Data in Community Health: Definition, Types, Sources & Statistical Methods

1. Definition of Data

• Data - raw, discrete observations (e.g., individual patient records, counts)
• Information - data transformed by reducing, summarizing, and adjusting for variations such as age and sex composition, enabling comparisons over time and place
• Intelligence - information further transformed through integration with experience, social and political values, to guide decision-makers, policy-makers, planners, administrators, and health care personnel

2. Types of Data

A. By Measurement Scale

B. By Nature

• Qualitative (Categorical) data - describes attributes (e.g., sex, religion, marital status)
• Quantitative (Numerical) data - measurable values:
  • Discrete - whole numbers (e.g., number of children, hospital admissions)
  • Continuous - any value within a range (e.g., height, weight, hemoglobin)

C. By Method of Collection

• Primary data - collected first-hand for a specific purpose (surveys, interviews, clinical examinations)
• Secondary data - already collected by others (census records, hospital registers, vital statistics)

3. Sources of Health Data

Routine / Ongoing Sources

1. Census - provides demographic data on population size, age, sex, occupation, literacy, and housing
2. Vital Registration System - births, deaths, marriages, divorces; forms the basis of vital statistics
3. Notification of diseases - legally notifiable communicable diseases reported by health workers and institutions
4. Hospital records / Health facility records - admissions, diagnoses, procedures, outcomes
5. Health surveys - National Family Health Surveys (NFHS), National Sample Surveys, Sample Registration System (SRS)

Domains of Health Information

1. Demography and vital events - population structure, births, deaths
2. Environmental health statistics - water quality, sanitation, pollution data
3. Health status - mortality, morbidity, disability, and quality of life
4. Health resources - facilities, beds, manpower
5. Utilization of health services - attendance, admissions, waiting lists
6. Indices of outcome of medical care
7. Financial statistics - costs and expenditure

Other Sources

• Special studies and registries - cancer registries, communicable disease surveillance
• Insurance and administrative records
• Environmental health data (air, water, food monitoring)
• Health manpower statistics
• Big data - electronic health records, wearables, health apps (increasingly relevant)

Requirements for a Good Health Information System (WHO)

1. Should be population-based
2. Should avoid unnecessary agglomeration of data
3. Should be problem-oriented
4. Should use functional and operational terms (episodes of illness, treatment regimens)
5. Should express information briefly and imaginatively (tables, charts, percentages)
6. Should make provision for feedback of data

4. Importance of Statistical Methods in Community Health

A. Measuring Health Status of the Community

• Rates, ratios, and proportions (birth rate, death rate, infant mortality rate, prevalence) quantify the burden of disease in a population
• Without statistics, it is impossible to know whether a community is healthy or sick

B. Identifying Health Problems and Priorities

• Statistical analysis of morbidity and mortality data helps identify the leading causes of illness and death
• Enables prioritization of limited resources toward high-burden conditions

C. Planning and Administration of Health Services

• Health planners use statistical data to decide where hospitals, PHCs, and sub-centres should be located
• Staffing requirements, bed strength, drug procurement - all are based on statistical projections

D. Evaluating Effectiveness of Health Programmes

• Before-and-after comparisons, control group studies, and time-trend analysis assess whether an intervention (vaccination, sanitation, nutrition programmes) actually worked
• Without statistical evaluation, success or failure of a programme cannot be determined

E. Epidemiological Research

• Statistical methods allow identification of associations between risk factors and disease (e.g., smoking and lung cancer)
• Measures of association (relative risk, odds ratio, attributable risk) are purely statistical constructs

F. Clinical Trials and Drug Evaluation

• Randomized controlled trials use statistical methods (randomization, blinding, significance testing) to establish the safety and efficacy of treatments

G. Demographic Analysis

• Fertility rates, mortality rates, population projections - all require statistical tools
• Life tables, survival analysis, and age-standardization are essential for comparing populations

H. Presentation and Communication of Data

• Frequency distribution tables - organize raw data into class intervals for easy comparison
• Bar charts, pie charts, histograms, frequency polygons - visual representation of data
• Measures of central tendency - mean, median, mode summarize data in a single value
• Measures of dispersion - range, standard deviation, variance show spread of data
• Correlation and regression - study relationships between variables
• Significance tests - chi-square, t-test, ANOVA - determine if observed differences are real or due to chance

I. Surveillance and Early Warning Systems

• Statistical thresholds define what counts as an "epidemic" or "outbreak"
• Trend analysis detects rising disease rates before they become emergencies

J. International and National Comparisons

• Age-standardized rates and disability-adjusted life years (DALYs) allow valid comparisons of health status across countries and over time

Summary Table