REPORT ON MEASUREMENTS OF OUTCOMES IN EPIDEMIOLOGY

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Report on Measurements of Outcomes in Epidemiology 15

REPORTON MEASUREMENTS OF OUTCOMES IN EPIDEMIOLOGY

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Tableof Contents

List of Figures 3

Introduction 4

The Basics in Measurements of Outcomes in Epidemiology 4

Activities on Measurements of Outcomes in Epidemiology 5

Measurements of Outcome in Epidemiology 5

Measurements of Morbidity 5

Prevalence 6

Incidence 7

Measurements of Mortality 10

Crude Death Rate 10

Infant Mortality Rate 10

Case-Fatality 11

Case-Fatality Rate 11

Years of Potential Life Lost (YPLL) 11

Tools of Measurement in Epidemiology 12

Counts 12

Proportions 12

Rates 13

Ratios 14

Conclusion 14

References 16

Listof Figures

Figure 1: Logical Sequence to the Practice of Epidemiology 5

Figure 2: Prevalence Rate Formula 6

Figure 3: Point Prevalence Formula 6

Figure 4: Incidence Risk Formula 7

Figure 5: Incidence Rate Formula 8

Figure 6: Illustration of Incidence Rate 8

Figure 7: Calculation of Cumulative Incidence 9

Figure 8: Incidence Rate Formula 9

Figure 9: Crude Death Rate Formula 10

Figure 10: Calculation of Infant Mortality Rate 11

Figure 11: Calculation of Case-Fatality Rate 11

Figure 12: Illustration of Counts 12

Figure 13: Calculation of Proportions 12

Figure 14: Illustration of Proportions 13

Figure 15: Formula for Calculating Rates 14

Figure 16: An Illustration of Ratios 14

REPORTON MEASUREMENTS OF OUTCOMES IN EPIDEMIOLOGY

Introduction

Inthe present age, accountability in medicine is essential and requiresdata concerning outcomes of care, which play a critical role inmaking medical decisions for a physician or any other healthcareprovider, patients, and the hospital administrators. As a result, theepidemiological techniques are crucial as they provide the basis forthe development of disease control and prevention measures for thevulnerable populations. Epidemiology refers to the study of thedeterminants, distribution, and the frequency of infections ordiseases in a given human population (Merrill 2015). It concerns thequestions such as, who acquires disease and why (Rugge, Fassan, andGraham 2015, p.23). In simpler terms, epidemiology studies the sickand the well individuals to help in the determination of theessential difference between the affected and the population spared(Adams et al. 2015, p.563). Epidemiological studies begin byidentifying the health problem and collecting data on the populationof interest as well as setting the control population (Hundal andShaffer 2014, p.99). Hypotheses are then formulated and tested byobservation or experimentation to give evidence for causalrelationships. As such, epidemiology provides the basis forestablishing disease control and prevention measures for a particulargroup in a population at risk (Zoni-Berisso et al. 2014, p.220). Ittranslates into designing a public health measure to prevent orcontrol diseases from spreading (Woodward 2013). However,epidemiology requires that samples of the population are taken toexamine the health issues, and the health outcome data can bepresented via multiple measures that are expressed in differentmetrics. This study, therefore, seeks to present a report on themeasurements of outcomes in epidemiology.

TheBasics in Measurements of Outcomes in Epidemiology

Thebasics of outcomes in epidemiology is to provide a framework toanalyze and comprehend the possible relationships between outcomesand the medical interventions (Canavan, West, and Card, 2014, p.71).The primary objective is to maximize the good results and minimizeadverse outcomes in populations (Hundal and Shaffer 2014, p.99). Inthis report, the focal point is in the occurrence of health anddisease in a given population that is, what happens to manyindividuals, which is unlike the clinical approach that concerns withwhat happens to one person (Grant et al. 2015, p.757). It is alsocritical to take note that human beings are social animals and thatmany diseases are caused only by the interactions of people withinand between populations (Moorhead et al. 2014). Besides, the diseasesand health outcome patterns are generated in and by populations andrequire description, explanations, and predictions in a populationsetting (Rugge, Fassan, and Graham 2015, p.23). A population refersto all the inhabitants of a particular region considered together, orgroups of individuals sharing a common thread such as clinical orsubgroups of the population in terms of age, race or any otherepidemiological classification of subgroups in a population (Friisand Sellers 2013).

