scholarly journals Income Levels and Mortality Rates in Patients with Chronic Kidney Disease in South Korea: a Population-Based Cohort Study Using National Health Insurance Data

2020 ◽  
Author(s):  
Young Choi
Keyword(s):  
Chronic Kidney Disease ◽  
Health Insurance ◽  
Kidney Disease ◽  
Mortality Rate ◽  
National Health Insurance ◽  
National Health ◽  
Mortality Rates ◽  
Dialysis Patients ◽  
Income Levels ◽  
Low Incomes

Background: To examine the association between income levels and mortality rates in patients with chronic kidney disease. Methods: We analyzed data obtained from 3,172 patients with chronic kidney disease obtained from the Korean National Health Insurance claims database (2003–2009). Each patient was monitored until December 2010 or until death, whichever came first. Individual income was estimated from the national health insurance premium. Information on mortality was obtained from the Korean National Statistical Office. Cox proportional hazard models were used to compare mortality rates between different income groups after adjusting for possible confounding risk factors. Results: A low income was significantly associated with a high mortality rate after adjusting for covariates (adjusted HR 1.298 [1.082–1.556]). In addition, dialysis patients who had low incomes were more likely to have higher mortality rates compared to those in dialysis patients who had high incomes (adjusted HR 1.528 [1.122–2.082]). Conclusion: The findings of this study indicate that chronic kidney disease patients with low incomes have the highest mortality risk. Promotion of targeted policies and priority health services for patients with low incomes may help reduce the mortality rate in this vulnerable group.

scholarly journals Changes in Prevalence and Health Checkup Coverage Rate of Chronic Kidney Disease (CKD) after Introduction of Prefecture-Wide CKD Initiative: Results of the Kagawa Association of CKD Initiatives

2021 ◽  
Vol 11 (11) ◽  
pp. 1121
Author(s):  
Tadashi Sofue ◽  
Taiga Hara ◽  
Yoko Nishijima ◽  
Satoshi Nishioka ◽  
Hiroyuki Watatani ◽  
...  
Keyword(s):  
Chronic Kidney Disease ◽  
Health Insurance ◽  
Kidney Disease ◽  
National Health Insurance ◽  
National Health ◽  
Coverage Rate ◽  
Medical Visit ◽  
Health Checkup ◽  
Insurance Database ◽  
Special Health

The National Health Insurance (NHI) special health checkup system in Japan targets the NHI population aged 40–74 years. Since 2015, the Kagawa NHI special health checkup was initiated in a prefecture-wide chronic kidney disease (CKD) initiative, including renal examination as an essential item in NHI health checkups. Here, we aimed to investigate the effects of the prefecture-wide CKD initiative. We conducted a retrospective cohort survey using the Kagawa National Health Insurance database created by the Kagawa National Health Insurance Organization. Results of the NHI health checkup (2015–2019) and prefecture-wide outcomes (2013–2019) were analyzed. The prevalence of CKD among examinees who underwent the NHI health checkup increased from 17.7% in 2015 to 23.2% in 2019. The percentage of examinees who completed a medical visit was 29.4% in 2015. After initiation of the initiative, the NHI health checkup coverage rate increased significantly, from a mean (standard deviation) of 40.8% (0.4%) to 43.2% (1.1%) (p = 0.04). After the start of the CKD initiative, we found an increase in the prevalence of CKD and the NHI health checkup coverage rate.

scholarly journals Machine Learning Prediction Models for Chronic Kidney Disease using National Health Insurance Claim Data in Taiwan

2020 ◽  
Author(s):  
Surya Krishnamurthy ◽  
Kapeleshh KS ◽  
Erik Dovgan ◽  
Mitja Luštrek ◽  
Barbara Gradišek Piletič ◽  
...  
Keyword(s):  
Machine Learning ◽  
Chronic Kidney Disease ◽  
Health Insurance ◽  
Kidney Disease ◽  
National Health Insurance ◽  
National Health ◽  
Performance Metrics ◽  
Prediction Models ◽  
Research Database ◽  
Number Of Patients

