Determinants of Quality Of Life of recent Ghanaian stroke survivors

Author

Dr. Ayisi-Boateng

Published

December 17, 2025

Import data

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df_paper_02 <- 
    dget("df_for_papers") %>% 
    mutate(
        d_st_type = as.character(d_st_type),
        d_st_type = case_when(
            d_st_type == "Ischemic With Hemorrhagic Transformation" ~ 
            "Ischemic Stroke", 
            TRUE ~ d_st_type), 
        d_st_type = factor(
            d_st_type, 
            levels = c(
                "Ischemic Stroke", 
                "Intracerebral Hemorrhagic Stroke", 
                "Untyped Stroke (no CT scan available)")))

Summary table: All data

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gtsummary::theme_gtsummary_compact()

Setting theme "Compact"

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table_0 <- 
    df_paper_02 %>%
    gtsummary::tbl_summary(
        include = c(
            a_agebase, a_gender, maristat, educ, a_livingsit, a_religion, 
            a_domicile, income, d_st_type, d_stroke_ct,d_stroke_loc, bmi,
            mobility, selfcare,  usual_act, pain_disc, anxiety, vas, vas_cat, 
            barthels_index),
        digits = gtsummary::all_categorical()~ c(0,1),
        statistic = gtsummary::all_categorical() ~ "{n} ({p})",
        missing_text = "Missing"
    ) %>% 
    gtsummary::bold_labels() %>% 
    gtsummary::modify_caption("**Table 1**: ") %>% 
    gtsummary::modify_spanning_header(
        gtsummary::all_stat_cols() ~ "****")

Tables

Table1

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

Setting theme "Compact"

Show the code

df_paper_02 %>%
    mutate(a_livingsit = droplevels(a_livingsit)) %>% 
    gtsummary::tbl_summary(
        include = c(
            a_agebase, a_gender, maristat, educ, a_livingsit, 
            a_religion, a_domicile, income, d_st_type, 
            d_stroke_ct,d_stroke_loc, bmi, nihss_scale),
        digits = gtsummary::all_categorical()~ c(0,1),
        statistic = list(
            gtsummary::all_categorical() ~ "{n} ({p})",
            gtsummary::all_continuous() ~ "{mean} ({sd})", 
            nihss_scale ~ "{median} ({p25}, {p75})"
            ),
        missing = "no",
        by = a_gender,
        label = list(
            a_livingsit ~ "Living Status",
            d_st_type ~ "Stroke Type")) %>% 
    gtsummary::bold_labels() %>% 
    gtsummary::modify_caption(
        "**Table 1**: Socio-demographic and clinical characteristics of the study participants"
        ) %>% 
    gtsummary::modify_spanning_header(
        gtsummary::all_stat_cols() ~ "**Sex of study participant**") %>% 
    gtsummary::add_overall(last = TRUE)%>% 
    gtsummary::add_p(
        pvalue_fun = function(x) style_pvalue(x, digits = 3)) %>%
    gtsummary::bold_p()

