ec02

Is the disease infectious? (EC01)

Module: EPM301 Epidemiology of Communicable Diseases

Course: PG Diploma/ MSc Epidemiology

This document contains a copy of the study material located within the computer assisted learning (CAL) session. The first three columns designate which page, card and screen position the text refers to. If you have any questions regarding this document or your course, please contact DLsupport via [email protected]. Important note: this document does not replace the CAL material found on your module CDROM. When studying this session, please ensure you work through the CDROM material first. This document can then be used for revision purposes to refer back to specific sessions. These study materials have been prepared by the London School of Hygiene & Tropical Medicine as part of the PG Diploma/MSc Epidemiology distance learning course. This material is not licensed either for resale or further copying.

London School of Hygiene & Tropical Medicine September 2013 v2.0

Section 1: EC02 Is the disease infectious? Aim To determine whether a disease is caused by an infectious agent or not.

Objectives By the end of this session, you should be able to: identify the factors that suggest that a disease has an infectious cause use these factors to determine whether a disease of unknown cause is likely to

be infectious. This session should take you about 2 to 3 hours to complete. Section 2: Introduction

How can you know whether a disease is caused by an infection or by exposure to some other factor? You may remember that Koch's postulates list a number of criteria that can be used to decide whether an infectious agent is responsible for a disease. Review these criteria and think about what their limitations are. (You might want to refer back to session FE13 to fully review the subject of causality.)

Koch's postulates (1892) The agent must be consistently demonstrable in diseased individuals The agent must be isolated from a diseased individual and grown in pure culture Inoculation of the agent must induce the disease experimentally

2.1: Introduction

Often it is not possible to fulfil Koch's postulates because no infectious agent has yet been identified, or it is not possible to culture a suspected agent, or there are no experimental hosts. In these cases, there are a number of epidemiological characteristics that might indicate an infectious aetiology.

In this session you will learn about some common features of infectious diseases that can help you assess whether a disease is infectious or not.

When might this be necessary?

Interaction: Hyperlink: aetiology: output (appears in new window) Aetiology

Aetiology is often used in epidemiology to refer to "cause".

The search for an infectious cause may begin when: a new disease syndrome emerges

Example button new knowledge about old diseases raises questions about possible infectious causes

Example button new technology opens-up new areas of investigation

Example button

Interaction: Button: Example: output (appears in new window) The search for the causative agent of AIDS began when the syndrome was recognised and became increasingly prevalent in the early 1980s.

Interaction: Button: Example: output (appears in new window) A variety of infectious agents have now been associated with diseases that were previously considered to be chronic conditions: human papillomavirus and cervical cancer see http://www.who.int/vaccine_research/diseases/viral_cancers/en/index3.html)

Helicobacter pylori and gastritis

parvovirus (see http://www.cdc.gov/parvovirusB19/about-parvovirus.html) and arthritis.

Interaction: Button: Example: output (appears in new window) Example 1: Since the advent of molecular biological tools such as the Polymerase Chain Reaction (PCR), it is possible to identify pathogens from smaller quantities of tissue. New techniques like Genome Wide Sequencing, Single Nucleotide

Polymorphisms (SNP) genotyping are now available for further studies of genetic variability in various pathogens.

Example 2: New agents are continually being recognised through advanced virological techniques (e.g. hepatitis C virus, hepatitis E virus).

Section 3: Epidemiological characteristics of infectious disease

What characteristics might suggest that a disease has an infectious cause? We can consider the characteristics of infectious aetiology under the following headings: Time when the disease occurs Place where the disease is distributed b Person who develops the disease On the next few pages each of these categories are considered in turn.

Section 4: Time when the disease occurs How can time be used to indicate an infectious aetiology? There are 4 main patterns, which are described on this page:

1. Do disease cases occur close together in time? (Temporal clustering) 2. Does disease occurrence change with the climate during the year?

(Seasonality)

3. Are there periodic changes in disease occurrence? (Cyclical patterns) 4. Is there a change in disease occurrence over long time periods (more than one year)? (Long-term trends)

4.1: Time when the disease occurs 1. Temporal Clustering: Do disease cases occur close together in time?

Temporal clustering is more easily recognised for rare diseases or when there are few sources of infection. It is especially important in identifying the causes of epidemics. Epidemiologists have developed a 'moving window' test to distinguish between random temporal clustering of cases and clustering due to an exposure.

This assesses the statistical probability of whether more cases occur within a specified time interval than would be expected by chance. By 'moving' the window continuously across the period of observation you can sum the number of cases occurring during the interval at different times.

4.2: Time when the disease occurs

1. Temporal Clustering: Do disease cases occur close together in time? The 'moving window' test By sliding the 28-day 'window' along the time-line opposite, you can see that the number of cases in the window changes.

Use the buttons beneath the graph to move the window left and right.

(buttons moves green line left or right) 4.3: Time when the disease occurs 1. Temporal Clustering:

Do disease cases occur close together in time? Knox and Lancashire (1982) used this method to show that pityriasis rosea a mild skin disease occurred in temporal clusters.

They found a maximum of 16 cases within a 28-day window. This was significantly higher than the average of 4.8 cases expected in any 28-day interval from the overall data. This supported the hypothesis of an infectious aetiology.

4.4: Time when the disease occurs

2. Seasonality :

Does disease occurrence change with the climate during the year?

The reasons for seasonality of cases are unclear. Seasonality may be associated with changes in temperature and humidity that determine the survival and transmissibility of the infectious agent, or the prevalence of vectors.

Behaviour can also vary with season, affecting the amount of contact with potential sources of infection.

Interaction: Hyperlink: vectors: output (appears in new window) Vector

A living carrier, such as an insect, that transports an infectious agent from an infected individual to a susceptible individual. Examples

Interaction: Tabs: Meningitis : output For meningococcal meningitis, in which season would more cases generally occur, and why might this be? Choose one of the seasons below:

Interaction: Button: temperate summer/tropical wet season: output (appears in new window) In fact, the incidence of droplet- or aerosol-transmitted diseases such as meningococcal meningitis tends to peak in the temperate winter or tropical dry season for a number of reasons. In temperate climates, there tends to be more crowding and less ventilation in winter. This leads to increased contact, re-circulation of air and dry nasal mucosa, providing a better environment for transmission. In the tropics, the free bacterial particles in droplets survive better at lower temperatures, and dry nasal mucosa provides a better environment for transmission.

Click the 'graph' button below to see an example.

Interaction: Button: temperate winter/tropical dry season: output (appears in new window) That's correct. The incidence of droplet- or aerosol-transmitted diseases such as meningococcal meningitis tends to peak in the temperate winter or tropical dry

season for a number of reasons. In temperate climates, there tends to be more crowding and less ventilation in winter. This leads to increased contact, re-circulation of air and dry nasal mucosa, providing a better environment for transmission. In the tropics, the free bacterial particles in droplets survive better at lower temperatures, and dry nasal mucosa provides a better environment for transmission.

Click the 'graph' button below to see an example.

Interaction: Button: graph: output (appears in new window)

This graph shows that the epidemic of meningococcal meningitis started towards the end of the dry season when the weather was hot, with the dry and dusty Harmattan wind blowing from the Sahara. The incidence of the disease declined as the absolute humidity rose and the epidemic stopped shortly after the wind stopped and the rains began.