ABSTRACTBackground and ObjectiveChronic kidney disease (CKD) represent a heavy burden on the healthcare system because of the increasing number of patients, high risk of progression to end-stage renal disease, and poor prognosis of morbidity and mortality. The aim of this study is to develop a machine-learning model that uses the comorbidity and medication data, obtained from Taiwan’s National Health Insurance Research Database, to forecast whether an individual will develop CKD within the next 6 or 12 months, and thus forecast the prevalence in the population.MethodsA total of 18,000 people with CKD and 72,000 people without CKD diagnosis along with the past two years of medication and comorbidity data matched by propensity score were used to build a predicting model. A series of approaches were tested, including Convoluted Neural Networks (CNN). 5-fold cross-validation was used to assess the performance metrics of the algorithms.ResultsBoth for the 6 month and 12-month models, the CNN approach performed best, with the AUROC of 0.957 and 0.954, respectively. The most prominent features in the tree-based models were identified, including diabetes mellitus, age, gout, and medications such as sulfonamides, angiotensins which had an impact on the progression of CKD.ConclusionsThe model proposed in this study can be a useful tool for the policy-makers helping them in predicting the trends of CKD in the population in the next 6 to 12 months. Information provided by this model can allow closely monitoring the people with risk, early detection of CKD, better allocation of resources, and patient-centric management

scholarly journals Prevalence of Chronic Kidney Disease and Variation of Its Risk Factors by the Regions in Okayama Prefecture

2022 ◽  
Vol 12 (1) ◽  
pp. 97
Author(s):  
Ryoko Umebayashi ◽  
Haruhito Adam Uchida ◽  
Natsumi Matsuoka-Uchiyama ◽  
Hitoshi Sugiyama ◽  
Jun Wada
Keyword(s):  
Risk Factors ◽  
Chronic Kidney Disease ◽  
Health Insurance ◽  
Kidney Disease ◽  
National Health Insurance ◽  
National Health ◽  
Health Problems ◽  
Ckd Progression ◽  
Increased Risk ◽  
Okayama Prefecture

Objective: The prevention of chronic kidney disease (CKD) progression is an important issue from health and financial perspectives. We conducted a single-year cross-sectional study to clarify the prevalence of CKD and its risk factors along with variations in these factors among five medical regions in Okayama Prefecture, Japan. Methods and Results: Data concerning the renal function and proteinuria as well as other CKD risk factors were obtained from the database of the Japanese National Health Insurance. The proportion of CKD patients at an increased risk of progression to end-stage renal disease (ESRD), classified as orange and red on the CKD heatmap, ranged from 6–9% and did not vary significantly by the regions. However, the causes of the increased severity differed between regions where renal dysfunction was predominant and regions where there were many patients with proteinuria. CKD risk factors, such as diabetes mellitus, hypertension, hyper low-density lipoprotein-cholesterolemia, obesity, smoking and lack of exercise, also differed among these regions, suggesting that different regions need tailored interventions that suit the characteristics of the region, such as an increased health checkup ratio, dietary guidance and promotion of exercise opportunities. Conclusions: Approximately 6–9% of people are at an increased risk of developing ESRD (orange or red on a CKD heatmap) among the population with National Health Insurance in Okayama Prefecture. The underlying health problems that cause CKD may differ among the regions. Thus, it is necessary to consider intervention methods for preventing CKD progression that are tailored to each region’s health problems.

