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Widely used as a measure of population health and the quality of health care, infant mortality is defined as the death of an infant before their first birthday. Infant mortality represents a long-standing concern of public health. In the US, for example, a substantial and statistically significant effect on IMR was associated with falls in income.
The increase was just as large for men 40 to 44 years old 0. The effect was similar for women, though smaller. The main finding of this paper is that there is a robust relationship between per capita GDP and infant mortality: on average, a one percent decrease in per capita GDP results in an increase in infant mortality of between 0.
Population growth is difficult to predict because unforeseen events can alter birth rates, death rates, migration, or the resource limits on population growth. Such policies could have a significant effect on global fertility rates. The vast majority of the countries with the highest fertility rates are in Africa, with Niger topping the list at 6. Niger also has the highest birth rate in the world of Overtime, the growth rates of populations change because birth rates and death rates increase or decrease.
If the adults in a population are not replaced by new births, the growth rate will be negative and the population will shrink. Rapid growth has led to uncontrolled urbanization, which has produced overcrowding, destitution, crime, pollution, and political turmoil. It is a widely used measure of health status because it reflects the health of the mother and infant during pregnancy and the year thereafter. The health of the mother and infant, in turn, reflects a wide variety of factors, including access to prenatal care, prevalence of prenatal maternal health behaviors such as alcohol or tobacco use and proper nutrition during pregnancy, etc.
Is the infant mortality rate a ratio? Is it a proportion? No, because some of the deaths in the numerator were among children born the previous year.
Consider the infant mortality rate in That year, 28, infants died and 4,, children were born, for an infant mortality rate of 6. Is the infant mortality rate truly a rate? They are not exactly the same, however, because the estimated number of infants residing in the United States on July 1, was slightly larger than the number of children born in the United States in , presumably because of immigration.
The neonatal period covers birth up to but not including 28 days. The numerator of the neonatal mortality rate therefore is the number of deaths among children under 28 days of age during a given time period.
The denominator of the neonatal mortality rate, like that of the infant mortality rate, is the number of live births reported during the same time period. The neonatal mortality rate is usually expressed per 1, live births. In , the neonatal mortality rate in the United States was 4. The postneonatal period is defined as the period from 28 days of age up to but not including 1 year of age. The numerator of the postneonatal mortality rate therefore is the number of deaths among children from 28 days up to but not including 1 year of age during a given time period.
The denominator is the number of live births reported during the same time period. The postneonatal mortality rate is usually expressed per 1, live births.
In , the postneonatal mortality rate in the United States was 2. The maternal mortality rate is really a ratio used to measure mortality associated with pregnancy. The numerator is the number of deaths during a given time period among women while pregnant or within 42 days of termination of pregnancy, irrespective of the duration and the site of the pregnancy, from any cause related to or aggravated by the pregnancy or its management, but not from accidental or incidental causes.
Maternal mortality rate is usually expressed per , live births. In , the U. A sex-specific mortality rate is a mortality rate among either males or females. Both numerator and denominator are limited to the one sex. A race-specific mortality rate is a mortality rate related to a specified racial group. Both numerator and denominator are limited to the specified race. For example, in , the death rate from diseases of the heart among women ages 45—54 years was These rates are a cause-, age-, and sex-specific rates, because they refer to one cause diseases of the heart , one age group 45—54 years , and one sex female or male.
Review the following rates. Determine what to call each one, then calculate it using the data provided in Table 3. In , a total of 15, homicide deaths occurred among males and 4, homicide deaths occurred among females. The estimated midyear populations for males and females were ,, and ,,, respectively.
Check your answer. These include language barriers, variations in methodologies and definitions, and sampling and reporting biases.
Government mistrust can also play a role in the formulation of inaccuracies. Ultimately, it is the cumulation of the inaccuracies mentioned above that can manifest as ill-advised decisions or interventions for health-related events.
If data is not available from a census, other means of data collection should be employed. Their advantages and disadvantages merit consideration, and their limitations require immediate action. The anticipation of foreseeable pitfalls, sound study designs, and effective countermeasures need to be in place to garner the most accurate picture of the health event and population in focus.
