 With this video, I'm going to present how to use the Consistency Data tab of the dashboard of the WHO Data Quality Tool. Let's click on the Consistency Data tab. For the presentation, let's set data to core, period to June, and organization unit to national with disaggregation to district. Set this way, the screens that you will see will match up with the screenshots in the guide. We can enlarge the chart by making the settings window disappear. And let's scroll down and look at the charts that appear on the Consistency Data tab. There are three of them. There are two scatter plots. And then a third chart that shows a series of histograms of bar charts with a bar for each district. Let's begin by looking at the two scatter plots. The first of these compares the district value of A and C first visits to the district value of A and C fourth visits. Again, with a scatter plot like this, each dot represents the values for one district. And you see that for most districts, there is a relationship between A and C first visits and A and C fourth visits that is represented by this dark line. It isn't to say that A and C one is close to A and C four, just that the ratio between these two values follows a slope that is given by this black line. For example, if a district had 40,000 A and C first visits, then it is likely to have somewhere around 7,500 A and C fourth visits. And most of the districts in the country follow this relationship. And therefore, most of the districts have dots that fall between these two gray lines. There is, however, an exception. You see this diamond shaped dot again for district B two, which had far more A and C first visits during this 12 month period, far more A and C first visits than A and C fourth visits. In fact, if we look at that, it is perhaps 20 times as many A and C first visits as A and C fourth visits, whereas the districts down here may have only five times as many A and C first visits as A and C fourth visits. Now let's think about it, district B two, we've seen this district appear before. In fact, when we looked at the consistency time charts, we saw that there was a facility in district B two, which had greatly over reported A and C first visits in January of 2020. So we have a pretty good explanation for why it is that A and C first visits might be so much greater than A and C fourth visits that this might be the result of the same error made in the reporting of A and C first visits. Let's scroll down to the second scatter plot. And this shows the relationship of A and C first visits this time to the number of first doses of PINTA vaccine. Well, you may be surprised at first that there is any relationship at all. But if you were told that this was a country where in almost every district, more than 90% of pregnant women got at least one anti-natal care visit, and more than 90% of eligible infants got their first dose of PINTA vaccine, you might begin to understand why the number of A and C first visits for a district is pretty closely related to the number of doses, first doses of PINTA vaccine. And you see that for almost all districts, this relationship is followed closely. And again, there are certain exceptions. Here's another exception. But the diamond shaped dot up here is once again for district B2. And we ask ourselves, why is the number of first anti-natal care visits for district B2 so much greater than the number of first doses of PINTA vaccine? Well, perhaps you can answer the question yourself by now. But each of these diamond shaped dots is worthy of some investigation. Because the relationship between anti-natal care first visits and PINTA first doses doesn't follow what we see in these other districts. Finally, we come to this last chart, which is not a scatterplot, but is a histogram showing one bar for each district. And for most districts, the bars are blue. These bars are showing the dropout rate between PINTA 1 and PINTA 3. What is the dropout rate? The dropout rate is simply the percentage of infants who got their first dose of PINTA vaccine, but who did not return for their third dose. So since an infant needs to receive their first dose before they can receive their third dose, we expect the dropout rate to be positive. That is, we expect the number of first doses of PINTA vaccine to be greater than the number of third doses. But look here. There are two districts where the dropout rate is negative, district A2 and district A3. In both of these districts, the number of third doses of PINTA vaccine was greater than the number of first doses of PINTA vaccine during the 12 month period shown here. This warrants some further investigation. Is it because of over-reporting of third doses of PINTA by a single health facility, which greatly over-reported third doses of PINTA vaccine? Or is it because many health facilities in one of these districts, or both of these districts, are consistently over-reporting of PINTA 3 doses? We cannot answer the question with the dashboard. We will see later how we can use the analysis function of the data quality tool to investigate further the phenomenon of negative dropout in these two districts.