Haven't watched the YouTube video yet, but quick questions/feedback/notes:
1) In the XLSX file "Underlying Cause of Death 2021 Provisional Stats and Ranking 2022 May 7" I am trying to figure out what category/age group is missing from the various pivot tables which causes the total deaths to be artificially low.
Examples: Sheet "COD Pivot Table", filtering to all 10 year age groups, sum of deaths is 2,891,658 when it should be 3,461,321 according to sheet "Age Groups", row 53, which sums total deaths for 2021 (3.46 million also ties out to other various CDC reports). What explains the 569K gap? I scanned ICD list and not seeing anything missing, and appears all age groups included.
I see similar gap in the sheet "Graph Prep COD" - each year appears to be missing roughly 500-600K deaths. What were you filtering out?
2) Have you run similar analysis for other countries? Granted my skills are at "Grade school Tee Ball" to your "New York Yankees", but lots of interesting things when digging into all-cause morality and excess deaths using mortality.org data and other countries vital statistics. Sweden not having any excess deaths ages 0-65 throughout 2020 and 2021 is interesting, South Korea reporting unusually low death rates even before the pandemic strikes me as odd considering their aging population (death rates roughly equal to Colorado when I would have expected them to mirror closer to Arizona), etc.
3) I'm embarrassed I never knew of the "Sparkline" feature in Excel (As seen in "Total Mortality Level by State 1999-2021 provisional 6 May 2022", Sheet "Crude Death Rates", Column Y. ) I wonder how new that is... I used to tediously make tiny pictures of my charts and paste into cells a decade+ ago to achieve the same thing. If I find out that was there all along in the late aughts I'm gonna pull out what's left of my hair :)
Haven't watched the YouTube video yet, but quick questions/feedback/notes:
1) In the XLSX file "Underlying Cause of Death 2021 Provisional Stats and Ranking 2022 May 7" I am trying to figure out what category/age group is missing from the various pivot tables which causes the total deaths to be artificially low.
Examples: Sheet "COD Pivot Table", filtering to all 10 year age groups, sum of deaths is 2,891,658 when it should be 3,461,321 according to sheet "Age Groups", row 53, which sums total deaths for 2021 (3.46 million also ties out to other various CDC reports). What explains the 569K gap? I scanned ICD list and not seeing anything missing, and appears all age groups included.
I see similar gap in the sheet "Graph Prep COD" - each year appears to be missing roughly 500-600K deaths. What were you filtering out?
2) Have you run similar analysis for other countries? Granted my skills are at "Grade school Tee Ball" to your "New York Yankees", but lots of interesting things when digging into all-cause morality and excess deaths using mortality.org data and other countries vital statistics. Sweden not having any excess deaths ages 0-65 throughout 2020 and 2021 is interesting, South Korea reporting unusually low death rates even before the pandemic strikes me as odd considering their aging population (death rates roughly equal to Colorado when I would have expected them to mirror closer to Arizona), etc.
3) I'm embarrassed I never knew of the "Sparkline" feature in Excel (As seen in "Total Mortality Level by State 1999-2021 provisional 6 May 2022", Sheet "Crude Death Rates", Column Y. ) I wonder how new that is... I used to tediously make tiny pictures of my charts and paste into cells a decade+ ago to achieve the same thing. If I find out that was there all along in the late aughts I'm gonna pull out what's left of my hair :)