scholarly journals Impact of comorbidity assessment methods to predict non-cancer mortality risk in cancer patients: a retrospective observational study using the National Health Insurance Service claims-based data in Korea

2021 ◽  
Vol 21 (1) ◽  
Author(s):  
Sanghee Lee ◽  
Yoon Jung Chang ◽  
Hyunsoon Cho
Keyword(s):  
Risk Assessment ◽  
Health Insurance ◽  
Cancer Patients ◽  
National Health Insurance ◽  
National Health ◽  
Cancer Mortality ◽  
Prediction Models ◽  
Assessment Methods ◽  
Chronic Obstructive ◽  
Mortality Risks

Abstract Background Cancer patients’ prognoses are complicated by comorbidities. Prognostic prediction models with inappropriate comorbidity adjustments yield biased survival estimates. However, an appropriate claims-based comorbidity risk assessment method remains unclear. This study aimed to compare methods used to capture comorbidities from claims data and predict non-cancer mortality risks among cancer patients. Methods Data were obtained from the National Health Insurance Service-National Sample Cohort database in Korea; 2979 cancer patients diagnosed in 2006 were considered. Claims-based Charlson Comorbidity Index was evaluated according to the various assessment methods: different periods in washout window, lookback, and claim types. The prevalence of comorbidities and associated non-cancer mortality risks were compared. The Cox proportional hazards models considering left-truncation were used to estimate the non-cancer mortality risks. Results The prevalence of peptic ulcer, the most common comorbidity, ranged from 1.5 to 31.0%, and the proportion of patients with ≥1 comorbidity ranged from 4.5 to 58.4%, depending on the assessment methods. Outpatient claims captured 96.9% of patients with chronic obstructive pulmonary disease; however, they captured only 65.2% of patients with myocardial infarction. The different assessment methods affected non-cancer mortality risks; for example, the hazard ratios for patients with moderate comorbidity (CCI 3–4) varied from 1.0 (95% CI: 0.6–1.6) to 5.0 (95% CI: 2.7–9.3). Inpatient claims resulted in relatively higher estimates reflective of disease severity. Conclusions The prevalence of comorbidities and associated non-cancer mortality risks varied considerably by the assessment methods. Researchers should understand the complexity of comorbidity assessments in claims-based risk assessment and select an optimal approach.

scholarly journals Consensus on Control of Risky Nonvariceal Upper Gastrointestinal Bleeding in Taiwan with National Health Insurance

2014 ◽  
Vol 2014 ◽  
pp. 1-8 ◽  
Author(s):  
Bor-Shyang Sheu ◽  
Chun-Ying Wu ◽  
Ming-Shiang Wu ◽  
Cheng-Tang Chiu ◽  
Chun-Che Lin ◽  
...  
Keyword(s):  
Health Insurance ◽  
High Risk ◽  
Gastrointestinal Bleeding ◽  
National Health Insurance ◽  
National Health ◽  
Upper Gastrointestinal Bleeding ◽  
Ulcer Bleeding ◽  
Chronic Obstructive ◽  
Research Database ◽  
Upper Gastrointestinal

Background and Aims. To compose upper gastrointestinal bleeding (UGIB) consensus from a nationwide scale to improve the control of UGIB, especially for the high-risk comorbidity group.Methods. The steering committee defined the consensus scope to cover preendoscopy, endoscopy, postendoscopy, and overview from Taiwan National Health Insurance Research Database (NHIRD) assessments for UGIB. The expert group comprised thirty-two Taiwan experts of UGIB to conduct the consensus conference by a modified Delphi process through two separate iterations to modify the draft statements and to vote anonymously to reach consensus with an agreement ≥80% for each statement and to set the recommendation grade.Results. The consensus included 17 statements to highlight that patients with comorbidities, including liver cirrhosis, end-stage renal disease, probable chronic obstructive pulmonary disease, and diabetes, are at high risk of peptic ulcer bleeding and rebleeding. Special considerations are recommended for such risky patients, including raising hematocrit to 30% in uremia or acute myocardial infarction, aggressive acid secretory control in high Rockall scores, monitoring delayed rebleeding in uremia or cirrhosis, considering cycloxygenase-2 inhibitors plus PPI for pain control, and early resumption of antiplatelets plus PPI in coronary artery disease or stroke.Conclusions. The consensus comprises recommendations to improve care of UGIB, especially for high-risk comorbidities.

Difference in strong opioid prescription among different cancer and care providers in advanced cancer patients in Taiwan: Analysis using national health insurance database.

2015 ◽  
Vol 33 (29_suppl) ◽  
pp. 55-55
Author(s):  
Ruey Kuen Hsieh ◽  
Yu-Lin Lin ◽  
Chao-Hsiun Tang
Keyword(s):  
Health Insurance ◽  
Cancer Patients ◽  
Advanced Cancer ◽  
National Health Insurance ◽  
National Health ◽  
Care Providers ◽  
Advanced Cancer Patients ◽  
Opioid Prescription ◽  
Strong Opioid ◽  
Strong Opioids

55 Background: Pain assessment and management had been adopted as an important criteria in hospital accreditation in Taiwan. National health insurance database may help to determine factors influencing patterns of strong opioid use in advanced cancer patients in their final 12 months of life. Methods: Cancer patients who died from cancer during 2008-2011 were included in the analysis. Data in prescription of strong opioids during their last 12 months of life were collected and analyzed using National Health Insurance Research Database (NHIRD). Patient’s characteristics, such as cancer types, birthdate and gender, as well as information on the provider’s characteristics, such as specialty, gender and age of the physician, the ownership and level of accreditation of the hospital, and the level of urbanization of the hospital where it is located, were also retrieved and included as the controlled variables in the analysis. Results: Of the 162,679 cancer deaths, 57,578 were prescribed strong opioids in their last year of life (35.4 percent). Strong opioid prescription steadily decreased with the corresponding increase in patient age. Besides there are difference in different cancer types. Association with prescription prevalence has also been noted among physician characteristics such as subspecialty, gender and age, as well among hospital characteristics, such as public vs private and accreditation level. Conclusions: There are significant difference in strong opioids prescription among different care providers for advanced cancer patients. Information from this study can guide further efforts in improving supportive care and education for advanced cancer care providers.

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

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