**Table 1**: Socio-demographic and clinical characteristics of the study participants
Characteristic	Sex of study participant		Overall N = 500¹	p-value²
Characteristic	Male N = 281¹	Female N = 219¹	Overall N = 500¹	p-value²
Age in years	58 (11)	59 (11)	58 (11)	0.083
Marital Status				<0.001
Currently Married	235 (83.6)	98 (44.7)	333 (66.6)
Previously Married	32 (11.4)	112 (51.1)	144 (28.8)
Never Married	14 (5.0)	9 (4.1)	23 (4.6)
Educational Status				<0.001
None	11 (3.9)	38 (17.4)	49 (9.8)
Primary	89 (31.7)	114 (52.1)	203 (40.6)
Secondary	114 (40.6)	51 (23.3)	165 (33.0)
Tertiary	67 (23.8)	16 (7.3)	83 (16.6)
Living Status				<0.001
Lives Alone	16 (5.7)	13 (5.9)	29 (5.8)
Lives With Spouse and Children	199 (70.8)	74 (33.8)	273 (54.6)
Lives With Spouse	18 (6.4)	12 (5.5)	30 (6.0)
Lives With Extended Family	30 (10.7)	42 (19.2)	72 (14.4)
Lives With Children	18 (6.4)	78 (35.6)	96 (19.2)
Religion				0.901
Christianity	250 (89.0)	198 (90.4)	448 (89.6)
Islam	29 (10.3)	20 (9.1)	49 (9.8)
Other	2 (0.7)	1 (0.5)	3 (0.6)
Domicile				0.421
Rural	16 (5.7)	17 (7.8)	33 (6.6)
Semi-Urban	99 (35.2)	67 (30.6)	166 (33.2)
Urban	166 (59.1)	135 (61.6)	301 (60.2)
Income in GHC				0.026
0-100	83 (29.5)	91 (42.1)	174 (35.0)
101-250	89 (31.7)	61 (28.2)	150 (30.2)
251-500	67 (23.8)	42 (19.4)	109 (21.9)
>500	42 (14.9)	22 (10.2)	64 (12.9)
Stroke Type				0.006
Ischemic Stroke	178 (70.6)	164 (82.8)	342 (76.0)
Intracerebral Hemorrhagic Stroke	71 (28.2)	32 (16.2)	103 (22.9)
Untyped Stroke (no CT scan available)	3 (1.2)	2 (1.0)	5 (1.1)
Stroke Subtype ( with results of Brain CT scan)				0.004
Ischaemic	161 (67.4)	152 (81.7)	313 (73.6)
Haemorrhage infarct	13 (5.4)	3 (1.6)	16 (3.8)
Haemorrhagic	55 (23.0)	29 (15.6)	84 (19.8)
Ischaemic and Haemorrhagic	10 (4.2)	2 (1.1)	12 (2.8)
Stroke Location				0.041
Anterior Circulation	149 (71.3)	132 (80.5)	281 (75.3)
Posterior Circulation	60 (28.7)	32 (19.5)	92 (24.7)
Body Mass Index	25.5 (4.8)	28.0 (6.1)	26.6 (5.5)	<0.001
NIH Stroke Scale	3.0 (0.0, 8.0)	3.0 (0.0, 7.5)	3.0 (0.0, 8.0)	0.757
¹ Mean (SD); n (%); Median (Q1, Q3)
² Wilcoxon rank sum test; Pearson’s Chi-squared test; Fisher’s exact test

Table 2

Show the code

# gtsummary::reset_gtsummary_theme()
# gtsummary::theme_gtsummary_compact()
df_paper_02 %>%
    gtsummary::tbl_summary(
        include = c(
            a_gender, mobility, selfcare,  usual_act, pain_disc, 
            anxiety, vas, 
            vas_cat, barthels_index),
        digits = gtsummary::all_categorical()~ c(0,1),
        statistic = list(
            gtsummary::all_categorical() ~ "{n} ({p})",
            gtsummary::all_continuous() ~ "{mean} ({sd})"
            ),
        type = list(
            c(mobility, selfcare,  usual_act, pain_disc, anxiety)~ 
                "continuous"),
        missing = "no",
        by = a_gender
    ) %>% 
    gtsummary::bold_labels() %>% 
    gtsummary::modify_caption(
        "**Table 2**: Descriptive statistics of EQ-5D, VAS and Barthel index scales"
        ) %>% 
    gtsummary::modify_spanning_header(
        gtsummary::all_stat_cols() ~ "**Sex of study participant**") %>% 
    gtsummary::add_overall(last = TRUE) %>% 
    gtsummary::add_p(pvalue_fun = function(x) style_pvalue(x, digits = 3)) %>%
    gtsummary::bold_p()

**Table 2**: Descriptive statistics of EQ-5D, VAS and Barthel index scales
Characteristic	Sex of study participant		Overall N = 500¹	p-value²
Characteristic	Male N = 281¹	Female N = 219¹	Overall N = 500¹	p-value²
Mobility	1.71 (0.67)	1.80 (0.66)	1.75 (0.66)	0.131
Self Care	1.67 (0.72)	1.70 (0.76)	1.69 (0.74)	0.734
Usual Activity	1.84 (0.72)	1.90 (0.68)	1.87 (0.71)	0.306
Pain Discomfort	1.59 (0.60)	1.71 (0.61)	1.64 (0.61)	0.024
Anxiety	1.44 (0.60)	1.44 (0.57)	1.44 (0.58)	0.787
Your Health Today	64 (23)	68 (22)	65 (23)	0.040
Categorised VAS				0.076
Below Median	132 (47.1)	85 (39.2)	217 (43.7)
Median & above	148 (52.9)	132 (60.8)	280 (56.3)
Barthels Index	68 (26)	65 (29)	66 (27)	0.458
¹ Mean (SD); n (%)
² Wilcoxon rank sum test; Pearson’s Chi-squared test

