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Multiple Choice Questions
4.1. In the investigation of allergy, which of the following statements are
correct?
A. Mast cell tryptase is labile in serum and therefore not a useful
biomarker of mast cell activation
B. Measurement of total IgE is not useful
C. Component resolved diagnostics can have predictive value in
some food allergies
D. Skin testing is not affected by antihistamine medication
E. Some antidepressant medications can lead to false-negative
skin test results
Answer: B, C and E.
CD8 T cells, not CD4 T cells, kill infected cells directly through the
production of pore-forming molecules such as perforin and release of
enzymes triggering apoptosis of the target cell. CD8 T cells are particularly important in defence against viral infection. Th1 (T-helper) cells typically produce IL-2, IFN-γ and TNF-α, and support the development of
delayed-type hypersensitivity responses. T-regulatory cells (T regs) are a
subset of specialised CD4+ lymphocytes important in actively suppressing activation of other cells and preventing autoimmune disease. They
produce cytokines such as TGF-beta and IL-10. Th17 cells are pro-inammatory cells dened by their production of IL-17. They have a key
role in defence against extracellular bacteria and fungi. They also have a
role in the development of autoimmune disease.
Answer: C and E.
Mast cell tryptase is stable in serum, making it a particularly convenient biomarker of mast cell activation. A total IgE can be helpful in
atopic patients as a high total level can be associated with false-positive
specic IgEs. Component resolved diagnostics uses puried native or
recombinant allergens to detect specic IgE directed against individual
allergenic molecules. CRD can discriminate genuine sensitisation from
sensitisation due to cross reactivity and in some cases can be used in
risk stratication, having predictive value, such as in peanut and some
nut allergy. A number of medications, including certain antidepressant
classes, can have antihistamine properties and thereby interfere with skin
testing. Patients should be advised to discontinue interacting medications in advance of testing.
4.2. Which of the following are required for naïve T-cell activation?
A. Antigen processing by antigen-presenting cells
B. Antigenic peptide presentation by pattern recognition
receptors
C. HLA class 1 for CD4 T cells
D. Co-stimulatory molecules
E. Intracellular T-cell signalling
Answer: A, D and E.
Unlike B cells, which recognise native antigen, T cells require antigen
processing through professional antigen-presenting cells, with presentation of antigenic peptide by self-HLA molecules expressed at the APC
surface. The Ag–HLA complex then interacts with the T-cell receptor.
Pattern recognition receptors are expressed by phagocytic cells and
recognise pathogen-associated molecular patterns on invading microorg anisms. They do not present antigen to T cells. CD4 T cells recognise
antigenic peptide presented by HLA class II molecules; CD8 T cells recognise antigenic peptide presented by HLA class I molecules. A second
signal, known as co-stimulation, is required for naïve T-cell activation.
Downstream intracellular T cells signalling then drives T-cell proliferation.
4.3. Which of the following statements are correct regarding T-cell
populations?
4.4. In tumour immunology, which of the following statements are
correct?
A. NK cells have an important role in immune surveillance
B. Tumour cells reliably express HLA molecules to allow immune
recognition by T cells
C. Immune checkpoint blockade includes anti-CTLA and
anti-PD1 pathways
D. Autoimmune disease is a recognised complication of immune
checkpoint blockade in tumour therapy
E. Tumour progression occurs rapidly if immune checkpoint
blockade is withdrawn
Answer: A, C and D.
NK cells have an important role in tumour surveillance especially as
tumour cells lose their HLA expression, thereby allowing NK cell activation. Loss of HLA expression is one of the mechanisms by which tumours
can evade T-cell immunity. Immune checkpoint blockade includes antibodies directed at the normally downregulatory pathways, thereby allowing immune cells to be active against the tumour. Some patients maintain
the anti-tumour effect of immune checkpoint blockade despite treatment
withdrawal in the event of drug toxicity.
4.5. In the context of organ transplantation, which of the following
statements are correct?
A. The major complications are graft rejection, drug toxicity and
infection
B. HLA incompatibility does not have a bearing on transplant
outcome
C. Acute cellular rejection is predominantly mediated by activated
B cells
D. Post-transplantation, failure to control viral infections
associated with malignant transformation leads to an increased
risk of malignancy
E. Co-stimulatory blockade has no role in post-transplant immune
suppression
Answer: A and D.
