Epidemiology, statistics and risk factors explained
Organisation • Animal tissues, organs and systems
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Key concepts
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Types of data and table construction
Data for disease studies appear as counts, rates or continuous measurements and must be recorded in clear frequency tables. The independent variable belongs in the first column and the dependent variable(s) follow; column headers include titles and units, and calculated means appear in the rightmost column . Tables use consistent decimal places or significant figures so numerical values match the stated precision and allow accurate calculation of incidence and risk ratios.
Choosing and drawing graphs
Bar charts compare categories using discrete x-axis labels; bars remain separated so categories stay distinct. Histograms display continuous data across intervals with touching bars to show ranges and frequency, suitable for disease measures such as age distributions or BMI groups . Line graphs trace continuous change over time and assist in showing trends in incidence at local, national or global scales.
Frequency diagrams, histograms and interpreting disease incidence
Frequency tables convert raw counts into grouped frequencies for histogram construction and calculation of incidence per population. Disease incidence converts between graphical and numerical forms by reading bar heights or bin frequencies and translating them into rates (cases per 1,000 or 100,000 population) for comparison between groups. Clear axis labels and intervals prevent misreading of incidence and support accurate extraction of numerical values from graphs .
Translating between graphical and numerical forms
Graphical elements such as bar height, histogram bin frequency or point coordinates correspond to numerical values in tables; converting between them involves reading axis scales and applying unit conversions where needed. Relative risk or percentage change derives from numerical values read from graphs, enabling comparisons of disease rates between exposed and unexposed groups. Accurate translation requires attention to axis units and consistent population denominators.
Scatter diagrams and correlation
Scatter diagrams plot paired numerical variables to reveal trends and the strength and direction of correlation: positive, negative or none. Trend lines and clustering of points indicate correlation strength but do not establish causation; confounding variables and spurious correlations require further testing and experimental or longitudinal evidence to establish cause-and-effect relationships . In epidemiology, scatter plots assist in identifying candidate risk factors that merit deeper investigation.
Principles of sampling in epidemiology
Sampling selects a subset of a population to estimate parameters for the whole population; representativeness is essential to avoid sampling bias. Random sampling gives each potential sample an equal chance of selection and larger sample sizes reduce random error; non-random or small samples introduce bias and limit the validity of incidence estimates . Systematic and stratified sampling methods can improve representativeness for subgroups and reduce confounding when comparing risk by age, sex or socioeconomic status.
Obesity as a risk factor for Type 2 diabetes
Excess body fat and lack of physical activity increase insulin resistance so body cells respond less effectively to insulin, raising blood glucose and increasing the incidence of Type 2 diabetes. Population-level increases in obesity correspond with higher Type 2 diabetes rates, making body mass index distributions and obesity prevalence key epidemiological metrics when assessing diabetes risk . Intervention studies and longitudinal data strengthen causal inference beyond observed correlations.
Lifestyle risk factors: diet, alcohol and smoking
Dietary patterns high in energy-dense foods and saturated fats increase risks of obesity and cardiovascular disease; excessive alcohol consumption damages the liver and brain and raises the risk of liver cirrhosis and other non-communicable diseases; smoking increases the risk of lung disease and multiple cancers fileciteturn0file4turn0file16. National and global incidence of non-communicable diseases reflects population-level exposure to these lifestyle factors; effective interpretation requires comparison of local prevalence, national surveillance data and international trends to identify where prevention is most needed.
Key notes
Important points to keep in mind