Interaction: Tabs: Malaria : output For malaria, in which season would more cases generally occur, and why might this be? Choose one of the seasons below: Interaction: Button: temperate summer/tropical wet season: output (appears in new window) That's correct, the incidence of vector-borne diseases such as malaria tends to peak in the temperate summer/tropical wet season because the mosquito vector abundance is dependent on warm temperatures and water pools for breeding.

Interaction: Button: temperate winter/tropical dry season: output (appears in new window)

In fact, incidence of vector-borne diseases such as malaria tends to peak in the temperate summer/tropical wet season because the mosquito vector abundance is dependent on warm temperatures and water pools for breeding.

(back to main card) Interaction: Tabs: Shigella : output For shigella (bacterial dysentery), in which season would more cases generally occur, and why might this be? Choose one of the seasons below: Interaction: Button: tropical wet season: output (appears in new window) That's correct. The incidence of faecal-oral diseases such as shigella tends to peak in the tropical wet season for a number of reasons. Higher temperatures lead to increased multiplication of bacteria in contaminated food, and there are likely to be more flies contaminating food. There is an increased possibility of water contamination, due to flooding, for example. Interaction: Button: tropical dry season: output (appears in new window) In fact, the incidence of faecal-oral diseases such as shigella tends to peak in the tropical wet season for a number of reasons. Higher temperatures lead to increased multiplication of bacteria in contaminated food, and there are likely to be more flies contaminating food. There is an increased possibility of water contamination, due to flooding, for example. 4.5: Time when the disease occurs

2. Seasonality :

Does disease occurrence change with the climate during the year?

Some infections do not strictly follow these patterns. Rotavirus is transmitted by the faecal-oral route, yet it is frequent in the winter in temperate climates, but is detected all year round in the tropics. This characteristic might support the hypothesis that it is also spread by aerosol transmission (Cook et al 1990).

Interaction: Hyperlink: Rotavirus: output (appears in new window) Rotavirus For more information about this infection, see http://www.cdc.gov/vaccines/pubs/pinkbook/rota.html However, it is likely that factors other than mode of transmission are also involved in determining seasonality. Click below to see graphs that show how different strains of the same virus can have different seasonal patterns. The incidence of parainfluenza virus type 3 peaks in summer, while types 1 and 2 peak in winter (Noah 1989).

Interaction: Hyperlink: parainfluenza virus: output (appears in new window) Parainfluenza virus

For more information about this infection see http://www.cdc.gov/vaccines/pubs/pinkbook/rota.html.

Interaction: Button: Graph: output

4.6: Time when the disease occurs 2. Seasonality :

Does disease occurrence change with the climate during the year? When looking for seasonality it is important to take into account changes in population size. For example, seasonal population migration or seasonal variation in birth rates (for a disease of infants) could falsely indicate a seasonally-transmitted disease because the number of cases might increase as the population size increases. Even if seasonality of the disease exists, there are a number of alternative aetiological explanations. Can you think of non-infectious exposures that might also vary seasonally?

Interaction: Button: Cloud: output (appears in new window) Nutritional status varies seasonally, especially in poorer countries or communities (e.g. Kigutha et al 1995). Exposure to toxins has also been shown to be associated with season, for example aflatoxin in West Africa (e.g. Wild et al 2000). You may have thought of other examples.

Interaction: Hyperlink: aflatoxin: output (appears in new window) Aflatoxin

A metabolic product of fungi of the Aspergillus spp. that grow on cereals and grains. It has been identified as a risk factor for liver cancer. 4.7: Time when the disease occurs 3. Cyclical patterns:

Are there periodic changes in disease occurrence? What other periodic events might influence the temporal pattern of a disease?

Interaction: Tabs: Example 1 : output Periodic visits to the market to sell goods may increase the potential for contact with infected individuals at particular times.

Seasonal movements from the highlands to the malaria-endemic lowlands for farming can expose non-immune individuals to malaria resulting in a peak of malaria cases at harvest time.

Interaction: Hyperlink: malaria: output (appears in new window) Malaria

For more information about this disease see http://www.cdc.gov/malaria/. (back to main card)

Interaction: Tabs: Example 2 : output Annual increases in measles transmission have been shown to coincide with the start of school terms, which provide an ideal opportunity for contact between infected and susceptible children (e.g. Fine & Clarkson 1982).

Interaction: Hyperlink: measles: output (appears in new window) Measles

For more information about this disease, see http://www.cdc.gov/measles/index.html. 4.8: Time when the disease occurs 3. Cyclical patterns:

Are there periodic changes in disease occurrence? In addition to the annual cycles, larger peaks of measles cases occurred at regular 2-year intervals in England and Wales before the national immunisation programme was introduced.

Click the 'plot' button below to see a plot that illustrates this.

Interaction: Button: Plot: output (appears in new window)

Can you think of a reason for this 2-year cycle? Bear in mind that measles infection confers lifelong immunity.

Interaction: Button: Cloud: output For infections that confer lifelong immunity, it may take a number of years until there are sufficient numbers of susceptible individuals born into the population to enable the infection to circulate widely.

This "critical threshold of susceptibles" will be discussed in session EC06. 4.9: Time when the disease occurs 3. Cyclical patterns:

Are there periodic changes in disease occurrence? Changes in climate may occur over longer periods of time. These can also cause variations in disease occurrence. The El Nio_ Southern Oscillation affects weather patterns in certain parts of the world at approximate intervals of 4 to 6 years.

It shows a close association with the between-year variation in malaria incidence in South America and the Indian subcontinent. This association is due to periods of intense drought or rainfall affecting the density of malaria mosquito vectors

Interaction: Hyperlink: El Nio: output (appears in new window) El Nio

The El Nio Southern Oscillation is a periodic climatic phenomenon associated with a warmer than average sea surface temperature in the eastern equatorial Pacific Ocean. 4.10: Time when the disease occurs

4. Long-term trends Is there a change in disease occurrence over long time periods (more than one year)? Long-term trends in a disease are useful if they can be associated with changes in the proposed mode of transmission. For example, the trend in mortality from cervical cancer in England and Wales was correlated with the trend in gonorrhoea incidence at the age of 20 for the same birth cohorts of women. Click below to see a graph of this trend.

This provided strong evidence that cervical cancer was a sexually transmitted disease. It has subsequently been associated with human papilloma virus infection (Beral 1974).

Interaction: Hyperlink: gonorrhoea: output (appears in new window) Gonorrhoea

For more information about this disease, see http://www.cdc.gov/std/Gonorrhea/

Interaction: Hyperlink: birth cohorts: output (appears in new window) Birth cohorts

Cases were grouped by year of birth, not year of onset.

Section 5: Place where the disease occurs

What spatial distributions can be characteristic of infectious diseases? 1. Do cases occur close together within a specified area? (Spatial Clustering) 2. Does the disease tend to occur only in specific geographical locations?

(Geographical Restriction) 5.1: Place where the disease occurs

1. Spatial Clustering:

Do cases occur close together within a specified area? Spatial clustering is useful to identify a common source of infection.

Can you remember an example of this from the epidemiological work of John Snow?

Interaction: Button: cloud: output (appears in new window)

John Snow plotted the residence of fatal cases of cholera http://www.cdc.gov/cholera/index.html on a map to identify the water pump responsible for the cholera epidemic in Soho, London in 1854. Review this from FE01. (appears on RHS) More recently, the cases of new variant Creutzfeld-Jacob disease were analysed to assess whether more cases lived near to meat rendering plants than would be expected by chance. There was no evidence that the distance from residence to the nearest rendering plant was a risk factor for the disease (Cousens et al 1999).

