Revascularization had the greatest impact on patient mortality. MSM showed that lower social support was associated with a higher risk of death. The logistic regression modelling results showed that patients’ support utilization was not significantly associated with death outcomes. Patients with a lower BMI had a higher risk of death, and those who underwent revascularization had a lower risk of death. The MSM showed that patients with low support utilization had a higher risk of death. 3.5. Subgroup analysis The results of subgroup analyses are presented in Table S2. In the younger group, patients with unfavorable SDoH, low social support, and low support utilization during follow-up had an increased risk of death. In the older groups, patients with unfavorable SDoH and low social support had an increased risk of death. There was no statistical association between support utilization and death in older patients. Subgroup analysis was stratified according to sex. Among male patients, the risk of death was higher in those with unfavorable SDoH, low social support, and low support utilization. Low social support had the highest impact on the risk of death. Among female patients, unfavorable SDoH and low social support were both associated with an increased risk of death. Among these, unfavorable SDoH played the greatest role in the risk of death. Low support utilization did not show a statistically significant association with death in female patients. Discussion Patients with CHF face additional challenges from social factors. The results showed that at baseline, only low support utilization increased the risk of death; however, during the course of the disease, unfavorable SDoH, low social support, and low support utilization all contributed to the death. This study is one of the few studies to comprehensively investigate the causality between SDoH throughout the course of disease and death in patients with CHF. Healthcare disparities related to SDoH considerably influence cardiovascular diseases [ 4 ] . This study provides new targets and ideas for residual risk to improve the prognosis of CHF by intervening in nontraditional risk factors of SDoH. RCTs face significant challenges when they come to social factors, while conventional observational cohort studies often lack adequate handling of time-dependent confounders. IPW of MSM can effectively reflect the true causality between SDoH and mortality outcomes in CHF patients. Previous studies have only assessed the impact of baseline SDoH on prognosis and yielded mixed results regarding the association between SDoH and CHF. This study performed causal analyses at both the baseline and post-follow-up and corrected the effects of the time-dependent confounders. At baseline, the results showed that SDoH was not associated with death in patients with CHF, similar to a cohort study in the United States [ 24 ] . Other studies showed that unfavorable SDoH was a risk factor for death in patients with cardiovascular disease [ 25 , 26 ] . The research results present paradoxes because they focus solely on cross-sectional studies at baseline, neglecting the influence of confounders or improper control of confounders. In this study, CHF-PROM was regularly collected during the follow-up period, which can accurately reflect the SDoH status of patients. Secondly, most studies that focused on the SDoH were real-world studies. Inevitably, multiple confounding factors in the analysis lead to biased risk-prediction results. MSM, a well-established causal inference model, addresses time-dependent confounders in longitudinal studies. The application of MSM ensures the reliability of results [ 27 , 28 ] . Third, some studies only assessed the social support received by patients, ignoring their utilization, which led to an incomplete reflection of SDoH. This study also confirmed the causality between low support utilization at baseline and death. The COACH secondary study noted that patients with low support utilization displayed poor self-care behaviors, which further affected the prognosis of patients with CHF [ 29 ] . This cohort study assessed the causal effect of SDoH, social support and support utilization in patients with CHF during the follow-up period. MSM was used to analyze the data. These results showed causality between SDoH, social support, support utilization, and death. This causality may be because the patient’s SDoH and social support at baseline did not impact death. However, during the course of the disease, unfavorable SDoH has cumulatively impacted patients with CHF [ 30 , 31 ] . The MSM results indicated that patients with more unfavorable SDoH had a higher risk of death. Patients with unfavorable SDoH are in unfriendly social environments; thus, they receive poor social support and medical resources [ 32 ] . In addition, during the course of the disease, unfavorable SDoH cause or aggravate the negative emotions of patients, which further affects the self-management of CHF and increases the risk of death [ 32 ] . An observational study of 119 patients with CHF over a 6-year follow-up found an association between social isolation and death [ 33 ] , which is consistent with our study. Our study had a larger sample size and focused on social risk factors. This study also confirmed that low support utilization by patients with CHF increased the risk of death. Higher utilization of social support plays a positive role in the self-management of patients with CHF by enhancing self-efficacy [ 34 ] . Caregivers and clinicians should prioritize patient support utilization while providing social support. This emphasis on support utilization could comprehensively reduce unfavorable SDoH and improve prognostic outcomes in patients with CHF. Overall, there is no causal relationship at baseline between patients’ unfavorable SDoH and mortality outcomes. However, the impact of unfavorable SDoH on patient outcomes is crucial for a considerable period after discharge. This finding underscores the importance of outpatient management and long-term attention to SDoH for patients with CHF. It is essential to pay more attention to patients in unfavorable social environments after discharge, as they may be more affected by various adverse factors. Furthermore, it is necessary to develop appropriate intervention strategies for risk factors associated with unfavorable SDoH, low social support, and low support utilization to improve patient outcomes. Screening for confounders associated with exposure and outcome is a critical step in MSM. In this study, the confounders identified might have been involved in mediating the causality between SDoH and mortality in patients with CHF. We found that confounders associated with all three exposures (unfavorable SDoH, low social support, and low support utilization) and death were low BMI without ACEIs/ARBs, statins, or revascularization. Confounders associated with outcomes and exposure included low BMI, low education, non-marital status, and the absence of ACEIs/ARBs, statins, or revascularization. Previous studies confirmed the phenomenon of an “obesity paradox” in CHF, viz. high BMI is associated with a better prognosis [ 35 ] . This finding is consistent with the results of the present study. BMI is associated with unfavorable SDoH in developed countries [ 36 , 37 ] . However, the Chinese Longitudinal Healthy Longevity Survey showed that patients with low BMI had more unfavorable SDoH [ 38 ] , which is consistent with the results of our study. Therefore, the relationship between BMI and SDoH varies across countries. Clinicians in China should pay more attention to patients with a low BMI. Our study also found that some therapeutic measures of patients affect their prognosis. Our study showed that using ACEIs/ARBs and statins improved outcomes in patients with CHF. The 2022 AHA/ACC/HFSA Guideline for the Management of Heart Failure suggests that ACEIs/ARBs improve the prognosis of heart failure. In contrast, statins are limited to patients with coronary artery disease combined with CHF [ 10 ] . This study suggested that patients who are not taking ACEIs/ARBs or statins have a lower socioeconomic status. Patients with a low socioeconomic status have poor access to health care, less motivation, and are unable to take medications as prescribed [ 39 ] . Additionally, this study found that revascularization is an effective measure for reducing patient mortality. There is insufficient evidence regarding the role of statins and revascularization on mortality in patients with CHF [ 10 , 40 ] . This research indicates that necessary medications and revascularization can substantially benefit patients. A cohort study of patients with CHF at the Tyumen Cardiology Research Center confirmed that most patients who underwent percutaneous coronary intervention had higher social support [ 41 ] , which is consistent with the findings of our study. The similarity in findings suggests the need for increased focus on patients who do not undergo revascularization. Therefore, outpatient management should be implemented to reduce the unfavorable SDoH throughout the entire course of CHF and improve prognosis. For example, effective social support policies should be formulated to promote patient health education and enhance patients’ utilization of social support. In the future, longitudinal causal mediation analysis could be conducted to propose interventions with causal evidence to reduce unfavorable SDoH. Despite the careful data collection and analysis design, this study had some limitations. First, the data in our study were primarily from the Shanxi Province of China, which limits generalizability and requires further validation in other populations. Second, 120 patients were lost to follow-up. This loss may have affected the internal validity of our results. Third, the MSM application assumed that all confounders were observed. However, unobserved confounders were present in the model. Conclusions In conclusion, this study using MSM provides causal evidence between SDoH and death in patients with CHF. We found that the long-term out-of-hospital causal effects, but not one-time effects of SDoH, are risk factors for CHF mortality. This increase in risk highlights the importance for clinicians and caregivers to address unfavorable SDoH, increase social support, and enhance support utilization throughout the course of the disease to improve the prognosis of patients with CHF. Declarations Funding This work was supported by the National Nature Science Foundation of China [Grant No. 82103958 and No. 82173631]; Shanxi Science and Technology Innovation Talent Team Project (Grant No. 202204051001026). Competing interests The authors have no relevant financial or non-financial interests to disclose. Ethics approval The cohort number registered in the China Clinical Trial Registry is ChiCTR2100043337. The study was conducted according to the Declaration of Helsinki, and the Ethics Committee of Shanxi Medical University approved the study protocol. Consent to Participate All participants provided written informed consent and received compensation for their time and effort. Data and Code availability The research data and code used in this study are available upon request. Please contact the corresponding author for access to the data and code used in this research. We strive to promote transparency and reproducibility in our research practices and are committed to making our data and code available to facilitate further analysis and validation of our findings. Authors’ contributions Jing Tian and Yanbo Zhang contributed to the conception and design of the study. Material preparation, data collection and analysis were performed by Yujing Wang, Yongfeng Lv, Jingjing Yan, Yajing Wang and Jing Tian. Yujing Wang wrote the first draft of the manuscript and all authors commented on previous versions of the manuscript. Guisheng Song was involved in revising this article. All authors read and approved the final manuscript. References Mcdonagh TA, Metra M, Adamo M, et al (2021) 2021 ESC Guidelines for the diagnosis and treatment of acute and chronic heart failure[J]. Eur Heart J, 42(36): 3599-3726. https://doi.org/10.1093/eurheartj/ehab368. Savarese G, Becher P, Lund L, et al (2023) Global burden of heart failure: a comprehensive and updated review of epidemiology[J]. Cardiovascular research, 118(17): 3272-3287. https://doi.org/10.1093/cvr/cvac013. Alkhalil M (2021) Mechanistic Insights to Target Atherosclerosis Residual Risk[J]. Curr Probl Cardiol, 46(3): 100432. https://doi.org/10.1016/j.cpcardiol.2019.06.004. Brandt EJ, Tobb K, Cambron JC, et al (2023) Assessing and Addressing Social Determinants of Cardiovascular Health: JACC State-of-the-Art Review[J]. J Am Coll Cardiol, 81(14): 1368-1385. https://doi.org/10.1016/j.jacc.2023.01.042. Marmot M, Friel S, Bell R, et al (2008) Closing the gap in a generation: health equity through action on the social determinants of health[J]. Lancet, 372(9650): 1661-1669. https://doi.org/10.1016/s0140-6736(08)61690-6. Frank J, Abel T, Campostrini S, et al (2020) The Social Determinants of Health: Time to Re-Think?