Disparities in Kidney Transplantation Access among Korean Patients Initiating Dialysis: A Population-Based Cohort Study Using National Health Insurance Data (2003-2013)

2016 ◽  
Vol 45 (1) ◽  
pp. 32-39 ◽  
Author(s):  
Young Choi ◽  
Jaeyong Shin ◽  
Jung Tak Park ◽  
Kyoung Hee Cho ◽  
Eun-Cheol Park ◽  
...  
Keyword(s):  
Chronic Kidney Disease ◽  
Socioeconomic Status ◽  
Kidney Transplantation ◽  
Health Insurance ◽  
Kidney Disease ◽  
South Korea ◽  
National Health Insurance ◽  
National Health ◽  
Income Level ◽  
Kidney Transplants

Background: The socioeconomic status of a person has an impact on his or her access to kidney transplantation as has been reported in western countries. This study examined the association between income level and kidney transplantation among chronic kidney disease patients undergoing dialysis in South Korea. Methods: We analyzed data from 1,792 chronic kidney disease patients undergoing dialysis and listed in the Korean National Health Insurance Claim Database (2003-2013). The likelihood of receiving the first kidney transplant over time was analyzed using competing risk proportional hazard models on time from initiating dialysis to receiving a transplant. Results: Of 1,792 patients on dialysis, only 184 patients (10.3%) received kidney transplants. Patients with medical aid had the lowest kidney transplantation rate (hazard ratio 0.29, 95% CI 0.16-0.51). A lower income level was significantly associated with a low kidney transplantation rate, after adjusting for covariates, compared to patients in the high-income level group. Conclusions: Our findings indicate that in South Korea, the total number of kidney transplants is remarkably low and there exists income disparity with regard to access to kidney transplantation. Thus, we suggest that plans be implemented to encourage organ donation and increase organ transplant accessibility for all patients irrespective of their socioeconomic status.

scholarly journals Machine Learning Prediction Models for Chronic Kidney Disease Using National Health Insurance Claim Data in Taiwan

Healthcare ◽  
2021 ◽  
Vol 9 (5) ◽  
pp. 546
Author(s):  
Surya Krishnamurthy ◽  
Kapeleshh KS ◽  
Erik Dovgan ◽  
Mitja Luštrek ◽  
Barbara Gradišek Piletič ◽  
...  
Keyword(s):  
Machine Learning ◽  
Chronic Kidney Disease ◽  
Health Insurance ◽  
Kidney Disease ◽  
National Health Insurance ◽  
National Health ◽  
Prediction Models ◽  
Research Database ◽  
Close Monitoring ◽  
Number Of Patients

Chronic kidney disease (CKD) represents a heavy burden on the healthcare system because of the increasing number of patients, high risk of progression to end-stage renal disease, and poor prognosis of morbidity and mortality. The aim of this study is to develop a machine-learning model that uses the comorbidity and medication data obtained from Taiwan’s National Health Insurance Research Database to forecast the occurrence of CKD within the next 6 or 12 months before its onset, and hence its prevalence in the population. A total of 18,000 people with CKD and 72,000 people without CKD diagnosis were selected using propensity score matching. Their demographic, medication and comorbidity data from their respective two-year observation period were used to build a predictive model. Among the approaches investigated, the Convolutional Neural Networks (CNN) model performed best with a test set AUROC of 0.957 and 0.954 for the 6-month and 12-month predictions, respectively. The most prominent predictors in the tree-based models were identified, including diabetes mellitus, age, gout, and medications such as sulfonamides and angiotensins. The model proposed in this study could be a useful tool for policymakers in predicting the trends of CKD in the population. The models can allow close monitoring of people at risk, early detection of CKD, better allocation of resources, and patient-centric management.

scholarly journals Mid-term survival of patients with chronic kidney disease after extracorporeal membrane oxygenation

2020 ◽  
Vol 31 (5) ◽  
pp. 595-602
Author(s):  
Yueh-An Lu ◽  
Shao-Wei Chen ◽  
Cheng-Chia Lee ◽  
Victor Chien-Chia Wu ◽  
Pei-Chun Fan ◽  
...  
Keyword(s):  
Chronic Kidney Disease ◽  
Kidney Disease ◽  
Mortality Rate ◽  
Extracorporeal Membrane Oxygenation ◽  
Hospital Mortality ◽  
Subgroup Analysis ◽  
Mortality Rates ◽  
Term Survival ◽  
Membrane Oxygenation ◽  
Ecmo Treatment