Click below to see the map from this study. Show Button

Interaction: Hyperlink: new variant: output (appears in new window) New variant Creutzfeld-Jacob disease For more information about this disease see http://www.hpa.org.uk/Topics/InfectiousDiseases/InfectionsAZ/CreutzfeldtJakobDisease/

Interaction: Hyperlink: Creutzfeld-Jacob disease: output (appears in new window) New variant Creutzfeld-Jacob disease

For more information about this disease see http://www.hpa.org.uk/Topics/InfectiousDiseases/InfectionsAZ/CreutzfeldtJakobDisease

Interaction: Hyperlink: rendering plants: output (appears in new window) Rendering plants are factories involved in the production of meat and bone meal from animal carcasses.

Interaction: Button: Show: output (appears in new window)

5.2: Place where the disease occurs

1. Spatial Clustering:

Do cases occur close together within a specified area?

It is important to remember that spatial clustering of cases can also indicate an environmental (non-infectious) cause. A point-source exposure, such as a toxic contaminated water source, would also produce a geographical cluster of cases.

5.3: Place where the disease occurs 1. Spatial Clustering:

Do cases occur close together within a specified area? Household clustering of cases can be a good indicator of infectious aetiology. Think about which non-infectious causes could also explain household clustering.

Interaction: Button: cloud: output (appears in new window) Individuals from the same family and household are likely to be exposed to the same genetic and environmental factors, such as socio-economic conditions, diet, and air pollution. Any of these could provide an alternative, non-infectious explanation for the disease.

Measures of household spread of infection will be discussed in EC03. 5.4: Place where the disease occurs

2. Geographical restriction: Does the disease tend to occur only in specific geographical locations? Climatic and ecological conditions are responsible for the geographical restriction of infectious diseases. Certain conditions favour the development of the infective agent, transmission of the infection, and breeding of vectors.

Examples

Interaction: Tabs: 1 : output Schistosomiasis is distributed in tropical and sub-tropical areas, because optimal survival of the parasite in water, infectivity to the intermediate snail host, and development in the snail occurs at 25_. Click below to see a map of the world distribution of this disease. http://www.cdc.gov/parasites/schistosomiasis/. Map Button

Interaction: Hyperlink: Schistosomiasis: output (appears in new window) Schistosomiasis

For more information about this disease see . (Back to main card)

Interaction: Button: Map: output (appears in new window)

Interaction: Tabs: 2 : output Sandflies, and therefore the Leishmania parasitic infection that they transmit, are distributed in the tropics and subtropics, and are extending into southern European climates.

Click below to see a map of the world distribution of this disease. Map Button

Interaction: Hyperlink: Leishmania: output (appears in new window) Leishmania

For more information about this infection see http://www.cdc.gov/parasites/leishmaniasis/index.html (Back to main card)

Interaction: Button: Map: output (appears in new window)

Interaction: Tabs: 3 : output Burkitt's lymphoma is geographically restricted to the lowlands of Africa, despite a wider distribution of Epstein-Barr virus, which is implicated in the disease. The association with areas of high rainfall and warm temperatures suggests that malaria might play a role in the aetiology of the disease (de Th 1979). Map Button

Interaction: Hyperlink: Leishmania: output (appears in new window) Burkitt's lymphoma

For more information about this disease.

Interaction: Button: Map: output (appears in new window) Burkitt's lymphoma and climate in Africa

The red overlay on this map shows the distribution of Burkitt's lymphoma. The unshaded areas of the map beneath represent regions of high rainfall and high mean temperature (in the coolest month). 5.5: Place where the disease occurs

2. Geographical restriction: Does the disease tend to occur only in specific geographical locations?

There are a number of characteristics that can help to associate a disease with a particular place. They are listed opposite.

1. The disease is equally common in all ethnic groups that live in the area; 2. the disease is much less common among people from similar groups that live elsewhere;

3. individuals migrating into the area become ill with the same or a greater frequency; 4. individuals migrating out of the area do not become ill with the same

frequency.

Section 6: Person who develops the disease How might particular groups of individuals be at greater risk of infection and disease? Some characteristics of particular groups may increase their exposure to infection or facilitate the onset of the disease. Groups to consider are:

1. Occupational

2. Behavioural

3. Socio-economic

4. Immunological status

6.1: Person who develops the disease 1. Occupational:

A number of infections are associated with occupational exposure. Occupation often acts to bring a specific group of individuals in contact with infection as a direct result of the activity. For example, butchers are more likely to develop warts. (Keefe et al 1994).

Interaction: Hyperlink: warts: output (appears in new window) Warts

For more information about this disease see www.cdc.gov/hpv. In the same way, it might be expected that cattle farmers in the UK should be more likely to develop new-variant Creutzfeld-Jakob disease because of their increased contact with cattle that have Bovine Spongiform Encephalitis. So far there have been too few cases to assess this risk. Interaction: Hyperlink: warts: output (appears in new window) New-variant Creutzfeld-Jakob disease

For more information about this disease see http://www.hpa.org.uk/Topics/InfectiousDiseases/InfectionsAZ/CreutzfeldtJakobDisease/.

1. Occupational: Occupational groups can also be at greater risk of disease because of associated behaviours. In the tropics, illegal night-time fishing and logging activities increase outdoor exposure to mosquito bites and therefore increase the risk of malaria infection.

Long-distance truck drivers tend to have a more promiscuous sexual behaviour, leading to an increased risk of HIV infection (Bwayo et al 1994).

6.2: Person who develops the disease 2. Behavioural:

Certain lifestyle or cultural behaviours may increase the risk of an infectious disease by increasing the likelihood or amount of exposure to the infectious agent. This aspect may be less easy to investigate, for reasons of cultural sensitivity or privacy, for example sexual behaviours.

Examples 1. Pneumocystis pneumonia and Kaposi sarcoma in homosexuals

2. Kuru and cannibalism

Interaction: Hyperlink: Pneumocystis pneumonia: output (appears in new window) Pneumocystis pneumonia

For more information about this disease see http://www.cdc.gov/fungal/pneumocystis-pneumonia/. Interaction: Hyperlink: Kaposi sarcoma: output (appears in new window) Kaposi sarcoma

For more information about this disease see http://www.cancer.gov/cancertopics/pdq/treatment/kaposis/HealthProfessional Interaction: Hyperlink: Kuru: output (appears in new window) Kuru

For more information about this disease see Collinge et at (2006) http://dx.doi.org/10.1016/S0140-6736(06)68930-7

6.3: Person who develops the disease 3. Socio-economic: Many infectious diseases are related to socio-economic status. This is often related to living-conditions that can affect transmission, such as levels of crowding, poor-sanitation, dampness and house-structure.

For example, tuberculosis is frequently associated with overcrowded and low-income households (Mangtani et al 1995).

Interaction: Hyperlink: tuberculosis: output (appears in new window) Tuberculosis

For more information about this disease see http://www.cdc.gov/tb/. Can you think of non-infectious causes of disease that might also be associated with socio-economic status?

Interaction: Button: cloud: output (appears in new window) Malnutrition and toxic exposure could both cause disease and are also likely to be related to socio-economic status. You might have thought of other exposures. 6.4: Person who develops the disease 4. Immunological status: High incidence of a disease among people lacking immunity or with an impaired immunological system is highly suggestive of an infectious cause.

