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Introduction and Datasets
Published in Andrew B. Lawson, Using R for Bayesian Spatial and Spatio-Temporal Health Modeling, 2021
Often individual-level patient-based data becomes available. Individual-level data is the fundamental data form that arises in spatial epidemiology and is the most fundamental level in most biomedical studies. The main difference in the case of disease mapping is that the individual subject has a geocoding defining their location (residential or otherwise). The finest level of geocoding is an exact address which offers a substitute for exposure or other locational effect. For example in a cluster study it may be important to assess the degree of exposure to a potential air pollution source. If so then exposure at a location near to the source could be important information associating case outcomes to air pollution insult. Equally, residential location could lead to evidence of a disease cluster. If cases are found to be located close to each other then a disease cluster might emerge. In both these examples residential address may be a surrogate for a common locationally specific effect. On the other hand, residential address may not be relevant when an exposure occurs during travel or in the workplace. Mesothelioma affected many shipyard workers in the eastern seaboard of the US in the early 20th century, but their workplaces (shipyards) were the exposure locale (Sanden and Jarvholm, 1991).
Describing what happens: Clinical case reports, case series, occurrence studies
Published in Milos Jenicek, Foundations of Evidence-Based Medicine, 2019
To state that a cholera epidemic exists does not usually require special quantitative techniques. If rare diseases such as tumors are involved, more sophisticated statistical techniques are required. If a ‘new’ disease appears, a study of its clusters may lead to etiological breakthroughs, such as the elucidation of angiosarcoma in workers exposed to vinyl chloride monomers, or of phocomelia after thalidomide exposure.89 Once clusters are discovered, further etiological research follows based on methodology, as described in the next chapter. A study of disease clusters is subjected to rigorous methodology, as is any study of disease outbreaks. It relies on important clinical considerations such as the operational definition of cases (a cluster must be an aggregation of one disease and not of a mix of poorly defined, obscure cases of many different diseases). Statistical methodology, as diversified as it is today, goes beyond the scope of this reading and can be reviewed elsewhere (see the appendix to Reference 89).
Health status of the elderly in the future: demography, epidemiology and prevention
Published in Gert P Westert, Lea Jabaaij, François G Schellevis, Morbidity, Performance and Quality in Primary Care, 2018
Nancy Hoeymans, Anneke van den Berg Jeths
These observed epidemiological trends in disease clusters are projected to 2020. Table 27.2 shows the number of patients based on the epidemiological trend, including the demographic changes. Diabetes and lung diseases show the largest increase: 83% and 92% respectively.
Trends in health and disability in Botswana. An analysis of the global burden of disease study
Published in Disability and Rehabilitation, 2021
Jill Hanass-Hancock, Bradley Carpenter
We conducted a descriptive analysis and examined trends in health over time and the contribution that years lived with disability make towards the global burden of disease, focusing on the Botswanan rates of disability-adjusted life years, years of life lost, and years lived with disability per 100 000 people. The Global Burden of Disease study clusters diseases into different “levels”. The top level, “broad causes”, of disease are: (A) Communicable, maternal, neonatal, and nutritional diseases; (B) Non-communicable diseases; and (C) Injuries. These causes are then further broken up into a second level of “22 disease clusters” (e.g., cardiovascular diseases, diabetes and kidney diseases), a third level of “169 diseases” (e.g., stroke, diabetes mellitus, etc.), and fourth level causes of 293 diseases and disease subtypes (e.g., ischemic stroke, diabetes mellitus type 1, etc.). This study makes use of the first two of these four levels.
Comorbidity patterns among people living with HIV: a hierarchical clustering approach through integrated electronic health records data in South Carolina
Published in AIDS Care, 2021
Xueying Yang, Jiajia Zhang, Shujie Chen, Sharon Weissman, Bankole Olatosi, Xiaoming Li
The hierarchical cluster algorithms were used to identify comorbidity clusters based on the 24 diagnosis groups. Cluster analysis is one of the most common used statistical methods to identify multimorbidity patterns among others and applied in several other studies (Ng et al., 2018; Prados-Torres et al., 2014). Given our expectation of multimorbidity among our study population, we applied a hierarchical cluster algorithm which was shown to be the most appropriate for classification problems where objects are related via some underlying systematic structure (Everitt et al., 2001). Specifically, we first computed the proximity between different comorbidities (“present” or “absent”) via the DGOWER method (Gower & Legendre, 1986) through the DISTANCE procedure. Second, we clustered the diagnosis groups based on their proximity using a hierarchical clustering algorithm, implemented by CLUSTER statement with Ward’s minimum-variance method. Because there is little consensus in the literature regarding how to determine the number and quality of clusters representing a meaningful grouping of objects, we used the following two clinically relevant criteria to identify disease clusters: (1) the groupings of the diseases (diagnosis groups) within the cluster match known epidemiological ties, and (2) most of the diseases within the cluster are known to respond to disease co-management approaches. Although these criteria are subjective, they were the commonly used criteria to determine the number of comorbidity clusters using hierarchical cluster analyses (Cornell et al., 2008). The number of concurrent comorbidity clusters was dichotomized (0–1 cluster vs. ≥ 2 clusters) to serve as a measure of multimorbidity in the current study.
Geographical clusters of amyotrophic lateral sclerosis and the Bradford Hill criteria
Published in Amyotrophic Lateral Sclerosis and Frontotemporal Degeneration, 2022
Elisa Longinetti, Elisabetta Pupillo, Chiara Belometti, Elisa Bianchi, Marco Poloni, Fang Fang, Ettore Beghi
A disease cluster is an aggregation of cases observed in an identifiable subpopulation (7) defined in terms of space, space-time, families, jobs, buildings, associations or groups of people with specific hobbies. A geographical cluster of disease is the identification of areas of a territory characterized by a significantly different number of observed and expected cases, in relation to one or more phenomena examined.