CASRAI Dictionary

Tag: epidemiology

  • Incidence vs Prevalence: Key Epidemiological Measures

    Incidence and prevalence are two foundational measures in epidemiology that answer different questions about how a condition affects a population. Incidence measures how many new cases of a condition arise in a population over a period of time, capturing the rate at which cases occur. Prevalence measures how many cases exist in a population at a point in time or over a defined period, capturing the burden present. Confusing the two leads to serious misinterpretation, so the distinction is a methodological essential rather than a matter of terminology.

    Both measures rest on the same underlying ideas of a case, a population at risk, and a time reference, but they assemble those ingredients differently. Getting the definitions right is the first step to choosing the correct measure for a given research or planning question.

    How incidence is calculated

    Incidence quantifies new cases relative to a population at risk over time, and it comes in two common forms. Cumulative incidence divides the number of new cases by the number of people at risk at the start of the period, giving a proportion that approximates the average risk of developing the condition over that period. Incidence rate, sometimes called incidence density, divides new cases by the total person-time at risk, which accounts for individuals being observed for different lengths of time and for people entering or leaving the population. Both forms require defining the population at risk precisely, excluding those who already have the condition, and stating the observation window clearly. The person-time approach is particularly useful in studies where people are followed for varying durations, because each individual contributes time at risk only for as long as they are observed and remain capable of developing the condition. Expressing the result, for example, as cases per 1,000 person-years makes the time dimension explicit and allows fair comparison between groups followed for different lengths of time.

    How prevalence is calculated

    Prevalence divides the number of existing cases by the total population, counting everyone who currently has the condition regardless of when it began. Point prevalence refers to a single point in time, answering how many cases exist right now, while period prevalence covers a defined interval and counts anyone who had the condition at any time during that interval. Because prevalence includes both long-standing and recently arisen cases, it reflects the accumulated stock of cases in the population rather than the flow of new ones.

    Incidence and prevalence compared

    Feature Incidence Prevalence
    What it counts New cases arising Existing cases present
    Time element Over a period (flow) At a point or period (stock)
    Denominator Population at risk or person-time Total population
    Best for Studying causes and risk Describing burden and planning

    Data sources and case ascertainment

    Both measures depend on how reliably cases are identified, a process known as case ascertainment. Cases may be captured through disease registers, routine health records, notification systems for certain conditions, or purpose-designed studies, and each source has its own coverage and biases. Incidence is especially sensitive to the timing and completeness of detection, because it counts new cases within a defined window; if detection is delayed or incomplete, new cases may be missed or assigned to the wrong period. Prevalence is sensitive to whether long-standing cases remain on the source from which counts are drawn. For both measures, a clearly stated and consistently applied case definition is essential, because changes in definition or in how actively cases are sought can move the numbers independently of any real change. This is why epidemiological reporting standards emphasise documenting the data source, the case definition and the ascertainment method together with the measure itself.

    The relationship between them

    Incidence and prevalence are linked, and the link is intuitive once framed as flow and stock. In broad terms, prevalence reflects both how quickly new cases arise (incidence) and how long cases persist (duration). When a condition lasts a long time, even a modest incidence can produce a high prevalence, because cases accumulate faster than they leave the population through recovery or death. When cases resolve quickly, prevalence stays low even if incidence is high, because cases flow out almost as fast as they arrive. This conceptual relationship explains why the two measures can move in different directions: a change that shortens how long cases persist can lower prevalence even while incidence is unchanged or rising. For that reason the two measures must never be used interchangeably.

    Common pitfalls in interpretation

    Because the two measures are so often reported side by side, several errors recur. Treating prevalence as if it indicated risk is a frequent mistake: a high prevalence may reflect that cases persist for a long time rather than that the condition arises frequently, so prevalence alone says little about the chance of developing a condition. Comparing an incidence figure from one study with a prevalence figure from another, as though they were the same quantity, produces meaningless conclusions. A further pitfall is failing to define the population at risk consistently; if people who already have the condition are not excluded from the incidence denominator, the calculated incidence will be understated. Finally, both measures are sensitive to how a case is defined and detected: broadening the case definition or improving detection can raise measured incidence or prevalence without any real change in the underlying occurrence, which is why the case definition should always be reported alongside the figure.

