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library("lubridate")
library("dplyr")
# data file
covid = read.csv("/Volumes/group/users/borchersLR/COVID/github/covid.csv")
# excluding participants with no cesdc
covid = covid %>%
filter(!is.na(CESDC_total.TC))
# N = 109
covid
### key
# tc = covid-19 assessment time point
## sex
# 1 is male; 2 is female
## race
# 1 is white; 2 is african american; 3 is hispanic; 4 is asian; 5 is biracial; 6 is other
# els = early life stress
# abstract
# number of male and female participants in the sample
# 1 is male; 2 is female
covid %>%
group_by(Child_Sex.T1) %>%
summarise(n=n())
## `summarise()` ungrouping output (override with `.groups` argument)
# age range of participants
min(covid$Child_Age.TC)
## [1] 12.82
max(covid$Child_Age.TC)
## [1] 19.98
# years between baseline and tc assessment
covid$years <- covid$Child_Age.TC-covid$Age_S1.T1
summary(covid$years)
## Min. 1st Qu. Median Mean 3rd Qu. Max.
## 3.020 4.510 5.070 5.064 5.810 6.540
mean(covid$years)
## [1] 5.064312
sd(covid$years)
## [1] 0.8592346
# correlation between els and depression
cor.test(covid$sumsev_type_t1, covid$CESDC_total.TC)
##
## Pearson's product-moment correlation
##
## data: covid$sumsev_type_t1 and covid$CESDC_total.TC
## t = 2.8112, df = 107, p-value = 0.005871
## alternative hypothesis: true correlation is not equal to 0
## 95 percent confidence interval:
## 0.07800399 0.42918713
## sample estimates:
## cor
## 0.2622577
# sex differences in depression
t.test(CESDC_total.TC ~ Child_Sex.T1, data = covid, rm.na=TRUE, var.equal=T)
##
## Two Sample t-test
##
## data: CESDC_total.TC by Child_Sex.T1
## t = -3.5573, df = 107, p-value = 0.0005598
## alternative hypothesis: true difference in means is not equal to 0
## 95 percent confidence interval:
## -11.569219 -3.289132
## sample estimates:
## mean in group 1 mean in group 2
## 16.11628 23.54545
# methods
dates <- read.csv('/Volumes/group/users/borchersLR/COVID/github/tc_dates.csv',sep=",",header=T)
dates_sample <-left_join(covid, dates, by="ELS_ID")
# time between
dates_sample$background_timestamp<-as.Date(dates_sample$background_timestamp, format = "%m/%d/%y")
summary(dates_sample$background_timestamp)
## Min. 1st Qu. Median Mean 3rd Qu. Max.
## "2020-04-03" "2020-04-04" "2020-04-07" "2020-04-08" "2020-04-12" "2020-04-23"
# days between shelter in place March 17
shelter<-as.Date(as.character("03/17/2020"), format="%m/%d/%y")
dates_sample$date_diff <- dates_sample$background_timestamp- shelter
summary(as.integer(dates_sample$date_diff))
## Min. 1st Qu. Median Mean 3rd Qu. Max.
## 17.00 18.00 21.00 22.57 26.00 37.00
mean(as.integer(dates_sample$date_diff), na.rm=T)
## [1] 22.56881
sd(as.integer(dates_sample$date_diff), na.rm=T)
## [1] 4.941073
# percent of participants who completed the covid-19 (TC) assessment
109/221
## [1] 0.4932127
# age of participants
min(covid$Child_Age.TC)
## [1] 12.82
max(covid$Child_Age.TC)
## [1] 19.98
mean(covid$Child_Age.TC)
## [1] 16.29789
sd(covid$Child_Age.TC)
## [1] 1.473223
# cdi
# one participant did not complete the cdi at baseline
min(covid$cdi_TOTAL.T1, na.rm = TRUE)
## [1] 0
max(covid$cdi_TOTAL.T1, na.rm = TRUE)
## [1] 10
mean(covid$cdi_TOTAL.T1, na.rm = TRUE)
## [1] 1.768519
sd(covid$cdi_TOTAL.T1, na.rm = TRUE)
## [1] 2.048964
# crobach's alpha for cdi at baseline
cdi_t1 = read.csv("/Volumes/group/users/borchersLR/COVID/github/cdi_t1.csv")
cdi_t1 = cdi_t1 %>%
filter(!is.na(cdi_1.T1))
cdi_t1 %>%
select(
cdi_1.T1,
cdi_2_R.T1,
cdi_3.T1,
cdi_4_R.T1,
cdi_5_R.T1,
cdi_6_R.T1,
cdi_7.T1,
cdi_8.T1,
cdi_9.T1,
cdi_10_R.T1
) %>%
alpha()
