What Are Inclusion and Exclusion Criteria in Systematic Reviews and Research Studies?

Understanding inclusion and exclusion criteria in research

What inclusion and exclusion criteria mean

• Inclusion and exclusion criteria are pre-set rules that researchers use to decide who qualifies to participate in a study and who does not qualify.
• Researchers write these criteria before recruitment or data collection to avoid changing rules midway, which can introduce bias.
• These criteria can apply to:
  • Participants in primary research such as surveys, experiments, and clinical studies.
  • Records in chart reviews and secondary data analysis.
  • Studies in systematic reviews, scoping reviews, and literature reviews.
• Together, they define the study population clearly and make the selection process consistent and defendable.

Inclusion criteria definition

• Inclusion criteria are the essential characteristics that must be present for someone or something to be eligible.
• Inclusion criteria describe the target group that the study is designed to understand.
• Inclusion criteria often specify:
  • Population characteristics such as age group, sex where relevant, job role, or level of training.
  • Condition or status such as having a diagnosis, being at risk of a condition, or having a specific experience.
  • Setting such as a hospital ward, community clinic, workplace, or geographic location.
  • Timeframe such as “within the last 12 months” or “during admission.”
  • Language and access such as ability to complete the study tools in a specific language or availability of complete records.
  • Consent and participation capacity such as ability to provide informed consent when human participants are involved.
• Inclusion criteria reduce confusion by stating clearly, “These are the exact types of participants or sources needed.”

Exclusion criteria definition

• Exclusion criteria are the specific conditions or characteristics that remove a person or data source even if the inclusion criteria are met.
• Exclusion criteria help prevent issues that could distort results or create avoidable risk.
• Exclusion criteria often cover:
  • Safety concerns such as excluding individuals for whom participation could be harmful.
  • Confounding factors such as conditions or treatments that strongly affect the outcome and make comparison unfair.
  • Practical barriers such as inability to complete required procedures or provide reliable responses.
  • Data quality problems such as missing key variables, incomplete records, or duplicate entries.
• Exclusion criteria reduce noise by stating clearly, “These cases could weaken accuracy or fairness, so they will not be included.”

How inclusion and exclusion criteria work together

• Inclusion criteria act as the first filter by creating the initial pool of eligible participants or sources.
• Exclusion criteria act as the second filter by removing cases that could compromise the study’s purpose, ethics, or validity.
• When combined, they help the researcher:
  • Select a sample that is relevant to the research question.
  • Keep participants or records comparable, which improves interpretation.
  • Ensure recruitment decisions are consistent, not based on preference or convenience.
• A simple way to express the relationship:
  • Inclusion criteria answer: Who should be included?
  • Exclusion criteria answer: Who should be removed, and why?

Why inclusion and exclusion criteria matter

• They improve research validity by ensuring the study actually examines the population the research question describes.
• They reduce selection bias because rules are applied the same way to everyone.
• They strengthen reliability and reproducibility because another researcher can follow the same rules and reach similar selection decisions.
• They protect participant safety and ethics by avoiding unnecessary risk and ensuring appropriate participation.
• They support clear interpretation because the reader understands exactly who the findings apply to.
• They improve study quality by reducing missing data, reducing confounding, and increasing the likelihood that results reflect true patterns rather than sampling errors.
• They help with generalization when criteria are balanced, meaning the sample is not so narrow that the findings only apply to a rare subgroup.

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Inclusion and exclusion criteria in the research process and systematic reviews

Where inclusion and exclusion criteria fit in the research process

• Inclusion and exclusion criteria are developed early, after the researcher defines the problem, aim, and research question.
• Criteria are typically finalized during the study design stage so recruitment and selection remain consistent.
• In primary research, criteria guide:
  • Sampling and recruitment, including where to recruit and who is eligible.
  • Ethics approval, because eligibility rules show how participants will be protected and selected fairly.
  • Data collection planning, because the researcher ensures participants or records will provide the needed variables.
• In reviews, criteria are set before searching to avoid selecting studies based on results.