[J]. Int J Environ Res Public Health, 17(16). https://doi.org/10.3390/ijerph17165856. Enard K, Coleman A, Yakubu R, et al (2023) Influence of Social Determinants of Health on Heart Failure Outcomes: A Systematic Review[J]. Journal of the American Heart Association, 12(3): e026590. https://doi.org/10.1161/jaha.122.026590. White-Williams C, Rossi LP, Bittner VA, et al (2020) Addressing Social Determinants of Health in the Care of Patients With Heart Failure: A Scientific Statement From the American Heart Association[J]. Circulation, 141(22): e841-e863. https://doi.org/10.1161/cir.0000000000000767. Olano-Lizarraga M, Wallström S, Martín-Martín J, et al (2022) Causes, experiences and consequences of the impact of chronic heart failure on the person´s social dimension: A scoping review[J]. Health Soc Care Community, 2022, 30(4): e842-e858. https://doi.org/10.1111/hsc.13680. Heidenreich PA, Bozkurt B, Aguilar D, et al (2022) 2022 AHA/ACC/HFSA Guideline for the Management of Heart Failure: A Report of the American College of Cardiology/American Heart Association Joint Committee on Clinical Practice Guidelines[J]. Circulation, 145(18): e895-e1032. https://doi.org/10.1161/CIR.0000000000001063. Manemann SM, Chamberlain AM, Roger VL, et al (2018) Perceived Social Isolation and Outcomes in Patients With Heart Failure[J]. J Am Heart Assoc, 7(11). https://doi.org/10.1161/jaha.117.008069. Tian J, Xue J, Hu X, et al (2018) CHF-PROM: validation of a patient-reported outcome measure for patients with chronic heart failure[J]. Health Qual Life Outcomes, 16(1): 51. https://doi.org/10.1186/s12955-018-0874-2. Graven LJ, Grant JS (2014) Social support and self-care behaviors in individuals with heart failure: an integrative review[J]. Int J Nurs Stud, 51(2): 320-333. https://doi.org/10.1016/j.ijnurstu.2013.06.013. Hernán MA, Robins JM (2016) Using Big Data to Emulate a Target Trial When a Randomized Trial Is Not Available[J]. Am J Epidemiol, 183(8): 758-764. https://doi.org/10.1093/aje/kwv254. Robins JM, Hernan MA, Brumback B (2000) Marginal structural models and causal inference in epidemiology[J]. Epidemiology (Cambridge, Mass.), 11(5): 550-560. https://doi.org/10.1097/00001648-200009000-00011. Gu X, Huang X, Zhang X, et al (2022) Development and Validation of a DNA Methylation-related Classifier of Circulating Tumour Cells to Predict Prognosis and to provide a therapeutic strategy in Lung Adenocarcinoma[J]. Int J Biol Sci, 18(13): 4984-5000. https://doi.org/10.7150/ijbs.75284. Mojaverian N, Moodie EE, Bliu A, et al (2015) The Impact of Sparse Follow-up on Marginal Structural Models for Time-to-Event Data[J]. Am J Epidemiol, 182(12): 1047-1055. https://doi.org/10.1093/aje/kwv152. Fox-Wasylyshyn SM, El-Masri MM (2005) Handling missing data in self-report measures[J]. Res Nurs Health, 28(6): 488-495. https://doi.org/10.1002/nur.20100. Hong S, Lynn HS (2020) Accuracy of random-forest-based imputation of missing data in the presence of non-normality, non-linearity, and interaction[J]. BMC Med Res Methodol, 20(1): 199. https://doi.org/10.1186/s12874-020-01080-1. Wang Y, Jiang J, Chen L, et al (2023) Is COPD mortality in South China causally linked to the long-term PM(1) exposure? Evidence from a large community-based cohort[J]. Ecotoxicol Environ Saf, 263: 115299. https://doi.org/10.1016/j.ecoenv.2023.115299. Mortimer KM, Neugebauer R, Van Der Laan M, et al (2005) An application of model-fitting procedures for marginal structural models[J]. Am J Epidemiol, 162(4): 382-388. https://doi.org/10.1093/aje/kwi208. Seaman SR, White IR (2013) Review of inverse probability weighting for dealing with missing data[J]. Stat Methods Med Res, 22(3): 278-295. https://doi.org/10.1177/0962280210395740. Seaman SR, White IR, Copas AJ, et al (2012) Combining multiple imputation and inverse-probability weighting[J]. Biometrics, 68(1): 129-137. https://doi.org/10.1111/j.1541-0420.2011.01666.x. Sterling MR, Ringel JB, Pinheiro LC, et al (2020) Social Determinants of Health and 90-Day Mortality After Hospitalization for Heart Failure in the REGARDS Study[J]. J Am Heart Assoc, 9(9): e014836. https://doi.org/10.1161/jaha.119.014836. Rangachari P, Govindarajan A, Mehta R, et al (2022) The relationship between Social Determinants of Health (SDoH) and death from cardiovascular disease or opioid use in counties across the United States (2009-2018)[J]. BMC Public Health, 22(1): 236. https://doi.org/10.1186/s12889-022-12653-8. Bundy JD, Mills KT, He H, et al (2023) Social determinants of health and premature death among adults in the USA from 1999 to 2018: a national cohort study[J]. Lancet Public Health, 8(6): e422-e431. https://doi.org/10.1016/s2468-2667(23)00081-6. Cabrera C, Quélen C, Ouwens M, et al (2022) Evaluating a Cox marginal structural model to assess the comparative effectiveness of inhaled corticosteroids versus no inhaled corticosteroid treatment in chronic obstructive pulmonary disease[J]. Ann Epidemiol, 67: 19-28. https://doi.org/10.1016/j.annepidem.2021.11.004. Yang Z, Toh S, Li X, et al (2022) Statin use is associated with lower risk of dementia in stroke patients: a community-based cohort study with inverse probability weighted marginal structural model analysis[J]. Eur J Epidemiol, 37(6): 615-627. https://doi.org/10.1007/s10654-022-00856-7. Gallagher R, Luttik ML, Jaarsma T (2011) Social support and self-care in heart failure[J]. J Cardiovasc Nurs, 26(6): 439-445. https://doi.org/10.1097/JCN.0b013e31820984e1. World Health Organization (2021) Social Determinants of Health. [EB/OL]. https://www.who.int/health-topics/social-determinants-of-health#tab=tab_1. Accessed 27 May 2023 Xie D, Yang W, Jepson C, et al (2017) Statistical Methods for Modeling Time-Updated Exposures in Cohort Studies of Chronic Kidney Disease[J]. Clin J Am Soc Nephrol, 12(11): 1892-1899. https://doi.org/10.2215/cjn.00650117. Yan J, Tian J, Yang H, et al (2022) The Causal Effects of Anxiety-Mediated Social Support on Death in Patients with Chronic Heart Failure: A Multicenter Cohort Study[J]. Psychol Res Behav Manag, 15: 3287-3296. https://doi.org/10.2147/prbm.S387222. Murberg TA (2004) Long-term effect of social relationships on mortality in patients with congestive heart failure[J]. Int J Psychiatry Med, 34(3): 207-217. https://doi.org/10.2190/gkj2-p8bd-v59x-mjnq. Calero-Molina E, Moliner P, Hidalgo E, et al (2022) Interplay between psychosocial and heart failure related factors may partially explain limitations in self-efficacy in patients with heart failure: Insights from a real-world cohort of 1,123 patients[J]. Int J Nurs Stud, 129: 104233. https://doi.org/10.1016/j.ijnurstu.2022.104233. Jones NR, Ordóñez-Mena JM, Roalfe AK, et al (2023) Body mass index and survival in people with heart failure[J]. Heart, https://doi.org/10.1136/heartjnl-2023-322459. Blasingame M, Samuels LR, Heerman WJ (2023) The Combined Effects of Social Determinants of Health on Childhood Overweight and Obesity[J]. Child Obes, https://doi.org/10.1089/chi.2022.0222. Howell CR, Zhang L, Yi N, et al (2022) Associations between cardiometabolic disease severity, social determinants of health (SDoH), and poor COVID-19 outcomes[J]. Obesity (Silver Spring), 30(7): 1483-1494. https://doi.org/10.1002/oby.23440. Yang L, Wang H, Cheng J (2022) Association of social capital with obesity among older adults in China: a cross-sectional analysis[J]. BMC Geriatr, 22(1): 871. https://doi.org/10.1186/s12877-022-03566-7. Savarese G, Bodegard J, Norhammar A, et al (2021) Heart failure drug titration, discontinuation, mortality and heart failure hospitalization risk: a multinational observational study (US, UK and Sweden)[J]. Eur J Heart Fail, 23(9): 1499-1511. https://doi.org/10.1002/ejhf.2271. Perera D, Clayton T, O'kane PD, et al (2022) Percutaneous Revascularization for Ischemic Left Ventricular Dysfunction[J]. N Engl J Med, 387(15): 1351-1360. https://doi.org/10.1056/NEJMoa2206606. Pushkarev G, Kuznetsov V, Yaroslavskaya E, et al (2019) Social support for patients with coronary artery disease after percutaneous coronary intervention[J]. J Psychosom Res, 119: 74-78. https://doi.org/10.1016/j.jpsychores.2019.02.011. Additional Declarations No competing interests reported. Supplementary Files SupplementaryFigure.jpg Supplementary Figure Causal network structure of SDoH, social support, and support utilization. Confounding factors related to 1) both death and the unfavorable SDoH group, 2) both death and the low social support group, 3) both death and the low support utilization group. The a) baseline b) and follow-up datasets were analyzed. Abbreviations: ACEIs, angiotensin-converting enzyme inhibitors; AF, atrial fibrillation; ARBs, angiotensin receptor blockers; BMI, body mass index; CABG, coronary artery bypass grafting; CRF, chronic renal failure; PCI, percutaneous coronary intervention; SDoH, Social Determinants of Health; VHD, valvular heart disease TabelS1.docx TabelS2.docx Cite Share Download PDF Status: Posted Version 1 posted You are reading this latest preprint version Research Square lets you share your work early, gain feedback from the community, and start making changes to your manuscript prior to peer review in a journal. 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Also discoverable on Platform About Our Team In Review Editorial Policies Advisory Board Help Center Resources Author Services Accessibility API Access RSS feed Manage Cookie Preferences © Research Square 2026 | ISSN 2693-5015 (online) Privacy Policy Terms of Service Do Not Sell My Personal Information {"props":{"pageProps":{"initialData":{"identity":"rs-4496796","acceptedTermsAndConditions":true,"allowDirectSubmit":true,"archivedVersions":[],"articleType":"Research Article","associatedPublications":[],"authors":[{"id":313001487,"identity":"9620b79f-b1eb-496b-ad0e-878d558fcbc1","order_by":0,"name":"Yujing Wang","email":"","orcid":"","institution":"Shanxi Medical University","correspondingAuthor":false,"prefix":"","firstName":"Yujing","middleName":"","lastName":"Wang","suffix":""},{"id":313001488,"identity":"ef901dda-29af-43c3-b4fb-5abbd0c35116","order_by":1,"name":"Guisheng Song","email":"","orcid":"","institution":"University of Minnesota","correspondingAuthor":false,"prefix":"","firstName":"Guisheng","middleName":"","lastName":"Song","suffix":""},{"id":313001489,"identity":"de9d76ec-3271-41e3-9c5a-bfe80d0996f8","order_by":2,"name":"Yongfeng Lv","email":"","orcid":"","institution":"Shanxi Medical University","correspondingAuthor":false,"prefix":"","firstName":"Yongfeng","middleName":"","lastName":"Lv","suffix":""},{"id":313001494,"identity":"2227e834-704b-4f9f-865f-81908d37d0aa","order_by":3,"name":"Jingjing Yan","email":"","orcid":"","institution":"Shanxi Medical University","correspondingAuthor":false,"prefix":"","firstName":"Jingjing","middleName":"","lastName":"Yan","suffix":""},{"id":313001495,"identity":"5f29a4ab-3425-4a68-a547-d654b2591b18","order_by":4,"name":"Yajing Wang","email":"","orcid":"","institution":"Shanxi Medical University","correspondingAuthor":false,"prefix":"","firstName":"Yajing","middleName":"","lastName":"Wang","suffix":""},{"id":313001497,"identity":"6a090e63-7527-4f3c-a56c-30bd35a0a80e","order_by":5,"name":"Yanbo Zhang","email":"","orcid":"","institution":"Shanxi Medical University","correspondingAuthor":false,"prefix":"","firstName":"Yanbo","middleName":"","lastName":"Zhang","suffix":""},{"id":313001500,"identity":"cfee9d40-03c6-460d-8249-3564d0541a8e","order_by":6,"name":"Jing Tian","email":"data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAZAAAAAyAQMAAABI0h/eAAAABlBMVEX///8AAABVwtN+AAAACXBIWXMAAA7EAAAOxAGVKw4bAAAAqUlEQVRIiWNgGAWjYNACHhsefv4GUnQc4EmTkZxxgCQtDIdtDBoSiFQtPyM7TfqDzHkeA4YDjB8+5hChxeDM2W0SB3hu85gzNzBLztxGjBb2XogWy4YDbMy8xGiRb+YFaTnHY3AggUgtDMfBthwgQQvQL5stzvAk80jOONhMnF/kZ+RuvFHZY2fPz9988MNHohwGAow9YLKBWPUg8IMUxaNgFIyCUTDiAADGiDXdlTL/hQAAAABJRU5ErkJggg==","orcid":"","institution":"the 1st Hospital of Shanxi Medical University","correspondingAuthor":true,"prefix":"","firstName":"Jing","middleName":"","lastName":"Tian","suffix":""}],"badges":[],"createdAt":"2024-05-29 11:41:54","currentVersionCode":1,"declarations":"","doi":"10.21203/rs.3.rs-4496796/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-4496796/v1","draftVersion":[],"editorialEvents":[],"editorialNote":"","failedWorkflow":false,"files":[{"id":58315997,"identity":"253e4820-20c5-4334-9c76-edaf367d2a94","added_by":"auto","created_at":"2024-06-13 21:00:39","extension":"png","order_by":1,"title":"Figure 1","display":"","copyAsset":false,"role":"figure","size":2372201,"visible":true,"origin":"","legend":"\u003cp\u003eFlow diagram illustrating the participants for each time point during follow-up\u003c/p\u003e","description":"","filename":"Figure1.png","url":"https://assets-eu.researchsquare.com/files/rs-4496796/v1/3eeae7e4815f173cf17cd559.png"},{"id":58314986,"identity":"eb8cd4f8-3d7a-4dc4-9723-2975161870ad","added_by":"auto","created_at":"2024-06-13 20:52:39","extension":"png","order_by":2,"title":"Figure 2","display":"","copyAsset":false,"role":"figure","size":398815,"visible":true,"origin":"","legend":"\u003cp\u003eDynamic change of SDoH \u0026nbsp;A) Changes in SDoH, social support, and support utilization scores in patients with CHF. The horizontal axis represents follow-up time, and the vertical axis represents SDoH, social support, and support utilization scores. B) Dynamic change of the percentage of patients in the favorable SDoH, high social support, and high support utilization groups. The horizontal axis represents follow-up time, and the vertical axis represents the percentage of patients in the high-scoring group.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAbbreviations: \u003c/strong\u003eCHF, chronic heart failure;\u003cstrong\u003e \u003c/strong\u003eSDoH, Social Determinants of Health\u003c/p\u003e","description":"","filename":"Figure2.png","url":"https://assets-eu.researchsquare.com/files/rs-4496796/v1/98c92e445508aff76990761b.png"},{"id":58314989,"identity":"da9ba6eb-bf42-48e1-9d31-31d48653512a","added_by":"auto","created_at":"2024-06-13 20:52:39","extension":"png","order_by":3,"title":"Figure 3","display":"","copyAsset":false,"role":"figure","size":1827846,"visible":true,"origin":"","legend":"\u003cp\u003eImpact of patient SDoH, social support, and support utilization during follow-up upon death\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAbbreviations:\u003c/strong\u003e ACEIs, angiotensin-converting enzyme inhibitors; ARBs, angiotensin receptor blockers; BMI, body mass index; CABG, coronary artery bypass grafting; CRF, chronic renal failure; PCI, percutaneous coronary intervention; VHD, valvular heart disease; SDoH, Social Determinants of Health\u003c/p\u003e","description":"","filename":"Figure3.png","url":"https://assets-eu.researchsquare.com/files/rs-4496796/v1/075ac6ea6bc521a5f22832b9.png"},{"id":58968962,"identity":"c7ab7c91-2dfc-4020-a165-7415d5886068","added_by":"auto","created_at":"2024-06-24 19:52:43","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":5910318,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-4496796/v1/1b038573-2dc3-43f2-b2de-1dfcfc2485e9.pdf"},{"id":58314990,"identity":"dc9ca642-84e8-4469-be33-01614dfd6114","added_by":"auto","created_at":"2024-06-13 20:52:39","extension":"jpg","order_by":1,"title":"","display":"","copyAsset":false,"role":"supplement","size":253198,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eSupplementary Figure \u003c/strong\u003e\u0026nbsp;Causal network structure of SDoH, social support, and support utilization. Confounding factors related to 1) both death and the unfavorable SDoH group, 2) both death and the low social support group, 3) both death and the low support utilization group. The a) baseline b) and follow-up datasets were analyzed.