scholarly journals Causal attribution fractions for epidemiological studies, applied to a UK Biobank study of smoking and BMI

2021 ◽  
Author(s):  
Anthony Webster
Keyword(s):  
Risk Factors ◽  
Proportional Hazards ◽  
Disease Risk ◽  
Causal Attribution ◽  
Disease Incidence ◽  
World Health Organisation ◽  
Epidemiological Studies ◽  
World Health ◽  
Proportional Hazard ◽  
Uk Biobank

Epidemiological studies often use proportional hazard models to estimate associations between potential risk factors and disease risk. It is emphasised that when the "backdoor criteria" from causal-inference applies, if diseases are sufficiently rare, then the proportional hazard model can be used to estimate causal associations. When the "frontdoor criteria" applies (allowing causal estimates with unmeasured confounders), similar estimates are found to mediation analyses with measured confounders. Reasons for this are discussed. An attribution fraction is constructed using the average causal effects (ACE) of exposures on the population, and simple methods for its evaluation are suggested. It differs from the attribution fraction used by the World Health Organisation (WHO), except for specific circumstances where the latter can agree or provide a bound. A counterfactual argument determines an individual's attribution fraction Af in terms of proportional hazard estimates, as Af = 1 − 1/R, where R is an individual's relative risk. Causally meaningful attribution fractions cannot be constructed for all known risk factors or confounders, but there are important cases where they can. As an example, systematic proportional hazards studies with UK Biobank data estimate the attribution fractions of smoking and BMI for 226 diseases. The attribution of risk is characterised in terms of disease chapters from the International Classification of Diseases (ICD-10), and the diseases most strongly attributed to smoking and BMI are identified. The result is a quantitative characterisation of the causal influence of smoking and BMI on the landscape of disease incidence in the UK Biobank population.

scholarly journals Characterisation, identification, clustering, and classification of disease

2020 ◽  
Author(s):  
A.J. Webster ◽  
K. Gaitskell ◽  
I. Turnbull ◽  
B.J. Cairns ◽  
R. Clarke
Keyword(s):  
Risk Factors ◽  
Statistical Power ◽  
Proportional Hazards ◽  
Proportional Hazards Model ◽  
Disease Risk ◽  
Neurological Diseases ◽  
Disease Incidence ◽  
Disease Onset ◽  
Laboratory Findings ◽  
Clustering And Classification

Data-driven classifications are improving statistical power and refining prognoses for a range of respiratory, infectious, autoimmune, and neurological diseases. Studies have used molecular information, age of disease incidence, and sequences of disease onset (“disease trajectories”). Here we consider whether easily measured risk factors such as height and BMI can usefully characterise diseases in UK Biobank data, combining established statistical methods in new but rigorous ways to provide clinically relevant comparisons and clusters of disease. Over 400 common diseases were selected for study on the basis of clinical and epidemiological criteria, and a conventional proportional hazards model was used to estimate associations with 12 established risk factors. Comparing men and women, several diseases had strongly sex-dependent associations of disease risk with BMI. Despite this, a large proportion of diseases affecting both sexes could be identified by their risk factors, and equivalent diseases tended to cluster adjacently. This included 10 diseases presently classified as “Symptoms, signs, and abnormal clinical and laboratory findings, not elsewhere classified”. Many clusters are associated with a shared, known pathogenesis, others suggest likely but presently unconfirmed causes. The specificity of associations and shared pathogenesis of many clustered diseases, provide a new perspective on the interactions between biological pathways, risk factors, and patterns of disease such as multimorbidity.

Concomitants of Excess Coronary Deaths — Major Risk Factor and Lifestyle Findings from 10,359 Men and Women in the Scottish Heart Health Study

1989 ◽  
Vol 34 (6) ◽  
pp. 550-555 ◽  
Author(s):  
W.C.S. Smith ◽  
H. Tunstall-Pedoe ◽  
I.K. Crombie ◽  
R. Tavendale
Keyword(s):  
Risk Factors ◽  
Coronary Heart Disease ◽  
Heart Disease ◽  
Disease Risk ◽  
World Health Organisation ◽  
International Standards ◽  
World Health ◽  
Health Study ◽  
Heart Health ◽  
Men And Women

