Hypertension/Stroke Knowledge Questionnaire (HKQ)

Summary table: All data

gtsummary::theme_gtsummary_compact()
df_paper_03 %>% 
    gtsummary::tbl_summary(
        include = c(
            a_agebase, a_gender, maristat, educ, a_livingsit, a_religion, 
            a_domicile, income, d_st_type, d_stroke_ct, bmi, barthels_index, 
            aa_bp_115_75:aa_strokeris, aa_hkq, aa_hkq_cat, hosp_cat),
        digits = gtsummary::all_categorical()~ c(0,1),
        statistic = gtsummary::all_categorical() ~ "{n} ({p})",
        missing_text = "Missing"
    ) %>% 
    gtsummary::bold_labels() %>% 
    gtsummary::modify_caption("General overview of data") 

Table 1: General overview of data

Characteristic	N = 5001
Age in years
Median (Q1, Q3)	58 (51, 67)
Mean (SD)	58 (11)
Min, Max	29, 88
Gender
Male	281 (56.2)
Female	219 (43.8)
Marital Status
Currently Married	333 (66.6)
Previously Married	144 (28.8)
Never Married	23 (4.6)
Educational Status
None	49 (9.8)
Primary	203 (40.6)
Secondary	165 (33.0)
Tertiary	83 (16.6)
Living Status
Lives Alone	29 (5.8)
Lives With Spouse and Children	273 (54.6)
Lives in a Nursing Home	0 (0.0)
Lives With Spouse	30 (6.0)
Lives With Extended Family	72 (14.4)
Lives With Children	96 (19.2)
Religion
Christianity	448 (89.6)
Islam	49 (9.8)
Other	3 (0.6)
Domicile
Rural	33 (6.6)
Semi-Urban	166 (33.2)
Urban	301 (60.2)
Income in GHC
0-100	174 (35.0)
101-250	150 (30.2)
251-500	109 (21.9)
>500	64 (12.9)
Missing	3
Stroke Type
Intracerebral Hemorrhagic Stroke	103 (20.6)
Ischemic Stroke	342 (68.4)
Untyped Stroke (no CT scan available)	55 (11.0)
Stroke Subtype ( with results of Brain CT scan)
Ischaemic	313 (73.6)
Haemorrhage infarct	16 (3.8)
Haemorrhagic	84 (19.8)
Ischaemic and Haemorrhagic	12 (2.8)
Missing	75
Body Mass Index
Median (Q1, Q3)	26.2 (22.7, 30.0)
Mean (SD)	26.6 (5.5)
Min, Max	11.4, 47.9
Missing	21
Barthels Index
Median (Q1, Q3)	80 (40, 90)
Mean (SD)	66 (27)
Min, Max	0, 90
Missing	16
(1) If someones blood pressure is 115/75. it is ......
High	52 (10.5)
Low	158 (31.8)
Normal	119 (23.9)
Dont Know	168 (33.8)
Missing	3
(2) If someones blood pressure is 160/100. It is....
High	330 (66.7)
Low	10 (2.0)
Normal	9 (1.8)
Dont Know	146 (29.5)
Missing	5
(3) Once someone has high blood pressure, it usually lasts for
A few years	76 (15.4)
5-10 Years	25 (5.1)
The Rest of their Life	178 (36.0)
Dont Know	216 (43.6)
Missing	5
(4) People with high blood pressure should take their medicine
Everyday	466 (95.3)
At Least a few Times a week	11 (2.2)
Only When They feel sick	12 (2.5)
Missing	11
(5) Losing weight usually makes blood pressure
Go up	39 (8.1)
Go Down	322 (66.5)
Stay the same	123 (25.4)
Missing	16
(6) Eating less salt usually makes blood pressure
Go Up	50 (10.2)
Go Down	369 (75.2)
Stay the Same	72 (14.7)
Missing	9
(7) High blood pressure can cause heart attacks
Yes	347 (70.0)
No	10 (2.0)
Dont Know	139 (28.0)
Missing	4
(8) High blood pressure can cause cancer
Yes	147 (29.6)
No	60 (12.1)
Dont Know	290 (58.4)
Missing	3
(9) High blood pressure can cause can kidney problems
Yes	239 (48.1)
No	23 (4.6)
Dont Know	235 (47.3)
Missing	3
(10) High blood pressure can cause strokes
Yes	394 (79.4)
No	8 (1.6)
Dont Know	94 (19.0)
Missing	4
(11) Someone who has had a stroke is at higher risk of having another
Yes	258 (52.0)
No	29 (5.8)
Dont Know	209 (42.1)
Missing	4
(12) If someone is not having headaches they can stop taking medications
Yes	92 (18.5)
No	278 (56.0)
Dont Know	126 (25.4)
Missing	4
(13) If someone is feeling good it is ok to miss doses of medication
Never	357 (71.8)
Once a Month	11 (2.2)
Once a week	7 (1.4)
Dont know	122 (24.5)
Missing	3
(14) Once someone has had a stroke, they will be at risk for stroke for ....
A Few Years	140 (28.3)
5-10 Years	21 (4.3)
The Rest of Their Life	62 (12.6)
Dont Know	271 (54.9)
Missing	6
Total HKQ Score
Median (Q1, Q3)	8 (6, 10)
Mean (SD)	8 (3)
Min, Max	0, 13
Missing	30
Categorised Total HKQ Score
Median & below	238 (50.6)
Above Median	232 (49.4)
Missing	30
Health institution category
Primary	148 (29.6)
Secondary	119 (23.8)
Tertiary	233 (46.6)
1 n (%)

