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@sco0psmcgoo - Scoops McGoo

BREAKING: the New Zealand government today released gross mortality statistics by covid vax dose (including unvaccinated) under Official Information Act request Combined with @BarryYoungNZ's leaked data, we now see mortality in each dose group (including 0 doses) #StoptheShots!

@sco0psmcgoo - Scoops McGoo

Spreadsheet https://docs.google.com/spreadsheets/d/1BwtabtrYjvSfAKlI3o_OTUlgPN_NrzCXTsPM_QT6gKI/ Mortality-by-dose-by-month (OIA request) https://fyi.org.nz/request/25021-number-of-covid19-vax-deaths-by-age-band-location-and-month#incoming-96520 NZ Stats (official metrics) https://infoshare.stats.govt.nz Min. Health git repo (for deducing unvaxxed group size from cumulative first-dose-by-date data) https://github.com/minhealthnz/nz-covid-data/

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Number of COVID19 vax deaths by age band, location and month - a Official Information Act request to Ministry of Health Can you please provide a count of the number of people vaccinated against COVID19 funded by the government of NZ and those who subsequently died of any cause - as measured by MoH record level patient or payment databases. Please provide this as a machine readable CSV file. Dataset 1 - Vaccinations The following breakdown of each column is required: 1) Month of vaccination - from the first vaccination until the end of Nov 2023 2) Vaccination number (eg 1=1st, 2=2nd, 3=1st booster, 4=2nd booster etc) using a coding scheme used within the MoH 3) Location - Territorial Local Authority (ie Council area) based grouped into The following categories: Auckland, Greater Wellington (incld Wairarapa), Christchurch, Dunedin, Rest of South Island, Lower North Island (including Taranaki and Gisborne but excluding Taupo and King Country), Uppoer Nirth Island. Ideally this is the physical vaccinating provider paid. Ideally derived from the location the injection was given (eg Rest Home for a mobile clinic - which is possible because the address of the patient’s NHI number will be the rest home, or the physical address of a physical site like a pharmacy/GP clinic). 4) Age Band of Patient - <19 years old inclusive, 20-39 inclusive, 40-59 inclusive, 60-74 inclusive, 75+ inclusive 5) Intentionally blank - null 6) Death Last Dose - <30 days inclusive, 31 days-90 days inclusive, 91-180 days, 181 days to 365 days, 366 days to 730 days inclusive, 731 to 1095 days inclusive, over 1096 days inclusive. Each of these categories should be calculated from the date of the patients’s most recent vaccine and a suffix appended depending upon the number of their last vaccine (eg “<30_5” would be coded against a patient who died within 30 days of their 5th jab/3rd booster 7) Intentionally blank - null 8) The count of the number of vaccination injections in each of the categories above when the underlying dataset has a Group By query applied using Count(Distinct NHI_Number) or similar for this column. 9) The count of the number of people who subsequently died after the vaccination month in each of the categories above when the underlying dataset has a Group By query applied using Count(Distinct NHI_Number) or similar for this column. So for example, a group of 1000 70 year old people who were injected in June 2023 with their 3rd injection where 7 people died after 4 months (in Oct 2023) after visiting an ER would appear in a record like the following: 202307, 3, “Auckland”, “60-74”,”>null_1”, “91-180_3”, “null”, 1000, “7” Similarly there be no recorded deaths for 366 days and over because this is in the future after this dataset is being summarised. If there were 1234 1st injections and no deaths within 30 days of Feb 2022 for teenagers, but 3 / 345 within 2 months and 10 / 456 in month 4 then the records might look like: 202202, 1, Dunedin, “<19”, “null”, “<30_1”, “N”, 1234, “0” 202202, 1, Dunedin, “<19”, “null”, “31-90_1”, “null”, 345, “S” (see below re use of “S”) 202202, 1, Dunedin, “<19”, “null”, “91_180_1”,“null”, 456, “10” Dataset 2 - Benchmark Total Vaccinations and Deaths The following breakdown of each column is required: 1) Month of vaccination - from the first vaccination until the end of Nov 2023 2) Vaccination number (eg 1=1st, 2=2nd, 3=1st booster, 4=2nd booster etc) using a coding scheme used within the MoH 3) Intentionally blank - null 4) Age Band of Patient - <19 years old inclusive, 20-39 inclusive, 40-59 inclusive, 60-74 inclusive, 75+ inclusive 5) Death First Dose - <30 days inclusive, 31 days-90 days inclusive, 91-180 days, 181 days to 365 days, 366 days to 730 days inclusive, 731 to 1095 days inclusive, over 1096 days inclusive. Each of these categories should be calculated from the date of the patients’s first injection and a suffix “_1” appended 6) Death Last Dose - <30 days inclusive, 31 days-90 days inclusive, 91-180 days, 181 days to 365 days, 366 days to 730 days inclusive, 731 to 1095 days inclusive, over 1096 days inclusive. Each of these categories should be calculated from the date of the patients’s most recent vaccine and a suffix appended depending upon the number of their last vaccine (eg “<30_5” would be coded against a patient who died within 30 days of their 5th jab/3rd booster) 7) ER Visit - a “E” if the patient visited a hospital Emergency Room for any reason between their last injection and their date of death inclusive. Otherwise “N”. 