Consumption of sugar sweetened beverages, artificially sweetened beverages, and fruit juice and incidence of type 2 diabetes: systematic review, meta-analysis, and estimation of population attributable fraction.

OBJECTIVES: To examine the prospective associations between consumption of sugar sweetened beverages, artificially sweetened beverages, and fruit juice with type 2 diabetes before and after adjustment for adiposity, and to estimate the population attributable fraction for type 2 diabetes from consumption of sugar sweetened beverages in the United States and United Kingdom. DESIGN: Systematic review and meta-analysis. DATA SOURCES AND ELIGIBILITY: PubMed, Embase, Ovid, and Web of Knowledge for prospective studies of adults without diabetes, published until February 2014. The population attributable fraction was estimated in national surveys in the USA, 2009-10 (n=4729 representing 189.1 million adults without diabetes) and the UK, 2008-12 (n=1932 representing 44.7 million). SYNTHESIS METHODS: Random effects meta-analysis and survey analysis for population attributable fraction associated with consumption of sugar sweetened beverages. RESULTS: Prespecified information was extracted from 17 cohorts (38,253 cases/10,126,754 person years). Higher consumption of sugar sweetened beverages was associated with a greater incidence of type 2 diabetes, by 18% per one serving/day (95% confidence interval 9% to 28%, I(2) for heterogeneity=89%) and 13% (6% to 21%, I(2)=79%) before and after adjustment for adiposity; for artificially sweetened beverages, 25% (18% to 33%, I(2)=70%) and 8% (2% to 15%, I(2)=64%); and for fruit juice, 5% (-1% to 11%, I(2)=58%) and 7% (1% to 14%, I(2)=51%). Potential sources of heterogeneity or bias were not evident for sugar sweetened beverages. For artificially sweetened beverages, publication bias and residual confounding were indicated. For fruit juice the finding was non-significant in studies ascertaining type 2 diabetes objectively (P for heterogeneity=0.008). Under specified assumptions for population attributable fraction, of 20.9 million events of type 2 diabetes predicted to occur over 10 years in the USA (absolute event rate 11.0%), 1.8 million would be attributable to consumption of sugar sweetened beverages (population attributable fraction 8.7%, 95% confidence interval 3.9% to 12.9%); and of 2.6 million events in the UK (absolute event rate 5.8%), 79,000 would be attributable to consumption of sugar sweetened beverages (population attributable fraction 3.6%, 1.7% to 5.6%). CONCLUSIONS: Habitual consumption of sugar sweetened beverages was associated with a greater incidence of type 2 diabetes, independently of adiposity. Although artificially sweetened beverages and fruit juice also showed positive associations with incidence of type 2 diabetes, the findings were likely to involve bias. None the less, both artificially sweetened beverages and fruit juice were unlikely to be healthy alternatives to sugar sweetened beverages for the prevention of type 2 diabetes. Under assumption of causality, consumption of sugar sweetened beverages over years may be related to a substantial number of cases of new onset diabetes

Causal inference with multi-state models - estimands and estimators of the population-attributable fraction

The population-attributable fraction (PAF) is a popular epidemiological measure for the burden of a harmful exposure within a population. It is often interpreted causally as proportion of preventable cases after an elimination of exposure. Originally, the PAF has been defined for cohort studies of fixed length with a baseline exposure or cross-sectional studies. An extension of the definition to complex time-to-event data is not straightforward. We revise the proposed approaches in literature and provide a clear concept of the PAF for these data situations. The conceptualization is achieved by a proper differentiation between estimands and estimators as well as causal effect measures and measures of association.Comment: A revised version of this manuscript has been submitted to a journal on March 8 201

Attributable Risk Function in the Proportional Hazards Model

As an epidemiological parameter, the population attributable fraction is an important measure to quantify the public health attributable risk of an exposure to morbidity and mortality. In this article, we extend this parameter to the attributable fraction function in survival analysis of time-to-event outcomes, and further establish its estimation and inference procedures based on the widely used proportional hazards models. Numerical examples and simulations studies are presented to validate and demonstrate the proposed methods

