Midwife Licensure Exam Community & Public Health — Epidemiology & BiostatisticsDetailed Explanation

Definition and Purpose of Epidemiology

Epidemiology is formally defined as the study of the distribution and determinants of health-related states or events in specified populations, and the application of this study to the control of health problems. Break this down: 'distribution' means describing who gets sick, where, and when — the classic epidemiologic triad of person, place, and time. 'Determinants' means identifying the causes and risk factors that influence whether a disease occurs. 'Control' means using the findings to prevent or reduce disease burden in the community. The community health nurse uses epidemiology every day: when mapping dengue cases in a barangay, conducting contact tracing for tuberculosis, computing the infant mortality rate of a municipality, or evaluating whether a maternal health program reduced maternal deaths. Epidemiology is not just for doctors or researchers — under RA 9173 (Philippine Nursing Act of 2002), community health nursing practice explicitly includes disease surveillance, case-finding, and community health assessment, all of which are epidemiologic functions. The three descriptive dimensions — person, place, and time — guide every community health assessment: - PERSON: Who is affected? Age group, sex, occupation, socioeconomic class, nutritional status, immunization history, behavior. - PLACE: Where? Specific barangay, province, urban vs. rural, near bodies of water, proximity to a market or health facility. - TIME: When? Season (dengue peaks June–November in the Philippines), trend over years, point in time vs. sustained period.

The Epidemiologic Triangle: Agent, Host, Environment

The epidemiologic triangle (also called the ecologic triangle) is the classic model explaining why communicable diseases occur. Disease happens when three elements interact in an IMBALANCED way: 1. AGENT — the cause of the disease. It can be: - Biologic: bacteria (M. tuberculosis), viruses (dengue virus, HIV), parasites (P. falciparum for malaria), fungi - Chemical: pesticides, heavy metals, toxins - Physical: radiation, heat, trauma - Nutritional: deficiency (e.g., iron deficiency anemia) or excess 2. HOST — the person or animal that can harbor the disease. Host factors affecting susceptibility include: - Age (infants and elderly are more susceptible) - Sex and genetic makeup - Nutritional and immunologic status - Behavior (smoking, sexual practices, hygiene) - Existing immunity (vaccination status) 3. ENVIRONMENT — all external conditions surrounding the host-agent interaction: - Physical: climate, geography, water supply, sanitation - Biologic: presence of vectors, reservoirs - Social: poverty, crowding, access to healthcare, cultural practices Disease DOES NOT occur when these three are in BALANCE (equilibrium). Disease OCCURS when the balance is disrupted — for example, when the agent becomes more virulent, the host becomes more susceptible, or the environment becomes more favorable for transmission. THE VECTOR is often added to the triangle (especially in the Philippines, where vector-borne diseases like dengue, malaria, and schistosomiasis are major public health problems). The vector (e.g., Aedes aegypti mosquito, Anopheles mosquito, Oncomelania snail) is the living organism that carries the agent from reservoir to host. Breaking any point of the triangle INTERRUPTS disease: - Target the AGENT: antibiotics, disinfection, sterilization - Target the HOST: immunization, nutrition, health education, chemoprophylaxis - Target the ENVIRONMENT: water sanitation, vector control, housing improvement - Target the VECTOR: larviciding, fogging, environmental sanitation