Table 3: Logistic regression

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table_vas_crude <- 
    df_paper_02 %>%
    filter(!is.na(vas_cat)) %>% 
    mutate(vas_cat = factor(vas_cat)) %>% 
    select(
        a_agebase, a_gender, maristat, educ, a_livingsit, a_religion, 
        a_domicile, income, d_st_type, bmi, barthels_index, vas_cat) %>% 
     tbl_uvregression(
        y = vas_cat, 
        method = glm,
        method.args = family(binomial),
        pvalue_fun = ~ gtsummary::style_pvalue(.x, digits = 3),
        exponentiate = T) %>% 
    gtsummary::bold_labels() %>% 
    gtsummary::bold_p()

table_vas_adj <- 
    df_paper_02 %>% 
    select(
        vas_cat, a_agebase, a_gender, a_livingsit, a_domicile, 
        income, bmi, barthels_index) %>% 
    mutate(vas_cat = factor(vas_cat), a_livingsit = a_livingsit) %>%
    glm(vas_cat ~ ., data = ., family = "binomial") %>%
    tbl_regression(
        pvalue_fun = function(x) style_pvalue(x, digits = 3),
        exponentiate = T, 
        label = list(a_livingsit ~ "Living Status")) %>% 
    bold_labels() %>% 
    bold_p() 

tbl_merge(
    list(table_vas_crude, table_vas_adj),
    tab_spanner = c("**Univariate**", "**Multivariate**")) %>%    
    modify_caption(
        caption = "**Table 3a**:Univartiate and multivariate logistic 
        regression for Quality of Life of study participants")

**Table 3a**:Univartiate and multivariate logistic regression for Quality of Life of study participants
Characteristic	Univariate				Multivariate
Characteristic	N	OR	95% CI	p-value	OR	95% CI	p-value
Age in years	497	0.97	0.96, 0.99	0.002	0.99	0.97, 1.00	0.121
Gender	497
Male		—	—		—	—
Female		1.39	0.97, 1.99	0.076	2.31	1.43, 3.78	<0.001
Marital Status	497
Currently Married		—	—
Previously Married		0.83	0.56, 1.24	0.368
Never Married		0.55	0.23, 1.29	0.170
Educational Status	497
None		—	—
Primary		1.63	0.87, 3.08	0.132
Secondary		1.71	0.90, 3.29	0.104
Tertiary		1.33	0.65, 2.74	0.429
Living Status	497
Lives Alone		—	—		—	—
Lives With Spouse and Children		0.67	0.28, 1.48	0.338	0.95	0.36, 2.33	0.907
Lives With Spouse		0.34	0.11, 0.98	0.050	0.57	0.17, 1.85	0.360
Lives With Extended Family		0.58	0.22, 1.42	0.245	0.70	0.24, 1.94	0.498
Lives With Children		0.40	0.16, 0.94	0.040	0.41	0.14, 1.10	0.084
Religion	497
Christianity		—	—
Islam		0.65	0.36, 1.18	0.158
Other		0.37	0.02, 3.88	0.417
Domicile	497
Rural		—	—		—	—
Semi-Urban		2.40	1.12, 5.34	0.026	3.42	1.46, 8.38	0.005
Urban		2.39	1.15, 5.18	0.022	3.67	1.61, 8.76	0.002
Income in GHC	494
0-100		—	—		—	—
101-250		2.50	1.60, 3.95	<0.001	2.50	1.50, 4.22	<0.001
251-500		2.69	1.64, 4.48	<0.001	2.48	1.41, 4.40	0.002
>500		1.21	0.67, 2.16	0.522	0.94	0.47, 1.89	0.858
d_st_type	448
Ischemic Stroke		—	—
Intracerebral Hemorrhagic Stroke		1.43	0.91, 2.27	0.120
Untyped Stroke (no CT scan available)		0.00		0.981
Body Mass Index	478	1.05	1.01, 1.08	0.011	1.03	0.99, 1.07	0.214
Barthels Index	483	1.02	1.01, 1.03	<0.001	1.02	1.01, 1.03	<0.001
Abbreviations: CI = Confidence Interval, OR = Odds Ratio