A. CD4 T cells kill virally infected cells through production of pore-
forming molecules such as perforin and release of enzymes
triggering apoptosis of the target cell
B. CD8 T cells are important in defence against viral infection
C. Th1 (T-helper) cells typically produce IL-2, IFN γ and TNF-α
D. T regs are regulatory CD4 T cells that promote activation of
other cells and augment autoimmune disease
E. Th-17 cells are pro-inammatory cells that produce IL-17 and
have a key role in defence against extracellular bacteria and
fungi
The major complications of transplantation are graft rejection, drug
toxicity and infection consequent to immunosuppression. Solid organ
transplantation stimulates an aggressive immune response by the recipient, unless the transplant is between monozygotic twins. The most
important genetic determinant is the difference between donor and
recipient HLA proteins. The polymorphism of these proteins means that
donor HLA antigens are almost invariably recognised as foreign by the
recipient immune system, unless an active attempt has been made to
minimise incompatibility. Acute cellular rejection is mediated by activated
T lymphocytes and results in deterioration in graft function. The risk of
post-transplant malignancy arises because T-cell suppression results in
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failure to control viral infections associated with malignant transformation, e.g. lymphoma associated with Epstein–Barr virus, Kaposi's sarcoma associated with human herpesvirus 8 and skin tumours associated
with human papillomavirus. Co-stimulatory blockade, using the CTLA-4
fusion protein belatacept, with high afnity for CD80/86 on T cells, selectively inhibits T-cell activation and has a role in post-transplant immune
suppression.
H Campbell
DA McAllister
Population health and
5
epidemiology
Global burden of disease and underlying risk factors 88
Life expectancy 88
Global causes of death and disability 88
Risk factors underlying disease 88
Social determinants of health 89
The hierarchy of systems – from molecules to ecologies 89
The life course 89
Preventive medicine 89
Principles of screening and immunisation 90
Screening 90
Immunisation 91
Epidemiology 91
Understanding causes and effect 91
Mendelian randomisation 91
Health data/informatics 94
Management of epidemics 95
88 P OPU L ATI O N H EALTH AN D EP I DE M IOL O GY
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The UK Faculty of Public Heath denes public health as ‘The science
and art of promoting and protecting health and well-being, preventing
ill-health and prolonging life through the organised efforts of society’.
This recognises that there is a collective responsibility for the health of
the population which requires partnerships between government, health
services and other partners to promote and protect health and prevent
disease. Population health has been dened as ‘the health outcomes of
a group of individuals, including the distribution of such outcomes within
the group’. Medical doctors can play a role in all these efforts to improve
health both as part of their clinical work but also through supporting
broader actions to improve public health.
Global burden of disease and underlying risk
factors
The Global Burden of Disease (GBD) exercise was initiated by the World
Bank in 1992, with rst estimates appearing in the World Development
Report in 1993. Regular updated estimates have been published since
that time together with projections of future disease burden. The aim of
the exercise was to produce reliable and internally consistent estimates
of disease burden for all diseases and injuries and to assess their physiological, behavioural and social risk factors so that this information could
be made available to health workers, researchers and policy-makers.
The GBD exercise adopted the metric ‘disability life year’ or DALY to
describe population health. This combines information about premature mortality in a population (measured as Years of Life Lost from an
‘expected’ life expectancy) and years of life lived with disability (Years
of Life lived with Disability (YLD), which is weighted by a severity factor).
The International Classication of Disease (ICD) rules, which assign one
cause to each death, are followed. All estimates are presented by age
and sex groups and by regions of the world. Many countries now also
report their own national burden of disease data.
Life expectancy
Global life expectancy at birth increased from 61.7 years in 1980 to 73.0
years in 2017, an increase of about 0.3 years per calendar year. This
change is due to a substantial fall in child mortality (mainly due to common infections) partly offset by rises in mortality from adult conditions
such as diabetes and chronic kidney disease. Some areas have not
shown these increases in life expectancy in men, often due to war and
interpersonal violence.
Global causes of death and disability
Box 5.1 shows a ranked list of the major causes of global deaths in 2019.
Communicable, maternal, neonatal and nutritional causes accounted for
about one-quarter of deaths worldwide – down from about one-third in
1990. In contrast, deaths from non-communicable diseases are increasing in importance and now account for about two-thirds of all deaths
globally,: including about 18.5 million from cardiovascular disease (ischaemic heart disease and stroke), 10 million from cancer and about 4 million
from chronic respiratory diseases. The age standardised death rates for
most diseases globally are falling. However, despite this, the numbers of
deaths from many diseases are rising due to global population growth
and the change in age structure of the population to older ages and
this is placing an increasing burden on health systems. For a few conditions (e.g. HIV/AIDS, diabetes mellitus and chronic kidney disease)
age-standardised death rates continue to rise. Within this overall pattern,
signicant regional variations exist – for example, communicable, maternal, neonatal and nutritional causes still account for about two-thirds of
premature mortality in sub-Saharan Africa.
