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Rates and Standardization
Published in Marcello Pagano, Kimberlee Gauvreau, Heather Mattie, Principles of Biostatistics, 2022
Marcello Pagano, Kimberlee Gauvreau, Heather Mattie
Demographic data and vital statistics are used to characterize and describe populations. Demographic data include the size of a population and its composition by sex, and age. Vital statistics describe the life of the population; they summarize events such as births, deaths, marriages, divorces, and occurrences of disease. Public health professionals and researchers use both types of data to report the health status of a population, spot trends, make projections, and plan for necessary services such as housing and medical care.
Methodological Issues In The Analysis Of Vital Statistics
Published in Michele Kiely, Reproductive and Perinatal Epidemiology, 2019
One of the major advantages of vital statistics is the availability of relatively comparable data over a long period of time for every county and place over 10,000 population in the U.S. Thus, it is possible to study and compare trends in infant mortality or low birthweight for specific areas. This advantage also leads to potential pitfalls in analysis. The most serious of these pitfalls relates to the random error associated with rates based on small numbers of observations. A single year’s increase in an area’s IMR is sometimes viewed as evidence of a serious problem in the perinatal care system. Yet, if the area has 50,000 births (the median among states) and a true IMR of 10 per 1000 there is a 40% chance of observing an increase in the IMR even if the true IMR decreased by 5%. In an area with 10,000 births the chance of observing an increase jumps to 47%. Thus, inferences for subnational areas need to be based on statistical methods which assess the uncertainties involved.
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Published in Filomena Pereira-Maxwell, Medical Statistics, 2018
Summary measures for human populations, regarding, in particular, their magnitude, characteristics and dynamics, and how these interplay with different socioeconomic and health-related factors. The subset of indicators that relates to births and deaths (and also marriages and divorces) and reflects the rate of growth and health of a population is termed vital statistics. Examples of the latter include the birth rate, fertility rate, mortality rate, stillbirth rate, infant mortality rate, neonatal mortality rate, perinatal mortality rate, child mortality rate and maternal mortality rate. See also standardization, which covers methodolog y often used to compare populations.
Prevalence of hypertensive disorders of pregnancy at or beyond 39 weeks gestational age and associated maternal complications
Published in Hypertension in Pregnancy, 2023
Elizabeth Fronek, Summer Martins, Stephen Contag
This was a population-based retrospective cohort study using live birth databases from the U.S. National Center for Health Statistics (10). The National Vital Statistics data set is compiled from data reported on all birth certificates shortly after the live birth of an infant including parental demographic information, maternal reproductive and pregnancy history, medical procedures, and infant birth weight. It is collected from all 50 states and the District of Columbia and New York City and makes up more than 99% of all births for a given year. Because the datasets are publicly available and do not contain direct personal identifiers, this study was considered exempt from review by the institutional review board at the University of Minnesota (IRB exemption STUDY00007628). Our study sample was restricted to births to nulliparous individuals who delivered between 2014 and 2018 with a singleton, cephalic presenting, non-anomalous fetus, who experienced spontaneous onset of labor and delivered at 39, 40, 41, or 42 weeks of gestation. A reference group of births at 36–38 weeks of gestation was included to establish a perspective of the rate of HDP ≥39 weeks of gestation. For purposes of this study, we only included pregnancies having either no significant medical comorbid conditions or well-controlled comorbid conditions that do not mandate urgent IOL. In accordance with this definition, we did include births complicated by pregestational diabetes, gestational diabetes, or chronic hypertension as these conditions, if well-controlled, are managed expectantly past 38 6/7 weeks and consistent with current guidelines (11).
The changing epidemiology of opioid overdose in Baltimore, Maryland, 2012–2017: insights from emergency medical services
Published in Annals of Medicine, 2022
Chen Dun, Sean T. Allen, Carl Latkin, Amy Knowlton, Brian W. Weir
Through identifying and describing changes in the epidemiology of opioid overdose, surveillance plays an essential role in informing prevention and treatment strategies. The dearth of local data is a major issue for surveillance of opioid overdose, making it difficult for local policymakers to alter policies and respond to changes in epidemics in a timely manner [6–11]. Hospital emergency department records and death records are two common surveillance methods for local opioid overdose epidemiology. Vital statistics data from the CDC [12] is a publicly accessible source of death records, but sample sizes are small for smaller jurisdictions, and lags in data accessibility a year or longer are common [13]. As a result, local researchers and policymakers cannot get accurate information on recent local trends from vital statistics data alone. Hospital emergency departments can provide important local data. However, administrative regions of interest to policymakers, such as city or county boundaries, may include the catchment areas of multiple hospitals, and separate electronic medical records and reporting systems may make hospital-based surveillance challenging.
Transfer from pediatric to adult healthcare services for home mechanical ventilation users
Published in Canadian Journal of Respiratory, Critical Care, and Sleep Medicine, 2021
Erika MacIntyre, Jeffrey A. Bakal, Sean Bagshaw, Joanna E. MacLean
Health administrative data was extracted from the Alberta Health Services Enterprise Data warehouse18and linked to the clinical data using the patient’s Unique Lifetime Identifier. Data was extracted from: 1) Discharge Abstract Database, which records the admission date, discharge date, most responsible diagnosis and up to 25 other diagnoses using relevant ICD 9 and ICD 10-CA codes for all acute care hospitalizations; 2) Ambulatory Care Database, which records all patient visits to hospital-based physician offices or emergency departments with coding for of up to 10 conditions; 3) Practitioner Claims Database, which tracks all physician claims for outpatient services and includes up to 3 diagnoses per encounter; and 4) Alberta Health Care Insurance Plan Registry, which tracks vital statistics, including deaths. Thus, data was extracted for the study cohort from Jan 1,2013–Dec 31, 2018.