Crude and standardized 10-year cumulative incidence, risk difference, and risk ratio estimates for death without a cancer diagnosis in 7,515 CNICS patients, averaged over 30 imputations.

BackgroundVery few studies have been published on the causes of death of upper tract urothelial carcinoma (UTUC). We sought to explore the mortality patterns of contemporary UTUC survivors.MethodsWe performed a retrospective cohort study involving patients with upper urinary tract carcinoma from the National Cancer Institute’s Surveillance, Epidemiology, and End Results (SEER) database (2000 and 2015). We used standardized mortality ratios (SMRs) to compare death rates among patients with UTUC in the general population and excess absolute risks (EARs) to quantify the disease-specific death burden.ResultsA total of 10,179 patients with UTUC, including 7,133 who died, were included in our study. In total, 302 (17.17%) patients with the localized disease died of UTUC; however, patients who died from other causes were 4.8 times more likely to die from UTUC (n = 1,457 [82.83%]). Cardiovascular disease was the most common non-cancer cause of death (n = 393 [22.34% of all deaths]); SMR, 1.22; 95% confidence intervals [CI], 1.1–1.35; EAR, 35.96). A total of 4,046 (69.99%) patients with regional stage died within their follow-up, 1,413 (34.92%) of whom died from UTUC and 1,082 (26.74%) of whom died from non-cancer causes. UTUC was the main cause of death (SMR, 242.48; 95% CI, 230–255.47; EAR, 542.47), followed by non-tumor causes (SMR, 1.18; 95% CI, 1.11–1.25; EAR, 63.74). Most patients (94.94%) with distant stage died within 3 years of initial diagnosis. Although UTUC was the leading cause of death (n = 721 [54.29%]), these patients also had a higher risk of death from non-cancer than the general population (SMR, 2.08; 95% CI, 1.67–2.56; EAR, 288.26).ConclusionsNon-UTUC deaths accounted for 82.48% of UTUC survivors among those with localized disease. Patients with regional/distant stages were most likely to die of UTUC; however, there is an increased risk of dying from non-cancer causes that cannot be ignored. These data provide the latest and most comprehensive assessment of the causes of death in patients with UTUC.

Hospital-level multivariable linear regression for risk standardized mortality ratio (n = 724).

Abbreviations: CI, Confidence Interval; ICD, International Classification of Diseases; SMR, Standardized Mortality Ratio; SCCS, Southern Community Cohort StudyPanel A: Ratio of the directly standardized rate in the 12 SCCS states (Alabama, Arkansas, Florida, Georgia, Kentucky, Louisiana, Mississippi, North Carolina, South Carolina, Tennessee, Virginia, and West Virginia) to the directly standardized rate in the remaining 38 states.Panel B: Standardized mortality ratio of SCCS population relative to mortality rates in the 12 SCCS states.Rate ratios for external causes of death among the population age 40 and over in the 12 SCCS states, and in the SCCS population, by race and sex.

Despite recent advances in care, stroke remains a life-threatening disease. Little is known about current hospital mortality with stroke and how it varies by hospital in a national clinical setting in Japan. Using the Diagnosis Procedure Combination database (a national inpatient database in Japan), we identified patients aged ≥20 years who were admitted to the hospital with a primary diagnosis of stroke within 3 days of stroke onset from April 2012 to March 2013. We constructed a multivariable logistic regression model to predict in-hospital death for each patient with patient-level factors, including age, sex, type of stroke, Japan Coma Scale, and modified Rankin Scale. We defined risk-standardized mortality ratio as the ratio of the actual number of in-hospital deaths to the expected number of such deaths for each hospital. A hospital-level multivariable linear regression was modeled to analyze the association between risk-standardized mortality ratio and hospital-level factors. We performed a patient-level Cox regression analysis to examine the association of in-hospital death with both patient-level and hospital-level factors. Of 176,753 eligible patients from 894 hospitals, overall in-hospital mortality was 10.8%. The risk-standardized mortality ratio for stroke varied widely among the hospitals; the proportions of hospitals with risk-standardized mortality ratio categories of ≤0.50, 0.51–1.00, 1.01–1.50, 1.51–2.00, and >2.00 were 3.9%, 47.9%, 41.4%, 5.2%, and 1.5%, respectively. Academic status, presence of a stroke care unit, higher hospital volume and availability of endovascular therapy had a significantly lower risk-standardized mortality ratio; distance from the patient’s residence to the hospital was not associated with the risk-standardized mortality ratio. Our results suggest that stroke-ready hospitals play an important role in improving stroke mortality in Japan.

