Burden of Percieved Stress Among Stroke Survivors

Please Note!

• Perceived stress was derived first by adding up all 8 items. Those who were less than or equal to the median were considered to have “Low”, and those above were considered to have “High”. This is still not clear so the authors should please call for further discussion.

• Categories under primary occupation are too many. You may need to recategorize it to make it more compact. I have seen the categorization done but I am not sure that will suffice.

• I think if the team wants to categorize the perceived stress as such ( 2 groups) then a logistic regression would be in order and not an ordinal logistic regression. The regression should then only be on the significantly associated variables. Please let me know what the authors think.

Table 1

df_paper_04 %>%
    filter(!is.na(perceived_stress_cat)) %>% 
    gtsummary::tbl_summary(
        include = c(
            a_agebase, a_gender, a_domicile, income, maristat, 
            a_livingsit, educ, a_religion, perceived_stress_cat, 
            a_poccup3),
        by = perceived_stress_cat,
        digits = gtsummary::all_categorical()~ c(0,1),
        statistic = gtsummary::all_categorical() ~ "{n} ({p})",
        missing_text = "Missing") %>% 
    gtsummary::bold_labels() %>% 
    gtsummary::modify_caption(
        "**Table 1**: Sociodemographic characteristics among survivors by level of 
        perceived stress") %>% 
    gtsummary::modify_spanning_header(
    gtsummary::all_stat_cols() ~ "**Perceived Stress**") %>% 
    gtsummary::add_overall(col_label = "**Total**", last = TRUE) %>% 
    gtsummary::add_p(
        pvalue_fun = ~gtsummary::style_pvalue(.x, digits = 3)) %>% 
    gtsummary::bold_p() %>% 
    gtsummary::modify_caption(
        "**Table 1**: Sociodemographic characteristics among survivors by 
        level of perceived stress")

Table 1: Sociodemographic characteristics among survivors by level of perceived stress

Characteristic	Perceived Stress		Total1	p-value2
	Low, N = 2821	High, N = 2041
Age in years	58 (50, 66)	58 (52, 68)	58 (51, 67)	0.398
Gender				0.440
Male	162 (57.4)	110 (53.9)	272 (56.0)
Female	120 (42.6)	94 (46.1)	214 (44.0)
Domicile				0.980
Rural	18 (6.4)	13 (6.4)	31 (6.4)
Semi-Urban	95 (33.7)	67 (32.8)	162 (33.3)
Urban	169 (59.9)	124 (60.8)	293 (60.3)
Income in GHC				<0.001
0-100	66 (23.7)	104 (51.0)	170 (35.3)
101-250	90 (32.4)	54 (26.5)	144 (29.9)
251-500	82 (29.5)	25 (12.3)	107 (22.2)
>500	40 (14.4)	21 (10.3)	61 (12.7)
Missing	4	0	4
Marital Status				0.608
Currently Married	194 (68.8)	133 (65.2)	327 (67.3)
Previously Married	77 (27.3)	60 (29.4)	137 (28.2)
Never Married	11 (3.9)	11 (5.4)	22 (4.5)
Living Status				0.516
Lives Alone	17 (6.0)	11 (5.4)	28 (5.8)
Lives With Spouse and Children	165 (58.5)	105 (51.5)	270 (55.6)
Lives in a Nursing Home	0 (0.0)	0 (0.0)	0 (0.0)
Lives With Spouse	14 (5.0)	14 (6.9)	28 (5.8)
Lives With Extended Family	36 (12.8)	33 (16.2)	69 (14.2)
Lives With Children	50 (17.7)	41 (20.1)	91 (18.7)
Educational Status				0.074
None	27 (9.6)	20 (9.8)	47 (9.7)
Primary	106 (37.6)	93 (45.6)	199 (40.9)
Secondary	92 (32.6)	67 (32.8)	159 (32.7)
Tertiary	57 (20.2)	24 (11.8)	81 (16.7)
Religion				>0.999
Christianity	252 (89.4)	183 (89.7)	435 (89.5)
Islam	28 (9.9)	20 (9.8)	48 (9.9)
Other	2 (0.7)	1 (0.5)	3 (0.6)
Primary Occupation				0.001
Skilled	154 (54.6)	79 (38.7)	233 (47.9)
Manual	53 (18.8)	52 (25.5)	105 (21.6)
Others	9 (3.2)	10 (4.9)	19 (3.9)
Retired	35 (12.4)	20 (9.8)	55 (11.3)
Unemployed	31 (11.0)	43 (21.1)	74 (15.2)
1 Median (IQR); n (%)
2 Wilcoxon rank sum test; Pearson’s Chi-squared test; Fisher’s exact test

Table 2

df_paper_04 %>%
    filter(!is.na(perceived_stress_cat)) %>% 
    gtsummary::tbl_summary(
        include = c(
             d_st_type_2, ranking, nihss_scale, hamd_cat, dm,
             hyperlipidemia, perceived_stress_cat),
        by = perceived_stress_cat,
        type = list(ranking ~ "continuous2"),
        digits = gtsummary::all_categorical()~ c(0,1),
        statistic = gtsummary::all_categorical() ~ "{n} ({p})",
        missing_text = "Missing") %>% 
    gtsummary::bold_labels() %>% 
    gtsummary::modify_caption(
        "**Table 2**: Clinical Characteristics of stroke survivors 
        stratified by perceived stress level ") %>% 
    gtsummary::modify_spanning_header(
    gtsummary::all_stat_cols() ~ "**Perceived Stress**") %>% 
    gtsummary::add_p(
        pvalue_fun = ~gtsummary::style_pvalue(.x, digits = 3)) %>% 
    gtsummary::bold_p()

