# ① Descriptive Epidemiological Analysis

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Descriptive study designs

Cohort studies represent one of the fundamental designs of epidemiology which are used in research in the fields of medicine , nursing , psychology , social science , and in any field reliant on 'difficult to reach' answers that are based on evidence statistics. In medicine for instance, while clinical trials are used primarily for assessing the safety of newly developed pharmaceuticals before they are approved for sale, epidemiological analysis on how risk factors affect the incidence of diseases is often used to identify the causes of diseases in the first place, and to help provide pre-clinical justification for the plausibility of protective factors treatments. Cohort studies differ from clinical trials in that no intervention, treatment, or exposure is administered to participants in a cohort design; and no control group is defined.

Rather, cohort studies are largely about the life histories of segments of populations and the individual people who constitute these segments. The study is controlled by including other common characteristics of the cohort in the statistical analysis. Participants are then followed over time to observe the incidence rate of the disease or outcome in question. Regression analysis can then be used to evaluate the extent to which the exposure or treatment variable contributes to the incidence of the disease, while accounting for other variables that may be at play. Double-blind randomized controlled trials RCTs are generally considered superior methodology in the hierarchy of evidence in treatment, because they allow for the most control over other variables that could affect the outcome, and the randomization and blinding processes reduce bias in the study design.

This minimizes the chance that results will be influenced by confounding variables, particularly ones that are unknown. However, educated hypotheses based on prior research and background knowledge are used to select variables to be included in the regression model for cohort studies, and statistical methods can be used to identify and account potential confounders from these variables. Bias can also be mitigated in a cohort study when selecting participants for the cohort.

It is also important to note that RCTs may not be suitable in all cases; such as when the outcome is a negative health effect and the exposure is hypothesized to be a risk factor for the outcome. Ethical standards, and morality, would prevent the use of risk factors in RCTs. The natural or incidental exposure to these risk factors e. Cohort studies can be retrospective looking back in time, thus using existing data such as medical records or claims database or prospective requiring the collection of new data. There are advantages to this design however, as retrospective studies are much cheaper and faster because the data has already been collected and stored. A cohort is a group of people who share a common characteristic or experience within a defined period e.

Thus a group of people who were born on a day or in a particular period, say , form a birth cohort. The comparison group may be the general population from which the cohort is drawn, or it may be another cohort of persons thought to have had little or no exposure to the substance under investigation, but otherwise similar. Alternatively, subgroups within the cohort may be compared with each other. In medicine, a cohort study is often undertaken to obtain evidence to try to refute the existence of a suspected association between cause and effect; failure to refute a hypothesis often strengthens confidence in it.

Crucially, the cohort is identified before the appearance of the disease under investigation. The study groups follow a group of people who do not have the disease for a period of time and see who develops the disease new incidence. The cohort cannot therefore be defined as a group of people who already have the disease. Prospective longitudinal cohort studies between exposure and disease strongly aid in studying causal associations, though distinguishing true causality usually requires further corroboration from further experimental trials.

The advantage of prospective cohort study data is that it can help determine risk factors for contracting a new disease because it is a longitudinal observation of the individual through time, and the collection of data at regular intervals, so recall error is reduced. However, cohort studies are expensive to conduct, are sensitive to attrition and take a long follow-up time to generate useful data. Prospective cohort studies are considered to yield the most reliable results in observational epidemiology. They enable a wide range of exposure-disease associations to be studied. Some cohort studies track groups of children from their birth, and record a wide range of information exposures about them.

The value of a cohort study depends on the researchers' capacity to stay in touch with all members of the cohort. Some studies have continued for decades. In a cohort study, the population under investigation consists of individuals who are at risk of developing a specific disease or health outcome. An example of an epidemiological question that can be answered using a cohort study is whether exposure to X say, smoking associates with outcome Y say, lung cancer. For example in , the British Doctors Study was started. Using a cohort which included both smokers the exposed group and non-smokers the unexposed group.