Table 3 - Linear regression version

Show the code

table_vas_crude <- 
    df_paper_02 %>%
    filter(!is.na(vas)) %>% 
    select(
        a_agebase, a_gender, maristat, educ, a_livingsit, a_religion,
        a_domicile, income, d_st_type, bmi,
        barthels_index, vas) %>% 
    tbl_uvregression(
        y = vas, 
        method = lm,
        pvalue_fun = ~ gtsummary::style_pvalue(.x, digits = 3)) %>% 
    modify_header(
        update = list(estimate ~ "**Estimate**", label ~ "**Variable**")) %>% 
    gtsummary::bold_labels() %>% 
#    gtsummary::add_global_p() %>% 
    gtsummary::bold_p()

table_vas_adj <- 
    df_paper_02 %>% 
    select(
        vas, a_agebase, a_gender, a_domicile, d_st_type, income,
        barthels_index, bmi,  a_livingsit) %>% 
    lm(vas ~ ., data = .) %>%
    tbl_regression(
        pvalue_fun = function(x) style_pvalue(x, digits = 3)) %>% 
    bold_labels() %>% 
    bold_p() 

tbl_merge(
    list(table_vas_crude, table_vas_adj),
    tab_spanner = c("**Univariate**", "**Multivariate**")) %>%    
    modify_caption(
        caption = "**Table 3b**:Univartiate and multivariate linear 
        regression for quality of life of study participants")

**Table 3b**:Univartiate and multivariate linear regression for quality of life of study participants
Variable	Univariate				Multivariate
Variable	N	Estimate	95% CI	p-value	Beta	95% CI	p-value
Age in years	497	-0.26	-0.44, -0.09	0.003	-0.17	-0.35, 0.02	0.074
Gender	497
Male		—	—		—	—
Female		3.9	-0.09, 8.0	0.056	8.9	4.3, 14	<0.001
Marital Status	497
Currently Married		—	—
Previously Married		-2.5	-7.0, 1.9	0.264
Never Married		-5.3	-15, 4.3	0.279
Educational Status	497
None		—	—
Primary		3.6	-3.6, 11	0.324
Secondary		5.1	-2.2, 12	0.173
Tertiary		4.8	-3.3, 13	0.244
Living Status	497
Lives Alone		—	—
Lives With Spouse and Children		-5.8	-14, 2.9	0.191
Lives With Spouse		-11	-23, 0.16	0.053
Lives With Extended Family		-8.3	-18, 1.5	0.097
Lives With Children		-11	-20, -1.4	0.024
Religion	497
Christianity		—	—
Islam		-4.6	-11, 2.1	0.179
Other		-2.6	-28, 23	0.846
Domicile	497
Rural		—	—		—	—
Semi-Urban		11	2.4, 19	0.012	11	3.0, 19	0.007
Urban		12	3.4, 20	0.006	13	5.1, 21	0.001
Income in GHC	494
0-100		—	—		—	—
101-250		10	5.2, 15	<0.001	6.4	1.3, 11	0.014
251-500		9.8	4.5, 15	<0.001	7.5	1.8, 13	0.010
>500		1.4	-5.0, 7.9	0.661	0.21	-7.0, 7.4	0.955
d_st_type	448
Ischemic Stroke		—	—		—	—
Intracerebral Hemorrhagic Stroke		4.5	-0.45, 9.5	0.074	-0.51	-5.6, 4.5	0.842
Untyped Stroke (no CT scan available)		-23	-43, -3.2	0.023	-18	-36, 0.28	0.054
Body Mass Index	478	0.34	-0.03, 0.71	0.073	0.05	-0.33, 0.44	0.778
Barthels Index	483	0.25	0.18, 0.32	<0.001	0.21	0.13, 0.29	<0.001
a_livingsit
Lives Alone					—	—
Lives With Spouse and Children					0.04	-8.9, 9.0	0.993
Lives With Spouse					-4.5	-16, 7.0	0.443
Lives With Extended Family					-3.6	-14, 6.5	0.484
Lives With Children					-7.3	-17, 2.6	0.148
Abbreviation: CI = Confidence Interval

