Lung disease risk-factor data

Data on new cases of lung disease can show whether people with different exposures have different risks of developing disease. For example, researchers might compare incidence among people who smoke and those who do not, or among populations exposed to different levels of pollution.

Incidence concerns new cases arising in a defined population over a stated period. It is different from the total number of people living with the disease, which includes both existing and new cases.

Comparing lung disease incidence

A larger number of new cases does not necessarily mean a greater risk: a larger population might have more cases simply because it contains more people. Comparisons must take account of the population sizes and the periods over which cases were recorded.

The populations and exposure measures must also be comparable. For example, a comparison involving smoking needs to consider how smoking was measured, such as whether people smoked at all or how much they smoked. Pollution comparisons likewise depend on how exposure was measured. Differences in these measures can affect what a comparison shows.

Association and causation

If higher exposure tends to occur alongside higher lung disease incidence, the variables are correlated or associated. This pattern alone does not establish that the exposure caused the disease.

Another factor may influence both exposure and disease incidence. Such a factor is a confounder. For example, if populations with different pollution exposures also differ in their smoking habits, smoking could influence the difference in lung disease incidence. The observed difference cannot automatically be attributed to pollution.

A causal interpretation therefore depends on more than finding a correlation. Researchers consider the study design, including how exposure and disease were measured and whether possible confounders were accounted for. They also consider the strength of the association and whether the findings are consistent across studies. Evidence from well-designed studies that address confounding and produce consistent findings provides a stronger basis for judging causation.

Reducing coronary heart disease risk

The coronary arteries supply the heart muscle with blood. In coronary heart disease, fatty deposits build up in these arteries, narrowing them and reducing blood flow to the heart muscle. Diet and physical activity can lower the risk of this narrowing developing.

Diet and coronary heart disease risk

A healthy diet helps maintain a healthy body mass and reduces conditions that put extra strain on the heart and blood vessels. In particular, limiting excessive intake of saturated fat can help reduce the build-up of fatty deposits in coronary arteries.

Exercise and coronary heart disease risk

Regular exercise improves fitness and helps the heart and circulatory system work efficiently. It can also help maintain a healthy body mass and reduce problems associated with inactivity. Together, these effects can lower the risk of coronary heart disease.

These lifestyle choices reduce risk rather than eliminate it: they make coronary heart disease less likely, but they do not guarantee that it will be prevented.

Risk factors and multifactorial disease

A risk factor is something associated with an increased chance or rate of a disease. Risk factors can include aspects of lifestyle, such as diet or smoking, or substances present in the body or the environment.

A risk factor changes probability, not certainty. An exposed person may never develop the disease, while someone without that exposure may still develop it.

Association and cause

If a disease occurs more often in people exposed to a factor, the factor is associated with that disease. This association alone does not prove that the factor causes the disease: other differences between the groups may help explain the pattern.

For some risk factors, evidence also shows a causal mechanism: a way in which the factor produces changes that can lead to disease. For example, substances in tobacco smoke can damage cells in the lungs, contributing to the development of lung cancer. Other factors are associated with disease even though a cause-and-effect mechanism has not been proven.

Several factors and disease incidence

Many diseases are multifactorial: they result from the interaction of several factors rather than one single cause. For example, more than one lifestyle or environmental risk may contribute to a person’s chance of developing a disease. This helps explain why the same exposure does not have the same outcome for everyone.

Disease incidence describes the rate of new cases in a population over a period of time. Incidence can differ between local, national and global populations partly because their exposure to lifestyle and environmental risks differs. Such differences do not, by themselves, prove which factor caused the difference in incidence.

Costs of non-communicable disease

Non-communicable diseases are diseases that cannot be passed from one person to another. Their costs include both human costs, such as pain and loss of quality of life, and financial costs, such as paying for treatment and care.

Costs to individuals and families

A person with a non-communicable disease may experience pain, disability or a reduced quality of life. Some diseases can also cause premature death. Treatment and care may create costs for the individual or their family. If the person cannot work, or a family member reduces their working hours to provide care, the family may also lose income.

Providing support can place demands on family members’ time and wellbeing, especially when care is needed over a long period.

Costs to communities and wider society

A local community may need to provide more care and support for people living with non-communicable diseases. Illness or premature death can also mean that people are less able to contribute to work and community life.

At national and global levels, treatment and long-term care place demands on health services and their funding. Illness can reduce economic productivity when people are unable to work as much, while premature death can cut short their working lives.

