CHAPTER -3 ��DEMOGRAPHY SURVEILLANCE &�INTERPRETATION OF DATA
DEMOGRAPHY
Introduction
• 'Demo' means population and 'graphy' means study.
• Demography is Study of human population.
• Three main aspects:
1. Population change (growth/decline)
2. Composition of population
3. Distribution by space.
Key Demographic Processes
• F- Fertility
• M- Marriage
• M- Migration
• M- Mortality
• M- Mobility
Sources of Demographic Data
High Stationary Stage
• High birth & death rates
• Slow or no population growth
• Poor healthcare & sanitation
• Agricultural economy
Early Expanding Stage
• Decline in death rates
• Birth rates remain high
• Rapid population growth
• Improved healthcare & sanitation
Late Expanding Stage
• Declining birth & death rates
• Slower population growth
• Industrialization & urbanization
• Family planning awareness
Low Stationary Stage
• Low birth & death rates
• Stable population growth
• High living standards & economy
• Advanced healthcare & education
Declining Stage
• Birth rate falls below death rate
• Population decline
• Aging population & economic challenges
• Increased healthcare needs
World Population Trends
• The global population is growing due to medical advancements.
• Estimated world population milestones:
- 2000: 4 billion
- 2007: 5 billion
- 2019: 6 billion
- Reach 7 billion by 2024.
• Most of the population lives in developing countries.
Most Populated Countries (July 2024)
1. China - 1.41 billion
2. India - 1.40 billion
3. USA - 336 million
4. Indonesia - 281 million
5. Pakistan - 252 million
6. Nigeria - 236 million
7. Brazil - 220 million
8. Bangladesh - 168 million
9. Russia - 140 million
10. Mexico - 130 million
World Population Projections
• UN’s World Population Prospects (2024):
- 2024: 8.2 billion
- 2080s: 10.3 billion
- 2100: Decline to 10.2 billion
• By 2080, people aged 65+ will outnumber children under 18.
Declining Birth Rate Factors
• Government policies on population control
• Spread of education
• Increased contraception availability
• Improved maternal and child health services
Sex Ratio
• Defined as the number of females per 1,000 males.
• Affected by:
- Mortality rates
- Sex-selective migration
- Sex ratio at birth
- Cultural preference for male children
Low Sex Ratio Implications
• Strong preference for male children
• Gender inequality
• Female infanticide & feticide
• Higher maternal mortality rates
Sex Ratio Trends in India
• 1981: 972 females per 1,000 males
• 2011: 940 females per 1,000 males
• 2023 (NFHS-5): 1,020 females per 1,000 males
States with Lowest & Highest Sex Ratios
Lowest:
1. Haryana - 926
2. Punjab - 938
3. Gujarat - 965
Highest:
1. Kerala - 1,121
2. Rajasthan - 1,099
3. Bihar - 1,090
Social Implications of Declining Sex Ratio
• Gender imbalance leading to marriage imbalances
• Increased crime and violence against women
• Objectification and trafficking of women
• Economic development challenges
Government Initiatives
• Beti Bachao, Beti Padhao: Promoting gender equality.
• Legal frameworks against gender-based discrimination.
• Strengthening healthcare services for women & children.
VITAL STATISTICS
Introduction
• Vital statistics is a branch of demography studying key human life events.
• Includes birth, death, marriage, divorce, adoption, and more.
• Provides essential data for policy-making and public health.
Definition
• Collection of numerical data related to vital events like births, deaths, and marriages through civil registration.
• Used to calculate vital rates and analyze population trends.
Key Sources of Vital Statistics in India
• Population Census
• Civil Registration System
• Demographic Sample Surveys (NSSO)
• Sample Registration System (SRS)
• Health Surveys (NFHS, DLHS-RCH)
Crude Birth Rate (CBR)
• Measures live births per 1,000 population per year.
• Formula: CBR = (Live Births in a Year / Total Population) × 1000
• Example: If 200,000 live births occur in a population of 10 million, CBR = 20.
Crude Death Rate (CDR)
• Measures deaths per 1,000 population per year.
• Formula: CDR = (Total Deaths in a Year / Total Population) × 1000
• Example: If 40,000 deaths occur in a population of 5 million, CDR = 8.
Infant Mortality Rate (IMR)
• Measures infant deaths (<1 year) per 1,000 live births.
