Basic Epidemiology

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Guidelines for the diagnosis of an initial episode of rheumatic fever Jones criteria, 4 one major and two minor manifestations, if supported by evidence of a preceding Group A streptococcal infectiona Major manifestations Minor manifestations Carditis Clinical findings Polyarthritis Arthralgia Chorea Fever Erythema marginatum Laboratory findings Subcutaneous nodules Elevated acute-phase reactants: — erythrocyte sedimentation rate — C-reactive protein Prolonged PR interval a Supporting evidence of antecedent Group A streptococcal infection: — positive throat culture or rapid streptococcal antigen test — elevated or rising streptococcal antibody titre.

Diagnostic criteria may change quite rapidly as knowledge increases or diagnostic techniques improve; they also often change according to the context in which they are being used. For example, the original WHO diagnostic criteria for myocardial infarctionfor use in epidemiological studies, weremodifiedwhenanobjectivemethod for assessing electrocardiograms the Minnesota Code was introduced in the s.

Unfortunately, epidemiologists have not yet reached complete agreement on the definitions of terms used in this field. Ideallythesenumbersshouldonlyinclude people who are potentially susceptible to the diseases being studied. For instance, men should not be included when calculating the frequency of cervical cancer Figure 2.

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Figure 2. For instance, occupational injuries occur only among working people, so the population at risk is the workforce; in some countries brucellosis occurs only among people handling infected animals, so the population at risk consists of those working on farms and in slaughterhouses. Measuring health and disease 17 Incidence and prevalence The incidence of disease represents the rate of occurrence of new cases arising in a given period in a specified population, while prevalence is the frequency of existing cases in a defined population at a given point in time.

These are fundamentally dif- ferent ways of measuring occurrence see Table 2. There may be low incidence and a high prevalence — as for diabetes — or a high incidence and a low prevalence — as for the common cold. Colds occur more frequently than diabetes but last only a short time, whereas diabetes is essentially lifelong.

Measuring prevalence and incidence involves the counting of cases in defined populations at risk. Reporting the number of cases without reference to the population at risk can be used to give an impression of the overall magnitude of a health problem, or of short-term trends in a population, for instance, during an epidemic.

The attack rate can be calculated as the number of people affected divided by the number exposed. For example, in the case of a foodborne disease outbreak, the attack rate can be calculated for each type of food eaten, and then these rates compared to identify the source of the infection.

Data on prevalence and incidence become much more useful if converted into rates see Table 1. A rate is calculated by dividing the number of cases by the corresponding number of people in the population at risk and is expressed as cases per 10n people.

In this book, we use the term Table 2. Useful in the study of the burden of chronic diseases and implication for health services Note: If incident cases are not resolved, but continue over time, then they become existing prevalent cases. Prevalence is often expressed as cases per percentage , or per popu- lation. In this case, P has to be multipliedP by the appropriate factor: 10n.

Apart from age, several factors determine prevalence Figure 2. Factors influencing prevalence Increased by: Longer duration of the disease Prolongation of life of patients without cure Increase in new cases increase in incidence In-migration of cases Out-migration of healthy people In-migration of susceptible people Improved diagnostic facilities better reporting Decreased by: Shorter duration of the disease High case-fatality rate from disease Decrease in new cases decrease in incidence In-migration of healthy people Out-migration of cases Improved cure rate of cases Since prevalence can be influenced by many factors unrelated to the cause of the disease, prevalence studies do not usually provide strong evidence of causality.

Measures of prevalence are, however, helpful in assessing the need for preventive action, healthcare and the planning of health services. Prevalence is a useful measure Measuring health and disease 19 The prevalence of type 2 diabetes has been measured in various populations using criteria proposed by WHO see Table 2. Incidence Incidence refers to the rate at which new events occur in a population. In the calculation of incidence, the numerator is the number of new events that occur in a defined time period, and the denominator is the population at risk of experiencing the event during this period.

The unitsf of incidence rate mustalwaysincludeaunitoftime casesper10n andperday,week,month,year,etc. Foreachindividualinthe population,thetimeofobservationisthe periodthatthe person remains disease-free. The denominator used for the calculation of incidence is therefore the sum of all the disease-free person-time periods during the period of observation of the population at risk.