The cause-specific mortality rate and standardized mortality ratio in patients with atrial fibrillation according to ICD-10 code (the first code).

Standardized mortality ratios, risk difference and attributable fractions, Kisumu County, 2019.

BackgroundPatients with atrial fibrillation are known to have a high risk of mortality. There is a paucity of population-based studies about the impact of atrial fibrillation on the mortality risk stratified by age, sex, and detailed causes of death.MethodsA total of 15,411 patients with atrial fibrillation from the Korean National Health Insurance Service-National Sample Cohort were enrolled, and causes of death were identified according to codes of the 10th revision of the International Classification of Diseases.ResultsFrom 2002 to 2013, a total of 4,479 (29%) deaths were confirmed, and the crude mortality rate for all-cause death was 63.3 per 1,000 patient-years. Patients with atrial fibrillation had a 3.7-fold increased risk of all-cause death compared with the general population. The standardized mortality ratio for all-cause death was the highest in young patients and decreased with increasing age (standardized mortality ratio 21.93, 95% confidence interval 7.60–26.26 in patients aged

CI = confidence intervals, RRR = relative risk ratio, SD = standard deviation, Mtb = Mycobacterium tuberculosis, LAM = lipoarabinomannan, CRAG = cryptococcal antigen.*N = 350 with complete records, Multinomial logistic regression model adjusted for gender, age, baseline direct smear positivity, currently on ART. Alive group as reference.

The dataset is about the Standardized Infection Ratio (SIR) for central line-associated bloodstream infection (CLABSI) wards which is used to track healthcare-associated infections (HAIs) over time, at national, state, or facility level. The SIR compares the actual number of HAIs at each hospital, to the predicted risk-adjusted number of infections based on national baseline data. Risk adjustment takes into account that some hospitals treat sicker patients than others.

This dataset is about the Standardized Infection Ratio (SIR) for central line-associated bloodstream infection (CLABSI) which is a statistic used to track healthcare-associated infections (HAIs) over time, at national, state, or facility level. The SIR compares the actual number of HAIs at each hospital, to the predicted risk-adjusted number of infections based on national baseline data. Risk adjustment takes into account that some hospitals treat sicker patients than others.

The Standardized Infection Ratio (SIR) is a statistic used to track healthcare associated infections (HAIs) over time, at a national, state, or facility level. The SIR compares the actual number of HAIs at each hospital, to the predicted number of infections. The predicted number is an estimate based on national baseline data, and it is risk adjusted. Risk adjustment takes into account that some hospitals treat sicker patients than others.

"OBJECTIVE: People with alcohol dependence suffer from poor health outcomes, including excessive suicide mortality. This study estimated the suicide rate and explored the risk and protective factors for suicide in a large-scale Asian population.

METHOD: We enrolled patients with alcohol dependence (ICD-9 code 303**) consecutively admitted to a psychiatric center in northern Taiwan from January 1, 1985, through December 31, 2008 (N = 2,793). Using patient linkage to the national mortality database (1985-2008), we determined that 960 patients died during the study period. Of those deaths, 65 patients died of suicide. On the basis of risk-set sampling for the selection of controls, we conducted a nested case-control study and collected the information by means of a standardized chart review process. We estimated the standardized mortality ratio (SMR) for suicide mortality. Conditional logistic regression was employed for exploring the risk and protective factors for suicide.