The study continued through By , the study provided convincing proof of the association between smoking and the incidence of lung cancer. In a cohort study, the groups are matched in terms of many other variables such as economic status and other health status so that the variable being assessed, the independent variable in this case, smoking can be isolated as the cause of the dependent variable in this case, lung cancer.

In this example, a statistically significant increase in the incidence of lung cancer in the smoking group as compared to the non-smoking group is evidence in favor of the hypothesis. However, rare outcomes, such as lung cancer, are generally not studied with the use of a cohort study, but are rather studied with the use of a case-control study. Shorter term studies are commonly used in medical research as a form of clinical trial , or means to test a particular hypothesis of clinical importance. Such studies typically follow two groups of patients for a period of time and compare an endpoint or outcome measure between the two groups. Randomized controlled trials , or RCTs, are a superior methodology in the hierarchy of evidence, because they limit the potential for bias by randomly assigning one patient pool to an intervention and another patient pool to non-intervention or placebo.

This minimizes the chance that the incidence of confounding variables will differ between the two groups. Nevertheless, it is sometimes not practical or ethical to perform RCTs to answer a clinical question. To take our example, if we already had reasonable evidence that smoking causes lung cancer then persuading a pool of non-smokers to take up smoking in order to test this hypothesis would generally be considered quite unethical. Two examples of cohort studies that have been going on for more than 50 years are the Framingham Heart Study and the National Child Development Study NCDS , the most widely researched of the British birth cohort studies. The Dunedin Longitudinal Study , started in , has been studying the thousand people born in Dunedin , New Zealand, in — The subjects are interviewed regularly, with Phase 45 starting in The largest cohort study in women is the Nurses' Health Study.

The central portion of a distribution, calculated as the difference between the third quartile and the first quartile; this range includes about one-half of the observations in the set, leaving one-quarter of the observations on each side. A period of subclinical or inapparent pathologic changes following exposure, ending with the onset of symptoms of chronic disease. The measure of central location commonly called the average. It is calculated by adding together all the individual values in a group of measurements and dividing by the number of values in the group. The mean or average of a set of data measured on a logarithmic scale.

A quantified relationship between exposure and disease; includes relative risk, rate ratio, odds ratio. A central value that best represents a distribution of data. Measures of central location include the mean, median, and mode. Also called the measure of central tendency. A measure of the spread of a distribution out from its central value. Measures of dispersion used in epidemiology include the interquartile range, variance, and the standard deviation. The measure of central location which divides a set of data into two equal parts. The monitoring of potentially exposed individuals to detect early symptoms of disease.

The halfway point or midpoint in a set of observations. For most types of data, it is calculated as the sum of the smallest observation and the largest observation, divided by two. For age data, one is added to the numerator. The midrange is usually calculated as an intermediate step in determining other measures. A measure of central location, the most frequently occurring value in a set of observations.

Any departure, subjective or objective, from a state of physiological or psychological well-being. A measure of the frequency of occurrence of death in a defined population during a specified interval of time. A ratio expressing the number of deaths among children under one year of age reported during a given time period divided by the number of births reported during the same time period. The infant mortality rate is usually expressed per 1, live births. A ratio expressing the number of deaths among children from birth up to but not including 28 days of age divided by the number of live births reported during the same time period.

The neonatal mortality rate is usually expressed per 1, live births. A ratio expressing the number of deaths among children from 28 days up to but not including 1 year of age during a given time period divided by the number of lives births reported during the same time period. The postneonatal mortality rate is usually expressed per 1, live births. The temporal course of disease from onset inception to resolution. A causal factor whose presence is required for the occurrence of the effect of disease. Classification into unordered qualitative categories; e. A bell-shaped curve that results when a normal distribution is graphed. The symmetrical clustering of values around a central location.