Individuals undergoing therapy with immunosuppressive drugs or suffering from an immunodeficiency are more susceptible to infectious agents.

Diseases among these groups are likely to be of an infectious aetiology, e.g. the increased incidence of lymphomas in transplant patients. 6.5: Person who develops the disease 4. Immunological status: Migrants moving from an area of low to high endemicity will be at greater risk of being infected and developing the disease, as they have not developed a protective immunological response. Example

Studies of migrants have shown that there is a critical age at exposure for multiple sclerosis, after which an individual is less likely to develop the disease (for example, see Gale & Martin 1995).

Interaction: Hyperlink: multiple sclerosis: output (appears in new window) Multiple sclerosis

Multiple sclerosis (also called disseminated sclerosis) is a chronic, often disabling disease of the central nervous system, characterised by impairment of transmission of nerve impulses, particularly those involved with vision, sensation, and the use of limbs. There appear to be multiple causes, possibly including viruses and environmental, genetic, and immune system factors. Section 7: Interactions

Often, identifying the cause of a disease is not straightforward. Interactions can occur between any of the factors already mentioned.

The main areas to consider are: Are cases clustered in both space and time? (Spatial-temporal clustering) Does the incidence vary by geographical region? (Regional variation) Are there other factors that are necessary or seem to pre-dispose an individual to disease? (Co-factors)

7.1: Interactions 1. Spatial-temporal clustering:

Are cases clustered in both space and time? Spatial-temporal clustering occurs because of person-to-person transmission, and can be investigated in a number of ways.

One way is to pair each individual with every other individual who has the disease. Distance in time and geographical distance between each possible pair can be plotted against each other to look for any correlation. By considering particular intervals of time and distance, a chi-squared test can be used to test for any association (Messenger et al 1982).

This method has been adapted to study diseases with long latent periods. Another adaptation involves recording all significant contacts between cases of a disease, and between appropriate controls - a difficult task! (Pike & Smith 1974)

7.2: Interactions

2. Regional variation: Does the incidence vary by geographical region?

Regional variations in infectious diseases are likely to be due to an interaction between geographical, socio-economic, immunological, dietary and cultural factors. Genetics and other environmental exposures also vary by place and it is important to exclude these as possible causes before specifying an infectious aetiology. Some infections are limited to particular countries. In the case of political boundaries, these variations in disease incidence are usually due to socio-economic factors such as the degree of financial investment in infrastructure (e.g. sanitation systems) and public health measures (e.g. immunisation programmes).

7.3: Interactions

3. Co-factors: Are there other factors that are necessary or seem to pre-dispose an individual to disease?

In the case of a number of malignant diseases, the infective agent may not be a sufficient cause of disease. There might be a sequence of epidemiological, immunological and molecular events that are additionally required to cause the disease.

Often, external (environmental) and/or internal (genetic and physiological) factors also play a necessary role. The involvement of these 'co-factors' makes the search for an infectious aetiology more difficult.

Interaction: Hyperlink: sufficient cause: output (appears in new window) Sufficient cause refers to a particular exposure providing the full explanation for an outcome. In this case, infection alone is not enough to cause disease.

Interaction: Tabs: Example 1 : output It is suspected that Hodgkin's disease occurs in only a proportion of individuals infected with Epstein-Barr virus because of some genetic or environmental co-factor (Stiller 1998).

Interaction: Hyperlink: Hodgkin's disease: output (appears in new window) Hodgkin's disease

For more information about this disease see http://www.who.int/vaccine_research/diseases/viral_cancers/en/index1.html

(Back to main page)

Interaction: Tabs: Example 2 : output Burkitt's lymphoma (BL) is a tumour whose epidemiological characteristics strongly suggested Epstein-Barr virus (EBV) as the cause. However, EBV is widespread and the restricted geographical distribution of BL suggested that malaria might be involved. A higher incidence of BL was found among migrants with lower immunity to malaria. This was consistent with malaria being the major co-factor necessary for the disease (de-Th_1979). Interaction: Hyperlink: Burkitt's lymphoma: output (appears in new window) Burkitt's lymphoma

For more information about this disease see http://www.who.int/vaccine_research/diseases/viral_cancers/en/index1.html 7.4: Interactions

Over the next few pages you will complete an exercise to determine whether or not a disease of unknown aetiology is infectious. You may like to take a break at this point, before going on to complete the exercise.

Section 8: Exercise: A disease of unknown aetiology

This exercise demonstrates how the epidemiological pattern of a disease can be defined by morbidity surveys. Using the information you have covered earlier in this session, interpret the data provided to decide whether the disease under investigation is infectious or not. Clinical information is not given, to show you that the answer can be reached by epidemiological assessment alone. On the following pages, you will be shown a number of tables. Examine each table and summarise the epidemiological features that you think are most important. Consider the information from each table separately, and then evaluate all the information together to determine the aetiology of this disease.

8.1: Exercise: A disease of unknown aetiology

Background The disease was recognised in the early 1900s and could be diagnosed with reasonable accuracy by experienced physicians. It was thought to be associated with poor and unsanitary living conditions.

Cases appeared to be localised in their geographical distribution. Multiple cases were frequently observed in the same family and recurrent attacks were common. A number of hypotheses were proposed for the aetiology of the disease. These included infection, genetic susceptibility, toxicity, etc.

8.2: Exercise: A disease of unknown aetiology To collect data on this disease in a defined population, a responsible agency undertook an extended survey. This was done in an area in the Northern Hemisphere where there had been a high incidence of the disease over a period of years. The data presented in this exercise were collected from 24 villages during a one-year period. The name, age, sex and marital status of each family member were recorded for all individuals in the villages surveyed. Each household was visited every two weeks. Reported symptoms and a clinical examination were used to determine the occurrence of 'the disease'. Questionable cases were referred to a more experienced physician who was directing the study. Note: The number of cases is the same in the first three tables but the population size is inconsistent, leading to different estimates of disease risk. The reason for this is unclear, but may be due to missing data on age and sex.

Section 9: Exercise: Table 1

Table 1 opposite shows the total number of cases and risk of 'the disease' in each village during the survey year. Summarise the data in Table 1: The village population size ranges from Calc 1 to Calc 2. The minimum number of cases in one village is Calc 3. The maximum number of cases in one village is Calc 4 The lowest risk of disease is Calc 5 per 1000, and the highest risk of disease is Calc 6 per 1000.

Interaction: Calculation: Calc 1 Correct response: Correct

That's right, the smallest population size is 284 individuals in village 'Fn'.

Incorrect response: Sorry, that's not right. The smallest population size is 284 individuals in village 'Fn'.


That's right, the largest population size is 1569 individuals in village 'Ola'. Incorrect response: Sorry, that's not right. The largest population size is 1569 individuals in village 'Ola'.

Interaction: Calculation: Calc 3 Correct response: Correct That's right, the minimum number of cases is 13, in village 'Gr'.

Incorrect response: Sorry, that's not right. The minimum number of cases is 13, in village 'Gr'.

Interaction: Calculation: Calc 4 Correct response: That's right, the maximum number of cases is 119, in village 'Rc'. in village 'Rc'. Incorrect response: Sorry, that's not right. The maximum number of cases is 119, in village 'Rc'.


