Smoking and COVID Living Review (v7): a bayesian analysis
David Simons, Lion Shahab, Jamie Brown and Olga Perski
Wednesday 26 August 2020
1 Data preparation
1.1 Load packages
- Load packages: load packages and useful scripts for the analysis
- Load scripts: We have written functions to handle much of the Bayesian analysis
- Load Bayesian datasets: The Bayesian approach relies on large simulated datasets from the observed data, for this report we will only present the results graphically from data we have simulated elsewhere. The scripts
bayes_scripts.R,bayesian_data_generation.Randbayesian_analysis.Rshould be able to be run locally if you want to generate your own simulated data. Alternatively you can use the pre-generated data which is stored in thedata_clean\bayesian_modelsfolder in the github repository here and then you can skip thebayesian_data_generation.Rstep.
1.2 Download data
We maintain a Google sheets document here which contains all eligible articles the research team have identified. This is currently viewable only and not open for editing. We have collected the general information for the search in the first sheet, the studies general information in the second, further information on study populations, outcomes of interest and quality of the studies are contained in the further sheets. These sheets are organised in a similar way to the manuscript and so are labelled table_1 to table_6.
sheets_id <- as_sheets_id("https://docs.google.com/spreadsheets/d/1HV2Zk_UD2_s1zzd47dCyb4UKRSM0dDg7zmUPA9CMdx4/edit?usp=sharing")
search_details <- read_sheet(sheets_id, range = 'article_screening')
data_study_general <- read_sheet(sheets_id, range = 'general_details')
table_1 <- read_sheet(sheets_id, sheet = 'pop_descriptives')
table_2 <- read_sheet(sheets_id, sheet = 'testing')
table_3 <- read_sheet(sheets_id, sheet = 'hospitalisation')
table_4 <- read_sheet(sheets_id, sheet = 'severity')
table_5 <- read_sheet(sheets_id, sheet = 'mortality')
table_6 <- read_sheet(sheets_id, sheet = 'updated_quality_appraisal')We have extracted data from 252 studies, however, some of these are duplicates and some have been removed from the analysis set. For example there have been multiple versions of the ISARIC report and several publications have been retracted. We aim to keep on top of this but there is some risk that in between versions data may be superseded.
1.3 Raw Data
The first sheet is Search details we use this sheet to keep track of the number of studies found in our search and keep track of the number of titles and abstracts screened and number of papers included in each review version. It presents the data in groups of search periods.
date_screening in format (2020-05-22) | ovid_number_results | ovid_screened | ovid_excluded | ovid_included | medrxiv_number_results | medrxiv_screened | medrxiv_included | other_source_included | total_included | superseded |
2020-04-21 | 15 | NA | NA | 4 | NA | NA | NA | 24 | 28 | NA |
2020-05-05 | 57 | 7 | 4 | 3 | 37 | 10 | 0 | 0 | 3 | NA |
2020-05-16 | 67 | 12 | 12 | 0 | 70 | 64 | 22 | 6 | 28 | NA |
2020-06-02 | 297 | 48 | 44 | 4 | 158 | 158 | 32 | 2 | 38 | NA |
2020-06-23 | 477 | 72 | 63 | 6 | 157 | 157 | 41 | 2 | 49 | NA |
2020-07-14 | 656 | 30 | 26 | 4 | 105 | 105 | 23 | 1 | 28 | 8 |
2020-08-25 | 347 | 118 | 111 | 7 | 251 | 251 | 49 | 5 | 61 | 1 |
The third sheet produces Table 1 this contains the details of the population of the studies in our review. Primarily includes smoking status but also the proportion of females in the study and the age of participants.
The fourth sheet produces Table 2 this contains the data extracted from relevant studies on testing data and smoking status. Most studies will not have data on this outcome however we filter these subsequently. These are sparse datasets as many studies do not report on all levels of smoking exposure.
The fifth sheet produces Table 3 this contains the data extracted from relevant studies on hospitalisation and smoking status.
The sixth sheet produces Table 4 this contains the data extracted from relevant studies disease severity data and smoking status. Disease severity is differentially reported across the studies in some there is reference to WHO severity score in others we have based the distinction on clinical characteristics or whether patients were admitted to Intensive or Critical care areas.
The seventh sheet produces Table 5 this contains the data extracted from relevant studies on COVID-19 related mortality and smoking status. A caution needed when interpreting this data is that most studies have not followed patients up for sufficient amounts of time to be able to accurately report on disease associated mortality.
The final sheet is used to collate data on Study quality we changed our method on assessing study quality in version 3 of the review. We use the following criteria:
- Good:
- <20% missing data on smoking status and used a reliable self-report measure that distinguished between current, former and never smoking status AND
- biochemical verification of smoking status and reported results from adjusted analyses OR
- reported data from a representative/random sample
- Fair
- <20% missing data on smoking status and used a reliable self-report measure that distinguished between current, former and never smoking status
- Poor
- The remainder of the studies
1.4 Clean data and save updated versions as RDS files
The data from the google sheets document is then cleaned using a script from the linelist package which has been developed by T. Jombart and Z. Kamvar within the R Epidemics Consortium The script clean_data() ensures consistency among column names, country names, dates and missing values in the included sheets. Data is also saved as .rds files in the /data_clean folder of the project repository.
search_details <- search_details %>%
clean_data() %>%
rename(date_screened = date_screening_in_format_2020_05_22) %>%
write_rds(here::here('data_clean', 'search_details.rds')) ## The title of the date screened column is to communicate among the review team how we need date formatted
protect_columns_1 <- names(data_study_general) %in% 'doi' ## As the DoI's are links we want to preserve their formatting when running the cleaning script
data_study_general <- data_study_general %>%
rename(date_published = `date_published_format(2020-05-12)`) %>%
clean_data(protect = protect_columns_1) %>%
mutate(study_id = 1:length(lead_author)) %>% ## We create a unique study ID to aid checking of the outputs later
write_rds(here::here('data_clean', 'data_study_general.rds'))
prisma_previous <- data_study_general
review_details <- data_study_general %>%
select(lead_author, date_published, country, review_version, study_id) %>% ## The version of the review in which included studies were incorporated is noted
write_rds(here::here('data_clean', 'review_details.rds'))
table_1 <- table_1 %>%
clean_data() %>%
mutate(., lower_range = sub('\\_.*', '', .$range)) %>%
mutate(., upper_range = sub('.*_', '',.$range,)) %>% ## We break apart the age range into an upper and lower level
select(lead_author, sample_size, female_sex_percent, data_source, median_age, mean_age, iqr_lower, iqr_upper, standard_deviation, lower_range,
upper_range, current_smoker, former_smoker, current_former_smoker, never_smoker, never_smoker_unknown, current_vaper, former_vaper,
current_former_vaper, not_stated, missing, total) %>%
filter(!is.na(lead_author)) %>%
left_join(., review_details, by = 'lead_author') %>%
write_rds(here::here('data_clean', 'table_1.rds'))
##For the following tables we're only interested in obtaining data for trials which report on the specific outcome of interest
table_2 <- table_2 %>%
clean_data() %>%
filter(data_on_testing == TRUE) %>%
left_join(., review_details, by = 'lead_author') %>%
write_rds(here::here('data_clean', 'table_2.rds'))
table_3 <- table_3 %>%
clean_data() %>%
filter(data_on_hospitalisation == TRUE) %>%
left_join(., review_details, by = 'lead_author') %>%
write_rds(here::here('data_clean', 'table_3.rds'))
table_4 <- table_4 %>%
clean_data() %>%
filter(data_disease_severity == TRUE) %>%
left_join(., review_details, by = 'lead_author') %>%
write_rds(here::here('data_clean', 'table_4.rds'))
table_5 <- table_5 %>%
clean_data() %>%
filter(data_on_deaths == TRUE) %>%
left_join(., review_details, by = 'lead_author') %>%
write_rds(here::here('data_clean', 'table_5.rds'))
protect_columns_2 <- !names(table_6) %in% 'lead_author'
table_6 <- table_6 %>%
clean_data(protect = protect_columns_2) %>%
left_join(., table_1 %>%
select(lead_author,
not_stated,
missing,
total),
by = 'lead_author') %>%
mutate(missingness = rowSums(.[8:9], na.rm = T),
missingness_percentage = (missingness/total)*100) %>% ## Later we want to assess the amount of missingness for each study as it would impact the inclusion of a study into the relevant meta-analyses
select(-c(not_stated, missing, total, missingness)) %>%
left_join(., review_details, by = 'lead_author') %>%
write_rds(here::here('data_clean', 'table_6.rds'))
a <- data_study_general %>%
select(lead_author, date_published, source, study_id) %>%
rename('Lead Author' = lead_author,
'Date Published' = date_published,
'Publication Source' = source,
'Study ID' = study_id)
a$`Lead Author` <- to_upper_camel_case(a$`Lead Author`, sep_out = ", ")
a$`Publication Source` <- to_title_case(a$`Publication Source`)
a$`Publication Source` <- if_else(str_length(a$`Publication Source`) < 5,
toupper(a$`Publication Source`),
to_title_case(a$`Publication Source`))
write_rds(data_study_general, here("data_clean", "data_study_general.rds"))The package flextable can save tables in a format that is easily manageable within word which we currently use to write the manuscript (until I get better with RMarkdown). At various points I save these to the output_data folder.
2 Preparing the descriptive analysis
2.1 Choosing which data we want to include in the current review?
date_of_update <- Sys.Date()
prev_versions <- c('v1', 'v2', 'v3', 'v4', 'v5', 'v6')
#This can categorise which studies we want to look at
current_version <- c('v7')
#This will categorise which studies we are including in the current report
analysed_versions <- c('v1', 'v2', 'v3', 'v4', 'v5', 'v6', 'v7')
#This will incorporate all studies into the current version of the reportWe define the current version of the report here. At the moment this needs to be changed directly when compiling the code. This report has been generated for version v7
exclude_from_analysis <- c('isaric_1', 'isaric_2', 'isaric_3', 'miyara_old', 'isaric_4', 'mehra', 'hopkinson', 'gaibazzi', 'miyara_updated', 'russell_old', "petrilli_old", "magagnoli_old", "niedzwiedz_old", "bello_chavolla_old", "kimmig_old", "russell_old", "zuo_zuo_old", "patel_old", "shi_zuo_old")We don’t want to remove studies from the dataset despite them no longer being included in the analysis. The excluded studies are defined in this chunk of code. We have excluded this version the following studies: -isaric_1, isaric_2, isaric_3, miyara_old, isaric_4, mehra, hopkinson, gaibazzi, miyara_updated, russell_old, petrilli_old, magagnoli_old, niedzwiedz_old, bello_chavolla_old, kimmig_old, russell_old, zuo_zuo_old, patel_old, and shi_zuo_old Generally we label these as _old in the Google sheet but there are exceptions to this so we manually define them at the moment.
2.2 Filter the included studies
Based on the version and excluded studies we produce the tables for analysis. This is a searchable table.
data_study_general <- data_study_general %>%
filter(review_version %in% analysed_versions) %>%
filter(!(lead_author %in% exclude_from_analysis)) %>%
write_rds(., here::here('data_clean', 'data_study_general.rds'))
review_details <- data_study_general %>%
select(lead_author, date_published, country, review_version, study_id) %>%
filter(!(lead_author %in% exclude_from_analysis))
table_1 <- table_1 %>%
filter(review_version %in% analysed_versions) %>%
select(-review_version) %>%
filter(!(lead_author %in% exclude_from_analysis))
table_2 <- table_2 %>%
filter(review_version %in% analysed_versions) %>%
select(-review_version) %>%
filter(!(lead_author %in% exclude_from_analysis))
table_3 <- table_3 %>%
filter(review_version %in% analysed_versions) %>%
select(-review_version) %>%
filter(!(lead_author %in% exclude_from_analysis))
table_4 <- table_4 %>%
filter(review_version %in% analysed_versions) %>%
select(-review_version) %>%
filter(!(lead_author %in% exclude_from_analysis))
table_5 <- table_5 %>%
filter(review_version %in% analysed_versions) %>%
select(-review_version) %>%
filter(!(lead_author %in% exclude_from_analysis))
table_6 <- table_6 %>%
filter(review_version %in% analysed_versions) %>%
select(-review_version) %>%
filter(!lead_author %in% exclude_from_analysis)
a <- data_study_general %>%
select(lead_author, date_published, source, study_id) %>%
rename('Lead Author' = lead_author,
'Date Published' = date_published,
'Publication Source' = source,
'Study ID' = study_id)
a$`Lead Author` <- to_upper_camel_case(a$`Lead Author`, sep_out = ", ")
a$`Publication Source` <- to_title_case(a$`Publication Source`)
a$`Publication Source` <- if_else(str_length(a$`Publication Source`) < 5,
toupper(a$`Publication Source`),
to_title_case(a$`Publication Source`))
datatable(a)3 Study descriptive analysis
3.1 The PRISMA diagram will be updated
The PRISMA reporting guidelines for systematic reviews the guidance is available here. This takes the format of the number of articles obtained from the search from OVID (login may be needed), medRxiv, and articles identified by the research team. These are then screened at the title and abstract level. Full texts are then read to extract relevant data, if articles are excluded at this level we describe the reason for this. All of the selected articles will contribute to the narrative synthesis but only those that are judged ‘good’ or ‘fair’ quality will be included in the Bayesian meta-analysis.
current_total <- search_details %>%
select(total_included) %>%
sum()
previous_total <- search_details %>%
filter(review_version %in% prev_versions) %>%
select(total_included) %>%
sum()
prisma <- search_details %>%
group_by(review_version) %>%
filter(review_version == current_version) %>%
mutate(title_abstract = sum(ovid_number_results, medrxiv_number_results, other_source_included),
full_texts_assessed = sum(ovid_screened, medrxiv_screened, other_source_included),
title_abstract_included = sum(ovid_included, medrxiv_included, other_source_included),
title_abstract_excluded = title_abstract - full_texts_assessed,
full_text_excluded = full_texts_assessed-total_included,
previous_total = previous_total,
previous_minus_superseded = previous_total-superseded,
current_total = current_total-superseded) %>%
select("Review version" = review_version,
"Date Screened" = date_screened,
"OVID results" = "ovid_number_results",
"medRxiv results" = "medrxiv_number_results",
"Other sources" = "other_source_included",
"Title/abstracts screened" = "title_abstract",
"Title/abstracts excluded" = "title_abstract_excluded",
"Full texts assessed" = "full_texts_assessed",
"Full texts excluded" = "full_text_excluded",
"Records carried forward" = "previous_minus_superseded",
"Supersed from previous version" = superseded,
"Studies in narrative synthesis" = "current_total")
flextable(prisma)Review version | Date Screened | OVID results | medRxiv results | Other sources | Title/abstracts screened | Title/abstracts excluded | Full texts assessed | Full texts excluded | Records carried forward | Supersed from previous version | Studies in narrative synthesis |
v7 | 2020-08-25 | 347 | 251 | 5 | 603 | 229 | 374 | 313 | 173 | 1 | 234 |
3.2 We summarise where the studies were conducted
As the pandemic continues studies are being reported from an increasing numbers of countries.