Chain of Infection: The Six Links

The chain of infection is the sequence of events that must occur for a communicable disease to spread from one person to another. There are SIX links in the chain. ALL six must be present and connected for disease transmission to occur. Breaking even ONE link stops the spread. 1. INFECTIOUS/ETIOLOGIC AGENT — the pathogen (bacterium, virus, parasite, fungus). Characteristics: pathogenicity, virulence, infectivity, mode of action. 2. RESERVOIR — the natural habitat where the agent lives, grows, and multiplies: - Human reservoir: carriers (typhoid Mary concept), cases (active TB patients) - Animal reservoir: zoonoses (rabies from dogs, leptospirosis from rats) - Environmental reservoir: soil (tetanus spores), water (Vibrio cholerae) 3. PORTAL OF EXIT — how the agent LEAVES the reservoir: - Respiratory tract (coughing, sneezing — TB, influenza, COVID-19) - Gastrointestinal tract (feces — cholera, typhoid, hepatitis A) - Genitourinary tract (urine, sexual secretions — HIV, gonorrhea) - Skin/wounds (drainage — staphylococcal infections) - Blood (needles, blood transfusion — HIV, hepatitis B) 4. MODE OF TRANSMISSION — how the agent travels from the reservoir to the new host: - CONTACT transmission: direct (person-to-person touch, sexual intercourse) or indirect (fomites — contaminated objects like doorknobs, syringes) - DROPLET transmission: large respiratory droplets (travel <1 meter) — influenza, meningitis - AIRBORNE transmission: tiny droplet nuclei (travel >1 meter, remain suspended) — TB, measles, varicella - VEHICLE-BORNE: contaminated food, water, blood, or drugs - VECTOR-BORNE: biological (dengue via Aedes aegypti) or mechanical (flies carrying typhoid) 5. PORTAL OF ENTRY — how the agent ENTERS the new host: - Respiratory tract (inhalation) - GI tract (ingestion) - Skin/mucous membranes (contact, wound entry) - Placenta (congenital transmission — TORCH infections) - Blood/parenteral 6. SUSCEPTIBLE HOST — a person who CANNOT resist the agent because of: - No immunity (unvaccinated) - Compromised immunity (HIV, malnutrition, cancer, elderly, infants) - Breaks in skin or mucous membranes NURSING INTERVENTIONS AT EACH LINK: - Agent: proper waste disposal, disinfection, antibiotics/antivirals - Reservoir: case treatment and isolation, animal control, environmental sanitation - Portal of exit: respiratory etiquette, PPE, wound care - Mode of transmission: handwashing (MOST EFFECTIVE general measure), vector control, safe water, food safety, condom use - Portal of entry: PPE (masks, gloves), skin integrity maintenance - Susceptible host: immunization (MOST SPECIFIC protection), nutrition improvement, health education

Levels of Disease Occurrence: Sporadic, Endemic, Epidemic, Pandemic

Epidemiologists classify the FREQUENCY of disease in a community to determine whether the situation is normal or alarming: 1. SPORADIC — the disease occurs occasionally, irregularly, and unpredictably. There is no pattern and cases are isolated. Example: tetanus cases in various provinces at different times. 2. ENDEMIC — the disease is CONSTANTLY PRESENT in a specific geographic area at a USUAL or EXPECTED level. The word 'endemic' comes from Greek 'en demos' (among the people). Example: malaria is endemic in certain municipalities of Palawan and Mindanao; schistosomiasis is endemic in the Visayas and parts of Mindanao. NOTE: A disease can be endemic at a HIGH level — 'endemic' does NOT mean the disease is minor or harmless. 3. EPIDEMIC (OUTBREAK) — the occurrence of cases CLEARLY IN EXCESS of what is normally EXPECTED in a community, region, or season. The key word is 'excess of expected' — even two or three cases of a rare disease can constitute an epidemic. Example: a sudden surge of dengue cases above the epidemic threshold in a municipality during rainy season. - The terms 'epidemic' and 'outbreak' are largely synonymous, though 'outbreak' is sometimes preferred when the excess is localized (e.g., a single community or institution). 4. PANDEMIC — an epidemic that spreads across MULTIPLE COUNTRIES or CONTINENTS. Example: COVID-19 declared a pandemic by WHO in March 2020; the 1918 influenza pandemic; HIV/AIDS. A pandemic is NOT defined by severity — it is defined by GEOGRAPHIC SPREAD. 5. HYPERENDEMIC — some textbooks also describe hyperendemic situations where a disease is present at a HIGH, sustained level affecting most of the population — important for NLE preparation as it may appear as a distractor. In Philippine practice, the DOH sets epidemic thresholds (alert thresholds) for notifiable diseases. When reported cases exceed the threshold, a CODE RED alert is declared and an outbreak investigation is triggered.