RESULTS: The study subjects had excessive suicide and all-cause deaths, with SMRs of 21.2 and 12.7, respectively. We pinpointed auditory hallucination (adjusted risk ratio [aRR] = 1.80, P = .04) and attempted suicide (aRR = 7.52, P = .001) as the risk factors associated with suicide. In contrast, protective factors included financial independence (aRR = 0.11, P = .005) and being married (aRR = 0.16, P = .02). Intriguingly, those with physical illnesses had a lower risk of suicide (aRR = 0.15, P = .01).

CONCLUSIONS: Compared with the general population, those with alcohol dependence faced excessive suicide mortality. For a comprehensive approach to suicide prevention, recognizing and improving the protective factors could have equal importance in mitigating the risk of suicide."

The dataset is about the Standardized Infection Ratio (SIR) for catheter-associated urinary tract infection (CAUTI) wards which is used to track healthcare-associated infections (HAIs) over time, at national, state, or facility level. The SIR compares the actual number of HAIs at each hospital, to the predicted risk-adjusted number of infections based on national baseline data. Risk adjustment takes into account that some hospitals treat sicker patients than others.

The dataset is about the Standardized Infection Ratio (SIR) for catheter-associated urinary tract infection (CAUTI) for Intensive Care Units which is used to track healthcare-associated infections (HAIs) over time, at national, state, or facility level. The SIR compares the actual number of HAIs at each hospital, to the predicted risk-adjusted number of infections based on national baseline data. Risk adjustment takes into account that some hospitals treat sicker patients than others.

Abbreviations: CI, confidence interval; E, expected case number; O, observed case number; SIR, standardized incidence ratioSensitivity analysis of the standardized incidence ratios (SIR) with 95% CI for the association between CHD and all cancer risk, with the inclusion and exclusion of data from first year of follow-up.

BackgroundBreast cancer (BC) survivors have an increased risk of developing second primary cancers (SPCs); however, it is still unclear if metastasis is a risk factor for developing SPCs. Usually, long-term cancer survivors face an increased risk of developing SPCs; however, less attention has been paid to SPCs in patients with metastatic cancer as the survival outcomes of the patients are greatly reduced.MethodsA total of 17,077 American women diagnosed with breast cancer between 2010 and 2018 were identified from Surveillance, Epidemiology, and End Results (SEER) database and were included in the study. The clinical characteristics, standardized incidence ratio (SIR), standardized mortality ratio (SMR), and patterns of SPCs in BC patients with no metastasis, regional lymph node metastasis, and distant metastasis were investigated. Kaplan-Meier method was used to compare the prognosis of BC patients after developing SPCs with different metastatic status. XGBoost, a high-precision machine learning algorithm, was used to create a prediction model to estimate the prognosis of metastatic breast cancer (MBC) patients with SPCs.ResultsThe results reveal that the SIR (1.01; 95% CI, 0.99–1.03, p>0.05) of SPCs in non-metastasis breast cancer (NMBC) patients was similar to the general population. Further, patients with regional lymph node metastasis showed an 8% increased risk of SPCs (SIR=1.08, 95%CI, 1.05–1.11, p

Results from Lasso penalization with the heterogeneity stopping rule and cross-validation, simple median, weighted median and MR-Egger methods are also presented.

The Standardized Infection Ratio (SIR) is a statistic used to track healthcare associated infections (HAIs) over time, at a national, state, or facility level. The SIR compares the actual number of HAIs at each hospital, to the predicted number of infections. The predicted number is an estimate based on national baseline data, and it is risk adjusted. Risk adjustment takes into account that some hospitals treat sicker patients than others.

The Standardized Infection Ratio (SIR) is a statistic used to track healthcare associated infections (HAIs) over time, at a national, state, or facility level. The SIR compares the actual number of HAIs at each hospital, to the predicted number of infections. The predicted number is an estimate based on national baseline data, and it is risk adjusted. Risk adjustment takes into account that some hospitals treat sicker patients than others.