Since it may not be possible to measure disease-free periods precisely, the denominator is often calculated approximately by multiplying the average size of the study population by the length of the study period. This is reasonably accurate if the size of the population is large and stable and incidence is low, for example, for stroke. In a study in the United States of America, the incidence rate of stroke was measured in women who were 30—55 years of age and free from coronary heart disease, stroke and cancer in see Table 2.

A total of stroke cases were identified in eight years of follow-up person-years. The overall stroke incidence rate was Cumulative incidence Cumulative incidence is a simpler measure of the occurrence of a disease or health status. Unlike incidence, it measures the denominator only at the beginning of a study. In a statistical sense, the cumulative incidence is the probability that individuals in the population get the disease during the specified period.

The period can be of any length but is usually several years, or even the whole lifetime. The simplicity of cumulative incidence rates makes them useful when communicating health information to the general public. Relationship between cigarette smoking and incidence rate of stroke in a cohort of women13 Smoking category Number of cases of stroke Person-years of observation over 8 years Stroke incidence rate per person- years Never smoked 70 Case fatality Case fatality is a measure of disease severity and is defined as the proportion of cases with a specified disease or condition who die within a specified time.

It is usually expressed as a percentage. Since incidence usually changes with age, age- specific incidence rates need to be calculated. The cumulative incidence rate is a useful approximation of incidence when the rate is low or when the study period is short. This hypothetical example is based on a study of seven people over seven years.

Calculation of disease occurrence 6 1 1 Individuals Years of follow-up 2 3 4 5 6 7 2 3 4 5 7 2 7 7 2 7 3 2 Totaltimeunderobservation years TT Healthy period Disease period Lost to follow-up Death In Figure 2. The formula given on page 22 for prevalence would give an estimated average prevalence of 30 cases per population 9. Using available information to measure health and disease Mortality Epidemiologists often investigate the health status of a population by starting with information that is routinely collected.

In many high-income countries the fact and cause of death are recorded on a standard death certificate, which also carries information on age, sex, and place of residence.

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The usefulness of the data depends on many factors, including the completeness of records and the accuracy in assigning the underlying causes of death—especially in elderly people for whom autopsy rates are often low. Epidemiologists rely heavily on death statistics for assessing the burden of dis- ease, as well as for tracking changes in diseases over time. However, in many countries basic mortality statistics are not available, usually because of a lack of resources to establish routine vital registration systems. The provision of accurate cause-of-death information is a priority for health services.

This classification is the latest in a series which originated in the s. The ICD has become the standard diagnostic classification for all general epidemiological and many health man- agement purposes. The ICD is used to classify diseases and other health problems recorded on many types of records, in- cluding death certificates and hospital charts. This clas- sification makes it possible for countries to store and retrieve diagnostic information for clinical and epidemi- ological purposes, and compile comparable national mortality and morbidity statistics.

Measuring health and disease 23 the average duration of disease is the total number of years of disease divided by the number of cases, i. Limitations of vital registration systems The WHO Mortality Database includes only one third of adult deaths in the world, and these are mainly in high-income and middle-income countries. In some countries, the vital registration system covers only part of the country urban areas, or only some provinces. In other countries, although the vital registration system covers the whole country, not all deaths are registered.

Some countries rely on validation of deaths from representative samples of the pop- ulation as in China and India ; in other countries, demographic surveillance sites provide mortality rates for selected populations. The diversity of tools and methods used makes it difficult to compare cause-of-death data between places over time. For these reasons, data comparisons between countries can be misleading. WHO works with countries to produce country-level estimates, which are then adjusted to account for these differences see Box 2.

Box 2. The country-level estimates that WHO produces adjust for differences in completeness and accuracy of data supplied by countries. Estimates are based on data from national vital registration systems that capture about Information from sample registration sys- tems, population laboratories and epidemiological studies are also used to improve these estimates.

Other factors contributing to unreliable registration systems include: late registration, missing data and errors in reporting or classifying the cause of death. It is not usually appropriate to use it for comparing different time periods or geographical areas.

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For example, patterns of death in newly occupied urban developments with many young families are likely to be very different from those in seaside resorts, where retired people may choose to live. Comparisons of mortality rates between groups of diverse age structure are usually based on age- standardized rates. Age-specific death rates Death rates can be expressed for specific groups in a population which are defined by age, race, sex, occupation or geographical location, or for specific causes of death.

