Calculating and Interpreting
Coverage Indicators

By the end of the session, participants will be able to:
• Identify sources of data for calculating coverage indicators
• Estimate denominators for routine coverage estimates
• Calculate and interpret coverage indicators from routine data
• Use online resources for estimating coverage indicators
• Assess the quality of relevant data sources
• Reconcile coverage estimates from different data sources

Maternal Health Coverage Indicators
• Proportion of pregnant women who received at least two antenatal care visits
• Proportion of deliveries occurring in a health facility
• Proportion of deliveries with skilled attendant at birth
• Proportion of women attended at least once during postpartum period (42 days after delivery) by skilled health personnel for reasons related to childbirth

Why Coverage Indicators
Are Important
• Understand how effective program is
• See if one target group is reached more effectively than another
• Identify underserved area/regions

Child Health Coverage
Indicators
• Immunization Programs
– DTP3 vaccine coverage
– Measles vaccine coverage
– BCG vaccine coverage
– OPV3 coverage
– HepB3 coverage
– Fully immunized child
• Nutrition programs?
• Control of diarrheal disease programs?

Coverage Indicators for HIV/AIDS
Care & Treatment Programs
• Number of clients receiving public/NGO
VCT services
• Number of clients provided with ARVs
• Percent of children in need receiving cotrimoxazole prophylaxis
• Percent of HIV patients receiving DOTS
• Coverage of PMTCT programs?

• Censuses
• Surveys
• Registrations
• Health management information systems
• Program statistics
• Patient registers

Indicators From Program
Statistics: Numerators
• HMIS and routine reports give information on numerators
• Numerators: number of deliveries in health facilities, measles vaccinations, pills distributed, voluntary counseling and testing clients etc.
• Denominators: ?

Example:
Importance of denominator
• Town A vaccinated 200 infants
• Town B vaccinated 400 infants
• Population size:
– Town A= 10,000
– Town B= 30,000
– Town C= 60,000
• Town C vaccinated
600 infants

Indicators From Program Statistics:
What Denominators Are Needed?
• Denominators: population composition
– Population composition
– How many women are of childbearing ages?
– How many children are under five?
– How many adolescents? 15-19? 20-24?
– How many men are 15-59 years?
– How many children are of school going age?
– How many infants are there?
– How many babies are born each year?

• Population registers
• Censuses
• Population projections
• Population growth rate (r)
• Rate of natural increase = crude birth rate (CBR) minus the crude death rate (CDR)
• Net migration rate: inmigration - outmigrants per
1000 population
• CBR: no. of births per 1000 population in 1 year
• CDR: no. of deaths per 1000 population in 1 yr
• Population growth = rate of natural increase + net migration rate

• DemProj: projects population of country/region by age and sex based on assumptions about fertility, mortality, and migration
– Urban and rural population projections can also be prepared
• EasyProj: supplies data needed to make a population projection from estimates provided by the Population Division of the UN www.tfgi.com

Spectrum

• Population at time t: P(t) = P(0) * exp(r*t), where:
– P(t) is the population size after t years
– P(0) is the population size at the last census
• Example:
– 300,000 people at census
– Growth rate = 3% (0.03),
– What is the population after 10 years?
– 404,958 people

Estimating Number of Live Births
• Where data on the number of live births are unavailable:
Total expected births = Total population x crude birth rate
• Where the crude birth rate (CBR) is unknown:
Total expected births = Total population x 0.035
Source: WHO 1999a; WHO 1999b

Estimating Number of
Surviving Infants
• Target population for childhood immunization:
Surviving infants <12 months of age in a year
• Where data on the number of surviving infants are unavailable:
Total expected number of surviving infants =
Total population x CBR x (1 – infant mortality rate)

Estimating Number of
Surviving Infants: CBR Known
Total population: 5,500,000
CBR: 30/1000
Infant mortality rate (IMR): 80/1000
Number of surviving infants =
Total population x CBR x (1 – IMR)
= 5,500,000 x 30/1000 x (1 - 0.080)
= 5,500,000 x 0.030 x 0.920
= 151,800
Source: Immunization Essentials: A Practical Field Guide (USAID, 2003)

Estimating Number of Surviving
Infants: CBR Unknown
• Where data on the number of surviving infants, CBR or IMR are unavailable, multiply total population by
4%:
Expected no. of surviving children < 12 months =
Total population x .04
• If the total population is 30,000, then the number of children under one year = 30,000 x 4/100 = 1200
Source: WHO, 2002b