GBD also provides estimates of disability from disease (Box 5.2). This
has raised awareness of the importance of conditions like depression
and other common mental health conditions, low back and neck pain
5.1 Global causes of death – top 15 ranked causes 2019
[rank in 1990]
1. Cardiovascular disease [1]
2. Neoplasms [2]
3. Chronic respiratory [6]
4. Respiratory infections and TB [3]
5. Diabetes and CKD [10]
6. Digestive diseases [8]
7. Neurological disorders [15]
8. Maternal and neonatal [15]
9. Unintentional injuries [9]
10. Enteric infections [5]
11. Transport injuries [13]
12. Self harm and violence [12]
13. Other non-communicable diseases [11]
14. HIV/AIDS and STIs [17]
15. NTDs and malaria [14]
(CKD = chronic kidney disease; TB = tuberculosis; STIs = sexually transmitted infections;
NTDs = neglected tropical diseases)
From GBD 2019. https://vizhub.healthdata.org/gbd-compare/.
5.2 Global disability – top 15 ranked causes 2019 [rank in 1990]*
1. Musculoskeletal disorders [1]
2. Mental disorders [2]
3. Other non-communicable diseases [3]
4. Sense organ diseases [5]
5. Neurological disorders [4]
6. Diabetes and CKD [12]
7. Skin diseases [7]
8. Unintentional injuries [8]
9. Nutritional deciencies [6]
10. Cardiovascular diseases [11]
11. Chronic respiratory diseases [9]
12. Substance use [13]
13. Maternal and neonatal conditions [17]
14. Transport injuries [16]
15. Digestive diseases [15]
*By years of life lived with disability (YLD).
(CKD = chronic kidney disease)
From GBD 2019. https://vizhub.healthdata.org/gbd-compare/.
and other musculo-skeletal conditions, and asthma, which account for a
relatively large disease burden but relatively few deaths. This in turn has
resulted in greater health policy priority given to these conditions. Since
the policy focus in national health systems is increasingly on keeping
people healthy rather than only on reducing premature deaths it is important to have measures of these health outcomes.
It is important to recognise that although these estimates represent
the best overall picture of burden of disease globally, they are based on
limited and imperfect data. Nevertheless, the quality of data underlying
the estimates and the modelling processes are improving steadily over
time and provide an increasingly robust basis for evidence-based health
planning and priority setting.
Risk factors underlying disease
Box 5.3 shows a ranked list of the main risk factors underlying GBD
in 2019 and how this ranking has changed over the past 29 years. A
number of key insights have been identied in this, the most recent, GBD
exercise:
Socio-demographic development has been progressing steadily
since 1990 but it has increased faster in countries with the highest
socio-demographic development index and thus gaps have been
widening.
So ci al de te r mi na nts o f h ea lth 89
5.3 Global risk factors – top 10 ranked causes 2019 [rank in
1990]*
1. High blood pressure [7]
2. Smoking/second hand smoke exposure [5]
3. High fasting blood glucose [11]
4. Low birth weight [2]
5. High BMI [16]
6. Short gestation [3]
7. Ambient particulate matter pollution [13]
8. High LDL cholesterol [14]
9. Alcohol use [15]
10. Household air pollution [4]
*Risk factors ranked by % of burden of disease they cause.
(BMI = body mass index; LDL = low density lipoprotein)
From GBD 2019 Diseases and Injuries Collaborators. Global burden of 369 diseases and
injuries in 204 countries and territories, 1990–2019: a systematic analysis for the Global
Burden of Disease Study 2019. Lancet 2020; 396:1204–1222.
Health systems need to transform to be better able to respond to
the changing pattern of NCDs and disabilities.
The Millennium Development Goal (MDG) programme from 2000 to
2015 has led to faster progress in reducing deaths from maternal,
child and neonatal conditions/TB/HIV/malaria but this level of attention now needs to be directed at NCDs.
Public health is not giving sufcient priority to important global risk
factors which are increasing over time, such as high blood pressure,
high fasting glucose, high BMI, ambient particulate matter pollution
and drug and alcohol use.
There are many challenges resulting from the change in global
population pyramid structures, which have become inverted over
recent decades and now pose many health, nancial and political
challenges.
Social determinants of health
Health emerges from a highly complex interaction between a person’s
genetic background and environmental factors (aspects of the physical,
biological (microbes), built and social environments and also distant inuences such as the global ecosystem) (Fig. 5.1).
The hierarchy of systems – from molecules to
ecologies
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Fig. 5.1 Hierarchy of systems that inuence population health. Adapted from
an original model by Whitehead M, Dahlgren G. What can be done about inequalities
in health? Lancet 1991; 338:1059–1063
5.4 ‘Hierarchy of systems’ applied to ischaemic heart disease
Level in the hierarchy Example of effect
Molecular ApoB mutation causing hypercholesterolaemia
Cellular Foam cells accumulate in vessel wall
Tissue Atheroma and thrombosis of coronary artery
Organ Ischaemia and infarction of myocardium
System Cardiac failure
Person Limited exercise capacity, impact on employment
Family Passive smoking, diet
Community Shops and leisure opportunities
Population Prevalence of obesity
Society Policies on smoking, screening for risk factors
Ecology Agriculture inuencing fat content in diet
5
Inuences on health exist at many levels and extend beyond the individual
to include the family, community, population and ecology. Box 5.4 shows
an example of this for determinants of coronary heart disease and demonstrates the importance of considering not only the disease process in a
patient but also its context. Health care is not the only determinant – and is
usually not the major determinant – of health status in the population. The
concept of ‘global health’ recognises the global dimension of health problems, whether these be, for example, emerging or pandemic infections or
global economic inuences on health internationally.