The size of these human and financial costs depends on the disease, how frequently it occurs and the resources available to prevent and treat it. For example, a disease that requires long-term care can create continuing demands on families and health services.

Smoking and cardiovascular disease

Smoking increases the risk of cardiovascular disease by damaging blood vessels and placing extra strain on the heart and circulation. Chemicals in tobacco smoke also reduce the amount of oxygen that blood can carry.

Damage to arteries

Chemicals in tobacco smoke damage the inner lining of blood vessels. This encourages fatty deposits to build up in artery walls, narrowing the space through which blood can flow.

If arteries supplying the heart become narrowed, less blood reaches the heart muscle. The heart muscle then receives less oxygen, increasing the risk of a heart attack.

Nicotine and carbon monoxide

Nicotine increases heart rate and blood pressure. The heart beats faster and blood pushes more strongly against vessel walls, placing additional strain on the cardiovascular system.

Carbon monoxide reduces the blood’s capacity to carry oxygen. As a result, less oxygen reaches body tissues, including the heart muscle.

Obesity and Type 2 diabetes risk

Obesity is a risk factor for Type 2 diabetes: it increases the chance of developing the condition, but does not make it inevitable.

Insulin is a hormone that signals body cells to take up glucose from the blood. Excess body fat is associated with reduced sensitivity of cells to insulin. This means that the cells respond less effectively to insulin’s signal and take up less glucose.

The body may produce more insulin to compensate. However, if it cannot produce enough to overcome the reduced response, blood glucose concentration can remain too high. This is a feature of Type 2 diabetes.

Other factors also contribute to the risk of Type 2 diabetes. A person with obesity will not necessarily develop it, and a person without obesity can still develop it.

Alcohol and liver disease

The liver processes alcohol carried in the blood, but drinking excessive amounts can damage liver cells. If this damage happens repeatedly, the liver can become inflamed and scarred. Extensive scarring of the liver is called cirrhosis.

As scarring becomes extensive, the damaged liver is less able to carry out its normal functions. These include processing substances in the blood, so severe liver damage reduces the liver’s ability to perform this task.

Alcohol and brain function

The brain and nervous system communicate using signals that help a person make decisions, control movements and respond to their surroundings. Alcohol disrupts this normal communication, so the brain may process information and control responses less effectively.

During intoxication, these effects can impair:

• Judgement: a person may make less careful decisions or have difficulty assessing risk.
• Coordination: movements may become less controlled, affecting balance and accuracy.
• Reaction time: responses to a stimulus may take longer.

These effects can occur while a person is intoxicated. Prolonged excessive alcohol consumption can also damage the brain, affecting functions such as memory.

Smoking-related lung disease

Tobacco smoke can damage the airways and the tiny air sacs in the lungs. This makes the lungs more vulnerable to infection and can reduce their ability to exchange gases. Smoking also increases the risk of lung cancer.

Damage to the airways

The airways produce mucus, which traps particles and microorganisms. Tiny hair-like structures called cilia normally move the mucus out of the airways. Tobacco smoke damages the cilia, so mucus is not cleared effectively and can accumulate. Microorganisms can remain in the airways, increasing the risk of infection.

Damage to alveoli

Alveoli are tiny air sacs where oxygen enters the blood and carbon dioxide leaves it. Smoking can damage and break down their walls. This reduces the surface area available for gas exchange, so less oxygen can enter the blood. This damage occurs in emphysema.

Smoking and lung cancer

Tobacco smoke contains carcinogens: substances that can cause cancer. These can damage the DNA in lung cells. Some changes to DNA can cause cells to divide uncontrollably, leading to lung cancer.

Smoking and alcohol during pregnancy

The placenta is the organ through which substances pass between a pregnant person's blood and their developing baby. Oxygen and nutrients cross the placenta, but harmful substances from tobacco smoke and alcohol can cross it too.

Smoking during pregnancy

Carbon monoxide from tobacco smoke reduces the amount of oxygen that blood can carry to the developing baby. Nicotine can narrow blood vessels, reducing blood flow to the placenta. Both effects can reduce the oxygen available to the baby for growth and development.

Smoking during pregnancy increases the risk of restricted fetal growth, meaning that the baby grows more slowly than expected. It also increases the risks of low birth weight and premature birth, when the baby is born too early.