Liver mortality attributable to chronic hepatitis C virus infection in Denmark and Scotland: using spontaneous resolvers as the benchmark comparator

Liver mortality among individuals with chronic hepatitis C (CHC) infection is common, but the relative contribution of CHC per se versus adverse health behaviors is uncertain. We explored data on spontaneous resolvers of hepatitis C virus (HCV) as a benchmark group to uncover the independent contribution of CHC on liver mortality. Using national HCV diagnosis and mortality registers from Denmark and Scotland, we calculated the liver mortality rate (LMR) for persons diagnosed with CHC infection (LMR chronic) and spontaneously resolved infection (LMR resolved), according to subgroups defined by age, sex, and drug use. Through these mortality rates, we determined subgroup-specific attributable fractions (AFs), defined as (LMR chronic - LMR resolved)/LMR chronic, and then calculated the total attributable fraction (TAF) as a weighted average of these AFs. Thus, the TAF represents the overall fraction (where 0.00 = not attributable at all; and 1.00 = entirely attributable) of liver mortality attributable to CHC in the diagnosed population. Our cohort comprised 7,005 and 21,729 persons diagnosed with HCV antibodies in Denmark and Scotland, respectively. Mean follow-up duration was 6.3-6.9 years. The TAF increased stepwise with age. It was lowest for death occurring at &lt;45 years of age (0.21 in Denmark; 0.26 in Scotland), higher for death occurring at 45-59 years (0.69 in Denmark; 0.69 in Scotland), and highest for death at 60+years (0.92 in Denmark; 0.75 in Scotland). Overall, the TAF was 0.66 (95% confidence interval [CI]: 0.55-0.78) in Denmark and 0.55 (95% CI: 0.44-0.66) in Scotland. Conclusions: In Denmark and Scotland, the majority of liver death in the CHC-diagnosed population can be attributed to CHC-nevertheless, an appreciable fraction cannot, cautioning that liver mortality in this population is a compound problem that can be reduced, but not solved, through antiviral therapy alone. </p

The population-attributable fraction for time-dependent exposures using dynamic prediction and landmarking

The public health impact of a harmful exposure can be quantified by the population-attributable fraction (PAF). The PAF describes the attributable risk due to an exposure and is often interpreted as the proportion of preventable cases if the exposure could be extinct. Difficulties in the definition and interpretation of the PAF arise when the exposure of interest depends on time. Then, the definition of exposed and unexposed individuals is not straightforward. We propose dynamic prediction and landmarking to define and estimate a PAF in this data situation. Two estimands are discussed which are based on two hypothetical interventions that could prevent the exposure in different ways. Considering the first estimand, at each landmark the estimation problem is reduced to a time-independent setting. Then, estimation is simply performed by using a generalized-linear model accounting for the current exposure state and further (time-varying) covariates. The second estimand is based on counterfactual outcomes, estimation can be performed using pseudo-values or inverse-probability weights. The approach is explored in a simulation study and applied on two data examples. First, we study a large French database of intensive care unit patients to estimate the population-benefit of a pathogen-specific intervention that could prevent ventilator-associated pneumonia caused by the pathogen Pseudomonas aeruginosa. Moreover, we quantify the population-attributable burden of locoregional and distant recurrence in breast cancer patients.Comment: A revised version has been submitte

Deaths attributable to diabetes in the United States: comparison of data sources and estimation approaches