Proportionate mortality does not express the risk of members of a population contracting or dying from a disease. However, unless the crude or age-group-specific mortality rates are Measuring health and disease 25 For example, proportionate mortality rates for cancer would be much greater in high-income countries with many old people than in low- and middle-income countries with few old people, even if the actual lifetime risk of cancer is the same.

Infant mortality The infant mortality rate is commonly used as an indicator of the level of health in a community. It measures the rate of death in children during the first year of life, the denominator being the number of live births in the same year. Infant mortality has declined in all regions of the world, but wide differences persist between and within countries see Figure 2. Injuries, malnutrition and infectious diseases are common causes of death in this age group.

The under-5 mortality rate describes the probability expressed per live births of a child dying before reaching 5 years of age. The areas of uncertainty around the esti- mates for middle-income and low-income countries are shown in parentheses. Data in Table 2.

Introduction to Epidemiology

Mortality rates per live births vary from as low as 4 for Japan based on precise data to for males in Sierra Leone with a wide range of 26 Chapter 2 Maternal mortality rate The maternal mortality rate refers to the risk of mothers dying from causes associated with delivering babies, complications of pregnancy or childbirth. This important Table 2. Adult mortality can also be approximated from household surveys if accurate infor- mation is not available. However, this is the only method that is applicable in some communities. Measurement of infant mortality in low-incomef communities is particularly important in helping planners to address the need for equity in health care.

Additionally, reducing child mortality rates is one of the Millennium Development Goals see Chapter Measuring health and disease 27 Adult mortality rate The adult mortality rate is defined as the probability of dying between the ages of 15 and 60 years per pop- ulation. The adult mortality rate offers a way to analyse health gaps between countries in the main working age groups. In Japan, less than 1 in 10 men and 1 in 20 women die in these pro- ductive age groups, compared with almost 2 in 3 men and 1 in 2 women in Angola see Table 2.

Life expectancy Life expectancy is another summary measure of the health status of a population. It is defined as the average number of years an individual of a given age is expected to live if current mortality rates continue. It is not always easy to interpret the reasons for the differences in life expectancy between countries; different patterns may emerge accord- ing to the measures that are used. For the world as a whole, life expectancy at birth has increased from Similar reversals in life expectancy have also occurred in middle-aged men in the former Soviet Union, where almost 1 in 2 men die between the ages of 15 and 60 years, largely due to changes in the use of alcohol and tobacco.

As the data are based on existing age-specific death rates, additional calculation is necessary to allow comparability between countries; the uncertainty of the estimates are shown in parentheses. Confidence intervals can be quite large — as in Zimbabwe — but quite precise in countries like Japan which has complete vital registration.

These data show the large variations in life expectan- cies between countries. For example, a girl born in Japan in can expect to live 86 years, whereas a girl born in Zimbabwe at the same time will live between 30 and 38 years. In almost all countries, women live longer than men. The standardization of rates can be done either directly or indirectly see Box 2. Age-standardized rates enable comparisons to be made between populations that have different age struc- tures. Standardization can also be done for variables other than age. This is necessary when comparing two or more populations that have different basic characteristics that independently influence the risk of death such as age, race, socioeconomic status, etc.

Direct and indirect standardization of disease rates The direct method of standardization is more frequently used, and is done by applying the disease rates of the populations being compared to a standard population. This method yields the number of cases that would be expected if the age-specific rates in the standard popu- lation were true for the study population. Standardized rates are used, whenever relevant, for morbidity as well as mortality. The choice of a standard population is arbitrary, but can be problematic when comparing rates of low-income and high-income countries.

Details on methods of standardizing rates can be found in: Teaching health statistics: lesson and seminar outlines. Worldwide trends in life expectancy, — Lifeexpectancyatbirth years Period 30 Africa World Europe Asia 70 60 50 40 55 60 65 70 80 85 90 95 North America Latin America and the Caribbean Oceania pp 80 Measuring health and disease 29 While each give different age-standardized rates see Table 2. For example, there is great variation between countries inthe reported crudemortality ratesfor heart disease as shown in Table 2. Finland has a crude heart disease death rate approximately three times that of Brazil, but the standardized rate is the same.

Similarly, the United States of America has a crude rate more than twice that of Brazil, yet again, age-standardized rates are similar. Thus the difference between these countries is not as large as it appears from the crude rates. High-income countries have a much greater propor- tion of older people in their populations than low- and middle-income countries—the older people have higher rates of cardiovascular disease compared with younger people.