Role of inclusion and exclusion criteria in systematic reviews

• In a systematic review, eligibility criteria define which studies will be included and which will be excluded.
• Criteria make the review transparent and reproducible, because another team can apply the same rules and reach similar decisions.
• Criteria support:
  • Accurate searching, by identifying keywords, populations, interventions, and settings.
  • Screening consistency, during title and abstract screening and full-text review.
  • Quality control, because rules can exclude studies with unsuitable designs or insufficient data.
  • Synthesis validity, because included studies are more comparable, which strengthens conclusions.

How criteria align with the review question

• Eligibility criteria should match the review question exactly, so the evidence collected answers the intended question.
• Alignment means the criteria specify:
  • The population of interest.
  • The intervention or exposure being examined.
  • The comparison group or alternative approach, if required.
  • The outcomes the review intends to evaluate.
  • The context such as setting, country, and timeframe.
• When criteria match the question, the review avoids including irrelevant studies that dilute findings.

Using frameworks like PICOT in eligibility criteria

• Why PICOT is used
  • PICOT is a planning tool that helps a reviewer convert a broad review question into specific, testable eligibility criteria.
  • PICOT ensures the review includes studies that directly answer the question and excludes studies that do not match the intended scope.
  • PICOT improves the quality of screening because each reviewer can apply the same rules consistently.
• How PICOT translates a review question into selection rules
  • The reviewer takes each PICOT element and turns it into inclusion criteria and exclusion criteria.
  • This process reduces vague decisions such as “include if relevant” and replaces them with measurable rules such as age range, setting, outcome definitions, and follow-up time.
• Population
  • Defines who the evidence must focus on.
  • Eligibility rules often specify:
    • Age group such as adults aged 18 years and above.
    • Clinical characteristics such as diagnosis, risk status, or symptom profile.
    • Setting such as hospitalized patients, intensive care units, emergency departments, or community care.
    • Special groups to include or exclude, such as immunocompromised patients or pregnant individuals, if relevant to the question.
  • A strong Population criterion prevents mixing populations that cannot be fairly compared.
• Intervention
  • Defines what is being done, introduced, or tested.
  • Eligibility rules often specify:
    • The exact intervention name or category such as an early warning score tool, education program, or clinical protocol.
    • Intervention components such as screening frequency, thresholds, or training requirements.
    • Implementation context such as routine care, pilot implementation, or controlled research conditions.
  • A strong Intervention criterion prevents including studies that only discuss related ideas without testing the intervention.
• Comparison
  • Defines what the intervention is compared to, if comparison is relevant to the question.
  • Common comparators include:
    • Usual care.
    • Another tool or approach.
    • Different thresholds of the same tool.
    • Pre-implementation versus post-implementation outcomes.
  • A clear Comparison criterion prevents including studies with no meaningful reference group when the review requires one.
• Outcome
  • Defines what effects or results the study must report.
  • Eligibility rules often specify:
    • Primary outcomes such as sepsis detection accuracy, time to antibiotics, mortality, intensive care unit admission, or length of stay.
    • Secondary outcomes such as escalation of care, clinical deterioration, or staff compliance.
    • Outcome measurement method such as sensitivity, specificity, receiver operating characteristic area under the curve, or hazard ratios.
  • A clear Outcome criterion prevents including studies that do not report outcomes relevant to your review aim.
• Time
  • Defines the timeframe for follow-up, measurement, or study period.
  • Eligibility rules often specify:
    • Minimum follow-up such as 24 hours, 72 hours, or 30 days.
    • Time to event outcomes such as time to recognition or time to treatment.
    • Study timeframe limits such as publications from the last 10 years.
  • A clear Time criterion prevents mixing studies with very different follow-up periods that could distort conclusions.
• How PICOT reduces reviewer disagreement
  • Reviewers disagree most when criteria are vague.
  • PICOT reduces disagreement by turning the question into clear yes or no decisions for each element.
  • PICOT also improves documentation because reviewers can explain exclusions using specific reasons such as wrong population or wrong outcome.

