1. Monitoring and Evaluation: Calculating and Interpreting Coverage Indicators

2. Learning Objectives
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
This session is about calculating and interpreting coverage indicators. By the end of this session, participants will be able to identify sources of data for calculating coverage indicators; estimate denominators for coverage estimates; calculate and interpret coverage indicators from routine data. Participants will also learn how to use online resources for deriving survey-based coverage estimates and assess the quality of relevant data sources. We will also learn how to reconcile coverage estimates from different data sources.
Activity:
Review the definition of coverage by asking participants to explain in their own words what that means. Possible answer: coverage indicates what proportion of the total population targeted really received a health service or product. Important aspects of this definition are extent, place (area, geography), time period, and jurisdiction (administrative unit)

3. 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
These are a few examples of indicators of the coverage of maternity care. They include antenatal and postpartum care coverage, deliveries at health facilities and presence of skilled personnel during childbirth.

4. 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
Why are coverage indicators important? Coverage is a measure of the effectiveness of program efforts or activities. Important aspects of this are the kinds of activities being undertaken and the areas or populations where these activities are intended to influence health outcomes. The latter can be expressed in geographical terms. One pertinent question is whether the program is working in the right places and reaching its target population(s) to have a maximum impact on health outcomes. Coverage indicators help us understand how effective a program is and whether one target group is reached more effectively than another. They also help to identify underserved areas or regions which need more attention. Coverage indicators are quantitative in nature. They may be expressed as cumulative numbers of people receiving a health service or product as well as in terms of the relative population served or reached by program activities and the total amount and type of activities. For example, a communication program might be interested in the areas or groups of people reached by a mass media campaign, as well as the range of topics treated or the depth to which a subject is discussed or reported. While coverage indicators do not address the quality of the interventions, they can be used to evaluate how program activities relate to national or global health priorities.

5. 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?
There are a number of coverage indicators for immunization programs. These basically tell you the percentage of the target population receiving a specific vaccine. Immunization coverage indicators include the measles and BCG coverage as well as coverage with the third doses of Diphtheria-Tetanus-Pertussis (DTP) and Oral Polio vaccine (OPV). They help measure the success of immunization programs and increase awareness of under and over immunized populations.
Activity:
What would be an appropriate coverage indicator for a nutrition program? What would be an appropriate coverage indicator for an iron fortification program? A vitamin A program? How about programs aimed at the control of diarrheal disease? Would oral rehydration therapy use (the proportion of children with diarrhea in the past two weeks who received ORS) be an indicator of coverage?

6. 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?
Here are a few examples of coverage indicators for HIV/AIDS care and treatment programs.
Activity:
What indicators can we use to measure coverage of PMTCT programs?

7. Where Do We Get the Data? Censuses Surveys Registrations
Health management information systems
Program statistics
Patient registers
We can see that there are several sources data for calculating indicators. These include health management information systems and program statistics, patient registers, population-based surveys, and vital registration systems. Some of these data sources like censuses, sample surveys, and registration systems have been around for a long time and are familiar to most people. While censuses and surveys both involve collecting data describing sets of conditions at the point in time when the census or survey is taken, registrations have to do with recording events as they occur or status as they are acquired at the time they occur, not at some time later. While censuses and surveys are forms of stock-taking, registrations are forms of record keeping. There are various types of registration systems. One is called civil or vital registration in which facts regarding individual civil or vital events (live births, deaths, marriages, adoptions, divorces) are recorded by a government agency. Another sets relates to other forms of public recordings such as population registers which contain statistics about all persons living in a community. Some sources of data like health management information systems are relatively recent sources of information in many countries. Some related sources of data are program statistics, which may come in many different forms depending on program activities, and patient registers. These sources of data have to do with recording events as they occur.
Class Activity:
All types of data sources rely on individuals providing information. What can you say about the quality of data from each of the sources listed above? What are the advantages and limitations of using these sources of information to estimate coverage indicators?