Furthermore, continued education from epidemiologists needs to be a priority. Through educational activism, epidemiologists can present their research and equip their audiences with the necessary background information to effectively understand and apply the epidemiology of a health event. Educational interactions also provide an opportunity for epidemiologists to qualify their claims as well as the limitations of a study. This approach ultimately ensures that the data produced is utilized in its intended manner.
Continued educational activism not only empowers the general population, but it also keeps organizations, people in power, and other epidemiologists in check. In fact, within the epidemiology department of CDC, their epidemic intelligence service EIS is not only responsible for research, field investigation, and surveillance, but also education via presentations on complex scientific topics, writing peer-reviewed journals and updating public health information.
Epidemiology can be broken down into two types: descriptive and analytic. Typically, descriptive epidemiology precedes analytic epidemiology. Descriptive epidemiology aims to develop foundational knowledge regarding the health event in focus.
This data can include rates of occurrences, populations affected, timing, and geographic-specific presentations of a health event. By studying population-specific characteristics, epidemiologists can start learning about the natural history, modes of transmission, risk factors, and even disparities of a health event that is present within a community. Descriptive epidemiology also utilizes information from continuous public health surveillance and ultimately initiates the process of developing hypotheses and directing field investigations to develop effective and informed analytic studies.
For example, in a breast cancer study, incidence and prevalence rates may be observed in different countries and in different age groups to learn about its etiologies, risk factors, and potential preventative measures.
Analytic epidemiology, on the other hand, builds from descriptive epidemiology. As hypotheses arise in descriptive epidemiology, analytic epidemiology aims to test its validity. It seeks to uncover potential associations and any other contributors between factors and outcomes using a control group. This hypothesis testing is possible via experimentation or observation. Experimental studies usually involve clinical trials within a controlled process; this includes using randomization procedures, implementations of placebos, and counterbalancing measures to counteract any potential confounding variables or bias.
The overarching goal of experimental studies is to establish a causal relationship between an exposure and an outcome. For example, in a parasitic infection study, the exposure would be the parasites, and the outcome would be host survival or host extinction. On the other hand, observational studies detect the onset of an outcome between those exposed and those not exposed, as well as any potentially related variables, with the hopes of determining associations.
There are three types of observational studies: cohort, case-control, and cross-sectional. Cohort studies observe both exposed and non-exposed individuals and record the number of outcomes between the two groups over a designated amount of time.
In these observations, if the outcomes are higher in those exposed than non-exposed control , then an association can be inferred. For example, in a study of obesity risk factors, over children were followed until 7 years of age. The primary outcome observed was the development of obesity BMI over the 95th percentile.
In this study, risk factors associated with an increased likelihood of developing obesity were found to include: parental obesity, early body mass index rebound, more than eight hours watching television per week at age three, catch-up growth, short sleep duration, and weight gain in the first year. Case-control studies involve a more retrospective approach, wherein the rates of exposures in individuals who present with the outcome get compared with those that do not control.
If there are more individuals exposed who also have the outcome of interest in these observations, then an association can be inferred.
For example, in a study of running-related injuries, it was found that people who have been active for less than 8. Lastly, cross-sectional studies focus more on a specific time-point than a more extended period, leading to data showing the prevalence or incidence of an outcome after an exposure. However, this format does not offer as much information as the first two types of observational studies and is usually better suited when focusing on the descriptive epidemiology of a larger population.
For example, in a study of medical student mental health, students in their first, third, and sixth years of education were surveyed. The study showed that first-year students identified workload and lack of feedback as stressors. In addition to giving us key information about maternal and infant health, the infant mortality rate is an important marker of the overall health of a society. In , the infant mortality rate in the United States was 5.
See Mortality in the United States, Almost 21, infants died in the United States in The five leading causes of infant death in were:. Healthy People external icon provides science-based, year national objectives for improving the health of all Americans. One of the Healthy People objectives is to reduce the rate of all infant deaths. In , 15 states met the Healthy People target of 5.
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