OBJECTIVE: The goal of this research was to identify the fraction of deaths attributable to diabetes in the United States. RESEARCH DESIGN AND METHODS: We estimated population attributable fractions (PAF) for cohorts aged 30±84 who were surveyed in the National Health Interview Survey (NHIS) between 1997 and 2009 (N = 282,322) and in the National Health and Nutrition Examination Survey (NHANES) between 1999 and 2010 (N = 21,814). Cohort members were followed prospectively for mortality through 2011. We identified diabetes status using self-reported diagnoses in both NHIS and NHANES and using HbA1c in NHANES. Hazard ratios associated with diabetes were estimated using Cox model adjusted for age, sex, race/ethnicity, educational attainment, and smoking status. RESULTS: We found a high degree of consistency between data sets and definitions of diabetes in the hazard ratios, estimates of diabetes prevalence, and estimates of the proportion of deaths attributable to diabetes. The proportion of deaths attributable to diabetes was estimated to be 11.5% using self-reports in NHIS, 11.7% using self-reports in NHANES, and 11.8% using HbA1c in NHANES. Among the sub-groups that we examined, the PAF was highest among obese persons at 19.4%. The proportion of deaths in which diabetes was assigned as the underlying cause of death (3.3±3.7%) severely understated the contribution of diabetes to mortality in the United States. CONCLUSIONS: Diabetes may represent a more prominent factor in American mortality than is commonly appreciated, reinforcing the need for robust population-level interventions aimed at diabetes prevention and care

SAS Macros for Calculation of Population Attributable Fraction in a Cohort Study Design

The population attributable fraction (PAF) is a useful measure for quantifying the impact of exposure to certain risk factors on a particular outcome at the population level. Recently, new model-based methods for the estimation of PAF and its confidence interval for different types of outcomes in a cohort study design have been proposed. In this paper, we introduce SAS macros implementing these methods and illustrate their application with a data example on the impact of different risk factors on type 2 diabetes incidence.

The population-attributable fraction for time-dependent exposures and competing risks - A discussion on estimands

The population-attributable fraction (PAF) quantifies the public health impact of a harmful exposure. Despite being a measure of significant importance an estimand accommodating complicated time-to-event data is not clearly defined. We discuss current estimands of the PAF used to quantify the public health impact of an internal time-dependent exposure for data subject to competing outcomes. To overcome some limitations, we proposed a novel estimand which is based on dynamic prediction by landmarking. In a profound simulation study, we discuss interpretation and performance of the various estimands and their estimators. The methods are applied to a large French database to estimate the health impact of ventilator-associated pneumonia for patients in intensive care.Comment: A revision has been submitte

Kritik an Population Attributable Fraction bei genauerem Hinsehen nicht gerechtfertigt

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Comorbidities only account for a small proportion of excess mortality after fracture: A record linkage study of individual fracture types

Background: Non-hip non-vertebral fractures (NHNV) constitute the majority of osteoporotic fractures but few studies have examined the association between these fractures, co-morbidity and mortality. Objective: To examine the relationship between individual non-hip non-vertebral fractures, co-morbidities and mortality. Methods: Prospective population-based cohort of 267,043 subjects (45 and Up Study, Australia) had baseline questionnaires linked to hospital administrative and all-cause mortality data from 2006 - 2013. Associations between fracture and mortality examined using multivariate, time dependent Cox models, adjusted for age, prior fracture, body mass index, smoking and co-morbidities (cardiovascular disease, diabetes, stroke, thrombosis and cancer) and survival function curves. Population attributable fraction calculated for each level of risk exposure. Results: During 1,490,651 person-years, women and men experienced 7,571 and 4,571 fractures and 7,064 deaths and 11,078 deaths, respectively. In addition to hip and vertebral fractures, pelvis, humerus, clavicle, rib, proximal tibia/fibula, elbow and distal forearm fractures in both sexes, and ankle fractures in men, were associated with increased multivariable adjusted mortality hazard ratios ranging from 1.3 to 3.4. Co-morbidity independently added to mortality such that a woman with a humeral fracture and one co-morbidity had a similarly reduced 5 year survival to that of a woman with a hip fracture and no co-morbidities. Population mortality attributable to any fracture without co-morbidity was 9.2% in women and 5.3% in men. Conclusion: All proximal non-hip, non-vertebral fractures in women and men were associated with increased mortality risk. Co-existent co-morbidities independently further increased mortality. Population attributable risk for mortality for fracture was similar to cardiovascular disease and diabetes, highlighting their importance and potential benefit for early intervention and treatment