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Week Overview
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Epidemiology is the science of figuring out who gets sick, how often, and why. Think of epidemiologists as detectives for disease: they collect data on health problems in populations to find patterns and causes, and then use that information to prevent future illness and injury. This week focuses on the mathematical tools epidemiologists use to measure disease in populations, and these tools are essential for understanding public health research that paramedics encounter throughout their careers.
Two of the most fundamental measures are incidence and prevalence. Incidence tells you how many new cases of a disease appear in a given time period (the rate at which people are getting sick), while prevalence tells you how many people currently have the disease at a specific point or during a period (the total burden of disease). To calculate incidence rates accurately, epidemiologists use the concept of person-time, which accounts for the fact that different people may be followed for different lengths of time in a study. For example, if 3 cases occur among people followed for a combined total of 35 person-years, the incidence rate is 3 divided by 35, or about 8.57 cases per 100 person-years. Understanding these calculations helps you make sense of health statistics you will encounter in clinical guidelines and research.
To determine whether an exposure (like working night shifts or smoking) increases the risk of disease, epidemiologists calculate measures of association such as the risk ratio and the odds ratio. A risk ratio (RR) compares the probability of disease in an exposed group to the probability in an unexposed group. An RR of 2.0 means the exposed group has twice the risk. The odds ratio (OR) is similar but compares odds rather than risks, and is particularly useful in case-control studies where you start with people who already have the disease and look backwards at their exposures. Different study designs, including cohort studies (following groups forward in time), case-control studies (comparing cases with controls looking back), cross-sectional studies (a snapshot at one point in time), and randomised controlled trials (experimental studies), each have strengths and limitations for answering different types of health questions.
Epidemiology is the foundational discipline of public health, concerned with the study of the distribution and determinants of health-related states and events in specified populations, and the application of this study to the control of health problems (Last, 2001). This week introduces the core quantitative measures used to characterise disease occurrence and to evaluate the strength and direction of associations between exposures and health outcomes.
Measures of disease frequency are categorised into incidence measures and prevalence measures. Incidence captures the occurrence of new cases over a defined period and can be expressed as cumulative incidence (risk) when person-time is not explicitly measured, or as an incidence rate (incidence density) when the denominator is person-time at risk. Person-time denominators are essential when follow-up periods vary across participants or when individuals exit the study at different times due to disease onset, death, or loss to follow-up. For example, if 40 individuals develop hypertension at year 2 and 60 at year 4 in a cohort of 800, the person-years contributed are calculated by multiplying each subgroup by their duration of follow-up (e.g., 40 x 2 + 60 x 4 + 680 x 8 = 6,720 person-years). Prevalence, by contrast, measures the proportion of a population affected by disease at a specific point in time (point prevalence) or during a defined period (period prevalence), and includes both new and pre-existing cases. It is influenced by both the incidence of disease and the duration of the condition.
Measures of association quantify the relationship between exposures and outcomes. The risk ratio (RR), also termed relative risk, is the ratio of the cumulative incidence (risk) in the exposed group to that in the unexposed group, and is calculated in cohort studies. The incidence rate ratio compares incidence rates (per person-time) between groups. The odds ratio (OR) compares the odds of exposure among cases to the odds of exposure among controls, and is the primary measure of association in case-control studies. An OR of 1.0 indicates no association, values greater than 1.0 suggest a positive association, and values less than 1.0 suggest a protective effect. Each study design has distinct methodological characteristics: cohort studies follow exposed and unexposed groups prospectively (or retrospectively) to compare disease incidence; case-control studies start with diseased (cases) and non-diseased (controls) individuals and look retrospectively at exposure history; cross-sectional studies assess exposure and disease status simultaneously in a population sample; and randomised controlled trials (RCTs) randomly allocate participants to exposure or control conditions, providing the strongest evidence for causal inference. Understanding these measures and designs enables paramedics and public health practitioners to critically appraise research evidence, interpret clinical guidelines, and contribute to evidence-based practice.
Key Terms
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Incidence
The number of new cases of a disease arising in a population over a specified period of time.
Prevalence
The proportion of a population found to have a condition at a specific point in time or over a specified period.
Risk Ratio (Relative Risk)
The ratio of the risk of disease in the exposed group to the risk in the unexposed group.
Odds Ratio
The ratio of the odds of exposure among cases compared to the odds of exposure among controls.
Cohort Study
An observational study design that follows a group of people over time to compare disease outcomes between exposed and unexposed individuals.
Case-Control Study
A study that compares people with a disease (cases) to people without the disease (controls) to identify past exposures.
Confounding
A distortion of the true association between an exposure and outcome caused by a third variable related to both.
Bias
A systematic error in study design, data collection, or analysis that leads to incorrect estimates of the association between exposure and outcome.
Sensitivity
The ability of a test to correctly identify individuals who have the disease (true positive rate).
Specificity
The ability of a test to correctly identify individuals who do not have the disease (true negative rate).
Randomised Controlled Trial (RCT)
An experimental study design in which participants are randomly assigned to receive either the intervention or a control, to measure the effect of the intervention.
Cross-Sectional Study
A study that measures both exposure and outcome at a single point in time in a defined population.
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Paramedicine Case Study
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