##
## Reliability analysis
## Call: alpha(x = .)
##
## raw_alpha std.alpha G6(smc) average_r S/N ase mean sd median_r
## 0.75 0.77 0.77 0.26 3.4 0.025 0.22 0.24 0.24
##
## lower alpha upper 95% confidence boundaries
## 0.7 0.75 0.8
##
## Reliability if an item is dropped:
## raw_alpha std.alpha G6(smc) average_r S/N alpha se var.r med.r
## cdi_1.T1 0.72 0.75 0.74 0.25 2.9 0.027 0.0082 0.24
## cdi_2_R.T1 0.74 0.76 0.76 0.26 3.2 0.026 0.0090 0.24
## cdi_3.T1 0.75 0.78 0.78 0.28 3.5 0.025 0.0071 0.28
## cdi_4_R.T1 0.73 0.74 0.74 0.24 2.9 0.027 0.0081 0.24
## cdi_5_R.T1 0.73 0.75 0.75 0.25 3.0 0.027 0.0083 0.24
## cdi_6_R.T1 0.74 0.76 0.76 0.26 3.2 0.026 0.0101 0.25
## cdi_7.T1 0.74 0.76 0.76 0.26 3.2 0.026 0.0102 0.27
## cdi_8.T1 0.71 0.74 0.74 0.24 2.9 0.029 0.0083 0.24
## cdi_9.T1 0.74 0.76 0.76 0.26 3.2 0.026 0.0094 0.26
## cdi_10_R.T1 0.72 0.74 0.73 0.24 2.8 0.028 0.0085 0.23
##
## Item statistics
## n raw.r std.r r.cor r.drop mean sd
## cdi_1.T1 211 0.60 0.63 0.58 0.48 0.185 0.39
## cdi_2_R.T1 211 0.57 0.54 0.46 0.40 0.332 0.50
## cdi_3.T1 211 0.35 0.41 0.29 0.25 0.071 0.26
## cdi_4_R.T1 211 0.60 0.64 0.60 0.51 0.095 0.29
## cdi_5_R.T1 210 0.58 0.60 0.54 0.47 0.114 0.35
## cdi_6_R.T1 211 0.61 0.54 0.46 0.41 0.488 0.63
## cdi_7.T1 211 0.55 0.52 0.43 0.37 0.346 0.52
## cdi_8.T1 211 0.67 0.66 0.62 0.53 0.223 0.48
## cdi_9.T1 211 0.54 0.53 0.45 0.38 0.242 0.44
## cdi_10_R.T1 211 0.63 0.67 0.64 0.55 0.090 0.29
##
## Non missing response frequency for each item
## 0 1 2 miss
## cdi_1.T1 0.82 0.18 0.00 0
## cdi_2_R.T1 0.68 0.30 0.01 0
## cdi_3.T1 0.93 0.07 0.00 0
## cdi_4_R.T1 0.91 0.09 0.00 0
## cdi_5_R.T1 0.90 0.10 0.01 0
## cdi_6_R.T1 0.58 0.35 0.07 0
## cdi_7.T1 0.68 0.30 0.02 0
## cdi_8.T1 0.81 0.17 0.03 0
## cdi_9.T1 0.76 0.23 0.00 0
## cdi_10_R.T1 0.91 0.09 0.00 0
# cesdc
# internal consistency
# df with all cesdc and pss items at tc
items = read.csv("/Volumes/group/users/borchersLR/COVID/github/pss_cesd_items.csv")
# pss
# one participant did not complete the pss at tc
# reverse code 4, 5, 7, 8, then sum
items <-
items %>%
mutate_at(
vars(
covid_pss_c_4, covid_pss_c_5, covid_pss_c_7, covid_pss_c_8
),
funs(
. %>%
dplyr::recode(
"0" = 4,
"1" = 3,
"2" = 2,
"3" = 1,
"4" = 0
)
)
)
## Warning: `funs()` is deprecated as of dplyr 0.8.0.
## Please use a list of either functions or lambdas:
##
## # Simple named list:
## list(mean = mean, median = median)
##
## # Auto named with `tibble::lst()`:
## tibble::lst(mean, median)
##
## # Using lambdas
## list(~ mean(., trim = .2), ~ median(., na.rm = TRUE))
## This warning is displayed once every 8 hours.
## Call `lifecycle::last_warnings()` to see where this warning was generated.
items %>%
select(
covid_pss_c_1,
covid_pss_c_2,
covid_pss_c_3,
covid_pss_c_4,
covid_pss_c_5,
covid_pss_c_6,
covid_pss_c_7,
covid_pss_c_8,
covid_pss_c_9,
covid_pss_c_10
) %>%
alpha()
##
## Reliability analysis
## Call: alpha(x = .)
##
## raw_alpha std.alpha G6(smc) average_r S/N ase mean sd median_r
## 0.84 0.84 0.87 0.35 5.3 0.016 1.8 0.66 0.33
##
## lower alpha upper 95% confidence boundaries
## 0.81 0.84 0.87
##
## Reliability if an item is dropped:
## raw_alpha std.alpha G6(smc) average_r S/N alpha se var.r med.r
## covid_pss_c_1 0.82 0.82 0.85 0.34 4.6 0.019 0.022 0.32
## covid_pss_c_2 0.83 0.83 0.86 0.35 4.9 0.018 0.021 0.32
## covid_pss_c_3 0.82 0.82 0.84 0.34 4.6 0.019 0.019 0.32
## covid_pss_c_4 0.83 0.83 0.86 0.36 5.1 0.017 0.020 0.34
## covid_pss_c_5 0.83 0.83 0.85 0.35 4.8 0.018 0.023 0.32
## covid_pss_c_6 0.83 0.83 0.86 0.35 4.9 0.018 0.023 0.34
## covid_pss_c_7 0.84 0.84 0.86 0.36 5.1 0.017 0.024 0.34
## covid_pss_c_8 0.83 0.82 0.85 0.34 4.7 0.018 0.021 0.32
## covid_pss_c_9 0.83 0.83 0.85 0.35 4.8 0.018 0.021 0.33
## covid_pss_c_10 0.81 0.81 0.83 0.32 4.2 0.020 0.020 0.29
##
## Item statistics
## n raw.r std.r r.cor r.drop mean sd
## covid_pss_c_1 108 0.69 0.69 0.65 0.60 1.8 0.97
## covid_pss_c_2 108 0.61 0.60 0.54 0.49 2.1 1.12
## covid_pss_c_3 108 0.70 0.68 0.65 0.60 2.1 1.08
## covid_pss_c_4 108 0.54 0.57 0.51 0.44 1.5 0.91
## covid_pss_c_5 108 0.60 0.62 0.58 0.50 2.0 0.91
## covid_pss_c_6 108 0.63 0.61 0.56 0.51 1.7 1.20
## covid_pss_c_7 108 0.54 0.56 0.48 0.43 1.8 0.90
## covid_pss_c_8 108 0.65 0.66 0.64 0.54 1.9 1.00
## covid_pss_c_9 108 0.64 0.63 0.58 0.52 1.9 1.11
## covid_pss_c_10 108 0.80 0.79 0.79 0.73 1.7 1.08
##
## Non missing response frequency for each item
## 0 1 2 3 4 miss
## covid_pss_c_1 0.09 0.25 0.44 0.19 0.04 0.48
## covid_pss_c_2 0.09 0.19 0.38 0.22 0.12 0.48
## covid_pss_c_3 0.06 0.21 0.35 0.26 0.11 0.48
## covid_pss_c_4 0.10 0.43 0.36 0.07 0.04 0.48
## covid_pss_c_5 0.04 0.28 0.40 0.25 0.04 0.48
## covid_pss_c_6 0.16 0.36 0.22 0.17 0.09 0.48
## covid_pss_c_7 0.06 0.32 0.42 0.17 0.03 0.48
## covid_pss_c_8 0.07 0.26 0.40 0.21 0.06 0.48
## covid_pss_c_9 0.16 0.18 0.35 0.28 0.04 0.48
## covid_pss_c_10 0.14 0.31 0.32 0.17 0.06 0.48
# cesdc
# reverse code 4, 8, 12, 16, then sum
items <-
items %>%
mutate_at(
vars(
covid_cesd_c_4, covid_cesd_c_8, covid_cesd_c_12, covid_cesd_c_16
),
funs(
. %>%
dplyr::recode(
"0" = 3,
"1" = 2,
"2" = 1,
"3" = 0
)
)
)
items %>%
select(
covid_cesd_c_1,
covid_cesd_c_2,
covid_cesd_c_3,
covid_cesd_c_4,
covid_cesd_c_5,
covid_cesd_c_6,
covid_cesd_c_7,
covid_cesd_c_8,
covid_cesd_c_9,
covid_cesd_c_10,
covid_cesd_c_11,
covid_cesd_c_12,
covid_cesd_c_13,
covid_cesd_c_14,
covid_cesd_c_15,
covid_cesd_c_16,
covid_cesd_c_17,
covid_cesd_c_18,
covid_cesd_c_19,
covid_cesd_c_20
) %>%
alpha()