That's right, the lowest risk of disease is 19.6 per 1000, in village 'Gr'. Incorrect response: Sorry, that's not right. The lowest risk of disease is 19.6 per 1000, in village 'Gr'.


The highest risk of disease is 100.8 per 1000, in village 'In'. Incorrect response: Sorry, that's not right. The highest risk of disease is 100.8 per 1000, in village 'In'.

9.1: Exercise: Table 1

RHS remains static (chart) Which epidemiological feature of 'the disease' can we assess from these data? Click on the answer from the options below:

Interaction: Hotspot: Clustering in ethnic groups (appears in new window)

No, the table does not give us any information on whether there is any ethnic variation by village - there is insufficient data presented to assess this feature.

Interaction: Hotspot: Geographical variations (appears in new window)

Yes, we can assess whether the cases are distributed randomly between the villages or whether they are clustered in particular villages

Interaction: Hotspot: Temporal trends (appears in new window)

No, there is no information in this table about how 'the disease' was distributed over time - the table shows the total number of cases diagnosed in a one-year period. 9.2: Exercise: Table 1 (RHS remains static) Is there spatial clustering of cases? Look at the data and write down your answer without doing any formal tests.

Interaction: Button: cloud: output (appears in new window) A number of villages have a risk of disease considerably higher or lower than the overall risk of 50.6 per 1000 (see final row of Table 1). This indicates that there might be clustering of cases. (appears on main card below original text) How can you tell if there is significant geographical variation? Think carefully about what statistical test you could use and then click on the box below.

Interaction: Button: cloud: output (appears in new window) A chi-squared test of the observed population size and number of cases would show whether the cases are distributed randomly between all the villages. The results of a chi-squared test are:

_ = 186.8, 23 degrees of freedom, P ~< 0.001. 9.3: Exercise: Table 1 (RHS remains static) How are cases distributed?

Interaction: Hotspot: Randomly (appears in new window)

In fact, the significant chi squared value indicates that cases are *not* randomly distributed among villages.

Interaction: Hotspot: Randomly (appears in new window) That's right. The chi-squared value indicates that there is a significant association between villages and cases. Some villages have more cases and some villages have fewer cases than would be expected if cases were randomly distributed. 9.4: Exercise: Table 1 Another approach to the distribution of cases is to consider population size as a risk factor for 'the disease'.

The best way to visualise this is to plot a graph of the data. Drag and drop the variables in the boxes below into the blue bays next to the appropriate axes opposite. Reset Button Interaction: Drag and Drop: Risk of disease Correct response: (drag to top box on RHS) That's right, the number of cases observed will be a function of the number of individuals in the village. We need to use risk of disease as our outcome variable to adjust for differences in the denominator population being observed. Incorrect response: (drag to bottom box on RHS) In fact it is more conventional to put the dependant or outcome variable on the Y-axis. Interaction: Drag and Drop: Number of cases Incorrect response: (drag to either box on RHS) Thats not quite right. The number of cases observed will be a function of the population size. For villages with the same risk, those with more individuals will have more cases. We need to use risk of disease to adjust for differences in the denominator population. Interaction: Drag and Drop: Population sizes Correct response: (drag to bottom box on RHS) That's correct, it is better to put the independent or 'exposure' variable on the x-axis. Incorrect response: (drag to top box on RHS) Actually it is more conventional to put the independent or exposure variable on the x-axis.

In fact it is more conventional to put the dependent or outcome variable on the y-axis. 9.5: Exercise: Table 1 Is there an association between population size and risk of 'the disease'?

Interaction: Hotspot: yes In fact the data are scattered randomly, implying that there is *no* association between population size and risk of 'the disease'.

Interaction: Hotspot: no That's correct - if there had been an association between population size and risk of 'the disease', the points would have formed more of an ellipse. (back to main text) Does this mean that over-crowding is a risk factor for 'the disease'?

Interaction: Hotspot: yes

In fact, population size is not the same as population density (number of people in a specified area), so we have no information on whether crowding might be a risk factor.

Interaction: Hotspot: no That's correct, population size is not the same as population density (number of people in a specified area), so we have no information on whether crowding might be a risk factor.


Exercise: Table 2 Table 2 opposite shows the risk of 'the disease' by age and gender. The data for males are shown first; click 'swap' to see the data for females. Notice that the age groups that have been used are not even. Complete Table 2 by calculating the missing risk value for males to one decimal place. Next click 'swap' and complete the corresponding table for females.

Then, look at the data in Table 2 and try to identify the main epidemiological features of 'the disease'. Think about how this data can be better visualised.

Interaction: Button: cloud: output (appears in new window) By plotting a graph of risk of 'the disease' by age group for each gender, the distribution of 'the disease' by age and the comparative trends for each gender will be clearer.

Interaction: Calculation: Calc 1 Correct response: That's right, the risk for males aged 5 to 9 years is (193 / 1574) x 1000 = 122.6.

Remember to also calculate the empty cells in the table for females. Incorrect response: Sorry, that's not correct. The risk for males aged 5 to 9 years is given by the number of cases divided by the population in that age group: Risk per 1000 = (193 / 1574) x 1000 = 122.6.

Try again with the empty cells in the table for females.

(Back to main page)

Interaction: Button: Swap: output (changes table on RHS)

Interaction: Calculation: Calc 1 Correct response: That's right, the risk for females aged 2 years is (16 / 365) x 1000 = 43.8.

Incorrect response: Sorry, that's not correct. The risk for females aged 2 years is given by the number of cases divided by the population in that age group:

Risk per 1000 = (16 / 365) x 1000 = 43.8

Interaction: Calculation: Calc 2 Correct response: That's right, the risk for females aged 25 to 29 years is (75 / 997) x 1000 = 75.2. Incorrect response: Sorry, that's not correct. The risk for females aged 25 to 29 years is given by the number of cases divided by the population in that age group:

Risk per 1000 = (75 / 997) x 1000 = 75.2. 9.7: Exercise: Table 1 (RHS remains static - chart)

The button below will show a graph of the data in Table 2. Look at the graph and identify the main characteristics of 'the disease'. Think about epidemiological inferences that are consistent with each of the characteristics.

Write down your answer before continuing.



The points listed opposite summarise the data shown in the graph. We can potentially make inferences from some of these points.

Click below if you would like to see the graph again.


1. There are no cases during the first year of life.

Interaction: Button: Inferences: output Inferences This feature is consistent with a number of potential inferences: The disease is caused by an infection with a long incubation period. Infants are protected from a nutritional deficiency by a breast milk diet. Infants are protected from an infection by passive immunity from the mother Infants are not exposed to the causal factor because of some behavioural difference. (back to main text) 2. There is a rapid rise in risk to a peak at age 5-9 that is similar for both genders.

Interaction: Button: Inferences: output Inferences A peak of disease among school-aged children is consistent with transmission of an aerosol infection, such as measles, chickenpox, etc. (back to main text) 3. The risk falls to a very low level among adolescents (15-19). 4. In adulthood there is a difference between the genders with males having a lower risk from 20-50 years, and then having excess risk above 60

years.

Interaction: Button: Inferences: output Inferences Women aged 20 50 years old are of childbearing age and may have a lower immunity to infection or a greater nutritional requirement.

Men above 60 years could have some behavioural difference in exposure but this is difficult to assess without sociological information. Section 10: Exercise: Table 3

Table 3 opposite shows the distribution of cases by month of onset, adjusted to a 31-day month.