Natural History of Disease and Levels of Prevention

The NATURAL HISTORY OF DISEASE describes the course of a disease from its earliest beginning (before any symptoms) to its final outcome (recovery, disability, or death) WITHOUT any medical intervention. Understanding this helps nurses know WHEN and HOW to intervene. Two main stages: 1. PRE-PATHOGENESIS PERIOD (before disease begins): - The person is susceptible but disease has not yet started - The agent exists in the environment; the host is exposed to risk factors - This is the TARGET of PRIMARY PREVENTION 2. PATHOGENESIS PERIOD (disease process begins): a. EARLY PATHOGENESIS / SUBCLINICAL STAGE — the agent is present and causing tissue changes, but the person has no symptoms yet. The disease is detectable by screening tests. This is the target of SECONDARY PREVENTION. b. CLINICAL STAGE — signs and symptoms appear (clinical disease). Treatment is needed. c. RESOLUTION — the disease ends in recovery, disability, or death. LEVELS OF PREVENTION (Leavell and Clark Model): PRIMARY PREVENTION (Pre-pathogenesis stage): Goal: PREVENT the disease from occurring Two sub-levels: - Health Promotion: general measures to improve overall health — nutrition counseling, health education, decent housing, exercise, mental health support, regular check-ups, family planning - Specific Protection: targeted measures against specific diseases — IMMUNIZATION (highest NLE priority), use of PPE, fluoridation of water, proper sanitation, chemoprophylaxis (e.g., malaria prophylaxis) SECONDARY PREVENTION (Early pathogenesis stage): Goal: DETECT EARLY and TREAT PROMPTLY to stop progression Two sub-levels: - Early Diagnosis and Prompt Treatment: screening programs (cancer screening, neonatal screening), case-finding (TB symptomatic screening), contact tracing - Disability Limitation: treatment to prevent complications and permanent damage TERTIARY PREVENTION (Late pathogenesis / outcome stage): Goal: REDUCE DISABILITY and REHABILITATE Two sub-levels: - Disability Limitation: preventing further deterioration (e.g., preventing contractures in stroke patients) - Rehabilitation: restoring function — physical, occupational, speech, vocational, social rehabilitation NLE KEY RULE: When the question involves IMMUNIZATION, the answer is ALWAYS Primary Prevention – Specific Protection. When it involves SCREENING (Papanicolaou smear, mammography, neonatal hearing test), the answer is Secondary Prevention.

Disease Surveillance in the Philippines: PIDSR, ESU, and Reporting

Surveillance is the ongoing, systematic collection, analysis, interpretation, and dissemination of health data for the purpose of public health action. It is the foundation of outbreak detection and disease control. In the Philippines, surveillance is legally mandated and operationalized through specific systems: PHILIPPINE INTEGRATED DISEASE SURVEILLANCE AND RESPONSE (PIDSR): - The national system for reporting notifiable diseases from all health facilities upward - Managed by the DOH Epidemiology Bureau - Reports flow from: Barangay Health Center → Rural Health Unit → City/Municipal Health Office → Provincial Health Office → Regional Health Office → DOH Epidemiology Bureau - Covers NOTIFIABLE DISEASES — diseases mandatorily reported by healthcare workers - Two categories of notifiable diseases: * Immediately Notifiable (within 24 hours): cholera, plague, SARS, measles, AFP, rabies, human cases of avian flu, viral hemorrhagic fevers * Weekly Notifiable (within 7 days): dengue, malaria, TB, typhoid fever, etc. EPIDEMIOLOGY AND SURVEILLANCE UNITS (ESUs): - Established at regional, provincial, and city/municipal levels - Functions: receive surveillance reports, analyze data, investigate outbreaks, provide technical support to local health offices - The nurse at the RHU level directly feeds data into ESUs FHSIS (Field Health Service Information System): - Collects routine service data from RHUs and health centers - Includes patient visits, immunization rates, prenatal care, family planning, nutrition data - The primary source of routine community health data at the grassroots level TYPES OF SURVEILLANCE: 1. PASSIVE SURVEILLANCE — routine reporting by health facilities as part of their regular duties. Most common and least expensive. Example: weekly PIDSR reports from RHUs. LIMITATION: underreporting is common. 2. ACTIVE SURVEILLANCE — health workers ACTIVELY GO OUT to find cases. More accurate but more resource-intensive. Example: nurse conducting door-to-door case-finding during a cholera outbreak. 3. SENTINEL SURVEILLANCE — selected representative sites (sentinel sites) provide detailed data for specific diseases. Example: flu sentinel surveillance at selected hospitals. Under RA 9173, nurses who observe notifiable diseases MUST REPORT — failure to report is a professional and legal violation. This applies to community nurses at RHUs, nurses in schools, occupational nurses, and nurses in hospitals. Community nurses must also understand REPORTING vs. RECORDING: - RECORDING: writing data in the appropriate forms and registers (e.g., individual health record, family health record, RHU registers) - REPORTING: transmitting aggregated data to the next level of the health system