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAbbreviations: \u003c/strong\u003eACEIs, angiotensin-converting enzyme inhibitors; AF, atrial fibrillation; ARBs, angiotensin receptor blockers; BMI, body mass index; CABG, coronary artery bypass grafting; CRF, chronic renal failure; PCI, percutaneous coronary intervention; SDoH, Social Determinants of Health; VHD, valvular heart disease\u003c/p\u003e","description":"","filename":"SupplementaryFigure.jpg","url":"https://assets-eu.researchsquare.com/files/rs-4496796/v1/d96b35ecac857a9ec01b0265.jpg"},{"id":58314991,"identity":"c3e3930b-073a-45a7-b14e-a92132dcaa5e","added_by":"auto","created_at":"2024-06-13 20:52:39","extension":"docx","order_by":2,"title":"","display":"","copyAsset":false,"role":"supplement","size":16234,"visible":true,"origin":"","legend":"","description":"","filename":"TabelS1.docx","url":"https://assets-eu.researchsquare.com/files/rs-4496796/v1/6be8eb9b0bd8b4e90db3ba24.docx"},{"id":58316646,"identity":"94ba86b5-1b29-463f-8fb1-027659899dec","added_by":"auto","created_at":"2024-06-13 21:08:39","extension":"docx","order_by":3,"title":"","display":"","copyAsset":false,"role":"supplement","size":16328,"visible":true,"origin":"","legend":"","description":"","filename":"TabelS2.docx","url":"https://assets-eu.researchsquare.com/files/rs-4496796/v1/50e4192e97f893cd82dea168.docx"}],"financialInterests":"No competing interests reported.","formattedTitle":"Causal Association of Social Determinants of Health and Dynamic Impact on Mortality in Patients with Chronic Heart Failure","fulltext":[{"header":"1. Introduction","content":"\u003cp\u003eChronic heart failure (CHF) is a complex clinical syndrome caused by structural and/or functional abnormalities of the heart, with a high prevalence and poor prognosis \u003csup\u003e[\u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e1\u003c/span\u003e]\u003c/sup\u003e. Data from the Global Burden of Disease Study show that heart failure affects more than 64\u0026nbsp;million people worldwide, with the age-standardized prevalence increasing by 0.6% over three years \u003csup\u003e[\u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2\u003c/span\u003e]\u003c/sup\u003e. Various pharmacological and mechanical therapies have been developed with improved treatments to address traditional risk factors. However, patients still have a substantial residual risk \u003csup\u003e[\u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e3\u003c/span\u003e]\u003c/sup\u003e. Evaluating the impact of nontraditional risk factors on mortality in patients with heart failure is important for developing effective management measures to reduce associated residual risk and mortality \u003csup\u003e[\u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e4\u003c/span\u003e]\u003c/sup\u003e.\u003c/p\u003e \u003cp\u003eSocial determinants of health (SDoH) are pivotal nontraditional risk factors linked to healthcare inequities and impacting disease outcomes significantly \u003csup\u003e[\u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e5\u003c/span\u003e, \u003cspan citationid=\"CR6\" class=\"CitationRef\"\u003e6\u003c/span\u003e]\u003c/sup\u003e. Recent research has focused on the role of SDoH in patients with CHF \u003csup\u003e[\u003cspan citationid=\"CR7\" class=\"CitationRef\"\u003e7\u003c/span\u003e]\u003c/sup\u003e. The American Heart Association emphasizes that the complexity of heart failure management is compounded by the number of patients who experience the adverse downstream effects of SDoH and recommends using health data other than medical records to evaluate patient SDoH \u003csup\u003e[\u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e8\u003c/span\u003e]\u003c/sup\u003e. Most current studies have applied generic scales of social domains to assess the social status of patients with CHF \u003csup\u003e[\u003cspan citationid=\"CR9\" class=\"CitationRef\"\u003e9\u003c/span\u003e]\u003c/sup\u003e. However, these scales are not heart failure-specific and cannot be used to assess patients. Guidelines for managing heart failure recommend patient-reported outcomes to assess SDoH in patients with heart failure \u003csup\u003e[\u003cspan citationid=\"CR10\" class=\"CitationRef\"\u003e10\u003c/span\u003e, \u003cspan citationid=\"CR11\" class=\"CitationRef\"\u003e11\u003c/span\u003e]\u003c/sup\u003e. The social domain of CHF-Patient-Reported Outcomes Measure (CHF-PROM) developed by our group is more comprehensive than other PROM. The reliability and validity of CHF-PROM have already been verified \u003csup\u003e[\u003cspan citationid=\"CR12\" class=\"CitationRef\"\u003e12\u003c/span\u003e]\u003c/sup\u003e.\u003c/p\u003e \u003cp\u003eClarifying the relationship between SDoH and mortality in patients with CHF may guide the development of effective disease management strategies. However, some deficiencies in previous studies may have interfered with the results. First, most previous studies on SDoH were cross-sectional, ignoring the dynamic changes in social factors during progression. Therefore, the causality between SDoH and death cannot be truly reflected \u003csup\u003e[\u003cspan citationid=\"CR13\" class=\"CitationRef\"\u003e13\u003c/span\u003e]\u003c/sup\u003e. CHF is a chronic and lingering disease. There are unavoidable fluctuations in the social environment during the course of the disease. One-time data cannot reflect the real status of SDoH. Second, SDoH is uncertain, making randomized controlled trials (RCTs) expensive.\u003c/p\u003e \u003cp\u003eMoreover, logistic and Cox regression methods cannot address the time-dependent confounding problem in longitudinal follow-up data and do not fully utilize the information on confounder changes over time. Causal inference can analyze real-world data and obtain effect estimates like RCTs \u003csup\u003e[\u003cspan citationid=\"CR14\" class=\"CitationRef\"\u003e14\u003c/span\u003e]\u003c/sup\u003e. The Marginal Structural Model (MSM) proposed by Robins removes the effects of confounders, achieves randomization by employing the inverse probability weighting (IPW) parameter estimation method, and allows accurate assessment of the time-dependent effects of exposure on outcomes \u003csup\u003e[\u003cspan citationid=\"CR15\" class=\"CitationRef\"\u003e15\u003c/span\u003e]\u003c/sup\u003e. We conducted a cohort study of patients with CHF in three hospitals in China. We collected information on the dynamic changes of SDoH during the course of the disease to determine the causal effect of SDoH on mortality in patients with CHF. The IPW of MSM was further applied to comprehensively infer the causal relationship between SDoH at baseline, follow-up, and death. The results of this study will provide new intervention targets of clinical treatment and management for CHF from a novel perspective.\u003c/p\u003e"},{"header":"2. Methods","content":"\u003cdiv id=\"Sec3\"\u003e\n \u003ch2\u003e2.1. Study population\u003c/h2\u003e\n \u003cp\u003eThis study was a multicenter prospective cohort study. Patients from three medical centers in the Shanxi Province of the People\u0026rsquo;s Republic of China were enrolled between September 1, 2017, and August 30, 2022. The cohort number registered in the China Clinical Trial Registry is ChiCTR2100043337. The study was conducted according to the Declaration of Helsinki, and the Ethics Committee of Shanxi Medical University approved the study protocol. All patients provided written informed consent before enrollment.\u003c/p\u003e\n \u003cp\u003ePatients were strictly selected according to the inclusion and exclusion criteria. Inclusion criteria were as follows: (1) age\u0026thinsp;\u0026ge;\u0026thinsp;18 years, (2) CHF diagnosis following the 2021 ESC Heart Failure Guidelines \u003csup\u003e[\u003cspan\u003e1\u003c/span\u003e]\u003c/sup\u003e, (3) NYHA Cardiac Function Class II-IV, and (4) HF treatment within the past month. The exclusion criteria were as follows: (1) acute cardiovascular events within the past two months, and (2) life expectancy of \u0026lt;\u0026thinsp;1 year due to other malignant diseases.\u003c/p\u003e\n\u003c/div\u003e\n\u003cdiv id=\"Sec4\"\u003e\n \u003ch2\u003e2.2. Data collection\u003c/h2\u003e\n \u003cp\u003eThe baseline was defined as the first hospitalization for patients diagnosed with CHF during the study period. General information and CHF-PROM were assessed at baseline during hospitalization. CHF-PROM and outcome were collected by face-to-face counseling or telephone at months 1, 3, and 6 after discharge and every 6 months after that. Professionally trained individuals entered all data into the system to ensure quality.\u003c/p\u003e\n \u003cdiv id=\"Sec5\"\u003e\n \u003ch2\u003e2.2.1. General information\u003c/h2\u003e\n \u003cp\u003eGeneral information included patient demographics, clinical information, comorbidities, and treatments. Demographics included age, sex, body mass index (BMI), marital status, education, occupation, household income, and health insurance type. Clinical information included heart rate, blood pressure, and medical history. Patient comorbidities included atrial fibrillation, coronary artery disease, hypertension, valvular disease, hyperlipidemia, diabetes mellitus, central nervous system disorders, chronic obstructive pulmonary disease, chronic renal insufficiency, and cancer. Treatments included medication and revascularization. Medications included antiplatelets, statins, nitrates, beta receptor inhibitors, angiotensin-converting enzyme inhibitors (ACEIs)/angiotensin II receptor blockers (ARBs), aldosterone receptor antagonists, diuretics, and digoxin. Revascularization included percutaneous coronary intervention (PCI) and coronary artery bypass grafting (CABG). Age, BMI, heart rate, and blood pressure were continuous variables, and all other variables were transformed into categorical variables, as shown in Table\u0026nbsp;\u003cspan\u003e1\u003c/span\u003e.\u003c/p\u003e\n \u003cdiv\u003e\n \u003ctable id=\"Tab1\" border=\"1\"\u003e\n \u003ccaption language=\"En\"\u003e\n \u003cdiv\u003eTable 1\u003c/div\u003e\n \u003cdiv\u003e\n \u003cp\u003eBaseline characteristics and clinical profiles of patients with CHF\u003c/p\u003e\n \u003c/div\u003e\n \u003c/caption\u003e\n \u003cthead\u003e\n \u003ctr\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eCharacteristics\u003c/p\u003e\n \u003cp\u003enumber (%)/median (IQR)\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eUnfavorable SDoH\u003c/p\u003e\n \u003cp\u003e(n\u0026thinsp;=\u0026thinsp;630)\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eFavorable SDoH\u003c/p\u003e\n \u003cp\u003e(n\u0026thinsp;=\u0026thinsp;747)\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003e\u003cem\u003e\u0026chi;\u003c/em\u003e\u003csup\u003e\u003cem\u003e2\u003c/em\u003e\u003c/sup\u003e\u003cem\u003e/Z\u003c/em\u003e\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003e\u003cem\u003eP\u003c/em\u003e\u003c/p\u003e\n \u003c/th\u003e\n \u003c/tr\u003e\n \u003c/thead\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cstrong\u003eAge\u003c/strong\u003e, median (IQR), years\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e67.47(59.02, 77.00)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e66.61(56.59, 76.07)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1.286\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.20\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cstrong\u003eSex\u003c/strong\u003e, n(%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e-\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e-\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e-\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e-\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eFemale\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e279(44.29%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e327(43.78%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.036\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.85\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cstrong\u003eBMI\u003c/strong\u003e, median (IQR), kg/m\u003csup\u003e2\u003c/sup\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e23.94(21.45,27.06)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e23.88(20.98,26.91)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1.515\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.13\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cstrong\u003eHeart rate\u003c/strong\u003e, median (IQR), bpm\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e75(67,86.25)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e75(66,87)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.103\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.92\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cstrong\u003eBlood pressure\u003c/strong\u003e, median (IQR), mmHg\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e-\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e-\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e-\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e-\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eSystolic blood pressure\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e124(111,139)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e125(111,140)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.083\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.93\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eDiastolic blood