How would you describe the distribution of the cases? How do you interpret this?

Interaction: Button: cloud: output (appears in new window) There is a dramatic peak of cases in May and June. The majority of cases occur between April and July. Over 50% of cases occur in May and June alone. This disease exhibits marked seasonality, which is likely to relate to warmer temperatures and increased sunlight hours in the Northern Hemisphere.


Table 4 opposite shows the relationship between risk of 'the disease' with sanitary rating at the village level.

These ratings are developed by sanitary engineers and are based on composite indices of general cleanliness, excreta disposal and water supply. The ratings are given on a scale of 000 where less than 40 is to be poor sanitation, and 70 and above is good sanitation. Relationship between risk of the disease and sanitary ratings

Unknown disease


Describe the data in Table 4 by selecting the correct words from the drop-down menus below: The highest risk of 'the disease' is in villages with the pulldown 1 sanitation. The lowest risk of 'the disease' is in villages with the pulldown 2 sanitary rating. The risk of 'the disease' pulldown 3

decrease with increasing sanitary rating.

Interaction: Pulldown: pulldown 1 (appears in new window) Correct response: best Yes, the risk is highest in the village with the highest sanitary rating. Incorrect response: intermediate No, the village with the intermediate sanitation (a rating of 40 69) does not have the highest risk of disease. Try again. Incorrect response: worst No, the village with the worst sanitation (a rating of less than 40) does not have the highest risk of disease. Try again.

Interaction: Pulldown: pulldown 2 (appears in new window) Correct response: intermediate Yes, the risk is lowest in the village with the intermediate sanitary rating (40 69). Incorrect response: best No, the village with the highest sanitary rating in fact has the highest risk of disease. Try again. Incorrect response: worst No, the village with the worst sanitation (a rating of less than 40) does not have the lowest risk of disease. Try again.

Interaction: Pulldown: pulldown 3 (appears in new window) Correct response: does not That's right, there is no evidence here that the risk of 'the disease' decreases with increasing sanitary rating. Incorrect response: does In fact the risk does not decrease with increasing sanitary rating, since the risk is higher in the village with the best sanitation than it is in the village with the worst sanitation.

11.2: Exercise: Table 4 (RHS remains static chart) Sanitation is a risk factor for the spread of a faecal-oral transmitted disease. Is the sanitary rating used here a good measure of the quality of sanitation? Think about how the investigators might have validated this rating score during their study.

Interaction: Button: cloud: output (appears in new window) To validate whether the rating used here is useful, you could compare it with a disease known to be spread by faecal-oral transmission. Information was collected on the risk of typhoid fever, which is an intestinal infection transmitted by the faecal-oral route. (appears below main text on LHS card) By looking at the data you can see that the risk of typhoid fever decreases as the quality of sanitation improves. This suggests that this sanitary rating is a good indicator of the quality of sanitation, which is related to the spread of faecal-oral transmitted infections. (appears on RHS card)

Typhoid fever

11.3: Exercise: Table 4 (RHS remains static)

Now think about how you would interpret the data from Table 4.

Interaction: Button: cloud: output (appears below main text on LHS) There does not appear to be any association between sanitation and risk of 'the disease' of unknown aetiology. This is surprising as sanitation levels are usually correlated with standards of living, hygiene, nutrition, etc., which are linked to many kinds of diseases. However, these ratings are allocated at the village level and may hide variations at the household level.


Table 5 opposite shows the risk of disease by economic status. The measure used is weekly family income per Adult Male Index (AMMAIN). By studying a large number of families in the survey area, the consumption of all types of goods, services and food was estimated for each family member. Males aged 25 years were found to consume the most. Adult females, children and older adults consumed less. The amount of consumption of a 25-year-old male was arbitrarily chosen as one unit, and other individuals were weighted according to this. In this way, family income was adjusted for family consumption.

Interaction: Hyperlink: AMMAIN: output (appears in new window) AMMAIN measures variation in the gross demand for articles of consumption among individuals of different ages and gender.

12.1: Exercise: Table 5 (RHS remains static - chart)

Is there an association between adjusted family income and risk of 'the disease'?

Think about how you would describe the epidemiological interpretation of these data. Write down your answer before continuing.

Interaction: Hyperlink: cloud: output (appears in new window) There is a clear and dramatic inverse association between risk of 'the disease' and adjusted weekly income. Risk declines as family income per AMMAIN increases.

(appears on LHS card below main text) Is this contradictory to the relationship with sanitary rating?

Interaction: Hyperlink: cloud: output (appears in new window) At first glance it seems that this is contradictory, because sanitation is likely to be positively associated with income. However, the sanitation ratings used here were at the village-level, while the income analysis was at the family level.

The mistaken assumption that differences between groups reflect differences between individuals is sometimes called the 'ecological fallacy'.

Section 13: Exercise: Overall evaluation Consider all the data together and decide what you think the aetiology of 'the disease' is. Write it down, together with any supporting evidence from the information given so far. Now review what each data table tells you about the epidemiology of 'the disease'.

13.1: Exercise: Overall evaluation

For each data source listed on the next page, select whether there is evidence in favour of or against the hypotheses listed on the chart opposite. For each data source listed below, select whether there is evidence for or against the hypotheses listed opposite. Note that for some hypotheses there is no evidence either for or against.

Interaction: Button: Geographic village distribution: output (appears on RHS) Data source: Geographic-village distribution For Against Insufficien

t evidence Hereditary ? (hotspot

1) ? (hotspot 7)

? (hotspot 13)

Infectious : aerosol

? (hotspot 2)

? (hotspot 8)

? (hotspot 14)

Infectious : faecal oral

? (hotspot 3)

? (hotspot 9)

? (hotspot 15)

Infectious : vector borne

? (hotspot 4)

? (hotspot 10)

? (hotspot 16)

Infectious : contact

? (hotspot 5)

? (hotspot 11)

? (hotspot 17)

Nutritional ? (hotspot 6)

? (hotspot 12)

? (hotspot 18)

Interaction: Hotspot: ? (hotspot 1) (from above table) Incorrect response: (appears in new window) In fact, a potentially fatal disease is unlikely to have such a high annual risk if it is hereditary.

Interaction: Hotspot: ? (hotspot 2) (from above table) Incorrect response: (appears in new window) No, in fact there is insufficient evidence or information to support this.





Interaction: Hotspot: ? (hotspot 7) (from above table) Correct response: (appears in new window) That's right, a potentially fatal disease is unlikely to have such a high annual risk if it is hereditary.






Interaction: Hotspot: ? (hotspot 13) (from above table) Incorrect response: (appears in new window) In fact there is sufficient evidence in this case. Please try again.

Interaction: Hotspot: ? (hotspot 14) (from above table) Incorrect response: (appears in new window) That's right, there is insufficient evidence to support or refute this.

Interaction: Hotspot: ? (hotspot 15) (from above table) Correct response: (appears in new window) That's right, there is insufficient evidence to support or refute this.



