45,772 research outputs found

    Evaluating hospital performance based on excess cause-specific incidence

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    Formal evaluation of hospital performance in specific types of care is becoming an indispensable tool for quality assurance in the health care system. When the prime concern lies in reducing the risk of a cause-specific event, we propose to evaluate performance in terms of an average excess cumulative incidence, referring to the center's observed patient mix. Its intuitive interpretation helps give meaning to the evaluation results and facilitates the determination of important benchmarks for hospital performance. We apply it to the evaluation of cerebrovascular deaths after stroke in Swedish stroke centers, using data from Riksstroke, the Swedish stroke registry

    Chronic kidney disease and cause-specific hospitalisation: a matched cohort study using primary and secondary care patient data.

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    BACKGROUND: Although chronic kidney disease (CKD) is associated with various outcomes, the burden of each condition for hospital admission is unknown. AIM: To quantify the association between CKD and cause-specific hospitalisation. DESIGN AND SETTING: A matched cohort study in primary care using Clinical Practice Research Datalink linked to Hospital Episode Statistics in England. METHOD: Patients with CKD (estimated glomerular filtration rate <60 mL/min/1.73 m2 for ≥3 months) and a comparison group of patients without known CKD (matched for age, sex, GP, and calendar time) were identified, 2004-2014. Outcomes were hospitalisations with 10 common conditions as the primary admission diagnosis: heart failure; urinary tract infection; pneumonia; acute kidney injury (AKI); myocardial infarction; cerebral infarction; gastrointestinal bleeding; hip fracture; venous thromboembolism; and intracranial bleeding. A difference in the incidence rate of first hospitalisation for each condition was estimated between matched patients with and without CKD. Multivariable Cox regression was used to estimate a relative risk for each outcome. RESULTS: In a cohort of 242 349 pairs of patients, with and without CKD, the rate difference was largest for heart failure at 6.6/1000 person-years (9.7/1000 versus 3.1/1000 person-years in patients with and without CKD, respectively), followed by urinary tract infection at 5.2, pneumonia at 4.4, and AKI at 4.1/1000 person-years. The relative risk was highest for AKI with a fully adjusted hazard ratio of 4.90, 95% confidence interval (CI) = 4.47 to 5.38, followed by heart failure with 1.66, 95% CI = 1.59 to 1.75. CONCLUSION: Hospitalisations for heart failure, infection, and AKI showed strong associations with CKD in absolute and(or) relative terms, suggesting targets for improved preventive care

    Cause-specific mortality as a sentinel indicator of current socioeconomic conditions in Italy

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    This study aims to assess whether simple, widely available demographic indexes, like mortality measures, may serve as sentinel indicators of the economic development and the social wellbeing in Italy. We analyze and compare the geographical patterns of all-cause mortality indexes and those of the mortality rates for leading causes of death, with the spatial pattern found for a more complex index, the vulnerability index, recently introduced by the Italian National Institute for Statistics, at provincial level in the contemporary Italy. We show that mortality data are a straightforward and powerful tool for driving policy makers in planning appropriate interventions

    Adverse socioeconomic conditions in childhood and cause specific adult mortality: prospective observational study

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    &lt;b&gt;Objective:&lt;/b&gt; To investigate the association between social circumstances in childhood and mortality from various causes of death in adulthood. Design: Prospective observational study. Setting: 27 workplaces in the west of Scotland. &lt;b&gt;Subjects:&lt;/b&gt; 5645 men aged 35-64 years at the time of examination. &lt;b&gt;Main outcome measures:&lt;/b&gt; Death from various causes. &lt;b&gt;Results:&lt;/b&gt; Men whose fathers had manual occupations when they were children were more likely as adults to have manual jobs and be living in deprived areas. Gradients in mortality from coronary heart disease, stroke, lung cancer, stomach cancer, and respiratory disease were seen (all P&#60;0.05), generally increasing from men whose fathers had professional and managerial occupations (social class I and II) to those whose fathers had semiskilled and unskilled manual occupations (social class IV and V). Relative rates of mortality adjusted for age for men with fathers in manual versus non-manual occupations were 1.52 (95&#37; confidence interval 1.24 to 1.87) for coronary heart disease, 1.83 (1.13 to 2.94) for stroke, 1.65 (1.12 to 2.43) for lung cancer, 2.06 (0.93 to 4.57) for stomach cancer, and 2.01 (1.17 to 3.48) for respiratory disease. Mortality from other cancers and accidental and violent death showed no association with fathers' social class. Adjustment for adult socioeconomic circumstances and risk factors did not alter results for mortality from stroke and stomach cancer, attenuated the increased risk of coronary heart disease and respiratory disease, and essentially eliminated the association with lung cancer. &lt;b&gt;Conclusions:&lt;/b&gt; Adverse socioeconomic circumstances in childhood have a specific influence on mortality from stroke and stomach cancer in adulthood, which is not due to the continuity of social disadvantage throughout life. Deprivation in childhood influences risk of mortality from coronary heart disease and respiratory disease in adulthood, although an additive influence of adulthood circumstances is seen in these cases. Mortality from lung cancer, other cancer, and accidents and violence is predominantly influenced by risk factors that are related to social circumstances in adulthood

    Disparities in Cause-Specific Cancer Survival by Census Tract Poverty Level in Idaho, U.S.