##
## Reliability analysis
## Call: alpha(x = .)
##
## raw_alpha std.alpha G6(smc) average_r S/N ase mean sd median_r
## 0.91 0.91 0.94 0.34 10 0.0091 1 0.56 0.34
##
## lower alpha upper 95% confidence boundaries
## 0.89 0.91 0.93
##
## Reliability if an item is dropped:
## raw_alpha std.alpha G6(smc) average_r S/N alpha se var.r med.r
## covid_cesd_c_1 0.91 0.91 0.94 0.34 10.0 0.0094 0.021 0.35
## covid_cesd_c_2 0.91 0.91 0.94 0.35 10.0 0.0093 0.021 0.35
## covid_cesd_c_3 0.90 0.90 0.94 0.33 9.5 0.0097 0.021 0.33
## covid_cesd_c_4 0.91 0.91 0.94 0.35 10.1 0.0092 0.021 0.35
## covid_cesd_c_5 0.91 0.91 0.94 0.34 9.9 0.0094 0.021 0.35
## covid_cesd_c_6 0.90 0.90 0.93 0.33 9.4 0.0098 0.019 0.33
## covid_cesd_c_7 0.90 0.91 0.94 0.34 9.6 0.0097 0.022 0.33
## covid_cesd_c_8 0.91 0.91 0.94 0.35 10.0 0.0094 0.021 0.35
## covid_cesd_c_9 0.91 0.91 0.94 0.35 10.0 0.0093 0.021 0.34
## covid_cesd_c_10 0.91 0.91 0.94 0.35 10.2 0.0093 0.020 0.35
## covid_cesd_c_11 0.91 0.91 0.94 0.35 10.4 0.0090 0.019 0.35
## covid_cesd_c_12 0.90 0.91 0.94 0.33 9.5 0.0097 0.021 0.33
## covid_cesd_c_13 0.91 0.91 0.94 0.34 9.8 0.0095 0.021 0.33
## covid_cesd_c_14 0.90 0.91 0.94 0.34 9.6 0.0097 0.019 0.34
## covid_cesd_c_15 0.91 0.91 0.94 0.34 9.9 0.0094 0.018 0.34
## covid_cesd_c_16 0.90 0.91 0.94 0.33 9.5 0.0097 0.021 0.33
## covid_cesd_c_17 0.90 0.90 0.93 0.33 9.3 0.0100 0.020 0.33
## covid_cesd_c_18 0.90 0.90 0.93 0.33 9.3 0.0099 0.019 0.33
## covid_cesd_c_19 0.90 0.91 0.93 0.33 9.6 0.0097 0.019 0.33
## covid_cesd_c_20 0.90 0.90 0.94 0.33 9.4 0.0100 0.021 0.33
##
## Item statistics
## n raw.r std.r r.cor r.drop mean sd
## covid_cesd_c_1 109 0.55 0.55 0.52 0.49 0.99 0.98
## covid_cesd_c_2 109 0.54 0.52 0.49 0.47 0.91 1.02
## covid_cesd_c_3 109 0.69 0.70 0.68 0.65 0.54 0.76
## covid_cesd_c_4 109 0.51 0.51 0.48 0.43 1.27 1.03
## covid_cesd_c_5 109 0.56 0.55 0.52 0.50 1.61 0.97
## covid_cesd_c_6 109 0.72 0.74 0.74 0.69 1.02 0.83
## covid_cesd_c_7 109 0.66 0.65 0.63 0.60 1.16 0.97
## covid_cesd_c_8 109 0.52 0.52 0.49 0.46 1.85 0.85
## covid_cesd_c_9 109 0.52 0.52 0.48 0.46 0.93 0.90
## covid_cesd_c_10 109 0.45 0.46 0.43 0.40 0.70 0.71
## covid_cesd_c_11 109 0.42 0.41 0.37 0.34 0.74 1.01
## covid_cesd_c_12 109 0.67 0.68 0.66 0.62 0.95 0.79
## covid_cesd_c_13 109 0.60 0.59 0.57 0.54 1.06 1.00
## covid_cesd_c_14 109 0.66 0.67 0.66 0.61 0.88 1.04
## covid_cesd_c_15 109 0.56 0.56 0.55 0.50 0.52 0.87
## covid_cesd_c_16 109 0.67 0.68 0.67 0.63 1.18 0.82
## covid_cesd_c_17 109 0.75 0.75 0.75 0.71 0.97 0.98
## covid_cesd_c_18 109 0.74 0.75 0.75 0.70 1.10 0.83
## covid_cesd_c_19 109 0.67 0.67 0.68 0.62 0.68 0.99
## covid_cesd_c_20 109 0.73 0.73 0.72 0.68 1.56 1.06
##
## Non missing response frequency for each item
## 0 1 2 3 miss
## covid_cesd_c_1 0.39 0.33 0.19 0.09 0.48
## covid_cesd_c_2 0.48 0.23 0.20 0.09 0.48
## covid_cesd_c_3 0.60 0.29 0.08 0.03 0.48
## covid_cesd_c_4 0.27 0.37 0.20 0.17 0.48
## covid_cesd_c_5 0.17 0.25 0.40 0.18 0.48
## covid_cesd_c_6 0.29 0.43 0.24 0.04 0.48
## covid_cesd_c_7 0.29 0.37 0.23 0.11 0.48
## covid_cesd_c_8 0.06 0.25 0.46 0.23 0.48
## covid_cesd_c_9 0.39 0.36 0.20 0.06 0.48
## covid_cesd_c_10 0.43 0.46 0.09 0.02 0.48
## covid_cesd_c_11 0.56 0.25 0.08 0.11 0.48
## covid_cesd_c_12 0.31 0.44 0.23 0.02 0.48
## covid_cesd_c_13 0.35 0.37 0.17 0.12 0.48
## covid_cesd_c_14 0.50 0.24 0.16 0.11 0.48
## covid_cesd_c_15 0.67 0.19 0.08 0.06 0.48
## covid_cesd_c_16 0.23 0.39 0.36 0.03 0.48
## covid_cesd_c_17 0.41 0.28 0.24 0.07 0.48
## covid_cesd_c_18 0.23 0.50 0.20 0.06 0.48
## covid_cesd_c_19 0.61 0.20 0.10 0.09 0.48
## covid_cesd_c_20 0.18 0.32 0.25 0.25 0.48
# calculating self-report covid symptoms
covid$covid_physs <-covid$Covid_child_fever.TC + covid$Covid_child_cough.TC + covid$Covid_child_short_breath.TC + covid$Covid_child_sore_throat.TC + covid$Covid_child_fatigue.TC + covid$Covid_child_loss_taste_smell.TC + covid$Covid_child_other_symptom.TC
# table 1
corr_covid_plot <-
covid %>%
dplyr::select(
'CDI-S Baseline' = cdi_TOTAL.T1,
'ELS Severity Baseline' = sumsev_type_t1,
'PSS COVID-19' = PSS_total.TC,
'CES-DC COVID-19' = CESDC_total.TC,
'Symptoms COVID-19' = covid_physs,
'Worry About Food COVID-19' = Worry_no_food.TC,
'Mental Health Impact COVID-19' = Mental_health_rating.TC,
'Difficulty Cancel Events COVID-19' = Cancelling_events_difficulty.TC,
