How ANC1 can exceed 100%?

Good day!

As part of my research, I am examining the validation of Maternal and Child Health (MCH) indicators in DHIS2. However, I’m unsure how to explain the phenomenon of ANC1 coverage exceeding 100%.

While I have come across a few publications that also report ANC1 coverage above 100%, none of them provide a detailed explanation of how such values can go beyond this threshold. In the context of a developing country setting, could you offer advice on how to interpret these situations? I would greatly appreciate any guidance you can provide.

Hi there,

denominators are more often than not a big problem, no matter the setting really. Underestimations, outdated population numbers, internal mouvements, lack of births and deaths registrations can give us data that at first glance could seem erroneous, but that in reality are trying to guide us towards the improvement of our baselines and/or of the collected data.

Coverages go over the 100% “limit” all teh times - there are no wrong or rigth answers here, as teh cause could be dependent on a variety of factors, but here soem questions you could use to guide and deepen your analysis:

  • Is your population data up to date and realistic? If my baseline tells me that my population has 100 pregnant women coming for their first ANC, but then I get 120 at the end of the month, your coverage will for sure be on the weird side of things.
  • Are the data collected correctly? Would be worth it to check also if the data collected for the number of women showing up for their ANC1 are correct. Is there any woman being recorded more than once? Would be worth it to check your registers with the service’s staff and check whether the numbers are related to any issue with recording patients, counting, or aggregating.

These are two very common reasons related to high coverage and would definitely be worth it to go and dig further in the data. Although they could seem “wrong” at first, these are just very common red flags that help us better understanding the numbers, the setting, and to improve our baselines and reporting.

Keep us updated and let us know if you find any odd numbers! In particular, if you are having denominator (population) data issues, we have plenty of resources on how to improve your population data via estimations and maps.



Being totally agree with Vittoria’s comments and guidance. There are few other techniques people are using in different countries:
Purpose: First of all calculating coverages from routine data is to compare progress in different times. keeping that in mind see the below methods/solutions.

  1. Adjusting the denominator: For example if you calculate target population using 4.5% of total population, increase the factor to 4.8%. look at some other national surveys (DHIS and/or other) for factors (i.e 5% of total population) for target population for ANC and use it.
  2. Use BCG data as denominator if that is available and gives better output.
  3. Use data quality factor if that is available. Most countries assess quality of data and produce a factor. For example, if the data quality factor is 85%. ANC1 * 85% = Corrected ANC1 data.
    Good Luck !
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