30.2 Epidemiology, Surveillance & Outbreaks: Measures of Disease Occurrence and Association, Rates, Endemic and Epidemic Levels, PIDSR and RA 11332 Notifiable Diseases, the Ten Steps of Outbreak Investigation and Epidemic Curves

Why this area matters

The TOS asks you to:

• declare outbreaks and epidemics using available parameters;
• carry out interventions during outbreaks;
• monitor outbreaks until they are declared over;
• interpret epidemiologic measures.

Dengue, measles, cholera, typhoid, leptospirosis and food-poisoning outbreaks are frequent in the Philippines. The municipal health officer is often the first to recognize them and must report them under RA 11332.

Measures of Disease Occurrence: Incidence vs. Prevalence

Quantifying disease in populations requires clear differentiation between newly emerging cases and existing morbidity.

Incidence: Quantifying New Events

Incidence measures the rate at which newly diagnosed cases develop in a susceptible population initially free of the target disease over a defined observation window:

• Cumulative Incidence (Incidence Proportion / Attack Rate): The proportion of initially at-risk individuals who develop the condition over a specified timeframe:

$\text{Cumulative Incidence} = \frac{\text{Number of new incident cases during time period}}{\text{Total population at risk at study baseline}}$

• Incidence Rate (Incidence Density): The true rate of disease occurrence taking into account variable periods of person-time observation:

$\text{Incidence Rate} = \frac{\text{Number of new incident cases}}{\text{Total person-time of observation at risk}}$

Denominator note: Individuals who already possess the disease, who are biologically immune, or who have undergone bilateral organ resection (e.g., prior bilateral oophorectomy in ovarian cancer studies) must be strictly excluded from the baseline population at risk.

Prevalence: Quantifying Total Disease Burden

Prevalence reflects the total burden of disease in a designated population at a specific cross-section of time, encompassing both newly incident cases and pre-existing chronic conditions:

• Point Prevalence: The proportion of a population affected by disease at a single precise instant in time:

$\text{Point Prevalence} = \frac{\text{Total existing cases (new + old) at a specific date}}{\text{Total population at that specific date}}$

• Period Prevalence: The proportion of individuals possessing the disease at any point during a defined interval (e.g., calendar year 2026).

The Steady-State Relationship Formula

In a stable population where disease incidence and duration remain constant over time (steady-state dynamics) and prevalence is relatively low ($P < 0.10$), prevalence is approximately equal to the product of incidence and the mean duration of illness:

$P \approx I \times D$

Where:

• $P = \text{Prevalence (dimensionless proportion)}$
• $I = \text{Incidence rate (cases per unit person-time)}$
• $D = \text{Average duration of illness (time)}$

High-Yield Board Exam Traps: Dynamics of P = I x D

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Measures of Association and Public Health Impact

Measures of association quantify the strength of statistical dependency between an exposure factor and a health outcome.

The Standard 2x2 Contingency Table

Relative Risk (Risk Ratio, RR)

Calculated exclusively in cohort studies and randomized controlled trials (RCTs) where baseline incidence rates in exposed and unexposed cohorts can be directly measured:

$RR = \frac{\text{Incidence in exposed cohort } (I_e)}{\text{Incidence in unexposed cohort } (I_u)} = \frac{a / (a + b)}{c / (c + d)}$

• Interpretation:
  • $RR = 1.0$: Null association; risk in exposed equals risk in unexposed.
  • $RR > 1.0$: Positive association; exposure increases the risk of disease (e.g., $RR = 3.0$ indicates exposed individuals have 3 times the risk of unexposed individuals).
  • $RR < 1.0$: Negative/protective association; exposure reduces disease risk (e.g., $RR = 0.40$ indicates a 60% risk reduction, characteristic of effective vaccination).

Odds Ratio (OR)

Calculated in case-control studies (where subjects are recruited based on disease outcome, precluding direct calculation of baseline population incidence) and cross-sectional studies:

$OR = \frac{\text{Odds of exposure in cases}}{\text{Odds of exposure in controls}} = \frac{a / c}{b / d} = \frac{a \times d}{b \times c}$

• The Rare Disease Assumption: In case-control studies, the Odds Ratio closely approximates the Relative Risk ($OR \approx RR$) when the disease under investigation is rare in the general population (prevalence $< 5\%$). Under this condition, $a$ is negligible relative to $b$ ($a + b \approx b$), and $c$ is negligible relative to $d$ ($c + d \approx d$), making the mathematical ratio identical.

Absolute Risk Reduction (ARR) & Attributable Risk (AR)

Attributable Risk (Risk Difference, AR) represents the absolute excess rate of disease directly attributable to the exposure among exposed individuals:

$AR = I_e - I_u = \frac{a}{a + b} - \frac{c}{c + d}$

When evaluating a beneficial therapeutic or preventive intervention in an RCT, this absolute difference is termed the Absolute Risk Reduction (ARR):

$ARR = I_{\text{control}} - I_{\text{experimental}}$

Relative Risk Reduction (RRR) and Attributable Risk Percent (AR%)

Quantifies the proportion of disease in the exposed cohort that would be eliminated if the exposure were completely abolished:

$AR\% = \frac{I_e - I_u}{I_e} \times 100\% = \frac{RR - 1}{RR} \times 100\%$

For therapeutic trials:

$RRR = \frac{I_{\text{control}} - I_{\text{experimental}}}{I_{\text{control}}} \times 100\% = 1 - RR$

Number Needed to Treat (NNT) and Number Needed to Harm (NNH)

The Number Needed to Treat (NNT) represents the number of patients who must receive a specific therapeutic intervention over a defined time period to prevent one additional adverse clinical outcome:

$NNT = \frac{1}{ARR} = \frac{1}{I_{\text{control}} - I_{\text{experimental}}}$

The Number Needed to Harm (NNH) represents the number of patients exposed to a hazardous agent or adverse drug reaction required to produce one additional adverse event:

$NNH = \frac{1}{ARI} = \frac{1}{I_{\text{exposed}} - I_{\text{unexposed}}}$

Strict Board Exam Calculation Rule: NNT and NNH values are always rounded upward to the nearest whole integer (e.g., an NNT of 12.1 is reported as 13 patients), because treating 12 patients would achieve sub-threshold clinical benefit.

