Comparison of new psychiatric diagnoses among children and adolescents before and during the COVID-19 pandemic: A nationwide register-based study

Introduction

This document shows how to perform the analyses and vizualize the results as described in the manuscript ‘Comparison of new psychiatric diagnoses among children and adolescents before and during the COVID-19 pandemic: A nationwide register-based study’.

Reproducing analyses

Clone the repository from https://github.com/davgyl/covid_psyserv.git.

Prepare the denominator

The program to edit the data containing the number at risk is found in code/01_prep_denominator.R.

Time series data

The time series data used for running the analyses is found in data/ts_aggregated.rds and it has the following structure.

source("code//00_pkgs_utils.R")

ts_m <- read_rds("data/ts_aggregated.rds")
ts_m %>% glimpse()

## Rows: 798
## Columns: 14
## $ ts            <chr> "01_any", "01_any", "01_any", "01_any", "01_any", "01_an…
## $ lab_ts        <chr> "Any diagnosis", "Any diagnosis", "Any diagnosis", "Any …
## $ order_outcome <dbl> 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1,…
## $ lab_sex       <chr> NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, …
## $ lab_age       <chr> NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, …
## $ lab_area      <chr> NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, …
## $ lab_outcome   <chr> NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, …
## $ yearmonday    <date> 2017-01-15, 2017-02-15, 2017-03-15, 2017-04-15, 2017-05…
## $ YEAR          <dbl> 2017, 2017, 2017, 2017, 2017, 2017, 2017, 2017, 2017, 20…
## $ MONTH         <dbl> 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 1, 2, 3, 4, 5, 6,…
## $ n             <dbl> 1505, 1400, 1564, 1349, 1624, 1522, 605, 1343, 1477, 149…
## $ nrisk         <dbl> 1066261, 1066261, 1066261, 1066261, 1066261, 1066261, 10…
## $ rate1000      <dbl> 1.4114743, 1.3129994, 1.4668078, 1.2651687, 1.5230792, 1…
## $ ln_risk       <dbl> 13.87967, 13.87967, 13.87967, 13.87967, 13.87967, 13.879…

The variables ts and lab_ts clusters the time series to
- One time serie of any diagnosis without stratification (01_any)
- Two time series of any diagnosis stratified by sex (02_sex)
- Two time series of any diagnosis stratified by age group (03_age)
- Two time series of any diagnosis stratified by geomgraphic area (04_area)
- Seven time series of diagnostic groups as outcomes (04_area)

ts	lab_ts
01_any	Any diagnosis
02_sex	Any diagnosis by sex
03_age	Any diagnosis by age group
04_area	Any diagnosis by area
05_out	Diagnostic groups

Modelling and visualization

• The program for modelling data is found in code/02_model_functions.R.
• To model the associations, run code/03_model_all_ts.R.
• To edit tables, run code/04_viz_table1_descr.R and code/05_viz_table2.R.
• To visualize the associations as figures, run code/06_viz_fig_aim1.R for Figure 1 and code/07_viz_fig_aim2.R for Figure 2.
• To plot supplemental figures, run code/08_covid_patients_hospital.R and code/10_stratification.R.
• To examine immigration and emigration, run code/09_emigration_immigration.R

Immigration and emigration

The number and percentage of youth immigrating and emigrating per year is shown in the table below as described in detail in code/09_emigration_immigration.R.

Year	Total_immigration	Total_emigration	Total_population	Percent_immigration	Percent_emigration
2017	8287	2886	1186479	0.6984532	0.2432407
2018	7361	3219	1178401	0.6246600	0.2731668
2019	7710	3173	1167707	0.6602684	0.2717291
2020	7570	2432	1159093	0.6530969	0.2098192

Versions

R version: 3.6.3. Version of packages are shown in the table below.

Package	Version
MASS	7.3.51.5
tidyverse	1.3.0
stringr	1.4.0
openxlsx	4.1.4
knitr	1.28
lubridate	1.7.4
codebook	0.9.2
haven	2.4.3
broom	0.5.5