    When to use which

    Use incidence when studying the development of a condition, investigating its causes, or evaluating risk, because it captures the flow of new cases and is the natural measure for cause-and-effect questions. Use prevalence when describing the existing burden, planning services and resources, or characterising how widespread a condition is at a moment in time, because it reflects the total caseload a system must manage. Reporting which measure was used, together with its denominator and time frame, is critical, and reporting guidelines such as STROBE prompt exactly this kind of clarity for observational studies.

    Both measures depend on accurate population denominators, which come from a census or population register, underscoring their place in research data infrastructure. The same denominators underpin death rates. Consistent terminology drawn from the CASRAI dictionary helps keep these definitions stable across studies, and authors can consult the guidance for authors when reporting them.

    Frequently asked questions

    Can incidence be higher than prevalence?

    It can, particularly for conditions that resolve quickly. Because prevalence reflects cases that persist, a condition with short duration may show high incidence but low prevalence, since new cases leave the population almost as fast as they arrive and do not accumulate.

    Why is the denominator different for each?

    Incidence uses the population at risk or person-time, because only those who can newly develop the condition are relevant to counting new cases. Prevalence uses the total population, because it counts all existing cases regardless of when they arose.

    Which measure should a study report?

    It depends on the question. Studies of causation and risk report incidence; studies of burden, planning and service provision report prevalence. The chosen measure, its denominator and its time frame should always be stated explicitly so readers can interpret it correctly.

  • The STROBE Statement for Observational Epidemiology

    The STROBE Statement, short for Strengthening the Reporting of Observational Studies in Epidemiology, is a reporting guideline that specifies the information an observational study should include so that readers can judge its validity and reuse its findings. STROBE is a checklist for authors, reviewers and editors; it does not dictate how a study should be designed or analysed, only what must be reported transparently once the work is done.

    Observational studies make up a large share of epidemiological evidence, yet they cannot randomise who is exposed to what. Their credibility therefore rests unusually heavily on the clarity of their reporting, and STROBE exists to make that clarity a shared, citable expectation rather than a matter of individual habit.

    What STROBE covers

    The STROBE checklist enumerates items spanning the standard sections of a research paper: title and abstract, introduction, methods, results and discussion. The methods items are central, asking authors to describe the study setting, eligibility criteria, the variables and how they were measured, the data sources, efforts to address potential sources of bias, how the study size was determined, how confounding was handled, and the statistical methods used. The results items ask for the flow of participants through the study, descriptive data on the participants, and the main estimates reported with measures of uncertainty such as confidence intervals. Several items deal specifically with how the study handled missing data, how continuous variables were grouped or modelled, and whether any sensitivity analyses were performed, because each of these choices can materially change a result and each is easy to leave undescribed. A short, structured abstract is also expected, so that readers scanning the literature can grasp the design, population and main findings before reading the full text.

    The aim is completeness rather than a particular conclusion. When every relevant item is reported, a reader can assess whether the conclusions are supported by the design and data, and another team can attempt to reproduce the work or pool it in a synthesis. This emphasis on transparent, reusable reporting aligns directly with the wider goals of reproducibility in research, where undocumented methods are a primary obstacle to replication.

    Three observational study designs

    STROBE is written to cover the three principal observational designs, with a common core checklist plus design-specific guidance where the designs diverge.

    Design How it observes Typical question
    Cohort Follows groups over time by exposure status What outcomes follow an exposure?
    Case-control Compares exposure histories of cases and controls What exposures preceded an outcome?
    Cross-sectional Measures exposure and outcome at one point What is associated at a given time?