Table 2: Clinical Characteristics of stroke survivors stratified by perceived stress level

Characteristic	Perceived Stress		p-value2
	Low, N = 2821	High, N = 2041
Stroke Type (Choose One)			0.060
Ischemic Stroke	177 (71.1)	130 (77.8)
Intracerebral Hemorrhagic Stroke	68 (27.3)	31 (18.6)
Ischemic With Hemorrhagic Transformation	4 (1.6)	6 (3.6)
Missing	33	37
Modified Ranking Score			<0.001
Median (IQR)	2.00 (1.00, 3.00)	3.00 (2.00, 4.00)
NIH Stroke Scale	1.0 (0.0, 5.0)	6.0 (2.0, 11.0)	<0.001
Missing	6	3
Depression (HAMD)			<0.001
Normal	244 (87.5)	110 (54.2)
Mild Depression	28 (10.0)	59 (29.1)
Moderate Depression	7 (2.5)	22 (10.8)
Severe Depression	0 (0.0)	7 (3.4)
Very Severe Depression	0 (0.0)	5 (2.5)
Missing	3	1
Diabetes Mellitus	90 (31.9)	70 (34.3)	0.579
Hyperlipidemia	95 (33.7)	61 (29.9)	0.378
1 n (%); Median (IQR)
2 Fisher’s exact test; Wilcoxon rank sum test; Pearson’s Chi-squared test

Table 3

tbl_3a <- 
    df_paper_04 %>%
    drop_na(perceived_stress_cat) %>% 
    select(
        d_st_type_2, perceived_stress_cat, nihss_scale, hamd_cat_2, educ, 
        a_livingsit2, a_poccup3
        ) %>% 
    tbl_uvregression(
        method = glm,
        y = perceived_stress_cat,
        method.args = family(binomial),
        exponentiate = TRUE,
        pvalue_fun = function(x) style_pvalue(x, digits = 3)
    ) %>% 
    modify_header(
        update = list(estimate ~ "**OR**", label ~ "**Variable**")
        ) %>% 
    gtsummary::bold_labels() %>% 
    gtsummary::add_global_p() %>% 
    gtsummary::bold_p(t = 0.1)

tbl_3b <- 
    df_paper_04 %>%
    drop_na(perceived_stress_cat) %>% 
    select(d_st_type_2, perceived_stress_cat, nihss_scale, hamd_cat_2, educ, a_poccup3) %>% 
    glm(
        perceived_stress_cat ~ ., data = ., family = binomial) %>% 
    tbl_regression(
        exponentiate = T, 
        pvalue_fun = function(x) style_pvalue(x, digits = 3)) %>% 
     modify_header(
        update = list(estimate ~ "**OR**", label ~ "**Variable**")
        ) %>%
    gtsummary::bold_labels() %>% 
    gtsummary::bold_p()

gtsummary::tbl_merge(
    tbls = list(tbl_3a, tbl_3b),
    tab_spanner = c("**Crude**", "**Adjusted**")) %>% 
    gtsummary::bold_labels() %>% 
    gtsummary::modify_caption(
        "Table 3: Association between Baseline Characteristics and Percieved Stress")

Table 3: Association between Baseline Characteristics and Percieved Stress

Variable	Crude				Adjusted
	N	OR1	95% CI1	p-value	OR1	95% CI1	p-value
Stroke Type (Choose One)	416			0.063
Ischemic Stroke		—	—		—	—
Intracerebral Hemorrhagic Stroke		0.62	0.38, 1.00		0.99	0.56, 1.72	0.967
Ischemic With Hemorrhagic Transformation		2.04	0.57, 8.12		1.16	0.26, 5.51	0.848
NIH Stroke Scale	477	1.18	1.13, 1.24	<0.001	1.14	1.08, 1.20	<0.001
Depression (HAMD)	482			<0.001
Normal		—	—		—	—
Mild Depression		4.67	2.85, 7.82		3.36	1.88, 6.08	<0.001
Moderate_Severe		10.8	4.90, 27.2		4.75	1.90, 13.1	0.001
Educational Status	486			0.068
None		—	—		—	—
Primary		1.18	0.63, 2.27		1.57	0.67, 3.87	0.315
Secondary		0.98	0.51, 1.92		1.92	0.79, 4.88	0.158
Tertiary		0.57	0.27, 1.20		1.31	0.49, 3.63	0.599
Living Status	458			0.807
Lives with family		—	—
Lives without family		0.91	0.40, 1.96
Primary Occupation	486			0.001
Skilled		—	—		—	—
Manual		1.91	1.20, 3.06		1.34	0.72, 2.49	0.349
Others		2.17	0.84, 5.66		1.30	0.37, 4.23	0.664
Retired		1.11	0.60, 2.04		0.92	0.43, 1.90	0.827
Unemployed		2.70	1.59, 4.65		1.34	0.64, 2.78	0.438
1 OR = Odds Ratio, CI = Confidence Interval