Table 4a - Linear regression for males

Show the code

table_vas_crude <- 
    df_paper_02 %>%
    filter(!is.na(vas) & a_gender == "Male") %>% 
    select(
        a_agebase, maristat, educ, a_livingsit, a_religion,
        a_domicile, income, d_st_type, bmi,
        barthels_index, vas) %>% 
    tbl_uvregression(
        y = vas, 
        method = lm,
        pvalue_fun = ~ gtsummary::style_pvalue(.x, digits = 3)) %>% 
    modify_header(
        update = list(estimate ~ "**Estimate**", label ~ "**Variable**")) %>%
    gtsummary::bold_labels() %>% 
#    gtsummary::add_global_p() %>% 
    gtsummary::bold_p(t = 0.1)

table_vas_adj <- 
    df_paper_02 %>%
    filter(!is.na(vas) & a_gender == "Male") %>% 
    select(
        vas, a_agebase, a_domicile, income, d_st_type, a_livingsit,
        barthels_index) %>% 
    lm(vas ~ ., data = .) %>%
    tbl_regression(
        pvalue_fun = function(x) style_pvalue(x, digits = 3)) %>% 
    bold_labels() %>% 
    bold_p() 

tbl_merge(
    list(table_vas_crude, table_vas_adj),
    tab_spanner = c("**Univariate**", "**Multivariate**")) %>%    
    modify_caption(
        caption = "**Table 4a**:Univartiate and multivariate linear 
        regression for quality of life of study participants (Males)")

**Table 4a**:Univartiate and multivariate linear regression for quality of life of study participants (Males)
Variable	Univariate				Multivariate
Variable	N	Estimate	95% CI	p-value	Beta	95% CI	p-value
Age in years	280	-0.30	-0.53, -0.06	0.014	-0.15	-0.41, 0.10	0.229
Marital Status	280
Currently Married		—	—
Previously Married		-0.27	-8.8, 8.3	0.950
Never Married		-8.8	-21, 3.7	0.166
Educational Status	280
None		—	—
Primary		-0.54	-15, 14	0.942
Secondary		3.1	-11, 18	0.668
Tertiary		2.5	-12, 17	0.742
Living Status	280
Lives Alone		—	—
Lives With Spouse and Children		-7.7	-19, 4.1	0.200
Lives With Spouse		-16	-32, -0.87	0.039
Lives With Extended Family		-9.3	-23, 4.7	0.191
Lives With Children		-16	-31, -0.14	0.048
Religion	280
Christianity		—	—
Islam		-3.0	-12, 5.9	0.504
Other		6.0	-26, 38	0.714
Domicile	280
Rural		—	—		—	—
Semi-Urban		20	8.2, 32	0.001	20	8.2, 32	<0.001
Urban		16	3.8, 27	0.010	18	6.4, 29	0.002
Income in GHC	280
0-100		—	—		—	—
101-250		11	4.2, 18	0.002	8.2	1.2, 15	0.021
251-500		13	5.4, 20	<0.001	10	2.4, 18	0.011
>500		7.6	-0.92, 16	0.080	4.3	-5.0, 14	0.361
d_st_type	252
Ischemic Stroke		—	—		—	—
Intracerebral Hemorrhagic Stroke		5.8	-0.56, 12	0.074	1.2	-5.3, 7.6	0.725
Untyped Stroke (no CT scan available)		-17	-43, 9.5	0.209	-8.8	-34, 16	0.491
Body Mass Index	273	-0.23	-0.81, 0.35	0.439
Barthels Index	269	0.28	0.17, 0.38	<0.001	0.21	0.09, 0.32	<0.001
a_livingsit
Lives Alone					—	—
Lives With Spouse and Children					1.1	-11, 13	0.849
Lives With Spouse					-5.6	-21, 9.8	0.475
Lives With Extended Family					6.5	-7.6, 21	0.364
Lives With Children					-3.1	-19, 12	0.695
Abbreviation: CI = Confidence Interval