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    Objective. This population-based study compared cause-specific cancer survival by socioeconomic status using methods to more accurately assign cancer deaths to primary site. Methods. The current study analyzed Idaho data used in the Accuracy of Cancer Mortality Statistics Based on Death Certificates (ACM) study supplemented with additional information to measure cause-specific cancer survival by census tract poverty level. Results. The distribution of cases by primary site group differed significantly by poverty level (chi-square = 265.3, 100 df, p In the life table analyses, for 8 of 24 primary site groups investigated, and all sites combined, there was a significant gradient relating higher poverty with poorer survival. For all sites combined, the absolute difference in 5-year cause-specific survival rate was 13.6% between the lowest and highest poverty levels. Conclusions. This study shows striking disparities in cause-specific cancer survival related to the poverty level of the area a person resides in at the time of diagnosis

    Association of BMI with overall and cause-specific mortality: a population-based cohort study of 3·6 million adults in the UK.

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    BACKGROUND: BMI is known to be strongly associated with all-cause mortality, but few studies have been large enough to reliably examine associations between BMI and a comprehensive range of cause-specific mortality outcomes. METHODS: In this population-based cohort study, we used UK primary care data from the Clinical Practice Research Datalink (CPRD) linked to national mortality registration data and fitted adjusted Cox regression models to examine associations between BMI and all-cause mortality, and between BMI and a comprehensive range of cause-specific mortality outcomes (recorded by International Classification of Diseases, 10th revision [ICD-10] codes). We included all individuals with BMI data collected at age 16 years and older and with subsequent follow-up time available. Follow-up began at whichever was the latest of: start of CPRD research-standard follow up, the 5-year anniversary of the first BMI record, or on Jan 1, 1998 (start date for death registration data); follow-up ended at death or on March 8, 2016. Fully adjusted models were stratified by sex and adjusted for baseline age, smoking, alcohol use, diabetes, index of multiple deprivation, and calendar period. Models were fitted in both never-smokers only and the full study population. We also did an extensive range of sensitivity analyses. The expected age of death for men and women aged 40 years at baseline, by BMI category, was estimated from a Poisson model including BMI, age, and sex. FINDINGS: 3 632 674 people were included in the full study population; the following results are from the analysis of never-smokers, which comprised 1 969 648 people and 188 057 deaths. BMI had a J-shaped association with overall mortality; the estimated hazard ratio per 5 kg/m2 increase in BMI was 0·81 (95% CI 0·80-0·82) below 25 kg/m2 and 1·21 (1·20-1·22) above this point. BMI was associated with all cause of death categories except for transport-related accidents, but the shape of the association varied. Most causes, including cancer, cardiovascular diseases, and respiratory diseases, had a J-shaped association with BMI, with lowest risk occurring in the range 21-25 kg/m2. For mental and behavioural, neurological, and accidental (non-transport-related) causes, BMI was inversely associated with mortality up to 24-27 kg/m2, with little association at higher BMIs; for deaths from self-harm or interpersonal violence, an inverse linear association was observed. Associations between BMI and mortality were stronger at younger ages than at older ages, and the BMI associated with lowest mortality risk was higher in older individuals than in younger individuals. Compared with individuals of healthy weight (BMI 18·5-24·9 kg/m2), life expectancy from age 40 years was 4·2 years shorter in obese (BMI ≥30·0 kg/m2) men and 3·5 years shorter in obese women, and 4·3 years shorter in underweight (BMI <18·5 kg/m2) men and 4·5 years shorter in underweight women. When smokers were included in analyses, results for most causes of death were broadly similar, although marginally stronger associations were seen among people with lower BMI, suggesting slight residual confounding by smoking. INTERPRETATION: BMI had J-shaped associations with overall mortality and most specific causes of death; for mental and behavioural, neurological, and external causes, lower BMI was associated with increased mortality risk. FUNDING: Wellcome Trust

    Migration Age Patterns: II. Cause-Specific Profiles

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    This paper seeks to illuminate the role played by various reasons for migration in accounting for observed-variations of age-specific migration rates. The focus is on the levels and age profiles of different cause-specific migration schedules and on their contribution to aggregate migration age curves that change over time and space

    Cause-specific measures of life years lost

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    Background: A new measure of the number of life years lost due to specific causes of death is introduced. Methods: This measure is based on the cumulative incidence of death, it does not require "independence" of causes, and it satisfies simple balance equations: "total number of life years lost = sum of cause-specific life years lost", and "total number of life years lost before age x + temporary life expectancy between birth and age x = x". Results: The measure is contrasted to alternatives suggested in the demographic literature and allmethods are illustrated using Danish and Russian multiple decrement life-tables

    Trends and forecasts in cause-specific mortality

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    Mortality forecasting models are typically limited in that they pertain only to national death rates, predict only all-cause mortality, or do not capture and utilize the correlation among diseases. I have developed a novel Bayesian hierarchical model that jointly forecasts cause- specific death rates for geographic subunits. I examined the model’s effectiveness by applying it to United States vital statistics data from 1982 to 2011 that I prepared using a new cause of death reassignment algorithm. I found that the model not only generated coherent forecasts for mutually exclusive causes of death, but it also exhibited lower out-of-sample error than alternative commonly-used models for forecasting mortality. I then used the model to produce forecasts of US cause-specific mortality through 2025 and analysed the resulting trends. I found that total death rates in the US were likely to continue their decline, but at a slower rate of improvement than has been observed for the past several decades. While death rates due to major causes of death like ischaemic heart disease, stroke, and lung cancer were projected to continue trending downward, increases in causes such as unintentional injuries and mental and neurological conditions offset many of these gains. These findings suggest that the US health system will need to adapt to a changing cause composition of disease burden as its population ages in the coming decade. Forecasting research should continue to consider how to best incorporate and balance the many dimensions of mortality when producing projections.Open Acces
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