'Stress of Restrictions COVID-19' = Stress_of_restrictions.TC,
'Stress Change Family Contact COVID-19' = Stress_relations_family_change.TC,
'Stress Change Social Contact COVID-19' = Stress_relations_friends_change.TC,
) %>%
cor(use = "complete.obs", method = "pearson")
ggcorrplot(corr_covid_plot, hc.order = FALSE, type = "upper", lab = TRUE,
outline.col = "white", sig.level=0.05, lab_size = 2, p.mat = NULL,
insig = c("pch", "blank"), pch = 1, pch.col = "black", pch.cex =1,
tl.cex = 14) +
theme(axis.text.x=element_text(size=9, angle=60, vjust=1, hjust=1,
margin=margin(-3,0,0,0)),
axis.text.y=element_text(size=9, margin=margin(0,-3,0,0)),
panel.grid.major=element_blank())
# ggsave("~/Desktop/corr_covid_plot_new.tiff", height = 5, width = 6)
### table 2
## age baseline
t.test(Age_S1.T1 ~ Child_Sex.T1, data = covid, var.equal = T)
##
## Two Sample t-test
##
## data: Age_S1.T1 by Child_Sex.T1
## t = 3.9542, df = 107, p-value = 0.0001382
## alternative hypothesis: true difference in means is not equal to 0
## 95 percent confidence interval:
## 0.355392 1.069992
## sample estimates:
## mean in group 1 mean in group 2
## 11.66512 10.95242
# 1 is male; 2 is female
covid %>%
group_by(Child_Sex.T1) %>%
summarise(sd = sd(Age_S1.T1))
## `summarise()` ungrouping output (override with `.groups` argument)
## els severity baseline
t.test(sumsev_type_t1 ~ Child_Sex.T1, data = covid, var.equal = T)
##
## Two Sample t-test
##
## data: sumsev_type_t1 by Child_Sex.T1
## t = -0.28764, df = 107, p-value = 0.7742
## alternative hypothesis: true difference in means is not equal to 0
## 95 percent confidence interval:
## -1.999400 1.492705
## sample estimates:
## mean in group 1 mean in group 2
## 5.686047 5.939394
# 1 is male; 2 is female
covid %>%
group_by(Child_Sex.T1) %>%
summarise(sd = sd(sumsev_type_t1))
## `summarise()` ungrouping output (override with `.groups` argument)
## cdi baseline
# one male participant missing cdi at baseline
t.test(cdi_TOTAL.T1 ~ Child_Sex.T1, data = covid, rm.na = TRUE, var.equal = T)
##
## Two Sample t-test
##
## data: cdi_TOTAL.T1 by Child_Sex.T1
## t = -2.1506, df = 106, p-value = 0.03378
## alternative hypothesis: true difference in means is not equal to 0
## 95 percent confidence interval:
## -1.63721329 -0.06654342
## sample estimates:
## mean in group 1 mean in group 2
## 1.255814 2.107692
# 1 is male; 2 is female
covid %>%
group_by(Child_Sex.T1) %>%
summarise(sd = sd(cdi_TOTAL.T1, na.rm = TRUE))
## `summarise()` ungrouping output (override with `.groups` argument)
## race
# 1 is white; 2 is african american; 3 is hispanic; 4 is asian; 5 is biracial; 6 is other
race <- data.frame(covid$Child_Sex.T1, covid$KSADS_Child_Race_by_P.T1)
race = table(covid$Child_Sex.T1, covid$KSADS_Child_Race_by_P.T1)
print(race)
##
## 1 2 3 4 5 6
## 1 20 3 3 7 7 3
## 2 26 1 7 13 15 4
print(chisq.test(race))
## Warning in stats::chisq.test(x, y, ...): Chi-squared approximation may be
## incorrect
##
## Pearson's Chi-squared test
##
## data: race
## X-squared = 3.5389, df = 5, p-value = 0.6175
covid %>%
count(KSADS_Child_Race_by_P.T1, Child_Sex.T1, sort = TRUE) %>%
arrange(KSADS_Child_Race_by_P.T1, Child_Sex.T1)
## parental income
covid_income <-
covid %>%
mutate_at(
vars(Parent_Income.T1
),
funs(
. %>%
dplyr::recode(
"10" = 3,
"9" = 2,
"8" = 2,
"7" = 2,
"6" = 2,
"5" = 1,
"4" = 1,
"3" = 1,
"2" = 1,
"1" = 1,
)
)
)
covid_income <- covid_income %>%
mutate(Parent_Income.T1 = if_else(is.na(Parent_Income.T1), 0, Parent_Income.T1))
income = table(covid_income$Child_Sex.T1, covid_income$Parent_Income.T1)
print(income)
##
## 0 1 2 3
## 1 3 1 19 20
## 2 5 3 31 27
print(chisq.test(income))
## Warning in stats::chisq.test(x, y, ...): Chi-squared approximation may be
## incorrect
##
## Pearson's Chi-squared test
##
## data: income
## X-squared = 0.59587, df = 3, p-value = 0.8974
covid_income %>%
count(Parent_Income.T1, Child_Sex.T1, sort = TRUE) %>%
arrange(Parent_Income.T1, Child_Sex.T1)
## age tc
t.test(Child_Age.TC ~ Child_Sex.T1, data = covid, var.equal = T)
##
## Two Sample t-test
##
## data: Child_Age.TC by Child_Sex.T1
## t = 3.1595, df = 107, p-value = 0.002055
## alternative hypothesis: true difference in means is not equal to 0
## 95 percent confidence interval:
## 0.3265441 1.4264228
## sample estimates:
## mean in group 1 mean in group 2
## 16.82860 15.95212
# 1 is male; 2 is female
covid %>%