pressure\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e75(67,84)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e75(67,83)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e-1.023\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.31\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cstrong\u003eMarriage\u003c/strong\u003e, n(%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e-\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e-\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e-\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e-\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eSingle\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e10(1.59%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e15(2.01%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1.587\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.66\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eMarried\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e539(85.56%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e636(85.14%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e-\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e-\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eDivorced\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e6(0.95%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e12(1.61%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e-\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e-\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eWidowed\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e75(11.90%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e84(11.24%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e-\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e-\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cstrong\u003eEducation\u003c/strong\u003e, n(%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e-\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e-\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e-\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e-\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eIlliterate\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e42(6.67%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e67(8.97%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e2.532\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.28\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eLow-level\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e184(29.21%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e209(27.98%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e-\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e-\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eHigh-level\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e404(64.13%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e471(63.05%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e-\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e-\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cstrong\u003eOccupation\u003c/strong\u003e, n(%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e-\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e-\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e-\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e-\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eManual labor\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e347(55.08%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e428(57.30%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.682\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.41\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eNon-manual labor\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e283(44.92%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e319(42.70%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e-\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e-\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cstrong\u003eHousehold income\u003c/strong\u003e, n(%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e-\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e-\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e-\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e-\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eLow-level\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e299(47.46%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e395(52.88%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e5.050\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.08\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eModerate-level\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e324(51.43%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e348(46.59%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e-\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e-\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eHigh-level\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e7(1.11%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e4(0.54%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e-\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e-\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cstrong\u003eMedical insurance\u003c/strong\u003e, n(%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e-\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e-\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e-\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e-\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eUrban\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e383(60.79%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e463(61.98%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e3.942\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.14\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eRural\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e243(38.57%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e271(36.28%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e-\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e-\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eOwn expense\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e4(0.63%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e13(1.74%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e-\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e-\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cstrong\u003eFamily history\u003c/strong\u003e, n(%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e156(24.76%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e193(25.84%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.209\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.65\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cstrong\u003ePast history\u003c/strong\u003e, n(%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e418(66.35%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e454(60.78%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e4.570\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.03\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cstrong\u003eSmoking\u003c/strong\u003e, n(%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e-\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e-\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e-\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e-\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eNever\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e305(48.41%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e374(50.07%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1.645\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.44\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eFormer\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e193(30.63%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e237(31.73%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e-\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e-\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eCurrent\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e132(20.95%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e136(18.21%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e-\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e-\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cstrong\u003eDrinking\u003c/strong\u003e, n(%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e-\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e-\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e-\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e-\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eNever\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e421(66.83%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e514(68.81%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.631\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.73\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eFormer\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e116(18.41%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e128(17.14%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e-\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e-\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eCurrent\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e93(14.76%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e105(14.06%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e-\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e-\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cstrong\u003eComplications\u003c/strong\u003e, n(%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e-\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e-\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e-\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e-\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eAtrial fibrillation\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e228(36.19%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e300(40.16%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e2.279\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.13\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eCoronary heart disease\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e422(66.98%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e465(62.25%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e3.343\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.07\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eHypertension\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e454(72.06%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e483(64.66%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e8.619\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.003\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eValvular heart disease\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e325(51.59%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e402(53.82%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.681\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.41\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eHyperlipidemia\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e425(67.46%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e482(64.52%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1.310\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.25\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eDiabetes\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e218(34.60%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e251(33.60%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.153\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.70\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eEncephalopathy\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e104(16.51%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e126(16.87%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.032\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.86\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eCOPD\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e142(22.54%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e139(18.61%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e3.253\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.07\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eChronic renal failure\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e237(37.62%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e301(40.29%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1.028\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.31\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eCancers\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e7(1.11%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e14(1.87%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1.325\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.25\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cstrong\u003eDrugs\u003c/strong\u003e, n(%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e-\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e-\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e-\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e-\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eAntiplatelet drugs\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e450(71.43%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e487(65.19%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e6.109\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.01\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eStatins\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e467(74.13%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e517(69.21%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e4.051\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.04\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eNitrates\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e263(41.75%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e287(38.42%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1.576\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.21\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u0026beta;-receptor blockers\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e470(74.60%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e571(76.44%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.624\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.43\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eACEIs/ARBs\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e251(39.84%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e247(33.07%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e6.796\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.009\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eAldosterone