Tables

Table 1

gtsummary::reset_gtsummary_theme()
gtsummary::theme_gtsummary_compact()

table_1 <-
    df_paper_03 %>% 
    select(matches("(aa)*(correct)")) %>% 
    gtsummary::tbl_summary(
        missing = "no"
    ) %>% 
    bold_labels() %>% 
    add_n() %>% 
    gtsummary::modify_caption("Item response rate to the HKQ")

table_1
file.remove("paper_3_table_1.docx")

[1] TRUE

table_1 %>% 
    gtsummary::as_gt() %>% 
    gt::gtsave(filename = "paper_3_table_1.docx")

Table 2: Item response rate to the HKQ

Characteristic	N	N = 5001
If someones blood pressure is 160/100. It is	495	330 (67%)
Once someone has high blood pressure, it usually lasts for	495	178 (36%)
People with high blood pressure should take their medicine	489	466 (95%)
Losing weight usually makes blood pressure	484	322 (67%)
Eating less salt usually makes blood pressure	491	369 (75%)
High blood pressure can cause heart attacks	496	347 (70%)
High blood pressure can cause cancer	497	60 (12%)
High blood pressure can cause strokes	496	394 (79%)
High blood pressure can cause can kidney problems	497	239 (48%)
Someone who has had a stroke is at higher risk of having another	496	258 (52%)
If someone is not having headaches they can stop taking medications	496	278 (56%)
If someone is feeling good it is ok to miss doses of medication	497	357 (72%)
Once someone has had a stroke, they will be at risk for stroke for	494	62 (13%)
1 n (%)

Table 2

gtsummary::theme_gtsummary_compact()
table_2 <- 
    df_paper_03 %>% 
    filter(!is.na(aa_hkq_cat)) %>% 
    gtsummary::tbl_summary(
        type = list(ranking ~ "continuous2"),
        include = c(
            a_agebase, male, educ, a_religion,  a_domicile, income, d_st_type, 
            ranking, nihss_scale, ee_sbp_0, ee_dbp_0, bmi, aa_hkq_cat, hosp_cat),
        digits = gtsummary::all_categorical()~ c(0,1),
        statistic = gtsummary::all_categorical() ~ "{n} ({p})",
        missing = "no",
        by = aa_hkq_cat
    ) %>% 
    gtsummary::bold_labels() %>% 
    gtsummary::modify_caption(
        "Baseline Demographic & Clinical Characteristics According 
        to Scores Obtained on the HKQ") %>% 
    gtsummary::modify_spanning_header(
    gtsummary::all_stat_cols() ~ "**Categorised Total HKQ Score**") %>% 
    gtsummary::add_overall(last = T) %>% 
    gtsummary::add_p(pvalue_fun = ~ gtsummary::style_pvalue(.x, digits = 3))

table_2
file.remove("paper_3_table_2.docx")