Table 4b - Linear regression for Females

Show the code

table_vas_crude <- 
    df_paper_02 %>%
    filter(!is.na(vas) & a_gender == "Female") %>% 
    select(
        a_agebase, maristat, educ, a_livingsit, a_religion,
        a_domicile, income, d_st_type, bmi,
        barthels_index, vas) %>% 
    tbl_uvregression(
        y = vas, 
        method = lm,
        pvalue_fun = ~ gtsummary::style_pvalue(.x, digits = 3)) %>% 
    modify_header(
        update = list(estimate ~ "**Estimate**", label ~ "**Variable**")) %>%
    gtsummary::bold_labels() %>% 
#    gtsummary::add_global_p() %>% 
    gtsummary::bold_p()

table_vas_adj <- 
    df_paper_02 %>%
    filter(!is.na(vas) & a_gender == "Female") %>% 
    select(
        vas, a_agebase, a_domicile, d_st_type, income,
        barthels_index,  maristat, a_livingsit, educ) %>% 
    lm(vas ~ ., data = .) %>%
    tbl_regression(
        pvalue_fun = function(x) style_pvalue(x, digits = 3),
        label = d_st_type ~ "Stroke Type (Choose One)") %>% 
    bold_labels() %>% 
    bold_p() 

tbl_merge(
    list(table_vas_crude, table_vas_adj),
    tab_spanner = c("**Univariate**", "**Multivariate**")) %>%    
    modify_caption(
        caption = "**Table 4a**:Univartiate and multivariate linear 
        regression for quality of life of study participants (Females)")

**Table 4a**:Univartiate and multivariate linear regression for quality of life of study participants (Females)
Variable	Univariate				Multivariate
Variable	N	Estimate	95% CI	p-value	Beta	95% CI	p-value
Age in years	217	-0.26	-0.52, 0.00	0.052	-0.01	-0.30, 0.28	0.945
Marital Status	217
Currently Married		—	—		—	—
Previously Married		-7.9	-14, -1.9	0.010	-3.4	-14, 7.3	0.532
Never Married		-1.8	-17, 13	0.817	3.7	-14, 21	0.683
Educational Status	217
None		—	—		—	—
Primary		6.6	-1.6, 15	0.115	7.5	-0.80, 16	0.076
Secondary		8.4	-1.0, 18	0.080	7.2	-2.4, 17	0.139
Tertiary		12	-0.55, 25	0.060	10	-2.8, 23	0.125
Living Status	217
Lives Alone		—	—
Lives With Spouse and Children		-0.18	-13, 13	0.978
Lives With Spouse		-3.8	-21, 13	0.662
Lives With Extended Family		-7.7	-21, 6.0	0.268
Lives With Children		-10	-23, 2.8	0.125
Religion	217
Christianity		—	—
Islam		-6.5	-17, 3.7	0.210
Other		-18	-62, 25	0.409
Domicile	217
Rural		—	—		—	—
Semi-Urban		1.1	-11, 13	0.859	-0.85	-12, 10	0.882
Urban		8.8	-2.3, 20	0.119	5.8	-4.9, 16	0.285
Income in GHC	214
0-100		—	—		—	—
101-250		11	4.0, 18	0.002	3.8	-3.6, 11	0.310
251-500		8.0	0.16, 16	0.046	4.9	-3.9, 14	0.277
>500		-6.3	-16, 3.8	0.222	-9.1	-20, 1.3	0.087
d_st_type	196
Ischemic Stroke		—	—
Intracerebral Hemorrhagic Stroke		4.5	-3.8, 13	0.283
Untyped Stroke (no CT scan available)		-32	-62, -1.1	0.042
Body Mass Index	205	0.65	0.16, 1.1	0.010
Barthels Index	214	0.23	0.14, 0.33	<0.001	0.22	0.12, 0.33	<0.001
Stroke Type (Choose One)
Ischemic Stroke					—	—
Intracerebral Hemorrhagic Stroke					-0.37	-8.5, 7.8	0.928
Untyped Stroke (no CT scan available)					-29	-58, -0.66	0.045
a_livingsit
Lives Alone					—	—
Lives With Spouse and Children					-3.8	-20, 13	0.648
Lives With Spouse					-7.0	-26, 12	0.479
Lives With Extended Family					-12	-27, 2.0	0.092
Lives With Children					-12	-25, 1.2	0.075
Abbreviation: CI = Confidence Interval