library('ggmap')
#Countries
country <- table_1$country %>%
to_upper_camel_case() %>%
recode(., 'Usa' = 'USA', 'Uk' = 'UK', 'SaudiArabia' = 'Saudi Arabia', "SouthKorea" = "South Korea") %>%
table() %>%
as.data.frame() %>%
arrange(desc(Freq)) %>%
rename('Country' = 1,
'Number' = 2) %>%
mutate(Country = as.character(Country))
ggplot(country, aes(x = reorder(Country, desc(-Number)), y = Number))+
geom_col()+
coord_flip()+
theme_bw()+
labs(title = 'Countries where studies were performed',
y = 'Number of studies',
x = 'Country')
Country | Number |
USA | 62 |
China | 53 |
UK | 27 |
Spain | 13 |
Mexico | 12 |
France | 11 |
Italy | 7 |
Multiple | 6 |
Brazil | 4 |
Iran | 4 |
Israel | 3 |
Turkey | 3 |
Bangladesh | 2 |
Chile | 2 |
Denmark | 2 |
Finland | 2 |
India | 2 |
Japan | 2 |
Qatar | 2 |
Australia | 1 |
Austria | 1 |
Canada | 1 |
Colombia | 1 |
Germany | 1 |
Hungary | 1 |
Kuwait | 1 |
Netherlands | 1 |
Poland | 1 |
Portugal | 1 |
Saudi Arabia | 1 |
South Korea | 1 |
Sweden | 1 |
Switzerland | 1 |
Thailand | 1 |
Currently studies are reported from 33
country <- mutate_geocode(country, Country)
country_bounds <- country %>%
select(lon, lat) %>%
tidyr::drop_na() %>%
sp::SpatialPointsDataFrame(coords = .[,1:2], data = .) %>%
sp::bbox() * 1.1
map_world <- map_data(map = "world") %>%
filter(region != "Antarctica")
save_as_docx(a, path = here('output_data', 'Countries_performing_studies.docx')) 3.3 A map is produced for where the studies are performed
library(rgdal)
library(leaflet)
library(coronavirus)
update_dataset()
my_spdf <- readOGR(
dsn= paste0(here::here("data_clean", "world_shape_file")),
layer="TM_WORLD_BORDERS_SIMPL-0.3",
verbose=FALSE
)
from_countries <- c("USA", "UK", "Iran", "South Korea")
to_countries <- c("United States", "United Kingdom", "Iran (Islamic Republic of)", "Korea, Republic of")
country_map <- country %>%
mutate(Country = plyr::mapvalues(Country, from = from_countries,
to = to_countries))
from_countries <- c("US", "Iran", "Korea, South", "Czech Republic")
to_countries <- c("United States", "Iran (Islamic Republic of)", "Korea, Republic of", "Czechia")
data("coronavirus")
summary_df <- coronavirus %>%
filter(type == "confirmed") %>%
group_by(country) %>%
summarise(total_cases = sum(cases)) %>%
arrange(-total_cases) %>%
rename("NAME" = country) %>%
mutate(NAME = plyr::mapvalues(NAME, from = from_countries,
to = to_countries))
my_spdf@data <- left_join(my_spdf@data, country_map %>%
rename(NAME = Country),
by = "NAME") %>%
left_join(., summary_df %>%
mutate(total_cases = total_cases/1000),
by = "NAME") %>%
mutate(NAME = as.factor(NAME))
my_spdf@data$Number[ which(my_spdf@data$Number == 0)] = NA
my_spdf@data$total_cases[ which(is.na(my_spdf@data$total_cases))] = 0
my_bins <- c(0, 1, 5, 10, 20, 30, 40, 70)
mypalette <- colorBin(palette="YlOrBr", domain=my_spdf@data$Number, na.color="transparent", bins=my_bins)
mytext <- paste(
"Country: ", my_spdf@data$NAME,"<br/>",
"Number of studies included: ", my_spdf@data$Number, "<br/>",
"COVID-19 reported cases (thousands): ", round(my_spdf@data$total_cases, 2),
sep="") %>%
lapply(htmltools::HTML)
html_map <- leaflet(my_spdf) %>%
addTiles() %>%
setView(lat=10, lng=0 , zoom=2) %>%
addPolygons(
fillColor = ~mypalette(Number),
stroke=TRUE,
fillOpacity = 0.9,
color="white",
weight=0.3,
label = mytext,
labelOptions = labelOptions(
style = list("font-weight" = "normal", padding = "3px 8px"),
textsize = "13px",
direction = "auto"
)
) %>%
addLegend( pal=mypalette, values=~Number, opacity=0.9, title = "Number of studies included", position = "bottomright" )
html_mapcoronavirus package. The number of cases is updated when the report is generated so will not remain in date, it is current as of 2020-08-26 16:11:19. Countries displaying 0 cases are most likely due to naming mismatches.
3.4 We sumarise the settings in which they were conducted
Most studies are performed in hospital settings. Some studies are conducted in both such as those in emergency departments where patients are discharged home and followed up. Some registry studies utilising primary care networks are solely conducted in the community.
#Setting
setting <- to_upper_camel_case(data_study_general$study_setting, sep_out = ' ') %>%
table(.) %>%
sort('Number of studies', decreasing = T) %>%
as.data.frame() %>%
arrange(desc(Freq)) %>%
rename('Setting' = 1,
'Number' = 2)
a <- flextable(setting) %>%
set_table_properties(width = 0.5, layout = 'autofit')
aSetting | Number |
Hospital | 155 |
Community And Hospital | 63 |
Community | 14 |
Not Stated | 1 |
Quarantine Centre | 1 |
3.5 We summarise the design of the included studies
The majority were retrospective cohorts. There were fewer case-control and cross-sectional studies. There were some prospective cohorts established and reported.
design <- to_upper_camel_case(data_study_general$study_design, sep_out = ' ') %>%
recode("Rct" = "RCT") %>%
table(.) %>%
sort('Number of studies', decreasing = T) %>%
as.data.frame() %>%
arrange(desc(Freq)) %>%
rename('Design' = 1,
'Number' = 2)
a <- flextable(design) %>%
set_table_properties(width = 0.5, layout = 'autofit')
aDesign | Number |
Retrospective Cohort | 165 |
Prospective Cohort | 44 |
Cross Sectional | 17 |
Case Control | 3 |
Matched Case Control | 3 |
RCT | 2 |
save_as_docx(a, path = here('output_data', 'Study_designs.docx'))
##For supplementary tables
study_design <- data_study_general %>%
select(lead_author, study_design) %>%
mutate(study_design = to_upper_camel_case(study_design, sep_out = " "),
study_design = ifelse(study_design == "Rct", "Randomised Controlled Trial", study_design))3.6 We update the number of participants we have included
- Summary statistics: this table will give us the mean, median and IQR of the samples in the review
## Min. 1st Qu. Median Mean 3rd Qu. Max.
## 6 115 404 121141 1631 17425445- Total number of participants: 28346947
3.7 We want to know if they use a CRF or electronic health record
The majority of data on smoking outcomes is extracted from electronic health records that are routinely collected in the provision of healthcare. Some of the studies utilise specifically designed Case Report Forms (CRFs). The source of information on smoking status is not always reported.
#Source of data
table_1$data_source %>%
recode('electronic_health_records' = 'Electronic health record',
'case_report_form' = 'Case report form',
'not_stated' = 'Not stated') %>%
table() %>%
as.data.frame() %>%
arrange(desc(Freq)) %>%
rename('Data source' = 1,
'Number' = 2) %>%
flextable() %>%
set_table_properties(width = 0.5, layout = 'autofit')Data source | Number |
Electronic health record | 145 |
Case report form | 60 |
Not stated | 29 |
4 Smoking descriptive data
- Current smokers: 143 studies report on the numbers of current smokers
- Former smokers: 71 studies report on the numbers of former smokers
- Never smokers: 75 studies report on the numbers of never smokers
#Studies reporting current/former smokers
current_former_smok <- table_1 %>%
filter(., current_former_smoker != 'NA')- Current/former smokers: 91 studies report on the numbers of current/former smokers without distinguishing between them, often termed as yes to a smoking history question.
#Studies reporting missing data
missing_smok <- table_1 %>%
filter(., missing != 'NA') %>%
mutate(percent = missing/total*100)
minimum <- min(missing_smok$percent)
maximum <- max(missing_smok$percent)
#Studies reporting never/unknown
never_smok_unknown <- table_1 %>%
filter(., never_smoker_unknown != 'NA')
#Studies with not stated
smok_not_stated <- table_1 %>%
filter(., not_stated != 'NA')- Missing, never/unknown and not stated: studies report on the numbers of missing (73, 31.2%), never/unknown (24) or not stated (134) smoking history. This is often framed as absence of a smoking history.
- Minimum missing: 0.08%
- Maximum missing: 96.36%
- Minimum missing: 0.08%
4.1 Studies reporting current, former and never smoking status
#Studies reporting current, former and never smoking status
full_smoking_status <- table_1 %>%
filter(lead_author %in% current_smok$lead_author) %>%
filter(lead_author %in% former_smok$lead_author) %>%
filter(lead_author %in% never_smok$lead_author)
b <- full_smoking_status %>%
select(lead_author, sample_size, current_smoker, former_smoker, never_smoker, study_id) %>%
rename('Lead author' = lead_author,
'Sample size' = sample_size,
'Current smokers' = current_smoker,
'Former smokers' = former_smoker,
'Never smokers' = never_smoker,
'Study ID' = study_id)
b$`Lead author` <- to_upper_camel_case(b$`Lead author`, sep_out = ", ")
b$`Lead author` <- plyr::mapvalues(b$`Lead author`,
from = cleaned_names,
to = correct_names)
datatable(b,
caption = "Studies providing data on current, former and never smokers") %>%
formatRound(columns = c(2:5), digits = 0, interval = 3, mark = ",")#These are the studies with data on current, former and never smokers
b <- b %>%
select(-"Study ID") %>%
flextable() %>%
set_caption(caption = 'Studies reporting complete smoking status') %>%
colformat_num(col_keys = c("Sample size", "Current smokers", "Former smokers", "Never smokers"),
digits = 0, na_str = '-', big.mark = ',') %>%
set_table_properties(width = 1, layout = 'autofit')
save_as_docx(b, path = here('output_data', 'Supplementary_1a.docx'))- Studies with complete smoking status: 57, 24.36%
4.2 Studies providing partially complete data on smoking status
#Studies reporting current or current/former and never smoking
semi_full_smoking_status <- table_1 %>%
filter(lead_author %in% current_former_smok$lead_author) %>%
filter(lead_author %in% never_smok$lead_author) %>%
filter(!lead_author %in% full_smoking_status$lead_author)
b <- semi_full_smoking_status %>%
select(lead_author, sample_size, current_former_smoker, never_smoker, study_id) %>%
rename('Lead author' = lead_author,
'Sample size' = sample_size,
'Current/former smokers' = current_former_smoker,
'Never smokers' = never_smoker,
'Study ID' = study_id)
b$`Lead author` <- to_upper_camel_case(b$`Lead author`, sep_out = ", ")
b$`Lead author` <- plyr::mapvalues(b$`Lead author`,
from = cleaned_names,
to = correct_names)
datatable(b,
caption = "Studies providing partially complete data on smoking status") %>%
formatRound(columns = c(2:4), digits = 0, interval = 3, mark = ",")b <- b %>%
select(-"Study ID") %>%
flextable() %>%
set_caption(caption = 'Studies reporting complete smoking status') %>%
colformat_num(col_keys = c("Sample size", "Current/former smokers", "Never smokers"),
digits = 0, na_str = '-', big.mark = ',') %>%
set_table_properties(width = 1, layout = 'autofit')
save_as_docx(b, path = here('output_data', 'Supplementary_1b.docx'))- Studies reporting partially incomplete smoking data: 17
These studies typically report smoking history as the measure of exposure which is why they have grouped values for current/former smokers
4.3 Studies providing incomplete data on smoking status
#Remaining studies
incomplete_smoking_status <- table_1 %>%
filter(!lead_author %in% full_smoking_status$lead_author) %>%
filter(!lead_author %in% semi_full_smoking_status$lead_author)
b <- incomplete_smoking_status %>%
select(lead_author, sample_size, current_smoker, current_former_smoker, former_smoker, never_smoker, never_smoker_unknown, not_stated, missing, study_id) %>%
rename('Lead author' = lead_author,
'Sample size' = sample_size,
'Current smokers' = current_smoker,
'Current/former smokers' = current_former_smoker,
'Former smoker' = former_smoker,
'Never smokers' = never_smoker,
'Never smoker/unknown' = never_smoker_unknown,
'Not stated' = not_stated,
'Missing' = missing,
'Study ID' = study_id)
b$`Lead author` <- to_upper_camel_case(b$`Lead author`, sep_out = ", ")
b$`Lead author` <- plyr::mapvalues(b$`Lead author`,
from = cleaned_names,
to = correct_names)
datatable(b,
caption = "Studies providing incomplete data on smoking status") %>%
formatRound(columns = c(2:8), digits = 0, interval = 3, mark = ",")numeric_names <- colnames(b[2:9])
b <- b %>%
select(-"Study ID") %>%
flextable() %>%
set_caption(caption = 'Studies reporting complete smoking status') %>%
colformat_num(col_keys = numeric_names,
digits = 0, na_str = '-', big.mark = ',') %>%
set_table_properties(width = 1, layout = 'autofit')
save_as_docx(b, path = here('output_data', 'Supplementary_1c.docx'))- Studies missing key smoking data: 160
5 Smoking prevalence by country
#Smoking prevalence by country
country_prevalence_list <- table_1 %>%
group_by(country, add = T) %>%
mutate(., current_smok_percentage = current_smoker/total*100) %>%
mutate(., former_smok_percentage = former_smoker/total*100) %>%
mutate(., missing_percentage = missing/total*100) %>%
mutate(., not_stated_percentage = not_stated/total*100) %>%
mutate(never_smoker_percentage = never_smoker/total*100) %>%
group_split()We have split the data into a list where each country can be analysed separately if required
Confidence intervals are obtained through bootstrap estimates of a thousand repetitions of the included studies. Following the production of the confidence intervals from bootstrapping in olgas_script.R the prevalence graphs are produced in the smoking_plots.R script. This uses data extracted on current and former smoking prevalence data from countries which have included studies in this review.
5.1 Graph for smoking prevalence
Current smoking prevalence The studies are grouped by the country in which they were conducted. The solid line represents national current smoking prevalence obtained from national smoking survey estimates. Studies are further grouped by the setting in which they were conducted, Hospital (Triangle), Community (Square) and Community and Hospital (Circle). The size of the shape corresponds to the relative size of the study sample. The weighted mean current smoking prevalence is displayed for each setting. 
5.2 Graph for former smoking prevalence
Former smoker prevalence This graph is for former smoking prevalence. The data is more sparse for this exposure and therefore several countries are missing for the analysis. Studies are further grouped by the setting in which they were conducted, Hospital (Triangle), Community (Square) and Community and Hospital (Circle). The size of the shape corresponds to the relative size of the study sample. The weighted mean current smoking prevalence is displayed for each setting. 