Outbreak Investigation: Steps and Epidemic Curves

When a potential epidemic is detected, a systematic outbreak investigation is conducted to identify the source, confirm the cause, describe the spread, and implement control measures. The steps are sequential but in practice may occur simultaneously. STEPS IN OUTBREAK INVESTIGATION: 1. PREPARE FOR FIELD WORK — organize the investigation team, gather supplies, review background data about the area and disease. 2. ESTABLISH/VERIFY THE DIAGNOSIS — confirm that the cases are what they appear to be. Collect specimens (stool, blood, swabs), conduct lab tests. Prevent over-reporting due to misdiagnosis. 3. CONFIRM THE EXISTENCE OF AN OUTBREAK — compare CURRENT case counts with BASELINE EXPECTED numbers. Is this truly an excess? Use historical data, expected seasonal levels, and alert thresholds. 4. DEFINE AND IDENTIFY CASES (Case Definition): - Create a CASE DEFINITION: a set of standard clinical, laboratory, and epidemiologic criteria to decide who counts as a case - A good case definition is SENSITIVE enough to find most real cases but SPECIFIC enough to exclude non-cases - Case definitions may be 'confirmed,' 'probable,' or 'suspected' - Conduct CASE FINDING: use passive (review medical records) and active (community surveys) methods 5. DESCRIBE THE OUTBREAK BY PERSON, PLACE, AND TIME: - PERSON: age, sex, occupation, common exposures (what did they eat? where did they go?) - PLACE: spot map showing where cases cluster - TIME: construct an EPIDEMIC CURVE 6. DEVELOP AND TEST HYPOTHESES: - Based on descriptive findings, formulate a hypothesis about the SOURCE and MODE of transmission - Test the hypothesis using analytic studies (cohort or case-control study of the exposed group) 7. IMPLEMENT CONTROL AND PREVENTION MEASURES: - Measures should begin AS EARLY AS POSSIBLE — do NOT wait for all steps to be completed - Measures may include: case isolation, food recall, water chlorination, mass treatment, vaccination 8. COMMUNICATE FINDINGS AND WRITE A REPORT: - Disseminate findings to health authorities, the community, and relevant stakeholders - Write an official outbreak investigation report - Continue enhanced surveillance to detect new cases THE EPIDEMIC CURVE: An epidemic curve is a HISTOGRAM showing the number of cases on the Y-axis and time (date of onset) on the X-axis. The shape of the curve reveals the type of outbreak: - POINT-SOURCE EPIDEMIC: all cases exposed to the SAME SOURCE at the SAME TIME. The curve shows a SINGLE, SHARP PEAK followed by a rapid decline. The peak is usually within one incubation period. Example: food poisoning at a single event (wedding reception with contaminated food). - CONTINUOUS/ONGOING SOURCE: exposure to a CONTAMINATED SOURCE continues over time. The curve shows a plateau or gradual rise and fall. Example: contaminated water supply affecting a community over weeks. - PROPAGATED (PERSON-TO-PERSON) OUTBREAK: disease spreads from person to person. The curve shows MULTIPLE PEAKS (successive waves) with each wave about one incubation period apart, and gradually increasing case numbers. Example: a measles or chickenpox outbreak in a school.