Interaction: Hotspot: ? (hotspot 18) (from above table) Correct response: (appears in new window) That's right, there is insufficient evidence to support or refute this. Table is as follows after all hotspots have been clicked on:

Interaction: Button: Age-sex distribution: output

(table changes on RHS)

Data source: Age-sex distribution For Against Insufficien


1) ? (hotspot 7)

? (hotspot 13)


? (hotspot 2)

? (hotspot 8)

? (hotspot 14)


? (hotspot 3)

? (hotspot 9)

? (hotspot 15)


? (hotspot 4)

? (hotspot 10)

? (hotspot 16)


? (hotspot 5)

? (hotspot 11)

? (hotspot 17)


? (hotspot 12)

? (hotspot 18)


Interaction: Hotspot: ? (hotspot 2) (from above table) Correct response: (appears in new window) That's right. The peak risk among school-aged children is consistent with an infection spread by aerosol-contact, such as measles, chickenpox, etc.

Interaction: Hotspot: ? (hotspot 3) (from above table) Incorrect response: (appears in new window) In this case the evidence is inconsistent.

The decline in risk among older children is consistent with reduced transmission of faecal-oral transmitted diseases in the older age groups. Younger children tend to have less hygienic behaviours and therefore have a higher risk of faecal-oral transmitted diseases. However the increase with age in adulthood is not consistent with risk of faecal-oral transmitted diseases.



Interaction: Hotspot: ? (hotspot 6) (from above table) Correct response: (appears in new window) That's right. The high risk among women of child-bearing age is consistent with a nutritional factor as they have greater nutritional requirements during pregnancy

and lactation. The absence of cases among infants could be because they are provided with sufficient nutrition from breast-milk. In this case the peak risk among young children could be explained by patterns of food distribution in the household or a higher nutritional requirement in this age group.


Interaction: Hotspot: ? (hotspot 8) (from above table) Incorrect response: (appears in new window) In fact, the evidence of peak risk among school-aged children is consistent with an infection spread by aerosol-contact, such as measles, chickenpox, etc.





Interaction: Hotspot: ? (hotspot 12) (from above table) Incorrect response: (appears in new window) In fact, the high risk among women of child-bearing age is consistent with a nutritional factor as they have greater nutritional requirements during pregnancy and lactation. The absence of cases among infants could be because they are provided with sufficient nutrition from breast-milk. In this case the peak risk among young children could be explained by patterns of food distribution in the household or a higher nutritional requirement in this age group.


Interaction: Hotspot: ? (hotspot 14) (from above table)

Incorrect response: (appears in new window) In fact, there is insufficient evidence in this case. Please try again.





Interaction: Hotspot: ? (hotspot 18) (from above table) Incorrect response: (appears in new window) In fact, there is insufficient evidence in this case. Please try again. Table is as follows after all hotspots have been clicked on:

Interaction: Button: Seasonal distribution: output


Data source: Seasonal distribution For Against Insufficien


1) ? (hotspot 7)

? (hotspot 13)


? (hotspot 2)

? (hotspot 8)

? (hotspot 14)


? (hotspot 3)

? (hotspot 9)

? (hotspot 15)


? (hotspot 4)

? (hotspot 10)

? (hotspot 16)


? (hotspot 5)

? (hotspot 11)

? (hotspot 17)


? (hotspot 12)

? (hotspot 18)

Interaction: Hotspot: ? (hotspot 1) (from above table) Incorrect response: (appears in new window) In fact, seasonality of cases is not consistent with a hereditary disease.


Interaction: Hotspot: ? (hotspot 3) (from above table) Correct response: (appears in new window) Yes, the peak of cases in summer is consistent with a faecal-oral transmitted infection.



Interaction: Hotspot: ? (hotspot 6) (from above table) Correct response: (appears in new window) Yes, the peak of cases prior to the harvesting of crops in July and August is consistent with a nutritional deficiency.

Interaction: Hotspot: ? (hotspot 7) (from above table) Correct response: (appears in new window) That's right, seasonality of cases is inconsistent with a hereditary disease.

Interaction: Hotspot: ? (hotspot 8) (from above table) Correct response: (appears in new window) Correct, the peak of cases in summer is not consistent with an aerosol-transmitted infection. Interaction: Hotspot: ? (hotspot 9) (from above table) Incorrect response: (appears in new window) In fact, the peak of cases in summer is consistent with a faecal-oral transmitted infection.



Interaction: Hotspot: ? (hotspot 12) (from above table) Incorrect response: (appears in new window) In fact, the peak of cases prior to the harvesting of crops in July and August is consistent with a nutritional deficiency.

Interaction: Hotspot: ? (hotspot 13) (from above table) Correct response: (appears in new window) In fact, there is insufficient evidence in this case. Please try again

Interaction: Hotspot: ? (hotspot 14) (from above table) Incorrect response: (appears in new window) In fact, there is insufficient evidence in this case. Please try again.





(back to LHS card)

Interaction: Button: Relationship with sanitary ratings: output


Data source: Relationship with sanitary ratings For Against Insufficien


1) ? (hotspot 7)

? (hotspot 13)


? (hotspot 2)

? (hotspot 8)

? (hotspot 14)


? (hotspot 3)

? (hotspot 9)

? (hotspot 15)


? (hotspot 4)

? (hotspot 10)

? (hotspot 16)


? (hotspot 5)

? (hotspot 11)

? (hotspot 17)


? (hotspot 12)

? (hotspot 18)


Interaction: Hotspot: ? (hotspot 2) (from above table) correct response: (appears in new window) Thats right, there is sufficient evidence to support or refute this.

Interaction: Hotspot: ? (hotspot 3) (from above table) Incorrect response: (appears in new window) In fact, the lack of any association with village sanitary ratings suggests that this is not a faecal-oral transmitted infection.





Interaction: Hotspot: ? (hotspot 8) (from above table) Incorrect response: (appears in new window) No, in fact there is insufficient evidence or information to support this. Interaction: Hotspot: ? (hotspot 9) (from above table) Correct response: (appears in new window) That's right, the lack of any association with village sanitary ratings suggests that this is not a faecal-oral transmitted infection.









Interaction: Hotspot: ? (hotspot 18) (from above table) Correct response: (appears in new window) That's right, there is insufficient evidence to support or refute this. Table is as follows after all hotspots have been clicked on:

Interaction: Button: Relationship with sanitary ratings: output

(table changes on RHS) Data source: Relationship with economic status

For Against Insufficient evidence

Hereditary ? (hotspot 1)

? (hotspot 7)

? (hotspot 13)


? (hotspot 2)

? (hotspot 8)

? (hotspot 14)


? (hotspot 3)

? (hotspot 9)

? (hotspot 15)


? (hotspot 4)

? (hotspot 10)

? (hotspot 16)


? (hotspot 5)

? (hotspot 11)

? (hotspot 17)


? (hotspot 12)

? (hotspot 18)



Interaction: Hotspot: ? (hotspot 3) (from above table) Incorrect response: (appears in new window)

An inverse correlation with socio-economic status could indicate an inverse correlation with household-level sanitation. This would be consistent with a faecal-oral transmitted infection. However there is insufficient information from the economic data to support this.



Interaction: Hotspot: ? (hotspot 6) (from above table) Correct response: (appears in new window) The inverse correlation with socio-economic status could indicate an inverse correlation with consumption, and this would be consistent with a disease due to a nutritional deficiency.


Interaction: Hotspot: ? (hotspot 8) (from above table) Incorrect response: (appears in new window) No, in fact there is insufficient evidence or information to support this. Interaction: Hotspot: ? (hotspot 9) (from above table) Incorrect response: (appears in new window) An inverse correlation with socio-economic status would indicate an inverse correlation with household-level sanitation, and this would be consistent with a faecal-oral transmitted infection. However there is insufficient information from the economic data to support this. Interaction: Hotspot: ? (hotspot 10) (from above table) Incorrect response: (appears in new window) No, in fact there is insufficient evidence or information to support this.