6 Final results tables and meta-analyses
6.1 Table 1: Included studies
#Updating table 1
table_1_word <- table_1 %>%
mutate(., current_percentage = current_smoker/total*100,
former_percentage = former_smoker/total*100,
current_former_percentage = current_former_smoker/total*100,
never_smoker_percentage = never_smoker/total*100,
never_smoker_unknown_percentage = never_smoker_unknown/total*100,
not_stated_percentage = not_stated/total*100,
missing_percentage = missing/total*100) %>%
select(lead_author, date_published, country, sample_size, median_age, iqr_lower, iqr_upper, mean_age, lower_range,
upper_range, standard_deviation, female_sex_percent, current_percentage, former_percentage, current_former_percentage,
never_smoker_percentage, never_smoker_unknown_percentage, not_stated_percentage, missing_percentage, study_id) %>%
replace_na(., list(not_stated_percentage = 0, missing_percentage = 0)) %>%
mutate(., missing_percentage = not_stated_percentage + missing_percentage,
missing_percentage = formatC(missing_percentage, digits = 2, format = "f"))
#Smoking completeness
smoking_status <- as_tibble(full_smoking_status$lead_author) %>%
rename(lead_author = 1) %>%
mutate(complete_status = c("Yes")) %>%
bind_rows(., semi_full_smoking_status %>%
select(lead_author)) %>%
bind_rows(., incomplete_smoking_status %>%
select(lead_author)) %>%
distinct() %>%
mutate(complete_status = replace_na(complete_status, "No"))
#Data missingness
missingness <- table_6 %>%
select(lead_author) %>%
left_join(., table_1_word %>%
select(lead_author, missing_percentage)) %>%
mutate(missing_percentage = as.numeric(missing_percentage),
low_missingness = ifelse(missing_percentage >= 20, "No", "Yes"))
#Defining poor quality
poor_quality <- missingness %>%
select(-missing_percentage) %>%
left_join(., smoking_status, by = "lead_author") %>%
mutate(study_quality = ifelse(low_missingness == "No" | complete_status == "No", "Poor", "Not_poor"))
good_quality <- table_6 %>%
left_join(., poor_quality, by = "lead_author") %>%
select(lead_author, study_quality, biochemical_verification, random_representative_sample) %>%
mutate(study_quality_final = ifelse(study_quality == "Not_poor" & (biochemical_verification == "Yes" | random_representative_sample == "Yes"), "good",
ifelse(study_quality == "Poor", "poor", "fair")))
quality_rating <- good_quality %>%
select(lead_author, study_quality_final) %>%
rename("overall_quality" = study_quality_final)
table_1_word <- left_join(table_1_word, good_quality %>%
select(lead_author, study_quality_final))
a <- data_study_general %>%
select(study_id, study_setting)
table_1_word <- left_join(table_1_word, a, by = 'study_id') %>%
select(1:4, 22, 5:19, 21, 20)
table_1_word$date_published <- as.Date.character(table_1_word$date_published)
write_rds(table_1_word, here::here('data_clean', 'table_1_word.rds'))
table_1_word <- table_1_word %>%
mutate(median_mean = ifelse(is.na(median_age), mean_age, median_age))
a <- table_1_word %>%
mutate(median_mean = ifelse(is.na(median_age), mean_age, median_age),
mean_used = ifelse(is.na(mean_age), '','^'),
iqr = ifelse(is.na(iqr_lower), NA, paste(iqr_lower, iqr_upper, sep = '-')),
range_combined = paste(lower_range, upper_range, sep = '-'),
range_combined = na_if(range_combined, 'NA-NA'),
standard_deviation = as.numeric(standard_deviation),
st_dev = paste((as.integer(median_mean-standard_deviation)), as.integer((median_mean+standard_deviation)), sep = '-'),
st_dev = na_if(st_dev, 'NA-NA'))
a$iqr <- coalesce(a$iqr, a$range_combined, a$st_dev)
a <- a %>%
select(study_id, lead_author, date_published, country, sample_size, study_setting, median_mean, mean_used, iqr,
female_sex_percent, current_percentage, former_percentage, current_former_percentage, never_smoker_percentage,
never_smoker_unknown_percentage, missing_percentage, study_quality_final) %>%
mutate(median_mean = paste(median_mean, mean_used, sep = ''),
median_mean = na_if(median_mean, NA)) %>%
select(-mean_used) %>%
mutate(median_mean = ifelse(median_mean == 'NA', 'NA', paste(paste(median_mean, iqr, sep = ' ('),')', sep = ''))) %>%
select(-iqr) %>%
rename('Study ID' = study_id,
'Lead author' = lead_author,
'Date published' = date_published,
'Country' = country,
'Sample size' = sample_size,
'Study setting' = study_setting,
'Median (IQR)' = median_mean,
'Female %' = female_sex_percent,
'Current smoker %' = current_percentage,
'Former smokers %' = former_percentage,
'Current/former smokers %' = current_former_percentage,
'Never smokers %' = never_smoker_percentage,
'Never/unknown smokers %' = never_smoker_unknown_percentage,
'Missing %' = missing_percentage,
"Study quality" = study_quality_final)
a$`Lead author` <- to_upper_camel_case(a$`Lead author`, sep_out = ", ")
author_list <- plyr::mapvalues(a$`Lead author`,
from = cleaned_names,
to = correct_names)
a$`Lead author` <- author_list
a$`Study setting` <-to_title_case(a$`Study setting`, sep_out = " ")
a$`Country` <-to_title_case(a$`Country`, sep_out = " ")
a$`Country` <- a$Country %>%
recode('Usa' = 'USA',
'Uk' = 'UK')
numeric_columns <- c('Median (IQR)', 'Female %', 'Current smoker %','Former smokers %', 'Current/former smokers %',
'Never smokers %', 'Never/unknown smokers %', 'Missing %')The total number of included studies is 234. This table is searchable
datatable(a) %>%
formatRound(
columns = c(8:14),
digits = 2,
interval = 3,
mark = ",",
dec.mark = getOption("OutDec")
)a <- flextable(a) %>%
set_caption(caption = 'Characteristics of included studies') %>%
colformat_num(col_keys = numeric_columns, digits = 1, na_str = '-', big.mark = ',') %>%
colformat_num(col_keys = 'Sample size', digits = 0, na_str = '-', big.mark = ',') %>%
set_table_properties(width = 1, layout = 'autofit')
save_as_docx(a, path = here('output_data', 'Table_1.docx'))6.2 Table 2: Smoking and COVID-19 diagnostic testing
#Table 2
table_2_word <- table_2 %>%
mutate(., sample = contributing_sample) %>%
mutate(., negative_test_percentage =formatC(negative_test/sample*100, digits = 2, format = "f"),
negative_current_percentage = formatC(negative_current_smoker/negative_test*100, digits = 2, format = "f"),
negative_former_smoker_percentage = formatC(negative_former_smoker/negative_test*100, digits = 2, format = "f"),
negative_current_former_smoker_percentage = formatC(negative_current_former_smoker/negative_test*100, digits = 2, format = "f"),
negative_never_smoker_percentage = formatC(negative_never_smoker/negative_test*100, digits = 2, format = "f"),
negative_not_stated_percentage = formatC(negative_not_stated/negative_test*100, digits = 2, format = "f"),
positive_test_percentage = formatC(positive_test/sample*100, digits = 2, format = "f"),
positive_current_smoker_percentage = formatC(positive_current_smoker/positive_test*100, digits = 2, format = "f"),
positive_former_smoker_percentage = formatC(positive_former_smoker/positive_test*100, digits = 2, format = "f"),
positive_current_former_smoker_percentage = formatC(positive_current_former_smoker/positive_test*100, digits = 2, format = "f"),
positive_never_smoker_percentage = formatC(positive_never_smoker/positive_test*100, digits = 2, format = "f"),
positive_not_stated_percentage = formatC(positive_not_stated/positive_test*100, digits = 2, format = "f")) %>%
select(-data_on_testing, -missing, -date_published, -sample) %>%
write_rds(here::here('data_clean', 'table_2_word.rds'))
quality_table_2 <- table_2_word %>%
left_join(., quality_rating, by = 'lead_author')
a <- table_2_word %>%
filter(contributing_sample >= 1) %>%
mutate(Author = lead_author,
Population_tested = contributing_sample,
SARS_CoV_2_negative = paste(
paste(negative_test,
(negative_test_percentage), sep = " ("), "%)", sep = ""),
N_current_smoker = paste(paste(negative_current_smoker, (negative_current_percentage), sep = " (")
, "%)", sep = ""),
N_current_smoker = na_if(N_current_smoker, "NA ( NA%)"),
N_former_smoker = paste(paste(negative_former_smoker, (negative_former_smoker_percentage), sep = " (")
, "%)", sep = ""),
N_former_smoker = na_if(N_former_smoker, "NA ( NA%)"),
N_current_former_smoker = paste(paste(negative_current_former_smoker,
(negative_current_former_smoker_percentage), sep = " (")
, "%)", sep = ""),
N_current_former_smoker = na_if(N_current_former_smoker, "NA ( NA%)"),
N_never_smoker = paste(paste(negative_never_smoker,
(negative_never_smoker_percentage), sep = " (")
, "%)", sep = ""),
N_never_smoker = na_if(N_never_smoker, "NA ( NA%)"),
N_not_stated = paste(paste(negative_not_stated,
(negative_not_stated_percentage), sep = " (")
, "%)", sep = ""),
N_not_stated = na_if(N_not_stated, "NA ( NA%)")) %>%
mutate(SARS_CoV_2_positive = paste(
paste(positive_test,
(positive_test_percentage), sep = " ("), "%)", sep = ""),
P_current_smoker = paste(paste(positive_current_smoker, (positive_current_smoker_percentage), sep = " (")
, "%)", sep = ""),
P_current_smoker = na_if(P_current_smoker, "NA ( NA%)"),
P_former_smoker = paste(paste(positive_former_smoker, (positive_former_smoker_percentage), sep = " (")
, "%)", sep = ""),
P_former_smoker = na_if(P_former_smoker, "NA ( NA%)"),
P_current_former_smoker = paste(paste(positive_current_former_smoker,
(positive_current_former_smoker_percentage), sep = " (")
, "%)", sep = ""),
P_current_former_smoker = na_if(P_current_former_smoker, "NA ( NA%)"),
P_never_smoker = paste(paste(positive_never_smoker,
(positive_never_smoker_percentage), sep = " (")
, "%)", sep = ""),
P_never_smoker = na_if(P_never_smoker, "NA ( NA%)"),
P_not_stated = paste(paste(positive_not_stated,
(positive_not_stated_percentage), sep = " (")
, "%)", sep = ""),
P_not_stated = na_if(P_not_stated, "NA ( NA%)")) %>%
select(Author, Population_tested, SARS_CoV_2_negative, N_current_smoker, N_former_smoker, N_current_former_smoker,
N_never_smoker, N_not_stated, SARS_CoV_2_positive, P_current_smoker, P_former_smoker, P_current_former_smoker,
P_never_smoker, P_not_stated)
a$Author <- to_upper_camel_case(a$Author, sep_out = ", ")
a$Author <- a$Author %>%
recode("Bello, Chavolla" = "Bello-Chavolla",
"De, Lusignan" = "de Lusignan",
"Del, Valle" = "del Valle")
numeric_columns <- c('Population_tested', 'SARS_CoV_2_negative', 'N_current_smoker', 'N_former_smoker',
'N_current_former_smoker', 'N_never_smoker', 'N_not_stated', 'SARS_CoV_2_positive', 'P_current_smoker',
'P_former_smoker', 'P_current_former_smoker', 'P_never_smoker', 'P_not_stated')
a <- flextable(a) %>%
set_caption(caption = 'SARS-CoV-2 infection by smoking status') %>%
colformat_num(j = numeric_columns, digits = 0, na_str = '-', big.mark = ',')The number of studies reporting on infection by smoking status is: 45
a <- set_header_labels(a,
Population_tested = 'Total population tested',
SARS_CoV_2_negative = "N (%)",
N_current_smoker = "Current smoker (%)",
N_former_smoker = "Former smoker (%)",
N_current_former_smoker = "Current/former smoker (%)",
N_never_smoker = "Never smoker (%)",
N_not_stated = "Not stated (%)",
SARS_CoV_2_positive = "N (%)",
P_current_smoker = "Current smoker (%)",
P_former_smoker = "Former smoker (%)",
P_current_former_smoker = "Current/former smoker (%)",
P_never_smoker = "Never smoker (%)",
P_not_stated = "Not stated (%)") %>%
add_header_row(top = TRUE, values = c("","SARS-CoV-2 negative", "SARS-CoV-2 positive" ), colwidths = c(2, 6, 6)) %>%
theme_booktabs() %>%
fix_border_issues() %>%
set_table_properties(width = 1, layout = 'autofit') %>%
colformat_char(na_str = '-')
aSARS-CoV-2 negative | SARS-CoV-2 positive | ||||||||||||
Author | Total population tested | N (%) | Current smoker (%) | Former smoker (%) | Current/former smoker (%) | Never smoker (%) | Not stated (%) | N (%) | Current smoker (%) | Former smoker (%) | Current/former smoker (%) | Never smoker (%) | Not stated (%) |
Rentsch | 3528 | 2974 (84.30%) | 1444 (48.55%) | 704 (23.67%) | - | 826 (27.77%) | - | 554 (15.70%) | 159 (28.70%) | 179 (32.31%) | - | 216 (38.99%) | - |