[1] TRUE

table_2 %>% 
    gtsummary::as_gt() %>% 
    gt::gtsave(filename = "paper_3_table_2.docx")

Table 3: Baseline Demographic & Clinical Characteristics According to Scores Obtained on the HKQ

Characteristic	Categorised Total HKQ Score		Overall N = 4701	p-value2
	Median & below N = 2381	Above Median N = 2321
Age in years	58 (50, 68)	58 (52, 65)	58 (51, 67)	0.708
Male sex	115 (48.3)	145 (62.5)	260 (55.3)	0.002
Educational Status				<0.001
None	32 (13.4)	12 (5.2)	44 (9.4)
Primary	115 (48.3)	77 (33.2)	192 (40.9)
Secondary	72 (30.3)	85 (36.6)	157 (33.4)
Tertiary	19 (8.0)	58 (25.0)	77 (16.4)
Religion				0.082
Christianity	207 (87.0)	213 (91.8)	420 (89.4)
Islam	28 (11.8)	19 (8.2)	47 (10.0)
Other	3 (1.3)	0 (0.0)	3 (0.6)
Domicile				0.316
Rural	13 (5.5)	19 (8.2)	32 (6.8)
Semi-Urban	86 (36.1)	72 (31.0)	158 (33.6)
Urban	139 (58.4)	141 (60.8)	280 (59.6)
Income in GHC				<0.001
0-100	108 (45.6)	58 (25.2)	166 (35.5)
101-250	63 (26.6)	79 (34.3)	142 (30.4)
251-500	42 (17.7)	61 (26.5)	103 (22.1)
>500	24 (10.1)	32 (13.9)	56 (12.0)
Stroke Type				0.055
Intracerebral Hemorrhagic Stroke	50 (21.0)	45 (19.4)	95 (20.2)
Ischemic Stroke	154 (64.7)	169 (72.8)	323 (68.7)
Untyped Stroke (no CT scan available)	34 (14.3)	18 (7.8)	52 (11.1)
Modified Ranking Score				0.016
Median (Q1, Q3)	2.00 (1.00, 3.00)	2.00 (1.00, 3.00)	2.00 (1.00, 3.00)
NIH Stroke Scale	4.0 (0.0, 9.0)	2.0 (0.0, 6.0)	3.0 (0.0, 8.0)	<0.001
Systolic blood pressure (mm Hg)-Baseline	155 (147, 172)	154 (146, 169)	155 (146, 170)	0.297
Diastolic Blood Pressure	96 (89, 106)	94 (87, 103)	95 (88, 105)	0.093
Body Mass Index	25.8 (22.2, 28.7)	26.5 (23.0, 30.9)	26.2 (22.7, 30.1)	0.040
Health institution category				0.552
Primary	75 (31.5)	67 (28.9)	142 (30.2)
Secondary	59 (24.8)	52 (22.4)	111 (23.6)
Tertiary	104 (43.7)	113 (48.7)	217 (46.2)
1 Median (Q1, Q3); n (%)
2 Wilcoxon rank sum test; Pearson’s Chi-squared test; Fisher’s exact test

Linear regression of Total HKQ and covariates

tbl1 <- 
    df_paper_03 %>% 
    select(
        a_agebase, male, educ, a_religion,  a_domicile, income, d_st_type, 
        ranking, nihss_scale, ee_sbp_0, ee_dbp_0, bmi, aa_hkq, hosp_cat) %>% 
    tbl_uvregression(
        y = aa_hkq, 
        method = lm,
        pvalue_fun = function(x) style_pvalue(x, digits = 3)) %>% 
    modify_header(
        update = list(estimate ~ "**Estimate**", label ~ "**Variable**")) %>% 
    bold_labels() %>% 
    bold_p()

Warning: The `update` argument of `modify_header()` is deprecated as of gtsummary 2.0.0.
ℹ Use `modify_header(...)` input instead. Dynamic dots allow for syntax like
  `modify_header(!!!list(...))`.
ℹ The deprecated feature was likely used in the gtsummary package.
  Please report the issue at <https://github.com/ddsjoberg/gtsummary/issues>.

