China
Africa
Explore chapters and articles related to this topic
Epidemiology
Published in Samuel C. Morris, Cancer Risk Assessment, 2020
As opposed to incidence, which is the number of new cases developing in a period in a given population, prevalence is the number of people with the disease at a given time in a given population. The cancer prevalence rate is always larger than the cancer incidence rate, since it includes not only those cases which have developed in the immediate year, but all the cases developed in past years who are still alive and in the population. Since it incorporates factors affecting the survival rate following diagnosis and well as possible in-or out-migration from the area following diagnosis of the disease, prevalence rates are more difficult to translate into risk than incidence rates and so are not as useful for risk assessment. The advantage of prevalence rate is that it is easier to ascertain than incidence rates in surveys.
Feasibility Study to Relate Arsenic in Drinking Water to Skin Cancer in the United States
Published in Frederick C. Kopfler, Gunther F. Craun, Environmental Epidemiology, 2019
Julian B. Andelman, Margot Barnett
There are two basic study designs which are possible alternatives to the prevalence study of Hanford City discussed above. They are a prospective cohort study and a multi-city prevalence study. Retrospective studies are not generally feasible for populations like those of Hanford City because historic information on skin cancer would not be reliable. Furthermore, thoroughly defining the population and tracing the health status of everybody who had ever lived in such an area would be a near-impossible task. Of the two alternative designs, the prospective cohort design would be the more feasible. In power calculations done above, numbers of people would be replaced by numbers of person-years. Thus, following prospectively the population of Hanford City and an appropriate un-exposed population for 10 years would theoretically give a study with reasonable power, assuming for example, an arsenic concentration of 100 g/l. The focus would be to detect incidence of the disease rather than prevalence. Generally, incidence is more useful in researching etiology of a disease than prevalence. One disadvantage is that it would not be the most useful measure in a comparison with the Taiwan study results. Incidence of the disease in Taiwan is not known. It can be estimated using the EPA model, but the actual incidence is not known. Comparing United States incidence directly with Taiwan prevalence would obviously be irrelevant.
Environmental Epidemiology
Published in Lorris G. Cockerham, Barbara S. Shane, Basic Environmental Toxicology, 2019
In studying nonfatal (morbidity) diseases, two types of rates can be determined: incidence and prevalence. Incidence refers to number of new cases of disease (numerator) that has occurred within some specified time, such as a year. Prevalence rates depend on the number of existing cases in a population at a particular time. Prevalence is related to incidence in so far as prevalence (P) varies as incidence (I) and duration (t) vary (P ≈ I × t). For some types of illnesses, it is difficult to determine when a disease is manifest, therefore the best way to measure the disease is by the prevalence rate. For example, some chemicals in the work place affect the respiratory system. Studies of decrements in respiratory function attempt to identify persons with deficits because without continued surveillance it is not possible to know when the changes in respiratory function may have occurred. However, incidence or mortality are usually the preferred measures of disease in situations where etiological analyses or risk assessments are needed.
Competing risks models for the deterioration of highway pavement subject to hurricane events
Published in Structure and Infrastructure Engineering, 2019
Sylvester Inkoom, John O. Sobanjo
The variations among the different modes of failure were investigated using different statistics from the Kaplan–Meier estimates and cumulative incidence function outcomes. The cumulative incidence function is a summary statistic for the evaluating the cumulative failure rates over time due to a particular cause. It estimates the cause-specific hazard function of all causes (Andersen, Klein, & Rosthøj, 2003; Klein, Gerster, Andersen, Tarima, & Perme, 2008; Scheike & Zhang, 2002). The various risks are compared using the log rank test and the hazard ratio. The log-rank test is a hypothesis test used to compare the survival distributions of two samples. It is a nonparametric test and usually employed when the data are right skewed. The statistic which yields the chi-square estimate and corresponding -value are used in this study to determine whether the survival of the pavement due to crack deterioration and the hurricane failure are significantly different along with the Kaplan–Meier curves and estimates.
