NLE Community Health Nursing — Epidemiology & BiostatisticsCheat Sheet

Term

Epidemiology

Example

Tracking TB cases across Barangay X to identify transmission sources and implement DOH interventions.

Definition

Study of the distribution and determinants of health-related states/events in populations, applied to control of health problems.

Term

Epidemiologic Triangle

Example

Dengue: virus (agent) + mosquito vector + stagnant water (environment) + unimmunized person (host) = outbreak.

Definition

Three interacting elements causing disease: Agent (cause), Host (susceptibility), Environment (conditions). Breaking any point stops disease transmission.

Term

Chain of Infection

Example

COVID: virus (agent) → infected person (reservoir) → respiratory droplets (exit/transmission) → nasal mucosa (entry) → unvaccinated person (host).

Definition

Six sequential links: etiologic agent → reservoir → portal of exit → mode of transmission → portal of entry → susceptible host. Nursing breaks any link.

Term

Sporadic Disease

Example

A single case of plague in a province; not typical for that area.

Definition

Occasional, irregular occurrence of disease with no clear pattern.

Term

Endemic Disease

Example

Malaria endemic in Mindanao; dengue endemic in Metro Manila; filariasis in Visayas.

Definition

Constant, usual presence of a disease in a given geographic area or population.

Term

Epidemic / Outbreak

Example

Sudden spike in measles cases in a school when expected baseline is zero.

Definition

Occurrence of cases clearly in EXCESS of the expected number in a community or region.

Term

Pandemic

Example

COVID-19 pandemic (2020–present); 1918 Spanish flu pandemic.

Definition

Epidemic spreading across multiple countries or continents.

Term

Incidence Rate

Example

10 new TB cases per 100,000 population per year in a region.

Definition

Frequency of NEW cases of disease in a population at risk during a specific period. Measures RISK.

Term

Prevalence Rate

Example

50 total HTN cases (diagnosed + undiagnosed) per 1,000 adults at a single point in time.

Definition

Frequency of ALL EXISTING cases (new + old) at a point or period in a population. Measures BURDEN.

Term

Attack Rate

Example

In a food poisoning outbreak: 45 people ill out of 150 exposed = 30% attack rate.

Definition

Special incidence rate used in OUTBREAKS. Proportion of exposed population that becomes ill. Expressed as percentage (×100).

Term

Case Fatality Rate (CFR)

Example

COVID CFR in 2020: ~1–2% globally; measles CFR in unvaccinated: ~0.2%.

Definition

Proportion of diagnosed cases that result in DEATH. Measures disease SEVERITY/LETHALITY (expressed %).

Term

Vital Statistics

Example

Philippine Statistics Authority (PSA) compiles national vital statistics from civil registrars.

Definition

Data on vital events (births, deaths, marriages, fetal deaths) registered under civil registry law. Primary source: Birth and Death Certificates.

Term

Surveillance (Epidemiologic)

Example

PIDSR (Philippine Integrated Disease Surveillance and Response) monitors notifiable diseases; RHUs report weekly to municipal health offices.

Definition

Ongoing, systematic collection, analysis, interpretation, and dissemination of health data for PUBLIC HEALTH ACTION.

Term

PIDSR

Example

RHU nurse reports 5 measles cases to municipal health office within 24 hours; municipality reports to provincial ESU.

Definition

Philippine system for reporting notifiable diseases from health facilities upward to DOH Epidemiology Bureau. Nurses must report promptly (legal duty under RA 9173).

Formula

Crude Birth Rate (CBR) = (Total live births in a year / Midyear population) × 1,000

Meaning

Total live births = registered births; Midyear population = (population Jan 1 + Dec 31) ÷ 2; Constant = 1,000 per person.

Watch Out

Student uses END-OF-YEAR population or uses constant 100,000 instead of 1,000. ALWAYS check the denominator in the question.

When To Use

Asked for birth rate of a region or country for a given year. Use midyear population as denominator.

Formula

Crude Death Rate (CDR) = (Total deaths in a year / Midyear population) × 1,000

Meaning

Total deaths = all deaths from all causes; Midyear population = average population; Constant = 1,000.

Watch Out

Confusing CDR with cause-specific death rate (which uses ×100,000). Read the question carefully.