• Formula: IMR = (Infant Deaths / Live Births) × 1000
• Example: If 150 infants die out of 10,000 births, IMR = 15.
Total Fertility Rate (TFR)
• Measures the average number of children a woman would have in reproductive years.
• Formula: TFR = Σ ASFRa (Sum of age-specific fertility rates).
Steps in Census Process
1. Planning & Preparation
2. Legal Framework
3. Public Awareness
4. Training of Enumerators
5. Data Collection
6. Data Processing
7. Data Analysis
8. Publication & Dissemination
9. Post-Census Evaluation
Registration of Vital Events
• Official recording of life events: birth, death, marriage, divorce, migration.
• Ensures real-time demographic monitoring.
• Requires accuracy and completeness.
Importance of Vital Registration
• Provides accurate demographic data.
• Used in policymaking and public health.
• Challenges: Incomplete data, lack of awareness, rural-urban variations.
Civil Registration System
• Civil registration system includes the birth and death registration system (ERS).
• The Central Birth and Death Act was passed in 1969 and came into force on 1 April 1970.
• The act required compulsory registration of births, deaths, and marriages to maintain uniformity and comparability of data.
• Birth and death must be registered within 21 days.
- A late fee is charged after 21 days up to 30 days.
- After 30 days and up to one year, a late fee is charged with permission.
- After one year, late fees may be imposed with an order from the executive magistrate.
- Free within 12 months.
- ₹50 fee if registered after 12 months up to 15 years.
Sample Registration System (SRS)
• Launched in 1964-65 and expanded nationwide by 1966.
• Provides reliable estimates of fertility and mortality rates.
• Operates as a dual recording system:
- Continuous Enumeration: Field investigators track births and deaths.
- Retrospective Survey: Independent investigators verify and update records every six months.
• Provides key data on:
- Crude Birth Rate
- Crude Death Rate
- Natural Growth Rate
- Fetal Mortality Rate
• Advantages:
- Conducted annually.
- Eliminates duplication errors.
- Self-evaluating technique.
- Dual reporting system.
- Sampling frame updated every 10 years.
Morbidity and Mortality Indicators
• Essential metrics in epidemiology and public health.
• Measure incidence and prevalence of diseases and associated risks of death.
• Help public health officials track disease trends and assess interventions.
• Morbidity: Any departure from physical well-being; illness rate.
• Mortality: Measure of death within a population.
Morbidity: Definition and Calculation
• Morbidity: Extent of illness, injury, or disability in a population.
- J B Stallman: 'Extent of illness in a defined population.'
Uses of Morbidity Data
• Identify disease burden in a community.
• Establish priorities for healthcare interventions.
• Provide accurate clinical data for research.
• Conduct etiological studies to understand disease causes.
• Support disease prevention programs.
• Monitor and evaluate disease control activities.
• WHO Calculation:
- Number of people who were ill (frequency).
- Number of illness episodes per person (severity).
- Duration of illness.
• Morbidity rates assessed through:
- Incidence Rate
- Prevalence Rate
INCIDENCE RATE
Definition of Incidence Rate
Example Calculation
Frequency of Illness Measurement
Elements of Incidence Rate
• Only considers new cases of disease.
• Calculated over a specific time period.
• Population at risk is used as the denominator.
• Helps track new disease cases.
Uses of Incidence Rate
• Helps in disease control and prevention.
• Determines disease distribution.
• Assesses the efficacy of preventive and therapeutic measures.
• Increase suggests a need for improvement in control programs.
• Fluctuations indicate changes in disease etiology, host, agent, and environment.
PREVALENCE RATE
Definition of Prevalence Rate
Types of Prevalence
• Point Prevalence
• Period Prevalence
Point Prevalence
Period Prevalence
Prevalence vs. Incidence
Key Insights
• High prevalence if disease duration is long.
• If duration is short, incidence is higher than prevalence.
• Changes in treatment can affect prevalence rates.
• Ineffective treatment increases prevalence.
• Incidence is like new visitors; prevalence is those who stay.
Uses of Prevalence Rate
• Helps calculate disease magnitude.
• Identifies high-risk populations.
• Aids in health planning and resource allocation.
• Estimates needs for hospital beds, workforce, and facilities.
MORTALITY INDICATORS
Measurement of Mortality
• Mortality rate is the number of deaths in a population over a specific time.