The Scottish Heart Health Study is a study of lifestyle and coronary heart disease risk factors in 10,359 men and women aged 40–59 years, in 22 districts of Scotland. The study was conducted during 1984–86, when Scotland had the highest national coronary heart disease mortality reported by the World Health Organisation. A self-completed questionnaire, complemented by a 40 minute visit to a survey clinic, staffed by nurses, enabled the classical major risk factors and some more newly described ones to be measured. The study emphasised quality control and representativeness, and incorporated a World Health Organisation protocol for measurement of key items to allow comparisons in place and time, and therefore also to provide a definitive baseline against which interventions can be assessed. This paper describes the overall findings. Current cigarette smokers constitute 39% of men and 38% of women, higher levels than those reported in England but lower than previous Scottish reports. Mean blood pressure levels were 134/84 mmHg for men and 131/81 mmHg in women, lower than in British studies of the 1960s and 1970s. Mean body mass index levels, 26.1 Kg/m2 in men and 25.7 Kg/m2 in women, were not high by international standards. However, mean serum cholesterol levels were 6.4 mmol/l in men and 6.6 mmol/l in women — as high as those in previous British studies and high by international standards. Levels of high density lipoprotein cholesterol, non-fasting triglycerides and fibrinogen are also reported. Physical activity both at work and in leisure time was low. Many participants did not eat fresh fruit or green vegetables. High cholesterol and cigarette smoking levels provide a classical explanation for the excess of coronary deaths in Scotland, justifying action, but other factors, such as the dietary deficiencies, also merit further investigation.

scholarly journals Characterisation, identification, clustering, and classification of disease

2021 ◽  
Vol 11 (1) ◽  
Author(s):  
A. J. Webster ◽  
K. Gaitskell ◽  
I. Turnbull ◽  
B. J. Cairns ◽  
R. Clarke
Keyword(s):  
Risk Factors ◽  
Statistical Power ◽  
Proportional Hazards ◽  
Disease Risk ◽  
Neurological Diseases ◽  
Disease Incidence ◽  
Disease Onset ◽  
Laboratory Findings ◽  
Clustering And Classification ◽  
New Perspective

AbstractThe importance of quantifying the distribution and determinants of multimorbidity has prompted novel data-driven classifications of disease. Applications have included improved statistical power and refined prognoses for a range of respiratory, infectious, autoimmune, and neurological diseases, with studies using molecular information, age of disease incidence, and sequences of disease onset (“disease trajectories”) to classify disease clusters. Here we consider whether easily measured risk factors such as height and BMI can effectively characterise diseases in UK Biobank data, combining established statistical methods in new but rigorous ways to provide clinically relevant comparisons and clusters of disease. Over 400 common diseases were selected for analysis using clinical and epidemiological criteria, and conventional proportional hazards models were used to estimate associations with 12 established risk factors. Several diseases had strongly sex-dependent associations of disease risk with BMI. Importantly, a large proportion of diseases affecting both sexes could be identified by their risk factors, and equivalent diseases tended to cluster adjacently. These included 10 diseases presently classified as “Symptoms, signs, and abnormal clinical and laboratory findings, not elsewhere classified”. Many clusters are associated with a shared, known pathogenesis, others suggest likely but presently unconfirmed causes. The specificity of associations and shared pathogenesis of many clustered diseases provide a new perspective on the interactions between biological pathways, risk factors, and patterns of disease such as multimorbidity.

scholarly journals The Impact of Ultraviolet Radiation on the Aetiology and Development of Uveal Melanoma

Cancers ◽  
2021 ◽  
Vol 13 (7) ◽  
pp. 1700
Author(s):  
Melissa Chalada ◽  
Charmaine A. Ramlogan-Steel ◽  
Bijay P. Dhungel ◽  
Christopher J. Layton ◽  
Jason C. Steel
Keyword(s):  
Risk Factors ◽  
Ultraviolet Radiation ◽  
Uveal Melanoma ◽  
World Health Organisation ◽  
World Health ◽  
Genetic Mutations ◽  
High Incidence ◽  
The World ◽  
The Impact