When To Use

Asked for overall death rate. Use midyear population, same as CBR.

Formula

Infant Mortality Rate (IMR) = (Deaths under 1 year of age / Total live births) × 1,000

Meaning

Deaths under 1 year = neonatal + post-neonatal deaths; Total live births = denominator; Constant = 1,000 (NOT midyear population).

Watch Out

Using midyear population as denominator (WRONG). IMR, neonatal rate, and fetal rate all use LIVE BIRTHS as denominator, not population.

When To Use

Measuring child survival/health system quality. Denominator is LIVE BIRTHS, not population.

Formula

Neonatal Mortality Rate = (Deaths under 28 days / Total live births) × 1,000

Meaning

Deaths under 28 days (first 4 weeks of life); Total live births = denominator; Constant = 1,000.

Watch Out

Post-neonatal mortality (28 days to 1 year) is NOT included; IMR includes both neonatal and post-neonatal.

When To Use

Measuring newborn/maternal care quality. Early neonatal deaths are key quality indicator.

Formula

Maternal Mortality Rate/Ratio (MMR) = (Maternal deaths / Total live births) × 100,000

Meaning

Maternal deaths = deaths during pregnancy, childbirth, or 42 days postpartum; Total live births = denominator; Constant = 100,000 (may vary—read question).

Watch Out

Some questions use ×10,000 or ×1,000 instead of ×100,000. CHECK THE CONSTANT IN EVERY QUESTION. This is a high-frequency error.

When To Use

Measuring maternal health/obstetric care quality. A key MDG/SDG indicator.

Formula

Fetal Death Rate = (Fetal deaths / [Total live births + Fetal deaths]) × 1,000

Meaning

Fetal deaths = stillbirths (>20 weeks or ≥350g); Denominator includes BOTH live births AND fetal deaths; Constant = 1,000.

Watch Out

Denominator is NOT midyear population; it is (live births + fetal deaths). Easy to miss the addition of fetal deaths to denominator.

When To Use

Measuring intrauterine loss. Denominator is different from IMR (includes fetal deaths in denominator).

Formula

Cause-of-Death (Specific Death) Rate = (Deaths from specific cause / Midyear population) × 100,000

Meaning

Deaths from specific cause = e.g., TB deaths, cancer deaths, pneumonia deaths; Midyear population = population at risk; Constant = 100,000.

Watch Out

Using 1,000 instead of 100,000 (WRONG). This rate uses 100,000, not 1,000. The large constant reflects that cause-specific rates are typically smaller.

When To Use

Measuring the burden of a specific disease as a cause of death in a population.

Formula

Proportionate Mortality Rate = (Deaths from specific cause / Total deaths) × 100

Meaning

Deaths from specific cause = e.g., TB deaths; Total deaths = all-cause deaths in the period; Constant = 100 (expressed as percentage).

Watch Out

This is a PROPORTION, not a true rate (no time-at-risk denominator). Denominator is total deaths, not population. Do NOT confuse with cause-specific death rate.

When To Use

Showing what proportion of all deaths are due to a specific cause. Does NOT include population; compares within deaths only.

Formula

Incidence Rate = (NEW cases of disease during a period / Population at risk) × constant

Meaning

NEW cases = cases appearing for the first time in the specified period; Population at risk = those who can contract the disease; Constant = 1,000, 10,000, or 100,000 depending on frequency.

Watch Out

Including old/prevalent cases in the numerator (WRONG—incidence counts NEW cases only). Confusing with prevalence. Check that denominator is population at risk (not just patients already diagnosed).

When To Use

Measuring the RISK or probability of developing disease over time. Compare two populations or time periods for disease risk.

Formula

Prevalence Rate = (ALL EXISTING cases at a point or period in time / Total population) × constant

Meaning

ALL existing cases = new + old; Point/period = snapshot at one time (point prevalence) or during a period (period prevalence); Constant = typically 1,000 or 100,000.

Watch Out

Including only new cases (WRONG—prevalence includes all cases). Confusing with incidence. If disease is chronic (long duration), prevalence is much higher than incidence.

When To Use

Measuring the BURDEN or overall load of disease in a population. Planning services (how many patients to treat?). Affected by BOTH incidence and duration of disease.