• Every country registers deaths, making data collection easier.
Limitations of Mortality Data
• Inaccuracy in recording age and cause of death.
• Incomplete death reporting in some regions.
• Lack of uniform data collection methods.
• Missing details on comorbidities and social factors.
• Changing diagnostic criteria affect accuracy.
Uses of Mortality Data
• Provides data for epidemiological studies.
• Identifies leading causes of death.
• Helps allocate healthcare resources.
• Supports the design of intervention programs.
• Monitors public health programs.
Mortality Rates and Ratios
• Mortality rate: Number of deaths in a population over time.
• Mortality ratio: Compares mortality rates between groups.
Common measures:
1. Crude Death Rate
2. Infant Mortality Rate (IMR)
3. Maternal Mortality Rate (MMR)
4. Specific Death Rate
5. Case Fatality Rate
6. Proportional Mortality Rate
7. Survival Rate
Crude Death Rate (CDR)
• Total number of deaths from all causes per 1,000 people per year.
• Formula: (Total deaths / Mid-year population) × 1000
• Drawbacks:
- Does not consider age distribution.
- Cannot explain causes of death.
- Affected by migration and disasters.
Infant & Maternal Mortality Rates
• Infant Mortality Rate (IMR): Deaths of children under 1 year per 1,000 live births.
- India’s IMR (2022): 25.5 per 1,000 live births.
• Maternal Mortality Rate (MMR): Maternal deaths per 100,000 live births.
- India’s MMR reduced from 130 (2014-16) to 97 (2018-20).
Specific Death Rates
• Mortality rate for a specific group or cause:
- Age-specific: Deaths in a particular age group.
- Disease-specific: Deaths due to a specific disease.
- Sex-specific: Deaths categorized by gender.
• Helps identify high-risk populations and needed interventions.
Case Fatality Rate (CFR)
• Measures disease severity by showing the proportion of deaths among diagnosed cases.
• Formula: (Deaths from a disease / Total diagnosed cases) × 100
• Used to assess prognosis and treatment effectiveness.
Proportional Mortality Rate
• Measures deaths due to a specific disease out of total deaths.
• Formula: (Deaths from specific disease / Total deaths) × 1000
• Helps identify the most fatal diseases in an area.
Survival Rates
• Proportion of people surviving a disease over time.
• Used to evaluate prognosis and treatment effectiveness.
• Helps track disease trends and improve healthcare strategies.
SURVEILLANCE IN PUBLIC HEALTH
Surveillance
Types of Surveillance
- Active Surveillance: Regular contact with healthcare providers.
- Passive Surveillance: Reports submitted by hospitals and clinics.
- Integrated Surveillance: Combination of active and passive systems.
Components of Surveillance
- Data analysis
- Data interpretation
- Reporting
- Response actions
- Monitoring and evaluation
- Feedback mechanism
Tools Used in Surveillance
- Case reports
- Health information systems
- Surveillance forms
- Epidemiological surveys
- Laboratory reports
- Sentinel surveillance
- Health registries
- GIS mapping
Challenges in Surveillance
- Data quality issues
- Resource limitations
- Delayed reporting
- Privacy concerns
- Data integration difficulties
- Need for personnel training
- Health system constraints
- Low public awareness
INTEGRATED DISEASE SURVEILLANCE PROGRAM (IDSP)
Introduction
• Launched in November 2004 and March 2010 by the Ministry of Health and Family Welfare
• Restructured in March 2012 and converted into IDSP under the National Health Mission
• Aims to strengthen disease surveillance and response systems
• Central, State, and District Surveillance Units established
Objectives
• Strengthen a decentralized, laboratory-enabled disease surveillance system
• Monitor disease trends
• Detect and respond to outbreaks through trained Rapid Response Teams (RRTs)
Program Components
• Integration and decentralization of surveillance activities
• Human resource development through training
• Use of information technology for data collection and analysis
• Strengthening of public health laboratories
• Inter-sectoral coordination for zoonotic diseases
Organizational Structure
• Central Surveillance Unit (CSU) at NCDC, Delhi
• State Surveillance Units (SSU) at state/UT headquarters
• District Surveillance Units (DSU) in all districts
• Coordinated efforts for disease monitoring and outbreak response
Functions of Central Surveillance Unit (CSU)
• Monitors disease outbreaks and health trends
• Analyzes surveillance data
• Coordinates with state and district units
• Provides technical support and capacity building
