Transmission of SARS-CoV-2 to healthcare workers - Poland
MT
17 January, 2022, 12:58
Initial analysis
Initially I combined all the datasets. I calculated the times between positive COVID test in healthcare worker (earliest positive test) and each of this healthcare worker intubation procedure. I kept those pair intubation - positive COVID test with the shortest time between them for each positive healthcare worker and this is what I got:
| Intubation to positive COVID test pairs | ||||||
|---|---|---|---|---|---|---|
| With lowest number of days from intubation to positive COVID test for each healthcare worker that tested positive | ||||||
| no | intubation_id | account_id | intubation_date | date_of_outcome | days_intub_to_pos_covid | patient_s_covid_19_status |
| 1 | 8388 | 5098 | 2020-11-08 | 2020-11-09 | 1 | Confirmed |
| 2 | 8282 | 5127 | 2020-10-31 | 2020-11-04 | 4 | Confirmed |
| 3 | 8263 | 5116 | 2020-10-21 | 2020-10-27 | 6 | Confirmed |
| 4 | 8195 | 5082 | 2020-10-21 | 2020-10-28 | 7 | Suspected |
| 5 | 8304 | 5068 | 2020-11-04 | 2020-11-11 | 7 | Confirmed |
| 6 | 8989 | 3729 | 2020-12-11 | 2020-12-18 | 7 | Confirmed |
| 7 | 8839 | 5132 | 2020-11-20 | 2020-11-30 | 10 | Confirmed |
| 8 | 8858 | 4439 | 2020-12-02 | 2020-12-12 | 10 | Confirmed |
| 9 | 8834 | 5175 | 2020-11-29 | 2020-12-11 | 12 | Confirmed |
| 10 | 8578 | 5130 | 2020-11-06 | 2020-11-29 | 23 | Confirmed |
| 11 | 8257 | 5114 | 2020-10-30 | 2020-11-27 | 28 | Confirmed |
| 12 | 8917 | 5259 | 2020-10-09 | 2020-11-07 | 29 | Confirmed |
| 13 | 8166 | 5065 | 2020-10-22 | 2020-12-18 | 57 | Confirmed |
| 14 | 3503 | 3733 | 2020-04-17 | 2020-11-23 | 220 | Suspected |
Should we remove those with patient’s COVID_19 status suspected?
57 and 220 days from exposure to positive test doesn’t make too much sense so I filter out those records and we are left with 12 healthcare workers that have positive COVID after intubation. I will be using these 12 workers for the rest of analysis.
Cumulative incidence curve
Also, on the top of removing 2 healthcare workers that had + test > 30 days from intubation, I removed all healthcare workers that did not have any follow up (we don’t know if they are COVID+ or COVID-, so cannot put them in any of these categories) and we are left with total of 62
Table with outcomes
Here is our healthcare worker data divided into those with outcome and no outcome:
| Characteristic | Overall, N = 621 | No Outcome, N = 501 | Outcome, N = 121 | p-value2 |
|---|---|---|---|---|
| age | 38 (8) | 38 (8) | 39 (9) | 0.6 |
| gender | 0.5 | |||
| Female | 26 (42%) | 20 (40%) | 6 (50%) | |
| Male | 36 (58%) | 30 (60%) | 6 (50%) | |
| specialty | 0.4 | |||
| Anaesthetics | 42 (68%) | 34 (68%) | 8 (67%) | |
| Emergency Medicine | 1 (1.6%) | 1 (2.0%) | 0 (0%) | |
| Intensive Care Medicine | 18 (29%) | 15 (30%) | 3 (25%) | |
| Pre-Hospital Emergency Medicine | 1 (1.6%) | 0 (0%) | 1 (8.3%) | |
| grade | 0.5 | |||
| Advanced Critical Care Practitioner (ACCP) | 1 (1.6%) | 1 (2.0%) | 0 (0%) | |
| Cons / SAS / Attending | 33 (53%) | 26 (52%) | 7 (58%) | |
| Other | 1 (1.6%) | 1 (2.0%) | 0 (0%) | |
| Paramedic | 1 (1.6%) | 0 (0%) | 1 (8.3%) | |
| Registered Nurse | 1 (1.6%) | 1 (2.0%) | 0 (0%) | |
| Trainee grades | 25 (40%) | 21 (42%) | 4 (33%) | |
| intubator | 58 (94%) | 47 (94%) | 11 (92%) | >0.9 |
|
1
Mean (SD); n (%)
2
Wilcoxon rank sum test; Pearson's Chi-squared test; Fisher's exact test
|
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Will do Kaplan-Mayers soon, altough I don’t think we will get a lot of significance in Cox regression because we have so few + outcomes.