The life course
The determinants of health operate over the whole lifespan. Values and
behaviours acquired during childhood and adolescence have a profound inuence on educational outcomes, job prospects and risk of
disease. These can have a strong inuence, for example, on whether
a young person takes up a damaging behaviour like smoking, risky
sexual activity and drug misuse. Inuences on health can even operate
before birth. Low birth weight can lead to higher risk hypertension and
type 2 diabetes in young adults and of cardiovascular disease in mid-
dle age. It has been suggested that under-nutrition during middle to
late gestation permanently ‘programmes’ cardiovascular and metabolic
responses.
This ‘life course’ perspective highlights the cumulative effect (through
each stage of life) on health of exposures to illness, adverse environmental conditions and behaviours that damage health.
Preventive medicine
The complexity of the interactions between physical, social and economic determinants of health means that successful prevention is often
difcult. Moreover, the life course perspective illustrates that it may be
necessary to intervene early in life or even before birth, to prevent important disease in later life. Successful prevention is likely to require many
interventions across the life course and at several levels in the hierarchy
of systems. The examples below illustrate this principle.
90 P OPU L ATI O N H EALTH AN D EP I DE M IOL O GY
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Alcohol
Alcohol use is an increasingly important risk factor underlying global burden of disease (see
related harm vary by place and time but include the falling price of alcohol
(in real terms), increased availability and cultural change fostering higher
levels of consumption. Public, professional and governmental concern
has now led to a minimum price being charged for a unit of alcohol,
tightening of licensing regulations and curtailment of some promotional
activity in many countries. However, even more aggressive public health
measures will be needed to reverse the levels of harm in the population.
The approach for individual patients suffering adverse effects of alcohol
is described on pages 892 and 1240.
Box 5.3). Reasons for increasing rates of alcohol-
Smoking
Smoking is also one of the top three risk factors underlying global burden of disease (see Box 5.3). It is responsible for a substantial majority of cases of lung cancer and chronic obstructive pulmonary disease
(COPD), and most smokers die either from these respiratory diseases or
from ischaemic heart disease. Smoking also causes cancers of the upper
respiratory and gastrointestinal tracts, pancreas, bladder and kidney, and
increases risks of peripheral vascular disease, stroke and peptic ulceration. Maternal smoking is an important cause of fetal growth retardation. Moreover, there is increasing evidence that passive (‘second hand’)
smoking has adverse effects on cardiovascular and respiratory health.
The decline in smoking rates in many high-income countries has been
achieved not only by warning people of the health risks but also increased
taxation of tobacco, banning of advertising, banning of smoking in public
places and support for smoking cessation to maintain a decline in smoking rates. However, smoking rates remain high in many poorer areas and
are increasing amongst young women. In many low-income countries,
tobacco companies have found new markets and rates are rising.
There is a complex hierarchy of systems that interact to cause smokers
to initiate and maintain their habit. At the molecular and cellular levels, nicotine acts on the nervous system to create dependence and acts to maintain the smoking habit. There are also strong inuences at the personal and
social level, such as young female smokers being motivated to ‘stay thin’
or ‘look cool’ and peer pressure. Other important inuences in the wider
environment include cigarette advertising, with the advertising budget of
the tobacco industry being much greater than that of health services.
Strategies to help individuals quit smoking (such as nicotine replacement
therapy, anti-smoking advice and behavioural support) are cost-effective
and form an important part of the overall anti-tobacco strategy.
Typically, with industrialisation, the pattern changes: low birth rates, low
death rates and longer life expectancy. Instead of infections, chronic
conditions such as heart disease dominate in an older population.
Adverse health consequences of excessive afuence are also becoming
apparent. Despite experiencing sustained economic growth for the last
50 years, people in many high-income countries are not growing any
happier and the litany of socioeconomic problems – crime, congestion,
inequality, mental health problems – persists.
Many countries are now experiencing a ‘double burden’. They have
large populations still living in poverty who are suffering from problems
such as diarrhoea and malnutrition, alongside afuent populations (often
in cities) who suffer from chronic illness such as diabetes and heart
disease.
Atmospheric pollution
Emissions from industry, power plants and motor vehicles of sulphur
oxides, nitrogen oxides, respirable particles and metals are severely polluting cities and towns in Asia, Africa, Latin America and Eastern Europe.
