scholarly journals Spatially varying effects of measured confounding variables on disease risk

2021 ◽  
Vol 20 (1) ◽  
Author(s):  
Chih-Chieh Wu ◽  
Yun-Hsuan Chu ◽  
Sanjay Shete ◽  
Chien-Hsiun Chen
Keyword(s):  
Risk Factors ◽  
Disease Risk ◽  
Disease Incidence ◽  
Spatial Scan Statistic ◽  
Scan Statistic ◽  
Disease Clusters ◽  
Peak Incidence ◽  
Confounding Variables ◽  
And Gender ◽  
Spatially Varying

Abstract Background The presence of considerable spatial variability in incidence intensity suggests that risk factors are unevenly distributed in space and influence the geographical disease incidence distribution and pattern. As most human common diseases that challenge investigators are complex traits and as more factors associated with increased risk are discovered, statistical spatial models are needed that investigate geographical variability in the association between disease incidence and confounding variables and evaluate spatially varying effects on disease risk related to known or suspected risk factors. Information on geography that we focus on is geographical disease clusters of peak incidence and paucity of incidence. Methods We proposed and illustrated a statistical spatial model that incorporates information on known or hypothesized risk factors, previously detected geographical disease clusters of peak incidence and paucity of incidence, and their interactions as covariates into the framework of interaction regression models. The spatial scan statistic and the generalized map-based pattern recognition procedure that we recently developed were both considered for geographical disease cluster detection. The Freeman-Tukey transformation was applied to improve normality of distribution and approximately stabilize the variance in the model. We exemplified the proposed method by analyzing data on the spatial occurrence of sudden infant death syndrome (SIDS) with confounding variables of race and gender in North Carolina. Results The analysis revealed the presence of spatial variability in the association between SIDS incidence and race. We differentiated spatial effects of race on SIDS incidence among previously detected geographical disease clusters of peak incidence and incidence paucity and areas outside the geographical disease clusters, determined by the spatial scan statistic and the generalized map-based pattern recognition procedure. Our analysis showed the absence of spatial association between SIDS incidence and gender. Conclusion The application to the SIDS incidence data demonstrates the ability of our proposed model to estimate spatially varying associations between disease incidence and confounding variables and distinguish spatially related risk factors from spatially constant ones, providing valuable inference for targeted environmental and epidemiological surveillance and management, risk stratification, and thorough etiologic studies of disease.

scholarly journals Differentiating anomalous disease intensity with confounding variables in space

2020 ◽  
Vol 19 (1) ◽  
Author(s):  
Chih-Chieh Wu ◽  
Sanjay Shete
Keyword(s):  
Risk Factors ◽  
Disease Incidence ◽  
Statistical Significance ◽  
Poisson Model ◽  
Small Region ◽  
Sudden Infant ◽  
Disease Clusters ◽  
Related Risk ◽  
Confounding Variables ◽  
Risk Areas

Abstract Background The investigation of perceived geographical disease clusters serves as a preliminary step that expedites subsequent etiological studies and analysis of epidemicity. With the identification of disease clusters of statistical significance, to determine whether or not the detected disease clusters can be explained by known or suspected risk factors is a logical next step. The models allowing for confounding variables permit the investigators to determine if some risk factors can explain the occurrence of geographical clustering of disease incidence and to investigate other hidden spatially related risk factors if there still exist geographical disease clusters, after adjusting for risk factors. Methods We propose to develop statistical methods for differentiating incidence intensity of geographical disease clusters of peak incidence and low incidence in a hierarchical manner, adjusted for confounding variables. The methods prioritize the areas with the highest or lowest incidence anomalies and are designed to recognize hierarchical (in intensity) disease clusters of respectively high-risk areas and low-risk areas within close geographic proximity on a map, with the adjustment for known or suspected risk factors. The data on spatial occurrence of sudden infant death syndrome with a confounding variable of race in North Carolina counties were analyzed, using the proposed methods. Results The proposed Poisson model appears better than the one based on SMR, particularly at facilitating discrimination between the 13 counties with no cases. Our study showed that the difference in racial distribution of live births explained, to a large extent, the 3 previously identified hierarchical high-intensity clusters, and a small region of 4 mutually adjacent counties with the higher race-adjusted rates, which was hidden previously, emerged in the southwest, indicating that unobserved spatially related risk factors may cause the elevated risk. We also showed that a large geographical cluster with the low race-adjusted rates, which was hidden previously, emerged in the mid-east. Conclusion With the information on hierarchy in adjusted intensity levels, epidemiologists and public health officials can better prioritize the regions with the highest rates for thorough etiologic studies, seeking hidden spatially related risk factors and precisely moving resources to areas with genuine highest abnormalities.