Activitieson Measurements of Outcomes in Epidemiology

Thereare different types of activities and practices undertaken inepidemiology to design disease control and prevention measures forthe groups at risk of acquiring a disease (Merrill 2015). Theactivities comprise of descriptive epidemiology that comprises of theassessment, identification and reporting on the frequencies anddistribution of a disease in a given population as well as learningthe basic features of its distribution (Moorhead et al. 2014). Theother activity is the analytical epidemiology that involves theidentification of factors underlying disease or health issues andtesting the hypothesis through studying how the exposure relate tothe outcomes (Zimlichman et al. 2013, p.2039). Additionally, thedevelopment of the interventions to minimize disease or improvehealth in the community through making use of the information fromthe analytical study, developing strategies that concentrate on acrucial exposure factor and testing the strategies with clinicaltrials is the core element in an epidemiology study (Zimlichman etal. 2013, p.2039). The end outcome is the program evaluation, whichassesses how effective the program has been for disease control andprevention in the community (Koepsell and Weiss 2014). The followingdiagram can summarize the logical sequence to the practice ofepidemiology in the prevention of disease:

Figure1: LogicalSequence to the Practice of Epidemiology

Measurementsof Outcome in Epidemiology

Becausethe prime purpose of epidemiology is to identify, make a descriptionand explanations of the difference on how the disease is distributedof or any other health outcome of concern between variouspopulations, this study examines the various health outcome measuresin epidemiology (Rudan et al. 2015). The health outcome measurementscomprise of “morbidity, mortality, infectious disease incidence,congenital disabilities, disability, injuries, vaccines efficiency,and the use of hospital services.” (Grant et al. 2015, p.759). Themeasurements of disease frequencies are utilized to help indescribing how the common disease is, with regard to “the size ofthe population at risk and a measure of time” (Doherty et al. 2014,p.140)

Measurementsof Morbidity

Inepidemiology, morbidity refers to any the withdrawal from a state ofphysical or emotional well-being (Nast et al. 2013, p.317). Itincludes injuries, diseases, and any other form of disability (Vos etal. 2013, p.2163). In this report, Morbidity refers to the number ofindividuals who are ill as well as the periods of the illness thatthese people have experienced, or the periods of the illnesses (Rudanet al. 2015). The measures of morbidity characterize the sum ofpeople in a given population who become ill or are sick at aspecified time (Barnett et al. 2012, p.37). The common measures ofmorbidity include prevalence and incidence rates as discussed in thefollowing section.

Prevalence

Theprevalence is used to measure the proportions of the persons in aparticular population at risk of acquiring an infection or any otherhealth outcome of concern at a given point (also known as “pointprevalence”) in time or during a particular period (also called“period prevalence”) (Zimlichman et al. 2013, p.2039). Theprevalence rate can be evaluated as shown in figure 2.

Figure2:Prevalence Rate Formula

PointPrevalence:Is evaluated by dividing the sum of cases in a specified populationat a particular point within a particular time then dividing by thesum of individuals in a specified population at a similar point intime (Haycock et al. 2014, p.4227). For instance, considering a citywith 10,000 female residents on 3rd February 2016, 1,000 havetuberculosis. Then the prevalence of tuberculosis in the city on theparticular date is evaluated as follows:

1,000/10,000=0.1or10%.

Figure3: PointPrevalence Formula

Prevalenceis an important measure for the quantification of the “burden ofdisease” in a given population and a particular point in a giventime (Zimlichman et al. 2013, p.2039). When planning healthhealthcare services, the calculation of the prevalence of differentcircumstances across various geographical localities or among varioussubgroups of the population and then assessing the prevalence ofother possible risk elements is essential (Jung et al 2014, p.123).However, it is also crucial to take note that prevalence is not animportant metric for developing the elements of an infection in agiven population (Haagsma et al. 2016, p.3).

Incidence

Theincidence measure of outcomes in epidemiology is a contrast to theprevalence as it measures the sum of new cases of an infection orother health outcomes of concern that emerges in a particular peopleat risk during a given period (Haagsma et al. 2016, p.5). It can beevaluated in terms of Incidence Risk or cumulative incidence andIncidence Rate.

IncidenceRisk: TheIncidence Risk is associated with the people at risk at the start ofthe period of investigation (Canoy 2015). The Incidence Rate risk isassociated with a more accurate measure of the people at riskthroughout the study period, and it is measured in “person-timeunits” (Znaor et al. 2015, p.530) The “Incidence Risk” alsorefers to the fraction of persons in a population who were initiallyfree of disease and have now developed the infection within aspecific time interval (Graber 2013, p.21). It is expressed as apercentage, and in case the population is small, then it is expressedas a percentage per 1000 individuals. It is evaluated as follows:

Figure4: IncidenceRisk Formula

Whencalculating the incidence risk, it is important to make an assumptionthat the whole populations at danger of acquiring the disease at thestart of the investigation period have been monitored for theparticular period for the proper establishment of the outcome understudy (Robertson 2015). However, in some cases of a cohort study, thesubjects might be lost during the follow-up because some membersunder the study might have migrated, die, refuse to continue with thestudy, develop the outcome under study, or even enter theinvestigation at some period after it begins (McNutt et al. 2013,p.943). As a result, these variations need to be counted for duringfollow-up using a more accurate measure, known as the Incident Rate.