Interaction: Hotspot: ? (hotspot 12) (from above table)

Incorrect response: (appears in new window) In fact, the inverse correlation with socio-economic status could indicate an inverse correlation with consumption, and this would be consistent with a disease due to a nutritional deficiency.







13.2: Exercise: Overall evaluation

What do you consider the aetiology of 'the disease' to be now? Write down your answer, then look at the hints given below.

Interaction: Button: Hint 1: output (appears in new window) Hint 1

The disease is highly seasonal, and starts to decline at the time of crop harvest in July and August.


Income is linked to consumption. There is a strong association between income and risk of 'the disease'.


Men aged 25 years old have the highest consumption of goods and food. They also have the lowest risk of 'the disease'.

Has your opinion changed after seeing the hints? Check whether you were correct by clicking on the button below.

Interaction: Button: Show: output (appears on RHS)

Despite having many epidemiological characteristics of an infectious disease, 'the disease' is in fact due to a nutritional deficiency. It is called pellagra, from Italian pelle (skin) and agro (rough) because of the distinctive photosensitive dermatitis. The clinical presentations are:

diarrhoea dermatitis

dementia.

Interaction: Hyperlink: photosensitive dermatitis: output (appears in new window)

On parts of the body exposed to sunlight, the skin develops a rash that later develops into the characteristic pellagrous lesions. This may be another reason why more cases were diagnosed at the height of the summer in May and June. 13.3: Exercise: Overall evaluation Pellagra is caused by a deficiency of tryptophan and/or nicotinic acid.

Maize contains nicotinic acid but in an unavailable form, so that a maize-rich diet can be associated with pellagra. Good sources of nicotinic acid include meat, wheatgerm and yeast.

The data presented here are from surveys conducted in the Southern USA under the supervision of J. Goldberger. See his essay "Considerations on Pellagra", for information on the debate surrounding the aetiology of the disease prior to these studies.

Interaction: Hyperlink: Considerations on Pellagra: output (appears in new window)

This is on pages 99-102 of one of the course books: "The Challenge of Epidemiology: Issues and Selected Readings" Eds. Buck et al. (1989). 13.4: Exercise: Overall evaluation This exercise enabled you to use the information learned in the session to investigate whether or not the case data supplied were due to an infectious aetiology. In this case the disease was not infectious despite sharing many epidemiological characteristics of infectious diseases.

By contrast, Beral's comparison of mortality patterns for cancer of the cervix, with trends in incidence of sexually transmitted diseases, showed associations between the temporal, social class, occupational, and geographic distributions for these diseases (Beral, 1974). She proposed that the cause of cervical cancer was a sexually transmitted infection, when many considered it to be a non-infectious disease. Human papilloma virus has since been associated with cases of the disease. See Beral (1974) in your workbooks, for the evidence of an infectious aetiology for cervical cancer. 13.5: Exercise: Overall evaluation There are a number of diseases for which the aetiology is still not fully understood. In many cases the evidence points to the need for a number of co-factors to be present together before an individual develops the disease. The cause of multiple sclerosis still remains unknown, but it appears to involve a complex interaction of genetic and environmental factors. There is also some evidence that a viral infection may be involved in triggering the disease in genetically susceptible individuals. For more information on this topic read the first two pages of the review by Noseworthy (1999) in your reader. 13.6: Exercise: Overall evaluation The aetiology of childhood leukaemia is also still under debate. It has been associated with exposure to X-rays in utero and to ionising radiation. In a few cases a link has been found with genetic abnormalities such as Down's syndrome. Over 80% of cases are still unexplained, and an infectious aetiology has been proposed for this. For further information on this topic read the review by Kinlen (1996) on the evidence supporting the hypothesis of an infectious aetiology of leukaemia in your reader. Section 14: Summary This is the end of EC02. When you are happy with the material covered here please move on to session EC03. The main points of this session will appear below as you click on the relevant title. Characteristics of infectious aetiology (link returns RHS to relevant page)

During this session we identified a number of epidemiological features that are associated with, but not exclusive to, infectious diseases. These fall into three distinct categories: Time Place Person

Time (link returns RHS to relevant page) The temporal distribution of cases can be divided into the following patterns: (i) Temporal clustering associated with a common source or sources of infection. (ii) Seasonality regulated by climatic changes that restrict transmission of the infection. (iii) Cyclical patterns associated with recurrent social events, periodic climatic phenomena, or the interval to reach the critical threshold of susceptibles. (iv) Long-term trends that may indicate associations with an exposure. Place (link returns RHS to relevant page) The spatial distribution of cases can occur on two levels: (i) Spatial clustering associated with a common source or sources of infection. (ii) Geographical restriction regulated by ecological and climatic conditions that restrict transmission of the infection. Person (link returns RHS to relevant page) The disease may be exclusive to, or more common among, a particular group of individuals: (i) Occupational groups tend to have

special behaviours and known exposures that can be associated with the infection. (ii) Behaviours may increase exposure to the infection and can be an important area for focusing public health interventions. (iii) Socio-economic status is associated with many infectious diseases, as poor living conditions often provide favourable environments for transmission of infection. (iv) Immunological status can provide a clear indication of an infectious aetiology. Final tips (link returns RHS to relevant page)

Interaction: Tabs: 1 : output When faced with epidemiological data about a disease, think about what each piece of information might be telling you about the cause. Remember that some epidemiological features may indicate different aetiologies. Spatial clustering may be due to an infectious or environmental cause, and at the family level it can even be genetic.

Interaction: Tabs: 2 : output Also consider that lack of conclusive evidence for a proposed cause does not necessarily mean that it is not involved. It could suggest that it is only one of a number of factors required to cause the disease. Other potential co-factors may need to be investigated to fully understand the aetiology of the disease and the role, if any, of infectious agents.

2.1: Introduction4.1: Time when the disease occurs4.2: Time when the disease occurs4.3: Time when the disease occurs4.4: Time when the disease occurs4.5: Time when the disease occurs4.6: Time when the disease occurs4.7: Time when the disease occurs4.8: Time when the disease occurs4.9: Time when the disease occurs4.10: Time when the disease occurs5.1: Place where the disease occurs5.2: Place where the disease occurs5.3: Place where the disease occurs5.4: Place where the disease occurs5.5: Place where the disease occurs6.1: Person who develops the disease6.2: Person who develops the disease6.3: Person who develops the disease6.4: Person who develops the disease6.5: Person who develops the disease7.1: Interactions7.2: Interactions7.3: Interactions8.1: Exercise: A disease of unknown aetiology8.2: Exercise: A disease of unknown aetiology9.1: Exercise: Table 19.2: Exercise: Table 19.3: Exercise: Table 19.4: Exercise: Table 19.5: Exercise: Table 19.6: Exercise: Table 19.7: Exercise: Table 19.8: Exercise: Table 111.1: Exercise: Table 411.2: Exercise: Table 411.3: Exercise: Table 412.1: Exercise: Table 513.1: Exercise: Overall evaluation13.2: Exercise: Overall evaluation13.3: Exercise: Overall evaluation13.4: Exercise: Overall evaluation13.5: Exercise: Overall evaluation13.6: Exercise: Overall evaluation

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