• Disseminates information and manages response to outbreaks
Functions of State Surveillance Unit (SSU)
• Oversees disease surveillance across the state
• Collects and analyzes district-level data
• Provides technical support and training
• Manages outbreaks and allocates resources
Functions of District Surveillance Unit (DSU)
• Collects local disease data
• Monitors health trends
• Reports disease data to the SSU
• Investigates and manages outbreaks
• Conducts training for local health workers
Data Management
• Data collected through Integrated Health Information Platform (IHIP)
• Three reporting formats:
- Suspected cases
- Presumptive cases
- Laboratory-confirmed cases
• Rapid Response Teams (RRTs) analyze and act on outbreaks
Outbreak Surveillance and Response
• Daily outbreak reports from districts to IDSP
• Media Scanning and Verification Cell detects early warning signals
• Rapid communication and coordinated response to control outbreaks
Role of ICT in IDSP
• Integrated Health Information Platform (IHIP) launched on April 5, 2021
• Real-time data reporting via mobile application
• GIS-enabled data visualization and dashboard
• Data integration with other health programs
Training and Laboratory Strengthening
• Three-tiered training system:
- Master Trainers (State & District Officers, RRT members)
- District Surveillance Officers and health professionals
- Medical officers, paramedics, and lab technicians
• 114 district labs strengthened; state-based referral lab network in 23 states
HEALTH MANAGEMENT INFORMATION SYSTEM (HMIS) & REPRODUCTIVE AND CHILD HEALTH (RCH) PORTAL
Introduction to HMIS Portal
- HMIS is a web-based system for collecting and reporting healthcare data.
- It enables real-time data flow from sub-centers to the ministry.
- Helps in efficient program management and decision-making.
Introduction to RCH Portal
- The RCH portal tracks women’s reproductive health lifecycle.
- It ensures timely maternal and child healthcare services.
- Helps in planning and monitoring reproductive, maternal, newborn, and child health programs.
Key Features of RCH Portal
- Assists in decision-making and health scheme implementation.
- Helps in tracking high-risk pregnancies.
- Supports antenatal, postnatal, and immunization services.
- Generates service delivery plans for health workers.
MOTHER AND CHILD TRACKING SYSTEM (MCTS) IN INDIA
Introduction to MCTS
- MCTS is a name-based tracking system for mothers and children.
- Launched in December 2009 under the National Health Mission.
- Tracks pregnant women from conception to 42 days postpartum.
- Tracks newborns up to 5 years of age.
- Declared a Mission Mode Project under NeGP in 2011.
Objectives of MCTS
- Ensure full health coverage and immunization for mothers and children.
- Establish two-way communication between service providers and beneficiaries.
- Send alerts to mothers and ASHA workers about due services.
- Monitor service delivery and ensure quality healthcare.
- Support evidence-based planning and assessment of healthcare programs.
Services Provided under MCTS
- Registration of pregnant women.
- Antenatal care (ANC), delivery, and postnatal care (PNC) services.
- Registration of children for immunization.
- Immunization services for children.
- Integration with Public Financial Management System (PFMS) and other applications.
- Use of USSD technology for live updates on MCTS portal.
Benefits to Beneficiaries
- Information about government schemes and benefits.
- Advance notifications about due healthcare services.
- Ensures timely delivery of healthcare services.
- Facilitates better interaction with healthcare providers.
- Allows access to services from any healthcare center.
- Free consultation through a toll-free helpline.
COLLECTION, ANALYSIS, INTERPRETATION, AND USE OF DATA
Overview
- Data collection helps in research and decision-making.
- Identifies patterns, trends, and relationships in healthcare.
- Evaluates effectiveness of health programs and interventions.
- Assists policymakers in making informed decisions.
- Helps in assessing and improving maternal and child healthcare services.
Sources of Data
Primary Data:
- Collected fresh and considered original.
Secondary Data:
- Previously collected by someone else.
Collection of Primary Data
• In surveys and descriptive research, data is collected via direct observation or communication.
• Observation helps in understanding phenomena through systematic 'seeing' or recording events.
Observation Methods
• Observation involves recording behavior or events, with or without devices.