receptor antagonist\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e382(60.63%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e479(64.12%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1.775\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.18\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eDiuretics\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e429(68.10%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e523(70.01%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.589\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.44\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eDigoxin\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e116(18.41%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e145(19.41%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.222\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.64\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cstrong\u003ePCI/CABG\u003c/strong\u003e, n(%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e157(24.92%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e171(22.89%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.775\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.38\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n \u003c/table\u003e\n \u003c/div\u003e\n \u003cp\u003e\u003cstrong\u003eAbbreviations:\u003c/strong\u003e ACEIs, angiotensin-converting enzyme inhibitors; ARBs, angiotensin receptor blockers; BMI, body mass index; CABG, coronary artery bypass grafting; CHF, chronic heart failure; COPD, chronic obstructive pulmonary disease; IQR, interquartile range; PCI, percutaneous coronary intervention\u0026nbsp;\u003c/p\u003e\n \u003cdiv\u003e\n \u003ctable id=\"Tab3\" border=\"1\"\u003e\n \u003ccaption language=\"En\"\u003e\n \u003cdiv\u003eTable 2\u003c/div\u003e\n \u003cdiv\u003e\n \u003cp\u003eImpact of patient SDoH, social support, and support utilization scores at baseline upon death\u003c/p\u003e\n \u003c/div\u003e\n \u003c/caption\u003e\n \u003cthead\u003e\n \u003ctr\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eExposure\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eModel\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003e\u003cem\u003e\u0026beta;\u003c/em\u003e\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eSE\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eWald \u003cem\u003e\u0026chi;\u003c/em\u003e\u003csup\u003e2\u003c/sup\u003e\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003e\u003cem\u003eP\u003c/em\u003e\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003e\u003cem\u003eHR\u003c/em\u003e (95%\u003cem\u003eCI\u003c/em\u003e)\u003c/p\u003e\n \u003c/th\u003e\n \u003c/tr\u003e\n \u003c/thead\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cstrong\u003eSDoH\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cstrong\u003e-\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e-\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e-\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e-\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e-\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e-\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cstrong\u003eLogistic regression\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e-\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e-\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e-\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e-\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e-\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eStatins\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.268\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.170\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e2.472\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.12\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1.307(0.936,1.826)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eACEIs/ARBs\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.600\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.184\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e10.626\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1.821(1.270,2.612)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eSDoH\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.135\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.160\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.706\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.40\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1.144(0.836,1.567)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eConstant\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e-2.436\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.183\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e177.091\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.088\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cstrong\u003eMSM\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e-\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e-\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e-\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e-\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e-\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eSDoH\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e-0.134\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.124\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1.421\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.23\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.875(0.702,1.090)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eConstant\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e-1.809\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.775\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e545.117\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.164(0.141,0.191)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cstrong\u003eSocial support\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e-\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e-\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e-\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e-\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e-\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e-\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cstrong\u003eLogistic regression\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e-\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e-\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e-\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e-\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e-\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eSocial support\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e-0.015\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.161\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.009\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.93\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.985(0.719,1.350)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eConstant\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e-1.867\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.122\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e232.405\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.155\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cstrong\u003eMSM\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e-\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e-\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e-\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e-\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e-\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eSocial support\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e-0.015\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.161\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.009\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.93\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.985(0.719,1.350)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eConstant\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e-1.867\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.122\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e232.405\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.155\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cstrong\u003eSupport utilization\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e-\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e-\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e-\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e-\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e-\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e-\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cstrong\u003eLogistic regression\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e-\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e-\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e-\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e-\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e-\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eAge\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.042\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.007\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e35.018\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1.043(1.029,1.058)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eAtrial fibrillation\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e-0.095\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.171\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.309\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.58\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.909(0.650,1.272)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eStatins\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.464\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.184\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e6.361\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.01\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1.590(1.109,2.280)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eACEIs/ARBs\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.368\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.190\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e3.775\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.05\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1.445(0.997,2.096)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eSupport utilization\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e-0.318\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.183\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e3.027\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.08\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.728(0.509,1.041)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eConstant\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e-4.998\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.546\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e83.851\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.007\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cstrong\u003eMSM\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e-\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e-\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e-\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e-\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e-\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eSupport utilization\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.367\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.1742\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e4.442\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.04\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1.444(1.026,2.031)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eConstant\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e-2.141\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.1485\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e207.804\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.118(0.088,0.157)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n \u003c/table\u003e\n \u003c/div\u003e\n \u003cdiv\u003e\u003cstrong\u003eAbbreviations:\u0026nbsp;\u003c/strong\u003eACEIs, angiotensin-converting enzyme inhibitors; ARBs, angiotensin receptor blockers; CI, confidence interval; HR, hazard ratio; MSM, marginal structural model; SDoH, Social Determinants of Health; SE, standard error\u003c/div\u003e\n \u003c/div\u003e\n \u003cdiv id=\"Sec6\"\u003e\n \u003ch2\u003e2.2.2. SDoH\u003c/h2\u003e\n \u003cp\u003eCHF-PROM is divided into four domains, 12 dimensions, and 57 entries \u003csup\u003e[\u003cspan\u003e12\u003c/span\u003e]\u003c/sup\u003e. Specific scale information is detailed in Table \u003cspan\u003eS1\u003c/span\u003e. In this study, the social domain of CHF-PROM was used to assess the SDoH status of patients. The social domain was divided into social support and support utilization. The items for social support included SOY1 (My family members care about my illness), SOY2 (My relatives, neighbors, and friends have asked about my illness), SOY3 (My colleagues care about my illness), SOY4 (I have received financial support from my relatives and friends), and SOY5 (I had received comfort and care from my family, relatives, and friends when I was in trouble). The items for support utilization included SOY6 (I am deeply involved in controlling the risk factors of heart failure), SOY7 (I talk to others voluntarily when I am in trouble), and SOY8 (I ask for help from others when I am in trouble) \u003csup\u003e[\u003cspan\u003e12\u003c/span\u003e]\u003c/sup\u003e. Each item was measured on a five-point Likert scale ranging from 0 to 4 to reflect the frequency of occurrence of each issue during the past two weeks (0\u0026thinsp;=\u0026thinsp;never, 1\u0026thinsp;=\u0026thinsp;occasionally, 2\u0026thinsp;=\u0026thinsp;approximately half of the time, 3\u0026thinsp;=\u0026thinsp;often, and 4\u0026thinsp;=\u0026thinsp;almost every day). Items were set as positive or negative to facilitate patient understanding. Positively scored items were recorded as the original score plus 1, whereas negatively scored items were recorded as five minus the original score. The final scores of the SDoH, social support, and social utilization of the CHF-PROM were calculated by adding the scores of the corresponding items. A higher score represented a better SDoH status.