Abstract OBJECTIVES Chronic kidney disease (CKD) impairs the elimination of fluids, electrolytes and metabolic wastes, which can affect the outcomes of extracorporeal membrane oxygenation (ECMO) treatment. This study aimed to elucidate the impact of CKD on in-hospital mortality and mid-term survival of adult patients who received ECMO treatment. METHODS Patients who received first-time ECMO treatment between 1 January 2003 and 31 December 2013 were included. Those with CKD were identified and matched to patients without CKD using a 1:2 ratio and were followed for 3 years. The study outcomes included in-hospital outcomes and the 3-year mortality rate. A subgroup analysis was conducted by comparing the dialytic patients with the non-dialytic CKD patients. RESULTS The study comprised 1008 CKD patients and 2016 non-CKD patients after propensity score matching. The CKD patients had higher in-hospital mortality rates [69.5% vs 62.2%; adjusted odds ratio 1.41; 95% confidence interval (CI) 1.15–1.72] than the non-CKD patients. The 3-year mortality rate was 80.4% in the CKD group and 68% in the non-CKD group (adjusted hazard ratio 1.17; 95% CI 1.06–1.28). The subgroup analysis showed that the 3-year mortality rates were 84.5% and 78.4% in the dialytic and non-dialytic patients, respectively. No difference in the 3-year mortality rate was noted between the 2 CKD subgroups (P = 0.111). CONCLUSIONS CKD was associated with increased risks of in-hospital and mid-term mortalities in patients who received ECMO treatment. Furthermore, no difference in survival was observed between the patients with end-stage renal disease and non-dialytic CKD patients.

scholarly journals The impact of social disadvantage in moderate-to-severe chronic kidney disease: an equity-focused systematic review*

2015 ◽  
Vol 31 (1) ◽  
pp. 46-56 ◽  
Author(s):  
Rachael Lisa Morton ◽  
Iryna Schlackow ◽  
Borislava Mihaylova ◽  
Natalie Dawn Staplin ◽  
Alastair Gray ◽  
...  
Keyword(s):  
Systematic Review ◽  
Chronic Kidney Disease ◽  
Health Insurance ◽  
Kidney Disease ◽  
Cardiovascular Events ◽  
Home Ownership ◽  
Dialysis Patients ◽  
Low Education ◽  
Severe Chronic Kidney Disease ◽  
Event Rates

Abstract It is unclear whether a social gradient in health outcomes exists for people with moderate-to-severe chronic kidney disease (CKD). We critically review the literature for evidence of social gradients in health and investigate the ‘suitability’ of statistical analyses in the primary studies. In this equity-focused systematic review among adults with moderate-to-severe CKD, factors of disadvantage included gender, race/ethnicity, religion, education, socio-economic status or social capital, occupation and place of residence. Outcomes included access to healthcare, kidney disease progression, cardiovascular events, all-cause mortality and suitability of analyses. Twenty-four studies in the pre-dialysis population and 34 in the dialysis population representing 8.9 million people from 10 countries were included. In methodologically suitable studies among pre-dialysis patients, a significant social gradient was observed in access to healthcare for those with no health insurance and no home ownership. Low income and no home ownership were associated with higher cardiovascular event rates and higher mortality [HR 1.94, 95% confidence interval (CI) 1.27–2.98; HR 1.28, 95% CI 1.04–1.58], respectively. In methodologically suitable studies among dialysis patients, females, ethnic minorities, those with low education, no health insurance, low occupational level or no home ownership were significantly less likely to access cardiovascular healthcare than their more advantaged dialysis counterparts. Low education level and geographic remoteness were associated with higher cardiovascular event rates and higher mortality (HR 1.54, 95% CI 1.01–2.35; HR 1.21, 95% CI 1.08–1.37), respectively. Socially disadvantaged pre-dialysis and dialysis patients experience poorer access to specialist cardiovascular health services, and higher rates of cardiovascular events and mortality than their more advantaged counterparts.

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