How would you count “Procedures with outcome” as part of your table 1, as we do not really know which intubation caused the outcome? You had n=14 out of 292 all intubations in your table
Please double check my analysis with the raw data as I there might be mistakes!
And let me know what changes you would suggest and what you would like me to do next.
Most common combination of PPD
| Most common PPD combinations | ||||||||||
|---|---|---|---|---|---|---|---|---|---|---|
| For more or equal than 5 intubation procedures | ||||||||||
| eyewear | hat | gown | apron | gloves | surgical_mask | ffp2 | ffp3 | papr | plastic_drape_box | count |
| 1 | 1 | 1 | 0 | 1 | 0 | 0 | 1 | 0 | 0 | 90 |
| 1 | 1 | 1 | 1 | 1 | 0 | 0 | 1 | 0 | 0 | 36 |
| 1 | 1 | 1 | 0 | 1 | 0 | 1 | 0 | 0 | 0 | 29 |
| 1 | 1 | 1 | 1 | 1 | 0 | 1 | 0 | 0 | 0 | 18 |
| 1 | 0 | 1 | 0 | 1 | 1 | 0 | 1 | 0 | 0 | 15 |
| 1 | 1 | 0 | 1 | 1 | 0 | 0 | 1 | 0 | 0 | 13 |
| 1 | 1 | 1 | 0 | 1 | 1 | 1 | 0 | 0 | 0 | 11 |
| 1 | 0 | 1 | 0 | 1 | 0 | 0 | 1 | 0 | 0 | 7 |
| 1 | 1 | 1 | 0 | 1 | 1 | 0 | 1 | 0 | 0 | 7 |
| 1 | 0 | 1 | 1 | 1 | 0 | 0 | 1 | 0 | 0 | 6 |
| 1 | 1 | 0 | 0 | 1 | 0 | 0 | 1 | 0 | 0 | 6 |
| 1 | 1 | 1 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 5 |
Or do we want to do combinations with less PPD elements than we have now (we have 10)?
Symptoms
| Symptoms in healthcare workers with + follow ups | |||||||||||||
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
| no | account_id | fever | cough | sore_throat | sob | headache | photophobia | myalgia | fatigue | nausea_vomiting | diarrhoea | abdominal_pain | loss_of_smell_taste |
| 1 | 3729 | 0 | 0 | 1 | 0 | 0 | 0 | 1 | 1 | 0 | 0 | 0 | 0 |
| 2 | 3729 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 1 | 0 | 0 | 0 | 0 |
| 3 | 3729 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 |
| 4 | 3733 | 1 | 1 | 0 | 1 | 1 | 0 | 0 | 1 | 0 | 0 | 0 | 0 |
| 5 | 4439 | 0 | 0 | 1 | 0 | 1 | 0 | 1 | 1 | 0 | 1 | 0 | 0 |
| 6 | 5065 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 |
| 7 | 5068 | 0 | 1 | 1 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 1 |
| 8 | 5082 | 1 | 1 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 |
| 9 | 5098 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 |
| 10 | 5114 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 |
| 11 | 5114 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 |
| 12 | 5116 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 1 | 0 | 0 | 0 | 0 |
| 13 | 5116 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 1 | 0 | 0 | 0 | 0 |
| 14 | 5127 | 1 | 0 | 0 | 0 | 1 | 0 | 1 | 1 | 0 | 0 | 0 | 0 |
| 15 | 5130 | 1 | 0 | 0 | 0 | 1 | 0 | 1 | 1 | 0 | 0 | 0 | 1 |
| 16 | 5132 | 1 | 1 | 0 | 0 | 1 | 0 | 1 | 1 | 0 | 0 | 0 | 1 |
| 17 | 5175 | 0 | 1 | 0 | 0 | 1 | 0 | 1 | 1 | 0 | 0 | 0 | 1 |
| 18 | 5259 | 1 | 1 | 0 | 1 | 0 | 1 | 0 | 1 | 0 | 1 | 0 | 1 |
Some healthcare have different symptoms on different follow up records (ex account_id=3729), so I guess we have to mark the symptom as “1” if that person had it in at least 1 follow up?