Vital Statistics: Key Rates and Formulas

Vital statistics are quantitative data about vital events — births, deaths, marriages, divorces, and fetal deaths. These are collected through the CIVIL REGISTRATION SYSTEM (Philippine Statistics Authority — PSA is responsible for registration and compilation). Vital statistics are the primary source of birth rate, death rate, and mortality data for the Philippines. Understanding rates, ratios, and proportions is fundamental: - RATE: measures the frequency of an event in a defined population OVER TIME. Formula: (Number of events / Population at risk) × constant multiplier. The multiplier (1,000; 10,000; 100,000) is stated — always use what the problem gives you. - RATIO: comparison of two numbers (numerator NOT included in denominator) - PROPORTION: numerator IS included in the denominator (a fraction — always between 0 and 1, often expressed as a percentage) KEY VITAL STATISTICS FORMULAS: 1. CRUDE BIRTH RATE (CBR): Formula: (Total live births in a year / Midyear population) × 1,000 Measures: The overall frequency of births in a population Denominator: MIDYEAR POPULATION (population estimated at July 1 of the year) 2. CRUDE DEATH RATE (CDR): Formula: (Total deaths in a year / Midyear population) × 1,000 Measures: The overall frequency of deaths in a population Denominator: MIDYEAR POPULATION 3. INFANT MORTALITY RATE (IMR): Formula: (Deaths under 1 year of age / Total live births) × 1,000 Measures: Risk of dying BEFORE reaching age 1; a key indicator of maternal-child health and sanitation Denominator: TOTAL LIVE BIRTHS (NOT midyear population) NOTE: IMR is a SENSITIVE indicator of a country's overall health system quality 4. NEONATAL MORTALITY RATE: Formula: (Deaths under 28 days of age / Total live births) × 1,000 Measures: Risk of dying in the first 28 days of life Denominator: TOTAL LIVE BIRTHS 5. MATERNAL MORTALITY RATE/RATIO (MMR): Formula: (Maternal deaths / Total live births) × 100,000 (Some references use × 1,000 or × 10,000 — always use the constant specified in the question) Definition: Maternal death = death of a woman WHILE PREGNANT or within 42 DAYS of termination of pregnancy, from causes related to or aggravated by the pregnancy or its management Denominator: TOTAL LIVE BIRTHS (technically makes it a RATIO — hence 'Maternal Mortality Ratio') 6. FETAL DEATH RATE: Formula: (Fetal deaths / [Total live births + Fetal deaths]) × 1,000 Note: The denominator includes BOTH live births AND fetal deaths (total deliveries) 7. CAUSE-SPECIFIC DEATH RATE: Formula: (Deaths from a specific cause / Midyear population) × 100,000 Denominator: MIDYEAR POPULATION 8. PROPORTIONATE MORTALITY RATE: Formula: (Deaths from a specific cause / Total deaths from all causes) × 100 Measures: The PROPORTION (percentage) of ALL deaths attributed to a specific cause Note: This is a PROPORTION, not a true rate — it does NOT measure risk, only relative contribution MEMORY TIP FOR DENOMINATORS: - MIDYEAR POPULATION: CBR, CDR, Cause-specific death rate - LIVE BIRTHS: IMR, Neonatal MR, MMR, Fetal Death Rate - TOTAL DEATHS: Proportionate Mortality Rate

