Vital Statistics

Definition & Scope of Vital Statistics

• Application of statistical methods to vital facts/events: births, deaths, marriages, migration, illnesses.
• Also denotes the government database (civil register) where these events are officially recorded.
• NOT to be confused with body-measurement usage (“vital statistics” = bust-waist-hip).

Morbidity vs. Mortality

• Morbidity
  • State or incidence of disease/unhealthiness within a population.
  • Data classified by disease type, gender, age, area.
  • Morbidity scores or predicted morbidity may be assigned using scoring systems.
• Mortality
  • Incidence of death within a population.
  • Expressed through several rates: crude, perinatal, maternal, infant, child, age-specific, standardized, etc.
  • Generally stated per 1 000 individuals per year.

Importance of Vital Statistics

• Reveal healthfulness of a community & gauge success/failure of health services.
• Provide clues to what type of health work is required.
• Death: unique, universal, final → age & cause give instant population-health picture.
• In high-mortality settings, cause-specific trends highlight program progress.
• With falling mortality, morbidity indicators (chronic disease prevalence, disability) gain importance.

Sources of Vital Statistics in the Philippines

• Philippine Statistics Authority (PSA) – central statistical authority (created 12 Sep 2013 via RA 10625 “Philippine Statistical Act of 2013”).
  • Merged:
  • National Statistics Office (NSO)
  • National Statistical Coordination Board (NSCB)
  • Bureau of Agricultural Statistics (BAS)
  • Bureau of Labor & Employment Statistics (BLES)
  • Conducts censuses (population, housing, agriculture, fisheries, business, etc.).
• Historical lineage
  • PD 418 → National Census & Statistics Office (NCSO) under NEDA.
  • EO 121 → NCSO renamed NSO (Office of the President).
  • EO 149 → NSO back under NEDA supervision.
• Bureau of Agricultural Statistics (BAS)
  • Central information source/server for the Department of Agriculture’s National Information Network (NIN); provides market & tech data.
• National Statistical Coordination Board (NSCB)
  • Created EO 121 (30 Jan 1987); highest policy-making/coordinating body for statistics; chaired by NEDA Director-General.

Philippine National Census

• First official census: 1878 (Spanish colonial) → population = 5 567 685 (as of 31 Dec 1877).
• Last decennial census discussed: May 2010 → population = 92.34 M (2007: 88.55 M).
• RA 10625 mandates periodic censuses across sectors.
• Proclamation 1031 (Pres. B. S. Aquino III) declared Aug 2015 as National Census Month.
• Updated population size → basis for socio-economic planning.

Civil Registration Essentials

• Birth registration
  • Mandatory; handled by local civil registrar.
  • Birth certificate contains: date/place of birth, child’s name & sex, parents’ name/age/birthplace/residence/occupation, attendant’s signature.
• Death registration
  • Required before burial; accomplished by physician or undertaker.
  • Death certificate lists: name/age/nationality/civil status/occupation of deceased, date & cause of death.

Major Sources of Mortality Information

• National vital registration systems (developed countries).
• Sample/Model registration systems.
• Hospital surveys.
• Household surveys (esp. infant & child mortality estimation).
• Special longitudinal studies (e.g., maternal mortality).

Death Registration: Counting Events

• Official notification of a death (legal prerequisite for burial/cremation).
• Tabulated by age, sex, location, time → invaluable public-health data.
• Developing-country challenges: variable laws, poor compliance, inconsistent definitions, resource constraints, few trained personnel, infrequent analysis, under-utilisation.

Estimating Future Population Size

• Requires a known earlier population count (often from census).
• Used for non-censal years via:
  1. Arithmetic Increase Method
     $P<em>f = P</em>p + n k$
     where
     • $P<em>f$ = future population • $P</em>p$ = present (base) population
     • $n$ = number of years into future
     • $k$ = constant annual increase (absolute persons/year)
  2. Geometric Increase Method
     $P<em>f = P</em>p (1 + k)^n$
     where $k$ = constant proportional growth rate (fraction per year).
  3. Other modelling techniques (e.g., logistic, exponential smoothing, cohort-component) may be used when appropriate.

Concept of a Rate

• Measures occurrence of events over a specified time interval.
• Suitable for dynamic events; quantifies speed of change.

Fertility Rates

• Crude Birth Rate (CBR) – births per total population.
  $\text{CBR} = \frac{\text{No. of live births in a year}}{\text{Mid-year population}} \times 1 000$
• General Fertility Rate (GFR) – births per women of reproductive age (15-44 y).
  $\text{GFR} = \frac{\text{No. of live births in a year}}{\text{Mid-year women aged 15–44}} \times 1 000$

