PENENTUAN PREMI DAN CADANGAN PADA ASURANSI PENDIDIKAN DENGAN MEMERHATIKAN PELUANG HIDUP ANAK

Education insurance provides services in the field of education. In education insurance, the insured not only gets protection benefits but also education funds. These benefits will be received if they have paid premiums. Insurance companies also need to set the exact amount of policy value. The purpose of this study is to determine the premium and policy value of education insurance by taking into account the child's life chances. In this study, used secondary data from the 2011 Indonesian Mortality Table and illustrated data in the form of education fund data. Premium is obtained using the equivalence principle and policy value is obtained using the prospective method. In the calculation of premiums and policy values for education insurance premiums by taking into account the child's life chances, modifications are made, the amount of education funds multiplied by the child's life chances. The results given in this study are the amount of education insurance premium by taking into account the child's life chances is Rp 6.946.456,00. Policy value increases during the disbursement of education funds and decreases at the end of coverage.

Thrombin Generation to Predict the Risk of Venous Thromboembolism Recurrence, Major Bleeding and Death in the Elderly: A Prospective Multicenter Cohort Study

Introduction: Venous thromboembolism (VTE), comprising deep vein thrombosis (DVT) and pulmonary embolism (PE), constitutes a worldwide major health issue, and a leading cause of death. VTE incidence increases exponentially with age mainly due to the accumulation of risk factors and comorbidities predisposing to thrombosis. This leads to greater morbidity impact of VTE on elderly patients, who are also at higher risk of bleeding. Consequently, identification of older patients who might benefit from indefinite anticoagulation treatment is paramount. In order to facilitate the identification of these patients, the benefit/risk ratio should be carefully evaluated by considering clinical and laboratory information. Thrombin activity can be recorded by continuously measuring cleavage of a fluorescent substrate, resulting in a thrombin generation (TG) curve. Recent association studies show promising data of thrombin generation parameters predicting first VTE in elderly (Wang H et. al. RPTH 2021, 5:e12536). However, the predictive ability of thrombin generation for recurrent VTE, major bleeding and mortality in the elderly is unknown. The goal of this study was to prospectively investigate the performance of the TG assay one year after index VTE in predicting the risk of VTE, recurrence, major bleeding and mortality up to 2 years in elderly population. Methods: The study was conducted as part of the Swiss Cohort of Elderly Patients with VTE (SWITCO65+), a prospective multicenter cohort study to assess medical outcomes and quality of life in elderly patients with acute VTE in Switzerland. For the present study, the clinical outcomes were VTE recurrence, major bleeding and mortality, which were assessed parallel to clinical data of thrombosis and other general laboratory parameters including thrombophilia testing in over a 3-year period. Blood samples for assessment of TG parameters were drawn 12 month after the index VTE. Venous blood was drawn after minimal venostasis and processed by double centrifugation according to the recommendation of the subcommittee of the Scientific and Standardization Committee of ISTH. TG measurements were performed with the calibrated automated thrombogramm assay (Stago, Asnières-sur-Seine, France) in two experimental settings: 1pM tissue factor (TF) with/without thrombomodulin (TM) and 13.6 pM TF with/without activated protein C (APC). In addition, reference plasma (Cryocheck Reference Control Normal, PrecisionBiologic, Dartmouth, Canada) was tested in all experiments in order to correct day-to-day variations. Lag time, velocity index, time to peak, peak height, endogenous thrombin potential (ETP) were measured and peak and ETP ratio obtained in presence/absence of TM or APC were calculated. Results from the reference plasma were used to calculate the normalized ETP and peak ratio in 1 pM setting and peak ratio in 13.6 pM TF setting. Results: TG was assessed in 565 patients 12 months after the index VTE. At this time, 59% of patients were still anticoagulated. Eleven percent of them had cancer-related VTE, 20% provoked VTE and 68% unprovoked VTE. The prevalence of inherited risk factors for VTE was in line with previous reports on European patients with VTE. Patients still anticoagulated 12 months after the index VTE were less likely to develop recurrent VTE in the next 24 months than patients without anticoagulation. However, the incidence of major bleeding and mortality was comparable in anticoagulated and non-anticoagulated patients. TG was faster and lower in anticoagulated than in non-anticoagulated patients. Some thrombin generation parameters measured 12 months after the index VTE (Figure 1) were discriminatory for VTE recurrence, major bleeding and mortality (Table 1). In addition, several thrombin generation parameters measured in patients not under anticoagulation 12 months after the index VTE were associated with an increased risk of VTE recurrence, major bleeding and mortality up to 24 months. These associations remained after adjustment for potential confounding factors for the risk of VTE recurrence, major bleeding and mortality (Table 2). Conclusion: In elderly patients, several parameters of thrombin generation were associated with VTE recurrence, major bleeding and/or mortality. These findings may serve as the basis for validation in a prospective interventional outcome trial. Figure 1 Figure 1. Disclosures No relevant conflicts of interest to declare.