Morbidity Measures: Incidence, Prevalence, Attack Rate, and Case Fatality Rate

Morbidity measures quantify the FREQUENCY OF DISEASE in a population. They are distinct from mortality measures (which count deaths) and are essential for understanding disease burden. 1. INCIDENCE RATE: Formula: (NEW cases of a disease during a specified period / Population at risk during that period) × constant Measures: The RISK or PROBABILITY of developing a disease; the RATE OF NEW OCCURRENCE Key word: NEW cases ONLY Example: 200 new dengue cases in Cebu City in July among 100,000 at-risk residents = Incidence rate of 200 per 100,000. Clinical relevance: High incidence = disease is spreading rapidly; indicates need for prevention 2. PREVALENCE RATE: Formula: (ALL existing cases at a point in time or during a period / Total population) × constant Types: a. POINT PREVALENCE: all cases at a SINGLE POINT in time (e.g., on June 1, 2024, how many people have hypertension?) b. PERIOD PREVALENCE: all cases during a PERIOD of time (e.g., how many people had influenza during January 2024?) Measures: The BURDEN or LOAD of disease in a population; includes both OLD and NEW cases Key word: ALL existing cases Clinical relevance: High prevalence = large disease burden; indicates need for treatment and long-term management resources RELATIONSHIP between Incidence and Prevalence: Prevalence ≈ Incidence × Duration of disease - A disease with HIGH incidence but SHORT duration (e.g., the common cold) may have LOW prevalence - A disease with MODERATE incidence but LONG duration (e.g., diabetes, HIV) will have HIGH prevalence - Effective TREATMENT that shortens disease duration REDUCES prevalence without changing incidence 3. ATTACK RATE (AR): Formula: (Number of cases / Population at risk during an outbreak) × 100 Measures: A special form of INCIDENCE RATE used specifically during OUTBREAKS, expressed as a PERCENTAGE Example: During a food poisoning outbreak at a fiesta, 30 of 80 people who attended got sick. AR = (30/80) × 100 = 37.5% Secondary Attack Rate: measures spread from index cases to close contacts (e.g., household members) 4. CASE FATALITY RATE (CFR): Formula: (Deaths caused by a specific disease / Number of CASES of that disease) × 100 Measures: The SEVERITY or LETHALITY of a disease — what proportion of those WHO HAVE the disease DIE from it Note: This is a PROPORTION, not a true rate (no time element), but conventionally called a 'rate' Example: COVID-19 with 1,000 confirmed cases and 30 deaths: CFR = (30/1,000) × 100 = 3% High CFR = very deadly disease (e.g., rabies has ~100% CFR without post-exposure prophylaxis); Low CFR = less lethal NLE CLASSIC DISTINCTION — INCIDENCE vs. PREVALENCE: - INCIDENCE = NEW cases (measures RISK — likelihood of getting the disease) - PREVALENCE = ALL existing cases (measures BURDEN — how many people currently have the disease) - Think of a swimming pool: incidence = people jumping IN (new cases); prevalence = all people currently IN the pool (existing cases) INCIDENCE vs. PREVALENCE in Study Design: - Cohort studies measure INCIDENCE (they follow people over time and count new cases) - Cross-sectional surveys measure PREVALENCE (they capture a snapshot in time)

Screening Tests: Sensitivity, Specificity, and Predictive Values

SCREENING is the application of a test or examination to apparently HEALTHY (asymptomatic) people to detect early signs of disease or identify those at high risk. It is an activity of SECONDARY PREVENTION (early diagnosis). Important examples in the Philippines: neonatal screening (R.A. 9288), cervical cancer screening (Pap smear, VIA), breast cancer screening (mammography), TB symptomatic screening at RHUs. A screening test is evaluated by its VALIDITY (accuracy) — how well does it correctly identify those who have the disease and those who don't? FOUR OUTCOMES OF A SCREENING TEST (2×2 Table): | Disease+ | Disease− Test+ | True Pos | False Pos (Type I error) Test− | False Neg | True Neg (Type II error) | (TP) | (FP) Test− | (FN) | (TN) 1. SENSITIVITY: Formula: TP / (TP + FN) × 100 Meaning: The ability of the test to correctly identify individuals WHO HAVE the disease (true positive rate) High sensitivity = FEW FALSE NEGATIVES = test misses very few real cases Clinical use: HIGH SENSITIVITY is important when missing a case is DANGEROUS (e.g., HIV screening, cancer screening) Memory aid: SeNsitivity — 'N' for Negative — a SENSITIVE test, when NEGATIVE, rules the disease OUT (SnNout) 2. SPECIFICITY: Formula: TN / (TN + FP) × 100 Meaning: The ability of the test to correctly identify individuals WHO DO NOT HAVE the disease (true negative rate) High specificity = FEW FALSE POSITIVES = test rarely labels healthy people as sick Clinical use: HIGH SPECIFICITY is important when a false positive causes serious consequences (e.g., before invasive confirmatory tests) Memory aid: SpEcificity — 'P' for Positive — a SPECIFIC test, when POSITIVE, rules the disease IN (SpPin) 3. POSITIVE PREDICTIVE VALUE (PPV): Formula: TP / (TP + FP) × 100 Meaning: Given a POSITIVE test result, what is the probability that the person TRULY HAS the disease? PPV is INFLUENCED BY PREVALENCE — in a low-prevalence population, even a specific test has a LOW PPV (many false positives relative to true positives) 4. NEGATIVE PREDICTIVE VALUE (NPV): Formula: TN / (TN + FN) × 100 Meaning: Given a NEGATIVE test result, what is the probability that the person TRULY DOES NOT have the disease? CRITERIA FOR A GOOD SCREENING PROGRAM: - The disease must be important (significant morbidity/mortality) - There must be a recognizable early-stage, latent, or presymptomatic phase - Effective treatment must be available - The test must be acceptable, safe, reliable, and inexpensive - Treatment at the early stage must provide better outcomes than treatment at the clinical stage SENSITIVITY vs. SPECIFICITY TRADE-OFF: Increasing sensitivity DECREASES specificity, and vice versa. The CUT-OFF POINT of a test determines this trade-off. For mass screening (population level), HIGH SENSITIVITY is preferred (do not miss cases). For confirmatory testing, HIGH SPECIFICITY is preferred (do not falsely label healthy people).