Alcohol during pregnancy

Alcohol can pass through the placenta to the developing baby and disrupt development, including development of the brain. Its effects can last after birth and may affect growth, learning and behaviour.

The range of lasting effects caused by alcohol exposure before birth is described as fetal alcohol spectrum disorders (FASD). The effects can vary between individuals.

Carcinogens and cancer risk

A carcinogen is an agent that increases the risk of cancer. Some carcinogens damage DNA, causing changes in its information called mutations.

DNA contains information that helps control cell division. If mutations affect this control, cells may divide uncontrollably and form a cancer. Ionising radiation is a physical carcinogen that can cause DNA damage and mutations.

Tobacco smoke contains carcinogenic substances. Exposure to a carcinogen increases the risk of cancer; it does not guarantee that cancer will develop.

Sampling in risk-factor studies

A risk-factor study often cannot investigate every person in the population of interest. Instead, researchers study a sample: a subset of that population. They use the results to look for patterns that may also apply to the wider population.

Sample size and chance variation

A sample needs to be large enough that results are not dominated by chance differences between the people selected. For example, a small sample might contain an unusually high proportion of people with a particular exposure or disease simply by chance. A larger sample reduces the effect of this chance variation, although it does not eliminate it.

Selection and representation

The people selected should represent the population being studied. If they differ from it systematically, estimates of how common an exposure or disease is may be misleading. For example, a sample drawn only from one age group may give a misleading estimate for a population containing several age groups.

Random selection gives people in the population a chance of being selected and can reduce selection bias: systematic differences caused by how the sample is chosen. However, even a randomly selected sample may, by chance, differ from the wider population. A large sample is not necessarily representative if the way it was selected favours certain people.

When interpreting a risk-factor study, consider both the sample size and how participants were selected. These affect how confidently its findings can be applied to the wider population.

Presenting and interpreting risk-factor data

Risk-factor data show how a measure of health, such as the percentage of people developing a disease, varies between groups or over time. The data may be given as numbers in a table or displayed in a chart or graph.

Reading and comparing risk-factor data

First identify what each variable measures. For example, one variable might describe a risk factor, while another gives the percentage of people who developed a disease. Read the headings, units and scales before extracting the values needed for a comparison.

Check that the values use compatible measures, populations and time periods. A percentage developing a disease over one year, for example, should not be directly compared with a percentage developing it over ten years.

Suppose a fictional data set records the percentage of initially disease-free adults who develop a disease over ten years:

• Group with lower exposure to a risk factor: $$10%$$
• Group with higher exposure to the risk factor: $$20%$$

The recorded percentage is higher in the higher-exposure group by 10 percentage points. This describes an association in the data; the comparison alone does not establish that the risk factor caused the disease.

Converting between numerical data and graphs

Choose a display that suits the variables. A bar chart can compare separate groups, while a line graph can show how a measure changes across a continuous variable such as time. Label the axes, include units where needed, and choose a scale that covers the data with equal, readable intervals. Plot each value accurately.

For the fictional data above, a bar chart could have the two exposure groups on the horizontal axis and the percentage developing the disease on the vertical axis. A vertical scale from $$0%$$ to $$20%$$ in intervals of $$5$$ percentage points would allow the bars to be plotted at $$10%$$ and $$20%$$.

To convert a graph back into numerical data, locate each group or point and read its value against the labelled scale. Record the value with its unit or percentage. If a point falls between scale markings, estimate its value rather than treating the reading as exact.

Scatter diagrams and correlation

A scatter diagram shows the relationship between two variables. One variable is plotted on each axis. Each point represents a pair of values: its position shows the value of one variable and the corresponding value of the other.

For example, if each person has a measurement for both variables, each person contributes one point. Looking at the pattern of all the points shows whether the variables tend to change together.

Reading the pattern

• Positive correlation: as one variable increases, the other tends to increase.
• Negative correlation: as one variable increases, the other tends to decrease.
• Little or no correlation: there is no clear pattern in how the variables change together.

The closer the points lie to a trend, the stronger the association appears. If the points are widely scattered around the trend, the association is weaker. An outlier is a point that lies unusually far from the general pattern; it is not necessarily a mistake.

Correlation and causation

A scatter diagram can show an association between a risk factor and a disease, but it cannot show that the risk factor caused the disease. Another factor could affect both variables, or the observed association could arise by chance. The pattern alone is therefore not enough to establish causation.