Commonly used rates and levels of disease

• Levels of disease occurrence:
  • sporadic: occasional cases;
  • endemic: the usual, constant presence in an area, such as malaria in Palawan;
  • hyperendemic: persistently high levels;
  • epidemic or outbreak: occurrence clearly in excess of what is expected for that place, time and population;
  • pandemic: an epidemic across several countries or continents.
• Detecting an epidemic: the expected level is set from previous years, for example with an epidemic threshold such as the 5-year mean plus 2 standard deviations. A single case of a disease that is absent or targeted for elimination (polio, diphtheria in a vaccinated area, human avian influenza) is treated as an outbreak.
• Declaring an outbreak over: usually after two maximum incubation periods pass without new cases.
• Herd immunity: when enough of a population is immune, transmission stops even among the unvaccinated. The threshold is about 1 minus 1/R0. For measles, with an R0 of 12 to 18, about 95% coverage is needed.

Disease Surveillance in the Philippines: PIDSR Framework

Disease surveillance is governed nationally by the Philippine Integrated Disease Surveillance and Response (PIDSR) system under the Department of Health (DOH) Epidemiology Bureau, anchored by the legal authority of Republic Act No. 11332 (Mandatory Reporting of Notifiable Diseases and Health Events of Public Health Concern Act).

Statutory Disease Categories under RA 11332

The revised Implementing Rules and Regulations of RA 11332 (2020) list the notifiable diseases in two categories:

Any cluster or unusual event of public health concern is reported at once through event-based surveillance. Failure to report, or non-cooperation with authorities, is penalized under RA 11332. The penalty is a fine of 20,000 to 50,000 pesos, imprisonment of 1 to 6 months, or both.

How surveillance works in practice:

• PIDSR uses disease reporting units: hospitals, RHUs and clinics, staffed by surveillance officers.
• Epidemiology and surveillance units (municipal or city, provincial and regional) collect, analyze and act on reports.
• Event-based surveillance captures unusual events and rumors.
• Surveillance can be:
  • passive: routine reports from health facilities;
  • active: staff seek cases, for example AFP case-finding;
  • sentinel: selected sites, for example influenza;
  • syndromic, for example SPEED after disasters.

Outbreak Investigation: The 10-Step Standard Protocol

When epidemic clustering occurs, field epidemiologists execute a standardized 10-step protocol:

1. Prepare for Fieldwork: Secure administrative authorizations, assemble multidisciplinary investigation team, procure field diagnostic supplies and personal protective equipment (PPE).
2. Establish the Existence of an Outbreak: Confirm that observed case counts significantly exceed expected baseline endemic incidence for that specific geographic region and timeframe.
3. Verify the Diagnosis: Review clinical charts, interview attending clinicians, and collect biological specimens to verify etiologic diagnosis via reference laboratory testing.
4. Construct a Working Case Definition: Develop standardized criteria based on person, place, time, and clinical/laboratory features, categorizing cases as Confirmed, Probable, or Suspect.
5. Find Cases Systematically and Record Information: Conduct active case finding across clinics and communities; record demographic, clinical, and exposure details in a standardized line listing.
6. Perform Descriptive Epidemiology: Characterize the outbreak by time (epidemic curve), place (spot map of cases), and person (age, sex, attack rates by subgroup).
7. Develop Hypotheses: Formulate hypotheses regarding the source of the pathogen, vehicle of transmission, and exposure route based on descriptive data and biological plausibility.
8. Evaluate Hypotheses Analytically: Conduct an analytical observational study (a retrospective cohort study comparing attack rates and relative risks if the population is well-defined, e.g., a catered banquet; or a case-control study comparing odds ratios if the population is open and undefined).
9. Implement Immediate Control and Prevention Measures: Eliminate the source (e.g., close contaminated water wells, recall contaminated food), interrupt transmission (vector control, sanitary disinfection), and protect susceptible hosts (vaccination, chemoprophylaxis, barrier precautions). Critical rule: Control measures must be executed immediately when evidence emerges, without delaying for analytical study completion.
10. Communicate Findings and Initiate Ongoing Surveillance: Disseminate a comprehensive written report to public health authorities and the public; establish ongoing post-outbreak surveillance.

Epidemic Curve Patterns

• Point Source Epidemic: Cases rise sharply, peak rapidly within a single incubation period, and decline gradually. Reflects simultaneous exposure of a cohort to a single common vehicle (e.g., staphylococcal enterotoxin food poisoning).
• Continuous Common Source Epidemic: Cases rise gradually and maintain a prolonged plateau without a sharp peak, reflecting persistent ongoing exposure to a contaminated source (e.g., ongoing contamination of a municipal water reservoir).
• Propagated (Person-to-Person) Epidemic: A series of progressively taller, undulating peaks separated by an average incubation period interval, reflecting successive waves of human-to-human transmission (e.g., measles, pertussis, SARS-CoV-2).

Typical vehicles of food and water outbreaks:

• S. aureus and B. cereus: short incubation;
• Salmonella;
• cholera and typhoid: contaminated water systems;
• hepatitis A.

In a food-borne outbreak, food-specific attack rates are compared. The food with the largest difference in attack rate (or highest relative risk) between people who ate it and those who did not is the likely vehicle, especially if most cases ate it.