Fontanet | 661 | 490 (74.13%) | 64 (13.06%) | - | - | 426 (86.94%) | - | 171 (25.87%) | 5 (2.92%) | - | - | 166 (97.08%) | - |
Cho | 1331 | 793 (59.58%) | 142 (17.91%) | 214 (26.99%) | - | 437 (55.11%) | - | 538 (40.42%) | 111 (20.63%) | 145 (26.95%) | - | 282 (52.42%) | - |
Shah | 243 | 212 (87.24%) | 52 (24.53%) | 47 (22.17%) | - | 113 (53.30%) | - | 29 (11.93%) | 0 (0.00%) | 9 (31.03%) | - | 20 (68.97%) | - |
Kolin | 1474 | 805 (54.61%) | 141 (17.52%) | 307 (38.14%) | - | 354 (43.98%) | 3 (0.37%) | 669 (45.39%) | 72 (10.76%) | 285 (42.60%) | - | 303 (45.29%) | 9 (1.35%) |
de Lusignan | 3291 | 2740 (83.26%) | 366 (13.36%) | 1450 (52.92%) | - | 924 (33.72%) | - | 551 (16.74%) | 47 (8.53%) | 303 (54.99%) | - | 201 (36.48%) | - |
Valenti | 789 | 689 (87.33%) | 197 (28.59%) | - | - | - | 492 (71.41%) | 40 (5.07%) | 7 (17.50%) | - | - | - | 33 (82.50%) |
Parrotta | 76 | 39 (51.32%) | 1 (2.56%) | 10 (25.64%) | - | 27 (69.23%) | 1 (2.56%) | 37 (48.68%) | 1 (2.70%) | 10 (27.03%) | - | 25 (67.57%) | 1 (2.70%) |
Berumen | 102875 | 71353 (69.36%) | - | - | 7173 (10.05%) | 64180 (89.95%) | - | 31522 (30.64%) | - | - | 2748 (8.72%) | 28774 (91.28%) | - |
Israel | 24906 | 20755 (83.33%) | 3783 (18.23%) | 2671 (12.87%) | - | 14301 (68.90%) | - | 41151 (165.23%) | 406 (0.99%) | 483 (1.17%) | - | 3262 (7.93%) | - |
del Valle | 1108 | 143 (12.91%) | 27 (18.88%) | 53 (37.06%) | - | - | 63 (44.06%) | 965 (87.09%) | 55 (5.70%) | 293 (30.36%) | - | - | 617 (63.94%) |
Romao | 34 | 20 (58.82%) | - | - | 5 (25.00%) | - | 15 (75.00%) | 14 (41.18%) | - | - | 4 (28.57%) | - | 10 (71.43%) |
Ramlall | 11116 | 4723 (42.49%) | - | - | - | - | - | 6393 (57.51%) | - | - | 1643.001 (25.70%) | 4749.999 (74.30%) | - |
Sharma | 501 | 267 (53.29%) | - | - | 1 (0.37%) | - | 266 (99.63%) | 234 (46.71%) | - | - | 20 (8.55%) | - | 214 (91.45%) |
Eugen, Olsen | 407 | 290 (71.25%) | 76 (26.21%) | 104 (35.86%) | - | 102 (35.17%) | - | 117 (28.75%) | 8 (6.84%) | 46 (39.32%) | - | 59 (50.43%) | - |
Raisi, Estabragh | 4510 | 3184 (70.60%) | - | - | 1653 (51.92%) | - | 1531 (48.08%) | 1326 (29.40%) | - | - | 683 (51.51%) | - | 643 (48.49%) |
Houlihan | 177 | 97 (54.80%) | 14 (14.43%) | 14 (14.43%) | - | 69 (71.13%) | - | 80 (45.20%) | 7 (8.75%) | 19 (23.75%) | - | 54 (67.50%) | - |
Mcqueenie | 428199 | 424355 (99.10%) | - | - | 189299 (44.61%) | 235056 (55.39%) | - | 1311 (0.31%) | - | - | 669 (51.03%) | 642 (48.97%) | - |
Woolford | 4474 | 3161 (70.65%) | 441 (13.95%) | 1194 (37.77%) | - | 1526 (48.28%) | - | 1313 (29.35%) | 145 (11.04%) | 525 (39.98%) | - | 643 (48.97%) | - |
Lan | 104 | 83 (79.81%) | - | - | 24 (28.92%) | - | 59 (71.08%) | 21 (20.19%) | - | - | 1 (4.76%) | - | 20 (95.24%) |
Hernandez, Garduno | 32583 | 20279 (62.24%) | - | - | 2399 (11.83%) | 17861 (88.08%) | - | 12304 (37.76%) | - | - | 1191 (9.68%) | 11083 (90.08%) | - |
Govind | 6215 | 6207 (99.87%) | 4104 (66.12%) | 1669 (26.89%) | - | 342 (5.51%) | - | 102 (1.64%) | 78 (76.47%) | 20 (19.61%) | - | 2 (1.96%) | - |
Gu | 4699 | 3815 (81.19%) | 360 (9.44%) | 1142 (29.93%) | - | 2313 (60.63%) | - | 884 (18.81%) | 40 (4.52%) | 264 (29.86%) | - | 580 (65.61%) | - |
Kibler | 702 | 680 (96.87%) | 25 (3.68%) | - | - | - | 655 (96.32%) | 22 (3.13%) | 1 (4.55%) | - | - | - | 21 (95.45%) |
Auvinen | 61 | 33 (54.10%) | 10 (30.30%) | 8 (24.24%) | - | 15 (45.45%) | - | 28 (45.90%) | 1 (3.57%) | 9 (32.14%) | - | 18 (64.29%) | - |
Favara | 70 | 55 (78.57%) | 5 (9.09%) | - | - | - | 50 (90.91%) | 15 (21.43%) | 2 (13.33%) | - | - | - | 13 (86.67%) |
Antonio, Villa | 34263 | 23338 (68.11%) | 2293 (9.83%) | - | - | - | 21045 (90.17%) | 10925 (31.89%) | 1023 (9.36%) | - | - | - | 9902 (90.64%) |
Merzon | 7807 | 7025 (89.98%) | - | - | 1136 (16.17%) | - | 5889 (83.83%) | 782 (10.02%) | - | - | 127 (16.24%) | - | 655 (83.76%) |
Trubiano | 2676 | 2827 (105.64%) | - | - | 256 (9.06%) | - | 2586 (91.48%) | 108 (4.04%) | - | - | 3 (2.78%) | - | 105 (97.22%) |
Shi, Resurreccion | 1521 | 1265 (83.17%) | - | - | 681 (53.83%) | - | 584 (46.17%) | 256 (16.83%) | - | - | 154 (60.16%) | - | 102 (39.84%) |
Riley | 120620 | 120461 (99.87%) | 2594 (2.15%) | - | - | 19914 (16.53%) | 97953 (81.32%) | 159 (0.13%) | 3 (1.89%) | - | - | 17 (10.69%) | 139 (87.42%) |
Alizadehsani | 319 | 196 (61.44%) | - | - | - | - | 196 (100.00%) | 123 (38.56%) | - | - | 1 (0.81%) | - | 122 (99.19%) |
Merkely | 10474 | 10336 (98.68%) | 2904 (28.10%) | 2107 (20.39%) | - | 5310 (51.37%) | 15 (0.15%) | 70 (0.67%) | 16 (22.86%) | 15 (21.43%) | - | 38 (54.29%) | 1 (1.43%) |
Mcgrail | 209 | 118 (56.46%) | - | - | 31 (26.27%) | - | 87 (73.73%) | 91 (43.54%) | - | - | 8 (8.79%) | - | 83 (91.21%) |
Izquierdo | 71192 | NA ( NA%) | - | - | - | - | - | 1006 (1.41%) | 111 (11.03%) | - | - | - | 895 (88.97%) |
Ward | 99908 | 94416 (94.50%) | 10202 (10.81%) | - | - | - | 84214 (89.19%) | 5492 (5.50%) | 433 (7.88%) | - | - | - | 5059 (92.12%) |
Ebinger | 6062 | 5850 (96.50%) | 99 (1.69%) | - | - | - | 5668 (96.89%) | 212 (3.50%) | 3 (1.42%) | - | - | - | 205 (96.70%) |
Salerno | 15920 | 14753 (92.67%) | - | - | 5517 (37.40%) | 8278 (56.11%) | 958 (6.49%) | 1167 (7.33%) | - | - | 339 (29.05%) | 626 (53.64%) | 202 (17.31%) |
Iversen | 28792 | 27629 (95.96%) | 4430 (16.03%) | 1799 (6.51%) | - | 21217 (76.79%) | 246 (0.89%) | 1163 (4.04%) | 177 (15.22%) | 78 (6.71%) | - | 898 (77.21%) | 10 (0.86%) |
Hippisley, Cox | 8275949 | NA ( NA%) | - | - | - | - | - | 19486 (0.24%) | 1354 (6.95%) | 5715 (29.33%) | - | 12036 (61.77%) | 381 (1.96%) |
Fillmore | 22914 | 21120 (92.17%) | 8137 (38.53%) | 8416 (39.85%) | - | 3227 (15.28%) | 1340 (6.34%) | 1794 (7.83%) | 452 (25.20%) | 899 (50.11%) | - | 322 (17.95%) | 121 (6.74%) |
Alkurt | 119 | NA ( NA%) | - | - | - | - | - | 119 (100.00%) | 14 (11.76%) | - | - | - | 105 (88.24%) |
Petrilli | 10620 | 5341 (50.29%) | 3454 (64.67%) | 816 (15.28%) | - | 541 (10.13%) | 530 (9.92%) | 5279 (49.71%) | 3268 (61.91%) | 902 (17.09%) | - | 288 (5.46%) | 821 (15.55%) |
Bello-Chavolla | 150200 | 98567 (65.62%) | - | - | 9624 (9.76%) | - | 88943 (90.24%) | 51633 (34.38%) | - | - | 4366 (8.46%) | - | 47267 (91.54%) |
6.2.1 Smoking and testing meta-analysis
The steps to reproduce the Bayesian analysis are available above. We generate the treatment effect (mu) and standard deviations (tau) from the studies using the meta package
source(here::here('scripts', 'rr_function.R'))
table_2 <- table_2_word
included_studies <- quality_table_2 %>%
filter(overall_quality != 'poor') %>%
select(lead_author) %>%
filter(lead_author != "reiter")
meta <- tibble('author' = table_2$lead_author,
'negative_smoker' = table_2$negative_current_smoker,
'negative_never_smoker' = table_2$negative_never_smoker,
'positive_smoker' = table_2$positive_current_smoker,
'positive_never_smoker' = table_2$positive_never_smoker,
'negative_former_smoker' = table_2$negative_former_smoker,
'positive_former_smoker' = table_2$positive_former_smoker) %>%
filter(author %in% included_studies$lead_author)
meta$author <- to_upper_camel_case(meta$author, sep_out = ", ")
meta$author <- plyr::mapvalues(meta$author,
from = cleaned_names,
to = correct_names)
testing_rr <- list()
testing_rr[[1]] <- RR_testing('Rentsch', 'current')
testing_rr[[2]] <- RR_testing('Rentsch', 'former')
testing_rr[[3]] <- RR_testing('Cho', 'current')
testing_rr[[4]] <- RR_testing('Cho', 'former')
testing_rr[[5]] <- RR_testing('Kolin', 'current')
testing_rr[[6]] <- RR_testing('Kolin', 'former')
testing_rr[[7]] <- RR_testing('de Lusignan', 'current')
testing_rr[[8]] <- RR_testing('de Lusignan', 'former')
testing_rr[[9]] <- RR_testing('Parrotta', 'current')
testing_rr[[10]] <- RR_testing('Parrotta', 'former')
testing_rr[[11]] <- RR_testing('Israel', 'current')
testing_rr[[12]] <- RR_testing('Israel', 'former')
testing_rr[[13]] <- RR_testing('Eugen-Olsen', 'current')
testing_rr[[14]] <- RR_testing('Eugen-Olsen', 'former')
testing_rr[[15]] <- RR_testing('Houlihan', 'current')
testing_rr[[16]] <- RR_testing('Houlihan', 'former')
testing_rr[[17]] <- RR_testing('Woolford', 'current')
testing_rr[[18]] <- RR_testing('Woolford', 'former')
testing_rr[[19]] <- RR_testing('Govind', 'current')
testing_rr[[20]] <- RR_testing('Govind', 'former')
testing_rr[[21]] <- RR_testing('Gu', 'current')
testing_rr[[22]] <- RR_testing('Gu', 'former')
testing_rr[[23]] <- RR_testing('Petrilli', 'current')
testing_rr[[24]] <- RR_testing('Petrilli', 'former')
testing_rr[[25]] <- RR_testing('Auvinen', 'current')
testing_rr[[26]] <- RR_testing('Auvinen', 'former')
testing_rr[[27]] <- RR_testing('Merkely', 'current')
testing_rr[[28]] <- RR_testing('Merkely', 'former')
testing_rr[[29]] <- RR_testing('Iversen', 'current')
testing_rr[[30]] <- RR_testing('Iversen', 'former')
testing_rr[[31]] <- RR_testing('Fillmore', 'current')
testing_rr[[32]] <- RR_testing('Fillmore', 'former')
data <- testing_rr
k <- do.call(rbind.data.frame, data)
#SEs for Niedzwiedz et al. 2020
#current vs. never
niedz_log_RR_1<-log(1.15)
niedz_log_SE_1<-(log(1.54)-log(0.86))/3.92
k <- k %>%
add_row(., study = 'Niedzwiedz', smoking_status = 'current', log_RR = niedz_log_RR_1, log_SE = niedz_log_SE_1)
#former vs. never
niedz_log_RR_2<-log(1.42)
niedz_log_SE_2<-(log(1.69)-log(1.19))/3.92
k <- k %>%
add_row(., study = 'Niedzwiedz', smoking_status = 'former', log_RR = niedz_log_RR_2, log_SE = niedz_log_SE_2)
numbers_in_analysis <- table_2_word %>%
left_join(., quality_rating, by = 'lead_author') %>%
filter(., overall_quality != 'poor') %>%
add_row(lead_author = 'niedzwiedz', contributing_sample = 1474) %>%
replace_na(list(negative_not_stated = 0, positive_not_stated = 0)) %>%
mutate(contributing_sample = (contributing_sample - (negative_not_stated+positive_not_stated))) %>%
select(lead_author, contributing_sample) %>%
na.omit()
numbers_in_analysis$lead_author <- to_upper_camel_case(numbers_in_analysis$lead_author, sep_out = ", ")
numbers_in_analysis$lead_author <- plyr::mapvalues(numbers_in_analysis$lead_author,
from = cleaned_names,
to = correct_names)
running_meta_count <- k %>%
group_by(study) %>%
summarise()
ecdf <- read_rds(here("data_clean", "bayesian_models", "ecdf_list.rds"))
m1_a_ecdf <- ecdf[[1]]
m2_a_ecdf <- ecdf[[2]]
m3_a_ecdf <- ecdf[[3]]
m4_a_ecdf <- ecdf[[4]]
m5_a_ecdf <- ecdf[[5]]
m6_a_ecdf <- ecdf[[6]]
m7_a_ecdf <- ecdf[[7]]
m8_a_ecdf <- ecdf[[8]]The studies included in meta-analysis are rentsch, cho, kolin, de_lusignan, parrotta, israel, eugen_olsen, houlihan, woolford, govind, gu, auvinen, merkely, iversen, hippisley_cox, fillmore, petrilli, and niedzwiedz.
6.2.1.1 Current versus never smokers for testing positive
#current vs. never smokers
current_never_meta <- k %>%
filter(smoking_status == 'current') %>%
left_join(., numbers_in_analysis %>%
rename("study" = lead_author),
by = c("study")) %>%
mutate(study = paste(study, contributing_sample, sep = ", n = ")) %>%
select(-contributing_sample)
a <-metagen(current_never_meta$log_RR,
current_never_meta$log_SE,
studlab = current_never_meta$study,
sm="RR",
comb.fixed = F, comb.random = T)
write_rds(a, here("data_clean", "bayes_testing_current.rds"))
The ECDF shows the probability of the risk being less than or equal to RR = 0.9, 95.44%
6.2.1.2 Former versus never smokers
#former vs. never smokers
former_never_meta <- k %>%
filter(smoking_status == 'former') %>%
left_join(., numbers_in_analysis %>%
rename("study" = lead_author),
by = c("study")) %>%
mutate(study = paste(study, contributing_sample, sep = ", n = ")) %>%
select(-contributing_sample)
a<-metagen(former_never_meta$log_RR,
former_never_meta$log_SE,
studlab = former_never_meta$study,
sm="RR", comb.fixed = F, comb.random = T)
write_rds(a, here("data_clean", "bayes_testing_former.rds"))
The ECDF shows the probability of the risk being greater than or equal to RR = 1.1, 20.85%
6.3 Table 3: Smoking and COVID-19 hospitalisation
#Table 3
table_3_word <- table_3 %>%
mutate(., sample = sample_with_outcome) %>%