\u003c/p\u003e\n \u003cp\u003eThis study defined exposure as an unfavorable SDoH status in patients with CHF. The overall social domain score of the scale was used to reflect the SDoH status of patients.\u003c/p\u003e\n \u003cp\u003eThe overall scores for the SDoH, social support dimension, and support utilization dimension were 40, 25, and 15, respectively. The median scores were used as nodes \u003csup\u003e[\u003cspan\u003e16\u003c/span\u003e]\u003c/sup\u003e. According to the nodes, patients were divided into unfavorable SDoH (\u0026lt;\u0026thinsp;27) and favorable SDoH (\u0026ge;\u0026thinsp;27) groups; low social support (\u0026lt;\u0026thinsp;17) and high social support (\u0026ge;\u0026thinsp;17) groups; and low support utilization (\u0026lt;\u0026thinsp;11) and high support utilization (\u0026ge;\u0026thinsp;11) groups. The lowest score was defined as the exposure group. Cronbach\u0026rsquo;s alpha coefficient assessed the quality of CHF-PROM data, yielding coefficients of 0.868, 0.726, and 0.809 for the SDoH, social support, and support utilization dimensions, respectively.\u003c/p\u003e\n \u003c/div\u003e\n \u003cdiv id=\"Sec7\"\u003e\n \u003ch2\u003e2.2.3. Outcomes\u003c/h2\u003e\n \u003cp\u003eThe outcome was defined as all-cause death during the follow-up period, including both cardiac (due to cardiovascular diseases such as heart failure, acute myocardial infarction, and arrhythmias) and non-cardiac death unrelated to organic and functional cardiac disease. The death information was obtained from regular telephone follow-ups and inquiries to the death information system for reports on the cause of death registrations in Shanxi Province based on the patient\u0026rsquo;s identification number.\u003c/p\u003e\n \u003c/div\u003e\n\u003c/div\u003e\n\u003cdiv id=\"Sec8\"\u003e\n \u003ch2\u003e2.3. Missing values processing\u003c/h2\u003e\n \u003cp\u003eMarginal structural models require sufficient repeated-measures data points to estimate model parameters for reliable results, and deleting patients with \u0026lt;\u0026thinsp;3 follow-up visits avoid model overfitting, which produces unstable results \u003csup\u003e[\u003cspan\u003e17\u003c/span\u003e]\u003c/sup\u003e. Patients with \u0026lt;\u0026thinsp;3 follow-up visits, \u0026gt;\u0026thinsp;30% missing PROM data, or those who refused follow-up were excluded. Missing data included both non-time-varying and time-varying variables. For missing values in time-varying PROM variables, the average value of the two points in time before and after deletion was used to fill in the values \u003csup\u003e[\u003cspan\u003e18\u003c/span\u003e]\u003c/sup\u003e. Missing values of non-time-varying variables were implemented using the MissForest package in R 4.3.2 software \u003csup\u003e[\u003cspan\u003e19\u003c/span\u003e]\u003c/sup\u003e.\u003c/p\u003e\n\u003c/div\u003e\n\u003cdiv id=\"Sec9\"\u003e\n \u003ch2\u003e2.4. Descriptive analysis\u003c/h2\u003e\n \u003cp\u003eData were described and analyzed using SPSS 26 software. Means and standard deviations (SDs) were used to describe normally distributed data. Medians and interquartile ranges were used to describe data that were not normally distributed. Qualitative data are presented as numbers (percentages). Depending on the type of data distribution, \u003cem\u003et\u003c/em\u003e-tests, chi-square tests, or Mann-Whitney U tests were used to compare the differences between variables in the death and survival groups. All statistical analyses were performed using two-tailed tests.\u003c/p\u003e\n\u003c/div\u003e\n\u003cdiv id=\"Sec10\"\u003e\n \u003ch2\u003e2.5. Screening confounding factors\u003c/h2\u003e\n \u003cp\u003eIn this study, we performed a correlation analysis to screen for general information on variables associated with both exposure and outcome. We defined clusters of confounders screened for associations with unfavorable SDoH, low social support, and low support utilization with mortality as Clusters 1, 2, and 3, respectively. Cluster (a) represented baseline confounders, and cluster (b) represented confounders during long-term follow-up. The screened confounders were used to construct the MSM.\u003c/p\u003e\n\u003c/div\u003e\n\u003cdiv id=\"Sec11\"\u003e\n \u003ch2\u003e2.6 Marginal structural model\u003c/h2\u003e\n \u003cp\u003eThis model has been widely used for causal inference in real-world research, especially for analyzing repeated measurements in longitudinal studies \u003csup\u003e[\u003cspan\u003e20\u003c/span\u003e]\u003c/sup\u003e. The core step of MSM is to control for confounders by assigning each individual a certain weight using IPW to obtain a virtual population. Analyzing this virtual population provides an unbiased estimation of treatment effects. This analysis ensures that the relationship between treatment and outcomes remains consistent with the original population rather than affected by confounders \u003csup\u003e[\u003cspan\u003e15\u003c/span\u003e, \u003cspan\u003e21\u003c/span\u003e]\u003c/sup\u003e.\u003c/p\u003e\n \u003cp\u003eThe probability of treatment levels for individuals must be estimated before using IPW. This study used classical logistic regression analysis to estimate treatment probability. Predicted probabilities of patients receiving treatment were calculated using each confounding factor as an independent variable and the exposure factor as the outcome \u003csup\u003e[\u003cspan\u003e22\u003c/span\u003e]\u003c/sup\u003e.\u003c/p\u003e\n \u003cp\u003eBased on the probability estimation, the IPWs of patients were further calculated to balance confounding factors and construct a comparable virtual population. The formula is: A(k) indicates whether an individual receives exposure at time point k (unfavorable SDoH\u0026thinsp;=\u0026thinsp;1, low social support\u0026thinsp;=\u0026thinsp;1, low support utilization\u0026thinsp;=\u0026thinsp;1). X represents baseline confounders. L represents time-dependent confounders. Hence, the numerator and denominator represent the probability that an individual receives exposure at time point k under the influence of baseline and time-dependent confounders, respectively. The IPW can be adjusted for treatment-level groups (e.g., unfavorable SDoH and high SDoH groups) to have the same distribution in population subgroups characterized by different confounders.\u003c/p\u003e\n \u003cdiv id=\"Equa\"\u003e\n \u003cdiv id=\"FileID_Equa\" name=\"EquationSource\"\u003e$$SW={\\prod }_{k=0}^{t}\\frac{\\left.f\\left[A\\left(k\\right)\\right]\\right|\\stackrel{-}{A}\\left(k-1\\right),X]}{\\left.f[A（k）\\right|\\stackrel{-}{A}\\left(k-1\\right),\\stackrel{-}{L}（k）]}$$\u003c/div\u003e\n \u003c/div\u003e\n \u003cp\u003ePercentile truncation was applied to prevent extreme weights from occurring during the calculations. The IPW obtained above was truncated (replacing the extreme values on both sides of the 2.5 and 97.5 percentiles with the value of that percentile point) to obtain stabilized weights (SWs) for each individual \u003csup\u003e[\u003cspan\u003e23\u003c/span\u003e]\u003c/sup\u003e.\u003c/p\u003e\n\u003c/div\u003e\n\u003cdiv id=\"Sec12\"\u003e\n \u003ch2\u003e2.7. Estimate effect\u003c/h2\u003e\n \u003cp\u003eWe further applied a generalized estimating equation model to estimate the effect values and 95% confidence intervals (95% CI) between conditions such as SDoH and outcome events of patients. Unfavorable SDoH, low social support, and low support utilization were used as independent variables, respectively, patient death as the outcome, and truncated SW was included in the model. The effect values obtained from MSM represent the true causal effects after controlling time-dependent confounders.\u003c/p\u003e\n\u003c/div\u003e\n\u003cdiv id=\"Sec13\"\u003e\n \u003ch2\u003e2.8. Subgroup analysis\u003c/h2\u003e\n \u003cp\u003eIn this study, patients with CHF were divided into subgroups according to age and sex, with long-term follow-up. In the subgroups, the relationships between unfavorable SDoH, low social support, low support utilization, and death were estimated using the IPW of MSM.\u003c/p\u003e\n\u003c/div\u003e"},{"header":"3. Results","content":"\u003cdiv id=\"Sec15\" class=\"Section2\"\u003e \u003ch2\u003e3.1. Descriptive analysis\u003c/h2\u003e \u003cp\u003eA total of 1497 patients participated at baseline. During this period, 120 patients (8.02%) were excluded from follow-up owing to refusal of follow-up visits (n\u0026thinsp;=\u0026thinsp;48) and died prematurely (n\u0026thinsp;=\u0026thinsp;24). Before the end of the study, \u0026lt;\u0026thinsp;3 follow-up visits (n\u0026thinsp;=\u0026thinsp;32), patients felt trial was too demanding and long (n\u0026thinsp;=\u0026thinsp;10) and inability to contact patients by phone (n\u0026thinsp;=\u0026thinsp;6). Finally, data from 1377 (91.98%) patients were analyzed.\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003eThe baseline patient characteristics are shown in Table\u0026nbsp;\u003cspan refid=\"Tab1\" class=\"InternalRef\"\u003e1\u003c/span\u003e. A total of 183 patients (13.29%) died during the follow-up period. Deceased individuals were older, had a lower BMI, were more likely to have a lower educational level, and had a higher likelihood of being unmarried or widowed. They had a higher prevalence of comorbidities, such as atrial fibrillation, valvular heart disease, chronic renal failure, and cancer. Patients who died were less likely to receive statins, β-receptor blockers, ACEIs/ARBs, or revascularization; however, they were more likely to be treated with diuretics.\u003c/p\u003e\u003cp\u003e\u003cem\u003e3.2. Dynamic changes in SDoH scores\u003c/em\u003e\u003c/p\u003e\n\u003cp\u003eThe dynamic changes in SDoH reflected by the social domain scores of the CHF-PROM and the proportion of patients are shown in Fig. 2. Fig. 2A shows that the scores for SDoH, social support, and support utilization initially increased, continued to increase gradually, peaked at the 42nd-month post-discharge, and then sharply declined. This trend in Fig. 2B synchronized with the change in SDoH scores.\u003c/p\u003e\n\u003cp\u003e\u003cem\u003e3.3. Screening of confounding factors\u0026nbsp;\u003c/em\u003e\u003c/p\u003e\n\u003cp\u003eThe results of screening for confounding factors are shown in supplementary figure. The results of\u0026nbsp;the univariate analysis at baseline showed that the confounders in Cluster 1(a) were statins and ACEIs/ARBs.\u0026nbsp;Those\u0026nbsp;in Cluster 2(a) were empty, and\u0026nbsp;those in Cluster 3(a) were age, atrial fibrillation, statins, and ACEIs/ARBs.\u003c/p\u003e\n\u003cp\u003eThe results of the univariate analysis\u0026nbsp;during follow-up showed that the confounders in Cluster 1(b) were BMI, education, valvular heart disease, ACEIs/ARBs, statins, diuretics, and revascularization.\u0026nbsp;Cluster 2(b) confounders were age, BMI, valvular heart disease, cancers, ACEIs/ARBs, statins, and revascularization.\u0026nbsp;Cluster 3(b) confounders were BMI, marital status, education, chronic renal failure, ACEIs/ARBs, statins, diuretics, and revascularization.\u003c/p\u003e\n\u003cp\u003e\u003cem\u003e3.4. Causality estimation\u003c/em\u003e\u003c/p\u003e\n\u003cp\u003eThe results of the impact of patient SDoH, social support,\u0026nbsp;and support utilization on death\u0026nbsp;at baseline are shown in Table 2. The logistic regression model and IPW of MSM showed that SDoH and social support were not associated with mortality. The logistic regression model showed that support utilization was not associated with\u0026nbsp;death.\u0026nbsp;MSM showed that low support utilization increased the risk of death (\u003cem\u003eHR\u0026nbsp;\u003c/em\u003e= 1.444, \u003cem\u003eP\u0026nbsp;\u003c/em\u003e= 0.035).\u003c/p\u003e\n\u003cp\u003e\u0026nbsp;The results of SDoH, social support, and support utilization during follow-up are presented in Fig. 3. Logistic regression modeling showed that patients with a favorable SDoH had an increased mortality risk. Revascularization had the greatest effect on patient mortality. MSM showed that patients with unfavorable SDoH had an increased risk of death. Logistic regression modeling results showed that patients with higher social support had a higher risk of death. Revascularization had the greatest impact on patient mortality. MSM showed that lower social support was associated with a higher risk of death. The logistic regression modelling results showed that patients\u0026rsquo; support utilization was not significantly associated with death outcomes. Patients with a lower BMI had a higher risk of death, and those who underwent revascularization had a lower risk of death. The MSM showed that patients with low support utilization had a higher risk of death.\u003c/p\u003e\n\u003cp\u003e\u003cem\u003e3.5. Subgroup analysis\u003c/em\u003e\u003c/p\u003e\n\u003cp\u003eThe results of subgroup analyses are presented in Table S2. In the younger group, patients with unfavorable SDoH, low social support, and low support utilization during follow-up\u0026nbsp;had an\u0026nbsp;increased risk of death. In the older groups, patients with unfavorable SDoH and low social support\u0026nbsp;had an increased\u0026nbsp;risk of death. There was no statistical association between support utilization and death in older patients.\u003c/p\u003e\n\u003cp\u003eSubgroup analysis was stratified according to sex. Among male patients, the risk of death was higher in those with unfavorable SDoH, low social support, and low support utilization. Low social support had the highest impact on the risk of death. Among female patients, unfavorable SDoH and low social support were both associated with an increased risk of death. Among these, unfavorable SDoH played the greatest role in the risk of death. Low support utilization did not show a statistically significant association with death in female patients.