Increased death rates from respiratory and cardiovascular disease occur
in vulnerable adults, such as those with established respiratory disease
and older people, while children experience an increase in bronchitic
symptoms. Low-income countries also suffer high rates of respiratory
disease as a result of indoor pollution caused mainly by heating and
cooking combustion.
Carbon dioxide and global warming
Climate change is arguably the world’s most important environmental
health issue. A combination of increased production of carbon dioxide
and habitat destruction, both caused primarily by human activity, seems
to be the main cause. The temperature of the globe is rising, climate is
being affected, and if the trend continues, sea levels will rise and rainfall
patterns will be altered so that both droughts and oods will become
more common. These have already claimed millions of lives during the
past 20 years and have adversely affected the lives of many more. The
economic costs of property damage and the impact on agriculture, food
supplies and prosperity have also been substantial. The health impacts
of global warming will also include changes in the geographical range
of some vector-borne infectious diseases. Currently, politicians cannot agree on an effective framework of actions to tackle the problem.
Meanwhile, the industrialised world continues with lifestyles and levels of
waste that are beyond the planet’s ability to sustain.
Obesity
Obesity is an increasingly important risk factor underlying global burden
of disease (see Box 5.3). The weight distribution of almost the whole
population is shifting upwards – the slim are becoming less slim while the
overweight and obese are becoming more so. In the UK, this translates
into a 1-kilogram increase in weight per adult per year (on average over
the adult population). The current obesity epidemic cannot be explained
simply by individual behaviour and poor choice but also requires an
understanding of the obesogenic environment that encourages people
to eat more and exercise less. This includes the availability of cheap
and heavily marketed energy-rich foods, the increase in labour-saving
devices (e.g. elevators and remote controls) and the increase in passive
transport (cars as opposed to walking, cycling, or walking to public transport hubs). To combat the health impact of obesity, therefore, we need to
help those who are already obese but also develop strategies that impact
on the whole population and reverse the obesogenic environment.
Poverty and afuence
The adverse health and social consequences of poverty are well documented: high birth rates, high death rates and short life expectancy.
Principles of screening and immunisation
Screening
Screening is the application of a screening test to a large number of
asymptomatic people with the aim of reducing morbidity or mortality
from a disease. WHO have identied a set of (Wilson and Jungner) criteria to guide health systems in deciding when it is appropriate to implement screening programmes. The essential criteria are:
Is the disease an important public health problem?
Is there a suitable screening test available?
Is there a recognisable latent or early stage?
Is there effective treatment for the disease at this stage which
improves prognosis?
A suitable screening test is one that is cheap, acceptable, easy to
perform, safe and gives a valid result in terms of sensitivity and specicity. Screening programmes should always be evaluated in trials so that
robust evidence is provided in favour of their adoption. These evaluations
are prone to several biases – self-selection bias, lead-time bias and length
Epidem iolog y 91
bias – and these need to be accounted for in the analysis. Examples of
large-scale screening programmes in the UK include breast, colorectal
and cervical cancer national screening programmes ( https://www.gov.
uk/topic/population-screening-programmes) and a number of screening
tests carried out in pregnancy and in the newborn, such as the:
diabetic eye screening programme
fetal anomaly screening programme
infectious diseases in pregnancy screening programme
newborn and infant physical examination screening programme
newborn blood spot screening programme
newborn hearing screening programme
sickle-cell anaemia and thalassaemia screening programme.
These are illustrated in Figure 5.2
Problems with screening include:
over-diagnosis (of a disease that would not have come to clinical
attention on its own or would not have led to death)
false reassurance
diversion of resources from investments that could control the dis-
ease more cost-effectively.
Immunisation
Immunisation can confer immunity to specic infectious diseases and
be either passive (through injected antibodies, such as the monoclonal palivizimab against respiratory syncytial virus (RSV) infection given
to premature infants) or active (through administration of a vaccine).
Immunisation invokes antibody and/or cell-mediated immunity and
can lead to both short- and longer-term protection in the person who
is vaccinated. Immunisation has also been used to eradicate a disease such as occurred in the smallpox eradication programme and
is currently being targeted in the polio eradication programme. As
well as direct effects of vaccination a number of indirect effects can
occur – such as protection of individuals who are vaccinated through
altering disease transmission leading to ‘herd immunity’; or reduction
of antibiotic resistance through selective reduction of pneumococcal
serogroups that are associated with antibiotic resistance. The UK
immunisation schedule is described in detail and regularly updated in
the UK government publication ‘Immunisation against infectious disease’ (Green Book).
Epidemiology
Epidemiologists study disease in free-living humans, seeking to describe
patterns of health and disease and to understand how different exposures cause or prevent disease (Box 5.5). Chronic diseases and risk
factors (e.g. smoking, obesity etc.) are often described in terms of their
prevalence. A prevalence is simply a proportion, for example the prevalence of diabetes among people aged 80 and older in developed countries is around 10%.