Mortality Rates

• Crude Death Rate (CDR) – deaths per total population. $\text{CDR} = \frac{\text{No. of deaths in a calendar year}}{\text{Mid-year population}} \times 1 000$
  • Sensitive to age structure; ageing populations can show higher CDR despite better health.
• Specific Mortality Rate – subgroup death rate.
  $\text{Specific} = \frac{\text{Deaths in specified group}}{\text{Mid-year pop. of same group}} \times 1 000$
• Age-Specific Death Rate (ASDR) – same formula but denominator & numerator restricted to an age band.
  • Facilitates cross-age & cross-area comparisons; input to life tables.
• Cause-of-Death (Cause-Specific) Rate
  $\text{Cause-specific Rate} = \frac{\text{Deaths from specific cause}}{\text{Mid-year population}} \times 1 000$ (or × 100 000 for rare causes)
• Infant Mortality Rate (IMR)
  \text{IMR} = \frac{\text{Deaths <1 year during year}}{\text{Live births during same year}} \times 1 000
• Case-Fatality Rate (CFR) – proportion of cases that end fatally.
  $\text{CFR} = \frac{\text{Deaths from specified disease}}{\text{Cases of same disease}} \times 100$ (percentage)

Morbidity Rates

• Incidence Rate – new cases arising.
  $\text{Incidence} = \frac{\text{New cases during period}}{\text{Population at risk during period}}$
• Prevalence Rate – existing (old + new) cases at a point/period.
  $\text{Prevalence} = \frac{\text{All existing cases}}{\text{Population examined}}$

Epidemiological Study Designs (for Morbidity)

• Prospective/Cohort Study – forward follow-up from exposure to disease development.
• Retrospective/Case-Control Study – look back in time, compare diseased vs. non-diseased regarding past exposures; data may be vague.

Life Expectancy & Primary Health Care

• Life expectancy at birth varies with era, geography, application of disease-control knowledge.
• Major 20th-century gains due to sanitation (water filtration/chlorination, sewage disposal), milk pasteurisation, nutrition, immunisation, chemotherapy, medical & surgical advances.
• Primary Health Care (PHC) package: health education, nutrition, immunisation, water & sanitation, maternal/child & family planning services, endemic-disease control, common-disease treatment, essential drugs.
• Prevention of deaths from one disease ⇒ gains in life expectancy are less than proportional (depends on age distribution of prevented deaths).

Sample Problem 1 (Philippines 1985)

Given data (population = 54 668 332; live births = 1 437 154; deaths = 334 663; women 15–44 = 12 913 036; TB deaths = 31 650; TB cases = 153 406; infant deaths = 54 613 (>28 d – <1 y) + 22 343 (<28 d)).

Computed indicators:

• CBR: $\frac{1 437 154}{54 668 332}\times1 000 = 26.29/1000$
• CDR: $\frac{334 663}{54 668 332}\times1 000 = 6.12/1000$
• GFR: $\frac{1 437 154}{12 913 036}\times1 000 = 111.30/1000$
• IMR: $\frac{54 613+22 343}{1 437 154}\times1 000 = 53.55/1000$
• TB Cause-specific Mortality Rate: $\frac{31 650}{54 668 332}\times100 000 = 57.9/100 000$
• TB Morbidity (Prevalence) Rate: $\frac{153 406}{54 668 332}\times1 000 = 2.81/1000$
• TB CFR: $\frac{31 650}{153 406}\times100 = 20.63\%$

Interpretations:

• ~26 births and 6 deaths per 1 000 population indicate a young, growing population.
• Each woman aged 15–44 delivered on average 111 births per 1 000 (≈0.111 births per woman) that year.
• IMR ≈ 54/1 000 – significant infant-health concern.
• ~58 TB deaths per 100 000 & CFR of 20.6 % point to high TB fatality & need for control measures.

Sample Problem 2

A. State (1943): population = 2 000 000; TB cases = 3 895; TB deaths = 2 305.

• Case Rate: $\frac{3 895}{2 000 000}\times1 000 = 1.95/1 000$
• Death Rate: $\frac{2 305}{2 000 000}\times1 000 = 1.15/1 000$
• Fatality Rate: $\frac{2 305}{3 895}\times1 000 = 59.18/1000 \text{ or } 5.918\%$

B. City population growth (98 344 in 1980 → 110 855 in 1986)

1. Arithmetic growth

   • $k = \frac{110 855 - 98 344}{6} = 2 085.17 \text{ persons/year}$

2. Geometric growth rate

   • $110 855 = 98 344 (1+k)^6$ → $1+k = (1.127)^{1/6} = 1.020$ → $k \approx 0.020 = 2\%$

   Populations estimated:

   • 1984 (arithmetic): $P = 98 344 + 4\times2 085.17 = 106 684.68$
   • 1985 (arithmetic): $P = 98 344 + 5\times2 085.17 = 108 769.85$

   (Geometric estimates give slightly different values due to compounding.)

Ethical & Practical Implications

• Accurate vital statistics underpin resource allocation, epidemic response, and long-term planning.
• Under-registration skews indicators, leading to misinformed policies.
• Standardised definitions & trained registrars improve data quality.
• Privacy & data security are key when handling individual-level vital records.

Connections to Broader Public Health Engineering

• Vital statistics inform water-sanitation projects (e.g., linking diarrhoeal mortality to water supply quality).
• Mortality/morbidity trends justify infrastructure investments (hospitals, waste systems).
• Fertility & population projections guide urban planning, housing, and environmental impact assessments.