tbl2 <- 
    df_paper_03 %>% 
    select(
        male, educ, a_religion,  ranking, nihss_scale, bmi, aa_hkq, hosp_cat) %>% 
    lm(aa_hkq ~ ., data = .) %>% 
    tbl_regression(pvalue_fun = function(x) style_pvalue(x, digits = 3)) %>% 
    modify_header(
        update = list(estimate ~ "**Estimate**", label ~ "**Variable**")) %>% 
    bold_labels() %>% 
    bold_p()

 
table_3 <- 
    tbl_merge(
    list(tbl1, tbl2),
    tab_spanner = c("**Univariate**", "**Multivariate**")) %>%    
    modify_caption(
        caption = "Univartiate and multivariate linear regression predicting HKQ") 

The number rows in the tables to be merged do not match, which may result in
rows appearing out of order.
ℹ See `tbl_merge()` (`?gtsummary::tbl_merge()`) help file for details. Use
  `quiet=TRUE` to silence message.

table_3
file.remove("paper_3_table_3.docx")

[1] TRUE

table_3 %>% 
    gtsummary::as_gt() %>% 
    gt::gtsave(filename = "paper_3_table_3.docx")

Table 4: Univartiate and multivariate linear regression predicting HKQ

Variable	Univariate				Multivariate
	N	Estimate	95% CI	p-value	Estimate	95% CI	p-value
Age in years	470	-0.02	-0.04, 0.01	0.149
Male sex	470
No		—	—		—	—
Yes		0.69	0.12, 1.3	0.017	0.45	-0.15, 1.1	0.144
Educational Status	470
None		—	—		—	—
Primary		1.5	0.55, 2.5	0.002	1.1	0.10, 2.1	0.031
Secondary		2.2	1.2, 3.2	<0.001	1.6	0.56, 2.7	0.003
Tertiary		3.3	2.2, 4.4	<0.001	2.5	1.3, 3.7	<0.001
Religion	470
Christianity		—	—		—	—
Islam		-1.5	-2.5, -0.61	0.001	-1.3	-2.3, -0.37	0.006
Other		-3.6	-7.1, -0.09	0.044	-3.5	-7.5, 0.57	0.093
Domicile	470
Rural		—	—
Semi-Urban		-1.1	-2.3, 0.07	0.066
Urban		-1.1	-2.2, 0.03	0.056
Income in GHC	467
0-100		—	—
101-250		0.75	0.05, 1.4	0.035
251-500		0.89	0.13, 1.7	0.022
>500		1.2	0.24, 2.1	0.014
Stroke Type	470
Intracerebral Hemorrhagic Stroke		—	—
Ischemic Stroke		0.29	-0.41, 0.99	0.419
Untyped Stroke (no CT scan available)		-1.6	-2.6, -0.57	0.002
Modified Ranking Score	470	-0.39	-0.62, -0.17	<0.001	0.10	-0.19, 0.40	0.497
NIH Stroke Scale	461	-0.14	-0.20, -0.09	<0.001	-0.17	-0.24, -0.09	<0.001
Systolic blood pressure (mm Hg)-Baseline	469	-0.01	-0.03, 0.00	0.117
Diastolic Blood Pressure	469	-0.01	-0.03, 0.01	0.285
Body Mass Index	454	0.10	0.05, 0.15	<0.001	0.08	0.03, 0.13	0.001
Health institution category	470
Primary		—	—		—	—
Secondary		0.81	0.04, 1.6	0.040	0.51	-0.25, 1.3	0.187
Tertiary		0.58	-0.08, 1.2	0.085	-0.41	-1.1, 0.31	0.262
Abbreviation: CI = Confidence Interval