Excess Deaths Reveal the Substantial Impact of COVID-19 Pandemic on Mortality in the State of Florida

Our study found that Florida experienced 19 241 excess deaths from March to September 2020.1 Official numbers link 14 317 of these deaths to COVID-19, suggesting that 4924 excess deaths are unexplained. In 2020, Florida experienced a significant increase in all-cause mortality (Table 1), and prior research indicated substantial excess deaths during the pandemic.2–4 Little is known about the change in deaths from non-COVID-19 causes during the pandemic; however, suicidal ideation and substance use increased nationally, for example, which may have led to increased deaths from these causes.5 On the other hand, studies suggest that avoidance of health care services, lack or restricted access to care, limited availability of COVID-19 diagnostic tests, and severe lack of contact tracing may have resulted in several deaths that were not counted in official COVID-19 death records, especially in the beginning of the pandemic.3,4,6 (Am J Public Health. Published online ahead of print June 10, 2021: e1–e2. https://doi.org/10.2105/AJPH.2021.306340 )

Procrustes Analysis of Indonesian Mortality Table Iv and Indonesia's Death Rate During Covid-19 Pandemic

The level of accuracy to calculate the premium is one of the main points for an actuary to determine the criteria of product which is offered by an insurance company to customers. The main reference in this accuracy is the mortality table. The last mortality table made by AAJI (Asosiasi Asuransi Jiwa Indonesia) was Mortality Table Indonesia (MTI) IV which was published in 2019. However, unexpectedly, the Covid-19 pandemic occurred in early 2020 which caused the death rate to be higher than normal situation. This study aims to compare MTI IV which was made with assumptions before the Covid-19 pandemic according to the death rate in Indonesia during the Covid-19 pandemic. This study uses secondary data, by finding the probability of death in Indonesia by calculating the death rate in Indonesia based on population data according to age group classifications obtained from BPS (Badan Pusat Statistik) Indonesia. Furthermore, both data were compared using Procrustes analysis to calculated the level of conformity. The results showed that 75.97% of the data matched MTI IV with the death rate during the pandemic. If the insurance company wants more accurate results, they can be adjusted to the Indonesian Mortality Table using data during the pandemic. If it is quite satisfied with the accuracy of 75.97%, the company can continue to use MTI IV.

Overall and cardiac-specific mortality following serious cardiovascular events in survivors of childhood cancer: A report from the Childhood Cancer Survivor Study (CCSS).