Uveal melanoma (UM) is currently classified by the World Health Organisation as a melanoma caused by risk factors other than cumulative solar damage. However, factors relating to ultraviolet radiation (UVR) susceptibility such as light-coloured skin and eyes, propensity to burn, and proximity to the equator, frequently correlate with higher risk of UM. These risk factors echo those of the far more common cutaneous melanoma (CM), which is widely accepted to be caused by excessive UVR exposure, suggesting a role of UVR in the development and progression of a proportion of UM. Indeed, this could mean that countries, such as Australia, with high UVR exposure and the highest incidences of CM would represent a similarly high incidence of UM if UVR exposure is truly involved. Most cases of UM lack the typical genetic mutations that are related to UVR damage, although recent evidence in a small minority of cases has shown otherwise. This review therefore reassesses statistical, environmental, anatomical, and physiological evidence for and against the role of UVR in the aetiology of UM.

scholarly journals Cardiovascular Risk Assessment in Angolan Adults: A Descriptive Analysis from CardioBengo, a Community-Based Survey

2018 ◽  
Vol 2018 ◽  
pp. 1-6 ◽  
Author(s):  
João M. Pedro ◽  
Miguel Brito ◽  
Henrique Barros
Keyword(s):  
Cardiovascular Risk ◽  
Risk Prediction ◽  
Disease Risk ◽  
Descriptive Analysis ◽  
World Health Organisation ◽  
World Health ◽  
Lipid Lowering ◽  
Community Based ◽  
Risk Factor Surveillance ◽  
Using Data

From a community-based survey conducted in Angola, 468 individuals aged 40 to 64 years and not using drug therapy were evaluated according to the World Health Organisation STEPwise Approach to Chronic Disease Risk Factor Surveillance. Using data from tobacco use, blood pressure, blood glucose, and total cholesterol levels, we estimated the 10-year risk of a fatal or nonfatal major cardiovascular event and computed the proportion of untreated participants eligible for pharmacological treatment according to clinical values alone and total cardiovascular risk. The large majority of participants were classified as having a low (<10%) 10-year cardiovascular risk (87.6%), with only 4.5% having a high (≥ 20%) cardiovascular risk. If we consider the single criteria for hypertension, 48.7% of the population should be considered for treatment. This value decreases to 22.0% if we apply the risk prediction chart. The use of hypoglycaemic drugs does not present any differences (19.0% in both situations). The use of lipid-lowering drugs (3.8%) is only recommended by the risk prediction chart. This study reveals the need of integrated approaches for the treatment of cardiovascular disorders in this population. Risk prediction charts can be used as a way to promote a better use of limited resources.

scholarly journals Revised World Health Organization (WHO)’s causality assessment of adverse events following immunization—a critique

F1000Research ◽  
2018 ◽  
Vol 7 ◽  
pp. 243 ◽  
Author(s):  
Jacob Puliyel ◽  
Pathik Naik
Keyword(s):  
Heart Disease ◽  
Adverse Events ◽  
Vaccine Safety ◽  
World Health Organisation ◽  
Epidemiological Studies ◽  
World Health ◽  
Causal Association ◽  
Risk Of Death ◽  
Post Marketing Surveillance ◽  
Definition Of

The World Health Organisation (WHO) has recently revised how adverse events after immunization (AEFI) are classified. Only reactions that have previously been acknowledged in epidemiological studies to be caused by the vaccine, are classified as a vaccine-product–related-reaction. Deaths observed during post-marketing surveillance are not considered as “consistent with causal association with vaccine”, if there was no statistically significant increase in deaths recorded during the small Phase 3 trials that preceded it. Of course, vaccines that caused deaths in the control-trials stage would not be licensed. After licensure, deaths and all new serious adverse reactions are labelled as ‘coincidental deaths’ or ‘unclassifiable’, and the association with vaccine is not acknowledged. The resulting paradox is evident. The definition of causal association has also been changed. It is now used only if there is “no other factor intervening in the processes.” Therefore, if a child with an underlying congenital heart disease (other factor), develops fever and cardiac decompensation after vaccination, the cardiac failure would not be considered causally related to the vaccine. The Global Advisory Committee on Vaccine Safety has documented many deaths in children with pre-existing heart disease after they were administered the Pentavalent vaccine. The WHO now advises precautions when vaccinating such children and this has reduced the risk of death. Using the new definition of causal association, this relationship would not be acknowledged and lives would be put at risk. In view of the above, it is necessary that the AEFI manual be revaluated and revised urgently. AEFI reporting is said to be for vaccine safety. Child safety (safety of children) rather than vaccine safety (safety for vaccines) needs to be the emphasis.