Events such as deaths, hospitalisations and rst occurrences of a disease are described using incidence rates, so, for example, if there are
100 new cases of a disease in a single year in a population of 1000,
the incidence rate is 105 per 1000 person-years. The rate is 105 rather
than 100 because the denominator is person-time, the sum of the total
‘exposed’ time for the population, which in this example is 950 person-years. Person-time is the sum of the total ‘exposed’ time for the
population and in this example is 950 person-years. The reason the person-time is less than 1000 is that 100 people experienced the event.
These 100 people are assumed to have had an event, on average, halfway through the time-period, removing 100 ×0.5 person-years from the
exposure-time (as it is not possible to have a rst occurrence of a disease
twice).
A similar measure to the incidence rate is the cumulative incidence
or risk, which is the number of new cases as a proportion of the total
people at risk at the beginning of the exposure time. If in the example
above the same 1000 people were observed for a year (i.e. with no one
joining or leaving the group) then the one-year risk is 10% (100/1000).
The time-period should always be specied for risks.
These rates and proportions are used to describe how diseases (and
risk factors) vary according to time, person and place. Temporal variation may occur seasonally; for example, malaria occurs in the wet season but not the dry, or as longer-term ‘secular’ trends, e.g. malaria may
re-emerge due to drug resistance. Person comparisons include age, sex,
socio-economic status, employment, and lifestyle characteristics. Place
comparisons include the local environment (e.g. urban versus rural) and
international comparisons.
Understanding causes and effect
Epidemiological research complements that based on animal, cell and
tissue models, the ndings of which do not always translate to humans.
For example, only a minority of drug discoveries from laboratory research
are found to be effective when tested in people.
However, differentiating causes from mere non-causal associations is
a considerable challenge for epidemiology. This is because while laboratory researchers can directly manipulate conditions to isolate and understand causes, such approaches are impossible in free-living populations.
Epidemiologists have developed a different approach, based around a
number of study designs (Box 5.6). Of these, the clinical trial is closest to
the laboratory experiment. An early example of a clinical trial is shown in
Figure 5.3, along with ‘effect measures’ which are used to quantify the
difference in rates and risks.
In clinical trials, patients are usually randomly allocated to treatments
so that, on average, groups are similar apart from the intervention of
interest. Nevertheless, for any particular trial, especially a small trial, the
laws of probability mean that differences can and do occur by chance.
Poorly designed or executed trials can also limit comparability between
groups. Allocation may not truly be random (e.g. because of inadequate
concealment of the randomisation sequence), and there may be systematic differences (biases) in the way people allocated to different groups
are treated or studied.
Such biases also occur in observational epidemiological study
designs, such as cohort, case–control and cross-sectional studies (see
Box 5.6). These designs are also much more subject to the problem of
confounding than are randomised trials.
Confounding is where the relationship between an exposure and outcome of interest is confused by the presence of some other causal factor.
For example, coffee consumption may be associated with lung cancer
because smoking is commoner among coffee-drinkers. Here, smoking is
said to confound the association between coffee and lung cancer.
Despite these limitations, for most causes of diseases, randomised
controlled trials are not feasible because of ethical, or more often practical, considerations. Epidemiologists therefore seek to minimise bias
and confounding by good study design and analysis. Epidemiologists
subsequently make causal inferences by balancing the probability that
an observed association has been caused by chance, bias and/or confounding against the alternative probability that the relationship is causal.
This weighing-up requires an understanding of the frequency and importance of different sources of bias and confounding as well as the scientic rationale of the putative causal relationship. It was this approach,
collectively and over a number of years, that settled the fact that smoking
causes lung cancer, and, subsequently, heart disease.
Mendelian randomisation
Mendelian randomisation (MR) is a method to study whether the relationship between a (modiable) risk factor and a disease may be causal.
It uses genetic variation in a gene that inuences the level of the risk
factor under consideration and studies the impact of this variation on
5
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Pre-conception
Antenatal
Blood for syphilis, hepatitis B, HIV and
rubella susceptibility as early as possible,
or at any stage of the pregnancy,
including labour
Blood for haemoglobin,
group, rhesus and
antibodies as early as
possible, or as soon
as a woman arrives for
care, including labour
Re-offer screening for
infectious diseases if
initially declined
Newborn
For babies of hepatitis B-positive
mothers, give hepatitis B vaccination
± immunoglobulin within 24 hrs*
Blood for sickle cell
and thalassaemia
(quadruple test)
Commence
folic acid
Blood for T21,
T18 and T13
(combined test)
Blood for T21
Repeat
haemoglobin
and antibodies
Newborn
physical
examination
by 72 hrs
Newborn
hearing
screen
Infant physical
examination
at 6–8 weeks
Week
0
1
2
3
4
5
6
7
8
9
10
11
12
13
14
15
16
17
18
19
20
21
22
23
24
25
26
27
28
29
30
31
32
33
34
35
36
Birth
+1
+2
+3
+4
+5
+6
Women with type 1 or type 2
diabetes are offered diabetic
eye (DE) screening annually.