Logistit regression - Item 11

Item # 11: If someone has had a stroke is at a higher risk of having another stroke

tbl3 <- 
    df_paper_03 %>% 
    mutate(aa_highrisk_correct = factor(aa_highrisk_correct)) %>% 
    select(
        a_agebase, male, educ, a_religion,  a_domicile, income, d_st_type, 
        ranking, nihss_scale, ee_sbp_0, ee_dbp_0, bmi, aa_highrisk_correct, hosp_cat) %>% 
    tbl_uvregression(
        y = aa_highrisk_correct, 
        method = glm,
        method.args = family(binomial),
        exponentiate = TRUE,
        pvalue_fun = function(x) style_pvalue(x, digits = 3)) %>% 
    modify_header(
        update = list(estimate ~ "**cOR**", label ~ "**Variable**")) %>% 
    bold_labels() %>% 
    bold_p()


tbl4 <- 
    df_paper_03 %>% 
    mutate(aa_highrisk_correct = factor(aa_highrisk_correct)) %>% 
    select(
        a_agebase, male, educ, a_religion, income, nihss_scale, bmi, hosp_cat, 
        aa_highrisk_correct) %>% 
    glm(aa_highrisk_correct ~ ., data = ., family=binomial) %>%
    tbl_regression(
        exponentiate = T, 
        pvalue_fun = function(x) style_pvalue(x, digits = 3)) %>% 
    modify_header(
        update = list(estimate ~ "**aOR**", label ~ "**Variable**")) %>% 
    bold_labels() %>% 
    bold_p()

table_4 <-
    tbl_merge(
    list(tbl3, tbl4),
    tab_spanner = c("**Univariate**", "**Multivariate**")) %>%    
    modify_caption(
        caption = "Univartiate and multivariate logistic regression of Item 11 of HKQ") 

The number rows in the tables to be merged do not match, which may result in
rows appearing out of order.
ℹ See `tbl_merge()` (`?gtsummary::tbl_merge()`) help file for details. Use
  `quiet=TRUE` to silence message.

table_4
file.remove("paper_3_table_4.docx")

[1] TRUE

table_4 %>% 
    gtsummary::as_gt() %>% 
    gt::gtsave(filename = "paper_3_table_4.docx")

Table 5: Univartiate and multivariate logistic regression of Item 11 of HKQ

Variable	Univariate				Multivariate
	N	cOR	95% CI	p-value	aOR	95% CI	p-value
Age in years	496	0.98	0.97, 1.00	0.044	1.00	0.98, 1.02	0.817
Male sex	496
No		—	—		—	—
Yes		1.87	1.30, 2.68	<0.001	1.68	1.08, 2.61	0.022
Educational Status	496
None		—	—		—	—
Primary		2.97	1.45, 6.61	0.004	2.37	1.09, 5.53	0.035
Secondary		5.78	2.78, 13.0	<0.001	4.07	1.81, 9.80	0.001
Tertiary		8.58	3.82, 20.8	<0.001	5.37	2.13, 14.4	<0.001
Religion	496
Christianity		—	—		—	—
Islam		0.50	0.27, 0.91	0.025	0.76	0.37, 1.52	0.435
Other		0.43	0.02, 4.51	0.491	1.15	0.04, 30.4	0.922
Domicile	496
Rural		—	—
Semi-Urban		1.38	0.64, 2.97	0.411
Urban		1.18	0.57, 2.48	0.657
Income in GHC	493
0-100		—	—		—	—
101-250		1.37	0.89, 2.13	0.155	0.91	0.54, 1.51	0.706
251-500		1.68	1.04, 2.75	0.035	0.76	0.41, 1.40	0.382
>500		1.51	0.84, 2.74	0.166	0.86	0.42, 1.76	0.672
Stroke Type	496
Intracerebral Hemorrhagic Stroke		—	—
Ischemic Stroke		1.04	0.66, 1.62	0.866
Untyped Stroke (no CT scan available)		0.38	0.19, 0.75	0.006
Modified Ranking Score	496	0.94	0.82, 1.08	0.389
NIH Stroke Scale	486	0.96	0.93, 0.99	0.015	0.98	0.94, 1.02	0.283
Systolic blood pressure (mm Hg)-Baseline	495	1.00	0.99, 1.01	0.874
Diastolic Blood Pressure	495	1.00	0.99, 1.01	0.881
Body Mass Index	478	1.05	1.02, 1.09	0.002	1.06	1.02, 1.10	0.004
Health institution category	496
Primary		—	—		—	—
Secondary		1.57	0.96, 2.57	0.071	1.38	0.80, 2.38	0.250
Tertiary		2.15	1.42, 3.29	<0.001	1.83	1.08, 3.13	0.025
Abbreviations: CI = Confidence Interval, OR = Odds Ratio