\u003c/p\u003e"},{"header":"Discussion","content":"\u003cp\u003ePatients with CHF face additional challenges from social factors. The results showed that at baseline, only low support utilization increased the risk of death; however, during the course of the disease, unfavorable SDoH, low social support, and low support utilization all contributed to the death. This study is one of the few studies to comprehensively investigate the causality between SDoH throughout the course of disease and death in patients with CHF. Healthcare disparities related to SDoH considerably influence cardiovascular diseases \u003csup\u003e[\u003c/sup\u003e\u003csup\u003e4\u003c/sup\u003e\u003csup\u003e]\u003c/sup\u003e. This study provides new targets and ideas for residual risk to improve the prognosis of CHF by intervening in nontraditional risk factors of SDoH. \u003c/p\u003e\n\u003cp\u003eRCTs face significant challenges when they come to social factors, while conventional observational cohort studies often lack adequate handling of time-dependent confounders. IPW of MSM can effectively reflect the true causality between SDoH and mortality outcomes in CHF patients. Previous studies have only assessed the impact of baseline SDoH on prognosis and yielded mixed results regarding the association between SDoH and CHF. This study performed causal analyses at both the baseline and post-follow-up and corrected the effects of the time-dependent confounders. At baseline, the results showed that SDoH was not associated with death in patients with CHF, similar to a cohort study in the United States \u003csup\u003e[\u003c/sup\u003e\u003csup\u003e24\u003c/sup\u003e\u003csup\u003e]\u003c/sup\u003e. Other studies showed that unfavorable SDoH was a risk factor for death in patients with cardiovascular disease \u003csup\u003e[\u003c/sup\u003e\u003csup\u003e25\u003c/sup\u003e\u003csup\u003e,\u003c/sup\u003e\u003csup\u003e26\u003c/sup\u003e\u003csup\u003e]\u003c/sup\u003e. The research results present paradoxes because they focus solely on cross-sectional studies at baseline, neglecting the influence of confounders or improper control of confounders. In this study, CHF-PROM was regularly collected during the follow-up period, which can accurately reflect the SDoH status of patients. Secondly, most studies that focused on the SDoH were real-world studies. Inevitably, multiple confounding factors in the analysis lead to biased risk-prediction results. MSM, a well-established causal inference model, addresses time-dependent confounders in longitudinal studies. The application of MSM ensures the reliability of results \u003csup\u003e[\u003c/sup\u003e\u003csup\u003e27\u003c/sup\u003e\u003csup\u003e,\u003c/sup\u003e\u003csup\u003e28\u003c/sup\u003e\u003csup\u003e]\u003c/sup\u003e. Third, some studies only assessed the social support received by patients, ignoring their utilization, which led to an incomplete reflection of SDoH. This study also confirmed the causality between low support utilization at baseline and death. The COACH secondary study noted that patients with low support utilization displayed poor self-care behaviors, which further affected the prognosis of patients with CHF \u003csup\u003e[\u003c/sup\u003e\u003csup\u003e29\u003c/sup\u003e\u003csup\u003e]\u003c/sup\u003e. \u003c/p\u003e\n\u003cp\u003eThis cohort study assessed the causal effect of SDoH, social support and support utilization in patients with CHF during the follow-up period. MSM was used to analyze the data. These results showed causality between SDoH, social support, support utilization, and death. This causality may be because the patient\u0026rsquo;s SDoH and social support at baseline did not impact death. However, during the course of the disease, unfavorable SDoH has cumulatively impacted patients with CHF \u003csup\u003e[\u003c/sup\u003e\u003csup\u003e30\u003c/sup\u003e\u003csup\u003e,\u003c/sup\u003e\u003csup\u003e31\u003c/sup\u003e\u003csup\u003e]\u003c/sup\u003e. The MSM results indicated that patients with more unfavorable SDoH had a higher risk of death. Patients with unfavorable SDoH are in unfriendly social environments; thus, they receive poor social support and medical resources \u003csup\u003e[\u003c/sup\u003e\u003csup\u003e32\u003c/sup\u003e\u003csup\u003e]\u003c/sup\u003e. In addition, during the course of the disease, unfavorable SDoH cause or aggravate the negative emotions of patients, which further affects the self-management of CHF and increases the risk of death \u003csup\u003e[\u003c/sup\u003e\u003csup\u003e32\u003c/sup\u003e\u003csup\u003e]\u003c/sup\u003e. An observational study of 119 patients with CHF over a 6-year follow-up found an association between social isolation and death \u003csup\u003e[\u003c/sup\u003e\u003csup\u003e33\u003c/sup\u003e\u003csup\u003e]\u003c/sup\u003e, which is consistent with our study. Our study had a larger sample size and focused on social risk factors. This study also confirmed that low support utilization by patients with CHF increased the risk of death. Higher utilization of social support plays a positive role in the self-management of patients with CHF by enhancing self-efficacy \u003csup\u003e[\u003c/sup\u003e\u003csup\u003e34\u003c/sup\u003e\u003csup\u003e]\u003c/sup\u003e. Caregivers and clinicians should prioritize patient support utilization while providing social support. This emphasis on support utilization could comprehensively reduce unfavorable SDoH and improve prognostic outcomes in patients with CHF. Overall, there is no causal relationship at baseline between patients\u0026rsquo; unfavorable SDoH and mortality outcomes. However, the impact of unfavorable SDoH on patient outcomes is crucial for a considerable period after discharge. This finding underscores the importance of outpatient management and long-term attention to SDoH for patients with CHF. It is essential to pay more attention to patients in unfavorable social environments after discharge, as they may be more affected by various adverse factors. Furthermore, it is necessary to develop appropriate intervention strategies for risk factors associated with unfavorable SDoH, low social support, and low support utilization to improve patient outcomes.\u003c/p\u003e\n\u003cp\u003eScreening for confounders associated with exposure and outcome is a critical step in MSM. In this study, the confounders identified might have been involved in mediating the causality between SDoH and mortality in patients with CHF. We found that confounders associated with all three exposures (unfavorable SDoH, low social support, and low support utilization) and death were low BMI without ACEIs/ARBs, statins, or revascularization. Confounders associated with outcomes and exposure included low BMI, low education, non-marital status, and the absence of ACEIs/ARBs, statins, or revascularization. Previous studies confirmed the phenomenon of an \u0026ldquo;obesity paradox\u0026rdquo; in CHF, viz. high BMI is associated with a better prognosis \u003csup\u003e[\u003c/sup\u003e\u003csup\u003e35\u003c/sup\u003e\u003csup\u003e]\u003c/sup\u003e. This finding is consistent with the results of the present study. BMI is associated with unfavorable SDoH in developed countries \u003csup\u003e[\u003c/sup\u003e\u003csup\u003e36\u003c/sup\u003e\u003csup\u003e,\u003c/sup\u003e\u003csup\u003e37\u003c/sup\u003e\u003csup\u003e]\u003c/sup\u003e. However, the Chinese Longitudinal Healthy Longevity Survey showed that patients with low BMI had more unfavorable SDoH \u003csup\u003e[\u003c/sup\u003e\u003csup\u003e38\u003c/sup\u003e\u003csup\u003e]\u003c/sup\u003e, which is consistent with the results of our study.\u003c/p\u003e\n\u003cp\u003eTherefore, the relationship between BMI and SDoH varies across countries. Clinicians in China should pay more attention to patients with a low BMI. Our study also found that some therapeutic measures of patients affect their prognosis. Our study showed that using ACEIs/ARBs and statins improved outcomes in patients with CHF. The 2022 AHA/ACC/HFSA Guideline for the Management of Heart Failure suggests that ACEIs/ARBs improve the prognosis of heart failure.\u003c/p\u003e\n\u003cp\u003eIn contrast, statins are limited to patients with coronary artery disease combined with CHF \u003csup\u003e[\u003c/sup\u003e\u003csup\u003e10\u003c/sup\u003e\u003csup\u003e]\u003c/sup\u003e. This study suggested that patients who are not taking ACEIs/ARBs or statins have a lower socioeconomic status. Patients with a low socioeconomic status have poor access to health care, less motivation, and are unable to take medications as prescribed \u003csup\u003e[\u003c/sup\u003e\u003csup\u003e39\u003c/sup\u003e\u003csup\u003e]\u003c/sup\u003e.\u003c/p\u003e\n\u003cp\u003eAdditionally, this study found that revascularization is an effective measure for reducing patient mortality. There is insufficient evidence regarding the role of statins and revascularization on mortality in patients with CHF \u003csup\u003e[\u003c/sup\u003e\u003csup\u003e10\u003c/sup\u003e\u003csup\u003e,\u003c/sup\u003e\u003csup\u003e40\u003c/sup\u003e\u003csup\u003e]\u003c/sup\u003e. This research indicates that necessary medications and revascularization can substantially benefit patients. A cohort study of patients with CHF at the Tyumen Cardiology Research Center confirmed that most patients who underwent percutaneous coronary intervention had higher social support \u003csup\u003e[\u003c/sup\u003e\u003csup\u003e41\u003c/sup\u003e\u003csup\u003e]\u003c/sup\u003e, which is consistent with the findings of our study. The similarity in findings suggests the need for increased focus on patients who do not undergo revascularization.\u003c/p\u003e\n\u003cp\u003eTherefore, outpatient management should be implemented to reduce the unfavorable SDoH throughout the entire course of CHF and improve prognosis. For example, effective social support policies should be formulated to promote patient health education and enhance patients\u0026rsquo; utilization of social support. In the future, longitudinal causal mediation analysis could be conducted to propose interventions with causal evidence to reduce unfavorable SDoH.\u003c/p\u003e\n\u003cp\u003eDespite the careful data collection and analysis design, this study had some limitations. First, the data in our study were primarily from the Shanxi Province of China, which limits generalizability and requires further validation in other populations. Second, 120 patients were lost to follow-up. This loss may have affected the internal validity of our results. Third, the MSM application assumed that all confounders were observed. However, unobserved confounders were present in the model.\u003c/p\u003e"},{"header":"Conclusions","content":"\u003cp\u003eIn conclusion, this study using MSM provides causal evidence between SDoH and death in patients with CHF. We found that the long-term out-of-hospital causal effects, but not one-time effects of SDoH, are risk factors for CHF mortality. This increase in risk highlights the importance for clinicians and caregivers to address unfavorable SDoH, increase social support, and enhance support utilization throughout the course of the disease to improve the prognosis of patients with CHF.\u003c/p\u003e\n"},{"header":"Declarations","content":"\u003cp\u003e\u003cem\u003eFunding\u003c/em\u003e\u003c/p\u003e\n\u003cp\u003eThis work was supported by the National Nature Science Foundation of China [Grant No. 82103958 and No. 82173631]; Shanxi Science and Technology Innovation Talent Team Project (Grant No. 202204051001026).\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u0026nbsp;\u003cem\u003eCompeting interests\u003c/em\u003e\u003c/p\u003e\n\u003cp\u003eThe authors have no relevant financial or non-financial interests to disclose.\u003c/p\u003e\n\u003cp\u003e\u0026nbsp;\u003cem\u003eEthics approval\u003c/em\u003e\u003c/p\u003e\n\u003cp\u003eThe cohort number registered in the China Clinical Trial Registry is ChiCTR2100043337. The study was conducted according to the Declaration of Helsinki, and the Ethics Committee of Shanxi Medical University approved the study protocol.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u0026nbsp;\u003cem\u003eConsent to Participate\u003c/em\u003e\u003c/p\u003e\n\u003cp\u003eAll participants provided written informed consent and received compensation for their time and effort.\u003c/p\u003e\n\u003cp\u003e\u0026nbsp;\u003cem\u003eData and Code availability\u003c/em\u003e\u003c/p\u003e\n\u003cp\u003eThe research data and code used in this study are available upon request. Please contact the corresponding author for access to the data and code used in this research. We strive to promote transparency and reproducibility in our research practices and are committed to making our data and code available to facilitate further analysis and validation of our findings.\u003c/p\u003e\n\u003cp\u003e\u0026nbsp;\u003cem\u003eAuthors\u0026rsquo; contributions\u003c/em\u003e\u003c/p\u003e\n\u003cp\u003eJing Tian and Yanbo Zhang contributed to the conception and design of the study. Material preparation, data collection and analysis were performed by Yujing Wang, Yongfeng Lv, Jingjing Yan, Yajing Wang and Jing Tian. Yujing Wang wrote the first draft of the manuscript and all authors commented on previous versions of the manuscript. Guisheng Song was involved in revising this article. All authors read and approved the final manuscript.\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\n\u003cli\u003eMcdonagh TA, Metra M, Adamo M, et al (2021) 2021 ESC Guidelines for the diagnosis and treatment of acute and chronic heart failure[J]. Eur Heart J, 42(36): 3599-3726. https://doi.org/10.1093/eurheartj/ehab368.\u003c/li\u003e\n\u003cli\u003eSavarese G, Becher P, Lund L, et al (2023) Global burden of heart failure: a comprehensive and updated review of epidemiology[J]. Cardiovascular research, 118(17): 3272-3287. https://doi.org/10.1093/cvr/cvac013.\u003c/li\u003e\n\u003cli\u003eAlkhalil M (2021) Mechanistic Insights to Target Atherosclerosis Residual Risk[J]. Curr Probl Cardiol, 46(3): 100432. https://doi.org/10.1016/j.cpcardiol.2019.06.004.\u003c/li\u003e\n\u003cli\u003eBrandt EJ, Tobb K, Cambron JC, et al (2023) Assessing and Addressing Social Determinants of Cardiovascular Health: JACC State-of-the-Art Review[J]. J Am Coll Cardiol, 81(14): 1368-1385. https://doi.org/10.1016/j.jacc.2023.01.042.