Part 1. Data analysis

Task 1. Flag all the suspicious values. (Outliers, repetitions, etc.) (spend max 15 minutes)

All outliers or suspected values are highlighted in the below mentioned table.

In the below data seven districts have reported over all >100% coverage which is not possible. Month wise data also showing high coverage against target. Two districts (District 3 and District 14) reported more than 200% coverage and 4 districts (District 1,4,10 and 15) have reported more than 100% coverage and the trend of the coverage is also not clear as district 13 reported very low coverage in the month of Jun and Nov and finally it is effecting the total coverage of Grandtown. There are duplicate values also in the month of Feb & March in approx. all districts.

Task 2. Review the national and subnational coverage for MR1. Your data manager produces the following tables. What can you conclude from the administrative data?

MR1 coverage is >100% in Grandtown it seems it inflated coverage. Here can be some problem with the denominators of the districts as the population growth rate is not seems to be the same for every year. Surviving infants of Grandtown seems to be ok for every year.

As data shows that some of the states like Nemo & Westtan are having very low coverage for MR1 and so migration may be a reason of the high number of measles cases

Task 3. Review coverage evaluation survey data. You remember that in 2013, there was a coverage evaluation survey. You pull up the data for that. Does this change your view about coverage at national level? For any of the regions?

As per the survey data, it seems that it is quite difficult to rely on the administrative data as in 2012 and 2013 some of the regions shows very low coverage and some shows very high coverage in the administrative data however the survey shows that it is not the actual situation so it shows that data quality issues are there in the administrative data because there are not much difference in data at national level.

Task 4. Review the chart with the age distribution of measles cases. Does that tell you anything additional about coverage?

Approx. 90 % of the measles cases are in the age group of >1 year so it appears that the coverage of MR2 would be very low and vaccine hesitancy could also be a reason for increasing the cases in 2018 as it is also mentioned in the case study that vaccine hesitancy has been increasing after 2017.

Part 2. Brief the Minister

Task 5. Brief the Minister (spend max 1/2 hour on this section). Summarize the situation in three bullet points.

80% cases of 2018 found unvaccinated which need to immediate a campaign for all age groups including adults (in total 90% cases are more than 1> year age group) should be conducted in a focused approach on the children less than 5 years of age. Age break up of Measles cases shows that there should be a big drop out of MR1 and MR2 coverage.
There are data quality issues in sub division level therefore supervisory cadre needs to improve. It seems that data quality issues are in some of the regions shows very high coverage and some shows very low coverage and the same condition is there in the Grandtown also. It shows that RI activities are not up to the mark.
Recording and reporting system in the Vacciland is paper based only which leads to errors and wrong calculations / aggregations by the health workers at the time of reporting which ultimately leads to wrong immunization coverage.

Task 6. Brief the Minister. Propose three actions to respond to the outbreak.

Determine priority areas needing focused support, detect, Identify the pockets where beneficiaries are left out from MR doses because of geographical or social factors.
MR Campaigns can be done immediately to stop the transmission
Rigorous Monitoring needs to be done of the campaign as well as the regular RI activities

Task 7. Formulate recommendations. List your top 3-5 recommendations specific to data strengthening you would prioritize as the EPI and surveillance teams in Vacciland

Headcount survey by any link worker / front line worker would be a better option in order to get the accurate targets, it will help to get the actual picture of the coverage.
Availability of updated recording and reporting tools is very important to ensure the good and accurate coverage data
Capacity building of health workers on recording and reporting and if possible, try to introduce artificial intelligence for recording and reporting of data
Regular data quality assessment (DQA) to review the reported data and coverage data can also be matched with the stock consumption for highlighting the reporting error so that any discrepancy could be flagged immediately