Common protocol elements that specify criteria

• Purpose of protocol elements
  • Protocol elements record eligibility criteria in a structured way to ensure transparency and consistency.
  • A protocol makes it easier to justify decisions to readers, supervisors, and examiners.
• Types of participants or population
  • Specifies participant characteristics such as:
    • Age and clinical status.
    • Setting of care.
    • Inclusion and exclusion subgroups.
  • May also define diagnostic standards used to confirm a condition.
• Types of interventions or exposures
  • Specifies what qualifies as the intervention or exposure.
  • Defines intervention boundaries such as:
    • Tool type and version.
    • Implementation process.
    • Who delivers the intervention and how often it is used.
  • For exposure-based reviews, it defines exposure categories and how exposure is measured.
• Types of comparators
  • Specifies acceptable comparison groups, such as usual care or alternative tools.
  • May state that studies without a comparator are excluded, or may allow single-arm studies depending on the review purpose.
• Types of outcomes and outcome measures
  • Lists primary and secondary outcomes.
  • Defines how outcomes must be measured, including acceptable indicators and reporting formats.
  • Specifies whether studies must report outcomes quantitatively, qualitatively, or both.
• Types of study designs included or excluded
  • Specifies eligible designs such as randomized controlled trials, cohort studies, case-control studies, qualitative studies, or mixed methods.
  • States exclusions such as editorials, opinion pieces, case reports, or conference abstracts without full data.
  • Aligns design selection with the review goal, such as effectiveness, experiences, implementation, or diagnostic accuracy.
• Setting and context
  • Defines the care context such as acute hospital wards, emergency departments, intensive care units, or community settings.
  • May limit geography such as one country, region, or health system type.
• Publication years and language limits
  • Defines the time window for included studies such as 2014 to 2026.
  • Specifies language restrictions, if any, and provides a rationale because language limits can introduce bias.
• Publication type
  • Defines what publication sources are eligible, such as peer-reviewed journal articles only, or includes gray literature.
  • Gray literature can include dissertations, policy reports, and government documents, depending on the review purpose.
• Minimum data requirements
  • States the minimum information needed for inclusion, such as:
    • Full text availability.
    • Clear description of population and intervention.
    • Extractable outcome data.
  • May also specify handling of duplicates, missing data, or unclear reporting, including contacting authors when appropriate.

Inclusion and exclusion criteria in primary research studies

• Inclusion and exclusion criteria are the eligibility rules used to select participants, records, or cases for primary research such as surveys, cohort studies, case-control studies, and clinical trials.
• These criteria should be written before recruitment starts to reduce selection bias and improve consistency.
• Strong criteria ensure the sample matches the research aim, protects participant safety, and supports valid analysis.

How to write strong inclusion criteria

Step 1: Defining the target population clearly

• Start by stating the population in direct, measurable terms.
• Include key characteristics that define “who” the study is about, such as:
  • Age range and life stage.
  • Clinical condition or risk category.
  • Level of severity or stage of disease, where relevant.
  • Required setting, such as inpatient adult medical wards or outpatient clinics.
• Use operational definitions, such as diagnostic criteria, laboratory thresholds, or documented clinical diagnoses, to avoid subjective selection.
• If the study targets a specific group, explain what evidence is required to confirm membership in that group, such as a diagnosis recorded in the medical record.

Step 2: Setting boundaries for interventions or exposures

• State what intervention or exposure must be present for eligibility, using precise descriptions.
• For interventions, specify:
  • The intervention name and version, if applicable.
  • The timing and intensity, such as frequency, duration, and delivery method.
  • Who delivers the intervention, such as nurses, physicians, or trained research staff.
• For exposures, specify:
  • How exposure is defined and measured.
  • The required level or duration of exposure.
  • The time window when exposure must occur.
• These boundaries prevent mixing participants who received different or unclear interventions that would weaken comparisons.

Step 3: Specifying outcomes and measures

• Define the primary outcome and any required secondary outcomes that participants must be able to contribute data to.
• Specify how outcomes will be measured, such as:
  • A validated scale, clinical diagnostic criteria, laboratory values, or administrative codes.
  • Timing of measurement, such as baseline, 24 hours, discharge, or 30-day follow-up.
• Ensure inclusion criteria support outcome capture. For example, if the outcome requires follow-up data, include a requirement that follow-up is feasible or that records contain the necessary variables.