mutate(., community_percentage = formatC(number_community/sample*100, digits = 2, format = "f")) %>%
mutate(., community_current_smoker_percent = formatC(community_current_smoker/number_community*100, digits = 2, format = "f")) %>%
mutate(., community_former_smoker_percent = formatC(community_former_smoker/number_community*100, digits = 2, format = "f")) %>%
mutate(., community_current_former_smoker_percent = formatC(community_current_former_smoker/number_community*100, digits = 2, format = "f")) %>%
mutate(., community_never_smoker_percent = formatC(community_never_smoker/number_community*100, digits = 2, format = "f")) %>%
mutate(., community_never_unknown_smoker_percent = formatC(community_never_unknown_smoker/number_community*100, digits = 2, format = "f")) %>%
mutate(., community_not_stated_percent = formatC(community_not_stated/number_community*100, digits = 2, format = "f")) %>%
mutate(., number_hospitalised_percent = formatC(number_hospitalised/sample*100, digits = 2, format = "f")) %>%
mutate(., hospitalised_current_smoker_percent = formatC(hospitalised_current_smoker/number_hospitalised*100, digits = 2, format = "f")) %>%
mutate(., hospitalised_former_smoker_percent = formatC(hospitalised_former_smoker/number_hospitalised*100, digits = 2, format = "f")) %>%
mutate(., hospitalised_current_former_smoker_percent = formatC(hospitalised_current_former_smoker/number_hospitalised*100, digits = 2, format = "f")) %>%
mutate(., hospitalised_never_smoker_percent = formatC(hospitalised_never_smoker/number_hospitalised*100, digits = 2, format = "f")) %>%
mutate(., hospitalised_never_unknown_smoker_percent = formatC(hospitalised_never_unknown_smoker/number_hospitalised*100, digits = 2, format = "f")) %>%
mutate(., hospitalised_not_stated_percent = formatC(hospitalised_not_stated/number_hospitalised*100, digits = 2, format = "f")) %>%
select(lead_author, sample_with_outcome, number_community, community_percentage,
community_current_smoker, community_current_smoker_percent, community_former_smoker,
community_former_smoker_percent, community_current_former_smoker,
community_current_former_smoker_percent, community_never_smoker, community_never_smoker_percent,
community_never_unknown_smoker, community_never_unknown_smoker_percent,
community_not_stated, community_not_stated_percent, number_hospitalised, number_hospitalised_percent,
hospitalised_current_smoker, hospitalised_current_smoker_percent, hospitalised_former_smoker,
hospitalised_former_smoker_percent, hospitalised_current_former_smoker, hospitalised_current_former_smoker_percent,
hospitalised_never_smoker, hospitalised_never_smoker_percent, hospitalised_never_unknown_smoker,
hospitalised_never_unknown_smoker_percent, hospitalised_not_stated, hospitalised_not_stated_percent) %>%
write_rds(here::here('data_clean', 'table_3_word.rds'))The number of studies reporting on hospitalisation by smoking status is: 29
quality_table_3 <- table_3_word %>%
left_join(., quality_rating, by = 'lead_author') %>%
select(lead_author, overall_quality)
a <- table_3_word %>%
filter(number_community >= 1) %>%
mutate(Author = lead_author,
Population = sample_with_outcome,
Community = paste(
paste(number_community,
as.integer(community_percentage), sep = " ("), "%)", sep = ""),
C_current_smoker = paste(paste(community_current_smoker, (community_current_smoker_percent), sep = " (")
, "%)", sep = ""),
C_current_smoker = na_if(C_current_smoker, "NA ( NA%)"),
C_former_smoker = paste(paste(community_former_smoker, (community_former_smoker_percent), sep = " (")
, "%)", sep = ""),
C_former_smoker = na_if(C_former_smoker, "NA ( NA%)"),
C_current_former_smoker = paste(paste(community_current_former_smoker,
(community_current_former_smoker_percent), sep = " (")
, "%)", sep = ""),
C_current_former_smoker = na_if(C_current_former_smoker, "NA ( NA%)"),
C_never_smoker = paste(paste(community_never_smoker,
(community_never_smoker_percent), sep = " (")
, "%)", sep = ""),
C_never_smoker = na_if(C_never_smoker, "NA ( NA%)"),
C_never_unknown_smoker = paste(paste(community_never_unknown_smoker,
(community_never_unknown_smoker_percent), sep = " (")
, "%)", sep = ""),
C_never_unknown_smoker = na_if(C_never_unknown_smoker, "NA ( NA%)"),
C_not_stated = paste(paste(community_not_stated,
(community_not_stated_percent), sep = " (")
, "%)", sep = ""),
C_not_stated = na_if(C_not_stated, "NA ( NA%)")) %>%
mutate(Hospitalised = paste(
paste(number_hospitalised,
as.integer(number_hospitalised_percent), sep = " ("), "%)", sep = ""),
H_current_smoker = paste(paste(hospitalised_current_smoker, (hospitalised_current_smoker_percent), sep = " (")
, "%)", sep = ""),
H_current_smoker = na_if(H_current_smoker, "NA ( NA%)"),
H_former_smoker = paste(paste(hospitalised_former_smoker, (hospitalised_former_smoker_percent), sep = " (")
, "%)", sep = ""),
H_former_smoker = na_if(H_former_smoker, "NA ( NA%)"),
H_current_former_smoker = paste(paste(hospitalised_current_former_smoker,
(hospitalised_current_former_smoker_percent), sep = " (")
, "%)", sep = ""),
H_current_former_smoker = na_if(H_current_former_smoker, "NA ( NA%)"),
H_never_smoker = paste(paste(hospitalised_never_smoker,
(hospitalised_never_smoker_percent), sep = " (")
, "%)", sep = ""),
H_never_smoker = na_if(H_never_smoker, "NA ( NA%)"),
H_never_unknown_smoker = paste(paste(hospitalised_never_unknown_smoker,
(hospitalised_never_unknown_smoker_percent), sep = " (")
, "%)", sep = ""),
H_never_unknown_smoker = na_if(H_never_unknown_smoker, "NA ( NA%)"),
H_not_stated = paste(paste(hospitalised_not_stated,
(hospitalised_not_stated_percent), sep = " (")
, "%)", sep = ""),
H_not_stated = na_if(H_not_stated, "NA ( NA%)")) %>%
select(Author, Population, Community, C_current_smoker, C_former_smoker, C_current_former_smoker,
C_never_smoker,C_never_unknown_smoker,C_not_stated, Hospitalised, H_current_smoker, H_former_smoker,
H_current_former_smoker, H_never_smoker, H_never_unknown_smoker, H_not_stated)
a$Author <- to_upper_camel_case(a$Author, sep_out = ", ")
a$Author <- plyr::mapvalues(a$Author,
from = cleaned_names,
to = correct_names)
numeric_columns <- 'Population'
a <- flextable(a) %>%
set_caption(caption = 'COVID-19 hospitalisation by smoking status') %>%
colformat_num(col_keys = numeric_columns, digits = 0, na_str = '-', big.mark = ',') %>%
colformat_char(na_str = '-')
a <- set_header_labels(a,
Population = 'Population with outcome',
Community = "N (%)",
C_current_smoker = "Current smoker (%)",
C_former_smoker = "Former smoker (%)",
C_current_former_smoker = "Current/former smoker (%)",
C_never_smoker = "Never smoker (%)",
C_never_unknown_smoker = "Never/unknown smoker (%)",
C_not_stated = "Not stated (%)",
Hospitalised = "N (%)",
H_current_smoker = "Current smoker (%)",
H_former_smoker = "Former smoker (%)",
H_current_former_smoker = "Current/former smoker (%)",
H_never_smoker = "Never smoker (%)",
H_never_unknown_smoker = "Never/unknown smoker (%)",
H_not_stated = "Not stated (%)") %>%
add_header_row(top = TRUE, values = c("","Community", "Hospitalised" ), colwidths = c(2, 7, 7)) %>%
theme_booktabs() %>%
fix_border_issues() %>%
set_table_properties(width = 1, layout = 'autofit')
aCommunity | Hospitalised | ||||||||||||||
Author | Population with outcome | N (%) | Current smoker (%) | Former smoker (%) | Current/former smoker (%) | Never smoker (%) | Never/unknown smoker (%) | Not stated (%) | N (%) | Current smoker (%) | Former smoker (%) | Current/former smoker (%) | Never smoker (%) | Never/unknown smoker (%) | Not stated (%) |
Rentsch | 554 | 269 (48%) | 69 (25.65%) | 90 (33.46%) | - | 110 (40.89%) | - | - | 285 (51%) | 90 (31.58%) | 89 (31.23%) | - | 106 (37.19%) | - | - |
Chow (US CDC) | 6637 | 5143 (77%) | 61 (1.19%) | 80 (1.56%) | - | - | - | 5002 (97.26%) | 1494 (22%) | 27 (1.81%) | 78 (5.22%) | - | - | - | 1389 (92.97%) |
Argenziano | 1000 | 151 (15%) | 14 (9.27%) | 18 (11.92%) | - | 119 (78.81%) | - | - | 849 (84%) | 35 (4.12%) | 161 (18.96%) | - | 653 (76.91%) | - | - |
Lubetzky | 54 | 15 (27%) | - | - | 4 (26.67%) | - | - | 11 (73.33%) | 39 (72%) | - | - | 8 (20.51%) | - | - | 31 (79.49%) |
Carillo-Vega | 9946 | 3922 (39%) | 408 (10.40%) | - | - | - | - | 3514 (89.60%) | 6024 (60%) | 486 (8.07%) | - | - | - | - | 5538 (91.93%) |
Yanover | 4353 | 4180 (96%) | 484 (11.58%) | 118 (2.82%) | - | 3578 (85.60%) | - | - | 173 (3%) | 30 (17.34%) | 11 (6.36%) | - | 132 (76.30%) | - | - |
Hamer | 387109 | 386349 (99%) | 37333 (9.66%) | 134542 (34.82%) | - | 214474 (55.51%) | - | - | 760 (0%) | 93 (12.24%) | 313 (41.18%) | - | 354 (46.58%) | - | - |
Heili-Frades | 4712 | 1973 (41%) | 121 (6.13%) | 222 (11.25%) | - | - | 1630 (82.62%) | 1630 (82.62%) | 2739 (58%) | 112 (4.09%) | 598 (21.83%) | - | - | 2029 (74.08%) | - |
Freites | 123 | 69 (56%) | 1 (1.45%) | - | - | - | - | 68 (98.55%) | 54 (43%) | 3 (5.56%) | - | - | - | - | 51 (94.44%) |
Berumen | 102875 | 18832 (18%) | - | - | 1546 (8.21%) | - | 17286 (91.79%) | - | 12690 (12%) | - | - | 1202 (9.47%) | - | 11488 (90.53%) | - |
Gianfrancesco | 600 | 323 (53%) | - | - | 61 (18.89%) | - | - | 262 (81.11%) | 277 (46%) | - | - | 68 (24.55%) | - | - | 209 (75.45%) |
Chaudhry | 40 | 19 (47%) | - | - | 0 (0.00%) | - | - | 19 (100.00%) | 21 (52%) | - | - | 6 (28.57%) | - | - | 15 (71.43%) |
Giannouchos | 89756 | 58485 (65%) | 4679 (8.00%) | - | - | - | 53806 (92.00%) | - | 31271 (34%) | 2721 (8.70%) | - | - | - | 28550 (91.30%) | - |
Wang, Oekelen | 57 | 22 (38%) | - | - | 6 (27.27%) | - | - | 16 (72.73%) | 36 (63%) | - | - | 15 (41.67%) | - | - | 20 (55.56%) |
Miyara | 470 | 132 (28%) | 14 (10.61%) | 41 (31.06%) | - | 77 (58.33%) | - | - | 338 (71%) | 18 (5.33%) | 111 (32.84%) | - | 209 (61.83%) | - | - |
Suleyman | 463 | 108 (23%) | - | - | 23 (21.30%) | - | - | 85 (78.70%) | 355 (76%) | - | - | 137 (38.59%) | - | - | 218 (61.41%) |
Garassino | 196 | 48 (24%) | 10 (20.83%) | 27 (56.25%) | - | 11 (22.92%) | - | - | 152 (77%) | 38 (25.00%) | 84 (55.26%) | - | 26 (17.11%) | - | - |
Siso-Almirall | 260 | 119 (45%) | - | - | 31 (26.05%) | - | - | 88 (73.95%) | 141 (54%) | - | - | 50 (35.46%) | - | - | 91 (64.54%) |
Gu | 884 | 511 (57%) | 30 (5.87%) | 126 (24.66%) | - | 355 (69.47%) | - | - | 373 (42%) | 10 (2.68%) | 138 (37.00%) | - | 225 (60.32%) | - | - |
Killerby | 531 | 311 (58%) | - | - | 37 (11.90%) | 222 (71.38%) | - | 52 (16.72%) | 220 (41%) | - | - | 54 (24.55%) | 157 (71.36%) | - | 9 (4.09%) |
Nguyen | 689 | 333 (48%) | - | - | 57 (17.12%) | - | - | 276 (82.88%) | 356 (51%) | - | - | 114 (32.02%) | - | - | 242 (67.98%) |
Mendy | 689 | 473 (68%) | - | - | 84 (17.76%) | - | - | 389 (82.24%) | 216 (31%) | - | - | 86 (39.81%) | - | - | 130 (60.19%) |
Soares | 10713 | 9561 (89%) | 132 (1.38%) | - | - | - | 9429 (98.62%) | - | 1152 (10%) | 77 (6.68%) | - | - | - | 1075 (93.32%) | - |
Zobairy | 203 | 65 (32%) | 1 (1.54%) | - | - | - | 64 (98.46%) | - | 138 (67%) | 11 (7.97%) | - | - | - | 127 (92.03%) | - |
Izquierdo | 1006 | 743 (73%) | 52 (7.00%) | - | - | - | 691 (93.00%) | - | 263 (26%) | 16 (6.08%) | - | - | - | 247 (93.92%) | - |
Rizzo | 76819 | 60039 (78%) | 3931 (6.55%) | 11379 (18.95%) | - | 30042 (50.04%) | - | 14687 (24.46%) | 16780 (21%) | 1254 (7.47%) | 4585 (27.32%) | - | 8693 (51.81%) | - | 2248 (13.40%) |
Dashti | 4140 | 2759 (66%) | - | - | 600 (21.75%) | 1541 (55.85%) | - | 618 (22.40%) | 1381 (33%) | - | - | 577 (41.78%) | - | 596 (43.16%) | 208 (15.06%) |
Pan | 12084 | 8548 (70%) | - | - | 1263 (14.78%) | - | - | 7285 (85.22%) | 3536 (29%) | - | - | 874 (24.72%) | - | - | 2662 (75.28%) |
Petrilli | 5279 | 2538 (48%) | 147 (5.79%) | 337 (13.28%) | - | 1678 (66.12%) | - | 376 (14.81%) | 2741 (51%) | 141 (5.14%) | 565 (20.61%) | - | 1590 (58.01%) | - | 445 (16.23%) |
6.3.1 Smoking and hospitalisation meta-analysis
The studies included in meta-analysis are rentsch, argenziano, yanover, hamer, miyara_medrxiv, garassino, gu, and petrilli.
