Dataset Information

Revisiting sequential attributable fractions.

ABSTRACT:

Background

In 1995, Eide and Gefeller introduced the concepts of sequential and average attributable fractions as methods to partition the risk of disease among differing exposures. In particular, sequential attributable fractions are interpreted in terms of an incremental reduction in disease prevalence associated with removing a particular risk factor from the population, having removed other risk factors. Clearly, both concepts are causal entities, but are not usually estimated within a causal inference framework.

Methods

We propose causal definitions of sequential and average attributable fractions using the potential outcomes framework. To estimate these quantities in practice, we model exposure-exposure and exposure-disease interrelationships using a causal Bayesian network, assuming no unmeasured latent confounders. This allows us to model not only the direct impact of removing a risk factor on disease, but also the indirect impact through the effect on the prevalence of causally downstream risk factors that are typically ignored when calculating sequential and average attributable fractions. The procedure for calculating sequential attributable fractions involves repeated applications of Pearl's do-operator over a fitted Bayesian network, and simulation from the resulting joint probability distributions.

Results

The methods are applied to the INTERSTROKE study, which was designed to quantify disease burden attributable to the major risk factors for stroke. The resulting sequential and average attributable fractions are compared with results from a prior estimation approach which uses a single logistic model and which does not properly account for differing causal pathways.

Conclusions

In contrast to estimation using a single regression model, the proposed approaches allow consistent estimation of sequential, joint and average attributable fractions under general causal structures.

SUBMITTER: Ferguson J

PROVIDER: S-EPMC7374857 | biostudies-literature | 2020

REPOSITORIES: biostudies-literature

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Publications

Revisiting sequential attributable fractions.

Ferguson John J O'Connell Maurice M O'Donnell Martin M

Archives of public health = Archives belges de sante publique 20200721

<h4>Background</h4>In 1995, Eide and Gefeller introduced the concepts of sequential and average attributable fractions as methods to partition the risk of disease among differing exposures. In particular, sequential attributable fractions are interpreted in terms of an incremental reduction in disease prevalence associated with removing a particular risk factor from the population, having removed other risk factors. Clearly, both concepts are causal entities, but are not usually estimated within ...[more]

PMID: 32704369

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Project description:Risk factors for hepatocellular carcinoma (HCC) include hepatitis B and C viruses (HBV, HCV), excessive alcohol consumption, rare genetic disorders and diabetes/obesity. The population attributable fractions (PAF) of these factors, however, have not been investigated in population-based studies in the United States.Persons ?68 years diagnosed with HCC (n=6,991) between 1994 and 2007 were identified in the SEER-Medicare database. A 5% random sample (n=255,702) of persons residing in SEER locations were selected for comparison. For each risk factor, odds ratios (ORs), 95% confidence intervals (95% CI) and PAFs were calculated.As anticipated, the risk of HCC was increased in relationship to each factor: HCV (OR 39.89, 95% CI: 36.29-43.84), HBV (OR 11.17, 95% CI: 9.18-13.59), alcohol-related disorders (OR 4.06, 95% CI: 3.82-4.32), rare metabolic disorders (OR 3.45, 95% CI: 2.97-4.02), and diabetes and/or obesity (OR 2.47, 95% CI: 2.34-2.61). The PAF of all factors combined was 64.5% (males 65.6%; females 62.2%). The PAF was highest among Asians (70.1%) and lowest among black persons (52.4%). Among individual factors, diabetes/obesity had the greatest PAF (36.6%), followed by alcohol-related disorders (23.5%), HCV (22.4%), HBV (6.3%) and rare genetic disorders (3.2%). While diabetes/obesity had the greatest PAF among both males (36.4%) and females (36.7%), alcohol-related disorders had the second greatest PAF among males (27.8%) and HCV the second greatest among females (28.1%). Diabetes/obesity had the greatest PAF among whites (38.9%) and Hispanics (38.1%), while HCV had the greatest PAF among Asians (35.4%) and blacks (34.9%). The second greatest PAF was alcohol-related disorders in whites (25.6%), Hispanics (30.1%) and blacks (and 18.5%) and HBV in Asians (28.5%).The dominant risk factors for HCC in the United States among persons ?68 years differ by sex and race/ethnicity. Overall, eliminating diabetes/obesity could reduce the incidence of HCC more than the elimination of any other factor.

Project description:BackgroundAsymptomatic carriage of malaria parasites is common in high transmission intensity areas and confounds clinical case definitions for research studies. This is important for investigations that aim to identify immune correlates of protection from clinical malaria. The proportion of fevers attributable to malaria parasites is widely used to define different thresholds of parasite density associated with febrile episodes. The varying intensity of malaria transmission was investigated to check whether it had a significant impact on the parasite density thresholds. The same dataset was used to explore an alternative statistical approach, using the probability of developing fevers as a choice over threshold cut-offs. The former has been reported to increase predictive power.MethodsData from children monitored longitudinally between 2005 and 2017 from Junju and Chonyi in Kilifi, Kenya were used. Performance comparison of Bayesian-latent class and logistic power models in estimating malaria attributable fractions and probabilities of having fever given a parasite density with changing malaria transmission intensity was done using Junju cohort. Zero-inflated beta regressions were used to assess the impact of using probabilities to evaluate anti-merozoite antibodies as correlates of protection, compared with multilevel binary regression using data from Chonyi and Junju.ResultsMalaria transmission intensity declined from over 49% to 5% between 2006 and 2017, respectively. During this period, malaria attributable fraction varied between 27-59% using logistic regression compared to 10-36% with the Bayesian latent class approach. Both models estimated similar patterns of fevers attributable to malaria with changing transmission intensities. The Bayesian latent class model performed well in estimating the probabilities of having fever, while the latter was efficient in determining the parasite density threshold. However, compared to the logistic power model, the Bayesian algorithm yielded lower estimates for both attributable fractions and probabilities of fever. In modelling the association of merozoite antibodies and clinical malaria, both approaches resulted in comparable estimates, but the utilization of probabilities had a better statistical fit.ConclusionsMalaria attributable fractions, varied with an overall decline in the malaria transmission intensity in this setting but did not significantly impact the outcomes of analyses aimed at identifying immune correlates of protection. These data confirm the statistical advantage of using probabilities over binary data.

Dataset Information

Revisiting sequential attributable fractions.

Background

Methods

Results

Conclusions

Publications

Revisiting sequential attributable fractions.

Similar Datasets

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