In pregnancy women with
type 1 or type 2 diabetes are
offered a DE screen when
they first present for care
Early pregnancy scan to
support T21, T18 and
T13 screening
Detailed ultrasound
scan for structural
abnormalities, including
T18 and T13
Give and discuss
newborn screening
information
Newborn blood spot screens
(ideally on day 5) for:
sickle cell disease (SCD),
cystic fibrosis (CF), congenital
hypothyroidism (CHT) and inherited
metabolic diseases (PKU, MCADD,
MSUD, IVA, GA1 and HCU)
Note that babies who missed
the screen can be tested up to
1 year (except CF offered up
to 8 weeks)
Follow-up DE screen for
women with type 1 or 2
diabetes found to have
diabetic retinopathy
Further DE screen for
women with type 1 or 2
diabetes
Give screening
information as
soon as possible
Key
Fetal anomaly (Down syndrome/T21,
Edwards syndrome/T18, Patau
syndrome/T13, and fetal anomaly
ultrasound)
Sickle cell and thalassaemia
Newborn and infant
physical examination
Newborn blood spot
Fig. 5.2 UK NHS pregnancy and newborn screening programmes. Antenatal and newborn screening timeline. *To stop mother-to-baby transmission of infection follow
up all infection screens in pregnancy that are positive: carry out paediatric assessment and follow-up of mothers who are found to be HIV-positive or had syphilis treatment in
pregnancy; and if mothers are found to be susceptible to rubella then offer the mother MMR vaccination postnatally and refer to GP for second dose. (GA1 = glutaric aciduria
type 1; HCU = homocystinuria; IVA = isovaleric acidaemia; MCADD = medium-chain acyl-CoA dehydrogenase deciency; MSUD = maple syrup urine deciency;
PKU = phenylketonuria) Based on Version 8.4, January 2019. Gateway Ref: 20144696 . www.gov.uk/phe/screening.
Newborn hearing
Infectious diseases
in pregnancy
Diabetic eye
Epidem iolog y 93
5.5 Calculation of risk using descriptive epidemiology
Prevalence
The ratio of the number of people with a longer-term disease or condition at a
specied time, to the number of people in the population who are at risk
Incidence
The number of events (new cases or episodes) occurring in the population at
risk during a dened period of time
Attributable risk
The difference between the risk (or incidence) of disease in exposed and non-
exposed populations
Attributable fraction
The ratio of the attributable risk to the incidence
Relative risk
The ratio of the risk (or incidence) in the exposed population to the risk (or
incidence) in the non-exposed population
5.6 Epidemiological study designs
Design Description Example
Clinical trial Enrols a sample from
a population and
compares outcomes
after randomly
allocating patients to an
The Medical Research
Council (MRC) streptomycin
trial – demonstrated
effectiveness of streptomycin
in tuberculosis
intervention
Cohort Enrols a sample from
a population and
compares outcomes
The Framingham Study –
identied risk factors for
cardiovascular disease
according to exposures
Case–control Enrols cases with an
outcome of interest and
controls without that
outcome, and compares
exposures between the
Doll and Hill’s study on
smoking and carcinoma
of the lung (BMJ 1950, 2)
demonstrated that smoking
caused lung cancer
groups
Cross-sectional Enrols a cross-section
(sample) of people
from the population of
interest. Obtains data on
exposures and outcomes
World Health Organization
Demographic and Health
Survey. Captures risk factor
data in a uniform way across
many countries
Enrolled 107 patients
with tuberculosis
Random allocation
Streptomycin
55 patients
Follow-up and count deaths
Events 4
Risk 7.3%
Odds 0.068
Effect measures
Risk ratio (relative risk, RR)
Absolute risk reduction (ARR)
Relative risk reduction (RRR)
Number needed to treat to prevent
one death (NNT= 1/ARR)
Bed rest
52 patients
Events 15
Risk 28.8%
Odds 0.224
Odds ratio (OR)
0.25
0.30
21.6%
74.8%
4.6
Fig. 5.3 An example of a clinical trial: streptomycin versus bed rest in
tuberculosis. Both prevalences and risks are, in fact, proportions and are therefore
frequently expressed as odds. The reasons for doing so are beyond the scope of
this text.
Population
Random allocation of alleles
Genotype group A
LDL-C lower LDL-C unchanged
Genotype group B
5
disease risk (see Fig. 5.4 for an example). The genetic variant (or multiple variants or genetic risk score) is used as an instrumental variable
under certain assumptions. MR investigates the effect of differences
in the risk factor level through the life course which have been determined by the genetic variants. This approach uses observational data
to test a proposed causal relationship and to estimate the size of effect.