Estimating the Monthly Target
Population
Monitoring immunization and vitamin A coverage should be done monthly at the facility and district levels, requiring estimations of the monthly target population
Monthly target population = Estimated number of children under 1 year of age divided by 12
Example:
• Annual target population of children < 12 months = 1200
• Monthly target = 1200/12 = 100

Example: Immunization
Coverage From Routine Data
• Total population of district in 1990 = 99,000
• CBR = 40 per thousand
• IMR = 80 per thousand
• Population growth (r) = 3% per year
• 3,000 measles vaccinations were given to infants in district in 1998
• What is the measles coverage rate for 1998?
– Numerator: No. immunized by 12 months in a given year
– Denominator: Total no. of surviving infants < 12 months in same year

Immunization Coverage From
Routine Data: Answer
• Estimate district total population in 1998
Pop
1998
= 99,000 * exp(.03*8) = 125,410
• Estimate number of surviving infants in 1998
125,410 x (40/1000) x (1 - .080) = 4615
• Estimate measles coverage rate
Measles coverage = 3000/4615 x 100 = 65%

Case Study 1: Immunization
Coverage from Facility Data
• Estimate total population in 2003
• Calculate coverage for DTP1, DPT3, and measles vaccine in 2003
• Evaluate trends in coverage
• Estimate drop-out rates
• Analyze the problems in 2003
– Is coverage low or falling?
– What are possible causes?
– What are the differences in coverage in different areas?
• What action can managers take if coverage data indicate problems?

Challenges in Estimating
Coverage from Routine Data
• Limited knowledge of target pop/denominators
• Low timeliness & completeness of reporting
• Poor data quality
– Lack of written standard reporting procedures
– No systematic supervision on data management
• Dual reporting systems (EPI, HMIS)
• Inclusion of data from private sector

Assessing Reliability of
Routine Coverage Indicators
• Understand how denominators are derived
• Understand the process of collecting the information
• Look for inconsistencies and surprises

Assessing Reliability of
Routine Coverage Indicators
• Look for reliable data from other sources to use as a basis for comparison
• Cross-check

Survey Tools for Coverage
Estimation
• WHO-EPI surveys
• Lot quality coverage surveys
• Large-scale population-based surveys
• USAID Demographic and Health Surveys
• UNICEF Multiple Indicator Cluster Survey
• Arab League PAPCHILD surveys
• CDC Reproductive Health Surveys
• Seventy-five household survey
• Knowledge-Practice-Coverage Surveys
• Other local surveys

How Do Administrative Data
Compare With Survey Data?
50
40
30
20
10
0
100
90
80
70
60
Nairobi Central Coast Eastern N/ Eastern Nyanza Rift Valley Western
Survey (2002) Routine Cumm Sep 2002

Reconciling Coverage Estimates
From Different Data Sources
• Age group & geographic scope
• Health cards versus recall
• Different sources for different purposes
• Not all coverage data can be compared in constructive way
• Differences in inclusion of private sector
• Selectivity

On-line Resource: STATcompiler
• Innovative online database tool
• Allows users to select numerous countries and hundreds of indicators to create customized tables that serve specific needs
• Accesses nearly all population and health indicators published in DHS final reports http://www.measuredhs.com/statcompiler

STATcompiler

• DOLPHN: Data Online for Population, Health and Nutrition
• Online statistical data resource
• Quick access to frequently used indicators from multiple sources, including:
– DHS, BUCEN, CDC, UNAIDS, UNESCO,
UNICEF, World Bank, WHO www.phnip.com/dolphn

Advantages and Disadvantages of Routine-based Coverage
Advantages
• Provides information on more timely basis
• Makes use of data routinely collected
• Can be used to detect and correct problems in service delivery
Disadvantages
• Denominator errors
• Poor quality reporting

Advantages and Disadvantages of Survey-based Coverage
Advantages
• Avoids problems with denominators
• Includes information from non-reporting facilities
Disadvantages
• Coverage survey has low precision
• Larger standard errors at sub-national levels
• Irregular and expensive
• Survey timing may affect coverage rates

Case Study 2: Estimating
Vitamin A Coverage
• Calculate coverage from routine data
• Use tally sheets to determine number of children who received vitamin A compared to target population
• Compare coverage estimates from routine data with estimates from survey data
• Estimate missed opportunities

• WHO. 1999a
. Indicators to Monitor Maternal
Health Goals: Report of a Technical Working
Group , Geneva, 8-12 November 1993. Division of Family Health Geneva: WHO.
• WHO. 1999b.
Reduction of Maternal
Mortality: A Joint WHO, UNFPA, UNICEF,
World Bank Statement . Geneva: WHO.
• WHO (2002)
Increasing Immunization at the
Health Facility Level . Geneva, Switzerland:
World Health Organization