Formula

Attack Rate = (Number of cases [among exposed] / Number of people exposed) × 100

Meaning

Number of cases = people who became ill (in outbreak); Number exposed = people at risk in the outbreak; Constant = 100 (expressed as percentage).

Watch Out

Using as a true rate (with time denominators); it is a PROPORTION. Only used in outbreak contexts. Do NOT confuse with incidence rate (which has a time-period denominator).

When To Use

OUTBREAK investigation ONLY. Describes what proportion of exposed people became ill. Always expressed as a percentage.

Formula

Case Fatality Rate (CFR) = (Deaths from a specific disease / Number of cases of that disease) × 100

Meaning

Deaths from specific disease = persons who died; Number of cases = all diagnosed cases; Constant = 100 (expressed as percentage). Describes LETHALITY.

Watch Out

Using population as denominator (WRONG—denominator is cases, not population). Confusing with cause-specific death rate (which uses population as denominator). CFR measures case severity; cause-specific rate measures disease burden in population.

When To Use

Measuring disease SEVERITY. High CFR = severe disease (e.g., rabies ~100%, measles ~0.2% unvaccinated). Indicates case management quality.

Formula

Sensitivity = (True Positives / [True Positives + False Negatives]) × 100

Meaning

True Positives (TP) = test positive AND disease present; False Negatives (FN) = test negative BUT disease present; Sensitivity = ability to DETECT disease (rule OUT when negative).

Watch Out

High sensitivity = few false negatives (few missed cases). BUT high sensitivity may include false positives (false alarms). Do NOT confuse with specificity.

When To Use

Choosing a screening test when you want to IDENTIFY ALL DISEASED people (minimize missed cases). For serious diseases (e.g., cancer, TB), use high-sensitivity test.

Formula

Specificity = (True Negatives / [True Negatives + False Positives]) × 100

Meaning

True Negatives (TN) = test negative AND no disease; False Positives (FP) = test positive BUT no disease; Specificity = ability to EXCLUDE disease (rule IN when positive).

Watch Out

High specificity = few false positives (few false alarms). BUT may miss some true cases. Do NOT confuse with sensitivity. Sensitivity & specificity are inversely related (usually).

When To Use

Choosing a diagnostic (confirmatory) test when you want to CONFIRM disease (minimize false alarms). For high-anxiety diseases (e.g., HIV), use high-specificity confirmatory test.

Formula

Positive Predictive Value (PPV) = (True Positives / [True Positives + False Positives]) × 100

Meaning

TP = test positive AND disease; FP = test positive BUT no disease; PPV = probability that a POSITIVE TEST truly indicates disease. DEPENDS ON DISEASE PREVALENCE.

Watch Out

PPV is NOT intrinsic to the test (like sensitivity/specificity); it CHANGES with disease prevalence in the population. A test with high sensitivity/specificity may have LOW PPV in a low-prevalence population (many false positives).

When To Use

Interpreting a positive test result in a specific population. High prevalence = higher PPV (more true positives relative to false positives).

Term

Screening

Example

Mammography screening for breast cancer in asymptomatic women; Pap smear for cervical cancer screening.

Definition

Application of a test to apparently HEALTHY people to detect disease early (secondary prevention). Different from diagnostic testing (which confirms suspected disease).

Term

Sensitivity

Example

A TB test with 95% sensitivity correctly identifies 95 of 100 TB patients (5 missed).

Definition

Ability of a test to correctly identify those WHO HAVE the disease (true positives). High sensitivity = few false negatives = RULE OUT disease when negative.

Term

Specificity

Example

A TB confirmatory test with 98% specificity correctly identifies 98 of 100 non-TB people as negative (2 false alarms).

Definition

Ability of a test to correctly identify those WHO DO NOT HAVE the disease (true negatives). High specificity = few false positives = RULE IN disease when positive.

Term

Positive Predictive Value (PPV)

Example

HIV test with 99% sensitivity and specificity: In high-prevalence group, PPV is 99%; in low-prevalence group, PPV is 50%.

Definition

Probability that a POSITIVE test result actually indicates DISEASE presence. DEPENDS ON DISEASE PREVALENCE in the population tested.

