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Community and environment as determinants of health
Published in Ben Y.F. Fong, Martin C.S. Wong, The Routledge Handbook of Public Health and the Community, 2021
Thomas Man-chi Dao, Bean S.N. Fu
The most elementary description of community structure is age and sex distribution. It is usually surveyed by population census every 10 years in most countries. Stakeholders require this information to assess community health care needs and disease burden to manage resources effectively. For example, most governments run specific public health services on maternal and child health, elderly home planning according to women population in their child-bearing age, birth rate and the older people population. With the resource allocation and enhancement of maternal-child care in most countries, we have achieved a global success of a large decline in age-under-5 mortality in the past decades. The mortality rate dropped from 216.0 deaths per 1000 live births in 1950 to 38.9 deaths per 1000 live births in 2017 (Abbastabar et al., 2018).
Extreme Events, Population, and Risk: An Integrated Modeling Approach
Published in Vyacheslav Lyubchich, Yulia R. Gel, K. Halimeda Kilbourne, Thomas J. Miller, Nathaniel K. Newlands, Adam B. Smith, Evaluating Climate Change Impacts, 2020
Lelys Bravo de Guenni, Desireé Villalta, Andrés Sajo-Castelli
Rainfall data from 26 climatological stations were obtained from the Argus/CEsMA repository (Bravo et al, 2014; Centro de Estadística y Matemáticas Aplicadas, 2015), which includes meteorological data from several official sources (Figure 11.8). Monthly data on the total number of casualties were obtained from Desinventar (2014); CRED (2013); FUNVISIS (2013). Population census data for years 2001 and 2010 were obtained from the National Institute of Statistics (INE). An exponential growth model was used to interpolate and extrapolate population values for the remaining years to generate population maps for the whole study domain.
Infectious Disease Data from Surveillance, Outbreak Investigation, and Epidemiological Studies
Published in Leonhard Held, Niel Hens, Philip O’Neill, Jacco Wallinga, Handbook of Infectious Disease Data Analysis, 2019
Denominator data. A key source of non-health data which is important for surveillance is demographic data on population denominators. In many countries this is collected and made available by a national statistics bureau, either derived from a population census or, in some countries, from a population register. Denominator data is crucial to be able to calculate incidence rates and to assess how representative surveillance data is of the general population.
Early Marriage Among Young Girls in Eastern Ethiopia: Trends From 2008 to 2018
Published in Women's Reproductive Health, 2023
Dureti Abdurahman, Nega Assefa, Yemane Berhane
Kersa HDSS is located in the East Hararghe zone of the Oromia Regional State in Ethiopia. It is a demographic and health surveillance and research center established in 2007 by Haramaya University to serve as research center and source of health and demographic data for Eastern Ethiopia to create a framework for research at the community level. The HDSS monitors demographic events such as birth, death, and marital status change as well as health-related conditions such as family planning and morbidity events. The initial baseline household and population census was conducted in 2007, and the database is updated every 6 months (Assefa et al., 2016). The data are collected by trained interviewers who are residents of the study sites. From the beginning, and in each round of data collection (the Kersa database is updated every 6 months), the head of the family (father or mother) or another adult member of the household is interviewed to capture a specific demographic or health event using structured forms; this includes the marriage status of people the specific household. The Kersa DHSS population is predominantly Muslim and lives on subsistence farming, although some farmers cultivate cash crops. The society is patriarchal and, as a result, traditional values dominate in producing social norms (Ethiopia Office of the Population & Housing Census Commission, 2007).
Epidemiology and Clinical Characteristics of Presumed Ocular Histoplasmosis in Olmsted County, Minnesota
Published in Ocular Immunology and Inflammation, 2022
Timothy T. Xu, Margaret M. Reynolds, David O. Hodge, Wendy M. Smith
Overall incidence was estimated using the age- and sex-specific population figures for Olmsted County census data from 2006 to 2015. Prevalence rates were estimated using the population census figures in 2010. Age was stratified into the following groups by years: 0 to 14, 15 to 24, 25 to 44, 45 to 64, and 65 to 110. Population estimates for individual years between census years were determined using linear interpolation. Because the Olmsted County, Minnesota population is approximately 85% White, incidence and prevalence rates were also age- and/or sex-adjusted to the 2010 census figures for the U.S. White population so data could be compared to national estimates. Incident and prevalent cases were combined for prevalence calculations. The 95% confidence intervals (95% CI) for the rates were calculated assuming a Poisson error distribution.
Assessment of longevity risk: credibility approach
Published in Journal of Applied Statistics, 2021
Bükre Yıldırım Külekci, A. Sevtap Selcuk-Kestel
Turkish mortality studies are scarce because of insufficient history on mortality and population census data. After the implementation of Address Based Population Registration System (ABPRS) in 2007, the population dynamics are available but not long enough to construct a reliable mortality table. In this study, regression levels from the Construction of Turkish Life and Annuity Tables Project [15] are used as the primary input between the years 1931 and 2015. The project assumes that Turkish Life Tables and the Cole-Demeny West Model Life Tables [9] are alike. The data used in the project is derived initially from the Turkish Population Census Data. Using these regression levels, we regenerate Turkish mortality life tables over the years 1931 to 2015. The data for Germany between 1990 and 2017 and Japan between 1947 and 2016 were obtained from the Human Mortality Database [27]. Even though the time periods for each country do not coincide, the projections done based on these data sets are expected to reflect country-specific behavior. For this reason, due to the availability of the data, we remain on the maximum available time periods to set up the models.