The properties of a normal distribution include the following: 1 It is a continuous, symmetrical distribution; both tails extend to infinity; 2 the arithmetic mean, mode, and median are identical; and, 3 its shape is completely determined by the mean and standard deviation. The upper portion of a fraction. Epidemiological study in situations where nature is allowed to take its course. Changes or differences in one characteristic are studied in relation to changes or differences in others, without the intervention of the investigator. A measure of association which quantifies the relationship between an exposure and health outcome from a comparative study; also known as the cross-product ratio. Classification into ordered qualitative categories; e.

Synonymous with epidemic. Sometimes the preferred word, as it may escape sensationalism associated with the word epidemic. Alternatively, a localized as opposed to generalized epidemic. An epidemic occurring over a very wide area several countries or continents and usually affecting a large proportion of the population. The proportion of persons infected, after exposure to a causative agent, who then develop clinical disease. The amount a particular disease present in a population over a period of time. A measure of the incidence rate of an event, e. The amount of a particular disease present in a population at a single point in time.

The total number of inhabitants of a given area or country. In sampling, the population may refer to the units from which the sample is drawn, not necessarily the total population of people. A measure of the predictive value of a reported case or epidemic; the proportion of cases reported by a surveillance system or classified by a case definition which are true cases. The number or proportion of cases or events or conditions in a given population. The proportion of persons in a population who have a particular disease or attribute at a specified point in time or over a specified period of time.

An outbreak that does not have a common source, but instead spreads from person to person. A type of ratio in which the numerator is included in the denominator. The proportion of deaths in a specified population over a period of time attributable to different causes. These proportions are not mortality rates, since the denominator is all deaths, not the population in which the deaths occurred. The systematic collection, analysis, interpretation, and dissemination of health data on an ongoing basis, to gain knowledge of the pattern of disease occurrence and potential in a community, in order to control and prevent disease in the community.

A mortality rate limited to a specified racial group. Both numerator and denominator are limited to the specified group. A sample derived by selecting individuals such that each individual has the same probability of selection. In statistics, the difference between the largest and smallest values in a distribution. In common use, the span of values from smallest to largest. An expression of the frequency with which an event occurs in a defined population. A comparison of two groups in terms of incidence rates, person-time rates, or mortality rates. The value obtained by dividing one quantity by another. A comparison of the risk of some health-related event such as disease or death in two groups.

A sample whose characteristics correspond to those of the original population or reference population. The habitat in which an infectious agent normally lives, grows and multiplies; reservoirs include human reservoirs, animals reservoirs, and environmental reservoirs. The probability that an event will occur, e. An aspect of personal behavior or lifestyle, an environmental exposure, or an inborn or inherited characteristic that is associated with an increased occurrence of disease or other health-related event or condition.

A selected subset of a population. A sample may be random or non-random and it may be representative or non-representative. A graph in which each dot represents paired values for two continuous variables, with the x-axis representing one variable and the y-axis representing the other; used to display the relationship between the two variables; also called a scattergram. Change in physiological status or in disease occurrence that conforms to a regular seasonal pattern.

Ratio scales are similar to interval scales, in that equal differences between scale values have equal quantitative meaning. However, ratio scales also have a true zero point, which gives them an additional property. For example, the system of centimetres is an example of a ratio scale. There is a true zero point and the value of 0 cm means a complete absence of length. The thyromental distance of 6 cm in an adult may be twice that of a child in whom it may be 3 cm. Descriptive statistics[ 4 ] try to describe the relationship between variables in a sample or population.

Descriptive statistics provide a summary of data in the form of mean, median and mode. Inferential statistics[ 4 ] use a random sample of data taken from a population to describe and make inferences about the whole population. It is valuable when it is not possible to examine each member of an entire population. The examples if descriptive and inferential statistics are illustrated in Table 1. The extent to which the observations cluster around a central location is described by the central tendency and the spread towards the extremes is described by the degree of dispersion. The measures of central tendency are mean, median and mode. Mean may be influenced profoundly by the extreme variables.