Term

Outbreak Investigation

Example

2023 measles outbreak in a school: verify diagnosis, count cases, plot epidemic curve, interview cases for exposure (assembly? lunch hall?), implement vaccination, report to DOH.

Definition

Systematic multi-step process to identify source and mode of transmission during an epidemic. Key steps: verify diagnosis, define cases, describe by person/place/time, formulate hypothesis, test hypothesis, implement control measures, communicate findings.

Term

Epidemic Curve (Epi Curve)

Example

Food poisoning from one catered event = point-source (all ill within 1–2 days, single peak). Measles in unvaccinated school = propagated (successive generations of cases, multiple peaks).

Definition

Histogram of case counts over TIME. SHAPE distinguishes outbreak type: POINT-SOURCE (single sharp peak) vs. PROPAGATED (successive waves/person-to-person).

Term

Point-Source Outbreak

Example

Hotel food poisoning event on Jan 15 → cases appear Jan 16–17 (incubation 1–2 days) → curve peaks Jan 17 → no new cases after Jan 18.

Definition

Cases result from SINGLE, SHORT exposure to a source (e.g., contaminated food, one event). Epi curve shows SINGLE SHARP PEAK. Incubation period determines peak duration.

Term

Propagated (Person-to-Person) Outbreak

Example

Measles in school: Gen 1 (5 cases) → Gen 2 (20 cases 2 weeks later) → Gen 3 (60 cases 4 weeks later). Multiple peaks on epi curve.

Definition

Cases result from PERSON-TO-PERSON transmission over SUCCESSIVE GENERATIONS. Epi curve shows SUCCESSIVE WAVES (multiple peaks). Duration depends on generation time and control measures.

Term

Case Definition

Example

Measles case definition: fever ≥38°C × 3 days + cough/coryza/conjunctivitis + rash, OR lab confirmation. Used to count suspected & confirmed cases consistently.

Definition

Standardized criteria (symptoms, timeline, lab findings, exposure) used to IDENTIFY and COUNT cases uniformly during outbreak investigation.

Term

PIDSR (Philippine Integrated Disease Surveillance and Response)

Example

RHU identifies TB case → completes TB notification form → reports to municipal health office within 24 hrs → municipality to provincial ESU → province to DOH Epi Bureau.

Definition

National system for reporting NOTIFIABLE diseases from health facilities upward to DOH Epidemiology Bureau. Passive & active surveillance. Nurses must report promptly (RA 9173 duty).

Term

ESU (Epidemiology and Surveillance Unit)

Example

Municipal ESU coordinates PIDSR reporting from all health facilities in the municipality; receives reports from RHUs, processes, forwards to provincial ESU.

Definition

Surveillance units established at regional, provincial, city/municipal levels to coordinate disease reporting, outbreak response, and health surveillance.

Term

Passive Surveillance

Example

RHU nurse reports measles cases weekly to municipal health office as part of standard PIDSR reporting.

Definition

Health facilities ROUTINELY REPORT cases of notifiable diseases to higher levels (RHU → Municipal → Provincial → DOH). Passive = facilities self-report.

Term

Active Surveillance

Example

During cholera outbreak, public health nurse visits homes in affected area, screens residents, identifies suspected cases.

Definition

Health workers ACTIVELY SEEK and identify cases (not waiting for facility reports). Used during outbreaks or for targeted diseases.

Term

Vital Statistics

Example

Philippine Statistics Authority (PSA) compiles national vital statistics from municipal civil registrars; used to calculate CBR, CDR, IMR, MMR.

Definition

Data on vital events: births, deaths, marriages, fetal deaths, divorces. Registered under civil registry law. PRIMARY SOURCE: Birth and Death Certificates from civil registrars.

Term

FHSIS (Field Health Service Information System)

Example

RHU submits FHSIS report monthly to municipal health office showing number of ANC visits, immunizations given, TB cases treated.

Definition

Routine service data system collecting data from RHUs and health centers on outpatient visits, immunizations, deliveries, consultations. Used for monitoring service load and coverage.

Term

Census

Example

Philippine Census conducted every 5 years (2010, 2015, 2020); used as basis for population projections and rate denominators.

Definition

PERIODIC COMPLETE COUNT of population. Conducted by Philippine Statistics Authority (PSA). Source of denominator for rates; identifies demographic trends.