Epidemiologic Study Designs

Epidemiologic studies can be classified as DESCRIPTIVE (describe who, where, when without testing a hypothesis) or ANALYTIC (test a hypothesis about cause-effect relationships). DESCRIPTIVE STUDIES: 1. CASE REPORT: description of a single interesting/unusual case. Lowest level of evidence. Generates hypotheses. 2. CASE SERIES: description of a group of cases with similar characteristics. 3. CROSS-SECTIONAL (PREVALENCE) STUDY: - Data on exposure and disease status are collected at the SAME POINT IN TIME - Measures PREVALENCE (not incidence) - Relatively quick and inexpensive - Cannot establish temporal relationship (cannot prove cause precedes effect) - Example: a community survey measuring blood pressure AND dietary salt intake of residents on the same day ANALYTIC STUDIES (Observational): 4. COHORT STUDY: - Follow a group of EXPOSED and a group of NON-EXPOSED individuals FORWARD in time - Measure and compare INCIDENCE of disease between groups - Yields: RELATIVE RISK (RR) — how many times more likely is the exposed group to develop disease vs. the unexposed group? - RR > 1 = exposure increases risk; RR < 1 = exposure is protective; RR = 1 = no association - Good for: common diseases, establishing temporal relationship (exposure precedes disease) - Example: following smokers vs. non-smokers for 10 years to compare lung cancer incidence 5. CASE-CONTROL STUDY: - Start with CASES (people who HAVE the disease) and CONTROLS (people WITHOUT the disease) - Look BACKWARDS in time to compare PAST EXPOSURES - Yields: ODDS RATIO (OR) — approximates relative risk; OR > 1 = exposure associated with disease - Good for: RARE diseases (cannot afford to wait for rare disease to develop in a cohort) - Example: comparing HIV-positive patients (cases) vs. HIV-negative controls for past needle-sharing behavior EXPERIMENTAL STUDIES (Interventional): 6. RANDOMISED CONTROLLED TRIAL (RCT): - Investigator ASSIGNS the intervention (treatment or control) - Randomisation eliminates confounding bias - HIGHEST LEVEL OF EVIDENCE for causation - Example: randomly assigning patients to a new TB drug vs. standard treatment STRENGTH OF EVIDENCE (Hierarchy): RCT > Cohort > Case-Control > Cross-sectional > Case Series > Case Report MEASURES OF ASSOCIATION: - RELATIVE RISK (RR): used in cohort studies. RR = (incidence in exposed / incidence in unexposed) - ODDS RATIO (OR): used in case-control studies. OR = (exposure odds in cases / exposure odds in controls) - ATTRIBUTABLE RISK (AR): excess risk attributable to the exposure (incidence in exposed − incidence in unexposed)