group_by(Child_Sex.T1) %>%
summarise(sd = sd(Child_Age.TC))
## `summarise()` ungrouping output (override with `.groups` argument)
## pss tc
# one participant missing pss at tc
t.test(PSS_total.TC ~ Child_Sex.T1, data = covid, var.equal = T)
##
## Two Sample t-test
##
## data: PSS_total.TC by Child_Sex.T1
## t = -3.7252, df = 106, p-value = 0.0003148
## alternative hypothesis: true difference in means is not equal to 0
## 95 percent confidence interval:
## -7.047525 -2.151610
## sample estimates:
## mean in group 1 mean in group 2
## 15.64286 20.24242
# 1 is male; 2 is female
covid %>%
group_by(Child_Sex.T1) %>%
summarise(sd = sd(PSS_total.TC, na.rm = TRUE))
## `summarise()` ungrouping output (override with `.groups` argument)
## cesdc tc
# one participant missing pss at tc
t.test(CESDC_total.TC ~ Child_Sex.T1, data = covid, var.equal = T)
##
## Two Sample t-test
##
## data: CESDC_total.TC by Child_Sex.T1
## t = -3.5573, df = 107, p-value = 0.0005598
## alternative hypothesis: true difference in means is not equal to 0
## 95 percent confidence interval:
## -11.569219 -3.289132
## sample estimates:
## mean in group 1 mean in group 2
## 16.11628 23.54545
# 1 is male; 2 is female
covid %>%
group_by(Child_Sex.T1) %>%
summarise(sd = sd(CESDC_total.TC))
## `summarise()` ungrouping output (override with `.groups` argument)
### sample characteristics
## race
# 1 is white; 2 is african american; 3 is hispanic; 4 is asian; 5 is biracial; 6 is other
# binarizing race
# covid <-
# covid %>%
# mutate_at(
# vars(KSADS_Child_Race_by_P.T1
# ),
# funs(
# . %>%
# dplyr::recode(
# "1" = 1,
# "2" = 0,
# "3" = 0,
# "4" = 0,
# "5" = 0,
# "6" = 0
# )
# )
# )
#
# covid %>%
# group_by(KSADS_Child_Race_by_P.T1) %>%
# summarise(n = n())
# calculation of non-white participants
63/109
## [1] 0.5779817
## income
# 2 is less than 10k; 10 is more than 150k
min(covid$Parent_Income.T1, na.rm = TRUE)
## [1] 2
max(covid$Parent_Income.T1, na.rm = TRUE)
## [1] 10
### sex differences crisis
x <- which(names(covid) == "Child_Sex.T1") # name of grouping variable
y <- which(names(covid) == "Mental_health_rating.TC" # names of variables to test
| names(covid) == "Worry_self_infected.TC"
| names(covid) == "Worry_fam_infected.TC"
| names(covid) == "Self_phys_health_influence.TC"
| names (covid) == "Self_mental_health_influence.TC"
| names (covid) == "Read_talk_virus.TC"
| names (covid) == "Covid_positive_changes.TC"
| names (covid) == "Stress_of_restrictions.TC"
| names (covid) == "Contacts_outside_change.TC"
| names (covid) == "Diff_following_contact_rules.TC"
| names (covid) == "Quality_relations_family_change.TC"
| names (covid) == "Stress_relations_family_change.TC"
| names (covid) == "Quality_relations_friends_change.TC"
| names (covid) == "Stress_relations_friends_change.TC"
| names (covid) == "Cancelling_events_difficulty.TC"
| names (covid) == "Family_financial_problems.TC"
| names (covid) == "Worry_living_stability.TC"
| names (covid) == "Hope_crisis_end.TC"
| names (covid) == "covid_physs")
method <- "t.test" # one of "wilcox.test" or "t.test"
paired <- FALSE
for (i in y) {
for (j in x) {
ifelse(paired == TRUE,
p <- ggpaired(covid,
x = colnames(covid[j]), y = colnames(covid[i]),
color = colnames(covid[j]), line.color = "gray", line.size = 0.4,
palette = "npg",
legend = "none",
xlab = colnames(covid[j]),
ylab = colnames(covid[i]),
add = "jitter"
),
p <- ggboxplot(covid,
x = colnames(covid[j]), y = colnames(covid[i]),
color = colnames(covid[j]),
palette = "npg",
legend = "none",
add = "jitter"
)
)
# Add p-value
print(p + stat_compare_means(aes(label = paste0(..method.., ", p-value = ", ..p.format..)),
method = method,
paired = paired,
# group.by = NULL,
ref.group = NULL
))
}
}
# significant sex differences on the crisis
t.test(covid_physs ~ Child_Sex.T1, data = covid, var.equal = TRUE)
##
## Two Sample t-test
##
## data: covid_physs by Child_Sex.T1
## t = -2.4475, df = 107, p-value = 0.01601
## alternative hypothesis: true difference in means is not equal to 0
## 95 percent confidence interval:
## -0.70536132 -0.07406081
## sample estimates:
## mean in group 1 mean in group 2
## 0.1860465 0.5757576
t.test(Worried_2weeks.TC ~ Child_Sex.T1, data = covid, var.equal = TRUE)