• Types of Observation:
- Structured (systematic, controlled)
- Unstructured (holistic, participant observation)
Structured Observation Techniques
• Uses pre-determined checklists and rating scales.
• Checklists:
- Sign Checklist System (tally frequency)
- Category System (mutually exclusive, exhaustive)
• Rating Scales:
- Numerical, Forced-Choice, and Graphic Rating Scales
Unstructured & Participant Observation
• Unstructured Observation:
- No pre-determined guide; used in qualitative research.
• Participant Observation:
- Researcher actively engages in the setting to gain deeper insights.
Questionnaire & Interview Schedules
Questionnaires:
- Closed-ended, Open-ended, or Partially closed questions
Interview Schedules:
- Structured (oral questionnaires), Semi-structured, or In-depth (unstructured)
Interview Methods
• Structured Interviews:
- Predetermined set of questions; standardized format.
• Semi-Structured Interviews:
- Flexible, allowing for probing and discussion.
• Unstructured Interviews:
- Open-ended, conversational approach.
Projective Techniques
• Techniques to uncover underlying motives through projection:
- Rorschach Ink-blot Test
- Thematic Apperception Test (TAT)
- Word Association Test
- Sentence Completion Test
- Doll Play / Play Techniques
- Story Completion & Drawing Tests
Collection of Secondary Data
Types of Secondary Data
Published Data
Government publications
Nursing journals
Books, magazines, newspapers
Reports & publications of associations
Research theses & university reports
Public records & statistics
Types of Secondary Data
Unpublished Data
Diaries, letters
Unpublished biographies & autobiographies
Unpublished research projects & reports
Existing Records
Records are valuable sources of nursing research
data. They can be found in:
- Hospitals
- Offices
- Museums
- Personal diaries, letters, speeches, articles, and documents.
Analysis & Interpretation of Data
Measurement of Central Tendency
It refers to values clustering around a central point.
Main measures:
- Mean
- Median
- Mode
Mean
Median
Mode
Measurement of Dispersion
Chi-Square Test
Used to determine association between two
categorical variables.
Conditions:
- Random sample
- Qualitative data
- Sample size > 30
- Expected frequency ≥ 5
Regression Analysis
Correlation Coefficient
Measures strength and direction of relationship
between two variables.
Values:
+1 = Perfect positive
-1 = Perfect negative
0 = No correlation
INTERPRETATION AND PRESENTATION OF DATA
Interpretation of Data
Analysis must align with study objectives,
theoretical framework, and research limitations.
Distinguish causality vs. coincidence.
Consider all factors influencing results.
Ensure validity and reliability of data.
Steps in Data Interpretation
1. Meaning of Results
2. Generalization of Results
3. Credibility of Results
4. Importance of Results
5. Implications of Results
Presentation of Data
Data can be presented in various formats:
- Tables
- Charts (Flowchart, Graphs)
- Graph Types: Bar Graph, Line Graph, Pie Chart, Histogram, Scatter Plot
Uses of Data
- Understanding health status
- Comparing national and international data
- Planning health programs
- Evaluating program success
- Supporting research and policy decisions
- Personalized healthcare planning
Discussion of Data
- Communicate results effectively
- Choose appropriate methods (Reports, Oral
Presentations, Posters)
- Breakdown discussion:
1. Introduction
2. Key Findings
3. Comparisons
4. Statistical Analysis
5. Outliers and Anomalies
6. Implications
7. Conclusion
COMMON SAMPLING TECHNIQUES & DISAGGREGATION OF DATA
Common Sampling Techniques
• Sampling is the process of selecting a subset of
individuals from a population.
• It is essential for research as it saves time and
resources.
• Sampling techniques are categorized into
Probability and Non-Probability Sampling.
Probability Sampling
• Probability sampling ensures that every individual
in the population has a known chance of selection.
• It reduces bias and improves the generalizability of
results.
Non-Probability Sampling
• Non-probability sampling does not give every
individual a known chance of selection.
• It is useful when probability sampling is not
feasible.
Disaggregation of Data
• Breaking down data into smaller units for better
analysis.
• Helps in identifying disparities and trends.
• Enables targeted interventions and resource
allocation.
Uses of Data Disaggregation
• Identifying affected populations.
• Understanding the impact of factors such as age,
gender, and geography.
• Supporting informed decision-making in health
and social policies.