Basic Statistics: Measures of Central Tendency

Basic descriptive statistics help the community health nurse summarize and interpret health data from the community. MEASURES OF CENTRAL TENDENCY describe the CENTER or typical value of a dataset: 1. MEAN (Arithmetic Average): Formula: Sum of all values / Number of values Properties: - Uses ALL data points - SENSITIVE TO OUTLIERS (extreme values pull the mean up or down) - Best for DATA THAT IS NORMALLY DISTRIBUTED (symmetrical, no extreme values) Example: Mean age of 5 children: 2, 3, 4, 5, 6 = (2+3+4+5+6)/5 = 20/5 = 4 years Nursing use: Mean birth weight of babies in a barangay, mean blood pressure readings 2. MEDIAN: Definition: The MIDDLE VALUE when data are arranged in ORDER For ODD number of values: the single middle value For EVEN number of values: the average of the two middle values Properties: - NOT affected by outliers - BEST FOR SKEWED DATA or when extreme values are present - Best measure for ORDINAL data or when the distribution is asymmetric Example: Ages: 2, 3, 4, 5, 100 → Median = 4 (middle value), Mean = 22.8 (distorted by the outlier 100) Nursing use: Median household income, median waiting time at health centers 3. MODE: Definition: The value that OCCURS MOST FREQUENTLY in the dataset Properties: - Can have no mode, one mode (unimodal), or multiple modes (bimodal, multimodal) - Least affected by outliers - Best for NOMINAL DATA and to identify the most common value Example: Blood types of 10 patients: A, A, A, B, B, O, O, O, O, AB → Mode = O (occurs 4 times) Nursing use: Most common diagnosis in a barangay, most frequent complaint at an RHU MEASURES OF DISPERSION (additional statistics): - RANGE: difference between highest and lowest values (simple but crude measure of spread) - STANDARD DEVIATION (SD): average distance of each value from the mean; measures how spread out the data is - VARIANCE: SD squared NORMAL DISTRIBUTION: - Bell-shaped, symmetrical curve - In a normal distribution: Mean = Median = Mode - 68% of data falls within ±1 SD of the mean - 95% within ±2 SD - 99.7% within ±3 SD PHILIPPINE NURSING APPLICATION: The community health nurse uses these measures when: - Analyzing nutrition survey data (mean weight-for-age, median MUAC) - Interpreting vital statistics reports - Summarizing community diagnosis data for BHC/RHU reports - Presenting health program evaluation results to the LGU

STEP 1: Identify what each rate measures and its denominator. CBR and CDR use MIDYEAR POPULATION (75,000). IMR and MMR use LIVE BIRTHS (1,200). Cause-specific death rate uses MIDYEAR POPULATION (75,000). STEP 2: Apply the correct multiplier — ×1,000 for CBR, CDR, IMR; ×100,000 for MMR and cause-specific death rate. STEP 3: Substitute the values. This is the most common type of NLE computation question for vital statistics — practice identifying the correct denominator first before computing.

This comprehensive problem tests multiple formulas simultaneously. KEY DISTINCTIONS: (a) Incidence uses NEW cases (250) over population AT RISK (5,000). (b) Prevalence uses ALL EXISTING cases at a point (800 at START of month) over TOTAL population (5,000). (c) Attack rate = incidence expressed as a percentage — same numerator and denominator as incidence. (d) CFR denominator is the number of CASES (250), NOT the total population. (e) Sensitivity = TP / all REAL POSITIVES = 200/(200+50). (f) Specificity = TN / all REAL NEGATIVES = 4,650/(4,650+100). The NS1 test has good specificity (97.9%) but only 80% sensitivity — meaning it misses 20% of true dengue cases.

This is a classic FOOD-BORNE OUTBREAK investigation question. Attack rates are computed separately for each food to identify the suspect vehicle. The food with the HIGHEST ATTACK RATE among those who ate it AND LOWEST ATTACK RATE among those who did NOT eat it is the most likely culprit. Here, kare-kare meets both criteria: 75.6% attack rate for eaters vs. only 13.3% for non-eaters. This food-specific attack rate analysis is the KEY step in hypothesis testing for food poisoning outbreaks — a high-yield NLE application of the attack rate concept.

In this dataset, 200 mmHg is an outlier (likely a hypertensive crisis patient). The mean (137.2) is pulled higher than 7 of the 9 actual readings, making it misleading. The median (130) better represents the 'typical' blood pressure in this group. This principle — that the median is preferred for SKEWED DATA with outliers — is a classic NLE biostatistics question. When data is normally distributed (no extreme values), the mean is appropriate. When data is skewed or has outliers, use the median.

This sequencing question is a high-yield NLE topic. The key message is that CONTROL MEASURES ARE NOT THE LAST STEP — they should be initiated as soon as the evidence is sufficient to guide action. In a gastroenteritis outbreak, the nurse can begin promoting handwashing, safe water practices, and oral rehydration therapy WHILE still completing the investigation. The final step (communicate findings) comes AFTER control measures have been initiated, not before. Remember: the goal of outbreak investigation is ultimately to PROTECT PUBLIC HEALTH, not just to complete paperwork.