##
## Two Sample t-test
##
## data: Worried_2weeks.TC by Child_Sex.T1
## t = -2.2627, df = 107, p-value = 0.02568
## alternative hypothesis: true difference in means is not equal to 0
## 95 percent confidence interval:
## -0.74635316 -0.04927757
## sample estimates:
## mean in group 1 mean in group 2
## 2.162791 2.560606
t.test(Happy_sad_2weeks.TC ~ Child_Sex.T1, data = covid, var.equal = TRUE)
##
## Two Sample t-test
##
## data: Happy_sad_2weeks.TC by Child_Sex.T1
## t = 3.4525, df = 107, p-value = 0.0007966
## alternative hypothesis: true difference in means is not equal to 0
## 95 percent confidence interval:
## 0.2681199 0.9912177
## sample estimates:
## mean in group 1 mean in group 2
## 3.372093 2.742424
t.test(Relaxed_anxious_2weeks.TC ~ Child_Sex.T1, data = covid, var.equal = TRUE)
##
## Two Sample t-test
##
## data: Relaxed_anxious_2weeks.TC by Child_Sex.T1
## t = -2.075, df = 107, p-value = 0.04039
## alternative hypothesis: true difference in means is not equal to 0
## 95 percent confidence interval:
## -0.75720803 -0.01728105
## sample estimates:
## mean in group 1 mean in group 2
## 2.627907 3.015152
t.test(Fatigued_tired_2weeks.TC ~ Child_Sex.T1, data = covid, var.equal = TRUE)
##
## Two Sample t-test
##
## data: Fatigued_tired_2weeks.TC by Child_Sex.T1
## t = -3.4128, df = 107, p-value = 0.0009088
## alternative hypothesis: true difference in means is not equal to 0
## 95 percent confidence interval:
## -0.9965369 -0.2642101
## sample estimates:
## mean in group 1 mean in group 2
## 1.930233 2.560606
t.test(Loneliness_2weeks.TC ~ Child_Sex.T1, data = covid, var.equal = TRUE)
##
## Two Sample t-test
##
## data: Loneliness_2weeks.TC by Child_Sex.T1
## t = -2.8297, df = 107, p-value = 0.005566
## alternative hypothesis: true difference in means is not equal to 0
## 95 percent confidence interval:
## -1.0713971 -0.1886452
## sample estimates:
## mean in group 1 mean in group 2
## 2.279070 2.909091
t.test(Concentration_2weeks.TC ~ Child_Sex.T1, data = covid, var.equal = TRUE)
##
## Two Sample t-test
##
## data: Concentration_2weeks.TC by Child_Sex.T1
## t = -2.5794, df = 107, p-value = 0.01126
## alternative hypothesis: true difference in means is not equal to 0
## 95 percent confidence interval:
## -1.0039271 -0.1313795
## sample estimates:
## mean in group 1 mean in group 2
## 3.023256 3.590909
t.test(Neg_thoughts_2weeks.TC ~ Child_Sex.T1, data = covid, var.equal = TRUE)
##
## Two Sample t-test
##
## data: Neg_thoughts_2weeks.TC by Child_Sex.T1
## t = -2.0837, df = 107, p-value = 0.03957
## alternative hypothesis: true difference in means is not equal to 0
## 95 percent confidence interval:
## -0.86704455 -0.02160943
## sample estimates:
## mean in group 1 mean in group 2
## 2.116279 2.560606
t.test(Self_mental_health_influence.TC ~ Child_Sex.T1, data = covid, var.equal = TRUE)
##
## Two Sample t-test
##
## data: Self_mental_health_influence.TC by Child_Sex.T1
## t = -2.9393, df = 107, p-value = 0.004031
## alternative hypothesis: true difference in means is not equal to 0
## 95 percent confidence interval:
## -1.1245578 -0.2186416
## sample estimates:
## mean in group 1 mean in group 2
## 2.116279 2.787879
t.test(Mental_health_rating.TC ~ Child_Sex.T1, data = covid, var.equal = TRUE)
##
## Two Sample t-test
##
## data: Mental_health_rating.TC by Child_Sex.T1
## t = -2.5713, df = 107, p-value = 0.01151
## alternative hypothesis: true difference in means is not equal to 0
## 95 percent confidence interval:
## -0.9547513 -0.1234728
## sample estimates:
## mean in group 1 mean in group 2
## 2.279070 2.818182
t.test(Cancelling_events_difficulty.TC ~ Child_Sex.T1, data = covid, var.equal = TRUE)
##
## Two Sample t-test
##
## data: Cancelling_events_difficulty.TC by Child_Sex.T1
## t = -3.6008, df = 107, p-value = 0.0004826
## alternative hypothesis: true difference in means is not equal to 0
## 95 percent confidence interval:
## -1.3101518 -0.3797707
## sample estimates:
## mean in group 1 mean in group 2
## 2.488372 3.333333
t.test(Worry_no_food.TC ~ Child_Sex.T1, data = covid, var.equal = TRUE)
##
## Two Sample t-test
##
## data: Worry_no_food.TC by Child_Sex.T1
## t = -2.2379, df = 107, p-value = 0.0273
## alternative hypothesis: true difference in means is not equal to 0
## 95 percent confidence interval:
## -0.20001257 -0.01210864
## sample estimates:
## mean in group 1 mean in group 2
## 0.0000000 0.1060606
t.test(Anhedonia_2weeks.TC ~ Child_Sex.T1, data = covid, var.equal = TRUE)