scholarly journals The relationship between cardiovascular disease risk factors and gender

Health ◽  
2012 ◽  
Vol 04 (06) ◽  
pp. 309-315 ◽  
Author(s):  
Chung-Min Cho ◽  
Young-Mi Lee
Keyword(s):  
Risk Factors ◽  
Cardiovascular Disease ◽  
Disease Risk ◽  
Cardiovascular Disease Risk ◽  
Cardiovascular Disease Risk Factors ◽  
And Gender ◽  
The Relationship

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.

scholarly journals Cardiovascular disease risk factors are elevated among a cohort of young sexual and gender minorities in Chicago

2019 ◽  
Vol 42 (6) ◽  
pp. 1073-1081
Author(s):  
Ethan Morgan ◽  
Richard D’Aquila ◽  
Mercedes R. Carnethon ◽  
Brian Mustanski
Keyword(s):  
Risk Factors ◽  
Cardiovascular Disease ◽  
Disease Risk ◽  
Cardiovascular Disease Risk ◽  
Cardiovascular Disease Risk Factors ◽  
Sexual And Gender Minorities ◽  
Gender Minorities ◽  
And Gender

A flexible spatial scan statistic with a restricted likelihood ratio for detecting disease clusters

2012 ◽  
Vol 31 (30) ◽  
pp. 4207-4218 ◽  
Author(s):  
Toshiro Tango ◽  
Kunihiko Takahashi
Keyword(s):  
Likelihood Ratio ◽  
Spatial Scan Statistic ◽  
Scan Statistic ◽  
Disease Clusters ◽  
Restricted Likelihood ◽  
Spatial Scan

scholarly journals Renal function, cardiovascular disease risk factors' prevalence and 5-year disease incidence; the role of diet, exercise, lipids and inflammation markers: the ATTICA study

QJM ◽  
2010 ◽  
Vol 103 (6) ◽  
pp. 413-422 ◽  
Author(s):  
C. Chrysohoou ◽  
D. B. Panagiotakos ◽  
C. Pitsavos ◽  
J. Skoumas ◽  
M. Toutouza ◽  
...  
Keyword(s):  
Risk Factors ◽  
Cardiovascular Disease ◽  
Renal Function ◽  
Disease Risk ◽  
Disease Incidence ◽  
Cardiovascular Disease Risk ◽  
Cardiovascular Disease Risk Factors ◽  
Inflammation Markers

scholarly journals Comparisons of Disease Cluster Patterns, Prevalence and Health Factors in the USA, Canada, England and Ireland.

2021 ◽  
Author(s):  
Belinda Hernandez ◽  
MA Stacey Voll ◽  
MA Nathan Lewis ◽  
Cathal McCrory ◽  
Arthur White ◽  
...  
Keyword(s):  
Risk Factors ◽  
At Risk ◽  
Longitudinal Study ◽  
Latent Class ◽  
Disease Incidence ◽  
Management Strategies ◽  
Smoking History ◽  
Complex Nature ◽  
Disease Clusters ◽  
The U.S

