Maps of program areas are not always available or when they are, they are not of useful scale or have incomplete village locations which make them unusable for any of the mapping approaches described in the module on mapping of beneficiary locations. Mapping may also prove to be difficult particularly in urban, peri-urban, or ‘shanty’ areas. In these situations, the use of lists and tables of home locations, distance (or time-to-travel as proxy), number of admissions and number of defaulters can be used as alternative.
Data on home locations, time-to-travel, admissions and defaulters can be collected from beneficiary records. This tool also requires having a complete list of locations (e.g., villages) in the catchment area of a program or program site.
Analysis of data
A table can be used to investigate the effect of distance (travel time) on admissions and defaulting as shown in Table 1.
Data courtesy of Lusaka District Health Management Team
Listing, on the other hand, is a useful and simple way of identifying locations where coverage is likely to be poor (i.e., locations from which there are very few or no admissions) or defaulting is likely to be high. A simple listing can be done with a column for the complete list of villages in the catchment area of program or program site, a column for distance, time-to-travel, or fuzzy class (e.g., ‘very near’, ‘far’, etc.) and respective columns for counts of admissions and defaulters taken from beneficiary record cards. Table 2 shows an example of this approach used in assessing the coverage of a CMAM program in a local government area in the north of Nigeria. A template to use for lists and tables approach is available on the accessory tools section of the resources page.
Data courtesy of Gombe State Ministry of Health
The data in Table 1 suggests that, in this program, distance has an effect on both admissions (higher close to the clinic) and defaulting (higher further from the clinic). For the data in Table 2, the effect of distance on admissions is clear with villages at or very near the health centre having high admission numbers. However, for defaulting, the effect of distance is not clear as high number of defaulters came from very near the health centre as well.