For example, the average stay of organophosphorus poisoning patients in ICU may be influenced by a single patient who stays in ICU for around 5 months because of septicaemia. The extreme values are called outliers. The formula for the mean is. Median[ 6 ] is defined as the middle of a distribution in a ranked data with half of the variables in the sample above and half below the median value while mode is the most frequently occurring variable in a distribution. Range defines the spread, or variability, of a sample. If we rank the data and after ranking, group the observations into percentiles, we can get better information of the pattern of spread of the variables. In percentiles, we rank the observations into equal parts.

The median is the 50 th percentile. Variance[ 7 ] is a measure of how spread out is the distribution. It gives an indication of how close an individual observation clusters about the mean value. The variance of a population is defined by the following formula:. The variance of a sample is defined by slightly different formula:. Each observation is free to vary, except the last one which must be a defined value.

The variance is measured in squared units. To make the interpretation of the data simple and to retain the basic unit of observation, the square root of variance is used. The square root of the variance is the standard deviation SD. The SD of a sample is defined by slightly different formula:. An example for calculation of variation and SD is illustrated in Table 2. Most of the biological variables usually cluster around a central value, with symmetrical positive and negative deviations about this point. It is a distribution with an asymmetry of the variables about its mean. In a negatively skewed distribution [ Figure 3 ], the mass of the distribution is concentrated on the right of Figure 1. In a positively skewed distribution [ Figure 3 ], the mass of the distribution is concentrated on the left of the figure leading to a longer right tail.

In inferential statistics, data are analysed from a sample to make inferences in the larger collection of the population. The purpose is to answer or test the hypotheses. A hypothesis plural hypotheses is a proposed explanation for a phenomenon. Hypothesis tests are thus procedures for making rational decisions about the reality of observed effects. Probability is the measure of the likelihood that an event will occur.

Probability is quantified as a number between 0 and 1 where 0 indicates impossibility and 1 indicates certainty. Alternative hypothesis H 1 and H a denotes that a statement between the variables is expected to be true. The P value or the calculated probability is the probability of the event occurring by chance if the null hypothesis is true. The P value is a numerical between 0 and 1 and is interpreted by researchers in deciding whether to reject or retain the null hypothesis [ Table 3 ]. However, if null hypotheses H0 is incorrectly rejected, this is known as a Type I error. Numerical data quantitative variables that are normally distributed are analysed with parametric tests.

The assumption of normality which specifies that the means of the sample group are normally distributed. The assumption of equal variance which specifies that the variances of the samples and of their corresponding population are equal. However, if the distribution of the sample is skewed towards one side or the distribution is unknown due to the small sample size, non-parametric[ 14 ] statistical techniques are used. Non-parametric tests are used to analyse ordinal and categorical data. The parametric tests assume that the data are on a quantitative numerical scale, with a normal distribution of the underlying population. The samples have the same variance homogeneity of variances. The samples are randomly drawn from the population, and the observations within a group are independent of each other.

Student's t -test is used to test the null hypothesis that there is no difference between the means of the two groups. It is used in three circumstances:. To test if a sample mean as an estimate of a population mean differs significantly from a given population mean this is a one-sample t -test. The formula for one sample t -test is. To test if the population means estimated by two independent samples differ significantly the unpaired t -test. The formula for unpaired t -test is:. To test if the population means estimated by two dependent samples differ significantly the paired t -test.

A usual setting for paired t -test is when measurements are made on the same subjects before and after a treatment. The group variances can be compared using the F -test. If F differs significantly from 1. The Student's t -test cannot be used for comparison of three or more groups. The purpose of ANOVA is to test if there is any significant difference between the means of two or more groups. The within-group variability error variance is the variation that cannot be accounted for in the study design.

It is based on random differences present in our samples. However, the between-group or effect variance is the result of our treatment. These two estimates of variances are compared using the F-test. However, a repeated measure ANOVA is used when all variables of a sample are measured under different conditions or at different points in time. As the variables are measured from a sample at different points of time, the measurement of the dependent variable is repeated. When the assumptions of normality are not met, and the sample means are not normally, distributed parametric tests can lead to erroneous results.

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