Step 1: Calculate the number of underweight children using the child population and underweight prevalence
                    - i.e. multiply 10,000 by 0.4 to get 4,000 underweight children. 

        Step 2: Then calculate the prevalence of underweight using the total number of underweight children and the total child population
                    - i.e.divide 12,000 by 50,000 and then multiply by 100% to get a regional prevalence of underweight of 24%. 

When the only information given is the total population and underweight prevalence data for each district then the process is slightly different. 

Given the following district level information calculate the prevalence of underweight for the province.

   CURRENT PROGRAM TARGETING: Service Coverage of Nutrition Related Programs

When making recommendations for nutrition related programs it is important to assess targeting of current programs, and perhaps retarget current resources as opposed to creating a new program.  In the Eastern Kenya data set, if we run a compare means with prevalence of underweight as the dependent variable and recorded measles immunization as the independent we get the following output:

From this we can then create a dummy table in the following manner:

From the above output we can fill in the empty spaces of our dummy table.  For example, 22.03% of those children not in a vaccination program are malnourished (.2203 X 177 = 39) while 32.80% of the children participating the vaccination program are malnourished (.3280 X 503 = 165).  Simple subtraction allows us to complete the table.

Simple subtraction allows us to complete the table (503 - 165 = 338  and 177 - 39 = 138):

Population Prevalence: prevalence of underweight in the population {(a+c) / (a+b+c+d)} 
204/680 = 30.0%
Population Coverage: the proportion of the whole population participating in the program {(a+b) / (a+b+c+d)}
503/680 = 73.9%
Malnourished Coverage:  the proportion of the malnourished children participating in the program  {a / (a+c)}
165/204 = 80.9%
Focusing (Program Coverage):  proportion of the those in participating in the program who are malnourished  {a/ (a+b) }
165/503 = 32.8%
F/PP Ratio:  A way to test for targeting is to determine the ratio of those in the program who are malnourished (focus)
to the population prevalence (PP).  [a/(a+b)] / [(a+c)/(a+b+c+d)]  If the ratio is greater than one, then program
targeting is happening.
       F/PP = (165/503) / (204/680) = 1.09

INTERPRETATION: 

Thirty percent of the population is underweight and only 7.4% of those participate in the program.  The malnourished, however, make up 32.8% of the children who are participating in the program. By looking at these results, we can see that the measles vaccination program is well targeting towards those worse off.

1.  In order to assess targeting and program delivery, it is useful to examine outcomes such as prevalence of low arm circumference or diarrhea in relation to indicators of relevant interventions such as access to service. For example, in the Bangladesh data set, we can look at the scatterplot of diarrhea prevalence and prevalence of diarrhea treatment by a trained health worker.

Try an exercise to produce a SCATTERPLOT of the prevalence of diarrhea by treatment of diarrhea.

The basic relationship looks like this.

INTERPRETATION:

This graph shows that there is not a strong relationship between diarrhea prevalence and access to trained health workers, but that is not what we are looking for.  The research question is:  "Which districts have a high prevalence of diarrhea and poor access to trained health workers?" and then:  "What are the prospects of shifting resources to ensure the high diarrhea prevalence districts get more equitable access to trained health workers?"  The districts with high diarrhea prevalence and low trained health worker coverage are cases such as 25, 11, 46, 2, 1, etc., found in the top left hand corner of the graph.

2.  The next step will be to define some cutpoints for high/low coverage and high/low prevalence, resulting in four categories. It is most useful to define these cut-points using the mean for each variable.

Try this exercise on DEFINING CUT-POINTS to create categories of illness and categories of health service.

It is useful to roundup from the means to logical cut-off points. In this case we will roundup and use below 15% diarrhea prevalence and below 20% health worker coverage as our cut-offs.

3. Dichotomizing the variables using these cut-offs will make it possible to separate those that are most in need and also make it easier to present the situation in a two-by-two table.

Try an exercise on DICHOTOMIZING THE VARIABLES.

In this table, there are two categories for the diarrhea treatment by a health worker (HLDITRHW:  represents a high/ low diarrhea treatment by health worker) that are coded 1 for <20% (low) and 2 for >=20% (high).  There are also two categories for prevalence of diarrhea (HLDIAPRV:  represents a variable high/ low prevalence of diarrhea) that are coded 1 for <15% (low) and 2 for >=15% (high). It is easy now to see where the deficiencies are in targeting, and to re-target the healthworkers from the high coverage/ low prevalence areas to those areas that are high prevalence/ low coverage. This will be the covered in the next lesson.

PRESENTING THE INFORMATION

Presenting this information with Microsoft Word or other word processing package might result in more attractive 2x2 tables, if it is important to have presentable information. Also notice that the cells have been shifted so that the upper left cell is the high diarrhea prevalence and low health worker coverage districts (lower left above).

Diarrhea treatment by health worker * Diarrhea prevalence categories Crosstabulation

• According to this information, is this program well-targeted? 
  WARNING:  in order to "blindly" use the equations you must set up your 2X2 tables so that box A represents high prevalence of diarrhea and high coverage;  box B is low prevalence, high coverage; box C high prevalence, low coverage; and box D is low prevalence and low coverage.
  
• You may want to re-do the presentation in the following manner to aid in the calculation of the program coverage.

• To determine if the program is well targeted, perform the following calcualtions:
  
  Population Prevalence: prevalence of underweight in the population {(a+c) / (a+b+c+d)}                                                 PopP= 29.69%
                                  
  Population Coverage: the proportion of the whole population participating in the program {(a+b) / (a+b+c+d)}                         PopC = 34.38%
  
  Malnourished Coverage:  the proportion of the malnourished children participating in the program  {a / (a+c)}                         MalC = 26.32%
  
  Focusing (Program Coverage):  proportion of the those in participating in the program who are malnourished  {a/ (a+b) }    ProC = 22.73%
  
  F/PP Ratio:  A way to test for targeting is to determine the ratio of those in the program who are malnourished (focus)          F/PP = .7655 (<1 is poor targeting)
  to the population prevalence (PP).  [a/(a+b)] / [(a+c)/(a+b+c+d)]  If the ratio is greater than one, then program
  targeting is happening.

• This information can then be used to re-target services from areas of low diarrhea prevalence and high health worker coverage to areas of high diarrhea prevalence and low health worker coverage.

Retarget Health Workers

The health workers from the blue cell are in an area where the need is low, but the coverage is high.  These health workers would be better used in an area where more children are needing care (high prevalence), therefore the workers from 14 of the 17 districts with low prevalence/ high coverage move (indicated by the blue arrow) to the high prevalence/ low coverage areas (which would make them high prevalence /high coverage areas).  The 14 districts with low prevalence / high coverage that sent their health workers to high prevalence / low coverage areas are now reclassified to low prevalence and low coverage areas (since they have sent their health workers to help others).  So 14 districts are reclassified as low /low and that makes a total of 42 low/low districts (indicated by the white arrow on the right) and the 14 districts that receive health care assistance are now reclassified as high prevalence/ high coverage districts, which makes a total of 19 districts high/ high (indicated by the white arrow on the left).

By re-targeting health worker coverage to areas with high diarrhea and low coverage, these areas become high diarrhea/high coverage. It is optimal to have 0 districts with high diarrhea and low coverage. In this case, health workers were redistributed from 14 low prevalence/high coverage districts to 14 high prevalence/low coverage districts. The results are shown below:

After retargeting the health workers:

RE-TARGETING HEALTH CARE WORKERS exercise will give more details on redistributing the health workers between high and
low coverage areas. 

Return to Top of Page