    Because these designs differ in how data are gathered and in the biases they are prone to, certain checklist items are tailored to each. A case-control study, for example, must report carefully how cases and controls were selected, while a cohort study must report how participants were followed and how losses to follow-up were handled. Reporting which design was used, and reporting it accurately, is itself a STROBE requirement and a prerequisite for sound interpretation, since the same association means different things under different designs.

    Extensions and related reporting tools

    The core STROBE checklist has been supplemented by extensions that address particular fields and data types while keeping the same philosophy of transparent reporting. These extensions adapt the checklist for areas such as molecular and genetic epidemiology, nutritional epidemiology, infectious-disease studies and research that reuses routinely collected health data, where additional reporting items are needed to let readers judge validity. The proliferation of extensions reflects a general principle: the more specialised or complex the data source, the more there is to report before a study can be appraised or reproduced. Authors should check whether an extension exists for their study type, because using the most specific applicable guideline captures reporting items that the generic checklist would miss. This mirrors the broader move in research toward documenting not just results but the full provenance of the data and analysis behind them.

    STROBE and the EQUATOR Network

    STROBE is one of the most widely used guidelines hosted by the EQUATOR Network, an international initiative that curates reporting guidelines to improve the reliability and value of the health research literature. EQUATOR organises guidelines by study type, so STROBE sits alongside guidelines such as CONSORT for randomised trials and PRISMA for systematic reviews. Locating a guideline through EQUATOR helps authors choose the correct checklist for their study type rather than reaching for a familiar but inappropriate one.

    Within an evidence ecosystem, having a named, citable reporting standard makes expectations explicit for everyone involved. It also connects observational studies to the population measures they rely on, such as incidence and prevalence and the denominators drawn from a census. Consistent terminology drawn from the CASRAI dictionary further helps keep the language of reporting stable across studies and journals.

    How STROBE is used in practice

    In practice, STROBE is most useful when it is consulted at the writing stage and again at peer review. Authors typically complete the checklist and indicate, for each item, the page or section where it is addressed, submitting this alongside the manuscript so that editors and reviewers can verify coverage quickly. Many journals reference STROBE in their instructions to authors for observational research, which gives the guideline practical force rather than leaving it as an optional ideal. Importantly, STROBE is not a quality score: a study can be reported completely yet still have design limitations, and conversely a strong study reported poorly is hard to trust. The checklist’s role is to ensure that whatever the study did, the reader can see it clearly. Used this way, it improves the appraisal, synthesis and reuse of observational evidence without constraining how researchers choose to investigate their questions.

    Why transparent reporting matters

    Observational studies cannot randomise exposure, so their credibility rests heavily on how clearly the methods, data sources and analytical choices are reported. Incomplete reporting makes it impossible to judge whether bias or confounding could explain the findings, and it makes the work difficult or impossible to reproduce, which weakens the cumulative evidence base. Transparent, STROBE-compliant reporting supports critical appraisal by readers, enables evidence synthesis by reviewers, and allows secondary analysts to reuse the work with confidence. Authors preparing observational manuscripts can consult the guidance for authors to align their reporting with these expectations from the outset rather than retrofitting it at submission.

    Frequently asked questions

    Is STROBE a way to design a study?

    No. STROBE is a reporting guideline, not a design or analysis protocol. It tells authors what to report so readers can evaluate the work; the design and statistical choices remain the researchers’ responsibility and are made before STROBE applies.

    Which studies should use STROBE?

    STROBE applies to observational designs, specifically cohort, case-control and cross-sectional studies. Randomised trials, systematic reviews and other study types have their own guidelines, which can be located through the EQUATOR Network’s catalogue.

    How does STROBE relate to reproducibility?

    Complete, transparent reporting is a precondition for reproducibility. By prompting authors to describe data sources, variables, bias and methods fully, STROBE makes it possible for others to appraise, synthesise and attempt to replicate observational findings.