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Biostatistics, Demography, and Research Methodology

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Biostatistics, Demography, and Research Methodology — Key Facts for FMGE Core concept: Demography studies population dynamics; biostatistics provides tools to analyze data; research methodology ensures valid scientific conclusions High-yield point: Vital statistics (birth/death rates), population pyramid interpretation, and basic statistical tests are important ⚡ Exam tip: Know the difference between correlation and causation; understand the principles of a good research study design

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Biostatistics, Demography, and Research Methodology — FMGE Study Guide

Demography and Population Studies

Demographic Parameters

Crude Birth Rate (CBR):

  • CBR = Total live births / Total population × 1000
  • India’s CBR: ~20 per 1000 (declining)

Crude Death Rate (CDR):

  • CDR = Total deaths / Total population × 1000
  • India’s CDR: ~7 per 1000

Growth Rate:

  • Natural growth = CBR - CDR
  • India’s growth rate: ~1.5% (declining)

Total Fertility Rate (TFR):

  • Average number of children per woman of reproductive age
  • Replacement level: 2.1
  • India’s TFR: ~2.2 (approaching replacement)

Reproductive Rate:

  • Net Reproduction Rate: Daughters born per woman (accounts for mortality)

Population Pyramid

Expansive pyramid (developing countries):

  • Wide base (high fertility, high mortality)
  • Tapers rapidly (young population)
  • Example: India, sub-Saharan Africa

Stationary pyramid (developed countries):

  • Relatively equal distribution
  • Narrower base (low fertility)
  • Example: Japan, Germany

Constrictive pyramid (very low fertility):

  • Narrows at base
  • Bulge in older age groups
  • Example: Russia, Italy

Life Table

Mortality table: Shows survival and death rates at each age

Life expectancy: Average number of years a person can expect to live

Survival curve:

  • Type I: Developed countries (most survive to old age)
  • Type II: Equal mortality at all ages
  • Type III: Developing countries (high infant/child mortality)

Population Dynamics

Malthusian theory: Population grows faster than food supply (checked by preventive/ppositive checks) Demographic transition: Shift from high birth/death rates to low birth/death rates with economic development

Population momentum: Continued growth even after fertility falls to replacement level (due to age structure)

Vital Statistics

Sources of Vital Statistics

Civil Registration System (CRS):

  • Registration of births and deaths by law
  • Incomplete in India (especially rural areas)

Census:

  • Complete enumeration every 10 years
  • Gives population structure, growth
  • Census 2021 (postponed due to COVID)

Sample Registration System (SRS):

  • Continuous registration in sample villages
  • Gives birth/death rates, fertility rates
  • Established 1969-70

Health Statistics

Sources in India:

  • Civil Registration: Birth/death data
  • Sample Registration System (SRS): Fertility, mortality estimates
  • NFHS (National Family Health Survey): Health and nutrition; conducted every 5 years (NFHS 5: 2019-21)
  • National Sample Survey (NSS): Social and economic indicators
  • HMIS (Health Management Information System): Service statistics from health facilities

Important indicators:

  • IMR: Infant mortality rate - key indicator of health status
  • MMR: Maternal mortality ratio - measures maternal health
  • TFR: Total fertility rate - population growth indicator
  • CBR/CDR: Crude birth/death rates
  • Life expectancy: Average lifespan

Research Methodology

Types of Research

Descriptive:

  • Describes characteristics of populations/situations
  • No manipulation of variables
  • Examples: Cross-sectional surveys, case reports

Analytical:

  • Examines associations between variables
  • Case-control, cohort studies

Experimental:

  • Involves intervention/manipulation
  • RCT is gold standard
  • Randomization eliminates confounding

Research Design

Observational:

  • Cross-sectional: Data at one point in time (prevalence studies)
  • Case-control: Start with disease → look for past exposures (retrospective)
  • Cohort: Start with exposure → follow for disease (prospective or retrospective)

Experimental:

  • Randomized Controlled Trial (RCT): Random allocation to groups; gold standard for intervention studies
  • Field trials: Community-level interventions
  • Quasi-experimental: No randomization (practical limitations)

Sampling

Probability sampling:

  • Simple random: Each member has equal chance
  • Stratified: Divide into strata, random within each
  • Systematic: Every nth person from list
  • Cluster: Random clusters, sample all in cluster

Non-probability sampling:

  • Convenience: Readily available subjects
  • Purposive: Based on specific criteria
  • Snowball: Used for hard-to-reach populations

Sample Size

Factors affecting sample size:

  • Expected effect size (smaller effects need larger samples)
  • Desired power (typically 80%)
  • Significance level (typically 5%)
  • Expected variability

Formula-based calculations for means, proportions

Data Collection

Questionnaire design:

  • Clear, simple language
  • Avoid leading questions
  • Pre-test the instrument
  • Confidentiality assurance

Interview techniques:

  • Structured, semi-structured, unstructured
  • Pilot testing

Observation:

  • Participant vs non-participant
  • Structured vs unstructured

Data Analysis

Descriptive statistics:

  • Frequency distributions
  • Measures of central tendency and dispersion

Inferential statistics:

  • Parametric tests: t-test (comparing means), ANOVA (multiple groups), correlation
  • Non-parametric tests: Chi-square (comparing proportions), Mann-Whitney
  • Regression: Linear, logistic

Statistical significance: P-value < 0.05

Health Research Ethics

Principles

Respect for persons: Informed consent, confidentiality Beneficence: Maximize benefits, minimize harms Justice: Fair distribution of benefits and burdens

  • Voluntary participation
  • Adequate information about study
  • Understanding of risks and benefits
  • Right to withdraw

IEC (Institutional Ethics Committee)

  • Reviews research proposals
  • Ensures ethical conduct
  • Monitors ongoing research

Helsinki Declaration

  • World Medical Association guidelines
  • Key reference for research ethics

ICMR Guidelines

  • Indian Council of Medical Research ethical guidelines
  • Specific for Indian context

Tests of Significance

Chi-Square Test

  • Compares proportions between groups
  • Used for categorical data
  • Example: Association between smoking and lung cancer

t-Test

  • Compares means of two groups
  • Example: Difference in BP between two groups

ANOVA (Analysis of Variance)

  • Compares means of three or more groups
  • Example: Effect of three different diets on weight

Correlation and Regression

Correlation coefficient (r):

  • Measures strength and direction of linear relationship
  • Ranges from -1 to +1
  • r = 0: No correlation; r = ±1: Perfect correlation

Linear regression:

  • Predicts one variable from another
  • Y = a + bX (Y predicted from X)

Logistic regression:

  • Predicts binary outcome
  • Used when outcome is disease/no disease

Errors in Hypothesis Testing

Type I error (α): Rejecting true null hypothesis (false positive) Type II error (β): Accepting false null hypothesis (false negative) Power of study: 1 - β; ability to detect true difference

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