Term

Primary Prevention

Example

Health education on nutrition (promotion); immunization against measles (specific protection); sanitation improvement (specific protection).

Definition

Interventions BEFORE disease occurs. Includes HEALTH PROMOTION (general wellness) and SPECIFIC PROTECTION (disease-specific prevention). Target: susceptible population.

Term

Secondary Prevention

Example

Screening for TB (chest X-ray, sputum exam); case-finding for hypertension (BP screening); early treatment of diagnosed TB.

Definition

Interventions during EARLY STAGE of disease (subclinical pathogenesis). Early DETECTION and PROMPT TREATMENT to limit progression. Target: early-stage diseased individuals.

Term

Tertiary Prevention

Example

Physical therapy for stroke survivors (limit disability); vocational rehabilitation for TB patients (promote function).

Definition

Interventions AFTER disease is clinically established. Goal: LIMIT DISABILITY and PROMOTE REHABILITATION. Target: symptomatic diseased individuals.

Term

Prepathogenesis

Example

Unvaccinated child before measles infection; person without HTN before blood pressure elevation.

Definition

Stage BEFORE disease occurs. Person is susceptible but no disease present. Intervention: primary prevention (health ed, immunization, sanitation).

Term

Pathogenesis

Example

TB: subclinical = infection present but no cough/symptoms; clinical = cough, fever, weight loss.

Definition

Stage of disease development. Includes SUBCLINICAL PHASE (no symptoms; disease processes underway) and CLINICAL PHASE (symptoms present). Interventions: secondary (early) and tertiary (late).

Values

• NEW cases only (appearing during the period)
• ALL existing cases (new + old) at a point/period

Property

What does it count?

Values

• RISK of developing disease
• BURDEN of disease in population

Property

What does it measure?

Values

• New cases during the period
• All cases present at point/period in time

Property

Numerator

Values

• Population at risk (disease-free at start)
• Total population (includes diseased + non-diseased)

Property

Denominator

Values

• Expressed per unit TIME (rate)
• Point or period in time (snapshot)

Property

Time element

Values

• Incidence only (new cases)
• Incidence + duration + recovery/death rate

Property

Affected by

Values

• Comparing RISK between populations/time periods; identifying causes
• Planning services (how many patients to treat?); understanding disease burden

Property

Useful for

Values

• 10 NEW TB cases per 100,000 population per YEAR
• 50 total TB cases (old + new) per 100,000 population AT A POINT IN TIME

Property

Example

Values

• Ability to correctly ID those WITH disease (true positives)
• Ability to correctly ID those WITHOUT disease (true negatives)
• Probability that a POSITIVE test truly indicates disease

Property

Definition

Values

• TP / (TP + FN) × 100
• TN / (TN + FP) × 100
• TP / (TP + FP) × 100

Property

Formula

Values

• INTRINSIC to test (fixed property)
• INTRINSIC to test (fixed property)
• Test properties + disease PREVALENCE in population

Property

What it depends on

Values

• High sensitivity for screening (catch all diseased people; minimize missed cases)
• High specificity for diagnosis (confirm disease; minimize false alarms)
• Interpret positive result; depends on how common disease is in tested population

Property

Goal in practice

Values

• Usually inversely related to specificity
• Usually inversely related to sensitivity
• Increases with disease prevalence

Property

Inverse relationship

Values

• High sensitivity = few FN (rarely miss cases); can have many FP
• High specificity = few FP (rarely false-alarm); can have many FN
• High PPV = positive test is trustworthy. Low PPV = positive test unreliable (need confirmation)

Property

Interpretation

Values

• TB screening: 95% sensitivity misses 5 TB cases per 100
• TB diagnosis (GeneXpert): 98% specificity; only 2 false-positive per 100 non-TB people
• HIV rapid test in low-prevalence area: high sensitivity/specificity but LOW PPV (need confirmatory test)

Property

Example

Values

• Constant, usual presence of disease in a given area
• Cases CLEARLY IN EXCESS of expected number in a community/region
• Epidemic spreading across MULTIPLE COUNTRIES or CONTINENTS

Property

Definition

Values

• Cases are EXPECTED; normal for that area
• Cases are UNEXPECTED (exceed baseline)
• Crosses international/global boundaries