IncidentRate: TheIncident Rate measures the frequency of new cases of a health outcomein a population but takes into account the number of the times thatevery person remains under observation and at risk of acquiring theoutcome being studied (Klompas, Kleinman, and Murphy 2014, p.502).The Incidence Rate is expressed as follows:

Figure5: IncidenceRate Formula

Whencalculating a person-time at risk, it is essential to note that thedenominator is the sum of each person’s time in danger of acquiringthe disease and is expressed in “person-per-years at risk”(Zoni-Berisso et al. 2014, p.220). This fact illustrates why theincidence rate is the rate of contracting a disease among theindividuals still at risk (Nieuwenhuijsen 2015). The incidence rateensures that when a participant develops the disease, dies or abandonthe investigation, they are not at risk and will, therefore, do notcontribute to the person-time units at risk (Robertson 2015). The“person-time at risk” measures that the number of individuals atrisk during the specified period (Morley, Anker, and von Haehling2014, p.253). The following graph can also represent the incidencerate:

Figure6: Illustrationof Incidence Rate

WhereN-d represents the various numbers of individuals at risk duringtime-period t. The Y line represents the total person-time at risk,while d represents the individuals who have acquired the disease andare no longer taken into consideration at risk because they alreadypossess the disease (Barnett et al. 2012, p.40). When given N whichrepresents the initial population at risk, Y, the person-years atrisk, D, the number of new cases, the incidence rate is evaluated as:

IncidenceRate =d/Y

Risk(Cumulative Incidence)= d/N

Whenpresented with the following data in person-time (years) at risk forfive persons in a theoretical group study between 2000 and 2004, theincidence rate for the disease X can be evaluated.

Figure7: Calculationof Cumulative Incidence

Where

-represent the time at risk, X represents the disease, L representsthe individual lost to follow-up.

The“incidence rate” for the disease X is evaluated as:

“3/18= 0.167 per person-years” or 16percent per 100 person-years.”

Figure8: IncidenceRate Formula

Inthis evaluation, it is critical to recognize that if the persons notin danger of acquiring the disease are incorporated in thedenominator which is the people at risk, then the resultant metricof the frequency of the disease will undermine the actual incidenceof the infection under study in the investigated population(Nieuwenhuijsen 2015). As a result, the persons who are presentlypossessing the disease or those who are immune should not be includedin the denominator (Williet, Sandborn, and Peyrin–Biroulet 2014,p.1246).

Measurementsof Mortality

Mortalityis the final outcome that might befall a patient affected with aparticular disease. Mortality rates vary depending on geographicallocation, demography, and the method of diagnosis of a particulardisease, the thoroughness of follow-up, and the status of the diseasein question (Cohen-Mansfield and Perach 2012). Population-basedinvestigations majorly assess trends in mortality of time beforemaking conclusions on the associated cause (Atkin et al. 2012,p.1460). Mortality rate refers to the number of deaths as a result ofan infection divided by the sum of the population (World HealthOrganization 2013). For example, there are 25 tuberculosis deaths ina year with a population of 30,000. The mortality rate due totuberculosis is 83% on every 100,000 individuals. Mortality ismeasured using various rates as discussed in the next section.

CrudeDeath Rate

CrudeBirth Rate represents the simplest and the common indicator ofmortality in a specified population (Malvezzi et al. 2013, p.138). Itis the sum of deaths observed in a given population in a year dividedby the population in danger of dying in the same year per 1,000individuals (Nelson et al. 2013, p.648). It is also influenced bythe number of individuals who in danger of dying. The number ofdeaths can be extracted from the death registers, and the informationon the individuals at risk of dying are extracted from census data(Delfino et al. 2014, p.48). It can be evaluated as follow:

Figure9: CrudeDeath Rate Formula

InfantMortality Rate

Infantmortality rate represents the number of infants below one-year-oldwho die in every 1,000 live births (Kurina et al 2013, p.361). Thismeasure is commonly used to determine the level of health in a givenlocality or country (Gimeno-Santos et al. 2014, p.731). It iscalculated as:

Figure10: Calculationof Infant Mortality Rate

Case-Fatality

Case-Fatalityrefers to the frequency by which the reported cases of an infectiondie from that infection (Wang 2014, p.4490). For example, 50 cases oftuberculosis died last month.

