Socioeconomic status and mortality among dialysis patients: a systematic review and meta-analysis

Tao, Sibei; Zeng, Xiaoxi; Liu, Jing; Fu, Ping

doi:10.1007/s11255-019-02078-5

Cited by 24 publications

(24 citation statements)

References 52 publications

Supporting

Mentioning

Contrasting

Unclassified

Order By: Relevance

“…Also, similar to the present study, Tsur's study confirmed that higher BMI, was significantly associated with longer survival [17], hence, high BMI is protective in these patients. Further, based on the present study, occupation is one of the most important factors affecting dialysis patients' survival that its effect is confirmed in past studies [18].…”

Section: Discussionsupporting

confidence: 86%

Survival Analysis in The Presence of a Cure Fraction Using Data of Dialysis Patients: A Bayesian Approach

Rafati

Baneshi

Hassani

et al. 2021

Preprint

View full text Add to dashboard Cite

Background: The aim of this study was to evaluate the goodness of fit of Bayesian mixture and non-mixture cure models to find the factors affecting dialysis patient’s survival time where a significant proportion of the population has a long-term survival.Study Design: A retrospective cohort study. Methods: The data of 252 dialysis patients were used among whom 35 cases died. Since in this study a part of the population had long-term survival, Bayesian cure models were used and evaluated using DIC index. The data were analyzed by R and Openbugs Softwares. Results: Of the 252 dialysis patients, 136(54%) were males and the mean (SD) age was 53.39 (18.09) years. The patient’s follow-up time mean (SD) was 10.93(7.82) years. The 10 and 20-year survival rate of these patients were 87% and 73%, respectively. The findings show that the best fitting belonged to the Bayesian Non-mixture Cure Model (BNCM) with Dagum distribution. The variables of age, Body Mass Index, dialysis duration, frequency of dialysis, age of onset of dialysis, and occupation affected patients' survival based on BNCM with Dagum distribution.Conclusions: The results demonstrated that the BNCM with Dagum distribution can be a good selection model to analyze survival data, where there is the possibility of a fraction of cure.

show abstract

Section: Discussionsupporting

confidence: 86%

Survival Analysis in The Presence of a Cure Fraction Using Data of Dialysis Patients: A Bayesian Approach

Rafati

Baneshi

Hassani

et al. 2021

Preprint

View full text Add to dashboard Cite

show abstract

“…Meta-analysis found that lower level of education was a risk factor for increased mortality. 25 However, Kim et al demonstrated that lower education did not predict increased mortality in PD patients if patients received comprehensive training. 26 Charlson comorbidity index was significantly higher in patients with diabetic nephropathy compared to glomerulonephritis (6.2 ± 1.6 vs. 2.7 ± 1.1, p < 0.001).…”

Section: Discussionmentioning

confidence: 99%

Ten-year survival of patients treated with peritoneal dialysis: A prospective observational cohort study

Xia

Qiu

et al. 2020

Perit Dial Int

View full text Add to dashboard Cite

Background: Few studies evaluated over 5-year outcomes of dialysis patients. This study examined 10-year all-cause mortality and death-censored technique failure in a cohort of incident peritoneal dialysis (PD) individuals. Methods: Five hundred and thirty-three incident PD individuals from 2006 to 2008 were prospectively followed up for more than 10 years until 2018. Clinical characteristics at PD initiation were collected. The primary outcome was all-cause mortality, and the secondary outcome was death-censored technique failure. Cox hazards models were fit using clinical characteristics at PD initiation. Results: The mean age starting PD for these participants was 48 ± 16 years; 130 (24%) patients had diabetic nephropathy. During follow-up, 208 patients died, and 84 patients experienced technique failures. The 1, 3, 5, and 10 years’ survival rates for incident PD patients were 93%, 81%, 64%, and 36%, respectively, and the technical survival rates were 98%, 93%, 85%, and 62%, respectively. Mortality risk was much higher after 3 years on PD. The peritonitis rate was 0.19 episodes per patient-year, and 7 (1.3%) patients had encapsulating peritoneal sclerosis (EPS) giving an incidence rate of 3.1 of 1000 patient-years. The main causes of death were cardiovascular events (97 of 208, 47%), and technique failure was mainly due to peritonitis (41 of 84, 49%). Older age, higher Charlson comorbidity index, and lower level of education were strongly associated with mortality, and diabetic nephropathy was an independent risk factor for technique failure. Conclusions: The 10-year’s survival and technique survival rates of incident PD patients were 36% and 62%. Long-term PD can be continued successfully with improved outcomes and low risk for EPS.

show abstract

“…For example, racial difference and vocational activity may affect the mortality rate [18]. Recent meta-analysis by Tao et al [19] demonstrated that low SES was associated with higher mortality in HD patients, but the magnitude of the association may vary for different individual indicators of SES, different geographic locations, and different study designs. Meanwhile, Korea has a single-payer system in which every resident is eligible for medical service and there is no economic barrier to utilize medical service.…”

Section: Discussionmentioning

confidence: 99%

Health Insurance Status Is Related to Risk of Mortality and Hospitalization in Korean Maintenance Hemodialysis Patients: A Longitudinal Cohort Study

et al. 2020

View full text Add to dashboard Cite

Background: There has been an increasing incidence of hemodialysis (HD) due to old age and comorbid condition such as diabetes. In general, socioeconomic status (SES) is known as one of the most important risk factors for patient mortality and morbidity. Whether low SES is associated with poorer outcome in HD patients is controversial. This study was performed to evaluate the association of health insurance status as a proxy indicator for SES upon mortality and hospitalization in maintenance HD patients. Methods: We used HD-quality assessment data from the year of 2015 for collecting demographic and clinical data. The subjects were classified into Medical Aid (MA) recipients (low SES) and National Health Insurance (NHI) beneficiary (high SES). We analyzed mortality and hospitalization risk based on health insurance status using Cox proportional hazard model. A total of 35,454 adult HD patients ≥18 years old who received HD treatment more than twice weekly were included in the analysis. Results: The ratio between MA recipient and NHI beneficiary was 76.7 versus 23.3%. The MA recipient group demonstrated younger age and lower proportion of female, diabetes, hypertension, and cerebrovascular accidents compared to the NHI beneficiary group. After adjusting for age, gender, comorbidity, and laboratory parameters, the MA recipient group showed a significantly higher mortality risk compared to the NHI beneficiary group (hazard ratio 1.073 [1.009–1.14], p = 0.025). The MA recipient group was also an independent risk factor for hospitalization after adjusting for age, gender, comorbidities, and laboratory parameters (hazard ratio 1.142 [1.108–1.178], p < 0.001). Conclusion: Low SES as measured by health insurance status was associated with an increased risk of patient mortality and hospitalization in Korean maintenance HD patients.

show abstract

Socioeconomic status and mortality among dialysis patients: a systematic review and meta-analysis

Cited by 24 publications

References 52 publications

Survival Analysis in The Presence of a Cure Fraction Using Data of Dialysis Patients: A Bayesian Approach

Survival Analysis in The Presence of a Cure Fraction Using Data of Dialysis Patients: A Bayesian Approach

Ten-year survival of patients treated with peritoneal dialysis: A prospective observational cohort study

Health Insurance Status Is Related to Risk of Mortality and Hospitalization in Korean Maintenance Hemodialysis Patients: A Longitudinal Cohort Study

Contact Info

Product

Resources

About