Step 4: Choosing eligible study designs and settings

• While study design applies more directly to reviews, primary studies also need design and setting boundaries in the eligibility criteria.
• Specify the setting clearly because setting changes risk, care processes, and outcome patterns.
• Examples of setting boundaries include:
  • Acute care hospital only versus community clinics.
  • Intensive care unit only versus mixed wards.
  • Single site versus multi-site recruitment.
• Define the recruitment approach that fits the design, such as consecutive admissions, random sampling, or purposive sampling in qualitative work.
• These choices should reflect the research question and feasibility constraints.

How to write strong exclusion criteria

Step 1: Avoiding unnecessary exclusions

• Exclusion criteria should be limited to factors that protect participants, prevent major confounding, or prevent meaningful participation.
• Avoid exclusions that reduce representativeness without a strong reason, such as excluding older adults with common chronic conditions in studies about older populations.
• Each exclusion should have a clear justification linked to safety, ethics, data quality, or interpretability.

Step 2: Handling comorbidities and special populations

• Decide whether comorbidities are part of the real-world population you want to study or whether they introduce unacceptable confounding.
• Use clear rules, such as:
  • Exclude only specific comorbidities that directly change the outcome pathway.
  • Stratify or adjust for comorbidities rather than excluding large groups.
  • Define severity thresholds for exclusion, such as end-stage disease or hospice care.
• For special populations, state the ethical and scientific rationale. Examples may include:
  • Pregnancy, when interventions carry unknown risks.
  • Cognitive impairment, when consent cannot be obtained and no proxy process exists.
  • Immunocompromised status, when outcomes differ strongly from the general population.
• If special populations are excluded, note how that affects generalizability.

Step 3: Managing language, geography, and publication type limits

• In primary research, language limits often relate to participant communication and informed consent.
• If the study requires English-only materials, state this clearly and justify it based on available resources, translation feasibility, and participant safety.
• Geography limits should be based on the study’s recruitment sites and the context being investigated, not convenience alone.
• Publication type limits mostly apply to reviews, but primary studies may limit data sources, such as using only electronic health records from a specific health system. State the scope and explain why.

Step 4: Addressing duplicate data and overlapping samples

• In primary research, duplicates can occur through repeated records, multiple admissions by the same patient, or data merged from different systems.
• Create explicit rules to prevent double counting, such as:
  • Include only the first admission per patient during the study period.
  • Use a unique identifier to remove repeated entries.
  • Define whether readmissions count as separate cases or as follow-up events.
• For overlapping samples across sites or datasets, specify how you will detect overlap and which dataset takes priority.
• These rules protect the validity of statistical estimates and prevent inflated sample sizes.

Practical Examples of Inclusion and Exclusion Criteria

Examples of inclusion and exclusion criteria for quantitative studies

• Context: A prospective cohort study evaluating an early warning score for deterioration in adults with heart failure.
• Establishing inclusion and exclusion criteria based on your research question helps the investigator define the criteria that will be used to recruit study participants and to protect the study results.
• Inclusion criteria may include:
  • Adults aged 18 years and above admitted with a documented diagnosis of heart failure.
  • A confirmed type and stage or type and stage of disease recorded in the medical record.
  • Ability to participate in the study, including capacity to consent and ability to complete baseline assessment.
  • Documented previous treatment history, including current medication list at admission.
  • Agreement to complete a follow-up study at 30 days to assess results of the study.
  • Additional characteristics such as stable contact information to support follow-up.
• Exclusion criteria must be clear, and exclusion criteria are defined to reduce bias and prevent factors that may interfere with outcome measurement:
  • Pregnant women, if physiological changes may interfere with the score’s thresholds and interpretation.
  • Patients receiving end-of-life care, because goals of care may interfere with escalation decisions.
  • Patients with incomplete medical records that prevent accurate measurement of medication exposure or outcomes.
  • Patients transferred from another facility without reliable baseline data, because missing information can interfere with accurate risk classification.