12073 Background: The direct impact of a major cardiovascular (CV) event on mortality among childhood cancer survivors is not well described. We hypothesized that mortality following a major CV event would be higher among survivors compared with siblings and that mortality would be influenced by primary cancer treatment. Methods: The CCSS cohort has conducted longitudinal follow-up of 25,658 survivors of childhood cancer and 5,051 siblings. All-cause and CV-cause specific mortality after a first event of heart failure (HF), coronary artery disease (CAD), or stroke occurring at least 5 years after cancer diagnosis, was estimated using the Kaplan-Meier method. The relative hazards (HR) and 95% confidence intervals (CI) between survivors and siblings as well as the influence of demographic (sex, age, race/ethnicity) and cancer treatment factors were estimated via Cox regression. Results: In total, 1780 survivors and 91 siblings experienced a serious CV event. Total deaths included 706 survivors (271 cardiac causes, 381 non-cardiac causes, 54 unknown causes) and 14 siblings. Survivors were a median age of 31.5 years (range 6.5-61.5) and 20.0 years (range 5.0-44.6) since cancer diagnosis at time of CV event. After a CV event, estimated 10- and 20-y all-cause mortality was significantly higher among survivors than siblings (Table). The HR for all-cause mortality was significantly higher among survivors than siblings after HF (HR 5.2, CI 2.1-13.0), CAD (HR 4.2, CI 2.0-9.0), and stroke (HR 4.6, CI 1.5-14.6). HF and stroke-specific mortality were not significantly increased among survivors versus siblings, in contrast to CAD-specific mortality (HR 3.5, CI 1.1-11.0). Among survivors, heart dose from radiotherapy (per 10 Gy) was associated with increased all-cause and cause-specific mortality after HF (HR 1.2, CI 1.0-1.3; HR 1.3, CI 1.0-1.7), all-cause mortality after CAD (HR 1.2, CI 1.0-1.3), and cause-specific mortality after stroke (HR 2.5, CI 1.2-4.9). Brain dose from radiotherapy was associated with increased all-cause mortality (HR 1.1, CI, 1.0-1.2, per 10 Gy) after stroke. Anthracycline dose was not associated with increased overall or cause-specific mortality risk after a CV event. Conclusions: After a CV event, mortality is higher among survivors than siblings. In survivors, mortality is primarily driven by non-cardiac causes. CAD and prior radiotherapy exposure to the heart and brain also influenced mortality.[Table: see text]

Augmenting machine learning algorithms to predict mortality using patient-reported outcomes in oncology.

1510 Background: Machine learning (ML) algorithms based on electronic health record (EHR) data have been shown to accurately predict mortality risk among patients with cancer, with areas under the curve (AUC) generally greater than 0.80. While patient-reported outcomes (PROs) may also predict mortality among patients with cancer, it is unclear whether routinely-collected PROs improve the predictive performance of EHR-based ML algorithms. Methods: This cohort study included 8600 patients with cancer who had an outpatient encounter at one of 18 medical oncology practices in a large academic health system between July 1st, 2019 and January 1st, 2020. 4692 (54.9%) patients completed assessments of symptoms, performance status, and quality of life from the PRO version of the Common Terminology Criteria for Adverse Events and the Patient-Reported Outcomes Measurement Information System Global v.1.2 scales. We hypothesized that ML models predicting 180-day all-cause mortality based on EHR + PRO data would improve AUC compared to ML models based on EHR data alone. We assessed univariate and adjusted associations between each PRO and 180-day mortality. To train the EHR-only model, we fit a Least Absolute Shrinkage and Selection Operator (LASSO) regression using 192 EHR demographic, comorbidity, and laboratory variables. To train the EHR + PRO model, we used a two-phase approach to fit a model using EHR data for all patients and PRO data for those who completed assessments. To test our hypothesis, we compared the bootstrapped AUC, area under the precision-recall curve (AUPRC), and sensitivity at a 20% risk threshold for both models. Results: 464 (5.4%) patients died within 180 days of the encounter. Decreased quality of life, functional status, and appetite were associated with greater 180-day mortality (Table). Compared to the EHR-only model, the EHR + PRO model significantly improved AUC (0.86 [95% CI 0.85-0.86] vs. 0.80 [95% CI 0.80-0.81]), AUPRC (0.40 [95% CI 0.37-0.42] vs. 0.30 [95% CI 0.28-0.32]), and sensitivity (0.45 [95% CI 0.42-0.48] vs. 0.33 [95% CI 0.30-0.35]). Conclusions: Routinely collected PROs augment EHR-based ML mortality risk algorithms. ML algorithms based on EHR and PRO data may facilitate earlier supportive care for patients with cancer. Association of PROs with 180-day mortality.[Table: see text]

Acute thromboembolic events (TE) within 30 days of COVID-19 infection in cancer patients.

