 I'll, too, use the Consistency Time page of the dashboard of the WHO Data Quality Tool. Let's click on the tab for Consistency Time. And then so that our screen will match up with the screenshots and the guide will set data to core, period to April, and disaggregation to district. Now, if we want to free up some space on our screen, we can make the Settings window disappear by clicking on the Settings icon. Let's look at the charts on this page. We see that there are charts for several indicators, A&C first visits. If we scroll down A&C fourth visits, HIV tests done. Institutional deliveries, 10 to 3 given before the age of 12 months. And under 5, OPD malaria has a percentage of total outpatient malaria. For each of these indicators, the page has two charts. The chart on the left has three different lines in different colors. Let's explore these lines. The dark blue line is showing the trend in the number of A&C first visits from May 2019 through April of 2020, which is the period that we selected. And then what is this light blue line? Well, the light blue line is also showing the trend over a 12 month period. But in this case, it is the months from May of 2018 to April of 2019. And then the orange line shows the trend over another 12 month period, which is May of 2017 to April of 2018. So each of the lines is showing the trend over a 12 month period, whether it's between one and 11 months previously, or 12 and 23 months previously, or 24 and 35 months previously. This is an example of what is called a year on year chart because we have placed the findings for one year on top of the findings for other years. And we notice from these charts that the values of A&C first visits go up and down a bit, but most months they don't jump up or down a great deal. But we see two exceptions. There's this outlier, this value that's very different from other months in January of 2020. Back here in May of 2019, we also see a jump up. So the year on year chart is good to show how the value for a single month can be quite different, not only different from the months in that year, but different from the value for months in other years. Let's look at now at the chart on the left, on the right side of the page. This is special kind of chart also. It's called a scatter plot. This is because the values are scattered and each of these dots represents the values for a single district. If we place the cursor on the dot, then a window appears showing that this is for district C5. And what does the value along the vertical axis represent? Well, as we see, the value on the vertical axis is the number of first A&C visits in April of 2020, which was 3,917. What does the value on the horizontal axis represent? Horizontal axis is the average value of A&C visits for that district during the 11 months prior to April 2020. So this would be from May of 2019 until March of 2020. And the two values, they aren't the same, but they are close to each other and that's why this dot is close to the line. This line represents districts which have exactly or almost exactly the same value in April 2020 as they had during the average of the previous 11 months. And all of the dots where the two values, the value for April 2020 and the value for the previous 11 months, where those two values are almost the same, they're close to this black line and they fall between the two gray lines. Now there's one dot where there's an exception. There's this dot down here and notice that the dot has a diamond shape rather than a circular shape of the dots that appear between the two gray lines. So dots which are suspicious have a different shape. This is for district B2, which in April of 2020 reported a total of 1,425 first A&C visits, whereas the average reported by this district during the previous 11 months was 4,595. So these are quite different. The value on the vertical axis is quite different from the value on the horizontal axis. So this dot is placed outside and identified as an outlier. Let me show you how we might investigate this outlier. By changing the period, the outlier appears in January of 2020. So what if we change the period of these charts to January of 2020? Again, let's free up space by making the settings window disappear. And notice how the charts have changed. Now we still see trends, trend lines in the chart on the left, but in this case the trend for the blue line is from February of 2019 to January of 2020, the period that we select and we see our outlier here. Look at the chart on the right now. Well, apart from these districts which had values in January of 2020, which were reasonably close to the average for the previous 11 months, there's a single dot appear and it is for District B2. So in January of 2020, District B2 reported a very large and probably incorrect value of 36,835 first A&C visits. When you compare this to the average of the previous 11 months, it reported only 1,363 first A&C visits during those previous 11 months. So this dot is shown far from the reference line, far from the line showing equal values. And can you explain now why it is that we saw, let's go back to April. Keep in mind the value reported for District B2 in January of 2020, almost 37,000, if we change the period back to April, can you explain why the dot was below the line in April? Well, in fact, this dot is below the line in April because the average of the previous 11 months, when we are looking at the chart based on April, the average includes this very high and incorrect value that was reported in January. And so that increases so much, the average over the previous 11 months that it pulls this dot away from the reference line. So that's an example of how you can do some investigation by changing the period. And you could also do some investigation by changing the organization unit and drill down. However, we will see how it is much easier to drill down and do investigations when we talk about the outlier page. Before we leave the consistency time page, I want to show you how we can change data and what happens to the consistency time page when we change data. At present, data includes, as you see, one or two indicators from multiple programs, anti-natal care program, HIV program. This is labor and delivery program immunization. And a malaria indicator. Notice that there's only a single immunization indicator now included when we have data set to core. But what happens if we change core to immunization? We wait a while for the data to be processed. And now let's scroll down the page and see which indicators are included on the consistency time page. Well, we see a new indicator, a new immunization indicator, BCG, measles, OPV1, OPV2, 3, PENTA1, 2, 3, rotavirus 1 and 2. So by changing to a different group, changing data to one of these other groups, we can look at a larger group of program-specific indicators. And we can do a data quality review that is specific to that program. Here, if we change to the HIV program, we see a larger number of HIV indicators on the dashboard. So that's an example of how data, changing data, allows us to provide a more detailed look at a more extensive set of indicators. And later, we will practice changing this to the district group, which has been especially designed for data quality review of indicators of greatest relevance at the district level. Thank you.