scholarly journals Etiology and Risk Factors for Rheumatoid Arthritis: A State-of-the-Art Review

2021 ◽  
Vol 8 ◽  
Author(s):  
Vasco C. Romão ◽  
João Eurico Fonseca
Keyword(s):  
Rheumatoid Arthritis ◽  
Risk Factors ◽  
Disease Risk ◽  
State Of The Art ◽  
Current Knowledge ◽  
Epidemiological Studies ◽  
Complex Interactions ◽  
Significant Burden ◽  
Risk Of Disease ◽  
Societal Level

Rheumatoid arthritis (RA) is the most common systemic inflammatory rheumatic disease. It is associated with significant burden at the patient and societal level. Extensive efforts have been devoted to identifying a potential cause for the development of RA. Epidemiological studies have thoroughly investigated the association of several factors with the risk and course of RA. Although a precise etiology remains elusive, the current understanding is that RA is a multifactorial disease, wherein complex interactions between host and environmental factors determine the overall risk of disease susceptibility, persistence and severity. Risk factors related to the host that have been associated with RA development may be divided into genetic; epigenetic; hormonal, reproductive and neuroendocrine; and comorbid host factors. In turn, environmental risk factors include smoking and other airborne exposures; microbiota and infectious agents; diet; and socioeconomic factors. In the present narrative review, aimed at clinicians and researchers in the field of RA, we provide a state-of-the-art overview of the current knowledge on this topic, focusing on recent progresses that have improved our comprehension of disease risk and development.

scholarly journals Underlying trend, seasonality, prediction, forecasting and the contribution of risk factors: an analysis of globally reported cases of Middle East Respiratory Syndrome Coronavirus

2018 ◽  
Vol 146 (11) ◽  
pp. 1343-1349 ◽  
Author(s):  
Omar B. Da'ar ◽  
Anwar E. Ahmed
Keyword(s):  
Risk Factors ◽  
Middle East ◽  
Moving Average ◽  
World Health Organisation ◽  
Middle East Respiratory Syndrome ◽  
World Health ◽  
Empirical Literature ◽  
Predictive Capacity ◽  
Underlying Trend ◽  
Over Time

AbstractThis study set out to identify and analyse trends and seasonal variations of monthly global reported cases of the Middle East respiratory syndrome coronavirus (MERS-CoV). It also made a prediction based on the reported and extrapolated into the future by forecasting the trend. Finally, the study assessed contributions of various risk factors in the reported cases. The motivation for this study is that MERS-CoV remains among the list of blueprint priority and potential pandemic diseases globally. Yet, there is a paucity of empirical literature examining trends and seasonality as the available evidence is generally descriptive and anecdotal. The study is a time series analysis using monthly global reported cases of MERS-CoV by the World Health Organisation between January 2015 and January 2018. We decomposed the series into seasonal, irregular and trend components and identified patterns, smoothened series, generated predictions and employed forecasting techniques based on linear regression. We assessed contributions of various risk factors in MERS-CoV cases over time. Successive months of the MERS-CoV cases suggest a significant decreasing trend (P = 0.026 for monthly series and P = 0.047 for Quarterly series). The MERS-CoV cases are forecast to wane by end 2018. Seasonality component of the cases oscillated below or above the baseline (the centred moving average), but no association with the series over time was noted. The results revealed contributions of risk factors such as camel contact, male, old age and being from Saudi Arabia and Middle East regions to the overall reported cases of MERS-CoV. The trend component and several risk factors for global MERS-CoV cases, including camel contact, male, age and geography/region significantly affected the series. Our statistical models appear to suggest significant predictive capacity and the findings may well inform healthcare practitioners and policymakers about the underlying dynamics that produced the globally reported MERS-CoV cases.

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