6.3.1.1 Current versus never smoking for hospitalisation
# Data --------------------------------------------------------------------
meta <- tibble('author' = table_3$lead_author,
'community_smoker' = table_3$community_current_smoker,
'community_never_smoker' = table_3$community_never_smoker,
'hospitalised_smoker' = table_3$hospitalised_current_smoker,
'hospitalised_never_smoker' = table_3$hospitalised_never_smoker,
'community_former_smoker' = table_3$community_former_smoker,
'hospitalised_former_smoker' = table_3$hospitalised_former_smoker) %>%
filter(author %in% included_studies$lead_author)
meta$author <- to_upper_camel_case(meta$author, sep_out = ", ")
meta$author <- plyr::mapvalues(meta$author,
from = cleaned_names,
to = correct_names)
# Current smoker hospitalisation ------------------------------------------
event_rates_smoker <- meta %>%
mutate(., Ee = hospitalised_smoker) %>%
mutate(., Ne = (hospitalised_smoker+community_smoker)) %>%
mutate(., Ec = hospitalised_never_smoker) %>%
mutate(., Nc = (hospitalised_never_smoker+community_never_smoker)) %>%
rename('Author' = author) %>%
select(Author, Ee, Ne, Ec, Nc)
event_rates_smoker <- metabin(Ee,
Ne,
Ec,
Nc,
data = event_rates_smoker,
studlab = paste(Author),
comb.fixed = F,
comb.random = T,
method.tau = 'SJ',
hakn = T,
prediction = F,
incr = 0.1,
sm = 'RR')
write_rds(event_rates_smoker, here("data_clean", "bayes_hospital_current.rds"))
Current smoking against never smoking for hospitalisation
ECDF for current smoking against never smoking for hospitalisation
The ECDF shows the probability of the risk being greater than or equal to RR = 1.1, 34.93%
6.3.1.2 Former versus never smoking for hospitalisation
# Former smoker hospitalisation -------------------------------------------
event_rates_former <- meta %>%
mutate(., Ee = hospitalised_former_smoker) %>%
mutate(., Ne = (hospitalised_former_smoker+community_former_smoker)) %>%
mutate(., Ec = hospitalised_never_smoker) %>%
mutate(., Nc = (hospitalised_never_smoker+community_never_smoker)) %>%
rename('Author' = author) %>%
select(Author, Ee, Ne, Ec, Nc)
event_rates_former <- metabin(Ee,
Ne,
Ec,
Nc,
data = event_rates_former,
studlab = paste(Author),
comb.fixed = F,
comb.random = T,
method.tau = 'SJ',
hakn = T,
prediction = F,
incr = 0.1,
sm = 'RR')
running_meta_count <- running_meta_count %>%
full_join(., meta %>%
rename("study" = author), by = "study") %>%
select(study)
write_rds(event_rates_former, here("data_clean", "bayes_hospital_former.rds"))
The ECDF shows the probability of the risk being greater than or equal to RR = 1.1, 89.09%
6.4 Table 4: Smoking and COVID-19 disease severity
#Table 4
table_4_word <- table_4 %>%
mutate(., sample = sample_with_severity) %>%
mutate(., non_severe_disease_percentage = formatC(non_severe_disease/sample*100, digits = 2, format = "f")) %>%
mutate(., non_severe_current_smoker_percent = formatC(non_severe_current_smoker/non_severe_disease*100, digits = 2, format = "f")) %>%
mutate(., non_severe_former_smoker_percent = formatC(non_severe_former_smoker/non_severe_disease*100, digits = 2, format = "f")) %>%
mutate(., non_severe_current_former_smoker_percent = formatC(non_severe_current_former_smoker/non_severe_disease*100, digits = 2, format = "f")) %>%
mutate(., non_severe_never_smoker_percent = formatC(non_severe_never_smoker/non_severe_disease*100, digits = 2, format = "f")) %>%
mutate(., non_severe_never_unknown_smoker_percent = formatC(non_severe_never_unknown_smoker/non_severe_disease*100, digits = 2, format = "f")) %>%
mutate(., non_severe_not_stated_percent = formatC(non_severe_not_stated/non_severe_disease*100, digits = 2, format = "f")) %>%
mutate(., severe_disease_number_percent = formatC(severe_disease_number/sample*100, digits = 2, format = "f")) %>%
mutate(., severe_disease_current_smoker_percent = formatC(severe_disease_current_smoker/severe_disease_number*100, digits = 2, format = "f")) %>%
mutate(., severe_disease_former_smoker_percent = formatC(severe_disease_former_smoker/severe_disease_number*100, digits = 2, format = "f")) %>%
mutate(., severe_disease_current_former_smoker_percent = formatC(severe_disease_current_former_smoker/severe_disease_number*100, digits = 2, format = "f")) %>%
mutate(., severe_disease_never_smoker_percent = formatC(severe_disease_never_smoker/severe_disease_number*100, digits = 2, format = "f")) %>%
mutate(., severe_disease_never_unknown_percent = formatC(severe_disease_never_unknown/severe_disease_number*100, digits = 2, format = "f")) %>%
mutate(., severe_disease_not_stated_percent = formatC(severe_disease_not_stated/severe_disease_number*100, digits = 2, format = "f")) %>%
select(lead_author, sample, non_severe_disease, non_severe_disease_percentage, non_severe_current_smoker,
non_severe_current_smoker_percent, non_severe_former_smoker, non_severe_former_smoker_percent,
non_severe_current_former_smoker, non_severe_current_former_smoker_percent, non_severe_never_smoker,
non_severe_never_smoker_percent, non_severe_never_unknown_smoker, non_severe_never_unknown_smoker_percent,
non_severe_not_stated, non_severe_not_stated_percent, severe_disease_number, severe_disease_number_percent,
severe_disease_current_smoker, severe_disease_current_smoker_percent,
severe_disease_former_smoker, severe_disease_former_smoker_percent, severe_disease_current_former_smoker,
severe_disease_current_former_smoker_percent, severe_disease_never_smoker, severe_disease_never_smoker_percent,
severe_disease_never_unknown, severe_disease_never_unknown_percent, severe_disease_not_stated,
severe_disease_not_stated_percent) %>%
write_rds(here::here('data_clean', 'table_4_word.rds'))The number of studies reporting on hospitalisation by smoking status is: 60
quality_table_4 <- table_4_word %>%
left_join(., quality_rating, by = 'lead_author') %>%
select(lead_author, overall_quality)
a <- table_4_word %>%
mutate(Author = lead_author,
Population = sample,
non_severe_disease = paste(
paste(non_severe_disease,
as.integer(non_severe_disease_percentage), sep = " ("), "%)", sep = ""),
ns_current_smoker = paste(paste(non_severe_current_smoker, (non_severe_current_smoker_percent), sep = " (")
, "%)", sep = ""),
ns_current_smoker = na_if(ns_current_smoker, "NA ( NA%)"),
ns_former_smoker = paste(paste(non_severe_former_smoker, (non_severe_former_smoker_percent), sep = " (")
, "%)", sep = ""),
ns_former_smoker = na_if(ns_former_smoker, "NA ( NA%)"),
ns_current_former_smoker = paste(paste(non_severe_current_former_smoker,
(non_severe_current_former_smoker_percent), sep = " (")
, "%)", sep = ""),
ns_current_former_smoker = na_if(ns_current_former_smoker, "NA ( NA%)"),
ns_never_smoker = paste(paste(non_severe_never_smoker,
(non_severe_never_smoker_percent), sep = " (")
, "%)", sep = ""),
ns_never_smoker = na_if(ns_never_smoker, "NA ( NA%)"),
ns_never_unknown_smoker = paste(paste(non_severe_never_unknown_smoker,
(non_severe_never_unknown_smoker_percent), sep = " (")
, "%)", sep = ""),
ns_never_unknown_smoker = na_if(ns_never_unknown_smoker, "NA ( NA%)"),
ns_not_stated = paste(paste(non_severe_not_stated,
(non_severe_not_stated_percent), sep = " (")
, "%)", sep = ""),
ns_not_stated = na_if(ns_not_stated, "NA ( NA%)"),
) %>%
mutate(severe_disease = paste(
paste(severe_disease_number,
as.integer(severe_disease_number_percent), sep = " ("), "%)", sep = ""),
s_current_smoker = paste(paste(severe_disease_current_smoker, (severe_disease_current_smoker_percent), sep = " (")
, "%)", sep = ""),
s_current_smoker = na_if(s_current_smoker, "NA ( NA%)"),
s_former_smoker = paste(paste(severe_disease_former_smoker, (severe_disease_former_smoker_percent), sep = " (")
, "%)", sep = ""),
s_former_smoker = na_if(s_former_smoker, "NA ( NA%)"),
s_current_former_smoker = paste(paste(severe_disease_current_former_smoker,
(severe_disease_current_former_smoker_percent), sep = " (")
, "%)", sep = ""),
s_current_former_smoker = na_if(s_current_former_smoker, "NA ( NA%)"),
s_never_smoker = paste(paste(severe_disease_never_smoker,
(severe_disease_never_smoker_percent), sep = " (")
, "%)", sep = ""),
s_never_smoker = na_if(s_never_smoker, "NA ( NA%)"),
s_never_unknown_smoker = paste(paste(severe_disease_never_unknown,
(severe_disease_never_unknown_percent), sep = " (")
, "%)", sep = ""),
s_never_unknown_smoker = na_if(s_never_unknown_smoker, "NA ( NA%)"),
s_not_stated = paste(paste(severe_disease_not_stated,
(severe_disease_not_stated_percent), sep = " (")
, "%)", sep = ""),
s_not_stated = na_if(s_not_stated, "NA ( NA%)")) %>%
select(Author, Population, non_severe_disease, ns_current_smoker, ns_former_smoker,
ns_current_former_smoker, ns_never_smoker, ns_never_unknown_smoker, ns_not_stated,
severe_disease, s_current_smoker, s_former_smoker, s_current_former_smoker, s_never_smoker, s_never_unknown_smoker,
s_not_stated)
a$Author <- to_upper_camel_case(a$Author, sep_out = ", ")
a$Author <- plyr::mapvalues(a$Author,
from = cleaned_names,
to = correct_names)
numeric_columns <- 'Population'
a <- flextable(a) %>%
set_caption(caption = 'COVID-19 severity by smoking status') %>%
colformat_num(col_keys = numeric_columns, digits = 0, na_str = '-', big.mark = ',') %>%
colformat_char(na_str = '-')
a <- set_header_labels(a,
Population = "Population with severity",
non_severe_disease = "N (%)",
ns_current_smoker = "Current smoker (%)",
ns_former_smoker = "Former smoker (%)",
ns_current_former_smoker = "Current/former smoker (%)",
ns_never_unknown_smoker = "Never/unknown smoker (%)",
ns_never_smoker = "Never smoker (%)",
ns_not_stated = "Not stated (%)",
severe_disease = "N (%)",
s_current_smoker = "Current smoker (%)",
s_former_smoker = "Former smoker (%)",
s_current_former_smoker = "Current/former smoker (%)",
s_never_unknown_smoker = "Never/unknown smoker (%)",
s_never_smoker = "Never smoker (%)",
s_not_stated = "Not stated (%)") %>%
add_header_row(top = TRUE, values = c("","Non severe disease", "Severe disease" ), colwidths = c(2, 7, 7)) %>%
theme_booktabs() %>%
fix_border_issues() %>%
set_table_properties(width = 1, layout = 'autofit')
aNon severe disease | Severe disease | ||||||||||||||
Author | Population with severity | N (%) | Current smoker (%) | Former smoker (%) | Current/former smoker (%) | Never smoker (%) | Never/unknown smoker (%) | Not stated (%) | N (%) | Current smoker (%) | Former smoker (%) | Current/former smoker (%) | Never smoker (%) | Never/unknown smoker (%) | Not stated (%) |
Guan, Ni | 1085 | 913 (84%) | 108 (11.83%) | 12 (1.31%) | - | 793 (86.86%) | - | - | 172 (15%) | 29 (16.86%) | 9 (5.23%) | - | 134 (77.91%) | - | - |
Zhang, Dong | 9 | 3 (33%) | 0 (0.00%) | 3 (100.00%) | - | 0 (0.00%) | - | - | 6 (66%) | 2 (33.33%) | 4 (66.67%) | - | 0 (0.00%) | - | - |
Wan | 9 | 8 (88%) | 8 (100.00%) | 0 (0.00%) | - | 0 (0.00%) | - | - | 1 (11%) | 1 (100.00%) | 0 (0.00%) | - | 0 (0.00%) | - | - |
Huang, Wang | 3 | 3 (100%) | 3 (100.00%) | 0 (0.00%) | - | 0 (0.00%) | - | - | 0 (0%) | 0 (NaN%) | 0 (NaN%) | - | 0 (NaN%) | - | - |
Rentsch | 285 | 168 (58%) | 47 (27.98%) | 53 (31.55%) | - | 68 (40.48%) | - | - | 117 (41%) | 43 (36.75%) | 36 (30.77%) | - | 38 (32.48%) | - | - |
Hu | 323 | 151 (46%) | - | - | 12 (7.95%) | - | 139 (92.05%) | - | 172 (53%) | - | - | 26 (15.12%) | - | 146 (84.88%) | - |
Wang, Pan | 125 | 100 (80%) | - | - | 9 (9.00%) | - | 91 (91.00%) | - | 25 (20%) | - | - | 7 (28.00%) | - | 18 (72.00%) | - |
Kim | 27 | 21 (77%) | 3 (14.29%) | - | - | - | 18 (85.71%) | - | 6 (22%) | 2 (33.33%) | 0 (0.00%) | - | - | 4 (66.67%) | - |
Shi, Yu | 474 | 425 (89%) | - | - | 34 (8.00%) | - | 391 (92.00%) | - | 49 (10%) | - | - | 6 (12.24%) | - | 43 (87.76%) | - |
Liao, Feng | 148 | 92 (62%) | - | - | 5 (5.43%) | - | - | 87 (94.57%) | 56 (37%) | 3 (5.36%) | - | - | - | - | 53 (94.64%) |
Shi, Ren | 134 | 88 (65%) | - | - | 8 (9.09%) | - | - | 80 (90.91%) | 46 (34%) | - | - | 6 (13.04%) | - | - | 40 (86.96%) |
Hadjadj | 50 | 15 (30%) | 1 (6.67%) | 2 (13.33%) | - | 12 (80.00%) | - | - | 35 (70%) | 0 (0.00%) | 7 (20.00%) | - | 28 (80.00%) | - | - |
Zheng, Xiong | 73 | 43 (58%) | - | - | 6 (13.95%) | 37 (86.05%) | - | - | 30 (41%) | - | - | 2 (6.67%) | 28 (93.33%) | - | - |
de la Rica | 48 | 26 (54%) | - | - | 6 (23.08%) | - | - | 20 (76.92%) | 20 (41%) | - | - | 4 (20.00%) | - | - | 16 (80.00%) |
Yin, Yang | 106 | 47 (44%) | - | - | 6 (12.77%) | - | - | 41 (87.23%) | 59 (55%) | - | - | 12 (20.34%) | - | - | 47 (79.66%) |
Allenbach | 147 | 100 (68%) | - | - | 9 (9.00%) | - | - | 91 (91.00%) | 47 (31%) | - | - | 0 (0.00%) | - | - | 47 (100.00%) |
Goyal | 393 | 263 (66%) | 14 (5.32%) | - | - | - | - | 249 (94.68%) | 130 (33%) | 6 (4.62%) | - | - | - | - | 124 (95.38%) |
Feng | 454 | 333 (73%) | 27 (8.11%) | - | - | - | - | 306 (91.89%) | 121 (26%) | 17 (14.05%) | - | - | - | - | 104 (85.95%) |
Yao | 108 | 83 (76%) | 1 (1.20%) | - | - | - | - | 82 (98.80%) | 25 (23%) | 3 (12.00%) | - | - | - | - | 22 (88.00%) |
Sami | 490 | 400 (81%) | 53 (13.25%) | - | - | - | - | 347 (86.75%) | 90 (18%) | 16 (17.78%) | - | - | - | - | 74 (82.22%) |
Regina | 200 | 163 (81%) | 9 (5.52%) | - | - | - | - | 154 (94.48%) | 37 (18%) | 0 (0.00%) | - | - | - | - | 37 (100.00%) |
Feuth | 28 | 21 (75%) | 1 (4.76%) | 7 (33.33%) | - | 13 (61.90%) | - | - | 7 (25%) | 2 (28.57%) | 1 (14.29%) | - | 4 (57.14%) | - | - |