8. Estimating Coverage From Routine Data
We will now consider turn to estimating coverage from routine data.

9. 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: ?
If we are estimating coverage indicators from routine data, otherwise known as program statistics, where would our numerators come from? They come from HMIS and routine reports. Depending on the indicator, our numerator may include things like the number of deliveries in health facilities, measles vaccinations administered, pills distributed, voluntary counseling and testing clients, etc. What about our denominators?

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

11. 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 years?
How many children are of school going age?
How many infants are there?
How many babies are born each year?
What denominators do we need if we are estimating coverage indicators from program statistics? Depending on our indicator, we may need data on population composition. We may want to know: how many women are of childbearing age; how many children are under the age of five years; how many adolescents are in a given population or geographical area. We may want to know how many men are aged 15-59; how many children are of school-going age; how many infants are there; how many babies are born each year.
Activity:
Can we think of indicators that can be estimated from program statistics and for which our denominator would be the number of women of childbearing age? The number of children under five? The number of infants? The number of babies born each year?

12. How Do We Get Denominators?
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
As we know, censuses are taken at infrequent intervals. Yet we might want to estimate our coverage indicator at different times, such as in the period between censuses. How do we handle this situation? In some countries, national governments and some private agencies can issue regular population estimates for noncensus periods. But what if these estimates are not available for the administrative area covered by our program? Demographers have come up with various ways to estimate this information by projecting the population based on data from the last census. These population estimates since the last census was taken are most accurate soon after the census and get weaker as the date departs from the last census date. In each case, the person doing the projections must make certain assumption about the rate of growth of the population. Because developing countries collect relatively little data on population and the data they collect are often inadequate, there exists a whole battery of estimation techniques geared to these places. A fundamental aspect of these techniques is the dependence on the relationships between births, deaths, migrations, age structure, and population size (read out relationships in slide).

13. Spectrum Model
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
If you don’t want to estimate the population for non-census periods directly using the various mathematical formula that have been designed for that purpose, you may use the SPECTRUM model. The SPECTRUM model has a component called DemProj. It projects the population for an entire country or region by age and sex based on assumptions about fertility, mortality, and migration. A full set of demographic indicators can be displayed for up to 50 years into the future. Urban and rural populations can also be prepared. A companion model, EasyProj, supplies data needed to make a population projection from the estimates provided by the Population Division of the United Nations. The Spectrum model can be obtained online from the Futures Group International (

14. Spectrum

15. Calculating Denominators
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
If you don’t have the SPECTRUM model and cannot run to the census office for assistance, then you can use a basic formula to estimate the population size after a given number of years from the last census. (Present formula and have the class do the given exercise)

16. 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
If data on the number of live births are unavailable, you can calculate total estimated live births using census data for the total population and crude birth rate in a specified area. In settings where the crude birth rate is unknown, WHO recommends using 3.5 percent of the total population as an estimate of the number of live births or number of pregnant women. In reality, the multiplier for the number of pregnant women should be higher than the multiplier for the number of live births because not all pregnancies end in a live birth. It is important to remember, therefore, that these are just rough estimates. If you know a more precise percentage of pregnant women for your country or region, use this number instead.
Source: WHO 1999a; WHO 1999b

17. 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)
If data on the numbers of surviving infants are unavailable, you can estimate the total live births from the total population and crude birth rate and infant mortality rate in a geographic area. (Read out the formula.)