All these death rates are influenced by the quality of the original data on the causes of death. Morbidity Death rates are particularly useful for investigating diseases with a high case-fatality. However, many diseases have low case-fatality, for example, most mental illnesses, musculoskeletal diseases, rheumatoid arthritis, chickenpox and mumps. In this situ- ation, data on morbidity illness are more useful than mortality rates. Morbidity data are often helpful in clarifying the reasons for particular trends in mortality.

Changes in death rates could be due to changes in morbidity rates or in case-fatality. For example, the recent decline in cardiovascular disease mortality rates in many developed countries could be due to a fall in either incidence suggesting improvements in primary prevention or in case-fatality suggesting improvements in treatment. Because population age structures change with time, time-trend analyses should be based on age-standardized morbidity and mortality rates. Other sources of morbidity data include: Table 2. Directly standardized male death rates from respiratory infections, and the ranking of five countries using three different standard populations30 Country Age-standardized rate per Ranking of countries by age-standardized rate Segi European WHO world Segi European WHO world Australia 6.

Crude and age-standardized death rates per for heart disease in three selected countries men and women combined , Country Crude death rate Age-standardized death rate Brazil 79 Finland USA 30 Chapter 2 To be useful for epidemiological studies, the data must be relevant and easily acces- sible. In some countries, the confidential nature of patient medical records may make hospital data inaccessible for epidemiological studies. A recording system focusing on administrative or financial data, rather than on diagnostic and individual charac- teristics may diminish the epidemiological value of routine health service data.

Hospital admission rates are influenced by factors other than the morbidity of the pop- ulation, such as the availability of beds, hospital admission policies and social factors. Because of the numerous limitations of routinely recorded morbidity data, many epidemiological studies of morbidity rely on the collection of new data using specially designed questionnaires and screening methods.

This enables investigators to have more confidence in the data and the rates calculated from them. Disability Epidemiologists are concerned not only with the occurrence of disease, but also with the consequences of disease: impairments, disabilities and handicaps. ICF is a useful tool for understanding and measuring health outcomes. It can be used in clinical settings, health services or surveys, at the indi- vidual or population level.

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The key parameters of ICF are as follows: impairment: any loss or abnormality of psychological, physiological or anatomical structure or function; disability: any restriction or lack resulting from an impairment of ability to perform an activity in the manner or within the range considered normal for a human being; handicap: a disadvantage for a given individual, resulting from an impairment or a disability, that limits or prevents the fulfilment of a role that is normal depending on age, sex, and social and cultural factors for that individual.

The relationship between the different non-fatal outcomes is shown in Box 2. It is difficult to measure the prevalence of disability, but it is becoming increas- ingly important in societies where acute morbidity and fatal illness are decreasing, and where there is an increasing number of aged people living with disabilities. Health determinants, indicators, and risk factors Health determinants Health determinants are generally defined as the underlying social, economic, cultural and environmental factors that are responsible for health and disease, most of which are outside the health sector.

WHO presents the most recent data for 50 health indicators each year. Risk factors A risk factor refers to an aspect of personal habits or an environmental exposure, that is associated with an increased probability of occurrence of a disease. Since risk factors can usually be modified, intervening to alter them in a favourable direction can reduce the probability of occurrence of disease. The impact of these interventions can be determined by repeated measures using the same methods and definitions see Box 2.

Other summary measures of population health Policy-makers face the challenge of responding to current disease prevention and controlpriorities,whilebeingresponsibleforpredictingfuturepriorities. Suchdecisions Box 2.

Measuring risk factors Risk factors can include tobacco and alcohol use, diet, physical inactivity, blood pressure and obesity. Since risk factors can be used to predict future disease, their measurement at a population level is important, but also challenging. However, different surveys use different methods, often with different measurement techniques and criteria for detecting a risk factor or clinical outcome for example, diabetes or hypertension. Show details. Buy the selected items together This item: Basic Epidemiology by R.

Basic Epidemiology

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  8. Share your thoughts with other customers. Write a customer review. Showing of 6 reviews. Top Reviews Most recent Top Reviews. There was a problem filtering reviews right now. Please try again later. Format: Paperback Verified Purchase. Very useful book, used it when I had science Olympiad, but I'm not really the person who like statistics, but I would really recommend this book to a student who's in this field.

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