Mejia-Vilet | 329 | 214 (65%) | - | - | 13 (6.07%) | - | - | 201 (93.93%) | 115 (34%) | - | - | 10 (8.70%) | - | - | 105 (91.30%) |
Chen, Jiang | 135 | 54 (40%) | - | - | 4 (7.41%) | - | - | 50 (92.59%) | 81 (60%) | - | - | 9 (11.11%) | - | - | 72 (88.89%) |
Vaquero-Roncero | 146 | 75 (51%) | - | - | 4 (5.33%) | - | - | 71 (94.67%) | 71 (48%) | - | - | 6 (8.45%) | - | - | 65 (91.55%) |
Kim, Garg | 2490 | 1692 (67%) | 112 (6.62%) | 395 (23.35%) | - | - | 1185 (70.04%) | - | 798 (32%) | 38 (4.76%) | 247 (30.95%) | - | - | 512 (64.16%) | - |
Wu | 174 | 92 (52%) | - | - | 47 (51.09%) | - | 45 (48.91%) | - | 82 (47%) | 11 (13.41%) | - | - | - | 71 (86.59%) | - |
Chaudhry | 40 | 34 (85%) | - | - | 5 (14.71%) | - | - | 29 (85.29%) | 6 (15%) | - | - | 1 (16.67%) | - | - | 5 (83.33%) |
Garibaldi | 832 | 532 (63%) | 25 (4.70%) | 107 (20.11%) | - | - | - | 400 (75.19%) | 300 (36%) | 21 (7.00%) | 81 (27.00%) | - | - | - | 198 (66.00%) |
Kuderer | 928 | 686 (73%) | 35 (5.10%) | 210 (30.61%) | - | 370 (53.94%) | - | 29 (4.23%) | 242 (26%) | 8 (3.31%) | 116 (47.93%) | - | 99 (40.91%) | 15 (6.20%) | 4 (1.65%) |
Romao | 14 | 14 (100%) | - | - | 4 (28.57%) | - | - | 10 (71.43%) | 0 (0%) | - | - | - | - | - | - |
Giannouchos | 89756 | 78050 (86%) | 6322 (8.10%) | - | - | - | 71728 (91.90%) | - | 11706 (13%) | 1089 (9.30%) | - | - | - | 10617 (90.70%) | - |
Cen | 1007 | 720 (71%) | - | - | 70 (9.72%) | - | - | 650 (90.28%) | 287 (28%) | - | - | 18 (6.27%) | - | - | 269 (93.73%) |
Maraschini | 132 | 89 (67%) | - | 11 (12.36%) | - | 78 (87.64%) | - | - | 43 (32%) | - | 3 (6.98%) | - | 40 (93.02%) | - | - |
Russell | 156 | 128 (82%) | 9 (7.03%) | 31 (24.22%) | - | 51 (39.84%) | - | 37 (28.91%) | 28 (17%) | 2 (7.14%) | 8 (28.57%) | - | 8 (28.57%) | - | 10 (35.71%) |
Siso-Almirall | 260 | 212 (81%) | - | - | 60 (28.30%) | - | - | 152 (71.70%) | 48 (18%) | - | - | 21 (43.75%) | - | - | 27 (56.25%) |
Gu | 884 | 511 (57%) | 30 (5.87%) | 126 (24.66%) | - | 355 (69.47%) | - | - | 134 (15%) | 3 (2.24%) | 61 (45.52%) | - | 70 (52.24%) | - | - |
Mendy | 689 | 598 (86%) | - | - | 133 (22.24%) | - | - | 465 (77.76%) | 91 (13%) | - | - | 37 (40.66%) | - | - | 54 (59.34%) |
Pongpirul | 193 | 161 (83%) | - | - | 25 (15.53%) | 106 (65.84%) | - | 30 (18.63%) | 32 (16%) | - | - | 4 (12.50%) | 21 (65.62%) | - | 7 (21.88%) |
Jin, Gu | 6 | 2 (33%) | - | - | 0 (0.00%) | - | - | 4 (200.00%) | 4 (66%) | - | - | 2 (50.00%) | - | - | 2 (50.00%) |
Senkal | 611 | 446 (73%) | 48 (10.76%) | - | - | - | - | 398 (89.24%) | 165 (27%) | 21 (12.73%) | - | - | - | - | 144 (87.27%) |
Patel | 129 | 89 (68%) | 26 (29.21%) | - | - | - | 58 (65.17%) | 5 (5.62%) | 40 (31%) | 22 (55.00%) | - | - | - | 14 (35.00%) | 4 (10.00%) |
Maucourant | 27 | 10 (37%) | 1 (10.00%) | 2 (20.00%) | - | 2 (20.00%) | - | 5 (50.00%) | 17 (62%) | 2 (11.76%) | 5 (29.41%) | - | 9 (52.94%) | - | 1 (5.88%) |
Xie | 619 | 469 (75%) | - | - | 32 (6.82%) | - | - | 437 (93.18%) | 150 (24%) | - | - | 19 (12.67%) | - | - | 131 (87.33%) |
Fox | 55 | 30 (54%) | 1 (3.33%) | 4 (13.33%) | - | 17 (56.67%) | - | 8 (26.67%) | 25 (45%) | 0 (0.00%) | 2 (8.00%) | - | 14 (56.00%) | - | 9 (36.00%) |
Zhang, Cao | 240 | 162 (67%) | 2 (1.23%) | 6 (3.70%) | - | - | - | 154 (95.06%) | 78 (32%) | 4 (5.13%) | 4 (5.13%) | - | - | - | 70 (89.74%) |
Kurashima | 53 | 10 (18%) | - | - | 3 (30.00%) | - | - | 7 (70.00%) | 43 (81%) | - | - | 24 (55.81%) | - | - | 19 (44.19%) |
Zhan | 75 | NA (NA%) | - | - | - | - | - | - | 75 (100%) | - | - | 9 (12.00%) | - | - | 66 (88.00%) |
Omrani | 858 | 806 (93%) | - | - | 121 (15.01%) | - | - | 685 (84.99%) | 52 (6%) | - | - | 9 (17.31%) | - | - | 43 (82.69%) |
Marcos | 918 | 555 (60%) | 38 (6.85%) | - | 69 (12.43%) | - | - | 448 (80.72%) | 363 (39%) | 18 (4.96%) | - | 71 (19.56%) | - | - | 292 (80.44%) |
Hoertel, Sanchez, Rico | 7345 | 6014 (81%) | 433 (7.20%) | - | - | - | - | 5581 (92.80%) | 1331 (18%) | 190 (14.27%) | - | - | - | - | 1141 (85.73%) |
Qi | 267 | 217 (81%) | 22 (10.14%) | - | - | - | 195 (89.86%) | - | 50 (18%) | 31 (62.00%) | - | - | - | 19 (38.00%) | - |
Monteiro | 112 | 84 (75%) | 3 (3.57%) | 14 (16.67%) | - | 63 (75.00%) | - | 4 (4.76%) | 28 (25%) | 4 (14.29%) | 6 (21.43%) | - | 14 (50.00%) | - | 4 (14.29%) |
Dashti | 1381 | 619 (44%) | - | - | 239 (38.61%) | 292 (47.17%) | - | 88 (14.22%) | 762 (55%) | - | - | 338 (44.36%) | 304 (39.90%) | - | 120 (15.75%) |
Morshed | 103 | 87 (84%) | 28 (32.18%) | - | - | - | 59 (67.82%) | - | 16 (15%) | 4 (25.00%) | - | - | - | 12 (75.00%) | - |
Zhou, Sun | 144 | 108 (75%) | 11 (10.19%) | - | - | - | - | 97 (89.81%) | 36 (25%) | 2 (5.56%) | - | - | - | - | 34 (94.44%) |
Hippisley, Cox | - | NA (NA%) | - | - | - | - | - | - | 1286 (NA%) | 56 (4.35%) | 427 (33.20%) | - | 791 (61.51%) | - | 12 (0.93%) |
Zhao, Chen | 641 | 398 (62%) | 87 (21.86%) | - | - | - | - | 311 (78.14%) | 195 (30%) | 52 (26.67%) | - | - | - | - | 143 (73.33%) |
Qu | 246 | 226 (91%) | 90 (39.82%) | - | - | - | - | 136 (60.18%) | 20 (8%) | 14 (70.00%) | - | - | - | - | 6 (30.00%) |
Petrilli | 2729 | 1739 (63%) | 97 (5.58%) | 325 (18.69%) | - | 1067 (61.36%) | - | 250 (14.38%) | 990 (36%) | 44 (4.44%) | 236 (23.84%) | - | 517 (52.22%) | - | 193 (19.49%) |
6.4.1 Smoking and severity meta-analysis
The studies included in meta-analysis are guan_ni, rentsch, hadjadj, feuth, kuderer, gu, monteiro, hippisley_cox, and petrilli.
6.4.1.1 Current versus never smoking for risk of severe disease
# Data --------------------------------------------------------------------
meta <- tibble('author' = table_4$lead_author,
'non_severe_smoker' = table_4$non_severe_current_smoker,
'non_severe_never_smoker' = table_4$non_severe_never_smoker,
'severe_smoker' = table_4$severe_disease_current_smoker,
'severe_never_smoker' = table_4$severe_disease_never_smoker,
'non_severe_former_smoker' = table_4$non_severe_former_smoker,
'severe_former_smoker' = table_4$severe_disease_former_smoker) %>%
filter(author %in% included_studies$lead_author)
meta$author <- to_upper_camel_case(meta$author, sep_out = ", ")
meta$author <- plyr::mapvalues(meta$author,
from = cleaned_names,
to = correct_names)
# Current smoker severity ------------------------------------------
event_rates_smoker <- meta %>%
mutate(., Ee = severe_smoker) %>%
mutate(., Ne = (severe_smoker+non_severe_smoker)) %>%
mutate(., Ec = severe_never_smoker) %>%
mutate(., Nc = (severe_never_smoker+non_severe_never_smoker)) %>%
rename('Author' = author) %>%
select(Author, Ee, Ne, Ec, Nc)
event_rates_smoker <- metabin(Ee,
Ne,
Ec,
Nc,
data = event_rates_smoker,
studlab = paste(Author),
comb.fixed = F,
comb.random = T,
method.tau = 'SJ',
hakn = F,
prediction = F,
incr = 0.1,
sm = 'RR')
running_meta_count <- running_meta_count %>%
full_join(., meta %>%
rename("study" = author), by = "study") %>%
select(study)
write_rds(event_rates_smoker, here("data_clean", "bayes_severity_current.rds"))
The ECDF shows the probability of the risk being greater than or equal to RR = 1.1, 78.96%
6.4.1.2 Former versus never smoking for risk of severe disease
# Former smoker severity -------------------------------------------
event_rates_former <- meta %>%
mutate(., Ee = severe_former_smoker) %>%
mutate(., Ne = (severe_former_smoker+non_severe_former_smoker)) %>%
mutate(., Ec = severe_never_smoker) %>%
mutate(., Nc = (severe_never_smoker+non_severe_never_smoker)) %>%
rename('Author' = author) %>%
select(Author, Ee, Ne, Ec, Nc)
event_rates_former <- metabin(Ee,
Ne,
Ec,
Nc,
data = event_rates_former,
studlab = paste(Author),
comb.fixed = F,
comb.random = T,
method.tau = 'SJ',
hakn = F,
prediction = F,
incr = 0.1,
sm = 'RR')
write_rds(event_rates_former, here("data_clean", "bayes_severity_former.rds"))
The ECDF shows the probability of the risk being greater than or equal to RR = 1.1, 98.02%
6.5 Table 5: Smoking and COVID-19 mortality
#Table 5
table_5_word <- table_5 %>%
mutate(., sample = sample_with_deaths) %>%
mutate(., deaths_percentage = formatC(deaths/sample*100, digits = 2, format = "f")) %>%
mutate(., death_current_smokers_percent = formatC(death_current_smokers/deaths*100, digits = 2, format = "f")) %>%
mutate(., death_former_smokers_percent = formatC(death_former_smokers/deaths*100, digits = 2, format = "f")) %>%
mutate(., death_current_former_smokers_percent = formatC(death_current_former_smokers/deaths*100, digits = 2, format = "f")) %>%
mutate(., death_never_smokers_percent = formatC(death_never_smokers/deaths*100, digits = 2, format = "f")) %>%
mutate(., death_never_unknown_smokers_percent = formatC(death_never_unknown_smokers/deaths*100, digits = 2, format = "f")) %>%
mutate(., death_not_stated_percent = formatC(death_not_stated/deaths*100, digits = 2, format = "f")) %>%
mutate(., recovered_percentage = formatC(recovered/sample*100, digits = 2, format = "f")) %>%
mutate(., recovered_current_smokers_percent = formatC(recovered_current_smoking/recovered*100, digits = 2, format = "f")) %>%
mutate(., recovered_former_smokers_percent = formatC(recovered_former_smoker/recovered*100, digits = 2, format = "f")) %>%
mutate(., recovered_current_former_smokers_percent = formatC(recovered_current_former_smokers/recovered*100, digits = 2, format = "f")) %>%
mutate(., recovered_never_smokers_percent = formatC(recovered_never_smoker/recovered*100, digits = 2, format = "f")) %>%
mutate(., recovered_never_unknown_smokers_percent = formatC(recovered_never_unknown_smoker/recovered*100, digits = 2, format = "f")) %>%
mutate(., recovered_not_stated_percent = formatC(recovered_not_stated/recovered*100, digits = 2, format = "f")) %>%
select(lead_author, sample, recovered, recovered_percentage, recovered_current_smoking, recovered_current_smokers_percent,
recovered_former_smoker, recovered_former_smokers_percent, recovered_current_former_smokers,
recovered_current_former_smokers_percent, recovered_never_smoker, recovered_never_smokers_percent,
recovered_never_unknown_smoker, recovered_never_unknown_smokers_percent, recovered_not_stated,
recovered_not_stated_percent,
deaths, deaths_percentage, death_current_smokers, death_current_smokers_percent,
death_former_smokers, death_former_smokers_percent, death_current_former_smokers,
death_current_former_smokers_percent, death_never_smokers, death_never_smokers_percent,
death_never_unknown_smokers, death_never_unknown_smokers_percent, death_not_stated, death_not_stated_percent) %>%
write_rds(here::here('data_clean', 'table_5_word.rds'))The number of studies reporting on death by smoking status is: 50
quality_table_5 <- table_5_word %>%
left_join(., quality_rating, by = 'lead_author') %>%
select(lead_author, overall_quality)
a <- table_5_word %>%
mutate(Author = lead_author,
Population = sample,
recovered = paste(
paste(recovered,
as.integer(recovered_percentage), sep = " ("), "%)", sep = ""),
recovered_current_smoker = paste(paste(recovered_current_smoking,
(recovered_current_smokers_percent), sep = " (")
, "%)", sep = ""),
recovered_current_smoker = na_if(recovered_current_smoker, "NA ( NA%)"),
recovered_former_smoker = paste(paste(recovered_former_smoker,
(recovered_former_smokers_percent), sep = " (")
, "%)", sep = ""),
recovered_former_smoker = na_if(recovered_former_smoker, "NA ( NA%)"),
recovered_current_former_smoker = paste(paste(recovered_current_former_smokers,
(recovered_current_former_smokers_percent), sep = " (")
, "%)", sep = ""),
recovered_current_former_smoker = na_if(recovered_current_former_smoker, "NA ( NA%)"),
recovered_never_smoker = paste(paste(recovered_never_smoker,
(recovered_never_smokers_percent), sep = " (")
, "%)", sep = ""),
recovered_never_smoker = na_if(recovered_never_smoker, "NA ( NA%)"),
recovered_never_unknown_smoker = paste(paste(recovered_never_unknown_smoker,
(recovered_never_unknown_smokers_percent), sep = " (")
, "%)", sep = ""),
recovered_never_unknown_smoker = na_if(recovered_never_unknown_smoker, "NA ( NA%)"),
recovered_not_stated = paste(paste(recovered_not_stated,
(recovered_not_stated_percent), sep = " (")
, "%)", sep = ""),
recovered_not_stated = na_if(recovered_not_stated, "NA ( NA%)")) %>%
mutate(deaths = paste(
paste(deaths,
as.integer(deaths_percentage), sep = " ("), "%)", sep = ""),
death_current_smoker = paste(paste(death_current_smokers,
(death_current_smokers_percent), sep = " (")
, "%)", sep = ""),
death_current_smoker = na_if(death_current_smoker, "NA ( NA%)"),
death_former_smoker = paste(paste(death_former_smokers,
(death_former_smokers_percent), sep = " (")
, "%)", sep = ""),
death_former_smoker = na_if(death_former_smoker, "NA ( NA%)"),
death_current_former_smoker = paste(paste(death_current_former_smokers,
(death_current_former_smokers_percent), sep = " (")
, "%)", sep = ""),
death_current_former_smoker = na_if(death_current_former_smoker, "NA ( NA%)"),
death_never_smoker = paste(paste(death_never_smokers,
(death_never_smokers_percent), sep = " (")
, "%)", sep = ""),
death_never_smoker = na_if(death_never_smoker, "NA ( NA%)"),
death_never_unknown_smoker = paste(paste(death_never_unknown_smokers,
(death_never_unknown_smokers_percent), sep = " (")
, "%)", sep = ""),
death_never_unknown_smoker = na_if(death_never_unknown_smoker, "NA ( NA%)"),
death_not_stated = paste(paste(death_not_stated,
(death_not_stated_percent), sep = " (")
, "%)", sep = ""),
death_not_stated = na_if(death_not_stated, "NA ( NA%)")) %>%