18. 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 ( )
= 5,500,000 x x 0.920
= 151,800
Let us consider this example. The total population is 5.5 million, the crude birth rate is 30 per 1000 and the infant mortality rate, 80 per Applying the formula from the last slide, we can see that the estimated number of surviving children is 151,800.
Source: Immunization Essentials: A Practical Field Guide (USAID, 2003)

19. 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
As we mentioned earlier, you can use existing population figures for children under one year of age obtained from official census data or your own community census. If you do not have these numbers, and do not have data on the crude birth rate and infant mortality rate, you can estimate the number of children under one year in the population by multiplying the total population by 4%. In the previous slide, we used 3.5%. Here, this multiplier has been rounded to 4%. If you know a more precise percentage of children under one year for your country or region, use this number instead. For now, we will use 4% as the estimate to calculate the percentage of children less than one year of age in a population. For example if the total population is 30,000 then the number of children under one year is 30,000 x 4/100 = 1200.
Source: WHO, 2002b

20. 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
Some programs such as immunization programs and micronutrient programs monitor coverage on a monthly basis. At the end of each month, they review their registers and program records, and compile the number of vaccinations administered or the number of vitamin A capsules given. To calculate coverage on a monthly basis, these programs need to estimate the monthly target population. To get a monthly target population, divide the number of children under one year of age by 12. For example, if the annual target under one year is 1200, the monthly target is 1200/12 = 100.

21. 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
Let us consider this example. It is February You are the monitoring and evaluation officer for the district EPI program and have to update annual immunization coverage estimates for your district. At the time of the last census, which was held in 1990, the district had a total population of 99,000. The crude birth rate was 40 per thousand, the infant mortality rate was 80 per thousand and the population was growing at a rate of 3 percent per year. In the district, 3000 measles vaccinations were given to infants in What is the measles coverage rate for 1998?
As some of you may know, the numerator for the measles coverage rate is the number immunized by age 12 months with measles vaccine in a given year. The denominator is the total number of surviving infants less than 12 months of age (that is, age 0-11 months) in the same year.

22. Immunization Coverage From Routine Data: Answer
Estimate district total population in 1998
Pop1998 = 99,000 * exp(.03*8) = 125,854
Estimate number of surviving infants in 1998
125,854 x (40/1000) x ( ) = 4615
Estimate measles coverage rate
Measles coverage = 3000/4615 x 100 = 65%

23. 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
There are various challenges in estimating coverage indicators from routine data in most developing countries. First knowledge of the catchment or target population is limited, especially at lower levels, and here, we are talking about the sub-regional or sub-district level. You may even find different denominators being used for the same area at the national and district levels. Second, there is poor timeliness and completeness of reporting. The process of transmitting , compiling, analyzing and presenting the data is so tedious that by the time the report is prepared, the data are frequently obsolete. Third, the quality of the data are often poor. Health workers receive little if any training on data collection methods and rarely have standardized guidelines on how to collect the data. Another reason why data quality is poor is lack of motivation. Health workers rarely receive feedback on data reported to higher levels; so they have little incentive to ensure data quality or comply with reporting requirements. Fourth, duplication is common. Often donor agencies or national programs develop their own specialized information systems. Thus, varying information systems can exist side by side, like the EPI and a general HMIS system, instead of addressing routine data collection in a unified way. Because data are often not cross-referenced among different systems, the data may be redundant or overlapping. Finally, it is sometimes unclear whether data from the private sector are included in routine reports.

24. Assessing Reliability of Routine Coverage Indicators
Understand how denominators are derived
Understand the process of collecting the information
Look for inconsistencies and surprises
Because health information systems in many developing countries are inadequate and because censuses are taken at infrequent intervals and registration systems are often incomplete, estimates of the denominators for routine coverage indicators are based on counts by health workers or on projections from the latest census data. These projections usually involve a number of assumptions about the way the population is going to change over time. The further one forecasts away from the date of the last census, the more the assumptions are likely to be wrong. There may be unforeseen changes in the birth rate, mortality rate, and migration.
Therefore, it is especially important to assess the reliability of routine coverage indicators. (1) Make sure you know which population figures are used for the denominator and the time period to which they refer. Some managers inflate the size of the target population to make sure that they receive an adequate supply of commodities. In some cases, they may deflate the denominator to make coverage rates look high. (2) Next, you should understand the process of collecting the information. How is the numerator derived? Locate information sources. Know where requests for information comes, where it goes, what is kept at each level, who compiles it, how much time passes from the occurrence of an event to the compilation of the records, and the motivations for collecting and analyzing the information. Let us take the example of immunization coverage, you may want to know what method is used to collect data on doses administered. What percent of sites submit reports? Are doses from the private sector included? Are doses administered during campaigns included in routine coverage? (3) Next, look for inconsistencies and surprises. Are coverage rates greater than 100%, for example? Check the validity of the data and try to see if the process of data collection or some other factors may explain the inconsistencies.