Logistit regression - Item 14

Item # 14: If someone has had a stroke is at a higher risk of having another stroke

tbl5 <- 
    df_paper_03 %>% 
    mutate(aa_strokeris_correct = factor(aa_strokeris_correct)) %>% 
    select(
        a_agebase, male, educ, a_religion,  a_domicile, income, d_st_type, 
        ranking, nihss_scale, ee_sbp_0, ee_dbp_0, bmi, aa_strokeris_correct, hosp_cat) %>% 
    tbl_uvregression(
        y = aa_strokeris_correct, 
        method = glm,
        method.args = family(binomial),
        exponentiate = TRUE,
        pvalue_fun = function(x) style_pvalue(x, digits = 3)) %>% 
    modify_header(
        update = list(estimate ~ "**cOR**", label ~ "**Variable**")) %>% 
    bold_labels() %>% 
    bold_p()


tbl6 <- 
    df_paper_03 %>% 
    mutate(aa_strokeris_correct = factor(aa_strokeris_correct)) %>% 
    select(a_domicile, income, ranking, hosp_cat, aa_strokeris_correct) %>% 
    glm(aa_strokeris_correct ~ ., data = ., family=binomial) %>%
    tbl_regression(
        exponentiate = T, 
        pvalue_fun = function(x) style_pvalue(x, digits = 3)) %>% 
    modify_header(
        update = list(estimate ~ "**aOR**", label ~ "**Variable**")) %>% 
    bold_labels() %>% 
    bold_p()

table_5 <-
    tbl_merge(
    list(tbl5, tbl6),
    tab_spanner = c("**Univariate**", "**Multivariate**")) %>%    
    modify_caption(
        caption = "Univartiate and multivariate logistic regression of Item 14 of HKQ") 

table_5
file.remove("paper_3_table_5.docx")

[1] TRUE

table_5 %>% 
    gtsummary::as_gt() %>% 
    gt::gtsave(filename = "paper_3_table_5.docx")

Table 6: Univartiate and multivariate logistic regression of Item 14 of HKQ

Variable	Univariate				Multivariate
	N	cOR	95% CI	p-value	aOR	95% CI	p-value
Age in years	494	1.00	0.97, 1.02	0.799
Male sex	494
No		—	—
Yes		1.73	0.99, 3.10	0.058
Educational Status	494
None		—	—
Primary		0.47	0.18, 1.41	0.152
Secondary		1.59	0.66, 4.47	0.333
Tertiary		1.17	0.42, 3.58	0.768
Religion	494
Christianity		—	—
Islam		0.96	0.35, 2.21	0.932
Other		0.00		0.987
Domicile	494
Rural		—	—		—	—
Semi-Urban		0.34	0.14, 0.88	0.021	0.43	0.17, 1.17	0.089
Urban		0.35	0.15, 0.84	0.014	0.46	0.19, 1.20	0.099
Income in GHC	491
0-100		—	—		—	—
101-250		1.52	0.76, 3.09	0.238	2.30	1.08, 4.99	0.032
251-500		1.01	0.43, 2.29	0.978	1.54	0.61, 3.71	0.346
>500		3.41	1.58, 7.40	0.002	2.75	1.22, 6.19	0.014
Stroke Type	494
Intracerebral Hemorrhagic Stroke		—	—
Ischemic Stroke		1.07	0.57, 2.14	0.849
Untyped Stroke (no CT scan available)		0.39	0.09, 1.28	0.157
Modified Ranking Score	494	0.77	0.61, 0.95	0.017	0.81	0.65, 1.01	0.066
NIH Stroke Scale	484	1.00	0.95, 1.05	0.975
Systolic blood pressure (mm Hg)-Baseline	493	1.00	0.99, 1.01	0.813
Diastolic Blood Pressure	493	1.00	0.99, 1.02	0.685
Body Mass Index	476	1.02	0.97, 1.07	0.405
Health institution category	494
Primary		—	—		—	—
Secondary		1.83	0.98, 3.48	0.060	1.71	0.87, 3.37	0.119
Tertiary		0.39	0.19, 0.79	0.010	0.36	0.16, 0.77	0.009
Abbreviations: CI = Confidence Interval, OR = Odds Ratio