select(Author, Population, recovered, recovered_current_smoker, recovered_former_smoker, recovered_current_former_smoker,
recovered_never_smoker, recovered_never_unknown_smoker, recovered_not_stated, deaths, death_current_smoker,
death_former_smoker, death_current_former_smoker, death_never_smoker, death_never_unknown_smoker,
death_not_stated)
a$Author <- to_upper_camel_case(a$Author, sep_out = ", ")
a$Author <- plyr::mapvalues(a$Author,
from = cleaned_names,
to = correct_names)
numeric_columns <- 'Population'
a <- flextable(a) %>%
set_caption(caption = 'COVID-19 mortality by smoking status') %>%
colformat_num(col_keys = numeric_columns, digits = 0, na_str = '-', big.mark = ',') %>%
colformat_char(na_str = '-')
a <- set_header_labels(a,
Population = "Population with mortality",
recovered = "N (%)",
recovered_current_smoker = "Current smoker (%)",
recovered_former_smoker = "Former smoker (%)",
recovered_current_former_smoker = "Current/former smoker (%)",
recovered_never_unknown_smoker = "Never/unknown smoker (%)",
recovered_never_smoker = "Never smoker (%)",
recovered_not_stated = "Not stated (%)",
deaths = "N (%)",
death_current_smoker = "Current smoker (%)",
death_former_smoker = "Former smoker (%)",
death_current_former_smoker = "Current/former smoker (%)",
death_never_unknown_smoker = "Never/unknown smoker (%)",
death_never_smoker = "Never smoker (%)",
death_not_stated = "Not stated (%)") %>%
add_header_row(top = TRUE, values = c("","Recovered", "Died" ), colwidths = c(2, 7, 7)) %>%
theme_booktabs() %>%
fix_border_issues() %>%
set_table_properties(width = 1, layout = 'autofit')
aRecovered | Died | ||||||||||||||
Author | Population with mortality | N (%) | Current smoker (%) | Former smoker (%) | Current/former smoker (%) | Never smoker (%) | Never/unknown smoker (%) | Not stated (%) | N (%) | Current smoker (%) | Former smoker (%) | Current/former smoker (%) | Never smoker (%) | Never/unknown smoker (%) | Not stated (%) |
Chen | 274 | 161 (58%) | 5 (3.11%) | 5 (3.11%) | - | - | - | 151 (93.79%) | 113 (41%) | 7 (6.19%) | 2 (1.77%) | - | - | - | 104 (92.04%) |
Zhou, Yu | 191 | 137 (71%) | 6 (4.38%) | - | - | - | - | 131 (95.62%) | 54 (28%) | 5 (9.26%) | - | - | - | - | 49 (90.74%) |
Yang, Yu | 52 | 20 (38%) | 2 (10.00%) | - | - | - | 18 (90.00%) | - | 32 (61%) | - | - | - | - | 32 (100.00%) | - |
Borobia | 2226 | 1766 (79%) | 113 (6.40%) | - | - | - | - | 1653 (93.60%) | 460 (20%) | 44 (9.57%) | - | - | - | - | 416 (90.43%) |
Giacomelli | 233 | 185 (79%) | - | - | 53 (28.65%) | 132 (71.35%) | - | - | 48 (20%) | - | - | 17 (35.42%) | 31 (64.58%) | - | 0 (0.00%) |
Yao | 108 | 96 (88%) | 1 (1.04%) | - | - | - | - | 95 (98.96%) | 12 (11%) | 3 (25.00%) | - | - | - | - | 9 (75.00%) |
Carillo-Vega | 9946 | 8983 (90%) | 795 (8.85%) | - | - | - | - | 8188 (91.15%) | 963 (9%) | 99 (10.28%) | - | - | - | - | 864 (89.72%) |
Ge | 51 | 39 (76%) | 6 (15.38%) | - | - | - | - | 33 (84.62%) | 12 (23%) | 1 (8.33%) | - | - | - | - | 11 (91.67%) |
Chen, Jiang | 135 | NA (NA%) | - | - | - | - | - | - | 31 (22%) | - | - | 4 (12.90%) | - | - | 27 (87.10%) |
Heili-Frades | 4712 | 4086 (86%) | 210 (5.14%) | 659 (16.13%) | - | - | 3217 (78.73%) | - | 626 (13%) | 23 (3.67%) | 161 (25.72%) | - | - | 442 (70.61%) | - |
Kim, Garg | 2490 | 2070 (83%) | 128 (6.18%) | 481 (23.24%) | - | - | 1461 (70.58%) | - | 420 (16%) | 22 (5.24%) | 161 (38.33%) | - | - | 236 (56.19%) | - |
Al-Hindawi | 31 | 15 (48%) | 0 (0.00%) | 10 (66.67%) | - | 5 (33.33%) | - | - | 16 (51%) | 1 (6.25%) | 12 (75.00%) | - | 3 (18.75%) | - | - |
Louis | 22 | 16 (72%) | - | - | 7 (43.75%) | - | - | 9 (56.25%) | 6 (27%) | - | - | 3 (50.00%) | - | - | 3 (50.00%) |
Soto-Mota | 400 | 200 (50%) | - | - | 23 (11.50%) | - | - | 177 (88.50%) | 200 (50%) | - | - | 25 (12.50%) | - | - | 175 (87.50%) |
Garibaldi | 747 | 634 (84%) | 36 (5.68%) | 129 (20.35%) | - | - | - | 469 (73.97%) | 113 (15%) | 6 (5.31%) | 36 (31.86%) | - | - | - | 71 (62.83%) |
Docherty | 13364 | 8199 (61%) | 370 (4.51%) | 1832 (22.34%) | - | 4179 (50.97%) | - | 1818 (22.17%) | 5165 (38%) | 214 (4.14%) | 1350 (26.14%) | - | 2105 (40.76%) | - | 1496 (28.96%) |
Kuderer | 928 | 807 (86%) | 38 (4.71%) | 262 (32.47%) | - | 425 (52.66%) | - | 31 (3.84%) | 121 (13%) | 5 (4.13%) | 64 (52.89%) | - | 44 (36.36%) | - | 2 (1.65%) |
Ramlall | 11116 | 10498 (94%) | - | - | 2771 (26.40%) | 7727 (73.60%) | - | - | 618 (5%) | - | - | 208 (33.66%) | 410 (66.34%) | - | - |
Wang, Oekelen | 57 | 43 (75%) | - | - | 14 (32.56%) | - | - | 29 (67.44%) | 14 (24%) | - | - | 7 (50.00%) | - | - | 7 (50.00%) |
Martinez-Portilla | 224 | 217 (96%) | - | - | 7 (3.23%) | - | - | 210 (96.77%) | 7 (3%) | - | - | 0 (0.00%) | - | - | 7 (100.00%) |
Cen | 1007 | 964 (95%) | - | - | 87 (9.02%) | - | - | 877 (90.98%) | 43 (4%) | - | - | 1 (2.33%) | - | - | 42 (97.67%) |
Klang | 3406 | 2270 (66%) | - | - | 492 (21.67%) | - | - | 1778 (78.33%) | 1136 (33%) | - | - | 301 (26.50%) | - | - | 835 (73.50%) |
Wang, Zhong | 5510 | 4874 (88%) | 247 (5.07%) | 1083 (22.22%) | - | 3544 (72.71%) | - | - | 636 (11%) | 28 (4.40%) | 214 (33.65%) | - | 394 (61.95%) | - | - |
Miyara | 338 | 211 (62%) | 13 (6.16%) | 58 (27.49%) | - | 141 (66.82%) | - | - | 46 (13%) | 1 (2.17%) | 23 (50.00%) | - | 21 (45.65%) | - | - |
Rajter | 255 | 209 (81%) | - | - | 28 (13.40%) | 181 (86.60%) | - | - | 53 (20%) | - | - | 18 (33.96%) | 28 (52.83%) | - | - |
Zeng | 1031 | 866 (84%) | - | - | 69 (7.97%) | - | - | 797 (92.03%) | 165 (16%) | - | - | 36 (21.82%) | - | - | 129 (78.18%) |
Chen, Yu | 1859 | 1651 (88%) | 32 (1.94%) | 54 (3.27%) | - | 1565 (94.79%) | - | - | 208 (11%) | 13 (6.25%) | 12 (5.77%) | - | 183 (87.98%) | - | - |
Garassino | 190 | 124 (65%) | - | - | 92 (74.19%) | 32 (25.81%) | - | - | 66 (34%) | - | 61 (92.42%) | - | 5 (7.58%) | - | - |
Gu | 884 | 864 (97%) | 40 (4.63%) | 250 (28.94%) | - | 219 (25.35%) | - | - | 20 (2%) | 0 (0.00%) | 14 (70.00%) | - | 6 (30.00%) | - | - |
Zhou, He | - | NA (NA%) | - | - | - | - | - | - | NA (NA%) | - | - | - | - | - | - |
Sigel | 88 | 70 (79%) | - | - | 37 (52.86%) | - | - | 33 (47.14%) | 18 (20%) | - | - | 11 (61.11%) | - | - | 7 (38.89%) |
Nguyen | 356 | 308 (86%) | - | - | 91 (29.55%) | - | - | 217 (70.45%) | 45 (12%) | - | - | 23 (51.11%) | - | - | 22 (48.89%) |
de Souza | 8443 | 7826 (92%) | - | - | 95 (1.21%) | - | 7571 (96.74%) | 160 (2.04%) | 617 (7%) | - | - | 47 (7.62%) | - | 560 (90.76%) | 10 (1.62%) |
Mendy | 532 | 663 (124%) | - | - | 160 (24.13%) | - | - | 502 (75.72%) | 26 (4%) | - | - | 10 (38.46%) | - | - | 16 (61.54%) |
Shi, Resurreccion | 256 | 210 (82%) | - | - | 128 (60.95%) | - | - | 82 (39.05%) | 46 (17%) | - | - | 26 (56.52%) | - | - | 20 (43.48%) |
Xie | 619 | 591 (95%) | - | - | 43 (7.28%) | - | - | 548 (92.72%) | 28 (4%) | - | - | 8 (28.57%) | - | - | 20 (71.43%) |
Fox | 54 | 35 (64%) | 1 (2.86%) | 4 (11.43%) | - | 18 (51.43%) | - | 12 (34.29%) | 19 (35%) | 0 (0.00%) | 2 (10.53%) | - | 12 (63.16%) | - | 5 (26.32%) |
Zhang, Cao | 289 | 240 (83%) | 10 (4.17%) | 6 (2.50%) | - | - | - | 224 (93.33%) | 49 (16%) | 4 (8.16%) | 8 (16.33%) | - | - | - | 37 (75.51%) |
Gupta | 496 | 255 (51%) | - | - | 15 (5.88%) | - | 80 (31.37%) | 160 (62.75%) | 241 (48%) | - | - | 21 (8.71%) | 77 (31.95%) | - | 143 (59.34%) |
Soares | 1075 | 696 (64%) | 38 (5.46%) | - | - | - | 658 (94.54%) | - | 456 (42%) | 39 (8.55%) | - | - | - | 417 (91.45%) | - |
Thompson | 470 | 301 (64%) | 39 (12.96%) | 79 (26.25%) | - | 183 (60.80%) | - | - | 169 (35%) | 27 (15.98%) | 49 (28.99%) | - | 93 (55.03%) | - | - |
Bernaola | 1645 | 1382 (84%) | 35 (2.53%) | 146 (10.56%) | - | 1201 (86.90%) | - | - | 263 (15%) | 6 (2.28%) | 33 (12.55%) | - | 218 (82.89%) | - | - |
Islam | 654 | 631 (96%) | 103 (16.32%) | - | - | - | - | 507 (80.35%) | 23 (3%) | 3 (13.04%) | - | - | - | - | - |
Philipose | 466 | 267 (57%) | 19 (7.12%) | 204 (76.40%) | - | 44 (16.48%) | - | - | 199 (42%) | 9 (4.52%) | 137 (68.84%) | - | 33 (16.58%) | - | 20 (10.05%) |
Dashti | 4140 | 3953 (95%) | - | - | 1068 (27.02%) | 2078 (52.57%) | - | 804 (20.34%) | 187 (4%) | - | - | 109 (58.29%) | 56 (29.95%) | - | 22 (11.76%) |
Fillmore | 1794 | 1566 (87%) | 408 (26.05%) | 758 (48.40%) | - | 279 (17.82%) | - | 98 (6.26%) | 228 (12%) | 44 (19.30%) | 141 (61.84%) | - | 43 (18.86%) | - | 23 (10.09%) |
Pan | 3536 | 3302 (93%) | - | - | 862 (26.11%) | - | - | 2440 (73.89%) | 234 (6%) | - | - | 82 (35.04%) | - | - | 152 (64.96%) |
Zhao, Chen | 474 | 398 (83%) | 87 (21.86%) | - | - | - | - | 311 (78.14%) | 82 (17%) | 36 (43.90%) | - | - | - | - | 46 (56.10%) |
Holman | 10989 | NA (NA%) | - | - | - | - | - | - | 10989 (100%) | 609 (5.54%) | 4684 (42.62%) | - | 5386 (49.01%) | - | 310 (2.82%) |
Chand | 300 | 143 (47%) | 23 (16.08%) | - | - | - | - | 120 (83.92%) | 157 (52%) | 44 (28.03%) | - | - | - | - | 113 (71.97%) |
6.5.1 Smoking and COVID-19 mortality meta-analysis
The studies included in meta-analysis are al_hindawi, kuderer, miyara_medrxiv, chen_yu, gu, thompson, bernaola, philipose, and fillmore.
6.5.1.1 Current versus never smoking for disease mortality
# Data --------------------------------------------------------------------
meta <- tibble('author' = table_5$lead_author,
'recovered_current_smoker' = table_5$recovered_current_smoking,
'recovered_never_smoker' = table_5$recovered_never_smoker,
'death_current_smoker' = table_5$death_current_smokers,
'death_never_smoker' = table_5$death_never_smokers,
'recovered_former_smoker' = table_5$recovered_former_smoker,
'death_former_smoker' = table_5$death_former_smokers) %>%
filter(author %in% included_studies$lead_author)
meta$author <- to_upper_camel_case(meta$author, sep_out = ", ")
meta$author <- plyr::mapvalues(meta$author,
from = cleaned_names,
to = correct_names)
# Current smoker mortality ------------------------------------------
event_rates_smoker <- meta %>%
mutate(., Ee = death_current_smoker) %>%
mutate(., Ne = (death_current_smoker+recovered_current_smoker)) %>%
mutate(., Ec = death_never_smoker) %>%
mutate(., Nc = (death_never_smoker+recovered_never_smoker)) %>%
rename('Author' = author) %>%
select(Author, Ee, Ne, Ec, Nc)
event_rates_smoker <- metabin(Ee,
Ne,
Ec,
Nc,
data = event_rates_smoker,
studlab = paste(Author),
comb.fixed = F,
comb.random = T,
method.tau = 'SJ',
hakn = F,
prediction = F,
incr = 0.1,
sm = 'RR')
write_rds(event_rates_smoker, here("data_clean", "bayes_mortality_current.rds"))
The ECDF shows the probability of the risk being greater than or equal to RR = 1.1, 69.54%
6.5.1.2 Former versus never smoking for disease mortality
# Former smoker mortality ------------------------------------------
event_rates_former <- meta %>%
mutate(., Ee = death_former_smoker) %>%
mutate(., Ne = (death_former_smoker+recovered_former_smoker)) %>%
mutate(., Ec = death_never_smoker) %>%
mutate(., Nc = (death_never_smoker+recovered_never_smoker)) %>%
rename('Author' = author) %>%
select(Author, Ee, Ne, Ec, Nc)
event_rates_former <- metabin(Ee,
Ne,
Ec,
Nc,
data = event_rates_former,
studlab = paste(Author),
comb.fixed = F,
comb.random = T,
method.tau = 'SJ',
hakn = F,
prediction = F,
incr = 0.1,
sm = 'RR')
write_rds(event_rates_former, here("data_clean", "bayes_mortality_former.rds"))
running_meta_count <- running_meta_count %>%
full_join(., meta %>%
rename("study" = author), by = "study") %>%
select(study)
The ECDF shows the probability of the risk being greater than or equal to RR = 1.1, 97.23%
6.6 Study quality assessment
#Table 6
fair <- filter(quality_rating, quality_rating$overall_quality == "fair" ) %>%
filter(!lead_author %in% exclude_from_analysis) %>%
nrow()
poor <- filter(quality_rating, quality_rating$overall_quality == "poor" ) %>%
filter(!lead_author %in% exclude_from_analysis) %>%
nrow()The number of Fair quality studies are 46 with 187 Poor quality studies
The number of studies included in Meta-analysis are 33
7 Final PRISMA table
prisma <- prisma %>%
mutate("Studies included in meta-analysis" = nrow(running_meta_count))
flextable(prisma)Review version | Date Screened | OVID results | medRxiv results | Other sources | Title/abstracts screened | Title/abstracts excluded | Full texts assessed | Full texts excluded | Records carried forward | Supersed from previous version | Studies in narrative synthesis | Studies included in meta-analysis |
v7 | 2020-08-25 | 347 | 251 | 5 | 603 | 229 | 374 | 313 | 173 | 1 | 234 | 33 |
8 Supplementary tables
8.1 Supplementary tables
8.1.1 Data on clinical or virological diagnosis and studies stratifying smoking status
a <- data_study_general %>%
filter(review_version %in% analysed_versions) %>%
select(lead_author, study_design, clinical_diagnosis, stratified_smoking) %>%
filter(!lead_author %in% exclude_from_analysis) %>%
rename("Lead author" = "lead_author",
"Study design" = "study_design",
"Clinical diagnoses included" = "clinical_diagnosis",
"Stratified smoking reported" = "stratified_smoking") %>%
mutate("Lead author" = to_upper_camel_case(`Lead author`, sep_out = ", "),
"Lead author" = plyr::mapvalues(`Lead author`,
from = cleaned_names,
to = correct_names),
"Study design" = to_upper_camel_case(`Study design`, sep_out = " "))
a <- a %>%
flextable() %>%
set_caption(caption = 'Studies design, inclusion of clinical diagnoses and stratified smoking') %>%
set_table_properties(width = 1, layout = 'autofit')
save_as_docx(a, path = here('output_data', 'Supplementary_2.docx'))