##
## Two Sample t-test
##
## data: Anhedonia_2weeks.TC by Child_Sex.T1
## t = 2.5575, df = 107, p-value = 0.01194
## alternative hypothesis: true difference in means is not equal to 0
## 95 percent confidence interval:
## 0.1119646 0.8838071
## sample estimates:
## mean in group 1 mean in group 2
## 2.906977 2.409091
# no sex differences
t.test(Physical_health_rating.TC ~ Child_Sex.T1, data = covid, var.equal = TRUE)
##
## Two Sample t-test
##
## data: Physical_health_rating.TC by Child_Sex.T1
## t = -1.382, df = 107, p-value = 0.1698
## alternative hypothesis: true difference in means is not equal to 0
## 95 percent confidence interval:
## -0.54812725 0.09781012
## sample estimates:
## mean in group 1 mean in group 2
## 2.093023 2.318182
t.test(Fidgety_restless_2weeks.TC ~ Child_Sex.T1, data = covid, var.equal = TRUE)
##
## Two Sample t-test
##
## data: Fidgety_restless_2weeks.TC by Child_Sex.T1
## t = -1.6416, df = 107, p-value = 0.1036
## alternative hypothesis: true difference in means is not equal to 0
## 95 percent confidence interval:
## -0.85566977 0.08047597
## sample estimates:
## mean in group 1 mean in group 2
## 2.279070 2.666667
t.test(Irritability_2weeks.TC ~ Child_Sex.T1, data = covid, var.equal = TRUE)
##
## Two Sample t-test
##
## data: Irritability_2weeks.TC by Child_Sex.T1
## t = -1.5114, df = 107, p-value = 0.1336
## alternative hypothesis: true difference in means is not equal to 0
## 95 percent confidence interval:
## -0.7558909 0.1019093
## sample estimates:
## mean in group 1 mean in group 2
## 2.279070 2.606061
# data file for group comparisons of responders/non-responders
completers_noncompleters = read.csv("/Volumes/group/users/borchersLR/COVID/github/completers_noncompleters.csv")
completers_noncompleters = completers_noncompleters %>%
mutate(subsample = !is.na(CESDC_total.TC))
# 109 included in sample; 115 did not complete tc assessment
completers_noncompleters %>%
group_by(subsample) %>%
summarise(n=n())
## `summarise()` ungrouping output (override with `.groups` argument)
## group comparisons covid-19 survey completers vs. non-completers
# age at baseline
t.test(Age_S1.T1 ~ subsample, data = completers_noncompleters, var.equal=T)
##
## Two Sample t-test
##
## data: Age_S1.T1 by subsample
## t = 1.5736, df = 222, p-value = 0.117
## alternative hypothesis: true difference in means is not equal to 0
## 95 percent confidence interval:
## -0.05545692 0.49490965
## sample estimates:
## mean in group FALSE mean in group TRUE
## 11.45330 11.23358
# race
# 1 is white; 2 is african american; 3 is hispanic; 4 is asian; 5 is biracial; 6 is other
race <- data.frame(completers_noncompleters$subsample, completers_noncompleters$KSADS_Child_Race_by_P.T1)
race = table(completers_noncompleters$subsample, completers_noncompleters$KSADS_Child_Race_by_P.T1)
print(race)
##
## 1 2 3 4 5 6
## FALSE 54 15 10 4 25 7
## TRUE 46 4 10 20 22 7
print(chisq.test(race))
##
## Pearson's Chi-squared test
##
## data: race
## X-squared = 17.719, df = 5, p-value = 0.003321
# els baseline
t.test(sumsev_type_t1 ~ subsample, data = completers_noncompleters, var.equal=T)
##
## Two Sample t-test
##
## data: sumsev_type_t1 by subsample
## t = 2.8344, df = 221, p-value = 0.005017
## alternative hypothesis: true difference in means is not equal to 0
## 95 percent confidence interval:
## 0.6235654 3.4694653
## sample estimates:
## mean in group FALSE mean in group TRUE
## 7.885965 5.839450
t.test(cdi_TOTAL.T1 ~ subsample, data = completers_noncompleters, rm.na=TRUE, var.equal=T)
##
## Two Sample t-test
##
## data: cdi_TOTAL.T1 by subsample
## t = 2.4216, df = 218, p-value = 0.01627
## alternative hypothesis: true difference in means is not equal to 0
## 95 percent confidence interval:
## 0.1427805 1.3916110
## sample estimates:
## mean in group FALSE mean in group TRUE
## 2.535714 1.768519
# parental income
covid_income_all <-
completers_noncompleters %>%
mutate_at(
vars(Parent_Income.T1
),
funs(
. %>%
dplyr::recode(
"10" = 3,
"9" = 2,
"8" = 2,
"7" = 2,
"6" = 2,
"5" = 1,
"4" = 1,
"3" = 1,
"2" = 1,
"1" = 1,
)
)
)
## Warning: Problem with `mutate()` input `Parent_Income.T1`.
## x Unreplaced values treated as NA as .x is not compatible. Please specify replacements exhaustively or supply .default