Examples of inclusion and exclusion criteria for qualitative studies

• Context: An interview study exploring nurses’ experiences implementing a new tool for early recognition of deterioration.
• The research team may think about criteria that ensure participants can provide rich, relevant perspectives.
• Inclusion criteria may include:
  • Registered nurses who were included in a study site during the pilot period.
  • Nurses who used the tool at least five times and can describe implementation barriers and facilitators.
  • Representation across demographic variables such as years of experience and unit type, as used to identify variation in perceptions.
• Exclusion criteria must focus on protecting data quality:
  • Staff who had no direct exposure to the tool, because they cannot describe actual use.
  • Agency staff who worked fewer than two shifts in the period, because limited exposure may interfere with depth of data.

Examples of inclusion and exclusion criteria for mixed methods studies

• Context: A mixed methods evaluation combining chart review outcomes with staff interviews.
• Appropriate inclusion and exclusion criteria align both the quantitative and qualitative components so the sample is coherent.
• Inclusion criteria may include:
  • Adult inpatients meeting the inclusion criteria for the chart review, plus staff directly involved in the intervention.
  • Clear type and stage of disease documentation and complete medication data.
• Exclusion criteria must include:
  • Duplicate patient entries across admissions unless the protocol specifies how repeated admissions are handled.
  • Staff without direct involvement, because they cannot contribute valid interview data.

Example for a healthcare systematic review

• Review based on your research and based on your research question, for example: effectiveness of early warning scores in adults at risk of sepsis.
• Establishing inclusion and exclusion criteria supports inclusion in a literature review by defining which studies are eligible for inclusion.
• The reviewer will select articles from each database, then select articles for your literature by screening titles, abstracts, and full texts.
• Inclusion criteria may include:
  • Adult hospital populations, including studies on heart failure patients if they report deterioration outcomes relevant to sepsis recognition processes.
  • Studies reporting diagnostic accuracy or clinical outcomes.
  • Prospective or retrospective observational studies and trials.
• Exclusion criteria must include:
  • Studies in children or pregnant women if outside the defined population.
  • Studies without extractable outcome data or missing key elements of an article needed for appraisal.
• The protocol should describe how articles are used to select articles, and how reviewers confirm studies meet the inclusion criteria.

Example for an education research study

• Context: Evaluating a training program for teachers to improve classroom assessment practices.
• Inclusion criteria may include:
  • Teachers at selected schools who agree to participate in the study.
  • Schools implementing the training during the study period.
  • Collection of baseline and post-training outcomes to collect data for comparison.
• Exclusion criteria must include:
  • Schools that already received similar training in the prior year as previous treatment history, because earlier exposure may interfere with attributing change to the current program.

Example for a public health intervention evaluation

• Context: Community smoking cessation program evaluation.
• Inclusion criteria may include:
  • Adults who report smoking at baseline and enroll in the program.
  • Participants willing to complete an intermediate follow-up at 6 weeks and final follow-up at 12 weeks.
• Exclusion criteria must include:
  • Individuals using another intensive cessation program concurrently, because this may interfere with estimating the program’s effect.
• If criteria are too narrow, external validity may decrease and findings may be less generalizable.
• If criteria match real-world conditions, the study results are more likely to reflect typical practice and support the success of the study.

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Common mistakes when developing inclusion and exclusion criteria and how to avoid them

Criteria that are too broad or too narrow

• What the mistake looks like
  • Criteria are so broad that many different participant types are included, which creates an uneven sample and weakens interpretation.
  • Criteria are so narrow that too few people qualify, recruitment fails, and results are not generalizable.
• Why it matters
  • Very broad criteria can increase confounding and make it hard to explain why outcomes occurred.
  • Very narrow criteria can reduce external validity because the sample no longer reflects the real population.
• How to avoid it
  • Link each criterion directly to the research question and primary outcome.
  • Pilot test criteria on a small set of records or potential participants to estimate how many will be eligible.
  • Use “must-have” criteria only, then handle other characteristics through stratification or statistical adjustment rather than exclusions.
  • Review whether common real-world characteristics, such as comorbidities in adults, should be included rather than excluded.

