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Cancer Epidemiology
Published in Trevor F. Cox, Medical Statistics for Cancer Studies, 2022
We end this chapter touching on the topic of spatial epidemiology that brings together spatial statistics and epidemiology. The book edited by Lawson[35] might be of interest to the reader. We will only give one very simple example.
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).
E
Published in Filomena Pereira-Maxwell, Medical Statistics, 2018
The branch of medical science that studies disease distribution in populations, i.e. who gets diseased, where and when. Epidemiology is also concerned with the study of disease determinants, i.e. the reasons why people are affected by disease. Other health-related outcomes such as causes of death, disease outcomes, behaviours and provision and use of health services are also the focus of epidemiology (PORTA (ed.), 2014). The main areas of epidemiological study are disease surveillance and analytical research, the latter entailing observational and intervention studies. An important goal of epidemiological research is to identify the causative factors for different diseases, which can lead to the development and implementation of appropriate disease control and disease prevention measures. See also spatial epidemiology, clinical epidemiology, epidemiological study.
Impact of occupational categories on the incidence of amyotrophic lateral sclerosis in Sardinia Island, Italy
Published in Amyotrophic Lateral Sclerosis and Frontotemporal Degeneration, 2023
Vincenzo Pierri, Giuseppe Borghero, Francesca Pili, Tommaso Ercoli, Angelo Fabio Gigante, Luigi Isaia Lecca, Rosario Vasta, Marcello Campagna, Adriano Chiò, Giovanni Defazio
Attempts to identify non-genetic risk factors were mostly made by case-control studies (3,4), a design often used in rare diseases that can be nevertheless biased by several factors, including defects in the collection of cases (as only selected cases, not representative of the general population, are included) and controls (that may not be drawn from the same frame as cases, or be equally motivated to provide accurate information) or incomplete information from clinical records. When searching for putative environmental causes of a disease, an alternative approach may be to study the incidence of the disease in those exposed to an environmental factor. An increased incidence of the disease in the exposed population would support the association between disease and exposure. Under this design, spatial epidemiology could also give important clues (5).
Spatial analysis of HIV infection and the associated correlates among transgender persons in the United States
Published in AIDS Care, 2022
Babayemi O. Olakunde, Jennifer R. Pharr, Daniel A. Adeyinka, Donaldson F. Conserve, Dustin T. Duncan
Identifying the geographic hotspots of HIV infection for targeted prevention and treatment interventions is critical to ending the HIV epidemic in the U.S. (Fauci et al., 2019). While some geographic areas (48 counties; Washington, DC; and San Juan, Puerto Rico with a high burden of new HIV diagnoses, and seven states with a high proportion of HIV diagnoses in rural areas) have been prioritized for the first phase of the “Ending the HIV Epidemic in the U.S (EHE)” Plan (HIV.gov, 2021), there is a paucity of research on the spatial epidemiology of HIV among transgender people. Previous studies on the spatial epidemiology of HIV infection in the U.S. have focused on other populations such as injection drug users (Martinez et al., 2014), prison inmates (Bose, 2018), U.S. military applicants (Bautista et al., 2008), or were restricted to one state (Gray et al., 2016; Hixson et al., 2011; Shepard et al., 2011; Stopka et al., 2018). Additionally, the majority of the studies on HIV risk factors among transgender population in the U.S. often do not use spatial analytical techniques which are important to accurately quantify spatial relationships.