8) The count of the number of vaccination injections in each of the categories above when the underlying dataset has a Group By query applied using Count(Distinct NHI_Number) or similar for this column. 9) The count of the number of people who subsequently died after the vaccination month in each of the categories above when the underlying dataset has a Group By query applied using Count(Distinct NHI_Number) or similar for this column. Dataset 3 - Control Group For anyone in the NHI dataset who is NOT in the COVID Vax Pay per Dose database but is enrolled with a GP Practice in NZ, please produce the following dataset. The following breakdown of each column is required: 1) Date of 1st COVID19 injection in NZ - this will be the same value for each row 2) Vaccination number - 0 - the same for all rows 4) Age Band of Patient - <19 years old inclusive, 20-39 inclusive, 40-59 inclusive, 60-74 inclusive, 75+ inclusive 5) Death Period - <30 days inclusive, 31 days-90 days inclusive, 91-180 days, 181 days to 365 days, 366 days to 730 days inclusive, 731 to 1095 days inclusive, over 1096 days inclusive. Each of these categories should be calculated from the date in column (1) of this dataset and a suffix “_0” appended 8) Size of Control at start - The count of the number of people in each of the categories above when the underlying dataset has a Group By query applied using Count(Distinct NHI_Number) or similar for this column. 9) Death count - The count of the number of people who subsequently died after the date in column (1) in each of the categories above when the underlying dataset has a Group By query applied using Count(Distinct NHI_Number) or similar for this column. For all Datasets please provide a list of codes used and descriptions, and a description of the file format. A Reasonable OIA with Precedent This OIA should be possible by using simple queries on the entire datasets of COVID Vax Pay per Dose and National Minimum Dataset of Hospital data - matched using the NHI number. My understanding is the Pay Per Dose dataset is about 12m records. This OIA does not ask for the creation of any information, just the aggregation of a data set the MoH already collects as a normal course of its business. This OIA does not seek any personal or commercial information about patients or suppliers. Where a cell count in column (7) or (8) is less than 5 please mark as “S”. Where a cell count in column (7) is truely zero this should not occur in the dataset. I am a NZ citizen, resident and taxpayer and am entitled to use the OIA. There is precedent for the MoH answering previous data requests of this type as per this one: https://fyi.org.nz/request/12581-deaths-in-aged-care-residential-facilities-5-years-of-monthly-data Ways of Working Under the OIA the MoH is obliged to consult with me if there are any questions or where there may be a way for me to simplify the request so that it is reasonable. This must be done before refusing no matter what your belief is because you do not understand my purpose and what trade offs are possible. However I put it to you ahead of time that it would take a competent SQL developer + checking the numbers a shorter/similar amount of time than if I requested any relevant documents/emails and they all had to be reviewed for redaction. Please provide contact details if you want to talk through any questions and then we will document the outcome in writing through this channel. We have used this method before at the request of the Ombudsman so it would seem like a good way to assist the MoH to meet its KPIs for OIAs. Please raise any questions ASAP and do not leave it until near the end of the 20 working day period to respond. Yours faithfully, Chris Johnston fyi.org.nz

Browse - Infoshare - Statistics New Zealand Infoshare: Connecting you to a wealth of information. A free service provided by Statistics New Zealand to allow viewing our survey data. infoshare.stats.govt.nz

GitHub - minhealthnz/nz-covid-data: COVID-19 data for New Zealand COVID-19 data for New Zealand. Contribute to minhealthnz/nz-covid-data development by creating an account on GitHub. github.com

@sco0psmcgoo - Scoops McGoo

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