Online sequential monitoring of spatio-temporal disease incidence rates
Published in IISE Transactions, 2020
Due to the importance of early detection of infectious disease outbreaks, some global, national and regional disease reporting systems have been established to collect/provide data about certain measurements of some important diseases. Commonly used measures of disease frequency include prevalence and incidence (Noordzij et al., 2010). The prevalence reflects the total number of existing disease cases, whereas the incidence refers to the number of newly diagnosed cases of a disease. Compared with the prevalence, the incidence is more useful in understanding disease etiology and providing guiding principles for targeting interventions. Thus, it is preferred in many disease surveillance systems. Also, two main types of disease incidence data are available in practice, including (i) number of newly confirmed cases and (ii) incidence rate of a disease. Even though the number of disease cases can provide some useful information about the current burden of a disease, it suffers from the limitation that it cannot distinguish a large population with a low disease rate from a small population with a high disease rate. To overcome this limitation, we can consider the disease incidence rate, defined as the total number of new cases in a region divided by the population of the region in a specific observation time period. This article is motivated by the incidence rate data of the Influenza-Like Illness (ILI), which is a respiratory infection caused by a variety of influenza viruses. A suspect ILI case is defined as a severe respiratory illness with fever (), cough, sore throat, and difficulty in breathing. It is estimated that 15–40% of the population develop illness from influenza each year in the US. About 36 000 people per year die from influenza infection, and about 114 000 people per year have to be admitted to hospital due to influenza infection (Fiore et al., 2010). A traditional method to estimate the incidence rate of ILI is to carry out repeated seroprevalence surveys. However, such surveys are resource-intensive and slow. Thus, they are unfeasible for early detection of disease outbreaks. To overcome that difficulty, the Florida Department of Health has built an Electronic Surveillance System for the Early Notification of Community-based Epidemics at Florida (ESSENCE-FL) recently, which is a syndromic surveillance system for collecting near real-time pre-diagnostic data from participating hospitals and urgent care centers in Florida. Currently, the system collects data from acute care visits to 229 emergency departments and 35 urgent care centers distributed in all counties of Florida, and the collected data are updated once a day. Figure 1 presents the observed incidence rates of ILI for all 67 counties of Florida on June 1, 2012 (a summer time) and December 1, 2012 (a winter time). From the two plots, we can see that the ILI incidence rates in winter were generally higher than those in summer, and the ILI epidemic in some counties (e.g., Liberty county in northwestern Florida) was serious in the winter time.
Dengue outbreaks in a city with recent transmission in São Paulo state, Brazil
Published in International Journal of Environmental Health Research, 2023
Luiz Euribel Prestes-Carneiro, Alana Barbosa Souza, Gabriella Lima Belussi, Guilherme Henrique Dalaqua Grande, Elaine Aparecida Maldonado Bertacco, André Gonçalves Vieira, Edilson Ferreira Flores
Kernel density was used to show hotspots of dengue in Presidente Prudente. Kernel density calculates the density of point features, creating a raster output grid with the intensity of the phenomenon. The method uses a smoothly curved surface over each occurrence point, presenting higher values at the geolocation and diminishing in the opposite direction. This study used a pixel size output of 100 m. The density of each output raster cell (pixels) was calculated by adding the values of all surfaces in the kernel where they overlap the center of the raster cell. An Excel spreadsheet containing the addresses (places of residence) of positive cases of dengue in 2015, 2020, and 2021 in the urban area of Presidente Prudente was provided. This file was exported to GIS ArcGIS (ArcGIS 10-7-1 software) and the column of addresses was geocoded, generating Points Shape. The number of infected individuals and deaths was obtained from the Municipal Surveillance Department of Presidente Prudente, and the estimated number of people living in the city from 2015 to 2021 was obtained from IBGE. The incidence was calculated by dividing the number of cases by the population corresponding to the year and multiplied by 1000. Multivariate analyses of dengue cases and SWDs in 2015, 2020, and 2021 were determined by ArcGIS 10.3 Band Collection Statistics, which provides statistics for multivariate analysis of a set of raster layers. Using the option of computational covariance and correlation matrices, the covariance and correlation matrices, as well as the basic statistical parameters, such as the minimum, maximum, mean, and standard deviation values for each layer, are presented in tables. The correlation matrix shows the values of correlation coefficients that depict a relationship between two sets of data. In the case of a set of raster layers, the correlation matrix displays the cell values of one raster layer as they relate to the cell values of another layer. The correlation ranges from+1 to−1. A positive correlation indicates a direct relationship between two layers, e.g. when the cell values of one layer increase, the cell values of another layer are also likely to increase. A negative correlation means that one variable changes inversely to the other. A correlation of zero means that two layers are independent of each other. The analysis of disposal of solid waste, water drainage, and forest fragments was performed with the ArcGIS (ESRI, Redlands, CA) geographic information system software, using the Universal Transverse Mercator coordinate system and Datum SIRGAS 2000 as a reference, with a scale of 1:100,000 (The R Core Team 2021). Based on the presence of increased numbers of individuals infected with dengue, small rivers, forest fragments, and SWDs, environmental risk factors that favor the development and maintenance of Aedes aegypti vectors, spheres of influence of 1.2 km were established (represented by a circle). The density results were stored in a raster file in a grid format. Ten-meter pixels were set as the spatial resolution using a scale of 1:1200 m. In areas where the hydrographic basin did not include forest fragments, artificial canals were constructed previously for one raster layer as they related to the cell values of another layer.