Property

Expected baseline

Values

• Limited to specific area/region
• Single community, city, or region
• Multiple countries or continents

Property

Geographic scope

Values

• Ongoing, year-round or seasonal pattern
• Temporary surge; usually controlled within weeks/months
• May persist months to years across globe

Property

Duration

Values

• Routine prevention (immunization, sanitation); accepted as normal
• Active outbreak investigation and control measures
• International coordination, global health response

Property

Response needed

Values

• Malaria endemic in Mindanao; dengue endemic in Metro Manila
• Measles outbreak in school (2023); cholera outbreak in a barangay
• COVID-19 pandemic (2020–present); 1918 Spanish flu pandemic

Property

Philippine example

Values

• SINGLE source; all exposed at same time (or very close time window)
• PERSON-TO-PERSON transmission across successive GENERATIONS

Property

Source of cases

Values

• SHORT exposure period (hours to 1–2 days)
• Exposure spans MULTIPLE WEEKS/MONTHS (ongoing transmission)

Property

Duration of exposure

Values

• SINGLE SHARP PEAK; all cases appear within one incubation period
• SUCCESSIVE WAVES or multiple peaks; each peak = new generation of cases

Property

Epidemic curve shape

Values

• All cases appear within incubation period of source exposure (e.g., 1–2 days for food poisoning, 10–14 days for measles)
• Cases spread over multiple generation times (e.g., 1–2 weeks apart for measles, 2–3 days for flu)

Property

Timeline

Values

• Remove/control source (discard contaminated food, close restaurant); quarantine is less critical
• Isolation + quarantine CRITICAL; vaccine/immunization important

Property

Control measures

Values

• Common exposure (place, event, food) among all cases
• Cases LINKED in a chain; secondary cases have epidemiologic link to primary case

Property

Investigation clue

Values

• Food poisoning from hotel catered event on Jan 15 → all become ill Jan 16–17 (incubation 1–2 days) → epi curve peaks Jan 17 with single sharp peak
• Measles in school: Gen 1 (5 students) → Gen 2 (20 students, 2 weeks later) → Gen 3 (60+ students, 4 weeks later) → multiple waves on epi curve

Property

Example

Values

• BEFORE disease occurs (prepathogenesis)
• Health promotion + specific protection
• Health education, immunization, sanitation, safe food/water, nutrition, exercise, occupational safety, smoking cessation
• Susceptible (disease-free) population

Property

PRIMARY

Values

• EARLY stage of disease (subclinical pathogenesis)
• EARLY DETECTION + PROMPT TREATMENT
• Screening (Pap smear, mammography, BP check, TB sputum), case-finding, early diagnosis, prompt treatment to limit progression
• Asymptomatic or early-stage diseased individuals

Property

SECONDARY

Values

• AFTER disease is clinically established (clinical pathogenesis + outcomes)
• Disability LIMITATION + REHABILITATION
• Surgery, medications, physiotherapy, occupational therapy, vocational training, disability support, chronic disease management
• Symptomatic diseased individuals (chronic conditions)

Property

TERTIARY

Values

• EPIDEMIOLOGIC TRIANGLE: Agent + Host + Environment (chain of infection)
• WEB OF CAUSATION: Multiple interacting risk factors; NO single cause

Property

Causation model

Values

• SPECIFIC infectious agent (bacteria, virus, parasite, fungus)
• MULTIPLE risk factors interact (genetic, lifestyle, environmental, behavioral)

Property

Cause

Values

• TB (Mycobacterium tuberculosis), measles (virus), malaria (Plasmodium parasite), dengue (virus transmitted by mosquito)
• Hypertension (diet + genetics + stress + obesity + age), diabetes (genetics + obesity + physical inactivity), cancer (smoking + genetics + exposure + age)

Property

Examples

Values

• 6-link chain: agent → reservoir → portal of exit → transmission → portal of entry → host
• NOT applicable; multiple risk factors converge over time

Property

Chain of infection

Values

• Break the chain at any link: isolate (break transmission), vaccinate (protect host), sanitation (reduce exposure), treat early
• Address multiple risk factors: lifestyle modification, screening, early detection, chronic disease management

Property

Prevention approach