Case-FatalityRate

Case-FatalityRate refers to the proportion of the individuals with an infection ina particular period die from the disease (Murray et al. 2014,p.1005). This rate is reported as a percentage. For example, the“case-fatality rate&quot of individuals with tuberculosis lastmonth was 50%. It is calculated as shown in the figure below.

Figure11: Calculationof Case-Fatality Rate

Yearsof Potential Life Lost (YPLL)

The“Years of Potential Life Lost (YPLL)” estimates the loss offuture productive years as a result of a particular cause of death(Zivin et al. 2012, p.823). This measure shows that possible burdenassociated with the occurrence of death. The measure is highest whenthe cause of death is common or somewhat common, and an individualdies at a younger age (Scosyrev et. al. 2012, p.62).

Toolsof Measurement in EpidemiologyCounts

Countsrefer to the sum of reported cases of a particular disease or anyother health issue of concern within in a definite period of time(Atkin et al. 2012, p.1460). For example, the number of cases oftuberculosis in California in 2016. A count may be used to help inthe allocation of health resources (Bhopal 2016). However, it can beof limited use if the size of the total population is unknown(Stevenson et al. 2014, p.625). However, counts are useful inepidemiology because measuring diseases or the healthcare frequenciesbegin with counting the cases (Brauer et al. 2015). The figure belowillustrates an example of counts for cities A and B.

Figure12: Illustrationof Counts

Proportions

Proportionsare ratios that relate parts of a population to a whole. In aproportion, the individuals included in the numerator are alwaysincorporated in the denominator as shown in the following formula ofcalculating proportions.

Figure13: Calculationof Proportions

Inepidemiology, a proportion indicates the fraction of the affectedpopulation (Wissel, Manack, and Brainin 2013, p.14). For instance, aninstitution with 2000 males and 8000 females, the total size of theorganization is 10,000, and the proportion of men is 2,000/10,000 ormerely 20%. The proportion of females is 8,000/10,000 or merely 80%.It is clear that in both of these two proportions, the size of a partof the institution is being associated with the size of the wholeorganization. Another good example of proportion is shown in thefigure below:

Figure14: Illustrationof Proportions

Inthe above example, the two populations, that is, those withtuberculosis and those without tuberculosis are represented byletters A and B respectively. The total population is (A+B). Theproportion of A = A / (A+B), and the proportion of B = B / (A+B).

Rates

Arate measures the frequency with which a given event happens in aparticular population at a specified time, for instance, the numberof deaths per 100 US citizens in a year (Nelson et al. 2013, p.648).A rate has a time dimension such as the number of US citizenssuffering from tuberculosis divided by the total population, whereasa proportion does not have a time dimension (DiSipio et al. 2013,p.515). Precisely, a rate is a ratio in which time forms part of thedenominator. An epidemiologic rate comprises a health issue&quotfrequency in the numerator, a unit size of the population, andthe time period&quot when an event happens as the denominator(Austin 2013, p.2839). An important note is that rates form the basictool of epidemiological practice. A rate provides a completeinformation that helps in describing or assessing the implicationsimpacts of a health issue in a given society of population (Adams etal. 2015, p.563). A rate is evaluated as follows:

Figure15: Formulafor Calculating Rates

Ratios

Aratio refers to the value obtained by dividing one quantity byanother. For instance, male to female ratio in California. Inepidemiology, a ratio gives a comparison of two rates. For example,making a comparison of the death rates for women and men at aparticular age (Rudan 2015). The significance difference between arate and a ratio is that a rate has its numerator incorporated inthe denominator (Austin 2013, p.2849). For example, the new cases ofan infection divided by the total population, while on the otherhand, a ratio has separate numerator and denominator and withdifferent quantities, neither being incorporated in the other, forexample, the ratio of men to women in California. In short, a ratiois a fraction, but without a particular association between thenumerator and the denominator (Bloom et al. 2015). An example can begiven as follows:

Inan institution, the reported male cases of a particular infection is360, and that of the female is 120, then the ratio of male to thefemale case is calculated as:

Figure16: AnIllustration of Ratios

Inthe above figure, the ratio of male cases to the female case is 3:1.This ratio means that in every three male cases of reportedinfection, there is one female with the same infection.

Conclusion

Fromthe report, it is clear that measurements of the outcomes inepidemiology can increase the overall comprehension of a disease andspecifically, how the disease is transmitted even in the instanceswhere the cause is not known. It can also be concluded thatepidemiology also assumes that a disease does not occur randomly, butfollows a pattern that can be predicted that can be investigated andexpressed in terms of “what, who, where, when, how, and what next.”As a result, it offers physicians with a better understanding of thedisease or health issue causing deaths in a given population anddeveloping prevention interventions.

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