Guidelines such as STROBE-MR have been published for the proper
conduct of these studies and analytic software packages are now available which contain a range of methods and tools. The selection of the
most appropriate method depends on the research question and the
data structure.
The MR approach requires very large sample sizes to have sufcient
power and very large databases of genetic and health data such as UK
Biobank are often used. MR can be conducted using either individual
level data or summary data from genome-wide association studies
(GWAS); and data from one study (one sample) or two studies with the
variant–risk factor association measure from one and the risk factor–
outcome association from the other (two sample).
CV event rate lower CV event rate unchanged
Fig. 5.4 Mendelian randomisation. An example showing comparison of a
conventional trial with a Mendelian randomisation study. (CV = cardiovascular;
LDL-C = low-density lipoprotein cholesterol) Adapted with permission from
Bennett DA, Holmes MV. Heart 2017; 103:1400–1407.
Correct interpretation of MR results is challenging and multiple analytic methods are often employed. This includes methods to detect and
adjust for pleiotropy (having more than one effect) which is a common
problem in data interpretation. The strength of the conclusions depends
on the degree to which instrumental variable assumptions are met and
the level of consistency of ndings across different methods. MR has
proven useful in both identifying new causal relationships or conrming
trial results but also in redirecting research interest away from relationships that have been shown not to be causal. MR can be considered
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INTERNATIONAL FORM OF MEDICAL CERTIFICATE OF CAUSE OF DEATH
Cause of death
I
Disease or condition directly
leading to death*
Antecedent causes
Morbid conditions, if any,
giving rise to the above cause,
stating the underlying
condition last
II
Other significant conditions
contributing to the death, but
not related to the disease or
condition causing it
*This does not mean the mode of dying, e.g. heart failure, respiratory failure.
It means the disease, injury, or complication that caused death.
(a)
due to (or as a consequence of)
(b)
due to (or as a consequence of)
(c)
due to (or as a consequence of)
(d)
Approximate
interval between
onset and death
I21.9
E78.0
J47
Fig. 5.5 Completed death certicate. International Classication of Diseases 10 (ICD-10) codes are appended in red. Based on World Health Organization, ICD-10, vol. 2.
Geneva: WHO; 1990. Form retrieved from https://commons.m.wikimedia.org/wiki/File:International_form_of_medical_certicate_of_cause_of_death.png.
to provide further evidence for or against a causal relationship but care
should be taken in interpretation of the size of the expected impact from
an intervention.
Health data/informatics
As patients pass through health and social care systems, data are
recorded concerning their family background, lifestyle and disease
states, which is of potential interest to health-care organisations seeking to deliver services, policy-makers concerned with improving health,
scientic researchers seeking to understand health, and also to pharmaceutical and other commercial organisations seeking to identify
markets.
There is a long tradition of maintaining health information systems.
In most countries, the registration of births and deaths is required by
law, and in the majority, the cause of death is also recorded (Fig. 5.5).
There are numerous challenges in ensuring such data are useful, especially for making comparisons across time and place. First, a system of
standard terminologies is needed, such as the World Health Organization
International Classication of diseases, which provides a list of diagnostic codes attempting to cover every diagnostic entity. Secondly, these
terms must be understood to refer to the same, or at least similar diseases in different places. Thirdly, access to diagnostic skill and facilities
is required, fourthly standard protocols for assigning clinical diagnoses
to ICD-10 codes are needed and fthly, robust quality control processes
are needed to maintain some level of data completeness and accuracy.
Many countries employ similar systems for hospitalisations, either to
allow recovery of healthcare utilisation costs, or to manage and plan services. Similar data are, however, rarely collected for community-based
healthcare. Nor are detailed data on health-care process generally
included in national data systems.
Consequently, there has been considerable interest in using data from
information technology systems used to deliver care – such as electronic
patient records, drug-dispensing databases, radiological software, and
clinical laboratory information systems.
Data from such systems are, of course, much less structured than
those obtained from vital registrations. Moreover, the completeness of
such data depends greatly on local patterns of healthcare utilisation as
well as how clinicians and others use IT systems within different settings.
As such, deriving useful unbiased information from such data is a considerable challenge.
Much of the discipline of health informatics is concerned with addressing this challenge. One approach has been to develop comprehensive
standard classication systems such as SNOMED-CT ‘a standardised,
multilingual vocabulary of terms relating to the care of the individual’
which has been designed for electronic health-care records. An alternative has been to use statistical methods such as natural language processing to automatically derive information from free text (such as culling
diagnoses from radiological reports), or to employ ‘machine learning’, in
which software algorithms are applied to data in order to derive useful
insights. Such approaches are suited to large, messy data where the
costs of systematisation would be prohibitive. It is likely that such innovations will over the coming years provide useful information to complement that obtained from more traditional health information systems.
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