Criteria that change during screening

• What the mistake looks like
  • The research team revises criteria mid-recruitment or mid-screening because too many or too few cases are found.
  • Reviewers add new rules informally during title and abstract screening because some studies seem “almost relevant.”
• Why it matters
  • Changes can introduce bias and reduce trust in the study results because selection becomes influenced by convenience or emerging patterns.
  • It harms reproducibility because another investigator cannot repeat the selection process.
• How to avoid it
  • Finalize criteria before recruitment or database searching begins, then document them in a protocol.
  • If changes are unavoidable, record the reason, the date, what changed, and how many cases were affected.
  • In systematic reviews, use a calibration exercise where reviewers screen a sample together to confirm shared understanding before full screening starts.

Criteria that introduce selection bias

• What the mistake looks like
  • Eligibility rules systematically favor one group, such as only including participants who can attend daytime clinic visits.
  • Excluding participants with limited literacy or limited language access without considering translation options.
  • Using referral-based recruitment only, which selects participants already connected to care.
• Why it matters
  • Selection bias can distort effect estimates and reduce generalizability.
  • Results may reflect access patterns rather than true relationships between exposure and outcome.
• How to avoid it
  • Check whether each criterion could systematically exclude particular demographic groups.
  • Use consecutive sampling when feasible, especially in clinical settings.
  • Provide practical supports that reduce exclusion, such as flexible interview times, translated materials, or remote follow-up options when ethical and feasible.
  • Distinguish between exclusions needed for safety versus exclusions based on convenience.

Poorly defined terms and ambiguous thresholds

• What the mistake looks like
  • Terms like “severe illness,” “high risk,” “older adult,” or “stable condition” are used without definitions.
  • Thresholds are unclear, such as “recent treatment” without a time window.
  • Outcomes are listed without measurement methods, such as “improved health” without a scale or objective measure.
• Why it matters
  • Ambiguity increases disagreement between reviewers and inconsistent decisions.
  • It creates measurement error and weakens validity.
• How to avoid it
  • Define key terms using operational definitions, such as diagnostic codes, laboratory cut-offs, or validated instruments.
  • Specify time windows, such as “previous treatment history within the past 6 months.”
  • State exact thresholds, tools, and timing for outcome measurement.
  • Use established frameworks such as Population, Intervention, Comparison, Outcome, and Time to force precision.

Quick inclusion and exclusion criteria checklist for researchers

Checklist for planning criteria

• Confirm the research question is specific enough to guide eligibility decisions.
• Define the target population using measurable characteristics, including key demographic elements when relevant.
• Specify the setting and recruitment source, such as outpatient clinic, inpatient ward, or school district.
• Specify the intervention or exposure and its boundaries, including timing and dose where relevant.
• Identify primary outcomes and how they will be measured, including timing.
• Decide which comorbidities and special populations are part of the real-world population and which should be excluded for safety or major confounding.
• Pilot test criteria on a small sample to estimate the eligible pool and refine feasibility.
• Document the final criteria in a protocol and align them with ethics materials.

Checklist for screening and documentation

• Train screeners or recruiters and use standardized forms.
• Run a calibration exercise so all screeners apply criteria consistently.
• Record a specific reason for every exclusion using predefined categories.
• Track duplicates and define how repeated records, readmissions, or overlapping samples will be handled.
• Maintain version control for the criteria document so changes are visible and date-stamped.
• If changes occur, document why, when, and how many cases were impacted.

Checklist for reporting in the final manuscript

• Report inclusion and exclusion criteria in a dedicated section, such as Methods, Participants, or Eligibility.
• Provide clear operational definitions for key terms and thresholds.
• Report setting, timeframe, recruitment strategy, and sampling method.
• Report how many were assessed, included, and excluded, with reasons.
• For reviews, include a flow diagram showing selection steps.
• Explain how missing data were handled and whether missingness affected eligibility.

How to report inclusion and exclusion criteria transparently

• Present criteria exactly as used, with clear headings for inclusion and exclusion.
• Use simple, measurable language and avoid undefined terms.
• Describe the rationale for major exclusions, especially those that may affect external validity.
• Describe the screening process, including how many screeners were involved and how disagreements were resolved.
• Report selection numbers and exclusion reasons in a structured format so readers can judge whether the final sample fits the research question and whether results are generalizable.