25. Assessing Reliability of Routine Coverage Indicators
Look for reliable data from other sources to use as a basis for comparison
Cross-check
Next, look for reliable data from other sources to use as a basis for comparison. Going back to the immunization example, you may want to see how immunization coverage compare with disease trends. Last but not least, cross-checking is important. You may want to compare immunization cards of children presenting at the facility with patient registers. As an alternative, you may want to collect a sample of the health facility information from birth or patient (maternity registers) to cross-check information sent to a higher level.

26. Estimating Coverage from Survey Data
Let us now turn to estimating coverage from survey data.
Activity:
What are some of the large-scale surveys from which we can derive coverage indicators? What are the strengths and weaknesses of survey data for coverage estimation?

27. 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
Many tools are now available for estimating coverage from survey data. A standard WHO methodology for estimating immunization coverage is based on a small number of individuals (210 in 30 clusters of 7 individuals each). The survey uses cluster sampling technique to ensure that data from a small number of homes can be generalized to a larger population. The EPI cluster surveys are particularly suitable for estimating immunization coverage for sub-national areas. Lot quality assurance sampling may also be used. This technique is used to assess coverage, quality, or both with the purpose of directing attention to the facilities or areas that need it most. It is often used in areas that do not correspond to official reporting sites, such as urban slums. The Demographic and Health Surveys, the Multiple Indicator Cluster Surveys, the Arab League PAPCHILD surveys, and the CDC Reproductive Health Survey are good sources of data on national health sectors and target populations. Another survey tool that is sometimes used to estimate immunization coverage is the seventy-five household survey. This survey focuses on households that have easy access to health facilities. The theory is that if people in the households closest to health facilities are not receiving services, then use of services in the wider catchment area must be poor indeed. It is useful where the population is stable and coverage is unknown. Knowledge-Practice-Coverage are smaller versions of the DHS and MICS. Other local coverage surveys may also exist.

28. How Do Administrative Data Compare With Survey Data?
Survey data may tell a different story from routine data. So managers and other users need to figure out which data are most suitable to use in a given situation. The data presented above is from Kenya and highlights the complexities of coming up with clear figures for estimating coverage. As can be seen , coverage estimates can vary considerably by source of data. In this instance, the two sources of data cover approximately the same period. In every region, the survey yields higher coverage estimates than the routine data.
Activity:
What are the possible reasons for this discrepancy? (Answer: Survey data should report coverage based only on vaccinations that children receive up to 12 months of age. This is the recommended methodology & routine data report only vaccinations of children aged 0-11 months. However, most surveys coverage estimates based on children aged months include vaccinations that children receive up to the time of the survey. This means that vaccinations given to children when they were older than 12 months might be included, leading to higher survey-based coverage estimates. Surveys may also detect vaccinations provided y the private sector that may be missed by facility-based tallies that are used to estimate routine coverage. Surveys may include campaign doses while facility-based tallies on which routine coverage estimates are based do not. Thus surveys may capture doses received by children who are not brought to health centers).