\u003c/li\u003e\n\u003cli\u003eMarmot M, Friel S, Bell R, et al (2008) Closing the gap in a generation: health equity through action on the social determinants of health[J]. Lancet, 372(9650): 1661-1669. https://doi.org/10.1016/s0140-6736(08)61690-6.\u003c/li\u003e\n\u003cli\u003eFrank J, Abel T, Campostrini S, et al (2020) The Social Determinants of Health: Time to Re-Think?[J]. Int J Environ Res Public Health, 17(16). https://doi.org/10.3390/ijerph17165856.\u003c/li\u003e\n\u003cli\u003eEnard K, Coleman A, Yakubu R, et al (2023) Influence of Social Determinants of Health on Heart Failure Outcomes: A Systematic Review[J]. Journal of the American Heart Association, 12(3): e026590. https://doi.org/10.1161/jaha.122.026590.\u003c/li\u003e\n\u003cli\u003eWhite-Williams C, Rossi LP, Bittner VA, et al (2020) Addressing Social Determinants of Health in the Care of Patients With Heart Failure: A Scientific Statement From the American Heart Association[J]. Circulation, 141(22): e841-e863. https://doi.org/10.1161/cir.0000000000000767.\u003c/li\u003e\n\u003cli\u003eOlano-Lizarraga M, Wallstr\u0026ouml;m S, Mart\u0026iacute;n-Mart\u0026iacute;n J, et al (2022) Causes, experiences and consequences of the impact of chronic heart failure on the person\u0026acute;s social dimension: A scoping review[J]. Health Soc Care Community, 2022, 30(4): e842-e858. https://doi.org/10.1111/hsc.13680. \u003c/li\u003e\n\u003cli\u003eHeidenreich PA, Bozkurt B, Aguilar D, et al (2022) 2022 AHA/ACC/HFSA Guideline for the Management of Heart Failure: A Report of the American College of Cardiology/American Heart Association Joint Committee on Clinical Practice Guidelines[J]. Circulation, 145(18): e895-e1032. https://doi.org/10.1161/CIR.0000000000001063.\u003c/li\u003e\n\u003cli\u003eManemann SM, Chamberlain AM, Roger VL, et al (2018) Perceived Social Isolation and Outcomes in Patients With Heart Failure[J]. J Am Heart Assoc, 7(11). https://doi.org/10.1161/jaha.117.008069.\u003c/li\u003e\n\u003cli\u003eTian J, Xue J, Hu X, et al (2018) CHF-PROM: validation of a patient-reported outcome measure for patients with chronic heart failure[J]. Health Qual Life Outcomes, 16(1): 51. https://doi.org/10.1186/s12955-018-0874-2.\u003c/li\u003e\n\u003cli\u003eGraven LJ, Grant JS (2014) Social support and self-care behaviors in individuals with heart failure: an integrative review[J]. Int J Nurs Stud, 51(2): 320-333. https://doi.org/10.1016/j.ijnurstu.2013.06.013.\u003c/li\u003e\n\u003cli\u003eHern\u0026aacute;n MA, Robins JM (2016) Using Big Data to Emulate a Target Trial When a Randomized Trial Is Not Available[J]. Am J Epidemiol, 183(8): 758-764. https://doi.org/10.1093/aje/kwv254.\u003c/li\u003e\n\u003cli\u003eRobins JM, Hernan MA, Brumback B (2000) Marginal structural models and causal inference in epidemiology[J]. Epidemiology (Cambridge, Mass.), 11(5): 550-560. https://doi.org/10.1097/00001648-200009000-00011.\u003c/li\u003e\n\u003cli\u003eGu X, Huang X, Zhang X, et al (2022) Development and Validation of a DNA Methylation-related Classifier of Circulating Tumour Cells to Predict Prognosis and to provide a therapeutic strategy in Lung Adenocarcinoma[J]. Int J Biol Sci, 18(13): 4984-5000. https://doi.org/10.7150/ijbs.75284.\u003c/li\u003e\n\u003cli\u003eMojaverian N, Moodie EE, Bliu A, et al (2015) The Impact of Sparse Follow-up on Marginal Structural Models for Time-to-Event Data[J]. Am J Epidemiol, 182(12): 1047-1055. https://doi.org/10.1093/aje/kwv152.\u003c/li\u003e\n\u003cli\u003eFox-Wasylyshyn SM, El-Masri MM (2005) Handling missing data in self-report measures[J]. Res Nurs Health, 28(6): 488-495. https://doi.org/10.1002/nur.20100.\u003c/li\u003e\n\u003cli\u003eHong S, Lynn HS (2020) Accuracy of random-forest-based imputation of missing data in the presence of non-normality, non-linearity, and interaction[J]. BMC Med Res Methodol, 20(1): 199. https://doi.org/10.1186/s12874-020-01080-1.\u003c/li\u003e\n\u003cli\u003eWang Y, Jiang J, Chen L, et al (2023) Is COPD mortality in South China causally linked to the long-term PM(1) exposure? Evidence from a large community-based cohort[J]. Ecotoxicol Environ Saf, 263: 115299. https://doi.org/10.1016/j.ecoenv.2023.115299.\u003c/li\u003e\n\u003cli\u003eMortimer KM, Neugebauer R, Van Der Laan M, et al (2005) An application of model-fitting procedures for marginal structural models[J]. Am J Epidemiol, 162(4): 382-388. https://doi.org/10.1093/aje/kwi208.\u003c/li\u003e\n\u003cli\u003eSeaman SR, White IR (2013) Review of inverse probability weighting for dealing with missing data[J]. Stat Methods Med Res, 22(3): 278-295. https://doi.org/10.1177/0962280210395740.\u003c/li\u003e\n\u003cli\u003eSeaman SR, White IR, Copas AJ, et al (2012) Combining multiple imputation and inverse-probability weighting[J]. Biometrics, 68(1): 129-137. https://doi.org/10.1111/j.1541-0420.2011.01666.x.\u003c/li\u003e\n\u003cli\u003eSterling MR, Ringel JB, Pinheiro LC, et al (2020) Social Determinants of Health and 90-Day Mortality After Hospitalization for Heart Failure in the REGARDS Study[J]. J Am Heart Assoc, 9(9): e014836. https://doi.org/10.1161/jaha.119.014836.\u003c/li\u003e\n\u003cli\u003eRangachari P, Govindarajan A, Mehta R, et al (2022) The relationship between Social Determinants of Health (SDoH) and death from cardiovascular disease or opioid use in counties across the United States (2009-2018)[J]. BMC Public Health, 22(1): 236. https://doi.org/10.1186/s12889-022-12653-8.\u003c/li\u003e\n\u003cli\u003eBundy JD, Mills KT, He H, et al (2023) Social determinants of health and premature death among adults in the USA from 1999 to 2018: a national cohort study[J]. Lancet Public Health, 8(6): e422-e431. https://doi.org/10.1016/s2468-2667(23)00081-6.\u003c/li\u003e\n\u003cli\u003eCabrera C, Qu\u0026eacute;len C, Ouwens M, et al (2022) Evaluating a Cox marginal structural model to assess the comparative effectiveness of inhaled corticosteroids versus no inhaled corticosteroid treatment in chronic obstructive pulmonary disease[J]. Ann Epidemiol, 67: 19-28. https://doi.org/10.1016/j.annepidem.2021.11.004.\u003c/li\u003e\n\u003cli\u003eYang Z, Toh S, Li X, et al (2022) Statin use is associated with lower risk of dementia in stroke patients: a community-based cohort study with inverse probability weighted marginal structural model analysis[J]. Eur J Epidemiol, 37(6): 615-627. https://doi.org/10.1007/s10654-022-00856-7.\u003c/li\u003e\n\u003cli\u003eGallagher R, Luttik ML, Jaarsma T (2011) Social support and self-care in heart failure[J]. J Cardiovasc Nurs, 26(6): 439-445. https://doi.org/10.1097/JCN.0b013e31820984e1.\u003c/li\u003e\n\u003cli\u003eWorld Health Organization (2021) Social Determinants of Health. [EB/OL]. https://www.who.int/health-topics/social-determinants-of-health#tab=tab_1. Accessed 27 May 2023\u003c/li\u003e\n\u003cli\u003eXie D, Yang W, Jepson C, et al (2017) Statistical Methods for Modeling Time-Updated Exposures in Cohort Studies of Chronic Kidney Disease[J]. Clin J Am Soc Nephrol, 12(11): 1892-1899. https://doi.org/10.2215/cjn.00650117.\u003c/li\u003e\n\u003cli\u003eYan J, Tian J, Yang H, et al (2022) The Causal Effects of Anxiety-Mediated Social Support on Death in Patients with Chronic Heart Failure: A Multicenter Cohort Study[J]. Psychol Res Behav Manag, 15: 3287-3296. https://doi.org/10.2147/prbm.S387222.\u003c/li\u003e\n\u003cli\u003eMurberg TA (2004) Long-term effect of social relationships on mortality in patients with congestive heart failure[J]. Int J Psychiatry Med, 34(3): 207-217. https://doi.org/10.2190/gkj2-p8bd-v59x-mjnq.\u003c/li\u003e\n\u003cli\u003eCalero-Molina E, Moliner P, Hidalgo E, et al (2022) Interplay between psychosocial and heart failure related factors may partially explain limitations in self-efficacy in patients with heart failure: Insights from a real-world cohort of 1,123 patients[J]. Int J Nurs Stud, 129: 104233. https://doi.org/10.1016/j.ijnurstu.2022.104233.\u003c/li\u003e\n\u003cli\u003eJones NR, Ord\u0026oacute;\u0026ntilde;ez-Mena JM, Roalfe AK, et al (2023) Body mass index and survival in people with heart failure[J]. Heart, https://doi.org/10.1136/heartjnl-2023-322459.\u003c/li\u003e\n\u003cli\u003eBlasingame M, Samuels LR, Heerman WJ (2023) The Combined Effects of Social Determinants of Health on Childhood Overweight and Obesity[J]. Child Obes, https://doi.org/10.1089/chi.2022.0222.\u003c/li\u003e\n\u003cli\u003eHowell CR, Zhang L, Yi N, et al (2022) Associations between cardiometabolic disease severity, social determinants of health (SDoH), and poor COVID-19 outcomes[J]. Obesity (Silver Spring), 30(7): 1483-1494. https://doi.org/10.1002/oby.23440.\u003c/li\u003e\n\u003cli\u003eYang L, Wang H, Cheng J (2022) Association of social capital with obesity among older adults in China: a cross-sectional analysis[J]. BMC Geriatr, 22(1): 871. https://doi.org/10.1186/s12877-022-03566-7.\u003c/li\u003e\n\u003cli\u003eSavarese G, Bodegard J, Norhammar A, et al (2021) Heart failure drug titration, discontinuation, mortality and heart failure hospitalization risk: a multinational observational study (US, UK and Sweden)[J]. Eur J Heart Fail, 23(9): 1499-1511. https://doi.org/10.1002/ejhf.2271.\u003c/li\u003e\n\u003cli\u003ePerera D, Clayton T, O\u0026apos;kane PD, et al (2022) Percutaneous Revascularization for Ischemic Left Ventricular Dysfunction[J]. N Engl J Med, 387(15): 1351-1360. https://doi.org/10.1056/NEJMoa2206606.\u003c/li\u003e\n\u003cli\u003ePushkarev G, Kuznetsov V, Yaroslavskaya E, et al (2019) Social support for patients with coronary artery disease after percutaneous coronary intervention[J]. J Psychosom Res, 119: 74-78. https://doi.org/10.1016/j.jpsychores.2019.02.011.\u003c/li\u003e\n\u003c/ol\u003e"}],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":true,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":false,"hideJournal":true,"highlight":"","institution":"","isAcceptedByJournal":false,"isAuthorSuppliedPdf":false,"isDeskRejected":"","isHiddenFromSearch":false,"isInQc":false,"isInWorkflow":false,"isPdf":false,"isPdfUpToDate":true,"isWithdrawnOrRetracted":false,"journal":{"display":true,"email":"[email protected]","identity":"researchsquare","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":true,"externalIdentity":"","sideBox":"","snPcode":"","submissionUrl":"/submission","title":"Research Square","twitterHandle":"researchsquare","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"","reportingPortfolio":"","inReviewEnabled":false,"inReviewRevisionsEnabled":true},"keywords":"Social determinants of health, Chronic heart failure, Inverse probability weighting, Marginal structural models, Chronic Heart Failure Patient-Reported Outcomes Measure","lastPublishedDoi":"10.21203/rs.3.rs-4496796/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-4496796/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003ch2\u003ePurpose\u003c/h2\u003e \u003cp\u003eThe causality between social determinants of health (SDoH) and mortality in patients with chronic heart failure (CHF) is uncertain. Herein, we assessed the causality using inverse probability weighting (IPW) of marginal structural models (MSMs) during the course of CHF.\u003c/p\u003e\u003ch2\u003eMethod\u003c/h2\u003e \u003cp\u003eA multicenter, prospective cohort study of 1377 patients with CHF were enrolled from September 2017. The social domain and two dimensions of Chronic Heart Failure Patient-Reported Outcomes Measure (CHF-PROM) was used to assess the SDoH, social support, and support utilization of patients with CHF. CHF-PROM and mortality information were obtained at 1, 3, and 6 months following patient discharge, and every 6 months thereafter at regular follow-ups. Logistic regression and IPW of MSMs were applied to analyze the SDoH, social support, and support utilization on mortality in patients with CHF.\u003c/p\u003e\u003ch2\u003eResults\u003c/h2\u003e \u003cp\u003eLogistic regression showed that at baseline, the SDoH, social support, and support utilization were not associated with mortality in patients with CHF. After adjusting for confounders, MSMs showed that the SDoH and social support were not associated with mortality at baseline. In contrast, low support utilization at baseline and unfavorable SDoH, low social support, and low support utilization during follow-up increased the risk of death in patients with CHF.\u003c/p\u003e\u003ch2\u003eConclusion\u003c/h2\u003e \u003cp\u003eThrough follow-up data and MSMs analysis, we found that the long-term out-of-hospital causal effects, but not one-time effects of SDoH, are risk factors for CHF mortality. SDoH should be taken seriously during the entire CHF process to prolong patients\u0026rsquo; survival.\u003c/p\u003e\u003ch2\u003eTrial registration:\u003c/h2\u003e \u003cp\u003eThe cohort number registered in the China Clinical Trial Registry is ChiCTR2100043337. URL: \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://www.chictr.org.cn/showproj.html?proj=64980\u003c/span\u003e\u003cspan address=\"https://www.chictr.org.cn/showproj.html?proj=64980\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e Registered on February 11, 2021\u003c/p\u003e","manuscriptTitle":"Causal Association of Social Determinants of Health and Dynamic Impact on Mortality in Patients with Chronic Heart Failure","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2024-06-13 20:52:34","doi":"10.21203/rs.3.rs-4496796/v1","editorialEvents":[{"type":"communityComments","content":0}],"status":"published","journal":{"display":true,"email":"[email protected]","identity":"researchsquare","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":true,"externalIdentity":"","sideBox":"","snPcode":"","submissionUrl":"/submission","title":"Research Square","twitterHandle":"researchsquare","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"","reportingPortfolio":"","inReviewEnabled":false,"inReviewRevisionsEnabled":true}}],"origin":"","ownerIdentity":"8f372803-0744-48fa-a5db-dd51e9906ea5","owner":[],"postedDate":"June 13th, 2024","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"posted","subjectAreas":[],"tags":[],"updatedAt":"2024-06-24T19:44:32+00:00","versionOfRecord":[],"versionCreatedAt":"2024-06-13 20:52:34","video":"","vorDoi":"","vorDoiUrl":"","workflowStages":[]},"version":"v1","identity":"rs-4496796","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-4496796","identity":"rs-4496796","version":["v1"]},"buildId":"qtupq5eGEP_6zYnWcrvyt","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}