Health and disease

Health is a state of physical and mental well-being. It is more than simply being free from disease: a person may have no diagnosed disease but still experience poor physical or mental well-being.

Diseases can cause ill health. They can be grouped according to whether they spread as infections:

• Communicable diseases are caused by pathogens, such as bacteria or viruses, and can spread from one organism to another.
• Non-communicable diseases do not spread from one organism to another as infections.

Both types of disease can affect a person’s health.

Other influences on health

Health is also affected by factors beyond disease. Diet influences whether the body receives the nutrients it needs to function properly. Stress can affect mental well-being and may also disturb physical functions, such as sleep.

Life situations, such as financial difficulties or problems at home, can affect both physical and mental well-being. For example, they may cause stress or make it harder for a person to obtain a healthy diet.

Interactions between diseases

One disease or form of ill health can change the likelihood that another will develop. These interactions are possible outcomes, not inevitable ones.

A defect in the immune system can weaken the body’s defence against pathogens. This makes the person more susceptible to infectious diseases, because pathogens are less effectively controlled or removed.

Some viruses live inside cells and can cause changes that lead to uncontrolled cell division. This can trigger cancer, although a viral infection does not always do so.

An immune reaction initially caused by a pathogen can also trigger an allergy. This may appear as a skin rash or asthma.

Severe physical ill health can affect mental health. For example, the effects of a serious illness on daily life can contribute to depression or another mental illness, although not everyone with severe physical ill health develops one.

Sampling scientific data

Scientists often cannot measure every member of the group they want to study. Instead, they collect data from a sample: a subset of that group. The whole group about which they want information is the population.

The sample must be chosen with the population in mind. For example, if a study aims to estimate how common a condition is among adults in a town, its population is the adults living in that town. The sample should be as representative as possible: it should reflect the relevant characteristics of those adults, rather than overrepresenting some groups and leaving others out.

Selecting a sample

A selection method is biased if it systematically favours some members of the population over others. For example, selecting only adults who attend a gym could give a misleading picture of all adults in the town.

Random selection can reduce selection bias by giving members of the population a chance of being chosen without the researcher favouring particular individuals. It does not guarantee that the sample will perfectly represent the population.

Sample size and conclusions

A larger sample generally reduces the effect of random variation: unusual results from a few individuals have less influence on the overall result. However, increasing the sample size does not by itself correct a biased selection method. A large sample taken only from gym attendees could still misrepresent the town’s adults.

A conclusion drawn from a sample is therefore an estimate of what is true for the population. Even a large, randomly selected sample may not describe the whole population exactly.

Representing disease incidence and frequency data

Disease incidence describes the new cases of a disease occurring in a specified population over a specified period. The population and period are needed to understand what the figure describes. For example, 30 new cases among residents of a town during 2024 is an incidence count for that population and year.

Incidence data can be given as numbers or shown on a graph. To move between these forms, check the axis labels, units and scale. A graph may show a count of new cases or a rate stated using a population unit. Read or plot each value against the correct period and population unit.

Frequency tables and bar charts

A frequency table records how many observations fall in each category or interval. For example, a table of new cases among the town’s residents might show:

[Use the table shape for tabular content — GFM tables are disabled in md_block]YearFrequency of new cases202312202430

A frequency diagram shows frequencies graphically. One form is a bar chart, which uses separate bars for categories or discrete values. To plot the table above, put the years on one axis and the number of new cases on the other. Draw separate bars with heights of 12 and 30. To read the chart back into a table, match each bar to its year and use the frequency scale to find its value.

Histograms for grouped continuous data

A histogram shows frequencies for continuous data grouped into intervals, such as age intervals. The bars touch because the intervals are adjacent. Each bar covers its interval along the horizontal axis.

In a histogram, bar area represents frequency. When intervals have different widths, bar height alone cannot be used to compare frequencies. Instead, the vertical axis shows frequency density:

For example, suppose the 30 new cases in 2024 included 20 people aged from 0 to under 10 years and 10 people aged from 10 to under 30 years. The first interval has width 10 years, so its frequency density is $$20 \div 10 = 2$$. The second has width 20 years, so its frequency density is $$10 \div 20 = 0.5$$.

To draw the histogram, plot each interval along the age axis and give its bar the calculated height. The areas are $$10 \times 2 = 20$$ and $$20 \times 0.5 = 10$$, matching the frequencies. To read a histogram, multiply each bar’s width by its frequency density to recover its frequency, using the labelled scales on both axes.