29. 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
As can be seen from the previous slide, coverage estimates can vary greatly by the source of the data. To make sense of any of these data, users need to be aware that the age group and geographic scope can differ among data sources. For example, are consistent age definitions used to count those immunized (that is, in surveys, are coverage estimates for children aged months based on vaccinations received before the age of 12 months, or at any time before the survey?
Some surveys report coverage only on what is reported in health cards, while others include data from recall. Recall information is not always reliable. In countries where a large proportion of mothers are unable to produce the health cards, survey-based coverage based on both card and recall may be under-estimated. Different sources can be used for different purposes. For example, population-based data should be used to estimate the population reached by a service or product but estimates based on routine data can be used to detect year to year changes in coverage. Not all coverage data can be compared in a constructive way. It would be meaningless to compare the DHS from 1997 and the 1999 routine EPI data because both the sources of data ad the time periods are different. Therefore, when comparing coverage from program statistics with those measured in household surveys, you should always ask yourself: “During which months was the survey conducted and how does this compare with the time frame reported by the program? Did any large scale immunization (or vitamin A) campaign take place before or after the survey, and how is this reflected in either data set?”
Population-based data will detect services provided by the private sector that can be used by the facility-based tallies from which routine coverage estimates are derived. In countries where utilization of health services is low, people who use health facilities are a select group and coverage estimates from routine data may not be representative of the general population.

30. 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

31. STATcompiler

32. On-line Resource: DOLPHN
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

33. Advantages and Disadvantages of Routine-based Coverage
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
There are a number of advantages to using routine data to estimate coverage indicators. Routine data provide information on a more timely basis. They make use of data routinely collected by programs and can be used to detect and correct problems in service delivery. However, there are difficulties in providing accurate denominators for routine-based coverage estimates. The quality of routine-based coverage may also be poor due to a lack of interest in record keeping and reporting and poor timeliness and completeness of reporting.

34. Advantages and Disadvantages of Survey-based Coverage
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
With survey-based estimates, you don’t have the problems with denominators. Also, surveys include vaccinations from non-reporting facilities and vaccinations administered to children during campaigns. However, every survey estimate is subject to sampling errors, resulting from the fact that many samples drawn from the same population can produce slightly different estimates. The sampling errors are used to construct confidence intervals, usually at the 95 percent level, to indicate a high probability that the true estimate lies within these intervals. Standard errors tend to be larger at sub-national level, making it more difficult to compare different regions or to measure change over timing. The EPI surveys in particular tend to have low precision because they are based on a small number of children. Furthermore, surveys are irregular and expensive. Surveys like the DHS and MICS are conducted every three to five years but managers need data at much more frequent intervals in order to make program decisions. The timing of the survey may affect coverage rates if national immunization days for polio immunization or large-scale campaigns for measles immunization or National Vitamin A weeks occur just before or during the period the survey is being conducted in the field or just after the end of the survey. In the first case, the coverage rates may be biased upwards, especially if efforts are not sustained. If the campaign is conducted right after the survey, the survey coverage estimates will be of little use.

35. References
WHO a. Indicators to Monitor Maternal Health Goals: Report of a Technical Working Group, Geneva, 8-12 November 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

36. 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?
The goal of this exercise is to give participants hands-on experience in calculating coverage indicators from routine data and to interpreting and using coverage indicators for program action. You will use the handout provided for the Eastern district to estimate levels and trends in immunization coverage. The handout provides the target population for 1990, doses of vaccines administered in health facilities in the district in 1990 by area and immunization coverage rates for The population of the district is growing at an average rate of 3 percent per annum. You have just finished compiling the number of doses administered in the district in 2003 and you need to estimate immunization coverage rates for 2003.
Once this exercise is complete, we will examine trends in coverage levels. Is coverage low or falling? What are the possible causes? Next, we will analyze the problems in What are the differences in coverage between different areas and possible causes? What action can managers take if coverage indicate problems? Are unreached areas identified and what strategies can be used to reach them?
For this activity the class may be divided into three groups. One first group can focus on DTP1 coverage, the second group on DTP3 coverage, and the third group on measles coverage. Each group should estimate the number unimmunized and drop-out rates, identify the problem, and prioritize areas for intervention. If immunization coverage rate are greater than 100%, the reasons should be identified (e.g., inadequate target population data, number of immunized children includes other age groups than the target one or children from other areas).