e18691 Background: Increased rates of TE have been reported in patients (pts) with coronavirus disease (COVID-19), even without prior predisposition to thrombosis. Patients with cancer are already predisposed to a hypercoagulable state. We aimed to assess whether COVID-19 further increased the risk of TE in pts with active cancer at Montefiore Medical Center, Bronx, NY. Methods: The EMR of 90 cancer pts diagnosed with COVID-19 from March 15th to April 10th, 2020 were reviewed. COVID-19 testing was performed by PCR of nasal swab samples. Active cancer was defined as disease treated <1 year. Reports of imaging studies performed <30 days of the COVID-19+ test, either for new symptoms or for other reasons, were reviewed for new arterial (ATE) and/or venous thromboses (VTE). Patient were followed for 30 days from the date of COVID-19+ test for development of TE, hospital length of stay (LOS) and mortality. Results: Of 90 pts, 11 (12.2%) were found to have 13 new TE within 30 days of COVID-19+ test, 8 (8.9%) arterial and 5 (5.6%) venous. Of the 8 ATE, 7 were new strokes and/or microvascular cerebral disease (MCD) and 1 was a spleen infarct (SI). Of the 5 VTE, 3 were deep venous thrombosis, 1 pulmonary embolism (PE) and 1 patient presented with a superficial VTE. Two patients had 2 new TE each; stroke/PE and MCD/SI, respectively. Peak D-dimer (DD) value was higher in the TE group; mean DD (SD), TE vs no TE, 7.1 (3.4) vs 6.4 (7) ug/mL, p=0.03. Pts on either prophylactic or therapeutic anticoagulation (AC) had less TE; AC vs no AC, 9.1% vs 90.9%, p=0.0003. Only 1 pt on Enoxaparin prophylaxis developed TE. Of the 20 pts on therapeutic AC, 25% were newly started due to concern for thrombosis; the rest were already receiving AC for other reasons. Mortality was higher in the TE group; HR, TE vs no TE, 2.6, 95% CI (1.2 - 5.6), p=0.009. There was no correlation of cancer type, disease stage (metastatic or not), administration of prior chemotherapy or immunotherapy, common comorbidities, patient setting (inpatient, ICU, outpatient, ED visit), LOS or ventilation status with increased incidence of TE. Conclusions: Pts with COVID-19 have high rates of TE, and this is true for our pts with cancer. A high incidence of ATE was noted. TE was associated with increased mortality.[Table: see text]

A systematic review of post-transplant lymphoproliferative disorder after renal transplantation.

e19577 Background: Post Transplant Lymphoproliferative Disorder (PTLD) is a rare but severe complication following renal transplant. This study aims to explore the treatment modalities, histological types, and risk factors related to PTLD. Methods: Following the PRISMA guideline, we searched the literature on PubMed, Cochrane, Embase & clinicaltrials.gov. A total of 1741 articles were screened and 16 studies were included. Results: We reviewed 275915 adult patients who underwent renal transplantation out of which 2484 (0.9%) patients developed PTLD. Data for gender shows that 61.1% were males and 38.9% females. 576/2484 (23%) cases were EBV positive post-transplant. Seven studies showed the median duration from transplant to the development of PTLD was 80 months (5m-22yrs). Monomorphic PTLD was reported in 585 cases as the most common histological type. 5 studies suggested mortality due to PTLD was 41.38%. OS at 5 and 10 years was 55% and 41% respectively. Conclusions: Our study shows that PTLD is a rare complication after renal transplant which was more common in males. EBV did not show association with PTLD. Monomorphic is the most common histological type of PTLD after renal transplant. Our results show that it is associated with significantly high mortality. [Table: see text]

Financial Resources and Later-Year Relocation From the Annuitized Assessment of Income and Assets

Solid evidence has shown financial resources play important roles in housing decisions among older adults. Despite the growing research on the joint assessment of income and assets as valid economic well-being, little attention is paid to its role in relocation in old age. Drawing from the Behavioral Model of Elderly Migration, this study examined to what extent financial resources are associated with the likelihood of moving in later years. The data came from the 2017 Panel Study of Income Dynamic (PSID). A sample of 1354 people, 65 years and older, was used in the analyses. We used the annuitized approach, which is different from conventional approaches that assume people draw down all available assets to satisfy daily needs and leave no assets for use in later years. We (1) assessed annuitized assets based on the 2019 IRS Mortality Table, (2) assessed yearly income using supplementary income (i.e. income plus non-discretionary expense). A final indicator of the summed score was used in a logistic regression to predict the likelihood of moving. A set of covariates known to affect later- year relocation at an individual level (e.g. health condition, living arrangement change), environmental level (e.g. rural, non-metro area) are controlled for. In clear conflict with previous studies, we found annual financial resources did not significantly influence relocation among older adults. The notable absence of the well-known role of the economic factor provides critical initial evidence about the importance of simultaneous assessment of financial resources for the literature on later year relocation.