Abstract Background Identification of those who are most at risk of developing specific patterns of disease across different populations is required for directing public health policy. Here, we contrast prevalence and patterns of cross-national disease incidence, co-occurrence and related risk factors across population samples from the U.S., Canada, England and Ireland. Methods Participants (n=62,111) were drawn from the US Health and Retirement Study (n=10,858); the Canadian Longitudinal Study on Ageing (n=36,647); the English Longitudinal Study of Ageing (n=7,938) and The Irish Longitudinal Study on Ageing (n=6,668). Self-reported lifetime prevalence of 10 medical conditions, predominant clusters of multimorbidity and their specific risk factors were compared across countries using latent class analysis. Results The U.S. had significantly higher prevalence of multimorbid disease patterns and nearly all diseases when compared to the three other countries, even after adjusting for age, sex, BMI, income, employment status, education, alcohol consumption and smoking history. For the U.S. the most at-risk group were younger on average compared to Canada, England and Ireland. Socioeconomic gradients for specific disease combinations were more pronounced for the U.S., Canada and England than they were for Ireland. The rates of obesity trends over the last 50 years align with the prevalence of eight of the ten diseases examined. While patterns of disease clusters and the risk factors related to each of the disease clusters were similar, the probabilities of the diseases within each cluster differed across countries. Conclusions This information can be used to better understand the complex nature of multimorbidity and identify appropriate prevention and management strategies for treating multimorbidity across countries.

scholarly journals A catalogue of omics biological ageing clocks reveals substantial commonality and associations with disease risk

2021 ◽  
Author(s):  
Erin Macdonald-Dunlop ◽  
Nele Taba ◽  
Lucija Klaric ◽  
Azra Frkatovic ◽  
Rosie Walker ◽  
...  
Keyword(s):  
Risk Factors ◽  
Health Outcomes ◽  
Functional Capacity ◽  
Disease Risk ◽  
Disease Incidence ◽  
Chronological Age ◽  
Biological Ageing ◽  
Health Measures ◽  
The Difference ◽  
Future Work

AbstractBiological age (BA), a measure of functional capacity and prognostic of health outcomes that discriminates between individuals of the same chronological age (chronAge), has been estimated using a variety of biomarkers. Previous comparative studies have mainly used epigenetic models (clocks), we use ~1000 participants to create eleven omics ageing clocks, with correlations of 0.45-0.97 with chronAge, even with substantial sub-setting of biomarkers. These clocks track common aspects of ageing with 94% of the variance in chronAge being shared among clocks. The difference between BA and chronAge - omics clock age acceleration (OCAA) - often associates with health measures. One year’s OCAA typically has the same effect on risk factors/10-year disease incidence as 0.46/0.45 years of chronAge. Epigenetic and IgG glycomics clocks appeared to track generalised ageing while others capture specific risks. We conclude BA is measurable and prognostic and that future work should prioritise health outcomes over chronAge.

scholarly journals Carotid Intima Media Thickness as a Measure of Cardiovascular Disease Burden in Nigerian Africans with Hypertension and Diabetes Mellitus

2011 ◽  
Vol 2011 ◽  
pp. 1-4 ◽  
Author(s):  
Basil N. Okeahialam ◽  
Benjamin A. Alonge ◽  
Stephen D. Pam ◽  
Fabian H. Puepet
Keyword(s):  
Risk Factors ◽  
Cardiovascular Disease ◽  
Disease Risk ◽  
Cardiovascular Disease Risk ◽  
Intima Media Thickness ◽  
Normal Subjects ◽  
Carotid Intima Media Thickness ◽  
Media Thickness ◽  
Need To Evaluate ◽  
And Gender

As part of a larger study of cardiovascular risk factors in nonhypertensive type 2 diabetes patients, we subjected a cohort of diabetics to B mode ultrasonography of the carotid artery to measure the intima media thickness (IMT) and compared it with values in hypertensives and apparently normal controls matched reasonably for gender and age. All groups were comparable in terms of age and gender representation. The mean (SD) of carotid IMT right and left was 0.94 mm (0.12), 0.94 mm (0.16); 0.93 mm (0.21), 0.93 mm (0.15); 0.91 mm (0.17), 0.91 mm (0.13) for diabetic, hypertensive, and normal groups, respectively. There was a nonsignificant tendency to raised IMT for the disease groups from the normal ones. Diabetic and hypertensive Nigerians are equally burdened by cardiovascular disease risk factors. Apparently normal subjects have a reasonable degree of burden suggesting the need to evaluate them for other traditional and emerging risk factors.

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.

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