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.

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.

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.

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.

Lifestyle and genetic cancer risk

A risk factor is something that increases the likelihood of developing a disease. It does not mean that the disease is certain to occur. Both lifestyle and inherited genetic variants can affect a person’s risk of developing some cancers.

Lifestyle and cancer risk

Lifestyle factors are patterns of behaviour that can change a person’s exposure to carcinogens: agents that can cause cancer. Carcinogens can damage DNA, increasing the chance of changes that disrupt the control of cell growth and division.

Different cancers are associated with different risk factors. For example, smoking increases the risk of lung cancer because tobacco smoke contains carcinogens. Frequent exposure to ultraviolet radiation from the Sun increases the risk of skin cancer.

Inherited genetic susceptibility

A person can inherit a genetic variant that increases their susceptibility to certain cancers. Some inherited variants affect processes that normally control cell growth and division, making it more likely that cells will grow and divide uncontrollably.

Inheriting susceptibility is not the same as inheriting cancer. The variant increases risk, but cancer is not certain to develop. Lifestyle and inherited genetic factors can both contribute to a person’s risk.