## ℹ Input `Parent_Income.T1` is ``%>%`(...)`.
## Warning: Unreplaced values treated as NA as .x is not compatible. Please specify
## replacements exhaustively or supply .default
# setting na values for income to 0
covid_income_all <- covid_income_all %>%
mutate(Parent_Income.T1 = if_else(is.na(Parent_Income.T1), 0, Parent_Income.T1))
income <- data.frame(covid_income_all$subsample, covid_income_all$Parent_Income.T1)
income = table(covid_income_all$subsample, covid_income_all$Parent_Income.T1)
print(income)
##
## 0 1 2 3
## FALSE 12 16 56 31
## TRUE 8 4 50 47
print(chisq.test(income))
##
## Pearson's Chi-squared test
##
## data: income
## X-squared = 11.469, df = 3, p-value = 0.009441
# setting theme for graphs
base_size=9
theme_set(
theme_minimal(
base_size = 11,
base_family = "",
base_line_size = base_size/22,
base_rect_size = base_size/22
))
# figure 1a
cor.test(covid$PSS_total.TC, covid$sumsev_type_t1)
##
## Pearson's product-moment correlation
##
## data: covid$PSS_total.TC and covid$sumsev_type_t1
## t = 3.8394, df = 106, p-value = 0.0002102
## alternative hypothesis: true correlation is not equal to 0
## 95 percent confidence interval:
## 0.1717782 0.5050365
## sample estimates:
## cor
## 0.3494098
covid = covid %>%
mutate(Child_Sex.T1 = as.factor(Child_Sex.T1))
covid %>%
ggplot(mapping = aes(x = sumsev_type_t1,
y = PSS_total.TC,
color = Child_Sex.T1)) +
geom_point(alpha = .6, size = 3)+
geom_smooth(method = "lm", alpha=.1, size = 2) +
labs(x = "ELS Severity (Baseline)") +
labs(y = "Perceived Stress (COVID-19)") +
ggtitle(~italic(r)~"(106)=0.35,"~italic(p)~"<.001") +
theme(plot.title = element_text(hjust = 0.5, size=30)) +
theme(axis.line = element_line(size = 1)) +
theme(legend.title = element_blank()) +
theme(legend.position = c(.92, .85)) +
theme(axis.text=element_text(size=20)) +
theme(axis.title=element_text(size=18)) +
geom_hline(yintercept=14.2, color = "gray",
linetype="dashed") +
annotate("text", x = 17, y = 12, label = "Avg PSS in Adolescents", color="dark gray") +
scale_color_manual(labels = c("Male", "Female"), values = c("#4d4f53", "#8c1515"))+
xlim(0,20) +
ylim(0, 40)
## `geom_smooth()` using formula 'y ~ x'
## Warning: Removed 1 rows containing non-finite values (stat_smooth).
## Warning: Removed 1 rows containing missing values (geom_point).
# ggsave("~/Desktop/els_pss.tiff", height = 5, width = 6)
cor.test(covid$CESDC_total.TC, covid$sumsev_type_t1)
##
## Pearson's product-moment correlation
##
## data: covid$CESDC_total.TC and covid$sumsev_type_t1
## t = 2.8112, df = 107, p-value = 0.005871
## alternative hypothesis: true correlation is not equal to 0
## 95 percent confidence interval:
## 0.07800399 0.42918713
## sample estimates:
## cor
## 0.2622577
covid %>%
ggplot(mapping = aes(x = sumsev_type_t1,
y = CESDC_total.TC,
color = Child_Sex.T1)) +
geom_point(alpha = .6, size = 3)+
geom_smooth(method = "lm", alpha=.1, size = 2) +
labs(x = "ELS Severity (Baseline)") +
labs(y = "Depression Symptoms (COVID-19)") +
ggtitle(~italic(r)~"(107)=0.26,"~italic(p)~"=.006") +
theme(plot.title = element_text(hjust = 0.5, size=30)) +
theme(axis.line = element_line(size = 1)) +
theme(legend.title = element_blank()) +
theme(legend.position = c(.92, .85)) +
theme(axis.text=element_text(size=20)) +
theme(axis.title=element_text(size=18)) +
geom_hline(yintercept=15, color = "gray",
linetype="dashed") +
annotate("text", x = 18, y = 12, label = "CESDC cut-off score", color="dark gray") +
scale_color_manual(labels = c("Male", "Female"), values = c("#4d4f53", "#8c1515"))+
xlim(0,20) +
ylim(0, 60)
## `geom_smooth()` using formula 'y ~ x'
# ggsave("~/Desktop/els_cesdc.tiff", height = 5, width = 6)
# comparing scores in our sample to validated sample / clinical cutoff
mean(covid$PSS_total.TC, na.rm = TRUE)
## [1] 18.4537
# mean of pss in validated sample of 645 adolescents between 18-29 years old
14.2
## [1] 14.2
# difference between pss in our sample and the validated sample
18.45-14.2
## [1] 4.25
# number of participants above clinical cutoff of 15 on the cesdc
sum(covid$CESDC_total.TC > 15)
## [1] 69
# percent above cutoff in our sample
69/109
## [1] 0.6330275
## mediation analysis
# centering variables
covid$Age_S1.T1_z=scale(covid$Age_S1.T1, center=TRUE, scale =TRUE)
covid$CDI_total.T1_z=scale(covid$cdi_TOTAL.T1, center=TRUE, scale =TRUE)
covid$sumsev_type_t1_z=scale(covid$sumsev_type_t1, center=TRUE, scale =TRUE)
covid$Child_Age.TC_z=scale(covid$Child_Age.TC, center=TRUE, scale =TRUE)
covid$PSS_total.TC_z=scale(covid$PSS_total.TC, center=TRUE, scale =TRUE)
covid$CESDC_total.TC_z=scale(covid$CESDC_total.TC, center=TRUE, scale =TRUE)
covid = covid %>%
mutate(Child_Sex.T1 = as.numeric(Child_Sex.T1))
# # mediation
# psych::mediate(CESDC_total.TC_z ~ sumsev_type_t1_z + (PSS_total.TC_z) - CDI_total.T1_z - Child_Sex.T1 - Age_S1.T1_z - Child_Age.TC_z - KSADS_Child_Race_by_P.T1 - income_3levels,
# data = covid) %>% print(short = FALSE)